From 356264fa9f566e8edecbcbd4f7a7952e5a161c9d Mon Sep 17 00:00:00 2001 From: yonishelach Date: Wed, 8 Mar 2023 11:01:28 +0200 Subject: [PATCH 01/38] [Build] Fix html links, Add .html as source in documentation --- cli/marketplace/build.py | 116 ++++++++++++++++++++++++++------------- 1 file changed, 78 insertions(+), 38 deletions(-) diff --git a/cli/marketplace/build.py b/cli/marketplace/build.py index 1a6c56b76..bc6d91293 100644 --- a/cli/marketplace/build.py +++ b/cli/marketplace/build.py @@ -18,7 +18,7 @@ import subprocess import uuid from pathlib import Path -from typing import Union, Optional, Set, Dict, List +from typing import Dict, List, Optional, Set, Union import click import yaml @@ -26,13 +26,8 @@ from sphinx.cmd.build import main as sphinx_build_cmd from sphinx.ext.apidoc import main as sphinx_apidoc_cmd -from cli.helpers import ( - is_item_dir, - render_jinja, - PROJECT_ROOT, - get_item_yaml_values, - get_mock_requirements, -) +from cli.helpers import (PROJECT_ROOT, get_item_yaml_values, + get_mock_requirements, is_item_dir, render_jinja) from cli.marketplace.changelog import ChangeLog from cli.path_iterator import PathIterator @@ -68,6 +63,14 @@ default=False, help="When this flag is set, the process will output extra information", ) +@click.option( + "-f", + "--force-update", + "force_update_items", + is_flag=True, + default=False, + help="When this flag is set, item pages will be created even if the item did not changed", +) def build_marketplace_cli( source_dir: str, source_name: str, @@ -75,6 +78,7 @@ def build_marketplace_cli( temp_dir: str, channel: str, verbose: bool, + force_update_items: bool, ): build_marketplace( source_dir, @@ -83,6 +87,7 @@ def build_marketplace_cli( temp_dir, channel, verbose, + force_update_items, ) @@ -93,6 +98,7 @@ def build_marketplace( temp_dir: str = "/tmp", channel: str = "development", verbose: bool = False, + force_update_items: bool = False, ): """Main entry point to marketplace building @@ -103,6 +109,8 @@ def build_marketplace( if not provided '/tmp/' will be used :param channel: The name of the marketplace channel to write to :param verbose: When True, additional debug information will be written to stdout + :param force_update_items: If True, items will be updated unrelated if they are not changed. + The purpose of this flag is to fix existed broken pages (e.g. broken links) """ global _verbose _verbose = verbose @@ -152,9 +160,15 @@ def build_marketplace( render_html_files(temp_docs) change_log = ChangeLog() - copy_static_resources(marketplace_dir, temp_docs) + copy_resources(marketplace_dir, temp_docs) - update_or_create_items(source_dir, marketplace_dir, temp_docs, change_log) + update_or_create_items( + source_dir, + marketplace_dir, + temp_docs, + change_log, + force_update=force_update_items, + ) build_catalog_json( marketplace_dir=marketplace_dir, catalog_path=(marketplace_root / "catalog.json"), @@ -212,18 +226,22 @@ def write_index_html(marketplace_root: Union[str, Path]): shutil.copy(template_path, index_path) -def copy_static_resources(marketplace_dir, temp_docs): +def copy_resources(marketplace_dir, temp_docs): marketplace_static = marketplace_dir / "_static" - if not marketplace_static.exists(): - click.echo("Copying static resources...") - shutil.copytree(temp_docs / "_build/_static", marketplace_static) - shutil.copytree(temp_docs / "_build/_modules", marketplace_dir / "_modules") + click.echo("Copying static resources...") + shutil.copytree( + temp_docs / "_build/_static", marketplace_static, dirs_exist_ok=True + ) -def update_or_create_items(source_dir, marketplace_dir, temp_docs, change_log): +def update_or_create_items( + source_dir, marketplace_dir, temp_docs, change_log, force_update: bool = False +): click.echo("Creating items...") for item_dir in PathIterator(root=source_dir, rule=is_item_dir, as_path=True): - update_or_create_item(item_dir, marketplace_dir, temp_docs, change_log) + update_or_create_item( + item_dir, marketplace_dir, temp_docs, change_log, force_update + ) def build_catalog_json( @@ -329,17 +347,22 @@ def add_assets(item_yaml: dict): def update_or_create_item( - item_dir: Path, marketplace_dir: Path, temp_docs: Path, change_log: ChangeLog + item_dir: Path, + marketplace_dir: Path, + temp_docs: Path, + change_log: ChangeLog, + force_update: bool = False, ): # Copy source directories to target directories, if target already has the directory, archive previous version item_yaml = yaml.full_load(open(item_dir / "item.yaml", "r")) source_version = item_yaml["version"] + relative_path = "../../../" marketplace_item = marketplace_dir / item_dir.stem target_latest = marketplace_item / "latest" target_version = marketplace_item / source_version - if target_version.exists(): + if target_version.exists() and not force_update: latest_item_yaml = yaml.full_load( open(target_latest / "src" / "item.yaml", "r") ) @@ -351,16 +374,21 @@ def update_or_create_item( example_html_name = f"{item_dir.stem}_example.html" build_path = temp_docs / "_build" - source_html = marketplace_dir / "_modules" / item_dir.stem / f"{item_dir.stem}.html" - update_html_resource_paths(source_html, relative_path="../") + source_html = ( + temp_docs / "_build" / "_modules" / item_dir.stem / f"{item_dir.stem}.html" + ) + update_html_resource_paths(source_html, relative_path=relative_path) documentation_html = build_path / documentation_html_name update_html_resource_paths( - documentation_html, relative_path="../../../", with_download=False, item_name=item_dir.stem + documentation_html, + relative_path=relative_path, + with_download=False, + item_name=item_dir.stem, ) example_html = build_path / example_html_name - update_html_resource_paths(example_html, relative_path="../../../") + update_html_resource_paths(example_html, relative_path=relative_path) latest_src = target_latest / "src" version_src = target_version / "src" @@ -445,34 +473,38 @@ def update_or_create_item( def update_html_resource_paths( - html_path: Path, relative_path: str, with_download: bool = True, item_name: str = None + html_path: Path, + relative_path: str, + with_download: bool = True, + item_name: str = None, ): if html_path.exists(): with open(html_path, "r", encoding="utf8") as html: parsed = BeautifulSoup(html.read(), features="html.parser") # Update back to docs link (from source page) - back_to_docs_nodes = parsed.find_all(lambda node: "viewcode-back" in node.get("class", "")) - pattern = r"^.*?(?=.html)" + back_to_docs_nodes = parsed.find_all( + lambda node: "viewcode-back" in node.get("class", "") + ) + pattern = r"^.*?(?={})" for node in back_to_docs_nodes: - node["href"] = re.sub(pattern, "documentation", node["href"]) + node["href"] = re.sub( + pattern.format(".html"), "documentation", node["href"] + ) - # Remove _static from links and replace with src + # Fix links with relative paths: nodes = parsed.find_all( - lambda node: node.name == "link" and "_static" in node.get("href", "") + lambda node: "_static" in node.get("src", "") + or "_static" in node.get("href", "") ) for node in nodes: - node["href"] = f"{relative_path}{node['href']}" + key = "href" if "_static" in node.get("href", "") else "src" + node[key] = re.sub(pattern.format("_static"), relative_path, node[key]) - nodes = parsed.find_all( - lambda node: node.name == "script" - and node.get("src", "").startswith("_static") - ) - for node in nodes: - node["src"] = f"{relative_path}{node['src']}" if with_download: nodes = parsed.find_all(lambda node: "_sources" in node.get("href", "")) for node in nodes: + # fix path and remove example from name: node[ "href" ] = f'../{node["href"].replace("_sources", "src").replace("_example", "")}' @@ -487,7 +519,9 @@ def update_html_resource_paths( # Fix links in source page: if item_name: - nodes = parsed.find_all(lambda node: node.name == "a" and "_modules" in node.get("href", "")) + nodes = parsed.find_all( + lambda node: node.name == "a" and "_modules" in node.get("href", "") + ) for node in nodes: node["href"] = node["href"].replace(f"_modules/{item_name}/", "") @@ -653,4 +687,10 @@ def build_temp_docs(temp_root, temp_docs): if __name__ == "__main__": # build_marketplace_cli() - build_marketplace("../../", "../../../marketp", verbose=True) + build_marketplace( + source_dir="../../../functions", + marketplace_dir="../../../marketplace", + verbose=True, + channel="development", + force_update_items=True, + ) From 2202cafb5adb8fcc8cc93bb21627143a7df494fa Mon Sep 17 00:00:00 2001 From: yonishelach Date: Wed, 19 Apr 2023 09:36:25 +0300 Subject: [PATCH 02/38] Update CI temporarily and update index --- .github/workflows/test-all.yaml | 2 +- cli/marketplace/index.html | 13 +++++++++++++ 2 files changed, 14 insertions(+), 1 deletion(-) diff --git a/.github/workflows/test-all.yaml b/.github/workflows/test-all.yaml index e946b11b3..d0eb03579 100644 --- a/.github/workflows/test-all.yaml +++ b/.github/workflows/test-all.yaml @@ -167,7 +167,7 @@ jobs: pwd git pull origin cd .. - python functions/functions.py build-marketplace -s functions -m marketplace -c $CHANNEL -v + python functions/functions.py build-marketplace -s functions -m marketplace -c $CHANNEL -v -f - name: Publish marketplace release env: GITHUB_TOKEN: ${{ secrets.MARKETPLACE_ACCESS_TOKEN_V3 }} diff --git a/cli/marketplace/index.html b/cli/marketplace/index.html index b6e38cfa3..d1030e92f 100644 --- a/cli/marketplace/index.html +++ b/cli/marketplace/index.html @@ -115,6 +115,7 @@ item.example = item.example ? `${base_url}/static/example.html` : null; item.functionPath = `${base_url}/static/function.html`; item.itemPath = `${base_url}/static/item.html`; + item.code = `${base_url}/static/${item.rawName}.html`; table.addRow(item); } @@ -189,6 +190,18 @@ }, }, width: 150 + }, + { + title: "Source Code", + field: "code", + headerSort: false, + formatter: "link", + formatterParams: { + label: (cell) => { + return (cell._cell.value === undefined ? '': 'View'); + }, + }, + width: 150 }, { title: "Deployment", From 8b17c8f3dea43adcde9f9a389f16564eac082dca Mon Sep 17 00:00:00 2001 From: yonishelach Date: Wed, 19 Apr 2023 10:20:07 +0300 Subject: [PATCH 03/38] [XGB-Custom] Fix test artifact key name --- xgb_custom/test_xgb_custom.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/xgb_custom/test_xgb_custom.py b/xgb_custom/test_xgb_custom.py index 6cdada74b..81b77a2e0 100644 --- a/xgb_custom/test_xgb_custom.py +++ b/xgb_custom/test_xgb_custom.py @@ -41,7 +41,7 @@ def test_local_xgb_custom(): "verbose_eval": False, "XGB_max_depth": 2, "XGB_subsample": 0.9, - "test_set_key": "./artifacts/inputs/test-set", + "test_set_key": "test-set", }, inputs={"dataset": run.artifact('xgb-outs').url}, handler="fit", From a469dcaebeea7cb8874fc05eb18b9728019cde84 Mon Sep 17 00:00:00 2001 From: yonishelach Date: Wed, 19 Apr 2023 13:43:33 +0300 Subject: [PATCH 04/38] [XGB-Serving][XGB-Test][XGB-Trainer] Fix tests - artifact key --- xgb_serving/test_xgb_serving.py | 3 +-- xgb_test/test_xgb_test.py | 2 -- xgb_trainer/test_xgb_trainer.py | 7 +++---- 3 files changed, 4 insertions(+), 8 deletions(-) diff --git a/xgb_serving/test_xgb_serving.py b/xgb_serving/test_xgb_serving.py index 9f4dd8244..ce5e8aaa8 100644 --- a/xgb_serving/test_xgb_serving.py +++ b/xgb_serving/test_xgb_serving.py @@ -37,8 +37,7 @@ def test_local_xgb_serving(): "CLASS_objective": "binary:logistic", "CLASS_booster": "gbtree", "FIT_verbose": 0, - "label_column": "labels", - "test_set": "./"}, + "label_column": "labels"}, local=True, inputs={"dataset": gen_data_run.artifact('classifier-data').url}, artifact_path='./') diff --git a/xgb_test/test_xgb_test.py b/xgb_test/test_xgb_test.py index 71dfee3e2..e56b9db01 100644 --- a/xgb_test/test_xgb_test.py +++ b/xgb_test/test_xgb_test.py @@ -51,7 +51,6 @@ def xgb_trainer(): "CLASS_booster": "gbtree", "FIT_verbose": 0, "label_column": "labels", - "test_set": "./artifacts/test-set", }, local=True, inputs={"dataset": data}, @@ -111,7 +110,6 @@ def test_local_xgb_test_import_local_function(): "CLASS_booster": "gbtree", "FIT_verbose": 0, "label_column": "labels", - "test_set": "./artifacts/test-set", }, local=True, inputs={"dataset": data}, diff --git a/xgb_trainer/test_xgb_trainer.py b/xgb_trainer/test_xgb_trainer.py index 1356f72c7..52df8db48 100644 --- a/xgb_trainer/test_xgb_trainer.py +++ b/xgb_trainer/test_xgb_trainer.py @@ -29,6 +29,7 @@ def get_class_data(): 'file_ext': 'csv'}, local=True, artifact_path='./') return run + def test_xgb_trainer_code_to_function(): gen_data_run = get_class_data() fn = code_to_function(name='test_xgb_trainer', @@ -41,8 +42,7 @@ def test_xgb_trainer_code_to_function(): 'CLASS_objective': 'binary:logistic', 'CLASS_booster': 'gbtree', 'FIT_verbose': 0, - 'label_column': 'labels', - 'test_set': './'}, + 'label_column': 'labels'}, local=False, inputs={'dataset': gen_data_run.artifact('classifier-data').url}) @@ -59,8 +59,7 @@ def test_local_xgb_trainer_import_function(): 'CLASS_objective': 'binary:logistic', 'CLASS_booster': 'gbtree', 'FIT_verbose': 0, - 'label_column': 'labels', - 'test_set': './'}, + 'label_column': 'labels'}, local=True, inputs={'dataset': gen_data_run.artifact('classifier-data').url}) assert (run.artifact('model')) \ No newline at end of file From 3301415200e52326bade1e17f99cb6b6d3880860 Mon Sep 17 00:00:00 2001 From: Yoni Shelach <92271540+yonishelach@users.noreply.github.com> Date: Wed, 19 Apr 2023 19:05:40 +0300 Subject: [PATCH 05/38] [Build] Install python 3.9 when testing (#618) --- cli/helpers.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cli/helpers.py b/cli/helpers.py index 64d5505c5..75661f345 100644 --- a/cli/helpers.py +++ b/cli/helpers.py @@ -65,7 +65,7 @@ def install_pipenv(): def install_python(directory: Union[str, Path]): print(f"Installing python for {directory}...") python_install: subprocess.CompletedProcess = subprocess.run( - f"pipenv --rm;pipenv --python 3.7", + f"pipenv --rm;pipenv --python 3.9.13", stdout=sys.stdout, stderr=subprocess.PIPE, cwd=directory, From 0cd1f1585a618c253f201b6f5a63502cdbddb591 Mon Sep 17 00:00:00 2001 From: Yoni Shelach <92271540+yonishelach@users.noreply.github.com> Date: Wed, 19 Apr 2023 19:41:19 +0300 Subject: [PATCH 06/38] [Build] Update python version in CI (#620) * [Build] Install python 3.9 when testing * [Build] Update python version in CI * . --- .github/workflows/ci.yaml | 2 +- .github/workflows/test-all.yaml | 6 +++--- cli/helpers.py | 2 +- 3 files changed, 5 insertions(+), 5 deletions(-) diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index 155634dfc..1ab67ffc7 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -107,7 +107,7 @@ jobs: - name: Install python uses: actions/setup-python@v2 with: - python-version: 3.7 + python-version: 3.9 # Install dependencies - uses: actions/cache@v1 id: cache diff --git a/.github/workflows/test-all.yaml b/.github/workflows/test-all.yaml index d0eb03579..fe7248bcd 100644 --- a/.github/workflows/test-all.yaml +++ b/.github/workflows/test-all.yaml @@ -70,7 +70,7 @@ jobs: - name: Install python uses: actions/setup-python@v2 with: - python-version: 3.7 + python-version: 3.9 # Install dependencies - uses: actions/cache@v1 id: cache @@ -106,7 +106,7 @@ jobs: # - name: Install python # uses: actions/setup-python@v2 # with: -# python-version: 3.7 +# python-version: 3.9 # # Install dependencies # - uses: actions/cache@v1 # id: cache @@ -153,7 +153,7 @@ jobs: - name: Install python uses: actions/setup-python@v2 with: - python-version: 3.7 + python-version: 3.9 - name: Install requirements run: | cd functions diff --git a/cli/helpers.py b/cli/helpers.py index 75661f345..b44ebc92e 100644 --- a/cli/helpers.py +++ b/cli/helpers.py @@ -65,7 +65,7 @@ def install_pipenv(): def install_python(directory: Union[str, Path]): print(f"Installing python for {directory}...") python_install: subprocess.CompletedProcess = subprocess.run( - f"pipenv --rm;pipenv --python 3.9.13", + f"pipenv --rm;pipenv --python 3.9", stdout=sys.stdout, stderr=subprocess.PIPE, cwd=directory, From 33e7ab8c43579b8609ed4f9654cc7b0d0f06671a Mon Sep 17 00:00:00 2001 From: Yoni Shelach <92271540+yonishelach@users.noreply.github.com> Date: Wed, 19 Apr 2023 19:47:50 +0300 Subject: [PATCH 07/38] Revert "[Build] Update python version in CI (#620)" (#621) This reverts commit 0cd1f1585a618c253f201b6f5a63502cdbddb591. --- .github/workflows/ci.yaml | 2 +- .github/workflows/test-all.yaml | 6 +++--- cli/helpers.py | 2 +- 3 files changed, 5 insertions(+), 5 deletions(-) diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index 1ab67ffc7..155634dfc 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -107,7 +107,7 @@ jobs: - name: Install python uses: actions/setup-python@v2 with: - python-version: 3.9 + python-version: 3.7 # Install dependencies - uses: actions/cache@v1 id: cache diff --git a/.github/workflows/test-all.yaml b/.github/workflows/test-all.yaml index fe7248bcd..d0eb03579 100644 --- a/.github/workflows/test-all.yaml +++ b/.github/workflows/test-all.yaml @@ -70,7 +70,7 @@ jobs: - name: Install python uses: actions/setup-python@v2 with: - python-version: 3.9 + python-version: 3.7 # Install dependencies - uses: actions/cache@v1 id: cache @@ -106,7 +106,7 @@ jobs: # - name: Install python # uses: actions/setup-python@v2 # with: -# python-version: 3.9 +# python-version: 3.7 # # Install dependencies # - uses: actions/cache@v1 # id: cache @@ -153,7 +153,7 @@ jobs: - name: Install python uses: actions/setup-python@v2 with: - python-version: 3.9 + python-version: 3.7 - name: Install requirements run: | cd functions diff --git a/cli/helpers.py b/cli/helpers.py index b44ebc92e..75661f345 100644 --- a/cli/helpers.py +++ b/cli/helpers.py @@ -65,7 +65,7 @@ def install_pipenv(): def install_python(directory: Union[str, Path]): print(f"Installing python for {directory}...") python_install: subprocess.CompletedProcess = subprocess.run( - f"pipenv --rm;pipenv --python 3.9", + f"pipenv --rm;pipenv --python 3.9.13", stdout=sys.stdout, stderr=subprocess.PIPE, cwd=directory, From 7a7473b8f41e80032f381d927214a9076a4a55b8 Mon Sep 17 00:00:00 2001 From: Yoni Shelach <92271540+yonishelach@users.noreply.github.com> Date: Wed, 19 Apr 2023 19:48:09 +0300 Subject: [PATCH 08/38] Revert "[Build] Install python 3.9 when testing (#618)" (#619) This reverts commit 3301415200e52326bade1e17f99cb6b6d3880860. --- cli/helpers.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cli/helpers.py b/cli/helpers.py index 75661f345..64d5505c5 100644 --- a/cli/helpers.py +++ b/cli/helpers.py @@ -65,7 +65,7 @@ def install_pipenv(): def install_python(directory: Union[str, Path]): print(f"Installing python for {directory}...") python_install: subprocess.CompletedProcess = subprocess.run( - f"pipenv --rm;pipenv --python 3.9.13", + f"pipenv --rm;pipenv --python 3.7", stdout=sys.stdout, stderr=subprocess.PIPE, cwd=directory, From 81437da88e99ed48a9e1b0b0b367c4c02db80140 Mon Sep 17 00:00:00 2001 From: Yoni Shelach <92271540+yonishelach@users.noreply.github.com> Date: Wed, 19 Apr 2023 20:26:27 +0300 Subject: [PATCH 09/38] [Build] Build with python 3.9 (#622) * [Build] Build with python 3.9 * . --- .github/workflows/ci.yaml | 5 +++++ .github/workflows/test-all.yaml | 4 ++-- 2 files changed, 7 insertions(+), 2 deletions(-) diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index 155634dfc..5b4bfcd79 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -108,6 +108,11 @@ jobs: uses: actions/setup-python@v2 with: python-version: 3.7 + # Install python 3.9 + - name: Install python 3.9 + uses: actions/setup-python@v2 + with: + python-version: 3.9 # Install dependencies - uses: actions/cache@v1 id: cache diff --git a/.github/workflows/test-all.yaml b/.github/workflows/test-all.yaml index d0eb03579..5eff03b0f 100644 --- a/.github/workflows/test-all.yaml +++ b/.github/workflows/test-all.yaml @@ -150,10 +150,10 @@ jobs: with: repository: mlrun/marketplace path: marketplace - - name: Install python + - name: Install python 3.9 uses: actions/setup-python@v2 with: - python-version: 3.7 + python-version: 3.9 - name: Install requirements run: | cd functions From 88727986ffa91662593958023be8ac3ccef2cab0 Mon Sep 17 00:00:00 2001 From: Avi Asulin Date: Sun, 7 Apr 2024 21:10:55 +0300 Subject: [PATCH 10/38] [onnx utils] update onnx utils packages --- onnx_utils/function.yaml | 42 +++++++++++++++++------------------ onnx_utils/item.yaml | 16 ++++++------- onnx_utils/requirements.txt | 12 +++++----- onnx_utils/test_onnx_utils.py | 4 ++-- 4 files changed, 36 insertions(+), 38 deletions(-) diff --git a/onnx_utils/function.yaml b/onnx_utils/function.yaml index 7a0054c4d..88f810fb4 100644 --- a/onnx_utils/function.yaml +++ b/onnx_utils/function.yaml @@ -2,7 +2,7 @@ kind: job metadata: name: onnx-utils tag: '' - hash: 0c4a6491b976d5220d3ebfb83a3905dd47e86be2 + hash: fd6cd909ef6e055c348b44a0313e190513cd755b project: '' labels: author: guyl @@ -16,16 +16,16 @@ spec: functionSourceCode: # Copyright 2019 Iguazio
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from typing import Any, Callable, Dict, List, Tuple

import mlrun


class _ToONNXConversions:
    """
    An ONNX conversion functions library class.
    """

    @staticmethod
    def tf_keras_to_onnx(
        model_handler,
        onnx_model_name: str = None,
        optimize_model: bool = True,
        input_signature: List[Tuple[Tuple[int], str]] = None,
    ):
        """
        Convert a TF.Keras model to an ONNX model and log it back to MLRun as a new model object.

        :param model_handler:   An initialized TFKerasModelHandler with a loaded model to convert to ONNX.
        :param onnx_model_name: The name to use to log the converted ONNX model. If not given, the given `model_name`
                                will be used with an additional suffix `_onnx`. Defaulted to None.
        :param optimize_model:  Whether or not to optimize the ONNX model using 'onnxoptimizer' before saving the model.
                                Defaulted to True.
        :param input_signature: A list of the input layers shape and data type properties. Expected to receive a list
                                where each element is an input layer tuple. An input layer tuple is a tuple of:
                                [0] = Layer's shape, a tuple of integers.
                                [1] = Layer's data type, a mlrun.data_types.ValueType string.
                                If None, the input signature will be tried to be read from the model artifact. Defaulted
                                to None.
        """
        # Import the framework and handler:
        import tensorflow as tf
        from mlrun.frameworks.tf_keras import TFKerasUtils

        # Check the given 'input_signature' parameter:
        if input_signature is None:
            # Read the inputs from the model:
            try:
                model_handler.read_inputs_from_model()
            except Exception as error:
                raise mlrun.errors.MLRunRuntimeError(
                    f"Please provide the 'input_signature' parameter. The function tried reading the input layers "
                    f"information automatically but failed with the following error: {error}"
                )
        else:
            # Parse the 'input_signature' parameter:
            input_signature = [
                tf.TensorSpec(
                    shape=shape,
                    dtype=TFKerasUtils.convert_value_type_to_tf_dtype(
                        value_type=value_type
                    ),
                )
                for (shape, value_type) in input_signature
            ]

        # Convert to ONNX:
        model_handler.to_onnx(
            model_name=onnx_model_name,
            input_signature=input_signature,
            optimize=optimize_model,
        )

    @staticmethod
    def pytorch_to_onnx(
        model_handler,
        onnx_model_name: str = None,
        optimize_model: bool = True,
        input_signature: List[Tuple[Tuple[int, ...], str]] = None,
        input_layers_names: List[str] = None,
        output_layers_names: List[str] = None,
        dynamic_axes: Dict[str, Dict[int, str]] = None,
        is_batched: bool = True,
    ):
        """
        Convert a PyTorch model to an ONNX model and log it back to MLRun as a new model object.

        :param model_handler:       An initialized PyTorchModelHandler with a loaded model to convert to ONNX.
        :param onnx_model_name:     The name to use to log the converted ONNX model. If not given, the given
                                    `model_name` will be used with an additional suffix `_onnx`. Defaulted to None.
        :param optimize_model:      Whether or not to optimize the ONNX model using 'onnxoptimizer' before saving the
                                    model. Defaulted to True.
        :param input_signature:     A list of the input layers shape and data type properties. Expected to receive a
                                    list where each element is an input layer tuple. An input layer tuple is a tuple of:
                                    [0] = Layer's shape, a tuple of integers.
                                    [1] = Layer's data type, a mlrun.data_types.ValueType string.
                                    If None, the input signature will be tried to be read from the model artifact.
                                    Defaulted to None.
        :param input_layers_names:  List of names to assign to the input nodes of the graph in order. All of the other
                                    parameters (inner layers) can be set as well by passing additional names in the
                                    list. The order is by the order of the parameters in the model. If None, the inputs
                                    will be read from the handler's inputs. If its also None, it is defaulted to:
                                    "input_0", "input_1", ...
        :param output_layers_names: List of names to assign to the output nodes of the graph in order. If None, the
                                    outputs will be read from the handler's outputs. If its also None, it is defaulted
                                    to: "output_0" (for multiple outputs, this parameter must be provided).
        :param dynamic_axes:        If part of the input / output shape is dynamic, like (batch_size, 3, 32, 32) you can
                                    specify it by giving a dynamic axis to the input / output layer by its name as
                                    follows: {
                                        "input layer name": {0: "batch_size"},
                                        "output layer name": {0: "batch_size"},
                                    }
                                    If provided, the 'is_batched' flag will be ignored. Defaulted to None.
        :param is_batched:          Whether to include a batch size as the first axis in every input and output layer.
                                    Defaulted to True. Will be ignored if 'dynamic_axes' is provided.
        """
        # Import the framework and handler:
        import torch
        from mlrun.frameworks.pytorch import PyTorchUtils

        # Parse the 'input_signature' parameter:
        if input_signature is not None:
            input_signature = tuple(
                [
                    torch.zeros(
                        size=shape,
                        dtype=PyTorchUtils.convert_value_type_to_torch_dtype(
                            value_type=value_type
                        ),
                    )
                    for (shape, value_type) in input_signature
                ]
            )

        # Convert to ONNX:
        model_handler.to_onnx(
            model_name=onnx_model_name,
            input_sample=input_signature,
            optimize=optimize_model,
            input_layers_names=input_layers_names,
            output_layers_names=output_layers_names,
            dynamic_axes=dynamic_axes,
            is_batched=is_batched
        )


# Map for getting the conversion function according to the provided framework:
_CONVERSION_MAP = {
    "tensorflow.keras": _ToONNXConversions.tf_keras_to_onnx,
    "torch": _ToONNXConversions.pytorch_to_onnx,
}  # type: Dict[str, Callable]


def to_onnx(
    context: mlrun.MLClientCtx,
    model_path: str,
    onnx_model_name: str = None,
    optimize_model: bool = True,
    framework_kwargs: Dict[str, Any] = None,
):
    """
    Convert the given model to an ONNX model.

    :param context:          The MLRun function execution context
    :param model_path:       The model path store object.
    :param onnx_model_name:  The name to use to log the converted ONNX model. If not given, the given `model_name` will
                             be used with an additional suffix `_onnx`. Defaulted to None.
    :param optimize_model:   Whether to optimize the ONNX model using 'onnxoptimizer' before saving the model. Defaulted
                             to True.
    :param framework_kwargs: Additional arguments each framework may require in order to convert to ONNX. To get the doc
                             string of the desired framework onnx conversion function, pass "help".
    """
    from mlrun.frameworks.auto_mlrun.auto_mlrun import AutoMLRun

    # Get a model handler of the required framework:
    model_handler = AutoMLRun.load_model(model_path=model_path, context=context)

    # Get the model's framework:
    framework = model_handler.FRAMEWORK_NAME

    # Use the conversion map to get the specific framework to onnx conversion:
    if framework not in _CONVERSION_MAP:
        raise mlrun.errors.MLRunInvalidArgumentError(
            f"The following framework: '{framework}', has no ONNX conversion."
        )
    conversion_function = _CONVERSION_MAP[framework]

    # Check if needed to print the function's doc string ("help" is passed):
    if framework_kwargs == "help":
        print(conversion_function.__doc__)
        return

    # Set the default empty framework kwargs if needed:
    if framework_kwargs is None:
        framework_kwargs = {}

    # Run the conversion:
    try:
        conversion_function(
            model_handler=model_handler,
            onnx_model_name=onnx_model_name,
            optimize_model=optimize_model,
            **framework_kwargs,
        )
    except TypeError as exception:
        raise mlrun.errors.MLRunInvalidArgumentError(
            f"ERROR: A TypeError exception was raised during the conversion:\n{exception}. "
            f"Please read the {framework} framework conversion function doc string by passing 'help' in the "
            f"'framework_kwargs' dictionary parameter."
        )


def optimize(
    context: mlrun.MLClientCtx,
    model_path: str,
    optimizations: List[str] = None,
    fixed_point: bool = False,
    optimized_model_name: str = None,
):
    """
    Optimize the given ONNX model.

    :param context:              The MLRun function execution context.
    :param model_path:           Path to the ONNX model object.
    :param optimizations:        List of possible optimizations. To see what optimizations are available, pass "help".
                                 If None, all of the optimizations will be used. Defaulted to None.
    :param fixed_point:          Optimize the weights using fixed point. Defaulted to False.
    :param optimized_model_name: The name of the optimized model. If None, the original model will be overridden.
                                 Defaulted to None.
    """
    # Import the model handler:
    import onnxoptimizer
    from mlrun.frameworks.onnx import ONNXModelHandler

    # Check if needed to print the available optimizations ("help" is passed):
    if optimizations == "help":
        available_passes = "\n* ".join(onnxoptimizer.get_available_passes())
        print(f"The available optimizations are:\n* {available_passes}")
        return

    # Create the model handler:
    model_handler = ONNXModelHandler(
        model_path=model_path, context=context
    )

    # Load the ONNX model:
    model_handler.load()

    # Optimize the model using the given configurations:
    model_handler.optimize(optimizations=optimizations, fixed_point=fixed_point)

    # Rename if needed:
    if optimized_model_name is not None:
        model_handler.set_model_name(model_name=optimized_model_name)

    # Log the optimized model:
    model_handler.log()
 base_image: mlrun/mlrun commands: [] - code_origin: https://github.com/yonishelach/functions.git#f84b9565a33d8159315992ebba5838d41f6cc112:/Users/Yonatan_Shelach/projects/functions/onnx_utils/onnx_utils.py - origin_filename: /Users/Yonatan_Shelach/projects/functions/onnx_utils/onnx_utils.py + code_origin: '' + origin_filename: '' with_mlrun: false auto_build: true requirements: - - onnx~=1.13.0 - - onnxruntime~=1.14.0 - - onnxoptimizer~=0.3.0 - - onnxmltools~=1.11.0 - - tf2onnx~=1.13.0 + - onnx~=1.15.0 + - onnxruntime~=1.8.1 + - onnxoptimizer~=0.2.0 + - onnxmltools~=1.9.0 + - tf2onnx~=1.16.0 entry_points: tf_keras_to_onnx: name: tf_keras_to_onnx @@ -35,7 +35,6 @@ spec: - name: model_handler doc: An initialized TFKerasModelHandler with a loaded model to convert to ONNX. - default: '' - name: onnx_model_name type: str doc: The name to use to log the converted ONNX model. If not given, the given @@ -55,9 +54,10 @@ spec: data type, a mlrun.data_types.ValueType string. If None, the input signature will be tried to be read from the model artifact. Defaulted to None.' default: null - outputs: - - default: '' + outputs: [] lineno: 26 + has_varargs: false + has_kwargs: false pytorch_to_onnx: name: pytorch_to_onnx doc: Convert a PyTorch model to an ONNX model and log it back to MLRun as a @@ -66,7 +66,6 @@ spec: - name: model_handler doc: An initialized PyTorchModelHandler with a loaded model to convert to ONNX. - default: '' - name: onnx_model_name type: str doc: The name to use to log the converted ONNX model. If not given, the given @@ -114,9 +113,10 @@ spec: doc: Whether to include a batch size as the first axis in every input and output layer. Defaulted to True. Will be ignored if 'dynamic_axes' is provided. default: true - outputs: - - default: '' + outputs: [] lineno: 81 + has_varargs: false + has_kwargs: false to_onnx: name: to_onnx doc: Convert the given model to an ONNX model. @@ -124,11 +124,9 @@ spec: - name: context type: MLClientCtx doc: The MLRun function execution context - default: '' - name: model_path type: str doc: The model path store object. - default: '' - name: onnx_model_name type: str doc: The name to use to log the converted ONNX model. If not given, the given @@ -146,9 +144,10 @@ spec: ONNX. To get the doc string of the desired framework onnx conversion function, pass "help". default: null - outputs: - - default: '' + outputs: [] lineno: 160 + has_varargs: false + has_kwargs: false optimize: name: optimize doc: Optimize the given ONNX model. @@ -156,11 +155,9 @@ spec: - name: context type: MLClientCtx doc: The MLRun function execution context. - default: '' - name: model_path type: str doc: Path to the ONNX model object. - default: '' - name: optimizations type: List[str] doc: List of possible optimizations. To see what optimizations are available, @@ -176,9 +173,10 @@ spec: doc: The name of the optimized model. If None, the original model will be overridden. Defaulted to None. default: null - outputs: - - default: '' + outputs: [] lineno: 219 + has_varargs: false + has_kwargs: false description: ONNX intigration in MLRun, some utils functions for the ONNX framework, optimizing and converting models from different framework to ONNX using MLRun. default_handler: to_onnx diff --git a/onnx_utils/item.yaml b/onnx_utils/item.yaml index 36335837a..84486d9f8 100644 --- a/onnx_utils/item.yaml +++ b/onnx_utils/item.yaml @@ -12,9 +12,9 @@ labels: author: guyl maintainers: [] marketplaceType: '' -mlrunVersion: 1.1.0 +mlrunVersion: 1.6.3 name: onnx_utils -platformVersion: 3.5.0 +platformVersion: 3.5.4 spec: extra_spec: allow_empty_resources: true @@ -26,10 +26,10 @@ spec: image: mlrun/mlrun kind: job requirements: - - onnx~=1.13.0 - - onnxruntime~=1.14.0 - - onnxoptimizer~=0.3.0 - - onnxmltools~=1.11.0 - - tf2onnx~=1.13.0 + - onnx~=1.15.0 + - onnxruntime~=1.8.1 + - onnxoptimizer~=0.2.0 + - onnxmltools~=1.9.0 + - tf2onnx~=1.16.0 url: '' -version: 1.2.0 +version: 1.3.0 diff --git a/onnx_utils/requirements.txt b/onnx_utils/requirements.txt index dc7ff1e7b..a9acd7371 100644 --- a/onnx_utils/requirements.txt +++ b/onnx_utils/requirements.txt @@ -1,11 +1,11 @@ tqdm~=4.62.3 -tensorflow~=2.7.0 +tensorflow~=2.13.0 torch~=1.10.0 torchvision~=0.11.1 -onnx~=1.10.1 -onnxruntime~=1.8.1 -onnxoptimizer~=0.2.0 +onnx~=1.15.0 +onnxruntime~=1.12.1 +onnxoptimizer~=0.3.0 onnxmltools~=1.9.0 -tf2onnx~=1.9.0 +tf2onnx~=1.16.0 plotly~=5.4.0 -wrapt<1.15.0 # wrapt==1.15.0 fails tensorflow 2.7 Todo: please remove when updating tensorflow \ No newline at end of file +#wrapt<1.15.0 # wrapt==1.15.0 fails tensorflow 2.7 Todo: please remove when updating tensorflow \ No newline at end of file diff --git a/onnx_utils/test_onnx_utils.py b/onnx_utils/test_onnx_utils.py index 35b224c4a..aaae96372 100644 --- a/onnx_utils/test_onnx_utils.py +++ b/onnx_utils/test_onnx_utils.py @@ -257,7 +257,7 @@ def test_pytorch_to_onnx(): filename="test_onnx_utils.py", name="log_model", kind="job", - image="mlrun/ml-models", + image="mlrun/mlrun", ) # Run the function to log the model: @@ -341,7 +341,7 @@ def test_optimize(): filename="test_onnx_utils.py", name="log_model", kind="job", - image="mlrun/ml-models", + image="mlrun/mlrun", ) # Run the function to log the model: From 3b34fef26127e5cd46b19c0d9b5085aff32700c1 Mon Sep 17 00:00:00 2001 From: Yonatan Shelach <92271540+yonishelach@users.noreply.github.com> Date: Wed, 5 Jun 2024 13:24:52 +0300 Subject: [PATCH 11/38] [Noise-reduction] Add new function to hub (#765) * [Noise-reduction] Add new function to hub * fix test * added multiprocessing and silence removal to function --- noise_reduction/data/test_data.mp3 | Bin 0 -> 27972 bytes noise_reduction/data/test_data.wav | Bin 0 -> 179672 bytes noise_reduction/function.yaml | 194 +++++ noise_reduction/item.yaml | 29 + noise_reduction/noise_reduction.ipynb | 942 ++++++++++++++++++++++++ noise_reduction/noise_reduction.py | 625 ++++++++++++++++ noise_reduction/requirements.txt | 5 + noise_reduction/test_noise_reduction.py | 75 ++ 8 files changed, 1870 insertions(+) create mode 100644 noise_reduction/data/test_data.mp3 create mode 100644 noise_reduction/data/test_data.wav create mode 100644 noise_reduction/function.yaml create mode 100644 noise_reduction/item.yaml create mode 100644 noise_reduction/noise_reduction.ipynb create mode 100644 noise_reduction/noise_reduction.py create mode 100644 noise_reduction/requirements.txt create mode 100644 noise_reduction/test_noise_reduction.py diff --git a/noise_reduction/data/test_data.mp3 b/noise_reduction/data/test_data.mp3 new file mode 100644 index 0000000000000000000000000000000000000000..a330f9804f67205e2af72652151f4721ec16ff74 GIT binary patch literal 27972 zcmce-g;&(=_C7px_t4!TU4nFXGjw-ImmuBUjdYiEcXxLqC5?)JfG|IJp0A#B-ap`- zwScu|fX}u2y7%5U_&VGS@c(^jS=(E_yodJkRRRFmi36bF;1N+!F|dFH6f|#G*|>Ok z`9;O0l~h35x`rmE7S=X)4vsGF9$wzQ{z1WEkx?=63CStxS)X$A3yaIjt7>ZN>gwwo 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z!G4z`R{8B_tqOfFT%%Xay6yUXwYZ8%roGmdGrgUwBvtoT)hd>r3D=T);A)+v|5Z^| zp4Y9f)O?OmG3s7cm5w{_rmoZVGtb`Z(WP1I&DqXvoz-4_ob_&7W3*S-J8t!BoTFGd znY!m~W~nRw-K#8~?|E)tH}lZ3yH9GFZLjv1{du)}rVD+y*@%Y#0tg_000IagfB*srAb=2.1.2 + entry_points: + reduce_noise: + name: reduce_noise + doc: 'Reduce noise from audio file or directory containing audio files. + + The audio files must be in .wav format. + + The cleaned audio files will be saved in the target_directory. + + For information about the noise reduction algorithm see: + + https://github.com/timsainb/noisereduce + + Notice that the saved files are in wav format, even if the original files + are in other format.' + parameters: + - name: audio_source + type: str + doc: path to audio file or directory containing audio files + - name: target_directory + type: str + doc: path to directory to save the cleaned audio files. + - name: sample_rate + type: int + doc: Number of samples in one second in the audio file. Pass `None` to keep + the original sample rate. + default: 16000 + - name: duration + type: int + doc: Duration of the audio file to clean in seconds. Pass `None` to keep the + original duration. + default: null + - name: channel + type: int + doc: Channel to clean. Pass the number of the channel to clean. To clean all + channels pass None. + default: null + - name: silence_threshold + type: float + doc: The threshold to remove silence from the audio, in dB. If None, no silence + removal is performed. + default: null + - name: use_multiprocessing + type: int + doc: Number of processes to use for cleaning the audio files. If 0, no multiprocessing + is used. + default: 0 + - name: verbose + type: bool + doc: Verbosity level. If True, display progress bar. + default: true + outputs: [] + lineno: 388 + has_varargs: false + has_kwargs: false + clean_audio: + name: clean_audio + doc: '' + parameters: + - name: self + - name: data + type: Tensor + outputs: + - type: torch.Tensor + lineno: 276 + has_varargs: false + has_kwargs: false + save_audio: + name: save_audio + doc: '' + parameters: + - name: self + - name: audio + type: ndarray + - name: target_path + type: Path + outputs: [] + lineno: 256 + has_varargs: false + has_kwargs: false + load_audio: + name: load_audio + doc: '' + parameters: + - name: self + - name: file + type: str + outputs: + - type: torch.Tensor + lineno: 268 + has_varargs: false + has_kwargs: false + update_to_wav_suffix: + name: update_to_wav_suffix + doc: '' + parameters: + - name: self + - name: audio_file + type: Path + outputs: [] + lineno: 125 + has_varargs: false + has_kwargs: false + remove_silence: + name: remove_silence + doc: Remove silence sections from the audio. + parameters: + - name: self + - name: audio + type: ndarray + doc: The audio to remove silence from. + outputs: + - doc: The audio without silence. + lineno: 134 + has_varargs: false + has_kwargs: false + reduce_noise_dfn: + name: reduce_noise_dfn + doc: 'Reduce noise from audio files using DeepFilterNet. + + For more information about the noise reduction algorithm see: + + https://github.com/Rikorose/DeepFilterNet + + Notice that the saved files are in wav format, even if the original files + are in other format.' + parameters: + - name: audio_source + type: str + doc: path to audio file or directory of audio files + - name: target_directory + type: str + doc: path to target directory to save cleaned audio files + - name: pad + type: bool + doc: whether to pad the audio file with zeros before cleaning + default: true + - name: atten_lim_db + type: int + doc: maximum attenuation in dB + default: null + - name: silence_threshold + type: float + doc: the threshold to remove silence from the audio, in dB. If None, no silence + removal is performed. + default: null + - name: use_multiprocessing + type: int + doc: Number of processes to use for cleaning the audio files. If 0, no multiprocessing + is used. + default: 0 + - name: verbose + type: bool + doc: verbosity level. If True, display progress bar and logs. + default: true + outputs: [] + lineno: 322 + has_varargs: false + has_kwargs: true + description: Reduce noise from audio files + default_handler: reduce_noise + disable_auto_mount: false + clone_target_dir: '' + env: [] + priority_class_name: '' + preemption_mode: prevent + affinity: null + tolerations: null + security_context: {} +verbose: false diff --git a/noise_reduction/item.yaml b/noise_reduction/item.yaml new file mode 100644 index 000000000..8ddc63f4f --- /dev/null +++ b/noise_reduction/item.yaml @@ -0,0 +1,29 @@ +apiVersion: v1 +categories: + - data-preparation + - machine-learning +description: Reduce noise from audio files +doc: '' +example: noise_reduction.ipynb +generationDate: 2024-03-04:17-30 +hidden: false +icon: '' +labels: + author: yonatans +maintainers: [] +mlrunVersion: 1.5.2 +name: noise-reduction +platformVersion: 3.5.3 +spec: + filename: noise_reduction.py + handler: reduce_noise + image: mlrun/mlrun + kind: job + requirements: [ + librosa, + noisereduce, + deepfilternet, + torchaudio>=2.1.2, + ] +url: '' +version: 1.0.0 \ No newline at end of file diff --git a/noise_reduction/noise_reduction.ipynb b/noise_reduction/noise_reduction.ipynb new file mode 100644 index 000000000..e4fa0a534 --- /dev/null +++ b/noise_reduction/noise_reduction.ipynb @@ -0,0 +1,942 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "4e0abc60-b718-4f45-a82a-0b8759f19d3f", + "metadata": {}, + "source": [ + "# Noise Reduction\n", + "\n", + "## Table of Contents\n", + "\n", + "1. [Introduction](#Introduction)\n", + "2. [Project Setup](#Setting-up-a-project)\n", + "3. [Noise Reduction Techniques](#Noise-Reduction-Techniques)\n", + " 1. [DeepFilterNet](#DeepFilterNet)\n", + " 2. [Spectral Gating](#SpectralGating)" + ] + }, + { + "cell_type": "markdown", + "id": "9af33629-965f-4f73-9e4a-89cc4c3dacf1", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "Noise reduction is a crucial signal processing technique used to enhance the quality of signals by minimizing unwanted or irrelevant noise. This technique finds applications in various fields such as audio processing, image processing, telecommunications, and more. The goal is to extract the useful information from a signal while suppressing undesirable background noise." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "f9cd530d-36a7-47b1-96f8-498d338b3a1a", + "metadata": {}, + "outputs": [], + "source": [ + "import mlrun" + ] + }, + { + "cell_type": "markdown", + "id": "c659289f-01f2-4e02-b843-b39cfc0c1d63", + "metadata": {}, + "source": [ + "## Setting up a project\n", + "\n", + "First of all we need to create a project with the `noise-reduction` function" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "c4217272-85b8-4af7-afee-bc97c6c73bd9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> 2024-03-04 15:54:53,561 [info] Project loaded successfully: {'project_name': 'noise-reduction'}\n" + ] + } + ], + "source": [ + "# Creating a project\n", + "project = mlrun.get_or_create_project(\"noise-reduction\")\n", + "# Importing the function from hub\n", + "noise_reduction_function = project.set_function(\"hub://noise_reduction\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "f7df4c3e-4e5b-47bd-a298-527d9c6fcb8f", + "metadata": {}, + "outputs": [], + "source": [ + "# Audio source can be either a single file or a directory of audio files\n", + "audio_source = \"data\"" + ] + }, + { + "cell_type": "markdown", + "id": "6c1c5109-6380-4364-b016-728523ed0ea1", + "metadata": {}, + "source": [ + "## Noise Reduction Techniques" + ] + }, + { + "attachments": { + "e48ce103-14f3-421d-82a4-823344895241.png": { + "image/png": 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+ } + }, + "cell_type": "markdown", + "id": "5c81ecee-851c-4ee8-ad3a-4d372a1bfd97", + "metadata": {}, + "source": [ + "\n", + "### 1. DeepFilterNet\n", + "![image.png](attachment:e48ce103-14f3-421d-82a4-823344895241.png)\n", + "\n", + "In order to use this technique, you simply need to use the `reduce_noise_dfn` handler.\n", + "\n", + "Reduce noise from audio files using DeepFilterNet. For more information about the noise reduction algorithm, see [DeepFilterNet GitHub](https://github.com/Rikorose/DeepFilterNet). Notice that the saved files are in wav format, even if the original files are in other formats.\n", + "\n", + "### Parameters:\n", + "\n", + "- `audio_source`: path to the audio file or directory of audio files\n", + "- `target_directory`: path to the target directory to save cleaned audio files\n", + "- `pad`: whether to pad the audio file with zeros before cleaning\n", + "- `atten_lim_db`: maximum attenuation in dB\n", + "- `silence_threshold`: the threshold to remove silence from the audio, in dB. If None, no silence removal is performed.\n", + "- `use_multiprocessing`: Number of processes to use for cleaning the audio files. If 0, no multiprocessing is used.\n", + "- `verbose`: verbosity level. If True, display progress bar and logs.\n", + "- `kwargs`: additional arguments to pass to `torchaudio.load()`. For more information, see [torchaudio.load()](https://pytorch.org/audio/stable/generated/torchaudio.load.html).\n", + "\n", + "\n", + "In the examples below, the function is running locally, for running remotely, it is required to build the function's image first (need to execute only once):\n", + "```python\n", + "noise_reduction_function.apply(mlrun.auto_mount()) # required for local files\n", + "project.build_function(\"noise-reduction\")\n", + "```\n", + "\n", + "#### 1.1. Example" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "16113524-8597-48d4-8172-76b897fee3f2", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> 2024-03-04 15:54:56,999 [info] Storing function: {'name': 'noise-reduce-reduce-noise-dfn', 'uid': '9732dac831784a6a8b53acab5ff83a08', 'db': 'http://mlrun-api:8080'}\n", + "> 2024-03-04 15:55:07,525 [info] logging run results to: http://mlrun-api:8080\n", + "> 2024-03-04 15:55:07,702 [info] Reducing noise from audio files.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Noise-reduction: 0%| | 0/2 [00:00 2024-03-04 15:55:08,437 [info] Loading DeepFilterNet2 model.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "`torchaudio.backend.common.AudioMetaData` has been moved to `torchaudio.AudioMetaData`. Please update the import path.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-03-04 15:55:08 | INFO | DF | Running on torch 2.1.2+cu121\n", + "2024-03-04 15:55:08 | INFO | DF | Running on host jupyter-yoni-d56767c87-678n2\n", + "> 2024-03-04 15:55:08,464 [info] Loading DeepFilterNet2 model.\n", + "2024-03-04 15:55:08 | INFO | DF | Running on torch 2.1.2+cu121\n", + "2024-03-04 15:55:08 | INFO | DF | Running on host jupyter-yoni-d56767c87-678n2\n", + "2024-03-04 15:55:08 | INFO | DF | Loading model settings of DeepFilterNet3\n", + "2024-03-04 15:55:08 | INFO | DF | Using DeepFilterNet3 model at /igz/.cache/DeepFilterNet/DeepFilterNet3\n", + "2024-03-04 15:55:08 | INFO | DF | Initializing model `deepfilternet3`\n", + "2024-03-04 15:55:08 | INFO | DF | Loading model settings of DeepFilterNet3\n", + "2024-03-04 15:55:08 | INFO | DF | Using DeepFilterNet3 model at /igz/.cache/DeepFilterNet/DeepFilterNet3\n", + "2024-03-04 15:55:08 | INFO | DF | Initializing model `deepfilternet3`\n", + "2024-03-04 15:55:08 | INFO | DF | Found checkpoint /igz/.cache/DeepFilterNet/DeepFilterNet3/checkpoints/model_120.ckpt.best with epoch 120\n", + "2024-03-04 15:55:08 | INFO | DF | Found checkpoint /igz/.cache/DeepFilterNet/DeepFilterNet3/checkpoints/model_120.ckpt.best with epoch 120\n", + "2024-03-04 15:55:08 | INFO | DF | Running on device cpu\n", + "2024-03-04 15:55:08 | INFO | DF | Running on device cpu\n", + "2024-03-04 15:55:08 | INFO | DF | Model loaded\n", + "2024-03-04 15:55:08 | INFO | DF | Model loaded\n", + "> 2024-03-04 15:55:08,635 [info] Reducing noise from test_data.mp3.\n", + "> 2024-03-04 15:55:08,636 [info] Reducing noise from test_data.wav.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2024-03-04 15:55:08\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mDF\u001b[0m | \u001b[33m\u001b[1mAudio sampling rate does not match model sampling rate (16000, 48000). Resampling...\u001b[0m\n", + "\"sinc_interpolation\" resampling method name is being deprecated and replaced by \"sinc_interp_hann\" in the next release. The default behavior remains unchanged.\n", + "The MPEG_LAYER_III subtype is unknown to TorchAudio. As a result, the bits_per_sample attribute will be set to 0. If you are seeing this warning, please report by opening an issue on github (after checking for existing/closed ones). You may otherwise ignore this warning.\n", + "\u001b[32m2024-03-04 15:55:08\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mDF\u001b[0m | \u001b[33m\u001b[1mAudio sampling rate does not match model sampling rate (16000, 48000). Resampling...\u001b[0m\n", + "\"sinc_interpolation\" resampling method name is being deprecated and replaced by \"sinc_interp_hann\" in the next release. The default behavior remains unchanged.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> 2024-03-04 15:55:16,701 [info] Saved cleaned audio file to clean_data/test_data.wav.\n", + "> 2024-03-04 15:55:16,706 [info] Saved cleaned audio file to clean_data/test_data_mp3.wav.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Noise-reduction: 100%|██████████| 2/2 [00:09<00:00, 4.51s/file]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> 2024-03-04 15:55:16,791 [info] Summarizing the results.\n", + "> 2024-03-04 15:55:16,792 [info] Done (2/2)\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "
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projectuiditerstartstatenamelabelsinputsparametersresultsartifacts
noise-reduction0Mar 04 15:54:57completednoise-reduce-reduce-noise-dfn
v3io_user=yonis
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owner=yonis
host=jupyter-yoni-d56767c87-678n2
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target_directory=./clean_data
use_multiprocessing=2
silence_threshold=50
atten_lim_db=10
successes
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\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/html": [ + " > to track results use the .show() or .logs() methods or click here to open in UI" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> 2024-03-04 15:55:17,976 [info] Run execution finished: {'status': 'completed', 'name': 'noise-reduce-reduce-noise-dfn'}\n" + ] + } + ], + "source": [ + "dfn_run = noise_reduction_function.run(\n", + " handler=\"reduce_noise_dfn\",\n", + " inputs={\"audio_source\": audio_source},\n", + " params={\n", + " \"target_directory\": \"./clean_data\",\n", + " \"use_multiprocessing\": 2,\n", + " \"silence_threshold\": 50,\n", + " \"atten_lim_db\": 10,\n", + " },\n", + " returns=[\"successes: file\", \"errors: file\"],\n", + " local=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "a71ba944-1fc2-48be-b789-d57c59201939", + "metadata": {}, + "source": [ + "### Looking at the result" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "19b04cf6-5a4d-4d74-b66e-193540a900a1", + "metadata": {}, + "outputs": [ + { + "data": { + "application/json": { + "test_data.mp3": "clean_data/test_data_mp3.wav", + "test_data.wav": "clean_data/test_data.wav" + }, + "text/plain": [ + "" + ] + }, + "metadata": { + "application/json": { + "expanded": false, + "root": "root" + } + }, + "output_type": "display_data" + }, + { + "data": { + "application/json": {}, + "text/plain": [ + "" + ] + }, + "metadata": { + "application/json": { + "expanded": false, + "root": "root" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "dfn_run.artifact(\"successes\").show()\n", + "dfn_run.artifact(\"errors\").show()" + ] + }, + { + "attachments": { + "68c16acf-c28e-4bb8-a453-abbebc0137ce.png": { + "image/png": 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+ } + }, + "cell_type": "markdown", + "id": "4576b576-4ac0-433a-9d1d-f39225a6648d", + "metadata": {}, + "source": [ + "\n", + "### 2. Spectral Gating\n", + "![image.png](attachment:68c16acf-c28e-4bb8-a453-abbebc0137ce.png)\n", + "\n", + "In order to use this technique, you simply need to use the `reduce_noise` handler.\n", + "\n", + "Spectral gating selectively filters signal frequencies based on amplitude, offering targeted noise reduction or feature enhancement in signal processing applications.\n", + "\n", + "Reduce noise from an audio file or directory containing audio files. The audio files must be in .wav format. The cleaned audio files will be saved in the target directory. For information about the noise reduction algorithm, see [noisereduce GitHub](https://github.com/timsainb/noisereduce). Notice that the saved files are in .wav format, even if the original files are in another format.\n", + "\n", + "### Parameters:\n", + "\n", + "- `audio_source`: path to the audio file or directory containing audio files\n", + "- `target_directory`: path to the directory to save the cleaned audio files.\n", + "- `sample_rate`: Number of samples in one second in the audio file. Pass `None` to keep the original sample rate.\n", + "- `duration`: Duration of the audio file to clean in seconds. Pass `None` to keep the original duration.\n", + "- `channel`: Channel to clean. Pass the number of the channel to clean. To clean all channels, pass `None`.\n", + "- `silence_threshold`: The threshold to remove silence from the audio, in dB. If `None`, no silence removal is performed.\n", + "- `use_multiprocessing`: Number of processes to use for cleaning the audio files. If 0, no multiprocessing is used.\n", + "- `verbose`: Verbosity level. If True, display a progress bar.\n", + "\n", + "#### 2.1. Example" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "f10a5ecd-bf90-4650-a42e-d3fbfff78e52", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> 2024-03-04 16:07:39,378 [info] Storing function: {'name': 'noise-reduce-reduce-noise', 'uid': '6e6d6f7c3f8243b995dc1bbcf66f7544', 'db': 'http://mlrun-api:8080'}\n", + "> 2024-03-04 16:07:39,541 [info] Reducing noise from audio files.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Noise-reduction: 0%| | 0/2 [00:00 2024-03-04 16:07:39,565 [info] Reducing noise from test_data.mp3.\n", + "> 2024-03-04 16:07:39,566 [info] Reducing noise from test_data.wav.\n", + "> 2024-03-04 16:07:46,174 [info] Saved cleaned audio file to clean_data/test_data.wav.\n", + "> 2024-03-04 16:07:46,175 [info] Saved cleaned audio file to clean_data/test_data_mp3.wav.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Noise-reduction: 100%|██████████| 2/2 [00:06<00:00, 3.31s/file]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> 2024-03-04 16:07:46,211 [info] Summarizing the results.\n", + "> 2024-03-04 16:07:46,212 [info] Done (2/2)\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "
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projectuiditerstartstatenamelabelsinputsparametersresultsartifacts
noise-reduction0Mar 04 16:07:39completednoise-reduce-reduce-noise
v3io_user=yonis
kind=local
owner=yonis
host=jupyter-yoni-d56767c87-678n2
audio_source
target_directory=./clean_data
use_multiprocessing=2
silence_threshold=50
successes
errors
\n", + "
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\n", + " Title\n", + " ×\n", + "
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\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/html": [ + " > to track results use the .show() or .logs() methods or click here to open in UI" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> 2024-03-04 16:07:46,389 [info] Run execution finished: {'status': 'completed', 'name': 'noise-reduce-reduce-noise'}\n" + ] + } + ], + "source": [ + "noise_reduction_run = noise_reduction_function.run(\n", + " handler=\"reduce_noise\",\n", + " inputs={\"audio_source\": audio_source},\n", + " params={\n", + " \"target_directory\": \"./clean_data\",\n", + " \"use_multiprocessing\": 2,\n", + " \"silence_threshold\": 50,\n", + " },\n", + " local=True,\n", + " returns=[\"successes: file\", \"errors: file\"],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "699615d7-bba1-4147-ad3d-d295d794f866", + "metadata": {}, + "source": [ + "### Looking at the result" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "47c4f66a-d5d0-47e5-9842-abbe6653526b", + "metadata": {}, + "outputs": [ + { + "data": { + "application/json": { + "test_data.mp3": "clean_data/test_data_mp3.wav", + "test_data.wav": "clean_data/test_data.wav" + }, + "text/plain": [ + "" + ] + }, + "metadata": { + "application/json": { + "expanded": false, + "root": "root" + } + }, + "output_type": "display_data" + }, + { + "data": { + "application/json": {}, + "text/plain": [ + "" + ] + }, + "metadata": { + "application/json": { + "expanded": false, + "root": "root" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "dfn_run.artifact(\"successes\").show()\n", + "dfn_run.artifact(\"errors\").show()" + ] + }, + { + "cell_type": "markdown", + "id": "6eeae1bb-c714-491b-91dd-f22148cd0970", + "metadata": {}, + "source": [ + "The output of this function is the same as the first one." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mlrun-base", + "language": "python", + "name": "conda-env-mlrun-base-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/noise_reduction/noise_reduction.py b/noise_reduction/noise_reduction.py new file mode 100644 index 000000000..f0fff5504 --- /dev/null +++ b/noise_reduction/noise_reduction.py @@ -0,0 +1,625 @@ +import logging +from abc import ABCMeta, abstractmethod +from multiprocessing import Process, Queue +from pathlib import Path +from typing import List, Tuple, Type, Union + +import librosa +import numpy as np +import torch +from scipy.io import wavfile +from tqdm import tqdm + +#: The value to send into multiprocessing queues to stop the process: +_MULTIPROCESSING_STOP_MARK = "STOP" + +# Get the global logger: +try: + import mlrun + + _LOGGER = mlrun.get_or_create_ctx("noise_reduce").logger +except ModuleNotFoundError: + _LOGGER = logging.getLogger() + + +class ReduceNoiseBase(metaclass=ABCMeta): + """ + Base class for noise reduction. + This class is aimed to be inherited by specific noise reduction algorithms. + You must implement the following methods: + - clean_audio: The method to clean the audio, where the noise reduction algorithm is implemented. + - save_audio: The method to save the audio to a file. + - load_audio: The method to load the audio from a file. + + After implementing the above methods, you can use the reduce_noise method to reduce noise from audio files. + """ + def __init__( + self, + target_directory: Path, + verbose: bool = True, + silence_threshold: float = None, + ): + self.target_directory = Path(target_directory) + self.verbose = verbose + self.silence_threshold = silence_threshold + + def reduce_noise(self, audio_file: Path) -> Tuple[bool, Tuple[str, str]]: + """ + Reduce noise from the given audio file. + + :param audio_file: The audio file to reduce noise from. + + :returns: A tuple of: + - a boolean indicating whether an error occurred + - a tuple of: + - audio file name + - target path in case of success / error message in case of failure. + """ + try: + if self.verbose: + _LOGGER.info(f"Reducing noise from {audio_file.name}.") + + # Load audio data: + audio = self.load_audio(file=str(audio_file)) + + # Perform noise reduction: + reduced_noise = self.clean_audio(data=audio) + + # Remove silence from the audio if necessary: + reduced_noise = self.remove_silence(audio=reduced_noise) + + # Prepare target path: + target_path = self.update_to_wav_suffix(audio_file=audio_file) + + # Save file: + self.save_audio( + audio=reduced_noise, + target_path=target_path, + ) + + if self.verbose: + _LOGGER.info(f"Saved cleaned audio file to {target_path}.") + + return False, (audio_file.name, str(target_path)) + except Exception as exception: + if self.verbose: + _LOGGER.error(f"Failed to reduce noise from {audio_file.name}.") + _LOGGER.error(f"Error: {exception}") + # Collect the error: + return True, (audio_file.name, str(exception)) + + @abstractmethod + def clean_audio(self, data) -> Union[np.ndarray, torch.Tensor]: + """ + Clean the audio from noise. Here you should implement the noise reduction algorithm. + + :param data: The audio data to clean. + + :returns: The cleaned audio. + """ + pass + + @abstractmethod + def save_audio(self, audio: np.ndarray, target_path: Path): + """ + Save the audio to a file. + + :param audio: The audio to save. + :param target_path: The target path to save the audio to. + """ + pass + + @abstractmethod + def load_audio(self, file: str) -> Tuple[Union[np.ndarray, torch.Tensor], int]: + """ + Load the audio from a file. + + :param file: The file to load the audio from. + + :returns: A tuple of: + - the audio data + - the sample rate + """ + pass + + def update_to_wav_suffix(self, audio_file: Path): + target_path = self.target_directory / audio_file.name + if target_path.suffix != ".wav": + old_suffix = target_path.suffix[1:] + target_path = target_path.with_stem(target_path.stem + f"_{old_suffix}") + return target_path.with_suffix(".wav") + else: + return target_path + + def remove_silence( + self, + audio: np.ndarray, + ): + """ + Remove silence sections from the audio. + + :param audio: The audio to remove silence from. + + :returns: The audio without silence. + """ + if self.silence_threshold is None: + return audio + + # Get the indices of the non-silent frames: + non_silent_indices = librosa.effects.split( + y=audio, + top_db=self.silence_threshold, + frame_length=2048, + hop_length=256, + ) + + # Get the non-silent audio: + non_silent_audio = np.concatenate( + [audio[:, start:end] for start, end in non_silent_indices], axis=1 + ) + + return non_silent_audio + + +class ReduceNoise(ReduceNoiseBase): + def __init__( + self, + target_directory: Path, + verbose: bool = True, + silence_threshold: float = None, + sample_rate: int = 16000, + duration: int = None, + channel: int = None, + ): + super().__init__(target_directory, verbose, silence_threshold) + self.sample_rate = sample_rate + self.duration = duration + self.channel = channel + + def save_audio(self, audio: np.ndarray, target_path: Path): + # If the audio has more than one channel, transpose it in order to save it: + if len(audio) > 1: + audio = audio.T + + wavfile.write( + filename=target_path, + rate=self.sample_rate, + data=audio, + ) + + def load_audio(self, file: str) -> np.ndarray: + data, sr = librosa.load( + path=file, + sr=self.sample_rate, + mono=False, # keep channels separate + duration=self.duration, + ) + # set sample rate: + self.sample_rate = int(sr) + + # convert to int with scaling for 16-bit integer + data *= 32767 / np.max(np.abs(data)) # re-scaling + data = data.astype(np.int16) # change data type + + # select channel + data_to_reduce = data[self.channel] if self.channel is not None else data + return data_to_reduce + + def clean_audio(self, data: np.ndarray) -> np.ndarray: + try: + import noisereduce + except ImportError as e: + raise ImportError("Please install noisereduce package") from e + + reduced_noise = noisereduce.reduce_noise(y=data, sr=self.sample_rate) + + # add channel back after noise reduction + if self.channel is not None: + # putting the channel back in the data + data[self.channel] = reduced_noise + # updating the data to save + reduced_noise = data + + return reduced_noise + + +class DFN(ReduceNoiseBase): + def __init__( + self, + target_directory: Path, + verbose: bool = True, + silence_threshold: float = None, + pad: bool = True, + atten_lim_db: int = None, + **kwargs, + ): + super().__init__(target_directory, verbose, silence_threshold) + self.pad = pad + self.atten_lim_db = atten_lim_db + self.kwargs = kwargs + + # import required packages + try: + from df.enhance import init_df + except ImportError as e: + raise ImportError("Please install deepfilternet packages") from e + + if self.verbose: + _LOGGER.info("Loading DeepFilterNet2 model.") + + # Load the model: + model, df_state, _ = init_df() + self.model = model + self.df_state = df_state + self.sample_rate = self.df_state.sr() + + def save_audio(self, audio: np.ndarray, target_path: Path): + try: + from df.enhance import save_audio + except ImportError as e: + raise ImportError("Please install deepfilternet package") from e + save_audio( + file=target_path.name, + audio=audio, + sr=self.sample_rate, + output_dir=str(self.target_directory), + ) + + def load_audio(self, file: str) -> torch.Tensor: + try: + from df.enhance import load_audio + except ImportError as e: + raise ImportError("Please install deepfilternet package") from e + audio, _ = load_audio(file=file, sr=self.sample_rate, **self.kwargs) + return audio + + def clean_audio(self, data: torch.Tensor) -> torch.Tensor: + try: + from df.enhance import enhance + except ImportError as e: + raise ImportError("Please install deepfilternet package") from e + return enhance( + model=self.model, + df_state=self.df_state, + audio=data, + pad=self.pad, + atten_lim_db=self.atten_lim_db, + ) + + +def _multiprocessing_complete_tasks( + noise_reduce_type: Type[ReduceNoiseBase], + noise_reduce_arguments: dict, + tasks_queue: Queue, + results_queue: Queue, +): + """ + Complete the tasks in the given queue and put the results in the given results queue. The function will stop when + the given tasks queue will receive the stop mark. It is aimed to be used with multiprocessing as a process. + + :param noise_reduce_type: The noise reduce type to use. + :param noise_reduce_arguments: The noisereduce initialization kwargs. + :param tasks_queue: A queue to get the tasks from. + :param results_queue: A queue to put the results in. + """ + # Initialize the reduce noise object + noise_reducer = noise_reduce_type(**noise_reduce_arguments) + + # Start listening to the tasks queue: + while True: + # Get the audio_file: + audio_file = tasks_queue.get() + if audio_file == _MULTIPROCESSING_STOP_MARK: + break + audio_file = Path(audio_file) + # Apply noise reduction and collect the result: + results_queue.put(noise_reducer.reduce_noise(audio_file=audio_file)) + + # Mark the end of the tasks: + results_queue.put(_MULTIPROCESSING_STOP_MARK) + + +def reduce_noise_dfn( + audio_source: str, + target_directory: str, + pad: bool = True, + atten_lim_db: int = None, + silence_threshold: float = None, + use_multiprocessing: int = 0, + verbose: bool = True, + **kwargs, +): + """ + Reduce noise from audio files using DeepFilterNet. + For more information about the noise reduction algorithm see: + https://github.com/Rikorose/DeepFilterNet + Notice that the saved files are in wav format, even if the original files are in other format. + + :param audio_source: path to audio file or directory of audio files + :param target_directory: path to target directory to save cleaned audio files + :param pad: whether to pad the audio file with zeros before cleaning + :param atten_lim_db: maximum attenuation in dB + :param silence_threshold: the threshold to remove silence from the audio, in dB. If None, no silence removal is + performed. + :param use_multiprocessing: Number of processes to use for cleaning the audio files. + If 0, no multiprocessing is used. + :param verbose: verbosity level. If True, display progress bar and logs. + :param kwargs: additional arguments to pass to torchaudio.load(). For more information see: + https://pytorch.org/audio/stable/generated/torchaudio.load.html + """ + if verbose: + _LOGGER.info("Reducing noise from audio files.") + + # create target directory: + target_directory = _create_target_directory(target_directory) + + # get audio files: + audio_files = _get_audio_files(audio_source) + + noise_reduce_arguments = { + "target_directory": target_directory, + "pad": pad, + "atten_lim_db": atten_lim_db, + "silence_threshold": silence_threshold, + **kwargs, + } + + if use_multiprocessing: + results = _parallel_run( + noise_reduce_type=DFN, + noise_reduce_arguments=noise_reduce_arguments, + n_workers=use_multiprocessing, + audio_files=audio_files, + description="Noise-reduction", + verbose=verbose, + ) + else: + results = _run( + noise_reduce_type=DFN, + noise_reduce_arguments=noise_reduce_arguments, + audio_files=audio_files, + description="Noise-reduction", + verbose=verbose, + ) + + return _process_results(results, verbose) + + +def reduce_noise( + audio_source: str, + target_directory: str, + sample_rate: int = 16000, + duration: int = None, + channel: int = None, + silence_threshold: float = None, + use_multiprocessing: int = 0, + verbose: bool = True, +): + """ + Reduce noise from audio file or directory containing audio files. + The audio files must be in .wav format. + The cleaned audio files will be saved in the target_directory. + For information about the noise reduction algorithm see: + https://github.com/timsainb/noisereduce + Notice that the saved files are in wav format, even if the original files are in other format. + + :param audio_source: path to audio file or directory containing audio files + :param target_directory: path to directory to save the cleaned audio files. + :param sample_rate: Number of samples in one second in the audio file. + Pass `None` to keep the original sample rate. + :param duration: Duration of the audio file to clean in seconds. + Pass `None` to keep the original duration. + :param channel: Channel to clean. Pass the number of the channel to clean. + To clean all channels pass None. + :param silence_threshold: The threshold to remove silence from the audio, in dB. + If None, no silence removal is performed. + :param use_multiprocessing: Number of processes to use for cleaning the audio files. + If 0, no multiprocessing is used. + :param verbose: Verbosity level. If True, display progress bar. + """ + if verbose: + _LOGGER.info("Reducing noise from audio files.") + + # create target directory: + target_directory = _create_target_directory(target_directory) + + # get audio files: + audio_files = _get_audio_files(audio_source) + + # Create the reduce noise object: + noise_reduce_arguments = { + "target_directory": target_directory, + "sample_rate": sample_rate, + "duration": duration, + "channel": channel, + "silence_threshold": silence_threshold, + } + + if use_multiprocessing: + results = _parallel_run( + noise_reduce_type=ReduceNoise, + noise_reduce_arguments=noise_reduce_arguments, + n_workers=use_multiprocessing, + audio_files=audio_files, + description="Noise-reduction", + verbose=verbose, + ) + else: + results = _run( + noise_reduce_type=ReduceNoise, + noise_reduce_arguments=noise_reduce_arguments, + audio_files=audio_files, + description="Noise-reduction", + verbose=verbose, + ) + + return _process_results(results, verbose) + + +def _create_target_directory(target_directory: str) -> str: + target_directory = Path(target_directory) + if not target_directory.exists(): + target_directory.mkdir(parents=True, exist_ok=True) + return str(target_directory) + + +def _get_audio_files(audio_source: str): + audio_source = Path(audio_source) + audio_files = [] + if audio_source.is_dir(): + audio_files = list(audio_source.glob("*.*")) + elif audio_source.is_file(): + audio_files.append(audio_source) + else: + raise ValueError( + f"audio_source must be a file or a directory, got {audio_source}" + ) + return audio_files + + +def _parallel_run( + noise_reduce_type: Type[ReduceNoiseBase], + noise_reduce_arguments: dict, + n_workers: int, + audio_files: List[Path], + description: str, + verbose: bool, +) -> List[Tuple[bool, Tuple[str, str]]]: + """ + Run multiple noise reduce workers with multiprocessing to complete the tasks that will be created on the provided + files using the given task creator. + + :param noise_reduce_type: The noise reduce type to use. + :param n_workers: The number of workers to use. + :param audio_files: The audio files to use. + :param description: The description to use for the progress bar. + :param verbose: Verbosity. + + :returns: The collected results. + """ + # Check the number of workers: + if n_workers > len(audio_files): + _LOGGER.warning( + f"The number of workers ({n_workers}) is larger than the number of audio files ({len(audio_files)}). " + f"Setting the number of workers to {len(audio_files)}." + ) + n_workers = len(audio_files) + + # Initialize the multiprocessing queues: + tasks_queue = Queue() + results_queue = Queue() + + # Initialize the multiprocessing processes: + task_completion_processes = [ + Process( + target=_multiprocessing_complete_tasks, + kwargs={ + "noise_reduce_type": noise_reduce_type, + "noise_reduce_arguments": noise_reduce_arguments, + "tasks_queue": tasks_queue, + "results_queue": results_queue, + }, + ) + for _ in range(n_workers) + ] + + # Start the multiprocessing processes: + for p in task_completion_processes: + p.start() + + # Put the tasks in the queue: + for audio_file in audio_files: + # tasks_queue.put(task_creator.create_task(audio_file=audio_file).to_tuple()) + tasks_queue.put(audio_file) + + # Put the stop marks in the queue: + for _ in range(n_workers): + tasks_queue.put(_MULTIPROCESSING_STOP_MARK) + + # Collect the results: + results = [] + stop_marks_counter = 0 + with tqdm( + desc=description, + unit="file", + total=len(audio_files), + disable=not verbose, + ) as progressbar: + while True: + # Get a result from the queue: + result: Tuple[bool, Tuple[str, str]] = results_queue.get() + if result == _MULTIPROCESSING_STOP_MARK: + stop_marks_counter += 1 + if stop_marks_counter == n_workers: + break + else: + # Collect the result: + results.append(result) + progressbar.update(1) + + # Wait for the processes to finish: + for p in task_completion_processes: + p.join() + + return results + + +def _run( + noise_reduce_type: Type[ReduceNoiseBase], + noise_reduce_arguments: dict, + audio_files: List[Path], + description: str, + verbose: bool, +) -> List[Tuple[bool, Tuple[str, str]]]: + """ + Run the noise reduce algorithm on the given audio files and collect the results. + + :param noise_reduce_type: The noise reduce type to use. + :param noise_reduce_arguments: The noisereduce initialization kwargs. + :param audio_files: The audio files to use. + :param description: The description to use for the progress bar. + :param verbose: Verbosity. + + :returns: The collected results. + """ + # Create the reduce noise object: + noise_reducer = noise_reduce_type(**noise_reduce_arguments) + + # Run the noise reduce algorithm on the audio files and collect the results: + results = [] + for audio_file in tqdm( + audio_files, + desc=description, + unit="file", + total=len(audio_files), + disable=not verbose, + ): + results.append(noise_reducer.reduce_noise(audio_file=audio_file)) + + return results + + +def _process_results( + results: List[Tuple[bool, Tuple[str, str]]], verbose: bool +) -> Tuple[dict, dict]: + """ + Process the results of the tasks. + + :param results: The results to process. + :param verbose: Verbosity. + + :returns: The processed results as a tuple of successes and errors. + """ + if verbose: + _LOGGER.info("Summarizing the results.") + successes = {} + errors = {} + for is_error, result in results: + if is_error: + errors[result[0]] = result[1] + else: + successes[result[0]] = result[1] + if verbose: + _LOGGER.info(f"Done ({len(successes)}/{len(successes) + len(errors)})\n") + + return successes, errors diff --git a/noise_reduction/requirements.txt b/noise_reduction/requirements.txt new file mode 100644 index 000000000..30934ad7c --- /dev/null +++ b/noise_reduction/requirements.txt @@ -0,0 +1,5 @@ +tqdm +deepfilternet +librosa +noisereduce +torchaudio>=2.1.2 \ No newline at end of file diff --git a/noise_reduction/test_noise_reduction.py b/noise_reduction/test_noise_reduction.py new file mode 100644 index 000000000..a77377565 --- /dev/null +++ b/noise_reduction/test_noise_reduction.py @@ -0,0 +1,75 @@ +import tempfile + +import mlrun +import pytest + + +@pytest.mark.parametrize( + "audio_source", + [ + "data/test_data.wav", + "data/test_data.mp3", + "data", + ], +) +def test_reduce_noise(audio_source): + # set up the project and function + artifact_path = tempfile.TemporaryDirectory().name + project = mlrun.new_project("noise-reduction") + noise_reduction_function = project.set_function( + func="function.yaml", + name="reduce_noise", + kind="job", + image="mlrun/mlrun", + ) + + # run the function + noise_reduction_run = noise_reduction_function.run( + handler="reduce_noise", + inputs={"audio_source": audio_source}, + params={ + "target_directory": artifact_path + "/data", + "sample_rate": None, + }, + local=True, + artifact_path=artifact_path, + returns=["successes: file", "errors: file"], + ) + + assert noise_reduction_run.outputs["successes"] + + +@pytest.mark.parametrize( + "audio_source", + [ + "data/test_data.wav", + "data/test_data.mp3", + "data", + ], +) +def test_reduce_noise_dfn(audio_source): + # set up the project and function + artifact_path = tempfile.TemporaryDirectory().name + project = mlrun.new_project("noise-reduction") + noise_reduction_function = project.set_function( + func="function.yaml", + name="reduce_noise", + kind="job", + image="mlrun/mlrun", + ) + + # run the function + noise_reduction_run = noise_reduction_function.run( + handler="reduce_noise_dfn", + inputs={"audio_source": audio_source}, + params={ + "target_directory": artifact_path + "/data", + "atten_lim_db": 50, + }, + local=True, + artifact_path=artifact_path, + returns=["successes: file", "errors: file"], + ) + + # assert that the function run completed successfully + assert noise_reduction_run.outputs["successes"] From 1e0cb66aba9cbe29c1066ef2b27b9e23d09a89d2 Mon Sep 17 00:00:00 2001 From: Eyal Danieli Date: Wed, 28 Aug 2024 11:52:53 +0300 Subject: [PATCH 12/38] delete `load_dask` (#822) --- README.md | 1 - catalog.json | 2 +- catalog.yaml | 9 -- load_dask/function.yaml | 75 --------- load_dask/item.yaml | 25 --- load_dask/load_dask.ipynb | 309 -------------------------------------- load_dask/load_dask.py | 68 --------- 7 files changed, 1 insertion(+), 488 deletions(-) delete mode 100644 load_dask/function.yaml delete mode 100644 load_dask/item.yaml delete mode 100644 load_dask/load_dask.ipynb delete mode 100644 load_dask/load_dask.py diff --git a/README.md b/README.md index 9a1e74821..1136c963d 100644 --- a/README.md +++ b/README.md @@ -21,7 +21,6 @@ it is expected that contributors follow certain guidelines/protocols (please chi | [feature-selection](feature_selection/feature_selection.ipynb) | job | Select features through multiple Statistical and Model filters | data-prep, ml | | [gen-class-data](gen_class_data/gen_class_data.ipynb) | job | Create a binary classification sample dataset and save. | data-prep | | [github-utils](github_utils/github_utils.ipynb) | job | add comments to github pull request | notifications, utils | -| [load-dask](load_dask/load_dask.ipynb) | dask | load dask cluster with data | data-movement, utils | | [load-dataset](load_dataset/load_dataset.ipynb) | job | load a toy dataset from scikit-learn | data-source, ml | | [model-monitoring-batch](model_monitoring_batch/model_monitoring_batch.ipynb) | job | | | | [model-monitoring-stream](model_monitoring_stream/model_monitoring_stream.ipynb) | nuclio | | | diff --git a/catalog.json b/catalog.json index 6fff9830c..4bcc4022d 100644 --- a/catalog.json +++ b/catalog.json @@ -1 +1 @@ -{"aggregate": {"description": "Rolling aggregation over Metrics and Lables according to specifications", "categories": ["data-prep"], "kind": "job", "docfile": "aggregate/aggregate.ipynb", "versions": {"latest": "aggregate/function.yaml"}}, "arc-to-parquet": {"description": "retrieve remote archive, open and save as parquet", "categories": ["data-movement", "utils"], "kind": "job", "docfile": "arc_to_parquet/arc_to_parquet.ipynb", "versions": {"latest": "arc_to_parquet/function.yaml"}}, "bert-embeddings": {"description": "Get BERT based embeddings for given text", "categories": ["NLP", "BERT", "embeddings"], "kind": "remote", "docfile": "bert_embeddings/bert_embeddings.ipynb", "versions": {"latest": "bert_embeddings/function.yaml"}}, "churn-server": {"description": "churn classification and predictor", "categories": ["serving", "ml"], "kind": "serving", "docfile": "churn_server/churn_server.ipynb", "versions": {"latest": "churn_server/function.yaml"}}, "concept-drift": {"description": "Deploy a streaming Concept Drift detector on a labeled stream", "categories": ["ml", "serve"], "kind": "job", "docfile": "concept_drift/concept_drift.ipynb", "versions": {"latest": "concept_drift/function.yaml"}}, "concept-drift-streaming": {"description": "Deploy a streaming Concept Drift detector on a labeled stream. the nuclio part of the concept_drift function", "categories": ["ml", "serve"], "kind": "remote", "docfile": "concept_drift_streaming/concept_drift_streaming.ipynb", "versions": {"latest": "concept_drift_streaming/function.yaml"}}, "coxph-test": {"description": "Test cox proportional hazards model", "categories": ["ml", "test"], "kind": "job", "docfile": "coxph_test/coxph_test.ipynb", "versions": {"latest": "coxph_test/function.yaml"}}, "coxph-trainer": {"description": "cox proportional hazards, kaplan meier plots", "categories": ["training", "ml"], "kind": "job", "docfile": "coxph_trainer/coxph_trainer.ipynb", "versions": {"latest": "coxph_trainer/function.yaml"}}, "describe": {"description": "describe and visualizes dataset stats", "categories": ["analysis"], "kind": "job", "docfile": "describe/describe.ipynb", "versions": {"latest": "describe/function.yaml"}}, "describe-dask": {"description": "describe and visualizes dataset stats", "categories": ["analysis"], "kind": "job", "docfile": "describe_dask/describe_dask.ipynb", "versions": {"latest": "describe_dask/function.yaml"}}, "describe-spark": {"description": "", "categories": [], "kind": "job", "docfile": "describe_spark/describe_spark.ipynb", "versions": {"latest": "describe_spark/function.yaml"}}, "feature-perms": {"description": "estimate feature importances using permutations", "categories": ["analysis"], "kind": "job", "docfile": "feature_perms/feature_perms.ipynb", "versions": {"latest": "feature_perms/function.yaml"}}, "feature-selection": {"description": "Select features through multiple Statistical and Model filters", "categories": ["data-prep", "ml"], "kind": "job", "docfile": "feature_selection/feature_selection.ipynb", "versions": {"latest": "feature_selection/function.yaml"}}, "gen-class-data": {"description": "Create a binary classification sample dataset and save.", "categories": ["data-prep"], "kind": "job", "docfile": "gen_class_data/gen_class_data.ipynb", "versions": {"latest": "gen_class_data/function.yaml"}}, "github-utils": {"description": "add comments to github pull request", "categories": ["notifications", "utils"], "kind": "job", "docfile": "github_utils/github_utils.ipynb", "versions": {"latest": "github_utils/function.yaml"}}, "load-dask": {"description": "load dask cluster with data", "categories": ["data-movement", "utils"], "kind": "dask", "docfile": "load_dask/load_dask.ipynb", "versions": {"latest": "load_dask/function.yaml"}}, "load-dataset": {"description": "load a toy dataset from scikit-learn", "categories": ["data-source", "ml"], "kind": "job", "docfile": "load_dataset/load_dataset.ipynb", "versions": {"latest": "load_dataset/function.yaml"}}, "model-monitoring-batch": {"description": "", "categories": [], "kind": "job", "docfile": "model_monitoring_batch/model_monitoring_batch.ipynb", "versions": {"latest": "model_monitoring_batch/function.yaml"}}, "model-monitoring-stream": {"description": "", "categories": [], "kind": "remote", "docfile": "model_monitoring_stream/model_monitoring_stream.ipynb", "versions": {"latest": "model_monitoring_stream/function.yaml"}}, "model-server": {"description": "generic sklearn model server", "categories": ["serving", "ml"], "kind": "remote", "docfile": "model_server/model_server.ipynb", "versions": {"latest": "model_server/function.yaml"}}, "model-server-tester": {"description": "test model servers", "categories": ["ml", "test"], "kind": "job", "docfile": "model_server_tester/model_server_tester.ipynb", "versions": {"latest": "model_server_tester/function.yaml"}}, "open-archive": {"description": "Open a file/object archive into a target directory", "categories": ["data-movement", "utils"], "kind": "job", "docfile": "open_archive/open_archive.ipynb", "versions": {"latest": "open_archive/function.yaml"}}, "pandas-profiling-report": {"description": "Create Pandas Profiling Report from Dataset", "categories": ["analysis"], "kind": "job", "docfile": "pandas_profiling_report/pandas_profiling_report.ipynb", "versions": {"latest": "pandas_profiling_report/function.yaml"}}, "project-runner": {"description": "Nuclio based - Cron scheduler for running your MLRun projects", "categories": ["utils"], "kind": "remote", "docfile": "project_runner/project_runner.ipynb", "versions": {"latest": "project_runner/function.yaml"}}, "rnn-serving": {"description": "deploy an rnn based stock analysis model server.", "categories": ["model-serving"], "kind": "serving", "docfile": "rnn_serving/rnn_serving.ipynb", "versions": {"latest": "rnn_serving/function.yaml"}}, "send-email": {"description": "Send Email messages through SMTP server", "categories": ["notifications"], "kind": "job", "docfile": "send_email/send_email.ipynb", "versions": {"latest": "send_email/function.yaml"}}, "sentiment-analysis-serving": {"description": "BERT based sentiment classification model", "categories": ["serving", "NLP", "BERT", "sentiment analysis"], "kind": "serving", "docfile": "sentiment_analysis_serving/sentiment_analysis_serving.ipynb", "versions": {"latest": "sentiment_analysis_serving/function.yaml"}}, "sklearn-classifier": {"description": "train any classifier using scikit-learn's API", "categories": ["ml", "training"], "kind": "job", "docfile": "sklearn_classifier/sklearn_classifier.ipynb", "versions": {"latest": "sklearn_classifier/function.yaml"}}, "sklearn-classifier-dask": {"description": "train any classifier using scikit-learn's API over Dask", "categories": ["ml", "training", "dask"], "kind": "job", "docfile": "sklearn_classifier_dask/sklearn_classifier_dask.ipynb", "versions": {"latest": "sklearn_classifier_dask/function.yaml"}}, "slack-notify": {"description": "Send Slack notification", "categories": ["ops"], "kind": "job", "docfile": "slack_notify/slack_notify.ipynb", "versions": {"latest": "slack_notify/function.yaml"}}, "spark-submit": {"description": "", "categories": [], "kind": "job", "docfile": "spark_submit/spark_submit.ipynb", "versions": {"latest": "spark_submit/function.yaml"}}, "sql-to-file": {"description": "SQL To File - Ingest data using SQL query", "categories": ["data-prep"], "kind": "job", "docfile": "sql_to_file/sql_to_file.ipynb", "versions": {"latest": "sql_to_file/function.yaml"}}, "stream-to-parquet": {"description": "Saves a stream to Parquet and can lunch drift detection task on it", "categories": ["ml", "serve"], "kind": "remote", "docfile": "stream_to_parquet/stream_to_parquet.ipynb", "versions": {"latest": "stream_to_parquet/function.yaml"}}, "test-classifier": {"description": "test a classifier using held-out or new data", "categories": ["ml", "test"], "kind": "job", "docfile": "test_classifier/test_classifier.ipynb", "versions": {"latest": "test_classifier/function.yaml"}}, "tf1-serving": {"description": "tf1 image classification server", "categories": ["serving", "dl"], "kind": "remote", "docfile": "tf1_serving/tf1_serving.ipynb", "versions": {"latest": "tf1_serving/function.yaml"}}, "tf2-serving": {"description": "tf2 image classification server", "categories": ["serving", "dl"], "kind": "remote", "docfile": "tf2_serving/tf2_serving.ipynb", "versions": {"latest": "tf2_serving/function.yaml"}}, "tf2-serving-v2": {"description": "tf2 image classification server v2", "categories": ["serving", "dl"], "kind": "serving", "docfile": "tf2_serving_v2/tf2_serving_v2.ipynb", "versions": {"latest": "tf2_serving_v2/function.yaml"}}, "v2-model-server": {"description": "generic sklearn model server", "categories": ["serving", "ml"], "kind": "serving", "docfile": "v2_model_server/v2_model_server.ipynb", "versions": {"latest": "v2_model_server/function.yaml"}}, "v2-model-tester": {"description": "test v2 model servers", "categories": ["ml", "test"], "kind": "job", "docfile": "v2_model_tester/v2_model_tester.ipynb", "versions": {"latest": "v2_model_tester/function.yaml"}}, "virtual-drift": {"description": "Compute drift magnitude between Time-Samples T and U", "categories": ["ml", "serve", "concept-drift"], "kind": "job", "docfile": "virtual_drift/virtual_drift.ipynb", "versions": {"latest": "virtual_drift/function.yaml"}}, "xgb-custom": {"description": "simulate data with outliers.", "categories": ["model-testing"], "kind": "job", "docfile": "xgb_custom/xgb_custom.ipynb", "versions": {"latest": "xgb_custom/function.yaml"}}, "xgb-serving": {"description": "deploy an XGBoost model server.", "categories": ["model-serving"], "kind": "remote", "docfile": "xgb_serving/xgb_serving.ipynb", "versions": {"latest": "xgb_serving/function.yaml"}}, "xgb-test": {"description": "Test one or more classifier models against held-out dataset.", "categories": ["model-test"], "kind": "job", "docfile": "xgb_test/xgb_test.ipynb", "versions": {"latest": "xgb_test/function.yaml"}}, "xgb-trainer": {"description": "train multiple model types using xgboost.", "categories": ["model-prep"], "kind": "job", "docfile": "xgb_trainer/xgb_trainer.ipynb", "versions": {"latest": "xgb_trainer/function.yaml"}}} \ No newline at end of file +{"aggregate": {"description": "Rolling aggregation over Metrics and Lables according to specifications", "categories": ["data-prep"], "kind": "job", "docfile": "aggregate/aggregate.ipynb", "versions": {"latest": "aggregate/function.yaml"}}, "arc-to-parquet": {"description": "retrieve remote archive, open and save as parquet", "categories": ["data-movement", "utils"], "kind": "job", "docfile": "arc_to_parquet/arc_to_parquet.ipynb", "versions": {"latest": "arc_to_parquet/function.yaml"}}, "bert-embeddings": {"description": "Get BERT based embeddings for given text", "categories": ["NLP", "BERT", "embeddings"], "kind": "remote", "docfile": "bert_embeddings/bert_embeddings.ipynb", "versions": {"latest": "bert_embeddings/function.yaml"}}, "churn-server": {"description": "churn classification and predictor", "categories": ["serving", "ml"], "kind": "serving", "docfile": "churn_server/churn_server.ipynb", "versions": {"latest": "churn_server/function.yaml"}}, "concept-drift": {"description": "Deploy a streaming Concept Drift detector on a labeled stream", "categories": ["ml", "serve"], "kind": "job", "docfile": "concept_drift/concept_drift.ipynb", "versions": {"latest": "concept_drift/function.yaml"}}, "concept-drift-streaming": {"description": "Deploy a streaming Concept Drift detector on a labeled stream. the nuclio part of the concept_drift function", "categories": ["ml", "serve"], "kind": "remote", "docfile": "concept_drift_streaming/concept_drift_streaming.ipynb", "versions": {"latest": "concept_drift_streaming/function.yaml"}}, "coxph-test": {"description": "Test cox proportional hazards model", "categories": ["ml", "test"], "kind": "job", "docfile": "coxph_test/coxph_test.ipynb", "versions": {"latest": "coxph_test/function.yaml"}}, "coxph-trainer": {"description": "cox proportional hazards, kaplan meier plots", "categories": ["training", "ml"], "kind": "job", "docfile": "coxph_trainer/coxph_trainer.ipynb", "versions": {"latest": "coxph_trainer/function.yaml"}}, "describe": {"description": "describe and visualizes dataset stats", "categories": ["analysis"], "kind": "job", "docfile": "describe/describe.ipynb", "versions": {"latest": "describe/function.yaml"}}, "describe-dask": {"description": "describe and visualizes dataset stats", "categories": ["analysis"], "kind": "job", "docfile": "describe_dask/describe_dask.ipynb", "versions": {"latest": "describe_dask/function.yaml"}}, "describe-spark": {"description": "", "categories": [], "kind": "job", "docfile": "describe_spark/describe_spark.ipynb", "versions": {"latest": "describe_spark/function.yaml"}}, "feature-perms": {"description": "estimate feature importances using permutations", "categories": ["analysis"], "kind": "job", "docfile": "feature_perms/feature_perms.ipynb", "versions": {"latest": "feature_perms/function.yaml"}}, "feature-selection": {"description": "Select features through multiple Statistical and Model filters", "categories": ["data-prep", "ml"], "kind": "job", "docfile": "feature_selection/feature_selection.ipynb", "versions": {"latest": "feature_selection/function.yaml"}}, "gen-class-data": {"description": "Create a binary classification sample dataset and save.", "categories": ["data-prep"], "kind": "job", "docfile": "gen_class_data/gen_class_data.ipynb", "versions": {"latest": "gen_class_data/function.yaml"}}, "github-utils": {"description": "add comments to github pull request", "categories": ["notifications", "utils"], "kind": "job", "docfile": "github_utils/github_utils.ipynb", "versions": {"latest": "github_utils/function.yaml"}}, "load-dataset": {"description": "load a toy dataset from scikit-learn", "categories": ["data-source", "ml"], "kind": "job", "docfile": "load_dataset/load_dataset.ipynb", "versions": {"latest": "load_dataset/function.yaml"}}, "model-monitoring-batch": {"description": "", "categories": [], "kind": "job", "docfile": "model_monitoring_batch/model_monitoring_batch.ipynb", "versions": {"latest": "model_monitoring_batch/function.yaml"}}, "model-monitoring-stream": {"description": "", "categories": [], "kind": "remote", "docfile": "model_monitoring_stream/model_monitoring_stream.ipynb", "versions": {"latest": "model_monitoring_stream/function.yaml"}}, "model-server": {"description": "generic sklearn model server", "categories": ["serving", "ml"], "kind": "remote", "docfile": "model_server/model_server.ipynb", "versions": {"latest": "model_server/function.yaml"}}, "model-server-tester": {"description": "test model servers", "categories": ["ml", "test"], "kind": "job", "docfile": "model_server_tester/model_server_tester.ipynb", "versions": {"latest": "model_server_tester/function.yaml"}}, "open-archive": {"description": "Open a file/object archive into a target directory", "categories": ["data-movement", "utils"], "kind": "job", "docfile": "open_archive/open_archive.ipynb", "versions": {"latest": "open_archive/function.yaml"}}, "pandas-profiling-report": {"description": "Create Pandas Profiling Report from Dataset", "categories": ["analysis"], "kind": "job", "docfile": "pandas_profiling_report/pandas_profiling_report.ipynb", "versions": {"latest": "pandas_profiling_report/function.yaml"}}, "project-runner": {"description": "Nuclio based - Cron scheduler for running your MLRun projects", "categories": ["utils"], "kind": "remote", "docfile": "project_runner/project_runner.ipynb", "versions": {"latest": "project_runner/function.yaml"}}, "rnn-serving": {"description": "deploy an rnn based stock analysis model server.", "categories": ["model-serving"], "kind": "serving", "docfile": "rnn_serving/rnn_serving.ipynb", "versions": {"latest": "rnn_serving/function.yaml"}}, "send-email": {"description": "Send Email messages through SMTP server", "categories": ["notifications"], "kind": "job", "docfile": "send_email/send_email.ipynb", "versions": {"latest": "send_email/function.yaml"}}, "sentiment-analysis-serving": {"description": "BERT based sentiment classification model", "categories": ["serving", "NLP", "BERT", "sentiment analysis"], "kind": "serving", "docfile": "sentiment_analysis_serving/sentiment_analysis_serving.ipynb", "versions": {"latest": "sentiment_analysis_serving/function.yaml"}}, "sklearn-classifier": {"description": "train any classifier using scikit-learn's API", "categories": ["ml", "training"], "kind": "job", "docfile": "sklearn_classifier/sklearn_classifier.ipynb", "versions": {"latest": "sklearn_classifier/function.yaml"}}, "sklearn-classifier-dask": {"description": "train any classifier using scikit-learn's API over Dask", "categories": ["ml", "training", "dask"], "kind": "job", "docfile": "sklearn_classifier_dask/sklearn_classifier_dask.ipynb", "versions": {"latest": "sklearn_classifier_dask/function.yaml"}}, "slack-notify": {"description": "Send Slack notification", "categories": ["ops"], "kind": "job", "docfile": "slack_notify/slack_notify.ipynb", "versions": {"latest": "slack_notify/function.yaml"}}, "spark-submit": {"description": "", "categories": [], "kind": "job", "docfile": "spark_submit/spark_submit.ipynb", "versions": {"latest": "spark_submit/function.yaml"}}, "sql-to-file": {"description": "SQL To File - Ingest data using SQL query", "categories": ["data-prep"], "kind": "job", "docfile": "sql_to_file/sql_to_file.ipynb", "versions": {"latest": "sql_to_file/function.yaml"}}, "stream-to-parquet": {"description": "Saves a stream to Parquet and can lunch drift detection task on it", "categories": ["ml", "serve"], "kind": "remote", "docfile": "stream_to_parquet/stream_to_parquet.ipynb", "versions": {"latest": "stream_to_parquet/function.yaml"}}, "test-classifier": {"description": "test a classifier using held-out or new data", "categories": ["ml", "test"], "kind": "job", "docfile": "test_classifier/test_classifier.ipynb", "versions": {"latest": "test_classifier/function.yaml"}}, "tf1-serving": {"description": "tf1 image classification server", "categories": ["serving", "dl"], "kind": "remote", "docfile": "tf1_serving/tf1_serving.ipynb", "versions": {"latest": "tf1_serving/function.yaml"}}, "tf2-serving": {"description": "tf2 image classification server", "categories": ["serving", "dl"], "kind": "remote", "docfile": "tf2_serving/tf2_serving.ipynb", "versions": {"latest": "tf2_serving/function.yaml"}}, "tf2-serving-v2": {"description": "tf2 image classification server v2", "categories": ["serving", "dl"], "kind": "serving", "docfile": "tf2_serving_v2/tf2_serving_v2.ipynb", "versions": {"latest": "tf2_serving_v2/function.yaml"}}, "v2-model-server": {"description": "generic sklearn model server", "categories": ["serving", "ml"], "kind": "serving", "docfile": "v2_model_server/v2_model_server.ipynb", "versions": {"latest": "v2_model_server/function.yaml"}}, "v2-model-tester": {"description": "test v2 model servers", "categories": ["ml", "test"], "kind": "job", "docfile": "v2_model_tester/v2_model_tester.ipynb", "versions": {"latest": "v2_model_tester/function.yaml"}}, "virtual-drift": {"description": "Compute drift magnitude between Time-Samples T and U", "categories": ["ml", "serve", "concept-drift"], "kind": "job", "docfile": "virtual_drift/virtual_drift.ipynb", "versions": {"latest": "virtual_drift/function.yaml"}}, "xgb-custom": {"description": "simulate data with outliers.", "categories": ["model-testing"], "kind": "job", "docfile": "xgb_custom/xgb_custom.ipynb", "versions": {"latest": "xgb_custom/function.yaml"}}, "xgb-serving": {"description": "deploy an XGBoost model server.", "categories": ["model-serving"], "kind": "remote", "docfile": "xgb_serving/xgb_serving.ipynb", "versions": {"latest": "xgb_serving/function.yaml"}}, "xgb-test": {"description": "Test one or more classifier models against held-out dataset.", "categories": ["model-test"], "kind": "job", "docfile": "xgb_test/xgb_test.ipynb", "versions": {"latest": "xgb_test/function.yaml"}}, "xgb-trainer": {"description": "train multiple model types using xgboost.", "categories": ["model-prep"], "kind": "job", "docfile": "xgb_trainer/xgb_trainer.ipynb", "versions": {"latest": "xgb_trainer/function.yaml"}}} \ No newline at end of file diff --git a/catalog.yaml b/catalog.yaml index 2b9d7c6b0..c3364fefa 100644 --- a/catalog.yaml +++ b/catalog.yaml @@ -128,15 +128,6 @@ github-utils: kind: job versions: latest: github_utils/function.yaml -load-dask: - categories: - - data-movement - - utils - description: load dask cluster with data - docfile: load_dask/load_dask.ipynb - kind: dask - versions: - latest: load_dask/function.yaml load-dataset: categories: - data-source diff --git a/load_dask/function.yaml b/load_dask/function.yaml deleted file mode 100644 index a0f73c5fe..000000000 --- a/load_dask/function.yaml +++ /dev/null @@ -1,75 +0,0 @@ -kind: dask -metadata: - name: load-dask - tag: '' - hash: 2af86b4c6ce0bc3e3d0468c1b66a5358482f383e - project: '' - labels: - author: yjb - categories: - - data-preparation - - etl -spec: - command: '' - image: mlrun/ml-models - env: [] - build: - functionSourceCode: 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 - commands: [] - code_origin: https://github.com/daniels290813/functions.git#55a79c32be5d233cc11efcf40cd3edbe309bfdef:/home/kali/functions/load_dask/load_dask.py - default_handler: load_dask - entry_points: - load_dask: - name: load_dask - doc: 'Load dataset into an existing dask cluster - - - dask jobs define the dask client parameters at the job level, this method - will raise an error if no client is detected.' - parameters: - - name: context - type: MLClientCtx - doc: the function context - default: '' - - name: src_data - type: DataItem - doc: url of the data file or partitioned dataset as either artifact DataItem, - string, or path object (similar to pandas read_csv) - default: '' - - name: dask_key - type: str - doc: destination key of data on dask cluster and artifact store - default: dask_key - - name: inc_cols - type: Optional[List[str]] - doc: include only these columns (very fast) - default: null - - name: index_cols - type: Optional[List[str]] - doc: list of index column names (can be a long-running process) - default: null - - name: dask_persist - type: bool - doc: (True) should the data be persisted (through the `client.persist` op) - default: true - - name: refresh_data - type: bool - doc: (False) if the dask_key already exists in the dask cluster, this will - raise an Exception. Set to True to replace the existing cluster data. - default: true - - name: scheduler_key - type: str - doc: (scheduler) the dask scheduler configuration, json also logged as an - artifact - default: scheduler - outputs: - - default: '' - lineno: 7 - description: load dask cluster with data - remote: true - nthreads: 1 - min_replicas: 0 - max_replicas: 16 - scheduler_timeout: 60 minutes - affinity: null -verbose: false diff --git a/load_dask/item.yaml b/load_dask/item.yaml deleted file mode 100644 index 3d923e370..000000000 --- a/load_dask/item.yaml +++ /dev/null @@ -1,25 +0,0 @@ -apiVersion: v1 -categories: -- data-preparation -- etl -description: load dask cluster with data -doc: '' -example: load_dask.ipynb -generationDate: 2022-08-28:17-25 -hidden: false -icon: '' -labels: - author: yjb -maintainers: [] -marketplaceType: '' -mlrunVersion: 1.1.0 -name: load-dask -platformVersion: 3.5.0 -spec: - filename: load_dask.py - handler: load_dask - image: mlrun/ml-models - kind: dask - requirements: [] -url: '' -version: 1.1.0 diff --git a/load_dask/load_dask.ipynb b/load_dask/load_dask.ipynb deleted file mode 100644 index 3dfcdddb5..000000000 --- a/load_dask/load_dask.ipynb +++ /dev/null @@ -1,309 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# load dask cluster with data\n", - "load a parquet dataset into a dask cluster" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# nuclio: ignore\n", - "import nuclio" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%nuclio config kind = \"dask\"\n", - "%nuclio config spec.image = \"mlrun/ml-models\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import json\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "import dask\n", - "import dask.dataframe as dd\n", - "from dask.distributed import Client, LocalCluster\n", - "\n", - "from mlrun.execution import MLClientCtx\n", - "from mlrun.datastore import DataItem\n", - "\n", - "from typing import List, Optional\n", - "\n", - "def load_dask(\n", - " context: MLClientCtx,\n", - " src_data: DataItem,\n", - " dask_key: str = \"dask_key\",\n", - " inc_cols: Optional[List[str]] = None,\n", - " index_cols: Optional[List[str]] = None,\n", - " dask_persist: bool = True,\n", - " refresh_data: bool = True,\n", - " scheduler_key: str = \"scheduler\"\n", - ") -> None:\n", - " \"\"\"Load dataset into an existing dask cluster\n", - " \n", - " dask jobs define the dask client parameters at the job level, this method will raise an error if no client is detected.\n", - " \n", - " :param context: the function context\n", - " :param src_data: url of the data file or partitioned dataset as either\n", - " artifact DataItem, string, or path object (similar to \n", - " pandas read_csv)\n", - " :param dask_key: destination key of data on dask cluster and artifact store\n", - " :param inc_cols: include only these columns (very fast)\n", - " :param index_cols: list of index column names (can be a long-running process)\n", - " :param dask_persist: (True) should the data be persisted (through the `client.persist` op)\n", - " :param refresh_data: (False) if the dask_key already exists in the dask cluster, this will \n", - " raise an Exception. Set to True to replace the existing cluster data.\n", - " :param scheduler_key: (scheduler) the dask scheduler configuration, json also logged as an artifact\n", - " \"\"\"\n", - " if hasattr(context, \"dask_client\"):\n", - " dask_client = context.dask_client\n", - " else:\n", - " raise Exception(\"a dask client was not found in the execution context\")\n", - " \n", - " df = src_data.as_df(df_module=dd)\n", - "\n", - " if dask_persist:\n", - " df = dask_client.persist(df)\n", - " if dask_client.datasets and dask_key in dask_client.datasets:\n", - " dask_client.unpublish_dataset(dask_key)\n", - " dask_client.publish_dataset(df, name=dask_key)\n", - " \n", - " if context:\n", - " context.dask_client = dask_client\n", - " \n", - " # share the scheduler, whether data is persisted or not\n", - " dask_client.write_scheduler_file(scheduler_key+\".json\")\n", - " \n", - " # we don't use log_dataset here until it can take into account\n", - " # dask origin and apply dask describe.\n", - " context.log_artifact(scheduler_key, local_path=scheduler_key+\".json\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# nuclio: end-code" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### mlconfig" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from mlrun import mlconf\n", - "import os\n", - "\n", - "mlconf.dbpath = mlconf.dbpath or 'http://mlrun-api:8080'\n", - "mlconf.artifact_path = mlconf.artifact_path or f'{os.environ[\"HOME\"]}/artifacts'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### save" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from mlrun import code_to_function \n", - "# create job function object from notebook code\n", - "fn = code_to_function(\"load_dask\", handler='load_dask')\n", - "\n", - "# add metadata (for templates and reuse)\n", - "fn.spec.description = \"load dask cluster with data\"\n", - "fn.metadata.categories = [\"data-movement\", \"utils\"]\n", - "fn.metadata.labels = {\"author\": \"yjb\"}\n", - "fn.spec.remote = True\n", - "fn.spec.replicas = 4\n", - "fn.spec.max_replicas = 8\n", - "fn.spec.service_type = \"NodePort\"\n", - "fn.export(\"function.yaml\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### test" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# load function from marketplacen\n", - "from mlrun import import_function\n", - "\n", - "# vcs_branch = 'development'\n", - "# base_vcs = f'https://raw.githubusercontent.com/mlrun/functions/{vcs_branch}/'\n", - "# mlconf.hub_url = mlconf.hub_url or base_vcs + f'{name}/function.yaml'\n", - "# fn = import_function(\"hub://load_dask\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "if \"V3IO_HOME\" in list(os.environ):\n", - " from mlrun import mount_v3io\n", - " fn.apply(mount_v3io())\n", - "else:\n", - " # is you set up mlrun using the instructions at https://github.com/mlrun/mlrun/blob/master/hack/local/README.md\n", - " from mlrun.platforms import mount_pvc\n", - " fn.apply(mount_pvc('nfsvol', 'nfsvol', '/home/joyan/data'))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from mlrun import NewTask, run_local\n", - "\n", - "task_params = {\n", - " \"name\": \"tasks load dask cluster with data\",\n", - " \"params\" : {\n", - " \"persist\" : True,\n", - " \"refresh_data\" : True,\n", - " \"dask_key\" : \"dask_key\"}}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run = fn.run(NewTask(**task_params), \n", - " handler=load_dask, \n", - " inputs={\"src_data\" : os.path.join(mlconf.artifact_path, 'iris.parquet') },\n", - " artifact_path=mlconf.artifact_path)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "func.status.to_dict()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import dask\n", - "import dask.dataframe as dd\n", - "from dask.distributed import Client, LocalCluster" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### TODO: this client dash board wont work -- wrong port!\n", - "\n", - "...even though its the correct client" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "client = Client(func.status.to_dict()['scheduler_address'])\n", - "client" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "list(client.list_datasets())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "client.datasets['dask_key']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/load_dask/load_dask.py b/load_dask/load_dask.py deleted file mode 100644 index 76c53c216..000000000 --- a/load_dask/load_dask.py +++ /dev/null @@ -1,68 +0,0 @@ -# Copyright 2019 Iguazio -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -from mlrun.execution import MLClientCtx -from mlrun.datastore import DataItem - -from typing import List, Optional - - -def load_dask( - context: MLClientCtx, - src_data: DataItem, - dask_key: str = "dask_key", - inc_cols: Optional[List[str]] = None, - index_cols: Optional[List[str]] = None, - dask_persist: bool = True, - refresh_data: bool = True, - scheduler_key: str = "scheduler" -) -> None: - """Load dataset into an existing dask cluster - - dask jobs define the dask client parameters at the job level, this method will raise an error if no client is detected. - - :param context: the function context - :param src_data: url of the data file or partitioned dataset as either - artifact DataItem, string, or path object (similar to - pandas read_csv) - :param dask_key: destination key of data on dask cluster and artifact store - :param inc_cols: include only these columns (very fast) - :param index_cols: list of index column names (can be a long-running process) - :param dask_persist: (True) should the data be persisted (through the `client.persist` op) - :param refresh_data: (False) if the dask_key already exists in the dask cluster, this will - raise an Exception. Set to True to replace the existing cluster data. - :param scheduler_key: (scheduler) the dask scheduler configuration, json also logged as an artifact - """ - if hasattr(context, "dask_client"): - dask_client = context.dask_client - else: - raise Exception("a dask client was not found in the execution context") - - df = src_data.as_df(df_module=dd) - - if dask_persist: - df = dask_client.persist(df) - if dask_client.datasets and dask_key in dask_client.datasets: - dask_client.unpublish_dataset(dask_key) - dask_client.publish_dataset(df, name=dask_key) - - if context: - context.dask_client = dask_client - - # share the scheduler, whether data is persisted or not - dask_client.write_scheduler_file(scheduler_key + ".json") - - # we don't use log_dataset here until it can take into account - # dask origin and apply dask describe. - context.log_artifact(scheduler_key, local_path=scheduler_key + ".json") \ No newline at end of file From 6fdc1b1adb171283d70b2e5458d0c943d93c4f8e Mon Sep 17 00:00:00 2001 From: Avi Asulin Date: Thu, 29 Aug 2024 13:25:11 +0300 Subject: [PATCH 13/38] [feature selection] update function yaml --- feature_selection/function.yaml | 75 +++++++++++++-------------------- feature_selection/item.yaml | 6 +-- 2 files changed, 32 insertions(+), 49 deletions(-) diff --git a/feature_selection/function.yaml b/feature_selection/function.yaml index 0851f54d3..d4e95f1e9 100644 --- a/feature_selection/function.yaml +++ b/feature_selection/function.yaml @@ -1,58 +1,33 @@ kind: job -metadata: - name: feature-selection - tag: '' - hash: 6dba16d062d81f78d3d210fee75edfe8b1def9b3 - project: '' - labels: - author: orz - categories: - - data-preparation - - machine-learning +verbose: false spec: + disable_auto_mount: false command: '' - args: [] - image: mlrun/mlrun - build: - functionSourceCode: # Copyright 2019 Iguazio
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import json
import os

import matplotlib.pyplot as plt
import mlrun
import mlrun.datastore
import mlrun.utils
import mlrun.feature_store as fs
import numpy as np
import pandas as pd
import seaborn as sns
from mlrun.artifacts import PlotArtifact
from mlrun.datastore.targets import ParquetTarget
# MLRun utils
from mlrun.utils.helpers import create_class
# Feature selection strategies
from sklearn.feature_selection import SelectFromModel, SelectKBest
# Scale feature scoresgit st
from sklearn.preprocessing import MinMaxScaler
# SKLearn estimators list
from sklearn.utils import all_estimators

DEFAULT_STAT_FILTERS = ["f_classif", "mutual_info_classif", "chi2", "f_regression"]
DEFAULT_MODEL_FILTERS = {
    "LinearSVC": "LinearSVC",
    "LogisticRegression": "LogisticRegression",
    "ExtraTreesClassifier": "ExtraTreesClassifier",
}


def _clear_current_figure():
    """
    Clear matplotlib current figure.
    """
    plt.cla()
    plt.clf()
    plt.close()


def show_values_on_bars(axs, h_v="v", space=0.4):
    def _show_on_single_plot(ax_):
        if h_v == "v":
            for p in ax_.patches:
                _x = p.get_x() + p.get_width() / 2
                _y = p.get_y() + p.get_height()
                value = int(p.get_height())
                ax_.text(_x, _y, value, ha="center")
        elif h_v == "h":
            for p in ax_.patches:
                _x = p.get_x() + p.get_width() + float(space)
                _y = p.get_y() + p.get_height()
                value = int(p.get_width())
                ax_.text(_x, _y, value, ha="left")

    if isinstance(axs, np.ndarray):
        for idx, ax in np.ndenumerate(axs):
            _show_on_single_plot(ax)
    else:
        _show_on_single_plot(axs)


def plot_stat(context, stat_name, stat_df):
    _clear_current_figure()

    # Add chart
    ax = plt.axes()
    stat_chart = sns.barplot(
        x=stat_name,
        y="index",
        data=stat_df.sort_values(stat_name, ascending=False).reset_index(),
        ax=ax,
    )
    plt.tight_layout()

    for p in stat_chart.patches:
        width = p.get_width()
        plt.text(
            5 + p.get_width(),
            p.get_y() + 0.55 * p.get_height(),
            "{:1.2f}".format(width),
            ha="center",
            va="center",
        )

    context.log_artifact(
        PlotArtifact(f"{stat_name}", body=plt.gcf()),
        local_path=os.path.join("plots", "feature_selection", f"{stat_name}.html"),
    )
    _clear_current_figure()


def feature_selection(
    context,
    df_artifact,
    k: int = 5,
    min_votes: float = 0.5,
    label_column: str = None,
    stat_filters: list = None,
    model_filters: dict = None,
    max_scaled_scores: bool = True,
    sample_ratio: float = None,
    output_vector_name: float = None,
    ignore_type_errors: bool = False,
    is_feature_vector: bool = False,
):
    """
    Applies selected feature selection statistical functions or models on our 'df_artifact'.

    Each statistical function or model will vote for it's best K selected features.
    If a feature has >= 'min_votes' votes, it will be selected.

    :param context:             the function context.
    :param df_artifact:         dataframe to pass as input.
    :param k:                   number of top features to select from each statistical
                                function or model.
    :param min_votes:           minimal number of votes (from a model or by statistical
                                function) needed for a feature to be selected.
                                Can be specified by percentage of votes or absolute
                                number of votes.
    :param label_column:        ground-truth (y) labels.
    :param stat_filters:        statistical functions to apply to the features
                                (from sklearn.feature_selection).
    :param model_filters:       models to use for feature evaluation, can be specified by
                                model name (ex. LinearSVC), formalized json (contains 'CLASS',
                                'FIT', 'META') or a path to such json file.
    :param max_scaled_scores:   produce feature scores table scaled with max_scaler.
    :param sample_ratio:        percentage of the dataset the user whishes to compute the feature selection process on.
    :param output_vector_name:  creates a new feature vector containing only the identifies features.
    :param ignore_type_errors:  skips datatypes that are neither float nor int within the feature vector.
    :param is_feature_vector:   bool stating if the data is passed as a feature vector.
    """
    stat_filters = stat_filters or DEFAULT_STAT_FILTERS
    model_filters = model_filters or DEFAULT_MODEL_FILTERS
    # Check if df.meta is valid, if it is, look for a feature vector
    store_uri_prefix, _ = mlrun.datastore.parse_store_uri(df_artifact.artifact_url)
    is_feature_vector = mlrun.utils.StorePrefix.FeatureVector == store_uri_prefix

    # Look inside meta.spec.label_feature to identify the label_column if the user did not specify it
    if label_column is None:
        if is_feature_vector:
            label_column = df_artifact.meta.spec.label_feature.split(".")[1]
        else:
            raise ValueError("No label_column was given, please add a label_column.")

    # Use the feature vector as dataframe
    df = df_artifact.as_df()

    # Ensure k is not bigger than the total number of features
    if k > df.shape[1]:
        raise ValueError(
            f"K cannot be bigger than the total number of features ({df.shape[1]}). Please choose a smaller K."
        )
    elif k < 1:
        raise ValueError("K cannot be smaller than 1. Please choose a bigger K.")

    # Create a sample dataframe of the original feature vector
    if sample_ratio:
        df = (
            df.groupby(label_column)
            .apply(lambda x: x.sample(frac=sample_ratio))
            .reset_index(drop=True)
        )
        df = df.dropna()

    # Set feature vector and labels
    y = df.pop(label_column)
    X = df

    if np.object_ in list(X.dtypes) and ignore_type_errors is False:
        raise ValueError(
            f"{df.select_dtypes(include=['object']).columns.tolist()} are neither float or int."
        )

    # Create selected statistical estimators
    stat_functions_list = {
        stat_name: SelectKBest(
            score_func=create_class(f"sklearn.feature_selection.{stat_name}"), k=k
        )
        for stat_name in stat_filters
    }
    requires_abs = ["chi2"]

    # Run statistic filters
    selected_features_agg = {}
    stats_df = pd.DataFrame(index=X.columns).dropna()

    for stat_name, stat_func in stat_functions_list.items():
        try:
            params = (X, y) if stat_name in requires_abs else (abs(X), y)
            stat = stat_func.fit(*params)

            # Collect stat function results
            stat_df = pd.DataFrame(
                index=X.columns, columns=[stat_name], data=stat.scores_
            )
            plot_stat(context, stat_name, stat_df)
            stats_df = stats_df.join(stat_df)

            # Select K Best features
            selected_features = X.columns[stat_func.get_support()]
            selected_features_agg[stat_name] = selected_features

        except Exception as e:
            context.logger.info(f"Couldn't calculate {stat_name} because of: {e}")

    # Create models from class name / json file / json params
    all_sklearn_estimators = dict(all_estimators()) if len(model_filters) > 0 else {}
    selected_models = {}
    for model_name, model in model_filters.items():
        if ".json" in model:
            current_model = json.load(open(model, "r"))
            classifier_class = create_class(current_model["META"]["class"])
            selected_models[model_name] = classifier_class(**current_model["CLASS"])
        elif model in all_sklearn_estimators:
            selected_models[model_name] = all_sklearn_estimators[model_name]()

        else:
            try:
                current_model = json.loads(model)
                classifier_class = create_class(current_model["META"]["class"])
                selected_models[model_name] = classifier_class(**current_model["CLASS"])
            except Exception as e:
                context.logger.info(f"unable to load {model} because of: {e}")

    # Run model filters
    models_df = pd.DataFrame(index=X.columns)
    for model_name, model in selected_models.items():

        if model_name == "LogisticRegression":
            model.set_params(solver="liblinear")

        # Train model and get feature importance
        select_from_model = SelectFromModel(model).fit(X, y)
        feature_idx = select_from_model.get_support()
        feature_names = X.columns[feature_idx]
        selected_features_agg[model_name] = feature_names.tolist()

        # Collect model feature importance
        if hasattr(select_from_model.estimator_, "coef_"):
            stat_df = select_from_model.estimator_.coef_
        elif hasattr(select_from_model.estimator_, "feature_importances_"):
            stat_df = select_from_model.estimator_.feature_importances_

        stat_df = pd.DataFrame(index=X.columns, columns=[model_name], data=stat_df[0])
        models_df = models_df.join(stat_df)

        plot_stat(context, model_name, stat_df)

    # Create feature_scores DF with stat & model filters scores
    result_matrix_df = pd.concat([stats_df, models_df], axis=1, sort=False)
    context.log_dataset(
        key="feature_scores",
        df=result_matrix_df,
        local_path="feature_scores.parquet",
        format="parquet",
    )
    if max_scaled_scores:
        normalized_df = result_matrix_df.replace([np.inf, -np.inf], np.nan).values
        min_max_scaler = MinMaxScaler()
        normalized_df = min_max_scaler.fit_transform(normalized_df)
        normalized_df = pd.DataFrame(
            data=normalized_df,
            columns=result_matrix_df.columns,
            index=result_matrix_df.index,
        )
        context.log_dataset(
            key="max_scaled_scores_feature_scores",
            df=normalized_df,
            local_path="max_scaled_scores_feature_scores.parquet",
            format="parquet",
        )

    # Create feature count DataFrame
    for test_name in selected_features_agg:
        result_matrix_df[test_name] = [
            1 if x in selected_features_agg[test_name] else 0 for x in X.columns
        ]
    result_matrix_df.loc[:, "num_votes"] = result_matrix_df.sum(axis=1)
    context.log_dataset(
        key="selected_features_count",
        df=result_matrix_df,
        local_path="selected_features_count.parquet",
        format="parquet",
    )

    # How many votes are needed for a feature to be selected?
    if isinstance(min_votes, int):
        votes_needed = min_votes
    else:
        num_filters = len(stat_filters) + len(model_filters)
        votes_needed = int(np.floor(num_filters * max(min(min_votes, 1), 0)))
    context.logger.info(f"votes needed to be selected: {votes_needed}")

    # Create final feature dataframe
    selected_features = result_matrix_df[
        result_matrix_df.num_votes >= votes_needed
    ].index.tolist()
    good_feature_df = df.loc[:, selected_features]
    final_df = pd.concat([good_feature_df, y], axis=1)
    context.log_dataset(
        key="selected_features",
        df=final_df,
        local_path="selected_features.parquet",
        format="parquet",
    )

    # Creating a new feature vector containing only the identified top features
    if is_feature_vector and df_artifact.meta.spec.features and output_vector_name:
        # Selecting the top K features from our top feature dataframe
        selected_features = result_matrix_df.head(k).index

        # Match the selected feature names to the FS Feature annotations
        matched_selections = [
            feature
            for feature in list(df_artifact.meta.spec.features)
            for selected in list(selected_features)
            if feature.endswith(selected)
        ]

        # Defining our new feature vector
        top_features_fv = fs.FeatureVector(
            output_vector_name,
            matched_selections,
            label_feature="labels.label",
            description="feature vector composed strictly of our top features",
        )

        # Saving
        top_features_fv.save()
        fs.get_offline_features(top_features_fv, target=ParquetTarget())

        # Logging our new feature vector URI
        context.log_result("top_features_vector", top_features_fv.uri)
 - commands: [] - code_origin: '' - origin_filename: '' - requirements: [] entry_points: show_values_on_bars: - name: show_values_on_bars - doc: '' + lineno: 54 parameters: - name: axs - name: h_v default: v - name: space default: 0.4 - outputs: [] - lineno: 54 + name: show_values_on_bars has_varargs: false has_kwargs: false - plot_stat: - name: plot_stat doc: '' + plot_stat: + lineno: 76 parameters: - name: context - name: stat_name - name: stat_df - outputs: [] - lineno: 76 + name: plot_stat has_varargs: false has_kwargs: false + doc: '' feature_selection: - name: feature_selection - doc: 'Applies selected feature selection statistical functions or models on - our ''df_artifact''. - - - Each statistical function or model will vote for it''s best K selected features. - - If a feature has >= ''min_votes'' votes, it will be selected.' + lineno: 106 parameters: - name: context doc: the function context. @@ -103,18 +78,26 @@ spec: type: bool doc: bool stating if the data is passed as a feature vector. default: false - outputs: [] - lineno: 106 + name: feature_selection has_varargs: false has_kwargs: false - description: Select features through multiple Statistical and Model filters + doc: 'Applies selected feature selection statistical functions or models on + our ''df_artifact''. + + + Each statistical function or model will vote for it''s best K selected features. + + If a feature has >= ''min_votes'' votes, it will be selected.' + build: + origin_filename: '' + functionSourceCode: # Copyright 2019 Iguazio
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import json
import os

import matplotlib.pyplot as plt
import mlrun
import mlrun.datastore
import mlrun.utils
import mlrun.feature_store as fs
import numpy as np
import pandas as pd
import seaborn as sns
from mlrun.artifacts import PlotArtifact
from mlrun.datastore.targets import ParquetTarget
# MLRun utils
from mlrun.utils.helpers import create_class
# Feature selection strategies
from sklearn.feature_selection import SelectFromModel, SelectKBest
# Scale feature scoresgit st
from sklearn.preprocessing import MinMaxScaler
# SKLearn estimators list
from sklearn.utils import all_estimators

DEFAULT_STAT_FILTERS = ["f_classif", "mutual_info_classif", "chi2", "f_regression"]
DEFAULT_MODEL_FILTERS = {
    "LinearSVC": "LinearSVC",
    "LogisticRegression": "LogisticRegression",
    "ExtraTreesClassifier": "ExtraTreesClassifier",
}


def _clear_current_figure():
    """
    Clear matplotlib current figure.
    """
    plt.cla()
    plt.clf()
    plt.close()


def show_values_on_bars(axs, h_v="v", space=0.4):
    def _show_on_single_plot(ax_):
        if h_v == "v":
            for p in ax_.patches:
                _x = p.get_x() + p.get_width() / 2
                _y = p.get_y() + p.get_height()
                value = int(p.get_height())
                ax_.text(_x, _y, value, ha="center")
        elif h_v == "h":
            for p in ax_.patches:
                _x = p.get_x() + p.get_width() + float(space)
                _y = p.get_y() + p.get_height()
                value = int(p.get_width())
                ax_.text(_x, _y, value, ha="left")

    if isinstance(axs, np.ndarray):
        for idx, ax in np.ndenumerate(axs):
            _show_on_single_plot(ax)
    else:
        _show_on_single_plot(axs)


def plot_stat(context, stat_name, stat_df):
    _clear_current_figure()

    # Add chart
    ax = plt.axes()
    stat_chart = sns.barplot(
        x=stat_name,
        y="index",
        data=stat_df.sort_values(stat_name, ascending=False).reset_index(),
        ax=ax,
    )
    plt.tight_layout()

    for p in stat_chart.patches:
        width = p.get_width()
        plt.text(
            5 + p.get_width(),
            p.get_y() + 0.55 * p.get_height(),
            "{:1.2f}".format(width),
            ha="center",
            va="center",
        )

    context.log_artifact(
        PlotArtifact(f"{stat_name}", body=plt.gcf()),
        local_path=os.path.join("plots", "feature_selection", f"{stat_name}.html"),
    )
    _clear_current_figure()


def feature_selection(
    context,
    df_artifact,
    k: int = 5,
    min_votes: float = 0.5,
    label_column: str = None,
    stat_filters: list = None,
    model_filters: dict = None,
    max_scaled_scores: bool = True,
    sample_ratio: float = None,
    output_vector_name: float = None,
    ignore_type_errors: bool = False,
    is_feature_vector: bool = False,
):
    """
    Applies selected feature selection statistical functions or models on our 'df_artifact'.

    Each statistical function or model will vote for it's best K selected features.
    If a feature has >= 'min_votes' votes, it will be selected.

    :param context:             the function context.
    :param df_artifact:         dataframe to pass as input.
    :param k:                   number of top features to select from each statistical
                                function or model.
    :param min_votes:           minimal number of votes (from a model or by statistical
                                function) needed for a feature to be selected.
                                Can be specified by percentage of votes or absolute
                                number of votes.
    :param label_column:        ground-truth (y) labels.
    :param stat_filters:        statistical functions to apply to the features
                                (from sklearn.feature_selection).
    :param model_filters:       models to use for feature evaluation, can be specified by
                                model name (ex. LinearSVC), formalized json (contains 'CLASS',
                                'FIT', 'META') or a path to such json file.
    :param max_scaled_scores:   produce feature scores table scaled with max_scaler.
    :param sample_ratio:        percentage of the dataset the user whishes to compute the feature selection process on.
    :param output_vector_name:  creates a new feature vector containing only the identifies features.
    :param ignore_type_errors:  skips datatypes that are neither float nor int within the feature vector.
    :param is_feature_vector:   bool stating if the data is passed as a feature vector.
    """
    stat_filters = stat_filters or DEFAULT_STAT_FILTERS
    model_filters = model_filters or DEFAULT_MODEL_FILTERS
    # Check if df.meta is valid, if it is, look for a feature vector
    store_uri_prefix, _ = mlrun.datastore.parse_store_uri(df_artifact.artifact_url)
    is_feature_vector = mlrun.utils.StorePrefix.FeatureVector == store_uri_prefix

    # Look inside meta.spec.label_feature to identify the label_column if the user did not specify it
    if label_column is None:
        if is_feature_vector:
            label_column = df_artifact.meta.spec.label_feature.split(".")[1]
        else:
            raise ValueError("No label_column was given, please add a label_column.")

    # Use the feature vector as dataframe
    df = df_artifact.as_df()

    # Ensure k is not bigger than the total number of features
    if k > df.shape[1]:
        raise ValueError(
            f"K cannot be bigger than the total number of features ({df.shape[1]}). Please choose a smaller K."
        )
    elif k < 1:
        raise ValueError("K cannot be smaller than 1. Please choose a bigger K.")

    # Create a sample dataframe of the original feature vector
    if sample_ratio:
        df = (
            df.groupby(label_column)
            .apply(lambda x: x.sample(frac=sample_ratio))
            .reset_index(drop=True)
        )
        df = df.dropna()

    # Set feature vector and labels
    y = df.pop(label_column)
    X = df

    if np.object_ in list(X.dtypes) and ignore_type_errors is False:
        raise ValueError(
            f"{df.select_dtypes(include=['object']).columns.tolist()} are neither float or int."
        )

    # Create selected statistical estimators
    stat_functions_list = {
        stat_name: SelectKBest(
            score_func=create_class(f"sklearn.feature_selection.{stat_name}"), k=k
        )
        for stat_name in stat_filters
    }
    requires_abs = ["chi2"]

    # Run statistic filters
    selected_features_agg = {}
    stats_df = pd.DataFrame(index=X.columns).dropna()

    for stat_name, stat_func in stat_functions_list.items():
        try:
            params = (X, y) if stat_name in requires_abs else (abs(X), y)
            stat = stat_func.fit(*params)

            # Collect stat function results
            stat_df = pd.DataFrame(
                index=X.columns, columns=[stat_name], data=stat.scores_
            )
            plot_stat(context, stat_name, stat_df)
            stats_df = stats_df.join(stat_df)

            # Select K Best features
            selected_features = X.columns[stat_func.get_support()]
            selected_features_agg[stat_name] = selected_features

        except Exception as e:
            context.logger.info(f"Couldn't calculate {stat_name} because of: {e}")

    # Create models from class name / json file / json params
    all_sklearn_estimators = dict(all_estimators()) if len(model_filters) > 0 else {}
    selected_models = {}
    for model_name, model in model_filters.items():
        if ".json" in model:
            current_model = json.load(open(model, "r"))
            classifier_class = create_class(current_model["META"]["class"])
            selected_models[model_name] = classifier_class(**current_model["CLASS"])
        elif model in all_sklearn_estimators:
            selected_models[model_name] = all_sklearn_estimators[model_name]()

        else:
            try:
                current_model = json.loads(model)
                classifier_class = create_class(current_model["META"]["class"])
                selected_models[model_name] = classifier_class(**current_model["CLASS"])
            except Exception as e:
                context.logger.info(f"unable to load {model} because of: {e}")

    # Run model filters
    models_df = pd.DataFrame(index=X.columns)
    for model_name, model in selected_models.items():

        if model_name == "LogisticRegression":
            model.set_params(solver="liblinear")

        # Train model and get feature importance
        select_from_model = SelectFromModel(model).fit(X, y)
        feature_idx = select_from_model.get_support()
        feature_names = X.columns[feature_idx]
        selected_features_agg[model_name] = feature_names.tolist()

        # Collect model feature importance
        if hasattr(select_from_model.estimator_, "coef_"):
            stat_df = select_from_model.estimator_.coef_
        elif hasattr(select_from_model.estimator_, "feature_importances_"):
            stat_df = select_from_model.estimator_.feature_importances_

        stat_df = pd.DataFrame(index=X.columns, columns=[model_name], data=stat_df[0])
        models_df = models_df.join(stat_df)

        plot_stat(context, model_name, stat_df)

    # Create feature_scores DF with stat & model filters scores
    result_matrix_df = pd.concat([stats_df, models_df], axis=1, sort=False)
    context.log_dataset(
        key="feature_scores",
        df=result_matrix_df,
        local_path="feature_scores.parquet",
        format="parquet",
    )
    if max_scaled_scores:
        normalized_df = result_matrix_df.replace([np.inf, -np.inf], np.nan).values
        min_max_scaler = MinMaxScaler()
        normalized_df = min_max_scaler.fit_transform(normalized_df)
        normalized_df = pd.DataFrame(
            data=normalized_df,
            columns=result_matrix_df.columns,
            index=result_matrix_df.index,
        )
        context.log_dataset(
            key="max_scaled_scores_feature_scores",
            df=normalized_df,
            local_path="max_scaled_scores_feature_scores.parquet",
            format="parquet",
        )

    # Create feature count DataFrame
    for test_name in selected_features_agg:
        result_matrix_df[test_name] = [
            1 if x in selected_features_agg[test_name] else 0 for x in X.columns
        ]
    result_matrix_df.loc[:, "num_votes"] = result_matrix_df.sum(axis=1)
    context.log_dataset(
        key="selected_features_count",
        df=result_matrix_df,
        local_path="selected_features_count.parquet",
        format="parquet",
    )

    # How many votes are needed for a feature to be selected?
    if isinstance(min_votes, int):
        votes_needed = min_votes
    else:
        num_filters = len(stat_filters) + len(model_filters)
        votes_needed = int(np.floor(num_filters * max(min(min_votes, 1), 0)))
    context.logger.info(f"votes needed to be selected: {votes_needed}")

    # Create final feature dataframe
    selected_features = result_matrix_df[
        result_matrix_df.num_votes >= votes_needed
    ].index.tolist()
    good_feature_df = df.loc[:, selected_features]
    final_df = pd.concat([good_feature_df, y], axis=1)
    context.log_dataset(
        key="selected_features",
        df=final_df,
        local_path="selected_features.parquet",
        format="parquet",
    )

    # Creating a new feature vector containing only the identified top features
    if is_feature_vector and df_artifact.meta.spec.features and output_vector_name:
        # Selecting the top K features from our top feature dataframe
        selected_features = result_matrix_df.head(k).index

        # Match the selected feature names to the FS Feature annotations
        matched_selections = [
            feature
            for feature in list(df_artifact.meta.spec.features)
            for selected in list(selected_features)
            if feature.endswith(selected)
        ]

        # Defining our new feature vector
        top_features_fv = fs.FeatureVector(
            output_vector_name,
            matched_selections,
            label_feature="labels.label",
            description="feature vector composed strictly of our top features",
        )

        # Saving
        top_features_fv.save()
        fs.get_offline_features(top_features_fv, target=ParquetTarget())

        # Logging our new feature vector URI
        context.log_result("top_features_vector", top_features_fv.uri)
 + code_origin: '' default_handler: feature_selection - disable_auto_mount: false - clone_target_dir: '' - env: [] - priority_class_name: '' - preemption_mode: prevent - affinity: null - tolerations: null - security_context: {} -verbose: false + description: Select features through multiple Statistical and Model filters + image: mlrun/mlrun +metadata: + categories: + - data-preparation + - machine-learning + tag: '' + name: feature-selection diff --git a/feature_selection/item.yaml b/feature_selection/item.yaml index 7e80a417b..1c79e6f12 100644 --- a/feature_selection/item.yaml +++ b/feature_selection/item.yaml @@ -12,9 +12,9 @@ labels: author: orz maintainers: [] marketplaceType: '' -mlrunVersion: 1.1.0 +mlrunVersion: 1.7.0 name: feature-selection -platformVersion: 3.5.0 +platformVersion: 3.6.0 spec: filename: feature_selection.py handler: feature_selection @@ -22,4 +22,4 @@ spec: kind: job requirements: [] url: '' -version: 1.4.0 +version: 1.5.0 From 64907af0011894ceffe6e7e8471a306eb215221f Mon Sep 17 00:00:00 2001 From: Avi Asulin Date: Mon, 2 Sep 2024 14:52:10 +0300 Subject: [PATCH 14/38] [feature selection] update function yaml --- feature_selection/function.yaml | 56 ++++++++++++++++----------------- feature_selection/item.yaml | 2 +- 2 files changed, 29 insertions(+), 29 deletions(-) diff --git a/feature_selection/function.yaml b/feature_selection/function.yaml index d4e95f1e9..f1bf53b8a 100644 --- a/feature_selection/function.yaml +++ b/feature_selection/function.yaml @@ -1,33 +1,43 @@ +metadata: + name: feature-selection + tag: '' + categories: + - data-preparation + - machine-learning kind: job -verbose: false spec: - disable_auto_mount: false - command: '' entry_points: show_values_on_bars: - lineno: 54 + doc: '' + has_kwargs: false parameters: - name: axs - name: h_v default: v - name: space default: 0.4 - name: show_values_on_bars + lineno: 54 has_varargs: false - has_kwargs: false - doc: '' + name: show_values_on_bars plot_stat: - lineno: 76 + doc: '' + has_kwargs: false parameters: - name: context - name: stat_name - name: stat_df - name: plot_stat + lineno: 76 has_varargs: false - has_kwargs: false - doc: '' + name: plot_stat feature_selection: - lineno: 106 + doc: 'Applies selected feature selection statistical functions or models on + our ''df_artifact''. + + + Each statistical function or model will vote for it''s best K selected features. + + If a feature has >= ''min_votes'' votes, it will be selected.' + has_kwargs: false parameters: - name: context doc: the function context. @@ -78,26 +88,16 @@ spec: type: bool doc: bool stating if the data is passed as a feature vector. default: false - name: feature_selection + lineno: 106 has_varargs: false - has_kwargs: false - doc: 'Applies selected feature selection statistical functions or models on - our ''df_artifact''. - - - Each statistical function or model will vote for it''s best K selected features. - - If a feature has >= ''min_votes'' votes, it will be selected.' + name: feature_selection + disable_auto_mount: false + command: '' build: origin_filename: '' functionSourceCode: # Copyright 2019 Iguazio
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import json
import os

import matplotlib.pyplot as plt
import mlrun
import mlrun.datastore
import mlrun.utils
import mlrun.feature_store as fs
import numpy as np
import pandas as pd
import seaborn as sns
from mlrun.artifacts import PlotArtifact
from mlrun.datastore.targets import ParquetTarget
# MLRun utils
from mlrun.utils.helpers import create_class
# Feature selection strategies
from sklearn.feature_selection import SelectFromModel, SelectKBest
# Scale feature scoresgit st
from sklearn.preprocessing import MinMaxScaler
# SKLearn estimators list
from sklearn.utils import all_estimators

DEFAULT_STAT_FILTERS = ["f_classif", "mutual_info_classif", "chi2", "f_regression"]
DEFAULT_MODEL_FILTERS = {
    "LinearSVC": "LinearSVC",
    "LogisticRegression": "LogisticRegression",
    "ExtraTreesClassifier": "ExtraTreesClassifier",
}


def _clear_current_figure():
    """
    Clear matplotlib current figure.
    """
    plt.cla()
    plt.clf()
    plt.close()


def show_values_on_bars(axs, h_v="v", space=0.4):
    def _show_on_single_plot(ax_):
        if h_v == "v":
            for p in ax_.patches:
                _x = p.get_x() + p.get_width() / 2
                _y = p.get_y() + p.get_height()
                value = int(p.get_height())
                ax_.text(_x, _y, value, ha="center")
        elif h_v == "h":
            for p in ax_.patches:
                _x = p.get_x() + p.get_width() + float(space)
                _y = p.get_y() + p.get_height()
                value = int(p.get_width())
                ax_.text(_x, _y, value, ha="left")

    if isinstance(axs, np.ndarray):
        for idx, ax in np.ndenumerate(axs):
            _show_on_single_plot(ax)
    else:
        _show_on_single_plot(axs)


def plot_stat(context, stat_name, stat_df):
    _clear_current_figure()

    # Add chart
    ax = plt.axes()
    stat_chart = sns.barplot(
        x=stat_name,
        y="index",
        data=stat_df.sort_values(stat_name, ascending=False).reset_index(),
        ax=ax,
    )
    plt.tight_layout()

    for p in stat_chart.patches:
        width = p.get_width()
        plt.text(
            5 + p.get_width(),
            p.get_y() + 0.55 * p.get_height(),
            "{:1.2f}".format(width),
            ha="center",
            va="center",
        )

    context.log_artifact(
        PlotArtifact(f"{stat_name}", body=plt.gcf()),
        local_path=os.path.join("plots", "feature_selection", f"{stat_name}.html"),
    )
    _clear_current_figure()


def feature_selection(
    context,
    df_artifact,
    k: int = 5,
    min_votes: float = 0.5,
    label_column: str = None,
    stat_filters: list = None,
    model_filters: dict = None,
    max_scaled_scores: bool = True,
    sample_ratio: float = None,
    output_vector_name: float = None,
    ignore_type_errors: bool = False,
    is_feature_vector: bool = False,
):
    """
    Applies selected feature selection statistical functions or models on our 'df_artifact'.

    Each statistical function or model will vote for it's best K selected features.
    If a feature has >= 'min_votes' votes, it will be selected.

    :param context:             the function context.
    :param df_artifact:         dataframe to pass as input.
    :param k:                   number of top features to select from each statistical
                                function or model.
    :param min_votes:           minimal number of votes (from a model or by statistical
                                function) needed for a feature to be selected.
                                Can be specified by percentage of votes or absolute
                                number of votes.
    :param label_column:        ground-truth (y) labels.
    :param stat_filters:        statistical functions to apply to the features
                                (from sklearn.feature_selection).
    :param model_filters:       models to use for feature evaluation, can be specified by
                                model name (ex. LinearSVC), formalized json (contains 'CLASS',
                                'FIT', 'META') or a path to such json file.
    :param max_scaled_scores:   produce feature scores table scaled with max_scaler.
    :param sample_ratio:        percentage of the dataset the user whishes to compute the feature selection process on.
    :param output_vector_name:  creates a new feature vector containing only the identifies features.
    :param ignore_type_errors:  skips datatypes that are neither float nor int within the feature vector.
    :param is_feature_vector:   bool stating if the data is passed as a feature vector.
    """
    stat_filters = stat_filters or DEFAULT_STAT_FILTERS
    model_filters = model_filters or DEFAULT_MODEL_FILTERS
    # Check if df.meta is valid, if it is, look for a feature vector
    store_uri_prefix, _ = mlrun.datastore.parse_store_uri(df_artifact.artifact_url)
    is_feature_vector = mlrun.utils.StorePrefix.FeatureVector == store_uri_prefix

    # Look inside meta.spec.label_feature to identify the label_column if the user did not specify it
    if label_column is None:
        if is_feature_vector:
            label_column = df_artifact.meta.spec.label_feature.split(".")[1]
        else:
            raise ValueError("No label_column was given, please add a label_column.")

    # Use the feature vector as dataframe
    df = df_artifact.as_df()

    # Ensure k is not bigger than the total number of features
    if k > df.shape[1]:
        raise ValueError(
            f"K cannot be bigger than the total number of features ({df.shape[1]}). Please choose a smaller K."
        )
    elif k < 1:
        raise ValueError("K cannot be smaller than 1. Please choose a bigger K.")

    # Create a sample dataframe of the original feature vector
    if sample_ratio:
        df = (
            df.groupby(label_column)
            .apply(lambda x: x.sample(frac=sample_ratio))
            .reset_index(drop=True)
        )
        df = df.dropna()

    # Set feature vector and labels
    y = df.pop(label_column)
    X = df

    if np.object_ in list(X.dtypes) and ignore_type_errors is False:
        raise ValueError(
            f"{df.select_dtypes(include=['object']).columns.tolist()} are neither float or int."
        )

    # Create selected statistical estimators
    stat_functions_list = {
        stat_name: SelectKBest(
            score_func=create_class(f"sklearn.feature_selection.{stat_name}"), k=k
        )
        for stat_name in stat_filters
    }
    requires_abs = ["chi2"]

    # Run statistic filters
    selected_features_agg = {}
    stats_df = pd.DataFrame(index=X.columns).dropna()

    for stat_name, stat_func in stat_functions_list.items():
        try:
            params = (X, y) if stat_name in requires_abs else (abs(X), y)
            stat = stat_func.fit(*params)

            # Collect stat function results
            stat_df = pd.DataFrame(
                index=X.columns, columns=[stat_name], data=stat.scores_
            )
            plot_stat(context, stat_name, stat_df)
            stats_df = stats_df.join(stat_df)

            # Select K Best features
            selected_features = X.columns[stat_func.get_support()]
            selected_features_agg[stat_name] = selected_features

        except Exception as e:
            context.logger.info(f"Couldn't calculate {stat_name} because of: {e}")

    # Create models from class name / json file / json params
    all_sklearn_estimators = dict(all_estimators()) if len(model_filters) > 0 else {}
    selected_models = {}
    for model_name, model in model_filters.items():
        if ".json" in model:
            current_model = json.load(open(model, "r"))
            classifier_class = create_class(current_model["META"]["class"])
            selected_models[model_name] = classifier_class(**current_model["CLASS"])
        elif model in all_sklearn_estimators:
            selected_models[model_name] = all_sklearn_estimators[model_name]()

        else:
            try:
                current_model = json.loads(model)
                classifier_class = create_class(current_model["META"]["class"])
                selected_models[model_name] = classifier_class(**current_model["CLASS"])
            except Exception as e:
                context.logger.info(f"unable to load {model} because of: {e}")

    # Run model filters
    models_df = pd.DataFrame(index=X.columns)
    for model_name, model in selected_models.items():

        if model_name == "LogisticRegression":
            model.set_params(solver="liblinear")

        # Train model and get feature importance
        select_from_model = SelectFromModel(model).fit(X, y)
        feature_idx = select_from_model.get_support()
        feature_names = X.columns[feature_idx]
        selected_features_agg[model_name] = feature_names.tolist()

        # Collect model feature importance
        if hasattr(select_from_model.estimator_, "coef_"):
            stat_df = select_from_model.estimator_.coef_
        elif hasattr(select_from_model.estimator_, "feature_importances_"):
            stat_df = select_from_model.estimator_.feature_importances_

        stat_df = pd.DataFrame(index=X.columns, columns=[model_name], data=stat_df[0])
        models_df = models_df.join(stat_df)

        plot_stat(context, model_name, stat_df)

    # Create feature_scores DF with stat & model filters scores
    result_matrix_df = pd.concat([stats_df, models_df], axis=1, sort=False)
    context.log_dataset(
        key="feature_scores",
        df=result_matrix_df,
        local_path="feature_scores.parquet",
        format="parquet",
    )
    if max_scaled_scores:
        normalized_df = result_matrix_df.replace([np.inf, -np.inf], np.nan).values
        min_max_scaler = MinMaxScaler()
        normalized_df = min_max_scaler.fit_transform(normalized_df)
        normalized_df = pd.DataFrame(
            data=normalized_df,
            columns=result_matrix_df.columns,
            index=result_matrix_df.index,
        )
        context.log_dataset(
            key="max_scaled_scores_feature_scores",
            df=normalized_df,
            local_path="max_scaled_scores_feature_scores.parquet",
            format="parquet",
        )

    # Create feature count DataFrame
    for test_name in selected_features_agg:
        result_matrix_df[test_name] = [
            1 if x in selected_features_agg[test_name] else 0 for x in X.columns
        ]
    result_matrix_df.loc[:, "num_votes"] = result_matrix_df.sum(axis=1)
    context.log_dataset(
        key="selected_features_count",
        df=result_matrix_df,
        local_path="selected_features_count.parquet",
        format="parquet",
    )

    # How many votes are needed for a feature to be selected?
    if isinstance(min_votes, int):
        votes_needed = min_votes
    else:
        num_filters = len(stat_filters) + len(model_filters)
        votes_needed = int(np.floor(num_filters * max(min(min_votes, 1), 0)))
    context.logger.info(f"votes needed to be selected: {votes_needed}")

    # Create final feature dataframe
    selected_features = result_matrix_df[
        result_matrix_df.num_votes >= votes_needed
    ].index.tolist()
    good_feature_df = df.loc[:, selected_features]
    final_df = pd.concat([good_feature_df, y], axis=1)
    context.log_dataset(
        key="selected_features",
        df=final_df,
        local_path="selected_features.parquet",
        format="parquet",
    )

    # Creating a new feature vector containing only the identified top features
    if is_feature_vector and df_artifact.meta.spec.features and output_vector_name:
        # Selecting the top K features from our top feature dataframe
        selected_features = result_matrix_df.head(k).index

        # Match the selected feature names to the FS Feature annotations
        matched_selections = [
            feature
            for feature in list(df_artifact.meta.spec.features)
            for selected in list(selected_features)
            if feature.endswith(selected)
        ]

        # Defining our new feature vector
        top_features_fv = fs.FeatureVector(
            output_vector_name,
            matched_selections,
            label_feature="labels.label",
            description="feature vector composed strictly of our top features",
        )

        # Saving
        top_features_fv.save()
        fs.get_offline_features(top_features_fv, target=ParquetTarget())

        # Logging our new feature vector URI
        context.log_result("top_features_vector", top_features_fv.uri)
 code_origin: '' default_handler: feature_selection - description: Select features through multiple Statistical and Model filters image: mlrun/mlrun -metadata: - categories: - - data-preparation - - machine-learning - tag: '' - name: feature-selection + description: Select features through multiple Statistical and Model filters +verbose: false diff --git a/feature_selection/item.yaml b/feature_selection/item.yaml index 1c79e6f12..c7400a7f0 100644 --- a/feature_selection/item.yaml +++ b/feature_selection/item.yaml @@ -22,4 +22,4 @@ spec: kind: job requirements: [] url: '' -version: 1.5.0 +version: 1.7.0 From d1d61a0f1765749cdd5ee0d4225aea1c60093f88 Mon Sep 17 00:00:00 2001 From: Avi Asulin Date: Mon, 2 Sep 2024 16:09:01 +0300 Subject: [PATCH 15/38] Revert "[onnx utils] update onnx utils packages" This reverts commit 88727986ffa91662593958023be8ac3ccef2cab0. --- onnx_utils/function.yaml | 42 ++++++++++++++++++----------------- onnx_utils/item.yaml | 16 ++++++------- onnx_utils/requirements.txt | 12 +++++----- onnx_utils/test_onnx_utils.py | 4 ++-- 4 files changed, 38 insertions(+), 36 deletions(-) diff --git a/onnx_utils/function.yaml b/onnx_utils/function.yaml index 88f810fb4..7a0054c4d 100644 --- a/onnx_utils/function.yaml +++ b/onnx_utils/function.yaml @@ -2,7 +2,7 @@ kind: job metadata: name: onnx-utils tag: '' - hash: fd6cd909ef6e055c348b44a0313e190513cd755b + hash: 0c4a6491b976d5220d3ebfb83a3905dd47e86be2 project: '' labels: author: guyl @@ -16,16 +16,16 @@ spec: functionSourceCode: # Copyright 2019 Iguazio
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from typing import Any, Callable, Dict, List, Tuple

import mlrun


class _ToONNXConversions:
    """
    An ONNX conversion functions library class.
    """

    @staticmethod
    def tf_keras_to_onnx(
        model_handler,
        onnx_model_name: str = None,
        optimize_model: bool = True,
        input_signature: List[Tuple[Tuple[int], str]] = None,
    ):
        """
        Convert a TF.Keras model to an ONNX model and log it back to MLRun as a new model object.

        :param model_handler:   An initialized TFKerasModelHandler with a loaded model to convert to ONNX.
        :param onnx_model_name: The name to use to log the converted ONNX model. If not given, the given `model_name`
                                will be used with an additional suffix `_onnx`. Defaulted to None.
        :param optimize_model:  Whether or not to optimize the ONNX model using 'onnxoptimizer' before saving the model.
                                Defaulted to True.
        :param input_signature: A list of the input layers shape and data type properties. Expected to receive a list
                                where each element is an input layer tuple. An input layer tuple is a tuple of:
                                [0] = Layer's shape, a tuple of integers.
                                [1] = Layer's data type, a mlrun.data_types.ValueType string.
                                If None, the input signature will be tried to be read from the model artifact. Defaulted
                                to None.
        """
        # Import the framework and handler:
        import tensorflow as tf
        from mlrun.frameworks.tf_keras import TFKerasUtils

        # Check the given 'input_signature' parameter:
        if input_signature is None:
            # Read the inputs from the model:
            try:
                model_handler.read_inputs_from_model()
            except Exception as error:
                raise mlrun.errors.MLRunRuntimeError(
                    f"Please provide the 'input_signature' parameter. The function tried reading the input layers "
                    f"information automatically but failed with the following error: {error}"
                )
        else:
            # Parse the 'input_signature' parameter:
            input_signature = [
                tf.TensorSpec(
                    shape=shape,
                    dtype=TFKerasUtils.convert_value_type_to_tf_dtype(
                        value_type=value_type
                    ),
                )
                for (shape, value_type) in input_signature
            ]

        # Convert to ONNX:
        model_handler.to_onnx(
            model_name=onnx_model_name,
            input_signature=input_signature,
            optimize=optimize_model,
        )

    @staticmethod
    def pytorch_to_onnx(
        model_handler,
        onnx_model_name: str = None,
        optimize_model: bool = True,
        input_signature: List[Tuple[Tuple[int, ...], str]] = None,
        input_layers_names: List[str] = None,
        output_layers_names: List[str] = None,
        dynamic_axes: Dict[str, Dict[int, str]] = None,
        is_batched: bool = True,
    ):
        """
        Convert a PyTorch model to an ONNX model and log it back to MLRun as a new model object.

        :param model_handler:       An initialized PyTorchModelHandler with a loaded model to convert to ONNX.
        :param onnx_model_name:     The name to use to log the converted ONNX model. If not given, the given
                                    `model_name` will be used with an additional suffix `_onnx`. Defaulted to None.
        :param optimize_model:      Whether or not to optimize the ONNX model using 'onnxoptimizer' before saving the
                                    model. Defaulted to True.
        :param input_signature:     A list of the input layers shape and data type properties. Expected to receive a
                                    list where each element is an input layer tuple. An input layer tuple is a tuple of:
                                    [0] = Layer's shape, a tuple of integers.
                                    [1] = Layer's data type, a mlrun.data_types.ValueType string.
                                    If None, the input signature will be tried to be read from the model artifact.
                                    Defaulted to None.
        :param input_layers_names:  List of names to assign to the input nodes of the graph in order. All of the other
                                    parameters (inner layers) can be set as well by passing additional names in the
                                    list. The order is by the order of the parameters in the model. If None, the inputs
                                    will be read from the handler's inputs. If its also None, it is defaulted to:
                                    "input_0", "input_1", ...
        :param output_layers_names: List of names to assign to the output nodes of the graph in order. If None, the
                                    outputs will be read from the handler's outputs. If its also None, it is defaulted
                                    to: "output_0" (for multiple outputs, this parameter must be provided).
        :param dynamic_axes:        If part of the input / output shape is dynamic, like (batch_size, 3, 32, 32) you can
                                    specify it by giving a dynamic axis to the input / output layer by its name as
                                    follows: {
                                        "input layer name": {0: "batch_size"},
                                        "output layer name": {0: "batch_size"},
                                    }
                                    If provided, the 'is_batched' flag will be ignored. Defaulted to None.
        :param is_batched:          Whether to include a batch size as the first axis in every input and output layer.
                                    Defaulted to True. Will be ignored if 'dynamic_axes' is provided.
        """
        # Import the framework and handler:
        import torch
        from mlrun.frameworks.pytorch import PyTorchUtils

        # Parse the 'input_signature' parameter:
        if input_signature is not None:
            input_signature = tuple(
                [
                    torch.zeros(
                        size=shape,
                        dtype=PyTorchUtils.convert_value_type_to_torch_dtype(
                            value_type=value_type
                        ),
                    )
                    for (shape, value_type) in input_signature
                ]
            )

        # Convert to ONNX:
        model_handler.to_onnx(
            model_name=onnx_model_name,
            input_sample=input_signature,
            optimize=optimize_model,
            input_layers_names=input_layers_names,
            output_layers_names=output_layers_names,
            dynamic_axes=dynamic_axes,
            is_batched=is_batched
        )


# Map for getting the conversion function according to the provided framework:
_CONVERSION_MAP = {
    "tensorflow.keras": _ToONNXConversions.tf_keras_to_onnx,
    "torch": _ToONNXConversions.pytorch_to_onnx,
}  # type: Dict[str, Callable]


def to_onnx(
    context: mlrun.MLClientCtx,
    model_path: str,
    onnx_model_name: str = None,
    optimize_model: bool = True,
    framework_kwargs: Dict[str, Any] = None,
):
    """
    Convert the given model to an ONNX model.

    :param context:          The MLRun function execution context
    :param model_path:       The model path store object.
    :param onnx_model_name:  The name to use to log the converted ONNX model. If not given, the given `model_name` will
                             be used with an additional suffix `_onnx`. Defaulted to None.
    :param optimize_model:   Whether to optimize the ONNX model using 'onnxoptimizer' before saving the model. Defaulted
                             to True.
    :param framework_kwargs: Additional arguments each framework may require in order to convert to ONNX. To get the doc
                             string of the desired framework onnx conversion function, pass "help".
    """
    from mlrun.frameworks.auto_mlrun.auto_mlrun import AutoMLRun

    # Get a model handler of the required framework:
    model_handler = AutoMLRun.load_model(model_path=model_path, context=context)

    # Get the model's framework:
    framework = model_handler.FRAMEWORK_NAME

    # Use the conversion map to get the specific framework to onnx conversion:
    if framework not in _CONVERSION_MAP:
        raise mlrun.errors.MLRunInvalidArgumentError(
            f"The following framework: '{framework}', has no ONNX conversion."
        )
    conversion_function = _CONVERSION_MAP[framework]

    # Check if needed to print the function's doc string ("help" is passed):
    if framework_kwargs == "help":
        print(conversion_function.__doc__)
        return

    # Set the default empty framework kwargs if needed:
    if framework_kwargs is None:
        framework_kwargs = {}

    # Run the conversion:
    try:
        conversion_function(
            model_handler=model_handler,
            onnx_model_name=onnx_model_name,
            optimize_model=optimize_model,
            **framework_kwargs,
        )
    except TypeError as exception:
        raise mlrun.errors.MLRunInvalidArgumentError(
            f"ERROR: A TypeError exception was raised during the conversion:\n{exception}. "
            f"Please read the {framework} framework conversion function doc string by passing 'help' in the "
            f"'framework_kwargs' dictionary parameter."
        )


def optimize(
    context: mlrun.MLClientCtx,
    model_path: str,
    optimizations: List[str] = None,
    fixed_point: bool = False,
    optimized_model_name: str = None,
):
    """
    Optimize the given ONNX model.

    :param context:              The MLRun function execution context.
    :param model_path:           Path to the ONNX model object.
    :param optimizations:        List of possible optimizations. To see what optimizations are available, pass "help".
                                 If None, all of the optimizations will be used. Defaulted to None.
    :param fixed_point:          Optimize the weights using fixed point. Defaulted to False.
    :param optimized_model_name: The name of the optimized model. If None, the original model will be overridden.
                                 Defaulted to None.
    """
    # Import the model handler:
    import onnxoptimizer
    from mlrun.frameworks.onnx import ONNXModelHandler

    # Check if needed to print the available optimizations ("help" is passed):
    if optimizations == "help":
        available_passes = "\n* ".join(onnxoptimizer.get_available_passes())
        print(f"The available optimizations are:\n* {available_passes}")
        return

    # Create the model handler:
    model_handler = ONNXModelHandler(
        model_path=model_path, context=context
    )

    # Load the ONNX model:
    model_handler.load()

    # Optimize the model using the given configurations:
    model_handler.optimize(optimizations=optimizations, fixed_point=fixed_point)

    # Rename if needed:
    if optimized_model_name is not None:
        model_handler.set_model_name(model_name=optimized_model_name)

    # Log the optimized model:
    model_handler.log()
 base_image: mlrun/mlrun commands: [] - code_origin: '' - origin_filename: '' + code_origin: https://github.com/yonishelach/functions.git#f84b9565a33d8159315992ebba5838d41f6cc112:/Users/Yonatan_Shelach/projects/functions/onnx_utils/onnx_utils.py + origin_filename: /Users/Yonatan_Shelach/projects/functions/onnx_utils/onnx_utils.py with_mlrun: false auto_build: true requirements: - - onnx~=1.15.0 - - onnxruntime~=1.8.1 - - onnxoptimizer~=0.2.0 - - onnxmltools~=1.9.0 - - tf2onnx~=1.16.0 + - onnx~=1.13.0 + - onnxruntime~=1.14.0 + - onnxoptimizer~=0.3.0 + - onnxmltools~=1.11.0 + - tf2onnx~=1.13.0 entry_points: tf_keras_to_onnx: name: tf_keras_to_onnx @@ -35,6 +35,7 @@ spec: - name: model_handler doc: An initialized TFKerasModelHandler with a loaded model to convert to ONNX. + default: '' - name: onnx_model_name type: str doc: The name to use to log the converted ONNX model. If not given, the given @@ -54,10 +55,9 @@ spec: data type, a mlrun.data_types.ValueType string. If None, the input signature will be tried to be read from the model artifact. Defaulted to None.' default: null - outputs: [] + outputs: + - default: '' lineno: 26 - has_varargs: false - has_kwargs: false pytorch_to_onnx: name: pytorch_to_onnx doc: Convert a PyTorch model to an ONNX model and log it back to MLRun as a @@ -66,6 +66,7 @@ spec: - name: model_handler doc: An initialized PyTorchModelHandler with a loaded model to convert to ONNX. + default: '' - name: onnx_model_name type: str doc: The name to use to log the converted ONNX model. If not given, the given @@ -113,10 +114,9 @@ spec: doc: Whether to include a batch size as the first axis in every input and output layer. Defaulted to True. Will be ignored if 'dynamic_axes' is provided. default: true - outputs: [] + outputs: + - default: '' lineno: 81 - has_varargs: false - has_kwargs: false to_onnx: name: to_onnx doc: Convert the given model to an ONNX model. @@ -124,9 +124,11 @@ spec: - name: context type: MLClientCtx doc: The MLRun function execution context + default: '' - name: model_path type: str doc: The model path store object. + default: '' - name: onnx_model_name type: str doc: The name to use to log the converted ONNX model. If not given, the given @@ -144,10 +146,9 @@ spec: ONNX. To get the doc string of the desired framework onnx conversion function, pass "help". default: null - outputs: [] + outputs: + - default: '' lineno: 160 - has_varargs: false - has_kwargs: false optimize: name: optimize doc: Optimize the given ONNX model. @@ -155,9 +156,11 @@ spec: - name: context type: MLClientCtx doc: The MLRun function execution context. + default: '' - name: model_path type: str doc: Path to the ONNX model object. + default: '' - name: optimizations type: List[str] doc: List of possible optimizations. To see what optimizations are available, @@ -173,10 +176,9 @@ spec: doc: The name of the optimized model. If None, the original model will be overridden. Defaulted to None. default: null - outputs: [] + outputs: + - default: '' lineno: 219 - has_varargs: false - has_kwargs: false description: ONNX intigration in MLRun, some utils functions for the ONNX framework, optimizing and converting models from different framework to ONNX using MLRun. default_handler: to_onnx diff --git a/onnx_utils/item.yaml b/onnx_utils/item.yaml index 84486d9f8..36335837a 100644 --- a/onnx_utils/item.yaml +++ b/onnx_utils/item.yaml @@ -12,9 +12,9 @@ labels: author: guyl maintainers: [] marketplaceType: '' -mlrunVersion: 1.6.3 +mlrunVersion: 1.1.0 name: onnx_utils -platformVersion: 3.5.4 +platformVersion: 3.5.0 spec: extra_spec: allow_empty_resources: true @@ -26,10 +26,10 @@ spec: image: mlrun/mlrun kind: job requirements: - - onnx~=1.15.0 - - onnxruntime~=1.8.1 - - onnxoptimizer~=0.2.0 - - onnxmltools~=1.9.0 - - tf2onnx~=1.16.0 + - onnx~=1.13.0 + - onnxruntime~=1.14.0 + - onnxoptimizer~=0.3.0 + - onnxmltools~=1.11.0 + - tf2onnx~=1.13.0 url: '' -version: 1.3.0 +version: 1.2.0 diff --git a/onnx_utils/requirements.txt b/onnx_utils/requirements.txt index a9acd7371..dc7ff1e7b 100644 --- a/onnx_utils/requirements.txt +++ b/onnx_utils/requirements.txt @@ -1,11 +1,11 @@ tqdm~=4.62.3 -tensorflow~=2.13.0 +tensorflow~=2.7.0 torch~=1.10.0 torchvision~=0.11.1 -onnx~=1.15.0 -onnxruntime~=1.12.1 -onnxoptimizer~=0.3.0 +onnx~=1.10.1 +onnxruntime~=1.8.1 +onnxoptimizer~=0.2.0 onnxmltools~=1.9.0 -tf2onnx~=1.16.0 +tf2onnx~=1.9.0 plotly~=5.4.0 -#wrapt<1.15.0 # wrapt==1.15.0 fails tensorflow 2.7 Todo: please remove when updating tensorflow \ No newline at end of file +wrapt<1.15.0 # wrapt==1.15.0 fails tensorflow 2.7 Todo: please remove when updating tensorflow \ No newline at end of file diff --git a/onnx_utils/test_onnx_utils.py b/onnx_utils/test_onnx_utils.py index aaae96372..35b224c4a 100644 --- a/onnx_utils/test_onnx_utils.py +++ b/onnx_utils/test_onnx_utils.py @@ -257,7 +257,7 @@ def test_pytorch_to_onnx(): filename="test_onnx_utils.py", name="log_model", kind="job", - image="mlrun/mlrun", + image="mlrun/ml-models", ) # Run the function to log the model: @@ -341,7 +341,7 @@ def test_optimize(): filename="test_onnx_utils.py", name="log_model", kind="job", - image="mlrun/mlrun", + image="mlrun/ml-models", ) # Run the function to log the model: From 98d09427db1cc9bdd08e3c498289e0bb35664df6 Mon Sep 17 00:00:00 2001 From: Avi Asulin Date: Mon, 2 Sep 2024 16:12:12 +0300 Subject: [PATCH 16/38] [feature selection] update function yaml --- feature_selection/item.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/feature_selection/item.yaml b/feature_selection/item.yaml index c7400a7f0..1b25ec410 100644 --- a/feature_selection/item.yaml +++ b/feature_selection/item.yaml @@ -12,7 +12,7 @@ labels: author: orz maintainers: [] marketplaceType: '' -mlrunVersion: 1.7.0 +mlrunVersion: 1.6.4 name: feature-selection platformVersion: 3.6.0 spec: From 2fb20802033039178fcf94ad33eff63155e9ff37 Mon Sep 17 00:00:00 2001 From: Avi Asulin Date: Mon, 2 Sep 2024 16:34:04 +0300 Subject: [PATCH 17/38] [feature selection] update function yaml --- feature_selection/requirements.txt | 2 +- feature_selection/test_feature_selection.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/feature_selection/requirements.txt b/feature_selection/requirements.txt index 961f64ea4..70a079c7d 100644 --- a/feature_selection/requirements.txt +++ b/feature_selection/requirements.txt @@ -1,4 +1,4 @@ -scikit-learn~=1.0.2 +scikit-learn matplotlib seaborn scikit-plot diff --git a/feature_selection/test_feature_selection.py b/feature_selection/test_feature_selection.py index 6289648f2..d21e648ff 100644 --- a/feature_selection/test_feature_selection.py +++ b/feature_selection/test_feature_selection.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # -from mlrun import code_to_function +from mlrun import code_to_function, get_dataitem from pathlib import Path import shutil @@ -44,5 +44,5 @@ def test_run_local_feature_selection(): inputs={'df_artifact': 'data/metrics.pq'}, artifact_path='artifacts/', ) - assert run.artifact('feature_scores').get() and run.artifact('selected_features').get() + assert run.outputs['feature_scores'] and run.outputs['selected_features'] _delete_outputs({ARTIFACTS_PATH, RUNS_PATH, SCHEDULES_PATH}) From 55e1dc0b0a3f96d40e85672a879ae3064bcef895 Mon Sep 17 00:00:00 2001 From: Eyal Danieli Date: Mon, 9 Sep 2024 09:46:33 +0300 Subject: [PATCH 18/38] Delete unsupported functions from the hub (#824) * delete EOS functions * bring back validate_great_expectations * bring back load_dataset --- bert_embeddings/bert_embeddings.ipynb | 503 ---- bert_embeddings/bert_embeddings.py | 41 - bert_embeddings/function.yaml | 38 - bert_embeddings/item.yaml | 26 - bert_embeddings/requirements.txt | 1 - bert_embeddings/test_bert_embeddings.py | 32 - concept_drift/README.md | 132 - concept_drift/concept_drift.ipynb | 793 ------ concept_drift/concept_drift.py | 147 - concept_drift/function.yaml | 112 - concept_drift/item.yaml | 27 - concept_drift/requirements.txt | 1 - .../concept_drift_streaming.ipynb | 480 ---- .../concept_drift_streaming.py | 157 - concept_drift_streaming/function.yaml | 48 - concept_drift_streaming/item.yaml | 29 - concept_drift_streaming/requirements.txt | 1 - feature_perms/README.ipynb | 788 ----- feature_perms/feature_perms.ipynb | 1106 ------- feature_perms/feature_perms.py | 174 -- feature_perms/function.yaml | 63 - feature_perms/item.yaml | 25 - feature_perms/requirements.txt | 5 - feature_perms/test_feature_perms.py | 134 - get_offline_features/function.yaml | 127 - .../get_offline_features.ipynb | 1536 ---------- get_offline_features/get_offline_features.py | 142 - get_offline_features/item.yaml | 26 - .../test_get_offline_features.py | 239 -- hugging_face_classifier_trainer/function.yaml | 368 --- .../hugging_face_classifier_trainer.ipynb | 2533 ----------------- .../hugging_face_classifier_trainer.py | 832 ------ hugging_face_classifier_trainer/item.yaml | 31 - .../requirements.txt | 6 - .../test_hugging_face_classifier_trainer.py | 145 - huggingface_auto_trainer/function.yaml | 349 --- .../huggingface_auto_trainer.ipynb | 195 -- .../huggingface_auto_trainer.py | 855 ------ huggingface_auto_trainer/item.yaml | 25 - huggingface_auto_trainer/requirements.txt | 5 - .../test_huggingface_auto_trainer.py | 42 - ingest/function.yaml | 87 - ingest/ingest.ipynb | 762 ----- ingest/ingest.py | 84 - ingest/item.yaml | 27 - ingest/test_ingest.py | 171 -- model_monitoring_stream/function.yaml | 267 -- model_monitoring_stream/item.yaml | 23 - .../model_monitoring_stream.ipynb | 178 -- .../model_monitoring_stream.py | 768 ----- model_monitoring_stream/requirements.txt | 3 - pandas_profiling_report/README.md | 26 - pandas_profiling_report/function.yaml | 40 - pandas_profiling_report/item.yaml | 25 - .../pandas_profiling_report.ipynb | 794 ------ .../pandas_profiling_report.py | 41 - project_runner/function.yaml | 53 - project_runner/project_runner.ipynb | 340 --- rnn_serving/function.yaml | 46 - rnn_serving/item.yaml | 25 - rnn_serving/requirements.txt | 2 - rnn_serving/rnn_serving.ipynb | 285 -- rnn_serving/rnn_serving.py | 35 - rnn_serving/test_rnn_serving.py | 74 - slack_notify/README.md | 1 - slack_notify/function.yaml | 48 - slack_notify/item.yaml | 25 - slack_notify/slack_notify.ipynb | 293 -- slack_notify/slack_notify.py | 48 - snowflake_dask/README.md | 38 - snowflake_dask/config-template.yaml | 5 - snowflake_dask/function.yaml | 81 - .../img/iguazio-project-secrets.png | Bin 105122 -> 0 bytes snowflake_dask/img/snowflake-dask.png | Bin 58722 -> 0 bytes snowflake_dask/item.yaml | 25 - snowflake_dask/requirements.txt | 2 - snowflake_dask/snowflake-dask-mlrun.ipynb | 437 --- snowflake_dask/snowflake_dask.py | 125 - snowflake_dask/test_snowflake_dask.py | 24 - sql_to_file/function.yaml | 47 - sql_to_file/item.yaml | 24 - sql_to_file/requirements.txt | 2 - sql_to_file/sql_to_file.ipynb | 1567 ---------- sql_to_file/sql_to_file.py | 45 - sql_to_file/test_sql_to_file.py | 31 - stream_to_parquet/function.yaml | 45 - stream_to_parquet/item.yaml | 28 - stream_to_parquet/stream_to_parquet.ipynb | 698 ----- stream_to_parquet/stream_to_parquet.py | 96 - tf1_serving/function.yaml | 48 - tf1_serving/item.yaml | 28 - tf1_serving/requirements.txt | 2 - tf1_serving/tf1_serving.ipynb | 567 ---- tf1_serving/tf1_serving.py | 87 - tf2_serving_v2/function.yaml | 45 - tf2_serving_v2/item.yaml | 28 - tf2_serving_v2/requirements.txt | 2 - tf2_serving_v2/tf2_serving_v2.ipynb | 545 ---- tf2_serving_v2/tf2_serving_v2.py | 82 - virtual_drift/README.md | 56 - virtual_drift/function.yaml | 129 - virtual_drift/item.yaml | 28 - virtual_drift/virtual_drift.ipynb | 935 ------ virtual_drift/virtual_drift.py | 206 -- xgb_custom/function.yaml | 241 -- xgb_custom/item.yaml | 26 - xgb_custom/requirements.txt | 7 - xgb_custom/test_xgb_custom.py | 50 - xgb_custom/xgb_custom.ipynb | 922 ------ xgb_custom/xgb_custom.py | 239 -- xgb_serving/function.yaml | 40 - xgb_serving/item.yaml | 29 - xgb_serving/requirements.txt | 7 - xgb_serving/test_xgb_serving.py | 67 - xgb_serving/xgb_serving.ipynb | 421 --- xgb_serving/xgb_serving.py | 33 - 116 files changed, 25080 deletions(-) delete mode 100644 bert_embeddings/bert_embeddings.ipynb delete mode 100644 bert_embeddings/bert_embeddings.py delete mode 100644 bert_embeddings/function.yaml delete mode 100644 bert_embeddings/item.yaml delete mode 100644 bert_embeddings/requirements.txt delete mode 100644 bert_embeddings/test_bert_embeddings.py delete mode 100644 concept_drift/README.md delete mode 100644 concept_drift/concept_drift.ipynb delete mode 100644 concept_drift/concept_drift.py delete mode 100644 concept_drift/function.yaml delete mode 100644 concept_drift/item.yaml delete mode 100644 concept_drift/requirements.txt delete mode 100644 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embeddings for given textual sequences as a serverless function. Embeddings are meaningful, contextual representations of text in the form of ndarrays that are used frequently as input to various learning tasks in the field of NLP." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Embeddings without bert" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[One-Hot Encoding](https://en.wikipedia.org/wiki/One-hot) is a general method that can vectorize any categorical features. It is simple and fast to create and update the vectorization.
\n", - "in case of text embeddings, each row is a sentence and each column is a word/char/[n-gram](https://en.wikipedia.org/wiki/N-gram)." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# some sentences to do examine\n", - "sentences = ['the quick brown fox jumps over the lazy dog',\n", - " 'Hello I am Jacob',\n", - " 'Daniel visited Tel-Aviv last month']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "lets see the difference between bert embeddings and one-hot encoding" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['the', 'quick', 'brown', 'fox', 'jumps', 'over', 'lazy', 'dog', 'Hello', 'I', 'am', 'Jacob', 'Daniel', 'visited', 'Tel-Aviv', 'last', 'month']\n" - ] - } - ], - "source": [ - "# constructing a list of all the words (will be our columns) - make sure no duplicate words are set\n", - "tokens = []\n", - "for sentence in sentences:\n", - " for word in sentence.split():\n", - " tokens.append(word) if word not in tokens else \"\"\n", - "print(tokens)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# constructing the one hot vector\n", - "import pandas as pd\n", - "import numpy as np\n", - "\n", - "one_hot = pd.DataFrame(columns = range(len(tokens)))\n", - "# filling our empty dataframe with each sentence encoding\n", - "for sentence in sentences:\n", - " vector = np.zeros(len(tokens))\n", - " for word in sentence.split():\n", - " vector[tokens.index(word)]=1\n", - " one_hot = one_hot.append(pd.Series(vector),ignore_index=True)\n", - "one_hot.columns = tokens" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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\n", - "Note that the first input has an intermediate dimension of size 11 that corresponds to the number of max tokens in the input sequence after addition of two special tokens marking beginning and end of a sequence by the tokenizer.
\n", - "The last dimension for both is of size 768 which is the embedding dimension for this default configuration of bert.
\n", - "Now you tell me, which encoding are you gonna use in your project ??" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/bert_embeddings/bert_embeddings.py b/bert_embeddings/bert_embeddings.py deleted file mode 100644 index 109081b1b..000000000 --- a/bert_embeddings/bert_embeddings.py +++ /dev/null @@ -1,41 +0,0 @@ -# Copyright 2019 Iguazio -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -import json -import pickle - -import torch -from transformers import BertModel, BertTokenizer - - -def init_context(context): - tokenizer = BertTokenizer.from_pretrained("bert-base-uncased") - model = BertModel.from_pretrained("bert-base-uncased") - model.eval() - - setattr(context.user_data, "tokenizer", tokenizer) - setattr(context.user_data, "model", model) - - -def handler(context, event): - docs = json.loads(event.body) - docs = [doc.lower() for doc in docs] - docs = context.user_data.tokenizer.batch_encode_plus( - docs, pad_to_max_length=True, return_tensors="pt" - ) - - with torch.no_grad(): - embeddings = context.user_data.model(**docs) - embeddings = [embeddings[0].numpy(), embeddings[1].numpy()] - return pickle.dumps(embeddings) diff --git a/bert_embeddings/function.yaml b/bert_embeddings/function.yaml deleted file mode 100644 index 4a3fcf54f..000000000 --- a/bert_embeddings/function.yaml +++ /dev/null @@ -1,38 +0,0 @@ -kind: remote -metadata: - name: bert-embeddings - tag: '' - hash: 57a2ce8e0da1f6e813a8649e9ea6fcbb69a1ce5f - project: '' - labels: - framework: pytorch - categories: - - machine-learning - - data-preparation -spec: - command: '' - args: [] - image: mlrun/mlrun - build: - functionSourceCode: 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 - commands: [] - code_origin: http://github.com/aviaIguazio/functions.git#a1c9940e4c2420c88063768b4038e29b1f4e37a6:/Users/Avi_Asulin/PycharmProjects/mlrun/functions/bert_embeddings/bert_embeddings.py - origin_filename: /Users/Avi_Asulin/PycharmProjects/mlrun/functions/bert_embeddings/bert_embeddings.py - requirements: - - torch - description: Get BERT based embeddings for given text - default_handler: '' - disable_auto_mount: false - clone_target_dir: '' - env: [] - priority_class_name: '' - preemption_mode: prevent - min_replicas: 1 - max_replicas: 4 - source: '' - function_handler: bert_embeddings:handler - base_image_pull: false - affinity: null - tolerations: null - security_context: {} -verbose: false diff --git a/bert_embeddings/item.yaml b/bert_embeddings/item.yaml deleted file mode 100644 index f0eaed1c0..000000000 --- a/bert_embeddings/item.yaml +++ /dev/null @@ -1,26 +0,0 @@ -apiVersion: v1 -categories: -- machine-learning -- data-preparation -description: Get BERT based embeddings for given text -doc: '' -example: bert_embeddings.ipynb -generationDate: 2022-08-28:17-25 -hidden: false -icon: '' -labels: - framework: pytorch -maintainers: [] -marketplaceType: '' -mlrunVersion: 1.4.1 -name: bert-embeddings -platformVersion: 3.5.3 -spec: - filename: bert_embeddings.py - handler: handler - image: mlrun/mlrun - kind: nuclio - requirements: - - torch -url: '' -version: 1.2.0 diff --git a/bert_embeddings/requirements.txt b/bert_embeddings/requirements.txt deleted file mode 100644 index 747b7aa97..000000000 --- a/bert_embeddings/requirements.txt +++ /dev/null @@ -1 +0,0 @@ -transformers \ No newline at end of file diff --git a/bert_embeddings/test_bert_embeddings.py b/bert_embeddings/test_bert_embeddings.py deleted file mode 100644 index 7ad9101cc..000000000 --- a/bert_embeddings/test_bert_embeddings.py +++ /dev/null @@ -1,32 +0,0 @@ -# Copyright 2019 Iguazio -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -from bert_embeddings import init_context,handler -import nuclio -import json -import pickle -import numpy as np - -ARCHIVE = "https://archive.ics.uci.edu/ml/machine-learning-databases/00280/HIGGS.csv.gz" -ARTIFACTS_PATH = 'artifacts' - - -def test_bert_embeddings(): - event = nuclio.Event(body=json.dumps(['John loves Mary'])) - ctx = nuclio.Context() - init_context(ctx) - outputs = pickle.loads(handler(ctx, event)) - assert (True if abs(np.mean(outputs[0]) - -0.011996539) <= 0.0001 else False) is True - assert (True if abs(np.mean(outputs[0]) - -0.011996539) > 0 else False) is True - diff --git a/concept_drift/README.md b/concept_drift/README.md deleted file mode 100644 index 92e6d893e..000000000 --- a/concept_drift/README.md +++ /dev/null @@ -1,132 +0,0 @@ -# Concept Drift - -**Concept drift** is a change in the statistical properties of the **target variable** over time. - -When deploying our models to production, we must ensure our models perform as we expect them to - reaching the same level of performence we have seen on our test sets or at least performing in the same quality as when they were deployed. - -However, often this is not the case. there are many factors that can affect our model's performance like seasonality or any unkown root causes that will change the laws underlying our data and invalidate some assumptions made by the model. - -We offer this function to help combat Concept Drift with implementation of streaming DDM, EDDM and PH concept drift detectors. - -## How to integrate - -This function is made of two parts: - -1. Kubernetes job to instantiate the selected models with a provided base dataset (the test dataset could be used) -2. [Nuclio serverless function](../concept_drift_streaming/concept_drift_streaming.ipynb) listed on a _labeled stream_, which will be deployed from this function after the models initialization and run the models per event and provide necessary alerts. - -There are two steps to integrate sucessfully with your workflow: - -1. Provide a stream where each event containes the joined **label** and **prediction** for that specific event. -2. Add this function to the workflow with the following params: - -```markdown -:param context: MLRun context -:param base_dataset: Dataset containing label_col and prediction_col to initialize the detectors -:param input_stream: labeled stream to track. - Should contain label_col and prediction_col -:param output_stream: Output stream to push the detector's alerts -:param output_tsdb: Output TSDB table to allow analysis and display -:param tsdb_batch_size: Batch size of alerts to buffer before pushing to the TSDB -:param callbacks: Additional rest endpoints to send the alert data to -:param models: List of the detectors to deploy - Defaults to ['ddm', 'eddm', 'pagehinkley']. -:param models_dest: Location for saving the detectors - Defaults to 'models' (in relation to artifact_path). -:param pagehinkley_threshold: Drift level threshold for PH detector Defaults to 10. -:param ddm_warning_level: Warning level alert for DDM detector Defaults to 2. -:param ddm_out_control_level: Drift level alert for DDM detector Defaults to 3. -:param label_col: Label column to be used on base_dataset and input_stream - Defaults to 'label'. -:param prediction_col: Prediction column to be used on base_dataset and input_stream - Defaults to 'prediction'. -:param hub_url: hub_url in case the default is not used, concept_drift_streaming will be loaded - by this url - Defaults to mlconf.hub_url. -:param fn_tag: hub tag to use - Defaults to 'master' -``` - -## Algorithms - -We offer to deploy up to 3 concept drift streaming detectors - -### DDM - Drift Detection Method - -Models the **Number of errors** as a **binomial** variable. This enables us to confine the expected number of errors in a prediction stream window to within some standard deviation. - -- Good for **abrupt** drift changes - -
- -![$mu=np_t$](https://latex.codecogs.com/svg.latex?mu=np_t) - -![$\sigma=\sqrt{\frac{p_t(1-p_t)}{n}}$]() - -
- -**Alert** when: - -
- -![$p_t+\sigma_t\ge{p_{min}+3\sigma_{min}}$](https://latex.codecogs.com/svg.latex?p_t+\sigma_t\ge{p_{min}+3\sigma_{min}}) - -
- -### EDDM - Early Drift Detection Method - -Uses the distance between two consecutive errors. - -- works better for **gradual** drift changes. -- More sensitive then DDM for noise -- Requires Minimal number of errors to initialize the statistics. - -**Warning**: - -
- -![$\frac{p_t+2\sigma_t}{p_{max}+2\sigma_{max}}<0.95$](https://latex.codecogs.com/svg.latex?\frac{p_t+2\sigma_t}{p_{max}+2\sigma_{max}}<0.95) - -
- -**Alert**: - -
- -![$\frac{p_t+2\sigma_t}{p_{max}+2\sigma_{max}}<0.90$](https://latex.codecogs.com/svg.latex?\frac{p_t+2\sigma_t}{p_{max}+2\sigma_{max}}<0.90) - -
- -### PageHinkley Test: - -The PageHinkley test is a sequential analysis technique typically used for monitoring change detection. (The test was designed to detect change in avg. of a Gaussian signal). In this test we use: -x*1*, ..., x*n* - labeled dataset -δ - magnitude threshold -λ - detection threshold - -
- -![$\hat{x_T}=\frac{1}{T}\sum_{t=1}^{t}{x_t}$](https://latex.codecogs.com/svg.latex?\hat{x_T}=\frac{1}{T}\sum_{t=1}^{t}{x_t}) - -![$\sum_{t=1}^T{x_t-\hat{x_T}-\delta}$](https://latex.codecogs.com/svg.latex?U_T=\sum_{t=1}^T{x_t-\hat{x_T}-\delta}) - -![$m_T=min(U_t,t=1..T)$]() - -
- -**Alert**: - -
- -![$U_T-m_T>\lambda$](https://latex.codecogs.com/svg.latex?U_T-m_T>\lambda) - -
- -## Additional resources -[A Study on Change Detection Methods](https://pdfs.semanticscholar.org/bb6e/8a44c0efcd725aae1c0b1817561f6e278c2c.pdf), Raquel Sebasti˜ao1,2 and Jo˜ao Gama1,3, 1 LIAAD-INESC Porto L.A., University of Porto -Rua de Ceuta, 118 - 6, 4050-190 Porto, Portugal -2 Faculty of Science, University of Porto -3 Faculty of Economics, University of Porto -{raquel,jgama}@liaad.up.pt - -[MLOps Live #4 - How to Detect & Remediate Drift in Production with MLOps Automation](https://www.youtube.com/watch?v=66_Q7mJZOSc&t=1296s) diff --git a/concept_drift/concept_drift.ipynb b/concept_drift/concept_drift.ipynb deleted file mode 100644 index e9c063b66..000000000 --- a/concept_drift/concept_drift.ipynb +++ /dev/null @@ -1,793 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Concept Drift - Deployer\n", - "Deploy a streaming Concept Drift detector on a labeled stream. \n", - "It will initialize the selected drift detectors with the base_dataset's statistics and deploy the [concept_drift_streaming](https://github.com/mlrun/functions/blob/master/concept_drift_streaming/concept_drift_streaming.ipynb) function from the hub.
\n", - "adding [V3IOStreamTrigger](https://nuclio.io/docs/latest/reference/triggers/v3iostream/) in order to listen to the input_stream." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Steps**\n", - "\n", - "1. [Data exploration](#Data-exploration)\n", - "2. [Creating the input stream](#Creating-the-input-stream)\n", - "3. [Importing the function](#Importing-the-function)\n", - "4. [Running the function remotely](#Running-the-function-remotely)\n", - "5. [Testing the function](#Testing-the-function)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Data exploration**" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In order to know about the performance of a drift detector by measuring the different detection metrics, we need to know beforehand where a real drift occurs.
\n", - "This is only possible with synthetic datasets.
The scikit-multiflow framework allows generating several kinds of synthetic data to simulate the occurrence of drifts.
\n", - "[Harvard dataverse](https://dataverse.harvard.edu) provides futher explanations on the [used dataset](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/5OWRGB) along with different kinds of drifted datasets.
\n", - "mixed_0101_abrupto has 4 concepts and 3 drifts at time steps 10000, 20000, and 30000.
\n", - "Our dataset will be train-test-splitted, the train part (first 5000 examples) is used to train the model (that is generated easly using [sklearn_classifer](https://github.com/mlrun/functions/blob/master/sklearn_classifier/sklearn_classifier.ipynb)).
\n", - "The test part (which is already predicted by the model) will be pushed to the input stream in order to detect drifts." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " X1 X2 X3 X4 class\n", - "0 0.0 1.0 0.460101 0.592744 1.0\n", - "1 1.0 1.0 0.588788 0.574984 0.0\n", - "2 0.0 0.0 0.401641 0.679325 1.0\n", - "3 1.0 1.0 0.306076 0.182108 0.0\n", - "4 0.0 0.0 0.962847 0.579245 1.0" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import pandas as pd\n", - "data_path = 'https://s3.wasabisys.com/iguazio/data/function-marketplace-data/concept_drift/mixed_0101_abrupto.csv'\n", - "predicted_train_path = 'https://s3.wasabisys.com/iguazio/data/function-marketplace-data/concept_drift/predicted_abrupto_train.csv'\n", - "predicted_test_data_path = 'https://s3.wasabisys.com/iguazio/data/function-marketplace-data/concept_drift/predicted_abrupto_test.csv'\n", - "# You can find the model used here\n", - "models_path = 'https://s3.wasabisys.com/iguazio/models/function-marketplace-models/concept_drift/concept_drift_random_forest.pkl'\n", - "original_data = pd.read_csv(data_path)\n", - "original_data.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " X1 X2 X3 X4 class predicted_col\n", - "34995 0.0 0.0 0.010106 0.647269 0.0 1.0\n", - "34996 1.0 1.0 0.293651 0.737291 1.0 0.0\n", - "34997 0.0 0.0 0.848546 0.552337 0.0 1.0\n", - "34998 1.0 1.0 0.614754 0.859896 1.0 0.0\n", - "34999 1.0 0.0 0.265306 0.843716 0.0 1.0" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "predicted_test = pd.read_csv(predicted_test_data_path)\n", - "predicted_test.tail()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Creating the input stream**" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import os \n", - "\n", - "container = os.path.join('/',os.environ['V3IO_HOME'].split('/')[0])\n", - "user = os.environ[\"V3IO_USERNAME\"]\n", - "rel_path = os.getcwd()[6:] + '/artifacts'\n", - "\n", - "base_input_stream = os.path.join(user,rel_path) + \"/inputs_stream\"\n", - "base_output_stream = os.path.join(user,rel_path) + \"/output_stream\"\n", - "input_stream = os.path.join(container,base_input_stream)\n", - "output_stream = os.path.join(container,user,rel_path) + \"/output_stream\"\n", - "tsdb_path = os.path.join(container,user,rel_path) + \"/output_tsdb\"\n", - "\n", - "stream_consumer_group = 'cg45'" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "import v3io.dataplane\n", - "\n", - "client = v3io.dataplane.Client()\n", - "response = client.stream.create(container = container,\n", - " stream_path=base_input_stream,\n", - " shard_count=1,\n", - " raise_for_status = v3io.dataplane.RaiseForStatus.never)\n", - "response.raise_for_status([409, 204])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Importing the function**" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2021-10-25 10:27:04,105 [info] created and saved project function-marketplace\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Importing the function\n", - "import mlrun\n", - "mlrun.set_environment(project='function-marketplace')\n", - "\n", - "fn = mlrun.import_function(\"hub://concept_drift:development\")\n", - "fn.apply(mlrun.auto_mount())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Running the function remotely**" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2021-10-25 10:27:04,567 [info] starting run concept_drift uid=fa07c222e77d4eac86d2ce9317aaded1 DB=http://mlrun-api:8080\n", - "> 2021-10-25 10:27:04,709 [info] Job is running in the background, pod: concept-drift-ggxgb\n", - "> 2021-10-25 10:27:11,199 [info] Loading base dataset\n", - "> 2021-10-25 10:27:13,227 [info] Creating models\n", - "> 2021-10-25 10:27:13,227 [info] Streaming data to models\n", - "> 2021-10-25 10:27:13,347 [info] Logging ready models\n", - "> 2021-10-25 10:27:13,487 [info] Deploying Concept Drift Streaming function\n", - "> 2021-10-25 10:27:13,490 [info] Starting remote function deploy\n", - "2021-10-25 10:27:13 (info) Deploying function\n", - "2021-10-25 10:27:13 (info) Building\n", - "2021-10-25 10:27:13 (info) Staging files and preparing base images\n", - "2021-10-25 10:27:13 (info) Building processor image\n", - "2021-10-25 10:27:15 (info) Build complete\n", - "2021-10-25 10:27:21 (info) Function deploy complete\n", - "> 2021-10-25 10:27:21,797 [info] successfully deployed function: {'internal_invocation_urls': ['nuclio-function-marketplace-concept-drift-streaming.default-tenant.svc.cluster.local:8080'], 'external_invocation_urls': ['default-tenant.app.dev39.lab.iguazeng.com:31143']}\n", - "> 2021-10-25 10:27:21,868 [info] run executed, status=completed\n", - "final state: completed\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "
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projectuiditerstartstatenamelabelsinputsparametersresultsartifacts
function-marketplace0Oct 25 10:27:10completedconcept_drift
v3io_user=dani
kind=job
owner=dani
host=concept-drift-ggxgb
base_dataset
input_stream=/users/dani/test/functions/concept_drift/artifacts/inputs_stream
consumer_group=cg45
output_stream=/users/dani/test/functions/concept_drift/artifacts/output_stream
output_tsdb=/users/dani/test/functions/concept_drift/artifacts/output_tsdb
tsdb_batch_size=1
models=['ddm', 'eddm', 'pagehinkley']
label_col=class
prediction_col=predicted_col
fn_tag=development
eddm_concept_drift
pagehinkley_concept_drift
ddm_concept_drift
\n", - "
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\n", - " Title\n", - " ×\n", - "
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\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "data": { - "text/html": [ - " > to track results use the .show() or .logs() methods or click here to open in UI" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2021-10-25 10:27:23,031 [info] run executed, status=completed\n" - ] - } - ], - "source": [ - "drift_run = fn.run(name='concept_drift',\n", - " params={'input_stream' : input_stream,\n", - " 'consumer_group' : stream_consumer_group,\n", - " 'output_stream' : output_stream,\n", - " 'output_tsdb' : tsdb_path,\n", - " 'tsdb_batch_size' : 1,\n", - " 'models' : ['ddm', 'eddm', 'pagehinkley'], # defaults\n", - " 'label_col' : 'class',\n", - " 'prediction_col' : 'predicted_col',\n", - " 'fn_tag' : 'development'},\n", - " inputs={'base_dataset' : predicted_train_path},\n", - " artifact_path = os.path.join(os.getcwd(), 'artifacts'))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Testing the function**\n", - "> Mark that we are testing the deployed function - concept_drift_streaming" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'data': '{\"class\": 1.0, \"request\": {\"instances\": [{\"X1\": 0.0, \"X2\": 0.0, \"X3\": 0.0634475073, \"X4\": 0.4136568818}]}, \"resp\": [1], \"when\": \"2021-10-25 10:27:23.152584\", \"model\": \"sklearn.ensemble.RandomForestClassifier\"}'}" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import json\n", - "import datetime\n", - "\n", - "# Reshaping the data to V3IOStream format.\n", - "def restructure_stream_event(context, event):\n", - " instances = [dict()]\n", - " for key in predicted_test.keys():\n", - " if key not in ['when', 'class', 'model', 'worker', 'hostname', 'predicted_col']:\n", - " instances[0].update({key: event.pop(key)})\n", - " event['request'] = {'instances': instances}\n", - " event['resp'] = [int(event.pop('predicted_col'))]\n", - " event['when'] = datetime.datetime.strftime(datetime.datetime.now(), format=\"%Y-%m-%d %H:%M:%S.%f\")\n", - " event['model'] = 'sklearn.ensemble.RandomForestClassifier'\n", - " return event\n", - " \n", - " \n", - "records = json.loads(predicted_test.to_json(orient='records'))\n", - "records = [{'data': json.dumps(restructure_stream_event(context, record))} for record in records]\n", - "\n", - "# showing first record\n", - "records[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Creating v3io client\n", - "v3io_client = v3io.dataplane.Client()\n", - "\n", - "# Pushing some undrifted data to the input stream\n", - "response = v3io_client.stream.put_records(container=container,\n", - " stream_path=base_input_stream, \n", - " records=records[4900:5100])" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'SequenceNumber': 200,\n", - " 'Data': 'eyJjbGFzcyI6IDAuMCwgInJlcXVlc3QiOiB7Imluc3RhbmNlcyI6IFt7IlgxIjogMC4wLCAiWDIiOiAwLjAsICJYMyI6IDAuMzMzMTYzNjk4OSwgIlg0IjogMC40MjE2NzY1Njg3fV19LCAicmVzcCI6IFsxXSwgIndoZW4iOiAiMjAyMS0xMC0yNSAxMDoyNzoyMy4yOTM3OTgiLCAibW9kZWwiOiAic2tsZWFybi5lbnNlbWJsZS5SYW5kb21Gb3Jlc3RDbGFzc2lmaWVyIn0=',\n", - " 'ArrivalTimeSec': 1635157644,\n", - " 'ArrivalTimeNSec': 395309631}" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Getting earliest location in the shard\n", - "location = json.loads(v3io_client.stream.seek(container=container,\n", - " stream_path=base_input_stream,\n", - " shard_id=0,\n", - " seek_type='EARLIEST').body)['Location']\n", - "# Getting records from input stream\n", - "response = v3io_client.stream.get_records(container=container,\n", - " stream_path=base_input_stream,\n", - " shard_id=0, location=location)\n", - "# Showing the last sequence that is written to the input stream\n", - "json.loads(response.body)['Records'][-1]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Make sure some time has passed - the function needs to be triggered by the input stream, then it'll write to the output stream" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Getting earliest location in the shard\n", - "location = json.loads(v3io_client.stream.seek(container=container,\n", - " stream_path=base_output_stream,\n", - " shard_id=0,\n", - " seek_type='EARLIEST').body)['Location']\n", - "# Getting records from output stream\n", - "response = v3io_client.stream.get_records(container=container,\n", - " stream_path=base_output_stream,\n", - " shard_id=0, location=location)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "sequence number : 106, data : {'class': 0.0, 'request': {'instances': [{'X1': 0.0, 'X2': 0.0, 'X3': 0.9628473804, 'X4': 0.5792453402}]}, 'resp': [1], 'when': '2021-10-25 10:27:23.291145', 'model': 'sklearn.ensemble.RandomForestClassifier', 'ddm_warning_zone': 0, 'ddm_drift': 1, 'eddm_warning_zone': 0, 'eddm_drift': 0}\n", - "sequence number : 122, data : {'class': 0.0, 'request': {'instances': [{'X1': 0.0, 'X2': 0.0, 'X3': 0.4969765505, 'X4': 0.9784738351}]}, 'resp': [1], 'when': '2021-10-25 10:27:23.291558', 'model': 'sklearn.ensemble.RandomForestClassifier', 'ddm_warning_zone': 0, 'ddm_drift': 0, 'eddm_warning_zone': 0, 'eddm_drift': 1}\n" - ] - } - ], - "source": [ - "# Showing changed detected\n", - "import base64\n", - "for instance in json.loads(response.body)['Records']:\n", - " seq = instance[\"SequenceNumber\"]\n", - " data = json.loads(base64.b64decode(instance['Data']))\n", - " if(data['ddm_drift']==1 or data['eddm_drift']==1):\n", - " print(f'sequence number : {seq}, data : {data}')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that the system detected a change in the 106 instance, which is 10006 instance in the real dataset -
\n", - "5000 first instances are for train, we started pushing data from the 4900 instance of the test dataset (9900 from the real dataset), and we pushed only 200 instances.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[Back to the top](#Concept-Drift---Deployer)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python [conda env:root] *", - "language": "python", - "name": "conda-root-py" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/concept_drift/concept_drift.py b/concept_drift/concept_drift.py deleted file mode 100644 index 03355d3b5..000000000 --- a/concept_drift/concept_drift.py +++ /dev/null @@ -1,147 +0,0 @@ -# Copyright 2019 Iguazio -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# Generated by nuclio.export.NuclioExporter - -import skmultiflow.drift_detection # We will grab our PH, DDM, EDDM algorithms from here -import numpy as np -import pandas as pd -import os -from cloudpickle import dumps, load, dump - -from nuclio.triggers import V3IOStreamTrigger -from mlrun import DataItem, import_function, mlconf, MLClientCtx, mount_v3io - -import random - - -def concept_drift_deployer( - context: MLClientCtx, - base_dataset: DataItem, - input_stream: str, - consumer_group: str, - output_stream: str, - output_tsdb: str, - tsdb_batch_size: int, - callbacks: list, - models: list = ["ddm", "eddm", "pagehinkley"], - models_dest="models", - pagehinkley_threshold: float = 10, - ddm_warning_level: float = 2, - ddm_out_control_level: float = 3, - label_col="label", - prediction_col="prediction", - hub_url: str = mlconf.hub_url, - fn_tag: str = "master", -): - """Deploy a streaming Concept Drift detector on a labeled stream - This function is the Deployment step for the Streaming Concept Drift Detector. - It will load the selected drift detectors and initialize them with the - base_dataset's statistics. Then it will deploy the concept_drift_streaming - function and pass the models to it for streaming concept-drift detection on top - of a labeled stream. - - :param context: MLRun context - :param base_dataset: Dataset containing label_col and prediction_col to initialize the detectors - :param input_stream: labeled stream to track. - Should contain label_col and prediction_col - :param output_stream: Output stream to push the detector's alerts - :param output_tsdb: Output TSDB table to allow analysis and display - :param tsdb_batch_size: Batch size of alerts to buffer before pushing to the TSDB - :param callbacks: Additional rest endpoints to send the alert data to - :param models: List of the detectors to deploy - Defaults to ['ddm', 'eddm', 'pagehinkley']. - :param models_dest: Location for saving the detectors - Defaults to 'models' (in relation to artifact_path). - :param pagehinkley_threshold: Drift level threshold for PH detector Defaults to 10. - :param ddm_warning_level: Warning level alert for DDM detector Defaults to 2. - :param ddm_out_control_level: Drift level alert for DDM detector Defaults to 3. - :param label_col: Label column to be used on base_dataset and input_stream - Defaults to 'label'. - :param prediction_col: Prediction column to be used on base_dataset and input_stream - Defaults to 'prediction'. - :param hub_url: hub_url in case the default is not used, concept_drift_streaming will be loaded - by this url - Defaults to mlconf.hub_url. - :param fn_tag: hub tag to use - Defaults to 'master' - """ - - mlconf.dbpath = mlconf.dbpath or "http://mlrun-api:8080" - mlconf.hub_url = hub_url - fn = import_function(url=f"hub://concept_drift_streaming:{fn_tag}") - - context.logger.info("Loading base dataset") - base_df = base_dataset.as_df() - error_stream = np.where( - base_df[prediction_col].values == base_df[label_col].values, 0, 1 - ) - - context.logger.info("Creating models") - models = [ - model.strip() - for model in os.getenv("models", "pagehinkley, ddm, eddm").split(",") - ] - models = { - "eddm": skmultiflow.drift_detection.EDDM(), - "pagehinkley": skmultiflow.drift_detection.PageHinkley( - min_instances=len(error_stream), threshold=pagehinkley_threshold - ), - "ddm": skmultiflow.drift_detection.DDM( - min_num_instances=len(error_stream), - warning_level=ddm_warning_level, - out_control_level=ddm_out_control_level, - ), - } - - context.logger.info("Streaming data to models") - for i in range(len(error_stream)): - for model_name, model in models.items(): - model.add_element(error_stream[i]) - - context.logger.info("Logging ready models") - for name, model in models.items(): - data = dumps(model) - model_file = f"{name}.pkl" - context.log_model( - f"{name}_concept_drift", - body=data, - labels={"framework": "skmultiflow", "workflow": "concept-drift"}, - model_file=model_file, - model_dir=models_dest, - tag="latest", - ) - fn.set_envs( - { - f"{name}_model_path": os.path.join( - context.artifact_path, models_dest, model_file - ) - } - ) - - context.logger.info("Deploying Concept Drift Streaming function") - fn.set_envs( - { - "label_col": label_col, - "prediction_col": prediction_col, - "drift_stream": output_stream, - "tsdb_table": output_tsdb, - "pagehinkley_threshold": pagehinkley_threshold, - "ddm_warning_level": ddm_warning_level, - "ddm_out_control": ddm_out_control_level, - } - ) - fn.add_v3io_stream_trigger(stream_path = input_stream, name = 'stream', group = consumer_group) - fn.apply(mount_v3io()) - fn.deploy(project=context.project) diff --git a/concept_drift/function.yaml b/concept_drift/function.yaml deleted file mode 100644 index 071111c78..000000000 --- a/concept_drift/function.yaml +++ /dev/null @@ -1,112 +0,0 @@ -kind: job -metadata: - name: concept-drift - tag: '' - hash: 935da41196802875e19948974f32b6f00c29feb2 - project: '' - labels: - author: orz - framework: sklearn - categories: - - machine-learning - - model-serving -spec: - command: '' - args: [] - image: mlrun/ml-models - env: [] - default_handler: concept_drift_deployer - entry_points: - concept_drift_deployer: - name: concept_drift_deployer - doc: "Deploy a streaming Concept Drift detector on a labeled stream\n This\ - \ function is the Deployment step for the Streaming Concept Drift Detector.\n\ - \ It will load the selected drift detectors and initialize them with the\n\ - \ base_dataset's statistics. Then it will deploy the concept_drift_streaming\n\ - \ function and pass the models to it for streaming concept-drift detection\ - \ on top\n of a labeled stream." - parameters: - - name: context - type: MLClientCtx - doc: MLRun context - default: '' - - name: base_dataset - type: DataItem - doc: Dataset containing label_col and prediction_col to initialize the detectors - default: '' - - name: input_stream - type: str - doc: labeled stream to track. Should contain label_col and prediction_col - default: '' - - name: consumer_group - type: str - default: '' - - name: output_stream - type: str - doc: Output stream to push the detector's alerts - default: '' - - name: output_tsdb - type: str - doc: Output TSDB table to allow analysis and display - default: '' - - name: tsdb_batch_size - type: int - doc: Batch size of alerts to buffer before pushing to the TSDB - default: '' - - name: callbacks - type: list - doc: Additional rest endpoints to send the alert data to - default: '' - - name: models - type: list - doc: List of the detectors to deploy Defaults to ['ddm', 'eddm', 'pagehinkley']. - default: - - ddm - - eddm - - pagehinkley - - name: models_dest - doc: Location for saving the detectors Defaults to 'models' (in relation to - artifact_path). - default: models - - name: pagehinkley_threshold - type: float - doc: Drift level threshold for PH detector Defaults to 10. - default: 10 - - name: ddm_warning_level - type: float - doc: Warning level alert for DDM detector Defaults to 2. - default: 2 - - name: ddm_out_control_level - type: float - doc: Drift level alert for DDM detector Defaults to 3. - default: 3 - - name: label_col - doc: Label column to be used on base_dataset and input_stream Defaults to - 'label'. - default: label - - name: prediction_col - doc: Prediction column to be used on base_dataset and input_stream Defaults - to 'prediction'. - default: prediction - - name: hub_url - type: str - doc: hub_url in case the default is not used, concept_drift_streaming will - be loaded by this url Defaults to mlconf.hub_url. - default: <_ast.Name object at 0x7f48eda946d0> - - name: fn_tag - type: str - doc: hub tag to use Defaults to 'master' - default: master - outputs: - - default: '' - lineno: 15 - description: Deploy a streaming Concept Drift detector on a labeled stream - build: - functionSourceCode: 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- commands: - - python -m pip install scikit-multiflow - code_origin: https://github.com/daniels290813/functions.git#82bbfde4afa2eae77059e05c70bbebacf530fd0d:/User/test/functions/concept_drift/concept_drift.py - origin_filename: /User/test/functions/concept_drift/concept_drift.py - disable_auto_mount: false - affinity: null -verbose: false diff --git a/concept_drift/item.yaml b/concept_drift/item.yaml deleted file mode 100644 index 2ee37e386..000000000 --- a/concept_drift/item.yaml +++ /dev/null @@ -1,27 +0,0 @@ -apiVersion: v1 -categories: -- machine-learning -- model-serving -description: Deploy a streaming Concept Drift detector on a labeled stream -doc: '' -example: concept_drift.ipynb -generationDate: 2022-08-28:17-25 -hidden: false -icon: '' -labels: - author: orz - framework: sklearn -maintainers: [] -marketplaceType: '' -mlrunVersion: 1.1.0 -name: concept-drift -platformVersion: 3.5.0 -spec: - filename: concept_drift.py - handler: concept_drift_deployer - image: mlrun/ml-models - kind: job - requirements: - - scikit-multiflow -url: '' -version: 1.1.0 diff --git a/concept_drift/requirements.txt b/concept_drift/requirements.txt deleted file mode 100644 index fa0fddd88..000000000 --- a/concept_drift/requirements.txt +++ /dev/null @@ -1 +0,0 @@ -skmultiflow \ No newline at end of file diff --git a/concept_drift_streaming/concept_drift_streaming.ipynb b/concept_drift_streaming/concept_drift_streaming.ipynb deleted file mode 100644 index b916cb7a2..000000000 --- a/concept_drift_streaming/concept_drift_streaming.ipynb +++ /dev/null @@ -1,480 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Concept Drift Streaming" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import nuclio" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from pprint import pprint" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "%%nuclio cmd -c\n", - "python -m pip install scikit-multiflow==0.4.1\n", - "python -m pip install v3io_frames" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "%nuclio: setting kind to 'nuclio'\n", - "%nuclio: setting spec.build.baseImage to 'mlrun/ml-models'\n" - ] - } - ], - "source": [ - "# Define function spec\n", - "%nuclio config kind = \"nuclio\"\n", - "%nuclio config spec.build.baseImage = \"mlrun/ml-models\"\n", - "\n", - "# Add V3IO Mount\n", - "# %nuclio env %v3io" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# nuclio: ignore\n", - "env = {'label_col': 'resp',\n", - " 'prediction_col': 'prediction',\n", - " 'drift_stream': '/bigdata/network-operations/drift_stream',\n", - " 'tsdb_table': 'network-operations/drift_tsdb',\n", - " 'pagehinkley_threshold': 10,\n", - " 'models': ['pagehinkley', 'ddm', 'eddm'],\n", - " 'window_size': 10}\n", - "config = {'kind': 'nuclio',\n", - " 'spec.build.baseImage': 'mlrun/ml-models'}\n", - "cmd = ['python -m pip install scikit-multiflow',\n", - " 'python -m pip install v3io_frames']\n", - "v3io = True\n", - "config = nuclio.ConfigSpec(env=env,\n", - " config=config,\n", - " cmd=cmd,\n", - " v3io=v3io)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# nuclio: start-code" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "import skmultiflow.drift_detection\n", - "import numpy as np\n", - "import pandas as pd\n", - "import os\n", - "import json\n", - "import v3io.dataplane\n", - "import v3io_frames as v3f\n", - "import requests\n", - "from cloudpickle import load\n", - "\n", - "# For testing\n", - "import random" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "def split_path(mntpath=''):\n", - " if mntpath[0] == '/':\n", - " mntpath = mntpath[1:]\n", - " paths = mntpath.split('/')\n", - " container = paths[0]\n", - " subpath = ''\n", - " if len(paths) > 1:\n", - " subpath = mntpath[len(container):]\n", - " return container, subpath\n", - "\n", - "\n", - "def create_stream(context, path, shards=1):\n", - " # create a stream w/8 shards\n", - " container, stream_path = split_path(path)\n", - " context.logger.info(f'Creating stream in Container: {container} & Path {stream_path}')\n", - " response = context.v3io_client.create_stream(container=container,\n", - " path=stream_path, \n", - " shard_count=shards,\n", - " raise_for_status=v3io.dataplane.RaiseForStatus.never)\n", - " response.raise_for_status([409, 204])\n", - " \n", - " \n", - "def push_to_stream(context, stream_path, data):\n", - " records = [{'data': json.dumps(rec)} for rec in data]\n", - " container, stream_path = split_path(stream_path)\n", - " response = context.v3io_client.put_records(container=container,\n", - " path=stream_path, \n", - " records=records)\n", - "\n", - "\n", - "def construct_record(record):\n", - " label_col = os.getenv('label_col', 'label')\n", - " prediction_col = os.getenv('prediction_col', 'prediction')\n", - " res = dict([(k, record[k]) for k in ['when', 'class', 'model', 'resp', 'request']])\n", - " res['feature_vector'] = res.pop('request')['instances'][0]\n", - " res['timestamp'] = res.pop('when')\n", - " res['prediction'] = res['resp'][0]\n", - " return res" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "def init_context(context):\n", - " # create a v3io context object\n", - " v3io_client = v3io.dataplane.Client()\n", - " setattr(context, \"v3io_client\", v3io_client)\n", - " \n", - " # Setup windowing for TSDB writer\n", - " v3f_client = v3f.Client('framesd:8081', container='bigdata')\n", - " setattr(context, \"v3f\", v3f_client)\n", - " window = []\n", - " setattr(context, 'window', window)\n", - " setattr(context, 'window_size', int(os.getenv('window_size', 10)))\n", - " setattr(context, 'tsdb_table', os.getenv('tsdb_table', 'concept_drift_tsdb_1'))\n", - " try:\n", - " context.v3f.create('tsdb', context.tsdb_table, rate='1/s', if_exists=1)\n", - " except Exception as e:\n", - " context.logger.info(f'Creating context with rate= faile for {e}')\n", - " context.v3f.create('tsdb', context.tsdb_table, attrs={'rate': '1/s'}, if_exists=1)\n", - " \n", - " # Setup callbacks\n", - " callbacks = [callback.strip() for callback in os.getenv('callbacks', '').split(',')]\n", - " setattr(context, 'callbacks', callbacks)\n", - " \n", - " # Setup drift stream\n", - " setattr(context, 'drift_stream', os.getenv('drift_stream', '/bigdata/drift_stream'))\n", - " try:\n", - " create_stream(context, context.drift_stream, int(os.getenv('drift_stream_shards', 1)))\n", - " except:\n", - " context.logger.info(f'{context.drift_stream} already exists')\n", - " \n", - " # Load models\n", - " models = {}\n", - " model_types = ['pagehinkely', 'ddm', 'eddm']\n", - " path_suffix = '_model_path'\n", - " for model in model_types:\n", - " model_env = f'{model}{path_suffix}'\n", - " if model_env in os.environ:\n", - " with open(os.environ[model_env], 'rb') as f:\n", - " models[model] = load(f)\n", - " setattr(context, 'models', models)\n", - " \n", - " # Columns to check\n", - " setattr(context, 'label_col', os.getenv('label_col', 'label'))\n", - " setattr(context, 'prediction_col', os.getenv('prediction_col', 'prediction'))" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "def handler(context, event):\n", - " # Construct event\n", - " context.logger.info(f'event: {event.body}')\n", - " full_event = json.loads(event.body)\n", - " record = construct_record(full_event)\n", - " \n", - " # Is our prediction wrong?\n", - " is_error = record[context.label_col] != record[context.prediction_col]\n", - " context.logger.info(f'Adding {is_error}')\n", - " \n", - " # Process the {is_error} element with our algorithms\n", - " for name, model in context.models.items():\n", - " # Add element\n", - " results = {'timestamp': record['timestamp']}\n", - " results['algorithm'] = name\n", - " model.add_element(is_error)\n", - " \n", - " # Detect warning zone (if applicable to the algorithm)\n", - " if hasattr(model, 'detected_warning_zone') and model.detected_warning_zone():\n", - " context.logger.info(f'{name}\\tWarning zone detected')\n", - " results['warning_zone'] = 1\n", - " full_event[f'{name}_warning_zone'] = 1\n", - " else:\n", - " results['warning_zone'] = 0\n", - " full_event[f'{name}_warning_zone'] = 0\n", - " \n", - " # Detect drift\n", - " if model.detected_change():\n", - " context.logger.info('Change Detected')\n", - " results['change_detected'] = 1\n", - " full_event[f'{name}_drift'] = 1\n", - " else:\n", - " results['change_detected'] = 0\n", - " full_event[f'{name}_drift'] = 0\n", - " context.window.append(results)\n", - " \n", - " # Return results\n", - " # Write to stream\n", - " push_to_stream(context, context.drift_stream, [full_event])\n", - " \n", - " # Add to callbacks\n", - " if context.callbacks != ['']:\n", - " for callback in context.callbacks:\n", - " requests.post(url=callback,\n", - " json=full_event)\n", - " \n", - " if (len(context.window) / len(context.models)) >= context.window_size:\n", - " df = pd.DataFrame(context.window)\n", - " df['timestamp'] = pd.to_datetime(df['timestamp'])\n", - " df = df.set_index(['timestamp', 'algorithm'])\n", - " context.v3f.write('tsdb', context.tsdb_table, df)\n", - " context.window = []" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# nuclio: end-code" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Test " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "init_context(context)\n", - "event = nuclio.Event(body=json.dumps({'prediction': 0,\n", - " 'when': 'now',\n", - " 'class': 'ClassModel', \n", - " 'model': 'tester_v1', \n", - " 'resp': [0], \n", - " 'request': {'instances': [[1, 1.2, 3]]}}))\n", - "out = handler(context, event)\n", - "out" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Cluster" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%nuclio deploy -n network-operations-concept-drift -p network-operations" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Save function yaml" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "from os import path\n", - "from mlrun import run_local, NewTask, mlconf, import_function, mount_v3io, code_to_function, get_run_db\n", - "mlconf.dbpath = mlconf.dbpath or 'http://mlrun-api:8080'" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[mlrun] 2020-07-14 13:49:22,720 function spec saved to path: /User/functions/concept_drift_streaming/function.yaml\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# create job function object from notebook code\n", - "fn = code_to_function(\"concept_drift_streaming\", kind='nuclio')\n", - "\n", - "# add metadata (for templates and reuse)\n", - "fn.spec.default_handler = \"handler\"\n", - "fn.spec.description = \"Deploy a streaming Concept Drift detector on a labeled stream. the nuclio part of the concept_drift function\"\n", - "fn.metadata.categories = [\"ml\", \"serve\"]\n", - "fn.metadata.labels = {\"author\": \"orz\", \"framework\": \"sklearn\"}\n", - "fn.export(\"/User/functions/concept_drift_streaming/function.yaml\")" - ] - }, - { - "cell_type": "code", - "execution_count": 120, - "metadata": {}, - "outputs": [], - "source": [ - "stream_trigger = nuclio.triggers.V3IOStreamTrigger(url='/bigdata/network-operations/inference_stream@cd2')" - ] - }, - { - "cell_type": "code", - "execution_count": 121, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 121, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fn.add_trigger('labeled_stream', stream_trigger)" - ] - }, - { - "cell_type": "code", - "execution_count": 122, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 122, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fn.apply(mount_v3io()).with_v3io()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fn.export(\"function.yaml\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Stream testing" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [], - "source": [ - "fn = import_function('./function.yaml')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fn.deploy(project='network-operations')" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/concept_drift_streaming/concept_drift_streaming.py b/concept_drift_streaming/concept_drift_streaming.py deleted file mode 100644 index ebcbf8a1b..000000000 --- a/concept_drift_streaming/concept_drift_streaming.py +++ /dev/null @@ -1,157 +0,0 @@ -# Copyright 2019 Iguazio -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# Generated by nuclio.export.NuclioExporter - -import skmultiflow.drift_detection -import numpy as np -import pandas as pd -import os -import json -import v3io.dataplane -import v3io_frames as v3f -import requests -from cloudpickle import load - -import random - - -def split_path(mntpath=""): - if mntpath[0] == "/": - mntpath = mntpath[1:] - paths = mntpath.split("/") - container = paths[0] - subpath = "" - if len(paths) > 1: - subpath = mntpath[len(container) :] - return container, subpath - - -def create_stream(context, path, shards=1): - container, stream_path = split_path(path) - context.logger.info( - f"Creating stream in Container: {container} & Path {stream_path}" - ) - response = context.v3io_client.create_stream( - container=container, - path=stream_path, - shard_count=shards, - raise_for_status=v3io.dataplane.RaiseForStatus.never, - ) - response.raise_for_status([409, 204]) - - -def push_to_stream(context, stream_path, data): - records = [{"data": json.dumps(rec)} for rec in data] - container, stream_path = split_path(stream_path) - response = context.v3io_client.put_records( - container=container, path=stream_path, records=records - ) - - -def construct_record(record): - label_col = os.getenv("label_col", "label") - prediction_col = os.getenv("prediction_col", "prediction") - res = dict([(k, record[k]) for k in ["when", "class", "model", "resp", "request"]]) - res["feature_vector"] = res.pop("request")["instances"][0] - res["timestamp"] = res.pop("when") - res[prediction_col] = res["resp"][0] - return res - - -def init_context(context): - v3io_client = v3io.dataplane.Client() - setattr(context, "v3io_client", v3io_client) - - v3f_client = v3f.Client("framesd:8081", container="bigdata") - setattr(context, "v3f", v3f_client) - window = [] - setattr(context, "window", window) - setattr(context, "window_size", int(os.getenv("window_size", 10))) - setattr(context, "tsdb_table", os.getenv("tsdb_table", "concept_drift_tsdb_1")) - try: - context.v3f.create("tsdb", context.tsdb_table, rate="1/s", if_exists=1) - except Exception as e: - context.logger.info(f"Creating context with rate= faile for {e}") - context.v3f.create( - "tsdb", context.tsdb_table, attrs={"rate": "1/s"}, if_exists=1 - ) - - callbacks = [callback.strip() for callback in os.getenv("callbacks", "").split(",")] - setattr(context, "callbacks", callbacks) - - setattr(context, "drift_stream", os.getenv("drift_stream", "/bigdata/drift_stream")) - try: - create_stream( - context, context.drift_stream, int(os.getenv("drift_stream_shards", 1)) - ) - except: - context.logger.info(f"{context.drift_stream} already exists") - - models = {} - model_types = ["pagehinkely", "ddm", "eddm"] - path_suffix = "_model_path" - for model in model_types: - model_env = f"{model}{path_suffix}" - if model_env in os.environ: - with open(os.environ[model_env], "rb") as f: - models[model] = load(f) - setattr(context, "models", models) - - setattr(context, "label_col", os.getenv("label_col", "label")) - setattr(context, "prediction_col", os.getenv("prediction_col", "prediction")) - - -def handler(context, event): - context.logger.info(f"event: {event.body}") - full_event = json.loads(event.body) - record = construct_record(full_event) - - is_error = record[context.label_col] != record[context.prediction_col] - context.logger.info(f"Adding {is_error}") - - for name, model in context.models.items(): - results = {"timestamp": record["timestamp"]} - results["algorithm"] = name - model.add_element(is_error) - - if hasattr(model, "detected_warning_zone") and model.detected_warning_zone(): - context.logger.info(f"{name}\tWarning zone detected") - results["warning_zone"] = 1 - full_event[f"{name}_warning_zone"] = 1 - else: - results["warning_zone"] = 0 - full_event[f"{name}_warning_zone"] = 0 - - if model.detected_change(): - context.logger.info("Change Detected") - results["change_detected"] = 1 - full_event[f"{name}_drift"] = 1 - else: - results["change_detected"] = 0 - full_event[f"{name}_drift"] = 0 - context.window.append(results) - - push_to_stream(context, context.drift_stream, [full_event]) - - if context.callbacks != [""]: - for callback in context.callbacks: - requests.post(url=callback, json=full_event) - - if (len(context.window) / len(context.models)) >= context.window_size: - df = pd.DataFrame(context.window) - df["timestamp"] = pd.to_datetime(df["timestamp"]) - df = df.set_index(["timestamp", "algorithm"]) - context.v3f.write("tsdb", context.tsdb_table, df) - context.window = [] diff --git a/concept_drift_streaming/function.yaml b/concept_drift_streaming/function.yaml deleted file mode 100644 index 3001b1bf5..000000000 --- a/concept_drift_streaming/function.yaml +++ /dev/null @@ -1,48 +0,0 @@ -kind: remote -metadata: - name: concept-drift-streaming - tag: '' - hash: dc41ff41149be69f19b91a6d78a06571937063ae - project: '' - labels: - author: orz - framework: sklearn - categories: - - machine-learning - - monitoring -spec: - command: '' - args: [] - image: mlrun/ml-models - description: Deploy a streaming Concept Drift detector on a labeled stream. the - nuclio part of the concept_drift function - min_replicas: 1 - max_replicas: 4 - env: [] - base_spec: - apiVersion: nuclio.io/v1 - kind: Function - metadata: - name: concept-drift-streaming - labels: {} - annotations: - nuclio.io/generated_by: function generated from /User/test/functions/concept_drift_streaming/concept_drift_streaming.py - spec: - runtime: python:3.6 - handler: concept_drift_streaming:handler - env: [] - volumes: [] - build: - commands: [] - noBaseImagesPull: true - functionSourceCode: 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- source: '' - build: - commands: - - python -m pip install scikit-multiflow==0.4.1 v3io_frames - code_origin: https://github.com/daniels290813/functions.git#d96059851b5d51fd4583e982483eb973fccc47d2:/User/test/functions/concept_drift_streaming/concept_drift_streaming.py - origin_filename: /User/test/functions/concept_drift_streaming/concept_drift_streaming.py - default_handler: handler - disable_auto_mount: false - affinity: null -verbose: false diff --git a/concept_drift_streaming/item.yaml b/concept_drift_streaming/item.yaml deleted file mode 100644 index 91dcb9f4f..000000000 --- a/concept_drift_streaming/item.yaml +++ /dev/null @@ -1,29 +0,0 @@ -apiVersion: v1 -categories: -- machine-learning -- monitoring -description: Deploy a streaming Concept Drift detector on a labeled stream. the nuclio - part of the concept_drift function -doc: '' -example: concept_drift_streaming.ipynb -generationDate: 2022-08-28:17-25 -hidden: false -icon: '' -labels: - author: orz - framework: sklearn -maintainers: [] -marketplaceType: '' -mlrunVersion: 1.1.0 -name: concept-drift-streaming -platformVersion: 3.5.0 -spec: - filename: concept_drift_streaming.py - handler: handler - image: mlrun/ml-models - kind: nuclio - requirements: - - scikit-multiflow==0.4.1 - - v3io_frames -url: '' -version: 1.1.0 diff --git a/concept_drift_streaming/requirements.txt b/concept_drift_streaming/requirements.txt deleted file mode 100644 index fa0fddd88..000000000 --- a/concept_drift_streaming/requirements.txt +++ /dev/null @@ -1 +0,0 @@ -skmultiflow \ No newline at end of file diff --git a/feature_perms/README.ipynb b/feature_perms/README.ipynb deleted file mode 100644 index 0929a6f6a..000000000 --- a/feature_perms/README.ipynb +++ /dev/null @@ -1,788 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# feature importances" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There are a number of ways to compute feature importances and **the default estimates reported by scikit learn can be shown to be biased** under certain circumstances. In addition, many non-tree algorithms do not provide conveniently calculated feature importance estimates. The following demonstration is based on material that draws heavily from the following sources:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## references\n", - "\n", - "\n", - "### repos\n", - "\n", - "* **[Feature importances for scikit-learn machine learning models](https://github.com/parrt/random-forest-importances)**, [MIT License](https://github.com/parrt/random-forest-importances/blob/master/LICENSE)\n", - "* **[Scikit-Learn ensemble module - forests](https://github.com/scikit-learn/scikit-learn/blob/0.23.1/sklearn/ensemble/_forest.py)**, [BSD License](https://github.com/scikit-learn/scikit-learn/blob/fd237278e895b42abe8d8d09105cbb82dc2cbba7/sklearn/ensemble/_forest.py#L40)\n", - "* **[ELI5 - Permutation Importance](https://eli5.readthedocs.io/en/latest/blackbox/permutation_importance.html)** \n", - "\n", - "### articles\n", - "\n", - "Strobl, C., Boulesteix, A., Zeileis, A. et al. **[Bias in random forest variable importance measures: Illustrations, sources and a solution](https://link.springer.com/article/10.1186/1471-2105-8-25#citeas)**. BMC Bioinformatics 8, 25 (2007). https://doi.org/10.1186/1471-2105-8-25 \n", - "\n", - "Strobl, C., Boulesteix, A., Kneib, T. et al. **[Conditional variable importance for random forests](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-9-307#citeas)**. BMC Bioinformatics 9, 307 (2008). https://doi.org/10.1186/1471-2105-9-307 " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## what we'll do\n", - "\n", - "* demonstrate an issue with default feature importance estimates \n", - "* provide alternatives and compare to the default \n", - "* create a new function `feature_perms` that implements a computationally simple algorithm \n", - "* create a new function `dropcol_importances` that implements a computationally intensive algorithm that is more accurate\n", - "* test our new functions\n", - "\n", - "It should be noted that although we are developing this notebook using a classification example, an almost identical presentation can be done for regression." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## imports" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "import sklearn\n", - "from sklearn.base import clone\n", - "\n", - "from sklearn.ensemble import RandomForestClassifier as SomeModel\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "\n", - "from typing import Union, Callable, List" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## default feature importances\n", - "\n", - "This is a function that plots default feature importances from an estimated model object when available. It is taken from mlrun's current source-code implementation:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "def feature_importances(\n", - " model: SomeModel,\n", - " header: List[str], \n", - " figsz=(10, 5)\n", - ") -> None:\n", - " \"\"\"Display default model feature importances\n", - "\n", - " Only works for models with attribute 'feature_importances_`\n", - "\n", - " :param model: fitted model with a feature_importances_ attribute\n", - " :param header: feature labels\n", - " :param figsz: matplotlib figure size\n", - " \"\"\"\n", - " if not hasattr(model, \"feature_importances_\"):\n", - " raise Exception(\n", - " \"feature importances are only available for some models\")\n", - "\n", - " # create a feature importance table with desired labels\n", - " zipped = zip(model.feature_importances_, header)\n", - " feature_imp = pd.DataFrame(\n", - " sorted(zipped), columns=[\"freq\", \"feature\"]).sort_values(\n", - " by=\"freq\", ascending=False)\n", - "\n", - " plt.clf()\n", - " plt.figure(figsize=figsz)\n", - " sns.barplot(x=\"freq\", y=\"feature\", data=feature_imp)\n", - " plt.title(\"features\")\n", - " plt.tight_layout();\n", - " \n", - " return feature_imp" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## permuted features\n", - "\n", - "A proposed solution that has general applicability is randomly permuted features**[refs](#references)**: \n", - "* loop through the feature set \n", - "* shuffle one feature \n", - "* run predict\n", - "* compare the (marginal) change in accuracy (or other metric of interest) \n", - "\n", - "This approach is computationally more demanding than relying on the default values, however it can be easily parallelized. To perform the estimation we only need an estimated model and a held-out test set. The following was proposed in **[Beware Default Random Forest Importances](https://explained.ai/rf-importance/index.html)**:\n", - "\n", - "( the following 3 glue functions will no longer be publicly visible in the sklearn package from 0.24 onwards, consider this a temporary hack while we refactor these away)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**the following has been refactored in final version of function:**" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "from distutils.version import LooseVersion\n", - "import numpy as np\n", - "from sklearn.utils import check_random_state\n", - "\n", - "def _generate_sample_indices(random_state: int, n_samples: int, n_samples_bootstrap: int):\n", - " \"\"\"\n", - " Private function used to _parallel_build_trees function.\n", - " taken from:\n", - " https://github.com/scikit-learn/scikit-learn/blob/2253807bb488b6de73796aef2de38a6dcf282d86/sklearn/ensemble/_forest.py#L116\n", - " (public availability to be deprecated by sklearn v0.24)\n", - " \"\"\"\n", - " random_instance = check_random_state(random_state)\n", - " sample_indices = random_instance.randint(0, n_samples, n_samples_bootstrap)\n", - "\n", - " return sample_indices\n", - "\n", - "def _generate_unsampled_indices(random_state: int, n_samples: int, n_samples_bootstrap: int):\n", - " \"\"\"\n", - " Private function used to forest._set_oob_score function.\n", - " taken from: \n", - " https://github.com/scikit-learn/scikit-learn/blob/2253807bb488b6de73796aef2de38a6dcf282d86/sklearn/ensemble/_forest.py#L126\n", - " (public availability to be deprecated by sklearn v0.24)\n", - " \"\"\"\n", - " sample_indices = _generate_sample_indices(random_state, n_samples,\n", - " n_samples_bootstrap)\n", - " sample_counts = np.bincount(sample_indices, minlength=n_samples)\n", - " unsampled_mask = sample_counts == 0\n", - " indices_range = np.arange(n_samples)\n", - " unsampled_indices = indices_range[unsampled_mask]\n", - "\n", - " return unsampled_indices\n", - "\n", - "def _get_unsampled_indices(tree, n_samples: int):\n", - " \"\"\"\n", - " An interface to get unsampled indices regardless of sklearn version.\n", - " \"\"\"\n", - " import warnings\n", - " warnings.simplefilter(action=\"ignore\", category=FutureWarning)\n", - " if LooseVersion(sklearn.__version__) >= LooseVersion(\"0.22\"):\n", - " # Version 0.22 or newer uses 3 arguments.\n", - " from sklearn.ensemble.forest import _get_n_samples_bootstrap\n", - " n_samples_bootstrap = _get_n_samples_bootstrap(n_samples, n_samples)\n", - " return _generate_unsampled_indices(tree.random_state, n_samples,\n", - " n_samples_bootstrap)\n", - " else:\n", - " # Version 0.21 or older uses only two arguments.\n", - " return _generate_unsampled_indices(tree.random_state, n_samples)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The following function estimates classifier accuracy and has been borrowed from **[references](#references)**. See **[breitman on oob](https://www.stat.berkeley.edu/~breiman/OOBestimation.pdf)** for details on out-of-bag estimation:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "def oob_classifier_accuracy(rf, X_train: np.array, y_train: np.array) -> float:\n", - " \"\"\"\n", - " Compute out-of-bag (OOB) accuracy for a scikit-learn forest classifier.\n", - " \n", - " https://github.com/scikit-learn/scikit-learn/blob/a24c8b46/sklearn/ensemble/forest.py#L425\n", - " \"\"\"\n", - " X = X_train.values\n", - " y = y_train.values\n", - "\n", - " n_samples = len(X)\n", - " n_classes = len(np.unique(y))\n", - " predictions = np.zeros((n_samples, n_classes))\n", - " for tree in rf.estimators_:\n", - " unsampled_indices = _get_unsampled_indices(tree, n_samples)\n", - " tree_preds = tree.predict_proba(X[unsampled_indices, :])\n", - " predictions[unsampled_indices] += tree_preds\n", - "\n", - " predicted_class_indexes = np.argmax(predictions, axis=1)\n", - " predicted_classes = [rf.classes_[i] for i in predicted_class_indexes]\n", - "\n", - " oob_score = np.mean(y == predicted_classes)\n", - " \n", - " return oob_score" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Putting it all together:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "def permutation_importances(\n", - " model, \n", - " X_train: np.array,\n", - " y_train: np.array, \n", - " header: List[str],\n", - " metric: Callable = oob_classifier_accuracy,\n", - " figsz=(10, 5)\n", - ") -> np.array:\n", - " \"\"\"calculate change in metric from permuting feature columns\n", - " \n", - " modified from https://explained.ai/rf-importance/index.html\n", - " \n", - " uses a pre-estimated model\n", - "\n", - " :param X_train: training set features\n", - " :param y_train: training set ground truths, regression targets\n", - " :param header: column labels for X_train\n", - " :param figsz: matplotlib figure size\n", - " \n", - " \"\"\"\n", - " baseline = metric(model, X_train, y_train)\n", - " imp = []\n", - " for col in X_train.columns:\n", - " save = X_train[col].copy()\n", - " X_train[col] = np.random.permutation(X_train[col])\n", - " m = metric(model, X_train, y_train)\n", - " X_train[col] = save\n", - " imp.append(baseline - m)\n", - " \n", - " # create a feature importance table with desired labels\n", - " zipped = zip(imp, header)\n", - " feature_imp = pd.DataFrame(sorted(zipped), columns=[\"importance\", \"feature\"])\n", - " feature_imp.sort_values(by=\"importance\", ascending=False, inplace=True)\n", - "\n", - " plt.clf()\n", - " plt.figure(figsize=figsz)\n", - " sns.barplot(x=\"importance\", y=\"feature\", data=feature_imp)\n", - " plt.title(\"feature permutation importances\")\n", - " plt.tight_layout()\n", - "\n", - " return np.array(feature_imp)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## drop-column importances\n", - "\n", - "According to our **[references](#references)** a more accurate measure of feature importance would have us re-estimate the model after dropping a column. This is considered as being close to \"ideal\". Unfortunately, the entire model needs to be re-estimated for each column and without some approximating shortcut this is likely to be infeasible for large datasets.\n", - "\n", - "Here is the suggested implementation and **don't run this on big models!**:" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "def dropcol_importances(\n", - " model, \n", - " X_train: np.array,\n", - " y_train: np.array,\n", - " header: List[str] = [],\n", - " random_state: int = 1994,\n", - " figsz=(10, 5)\n", - ") -> pd.DataFrame:\n", - " \"\"\"drop columns and re-estimate model\n", - " \n", - " modified from https://explained.ai/rf-importance/index.html\n", - " \n", - " :param rf: model to fit\n", - " :param X_train: training set features\n", - " :param y_train: training set ground truth labels\n", - "\n", - " Returns:\n", - " pd.DataFrame: table of diffs vs baseline metric\n", - " \"\"\"\n", - " # cloning makes copy of model pre-fit\n", - " # calculate a baseline with all features\n", - " model_ = clone(model)\n", - " model_.random_state = random_state\n", - " model_.fit(X_train, y_train)\n", - " baseline = model_.oob_score_\n", - " \n", - " # now drop each colum, refit model and calc metric\n", - " imp = []\n", - " for col in X_train.columns:\n", - " X = X_train.drop(col, axis=1)\n", - " model_ = clone(model)\n", - " model_.random_state = random_state\n", - " model_.fit(X, y_train)\n", - " o = model_.oob_score_\n", - " imp.append(baseline - o)\n", - " \n", - " # put it all in a table\n", - " imp = np.array(imp)\n", - " feature_imps = pd.DataFrame(\n", - " data={'feature': X_train.columns,\n", - " 'importance': imp})\n", - " #feature_imps.set_index('feature', inplace=True)\n", - " feature_imps.sort_values('importance', ascending=True, inplace=True)\n", - " \n", - " plt.clf()\n", - " plt.figure(figsize=figsz)\n", - " sns.barplot(x=\"importance\", y=\"feature\", data=feature_imps)\n", - " plt.title(\"drop column feature importances\")\n", - " plt.tight_layout()\n", - " \n", - " return feature_imps" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## demonstration\n", - "\n", - "In this demonstratuon we are going to take a fraction of a fraction of **[Kaggle's RentHop rental listing interest competition](https://www.kaggle.com/c/two-sigma-connect-rental-listing-inquiries)**--the complete dataset is presently >80GB, we'll be looking at 5K rows. \n", - "\n", - "The competition's **[goal](https://www.kaggle.com/c/two-sigma-connect-rental-listing-inquiries)** was\n", - "> to predict the number of inquiries a new listing receives based on the listing’s creation date and other features. \n", - "\n", - "Doing so would help **[RentHop](https://www.renthop.com/)**\n", - "> better handle fraud control, identify potential listing quality issues, and allow owners and agents to better understand renters’ needs and preferences." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "data = \"/User/artifacts/two-sigma-connect-rental-listing-inquiries/\"\n", - "NFRAC = 0.1" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "sample dimensions (4935, 6)\n" - ] - }, - { - "data": { - "text/html": [ - "
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bathroomsbedroomspricelongitudelatitudeinterest_level
182351.001800-73.996040.71972
421401.023000-73.987940.76533
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" - ], - "text/plain": [ - " bathrooms bedrooms price longitude latitude interest_level\n", - "18235 1.0 0 1800 -73.9960 40.7197 2\n", - "42140 1.0 2 3000 -73.9879 40.7653 3\n", - "4677 1.0 1 1350 -73.8996 40.8549 2" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = pd.read_csv(data + 'rent.csv').sample(frac=NFRAC)\n", - "print(\"sample dimensions\", df.shape)\n", - "df.head(3)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "features = ['bathrooms', 'bedrooms', 'longitude', 'latitude', 'price']\n", - "dfr = df[features]\n", - "\n", - "# drop price column\n", - "X_train, y_train = dfr.drop('price', axis=1), dfr['price']\n", - "\n", - "# insert column with random values\n", - "X_train['random'] = np.random.random(size=len(X_train))\n", - "features = ['bathrooms', 'bedrooms', 'longitude', 'latitude', 'random']" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "RandomForestClassifier(n_jobs=-1, oob_score=True)" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# define model\n", - "model_params = {\n", - " \"n_estimators\" : 100, \n", - " \"min_samples_leaf\" : 1,\n", - " \"n_jobs\" : -1,\n", - " \"oob_score\" : True\n", - "}\n", - "\n", - "model = SomeModel(**model_params)\n", - "\n", - "# estimate\n", - "model.fit(X_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### to run this the model needs a default attribute" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "default feature_importances [0.01683784 0.03215169 0.29983429 0.30418813 0.34698806]\n" - ] - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "if hasattr(model, \"feature_importances_\"):\n", - " print(\"default feature_importances\", model.feature_importances_)\n", - " feature_importances(model, features)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## permutation importances\n", - "\n", - "No need to check for default attributes or functions, this can be run on any kind of model:" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.06545086119554205, 'longitude'],\n", - " [0.06160081053698076, 'latitude'],\n", - " [0.053495440729483285, 'bedrooms'],\n", - " [0.021681864235055734, 'bathrooms'],\n", - " [0.0004052684903748799, 'random']], dtype=object)" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "pi = permutation_importances(model, X_train, y_train, features)\n", - "pi" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## drop-column importances" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
featureimportance
4random-0.042756
0bathrooms0.001824
1bedrooms0.023100
2longitude0.049240
3latitude0.051874
\n", - "
" - ], - "text/plain": [ - " feature importance\n", - "4 random -0.042756\n", - "0 bathrooms 0.001824\n", - "1 bedrooms 0.023100\n", - "2 longitude 0.049240\n", - "3 latitude 0.051874" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "dc = dropcol_importances(model, X_train, y_train)\n", - "dc" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## conclusions" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So I would say location is a prime factor, then the number of bedrooms. Bathrooms often is gte bedrooms, and is likely correlated so one of them should likely be dropped." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.8" - }, - "toc-autonumbering": false, - "toc-showcode": false, - "toc-showmarkdowntxt": false - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/feature_perms/feature_perms.ipynb b/feature_perms/feature_perms.ipynb deleted file mode 100644 index 77da7b553..000000000 --- a/feature_perms/feature_perms.ipynb +++ /dev/null @@ -1,1106 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# permutation_importances as reusable function" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## function code" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# nuclio: ignore\n", - "import nuclio" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import numbers\n", - "\n", - "import sklearn\n", - "from sklearn.base import clone\n", - "from sklearn.utils import check_random_state\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "\n", - "from cloudpickle import load\n", - "\n", - "from mlrun.execution import MLClientCtx\n", - "from mlrun.datastore import DataItem\n", - "from mlrun.artifacts import get_model, PlotArtifact\n", - "from typing import Union, Callable, List\n", - "\n", - "def _get_n_samples_bootstrap(n_samples, max_samples) -> int:\n", - " \"\"\"get the number of samples in a bootstrap sample\n", - " \n", - " returns the total number of samples to draw for the bootstrap sample\n", - " \n", - " private api in sklearn >= v0.24, taken from sklearn.ensemble._forest.py\n", - "\n", - " :param n_samples: Number of samples in the dataset.\n", - " :param max_samples: \n", - " The maximum number of samples to draw from the total available:\n", - " - if float, this indicates a fraction of the total and should be\n", - " the interval `(0, 1)`;\n", - " - if int, this indicates the exact number of samples;\n", - " - if None, this indicates the total number of samples.\n", - " \"\"\"\n", - " if max_samples is None:\n", - " return n_samples\n", - "\n", - " if isinstance(max_samples, numbers.Integral):\n", - " if not (1 <= max_samples <= n_samples):\n", - " msg = \"`max_samples` must be in range 1 to {} but got value {}\"\n", - " raise ValueError(msg.format(n_samples, max_samples))\n", - " return max_samples\n", - "\n", - " if isinstance(max_samples, numbers.Real):\n", - " if not (0 < max_samples < 1):\n", - " msg = \"`max_samples` must be in range (0, 1) but got value {}\"\n", - " raise ValueError(msg.format(max_samples))\n", - " return int(round(n_samples * max_samples))\n", - "\n", - " msg = \"`max_samples` should be int or float, but got type '{}'\"\n", - " raise TypeError(msg.format(type(max_samples)))\n", - "\n", - "def _get_unsampled_ix(random_state, n_samples: int) -> np.array:\n", - " \"\"\"\n", - " future-proof get unsampled indices\n", - " \"\"\"\n", - " n_bootstrap = _get_n_samples_bootstrap(n_samples, n_samples)\n", - " random_instance = check_random_state(random_state)\n", - " sample_indices = random_instance.randint(0, n_samples, n_bootstrap)\n", - " sample_counts = np.bincount(sample_indices, minlength=n_samples)\n", - "\n", - " return np.arange(n_samples)[sample_counts==0]\n", - "\n", - "def _oob_classifier_accuracy(rf, X_train, y_train) -> float:\n", - " \"\"\"\n", - " Compute out-of-bag (OOB) accuracy for a scikit-learn forest classifier.\n", - " \n", - " https://github.com/scikit-learn/scikit-learn/blob/a24c8b46/sklearn/ensemble/forest.py#L425\n", - " \"\"\"\n", - " X = X_train.values if isinstance(X_train, pd.DataFrame) else X_train\n", - " y = y_train.values if isinstance(y_train, pd.Series) else y_train\n", - "\n", - " n_samples = len(X)\n", - " n_classes = len(np.unique(y))\n", - " predictions = np.zeros((n_samples, n_classes))\n", - " for tree in rf.estimators_:\n", - " unsampled_indices = _get_unsampled_ix(tree.random_state, n_samples)\n", - " tree_preds = tree.predict_proba(X[unsampled_indices, :])\n", - " predictions[unsampled_indices] += tree_preds\n", - "\n", - " predicted_class_indexes = np.argmax(predictions, axis=1)\n", - " predicted_classes = [rf.classes_[i] for i in predicted_class_indexes]\n", - "\n", - " oob_score = np.mean(y == predicted_classes)\n", - " \n", - " return oob_score\n", - "\n", - "def permutation_importances(\n", - " context: MLClientCtx,\n", - " model: DataItem,\n", - " dataset: DataItem,\n", - " labels: str,\n", - " figsz=(10, 5),\n", - " plots_dest: str = \"plots\",\n", - " fitype: str = \"permute\"\n", - ") -> pd.DataFrame:\n", - " \"\"\"calculate change in metric\n", - " \n", - " type 'permute' uses a pre-estimated model\n", - " type 'dropcol' uses a re-estimates model\n", - " \n", - " :param context: the function's execution context\n", - " :param model: a trained model\n", - " :param dataset: features and ground truths, regression targets\n", - " :param labels name of the ground truths column\n", - " :param figsz: matplotlib figure size\n", - " :param plots_dest: path within artifact store\n", - " :\n", - " \"\"\"\n", - " model_file, model_data, _ = get_model(model.url, suffix='.pkl')\n", - " model = load(open(str(model_file), \"rb\"))\n", - " \n", - " X = dataset.as_df()\n", - " y = X.pop(labels)\n", - " header = X.columns\n", - " \n", - " # this will be paramettrized next version, and include regression\n", - " metric = _oob_classifier_accuracy\n", - " \n", - " baseline = metric(model, X, y)\n", - " \n", - " imp = []\n", - " for col in X.columns:\n", - " if fitype is \"permute\":\n", - " save = X[col].copy()\n", - " X[col] = np.random.permutation(X[col])\n", - " m = metric(model, X, y)\n", - " X[col] = save\n", - " imp.append(baseline - m)\n", - " elif fitype is \"dropcol\":\n", - " X_ = X.drop(col, axis=1)\n", - " model_ = clone(model)\n", - " model_.random_state = random_state\n", - " model_.fit(X_, y)\n", - " o = model_.oob_score_\n", - " imp.append(baseline - o)\n", - " else:\n", - " raise ValueError(\"unknown fitype, only 'permute' or 'dropcol' permitted\")\n", - "\n", - " # create a feature importance table with desired labels\n", - " zipped = zip(imp, header)\n", - " feature_imp = pd.DataFrame(sorted(zipped), columns=[\"importance\", \"feature\"])\n", - " feature_imp.sort_values(by=\"importance\", ascending=False, inplace=True)\n", - "\n", - " plt.clf()\n", - " plt.figure(figsize=figsz)\n", - " sns.barplot(x=\"importance\", y=\"feature\", data=feature_imp)\n", - " plt.title(f\"feature importances-{fitype}\")\n", - " plt.tight_layout()\n", - "\n", - " context.log_artifact(PlotArtifact(f\"feature importances-{fitype}\", body=plt.gcf()),\n", - " local_path=f\"{plots_dest}/feature-permutations.html\")\n", - " context.log_dataset(f\"feature-importances-{fitype}-tbl\", df=feature_imp, index=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# nuclio: end-code" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## save function" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[mlrun] 2020-06-07 19:58:25,298 function spec saved to path: function.yaml\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from mlrun import code_to_function\n", - "from mlrun.platforms.other import auto_mount\n", - "\n", - "gpus = False\n", - "\n", - "# create job function object from notebook code\n", - "fn_params = {\n", - " \"name\" : \"feature-perms\",\n", - " \"handler\" : \"permutation_importances\",\n", - " \"kind\" : \"job\",\n", - " \"image\" : \"mlrun/ml-models\" if not gpus else \"mlrun/ml-models-gpu\",\n", - " \"description\" : \"estimate feature importances using permutations\",\n", - " \"categories\" : [\"analysis\"],\n", - " \"labels\" : {\"author\": \"yjb\"}\n", - "}\n", - "\n", - "perms_fn = code_to_function(**fn_params)\n", - "perms_fn.apply(auto_mount())\n", - "perms_fn.export(\"function.yaml\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## tests" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "from mlrun import import_function\n", - "from mlrun import NewTask, mlconf" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### get some data" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[mlrun] 2020-06-07 19:58:25,352 starting run tasks arc-to-parq uid=e9bc67f2189c418d96bfde754d369956 -> http://mlrun-api:8080\n", - "[mlrun] 2020-06-07 19:58:25,486 Job is running in the background, pod: tasks-arc-to-parq-xqkr7\n", - "[mlrun] 2020-06-07 19:58:29,118 starting local run: main.py # arc_to_parquet\n", - "[mlrun] 2020-06-07 19:58:29,169 downloading https://raw.githubusercontent.com/parrt/random-forest-importances/master/notebooks/data/rent.csv to local tmp\n", - "[mlrun] 2020-06-07 19:58:29,535 destination file does not exist, downloading\n", - "[mlrun] 2020-06-07 19:58:29,898 log artifact rent at /User/artifacts/rent.csv, size: 1492462, db: Y\n", - "\n", - "[mlrun] 2020-06-07 19:58:29,917 run executed, status=completed\n", - "final state: succeeded\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "
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projectuiditerstartstatenamelabelsinputsparametersresultsartifacts
default0Jun 07 19:58:29completedtasks arc-to-parq
v3io_user=admin
kind=job
owner=admin
host=tasks-arc-to-parq-xqkr7
archive_url
key=rent
stats=True
file_ext=csv
rent
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\n", - " Title\n", - " ×\n", - "
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\n", - "
\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "to track results use .show() or .logs() or in CLI: \n", - "!mlrun get run e9bc67f2189c418d96bfde754d369956 --project default , !mlrun logs e9bc67f2189c418d96bfde754d369956 --project default\n", - "[mlrun] 2020-06-07 19:58:31,666 run executed, status=completed\n" - ] - } - ], - "source": [ - "data_url = \"https://raw.githubusercontent.com/parrt/random-forest-importances/master/notebooks/data/rent.csv\"\n", - "\n", - "fn = import_function(\"hub://arc_to_parquet\", \"a2p\")\n", - "fn.apply(auto_mount())\n", - "\n", - "params = {\n", - " \"name\" : \"tasks arc-to-parq\",\n", - " \"params\" : {\"key\":\"rent\", \"stats\": True, \"file_ext\":\"csv\"}\n", - "}\n", - "acquire_run = fn.run(NewTask(**params),inputs={\"archive_url\" : data_url},\n", - " artifact_path=mlconf.artifact_path)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### train a model" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[mlrun] 2020-06-07 19:58:31,704 starting run tasks random forest uid=57af834167264641905a5bb5e6b0e263 -> http://mlrun-api:8080\n", - "[mlrun] 2020-06-07 19:58:31,861 Job is running in the background, pod: tasks-random-forest-vkjk5\n", - "[mlrun] 2020-06-07 19:58:35,390 starting local run: main.py # train_model\n", - "[mlrun] 2020-06-07 19:58:36,310 log artifact test_set at /User/artifacts/data/test_set.parquet, size: 24484, db: Y\n", - "[mlrun] 2020-06-07 19:58:37,153 log artifact confusion-matrix at /User/artifacts/model/plots/confusion-matrix.html, size: 27401, db: N\n", - "[mlrun] 2020-06-07 19:58:37,598 log artifact feature-importances at /User/artifacts/model/plots/feature-importances.html, size: 19685, db: N\n", - "[mlrun] 2020-06-07 19:58:37,806 log artifact precision-recall-multiclass at /User/artifacts/model/plots/precision-recall-multiclass.html, size: 74009, db: N\n", - "[mlrun] 2020-06-07 19:58:37,936 log artifact roc-multiclass at /User/artifacts/model/plots/roc-multiclass.html, size: 73053, db: N\n", - "[mlrun] 2020-06-07 19:58:38,079 log artifact model at /User/artifacts/model/, size: 10346780, db: Y\n", - "\n", - "[mlrun] 2020-06-07 19:58:38,106 run executed, status=completed\n", - "final state: succeeded\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "
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projectuiditerstartstatenamelabelsinputsparametersresultsartifacts
default0Jun 07 19:58:36completedtasks random forest
v3io_user=admin
kind=job
owner=admin
host=tasks-random-forest-vkjk5
class=sklearn.ensemble.RandomForestClassifier
dataset
sample=-5000
model_pkg_class=sklearn.ensemble.RandomForestClassifier
label_column=interest_level
CLASS_n_estimators=100
CLASS_min_samples_leaf=1
CLASS_n_jobs=-1
CLASS_oob_score=True
test-accuracy=0.6902857142857143
test-error=0.3097142857142857
auc-micro=0.8567196734693878
auc-weighted=0.7077200281488216
f1-score=0.44361444815007395
precision_score=0.4969837043184901
recall_score=0.42733978329897576
test_set
confusion-matrix
feature-importances
precision-recall-multiclass
roc-multiclass
model
\n", - "
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\n", - " Title\n", - " ×\n", - "
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\n", - "
\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "to track results use .show() or .logs() or in CLI: \n", - "!mlrun get run 57af834167264641905a5bb5e6b0e263 --project default , !mlrun logs 57af834167264641905a5bb5e6b0e263 --project default\n", - "[mlrun] 2020-06-07 19:58:41,115 run executed, status=completed\n" - ] - } - ], - "source": [ - "fn = import_function(\"hub://sklearn_classifier\", \"skrf\")\n", - "fn.apply(auto_mount())\n", - "\n", - "# define model\n", - "params = {\n", - " \"name\" : \"tasks random forest\",\n", - " \"params\" : {\n", - " \"sample\" : -5_000, # 5k random rows,\n", - " \"model_pkg_class\" : \"sklearn.ensemble.RandomForestClassifier\",\n", - " \"label_column\" : \"interest_level\",\n", - " \"CLASS_n_estimators\" : 100,\n", - " \"CLASS_min_samples_leaf\" : 1,\n", - " \"CLASS_n_jobs\" : -1,\n", - " \"CLASS_oob_score\" : True}\n", - "}\n", - "\n", - "train_run = fn.run(NewTask(**params), inputs={\"dataset\" : acquire_run.outputs[\"rent\"]},\n", - " artifact_path=mlconf.artifact_path)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "

Feature Importances

\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from IPython.display import HTML\n", - "HTML(filename=train_run.outputs['feature-importances'])" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "data = acquire_run.outputs[\"rent\"]\n", - "labels = \"interest_level\"\n", - "model = train_run.outputs[\"model\"]\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[mlrun] 2020-06-07 19:58:41,152 starting run features-permutation_importances uid=89235b15ac2a4213aefc906c178a1c5e -> http://mlrun-api:8080\n", - "[mlrun] 2020-06-07 19:58:41,312 Job is running in the background, pod: features-permutation-importances-dwxmt\n", - "[mlrun] 2020-06-07 19:58:44,871 starting local run: main.py # permutation_importances\n", - "[mlrun] 2020-06-07 19:58:48,714 log artifact feature importances-permute at /User/artifacts/plots/feature-permutations.html, size: 25694, db: Y\n", - "[mlrun] 2020-06-07 19:58:48,770 log artifact feature-importances-permute-tbl at /User/artifacts/feature-importances-permute-tbl.csv, size: 167, db: Y\n", - "\n", - "[mlrun] 2020-06-07 19:58:48,785 run executed, status=completed\n", - "final state: succeeded\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "
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projectuiditerstartstatenamelabelsinputsparametersresultsartifacts
default0Jun 07 19:58:45completedfeatures-permutation_importances
v3io_user=admin
kind=job
owner=admin
host=features-permutation-importances-dwxmt
model
dataset
labels=interest_level
plots_dest=plots
feature importances-permute
feature-importances-permute-tbl
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\n", - " Title\n", - " ×\n", - "
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\n", - "
\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "to track results use .show() or .logs() or in CLI: \n", - "!mlrun get run 89235b15ac2a4213aefc906c178a1c5e --project default , !mlrun logs 89235b15ac2a4213aefc906c178a1c5e --project default\n", - "[mlrun] 2020-06-07 19:58:50,488 run executed, status=completed\n" - ] - } - ], - "source": [ - "fi_perms = perms_fn.run(\n", - " NewTask(params={\"labels\": labels, \n", - " \"plots_dest\": \"plots\"}),\n", - " inputs={\"model\": model, \"dataset\": data},\n", - " artifact_path=mlconf.artifact_path)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from IPython.display import HTML\n", - "HTML(filename=fi_perms.outputs['feature importances-permute'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} \ No newline at end of file diff --git a/feature_perms/feature_perms.py b/feature_perms/feature_perms.py deleted file mode 100644 index 13caae32e..000000000 --- a/feature_perms/feature_perms.py +++ /dev/null @@ -1,174 +0,0 @@ -# Copyright 2019 Iguazio -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# Generated by nuclio.export.NuclioExporter - -import numpy as np -import pandas as pd -import numbers - -import sklearn -from sklearn.base import clone -from sklearn.utils import check_random_state - -import matplotlib.pyplot as plt -import seaborn as sns - -from cloudpickle import load - -from mlrun.execution import MLClientCtx -from mlrun.datastore import DataItem -from mlrun.artifacts import get_model, PlotArtifact -from typing import Union, Callable, List - - -def _get_n_samples_bootstrap(n_samples, max_samples) -> int: - """get the number of samples in a bootstrap sample - - returns the total number of samples to draw for the bootstrap sample - - private api in sklearn >= v0.24, taken from sklearn.ensemble._forest.py - - :param n_samples: Number of samples in the dataset. - :param max_samples: - The maximum number of samples to draw from the total available: - - if float, this indicates a fraction of the total and should be - the interval `(0, 1)`; - - if int, this indicates the exact number of samples; - - if None, this indicates the total number of samples. - """ - if max_samples is None: - return n_samples - - if isinstance(max_samples, numbers.Integral): - if not (1 <= max_samples <= n_samples): - msg = "`max_samples` must be in range 1 to {} but got value {}" - raise ValueError(msg.format(n_samples, max_samples)) - return max_samples - - if isinstance(max_samples, numbers.Real): - if not (0 < max_samples < 1): - msg = "`max_samples` must be in range (0, 1) but got value {}" - raise ValueError(msg.format(max_samples)) - return int(round(n_samples * max_samples)) - - msg = "`max_samples` should be int or float, but got type '{}'" - raise TypeError(msg.format(type(max_samples))) - - -def _get_unsampled_ix(random_state, n_samples: int) -> np.array: - """ - future-proof get unsampled indices - """ - n_bootstrap = _get_n_samples_bootstrap(n_samples, n_samples) - random_instance = check_random_state(random_state) - sample_indices = random_instance.randint(0, n_samples, n_bootstrap) - sample_counts = np.bincount(sample_indices, minlength=n_samples) - - return np.arange(n_samples)[sample_counts == 0] - - -def _oob_classifier_accuracy(rf, X_train, y_train) -> float: - """ - Compute out-of-bag (OOB) accuracy for a scikit-learn forest classifier. - - https://github.com/scikit-learn/scikit-learn/blob/a24c8b46/sklearn/ensemble/forest.py#L425 - """ - X = X_train.values if isinstance(X_train, pd.DataFrame) else X_train - y = y_train.values if isinstance(y_train, pd.Series) else y_train - - n_samples = len(X) - n_classes = len(np.unique(y)) - predictions = np.zeros((n_samples, n_classes)) - for tree in rf.estimators_: - unsampled_indices = _get_unsampled_ix(tree.random_state, n_samples) - tree_preds = tree.predict_proba(X[unsampled_indices, :]) - predictions[unsampled_indices] += tree_preds - - predicted_class_indexes = np.argmax(predictions, axis=1) - predicted_classes = [rf.classes_[i] for i in predicted_class_indexes] - - oob_score = np.mean(y == predicted_classes) - - return oob_score - - -def permutation_importance( - context: MLClientCtx, - model: DataItem, - dataset: DataItem, - labels: str, - figsz=(10, 5), - plots_dest: str = "plots", - fitype: str = "permute", -) -> pd.DataFrame: - """calculate change in metric - - type 'permute' uses a pre-estimated model - type 'dropcol' uses a re-estimates model - - :param context: the function's execution context - :param model: a trained model - :param dataset: features and ground truths, regression targets - :param labels name of the ground truths column - :param figsz: matplotlib figure size - :param plots_dest: path within artifact store - : - """ - model_file, model_data, _ = get_model(model.url, suffix=".pkl") - model = load(open(str(model_file), "rb")) - - X = dataset.as_df() - y = X.pop(labels) - header = X.columns - - metric = _oob_classifier_accuracy - - baseline = metric(model, X, y) - - imp = [] - for col in X.columns: - if fitype is "permute": - save = X[col].copy() - X[col] = np.random.permutation(X[col]) - m = metric(model, X, y) - X[col] = save - imp.append(baseline - m) - elif fitype is "dropcol": - X_ = X.drop(col, axis=1) - model_ = clone(model) - #model_.random_state = random_state - model_.fit(X_, y) - o = model_.oob_score_ - imp.append(baseline - o) - else: - raise ValueError("unknown fitype, only 'permute' or 'dropcol' permitted") - - zipped = zip(imp, header) - feature_imp = pd.DataFrame(sorted(zipped), columns=["importance", "feature"]) - feature_imp.sort_values(by="importance", ascending=False, inplace=True) - - plt.clf() - plt.figure(figsize=figsz) - sns.barplot(x="importance", y="feature", data=feature_imp) - plt.title(f"feature importances-{fitype}") - plt.tight_layout() - - context.log_artifact( - PlotArtifact(f"feature importances-{fitype}", body=plt.gcf()), - local_path=f"{plots_dest}/feature-permutations.html", - ) - context.log_dataset( - f"feature-importances-{fitype}-tbl", df=feature_imp, index=False - ) diff --git a/feature_perms/function.yaml b/feature_perms/function.yaml deleted file mode 100644 index 713981fdf..000000000 --- a/feature_perms/function.yaml +++ /dev/null @@ -1,63 +0,0 @@ -kind: job -metadata: - name: feature-perms - tag: '' - hash: 2e32234a73e2e48f029cf6c957b150ec2ffd4bc7 - project: '' - labels: - author: yjb - categories: - - data-analysis -spec: - command: '' - args: [] - image: mlrun/ml-models - env: [] - default_handler: permutation_importance - entry_points: - permutation_importance: - name: permutation_importance - doc: 'calculate change in metric - - - type ''permute'' uses a pre-estimated model - - type ''dropcol'' uses a re-estimates model' - parameters: - - name: context - type: MLClientCtx - doc: the function's execution context - default: '' - - name: model - type: DataItem - doc: a trained model - default: '' - - name: dataset - type: DataItem - doc: features and ground truths, regression targets - default: '' - - name: labels - type: str - default: '' - - name: figsz - doc: matplotlib figure size - default: - - 10 - - 5 - - name: plots_dest - type: str - doc: path within artifact store - default: plots - - name: fitype - type: str - default: permute - outputs: - - default: '' - lineno: 93 - description: estimate feature importances using permutations - build: - functionSourceCode: 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 - commands: [] - code_origin: https://github.com/daniels290813/functions.git#55a79c32be5d233cc11efcf40cd3edbe309bfdef:/home/kali/functions/feature_perms/feature_perms.py - affinity: null -verbose: false diff --git a/feature_perms/item.yaml b/feature_perms/item.yaml deleted file mode 100644 index bd909d3ee..000000000 --- a/feature_perms/item.yaml +++ /dev/null @@ -1,25 +0,0 @@ -apiVersion: v1 -categories: -- data-analysis -description: estimate feature importances using permutations -doc: '' -example: feature_perms.ipynb -generationDate: 2022-08-28:17-25 -hidden: false -icon: '' -labels: - author: yjb -maintainers: [] -marketplaceType: '' -mlrunVersion: 1.1.0 -name: feature-perms -platformVersion: 3.5.0 -spec: - filename: feature_perms.py - handler: permutation_importance - image: mlrun/ml-models - kind: job - requirements: [] -url: '' -version: 1.1.0 -test_valid : False diff --git a/feature_perms/requirements.txt b/feature_perms/requirements.txt deleted file mode 100644 index 70a079c7d..000000000 --- a/feature_perms/requirements.txt +++ /dev/null @@ -1,5 +0,0 @@ -scikit-learn -matplotlib -seaborn -scikit-plot - diff --git a/feature_perms/test_feature_perms.py b/feature_perms/test_feature_perms.py deleted file mode 100644 index a59891ea8..000000000 --- a/feature_perms/test_feature_perms.py +++ /dev/null @@ -1,134 +0,0 @@ -# Copyright 2019 Iguazio -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -from mlrun import code_to_function, import_function -from pathlib import Path -import os - -ARTIFACTS_PATH = 'artifacts' -DATA_URL = "https://raw.githubusercontent.com/parrt/random-forest-importances/master/notebooks/data/rent.csv" -FEATURE_OUTPUT = "feature-importances-permute-tbl" - - -def arc_to_parquet(): - from mlrun import import_function - - archive_func = import_function('hub://arc_to_parquet') - - archive_run = archive_func.run( - handler="arc_to_parquet", - params={"key": "rent", "stats": True, "file_ext": "csv"}, - inputs={"archive_url": DATA_URL}, - artifact_path=os.getcwd() + '/artifacts', - local=True, - ) - - return archive_run.artifact('rent').url - - -def sklearn_classifier(run): - cwd = os.getcwd() - file_path = str(Path(cwd).parent.absolute()) + "/sklearn_classifier/sklearn_classifier.py" - fn = code_to_function( - name='test_sklearn_classifier', - filename=file_path, - handler="train_model", - kind="local", - ) - - fn.spec.command = file_path - fn.run( - params={ - "sample": -5_000, # 5k random rows, - "model_pkg_class": "sklearn.ensemble.RandomForestClassifier", - "label_column": "interest_level", - "CLASS_n_estimators": 100, - "CLASS_min_samples_leaf": 1, - "CLASS_n_jobs": -1, - "CLASS_oob_score": True, - }, - handler="train_model", - inputs={"dataset": run.outputs["rent"]}, - artifact_path='artifacts', - ) - - -def train_model(data): - from mlrun import import_function - - train = import_function('hub://sklearn_classifier') - - train_run = train.run( - inputs={"dataset": data}, - params={ - "sample": -5_000, # 5k random rows, - "model_pkg_class": "sklearn.ensemble.RandomForestClassifier", - "label_column": "interest_level", - "CLASS_n_estimators": 100, - "CLASS_min_samples_leaf": 1, - "CLASS_n_jobs": -1, - "CLASS_oob_score": True, - }, - local=True - ) - - return train_run.artifact('model').url - - -def test_feature_selection_run_local(): - data = arc_to_parquet() - model = train_model(data) - labels = "interest_level" - fn = code_to_function( - name='test_run_local_feature_perms', - filename="feature_perms.py", - handler="permutation_importance", - kind="local", - ) - fn.spec.command = "feature_perms.py" - - run = fn.run( - params={ - "labels": labels, - "plots_dest": "plots", - }, - inputs={ - "model": model, - "dataset": data, - }, - artifact_path='artifacts', - ) - - assert run.artifact(FEATURE_OUTPUT).get() - - -def test_feature_perms_import_function(): - data = arc_to_parquet() - model = train_model(data) - labels = "interest_level" - fn = import_function("function.yaml") - - run = fn.run( - params={ - "labels": labels, - "plots_dest": "plots" - }, - inputs={ - "model": model, - "dataset": data}, - artifact_path=os.getcwd() + '/artifacts', - local=True, - ) - - assert run.artifact(FEATURE_OUTPUT).get() diff --git a/get_offline_features/function.yaml b/get_offline_features/function.yaml deleted file mode 100644 index 3c6a8a87a..000000000 --- a/get_offline_features/function.yaml +++ /dev/null @@ -1,127 +0,0 @@ -kind: job -metadata: - name: get-offline-features - tag: '' - hash: 5ac6c4e2b67440b464710c072708a3581125c2f8 - project: '' - labels: - author: yonish - categories: - - data-preparation - - data-analysis - - feature-store -spec: - command: '' - args: [] - image: mlrun/mlrun - build: - functionSourceCode: # Copyright 2019 Iguazio
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from typing import Union, List, Dict

import mlrun
import mlrun.feature_store as fs
from mlrun.datastore.store_resources import is_store_uri, parse_store_uri
from mlrun.datastore.targets import get_target_driver, kind_to_driver
from mlrun.datastore.base import DataItem
from mlrun.execution import MLClientCtx
from mlrun.utils import StorePrefix, parse_versioned_object_uri
from mlrun.errors import MLRunInvalidArgumentError


def get_offline_features(
    context: MLClientCtx,
    feature_vector: str,
    features: Union[List[str], None] = None,
    label_feature: str = None,
    description: str = None,
    entity_rows: DataItem = None,
    entity_timestamp_column: str = None,
    target: Union[str, Dict] = None,
    run_config: Union[str, Dict] = None,
    drop_columns: List[str] = None,
    start_time: str = None,
    end_time: str = None,
    with_indexes: bool = False,
    update_stats: bool = False,
):
    """retrieve offline feature vector results

    specify a feature vector object/uri and retrieve the desired features, their metadata
    and statistics. returns :py:class:`~mlrun.feature_store.OfflineVectorResponse`,
    results can be returned as a dataframe or written to a target.
    If feature vector does not exist, a new one will be created and saved with the given features.

    The start_time and end_time attributes allow filtering the data to a given time range, they accept
    string values or pandas `Timestamp` objects, string values can also be relative, for example:
    "now", "now - 1d2h", "now+5m", where a valid pandas Timedelta string follows the verb "now",
    for time alignment you can use the verb "floor" e.g. "now -1d floor 1H" will align the time to the last hour
    (the floor string is passed to pandas.Timestamp.floor(), can use D, H, T, S for day, hour, min, sec alignment)


    :param context:        MLRun context
    :param feature_vector: feature vector uri
    :param features:       Relevant only if feature_vector not exist. list of feature to collect to this vector
                           format [<project>/]<feature_set>.<feature_name or *> [as <alias>]
    :param label_feature:  feature name to be used as label data
    :param description:    text description of the vector
    :param entity_rows:    URI of the data entity rows to join with
    :param target:         where to write the results to
    :param drop_columns:   list of columns to drop from the final result
    :param entity_timestamp_column: timestamp column name in the entity rows dataframe
    :param run_config:     function and/or run configuration
                           see :py:class:`~mlrun.feature_store.RunConfig`
    :param start_time:      datetime, low limit of time needed to be filtered. Optional
        entity_timestamp_column must be passed when using time filtering
    :param end_time:        datetime, high limit of time needed to be filtered. Optional
        entity_timestamp_column must be passed when using time filtering
    :param with_indexes:    return vector with index columns (default False)
    :param update_stats:    update features statistics from the requested feature sets on the vector. Default is False.

    :returns feature_vector input
    """

    if features:
        # Creating a new FeatureVector and saving:
        if is_store_uri(feature_vector):
            prefix, new_uri = parse_store_uri(feature_vector)
            if prefix != StorePrefix.FeatureVector:
                raise MLRunInvalidArgumentError(
                    f"provided store uri ({feature_vector}) does not represent a feature vector (prefix={prefix})"
                )
            feature_vector = new_uri

        context.logger.info(f"Creating FeatureVector {feature_vector}")
        project, name, tag, _ = parse_versioned_object_uri(feature_vector, mlrun.mlconf.default_project)
        vector = fs.FeatureVector(name, features, label_feature=label_feature, description=description)
        vector.metadata.project = project
        vector.metadata.tag = tag
        vector.save()
        feature_vector_uri = vector.uri
    else:
        if is_store_uri(feature_vector):
            feature_vector_uri = feature_vector
        else:
            vector = fs.get_feature_vector(feature_vector)
            feature_vector_uri = vector.uri

    # Preparing entity_rows:
    if entity_rows is not None:
        context.logger.info(f"Creating DataFrame from entity_rows = {entity_rows}")
        entity_rows = entity_rows.as_df()

    # Preparing target:
    if target:
        if isinstance(target, str):
            target = kind_to_driver[target]()

        name = target.name if hasattr(target, "name") else target["name"]
        context.logger.info(f"Preparing '{name}' target")
        target = get_target_driver(target)
    if hasattr(target, 'path') and target.path:
        context.log_result("target", target.path)

    # Preparing run_config:
    if run_config and isinstance(run_config, dict):
        context.logger.info("Preparing run configuration")
        run_config = fs.RunConfig(**run_config)

    # Calling get_offline_features:
    context.logger.info(
        f"getting offline features from the FeatureVector {feature_vector}"
    )
    fs.get_offline_features(
        feature_vector=feature_vector_uri,
        entity_rows=entity_rows,
        entity_timestamp_column=entity_timestamp_column,
        target=target,
        run_config=run_config,
        drop_columns=drop_columns,
        start_time=start_time,
        end_time=end_time,
        with_indexes=with_indexes,
        update_stats=update_stats,
    )

    context.log_result("feature_vector", feature_vector)
    context.log_result("feature_vector_uri", feature_vector_uri)
 - commands: [] - code_origin: https://github.com/yonishelach/functions.git#82aab1724569d20c73cca114beb2fac0821d3383:/Users/Yonatan_Shelach/projects/functions/get_offline_features/get_offline_features.py - origin_filename: /Users/Yonatan_Shelach/projects/functions/get_offline_features/get_offline_features.py - entry_points: - get_offline_features: - name: get_offline_features - doc: 'retrieve offline feature vector results - - - specify a feature vector object/uri and retrieve the desired features, their - metadata - - and statistics. returns :py:class:`~mlrun.feature_store.OfflineVectorResponse`, - - results can be returned as a dataframe or written to a target. - - If feature vector does not exist, a new one will be created and saved with - the given features. - - - The start_time and end_time attributes allow filtering the data to a given - time range, they accept - - string values or pandas `Timestamp` objects, string values can also be relative, - for example: - - "now", "now - 1d2h", "now+5m", where a valid pandas Timedelta string follows - the verb "now", - - for time alignment you can use the verb "floor" e.g. "now -1d floor 1H" will - align the time to the last hour - - (the floor string is passed to pandas.Timestamp.floor(), can use D, H, T, - S for day, hour, min, sec alignment)' - parameters: - - name: context - type: MLClientCtx - doc: MLRun context - default: '' - - name: feature_vector - type: str - doc: feature vector uri - default: '' - - name: features - type: Union[List[str], ] - doc: Relevant only if feature_vector not exist. list of feature to collect - to this vector format [/]. [as - ] - default: null - - name: label_feature - type: str - doc: feature name to be used as label data - default: null - - name: description - type: str - doc: text description of the vector - default: null - - name: entity_rows - type: DataItem - doc: URI of the data entity rows to join with - default: null - - name: entity_timestamp_column - type: str - doc: timestamp column name in the entity rows dataframe - default: null - - name: target - type: Union[str, Dict] - doc: where to write the results to - default: null - - name: run_config - type: Union[str, Dict] - doc: function and/or run configuration see :py:class:`~mlrun.feature_store.RunConfig` - default: null - - name: drop_columns - type: List[str] - doc: list of columns to drop from the final result - default: null - - name: start_time - type: str - doc: datetime, low limit of time needed to be filtered. Optional entity_timestamp_column - must be passed when using time filtering - default: null - - name: end_time - type: str - doc: datetime, high limit of time needed to be filtered. Optional entity_timestamp_column - must be passed when using time filtering - default: null - - name: with_indexes - type: bool - doc: return vector with index columns (default False) - default: false - - name: update_stats - type: bool - doc: update features statistics from the requested feature sets on the vector. - Default is False. - default: false - outputs: - - default: '' - lineno: 27 - description: retrieve offline feature vector results - default_handler: get_offline_features - disable_auto_mount: false - env: [] - priority_class_name: '' - preemption_mode: prevent - affinity: null - tolerations: null - security_context: {} -verbose: false diff --git a/get_offline_features/get_offline_features.ipynb b/get_offline_features/get_offline_features.ipynb deleted file mode 100644 index d97402a2e..000000000 --- a/get_offline_features/get_offline_features.ipynb +++ /dev/null @@ -1,1536 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# `get_offline_features()` from MLRun FeatureStore\n", - "\n", - "This MLRun Function has the following `params`:\n", - "\n", - "- `feature_vector: str`, feature vector uri.\n", - "\n", - "- `entity_rows: DataItem` = None, URI of the data entity rows to join with.\n", - "\n", - "- `entity_timestamp_column: str = None`, timestamp column name in the entity rows dataframe.\n", - "\n", - "- `target: Union[str, Dict] = None`, where to write the results to.\n", - "\n", - "- `run_config: Union[str, Dict] = None`, function and/or run configuration see :py:class:`~mlrun.feature_store.RunConfig`.\n", - "\n", - "- `drop_columns: List[str] = None`, list of columns to drop from the final result. \n", - "\n", - "- `start_time: str = None`, datetime, low limit of time needed to be filtered. Optional. `entity_timestamp_column` must be passed when using time filtering.\n", - "\n", - "- `end_time: str = None`, datetime, high limit of time needed to be filtered. Optional. `entity_timestamp_column` must be passed when using time filtering.\n", - "\n", - "- `with_indexes: bool = False`, return vector with index columns (default False).\n", - "\n", - "- `update_stats: bool = False`, update features statistics from the requested feature sets on the vector. Default is False." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import mlrun\n", - "import mlrun.feature_store as fstore\n", - "from mlrun.datastore.targets import CSVTarget\n", - "from mlrun.datastore.sources import CSVSource\n", - "from mlrun.run import get_dataitem\n", - "from mlrun.feature_store.steps import *\n", - "from mlrun.features import MinMaxValidator\n", - "import pandas as pd\n", - "import datetime\n", - "import os" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2022-01-31 14:41:48,288 [info] loaded project get-offline-features from MLRun DB\n" - ] - } - ], - "source": [ - "ABS_PATH = 'v3io://users/{}/get_offline_features/'.format(os.environ['V3IO_USERNAME'])\n", - "# Initialize the MLRun project object\n", - "project = mlrun.get_or_create_project('get-offline-features', context=\"./\", user_project=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Generating the Same FeatureSets and FeatureVecotrs Based on the Stocks Example" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create Sample Data For Demo" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "quotes = pd.DataFrame(\n", - " {\n", - " \"time\": [\n", - " pd.Timestamp(\"2016-05-25 13:30:00.023\"),\n", - " pd.Timestamp(\"2016-05-25 13:30:00.023\"),\n", - " pd.Timestamp(\"2016-05-25 13:30:00.030\"),\n", - " pd.Timestamp(\"2016-05-25 13:30:00.041\"),\n", - " pd.Timestamp(\"2016-05-25 13:30:00.048\"),\n", - " pd.Timestamp(\"2016-05-25 13:30:00.049\"),\n", - " pd.Timestamp(\"2016-05-25 13:30:00.072\"),\n", - " pd.Timestamp(\"2016-05-25 13:30:00.075\")\n", - " ],\n", - " \"ticker\": [\n", - " \"GOOG\",\n", - " \"MSFT\",\n", - " \"MSFT\",\n", - " \"MSFT\",\n", - " \"GOOG\",\n", - " \"AAPL\",\n", - " \"GOOG\",\n", - " \"MSFT\"\n", - " ],\n", - " \"bid\": [720.50, 51.95, 51.97, 51.99, 720.50, 97.99, 720.50, 52.01],\n", - " \"ask\": [720.93, 51.96, 51.98, 52.00, 720.93, 98.01, 720.88, 52.03]\n", - " }\n", - ")\n", - "\n", - "trades = pd.DataFrame(\n", - " {\n", - " \"time\": [\n", - " pd.Timestamp(\"2016-05-25 13:30:00.023\"),\n", - " pd.Timestamp(\"2016-05-25 13:30:00.038\"),\n", - " pd.Timestamp(\"2016-05-25 13:30:00.048\"),\n", - " pd.Timestamp(\"2016-05-25 13:30:00.048\"),\n", - " pd.Timestamp(\"2016-05-25 13:30:00.048\")\n", - " ],\n", - " \"ticker\": [\"MSFT\", \"MSFT\", \"GOOG\", \"GOOG\", \"AAPL\"],\n", - " \"price\": [51.95, 51.95, 720.77, 720.92, 98.0],\n", - " \"quantity\": [75, 155, 100, 100, 100]\n", - " }\n", - ")\n", - "\n", - "stocks = pd.DataFrame(\n", - " {\n", - " \"ticker\": [\"MSFT\", \"GOOG\", \"AAPL\"],\n", - " \"name\": [\"Microsoft Corporation\", \"Alphabet Inc\", \"Apple Inc\"],\n", - " \"exchange\": [\"NASDAQ\", \"NASDAQ\", \"NASDAQ\"]\n", - " }\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "def move_date(df, col):\n", - " max_date = df[col].max()\n", - " now_date = datetime.datetime.now()\n", - " delta = now_date - max_date \n", - " df[col] = df[col] + delta \n", - " return df\n", - "\n", - "quotes = move_date(quotes, \"time\")\n", - "trades = move_date(trades, \"time\")\n", - "trades.to_csv('trades.csv', index=False)\n", - "data_uri = os.path.join(ABS_PATH, 'trades.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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timetickerbidask
02022-01-31 14:41:48.260566GOOG720.50720.93
12022-01-31 14:41:48.260566MSFT51.9551.96
22022-01-31 14:41:48.267566MSFT51.9751.98
32022-01-31 14:41:48.278566MSFT51.9952.00
42022-01-31 14:41:48.285566GOOG720.50720.93
52022-01-31 14:41:48.286566AAPL97.9998.01
62022-01-31 14:41:48.309566GOOG720.50720.88
72022-01-31 14:41:48.312566MSFT52.0152.03
\n", - "
" - ], - "text/plain": [ - " time ticker bid ask\n", - "0 2022-01-31 14:41:48.260566 GOOG 720.50 720.93\n", - "1 2022-01-31 14:41:48.260566 MSFT 51.95 51.96\n", - "2 2022-01-31 14:41:48.267566 MSFT 51.97 51.98\n", - "3 2022-01-31 14:41:48.278566 MSFT 51.99 52.00\n", - "4 2022-01-31 14:41:48.285566 GOOG 720.50 720.93\n", - "5 2022-01-31 14:41:48.286566 AAPL 97.99 98.01\n", - "6 2022-01-31 14:41:48.309566 GOOG 720.50 720.88\n", - "7 2022-01-31 14:41:48.312566 MSFT 52.01 52.03" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "quotes" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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timetickerpricequantity
02022-01-31 14:41:48.288476MSFT51.9575
12022-01-31 14:41:48.303476MSFT51.95155
22022-01-31 14:41:48.313476GOOG720.77100
32022-01-31 14:41:48.313476GOOG720.92100
42022-01-31 14:41:48.313476AAPL98.00100
\n", - "
" - ], - "text/plain": [ - " time ticker price quantity\n", - "0 2022-01-31 14:41:48.288476 MSFT 51.95 75\n", - "1 2022-01-31 14:41:48.303476 MSFT 51.95 155\n", - "2 2022-01-31 14:41:48.313476 GOOG 720.77 100\n", - "3 2022-01-31 14:41:48.313476 GOOG 720.92 100\n", - "4 2022-01-31 14:41:48.313476 AAPL 98.00 100" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "trades" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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tickernameexchange
0MSFTMicrosoft CorporationNASDAQ
1GOOGAlphabet IncNASDAQ
2AAPLApple IncNASDAQ
\n", - "
" - ], - "text/plain": [ - " ticker name exchange\n", - "0 MSFT Microsoft Corporation NASDAQ\n", - "1 GOOG Alphabet Inc NASDAQ\n", - "2 AAPL Apple Inc NASDAQ" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "stocks" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Build & Ingest Simple Feature Set (stocks)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
nameexchange
ticker
MSFTMicrosoft CorporationNASDAQ
GOOGAlphabet IncNASDAQ
AAPLApple IncNASDAQ
\n", - "
" - ], - "text/plain": [ - " name exchange\n", - "ticker \n", - "MSFT Microsoft Corporation NASDAQ\n", - "GOOG Alphabet Inc NASDAQ\n", - "AAPL Apple Inc NASDAQ" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# add feature set without time column (stock ticker metadata) \n", - "stocks_set = fstore.FeatureSet(\"stocks\", entities=[fstore.Entity(\"ticker\")])\n", - "fstore.ingest(stocks_set, stocks, infer_options=fstore.InferOptions.default())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Build Advanced feature set - with feature engineering pipeline" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "quotes_set = fstore.FeatureSet(\"stock-quotes\", entities=[fstore.Entity(\"ticker\")])" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "class MyMap(MapClass):\n", - " def __init__(self, multiplier=1, **kwargs):\n", - " super().__init__(**kwargs)\n", - " self._multiplier = multiplier\n", - "\n", - " def do(self, event):\n", - " event[\"multi\"] = event[\"bid\"] * self._multiplier\n", - " return event" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "mlrun-flow\n", - "\n", - "\n", - "\n", - "_start\n", - "\n", - "start\n", - "\n", - "\n", - "\n", - "MyMap\n", - "\n", - "MyMap\n", - "\n", - "\n", - "\n", - "_start->MyMap\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "storey.Extend\n", - "\n", - "storey.Extend\n", - "\n", - "\n", - "\n", - "MyMap->storey.Extend\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "filter\n", - "\n", - "filter\n", - "\n", - "\n", - "\n", - "storey.Extend->filter\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "FeaturesetValidator\n", - "\n", - "FeaturesetValidator\n", - "\n", - "\n", - "\n", - "filter->FeaturesetValidator\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Aggregates\n", - "\n", - "Aggregates\n", - "\n", - "\n", - "\n", - "FeaturesetValidator->Aggregates\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "parquet\n", - "\n", - "\n", - "parquet\n", - "\n", - "\n", - "\n", - "Aggregates->parquet\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "nosql\n", - "\n", - "\n", - "nosql\n", - "\n", - "\n", - "\n", - "Aggregates->nosql\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "quotes_set.graph.to(\"MyMap\", multiplier=3)\\\n", - " .to(\"storey.Extend\", _fn=\"({'extra': event['bid'] * 77})\")\\\n", - " .to(\"storey.Filter\", \"filter\", _fn=\"(event['bid'] > 51.92)\")\\\n", - " .to(FeaturesetValidator())\n", - "\n", - "quotes_set.add_aggregation(\"ask\", [\"sum\", \"max\"], \"1h\", \"10m\", name=\"asks1\")\n", - "quotes_set.add_aggregation(\"ask\", [\"sum\", \"max\"], \"5h\", \"10m\", name=\"asks5\")\n", - "quotes_set.add_aggregation(\"bid\", [\"min\", \"max\"], \"1h\", \"10m\", name=\"bids\")\n", - "\n", - "# add feature validation policy\n", - "quotes_set[\"bid\"] = fstore.Feature(validator=MinMaxValidator(min=52, severity=\"info\"))\n", - "\n", - "# add default target definitions and plot\n", - "quotes_set.set_targets()\n", - "quotes_set.plot(rankdir=\"LR\", with_targets=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Ingest Data Into Offline And Online Stores" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "info! bid value is smaller than min, key=['MSFT'] time=2022-01-31 14:41:51.377248+00:00 args={'min': 52, 'value': 51.95}\n", - "info! bid value is smaller than min, key=['MSFT'] time=2022-01-31 14:41:51.377927+00:00 args={'min': 52, 'value': 51.97}\n", - "info! bid value is smaller than min, key=['MSFT'] time=2022-01-31 14:41:51.378103+00:00 args={'min': 52, 'value': 51.99}\n", - "info! bid value is smaller than min, key=['MSFT'] time=2022-01-31 14:41:51.578640+00:00 args={'min': 52, 'value': 51.95}\n", - "info! bid value is smaller than min, key=['MSFT'] time=2022-01-31 14:41:51.581692+00:00 args={'min': 52, 'value': 51.97}\n", - "info! bid value is smaller than min, key=['MSFT'] time=2022-01-31 14:41:51.584351+00:00 args={'min': 52, 'value': 51.99}\n" - ] - }, - { - "data": { - "text/html": [ - "
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asks1_max_1hasks1_sum_1hasks5_max_5hasks5_sum_5hbids_max_1hbids_min_1htimebidaskmultiextra
ticker
GOOG720.93720.93720.93720.93720.50720.502022-01-31 14:41:48.260566720.50720.932161.5055478.50
MSFT51.9651.9651.9651.9651.9551.952022-01-31 14:41:48.26056651.9551.96155.854000.15
MSFT51.98103.9451.98103.9451.9751.952022-01-31 14:41:48.26756651.9751.98155.914001.69
MSFT52.00155.9452.00155.9451.9951.952022-01-31 14:41:48.27856651.9952.00155.974003.23
GOOG720.931441.86720.931441.86720.50720.502022-01-31 14:41:48.285566720.50720.932161.5055478.50
AAPL98.0198.0198.0198.0197.9997.992022-01-31 14:41:48.28656697.9998.01293.977545.23
GOOG720.932162.74720.932162.74720.50720.502022-01-31 14:41:48.309566720.50720.882161.5055478.50
MSFT52.03207.9752.03207.9752.0151.952022-01-31 14:41:48.31256652.0152.03156.034004.77
\n", - "
" - ], - "text/plain": [ - " asks1_max_1h asks1_sum_1h asks5_max_5h asks5_sum_5h bids_max_1h \\\n", - "ticker \n", - "GOOG 720.93 720.93 720.93 720.93 720.50 \n", - "MSFT 51.96 51.96 51.96 51.96 51.95 \n", - "MSFT 51.98 103.94 51.98 103.94 51.97 \n", - "MSFT 52.00 155.94 52.00 155.94 51.99 \n", - "GOOG 720.93 1441.86 720.93 1441.86 720.50 \n", - "AAPL 98.01 98.01 98.01 98.01 97.99 \n", - "GOOG 720.93 2162.74 720.93 2162.74 720.50 \n", - "MSFT 52.03 207.97 52.03 207.97 52.01 \n", - "\n", - " bids_min_1h time bid ask multi \\\n", - "ticker \n", - "GOOG 720.50 2022-01-31 14:41:48.260566 720.50 720.93 2161.50 \n", - "MSFT 51.95 2022-01-31 14:41:48.260566 51.95 51.96 155.85 \n", - "MSFT 51.95 2022-01-31 14:41:48.267566 51.97 51.98 155.91 \n", - "MSFT 51.95 2022-01-31 14:41:48.278566 51.99 52.00 155.97 \n", - "GOOG 720.50 2022-01-31 14:41:48.285566 720.50 720.93 2161.50 \n", - "AAPL 97.99 2022-01-31 14:41:48.286566 97.99 98.01 293.97 \n", - "GOOG 720.50 2022-01-31 14:41:48.309566 720.50 720.88 2161.50 \n", - "MSFT 51.95 2022-01-31 14:41:48.312566 52.01 52.03 156.03 \n", - "\n", - " extra \n", - "ticker \n", - "GOOG 55478.50 \n", - "MSFT 4000.15 \n", - "MSFT 4001.69 \n", - "MSFT 4003.23 \n", - "GOOG 55478.50 \n", - "AAPL 7545.23 \n", - "GOOG 55478.50 \n", - "MSFT 4004.77 " - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# save ingest data and print the FeatureSet spec\n", - "fstore.ingest(quotes_set, quotes)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Get an Offline Feature Vector" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "features = [\n", - " \"stock-quotes.multi\",\n", - " \"stock-quotes.asks5_sum_5h as total_ask\",\n", - " \"stock-quotes.bids_min_1h\",\n", - " \"stock-quotes.bids_max_1h\",\n", - " \"stocks.*\",\n", - "]\n", - "\n", - "vector = fstore.FeatureVector(\"stocks-vec\", features)\n", - "vector.save()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "target_dict = CSVTarget('mycsv',path=os.path.join(ABS_PATH, 'my_csv.csv')).to_dict()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Using `get_offline_features()` " - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "get_offline_features_fn = mlrun.import_function('hub://get_offline_features:development')" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2022-01-31 14:41:52,066 [info] starting run get-offline-features-get_offline_features uid=956663b9a9ba448c9ea65e8e9245718e DB=http://mlrun-api:8080\n", - "> 2022-01-31 14:41:52,214 [info] Creating DataFrame from entity_rows = v3io://users/yonatan/get_offline_features/trades.csv\n", - "> 2022-01-31 14:41:52,292 [info] Preparing 'mycsv' target\n", - "> 2022-01-31 14:41:52,294 [info] getting offline features from the FeatureVector store://feature-vectors/get-offline-features-yonatan/stocks-vec\n", - "> 2022-01-31 14:41:52,708 [info] wrote target: {'name': 'mycsv', 'kind': 'csv', 'path': 'v3io://users/yonatan/get_offline_features/my_csv.csv', 'status': 'ready', 'updated': '2022-01-31T14:41:52.708534+00:00'}\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "
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get-offline-features-yonatan0Jan 31 14:41:52completedget-offline-features-get_offline_features
v3io_user=yonatan
kind=
owner=yonatan
host=jupyter-yoni-647b99c95d-w4jlc
entity_rows
feature_vector=store://feature-vectors/get-offline-features-yonatan/stocks-vec
target={'name': 'mycsv', 'kind': 'csv', 'path': 'v3io://users/yonatan/get_offline_features/my_csv.csv', 'partitioned': False}
entity_timestamp_column=time
target=v3io://users/yonatan/get_offline_features/my_csv.csv
feature_vector=store://feature-vectors/get-offline-features-yonatan/stocks-vec
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Unnamed: 0pricequantitymultitotal_askbids_min_1hbids_max_1hnameexchange
0051.9575155.8551.9651.9551.95Microsoft CorporationNASDAQ
1151.9575155.91103.9451.9551.97Microsoft CorporationNASDAQ
2251.9575155.97155.9451.9551.99Microsoft CorporationNASDAQ
3351.9575156.03207.9751.9552.01Microsoft CorporationNASDAQ
4451.95155155.8551.9651.9551.95Microsoft CorporationNASDAQ
5551.95155155.91103.9451.9551.97Microsoft CorporationNASDAQ
6651.95155155.97155.9451.9551.99Microsoft CorporationNASDAQ
7751.95155156.03207.9751.9552.01Microsoft CorporationNASDAQ
88720.771002161.50720.93720.50720.50Alphabet IncNASDAQ
99720.771002161.501441.86720.50720.50Alphabet IncNASDAQ
1010720.771002161.502162.74720.50720.50Alphabet IncNASDAQ
1111720.921002161.50720.93720.50720.50Alphabet IncNASDAQ
1212720.921002161.501441.86720.50720.50Alphabet IncNASDAQ
1313720.921002161.502162.74720.50720.50Alphabet IncNASDAQ
141498.00100293.9798.0197.9997.99Apple IncNASDAQ
\n", - "
" - ], - "text/plain": [ - " Unnamed: 0 price quantity multi total_ask bids_min_1h \\\n", - "0 0 51.95 75 155.85 51.96 51.95 \n", - "1 1 51.95 75 155.91 103.94 51.95 \n", - "2 2 51.95 75 155.97 155.94 51.95 \n", - "3 3 51.95 75 156.03 207.97 51.95 \n", - "4 4 51.95 155 155.85 51.96 51.95 \n", - "5 5 51.95 155 155.91 103.94 51.95 \n", - "6 6 51.95 155 155.97 155.94 51.95 \n", - "7 7 51.95 155 156.03 207.97 51.95 \n", - "8 8 720.77 100 2161.50 720.93 720.50 \n", - "9 9 720.77 100 2161.50 1441.86 720.50 \n", - "10 10 720.77 100 2161.50 2162.74 720.50 \n", - "11 11 720.92 100 2161.50 720.93 720.50 \n", - "12 12 720.92 100 2161.50 1441.86 720.50 \n", - "13 13 720.92 100 2161.50 2162.74 720.50 \n", - "14 14 98.00 100 293.97 98.01 97.99 \n", - "\n", - " bids_max_1h name exchange \n", - "0 51.95 Microsoft Corporation NASDAQ \n", - "1 51.97 Microsoft Corporation NASDAQ \n", - "2 51.99 Microsoft Corporation NASDAQ \n", - "3 52.01 Microsoft Corporation NASDAQ \n", - "4 51.95 Microsoft Corporation NASDAQ \n", - "5 51.97 Microsoft Corporation NASDAQ \n", - "6 51.99 Microsoft Corporation NASDAQ \n", - "7 52.01 Microsoft Corporation NASDAQ \n", - "8 720.50 Alphabet Inc NASDAQ \n", - "9 720.50 Alphabet Inc NASDAQ \n", - "10 720.50 Alphabet Inc NASDAQ \n", - "11 720.50 Alphabet Inc NASDAQ \n", - "12 720.50 Alphabet Inc NASDAQ \n", - "13 720.50 Alphabet Inc NASDAQ \n", - "14 97.99 Apple Inc NASDAQ " - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mlrun.get_dataitem(gof_run.outputs['feature_vector']).as_df()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/get_offline_features/get_offline_features.py b/get_offline_features/get_offline_features.py deleted file mode 100644 index a48faa9c3..000000000 --- a/get_offline_features/get_offline_features.py +++ /dev/null @@ -1,142 +0,0 @@ -# Copyright 2019 Iguazio -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -from typing import Union, List, Dict - -import mlrun -import mlrun.feature_store as fs -from mlrun.datastore.store_resources import is_store_uri, parse_store_uri -from mlrun.datastore.targets import get_target_driver, kind_to_driver -from mlrun.datastore.base import DataItem -from mlrun.execution import MLClientCtx -from mlrun.utils import StorePrefix, parse_versioned_object_uri -from mlrun.errors import MLRunInvalidArgumentError - - -def get_offline_features( - context: MLClientCtx, - feature_vector: str, - features: Union[List[str], None] = None, - label_feature: str = None, - description: str = None, - entity_rows: DataItem = None, - entity_timestamp_column: str = None, - target: Union[str, Dict] = None, - run_config: Union[str, Dict] = None, - drop_columns: List[str] = None, - start_time: str = None, - end_time: str = None, - with_indexes: bool = False, - update_stats: bool = False, -): - """retrieve offline feature vector results - - specify a feature vector object/uri and retrieve the desired features, their metadata - and statistics. returns :py:class:`~mlrun.feature_store.OfflineVectorResponse`, - results can be returned as a dataframe or written to a target. - If feature vector does not exist, a new one will be created and saved with the given features. - - The start_time and end_time attributes allow filtering the data to a given time range, they accept - string values or pandas `Timestamp` objects, string values can also be relative, for example: - "now", "now - 1d2h", "now+5m", where a valid pandas Timedelta string follows the verb "now", - for time alignment you can use the verb "floor" e.g. "now -1d floor 1H" will align the time to the last hour - (the floor string is passed to pandas.Timestamp.floor(), can use D, H, T, S for day, hour, min, sec alignment) - - - :param context: MLRun context - :param feature_vector: feature vector uri - :param features: Relevant only if feature_vector not exist. list of feature to collect to this vector - format [/]. [as ] - :param label_feature: feature name to be used as label data - :param description: text description of the vector - :param entity_rows: URI of the data entity rows to join with - :param target: where to write the results to - :param drop_columns: list of columns to drop from the final result - :param entity_timestamp_column: timestamp column name in the entity rows dataframe - :param run_config: function and/or run configuration - see :py:class:`~mlrun.feature_store.RunConfig` - :param start_time: datetime, low limit of time needed to be filtered. Optional - entity_timestamp_column must be passed when using time filtering - :param end_time: datetime, high limit of time needed to be filtered. Optional - entity_timestamp_column must be passed when using time filtering - :param with_indexes: return vector with index columns (default False) - :param update_stats: update features statistics from the requested feature sets on the vector. Default is False. - - :returns feature_vector input - """ - - if features: - # Creating a new FeatureVector and saving: - if is_store_uri(feature_vector): - prefix, new_uri = parse_store_uri(feature_vector) - if prefix != StorePrefix.FeatureVector: - raise MLRunInvalidArgumentError( - f"provided store uri ({feature_vector}) does not represent a feature vector (prefix={prefix})" - ) - feature_vector = new_uri - - context.logger.info(f"Creating FeatureVector {feature_vector}") - project, name, tag, _ = parse_versioned_object_uri(feature_vector, mlrun.mlconf.default_project) - vector = fs.FeatureVector(name, features, label_feature=label_feature, description=description) - vector.metadata.project = project - vector.metadata.tag = tag - vector.save() - feature_vector_uri = vector.uri - else: - if is_store_uri(feature_vector): - feature_vector_uri = feature_vector - else: - vector = fs.get_feature_vector(feature_vector) - feature_vector_uri = vector.uri - - # Preparing entity_rows: - if entity_rows is not None: - context.logger.info(f"Creating DataFrame from entity_rows = {entity_rows}") - entity_rows = entity_rows.as_df() - - # Preparing target: - if target: - if isinstance(target, str): - target = kind_to_driver[target]() - - name = target.name if hasattr(target, "name") else target["name"] - context.logger.info(f"Preparing '{name}' target") - target = get_target_driver(target) - if hasattr(target, 'path') and target.path: - context.log_result("target", target.path) - - # Preparing run_config: - if run_config and isinstance(run_config, dict): - context.logger.info("Preparing run configuration") - run_config = fs.RunConfig(**run_config) - - # Calling get_offline_features: - context.logger.info( - f"getting offline features from the FeatureVector {feature_vector}" - ) - fs.get_offline_features( - feature_vector=feature_vector_uri, - entity_rows=entity_rows, - entity_timestamp_column=entity_timestamp_column, - target=target, - run_config=run_config, - drop_columns=drop_columns, - start_time=start_time, - end_time=end_time, - with_indexes=with_indexes, - update_stats=update_stats, - ) - - context.log_result("feature_vector", feature_vector) - context.log_result("feature_vector_uri", feature_vector_uri) diff --git a/get_offline_features/item.yaml b/get_offline_features/item.yaml deleted file mode 100644 index 17241f6e4..000000000 --- a/get_offline_features/item.yaml +++ /dev/null @@ -1,26 +0,0 @@ -apiVersion: v1 -categories: -- data-preparation -- data-analysis -- feature-store -description: retrieve offline feature vector results -doc: '' -example: get_offline_features.ipynb -generationDate: 2022-08-28:17-25 -hidden: false -icon: '' -labels: - author: yonish -maintainers: [] -marketplaceType: '' -mlrunVersion: 1.1.0 -name: get_offline_features -platformVersion: 3.5.0 -spec: - filename: get_offline_features.py - handler: get_offline_features - image: mlrun/mlrun - kind: job - requirements: [] -url: '' -version: 1.2.0 diff --git a/get_offline_features/test_get_offline_features.py b/get_offline_features/test_get_offline_features.py deleted file mode 100644 index 21913e011..000000000 --- a/get_offline_features/test_get_offline_features.py +++ /dev/null @@ -1,239 +0,0 @@ -# Copyright 2019 Iguazio -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -import os -import tempfile -import shutil -import datetime - -import pytest -import mlrun -import mlrun.feature_store as fstore -from mlrun.datastore.targets import CSVTarget -from mlrun.feature_store.steps import * -from mlrun.features import MinMaxValidator -from mlrun.run import get_dataitem - - -REQUIRED_ENV_VARS = [ - "MLRUN_DBPATH", - "MLRUN_ARTIFACT_PATH", - "V3IO_USERNAME", - "V3IO_API", - "V3IO_ACCESS_KEY", -] - - -def _validate_environment_variables() -> bool: - """ - Checks that all required Environment variables are set. - """ - environment_keys = os.environ.keys() - return all(key in environment_keys for key in REQUIRED_ENV_VARS) - - -def _set_environment(): - """ - Creating project and temp dir for the project. - """ - artifact_path = tempfile.TemporaryDirectory().name - os.makedirs(artifact_path) - project = mlrun.get_or_create_project( - "get-offline-features-test", context="./", user_project=True - ) - return artifact_path, project - - -def _cleanup_environment(artifact_path: str): - """ - Cleanup the test environment, deleting files and artifacts created during the test. - - :param artifact_path: The artifact path to delete. - """ - # Clean the local directory: - for test_output in [ - *os.listdir(artifact_path), - "schedules", - "runs", - "artifacts", - "functions", - ]: - test_output_path = os.path.abspath(f"./{test_output}") - if os.path.exists(test_output_path): - if os.path.isdir(test_output_path): - shutil.rmtree(test_output_path) - else: - os.remove(test_output_path) - - # Clean the artifacts directory: - shutil.rmtree(artifact_path) - - -def create_dataframes() -> (pd.DataFrame, pd.DataFrame, pd.DataFrame): - """ - Creates all the necessary DataFrames to the test. - """ - - def move_date(df, col): - max_date = df[col].max() - now_date = datetime.datetime.now() - delta = now_date - max_date - df[col] = df[col] + delta - return df - - stocks = pd.DataFrame( - { - "ticker": ["MSFT", "GOOG", "AAPL"], - "name": ["Microsoft Corporation", "Alphabet Inc", "Apple Inc"], - "exchange": ["NASDAQ", "NASDAQ", "NASDAQ"], - } - ) - - quotes = pd.DataFrame( - { - "time": [ - pd.Timestamp("2016-05-25 13:30:00.023"), - pd.Timestamp("2016-05-25 13:30:00.023"), - pd.Timestamp("2016-05-25 13:30:00.030"), - pd.Timestamp("2016-05-25 13:30:00.041"), - pd.Timestamp("2016-05-25 13:30:00.048"), - pd.Timestamp("2016-05-25 13:30:00.049"), - pd.Timestamp("2016-05-25 13:30:00.072"), - pd.Timestamp("2016-05-25 13:30:00.075"), - ], - "ticker": ["GOOG", "MSFT", "MSFT", "MSFT", "GOOG", "AAPL", "GOOG", "MSFT"], - "bid": [720.50, 51.95, 51.97, 51.99, 720.50, 97.99, 720.50, 52.01], - "ask": [720.93, 51.96, 51.98, 52.00, 720.93, 98.01, 720.88, 52.03], - } - ) - - trades = pd.DataFrame( - { - "time": [ - pd.Timestamp("2016-05-25 13:30:00.023"), - pd.Timestamp("2016-05-25 13:30:00.038"), - pd.Timestamp("2016-05-25 13:30:00.048"), - pd.Timestamp("2016-05-25 13:30:00.048"), - pd.Timestamp("2016-05-25 13:30:00.048"), - ], - "ticker": ["MSFT", "MSFT", "GOOG", "GOOG", "AAPL"], - "price": [51.95, 51.95, 720.77, 720.92, 98.0], - "quantity": [75, 155, 100, 100, 100], - } - ) - quotes = move_date(quotes, "time") - trades = move_date(trades, "time") - return quotes, trades, stocks - - -class MyMap(MapClass): - def __init__(self, multiplier=1, **kwargs): - super().__init__(**kwargs) - self._multiplier = multiplier - - def do(self, event): - event["multi"] = event["bid"] * self._multiplier - return event - - -def _create_feature_set(): - """ - Creating all the necessary FeatureSets for the test. - """ - stocks_set = fstore.FeatureSet("stocks", entities=[fstore.Entity("ticker")]) - - quotes_set = fstore.FeatureSet("stock-quotes", entities=[fstore.Entity("ticker")]) - - quotes_set.graph.to("MyMap", multiplier=3).to( - "storey.Extend", _fn="({'extra': event['bid'] * 77})" - ).to("storey.Filter", "filter", _fn="(event['bid'] > 51.92)").to( - FeaturesetValidator() - ) - - quotes_set.add_aggregation("ask", ["sum", "max"], "1h", "10m", name="asks1") - quotes_set.add_aggregation("ask", ["sum", "max"], "5h", "10m", name="asks5") - quotes_set.add_aggregation("bid", ["min", "max"], "1h", "10m", name="bids") - - # add feature validation policy - quotes_set["bid"] = fstore.Feature( - validator=MinMaxValidator(min=52, severity="info") - ) - - # add default target definitions and plot - quotes_set.set_targets() - return quotes_set, stocks_set - - -@pytest.mark.skipif( - condition=not _validate_environment_variables(), - reason="Project's environment variables are not set", -) -def test_get_offline_vector(): - # Creating project: - artifact_path, project = _set_environment() - - # Importing the marketplace function: - gof_fn = mlrun.import_function("function.yaml") - - # Creating the dataframes: - quotes, trades, stocks = create_dataframes() - - # Defining features for the FeatureVector: - features = [ - "stock-quotes.multi", - "stock-quotes.asks5_sum_5h as total_ask", - "stock-quotes.bids_min_1h", - "stock-quotes.bids_max_1h", - "stocks.*", - ] - - # Creating the FeatureSets and ingesting them: - quotes_set, stocks_set = _create_feature_set() - fstore.ingest(stocks_set, stocks) - fstore.ingest(quotes_set, quotes) - - # Saving the trades dataframe as a csv to use as entity_rows: - trades_uri = os.path.join(artifact_path, "trades.csv") - trades.to_csv(trades_uri, index=False) - - # Creating target for the FeatureVector: - target_dict = CSVTarget( - "mycsv", path=os.path.join(artifact_path, "my_csv.csv") - ).to_dict() - - # Running the getting_offline_features function: - gof_run = None - try: - gof_run = gof_fn.run( - handler="get_offline_features", - inputs={"entity_rows": trades_uri}, - params={ - "feature_vector": "stocks-vec", - "features": features, - "target": target_dict, - "entity_timestamp_column": "time", - }, - local=True, - ) - - except Exception as e: - print(f"- The test failed - raised the following error:\n- {e}") - - target_df = get_dataitem(gof_run.outputs["target"]).as_df() - vector_df = get_dataitem(gof_run.outputs["feature_vector"]).as_df() - - # Asserting that the target and FeatureVector dataframes are the same: - assert mlrun.datastore.is_store_uri(gof_run.outputs["feature_vector_uri"]) - assert vector_df.equals(target_df), "Target and feature vector are not the same" - _cleanup_environment(artifact_path) diff --git a/hugging_face_classifier_trainer/function.yaml b/hugging_face_classifier_trainer/function.yaml deleted file mode 100644 index eb223b2bf..000000000 --- a/hugging_face_classifier_trainer/function.yaml +++ /dev/null @@ -1,368 +0,0 @@ -kind: job -metadata: - name: hugging-face-classifier-trainer - tag: '' - hash: e8113e81f04c96fc9a8a94e717dea81ee3e05a18 - project: '' - labels: - author: davids - categories: - - machine-learning - - model-training -spec: - command: '' - args: [] - image: '' - build: - functionSourceCode: import os
import shutil
import tempfile
import zipfile
from abc import ABC
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import mlrun
import mlrun.datastore
import mlrun.utils
import numpy as np
import pandas as pd
import transformers
from datasets import Dataset, load_dataset, load_metric
from mlrun import MLClientCtx
from mlrun import feature_store as fs
from mlrun.artifacts import Artifact, PlotlyArtifact
from mlrun.datastore import DataItem
from mlrun.frameworks._common import CommonTypes, MLRunInterface
from mlrun.utils import create_class
from plotly import graph_objects as go
from sklearn.model_selection import train_test_split
from transformers import (
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    PreTrainedModel,
    PreTrainedTokenizer,
    Trainer,
    TrainerCallback,
    TrainerControl,
    TrainerState,
    TrainingArguments,
)


# ----------------------from MLRUN--------------------------------
class HFORTOptimizerMLRunInterface(MLRunInterface, ABC):
    """
    Interface for adding MLRun features for tensorflow keras API.
    """

    # MLRun's context default name:
    DEFAULT_CONTEXT_NAME = "mlrun-huggingface"

    # Attributes to be inserted so the MLRun interface will be fully enabled.
    _PROPERTIES = {
        "_auto_log": False,
        "_context": None,
        "_model_name": "model",
        "_tag": "",
        "_labels": None,
        "_extra_data": None,
    }
    _METHODS = ["enable_auto_logging"]
    # Attributes to replace so the MLRun interface will be fully enabled.
    _REPLACED_METHODS = [
        "optimize",
    ]

    @classmethod
    def add_interface(
        cls,
        obj,
        restoration: CommonTypes.MLRunInterfaceRestorationType = None,
    ):
        """
        Enrich the object with this interface properties, methods and functions, so it will have this TensorFlow.Keras
        MLRun's features.
        :param obj:                     The object to enrich his interface.
        :param restoration: Restoration information tuple as returned from 'remove_interface' in order to
                                        add the interface in a certain state.
        """
        super(HFORTOptimizerMLRunInterface, cls).add_interface(
            obj=obj, restoration=restoration
        )

    @classmethod
    def mlrun_optimize(cls):
        """
        MLRun's tf.keras.Model.fit wrapper. It will setup the optimizer when using horovod. The optimizer must be
        passed in a keyword argument and when using horovod, it must be passed as an Optimizer instance, not a string.

        raise MLRunInvalidArgumentError: In case the optimizer provided did not follow the instructions above.
        """

        def wrapper(self, *args, **kwargs):
            save_dir = cls._get_function_argument(
                self.optimize,
                argument_name="save_dir",
                passed_args=args,
                passed_kwargs=kwargs,
            )[0]

            # Call the original optimize method:
            result = self.original_optimize(*args, **kwargs)

            if self._auto_log:
                # Log the onnx model:
                self._context.log_model(
                    key="model",
                    db_key=self._model_name,
                    model_file=f"{save_dir}/model_optimized.onnx",
                    tag=self._tag,
                    framework="ONNX",
                    labels=self._labels,
                    extra_data=self._extra_data,
                )

            return result

        return wrapper

    def enable_auto_logging(
        self,
        context: mlrun.MLClientCtx,
        model_name: str = "model",
        tag: str = "",
        labels: Dict[str, str] = None,
        extra_data: dict = None,
    ):
        self._auto_log = True

        self._context = context
        self._model_name = model_name
        self._tag = tag
        self._labels = labels
        self._extra_data = extra_data


class HFTrainerMLRunInterface(MLRunInterface, ABC):
    """
    Interface for adding MLRun features for tensorflow keras API.
    """

    # MLRuns context default name:
    DEFAULT_CONTEXT_NAME = "mlrun-huggingface"

    # Attributes to replace so the MLRun interface will be fully enabled.
    _REPLACED_METHODS = [
        "train",
        # "evaluate"
    ]

    @classmethod
    def add_interface(
        cls,
        obj: Trainer,
        restoration: CommonTypes.MLRunInterfaceRestorationType = None,
    ):
        """
        Enrich the object with this interface properties, methods and functions, so it will have this TensorFlow.Keras
        MLRuns features.
        :param obj:                     The object to enrich his interface.
        :param restoration: Restoration information tuple as returned from 'remove_interface' in order to
                                        add the interface in a certain state.
        """

        super(HFTrainerMLRunInterface, cls).add_interface(
            obj=obj, restoration=restoration
        )

    @classmethod
    def mlrun_train(cls):

        """
        MLRuns tf.keras.Model.fit wrapper. It will setup the optimizer when using horovod. The optimizer must be
        passed in a keyword argument and when using horovod, it must be passed as an Optimizer instance, not a string.

        raise MLRunInvalidArgumentError: In case the optimizer provided did not follow the instructions above.
        """

        def wrapper(self: Trainer, *args, **kwargs):
            # Restore the evaluation method as `train` will use it:
            # cls._restore_attribute(obj=self, attribute_name="evaluate")

            # Call the original fit method:
            result = self.original_train(*args, **kwargs)

            # Replace the evaluation method again:
            # cls._replace_function(obj=self, function_name="evaluate")

            return result

        return wrapper


class MLRunCallback(TrainerCallback):
    """
    Callback for collecting logs during training / evaluation of the `Trainer` API.
    """

    def __init__(
        self,
        context: mlrun.MLClientCtx = None,
        model_name: str = "model",
        tag: str = "",
        labels: Dict[str, str] = None,
        extra_data: dict = None,
    ):
        super().__init__()

        # Store the configurations:
        self._context = (
            context
            if context is not None
            else mlrun.get_or_create_ctx("./mlrun-huggingface")
        )
        self._model_name = model_name
        self._tag = tag
        self._labels = labels
        self._extra_data = extra_data if extra_data is not None else {}

        # Set up the logging mode:
        self._is_training = False
        self._steps: List[List[int]] = []
        self._metric_scores: Dict[str, List[float]] = {}
        self._artifacts: Dict[str, Artifact] = {}

    def on_epoch_begin(
        self,
        args: TrainingArguments,
        state: TrainerState,
        control: TrainerControl,
        **kwargs,
    ):
        self._steps.append([])

    def on_epoch_end(
        self,
        args: TrainingArguments,
        state: TrainerState,
        control: TrainerControl,
        **kwargs,
    ):
        self._log_metrics()

    def on_log(
        self,
        args: TrainingArguments,
        state: TrainerState,
        control: TrainerControl,
        logs: Dict[str, float] = None,
        **kwargs,
    ):
        recent_logs = state.log_history[-1].copy()

        recent_logs.pop("epoch")
        current_step = int(recent_logs.pop("step"))
        if current_step not in self._steps[-1]:
            self._steps[-1].append(current_step)

        for metric_name, metric_score in recent_logs.items():
            if metric_name.startswith("train_"):
                if metric_name.split("train_")[1] not in self._metric_scores:
                    self._metric_scores[metric_name] = [metric_score]
                continue
            if metric_name not in self._metric_scores:
                self._metric_scores[metric_name] = []
            self._metric_scores[metric_name].append(metric_score)

    def on_train_begin(
        self,
        args: TrainingArguments,
        state: TrainerState,
        control: TrainerControl,
        **kwargs,
    ):
        self._is_training = True

    def on_train_end(
        self,
        args: TrainingArguments,
        state: TrainerState,
        control: TrainerControl,
        model: PreTrainedModel = None,
        tokenizer: PreTrainedTokenizer = None,
        **kwargs,
    ):
        self._log_metrics()

        temp_directory = tempfile.gettempdir()

        # Save and log the tokenizer:
        if tokenizer is not None:
            # Save tokenizer:
            tokenizer_dir = os.path.join(temp_directory, "tokenizer")
            tokenizer.save_pretrained(save_directory=tokenizer_dir)
            # Zip the tokenizer directory:
            tokenizer_zip = shutil.make_archive(
                base_name="tokenizer",
                format="zip",
                root_dir=tokenizer_dir,
            )
            # Log the zip file:
            self._artifacts["tokenizer"] = self._context.log_artifact(
                item="tokenizer", local_path=tokenizer_zip
            )

        # Save the model:
        model_dir = os.path.join(temp_directory, "model")
        model.save_pretrained(save_directory=model_dir)

        # Zip the model directory:
        shutil.make_archive(
            base_name="model",
            format="zip",
            root_dir=model_dir,
        )

        # Log the model:
        self._context.log_model(
            key="model",
            db_key=self._model_name,
            model_file="model.zip",
            tag=self._tag,
            framework="Hugging Face",
            labels=self._labels,
            extra_data={**self._artifacts, **self._extra_data},
        )

    def on_evaluate(
        self,
        args: TrainingArguments,
        state: TrainerState,
        control: TrainerControl,
        **kwargs,
    ):
        self._log_metrics()

        if self._is_training:
            return

        # TODO: Update the model object

    def _log_metrics(self):
        for metric_name, metric_scores in self._metric_scores.items():
            self._context.log_result(key=metric_name, value=metric_scores[-1])
            if len(metric_scores) > 1:
                self._log_metric_plot(name=metric_name, scores=metric_scores)
        self._context.commit(completed=False)

    def _log_metric_plot(self, name: str, scores: List[float]):
        # Initialize a plotly figure:
        metric_figure = go.Figure()

        # Add titles:
        metric_figure.update_layout(
            title=name.capitalize().replace("_", " "),
            xaxis_title="Samples",
            yaxis_title="Scores",
        )

        # Draw:
        metric_figure.add_trace(
            go.Scatter(x=np.arange(len(scores)), y=scores, mode="lines")
        )

        # Create the plotly artifact:
        artifact_name = f"{name}_plot"
        artifact = PlotlyArtifact(key=artifact_name, figure=metric_figure)
        self._artifacts[artifact_name] = self._context.log_artifact(artifact)


def _apply_mlrun_on_trainer(
    trainer: transformers.Trainer,
    model_name: str = None,
    tag: str = "",
    context: mlrun.MLClientCtx = None,
    auto_log: bool = True,
    labels: Dict[str, str] = None,
    extra_data: dict = None,
    **kwargs,
):
    # Get parameters defaults:
    if context is None:
        context = mlrun.get_or_create_ctx(HFTrainerMLRunInterface.DEFAULT_CONTEXT_NAME)

    HFTrainerMLRunInterface.add_interface(obj=trainer)

    if auto_log:
        trainer.add_callback(
            MLRunCallback(
                context=context,
                model_name=model_name,
                tag=tag,
                labels=labels,
                extra_data=extra_data,
            )
        )


def _apply_mlrun_on_optimizer(
    optimizer,
    model_name: str = None,
    tag: str = "",
    context: mlrun.MLClientCtx = None,
    auto_log: bool = True,
    labels: Dict[str, str] = None,
    extra_data: dict = None,
    **kwargs,
):
    # Get parameters defaults:
    if context is None:
        context = mlrun.get_or_create_ctx(
            HFORTOptimizerMLRunInterface.DEFAULT_CONTEXT_NAME
        )

    HFORTOptimizerMLRunInterface.add_interface(obj=optimizer)

    if auto_log:
        optimizer.enable_auto_logging(
            context=context,
            model_name=model_name,
            tag=tag,
            labels=labels,
            extra_data=extra_data,
        )


def apply_mlrun(
    huggingface_object,
    model_name: str = None,
    tag: str = "",
    context: mlrun.MLClientCtx = None,
    auto_log: bool = True,
    labels: Dict[str, str] = None,
    extra_data: dict = None,
    **kwargs,
):
    """
    Wrap the given model with MLRun's interface providing it with mlrun's additional features.
    :param huggingface_object: The model to wrap. Can be loaded from the model path given as well.
    :param model_name:         The model name to use for storing the model artifact. Default: "model".
    :param tag:                The model's tag to log with.
    :param context:            MLRun context to work with. If no context is given it will be retrieved via
                               'mlrun.get_or_create_ctx(None)'
    :param auto_log:           Whether to enable MLRun's auto logging. Default: True.
    """

    if isinstance(huggingface_object, transformers.Trainer):
        return _apply_mlrun_on_trainer(
            trainer=huggingface_object,
            model_name=model_name,
            tag=tag,
            context=context,
            auto_log=auto_log,
            labels=labels,
            extra_data=extra_data,
        )
    import optimum.onnxruntime as optimum_ort

    if isinstance(huggingface_object, optimum_ort.ORTOptimizer):
        return _apply_mlrun_on_optimizer(
            optimizer=huggingface_object,
            model_name=model_name,
            tag=tag,
            context=context,
            auto_log=auto_log,
            labels=labels,
            extra_data=extra_data,
        )
    raise mlrun.errors.MLRunInvalidArgumentError


# ---------------------- from auto_trainer--------------------------------
class KWArgsPrefixes:
    MODEL_CLASS = "CLASS_"
    FIT = "FIT_"
    TRAIN = "TRAIN_"
    PREDICT = "PREDICT_"


def _get_sub_dict_by_prefix(src: Dict, prefix_key: str) -> Dict[str, Any]:
    """
    Collect all the keys from the given dict that starts with the given prefix and creates a new dictionary with these
    keys.

    :param src:         The source dict to extract the values from.
    :param prefix_key:  Only keys with this prefix will be returned. The keys in the result dict will be without this
                        prefix.
    """
    return {
        key.replace(prefix_key, ""): val
        for key, val in src.items()
        if key.startswith(prefix_key)
    }


def _get_dataframe(
    context: MLClientCtx,
    dataset: DataItem,
    label_columns: Optional[Union[str, List[str]]] = None,
    drop_columns: Union[str, List[str], int, List[int]] = None,
) -> Tuple[pd.DataFrame, Optional[Union[str, List[str]]]]:
    """
    Getting the DataFrame of the dataset and drop the columns accordingly.

    :param context:         MLRun context.
    :param dataset:         The dataset to train the model on.
                            Can be either a list of lists, dict, URI or a FeatureVector.
    :param label_columns:   The target label(s) of the column(s) in the dataset. for Regression or
                            Classification tasks.
    :param drop_columns:    str/int or a list of strings/ints that represent the column names/indices to drop.
    """
    if isinstance(dataset, (list, dict)):
        dataset = pd.DataFrame(dataset)
        # Checking if drop_columns provided by integer type:
        if drop_columns:
            if isinstance(drop_columns, str) or (
                isinstance(drop_columns, list)
                and any(isinstance(col, str) for col in drop_columns)
            ):
                context.logger.error(
                    "drop_columns must be an integer/list of integers if not provided with a URI/FeatureVector dataset"
                )
                raise ValueError
            dataset.drop(drop_columns, axis=1, inplace=True)

        return dataset, label_columns

    store_uri_prefix, _ = mlrun.datastore.parse_store_uri(dataset.artifact_url)
    if mlrun.utils.StorePrefix.FeatureVector == store_uri_prefix:
        # feature-vector case:
        label_columns = label_columns or dataset.meta.status.label_column
        dataset = fs.get_offline_features(
            dataset.meta.uri, drop_columns=drop_columns
        ).to_dataframe()

        context.logger.info(f"label columns: {label_columns}")
    else:
        # simple URL case:
        dataset = dataset.as_df()
        if drop_columns:
            if all(col in dataset for col in drop_columns):
                dataset = dataset.drop(drop_columns, axis=1)
            else:
                context.logger.info(
                    "not all of the columns to drop in the dataset, drop columns process skipped"
                )
    return dataset, label_columns


# ---------------------- Hugging Face Trainer --------------------------------


def _create_compute_metrics(metrics: List[str]) -> Callable[[EvalPrediction], Dict]:
    """
    This function create and returns a function that will be used to compute metrics at evaluation.
    :param metrics: List of different metrics for evaluate the model such as f1, accuracy etc.

    :returns: Function that will be used to compute metrics at evaluation.
             Must take a [`EvalPrediction`] and return a dictionary string to metric values.
    """

    def _compute_metrics(eval_pred):
        logits, labels = eval_pred
        predictions = np.argmax(logits, axis=-1)
        metric_dict_results = {}
        for metric in metrics:
            load_met = load_metric(metric)
            metric_res = load_met.compute(predictions=predictions, references=labels)[
                metric
            ]
            metric_dict_results[metric] = metric_res

        return metric_dict_results

    return _compute_metrics


def _edit_columns(
    dataset: Dataset,
    drop_columns: List[str] = None,
    rename_columns: [str, str] = None,
) -> Dataset:
    """
    Drop and renames that columns of the given dataset
    :param dataset:         Dataset to process
    :param drop_columns:    The columns to drop from the dataset.
    :param rename_columns:  Dict of columns ro rename : {<old_name>: <new_name>, ...}

    :returns: The dataset after the desired process
    """
    if drop_columns:
        dataset = dataset.remove_columns(drop_columns)
    if rename_columns:
        dataset = dataset.rename_columns(rename_columns)
    return dataset


def _prepare_dataset(
    context: MLClientCtx,
    dataset_name: str,
    label_name: str = None,
    drop_columns: Optional[List[str]] = None,
    num_of_train_samples: int = None,
    train_test_split_size: float = None,
    random_state: int = None,
) -> Tuple[Dataset, Dataset]:
    """
    Loading the dataset and editing the columns

    :param context:                 MLRun contex
    :param dataset_name:            The name of the dataset to get from the HuggingFace hub
    :param label_name:              The target label of the column in the dataset.
    :param drop_columns:            The columns to drop from the dataset.
    :param num_of_train_samples:    Max number of training samples, for debugging.
    :param train_test_split_size:   Should be between 0.0 and 1.0 and represent the proportion of the dataset to include
                                    in the test split.
    :param random_state:            Random state for train_test_split

    """

    context.logger.info(
        f"Loading and editing {dataset_name} dataset from Hugging Face hub"
    )
    rename_cols = {label_name: "labels"}

    # Loading and editing dataset:
    dataset = load_dataset(dataset_name)

    # train set
    train_dataset = dataset["train"]
    if num_of_train_samples:
        train_dataset = train_dataset.shuffle(seed=random_state).select(
            list(range(num_of_train_samples))
        )
    train_dataset = _edit_columns(train_dataset, drop_columns, rename_cols)

    # test set
    test_dataset = dataset["test"]
    if train_test_split_size or num_of_train_samples:
        train_test_split_size = train_test_split_size or 0.2
        num_of_test_samples = int(
            (train_dataset.num_rows * train_test_split_size)
            // (1 - train_test_split_size)
        )
        test_dataset = test_dataset.shuffle(seed=random_state).select(
            list(range(num_of_test_samples))
        )
    test_dataset = _edit_columns(test_dataset, drop_columns, rename_cols)

    return train_dataset, test_dataset


def train(
    context: MLClientCtx,
    hf_dataset: str = None,
    dataset: DataItem = None,
    test_set: DataItem = None,
    drop_columns: Optional[List[str]] = None,
    pretrained_tokenizer: str = None,
    pretrained_model: str = None,
    model_class: str = None,
    model_name: str = "huggingface-model",
    label_name: str = "labels",
    text_col: str = "text",
    num_of_train_samples: int = None,
    train_test_split_size: float = None,
    metrics: List[str] = None,
    random_state: int = None,
):
    """
    Training and evaluating a pretrained model with a pretrained tokenizer over a dataset.
    The dataset can be either be the name of the dataset that contains in the HuggingFace hub,
    or a URI or a FeatureVector

    :param context:                 MLRun context
    :param hf_dataset:              The name of the dataset to get from the HuggingFace hub
    :param dataset:                 The dataset to train the model on. Can be either a URI or a FeatureVector
    :param test_set:                The test set to train the model with.
    :param drop_columns:            The columns to drop from the dataset.
    :param pretrained_tokenizer:    The name of the pretrained tokenizer from the HuggingFace hub.
    :param pretrained_model:        The name of the pretrained model from the HuggingFace hub.
    :param model_name:              The model's name to use for storing the model artifact, default to 'model'
    :param model_class:             The class of the model, e.g. `transformers.AutoModelForSequenceClassification`
    :param label_name:              The target label of the column in the dataset.
    :param text_col:                The input text column un the dataset.
    :param num_of_train_samples:    Max number of training samples, for debugging.
    :param train_test_split_size:   Should be between 0.0 and 1.0 and represent the proportion of the dataset to include
                                    in the test split.
    :param metrics:                 List of different metrics for evaluate the model such as f1, accuracy etc.
    :param random_state:            Random state for train_test_split
    """

    if train_test_split_size is None and test_set is None:
        context.logger.info(
            "'train_test_split_size' is not provided, setting train_test_split_size to 0.2"
        )
        train_test_split_size = 0.2

    # Creating tokenizer:
    tokenizer = AutoTokenizer.from_pretrained(pretrained_tokenizer)

    def preprocess_function(examples):
        return tokenizer(examples[text_col], truncation=True)

    # prepare data for training
    if hf_dataset:
        train_dataset, test_dataset = _prepare_dataset(
            context,
            hf_dataset,
            label_name,
            drop_columns,
            num_of_train_samples,
            train_test_split_size,
            random_state=random_state,
        )
    elif dataset:
        # Get DataFrame by URL or by FeatureVector:
        train_dataset, label_name = _get_dataframe(
            context=context,
            dataset=dataset,
            label_columns=label_name,
            drop_columns=drop_columns,
        )
        if test_set:
            test_dataset, _ = _get_dataframe(
                context=context,
                dataset=test_set,
                label_columns=label_name,
                drop_columns=drop_columns,
            )
        else:
            train_dataset, test_dataset = train_test_split(
                train_dataset,
                test_size=train_test_split_size,
                random_state=random_state,
            )
        train_dataset = Dataset.from_pandas(train_dataset)
        test_dataset = Dataset.from_pandas(test_dataset)
    else:
        raise mlrun.errors.MLRunInvalidArgumentError(
            "Training data was not provided. A training dataset is mandatory for training."
            " Please provide a training set using one of the arguments 'hf_dataset' or 'dataset'."
        )

    # Mapping datasets with the tokenizer:
    tokenized_train = train_dataset.map(preprocess_function, batched=True)
    tokenized_test = test_dataset.map(preprocess_function, batched=True)

    # Creating data collator for batching:
    data_collator = DataCollatorWithPadding(tokenizer=tokenizer)

    # Parsing kwargs:
    train_kwargs = _get_sub_dict_by_prefix(
        src=context.parameters, prefix_key=KWArgsPrefixes.TRAIN
    )
    model_class_kwargs = _get_sub_dict_by_prefix(
        src=context.parameters, prefix_key=KWArgsPrefixes.MODEL_CLASS
    )

    # Loading our pretrained model:
    model_class_kwargs["pretrained_model_name_or_path"] = (
        model_class_kwargs.get("pretrained_model_name_or_path") or pretrained_model
    )
    train_kwargs["hub_token"] = train_kwargs.get("hub_token") or pretrained_tokenizer
    if not model_class_kwargs["pretrained_model_name_or_path"]:
        raise mlrun.errors.MLRunRuntimeError(
            "Must provide pretrained_model name as "
            "function argument or in extra params"
        )
    model = create_class(model_class).from_pretrained(**model_class_kwargs)

    # Preparing training arguments:
    training_args = TrainingArguments(
        **train_kwargs,
    )

    compute_metrics = _create_compute_metrics(metrics) if metrics else None
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=tokenized_train,
        eval_dataset=tokenized_test,
        tokenizer=tokenizer,
        data_collator=data_collator,
        compute_metrics=compute_metrics,
    )

    apply_mlrun(trainer, model_name=model_name)

    # Apply training with evaluation:
    context.logger.info(f"training '{model_name}'")
    trainer.train()


def _get_model_dir(model_uri: str):
    model_file, _, _ = mlrun.artifacts.get_model(model_uri)
    model_dir = tempfile.gettempdir()
    # Unzip the Model:
    with zipfile.ZipFile(model_file, "r") as zip_file:
        zip_file.extractall(model_dir)

    return model_dir


def optimize(
    model_path: str,
    model_name: str = "optimized_model",
    target_dir: str = "./optimized",
    optimization_level: int = 1,
):
    """
    Optimizing the transformer model using ONNX optimization.


    :param model_path:          The path of the model to optimize.
    :param model_name:          Name of the optimized model.
    :param target_dir:          The directory to save the ONNX model.
    :param optimization_level:  Optimization level performed by ONNX Runtime of the loaded graph. (default is 1)
    """
    # We import these in the function scope so ONNX won't be mandatory for the other handlers:
    from optimum.onnxruntime import ORTModelForSequenceClassification, ORTOptimizer
    from optimum.onnxruntime.configuration import OptimizationConfig

    model_dir = _get_model_dir(model_uri=model_path)
    # Creating configuration for optimization step:
    optimization_config = OptimizationConfig(optimization_level=optimization_level)

    # Converting our pretrained model to an ONNX-Runtime model:
    ort_model = ORTModelForSequenceClassification.from_pretrained(
        model_dir, from_transformers=True
    )

    # Creating an ONNX-Runtime optimizer from ONNX model:
    optimizer = ORTOptimizer.from_pretrained(ort_model)

    apply_mlrun(optimizer, model_name=model_name)
    # Optimizing and saving the ONNX model:
    optimizer.optimize(save_dir=target_dir, optimization_config=optimization_config)
 - base_image: mlrun/mlrun - commands: [] - code_origin: '' - origin_filename: '' - requirements: - - onnx~=1.14.1 - - onnxruntime~=1.16.1 - - optimum~=1.6.4 - - transformers~=4.26.1 - - datasets~=2.10.1 - - scikit-learn~=1.0.2 - entry_points: - add_interface: - name: add_interface - doc: 'Enrich the object with this interface properties, methods and functions, - so it will have this TensorFlow.Keras - - MLRuns features.' - parameters: - - name: cls - - name: obj - type: Trainer - doc: The object to enrich his interface. - - name: restoration - type: MLRunInterfaceRestorationType - doc: Restoration information tuple as returned from 'remove_interface' in - order to add the interface in a certain state. - default: null - outputs: [] - lineno: 146 - has_varargs: false - has_kwargs: false - mlrun_optimize: - name: mlrun_optimize - doc: 'MLRun''s tf.keras.Model.fit wrapper. It will setup the optimizer when - using horovod. The optimizer must be - - passed in a keyword argument and when using horovod, it must be passed as - an Optimizer instance, not a string. - - - raise MLRunInvalidArgumentError: In case the optimizer provided did not follow - the instructions above.' - parameters: - - name: cls - outputs: [] - lineno: 79 - has_varargs: false - has_kwargs: false - wrapper: - name: wrapper - doc: '' - parameters: - - name: self - type: Trainer - outputs: [] - lineno: 173 - has_varargs: true - has_kwargs: true - enable_auto_logging: - name: enable_auto_logging - doc: '' - parameters: - - name: self - - name: context - type: MLClientCtx - - name: model_name - type: str - default: model - - name: tag - type: str - default: '' - - name: labels - type: Dict[str, str] - default: null - - name: extra_data - type: dict - default: null - outputs: [] - lineno: 114 - has_varargs: false - has_kwargs: false - mlrun_train: - name: mlrun_train - doc: 'MLRuns tf.keras.Model.fit wrapper. It will setup the optimizer when using - horovod. The optimizer must be - - passed in a keyword argument and when using horovod, it must be passed as - an Optimizer instance, not a string. - - - raise MLRunInvalidArgumentError: In case the optimizer provided did not follow - the instructions above.' - parameters: - - name: cls - outputs: [] - lineno: 164 - has_varargs: false - has_kwargs: false - on_epoch_begin: - name: on_epoch_begin - doc: '' - parameters: - - name: self - - name: args - type: TrainingArguments - - name: state - type: TrainerState - - name: control - type: TrainerControl - outputs: [] - lineno: 220 - has_varargs: false - has_kwargs: true - on_epoch_end: - name: on_epoch_end - doc: '' - parameters: - - name: self - - name: args - type: TrainingArguments - - name: state - type: TrainerState - - name: control - type: TrainerControl - outputs: [] - lineno: 229 - has_varargs: false - has_kwargs: true - on_log: - name: on_log - doc: '' - parameters: - - name: self - - name: args - type: TrainingArguments - - name: state - type: TrainerState - - name: control - type: TrainerControl - - name: logs - type: Dict[str, float] - default: null - outputs: [] - lineno: 238 - has_varargs: false - has_kwargs: true - on_train_begin: - name: on_train_begin - doc: '' - parameters: - - name: self - - name: args - type: TrainingArguments - - name: state - type: TrainerState - - name: control - type: TrainerControl - outputs: [] - lineno: 262 - has_varargs: false - has_kwargs: true - on_train_end: - name: on_train_end - doc: '' - parameters: - - name: self - - name: args - type: TrainingArguments - - name: state - type: TrainerState - - name: control - type: TrainerControl - - name: model - type: PreTrainedModel - default: null - - name: tokenizer - type: PreTrainedTokenizer - default: null - outputs: [] - lineno: 271 - has_varargs: false - has_kwargs: true - on_evaluate: - name: on_evaluate - doc: '' - parameters: - - name: self - - name: args - type: TrainingArguments - - name: state - type: TrainerState - - name: control - type: TrainerControl - outputs: [] - lineno: 322 - has_varargs: false - has_kwargs: true - apply_mlrun: - name: apply_mlrun - doc: Wrap the given model with MLRun's interface providing it with mlrun's additional - features. - parameters: - - name: huggingface_object - doc: The model to wrap. Can be loaded from the model path given as well. - - name: model_name - type: str - doc: 'The model name to use for storing the model artifact. Default: "model".' - default: null - - name: tag - type: str - doc: The model's tag to log with. - default: '' - - name: context - type: MLClientCtx - doc: MLRun context to work with. If no context is given it will be retrieved - via 'mlrun.get_or_create_ctx(None)' - default: null - - name: auto_log - type: bool - doc: 'Whether to enable MLRun''s auto logging. Default: True.' - default: true - - name: labels - type: Dict[str, str] - default: null - - name: extra_data - type: dict - default: null - outputs: [] - lineno: 421 - has_varargs: false - has_kwargs: true - train: - name: train - doc: 'Training and evaluating a pretrained model with a pretrained tokenizer - over a dataset. - - The dataset can be either be the name of the dataset that contains in the - HuggingFace hub, - - or a URI or a FeatureVector' - parameters: - - name: context - type: MLClientCtx - doc: MLRun context - - name: hf_dataset - type: str - doc: The name of the dataset to get from the HuggingFace hub - default: null - - name: dataset - type: DataItem - doc: The dataset to train the model on. Can be either a URI or a FeatureVector - default: null - - name: test_set - type: DataItem - doc: The test set to train the model with. - default: null - - name: drop_columns - type: Optional[List[str]] - doc: The columns to drop from the dataset. - default: null - - name: pretrained_tokenizer - type: str - doc: The name of the pretrained tokenizer from the HuggingFace hub. - default: null - - name: pretrained_model - type: str - doc: The name of the pretrained model from the HuggingFace hub. - default: null - - name: model_class - type: str - doc: The class of the model, e.g. `transformers.AutoModelForSequenceClassification` - default: null - - name: model_name - type: str - doc: The model's name to use for storing the model artifact, default to 'model' - default: huggingface-model - - name: label_name - type: str - doc: The target label of the column in the dataset. - default: labels - - name: text_col - type: str - doc: The input text column un the dataset. - default: text - - name: num_of_train_samples - type: int - doc: Max number of training samples, for debugging. - default: null - - name: train_test_split_size - type: float - doc: Should be between 0.0 and 1.0 and represent the proportion of the dataset - to include in the test split. - default: null - - name: metrics - type: List[str] - doc: List of different metrics for evaluate the model such as f1, accuracy - etc. - default: null - - name: random_state - type: int - doc: Random state for train_test_split - default: null - outputs: [] - lineno: 647 - has_varargs: false - has_kwargs: false - preprocess_function: - name: preprocess_function - doc: '' - parameters: - - name: examples - outputs: [] - lineno: 696 - has_varargs: false - has_kwargs: false - optimize: - name: optimize - doc: Optimizing the transformer model using ONNX optimization. - parameters: - - name: model_path - type: str - doc: The path of the model to optimize. - - name: model_name - type: str - doc: Name of the optimized model. - default: optimized_model - - name: target_dir - type: str - doc: The directory to save the ONNX model. - default: ./optimized - - name: optimization_level - type: int - doc: Optimization level performed by ONNX Runtime of the loaded graph. (default - is 1) - default: 1 - outputs: [] - lineno: 799 - has_varargs: false - has_kwargs: false - description: Automatic train and optimize functions for HuggingFace framework - default_handler: train - disable_auto_mount: false - clone_target_dir: '' - env: [] - priority_class_name: '' - preemption_mode: prevent - affinity: null - tolerations: null - security_context: {} -verbose: false diff --git a/hugging_face_classifier_trainer/hugging_face_classifier_trainer.ipynb b/hugging_face_classifier_trainer/hugging_face_classifier_trainer.ipynb deleted file mode 100644 index 2768d2dc1..000000000 --- a/hugging_face_classifier_trainer/hugging_face_classifier_trainer.ipynb +++ /dev/null @@ -1,2533 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "\n", - "# MLRun Hugging Face Classifier Trainer Tutorial" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "This notebook shows how to use the handlers of the Hugging Face classifier trainer.\n", - "the following handlers are:\n", - "- `train`\n", - "- `optimize`\n", - "\n", - "All you need is simply **HF model type** and a **HF dataset name**." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "scrolled": true, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: onnx~=1.14.1 in /User/.pythonlibs/mlrun-base/lib/python3.9/site-packages (from -r requirements.txt (line 1)) (1.14.1)\n", - "Requirement already satisfied: onnxruntime==1.16.1 in /User/.pythonlibs/mlrun-base/lib/python3.9/site-packages (from -r requirements.txt (line 2)) (1.16.1)\n", - "Requirement already satisfied: optimum~=1.6.4 in /User/.pythonlibs/mlrun-base/lib/python3.9/site-packages (from -r requirements.txt (line 3)) (1.6.4)\n", - "Requirement already satisfied: transformers~=4.26.1 in /User/.pythonlibs/mlrun-base/lib/python3.9/site-packages (from -r requirements.txt (line 4)) (4.26.1)\n", - 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"outputs": [], - "source": [ - "import mlrun" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2024-03-24 17:10:17,091 [info] Project loaded successfully: {'project_name': 'hugging-face-trainer'}\n" - ] - } - ], - "source": [ - "project = mlrun.get_or_create_project('hugging-face-trainer', context=\"./\", user_project=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "### **Importing the hugging_face_classifier_trainer function from the Marketplace**" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "hugging_face_classifier_trainer = mlrun.import_function(\"hub://hugging_face_classifier_trainer\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "### **Training a model**\n", - "\n", - "Choosing the `train` handler" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "#### Define task parameters¶\n", - "* Class parameters should contain the prefix `CLASS_`\n", - "* Train parameters should contain the prefix `TRAIN_`" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "model_class = \"transformers.AutoModelForSequenceClassification\"\n", - "additional_parameters = {\n", - " \"TRAIN_output_dir\": \"finetuning-sentiment-model-3000-samples\",\n", - " \"TRAIN_learning_rate\": 2e-5,\n", - " \"TRAIN_per_device_train_batch_size\": 16,\n", - " \"TRAIN_per_device_eval_batch_size\": 16,\n", - " \"TRAIN_num_train_epochs\": 3,\n", - " \"TRAIN_weight_decay\": 0.01,\n", - " \"TRAIN_push_to_hub\": False,\n", - " \"TRAIN_evaluation_strategy\": \"epoch\",\n", - " \"TRAIN_eval_steps\": 1,\n", - " \"TRAIN_logging_steps\": 1,\n", - " \"CLASS_num_labels\": 2\n", - "}" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "#### Running the Training job with the \"train\" handler" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "pycharm": { - "name": "#%%\n" - }, - "scrolled": true, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2024-03-24 17:10:21,025 [info] Storing function: {'name': 'hugging-face-classifier-trainer-train', 'uid': '514d8d5530c842238b1cc81983cd943e', 'db': 'http://mlrun-api:8080'}\n", - "> 2024-03-24 17:11:03,727 [info] 'train_test_split_size' is not provided, setting train_test_split_size to 0.2\n", - "> 2024-03-24 17:11:03,882 [info] Loading and editing Shayanvsf/US_Airline_Sentiment dataset from Hugging Face hub\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Found cached dataset parquet (/igz/.cache/huggingface/datasets/Shayanvsf___parquet/Shayanvsf--US_Airline_Sentiment-1319c42f87c44b2f/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec)\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "f43b1388d0b344888323bec590baadee", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/3 [00:00 2024-03-24 17:11:08,938 [info] training 'huggingface-model'\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "The following columns in the training set don't have a corresponding argument in `DistilBertForSequenceClassification.forward` and have been ignored: text. If text are not expected by `DistilBertForSequenceClassification.forward`, you can safely ignore this message.\n", - "This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n", - "***** Running training *****\n", - " Num examples = 100\n", - " Num Epochs = 3\n", - " Instantaneous batch size per device = 16\n", - " Total train batch size (w. parallel, distributed & accumulation) = 16\n", - " Gradient Accumulation steps = 1\n", - " Total optimization steps = 21\n", - " Number of trainable parameters = 66955010\n", - "You're using a DistilBertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "
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EpochTraining LossValidation LossAccuracyF1
10.7389000.5153110.7916670.000000
20.5259000.4815630.7916670.000000
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hugging-face-trainer-avia0Mar 24 17:10:21completedhugging-face-classifier-trainer-train
v3io_user=avia
kind=local
owner=avia
host=jupyter-avia-6454bdd4c5-xz8cg
hf_dataset=Shayanvsf/US_Airline_Sentiment
drop_columns=['airline_sentiment_confidence', 'negativereason_confidence']
pretrained_tokenizer=distilbert-base-uncased
pretrained_model=distilbert-base-uncased
model_class=transformers.AutoModelForSequenceClassification
label_name=airline_sentiment
num_of_train_samples=100
metrics=['accuracy', 'f1']
random_state=42
TRAIN_output_dir=finetuning-sentiment-model-3000-samples
TRAIN_learning_rate=2e-05
TRAIN_per_device_train_batch_size=16
TRAIN_per_device_eval_batch_size=16
TRAIN_num_train_epochs=3
TRAIN_weight_decay=0.01
TRAIN_push_to_hub=False
TRAIN_evaluation_strategy=epoch
TRAIN_eval_steps=1
TRAIN_logging_steps=1
CLASS_num_labels=2
loss=0.4908
learning_rate=0.0
eval_loss=0.47167453169822693
eval_accuracy=0.7916666666666666
eval_f1=0.0
eval_runtime=0.5186
eval_samples_per_second=46.276
eval_steps_per_second=3.856
train_runtime=17.6054
train_samples_per_second=17.04
train_steps_per_second=1.193
total_flos=3327208489680.0
loss_plot
learning_rate_plot
eval_loss_plot
eval_accuracy_plot
eval_f1_plot
eval_runtime_plot
eval_samples_per_second_plot
eval_steps_per_second_plot
tokenizer
model
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\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "data": { - "text/html": [ - " > to track results use the .show() or .logs() methods or click here to open in UI" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2024-03-24 17:12:01,880 [info] Run execution finished: {'status': 'completed', 'name': 'hugging-face-classifier-trainer-train'}\n" - ] - } - ], - "source": [ - "train_run = hugging_face_classifier_trainer.run(params={\n", - " \"hf_dataset\": \"Shayanvsf/US_Airline_Sentiment\",\n", - " \"drop_columns\": [\n", - " \"airline_sentiment_confidence\",\n", - " \"negativereason_confidence\",\n", - " ],\n", - " \"pretrained_tokenizer\": \"distilbert-base-uncased\",\n", - " \"pretrained_model\": \"distilbert-base-uncased\",\n", - " \"model_class\": \"transformers.AutoModelForSequenceClassification\",\n", - " \"label_name\": \"airline_sentiment\",\n", - " \"num_of_train_samples\": 100,\n", - " \"metrics\": [\"accuracy\", \"f1\"],\n", - " \"random_state\": 42,\n", - " **additional_parameters\n", - " },\n", - " handler=\"train\",\n", - " local=True,\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "#### The result of the train run" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'loss': 0.4908,\n", - " 'learning_rate': 0.0,\n", - " 'eval_loss': 0.47167453169822693,\n", - " 'eval_accuracy': 0.7916666666666666,\n", - " 'eval_f1': 0.0,\n", - " 'eval_runtime': 0.5186,\n", - " 'eval_samples_per_second': 46.276,\n", - " 'eval_steps_per_second': 3.856,\n", - " 'train_runtime': 17.6054,\n", - " 'train_samples_per_second': 17.04,\n", - " 'train_steps_per_second': 1.193,\n", - " 'total_flos': 3327208489680.0,\n", - " 'loss_plot': 'v3io:///projects/hugging-face-trainer-avia/artifacts/hugging-face-classifier-trainer-train/0/loss_plot.html',\n", - " 'learning_rate_plot': 'v3io:///projects/hugging-face-trainer-avia/artifacts/hugging-face-classifier-trainer-train/0/learning_rate_plot.html',\n", - " 'eval_loss_plot': 'v3io:///projects/hugging-face-trainer-avia/artifacts/hugging-face-classifier-trainer-train/0/eval_loss_plot.html',\n", - " 'eval_accuracy_plot': 'v3io:///projects/hugging-face-trainer-avia/artifacts/hugging-face-classifier-trainer-train/0/eval_accuracy_plot.html',\n", - " 'eval_f1_plot': 'v3io:///projects/hugging-face-trainer-avia/artifacts/hugging-face-classifier-trainer-train/0/eval_f1_plot.html',\n", - " 'eval_runtime_plot': 'v3io:///projects/hugging-face-trainer-avia/artifacts/hugging-face-classifier-trainer-train/0/eval_runtime_plot.html',\n", - " 'eval_samples_per_second_plot': 'v3io:///projects/hugging-face-trainer-avia/artifacts/hugging-face-classifier-trainer-train/0/eval_samples_per_second_plot.html',\n", - " 'eval_steps_per_second_plot': 'v3io:///projects/hugging-face-trainer-avia/artifacts/hugging-face-classifier-trainer-train/0/eval_steps_per_second_plot.html',\n", - " 'tokenizer': 'store://artifacts/hugging-face-trainer-avia/hugging-face-classifier-trainer-train_tokenizer@514d8d5530c842238b1cc81983cd943e',\n", - " 'model': 'store://artifacts/hugging-face-trainer-avia/huggingface-model@514d8d5530c842238b1cc81983cd943e'}" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "train_run.outputs" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "
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\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "train_run.artifact('loss_plot').show()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "#### Getting the model for evaluating and predicting" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "model_path = train_run.outputs['model']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Optimize the model**\n", - "\n", - "Choosing the `optimize` handler\n", - "\n", - "The result of using this handled is an onnx optimized model." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "scrolled": true, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2024-03-24 17:12:02,020 [info] Storing function: {'name': 'hugging-face-classifier-trainer-optimize', 'uid': 'fbee1ead18444824a4b5c0308a677bf4', 'db': 'http://mlrun-api:8080'}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/User/.pythonlibs/mlrun-base/lib/python3.9/site-packages/optimum/onnxruntime/configuration.py:726: FutureWarning:\n", - "\n", - "disable_embed_layer_norm will be deprecated soon, use disable_embed_layer_norm_fusion instead, disable_embed_layer_norm_fusion is set to True.\n", - "\n", - "loading configuration file /tmp/config.json\n", - "Model config DistilBertConfig {\n", - " \"_name_or_path\": \"/tmp/config.json\",\n", - " \"activation\": \"gelu\",\n", - " \"architectures\": [\n", - " \"DistilBertForSequenceClassification\"\n", - " ],\n", - " \"attention_dropout\": 0.1,\n", - " \"dim\": 768,\n", - " \"dropout\": 0.1,\n", - " \"hidden_dim\": 3072,\n", - " \"initializer_range\": 0.02,\n", - " \"max_position_embeddings\": 512,\n", - " \"model_type\": \"distilbert\",\n", - " \"n_heads\": 12,\n", - " \"n_layers\": 6,\n", - " \"pad_token_id\": 0,\n", - " \"problem_type\": \"single_label_classification\",\n", - " \"qa_dropout\": 0.1,\n", - " \"seq_classif_dropout\": 0.2,\n", - " \"sinusoidal_pos_embds\": false,\n", - " \"tie_weights_\": true,\n", - " \"torch_dtype\": \"float32\",\n", - " \"transformers_version\": \"4.26.1\",\n", - " \"vocab_size\": 30522\n", - "}\n", - "\n", - "loading configuration file /tmp/config.json\n", - "Model config DistilBertConfig {\n", - " \"_name_or_path\": \"/tmp\",\n", - " \"activation\": \"gelu\",\n", - " \"architectures\": [\n", - " \"DistilBertForSequenceClassification\"\n", - " ],\n", - " \"attention_dropout\": 0.1,\n", - " \"dim\": 768,\n", - " \"dropout\": 0.1,\n", - " \"hidden_dim\": 3072,\n", - " \"initializer_range\": 0.02,\n", - " \"max_position_embeddings\": 512,\n", - " \"model_type\": \"distilbert\",\n", - " \"n_heads\": 12,\n", - " \"n_layers\": 6,\n", - " \"pad_token_id\": 0,\n", - " \"problem_type\": \"single_label_classification\",\n", - " \"qa_dropout\": 0.1,\n", - " \"seq_classif_dropout\": 0.2,\n", - " \"sinusoidal_pos_embds\": false,\n", - " \"tie_weights_\": true,\n", - " \"torch_dtype\": \"float32\",\n", - " \"transformers_version\": \"4.26.1\",\n", - " \"vocab_size\": 30522\n", - "}\n", - "\n", - "loading weights file /tmp/pytorch_model.bin\n", - "All model checkpoint weights were used when initializing DistilBertForSequenceClassification.\n", - "\n", - "All the weights of DistilBertForSequenceClassification were initialized from the model checkpoint at /tmp.\n", - "If your task is similar to the task the model of the checkpoint was trained on, you can already use DistilBertForSequenceClassification for predictions without further training.\n", - "/User/.pythonlibs/mlrun-base/lib/python3.9/site-packages/transformers/models/distilbert/modeling_distilbert.py:218: TracerWarning:\n", - "\n", - "torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.\n", - "\n", - "Configuration saved in /tmp/tmp79wjp8m8/config.json\n", - "Could not locate the tokenizer configuration file, will try to use the model config instead.\n", - "loading configuration file /tmp/config.json\n", - "Model config DistilBertConfig {\n", - " \"_name_or_path\": \"/tmp\",\n", - " \"activation\": \"gelu\",\n", - " \"architectures\": [\n", - " \"DistilBertForSequenceClassification\"\n", - " ],\n", - " \"attention_dropout\": 0.1,\n", - " \"dim\": 768,\n", - " \"dropout\": 0.1,\n", - " \"hidden_dim\": 3072,\n", - " \"initializer_range\": 0.02,\n", - " \"max_position_embeddings\": 512,\n", - " \"model_type\": \"distilbert\",\n", - " \"n_heads\": 12,\n", - " \"n_layers\": 6,\n", - " \"pad_token_id\": 0,\n", - " \"problem_type\": \"single_label_classification\",\n", - " \"qa_dropout\": 0.1,\n", - " \"seq_classif_dropout\": 0.2,\n", - " \"sinusoidal_pos_embds\": false,\n", - " \"tie_weights_\": true,\n", - " \"torch_dtype\": \"float32\",\n", - " \"transformers_version\": \"4.26.1\",\n", - " \"vocab_size\": 30522\n", - "}\n", - "\n", - "loading configuration file /tmp/config.json\n", - "Model config DistilBertConfig {\n", - " \"_name_or_path\": \"/tmp\",\n", - " \"activation\": \"gelu\",\n", - " \"architectures\": [\n", - " \"DistilBertForSequenceClassification\"\n", - " ],\n", - " \"attention_dropout\": 0.1,\n", - " \"dim\": 768,\n", - " \"dropout\": 0.1,\n", - " \"hidden_dim\": 3072,\n", - " \"initializer_range\": 0.02,\n", - " \"max_position_embeddings\": 512,\n", - " \"model_type\": \"distilbert\",\n", - " \"n_heads\": 12,\n", - " \"n_layers\": 6,\n", - " \"pad_token_id\": 0,\n", - " \"problem_type\": \"single_label_classification\",\n", - " \"qa_dropout\": 0.1,\n", - " \"seq_classif_dropout\": 0.2,\n", - " \"sinusoidal_pos_embds\": false,\n", - " \"tie_weights_\": true,\n", - " \"torch_dtype\": \"float32\",\n", - " \"transformers_version\": \"4.26.1\",\n", - " \"vocab_size\": 30522\n", - "}\n", - "\n", - "Could not locate the tokenizer configuration file, will try to use the model config instead.\n", - "loading configuration file /tmp/config.json\n", - "Model config DistilBertConfig {\n", - " \"_name_or_path\": \"/tmp\",\n", - " \"activation\": \"gelu\",\n", - " \"architectures\": [\n", - " \"DistilBertForSequenceClassification\"\n", - " ],\n", - " \"attention_dropout\": 0.1,\n", - " \"dim\": 768,\n", - " \"dropout\": 0.1,\n", - " \"hidden_dim\": 3072,\n", - " \"initializer_range\": 0.02,\n", - " \"max_position_embeddings\": 512,\n", - " \"model_type\": \"distilbert\",\n", - " \"n_heads\": 12,\n", - " \"n_layers\": 6,\n", - " \"pad_token_id\": 0,\n", - " \"problem_type\": \"single_label_classification\",\n", - " \"qa_dropout\": 0.1,\n", - " \"seq_classif_dropout\": 0.2,\n", - " \"sinusoidal_pos_embds\": false,\n", - " \"tie_weights_\": true,\n", - " \"torch_dtype\": \"float32\",\n", - " \"transformers_version\": \"4.26.1\",\n", - " \"vocab_size\": 30522\n", - "}\n", - "\n", - "Could not locate the tokenizer configuration file, will try to use the model config instead.\n", - "loading configuration file /tmp/tmp79wjp8m8/config.json\n", - "Model config DistilBertConfig {\n", - " \"_name_or_path\": \"/tmp/tmp79wjp8m8\",\n", - " \"activation\": \"gelu\",\n", - " \"architectures\": [\n", - " \"DistilBertForSequenceClassification\"\n", - " ],\n", - " \"attention_dropout\": 0.1,\n", - " \"dim\": 768,\n", - " \"dropout\": 0.1,\n", - " \"hidden_dim\": 3072,\n", - " \"initializer_range\": 0.02,\n", - " \"max_position_embeddings\": 512,\n", - " \"model_type\": \"distilbert\",\n", - " \"n_heads\": 12,\n", - " \"n_layers\": 6,\n", - " \"pad_token_id\": 0,\n", - " \"problem_type\": \"single_label_classification\",\n", - " \"qa_dropout\": 0.1,\n", - " \"seq_classif_dropout\": 0.2,\n", - " \"sinusoidal_pos_embds\": false,\n", - " \"tie_weights_\": true,\n", - " \"torch_dtype\": \"float32\",\n", - " \"transformers_version\": \"4.26.1\",\n", - " \"vocab_size\": 30522\n", - "}\n", - "\n", - "loading configuration file /tmp/tmp79wjp8m8/config.json\n", - "Model config DistilBertConfig {\n", - " \"_name_or_path\": \"/tmp/tmp79wjp8m8\",\n", - " \"activation\": \"gelu\",\n", - " \"architectures\": [\n", - " \"DistilBertForSequenceClassification\"\n", - " ],\n", - " \"attention_dropout\": 0.1,\n", - " \"dim\": 768,\n", - " \"dropout\": 0.1,\n", - " \"hidden_dim\": 3072,\n", - " \"initializer_range\": 0.02,\n", - " \"max_position_embeddings\": 512,\n", - " \"model_type\": \"distilbert\",\n", - " \"n_heads\": 12,\n", - " \"n_layers\": 6,\n", - " \"pad_token_id\": 0,\n", - " \"problem_type\": \"single_label_classification\",\n", - " \"qa_dropout\": 0.1,\n", - " \"seq_classif_dropout\": 0.2,\n", - " \"sinusoidal_pos_embds\": false,\n", - " \"tie_weights_\": true,\n", - " \"torch_dtype\": \"float32\",\n", - " \"transformers_version\": \"4.26.1\",\n", - " \"vocab_size\": 30522\n", - "}\n", - "\n", - "Could not locate the tokenizer configuration file, will try to use the model config instead.\n", - "loading configuration file /tmp/tmp79wjp8m8/config.json\n", - "Model config DistilBertConfig {\n", - " \"_name_or_path\": \"/tmp/tmp79wjp8m8\",\n", - " \"activation\": \"gelu\",\n", - " \"architectures\": [\n", - " \"DistilBertForSequenceClassification\"\n", - " ],\n", - " \"attention_dropout\": 0.1,\n", - " \"dim\": 768,\n", - " \"dropout\": 0.1,\n", - " \"hidden_dim\": 3072,\n", - " \"initializer_range\": 0.02,\n", - " \"max_position_embeddings\": 512,\n", - " \"model_type\": \"distilbert\",\n", - " \"n_heads\": 12,\n", - " \"n_layers\": 6,\n", - " \"pad_token_id\": 0,\n", - " \"problem_type\": \"single_label_classification\",\n", - " \"qa_dropout\": 0.1,\n", - " \"seq_classif_dropout\": 0.2,\n", - " \"sinusoidal_pos_embds\": false,\n", - " \"tie_weights_\": true,\n", - " \"torch_dtype\": \"float32\",\n", - " \"transformers_version\": \"4.26.1\",\n", - " \"vocab_size\": 30522\n", - "}\n", - "\n", - "Configuration saved in optimized/config.json\n", - "Could not locate the tokenizer configuration file, will try to use the model config instead.\n", - "loading configuration file /tmp/tmp79wjp8m8/config.json\n", - "Model config DistilBertConfig {\n", - " \"_name_or_path\": \"/tmp/tmp79wjp8m8\",\n", - " \"activation\": \"gelu\",\n", - " \"architectures\": [\n", - " \"DistilBertForSequenceClassification\"\n", - " ],\n", - " \"attention_dropout\": 0.1,\n", - " \"dim\": 768,\n", - " \"dropout\": 0.1,\n", - " \"hidden_dim\": 3072,\n", - " \"initializer_range\": 0.02,\n", - " \"max_position_embeddings\": 512,\n", - " \"model_type\": \"distilbert\",\n", - " \"n_heads\": 12,\n", - " \"n_layers\": 6,\n", - " \"pad_token_id\": 0,\n", - " \"problem_type\": \"single_label_classification\",\n", - " \"qa_dropout\": 0.1,\n", - " \"seq_classif_dropout\": 0.2,\n", - " \"sinusoidal_pos_embds\": false,\n", - " \"tie_weights_\": true,\n", - " \"torch_dtype\": \"float32\",\n", - " \"transformers_version\": \"4.26.1\",\n", - " \"vocab_size\": 30522\n", - "}\n", - "\n", - "loading configuration file /tmp/tmp79wjp8m8/config.json\n", - "Model config DistilBertConfig {\n", - " \"_name_or_path\": \"/tmp/tmp79wjp8m8\",\n", - " \"activation\": \"gelu\",\n", - " \"architectures\": [\n", - " \"DistilBertForSequenceClassification\"\n", - " ],\n", - " \"attention_dropout\": 0.1,\n", - " \"dim\": 768,\n", - " \"dropout\": 0.1,\n", - " \"hidden_dim\": 3072,\n", - " \"initializer_range\": 0.02,\n", - " \"max_position_embeddings\": 512,\n", - " \"model_type\": \"distilbert\",\n", - " \"n_heads\": 12,\n", - " \"n_layers\": 6,\n", - " \"pad_token_id\": 0,\n", - " \"problem_type\": \"single_label_classification\",\n", - " \"qa_dropout\": 0.1,\n", - " \"seq_classif_dropout\": 0.2,\n", - " \"sinusoidal_pos_embds\": false,\n", - " \"tie_weights_\": true,\n", - " \"torch_dtype\": \"float32\",\n", - " \"transformers_version\": \"4.26.1\",\n", - " \"vocab_size\": 30522\n", - "}\n", - "\n", - "Could not locate the tokenizer configuration file, will try to use the model config instead.\n", - "loading configuration file /tmp/tmp79wjp8m8/config.json\n", - "Model config DistilBertConfig {\n", - " \"_name_or_path\": \"/tmp/tmp79wjp8m8\",\n", - " \"activation\": \"gelu\",\n", - " \"architectures\": [\n", - " \"DistilBertForSequenceClassification\"\n", - " ],\n", - " \"attention_dropout\": 0.1,\n", - " \"dim\": 768,\n", - " \"dropout\": 0.1,\n", - " \"hidden_dim\": 3072,\n", - " \"initializer_range\": 0.02,\n", - " \"max_position_embeddings\": 512,\n", - " \"model_type\": \"distilbert\",\n", - " \"n_heads\": 12,\n", - " \"n_layers\": 6,\n", - " \"pad_token_id\": 0,\n", - " \"problem_type\": \"single_label_classification\",\n", - " \"qa_dropout\": 0.1,\n", - " \"seq_classif_dropout\": 0.2,\n", - " \"sinusoidal_pos_embds\": false,\n", - " \"tie_weights_\": true,\n", - " \"torch_dtype\": \"float32\",\n", - " \"transformers_version\": \"4.26.1\",\n", - " \"vocab_size\": 30522\n", - "}\n", - "\n", - "Failed to remove node input: \"/distilbert/transformer/layer.0/attention/Transpose_output_0\"\n", - "input: \"/distilbert/transformer/layer.0/attention/Constant_11_output_0\"\n", - "output: \"/distilbert/transformer/layer.0/attention/Div_output_0\"\n", - "name: \"/distilbert/transformer/layer.0/attention/Div\"\n", - "op_type: \"Div\"\n", - "\n", - "Failed to remove node input: \"/distilbert/transformer/layer.1/attention/Transpose_output_0\"\n", - "input: \"/distilbert/transformer/layer.1/attention/Constant_11_output_0\"\n", - "output: \"/distilbert/transformer/layer.1/attention/Div_output_0\"\n", - "name: \"/distilbert/transformer/layer.1/attention/Div\"\n", - "op_type: \"Div\"\n", - "\n", - "Failed to remove node input: \"/distilbert/transformer/layer.2/attention/Transpose_output_0\"\n", - "input: \"/distilbert/transformer/layer.2/attention/Constant_11_output_0\"\n", - "output: \"/distilbert/transformer/layer.2/attention/Div_output_0\"\n", - "name: \"/distilbert/transformer/layer.2/attention/Div\"\n", - "op_type: \"Div\"\n", - "\n", - "Failed to remove node input: \"/distilbert/transformer/layer.3/attention/Transpose_output_0\"\n", - "input: \"/distilbert/transformer/layer.3/attention/Constant_11_output_0\"\n", - "output: \"/distilbert/transformer/layer.3/attention/Div_output_0\"\n", - "name: \"/distilbert/transformer/layer.3/attention/Div\"\n", - "op_type: \"Div\"\n", - "\n", - "Failed to remove node input: \"/distilbert/transformer/layer.4/attention/Transpose_output_0\"\n", - "input: \"/distilbert/transformer/layer.4/attention/Constant_11_output_0\"\n", - "output: \"/distilbert/transformer/layer.4/attention/Div_output_0\"\n", - "name: \"/distilbert/transformer/layer.4/attention/Div\"\n", - "op_type: \"Div\"\n", - "\n", - "Failed to remove node input: \"/distilbert/transformer/layer.5/attention/Transpose_output_0\"\n", - "input: \"/distilbert/transformer/layer.5/attention/Constant_11_output_0\"\n", - "output: \"/distilbert/transformer/layer.5/attention/Div_output_0\"\n", - "name: \"/distilbert/transformer/layer.5/attention/Div\"\n", - "op_type: \"Div\"\n", - "\n", - "Configuration saved in optimized/config.json\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "
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projectuiditerstartstatenamelabelsinputsparametersresultsartifacts
hugging-face-trainer-avia0Mar 24 17:12:02completedhugging-face-classifier-trainer-optimize
v3io_user=avia
kind=local
owner=avia
host=jupyter-avia-6454bdd4c5-xz8cg
model_path=store://artifacts/hugging-face-trainer-avia/huggingface-model@514d8d5530c842238b1cc81983cd943e
model
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\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "data": { - "text/html": [ - " > to track results use the .show() or .logs() methods or click here to open in UI" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2024-03-24 17:12:22,721 [info] Run execution finished: {'status': 'completed', 'name': 'hugging-face-classifier-trainer-optimize'}\n" - ] - } - ], - "source": [ - "optimize_run = hugging_face_classifier_trainer.run(params={\n", - " \"model_path\": str(model_path)\n", - " },\n", - " handler=\"optimize\",\n", - " local=True,\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'model': 'store://artifacts/hugging-face-trainer-avia/optimized_model@fbee1ead18444824a4b5c0308a677bf4'}" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "optimize_run.outputs" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Running the training remotely**\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "scrolled": true, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/User/.pythonlibs/mlrun-base/lib/python3.9/site-packages/mlrun/projects/operations.py:276: OverwriteBuildParamsWarning:\n", - "\n", - "The `overwrite_build_params` parameter default will change from 'False' to 'True' in 1.8.0.\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2024-03-24 17:14:22,792 [info] Started building image: .mlrun/func-hugging-face-trainer-avia-hugging-face-classifier-trainer:latest\n", - "\u001b[36mINFO\u001b[0m[0000] Retrieving image manifest mlrun/mlrun:1.6.1 \n", - "\u001b[36mINFO\u001b[0m[0000] Retrieving image mlrun/mlrun:1.6.1 from registry index.docker.io \n", - "\u001b[36mINFO\u001b[0m[0000] Built cross stage deps: map[] \n", - "\u001b[36mINFO\u001b[0m[0000] Retrieving image manifest mlrun/mlrun:1.6.1 \n", - "\u001b[36mINFO\u001b[0m[0000] Returning cached image manifest \n", - "\u001b[36mINFO\u001b[0m[0000] Executing 0 build triggers \n", - "\u001b[36mINFO\u001b[0m[0000] Building stage 'mlrun/mlrun:1.6.1' [idx: '0', base-idx: '-1'] \n", - "\u001b[36mINFO\u001b[0m[0000] Unpacking rootfs as cmd RUN echo 'Installing /empty/requirements.txt...'; cat /empty/requirements.txt requires it. \n", - "\u001b[36mINFO\u001b[0m[0047] RUN echo 'Installing /empty/requirements.txt...'; cat /empty/requirements.txt \n", - "\u001b[36mINFO\u001b[0m[0047] Initializing snapshotter ... \n", - "\u001b[36mINFO\u001b[0m[0047] Taking snapshot of full filesystem... \n", - "\u001b[36mINFO\u001b[0m[0074] Cmd: /bin/sh \n", - "\u001b[36mINFO\u001b[0m[0074] Args: [-c echo 'Installing /empty/requirements.txt...'; cat /empty/requirements.txt] \n", - "\u001b[36mINFO\u001b[0m[0074] Running: [/bin/sh -c echo 'Installing /empty/requirements.txt...'; cat /empty/requirements.txt] \n", - "Installing /empty/requirements.txt...\n", - "mlrun[complete]==1.6.1\n", - "onnx~=1.14.1\n", - "onnxruntime~=1.16.1\n", - "optimum~=1.6.4\n", - "transformers~=4.26.1\n", - "datasets~=2.10.1\n", - "scikit-learn~=1.0.2\n", - "\u001b[36mINFO\u001b[0m[0074] Taking snapshot of full filesystem... \n", - "\u001b[36mINFO\u001b[0m[0078] No files were changed, appending empty layer to config. No layer added to image. \n", - "\u001b[36mINFO\u001b[0m[0078] RUN python -m pip install -r /empty/requirements.txt \n", - "\u001b[36mINFO\u001b[0m[0078] Cmd: /bin/sh \n", - "\u001b[36mINFO\u001b[0m[0078] Args: [-c python -m pip install -r /empty/requirements.txt] \n", - "\u001b[36mINFO\u001b[0m[0078] Running: [/bin/sh -c python -m pip install -r /empty/requirements.txt] \n", - "Requirement already satisfied: mlrun[complete]==1.6.1 in /opt/conda/lib/python3.9/site-packages (from -r /empty/requirements.txt (line 1)) (1.6.1)\n", - "Collecting onnx~=1.14.1 (from -r /empty/requirements.txt (line 2))\n", - " Obtaining dependency information for onnx~=1.14.1 from https://files.pythonhosted.org/packages/ff/24/0e522fdcadf0e15fc304145a5b6e5d7246d7f2c507fd9bfe6e1fafb2aa95/onnx-1.14.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata\n", - " Downloading onnx-1.14.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (15 kB)\n", - "Collecting onnxruntime~=1.16.1 (from -r /empty/requirements.txt (line 3))\n", - " Obtaining dependency information for onnxruntime~=1.16.1 from https://files.pythonhosted.org/packages/de/ab/ed3ae0d649cee41e870f8b1653cf4a1c1fc321e0ded4e3e1a3d4a25c0131/onnxruntime-1.16.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata\n", - " Downloading onnxruntime-1.16.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.3 kB)\n", - "Collecting optimum~=1.6.4 (from -r /empty/requirements.txt (line 4))\n", - " Obtaining dependency information for optimum~=1.6.4 from https://files.pythonhosted.org/packages/31/72/a7e3b2c57d6368c5f4bb6fba54a85cbf07d25c385a2db3f1a638f3c0ddb2/optimum-1.6.4-py3-none-any.whl.metadata\n", - " Downloading optimum-1.6.4-py3-none-any.whl.metadata (17 kB)\n", - "Collecting transformers~=4.26.1 (from -r /empty/requirements.txt (line 5))\n", - " Obtaining dependency information for transformers~=4.26.1 from https://files.pythonhosted.org/packages/1e/e2/60c3f4691b16d126ee9cfe28f598b13c424b60350ab339aba81aef054b8f/transformers-4.26.1-py3-none-any.whl.metadata\n", - " Downloading transformers-4.26.1-py3-none-any.whl.metadata (100 kB)\n", - " ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100.3/100.3 kB 6.2 MB/s eta 0:00:00\n", - "Collecting datasets~=2.10.1 (from -r /empty/requirements.txt (line 6))\n", - " Obtaining dependency information for datasets~=2.10.1 from https://files.pythonhosted.org/packages/fe/17/5825fdf034ff1a315becdbb9b6fe5a2bd9d8e724464535f18809593bf9c2/datasets-2.10.1-py3-none-any.whl.metadata\n", - " Downloading datasets-2.10.1-py3-none-any.whl.metadata (20 kB)\n", - "Collecting scikit-learn~=1.0.2 (from -r /empty/requirements.txt (line 7))\n", - " Obtaining dependency information for scikit-learn~=1.0.2 from https://files.pythonhosted.org/packages/57/aa/483fbe6b5314bce2d49801e6cec1f2139a9c220d0d51494788fff47233b3/scikit_learn-1.0.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata\n", - " Downloading scikit_learn-1.0.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (10 kB)\n", - "Requirement already satisfied: urllib3<1.27,>=1.26.9 in /opt/conda/lib/python3.9/site-packages (from mlrun[complete]==1.6.1->-r /empty/requirements.txt (line 1)) (1.26.18)\n", - 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"WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\n", - "\u001b[36mINFO\u001b[0m[0238] Taking snapshot of full filesystem... \n", - "\u001b[36mINFO\u001b[0m[0463] Pushing image to docker-registry.default-tenant.app.app-lab-2-b688.iguazio-cd2.com/mlrun/func-hugging-face-trainer-avia-hugging-face-classifier-trainer:latest \n", - "\u001b[36mINFO\u001b[0m[0493] Pushed docker-registry.default-tenant.app.app-lab-2-b688.iguazio-cd2.com/mlrun/func-hugging-face-trainer-avia-hugging-face-classifier-trainer@sha256:691d0bb3c23487b4b5d2f84ab323c24735626ee81681475f53a4158b72d4cfee \n" - ] - }, - { - "data": { - "text/plain": [ - "BuildStatus(ready=True, outputs={'image': '.mlrun/func-hugging-face-trainer-avia-hugging-face-classifier-trainer:latest'})" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "project.build_function(\"hugging-face-classifier-trainer\",with_mlrun=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "scrolled": true, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2024-03-24 17:22:42,252 [info] Storing function: {'name': 'hugging-face-classifier-trainer-train', 'uid': '53252ce7aacb4b1aacf86bf3b862daa2', 'db': 'http://mlrun-api:8080'}\n", - "> 2024-03-24 17:22:42,536 [info] Job is running in the background, pod: hugging-face-classifier-trainer-train-dqqfr\n", - "> 2024-03-24 17:24:43,288 [info] 'train_test_split_size' is not provided, setting train_test_split_size to 0.2\n", - "> 2024-03-24 17:24:43,847 [info] Loading and editing Shayanvsf/US_Airline_Sentiment dataset from Hugging Face hub\n", - "Downloading metadata: 100%|██████████| 1.03k/1.03k [00:00<00:00, 6.77MB/s]\n", - "Downloading and preparing dataset None/None (download: 265.13 KiB, generated: 1.50 MiB, post-processed: Unknown size, total: 1.76 MiB) to /root/.cache/huggingface/datasets/Shayanvsf___parquet/Shayanvsf--US_Airline_Sentiment-1319c42f87c44b2f/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec...\n", - "Downloading data files: 0%| | 0/3 [00:00 2024-03-24 17:24:47,076 [info] training 'huggingface-model'\n", - "The following columns in the training set don't have a corresponding argument in `DistilBertForSequenceClassification.forward` and have been ignored: text. If text are not expected by `DistilBertForSequenceClassification.forward`, you can safely ignore this message.\n", - "This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n", - "***** Running training *****\n", - " Num examples = 100\n", - " Num Epochs = 3\n", - " Instantaneous batch size per device = 16\n", - " Total train batch size (w. parallel, distributed & accumulation) = 16\n", - " Gradient Accumulation steps = 1\n", - " Total optimization steps = 21\n", - " Number of trainable parameters = 66955010\n", - "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", - "To disable this warning, you can either:\n", - "\t- Avoid using `tokenizers` before the fork if possible\n", - "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n", - " 0%| | 0/21 [00:00 2024-03-24 17:26:00,230 [info] To track results use the CLI: {'info_cmd': 'mlrun get run 53252ce7aacb4b1aacf86bf3b862daa2 -p hugging-face-trainer-avia', 'logs_cmd': 'mlrun logs 53252ce7aacb4b1aacf86bf3b862daa2 -p hugging-face-trainer-avia'}\n", - "> 2024-03-24 17:26:00,231 [info] Or click for UI: {'ui_url': 'https://dashboard.default-tenant.app.app-lab-2-b688.iguazio-cd2.com/mlprojects/hugging-face-trainer-avia/jobs/monitor/53252ce7aacb4b1aacf86bf3b862daa2/overview'}\n", - "> 2024-03-24 17:26:00,231 [info] Run execution finished: {'status': 'completed', 'name': 'hugging-face-classifier-trainer-train'}\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "
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projectuiditerstartstatenamelabelsinputsparametersresultsartifacts
hugging-face-trainer-avia0Mar 24 17:24:39completedhugging-face-classifier-trainer-train
v3io_user=avia
kind=job
owner=avia
mlrun/client_version=1.6.1
mlrun/client_python_version=3.9.16
host=hugging-face-classifier-trainer-train-dqqfr
hf_dataset=Shayanvsf/US_Airline_Sentiment
drop_columns=['airline_sentiment_confidence', 'negativereason_confidence']
pretrained_tokenizer=distilbert-base-uncased
pretrained_model=distilbert-base-uncased
model_class=transformers.AutoModelForSequenceClassification
label_name=airline_sentiment
num_of_train_samples=100
metrics=['accuracy', 'f1']
random_state=42
TRAIN_output_dir=finetuning-sentiment-model-3000-samples
TRAIN_learning_rate=2e-05
TRAIN_per_device_train_batch_size=16
TRAIN_per_device_eval_batch_size=16
TRAIN_num_train_epochs=3
TRAIN_weight_decay=0.01
TRAIN_push_to_hub=False
TRAIN_evaluation_strategy=epoch
TRAIN_eval_steps=1
TRAIN_logging_steps=1
CLASS_num_labels=2
loss=0.5215
learning_rate=0.0
eval_loss=0.4750453531742096
eval_accuracy=0.7916666666666666
eval_f1=0.0
eval_runtime=1.0524
eval_samples_per_second=22.806
eval_steps_per_second=1.9
train_runtime=55.1543
train_samples_per_second=5.439
train_steps_per_second=0.381
total_flos=3327208489680.0
loss_plot
learning_rate_plot
eval_loss_plot
eval_accuracy_plot
eval_f1_plot
eval_runtime_plot
eval_samples_per_second_plot
eval_steps_per_second_plot
tokenizer
model
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\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "data": { - "text/html": [ - " > to track results use the .show() or .logs() methods or click here to open in UI" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2024-03-24 17:26:09,792 [info] Run execution finished: {'status': 'completed', 'name': 'hugging-face-classifier-trainer-train'}\n" - ] - } - ], - "source": [ - "train_run = hugging_face_classifier_trainer.run(params={\n", - " \"hf_dataset\": \"Shayanvsf/US_Airline_Sentiment\",\n", - " \"drop_columns\": [\n", - " \"airline_sentiment_confidence\",\n", - " \"negativereason_confidence\",\n", - " ],\n", - " \"pretrained_tokenizer\": \"distilbert-base-uncased\",\n", - " \"pretrained_model\": \"distilbert-base-uncased\",\n", - " \"model_class\": \"transformers.AutoModelForSequenceClassification\",\n", - " \"label_name\": \"airline_sentiment\",\n", - " \"num_of_train_samples\": 100,\n", - " \"metrics\": [\"accuracy\", \"f1\"],\n", - " \"random_state\": 42,\n", - " **additional_parameters\n", - " },\n", - " handler=\"train\", \n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "[Back to the top](#top)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "mlrun-base", - "language": "python", - "name": "conda-env-mlrun-base-py" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.16" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/hugging_face_classifier_trainer/hugging_face_classifier_trainer.py b/hugging_face_classifier_trainer/hugging_face_classifier_trainer.py deleted file mode 100755 index 29d070395..000000000 --- a/hugging_face_classifier_trainer/hugging_face_classifier_trainer.py +++ /dev/null @@ -1,832 +0,0 @@ -import os -import shutil -import tempfile -import zipfile -from abc import ABC -from typing import Any, Callable, Dict, List, Optional, Tuple, Union - -import mlrun -import mlrun.datastore -import mlrun.utils -import numpy as np -import pandas as pd -import transformers -from datasets import Dataset, load_dataset, load_metric -from mlrun import MLClientCtx -from mlrun import feature_store as fs -from mlrun.artifacts import Artifact, PlotlyArtifact -from mlrun.datastore import DataItem -from mlrun.frameworks._common import CommonTypes, MLRunInterface -from mlrun.utils import create_class -from plotly import graph_objects as go -from sklearn.model_selection import train_test_split -from transformers import ( - AutoTokenizer, - DataCollatorWithPadding, - EvalPrediction, - PreTrainedModel, - PreTrainedTokenizer, - Trainer, - TrainerCallback, - TrainerControl, - TrainerState, - TrainingArguments, -) - - -# ----------------------from MLRUN-------------------------------- -class HFORTOptimizerMLRunInterface(MLRunInterface, ABC): - """ - Interface for adding MLRun features for tensorflow keras API. - """ - - # MLRun's context default name: - DEFAULT_CONTEXT_NAME = "mlrun-huggingface" - - # Attributes to be inserted so the MLRun interface will be fully enabled. - _PROPERTIES = { - "_auto_log": False, - "_context": None, - "_model_name": "model", - "_tag": "", - "_labels": None, - "_extra_data": None, - } - _METHODS = ["enable_auto_logging"] - # Attributes to replace so the MLRun interface will be fully enabled. - _REPLACED_METHODS = [ - "optimize", - ] - - @classmethod - def add_interface( - cls, - obj, - restoration: CommonTypes.MLRunInterfaceRestorationType = None, - ): - """ - Enrich the object with this interface properties, methods and functions, so it will have this TensorFlow.Keras - MLRun's features. - :param obj: The object to enrich his interface. - :param restoration: Restoration information tuple as returned from 'remove_interface' in order to - add the interface in a certain state. - """ - super(HFORTOptimizerMLRunInterface, cls).add_interface( - obj=obj, restoration=restoration - ) - - @classmethod - def mlrun_optimize(cls): - """ - MLRun's tf.keras.Model.fit wrapper. It will setup the optimizer when using horovod. The optimizer must be - passed in a keyword argument and when using horovod, it must be passed as an Optimizer instance, not a string. - - raise MLRunInvalidArgumentError: In case the optimizer provided did not follow the instructions above. - """ - - def wrapper(self, *args, **kwargs): - save_dir = cls._get_function_argument( - self.optimize, - argument_name="save_dir", - passed_args=args, - passed_kwargs=kwargs, - )[0] - - # Call the original optimize method: - result = self.original_optimize(*args, **kwargs) - - if self._auto_log: - # Log the onnx model: - self._context.log_model( - key="model", - db_key=self._model_name, - model_file=f"{save_dir}/model_optimized.onnx", - tag=self._tag, - framework="ONNX", - labels=self._labels, - extra_data=self._extra_data, - ) - - return result - - return wrapper - - def enable_auto_logging( - self, - context: mlrun.MLClientCtx, - model_name: str = "model", - tag: str = "", - labels: Dict[str, str] = None, - extra_data: dict = None, - ): - self._auto_log = True - - self._context = context - self._model_name = model_name - self._tag = tag - self._labels = labels - self._extra_data = extra_data - - -class HFTrainerMLRunInterface(MLRunInterface, ABC): - """ - Interface for adding MLRun features for tensorflow keras API. - """ - - # MLRuns context default name: - DEFAULT_CONTEXT_NAME = "mlrun-huggingface" - - # Attributes to replace so the MLRun interface will be fully enabled. - _REPLACED_METHODS = [ - "train", - # "evaluate" - ] - - @classmethod - def add_interface( - cls, - obj: Trainer, - restoration: CommonTypes.MLRunInterfaceRestorationType = None, - ): - """ - Enrich the object with this interface properties, methods and functions, so it will have this TensorFlow.Keras - MLRuns features. - :param obj: The object to enrich his interface. - :param restoration: Restoration information tuple as returned from 'remove_interface' in order to - add the interface in a certain state. - """ - - super(HFTrainerMLRunInterface, cls).add_interface( - obj=obj, restoration=restoration - ) - - @classmethod - def mlrun_train(cls): - - """ - MLRuns tf.keras.Model.fit wrapper. It will setup the optimizer when using horovod. The optimizer must be - passed in a keyword argument and when using horovod, it must be passed as an Optimizer instance, not a string. - - raise MLRunInvalidArgumentError: In case the optimizer provided did not follow the instructions above. - """ - - def wrapper(self: Trainer, *args, **kwargs): - # Restore the evaluation method as `train` will use it: - # cls._restore_attribute(obj=self, attribute_name="evaluate") - - # Call the original fit method: - result = self.original_train(*args, **kwargs) - - # Replace the evaluation method again: - # cls._replace_function(obj=self, function_name="evaluate") - - return result - - return wrapper - - -class MLRunCallback(TrainerCallback): - """ - Callback for collecting logs during training / evaluation of the `Trainer` API. - """ - - def __init__( - self, - context: mlrun.MLClientCtx = None, - model_name: str = "model", - tag: str = "", - labels: Dict[str, str] = None, - extra_data: dict = None, - ): - super().__init__() - - # Store the configurations: - self._context = ( - context - if context is not None - else mlrun.get_or_create_ctx("./mlrun-huggingface") - ) - self._model_name = model_name - self._tag = tag - self._labels = labels - self._extra_data = extra_data if extra_data is not None else {} - - # Set up the logging mode: - self._is_training = False - self._steps: List[List[int]] = [] - self._metric_scores: Dict[str, List[float]] = {} - self._artifacts: Dict[str, Artifact] = {} - - def on_epoch_begin( - self, - args: TrainingArguments, - state: TrainerState, - control: TrainerControl, - **kwargs, - ): - self._steps.append([]) - - def on_epoch_end( - self, - args: TrainingArguments, - state: TrainerState, - control: TrainerControl, - **kwargs, - ): - self._log_metrics() - - def on_log( - self, - args: TrainingArguments, - state: TrainerState, - control: TrainerControl, - logs: Dict[str, float] = None, - **kwargs, - ): - recent_logs = state.log_history[-1].copy() - - recent_logs.pop("epoch") - current_step = int(recent_logs.pop("step")) - if current_step not in self._steps[-1]: - self._steps[-1].append(current_step) - - for metric_name, metric_score in recent_logs.items(): - if metric_name.startswith("train_"): - if metric_name.split("train_")[1] not in self._metric_scores: - self._metric_scores[metric_name] = [metric_score] - continue - if metric_name not in self._metric_scores: - self._metric_scores[metric_name] = [] - self._metric_scores[metric_name].append(metric_score) - - def on_train_begin( - self, - args: TrainingArguments, - state: TrainerState, - control: TrainerControl, - **kwargs, - ): - self._is_training = True - - def on_train_end( - self, - args: TrainingArguments, - state: TrainerState, - control: TrainerControl, - model: PreTrainedModel = None, - tokenizer: PreTrainedTokenizer = None, - **kwargs, - ): - self._log_metrics() - - temp_directory = tempfile.gettempdir() - - # Save and log the tokenizer: - if tokenizer is not None: - # Save tokenizer: - tokenizer_dir = os.path.join(temp_directory, "tokenizer") - tokenizer.save_pretrained(save_directory=tokenizer_dir) - # Zip the tokenizer directory: - tokenizer_zip = shutil.make_archive( - base_name="tokenizer", - format="zip", - root_dir=tokenizer_dir, - ) - # Log the zip file: - self._artifacts["tokenizer"] = self._context.log_artifact( - item="tokenizer", local_path=tokenizer_zip - ) - - # Save the model: - model_dir = os.path.join(temp_directory, "model") - model.save_pretrained(save_directory=model_dir) - - # Zip the model directory: - shutil.make_archive( - base_name="model", - format="zip", - root_dir=model_dir, - ) - - # Log the model: - self._context.log_model( - key="model", - db_key=self._model_name, - model_file="model.zip", - tag=self._tag, - framework="Hugging Face", - labels=self._labels, - extra_data={**self._artifacts, **self._extra_data}, - ) - - def on_evaluate( - self, - args: TrainingArguments, - state: TrainerState, - control: TrainerControl, - **kwargs, - ): - self._log_metrics() - - if self._is_training: - return - - # TODO: Update the model object - - def _log_metrics(self): - for metric_name, metric_scores in self._metric_scores.items(): - self._context.log_result(key=metric_name, value=metric_scores[-1]) - if len(metric_scores) > 1: - self._log_metric_plot(name=metric_name, scores=metric_scores) - self._context.commit(completed=False) - - def _log_metric_plot(self, name: str, scores: List[float]): - # Initialize a plotly figure: - metric_figure = go.Figure() - - # Add titles: - metric_figure.update_layout( - title=name.capitalize().replace("_", " "), - xaxis_title="Samples", - yaxis_title="Scores", - ) - - # Draw: - metric_figure.add_trace( - go.Scatter(x=np.arange(len(scores)), y=scores, mode="lines") - ) - - # Create the plotly artifact: - artifact_name = f"{name}_plot" - artifact = PlotlyArtifact(key=artifact_name, figure=metric_figure) - self._artifacts[artifact_name] = self._context.log_artifact(artifact) - - -def _apply_mlrun_on_trainer( - trainer: transformers.Trainer, - model_name: str = None, - tag: str = "", - context: mlrun.MLClientCtx = None, - auto_log: bool = True, - labels: Dict[str, str] = None, - extra_data: dict = None, - **kwargs, -): - # Get parameters defaults: - if context is None: - context = mlrun.get_or_create_ctx(HFTrainerMLRunInterface.DEFAULT_CONTEXT_NAME) - - HFTrainerMLRunInterface.add_interface(obj=trainer) - - if auto_log: - trainer.add_callback( - MLRunCallback( - context=context, - model_name=model_name, - tag=tag, - labels=labels, - extra_data=extra_data, - ) - ) - - -def _apply_mlrun_on_optimizer( - optimizer, - model_name: str = None, - tag: str = "", - context: mlrun.MLClientCtx = None, - auto_log: bool = True, - labels: Dict[str, str] = None, - extra_data: dict = None, - **kwargs, -): - # Get parameters defaults: - if context is None: - context = mlrun.get_or_create_ctx( - HFORTOptimizerMLRunInterface.DEFAULT_CONTEXT_NAME - ) - - HFORTOptimizerMLRunInterface.add_interface(obj=optimizer) - - if auto_log: - optimizer.enable_auto_logging( - context=context, - model_name=model_name, - tag=tag, - labels=labels, - extra_data=extra_data, - ) - - -def apply_mlrun( - huggingface_object, - model_name: str = None, - tag: str = "", - context: mlrun.MLClientCtx = None, - auto_log: bool = True, - labels: Dict[str, str] = None, - extra_data: dict = None, - **kwargs, -): - """ - Wrap the given model with MLRun's interface providing it with mlrun's additional features. - :param huggingface_object: The model to wrap. Can be loaded from the model path given as well. - :param model_name: The model name to use for storing the model artifact. Default: "model". - :param tag: The model's tag to log with. - :param context: MLRun context to work with. If no context is given it will be retrieved via - 'mlrun.get_or_create_ctx(None)' - :param auto_log: Whether to enable MLRun's auto logging. Default: True. - """ - - if isinstance(huggingface_object, transformers.Trainer): - return _apply_mlrun_on_trainer( - trainer=huggingface_object, - model_name=model_name, - tag=tag, - context=context, - auto_log=auto_log, - labels=labels, - extra_data=extra_data, - ) - import optimum.onnxruntime as optimum_ort - - if isinstance(huggingface_object, optimum_ort.ORTOptimizer): - return _apply_mlrun_on_optimizer( - optimizer=huggingface_object, - model_name=model_name, - tag=tag, - context=context, - auto_log=auto_log, - labels=labels, - extra_data=extra_data, - ) - raise mlrun.errors.MLRunInvalidArgumentError - - -# ---------------------- from auto_trainer-------------------------------- -class KWArgsPrefixes: - MODEL_CLASS = "CLASS_" - FIT = "FIT_" - TRAIN = "TRAIN_" - PREDICT = "PREDICT_" - - -def _get_sub_dict_by_prefix(src: Dict, prefix_key: str) -> Dict[str, Any]: - """ - Collect all the keys from the given dict that starts with the given prefix and creates a new dictionary with these - keys. - - :param src: The source dict to extract the values from. - :param prefix_key: Only keys with this prefix will be returned. The keys in the result dict will be without this - prefix. - """ - return { - key.replace(prefix_key, ""): val - for key, val in src.items() - if key.startswith(prefix_key) - } - - -def _get_dataframe( - context: MLClientCtx, - dataset: DataItem, - label_columns: Optional[Union[str, List[str]]] = None, - drop_columns: Union[str, List[str], int, List[int]] = None, -) -> Tuple[pd.DataFrame, Optional[Union[str, List[str]]]]: - """ - Getting the DataFrame of the dataset and drop the columns accordingly. - - :param context: MLRun context. - :param dataset: The dataset to train the model on. - Can be either a list of lists, dict, URI or a FeatureVector. - :param label_columns: The target label(s) of the column(s) in the dataset. for Regression or - Classification tasks. - :param drop_columns: str/int or a list of strings/ints that represent the column names/indices to drop. - """ - if isinstance(dataset, (list, dict)): - dataset = pd.DataFrame(dataset) - # Checking if drop_columns provided by integer type: - if drop_columns: - if isinstance(drop_columns, str) or ( - isinstance(drop_columns, list) - and any(isinstance(col, str) for col in drop_columns) - ): - context.logger.error( - "drop_columns must be an integer/list of integers if not provided with a URI/FeatureVector dataset" - ) - raise ValueError - dataset.drop(drop_columns, axis=1, inplace=True) - - return dataset, label_columns - - store_uri_prefix, _ = mlrun.datastore.parse_store_uri(dataset.artifact_url) - if mlrun.utils.StorePrefix.FeatureVector == store_uri_prefix: - # feature-vector case: - label_columns = label_columns or dataset.meta.status.label_column - dataset = fs.get_offline_features( - dataset.meta.uri, drop_columns=drop_columns - ).to_dataframe() - - context.logger.info(f"label columns: {label_columns}") - else: - # simple URL case: - dataset = dataset.as_df() - if drop_columns: - if all(col in dataset for col in drop_columns): - dataset = dataset.drop(drop_columns, axis=1) - else: - context.logger.info( - "not all of the columns to drop in the dataset, drop columns process skipped" - ) - return dataset, label_columns - - -# ---------------------- Hugging Face Trainer -------------------------------- - - -def _create_compute_metrics(metrics: List[str]) -> Callable[[EvalPrediction], Dict]: - """ - This function create and returns a function that will be used to compute metrics at evaluation. - :param metrics: List of different metrics for evaluate the model such as f1, accuracy etc. - - :returns: Function that will be used to compute metrics at evaluation. - Must take a [`EvalPrediction`] and return a dictionary string to metric values. - """ - - def _compute_metrics(eval_pred): - logits, labels = eval_pred - predictions = np.argmax(logits, axis=-1) - metric_dict_results = {} - for metric in metrics: - load_met = load_metric(metric) - metric_res = load_met.compute(predictions=predictions, references=labels)[ - metric - ] - metric_dict_results[metric] = metric_res - - return metric_dict_results - - return _compute_metrics - - -def _edit_columns( - dataset: Dataset, - drop_columns: List[str] = None, - rename_columns: [str, str] = None, -) -> Dataset: - """ - Drop and renames that columns of the given dataset - :param dataset: Dataset to process - :param drop_columns: The columns to drop from the dataset. - :param rename_columns: Dict of columns ro rename : {: , ...} - - :returns: The dataset after the desired process - """ - if drop_columns: - dataset = dataset.remove_columns(drop_columns) - if rename_columns: - dataset = dataset.rename_columns(rename_columns) - return dataset - - -def _prepare_dataset( - context: MLClientCtx, - dataset_name: str, - label_name: str = None, - drop_columns: Optional[List[str]] = None, - num_of_train_samples: int = None, - train_test_split_size: float = None, - random_state: int = None, -) -> Tuple[Dataset, Dataset]: - """ - Loading the dataset and editing the columns - - :param context: MLRun contex - :param dataset_name: The name of the dataset to get from the HuggingFace hub - :param label_name: The target label of the column in the dataset. - :param drop_columns: The columns to drop from the dataset. - :param num_of_train_samples: Max number of training samples, for debugging. - :param train_test_split_size: Should be between 0.0 and 1.0 and represent the proportion of the dataset to include - in the test split. - :param random_state: Random state for train_test_split - - """ - - context.logger.info( - f"Loading and editing {dataset_name} dataset from Hugging Face hub" - ) - rename_cols = {label_name: "labels"} - - # Loading and editing dataset: - dataset = load_dataset(dataset_name) - - # train set - train_dataset = dataset["train"] - if num_of_train_samples: - train_dataset = train_dataset.shuffle(seed=random_state).select( - list(range(num_of_train_samples)) - ) - train_dataset = _edit_columns(train_dataset, drop_columns, rename_cols) - - # test set - test_dataset = dataset["test"] - if train_test_split_size or num_of_train_samples: - train_test_split_size = train_test_split_size or 0.2 - num_of_test_samples = int( - (train_dataset.num_rows * train_test_split_size) - // (1 - train_test_split_size) - ) - test_dataset = test_dataset.shuffle(seed=random_state).select( - list(range(num_of_test_samples)) - ) - test_dataset = _edit_columns(test_dataset, drop_columns, rename_cols) - - return train_dataset, test_dataset - - -def train( - context: MLClientCtx, - hf_dataset: str = None, - dataset: DataItem = None, - test_set: DataItem = None, - drop_columns: Optional[List[str]] = None, - pretrained_tokenizer: str = None, - pretrained_model: str = None, - model_class: str = None, - model_name: str = "huggingface-model", - label_name: str = "labels", - text_col: str = "text", - num_of_train_samples: int = None, - train_test_split_size: float = None, - metrics: List[str] = None, - random_state: int = None, -): - """ - Training and evaluating a pretrained model with a pretrained tokenizer over a dataset. - The dataset can be either be the name of the dataset that contains in the HuggingFace hub, - or a URI or a FeatureVector - - :param context: MLRun context - :param hf_dataset: The name of the dataset to get from the HuggingFace hub - :param dataset: The dataset to train the model on. Can be either a URI or a FeatureVector - :param test_set: The test set to train the model with. - :param drop_columns: The columns to drop from the dataset. - :param pretrained_tokenizer: The name of the pretrained tokenizer from the HuggingFace hub. - :param pretrained_model: The name of the pretrained model from the HuggingFace hub. - :param model_name: The model's name to use for storing the model artifact, default to 'model' - :param model_class: The class of the model, e.g. `transformers.AutoModelForSequenceClassification` - :param label_name: The target label of the column in the dataset. - :param text_col: The input text column un the dataset. - :param num_of_train_samples: Max number of training samples, for debugging. - :param train_test_split_size: Should be between 0.0 and 1.0 and represent the proportion of the dataset to include - in the test split. - :param metrics: List of different metrics for evaluate the model such as f1, accuracy etc. - :param random_state: Random state for train_test_split - """ - - if train_test_split_size is None and test_set is None: - context.logger.info( - "'train_test_split_size' is not provided, setting train_test_split_size to 0.2" - ) - train_test_split_size = 0.2 - - # Creating tokenizer: - tokenizer = AutoTokenizer.from_pretrained(pretrained_tokenizer) - - def preprocess_function(examples): - return tokenizer(examples[text_col], truncation=True) - - # prepare data for training - if hf_dataset: - train_dataset, test_dataset = _prepare_dataset( - context, - hf_dataset, - label_name, - drop_columns, - num_of_train_samples, - train_test_split_size, - random_state=random_state, - ) - elif dataset: - # Get DataFrame by URL or by FeatureVector: - train_dataset, label_name = _get_dataframe( - context=context, - dataset=dataset, - label_columns=label_name, - drop_columns=drop_columns, - ) - if test_set: - test_dataset, _ = _get_dataframe( - context=context, - dataset=test_set, - label_columns=label_name, - drop_columns=drop_columns, - ) - else: - train_dataset, test_dataset = train_test_split( - train_dataset, - test_size=train_test_split_size, - random_state=random_state, - ) - train_dataset = Dataset.from_pandas(train_dataset) - test_dataset = Dataset.from_pandas(test_dataset) - else: - raise mlrun.errors.MLRunInvalidArgumentError( - "Training data was not provided. A training dataset is mandatory for training." - " Please provide a training set using one of the arguments 'hf_dataset' or 'dataset'." - ) - - # Mapping datasets with the tokenizer: - tokenized_train = train_dataset.map(preprocess_function, batched=True) - tokenized_test = test_dataset.map(preprocess_function, batched=True) - - # Creating data collator for batching: - data_collator = DataCollatorWithPadding(tokenizer=tokenizer) - - # Parsing kwargs: - train_kwargs = _get_sub_dict_by_prefix( - src=context.parameters, prefix_key=KWArgsPrefixes.TRAIN - ) - model_class_kwargs = _get_sub_dict_by_prefix( - src=context.parameters, prefix_key=KWArgsPrefixes.MODEL_CLASS - ) - - # Loading our pretrained model: - model_class_kwargs["pretrained_model_name_or_path"] = ( - model_class_kwargs.get("pretrained_model_name_or_path") or pretrained_model - ) - train_kwargs["hub_token"] = train_kwargs.get("hub_token") or pretrained_tokenizer - if not model_class_kwargs["pretrained_model_name_or_path"]: - raise mlrun.errors.MLRunRuntimeError( - "Must provide pretrained_model name as " - "function argument or in extra params" - ) - model = create_class(model_class).from_pretrained(**model_class_kwargs) - - # Preparing training arguments: - training_args = TrainingArguments( - **train_kwargs, - ) - - compute_metrics = _create_compute_metrics(metrics) if metrics else None - trainer = Trainer( - model=model, - args=training_args, - train_dataset=tokenized_train, - eval_dataset=tokenized_test, - tokenizer=tokenizer, - data_collator=data_collator, - compute_metrics=compute_metrics, - ) - - apply_mlrun(trainer, model_name=model_name) - - # Apply training with evaluation: - context.logger.info(f"training '{model_name}'") - trainer.train() - - -def _get_model_dir(model_uri: str): - model_file, _, _ = mlrun.artifacts.get_model(model_uri) - model_dir = tempfile.gettempdir() - # Unzip the Model: - with zipfile.ZipFile(model_file, "r") as zip_file: - zip_file.extractall(model_dir) - - return model_dir - - -def optimize( - model_path: str, - model_name: str = "optimized_model", - target_dir: str = "./optimized", - optimization_level: int = 1, -): - """ - Optimizing the transformer model using ONNX optimization. - - - :param model_path: The path of the model to optimize. - :param model_name: Name of the optimized model. - :param target_dir: The directory to save the ONNX model. - :param optimization_level: Optimization level performed by ONNX Runtime of the loaded graph. (default is 1) - """ - # We import these in the function scope so ONNX won't be mandatory for the other handlers: - from optimum.onnxruntime import ORTModelForSequenceClassification, ORTOptimizer - from optimum.onnxruntime.configuration import OptimizationConfig - - model_dir = _get_model_dir(model_uri=model_path) - # Creating configuration for optimization step: - optimization_config = OptimizationConfig(optimization_level=optimization_level) - - # Converting our pretrained model to an ONNX-Runtime model: - ort_model = ORTModelForSequenceClassification.from_pretrained( - model_dir, from_transformers=True - ) - - # Creating an ONNX-Runtime optimizer from ONNX model: - optimizer = ORTOptimizer.from_pretrained(ort_model) - - apply_mlrun(optimizer, model_name=model_name) - # Optimizing and saving the ONNX model: - optimizer.optimize(save_dir=target_dir, optimization_config=optimization_config) diff --git a/hugging_face_classifier_trainer/item.yaml b/hugging_face_classifier_trainer/item.yaml deleted file mode 100755 index 3c0877659..000000000 --- a/hugging_face_classifier_trainer/item.yaml +++ /dev/null @@ -1,31 +0,0 @@ -apiVersion: v1 -categories: -- machine-learning -- model-training -description: Automatic train and optimize functions for HuggingFace framework -doc: '' -example: hugging_face_classifier_trainer.ipynb -generationDate: 2022-08-28:17-25 -hidden: false -icon: '' -labels: - author: davids -maintainers: [] -marketplaceType: '' -mlrunVersion: 1.6.1 -name: hugging_face_classifier_trainer -platformVersion: 3.5.5 -spec: - filename: hugging_face_classifier_trainer.py - handler: train - image: mlrun/mlrun - kind: job - requirements: - - onnx~=1.14.1 - - onnxruntime~=1.16.1 - - optimum~=1.6.4 - - transformers~=4.26.1 - - datasets~=2.10.1 - - scikit-learn~=1.0.2 -url: '' -version: 0.2.0 diff --git a/hugging_face_classifier_trainer/requirements.txt b/hugging_face_classifier_trainer/requirements.txt deleted file mode 100644 index 9d0db7b43..000000000 --- a/hugging_face_classifier_trainer/requirements.txt +++ /dev/null @@ -1,6 +0,0 @@ -onnx~=1.14.1 -onnxruntime~=1.16.1 -optimum~=1.6.4 -transformers~=4.26.1 -datasets~=2.10.1 -scikit-learn~=1.0.2 \ No newline at end of file diff --git a/hugging_face_classifier_trainer/test_hugging_face_classifier_trainer.py b/hugging_face_classifier_trainer/test_hugging_face_classifier_trainer.py deleted file mode 100644 index a5e0fee9b..000000000 --- a/hugging_face_classifier_trainer/test_hugging_face_classifier_trainer.py +++ /dev/null @@ -1,145 +0,0 @@ -# Copyright 2019 Iguazio -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -import os - -import mlrun -import pytest -from mlrun import import_function - -REQUIRED_ENV_VARS = [ - "MLRUN_DBPATH", - "MLRUN_ARTIFACT_PATH", - "V3IO_USERNAME", - "V3IO_API", - "V3IO_ACCESS_KEY", -] - -ADDITIONAL_PARAM_FOR_TRAIN = { - "TRAIN_output_dir": "finetuning-sentiment-model-3000-samples", - "TRAIN_learning_rate": 2e-5, - "TRAIN_per_device_train_batch_size": 16, - "TRAIN_per_device_eval_batch_size": 16, - "TRAIN_num_train_epochs": 2, - "TRAIN_weight_decay": 0.01, - "TRAIN_push_to_hub": False, - "TRAIN_evaluation_strategy": "epoch", - "TRAIN_eval_steps": 1, - "TRAIN_logging_steps": 1, - "CLASS_num_labels": 2, -} - - -def _validate_environment_variables() -> bool: - """ - Checks that all required Environment variables are set. - """ - environment_keys = os.environ.keys() - return all(key in environment_keys for key in REQUIRED_ENV_VARS) - - -def _set_environment(env_file=None): - if env_file: - mlrun.set_env_from_file(env_file) - mlrun.get_or_create_project( - "hugging-face-classifier-trainer-test", context="./", user_project=True - ) - - -@pytest.mark.skipif( - condition=not _validate_environment_variables(), - reason="Project's environment variables are not set", -) -def test_train_sequence_classification(): - _set_environment() - - # Importing function: - fn = import_function("function.yaml") - - train_run = None - - try: - train_run = fn.run( - params={ - "hf_dataset": "Shayanvsf/US_Airline_Sentiment", - "drop_columns": [ - "airline_sentiment_confidence", - "negativereason_confidence", - ], - "pretrained_tokenizer": "distilbert-base-uncased", - "pretrained_model": "distilbert-base-uncased", - "model_class": "transformers.AutoModelForSequenceClassification", - "label_name": "airline_sentiment", - "num_of_train_samples": 100, - "metrics": ["accuracy", "f1"], - "random_state": 42, - **ADDITIONAL_PARAM_FOR_TRAIN, - }, - handler="train", - local=True, - ) - except Exception as exception: - print(f"- The test failed - raised the following error:\n- {exception}") - assert train_run and all( - key in train_run.outputs for key in ["model", "loss"] - ), "outputs should include more data" - - -@pytest.mark.skipif( - condition=not _validate_environment_variables(), - reason="Project's environment variables are not set", -) -def test_train_and_optimize_sequence_classification(): - _set_environment() - - # Importing function: - fn = import_function("function.yaml") - - train_run = None - optimize_run = None - - try: - train_run = fn.run( - params={ - "hf_dataset": "Shayanvsf/US_Airline_Sentiment", - "drop_columns": [ - "airline_sentiment_confidence", - "negativereason_confidence", - ], - "pretrained_tokenizer": "distilbert-base-uncased", - "pretrained_model": "distilbert-base-uncased", - "model_class": "transformers.AutoModelForSequenceClassification", - "label_name": "airline_sentiment", - "num_of_train_samples": 100, - "metrics": ["accuracy", "f1"], - "random_state": 42, - **ADDITIONAL_PARAM_FOR_TRAIN, - }, - handler="train", - local=True, - ) - - optimize_run = fn.run( - params={"model_path": train_run.outputs["model"]}, - handler="optimize", - local=True, - ) - except Exception as exception: - print(f"- The test failed - raised the following error:\n- {exception}") - assert train_run and all( - key in train_run.outputs for key in ["model", "loss"] - ), "outputs should include more data" - assert optimize_run and all( - key in optimize_run.outputs for key in ["model"] - ), "outputs should include more data" diff --git a/huggingface_auto_trainer/function.yaml b/huggingface_auto_trainer/function.yaml deleted file mode 100644 index eff09b4cf..000000000 --- a/huggingface_auto_trainer/function.yaml +++ /dev/null @@ -1,349 +0,0 @@ -kind: job -metadata: - name: huggingface-auto-trainer - tag: '' - hash: 4459f0b675c36a20c8f542126a96b98b0ac82271 - project: '' - labels: - author: Zeevr - categories: - - machine-learning - - model-training -spec: - command: '' - args: [] - image: mlrun/mlrun - build: - functionSourceCode: import importlib
import os
import shutil
import tempfile
import zipfile
from abc import ABC
from typing import Dict, List, Tuple, Union

import mlrun
import numpy as np
import pandas as pd
import peft
import torch
import transformers
from datasets import Dataset, load_dataset
from mlrun.artifacts.manager import Artifact, PlotlyArtifact
from mlrun.datastore import is_store_uri
from mlrun.frameworks._common import CommonTypes, MLRunInterface
from mlrun.utils import logger
from peft import (LoraConfig, PeftModel, get_peft_model,
                  prepare_model_for_kbit_training)
from plotly import graph_objects as go
from transformers import (AutoModelForCausalLM, AutoTokenizer,
                          BitsAndBytesConfig, DataCollatorForLanguageModeling,
                          PreTrainedModel, PreTrainedTokenizer, Trainer,
                          TrainerCallback, TrainerControl, TrainerState,
                          TrainingArguments)

supported_tasks = [
    "question-answering",
    "summarization",
    "table-question-answering",
    "text2text-generation",
    "text-classification",
    "sentiment-analysis",
    "text-generation",
    "token-classification",
    "translation",
    "translation_xx_to_yy",
]


class ConfigKeys:
    deepspeed = "deepspeed"
    quantization = "quantization"
    lora = "lora"
    training = "training"
    tokenizer_pretrained = "tokenizer_pretrained"
    model_pretrained = "model_pretrained"
    data_collator = "data_collator"


# ----------------------from MLRUN--------------------------------
class HFTrainerMLRunInterface(MLRunInterface, ABC):
    """
    This is temporary and will be built in mlrun 1.5.0
    Interface for adding MLRun features for tensorflow keras API.
    """

    # MLRuns context default name:
    DEFAULT_CONTEXT_NAME = "mlrun-huggingface"

    # Attributes to replace so the MLRun interface will be fully enabled.
    _REPLACED_METHODS = [
        "train",
        # "evaluate"
    ]

    @classmethod
    def add_interface(
        cls,
        obj: Trainer,
        restoration: CommonTypes.MLRunInterfaceRestorationType = None,
    ):
        super(HFTrainerMLRunInterface, cls).add_interface(
            obj=obj, restoration=restoration
        )

    @classmethod
    def mlrun_train(cls):
        def wrapper(self: Trainer, *args, **kwargs):
            # Restore the evaluation method as `train` will use it:
            # cls._restore_attribute(obj=self, attribute_name="evaluate")

            # Call the original fit method:
            result = self.original_train(*args, **kwargs)

            # Replace the evaluation method again:
            # cls._replace_function(obj=self, function_name="evaluate")

            return result

        return wrapper


class MLRunCallback(TrainerCallback):
    """
    This is temporary and will be built in mlrun 1.5.0
    Callback for collecting logs during training / evaluation of the `Trainer` API.
    """

    def __init__(
        self,
        context: mlrun.MLClientCtx = None,
        model_name: str = "model",
        tag: str = "",
        labels: Dict[str, str] = None,
        extra_data: dict = None,
    ):
        super().__init__()

        # Store the configurations:
        self._context = (
            context
            if context is not None
            else mlrun.get_or_create_ctx("./mlrun-huggingface")
        )
        self._model_name = model_name
        self._tag = tag
        self._labels = labels
        self._extra_data = extra_data if extra_data is not None else {}

        # Set up the logging mode:
        self._is_training = False
        self._steps: List[List[int]] = []
        self._metric_scores: Dict[str, List[float]] = {}
        self._artifacts: Dict[str, Artifact] = {}

    def on_epoch_begin(
        self,
        args: TrainingArguments,
        state: TrainerState,
        control: TrainerControl,
        **kwargs,
    ):
        if not state.is_world_process_zero:
            return
        self._steps.append([])

    def on_epoch_end(
        self,
        args: TrainingArguments,
        state: TrainerState,
        control: TrainerControl,
        **kwargs,
    ):
        if not state.is_world_process_zero:
            return
        self.log_metrics()

    def on_log(
        self,
        args: TrainingArguments,
        state: TrainerState,
        control: TrainerControl,
        logs: Dict[str, float] = None,
        **kwargs,
    ):
        if not state.is_world_process_zero:
            return
        recent_logs = state.log_history[-1].copy()

        recent_logs.pop("epoch")
        current_step = int(recent_logs.pop("step"))
        if current_step not in self._steps[-1]:
            self._steps[-1].append(current_step)

        for metric_name, metric_score in recent_logs.items():
            if metric_name.startswith("train_"):
                if metric_name.split("train_")[1] not in self._metric_scores:
                    self._metric_scores[metric_name] = [metric_score]
                continue
            if metric_name not in self._metric_scores:
                self._metric_scores[metric_name] = []
            self._metric_scores[metric_name].append(metric_score)

    def on_train_begin(
        self,
        args: TrainingArguments,
        state: TrainerState,
        control: TrainerControl,
        **kwargs,
    ):
        if not state.is_world_process_zero:
            return
        self._is_training = True

    def on_train_end(
        self,
        args: TrainingArguments,
        state: TrainerState,
        control: TrainerControl,
        model: PreTrainedModel = None,
        tokenizer: PreTrainedTokenizer = None,
        **kwargs,
    ):
        if not state.is_world_process_zero:
            return
        self.log_metrics()

    def on_evaluate(
        self,
        args: TrainingArguments,
        state: TrainerState,
        control: TrainerControl,
        **kwargs,
    ):
        if not state.is_world_process_zero:
            return
        self.log_metrics()

        if self._is_training:
            return

    def log_metrics(self):
        for metric_name, metric_scores in self._metric_scores.items():
            self._context.log_result(key=metric_name, value=metric_scores[-1])
            if len(metric_scores) > 1:
                self.log_metric_plot(name=metric_name, scores=metric_scores)
        self._context.commit(completed=False)

    def log_metric_plot(self, name: str, scores: List[float]):
        # Initialize a plotly figure:
        metric_figure = go.Figure()

        # Add titles:
        metric_figure.update_layout(
            title=name.capitalize().replace("_", " "),
            xaxis_title="Samples",
            yaxis_title="Scores",
        )

        # Draw:
        metric_figure.add_trace(
            go.Scatter(x=np.arange(len(scores)), y=scores, mode="lines")
        )

        # Create the plotly artifact:
        artifact_name = f"{name}_plot"
        artifact = PlotlyArtifact(key=artifact_name, figure=metric_figure)
        self._artifacts[artifact_name] = self._context.log_artifact(artifact)


def apply_mlrun(
    trainer: transformers.Trainer,
    model_name: str = None,
    tag: str = "",
    context: mlrun.MLClientCtx = None,
    auto_log: bool = True,
    labels: Dict[str, str] = None,
    extra_data: dict = None,
    **kwargs,
):
    """
    This is temporary and will be built in mlrun 1.5.0
    """
    # Get parameters defaults:
    if context is None:
        context = mlrun.get_or_create_ctx(HFTrainerMLRunInterface.DEFAULT_CONTEXT_NAME)

    HFTrainerMLRunInterface.add_interface(obj=trainer)

    if auto_log:
        trainer.add_callback(
            MLRunCallback(
                context=context,
                model_name=model_name,
                tag=tag,
                labels=labels,
                extra_data=extra_data,
            )
        )


# ----------------------end from MLRUN--------------------------------


def _print_trainable_parameters(model):
    """
    Prints the number of trainable parameters in the model.
    """
    trainable_params = 0
    all_param = 0
    for _, param in model.named_parameters():
        all_param += param.numel()
        if param.requires_grad:
            trainable_params += param.numel()
    print(
        f"trainable params: {trainable_params} || all params: {all_param} || trainable%:"
        f" {100 * trainable_params / all_param}"
    )


# default configs
# will be used if user provides "True" with config name as input
QUANTIZATION_CONFIG = transformers.BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
)

LORA_CONFIG = peft.LoraConfig(
    r=8,
    lora_alpha=32,
    target_modules=["query_key_value"],
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

DEEPSPEED_CONFIG = {
    "train_micro_batch_size_per_gpu": "auto",
    "fp16": {"enabled": True},
    "autotuning": {
        "enabled": True,
        "arg_mappings": {
            "train_micro_batch_size_per_gpu": "--per_device_train_batch_size",
            "gradient_accumulation_steps ": "--gradient_accumulation_steps",
        },
    },
    "zero_optimization": {
        "stage": 2,
    },
}


def _update_config(src: dict, dst: dict):
    """
    update configs according to user, this way the user can add/modify values in default configs for e.g.

    goes over all configs and corresponding prefixes, collect all the keys from the given dict that start
     with the prefix and add them to appropriate config

    :param src: dict of all candidate values to update dict.
    :param dst: dict containing all configs to update.
    """

    for config_name, config in dst.items():

        # If given True we use default dict
        # Can also be False or a config dict given from user, so we check specifically fo True
        if config is True and config_name == "quantization":
            config = QUANTIZATION_CONFIG

        if config is True and config_name == "lora":
            config = LORA_CONFIG

        if config is True and config_name == "deepspeed":
            config = DEEPSPEED_CONFIG

        # in some cases we can get a boolean value, in that case no need to look for args
        if isinstance(config, bool):
            config = None

        elif isinstance(config, dict):
            for key, val in src.items():
                if key.startswith(config_name):
                    config[key.replace(f"{config_name}_", "")] = val

        # update by config name
        else:
            for key, val in src.items():
                if key.startswith(config_name):
                    setattr(config, key.replace(f"{config_name}_", ""), val)

        dst.update({config_name: config})


def _get_class_object(class_path: str) -> type:
    """
    given a full class name, this function returns the correct class

    :param class_path: a full class name (ex. 'transformers.AutoModelForCausalLM')

    :return the wanted class object
    """
    module_path, class_name = class_path.rsplit(".", 1)
    module = importlib.import_module(module_path)
    return getattr(module, class_name)


def _set_model_and_tokenizer(
    model: Union[str, List[str]],
    tokenizer: Union[str, List[str]],
    task: str,
    framework: str,
    lora_config: dict,
    quantization_config: dict,
    use_cuda: bool,
    tokenizer_pretrained_config,
    model_pretrained_config,
    device_map: str,
):
    """
    get the correct model and tokenizer according to given user inputs

    :param model: a tuple containing model name and class, or str with model name or path
    :param tokenizer: a tuple containing tokenizer name and class, or str with tokenizer name or path
    :param task: a supported nlp task, used to choose model if not provided
    :param framework: pt or tf
    :param lora_config: lora config or None, to load model in appropriate way
    :param quantization_config: quantization config or None, to load model in appropriate way
    :param use_cuda: use gpu or not
    :param tokenizer_pretrained_config: config to load the pretrained tokenizer
    :param model_pretrained_config: config to load the pretrained model
    :param device_map: a device map for model training if using number of gpu's

    :returns: model and tokenizer
    """
    # if task is not supported and no model was given we can't choose one
    if task and task not in supported_tasks and not model:
        logger.error("unsupported task option chosen")
        raise

    # load model from store
    if isinstance(model, str) and is_store_uri(model):
        pass
        # TODO: load both model and tokenizer and return, need guy's help

    # if it's a tuple them we assume it contains of both name and class
    if isinstance(model, list):
        model_name, model_class = model
        model_class = _get_class_object(model_class)

    # in the case we don't get the model class we need the task in order to choose the correct model
    else:
        if task is None:
            logger.error("task must be chosen in order to determine the correct model")
            raise Exception(
                "this function requires either a supported task or a model and model class to be chosen"
            )

        _, available_classes, task_options = transformers.pipelines.check_task(task)

        if isinstance(model, str):
            model_name = model

        # if model is not given, we take the default model for the given task
        else:
            model_name, _ = transformers.pipelines.get_default_model_and_revision(
                available_classes, framework, task_options
            )
        if not available_classes.get(framework, tuple()):
            logger.error(
                "given task's default model is not supported in specified framework"
            )
            raise Exception(
                "this function requires either a supported task or a model and model class to be chosen"
            )

        model_class = available_classes[framework][0]

    # load the pretrained model
    if use_cuda:
        device_map = device_map
    else:
        device_map = None

    model = model_class.from_pretrained(
        model_name,
        quantization_config=quantization_config,
        device_map=device_map,
        **model_pretrained_config,
    )

    # If quantization config is given we will load a quantized model, if not a regular one
    if quantization_config:
        model.gradient_checkpointing_enable()
        model = peft.prepare_model_for_kbit_training(model)

    # If lora config was given we want to do lora fine tune, we update model here
    if lora_config:
        model = peft.get_peft_model(model, lora_config)

    # if not specified we choose the default tokenizer that corresponding to the model
    if tokenizer is None:
        tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
        return model_name, model, tokenizer

    if isinstance(tokenizer, str):
        tokenizer_name = tokenizer
        tokenizer_class = transformers.AutoTokenizer

    # if it's not a str then it's a tuple of both name and class
    else:
        tokenizer_name, tokenizer_class = tokenizer
        tokenizer_class = _get_class_object(tokenizer_class)

    tokenizer = tokenizer_class.from_pretrained(
        tokenizer_name, **tokenizer_pretrained_config
    )

    tokenizer.pad_token = tokenizer.eos_token

    return model_name, model, tokenizer


def _dataset_loader(dataset: str, is_train: bool = True, **kwargs) -> Dataset:
    """
    loads the specific dataset provided by the user

    :param dataset: name or path of dataset to load
    :param is_train: bool that indicates the purpose of the dataset
    :param kwargs: other kwargs for loading the dataset

    :returns: loaded dataset
    """
    # if split in kwargs then the user decides how to split the dataset
    if "split" in kwargs:
        return load_dataset(dataset, **kwargs)

    # if it's a dataset for train we split with train
    if is_train:
        return load_dataset(dataset, split="train", **kwargs)

    # if it's eval dataset, then a lot of names are acceptable for the set and we check all of them
    dataset = load_dataset(dataset, **kwargs)
    if "test" in dataset:
        return dataset.get("test")
    elif "eval" in dataset:
        return dataset.get("eval")
    elif "validation" in dataset:
        return dataset.get("validation")


def _prepare_dataset(
    train_dataset: str,
    eval_dataset: str,
    train_load_dataset_kwargs,
    eval_load_dataset_kwargs,
    tokenizer,
    dataset_columns_to_train: Union[str, list],
) -> (Dataset, Union[Dataset, None]):
    """
    Loads the train and eval datasets (if provided) passes them through the tokenizer and
    returns them ready to use in training

    :param train_dataset: the name or path to the train dataset
    :param eval_dataset: the name or path to the eval dataset
    :param dataset_columns_to_train: which columns to pass to the model as inputs
                                        (need to pass through the tokenizer first)
    :param train_load_dataset_kwargs: kwargs for dataset loading
    :param eval_load_dataset_kwargs: kwargs for dataset loading
    :param tokenizer: the tokenizer to pass the data through

    :returns: tokenized datasets
    """
    if not tokenizer.pad_token:
        tokenizer.pad_token = tokenizer.eos_token

    # we take col name/s in a list for easy generalization
    if isinstance(dataset_columns_to_train, str):
        dataset_columns_to_train = [dataset_columns_to_train]

    if isinstance(train_dataset, mlrun.datastore.DataItem):
        train_dataset = Dataset.from_pandas(train_dataset.as_df())
        return (
            train_dataset.map(
                lambda examples: tokenizer(
                    *[examples[col] for col in dataset_columns_to_train],
                    truncation=True,
                    padding=True,
                ),
                batched=True,
            ),
            None,
        )

    # Load datasets
    # if provided two paths/names we load each separately using designated func
    if eval_dataset:
        train_dataset = _dataset_loader(
            dataset=train_dataset, is_train=True, **train_load_dataset_kwargs
        )
        eval_dataset = _dataset_loader(
            dataset=eval_dataset, is_train=False, **eval_load_dataset_kwargs
        )

    # if only on path is given then we must check if it contains both dataset or if only one should be used
    else:
        dataset = load_dataset(train_dataset, **train_load_dataset_kwargs)
        if "train" in dataset:
            train_dataset = dataset.get("train")
            if "test" in dataset:
                eval_dataset = dataset.get("test")
            elif "eval" in dataset:
                eval_dataset = dataset.get("eval")
            elif "validation" in dataset:
                eval_dataset = dataset.get("validation")
            else:
                # only train dataset given, tokenize and return it
                return (
                    train_dataset.map(
                        lambda examples: tokenizer(
                            *[examples[col] for col in dataset_columns_to_train],
                            truncation=True,
                            padding=True,
                        ),
                        batched=True,
                    ),
                    None,
                )
        else:
            logger.error("train dataset is mandatory")
            raise KeyError("no train dataset found in given dataset")

    # Tokenize the data so the model can understand it
    tokenized_train_dataset = train_dataset.map(
        lambda examples: tokenizer(
            *[examples[col] for col in dataset_columns_to_train],
            truncation=True,
            padding=True,
        ),
        batched=True,
    )

    tokenized_eval_dataset = eval_dataset.map(
        lambda examples: tokenizer(
            *[examples[col] for col in dataset_columns_to_train],
            truncation=True,
            padding=True,
        ),
        batched=True,
    )

    return tokenized_train_dataset, tokenized_eval_dataset


def finetune_llm(
    context: mlrun.MLClientCtx,
    train_dataset: Union[str, mlrun.datastore.DataItem],
    eval_dataset: str = None,
    train_load_dataset_kwargs: dict = {},
    eval_load_dataset_kwargs: dict = {},
    dataset_columns_to_train: Union[str, list] = "text",
    model: Union[str, List[str]] = "huggingface-model",
    tokenizer: Union[str, List[str]] = None,
    deepspeed_config: Union[dict, bool] = False,
    quantization_config: Union[dict, bool] = False,
    lora_config: Union[dict, bool] = False,
    training_config: dict = {},
    model_pretrained_config: dict = {},
    tokenizer_pretrained_config: dict = {},
    data_collator_config: dict = {},
    task: str = "text-generation",
    use_cuda: bool = True,
    framework: str = "pt",
    device_map: str = "auto",
    **kwargs,
):
    """
    Fine-tunes a Language Model (LLM) on a specific task using the provided dataset.
     The function takes various configuration parameters to customize the training process
     and adapt the model to specific tasks using a provided dataset.

    :param context: mlrun context in order to log trained model
    :param dataset_columns_to_train: which columns to pass to the model as inputs
    :param eval_load_dataset_kwargs: kwargs for dataset loading
    :param train_load_dataset_kwargs: kwargs for dataset loading
    :param framework: pt ot tf
    :param use_cuda: use gpu or not
    :param tokenizer_pretrained_config: config to load the pretrained tokenizer
    :param model_pretrained_config: config to load the pretrained model
    :param tokenizer: a tuple containing tokenizer name and class, or str with tokenizer name or path
    :param model: a tuple containing model name and class, or str with model name or path
    :param train_dataset: The train dataset used for fine-tuning the language model.
    :param eval_dataset: The eval dataset used for evaluate the language model during training.
    :param deepspeed_config: Configuration options for DeepSpeed (optional).
    :param quantization_config: Configuration options for model quantization (optional).
    :param lora_config: Configuration options for Low-Rank Approximation (LoRA) (optional).
    :param training_config: Configuration options specific to the fine-tuning training process (optional).
    :param data_collator_config: Configuration options for data collation during training (optional).
    :param task: A description of the specific task the model is being fine-tuned for.
    :param kwargs: Additional keyword arguments.
    """

    # TODO: match forward.keyword to dataset.keyword - check if relevant in new design
    # TODO: add warning for label, and add option to modify dataset col names - check if relevant in new design

    # Look for updates to configs given in kwargs
    configs = {
        ConfigKeys.deepspeed: deepspeed_config,
        ConfigKeys.quantization: quantization_config,
        ConfigKeys.lora: lora_config,
        ConfigKeys.training: training_config,
        ConfigKeys.model_pretrained: model_pretrained_config,
        ConfigKeys.tokenizer_pretrained: tokenizer_pretrained_config,
        ConfigKeys.data_collator: data_collator_config,
    }
    _update_config(dst=configs, src=kwargs)

    # check gpu permission and availability
    if use_cuda:
        if torch.cuda.is_available():
            # Clean gpu cache
            torch.cuda.empty_cache()
        else:
            logger.warning("'use_cuda' is set to True, but no cuda device is available")

    # get model and tokenizer
    model_name, model, tokenizer = _set_model_and_tokenizer(
        model=model,
        tokenizer=tokenizer,
        task=task,
        framework=framework,
        lora_config=configs[ConfigKeys.lora],
        quantization_config=configs[ConfigKeys.quantization],
        use_cuda=use_cuda,
        tokenizer_pretrained_config=tokenizer_pretrained_config,
        model_pretrained_config=configs[ConfigKeys.model_pretrained],
        device_map=device_map,
    )

    # Load datasets
    tokenized_train, tokenized_eval = _prepare_dataset(
        train_dataset=train_dataset,
        eval_dataset=eval_dataset,
        train_load_dataset_kwargs=train_load_dataset_kwargs,
        eval_load_dataset_kwargs=eval_load_dataset_kwargs,
        tokenizer=tokenizer,
        dataset_columns_to_train=dataset_columns_to_train,
    )

    # Initialize the data collator for the trainer to use in order to create batches of data
    data_collator = transformers.DataCollatorForLanguageModeling(
        tokenizer=tokenizer, mlm=False, **data_collator_config
    )

    # Initialize training kwargs from user kwargs:
    train_kwargs = configs[ConfigKeys.training]

    # If deepspeed config given we add it to training kwargs
    if configs[ConfigKeys.deepspeed]:
        train_kwargs["deepspeed"] = configs[ConfigKeys.deepspeed]

    # Take a look at the trainable parameters in the model
    _print_trainable_parameters(model)

    # Preparing training arguments:
    training_args = transformers.TrainingArguments(
        output_dir=tempfile.mkdtemp(),
        **train_kwargs,
    )

    trainer = transformers.Trainer(
        model=model,
        train_dataset=tokenized_train,
        eval_dataset=tokenized_eval,
        tokenizer=tokenizer,
        data_collator=data_collator,
        args=training_args,
    )

    apply_mlrun(trainer, model_name=model_name.split("/")[-1])
    model.config.use_cache = (
        False  # silence the warnings. Please re-enable for inference!
    )

    # Apply training with evaluation:
    context.logger.info(f"training '{model_name}'")
    trainer.train()

    temp_directory = tempfile.TemporaryDirectory().name
    trainer.save_model(temp_directory)

    # Zip the model directory:
    shutil.make_archive(
        base_name="model",
        format="zip",
        root_dir=temp_directory,
    )

    # Log the model:
    context.log_model(
        key="model",
        db_key=model_name.split("/")[-1],
        model_file="model.zip",
        tag="",
        framework="Hugging Face",
    )


def evaluate(
    context,
    model_path,
    data: pd.DataFrame,
    model_name: str = None,
    tokenizer_name: str = None,
):
    """
    Evaluating the model using perplexity, for more information visit:
    https://huggingface.co/docs/transformers/perplexity

    :param context:     mlrun context
    :param model_path:  path to the model directory
    :param data:        the data to evaluate the model
    :param model_name:  name of base model
    :param tokenizer_name: name of base tokenizer
    """
    # Get the model artifact and file:
    (
        model_file,
        model_artifact,
        extra_data,
    ) = mlrun.artifacts.get_model(model_path)

    # Read the name:
    _model_name = model_artifact.spec.db_key

    # Extract logged model files:
    model_directory = os.path.join(os.path.dirname(model_file), _model_name)
    with zipfile.ZipFile(model_file, "r") as zip_file:
        zip_file.extractall(model_directory)

    # Loading the saved pretrained tokenizer and model:
    dataset = Dataset.from_pandas(data)
    tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
    pad_token_id = tokenizer.eos_token_id
    model = AutoModelForCausalLM.from_pretrained(
        model_name, device_map="cuda:0", trust_remote_code=True, load_in_8bit=True
    )
    model = PeftModel.from_pretrained(model, model_directory)
    model.eval()
    encodings = tokenizer("\n\n".join(dataset["text"][:5]), return_tensors="pt")

    max_length = 1024
    stride = 512
    seq_len = encodings.input_ids.size(1)

    nlls = []
    prev_end_loc = 0
    for begin_loc in range(0, seq_len, stride):
        end_loc = min(begin_loc + max_length, seq_len)
        trg_len = end_loc - prev_end_loc  # may be different from stride on last loop
        input_ids = encodings.input_ids[:, begin_loc:end_loc]
        target_ids = input_ids.clone()
        target_ids[:, :-trg_len] = -100

        with torch.no_grad():
            outputs = model(input_ids.cuda(), labels=target_ids)

            # loss is calculated using CrossEntropyLoss which averages over valid labels
            # N.B. the model only calculates loss over trg_len - 1 labels, because it internally shifts the labels
            # to the left by 1.
            neg_log_likelihood = outputs.loss

        nlls.append(neg_log_likelihood)

        prev_end_loc = end_loc
        if end_loc == seq_len:
            break

    ppl = torch.exp(torch.stack(nlls).mean()).item()
    context.log_result("perplexity", ppl)
 - commands: [] - code_origin: https://github.com/ZeevRispler/functions.git#a63a647cf6bc3015a8dcbd18903f9db44bdf0b66:/Users/Zeev_Rispler/PycharmProjects/functions/huggingface_auto_trainer/huggingface_auto_trainer.py - origin_filename: /Users/Zeev_Rispler/PycharmProjects/functions/huggingface_auto_trainer/huggingface_auto_trainer.py - requirements: [] - entry_points: - add_interface: - name: add_interface - doc: '' - parameters: - - name: cls - default: '' - - name: obj - type: Trainer - default: '' - - name: restoration - type: MLRunInterfaceRestorationType - default: null - outputs: - - default: '' - lineno: 70 - mlrun_train: - name: mlrun_train - doc: '' - parameters: - - name: cls - default: '' - outputs: - - default: '' - lineno: 80 - wrapper: - name: wrapper - doc: '' - parameters: - - name: self - type: Trainer - default: '' - outputs: - - default: '' - lineno: 81 - on_epoch_begin: - name: on_epoch_begin - doc: '' - parameters: - - name: self - default: '' - - name: args - type: TrainingArguments - default: '' - - name: state - type: TrainerState - default: '' - - name: control - type: TrainerControl - default: '' - outputs: - - default: '' - lineno: 129 - on_epoch_end: - name: on_epoch_end - doc: '' - parameters: - - name: self - default: '' - - name: args - type: TrainingArguments - default: '' - - name: state - type: TrainerState - default: '' - - name: control - type: TrainerControl - default: '' - outputs: - - default: '' - lineno: 140 - on_log: - name: on_log - doc: '' - parameters: - - name: self - default: '' - - name: args - type: TrainingArguments - default: '' - - name: state - type: TrainerState - default: '' - - name: control - type: TrainerControl - default: '' - - name: logs - type: Dict[str, float] - default: null - outputs: - - default: '' - lineno: 151 - on_train_begin: - name: on_train_begin - doc: '' - parameters: - - name: self - default: '' - - name: args - type: TrainingArguments - default: '' - - name: state - type: TrainerState - default: '' - - name: control - type: TrainerControl - default: '' - outputs: - - default: '' - lineno: 177 - on_train_end: - name: on_train_end - doc: '' - parameters: - - name: self - default: '' - - name: args - type: TrainingArguments - default: '' - - name: state - type: TrainerState - default: '' - - name: control - type: TrainerControl - default: '' - - name: model - type: PreTrainedModel - default: null - - name: tokenizer - type: PreTrainedTokenizer - default: null - outputs: - - default: '' - lineno: 188 - on_evaluate: - name: on_evaluate - doc: '' - parameters: - - name: self - default: '' - - name: args - type: TrainingArguments - default: '' - - name: state - type: TrainerState - default: '' - - name: control - type: TrainerControl - default: '' - outputs: - - default: '' - lineno: 201 - log_metrics: - name: log_metrics - doc: '' - parameters: - - name: self - default: '' - outputs: - - default: '' - lineno: 215 - log_metric_plot: - name: log_metric_plot - doc: '' - parameters: - - name: self - default: '' - - name: name - type: str - default: '' - - name: scores - type: List[float] - default: '' - outputs: - - default: '' - lineno: 222 - apply_mlrun: - name: apply_mlrun - doc: This is temporary and will be built in mlrun 1.5.0 - parameters: - - name: trainer - type: Trainer - default: '' - - name: model_name - type: str - default: null - - name: tag - type: str - default: '' - - name: context - type: MLClientCtx - default: null - - name: auto_log - type: bool - default: true - - name: labels - type: Dict[str, str] - default: null - - name: extra_data - type: dict - default: null - outputs: - - default: '' - lineno: 244 - finetune_llm: - name: finetune_llm - doc: "Fine-tunes a Language Model (LLM) on a specific task using the provided\ - \ dataset.\n The function takes various configuration parameters to customize\ - \ the training process\n and adapt the model to specific tasks using a provided\ - \ dataset." - parameters: - - name: context - type: MLClientCtx - doc: mlrun context in order to log trained model - default: '' - - name: train_dataset - type: Union[str, mlrun.datastore.DataItem] - doc: The train dataset used for fine-tuning the language model. - default: '' - - name: eval_dataset - type: str - doc: The eval dataset used for evaluate the language model during training. - default: null - - name: train_load_dataset_kwargs - type: dict - doc: kwargs for dataset loading - default: {} - - name: eval_load_dataset_kwargs - type: dict - doc: kwargs for dataset loading - default: {} - - name: dataset_columns_to_train - type: Union[str, list] - doc: which columns to pass to the model as inputs - default: text - - name: model - type: Union[str, List[str]] - doc: a tuple containing model name and class, or str with model name or path - default: huggingface-model - - name: tokenizer - type: Union[str, List[str]] - doc: a tuple containing tokenizer name and class, or str with tokenizer name - or path - default: null - - name: deepspeed_config - type: Union[dict, bool] - doc: Configuration options for DeepSpeed (optional). - default: false - - name: quantization_config - type: Union[dict, bool] - doc: Configuration options for model quantization (optional). - default: false - - name: lora_config - type: Union[dict, bool] - doc: Configuration options for Low-Rank Approximation (LoRA) (optional). - default: false - - name: training_config - type: dict - doc: Configuration options specific to the fine-tuning training process (optional). - default: {} - - name: model_pretrained_config - type: dict - doc: config to load the pretrained model - default: {} - - name: tokenizer_pretrained_config - type: dict - doc: config to load the pretrained tokenizer - default: {} - - name: data_collator_config - type: dict - doc: Configuration options for data collation during training (optional). - default: {} - - name: task - type: str - doc: A description of the specific task the model is being fine-tuned for. - default: text-generation - - name: use_cuda - type: bool - doc: use gpu or not - default: true - - name: framework - type: str - doc: pt ot tf - default: pt - - name: device_map - type: str - default: auto - outputs: - - default: '' - lineno: 630 - evaluate: - name: evaluate - doc: 'Evaluating the model using perplexity, for more information visit: - - https://huggingface.co/docs/transformers/perplexity' - parameters: - - name: context - doc: mlrun context - default: '' - - name: model_path - doc: path to the model directory - default: '' - - name: data - type: DataFrame - doc: the data to evaluate the model - default: '' - - name: model_name - type: str - doc: name of base model - default: null - - name: tokenizer_name - type: str - doc: name of base tokenizer - default: null - outputs: - - default: '' - lineno: 784 - description: fine-tune llm model with ease - default_handler: finetune_llm - disable_auto_mount: false - clone_target_dir: '' - env: [] - priority_class_name: '' - preemption_mode: prevent - affinity: null - tolerations: null - security_context: {} -verbose: false diff --git a/huggingface_auto_trainer/huggingface_auto_trainer.ipynb b/huggingface_auto_trainer/huggingface_auto_trainer.ipynb deleted file mode 100644 index 847fa98d6..000000000 --- a/huggingface_auto_trainer/huggingface_auto_trainer.ipynb +++ /dev/null @@ -1,195 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "a2c5dc6d-33d0-4e74-a875-6eab556e3b2d", - "metadata": {}, - "source": [ - "# Llm auto trainer" - ] - }, - { - "cell_type": "markdown", - "id": "cc7aa261-17b2-4362-bf6a-34af79b0230b", - "metadata": {}, - "source": [ - "## Notebook Introduction: Fine-Tuning a Large Language Model with Ease\n", - "\n", - "Welcome to this example notebook that demonstrates a simplified yet powerful approach to fine-tuning a Large Language Model (LLM) effortlessly. Fine-tuning is a crucial technique that allows you to adapt pre-trained language models to specific tasks, making them more contextually relevant and useful.\n", - "\n", - "In this notebook, we will walk you through a step-by-step process of fine-tuning a state-of-the-art language model using a user-friendly and efficient method. You don't need to be an expert in machine learning or natural language processing to follow along – our approach focuses on simplicity and effectiveness." - ] - }, - { - "cell_type": "markdown", - "id": "425249e9-f43f-45e6-aa25-9f53099049cd", - "metadata": {}, - "source": [ - "### First, we will select the model we wish to fine-tune and take the matching tokenizer and appropriate config" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3410e9c2-0557-4961-995e-0ef0cc07bf82", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig\n", - "from transformers import logging\n", - "\n", - "logging.set_verbosity(\"CRITICAL\")\n", - "\n", - "model_name = \"tiiuae/falcon-7b\"\n", - "tokenizer = model_name\n", - "generation_config = GenerationConfig.from_pretrained(model_name)" - ] - }, - { - "cell_type": "markdown", - "id": "f33f3c35-cf61-4b0f-8da9-1c30d3b53230", - "metadata": {}, - "source": [ - "### Then, in order to use with mlrun, we will create an mlrun project and create an mlrun function" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a8ee7c35-adf7-4ed8-9e7e-e659b9461cd5", - "metadata": {}, - "outputs": [], - "source": [ - "import mlrun\n", - "\n", - "project = mlrun.get_or_create_project(\n", - " name=\"auto-trainer-test\",\n", - " context=\"./\",\n", - " user_project=True,\n", - " parameters={\n", - " \"default_image\": \"yonishelach/mlrun-llm\",\n", - " },\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d56b834f-adf6-4736-8de7-3348e050f561", - "metadata": {}, - "outputs": [], - "source": [ - "project.set_function(\n", - " \"auto-trainer.py\",\n", - " name=\"auto-trainer\",\n", - " kind=\"job\",\n", - " image=\"yonishelach/mlrun-llm\",\n", - " handler=\"finetune_llm\",\n", - ")\n", - "project.save()" - ] - }, - { - "cell_type": "markdown", - "id": "f42315db-6ddd-4dc1-89f3-c732f92d0d47", - "metadata": {}, - "source": [ - "### we can set the every config or parameter we want, including training arguments, hyper parameters and more, and pass to the function" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8e62e577-15fb-477d-9c56-fa9fb4c2669b", - "metadata": {}, - "outputs": [], - "source": [ - "import transformers\n", - "\n", - "training_arguments = {\n", - " \"per_device_train_batch_size\": 4,\n", - " \"gradient_accumulation_steps\": 1,\n", - " \"warmup_steps\": 2,\n", - " \"max_steps\": 10,\n", - " \"learning_rate\": 2e-4,\n", - " \"fp16\": True,\n", - " \"logging_steps\": 1,\n", - " \"optim\": \"paged_adamw_8bit\",\n", - "}" - ] - }, - { - "cell_type": "markdown", - "id": "284a5772-f88d-46c9-87bc-fc14e434c1b4", - "metadata": {}, - "source": [ - "### Now we simply run the function" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "11ab5888-5870-4bf8-9657-db930adecd77", - "metadata": {}, - "outputs": [], - "source": [ - "training_run = mlrun.run_function(\n", - " function=\"auto-trainer\",\n", - " name=\"auto-trainer\",\n", - " local=True,\n", - " params={\n", - " \"model\": (model_name, \"transformers.AutoModelForCausalLM\"),\n", - " \"tokenizer\": tokenizer,\n", - " \"train_dataset\": \"Abirate/english_quotes\",\n", - " \"training_config\": training_arguments,\n", - " \"quantization_config\": True,\n", - " \"lora_config\": True,\n", - " \"dataset_columns_to_train\": \"quote\",\n", - " \"lora_target_modules\": [\"query_key_value\"],\n", - " \"model_pretrained_config\": {\"trust_remote_code\": True, \"use_cache\": False},\n", - " },\n", - " handler=\"finetune_llm\",\n", - " outputs=[\"model\"],\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0e674d25-5f1f-4ea8-af02-7d22c2fb6760", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7a4dfe9b-407a-43c0-9c5e-56de106477ac", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "mlrun-base", - "language": "python", - "name": "conda-env-mlrun-base-py" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.16" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/huggingface_auto_trainer/huggingface_auto_trainer.py b/huggingface_auto_trainer/huggingface_auto_trainer.py deleted file mode 100644 index d1166318c..000000000 --- a/huggingface_auto_trainer/huggingface_auto_trainer.py +++ /dev/null @@ -1,855 +0,0 @@ -import importlib -import os -import shutil -import tempfile -import zipfile -from abc import ABC -from typing import Dict, List, Tuple, Union - -import mlrun -import numpy as np -import pandas as pd -import peft -import torch -import transformers -from datasets import Dataset, load_dataset -from mlrun.artifacts.manager import Artifact, PlotlyArtifact -from mlrun.datastore import is_store_uri -from mlrun.frameworks._common import CommonTypes, MLRunInterface -from mlrun.utils import logger -from peft import (LoraConfig, PeftModel, get_peft_model, - prepare_model_for_kbit_training) -from plotly import graph_objects as go -from transformers import (AutoModelForCausalLM, AutoTokenizer, - BitsAndBytesConfig, DataCollatorForLanguageModeling, - PreTrainedModel, PreTrainedTokenizer, Trainer, - TrainerCallback, TrainerControl, TrainerState, - TrainingArguments) - -supported_tasks = [ - "question-answering", - "summarization", - "table-question-answering", - "text2text-generation", - "text-classification", - "sentiment-analysis", - "text-generation", - "token-classification", - "translation", - "translation_xx_to_yy", -] - - -class ConfigKeys: - deepspeed = "deepspeed" - quantization = "quantization" - lora = "lora" - training = "training" - tokenizer_pretrained = "tokenizer_pretrained" - model_pretrained = "model_pretrained" - data_collator = "data_collator" - - -# ----------------------from MLRUN-------------------------------- -class HFTrainerMLRunInterface(MLRunInterface, ABC): - """ - This is temporary and will be built in mlrun 1.5.0 - Interface for adding MLRun features for tensorflow keras API. - """ - - # MLRuns context default name: - DEFAULT_CONTEXT_NAME = "mlrun-huggingface" - - # Attributes to replace so the MLRun interface will be fully enabled. - _REPLACED_METHODS = [ - "train", - # "evaluate" - ] - - @classmethod - def add_interface( - cls, - obj: Trainer, - restoration: CommonTypes.MLRunInterfaceRestorationType = None, - ): - super(HFTrainerMLRunInterface, cls).add_interface( - obj=obj, restoration=restoration - ) - - @classmethod - def mlrun_train(cls): - def wrapper(self: Trainer, *args, **kwargs): - # Restore the evaluation method as `train` will use it: - # cls._restore_attribute(obj=self, attribute_name="evaluate") - - # Call the original fit method: - result = self.original_train(*args, **kwargs) - - # Replace the evaluation method again: - # cls._replace_function(obj=self, function_name="evaluate") - - return result - - return wrapper - - -class MLRunCallback(TrainerCallback): - """ - This is temporary and will be built in mlrun 1.5.0 - Callback for collecting logs during training / evaluation of the `Trainer` API. - """ - - def __init__( - self, - context: mlrun.MLClientCtx = None, - model_name: str = "model", - tag: str = "", - labels: Dict[str, str] = None, - extra_data: dict = None, - ): - super().__init__() - - # Store the configurations: - self._context = ( - context - if context is not None - else mlrun.get_or_create_ctx("./mlrun-huggingface") - ) - self._model_name = model_name - self._tag = tag - self._labels = labels - self._extra_data = extra_data if extra_data is not None else {} - - # Set up the logging mode: - self._is_training = False - self._steps: List[List[int]] = [] - self._metric_scores: Dict[str, List[float]] = {} - self._artifacts: Dict[str, Artifact] = {} - - def on_epoch_begin( - self, - args: TrainingArguments, - state: TrainerState, - control: TrainerControl, - **kwargs, - ): - if not state.is_world_process_zero: - return - self._steps.append([]) - - def on_epoch_end( - self, - args: TrainingArguments, - state: TrainerState, - control: TrainerControl, - **kwargs, - ): - if not state.is_world_process_zero: - return - self.log_metrics() - - def on_log( - self, - args: TrainingArguments, - state: TrainerState, - control: TrainerControl, - logs: Dict[str, float] = None, - **kwargs, - ): - if not state.is_world_process_zero: - return - recent_logs = state.log_history[-1].copy() - - recent_logs.pop("epoch") - current_step = int(recent_logs.pop("step")) - if current_step not in self._steps[-1]: - self._steps[-1].append(current_step) - - for metric_name, metric_score in recent_logs.items(): - if metric_name.startswith("train_"): - if metric_name.split("train_")[1] not in self._metric_scores: - self._metric_scores[metric_name] = [metric_score] - continue - if metric_name not in self._metric_scores: - self._metric_scores[metric_name] = [] - self._metric_scores[metric_name].append(metric_score) - - def on_train_begin( - self, - args: TrainingArguments, - state: TrainerState, - control: TrainerControl, - **kwargs, - ): - if not state.is_world_process_zero: - return - self._is_training = True - - def on_train_end( - self, - args: TrainingArguments, - state: TrainerState, - control: TrainerControl, - model: PreTrainedModel = None, - tokenizer: PreTrainedTokenizer = None, - **kwargs, - ): - if not state.is_world_process_zero: - return - self.log_metrics() - - def on_evaluate( - self, - args: TrainingArguments, - state: TrainerState, - control: TrainerControl, - **kwargs, - ): - if not state.is_world_process_zero: - return - self.log_metrics() - - if self._is_training: - return - - def log_metrics(self): - for metric_name, metric_scores in self._metric_scores.items(): - self._context.log_result(key=metric_name, value=metric_scores[-1]) - if len(metric_scores) > 1: - self.log_metric_plot(name=metric_name, scores=metric_scores) - self._context.commit(completed=False) - - def log_metric_plot(self, name: str, scores: List[float]): - # Initialize a plotly figure: - metric_figure = go.Figure() - - # Add titles: - metric_figure.update_layout( - title=name.capitalize().replace("_", " "), - xaxis_title="Samples", - yaxis_title="Scores", - ) - - # Draw: - metric_figure.add_trace( - go.Scatter(x=np.arange(len(scores)), y=scores, mode="lines") - ) - - # Create the plotly artifact: - artifact_name = f"{name}_plot" - artifact = PlotlyArtifact(key=artifact_name, figure=metric_figure) - self._artifacts[artifact_name] = self._context.log_artifact(artifact) - - -def apply_mlrun( - trainer: transformers.Trainer, - model_name: str = None, - tag: str = "", - context: mlrun.MLClientCtx = None, - auto_log: bool = True, - labels: Dict[str, str] = None, - extra_data: dict = None, - **kwargs, -): - """ - This is temporary and will be built in mlrun 1.5.0 - """ - # Get parameters defaults: - if context is None: - context = mlrun.get_or_create_ctx(HFTrainerMLRunInterface.DEFAULT_CONTEXT_NAME) - - HFTrainerMLRunInterface.add_interface(obj=trainer) - - if auto_log: - trainer.add_callback( - MLRunCallback( - context=context, - model_name=model_name, - tag=tag, - labels=labels, - extra_data=extra_data, - ) - ) - - -# ----------------------end from MLRUN-------------------------------- - - -def _print_trainable_parameters(model): - """ - Prints the number of trainable parameters in the model. - """ - trainable_params = 0 - all_param = 0 - for _, param in model.named_parameters(): - all_param += param.numel() - if param.requires_grad: - trainable_params += param.numel() - print( - f"trainable params: {trainable_params} || all params: {all_param} || trainable%:" - f" {100 * trainable_params / all_param}" - ) - - -# default configs -# will be used if user provides "True" with config name as input -QUANTIZATION_CONFIG = transformers.BitsAndBytesConfig( - load_in_4bit=True, - bnb_4bit_use_double_quant=True, - bnb_4bit_quant_type="nf4", - bnb_4bit_compute_dtype=torch.bfloat16, -) - -LORA_CONFIG = peft.LoraConfig( - r=8, - lora_alpha=32, - target_modules=["query_key_value"], - lora_dropout=0.05, - bias="none", - task_type="CAUSAL_LM", -) - -DEEPSPEED_CONFIG = { - "train_micro_batch_size_per_gpu": "auto", - "fp16": {"enabled": True}, - "autotuning": { - "enabled": True, - "arg_mappings": { - "train_micro_batch_size_per_gpu": "--per_device_train_batch_size", - "gradient_accumulation_steps ": "--gradient_accumulation_steps", - }, - }, - "zero_optimization": { - "stage": 2, - }, -} - - -def _update_config(src: dict, dst: dict): - """ - update configs according to user, this way the user can add/modify values in default configs for e.g. - - goes over all configs and corresponding prefixes, collect all the keys from the given dict that start - with the prefix and add them to appropriate config - - :param src: dict of all candidate values to update dict. - :param dst: dict containing all configs to update. - """ - - for config_name, config in dst.items(): - - # If given True we use default dict - # Can also be False or a config dict given from user, so we check specifically fo True - if config is True and config_name == "quantization": - config = QUANTIZATION_CONFIG - - if config is True and config_name == "lora": - config = LORA_CONFIG - - if config is True and config_name == "deepspeed": - config = DEEPSPEED_CONFIG - - # in some cases we can get a boolean value, in that case no need to look for args - if isinstance(config, bool): - config = None - - elif isinstance(config, dict): - for key, val in src.items(): - if key.startswith(config_name): - config[key.replace(f"{config_name}_", "")] = val - - # update by config name - else: - for key, val in src.items(): - if key.startswith(config_name): - setattr(config, key.replace(f"{config_name}_", ""), val) - - dst.update({config_name: config}) - - -def _get_class_object(class_path: str) -> type: - """ - given a full class name, this function returns the correct class - - :param class_path: a full class name (ex. 'transformers.AutoModelForCausalLM') - - :return the wanted class object - """ - module_path, class_name = class_path.rsplit(".", 1) - module = importlib.import_module(module_path) - return getattr(module, class_name) - - -def _set_model_and_tokenizer( - model: Union[str, List[str]], - tokenizer: Union[str, List[str]], - task: str, - framework: str, - lora_config: dict, - quantization_config: dict, - use_cuda: bool, - tokenizer_pretrained_config, - model_pretrained_config, - device_map: str, -): - """ - get the correct model and tokenizer according to given user inputs - - :param model: a tuple containing model name and class, or str with model name or path - :param tokenizer: a tuple containing tokenizer name and class, or str with tokenizer name or path - :param task: a supported nlp task, used to choose model if not provided - :param framework: pt or tf - :param lora_config: lora config or None, to load model in appropriate way - :param quantization_config: quantization config or None, to load model in appropriate way - :param use_cuda: use gpu or not - :param tokenizer_pretrained_config: config to load the pretrained tokenizer - :param model_pretrained_config: config to load the pretrained model - :param device_map: a device map for model training if using number of gpu's - - :returns: model and tokenizer - """ - # if task is not supported and no model was given we can't choose one - if task and task not in supported_tasks and not model: - logger.error("unsupported task option chosen") - raise - - # load model from store - if isinstance(model, str) and is_store_uri(model): - pass - # TODO: load both model and tokenizer and return, need guy's help - - # if it's a tuple them we assume it contains of both name and class - if isinstance(model, list): - model_name, model_class = model - model_class = _get_class_object(model_class) - - # in the case we don't get the model class we need the task in order to choose the correct model - else: - if task is None: - logger.error("task must be chosen in order to determine the correct model") - raise Exception( - "this function requires either a supported task or a model and model class to be chosen" - ) - - _, available_classes, task_options = transformers.pipelines.check_task(task) - - if isinstance(model, str): - model_name = model - - # if model is not given, we take the default model for the given task - else: - model_name, _ = transformers.pipelines.get_default_model_and_revision( - available_classes, framework, task_options - ) - if not available_classes.get(framework, tuple()): - logger.error( - "given task's default model is not supported in specified framework" - ) - raise Exception( - "this function requires either a supported task or a model and model class to be chosen" - ) - - model_class = available_classes[framework][0] - - # load the pretrained model - if use_cuda: - device_map = device_map - else: - device_map = None - - model = model_class.from_pretrained( - model_name, - quantization_config=quantization_config, - device_map=device_map, - **model_pretrained_config, - ) - - # If quantization config is given we will load a quantized model, if not a regular one - if quantization_config: - model.gradient_checkpointing_enable() - model = peft.prepare_model_for_kbit_training(model) - - # If lora config was given we want to do lora fine tune, we update model here - if lora_config: - model = peft.get_peft_model(model, lora_config) - - # if not specified we choose the default tokenizer that corresponding to the model - if tokenizer is None: - tokenizer = transformers.AutoTokenizer.from_pretrained(model_name) - return model_name, model, tokenizer - - if isinstance(tokenizer, str): - tokenizer_name = tokenizer - tokenizer_class = transformers.AutoTokenizer - - # if it's not a str then it's a tuple of both name and class - else: - tokenizer_name, tokenizer_class = tokenizer - tokenizer_class = _get_class_object(tokenizer_class) - - tokenizer = tokenizer_class.from_pretrained( - tokenizer_name, **tokenizer_pretrained_config - ) - - tokenizer.pad_token = tokenizer.eos_token - - return model_name, model, tokenizer - - -def _dataset_loader(dataset: str, is_train: bool = True, **kwargs) -> Dataset: - """ - loads the specific dataset provided by the user - - :param dataset: name or path of dataset to load - :param is_train: bool that indicates the purpose of the dataset - :param kwargs: other kwargs for loading the dataset - - :returns: loaded dataset - """ - # if split in kwargs then the user decides how to split the dataset - if "split" in kwargs: - return load_dataset(dataset, **kwargs) - - # if it's a dataset for train we split with train - if is_train: - return load_dataset(dataset, split="train", **kwargs) - - # if it's eval dataset, then a lot of names are acceptable for the set and we check all of them - dataset = load_dataset(dataset, **kwargs) - if "test" in dataset: - return dataset.get("test") - elif "eval" in dataset: - return dataset.get("eval") - elif "validation" in dataset: - return dataset.get("validation") - - -def _prepare_dataset( - train_dataset: str, - eval_dataset: str, - train_load_dataset_kwargs, - eval_load_dataset_kwargs, - tokenizer, - dataset_columns_to_train: Union[str, list], -) -> (Dataset, Union[Dataset, None]): - """ - Loads the train and eval datasets (if provided) passes them through the tokenizer and - returns them ready to use in training - - :param train_dataset: the name or path to the train dataset - :param eval_dataset: the name or path to the eval dataset - :param dataset_columns_to_train: which columns to pass to the model as inputs - (need to pass through the tokenizer first) - :param train_load_dataset_kwargs: kwargs for dataset loading - :param eval_load_dataset_kwargs: kwargs for dataset loading - :param tokenizer: the tokenizer to pass the data through - - :returns: tokenized datasets - """ - if not tokenizer.pad_token: - tokenizer.pad_token = tokenizer.eos_token - - # we take col name/s in a list for easy generalization - if isinstance(dataset_columns_to_train, str): - dataset_columns_to_train = [dataset_columns_to_train] - - if isinstance(train_dataset, mlrun.datastore.DataItem): - train_dataset = Dataset.from_pandas(train_dataset.as_df()) - return ( - train_dataset.map( - lambda examples: tokenizer( - *[examples[col] for col in dataset_columns_to_train], - truncation=True, - padding=True, - ), - batched=True, - ), - None, - ) - - # Load datasets - # if provided two paths/names we load each separately using designated func - if eval_dataset: - train_dataset = _dataset_loader( - dataset=train_dataset, is_train=True, **train_load_dataset_kwargs - ) - eval_dataset = _dataset_loader( - dataset=eval_dataset, is_train=False, **eval_load_dataset_kwargs - ) - - # if only on path is given then we must check if it contains both dataset or if only one should be used - else: - dataset = load_dataset(train_dataset, **train_load_dataset_kwargs) - if "train" in dataset: - train_dataset = dataset.get("train") - if "test" in dataset: - eval_dataset = dataset.get("test") - elif "eval" in dataset: - eval_dataset = dataset.get("eval") - elif "validation" in dataset: - eval_dataset = dataset.get("validation") - else: - # only train dataset given, tokenize and return it - return ( - train_dataset.map( - lambda examples: tokenizer( - *[examples[col] for col in dataset_columns_to_train], - truncation=True, - padding=True, - ), - batched=True, - ), - None, - ) - else: - logger.error("train dataset is mandatory") - raise KeyError("no train dataset found in given dataset") - - # Tokenize the data so the model can understand it - tokenized_train_dataset = train_dataset.map( - lambda examples: tokenizer( - *[examples[col] for col in dataset_columns_to_train], - truncation=True, - padding=True, - ), - batched=True, - ) - - tokenized_eval_dataset = eval_dataset.map( - lambda examples: tokenizer( - *[examples[col] for col in dataset_columns_to_train], - truncation=True, - padding=True, - ), - batched=True, - ) - - return tokenized_train_dataset, tokenized_eval_dataset - - -def finetune_llm( - context: mlrun.MLClientCtx, - train_dataset: Union[str, mlrun.datastore.DataItem], - eval_dataset: str = None, - train_load_dataset_kwargs: dict = {}, - eval_load_dataset_kwargs: dict = {}, - dataset_columns_to_train: Union[str, list] = "text", - model: Union[str, List[str]] = "huggingface-model", - tokenizer: Union[str, List[str]] = None, - deepspeed_config: Union[dict, bool] = False, - quantization_config: Union[dict, bool] = False, - lora_config: Union[dict, bool] = False, - training_config: dict = {}, - model_pretrained_config: dict = {}, - tokenizer_pretrained_config: dict = {}, - data_collator_config: dict = {}, - task: str = "text-generation", - use_cuda: bool = True, - framework: str = "pt", - device_map: str = "auto", - **kwargs, -): - """ - Fine-tunes a Language Model (LLM) on a specific task using the provided dataset. - The function takes various configuration parameters to customize the training process - and adapt the model to specific tasks using a provided dataset. - - :param context: mlrun context in order to log trained model - :param dataset_columns_to_train: which columns to pass to the model as inputs - :param eval_load_dataset_kwargs: kwargs for dataset loading - :param train_load_dataset_kwargs: kwargs for dataset loading - :param framework: pt ot tf - :param use_cuda: use gpu or not - :param tokenizer_pretrained_config: config to load the pretrained tokenizer - :param model_pretrained_config: config to load the pretrained model - :param tokenizer: a tuple containing tokenizer name and class, or str with tokenizer name or path - :param model: a tuple containing model name and class, or str with model name or path - :param train_dataset: The train dataset used for fine-tuning the language model. - :param eval_dataset: The eval dataset used for evaluate the language model during training. - :param deepspeed_config: Configuration options for DeepSpeed (optional). - :param quantization_config: Configuration options for model quantization (optional). - :param lora_config: Configuration options for Low-Rank Approximation (LoRA) (optional). - :param training_config: Configuration options specific to the fine-tuning training process (optional). - :param data_collator_config: Configuration options for data collation during training (optional). - :param task: A description of the specific task the model is being fine-tuned for. - :param kwargs: Additional keyword arguments. - """ - - # TODO: match forward.keyword to dataset.keyword - check if relevant in new design - # TODO: add warning for label, and add option to modify dataset col names - check if relevant in new design - - # Look for updates to configs given in kwargs - configs = { - ConfigKeys.deepspeed: deepspeed_config, - ConfigKeys.quantization: quantization_config, - ConfigKeys.lora: lora_config, - ConfigKeys.training: training_config, - ConfigKeys.model_pretrained: model_pretrained_config, - ConfigKeys.tokenizer_pretrained: tokenizer_pretrained_config, - ConfigKeys.data_collator: data_collator_config, - } - _update_config(dst=configs, src=kwargs) - - # check gpu permission and availability - if use_cuda: - if torch.cuda.is_available(): - # Clean gpu cache - torch.cuda.empty_cache() - else: - logger.warning("'use_cuda' is set to True, but no cuda device is available") - - # get model and tokenizer - model_name, model, tokenizer = _set_model_and_tokenizer( - model=model, - tokenizer=tokenizer, - task=task, - framework=framework, - lora_config=configs[ConfigKeys.lora], - quantization_config=configs[ConfigKeys.quantization], - use_cuda=use_cuda, - tokenizer_pretrained_config=tokenizer_pretrained_config, - model_pretrained_config=configs[ConfigKeys.model_pretrained], - device_map=device_map, - ) - - # Load datasets - tokenized_train, tokenized_eval = _prepare_dataset( - train_dataset=train_dataset, - eval_dataset=eval_dataset, - train_load_dataset_kwargs=train_load_dataset_kwargs, - eval_load_dataset_kwargs=eval_load_dataset_kwargs, - tokenizer=tokenizer, - dataset_columns_to_train=dataset_columns_to_train, - ) - - # Initialize the data collator for the trainer to use in order to create batches of data - data_collator = transformers.DataCollatorForLanguageModeling( - tokenizer=tokenizer, mlm=False, **data_collator_config - ) - - # Initialize training kwargs from user kwargs: - train_kwargs = configs[ConfigKeys.training] - - # If deepspeed config given we add it to training kwargs - if configs[ConfigKeys.deepspeed]: - train_kwargs["deepspeed"] = configs[ConfigKeys.deepspeed] - - # Take a look at the trainable parameters in the model - _print_trainable_parameters(model) - - # Preparing training arguments: - training_args = transformers.TrainingArguments( - output_dir=tempfile.mkdtemp(), - **train_kwargs, - ) - - trainer = transformers.Trainer( - model=model, - train_dataset=tokenized_train, - eval_dataset=tokenized_eval, - tokenizer=tokenizer, - data_collator=data_collator, - args=training_args, - ) - - apply_mlrun(trainer, model_name=model_name.split("/")[-1]) - model.config.use_cache = ( - False # silence the warnings. Please re-enable for inference! - ) - - # Apply training with evaluation: - context.logger.info(f"training '{model_name}'") - trainer.train() - - temp_directory = tempfile.TemporaryDirectory().name - trainer.save_model(temp_directory) - - # Zip the model directory: - shutil.make_archive( - base_name="model", - format="zip", - root_dir=temp_directory, - ) - - # Log the model: - context.log_model( - key="model", - db_key=model_name.split("/")[-1], - model_file="model.zip", - tag="", - framework="Hugging Face", - ) - - -def evaluate( - context, - model_path, - data: pd.DataFrame, - model_name: str = None, - tokenizer_name: str = None, -): - """ - Evaluating the model using perplexity, for more information visit: - https://huggingface.co/docs/transformers/perplexity - - :param context: mlrun context - :param model_path: path to the model directory - :param data: the data to evaluate the model - :param model_name: name of base model - :param tokenizer_name: name of base tokenizer - """ - # Get the model artifact and file: - ( - model_file, - model_artifact, - extra_data, - ) = mlrun.artifacts.get_model(model_path) - - # Read the name: - _model_name = model_artifact.spec.db_key - - # Extract logged model files: - model_directory = os.path.join(os.path.dirname(model_file), _model_name) - with zipfile.ZipFile(model_file, "r") as zip_file: - zip_file.extractall(model_directory) - - # Loading the saved pretrained tokenizer and model: - dataset = Dataset.from_pandas(data) - tokenizer = AutoTokenizer.from_pretrained(tokenizer_name) - pad_token_id = tokenizer.eos_token_id - model = AutoModelForCausalLM.from_pretrained( - model_name, device_map="cuda:0", trust_remote_code=True, load_in_8bit=True - ) - model = PeftModel.from_pretrained(model, model_directory) - model.eval() - encodings = tokenizer("\n\n".join(dataset["text"][:5]), return_tensors="pt") - - max_length = 1024 - stride = 512 - seq_len = encodings.input_ids.size(1) - - nlls = [] - prev_end_loc = 0 - for begin_loc in range(0, seq_len, stride): - end_loc = min(begin_loc + max_length, seq_len) - trg_len = end_loc - prev_end_loc # may be different from stride on last loop - input_ids = encodings.input_ids[:, begin_loc:end_loc] - target_ids = input_ids.clone() - target_ids[:, :-trg_len] = -100 - - with torch.no_grad(): - outputs = model(input_ids.cuda(), labels=target_ids) - - # loss is calculated using CrossEntropyLoss which averages over valid labels - # N.B. the model only calculates loss over trg_len - 1 labels, because it internally shifts the labels - # to the left by 1. - neg_log_likelihood = outputs.loss - - nlls.append(neg_log_likelihood) - - prev_end_loc = end_loc - if end_loc == seq_len: - break - - ppl = torch.exp(torch.stack(nlls).mean()).item() - context.log_result("perplexity", ppl) diff --git a/huggingface_auto_trainer/item.yaml b/huggingface_auto_trainer/item.yaml deleted file mode 100644 index e556c11df..000000000 --- a/huggingface_auto_trainer/item.yaml +++ /dev/null @@ -1,25 +0,0 @@ -apiVersion: v1 -categories: -- machine-learning -- model-training -description: fine-tune llm model with ease -doc: '' -example: huggingface_auto_trainer.ipynb -generationDate: 2023-08-21:17-25 -hidden: false -icon: '' -labels: - author: Zeevr -maintainers: [] -marketplaceType: '' -mlrunVersion: 1.4.0 -name: huggingface-auto-trainer -platformVersion: 3.5.0 -spec: - filename: huggingface_auto_trainer.py - handler: finetune_llm - image: mlrun/mlrun - kind: job - requirements: [] -url: '' -version: 1.0.0 diff --git a/huggingface_auto_trainer/requirements.txt b/huggingface_auto_trainer/requirements.txt deleted file mode 100644 index 1376b1d00..000000000 --- a/huggingface_auto_trainer/requirements.txt +++ /dev/null @@ -1,5 +0,0 @@ -peft -transformers -torch -datasets -plotly diff --git a/huggingface_auto_trainer/test_huggingface_auto_trainer.py b/huggingface_auto_trainer/test_huggingface_auto_trainer.py deleted file mode 100644 index 53576e4e7..000000000 --- a/huggingface_auto_trainer/test_huggingface_auto_trainer.py +++ /dev/null @@ -1,42 +0,0 @@ -import tempfile - -import mlrun - - -def test_train(): - - model_name = "distilgpt2" - tokenizer = model_name - auto_trainer = mlrun.import_function("function.yaml") - - training_arguments = { - "per_device_train_batch_size": 4, - "gradient_accumulation_steps": 1, - "warmup_steps": 2, - "max_steps": 10, - "learning_rate": 2e-4, - "logging_steps": 1, - } - - params = { - "model": (model_name, "transformers.AutoModelForCausalLM"), - "tokenizer": tokenizer, - "train_dataset": "Abirate/english_quotes", - "training_config": training_arguments, - "dataset_columns_to_train": "quote", - "model_pretrained_config": {"use_cache": False}, - "use_cuda": False, - } - - try: - with tempfile.TemporaryDirectory() as test_directory: - auto_trainer.run( - local=True, - params=params, - handler="finetune_llm", - returns=["model"], - workdir=test_directory, - ) - - except Exception as exception: - print(f"- The training failed - raised the following error:\n- {exception}") diff --git a/ingest/function.yaml b/ingest/function.yaml deleted file mode 100644 index a05ca6698..000000000 --- a/ingest/function.yaml +++ /dev/null @@ -1,87 +0,0 @@ -kind: job -metadata: - name: ingest - tag: '' - hash: 7e28700a86ebdd18d887fe588492201a1e3ef2f6 - project: '' - labels: - author: yonish - categories: - - data-preparation - - data-analysis - - feature-store -spec: - command: '' - args: [] - image: mlrun/mlrun - build: - functionSourceCode: ZnJvbSB0eXBpbmcgaW1wb3J0IFVuaW9uLCBMaXN0LCBEaWN0CgppbXBvcnQgbWxydW4uZmVhdHVyZV9zdG9yZSBhcyBmcwpmcm9tIG1scnVuLmV4ZWN1dGlvbiBpbXBvcnQgTUxDbGllbnRDdHgKZnJvbSBtbHJ1bi5kYXRhX3R5cGVzIGltcG9ydCBJbmZlck9wdGlvbnMKCgpkZWYgaW5nZXN0KAogICAgY29udGV4dDogTUxDbGllbnRDdHgsCiAgICBmZWF0dXJlc2V0OiBzdHIsCiAgICBzb3VyY2U6IHN0ciwKICAgIHRhcmdldHM6IExpc3RbVW5pb25bc3RyLCBEaWN0XV0gPSBOb25lLAogICAgbmFtZXNwYWNlPU5vbmUsCiAgICBpbmZlcl9vcHRpb25zPU5vbmUsCiAgICBydW5fY29uZmlnOiBVbmlvbltzdHIsIERpY3RdID0gTm9uZSwKICAgIHNwYXJrX2NvbnRleHQ9Tm9uZSwKICAgIG92ZXJ3cml0ZT1Ob25lLAopOgogICAgIiIiUmVhZCBsb2NhbCBEYXRhRnJhbWUsIGZpbGUsIFVSTCwgb3Igc291cmNlIGludG8gdGhlIGZlYXR1cmUgc3RvcmUKICAgIEluZ2VzdCByZWFkcyBmcm9tIHRoZSBzb3VyY2UsIHJ1biB0aGUgZ3JhcGggdHJhbnNmb3JtYXRpb25zLCBpbmZlcnMgIG1ldGFkYXRhIGFuZCBzdGF0cwogICAgYW5kIHdyaXRlcyB0aGUgcmVzdWx0cyB0byB0aGUgZGVmYXVsdCBvZiBzcGVjaWZpZWQgdGFyZ2V0cwoKICAgIHdoZW4gdGFyZ2V0cyBhcmUgbm90IHNwZWNpZmllZCBkYXRhIGlzIHN0b3JlZCBpbiB0aGUgY29uZmlndXJlZCBkZWZhdWx0IHRhcmdldHMKICAgICh3aWxsIHVzdWFsbHkgYmUgTm9TUUwgZm9yIHJlYWwtdGltZSBhbmQgUGFycXVldCBmb3Igb2ZmbGluZSkuCgogICAgZXhhbXBsZTo6CgogICAgICAgIHN0b2Nrc19zZXQgPSBGZWF0dXJlU2V0KCJzdG9ja3MiLCBlbnRpdGllcz1bRW50aXR5KCJ0aWNrZXIiKV0pCiAgICAgICAgc3RvY2tzID0gcGQucmVhZF9jc3YoInN0b2Nrcy5jc3YiKQogICAgICAgIGRmID0gaW5nZXN0KHN0b2Nrc19zZXQsIHN0b2NrcywgaW5mZXJfb3B0aW9ucz1mc3RvcmUuSW5mZXJPcHRpb25zLmRlZmF1bHQoKSkKCiAgICAgICAgIyBmb3IgcnVubmluZyBhcyByZW1vdGUgam9iCiAgICAgICAgY29uZmlnID0gUnVuQ29uZmlnKGltYWdlPSdtbHJ1bi9tbHJ1bicpLmFwcGx5KG1vdW50X3YzaW8oKSkKICAgICAgICBkZiA9IGluZ2VzdChzdG9ja3Nfc2V0LCBzdG9ja3MsIHJ1bl9jb25maWc9Y29uZmlnKQoKICAgICAgICAjIHNwZWNpZnkgc291cmNlIGFuZCB0YXJnZXRzCiAgICAgICAgc291cmNlID0gQ1NWU291cmNlKCJteWNzdiIsIHBhdGg9Im1lYXN1cmVtZW50cy5jc3YiKQogICAgICAgIHRhcmdldHMgPSBbQ1NWVGFyZ2V0KCJteWNzdiIsIHBhdGg9Ii4vbXljc3YuY3N2IildCiAgICAgICAgaW5nZXN0KG1lYXN1cmVtZW50cywgc291cmNlLCB0YXJnZXRzKQoKICAgIDpwYXJhbSBjb250ZXh0OiAgICAgICBNTFJ1biBjb250ZXh0CiAgICA6cGFyYW0gZmVhdHVyZXNldDogICAgZmVhdHVyZSBzZXQgb2JqZWN0IG9yIGZlYXR1cmVzZXQudXJpLiAodXJpIG11c3QgYmUgb2YgYSBmZWF0dXJlIHNldCB0aGF0IGlzIGluIHRoZSBEQiwKICAgICAgICAgICAgICAgICAgICAgICAgICBjYWxsIGAuc2F2ZSgpYCBpZiBpdCdzIG5vdCkKICAgIDpwYXJhbSBzb3VyY2U6ICAgICAgICBzb3VyY2UgZGF0YWZyYW1lIG9yIGZpbGUgcGF0aAogICAgOnBhcmFtIHRhcmdldHM6ICAgICAgIG9wdGlvbmFsIGxpc3Qgb2YgZGF0YSB0YXJnZXQgb2JqZWN0cwogICAgOnBhcmFtIG5hbWVzcGFjZTogICAgIG5hbWVzcGFjZSBvciBtb2R1bGUgY29udGFpbmluZyBncmFwaCBjbGFzc2VzCiAgICA6cGFyYW0gaW5mZXJfb3B0aW9uczogc2NoZW1hIGFuZCBzdGF0cyBpbmZlciBvcHRpb25zCiAgICA6cGFyYW0gcnVuX2NvbmZpZzogICAgZnVuY3Rpb24gYW5kL29yIHJ1biBjb25maWd1cmF0aW9uIGZvciByZW1vdGUgam9icywKICAgICAgICAgICAgICAgICAgICAgICAgICBzZWUgOnB5OmNsYXNzOmB+bWxydW4uZmVhdHVyZV9zdG9yZS5SdW5Db25maWdgCiAgICA6cGFyYW0gc3BhcmtfY29udGV4dDogbG9jYWwgc3Bhcmsgc2Vzc2lvbiBmb3Igc3BhcmsgaW5nZXN0aW9uLCBleGFtcGxlIGZvciBjcmVhdGluZyB0aGUgc3BhcmsgY29udGV4dDoKICAgICAgICAgICAgICAgICAgICAgICAgICBgc3BhcmsgPSBTcGFya1Nlc3Npb24uYnVpbGRlci5hcHBOYW1lKCJTcGFyayBmdW5jdGlvbiIpLmdldE9yQ3JlYXRlKClgCiAgICAgICAgICAgICAgICAgICAgICAgICAgRm9yIHJlbW90ZSBzcGFyayBpbmdlc3Rpb24sIHRoaXMgc2hvdWxkIGNvbnRhaW4gdGhlIHJlbW90ZSBzcGFyayBzZXJ2aWNlIG5hbWUKICAgIDpwYXJhbSBvdmVyd3JpdGU6ICAgICBkZWxldGUgdGhlIHRhcmdldHMnIGRhdGEgcHJpb3IgdG8gaW5nZXN0aW9uCiAgICAgICAgICAgICAgICAgICAgICAgICAgKGRlZmF1bHQ6IFRydWUgZm9yIG5vbi1zY2hlZHVsZWQgaW5nZXN0IC0gZGVsZXRlcyB0aGUgdGFyZ2V0cyB0aGF0IGFyZSBhYm91dCB0byBiZSBpbmdlc3RlZC4KICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgRmFsc2UgZm9yIHNjaGVkdWxlZCBpbmdlc3QgLSBkb2VzIG5vdCBkZWxldGUgdGhlIHRhcmdldCkKCiAgICAiIiIKICAgICMgU2V0dGluZyBpbmZlcl9vcHRpb25zIHRvIGRlZmF1bHQ6CiAgICBjb250ZXh0Ll9wYXJhbWV0ZXJzWyJpbmZlcl9vcHRpb25zIl0gPSBpbmZlcl9vcHRpb25zIG9yIEluZmVyT3B0aW9ucy5kZWZhdWx0KCkKCiAgICBjb250ZXh0LmxvZ2dlci5pbmZvKGYiQ2FsbGluZyBpbmdlc3Rpb24gdGFzayB3aXRoOiB7ZmVhdHVyZXNldH0iKQoKICAgICMgaW5nZXN0IGNhbGxlZCB3aXRoIG1scnVuX2NvbnRleHQsIGZlYXR1cmVfc2V0LCBzb3VyY2UgYW5kIHRhcmdldHMgcGFzc2VkIHdpdGggY29udGV4dAogICAgIyBUaGlzIHBhcmFtcyBoZXJlIGZvciBkb2N1bWVudGF0aW9uIHB1cnBvc2VzIG9ubHkKICAgIGZzLmluZ2VzdCgKICAgICAgICBtbHJ1bl9jb250ZXh0PWNvbnRleHQsCiAgICAgICAgbmFtZXNwYWNlPW5hbWVzcGFjZSwKICAgICAgICBzcGFya19jb250ZXh0PXNwYXJrX2NvbnRleHQsCiAgICApCiAgICBjb250ZXh0LmxvZ19yZXN1bHQoImZlYXR1cmVzZXQiLCBmZWF0dXJlc2V0KQo= - commands: [] - code_origin: https://github.com/mlrun/functions.git#886a88217c2a2570c81a14877f9c1dfb1ac8a244:C:\Users\yonatans\projects\functions\ingest\ingest.py - origin_filename: C:\Users\yonatans\projects\functions\ingest\ingest.py - entry_points: - ingest: - name: ingest - doc: "Read local DataFrame, file, URL, or source into the feature store\nIngest\ - \ reads from the source, run the graph transformations, infers metadata and\ - \ stats\nand writes the results to the default of specified targets\n\nwhen\ - \ targets are not specified data is stored in the configured default targets\n\ - (will usually be NoSQL for real-time and Parquet for offline).\n\nexample::\n\ - \n stocks_set = FeatureSet(\"stocks\", entities=[Entity(\"ticker\")])\n\ - \ stocks = pd.read_csv(\"stocks.csv\")\n df = ingest(stocks_set, stocks,\ - \ infer_options=fstore.InferOptions.default())\n\n # for running as remote\ - \ job\n config = RunConfig(image='mlrun/mlrun').apply(mount_v3io())\n \ - \ df = ingest(stocks_set, stocks, run_config=config)\n\n # specify source\ - \ and targets\n source = CSVSource(\"mycsv\", path=\"measurements.csv\"\ - )\n targets = [CSVTarget(\"mycsv\", path=\"./mycsv.csv\")]\n ingest(measurements,\ - \ source, targets)" - parameters: - - name: context - type: MLClientCtx - doc: MLRun context - default: '' - - name: featureset - type: str - doc: feature set object or featureset.uri. (uri must be of a feature set that - is in the DB, call `.save()` if it's not) - default: '' - - name: source - type: str - doc: source dataframe or file path - default: '' - - name: targets - type: List[Union[str, Dict]] - doc: optional list of data target objects - default: null - - name: namespace - doc: namespace or module containing graph classes - default: null - - name: infer_options - doc: schema and stats infer options - default: null - - name: run_config - type: Union[str, Dict] - doc: function and/or run configuration for remote jobs, see :py:class:`~mlrun.feature_store.RunConfig` - default: null - - name: spark_context - doc: 'local spark session for spark ingestion, example for creating the spark - context: `spark = SparkSession.builder.appName("Spark function").getOrCreate()` - For remote spark ingestion, this should contain the remote spark service - name' - default: null - - name: overwrite - doc: 'delete the targets'' data prior to ingestion (default: True for non-scheduled - ingest - deletes the targets that are about to be ingested. False for scheduled - ingest - does not delete the target)' - default: null - outputs: - - default: '' - lineno: 8 - description: Feature Store ingest function that runs the transformation graph on - the source of the featureset. - default_handler: ingest - disable_auto_mount: false - env: [] - priority_class_name: '' - affinity: null -verbose: false diff --git a/ingest/ingest.ipynb b/ingest/ingest.ipynb deleted file mode 100644 index 7da398b4f..000000000 --- a/ingest/ingest.ipynb +++ /dev/null @@ -1,762 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Feature Store Ingest" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Read local DataFrame, file, URL, or source into the feature store\n", - "Ingest reads from the source, run the graph transformations, infers metadata and stats\n", - "and writes the results to the default of specified targets." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Creating Project" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import mlrun\n", - "import mlrun.feature_store as fstore\n", - "from mlrun.datastore.sources import CSVSource\n", - "from mlrun.feature_store.steps import *\n", - "from mlrun.features import MinMaxValidator\n", - "import pandas as pd\n", - "import datetime" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2022-01-31 13:52:16,939 [info] loaded project ingest from MLRun DB\n" - ] - } - ], - "source": [ - "# Initialize the MLRun project object\n", - "project = mlrun.get_or_create_project('ingest', context=\"./\", user_project=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create Sample Data For Demo" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "quotes = pd.DataFrame(\n", - " {\n", - " \"time\": [\n", - " pd.Timestamp(\"2016-05-25 13:30:00.023\"),\n", - " pd.Timestamp(\"2016-05-25 13:30:00.023\"),\n", - " pd.Timestamp(\"2016-05-25 13:30:00.030\"),\n", - " pd.Timestamp(\"2016-05-25 13:30:00.041\"),\n", - " pd.Timestamp(\"2016-05-25 13:30:00.048\"),\n", - " pd.Timestamp(\"2016-05-25 13:30:00.049\"),\n", - " pd.Timestamp(\"2016-05-25 13:30:00.072\"),\n", - " pd.Timestamp(\"2016-05-25 13:30:00.075\"),\n", - " ],\n", - " \"ticker\": [\"GOOG\", \"MSFT\", \"MSFT\", \"MSFT\", \"GOOG\", \"AAPL\", \"GOOG\", \"MSFT\"],\n", - " \"bid\": [720.50, 51.95, 51.97, 51.99, 720.50, 97.99, 720.50, 52.01],\n", - " \"ask\": [720.93, 51.96, 51.98, 52.00, 720.93, 98.01, 720.88, 52.03],\n", - " }\n", - ")\n", - "\n", - "# move date:\n", - "max_date = quotes[\"time\"].max()\n", - "now_date = datetime.datetime.now()\n", - "delta = now_date - max_date\n", - "quotes[\"time\"] = quotes[\"time\"] + delta" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " time ticker bid ask\n", - "0 2022-01-31 13:52:16.905388 GOOG 720.50 720.93\n", - "1 2022-01-31 13:52:16.905388 MSFT 51.95 51.96\n", - "2 2022-01-31 13:52:16.912388 MSFT 51.97 51.98\n", - "3 2022-01-31 13:52:16.923388 MSFT 51.99 52.00\n", - "4 2022-01-31 13:52:16.930388 GOOG 720.50 720.93\n", - "5 2022-01-31 13:52:16.931388 AAPL 97.99 98.01\n", - "6 2022-01-31 13:52:16.954388 GOOG 720.50 720.88\n", - "7 2022-01-31 13:52:16.957388 MSFT 52.01 52.03" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "quotes" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Build Advanced Feature Set - With Feature Engineering Pipeline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Define a custom pipeline step (python class)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "class MyMap(MapClass):\n", - " def __init__(self, multiplier=1, **kwargs):\n", - " super().__init__(**kwargs)\n", - " self._multiplier = multiplier\n", - "\n", - " def do(self, event):\n", - " event[\"multi\"] = event[\"bid\"] * self._multiplier\n", - " return event" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Build and show the transformatiom pipeline" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "mlrun-flow\n", - "\n", - "\n", - "\n", - "_start\n", - "\n", - "start\n", - "\n", - "\n", - "\n", - "map.MyMap\n", - "\n", - "map.MyMap\n", - "\n", - "\n", - "\n", - "_start->map.MyMap\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "storey.Extend\n", - "\n", - "storey.Extend\n", - "\n", - "\n", - "\n", - "map.MyMap->storey.Extend\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "filter\n", - "\n", - "filter\n", - "\n", - "\n", - "\n", - "storey.Extend->filter\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "FeaturesetValidator\n", - "\n", - "FeaturesetValidator\n", - "\n", - "\n", - "\n", - "filter->FeaturesetValidator\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Aggregates\n", - "\n", - "Aggregates\n", - "\n", - "\n", - "\n", - "FeaturesetValidator->Aggregates\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "parquet\n", - "\n", - "\n", - "parquet\n", - "\n", - "\n", - "\n", - "Aggregates->parquet\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "nosql\n", - "\n", - "\n", - "nosql\n", - "\n", - "\n", - "\n", - "Aggregates->nosql\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "quotes_set = fstore.FeatureSet(\"stock-quotes\", entities=[fstore.Entity(\"ticker\")])\n", - "\n", - "quotes_set.graph.to(\"map.MyMap\", multiplier=3).to(\n", - " \"storey.Extend\", _fn=\"({'extra': event['bid'] * 77})\"\n", - ").to(\"storey.Filter\", \"filter\", _fn=\"(event['bid'] > 51.92)\").to(\n", - " FeaturesetValidator()\n", - ")\n", - "\n", - "quotes_set.add_aggregation(\"ask\", [\"sum\", \"max\"], \"1h\", \"10m\", name=\"asks1\")\n", - "quotes_set.add_aggregation(\"ask\", [\"sum\", \"max\"], \"5h\", \"10m\", name=\"asks5\")\n", - "quotes_set.add_aggregation(\"bid\", [\"min\", \"max\"], \"1h\", \"10m\", name=\"bids\")\n", - "\n", - "# add feature validation policy\n", - "quotes_set[\"bid\"] = fstore.Feature(\n", - " validator=MinMaxValidator(min=52, severity=\"info\")\n", - ")\n", - "\n", - "# add default target definitions and plot\n", - "quotes_set.set_targets()\n", - "quotes_set.plot(rankdir=\"LR\", with_targets=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Saving the feature set in the feature store " - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "quotes_set.save()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Creating the data source of the feature set to apply the ingest on:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "data_uri = 'quotes.csv'\n", - "quotes.to_csv(data_uri, index=False)\n", - "source = CSVSource('quotes', data_uri).to_dict()\n", - "source" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Import ingest function" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "ingest_fn = mlrun.import_function(\"function.yaml\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Running the function locally" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2022-01-31 13:52:17,201 [info] starting run ingest-ingest uid=4bd5d12691a8439d90bf53847f59df1a DB=http://mlrun-api:8080\n", - "> 2022-01-31 13:52:17,354 [info] Ingesting the FeatureSet: store://feature-sets/ingest-yonatan/stock-quotes\n", - "> 2022-01-31 13:52:17,427 [info] starting ingestion task to store://feature-sets/ingest-yonatan/stock-quotes:latest.\n", - "info! bid value is smaller than min, key=['MSFT'] time=2022-01-31 13:52:19.466055 args={'min': 52, 'value': 51.95}\n", - "info! bid value is smaller than min, key=['MSFT'] time=2022-01-31 13:52:19.466072 args={'min': 52, 'value': 51.97}\n", - "info! bid value is smaller than min, key=['MSFT'] time=2022-01-31 13:52:19.466085 args={'min': 52, 'value': 51.99}\n", - "info! bid value is smaller than min, key=['MSFT'] time=2022-01-31 13:52:19.671677 args={'min': 52, 'value': 51.95}\n", - "info! bid value is smaller than min, key=['MSFT'] time=2022-01-31 13:52:19.671692 args={'min': 52, 'value': 51.97}\n", - "info! bid value is smaller than min, key=['MSFT'] time=2022-01-31 13:52:19.671708 args={'min': 52, 'value': 51.99}\n", - "> 2022-01-31 13:52:19,915 [info] ingestion task completed, targets:\n", - "> 2022-01-31 13:52:19,915 [info] [{'name': 'parquet', 'kind': 'parquet', 'path': 'v3io:///projects/ingest-yonatan/FeatureStore/stock-quotes/parquet/sets/stock-quotes-latest', 'status': 'created', 'updated': '2022-01-31T13:52:19.649303+00:00', 'last_written': datetime.datetime(2022, 1, 31, 13, 52, 19, 671753)}, {'name': 'nosql', 'kind': 'nosql', 'path': 'v3io:///projects/ingest-yonatan/FeatureStore/stock-quotes/nosql/sets/stock-quotes-latest', 'status': 'created', 'updated': '2022-01-31T13:52:19.650044+00:00'}]\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "
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projectuiditerstartstatenamelabelsinputsparametersresultsartifacts
ingest-yonatan0Jan 31 13:52:17completedingest-ingest
v3io_user=yonatan
kind=
owner=yonatan
host=jupyter-yoni-647b99c95d-w4jlc
featureset=store://feature-sets/ingest-yonatan/stock-quotes
source={'kind': 'csv', 'name': 'quotes', 'path': 'quotes.csv'}
infer_options=63
overwrite=None
targets=None
featureset=store://feature-sets/ingest-yonatan/stock-quotes
\n", - "
\n", - "
\n", - "
\n", - " Title\n", - " ×\n", - "
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\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "data": { - "text/html": [ - " > to track results use the .show() or .logs() methods or click here to open in UI" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2022-01-31 13:52:20,045 [info] run executed, status=completed\n" - ] - } - ], - "source": [ - "ingest_run = ingest_fn.run(\n", - " handler=\"ingest\",\n", - " params={\n", - " \"featureset\": quotes_set.uri,\n", - " \"source\": source,\n", - " },\n", - " local=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## View of the targets' state after run" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'created'" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fstore.get_feature_set(ingest_run.outputs['featureset']).status.state" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/ingest/ingest.py b/ingest/ingest.py deleted file mode 100644 index 1412cbaf5..000000000 --- a/ingest/ingest.py +++ /dev/null @@ -1,84 +0,0 @@ -# Copyright 2019 Iguazio -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -from typing import Union, List, Dict - -import mlrun.feature_store as fs -from mlrun.execution import MLClientCtx -from mlrun.data_types import InferOptions - - -def ingest( - context: MLClientCtx, - featureset: str, - source: str, - targets: List[Union[str, Dict]] = None, - namespace=None, - infer_options=None, - run_config: Union[str, Dict] = None, - spark_context=None, - overwrite=None, -): - """Read local DataFrame, file, URL, or source into the feature store - Ingest reads from the source, run the graph transformations, infers metadata and stats - and writes the results to the default of specified targets - - when targets are not specified data is stored in the configured default targets - (will usually be NoSQL for real-time and Parquet for offline). - - example:: - - stocks_set = FeatureSet("stocks", entities=[Entity("ticker")]) - stocks = pd.read_csv("stocks.csv") - df = ingest(stocks_set, stocks, infer_options=fstore.InferOptions.default()) - - # for running as remote job - config = RunConfig(image='mlrun/mlrun').apply(mount_v3io()) - df = ingest(stocks_set, stocks, run_config=config) - - # specify source and targets - source = CSVSource("mycsv", path="measurements.csv") - targets = [CSVTarget("mycsv", path="./mycsv.csv")] - ingest(measurements, source, targets) - - :param context: MLRun context - :param featureset: feature set object or featureset.uri. (uri must be of a feature set that is in the DB, - call `.save()` if it's not) - :param source: source dataframe or file path - :param targets: optional list of data target objects - :param namespace: namespace or module containing graph classes - :param infer_options: schema and stats infer options - :param run_config: function and/or run configuration for remote jobs, - see :py:class:`~mlrun.feature_store.RunConfig` - :param spark_context: local spark session for spark ingestion, example for creating the spark context: - `spark = SparkSession.builder.appName("Spark function").getOrCreate()` - For remote spark ingestion, this should contain the remote spark service name - :param overwrite: delete the targets' data prior to ingestion - (default: True for non-scheduled ingest - deletes the targets that are about to be ingested. - False for scheduled ingest - does not delete the target) - - """ - # Setting infer_options to default: - context._parameters["infer_options"] = infer_options or InferOptions.default() - - context.logger.info(f"Calling ingestion task with: {featureset}") - - # ingest called with mlrun_context, feature_set, source and targets passed with context - # This params here for documentation purposes only - fs.ingest( - mlrun_context=context, - namespace=namespace, - spark_context=spark_context, - ) - context.log_result("featureset", featureset) diff --git a/ingest/item.yaml b/ingest/item.yaml deleted file mode 100644 index 8665e88f4..000000000 --- a/ingest/item.yaml +++ /dev/null @@ -1,27 +0,0 @@ -apiVersion: v1 -categories: -- data-preparation -- data-analysis -- feature-store -description: Feature Store ingest function that runs the transformation graph on the - source of the featureset. -doc: '' -example: ingest.ipynb -generationDate: 2022-08-28:17-25 -hidden: false -icon: '' -labels: - author: yonish -maintainers: [] -marketplaceType: '' -mlrunVersion: 1.1.0 -name: ingest -platformVersion: 3.5.0 -spec: - filename: ingest.py - handler: ingest - image: mlrun/mlrun - kind: job - requirements: [] -url: '' -version: 1.1.0 diff --git a/ingest/test_ingest.py b/ingest/test_ingest.py deleted file mode 100644 index 224f520b4..000000000 --- a/ingest/test_ingest.py +++ /dev/null @@ -1,171 +0,0 @@ -# Copyright 2019 Iguazio -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -import os -import tempfile -import shutil -import datetime -import pytest - -import mlrun -import mlrun.feature_store as fstore -from mlrun.datastore.sources import CSVSource -from mlrun.feature_store.steps import * -from mlrun.features import MinMaxValidator -import pandas as pd - -REQUIRED_ENV_VARS = [ - "MLRUN_DBPATH", - "MLRUN_ARTIFACT_PATH", - "V3IO_USERNAME", - "V3IO_API", - "V3IO_ACCESS_KEY", -] - - -def _validate_environment_variables() -> bool: - """ - Checks that all required Environment variables are set. - """ - environment_keys = os.environ.keys() - return all(key in environment_keys for key in REQUIRED_ENV_VARS) - - -def _set_environment(): - artifact_path = tempfile.TemporaryDirectory().name - os.makedirs(artifact_path) - project = mlrun.new_project("ingest-test") - return artifact_path, project - - -def _cleanup_environment(artifact_path: str): - """ - Cleanup the test environment, deleting files and artifacts created during the test. - - :param artifact_path: The artifact path to delete. - """ - # Clean the local directory: - for test_output in [ - *os.listdir(artifact_path), - "schedules", - "runs", - "artifacts", - "functions", - ]: - test_output_path = os.path.abspath(f"./{test_output}") - if os.path.exists(test_output_path): - if os.path.isdir(test_output_path): - shutil.rmtree(test_output_path) - else: - os.remove(test_output_path) - - # Clean the artifacts' directory: - shutil.rmtree(artifact_path) - - -def create_dataframes() -> (pd.DataFrame, pd.DataFrame): - quotes = pd.DataFrame( - { - "time": [ - pd.Timestamp("2016-05-25 13:30:00.023"), - pd.Timestamp("2016-05-25 13:30:00.023"), - pd.Timestamp("2016-05-25 13:30:00.030"), - pd.Timestamp("2016-05-25 13:30:00.041"), - pd.Timestamp("2016-05-25 13:30:00.048"), - pd.Timestamp("2016-05-25 13:30:00.049"), - pd.Timestamp("2016-05-25 13:30:00.072"), - pd.Timestamp("2016-05-25 13:30:00.075"), - ], - "ticker": ["GOOG", "MSFT", "MSFT", "MSFT", "GOOG", "AAPL", "GOOG", "MSFT"], - "bid": [720.50, 51.95, 51.97, 51.99, 720.50, 97.99, 720.50, 52.01], - "ask": [720.93, 51.96, 51.98, 52.00, 720.93, 98.01, 720.88, 52.03], - } - ) - - # move date: - max_date = quotes["time"].max() - now_date = datetime.datetime.now() - delta = now_date - max_date - quotes["time"] = quotes["time"] + delta - - return quotes - - -class MyMap(MapClass): - def __init__(self, multiplier=1, **kwargs): - super().__init__(**kwargs) - self._multiplier = multiplier - - def do(self, event): - event["multi"] = event["bid"] * self._multiplier - return event - - -def _create_feature_set(): - quotes_set = fstore.FeatureSet("stock-quotes", entities=[fstore.Entity("ticker")]) - - quotes_set.graph.to("test_ingest.MyMap", multiplier=3).to( - "storey.Extend", _fn="({'extra': event['bid'] * 77})" - ).to("storey.Filter", "filter", _fn="(event['bid'] > 51.92)").to( - FeaturesetValidator() - ) - - quotes_set.add_aggregation("ask", ["sum", "max"], "1h", "10m", name="asks1") - quotes_set.add_aggregation("ask", ["sum", "max"], "5h", "10m", name="asks5") - quotes_set.add_aggregation("bid", ["min", "max"], "1h", "10m", name="bids") - - # add feature validation policy - quotes_set["bid"] = fstore.Feature( - validator=MinMaxValidator(min=52, severity="info") - ) - - # add default target definitions - quotes_set.set_targets() - return quotes_set - - -@pytest.mark.skipif( - condition=not _validate_environment_variables(), - reason="Project's environment variables are not set", -) -def test_ingest(): - artifact_path, project = _set_environment() - ingest_fn = mlrun.import_function("function.yaml") - quotes = create_dataframes() - - quotes_set = _create_feature_set() - quotes_set.save() - - data_uri = os.path.join(artifact_path, "quotes.csv") - quotes.to_csv(data_uri, index=False) - source = CSVSource("quotes", data_uri).to_dict() - - ingest_run = None - try: - ingest_run = ingest_fn.run( - handler="ingest", - params={ - "featureset": quotes_set.uri, - "source": source, - }, - local=True, - ) - - except Exception as exception: - print(f"- The test failed - raised the following error:\n- {exception}") - assert ( - fstore.get_feature_set(ingest_run.outputs["featureset"]).status.state - == "created" - ), "Targets not created successfully" - _cleanup_environment(artifact_path) diff --git a/model_monitoring_stream/function.yaml b/model_monitoring_stream/function.yaml deleted file mode 100644 index 07a21c40b..000000000 --- a/model_monitoring_stream/function.yaml +++ /dev/null @@ -1,267 +0,0 @@ -kind: remote -metadata: - name: model-monitoring-stream - tag: '' - hash: 33f4d6de0858b3dfc9d150fc82fbed6feb05534c - project: '' - categories: - - monitoring -spec: - command: '' - args: [] - image: livsmichael/mlrun-api:automation - entry_points: - consume: - name: consume - doc: '' - parameters: - - name: self - default: '' - - name: event - type: Dict - default: '' - outputs: - - default: '' - lineno: 293 - compute_predictions_per_second: - name: compute_predictions_per_second - doc: '' - parameters: - - name: event - type: dict - default: '' - outputs: - - default: '' - lineno: 311 - process_before_kv: - name: process_before_kv - doc: '' - parameters: - - name: self - default: '' - - name: event - type: dict - default: '' - outputs: - - default: '' - lineno: 316 - process_before_events_tsdb: - name: process_before_events_tsdb - doc: '' - parameters: - - name: event - type: Dict - default: '' - outputs: - - default: '' - lineno: 325 - process_before_parquet: - name: process_before_parquet - doc: '' - parameters: - - name: event - type: dict - default: '' - outputs: - - default: '' - lineno: 362 - set_none_if_empty: - name: set_none_if_empty - doc: '' - parameters: - - name: _event - type: dict - default: '' - - name: keys - type: List[str] - default: '' - outputs: - - default: '' - lineno: 364 - drop_if_exists: - name: drop_if_exists - doc: '' - parameters: - - name: _event - type: dict - default: '' - - name: keys - type: List[str] - default: '' - outputs: - - default: '' - lineno: 369 - unpack_if_exists: - name: unpack_if_exists - doc: '' - parameters: - - name: _event - type: dict - default: '' - - name: keys - type: List[str] - default: '' - outputs: - - default: '' - lineno: 373 - do: - name: do - doc: '' - parameters: - - name: self - default: '' - - name: event - type: Dict - default: '' - outputs: - - default: '' - lineno: 702 - resume_state: - name: resume_state - doc: '' - parameters: - - name: self - default: '' - - name: endpoint_id - default: '' - outputs: - - default: '' - lineno: 475 - is_valid: - name: is_valid - doc: '' - parameters: - - name: self - default: '' - - name: endpoint_id - type: str - default: '' - - name: validation_function - default: '' - - name: field - type: Any - default: '' - - name: dict_path - type: List[str] - default: '' - outputs: - - default: '' - lineno: 495 - handle_errors: - name: handle_errors - doc: '' - parameters: - - name: self - default: '' - - name: endpoint_id - default: '' - - name: event - default: '' - outputs: - - default: '' - type: bool - lineno: 503 - enrich_even_details: - name: enrich_even_details - doc: '' - parameters: - - name: event - default: '' - outputs: - - default: '' - lineno: 511 - is_not_none: - name: is_not_none - doc: '' - parameters: - - name: field - type: Any - default: '' - - name: dict_path - type: List[str] - default: '' - outputs: - - default: '' - lineno: 536 - is_list_of_numerics: - name: is_list_of_numerics - doc: '' - parameters: - - name: field - type: List[Union[int, float, dict, list]] - default: '' - - name: dict_path - type: List[str] - default: '' - outputs: - - default: '' - lineno: 545 - get_endpoint_record: - name: get_endpoint_record - doc: '' - parameters: - - name: kv_container - type: str - default: '' - - name: kv_path - type: str - default: '' - - name: endpoint_id - type: str - default: '' - - name: access_key - type: str - default: '' - outputs: - - default: '' - lineno: 717 - init_context: - name: init_context - doc: '' - parameters: - - name: context - type: MLClientCtx - default: '' - outputs: - - default: '' - lineno: 743 - handler: - name: handler - doc: '' - parameters: - - name: context - type: MLClientCtx - default: '' - - name: event - type: Event - default: '' - outputs: - - default: '' - lineno: 751 - description: '' - min_replicas: 1 - max_replicas: 4 - env: [] - base_spec: - apiVersion: nuclio.io/v1 - kind: Function - metadata: - name: model-monitoring-stream - labels: {} - annotations: - nuclio.io/generated_by: function generated from /home/michaell/projects/functions/model_monitoring_stream/model_monitoring_stream.py - spec: - runtime: python:3.6 - handler: model_monitoring_stream:handler - env: [] - volumes: [] - build: - commands: [] - noBaseImagesPull: true - functionSourceCode: import json
import os
from collections import defaultdict
from datetime import datetime
from os import environ
from typing import Dict, List, Set, Optional, Any, Union

import pandas as pd
import v3io
from mlrun.config import config
from mlrun.run import MLClientCtx
from mlrun.utils import logger
from mlrun.utils.model_monitoring import (
    parse_model_endpoint_store_prefix,
    create_model_endpoint_id,
)
from mlrun.utils.v3io_clients import get_v3io_client, get_frames_client
from nuclio import Event
from storey import (
    FieldAggregator,
    NoopDriver,
    Table,
    Map,
    MapClass,
    AggregateByKey,
    build_flow,
    Filter,
    FlatMap,
    TSDBTarget,
    ParquetTarget,
    SyncEmitSource,
)
from storey.dtypes import SlidingWindows
from storey.steps import SampleWindow
# Constants
from v3io.dataplane import RaiseForStatus

ISO_8061_UTC = "%Y-%m-%d %H:%M:%S.%f%z"
FUNCTION_URI = "function_uri"
MODEL = "model"
VERSION = "version"
VERSIONED_MODEL = "versioned_model"
MODEL_CLASS = "model_class"
TIMESTAMP = "timestamp"
ENDPOINT_ID = "endpoint_id"
REQUEST_ID = "request_id"
LABELS = "labels"
UNPACKED_LABELS = "unpacked_labels"
LATENCY_AVG_5M = "latency_avg_5m"
LATENCY_AVG_1H = "latency_avg_1h"
PREDICTIONS_PER_SECOND = "predictions_per_second"
PREDICTIONS_COUNT_5M = "predictions_count_5m"
PREDICTIONS_COUNT_1H = "predictions_count_1h"
FIRST_REQUEST = "first_request"
LAST_REQUEST = "last_request"
ERROR_COUNT = "error_count"
ENTITIES = "entities"
FEATURE_NAMES = "feature_names"
LABEL_COLUMNS = "label_columns"
LATENCY = "latency"
RECORD_TYPE = "record_type"
FEATURES = "features"
PREDICTION = "prediction"
PREDICTIONS = "predictions"
NAMED_FEATURES = "named_features"
NAMED_PREDICTIONS = "named_predictions"
BASE_METRICS = "base_metrics"
CUSTOM_METRICS = "custom_metrics"
ENDPOINT_FEATURES = "endpoint_features"
METRICS = "metrics"
BATCH_TIMESTAMP = "batch_timestamp"
TIME_FORMAT: str = "%Y-%m-%d %H:%M:%S.%f"  # ISO 8061


# Stream processing code
class EventStreamProcessor:
    def __init__(
        self,
        project: str,
        sample_window: int = 10,
        tsdb_batching_max_events: int = 10,
        tsdb_batching_timeout_secs: int = 60 * 5,  # Default 5 minutes
        parquet_batching_max_events: int = 10_000,
        parquet_batching_timeout_secs: int = 60 * 60,  # Default 1 hour
        aggregate_count_windows: Optional[List[str]] = None,
        aggregate_count_period: str = "30s",
        aggregate_avg_windows: Optional[List[str]] = None,
        aggregate_avg_period: str = "30s",
        v3io_access_key: Optional[str] = None,
        v3io_framesd: Optional[str] = None,
        v3io_api: Optional[str] = None,
    ):
        self.project = project
        self.sample_window = sample_window
        self.tsdb_batching_max_events = tsdb_batching_max_events
        self.tsdb_batching_timeout_secs = tsdb_batching_timeout_secs
        self.parquet_batching_max_events = parquet_batching_max_events
        self.parquet_batching_timeout_secs = parquet_batching_timeout_secs
        self.aggregate_count_windows = aggregate_count_windows or ["5m", "1h"]
        self.aggregate_count_period = aggregate_count_period
        self.aggregate_avg_windows = aggregate_avg_windows or ["5m", "1h"]
        self.aggregate_avg_period = aggregate_avg_period

        self.v3io_framesd = v3io_framesd or config.v3io_framesd
        self.v3io_api = v3io_api or config.v3io_api

        self.v3io_access_key = v3io_access_key or environ.get("V3IO_ACCESS_KEY")
        self.model_monitoring_access_key = (
            os.environ.get("MODEL_MONITORING_ACCESS_KEY") or self.v3io_access_key
        )

        template = config.model_endpoint_monitoring.store_prefixes.default

        kv_path = template.format(project=project, kind="endpoints")
        _, self.kv_container, self.kv_path = parse_model_endpoint_store_prefix(kv_path)

        tsdb_path = template.format(project=project, kind="events")
        _, self.tsdb_container, self.tsdb_path = parse_model_endpoint_store_prefix(
            tsdb_path
        )
        self.tsdb_path = f"{self.tsdb_container}/{self.tsdb_path}"

        self.parquet_path = config.model_endpoint_monitoring.store_prefixes.user_space.format(
            project=project, kind="parquet"
        )

        logger.info(
            "V3IO Configuration",
            v3io_access_key=self.v3io_access_key,
            model_monitoring_access_key=self.model_monitoring_access_key,
            default_store_prefix=config.model_endpoint_monitoring.store_prefixes.default,
            user_space_store_prefix=config.model_endpoint_monitoring.store_prefixes.user_space,
            v3io_api=self.v3io_api,
            v3io_framesd=self.v3io_framesd,
            kv_container=self.kv_container,
            kv_path=self.kv_path,
            tsdb_container=self.tsdb_container,
            tsdb_path=self.tsdb_path,
            parquet_path=self.parquet_path,
        )

        self._kv_keys = [
            FUNCTION_URI,
            MODEL,
            MODEL_CLASS,
            TIMESTAMP,
            ENDPOINT_ID,
            LABELS,
            UNPACKED_LABELS,
            LATENCY_AVG_5M,
            LATENCY_AVG_1H,
            PREDICTIONS_PER_SECOND,
            PREDICTIONS_COUNT_5M,
            PREDICTIONS_COUNT_1H,
            FIRST_REQUEST,
            LAST_REQUEST,
            ERROR_COUNT,
        ]

        self._flow = build_flow(
            [
                SyncEmitSource(),
                ProcessEndpointEvent(
                    kv_container=self.kv_container,
                    kv_path=self.kv_path,
                    v3io_access_key=self.v3io_access_key,
                ),
                FilterNotNone(),
                FlatMap(lambda x: x),
                MapFeatureNames(
                    kv_container=self.kv_container,
                    kv_path=self.kv_path,
                    access_key=self.v3io_access_key,
                ),
                # Branch 1: Aggregate events, count averages and update TSDB and KV
                [
                    AggregateByKey(
                        aggregates=[
                            FieldAggregator(
                                PREDICTIONS,
                                ENDPOINT_ID,
                                ["count"],
                                SlidingWindows(
                                    self.aggregate_count_windows,
                                    self.aggregate_count_period,
                                ),
                            ),
                            FieldAggregator(
                                LATENCY,
                                LATENCY,
                                ["avg"],
                                SlidingWindows(
                                    self.aggregate_avg_windows,
                                    self.aggregate_avg_period,
                                ),
                            ),
                        ],
                        table=Table("notable", NoopDriver()),
                    ),
                    SampleWindow(
                        self.sample_window
                    ),  # Add required gap between event to apply sampling
                    Map(self.compute_predictions_per_second),
                    # Branch 1.1: Updated KV
                    [
                        Map(self.process_before_kv),
                        WriteToKV(container=self.kv_container, table=self.kv_path),
                        InferSchema(
                            v3io_access_key=self.v3io_access_key,
                            v3io_framesd=self.v3io_framesd,
                            container=self.kv_container,
                            table=self.kv_path,
                        ),
                    ],
                    # Branch 1.2: Update TSDB
                    [
                        # Map the event into taggable fields, add record type to each field
                        Map(self.process_before_events_tsdb),
                        [
                            FilterKeys(BASE_METRICS),
                            UnpackValues(BASE_METRICS),
                            TSDBTarget(
                                path=self.tsdb_path,
                                rate="10/m",
                                time_col=TIMESTAMP,
                                container=self.tsdb_container,
                                access_key=self.v3io_access_key,
                                v3io_frames=self.v3io_framesd,
                                index_cols=[ENDPOINT_ID, RECORD_TYPE],
                                # Settings for _Batching
                                max_events=self.tsdb_batching_max_events,
                                timeout_secs=self.tsdb_batching_timeout_secs,
                                key=ENDPOINT_ID,
                            ),
                        ],
                        [
                            FilterKeys(ENDPOINT_FEATURES),
                            UnpackValues(ENDPOINT_FEATURES),
                            TSDBTarget(
                                path=self.tsdb_path,
                                rate="10/m",
                                time_col=TIMESTAMP,
                                container=self.tsdb_container,
                                access_key=self.v3io_access_key,
                                v3io_frames=self.v3io_framesd,
                                index_cols=[ENDPOINT_ID, RECORD_TYPE],
                                # Settings for _Batching
                                max_events=self.tsdb_batching_max_events,
                                timeout_secs=self.tsdb_batching_timeout_secs,
                                key=ENDPOINT_ID,
                            ),
                        ],
                        [
                            FilterKeys(CUSTOM_METRICS),
                            FilterNotNone(),
                            UnpackValues(CUSTOM_METRICS),
                            TSDBTarget(
                                path=self.tsdb_path,
                                rate="10/m",
                                time_col=TIMESTAMP,
                                container=self.tsdb_container,
                                access_key=self.v3io_access_key,
                                v3io_frames=self.v3io_framesd,
                                index_cols=[ENDPOINT_ID, RECORD_TYPE],
                                # Settings for _Batching
                                max_events=self.tsdb_batching_max_events,
                                timeout_secs=self.tsdb_batching_timeout_secs,
                                key=ENDPOINT_ID,
                            ),
                        ],
                    ],
                ],
                # Branch 2: Batch events, write to parquet
                [
                    Map(self.process_before_parquet),
                    ParquetTarget(
                        path=self.parquet_path,
                        partition_cols=["$key", "$year", "$month", "$day", "$hour"],
                        infer_columns_from_data=True,
                        # Settings for _Batching
                        max_events=self.parquet_batching_max_events,
                        timeout_secs=self.parquet_batching_timeout_secs,
                        # Settings for v3io storage
                        storage_options={
                            "v3io_api": self.v3io_api,
                            "v3io_access_key": self.model_monitoring_access_key,
                        },
                    ),
                ],
            ]
        ).run()

    def consume(self, event: Dict):
        events = []
        if "headers" in event and "values" in event:
            for values in event["values"]:
                events.append({k: v for k, v in zip(event["headers"], values)})
        else:
            events.append(event)

        for enriched in map(enrich_even_details, events):
            if enriched is not None:
                self._flow.emit(
                    enriched,
                    key=enriched[ENDPOINT_ID],
                    event_time=datetime.strptime(enriched["when"], ISO_8061_UTC),
                )
            else:
                pass

    @staticmethod
    def compute_predictions_per_second(event: dict):
        event[PREDICTIONS_PER_SECOND] = float(event[PREDICTIONS_COUNT_5M]) / 600
        return event

    def process_before_kv(self, event: dict):
        # Filter relevant keys
        e = {k: event[k] for k in self._kv_keys}
        # Unpack labels dictionary
        e = {**e, **e.pop(UNPACKED_LABELS, {})}
        # Write labels to kv as json string to be presentable later
        e[LABELS] = json.dumps(e[LABELS])
        return e

    @staticmethod
    def process_before_events_tsdb(event: Dict):
        base_fields = [TIMESTAMP, ENDPOINT_ID]

        base_event = {k: event[k] for k in base_fields}
        base_event[TIMESTAMP] = pd.to_datetime(
            base_event[TIMESTAMP], format=TIME_FORMAT
        )

        base_metrics = {
            RECORD_TYPE: BASE_METRICS,
            PREDICTIONS_PER_SECOND: event[PREDICTIONS_PER_SECOND],
            PREDICTIONS_COUNT_5M: event[PREDICTIONS_COUNT_5M],
            PREDICTIONS_COUNT_1H: event[PREDICTIONS_COUNT_1H],
            LATENCY_AVG_5M: event[LATENCY_AVG_5M],
            LATENCY_AVG_1H: event[LATENCY_AVG_1H],
            **base_event,
        }

        endpoint_features = {
            RECORD_TYPE: ENDPOINT_FEATURES,
            **event[NAMED_PREDICTIONS],
            **event[NAMED_FEATURES],
            **base_event,
        }

        processed = {BASE_METRICS: base_metrics, ENDPOINT_FEATURES: endpoint_features}

        if event[METRICS]:
            processed[CUSTOM_METRICS] = {
                RECORD_TYPE: CUSTOM_METRICS,
                **event[METRICS],
                **base_event,
            }

        return processed

    @staticmethod
    def process_before_parquet(event: dict):
        def set_none_if_empty(_event: dict, keys: List[str]):
            for key in keys:
                if not _event.get(key):
                    _event[key] = None

        def drop_if_exists(_event: dict, keys: List[str]):
            for key in keys:
                _event.pop(key, None)

        def unpack_if_exists(_event: dict, keys: List[str]):
            for key in keys:
                value = _event.get(key)
                if value is not None:
                    _event = {**value, **event}

        drop_if_exists(event, [UNPACKED_LABELS, FEATURES])
        unpack_if_exists(event, [ENTITIES])
        set_none_if_empty(event, [LABELS, METRICS, ENTITIES])
        return event


class ProcessEndpointEvent(MapClass):
    def __init__(self, kv_container: str, kv_path: str, v3io_access_key: str, **kwargs):
        super().__init__(**kwargs)
        self.kv_container: str = kv_container
        self.kv_path: str = kv_path
        self.v3io_access_key: str = v3io_access_key
        self.first_request: Dict[str, str] = dict()
        self.last_request: Dict[str, str] = dict()
        self.error_count: Dict[str, int] = defaultdict(int)
        self.endpoints: Set[str] = set()

    def do(self, event: dict):
        function_uri = event[FUNCTION_URI]
        versioned_model = event[VERSIONED_MODEL]
        endpoint_id = event[ENDPOINT_ID]

        # In case this process fails, resume state from existing record
        self.resume_state(endpoint_id)

        # Handle errors coming from stream
        found_errors = self.handle_errors(endpoint_id, event)
        if found_errors:
            return None

        # Validate event fields
        model_class = event.get("model_class") or event.get("class")
        timestamp = event.get("when")
        request_id = event.get("request", {}).get("id")
        latency = event.get("microsec")
        features = event.get("request", {}).get("inputs")
        predictions = event.get("resp", {}).get("outputs")

        if not self.is_valid(endpoint_id, is_not_none, timestamp, ["when"],):
            return None

        if endpoint_id not in self.first_request:
            self.first_request[endpoint_id] = timestamp
        self.last_request[endpoint_id] = timestamp

        if not self.is_valid(endpoint_id, is_not_none, request_id, ["request", "id"],):
            return None
        if not self.is_valid(endpoint_id, is_not_none, latency, ["microsec"],):
            return None
        if not self.is_valid(
            endpoint_id, is_not_none, features, ["request", "inputs"],
        ):
            return None
        if not self.is_valid(
            endpoint_id, is_not_none, predictions, ["resp", "outputs"],
        ):
            return None

        unpacked_labels = {f"_{k}": v for k, v in event.get(LABELS, {}).items()}

        # Separate each model invocation into sub events
        events = []
        for i, (feature, prediction) in enumerate(zip(features, predictions)):
            if not self.is_valid(
                endpoint_id,
                is_list_of_numerics,
                feature,
                ["request", "inputs", f"[{i}]"],
            ):
                return None

            if not isinstance(prediction, list):
                prediction = [prediction]

            events.append(
                {
                    FUNCTION_URI: function_uri,
                    MODEL: versioned_model,
                    MODEL_CLASS: model_class,
                    TIMESTAMP: timestamp,
                    ENDPOINT_ID: endpoint_id,
                    REQUEST_ID: request_id,
                    LATENCY: latency,
                    FEATURES: feature,
                    PREDICTION: prediction,
                    FIRST_REQUEST: self.first_request[endpoint_id],
                    LAST_REQUEST: self.last_request[endpoint_id],
                    ERROR_COUNT: self.error_count[endpoint_id],
                    LABELS: event.get(LABELS, {}),
                    METRICS: event.get(METRICS, {}),
                    ENTITIES: event.get("request", {}).get(ENTITIES, {}),
                    UNPACKED_LABELS: unpacked_labels,
                }
            )
        return events

    def resume_state(self, endpoint_id):
        # Make sure process is resumable, if process fails for any reason, be able to pick things up close to where we
        # left them
        if endpoint_id not in self.endpoints:
            logger.info("Trying to resume state", endpoint_id=endpoint_id)
            endpoint_record = get_endpoint_record(
                kv_container=self.kv_container,
                kv_path=self.kv_path,
                endpoint_id=endpoint_id,
                access_key=self.v3io_access_key,
            )
            if endpoint_record:
                first_request = endpoint_record.get(FIRST_REQUEST)
                if first_request:
                    self.first_request[endpoint_id] = first_request
                error_count = endpoint_record.get(ERROR_COUNT)
                if error_count:
                    self.error_count[endpoint_id] = error_count
            self.endpoints.add(endpoint_id)

    def is_valid(
        self, endpoint_id: str, validation_function, field: Any, dict_path: List[str]
    ):
        if validation_function(field, dict_path):
            return True
        self.error_count[endpoint_id] += 1
        return False

    def handle_errors(self, endpoint_id, event) -> bool:
        if "error" in event:
            self.error_count[endpoint_id] += 1
            return True

        return False


def enrich_even_details(event) -> Optional[dict]:
    function_uri = event.get(FUNCTION_URI)

    if not is_not_none(function_uri, [FUNCTION_URI]):
        return None

    model = event.get(MODEL)
    if not is_not_none(model, [MODEL]):
        return None

    version = event.get(VERSION)
    versioned_model = f"{model}:{version}" if version else f"{model}:latest"

    endpoint_id = create_model_endpoint_id(
        function_uri=function_uri, versioned_model=versioned_model,
    )

    endpoint_id = str(endpoint_id)

    event[VERSIONED_MODEL] = versioned_model
    event[ENDPOINT_ID] = endpoint_id

    return event


def is_not_none(field: Any, dict_path: List[str]):
    if field is not None:
        return True
    logger.error(
        f"Expected event field is missing: {field} [Event -> {''.join(dict_path)}]"
    )
    return False


def is_list_of_numerics(
    field: List[Union[int, float, dict, list]], dict_path: List[str]
):
    if all(isinstance(x, int) or isinstance(x, float) for x in field):
        return True
    logger.error(
        f"Expected event field is missing: {field} [Event -> {''.join(dict_path)}]"
    )
    return False


class FilterNotNone(Filter):
    def __init__(self, **kwargs):
        super().__init__(fn=lambda event: event is not None, **kwargs)


class FilterKeys(MapClass):
    def __init__(self, *args, **kwargs):
        super().__init__(**kwargs)
        self.keys = list(args)

    def do(self, event):
        new_event = {}
        for key in self.keys:
            if key in event:
                new_event[key] = event[key]

        return new_event if new_event else None


class UnpackValues(MapClass):
    def __init__(self, *args, **kwargs):
        super().__init__(**kwargs)
        self.keys_to_unpack = set(args)

    def do(self, event):
        unpacked = {}
        for key in event.keys():
            if key in self.keys_to_unpack:
                unpacked = {**unpacked, **event[key]}
            else:
                unpacked[key] = event[key]
        return unpacked


class MapFeatureNames(MapClass):
    def __init__(self, kv_container: str, kv_path: str, access_key: str, **kwargs):
        super().__init__(**kwargs)
        self.kv_container = kv_container
        self.kv_path = kv_path
        self.access_key = access_key
        self.feature_names = {}
        self.label_columns = {}

    def do(self, event: Dict):
        endpoint_id = event[ENDPOINT_ID]

        if endpoint_id not in self.feature_names:
            endpoint_record = get_endpoint_record(
                kv_container=self.kv_container,
                kv_path=self.kv_path,
                endpoint_id=endpoint_id,
                access_key=self.access_key,
            )
            feature_names = endpoint_record.get(FEATURE_NAMES)
            feature_names = json.loads(feature_names) if feature_names else None

            label_columns = endpoint_record.get(LABEL_COLUMNS)
            label_columns = json.loads(label_columns) if label_columns else None

            if not feature_names:
                logger.warn(
                    f"Feature names are not initialized, they will be automatically generated",
                    endpoint_id=endpoint_id,
                )
                feature_names = [f"f{i}" for i, _ in enumerate(event[FEATURES])]
                get_v3io_client().kv.update(
                    container=self.kv_container,
                    table_path=self.kv_path,
                    access_key=self.access_key,
                    key=event[ENDPOINT_ID],
                    attributes={FEATURE_NAMES: json.dumps(feature_names)},
                    raise_for_status=RaiseForStatus.always,
                )

            if not label_columns:
                logger.warn(
                    f"label column names are not initialized, they will be automatically generated",
                    endpoint_id=endpoint_id,
                )
                label_columns = [f"p{i}" for i, _ in enumerate(event[PREDICTION])]
                get_v3io_client().kv.update(
                    container=self.kv_container,
                    table_path=self.kv_path,
                    access_key=self.access_key,
                    key=event[ENDPOINT_ID],
                    attributes={LABEL_COLUMNS: json.dumps(label_columns)},
                    raise_for_status=RaiseForStatus.always,
                )

            self.label_columns[endpoint_id] = label_columns
            self.feature_names[endpoint_id] = feature_names

            logger.info(
                "Label columns", endpoint_id=endpoint_id, label_columns=label_columns
            )
            logger.info(
                "Feature names", endpoint_id=endpoint_id, feature_names=feature_names
            )

        feature_names = self.feature_names[endpoint_id]
        features = event[FEATURES]
        event[NAMED_FEATURES] = {
            name: feature for name, feature in zip(feature_names, features)
        }

        label_columns = self.label_columns[endpoint_id]
        prediction = event[PREDICTION]
        event[NAMED_PREDICTIONS] = {
            name: prediction for name, prediction in zip(label_columns, prediction)
        }
        logger.info("Mapped event", event=event)
        return event


class WriteToKV(MapClass):
    def __init__(self, container: str, table: str, **kwargs):
        super().__init__(**kwargs)
        self.container = container
        self.table = table

    def do(self, event: Dict):
        get_v3io_client().kv.update(
            container=self.container,
            table_path=self.table,
            key=event[ENDPOINT_ID],
            attributes=event,
        )
        return event


class InferSchema(MapClass):
    def __init__(
        self,
        v3io_access_key: str,
        v3io_framesd: str,
        container: str,
        table: str,
        **kwargs,
    ):
        super().__init__(**kwargs)
        self.container = container
        self.v3io_access_key = v3io_access_key
        self.v3io_framesd = v3io_framesd
        self.table = table
        self.keys = set()

    def do(self, event: Dict):
        key_set = set(event.keys())
        if not key_set.issubset(self.keys):
            self.keys.update(key_set)
            get_frames_client(
                token=self.v3io_access_key,
                container=self.container,
                address=self.v3io_framesd,
            ).execute(backend="kv", table=self.table, command="infer_schema")
            logger.info(
                "Found new keys, inferred schema", table=self.table, event=event
            )
        return event


def get_endpoint_record(
    kv_container: str, kv_path: str, endpoint_id: str, access_key: str
) -> Optional[dict]:
    logger.info(
        f"Grabbing endpoint data",
        container=kv_container,
        table_path=kv_path,
        key=endpoint_id,
    )
    try:
        endpoint_record = (
            get_v3io_client()
            .kv.get(
                container=kv_container,
                table_path=kv_path,
                key=endpoint_id,
                access_key=access_key,
                raise_for_status=v3io.dataplane.RaiseForStatus.always,
            )
            .output.item
        )
        return endpoint_record
    except Exception:
        return None


def init_context(context: MLClientCtx):
    context.logger.info("Initializing EventStreamProcessor")
    parameters = environ.get("MODEL_MONITORING_PARAMETERS")
    parameters = json.loads(parameters) if parameters else {}
    stream_processor = EventStreamProcessor(**parameters)
    setattr(context, "stream_processor", stream_processor)


def handler(context: MLClientCtx, event: Event):
    event_body = json.loads(event.body)
    context.logger.debug(event_body)
    context.stream_processor.consume(event_body)
 - source: '' - build: - commands: [] - code_origin: https://github.com/Michaelliv/functions.git#202b4c489e4c02c3025742ea237f1a042b7c6043:/home/michaell/projects/functions/model_monitoring_stream/model_monitoring_stream.py - default_handler: handler -verbose: false diff --git a/model_monitoring_stream/item.yaml b/model_monitoring_stream/item.yaml deleted file mode 100644 index 219fa5286..000000000 --- a/model_monitoring_stream/item.yaml +++ /dev/null @@ -1,23 +0,0 @@ -apiVersion: v1 -categories: -- monitoring -description: '' -doc: '' -example: model_monitoring_stream.ipynb -generationDate: 2022-08-28:17-25 -hidden: false -icon: '' -labels: {} -maintainers: [] -marketplaceType: '' -mlrunVersion: 1.1.0 -name: model-monitoring-stream -platformVersion: 3.5.0 -spec: - filename: model_monitoring_stream.py - handler: handler - image: livsmichael/mlrun-api:automation - kind: nuclio - requirements: [] -url: '' -version: 1.1.0 diff --git a/model_monitoring_stream/model_monitoring_stream.ipynb b/model_monitoring_stream/model_monitoring_stream.ipynb deleted file mode 100644 index 93d8c92e4..000000000 --- a/model_monitoring_stream/model_monitoring_stream.ipynb +++ /dev/null @@ -1,178 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "source": [ - "# Model Monitoring\n", - "\n", - "## Initial set up (and pre-requisites)\n", - "1. Make sure you have the `mlrun-api` datasource available in your Grafana instance, otherwise add it by:\n", - " 1. Open your grafana instance\n", - " 2. Navigate to `Configuration -> Data Sources`\n", - " 3. Press `Add data source` and configure the following parameters\n", - " ```\n", - " Name: mlrun-api\n", - " URL: http://mlrun-api:8080/api/grafana-proxy/model-endpoints\n", - " Access: Server (default)\n", - "\n", - " ## Add a custom header of:\n", - " X-V3io-Session-Key: \n", - " ```\n", - " 4. Press `Save & Test` to make sure it works, a confirmation message should appear when this button is pressed\n", - "\n", - "2. Import the available dashboards `(./dashboards/*)` to you Grafana instance\n", - "3. To allow the system to utilize drift measurement, make sure you supply the train set when logging the model on the\n", - " training step\n", - "\n", - " ```python\n", - " # Log model\n", - " context.log_model(\n", - " \"model\",\n", - " body=dumps(model),\n", - " artifact_path=context.artifact_subpath(\"models\"),\n", - " extra_data=eval_metrics,\n", - " model_file=\"model.pkl\",\n", - " metrics=context.results,\n", - " training_set=X_test, # <- make sure this is passed into log_model\n", - " labels={\"class\": \"sklearn.linear_model.LogisticRegression\"}\n", - " )\n", - " ```\n", - "4. When serving a model, make sure that the Nuclio function is deployed with tracking enabled by applying\n", - " `fn.set_tracking()`\n", - "\n", - "## Configuration\n", - "The stream processing portion of the model monitoring, can be deployed under multiple configuration options. The\n", - "available configurations can be found under `stream.Config`. Once configured it should be supplied as environment\n", - "parameters to the Nuclio function by setting `fn.set_envs`\n", - "\n", - "```python\n", - "project: str # project name\n", - "sample_window: int # The sampling window for the data that flows into the TSDB and the KV\n", - "kv_path_template: str # Path template for the kv table\n", - "tsdb_path_template: str # Path template for the tsdb table\n", - "parquet_path_template: str # v3io parquets path template, assumes v3io is mounted\n", - "tsdb_batching_max_events: int # The max amount of event to batch before writing the batch to tsdb\n", - "tsdb_batching_timeout_secs: int # The max amount of seconds a given batch can be gathered before being emitted\n", - "parquet_batching_max_events: int # The max amount of event to batch before writing the batch to parquet\n", - "parquet_batching_timeout_secs: int # The max amount of seconds, a given batch can be gathered before being written to parquet\n", - "container: str # container name\n", - "v3io_access_key: str # V3IO Access key\n", - "v3io_framesd: str # V3IO framesd URL\n", - "time_format: str # The time format into which time related fields will be converted\n", - "aggregate_count_windows: List[str] # List of window sizes for predictions count\n", - "aggregate_count_period: str # Period of predictions count windows\n", - "aggregate_avg_windows: List[str] # List of window sizes for average latency\n", - "aggregate_avg_period: str # Period of average latency windows\n", - "```" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "## Export function yaml" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "from mlrun import code_to_function\n", - "from mlrun.runtimes import RemoteRuntime\n", - "\n", - "\n", - "fn: RemoteRuntime = code_to_function(\n", - " name=\"model-monitoring-stream\",\n", - " kind=\"nuclio\",\n", - " image=\"mlrun/mlrun\",\n", - " filename=\"model_monitoring_stream.py\",\n", - " handler=\"handler\",\n", - ")\n", - "fn.export(\"model_monitoring_stream.yaml\")\n" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "## Deploy Stream Processing" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "import os\n", - "\n", - "from mlrun import import_function\n", - "from mlrun.platforms import mount_v3io\n", - "from mlrun.runtimes import RemoteRuntime\n", - "import json\n", - "\n", - "# Set project name\n", - "project = \"\"\n", - "\n", - "fn: RemoteRuntime = import_function(\"hub://model_monitoring_stream\")\n", - "\n", - "fn.add_v3io_stream_trigger(\n", - " stream_path=f\"projects/{project}/model-endpoints/stream\",\n", - " name=\"monitoring_stream_trigger\",\n", - ")\n", - "\n", - "fn.set_env(\"MODEL_MONITORING_PARAMETERS\", json.dumps({\"project\": project, \"v3io_framesd\": os.environ.get(\"V3IO_FRAMESD\")}))\n", - "\n", - "fn.metadata.project = project\n", - "fn.apply(mount_v3io())\n", - "fn.deploy()" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/model_monitoring_stream/model_monitoring_stream.py b/model_monitoring_stream/model_monitoring_stream.py deleted file mode 100644 index 90c8b92c2..000000000 --- a/model_monitoring_stream/model_monitoring_stream.py +++ /dev/null @@ -1,768 +0,0 @@ -# Copyright 2019 Iguazio -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -import json -import os -from collections import defaultdict -from datetime import datetime -from os import environ -from typing import Dict, List, Set, Optional, Any, Union - -import pandas as pd -import v3io -from mlrun.config import config -from mlrun.run import MLClientCtx -from mlrun.utils import logger -from mlrun.utils.model_monitoring import ( - parse_model_endpoint_store_prefix, - create_model_endpoint_id, -) -from mlrun.utils.v3io_clients import get_v3io_client, get_frames_client -from nuclio import Event -from storey import ( - FieldAggregator, - NoopDriver, - Table, - Map, - MapClass, - AggregateByKey, - build_flow, - Filter, - FlatMap, - TSDBTarget, - ParquetTarget, - SyncEmitSource, -) -from storey.dtypes import SlidingWindows -from storey.steps import SampleWindow -# Constants -from v3io.dataplane import RaiseForStatus - -ISO_8061_UTC = "%Y-%m-%d %H:%M:%S.%f%z" -FUNCTION_URI = "function_uri" -MODEL = "model" -VERSION = "version" -VERSIONED_MODEL = "versioned_model" -MODEL_CLASS = "model_class" -TIMESTAMP = "timestamp" -ENDPOINT_ID = "endpoint_id" -REQUEST_ID = "request_id" -LABELS = "labels" -UNPACKED_LABELS = "unpacked_labels" -LATENCY_AVG_5M = "latency_avg_5m" -LATENCY_AVG_1H = "latency_avg_1h" -PREDICTIONS_PER_SECOND = "predictions_per_second" -PREDICTIONS_COUNT_5M = "predictions_count_5m" -PREDICTIONS_COUNT_1H = "predictions_count_1h" -FIRST_REQUEST = "first_request" -LAST_REQUEST = "last_request" -ERROR_COUNT = "error_count" -ENTITIES = "entities" -FEATURE_NAMES = "feature_names" -LABEL_COLUMNS = "label_columns" -LATENCY = "latency" -RECORD_TYPE = "record_type" -FEATURES = "features" -PREDICTION = "prediction" -PREDICTIONS = "predictions" -NAMED_FEATURES = "named_features" -NAMED_PREDICTIONS = "named_predictions" -BASE_METRICS = "base_metrics" -CUSTOM_METRICS = "custom_metrics" -ENDPOINT_FEATURES = "endpoint_features" -METRICS = "metrics" -BATCH_TIMESTAMP = "batch_timestamp" -TIME_FORMAT: str = "%Y-%m-%d %H:%M:%S.%f" # ISO 8061 - - -# Stream processing code -class EventStreamProcessor: - def __init__( - self, - project: str, - sample_window: int = 10, - tsdb_batching_max_events: int = 10, - tsdb_batching_timeout_secs: int = 60 * 5, # Default 5 minutes - parquet_batching_max_events: int = 10_000, - parquet_batching_timeout_secs: int = 60 * 60, # Default 1 hour - aggregate_count_windows: Optional[List[str]] = None, - aggregate_count_period: str = "30s", - aggregate_avg_windows: Optional[List[str]] = None, - aggregate_avg_period: str = "30s", - v3io_access_key: Optional[str] = None, - v3io_framesd: Optional[str] = None, - v3io_api: Optional[str] = None, - ): - self.project = project - self.sample_window = sample_window - self.tsdb_batching_max_events = tsdb_batching_max_events - self.tsdb_batching_timeout_secs = tsdb_batching_timeout_secs - self.parquet_batching_max_events = parquet_batching_max_events - self.parquet_batching_timeout_secs = parquet_batching_timeout_secs - self.aggregate_count_windows = aggregate_count_windows or ["5m", "1h"] - self.aggregate_count_period = aggregate_count_period - self.aggregate_avg_windows = aggregate_avg_windows or ["5m", "1h"] - self.aggregate_avg_period = aggregate_avg_period - - self.v3io_framesd = v3io_framesd or config.v3io_framesd - self.v3io_api = v3io_api or config.v3io_api - - self.v3io_access_key = v3io_access_key or environ.get("V3IO_ACCESS_KEY") - self.model_monitoring_access_key = ( - os.environ.get("MODEL_MONITORING_ACCESS_KEY") or self.v3io_access_key - ) - - template = config.model_endpoint_monitoring.store_prefixes.default - - kv_path = template.format(project=project, kind="endpoints") - _, self.kv_container, self.kv_path = parse_model_endpoint_store_prefix(kv_path) - - tsdb_path = template.format(project=project, kind="events") - _, self.tsdb_container, self.tsdb_path = parse_model_endpoint_store_prefix( - tsdb_path - ) - self.tsdb_path = f"{self.tsdb_container}/{self.tsdb_path}" - - self.parquet_path = config.model_endpoint_monitoring.store_prefixes.user_space.format( - project=project, kind="parquet" - ) - - logger.info( - "V3IO Configuration", - v3io_access_key=self.v3io_access_key, - model_monitoring_access_key=self.model_monitoring_access_key, - default_store_prefix=config.model_endpoint_monitoring.store_prefixes.default, - user_space_store_prefix=config.model_endpoint_monitoring.store_prefixes.user_space, - v3io_api=self.v3io_api, - v3io_framesd=self.v3io_framesd, - kv_container=self.kv_container, - kv_path=self.kv_path, - tsdb_container=self.tsdb_container, - tsdb_path=self.tsdb_path, - parquet_path=self.parquet_path, - ) - - self._kv_keys = [ - FUNCTION_URI, - MODEL, - MODEL_CLASS, - TIMESTAMP, - ENDPOINT_ID, - LABELS, - UNPACKED_LABELS, - LATENCY_AVG_5M, - LATENCY_AVG_1H, - PREDICTIONS_PER_SECOND, - PREDICTIONS_COUNT_5M, - PREDICTIONS_COUNT_1H, - FIRST_REQUEST, - LAST_REQUEST, - ERROR_COUNT, - ] - - self._flow = build_flow( - [ - SyncEmitSource(), - ProcessEndpointEvent( - kv_container=self.kv_container, - kv_path=self.kv_path, - v3io_access_key=self.v3io_access_key, - ), - FilterNotNone(), - FlatMap(lambda x: x), - MapFeatureNames( - kv_container=self.kv_container, - kv_path=self.kv_path, - access_key=self.v3io_access_key, - ), - # Branch 1: Aggregate events, count averages and update TSDB and KV - [ - AggregateByKey( - aggregates=[ - FieldAggregator( - PREDICTIONS, - ENDPOINT_ID, - ["count"], - SlidingWindows( - self.aggregate_count_windows, - self.aggregate_count_period, - ), - ), - FieldAggregator( - LATENCY, - LATENCY, - ["avg"], - SlidingWindows( - self.aggregate_avg_windows, - self.aggregate_avg_period, - ), - ), - ], - table=Table("notable", NoopDriver()), - ), - SampleWindow( - self.sample_window - ), # Add required gap between event to apply sampling - Map(self.compute_predictions_per_second), - # Branch 1.1: Updated KV - [ - Map(self.process_before_kv), - WriteToKV(container=self.kv_container, table=self.kv_path), - InferSchema( - v3io_access_key=self.v3io_access_key, - v3io_framesd=self.v3io_framesd, - container=self.kv_container, - table=self.kv_path, - ), - ], - # Branch 1.2: Update TSDB - [ - # Map the event into taggable fields, add record type to each field - Map(self.process_before_events_tsdb), - [ - FilterKeys(BASE_METRICS), - UnpackValues(BASE_METRICS), - TSDBTarget( - path=self.tsdb_path, - rate="10/m", - time_col=TIMESTAMP, - container=self.tsdb_container, - access_key=self.v3io_access_key, - v3io_frames=self.v3io_framesd, - index_cols=[ENDPOINT_ID, RECORD_TYPE], - # Settings for _Batching - max_events=self.tsdb_batching_max_events, - timeout_secs=self.tsdb_batching_timeout_secs, - key=ENDPOINT_ID, - ), - ], - [ - FilterKeys(ENDPOINT_FEATURES), - UnpackValues(ENDPOINT_FEATURES), - TSDBTarget( - path=self.tsdb_path, - rate="10/m", - time_col=TIMESTAMP, - container=self.tsdb_container, - access_key=self.v3io_access_key, - v3io_frames=self.v3io_framesd, - index_cols=[ENDPOINT_ID, RECORD_TYPE], - # Settings for _Batching - max_events=self.tsdb_batching_max_events, - timeout_secs=self.tsdb_batching_timeout_secs, - key=ENDPOINT_ID, - ), - ], - [ - FilterKeys(CUSTOM_METRICS), - FilterNotNone(), - UnpackValues(CUSTOM_METRICS), - TSDBTarget( - path=self.tsdb_path, - rate="10/m", - time_col=TIMESTAMP, - container=self.tsdb_container, - access_key=self.v3io_access_key, - v3io_frames=self.v3io_framesd, - index_cols=[ENDPOINT_ID, RECORD_TYPE], - # Settings for _Batching - max_events=self.tsdb_batching_max_events, - timeout_secs=self.tsdb_batching_timeout_secs, - key=ENDPOINT_ID, - ), - ], - ], - ], - # Branch 2: Batch events, write to parquet - [ - Map(self.process_before_parquet), - ParquetTarget( - path=self.parquet_path, - partition_cols=["$key", "$year", "$month", "$day", "$hour"], - infer_columns_from_data=True, - # Settings for _Batching - max_events=self.parquet_batching_max_events, - timeout_secs=self.parquet_batching_timeout_secs, - # Settings for v3io storage - storage_options={ - "v3io_api": self.v3io_api, - "v3io_access_key": self.model_monitoring_access_key, - }, - ), - ], - ] - ).run() - - def consume(self, event: Dict): - events = [] - if "headers" in event and "values" in event: - for values in event["values"]: - events.append({k: v for k, v in zip(event["headers"], values)}) - else: - events.append(event) - - for enriched in map(enrich_even_details, events): - if enriched is not None: - self._flow.emit( - enriched, - key=enriched[ENDPOINT_ID], - event_time=datetime.strptime(enriched["when"], ISO_8061_UTC), - ) - else: - pass - - @staticmethod - def compute_predictions_per_second(event: dict): - event[PREDICTIONS_PER_SECOND] = float(event[PREDICTIONS_COUNT_5M]) / 600 - return event - - def process_before_kv(self, event: dict): - # Filter relevant keys - e = {k: event[k] for k in self._kv_keys} - # Unpack labels dictionary - e = {**e, **e.pop(UNPACKED_LABELS, {})} - # Write labels to kv as json string to be presentable later - e[LABELS] = json.dumps(e[LABELS]) - return e - - @staticmethod - def process_before_events_tsdb(event: Dict): - base_fields = [TIMESTAMP, ENDPOINT_ID] - - base_event = {k: event[k] for k in base_fields} - base_event[TIMESTAMP] = pd.to_datetime( - base_event[TIMESTAMP], format=TIME_FORMAT - ) - - base_metrics = { - RECORD_TYPE: BASE_METRICS, - PREDICTIONS_PER_SECOND: event[PREDICTIONS_PER_SECOND], - PREDICTIONS_COUNT_5M: event[PREDICTIONS_COUNT_5M], - PREDICTIONS_COUNT_1H: event[PREDICTIONS_COUNT_1H], - LATENCY_AVG_5M: event[LATENCY_AVG_5M], - LATENCY_AVG_1H: event[LATENCY_AVG_1H], - **base_event, - } - - endpoint_features = { - RECORD_TYPE: ENDPOINT_FEATURES, - **event[NAMED_PREDICTIONS], - **event[NAMED_FEATURES], - **base_event, - } - - processed = {BASE_METRICS: base_metrics, ENDPOINT_FEATURES: endpoint_features} - - if event[METRICS]: - processed[CUSTOM_METRICS] = { - RECORD_TYPE: CUSTOM_METRICS, - **event[METRICS], - **base_event, - } - - return processed - - @staticmethod - def process_before_parquet(event: dict): - def set_none_if_empty(_event: dict, keys: List[str]): - for key in keys: - if not _event.get(key): - _event[key] = None - - def drop_if_exists(_event: dict, keys: List[str]): - for key in keys: - _event.pop(key, None) - - def unpack_if_exists(_event: dict, keys: List[str]): - for key in keys: - value = _event.get(key) - if value is not None: - _event = {**value, **event} - - drop_if_exists(event, [UNPACKED_LABELS, FEATURES]) - unpack_if_exists(event, [ENTITIES]) - set_none_if_empty(event, [LABELS, METRICS, ENTITIES]) - return event - - -class ProcessEndpointEvent(MapClass): - def __init__(self, kv_container: str, kv_path: str, v3io_access_key: str, **kwargs): - super().__init__(**kwargs) - self.kv_container: str = kv_container - self.kv_path: str = kv_path - self.v3io_access_key: str = v3io_access_key - self.first_request: Dict[str, str] = dict() - self.last_request: Dict[str, str] = dict() - self.error_count: Dict[str, int] = defaultdict(int) - self.endpoints: Set[str] = set() - - def do(self, event: dict): - function_uri = event[FUNCTION_URI] - versioned_model = event[VERSIONED_MODEL] - endpoint_id = event[ENDPOINT_ID] - - # In case this process fails, resume state from existing record - self.resume_state(endpoint_id) - - # Handle errors coming from stream - found_errors = self.handle_errors(endpoint_id, event) - if found_errors: - return None - - # Validate event fields - model_class = event.get("model_class") or event.get("class") - timestamp = event.get("when") - request_id = event.get("request", {}).get("id") - latency = event.get("microsec") - features = event.get("request", {}).get("inputs") - predictions = event.get("resp", {}).get("outputs") - - if not self.is_valid(endpoint_id, is_not_none, timestamp, ["when"],): - return None - - if endpoint_id not in self.first_request: - self.first_request[endpoint_id] = timestamp - self.last_request[endpoint_id] = timestamp - - if not self.is_valid(endpoint_id, is_not_none, request_id, ["request", "id"],): - return None - if not self.is_valid(endpoint_id, is_not_none, latency, ["microsec"],): - return None - if not self.is_valid( - endpoint_id, is_not_none, features, ["request", "inputs"], - ): - return None - if not self.is_valid( - endpoint_id, is_not_none, predictions, ["resp", "outputs"], - ): - return None - - unpacked_labels = {f"_{k}": v for k, v in event.get(LABELS, {}).items()} - - # Separate each model invocation into sub events - events = [] - for i, (feature, prediction) in enumerate(zip(features, predictions)): - if not self.is_valid( - endpoint_id, - is_list_of_numerics, - feature, - ["request", "inputs", f"[{i}]"], - ): - return None - - if not isinstance(prediction, list): - prediction = [prediction] - - events.append( - { - FUNCTION_URI: function_uri, - MODEL: versioned_model, - MODEL_CLASS: model_class, - TIMESTAMP: timestamp, - ENDPOINT_ID: endpoint_id, - REQUEST_ID: request_id, - LATENCY: latency, - FEATURES: feature, - PREDICTION: prediction, - FIRST_REQUEST: self.first_request[endpoint_id], - LAST_REQUEST: self.last_request[endpoint_id], - ERROR_COUNT: self.error_count[endpoint_id], - LABELS: event.get(LABELS, {}), - METRICS: event.get(METRICS, {}), - ENTITIES: event.get("request", {}).get(ENTITIES, {}), - UNPACKED_LABELS: unpacked_labels, - } - ) - return events - - def resume_state(self, endpoint_id): - # Make sure process is resumable, if process fails for any reason, be able to pick things up close to where we - # left them - if endpoint_id not in self.endpoints: - logger.info("Trying to resume state", endpoint_id=endpoint_id) - endpoint_record = get_endpoint_record( - kv_container=self.kv_container, - kv_path=self.kv_path, - endpoint_id=endpoint_id, - access_key=self.v3io_access_key, - ) - if endpoint_record: - first_request = endpoint_record.get(FIRST_REQUEST) - if first_request: - self.first_request[endpoint_id] = first_request - error_count = endpoint_record.get(ERROR_COUNT) - if error_count: - self.error_count[endpoint_id] = error_count - self.endpoints.add(endpoint_id) - - def is_valid( - self, endpoint_id: str, validation_function, field: Any, dict_path: List[str] - ): - if validation_function(field, dict_path): - return True - self.error_count[endpoint_id] += 1 - return False - - def handle_errors(self, endpoint_id, event) -> bool: - if "error" in event: - self.error_count[endpoint_id] += 1 - return True - - return False - - -def enrich_even_details(event) -> Optional[dict]: - function_uri = event.get(FUNCTION_URI) - - if not is_not_none(function_uri, [FUNCTION_URI]): - return None - - model = event.get(MODEL) - if not is_not_none(model, [MODEL]): - return None - - version = event.get(VERSION) - versioned_model = f"{model}:{version}" if version else f"{model}:latest" - - endpoint_id = create_model_endpoint_id( - function_uri=function_uri, versioned_model=versioned_model, - ) - - endpoint_id = str(endpoint_id) - - event[VERSIONED_MODEL] = versioned_model - event[ENDPOINT_ID] = endpoint_id - - return event - - -def is_not_none(field: Any, dict_path: List[str]): - if field is not None: - return True - logger.error( - f"Expected event field is missing: {field} [Event -> {''.join(dict_path)}]" - ) - return False - - -def is_list_of_numerics( - field: List[Union[int, float, dict, list]], dict_path: List[str] -): - if all(isinstance(x, int) or isinstance(x, float) for x in field): - return True - logger.error( - f"Expected event field is missing: {field} [Event -> {''.join(dict_path)}]" - ) - return False - - -class FilterNotNone(Filter): - def __init__(self, **kwargs): - super().__init__(fn=lambda event: event is not None, **kwargs) - - -class FilterKeys(MapClass): - def __init__(self, *args, **kwargs): - super().__init__(**kwargs) - self.keys = list(args) - - def do(self, event): - new_event = {} - for key in self.keys: - if key in event: - new_event[key] = event[key] - - return new_event if new_event else None - - -class UnpackValues(MapClass): - def __init__(self, *args, **kwargs): - super().__init__(**kwargs) - self.keys_to_unpack = set(args) - - def do(self, event): - unpacked = {} - for key in event.keys(): - if key in self.keys_to_unpack: - unpacked = {**unpacked, **event[key]} - else: - unpacked[key] = event[key] - return unpacked - - -class MapFeatureNames(MapClass): - def __init__(self, kv_container: str, kv_path: str, access_key: str, **kwargs): - super().__init__(**kwargs) - self.kv_container = kv_container - self.kv_path = kv_path - self.access_key = access_key - self.feature_names = {} - self.label_columns = {} - - def do(self, event: Dict): - endpoint_id = event[ENDPOINT_ID] - - if endpoint_id not in self.feature_names: - endpoint_record = get_endpoint_record( - kv_container=self.kv_container, - kv_path=self.kv_path, - endpoint_id=endpoint_id, - access_key=self.access_key, - ) - feature_names = endpoint_record.get(FEATURE_NAMES) - feature_names = json.loads(feature_names) if feature_names else None - - label_columns = endpoint_record.get(LABEL_COLUMNS) - label_columns = json.loads(label_columns) if label_columns else None - - if not feature_names: - logger.warn( - f"Feature names are not initialized, they will be automatically generated", - endpoint_id=endpoint_id, - ) - feature_names = [f"f{i}" for i, _ in enumerate(event[FEATURES])] - get_v3io_client().kv.update( - container=self.kv_container, - table_path=self.kv_path, - access_key=self.access_key, - key=event[ENDPOINT_ID], - attributes={FEATURE_NAMES: json.dumps(feature_names)}, - raise_for_status=RaiseForStatus.always, - ) - - if not label_columns: - logger.warn( - f"label column names are not initialized, they will be automatically generated", - endpoint_id=endpoint_id, - ) - label_columns = [f"p{i}" for i, _ in enumerate(event[PREDICTION])] - get_v3io_client().kv.update( - container=self.kv_container, - table_path=self.kv_path, - access_key=self.access_key, - key=event[ENDPOINT_ID], - attributes={LABEL_COLUMNS: json.dumps(label_columns)}, - raise_for_status=RaiseForStatus.always, - ) - - self.label_columns[endpoint_id] = label_columns - self.feature_names[endpoint_id] = feature_names - - logger.info( - "Label columns", endpoint_id=endpoint_id, label_columns=label_columns - ) - logger.info( - "Feature names", endpoint_id=endpoint_id, feature_names=feature_names - ) - - feature_names = self.feature_names[endpoint_id] - features = event[FEATURES] - event[NAMED_FEATURES] = { - name: feature for name, feature in zip(feature_names, features) - } - - label_columns = self.label_columns[endpoint_id] - prediction = event[PREDICTION] - event[NAMED_PREDICTIONS] = { - name: prediction for name, prediction in zip(label_columns, prediction) - } - logger.info("Mapped event", event=event) - return event - - -class WriteToKV(MapClass): - def __init__(self, container: str, table: str, **kwargs): - super().__init__(**kwargs) - self.container = container - self.table = table - - def do(self, event: Dict): - get_v3io_client().kv.update( - container=self.container, - table_path=self.table, - key=event[ENDPOINT_ID], - attributes=event, - ) - return event - - -class InferSchema(MapClass): - def __init__( - self, - v3io_access_key: str, - v3io_framesd: str, - container: str, - table: str, - **kwargs, - ): - super().__init__(**kwargs) - self.container = container - self.v3io_access_key = v3io_access_key - self.v3io_framesd = v3io_framesd - self.table = table - self.keys = set() - - def do(self, event: Dict): - key_set = set(event.keys()) - if not key_set.issubset(self.keys): - self.keys.update(key_set) - get_frames_client( - token=self.v3io_access_key, - container=self.container, - address=self.v3io_framesd, - ).execute(backend="kv", table=self.table, command="infer_schema") - logger.info( - "Found new keys, inferred schema", table=self.table, event=event - ) - return event - - -def get_endpoint_record( - kv_container: str, kv_path: str, endpoint_id: str, access_key: str -) -> Optional[dict]: - logger.info( - f"Grabbing endpoint data", - container=kv_container, - table_path=kv_path, - key=endpoint_id, - ) - try: - endpoint_record = ( - get_v3io_client() - .kv.get( - container=kv_container, - table_path=kv_path, - key=endpoint_id, - access_key=access_key, - raise_for_status=v3io.dataplane.RaiseForStatus.always, - ) - .output.item - ) - return endpoint_record - except Exception: - return None - - -def init_context(context: MLClientCtx): - context.logger.info("Initializing EventStreamProcessor") - parameters = environ.get("MODEL_MONITORING_PARAMETERS") - parameters = json.loads(parameters) if parameters else {} - stream_processor = EventStreamProcessor(**parameters) - setattr(context, "stream_processor", stream_processor) - - -def handler(context: MLClientCtx, event: Event): - event_body = json.loads(event.body) - context.logger.debug(event_body) - context.stream_processor.consume(event_body) diff --git a/model_monitoring_stream/requirements.txt b/model_monitoring_stream/requirements.txt deleted file mode 100644 index ef238930e..000000000 --- a/model_monitoring_stream/requirements.txt +++ /dev/null @@ -1,3 +0,0 @@ -storey -nuclio -v3io \ No newline at end of file diff --git a/pandas_profiling_report/README.md b/pandas_profiling_report/README.md deleted file mode 100644 index 40e0c9b22..000000000 --- a/pandas_profiling_report/README.md +++ /dev/null @@ -1,26 +0,0 @@ -## pandas_profiling_report - -Creates an html report with various graphs/statistics/correlations for a given dataset. See sample report [here](https://pandas-profiling.github.io/pandas-profiling/examples/master/titanic/titanic_report.html). Link to GitHub page [here](https://github.com/pandas-profiling/pandas-profiling). - - -Usage example: - -```python -import mlrun, os -mlrun.mlconf.dbpath = 'http://mlrun-api:8080' - -# Load pandas_profiling_report function from Github -func = mlrun.import_function("hub://pandas_profiling_report").apply(mlrun.mount_v3io()) - -# Build MLRun image (only needs to be run once) -func.deploy() - -# Create task -data = 'https://iguazio-sample-data.s3.amazonaws.com/datasets/iris_dataset.csv' - -task = NewTask(name="pandas-profiling-report", - inputs={"data": DATA_URL}) - -# Run task on cluster -run = func.run(task, artifact_path='/User/artifacts') -``` diff --git a/pandas_profiling_report/function.yaml b/pandas_profiling_report/function.yaml deleted file mode 100644 index ffdbbf837..000000000 --- a/pandas_profiling_report/function.yaml +++ /dev/null @@ -1,40 +0,0 @@ -kind: job -metadata: - name: pandas-profiling-report - tag: '' - hash: 79fe77fb2920a8ffecfef2f614a0be494c2ea43b - project: '' - labels: - author: nicks - categories: - - data-analysis -spec: - command: '' - args: [] - image: mlrun/mlrun - env: [] - default_handler: pandas_profiling_report - entry_points: - pandas_profiling_report: - name: pandas_profiling_report - doc: Create a Pandas Profiling Report for a dataset. - parameters: - - name: context - type: MLClientCtx - doc: the function context - default: '' - - name: data - type: DataItem - doc: Dataset to create report for - default: '' - outputs: - - default: '' - lineno: 10 - description: Create Pandas Profiling Report from Dataset - build: - functionSourceCode: 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 - commands: - - python -m pip install pandas_profiling - code_origin: https://github.com/daniels290813/functions.git#55a79c32be5d233cc11efcf40cd3edbe309bfdef:/home/kali/functions/pandas_profiling_report/pandas_profiling_report.py - affinity: null -verbose: false diff --git a/pandas_profiling_report/item.yaml b/pandas_profiling_report/item.yaml deleted file mode 100644 index 13d374369..000000000 --- a/pandas_profiling_report/item.yaml +++ /dev/null @@ -1,25 +0,0 @@ -apiVersion: v1 -categories: -- data-analysis -description: Create Pandas Profiling Report from Dataset -doc: '' -example: pandas_profiling_report.ipynb -generationDate: 2022-08-28:17-25 -hidden: false -icon: '' -labels: - author: nicks -maintainers: [] -marketplaceType: '' -mlrunVersion: 1.1.0 -name: pandas-profiling-report -platformVersion: 3.5.0 -spec: - filename: pandas_profiling_report.py - handler: pandas_profiling_report - image: mlrun/mlrun - kind: job - requirements: - - pandas_profiling -url: '' -version: 1.1.0 diff --git a/pandas_profiling_report/pandas_profiling_report.ipynb b/pandas_profiling_report/pandas_profiling_report.ipynb deleted file mode 100644 index 61aeba265..000000000 --- a/pandas_profiling_report/pandas_profiling_report.ipynb +++ /dev/null @@ -1,794 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Pandas Profiling Report" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# nuclio: ignore\n", - "import nuclio" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "%nuclio: setting kind to 'job'\n", - "%nuclio: setting spec.image to 'mlrun/mlrun'\n" - ] - } - ], - "source": [ - "%nuclio config kind = \"job\"\n", - "%nuclio config spec.image = \"mlrun/mlrun\"" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "%%nuclio cmd -c\n", - "pip install pandas_profiling" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import pandas_profiling\n", - "\n", - "from mlrun.execution import MLClientCtx\n", - "from mlrun.datastore import DataItem" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "def pandas_profiling_report(\n", - " context: MLClientCtx,\n", - " data: DataItem,\n", - ") -> None:\n", - " \"\"\"Create a Pandas Profiling Report for a dataset.\n", - " :param context: the function context\n", - " :param data: Dataset to create report for\n", - " \"\"\"\n", - " \n", - " # Load dataset\n", - " df = data.as_df()\n", - " \n", - " # Create Pandas Profiling Report\n", - " profile = df.profile_report(title='Pandas Profiling Report')\n", - " \n", - " # Save to MLRun DB\n", - " context.log_artifact('Pandas Profiling Report',\n", - " body=profile.to_html(),\n", - " local_path='pandas_profiling_report.html')" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# nuclio: end-code" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### mlconfig" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "from mlrun import mlconf\n", - "import os\n", - "\n", - "mlconf.dbpath = 'http://mlrun-api:8080'\n", - "mlconf.artifact_path = mlconf.artifact_path or f'{os.environ[\"HOME\"]}/artifacts'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### save" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2020-10-15 19:21:40,986 [info] function spec saved to path: function.yaml\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from mlrun import code_to_function\n", - "\n", - "# create job function object from notebook code\n", - "fn = code_to_function(\"pandas_profiling_report\", kind=\"job\")\n", - "\n", - "# add metadata (for templates and reuse)\n", - "fn.spec.default_handler = \"pandas_profiling_report\"\n", - "fn.spec.description = \"Create Pandas Profiling Report from Dataset\"\n", - "fn.metadata.categories = [\"analysis\"]\n", - "fn.metadata.labels = {\"author\": \"nicks\"}\n", - "fn.export(\"function.yaml\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## tests" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from mlrun.platforms import auto_mount\n", - "fn.apply(auto_mount())" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "from mlrun import NewTask, run_local\n", - "\n", - "DATA_URL = 'https://iguazio-sample-data.s3.amazonaws.com/datasets/iris_dataset.csv'" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "task = NewTask(name=\"pandas-profiling-report\", \n", - " handler=pandas_profiling_report, \n", - " inputs={\"data\": DATA_URL})" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### run locally" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2020-10-15 19:21:41,030 [warning] warning!, server (0.5.1) and client (0.5.2) ver dont match\n", - "> 2020-10-15 19:21:41,031 [info] starting run pandas-profiling-report uid=0894aed4f2854d96b776e25bdcaff80e -> http://mlrun-api:8080\n", - "> 2020-10-15 19:21:41,062 [warning] warning!, server (0.5.1) and client (0.5.2) ver dont match\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "86c3397cc7384565815af90bc5a6d10b", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(FloatProgress(value=0.0, description='Summarize dataset', max=19.0, style=ProgressStyle(descrip…" - 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projectuiditerstartstatenamelabelsinputsparametersresultsartifacts
default0Oct 15 19:21:41completedpandas-profiling-report
v3io_user=nicks
kind=handler
owner=nicks
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Pandas Profiling Report
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projectuiditerstartstatenamelabelsinputsparametersresultsartifacts
default0Oct 15 19:23:25completedpandas-profiling-report
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Pandas Profiling Report
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\n", - "
\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "to track results use .show() or .logs() or in CLI: \n", - "!mlrun get run 0ab5c8dbff95471da6018c1a7afd3b22 --project default , !mlrun logs 0ab5c8dbff95471da6018c1a7afd3b22 --project default\n", - "> 2020-10-15 19:23:36,481 [info] run executed, status=completed\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fn.run(task, inputs={\"data\": DATA_URL})" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/pandas_profiling_report/pandas_profiling_report.py b/pandas_profiling_report/pandas_profiling_report.py deleted file mode 100644 index c3d3d4d32..000000000 --- a/pandas_profiling_report/pandas_profiling_report.py +++ /dev/null @@ -1,41 +0,0 @@ -# Copyright 2019 Iguazio -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# Generated by nuclio.export.NuclioExporter - -import pandas as pd -import pandas_profiling - -from mlrun.execution import MLClientCtx -from mlrun.datastore import DataItem - - -def pandas_profiling_report( - context: MLClientCtx, - data: DataItem, -) -> None: - """Create a Pandas Profiling Report for a dataset. - :param context: the function context - :param data: Dataset to create report for - """ - - df = data.as_df() - - profile = df.profile_report(title="Pandas Profiling Report") - - context.log_artifact( - "Pandas Profiling Report", - body=profile.to_html(), - local_path="pandas_profiling_report.html", - ) diff --git a/project_runner/function.yaml b/project_runner/function.yaml deleted file mode 100644 index 1c43fd3b1..000000000 --- a/project_runner/function.yaml +++ /dev/null @@ -1,53 +0,0 @@ -kind: remote -metadata: - name: project-runner - tag: '' - hash: b7888996aa9a7833972928fa06fa238f674099b3 - project: '' - labels: - author: orz - categories: - - utils -spec: - command: '' - args: [] - image: '' - entry_points: - init_context: - name: init_context - doc: '' - parameters: - - name: context - outputs: [] - lineno: 8 - handler: - name: handler - doc: "Imports the latest project version and runs the \nspecified workflow" - parameters: - - name: context - - name: event - outputs: [] - lineno: 11 - description: Nuclio based - Cron scheduler for running your MLRun projects - min_replicas: 1 - max_replicas: 1 - env: [] - base_spec: - apiVersion: nuclio.io/v1 - kind: Function - metadata: - annotations: - nuclio.io/generated_by: function generated from 02-07-2020 by admin - labels: {} - name: project-runner - spec: - build: - baseImage: mlrun/mlrun - commands: [] - functionSourceCode: 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 - noBaseImagesPull: true - env: [] - handler: project_runner:handler - runtime: python:3.6 - volumes: [] - source: '' diff --git a/project_runner/project_runner.ipynb b/project_runner/project_runner.ipynb deleted file mode 100644 index 04bebea12..000000000 --- a/project_runner/project_runner.ipynb +++ /dev/null @@ -1,340 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Project runner\n", - "Imports the latest project version and runs the specified workflow" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "import nuclio" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "%nuclio: setting spec.build.baseImage to 'mlrun/mlrun'\n" - ] - } - ], - "source": [ - "%nuclio config spec.build.baseImage = \"mlrun/mlrun\"" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# nuclio: start-code" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "from mlrun import load_project\n", - "from mlrun import mlconf\n", - "import json\n", - "import os" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "def init_context(context):\n", - " setattr(context, 'hub_url', os.getenv('hub_url', None))" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "def handler(context, event):\n", - " \"\"\"Imports the latest project version and runs the \n", - " specified workflow\n", - " \"\"\"\n", - " context.logger.info('Pulling project and workflow details')\n", - " if isinstance(event.body, dict):\n", - " details = event.body\n", - " else:\n", - " details = json.loads(event.body)\n", - " context.logger.info(details)\n", - " project_url = details['project_url']\n", - " workflow = details['workflow']\n", - " artifact_path = details.get('artifact_path', os.environ.get('artifact_path', None))\n", - " hub_url = details.get('hub_url', context.hub_url)\n", - "\n", - " if hub_url:\n", - " mlconf.hub_url = hub_url\n", - "\n", - " project= load_project(os.path.abspath('./loaded_project'), url=project_url)\n", - " project.run(name=workflow,\n", - " arguments={},\n", - " artifact_path=artifact_path,\n", - " watch=False)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "# nuclio: end-code" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "runner_event = {'project_url': '/User/demo-network-operations/project.yaml',\n", - " 'workflow': 'main',\n", - " 'hub_url': '/User/functions/{name}/function.yaml',\n", - " 'artifact_path': '/User/functions/project_runner/artifacts/'}" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Python> 2020-07-01 14:36:45,368 [info] \n", - "Python> 2020-07-01 14:36:45,369 [info] {'project_url': '/User/demo-network-operations/project.yaml', 'workflow': 'main', 'hub_url': '/User/functions/{name}/function.yaml', 'artifact_path': '/User/functions/project_runner/artifacts/'}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/conda/lib/python3.6/site-packages/kfp/components/_data_passing.py:168: UserWarning: Missing type name was inferred as \"JsonArray\" based on the value \"['cpu_utilization', 'throughput', 'packet_loss', 'latency']\".\n", - " warnings.warn('Missing type name was inferred as \"{}\" based on the value \"{}\".'.format(type_name, str(value)))\n", - "/conda/lib/python3.6/site-packages/kfp/components/_data_passing.py:168: UserWarning: Missing type name was inferred as \"JsonArray\" based on the value \"['mean', 'sum', 'std', 'var', 'min', 'max', 'median']\".\n", - " warnings.warn('Missing type name was inferred as \"{}\" based on the value \"{}\".'.format(type_name, str(value)))\n", - "/conda/lib/python3.6/site-packages/kfp/components/_data_passing.py:168: UserWarning: Missing type name was inferred as \"Integer\" based on the value \"20\".\n", - " warnings.warn('Missing type name was inferred as \"{}\" based on the value \"{}\".'.format(type_name, str(value)))\n", - "/conda/lib/python3.6/site-packages/kfp/components/_data_passing.py:168: UserWarning: Missing type name was inferred as \"Float\" based on the value \"0.3\".\n", - " warnings.warn('Missing type name was inferred as \"{}\" based on the value \"{}\".'.format(type_name, str(value)))\n", - "/conda/lib/python3.6/site-packages/kfp/components/_data_passing.py:168: UserWarning: Missing type name was inferred as \"JsonArray\" based on the value \"[1, 0]\".\n", - " warnings.warn('Missing type name was inferred as \"{}\" based on the value \"{}\".'.format(type_name, str(value)))\n", - "/conda/lib/python3.6/site-packages/kfp/components/_data_passing.py:168: UserWarning: Missing type name was inferred as \"Integer\" based on the value \"-1\".\n", - " warnings.warn('Missing type name was inferred as \"{}\" based on the value \"{}\".'.format(type_name, str(value)))\n", - "/conda/lib/python3.6/site-packages/kfp/components/_data_passing.py:168: UserWarning: Missing type name was inferred as \"Float\" based on the value \"0.1\".\n", - " warnings.warn('Missing type name was inferred as \"{}\" based on the value \"{}\".'.format(type_name, str(value)))\n", - "/conda/lib/python3.6/site-packages/kfp/components/_data_passing.py:168: UserWarning: Missing type name was inferred as \"Float\" based on the value \"0.75\".\n", - " warnings.warn('Missing type name was inferred as \"{}\" based on the value \"{}\".'.format(type_name, str(value)))\n" - ] - }, - { - "data": { - "text/html": [ - "Experiment link here" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Run link here" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[mlrun] 2020-07-01 14:36:46,646 Pipeline run id=cf85ec1b-2df7-403c-b7b4-b9bdb8fcf92f, check UI or DB for progress\n" - ] - } - ], - "source": [ - "init_context(context)\n", - "event = nuclio.Event(body=json.dumps(runner_event))\n", - "out = handler(context, event)\n", - "out" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Deployment" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [], - "source": [ - "from mlrun import code_to_function, mount_v3io\n", - "from nuclio.triggers import CronTrigger" - ] - }, - { - "cell_type": "code", - "execution_count": 89, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[mlrun] 2020-07-02 09:30:36,014 function spec saved to path: function.yaml\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 89, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Saving the function for import via hub://project_runner\n", - "fn = code_to_function(name='project-runner',\n", - " kind='nuclio')\n", - "fn.spec.description = 'Nuclio based - Cron scheduler for running your MLRun projects'\n", - "fn.metadata.categories = [\"utils\"]\n", - "fn.metadata.labels = {'author': 'orz'}\n", - "fn.spec.maxReplicas = 1\n", - "fn.export('function.yaml')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### How to call from your project?\n", - "> **After** importing the function" - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 90, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cron_string = '* * 1 * *' # Regular cron string as in https://pypi.org/project/croniter/\n", - "\n", - "# Set defaults\n", - "fn.set_envs({'artifact_path': '/User/functions/project_runner/artifacts/',\n", - " 'hub_url': '/User/functions/{name}/function.yaml'})\n", - "\n", - "# Set project and workflow event\n", - "runner_event = {'project_url': '/User/demo-network-operations/project.yaml',\n", - " 'workflow': 'main'}\n", - "\n", - "# Add as a trigger\n", - "fn.add_trigger('cron', \n", - " CronTrigger(schedule=cron_string,\n", - " body=json.dumps(runner_event),\n", - " headers={'X-Nuclio-Target': 'project-runner'}))\n", - "\n", - "# Add mount for access to the different directories\n", - "fn.apply(mount_v3io())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[mlrun] 2020-07-02 09:31:16,905 deploy started\n", - "[nuclio] 2020-07-02 09:31:19,021 (info) Build complete\n" - ] - } - ], - "source": [ - "fn.deploy()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python [conda env:root] *", - "language": "python", - "name": "conda-root-py" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/rnn_serving/function.yaml b/rnn_serving/function.yaml deleted file mode 100644 index 7a09e1f46..000000000 --- a/rnn_serving/function.yaml +++ /dev/null @@ -1,46 +0,0 @@ -kind: serving -metadata: - name: rnn-serving - tag: '' - hash: 548cd27edfdc49aed0b069d94bd049435d484722 - project: '' - labels: - author: Daniel - categories: - - model-serving - - machine-learning -spec: - command: '' - args: [] - image: mlrun/ml-models - description: deploy an rnn based stock analysis model server. - min_replicas: 1 - max_replicas: 4 - env: [] - base_spec: - apiVersion: nuclio.io/v1 - kind: Function - metadata: - name: rnn-serving - labels: {} - annotations: - nuclio.io/generated_by: function generated from /User/test/functions/rnn_serving/rnn_serving.py - spec: - runtime: python:3.6 - handler: rnn_serving:handler - env: [] - volumes: [] - build: - commands: [] - noBaseImagesPull: true - functionSourceCode: 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 - source: '' - function_kind: serving_v2 - build: - commands: [] - code_origin: https://github.com/daniels290813/functions.git#97b63199864dd95681bca5af86835d177bf9d67b:/User/test/functions/rnn_serving/rnn_serving.py - origin_filename: /User/test/functions/rnn_serving/rnn_serving.py - secret_sources: [] - mount_applied: false - affinity: null -verbose: false diff --git a/rnn_serving/item.yaml b/rnn_serving/item.yaml deleted file mode 100644 index 5cc7b9367..000000000 --- a/rnn_serving/item.yaml +++ /dev/null @@ -1,25 +0,0 @@ -apiVersion: v1 -categories: -- model-serving -- machine-learning -description: deploy an rnn based stock analysis model server. -doc: '' -example: rnn_serving.ipynb -generationDate: 2022-08-28:17-25 -hidden: false -icon: '' -labels: - author: Daniel -maintainers: [] -marketplaceType: '' -mlrunVersion: 1.1.0 -name: rnn-serving -platformVersion: 3.5.0 -spec: - filename: rnn_serving.py - handler: handler - image: mlrun/ml-models - kind: serving - requirements: null -url: '' -version: 1.1.0 diff --git a/rnn_serving/requirements.txt b/rnn_serving/requirements.txt deleted file mode 100644 index ff480e35d..000000000 --- a/rnn_serving/requirements.txt +++ /dev/null @@ -1,2 +0,0 @@ -tensorflow==2.8.2 -wget \ No newline at end of file diff --git a/rnn_serving/rnn_serving.ipynb b/rnn_serving/rnn_serving.ipynb deleted file mode 100644 index dbdf3b874..000000000 --- a/rnn_serving/rnn_serving.ipynb +++ /dev/null @@ -1,285 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# **RNN Serving**" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The following section we create a new model serving function which wraps our class , and specify model and other resources.
\n", - "Deploying the serving function will provide us an http endpoint that can handle requests in real time.
\n", - "This function is part of the [stock-analysis demo](https://github.com/mlrun/demos/tree/master/stock-analysis).
\n", - "To see how the model is trained or how the data-set is generated, check out code folder in the demo repository." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Steps**\n", - "\n", - "1. [Setup function parameters](#Setup-function-parameters)\n", - "2. [Importing the function](#Importing-the-function)\n", - "3. [Testing the function locally](#Testing-the-function-locally)\n", - "4. [Testing the function remotely](#Testing-the-function-remotely)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import warnings\n", - "warnings.filterwarnings(\"ignore\")" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Following packages are required, make sure to install\n", - "# !pip install pip install torch==1.6.0\n", - "# !pip install tensorflow\n", - "# !pip install keras" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Setup function parameters**" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Setting up models path\n", - "rnn_model_path = 'https://s3.wasabisys.com/iguazio/models/function-marketplace-models/rnn_serving/rnn_model.h5'\n", - "data_path = 'https://s3.wasabisys.com/iguazio/data/function-marketplace-data/rnn_serving/stocks_data.pkl'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Importing the function**" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2021-10-17 10:43:46,363 [info] loaded project function-marketplace from MLRun DB\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import mlrun\n", - "mlrun.set_environment(project='function-marketplace')\n", - "\n", - "# Importing the function from the hub\n", - "fn = mlrun.import_function(\"hub://rnn_serving\")\n", - "fn.apply(mlrun.auto_mount())\n", - "\n", - "# Manually specifying needed packages \n", - "fn.spec.build.commands = ['pip install torch==1.6.0', 'pip install tensorflow', 'pip install keras']\n", - "\n", - "# Adding the model \n", - "fn.add_model(key='rnn_model', model_path=rnn_model_path ,class_name='RNN_Model_Serving')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Testing the function locally**" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2021-10-17 10:43:54,256 [info] model rnn_model was loaded\n", - "> 2021-10-17 10:43:54,257 [info] Initializing endpoint records\n", - "> 2021-10-17 10:43:54,276 [info] Loaded ['rnn_model']\n" - ] - } - ], - "source": [ - "# When mocking, class has to be present\n", - "from rnn_serving import *\n", - "\n", - "# Mocking function\n", - "server = fn.to_mock_server()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Getting the data\n", - "import cloudpickle as cp\n", - "from urllib.request import urlopen\n", - "\n", - "rnn_data = cp.load(urlopen(data_path))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "model used in this example take inputs with the shape `(None, None, 11)`.
\n", - "whereas the first dimenstion is the number of instances, the second dimenstion is the number of timestamps
\n", - "and the last dimenstion is the number of features the dataset have.
\n", - "our testing dataset has `(1,10,11)` means one instance to predict, with sequence length of 10, each step has 11 features." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'id': '1bf6a3dc4d204e6e8bfd5834f5d691f1',\n", - " 'model_name': 'rnn_model',\n", - " 'outputs': '[[0.43563252687454224]]'}" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import requests\n", - "\n", - "# KFServing protocol event\n", - "event_data = {\"inputs\": rnn_data}\n", - "\n", - "response = server.test(path='/v2/models/rnn_model/predict',body=event_data)\n", - "response" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Testing the function remotely**" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2021-10-17 10:43:57,192 [info] Starting remote function deploy\n", - "2021-10-17 10:43:57 (info) Deploying function\n", - "2021-10-17 10:43:57 (info) Building\n", - "2021-10-17 10:43:57 (info) Staging files and preparing base images\n", - "2021-10-17 10:43:57 (info) Building processor image\n", - "2021-10-17 10:43:58 (info) Build complete\n", - "2021-10-17 10:44:10 (info) Function deploy complete\n", - "> 2021-10-17 10:44:11,677 [info] successfully deployed function: {'internal_invocation_urls': ['nuclio-function-marketplace-rnn-serving.default-tenant.svc.cluster.local:8080'], 'external_invocation_urls': ['default-tenant.app.dev39.lab.iguazeng.com:30255']}\n" - ] - } - ], - "source": [ - "address = fn.deploy()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'id': '1bf6a3dc4d204e6e8bfd5834f5d691f1',\n", - " 'model_name': 'rnn_model',\n", - " 'outputs': '[[0.43563252687454224]]'}" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import json\n", - "import requests\n", - "\n", - "# using requests to predict\n", - "response = requests.put(address+\"/v2/models/rnn_model/predict\", json = json.dumps(event_data))\n", - "json.loads(response.text)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[Back to the top](#RNN-Serving)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/rnn_serving/rnn_serving.py b/rnn_serving/rnn_serving.py deleted file mode 100644 index d7e783d7a..000000000 --- a/rnn_serving/rnn_serving.py +++ /dev/null @@ -1,35 +0,0 @@ -# Copyright 2019 Iguazio -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -import mlrun -import numpy as np -from tensorflow import keras -import json - - -class RNN_Model_Serving(mlrun.serving.V2ModelServer): - def load(self): - """load and initialize the model and/or other elements""" - model_file, extra_data = self.get_model(suffix=".h5") - self.model = keras.models.load_model(model_file) - - def predict(self, body): - try: - """Generate model predictions from sample.""" - feats = np.asarray(body['inputs']) - result = self.model.predict(feats) - result = json.dumps(result.tolist()) - return result - except Exception as e: - raise Exception("Failed to predict %s" % e) \ No newline at end of file diff --git a/rnn_serving/test_rnn_serving.py b/rnn_serving/test_rnn_serving.py deleted file mode 100644 index fb2f49974..000000000 --- a/rnn_serving/test_rnn_serving.py +++ /dev/null @@ -1,74 +0,0 @@ -# Copyright 2019 Iguazio -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -import os -import wget -from mlrun import import_function -from os import path -from rnn_serving import * - -DATASET = np.array([[6.9955170e-01, 6.9952875e-01, 2.7922913e-02, 2.7853036e-02, - 6.9955170e-01, 7.0086759e-01, 7.0118028e-01, 7.0142627e-01, - 2.7922913e-02, 0.0000000e+00, 0.0000000e+00], - [6.9955170e-01, 6.9998503e-01, 1.6527303e-03, 2.7853036e-02, - 7.0000792e-01, 7.0085293e-01, 7.0118028e-01, 7.0203447e-01, - 1.6527303e-03, 0.0000000e+00, 0.0000000e+00], - [6.9955170e-01, 7.0025057e-01, 1.6904050e-04, 2.7853036e-02, - 7.0027345e-01, 7.0014298e-01, 7.0190376e-01, 7.0128226e-01, - 1.6904050e-04, 0.0000000e+00, 0.0000000e+00], - [6.9955170e-01, 7.0144778e-01, 1.6904050e-04, 2.7853036e-02, - 7.0147055e-01, 7.0178574e-01, 7.0236105e-01, 7.0295709e-01, - 7.3906886e-03, 0.0000000e+00, 0.0000000e+00], - [6.9955170e-01, 7.0324355e-01, 1.6904050e-04, 2.7853036e-02, - 7.0326620e-01, 7.0308524e-01, 7.0490342e-01, 7.0427048e-01, - 2.4815742e-03, 0.0000000e+00, 0.0000000e+00], - [6.9955170e-01, 7.0324355e-01, 1.6904050e-04, 2.7853036e-02, - 7.0191067e-01, 7.0173001e-01, 7.0354480e-01, 7.0291305e-01, - 2.9976186e-03, 0.0000000e+00, 0.0000000e+00], - [6.9955170e-01, 7.0324355e-01, 1.6904050e-04, 2.7853036e-02, - 7.0166123e-01, 7.0148063e-01, 7.0284635e-01, 7.0249581e-01, - 2.7904075e-03, 0.0000000e+00, 0.0000000e+00], - [6.9955170e-01, 7.0324355e-01, 1.6904050e-04, 2.7853036e-02, - 7.0133996e-01, 7.0143080e-01, 7.0297277e-01, 7.0250750e-01, - 4.1491759e-04, 0.0000000e+00, 0.0000000e+00], - [6.9955170e-01, 7.0324355e-01, 1.6904050e-04, 2.7853036e-02, - 7.0150572e-01, 7.0251614e-01, 7.0281982e-01, 7.0370042e-01, - 2.1256472e-03, 0.0000000e+00, 0.0000000e+00], - [6.9955170e-01, 7.0324355e-01, 1.6904050e-04, 2.7853036e-02, - 7.0272487e-01, 7.0258951e-01, 7.0429617e-01, 7.0376801e-01, - 1.4207334e-03, 0.0000000e+00, 0.0000000e+00]]).reshape(1, 10, 11).tolist() - - -def download_pretrained_model(model_path): - # Run this to download the pre-trained model to your `models` directory - model_location = 'https://s3.wasabisys.com/iguazio/models/rnn/rnn_model.h5' - saved_models_directory = model_path - # Create paths - os.makedirs(saved_models_directory, exist_ok=1) - model_filepath = os.path.join(saved_models_directory, os.path.basename(model_location)) - wget.download(model_location, model_filepath) - - -def test_rnn_serving(): - model_path = os.path.join(os.path.abspath('./'), 'models') - model = model_path + '/rnn_model.h5' - if not path.exists(model): - download_pretrained_model(model_path) - - fn = import_function('function.yaml') - fn.add_model('rnn_model', model_path=model, class_name='RNN_Model_Serving') - # create an emulator (mock server) from the function configuration) - server = fn.to_mock_server() - resp = server.test("/v2/models/rnn_model/infer", {"inputs": DATASET}) - assert (resp['outputs'] == '[[0.453309565782547]]') diff --git a/slack_notify/README.md b/slack_notify/README.md deleted file mode 100644 index 9bde32995..000000000 --- a/slack_notify/README.md +++ /dev/null @@ -1 +0,0 @@ -# Send Notification to Slack \ No newline at end of file diff --git a/slack_notify/function.yaml b/slack_notify/function.yaml deleted file mode 100644 index 95af087c0..000000000 --- a/slack_notify/function.yaml +++ /dev/null @@ -1,48 +0,0 @@ -kind: job -metadata: - name: slack-notify - tag: '' - hash: 3de7e78ed9b7928af192badf988055086431fb58 - project: '' - labels: - author: mdl - categories: - - utils -spec: - command: '' - args: [] - image: python:3.6-jessie - env: [] - default_handler: slack_notify - entry_points: - slack_notify: - name: slack_notify - doc: Summarize a table - parameters: - - name: context - type: MLClientCtx - doc: the function context - default: '' - - name: webhook_url - type: str - doc: 'Slack incoming webhook URL. Please read: https://api.slack.com/messaging/webhooks' - default: URL - - name: slack_blocks - type: List[str] - doc: Message blocks list. NOT IMPLEMENTED YET - default: [] - - name: notification_text - type: str - doc: Notification text - default: Notification - outputs: - - default: '' - lineno: 14 - description: Send Slack notification - build: - functionSourceCode: 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 - commands: - - python -m pip install requests - code_origin: https://github.com/daniels290813/functions.git#55a79c32be5d233cc11efcf40cd3edbe309bfdef:/home/kali/functions/slack_notify/slack_notify.py - affinity: null -verbose: false diff --git a/slack_notify/item.yaml b/slack_notify/item.yaml deleted file mode 100644 index 6bdfd2c83..000000000 --- a/slack_notify/item.yaml +++ /dev/null @@ -1,25 +0,0 @@ -apiVersion: v1 -categories: -- utils -description: Send Slack notification -doc: '' -example: slack_notify.ipynb -generationDate: 2022-08-28:17-25 -hidden: false -icon: '' -labels: - author: mdl -maintainers: [] -marketplaceType: '' -mlrunVersion: 1.1.0 -name: slack-notify -platformVersion: 3.5.0 -spec: - filename: slack_notify.py - handler: slack_notify - image: python:3.6-jessie - kind: job - requirements: - - requests -url: '' -version: 1.1.0 diff --git a/slack_notify/slack_notify.ipynb b/slack_notify/slack_notify.ipynb deleted file mode 100644 index 8119bb8cf..000000000 --- a/slack_notify/slack_notify.ipynb +++ /dev/null @@ -1,293 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "# nuclio: ignore\n", - "import nuclio" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "%nuclio: setting kind to 'job'\n", - "%nuclio: setting spec.image to 'python:3.6-jessie'\n" - ] - } - ], - "source": [ - "%nuclio config kind = \"job\"\n", - "%nuclio config spec.image = \"python:3.6-jessie\"" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "%%nuclio cmd -c \n", - "pip install requests" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "import warnings\n", - "warnings.simplefilter(action='ignore', category=FutureWarning)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import json\n", - "import requests\n", - "from mlrun.execution import MLClientCtx\n", - "from typing import List" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "def slack_notify(\n", - " context: MLClientCtx,\n", - " webhook_url: str = \"URL\",\n", - " slack_blocks: List[str] = [],\n", - " notification_text: str = \"Notification\"\n", - ") -> None:\n", - " \"\"\"Summarize a table\n", - " :param context: the function context\n", - " :param webhook_url: Slack incoming webhook URL. Please read: https://api.slack.com/messaging/webhooks\n", - " :param notification_text: Notification text\n", - " :param slack_blocks: Message blocks list. NOT IMPLEMENTED YET\n", - " \"\"\"\n", - " \n", - " data = {\n", - " 'text': notification_text\n", - " }\n", - " print(\"====\",webhook_url)\n", - " response = requests.post(webhook_url, data=json.dumps(\n", - " data), headers={'Content-Type': 'application/json'})\n", - "\n", - " print('Response: ' + str(response.text))\n", - " print('Response code: ' + str(response.status_code))" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [], - "source": [ - "# nuclio: end-code" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### mlconfig" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [], - "source": [ - "from mlrun import mlconf\n", - "import os\n", - "\n", - "mlconf.dbpath = 'http://mlrun-api:8080'\n", - "mlconf.artifact_path = mlconf.artifact_path or f'{os.environ[\"HOME\"]}/artifacts'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### save" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from mlrun import code_to_function\n", - "\n", - "# create job function object from notebook code\n", - "fn = code_to_function(\"slack_notify\")\n", - "# add metadata (for templates and reuse)\n", - "fn.spec.default_handler = \"slack_notify\"\n", - "fn.spec.description = \"Send Slack notification\"\n", - "fn.metadata.categories = [\"ops\"]\n", - "fn.metadata.labels = {\"author\": \"mdl\"}\n", - "fn.export(\"function.yaml\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## tests" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from mlrun import import_function\n", - "func = import_function(\"hub://slack_notify\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from mlrun import NewTask, run_local\n", - "\n", - "\n", - "#Slack incoming webhook URL. Please read: https://api.slack.com/messaging/webhooks\n", - "task_params = {\n", - " \"webhook_url\" : \"https://hooks.slack.com/services/xxxxxxxx/xxxxxxxxx/xxxxxxxxxxxxxx\",\n", - " \"notification_text\" : \"Test Notification\"\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "task = NewTask(\n", - " name=\"tasks slack notify\", \n", - " params = task_params,\n", - " handler=slack_notify)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### run local where artifact path is fixed " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run = run_local(task, artifact_path=mlconf.artifact_path)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### run remote where artifact path includes the run id" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "func.deploy()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "func.run(task, params=task_params, workdir=mlconf.artifact_path)" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "function: slack-notify\n", - "Send Slack notification\n", - "default handler: slack_notify\n", - "entry points:\n", - " slack_notify: Summarize a table\n", - " context(MLClientCtx) - the function context\n", - " webhook_url(str) - Slack incoming webhook URL. Please read: https://api.slack.com/messaging/webhooks, default=URL\n", - " slack_blocks(List[str]) - Message blocks list. NOT IMPLEMENTED YET\n", - " notification_text(str) - Notification text, default=Notification\n" - ] - } - ], - "source": [ - "func.doc()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/slack_notify/slack_notify.py b/slack_notify/slack_notify.py deleted file mode 100644 index 3208ffee1..000000000 --- a/slack_notify/slack_notify.py +++ /dev/null @@ -1,48 +0,0 @@ -# Copyright 2019 Iguazio -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# Generated by nuclio.export.NuclioExporter - -import warnings - -warnings.simplefilter(action="ignore", category=FutureWarning) - -import os -import json -import requests -from mlrun.execution import MLClientCtx -from typing import List - - -def slack_notify( - context: MLClientCtx, - webhook_url: str = "URL", - slack_blocks: List[str] = [], - notification_text: str = "Notification", -) -> None: - """Summarize a table - :param context: the function context - :param webhook_url: Slack incoming webhook URL. Please read: https://api.slack.com/messaging/webhooks - :param notification_text: Notification text - :param slack_blocks: Message blocks list. NOT IMPLEMENTED YET - """ - - data = {"text": notification_text} - print("====", webhook_url) - response = requests.post( - webhook_url, data=json.dumps(data), headers={"Content-Type": "application/json"} - ) - - print("Response: " + str(response.text)) - print("Response code: " + str(response.status_code)) diff --git a/snowflake_dask/README.md b/snowflake_dask/README.md deleted file mode 100644 index 70fa3c927..000000000 --- a/snowflake_dask/README.md +++ /dev/null @@ -1,38 +0,0 @@ -# **Data Preperation Function** - -## `Snowflake_dask` - -![](img/snowflake-dask.png) - -This function query the data from a snowflake database and process the results -in parallel in a Dask cluster. -It will publish the dask dataframe in the cluster for other process to use. -It can also write the results dataframe to parquet files. - -```markdown - -:param context: the function context -:param dask_client: dask cluster function name -:param connection_info: Snowflake database connection info (this will be in a secret later) -:param query: query to for Snowflake -:param parquet_out_dir: directory path for the output parquet files (default None, not write out) -:param publish_name: name of the dask dataframe to publish to the dask cluster (default None, not publish) -``` - -To use the function, you will need to either have the password or key pair authentication to Snowflake configured. - -To get the password, or generate key pair in Snowflake and configure Snowflake for key pair authentication, please follow Snowflake [documentation](https://docs.snowflake.com/en/user-guide/key-pair-auth.html) here. - -After obtained password or key pair, please set up the project secrets in your Iguazio cluster. - -If you are using password, you only need to add ```sfPassword``` secret to the project settings. - -If you are using the key pair authentication, you will need to add both ```pkPath``` and ```pkPassword``` to the project settings. - - where: - - ```pkPath``` is the file path to your private key file in the cluster, for example ```/User/rsa_key.p8``` - -```pkPassword``` is your private key encryption password. Please see the screenshot below for your reference. - -![Secrets Screenshot](img/iguazio-project-secrets.png) diff --git a/snowflake_dask/config-template.yaml b/snowflake_dask/config-template.yaml deleted file mode 100644 index fb46ac2e6..000000000 --- a/snowflake_dask/config-template.yaml +++ /dev/null @@ -1,5 +0,0 @@ -user: "..." -password: "..." -warehouse: "..." -account: "..." -application: "Iguazio" \ No newline at end of file diff --git a/snowflake_dask/function.yaml b/snowflake_dask/function.yaml deleted file mode 100644 index c9cc8d746..000000000 --- a/snowflake_dask/function.yaml +++ /dev/null @@ -1,81 +0,0 @@ -kind: job -metadata: - name: snowflake-dask - tag: '' - hash: a002c7743b4a7471c7befe00f5497de050ebe902 - project: snowflake-dask - labels: - author: xingsheng - categories: - - data-prep - credentials: - access_key: ec09bfc8-1cb4-466d-9049-852081973ce3 -spec: - command: '' - args: [] - image: .mlrun/func-snowflake-dask-snowflake-dask:latest - build: - functionSourceCode: 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- base_image: mlrun/mlrun - commands: - - python -m pip install bokeh snowflake-connector-python[pandas] mlrun~=0.9.1 - code_origin: https://github.com/xsqian/functions.git#6b31040e2ad762602f335b0589823a1c61a09975:snowflake_dask.py - origin_filename: snowflake_dask.py - entry_points: - load: - name: load - doc: A delayed load one batch. - parameters: - - name: batch - default: '' - outputs: - - default: '' - lineno: 15 - load_results: - name: load_results - doc: Snowflake Dask - Ingest Snaowflake data with Dask - parameters: - - name: context - type: MLClientCtx - doc: the function context - default: '' - - name: dask_client - type: str - doc: dask cluster function name - default: '' - - name: connection_info - type: str - doc: Snowflake database connection info (this will be in a secret later) - default: '' - - name: query - type: str - doc: query to for Snowflake - default: '' - - name: parquet_out_dir - doc: directory path for the output parquet files (default None, not write - out) 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-example: snowflake-dask-mlrun.ipynb -generationDate: 2022-08-28:17-25 -hidden: false -icon: '' -labels: - author: xingsheng - framework: dask -maintainers: [] -marketplaceType: '' -mlrunVersion: 1.4.1 -name: snowflake_dask -platformVersion: 3.5.0 -spec: - filename: snowflake_dask.py - handler: load_results - image: mlrun/mlrun - kind: job - requirements: [] -url: '' -version: 1.1.0 diff --git a/snowflake_dask/requirements.txt b/snowflake_dask/requirements.txt deleted file mode 100644 index 0bca2c92f..000000000 --- a/snowflake_dask/requirements.txt +++ /dev/null @@ -1,2 +0,0 @@ -bokeh -snowflake-connector-python[pandas] diff --git a/snowflake_dask/snowflake-dask-mlrun.ipynb b/snowflake_dask/snowflake-dask-mlrun.ipynb deleted file mode 100644 index 03936f2a5..000000000 --- a/snowflake_dask/snowflake-dask-mlrun.ipynb +++ /dev/null @@ -1,437 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# This notebook is to create a function to ingest data from snowflake with a Dask cluster" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The dask frameworks enables users to parallelize their python code and run it as a distributed process on Iguazio cluster and dramatically accelerate their performance.
\n", - "In this notebook we'll create an mlrun function running as a dask client to ingest data from snowflake.
\n", - "It also demonstrates how to run parallelize query against snowflake using Dask Delayed option to query a large data set from snowflake.
\n", - "The function will be published on the function marketplace.
\n", - "For more information on dask over kubernetes: https://kubernetes.dask.org/en/latest/" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Set up the enviroment" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import mlrun\n", - "import os\n", - "import warnings\n", - "import yaml\n", - "\n", - "project_name = \"snowflake-dask\"\n", - "dask_cluster_name=\"snowflake-dask-cluster\"\n", - "artifact_path = mlrun.set_environment(project=project_name,\n", - " artifact_path = os.path.join(os.path.abspath('/v3io/projects/'), project_name))\n", - "\n", - "warnings.filterwarnings(\"ignore\")\n", - "\n", - "print(f'artifact_path = {artifact_path}')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load snowflake configuration from config file. \n", - "This is for demo purpose, in the real production code, you would need to put the snowflake connection info into secrets use the secrets in the running pod to connect to snowflake" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Load connection info\n", - "with open(\".config.yaml\") as f:\n", - " connection_info = yaml.safe_load(f)\n", - "\n", - "# verify the config\n", - "print(connection_info['account'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create a python function" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This function querys data from snowflake using snowflake python connector for parallel processing of the query results.
\n", - "With snoeflake python connector, when you execute a query, the cursor will return the result batches.
\n", - "Using Dask Delayed it will return and process results set in parallel.
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### write the function to a py file" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile snowflake_dask.py\n", - "\"\"\"Snowflake Dask - Ingest Snowflake data with Dask\"\"\"\n", - "import warnings\n", - "import mlrun\n", - "from mlrun.execution import MLClientCtx\n", - "import snowflake.connector as snow\n", - "from dask.distributed import Client\n", - "from dask.dataframe import from_delayed\n", - "from dask import delayed\n", - "from dask import dataframe as dd\n", - "from cryptography.hazmat.backends import default_backend\n", - "from cryptography.hazmat.primitives import serialization\n", - "\n", - "warnings.filterwarnings(\"ignore\")\n", - "\n", - "@delayed\n", - "def load(batch):\n", - "\n", - " \"\"\"A delayed load one batch.\"\"\"\n", - "\n", - " try:\n", - " print(\"BATCHING\")\n", - " df_ = batch.to_pandas()\n", - " return df_\n", - " except Exception as e:\n", - " print(f\"Failed on {batch} for {e}\")\n", - " raise\n", - "\n", - "def load_results(context: MLClientCtx,\n", - " dask_client: str,\n", - " connection_info: str,\n", - " query: str,\n", - " parquet_out_dir = None,\n", - " publish_name = None\n", - " ) -> None:\n", - "\n", - " \"\"\"Snowflake Dask - Ingest Snowflake data with Dask\n", - "\n", - " :param context: the function context\n", - " :param dask_client: dask cluster function name\n", - " :param connection_info: Snowflake database connection info (this will be in a secret later)\n", - " :param query: query to for Snowflake\n", - " :param parquet_out_dir: directory path for the output parquet files\n", - " (default None, not write out)\n", - " :param publish_name: name of the dask dataframe to publish to the dask cluster\n", - " (default None, not publish)\n", - "\n", - " \"\"\"\n", - " context = mlrun.get_or_create_ctx('snawflake-dask-cluster')\n", - " sf_password = context.get_secret('sfPassword')\n", - " pk_path = context.get_secret('pkPath')\n", - " pk_password = context.get_secret('pkPassword')\n", - "\n", - " if pk_path and pk_password:\n", - " with open(pk_path, \"rb\") as key:\n", - " p_key= serialization.load_pem_private_key(\n", - " key.read(),\n", - " password=str(pk_password).encode(),\n", - " backend=default_backend()\n", - " )\n", - " pkb = p_key.private_bytes(\n", - " encoding=serialization.Encoding.DER,\n", - " format=serialization.PrivateFormat.PKCS8\n", - " ,encryption_algorithm=serialization.NoEncryption()\n", - " )\n", - " connection_info.pop('password', 'No password found')\n", - " connection_info['private_key'] = pkb\n", - " elif sf_password:\n", - " connection_info['password'] = sf_password\n", - " else:\n", - " raise Exception(\"\\nPlease set up the secret for Snowflake in your project!\\n\")\n", - "\n", - " # setup dask client from the MLRun dask cluster function\n", - " if dask_client:\n", - " client = mlrun.import_function(dask_client).client\n", - " context.logger.info(f'Existing dask client === >>> {client}\\n')\n", - " else:\n", - " client = Client()\n", - " context.logger.info(f'\\nNewly created dask client === >>> {client}\\n')\n", - "\n", - " conn = snow.connect(**connection_info)\n", - " cur = conn.cursor()\n", - " cur.execute(query)\n", - " batches = cur.get_result_batches()\n", - " context.logger.info(f'batches len === {len(batches)}\\n')\n", - "\n", - " dfs = []\n", - " for batch in batches:\n", - " if batch.rowcount > 0:\n", - " df = load(batch)\n", - " dfs.append(df)\n", - " ddf = from_delayed(dfs)\n", - "\n", - " # materialize the query results set for some sample compute\n", - "\n", - " ddf_describe = ddf.describe().compute()\n", - "\n", - " context.logger.info(f'query === >>> {query}\\n')\n", - " context.logger.info(f'ddf === >>> {ddf}\\n')\n", - " context.log_result('number of rows', len(ddf.index))\n", - " context.log_dataset(\"ddf_describe\", df=ddf_describe)\n", - "\n", - " if publish_name:\n", - " context.log_result('data_set_name', publish_name)\n", - " if not client.list_datasets():\n", - " ddf.persist(name = publish_name)\n", - " client.publish_dataset(publish_name=ddf)\n", - "\n", - " if parquet_out_dir:\n", - " dd.to_parquet(df=ddf, path=parquet_out_dir)\n", - " context.log_result('parquet directory', parquet_out_dir)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Convert the code to MLRun function" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Use code_to_function to convert the code to MLRun
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "fn = mlrun.code_to_function(name=\"snowflake-dask\", \n", - " kind='job', \n", - " filename='snowflake_dask.py',\n", - " image='mlrun/mlrun',\n", - " requirements='requirements.txt',\n", - " handler=\"load_results\", \n", - " description=\"Snowflake Dask - Ingest snowflake data in parallel with Dask cluster\",\n", - " categories=[\"data-prep\"],\n", - " labels={\"author\": \"xingsheng\"}\n", - " )\n", - "fn.apply(mlrun.platforms.auto_mount())\n", - "fn.deploy()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### export function to local `function.yaml` file for testing\n", - "in the real usage, we will import a function from hub" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fn.export('function.yaml')\n", - "# print(fn.to_yaml())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### import a function from local `function.yaml' for testing (Need to change it to import from hub before PR)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fn = mlrun.import_function(\"./function.yaml\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# fn = mlrun.import_function(\"hub://snowflake_dask\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fn.apply(mlrun.platforms.auto_mount()) # this is a very important line" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### create a dask cluster and specify the configuration for the dask process (e.g. replicas, memory etc)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# function URI is db:///\n", - "dask_uri = f'db://{project_name}/{dask_cluster_name}'\n", - "dask_uri" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dsf = mlrun.new_function(name=dask_cluster_name, \n", - " kind='dask', \n", - " image='mlrun/mlrun',\n", - " requirements=[\"bokeh\", \"snowflake-connector-python[pandas]\"]\n", - " )\n", - "dsf.apply(mlrun.mount_v3io())\n", - "dsf.spec.remote = True\n", - "dsf.spec.min_replicas = 1\n", - "dsf.spec.max_replicas = 10\n", - "dsf.spec.service_type = \"NodePort\"\n", - "dsf.with_requests(mem='4G', cpu='2')\n", - "# dsf.spec.node_port=30088\n", - "# dsf.spec.scheduler_timeout = \"5 days\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dsf.deploy()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "client = dsf.client" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Run the function" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When running the function you would see a remote dashboard link as part of the result. click on this link takes you to the dask monitoring dashboard" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "p = 'my-local-test'\n", - "parquet_path = f\"/v3io/bigdata/pq_from_sf_dask/{p}\"\n", - "\n", - "fn.run(handler = 'load_results',\n", - " params={\"dask_client\": dask_uri, \n", - " \"connection_info\": connection_info, \n", - " \"query\": \"SELECT * FROM SNOWFLAKE_SAMPLE_DATA.TPCH_SF1.CUSTOMER\",\n", - " \"parquet_out_dir\": parquet_path,\n", - " \"publish_name\": \"customer\",\n", - " }\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "client.close()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Track the progress in the UI" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Users can view the progress and detailed information in the mlrun UI by clicking on the uid above.
\n", - "Also, to track the dask progress in the dask UI click on the \"dashboard link\" above the \"client\" section" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python [conda env:root] *", - "language": "python", - "name": "conda-root-py" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/snowflake_dask/snowflake_dask.py b/snowflake_dask/snowflake_dask.py deleted file mode 100644 index 8846e821d..000000000 --- a/snowflake_dask/snowflake_dask.py +++ /dev/null @@ -1,125 +0,0 @@ -# Copyright 2019 Iguazio -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -"""Snowflake Dask - Ingest Snaowflake data with Dask""" - -import warnings -import mlrun -from mlrun.execution import MLClientCtx -import snowflake.connector as snow -from dask.distributed import Client -from dask.dataframe import from_delayed -from dask import delayed -from dask import dataframe as dd -from cryptography.hazmat.backends import default_backend -from cryptography.hazmat.primitives import serialization - -warnings.filterwarnings("ignore") - -@delayed -def load(batch): - - """A delayed load one batch.""" - - try: - print("BATCHING") - df_ = batch.to_pandas() - return df_ - except Exception as e: - print(f"Failed on {batch} for {e}") - raise - -def load_results(context: MLClientCtx, - dask_client: str, - connection_info: str, - query: str, - parquet_out_dir = None, - publish_name = None - ) -> None: - - """Snowflake Dask - Ingest Snowflake data with Dask - - :param context: the function context - :param dask_client: dask cluster function name - :param connection_info: Snowflake database connection info (this will be in a secret later) - :param query: query to for Snowflake - :param parquet_out_dir: directory path for the output parquet files - (default None, not write out) - :param publish_name: name of the dask dataframe to publish to the dask cluster - (default None, not publish) - - """ - context = mlrun.get_or_create_ctx('snawflake-dask-cluster') - sf_password = context.get_secret('sfPassword') - pk_path = context.get_secret('pkPath') - pk_password = context.get_secret('pkPassword') - - if pk_path and pk_password: - with open(pk_path, "rb") as key: - p_key= serialization.load_pem_private_key( - key.read(), - password=str(pk_password).encode(), - backend=default_backend() - ) - pkb = p_key.private_bytes( - encoding=serialization.Encoding.DER, - format=serialization.PrivateFormat.PKCS8 - ,encryption_algorithm=serialization.NoEncryption() - ) - connection_info.pop('password', 'No password found') - connection_info['private_key'] = pkb - elif sf_password: - connection_info['password'] = sf_password - else: - raise Exception("\nPlease set up the secret for Snowflake in your project!\n") - - # setup dask client from the MLRun dask cluster function - if dask_client: - client = mlrun.import_function(dask_client).client - context.logger.info(f'Existing dask client === >>> {client}\n') - else: - client = Client() - context.logger.info(f'\nNewly created dask client === >>> {client}\n') - - conn = snow.connect(**connection_info) - cur = conn.cursor() - cur.execute(query) - batches = cur.get_result_batches() - context.logger.info(f'batches len === {len(batches)}\n') - - dfs = [] - for batch in batches: - if batch.rowcount > 0: - df = load(batch) - dfs.append(df) - ddf = from_delayed(dfs) - - # materialize the query results set for some sample compute - - ddf_describe = ddf.describe().compute() - - context.logger.info(f'query === >>> {query}\n') - context.logger.info(f'ddf === >>> {ddf}\n') - context.log_result('number of rows', len(ddf.index)) - context.log_dataset("ddf_describe", df=ddf_describe) - - if publish_name: - context.log_result('data_set_name', publish_name) - if not client.list_datasets(): - ddf.persist(name = publish_name) - client.publish_dataset(publish_name=ddf) - - if parquet_out_dir: - dd.to_parquet(df=ddf, path=parquet_out_dir) - context.log_result('parquet directory', parquet_out_dir) diff --git a/snowflake_dask/test_snowflake_dask.py b/snowflake_dask/test_snowflake_dask.py deleted file mode 100644 index fc2d4c93a..000000000 --- a/snowflake_dask/test_snowflake_dask.py +++ /dev/null @@ -1,24 +0,0 @@ -# Copyright 2019 Iguazio -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -"""Snowflake Dask unit test""" -from mlrun import import_function - -def test_snowflake_dask(): - """An unit test""" - fn_to_test = import_function("function.yaml") - - # a fake assert to pass the unit test - if fn_to_test.to_yaml().__contains__('job'): - assert True diff --git a/sql_to_file/function.yaml b/sql_to_file/function.yaml deleted file mode 100644 index 10b332a58..000000000 --- a/sql_to_file/function.yaml +++ /dev/null @@ -1,47 +0,0 @@ -kind: job -metadata: - name: sql-to-file - tag: '' - hash: 61f616fe697994e05cf018f2ee94c4ea25ed8863 - project: '' - labels: - author: adih - categories: - - data-preparation -spec: - command: '' - args: [] - image: mlrun/mlrun - env: [] - default_handler: sql_to_file - entry_points: - sql_to_file: - name: sql_to_file - doc: SQL Ingest - Ingest data using SQL query - parameters: - - name: context - type: MLClientCtx - doc: the function context - default: '' - - name: sql_query - type: str - doc: the sql query used to retrieve the data - default: '' - - name: database_url - type: str - doc: database connection URL - default: '' - - name: file_ext - type: str - doc: ("parquet") format for result file - default: parquet - outputs: - - default: '' - lineno: 9 - description: SQL To File - Ingest data using SQL query - build: - functionSourceCode: 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 - commands: [] - code_origin: https://github.com/daniels290813/functions.git#55a79c32be5d233cc11efcf40cd3edbe309bfdef:/home/kali/functions/sql_to_file/sql_to_file.py - affinity: null -verbose: false diff --git a/sql_to_file/item.yaml b/sql_to_file/item.yaml deleted file mode 100644 index 2f6ae4c53..000000000 --- a/sql_to_file/item.yaml +++ /dev/null @@ -1,24 +0,0 @@ -apiVersion: v1 -categories: -- data-preparation -description: SQL To File - Ingest data using SQL query -doc: '' -example: sql_to_file.ipynb -generationDate: 2022-08-28:17-25 -hidden: false -icon: '' -labels: - author: adih -maintainers: [] -marketplaceType: '' -mlrunVersion: 1.1.0 -name: sql-to-file -platformVersion: 3.5.0 -spec: - filename: sql_to_file.py - handler: sql_to_file - image: mlrun/mlrun - kind: job - requirements: [] -url: '' -version: 1.1.0 diff --git a/sql_to_file/requirements.txt b/sql_to_file/requirements.txt deleted file mode 100644 index 822eabb88..000000000 --- a/sql_to_file/requirements.txt +++ /dev/null @@ -1,2 +0,0 @@ -pyhive -pymysql \ No newline at end of file diff --git a/sql_to_file/sql_to_file.ipynb b/sql_to_file/sql_to_file.ipynb deleted file mode 100644 index d4a084adb..000000000 --- a/sql_to_file/sql_to_file.ipynb +++ /dev/null @@ -1,1567 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# SQL Ingest - Ingest data using SQL query " - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'nuclio'", - "output_type": "error", - "traceback": [ - "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[1;31mModuleNotFoundError\u001B[0m Traceback (most recent call last)", - "\u001B[1;32m\u001B[0m in \u001B[0;36m\u001B[1;34m\u001B[0m\n\u001B[0;32m 1\u001B[0m \u001B[1;31m# nuclio: ignore\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m----> 2\u001B[1;33m \u001B[1;32mimport\u001B[0m \u001B[0mnuclio\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m 3\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n", - "\u001B[1;31mModuleNotFoundError\u001B[0m: No module named 'nuclio'" - ] - } - ], - "source": [ - "# nuclio: ignore\n", - "import nuclio" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "%nuclio: setting kind to 'job'\n", - "%nuclio: setting spec.image to 'mlrun/mlrun'\n" - ] - } - ], - "source": [ - "%nuclio config kind = \"job\"\n", - "%nuclio config spec.build.baseImage = \"mlrun/mlrun\"" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "%%nuclio cmd -c\n", - "pip install --no-cache-dir git+https://github.com/v3io/PyHive.git@v0.6.999 \n", - "pip install sqlalchemy==1.3.11\n", - "pip install PyMySQL==0.9.3" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import pyhive\n", - "from sqlalchemy.engine import create_engine\n", - "from mlrun.execution import MLClientCtx\n", - "\n", - "\n", - "def sql_to_file(\n", - " context: MLClientCtx,\n", - " sql_query: str,\n", - " database_url: str,\n", - " file_ext: str = \"parquet\",\n", - ") -> None:\n", - " \"\"\"SQL Ingest - Ingest data using SQL query\n", - "\n", - " :param context: the function context\n", - " :param sql_query: the sql query used to retrieve the data\n", - " :param database_url: database connection URL\n", - " :param file_ext: (\"parquet\") format for result file\n", - "\n", - "\"\"\"\n", - "\n", - " engine = create_engine(database_url)\n", - " df = pd.read_sql(sql_query, engine)\n", - "\n", - " context.log_dataset('query result',\n", - " df=df,\n", - " format=file_ext,\n", - " artifact_path=context.artifact_subpath('data'))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# nuclio: end-code" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### mlconfig" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "ename": "KeyError", - "evalue": "'HOME'", - "output_type": "error", - "traceback": [ - "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[1;31mKeyError\u001B[0m Traceback (most recent call last)", - "\u001B[1;32m\u001B[0m in \u001B[0;36m\u001B[1;34m\u001B[0m\n\u001B[0;32m 2\u001B[0m \u001B[1;32mimport\u001B[0m \u001B[0mos\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 3\u001B[0m \u001B[0mmlconf\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mdbpath\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mmlconf\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mdbpath\u001B[0m \u001B[1;32mor\u001B[0m \u001B[1;34m'http://mlrun-api:8080'\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m----> 4\u001B[1;33m \u001B[0mmlconf\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0martifact_path\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mmlconf\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0martifact_path\u001B[0m \u001B[1;32mor\u001B[0m \u001B[1;34mf'{os.environ[\"HOME\"]}/artifacts'\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m 5\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 6\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n", - "\u001B[1;32mC:\\Program Files\\Python37\\lib\\os.py\u001B[0m in \u001B[0;36m__getitem__\u001B[1;34m(self, key)\u001B[0m\n\u001B[0;32m 679\u001B[0m \u001B[1;32mexcept\u001B[0m \u001B[0mKeyError\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 680\u001B[0m \u001B[1;31m# raise KeyError with the original key value\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 681\u001B[1;33m \u001B[1;32mraise\u001B[0m \u001B[0mKeyError\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mkey\u001B[0m\u001B[1;33m)\u001B[0m \u001B[1;32mfrom\u001B[0m \u001B[1;32mNone\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m 682\u001B[0m \u001B[1;32mreturn\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mdecodevalue\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mvalue\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 683\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n", - "\u001B[1;31mKeyError\u001B[0m: 'HOME'" - ] - } - ], - "source": [ - "from mlrun import mlconf\n", - "import os\n", - "mlconf.dbpath = mlconf.dbpath or 'http://mlrun-api:8080'\n", - "mlconf.artifact_path = mlconf.artifact_path or f'{os.environ[\"HOME\"]}/artifacts'\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Save function" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "def mount_secret(\n", - " secret_name, volume_mount_path, volume_name='secret', items=None\n", - "):\n", - " def _mount_secret(task):\n", - " from kubernetes import client as k8s_client\n", - " vol = k8s_client.V1SecretVolumeSource(secret_name=secret_name, items=items)\n", - " return task.add_volume(\n", - " k8s_client.V1Volume(name=volume_name, secret=vol)\n", - " ).add_volume_mount(\n", - " k8s_client.V1VolumeMount(mount_path=volume_mount_path, name=volume_name)\n", - " )\n", - " return _mount_secret" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "from mlrun import code_to_function, NewTask\n", - "import os\n", - "\n", - "fn = code_to_function(name=\"sql_to_file\",\n", - " handler=\"sql_to_file\",\n", - " description=\"SQL To File - Ingest data using SQL query\",\n", - " categories=[\"data-prep\"],\n", - " labels={\"author\": \"adih\"})\n", - "\n", - "if \"V3IO_ACCESS_KEY\" in list(os.environ):\n", - " fn.apply(mount_secret(secret_name='presto-tls',\n", - " volume_mount_path= '/var/run/iguazio/secrets/'))\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Build the image" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[mlrun] 2020-06-29 12:42:44,100 starting remote build, image: .mlrun/func-default-sql-ingest-latest\n", - "\u001B[36mINFO\u001B[0m[0000] Resolved base name mlrun/mlrun:0.4.10 to mlrun/mlrun:0.4.10 \n", - "\u001B[36mINFO\u001B[0m[0000] Resolved base name mlrun/mlrun:0.4.10 to mlrun/mlrun:0.4.10 \n", - "\u001B[36mINFO\u001B[0m[0000] Retrieving image manifest mlrun/mlrun:0.4.10 \n", - "\u001B[36mINFO\u001B[0m[0000] Retrieving image manifest mlrun/mlrun:0.4.10 \n", - "\u001B[36mINFO\u001B[0m[0000] Built cross stage deps: map[] \n", - "\u001B[36mINFO\u001B[0m[0000] Retrieving image manifest mlrun/mlrun:0.4.10 \n", - "\u001B[36mINFO\u001B[0m[0000] Retrieving image manifest mlrun/mlrun:0.4.10 \n", - "\u001B[36mINFO\u001B[0m[0001] Unpacking rootfs as cmd RUN pip install --no-cache-dir git+https://github.com/v3io/PyHive.git@v0.6.999 requires it. \n", - "\u001B[36mINFO\u001B[0m[0027] Taking snapshot of full filesystem... \n", - "\u001B[36mINFO\u001B[0m[0039] Resolving paths \n", - "\u001B[36mINFO\u001B[0m[0046] RUN pip install --no-cache-dir git+https://github.com/v3io/PyHive.git@v0.6.999 \n", - "\u001B[36mINFO\u001B[0m[0046] cmd: /bin/sh \n", - "\u001B[36mINFO\u001B[0m[0046] args: [-c pip install --no-cache-dir git+https://github.com/v3io/PyHive.git@v0.6.999] \n", - "Collecting git+https://github.com/v3io/PyHive.git@v0.6.999\n", - " Cloning https://github.com/v3io/PyHive.git (to revision v0.6.999) to /tmp/pip-req-build-ycqhuolw\n", - " Running command git clone -q https://github.com/v3io/PyHive.git /tmp/pip-req-build-ycqhuolw\n", - "Requirement already satisfied: future in /usr/local/lib/python3.7/site-packages (from PyHive==0.6.1.dev0) (0.18.2)\n", - "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.7/site-packages (from PyHive==0.6.1.dev0) (2.8.1)\n", - "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/site-packages (from python-dateutil->PyHive==0.6.1.dev0) (1.15.0)\n", - "Building wheels for collected packages: PyHive\n", - " Building wheel for PyHive (setup.py): started\n", - " Building wheel for PyHive (setup.py): finished with status 'done'\n", - " Created wheel for PyHive: filename=PyHive-0.6.1.dev0-py3-none-any.whl size=46402 sha256=63dca405cbae83da4cfcabfd61fd00f1683bc008c8bfa2272eac7054ec283166\n", - " Stored in directory: /tmp/pip-ephem-wheel-cache-mwb52l_u/wheels/05/11/cd/4ac4df0fcee76e5ceb614c39c56fca1eead41c0ac32ff6285d\n", - "Successfully built PyHive\n", - "Installing collected packages: PyHive\n", - "Successfully installed PyHive-0.6.1.dev0\n", - "\u001B[36mINFO\u001B[0m[0048] Taking snapshot of full filesystem... \n", - "\u001B[36mINFO\u001B[0m[0048] Resolving paths \n", - "\u001B[36mINFO\u001B[0m[0053] RUN pip install sqlalchemy==1.3.11 \n", - "\u001B[36mINFO\u001B[0m[0053] cmd: /bin/sh \n", - "\u001B[36mINFO\u001B[0m[0053] args: [-c pip install sqlalchemy==1.3.11] \n", - "Collecting sqlalchemy==1.3.11\n", - " Downloading SQLAlchemy-1.3.11.tar.gz (6.0 MB)\n", - "Building wheels for collected packages: sqlalchemy\n", - " Building wheel for sqlalchemy (setup.py): started\n", - " Building wheel for sqlalchemy (setup.py): finished with status 'done'\n", - " Created wheel for sqlalchemy: filename=SQLAlchemy-1.3.11-cp37-cp37m-linux_x86_64.whl size=1216921 sha256=9dd22e89acfbb68df0c1d189d36907a16c9393e4174598eb4bf377ce57132f3c\n", - " Stored in directory: /root/.cache/pip/wheels/0a/60/60/f26cbd183a3bb0031ace108156036dd925ec0138ee1c496a16\n", - "Successfully built sqlalchemy\n", - "Installing collected packages: sqlalchemy\n", - " Attempting uninstall: sqlalchemy\n", - " Found existing installation: SQLAlchemy 1.3.17\n", - " Uninstalling SQLAlchemy-1.3.17:\n", - " Successfully uninstalled SQLAlchemy-1.3.17\n", - "Successfully installed sqlalchemy-1.3.11\n", - "\u001B[36mINFO\u001B[0m[0057] Taking snapshot of full filesystem... \n", - "\u001B[36mINFO\u001B[0m[0057] Resolving paths \n", - "\u001B[36mINFO\u001B[0m[0063] RUN pip install PyMySQL==0.9.3 \n", - "\u001B[36mINFO\u001B[0m[0063] cmd: /bin/sh \n", - "\u001B[36mINFO\u001B[0m[0063] args: [-c pip install PyMySQL==0.9.3] \n", - "Collecting PyMySQL==0.9.3\n", - " Downloading PyMySQL-0.9.3-py2.py3-none-any.whl (47 kB)\n", - "Installing collected packages: PyMySQL\n", - "Successfully installed PyMySQL-0.9.3\n", - "\u001B[36mINFO\u001B[0m[0064] Taking snapshot of full filesystem... \n", - "\u001B[36mINFO\u001B[0m[0064] Resolving paths \n" - ] - }, - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fn.deploy()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[mlrun] 2020-06-30 01:58:41,604 function spec saved to path: function.yaml\n" - ] - }, - { - "data": { - "text/plain": "" - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fn.export('function.yaml')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Test" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Reading from a public MySQL DB" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "mysql_url = 'mysql+pymysql://rfamro@mysql-rfam-public.ebi.ac.uk:4497/Rfam'\n", - "mysql_query = 'select rfam_acc,rfam_id,auto_wiki,description,author,seed_source FROM family'" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "from mlrun import NewTask, run_local\n", - "\n", - "sql_task = NewTask(name='sql',\n", - " handler=sql_to_file,\n", - " params={'sql_query': mysql_query,\n", - " 'database_url': mysql_url})\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[mlrun] 2020-06-29 12:43:59,253 starting run sql uid=b0914edaa58e45ee97c132200c6b60be -> http://mlrun-api:8080\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "

\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "to track results use .show() or .logs() or in CLI: \n", - "!mlrun get run b0914edaa58e45ee97c132200c6b60be --project default , !mlrun logs b0914edaa58e45ee97c132200c6b60be --project default\n", - "[mlrun] 2020-06-29 12:44:02,344 run executed, status=completed\n" - ] - } - ], - "source": [ - "sql_func = run_local(sql_task)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Run it on a cluster" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[mlrun] 2020-06-29 12:44:02,350 starting run sql uid=46ff7ef67e314be49353982cdd8d073a -> http://mlrun-api:8080\n", - "[mlrun] 2020-06-29 12:44:02,622 Job is running in the background, pod: sql-mplpz\n", - "[mlrun] 2020-06-29 12:44:09,070 run executed, status=completed\n", - "final state: succeeded\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "
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projectuiditerstartstatenamelabelsinputsparametersresultsartifacts
default0Jun 29 12:44:06completedsql
v3io_user=admin
kind=job
owner=admin
host=sql-mplpz
sql_query=select rfam_acc,rfam_id,auto_wiki,description,author,seed_source FROM family
database_url=mysql+pymysql://rfamro@mysql-rfam-public.ebi.ac.uk:4497/Rfam
query result
\n", - "
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\n", - " Title\n", - " ×\n", - "
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\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "to track results use .show() or .logs() or in CLI: \n", - "!mlrun get run 46ff7ef67e314be49353982cdd8d073a --project default , !mlrun logs 46ff7ef67e314be49353982cdd8d073a --project default\n", - "[mlrun] 2020-06-29 12:44:11,893 run executed, status=completed\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fn.run(sql_task)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### SQL query from Iguazio Key Value via Presto" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You need to create a table and set the sql_table path accordingly.
\n", - "you can find an example of creating such table in https://github.com/v3io/tutorials/blob/master/data-ingestion-and-preparation/basic-data-ingestion-and-preparation.ipynb" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# nuclio: ignore\n", - "import os\n", - "sql_table = os.path.join('v3io.users.\"'+str(os.getenv('V3IO_USERNAME'))+'/examples/stocks_tab\"')\n", - "sql_query_string = 'select * from '+sql_table+\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Done.\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
securitydescsecuritytypetimeisinminpricedateendpricenumberoftradesmnemoniccurrencysecurityidmaxpricetradedvolumestartprice
UBS I.ETF-DL G.SEL.DIV.ADETF08:27IE00BMP3HG278.4182018-03-26 00:00:00.0008.4181UBUMEUR25054508.4184038.418
GILEAD SCIENCES DL-,001Common stock08:00US375558103659.72018-03-26 00:00:00.00059.843GISEUR250649559.8474559.7
3M CO. DL-,01Common stock08:00US88579Y1010176.512018-03-26 00:00:00.000176.511MMMEUR2506577176.5139176.51
DIEBOLD NIXDORF INH.O.N.Common stock08:06DE000A0CAYB266.32018-03-26 00:00:00.00066.31WINEUR250428666.36066.3
XTR.II EUR.INF.LINK.BD 1CETF08:13LU0290358224218.972018-03-26 00:00:00.000218.971DBXKEUR2505840218.97110218.97
UBS-ETF-MSCI EMU S.C.EOADETF08:33LU0671493277100.22018-03-26 00:00:00.000100.21UEFDEUR2506045100.2180100.2
ASMALLWORLD AG SF 1Common stock08:23CH040488012912.72018-03-26 00:00:00.00012.711Q7EUR308912212.740012.7
IS.DJ GLOB.TITAN.50 U.ETFETF08:42DE000628938231.252018-03-26 00:00:00.00031.251EXI2EUR250502931.255031.25
ISHS IV-AGEING POPUL.ETFETF08:17IE00BYZK46694.9262018-03-26 00:00:00.0004.92612B77EUR25055524.926254.926
PORSCHE AUTOM.HLDG VZOCommon stock08:00DE000PAH003864.682018-03-26 00:00:00.00064.768PAH3EUR250481664.7669864.7
" - ], - "text/plain": [ - "[('UBS I.ETF-DL G.SEL.DIV.AD', 'ETF', '08:27', 'IE00BMP3HG27', 8.418, '2018-03-26 00:00:00.000', 8.418, 1, 'UBUM', 'EUR', 2505450, 8.418, 403, 8.418),\n", - " ('GILEAD SCIENCES DL-,001', 'Common stock', '08:00', 'US3755581036', 59.7, '2018-03-26 00:00:00.000', 59.84, 3, 'GIS', 'EUR', 2506495, 59.84, 745, 59.7),\n", - " ('3M CO. DL-,01', 'Common stock', '08:00', 'US88579Y1010', 176.51, '2018-03-26 00:00:00.000', 176.51, 1, 'MMM', 'EUR', 2506577, 176.51, 39, 176.51),\n", - " ('DIEBOLD NIXDORF INH.O.N.', 'Common stock', '08:06', 'DE000A0CAYB2', 66.3, '2018-03-26 00:00:00.000', 66.3, 1, 'WIN', 'EUR', 2504286, 66.3, 60, 66.3),\n", - " ('XTR.II EUR.INF.LINK.BD 1C', 'ETF', '08:13', 'LU0290358224', 218.97, '2018-03-26 00:00:00.000', 218.97, 1, 'DBXK', 'EUR', 2505840, 218.97, 110, 218.97),\n", - " ('UBS-ETF-MSCI EMU S.C.EOAD', 'ETF', '08:33', 'LU0671493277', 100.2, '2018-03-26 00:00:00.000', 100.2, 1, 'UEFD', 'EUR', 2506045, 100.2, 180, 100.2),\n", - " ('ASMALLWORLD AG SF 1', 'Common stock', '08:23', 'CH0404880129', 12.7, '2018-03-26 00:00:00.000', 12.7, 1, '1Q7', 'EUR', 3089122, 12.7, 400, 12.7),\n", - " ('IS.DJ GLOB.TITAN.50 U.ETF', 'ETF', '08:42', 'DE0006289382', 31.25, '2018-03-26 00:00:00.000', 31.25, 1, 'EXI2', 'EUR', 2505029, 31.25, 50, 31.25),\n", - " ('ISHS IV-AGEING POPUL.ETF', 'ETF', '08:17', 'IE00BYZK4669', 4.926, '2018-03-26 00:00:00.000', 4.926, 1, '2B77', 'EUR', 2505552, 4.926, 25, 4.926),\n", - " ('PORSCHE AUTOM.HLDG VZO', 'Common stock', '08:00', 'DE000PAH0038', 64.68, '2018-03-26 00:00:00.000', 64.76, 8, 'PAH3', 'EUR', 2504816, 64.76, 698, 64.7)]" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%sql select * from $sql_table limit 10" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "sql_task = NewTask(name='sql', \n", - " handler=sql_to_file,\n", - " params={'sql_query': sql_query_string,\n", - " 'database_url': os.getenv('DATABASE_URL')}\n", - " )\n" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[mlrun] 2020-06-29 12:44:14,406 starting run sql uid=d32a57bb990d4142bb1f63862e8906bf -> http://mlrun-api:8080\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "
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projectuiditerstartstatenamelabelsinputsparametersresultsartifacts
default0Jun 29 12:44:14completedsql
v3io_user=admin
kind=handler
owner=admin
host=jupyter-b9c7995f9-4fblj
sql_query=select * from v3io.users.\"admin/examples/stocks_tab\"
database_url=presto://admin:8278ee8e-0f31-4aea-a105-2eab202bec93@presto-api-presto.default-tenant.app.cs-mlrun-test.iguazio-c0.com:443/v3io?protocol=https&requests_kwargs=%7B%22verify%22%3A+%22%2Fvar%2Frun%2Figuazio%2Fsecrets%2Ftls.crt%22%2C+%22cert%22%3A+%5B%22%2Fvar%2Frun%2Figuazio%2Fsecrets%2Ftls.crt%22%2C+%22%2Fvar%2Frun%2Figuazio%2Fsecrets%2Ftls.key%22%5D%7D
query result
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projectuiditerstartstatenamelabelsinputsparametersresultsartifacts
default0Jun 29 12:44:21completedsql
v3io_user=admin
kind=job
owner=admin
host=sql-g7p4f
sql_query=select * from v3io.users.\"admin/examples/stocks_tab\"
database_url=presto://admin:8278ee8e-0f31-4aea-a105-2eab202bec93@presto-api-presto.default-tenant.app.cs-mlrun-test.iguazio-c0.com:443/v3io?protocol=https&requests_kwargs=%7B%22verify%22%3A+%22%2Fvar%2Frun%2Figuazio%2Fsecrets%2Ftls.crt%22%2C+%22cert%22%3A+%5B%22%2Fvar%2Frun%2Figuazio%2Fsecrets%2Ftls.crt%22%2C+%22%2Fvar%2Frun%2Figuazio%2Fsecrets%2Ftls.key%22%5D%7D
query result
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\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "to track results use .show() or .logs() or in CLI: \n", - "!mlrun get run db9507007f6d452e9ca020e4f483e33b --project default , !mlrun logs db9507007f6d452e9ca020e4f483e33b --project default\n", - "[mlrun] 2020-06-29 12:44:27,645 run executed, status=completed\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fn.run(sql_task)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} \ No newline at end of file diff --git a/sql_to_file/sql_to_file.py b/sql_to_file/sql_to_file.py deleted file mode 100644 index 6d5e152ba..000000000 --- a/sql_to_file/sql_to_file.py +++ /dev/null @@ -1,45 +0,0 @@ -# Copyright 2019 Iguazio -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# Generated by nuclio.export.NuclioExporter - -import pandas as pd -import pyhive -from sqlalchemy.engine import create_engine -from mlrun.execution import MLClientCtx - - -def sql_to_file( - context: MLClientCtx, - sql_query: str, - database_url: str, - file_ext: str = "parquet", -) -> None: - """SQL Ingest - Ingest data using SQL query - - :param context: the function context - :param sql_query: the sql query used to retrieve the data - :param database_url: database connection URL - :param file_ext: ("parquet") format for result file - """ - - engine = create_engine(database_url) - df = pd.read_sql(sql_query, engine) - - context.log_dataset( - "query result", - df=df, - format=file_ext, - artifact_path=context.artifact_subpath("data"), - ) diff --git a/sql_to_file/test_sql_to_file.py b/sql_to_file/test_sql_to_file.py deleted file mode 100644 index d636b86ca..000000000 --- a/sql_to_file/test_sql_to_file.py +++ /dev/null @@ -1,31 +0,0 @@ -# Copyright 2019 Iguazio -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -from mlrun import code_to_function - -mysql_url = 'mysql+pymysql://rfamro@mysql-rfam-public.ebi.ac.uk:4497/Rfam' -mysql_query = 'select rfam_acc,rfam_id,auto_wiki,description,author,seed_source FROM family' - - -def test_run_sql_to_file(): - fn = code_to_function(name='test_sql_to_file', - filename="sql_to_file.py", - handler="sql_to_file", - kind="job", - ) - run = fn.run(params={'sql_query': mysql_query, - 'database_url': mysql_url}, - local=True) - - assert(run.artifact("query result")) \ No newline at end of file diff --git a/stream_to_parquet/function.yaml b/stream_to_parquet/function.yaml deleted file mode 100644 index f8786cc92..000000000 --- a/stream_to_parquet/function.yaml +++ /dev/null @@ -1,45 +0,0 @@ -kind: remote -metadata: - name: stream-to-parquet - tag: '' - hash: 78316bfbe731714715c19f0bc6deabf8652f15c4 - project: '' - labels: - author: orz - categories: - - machine-learning - - data-preparation -spec: - command: '' - args: [] - image: mlrun/ml-models - description: Saves a stream to Parquet and can lunch drift detection task on it - min_replicas: 1 - max_replicas: 1 - env: [] - base_spec: - apiVersion: nuclio.io/v1 - kind: Function - metadata: - name: stream-to-parquet - labels: {} - annotations: - nuclio.io/generated_by: function generated from /User/test/functions/stream_to_parquet/stream_to_parquet.py - spec: - runtime: python:3.6 - handler: stream_to_parquet:handler - env: [] - volumes: [] - build: - commands: [] - noBaseImagesPull: true - functionSourceCode: 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 - source: '' - build: - commands: [] - code_origin: https://github.com/daniels290813/functions.git#3605c9b8dcadab89a5a45f7d16dcd2fcfeca8697:/User/test/functions/stream_to_parquet/stream_to_parquet.py - origin_filename: /User/test/functions/stream_to_parquet/stream_to_parquet.py - default_handler: handler - disable_auto_mount: false - affinity: null -verbose: false diff --git a/stream_to_parquet/item.yaml b/stream_to_parquet/item.yaml deleted file mode 100644 index cbd59376e..000000000 --- a/stream_to_parquet/item.yaml +++ /dev/null @@ -1,28 +0,0 @@ -apiVersion: v1 -categories: -- machine-learning -- data-preparation -description: Saves a stream to Parquet and can lunch drift detection task on it -doc: '' -example: stream_to_parquet.ipynb -generationDate: 2022-08-28:17-25 -hidden: false -icon: '' -labels: - author: orz -maintainers: [] -marketplaceType: '' -mlrunVersion: 1.1.0 -name: stream-to-parquet -platformVersion: 3.5.0 -spec: - customFields: - max_replicas: 1 - min_replicas: 1 - filename: stream_to_parquet.py - handler: handler - image: mlrun/ml-models - kind: nuclio - requirements: [] -url: '' -version: 1.1.0 diff --git a/stream_to_parquet/stream_to_parquet.ipynb b/stream_to_parquet/stream_to_parquet.ipynb deleted file mode 100644 index e47c6be92..000000000 --- a/stream_to_parquet/stream_to_parquet.ipynb +++ /dev/null @@ -1,698 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Stream to Parquet" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Part of the [network operations](https://github.com/mlrun/demos/tree/0.7.x/network-operations) demo pipeline, this function listens to a labeld stream and writes it as parquet files.
\n", - "This function also deploys the function [virtual_drift](https://github.com/mlrun/functions/tree/master/virtual_drift) from the hub, which computes drift magnitude metrics between base dataset t and dataset u,
\n", - "in our case (as well as in the demo) - base dataset (the one that the model trained on) and the dataset the model predicted.
\n", - "virtual_drift writes the output to TSDB." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Steps**\n", - "\n", - "1. [Data exploration](#Data-exploration)\n", - "2. [Creating the labeled stream](#Creating-the-labeled-stream)\n", - "3. [Importing the function](#Importing-the-function)\n", - "4. [Running the functioh remotely](#Running-the-function-remotely)\n", - "5. [Testing the function](#Testing-the-function)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Data exploration**" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In order to know about the performance of a drift detector by measuring the different detection metrics, we need to know beforehand where a real drift occurs.
\n", - "This is only possible with synthetic datasets.
The scikit-multiflow framework allows generating several kinds of synthetic data to simulate the occurrence of drifts.
\n", - "[Harvard dataverse](https://dataverse.harvard.edu) provides futher explanations on the [used dataset](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/5OWRGB) along with different kinds of drifted datasets.
\n", - "mixed_0101_abrupto has 4 concepts and 3 drifts at time steps 10000, 20000, and 30000.
\n", - "Our dataset will be train-test-splitted, the train part (first 5000 examples) is used to train the model (that is generated easly using [sklearn_classifer](https://github.com/mlrun/functions/blob/master/sklearn_classifier/sklearn_classifier.ipynb)).
\n", - "The test part (which is already predicted by the model) will be pushed to the input stream in order to detect drifts." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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X1X2X3X4class
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11.01.00.5887880.5749840.0
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31.01.00.3060760.1821080.0
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" - ], - "text/plain": [ - " X1 X2 X3 X4 class\n", - "0 0.0 1.0 0.460101 0.592744 1.0\n", - "1 1.0 1.0 0.588788 0.574984 0.0\n", - "2 0.0 0.0 0.401641 0.679325 1.0\n", - "3 1.0 1.0 0.306076 0.182108 0.0\n", - "4 0.0 0.0 0.962847 0.579245 1.0" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import pandas as pd\n", - "data_path = 'https://s3.wasabisys.com/iguazio/data/function-marketplace-data/concept_drift/mixed_0101_abrupto.csv'\n", - "base_dataset = 'https://s3.wasabisys.com/iguazio/data/function-marketplace-data/concept_drift/predicted_abrupto_train.csv'\n", - "# The predicted test data is pushed to the stream\n", - "predicted_test_data_path = 'https://s3.wasabisys.com/iguazio/data/function-marketplace-data/concept_drift/predicted_abrupto_test.csv'\n", - "# You can find the model used here\n", - "models_path = 'https://s3.wasabisys.com/iguazio/models/function-marketplace-models/concept_drift/concept_drift_random_forest.pkl'\n", - "original_data = pd.read_csv(data_path)\n", - "original_data.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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X1X2X3X4classpredicted_col
349950.00.00.0101060.6472690.01.0
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349970.00.00.8485460.5523370.01.0
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349991.00.00.2653060.8437160.01.0
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" - ], - "text/plain": [ - " X1 X2 X3 X4 class predicted_col\n", - "34995 0.0 0.0 0.010106 0.647269 0.0 1.0\n", - "34996 1.0 1.0 0.293651 0.737291 1.0 0.0\n", - "34997 0.0 0.0 0.848546 0.552337 0.0 1.0\n", - "34998 1.0 1.0 0.614754 0.859896 1.0 0.0\n", - "34999 1.0 0.0 0.265306 0.843716 0.0 1.0" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "predicted_test = pd.read_csv(predicted_test_data_path)\n", - "predicted_test.tail()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Creating the labeled stream**" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import os \n", - "\n", - "container = os.path.join('/',os.environ['V3IO_HOME'].split('/')[0])\n", - "user = os.environ[\"V3IO_USERNAME\"]\n", - "rel_path = os.getcwd()[6:] + '/artifacts'\n", - "\n", - "base_input_stream = os.path.join(user,rel_path) + \"/inputs_stream\"\n", - "base_output_stream = os.path.join(user,rel_path) + \"/output_stream\"\n", - "input_stream = os.path.join(container,base_input_stream)\n", - "tsdb_path = os.path.join(user,rel_path) + \"/output_tsdb\"\n", - "\n", - "stream_consumer_group = 's2p'" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "import v3io.dataplane\n", - "\n", - "client = v3io.dataplane.Client()\n", - "response = client.stream.create(container = container,\n", - " stream_path=base_input_stream,\n", - " shard_count=1,\n", - " raise_for_status = v3io.dataplane.RaiseForStatus.never)\n", - "response.raise_for_status([409, 204])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Importing the function**" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2021-10-26 14:37:45,224 [info] created and saved project function-marketplace\n" - ] - } - ], - "source": [ - "import mlrun\n", - "\n", - "# Importing the function\n", - "mlrun.set_environment(project='function-marketplace')\n", - "\n", - "fn = mlrun.import_function(\"hub://stream_to_parquet:development\")\n", - "fn.apply(mlrun.auto_mount())\n", - "\n", - "fn.add_v3io_stream_trigger(stream_path=input_stream, name='stream', group=stream_consumer_group)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Running the function remotely**" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2021-10-26 14:37:45,513 [info] Starting remote function deploy\n", - "2021-10-26 14:37:45 (info) Deploying function\n", - "2021-10-26 14:37:45 (info) Building\n", - "2021-10-26 14:37:45 (info) Staging files and preparing base images\n", - "2021-10-26 14:37:45 (info) Building processor image\n", - "2021-10-26 14:37:47 (info) Build complete\n", - "2021-10-26 14:37:55 (info) Function deploy complete\n", - "> 2021-10-26 14:37:55,689 [info] successfully deployed function: {'internal_invocation_urls': ['nuclio-function-marketplace-stream-to-parquet.default-tenant.svc.cluster.local:8080'], 'external_invocation_urls': ['default-tenant.app.dev39.lab.iguazeng.com:31445']}\n" - ] - }, - { - "data": { - "text/plain": [ - "'http://default-tenant.app.dev39.lab.iguazeng.com:31445'" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import json\n", - "fn.set_envs({'window': 200,\n", - " 'save_to': os.path.join(os.path.join('/User',rel_path), 'inference_pq'),\n", - " 'prediction_col': 'predicted_col',\n", - " 'label_col': 'class',\n", - " 'base_dataset': base_dataset,\n", - " 'results_tsdb_container': container[1:],\n", - " 'results_tsdb_table': tsdb_path,\n", - " 'mount_path': os.path.join(container,user),\n", - " 'mount_remote': container,\n", - " 'artifact_path': os.path.join('/User',rel_path)})\n", - "\n", - "fn.deploy()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Testing the function**" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'data': '{\"request\": {\"instances\": [{\"X1\": 0.0, \"X2\": 0.0, \"X3\": 0.0634475073, \"X4\": 0.4136568818, \"class\": 1.0, \"predicted_col\": 1.0}]}, \"resp\": [1], \"when\": \"2021-10-26 14:37:55.864974\", \"model\": \"sklearn.ensemble.RandomForestClassifier\"}'}" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import json\n", - "import datetime\n", - "\n", - "# Reshaping the data to V3IOStream format.\n", - "def restructure_stream_event(context, event):\n", - " instances = [dict()]\n", - " for key in predicted_test.keys():\n", - " if key not in ['when', 'model', 'worker', 'hostname', 'predicted_col']:\n", - " instances[0].update({key: event.pop(key)})\n", - " instances[0].update({key: event.get(key)}) \n", - " event['request'] = {'instances': instances}\n", - " event['resp'] = [int(event.pop('predicted_col'))]\n", - " event['when'] = datetime.datetime.strftime(datetime.datetime.now(), format=\"%Y-%m-%d %H:%M:%S.%f\")\n", - " event['model'] = 'sklearn.ensemble.RandomForestClassifier'\n", - " return event\n", - " \n", - " \n", - "records = json.loads(predicted_test.to_json(orient='records'))\n", - "records = [{'data': json.dumps(restructure_stream_event(context, record))} for record in records]\n", - "\n", - "# showing first record\n", - "records[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Pushing some data to the input stream\n", - "step = 500\n", - "for i in range(0,20000,step):\n", - " response = client.stream.put_records(container=container,\n", - " stream_path=base_input_stream, \n", - " records=records[i:i+step])" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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class_shift_helingerclass_shift_kldclass_shift_tvdprior_helingerprior_kldprior_tvdstream
time
2021-10-26 14:38:08.027000+00:000.0017590.0000250.0024881.010.01.0some_stream
2021-10-26 14:38:08.699000+00:000.0017590.0000250.0024881.010.01.0some_stream
2021-10-26 14:38:09.599000+00:000.0017590.0000250.0024881.010.01.0some_stream
2021-10-26 14:38:10.759000+00:000.0017590.0000250.0024881.010.01.0some_stream
2021-10-26 14:38:11.561000+00:000.0017590.0000250.0024881.010.01.0some_stream
........................
2021-10-26 14:39:42.037000+00:000.0017590.0000250.0024881.010.01.0some_stream
2021-10-26 14:39:42.191000+00:000.0017590.0000250.0024881.010.01.0some_stream
2021-10-26 14:39:42.586000+00:000.0017590.0000250.0024881.010.01.0some_stream
2021-10-26 14:39:42.816000+00:000.0017590.0000250.0024881.010.01.0some_stream
2021-10-26 14:39:49.180000+00:000.0017590.0000250.0024881.010.01.0some_stream
\n", - "

99 rows × 7 columns

\n", - "
" - ], - "text/plain": [ - " class_shift_helinger class_shift_kld \\\n", - "time \n", - "2021-10-26 14:38:08.027000+00:00 0.001759 0.000025 \n", - "2021-10-26 14:38:08.699000+00:00 0.001759 0.000025 \n", - "2021-10-26 14:38:09.599000+00:00 0.001759 0.000025 \n", - "2021-10-26 14:38:10.759000+00:00 0.001759 0.000025 \n", - "2021-10-26 14:38:11.561000+00:00 0.001759 0.000025 \n", - "... ... ... \n", - "2021-10-26 14:39:42.037000+00:00 0.001759 0.000025 \n", - "2021-10-26 14:39:42.191000+00:00 0.001759 0.000025 \n", - "2021-10-26 14:39:42.586000+00:00 0.001759 0.000025 \n", - "2021-10-26 14:39:42.816000+00:00 0.001759 0.000025 \n", - "2021-10-26 14:39:49.180000+00:00 0.001759 0.000025 \n", - "\n", - " class_shift_tvd prior_helinger prior_kld \\\n", - "time \n", - "2021-10-26 14:38:08.027000+00:00 0.002488 1.0 10.0 \n", - "2021-10-26 14:38:08.699000+00:00 0.002488 1.0 10.0 \n", - "2021-10-26 14:38:09.599000+00:00 0.002488 1.0 10.0 \n", - "2021-10-26 14:38:10.759000+00:00 0.002488 1.0 10.0 \n", - "2021-10-26 14:38:11.561000+00:00 0.002488 1.0 10.0 \n", - "... ... ... ... \n", - "2021-10-26 14:39:42.037000+00:00 0.002488 1.0 10.0 \n", - "2021-10-26 14:39:42.191000+00:00 0.002488 1.0 10.0 \n", - "2021-10-26 14:39:42.586000+00:00 0.002488 1.0 10.0 \n", - "2021-10-26 14:39:42.816000+00:00 0.002488 1.0 10.0 \n", - "2021-10-26 14:39:49.180000+00:00 0.002488 1.0 10.0 \n", - "\n", - " prior_tvd stream \n", - "time \n", - "2021-10-26 14:38:08.027000+00:00 1.0 some_stream \n", - "2021-10-26 14:38:08.699000+00:00 1.0 some_stream \n", - "2021-10-26 14:38:09.599000+00:00 1.0 some_stream \n", - "2021-10-26 14:38:10.759000+00:00 1.0 some_stream \n", - "2021-10-26 14:38:11.561000+00:00 1.0 some_stream \n", - "... ... ... \n", - "2021-10-26 14:39:42.037000+00:00 1.0 some_stream \n", - "2021-10-26 14:39:42.191000+00:00 1.0 some_stream \n", - "2021-10-26 14:39:42.586000+00:00 1.0 some_stream \n", - "2021-10-26 14:39:42.816000+00:00 1.0 some_stream \n", - "2021-10-26 14:39:49.180000+00:00 1.0 some_stream \n", - "\n", - "[99 rows x 7 columns]" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Reading from TSDB\n", - "import v3io_frames as v3f\n", - "\n", - "v3f_client = v3f.Client(os.environ[\"V3IO_FRAMESD\"],container=container[1:])\n", - "v3f_client.read(backend='tsdb',table=tsdb_path)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[Back to the top](#Stream-to-Parquet)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/stream_to_parquet/stream_to_parquet.py b/stream_to_parquet/stream_to_parquet.py deleted file mode 100644 index 175c12822..000000000 --- a/stream_to_parquet/stream_to_parquet.py +++ /dev/null @@ -1,96 +0,0 @@ -# Copyright 2019 Iguazio -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# Generated by nuclio.export.NuclioExporter - -import os -import pandas as pd -import numpy as np -import json -import datetime -import mlrun - - -def record_to_features(record): - features = record["request"]["instances"][0] - timestamp = record["when"] - prediction = record["resp"] - - record = {"timestamp": timestamp, **features, "predictions": prediction} - - return record - - -def init_context(context): - setattr(context, "batch", []) - setattr(context, "window", int(os.getenv("window", 10))) - setattr(context, "save_to", os.getenv("save_to", "/bigdata/inference_pq/")) - os.makedirs(context.save_to, exist_ok=True) - - mlrun.mlconf.dbpath = mlrun.mlconf.dbpath or "http://mlrun-api:8080" - artifact_path = os.getenv("artifact_path", None) - if artifact_path: - mlrun.mlconf.artifact_path = artifact_path - if "hub_url" in os.environ: - mlrun.mlconf.hub_url = os.environ["hub_url"] - virtual_drift_fn = mlrun.import_function("hub://virtual_drift") - virtual_drift_fn.apply(mlrun.auto_mount()) - setattr(context, "virtual_drift_fn", virtual_drift_fn) - - predictions_col = os.getenv("predictions", None) - label_col = os.getenv("label_col", None) - setattr(context, "base_dataset", os.getenv("base_dataset", "")) - setattr(context, "indexes", json.loads(os.environ.get("indexes", "[]"))) - setattr(context, "predictions_col", predictions_col) - setattr(context, "label_col", label_col) - setattr( - context, "results_tsdb_container", os.getenv("results_tsdb_container", None) - ) - setattr(context, "results_tsdb_table", os.getenv("results_tsdb_table", None)) - - -def handler(context, event): - - context.logger.info(f"Adding {event.body}") - context.batch.append(record_to_features(json.loads(event.body))) - - if len(context.batch) > context.window: - context.logger.info(context.batch[:1]) - context.logger.info(context.indexes) - df = pd.DataFrame(context.batch) - context.logger.info(f"df example: {df.head(1)}") - if context.indexes: - df = df.set_index(context.indexes) - df_path = os.path.join( - context.save_to, - f"{datetime.datetime.now().strftime('%Y-%m-%dT%H:%M:%S')}.pq", - ) - df.to_parquet(df_path,index=False) - - task = mlrun.NewTask( - name="drift_magnitude", - handler="drift_magnitude", - params={ - "label_col": context.label_col, - "prediction_col": context.predictions_col, - "results_tsdb_container": context.results_tsdb_container, - "results_tsdb_table": context.results_tsdb_table, - }, - inputs={"t": context.base_dataset, "u": df_path}, - artifact_path=mlrun.mlconf.artifact_path, - ) - - context.virtual_drift_fn.run(task, watch=False) - - context.batch = [] diff --git a/tf1_serving/function.yaml b/tf1_serving/function.yaml deleted file mode 100644 index e6a57c4b2..000000000 --- a/tf1_serving/function.yaml +++ /dev/null @@ -1,48 +0,0 @@ -kind: remote -metadata: - name: tf1-serving - tag: '' - hash: 20cdeb2119a67fc51e55474ac84d386c7b658db3 - project: '' - labels: - author: yaronh - categories: - - model-serving - - machine-learning -spec: - command: '' - args: [] - image: mlrun/mlrun - description: tf1 image classification server - min_replicas: 1 - max_replicas: 4 - env: - - name: MODEL_CLASS - value: TFModel - - name: ENABLE_EXPLAINER - value: false - base_spec: - apiVersion: nuclio.io/v1 - kind: Function - metadata: - name: tf1-serving - labels: {} - annotations: - nuclio.io/generated_by: function generated from /home/kali/functions/tf1_serving/tf1_serving.py - spec: - runtime: python:3.6 - handler: tf1_serving:handler - env: [] - volumes: [] - build: - commands: [] - noBaseImagesPull: true - functionSourceCode: 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 - source: '' - function_kind: serving - build: - commands: [] - code_origin: https://github.com/daniels290813/functions.git#55a79c32be5d233cc11efcf40cd3edbe309bfdef:/home/kali/functions/tf1_serving/tf1_serving.py - default_handler: handler - affinity: null -verbose: false diff --git a/tf1_serving/item.yaml b/tf1_serving/item.yaml deleted file mode 100644 index 6a5648ab0..000000000 --- a/tf1_serving/item.yaml +++ /dev/null @@ -1,28 +0,0 @@ -apiVersion: v1 -categories: -- model-serving -- machine-learning -description: tf1 image classification server -doc: '' -example: tf1_serving.ipynb -generationDate: 2022-08-28:17-25 -hidden: false -icon: '' -labels: - author: yaronh -maintainers: [] -marketplaceType: '' -mlrunVersion: 1.1.0 -name: tf1-serving -platformVersion: 3.5.0 -spec: - env: - ENABLE_EXPLAINER: false - MODEL_CLASS: TFModel - filename: tf1_serving.py - handler: handler - image: mlrun/mlrun - kind: nuclio:serving - requirements: [] -url: '' -version: 1.1.0 diff --git a/tf1_serving/requirements.txt b/tf1_serving/requirements.txt deleted file mode 100644 index 8d3d19557..000000000 --- a/tf1_serving/requirements.txt +++ /dev/null @@ -1,2 +0,0 @@ -pillow -tensorflow \ No newline at end of file diff --git a/tf1_serving/tf1_serving.ipynb b/tf1_serving/tf1_serving.ipynb deleted file mode 100644 index 1d42ee606..000000000 --- a/tf1_serving/tf1_serving.ipynb +++ /dev/null @@ -1,567 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Image Classification Model - Serving Function\n", - "\n", - "This notebook demonstrates how to deploy a Tensorflow model using MLRun & Nuclio.\n", - "\n", - "**In this notebook you will:**\n", - "* Write a Tensorflow-Model class to load and predict on the incoming data\n", - "* Deploy the model as a serverless function\n", - "* Invoke the serving endpoint with data as:\n", - " * URLs to images hosted on S3\n", - " * Direct image send\n", - " \n", - "**Steps:** \n", - "* [Define Nuclio function](#Define-Nuclio-function) \n", - " * [Install dependencies and set config](#Install-dependencies-and-set-config) \n", - " * [Model serving class](#Model-Serving-Class) \n", - "* [Deploy the serving function to the cluster](#Deploy-the-serving-function-to-the-cluster) \n", - "* [Define test parameters](#Define-test-parameters)\n", - "* [Test the deployed function on the cluster](#Test-the-deployed-function-on-the-cluster)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Define Nuclio Function" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To use the magic commands for deploying this jupyter notebook as a nuclio function we must first import nuclio \n", - "Since we do not want to import nuclio in the actual function, the comment annotation `nuclio: ignore` is used. This marks the cell for nuclio, telling it to ignore the cell's values when building the function." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# nuclio: ignore\n", - "import nuclio" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Install dependencies and set config\n", - "> Note: Since tensorflow 1.14 is being pulled from the baseimage it is not directly installed as a build command.\n", - "If it is not installed on your system please uninstall and install using the line: `pip install tensorflow==1.14 keras`" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "%nuclio: setting kind to 'nuclio:serving'\n", - "%nuclio: setting 'MODEL_CLASS' environment variable\n", - "%nuclio: setting spec.build.baseImage to 'mlrun/mlrun'\n" - ] - } - ], - "source": [ - "%nuclio config kind=\"nuclio:serving\"\n", - "%nuclio env MODEL_CLASS=TFModel\n", - "\n", - "# tensorflow version 1 requires a different version of python than \n", - "# the default (3.7), so we override the default tag here:\n", - "\n", - "%nuclio config spec.build.baseImage = \"mlrun/mlrun\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Since we are using packages which are not surely installed on our baseimage, or want to verify that a specific version of the package will be installed we use the `%nuclio cmd` annotation. \n", - ">`%nuclio cmd` works both locally and during deployment by default, but can be set with `-c` flag to only run the commands while deploying or `-l` to set the variable for the local environment only." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "%%nuclio cmd -c\n", - "pip install tensorflow==1.14 keras==2.3.1 'h5py<3.0.0'\n", - "pip install requests pillow" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Function Code" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "import warnings\n", - "warnings.simplefilter(action=\"ignore\", category=FutureWarning)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "import numpy as np\n", - "import requests\n", - "from tensorflow import keras\n", - "from keras.models import load_model\n", - "from keras.preprocessing import image\n", - "from keras.preprocessing.image import load_img\n", - "from os import environ, path\n", - "from PIL import Image\n", - "from io import BytesIO\n", - "from urllib.request import urlopen\n", - "import mlrun" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Model Serving Class" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We define the `TFModel` class which we will use to define data handling and prediction of our model. \n", - "\n", - "The class should consist of:\n", - "* `__init__(name, model_dir)` - Setup the internal parameters\n", - "* `load(self)` - How to load the model and broadcast it's ready for prediction\n", - "* `preprocess(self, body)` - How to handle the incoming event, forming the request to an `{'instances': []}` dictionary as requested by the protocol\n", - "* `predict(self, data)` - Receives and `{'instances': []}` and returns the model's prediction as a list\n", - "* `postprocess(self, data)` - Does any additional processing needed on the predictions." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "class TFModel(mlrun.runtimes.MLModelServer):\n", - " def __init__(self, name: str, model_dir: str):\n", - " super().__init__(name, model_dir)\n", - "\n", - " self.IMAGE_WIDTH = int(environ.get('IMAGE_WIDTH', '128'))\n", - " self.IMAGE_HEIGHT = int(environ.get('IMAGE_HEIGHT', '128'))\n", - " self.classes = None\n", - " try:\n", - " with open(environ['classes_map'], 'r') as f:\n", - " self.classes = json.load(f)\n", - " except:\n", - " pass\n", - " \n", - " def load(self):\n", - " model_file, extra_data = self.get_model('.h5')\n", - " self.model = load_model(open(model_file, 'rb'))\n", - " \n", - " def preprocess(self, body):\n", - " try:\n", - " output = {'instances': []}\n", - " instances = body.get('instances', [])\n", - " for byte_image in instances:\n", - " img = Image.open(byte_image)\n", - " img = img.resize((self.IMAGE_WIDTH, self.IMAGE_HEIGHT))\n", - "\n", - " # Load image\n", - " x = image.img_to_array(img)\n", - " x = np.expand_dims(x, axis=0)\n", - " output['instances'].append(x)\n", - " \n", - " # Format instances list\n", - " output['instances'] = [np.vstack(output['instances'])]\n", - " return output\n", - " except:\n", - " raise Exception(f'received: {body}')\n", - " \n", - "\n", - " def predict(self, data):\n", - " images = data.get('instances', [])\n", - "\n", - " # Predict\n", - " predicted_probability = self.model.predict(images)\n", - "\n", - " # return prediction\n", - " return predicted_probability\n", - " \n", - " def postprocess(self, predicted_probability):\n", - " if self.classes:\n", - " predicted_classes = np.around(predicted_probability, 1).tolist()[0]\n", - " predicted_probabilities = predicted_probability.tolist()[0]\n", - " return {\n", - " 'prediction': [self.classes[str(int(cls))] for cls in predicted_classes], \n", - " f'{self.classes[\"1\"]}-probability': predicted_probabilities\n", - " }\n", - " else:\n", - " return predicted_probability.tolist()[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To let our nuclio builder know that our function code ends at this point we will use the comment annotation `nuclio: end-code`. \n", - "\n", - "Any new cell from now on will be treated as if a `nuclio: ignore` comment was set, and will not be added to the funcion." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# nuclio: end-code" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Test the function locally" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Make sure your local TF / Keras version is the same as pulled in the nuclio image for accurate testing\n", - "\n", - "Set the served models and their file paths using: `SERVING_MODEL_ = `\n", - "\n", - "> Note: this notebook assumes the model and categories are under /User/mlrun/examples/" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "from PIL import Image\n", - "from io import BytesIO\n", - "import matplotlib.pyplot as plt\n", - "import os, requests" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Define test parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Test image:\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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ayjgKAlgsFsrQCDrRKA2VW6urZ1oVa6IwJs8KhQriJFKpySiKlMcVr9dut6txMzFNmzQNIa9SfOhlGlk3qxDQIAh85osFO8O+Mjqe50GeEoax8tJJNafiYCQbIuSizI2gInlWuEYJsp5kDYkxl9+FYUi321VjblkWYXhNYkvo4vs+V1dXNBoNRRDKvEpot01WOo6jslbT6ZT1ek2321VOURyErKflcqnmZ7PZKOcnSGcb7X3S9ZkwCrJQsyyj2+2qB9zeAGIthSkXyC4PHkURq9UK13UVxBKGXogxQRvCC5Sf4/H8+XNl/QVWOo7Dyy+/zHq9Jk1TFQ6UcLjLaDRSsF8mTMKEba+xXC7V4hBEEccxk8lEcQBFUShiC8C2bWzbBiCOFwr+isEKggDDMBgOhxiGwXw+VwhCOIVtjydZAiHEJPYfjUbcuXNHGS9JtxqGUZFWfRqNBmmakiQJy+VSGeXBYMBisWC1WinPKPer1+u89dZbfO9731Meq9lssre/y3w+V88qaVJ5BgnHvGaLdrfH7MkTWh0PP4zwg4jVakOcQRAnLFZXzCbn9Pt9Hjx6SLfd4eBgH69eo+45xEFAlMTUbEfpJcTpyMaWMFXCTOEb0jSl1WqhaRrj8Vg5pjRNaTabKs04nU4VAhwOh8oLSxZGeIQ4jlUGql6vK+QlhLcYciEykyQhSRLW6zWu67K3t6dCHN/3GY/HuK5Lr9dT4SlQhXLXYXYQBDdSn+12W82TaRqfvB8/rY3+x7myCn7Jho+iiOVySa/XA1CEzHw+vxbyVJtEvJB4Rhl8QQtRFCkDIAy3xGpRFOF5ber1uvL6wgC7rqsIKMliOI6jNr9kN+I4ptFoqOyD7/s4jqOIUwlNBOqLtxYOIk1TFecLmy+stqZpN7xXu91G0zSVOViv19i2rVCA53lomqYWtni77fGSlJdlWXS7XWXExBMKLyJpX/k9XEPSMAzxPA/P80rPrlkqKxLHMcPh8Ma8TadTxa5LWCebaT6fK9Ze0JVpX/NBq9WK6XRKEAREUUKuBSUvk6HuOZ2WoZa3qGOYGlkeYRsGFDpFJYQTzkmgthgIMb5CaMs8y5wJwbqd1hPjnySJMgIyPjJXEmZ5nsdsNiNNU0ajkeIRZH7kvrI+hcOStK6kumXuJH0t611QgCBgTdNUilMyDYDKsAnP84eVN3wmjIIs6M1mowiaZrNJv99X8HJbvJPnuWJVN5sN3W4Xx3EU4bYzGNBuNvnoo49wK/2Bv9koS29ommJ059Mpuq4TxzGd1iFRENGo13Fdm4uzk3LgtYzID0uP0BsA4JsbBcvDTYBj2ZiaQb/b5OnTp4qAjKvFLRthMBgQhqVll80gC1ZQiry2KApcpxS3uK6LoZUGptOqq7g0jkMso8Bx6yrXLeGGCKLk3+Kd47Dc7INeuyRUNwvqNRNTLw1BGEW0Wi0MLLK4YLKclZvJqKHbJovpisvzsTJYhlMqJtM0pd2pUxCz8efUXIONP2dvv08cx6xmU4oiQ9fBrOU0vBpRnOPUNXaGuyzXIWmaE24MPvrwPpNZmb589PgBeVGAlnNxdUmrVaZY/axgfTXBsQwupwv8KKQ1bvDqK6/QajaxTZNWqwF5gdvzmE9n1cbVGHT6zCdjNoFPr9vBNA1WqwlpYiieQSlYK56g3W6zWq1KRGCZkGckRU6ua0yXC2W47bpLHobkuka33SYIAmazmUJ8lmXh+z6dTgcoDVYQBKzXayV0cl1XCeGWyyWr1Yr9/X3a7TZJkjAej5nP5wppSmpVQocoimhX+2e9XpehWlFwdXFRGqXPOqega5qKp5rNJk+ePFExsugUgiBQMHU7zhWyajqdqnzybDZjsVjw+c9/njiOefPNN/n2t7+tYljxAALv79y5ozbTwcGBivN83wdKL9tqSk65TIN6lVCm/FlIHEdAQc31VEZD4GMYhsxmM6Vcazab+FVuWkhC27Y5OztT7LSo5wRqLhYLBSG3oeh27Co/k3hXQq7tZ3Ech8VsqbI7/X6fo6Mjzs/PVWpT0zRGoxFhcM20i9GVfL3k8OM4xnIMhfTE+7uui2EYSrHY6XTI6xlBsMGuWQx3ShQYRBGu16iMYin2WvsJjlvDWlt885vf5O692+RFQZyE7A6GzGZTwjCk0RkQRVGJqoqcJFiznM2xDAPLfJl202M+L9O494YdtY7+1L/6A7z//vs3hEci2Lq6XCoUIZ5WREau6yo+RM+v+a/tbINknGTzXl5eMpvNFLIUJCBpZTGswnPJuC2XSzzPUw6i2Wwyn8+ZTCYMh0PFETSbTRW2SdpbkMfjx49vcBMSfpdCq08ue/pMGIUsz6nX64r4kkkZDocURcFisVBZBxk0UW41m02VkvF9nzRNmc1mCsZNp1Pee+89Go2GuocQRRKKSDwtXlXkvhKLlcpE/0bGQWLTkgA0Kw9gqPSThDICNyVskM+XxSBIQbIVi8VCvUdgrngsIcYk5SrhQZ7nRJVmQ6C+QFsxIBJWAIqTEOJM7iEpSF3Xubq6Uqy5hEu1Wk2lM8MwVNyOZhTqsyQsE6Mtz21ZFlmuESUxhZaTpQUZlRa/0IjjtILWKUEQ0+v1sG2Tu3fvUhQZaZIQbHza7TbtdvuGilCvahvIU7QClfasOzVag5ZSxmqaQRiGPHzwmMViwd5woEIiUXlK2CZzJWHAarVSXEGSJGQUSvwUxzH1ep1arcZ4PCbPc2Vwto2+jNk2Ifn7w10xGtsOT0JaGV8JO7fHXOZH0pYSekrYIfMs6/czTzQmScKjR48wDIOPPvqInZ0d4jjmvffeU5tfILEgComLhLAbDAZ0u93KaxQqVfnmm2/y7rvvKlGTEI++Xy6we/fuKeXk0dGRMkwSc0m4Mtzp3tjcEhOmaYq+yUlTjSSJyDJTSUllciX9V6r/JsxmM+xaTTHbwvz3ej01cTs7O6RpyrLiUYRfkYW2TTDFcYxhuWpxF0WhdPcSY9ZqNUUkQrlxjo6OALi8vFSoRIip/f19HNtjuVwqIZeIxYSrEePqNtwbBrvV6hAEEYvFogqjNFarDV7NrRZ/jh+VYh80Ez9MAZ3pbMl8vuT5symr1YqPP37E7du3Kci4urqi0+ngWg7tRgO9ANN2yaosSF6khH65Ua4uL3k3zxl0O7z99tvs7e2BXs4/wHe/+11ee+01dN3EMAo2W9kIIXnFIKzXa0UACudxcXFB3fPoNMtwdjaeYBsmQZqRhBUZXoBjWkRpRrtd8laSGdtsNsznc27duqV0ESJ3FrQhqUWZY8Mw6Ha7KnW6bcQkHSlZM5HDHx4d4ft+mV0zDOLqGWVNf9L1mTAKVBBJlFeS9hJSZDQa0Ww22dnZueHhhM3PskxBaE3T2NvdZb1es9lsCIKAdrvNycmJEpwIzBY2VrIYMphCjL300ksEQVBZ/wxd18iylDwvB3e1WipG3/PqVSza5vz8XHkAKbQSjmHbO4thqVUGAlBFL6JuCyq9u6RcBabKs0pGxXJKQkx0+ZKKkj+CSFzXJfRLUY4gFMnwSFwrqs7AT1Sc7DjODa2/oKA4jqk368qriYcTr5qmKb7vU6vV8FdrsjwhB3w/wLTsEr0FGZZls1lHzKZLxa7L+N1/8CFpnLAzOMYwDKbTKXWnRq5rmIaJrhVEUZUmzAuVoRkDFxeXWJbDzvGAOCqh/cHBkUrJCgEscfm2LFu4BTGsYji63W5VnBUoLyyaAEGbYqCpEJimaYpTkLBKEIDMvfxsOzW8ncKVdVtumUKhMdu2lbMUByMIQ/7IPAjB+5knGsXjiqy22+2qYo8wDDk8PFQKs9FoRL/fV6TKdkGPpNukFuHq6uqG0Ec+Q8jL8/NztQjCMMRxHCX02Gw23L9/n+FwWMWVCzabDcPhUKkDBYZL1WBZDLTG930ODw/xfZ9nz56piez1ego6GpbFyckJm82GwWCgiEMR9Qi51Ww21XOen5+r6kaB6MKW1xtlpZ+w1o1GA9u2efLkCf1+v+QxKuXc3t4euq4rskpgpvAGkt5N4kIZgyiK2N3dVQt4MpmoLE0QRIpJD4JIIamyLiGuaiICHMeq4GzGau2z8UPQTbxGlzTNmc1XPHz0lDyH19/4Ah9//Ij5fE7NdrBdD8swSKMYs9DwnBr93R1ms1lZodls0nRd/CgkiUvjFIUxv/O773D/4SN+4if/XWXcRlppSKfjy1L8o1tKeLXZlHqZq6srhQo9z1NVpGJka7UaVlWVuFMhCdMwyLMMQ9fZVKlsMQZSZ+I4jor/DcPg3r17SnHoOA6LxYL1ek2n01GZKNd1FcIQWbyEw4vFQvFh8plSf7OYT0uVaoX0jg73yzWv65jGZzwlSVWMsk0girZ+Gw1I3C3lt5ZlKbUgoCSoVoUmBO6L5kHiXbi2ykICCZchWgdJz0l6NMviG2kpsebiFSQVKWToZDJRqVJZCGdnZyr+lFoL0QBILlzSWrKBt2XKAn8ldBLUFIYhfjhWJJeoNgX2y7MLF6OTKqJqG56K0lDKnpuNrjJWUmMhMa8YHYGvgk6ExxAoLBmV0oBapHkIWvk63TK5Gs8xTA+v0eLk5JQozND0gvF4rDQBrVaLLE6wDJONvyyzB70+/sanyHKaXgPTLpGmlhfUbJugcihQhqcff/wxtmnRaNTRdeOGWCiJYsV7uK6u1qFkbIS9FwJXnIqMjRCHIjDbjvPlj/AM2xWlvu8r1Wccx4psFOThOI4KC4IgUIpLUTFKWllety30K0luF8/zGI/HimT1PE850U+6PhNGQddK8mU8HlOrlYVC24MknvPi4oIwDLm8vFTst0yATEiWZdSqRSqLdbPZsLu7C5TSXfFwWZZxeHjI1dWV2mBSjCIxG6AmplTxFUSRj6aZiu0tc/oB5+eXKhyRnLLU2gsBJFWV9pa2XTZRXhGuMmlZlrGs1JGapvHqq6+SZZmqNNyW40ZhpBafiKxk8Uj2Q6pLiyxSYyoCGNHTixgriiIM3b/WylcEl3zmcDhUEHftB5ydnSkj3Gg06LRLWfh0M2UxXzIYDPCaHqt1mbpbrktS2Kl53Lp9j9/93nt84c2v8HvvvMfXv/F93P/gQ/YPD1hMZwwGfTqNJhfPT2m3Wniui6Hp9DsNYs9ViFIrwHPruJ7H6GpCkmSs/YBs6fPd7/4Ot4+PqNfrHB7skaYZbq2E+qEfsF4nuG5BFCXqOaT+xfM8VawmG/bZk6eYWmksDTTSan5lvdqGiZYXNDtthsNhVQTnqLGVuF7GXQyskMX1ep1nz54pPkrCRJH6h2GZIpe0tiBV4YXCMCRPIy4vFvR6PfrddlnUB9Ts2mc/+5DnmRJ4CCwSEk5SKWma8sorr1Cv1xmPx3zpS19iuVzy4YcfKvWiwPSg0jvIgEk5tUC/8p4lkrh37x6tVkvVGoggRwRLi8WiNDqaTRzlpEmpGbh393aZBl1HShTi2B6mVdYbiLRZ6iGExJLy4yCKaLfbCu2IUEieQd7fbreVjPbq6krFi/Iz8ViCWhqNBlEU8fz5c6XGlDhSkIFlFIp1Pzg4UHUTtm2r7/T06VPVH0JCLCmpbjQaLBbl5i6zBDb37t1T5bpSMCaoSQrKDNui2e7iOBZXk1Ky64cRf//XvsmHDx7z1vf9AEFQ5uDPL0ccH+zTud2i4daIwpBXX32V6dUYLc853j9gNL3AMQ28epswcCkKDXSdpEJxjUaLVfUMk8kEx7LZ39/FqzfZ+CtM06o4nbqqC7AsS6Uni6JELJeXl6p0fTt9KfyNOJjtoitJRa+qorUgCDg9PVXchPAJ22O+LUdvt9vs7u4qJaVwUlLiLTL8zWaj0s3iGCWtnUQblXETY+W6rkrHf9L1mTAKoKlUjsRr291s0jRV5b8iGxXYLNBblIe2bSu5pyj+RKcvHk/gn1RbSnGJWPDVaqU4Dglj4ihXAynfR7z6dDpV0JIwVaRemqZ0Oh2FOlzXZbValT0W4ph+v69QQUlcrlS4IHGhhCG7u7uKSNwuzJHwwKhievFGImUWQZhs6jRNaVR6h+0KPUE9Uv6raRrPTs7VM0t2QkIt8WxhGGI5Ndrttop9t0uAwzBUYYB4NwlnAHZ29vjVv/M/cnh4mzfeeIOnT5+pcYPrui/oJsgAACAASURBVIHh/gEaOXmSklXQu1azKYoqNHTKkAZdJ4giul2dPIe8KMiygk00URkM0zTRtfIZ6nUXXZPGKjUsy1FEsOu6dDodFouF4roEjQqHIq+V8EOQn0B4KW6S/0s4eV1NGqu0ofA60q1Lq0R2wl0J7yFrWDiL7RSjlHLruk69ZipDsl14Jgjvk67PhFGwLIv9/X1WqxXL5ZIHDx5QFAXHx8dqAbmui+/7jEYjdF3nu9/9rtKFCwwTAY14XTEoBwcHiiCTiRL2XHLH4v1EWahpGnt7e0r/3ql08U6tjl0zuZqOlJah2fFUyFF3muiaiefVq8WiEwQxlmXi+yvQEuJkQxQG5Fmd2XJDEmc06g7Dfkm4RVFElhY4NQcdFHw9Pz9Xxk44DCEkL8fnpQpPK0m2w+M9RqMRnV5TZS/Ozs4wq4W5nbPeJk5FxxEEAb3hQNUlaJrGfLUsC8eaDfr9sghJagPee/9djo6O6A96TKdTVUmZjzKOj49L9OBr3DreRytyJhdXdBwP/2rCXm/A973xZe4//hijVseoWbz2xpdYXo159aV7vPWlL/K//53/FUvXSeMNug6tQQeNFpPJhFarxXK55Hh3WPIypoFflKlgq9ckjmN+7+M1RVwwHs2ZLUI63T6mAWESslhM8FybPF5wdPeL2LbN48ePqdVM4thkvS7Y3e2jaRlRVHZp6vf7yquvVitFYm4Lmfr9Pp2woxzX7qCsj3jw4AH9fl/VkozHY3INhSa3y62Fj5J05rZsX9d1JWqS+hbJQBiGwSZIiFMwLJfeoCTg0xyipCCOk0/cj58Jo6BpZSpuW/Jbr9e5uLig0WhwfHysuATpaCQiHYHTQso1Gg1a1UBLfr/sS7AA+Kd6NmxLS6XHoIihzs7OlMUWAY/cU1JB0mhEoRmvRDP1ust6vVZNU6TJaRSHKqwRMtDfhNVzJ4RhabwcuzSCUdUuTgq/tuGneGZRv5mmqQqWJKQQL5Omaak9cBz0vFCoSDyPNOAQg+q6LtP5WvEyYkylVFgQnaRcO52OGgNBZ8KKb8PlxWKBTkWWaRrvvfcBr7zySslt1Dscxhm7u02+853v0G80+drX3uK//xs/z8HuAAMqXUfpiaOwbDgjxHCeVE1dGhah76twRpR8QRQqhBiGIXkWE0cbimoNBdV7xJvLeEsPDc/zrsVhYXYDggvnIu8RgnW7fdt6va50HC1VwyBefx34ivzVNE3VLEh4ImSj7/tKsCeVkrpe9gOR30vWQhCM8BDiQCTM+KTrM2EUTNNS3IFlWQpW/9iP/RjT6ZTvfOc7pGmqQgeJ/SSmEhZ2tVqVhmO9plYrIa0Us4hx2IZ2uq5zfHysqgylwEm4jE6no4qQvGZTtcOS+4pysoShdY6Ojgg3IWdnZximQLasarHVLDdQelPoMxgMmIxniuHWtNK49bplarRYZaqYRr7X4eGhUnkKyWTWHC4uLlSnp1arxa1bt7h//z7r9ZrJZMJbb72FaZo8+ui+gr0CbWVziYGdzWb0h/vKOwGKjW+1WlW7tjLsGo/HAJyfn6u+EkEQqJDs+fPn5HnOzI/YGfTJkpRZ1XBkupgzXcekuYld7/Dx0xPefWfN3bt3efurX+W/+2/+Wz7/8kskoQ+UCtYoLb2l53kcHR1xcXHBcDhkOZuXJG+WEzWbKoWXZRnddg1jbVAUGc9OnjC+ctnbH0KRMez30cnJs4STkxPFsUwmE+r1Ot1uV2WihMsh0xU/Jc5MKiBFoi8btSy8K9OaEjoIud3r9UryVy/DIOkqVhQF0+kUTSsb99ZqNXzfZzqdAmV/T6kFEg4iiiL1HaQxrmSjtkOdMlz9l9B56Y9zFcV1gZPEVcPhkI8//ljVJhiGoarMRFUnTG2SJKp5qcSBsrjFS8rrJCaUrjuSphRDIxJXyfmKhRWlmLRek4Ug3IZ8F9cuFWlZTjXpfVW4lec5QVgu1O0yVokvSy4jVykrSZvJs4gXF9h6eHio0I7j1HCsCnpmOXEYgV2QJSmWYRLlIc+fnapxE55FkJBAXxHwyGYXtlwktKZpVkVdoTK2lmUpsZikeWUhiscTGbiu65iOQ6fXJ40TLkZXtHu7vP3220wWG27ducP/849/nTe+8Bo/+7M/y3/4F36C+XRGEmxAy9ndHdJr9SpjbajvJwKePM+p11ySygtLyzrTqdFuNtCratvVasXe7oCaW1WIpmXtxSZcqmpDCQV0XVfckBhCx3TVRpOsgDgnQPFDwqHIZhVnICSuiJJEkCbaCNGMSJGbrBEhO6Vnh2QaJPPheZ5qPyAkr4jORJEq4ccnXZ8JoyCl02IhwzBULa5kYrZTL4CKoyRnvrOzo9I7WdU30TTL9u0iNZZQQyCZ5Oa73a4q7pEOQEVR8PDhQwXRXM9TElQhGaXjr5B1SZLQa/fKttujs4q5vqDb7bJYzMsQp1Z6cckknJ6e0mp2VIMXQCkFt3tICKciEDMMQ05OTrBtu+RDKqlyt9tV3MB0OlWh1507d1itVvR6PTaVrFuKaaQtXRRFqtvP3t4ehWapjS0EmtxXmp0KIrNtm1u3bqkU8HK5ZG9vTzWkWS6XaJaFYZRNWHTgo5MHFJrOm29+iQ8++ICT0xFJlvLn/9yfZTlf8Nf+6s/w7OPH+Os1+zs7zBfT0jD3usyXCzy3wdOnTxWRuTfcKbkkNNK4rJ9wXZfpdMp4tSLLM3qdDmeXl6xWK27fOsR1y/e0Gk2yJMWPSuMt4qH5fM7Z2Zmaa+AGcSdGVhCXOAqRy0saV7I2gsaEyATKdvD+dU2OaGO2pfSi1wmCgLt376oKSqUWrUKf5XKpMnm7u7tEUaT6MkroVGohPnk/fiaMgq7rPH36VFXTSRpvu9uRV21KafcudfDn5+c0K6jYbDZLqGXbPHr0iIODA2U0ZKDECgvJ6HmeamMmiEEMieM47O3tUavVWFRts6Qtm6QdV6uVgtNpmjKZTJRqsIx1U15//XUeP35EkiScPj9hOp2qhed5HoEvfRUzoqiMw2fTJyRJws7uQC1AsfoPHjxQ36PZbKpFm2UZaRxTr7I39VqNy4sLAIosY9Drqc5R0tREhE4CNSXFZZomB0d3VKZB5N/bjWDr9boixiSWlhj+4OBAGfQwDMvQqmq2cn5+wehqwqOnJxwc3yGKU17/wsu02z2+9847+Ks5Tx7dJ/R9+s02EfD89KREY3WHk5MT4jSi6bU4PDxUhsnUSmK57tbZ29tjs9koJn6+XNJoNtgslqznC9Is5f79h2w2G/Z3Ki7JreMVKMckDmBbQt9qlQVWjukyn88VcpQ1KGMpehEJCeLKSHmex9XVFfv7+6zXa77xjW8wGo2w3RrdbpfHjx+r14rjEIchXNvDhw/V31ITs1qtSoK50t1Ihk3SkaPRSK2hsvFO9In78TNhFLJKnyDx32g0otfrMZlM1IaQ+EwgrEBygXnSXSkMQ6aVGk3IMOmdkKapQhxSEiyfIZA9CAJeeeUVNamS3gyr1JHwCpPJRP1OauzjOCaISwsuPEWSRKzXaw4ODkrF4LKUqK5WK8WdjPOpSllCyXfYVk2VzYpiU4yWTLYUfW2nx7ar5yzLUm3vJfx6+PCh6iok6TBZ/OKNpLRcCCn5ufTElFblEsNK2CGEq9SwNBoN1SpvZ2eH6XSsDOI7v/cep6fP+cbbf4qsEP1Izg984+ucntzHNk2Gh/vEQchiOWPYH7C7O2S1WWO7Ncz0uuZFILVj2VV+Plbj0Gg0SijuufhhzO27d/HjCK0KC/QCkjhlPl+i5RmdfudavGVcl0Nvc017e3skYaZCI0ApY6VWQSpsf39losyFELiq+rTTVjUykp4WYns7fJBshHA6g8FAaSO2UZ0Y622id7vpiqZ/5nUKsLe3p5hyOblHPFAclyf1SP58G/JKr8U8z1Ufg6BCDVKBJiy7TIQ0MtkuCpH229uFJPP5nA8//LAUOX3ucziOw9XVFZZllem+6sCUOI5V56SdftlB9/nZs0r5168IoonK4W8rG8tQxqty4AW1WiU71stJ1o3rpqHSbEPkzkKG2bZNnmbEUcTz+eJGzLi7u8vz58+Joqg8PGbj31DWAaoIrVarqcKrdrutMjYSKu3s7Kj+DZqmcXZ2phrZStmw8C1FUSgp+nw+Lw26mdNs1JlOZ+X7BkO+9d3vcefWHeIs5R/9w9/i4OCAV++W9Sbnp8/YzJe0Gh6mXqLCg6NDWt0Oj58+YW+4c0OCHW5K8s2yLPwKpQyHwzJn71hcjSecja9oNRpM53OSJOY0TWi1X8XSNCynpjinIAhKwreqXJXMwnQ6LUVKRll5KmHDtmxZivDKvhueWsuiO9guVHvnnXfQNI2D4yPVh0LIxm3+Sz5bPs80yzNFjo6OlAZFWsRti9gkKydGQ1oQ/GGnwXwmjEJaFXlAWTIsQheRbsrgCKyyLEv1vN9ukCpwVYQkw+EQz/OYTqfKewmbLlZXioikNx7AaDS6riisKhNlYUun5v39fYVkhLSTIiNAHUYidRpywEhBpmSqUuiUxGllZLo3ZNuysYTMErZ5Gxoul0sODw/VYhHtu2yURqPB4eEhy+WS+bzkNZbLpeJTRDSzXRIttRzTaXlkmu/7qoJUoKyo6oIgUMZYdPvSOUiMDlRquzRBN6s0cp6zni8Z7hzghwHPnz/Htm2+/MUvsZk9YToZ8/z5M7rNFvWq1+aqImslnbo9n77vQ3XGZb1WxuqCIAFMHXaGAx6fPAO79PazxZzFojyxq1N1cxLeRJCBZKQAdeCO53nYRk3xUNttAgWhyPyJJkTmTfQzsjlnsxmmaTJ//31FJEr2SFCcIIXtGgf5TEG6QnhL6fs2hyaoRbgMEUt90vUvZBQ0TXsCrIAMSIui+KqmaT3gV4A7wBPg3yuKYvZHfA7rKo24Xq8VXBYvL/nhTqej0kwi3pFuQLJR8jxnMBhwenpKnpdNVvb29hTUF9JFcuniESRXL0fPiWUWWHxxcUG9Xuf4+Fid+ZDnuWqrNZ/P8X2fQbc87KSgHPynT59W5cetauKiGyFQkiRYpqMsfZ5XDVLcsjbh8vKSfr+vJjRJEnZ2yupA2eCmaaIVULMdtALSuDx3ol5zWS2WzCZlGst1arSbLZb+WpGSEqIIiSaHmkhjXCkfTpIyXSeQXI64KzmCc6XEy/Oc27dvYxhGKZaq8uthGGLkIXE2IU0T3njjDX7jN/8RR26dn/6pn+ZXfvkXuXXrFqenJ6zGT5hOp/Q6bXrNDr6/ZjabcPfuS9y7dw/brREmseKRhOPotkoSerVakSaJGq88zynSjDgrjdncD+l2OpyPLpjP54xGI9rNFrpmYBgo5axpmiwWCzqdjqofETQVrCOVMmy322ptXl5eqpSkoFjRb2zLmKFEx/P5vOz4NL0+AUz2hPABgjCLorgha47jWFX9yr6QsFGMiGTJRBOxs7NTGZ5PNyX5g0VRjLf+/3PA/1UUxV/XNO3nqv//tT/sA9yaqxRicjiIeK6iKFSXYzkGSzIHm82GxWKhat63F7dIg7ebjW7LfmVjivUX5l8mUNqAt1otpZEQ7ytxn6SrttvG5e3Sky2WMxXjaZrGkydPyknZHahFLCKqNCmt/Pn5Ba7rYRgGuzv7ZQvwyFcNVwQdie5euBa4ViZKmmob0o9GI0UkCokorcLEsG53bBISV8ZRQqP1eq3CF8mXb2s+AFX3IOlBSbHJJqk3WpyNnrG/v49hGHz+85/n3XffLcfVNNG06y5FaRQzHZXVkkdHR4xGIxabNUbNJopT9oYDJQmG667g8r0kvQqwe7DL+eiCi8mMVsvm5ORElabHUVpVqIYMdzo3PLKsCyGnhfD2V6EKuaQtXrvdZjweqw0dRZE6I0S8PqD4LjEYIioSZCHk73ZaXUhmKBvkbJfbi/ZAEKsgge1eHZJClvv+YdenET78KPCnq3//TeC3+COMQhLHpNWG1TQNvTryqt/vYxkGi0okNB1fqrp0KCFhqyOQOmY+jaqJuW7JJRay3W4zm83UQEqKabmcK6t6TZJZOI6F69bYbFakaUy93qBWFTTpQByGvPTSS5yfn5ekYhzjuS6YFpso5tnZJe12WzWktWqlCGoTaDQaXbQ8pu6WCEhztcp4XQurlqs5frCm1Wrg++uKAygN1TvvfE/lvI+PD9E0mK/nZWYhLFHAydkJnlcamJ2DHbV47LoNeaEWrvSPePb0yXXNRBjgr1eYVlkHgaahGwZoGkfHxyUxVvE5q9UKrQi5rPo99rsewWZBlgTkSYyfxBwf3y4Lkto2//dv/mMePnpGZ3CbN15+g3gV4N22yOOQZ6fPeP1Hfoj7foNXvvi1sgQ+zXDdGkkQcnZySnZ5yeHePq1Gk3SVk9kFLbtFOAu43FxS5DmDQQ+SHNOAYFMSohd5hOHY9Ns1njx9SsvK2SQZ/XaL+49PsJoD3GaLQW4TBhme18QwNPxgzWq9wK15dLtDao7L89Mx3U6Nvf2BQktyiHGv31LQ3ms4JHGmkIcUwwkXJUShpA9FBSmoV8JbQVtyzmWz7mHppaGYXpWno7lyrEBNU5kKcRrCqQnCLBHpp9dkpQB+XdO0AvgbRXmS9G5RFOcARVGca5q28we9Uds6in7QKw8k3S72kZRfv9/n/fffJ8syXn75JR48eHBDPit8gzDEpWdrq8Ne5bMkTBCvJ9kBx7kmIMWii5JNBE7SZ0DXde7evavSf5vNhpdffpnd3V2+/e1vl0ankj5/+ctfVuHK0VHZ6WdbFEQlXNku4jo8PFSxqTRRybJEISBJfQqxJWXgkv8G2N/fV5tVEMmtW7ewLIvxeEwcx/S7JS9z9+5dzs/PieOY27dvlw05qswNlJWkImDa399XRVAy3uLxkixR9QfdbpeDg31Go5HiibrdPuPxmEbT5lvf/i6G6dDdKXAcm3fffYcsjWi3m3Rbn+Mf/uZv4ftlEdXh4SEfP/yYw8NDXn/9ddB0gqoGoDBNak2P2bqEz4NBjy+8/gV0o9woy8UM27Spd1pYSYJTM8myBK/e5ODgiPPRBZfzS2y3QavR5Pz0GT/wr/0gcRyx2awIwnV5LmXFW+VZWXOyWfv4fohGqNS1aZoqsdLu7i66rqu04MJfKlXt7xcuiVZEZPWe59Hr9VgsFpycnNzImAFKAp0nqeJutrUmIsSTVLHb8JSzE85su9jq0zIK/0pRFGfVxv8NTdM+/Gd9Y7F1FP3L924X0nxC4iHZhBITClkixItc7XZbkULyoJIakvSaZVmK6BMiT4Qipqmr94maUSz7NgPcbJb56Nu3b2PbNh988IFi6q+urojjuNRFGJbqcZClKXXXwd+s1OJybJNup8XkanSjnFU8jpCVopJ0nOuzKATdSNpJUoGO46gDbeXZBEqKmEYWocDY7eP35N+SwpVYNEwLsjQm8NfEUUC71VDhUhwFWKZBv9+DPOLy8kpBUyF9BcaKwX76dESWlSdFvfrqq7z3/v1Kfh6TZymPHz1A0zPuHB0ShCGubbG7W2Yinp+eMZ5NSdOc3Z1Sfh2mCZugDB1ydOabFXmS4jU80iLFQKt0FRGGCUVxXXWpaRq3Do+w3AZXi4/Y29nl+ekJjVtDlSXQtIK6V2M4HBKFCatVQJFDu90hjhbKMErsv7Ozo05zEhJX1pSMgTgU+b8oIbeJWhHXSZGTyK6FoJY1CvxTMmZJR9q2zarKWkjVrehyVEXvJ1z/QkahKIqz6u9LTdN+Ffg6MNI0bb9CCfvA5R/1Obp2feKTZVlKtCH17FLE8fz5c5VdEKHSwcGBEh5J/L9YLJhMJhwcHGAYBrPZTPUSkME8ODjAsiwePryv8vWyiOU8P9mAJXSP1YY1TZM333yT8Xisil4Gg0FJltYtlVW4vLxUKSHZmDKp9+7dU6o34UeyLGN3d5ednR3F9sdxqH4nJd3ynURrsJ0W63a7zOdzVWDWbrcVXyBt1ybj8mzH6XTK0dGR0jIAahxM06TbLHUJ9+/f5+TkhFdeeUURpHI+ZK1WwzYdfD9QxOV8Xh7ko1FKzOU077TIGe7sM1+u+d7v/i5Pnpzw9a9/g/l8yvhqxPHeDmkaM766wLZtTp8+YbUOeenlVwiTlIPD26w2EW98+Ss8PXlGGq1p9vu4To24SBnNZrS8Oo6usX/7NkWWo00uscKQYD7F0HRcr4G1WjLo9fneu+9juXU8x+bZyROarQ61wqder9HuNJReIEkSdM2kKAyKHDodXZ0onqapIhqLolCNWMQwt1otRfQJOpVamW1eTBSNklURDkuyHdu9QQw0JSoT/qHf7yu0INycfA+pkZGam3LtfApGQdM0D9CLolhV//43gP8C+HvAfwz89ervv/tHfVZe5Ao6iZIRrpVlwn4vl5troUpF0smpTOJxS+GOUcXbx5imydXVlTo+TWSm0lRFshfbBI1MonhQCUME0p6envLbv/3bfPWrX1VGqt/vq4rI+XxOlkTEoa9Ku2WCJIxxHEf1FxSjJ0VbsrjyKj4Wmbc8g4RGIoWVCd+u0ReUJNASUAhDwjJhu0XKvVwuVVenXq9HEgX46yWNemmsQ7/sLaiTk6cxeQpx6LMMNxwdHSllaL8/UFJwIcCiKGLjB4RxSqPZ5oMPPsC2ayyXc+7ducXp00fkeZe3v/EN/s9/UB5W3u/36Q/K0On8/JIvffkrdId7xFHCrTt3IQ8UAtILcBwL07bp9npoOkRZiu24mJaDleeQZ+iGhmPZFFUzn7rXZDI/Y2cwwA/TqkQZBlbZOXk2L2sdak6dbndIUZ1teXh4qMIGgeaCAgAVGkZhpuC/hKibzUYRlsvlstzA5s0WcULOCrITQjOOYyK/bM3WbDbVGA+HQ0VEqiKsVlNVbEJZaSsh8acVPuwCv1p9uAn8YlEU/0DTtG8Df1vTtP8EOAF+4o/6IIFPUmxycHCgGoBIk8pSwDOg1WqV9QJVt6R3331XLewkSap0WckKT6dTpbKL4xjP81THIMlKmKauMhAS90uaRziITqfDaFSeUPzkyRMVwpyfn6s2cpKiOzg4IM9zLi4uVHWjdL+Bsmbji1/8ImEYlpV9y6Xq4SDtt4qiUC3rsyxRaSUJM6T4S/oHCmwUDkMau2qapmTaUVSm0A4ODmg3W0rqvD0Hot+QYi0JweSAGFFuCms/n8+ZTqc0GmXzGElNighnfDWpOhDFPHv2jA8ePSOMUv79f+fH+Jv/0/8MhsbV1Yj55JIvvPYaNdPgm3//1/nq17/G1dUVtuPy5a98nbOzc5x6m9Um4NbdFlpbZ2/vgCKPFDm3Wa357j/5NpPLK77/+7+GW6vantul+tRIC9IsxdVtGo0Wur/mzvExT09Pee3zr/D4yVPSLGcyCUjTmJpbks2aft2Oz/d90iTD90MMvUSOOzs7KlySkFdOsV6tVhi6oxSH0jhmNBop57S3t1emgZuNG3oD6Qgurd9lDQsi3s5OADx79kyFJZKhK/Tro/DEqYhC9g+RKfzzG4WiKB4DX/oDfj4BfuiP81lxFKvU0WAw4P3331dQSWoTXNflK1/5CicnJ6qsdLst1mQyUT31sqzg9u3bqtxYRETCEQjEyrIMx7FU4YywvlJcIjn6UoJdjqLEZ67rqjTj9sb+4L13aTabeG4Nre6q9vE6BXfv3mU+n3N5cc7h8S21qefzudq0kmsWCHhxccadO3cIw1ClWAeDgYo9RdM+nU5vwFUhnZ49e6ag5d27d0tthu6r0EIUmtKdWTT8EnLVajV2BqVOYqVrxGFAr9PmcH+Peq3s4NPv96nXPZUanExmN1KbksLUNIe80Pjdd95l6S/LysM0JM8yprMxk/MRtw6PGE1X/Fs/+uP8rb/1i6zWAXfuvsQP/pkf5v/91nd48vHj8rj6Z6esq6yMTilS6nUH7A13aDYa5YGvecbHH3/M1eWYllPDNnSKQk6NzqEoyJOUj957l5rXwI9C/CAiSSKarTp57lD3ypoEDQNdtwn8EMtysExDGdter6c0DHBd0JZlGflWibWEDsLLiDMLw5AovZY4l8TpQJ31KYIyCRefjp/yxhtv0Gg0+Na3vsWtW7duIGAJBYWXqtfrSsh2za998n78TCgaRRwkp+mIXl5kz1JsIwzta6+9xoMHD8pKvirdKPLSKIpYr31FAkpDFiFpNlXPPBl802yoFlXSMXcwGCiJqCCC6XSuWrlLPll6LsrBMlEUEemaar4iG1t0EaKbf/ToEaPRSMWrokBbLBbqVGwVP1bhiYQ0krKSkEJ4EpFCi/5CUMV27YLA3SSKFRsui3P7DEJphiL8guTORR0qHIcoFss6AVP1njDNiuA1rztD5XlOoemYps14NlfoJooihu02p09PeOn4NlmS4rW7/PL/8r/xfV/7On/u3/7z9Ho9fumXfgldMxmdn/HDP/zDvPvuu+zv7m01f0mp2TaWZRCHEadPT4BSvn779m1GT0+J8wzLLGshyHJM3aDdajKelyd7x0lGUmVXSp4pJU5KPcLhwTHrdYihmziOi65F6oi+fr+vxluIxe1wDrhB9AIKEQqkNx1bOSEhlWVsRd7c7Xap1+t8NJmqn0sWStYboBS0tltTnIPoVwSVf+ZLp23HVgrC0WjEvXv3ePbsmaqTl1TdBx98wJ07d7h16xZnZ2eqQlFCB8/zyLJMwXLpUeA4juqvJ9pyCRV0HRXXQRlrfe5zn1NnN4rYSTaudJTWdZ29vT0lJpKr3+uqNml5nmOZBpPJhGbD48H9jzg+PqbbadNod1XcKWnJfr8PlEZSGrgMh0N1SpHEnI8fP1axq4RZYuQEHYm6MIoi3n//fcVDdLtdyAulyhQeRlq9SXqz0WgwnYyJo5AsvRZt9brls2qGTt2tKeVikqS8/PLLCtGsViuytFClu8+fPye3hthuXXXtDsMAx9JxahYHB3sVaWlxdjXn6NZLAqZ9SwAAIABJREFU3Hv5C/yX/9V/zZ/9N3+E4XBIr93Bc2yKLOWtL72B4bbZbNZkcdlj0jJLhn2TLxgHpair1WjSqDcpgpjFrDx56mBngL2zy7Oz5xwdHXE2GrOaz6g3W6RaQlFIj4wU2ymPqndsl6IwlFEYDAbKoEt9gsyBOLeiKNCro+oAJYgSib1sTl3X0UxDCfYsy1IH0gphKMg1jmNee+01Hj16pPgvUduapnnjjM+g2j+S8pTU+qcqc/6TuqSgCMoUy0cffaS+uBShnJ+f88Ybr6uDXrrdLqenp4o4i6KIW7duVQPTUY1gpahH2PJtZVhZ2FOoDSTeUuJzOWJuZ2eH0ehKSZHT9Lohq1QqXlxclFCyfajq56VWoF6vK15EJlCq8CRlKE1di6JQhOJwOFQiFymg2iYsRaUpLdjE88rCAhTLvb+/z/3791kul8rDCokqaUNJiwZBcMPYSIpOiEtBSUJolv0dSoQxHo/ZbEp9RJrkFZIpjUrTs1kFZR/OnBwNaa+W89orr3Dy8DHHn/s89+68ws/8zE/zV37qr/CX/9O/xN/9e7/KrcMDhr0+//oP/WnCTdlDoOb1yhSjWyMLQ1Ve3djdpV67lhqLOKhm24wunqmGvqZpli3h6w5REtNut1nH/o36ABF0GYZBu91XLeDke2/rAASdSgrZsiw0LLUBt7MF8l6FKkAhQ0EJV1dXN3Qh2/0apKZGFKYSDkt4q2kam7Bc5zKngqbLGpxPvj4TRkHXNNrNsq/i4eEht44OGY/HJdlkGsynE7QiV+x+eVpvi3q9wXLhkyQLjo9v8+D+UzzP43vfe0e1zRbJryjJpJR6vV4rq9psNqvmFAuCIODhw8fcunWLi4tLDMNiNLoqm4RoGm+99RarqrfC9jFtd+/e5dGjR5xfjvE8T/X6ny3XTKZzVn4ZynitTrV4fOK4oFazcV2nakN3qyIsC1qtBpZlqhz4crlUxKnUgUhYVXrrC3S9QNdNNE3HcVza7VrVN2HB5eU/qU6XajOZlfDTrjksViWLfe9zLykptYQnha7R6nb58pe/zPPnz7l//yFWnNLwWnQafaga6fbbPSxNZzmes9PqslgtuDg/Y+/wiFXoczWb0tjfI5yv0dOUnU6HydUFb7/9Ns9PT+g0+9z/6GP6/T3c9i6Tp0/4q3/pL/OTP/7jzEfn3Nnb58/84A/RaDT4hV/4BX7qp36K2WzGgw9/h9FopHL+hmFwfHzMpCo2c73SWLquy2QygXhDbuq0Bn1qjk2z1abX63H3+CU+DB+Shhl2YfP/UfemQZJl53nec2/e3Pc9s/alu3rfZh8sMxgMYRAUF4CiqSBNAwqJhiMoR5gUzZB+MLhYP8QIh2yRtE2FJIoGuIBhahAAKZLYCAwGM4NpzNLd02vtW1bu+77cvNc/Tp4zPTQIMCxLMcqIierKqa7qyrz3nPN93/s+b6c3IndcIRgO4PKHCXpDWE4Y2wMcLihXD+j3gop8XS1XGA2GTEcTXIaBNpnSqTWU8QvEjelxiimWbY7QbROXw2Bqjgj63LQGI1VOgJggyHKh3RYjXqmvKJVKaqKwtLSkHLTyJCH7CKYtvD+yv+FwOEilUqIsnJ00v9vjPbEoyJGdXJnlCyOP4LITPh6P1J/fmfuKScK6a51QKEShUFB1cDQaxev1KhpRMBhUu53UnMufI3sIiUSCQqFAJBKhWCwqebR0NcqLT/49aQ+WfQq5a8j6Tk4N5BsjVWnyNCF3BzlqlF8nTVqySSR3F8lI8Hg8FAoF9TEcjqipijySyh0LUCeah5WfshchEWJyZCln5aFQiGq1yp07dxQ4pVKp0Gq1cLu9SksytcxZIywIukYqlSaaSNDqCsXlpNWhWCzy6Pn38c2XvkEoFCCRSHB4eMhkPFSl3/r6Op/85Cd57fXX6PV6XL9+XTESX3jhBbLZLD/5kz+p+BiFQoFarcby8jKWZZHL5ZSUXYq4pF9DNpelvHs46L/rd1XsBEvM9buTvjqeD4dDAk7hIXDwTq7kYDBQkxa3242hO97lldA0je5wIPQl1pSpPUMNplOiCatpDPpDpUuQWZ6yNPR6vepEI0VlAN1WWwmb5PsmexTya+RrIP/9gGIsaJqG9l7vKciFwO12s7+/r5iA0gsha9zRWByHptMpumYQj8cwzakIejE0AkEPg/2OUj/COzf8eDxWXAF5w8j/pOtPglNisRh7e3tqvCYdc8PhkLt376qsS9kAlMw/2aCU4yKn06mapnJ0KG/yyWSkbnR5UTUaDfWz5AUsa8D5+XmSySSlUonRaKT6GcFgkFarpTrVgLLzOhwO0uk06+vrGIah0qWTyaT6d0rZtPyZEmsvOt5DIhFxE3Q6HXK5HG63WyHn5THb4TCI+cXC1+m0BBtjNGQ0maLhoFptEgrHefXVV/nYxz7GN7/5DeZWFimXyywsLJBMJPE43ZRKJfb29vjN3/xNPvCBD5DNZvn0pz/NZz/7WR555BHW1ta4fv06mUyGt956i0wqwcWLl1UZ9uyzzzGdTnnw4IESw43HUkswxJraxGIJokEfmg29VJ9KpUan02N1ZY3s/ALtXptiscD20S6Gw0WpVMLtcbFgzBFJJ9FtS2QzGgb+WckbDoUUIq/TauN4aJPwh98RL8mE9NXVVZVE1mw28fl8ZBaWlSs3Ho8rvYsE5cq+j6Zp2DOEoCwTAFWiSBn9eDxmfmlRIeRl83l3d1csEt+jp+D4tV/7tf9U9/rf+vFb//J/+7VnnnwEl8tFIpFQARyRSIRgMChELPE4pXKeWq3KaDQU+PRKCa/HS6/Xne3gOpcvX6JUKiv0mtwx9vb2gHci3qW4RrrvKpWK6p7L5mKxWKRSqVCr1VhfX2d3d5dHHnmEZrPJwsKCcs3J0FcZhCsnBvV6nd3dXSqVijqZyP5FNBpRx3QQpUiz2SSdTuP1ehUuTY5LJUBDjkDb7bZi8gkB10BNAyQDIpFI0G63lWBFKtvAVlZeuVM+3D2Xx9BAwK8clCLaXkhuB/0h4/FE1amxaIxWW4jBXB4XO7u7aLrBSaFMrdHGRKdYrKCZ0O60qNWqtJp1KpUKiXgMw+HE0B3Uag3u3L5Lt9fl7NmzSsj23HPP4Xa72d7eVqK00WjERz/2USKxKD6/H4/PS7ffw7SmBMMhhqMRU8vC6/cRCAWxLZt6vcbNN99As8G2oFqtEQiEsNFEzoCmc+bcGUxzynE+jz/gw+l2MZ417EbDPsP+EI/bSyQWJRgIEovFcEtWotvDcDTCoTuYmCadbpfRxMTpcuPxeNEdBl6fH90hSjxdd+APBAmFI2Tm5pT+QE7jZB9HcjCkPiQ5Q85Lb4vH4yGbzSoBmtz4mDXTpZtTTkacTifXb7zNz//jX/z173Y/vidOCvJmkeM1Wb9L4YZ0oQWDQdFncLnw+4MMh0MuX7480yiMFQtA7rKSgSiR1+FwWIluJJX44emB3NFNU2Qk5HI5lpaW3iUSuXz5MicnJ2o6EYvFyOfz+P1+0uk00WiUk5MTBbeQ6DU5EZA7mNyppTpT3mDy9YhEIupzCU7x+/2KOSi1F/K4KssaSQCWJRigXkN5sbjd7+QWSm+J1BjI104ePyVRSJQ3YoGKRuL4fAE1PWlYdQXJdblcaLrB8fExhWqTydTGFQgzHJvkcjmWjUUis55COp3GMER3f2l+kXpdGKpsXTSSP/rRj5LJZPj2t7/N5uYmfr9f9Td+8Ed+hNe+9S0l95aNz0ajwfLyMoOBmBAtLS0Jh2Krjc8XwOvxq5OnUAhOMM0pCwtLlCs1lpdW1Xth2xrVaoXQzPMx6I/QvW6BQPO4qTcbjM2JkFmPx3THXSUQcmgC/uKfLaQC8x9SJ2DZtJblsJSWy4X5YRirEIgF1N+X43WpuJWLgywPZNPx+PhYjZFln0GWlvK6+W6P98hJ4X/9tY8885R6UeSbK19MqdaKxqM0Gk3G4wkaDnw+P81mi16vz2AwYm5unnqthdvtIpvNMhyKDAaZa9hut1X8vPRJHBwc4PF4qNVqajSZSqWUAEjeILlcTo0o5ThSoNUFou3MmTPcvHmTcrmsfA9yVi2bgnL02O12OTnJqfovmUwSDodVEK6cBMjvPR6PlRZjfn5eBYDouq76EG63EM7IbAzbthUcdG5uTs2lxQLQUyOxxcVFJZAxTVNNPKQgq1KpKuLTuXPnxZ9Ni4sXLxEIBCiXy6SyaarV0szyLWS/3d6AertHoVjFF4rS6Q7xGk7M6YRQKMhoKPokPq+HpcVlTo5zuFwehsMRP/ixH2Q0GnHq1Cn+5E/+hIWFBd7//vfj8/l4+eWXxekxEmFuYRGfP8Dc/ALzp07hchhYNqyfOoXP7ycWjxNMp9A8bhymxcbGaTrNFulEEnNiAhqhYIRgMEKxXMK2odvvUiyWmNhToYD0+6jX6/hmY0aX00m/16c77GFrYE4mDGZ4OjQBCXZ7ZrGBUxPN6aY/GBIMhylXqzRaLWLxBGg6lWoNc2rhdLvZ29ujXq+rk8LDzUNJtpKLeLPRUCe9drutTgEPTyJcLhe2Jnoby8vL7yo1dF3n5etv8gu/+D+9d08KsraXN4NhGOomkSaibrfLZOrG7wvicnqYTi1sG1wuQWuajKf0ugM0zaEkqXJXVQafaFQpFiU5+s6dO5w7d45ut6uEO0tLS8o2LFV5ciwlm32TyYRkMkmhUGD6EE5O/j955AOUSEjKXaU+QHodZA0oQS1yl5caDYmeLxaLilAld4N2uw2Arj9M6h0qaezDDEtZe8sYeenwrNVqAGrOLX0VDof+rrJCshkl3k42BzVD0Id0xHG1VK4SDIYZ5sqARqvZYTLrlsvfUwJgZVZCIZenUKhw4fwlajXBs3zzzTd5/vnn2dvbYzQasbm5ybPPPsvCwgK3bt0ilckqV6A1GzmfOnVKKF1jMcbDIbWc6CPNZTPcuXVLWMsHPUXjcru8DIYTpmhMLai36ty+fZuJPqVar+ENeXE6BWAlEAiAJXZmyyGa4pNZf8rpEP0l/aFd3+v1Mpk1i3O5nDoVtttt1SuQjVSpS5HXmNy4ZBNbXmPNZhPdRgnk5P0iF31Zpsp+18NCNrmhyTHz3/R4T5wU/o/f/s1f+9GPfhjbtoVIZYatvn//vrKk1ut1XG4vGk503clgMELTnJgTi/F4ynRqU6006XX7uD2ibkokEu+ISGbwknQ6TSQSodPp0Ol0WF9fV6VFrVZTxGj5AkoLq2ysyZtbqh2LxSKLi4scHBwoSXav16NarTKZTFRpIpV3csGr12tKESebiVLFOJqh0CeTCbVaTV2Q8uQkfRbJZFLdXL2eMFbJi00qJGVHWmYROhwOrl27qgAsUiMiL1C5qNXrdUajoWpCiqmG6GQnEyk6na4SiMWSUQaDoepqe9xe/KEQN27dJZcvEklm2D845tFLl6jWKgwGfYYD4XMxHDoH+4dEQmHOnj3PhQsXcXlcJJNJUqkUh4eH9Pt9rl+/TiwWY3FxEU0TixAOF+1OFxsNl9uD2+PF6w+geV3YE4vhaIzDcOL2eHFMbSbmmKX5BdrNJn6vH7fLw9HRMUfHOSzA7fbwrZe/xdLSMmPLxGHo5ItFUikxXtawGQ1GeL0+TEwMx4xzCaSSSXx+Hw6HgTmdYllTNF3D6fYRCofJZLO43G48Xi+RaBRN14nHE0wmJv3BgPFDDl4pYX8YOSinRaFQCH3WK5hMJgqpLz07ctLg8XhwedxKOzEcDtUG5/P5+MYr1/kff+EX3rsnBSnOkMKNer2uZrNylfP5fGg4cLkcaJoDy2oxGo2ZjMXNZGMzGHQxTYtFV0odw2TNKV84+bwcW8rewcOlS6lUUl19eXqROYwPh9devXpVhdKm02mF3g4EAiqyToatSnFWo9EgFAoRj8eVmlFKrzVNI5VKqWh62xYhu3IxAtQCJ0VbIBkB72Dq5K7QmB0z5e8t/04iERPmHtNUElk5tpPyZ+H3MHG7PWo6IqXn4rV0KjVjo1tn0OvjmoFOHbqTQr5Ic/b6OXSD0QxOK157U43L/H4/45GY1GxtbbEwv4Q+1pWCVOYgfPzjH+fg4OBdsmHDKSTxD58Kg8Eg/X6f+fn5d8aPwyGVXI54PM4f/dEf8eiVS4pIlEgk0B1utnb2OD7ZVjdfvV7H6/Mpc5nApYGNrZrXmkPHoTtUv0DSsmu1Gq1GYyaX7iukvrSoy2Zfq9VSPpbRUGxGEs4CqD6PFLs9POaW5ePDpaQ8Fcv+ksfvU6NO+XrLa/89732QjTNpHy6Xywqt/TCAst0RxqfhcMh4JLzhtWZDKNG64gUcDEbkcjnVaZdzXnm0lm+gXD2/+c1vMj8/r/wL8k0DmJ+fV6anfD6vduZms8nKygq3b99mNBqRSCQAlGPS5/ORTqdptVqK2ZdKpXC73ZycnJDP50kkYsrwJS23kiUhzSuj0Yj9/X1AmI7+elkjLbvhcJhMZk7p6H0+H8vLyywuLtLpdFRAjbyAisUi9XpdzdvltEday6UMt9831bxdvkfBYBCHLgRdUhJt26KGdczq2vMXr2LhwOl0kUikKJfLZLNZyuXyzOk3QLNNdUM/+cTT3HjjLX7xF/8JhXyJWFJY6D/zmc/woQ99iEKhwCuvvMLS0hJ/+qd/SjAY5JFHHuH1G6+ysbHB5cuX1cIAvMswJj0fyWRSvVbC/i03gDLDoQjsKVdrzM0t8Pbbb2MZtrrJup0+c3NrtJp1fG7hMRkMB0q1OB6JCIJGo4HXLco2yVC0ddEvkBoQOWHL5XIkk0ll8OvP7NRSMXr+/HlFkq5UKliWpezummUrPwugfm952pMp4ZIWLRdLSTOTPae/6fGeWBQ0TWdj4zy1Wo2trT1aLVFTLy2tzTT5YqfLziUIhd3E43E2d7aEky+iM7J6TJ1T0qlFdM3B9uZd1a2X5pJ4PE4sIjrz/W4f09QI+OJEfDo+l1t0eIPCV3Dl6iUCoRC9fodoWJQfXiOMzxUgHA4S9As+YTKTxJxO8cfE8a7b6xEJBYVlVvORTacY9nuEw2GuXr4koCZzWR48eCDgLENRJsgFoN/vk4jF6bY77O3skkqliEWiKpil0xKNUKfDIODzq4shnzth0BMqTXmkl00peRP4vR4sc0IiJoRKrpRTdf4nozEBnx+/14fP66PX6TI/P4/bK0Jrp1MTTQOPx0mvJxSQDsNkPOmIxdcbYtDtYY6nnF47h8fh4e7b99GwCQW9pBfnSM9l+cqffhkbyBdKrKysUK7WWD99lm9885v8xE/8BJphUWsVSSxH2bz/gNMX1jkuHnHu/DkODnLYGjjdHtyeAK12n2c/+H56vR5vvPZtgsEgmcycKIdcXqyJ4Gg2a4JZ0R8IcEsyGafVEZqRdDyBZdkU8wVKuX3iPoOdcpneqIbbGabRbWLpBp6Qn6N8Aa9Lw+dxMBq3ifqihNwhBVlp1jviJGVNiMcTVCoV/H4HXveY5YX0bKQoiMyjfouA14DpkHjEPzt5hTjJ5wnPYC2j8ZhUOo1lWayurfG1r32NZz/0IVHaVQqqx2NOh/gDbrJzSYDZQiMakHJCpuv6uxaOh5uO3+3xN8ua/jM+9Jk8NZvNKrWa1JJLx6A8ng8GA7V7yiOTbETKkWRvOEIznNi6QzSQ0CiUK1TqDTTDiTcQZGLZtLo9BhOL3sik1mrT7vVYXluj1RF5CocHx6p08fk8uA0HwaCfZDKuxntej4eA14dTdzCaHUkBVTLIHoOk9DxsipEnkod7FXKRkM0i+eYJT0FP+elBXAAyZk827yRxSY5w5VFXlhEP48AkOl5OXaRfXx4/M5kssVicTqeLy+VmOBwxGo3pdLqY5hTLsplOLaX3l7kRd+7cETdIf6hCfubmF1WJtLq6isPh4Pz58woRVqlUuH37Npqm8e1XXuX69escHx/z0z/90zSbTZy6gzt37vDoo4/y1NNPkEwk2L5/j2a1QjoZJx6JEvB6iEeimOaE0bDPaCwIzePxUAXpSCx8NpsF3gH5jEYC+qtZNi5DMAdGgyHjwRBrOqHdbKLZzMbmTiWhl/oS+fvL5+RYUYrKpENR9nceJjvLvo/slUh/xOHhoRK0ra6uUi6XFdE8Ho+r9zoSiahmoxxLSviObF5KhJu06r/nTwrYNru727PjqJtIZGEWXlLHMHQqlZKi3rrdbrLZLNWGaMDpgyHBYJT+cMJJ7oR2q4M/FMXl9VKbqfocre7MMXnCmzdvK+iE0+lkZx9cbieJaIT5aFpo9pMJNN3N2JySCgoBVSKaIJVKiaNX3yQS9DMxp3S7PQZdkbbsdbtpN9tcuHCB7e1t1UWPRCIcHQm6ssxxkCITGSgjjuG2cFQGgzz66KOMx2O2t7cV/l4KpcrlsmqISrBHp9MhlUopOrOmCZ6lNH9JkZRgNBRV81IeJWXdK4/htVqNZluIXtbW1pQxR5Zd8mILBoPYmhPTtJhM2jjdAr/e6Q146qmnePnbr3Hpscf4jd/4DZaSc0rVl0wmCYVCKqj25s2bPP/cc9Trdd737AfZ2dnjAx/4AH/4+7/Pk08+ycu7r/D888+z8+A+w4UFGvUWS8mocGDWawRCIeoFD2NzwvziMh6fX8BaGdEdNKlXhK/m2rVruAwng1nO5GAwwDUbIQ7GY0aDMfFIFEt3clw9wASSkRDJ+XmqlSKWy0nP28WaGu8Sfs3NxEcy01EyE+QESKg9OyqLUhrOQDQFo/EkumYTDgXwet6xUVuWRa/bJhoJcef2LXHT20LRKL+XnGhJj4ymaaKEcojGtRwvy6Rqqa79mx7viUVBjiOFBVf4D2TDTdbOsj7KZFJCszDjAVa2tvF6RSe3URdNJVvTMS0bl8erLvT+cARooDuo1htMKyJPYGJDv9PGH/ASjsXRHS7yJWG8SsSTeDw+NM1BpVyk3Xkn/luQi2Oz0eCs8WSJcVS5XFY3j/g3Z9SbIbkQspNs2/a7cHJyl5bGJ/mzpFtUKtkkoUcaxCRsNhqNKtafnGrIDIvRaKQUjg6HQ8me5XjS7/crOrBpmuSLBWKxGOnZMVZi3WQCtlxABmNYmKVUjUemGs/6AiK1Shq3ut0ui4tC3ry8vEylUhLH4MGA5557Tkmtv/rVr7KxscHnP/95Lp2/yP7+Po8//jjdVptMKonTYTAZD3Fr4Hca+NyCkTi/tITu8YgAnLbYELw+NxErxKAtdmCnQ4h8xrOFTXboez2hO/C4XFSrdYrVE8LBEMPxCCwbHY1Bt4cR8Cm3o9zhH4aiyhNePB7H7XZTLOYVI2QymSh5vOwHSTCLXChrtZra9eXoMpFIiJJ0NtJ0OjQ1WZLvlQSy9no95ufnhZq1O1SN1+FwSCqVUgv693q8JxaF4XDA8fEhp0+fZjIZUamU1E1vmmOeeuoJYfqJRiiXBb8xGBFjt8uXL/PgwY5SFY6NCflqE7PdU8wAaZ3N5/MUi0UikQiJRIJGp8/pC1e4d/c2nUafncMTKsUCg14Tv9fNtUsXWFk2CAR0rEmPfLEAwGQ8JT2XFeISS3gzXIZoanZazZmLUzj01tbWAIHLWlhYUPoCyRmYTCZKXNJut9na2sLj8XDp0iVMUzAKyuWymnpIYIbkIMTjcdXM7Pf7ahzZ6XQ4OTkhnU6LC962icfjBINBNfItFApqLCrBo4BSao4mJpPJlJ2dPd73vveRz+dVv6LbFYIx0Jmby+APBDncP6LfLzK1NZxuN1/60leIJJKsrp/ia994kUa3QSaVJBgMcuPGDXRN0KiS8QUlhLp16xYf/PCHhIhKd+B0GFw+dwFrOqVcLNFrizyQs2srtE9ydLpdguEQls/N0f4uU9siFIuTzSRw+n2U8nm63Sb7u3ssLi7iPXde/M6znVvTNFqdNs12S5SvvTHnz5ylWn0Z3TRZnZ+nXq/Snw5ZX1mdZXzYTLWh0lhIFa20ostydzKZUMybarGt1wXPQdrWY7GYam6//NKLPP300xg6xKNh8vm8GD/XKsQiISxzzNmNUyr+UMqgk8mkmhpFo1E6nQ77+/vkcjlSmQU1Fvd6vTQaDcUe/V6P90ZPYTZaqdVqavWVs/5Go8Hx8TH1ev1dEXHlchmZl7e6uqrqueFwSKvdptVuMzFNnC4X5UqFGzdvcpLP4/P7qdXrtDsdVlZX+fZ3XmfjzDkuXbrMm2/dxOnxEk+keenl1yiUqty9t8mtt+/icHpZWlkjlZljbnER05yytLgyc0m6yefzKtBWEpWr1SrNZpNKpaIkyxICK2tbad+WUw0JWjk6OuLBgwfU63USiYSCrcg6UuokyuWyOrqGQiH29/cpFosKmjIYDEgmk6yurqpa0+v1Uq/XiUajxGIx5ubmWFpaIhwOqylFs9kkm80qPPnm5qYSVcn+SL/fJxaLkUinKZVKMxKWKFu2t7cZT8REJZ/PY1lw6tQput0u0WiUaDTKlStXcLmE+vSVV17BsixOnTpFvzfg5W+9gq47uHr1Clvbm4wGAzrNJsGAn/XlZarlEoahk82kSCdTGJqOz+0hEYuTTKVwul1Men28M19AKBBAmy16Ur4tNSPtdptoPE57Np2q1RrEI1EW5+bxul3YlonX5abZaNHvDegPBiLHYjbNefi0Jl+jhyGp5XJZjcRTqRSxWIxMJkO/3+fg4IDhcKjk2FLrMhqN1Fg1n8+rsag078XjcVUGyx6F7CmlUuI0XalU3oXylxJ2MR5/j5cPlmXR7XUIG2Echk6311FHIofDg2VPcRi6gkj4/X4Gs+bRxJyi68J6WizUqdca1KpVdbzVNI3gQ29ePp8nlUrx3Ic+BEAo4OHLX/pzUR97PZSKRTbW11g/dYZvvfo6xkxEUms8w8bGhlIhYll85/UbjMYDqvUGk9GYCxcu4HCKmyaVShEOh9nZ2SEcFit/tSokw6dOnSIcDrO/v692eF3XCYfDisrb6XTMT9JQAAAgAElEQVRUmo+u64r0807mpKVuYLngdDodSqWS0h7Io60cs4odvqsQdpIo3Ov1uHnzpjKQeTwe4vE4xVINt8tLIu6k2Wjj9/vJHefFotc4xqE7aTU7nJTvkEql2D84ot5oUSyX6Q1EX8Pj9TMam3zwQ8/y0p//JafWVtXv1Ov1aDabnDm9jsvl4uDggNOnT+N0uSicnPAPP/X3efXllzm7cYbd7U1Ora3z9ls3aFfLhINBnG4vDsum0qyTzmZIbWwwqlXBshl1Rd7BsD8Q2pBAkEgojGVOldHIAmxNw+314nAaXH/lZZrtjlJa+vweBoMuqXicRreNLxRCNxy0egNCYT+1Wk0Rw5xOJ2+//bYqL+UoXDpnI5GIKtPkBjg3N8fW1hY7Ozvo2NhTU210kVCQk+MjdE1jNJilWfe6BAIBhSGUqlK/X/xb5DUhnba5fJlKpaJOgKlUilarNeszvccbjTbvZBhKsKQk4tq2rSSg+XwesN7JGjSFuKbTERSiWq1G/qRAq9VV38/hcGCORyTSadFcmYoO+b3bbxONRjm1mEGf9CkWi9iWiW5bHB4e0+/3ef/T7+P+/U3qrRF/9uVX0L/8KtceucSptVWuXLqI2+8nk0pTzB+zsChYA4tp4TPI5XKMx2OV2CShL1KlWK1WVXLU0dER6XSaK1euUK/XVT7m0tKSmn7IutflcpHP5wmFQjQaDSVK0XVd7QzyyDiZTFhcXKRQKHB0dKRAtw8ePFCIOynsikaj7O3tKQr2eDym3RnNRExTvF4/R0c5lT1x+vRphcvzRNzkcnm2tnYo5E5YO3WGyGRKpdXh1Vdf5Xd+93f5+z/7s8QMJycnJ4oP0ahX1cj02rVr7O3scO/ePZ778Ef4gR/4Ab74xS+iTacY6ER9PnRrSrteIeh24ovFuLW1yeOPP040mqQ3noCm447EqReLuN1eBr0hw+4Yj+ETCLp2R6R8z66BQqGA0+mk2W6xv7/Pzv4e5tSm22mRTcbRNJvRWPRjYrEIIxt6ozGpuSTmpK8Edw8bmcbjsdKqpFIppTLVdZ3d3d13cTAbjQYbGxvCdzJjhUhrvSwJ5Nc9zNpIJBJKFCf7RTK7U+pxZLNTfpSiJenElFOy7/Z4TywKhsPBcNinMktNksGu8heSN4ZQ9b0Tzy2Px4AiEo/HY3TNRsMCe4rhMAgFw3Q7LdVg8/s8DPpdDIdGcSePaU7xeVz4vH4CgRCDwYjz5y7y0rdewel0EYsnGIwm2JrG3bs7nOTLWNhcOH+aTq/LeGpiaxqaZquxkTRVyRwF+bNlN3pubk6VPNJHL3f0hxWPUnAlTwjSQZlIJJTxSu5MpmkSjUaVpbrb7bK0tKQaf4Ay2cjGWLVaZTAYUCqV1PhM6uglqUrmSxwdHSkgiDRpHR8fY7nctLsC8a4ZooHp8wdFOE0iSbsrVJ5Hu9tcOX8Rr9dLNpvlJHckUPAzSbjcwUulEk888QQ/+F99lL2tLV751ss8+xOfEAKmhUWcuoNUMsGPXr5Ms9nG4w/gnE7ZvHGTWCxGMpkG3QCPB/J5Wq0Wp0+fFv2WaAzDnLDi9eKZLRCtTptOv8fZyVn2DnPiGrQtugPpQ7HwRkLoFljYIkfCpSlJ+sPN2mg0qk4Jov5PK+Buu90mm82q06EUEw0GAyKhoDrdSqxeIBBQpY5kIoxGIyazMfBkMlENbfm1gOoZSEK5vK4kMv6/CHCrwyGOzoFAQOUlyNFOIBBQtXcgaNBo1JTJwzRNTp86xeHhCXatKZpVpQqDmYRXx8tU1xj0ROfc5xE0I3M8QrP92FOTZ55+guOTAoeHh9iWSa/bxh+IsLm5yT/42f+emzffptfrUag0MEdDOoMR9dYxxWKe+xdPEY9GuHzpDOVqiYX5LL1OVzUTJXI7Go0q66u8QHK5HIuLi7jdbgEamanbJHVJ1vVSvisbSpPJhCtXrmDbNtevX1dZmQsLC4r/WCgU1CJkGAbz8/Pq4pE6gaOjI5VRISXU8kLt9/vcuHGDcCSlQCyDwQC/369cpfF4XL1HuVye1157DZfhZmpa6JqTfEEkXXsCbhVXv7YksiszmYw6yXU6HVqtFoeHh7gMg6eeeorHn3iSF154gblsGsOy+dEf/iHe+M7rjAZDYXvXNazphN3jY3ETBvzYU4uN7AKa10u/XMMXCGC1utTKYuR7cnxMeWYuarZbtDptTgoF8sUCr7/1JolEQpxEdR2H06DVatDrNhkN2jg9bnq5HNmVVVweD+VqhUmvoRZmucjGYjHi8TjJZJJ4PM5gMKBcLqmbXO7ivZ4wZBUKBbLZLOl0mkLuiN5Mh5NIJOh22oxmjEW3y0mtWuHpp5/G7Xbz1q3bpNNp5Y14mBotQ2Pcbje1RkellC0uLpJMJhX38XtNIL5vo1HTtH+naVpZ07Q7Dz0X0zTtq5qmbc8+RmfPa5qm/ZamaTuapr2tadojf5tFQdcdvP/pD9JudtExsExwaE5SiQyWCUsLK+ztHDAcjDEcHgyXj3ZrgMcbYjSa4nA6CQS8NNoFBpM6Y8vE0mE0ndAd9ukN+soR2Ot00AGnw4HH5WI8HDEZ9HEbTiaDHvVamcLJHoYx4Q8++3/SbByztBhlORvh4rlV1pbm8Hs82JaDQr7D9e88YP+wR6vj5dadKt5ghN7IpDucMBpbnD1/gUqlhtPpptftEvD7SSYSaLqLqaVjazq7+wd8/cVvsLu/S2/YIT2XZPdgk73DLTTNQaPRot8XPIPhcMz29i71ehOXyyM4le0ut+/eo9Fqs7O3T384IpXJohtOmu0OtUYTp9uDren4AkEO8xWiqXkKlTomDsbmFK8/wOHhoRj/TsbUqhXq3TKbe/eotirE0nGcXi/lepN2z2RzJ8dk6qFUGdJotplaGol0mv38MX1zTHPQpVwt0em0WF9aIBONMDYtsksL1DsNvEEHtXYRzRhhaibNTou5pTVu3dnm1Re/QjoewGXYrJ8/zY0Hd+liUR4O6No624UK9aHN6noG6OPUJgR8TjRtCp02HiaMKidY3RIR5wB9UMIRDuEIhxg5dIxAAM3tod0fMJnaJBNpPG4fve6AevmYqTnA6QvRGE4ZuyOYRgivP4I2HjEf8qE1TnDqohR1YBPwuUglInhcEA66MfQJ9rSP36sr74p0xUqQrlyEJXHM5fVjuL0EwlEGYxOHy4OtG4RjCVrdPstrp/AFwzg9PsLhMK1WC8Mw1JRD2vxlTEAmk8HrduB1O5jLJDg62KFWKWCZQyzze5OX/jYnhf8L+N+Bzz703D8F/sq27d/QNO2fzj7/J8DHgNOz/54Efmf28Xs+3G43R0dHBINBgsEge3t7OJ1OyuUy586d4/j4WGCt94+V6s8wDJwOQ8VnHRwcKIONLCkmkwk6GpZjiqELTHt0FgM+7A+YjMZMzQGgEwqFiEQiDCcmhsPJcDxiOrWVnNTjdbG4NM/NmxVabbFLnL9wFsuy+PO/+DPOnj3L4uIi33jxkGtXrhIIiIToaqXO3MI8TsNBMBah027R6XTwB7wMhr0ZlUkc/3/kR36Yra0t7t65T7Ek5tuHh4csLy9zeHhIu92mXC4znYqQE8mfPDg4AF1AaWQOpq7rLCwsqCnIyckJ165do1wus762ojQGmUyG+/fvUy6XSaTSGC43hmUTjMS4e39LjIJDcSZTjeFggi8QIhSJcn9zG1tzzvDmPSKRCNlsFtvSODk5YTieKCWlIkxZmjop1WYlTbXeEI02hyH6CF/4Mza3t9A0jctXrnF0dMRwOOSll17ip//eT3P7jRtEZtzMzq260B7gxKE7CXnFzXL9tdfweQx6nTYfeu6DzC0uELBdSuE3nYzIpBLcvzul1aiRTQsX5NXLF2l0BNNgMJ5Qa4pG76DXw556adZLPPn4o9TrVXzRJIGA2JHjsSRnz20odeN0OqVarRKLxQiFoip4Vr4Wksshd/SbN29ycnKC0+lkbW1NCcPk9ZxIJLBtm7t376opVTAoyrNyuawiCw4ODpTOREYgLCws0O122djYYGtri0QiIaYP/zGGKNu2X9I0beWvPf1jwIdmf/4M8OJsUfgx4LO2KG5e0zQtos3CZr/Pz6BWq7GysqLkt9ISCqibPBgUtKV+v6+aKlvb25w/fx7DMCgVinT7PSxLGEUeRlKFQ2GYhZtg2cpcsrwyRygUYTqbvfeGA6qVGgFsTp3a4N79+9y6dYuVlTWazTof+cjzXL16ma9+9avcvPkWHo+HjY1TbG9vcuPGmzz7vsd49bXrfPD97xNKsmGPCxcugC1uEgFvmTIYDonF5tE0W8FYisUS+byAxkbCImAkFhYqxcPDQ+bn5+nNlHiTiYh/lwxIj8/7rgCRdrutmlwyQ0DyGhwOB8N+H49LyF4zmQx3797F6QnQaIsSYopBs9XBnGr0B0Mq1QaDgcg/SGfnGE9sKtW6ilIfj8eUS9UZIl803ZyGW5U8k4nIe5zaFqY1pd3uMhybxKJRpqatVJ7FYpGQF1ZWViiUS4xmo8Nf+qVf4nN/8Ic8+9QHWVlexjAMktEgGg567Q7D4ZiG3qRWq80AIza98ZBWu0t/OMB0+Mlm0gD0e10G/R4Bvw9sC2tq4jQcJOIxkqkIx/kCuUIRt9s9Q9q1cDps4pmk6vHIa7PXE/Jwt8dJKpVSidPS0SjDV+RUQYqW+v0+8XhcsRRkboNsDkqHayqVUorVUCjEyiwtzLIsBQeyLEuV2FJ1KonikhAtYUHJZFLIzf8TjCTT8ka3Rbp0avb8PHD80NflZs/9vxYFTdM+DXwaIBmPcvHiRdWEu3v3rlocJM76jTfeIJnKCPXYqE8mlWAwGnH54gViiTgej4vDo6e4c+cOR4ciH8GYNYBCwSCGruH2+nA7xYLRaNSZjEYUijmhKtMEaWlqw8LCAvOLS4xGI5544imy2Szj8ZCVlRU+97nPcXJywjPPfEA1Qb/2ta/NVHsu3rxxh1DAR63aIpmM89ijV2g0WtTqBS6ev8Cdu7dIpRIsLi6oBp5QCmocHBzh9wdF88wdYDyaEAmLpl6321UCpkgkQjKZVN4Qh8OBwylYfcfHxyq9OxaL8eSTT/Lqq68Sj8fZ29sjk8nQazdxYLGwsMxffOkrPPb4kzQ6PfoTm3K1xuLyKl/88tcxx8NZA61PpdrBtjTi8ST37m+zuLBMp9MhGksS0fy8+cYN0qksn/rUp/jMZ34fTXMQT8QZmWJyIbgPQpvR7Qbp9vrYtk7+pMzp0xt8+MM/wO/8q39Fvd7kfY9foVpv0u33GIxnmYmf/zzr6+vcevsG5mSE2R+yvrqAy+XizJnz+N1BavUm7XaH7qDPX37lL7l27Qq397bodrusZJeJBsVMX6ZztesVQj43GlN0Q6N0ckQ0HSfg9TCdjFmcn2NsCrhJIhmjXKlQrdpgmXiC0YeSuyaUihUVZCRNaYPBkO3tbTW2NAxDidok6UrmMCwsLCg4rs/nU7xQufmFQuLkeevWLYB30cOlGM3lctFsNpVydmVlhUKhoCZT0WiUUqkktDLWf77pw3dbfr5r8WLb9r8G/jXA2dNr9urVq3znq18VlN5Mhrm5OcWpazSEPboza8R0Oh2uXLvKdDohk03NEqTB5dDxOA1cbvFrOTQNXbNxYDMc9LAtE3MoZtDhUIipz4NpQbfTFQjuqQW6jtPpxtZ0vB4/sUQcv9/P/Qe32dvfIZGMEY2FefmVl6hWq5w9e5bnf+A5CoUC586dY3/rmPsP7rK9e0CtVid3csTK0iLnz57mm996mXQ6gWlalEolptMp6+vrjEbv4Od0zaDd6tNuNRSivlAoKJvx6uoqtVqNYrHIysqKQo2nwoIPWS6XSSaT6ij7pS99Se3CJycnIicCi5OTE6KJFIbTzQtf+AJv3bqD1x9Ed3nYPirS6A5xztx6S0tzMwaAsJ37vAHypRLz8/MMxiNajQrPPfccb711E81wks3O0+v3yWTm6PR7uF0eNHQGgx7D8ZjRZMpkMsXpdKE5TH7khz/BH//xH2NaU8LhIPe2tgiHw+TyJzz55BP0+30q1So3btzg4vop3nz9Oh/7yEeJBYXlu1lvMRiO+fqL32T/+JB/8On/jtYXP8+t+/e5s3ufQCjEretv8MyzH8DXc+FxG7zxxhvcvPEGS0tLLC4tsbOzQ7GQIxj2k82kqDaazC8tc/311xmORSZnMi5Om16PayZZ7qlJQ683IBIRXg6v10urJazp6+un2dnZURZomRwtmQ9ra2tYlsXx8TGRSIRMJqOER1LOL0s96Y/xeATjQobblstlMpmMamZLZmOpVCIWi3FycqJEUXL68b3SYP6/LgolWRZompYFyrPnc8DiQ1+3AOS/3zcbDgb8xec+h23bLC0tkclk1JF6OBzS6XQ4c+aMwGe53dTqdeypRSwSpVGtEYnHuHfvngqD1ey6ogxJvLYxw1z5Q6L+6raFlrzZ7YkxjWEIp5rDyc///D+m1+/z4MEDqvUGX/7yVzk82iafz7O6ukqlUlFUplwux+HhobiIczkS0SxXr17ltddexbJM3O4UlgVv3LjJ6bVVmk0hp41Fo1QqFXZ2dmm3u1y6dInjo5NZLZpQcFcpOFpaWqJcLqsYvJWVFfL5vDqSOhwODg8P1ShXHhuj0SjXr1/n2rVrCvpSLhYIBoO88u3r3Lp7n1K5iq05aHe6+MMuzKlNKp2lV68Tj8dnM/IZiNYfUt1rOT+fn1/kwYMHnD9/nsPcCbFYDKfLQyqVonjnLpFIRMzfx35l5PL5fEyGE1wu2Dh3VnXoI5EIzUYFuw1rp9a5v/mAbrvDyoLA0kWjUc6snyKZinNylKPeaNDs9Gl3ehRKRbB1tra2eOaZZ/D43Hzlr77EUx94P+NihRe//jXW19f5vd/7vZlD1WI6nVCrVdA0e+aATbJ7sI/X6ybo9ykKeCgYYdAf4vW58fmD2DgYjQez0kCg/G3bptcVfYPTp0/PvA9FZV9+ON1cBtNKxqIcN8uR5ubmplh0Z1AbyeQol8uUy2XloZGlx3AoJjOJRELxQjRNo9FoqGnY+vq6+lx3/P8/kvxT4FPAb8w+fvGh5/8HTdP+GNFgbH2/fgKAzydyBPL5vOoZ9Ho9CoUCw+GQjY0NcYyyLNxuFx63i+Ggj8Np0O126HY7BIMBUskEw9UV7tzdxzJNHJoTHR2n4SQ005/7Z+5IScmNRmNcvnyZdDbD/XubHOaO+ef//J/T6w85d+4cDza36Xa7nDl7ltOnzgh7a0jImGu1Ggvz87z66qusroqR0P27D3j88cdxedziTR32+fo3vsW1q5e5e3+HVqvFE489gtsVZC67IkaWkTQ3b9whlRJ4e4dhq0h4SeGV/obBYEAoFBJkIK9XAV7kwiQJvpVKhbm5Oe7fv8/CwgL7+/vs7u4yGo3IlwSopt3r0WgLX8R4CsOJiUkLyxaAklRcYPDm0nNYmkU6kaRarRKJRKjX6wz7OovzGba3t8TJIxoXI7fuAM2hc+PGTVqdDq+99h2CwTAjs0M+XyAajaNpTsq1Ek8/8RS/+iv/DK/PT3fQJ1/KEwyGaXZ7dPYPmVoTXA4HlgaXLl1gYWEOw9B5/bXX2Lm/Q7VRJxSOk5mbp9cfMpyY/N5nfp/1M+vUmnXuPdik0WpzKpGi0+vzgWeeZWxOCUdjivE4NqcsLC3jcLooFo7xuAw69Q5vvfWWuB6lJX8wZDATGQUDYTQcaDhmLtMwXq+HXq8jeje9IYbDhcvtpFAoEAqFFHz35OSEs2fPqrDe27dvs7CwwGAw4M0331TGqkZDnBYjERH0I8Gujz32GFtbW9TrdRYXF/F4PO9SzI7HY7UQjcdjFhcXVT6JTBf/j+IpaJr2OeDbwBlN03Kapv3D2WLwEU3TtoGPzD4H+AtgD9gB/g3wc9/v+wPYtqWaMdJH3u12abVa7xLb9Pt9eu3OLOKtQafVZjRbIZ26SHYOBALYM42AdJXJY5kUePT7fXqdLg5NZ35+nnA4zNbmDsViEdDxeQOKnDM/P8/ly5fZ3t5ld3efU6c2WFhYIhZLMD+/yMHBERcuXJoBY12cv3iOYjFPMOhnZ2+X4WCEZjjY3TvAaXiplBvs7OaoVlocH+Up5Mv4/QFlkllbXyGdTmJZJi7XO+BNmVMhYZ1S6NJoNMjlcso9KdVqkgItdwlpk67Vauwfn5ArlukOxqAbuDw+pSY1DAOnoeNxOwkHQ4yHI8bDEV63h16nTTadwuNyEg4GWF1eQrMtPB4fhiE8/hqOmRzdORuVady7d09AaPqCLiSmOT58Xj/Pf+Sj1JtNqhWBiXe73VjYjM0JY3OCwzAIx6IkEgmWlpbI5XKUyyVa7QZje4rm0BlPzRngVMh7z5+/SCGf59TaGmdObxCPxsidnPDmW2/xb/7tvyUcibC3v0+v36dQLDIcjXC6XNTqdfq9HpY5xTB0hoOeCtY1DIPp7LW00Ol0ekpoNx6Zs9OaAeiqdyATnqSBSTpWZW8IUM1FqWCUqLaHFYqSuiVFRxL/Byi1rMxBlQnq0jov+w7yfngYA/83Pf4204ef+hv+1/Pf5Wtt4B9931Xgrz0kdkyaheTFubi4qAAllmWhM6VYas/wWsKaevPtW6L7GvLj97qJhgWkpN/vg2VjTy2siXjTBjOPgcsQKT3JZJKRZaDrBgsLC3i9fibWlGQizcraKrpuEE8kqNVqbJw5xeHhIcfHJzMox4SPf/zH1Q24v7/PBz/4QTa33+IrX/kaxdKA0XDC4vwCum4QDoR568Ydstl53r61zVe/9HUee+wR3G4nhUKJM2dOE40GaDbr1Os10pk4LjcYmkclUiWTSWVokYlXe3t7hEIhFpfFRSM5iHKi4/P5uHnzJp1OhxdffFE4+twxRsPh7PjuQrfE6NXnduF2OXDpNrptsnF6HZfTQafTYXVlifF4SLVSmoWuBtjavEs6nSYUTuD3+2nUW7OFfUggZJBOZWl1O7z88qsz0K642bOZeSqVGqn0PA8ebIGtoxsOTgo5dGzWz5xnMJoAFktLiwSDftrdDjdu3CAdDtFpNHDrGp/+uX9ENBql1e5y7959kv0xlm2zvbfLE088wQ//8N/hP/yHP+XKtatc/9a3+OQnP8kLL7xAu93mp/7bT/Grv/qrPPLII6yns5TrTSKJFMdbt5lfWmZrc5NqUzAU3V4vY9PENC10l7hlpGV+OrUVq+Cdml8QoEUvRHhRFhYWFB/hypUrqrRwu91cu3aN4+NjJR7TtHdyUJxOJ6VSSW0I165dE7g4y1KNw0qlonwWkg5dLpcVnFdCiw3DoNlsisXmvW6IAhTWStbMkq8okdg+nw+Hjmqi+DweprOmi1z95PjH63XPtORTJpMR/UGXsBnA7/dijicEg34ikRDdbhuHRxyF05kMiYSDTr9Hv98nl8vz0ksvEYlGqdXqZLMZ9vb2iEQiLC8v8/Ef+3G+8IUvqBFTt9vF6/GzsXGKVCrFgwdbTIYmt269TdAfwhxZLMyvcPfuPa5cuozX5aTV7GFOx+gOQdxJpaM4XU66vRamOabdaRLwuBS3r9frKfutDH2RUI3NzU21m0iK0PHxsYrik/Vqr9djrE9xGC7cHh9OQ8BAHNZUpQ15PB6sqUmv3eHcxhk2N+9jmxM0y+bU2gq6rnPjxg0WFxfp93vKo7+/v084HMY0xZze7fJizoKBZV2s6/rMugwbGxt89rN/QDjgxWbKuXPnmE4ndPo90DUWFhYJhEMUTnIsZdP0xgJZ59Tg6mOP0Wg2WVtfJxiJU2u06e0fwlSMQD/87PP8i//lX/B3/+tPcOvGTVqtFr/8y7/MxYsX+fSnP81v//Zvq8Ceo6MjhcQbj8eKcBWLxeiOhIR9PHMaOp0G5tQm7A8o9P506mEyns5KOs+7KNwy/GU4HHJ4eEgsFlMaBTlql9Qmy7KUIe7hyVQikSCbzXJ0dCSus1nzeW5uTrFM5YlgNJqVN8GgCP+Nx5Xhbn5+Xp00NP09vii43e7ZzVcjEo0q6e/Ozo6AhkyndLpdUqkM85EEvV6PZGZRdGzDSU6fPo2u6xwcHGBoHhbm17ieu47XHyIYidPpjVkLZui02ixl50nF4/SaNQaDHj2vuMFqrSL7+4f80A/9MNs7+6QzUZaXF7l16xarq6u879oFtGFb9D1CHr7zza+iDdu879FH6XZFzNnv/M5v0Rh0FPxkMBiwtrbG1atXCQaDvPjii0SSHnaO7/LY40/x4MEDRpMBr77xBt9+6w3m0hk+8fEfZTQasbwQYGVlhVQmILIpLpylXC4zvygyDx57/Enefvtt0hknb775Jul4ikq+jtcvtAmd9pA337qD7nZy/8ED/MEAnd6QUChK1BEUwJNEGtu2KRdLZDMLVCoVHEOYz2RE46o3Ib0QoNxsk1lawh/ycmfrPsFgiOHUIleqCoHVrRt4fT5s3YE90XE6DVxTF7tHB3zgg8+ys3cAbjd6z4897hL2x5gMTH7mv/l7/NVf/SVzC1lyuSNOCiXOnz9P7sZNnn76abY27+OeTJmPJvDZDnrDCdn1BXweF7Vmg3Y1jzU8zb/73d+jPxqxtbvH8x/5CP6gwa/8z79CIBDgdz/zh9QbDU6vLYLLRalW5/9+4d8LDoLLSTIaZmdzi77fj2HoHBZKFKotzpzZmAUNh6g3WzhsJ4Ou4x3fQixKr98Fc0w4EaLcKDPVIjg9MQzLolLNEYvFOHPmigq6tSwxdZKBuEtLS8r0d/HiRaU3kAyQjY0NHjx4oHJEZMBxr9djbW3tXZg9j8ej/DZ7e3uYpok9mTJ0exn0evTtLqlYAoetgWmha+9x74M5s0TXajUWFxdxOp3s7e2JlbrbZX19fUbAzStyTS6XQ9d10um0mjTIiPlnnvkAR0dHjCZC03/tkSt0mi1Vp7VaLWKhEMFgkJ4h2AZTxoqOD6sAACAASURBVHz42Q9RPMnjc7qpVar81r/8TT7/+c8LoYhTI7uwyOVrj7Czs0N/NObilavEkilMG779ndcJhCM4g4L2JBs6o9GIF198UX0ejQqF2+3btzEMg0uXrvD2zRsKifbCCy8IqGm5KObSdwfUajVKlTqFglj5Y4kMb916m8ODIxHm0mhjTjSGE3Fq6XQ6lMpl9vf3sTSIJeIsLCzQ7or0pmZLuCLlbpTJZNja2uLHf/zHuX/nLvF4fEacTrC5ucmlS5cYDgdMrZGCtYSCEZpN0dsxZ27LWr1OKp0VwNBAgGw2qxpkkjAkX5NPfOITfOc731E06/F4zFNPPcUbb7zBx3/sx3jjjTeEN6LXxTB0XDoMxyNu3XqbRDRKJBzk4oWr/PsvfJE7Dx5guJyMrSkvv/oK2/tHhONxOsM+gVCE9Y3THOxv8ciVq+ho7O/v49Qd/ODf+SHyuRNCkTB3b99hZWUFl9MzY3L6OHV6g+6gj9vrY2t7m/5sciKTvWS5K65JB9hTXC4DdzyKxyOYFa+//rqaCHi9XnVjj0Yjhaz3+XxkMhkRR+dyCfHYTBUpaeaSlCXHyoCaTMi8z9FopDJBgsEghu4QorxZItj+4YFigdrfYyb5noCsaIjkoNOnT7/LzbW1tcXGxoZyRcpfSGq8h8OhqqXa7TaGYTA3NzfLPrDVG9ZqtYjH46rmCgaDnDlzhgsXLjCXyWCZJrY55f69e5QKRQ4PDnjkylX+2a//On/3p36K82fPks7McenyVYqlCqFwlKklqMR/9fUX8Xj9tNpdMlkR6ZbNZnn66ad58skn6Xa7rK6u8jM/8zP83M/9HOl0mt3dXQXFEFLYkAqFcbpcVKtVXnvtO7x6/TWK5QroDobjCW6Pj/5gRKFYYnNrl1qjycQEry9Ardul3u5QaTQ5LhQZmVN8wRD+QIj1tdMMB2OYanicXoXTz2QyqvmYyWSIx+McHR0Ri8WIRCKKATgYDBSApdvtquPo/8PcmwZJct/nmU9WVVZV1n1XV/V9d093YwZzAJgZAhgQBCiSokCQIlf3ilyJor0rWbIlOeSVvAxJQcV6l6uw5dUBWTZF0RQFS6IhkiABEAQHBDADzH10T0/fd9d931lVuR/+lQnAQVBa7YYD9QWY6YmensrKf/6O931e/bqoqkpDbVGpVg0BWrlcFgMwtwdzTyrudDp7MfY2fuZnfoann34an8/HpcuXmJycZHFxkSeffJLLly+L8J+eBkCAdToodictVcWqKFhtAu+/uLxMuVFjP3FIvlRkdXMLWRGsxmw+T6NVZ2Nrk7HRCV6/fIlKrcra2hrFStlox1qtFi6PG6fTSSAQYHh4GLdb4NJHR8eFSpE3w1nNVhFlqKdteb1e4vE4IG5U3enYbrc5PDwkk8kYwCA9Qbyvrw/gzaF5uUyhUGB9fZ1kMmnYp10uF4FAwAg1ajQahntWx+PpbZC+jrZYLPh8PpGH0rPW6zh/HSjb/QHW6XdJQtQffPY3/vn/Yqix6vW6YQ++evUqExMTbG5uks3mDOilnutQrVaNXe/m5iY+n4+21kW2yuwd7NPVuoRDYdbX1ikUcvjcbnweD1azwJ91220+9pEnqZYqpFNpJkbGsVvtjI2OUsqX+S9f+QqrK6uMz0zz0vnznHvve7l5e5H7HjjN2MQER+YXKJTKjE1MkCsU8Ps8xGJxtrd3SCaS9PcPkEgkWVxc4trVa6gtFbWlMjI2SaPZYunOEuPjE7g9bg4PE1jMFirVGl6vh2vXrrO8tk4ynWVze58r125Qb6rcXdnE4fSws3dAOltAtinkG3XqzRaFcoX9ZJJmq01XMqHYHUiY6OuLY5NtaF2w9OAr5WJJfKg9Xk4cP47ZbGZ0eIQrV64IvqHLi9vtottt02jUDVFYq6VSrdTI5fI9MpMgXrXUNrLNhkW2ksvnMZstDA2PsLW9g9PhYKA/9haIaYnFxUXW1td49NFHWVpa5Fd/9Vd57rnn0CQTtYogSLt7EXf7+3soDgculxvF6cBmV1jd2WZ1axNPIEA6X8Tp9dLqtDlMpwhFo7h9Xkq1CharBdlkYmt7hyNH5nG6nNhsdvZ2d5FtMjtb23Q6Xe7cXeaBs2c4du9xGs0me4cHVOt1Uuks6UyGZlOl2eNfunr8yVqtBlqHWCyGSRKbtFq1Ymy8nE6XIUDS4wrL5bJhknK73TgcYvvT6XRYWVkxoK+FQoFqtWpAi3Xpsl4pWCwW45DQU73eGrNY71XOSi8H0+VyGXOKZ188zz/7le+fOv2uqBSQBDUoEAhw4sQJgzNosViMYFidFKTruPUTUF/z6JNbh8NBpVI2AK+6Hz0Y9OP1eolGo704OdF7WyQTa8t3KRdLvOf0GdwOJ3Q1um0BPvU4XbTqDf72q8/wP/7sp7hw8Q2OHjuObLUjW+2kMzlsdgdmi5X3PPiwMUA6ceIEExMTXLp0yfBq6EDWfD5PJpPpYc26PPvss+zvC16iZBEp2LV6XfTpWDCZbdQbLba299g/SFIoVsjmCqTSeaw2hUq1TrlSo1xvIMlWFKcbyWzBZlNwuTy0mm3sNgeybEOWbcTjcYPV2N/fj9Vq5bXXXmNmZsaAjtZqtbf1t/o10GPwdLGN2+1GtlmpNepoEkbcnv6++3rVglNxGEE3kUiE5557zqhWlpeXOXbsGE8//bQxKJNlG1JPRl4slBkeGqVUKtNFQsNCrdniMJ0hVyqTyRfomiQk2Uqz3cHt89LoqHR7n6tmq8XtpTtMzc5w5+4ymUyGXCGPN+AHTEgWM9VGnbm5eUqlMmtra2xvb6OqHbLZrPAhNFq0NeGdsdrsxoDb6XQa1CidSaHTlgqFgtEe6C5JwEjx0mEroVCIYDBIJBKhv7/fGAhPTk7i9/vfhmjXv4+OVwOMp7+OH/D7/fT19SHbrMg2ETRbKBUpVcooTgdur4d2551Xku+KQ8HaExMlEgkuX75sEIn0fiqZTPLGG28YJVA4HDZ2svpk9/DwELvdztbWFhOTI/gDHhrNCu12g63tdSM38c6dO6ysrPSMRVVskpnNtXXajSYH27s0e2Xu4q3bxONxjh07xun3nOVTn/oUly9fJhKJEI1G+bEf+zFCoRAf+tCHDHJQMBjk6NF7UdUOiUSKfL7IwsJRHA4XNpuC1WrHbncQDkfJFopMzszS1rpIJgs7u/s4XG72dvfJ5PK0Wm1CoQiK202xWiWRyWGxK7S6EolMnoNUFllxgcWG0xugVK4Siw8QCIQYGBqm3YV4/yCTk9NIkplmvUWzoSJbbIbzU5/HZLNZ7rnnHl5++WVUVWVmZoaBAeErGBsbw+VyMTExYYBjK5WKQdpOJBJ0ul2SqRTRaJRStWLkO3hdbs6ePYvW6ZLLvJmJeeLECSRJMuYJDz/8MD/3cz9nOCjL5TJmq0wuV2A/kcSmONhPpmh1JZptjf1UhkK1iWS14wmGqakdhienqLdaDI+NYbZaCUfD7B/u4w/6abYamGUL+weH1BpN7qyusbyyxt2VNRrtDvlyBbvi5J7jJ1DbXSrVOoVyhUq1zvrGFvWmKmC5dgdWm93gIlitVjweD4VCwVB56i7JZDIp3KKNBslk0sjm0B2S+qag3W4byVH6gaqvKmu1GgMDAxweHhrZHPq1GxkZIRwOE41GmZyc7EUiFIxcER18rOdx6nGG29vbbGxsYOulS32/17ti0NhSVQ4PD3nfhz9MZnfXsMvq4Rj1ep2hoSG2t7cZHR0lHo8b6U9//ud/ztjYGIFAgHw+Ly5WPkunozIw0E86naFcqpDOJFFsDvp7se+6WMjWC+gwSaLH0mPUnG4XNruLTm8gs76+zsLCAsfuO45abRowDJ28s7q6yuuvv04+e2gAW/ULqNN+9ciuQCBA39AI58+f59y5c2w514zIOJtiN7BtktrC4/GRyWRot9uMjIwZ2oNGo8HMzBFsNhuHhwlmZ2bY29sjFovRF4myurSMq9cj9/f3Y7KYjXmGV/Eb76Heiy4vL3PixAnKvSj5U6dOUW+baDYFTj6dTtPuNEilUmi9UBRJEuEy2Wwai81KvlQ0Yux1ebTH6cJutVLprcJkWebMmTN84xtfM/IJpqen+dznPkcoFOrh43thtV3BkizXqihOF6GQg0q5iEm2Eo700c4mCYaE/j+bySNZzBTKoiVaX19HkiTjvZscnqRer7OzvUk4FKRcKOIPBvAF/BQLOUbGxzDJFmZmZlAUha3dPQ4TCQKBEKVKhVxBPGnNZpm+mBOrbDYkyzoZS3eiCiWizyAr6UNAnZmpU6R13qiOc3O5XG/jLS4vLxuuRkVRBEm6d5hYLBa2t7eNFa9ugtO9Eel0mlqjAaUSFqsVi9XK3sEBXSAQDP5/s07/93hZzGa8Xi8XvvMdgsEgg4ODxlQVYHNzs6ce0wwBhn5q/uiP/ii3bt3i2LFjbG1toSgKFbWKz+9lbn6ar/3dBl6v8JA77CLSO5fLYzeJC9Gwgi8oIsmbmoaGiXK9jiSb+eGf+ikONoRH3R0OQKtFvidZLRezWC1wsLfF4f4209PT9EUCzEyNGU+DtbU1pqamKJVKfPvb32Z4eJjr16/TaLRYunOXvlg/V65exaEoVBtNWo0Wit1OvlhC0zo4OgqZYpl4PC6YCLt7PV17i1azyfrqGmazWZSl9QaDoTBhn8h9WJidQTZJXLtyGbPZLA6LeJQ7d5d69mbNYD0GAgHaqsrx48fJptKsra0JRBrCIdnpdLh8+TJj40O9ktnFXvXAGB42u22cbhfVeg3ZYiNfFKnO9WqN0eER2i2VaDBEo1NndnaWJz7yAT772X8tsgo6Kq+99hqbm5ukUim8Xi+zs3P4e96QYi6PCtgdLhLpDOFAEIfDQbpQQXF5Wbp7l/HxUeYmJsnnRWamRod6vYbW6RLvjxEMBrnw2hsi6i8QRO3JmovVGodXr3P/qRP0x/txe3xIiDSutfV1TBYZk7WJ2uv/TRYZtbdetHgU0Zo0m9hkUYWWu23sdisCCUsvaMdk6Ap0MtPBwYGR0qRTyovFotFW6IeZfoDotmt9ZhAKhbh06RIgSEu61dpmszE5OWmY65xOp/Ez6oY4XQb9rsex6b1RvV43AjeTySRDQ0PIskw8HueNN95AkkwGxmtnZ4elpSWcTifT09O8+uqrWK1WSqUSuWoar9fP6dP3YzbJfO/li6TTKVwOF4pZRjaZ3zKVFW1IpwtOvx+Hy8NHH3scUw9EEYyG6XQ6JNbXuHjxIgBLS0tEIhEymYwRfxbyefnQ+x9neW2dRCJBMBhkdnaWxcVFUqmUoT8fGhoiGAzyvStX6XQ6LCwc5WtfewaP243VaqFeNyNbRKpUp9NhMNZHMnHYE3C56DQbRPxBOp0OQ3HheCuVSoR8HmrNBjYgn8ngslqxAKGgn3qzgcNpp6O1aXdVEokEk5OTmBFeiuRhgqnJSc6fP8/k2Dgmk4nDw0MKVZWFhTmuXb9MNBpld3cXr9dDo9HslboCctNsizJ4YGCIQr5EX1+celWs74ZGxYpycHAQr9vDxz/+cX7nt/93Q3r7W//6t/jUpz7FmTNnuHXrVi/A1sLW5rZRiiuKQrPVIRYfwG4XAa4Ws8z21iYT41MoDhvJgyQTE2MCRrJ0g8G+OIVCgYmRYV5//XWGR8dEpdaoIyGRSCUJBvy02iq+YIBIX5RkOkU+k8XldtMX72dlZQV373BK5fI4XS7sdgfegB9Tp24EvApPSg3sogXweVyGIGph4ZhQXRaLbGxskM1mmZmZoVgsGjRr3QRVrVYN09309DQAW1tbJBIJJEliamqqNzOrGFzIjY0N6vU6/f39tNttEomEoYCV7TbCjggOl/BSeHwCQhMIBQ2Z9fd7vStmCpqmCXoQGINAPdnG5/MZ5bge0Q4YZOJAIEAsFjNKbl1GOjExZhB1dU6dPqnVraiFQgHF5WR0bIyRsVEikQj+YID19XVye3uUc3kq9RovfvclTCbw+TxUq2X8fi+ZTIpIJMTExBjb25tcuvQ6Pp+HVDLDmdPvYXZmjqHBET765I/y3kfex8jwGNFIDJtVQbbYCAZCZPI5SqUS9933gOGgEweUAIKK1V6JcrnUk6mKAZ7Dae/tuxuGE3JjbR27WSYWitCo1jD3vCRTU1PYbDYajYaRnK3H0DebTex2u7F+1FWToVBIEIo6HdbW1oTiMxrF1dsE6KsufZKtdjsoLjEE1tsHnRysNsBpV1CsorSen5/nxRdfNNK/f/d3f1cEBt+9a+QoZHJZIwNDTz6Se9Fupp613WpXjOi0XCpHLBYjHA4L92NXM7ZT9WoNxWan1vPO6INgp9NJsdAL4+1AvdVkYWHBWNFOTExw7NgxUqkUSm/qHwiEsCp2sTru8R51F+5bV4SqqhqZoKqqIssyoVDICNxJJpOAAMnoSeI6el+vhPVUaX3wqw8c9VV2Pp9nbm7O0MTAmz4K3ewUCoWMvFLd86Ov6Vutd/mh0Gm3sVkk/B4nfeEAG6vLeJx2pK5Ks1YmebCL3+M0es7NzU2Gh4eN/u/GjRtMTEwY0VyF9SK59QJyy8Ls2BThSID4cJSx2VGaUpdyW6XQ6lLvWpFrJvyOINVinWA4imQ2oXaaBIZifPeFb/C3X/oCe8u3efbLXyG1uomlrtLvCzE/Oc/Q4ASjo0f48Z/+NL/wP/8q0cEpPv6JJ4lEA8wemWRmdoK7K4t0tRb3P3CC6Zlx5uanaXcaxP0e/uknfxZaDdw2mV/5pV/EjIRVsWNzOGkBtXab3Y19WtU2c1PzBD0hhmKDjA+N8fEnP8pgLI6mtnDarMycuof11C57+TSSw4bD48VidWDBznBsnFZJ4+zxh+kPDHPm4bMcpJJ0ZRstLBw/8zD5hkpDstBWbGxlkzj6/ExPTxvCo52dHZrNJrlcznBu6mhxh9WFWZNBs2A2y3S7GqlMGqfbheyAvsEYLr+b3Z1D7DYXhXyFbtfEr/3ab1Ap19G6JoKBCH5fCI/bT6taxmqGSNBH8mAXi9Qlm9pnY2OZTOYAn8/OrVuXcLhMaDRJ5RIUyiWWVza5tbhCvWUmV2owPj3HxSs36FpklB4qPZFI0Na62BQ7JqsZk1XmW9/5Nn/ztWfI1ypMTEwQCYfJpNK0Gk2cioNoMMLsxAxOmxO11iboCTEcHyISCOJWHJi7JqROl06jjQUJta4S8PrpC0colUoGXt/j8RCPxwmHw0bg7ZUrV4TWo1XH43YQCvoEJ/Rgl3wuTaNeYX5uBokOil2mkM/QaUv0RQe4s7RKrG+Q9z36QzQbHTzuAD5vCIvZjtvlxyrZcFpdtKoqtWKdwb4h7p0/js1kN7JCvt/rXdM+6AEm6XTa2Nm6XELiqyiK4BTuJY2+S2cG6Cz8dDoNCGrSUGgQVVXZONjBHwoyNTXB9165QDw+wEj/GJFQlJA/hMPm5KFHzvHC+ZeIjwxxcHDA5Ow00ckJvv5XT5PLZGn3shss9TbZfA5fIEij1cRuFWWs2+02VnDNpngavfDCC2QyGSKRCFtbW0Z4h6IojIyM9HT2KpevXCEYDPLaa6+RzmaYnZ3l0tUrdDodYv1xwzc/OTnJrVu3OH36NNVqlZWVFUOANDAwgM/n4/qd62JXbjIJ1aMvSK0q9tXLy8u4HG6+8Y1vGFZb3bxTKBYMnT9g5HY2m02iYeEgtdktlMtFtrbT2O02Y2qu8wJNVpuB4Bc6fCEiq1aroInqb3Nzk+npaeN6njgpeJEAZrP0to2S7pjVhUGaptHf38/29ja1Wo3bt29z5MgRHE4Lw0OjFApC8zA5MWP4X0KhEHfu3Hkz79Hc4x9KoiTXeYVFtWgoCRcXFzE1VEOhiUlUbhZZZWnlOi21YwwTd/f3OH7sXubm5nj55e+SzaVFSG29jtUiU6vVsFjMhKIi3bpYFH9PIBBgb2/P2Fro16PefVOcp1vT9UAhncSkV8c2m2Ig2d663dC1DZqmGQe3rrrUE7GsVquoIMzvfOu/K8RLf/SH//6zHzj3HjY3t3AoTkySmWKhhNYVpOdGo0k2m8NiFWsaXc1YKBQMoISec1gqlWhWmhw9fi8m2czu3g5Wm5WWqiLLFhq1Fq2mSkft4vX6aNZLeIJ+AtEwJ888QKvV5Ld/61/jUBRMXY0Pf/BDqM0W5VqTwbExvIEAIxNTxAYGmJ6dxmq1UK1VabUaPP30VzjY3+XGjRsMDw8bk/zBwUEWFxdZWlpieXm5F4W3R7FUor+/X6Dse8DNkbFRFEVhfWNdQDs9gi40Pj5uyGLHxsaoVqtEIhFjIt0F9vcPGBkZoVyqEvAFaTQaeN1eTpw4QbVS5tSp+7hy5QpDo/2EAiEiwSixaB9SF4IBP/Vqlf3dHVwOB9VymbGRKcxmE1evXsZkkgyyUCAQpNVUaTaFb7+ptnuo8zfnQ2KA5uFHPvwR/uIvvojVasNqs/OFL3yBsbExfuEzn+Zzn/scwWCAcFjs6Vtqk/2DffoikbcF7OrtTjAYZH5+3ijLO73DKxYTaLuhIYEfAwgEhZHN43Hh8/oJRyLs7OxQyBdwOh34vSKyHg0qlQqNRpOhoWFq9RqZXIH17U2y+Tz5Uhm106bT1fjJn/5pvvX8twhEQizMTuFyu1hZXaPVrOP3eelqXdA0FIcCmnhoub1ew9z31rZMzyk5ffq0eKglU2QyWSQkZNmK1WqjkC9gkkysr2/QqDeQZStul5v1jW1DR/LWdiOfzxOJRIw1p77d0TH0eoBzJBLhr7/2LL/0y7/8fcVL74pKQdM03D4v8UGhT1AcDkyyhZ39Pfr6+rDYrFQbdZxWq1Eh6CenPmXV34RgMIhdUshk0qiqSDCKRCKsr6+jaTAUH0NRxNOuUMrT7ISIRSIEQ2GajSZul4eQP0ghm2eof4B0IoVJk+gbEuiziZlZzGYJb8BPPpfD7nTwvdeeE4Rmh4iGP3nypBHH9v73v9/gTDocQsBTKpWo1Rp0ul0uXrxIKBQyJLcmWVwSi0UM8UanB2i1WmxsbxEKhag1G7S1LleuXyNXLDA0NERb63JwcMDQ0BAH+wkxoFO7nDhxgksXL5FMpohEIly69DpHjy4wPDDI9773KhIyo6NjdDoa1VIZqasRDYUpFvOEQgFWVlbodoV7NZ/PoqEaq7FQKEw2K3T2XcmExWzGKst02sIINjQwSLVSotvuYJZMlMolFKebYMiPyQxPPfUU0O2p+pzUGzUjWVkXpO3v7xuAUrfbbWj733jjDT7xiU+Qz6e5ffs2fX0DxPoEpWh4aBTJpJFOp7BarQwP92M2m0mmRGK3bBahOqbeASZJprcZ2FZXBVQnmUyiIaFpEm6bFYdk4pmvP8PZBx+k0Wxy5cpVBgZEnoZDsbG1sYLT6cDpEBVkSzVh7wUJ22w2o6rqdrt4vV6y2SzBYJBms8nAwADbmxsG5FZXIZpMJkMXoudDvlXarAfadrtdQ/XY6XQMY1Q6nTboXIqi0N8vhqeKotDutN/xfnxXHAr6BmF1dZXBwUHjHzI0NISmafT19YkPSkN9W1afLpl1u92cOnWKnZ0dnE4nQ7Fhsc/3OFHsVuoHFR48e5pkIs3tW3fJ5fL43EHKtSqNZonHnvywoYG/ePEiI8PDVMsV3nP/aXa3t5E6XdzhCIODg3Q7beGFT6dYXVmmWMxz/wMnqZXFAHRocJQvf/nLxCJRPvwLn+FrX/sah4eHXHjlVYaHh6kUSwwNDWG22uj2gkmXlpYIR8WTzBsQXMZWW3gKkslkz7Irk0gksNvFoCsejxsy2aWlJfz+IEtLy9x79DjXrt3g1/7Fr/Lss88SDUWZmBinXCpxZG5WBMEGQnz8iSe58NolGqVKT7kn2q+h4X4KqRSS2sFkN5FOZxkaGqJQyOF0ulDVFvl8gQfun2R62sLt27eRLOKp1e1igF1yuSzz8wtoCLJxJBLBapMZjPdjsVg43NvFaVeolAo0akIIJXU79IVDZDMZ/H6/sa7TU7B1O/iTTz7Jt771LT784Q8RiWSMNd2tm0uGMa5aKxGLR9nc3DaEWH3ROBJQrZYpZHPIspmO1sblclOv1/m7Z74GNrGqbrYErblWb3KYTTM8LD5TxWqFRqNBULGTTGcYGuinWi3jcHnw+jxYLWZMZpm+vvjbDrhUKoXL5TJ0K2azmVwuRzabxeVykU6JwNpyudqDrLiNGc74uEC7pVIpul2hiAyFQuTzeRYWFgiHw+zv7xstm67wtVqthqlQH8LHYjEODw+xyu8sXnpXtA9/8G9//7OPP3wGxeGgv3+AfKHA1NQ0xVKJbC6Hojgolcp4PKI6cLvdximoT1dDoZDR26ezGQ6Th5w6JcQ44aAfl8NNR+2QSmfxBwLIVivvffx9LJy4l3yxhMlkZmdrm/3dffoifaiNlmhhOhrTUzMMHz2CqrZZWlykpaqkUgkikTA+n5drly/j8XhYXLxFvdbk3Llz/N0zz7B4+zZ3l5fZ2triJ3/iJ7h58yYSoqctlMqGWUuSJGq9wJr+wQGhj6+Ip+bCkTmk3vop2tdHsVQy3HZ6rqTabqOqGqFgBNkiBEXNXpLxzvYWnW6beKyPSqVMs9nA5/Pw10//NW21Q9AfZO7IHB63m6XFJaamJohGwty8eR2H20+9XsNkglqtyt7eTi/Ny0elXKVcruB2u+mP9XG4v4/H5aZcLIKm4XG7mJs9QjwWY2tzA6ts4QMf+ADFQoF/8plf4PLl19G6HdwuJ8PDA6yu3OX4vfdSLhVoNJrMzMwAGFFpbrebS5cuceTIEZaXl1EUBZvNyubmNi6nB7PZQqOuEg5H6HY1oIvfJ7B5icQhVpuDcrnChz74AdLpDLLZzIkTJ8nnstRqdQqFPDarjVq3QxcwyzKYzJjMZoLhEJgkOloXVW0gmTTUegvZIlOpVjnznrNYzGbKpRKyTcbn8dFSVbp0DYmy2+2mhyTpugAAIABJREFUWq3S7XaFXwJxcx89epSBgQGKBZEHortXdSCLfhPr/MVCoYDZYjVEfbpKUudvejzCyLW/v0+5XDYozs1mE6fTyfj4OE6nk68++xy/9M++f/vwrtg+IEkEwxEyuTzJdIZcocjm9g6ZXJ7xySmaapu9g0ODlJvNCpGKPvRJJpPcvn2b3d1dw7UXiUS4du0arVZDRJG7nQQCPoJBPysrKzzyvkd58cUXMPXSenQY64kTJ7BbrUxOTvLGG2/w4PseJRiPsbchnmzWHgm63W5zcHDA2NgYI+NjjI6O8sgjjzI8PMx3v/tdw849OTmJ0+nk+eefByAWi/Hwww+LhOoeGEMfBnm9XiHYKRYNFWSukMcsW+iikcll0SRhIa43G9QadRqtJk21ZaC/Dg+TnD37INls3gBr6E8Om02m1Wrw6quvGrOMVqvF4aHwXUxOTtJut4nFYkZe5EMPPUQqlcLj8RhJ1uKGtBGNRo0VrziQO711ZafXcuS5efMmmibK29OnTzM4OMj3XjnP0NAQfr+faDRqxLFfuXKJra0tIpEIV69eNVB8mUyGvb09PvKRj7C0tMTMzAznzp3j/PnvMTIywszMLLVqw3AeBgIBHIoLj0fYlI8fP2kATq9dvUEkEmFgYIitrS1y2R6JSDLTaAi9SrujGSg0HVNnRsNmMeOw2XEpwsugdtrkiyWuXr3K6uoqUzPTtNUOO/t7gmFZbxkrSX2dWKlUDDSaoiiGRN9mU7DbHVitdoLBMHa7A5fLg6I4CQbDKIoTMBEIhAy0njBcOSmVRCJ4Lpdja2sLk8nEcC8bIxaLGRRp3Wnpcrl+YEKUpP2AL/73eh1bOKJ98+kv8NprrzE3NyegoZEI6XSa9fV1jhw5gqIoJA4OGR8fJ51Os729zQMPPMDi4iI7OzsMDw9zeHhIp9PhQ0/8CMnDQ1793suEw2HQusTj/XQ1M8urW/yb/+PzxAaHOThM4vMFeP/7HiOfyWLFxOz0DPefPCXKVq+btY11IUd1e3n11VeZnJxEVZvce+I47Xab3YN9bDZx0S9cuEBie5eBgQG+9KUvMTk5iSRJBAIBtra2BAuy57voyjKjY2O0Wi0h6241OTg4wB8KAuLGN5vN+JxuwyJut9uNQFKdiKzLY8slkdFw/Ni9ZDIZGvUahUKOe+bnqdUqOOwKSCJzsNlq0WyqPPieR9jc3EJRnGxtbYhJP23q9SomE2gWF7dv3yQY8rG2toLarqOqLebnF9jc2EaWBSFLQj/YxFxnZ3uPmZkZWq12T38hDscf/vCPUK1W+fVf/3VUtdWD1Epcu3aFubk50uk0/f0xMvmSYYnXNw96EK7+lB0bG8PtdlMuVUmnMyh2JyBs9+l0mqmpMZbvLjE6OsLlK28wNDzG448/zn/6sz/j1KlT3LpxTYTnFvLIsiwYB40aak+DoXV6wzqtI2z7ahNFTzFvt3E6fGidLmaLhKLYGOiPA106ap16tUY4EkJRbFgtJmZnZ/H5fIaGQJ+R6FSlSqVCJBSlXC5zcHBgAFOcTicbGxuG9XlxcVFI8+0O7r33XoaGhtjf3yeREOwNs9lsVNL5fL6Hnq8aYJjh4WEDy/aZX/9Nbi0tf1+x87uiUtA0WF1ZZ3JiGrfLS7OhkkpmqNeaxPr6CQbCVMo1Fo4eNaas/f397O3tGVBNXZji9/u5ePEiX/rylxkZGcHpUDBpkM9kaTXq5PNZFhYWWF1fY3x8nPn5eVKHCfLZHE5FODKz2SyrG+uY/D5qjQZDY6P816e/wsLsFM1qCa/bTTadYXt7G4fNTjw2xOFBmjcuXTOi2+bn54lEIoyMjLC+vk6tJqLLh4eHmZ2dZWJiwrCJ68IUPRfQarUyMjJCIBAgGo0iy7KBm9OHVrrQqFgskkqleM97HsJiEYGtLpeLvr4+IwpvcnKSvf0dY8hUbdRJ57KkshmSmTSZXA63z8t+4hCzLNOVQDMJZsXCwgLLy8uMjIwQi8UYGxtje3ubcDhsRPSpqorUS+KSZdkofXWXq90uoK5PPfUUX/ziFw1/iY7vn5qaIplMMjk5zv3332+ARvQMhJ2dHSwWC6urqxQKBVRVFR6BjLih1ZYA9eorvOHhYa5cuYKEmd3dXYaHRnE6nfze7/0eH//4J1heXmbuyEKvOvMb0QAmk/gc6WG8jVpNPIUVB16XG6/bg8vhxG6TeylUYDKLRKhSqcTIyAiTk9PEB/p7a3MM/Fk+nzeurdPpNKoHWZbx+/34fUGGh0bx+4LYbQ7aaheX04PNqtBpa6CZcDk9eD1+I3TZZDIZIUFer/fNIOZe/oNe2XV7yWhvXVF2f4BL8l0xU/h3//b3P/uJJz9MXyyGy+2m1VYJRcJIJhOr62tcv3EDn99P3/gYG3fuEA6HjQ/T+vq6YQXW33CT1UxLbWK3yszPzdFpqdy6dZt6o0lXEqaZUKSPw3SCB06e4ebVG9TKVdFTlqscO3GCI2ce4NqFCwyMjvCN577FI8fvxWYxo3U6HOztYrFYqdWbTNyzwHdeeIl6o8nP/+I/QeplNei8xHa7jcvlwmaz0dfXRyAQ4ODgAKvDQVfTDEGNXVGEVdvrFRSqXFY8lTodgqEQqXQayWSi0WxiMptRO22q9RqdbheT2cyVS9eZmZkhcZjE6/Xg9/k4ODjEbNLY29vh4YcewmyWaDYbxAYHeeKJj3Dr9iLz8wssLi3xI0/8CCurq3TpABrLd+9w+vRDfOlLf8Ho6DDBYIBGU+zeBwYGqJSrlEplstksEh0atQZej5u22uolYCep1arIFivxWIzpqUn29g9IJhOG8MlkhnA4SKvVYmpqgt3dXdLpNIpD6BNu377N448/TrVaJZ/PG6CcU6dOcf78eUZGRrl9+za2XtDu5ctXONg/JJvN8EM/9EO4XE6GhgTePh4fxOFw4lDshEJBquUKnW6b8bFxJAnDityRzEiA1AXZasGjOHC5FCRNo1Iu0qxW0TpdOpoFm83aSwaz09U65HNZEocHVCpluprYmkxNjhvbJd08VS6XDby7Dmfd2d57G++gXBZYP6X3uahUKgwMDBAIBGg0GwZBTPgrNAYHB8nlcgaXMR6PG+2jJElMTk6KyqhUwmw281+/+Ty/+A4zhXfF9gENw5Ou76Cj0SiZTIbh4WEjRHUwJnpGWZapVqvs7e0ZVCVVVbFYLAQCAeqdJo899hgbvVJ9Z2eHe+65h5u3l+iabZw5c4bf/3d/SLVR58aNGyQSCXxuD/FoH1NTU6J1Wd/k2rVrKF43bp+XSDhILl/k1o0bPPmxj1Go1Ak7HNAUUFWXy8W3n30RuV0lGo3yl3/5l4a33ePxUC6XuXFD9LOKotB+SxyY2Wym3mwYNnG73Y7b6zHWkpFIBJvNxvDwMHt7e3Q6HWPzommCMHXkyDxXrlzh/Y89zvLyEjarwNNPTo4BIoFIV9Z97Md/nOe+9QLVatX4sF67dk2U5lKHZFLExT/33HM89NBDtDtNw925s7ONz+fvYcdHuHDhgjhsGi0j0EXPLCwWiz0I7h7lcpm1zS2sViuVSoVA0GfYpG02G/v7+wbC/OTJk9y8eRO/388rr7xivEd6y/Tiiy8yNzfH4uIiTqcTv9/P0aP3MjY2wdLiHcqVEslkkuXlJcKRAG6323DYCqm0mf39fTLZFLLZQqUiID3xeJygWVQLtXIJi1nCabfRabeoVSs0qxU6Jk3Me8xCoShmW1Zk2YRit+PzuQgH/b0tQM2YGemCLJ3epM8WALxeL9sbe8b7pStF9fxI3fnYbrfZ3983Phu6vFmWZSMsWK8gdQjt7u4uo6Oj5PN5I3+iXq//QEPU3ztTkCTpPwI/DKQ0TZvv/d5ngZ8H0r0/9q80TXu297XfAP4noAP8kqZpz/19Z8LCkWnt6//5KbFWunULVVUNpkI0GmVxcRFN0ygVygZsRd/J6oq1vb09Tp8+Tb1eJ5FK0u12uXnzJrOzs4ZWvVAoGDbWmzdvoqoqV29v8cRHfpRMJsfZsw/i83i5ceUylUKesNfN5MgQ95w6RbtY4vUrV2l027S6Xaqqyoc/+jG+853v8thj7+fOnTtcv34dv9vF1NQUmiRx7doNnG4XaBKr62v4fAG+893vCq97R3gGvH4f127eQDKZsNllknsH2KxWIn4/dpuNZKmK1yfQdHbZysBAnN2dLaGvd4iniCjXMRiQ5XLZ2FVHo1HOnj3L2toaPp+PjY0N0okiVqsVu93KsWPHxHTf4+Kll17C7Xbz7W8/z0MPPUT6MGHo9qvVMorLSSKREG1Op2V4SaqVvHHAhcJREokETrebzZ1tAn6xP9ckmJua5/btm5hMgNQxhnnNRpehgVHCoTiZdJ5EclX4GGRFwEBMFtROF18wwNVr1/D5fMQHYrSKOaKRGHc2NnE4vewcJnH7vLQaNc7ef4JKuQBqTfwdXXGztxpNdre3GRsb42BvH6tZeCUURaFeq2GxS4ZgSh+WFgoF433W3Zs+t+tNu3JHpb+/n1APftJRmwadeWg4Rj6fF1ua/n7Dsq7ng5rNAqFfKecNTmd/f79BS1JVlVKphM/nw+VyiTWtx4HFLA4CEOa14eFRJEmirYq2wO12c5hM9hKqBQ/j/PmXKJVzqKrK55/6MmubO993pvCPjaIH+H1N0/7Pt/6GJElHgB8D5oA48G1JkqY0TXtnIBxgMQsjR6lUIhqNGjgpnUnn8/mMQYrdbieTyTA+Pt5TojUYHx+n3W5z8+ZNLBYLuz3RUywWo9FokE6LPXM0GqXZbDI5Ocng4CBLS0ts7GQpFnLEYlHq9SqlQp5SqcTs7AyPPPwQL379GdaXlljf2CJXyBMbGSKVSPDRT/wPXHn9DZL7e/zB5z/P4OAghxvbNEJeIpEQ3S4E/V7yRZE0dObsewiFQiQSB+TzRbpdiXgkbMA5KrUy4cgwa7VVJEmiXK1RLJapdTWOHj1Kt6PxxsULyLLM4OAw+XwWkwbhsGiZ+vsHWV9fp1QSxGkQ/ezBwYFhrvrgBz/I8ePH+fIX/4pEIoE5EGBjfb33ZyXe/9jjvPjii8wfWWBjbZMjM1Pk8/meuEfqZXaa2NnZMiC4arNFp6Viki3k83k6HRFqQlfDLluJBENs7e7Q6XT4zvPfxu0R1GSNNqFwmEajwdjsOMlkmu3rV+jri+P2KphljXZHPE1rdfHUy6Y26dQzdGwtknslLGqXO7ev4faFqVWKhAMBMIPL4SWRTuBU7Lz48isE/T5kq5N4PE4sFuPcQw9x4YJ4L4cHh8QB4PawsbHB+PgoHo/HkM3rZPHt7e2e6EwM7Er5nPG01TmKjUZDbBYkzchv0MOARbBuxdj2TE9P4/V6BVC3VKJUzLK1tUWz2SSRSBhqRb366nQ6RnucSiVBk3pSZ/B4PMLT0W7jcQsMnNlsJpPJoGliuLy+vso9R+dZWlrCbP4BMIV/yKHwDlH07/R6AviKpmlNYFOSpDXgPkTC1Du+zGaTMZyq1WpGhqJ+iuq9lq5ilCTJcJrpKb16KaZjsmdmZrh69SqVSoUf/+mfZmttjc3NTex2O5VKhVdeeYV2u82tW7c4Mr/A9tYG/bEBrl69ylC8n9XVVcJeN7LFxvDoCFaXi8uXL1OuVjgyN0cul2NosJ9cJktfIMT29i6zU5OMTI+wviYMW0okhNVu58knn2B5ZRWrTeaRh8+xtbNNq1njG998lnypSKgvSqGU5+rVq9x7770oVhvrd1do1OqoZgHTOHfuHKVC3hgaeb1+ZJOIFtMdi3quQl9fnzGACofDgMCSraysiHJWA7/Hi8NmZ6hfxJUNDg5w8eJFTvZo1fVKlXyxjMVqB0lDcTppV9rE43Hu3r3Tq7yEWcqEk06nQ7UhYtmV3kBL143orV2zWsGt2OlqXRSXQjgUIpkS8XabO7todKjUykQCAqK6u7tLJpNBtppxKg7UTgPFasEideg2qmCSObZwhEvXb3Lv8ftI5IvEBwfpYmJ4eIhkKsGP/8RPoCgKq8urrK2t0VZbJA8FC6KvL0pff4zdrW0cLgfNVsPYbnQ6HQOKopOYdQFQt9s1HJ1vnRNonY6oXjvCgaivHIvFIh6Px0C69/f34/P5jOAe3VWptwi6W7bbc8sWi0WDsah/fiPhKMFgkMPDZM9nYTV+Fh3wYzabMJksZDIZkslDRseGe0Nhcdi80+sftJLsHQpf/2/ah58FSsBl4F9ompaXJOnfAxc1TftS78/9GfBNTdP++gd9/6NzM9rffuH/xmazGYYZPehCN+jcuXOH0WEx+Z6bm+Pw8BCXy9ULJOkJf/r7KRQKpLMZQ+sdjUaZPXEC6nXUVovV1VWKxSIgpKIm2cV/+LM/54EzZ9nZEd6BkNfPwtwca3eXmJ+colgscljIsra2xqOPvY+m2haWWquNxOEhAaePbCqNbLHQNz5IMBTiuedewOVysXjnLmbZQigUYe/ggPFxQS1WHDJmi4WT951CM1u4fWeJl1/5HuViBY/Hg9NqRzaZKTaEcKevL0Ly8BBJkuiLRKhUS3RaKpKk4XQ6WVpaYmhoiJWVFUPAEggEWFtbM9Zoc3NzjIyM4LMLk823vvUtRkZGGBgY4OqVa3i9XuHDl2VmZo6QyKXY2toiFAxgMkGzJcQy2VSSQNBHuSyCaLRum2oPTGqxWDHJFvpiQl3n9vjY29vDZrPRLtSwKWI45/K68Pp9tNHY2d3HYrMTCIY5ODggdbCLx+0mlUpgkrpEQgEkrYsJMPWQQWbJRKfbolprMDM/z87ePidPP8jW3j6K28fVm7c4feYMVqsNWZa5e+0Gg4ODbG0JaM7m5iYWk9kQ9ug3pU2WjPCdYDBINps1Dl19M1Wv17FZzAZ2brA/htfrxdxb/ZUKOUDMCkJhL1NTU4yPjxths6FQCFVVOXLkCCsrK2INaTP32JSysbFwuVwUi0WKxaIxO5EkiWP3LvDC89/uZVM6yWQyxONiCJlJ54yE6f3DPdLptDHHUttNI5Lu53/lN1m8u/qPbh++3+uPgN9BBFr/DvB54FP8v4iilyTp08CnAWLRMPm8KNuPHDliZEjqrIDhkRFjX1sulwmHw4brUI9ir9fr5PN5mk3hdygWi4yOjrK/v8+rL7xANBolnU4zMDBAt9tlZWWFcDhMKnHA+vqquEjzR/H5fJw8dYqLr11ArZXZ2t2h1WqxtLLMqfvvo1SpIMtiZeaw2sgkU9SrZYb645w8cYKsWuVP//RP0TRoNGr0x6LYHApLd+4yPj7OztYGicQBjVqF0ckJLl68yOjUBE888QSzc0d46k/+A7LFQlNVaXSadDTo6+vj7t27+L2CRLW/vy+08WhkMlmsViuKojA2NobNJhyLly9fZnd315DU6v2oqqo8evYcW1tbWK0CBKpLxc1mM/fd9wBXrlzB4/EQ7hfuRpPFbKQeX7hwAZssUSmVkIByuSCemr2VJCC0FFWxgq1WKkaKVcjuQjZbMMsiqq9ea+Jwi8GyZpLYP9hGUZxoHROdtiSyIenSbKhYzRqybMVikumqbWw2BcXhpK/PhlmC8dERtrfWaTRUCqUK586dY/nuXSKxGHfv3iXuFolg7XaLO3cWBWG5WqXZqqOhEYmG8Pv93F1aNFgPeiaj3h7oSsNarYbVbDLYDHrb0FFVI6FLh7UCJJNJwuGwcS1u376N1+sFhHtScBCkN41evSollUoZzsh6vW4crktLS9Tr9V41kTYOGZ/PR6oX0KPLnGOxGA6n2Dr09fUZjJEflBD1j6oU3ulrvSEjmqb9Xu9rzwGf1TTtB7YP0+Mj2p9+/rcZHBwUFFpZ5vz58zidTqampoxQUrqS4ZK8desWQ0NDhg8iEAhw/vx5sYIxmxgfH8fr9fLSSy8Z4her1cr09DQej8cQw9TrTb7+zee5fv02P/zER6lW69hlO6dOnOSN117FabPi83mJDQ0imYQQJZVNY5Nlrl++SqVY4qd+8ifZXVmFdoer68tileRysrO9R6utIstW9vYPKZfLqN0Ofl+Qza1V2kjUmw1agE1RePCh93L16jW++MUv8hv/8l9xsLvH3mGCM2fO8PLLL2NG9OsBvxe/34cZQV1yuhSaTRFBVigUjPdVb8VCoRD1ep1oNEqlUmFhepZTp05x5co1PG4fu7u7TExMUq3WuX7tJufOnaNeryM7FeYXjvCFL/xHGvUaw0NxRoeGWb5zi43NVaSuhsvloFKpiX24BM2GimQ2iSe0zYbPFyCZTlGv1wk6RPJ3uVylg7C9x/v7yRVzpDJprFYZr88NbSc+nxeny0atkicY8NFuNpAAMzYsZituh5eqmmRkZIS9fRFcs59MMb9wlO+88iqlhorD7ePG4iLHj58kvbVBJpN5281RrlREBdNuG65DqdM2cjj0G1cPsNF6K2SbzUa1VDQYl32RELVajWw6TbVaxWLCCJSZnBoxWI2BQABN03j88cf54z/+Y4LBIHNzc+LvsJqIx+Ps7grxG2AAbo4fPy4+O6oqfk6Pg3yu0DuwPL1rLqTwDsXFyZMnuX37Nmq31gPeYAjBAn5hB/jIz3yaqzdu/f9XKUiSFHtLxPyTwO3e//8d8GVJkv4vxKBxEnjj7/t+mqYRjUYxm81sbwsM19mzZ41TWF+7RWIDuBoN8YTv8fz01U4qleL48eP09fWxub1lVBl+vx+Hw8H09DSVSsV4IuqDn1AoQDQcIhwOUsznqTdVAgMBnnrqKd7/6Htp1KscPXqMlfU1ZFvPRNLtcvfuXRSHjQ8+/jH+05/8EV63m6mJSSbHx0ikklgsVpKyEM843S4cig2bTSaRSLGyvITNYcPVYxPYrVZMZjOXLl2iVKnw0Y9+lGe//jW++tVvsLu7y/PPP8+pU6fIZ9Lk83kUxcbOzi7O3hpN0IVLxr9NVYWbUYfK6rMGfRW1tr3OxOwkIxOj7Ozska+UWNnY5IknnqBcb3Dt1m2OHz9OIptkc3tX4L3MFpoNlU5HRVHsSF2NbreNbLFgNovS1yLLdGXo9IZbojRvGZBStdvBJIv/Wm0KGiaWlpYZGR1gamyMrtYCk4mxkQW6Whu304Zs7adcLGCWXKhqG60to9idBH19yI5wj1cRF9DaaJS7y0tEQiFaqSx22cL46BgmJMqFPHTaeL1uWq0GZrOEbDXT6aoUyiLAOO6LEw+G31aNwpscxHq9bnAkTJogjauqanAeQoGAANpqYrbQ6XQMi7vubnS5XEYocLfbRVVVscb1udjb2zMqXr1S8fv9mEziIbe3t2d8D6fDRSaTIRaLiXmGJhk/q05v7tAw1p8Au7u7tJpi9tZsNt/5/v4HrCT/EjgHhIAk8L/1fn0M0RpsAb+gHxKSJP2viFaiDfyypmnf/PsOhYmRQe3f/OavGLiver2OoigsLy/zyU9+kvX1dRHG2ZWMXbXeZ01MTLC4uGhAJDRNwx8MGGVWIpEQISejo2xsbHD06FGcTiexWIxCocD1a1eoN9oUS1W++J//C2MTkwwPDIvdsEXGYpZwO13Ew1ExwFyY4/LlS4xNjPLG6xeg0+WRs2dJJhL43G6uLy1icyi0muLpU2+2SKVS7B0khD/BbmN3d49ao0apUgGLpVcpOHjg7MPcWlykWChjtyr09cX5zKd/gfvvX+Azn/nnLN66wZkzZ7hnfo4//pM/ROpqTE1Ncu3aNXx+lzHQs/Ys5oCRYahTfyORCJKsMTN9hFarjSzbuHVzEQkL8/P3YDbLIvZecVKpiw2QiQ52q4XLr1+gUSsxMz1Bs1aiXq9SKZVxOL2onXaPXt2i1mhgs9npHxzg8CAJZqGi8wTcdNtdTCYLrYZKV+0yNNBPwOdiaDCO221jZ2eLpqaitpvcf/9JHn3fgyJdqViiXlP53vnLrK5sUa10GRgYYHCwH5fdhtOpsLsjBsmJTJZcpcrdlTXKTQE8DThEcJBZtlCuCW2G8JQIMI9ZFlGEg6GouMFDIePprmMBdft7sVjE1cOypdNpKqUCbrcbp6IIcZAkWlun04nLbWNwcBCfz8fp06ffJlC6efMmtVpNIPOsb6LdC4XC24xfJ0+eJBwOc+HCBaFU9TpZWrzD5OQkmiYO/FQqQyqV4tH3PiYgv/v72J2m3kBUtI97ewcU8iVMJhP/9F9+lqWV7z9TeFd4H2YmxrS//JPP02yKVGF9s+DxeMjlckiSJKK8HW5UVTUQ3kePHuVv/uZv+MQnPsHGxgalUon5+XlKFREso1tJt7a2GBsbY2VlxbAi6/vglaXbmKx28oUKf/XXz2Cy2OjvH2Rve4f3PvIIz/7dM4yNjXHP+Cx2uxVJtjAwNMDNpZuMjAyRThwyP3eE5MEhit1Ot7dG9Xi8HCQTKHYnuUKey5evkisWKORFqEq9WaPR7oBJoqy2aXc1PP4w991/ho2NDVRVo1wsIfXIRZ/85CdZvHWDdDrNxtoqd+/eFe5Pl4tCoYDDKQNvkpMajQaqqhIMBtne3jYw4sFgkGC/cNv19cWRLQrz8/dQLtVIp7MMD43y4IMP8dWvPsPx48d46cVv0203CQV8lAs58tkkVjNUKwUsJolsLoPF4hD5iiZxKJgsFpT/h7k3j7HsTM/7fme7+75X3drXruqu6m6S3RxuPRzbw5nRSNHi8SjeHUuIYdkeCAgEyXaQCIhtyQakRLKFOPIYiTRRZFmakUajmdGIbFLchks3m72wu/auveru+3buPUv++O49khJJCWIj4AUIEmywq1n31nfe73mf5/d4fciqgtvlRVIV6vU6TaNJMpnG7wnill0E/SHS0QjzM5Pkz0+Q7R5PP32NxReXwDTpVs6xZBNFU9EHFrbt4vS0xuFBjrffvs9s9jLTU1PkTw9JxcPsbT8SSPh2i/uPNlE9XrZ29hlYFpgdYrEYuXyeUrWCruvMzs/R7nXR+31CoRDReIzGeRld14nHBRw3k8nQaDRbfebVAAAgAElEQVQAEWYb8QpcikyhUBDAn06LcrmMjBCvvW5h9fZ6vWguHILUiHswMSFclnNzc3zuc5/j4cOHuF2y0+1ZKBRIJpPOxFetCjv3yJcTT0Qo5IssLi5ydHSCZVmk02Ok02nOTnPU63UqlQora7O0mh0KhQJjY+MYhkXuvIBp2vyTn/l5Nnf3/tRD4WNhc/6lf/OLP/3F7/+c4/oKBsVKKp1OO803uVyOZqPJxYsXuXPnDl6vl60tId51u11qtRqLi4sUi0UurFwgFouxubmJqqrU63VOTk5IpVL0ej1nIjk/P6eYzzE7v0Cr1eGt777LwDQ4OjphbnaWfC7H5bVLdDodarkSLrcLC+jpPWRVRpIgGotSq1RoNhp4PC62t3ecuGwoGCSZSuNyuzBNi8zYGNgSH3zwAZpLxR8MgQSBYAjV7UYfmIQjUU5Pz8lkxqhVa4SCguv367/+63zhL/8Qzz33HL/9ta+JFqlhtt/n8zEY9BzY7Si1ONIRKhWhho/uxbOLc0PmQRVNczM/v0Cj3qTZbHHp0hqPHz8ml8vjcmmcnZ6QTqWRJRvJtqhWSujdDpYlcPSmZdLrCUqSqqmAhM/vR5ZlenqPYCAEsiSgrkE3tmmDLbF64RJ+n494JMKje/doNur8zb/2w4yvr9Ipb8Oghzvmx+WRURQb3TAxzQG27MEfCNEfWGw8OKJcrpCKR3nn3XeQsWg3mvQHA6KJOOVSlezkNMV8nvFMkmgsyuO9PaSh3wUJ2p0OHq9X6AimQeE0h67rjnjdbredcpVKRXgTer0eiiwJoKymEQoGaLfbuIfofFnCyU+AuEKM+KGj+vpRF+fe3h5nZ2fUaxXHGTtCt6mqSqUitgkjyEo4HCYUDuL3+cnn81iWoJJvbGwOSUtJTk5OxDq036JQyKNpgj0yNzc/5DYEufnmO/z9f/APPr42Z1FBpnB0dMiNGzfY3Nyk220TDsPJiUBIra2tcX5+TrPdYnp2hieeeIIHDx44d2p7B+LJBLFE3Gngefz4MUdHR+RyOf7KX/krvP3220xMTLCyssL774segKPTU84KJWZnZ3n+mad45513CPk9FAonNFsdNLdCo91E1Q2CnTBzY3Hu3LlNMBJkff2S8AN0WiiqwsL6Go8+OiAVn2TpwhIHx0d84/d+n1gyQaPT5vD4CG/Az+KlZdqmB08kwuneHoupaVYmJykWi2zcvYvX6yUTDSIP0sheL9/6g99jZmaGf/Yv/zmmafLijU/y4z/+4/zSL/5r7t69S6VS4fqTlxkMBpSLJcbSGVRF4vCwR+74gHQsTLNeJxT00e222b23gc8fJOD1Ew/F2NncJlcocf3pZ3jju2+RnZxidnmeD997ky9+8YdJxFOkUhlef/U1en2J/qDD9s4GyeksbVnDq/RQvV5sy0JSIBaOYpgDJNPE7AnXXsSt0tTbeDUfab+fkOzC7Pe5srTA01eyZGdiNLpbYBTwhNzItsmg1kZzhZFkN2GfDJJFNNLGwkDxhqBaZm/7Pju5R0wFIxh9C8XvZbuc56xWFkUsnQqRpI+TfAXzrEg0mRnSigxqtQpe1YVqW+SOj2g0GrhdorUqOlyHd3WdeCqJiY2kKvRNA1O36Zw1mZqaolwuYiKRSMTpdrs0mjUqzSaaLA3BM5ozzUWjUcePM7rqjQ6LSDRJNBoFYDw7TblSF1dYb5BcvsjJqXDpxmKiwKarN5mcFlu5k7NTZudnWFxe4Pbt22gemYHRo3w8zEaEFCKhGDIST1y9PCSG/9nkpY/FpPDv/u2//env/fSLTsZ8tKcdrddUVRgw1tfXCYVCnJ+fOzDQCxcuOH2Toz3v/fv3aQxhJHNzcywtLZHL5Zibm+Pk5ESg3b1eDg8PqddrhELhYUmGaEwuV2ooiophmEOxTuGZYZx6Z3cbv99PqVJC13uAKBUdIeFM3SYYCvLyzZuc5/PsHTymVCnTNwwKpTKn5+dUKmV0Q/4TBh/RKynurPl8fthlOABFodFoMDk5SW3Y6/i5z36WL3/5y/ydv/23RTmpqnF8fCCeQENRr9cV/ZXVilhZSuBkJjS3h57eR9VcBIIhgqEQSDKJZIrDoyMCfj+X1tZoNSrsPX6MMTB5/HifUqEoCnos8dQuFvPYlolliESkEB1lNFVF1VQG/T6KIjtCpKRKBL1+QoEwyWiEcinPU0+vM/nECkrAi2x1MS0bW5FRNDeWYSFZMpJLAySwbPS+gWkrDEyFwpGIB3eqbdLJNH3TxpZl2pZBriQQdKptYw9M1q88xerqKvG48G40GnVsWwCDDVOIfe12G5fL4zQwybLs1M6PNIXRk94aTgm1moDKVIcBtlarKfQcWVDKZUVyVptut9tBv48szaM4840bN8hms464WK1WCQQCSJJEpVJxvraqqqTSomV9BL5dX193QCu2bXPt2jW2t7eZGDIzRgyMkRVdkiR+5Te+yj/4h1/6+E4KsiJElk984hO89tprfOELX3DwY+12m2w2SygU4sGDB2QyGV789Kd54+ZN1tfX2d7eplAoUCqVePLJJ5046ugNaDabgiEw3B3ncjlM02R9fZ10Os3hwWPCEdH1MOpBGNV0pdJjQ7NHgEBYqMXXn/kErVYLc1tEeqvVKpYt7pntVguNIEdn5+RKZe5/9IAf/Ms/xHu3b1Gv1plZWKLWqIs7bCTt2GRH+YJut0ulUuHixYvkcjlKpRJer5cbN25wdHjI6uoqRweH3Lx5k0a1xj//H/4ZLpeLH/uxH+Nn/sVPMz4+zrkueAszMzPD8hPh/R/VkEciEaThE2p8LMNkdgyvP0il1mB7a4OV5SV29h6TfLzHrXsfMjczx+beDi/9xZfw+XxsPdpgcmKCdqNKr1nDq7npI/bqPq9X5BqGsdxRSnSUQAx6w1iDAbOT4zz46LssLy8ytpqlUzym1qhju1UebWzw1NPrKK4Aqs9CUmWwDLBsUDQwwpiWyqDvJpw5x+XzcuvtO6huN9FImnAiQcA9Ts8QT+JqvUkmJsjOzWaTldXl4YNGplwuOhmQkVlJU71EIhGHsTBqwdra2nKIV+Pj47hVhfffe49gMMj4eAbDsqkWC2BLKKqKaRhYw5hyp9P5E4CaWq1GLBYjGo2yubmJx+Ph3r17joYwOztLa7gurdfr6LpOJBKh0Wj8iZbp0Ybtww8/5KmnnuLo6Ijr169TrVaZnZ1lLJ1xXJjBoNDjHj58KK423d6f+fP4sTgU2q02KysrbGxs8Pzzz3NwcEAoFHJ+kBOJBJZlEY1GSSaT9NttksmkU0oyCo/s7+/jHd4P/X4/rVaLTqfD5OQkR0dHTp681WpRq9WcN+ns7MxZHVWr1WEnZUeszWybdDrA9va200T18OFDnn32E0SjgqfodbuwLRu3qrGxsUevr9Pp98mMZ/n9V24SCATY2t6l1e7i8XlJpDPO/8v+/j6PHj0iEonQ7/eZnZ11at0+//nPU2k1iMViFAs5zk9PGAx0tjYeous645kxQkqIw6N9AgHxZxxLp1BVld3dXVZXV50DsdvtOuQdj6Y4OPJOp0On1ycU8LG1s0cylaHb7bK7u8tLn/ss1UqNv/3X/xZf/uV/T8DtJZ1OM56MoWKxt/UITQK3z+/Uo4OwmstDXcU07T+aFCwFvdOgWi0TjvhYv7IItFFdCt5gkN3DM97/YJOLV67jGXiQ7B4elwtkG7s/QFI0XF4fiqmhKeAK+Kg3G6ApdPQeWruN7dLIXlhi0NOpNku0KlUYmAwGopi4WCw6n5kR+HfEkRwlMUcHwshyXK8LVFoikXCe3o8ebRKJxrFtk05H2KPjsQS2KQjTnXYT+KPpbFT9NhgMnFDfCBYrScKuXq/XndKiUdhqJIqPUPejUttRYfLk5KTDa7x27Rq+dJrNzU1mZ2fp90SuZkTyKhQKznpS+rh3SWqaxltvvcULL7zgfDMSiQSAszEIBALE43Hu3LnD7PKy0xEw6kNMp9MO3nvkSBvVnj9+/JjP/OAP8uZ3voPb7WZ9fd2p8ZqemhCU5E6HzFicarXKpUuXqFRqvPrqq7zwwgucnZ2xef8Bt+98QL/fc0pApyeyVCoVsjMzbGxsEA6GKNfFjrlwUiKVSTOQZDwBPxcuXabZbLK1d8jdR9uMp0SaMJVKOVy+arXqMPyTySSPHj3i5ddfE1gwVaQ808kkf+Ov/XVarRb3P7xLvVrjy//LL6OoYkd9fn4+FMJwKsXa7TaWYeD3ixan5NDdmc/nxVipqAxsiexYmvOzE6LhEEsL86hBjVg8wW/+1m8RTyS4dvkqr998lU69QrtWYTyVod/rYCF8/tg2Ho8Lj+ai2WqgKSqGIVBxvW6XeqlFdjLJzvYG//LnfhJ/xEu3cUJDV1C0AJYW4Z1be6ytN1m5kGF+cQIoATqS1gfJxrZ6DAwD3egytTDH48MDcKl0zAEhVabdEwddwO+nmMsT8HhRLYhGo5TLZSanssOV3TGRSGhISBZZBY/Hg8/rc6zAqqo6HESvVzRS9ft9dnd3sWWJdq/L9MQkJydHjGUnsG2TerUGikw0JlyGjaqoEBzF4INDlP+oTl7XdZaXlzFNk3g8jiRJjjA56oAY5UcGg4EDsG02m6ytrVEqlZiZmaFcLou4+fGx0+ExWturqsr8/DztdptSqSSIVx/33od/+z//0k//xJf+Pqenp84I3263KRaLqKrKzMyME2GNxWK889ZblMtl2u02Y2OiQLRUKpHJZGg2hQDk8XhQFMVRjWvFolPdNcKYtVotIpEwlmVzenrK6sU1sbXY2qZWq6NpwjOxsbEpuIymQTqTIhIOoyoyg75OwO+n3W4Rj0Qp5PO0+wNa3Rb+QIB6XVSp64M+Xp+fgWkSDId56aXP8qlP3nDCLpZlMTs7y6VLlxxl+t69ewJ37/cSCgVp1GpMT00J0apa5aN79zg9PaVaKeP1eJBkMa436rUhFl2MnoO+PuxeVJwwT6NexzJNIpEwsiLU83v3H5BMpVBUbRhQCzIw+xwcHIJpk0wkqZbKxKIxxtMpHj38iEDAR3+gEx3ixQVNSCYRj9Pv64xlMsjyELvv8VCrNoklQoxlE9z4zLMg65iyheYLgOLlNN/G5YsSdF9iYmIey7DxBTSwDaCLJfWRJBtJsVBVm7NSiZnpGY62D7BNi3g0ycA0Oc6dUygWMQ0DxbaxDBNXOEIqlaLRqA8fPCrt4fam3Wk5qU+/P+j0dBweHjpJ3dHVdrQ1CIej1OsNznI5Aj4/nW4P73AD49JcuNxuDMvAGvQwTdMpb8lkMs6Db3x8HMBpUFcUhWq1it/vJxQKOetP0zRJp9OOv6ZYFJHo8/PzYfI250BZqtUqV68KJJ9lmmxsbBCPx7Esi3K5zMTEBGtra/wfX/tdfuwf/qOPr6agDM0V2WyWhaUl9nZ2hj0Ft5ifn8ftdrOyssJrr71GIBDgmWee4eWXX3aKOU9PTzk9PXXuXSJDHnRO33K5LNJ7Xi+VSsW5b49gLbl8kbGhR36Ew15YWKDRbDu7Y0XTCHi9gv4TixOPRZAsE8s0GQw9Aa1mk3giiSRJ7B8e0Wo3SCZi1IeR24GuUyiV+LVf+zVSsaiTmvP5fBweHnJ2diZy+cMxVZZl6ucnjKczlPIFoY8oKhG/4PuFgkG0WEyIZZbtJEgHgwHdrtjqjNajtinS66NR3jAM6vU6tiQOi3g0TL/XI+TxgCwzOzNFtVPj6OCI7PQY6VSK1/7gFZ66+oRwkdoWsgUuzx9ZgEW010ur1RK7dUU8aUc7e8O2sGUbWZOxDQMp6MNjKrQNFUlSSCYTzM5NM5OYJZuFvu7CNrtIkgqoyBggmUOHpClaxHt9R0caDAbUmw0axgBJlkGW6LZ7eF0iR2CaJmPjae7evYvH43L6M/2BGTwejzM5ja6WoykuFAqxt7fnjO26ruP1BXAPk4sut5tOs0G+UODaU0/R7/fYePjI6az4416gUc4kEomg67oD0LEsi+npabrdLs2mIFr98d6HbrfrXHsqlYqzqszlcpydnTExMUGvJ9bSlUpFPGwMk5mZGRRFoVar4XIJLF46ncYYfMx7H0CEfKampnjt5k2CwSBHR0fOuJbLCdjHxYsXabfb7O3tsba25ryRMzMzxONx5ufnnQrver3O1NQUGxsbTtnpnTt3HG7D3NycmDCKecbGxggEAuzsHjhI8WbzjHK57JzwkiJj2haxZILFhUUCXg+1khjBq8PsfSwSpdxrcHBwgNvjI+D3kMvn8AfDVIs53F4fvU6HTCLumGNGyvLk5KSjgWxubjpvejQURNe7zMxMERgKXue5U/Reh2QyiWWYBHxejs9PKZfLpBLi9x0l8hRJwFTNoatRlmUkW5hg9G6XttYG4PP/xQ/y/u07JBIJh2S1+2iTs4MjlibneOv1N5idnQVg//iI7PQU1VoRr0um1+2TTqeJRaOcnh7jdYkfKL3bc75mq9kkEPdhqbB/eozkDdKtV/EEfWiqC1nSyIwF0dwujna3ePMdg+tPrQynBEBygamAjADSKBKJaIybf/AyxmDAQO9zWDkkkkxgDfpoPg8BRaau9zBkcKkuBzozOzvL9PQk5bIAAFcqFecp7dJ8TsBsFDR69913SafT9Pt956Gyv78/JCkJ0tHANIgEQxweHVEpl8nnC4TDQSJRgZ4bmccqlQqqqnL//n0Bixn/o36IS5cukU6nyefzZLNZ0XY2PDhG9K5sNkuheIZlWbhcLoLBINevX+fk5IRWq4XL5WJ3d5dAIOAQrD0ej8MhsW172Af6ZyNOPhbgVk1TnZNzbm6OUCjE3Nyc03coyzJTc3Pk83mnCSeRSPDBBx+wtLRELBbD7/cjDSGup6en4g32+ZxvzNWrVwXqPRRy4CyDwYBEIsHq6qqTnR+tRXVdp9frOSk5VVVJp9MOG2+Uthz0dMbGxhzIaKvZQO91abcaNKoV6tUq9WpZZDA8bq6uX0aSxH1/1PmwsLBAMBgU4Z7hm9toNJxy16mpKSzTFIh7l9sBm47WSyN4qqZpDoMgFos5U8PICTfqGFSHeQXLsuj3OrRaLYrFIrVKyTHoHDzeJZNM8dQTT7C0sMjy4hKNas2BrtYadcYnsoxPTRKLxXC5XI6xZ9Q1ORLqRpZrb8jLwBw+xTUPpiVjWyI85VJUPC6FSNiLJZcolbax7TqDQRfMAdga4AbLDaaGbGlUSmWODw4Z9HRHuR+BdGrNBq1Om75h0Ol1CQ/Zl1tbWw5WvlwuO6tHXddJp9PDz6PGxMQE09PT7O/vO27EWCxGJpMRus/4GJOTk0KgTqeZmJiiUq/x8OEG9Xqd9fV1Mplxzs7OqFarTufDqP15NPKPrpDz8/MEhsyFqakpACfzM/pvDMNwBPF4PO4cXqMJIZlM0mq1iMfjQlcJBCgWi2xvbzsw4HK5LMJSf86k8LHQFH753/3yT//Uf/eP2d/cdLrwRqLYlStXUFWV7c1NLn/iE9y7fdtpnw4Gg9RqNbrdLqFQiI/u3v0TpJq33niDsbEx6vW6syk4Pz/n0qVLDtAlEgmzvbNLLBajVhNsv5nZORRFxe0Ro3CpVKZcq3J6fML8/BxYNvfufkjA58XoDzCMAZWS2FM3OzUazSaSLIudvcdDMBDEtCwGA4Pt7W3GxzPYyM6HLBaLOahuAT3NOWRi0xyws71NLBbDrWrYtk27KWy1rUbTgdAk0ylRi6aqhEIh2sN9udslFGzvcNXqcrmwTANVVbFskBUFyxYQllg8jq73WVhc5OzsjPzJGZIFnXaH8cw4169f55133mFmdgrDGtDptpEVCZ/Ly87OztBX4qXZaNBsNQkGAvh8fxTNrnTrnOVOuLS2xuXVFXx+P5VqDduWaLTadPs6LpfK6lKa1ISfWNiHZXRQPB6wFLBUMFWwNIyBxP/+H36N85NTxmIZJMOiXmuxd7CPFgpQbzVpd9qEgmLMd3kCTE9Pc3p6gsvlIpc7ZzAQT/5WW9zHxXUhzoULF3jjjTc4PT11KNqjg+P09FRU2PUtcoU8yUSCTqdLX++hKDIet5v+oE9xqPQnIgHnST3KnrhcLgKBAKlUiieffJJGo0EoFOJgf9+hYo/CgIuLi7hcLvb29hzaWCwewev1Oh6Eubk5oZvVas46Utd1vB4v8/PzhEIhTk5OnMNieXmZX/2PX+MffeljDG6tVyp89N3vYhgG3W6X/f19BxUu8FHi6V05P6PWqNPudtAHfQzLZGVlhaOjIz68d1eEfXo9FtOL7O7uks1mnaftyDCSSqU4OTkRvL5+H9nuI1sS3337Dfz+APFYiIFhcXntAkdHj3G7wO2SUAOiU+DdD94hEY0RC4SIBPycnp5iGybWwEDv9mjoAwJKiEqjTqvT43t/6AcIhsN0jD5dfcDOwWMePXrEWDTFQG+ROzvk4QNh2/b5fBTzJj6PgiIZVEolJpLTLE0sUszn6ZsDFEXC61FR5CC2ZOJTPBh2i26zQSCoYRpubBMi4TQKEjISmmqhqoBtoPe7yJpIiXpdHTo9Hc0l0241SKTStAc2kfQkar5BOiQ6CZ74xHPk8mccnZ/StweUqhU0tw+fN4gkQb1UJJudRNe7mKaBooJPESagVqtNIp6hXu0j9SVWli9iWiD5NSrdJrHxCBIWPlNDb/eRMWg3NYKaD5CRZRdGp4fqcoOmgq7TrFZ57ZWb1PZyRN3iA19vtkSjk19DxiQTS4jSYa/A3VumTrF0yurqBR4+3KDZ6JJJZ2nWLRaX58nn81y7+ml29m/xh6+/wsRkRkwSmkxPF56B/qBHMOTFsvv0m01k26ZcEJHr1nBaSyQSjGeTHB0d0Ww2KTQGzM/P4/F4GEulCQT9BHx+mq06wZCbe/dvEQ6H6XRFW3SrXSGZTNI9rxMIurh7Tzhvs9ksxyd74lq3K5iPAAG/h9OTQ6Ymx1lanOPWrVvkc4K30Wg06Pf7zM/PMxgMODg4YGlpiYcPH2Iaf/b14WNxKLhcbnK5nLN5uHRJYBvOz88FuiqT4cGtWxSLRV588UXeeustms0mwWDQaQ8yTVMorpaF3hUUJsBp43n8+DH9ft/Z+5pD4a1azIsatFabZCJDMp2iWhPuyHQmxa3b75NIRqn3dFRZol5r0u/20YNdzJ5Bu97A1IVBxO/1Mp6d4CyfEw3D9SavvvoqtUYDU5FIpscIR8Upf3BwwOzsrNOaPaL87uyI7MTa2hqapnGWP3M2KYIr0WFgWPSNAbZtIssmqqrQH4jdtoSBYdq43BoyNgGfB1W2kBUby7SwbBnLEvd8l8sFsoKqubFUzxCGKzwMmWSCZETQih98dI90Oo0sw7Vr12g2apTyBVbW1vjoowdomoLXG6bVtGk0BL+h0Whg9AUu3jQHmKaB7FUZSDbRZIrDkwKz81P0bR3JtpAwkT0uZBtckoSiSNhGB0m2UdxuwKZ6ekjQH6LX6/DBB7doNi36fYNmW5TTev0B3F43oVCYSCyB2yt8Kc1mE6OvUypVUFUPxkB0ICwszg3zBj7c7gk8Xrco0un1HPfgyFkoGrbcBAKBYd1fw9kk6LpOPl9genqaWq1OrVan2+0RDIZwaxYSLixTodnoYQwkZqcXkCSNcChJtVqmWmmSSKTY39/H5/PhdnsZDEx0vY2quohEYrjdAtJr2zbxWMIpBWo2BA5A7/WRJYWpyWnH03Pv3gMH9DKqqO90OqTT6WFO5U9/fSw0BaQ/gkCM1iqj2uxarcZHt287iuzIVNJqtbjywgvOODcas0bpSIHylh0Pwqh8JJlMsnrxIslkknQ6ze7OIacnedwuv1OjtnxhkVK5wGDQZWIyhWX3nDthJBIhkUhwaViScpbLYUsQDIfw+HwcnZ5weHjIg0cP6QyZha1Wi+eeeZZENMbGxgaqJDsiqqZpzlXBtm1mZma4fPkyS0tLvPjii1xcWyGZjgu/vyYzjM0jSyqS7MK0ZFrtHqlEEsWWyI6PsbgwQyTsITMWI5mK4PFJaC7weGUCQSG4jWy3I/emW1Mo5nOsXVrl1T/4fVyawsMHH5Edzwjc+7e+zZUrV9jfe4zH42FmZpoHD+6zv78vqtSLRbHzH/QEWarXQde7DkdA0zTOyucUajWqHZ2zos5p0cSUMlhyHN0OILl8mC4NiRbdTgFJNWg2inRqeaxeg0g0yP37d/nyl7/MrTsfYJoWjUaTVquDhYRpS1i2RCqZQVE0DvaPCEfjTE7PsjB/kanJedqtHvF4glQqzb17d+npbf7uj/xN2p0av/07/4Hzszy2JSFLKudneSLhGLKkEgpG6OsG7VYXVXFhGgqNeo9atUNfh/GxGUxDoa+DaSj4vBFsS8MceKhVBjRqJnpXxhy4ODupEA2PYVsuAv4knbZJsSBEzlg0RbczoFJuEAkn8HqCnJ8V2dnep9Pu43b5kSTJaa22bZtyueysm9vtNuFw2BGqW60WDx8+ZGVlxakQtCwLRVb+zB/Hj8WkYFtCUJmdnaXb7ToGixFEZX9/n2vXrhFLxLl16xbLy8vCgDJsLhoJNoYhsgujVCDgtPL4fD6HkzcyoxSLRcbGsoJnmEyxu7vNydkpzXaLTCZFKCxalkulIqbtcdZCqiowZn/xpU9jDQyqxZL4d24Py2NrvHfrfVrdDpKssnd4wNzCAqVSibPzPBPjWc5y58xkp50/WyaTIRwOi1XRMPb88OHDYcJT2LQ1t0qtUcU0TYEVN0Dv9JxOgKA/hGTLeDQhmHY6HfRuD/dYUnQHYtPTW/QNkWh0uVxItiWqz2SQFA3dGBAJBdjc2iEzXmBg6A7E9ZlnnuZ3vvbbIpQUCtHv97l+7RqRcJDWsHrN7VYJBAI0qpWhqCmETYEqs3B7XIRDUY4OT3n2+Ri722f4PHG8XplBX8KrqmiSCi4DyxBTRjgcBtVDq1gBZG7efJ3Dw2NWV9Zp1Qbow3YqVXEhSUVhGiwAACAASURBVAogCZZAIMzVq1fxDWEky7MrmIaEbZ0BDSzL4oknnuDe/Q/56td+g+2dR1y/fp2dnS1HhB4BWQOBgHOdVRRFdFFmZsjnBTS1OxT1ksk42GIKSyaTeDwe4hHRdBVPRIlHorjdGpoqMzY2BpZotopGw5iWTjIpsISZzBjj48Jk1el0SSREWMq2xSZJkVWnxt7lcjmaw+TkpEN38vv9+P1BcYX6Yzb/EVxlJP7+aa+PxaGguQTYY2Jign6/7xST9Pt9stks169fx7IsPvzwQ4LBoFNP/vjxYyKRCMVikZmZGe7evcvMzAyBYaz0ypUrjqBoGAaZ8XESiQRbW1s0m038fj+X15/g4cMHJIfW46ULy3x47zaf//z34PG7KZXP8fo0tnbLQ21D1JX9xm/8JqlEkngsxuVLa6QSSfL5PGPZcS711ykUi+K+7vWgud34PF68Hg87m1usX72CKqlcuHAB27Z5/30Bpxp1WIyuN+l0mlgy5vQDyDYCbJo/F74AVcM0bRq6ztaDNouLiyh4UGWVJy5fo5DLiRyCLKaCaCTlXLNcmoC59jptJEVFNyySsRDtepVLK4s0qiXmpufY3dpkeXkZv9eHafRp5aq8/vrr/MD3f58DBdk8OSKeiFIonIsJodsWh5fiwjLBssUmwmp2ubd3m5XVy7z826/w3/73/w2DPmgSVFs69UYJzWWhKTqy5KZ4VCQYiHDr/bf41V/5dRqNFkFfFFUNcbTfcLoxQ+E4vYEIeEWicSLxFLsHh0xOTGHZsH75Cu+9foeTkyMSyTitZodgyM/Tn7jG73/n93B7JKamx3n9jVeYmpxnairjuGXT6bSz0UqlUkPLuE7A46PfN/H5gnzf9/0AL730ElNTU9y9e5c7d+5w8+ZNLMuiFspxcipISy5VQVEkVleWeePNl5mayBKJhFheXsa2Ily4eAk8HrrDsuNkIu1EqwN+cRC3223QbAzDYmJCmPRM0+T09BS32zvMa4gJdGxM/NkNw3CKgNrtNqurqx//Q8G2hACYHhvj4YMHXL9+nXK5zMLCAltbWxQKBad6ze12c3x8TCqVYnx8HJ/PRyaT4Y033uDatWu0220mJiYwDIOdoQmqWCzyzDPPcHp87EBEs9ks+/v7tOyewx+4evUqRyfHfPrTn+bmzZv0zT7j4+M8fvzYWZcWCgVkG6ZmZ3DLKj1dZ3NnW0BLZYmBZbK0tMTM7Cyvv/kWvV6PRqvFzt4efUPYWyvFEpVKjUKhQC6XY3V1VRSCtFpOcGlkp5Vdwn6bjAnXZqNeRZUlZFnB6Is+hKA/QDycplau0aw18fu9BH3C/dZuCwOWhI3X48IcTgqSJIl1ocuFaYMiCXyYYRjIyDx59TIXV9eIxSIc7h/QdjVYW71I7vSUqcms6EGo1fH5PcQTUSqV8tBS3aLf64jEoeom4A9hWSZenxt3XcaveejUWnSbx/z4f/1PubS2zt/9kR/mjVf+ENsskUyEqLeE3fvg4IjjoxyDvkmnPSAQiOAPxBn0TRTVRO8LPmW5XMbrD5AYSxEKR1lYXqE3sIjH4+SLJY6P38PjEk9OUS8o87nv+Qz/8l/9CxYXFymVc0iSqLAbHz44vvWtbzm+mK2tLRYXF53E5IsvvojPFWZhYYHLly/z7rvv8hM/8RN4PB6xzWi1SKfTgsnQEZsvVZPwRkX/Y7VWIBT2Eon6UFWbQumYgDfDnfffZ2Fhgf39fSYmJohnsyxeuOAcNqP3LBKJOIVIhUKBTqfDE088weHhIQCpVIqtrS0UReHRo0eEQiGWlpY4ORFAlnv37iEr/wkNUf9/vC5dWLLf/OZvUiwWMQzRLZDP59nd3eXq1avD8RdCkbDTN7i1tcWzzz5LNpvl/v37Do/w6eefJ390jNvt5vz8fFjfJcb+YDDI1aee4vzkhEajweLiIo/ubYg78vwc+fw5iVScWqPO7dvvMz45wfn5GW+//Ta5gk6j0eDVV1/Fo7lwqRrTk5OCnNsSVXEXLlzgU5/6JOfn50xOT3F0dMLJ2Rm5QoGtnR0kSWFze4uVlRUazbZzyI3o1deuXWN8fJx6vc7W1pYQwYIeFElmY+MRvW6bVr3BzPQkoYCP+dlZvB7B+zd0kcKLREMA1BoiS58vlIhGksiaC4/bj9vtRVHqYuXVbWP0xfq23mxh2RJXrj9Lt9dncfkCXd0Y8v9sJsezHBzsI2Hh9/oYzwoikYJELn/C8fER9VqJfr+HKotDzTZtbFvC7wsTi8QYi0W592CT/sDGH0rj8/pxedyEw24kBqhyh2a9wt6pKLPJZifpD2x6XR1kF7at0Gr3MAaWoD7LwjeRnZxmfnGBWr2NbgwIR+I8cf1pHj3c5OHGJnNzc3zjP36d559/Hq/PxeHhHpZtsL2zQTgcpFA8I5sd5/DwkIX5i3i9XidA1+0KIO7nP/95fvRHfxRZlvnc5z6HSxZRZIBKpUKjIYhaIw1lfHx8OO2eDUViH+lklEDARzwWxh/wsnZxmWpVmJkurbxANpt1glfHx8dMT0/jC4W4e/s2V556il6rhScep3K0T6VScZySuq47dKhRPkhVVebmRDwgkUhwcHDAD3zhC5SHrVHf+9d/lI3tP5289LE4FC4uL9rvvfx1ut0uyUyG1155hU997/ey/eGHZLNZJ/xULJdEEOSJJ8gdH1Ov1zFN0zEaxWIxisUiQb9Qi0d39Z2dHSYnJymXy+TzeWZnZ53159zUJGdnOeLD0hSPz0swEefhh/dpdtrs7OwA8PVvvMrh4SHhcJjNR4/IZrNUyxV8Ph9TU1OCLCQrXH1inWAwSG/Q58rlJ2g0Gjzc2ODevXvMzswTCIf49re/jS8YcrogR52EI63DNE0H7NltiPScpijMz04yPZUlEvbTalTpNKqEI0EmshmWVtZF5XivTafTodnpYtkS5VKL3d0TkDW8niiqoqG6+lhGH8sc0G42MAd9fIEgfcMkOT6FLxBic2uH5z75F0knkhweHggnXLnI2toa3U6LwvmZswHqD7psb29SLJxhmAN8bsGI9Lp9SJKMhIbfG8AluQhHI5SrVU5yORKpFItL81SKBdwuF7FgyImP981hLaAkocgaXb2PommomhiRS6Uymldsl5KpDKurl3jhU3+Bubl5/sd//W/Q+wbn+QKX168QikbYf7jB2NgYd+/eYXw8w8NHD6hUi0NnoMr5+SkTExOMZaZRFIVQKMSVK1f44he/yDe/+U2++c1vOiag+fl5Bj2dszNRKhOPx8nlcpTLZcbHx9F1nWKxKDIOygDNpeLzeUglIkQiYcbHkgRDfi6uLHB8fMjM7BTxyAzFYtERgROJBOfn58zNzTE1NcXZ2ZlzFc6OiUrFcDhMrVZz7NnVatUBIBeLRUxLdzSrkZ9namqKXq/HX/qhv8GDja3/rL0P/1lfHo/bISznz87w+Xyc7YguhrOzM6cEY2xsjEajQe742MFrn5+fOzHUTqcjeAQrqxwcHDin/UggqlarjjEERBBlY+MRsVicN996nWc+8Rz37t2jPzCJRqOMj0/Q6/Ud2+hovE4mkyiKgi/gxzYtBwjTaDTY2dkhnkry1FNP8Z3vfIcLFy44Tj/TNPnw9gesrKyQK5acGPeIuByNRh3YysipqBgGbs3FwtwskbAXr8fFvTu3iIb8vPjisywtzPPUE+vYXhVJlunrOp1ul26/j4RCudzlrbfucHpeolJuo+ttgqoI5FgyaIqMZKuOgFkbMjEb9SpbjzYwF/pMT09TKZd57dVX0BQB+fB4PE6t+UjkHbkm//hfsixhWzbtdpumPSAxkSWoQOd4l3pXwVanaOkt9k+KxENxopEIkmzjUVRMFNGNKNt4PCp9c+CsiyMJPz6fgNU+99xzBEJhvvrVr5KdnGR8fIJqrUG92cLj9xEKhXjxxRusr69zenpIdmKM/YM9BoMwsgLn56cEg2FKpQpul4Cg/OzP/iypVIqXXnpJWIsLBWKxGPV6nTfffBO/x8u1a9eQZZmTE2GImp2ddTZkoqjFS6/dQpGV4VpSxTIVZMlNr2vQbvVJJsfwesL09A61eoW5uTkCgQAzMzNoLoVINES702T/YE9E4V2K0/41qlIcfb5G2Z/R5Dk7N+lMCbIsk0qlnAJb+0+vYwE+JodCpyOw1qOcQyQScehIAI1GQ+QEhj2PoxErFouRSqWo1Wo88clPcvu117hx44ZYpQ2pM6MRL53JkJ6aAsPg3p07pNNpHjx4QCzs4Tx3QigU5PBon93dXVTNTTAYZtA3mJ6aQ1M9FEpt1tfX2dzcZG5mhq9//esiU4BNpycOmaULy6RTcR49euT4Db7xjW+QTKd5/PgxHrePWq1Gp9OhPRg4dtWRA3NkNpEkaSgcuVnMTjA5OUmpmEPvgd518T/93L9C77UY9Op4PRqV4gnelEtsFYJuVLeJ1rOwJYn0+BTTszP4AlFefeUdvvMHr2AZForLhd7rOLSqo5MTxsbGKDeqFEqlYZHOCamUmKCikRAej4eT0yMk2yQQEBHj+/fuoWiS8/6ZlkFfQtyn2z08Hi+hYAxzYKHbNsVGjXgyQigTRDfbnNWOMD0mql+l2ungjcTwKDaqqmCYJrYt4LZ6r4ttmciq+P5YtAmHs4yNjbGzs0d6LMPFi5c4y52zdnmF/TfeRJFVNM3N2tplfvIf/X3hELUGPNr4yHG+ttttxjKTIFn84i/+Il/76u/wzjvv8PM///OOJfz+/fuOgzCXE1HogM/D2blgIfYHPRRVYm39Ip/97Gfpdrt85StfAaCca9Hv9zD6Fq2GiUuzaTUHIJmUik10vcNAl3F7FC5cWBpOvBG63TaJRIxEIsY3vvENrly5gqJI+HweOk3x2RHBt65TNf/mm2861XMj89Lu7i7LQ9TA7u6u43n5824IH4tDweP14vL5iaXS9I0BjVqVubk5Bn1BnHG7NdA07AFEQkJX0Ls9FElGkRXOTk7pdbpcXFnl6OiItdk5vv3tb/PiX/iU6CDwuIf+eYW9nQ36gy6Hx/vMzE2haAHOHz1CklQKxzkuXLosbKfRCIWi4DgkklGuXJzn6OiYQa/O737jq8wuLLG8uoplws0/fB1fwE/LlAjaMrbHz3v3H4pDzefjrFIhNTXF5uFj8QTQNKRel3BCIL7GJ8fZ2dmhVCogIUbidCpBNBwh4gqiSQrzC5P0jQpPf3KNrlLG9hr4fBqWYdPtD1B0HdMcEIhlaLTr1Bt9klEfg26BQMiF7Woz90yY//LSZ/jKz/4KhZJB2JcCU0WWIRUPINFDkwf09QGF0yIrC+PkDzfwzs9huG0qxROaNRe63mZyekY8cTSFTqOKioRuymAqdAYmtq1h2DI93UTVuqJ/oNfgLGfg9rrwqVFcVoBOUSIaiqP6fITSorqt35cxLAs0iWaljG2BrIVQNJmAV2xNLl+9wv7jIyw0Wl2LCVeIXLFBT5f45re+w9zCDO8cvcfzn3yK3/zNr3Dt+pO8++67+P1+6rUm09PTglvgC7G+vs6P/MiP8Ku/8qucHh/Tqrepliqi10HvMjUxTrfbpl6vkkoGUFUTmx4Hhyd4PB4B9Ql4uXvvPd7+7qtYlsXP/MzPcPHiRX73d17md7/+28O+SD++oIeT3DnZsQybO/u88PyzFAo5FucWkfHTarSJhNNsb2+TTqe488F9el2DWq1BtVrnwoULpLMh8Hq58/rrLC2JWsPNnYcYts7y6iV2dnbo9XqUilUmsuM0GzWWlpZoNqKOx+HPe30sNIW11Qv2d1/+PQKBAL12i+Mj0ffXqFdZXltj76OPRFpRFv6ATCbD+fm5cyc/PT3lxo0b5HI5cZdFolQq4Xa78fh99I0BkXhMQEd6PVqdNteuXePx48c8/cwnOTo6olAoODXjIzTXxIQoXV1YWCB/ckYgFOSd997n/fdv88G9+5TKNSxbIp5Kow/6uDQPsWhwyOproeu6GL0rFbxeL8lk0hk1D4+PCIVCwtraaBKJRKhXqviHTMpUKoVbcxGQ3ESjYertPH/zv/ohZJeNRAdZMvFrbsz+AKtvMZBVXF4vCytPkS/XsC2LsEclGpDRQh5sSaIhqViyyltff4Obr7xJrzogGgpTKpxj0QYsyrUOA0PC7QoRTwSwJMFpsC2Jw+MjDMOkbxh4fH6HLZCKBp2n1sgrMgqTjSrrAoEAXVOi2WigaRrJZJJ2U9iH/R4R6omEwwQCAUKRoOP9j8aT1Go1Nrd2WF1dRXW52dvb4yd+6icpnpcIBoN8eO+hSLyWa5RrVVweNwtLgs15eLLPz/3czxHR/EQiEWKxGIPBgOXlZT7zmc8wvzDL3/t7f49qtcpgMCAZiw/JW+I96Q96WJZBtVp2RFzbtikUCg4IZWRsGzE2R4U8k5OTvPfuh6xfWsPt1kilE/ylv/Apnn/hORRsdna3KOZzNJp1PJrYbIiMjmfovxGCcDgisIJer3t4RWgxNTX1JwpqR5iA09NTp/RItgVNKp/PO9qXZVkUCgX+1pf+MR9tbP9/0xQkSZpE1NBnAAv4Zdu2f0GSpBjwG8AMohDmi8OSWQn4BeB7gA7wd2zbvvP/8DUoFcu4NDfb27t0Oy2CwTDHR6ekU2PUagJTlZmYBHCYd6NRKBAIcHx87OyTn776JOOTUxweH+H3+ykeHeEPR4jFkwTDIW7dusX+4TGa20un2RIpu56O1+VGlWS2t7ZhYQFNVggHghh6n4ODA9YuC2JTPB4XLkC3wKuNjCoXVlfQux3efPNN1tbWnGvAyKFZKBQ4OztjcXGRTkeAVX0+n/NhGE9nMIYY8BHfcHn9Mq1Wg0ZPRvO4UVQL0+4jWRJ6v4/e7WMPLI4LbZCaRDMWXd2Dz6UwMGwGAwvNdCEpCqrswrJVZpcu4n//Ic1KEUuSkV1uZEwGhhhFZUmYyXLFgrMH73R1seFRBbdg0Gw6acv6EJo7+nAOBgPM4dXNBhHbVhQiwRCuYWPRKJ34x9ejvZ6IWqfG0/QNg2Q4jD8YQtFUZgYGB0eHJDNjfN8PfD/f/v0/IOwLcuNTL9JoNATP0iecrY8P9vn0pz/NV77yFX7zt36d9StX8NlupqenCYfDHB7us7S0xKXLK/yv//5/4+7dD8lkMiQSCZ577llnDT4xOc7RkQDi1utVp2THtoWNeHQAigi1eGB1Oh2nBj6VSnHp0iq1WgWXWwXJ4Dvf+Q5vvf06k9kx0ukk83OzhMJBshkBC4qNjXG0s0Mw6Gd8bpLTx49RFAmPRxw+Dx48YGJiinK57NCkTFPg5DRNY3FxkZdffplkMomh95wrdKVSIRwOMzU1haZpqH8Oeen/zfXBQLRK35EkKQh8IEnSy4jW6Zu2bf+sJEk/BfwU8JPA5xB1cYvA04gy2qf/vC+gKCqzCxd4cPcOsWiS0NTMEFN1kUajw5PPfpJmsUilWSUcDhOZmkIvFh2kWjwep9frkcvluHLlCqeFMnNzc5SqTVrdAbMLyzQ7bWYXL4jp4NkbNJtNzs/P2dx8RK1WQ1EUjo8PCQQC/NW/+sPs7e2Rz5+jKApnZydcu/4kt2/f5pmnr/N73/o2E2PjeH0But0e/W6HVCrB/bv3CAQEWXpkrz4/P3ecaCMT0snw/t5qtfD5fMSjMSzLcjBdgIgiI3H37h0uX10nZERoNJu4fAqm3aff7WD1dRRJRUPjOKdy995DUvOfZnx8EhOdQjGP2TXIejSUUAif5qVnGCxf+QSzH+wxMHY43tthLJmgUTtmYFhYtoFpWrRaFboDcfi63G4MwxIuTQRXAsnCHHIN6/WWo92M/g4S2GAbJi7TQvUIjmb6j3EwR7ZybfhrI+I0qoytSCQzY/QGfYLRGP52j4VYHF8whKS5CEUjBDx+vvnNbyJJmgMfKRaL/MIv/Sw3brzE4tISX/ihv0yn0+H2Wx/idvlQFI3l5RW+9KUv8U/+6U8xNTXJ7t42Y+NJ4okwb739Br1ej1argSSbdDpi4puamiKdSTpIM1Vx02q1REZCUSkWisOadyEYT03O4HZ5OT87IOD3MzMzRaNRp1orobkSHB0dMDaWQte71Gpl+t0O+fw53Q9ucf3ppzg6PiDRbQx/vSoszRjIiujgHInw5XLZST6OWCJO9kTvDQXzcapDEnipVBLoAP0/Adw6rIM7H/5zU5KkDSALfD+iPg7gV4A/HB4K3w/8qi3uJe9KkhT5v3RP/t9ehmGC4qLZbBP0BQmnMhSOj/H7xAQQDhQ5OzvD5Re8gM3NTeLxuPjmT03R7XZZX19ndXWV/f19PP4I5XodSdOQXS4kRcXt8vCtb/8+N27c4P79+/h8Pp588ike3HmfdCrB2NgYp6enRCMhPrj9vmiV9gofQUnvEvB5WZxfwDBtPvnC86yvt7h99x7FUoV7Dx7AIUQTcf5P5t47SNL7Pu/8vG+//XbOuXt6ws7OzszOZuwCi0hEERLFIFEMEinJFJ1UCuezJYvS+SxKtq/sOlmlk8+6s3S6stKJokmKIiEBIAACWGAXwOa8s5PzTOec33B//HpesSxSdtlXV+gq1G71zs42evr9vd/wPJ9neXkZXdd57bXXaLfbFndh/6DodDoWAEVVVRwOB+OjY+zu7g6t0iI8JBgMMpLOMJ5Ic3Bqkin7GK1uB8XtoT8Y0OkPcNqdOJ1uuo0OB2eP8/alW/hCEabmUjRKLbRemXDEjc0OeruJ5pJwONzsVrscPvEgP/B9n+CX/oefpVhtgGGgGfpwE2LS7bVRHHb6gwHVZgOn042m62haH1MC05AwZYGTt9lt9IaeFHHI2yx2gGEYyA47PUPDaLUwh8O7breL1y0EOPseDH24wbh15x6nTp1Ctqv02m0RJGwIzcPJU6fodnvkSyU8Hg/9fp+x0SyBQICJA1M8+PBRfvYf/iJHD8/RaDQo7O6xtLTEL/zCL/HE06f58A/8CN2u8MIcmBynUMjzsY99BE3TKJfLNOo1gsEgPp/HSnzelwnfuX3POtgNQx5i/UycThdjY37hmDVNGo0OdruLblcjEolQr1VIp9P4fF72drdJpRPkd/fY2t6gkN8hm81aP3uP18Xe3h6qqpDL5ZiaOohh6FRrZUvhGw4luHPnjkUDP3jwILu7u7jdblqtFrdv3+a5556jVi7h8/lYWFgQ4bLhsKWBMPT/jyArw4Tpk8B7QGL/Qh/+Gh9+WQbY/I6/tjV87j//Xn9fkqTLkiRdrjUavP3aayIcdnUVBgMhjBlmQO73SfvAlGQyiWEYrK2tceXKFZLJJAyBI1NzcwTDIbx+HwtLixiYuL0eisUip06cJJhKc/DAJEF/gPxeztp2lIYfslAoZPWdDoeDSCQiZgzpNIah0e21KRdLFAo5nHZVsP2RsCkSm2vrVh/t94v+U/SnIpNxd3fXChGt1wWleX+N5B4CYfbR7v1+H6fTKUi95TKNhuhxB70+qurA5XbT6XRpNtrYFBW7CkgDllfusrNbp9OtYtCh3amAU8bmd2AywDAHRIJeHnhglkKxzG4+hzlMNGo0GnS6Xbp9gapTFHHP2I8s21+d2mw2HG6Xlaws2xRsih3FrmJXHdhVBwNNx6bYkWQb/YGGbFMsD4Gu60SjUYsx0O/3rRwDsUsfJxQSsNNGvUWxUCafL+L3+/nqV7/GV77yFYrFIlevX6M36HNoZppAKMjpU0f5h5//R3hcbgI+Pz/4Ax9ic32DH//MZxkfH+W3f/N3KZVKpFIpQqGQIGQ5HBSLZfp9zWpnEokE9Xqd69evW87ExcVlarUWkmSn0egI2XFPo9vp06i3KJeqtJodfN4A0Ugcu+IAUx5ejFFeeukllpaWBEJtVyh0W80ObrcXw4BkMkmxWOTObYGiBxmfz8fW1jbdbpdEPGUFCNvtdtxuscnad9YC5POi3Xv++eepVqtMTExY6/rDhw9b2ZfiZ2v/ntf5f/X2QZIkL/BVRGhs/a/LxL/5pd/lub8xzTRN83eB3wU4eeyoOXdklm996yVGUmlqlRK6PiAQj7K2sECzKS6a7byg2MzNzZFMJrl586YQE83Pk2k0SB46RHNnB5vDTblc4rnnnsXr8XDz5g2mD06xt7fHrbffpl6v8+gTT9Cq1zl65DDtloj5nj5zhl61SioZx+P3s7W+zoXzbzE1NcVffuXLfOgHP8K//53/k2g8hmSaRKIi1doE1tc3qJSLqG4xfKpUKjidTrLZLO122wq/3acq+Xw+RkZGyOfzLC0uAaD1BHfR4XBYbD/FMChVytx59wY/GHoWj8+Jqes4VSem08TQTBwON5gVPvujTzE1HWZn6wqS0SQZ9uBw6mA0QO9jmA76Oiwu1ohHkhiDPuFAkEIuj6p00U1TbGpkg4Gm0RnKqPvaAEwZA2Fykm0SkmFgYKL1dVxOFdMw0IazhEG/L2LjZRnF4SAcChEMBMimstSq1b82demiZSoXiqiqSjwep9PpUMoX0PsDZFnh2MkTeNw+Op0e5UKJ40ePoSgqKysrHDhwYJijmCAYDPLRj36C559/Hr/Xy/z8HR596CwzByf4jd/4Df7wD/54yN4cZ2trA13XCQUjjI+PAVAqlSjkqwx6bUrFGslkkpnpI9TrDdqtPbyeEAF/VKyPB3lGR8doNBroum4FIh8/fsLSKuyzPZeW7zM+msDnDeByOzC0AR5PgOzICKpDoVouEQpF2NjYRlEcKIqDdqtPJOwkEg6h6X12d3e5ePEKc3OzaAPhc+h2uxZJaWRkxEKz7WMNo9Eo/U7bSvwul8si2m4Yi2Ca/53eB0mS7MMD4U9M0/za8OncflsgSVIKyA+f3wKy3/HXR4Cdv+37dzodQtEwhw/P0G23OXfuTcbGxrh15RKpVIrlZg2H0z4Eg3rY3d1le3sbj8dDNBolEAhQLBYJ5HJ402ly6+t4PS46nQ6bm0Wy6RT+WARZMvG6PVy4cIF74r7/VwAAIABJREFU169RqVQIx4NEIhHhXVBVHLEY82+9RSwWw+PxWAowh8PBq6+8zKc+9QmuXbuBYndQqtTpD3RMw8BuF7i2RqdrlcPhcJiJiQkkSbJYgNVq1bJ/r62tDfMABT9x/y7a7XYtAG3K6UHSDdr1Bg7Fjt2mgAyyJCMjmIV2m4qi6PiyYZxqn0DQQ6/RQbXrOOyApGNqGvlSi+5Ap7Crs7u2xc1Lt9C1Ph6PC0NvY/ZFv9rtabS7A/q6ANkAyJKEarej6waGBKYEyjAnAcNElhTsisMSLemyjmITiDaH6kKWBMTV4XBYqsVIKGwFm2iahjEcmPnsTor5Es888wzVYpWKWSGdFOTjGzdvW4dppVQgHA4ze2SSX/1n/4pf+7Vfo9fp8MILL/Avfv3XkV3wS//0H2O32xkbz7KyssJbb7/J0aNHabdEv722uoHH46Neb3LgwAGq5Tw+n2/4szCR5Q6tZncIpnXi8QR45pkTeLwqY2NjJBIJkQTt91OpVMhms5baEKBYzLG4sMD8/F3W1lbotJuUimVcqoNwOIjNZscmq4yPHRi+B9BoNNnbyw0TqKpkMiOsrW6QyxVwuVxiTR6NWgpYl0sQwlwuF08/+yx4PNy7eBFFwqo6Q6GQ9TkOBoPYVfW//VAYbhN+H7hnmuZvfscffQP4SeBfD3/9i+94/mclSfoSYsBY+9vmCSAYjTevXyYYDLK0cA/VbuPg1Djffu0VNrdW8bnd7O1tozjdlntQlH1FRkdHyefz9Ho9oUFfWyMYDrCxty2SkkbHiCbiYAzoteoUtjfJRIf20mSM0YMHuD8/L2jPqoN+v08qniCfz6NpGidOn2ZrbY1es83MzAwenx+Px0W3NyCZjHN/cYmdnS1kWSEcDhFziAjy/Yn6nTt3LL/7PkFK0zQ2hzJt15AQ3Ww2aVRrBPx+/H4/qVRKpFPXGiiSzEgiRbveJuB34w8E0NFxyE5skg2Xw4vfbxBLJJBdXuz+ANVCD7/Thqz16DZadLU+3Z6dRtvgxsW3ufTeVVbvb5KIBAkG3LR7shj8aToDw8S02ZAlG4ox5DxiIMsSIKMNBtgQYcCqy4mqg6bY6ck9S/VpSjLGQMOUZfrtDrntHdqNJvIwumw/Acw0Taanp+l0OqytrorPXFWwK20mKJLM1vYW66vr+P1BMokkRn+ArEv86E98hiefeoy3Xn+PM2fO8Prrr/Pg6dP8yhd+mc/+2GcEHLc/4MjsYd689AadTod0JoFpGkwenKDb0XC7/YyNiorj7bcv8NCZYyiKcLCePfsIUzPjoEGnAy+++C2uXbvGxsYG7U6Je3fvMxgMrE3YPtErGo1aUYa5/K5AzKsqY+MjYJg8+dRjTB+c5LHHH+Hu7du0Wi3m7y2TSifJZidYWlrC5fRRKlaHODgn4bBQ0Rq6jVarxdbWFpOTk+zu7gqAryEMYJcvXuT0ww8z++CDlJaXrC3Xfgzhfsz93yJo/K+qFB4Ffhy4JUnS9eFzvzI8DL4sSdLngQ3gE8M/+yvEOnIJsZL83H/pHxCZfXZGJ8YYnZ3i/F+9yI0b14hGo1SrFcrlsrCxZrIWE2E/dmtrawvTNJmZmaFarVKv11FUma2tTVxOJ71+l1qpyKDbw+N2Uy0WCYRC9DrCM2/oGuIdMqnXa8PU3w4+n5ft7W3u37ktPPU+D4GJCW6/fd4CvBqyjMflZmRkhGazLQCaIbc1VZckiXA4zMjIiHU49HoitWega4RCIQKBAKVCUQiWhmx/RVGsXT8IZ6PL6WR1eZlozD+0TaugShiGCaZMp1GkLBt4oklsChj9Dpqsokomva5Bs6uzuVWiUK6zvryE2e9jDvoYuoZTVWi1BuIuL4kyQNc1kMQ2wZSAoZ5lv6Kx2exgEzoF2ZCsweL+f/v/D4ClHgzIMsaQbKxpIkkpFApZeZp2u+hzEzFBL75z+x4HDx5kbu4I6+vrOF0eioUisXiSbDrDk08+xq/88j/nUz/yo+zs7PDTP/3TNOt1zp49SyadYHdrG7sq8eKLLzI6myGZTLJwfwUJm0D+KR4C/iBXrlwjFotx5vRD/NzP/V1GsimwA3341//yt3jzzbeIRGLYZIV8Pi/mQVEn29vb1Go1ZFkmm82ytbVlUZoGgwHNZhOvx0+71RKE8WqdVrPJn33pP3FgfJSbN28yms3w5JNPUi4UrdSn0ewYkmTDNHV03aCQL4EpUy5V8frcHJ6bQpZlYWn3eKwt3OqQ8Xj90iXK5TInjx1lcXGRdDrNgQMHmJ+fp9vtWq/vv/lQME3zbb77nADgme/y9SbwM/+l7/udDxkTqddm/e4t+v0+iUiIg4cOcefWLbqtFg8+fppr165RrlXxeV3IUoxAIMDly5d57LHHWFlZwdD7+H1uJHSmTj1ALBYXAhOvD7uqUqpW2cwJMGo4HufunTvE43F29krUm0JktLW1BRQplUo897GPUW/2UJ1eciubxJIhFq9dIRiP0traJZ5O0+lq6Gs7zEzNcuPWHWxuJ5Mjk+zm9mhV9/B6vWxtbtPpCCipLMvopoY2FPrs7GyxtraCx+XF4/EQicTY2drlk5/8JIOBTrfdodwQ8WOqK8i9O/PUqk1+5NMfJhD0sNtZJRD0ItvL+N1hMQ23NTEGHdwOKJZL+HwZ2l2VTtPgja+9QqVcQ+tV0Ro1wgEFf0BFlw08wTQ4gnh8AkrS3dvDwI4sSQyafVRVoVVt4fV68Tnc2GUbhm6g1Tt4UyGRPdFqEwqFGHTFLEJVBXDELZs40anv7jE9dxi7Q2V9Y4NOv4eLID1TQsOGxx3C63Dh9PpIZEZQVDuVapWtpfuUG2JIpmPSunqBV779KjfPL+A2/NgNhU996of4xz//P9Ltdjh0VPT7piShuD20ahr1kpNes4PPkSEcCjAykubo7Cxnzz5IKBMGE/7NP/sX/Ktf+18oV0qEQgFUVWFndxOvx6RSuUelWkZRZA7NTHD73gKKYieZ8VAp19DNJplslEFfp1IVwzxVdZJv7eJ1ezAljb3KBl6Xi26vS62hktt1YfSr/PbVC6THxsR76/Px0EMPEQgEeO+99wiHwzTaDbLjWSLtCA6Hg2a1iWzIdJtddjZ2iMfjzE7NYrfbmZ+fJ7edIxaLsby6zQNnHiWfz9Pq6Hj9UYLhJKVKC+P9LnOWJJnNzS1GRkaQZZmRkSxXLl1GVVXOnn2YjY0NkskUDrebUqnMoTNnWL1+nUceeZR79+aHO1gdt9tNuVzhzoV3RIthV2k3W2wU1/H7/STjCQxNp9tqMz11CJuioHkNer2exdFvtVpks1kuvPIKmUyGeDxOt9vFH/TQbrfZ2tjk5q07PPTwY1y/fhtN64s98dQk5Wodl9tBsZi32huv102v10GWZSv4ZZ+WtJ+qNBiI1OPl5WUwJC5evEg6PUIkFEZXbIBBr6+RTGeo1Iq8/vpbJBIRDh85QKPaI54I0+2YqHYbNkOi1emjGyaSYef1V98kEpnihW+8gqkL0rLbJRMMhbCrKnt7e3T7PcLRKHaHyszMDIVCgbt37+ILhCyWo8PhwD7kSIIwk+0r97SBgWp34nIbGAaYpoTT6UYe3ks0A1qtDoOegIe6vR4xFygVURU7oVCIbCpDca+A3+PF5nWRTKcIBoM0hjj+8+fPC+LRSIb/4/d+h4//8Cc5EJvkV3/1V/n83/8cU1OTLC8vMXVoEkqwt1sgkx2h2egSDEQ5NH2Qxx9/lKc/8CQOVaFSKfPv//ff5mtf+wq1ahlVVVhcXMRUXKgOhXxRdLw2BbLZDG6vi0gsiizLbGxu4HYJcKquSfR6OpgKpgmtVpt+X8Pt9qLanegDg8JeAY/XRSaeEO1jv4PT4UbXTDweLzabwurqKsePH0eSJF5++WWefvppHnzwQZF8NjkJus5X/viPOX36NIZpUK3XWNtYFzMfWSKZTmEYhkX1zhcLmMgWxWx/JalpmuXG/F6P98Wh0OmKwdM+CLM+TFQ6cOAATp+PZFKQdbHJLC0tWWuxnZ0dYUjp963sg/2Vjd/vF6isTgdlmLIsyl6bVa4CQwhnzZKu7lthH3n8cZAkNleFd3329HG6lQrr65uMjIxQq1SYmTnEK6+8hqbp5HJ50RvOL/DgQw/xh3/0H5mensZld1oqP03T0IcILrfbbYldOm3hDTAxOXXiAYrFIidOnCKTSvP6yy8QjUZRbODyuDGlCG++cR6f30U09mmSySh+d5hKpYo5UHD7/GAKV6Jm63Lzxh3u33sF01DRdReBQAhdG2CaEtgU/IEQjl6Pfl/D5fHSqDVpNTsE/CEGw8Hf/ipyf4Oynwa1P8mutWvWmsw0DHCoqDZF0H0M02q3up2emCc4VJwOB94hWLVaroDfsFqQeDxOKpUSB+XSEoVCgWQsTqlY4t/91v/Gay+8QiVfRAuP8k9+4R8Ri0UYDERuQ7lcxjQk3C4v2gBOnTrN4dk5PvaRH0JVFTa31vnTP/kTavUKd2/fotPp4HEJl24ymSRXqeF0qkOQro3MSHrINYSNje1hgHEISVY4evT4MLl8k83NLarVKoqiEg7FhxuCXUYOCD6GXZFptTq0Wy2S8Rh2uwNJstHva7RaHSv+7fDhw2iaxrVr1/B6vYTDYe7cucPJkyd55JFHxGdVE23X/tB9b2/Pcv5KkmRpYro9MUPYb0UNw6BQKAz9RI7veT2+Lw4Fj8/HJ/7u59man+fOnTvMzc1hSBAazXLn0iVGRkZwetw43C6Lt5iZnuatF1/E4XBYPVIwGBRBmu22ta7c97Y7HA4ymYzlj9hnQUYCYUqFHIoMAZ+HUyeOoes6L33zLzhz5gz1apl0Mk6vUmF3aOtW+houl5Nut8cDD5zkm9/8S8DA6/WQG/rYT5w4RrfbpVDMYVdt0GU4fLSh6wPa7bZVNfiGB9/m2qa1riwWi+gDjdhoBptNolwsMTd2CGerwVHlQQb9Hm+8cpF+t4Nit1nJQ8GwD0VRyJfyNBotNE1G1x1IyIyPZajXGwQiYYvjWKnXGFR1BoO+5QoMBgIiebkrmAZ22WapMnVdR0Z88PZTm2OJhKiyGk1yuRwyEgMM9GEMuiLbUFUJfyhIuVpBlmX84Qhjo6OCTN0VB+bM7CzhUIhSo8KVK5colUrDwJc1fuC5Z/n0pz/Nb/7Gv6XZbJL0+CiVd5AkE28ghmI36Q/arKys8dgTT/FnX/oa1VqbxYVV/sN/+F2+9Kd/DMDYSJZTD5wgFPaxubmO0y1Wpjabje3tLUIhH+FwGEVRaDabLNxfJBKJUC6XCQXj2O12KpUKsmJy6+Y8d22LtJoidyQRz3LyhDhEPvvZnxTVqlsh6PcDBndu36JcLNBpNcDUkQwdWVJRbCpTU1OkUimq1Sq9Xo+RkRE2Nzex2Wwi8CibZXthQWzEsjPsbm2haRoPnjlLLpezVI6YMslEGk3TCAaDVtbJvr5nn8a1f4h8t8f74lDotFoUVzcIh6IcmTtGtVolFIywvbCMx+3D5fTQ64rS0263E48lWbp2k35PYyQziq7rVv6foUMoGOTGjRu4nE5Mw+DQ1BSKotBqNokOw043NzeZPnSIwl7BuijX19dZWVnh1EMPceTIESLRKKurq6LEHnRZXxOJvsV6mSc/+AOce/VVErEITzzxGJ1OjzfeeIOHHznLnbt3yOX2SKczSDbZwoYbhoEp7WPnRe7jfj6gaZqcPHkSl8PN3Nwc5XKVarWKP+4D3UCqVOgONAzThmHasCkO2rU+ToeXfruLxyUEV3s7NUx0TNlEtXvwenzk8hWOHZ1lZXmLUChCMBIWsestJ5Iiysh6s2GFrAL4vF66g76V0LX/0HUdh121xEyKohDwi0xPm2ynVmuIu9AQ2sqQN9EfaCBLuN1udNNke3OLYDhEIp7Crbqt+Pd6o0EsIQJTqtUqy8vL2G02IqEw/9M//QKXLgq16cTYOMVOTUB4HDKJZIxqvcYnPvEJPvHJz/Lt197k9//vP2R3p4iu60wdyuJw2EnG45TLZfb29shmhb1+ZWVFgGxlG76AH6/fRyaTodfrsbKyJrIyUUimssiyTDAUwzQlRkZGrGSvs2fPEolEcfqG79VwClfNN5i/f5dKqUwkFOHo3BH8XicOu0osGhRmq1KZUNzP6uoqMzMzPPTQQ1y+fBlJkpidnWVzc5PS+fPWgTQ6MY4pQa1RJxQJ0xnKmYPBIHt7e+KGo4jK2DAMvF4vBw4cGFay81bk/fd6vC9cknPTU+bFV7/B5uYmmUyGjY0NK1pe0zROnDhBs9lkaWHRuvuvra3x/PPPI7lcbCws4PP5rDXf+sYqjz32GNeuXePUqVNkMhlr/bdPy5EkSUyNk2kkSaJardJoNCwU3He2I/1+38oQ2NjaIplMMzKaZWN9C0W1c/KB07z11lu88sordAYab5x7k0BA2FQDwTAbG5vUW01ssp1Wp40k2ZAkcVH5fD4CPpGE7ff4WV5cYWZmhnK5ytTkQfzpAIlojH63x8riEqrdjt/hpdfpUCtViIUj5Pb2SGSEIk62Qb1ew0DoHXyBIKFIHNOQUFUn09OzNPvCkFQoFKjX66RSKS5fvozNZiMQCKCqKvfu3WO3KD5gsXBEtD5D0nQiFicWFmlD4XCYvVoVr9dL0Cci+eLRmIi28/up1WpsrK4BUK4ViUQijI2N4XC4WF5YJByOkspkh9FswvMxMpri4sWLKDYbboeT7c0ttjc3sUkybqeLXrtDOBSiYgiceXZsVKzbag1sNgfVWoe93QoDTSIYENyBeExCVVVrjqMoCru5HJVKxWqLgsEg8WiYbrfPkSNHOHnyJNOHZglEvBh9kEX8BK++dI6bN26ztbVFq9Wycht1XUe2YVVUAOGAD03TKJUKlAo5IpEwbpeDbrdNJpUgkYhhVxWe/9Cz3L17V2xzZJlTp07h9XoJDKu2e/fuMXvkCO+eP49zSFMCLPXlK6+8wuzsLJqmcejQIYrFItmZae5eElqfe/fuWbmSkUiE7/uRH+PazdvvZ/KSk3qjhepwcfvOPY4dO4amaVRrAmaaL5SGwzk3u7s5gsEgrVaH9fXN4YW+R7e7RiaTwe32YuoDnHaVkVSaV156mZ/6qZ+iUa3htKu4HU6+9eJLHDsm9tH24eHQ7/dJJBN0um10Q2NxSZRqZ86codlssrW9RzabZcQw6Pb7JA5OWXbbr3/tq9jtdp547FHOvfcu8XiURqOFaUrkFgTUtdVqCTGPTcZmE+7C/fKuXm1Qq9W4snmFo3PHSKfTzM0dZSSd4cLNd3C73XRbXcbGJlhfXaPaaxCPxsCQyJdrFMp12pqGJInciIFm0Bv0GU+JfENJVjg0PU2z0WZ1bY3ZE3NsbGwQjsaIxhNDUlCI3nC20O32SSbTDNDI5XKWcs+hqpYzdX+uI2zQCUu7f3JqWhC0dvc4ND1Lo1ZHtTu5ePEiqZE0jVqder3OqeMH8brc3Lp1B6/XP3T6AbKE6gCvR4SfbG+s02o2yWRSDHp9bJKM3W6j2+/g9DpptVpsbGzgdDqpVOu0Wj3CkZSInW+I9Zugdo1Sr9cFlMRmw9AM2u0ukmQjEApz/PhJPve5z+F1u4ZDZxlJgnq9x+rKDl6PsIe73W6e/b4nePaDT6B1QXHDoAV2D6BBs9FH13UCQRemAXpnQLMlZmSL9+fpD3oUiyL4aHN9mXKlhNfrZX5+3or1CwQCpFIpzp07ZylfU6kUtXJZbCMaDYLhsFiBdjogy0TjcWx2O+FolKvXrwvi2NYWiUSCYrHIkSNHuHDhAtFoVFRlvf73vB7fF4dCfzAgFotZuuxer2d57gOBAPfv3+fAgQPW/CAcDvOhD32ImzdvYpom0WiU9fV1NjY2eOihhzC0LlevXmVvb4+nn36ad999F0mSSKfTpCcmKBaFLuDo0aPcGHonVEXh1vUb1nQ96POTz+fJ7+6RTqcJzIb5+te/zg//yMe5cuUK9Dr0Om3iyQRgUCqVaHVaTEyMsrWzRbk8j6IoRKIhtIHBwDAY9HV0E5HjaPSHuPAOjZow2eyj286fP08mk6VarhANxllb2sDn9RL2hfD4/LgcburtDsVqjUQywdzx45RrRYrFIpVWC380zub2NrulOrNzR6jV67QHGtnJA9h2dri7cJ/Dhw8jSRKLCwvYkBjompD6FgrYJFkItTwe4QPpClGSfWiVBiyfQCgUwh1NEI1GOXbkKJqm4bSrTH9ymnffeYf79xdptNoEg2E2tjZxqg5KpRK1apWN9XUcNgWv24Pb7SYYjaEbBqX8Drqm0Wm28Pt82GQZ2a4gmQYGgC7j8QUw7Lahb6KJrpucOnUaj8dHMJTgz7/2V3S7LSLhJI8++jg/9JEPEAgG6fc1nB4FSYH8njhQJJud8fFxAmEF2QAMmfV1sYERytKyJU/fy+1iGAaZxCiXL19GloXtvd1pCoy7aicUClAoFMjn85jt5rDVkshmMzicdjqdFpOTE3S6bUZHR8hmM5x56AwgEP7BYNCKSwRYX1/n0Sef5NUXX2Rubg5VdVKp1KhW67TbXbrdLo888hjVanVo7Y8NM1DF5kGWZW7duoXHIxgYKysr9Aff+1B4X7QPp0+fMv/T7/+Opc1OJpN4vV7u3btHIpGwvOuqZKNULHLz5k28XhHcCVicwHq9zsmTJzn37ZeZmJggk8lw7Zrwyq+trTE5OYkyDGC9cuUK3//9349ig8uXLmEYBvF4XISZ1OuWR/7evXt4PB5ye1W+/0Mf4qWX/0r43qMRytUqjz7+CNhs/Lvf+i1GR0e5v7HOtes3KRRKVMpVEqk0lUoNZDvtVpdOr49iU+kPmlYis8shWhNTMxnLjg8TjqOsr67x8GOPCtORLJNOpwkGg7TbbaFQ03pWXPrNG1cIhUKk0iOEw1G2d3MEgyFu3rrDsZMn8Pv9wxi0EtMzh1haWhKrKk0nv5djY3WNTrvN9uYWPp9PbEWMFpVKhWpJDDFDwSBOpxOXw8nE6NhwfTzC+JETHDl82BIl5fdyXL16lXajSa/TtXI8dKVD0OfH6GvMTR/Godh56823GT0wSTyZwOH1YUgQcwnWQqlaYWVlhXK1gup0UiqVCEdF+zE9O8OB7AyGofHmW+dYXl6k0WoiSTZmDh/lJ3/i75HOjJLPlYnHo0haD7fbgeyCv/rmOe4vLPD6ubfo9frYVSfNdktQnxpiAOx0qezu7ojsTreDbq+FzSbCZh9//FHee+uKEFQ5nXzpz/4fyuUSr776LS5dfm+4rRAbJr/NRrfbpVoVr8Pr9yHJBoGAj3a7STIjNm5LS0uMjo7yxBNPkBji+x5++GGRL1oQgNl9F2at0WR6etoSyYVCIXI5UUErikIoFGJ3d5dyfg8QLUa73SYSibC4uEgikeCjP/55rt649f5tH7R+n2w2ixIIsLcsQjTv379Ps9nk8NGjYLezMz+P1u4yNjZGs9nE6XQSCAS4cuUKkUiEWCzG7u4uOzs7jI6OIssyu7u7FqNgbGzMYiGmDx6k2WzSbrfxhwP4fD5KpRJOp5NGo0EkEiEQj7O+sMDBgwcZHR3l3t0V9nZ2mJubs/pQw9S4dfMmhWH0e7lSotFo4A94abVa6LoP0zTxer00Wl3r99rAoNPVrXUqYN0VvvCFL/Ctb32L+fkFPvrRj7K5tU0qkWJza4PtbUEcVhSFXCknErQ6bXTDIBQJYrPJLK4s480XkG0qTz/7LIrTxQ9+5MPcunWLe/fusbW1RSKVoFKtitdcK7O7u4vf78fldFLI5anVaqK18tqsIaOiKFb2htftsWC6Ho+Hvb080WicaknE2J97Q3hXRkaEBH0w0On3NVSnIjI2EECVntGxlIzBYJCOpg+9H5o1id83T/V6PcLRCCcfOEUoFCKWTPB7v/d/4XI5KVcFaGTm8GE8Hg9PPv0c07PjIIPLGaXTgRdeeIHNzU3yhRKbO7tUqnXcPh9uj4NWu4tNtuP3BTFVH6ap43G7GBsbx+dzE4mGWF1bQlUVBoMe7753gYA/xvT0NP1Bly984ZfI5Xap1+v8z//8l9nd3eW1117F5/OidPs0m3ULdNtq1vEHherR5/dgYtBqN3n44YepVCriNebzBINBrl+/bg1gT58+zfb2NoVCgbHxg3Q7fWHLrtdFvF0wSCqZEQFJgTD5XBGvy8nC8DN88+bNYSUj4uu/c3j8nz/eF5XCyWNz5rXL52jkBBPxvffes7IlDx48yObmJvfv3yeaEGhrVVUZG81y4cIFUvEYvV6PZEIMt1wOJ2++dY6HH36Yd955h9OnT3P9+nV8PtETJhIJC53lcDiYnhlndVVYnjeG24WZqWkqlQo+X4Dl5WXcThf1bptYLMbq6iper1fIUUfFgOvixYt4PB6Rfn3nDuFQlLv372OaJusbO9hkOwbgdnmpNVsiJDQQY2e44tzPATQMwwrCqdWEUy+ezIhdfrXK5OQkFy5cYHJykna7zeHDgkn5wAMP4Pb6ePfddzlz5gxXL1/C5/MxPTXJ/Pw90qkUKytLjKTFh8au2oTibXmZkydPWqasQCDA7du3uX79OpOTk4zPiAtzeWGZXqfP7KE5XKqLiZEDJONpAoEAMjZ2ew3cbje1eoV0MsXVq1fQen3GRke4desW3/rWS0yMj2N3KERCYXxuD5sbGxY56MiJ48wcnmVjbwdd17ENhOW83W7TbrcBkaMAQldSKonDNxgRlUs8nqTRbmHoMuFolN7AJBZN4vUFmJiYFA7MkSAejw+/x4upG2BIBIJ2zD688Bcvs7G2LpytniiKojA1dQCX20G5nEd12Jmbm2by0DjmUGfSbDa5du0ajUaDVrPL7Vt3KZUqbGxs4fX4aDbbzM3N8bM/8zl+8Rf+yTC5qY026PHAA8fptts4nXZssrCtj0+mLUFYPBpjZ2eP8ey4mNvYxGB0fFTc2FJjY5imyeZ+1c0aAAAgAElEQVTmNpGICMdZX1+nVqvz3Mc+xsK1Gxyanub1N15ldHSUUNDPjRvXyKQStFpN2q0GP/2FX+Xmnfn3b6Wg6zqtQsGyGO9juVRVpVgsMjIyAoDdKfzkly5dwuV0MDk5SToRZ3FRbCUajQaqTyUSiVipzna7nbm5OYEX29sTWLfh+rFarTJ96gjlchmbJIg1DocwRe2z81VVJZaIY28KuXEuJySkk5OTgiEYCjEzM0Or1SKfzwuM+NauFdWlGzLtfhdTkhj0dZweL36/n92tXULhAIqicPbsWfx+P+fOneOdd8/T6/U4ceIEJjq3b9/G4/Hw4IMPWrvnbreLx+NhYjgfkWXZmrvE42KQVK2VWVkRtKfl5UVLyebz+eh0W+RyOWsmsO+wGwwG1Go1/EM2hdfrxRiI9alhiGRsh8NBvdVELhboDfo4FCdrObE18nm9uFwugbBzChfo+vo6sViM8fFxEinBnUQ3yOdymKZJKpWy8Px+rw9ZsVHY3LMQ5vsMhv1sTVmWLWdlzB6zeJrT07OcOHWadDqDPxAAGSQJen3oduH6rYsUCjeplitMjI/j8wZIRkXO5oc//kFhf+mBDtgcMOhAu6Oj6z2uXb/Kl7/8ZY4dn0PTNL72518hEokwNzfH5IEpnnv2IX7oYx/H5VPJbVcxDIM//IM/YmFhiS9+8Yv8+q//Os1mk3ffvUC5VOD+/TsEfD4cDgWHQ/wMQqEQOzs7OBwOJsYO4PU2GQwG5HI5Zg5N43Z7WFtbE05KRQjDRkezVKt1NrdE+FE8HqO8ucnYWJZyqcBTH/oQ969d4YUXXuDhhx8CQ7OMee/7SuH4kVnzxS//RyqVCqVSiYmJCUuSGRgZoZPPiw2BLlMoFAS5yOUkFothakL40mk3BTI9kWRzdYWNjQ28Xq+ljLty5QozMzNWurPf72dlZYV6rUgymaRWa2AYBqVSWbAMNKHcm52Zw+FwUKgWmT5+nHo+L+62djvZbJZWq8X29rb1A9NkmZXlNdY2N9na2mIvV6LXHeD1CyiHwy3k0npfGJAikYj1PiwsLBCLxXA4HESjUeG2tIkoso985CNcv36daFRIbVOpFC6Xi4WFBR5//HGuXr9mId3mZg+j6xr1SpVYPML66gqxWIzbt0Q0eSaTYX5+3oJwOBwilLbX6/Hmm29afEF/xM766gbNZpux0Qn6/QEg4/eEiMeT2GwK9XqdsalJZFkWrcIwnPXE8aO88MILbKyv8sgjjxCLxRgMhKJR7w9YWlwE3UDHZPTABE63CxQRvVfZLVIfch/3FXr7wNv9bMdAIIDDpWK32wkFIzidbpqtDq1WmzMPPcoHn38e1QXFXId8Pk+puGuFBu3t7dFqtdjd2kZVnfT7gllgs9moNzvD6kNYkze3Nuh0WgSDfgaaQMI7nU567Q7JZHJoBe9ZKc+lUglDh5MnT3LkyBHOPHiSw8fnGLS6bG6uc/G9dwiFfLz3zjtUqyUCftGS2R1i1pXJpOl1uwQCAZYX7pPJZDg8Oyta0FqVGzduYFNdli6h1+vjdrvZ3cnx9NNPU6s1uHNHkMSn5w7T6/XI50RbfeTwDM1mg8X7C/ziv/w33Lhz7/1bKQCsrq7idrtJpVLcvHmTbDZrrfLC4TBLS0vMnX2czc1NTp06xcb6GisrK7TqNR5//HG2NgUPcWH+PmcfeZhCoUC1WsXtFsKYj3zkI+Tzwiu/srJCPp/nxBNP8O7LLwxVhjrJZJJsdpRyqUomk6FUqhCMhKnX6yJIo1y2cgU3NzcpFosUhhVOr9dDkiRcHg+RSIT1YW6f3W6n2xH8hH3AJkAgEKDVatHr9ahUKuIAmp0lFosRDot/c2Njg3hyhGq1wgsvfNNy5D300EMUi4Xh1Plhvv3t13j40Ue5fPkyjUadnZ1tzl94C8kwURSZVCLJ1vYGU5MHKZVKDAYDJicnh7mKKvV6HYfDQaVSGQaTpsSAN+oCQ2JlZY1IJMLOzi5IYHc5CEbCYn0WFi2AqqqUCwU6nQ6pZFwARTVBj3IodrwuJzV9QLUkmBKdYRtlmqbQQZgGhiKjqHbMoMC67ezsiPfU5WJmZsZ6r/aFNycmTnD8+HGe/MDT9LUBqt0FThmzC92BQb2kEQi4CIfHWLjTo1qtIhsmE6NjGIbBT3zms7jdblZXV7l69Sob20KDYrfbrcjBdEZkQt6+fZNQKEI8JtKW3KqX9fVNq3pTFGWYUC3s/esby+zsbnDr9jX+zk/+BN1ul50dsSJ86qnHee6ZZ/j6179Koy5kyKGIoHC53W5UxcapB05Sr4j09FdeeRnD0Hji8cc5OHUA1eUmk85y/vx5KpUaExMTqA6F3b1tlpdWh+BfeOONNxgdHeXQ1CTVatWaJ4yPj9N/v4uXjs5Omy995Q+E8q3ft5hz+0O/XC7H0WPHuHj5Bpom8g3HRrMoisLBiXHefPNN3C4Hx44dY21lFckm02g0OHToEAsLC5w8eRI5EKCTz7O0tEQqlbJ4iXduXCOVEnvtcrlCrdlgZ3uPUCiCZJMZyYwSHhvj5oU38fl8lMtl/H4/r776Kk8++SSKopBKpURuYy7HbqnE8tIqS6ur1Ot1Nrf2qJRrlCoVtIGBZiIESwFhqNk3p7jdbrrdLrOzs9ZKqtPpkE4LBmUymbSCZ8+cOWPNWU6cOIGiKHS6fY4dO8b6+irf/MY3OHnyOINuj4WFeWLRKD6fh0xK6PDdbr9l6+52u2QyGatPvnpVgLedTifF/KYQi2k6it1OtV5HdbqpN1sMdGGPHp+Y5OjBaQxdH4p5GnRabaJhoa4zDQ2fz8czzzxDPJ3i6qXLrK2u4lDs9DpdkukUE1MHWd/axOX1UG3UMbqa5avYV+jlcjk8Hg9TU1PioO12GXQHwlDW1/EFA3Q6XZaWl/noRz/OyVOn6ff7XLp6hUK+SDufp1qtslfIo9jtyHYZySaTTKd48OxZHE47pVKJra1NUqkUH/zg8zidTu7fv8/y0irXrl3DMMT68dChQ4yNjHH06FG8Xi/Ly4ucv/Am6+vr9Ps9XG4HLpdIDNvZLiBLYpCczWYoFnL8g3/weSqlEnfv3uIzP/ZpwtksX/2jLxEOh4nFojTqVSYmsmyur1Ao5hjNpmm3m+Rzu0QiEbJjk6ytrWEYJul0Gl03KRbKlMtlRkfHUVVhbLu/vCq8ErrG2UceYXVxkXw+h6Hp/OTP/wILK6vv3yzJ0yePmZcvn+Py668zNTXFzo4YOM3OzrK8vGx9QManDjPo9Th//jxPPv2UMP5UK8LlqAvzx4HxCeRwiEGpZEWlr6+vk06nLXNULBaj0WiwurqKx26n3W5z9oMfZPPuPSq1GrKssL27w9zhoyTTAui6trrI+Pg4Y+PjbG9tsbOzY7kd93MaookEuWKRjfUtvvr1r2Oz2bh85QY723uYkoQ2MNARqcHNdoNut2uVwvuchXA4jH34mux2OzZE2Eg4HKZUKqFpmpWGXCqVrJyBVCZNsVik1+uR29uh0+mQTacYHx+n3WoIRZtTgGhDoRhut5tYLDY8JNzDbYnOlStXyGQyuFwuVhfvgSzhdHuYnp6mXKuL4WggQLsrtimyYsNjOEin02ysrVCr1bBJQpk4PjpCv98nn9vlAx/4AO2Bxu7ODt1WG1M38PmETwO7Da/Px04hh01RcNqdVhXRbDYtJaLIPnBZK7eQbzg7OjiJ0+miVq9z6tRpXnntVTrtHuWaSOPSNA2lpYk1s2pDk8RGw+5S8QcD1FsNUdG4VDLp5PCQ9qKqKjvbe+RyOR544DQPnjnL2toad+/eJRqIiVSvRhXD0DAMjUg0zHPPPcXExARXrl6iUqmwtLTF9WtX0TSNeDyKNuhx5MgMi/fv8/TTH+Dw7DTr6+v88Mc/zcsvv8xTTz1JtVJibW2JSinPkaOHqZQLuFwOLl18l09/5jPM353H7/fTbLZxOBxUK7UhXt8cgoIhHA5z5eYtstksGHDu3Dk+9YlP8PbbbzOWHeWH/s7f4/b8d8+StH3xi1/8//kI+JuP3/y3/+sXZ0cSPProo6ytrQkDlNOJN5Gg22iQnZlB1nVy+eJfk22cTuq1Gv5IBIeiUK0KhHW9VicUDFAtlaxU52q1SigUAsSkd21tjYMHDzI2OUktl6dYLtEqVYhEo4wfmkVRZMKhKB6fl35/gKI6CPq9GIZBq9m0YuNTqRR+vx+HwyEGcx4P3lCIeDTG1nB9pBtQKVcZaBqmCSZCUm1T7BiGic2mEAqF8Xp99Hp9XC433W4PSZKFHdzltfIB9/bEAG6fBr2PdEun05hIQzZgA5/HR6VSpV6rUa1WeObpZ+n1umxubOJwOHE6XZaJzOv1WlmE+2SodDrN7u4u9WoFXRO5mv5AAFV1kEgmKZdL+P0BksP9dy1fJBwKDrmLmpjiqwqqYkfX+6iqyrEjR6nW65TLZSQgEY8Tj8ZwOB20Wi0KpRK+oBi8ypIYgu2HAO+7NJ1OJ3Nzc0xNTdHv90mmRpCGs5D+YECpVGJja4tao8G9+/dottt4fV78gQCKIaE6VPr6AE3XsKk2QmGhKG11m+iGRrvdtMJiK5UqHo+HI0fnwJS4desW+UKOWq3K8ePHcNkdFAp5RjJpJiZGadRr7O7ssL29yY3r19EGfc6cPs3jTzxNtysqvUajTrVS5siRw4yPjfHNb34Dh2qnXq+TzmQ5efIk7733LslEnEQiRiQcIhwJcf3aVXw+L5o2wOt2Mz5+gPXVNdqtDqlkiuWlZbTBAK/by6HpaUqFIoV8nlgqxcjICP1+j+xolnZTZE86HQ7+5Kt/zs/83M//2ne7Ht8XMwUJifHxcavfrlQqwi49DIgFEbftdrvZ2NggEonQaDSEvNWuUCqVyOfzHDp0CJfDSWs4S3AlEtDvMzc8GHq9ntDoB4PYg0HodDA0kwcfeJArV65w9ANPUVxcJDo+QbdeQzfB4XIK7cPICNubm2RmZ7n33ntiXRiPc/PmTc4+8wz0ety6do2ZY8f48pe/gmEYPPHEE9xfWCEUjHD95k3qtSadviDyODx+ZMWGZFOoN7tiah+K4XC5kGx9a+oe8kTodDoW0Wef6z8YDEilUjjUeRyql5XldZHO3TPY3VrD4/HgcthJxDN8+ctfI5/Pc/rUSSsEt9VqWZJaTdMsUdd+aR4KhTAHghWh9XV2t3ap1WocP36cZx99AtUlJMbPP/kUr730bV5//TXcQzqzU7HR7/ZotxqC1CTLNGs1kZxVqzPo9dF7AyqlspUJGksmuHXvLqpTTN+/k4qtqiqLi4sYhsE777xDMBjE6/Xy1oV3ADhy7JiYHXk8dLtdRiYnOH76FJValcXFRXRdJz4qWB3tTpNmu4HdbqNcLWFv1ZFlA5fTBTaVTqeHoqgUCoVhcLCdZqtKIOhie2eVUqnEXm6dmD/B8ePH2dzcZHdvi2q1TDIZp9lsUi5VKRUrzN9bIhSN4/W4OX36ND/2Y59me2uDd955i/zengDSDAaMjY3xp1/6Q1wuFx/+8Ic5d+7bnDx1gtFDU2AY1GstbLKKods49+Y7AvsXi+F0ulhYWMTt8g6RbPCX3/xLJiYmcThcbG1vEItHSI2Psr6wZIXBNGrNv/V6fF8cCkgSBw4coNlsEovFLLNOIpHg8uXLREslotEowazQBfT7fasH7/V6KIqC0+mk3W7TrDdIZEfY2djAFY2CJvT72aFLbGFhgUOHDoGmsT10Sq5vCCjmlW+9gs2uojhduIcp04o/iKNUwdA0MhMTlFZXmX3wQYqrq7j9fqLRKPT7DIZ9f71eZ2pqik6/z8rKiliTqqoV6rkP3dRMxSLwptNpwuEwk5OTOBwOfD6fCImJRIgGEni9opTdt7s2GoI+nc8L0dDp06eJJeLE4xEkCVaXN9jYWEPr9ckX9tjdFuxAv89naTb2cye63S4+n498Ps/o6CgHDhyg1+tx7do1Al6xN9c0DY/NRrfT4f78PJVCXkTSV6vQ6+H3urHbbMLj73JSLBZxOgTTwj5cIZbLZXomwiVpV4ciKLdI2m418Ph9QpjWbrG+vv7/MvfeUXaf9b3u89u99zJtT++jkUa9WJa7ZXDDNoZjCKbYYB8CmOQeICShZOViIJwAoYSSY1MOwWAH4gI2xl0SsiWrjaZpet8zu/defuePd88O68ZwcnLvXcuzlpdtyTOSR7Pf+b3f7+fzPPU5SyqVqq8dt3Tr+XyeQCDAvfd/lNXVVY4dOwZABVkM/txO+gYFquyOO28nl8vx8x88xsLiPNdeezWyXGFpeYFKKY9KpcBo1KPWKEVSdGWzfoULBoO8+GKAoaEB+vp7ePXVkzicVhxOK1okzpw+yS233MJvf/s8hVyOdDKJXq+nkBW7TZ/Px/KGnyOXH+ZXv/oVP/iBn21DA3zoQx9gbmaGiYmLVCslzp07h9vrwuVycP78GcxmE3Nzc2iUChoaGrj55lvFatpgpq21A0mu1J2Q5XKVVCpFR2sHm5ubFHNFVJKCjtY2HGXhIdm+/4C4hinV9TDUH4apvUlmCrtHhuXv//3fUiwWMRqFIMRqtdI1MMDJl1+mqUkEOxzuJpR6PVQqFLMZQqEQqXgMj8eDvgbGWFlaBoVE1/btYkFtMJDZ3GRxcZGhoSGR+KoJPgKBAKp8mcefeIpmXwuhSIw//eQnmLp4kYH9+0gEgpQq4g9gc3kWo9GIx+NBp9OxsLDA6uoquVyO/fv34/R4iIXD2Ds6eOXpZ9EaDPT19TE9s4Db5UWt1aJUqFn1b/D6668TTRTrgSG9Xo9ara5/0edyOSYmJoSpKCqyG5lMpj6Jz+fztLa2cvLkSVpbW0W+o/YYmkqlcNjsqNVKFEClUkarEcSngb5+KpUS3kYbra2tKJVKnn32WSFaNRpxuwUcNJ/Pi1SjJKrR+VwGqSqTTYs5RntLMzqNuDIdPnyY8bl5Zi5NU6mUhF+xxnfQ1+jbZpMBk8lEKJmhUhLCl3ztUVapVOLraKdYLpHIZ6kik05m6oeCwLppxK/b3i4O1HKZsbExkiUFvX19TE1N0d3Tyb0f/CBjY2OcPHWSEydF3qO7u1toCf1xcvks1XKJO26/hW3DQwTWhUM0n01SKZUIhUL4NzNksincbhcmswGLxYDH60KlkpiYHEOn05BOp3HoHEiSAqPRSHdXLwMDQzz22C+YmZ5Dq9XR0tKKJEkU5BI333QjExMTXLhwDovZiMViYGlhgTvvvJ233HA98XiccMzP888/z549e/CvbdDX18dg3yBLS0sU8wVAwuNy1+LmBs6fP08ikWDnyG4uXryI2+0lGBCbsNZWIf/dccVuUrEYo+cvAtDX3YdKqSWXzfLWu97DhfE3bkm+KQ6Fof4e+YmffJ9SqVSvsFosFqrVal1Jn8lk0JuEqCUWi1Es5PF6vVw4ewaLxYLH7cRoNJLP5lCqxXpoa3C3VWy57IormBobo7e3l1gsxtraGvlwHKVKgFsKlSq7Dh4Qs4qmRsrZHOFolGq1ik4SgppSqUR7ezszMzP1x2ydTofRaAQglEjQ2NBMrraNMJpsrK36OfX662z4A8wuLDI/P49KI0AnNputPqNQq9V1eYckSWKNmRcVaIPBgF6vr0tjmpubmZmZobm5mXA4THOLOBwkhUwxX0CtVmLU6ymXS+g0WhxOG73dPSwsLFAoiarzVr23UqkQCATo7e1lfX2dVCrFwMAA+YwYdlKVyaaTGPUG1lZWKeQyNHuFezGXySCZLciVKrGYsBnrtGoqxRKpVBKzyURbmw+NRkO2AtWyOGRzKVEUSiQS5MslfG2tFCWZaDRKuVyth6dUKpWQDdfgM1sHmMViYWYtRDQa5cCBA2wGA5w69SpdvT285S1H+d2rJ5mYmMAf2MRg0JNLlFEqFditZhq8TixmE/FokEavhz27Rsim0xw7doxovEIymWTHyDZsdguFQoadu3aQSsWJxUNotRrOnTtHPrplRNeRTKQpl6tcd91RTr32OoVCEaPRjNPppH/7IKsryxSLRcxmI6VinlBogwP79jEzM8WVV1xOe3s70YSYF0WjUYx6A9lsnpHhEdbX1ynkRHHusoOH0BqNjJ45SUNDAyqleHpUq7Vo1NpahTtRJy2dmjglvpnanFSrVTrbOrHbXCTicW69+/2cuTD65j0U+ro65Ue+/012XXEFkaUlIpEIvX19jF28SENDA+72dmbOn0dRKdaDR6lUql7c2djYoLW1VfgGanKOUqlER0cHCrOZcg0qsQUFSSaTxGIx0TeoKLHabczOzjK0fZi19XXUWiEmyeRzSFvk5nyJqakpHC4nVqsVZY06tBEQd0Ob08HGxgbBxVWqyFitdlQqFdMzc8JTsRHA7/dz7MQJnE4nY9N+vF63IC9ZjPVVViaToloVZOd4IorL3kapVMLpstfDN11dnYTDwrjscjuYnZ3F47KI0phaVJvDwQjpdBa1UkNbSyulUkWEpewOQhtzYksRDNPa3obJaqFcER0ET2MDCqWSfL5IPplEoVDQ1t2OLMvMzc2QTCbJp9NIlSoKScbjdFGQFGi1WsoFsVJ2OZ1kMhnma5Kbq45cIbIX6aw4jORqHYG3RcAqFAoYzeKgUteSkUgSuVwOjU54G0vViqAImYwUCgXSyRJ/8zd/w6OPPlrvkOzfvx+Hw8HFixfx+/3ceeedNd7ACzXRrwhBWaymGglZbCisVjMul4vm9lb8fj+BDVEmctlE2UglKZArVbRqUR+vFsNsBkXbVqHU4nQ3IClUHDx0hAMHDjK/uExPTw+P/exH4grnX8XX7Aa5SnOjmx3D2/nXX/ySbQPbWF9f57qbb8Fms+Hz+Ugmk0xOTiJJEkNDQn/ndrvJZrPIskzPzh2ceeUV9BphMEvE4qhVKpqamlhdWiYYDIpymqbM/v37OX9ulFwuh8fTgMfTQDAQ4va772Vy+o2t02+K7cP3v/edz//tlx9ErunTtkCqkiSRyWRIxwTCy2Yx0dLRgX91ldbWVqxuN+VCQXwyVlfrwzlPXx9GhcgqqGQZtVZLqVhkdHSU1vZ2VpaXazx9NVqdgXA4THt7OxfHxuju7kZVWwlabeKw8G9soET8ATV4G1CqlKytrmIym4lGooTCYUqFIrFIlGqpQDgcoaHBw8LCPJvBDcKRIJVqhc1Nfw24EUapNWC3W9HqNMTiYZqaGimXi6hUAqFus1sE+l4nYbXpqcpFlEpwOCxYrSYBUqmWicWieDxuKhUVmWyRYr5MOpUjncyi1xlIp9I4XR462ttRqVTEEwmq5QJICjQ6LYlUhnKpQjKVJJ/P09TSTLFYZGVlGb1GiyxBMp2mVClRqcqolLW2ptnMjuEdKCUlZQmSySTJRAKr1YrX4xF4uZUVkfAri2CYSW8UiLZKhWKxSCadJl87DJRaNflikWK5TFUWc5NKjdlYKpexWCw43aIPk0ylaG9vx9fSwU9/+tP6NwqDwcDm5iYXLlyol4nSaaFt1+sNyHKF97znPWRzIo/R29tDqVRi27ZBotEoBoOB4OYapUKeSDDAYH8vPZ0d5DNpVBKUSwUUVKmUi2RzaWQU6PQG9EYzJouFtrZ2PN5GXnzxRbK5PLOzs+SzKQwGPdVqmXIpj0KSWF1ZYmF+nr179tDb3cvevXupIpFJp1EqFJiMRtwuF/sPHuT4K6/gcbvxr6/j9XjY8PuZnZrE5/PhsAnBSzaTQYI6wi+bzbKyskJTSwPBQIBgIMjVV13FytIKZqMZvU7PTx79Bf/1Tz/yhtuH/yOX5P9fb+l0Gv/0NJLBwMLCAkajEavdXpN1ilOyvbcXk8UCCgV2pxMUCvLpNAqVimK5jFxLE1rsdoKzs0TjcSqyTGGLlLy4yMDQEBcvXqShqYmp6WlKlQre9nYCgQDrm0Lwevr0aZxuN3a7XURF+/po9fmwWaz1gM/y4hIajYa1tTU6OztF+WhwkI6ODg5edQWDQ/0YjDqisTAGg55du3Zit1sFkadawmQ20NfXy/LKIoWi0L9rtWqcTnvtIBCpQofDgdGoI5WKMzDQTyQaZHVtmWw2i04nIr8eTwNebyMryxsgq+nsHsDlbCRXKJJOZ2lpbsXr9YJCgV6vpaHBg85gFIp3pYCtblXTC+USqVSKcDhMR0cHKpUGWZYJhUJEI2JF5/J4xe/L/G/V9a3O/la2oFAo1GcgitrhnM/nKWZzaJQq9CoNUk0+C4BSgaRQIKmUKNRi9l0ul0kkEhQKhfp2ZGumUq2K4ZpOo8VutRGPxoiEwug0WoYGBnnm109z6823YDGZeemFF/ncZz7L2OgFbBYr+WyO/t4+3nf3e3E5nLidLixmc/2v6UuTRIMB1EqJarnI0uI8dquFcrlIqZCjKpdRSDKFYrkGtRVQYJtVcB01Gg3XXnstBoOhrrW7cOFCPYnZ3NyM2+1meHiYeDxONBplaWmpFkYSIuKtKPqpkyc5cuQIsixz+MiRuk9027Zt5HI5nnvuOSKRCDMzM0QiEQKBAJVKhUOHDnH48GEB6a0IiO/42CSZmn8inU5R/SPauP/t9UGSJB/wY6ABqALfl2X5HyRJ+jzwQSBU+0//Upblp2vv82ngHkS/5GOyLD/7x36Nwb4e+cmfPCzy9s3NUCiwvrhYfxGGQkKXpdeo67y5rZlBuVwW9KKau69YLKLX60XKryZy7evrIxKJ4PT5SGxs1A1OmUyG+flFdu7axerKCuVKRcSph4ZoamtDLpdYXFxkdnaWvu4ewVUIBNh22WXkAgFiCaGAU9aGfGq1mt7BHlI1zFk6nSVXyDM5cQmFWkMqleJXv/41qVSa42fGMer1KJVKvvzlL/PII48IRNjmOgqFgu7uboLBICDT29vL6dOvs3fPQZxON/mcwKpfvDiO19tIT3cvXd2DdHV1MbJjWKwXzQqQYfT8BC+9+FtUKhVD2wZEmzESwWKx0CT9maEAACAASURBVNHVzhNPPMGZM2fEgVR7QW5xLcqFMlVAZ9CjUAAKiUI+S6VUpsnpRK1SYdQbqKoUzMzMoFEKBLxBrxd4vOUVNjc3mb00TVtbG51eH3aXU0Sr8+JzU6QqOg9qJVqdjngqiV6pFYalVKp+3VheXkal1VAoFHB53AIIsxnj/vvv5/nnn0ehULC0tIRer2dtbY39+/cTiUQYGBjg2LFjZNI53G43rW1ib69UKrn2uqtZX1/nxIljeL1e+vv7UVNmdHRUQFOR6psjrVZLOllzcGg0aM0mACwWC/39gwwMbkOWJcwmK5cuTRNLJgBwWAzEE1GGBvv4nz/6J7KZNDqNkAht3zaMXJYF4l8W6cStOdWePXto6Onh7Msv4/WKqLWmhuQ3WS34/X5cdgeFQoH+3j5ePXmSoaEhGj2iGyLZbBz/7dMolUqamppQKsXwN58vsmvXLoYvu4pLs/P/6e5DGfi/ZFk+J0mSGTgrSdJztZ/7mizL//3/cYgMAv8FGAKagOclSeqVZfkPuq9LpRLdPT3Mz80xU+Mt2mw2crkc3d3dNDU1sby8jNPlACAUCaNWqzE3NBFfW0Ot1ZDKpOs9hH379omwi0FPOpthMxgQK5z1NWw2G7/73e/YvXs3/s0NWtvamJubQ6EQ2jSz2SyGhuUygY1N9Fodne0dNHoEPrytrY2p116ju0bf3djYwGA2MT8/zxVXXAGlIul0mkuXLgmoxfwCXq+XUDRSL3udO3eOg/v3Y7fbefDBB7nnnnvqzomurq66a1GlUuFyNlDIV/G1dJFOFYhFlkmlcnR39XDwwJXsHNnNW996I1a7RLkMv/zlb5ibmWVpeY5oKMz1113N+z5wD1arGUlX8zAkcoyNjfG5z/4Nu3bt4tbbbiMUCjE2PkowGMTtdrO+vorX2UxzSzNGs4FwNEIul0FSqFAqZTR6HalYnGK+gMEmcGp6k4hub+Ht1Gq1gOnWYCCBDTFM0+h1yAqJbKlAWQEmpx2zwYBKp6UcixKJp+stPoVCQalUrr8YFxcXUdRgs7tHdvKjh39Qb3ru2bmLaDSKQgapKlMuFLl4/gJXHbmCl19+GZ1GxfzMLJIEHo+Hs6df5+jRowT868zPz5NOJHGYtQT86zQ3iBdioVAgnU7S2NCNUoGocktVtDrRqTGZLSApSSRSHDhwgNdePU1jYwNtbW0kk0lMRg2RaIinn366jrGTJAmPx0M4HKbJ20QymcRotaFSKHE5hPBldXmFaDiC1Sx8JamUSF2WCkXCiRgdHR2YDaKuPzo6Sm9vLz6fj+DGJpubm2IGkcmI2VRM/PelYhGTQSISCv/RF/x/xBC1AWwp51OSJE3xBmr533u7FfiZLMsFYFGSpDlgH/DqH3oHnVZHqVjEYrHUQSdmswCUrK+v07ljhyDOJBJEIhHMZrN45E2kyOWLVGMJqrKETm8kEAzz/Asv0d7eTiaTobu7m0KhwPr6Gjt37kRpd9DU7CMciaFSa9EZ9FRjUVKpJDuGt9fLQZjNmFMpofZqbSWbTaPXa1lfWaa7twf/+irexgYCm352tu1n3+5dTI5dxGrW09QsFGWPPPJzhndsZ2J8isuOXMGxY1+kUq3y7W//I9mKmo9+9KPceuutgmkYCOByeaiUJZQKLRazBZPJhMUoyDx2S5X3v++DeDweDHozKyt+Pvzhj7C2HOd/fP9nPPDJ+9BoNNxyyw0oFDcQi6b5p3/6Hh1dXZjMFk6ffp3f/OZpCoUCw8M76O3tRmMw8cxzz6HXaQkGN9HqRLYgFg6hU2uoSlCuVlhZXUepVVKsCNO0WqUhm82jUKrJ5gpozWJmsLEmjMdytYrf7yebzgg3Z2MjbW1t6CpKMvmcwL7rdKipYjQZAElIfmtXhOhmmGg0irrWipxfXMDlchGJx+js7GR1fQ23282ZM68jSRIdHe1YLGaOHz9GIpGgtbWV+fm5uhNydXUFl8tFIhFHo1HjaxVwYKfLylNPPYFer+Xeez/AsWPHiG1uMDy0XcwjshkWFxcpFstMTk0jqQRX0+V2ky0ruPzwFbjdboYGB9HrxCr98OHDKJVKTp9+nVOnThGPBZGpYLOaamlOGYdN9CdWl1fw+/3Y7XZ27xZcC7dLbMt27drFhn8Ni8WCXC2ztrZGqVTi0KFDZIriaSIeEZux9vZ28rkcc3Nz9AwM4u3vh3icpu4uxs+eZW5ujpFde1GgJBjcJBwKIP2RnML/UXhJkqR2YCdwCuGY/IgkSXcDZxBPE7HagfHa773bGm9wiEiS9CHgQwBNDV7UJhPampX49++kbrebciyGo7GxDt7Ytm2bKOyEBR14ixXQ2dlZ3+NvO3iQ2XPn2NjYqJN9JEmiEo/XtxBbM4uGhgampqZYWVlh+/btnD59mo72dhr7+6mcP8/U+ASNXk89XffS8y/Q5GvBbLWwc/9+EjWzL4g0ZlWW0Gq13H777fz66d9gNArS9MiOnbR1tHPvvfei0NuZnJzEZLKQzxdxu93o9UYi4YT4wnN6BBimoGHfvkPcdONNbGwEefLJXzM5McPk5CRdnd20tLRwzz0f5PA1g6RSRX7w43/mwtlzmMwG7v6Td6NWq7ntttvp7e0lGglx5MgRiqUSX/ryV3C5HaKSm06KR2KNjnQiTlGjYWhoiHS2gt3mIJVOY7XY0enEI2xwfZ219XXkahWpXEVt+DegazqdFlr6shh4ZTKZuo9DUZRBIb4YNQY9uUKe9t5ussWCKEMlE2i1Whan58V2Aur9iC3M2JY52e/3o5XENWN1ZYWhoSE6aqviDb9f9EnyeQr5PCajUbgwZYE0UypArVSxvLiEwSi6JU8+8QRXXnmE30UjnD9/Ho1OfB1aLBZaO9pFszOfR6PV4nS5GGzrpX9oG26HE29zI/lUnsXFRcIBUbp78cWXBGpPp2Tdv0pzkxe3283mhh+LxcL09DQWk5mu/i4MBgPj4+P09/czPDyMcDpTb9FuMTueffZZTDYb8soKU1NTbBsYrM9dHA6hDdxcXqZarbK2tsbwjhF0Wj0NDQ1Ui0W0WsHHdLlcKJX/H4SXJEkyAa8AX5Bl+ZeSJHmBMAJP8bdAoyzLH5Ak6dvAq7Is/6T2fg8BT8uy/Is/9LG3DfTJv3n0J6RSqbpmfG1tjcbGRsLhMJZactBud6A1Gjl76lSdy18sihBQPp9n+/btLCwsCBpNqVRn1+n1enFHrtGWtnL/RqORqiTXH+sUMsxcmmZtdZVDhw4R3BAdg0ZvAxfHzmEwGDAajbS0tTI3O8vE1BT79+8nlUmL35/TyeriCqGwQJw1NDSwUHNVvO097+XvPvc5FpdW2NjYII/4fUSjURx2FxaLFYvFwsEDl3HXXe/GZrPy7LPP8zeffxCXy0V7ezsOh4O9e/ZzzTVXYbLB177yMIFAQBSrTFW2bdvGDUePsr62wZ898AA33ngjH//YA4RDEWanp1msNTdfeOVlLCYzWp2GXEYUmLK5FFRl1EqhF8tkU1jtzbg8bjL5HDqDjlAohEQVh9VGPpmEqoxSkqhKYhCoVogKeiGfp1KpkEokKRQKDPUP0NHRQUuTD5/PR0WukisUhJ/AZMRoNhGMhLkwOopSqeTixCQ6nY7mlhbW19epIrgT65uCeaDV63jyySfZP7K3nl3weDxMT08Ti8UIh8P09/ej1WrR6/VUKhUkSUlvbzfj4+M4HDbC4TBKlVTrxQjiVF9fH4M9A7hcLp5/6UUqlQrFcgmtTofOaBCy4FYfIyMjWB2N6HTC/mXQ6WmsEb1eeuEFNtf9zMzMsL62Rji8yb79ewgFN7js4G4K+RypRISWpmZCgSCVYgWfz4dGq2JkZES0P0slLl26xG233cb58+dZWFhg//79BAIBLl68yI79B3G5XGSS4krhtDuYm50VV1+dQPyVy2WikUQt9NVKOBxGo1WTSMQwW4z817/8WyZm5v7zPAVJktTAL4B/lmX5lwCyLAd+7+f/CfhV7V/XAN/vvXsL4P9jH1+r1VIqCWvS5uameHEcPMjSkpjyt7S0iLRfIkFydZXG5mamZ2cZHBzEptHU75+jY2N4vV4ytTt5vlikpaWF+fl5kskkNpuNarVKd28vAKvLy/i6O8VvQq0mFwjWXQ8Gg4G2tjae/tWv8fl8JGJhcDjq2XuDSQwrT5w4wYWLoxSLRY4ePcrKop8b3voWnE5PzZWwzOrKCv/3X3wGq9XK2tqGIBAXijQ1NWG3ufj4A3+O2+3mm9/8No//669ZWw2QyeTw+Xw8/IPv4PV62dzc5POf/yzTMxd4/Kn/id1uZ2BgAK3BytGj7+Dvvv5VItFNQpENdu8d4IP330MmmUKtBbfbydRUleWageru93+At7/9KL9+6gV+/shP2b5tkEI+w/mzZ7j++usoFousri2j1joplcTVIBAS7MY9e/aQjsdIlEsYdXqUMmQL4hBwNzYRDAZr2xEdg4ODZDIZ2traBLa/Ck6vB4PJWL8jb6n6csk0lCts+DdQqVTMzMyIirNCQTQqroypVAqr1UqxWMTj8VAti4OnWCyyvLhEg8eL2WgSbohcnlwmi8Ihsg65XI7lxQUaGxuZnJigo6ODlZUlisU8pUKejrZ2xscuMntpicbGRm6743aWV1coVso0NTXR3d8notZGA2azmUpFWe+K5GpPRJFIhFwuz9raGvNzc7jdbgYGerA7rLT6mrh06RIjO7bT39vJhXPn2b1zF0adkVAohK+1GZ1O9GzUajVXXnklzzzzDLe8612srKxgsViIx+McOXIEg0MQwvIZoUV0djsx1UjNZ0+/zoHLLyefStHVqSaXy3Hy5AkCgQBWm4XGRi/5QpZS+f+FdVoSzzIPAVOyLH/19368sTZvALgNGK/985PATyVJ+ipi0NgDnP5jv8YWT1+j0dS9i8laZsHhcLC8vCyGKMEg20dGOHP6NFdcfz2vvvwyPT09FArCI9jY2Ii3s5NSPE5zdzcbCwtItS2E1WrF5XJhsdtZX1mp04sWZ2Zoa20lF4uxtrKCVqvFaDTy4nPPI0nCAqTRaNBoNCQSCfbt20coFGJ9w08wHCYej+PxeAiFQnzjG98gFk4xMTWJVivWSguLy1itVk6cfI3GxkYMBoN4hGvq4IorrqS7u5sf//jHLCwsoVSqGegfpFyu8ld/9Vd4PFa++rWvcvHiRfQGLc0tjXR3dwk0mVqir7+T5eVlfvij/8HHHvgIfX19PProo0xNTvLOd96JXJb5ylf+gTOnzpDN5hkeHGL//v3ceONRfvvbVzl16hR33HEHt97xVv7bR/8Mr1f0LE6ePIlWowJFiWK5VK932+3b6zYuYblSUi0Kg/aWqxOo9zysVmv90Pf5fEQSSVQaNdSkujqdTqj1akAcrVqDXBFU7UQigcPhYGZmhkQiIXIjDlEgyqVTNDc3k46JpqrNZuP06dN1EO5WiC2RSNRAsPm6+3IrKen3+3E6nVQqpfrHHxoagqoWtVasmwcHB8kVCxhq6Lstc5Vao0GtUpJOl+pr1UothRoKheju7uaaq6/m/PnzrKws0Nbuo621GUkuYDab62zMTCaDXiM2ZcP793PqpZe47PLLWVtZIRQSS73nH38cnU4n/ky0oqJuNpuZnp7GZhYhvmq1Wl/dNjQ0sDw3RzQaJZcto9drUShUXHXVVWRzGTo62nj+hd8iV//wDeE/spI8DBwHxhArSYC/BO4CRhDXhyXgvq1DQpKkvwI+gNhcfFyW5Wf+2K+xZ/cu+czx50nUklhGux20WmIrK1QqFaJR8Ti+c+dOisUiDodDFKZaWxmtacS2zEblcpmSArGaWl6hr6+PWDQqBogqsdI0GYx1ClK5IqrUbrdIF7obGylkMmxsiFbg1mEQ3ghhMpk4N3qB7u5ukpk0y2vrXBwbQ28yEo3HSKVSrG8IPHyhIGjNpWKVheUlfI0dqFUavvCFL6JSafjBD/6JpaUlfD4fOp2O/fv38yd/8id861vfYnx8vL4KUxkU+P1+BgYGatCVJt72trfR2tqKyWQiEAjw8ssv8+53v5tMJsePf/QTGhsbOXHid0xNTdPY0MzOnbt517vejVar5p//+RFi4SUGBgYYHh7m77/yZbq6urj/vg/x7nffVZ9+J5IxfC1tdHR0MH5pEpfXw8FDhxgdHWXD76fR5WH0/AVaW1qoKJTodDrSqRSBQACtWsOBAweIBEOiZNXRIf5fW700NTVhMZqIhgWBee7SNLlMVnyhT04JYG6rj7GxMXr6ejl37hw2h108WRgN2Gw2krWSWSaeQV17miiViqRSKcqVYq2mLKS3lYqQ+FZKeUwmE71d3XUTUz4rCmbV2gFnMpmw+9qQJDH47Onp4ciRIyiVSiH5rbERFUol2UoerVpXJ2rls2IOlk6kmZubY3pqWhymuhyrq6vIckWIcz1eZFlmz549nPjdMXp7eynk8vjaWknXDNv9/f0sLi7W6U86na5+2JjNZihX67MalUolREZeIfVJp4XPs6WlhUg0SaVSYffVV7F+6RKexgYuXbpELB7nw5/4a8Yn/5PgVlmWT/DGlaqn/8j7fAH4wv/uY9ffKmVS4TDWlhbyoRAUi4y+/rpoMyLw4n6/uIGYzWby+Twej4eNpaV6vNnu9UK5TKVUwh8J1Vdiy8vLFAuFOqRUo1LXAaWFQgGLVbQRZVnsi6nBPLY4+z6fD5PJxMi1O3j00UdRq9U8/uQT3HLrbZx+/XFMJhMTk1M4nU7CoQgbG2EOHTrM2NiEaFkqtfR29nLDDTcxODDEj3/8Y4rFMqOjZ7nxxhtpb2/nbW97G2q1mgcffLCOe1teXiYSidDc0czIyC7y+Tx33vlODh48iM2m47XXLrC5ucnQ0BC33nobf//f/wGlUsmlS5eYmJgUEBK7neuvv56bb74Zqw0++pHPsm3bNj7xqb8gGU9w//0fYtfIdgYGBnjggQcwGEz1oVdvby9erxej0Ugmk0GdSPDSSy/Vg06xWKw+z5EUEga9lrnZaSQZVCoFk+MXsVms5LJpCoUcNrMJJRKZZAqpKtcxcNF4nHQySaFUJJPPkcpm8BoMgsZ96jXRa/F48Hq9BCNhIpEIhaKwMOmUOgKBQC2AZROyEyU1P4JITY6MbCedTrO6MIdUFU8vW0+iZrMZtVpdD0X5fD6s3ibRyK2h0YLBIG01enK5XK73UraeELY6KqlUilwux6uvvsrCwgL+NYGSk/MbNchviXy+iEavw26xMjo6CoBeqyOwsVlnR77rvvtYnZxksH+AZDxRl+c4bHYSsTilQhGz0VRvBycSCQEeViqJRCLo9Xp6e3tFBH8zzL5rryXj32BmZganRwB0WyqVeu/ljd7eFNXpcqVS/yLbqgdvfdePx+PMzMzUdVmRSISzZ8/y9ne9i7W1NeDfnARbp6rP1wpaLeuzszQ3NfH666/T4PaIZF02h9lsxmwyUSmX6/h0tVpNKpViYWGB5uZm2traCIVCjI+PE4lEOMZxGhoaUOsNNDa1MDYxSWNLC7lsgfe+/wO8/vrrnD1/ERRKLo6PYbXaKRbLvO3229i7Zz9f/eo3+PUzTzM0uI2rrrma7z70jzz44IM8+cyv+OE//xij0VhnRsw+9xs0Gg07d+7ku9/9HkajmrGxaX75y1+Sz5W47fbrMZtsPPjQl4lEIhw6dIhz5y7gcDjo6OjgyJEjXH31FSgU8PDDP8Jqh9WVKJ/7/KcpFot8/ON/zqVLl7juumuIR2OcPXueufklrrvmamZmZti9Zz9qlZL5+XkCoSB9gwOUymWKlTI2p3ikTyeStLa3MTU9Tb6QFTZwrSAm+RobSKfTxKNh5EoJs15sJkr5DAspoZbX1OAqWw6N6cX5einsxRdfRK/Xc+XVV3Hx4kWy2SzJZJJCuVQbGorgWTwbr1foY7EYDoeDdI3p2dTUQLFYZHp6WhC3iiUy5RThYBiFQklXTy+FkqB2W+3iADBaLbS0+mhqaqK5uVmshC0WgPpBoJAExk2v0aOUxIDaYrEgyaIjc6J6oo7rr1Qq9HZ2EQyJYXlXVxc6nYELFy4I7J7VTKFQYGpqiuuuu45qtcry+DhtbW2g1da3Zs3NzVQqFZ599lm6u7uJRaIMDw8zMTHByMgIbW1tbGxsoNVq61SxM2fOUC5XGX9VzBMsFgvlQh5JqaBcKqCvpVHf6O1NcShUKpU63n0rv77FSBgfH+fw4cN1U07H9u0olUqOPf88hw8fZmlJAEWy2Wz97lguFIgHAmjUalAq6evuEad2pUq2mqFaM0pVKhXC4Si9O3czf/EixWIZk8lCMBimWCxTLlex28WwzeNyc+zEcW66+VYkpYKXT5wgkytQqlT51a+eRq3VMjA0TDKVB1nBtm3ba2m647z4wstsbgbZvXsvd999N2fOnOHa668jnRakH1sNfhoKhejq6qK1vY3vfe97tLf7WFsJ8NhjL+FwOHjPe97LysoKf/npL3Lu3DkSiURt1hLGZLRw2aHL+cAH3g9AoVBmcXGeD3/4vZx6bYy2dh9/9ucfE/mBZA6zxcTb3/4OEjHhJxwbG2N6do6DBw7Q1dXFc889h1ZvQK3WEgyEMZpN+AObdHZ2UypVKOdLKJVq0ukspUIKs8eDz9fMzPS08D94G1hfX0enUbOxsU4ikWCor78e5dWV9RQKBVKZNDqjAZfXg8Yg2rAdHR1IksTMzAzlchmHyynCT2shGhsbMZnFi8llddW6JBEcDjs+n49QOMDs7CyFQq4uUw2Hw5QLQtW+ZVvSaDRoDKLd6utoF41MrVjFejweEaWXJORyGUmphN+3bysUSFK1fgXd0hFUq1U+8IEPkEgkCGyIJ5h4eAH/2po4vLIZgoEQzc0+UqkU115zFeFgiBuuP4rb62FtbY3z58+zubmJ0Whk92WXgUYDpRKRtTX27dvHnssv5/RLLxOPC7jwyy+/THNzcz0MFYlE6OzspKenB7vdybFjxyhVylx55ZWs+/209vRgMpv/6JPCm6IluWfXiPzqb57k1KlT9ahnNBpl165dLCws0NTUhFarZW5uTvT1a08RRqORWC2tFQqFaubiKoVyCU9bG+tzcwT8GwwNDREJhdFqtWjUYiIbj9QKMLEQPp8Pd3MzawsL+P1+RkdH68OuQCBAd3c3kWgUi8XGiZOvksllsTk8PPGrX2Oz2YkmhX69UqnQ0TXAvffeyze+8Q3m5xa47LLLBW16zc8Pf/hDqhUE0ceorQ/atFote/fu5TOf+QzpdJrR0VGq1Sq33347agx88pOf5KWXXqoTmFwuF9lsloMHDzI8PMw997yfY8dOcuTIIUwmuPnmd5PNpWlo8PD1f/h79u7djVIpknSlUontw3v51re+RSIR5/4PfYj29laGBgaRZCEVKZeLtDQ1UyoXMZnNJLJprA475UqFzs5Onn/uOUrZPJViiUq5zMriFLt37yafzTE0NMSZU2KurFap6OnpgaroQ5j0Joy1QZvJYiYcjZDMZti+c4RILEq8JsBxWFy1erma9fV1imWxEZIVohGr1mgYHh4mGhCOx5aWFpxOBxMTE2xsrnPVVVexsrJEMBjE6xWOUjmVw+awMzyyA6PVglKtYtuuEZpbfbS1tyMpha9SVRHf8aXaAZCIx+tagK0fk6tVUAKysEsrlUqiYUHCDm0KBUF7a7vI2BRiGPR6UEAmEUOlULK2skiDx0s+lyIeDZNKpejq6cNkMvHYY4/VQ3xbw3CNRrR2t3ypTU1N9PX11ZH/drvIvGi12to8q0BPTw9TExNIksT+gweZnJxElsDb2EAqleJt77mPsck3ZjS+KQ6FncPb5F898kNKpRKxWAyfz0cul6tLStqHhyGdJlN7vFQqlYTDIqq51UEfHBwknRYd/S3+viRJGAwG0skUmXSazs5OpsYnkGW57jpApax7FmKxGIGaper48ePkcjmuuOIKVlZWCETDnDp9mmyhwI7tO0llCzz9m+dIZbK4PA0olGo+85nP8MF7Psjhw4fp7+/n6NGjPPDAn9XTaHa7nUKhJO7oOjVHjhzh6NGj7Nu3jy984Qvcdttt3HjjjTz00EN897vfpVqtsji/htPurPseUqkUX/rSl+pPT8WiQKa95YYbicfjuNwOvF4vX//6V3nggY8xOSX28qWSCLjc8JajHDp0NZ///Ocp1zwM/T3dHL3+Wl577TX8/nUcVgFhOXv2HAq1imK1wuHDh5menWF4eJiVpWVmp6dRIFHI5qiWxaowEYthMBhYXlwS8JSmZtrb2wXMVaHAbLJSqogikcFsIhqLodaJVKnBaCQUFfh5p8nJ4qKgcptMJpLpFLGYIG23tbWRroV6bEYbR44c4eTJk8zOzqDX67HZLfh8Prxed+06JkJug109eBsaKMtVegf6MdusmOxWtDodRpOJKlCWK2iqyn/39bkVJvr9N1khgywOWlmWKeSK6PRa4uEE1WqVkydOMjs7y8rSNGaTidbWFrq7OtGqlMQjATQqFQ6rEblSpqOjg2Q6w+rqKmazmebmZiYnJwXgt/Ya2Ll3L5fGxzl16hQ7du5kfHycVCrF7t27iUQEx2JgYICJiQlMJhMNDQ3kMhmsVqswmtVKZemsAPTe8p77/mD34U1xKAwP9suPPfSduhvP7XZjcbsZP3uW1tZWDAYDS0tLdfHs1lDHbDaTzWaJRCJ4vd76tiAQCLBj926Cfj8rS8sMDw+zWlvzlGsK7q2o6QvHX+HChQsMDg7S39+P1+vla1/7GiMjI8zOztbbehaXKJ8sr/pZXfcTjSVJZPL09g+i0ep517vezUc/9nGGt4m25LFjxygWy/X1nUKhECWkmrXqnnvu4R3vuJNPfeov2LZtG5dffjlf+tKXeOqpp2qk3rSAw3rayGazVKtVvvrVr+LxeIQ3sLGR48eP09rayr333ksul8Ng0PHOd76T02dRrwAAIABJREFU3bt386lPfVIIaipFiuU8bqebf/mXRxkZGWFo2wg2mw2dXkOjt4Fqtcx/+/M/4+Mfe4Dh7UOMjY0xMrydUrFKWa6iMegZ2bWTXEE81Zx+7RTVYon52TnymSzd3S31hGg4GEKJWOX29/XV5zQKGbQGPRaLBVPtz83ucqLWCpKRy+1mYmJCxHfDgiBldzrIZrOYLKKPcm70gliFqlTs3LmTQrrA5qbQujc1icSrTq8hFovR39/L8PAwPl+zQOS7G7Db7YQjETp6u3G4nEhKpeBkKhSUZZHC1KnU/7EvWklGrv7bgVHMC0DQ0rzI1hTzRTEvUcusryyTy2aRqyVcdhtKypgMBqwmHRsrKyiUEp6mFiqVCouLi2i1Wg4dOkS5XGZuTrAVm5qaOHv2LNlsFptDJFFNJhOvvPIKXV1ddHV1ibax34/D4SCXy1HMCrdGe2cH6XSaXLFAT2+viGPf+A4mLs2+eQ+FkW1D8hM/eaju0lOpVHWTtNVqJRKJEIvF6O7uFrtmpRK5NocAMJpMIEkk40KsoVaq6pkHo8lEMh5HIQvhjMkgpulOp5PG/n7W52ZpHhjg5Sef5MKFCzidTqampkROXZLqwhSN1cjC4hIXLo4JeQxq2jt7afG1cfbcKGazldvuuJNXXvwtL774IuVyGZ/Px2wtaWYyiTTkI488wvve9z46fR187nOfIxKJ8Mwzz/DYY4+RSqUoFgW0dSu66vI0MTIywr333suOHX1ks1Uefvhhent7ueuuu0imk3S0ddDU7GHv3r389Kc/xePxMDExhdVqpbGxkW9/+9sMDQ2xtLjCgYMH0BjE/1c8GuMzn/0r7nrnf+HmG9+CUqnEZjHX+QT5rEQ6m6Grt4emlmYSySQHDhxgYnycYr6A2WDk4oVRwnE/VrOlvi9vaRStvL6eXlEuk2WWl5eRFaIJGAgE6ko4jVot6NZKZb04ZdRZCAaDNPuELlCj03Ly5Ekcblc9J7B//35avC01jPocwWCAXbt24d9YI51OA1UOHDjA5ZdfRrlcxltTD2r1ekCAgLV6MWz7Ywo1gDd8jUgy1YpcLzhVSlVUKiW5tAhyqRQqdCYtVCGVTJDPpKmW8lCtoKiWcLsdhNZXWF9ZJp1KsvfgZYyNjdWLcVtrdmEgF3j/8fFxdDod/duGGB0dRafTceDAAU6dOsWVR48SDwRYXFxEVzN0d7W1UiyKVa1Gp6V3aIhMjZtxxa3vYnzqjSErb4pBo1KppK23FxQKqFYppdMsLCzQ0NDAysoKvcPDNNW4gVuhGdELT2O1WonHYthq24lqtYpKocRitbKyvCx4jx4P46dOiz1uVVwdLEYTVGWCwSAzMzMYjUY6OzvZtm0ba2tr+Hw+nnvuOWZnZ4W/UVklFBRXFqvdyfzCMn9+x51897vfw2y2c811R/nrv/5rPA475VIVp8vJzMxMnQd5zz33cPXVV3PXXXfxiU98gpuuv4H77rsPt9vNE088QSgaoq+7j3w+j81mY319HbfLwV/85adwuVw0NHo4dNmVpFIpvvjFL/Kpv/gESFV6uruIRqP0mtv40H338NRTTzAxMcaRI1cyNjZGX18fvpY2vvTFv+OXv/xXPG4vyVyQSCREY0MD73znnbz//e8lk8+QSSZYWSnR1NTE2voyrc1CLx8IBUEpvpvmS0Vi8Tg3XHc9P//pI2I+k0yjUGko5cV1JFcs0dfXSSgSZXF5hTZfK+v+Tfq29xFJxomlk+zZtZszZ85g1ImBoyzLNHi8ddN0pVJBo9Hw6quv4mkQq9GttKmyWq1fnarVKkajkf7+frGhSidwu910dLTVvZMNDQ0oNKKxCSIsp9b+WxKWqizEk/DHeKb/7m3rsNhC0JfLFfQmcdAE1oKcPTuH0WwkFg2TiMYwaFXYrSZKuTSlfAtz01NYjQaam5vrsuHBgwehVGKt1hbecnwqFAqam5upVqusrq5y7bXXilxEOo3FYiG0tla7nooBusfjIRIMkc5lBcpPq2NydBSzTTBB3+hKtPX2pnhSGOzrkV999qn6TGHru7zZbK7LSrxer7iLq9XMzc3h9XpxOp1sbGzUWHnaOoptaWFRhDyAttZWMpkM2VSafDbHiy++iNvtJrgh4tQFhSDWHDhwgJ///Od0dXVx5swZbr75Zp544ol6N/9fnnmSYqGEzmBGUqp5xzvfxYuvvEpjczPzC2K6nkilUEvy733Cq9xxxx3cdNNN/Ovjv2Dnzp089dRTJBJxtGWJ+fn5euhqfn6+vrZqaWnh/vvvZ2xsjLfedhsf/ehHOX36NB/+8IeRZZlvfvObQkOfTmMymfjTP/1Tvvb1L1EuV2uQkxJXXnEV119/AydOnKSjvZvHH3+cVEqIZdXGAnq9ngce+Chf/MKD6A1a5EqVfD5HS1MTKyviqqbGQaFURG3QsXffPhRqIb159JGfUS4UaW5opJgvoDLWAkS1EE0sHOHw4cMENwOsrq7icbq4/vrrefhnD9Pe3o7ZZEKn1jA3N0dXaztrq6tYTGYqxZI4sG0umpqamLw0xcbGBulsje8gCzmqRqvlwIEDBNeDddQ+yKJUZtDS1taGSiVKb11dHWQyGVQ174dGqaBaFcNClUowJ4D63+U3eGiQ+fevEQmZSllkFVQqFVtggGJOzHgKOaERdHncnDt3llg0TGuTl2qxQDoeZuf2YU6dfIW25kZ8HZ3k8+IqpFAoaG1vZ/ziRex2O8ViUXwcl4tCQajv9GaRyNxzzTW8/MQTdQXhoUOHCIfDzM3NiVxFNo3P5yOWFHMOnV5PJB4jk8nw3/72a0zNLrx5nxTUKhVWtx3KZZQK8YneiqQaDW7W19eJaxTo1Soq+TzDu7cTWF5iauI8rW1tqJQVMtkYhXJJiFKMWsqVPNlslmBYzcKCqN6ura2htWgpUKC1X3gmZs6PIVdlLpw+hcfhoFjI097ZwWtnXicny2DQ88KrJ1n0pxgaHCKdTjOyfYSfPfpL3vKWt1CtVvn1k49jMBhqeQkHxWKRoaEhuru7aW/18eMfPMzll1/OU48/zuTkJH19feiMRoorC4wtzNDS0oLDJ+K6R2+/hcOHD7OwuMjIZfu54S3Xsb6+zu7du9FolXznO99BpZYoFLMYTTqMJh3FUo5UUjy2Op1OTEYVd999NwMDA3zjG19neXmeldVZNBoNOr2MUqFDhYY9I/toavCxvLyI2WJEpzMSicUxmm2kMnmqlU3xCFvJsrYySyKRoKenh5tvvJbp6WmxG9drqeaL2J1OUuUqgTVxp3U7XSzMzYv1n17Hq6dPQapMfDWMrd2EUWtAj45KQaZaklheWhdtvkKF9cU5xmYv0d7ejslho6pSsL6+jkYjjOJKpZLR0VG8nham5xd45XcnMZlMHD58GI/Hg7uhikVrQlKpkRU6NHotUqFUK76JxQEKBXKhgqQWg8VKoYRSq6ZcydevBJIkISEhvwGlSCEXxKGCAuQKkqQAWYFapUCjUhPLxgiHovz8pz/A5XKglBSU0zFUSolsKsny0gLXXH0Vr544TkNrG3Pnx4QCripz/DfPoTMZBb2pv4/l5SWC8SgoJJq72okFQvR2d7N68SKRUIhGr5dwMEg+m8VsNKJVq2lqaiIe8DMxdhGz2YpOb2B6aZY73v4Ojv3uJPl88Q++Ht8UTwo7tw/JLz/xUyKRCD6fT2TodTqKSVF2qdSYfm6bg2AgQDQeEzxGnQBXSgoFSrudc8deoaOjg6WF1TrrsaOjgxdeeKEuJd2KiwqZho7oRpjZeQEynZyZIZPLEk9nyORyBMMRylXxKFtRCDvzfffdx2c/+1luuukmTp06xeLioujGr66KarXayDve8Q48Hg/r66usrq4yOioKU1qdhqamJo4fP47RZK5bnrq7u/F4PHz84x+nvb2dr3zlKzz99NNCH5dJc9ttt3H8+HFSqVQdOGuz2bj//vuZmZnhoYcewu3yEI1Gueeee9ixYwdf/OIXWVha4B+/9Y98+tOfZmRkhM3NTUKhEKVykeuvv55nn32m1krdRKXewqmJb3myLFOtiG+bRqORkZERxsfH62Ehi8WCLMtsbGygVxno7+8Xa0SVqv443NHRgd/vp6enRxzs4UDdSCWiyKl6IaqhoYFcTqDp1CY9qVSq/vmRJIl0Oo1SqcRoNNar1NFIql6DL9UMUVv8BrPZjE6no6enB5vNxkc+/OH6VU6tViOpJfF0UAEkKBWFvVxnE1eMraeDP/T6UFIEqbapqEhi6KjSMH5hguXlFeIxgVXbt2uAFl8zi3PzTI6P4XE7KeayBAMb7N2zm7npS+TyGdwGAarxNjUiyzIdO7YzNzmORqejdXgb5USchaUlrFYrSzMLFAoFBgcHWV5eJp/P43K56OzsFFdrq5Uf/a/23jzIsqu+8/yce+/bX77cMysrMytr36WSCslaLANCGgQE3XgdHGaM3XaE23aPu3GEY5qxewyN7Qg3Ed0d3e6ecXTbPWDCDNCAw6ZZbGBAMmAESCWVVPuWyqzc98y3v3vvmT/O/d133q0sgQ2SKoY8ES/ee/fdd8/+O7/f97f9p//EkYkxFhYWGBndjcKh3vLJF7roHRjk7f/LL3H+8h0MNN519LD+9J/+R1ZWVjh+3PiIK6UYO3iQ5uZmZLaq2Vrd5K57TvHCCy+YHAUpj81ymZmZGY4eP8bFixd54xvfyNe/8S1u3LjB4KBRS83Pz8fBWc6dO8ejjz7KF77wBeM16cPi0hKtULNVLtMMQlZWV6k1GqxvbZLJ5slms5y+70G01qytrXHy5Ek+85nP0Gwag5h0Ok1PTw9Hjx7lp3/qnVy/fp0/+7M/w3Vd5uZnOX78OFtbW4yPm0zB3d3dtHyfVCrFoUOHeN/73seZM2d4z3veQ29vL4tLi/T1mqzO//J/+y3+5E/+hFqtRhAETE1P8VM/+VM8/vjj/O7v/i71ej0y3qqbfIiR3FypVDh9+jRf+vKXGB4aZmFxgULe4CbvfOc7efTRR/nRRx4CYHT3CC2/cQtRCHyTpSkVRa7u6enB933uv/9+nnrqqTglX6vW5OTJk1y6ZOz9x8bGuHLlCvv378dxnDj9W0+P8SqcmZmJcaF8Ps/CwkKcvaurqwvHceLwdmEYkslk4vgaAkg2Gg2KhZ742UCcV1PkasEcuru7OX78BKdPn+bnfu7nmJiYwHHNRg7DMM68VSjkwQxfHG9SzJqTpVrZiPJWpHCUBzh4KZepyfkoJGCBeq1JMd3g/PkXWV5colGrkk653H3iOFub60zeuE4xlyXUPrv7hzh16hTPnX2edC5LrpAnnTfOUhuVMlNTU2ZzK0VPrseEp/M8Tpw4QSaT4fLly/FYiTv96IAh3Ckvzcr6GoODw2yVqxQKBX7uPf+S7zzz7LZE4Y4I3Koche+3aDYbrK6uEAQ+jqPYWlqkXq9RKnXhOIqRkVHOnT2HDhUD/UP4jYBCtkA+X+Qv/+Kv6O3tZ2lphXqrSbG7xOLKMi9eOG9Ch1UrVOo1evr7+NrffYNyrcr80iIzC4uU6w3wUmxWayyvrOLjUOjqZnjXbrRyabQCnChn4dGjR/nSl74Ugzz1ej1K2WXwg7/54l9z8dIF5uZnqVTLZLNZLl68GLtw57J5FheWaDQanDp1il/6pV9ibm6OD33oQxw5coRMJsPEngkGBwd55JFH+OhHPxprWaamp3jowYc4ceIEH/zgB1laXmLfvn3ML8zH/huiwRBvusOHDpPJZBjdPcqBAweoVCo8/vjjvOMd7+DQwUMcPnQ4NsuVDSABV3VkxSfBUzKZDFtbW5GDj2ZycjLekELIa7VanPZ8ZWWFRqMRv5erFfwwwE151Bp1tiplLl+9wvDILrp7e6g3G1RqVRqNRpwDpNVqdXze3NykWjWJVcvlchwXQ/JY1Ot1/IjgSn6IfD7P4sIy169Ncu3qDbY2K7QaIa1mgOs6dPUUKZTyoEEH5kWoIFQo7Wz7SmdyeKkcyvEINLT8kNXVLdY21ilXajz3/At86i8+zUc/+lEcx4ltb8SDVMy1xadiaX2VqbkZZhfmGZ/YA1F4wOXVFQYHB3nzW56gWCzSPzRIo9Vi99gYuUKBQGvWNzc5ePgwZ198kb97+mkeeOghxvbsYXNji77eftK5LMeOneDg4cM4jsOXv/xlVpcWb78f7wRO4ejBffrD//b3qdeNJ9vwsIlJFwRBrF5ZXV2lv2+EY8eO8eyzz1JrGNnP1yHz8/MsLC5y6NAhY1dQq/PlL3/Z6OKzWRqNBrt27TIGMZHzi0mbXmFpvcxGeYt8rshWpUyh0EW92cT3QxzPZWSXCTSyubbKL/7iL/L+97/foPM3b8a+/Y899hgPPPAAH//4x5mcvB5bWwaBjizTvDjVm+t47Nmzh5/4yX/M0NAQv/EbvxFHrF5bWyMMo2Apb3kLH/7wh6nVKvGGP3DgAG9961v5vd/7PaMGi6zpzIlInDoP4P777+fChQsEQcDY2Bg3b97k9a9/PW94wxv47d/+V4yMjABhZDOfAhVGxICYKLhOJs5+LVmqisUiWmvy+byJh+g49HaZEymXy5mISFFcRhEz5PTuHugBoFarmfD7kc9KPcpgnc0ar8N0qKhWq+RyuZgASDtE/DCZo7Kx6hoMDiUBcyRNgOcZ2KzeUHGwnYmJCXp6ejh27JjBiO65h1OnTjE2NkYmb+wUjEYhWqDbnKctHeAog1FobV6OAyvLZZRSTE/PcPnyZX7irT/GZz/7GVzlcO3KZfp6uylkM7gOsfiwVd7gH/3ETzAV5TzJFfLgGF+L4w88QH1t1cSmdBwmJiZ44dlz9PT0EARBbLczPT1tco/2GnPvmZkZmlvrDAwMMDMzw7333su1a9e4ev0aPT09/Nrv/D4Xr24PNN4ReR/+w7//d+//sfvvIZfPkUqncVyHow8/xFB3idm5OYaGh6hUK1y/NsXK6ipLS8tsrG8wOz/PtavXqDcarK6uMTn1EjOzs7iZLN09PdyYnKTl+3SVSpx57jmqtRrlSoXllRXWovBaTQ0aBz/wCXFo+T6VWh0NFPJdFLqKHDlyhK5igbNnz7K0tGQ2Qm8vlUqFvXv38va3v50zZ87wzDPP0GhWcV2H9fUNPM8lk8mSTqdYXzfq1Hy+wK//+q8zPDzI+973vlj8kDDmo6OjbG1tce3aNQA2NtbZ2tpCa82pU6f42Mc+ZvTgkZ+I60bRjhpNBgYGaDabeJ5HX19fHA5NApo8+uijfOQjH8GJIiTV65HrcGhOTHsjhGGI75tNJUFthWuQ084QvoCg1YojW0lMA/EHkOK6Ls2gs69yYoZhGNuneJ6HCgyBAWLuRUx4RewwDkqGe5DNrpSJvqyj8PFyCgdBgMaLr4l78dTUlDHyaTaZn58nCAK6S10UigVDB1wM5gCEfkgYhDiRxsL1HFqtgCAIUcohlYK1tQq5XJae7izNZsDo6G6+/uSXeN3rTrOxvk46lSKTSdPd1cX6+hrra2scP3aUo0ePMD1zk2qtZszKNza4+5FHuHj2LDnPIwxMAtxszjiczd2c5+TJkzQajViVubq6yvr6OhMTE8zOzlKpVAhbPpcuXWbP3j3U6nVQirSXIgxC/uqLX+E3/sV7ts37cEdwCof2Tej/8IH/g7U1oy4ZHx/H900E35mZmdiI6dBBA6ycP3+ebD6H7/u8dNOwsrUoNPZmpcz6lrEG3NraIpVKmdRk9TqAtaAMotxUxiFL62g3aAfHcwlDc/1/evwJKpUK58+9QCplNBmpCN39mZ/5GR555BF+8zd/M4770N0j/gxZstk8zYbP0tIK/f2DDA0O8973vpd//s/fg6MaJsdDJhMnys3n8+zatYsnnniCP/zDPzTJa11i24Xe3l4TkSgKcCqyrtaadCpLq9WKDa6azSazs7MMDQ1RKBSYnp6mp6cnUnGl0Frjuio6XVWMKQRBq20nkMrfUo9sUtn0QRDgYa41m8047L4k85F7lVLgGKMosUsQmT+fz8cnfiqVopDKxM/IZrMRgfLjBD7Sfwk7Jmy4EJdUKhV/F44hoG2oJNyEhGsrFArkcjmGh4fp7e5icHAwzuexvr6O53kcOXKEo0ePGq1RrcbkzRlGR0fZ2NhgaWmJQ4cOMzs7G4OZ3/jGNxgZGeGFZ7/J3NwMzXqDsd0jnLr7JI1qBXTA4sI8B/ZOsLm1TkuHvBAlI0qlUlQqFU6cOMHVq1d5+OGHcV2Xja1Nw51Wm3GC4XK5zN69e5mZmWFsbMwYj+3bx/z0NMvLqya58u4RxsfHOX/hRRq1OvV6nV/97Q9weXLqzlVJeqk0M7MLbGxsGLvvC5d57LHHWFlZYXnFsErnL1ymt2+YVD5LV59Z3OvlLWZmZ8kVCzSbTVa3TKSdgcHh+DQNw5CVlZWOvIn2QvX9FmEUhUYpF6VCquU6nueZRJ1+k0bdmFKPjo7S3d0dE62JiQn+4A/+gNnZWUZGoph9YYtQ++RyWZQyrPL997+ORiPg/vt+hOvXJ82G9Nw463OjYQjE4uIijz/+OJ/97Gdj+V07Jp3b8ePH+drXvkatVouRf9l4gsBLuLJCoRBbRi4tLZFKpbjvvvt49lkTZzKVSkeoehgRgvYG1rqd1AXa6Lv8LsRUCBBATE+j/9mxBuwS+D6B7xMGBqNBaxQQBgE6DHGUwon+I4ZFdv1A7JIMhvuw67VDqAsgKQROR/eF2kdHa6C+WaVaK7NV3sDzPNbWV8hLkF8dxv4wjuNwc+olXjz7fOxWXWn4fOC//msOHTrEE088wX//+MfjEHTVapWVpSU2o2Qvb3/723n6775JJp3iK1/5Cp6Ce07dxbFjx2hUK2xubpLOZnnogQdjn5xr166xOL/AgX37mZm+ie/7nLjvPr715JO0Gia35H333cfNm00KuQyFXIbF+VnK5TKXLpzj0KFDPPfcc2SzWUZGRoz7uOdR8X3C0N/W9kLKHcEp7B4e1r/+C+9mbm4ujg9w8+ZNXNdlbW3NeNUVi/jaoKtb5XJ8WszOz9EKTVLQ3n5j800YmbFGSHy9Xo9ZR8MVtPscOBF/GPnDO9o4UVWrdfbu3ctgv/FO26qZsGFyqv38z/88X/3qV3n++edjp5ONjQ2y+YBUKkV//yBbmxWCAJoNnz//84/zT3/lV1ldNQlkerrScYYlMJtMIhdPTU2ZtgUBw2NDrK+v8+CDD/LUU0+ZoKqVSrzg5fR0nUwcU6C7u5vFxcX4NM/lcjz88MN88pOfjMQKw947DhH77uMHzRhTALOBXCcTt0+Ij8jp9Xo9FiPSro5xBKWUCSRrcTNCgOtbBniV8HtioRhEREIs7aQOyTbleV78fOmzhO0T7YbRHhQ6rAztwCgtdMxxxFxiJGqJmlNrTVobsUwIS29vL7VazfyWThvT7HSarmIvx48fp7+/nxfPnaVcLjMxMcHGxhqZTIaXXnrJxNE8cZwXXniee0/dQ7NeY3lpgT2ju6nXKjQbdQ7t30c6Y8Sm/v5+5ufn2djYYGBggAP79tNoNFhaWmJjY4OJMeNyfeK+u6mtrXHmzJlYVLx69Srj4+MMDAwwPD5OZW2N+bU6169fp1Y1gW0X52bJZT3Srsc//Vd/wMXrk3cup9BstvjC33zZBOJotbj4oY/Q3d1NNps1UYIzedY3Kyyvm5iIxWKRUJmNX65UTJCLVIqVNeN81KobGVfTihaqY+iiAjdKJNKOoiOsLhBqtHJxHYcwaLF3zwSf/x+fNQFQcDhw4ACvf/3rOXbsGB/4wAdwXZf+/v4Y6DLtrZGLMj9ls1nuuuseBgeGede73kWr6RNGZtZdXXnW19cBYpCuWMwzNzeH5zl0d3ezubnJ4uIiJ06c4Itf/KJJKhNly4L26Wk2VUA645FKu5S6i1SqW2xtbTGxe5xcLsf6xirZXJpyZZNctjs6QSXpbhonEG6grYrT4a16ekkNL2HQtNY4KojtSdLpdAwYJksmnQatjTWh6+JEbH+xUIj9HlKpFJ7r4kd2JV0RsCligGAbAKnIalGHPkppPNfgDHKvjJFSKuIUQsIwsH4zr2w2E4c18+tNmk0nJjq1WiXmTMTEvl6vMzwwwuWL5yPQs86+ffv4ypXLZLIpBgcH6e3uJuUq5ufneeihh1iYm2d5cYGuoknT99LGGsePH2dzbRWNcY2em5vjwL79XLhwgdGR3Xzz69/g4MGDpJXLxO4x/GaLVqNJdWmRfHc3lU1DPDZWVziwd8J4BocBzz/7DEeOHKHsm6hSTpQdanBwkEzaoV6pbjs/Uu4IouCHAU7OoxyBYlpr6uvNmJIPDg7S8lsEWpHO5qk3I+rfCkh7GerVRgebmc5l0UArNFxBq9nqOLmUUngZA4LppkuoQ3TY1kcX8l0sLRpVULG7ZAyn+ntYX17g8P4J/vJTn0AFTRQuuZRDLpWDoEnGhWbNpe77pLQxdx3q7eebX/8aYbNCIZulUqlSKpTY2NiI2XyJc3D69Gk+8YlPxHb+XV1dpIIWBw8e5urV66yurpNOZ2NzZumvUq7BFLwAR3lsbpQJAygWSszOzPP444/z13/91+hQkcsWCHUdTYgmwA80RAShkwAoUCFi/6uBlu+DCmn5DbyUF528LULHIZvNknIcMl7KENxQGZBSG5bfcz3qnnlOKp+mHmiUckhn0tSkP26Kplb4UVq3XC5HEBGewHUIFeA6aMeJNqqi1miaZLnpNIHrgQuOcgharXgTe56HqrVwMYdDIzQqTpSHH8DqZjnmqJopzVa1bPCMlAnQ2opsHlSoSSmHQqmbpU0Tv8PFJVABV6evU6/XmZiYoNJqEFZMyjt3rcm18y9RLm9y6MABSqUiy4trdBV7mXppmt6+HjK5Ll6aX8Xz8jx36TpaeaxVG2S6e1nc2qRZb1BsGW1brVXH1yFnnztj3NoDPzaBHpvYY5LcDA2uyxfKAAAgAElEQVTy0s1p8j27efwtT3DxhRfitAZHTpxkYWEBN3V7b9A7gig0GiY+nVBwUTlpranX63HshGqlHlsmyiaXU0NYyzAM8UMDPsk9AjzZcrIQANeSXYVgbGyYoJ8LCwu0osUVhiFvfvOb+eAHP2gs36IAKcJmSltyuRzZbJY3vOENHDhwgD/6oz8CDBehlIpjHnqeF+e+FIRfvOTEIGlra4sDRw/z4osvGlVdFN0HOuV1GScg8hA09VUqFXbv3s3MzAx9fX0sLCwYTio2VGr3eTsx0gYVY7DQ+s3+T8zah4ZlL+ZNCHyCMLYzEA7Ctn8QEcR+yXjIaR3jAtHv8pvMr9QtBFY4Ba11HINRxAMBI7U2iWmUUgSRAdPm5iaFQiEm1s26sbFIe2b9pBw37qvv+ywuLppwblbQ4GvXrpFOpzl27BiDg4MM9g6ahDTFIi+eP8/wYD9+0KCn1MXwrn5WVlYol8tcjixje0oljh07ydnnnqdS3uT06dPs3mVyWoyPmzDwjgqpNpoMDO9iZWWF7lBz8MhR0t3daMcQwWq1yo2pJa5euBCHIxDPSgPGpm+Zbyl3BFEIw7bsZ5+AMnmysVvNILq/vTHshSnsrHinCKuZXMzJuu0NIhtMXFilHfv27TP5GjY3KRaLrK2txWowqSOWnSODpk9/+tMxqx2GIeUICxGATNojsr9gCQIS+r5PsVjkxo0b8f02VmKPgZyIEurbRDg2BG1qaipmzz3P68g4nAT07GJzVjYBkbHd7lqowg5RwIHYtDiIuA5bvWnXL5hEOp2J65d6tgM/bXwoSeTk/1IEu7B/b0ZgqXba4KZoRLTWOBjbh9CP8AuP2ChqaHggHuv19XUmJyfjvCKe53H9+nUTsMe7Rv+AccYqFQvMLS7gEJD2XC5dWiWT8hjeNUQxn8NzFDenptja2CCVdkmlXLq7CmQKObq7imysrbPcarGytgLKZWp6xlj2emm8VAZw6O7pM2Clu85DDx3k4sWLccaooaGhGGfxv5+8D69O2R6xloUiiT4zUYx83/djsE02o5wIWmt0ZKgZBEGssrJ95u2F5AS3mrIGQRAbJklYrOHhYZ555hl6e3tjz01ZlLb6LZvN8sQTT/C5z32OUqkUJ7np7e2Nre7ErFZOONE+aK3jDS+nnsiwUp+t+7c3lNgGAHEmrK6uLhYXF2NNQSqVMgFAWu0+C5cjHJhdxPBHNlwQBDGBS2pxRN4PIyOoeNwtWqOdNtch9fq+H3MD0idJwSdWjBJTMamJEKDR1kKACfqb/E3G255rWUutVlvd2Wg0TFSnnh5SjrHk7O4qGS/cZot6vW5iT+bSMafjeZ7JKVIqxWMqnEMtrFEup+ju7mJ2foaRXbuYm5tjfHyUk3cd52//9knG9ozz+GOP4jgOA339PPnkV1hbKnPvvadYWVpkY2WJs2fP0tVVMNzEwC6CIGBpbYtS3xDleoONSoNuL8vaVo3hfIlizwCVrUqcL+XEiROsrq6STpvM3TbIfcu83/aXqCilssBTGKtwD/ik1vp9Sql9wMeAPuBZ4Oe11k2lVAaTuv51wArwTq315HepJV5o9mIVQiFsc6VSiVRqqVh/bS/O2FxX6Vs2a7wwE6eOfRq21ZR+h85bIgwrZaIIix2ArR+XjTE7O8ulS5eiVPTlmJXb2NiIg8f09vbGC8dxHGq1Gr7vMzY2FoeDE8KzsLAQ+xiIBaA4DtknNCqK/Rjl4mw2m/T09LCwsMDx48e5efNmrLd33LbdgT1+yWL/ZosQNisv4LCw9C7SJnNPjCl4HvVWMzZCstWXQhRsQmGtvw4RQ4rYMMhvIooJIRAOTBLU6FabSwl1GM+d53m4TtuuQdbf+vo6LioO/ZfJZMilM/E4ifgiRB4wwUzSaZRSMeFPqSqV6gY35zTDA4PUGlXS2SyXrpisZc2W5vLlq/gtQ/iXeky+0p/8xXeztrAISvP8s2c4dOgAExMTzM/OMbb/GOvr6+QLy5S6+0zWq/PnqdaarK1vMTo2QTafwnNSXLlivGOfffZZ7r7bJM5dXFyMwfHtyvfi+9AA3qS1PoVJ/vIWpdSDwL/BpKI/BKwBvxzd/8vAmtb6IPDvo/u+p2Lr3GXhyUKRzWlYnzaAJItVFrjNGcjvwC2EwF7otpUcGJaxVquxtLQUG/8MDQ2ZwK/RCSDou/38IAgYHR2NFwjQYeQjEyF9qFQqcV9KpVIccWltzXiBbkZeooVCIYreVIzVffKSkzAMwyj5SRAv7q0tk0nJ932GhoZib0J7fOS0k5dtByD3ybiK3C4ETbgGUQvKeAuHIUTVcRyq1WqH/C/Ps+uTefQ8L+6LtEEMlaRddjYxW80o4xtzLtbzkyKGTeSEY5E+2GKTEBZps3CE4v8iHFilUomfJZaS1XolVr1WG1UWFhaYm5ujVqtRrVZZX1+nUCyxvLrCPafvRWtjlPXStRs0Wk1aTZ++wQE2Nrb47Gc/y/rmBpcvX6ZSqXDy5Mk4f8nY2BjZbDZWpy4tLjI9Pc3GxgaHjx/n8OHDpAYGIJejp6cH1709P/BdiYI2pRx9TUUvDbwJ+GR0/cPAj0ef3xF9J/r9MWUf1bcpyYmSd3uzJydLTiDbDFcWavL19ylSx+bmZnwiCZGSzSgnmywI2VBa69iDbXFxkVrNZGoulUr09JiEJVKH4ChAbOPf398fs3Zy6ku/t7a2YmIoxEApY9MvYKo8UxamxHoUq1CT3ToXG8nIphGiYHMgNqGU8ZZoQALy2ZydnNACEoszExC3TzZys9mk0WjE/7XnVoiF/bKJoG0SLXUmxU8BA8WTVcQQITxyv31YyG9J7ELepd5UKkW9XieXy8Wqc9/3Y/FBgE3ThiaNVp1ao2rGynXx0ilqjTrXJ6fo6u5lcuomjpvmc5//G144f5Hd43tZXtvi0pUb/N23v8PlK9fJFLo4ec9pxvcdIGj5LM4vQKjp7e7h+tVr9I7voVTsYm5mFrerxEs3JpmZmaFSqfC1r37V5FSZn+dClBpACPd25XtNMOsCzwAHgf8MXAPWtdbC59np5keB6WgwfaXUBtCPyVB9m9IpKyY/C3vuOp2yI9CxCeR/2jLASd7/vRZBsYUrSHIdQHyC2JZzImIIFpDL5W7hUmRDAR1cjqDfrVYr5ggkwU2xWIwXuIhOguprrUmns/F32ViyueRZQgiEfRfia8vZSVDRZt9FBpdxt/MexAQk6ourInFOtTkOza1AoLD5SbxAPtuHQ7xabgGL28Rju/44jkO9Vr8FUE4+65Z2JdpqEz8vlY3rVMpolcRqEIi5Ka1DHEcBGj/0CUKfoV0j9JRKNGp1qlFyorUtE0XLdTyy+QIH9u3jG9/4OvlCN6pLMzl1k/7+XvLFEuNjZqvNztyMCdxLZ583VrjjY3zqQ/83pVKJmZkZJiYm4oxe+bxJuzeyZw+NRv22a/97IgraONnfo5TqAf4COLbdbTK2L/ObPQG/AvwKGLWgzbLa2IL9MhFuOifbnmDbKMVq+y2LKtGS7fqLUirOzRcEAfl8HiA+dRzHia36JOy8cBSCCYiRkbjzysYUMUhOSJnYy5cvs3v3bvr6+pienjYenqEfb+q+vj601lQqlQ48ICnnx9iKtZGNIU4t6nLYOa60CZyMrc25xcZCqVQMAgK3iBxhGOLoNheUSqVic9owDHHTqY5TWcQJmyM0Y+J2qCxlrEEMtZx4fux22mtAvgsRLJVKMffUahlOBkd1EOwkdqGQddceF3lvtZpxoGERB2VuRbwx7t1bccRox3FM5Gjf59KVKwz09dHX14fruswurOLPLDA8PMzHP/VXTEyM09vdxcrKCrt3DTM0Ok61XOaprz/NqSNHGBoaYixKCJwrFFhdNvkjsuk0r7v7bs6dO8fExASP/viPszltgv08++yzMadjz3ey/L3iKWit14GvAg8CPUopISp2uvk4FX30ezewus2z/ovW+j6t9X2yOGwQMAkIyoQncQHppLDwSUJiE46Xe3aibR2ybLVajd24Bdyy2VQboPJ9P85XKGa7chraQJrN2UB7Ea+vr0cnv5HZZaHJYhez3Hw+H7sKC1cgz5F+2aeW7ce/XbFtAaRd0jb5TYhhcnxlzJJsuDzHFhvkZcv9yfndbn5sMSZ5CMgaEAJiE1ohiPJfW2Nlz7fUcTvR026TPMMu4rUqoqJk8EaFKKfNvQmxHBwcNEFx55dYWV1neGQXfQMDtFo+6xubBIFmdX2TrmI3xVI3k5NTOF6a4ydPkstnuHzlIpqA9Y1VLpx/gb6RIUbHRtAE7D11kp5eI65W5+bQWrOwsEAYhpRKJWZnZxF/n+3K95J1ehBoaa3XlVI54G8w4OEvAJ/SWn9MKfXHwFmt9f+plPpnwF1a619VSv0s8JNa6//55epIpzw90FOKvycXlUyArZGQyc9kMh2Lzpar7Qm3F4rNXuptABdb3pL7u3ImqpEEiK1Wqx14h9Qh3IAATXb7pQ9J2wm5J4yMaLTW9PX1GZfYjRr1ej3K65CPQb2krj9bNCo7SZ8np67I0kKQtNYo3Ykl2CKCtM11XZyoX/ZmFflZnpnNZmODKZtQ2GNss9l2n6GNNdicoet1qkq11h0YivRbNAASj0FANgFTZU3YMr5wV0A8b9vhCHZdMh4iRqZSKTzl4Ech3DKZDP29Ji/IoQMHGBgY4MWzLxg8qlhgcnKSbDYbq6gLhULMKebzJsFxV58BmatlI3oW8zkKxRwnjx0nlfLo6+3l6tXLdHd309NtUuFtrK1x7NhRGrU6w0MD1Ot16tVyzE129ZqAxtlslq4uY2AV+Jre3l4efOIfce7SPzzE+wjw4QhXcIBPaK3/h1LqPPAxpdTvA2eAP43u/1PgI0qpqxgO4We/exUqRqlt6myza0C8QOwJs++1T2Kb+9jOAMY+BZNFNoftpttoNOLFJwTJ5k6kniAwGarkXgGfbNTd5hLsdgOx0dLq6ipra2v09A/T1WXY1Hb481aHPK+Uot60jIrCkLQX+SYEIYEGxzDDhFH7BaRLntj25ojVeYmNYos9tVpt2/tsdbAAdPb4Sn1J9aOZ0zY7Lye62GdIW+X0t4mN3GuHcpc22QTAnnN7fch4JteC1GOLuBpFJjKNzufzcZQnx3HiyFStVgu/XObIkSMEQcDi4iLFYpEDBw7gOA7Xr19neHg4Tm7caDSobBk1diblkU53RzhSEwXMzS0wP7/IsaOp2AFr165dBC2fQwf388wzz3D16lXe9a538bWvfY19+/bxmc98hne/+91Uq3UqlQoju0a/K8b2vaSiPwvcu83168CPbHO9DvzMd3tu4l8dBGA79ixJIKK6OjiHpGwo98h7cjCUUuiXYaPsemxZXU5JmxOQk07Ui67rxtqLpEbF7oe9KB3HiQOXyAm/vLwcx0csFosdRM9mi32/vWBvJ0LJ70l038Zd7PGyCa78X9SaSdDVHqcOTszCPgTgtO+339uahE4cyHGcjr5K24TQJLkxez7sa/Y60RHeIu2T8b+dpsoWG4womYvFKlEZb21tUa1W2draioPc9A8Psbi4SLVqMnN7nkkf39PTY/wkKhUWFxfJFo1GSNTOpVKJlGfiLV65coWebpNdbGhoiI31Tfbt24fnGJ+TPXcdZumlG1QqFfbs2cP58+e59957OXv+HG9/+9spjIxQvnKNmZkZUl4mjmF5u3JHWDRq3fbPtyl2cnHZJ+p2eEPSz94mDiJS2LjE7SimzV7KPSnV9rewCYQsdsE1yuUy8/PzsRgDWACaEy8MWYwi90ofRN1XKpnovkFoFmolip7keR59PT20Wq0OF+rQWtAydrbNhs3RBEFb7JF22W2ICYrlOyIET8ZEZPhYM2RZFdqii/TxdriRLRLGdVvgpMyjAIzSFntNJLlBuz4pwokqpWJX+uTGsPsh3+3DxuYy1tbWKBW7YnFyaWnJRLqKxkX+f/ny5VgUtAH0RqNBoVDg2rVrpFIpulImB+aesXGmp6eZvHGNy5cvk89mKRRyNKI0dBsbG+RzBW7cuEGpWKTZbLC8uITfanD//fdTr5a5ceMGy8vLvPFtb+OpL3whWgshP/rWt7J6YypWK9+u3BFEQalbjYtsVNrGFJJEISkOQCdLaf/XZgXb/7sVa7XBqHhB0LbZhzamIYtLrkto8dXV1TiSkG1kJc+3zYrttgu6L3U3LWLZaNRwnFwEPBo3ZwjNy/gGm3ZbhCvmFpRtGdg+8ZJig7QneeLbokA2m40XVblc7uB0kpsyOT82ZyX9vGU+LS7l1rXSOS82R2QTattJzeYSbK5iu2ck8Y3tuAebixIxIfSN1qmrUDCxMssmcdGePXvQWrO5uYnWBpNZWlqKCWxvby9DQ0Ps3jOG7/tcvHgxxoWEsAdBQKVhDJkcx8FPGfB0aWmJnp5uMpkMx48d4XOf+xx9PSV27dpl1lGrRalUoru7m2vXbjB/+TKBb7AX9/u1U3jlS5tC2xZ50CnD2gZCNtWFW+VZaC8CWXjJid5OpJD77IUuz7SJlDzH1m6Ij4aoDuWEE8Q9ufDk1LNPdOEcBJdQumVYKa0Jw4DKlkmVl0mlaaVNRGOlIQg7VZKivZDQZ7Y9gdZtbMMeHynyHDEYs8dORCdJemuLMknCbPs0yCawN6tcFzFAtBLaIspyX5wyzlIbCmGTe2wVrWAlUr+tgrP7K/iKzKNYnYoBlmAZ0n4wKub+nt7YbqSrq4tsLmPS0UVqWzFGG9g1jOd5DA8PU61WaTab9PWZhEFHjhxhZmaG1dVVAmXSB/hNI5pVq3VSKZfBgWG2yhsErYChoV3RgbNsOJ9Uije96U2ce+FFsoUCBw8eZHnRBGl54Md+jIWbN6nVakxPT3P6jW/kha9/nZMn7jZexwnx3C53BFFQSsWDLwtY9NJyLXniJPXSdrE5guT9yROLsPM0vF3ZjiPZTgxJsp9CWOxTy2ZD7bbIJpPTJwgCNLLQ23X6QRMXl1TaJZc3MR4ztC0a0aB9E1TUjZhxpTE2BGFnnMWkPL5dv+1xTH6+HWH+bqKa1GurQSFyUgqat4gCMecU+Tckwc1kn+z/2/iB3T4RcSTgbE9PD4uLi/i+T1dXF0opVldXOyIxSdt934ewk/CLVkrUxul0mvX19Ti6ssRvlH5ev36d9fV1stksN6dnDSeRMQfFWJQQt9Vq0V3qRYc+L774IrlcjoGBPvbu3cuVS5fY2tpieXmZhdlZgiBgYGCAarXK6vx8HIptZWWF9akpDh06RLPZNFzInY4pQHuTCHgjKqTk4oJbWcZkSSLdssnku/2bUp0b43bPFJk06W+RJCg2lyGfbXk2SZwEZ7CfZftwqFDjOJ3xAJo1E8chm81SKhRRoabW8uN2QlvUStpDyG82lmBvdHuThbrtoixjJP4B2xG/5P/tuRJOLzn+tlm6EAjbg0/uFy5RIjzZ4y/jbZtuJ+u3OUt7fuX+er3O4uIi4+PjtFotJicnY58RSTcgY5vL5XA05PO5eBylD+IwNzQwiFKKzWqFSqXCoUOHKJfLrK2t0dvby+rqKhsbG4yMmIxQDX8V3/eZm1ugu7ubPWPjFIp5Ctkcly5dYGN9nV27dgEOq6urXLx4ESKO8PTp06yvrRjVdMb4jJw5c4ZAwe7duykUCiwuLpJOp9k1vDsGi29X7gii4EQoqi1rJ08WG7izicJ2ZTu77iSSLq9ay98WpEoWYY/tk0ueIWKA4ziUSiW2trbik0U2t83yJgmBLCrxBZDFb54hMreAmm2vwM3NjXhzFnL5mAOJOQ7AVZ1YBlp3iGHyntTYhGFIaM2DbRORHMvtAERbtLKNyoQI2eMieTjlerNV73i+zKkQwiAIYlsMe2yhrXq01cZKqThOQhLbsQlWOp3m+vXr5PN5Dhw4QBAEzM3NxaBqV1dXbGbuoigVu2LNiKvMM3IRQVtbWzP1RuHflpeXGRoaMvhApUK1arJBv/TSSwwNDfHmN7+FmZkZbly/GuXLTHHt6nXWV1c4dOgQhw8dYnV1NSI+TRNerb+fixcvUip28cLZ5+jv7yc90MfGxgbj4+MM7R6hWCxy7tw5yuU1wxW12jYrtyt3BFEQSh0vxgT4leyAfardbgNDp6mrjQXYJ7btWfhyahpZQPEJrtpYh7TJdU0IMbGBFxPjpBwscrSceMKC2gtUio2u25tSTnmR+72EijCJf9jFVknaWgn7//LZHuPt1JD2vUnikFx8ttXkdn1NXrPnaTvOwx4ze93YbU/OuzzDHiObG/E8L85bKSHgBc8QrkYphed6HSpRWWPCTUgwnGKxyPLyMl1dXWxubsYh/bPZLN3d3aRSKXK5HJubmyaAT6FEqVRieXEpUruaBDiiSgQ4fvxIHMDmqaee4vQ995LP5xkYGKC7u4vp6WlGR0cp9HRzc3o6Ar/zsaFVvV6/8zkFrYlZVR29HMeJw32be0w48OTiSIKKNsGwN5wsNBuolP+5joODRqFjW30VRePxxU5eOziOQjkuCuOc5SiTGKTVDAhdCANMpOnIjFVrTS4dRSFutY2EXF/TbNVju4NGo4ECMp4hUJ5yDAagFGk38i50Ik2C46CUiVPoaE0QhgRgQstHuvtcOkcQpGNbh5jYqUh9qRQ44HjRKY8mGtwYu1AKghAg2piui/YcFA5h7PwFjqNohZEBkmPuV8ohCEPclItyXUIid+ZmI4pf4MQq3Gw2a/wRfBOXERXG+JK9aYUA2B6POmqochyc6H43is2RchyUVqSVEXXKtTKhirKZuxE+QURcgwA/CKjWauQzWbKpNM1anUa1FhM3gMqmyWpFEFJr1qhVqm0i77gxhhAEAdm0AR6ddIaBAeN+L6nlJSze7t1daG28cRdmp2nVaywvLXL50jnuuecelDK5N1tByNrWFs3AHGJXr71Ed6lId3cXI8O78FIOi/OzlPIZVuam6e8ucOXcWSZO3o92cqyurTA02E9lY52wtkYqCFH6jscU6OAObOptX9sO0LKRZPu+JCubfK6wowISQVsOt01hY1Y77OROkmKGPFvCgQcRYNis1TsWuSygINCxW7R9+tsqNdd1SbmGUIg6SkVtR0faEUsel5PfPh2TYKC0I8lR3K4kxYLbnfT22CbnUopgEVKniAFyAiZNrbebb5t7CK0+iMORF2FEqch8PeUZ1r9QKBBEYeiCBFeRVIsmOR5b82SvF4fO+Ze+K6ViexMZn2azyfq6Ce+/tLTE2NgYy8vLaK3ZtWsXMzPTcdax+++/n9XVVUZGRjoiZ42OjjIxMcHayiJhYCJ6DQ30s7CwwNjYGHML84yPjHDy5Mk4KfP43j1MTV6L4kWWuHbpEq1GvYNDS5Y7hijIIrA3bXJBkvhuZOzOkGH2Z/kuZq+2Xl7AP9u6K0lQZNG7rkso6SGsjSHPEjHAcRwaUdTdpsjoQZRrwG9vJs9x0a6KA79KUFeRq2U8HMeJjWNCK2Sd4zhGq6A1WMZBQlRsDsEWd6Q4KHPCKky8xpg9uNV6U/4n8n4srnjtFG42ECn326pluSaxJIIgiJPI2gZQMl9J70epQ4q0UbAcpVSsxrQxDHuugiAg0G1xSSlju+F5XmyDEgTtTNW24Zr0V8ZE4k+mXC8+cIIwoFar4eRypNPpOFpYo2E4oEajEWfSHhoaolarMTExwerqKpOTkxw9fgzf9zl6/ATFUjd+qJmZm+fAgQN4nsfS0hIra+ssLC1TyLiUK5sQanKZNCnXY9fQIJNnnmFsdDeZYonhYZ8rU5P0jY+ya2iYWq3G6uIcSqk4UNDtyh1BFJSClGUtaANfNuW2T/DkqWSfIPYilWcI8bAtG4FYvSWLIolStxfnrVGH7RMxBrG8zlByjmr7PcREBB+8tgajXq/HYJYUexMC8UQGWqz+Og2KXEuuFmKQBASTHFeSWCRBwyQWsR1QZ5+k9oazn5N0Aku2JWknIWNqE+kOoibPsgiW1GckHYt7wVpDtI27zPMiLtKqM2g1b8EnbM7FXnehskRX2gCyzFWj0cBJtxPbyNh1dXXFfXJdl7GxMS5fvszAwACnT5+mr6+Pz3/+80xMTOA4DpVKJY47GYYh/UO72O3tZmV5ka6uEksLC1GIPheFw8LNmyilqFerPPvkk2ysreE6e+JoX61WA61v1bBJuUOIQhvBh04W3T79hbW3N2SShYdOpFxOpaQoIc8WIxW7Lba40S63stg2Yi+LJg58Em2UbCody+SxSKIcWrrTmlD05baTlai4HMfBS0enYiQz+6FhvUPdjgZlP0eebW9I6bcX9U/kcVvccJQigDi1W3I8tuMIkr75QoRtAut5XgzA2uMqOIjMhT2utk+CZIgSIu44DtUoVqXMeSqVIuUYgNCLiLgXxWbIZrP4EbvXCsz4tPzIDNtqj68N8Qis+fYck908DCMRMOWhNB1gXS6T7QgZuFHfMGOn24ZXpVIpdoXPZs39p0+fplqtUq5V6e3r49vf+Q6nTp1iYHCQb3372wwPDzM+Pk4lsn8Y37OHlZUlqtUyuwaHmJ2fZ9fQEEsrq4yMjjIzN0u5skWz2aTSgJ/6J/+Er332M1y4cI69e8ao1GukHBfPu32I9zsibVw2k9Z7hoc62EVhIcGKrKNudSSyF5DNRidlXMEQkqecrRqz9d1J2TKdageKlfaIWNLRbt/Ix3KSO5qOxam1Np6LqpMQAbFrtN1Ov96Mnmus5ZRrOIJmYIKv6Oj/2WjTiB9GtVqN7etFpIg3XcaozcQCTxycpH1yykmxLQ6Tp76w8RKgNgzDbaNNOY6DR1utLOKHPcZSZHxl09kilhAEgEYUL9LzPAPKptOkXc+oIbXhCpWmLSbqyIu1qwjA8tqqCT4TiXE2IZN6BaiGNoF1XZeM6xG0jGapWCyiAxO2zxPbDz8wmbhRjIyM0NPTE3MIMifSx1wuh6+hUqmwvr7OvhYVd38AAA/XSURBVH372NjY4J577uHZZ5/lrrvuYmxsjOeee46hoSHm56ZRaEZGhqmWN2k16hw5cICl5QUKmTTVqokbOTq+D9dVJiSg57GyssT+vftQDrzz136Li9fu4LRxrmNUeUm59VZwrNPIxmZD5R75TUoSAJJiy7s2WwttXbct18p3223YBiiTHI198oLJWtTmUnRsf2DXbwdVFaDKI4pQZHmS2uwyqm0rYW9QOVWT+IBSCi/y99DKAHNaRY7VBrbBxdwTbMMd2RxIUixJjrldhJVPGn3JRtxOJWyPvz1OybHOZDKxb4eiU8zwHLftyBXSQRzt+5Ki6nbrwi6+75Py2klrsmkTpTopCEl4PfF9EELoui49PT0sLS2ZpDI5k/n62Mm7mJqa4s1vfjNra2vsO3iIQqmbxZVVyrU66a0yynU4sG8fUy+9RL26SV9PL0sryxQKBaYmb3Dk0CGWlhbo7+1mdnaWI0eO8PzzZ+IDcM/4njsfU5DTE60xPj56W1zATbkdEyUcgpSkbGrLgHJf0gbC1qUnN2dHgFQ6ZUphkYWQCSrtppyO53oRMfAcK+6CE+DTKRpprWODJmGlfd+nr9RjTsO6CRbaaJnrfhBFFo7YZhFb5PSR+JJiKxGGYUwwgkicFA4g6f0pTk+1ZjsnY3I87TG04wwI2GljQrE4SKftgg3UClGwny/ci2xaux2e55GPwqF5nkcQhVpTkSu8g7mnVOxq2xi02r4sdqAaIUytVsukplPgug5aKUIFTioCvYFQRYZkITipduQvyR/qRxyuqFxFpLCBSjn4yuUy+XyesbExljaMuvOhhx4inU4zOjoat+nKlSvk83nGx8dN9OZ6i8nJl+jv7+P5qUnOnDnDQ/ffx4ED+1lYWOKRRx5haWWFG9eu8thjj/Hkk08yMb6HUqnEyOhurl+/Tub7yfvwahQZKLhV/dTxnuAekovudv+z77XvSf5HvouMbINcSRHDPom3e952JeYgXFCO2wEMJomUcCLlsslz6LiRFaSKuJ6wsy7bQnI7AM8WcfyWFeRWQELHiU7yWz0F7fG18Q6Zu+1O8lsxmfY82J/t4LM2lrTdnMhmE+tDHT2jXq+bFPdhGGtlVMTmC95ig9BBwnjHnjdbfLsdJyQco3C0MvY2mCz1Sn/klAbjUFUulxkeHmbv3r3U63WOn7iL7u5uvvDXX+Sxxx6j0fS5eOkKIyMjPPDgw1y9epUXXjyP67o88Lq7qVS3ePrpb1GtbLJreDeZXI5vf/vbPPSjD3Pp0hWGh4fxVMhTf/skW1tbPP/88+zfv590NsPy8nIHsb9lnd4JmEIundYHdu9GqbbdgO0vEJ/gTqetgs1GxwYtuhNdl3d7M0Nn4FAbiLtdcZTXQRSkTluk8DwPP2zFYoNSKk6OkvZSbT29Bu3e6rQjPh92e1o1437teCZnoZsyQTpqzYZJxgtRKvpbw9/DrZvYcRyajbafhIy1vRGE2yg3ah2cmP0Mew6ELRbOI8ldxL4WYad2R+Zb7ktyBbYIKf+ziadr1acwmEw2ZVD6TMoYRuWzubivlVqVWq1GiCEkwgk1o7UThiHK63TPT3KiMaaA4f5SqRRdXV1kUiYPBRGRzaRM3AyVSsdqTuEa8vk8xWKRbDZLs9lkYmKC/t176Ovrw/M8pqenjRt2lHRobm4OgNHRUUqlEi+c/Q6Xzl/gwMF9FHM5pqYmyaUcZm/eZO+ePezbu4discjMjSvs37+fWrNhIj7tP0i5WiGXK/CPf+FXuTZ1887FFELddtO1T4ukDKncToDLZldtim7Lz7C9hiL5PQl0yf+SrK59zXZSgkgV6iRUpnIKul7MViulaFhAqjzTDhkvv4mHXr0Z6fVTXrwB7T7b9gOyEW1sQ56rdZuFTrLnMp5JjKZjrhJE1YmAtduNa7IN9vzYxk/JZ9r/s2Vx4SxEdJJnyhi0MPYfSEStUMcbstYw/hJeuq3+vR1XI+3cToyVuc5lsrGnZT6KxGT3NQgCmq1qHCdR5rdYLFIul2m1Whw9ejQOqFOtGqJ1+PBhpqenmZ2dpVAoMDY2xtDQEOVymampKVqtFqOjo6ytreFi7HBajTo9PT3Mz8/z8EMPUK1WGR0dpV6vs7pugrM88CMP4m2mmJ6ewUvd6fEUFLRccByMTXnYDsuFbk9u3W/cslBFdk3KqslJttk6+x5bRZbEK+zroW472tjso0YTismoBqVv3UiO41DzmzihgxO0bgHb3AivcC3z5iizGw3d5oAqtSrlaiVmUyULcjqVphn4sRwrRjjSfuE+5N3NGbuMzc3NeHNIRChpb7PZpDubwfddGo0mrTBA+yEpz4TqFGZAKY9UqhNvSRJksf+gJb4KXsd8xT4MKvKMxcjt9vwGgeT3COI+Nfw29uM4Ls1Wy2gkHAc/DNDNRmzXUQtbsQWlaiSIJmbMlePQDMTQXpaeMSs33xVhCM2mj+84hFHo/YKr2KqUSaU8gmYTE/QmbVLmKYfA0bgqJHSVicKdy5LKZhgdHad/ZBdBoAkaPt2DRTzPY+rmDAPDwyxdusLh4/tIexme/rtv4tdaJixfVz8Te/uZn5vhwoUL9Ja6KFd90qk8u/aN87fffpGxsTHSbop02uPu+45w9oXn+MYzT5PLmAA5Ke8OT0UPbZbX1yb/oLGDt7AA3Zk0RN7FBkFYb8/zOmIzJDe3farYrGhHa26zwLcD2ZJlO6cqWwsg/7UJQ5urCA1hUO2EMUHkVyCoubRHnK+kLyKa2DYL4vMhbZLTqlFvR0hOjoXYAxQKBTxHRCQHNwwIU9BoNQmCED8+7RWaThEgOV7xWPnt6NHCoSS5OGlPklNMEvowDAl00DG2KVH7hp1OdUoZOwNoa5ZscaSDSIe3zl9ynqT+VkRo0AG+08B1HTKuRzodYQlBgPJMm/L5PMVSN1ob8/aW75vwbZUKmUzOsPszM/QODRiv0bSxdzh/4QJry2sM9Q9w7wP3MDc3R+A2+OY3v8n01CRjY2ORM1QfxUKBzc11VpdXcF2X0V39NBo1MpkMg4ODJhaFcFIvU+4QomCK1saHXxH5H2iLKIQax7vVXtvGC2zWVH5LssRStgMJ7UUi7bF/s+vcjrWW536vfW0/V7Jlg52bRohDTDQssSjZZkWb2AjR0NpoNOyYhHa/RTMBdOAjvu+TzWbJpMzGbbV8s1di7uZWkcsmANuNt9baOJ5ZLyFY23Fq24F90EnAbW5L6vU8z4CnYZv9B+KM1/aYJef05TAle77iZ9AGyR2lCUNDFKQdKIdMoUixWKSnpwcvnYmjXxeijF9CvJrNJr4OTYi1vn7CMCSfzzM1NYUKFccOH8HLpE0CmlyewcFB9u8zFo/PP/sMBw7so1arMTc3RyGXZ2tri0UnYGxsN7Ozs8zOzrJ3YsyIUbUazVbztn29I4iCUpasaa4AUVjyeHGATyf7bi8YSfqRBIfM89W2Cw7okPNt4hJvNqVuWXz291v7cuv17dSeHRvKGggZhxjAy7TRdjEOEkcq4SY8z2R0lv/YGYCS2gytNTo0YyDJZMIwjEPJgSEQPT09lIo5qtUq1WoNgnam5TD0Y0DNcRxC3clZ2UZASU4tyXlJxCJ7Lm1swRb3kv/NeJm4r1prClkTEblSLhO22hwBgON2+mrIM+3x9n0ftonZuR3xcBwnnjhDIHSHP4gZdye2idDaOMvVajXWNjbJ5fMMDAyxsbFBvd5kz/hehkZ2MbZvgoXFJZp+i30HD3D06FEeeuBhFhcWuHzuEqdPn+bM2acBYwPxzDPPcPDgQZaWFih1dbG6usrxh49RLBZ56folzp07x2OPvp63vvWtTN64GoeQf7lyR2gflFJLQIWXzTf5ipaB17DuH/b6f5j7/lrWP6G1HtzuhzuCKAAopb6jtb7vh63uH/b6f5j7fifUv135e+WS3Ck7Zaf8/7/sEIWdslN2Ske5k4jCf/khrfuHvf4f5r7fCfXfUu4YTGGn7JSdcmeUO4lT2Ck7ZafcAeU1JwpKqbcopS4ppa4qpd77KtU5qZR6QSn1nFLqO9G1PqXUF5VSV6L33h9gff9NKbWolHrRurZtfcqU/xiNx1ml1OlXoO73K6Vmov4/p5R6m/Xb/x7VfUkp9cT3U3f0vHGl1FeUUheUUueUUv8iuv6K9/9l6n5V+q+UyiqlvqWUej6q/19H1/cppZ6O+v5xpVQ6up6Jvl+Nft/7/dT/Dy5JY51X8wW4wDVgP5AGngeOvwr1TgIDiWsfBN4bfX4v8G9+gPW9HjgNvPjd6gPeBnweY8H1IPD0K1D3+4Hf2ube49EcZIB90dy432f9I8Dp6HMXcDmq5xXv/8vU/ar0P+pDMfqcAp6O+vQJ4Gej638M/Fr0+deBP44+/yzw8Vd6L2z3eq05hR8Brmqtr2utm8DHgHe8Rm15B/Dh6POHgR//QT1Ya/0UsPo91vcO4M+0Kd8EepRSIz/gum9X3gF8TGvd0FrfAK5i5ugfXLTWc1rrZ6PPW8AFYJRXof8vU/ftyg+0/1EfytHXVPTSwJuAT0bXk32XMfkk8Ji6nensK1hea6IwCkxb32/y8pP2gyoa+Bul1DNKqV+Jrg1rrefALCZg6BVuw+3qe7XG5H+N2PP/ZolKr2jdETt8L+bEfFX7n6gbXqX+K6VcpdRzwCLwRQz3sa61lnhodh1x/dHvG0D/91P/P6S81kRhOyr4aqhDflRrfRp4K/DPlFKvfxXq/F7LqzEm/xdwALgHmAP+7Stdt1KqCHwKeI/WevPlbv1Bt2Gbul+1/mutA631PcAYhus49jJ1vFb7oaO81kThJjBufR8DZl/pSrXWs9H7IvAXmMlaEDY1el98hZtxu/pe8THRWi9EizUE/ittFvkVqVsplcJsyj/XWn86uvyq9H+7ul/t/kd1rgNfxWAKPUop8Uqy64jrj37v5nsX/X5g5bUmCt8GDkVobBoDrvzVK1mhUqqglOqSz8CbgRejen8huu0XgL98JdvxMvX9FfDuCIV/ENgQNvsHVRIy+k9g+i91/2yEgu8DDgHf+j7rUsCfAhe01v/O+ukV7//t6n61+q+UGlRK9USfc8DjGFzjK8BPR7cl+y5j8tPA/6sj1PFVLa8FuplAaN+GQYWvAb/zKtS3H4MwPw+ckzoxstuXgSvRe98PsM7/B8OmtjCnwS/frj4MC/mfo/F4AbjvFaj7I9Gzz2IW4oh1/+9EdV8C3voD6PsjGBb4LPBc9Hrbq9H/l6n7Vek/cDdwJqrnReB3rTX4LQyQ+d+BTHQ9G32/Gv2+/5XeD9u9diwad8pO2Skd5bUWH3bKTtkpd1jZIQo7ZafslI6yQxR2yk7ZKR1lhyjslJ2yUzrKDlHYKTtlp3SUHaKwU3bKTukoO0Rhp+yUndJRdojCTtkpO6Wj/H/CPyt+k5pxYgAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Testing event\n", - "cat_image_url = 'https://s3.amazonaws.com/iguazio-sample-data/images/catanddog/cat.102.jpg'\n", - "response = requests.get(cat_image_url)\n", - "cat_image = response.content\n", - "img = Image.open(BytesIO(cat_image))\n", - "\n", - "print('Test image:')\n", - "plt.imshow(img)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Define Function specifications" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from mlrun import mlconf\n", - "import os\n", - "\n", - "# Model Server variables\n", - "model_class = 'TFModel'\n", - "model_name = 'cat_vs_dog_tfv1' # Define for later use in tests\n", - "models = {model_name: os.path.join(mlconf.artifact_path, 'tf1/cats_n_dogs.h5')}\n", - "\n", - "# Specific model variables\n", - "function_envs = {\n", - " 'IMAGE_HEIGHT': 128,\n", - " 'IMAGE_WIDTH': 128,\n", - " 'classes_map': os.path.join(mlconf.artifact_path, 'categories_map.json')\n", - "}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Deploy the serving function to the cluster" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from mlrun import new_model_server, mount_v3io" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[mlrun] 2020-05-04 21:22:18,924 function spec saved to path: function.yaml\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Setup the model server function\n", - "fn = new_model_server('tf1-serving', \n", - " model_class=model_class,\n", - " models=models)\n", - "fn.set_envs(function_envs)\n", - "fn.spec.description = \"tf1 image classification server\"\n", - "fn.metadata.categories = ['serving', 'dl']\n", - "fn.metadata.labels = {'author': 'yaronh'}\n", - "fn.export(\"function.yaml\")" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "if \"V3IO_HOME\" in list(os.environ):\n", - " from mlrun import mount_v3io\n", - " fn.apply(mount_v3io())\n", - "else:\n", - " # is you set up mlrun using the instructions at\n", - " # https://github.com/mlrun/mlrun/blob/master/hack/local/README.md\n", - " from mlrun.platforms import mount_pvc\n", - " fn.apply(mount_pvc('nfsvol', 'nfsvol', '/home/joyan/data'))" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[mlrun] 2020-04-30 20:52:15,886 deploy started\n", - "[nuclio] 2020-04-30 20:53:46,385 (info) Build complete\n", - "[nuclio] 2020-04-30 20:53:56,566 (info) Function deploy complete\n", - "[nuclio] 2020-04-30 20:53:56,573 done updating tensorflow-v1-2layers, function address: 3.135.130.246:30961\n" - ] - } - ], - "source": [ - "# Deploy the model server\n", - "addr = fn.deploy(project='cat-and-dog-servers')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Test the deployed function on the cluster" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Test the deployed function (with URL)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Sending event: {\"data_url\": \"https://s3.amazonaws.com/iguazio-sample-data/images/catanddog/cat.102.jpg\"}\n" - ] - }, - { - "data": { - "text/plain": [ - "b'[0.0]'" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# URL event\n", - "event_body = json.dumps({\"data_url\": cat_image_url})\n", - "print(f'Sending event: {event_body}')\n", - "\n", - "headers = {'Content-type': 'application/json'}\n", - "response = requests.post(url=addr + f'/{model_name}/predict', data=event_body, headers=headers)\n", - "response.content" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Test the deployed function (with Jpeg Image)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Sending image from https://s3.amazonaws.com/iguazio-sample-data/images/catanddog/cat.102.jpg\n" - ] - }, - { - "data": { - "text/plain": [ - "b'[0.0]'" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# URL event\n", - "event_body = cat_image\n", - "print(f'Sending image from {cat_image_url}')\n", - "plt.imshow(img)\n", - "\n", - "headers = {'Content-type': 'image/jpeg'}\n", - "response = requests.post(url=addr + f'/{model_name}/predict/', data=event_body, headers=headers)\n", - "response.content" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/tf1_serving/tf1_serving.py b/tf1_serving/tf1_serving.py deleted file mode 100644 index d9816c684..000000000 --- a/tf1_serving/tf1_serving.py +++ /dev/null @@ -1,87 +0,0 @@ -# Copyright 2019 Iguazio -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# Generated by nuclio.export.NuclioExporter - -import warnings - -warnings.simplefilter(action="ignore", category=FutureWarning) - -import json -import numpy as np -import requests -from tensorflow import keras -from keras.models import load_model -from keras.preprocessing import image -from keras.preprocessing.image import load_img -from os import environ, path -from PIL import Image -from io import BytesIO -from urllib.request import urlopen -import mlrun - - -class TFModel(mlrun.runtimes.MLModelServer): - def __init__(self, name: str, model_dir: str): - super().__init__(name, model_dir) - - self.IMAGE_WIDTH = int(environ.get("IMAGE_WIDTH", "128")) - self.IMAGE_HEIGHT = int(environ.get("IMAGE_HEIGHT", "128")) - self.classes = None - try: - with open(environ["classes_map"], "r") as f: - self.classes = json.load(f) - except: - pass - - def load(self): - model_file, extra_data = self.get_model(".h5") - self.model = load_model(open(model_file, "rb")) - - def preprocess(self, body): - try: - output = {"instances": []} - instances = body.get("instances", []) - for byte_image in instances: - img = Image.open(byte_image) - img = img.resize((self.IMAGE_WIDTH, self.IMAGE_HEIGHT)) - - x = image.img_to_array(img) - x = np.expand_dims(x, axis=0) - output["instances"].append(x) - - output["instances"] = [np.vstack(output["instances"])] - return output - except: - raise Exception(f"received: {body}") - - def predict(self, data): - images = data.get("instances", []) - - predicted_probability = self.model.predict(images) - - return predicted_probability - - def postprocess(self, predicted_probability): - if self.classes: - predicted_classes = np.around(predicted_probability, 1).tolist()[0] - predicted_probabilities = predicted_probability.tolist()[0] - return { - "prediction": [ - self.classes[str(int(cls))] for cls in predicted_classes - ], - f'{self.classes["1"]}-probability': predicted_probabilities, - } - else: - return predicted_probability.tolist()[0] diff --git a/tf2_serving_v2/function.yaml b/tf2_serving_v2/function.yaml deleted file mode 100644 index 4dbe9f3fc..000000000 --- a/tf2_serving_v2/function.yaml +++ /dev/null @@ -1,45 +0,0 @@ -kind: serving -metadata: - name: tf2-serving-v2 - tag: '' - hash: 8748deb1d9804f9b436c913322c84d5b46c82bd9 - project: '' - labels: - author: yaronh - categories: - - model-serving - - machine-learning -spec: - command: '' - args: [] - image: mlrun/mlrun - description: tf2 image classification server v2 - min_replicas: 1 - max_replicas: 4 - env: [] - base_spec: - apiVersion: nuclio.io/v1 - kind: Function - metadata: - name: tf2-serving-v2 - labels: {} - annotations: - nuclio.io/generated_by: function generated from /home/kali/functions/tf2_serving_v2/tf2_serving_v2.py - spec: - runtime: python:3.6 - handler: tf2_serving_v2:handler - env: [] - volumes: [] - build: - commands: [] - noBaseImagesPull: true - functionSourceCode: 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 - source: '' - function_kind: serving_v2 - build: - commands: - - python -m pip install requests pillow tensorflow>=2.1 - code_origin: https://github.com/daniels290813/functions.git#55a79c32be5d233cc11efcf40cd3edbe309bfdef:/home/kali/functions/tf2_serving_v2/tf2_serving_v2.py - secret_sources: [] - affinity: null -verbose: false diff --git a/tf2_serving_v2/item.yaml b/tf2_serving_v2/item.yaml deleted file mode 100644 index dc7640b0b..000000000 --- a/tf2_serving_v2/item.yaml +++ /dev/null @@ -1,28 +0,0 @@ -apiVersion: v1 -categories: -- model-serving -- machine-learning -description: tf2 image classification server v2 -doc: '' -example: tf2_serving_v2.ipynb -generationDate: 2022-08-28:17-25 -hidden: false -icon: '' -labels: - author: yaronh -maintainers: [] -marketplaceType: '' -mlrunVersion: 1.1.0 -name: tf2-serving-v2 -platformVersion: 3.5.0 -spec: - filename: tf2_serving_v2.py - handler: handler - image: mlrun/mlrun - kind: serving - requirements: - - requests - - pillow - - tensorflow>=2.1 -url: '' -version: 1.1.0 diff --git a/tf2_serving_v2/requirements.txt b/tf2_serving_v2/requirements.txt deleted file mode 100644 index 8d3d19557..000000000 --- a/tf2_serving_v2/requirements.txt +++ /dev/null @@ -1,2 +0,0 @@ -pillow -tensorflow \ No newline at end of file diff --git a/tf2_serving_v2/tf2_serving_v2.ipynb b/tf2_serving_v2/tf2_serving_v2.ipynb deleted file mode 100644 index 6a15b11a4..000000000 --- a/tf2_serving_v2/tf2_serving_v2.ipynb +++ /dev/null @@ -1,545 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Image Classification Model - Serving Function\n", - "\n", - "This notebook demonstrates how to deploy a Tensorflow model using MLRun & Nuclio.\n", - "\n", - "**In this notebook you will:**\n", - "* Write a Tensorflow-Model class to load and predict on the incoming data\n", - "* Deploy the model as a serverless function\n", - "* Invoke the serving endpoint with data as:\n", - " * URLs to images hosted on S3\n", - " * Direct image send\n", - " \n", - "**Steps:** \n", - "* [Define Nuclio function](#Define-Nuclio-function) \n", - " * [Install dependencies and set config](#Install-dependencies-and-set-config) \n", - " * [Model serving class](#Model-Serving-Class) \n", - "* [Deploy the serving function to the cluster](#Deploy-the-serving-function-to-the-cluster) \n", - "* [Define test parameters](#Define-test-parameters)\n", - "* [Test the deployed function on the cluster](#Test-the-deployed-function-on-the-cluster)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Define Nuclio Function" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To use the magic commands for deploying this jupyter notebook as a nuclio function we must first import nuclio \n", - "Since we do not want to import nuclio in the actual function, the comment annotation `nuclio: ignore` is used. This marks the cell for nuclio, telling it to ignore the cell's values when building the function." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The history saving thread hit an unexpected error (DatabaseError('database disk image is malformed')).History will not be written to the database.\n" - ] - } - ], - "source": [ - "# nuclio: ignore\n", - "import nuclio" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Install dependencies and set config\n", - "> Note: Since tensorflow is being pulled from the baseimage it is not directly installed as a build command.\n", - "If it is not installed on your system please uninstall and install using the line: `pip install tensorflow`" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "%nuclio: setting kind to 'serving'\n", - "%nuclio: setting spec.build.baseImage to 'mlrun/mlrun'\n" - ] - } - ], - "source": [ - "%nuclio config kind=\"serving\"\n", - "\n", - "# tensorflow 2 use the default serving image (or the mlrun/ml-models for a faster build)\n", - "\n", - "%nuclio config spec.build.baseImage = \"mlrun/mlrun\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Since we are using packages which are not surely installed on our baseimage, or want to verify that a specific version of the package will be installed we use the `%nuclio cmd` annotation. \n", - ">`%nuclio cmd` works both locally and during deployment by default, but can be set with `-c` flag to only run the commands while deploying or `-l` to set the variable for the local environment only." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "%%nuclio cmd -c\n", - "pip install tensorflow>=2.1\n", - "pip install requests pillow" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Function Code" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "import warnings\n", - "warnings.simplefilter(action=\"ignore\", category=FutureWarning)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2021-01-29 23:47:50,165 [warning] Failed resolving version info. Ignoring and using defaults\n", - "> 2021-01-29 23:47:51,342 [warning] Unable to parse server or client version. Assuming compatible: {'server_version': '0.6.0-rc9', 'client_version': 'unstable'}\n" - ] - } - ], - "source": [ - "import json\n", - "import numpy as np\n", - "import requests\n", - "from tensorflow import keras\n", - "from tensorflow.keras.models import load_model\n", - "from tensorflow.keras.preprocessing import image\n", - "from tensorflow.keras.preprocessing.image import load_img\n", - "from os import environ, path\n", - "from PIL import Image\n", - "from io import BytesIO\n", - "from urllib.request import urlopen\n", - "import mlrun" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Model Serving Class" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We define the `TFModel` class which we will use to define data handling and prediction of our model. \n", - "\n", - "The class should consist of:\n", - "* `__init__(name, model_dir)` - Setup the internal parameters\n", - "* `load(self)` - How to load the model and broadcast it's ready for prediction\n", - "* `preprocess(self, body)` - How to handle the incoming event, forming the request to an `{'instances': []}` dictionary as requested by the protocol\n", - "* `predict(self, data)` - Receives and `{'instances': []}` and returns the model's prediction as a list\n", - "* `postprocess(self, data)` - Does any additional processing needed on the predictions." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "class TFModel(mlrun.serving.V2ModelServer):\n", - "\n", - " def load(self):\n", - " self.IMAGE_WIDTH = int(environ.get('IMAGE_WIDTH', '128'))\n", - " self.IMAGE_HEIGHT = int(environ.get('IMAGE_HEIGHT', '128'))\n", - " \n", - " try:\n", - " with open(environ['classes_map'], 'r') as f:\n", - " self.classes = json.load(f)\n", - " except:\n", - " self.classes = None\n", - " \n", - " model_file, extra_data = self.get_model('.h5')\n", - " self.model = load_model(model_file)\n", - " \n", - " def preprocess(self, body, operation):\n", - " try:\n", - " output = {'inputs': []}\n", - " inputs = body.get('inputs', [])\n", - " for byte_image in inputs:\n", - " img = Image.open(byte_image)\n", - " img = img.resize((self.IMAGE_WIDTH, self.IMAGE_HEIGHT))\n", - "\n", - " # Load image\n", - " x = image.img_to_array(img)\n", - " x = np.expand_dims(x, axis=0)\n", - " output['inputs'].append(x)\n", - " \n", - " # Format inputs list\n", - " output['inputs'] = [np.vstack(output['inputs'])]\n", - " return output\n", - " except:\n", - " raise Exception(f'received: {body}')\n", - " \n", - "\n", - " def predict(self, data):\n", - " images = data.get('inputs', [])\n", - "\n", - " # Predict\n", - " predicted_probability = self.model.predict(images)\n", - "\n", - " # return prediction\n", - " return predicted_probability.tolist()[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To let our nuclio builder know that our function code ends at this point we will use the comment annotation `nuclio: end-code`. \n", - "\n", - "Any new cell from now on will be treated as if a `nuclio: ignore` comment was set, and will not be added to the funcion." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# nuclio: end-code" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Test the function locally" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Make sure your local TF / Keras version is the same as pulled in the nuclio image for accurate testing\n", - "\n", - "Set the served models and their file paths using: `SERVING_MODEL_ = `\n", - "\n", - "> Note: this notebook assumes the model and categories are under /User/mlrun/examples/" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "from PIL import Image\n", - "from io import BytesIO\n", - "import matplotlib.pyplot as plt\n", - "import os" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Define test parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Test image:\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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+0lit1+ug0AR/2263GGPCusk6iEyK5yiylec5WZYFg/dd471QCm6OTYEQy8mGEYtrrQ2aF55SixJuXGYFLl05D3CZZzl6SSPVdc1i4UMQuTcQlNP19TXH45HHx8cASEmuXjaKuOIiGLJhBfSR55mmkbZ9CjfG0dL3fXjX1WrFOI4hg1GWZXB1x9G77OJalmUZwgtJfWqtubq6wjkX4nVRLMAzjyJNUxaLBWVZhjkRDOJ4PM4Iuw7KVNxrUbzH45GXL1+yXC69YiIOm6Tve9brdVBacRyx2+2DMhS+hZ+DIcTc8m7jOIJK59x9C6rn7ds7jqcT57ICrajKmmEc6dqGrnUMQ8/kJoa+R2uH0YosibynYAwmMmjl51TkSTa7YDXiFcg6X252MRSXYaV4T6JgJEshnsM0TcHyH49H6rrmzZs3wbDI31x6GsKHkWcB76WJhyByKV6OyFqSJAHjUkqxWq0C2C3YmyiCSw/mu8Z7oRS0NuElgJDrFYsnGAMQNPE0TSF+FssvExVHEZExnM5nMknHtS191xHFMVZr+rbldD5Tl564xDSSpRl11WOtAjewe7zziG+W4CaNGyeWhU9ZNXWDsZphHBi6nsRGWKVxdJxP55C2mqaeqqyYxok4iVkUC4Q5KDiIWA5RVPI75xzWONyMWDP12CgmSyyLfI3SiqEfiAzYKA6urWQXZC6t9Qooz3PiyDL2DUPvSCKLURqmnjQ2MGnaqadrG9IkBZMxTVB2NZGNsCoiiTL65sT5UPrNrw3aTlhAaa+klB7phxrUQNuWFEXCMA5MfePfxSoiC1lmGcYJpQc2Vwv6wVFVLV0Lp1PD/nDCOXjz9g390FPVFWVVMjrPQG3agckpNI727cDjfk/X93z86iMWRYo1CUmcoI0mXWRMZlb4yxxnJsrziW6oWBQ51kazt+S9K1G24kWkaUqe5wETULNS66eRSUHdtZhxINMZSe6t8aQgjb0sn06nQFoSzEA8w8ViETy1uq4DUC1hp4S8wsURYFrSm4JZtG0bMhZt12HjGJskwTPrnaM+nWjmbN53jfdEKaiAhstCBCILPg4XpFVSeKKtgTCh4s51bUtZlmzWa7bbLUVR8MUXXwT3EPxmieaFzfP1U9wVRd56dW1we62NMPrJuiggjr0VFovv4/kOpSGKvIsoaHVVVVR9Rde29LOyE48hy7IgBLe3t8GrkXDHaI2a33GMInDe60jTOceuuzkmfQqzxOO5TAmKEE7TRN/5bE3ZnUmShKurK87nM+PoMwVRZzmfSrp2fApXMm/hszRDKz27ojnTNKKsnw9xn9u2IU0TtFagvLLI4pRIGbquJc1ilquC0U10fYcylslN9MNI0zb0bcRuv+dwOPLmzRu6vmEYR+rGs/XGoWcYemyU0Q8j4zRRNzVdV6OmCQVE1hBZPw/KKvLNgjiJ/UbKMvq+pe8i3ORp02nqler51IbQQt6nrp/4GiEtOY3oQVLMTx6hm2UrmUHh0+kULL1kx8S9b5omGMBLQplgLpfPcAk2ZlkWQhzJnAkeJEqkrCrKh4fAq1AzzqSNIZ7l77vGe6EUpskF4EqQbnHjtNY8PDw8Q4olLhMPYrFYhNis73sUcHV1FRhrct0XL14Ea3xJZ75kLkrsL26hTF7fNzP/309sFGmU1igsbavo+g5j/HeLogjutgiDkFBkk4q7LAoijmO2222gvEpuWZSYCJ6kliSPDd41bLsRhw44g4QFglFIPKqVo5ytndz/0pVer9ez0u2Joyyg9peuv4QJHj+JcfopE3PJ9hP8RbgRbphoWs8zcMz1B1pjTUTTdDTtyPlUsd93PD4+UlUlb9++JS/8BuvalvVmwzD4MCbNUtpuwE0T1mjc5MPJ29u3xJElT1OKYoGyiq7taNue87nky19/ibWazXpNXVXewPQ94zhR10OYM1Gm1trglQaindGBSyJZFfEIhJHrnCOy0bMUtYRuT9hNGxSKhGpiFAUYFH7CZcg6DEO4h6Q3F4tFABGTJAHzlBWSawIh1P6u8V4ohWEYuL29xVrL8XgMpKC3b9+GeF0EW8IKUQJiUYuiCOQM5tjZGMN2uw3km7quQ25+GAY2m00Ad47HI5vNJngfl6k/Y/ScKvTMvSe2mqXrelCeaef/RoV7y7UERMqyjPP5TFmWRBdEH1kwYSVK7hmgnkHEoiiekZSAYLX6vsdhAigqqS7ZlJc1CZdsyaurK6Io4nw+h+8K5nJ1dUWaFDw+PgagVGJfmZuQ2o004+TrJYyxxHFC348hNWstHhcBUJp+GGi7niT1QG/Xjzigqhoed0du3+6oqoY3b17PnpSPu682nuBV1SWME5GxpAufLZlG7z12TcN+fwDnWXNpkrLerhhmTCWOE47HE9vNGqV8UdfQt4zjAPifxfAI+3G5XIYMgXyeRDF56nGqrmkxStE7GPseDSjniIwFOwZGq8z/OI7ek91snhHuJFUqSkYUziWv5ZKkJ0pCMkMicwKCX2+3gS5t59BaFF3/x4QQ74VSgCeqs4BZMjkCRAkFWHLfQlCSTIBY+CiKSGdrdj6fgyJ4fHykbdtAQhKrKKMsy0BWEc/i008/RWvN27dvGIbOp7fGHufG2XrUF2yxKwAfE88ehqT3BBmOoojT6RRCo2EYQoGOKAeh2YrWn2ayjzA0BUi6zKFnWQbKUlZ1yMVfIugCWOZ5TpbGRFYHVPqyNuF4PNK2Lev1OmRbBOUWOrlwOQQYU1pRxAXGKMZxYhy7kOmQ9Twejx7onb04ZUCfz9g4JssLTucWo2PK6sjt7QP7/SlQojebDW1bM3Q9280GrRXHfce6WOCUIkpTtPL1Id3kQsVjWZb88suv0CbiN376Y1599pLBzrT1KEbh2O0Ovmiq7XDOez1iReUdRcbEc7TWst1uqZuGZvYucY62aXGxI008L6TvvJJiVrZCOxdFLt6opCWdc1xfXwd5FAUgKWwBhb/JstT6qUDrEoObpolpHHFzODWNI+0Muv9xFGd4T5SCCLekVwQYk7FcLomiiLquKcsyxFeSlpSJAu+6d20bGGuST5ZUobi+l4w2IRzJvfq+53Q68fbtW1ar1Uy1rgPNN4qiUNAirn+e57NrXwVQr21b9vsn5H2xWIRCK6cUh8MheEaXRUYy0jSlWCwoZqVSlmWwKKJEQtbFxD6fP6fDBLyUasTAaXC+UlDqGc7nc4hR5f/+GRQ4EwRNvBX5d1mWAb/out6XIGtF1/U+xNIaa2P6vmMYJk6nM3E8M+lGR9P2PO4OoA1ZvmAYFH0/cf+wZ5pgsVzyuHvwsqENRZajnGNoBxIbkSUpURwzTCNd37PIM4wCN83emjVUZc0Xv/wVTsPVyy3G2DlDNNC1LVV5whofvogMdd30jAkrALhYXMGt7PzuVVmyXCyelagLBtRfhKhCYBOFKRkdr/TaEGJK1argA5ebXHgo4glIxkOMqHiQPnzs6fsONw1M44DREctFQVmVRFaTptl37sf3Ril89NFHlGUZSqe/LfYSt0pcYSHsSFpHqt3i2ZuQ2FuYXSLMl6XZdV1TVZVH5udFkDSUEKfO5xNNU8+bvwjXjKKYOI6COyhZlOPxyP39fXD3tfaW7JLnoI0vt5baCEmZijUKYcWcTRDaqoQHomhCyDD5KsJLctIlaCsYgNYWowhkL1EsElKIgC6KBXnu3Vnh0EvV6WU1YNd1HoGfJsbRhazHYrEM+INSekbVE/qxm/kgI3QDZXUmjnuMzfijP/o1Xe/I0oR+6Fmt1r6oLFsTaYPGM0pXxYLNek3Xd7S9I84ylNYMfU+R+dT2qSxxDuq64Ve/+jXFKuPF9Q1FkZPEXglExqA19F1HEkdzaDiFGP1SEQoHQGRmmLMBsmlFviTe72euwSXwe8lfGYaB+/v7IMOSDpaQU2p+BDwWr3e5XIb1yrIspIXFaAo2BoosS1gul5RlOV+7J57xHSnj/7bxnigFFejKAgheVgPKRhItKyQX2VQS70lKcoBnfyOxFBCUziUB6nA4hPhMricx+TA3tUiSbI75YRhGrE1m5ZWjUDRNT9t6AE9AoUsOwSUTs2ka9OySyqa9pDmLFZmmiW4OGYSHIKlY8aaEyOIA58bwHt+ck4eHhxngLFBuDNeXyj8p+JEwzIdATSjgAcJmkZBFuCXHc8luv6NtfJwaJzF5VqC1Yr87UJYVWZqxXK1ou4Z+6Gm7jvJ4xJiEmxcf8Q9//kuStGCaHrl+8ZLj4ZHVekXfdmzWa/I05f7tG9I4YZHlpHHCerXgXHu3+lzWKAer5RIHtP1A1/UM40hZ1rx+/ZYi8+uyWa9IEzxIOo2M40RZ1rNRIIRJApqu12vSNA0g4jAMvLu9w2qNc9DblgZF17WMg5+fxEYweqWyvboKIe6lVyqZC5FVAQIvy+llvsWT+SavQTxCqcgVXKprG6ax57B7IMsyrjZrjscDE440tmRp/J378b1QCtOsfcHXsS8WixBniSadpokXL16QJAmn04lPP/0U5xxfffVVcJHF0vftE2tO6tovyRuXfIDNZhMq9oTqKtZXrOc4TsRRyjhAO3nUP1/nlGVJU/fBXTcmJkl8mvGyIAkIKHzo9zCnp4RNKMQTIdYI3TlJEnBP3H0RShHYpwYeNpBhgIBSSwwp3/Nx/RQqTzebDXEch3BMuPKPj7vgRYkChSewVhRPMdfwy30k/XZZlOYm/x5xmmAiizaK4+lEuz/SDwP7/Ylf/OEXZPmGNC2Ioph+6Nksl0TLFatFgRtHPn71MX3XkkYR282WbmxJxxjiGDd5L0WbiAnYrrfsTyemqsZY38jkcDiyXq+IosT3J1IaayOsjairCmOmADReepaPj4/PQF5jDGmSMI1TwLFwjnF4qsIVoHAcpyBP0i9DUouXGTSZXwlZFosF19fXz0hqsmYy1xLOSPGc3NPjV4a2LhnH4ZlHKAo9et+zD8453r59G9I/oqUFCRcBvyRniMYV5p7WOpBO1ut1oIrK9WRI3vmyLkAyGcId/yalN0kShn5imkAp3xcgjkeyLA+hQ9eV87VUsPbyvAIYiSu+2WxQM/gnRTJSBi2uudz7dDqhleJqtjYSy18qDa01KI2ZFYN4GhIXi3sr2YUi9wDoN+vwhQa7WCxYLla8fn0bnl3CBaGPC/BYVxVp5tmfTwzMMTz7OI4XhWEdw+i7EiltsMaQL9b8nz/7Geey5nd+53cxOgHd8fLlS7SDNI5ZLBYs0pS2rhi6nmlOISZJhCOdLawiihNG570EbSKiJKVpW6q6pCrPfPHFF/z4xz/mpz/5DYzWpElMnvneBicbkSQx0UwCE0p4URS8ffuW0+nE1ZUHkyW1eAnSitcpcisclKr1PTQE+7mkP4sREzxNiGfSD8G554VclzjDpQyLwWtnfg54nkYceYUjoYisi/zdd433QilY4+sRjscjfd/z7t27kGYU90hAFfmOpCulUlIQf1mYy1TNdrsNuXsZogjE8orLLJMdRVEoS66qGmMjpmnAGsvEyPHsF7ooCrLCcyymccLaBKMtSexjUGsNfd+htaFtK5zrAeMBMRdTns8M/UCRp6wWhU9tDr4yMoojmDGO7XbL8XgMHAapMRDLMIw9bhxQ2ruim+2KceqJZnAvjmPqqkYrAroOTz0DpclMqEvAkRUF1po5y2DoxwETWdL8KSYVJVre+c4/i2URrKNjomkbbm5ufNuwumFZLLFacz6cWGcL+rYnMREvrl9wPJ1xxqINLLOUoWl4eX3NDz9+xeuvvqRvGnAjJlakWUSW50zHCa19eJOnKX0/0LYdLS0qtrDMedjD211DW3VU54ZxND50UIaqaWjrM7F1aNexWl2R5xn39w+kaQSMZFlMHBus1bRtg3OeEyDzJ/iWKF1REJvNhmoGDq21rBZLwPHu3T2RjWByJFHM6XRkgqAwJIwVeQZCZbBwT8QwinEUT1AM6jhNNO3EhMaamGLhM1/9ONB0A+3j4bv345/Xxv6zDGM0n3zyCev1muPxGBqaCFBWFEUA6g6HQyghFvdYyB9iwaY53jLGhHBAylxlI0s14/l8DmQP0eSCXex2u4AKM82dlyLDOA2zq6pAOWxksINhUL5Rp5rd0rbzrp1S/h2H0a5uZAgAACAASURBVG8UbeyMdDd0XUtdNTNu4YVaa0MSp1RlNZOiTECcV6tVUGjC2S/L0mMU1mCsJp7bqEWxpW2bOS2pub65Ik0ShrYL4GogFs0kGAFrp8mBAmMtahhQWs3FRsZ3VZ2BrnGaaGdST1VVF/UEal6PBHD0fcc4Qdv2tNPANPpipZ//4R9SNgOr5Q1oTVbkRJHjdNjxkx/+iI9fXvPrX/yCsWtJ4pi2LbE2IslihnFgvd54l7yq6duWMRrI45jjNFLOhCM9DCzSguP5xPlcsd8fWK2WaANtfcCqiSw2VF1Lki+IY4u1vimt1orr66uA46TpinGcGNqn7lkSLklfD+E1iPf3zdL5yFqSOeVrtKZtWqqm9tmUeV2ltkIpFWpqJFQQb0K8OjEUAnIKjwdAKUOc+PoLlCbPFzinOM0Etm8b74VSEEKQuFJFUVAUBb/5m7/J8Xjk7du3Ie8ucbpMnFCRBQk/HA708yKIGwaEybvUtKIIhMwkE3uZ/YC5R8CczhTyStM0z/r7FUXBcrGkrVt2ux3n8sQw9Dg3EccRUWRJo5RxfCpD1lpzc33NMTpzPpdIlBPPDTratsUxhfcSF1LSmhJeaa3R1vKwe3yiwqYpH796xVdffx1AxM3MVjzMBVPidoogCa+i73varifLl8/CL+HpS/9CqcAUZSCUXkHlLzv/OOeoBkeeZ7hhoC4rXJrx8PjI7bs9/9hfekESxzzcvqNrT3z+g0/JspT//X/939gUGSqyNGWDjSKKRTFjL4qb6xfUdU0cRdRlxdD3TKPP0Q8zrhNFjkXkN9w0Dnz99Vc0zTXX1xu0sRRZjGGkH7whuCRpCZVYeAKBfj/qIEcif2KcZH6kL4a0oJMemEAoPAvl0oCem9qItyshhBi7SyaukKpkXYTkJyQ+8bYFJJZ0qHgyItvfNt4LpeDcU2NJiamttXz11Vd0XcfNzU1I9YkQi6CJovDcfV9lmCaJ78YzI++XsbdoU4mn5VpCRZVYUGrf+74nSX1sKlpasglCIpFqR+ccifXu3zD2WGvmEuPWK6u+A/WkEPwiLlBosizldDozDN4dFhpzkkR0fRfuIZ9L3z9J107OEVuPRfStL8rSWgcXtes6Xn/92j/j/K5AoO5KeCXvVJUlddOH1KXMTZqmITU6DEOYD0HXBVATBSrKa5omojhBOUeeF2RJyu3bW97dP+KU8V7i+cQnrz7iiy92/LV/6p/g9//e3+PHn33G490tQ+tDh812zWa7xc6bSzgA2fxcCl8v8jgDtOM40nQ9UaS5udqirOX2zVvquibPU662C5wb6YeBoR+py0PwCqQobblcstlsaJomNE5Jowxro5AalEyTbDaplxD5u6Q5eyD3MeBRWZZh44jJPTWKFY9WmKSCt0n68XQ6hUyDAJCX2IPQ3cWDk1IAqc78/4FScIHaK5tMOAZSHi3luDKpwmkQwZSX7vueaRiIZzBHYrNLltol2CMMNcElpmlivV4/IzdlWcZipkPL80j6RykVYjrgol/BicfdI4fDnjzPqKqSpq2x9qkRp3OOw/5InhfkuXQ+cvQztjFNE23XYO1T41lBqaVNunRpbtrWA3d5Htz5JI559eoVVVkSzy5llqa0F9x5UbJ5ngdFmaYp1zeWyXnlJaW2onyFRCbzKEVd2+02hCEyj8LB6PueEYjimDhOqM4VX379hn6cePXxpygFWZry5uuv+Mt/8S/wxT/8OR/dXLN/fIBposhzuq6m7zvO55KJCWsiSl3NGYCYZVFgtMFNvozaSirv7VuOVUU/jiRZxuF4pGlq1qsFi+JzEqtYLldMbqLuvFsdzwBnVVU8PDw8a8Xmra4O3hA8pTElpBCleNkNXDa8rKOkIdfrtS/qapvwXcHOBFi8TK9vt9vANhUsTTJsopCB0PNTDKF4GuLRfNd4P5QCBLdNhGy73bLb7cKkX2pFIeIAAeEWF26xWDAOAw/39wGoFJbeZessIDDLpEpN2o8LliDNLeI4ZpyRYVl0ASGl7wH4zMb+sCeOYtIsYzks0Vrx6tXLuR33kf1h9yz9p6O5S1HXMgwjbdvTm4Gq8gsZxZYoss/iTFF0cl9RoEopxjgmmj+Xis+6qkjimPVyyc3NDafTibu7u6AE5Xkui8PsnBYTZFxCAcloXHIc4KkvoXgzm80mrFnoZzGOFIsFp9OZx92B23cP5IsVcZKRJClJ4kle29WCX//yF+wfHtguVxBFdG1N2zSM48DpfGZyI3GUsCh8SnS5WJDGia950Bq2W6KZy7FZrTz6Po0wjsTWsjse+frrr1ktc642S4o8JY5T0nQI4ZIAz13XhbShGAOr4rDBZC2kFucytAWeVTXKMQRyD2lyM82HuAhT9nJdLolq0zSF4sCHh4egqKTkOs/zEDJfcn1k3URehCvxbeP9UApzbhaeDtSQw1UuhU0stUz+ZWNNwQv6vp+bbeiAzEpO9zIHLI1QRLOKGyoNT2QzrFYrtDGMs+aXOFNaXQnJJEkSutZvsp6efujmzePQ2vDixQuKRc44DYG04htuGpq6nd1PsLbDGAtu7pgcW7R+3r2nruvQqESwGBGyvusx1p961NQe+FoUBeMwsHvc8eWXXwbSkQifuLGXVNk0SUNRVnLhdUmsfJndAUL4IesitGo5i2C9XlOejxyOJ06nMz//h7/g9eu3fPTJZ7y+veOv/dN/nYf7e37yo8/ZP94ztC1XmzXV6UR1PFHkOcvVkn4+kcpEEeOMqfi43oeHisuTs3wG68XNDV3XUAwT6XJJP4ygFGM/UlcNVRzx9ddv0AqKZfEMqLu6unpWaJbnucdMep41KxHPDwjcAwntpLtTwH9mfsww+PNKjDEkWcrheAxhiMjkZRpROnULhiMH8UgXrEvjJPe6DHMvM3CisL5tvBdKAVRwNaVi8bI1mGAAkn8XxSANJyRXHPr4a008x11AEF6xhk8NR13gKch5C7JRhLhyd3eHA1azxyAhRVVVzyouBVzKYh+GPO4eUAoWi4Kq8qHO5MbgDoLEgJYoimfhG4giPwdaeXdRG0WWpcHbEcBRlIR4Kr7gpaU6l8+s13q9piqrUDdRVmUAxwTHkdp+qeMYx5Fo5iTIvEpc/cTydLx+/TqEdlJeLJyGaXqqwxAXOjKOIs948/Ydh8OROM/phomf/PSnKK04ziDx1Spmu1lweHikPB6ItJlLoydWyyWrqy1lXZFEccAx6rpGOcdquWLOUGKMP6shsgbcwP5w4lg3WKN9Lw03sd/tyfMUlURoa8Laixd5SSzSWocmK1lcBGN1SYiTWgmhrUsxlSgMmQv57M2bN3MKecvxfArhKhCqhS97Sgq3Bbz3fH19HfaFZI/E+Ikiu+S9SMj93vMUhsFzExaLRYiD2rYN/QFFqwmIYq3ldDqFsmCJ80UgrTGM84KkaUpVVaFtmnMuEICEtSYpPlEe9/f3obHm+XwmimMeHx+D+yyVigJ8CpFnGAbKo7e4282Wc+m77RwOR/yZiyNt98Su9GQUT2h6/fo16/V2VjJPrb/bqkFrFdKokh0R4PFwOPDJJ5+wWCyZxqcW8uL1yLNKvDnOZ2yIJYHnZyiKwi3LkrJqgzKVU6qko9R2u+Xm5ibgGjKPArZJ8ZkoEQCsQ2nLuSypm4a6bllvPAPx9evXNE3Di+srjvs77u++ZvfwSBYn5FlBlqZoK3005mMAshSj/WY4ugNMDqMNyax45Zms0awXBeMw8bA/kCYJVzc3PB72vHnzlixL+OEPPiVOIsCFNLUoAfm3EMCyLCNP8gCwCr4ia3ZJL5cNLCDfZU9RIDB3f/3VV+RFHkIUMXiirEVZXCpz+b2sm5w+JqG0zIGEyHJ/IUp91/gzKQWl1C+BEzACg3PuryilroD/DPgR8EvgX3LO7f7Y60ip5/ySl+lCYXlJquab2Qdx8yWmG4aBPE3Zz30IHh8fQ9oHnhBaidMEYxCFIhx3wTdkgzXzEW9SAy/AojxvVVUM/cBqsfJ1DlZD6bES58b52YcQY4t18acaSSeeGqX8eyRxNm9GbyUkuyJpJ1FCsvFxPstQa8M4jeRpRhontHXDXbDUflN1Qx/mGJ6ou0CwcsZYhpFnyPnd3R3As7ZkWvs28fJOApxZa8NZjcKoa+mZnA/bFssl0bEmzQs+/vgTxq6jWBTsdo+U+zse7++xxs6Zk5Zh6FlvrryljCJU21E3LcWcHUlm13qaxgCsBW8Qh9VemS2XC45Vg1aavvPzutvv+eEPPps5JuqZhyBovQDP0nGqa54YsxIiSer4EtQWxSuMQvnMWht6KSqlwpoIN0fwM1Gsssmrqgrhg4TN0udTPDPZN+JZSBgDhErdy2rcb44/D0/hn3XO3V/8/HvAf+ec+9tKqd+bf/53/rgLJHHMp5988pRNmGvJ4zim7zrMPIl1XYVNZY2maxvqqgzutDEGo5XvwTiXL8thGGLNxbXyTEnHND3lbqdpnN3GmKoqAUeaJnR9RzETSJI45mq7Zb/fh5ZuUiZb1zVZWtANI6fdbi4bHlAaHncnJjeRpl5rK6YZHc+JbDSHKvdBMD799DOurrZo7cgLn2F4eHhgHAeM0eR5htaKKLKAo+0aoiTCKU86SrIk5NclJx5FEcZaaKBrG86noz8ncowuwhqHVnEI3ZTyh550XRcO8BUMImRzGINSrduK4wEiGzH0PXmWE+W5J89oAMdXv/6SOF1RpAVXqw1j19M1NV1T0tYnhq7H5kvGvufY+HRjaiJu7+/Zn04+n59lLIsl2IxlumSzXBNZS1WWKOf7WsbaEMeWaepJigybJTR9Q91UnPf3JFaRJgl1M3D7cCJJY65XCdc3V3Njl5a6rnAu9sxQDEW+ROuIpj1RzR5nmiUkaUySRLStZBtqqrrC2pgkTgL5SLAzUaJinFarNZN7OudUPAj5jmTbxsHzL+JkPry360IadpEX86niUJUlk3PPWhxKilVSld81vo/w4W8A/8z87/8Y+O/5E5TCOIwc5xbf4vL3fY9WyjO/uo5xGKirMhTo+L/rw2b25aqOPEvDz8fjEeCZZRYvwbe6Hqiqcqb2+uvI8fD+qPGJuq58w9coJrKWoe9p6tqXNKep7ybkfHOPJElohwHqmlM5FxJF3tvIiqdDUrQxpNFcOBPJmZg5XTcEywyO0/mI0mr2IBR939F1Lff37wIbcb32dOOu80enaasZuoH9cU8/ehd1sVoE65XECZE1TFMWSqHbtmX3+BAKtJq6oh8nnPZH9HW9rzY087wJyOlJXDVDW9P3HuNZLXO0nsVqmhj6js3mimkYgYZ/8LOf87jbUSwjfvL5b2C1patrqvLI+fjAx6+u2O0GTJR45l8UE0WWsevYnU5UTUuaJKhJoTrF1eIGNWkebv3zx5FnIzLN4Gff0/YNIxGgSGNDFimG3ocWcRRxd//A6uYTlhgy2xEfK/IiJY5nVmjXkMQZRbHAmJjTsSLPE+LYBGss87jsiqdqUlswjSqEXOK5yDxLJe0wDKS5L/+W0FnK4y894SzL6FrfXUoo7qHWIYqIZ6/Dh2wGHT2dqyF9OKTHyB/XaOXPqhQc8N8opRzwHzh/kvRHzrk3fq+4N0qpl9/2h+riKPqbqw273S6g6hIHNU3DarUKWnO9Xj6bMHHFLnEBT99dhDhamI2ipSWW9u738y444nIK/iDx2jS54KZfX19TFEVIX3700UdkWcbPfvYzyrJCRwlJkgYN3fdP7cujOUV4PB5mprAKjEhpjSbsQpBzAZ6OOQ/KcMZQhANwWQq+Xq999ebsVobWamk69xlsydIsgLKCoEtZdiiYGSccPv11Pp+5uroKc+7Pe2g8e9A5lNIhZs2ynPV6w35/mMOsnjT1HtvkGn71qy/ph54otozTQFt2fPbpx+x3d1hjePvmLadzCcw9FCcoipyr7RUfvfqUcfC9FKMkYbXeUnUNQzWwWS15+ckrkijidDxSjWeUtURGM2mHtZq+70iSlJubF3RvbzlWDUkcE3cj+90jL1/cYIzjcDhQNyVX1+sAMspanseSum7AqSBfl99ZLpfPNl/dd9T1OXxXMgCHwyHInmRQ5LTv+/t79vt9CAfkO3meo5XCzfIv3ZnEi7gsv4/jmCTPQgczkd8kSQJl/rvGn1Up/HXn3Ot54/+3Sqmf/Wn/0F0cRf/TH//QycaU7IJ0bxavQeIviatEqIVdJ/GX/FtASGEyCmtRznwQd87TkONnlWPW2mfYgS9MycJJv5eHfV6m89brNTZOqeuGafKbxxrFNPbgNCaJyLIExZLq/EROkXy+HPB6f38f8stK+U7Jl8fAi7BI3ChIswja5bxdNvmA+SBXFKfTKbAi3717B/gsg5QNa2MZnO8OdT6dWK+WbNaeVuuzHnVQQtNQ8/Dgj5+LoucZHH/8nO+2/frtV9w/7Oj7kU8//YSf/cEX5NmCP/iDn8HU8vDuDbH1Z0IeDkcME1lReGWrDE5pUJaXn7wCYJwmdqcTKFhtN9RDD8aQrZaYNEbNstKNIwxeeeVZgXOKKHrkarMln6AdHyjShP3jA+uPtyHtezppsizh+vqavhup645p7CiKBXW9Dwbq8fExcFcEC5NUosT4EhpfnosqYLP3dt2zngvieYiHIPJ6Kcey9pf8GfE+bOSzQbKPhGsitTPfW/jgnHs9//9OKfV3gb8K3CqlPp69hI+Buz/pOpIiEwspZBwgpNyGYWC/3wdXKE1Tbm5uWK/X3N/fh7TgZa58tVoFNxeewogkSSiKgsNhz+GwD+kzsZSSx3/qYfgEwklKaTs3xZTFEAQ+z3PsbBEOxwO+IYsNKPzQ+yPJbm5eMAx9uIcc4bZYLJ49d9+3ARSSdwtFWhAIWeDz0wKKyTsK8nzZq6E8+bTVfr/n6uoqPLtgMzCfXRklc2r2zH6/49WrV0ExT+PIpGZMwyShXsXaiLr2523iFM4R8uhdP9F2A+vNds6c7Nis11T1maGrWS0XvLje4saBx67hsHukqRs22xtMFJOkBdpErK9ecC4r6vJIsVn7TMc0cihLlPFZgvXyGtyIOxh6NzA2HmCOE8/+LLKM/fEdzeiItOLd3S11VbGIJ9IsJs8TFARqs0IzjjrwR+q6DpmJPM/DHIpMyHx6JWsDLiBertCMBUSuW9/GXsKL0KcBgmHzhXk+VJWQRMhL0hNDfpYMloQJUk8hHsulofjm+H+sFJRSBaCdc6f53/8C8O8C/yXwrwF/e/7/f/EnXctNT/0LJe8shBzhbUs65ZJjLkIsn0layJfuesKQMYbj8RhoozIpsnDS3gp4xlsQdN27h0/Hjo3jGFrOf/7556E4SUBNay116Ss6rVYs1yuM0UEY7u7uAivucDiEqk8B9YRGLEfO53ka2INd1z3r7ychwmazCUIjnoEIisyh9KWIo4h+Ruol1bvdblkul9ze3oaejUYrtHKMfcsiz0hjS9dUWFMQGf+7pi6pziecG/joo48wxvDu3T3WemGW9nVuPg5+tz9xLlt+97f/En/4R7+i7zseHt9xvd3Qli3bm1eM/cDjwzs22w1FscTYmDhNQRt+46e/yYRmnODzlx+zXuUY47DGotzkW42lCUnsz8YozycWDn8IbVszjQNaQTeHklZrpqYhjSJMFDONvkXadrsmTX0pfVV7o7QoVsRxRmR9Cnl7dRU4GOIFyHxLuHo4HFB4HoqEAcIMffnyJUmScD6f/SG/ShElccBsxDOQDSzyPvQ9fdsFfogQmiQ7J3hB27UhWyZA+m6343A4hL/5rvFn8RQ+Av7uvBEt8J845/4rpdTfB/5zpdS/Afwa+Jt/0oUkJpbCjzRNQ3+Dp82RhwNGpIONcPUvmVziVYj1lRhY3C5hp/nFqYnjKNQPyMYULoB4FVmWU1XeWotyEXKTLKyUey+Xi/Bscl9JbSnlPR9ZTKkVkGcU/oBkTlar1RyGtKFAR7ABINQrXBJugGchkmAB0kVpvV6Tpb5iT7wMQbkv60QcT8w48XyEGpvnWajE83NgA47Rth3GDEyTozxXgC+53u12vLvf4TAkScrhsGeYPGbStTUvb26Io4g3X73lo49f4hxkRcGLlx9jowSUZbFckecLkjRjudoQWQ34/H95PnP3+g192/DyxQ3WapQCbS3jNMHknwOtiKKYRb7gxfU1++MvyRcrdqeSru94bAd/glVsGMYErfm/tdLruh6j+2CcZA1k/sS4VVWFwgaPQWRT1k/CCV/laoguKPnCtL26ugoGQMIQWV/BwKS/xmW3rzTNQKtAoLrkpVzyJL51P/5pNcA3h3Puj4C//C2fPwD/3D/StXg6YUespaS7pMtQlmVcXW1CM1VpiimLIg1CvDV3vHjxguPxGDaLdNERj0Nq4KVASYqxpAbjMmbbbrfEcfIMc1BKcXt7G4AerxQcdekr7IyC2BriOApCc319TWwtQ9eymgFKKamVTIlscGAWkCrU5wslVjwDgNvb22cbVipMpQWbhFHjOHJ9fc16vaGZPRo5ZFb6TYinIR2oA2BV5KFrcNfUrBYFm9WSpirRynsaURQHzoa3VgPT6Gjbhjj2hVf94Jic5vWbW05VSV5kKOM4Hvesi4w3X39NkRUMk+LHP/kp9w8PdMPED3/jh3z+45/ApLi7u+d0PPDu7h2TG6mq0qeltWa5XJAlMV3fo3WC0WpO8U0oB1pZxrGfOS744qlx5P7uFmVjuqahn1rivSXLEpLEkBS+DkdhiOOM8lyjlMZonrFZL5u9Sj0OCJnpyVWXZkGyXlJDMwwD5oL9uJzrVMRYCilpGAaaqg4hy93dHev1OoR1stmNtbQzl0bwCaFei3fzXeO9YDS2bcvr169Dv0RBVaULs3RBMsYTPuI45s2bN8HVkjhcKsLatp83chxcflmspmmCZyA4hngY0n1IuhyJFf/666+pqjrE6Je1BqKgBCx0zgVWmngAAqK+fPmScRy5vb0N3Z5lsa+urp4dgPpUtzE80/JSny91D/CEych9xfMSBqZsdoDDYU/bPPE1pDRXyn8lvJJCoMv7aP3UlVpiV/kcngS870fatkMrwXCyeb4MyljevntH33cYU9A2Dam13L55w6vrF8TGki02/PyLX/Nbv/Vb/PZv/Q43Ny/48ssvKU8VQ9fx+eefo6cRbRNeXF+TJP4ch6HvGXrPa3HjyDA4nPOdjcuyZho6sjQiSzPGYqBpalaLBWV9T2StL6Dq2rBpd7uB09lnXzabK/rOoZQhjhIiOyJHBwpBSAht8KTctX46nUvceDFykkU6Ho84RSi6uwSVT6fTjEHdkGUZ+8cdb9+8eZZFk9SyhBpt21I3Ddo+HRsge0HCS6lr+bbxXigF0V7SYWmz2QQOdxzHoUDp7u6WFy9esF6v+frrr5+l5ISY4Tfl00Emcl6fhANSjOKc43AgWDYJU9I05eOPPw4l1pdI+jiOHA6HkN6RgqinqsuOyPhY3hjDOB8z5wlHhi+//DWvXr1ivVqBNgzjGNJD0dxYRcIHYaklSRxCKllMOQpPctyi9QMte34eQbOlV6TWmiL3HtHpdHpWNyKH4XjWnz+gpjyfqErnQ5i2I4ojH6+PI0orrNHAU2+A9Xozl7GvKMuKaXT0vWdt3t3d0XY9yvhQxti5K1Qc+epE46tBjTKcyoa/+I//FX7nt3+bX37xR9zdP7BeLvn41UuU8wfjvvzRD0iyglPVkMwe4/l0ZP/44FmNlWcCerLZFamx3N++oa5rVkXGyxcvaNqG9XrF/W7P1Hekccykx2CQhqHFWL+ptDJMkyayKdZEMyj+VHEr1ly8vBCOokM4K1RywR3E/c/znGF6ophLeldCEkmTa62ZnPc4D4cDX375ZegQJsZTlEU5e4CCV4mcSNghiv7bxnuhFKZxIs8ypnEEYyjPZ6qqnC2VYb/bcXd3x+c/+mGwzMvlirIseXjY8fCwI7IeG3j58mNfYzCnwcRNkzSlpGYk4yBWvOt6fyZAkjLN3YeTuY3VarWm74fQj1BiQSBYV8kVZ8sCiaPHaSSOk1mT+wKZd+/uiSJLsVwRm5gkiWfqcj57KiX39w+M48iLFzfEcRSshYBI0hxG/pNF7zo5E6IPmZw4FhBWczyeaOqGOPYVhkLeMdbQtA1aG2wU+dLk0Z9DEEUR4zyfUTcfZdYP/rp4DMbqCKtjrLYc9wd2DztsFNH3oydBobh9ePBu9ORomwYNGBQGRWQsP/jB53RVw2ZzzW/97j/JT37nt/mf/sf/mZ/+5Df8WZFas1os+fxHPySLY6+kkpQfL9egNW1VkqcR29WCcRz8idIzpqOVosgyxqnnuH9gdI48if1BO7mnKPfDRJLn9KeBrm/o2p4kjbCRpzwPczGWcwrUiDYarZ6OJjgcDt5DmRykDhtplI0YJ3ATc/ctARpbjFbEmd+wRiv6cWR0T2dvSkgsYeD5fPZgYhxTZL7uwlPoXbjuJdDsgKZrgyG5VASCQ3zXeC+UAgrSJGYcevL1ipubG25vb9l1HW4cKeuacfBxr1i/NM04n6sAZt3cLCnLFjjz8HBHnDx5DkIwub6+RjoSezJT/OwQmK7zhUC73Z71ek3TtMjZg1Jr8emnn4Yjwi81+WKx4PHxkdOcv18ulwyTo247jueKpvMWIE7SOW3Yz5kT6dtvSNOVPzg1lVSTCkIRUOUZzJIQSATieDwxTX7zgwfTsmw+j7KueXj4yocYK8tp7l0YRRFt3/kWazPeIvHmOA50Q8/2+prPPvuM+/sHDocj3dDjsCRpAgrO55IiNdjI0pYNRZLQupbj6YhNUs5NQ1m3nPqOsXf0dU1qDG0PLzdX9F3LIl9R1z3r9Q2//Rd+l/Viyf/yP/xf1L1LrGVndt/32+/3Ofs87731Iotkky12s1vqjgJoYgTINEBGCZxRBgY8CZCpnVFGBjzKKCMPgiSDJHaQQQIEiARLHci2Iqsj5tLcAgAAIABJREFUWWr3k80qVtV9n/c5+/3O4NvfrltKM23IEsBsgCDrsliX9+y917fWf/0ff8TTszOKKMLzPMZBwHKx4Ob6lo8+/JC6LUgPO7788pdUZUneA87z+XwYb1zPoa4NDNMkSWKwdDpdxXQddMtE1XRcz2MymrCPEtROw9Fs0ihhv4sI5yGmZ6DbLp2mUHUVhqqS5QlKVw1bpDzNqIqSqgNT1+mqmqJuaOqasq4F3diaYOoqtAq1CroKqtpRVwW2qdNVUOZvRWxVVQ2ApWz/Jd4gR1tN04YxWeI5stswDIOWt8Smhy5SklvxVdfXoyj06LckdEiw7yHJQ8y+wolXhHfEpGlGXTeYpk0w8tBUnRcvXpAXKaE2HvAH6cokPxCJ0srTVNO0oQMZj8ccDgeCIGC/378z+0mc4GHrJW/QkydPhtZfblGkqtP3/aHDkO3cw59Nqu3kWCPHCYlPSNQa3jLgpPpTrppE+2gOAJf0EpCtsMyfFC3uW1GOoEqPB5xEFh3HEey6OE64ubmh68TsvN8fiKu4XzsqdHSUVUnT1NiGh6KpLJZLFprGercnrVri7Z7NdsfF5Fkv/YanT58SRydMow/7HY95/vxDxuPxYMO3Xq958uTJQCTb7/eClFXX7Pd7ri7fEEfHIatCEMr8fktjoevGcMqWZYGCgtlzL4q8QO8DcV3XIy1EhoVpm6RFximLhc6gFvfeDwQPoYXesaolk9uYXs6cZ7nodvvfYxgGUZpyPB3Z7LaDv+ZsMUfRVBFUk4l1seN61A+Ys9KlSa7PZbZGU9XEvWBPPhsSq5A4ma7r1L0XqBzNJVFKbqW+9s5L8gfruo7VajU82JJ0JLcDdVPRtDJuXmU2m1IUZX9a12i6sOSWAJDsFOSLKttuyVH4yy+g3P3ats3l5eU7phiKIlahaZoOrLC3vAgBHsq5/6HwShKSJGAnZ8m6LofiNGgO+g5ErCLbYZaUoiTDMLi/vx/IU/JlkHmUqqoN/400C3VddzhhZOCMYWjvqCwlBgIMhUnMn9UAXAr5txDbyP03CLBRRcHQVRRNFexDVQVNo64b8rwgihLG4yl39/c8efyY27trQCDzo0DwU1RNI0kER+Pzzz/H8z0++ugjPv74Y+7v75nNZizPz0XBuLzk8s0bVAWevfdebwdnslgsBKB8OglRWO+OHSe7noWqYFk2nm1hGTp13aKpW3TdYDKZEk4m5GVO03WUd1e0LUSniKouMU2DwPPQNZW2aTAME6/v1oIe6I6OR2Gjr+to/f0OgoCOjiQR44A4jGzSVFjLCV8Ol6ntDmxIyS2Q62xpZxdFEXQiq1LeV4lVyKIuV9tN2zDug4El2CxtAeQB91XX16IoKD3Y9RC0OxwOw8Mq1y/36xvyIus/BCjKAkMXxidXV5eEYciz956x2+0GbUDXCfrobrcbciLkyygNUGV7/pD9KFeXUgrreR7r9ZqLi4uh+3gYsmoYxtCJyLxKSU6Sc5982SWgKU9xYKjmMgVrt9ux2+3e0dbLrkRR3npUSmk5vC2iEpSSs6kkwYjvaw1kKgk+SUKUdLp6uDWxLCE5F4VaAeUtC880LWzL7k1nj1S1UHDe3N2iWw551Qhac90ync4pjtLNaEJVSU8AUZjp4OzsnB/84AdkRcZ0NmU6nbLZbBiNRpxdXPDLX/xCIO5Zhu/7PH/+PrZjDYYmVSNGtHAmNjmdquD4HhMFDjuF26sr1ne3zMIRyz59SY6QRiuKyHQ+paxrblZ3YvWsKVRlTZ4XxFGCqasYmk4QjAlGI0F+0zTyLMP1ROaFqigU/QpXM0w03cTzVDLJNaib3h1KxbZd4dHpeXiqNmzI5P2VLF3ZUQZBgKHpg1O0zJeQjMbdbidwFO1twZDPjAQ4Jdfhq66vR1GAoYWWdFE5/0i7KcuyyMqY6PpE18kYrZzzs4t+Fab1I4LHdrsFGDzx5LpI2oLd3t4OH5A83ZfL5VBVT6cTYRgOgTPyJZnNZnzrW9/iZz/72YDcL5dLTqcT0+mUxWJBnuccDod3KKcS5JQ3VqDLb910DMMY7L9lKyjtu+XoIr8ObzcFkgUqxp8MVdWGn/eh+YssaqLFVOm6duDYSxbeEGjbd1fA0AnJ+XU0GmNbDqqqYdsOWZZTNxVJKjZFWZnT9ThKuo/YHmPe3Nwxni0xTGcARqMoJk1OuI6Nqok153K+5J//838GCCbq8+fPGY/HTKdT8jznX//FX1BVFU+fPhWn83jM+v4e13MBQVXv2g5FVfA9n7qpByJRGFp0TcdoFLJdrWhbKKuauhaekR0aZb8puTh/TNvB6+tLyrYiS1J836WpWw6HI2HgMzufoWoah9ORum3QVU1I55OUIs9RUIbkaTcY9R2bxzic9KvA7p0IQFXT6TrQtbdJUXLElPoFubZ2XZc8zQaatRTmSQMcSY8uypKrq6thZJRxjJIA97V3XnpofyU/KJmAJDGGMAwZjcS8fzqeqKoWy7LZ7/do/YpoNltwPMTYtjPMydvtdhCNyJwG+cG1bTvgBlJsIouHVK1JjsPxeBwAUEkTlZsNOaNdXV2RJMngYyCFS5PJBOl8JMeI0+kwzIEy3v7hzyxXlNKcU44iFxcX5HnObrcbiockHmma/s7II01C5TpS1/VhqyOp4ZIfIYFUScQRRQ3SVJzKpmlydnaGqmq0TcdiseR0ithut2i6yqnM0QyDOIooipI0y4nTlJvbFV64ZLG44DDbYfS7c0lOK/Icx3I5HA7keYHr+Hz3u99lMpmgaRqvXr3CsiyRPuX7b233LYvHT57QdgKk7TSNspe0j8djQBw0aq88PTcMPMdGaRpoawxVwfcDmgZM0+V+vaVpWi4vL6nbhslkyik9oZkaRZFRlhamodN1ClEUo1fFO2g+bYeh64xCYX/XtS2GZYKqkmQ5umlRVzVtT0unKMnSjLIo8XyNu56EBm89MSUu9dCpXK6rJeYmDyzJkpTdr5qlNJ0YYWVhlc/qQ43Lr7q+FkVBzvayZbVte8hOlIy9oihABcv0GI2EZZmKhqZZRKeYTK+wzJMwLenZdXJNI9cvk8lkEAKJk2/Eq1evBnWiNG1dLpcDT0LeHGmAKV9cRRHJPVdXV8MHLf8uu4WHo4fcUNi2PQhjZOE6HA6DT4R86GXBeDhfSkKRLBJt27LZbPobLB4oKYWW30f6M0h5uSA4OQP2IIJXj4Mabz6f0zQNt7c3w55bWsRVVUXblGRZjuf5LBYLYUDaVdzfaRR9YlfbQd2qZLdrUDROUQqaRZqkZGlM01YDP0IWvzwTLfPH3/gmdVMP45V0zTIMg5ubG5bL5cBj8YIRiirDhAxUm8E3QNWFB0NDh6pptIpKkYtuj6bGMowe6O0oq4amE5qKU3zk9vYaXTfI8wI0ha5TqKqGSTih6+B0irC7twQjWqG/0E0DTdV6QxaxPWo6DcO02PQrWd/32Wy2BKMA1/MwTIvJJGS33VHV1fAMiA4nHO61fPE3640wHtK0ITNVHgLyUGvbFl3TB7BSYlryPZCA+VddX4uiIKW+IFhxco6SBSEMQ7bbLX4wwjBsuk6lrlK6TqMsGppGxLDf323xvIJw4qHp6tCCy9lbnvDSnGK32zEajbi/v39HRyDbbon+ytFB2m87jvNOLLlUasotg8Qx5Ikm20F5QorvFb/TFT0sjHIzIsFRKWmWN18KnKQMVxYS07QGcFG2nvDWH1B4Uow5O1sKSXQ/Kkn6tHywRJsvo87fKvQE7mIMBCpQcFwHx3NwXFeAYIrCuB2DllEUFUmagaJS1y2e53M6HsiLlLrKBQ25bftUp47ZbMZ0NkVVBO9C2obd3d2x2+14+vQpYRgOWx1V10mSlAzRPquagWE7dIYOKDQdoGqoXUdVVCJ4Z74gOR0xdR1N1Vmt1sRJJub8Dm5ubtF1g6YQdOZTFOH5NnmWE51OWL3oqqpr9Loe8jbEKOdDD2LLLYSmCFzm7PwCug7DNIfC4bhej32VtP39BwZRnnwOpEDKtm2hQ+nfG0k9lyCxJLzJsVCO3bIrkweX7ES+6vpaFAVJJHIcEVwqPRIlwm5ZFufn5xSl0DVoKjRNSpblKAjjjLpuRKpRVmHZGkWZDtUU3oJj8sSVYON8Puf29nZAY4/HI3meD2251A6EYTgoIbfbrWhfHz/m8vKSPM8Jw3Co5hILkdRqRVGGF1jGmpumObhWK4oy+EVMp9NBKdk0zTAHSuMZeAtKSkDRcRyCQESqSyOPOI45Ho/DHC8Bza5ryfNs4HtIN+KHnn3j8ZgwDMmylPE47E8ZweGvqpq6qodfHw4HGrWGtsPsTVsURWOz2bLb79FUHV03UTSdzXZDlmd0XdODxaLl1g2DFuF9oPV2+LK7+dGPfsTjx495/PjxsLMPw5C6afrNU9mvawVGIsVywq9RtNd5mlEkMbQNl19+iampjDxPbGhcF9NyOSU5N7d3qIrK/rjn9fUbbE9Etrdth+97g0gM+jQtTUXvHY/8UcD5mVB5RlFE2t/Duq5o2mY4sSWv5CH/QGooVFUZQHK5Ip9MJsDbsBlQhk3CQ8k88K6cHrAcEXknv5/seoGvf1GQKxXRwro94Oj1RByGtqvcRyTpiTiKBbHIMMnynLaDoihFm1c3rFYbmlaGetYYuo7an3RSsVaWFU1dk6cpjuvStSKFyLEtYTtmW1jWjK4TBKXVas18NsMyLY6n46ASVDVVjAFdh+XYb2WuqgpdRxTHuI7DbDZDUVW22w1JkmJbJl3bDUChoih0bUccxWR5NrR7+90ey7Z6q7NgKCwSTKz7bmQSTsSpU5YEgc9iPmcUBMRJTFGUA7+iLIsh81ESsB4KxSS2IJWRitLP5qoycB+qsuJ4PFHXQlDWdS2oQqJc1w1hGBL4I3TdZDQyqOuKqetS9eakdSMESpqu06EwnU55+cVLvv+930Y3NDo6Vut7Xr9+zXgc0iE+x9lsytXVFa9eveb84pw4zVgsz0XBAOqmge6t/0DXdjRaQ1c3qI3N6bjvx8YAo2eBFkVJ2yqEoxGbzQbPd3lz/RrHdYQeoevQ6SPdiwx0TYwFskXXDeggS3Pu7u7RVQ3LMvH9AM/3qWo4Hk+A0GE4joNpiAhE27Epi5wkqftOT2zefN9nNp/T9v4KVVWR96Otqek0qgAy5YkfRScURWc8Hg1biLrvDiQbVuqEgAGb+Krra1IUVMJwjnAGPhLHOWla4Dhe3xLVKEqOH7joBoShy/6wJ0oS1Lam6nJqrcV1QjRVZ7PboiktmqpBawyttudOANHaN3WH2rW4riXCR/Mc2zRI8oLpZITjubRtA+2IJC7QsFA7nXASomk6WZ6i6CqBP8IPA6qmIU1iwrGwWi/yjNl0gucK0PPi4oIoihgHPvv9ns1GzIaOPRqcc+I4FienqtE1LZZhoinqMFfXlWBc0nbYjjWMHfvtjrqsBv591cePSYcox3EIPDGSTXtRlms7Q/dgmRa14wo/RE0nS1Jm8xl+4A8gpqKAZQmVoWmpGCbUTUrbdnTolHlJXDdMRnNs0+G4OwmHaVNjEjo8eTLFsAzKQqNMRStsOT6jcM6rN28YzyaEsxFFnVApGT/+xY9QFJWZM8MJHAzDYrPb07XgOh51DU8eP8VxbU77HaZpMQqEaM4yHbqedNQUNV1Vk6cHmjrH8x0My0RVVOa+z367F+nU8QFXb4nLI51aEIQer65vaBUVO7A4pRltlWNbKkUV4+kTHM3BQIxTbdNSZBWtAZ5nE2WiYzH0hsVs1Lf1QFPQUGObCobaYto6eV7TtRpxkmD3gijDMPB7sNW2bX74wx+yWCzompquFS90XdfoBni+PTwjvu9zOBy4u1uRRNkgfJKdx2j09nn7qutrURR0Xeejjz4iiqIBwdc00UY+VB2WdTHM5Y7rkGQpKIq4IUWBoVd4Y591UQm3ZB3aU4Jl12wPJ2xbyGB1w6ZqBBpbodOoBvvDCd3QcF2bNC8pqprjcY9lmwSBh6V5zGYTgrGPYSqs1g2dIroDy7Bo6pSuAUUVbZkchS4vLwceuwRRsywbbow88SXIKjkJh8PhHcXk4XAYWmopfpnNZgMZS86WmqYN7j/S418yNmWCsjw9zs/P2e/3g0eEJL9IDGS+WGBZNpvNpldIxkP7KR7yflNiaqAwnE43N7fkRUFVViwu5rRtJ3wQLIv75HY44R5dXAyrWae31nddlx//6x/34JjB40ePiE4xh90BQ7d4+uQpF48eC3v2w57kIAxoFE2nyjMaVaOra7oWijIjihKieM/peE9d1XRtS+AFTMOQtm/Zsyyj6EHVal9C29JWNWWa0QCpqRHVBYFnkWcKrjXGcexhhJOjr+RLSCDZ8zwCz+ZwEDGE8rSWALB0whLrcZO8KIdQpIcMWMMwmM/nQjXsWGiKcG6SOQ/SYEV2wZZloWrqgJ8Bw3MgcYevfVEQe/KMqipQVYXxeNQjqQWK0pHnIpi1agQ6K3QFAuRR1Iy21Wg6he12g6romJZDUeRESUacviUlCYfdS0aBsDvTVJW6LnFdh/lsQjgKSIsc3VRoO2EdNhqNGY/GeLbH2dkUVdOo6gLbNKjbhjLLaaqKqq5Rmg5UWC6X7Ha7wSZObgl83x9WQ5KlttvtUHphjSwQco0pRwUp4ZZcdylflq2iBCglD15uKCRwKvkSco7d7/eDgYwEIGVrKYlcSZKiqG8BP8nHl27DcrbVDQPDtFFVnbpqUFSVLC9AUTg7P2N/OmEHI370ox89mItFzqVYr8V4jk3S07yvrq6wfZcsy3nyZM56tWI8GkML7z17j6qoOW63AjsALF0hrUqaLCdWFHTDZBxO0AwDVYNOrcmLVGwrHJfF2RnTcILSdcRpSlXXqP2Lg6KgtAigMy9oy5JOUfBNG82xqIqUIskpTJvETGmat/Fsvu8PUXCyNa+qiqY1B0wnz/PB+VkWE/nZO64viqdjo6mKGEdN4WRdVSW+57Ja3WObBnRviXfyOZLbMvmcj4IAzbCH7y0xNCnJl/jUr7q+FkVBWJyJ6AjLMgdnYenmLH+oqq4YjQJs28JyLFRN4+XLV7StcCNuGpU4jqiaDlQd1RAtVhwJso+iClrJ9e1KzFl+QJJldG1DOPL56IPnuLbO/hixnE+Zzc4Yh2M0pWN/2HCKD/0NLcmKgiAY9XOsuIn0LyKIdu14PA7AmKQjy3lXsislMiw3C5I1KV9Y6U0JDC/mw5dLbiikLd14PB7mUEnnlrTrqhJJXKfTacAsHm4dRqPRQODK86xfo4nxRWw0OgzD7Fe+GYeD+PksZ8RiviCcTImOMcejIJg5rsfV/RpF08jynNPpRBAEVGXJdDbj7u4O0zCgbXjv2TPBEahryAs++eYn3Fxf41g2nu2ymE3Jkxhd1cmTGNc2CQwdrRZy7MB3CcZjdMchLwqiJEZRVWxHZ7mc0VUZRZbj2uJEL/NcWO27ApA8no4opULXgK1b7A4HfMumU8CgQ1dV0ixHbQ00RaPtpc4SPJRqVcmBkYne6/UKTVWF+Wsvc5Y6H0mg0zQNTdcIw/EATIusEw3PFR1nmsTYlonveZRFNrg9S5BYCv3iOGY8HjMOQ6q6Y7lcslqtBgBcEub+Rjwa/zqvuq44nY6MRgFtW3M6HfsHW+QtPH78SMhEFZF9kOc5hmkMI8bNzZq8f0l32z2nKEHVDdIs5Xg4Dh/eZruhLEr8wBfdAirOeEaWxOyTguv7DRoNbV3wxrH45OMPAZUgcGi7lu1qJZx3ew59moosSNOwMHXRXWTpWwWijO+SnInlcjlsWuTJIU9/YKBjv3z5krOzs8Hb4HA4DFuEbf+iylFCkrLknys5DpIGK4lYD0lSkgwjTwypupSUWNHmWmi5AChPp4jFYonsDpoe+S/LClAZjcfopkmR5txv1qR5QVHVvHj5JYquYzsud+sNmqYzmYRkaSqcprpGgLuOjdWvgZMk4b35TPgv5AUqCtNwgmNZRIcjZdvhez6z0ZQ2SUijE7bj0JYWp32NltrotkXgOxiOQ5ZlJKcDm9UaXdPQNQ3TMGj601xVVfKyoG5FfmaWFixncyFuqhscT0ijHc9mPp2JXNCyRinE1kua7EqSm1DwirTy0+lEUzfolvjs5UEhX0jpBqbrOuvVPfPZHFMXRTrLMxTLJEsTLFPHNg0uzpaUVdnb0DEwGWUXKWMFT6eTIIL543d8NyTl/f8XNGcUBVVFyG11kQeQJgl5ITIKDEOEctiOx2azFgq6zRrTsnBch/Pzc95c3fTmIzmnOCZOhfZBrubkzOw4Dqv1hlMU80g3ifMtvucSBj7r7Z7AFYKZH/3kF8Rxyuf+S87PF3zjo6c8ee8D0VrHCXlRYhgQnY7YtjOkUMnq/5e1BVIoJV9QeUlZt2SqyVWalImLdaNgJq5Wq8HTz+jJN1VVMZ/Ph7Xmqi9ckj8vXX5kypPcU1dVRRiG73gHPrS567qOMJxQldXQFahKj+g3LWVR0jSCe+D6PtvtnizNSJKcJM3YbDfUTUvTNRyORzTdwPVcQefWdei6QYcyGi3ZrNfMplN83+fy8gq6ju9/73t8+PwDbq6uCFwP6oZHFxd0TcMvf/YTAsvCsww0VaXIMwzLZjSZYPk+6MLkRuuRerWDcTDCtizo52nDNNF0ndu7O7wg4Pb2liwvSJKMpqo4m0/plI74tENXFFRFpW07irKi6pmJsl2XFn5VVQ1WgFJsliTJYFzz7NmzYYyTStDRSPx/NU1N09TEcdYXb0+A0utyKOqaporYPd8niqJ3xgDJ+CyKgsPhSLxavePtKA8WEX33N2Pc+td2tW1LXuS9rFj4EmaDJbcBSoeiQN23xI7jUNYVh8OxNwtR+l2vOGm3uwPHU0RZCXTddWz0IEBRFbI049HFBRcXFxR1TVGV3N9es1/rYraNdMIgwPVGvLq8hbbl55+/4H615jvf+Y4gRHUaqmZxjBKKIifpR4bZfIamM8xugr22GTYLZVnieR7L5XLQRDw00RyPx8NeWc7+knQlx4KHL7CcZeXpfTgcBhm1LDaSHi1xCXmqPZxJZeGUnAXpExBFUlWqsLpfDyo+4e+YCTCvKLm7W6FpOnGcsj8cOZ5O7A8nkiRldn6BYVqMDCFcszSdxWJBVVUcD7vh5zN6z8iu6yibhvlsxvn5Oav7e9q6JktipuOQ1d0NdZ5jmZbwolBVyrZhMT3DGYfoqkpTNzSFwAPKvKDMhYu1bVkDMUpiPVkurNXrtuHy+pokL8jTBLoO0zIoywLXsamqEt0y0XSDrKzxbdGur9frQbgn3bylPZ4s6J7nvuP5Kb0WZfHebDYoCPampqroqoKhaxx2W7q2o2pqjH7kqfuuQHok2LY9yOjli6/rOn4QsNufhgQp6VwuwdGvPU9BPrCy1ZUfrjyxZJt7ipJe8edQ1wKI1A2LLEuHdny1WrHdHyj7D0fskTt812E2n5EmIqFpdXeLaVssJ2PGrsVuu0VTYOwHHA5HDN0gnCzZbXdc327YHn7ETz+/5FuffpPvfPYtzpZzmrpgZpmk8Ykg8FBVheVizul0YrVaDQIvyWyU3YIUNEnGZRRFnJ2dDcGx8rSfz+fDqOD7/iBi2u12Q0EBBtBKfgYSs5DdQ57nXF5eAjCfz9lsNkOradtCPyIxD00TaVVZmhPH7yLVx+Nbk5vJZIKqis+3qGtOUcL+cOT1mzeYpi28HbqOzXbL7/ytv8Uf/ot/QdO05LV4CcqypGvf+k/O5nNWd3ckScJiecann37KzfUNvuMQnU4sp1PS6EQaRziGgRv4ZEXGKJwwWS7RdPGslHVDlsbohkFRVMKrEEFFbiqxATBsa1CwRknMKY64vLri6uaaU5ISHQ+MfLffdFWomoKlG6Ao5FWNZZgDbVzO6A8LtizMk8kEQ1fo2mZwaJIyd9M0hyRv0xSmsaM+XSrLsuHEl4Q5UfjrATuSgUjy3jwcYSSZSb4/D42ApbZGFotfdX0tioKuadR1yf397VDFRYpvTlHo6LpB1zXkuWivyvJBSlIP0imKwna7FYVCU6nKBrXTsE1d2F13DS9/+UvathE8ebcDQ+Hu6kuapqWuGtqyINF1LMumaxVevb6irltaxSDOOtK7A9v9n/Ly1RXf+vRjPvzgGaORS1OVjI0xZZEPoI5ksElHaSlflgj+eDx+h9UmV0a6rjOdinTlPM8H4FLy37u+7X7y5AlxHLNer4eY+sPhMKy0ZACuPImkKYfMdpCz73a7fUCTFifYF198wXQ6xzDdHjsRcetXV9dDsTEMYVG/PxxI6obdds+rL1+T5wV1A4raayCqmrwoORyO7PY7zmYzoihiOp1y2G/R9X6F2rbstluWyyUfPP8A07DQVY276xvKNGU5Cdnt9hgKmLaNZ9uYwYjWtIiLkkpR0VqhhByFDl3bEYQ20W5PmWQsFwvOnjxB8zwaOvwgoKxriroSL3tZ4I9GVJ1Klqdi7DkdyJIINHA9B3RdaCl0A9N8a8MnW3IJPErw8XA4MJuMKEshaZcjpgSCPc8bXmLXsaHr+m6p7e9zQF2LMVRI/00M0x4KtewYsywbZAISjAYxKkumqjQckqawUmb/K9/Hv9nX/d/skixAcYqmg1WZZNCZpjBe9VxrGCtEocgYhyPyYj+0bLLK1nWNpii0dU1Ti927piqoqLRNRV1pGKrNe48vqOqGm9v73hhFx3Yc4ijj2599l/3hyGazI8lK6rrklOT89Be/5O7+li9eXPD++094dL4gzRNGgY+tCWttmQol50oJKsnIcMnalBuDyXTKYb/noR299P6XLaIcEy4uLtB1Y4h6l6Yn0pFpvV4P44qqqkO3IFtm2SWMRiPMHv+gbyeljPvNm9d4wQTTEDPxvke7pchLZBQIU5VXt/dcX133OEOHUTWkWQn96dk0DevNCtMw2e8POI7I77Ass7eQU1hFGIRQAAAgAElEQVSt19Q9cDYaj1mt7snTFLXrWC4X3NzcYABtz8No5jNaVaFRQDEN3CBgHE5Fgc8KNFUTa8pTRBLFHPd7LNvG1nXKpuby6orb+zvuVvd88fIleSEAOkXr2a9FynaXU5Uphm1StC1+OEXRFKIkpq1SPM/F78lBbdsyGgX4vs+TJ096fUsy8DCk2a7k2YzHY+JYxAFMJhPyNCGOTr3GwaGpKzTVJc4iVAXSJGYxfw/bcbm5ux/el4d0ZymdFl2pQV6+1dUsl0ug43A40vZd+Vddv7YoKIry3wD/AbDquu7b/demwD8G3gdeAf9x13X7/t/9F8DfARrgP++67nd/3fdQNY3z5SNevnyJawuUlk4V2n10PCdgf9gTjKa4ToBq6JziHMcNsCwP1y/Rj0ey8kBRHyjrlg6Noi6o4wY97ROhGoF2j3wf27LxbAfbMKnyCFNVUTSF9epGkH50k+32GlUzmExmuLZD05iY5rRH3wvevNlwfb3n+fPnPLq4YDQu+eDpGFXTiPMKXzWYLZZER9EyOraF3+dP3q92NC1Udc2bqyteX75B01Qs22S5XPDm6iU3t7dYhkfTdFiWYPUdDgdWq01vDlNjmjZxnHKKYrR+nrUsi+XZObv9nqwoBTAmjm90y2Z3OKGgEucV4XRBXlzjmibr1YpwPIau4+b2Gu2wIUsyDNNGU3WaBg5Riq5bqHpMWZlUlUKaFSSZcHq6ubrG8wLSLCcvCrSqZDEZkx+P6KqK4zl0XYViNET5Hse2iPOIKE0Zj6bo1ogXn/+CONoxXyx5/OQJ9+s1TV2RnFLm4wnjZUip2ow9B0wIRxaerdPkCXVekZ5O5KlYW1ptiaGVKK5DYxrUpkGZ1RRtS5znpFlB4I8wDJPD/kCR7KnrnE7VuD+dhITeNPFUkzrPOA8cNqt76s6lMTTM8YjAs/F9D8exCTwDTalQKRj5Jqrmcdgf8DxvkINLx+bVavVWaalqNKigqSR5QVW37E8xluWQRRGjyQzVsKj6EeFwOLwjfjMMY+gKZIRfs9sxm47QVJX720thLVjV0LWY+r+dHdt/C/zXwH//4Gt/H/j9ruv+oaIof7//9d9TFOVT4G8D3wIeAf9UUZSPu677ap0mQvS72+346JNPiI5Hrq+uBlRVsvZm0xmnKANFwdP9vuK2Q/vWNMKsUtM12q4ZWrCu7ejUFk1V+1gxG9MQNuVRFAFNT7MORYvetOR5QVGW1HXbi2BqTNPB90MBAB6Es+6TxWPKsuDy6pJTdOT8bElT+nz4/AN8L6CshN2ZdE42DJ8oOglfBcOk7Wp2u+1ge/bJJx8TxzE313ccT8J5qsgLXEeg0Gm/ypMKUsMQDtbb7ZambRmNR0PIqdozFne73XBKPXr0CEXVOOt32nmekds2bQdJFNN0ULcdmmmh6AbXN3dstztcxycMp9R1yylKWcw8Nrs9qAZar1QU6km/lxafKGuxx+8Ujbbtpd2KOrAvo36NmiQpt3e3BF7Ihx9+SBTFHDZXKNQE45Btz7g8Ho8sJnP2hwOe42DpJq1WY/sWaqNQJiW6anB9ec2rL1+SRCcuzhd88OH7zOcz7EZlsZijGyZVkbOYTzkedtxeXRH4Ll3b8OjinCQ7MRqNyIqSJM9pu7ZXctYkdcF8IlS8ujvCcQUd2XN9Li7OB6NdgRmJpG7XtIckMLnZkfiNNNy5vLxkv98PwUOSO6LrOqZl4fabLFlEZFYoMOSYSO+QIAgGklzXdbg9uUlyYGRX+W/l0dh13R8qivL+X/ryfwj8e/0//3fA/wn8vf7r/1PXdQXwpaIoXyBCZ/+v/6/v0fZt5pOLC/a2jdqr9+ReNcsyZrMZmi6whDSJUegwdI3NZs04DAkCn64RiUF1DQp9zoOhiXWmZWObJqZhYPU2Y6ap4/kOlmWLG9+05GWJqmo4rouq6my2W06nI57XYlo6H370vCelrLm8fDXIkfM85c/+7E+5P5tyeXnLx9/4CN9z6dqK5UJYf23amrIsaJpKMOUCj7OzBVUlqMNZlnM8njg7OycMJ9i2Q9e0FEXJfr9nsViw3+8HJx7P85hOp6IL6lq83nb+4aZDqB2zQXhVFAVd23DYrglGI/a7La4XsNsf6VSTY1pSFg2r7Ymr63viJEXXUk5xQVOLTI1woqBpBodT0m8wXGG2W9R4ns9+f4AOdN0c2tu2L86qrvWboxNZn9kQxxnvv/cRiqry4sUvaMqY87M5l9fX3K3Xg8PVfrfj+eP3CHwfRYE8SdGUjqg9kmoZpmGz3mw4nE7c3lyhOwb+bofjlVh2gOva0EFbFXR1RTjyUZUWTVEZ+Q7TMEC3HnO7WnFzt2K5WOC4LverW1RVZRKOSbMCTRfORh1v1bdFmfPs2ZN3nJWFSrNFpnFJxa3EfMIwHDAgiQlI9yuplJWZJ3kuQnvPzs6GdbeMLJRAtlzxHg4HJpMJ0+mUbc/+lKIo6WT2N+GncNZ13S1AJ9Kll/3XHwN//OD3XfVf+39diqL8XeDvAixmE2az2eAok2UZm81mIObIkzEYhWI91NSMRgHH04nz5RLX9/A8l8NhzxcvfkmRn0Qyr6pi9fO8ZZjYloWhiR85TROOh4LdXmQbarrQ4LeA7biEk2mfBRGwWIqEaNMSyVRRFLFcLnj6VMh5X71+JcggSsfV9T273YHjIeLZsyd88P5TqqqhbnLG4TnrzQrXsRkFHkWR94zGGl03OJ0iDMNiuz2gqRpxlOHYNtAN9GKpqz8/PxcjST8bKqqCbhjs9/uBmGIYBo8ePRq8/jabDXRCrHXYbXn06BHXt3egGmwOR4qyIclysqLkX//iBXmaYOgGbQeb3REVndnMI0kzxuMpmm6gamL12tRC2v3Ztz/jj//ln1CUNb4XYDoOnidWn20jKNNxbFPVDXXT0bU1Yejx/vsf8Bd//iNub1fMQ5/t4SAoyLrwDzgcDpzPF7z88gXH7QbfdlnMJiwWU7wnHrZtEcUJl5dXJHnKz774gs7USJsC3/VYhHMePzoTLNEy4+rNl8K9SulQ2pq2KomSE944wDINDF1jPB6RFyVJnKLgkN/eMZuOMXWdtmwGc5uqqjkeTlyp1ywWc9q2GeTbp1NMWVbvmPnKl1qSzuTGJ4qiAYyUmzhZJCaTCXVd8/r160EPBAwFQWJY0kej67pBvi95MZYlxk8ZT/9V11830Pirlp+/UnnRdd0/Av4RwG9844NuPJ/z4uc/J8/zAX13XXECSbDOsm00TSfZbAh8j4iOMBzTdhJE7IbU4a5T0XrJr64qKHS0dU1elZi62O8augaKgqYJq++uExyD6BSTFxWGbhJOQsIw5Ob2ktu7a8IwpGlKfv7zn9I0Dc+fP+fp08dUVcXjRxesbras1ndU9S1xHBNFR95//wnTcMSby8sejGvQe/n2k8dPyPOCqqpRFBVDN1mvdv3ab9qbqiTDTlvmGsgNh/RWnIQTjH4scByH3W6Hpmnc3NwMm42iKDgdD9RlQZzEVHVNVTf85Gc/4y9+/FP2pxg0g2A8IS0a6koQfEZjAeAZhkUQTtEMQQP+3ve+T11XRNdbHj9+QpKkaKZwZdL0kjCcYNo2juNi6CZpldPS0bQdddOgqgJAPjt7xP1qzdX1NYEfcIwiTsmJOE2Yz+e4jsNmuyU+HPEtizpLmX3yTSbjEUWSk0YJSVzwi89/yZ/9+b/is9/8LlGe8eLNK1bHDaEfMDYdPvnkIyHvrgq2m3t++tOfsVwu8X2PV69WpFmK6ZgEnks4HuEFI158+YqyrkhT8FyLqpYvtvBykPL+NM369e4ByzLJspzJZEIYTlivN8PW6e7ubqDwS/6JZJcKqn7zjveH1FNIjoNcU0omrLTSkw5ZD/0T5CpfHrRy4yRp1l91/VWLwr2iKBd9l3ABrPqvXwFPH/y+J8DNr/vD6rrmrvf6l+tFmdQsr4uLiyHkomlqNFXl0cU5ddtgOw6b7RpdV4Uufn2ibUVRUBCJRCXg2Da+K3bEbSNeiKrpMAwdpxeqBJbDtz/7DkmakSRCMPNnf/av2O1X7Ha7Yd8sKcFSzSiELg6OGfCNb3zM7d0N6+2WLBOzu+85LOYT5vMJo1FI2yjUVctmsxvszaqyIIpWg5nq6XQkDAWbMQiCYd0k15YSWJJil6z3SJAIt9x0bLdb5vN5jy/E7LZb1tsdv/f7P+DVm2t2x4gozSjqFtcb0akWumHTljW6bmEYNkHg9Kvdt/kK19fXTKcTDEME5nhewG63ZzwO0ZK0T1bqsExRzE3LJE4SOkDTLbq6xbIcnr33AT/8k39J3TQcT0fKMhdbINdlu9+z3W4IPI9DUfC9b3+b737nMxbTGafjkas3V/zi8y+wnIDXV1dsdwe+fPWaZ++/T5InvHj1im998jGuovDDP/ljPvzwQ968ecObN6+o6wLPszFNDccxKaucrqnoGoNJOCbJSo6HvejmWp0oTmjqirrxUVWDsqwG2ngYhjiOMHjtDFgslxiGRdqHwT404pW6FGBgqUregXy59/s98/lc4Eo9NXk+nw8chbIUI6VwpDYG79EgCAbCmiwu0ntTjg3Sou+rrr9qUfjfgP8U+If93//XB1//HxRF+a8QQOM3gD/5dX+Y0bPNHuY/yHh3RREmHIqioPfjgGUalFWFjjYQdwxNw/c8ZrMpX355I1BWBVR0EdFlGEzGI/HyWjZVWRIlGZ4lfAkXZ0vSJOfq9pY//MN/RhSnzOdzkjRju9sQTnyePX2Ppm0xdLGmO55O+J7HbrunqVuiU0yRX/Pes2eoqiIMY/KcFy9e8eTxI/b7I9vtkfff77g4P8N1Riho6Brstvs+c8ICWjRdzIDS4VcCRzIbU1KmpV4+joUASKZXR1HEfD7ncDjgui6vX7/mxYsXFGXFar3l5vaWNC/YHk50qFQt5GVFp+jopjhNRv5YcPdVHbcPMinLEs/1+gd3w8h3ORyOJEnK8+cXXN7cUhYVtusSxwnhdMrxeBIxc0rB8XTEsGx0TScuEt7/4COOp5gky1F0TXRFmk5alOS1yPkwNY2ma3n69DFn52fUdcWb1684bHa8fn2Jabn44wnHU8Q4nPDyy9fML+ZcXd9Q1jmff/4F7y+WuL7Pk2fP+PyXX6BqOrP5ghYoyopxOKHtOtI4EjmXecX19S1RHCNDfuuqomkE+9Tzgt4YR+np8y6+H/TW9R1NLUYL2RVIUZnRj3gPg4ju7+8Jw5AkSVitVgNNXmINQRAM4jjXdZnNZtzf31OW5RBVLx3DpM5BMmAFXT3szYJP71j5fdX1b7KS/B8RoOJcUZQr4L/si8E/URTl7wBvgP8IoOu6nyiK8k+AnwI18J/9us0DiLRdycmXQJqqqgMBZzIRrkJ1WVLkOYf9nqIscVyHtHeZsW0bx7aZhCG6ppLWIjPR7VeQI19QSLuuI01iurbD9zxGkzmj0YjTUVhpe67HfH5GBziOS1GULJdLrq5fU1cln3766eBhcDgceP36NefnjwdbeX+ksz9u0VTpXKSQFRVX1/cs5wuurjbsdim/8UkzsCAvHp1RFEIj8eTpI+L4RJ5nzOZjukaY1kq+OzC0f13XsdlsBLHFtvCDgOPxiKIo3N7eDj4Mkh0axzE//cUX3O9O1I3wJOwUXYTotC11B7qmYuoqqqIxsQOiKMLUNFQUqkJkQKq0hCMPlQbaqreBMwjHwuSmroWrcprlXDz2ePHihVBpNjWO65JnJY7tEYwMfvvf/R3+l//5H1OWNYoigmsbBaq2g7bBdiwWixmz0YjzsyW77QbygjQ60dQdfhiiKDooCpbj8lvf/z5ZVXJ184bn77/Pan3PxWLOdrNhtNvzk5/9HMt1Wa/XYmsFwu24qojSFF1pydKENC3YbNakadKHGBvkeUbTtlhNSxuJ8c0wTIq87P0lRPtvmkavZ+jjAa1uUL+eTiLF+uzsbMAYpE2aPBAly1NyQqbTaZ/WFQ9ahslkwmq1EsY5Pdi83+8HAFIWFenqJf+SwjlJivsrFYWu6/6Tr/hX//5X/P5/APyDX/fnPryqsuL+/h4ZlCrpmFI9KD+IrqlIkoqmrggCgTkcjwehHDM0bFN0A+ORcEui5yYIW6uWLBNfsy2b6XzCeLLAsAWBKIpjVFXHcl3CcMJoHKL0rke3d7fMFzNev37F8RjhODaqqvH+e8/54PmHNG3Lzc0Njx6d8+Xrn3F5eUWaxuiaiWs7TCYzVFRWqz3T6Yz16sDlqx/w+PEF47HP6RRxfrHo27uaKDrhuCZ1naGrzqC/l97+crTa7/dD8EwQCPsv6cvYdZ3gfbgup5NYg/7yl79kd4ioFIOyFfZgqqqjIJhwjmVimTq62mEZBo8uzok8j6qucG1zoMs2dUlV5hz2G+q6QFFEW3x9fYPaZ1mWZYWuGf3Du6brFOpa+E44juBY/MY3v83xGJHlFQodeZbRNAXBeEKnKOi6YE2ORmO6ruXNmzf4pkG82+FZFr/927/D+x9+TJLm3N2tiNMC23HYn46MRiMuHp1haBqzaUhb1syWZ9zcr3Fdh/PHT/mDP/gDPv7GNwhnKlXbYVo2h/srJprO/eqe/X6HYzvYnkdZV70Fm/j5ug7atusl6TVpkvWFusUwTBRFI4pioljcD+k3KqXopmkOeNl8Lqjx0tRXgogPx2ip+ZnNhIK0LEts2x7WtSBUl3ILIY2KpE+HdM2WgOfXXvugqspwmsmZWSKq0vw0yzJMTSFLUwF++T5F7wFgmCIZWUSFmYThiM16B11Lnqeoilhfjkcjurbl4vxcUHSPCVkpkOJxGGLbLkVVsd8fuLm9Z7vdMh6HXF5doesau92O+VzBMh081+PNm2vm8zmr1UroBlQNy9L59NNPsCyHtobdZsd+fyKLMzwn4P5+x3vvvUdlJ7x88QbL1lhv1nz7s2+iKA228xiUGkXRyYsYjQ4Rd2YN2nkpGQd4+fIlbdtye3/HZ599NpisSJDR8zxub2+5urrieDyS5hW1aaGbdt/CtpRFgaGp2P1Ma1smKipVnnO2mHN3d4uuKHSqWKM2TUN03EPbsNus0ayAtu344osXBOOx0PZnGZom4ti22xdDK6ugYJgmhq7x/e//O/ze7/6eUJGqHY7roKk2SVFgOQ6PHz9CU2G92TBybSw6sqxmMpvxrU8/xfVc3vvgA+q644uXr7m+vuP2bkWrtHzzm5/wT3//d1ksZ7z68jXjUcAf/8kPefL4CeNwwouXX5KkqSCXxSlJmpMXJVmW0u12xHEk1uBxCppO3bbohompa3QduJ7ba00KTFPYwe+2OzzfHajlXdeh6Qwg8Zs3b5hOp4PEHRgYrZom0qHCMHwnmAcQLM/RiMNBkKDG4/FwbyWzUeovJPW5rmtOp9NwsMruQvIcvvZFQdJmy7JkNB7j2DZ67wzcNg26YVA3DZbl4Y8F9uAGE4r9HsNwOD87p21bkTkYLlkuHvPjH79ENyzU1iQrDSaaT1kZhIHH2A0po4wii1Fcm8C3KeqEoqj4jU+/TZYVrNYb0jRht1uzmI55fDbn1StomgpHbWizE0ZbsBy7uNqcYhrw45/8mJvNLdDh+4HgrwcjPv74E8qy5Kc/+RmmbXC3fc352QWqq3PKYvZfHjmmMV98ueC3fvMzdF3j4tzlWfgY01R58+YVk+mUvMzwRwG77Q4/GLHfHxmHM/78z/9CxLu3grxVVhW3Nxu+fH1NC9ze39F2HUXdiWJgWui6hu8Iw9q4Asd2RG6hbuHbgkVZtjqdZlF2Co0qJN/HVOgmsjQnLxtAJdtvhRy5q6CtMQ2dotI5RhFuEJBXNUlZ0tYGXdNgaha2a1PkMTe3r9EMhboqqYqWMAyx24KPnn/IdrOiLnLm4Rhb1cjiI+EkRNU0rm6uyfKC7ybf4ac//Tlvvvw56+2W2fKMKM343/+PzymriuS6oCwKWuAYxwRxxPXNNVVV4Fomptqx2Wxpqoq2zDnEGUpSEU5C8rLENAS+Qa2SqdC2DUXZUfkdjaqAoaGaOsfkiGYCWoPj6uRFQ+AHLJbLIRpPcgikr4XMG5G42WQywbIsttstdV2zXC7J85z1es1utxvEa3EcD5sKOWpLqzfJbEyTlK5pyWIhn9c6BXM8Rmk6qqzAdv76gca/1ktWuKqquLi4wPd9Xr9+Pcxbjx496mdiIf7QVRGT5nk+pikQXuE8I0xPPvroI/7vP/0RZd3g+SPOzx9hGQZqJ5yDyrLCUDTOl2fUhoKua9R1y7NnT9is7lnMz1jO53zyjW9yOkWcjnvUrqbt2kG1Zts209kUP/Cpm4bbu1vOzs6olUYw+vrVY56Jmwr0UvAR0LLZbTAsA9dfEB2PrNcbDN3gD37wh7z37ClZWlBXHapWk2YxVdOSprloWZuOV6+vuLq6Yb1esT+c0FSd29WWuqnJsqxX/d0LN2LTIPA9DEuIlbpOyHerskRB4fzsXDxo4QRdVfFcj6Ks8UZjsrzg4uIRKA0tArwNRiNs22W93pGlKXmWYZgmXdv0XgVd/7NagiVa12iaToOKY1t0LT178UTT1CgKGKYxWM395m/+Jq9fvRJZnYpwy1IUaOqGw/4AtSCBLRbn/NEf/RE/+vGPOUYReVVwdX3J3XqLZpropompKpw9ekR83KEbBrbj0NFx2O95+vQJ+92OPM+4ubrGssXKcTobcXZ+ThTHZEWJnueDH6JsxauqQtN1LMdGUSFJUgxDRVE75rMQVVU4HPcoqorn+UNEgHTAkgQmXdexbWHWMpvNhjWlBAYl8eghoUkWAenJILNS5BpTang0U+t9NzNO0YnywTZisKD7FdfXoigAw7ggyUpy1/7BBx8M4ih419vQ9/130Ni2bQWAg0kQ+BxPMYoiaMqqahEGAY6hMxqNeXpxRlJlnPKEKDpxtrwgi1Nub+447SPmiyXb1Zrv/tb3MDSV9WbF6O5WoP6HA9EpEuq1QyRuomlTF0L9OJ/N/x/q3iTWtuy+z/t235/+3P6+tl5VkSw2kmCRFBwhsQcBEjhA5rENBIgzySyzjAJ4mkTDAA4CaKipJMOIHAmwTVFiI1Ji9c2r197u9Gef3fcZrL03iwnpRFQQVM7o1auHuq/u3Wedtf7r9/u+HpZ6c3uD67ocHR0JSe7dQkBNCyEnOT4+Zmfb3N3dEkYhsiTx6WdP+fTTz3jw4D7z4wmu57DaHoQVSxamo9e3S+IoJs8KJNVgH4RswogkTvoHWFINVODk5FRYhWSdsmrY+iIgMxxP8Pd7qqri8uKCi4sLPv3oY3GF5diMRiOCYE9dlwxHA3a7FUF4QFU1NFWns1YfggOmbZNmOXZ75RVFEd5wxGw6wtTVvri1226ZzWb87u/+Lr/3e/9Tey728TwRzf32t7/N6xcvBQLd0GnKggYIwhhZUQmTmMl4jKLoRGnK+x//kLvFgrKuKRvhsawlmYZGJFGbiuz6ClvXieOQ27s76rLA1A1qJK6urlFkCeSOTuVyfn6O43k0ksxoZvDq6ooa2uixhqLpYuDY1pWzvGmtW3UP6dV1rf11ymw27yVCkiT1PInb29t+YehuF7orxo7X2aVWy7Ls5T0di+OL2rhO7bdvf57DwQAJcYWt6TqSLJPlGYoqyodffnBrG2PtaqBfHIZ0qjCxSmZ9TLT7BnYk3S9alCzb4Oz8hDD5XESKG0EySqOQ0/kMrf1hDl2Psiw4ffgGZVlzd33H2B3guANcyyYME/7sT/6E8XSC5bn4QcjjR4+Js5wH8+M+FLLb7Xj05E3u7u5wUqtF1W+Fh9Hx8Pc+241ocnZgE0nRCKOY+Plzzs5Ouby4ZLFYtNHZEkPXePfd93CGLg8fPaRqB1qmYRJFCZcXl2y3B6JYPER5A2EsPtFub26F38D1MHQdVdHQLJ1CLciKEtcV5/vuAcyyrMeCXVxe8PSzp5yeX7T33gOSduHsuBdBECDL4kFWNY2yw8lLklCfqWIRUBUVRdYACalpGI3HVO21WufIjKKwZVg2fOtb3+Lm5obNbi8eZlXE0yVFJUoSDFVFMyySvEAvK55f3/Di5hpF1dhHEZphkJQlcZriDYdYrkNelcRJTCzJaKqGJCvohoplW/iHA1VZkAQheZ5xCAK+/o1vcHn/PlEUkW42SFVFFCXkhUDPZUVBBbie2894ylxCGw4wTJO6Kls/qSl2jmXT7y67uHnnL+16Kh2sV5Tdln14T3RDIobDYX/ccF23h6l0tesOjtPNIsTPKET5goqxt521arkvZoD+z68vxaLQDUk6PVsX8Oi+WV3/XlHC/n7+i469xWLRwyc8zyOIU+7dO+fFy5cUZUFR5DiWgyYLY5TtOIDEfrvD0FTkqmZxfcN8PME0HZBVmrLisPeRG9htdiimxdtfeYfVasV0JviJ8/mcIAj6aPEbb7zJ3/z1DwkCwTRMkpSbmxuqqm4TgSq+f2C93mA4IsyiKgovX73i7bfewjAN9rsd08mEIs9wvQFZWbE7iEzEwRcuAQmZqlHwffF1DlGIH6UYtsPQFLajPMuoGhnL8ihLGA4GFFlG3cRMp0KoaxsCH+7vxMP4zjvvYKgacSSuqwzDwPd3LflKgFWiWGxRm1rEa23bZrtZtVwIgyAMsB2PyXiM7Xo4touhGZR5IY4aaco3v/lNXr582Q9Moyji8eOHbLdbAXxpP5HrRmjWgjBiMJyw26wZuA61pBEkOVkQsY1iwb9QNVTLpkxTLNdpa9UNYRJDXZOVNcfzOYv1Gts0SdKE2USIf4umRlIU3nzrLTTD4Pr6hiCKyIqCyPdZLJcEYSyucSXQdIOmqdubHwVDE7avskg4Pz/r8XkinjztKVyyLPd0qe6mrTOh27b9BYuXyKPcu3ePq6ur/s91OEFJkvB9/xeAvt1NRfde6kQ/YRwJ6nhZ9CWopmn6K/Rf9vpSLApdJLO7S5/P5z1EJUkS0eff7SFXz7YAACAASURBVBgMRgJfPRj06OruzCXuh0X6y7R07t+/xPuZw2bjs9tv0VSNCkl0KAwTZToR9dG6Ynm7gKqmzApqrUKW1T52apomqmlycu+S29tbLMvi7OyM4+NjfN/nwYMHPH36VESOk4STk1N8X5wF67phNpv3gyMAw9CwLBvZsBhPxvi7HXKo8uLFS47mM6I4RjcMFAQYoypyXl7dtNFVWaDt0xzLjVA1g7yqkVWDsg64PDpGkiSyNGe5WGAaJq43IPAPuHZFXSPeQIp4MLzBgLIoiOK4f5A1XefJkyds9+KaS5CAQkzbpiizHhhTFDW6Jq5KVU0j3e+xXY+8qJAVBdcbMJ3OGQ6G1GWF0jYkPc/j9PSUjz/+SKDLZImLiwsePnzEj370w54epJsmaZLgByHjwYC8rJA1g1pSCJIMyzIparA8EdIZeWLXN5pOSdIU3TTY7nbCb9mAIskEcUxTVkLwaltIgOe6VA0cHx1zef8hSRITBKE4f0cx2/2esqpb4rJYqIDeNdoVoGRZAkv8OjW0lrdY4XkiptwFkLqt/xdrzp33Eeidpt2z3PV/OuFw98buFABdbLlbhLqjSFeW0nWd9XrNyckJhmmyb7MMo9HoV74fvxSLQtXWbAfjMVq75elwUZqm9fe2vr/n+PiEy8vLvuzx05/+FF3X+/RemqZ41gDHNTk9O2axXJEFQvAx9kZIgwGyIlNVNTIgNRWaqiBLitghtI1Nz7SwvSFHJ8ckaYbnOnz1H/4DhuMhZV6SJgnQ0NQ1vr8XRanDgSKPyTJxVWWaVjttFvTkIDi010getaqzXm/57ne/wycff0Tg+2y2O1GprmqKqkKKI9B0aCTyLMc0baqqxjItaGA2FWdTWWo4PT4lDiNm0ykPLi+JDwd0VXgOBwOPqhKK9jhLkHXxcDXtEW0w8Nhtt+LIE4RcXFzwxuM3KBoFRZE4HHYUZUoYHfr77yLPaWpxDj+EARUNaZ6haSZV565sGpqqpMiy1vOQCSXadMqHH4ruiGs7vP3223z/+9+jLMteAlMUJYos6MtJlmPqBpbjkaUZiq1jeyO2/o7BSOwsJVlwYppcLHb+wW///0TBzTFddrstcRjgOg5ZWZJXNYqqYVg25/fv0UgSg8EQVdNZbbcEYURdQ1nXxGlGXhTohik8F1L5c6ej2lKidZ2qpSRNp5N2oRW7ru7KfTAY4Pt+H2fuuInL5RLHcZhOp30u5fb29heKVNDW0vO8Xzy6I10H8ukQ82maErRN46ppaCSJ9WbTg2W7ReiXvb4Ui4KiCrPNfrNp7b1ObzPqVsn1WiDC5TbK252VHj9+zO3tbV8ssW0bSZPIypyzsyN+9MMUTROJNMsWjcLgEKBJCqYmo8pg2iam5VAh0ygqjawyOznhq9/6TbS22+4MXYo0I9qL867v+6SZUM7vtytm4yG6ImHZJ5yfX5DnOdfX1ziOw3a77Q3a2+2WMAyJigOSLPODH/xAtOaKnLoWK/0hDHvicCVnyIqCLMlEQYBumBRZAeMx+90W0zBRZAW97ebbqoZq1FycnIhh7XoLNHijMcPxkNWzNZ4u0FxZO4+wLbsviSmSmIcgydSyYADqhsHr188xDLU1Ghskyc/v2htZQtVUirKkIaduxGBQlTXKvCSJY46mMw6x31KJTKIoxLIsjo6OaJqm3/ElScL5+YzhcMh2u2W/2yM1InWZZjmOK8hMcVZSSyppXnByckKSxJRFjucOqOtSJAmrmtFoCJLM1dUteZaJnZIkYTkOaZbz/OVL3nj8CG8wwnIcZEmmCCOSJKMoKxRNoNdMU8w2GiBJU0xNaktFDZoiiyFjIm4gTF3rewemaaPrgglp23a/C06SpDeGd8j9LsvRDRU7bN4XhS9i9+H9wrHVNM1eAzCZTES6UtNwXJckTftFZ7ffo+vi2lr//4MMpit5dDj0bijX6dGur6+RJKVPdyVJ0jcA5/M5aZYxOzri7uYG2ZDQdZV33vkaH330lBfPX5OmMWki7D5iK47YpstC4lI3DZqpMz89497jN7DHE3TDREKCpuawXvPixQuyLOXTTz7tS0e2bTMdDhmPx5weH7Fcb3p562Qy6eux9+/fZ7FYcO/ePQ6HA89v78TZUpLZbLc0LS5OBHnaW5aqwtQFmSepaxQJDEVGNQ2oKlzLxLYc/MMBy3Kp84IyiSmyDEvV0CQJVZHI8gJFkymrnDSNUWK1V4p112PT6ZTVasXQG7RkH5+iUTAMrT3+VKRZRF1XPxfQ1FIfpsqaBkmW2kafiirryJKErEAURiKKqzY8fvyYq6sbPG+A6zp8+9vf5vd///fxvAFBIL5viqwQRzFpIkxUjQRFVSMrKoqmCzJRXnA4BFxenuF5Q/I0Z3Y8xTR0Xrx8hlzD0HWZT6a8eP1azGc0Eb82TJOgPdsXaYbrebieh6JquLbNs2fP0Ayd6XSKZposN1uSFmlv2jaW7aCS95Vk8SyUZGlJoipYhkgrNk2D6w76K8zNZtPr+jr4bpZlnJ6eYlliVxkEQU/8/iLjs7NLdUTusiz7W4yuTv/F9myWZSBLHB0fYdlihuTVIvosKwphFP7K9+OXYlHorl8G7ZS1c/IdDgeG4zE3V1ftGc7sZw+KovRFj/l8znq9Jm0HWa5pY5o6g4HH2dkpT58+R0IhCA5oksTQsglNC0VqODqecnxygqobFDXopgESWIZOI8vsAp9P3v+AsWPy6sVz4VqsBMnGtgwsU6csM9arBffu32c6nXPv3gOGw2EbOS745JNPuL29YzqZsVgsMQ2b4XCMf9jjuR6z2Yynn31CkWdtfFXv669NHbW2ZhlFljBNYZiWJFVUwdOE4cAjjUKGjs3pdMaHH30ottK1zPn5GcvVkjxPOYQ148mYnS+utahE7kKR5P5+3NQN0m4QlpXsdqJboesyRSrMyJIkiwx9VopbEanEsEzKvETXhLhW0cVwLTiE1KXQ6+maxte+9lX+8A//CE3TeOedd3j3Z+/iOA6Hg9/DbHf7HXleMp1M0DRNkKp0g7qsQFaQpPYGwcpxbRd/s2c8GjGfzVgsbimyHLklWVuGgaaqlAiYrKZpAhMnwd73mY7HZEXJIQx4eG/C3c0tWV5gmjab3ZYXV1dELcHasi1kVSXLM7yhg2HoZFlKmqUkcYRl6TityLcLE4lSU8HFxUXvcex2jWdnZ/1RIcsyNpsNcRz36cPBYPALMwjHcRgMBuJ7tBMFuo9b3MB0Ou0bxoqiiCtT2+rj0EBvIuuuQ3/V60uxKJRFQZZEnJwcYXWrXF2TFxl15ZJEIaOBi6pbKIrao7Ety8L3fZbLpZiCb7eiQrwXlBpbsXn84CEffvARkqwycIZQS6RNQ1KDV8qYjY5neMRlgW4ZeJMxpmvTyA0f/OwnPH36ObHvc1UKbJvtOBwNJ5iOg6xp2O6AyXSK63mCzaDCwT8wP5rjuBbPnz9nMHCx7AeiQ29oLJcLRo7F+fGMp5895Wgy4pvf+DrvvfeukLuqGiVQlyVhkGIbJkcnc7IsxzZsXMvl/OKC4HAgywosXcc2R6zXS8zQRbUtFFmhaWQ0xcC1hhwOAbPRBMcqMVyHumqI45S0bLi4/0jUcOuGpq6IqpzJ0GVoaqzXSxRFFv7KMqdpagzDRFUU0jpDlhUUNBRJpZYEkamuC/KyxLQt8jLF8Rycoctmv8O2PXbbA6qmMZnM+fGP/4qmES3DwUAcAcs8RwZGA2FQ1lUFVa5ZHzYocsO9e/e4vr7GG1ikech6vUFSL6hXGxbLLUlWkyQ5F/cmrLcb4iSlbGSKsqQqxNBQVmTquqKoSj7+7FP80Gc0HrfN0zYIlOVIDQwcD9cZUJQVcZZhGzbjwQhdV9lXNU2ei0Fq2SAjUZcVjmUzaGXBXXS/A+h2QaSOxj0cDlEVmcHARVNliiIjjoToRZElLi/OuLq+xtBVwiDAsj10zWK/O6DrFifHZ0RxLNK/io6EgiKDVMvURUMWZ0iNxHw8RzcMguDAeDD5le/HL8Wi0NQ1/n7Hq+dNTxIaj8e4rssnH7yPruvcv3fJYrWjbprehdiFZzoEeSdZHTtjFBSW+w1fefMtnr18zr/93veJ44TZ+AhZ02hkFdcbMZvMub1ZIJk6E9fBdWziJOFP/+RP2k6Aymw0oopS8rJAlmRR265qptMRF/cf0rTIc1VTWK+uefX6FZ9+9ok4x7V30iLfLjGbTdB1lTTN2fs+X337LbbbLYPhkG9+/Ru89+EHpFmGa1tiV6TqDAcjqCVmk5lA32c54SFAVVQ0R0ORJZIixxkOkDQNdzikLCpM3UZBRlMyBrZMFhVUSsVkNkGRNQ6HiIMfUjUS8+MTYeZKI2qpoawrzo8v2G7XIs1YFyT7EElqWiBNg6ZpRFGMrBtIyEiS0h//bNsRRwlFFsPZsmQ2O+LF81fkecHX3vkWz549JwxFVXrc+jZVpaRAtDU1VaHIMyRqLNMQnQy54fb2iuHQYzh0URS55WmWJEmGrGiUFZiWy9XNHWHo4w2GyIomZiVNzWa9xtQ1LMskjCJkRSErCj79/Cla2ZDlOXGSELRhIcNy2O99kfJ0HcqipChK5tMZ89mMjz76gCaq0QyDLEnRFZk4ijF0nfnxKbphsN1umc/nnJ2d9Z2ELqvhui6qImTHWm8DW6BrOuPJRBidTBPHsWkahbKk5V7K6JpBXcN0MmO1WhEEEVUlLOLT6VQMl8NYuDKRsS0Hzxn05bZf9vpSLApCeKmRJBmWJVqLUZRQ1yAhk2cFy8WKRlJ/ATfVGXKyLOsVWkmSUIQ5X/3qOxRyzeu7Ky7Ozrg4PyMKk1aBtkduVKLRiOvVEmvgMjs54v7jRyiayp/+2Z8RhyG2afHWkzdJogRfCzhus+qjyQTdtpkezSnygu1uj3/Y8erVS9arO3zfZzqd9np4gJcvX7JsNV4C/V6Qt5afpml4+eolpmVxfn7Oar1i13L2TFuhKEsmLfLdMA1msxlxHDMYDpAlsb3My4Ltfoum6miqjmXa1FVNg8RsNmW32eE4HofYZ+R6JEnOdDhm7I0oixpD1ahrmSDdUtcF/nbHyBaL0IsXPqoqwjO73RbX9drhV4Rl25TtgFQQs4VWLgwDwjCgKmvSNEGWFU6Oj1mtVoxGQx49esgf//Ef4XkCwmvbNkEoZCme/fNmaIemD4IAy7J68pTgT465unqNZdlMJzN03SSKIkzTRDc0iiJlPJ5gmCayIpwXVWvRNlSVshLR9cPhgGmanB6npJWYpyyWS7Z7nzBJqGUFSZF5/OZjXl9dochCDa8bOovFHZIk43oeErWwj0tiocqy1u3ZxvW7uHJX9GsasetRVZX9ditamS3eXzRNc4JDwMEXDUpdE96H7XbX3zh0bcu6rvsAX2/aapOOIkRV9h4RTdN67sYve30pFgVFVTm7uBDKK0Wlbn9vs9uhKErr2atQNaMvl3QR5y7L0J3BB4MBhmKyXC3IqrwlNJ0KItNigzG0sGyTqqmI8pTGnGGPhjx48gaSqvLxhx+xXW1QZJlHTx7gmjZRkDA4OsF1Pc5OTzBMA9MyOAQHbm5vuL6+Enz/vIC64eH9B4KiYxicn1/w6tVLdtutANIGQhleNRDFMev1Gtux2+q2TFrkYorf8hrOZkci1r0Xg6u75ZKoRWuhCBSYLMskWYpp2CwXK46PTynzlMvLS54/fY6maYzGI9arNWeXp5wcHfPi+SuiMMD1BqJEFSc0Tc3AdvH9DYal8/nnIhFq2zavr14gy01vNjo/v0TXxR161f4dDF14HJI4xbYdijzD1HWkpiFLYjRNYTIZcXF+xmeffoIiS2iacEDUVdnPAqr2Ae6m793C6jgORVEQxzGnp6e9N6Fq38iGkZNnJbblsNmuGI48xuMBSZqyXKwBCV3TaZr65+E4GsoyY7fbcXV1QxAGRHHMIQhEYlCWcMdD5DhhtduQlTlSKfPpZ5/1n96WZbNeH5CaGtPUqeqm/SSHpGUwjMdjfN8nDEOm0ym3t7d9lmA8HrP3fXF92AJVu/lA94Y/OzsTGY62f6Hrem/0sm27P5ooiiLanYrSm8o6K3tH7vr3pRnhS7IoaJrGZDKhKEvs1m3YzQ00XcexxfCmrBpBXq5E3bmLQruuK7ZJrV9x4AygkXANj0N0oKlKHj98QOiHXF3dURUNriO286Zn8uTr74jsfBQJKOtsThrFaIrK4uYObzLFO7tgdjRDVRRkSWKzW3P9+hVRdOCNNx5BI/4uUqPw6tUrmkYsDs+ePWOzWnNzdS3SaIDrOOiWjddy8va+GLKtNxsaGhFoKXUxH0kSpEaALsv2U043jN6rCbDZbNAMnbKsGI/GvHz5krff+ko7tFJa2pTB0fEcVVGYekM4v+STTz4n3O4ZjsYksRDoDEcuqaJiKCrWbMRicYvjuqLNJzcoiiBta6rKxcU5V1fXFFWJrhuUVUWRF1RlhSwJeIkkizq7rus0VcVhv+fi/Izr6ytGgwFFmbdodNFIHA5conaB7MA7HVtwNBoRhiIW/fTpUy4uzlBVjbLMcR2X3c7n/Pyc5XKJZdltkKegrn8eNTYNg7LMicuQRqqhqVE1lf1uz3uHgEZVkCQRNW8QXo74xXNUVSVJU1RNp6wrmixh4Lp4nktZSzjuAEVqUBUZRRX9GiS5F/Fut9uep9BRl3zf53A4iHxIFDEeTSiKgiCIeqJSFCXMZkcoitaGkcxfALceHR31IuUuxdhh9GVZ7mneAhAsFpwsyxgOh7/y/filWBSquibLxSe9bphoeYE3VEiThCwTyPXuqqu7Buw+Sbrwh9d6+IIgIIhDZEnh0flDqqYEGg5+wPnpKavlljiJMS2Hy0cPuP/kCYc0Qd7u2G02FFnJwB2iSSp3tws0ReX+eMr04hxZgv1uR1UUROGhjacalHmOqipEUQiNyte++lX+/Pvf57NPP2W5WqHIMm+88Qa3Nzc9DUlr3RJN06C1fXshdxXW6QIRMx4Px+SpgHUahkEYiXOu53lUtSjglFVFk5dYlkPTCGLUvk3z7fZbyqrg4f0HVFVBHIXc3d6y3fpkSYLjeJwcHaOrKq9fv8I25xjzI168fIU1EA9f2SbrtruV4FlaNmEUoSiCCSjLkmAEyjJ5VaJIMB4OmY5G5EnCZDRks9lwcnzEq9evOT055nv/7t9y794FRVlgmga3t9ccH81bUVUjehMtUKZrBna14w6VXlU1h0OAImuUZYWq6uR5SRwngIQia+R5yfXNNapqoKoab7/9NoeDz2a5RNNUFne3pGlC3Ma0y7psqVkykiyjKQqmJeY7pm2R5xllXVM0GlXdsA8CHj98iCI3XF+/hkb8Paq6QaoqirJEabs23Sd0dzVZVRXz+by/hu3iz106UZZlhsMhnTy4s0eD1AengP6olSRJz2LsSnHd97CDu4zHY66vr7/82jgAVdfZbXdohslmuxPXk4ZJWRTEqXDnubbR9w06tn5nWu6su47jIEkyum1ydXNFkaUcH82pq5owiFitdyRZxaM3HvPq+opagvlsxtgboiIxn0yRkZiOJ7x49pxvfuc3OLl/nyjPub655XDwmU4nFHmBqinM50fs91vhDhiOKLOKv/iLv2DbZsuHrR5+u91SFAXj0YiT42OyqsL3hdjFME2qJMaxbZSWP9np4uumxnJsirIkDcRQ1XVdiqqkiiPiKEKSZZpSFhzERuLo6ITNes2DBw/I0pSD79M0Na7r8PLVkuBTn4cPH5OlJaoiGnnz2ZymEY6C8/MT9oeAohEP5eb5CkVRBW26hYPWVUVViciz61gtpl4VR4iqxjQNojDgaevvnM9mfO2drxLFES9efM7xsfh6uqaxWNwSRSGyTCu/HbIPhAzHMAyCIKBpGi4uLliv1z3Q9PXrV7jugOFgTJFXLdpeYjgcURQihrzZrjg9OWO72+M4Q0F6ShJ0wySOQsIwRpIaZEmlLGtyZGS5RlFAQQCAaBpMTQy29TZDU5U1hzAkzTLRapQahp5LFB1YbcUHz3g8QlG1Hu7avXG7T/DO8CRmKg5pkqLrpthlICNJMuPxtJcSK4qKYVg0COjOfD5H/kLpqWtITiYTVFXtG5udkapbYLuw1K98L/5/8o7/v30J21OWCwaBZdstJ0EUgY6PjhmPxxx8vw8vdXTbu7u7/n/Y932qquLBo4eUZcHi7o6h53HwfVzH4rd+8zcwLZc//8sf8fr1K5zBkP0HH/Kdv/fbrG4WGIoGDxreePiIyXTKxYOH1JJ4UKsG9quWvHvwOT8/QzcMVuslhiEip7c3N6xu7sjzjP12S12LdNp0POHu7hZZ09lutyK9pqq4A2F0KtpPjjAM0aVe6yiGeWVJ1i6KXWBqu932+XYA2zTJs4qqrDg6PWW73VDkBVdXYosuSw1B4CMrYieSt32Hy3v32O32qJqG7+8ZTyYEoc/t3a2AjBQyi8UtnucSRX7/9QaDAbvtvrdVp6lA68uygqqoVGWJaejEccT19TVNXeONXHRV4cnjR/zBH/wBSRJzfHJMXZcc9nvKMmedxKIFm2e9/UrTNC4uLqjruh+OdR8Ag8GQ46MT8rykKgFkEbJKMyaTIdvdGs8dcHN7g+N6vPPO1/j8s6coskZWpiiyysATWYDcTMnbmxNoRGeCGqlpSKKIuiWIq4oigmaqhbBeSe2cYEyaF0iyQtPkSIpCmudo7eJgGEYvd3Vdt91hyT056fT0lCIv2O12vU9y4HlEcdTSsx3ubu/I8gW24/LkyRNc1xXp2Db/0D0fXXCqO1J3acmO4dCV+X7V60uxKDRNje+HjMdT6loiDBPqWhKDr8EYy3HJ8pLT83P2223v0Yui6Be64Z1Db7lasd1tuDg9JU9TbNNAkmRkSePoaM7JyQmfPn+BWbmcnZzR1DWL2zuePHxEeAgEsdmxkQyNcL8jDgLe/8lP+No775AFe2ZHxwS7PUmWMZnPMUyLn/zkT1ks7nh0fkocxyJ+3FJ2P/v0U4qiYDKZcNSm0BTLwg+C/jgUJTFI4oF3XJfBQJCTh4MR282GGsFNNGWLqhTb0qStyh7CgAeXj9E0gzAIGY3GjIZD9rsdk9FQ3Bps1yRJwsPHj1htNvhhyGR2zGg6IUoTRtMJ73/wHo5tEqchmqYTJzXz+ZwXL5+haRqz6RTDFBLf+dERWVq0Fq+05WEoPQvAcWwkSaZpKgxDZzab8r1/9z0R761KHNtmtVyg6xpNXSFLMqal49gWyXbP5b37AP1D38V/0zRlPp+3cpSKsqyIwpiiqLEs4W1UVJXFYoVhiG370fwI3TD54IMPOT89Ex2aoxN2uw0DT8Si97sd/uFALbcrcl1TVwVNXeGYlpg9KAoSDUWRt2/+RjQmkUjSjMuLMyxTx99vKYqcppFQVSF/6cC7tm33HQWgL0ONBhNMw+K1+prVakVV1qiqjqaW7Y2BKgjSctITnuM4btWHQq/YDWZt2/4FhHtZli0rwulr01/6K8m6btBNg8FoJAZMjZjWZlnGdrvl6lqwEMu2O9CdoY6OjlitVn3Ky3EccSYrUhRVJc0yRq5HkWVstzuyqhEMx8mY4zhhsVhzdnzBRx98zGGzZWC5DJ0hzmCI4Tg8e/Y5w+mEZy9ecHl8xHw6YbfbE/p7bHeAY9lopsWzZy9wnAH/0T98h+KwE+YhVeXu7g5Zlnsu3/n5OXmec3NzgypJqO3uoEurifqxCjT9vKRuaobjEZvNhhqh2JMUmTzLyFrFeSPBi1cv+cY736Isa7F9N4QlKs8z8rzh8vKSzWaNZZm8+fZbHPyInb/n4vwS/7Dg8RuP+euf/TVJloIEQRhwfHyfm5srjuZzoKRuirbaXveg0sPhgK6KAJrnesiKwqBdqMsypyxqphMbXVUpy4IwDPohomkaeJ6Drqvkedrv+mazGa7r8vr165/ftbdp1U6E8vnnnzObzkmSFFXRGQwmrNcbQXiqSh49eoysNKiqzPPnzzFNG8u2MS2L0WjI6m6BYZhMx2OSRGRcakBtoMgzqtZOpckShq6IgF2aUBU5IKHoFrohHBuyrFCVFTc3t2iqjHAMSTi2zWQy6ZOadesU7bR+3af3aDTC3wV9wU1YtEJ2u13PVOxizoZpkOV5D2+ta3Hks9q5Rzdv6W5qusp29z3P2uPOl36moCgyo4FLHAXYlkVdFsRhgO+LVV+VJfzdBtcwmE1n6LrGYrEgjmKitiTS1DWj0RhVVZBkiaPZnDA40LgeN3cLkCTKqkaW4OG9U26vX3M6n6BrCvsi5/TykqPzc37ru99BkiX+tz/6Q6azKaYq8+jynLnjsL65ZRscOL28xBt4oGkE/gFZkXnw8CEH32e7WFBVFS+vbjBME3c0QTFs6rrh85dXNECaVeRygm5YzE9OCcKQKIlQDNHTb+oGU9NRGiFlLcoCbzjCsW222w15Jlb7pq6ExbgQScePP36f05NT/MOGyWRM1ZSohs5oNKQoS1TT5Kd/8x7f/Z2/T56VNO3x5nDw+elP/6plV4SMRh7TicfrF8+QFZnDPkJXNCRZoy4bAW1RS1zXJokjaHKKIifNVDxvQFkVuLpCsDuQpQVpkbDcrahLuLm9QVEkHMfCtHR8fyfwb6nE6fE9EUWWdVZ3C2zDpi5otYAjirJku99zOPjMTy/YLJc0VY3jaiiGwaO33uLp558zcIas/R1SU6AqDW9/5U1uFiuOjo8Jg4D94cByvSZNE8q8oKlr7t0TiVM/iUjTBEWSsG2LIs8JA584ilAdDaO1cNUoWKaJoshUZUHe+il0y2DkOS0prKEsa6qyQTIUJGTR08hzirwiL3Ic26MsalbLJZvNGoB7l/cwLWHBrioFSW7QNIW6LsnzBMsyUeSGPIup6wZD18nzjOFwgCIr3N7ccnR81AqEZBzHoWlq1usVWZ4Rx9385Ze/fl0V/X8P/FfAqv1j7vZLiAAAIABJREFU/13TNP+q/Xd/axW9Isu4toWpi2GJpkhYhsbg/AxJkvpV8eWLFz1IRVVU6qpiNBxR1zW+72MctWCJQ4CqaSRRyvPgFaoqSlW+7wu3ngxff/sR7334lNvbK568+Rbz+SlPnrxJlOe8/9EH1FWFXBRUhz1H8zlyJSb9dV0TxBH+9SsuHzxksbhlPJny7PNnQrxi6DjugN/5+/8B251PWVXc3S1I84yjRuLFy1fIhk1NQy3JKIZJHgRoto1TV+Rxiq6ITyhLNyglFcPSWSwWRHGKqRtkqbirNnWLLE2xLQtJEjXhINoLyk8WCYBK6HF+71Lc7U+nBGHGX/34b1oOoNJ++ppUdYWiyNR1w+vX18ymU6SmIgkjcS3YWqMDXxzZFFmcrR3HIokLDENHlgWRKI5jdv5WhKl0kyzJUEuVgT2EpmqzJQFVrdNQcwgPTEcneO6YKMzYbK8YjT1cdyiu4qKEvIqYzKbEcUojKRSVCOtMh0P8KOFutSC5uaEG8ihgPh3iGCpVFpOVKbKqcHN326ZBD4wmY+LIIE9SwVo4xNR1hUqD2346V0VJFEaEYUIQRMK+pehkZYOlS+htu7cqc1zHYT6dirlLUyFLwqSlyBq+HwAyhm6hKjqGKzoJelmiKOLfV1XFZrNqQ1y+wLPbBpZloukKg8EAgDA8IMsCl+95NmUbv3dsE5CQPQ/bsgTu/3DoqVrT6ZjlckGShC3t6u92Jfn7/F9V9AC/1zTN//DF3/h1VfRd61FV1b533vXEO2pNN0TsGmPz+byXdQ4GA54/f85yuURVVVarFY7r9mCKbrtmmmaPt3rw4AGbfcTr6zXbzYbJZEYQHEgjcU6bjQZMj+asFrdEQUBRlCR5Dpq4Frp3eYm/27FeLHn/Z++Sxik0MHx8nySJcByb4cAljGKapub+5T10TQR/dvs9A2/AaDLm+auXyLJMTYWqqKRNjaIaJHFCmmSYgyFvP/kaRV6wXq8o8wzbFhIZRZIw2uqs43g9uCOKor780sFqumTbb/zGt/iLP/8BWZpQKAqLu7t2Gl3w8MEDPv3kU1JZXMeen56IRGFVUhQ5ZV21CDwxQ2hqEaAQ3AK5h950dWtD15EVhSAMkTKJYBOgKgrUFXVTopgmiioLRR01y+0KXbWQVGgokZUKVVMIgqXQ+AVLAn+PrMocmhC5ani9W4KqU0oqqDq6ZbeLW0VeNtwuFshLiTgpOD87w5tMuH95weJuIY48rc7NMAzqumLkekwmk95Q1lGMFEXpW7yj0Yi6EM+TWDB1Bu1/p65rdFXFtgUhaTgYohtab/Lq6tKTifCEdgj2OA4oy7ynktvt0aNzPnYzgKZpCKOIPCvaI0GNZYnejyTJ2JbL2dkZYRSx3W5JkhhFkYljg/FkRJYLDkiXc/hlr19XRf+rXr+Wir4bggiZatwvELqu98MTVVU5Ojrqk1qLxYL5fN7LT7pzU10LJfsbb74pGAyyzG/85m+y3WxYLpc9tSkMQ8ajIS9f3WKZOmF4IIkjPnt1xcgbiHP1wRfKessErWR52GOqDvPTE3x/z37vc9hvabKMSXdNV+YoikqaxBimBU3FaOBxdfWa2WzG3/ut32K9WaMbKreLOySEYOTV9TWKomCYpui61w1REFJFMc+fP+fNN99CkcVRwdBVlNEQ6pooDAW5V5F7ZVjnG+wI2ZqmCXS6aXJ5dsmkVZSpksRkOBTmYlUjPkS8/eRNXr++4vrmWmj1vAEgRClpFmMYeg+LifZbFFXFNkXfP04S4kgwBDRVQ9N0EXsuSlRdI4siBq5L1TRYLSA1STPOLi756OOnZHlBUVXYtommKSyXt4RhQFEISEsaBih1gVor1LEoY40HHnerDef3H1ErKqUkY1oOw+GAosg5PjsTjIS0II4jmroiT0XcfTgaMp/NOOx9PNdjs1lTVUrvzfC+8EbvgmRdt0OTjR6DZraBuqQsxdeoSgaDAcPhkDRLaRBAYdd1++p0F8nvas6ObTMcDvuFvZsjdNfv+/1epBazhCLPUFWtlcgE7XtI7UN9WSYSmh1Ve7NZc3NzxXAknn1V1Tpcxy99/V1mCv+NJEn/BPgr4L9tmmbH30JF/8VXXTd90KLLuNvtN6kDqmRZhuu5/Y1D0E7uj46OyPOc0WjEdDoVKu/tVhh1bBtNUVDaWrDYom1IkoTpdMrlvYecnd/nL//yR9QNbNZbLs4uGAw97t+/z83rlxxPpuwPB9KqRNY1jk5OMCyT1WpJkecYqozmuszH4tPFMg0GwyHXNzdkWc5qsyVOUrK8IEtj8rzgxcuXzOcCD//t3/5tNvs97sBlsVyyuFtSGAUD22U8NigQ5/zNZoNtWZSa1g4RBdXIbGG16/W6D/aoqtqeIwVV+fr6WmxXdZ3paMLpyTEH/8DzF8+xLYvBYMjrV68xTZPgEGBoOm88fkKUxey2O3RdUJOahtbuDXVZILVkJbkdotVV1SYjK2EIlyQkRUZTVTRVQzZNNEVGlXQs3aCpRH/l+uqavMiwXBvfP7BcrNFkSWjayhzb0qHMqctSpDtLSdS3ZYmkLjk/npDFe47OLgnTnLrMeP+9d3n05A1QVJq6QWoaZpMp282GQoKD7wtwTVukK6oSy7Gp66InJhstoh5EQe+LH1qG9nOLk9Fq3pQ2RhxEIsqu6zpRpDIYeD2Svfu5BIGQzRRFwWq1QtcV0dloBcFfvGYE+p+lYWjMz8/5/Onn7VxABhocxxbK+zzDqixkWULTVOI4bLmORt/7EAAj61e+H3/dReF/Bv45In37z4H/Efgv+Vuo6CVJ+mfAPwM4ORKWJdM0xX1tC4ugvV6ZTCasViuRMDMMtDZA0qUZO5CJpmmUZYlpmmxWK1zXZblYsFws+jv20Wgk7tl3O1zXY7lc0zQFn3z0AQ8ePeHi8oL5dMpiucQ/BBiqRlmXVDTM5nNUXaPIUjHciROyKGLsDrg4PUFTFV5u7virH/+QLM2om4bp7IgizwizmKa02G/X+NsNr19+ju2K+vX87ITvfve7ZHnObuvz3s/eZb/doasaqqLhDobc3i4w2uu7pqqwbQvHcdvvm4HjuGia2icfl8tlLwrpev3r9Zr33nuXJw/fYDQewoumnXBbWJYl5DLDEZqq4nlDji/OeS9+l+BwwLJ0TMvg9uaKqszYb1NkRabIchRFGL5F4KoiLRP8lvFomBZV22Z1FZWmqjEsE1XR2O8OZEXO1t9j2iZllZNlMXlaUkmiJOe1zkpNlXHcIU0NTVljmTa2rYNWY7sukqqT1wUD28CPEt566wlRmmG7LtvVGqOqudtvRQU8z5FkGZqGvEjF7szUQYZwH/Zat07E2m3zPc/ruxeqRM8myIui1xNK0CvaRqMRhiGq/oeDSMDWdd0Cg6Q2qCWkQVVZ9cG8ru/ReRzOz8/ZbrfCEyqDqim9a1JRtL4kJsviVsp1XepGHNHqukQ3xHF8MpmIlOx4jPx3OT78slfTNIsvvLn/F+Bftv/4/1hF3zTNvwD+BcDbTx413f1q1/TKc1GOMQwDpWXZ0cjYts1quRTXem1CqwNT/OxnP8NqZR+T6bQvlLiu2+PMuwDJaDRC03TG4wGnJ0dc39xhWwbvvf8uQ2/IaUtzilNxRTeaTpAkmaE3YHF3R9EOPseuyxtvPKJIYoJ9wmp5h9PWfKMoJokD8QA1NR9/+D41YBkaaSITRyFxnnG3XvLs5UsePnpCWZT843/6T/ng3ff56IOPCOIY1/VEajMS50FN1YjjBKkFsRZFQZomFIXSo8S72Uw3l+nup29ub7ANkzffeovpbIosK+z2OwbDEXlZs1ytuH//AftDQJCnfP0b3+SHP/hLkjRlMh7w4P59Nuslm/UCuWjQdI0kETJfXdOEih2o8hzNMFAVpb9CNi0T27RI0oyktYWrus54MCCMxZth5JpM3QkDb4ihiyCQaxuoskRVVEi1gqYYOJaHotc4A40GSYS8yhJ7MOLZqyuSIsfWDQ57H1MzCA8rDge/vdoTzcy8dSLUdcWLVy9wLBurRal1JPH+jZ/nvZdEURTKPOvnXY5tkqUpYTvY01W5b/Na9hhNE/Sqqqp68O+HH37Yh5K6r9NdW3Zyl+5nd3p62vshkyRGU7V+N23bHWo+JQgOuM4A0xSFwKLMsGwDkPrehW3Z5FnJwPx/mdEoSdJp0zQduuU/B95vf/1rqeibukaSZWRFYbVasVwumc+FQKNqz//73Q7TFKuvoij98HE4HPbHi/Pzc4EGa/v1WZYxaek9w+FQHC1Ms6cKh0HAwLM5Pzthu9sThQFxkjHyhvzVT3/C20+ekJUFnuOw32yxHBupQZzlg4CvfO1tpp7Hp+9/yGwsYqsP71+y2+1Is5zXccx6tRRbwbzANDRhjFqvkVVZLA5Z2jb9Kj7//HP2/oHrmzt+5zvf5T/+T/5TwjDiz//8e0wmU3RVFWfiLOVw8NlttmjtgykeOLN/oLtPug78Wbc9icHAY7lbYd06KKbGbrunrBqyuuHtt7+K9Plzbldrzi8uWG5XlFXNZDolTWLCIMJzdDzXJglN8ixBU2QqTUNCEjdCekNRVj3JKU4yVEXFdVwaqaHRFMqkwVA15KohiRImkyHnjx9jW5r4vpjiZ9xQUOQJcSyqwxVC6Go5Q8bzIxzPAKnAMA026zWSlJJHMSfTKS9vFsRpCFXD3t/iL8XnmKrIHA57ZEXGtCziJKRsqci6bWBpRr+gdjOqjqL8c1Cq6DOoqkoYhsRh0G7XxU61qsrWM5m2eDW7l+F0lXrPEwv9ZrNpE7kScSyQAL7vM5lMqOsaty2jzU5OuHrxAse1Remq7YbM50e8evWaohDimK7bIAakGrqutSGvks1mw2q5xnW9fhbxay0Kv0JF/x9KkvQtxNHgBfBfw6+voq/rmrvbW+Io6s9wWZZxe3uLpuu9xSjPCkEjCsWZrdsad3bdriVm2zaSLLZnZVmyXq9/oS/heZ7YFtKw3ayZTkacnZzw/R/8mOPTC4oyFw6G/R6amigImXhDNGSiQ4i/2zMaDQmDA9vFLbPjGXLTkKQxa38PsowMGLrKZDggjWPyNMWybWpdFQSdKCTOMqpWoGIicXQ6EyWbnc8f/8t/xVe/8jW+89vf5p/84/+Cf/Nvvsd77/4No+GQ87MzfvrTnwhEW7uQ6rrSQzt0XW8HSmo/IBPMCoUkTZA1iSCJGI3GnN2/4NXLa5IiR9Y0Lh48YLFYUtQ13mBIWZXohoVh6LzYLEljn+lkyGw64XDYkaUZtil2Z5IsUxRla/uuaeqaMi9Q21JOI9fUErgjQcR23QEXZ+ecHE05PpqgKDW77QrZqBlMXB4+epuHD84JwgNBFJFEJZ99+poP3vuc7bOA2eyY05Mj9EZn4I0JA5/JbMpivWbieexfXRPEKUkUYbU3T9SNcEs2AqYiKwqD4RBV11iulgSS2p/tu+eqc4p0zseiKGhKcYzYbrdQC2Cw2gmAZTEDEPYr0QPpmIvdTKELL3VXjYauEIbijer7fh9g6o59mq6LdqquYlkGpV8yHA7JspzhcEgYRq1KUZild/sYx7GwLLvnd1RVRRwlVJUQC/3ai8KvUNH/r/+eP/+3VtGrqtY7FjoRTBiGpGnKi1evKPOcOIpwbWFbUhSF/X7PyckJ7777rkg7tljx6XSKf/AZjUbcv3+/n8ROp1NevHjBarVis9kwGo04mk8FBstzGU9GDIdDFoslo+GYyWiMIsl8/PHHDB2XapJRFgJ6YQ9drl6/xvMcyiJns9ugSgKKUbXVVd0wcF0bx3F6uEVwECKYsqp676IkQZEXZKWPpOjcf/CINM0IDhFJkvKv//X/zvHxEf/oH/1nnJ4c896777Lf+6IaLNG35WRF6PG6XUHnFHRdgTTryMuj6QhZU7lbL1BNAwuJydGcPCu5ur3l7OyCew8fstvtePLkDX7y4x8RZxkD12I2n6NQ0pRiJyZLElVVIEkCwy9LErIkt9ekhvhUtywUTSxOURmhOxYDe0hd1BxNj3BNg4f3zinzmMBf85vf/Ab3v3bByfkUVZfI85hJpVLWE/JcZnw0QTI0nj27QXdMVN0lzSsc00EC0jihTHOSIGTkusRRiqFpSHXDeDwijCJIhO/SskzKpiZOItz/g7k3idUsvc/7fu+Zp2/+7lj31tRdxWZTlNWiLNoWiRgJEGeVrBJkGSD7bLKws+My2yBRgMhxYCcIkAC2EcSLWIgFqU2KUmJRpkj2UD1V1a2qO3zzcObhfbN4z3fUckgBliGjD9BAo3Cr6tZ3z3nPf3ie32P1iKKQbL3vtgpf7u01OWqKZVmMRyPyNNErx8EAoSRSNuQtD8Gx7M6ZCJDnOev1mg8++IDxeMxwOOy2Caenp22rLFsLedUlnWnXbMDt7W03cA9DH8Ogg/XojYnUGx/b0QPwqub27g2n96aUZcV+v2c0GmOZFpZls93u/txDwfze9773r/P8/qVc//1/999+7z/+D/8DiqIgz4vupg7DiKptBaqywrJsDddotfDr1apNe9K7W8/3KFuZ6GGqOxgMWCwWbFqS0QGjrZRitVqy3W6J+n3qWjGfL5gv5lxf3zAejSirErs1WiXbHYPhAMu2qGXNPt5hmkZ7EDRsd1tN7J2v9UlcVgyHY/r9AZJWc+HaKJQu7wwbxw9wvADPDzAsDSjRe3/o93qsV8vukPn+D74PAn7jN/4GeZZTlgVpkuL5HkoJdrsNo9GgzWSoaJqauqoZ9CKUbMizjKbWAbEaeGuTxCkCk+nRCQgDw7TwgoAgjMjyDKEadtstYRDgtZkGTaOBunmZY1o2tVJUlY5pN00L0Vp0bctujVGuRo2VJYarpd09v8fDy0eMegN6gcfi7g2jocff/Hd/na//1a/R6yksq8KyBZZnt4NlA8MEP/SYHA9wAsV6uWY9W+Magmy3o0xTlFSUTY1wLHZxguO5ZGmitQVKcXt3qw1X7ZQfwHEdXEeX/skuJs1S8pZRkLSkoqA1pzm2TaP0Gtl1XcbjEb6vq5CmrjFNo+NDFEWJlLqiEO1LY71ed6yQQ9TbbDYjS/UqUke8ed2soa4bDNPCEAZNoxiNR9q5KQTzxYI00Tkjq/W6NbwFrNcrpGxIkj1xnKCkDlY+Pj5pw5FdTk9P+c2/9z/ffO973/utf/V5/ErInJumpigqkiRlOtWsuabR9JokTjucddM0pHlGGIUcnxx32DPTNLm5uSEIw84oUtc60++Q9fjkyZNOPx5FkebZZTmL1YpdnGDbDvdOj/j02Ycg4ZNPPqTXHzIcDsnrEtNUYBkYtsHPfvoTGhru3TvX1GR0IGt/OGL2ZsN4NKA36JEXBR9/+hFSKNKi4IuXL3A9j3DYoyBA2trdOQhDjhxtNFovVzqheNBH9UPSquLV9RWDwYAPn33IfDnj23/11/nmr/wyf/wv/ojZbAZmwluTEU1TsNts8NvV3zpfEe82+I5DYeq3Xp1mrG4WuJ5PEPVQtSTe78nyksdvf42b21uiPMe2Bc8//4iLi/uEYQ/Hcnhh2Hz22acIt0eWVfiuRWPV2EJgOq6eCylB4IeAhLTBMQAkyoAKhW8IIssmECauVDw6P2f0yw948GSE2ysRxkuEYSNwaXKBYQYYIsC3wXclUZgT9huwe8hsxU/e/5TZ+jVv3XtMWQsaW7DJCxZJzCrbQVPjRz5FDdt9Rtgb4ssGw4CiyAkCHwtY3N6S5wWG0Ig/19eqxrKusBwb1YbQlk1N2dQ0bZhwUeZUTY3ruTSN9kfs4gQDRS/qUdUGhml0epuDaOmQ+6iUnpNYtkOvr4VMZVmy3SXkedVpTtaZdojmZcHx8ZSirBi2a/DFYsFoNMIPPObLO/IyQxiCLKk6MI9sFAI4PppqSvif09V/JQ6FAwjUcRwc1+Xi/n1WiwVCCM7OzrrYuEePHunQmDb44oCiOoictLIvRCrdNx7Wj+fn59R1zfn5Oa9fv9aDx+GQ9Voj0nTfqBHe9+7d482NHkodgLCu5XDvUrv2vvjiC+1TL9JOLur5LqZlslqtGEwmpEVBvqxZrJZ88fIlZVNRy4YkK7mZ6wg5r9XHH7QZh7Kx1+t1RJ7JZILf+u2rqiKJY4RSLBYL4t2eb33rWywXSz7+6CPWqzmBH1AVGhNvWwZ57rPbrKg8r9tEKCnb9GEL0YpbBr0ejqMBLEkcY5gmv/IrfwXqjF0SgzAZno8Jo4i33npMEu8IA4+7u9fYhpabQ/uWc6xWbGZTt/OhQ3y7EAaOsIh8F6Uyykpx+eg9Hnz9AcLKqIoleZ5h+ZYOay0lsi4wPRswkbWgqE0kA3rRW0yPDM4f5Fx/9hpMAyVpWzPJ/O6OyWRMGcdEQZ/h5FRTjtZLPv/8M5pGw3cWiwW2bVJWWq3oe/afiXs/fG6maXZyeiEEPd9rw1lqhFIkSYpsJHXdAAKJJmo1sgKiDpDitT+LA1NxPB6jlOLhw4eMRiPSNGW73XbiPL1xyLpQ5dV6yXCo0WuWZTEcDjsilVIafHx5ecnz589bcdOuy5c8vETLsmSxWPzC5/ErcSgcVjEPHjzg9vaWi/v3ES2jcLfb0W+5A/v9HsuyePTWW7x5pRWC6/Wabcu3O7gQszynbsU6Bzln2bIaNpsNy+WSR48e8fjRI8ajIZ4fcHX1CtlIHMfFsdsdda+vdeVeQNTvsdnvcAOfvhxixAZxvNcoNMeirip8P8QWEXlekBU5z69eMj2akq6WJEVFNBhheQEK6A2PMAzN00uSpCsvq6ri8ePHxHHMdrvFdmwePnjAdruld3FBU9fc3NyQ7PZ8+uwZf/2v/XW+853v8P3v/y55O73e7XYcTSf6MKwKPfBrGZaB72Pa2tgTRRH9KKIsSzabDRLBeDwiTvTWZLnboBoFhom/XYMhKIqS4WBAU+bMpcI0DHCtjklpmgYG+q2kS96mA5YGrmYqRKHLbnvLN7/5DtPLIXm6JMkT0jJjE+94+PgCOwgQjkQ4CmhQjURYDoaKoDFRjYPjx0TjW3bFp9ws5oThAMMJmU4mTNYrDAVJVqBcyatXr3Q2Y6D39JvNmjTVwSmWbYLQ0W4C0W2smqbptlhv3rzBcZwuj8FE8eb1GwaDnpblA3lZasx/69QU7UFykNh/eU18SJxerVaYpsnd3R3r9br7GqADpehZgp5Nua7d2QEOq/jNZkOv19MbuIsLitZB7DluZxc42KbfvHnT4Q5/0fWVOBTKSu+Ar6+vuby8hHZXaxhG9+Ed1kF1rbMJez0dorJq1YtCiO50/PIm4jDsGQwGvHjxolsjvX79mqdPn+D7Pot2h3x+75y72YzPv3hBmhVUtcS2HU6PT7i5uyMIfO7ubokibff1PZ8w0Fbc/X5HZZSs4iWz+ZzNbst6u+XV3UyTkQxBr9+nlIJdHCOsgMlkwvX1tYa3thxKjRnTFtd33nmHxWaN5dj0+xG9MOKzzz5jdnuDkpJd0/DDH/6Arz19ytuPH/PDP/j9js+wWCw4Pz/DMk2SPCfPMlxHQ0td26KpK+K9xroJ02TQ72kZ+GCIIQxW6zVP3/06q8Wak+kJf/KjH3MymfKNr79Lut/gGoL5m1eUVY7daj8E2vEqUAgUjusgm1o/CHWNqSyMBtbLGZf3J3z7O9/EjyRSCmwzZLmIef8HP+Xfc0554vWwzArTqrEsARStOtJHFjpW3YtGmKGHP+iRlDmSthU7OuLzF8+Z392R7fYUSUFlaVJRXZcd+k6TnRwaWbd0cAfLdLvtzUE/sF6vO1rSIaVpPl/Q6w9ACMqywvU8/RkIqMuiRQlq38hBbn5Yneuv0y+B3W6H7/vs9/sOxWbbNqPRiDAMCQK9PThEKAaB12kYDoPOw/frOA7RcEjy+jWnp6c4lg7S2e81bWqxWLThwNW/mffh38bl2BpSenF52W0gDv/Q+XxO3gpdDtCSYYub2u/3nYLx8KGLVvDktD16HOt4rKdf/zppUVBXFaPRqOMa+J6rh1BS0R+Mef78Bffu3ePTz77gzfVHPHr0iOfPn3P7+k2LMW/o93pMp2OGgz6mYXRTb0OYbOMMwzaRKKJ+H4nACwKKuiYrC27nC5I0Zbve8cEHH/D48ePOC5+mqY40b1OKb29v+fCTj8mLgn6vj1JS8x4fv0VZlOy3W7brNX/ww98nCHzqqtL4ulSbsNbrNUWhb0SjbVXyTEM6EDVpElOVpU6mSjN6UYBsKo5Pzgh6IbWS9AY9ZvMZ733rV7k4PuXZz37Gfr1kcXeDUApDAW3rZ5kmtm3iWDZZluA6DnWlKzbZNBRJRTgJqcqUX/v2X2E4DahJkZaLkg7C6fHZiyVHP11wNP0G06mDYcRIuUeYgNGAKmmEQokSP3SxAg/DsalyRWBbpHmGmSSau2BotoMQFrWnQa6OqzddNzdvGI1GVFVJmRZt6+PiOPrrDtyGA7bs8IAejHllXVPudwz7fbIsp9frU5Y5VVGihIHjulR1Q1OVXaV0kFQ7jtORpXa7HdOpzs48MDXm8zlCCIqi6F4Wh/8OLechIe0QNnuIi0vbFkEIQZrqcNt+v4/fApGzLOt4pr/o+kocCqZtcXl5yW63o2o/tKqqyLJMl2pt312WJScnJ/z4j/+YXq/HyclJJ/FcrVad7NmyLIIwxLJtysUCKSU3r18jpE6a9lv9d9qulWxbT/6Pjo64vLzk6tV1u++39bR4t9c6/1oR9ULc1h2nSTywXq113FqaURQZtZSEUUicpkgFu3iPEgZ1u/t/9Pht+kHA1dUVoHvuixZxP5vNUO3cIAgCAt9vo8MyHNvGECavrq50vHsra9VT6hIpG5pavwWUFO0NrG/qAyhUCMF6tdK+EMdlu1kzHI2YzRYEvT6uH+B7rj7slCJpQ2gPmP0sy/F8nyRJcVwXwxRY7feoszkhCAOEUIR+oLX3nodj26yrBNfzOL10KPoWAAAgAElEQVQ35vTyDGHpmHhhgGE7SCwmJxfsdhZVGZEm0B/0gBzDqFAUIBS2I+n1FWkpGU8nCNPAsg1sx6aoaq5vbrqXgRAChfZYTKdTZvNbgDaNquzK+zAMsEx9GBwwZ6vVqt0GuJyfn/P8+fOu9Hdcj91+x+3dnND3iJMM2zK056MyKEtBo7R79BA/cKhaD8DVA5b94LM4QGsO1XHWHuBpmraQ4ADDgPV63SWzB0HQxco1TUMcxzxq2R5lUXZbt4Nrttfr8fDhw45W9vOur8ShcHi7BYGm4zRtuXWg945Go84SDfD1d9/lpz/5CWdnZ5Rl2Q1ZhsMhq9WKKIo4Oj2FFg3+2WefdXvhg/Jxu90yn+mWYLPdMR6POjR7GIY8evSIz794zna7073/eMSgP+Ds9ISHD+4ziCINf9msyWL9xs3SlDAKqKqa2WLRotd90iwH00YJg7woeL7bM+z3OZpOu5IuTdMOVOr7fic8UkoxHg3ZbjaI9rMKXY9dK8gZDPosFgvSLG0fID0glU3VqfEcx6FpkfC2pfvcNEkoypIkTTEtGwyTpq5QsqYqC6ZHE9bxmtVsztnJGbJu+PGPf4xn2xjUjKYTVssKTLBbi3oURcjmT1O7BLSTde3S3G4yLM9iOB3R7/UQtsI2bJSyMQ2b45MjfuW9X2YavcPpqYeSiqausG0XZIEwJEI0WGaJ40h6oU1d6TxLUwmSNCWpKnKlkCiUgCTP6EUDGim5urrCMGg1MCmDQZ/pdMJg36csteHroPwsy5J+v/9ntlsHalGe5wyGE1xXt4/aCJaQ1BXHx0faZ5PE9PtDLAqNcrOsrqKVUhKGIWmadulhX2YqZlnWheAcqE0Ht6swIM3ibrB++PqDlLrf73eZlFWhX6JAV2lfXFxwcnbGp8+e/cLn8StxKBxw3kEQ8ObNm877EAS6lMsLbZ09lP1pkjAajbof4Pn5OVEUEUURoL3xWfuhHdyUoAGsUko+/fRT+v0+o/GYIs/avsxjudrqfIIWk1WWVadpEIaBME2ifo+Liwt8xyGL9yyrirRlJviuyzzZsFpvKIoKy4QyT5CNoshyvDBC1jW2rROZD4nPQggmk0mXiHRQbAZBgNeaoI6Pj/Bcl+dffMF+LwGF73lYpoHnOCRJzGq5IgrbN0ea4LoOptBDP6m0UxDAEFA3UlcZhslqteTpO++y3u4JfR9DKKqyJN3u2SxXHA0nvLq6wnUcfM9ju14wGI9oKJGyJonTjo+53Wi0vOu6VGWFaWoRkGwalCWJi4SkKDBcH6m0zdySJsqwGQwdnjx5wH5d8Nnz59y/OMJ1FRqQ6iCk/uZNYeAYAt912azXmMJA1g1xFmMFAbKqkIbACTyqImefpdhdqpOFaZmAxHUdlstF659JcF2PQb/XzWWGLR7w5cuXAN19cUiDBoGUSmdUSn2PrDdbyiLXsxvHxrK1NP0gSNpsNn/GgXkQl9m2zfHxcZvCte44CxpQ2+8Ok/F4SJ4nnbjJcRyGwyHr9bobis5ms272AfwZzLvneezbHMtfdBl/Cc/4v/YllQZI7HY7ja4+Pm5L4hq/bQOKsiRNU722iyKOj4+J45h79+4xPjri9OwMt+3Nnz9/zu3tLVnLrIuiiKdPn3YpOYethtEOai4vLzXoJc2I4wTHdVuenv5BGKZJWVcMR0OEYZAkKWEUUlYVqpFMj6YcTY8wDRNZl6imQskKWRfUZY5pKDzXpi4LJqMRliHY7XZdyCjoG+7gED1AZg7V0/3LS5SUzGYzHMvGNAyGg4EmAed6BalaX8Dt7a2OHW9bqT9NQqIT65imiWUaKCkpi5z57I7lYsFuq6lNUkoWd7cErsfXHr/N5fkF05HG2ud5ju26bOIdl48e8OjJ2x1SXFO2S/Jcr89k+4Y8SK9Nz2Sb7pDCQBo2UlrI2sQ0dbvi2HDvfExvmLNYfUQt50gVQ1OBskH5UHtQu4jaZbvcc/X8BbQORdu29Swpz9jGe3ZpQiUbavmn8e373b6rGJfLZcsdOMBPNGAnCALOz88xDINXr151P48wDDs7vx9GBGHI6zfXCMNkPJkQJymbjaZ7eZ5PVUsWSy2QO7gYTdMkTVPu7u7YtylUh2G5QgfoHJieh8SnL2dBHIJcptMprut2cwnHcTg+PkZKzW5o2llbkiTc3t52OauLxYLbN286eMvPu74alYJtMzk74+blyw5E4Xo682E4GJAkCcv5nMnJCfObmy6eOwhDZrMZaZ7jOjbLxVLjsVoM+t3dXbeFKMuyIxE9ffq0rQRKLMvgi88/p9frMxoPKe/m3L+8YLVcs95s9Qda5CzXBZ98qnCcr3Nze81idqtLaaH7+jzNkLKhLrUcuqlrXNvGtByEaZMVNUVVMJ/fEvX7uKaDaRqcn5/hOi677ZYiz/E9j32smRK2ZZPECZ/vdoRBQOgHJI2kyHOWywVJvEc2EsOA0Ui3Ttpl6rUhOhaWoUGwlhBYlqmt10riOBYKUG2vu1oumByf6hWipcNYl/MlUdRjtVziOT4P7t/n9u5Gcy2yHrajV5B+ELBaLrFbMM4uyyjzjMAPUAqKQuPKJIqiLAh7vdZNKXUV6EnyWlFLA9cLefLoiPjYodczUXUGlgMSlLJBAgiqPOPFZy/YrtY4tkPaJOz3a8rtBnw97ykA2/NwbY8wCgijgLu7W+qmIk0zyrJoUWgHSXiPXi/i+PiIm5sbtm1ehp5JWEjZsFgscD2X/W5LVVeMRgPqumK3K/A8VyPhs7SLdOv5Fp5jY1omlm0ShPrnGkUhQeATRSFVXWGaBovFjH6v38bZV+RFRhSFOpKwKhkOBziei2lpAjpKw2P1QbVttRRNC8X18Fyvc18eDiXf9/Xm5ku05//f8/hv57H/86+6qrh79arTcx+CU3zfb+PW9RonS2IMy+xko2EUtrkDGfO5pirZroPn+6yWS4Ig6Dj3B9vqZDJhPp/rjAXbwDbHCFnx8UcfEPV6TMYD1HLFaBiCKnEdgzQvUYZgtZ/z2QuLps7xLIdBGCHzkixJUXWDQJDvaozKpkpL+qMBp5cXDCYT1vGO+WrNNo7ZxXs8TJK91lgcQkXTRItmVFPgWD5lmWA0Nr7rka0TDFNgGyamZ5PlFQ05mIqiyDCw8EMb13NQjYmBgyVMLRGmxrEVliWo6gIpG1zHxrN1ZLztmjRVwfHxMTgBWWNSmyGGZ9OYDoPpMdvtGks1NEKRVxV+2KMoAFVjGja+H5LlGaahME2FZZua3ryXWGZAvKtItxmu5+vwVVVhuxD2XISQOI2gLkpMIWiEwyjqY5s2UqLpTbWuBGgadtsNH/70Z/zL3/8XBNIlyVK2RUwtFHkp8SyBbzv4owllVeP7LoFvkWYbhsMeSZKRxDlJnFGXNkEwwjQtHjy4ZHJs8+LFC03KFg2GqVCq6oJbhdFgWeCaFaaqSVvJspSSptZDw+GotbqnCVLVuNEEXIUdWvh9j8DzNMm675EV21bnUFLXGY3UyPc4kQSBzWJ50w0oZ/M31HVF1bbWvV4PQ9igasZtsvfaFLx5/ZKLi8uuMjw4M9frNefn58xms44v8vOur8ShIBvJYrHo2HUHroKUEgyDyekpm/kcYVuMplM++tnP6PV6ur3w/W5NtFwutWrMdZlMJgDd0O6gSDvg4H3fpypysrRkOj3i5m6BlGDZgpOTY8bjCbVseP/997Edk0rngzCfL6nSgnF/SNofku327c1stGUmFKXeBcdJwueff457e0ulJHGeg2G0O2ydF9jr9brNypfR293u2YSiNjBsC9d1SNI9UkkaqWhKbaJxbF8zHi0DJQ1kBbav+QaObWHQYBgNqIo0ragqHSdv+RYuUEv+1BPSNDx4cJ+7uzlNkbUK0jW2beF6DuPRmDTdI+uGi0eX3NzcoFDaHFYXrNcrvLbKqauGqqyp65IGE9O3UbbCDnu8ulny1pOHVKJpobMNOCY6l7XEtGyUTKmbEst2EaqhLApMDK7fXPGHf/j7XF3d4NgRSZax3e1xXM2q7I/G9AcjlpstgRBkacZ+l5DnJb2ew3ajy/ZHjx+ipMBxXE5OTlFK8uzTH7FarWhaJmKR59iOzX4fg1JEvR5VVbJYrHAcG9lIdts9ZVUSRfpw0OpGRb83wLIkthVhmSGqcSkyxWSotQXj4Sm2bTGbzYhCkywrqas11aAhz8t29uB3a/qyKHFsA9fWz8Z8piEt4/GYMNCpTxcXGmfSi/rMZvOOwnVoVTabDUBnkPt511fiUEDoh2Cz2bTZB/qBiPp91sslSUujafIcq20d/CBgeHTEeqZTmyxLf7hCCEzD/DOqsKLQEWK73Y7hcEjU67FcLMgTi+s315iGSxj2EUJLdUeTMVcvr3Bdi/FkwNWba/JKEXhtwk+vx2g8ZjVbUOUFrmlhuzbCMMjSTA+elCTLM8pUUq5W3H/0kLEX8OLVS4TQLRPo/fdhH36YNh/CPk5OTsjTgsViqclSroVMZJu8rduCummQdU0YuAgUQRRhGxZN3RD6AaYpqIoEqRoEBo3Ub1/DNDVEVRjIXJu6bm+uuffwbRazOybjMfEGvRI1BdevX/P4/rcxUHiuxajf79RyVVnSNBWmSburl21SlI6W01RjQVbnmKZNIQ3We8kmNgmjENtSSFIsq0GpCpoM2RRYlotsMhpVoyRYtsv11TW/+7u/y8efPIPGpSggSXPqWmLaYDsuvaiP7bjkWcFgNMI0HSxhsdluqCuptwZhq/w8mvK1p0+5enXFxx99yHa/aGGsBvv9XkN+DBvD1nmfVVmjlKCpIWk3Fp7n0u9NWsdrjs56dKlrhZAWWaKwTQhcEwOfIofAC5CNiTQslLSpS/B8C9N0qUpJnulKOQy0uW+11BXF+fkpVZmx3+87fkOW6WH5Adj7Zdv1IU3t4aNHrFcr/W9oq/BfdH0lDoWDNvuwfjl40bOWr7BcLnn7yRMQgsVywTtf/zqL+ZysVYMdcGw6mSgk3u27QM5DWEae54zHY+2pRzsFN1IQ+H1Wyy3RoMcu3lHLDM9PCUKPXj9gMIwQbyRKasZDP+q3LraI4NJHlpXGo3kBvSgibxruZnfMFnPyUguE/DBktVwRZzlRLyTNMgLf76bDB5PW6ammJx8Qc4vFgiTNsCwTz3JIsoSqqbSOo66o2u2I53kMoiGWJfA9H5T25Od5ShQFRFGIMAKqugByTENXGFYbZS+VQhk2u+2GCySvrl6SJommCx0fY1aC46Mpr65eYpgt7LUssWwLQ6gWOS5RChzHJt7vaIwKJaGpoapKpJBaOdgYfP7ZFdPpI27epJyd9whDk6YqETZ4jglCUhcxtRLaAyFNlncLnn30KT/453/Aiy9eYlkhjYQs0w+m7XgYpo1lOxRlhTJr3nn3GyAM1ssN/WBIGAxJ04S60kTw4bDP/QcXXL3+nJ/89E80Lo1eN28yDKNTCtq2zoQ0TY1bl5VBEiddv97U6O3GkUO/32c6PWIwGODaBoHvMxwNsAwDQygC3+PoaIpjW5iGwem9c5A1tm1q/YdtE13ozzhOElBwctJG0TeqzfDUocaGYZJlOS9evCSKQu0srvT3fHFx0aVrNa0g7rAKPQBif971lTgULNPqTEq0rrH5fE6apjx9+pR79+5RVxVJpkUch0j3w342jmP6/T5xHBNFEZ7vUZVVt9bRNJyG45aoexAI2ZbD2ek9rq/fAJoFcHQ0JcsTRqMhT56+zXI1424+p7qN8VwX27K5fnPDm6vXeLbLdDzm3tk50+MTDEMwDHzCfkQ46LNYrQmzHtskAaXXfOtlwWg8JvCDFpJRcHt721F4ylJDMw6KNcu1qeuKwPfxfZfdbs1uq98IQonO5LRqFJf3LnAMD8exGURD1qslGveiGQdhMMD39EqXduMj6wqEQaNM7CDARHF2PKVuJEfjCVkSMx6N9PYCRZ4kXG233Ds/oyxyDEOQZgm2bRInMXG8J89TUGAKE0PYukpRYNaS/W7D1tnw7E8+4dG9J6hE4Qc+6+2KQu6oA4UhSs3QSHfYts9HH3zCP/+93+fFi9dUhcS2POK0QcoGpcD1AgwpsR2PXm+IH/WI04yqapA0RFGfm9cz4niP77vkudaqvPX2I370o/+X6+tX9Achd7NronDA0dExWabfxofQlDwviCKtAhQYeG5AU+uN0enpKWdnZ1xcXJBlGV988QWr1Uo/uEaNYUK0DFFNjWxqTo6nLFc3TEZDRqMBQdTDMC2OT85oANFuiwBm8zlpkuB5Xgtm0YYrENgtJeog9hPC1PDWVgnZ6w86ifWB/dhIideaCn/h8/iX8Iz/a19SyQ57fZD5DgaD7s0ppdRtga0dea9fveqw74cU5Pl8znQ6bfurkP1ux3q97pRiZ2dnpFmG2a6mdEKyT5EmBIGP47ucnh2z2qwYjUd6pZll3H/wgC9evGa11i7L5WJJGidMxmN816OoauarFZ4fYNsWdlNzenrC9PiYL1685MXVFelsRi0lZV5yfHqCrGpme41pS9OU8XjcGbU8zyPLsq4sNG2BYRrkaUyeZaxXc1RLNXIsiygMCQMX1wrI9hmqkhBo843vBaA0LbssTExTo9fDUOgpdVVSKYkF1IWOkFdNRZHGPHj4iNPTc16+eMHs7pbpZELo+9y8XjIc9MlzDY1xHQvPd9ltN61/f09VFuRZiiEs+r0hZVnguz6eNNjvS/Z3a56tP+Tlh1qj/+//rX+HDz74Q7LkDt9XZEXK9e0tWVqw36fsdxlg47khruOS5xVVpVsN2wGp4Oj4lKg34PT8grA/5ONnnyKVIE5Skn2GbdoURYkQiocPHzE9GvHjH/9LkiShrisWC+14DfxhW6llDIfjzjEZhjZhGHF5ecl3v/td+sGIu7sZhmHw+eef80//6W935Xue53pmVVUI9riexenJCcNBhOvaSLknzXp43iV38+dEUcDZ6WMc2yUIQ9JE6xBs1+Xi8hKhFLP5vFOk9vtTRqNxd48c4EKvXr3Gti2iqMft7R273a7bRARB0A3yr5fLrr3+eddX41BoZLcuPFiobdvuYsc9T69WHM/t5Jy3t7e8++67Hd7qIIcejsdk+7hzhd3d3XUpU4ZhcN5yHMuyZDKasl6siHohJ+dnKCSDUZ8k1e3IdHqs+fqTY+paJ0vPZnOEVFRlzXg4xPc8Ni3J5vzeOSe9iLwsOL93D0wT07YxLIv1ZsN8sWIxm+P7HhKjS+85GFbOz885Pj4mSRLm87k2rtgO6W7ParVENjV5mjAeDuiHPY6mEwZ9PU+RtY1jhwShD0KRpCnpNiYvSjw3QvgOdS0Iwx5KGSTJnqZdR6qyRNYVVZGhmopkt2G9nLPb7ijynKoqqYqCN6sFVbsea+qqVUC2aPcsJd7vyPMMgdJ/rtSsBlkrelGfo/6UMm5I1lukD0YtefNizz/5RzNCH2yr4sVHL1lsd+RVxXAwwhQRtmEihI1sbPKspigaLNtpo9Ry+lGfh4/eoqwaVpstNSa//Cvv8eb6BhlnvPXWE15/ccV4POboeIJtG3z44Yc6TbwpOjVsHCegNDg4inrtvMDn9PSUp0+f8uTJE1zX5fvf/z6r2YbFYsl6vSJNM+q64gAzNwzRZXBImWu9ggVNkzEZ90kz8EODvNhRVjH7OGeUH9M0FevVAtMySXYalyelz3q15vTshGI8wLBMimTftsGCMDQxjIz1eotl6YNrtVp3z9bBYn19fc3Td96haVeThzb6511fiUNBKdXJcS1b50SeXVywXq87x2Oe58T7GIXi6OQEKWXn+BqPx2x3O1zH4fb6mqosCYOws8De3d3h+z5FUXD18mUHbNnvt9i2wLINHM/G9n2S7Zqw1+PRW19jt9+x3cacHJ/TyA2fPHvGdDIlS3QKcFFVNFIhm5rXtzesthviVLcypm0xmZ5wnBfsWhm2EBaNbHjx4gWipRR5ntcJVO7u7ro0qwOAI42lZjyWOYMo4O3LJ/qNYxtYQhL60O/79PsTbNenqkvysqA/DDk963Fzu2C12mPUOia+KEEYBxScnt/IpiEMfYRpIOsSYSpeXb3k/sO3eHD/Pq9fXfHy5QsC32M8HiEELOYzNqsVrutQVWWbqLSmKDXMVQCWqVuHvMiwTAtHmIxGY8p6SZyu8CILNwjZ7G7p9c6IwiELY07oCWy7QTUmCvDdgLJsUEL7KgyzII4TirrBD2yUEnhuwOWDM7b7mM++eMHN3YLVZsvx8Smj8QjXMHEcm6LI+NkHP2W9XrdhxTZRNCDLE8qyxrUlhmdyfDTm3sU93nvvPSzTYr1Z8w//4T9idjfTzlJJi2AvGY36NI1kPp91G6Q0jTEMUzs9DUjTAs8pqPsKKQVl2RDvEwxDW7VBcXt33b0YNXJwRVXpqmO9mneiKNOwNIl8GFHVFUnyGtt2QImWVG2097s2RB3gr2kcd/j5P+/6ShwK+mSOKIqC3XZLnmWsWqfYQXlVliVhLyIvS9I4ZjQada61oN9HCYGsa81juLikyDJevnxJr9djMpkwmUy4urrqYueEEGx2W5pKrwJ/8ic/4ujohOVqhWlp0ZFp2wz6Yx4+eMxq/SFJkmIYZlcaZoWOihsNhx2/fxfHmLbF9e0tn3z6BWEYsl6vdUS6abPZ7hgNhuStr6EsS6Io+jPAlUPST57nmFIy7EeMzs/xPZPj6ZC6TDiZRDx9+pivP32bk9Mj3J6PaZv6JklTGgWNstiuMz755IrPPr9mtdy3mnmt/DMEyKrAaYNNLMtis1rhRz12cYptebiWxWg0Yj674+OPP+Lxw4eMR0NtjXYcikIrTfMio6x0FSGU2fEabctCKY07T/OG09MT+sdDlldfsM4k0dEFMi959uIZnu1jCb0R8RwTqSRVCy2xHcjKjDTW8uLe0CUIRwhhcHp2jkLw8uUVwrLp94cUVU1VKyzbZh/vuTg7Zj6f8Ud/9C9QSieKDfpDpGrY7fYIAb4X8fDhIx48fMBbj99iNBrx27/9213mgm73MpbLFb0g5PT0hCgKu0rDtk12uz1NU2NZLpZlIisT0xAI5dHUNk1lI/Bw7R6WGeK6Fr43BAS7/YrhcMTJ+Tm2afLm9WsUNXkes1guOT4+RqAoi5okSajKujWr+fq+zgrivQ4DWi1XuJ7VQYoOPpiDMeor3z5UtU7lqZuG3W7HyckJtm0xbMMzNOyi6uYN2+2WKIro9fuEUcRqNuP08pLdcsnDhw8pywqn/UenaarZ/XXNxYP7yLph38pCsyzBNCCPM2oJd3e3vLm5JYr6jEZTHCUYj6Y4dshml1MWBYvlktGgz5/85KcotPCqqErGrqO1EapmuVq1U2ODTz75RB9kaUpdyQ5uIS2rg8ccNBSHVKCDxNl1XUaDIafHR9gWeK7B0XTMd3/jbzGIXKLAxvcsDFVgqAbX93FNEy90kZiYTsDF5SkXl/f49b8m+elPP+F3/tn7SNlgOA6ykC2/z2a3jxn6Abv9jkYpwOSTTz5mMOi1QhctYV6u5qhGC4KEENze3rDfb4j3sWZD1jUVkqZqsIyKIBC4jo+UDUVZsc0yBpOIMA5Iyz2rZI5tuShbEBc5vhthGwLH1kzC+kvIMqkKpNLsDcuxCSOP0fCYLC+oqprRaEJS5AwnQzbbHRKDfisJfvXqJe+//3skaULRYvWFENRlQ7835PLygr/xG7/B8fEJz5494wc/+EE35zk8THqjo3M0EZLtbgMoPM9nPBkxHA0Yj0fs9zFfPP9CVwxbnbcha0WeKcoC8qwhSxvWRkIQuPhehm2XmvYtNRhXYjOdTrAsk5cvX7ayZrur8KIoom7qrqI8tMuHLVy/36duNLT4QPMqioLA93X19VVfSSogznL6/R6O67JYakCI41iYQsdfVT2fRgq22x3ekfZGVHlBWRQgFfFqTS+MWK/X2Eoxv7nh4v4lru/TKIltGQjLZLackxcFjaoIBiPG42NW67UWTxUlFw8edzJQpRQISa/vcXE6RDQP+PjjnJ988DP6wx4PJo/Ii4rX1zfM45RMGUSRz3IX09RadnrQr1eGQVJnDE6mCMOgrCt6gz6ObWNbNrfXN+z3uy4tyPd9RoM+l0fnjMcD0mKN7dZ889feon9iYRoVjWiIc4ktLJRjI0oTZ3RC2VSkcUKvKbCMhOHIonfiYkwvsad/jR/+Xz8kT1LAQCibqkoxRU2WrijSingXY1oe41HIfvEKazqlSjcsZ29YL+8YjcdMplOkUqw3a6pkh6pqVA2yEuSl1lJUokGqnEbqTIS0TigXJbZ/SeiOaXKDamfhRz5lWRF6Lv1ehKANKmlqVBxTVTWl1OQlSzSEUcTF5SVV1WDYPr4ZIIXLbLED02Cxek0tK0zb5PHbl3zwJz9hdX1HkujUscl4ijYzNZwch3z3u9/lyZMn/N7v/R7v/873Wa1W9PsRsmqwDAGqRqia4cCjCjRZScqGOFniuh5NmvOTn/5Rh4Evy5LvfOc7fOMb3+CzT161mSUuu92GLI3JqxqFwXqb4Pkhm13GdDwhzyBLS3pRjW3D8fERq9UCzw3J85LZbKGDYsIQw/VY3FzTj3oIy2afbNgnGybTKU1Ts9lsuip4t9XkbcM08H1Ne9LAuJ9/fSUOBSkVwrBwbI8o6rHZ1BR5QZHlHB0fsW5XdQ2GRpm1KsC6rknTVIfFjkYdP89qWf5FUSIVLXzToaz1umaz3XLv3j32cUYURZRVxW6rNQ91XWO1vP9er8diPmc8HnN2ckoYhNR1w+vrW15f3/Dy6jVF1eAFIWmWtyTqPlWlV6DFl4AaYRThB4HeD7e06jiOkU1DXdW4rW3abEVFURThOS5e0CracPnWt99jOB6RZCmmIXENkzovMZXJrnDwc5uJYRCnBk3lYSsIPRshHIQy6UU+v/TN94hnFe//zg+pc4XrBuRFBu2gt8gLytrCMBq2hn6wlleeBT0AACAASURBVOsVy+Vaa/DrlH0cczefa9FVVdH3HZQAYRqoxkDWNUVVIRA0KG3LNAws26Gqaw238X2qsiYjx1CCqiyRdYNjOwShT1VXNEoxGE/Y7fas7mYcH5+AEBiWxf2Hj1CNotcbsN0lZK0Ja7dNsGwL13f4xi+9y831NT/+8Y9RmaZrHw788XjMo0cP+cYvfYOPPvyQ//Hv/V3WqxWe4yKEJn0LQ2CaRsv2SHFcq3tZxHGm3a4thLWu687v4DgO77//Pj/60Y/YblKevP2Us1Nd/f7yt3+d+/cvGI0HLOZ3VHlGnOwpywLHcfCDgOVywcnJMTc3N1iWyXgyatFrJpZlst/t8IJaG/yAJm/wfB8/yEmSuLPg07aFh/jA6XTahTn/G8mchRCX6Bj6U7Qd5beUUv+NEGIM/O/AQ3QgzH/ShswihPivgP8caID/Qin123/e32FbNrblEicp6/UOgUmR12zWK4QSLdLLwXUc8ra87jBVvV632guCQG8bgoijswviJEaZJmAgDRPbtTAcj6xs2OwSPC8gSxLS/V5HvHt9Qs9nNrsj8DxiqbBNC9U01HWJYQiCwOf+/QuSLEOJHZ5SWLZH1B8wmUwIA4+rq6tOiLXf71mv1220W94y+EfUsqEsS1zHxWit01WkBTOmYejhkO3QG/QpqhLbczk9Pyca9EEUxLs1RVZQpjmWsFheJ8Rlwjd+5RTbC3EMB1EUmMrAtD1MO8A1bcy+ybvvfZsPP77l9uUdyjCwgxBRSbJcD3KVrKgayWy5I8m1yCVOMoqypJE6RGWfJFi2dmzm6a493BvqpmlFNhIhoKkUluPgWwbHJydYls1ysejsvG6LWldKdXF3lmvTKEV/OEQYJsowwbJZLFecnJ5y//4D9mlKnuYI02a73XJ7N8N2DmrTEb/6a+/xT/7J/8nV6xeMBiOiaYhhanPQw0eXfOtb32K+uOPv//3/iRcvXtDIGsu0ePLWY/b7HYvlgjD025lWjUILvrrN1WTacQ9Ua9IyTZOmbsibgjRbo5SGEn/wwU949kzni8xmbzg6GvPg8oLpdMygDZ+djEacn53j+h7xbofj2jieS7zboBQcHU2o64r5fEYQRCAlKMV2t8MLQxzL4vz8HKWUzirp91DQia4OB9chku5AlPoLHQropKf/Uin1x0KIHvAjIcT/DfxnwO8opf5rIcTfAf4O8LeFEO8C/ynwDXR03D8TQjz985KilFLYjk+WxIRBr8sldCyHppaMj04o0pRGSQaDAXYYUrUrlXnLVzyoAEejEbIBYTnsU00JGk+n2uasFOvNhrN790nThHi34+7utgXCagPPaDTk3uWvMru7a7cTNtfXGwLfZ71ec3HvnO1uR5LmGOYN682OuqkJA5/1akkSay7e8fFx9z0dSLxfDnvtD7WwJIr07l3nWmjdwQG04jkuTV0xngyRoqCsK/ZJQqMy4n0MdYOJ9gvsU5Mff3jD8LTi6TvHCFkS7+8wkTiugRsG2JZL0whGpw94+933KIoPePnZp/R9F1klWjItG6qqoW6gklXrmrSom4a8LGgaqRWQKBACaQiS4kvqOKEXc0rnrWjPQiMRpt3Fu2ctJbuRkqquNR/BddvfpzSL0bToDYYYlkk0BGe7x4siBqMxludp+Kyn6dp5VhJGPcqqYTIa8fRrb/F3/4ffwjANHlzehwZmbxZYlsU7736Nt5884Td/8zd5/vxzGlmTJHu+9a33qOuG29trXaU0JVVlatl2VTEejekPok5xqpRAYGAIE8M2KZOEStbt4SiJwh6O7XapTsdHU6qqoCz12/z5889xXRPX0W//3W6LZRoIQzAeDUmSHZY10DL1qmK7XaPQ7VrTqI5mvtvtEOu1DkRqqz1A54W0JsBer9dVBoektAOq7S90KLSZkTft/++FEB+h4+X/I3ScHMA/AH4P+Nvtr/9vSqkCeC6E+Az4deAPftHfUVU1ZVXjhBGGYTIaDvXOXEpWyyUCzUDwIo+syLHzHLtN8DlAXo+Pj7vT0PN9StlQt5WEadnk+5jNdstwNGK1WiMElGVFluxxbJujY61isy2Toiq5d3GPLz7/nCSJqauKk6MjoijENG3Oz87w/BDLslG84vZuRrLfUzU1s9kdTdNwdXXVUXhHo1E3AT4o0w6xZGEYMej32bfti23bHQr85OSE0/EEyxIYTo1hmlRNQ5ZnpEWJa+rJc5VXOOGAXXaLsH0uH52S7zPWzRbfV3qfXxZIYSLMkEoZvPXONwm9CZ89+5yk0HqDRkoQ+o1f1RJhGdRNQ1FV1G2FIKU+DJSS1FKTmjENmqZuuQ0CYQgdG6cUUklM16GSjTbltL+eZRmmMDRl2tHbJ1oITJyk3H/0EMt1kVJxcnqMYbsMFZyd39MELsPAOoB3ghrX9Tk6PuHhw/v843/8f2CbFn7gUWU5VdnwS7/0TX7927/Gs2fP+F/+wf/K7e21Zh1aBvfvf00PBZMEA9HyE0ftz8igKDRBebPeMZ/PqJsa2/JxHBfDcPB9T681s5yiOPBEHTabmCBwsUzB+fkZcbynqQsMU7ShOls81+L09BShRMsXdRCGABTr9YqTE31fb7YrhBCcn50RBLp93mw2ncnvUCnnea7T005OUC2Kb7fbdRJty7JAiM578xc6FL58CSEeAu8B/w9wcgiZVUrdCCGO2y+7B/zhl37b6/bX/tU/q4uiv3d+RrL//5h7kx/L0vw87znzfO58b8w51NRVXVU9sEFSIgHbEkUCMg14Y+0MQfDSG6+9F+CV/gMvTcttSxApWTTFoSmaY7O72V1d1V2VlZmRkTHeuPOZ5+PFd+KyDbNpwA0TdYFEJaIiIzMy7/nOd37f+z5PhO25RJHAjBVZiiJJIr7cfVNpltK0NfFPUGOSJOHx48dojoPtupRVRVk1tK1Ekucs1yskWUY3TUaKiu37lB3tyDF05lXOeDSiaWp810FVZJIwoExibNPAsUyC7Q5JlpgeHLBZb7AMA4mGyWRElKREcUyRp6RZLtpsneH54ecPKreH9Fnbtqi6AIj2ej3aDuTywNoru8+RJIkiL7i5umMwdXH7Om3aIikKpmlT5SVF2SLLGoapYZgt6+0tr6+ukeuYtgloJRlJU5EMaCioGwPHcXjjzSdUcYNuWuRZApWIkTdVQ0Mjttqde7Kqa5pWBHOatkGSFGRVQe7yFQ0i99A2AhQiIVF0aPe6gbKqaWUZy3b2x2Hj8RilQ8DTMSIeFkOv32M8nqIbBnfze8IgJhcBCz7//Pl+9yK1LWkY8+jsKW+/8w62afG//7vfZjadkaaCfJxlMb/8j34ZWTL45je/yQ9+8AMcx6KuG+qqxrQN0iRHN0Twpy4rRqMhcZxwcXHBcDhiMp5xezcnjnNMU+joFVnddyjKou54nuJUTFWAVsYyTSxLQ1XEn9txbPo9F6mVsWyTphbFtjCMODk6Jk0EtHU6m+C5LqYpuhBhGFDXYnHXDQPTNPeReN/39x2f+XyOoiicnZ2haRr+oI/SPTo8cEWaRvxdP9Sxf6ZFQZIkF/hXwH/Xtm3w0NX+mz71b/jY/4MS+ZMq+g+//F5r2yavX3xOr+eTxqEgF5sGWZiSpoJvH2UhVWXu0W0P4IjVakWWZQymU+S2Rdc0oiTl0aMz8izj/n7OydkZWVWxuLpku93y6PFjiixhPBpC2yC1LeODGW1Z4jgivZinKXf3c3zP4/riFf3hkIuXL9G6OcCg3yfLS6Io5m4+J45CWlnZ5+VVVd137R9U5g+LhOu6jEYj8jxjtVyRdXdOs3t0eGjAUYsJ+N31DbNDH8MykCUF1TApGom2AV23QJN4990ZPb9hvX6J0qZMfQVFk0HSaFFoMIjjgCDKkGuVMs8xdJ3tMkKTxUIkqypK3VIVDUVZCIN3t12uG/HoIHcE66ZtRdxaV/enJlUtBLN0n/dQ3RU7pj5Flu+r7GUufs88SZEVBad7tIp2IfPbO8aTCY/PHhOEEWVeUtUN48mEqisEnRwfcv7iJZIs4boOv/Vv/h393oDTkxPOz1/w1Q8+5PjogD/41rf47vd/wO3tLaapsd1usG0Lx3XwPKcTwe7IsgpFarm+uhMls96IPK+IogxNNRkOTOqqJk4S+n1/D2TNc3EzeO+9o71sJcuEdq5uCnq+S9OIhaNtKiRJZTo9wHUdqrJBcXXKsgEU2lYmDGIsy8HRREM1z1csl0sePToTi1FV7U3qD92Hpmn2TeNev4/f86nyHFXX/29pYSGwqQWW4GdZFCRJ0roF4X9q2/Zfdx+ePyjpJUk6BO67j18Bpz/xy0+Am7/t67dti2mL7kESx9zNb7BMEyWWcG2bzFS5ubpCMXSSJNlLOS3L2vPw0zTFTVNs32e9WtE0LVmWoqsas/EQVQLqkr7vkicx8XZDnEQYpjgSHI6GOL6H1LZcfP555/vzmU0ngiEpyZR5xvtffo/b+T1lXaOmOWEUo6kiLTedTMjrZn8RPISyfrLg9YBHy7KMi4sLojASPoa6oejOnB8m2lEU0ZNVdGTu1huqrMCzhsiqqMO2CiiqimnYuLrGV798hOGpHB475HGBazSYpoQk1xRZyiKKuFmk3N9mvHp+ydWLS8LtGsvQaZqcusuEJFlFlteUtVgQaEFWVDRdE7kBoKHF0FSRB2laNFlC0iQ0RdtXqh+KbpbhoMpdGKujGwdBgO96+25L9hOtPdVoyeIMBlBmBWWa0/d6VHXNar4EJLZhgNQ2PH78mK989St86/f/iF/4+V9gs1pTFjn/xT/+xzx78Sn/82/8BpvdGsvRaClZrVf0+wO0TvyyWu4wzRxV1VBVhbpMGY2miFxGI/ibjcqjsxOGoyGe6+J6Hr2eS6/fo9/rd0PklN1uh6Kof234TlMkSVCbwmBHWeZE4Y4kDsW/eSNTFA1tI2PoNp6j7X/Pxf2aPBNNSN8bsl4FBEFCWTbourZ/BH0Iuj0gB84ePcKwbcIwJAlDWK0EaqAjLj28Nx+I0v+fFgVJbAn+R+DHbdv+i5/4X78F/FPgf+j++5s/8fHfkCTpXyAGjW8B3/5bfw9ZYrtdoUiQ5QmqKmOaGjc3VyQdvLUscwzHwjDM/YXzMLh6GDQGux3BbkcrtYKGXGbUbY3Vc1GkhrYuSaIUS1NQ2pqB7+H6nqgoh5HQrhUFmqIymEypspSjo2O26zWlIqM7Di1gmgJgqpsWd/N7yiKHtsW2HVzd2A928jzfs/ge/JayLLbM69UKRVX3Q8WyKCiyHK0LNQ36fQGaKSqaskSVZFb3Sx4/PcK2LAo1R5N18UMxMU2YHg5RLAfF1kllDVdr0eSWqiiIc0gS2Cy3PPv4U77/3U9Y3CyxDR3HkikQO4Gyrqm6xwC6bsR+hyBJSN2fX3ooqekaWg21XFN2XE2RmgOalrZuqIqCcLOlrWrU7o2pa9rejjQbTyiKglUH1tWKgjzNqPKCPMmIgpAgjHBdj57ria5JWuC7Pm+9/SZ/9d0fMJvOiJOEd959G0PV+eb/8k1enb/EtnRUQ+bu/hpFbXFci7atcVyHPCvp9z36vSGGYXJ1dcnJ0QmT6RTP8/ng/Q/RDTG999whq9WGH//4R9zdrrh8/RpZkYhC4YhUVJWXL14Sx9H+vZnnOU1b47libjSdjdFVlYPDCW88ecTBbEoYbCmrijBM8VwZx/bRVAPHsQXct6ixLRXP6wmug6Ts+aW6ru/DeQ8MxtVqxaFt4w9HglIVBHvA8cOOwjCMnxnx/kvAfw38UJKk73cf+++7xeCbkiT9N8Br4L8CaNv2E0mSvgn8CHFy8d/+bScPIHgKlm2ShCEHp0esbm8Jwi2OayO1LVGwxbZN0iRFVTXk7sjuwfak6/o+8lyWJYali8XAtpDblrYq2W02uI7DJokZ9H3hG+jOffWOoRdGIXVZUhQ5s+mE5TxneT9nuVxiGgZNpVFlObRgmRZydyTn93q0koSmGwwmAqkdBMF+wiu2p0HH/BOEI2QJ3TBwusegB22700lyLdsWZqmqpswKFCRefPY5b7x9hiwj4CRSTVW2SMioUklbxWiygUyFKrfdoEmmzGsWiy2fXqz55JPPefXjz4m3AVWWULY1im0hNcIjUSsSSDV190/2k4+JkiyhSgqKqiHLYsBYliVUHbmpaffDQiH4qZDF5JI8y3BcV5w0aRpVt2Ma9Pv7bsvD44Zruwx7fbI0Q5FVRv0hbdOyXCxBkkjSnLffeou/9/d+kT/7iz9HlYXI5YP3v8x3v/0d/u1v/VvyLCFPEyRqVEthdDTg6RuPePnigrqCpq7xvB6GbuH7fd555z1+9Vd/hXffeUqSZjz7/Bnz+Zy/+t4PefbsOZpmoHTP/7tgh6YXe5PXA/354URA2M0UVFUjCDKC3R2vzi9wXRtFltB1hcPZlLOzY3zP4Rs///PYlsugP8C2TMqiYL1ZUdcVpmlzfy92vpapi+PQyZCmaRiNRvtH0vV6vT8iNQyDNEk4Oznm/v5eoAYk4QEBmE6nP5s2rm3bP+ZvnhMA/MOf8mv+OfDP/9++9v7z65oy3GIpCm2aYOsanj9it92iyDLHh0fc3d2xWK/o+Q79vodjO9ze3jIaDUWwQ3EY9H3m8zneYIamyOSpiLMquoFqVURFTm82wbJtdtsthmZ2QNUWxzWJooS2aUiyCk23kWUd1TKJ02taWcKQZUzHIS1rLNUgCBM0zWIympKmJaZhUyUVDQ11XlPnFUEYkKSxmH8oMkUpZgpVUQINWZpgWw6qouI5PnGcc3R0hmXaYhretCiKjmH1ibYZn3z0jK9/430GfZemyfAcFeQI0zTQLdGYU+UGXWvJ05Yam7zUSYMdP/7Ll9xd3dImOVUSoqs1mq5SNCWoNranMrBMrm9uidMtLWKQWOUZmipTpCWmIcQuqqLSVA1tW6FZGlVVU9UFlmXQVOJoU5UkFEVClxrUtqIMQvzpBEWCNM8pJcjaFldR0U0H15XwLAdvMMDp9URvIY7ZLeasthsur69QdZ1Hjx/zi7/ySzx//opsW/LmW6ccHk/4oz/8Pb77ve8gGSVRsKORWlRNRdVssthm1TY4xoz+tMfTJ485mE754P33kXWZqij5zp/9BX/5p3/G1fWlqIWbGmG4o6py0jSjrApURWE8tkjyAg2wXZPtdkdLhu2YSCgE4QYtE6G1sNgiSS2yCVGxxTEN2qKlrCySaIkuF/zZH/0u/eGIs04ye/bojAN7InZSsoyqK/T7YgGQpRapkfG9PqqkItUStmFzeniKJEtcX1+zvFsyPTwkLWE6OyFOYqqywnYH6JpGmjfU7c/w+PB38ZJlibqsoGkwPR+pabm/m3esRnEX0TTBBxwORxiuS1kKeOXd3Zztdkscx+i6cE4urm8EJh6JPMvYbXfiotF0yrrGUVVGkwnBNsQ0LVQ1Jkkz0ZnQdUzT4vzFy87RoDEcjTEtwV+Mk5gwiugPx2wvb8g7FLdQc9mEYUIQBnsij2NbFEWOpokjP0mmG/KIO+qDXFfW5C7tKPH88xdMxhORcDMMJFE5xOsNuHh1haS0tHyJybRPVVUYpkpRVLStjCSpFEVN08og69xcL0Dq8fFHn3J9cU0Wi+2m77kkaUIUh+yiHbbrYrsOp6ePyPKSm5s7dNMCSe6OsjTapuk0eZJYGGRF5O+pO5WZ6BMgiUDag2S2bVqKLKfOa1pahkC/1yNdds0/2+ZgPCMJEwauhzscMJyMaVqBSM+6GUSaJJxNJ/yzf/ZP+ZM/+VP+4o++zT/5L/8Jigq/+a//DdtgzWI5RzOEEkBWVNq6RW9kDN3iq1/5Om8+fcKX3nkbXVN59vmn/Omf/J98/3vfI4oCgiAkKVvKMkeWJSSppaXCcSxkRceSNRRFJgwDvN6gmyUUFF1+A0lGkZWO16GRZaLFW9UFtmViGkJ4rMrCxVEUuaBhWxZFkbHbbWiaisVizpMnT5hMJmJXPOhj6Dqvz88ZDQekXYpX13WCMOTw8ADbdmjbh56QgNtut7s9tdmyLPxeD8M0+fijj/bD8L/p9YVYFJpaoL5NwyCPY0zTwO31cG0bv9djt92QlxWeaQijUbf9LLtAkq7rhGGEoohhlSQJmWsYhsRRLEJCjiOOK/McUzf2xzhxGrPb7faPIpIEk8l0r2urqwpJ9ZnMxkRBSBQlYoZRlhwdHnB1fYPrOuyub1gtFrSSgu06zOd3XeTVoK4F/aaqqv0w70E607aCIOVYYvgznk7F86JhMJtMub08x3PF86Xt+aC2fP75BdfX13z44bu8885TDmc98iKnSBUUSSXPU5brFc+fX/CX3/6Yq8sVaarQ1DquIzBmsqqhmzaurBLGEXGS0hsMqKoaVdFxHNFI/cn5jdQ5EQF0TcOxHVpaVrsVum6gaXq3cKioiiIeJ7qCjqIoNFXdqdjFiUQ/L1BUlWgXYisGmqpi2Tae79Ef9AnDgDgKWd7PqbKM04NDfvU/+Qf8+bf+iD/54z/G0m1+7/f/D/IyxzSE6XkyGbNYrrFMh+nBMe+992W+8XPf4PjwmLIsmd/e8r/9q/+V9WrJ5etXYo5RlaiKiGEXZYaqKiiKjK5r9Ps96qaiKHJWqwWGaeC4fdpGYTQc0ev3CHYhYRiz2WwpipJeb0QSx9R1w3AwIkliFAk0RaRye56D5/ndDUmnqmpMQxe9HU3b6+MvLi548uQJaZoymUyYTCYUuQgeVXWFIZukWcr9YoGqbrAsm6qqWXdfp+5mCg8nFLvOd+K6blfX/ptfX4hFQdFU3njvXcIgYH57S1lX2L6P2+sT7LYgK/SGAgmmqgIIqhoGSadce7jbmp3jsSgKrl6/3h/rbTYb2rZldnhIGkUsOopNXYoJeZZE2I6D59oC6CK1rJfCsiMBlu9Rpilt3eB5Pnle4roOcppxfHTIdrMVDsm6RNOFfWg2m7JcLonisAujIO74nVWobcWgqG3AMA2GoyFxmJCmCbPOvxDHMYZrU8ktdVnij3rIqoyqaoTBlu9/91NefHrBdDbtjqlULNsgLzIur6+4urqlbTXStMW2hriei6zI6JqL2dW2szynaGqyPOcBSfdwcpJ3sw5FkrtymCRCTt2ztCIraLqG43mYhkFVVkJuI8k0nYKtqWvUFmRZnLHnRUEcxfQGI05PTsizgiIvaWmZTmccHx1RUXN5ecF8Puf6+pr1YsGT01O+/rWv88lHH/HZZ58htS1JVlCVWfd9qch1Cy28/c6X+M/+03/EYDSm3xvwe7/7LX73P/wOd3d3qJLMeDwUOx1NxXFtmroCRCDLMLTOPSJ2bpvNFiRIkxRJ0tEUi7psidOE7TZGvZ6jKhqW7TCbHdPv9dF0ndl0xnA4QjUkoKEqSzFUzDPkVtCr5bbFMISt++jwCK8njq8ffJJJkohHCFUlryo0WabX6+O7nug/WBaWae8TirutWABsy+neDyKs9KARGA6H++tjvV7/1OvxC7EolEVJFibIDfhujyLPiXcRaZjgeS6D8UjYj25uoBEAirKqKIua0XDSnRVnGIZJ09S4rsf9/Vx4AGWZ2XSKJMvQNPT7/X2RSlNU8jQTYZrRUOw2dA2/16OuRP16sbhnvVnjei5FIaqqmqZxfHrK5cVr3nhyRs8XFurnL14yX65YLudkWY6sKNRNQ5alJGlCXYt4sBhIyR1iTgymqqpiNpth6CZvv/02sqyQZxl9f0hZ5Ww3G8I0Q5UUdMPDsWWyKKEudV58eito0G1N09aUVU5RFxSFwJc7toHvD9A0C8t1MJy/bvPleY6saeyCHbbt0DQtuqbjez5ZUXRBHRF0abtcgqzJ3Ta1wrJtHK8vPIlFAQgjstTZqOuqoshFmEtWW5F+bFvu53OGoxGu4+PanthRtS3b3Q7dNsiylCgK2axWuLZF3/P40//4H7m6vEKVZWzboZJbLEtFURoc16Dc5fznv/7ruN6AzSbgX/7L32S52OC6Fo4DtqUzHAxQFIXNZoOqqViOxWq5wnZs0rRAUiQ0Q7gvFUUlDEJx7KzbPH70mNFohOd5YkGxPfoDn16vj6ELT2SSheiG2Jo3tUSw3bHbbUizBEM1OJrMcGwTz7XxXBtNU6jKgoOjKVkhqMzvvvceq04wPB6PxQA9z8k7Z+VgOMTt96iKUtStEWW0B9DsZrNhMBqi6/peEDQej/f17wcw7U97fSEWBSQJSVWIwwDdEKTinu+LiWqWMZBlqjxHkWRsz2a1Wu17DpbrInXCzjzPyfOaKAxxbHsv3Oz3+9Rd+zHY7dhutyiKSl2W9P0ehi566qauodKyuL4mTRIW+RwA23YoczFv2O526LpJGocoMjiOzdHxMUVZkGYxaZGyWM2RJJm2FbHVh+2zosiUVdm168Q23HUc0XPoFrTlcsHV1RWKonJ8eAQamIZGT5IJNls0ScGzHIoioShlsdPJFWzHpUgiFEWibjWkOkNVa1TNYjAY07QKru9z+ugRRSOMxYZp4UsSsqJSFCVRGFNXLbquYVsutp10SjKDthFcSEUVd1LXcZCRsC2LOC9BVjA0A7/Xx7Zs8X2ZJrvtjnXn9MhLkY4cDAZi/rILyeIM07QxDw8JQlGscuSa168vqKuKo6MD0jDm+bPPKLIc33Voygq5bbogUItlGwLuIkl8/MMfcnV9z/XNkrKUUWTxWKOp4oSgKIo93zOqa7ZhhKRqGJbD9OAQz3PJ84LpZMrXv/51hsPOPylruJ6HaRrsNiEvz8/ZbXZstwHBNmY+v+f+fk4YCfR60whWhYRA4CuKRBpH1HWJoavYtoFtGVimwWg05IOvvoesSMznc8aTCePxWESYez16gwGL+Ryn3xczs/sFrudhm4LFqRsG569e0e/3sS2Lw8MjAPzhgLTjRcaJGKKbpslkMvnZcgp/Fy9N09gFEXle0iIzHIlz6yhOiZMMJPENREHEfL7ocgAWpmlTZkUHNU5PqQAAIABJREFUJmmwLJvBaMLd9WvSJKHMc+7jO2bjCbJhUpcVeZrx6uU5p6enqIpCURXYjkueJrie29VM4X4xpyxLnj59iuP3yLICu0sapmlGnIkhkaKqJEnEsO/x1htPiPOEINqxWCwpipL1dk3TCMy5LKvdkaqCqooKuK6J1XxRLdBVg/FozGazYdyd3a+3G4bjIb3+kKeP3+TFZ58TZznTw1OC7Za2apGSChSDuk0BlVaW0E0DVdeQFRVUg9OTM2zLIatKxrMpqGt0TSOOE1TdwHE9VsuVcEJILmdnj1BNgcYvikJIYzvBrKaJI0AJMC0LzxOadICTo2NMXUfXDXzP4/XFBY7j8+mnn6KZBkEYkiYJj04fUXcDzV5/IDBiskKaZ3iZSc93WdzfM7+9oypLVFlBViRxfGyJRSqMUrQy77bPKXXTkqYlluXz+PFjykJCUUx836LnSxSFwOI7nouqGbxjW+iGSMe++daXxN+RYVDVDYauomoaLS1ZmpFnJff3C16/vqGq8i7wNmIyG7LbhNiuyVvvPKUq62421aLrBjoSRZFR1xVZGlNWBcNBj9GoT5pEbLZryrLg9vYWx+0kSB2g9fr6mvv7e0zTZPoQ2PM8aKGqal5fXXN6eorfH3B8ckqe52RFyWYXMBoN2SwWYk6X5/jDIc+fPeP48JAsy1itVj/1evxCLAp1VYkEo6xQlAW649C0YNv2fstzcHgoNGLLJVVV0+/39mYoWVaIopDlcsXTN94QUdQu5fjh177GYrkUWQbLwrDE3aIsSw5nM6GXM0zyNGN1vxDZgLbB1HSqXHT8bcNERuXVixccHh0SRyH+cEDWpfpaWhQFNE1mMhlxfXPN/X3dmZNcirKmAfKsRPSJxPOrYPmlZGmGKqs4PZuiKNls1ui6kK5qtsH93RJLt6GVMEwLXbfIypK0LJlOZowPDkjSiLQWseT+eEoYRhhdQ7RpG9KyxBsYlEXGfCXMQpZhUtZiMZUVhTTLWC5XeK7L7GC6j2kXWS4ErqqK0fkJFVXBMkz6/T7e9JB+v8/hwaHIHagak9GYVxev2Gx3JFmG63rcLC6gFViwtq6FiVvXcW0H27JwOqxengSkcUSZCVFu0jViWwlquUVRZSzTpqc/ZCVqBoMh/f4Qw3TQDZfLyzmF2jAaTfi5r32FN58edVLgGt3QsT0TWpXFaokkK90zfEAc7ajLmjjOOT+/QJJbNpsNWZax2+24vHyNLMtMBgdsNmvyPCNNU6I4wDSFWdq2TXa7nRDRyhIyUNclvZ6H69k4js3h4RRJavF6Lo5jcXh4gNMh5B3H6dKRyv4Uq6prXl9ccHR8IohfVU3TtCyXK5TNltFoRBCGZGlKUQhXpiIJJ0hVVVy9eoXUCgHOQ1X9p72+EIuCqmkUVY3h+dS7HbKk4PT7mGGIoRu4nott20jI0NIx8wTRVkxZC0zTFsmtWJRKXNvCsWyuzl/tZTOe56GqKqfHJ4S7gHSY4NgW9/M7UbJRZIZDgfUeDgaCKxhHhNsti8WWN56+yefPn9FKsJrPsWyLOBZMxs16SV2VxElEWRVouka8DfB7JlXdYFs2VRXTVHUXAxYps7YRdxRNFTFg05AYj48EjHazZWJNOZjM0CSNu5s547EYKm43G0zPIchCPNdjHW1QTBXPtTg6PSVJMvKyZBvHgsbTNER5jud7uJ5LEidIisJwOKapGrTrW0bDMW3doioaYRiDxj6B+TDkdRwbQzdEldjzmM5mnLz9HqPRSOj6hiJo9MkPP2YxnyMhE0cJeVEiKQqWLVySu90OXdNYxjGpnZDlGVphUbY1miQhtw2WZaJVFcvlgqZt2Gy2OJ7Ll955h9nhAU0t3AdJmpDnGfP5PYvFisnsiPfe/Qr/8Fd+Dc/r4dqGMFNTk5cFd3drPv7kM25ubynrEmSJ8/NbXp2/YrtcA+IRKopCRqMBSRoCDU0r+IYffPBlJAl6PZ/lsuDrP/dVNE3l449/yO3dDZvtgiwTAB/fNGmrmjDKKIqUIMjJ85imzUQJLxTcg08+/YTRaMT7779Pv3tMcF2XXq9HUYjHvbqquLm+Js0ybNtmMBzsjdT3ywWj0Yhev08QCFlxkYravqIonJyc7LVxD/r6n3o9/h1d93/rqwVmh4eUTUPdWZt3y50411cVWkUhjGOSXYDnulxeXqLrwrZ7dXXFdDrFsiyxYkcirtw0dccrcDsnn9B2I0lYrkvQOSF0QyPLUjabDf1+nzzPGI1HDKdT5nd3bJdLer0euu6QxKJ3cXV1RV2VbDYptmMTroQEJU5Fecv3XbGd7baBQhiaCuqPZdPULWkWCsxcUXWVWQNFUviFX/gFsjRnuVjgOi5FVqD4CjfXN2S5qMXKikzVVDiOw2g8ZLvbgQrj4Zg0LdiEAYZh8d6773I3v+fs0SPuF/cEQcjN3ZxpI7yMTdMQ7nbcXt9gmCaHh4fUZcVyuUTRFHRT7U52aizTEvHrwQDHdjg6OMR1HPqDAbbtYlkOWZJwfn7Bi89foGsavf6Q5XK9P65TbY2mbdjttti6SZWVwo4tCaagrCpEcYqEwOev1mviOBKAE03lzS+9xcnZKZZtoxo6l+c3pFFOlqckSUx/0OfDD7/Cz//i3+fo6JgoWbPZrgi3MS8//4zLyyuKqmK12RFGCbppoRkGSAqGYXJ29pQ3H73ZlYtUkjRGVUHTFZarOVmWEMchz559ilzpXVZAI4p3ZFlKliV88OGX9xbrOI5IlmvyXNC/FRnqqkSyNGRZwvVdVE1BVmQGgxlxFO+J3rqu47ouQRBQFAVPnjzBcRyqqmE2PQTAdXyquqKuwbJMZEklCmOGgzHXV68BcQM9Pj7m9vaWw8ND0Q7u+g8/7fWFWBQkCVRTgzTFMjSW93dIsoSuKWiKRBJsuZ/PKcuGO2mB2+/jOgJtNplN8YcDTF1jMh0RBAGbzYLRaMh2s2F8cMCLFy/wgDjLsDp5rWZZbKKAod5DNnU022Kx3TAZTzFth6yo6I+mVGXNNgjRTBPdMUmSmMPTE9IsxXcdNE1jvdni9/rESQoN9Nw+Wz1AU3WCIEZRdMqiFNCUphV3CLtPXuTYhhgWaYpBU7c8e/acoiioqpqenzGdHbLe7pBllaOjEz7+5BNc38O2Lfp9kzQp6flDev6QxWKB53tIkoyu6ZiaGJxu5nPOP/+M0XCILcusb2+RJNjc3YmL3NRpuuacqkkimWhrDKc+ZRVTlQm6ouB7LuPBiPFgzGQ0ZdAfoOkiwbjb7pAQJy1NW5NlJY6tM59fc78Q7ALXdEUlWNXELqluMB0Tb+Di+g5JWaCbCkVScHk3J4wiyqpEN218v8dkOqHMa378o+8TRRGGLmYwk+mIOLWRZBXVMLm+m/Ps+asOt9dDUWWMvsUHR1/DsR0RvtJNhsMemqxxe3tPsAu6KLFgevg9D1luiaIdLRW2Y2JaGnEcEscR4S7h6uqSuhYN2O1my2Kx5JMfP2M4GHJ2dsZgeMD4A4cf/NX3CKNA5BVkGcv1kDUdRTVoaQlDISRSdZmqyQGT5fKessgEcbppuH59KfSCvgDNVHUl3lOmSdtIRGFMlhSMZlO2mx3D4ZSr69eMJ4c0rcJoPKVpRFFts1nT1F9wwWxd1ZRRSBSGnRAzR9M0JFlC6wxETV0yX4pTA82yMF2XVpLwfRdD1/F8lyLP8T2X2cGMIAwZTafIqsr04ID1es1gMEDTdZBlWknCsG0M3+d+taKVwO/30UyTXRihawWqpmM5HobdsgsDPM/jfrXAsiyOjo9JkoTDw0N0w+T6+powjBmPKm6ub6mrhqqsOkZCJJgOhoVpOfimSbAJ0FWRcDs5OcGyLK6vr7m9u6FpGg5mB8RpxIvzl5imJe7iNPSHQzRVRZFVfK9HGIbkFOyCENvxODo65OXz5yyjBUkYMJ1MmI0HvD5XSIMddVNT11WHejeZjUcM+719gef8pUDPFUWGLHvYpo5tmUitjOe6tC0EYUxV3JOlpRDd+hbD4QDf94Q30ffYbtZ8/PFH/OhHH+M4Jk+fPGE8HeHYNlmccPX6UlTIez6KphBEO1BkNEMlDSvuFss9pVjVDOI4Yf3ps45CJIhYjm2TZDFu4/LB+x/w6MlTbNsjSTMM02Q0GtEfjqkbic1uwW67pa4qbMvBtT00RSWNY/p9A1UxqYpGDBelkiRdkWcVRZFx/uoFQbBhMPRpabm5uaYqS3r9PrOp0MZZlk3TthiazXQ64/LyNZeX12TJjn/wa79GEsds1ktev37FajVH7waauiZyBbqhk2YJcRJzenJKkiZIQBSGDAcDVEUh7eZkdLIZVVVJ04TNZkuWFSiyiqKojEYj6qri3Q+/yuZ+zovnzzk5ORKnfF3W5GeuTv///2oJdjtWq5WoQLvuvtFV1DVSnmOaJoPhkLIU+nnhEWxIU9FQTO4EsNNzPXq9Hovlct9UtB2HvCurPMhXer0e8/ktV+fn2JZN0gie3m63o64q5G5LORpN9py7vGPcbTYbcYY8mbDZbPZDpQfOg2VZ9Ho9gjBCQjQNNd3YtznDMCTPM9q2wbYdXr9+BUgEwQ7DMPF9n7opaZoWRdOomxLD1ImisIt+G+i6RhgFRHHEaDRC0xXuF3fsdmvhfpRbEaNVZXa7LYOBcEtKkpCDbLfbvWvjwcD1ICMVAS6ZOAzZrDbUVcV4NCNKYuIkxzZden5DVpdUVc2p+5TNZs315WuKPKeuKjRV5vzlOVEY8uX33mM6mQB/XZpSVZWqKCiLgrIoRXlMkSkbcaT8cGTWti1hKHwVwJ4j4Ps+vX4PTdfo+33auuX1+QVJmtPrj3j7nS+JgBmSsFSlJRoyUiuxuV9wvn1OGISkScp2s+Hly3NxoUgi1KTpGmWRE0Y78jxD1zUkue1+rlNkGbp+y4/aHwt8RNN2WEGdg9khT5485p13vsRw+C7TgwlxGJImMacXR7Rtzac/+oSiSAVwR1Wpypbp5BDf90nTnNn0gLZpKIscVVNQVFAVmdV6Q5SEWKYgP1V1jWmYtFXOo7cfk8QJt9evadqWUSOi8tPphOVywdHBTNStq/KL731omobb29uuP2Bxe3vLbDYTtOA0FRPwosC0bYyqZjQacz+/FW4ETcX1fZIoZLVakoQRs4MDcWF1F2Cv1+Ps+FjESHXRf3h9eclwNiFeV+KO7Wk4rqA177YBYRiR5SVZnlNWAqZR5TlhGOK4AiUPcHt7C4iBnKaq9Ho9yqIijGNcxyFyUqo6omlqiqJCkkXf3nYt6qomzRNRZ9UN/J6PqqkMhn0RH16u0I0Ky7L55JMfiuOqpuHwULx5gjCg1/NZLOY0LWy3G1RVIU0irq4uqYqc8/PPcRwb0zQwDQNVUUkSEeIaj8c4jkOaijfnZrNBlmWOj49Fo0+rmU5gsVwxHk8Io4gsr9ClBnQZxdLRFZXNakXbtizmc8IgwPMcNFUliyM8x8Y2DXqeS1rkbNZr5je3lHmBaRhITYvnOOi6TtW2SKpYsFVJBIwelOsPSLs8z4UPYzDg7bfe4hvf+AaPH79BUZakWcE2CLBMh4PDIzRdQ1FlPMsg2GpEgQCMjLw+J4fHnTvEI88zPv3sM9IsozfooUiChZDlGZIMu01AHEd7AOpyuWK9XHN3e8N2t8W2LTRT8BjKsmA+v2K9nvPtb/8Jw9GYL739NuPxqHvk69PruZydHKFoKrdXnX3LcZlORE7CsU3Ozk55ffGKxf0di8Utu92GR48ecXw6Q1Y0ZEnm/n5BFMU4R8eYlsZ2c09dNaRJSl4WzJf3HB8dMhj2Wa8XbLdb0cuQZdHV+CmvL8SiQMs+rNE0De+88w5ZnlP9BFmp3++zurnf380d22Y4HDHo97i+uUaR4Pj4mDzNCKMQ3/f3dibXdXEGA5q2JYlj+sMhtmGQJjFVUdHqDePRmCRNmN/NSZIMVdNRNBUkGW805vZSnGI8OBJvb2/RdZ2joyN6vR5pmrLebLi4uWW1XKMqCqenp1Q1ZFlJlCQ0dUOaJpimJUJNikycxqIjYYqdked7eD0hzJVVGdMykBUJ3/b2O53BsN+9OZcoyim+7yNJ8p4DeP7yBY5jI7U1SZpgGBpxXFCXBYqiYRiWsAit1/u8vSRJ+11UmqbUVclut8AwTCzDIooigiAgKysWmzXnl69FkengiA/e+hJFnqNr4u56d71jMh5iWxaqLJElMRItx4dH1EXJQhLwHFVRcT0X0zTJixLNNNgEO5pCdEOGwyFBEBDHMWG3exiNRliWRV3XvHxxzt3NHElS0E2TLMuZ3y944823efr0TVpa7u7vWc3vKcKI3W5HmmVIioykSLQSHB4fcXRyjGHqxElM8uOY2XTGkydPGHsDVusVSRKx2WyI44S2Bc/1eP/LH/BLf/+XO4ZjShjuuJvfEuy2VHVJ09aEYUCeZ/zld76DoeuMx0NUBb783peoyhzDNDiYjnn3g1/n/m5BUzdomkoQxKzXG9qmwXEsjo9nLJc2SRKQZQ6eL+L/mi5zcnrQPQ6UzO9u8P0epqUzmU24WyzJ8pRqXXB0LLgg2+0Ws6uq/7TXF2JRUFSFwXRKsFphGGKbHXc25jxN997IJ0+eIEkyN9fXTKbTPf324OiIIo1pm4bZ4SG1IrO6FTsJTdO4vr7msa6TxLEgFVkW2zTl5upaMAdq8IdjgiBCVQ36fRtJVbEtG0VR2K1WBEHAo0eP6A9ES+34+HjPGsiyDNM0OTo+JqsbNFVH1XUuXl+SZmnXKwBVU2lagQLfbDciJmyZyLJCludYlomiimi0rCiYtoWm6ZiG1cVwU/G5aY6iqsiyyvzunqZpefL0DT766COKomA2m7FaLun7PoNBH0WWeP36gl25xXE8Dg6EZ7PuOIqr1WpPmn44qdE0lTIvqauG0WTCZDymbiBeLkSnpK6RFIksT7g4P0eSZILthnC3o8wzPEfIbMrcIIkjtstl59fYic6EqnUOBVivVuiWSVlXFHlOW7XUZbV/7nUcR2yxq2rPuozjGMdyWS7WeJ5PQ0uLRNvCH/7ht/id3/kPrLcbirKiLSrkvEJVFWRVRtYUDMvEtE2ub6/I/7zAdmws22Q6GXN7e80PP/4IWVZIkxTTtDg7PcP3Z7x8ec7t7Q1Kq3Zi3ZjZwZQ33njC2ekJ1ttPaduGi9fnhKGFLFs8++wZq9WKINgi0RBFOzzH4StfeZ84jnj57Bnvf+XrZGmOJMHrVy+61q+OaQ1RJDFbydIYTVOJoxBNU+j1exi6weuLC3TdZDIaoHSSWcMy0DQZXVdp6pb1asWw3ycKQzRN/+InGuu6ZrFc4naMPsfz6EsSqmmSpSnT42Pi7ZY0SdANE8dxhD1X0tBVDapqD/6kaXF6PqbrohkGuia8AGEQEHWi16auGU8mHB4ekex2XF9f05/M6A1GTA2roxnX6IaJrCisVgv6fTEFljpIiKIoGB1E07Is8TzZGYKm0ym33dHSZDwhDFOCMKQo6z0UQ5LVDrZq7wnPRVkRJzlpKtp4um7twZ5JknZ4L6EMOzk5YTQciWBNWvDq/IKyrEmSjPVqTRxFbFcbdruAD99/n35/yHKxQFXF38fDXGQ0GuG67h4VN+i6AcvFPaZhUZQFdVlh6CYnR0ecnpyw3KyRZAWrQ+Fdv74QOQVdQ1NksqokWK/B91BkCcexGfT75HVDkRdoXShqMhqDLJFmGZswAFUQmlEF/PVBIKxpGoZh0Ov16PV6e3DpoD8SVfUgIOtAuaqmI6kK2+WCsqmRVAlTs5DkCkVVQJbIyowmT9AsDVOVkBto24qyyNhs1l2zUOPs7BG+LyzOf/bnf4rnuYzHE778/juQi0iyJPfQdY1Pf/wjoijA9cTiYlsWX/vq11A1h9PTM3bbLYvFnJvr152yreUPfv8PGI+HnJ2dMp4ccHJyymq14unTJ5imTrBdY9k687sb2hZ8v4dt2ciyQhxFHTjIRZEVsiTBsRwODw5YrzZIqsbx6Qm6qpHEMcumom5qjo6OiIKQ3S74qdfjF2JRaFvRAWgeGo9ti6ooJNstareiaZqGoouGV9O2ZLmIag56ojGWZgmj0Qjf9SjKkiyK0F2XshY4rM1mg6qqewpNLUmMpjN0SUFTdeaX1/iTCYaaMjo4QAfqpqWVJZBk+oMBlmWhWhb319eir2CaRFHE+OCAtqq4u71FVVVePH9JXVU8OjvD8/rIis6Lly/ZbAMkWe6CPJ3EVtWp6hZJ1nAsF9vxkGWZfr8vjNuyQZqkGKZJWZQoiozjupRlyWw6E5p5WSLLcmhl2kairSUc2xOJNsPh1atLwjBiNJxRlQXz+bwzLptMp9N9jfzB8P3AlpTbRmQgWonl/YK6bvjwww85OTxC1QVZybZsfu+3f5/PP/sMq0v0GapCkWdkqbBdWaYJdU3bQpFlAh7TAk0r7namwUDts+jI247119mSByz5g1Tn/v4e27bRdR1J0iiqFt1xMD1v75E47fucPnlEEIXc3t5R5SW6pyJLwjAepxF5mVFUBVLSomkyjm2iGzrCkSLwZre3NwwGAwB0QyYI12x3Szabe84OHjMcerw8fymgOVWJZZkUecl2s0NWZC4vrxgMpzx58oQPP/wAw/g5ri5f8fz5M64vX4PEHv3/ne9+m8urC05OTrifX/LoyWN026aVIYkzFFWiquD1xRVVVXa1a42b3S225WDborD3+bPn6LrOxPcp8pQ8TdAta9+RGI/GzKW5CKf9lNcXYlGQAKmukRUF1RDps912u++Et7sdpm3jDIbEQSB493VNksTYpi622oZBWRSsVitM20I3DJHTl2W26/WeQrxarcTFbRg0WUERRqxXK16+PMdfrnj61ttEu4DBdEqVZRi2i2knOJbwMbRpyuzoiNX9PYqioGkabWfy7fV6ZE3L0dERw/GY65sbZEUny0uRQnM88cO28QdTHMdlNpvheR6+7wtzNaKlWFUVw+GAoT+m5/cpS7EbkmRJlGIWC3Y7IbN59Oix2DlMRpimQRQEAhqy3ZImKXEk0FuqpnJx/oIwEgOnB9WZruvc3d2h6zqDwaBT2gmrsa4ZwtVQNezWa3700Q8Z9H2ODg/xPI+zyZTRoMf89hpD0/b5kbapcG0bTVFom4bdZkvRmbVN3YAWFEkSx9FVgmaZ+J7PLgpIkwxDfzhyE4yMuq4ZDod701YURfTHMzzHxzRN4iTm+vaWvMixZIvpbMLho2M+/PpX2K53hKsYSWqFTiAJeH3xiijcURQplqnhOiZ13RAmOaqiUzQ5t7e3rNcrej2P6WxMmgWkeUKcKlxfvkBVNd58espms+PVq1coEpimhdzKtBVMJjN02yYvCv79v/9tyjLn+OiAD95/n69/9SuE4RZNVVguF5imxm63oihiHj96zP3dLYam8eTpU9597wMur67I4gVHh8ckcSjKf0kGrcR6ueHpkzcAMRSdPDmgSjMaQyKKQvqGwezwAFpZNG97fZrqCx5zlmSJ66srBuMxatPs2W+mbbNdr/dKejPPcH2fpq4p8xzHtsiSRKTDXHFBZXFCFIaMDw74v5h7s1jNsvM871l73v88/2c+p6q6q6qrupo9sNlsskVSo8PQsC0HAZILx0CCOBcJggC5iq8E6DKxBMNGjFDxhSTIEjRRpESZkkjJJEWKQ1cP7KHmOvP0z8Oex1ysXUekQjIIGAe9b6rq1Pnr1Dn/3mt96/ve93lFnqMUoSvTyUS6GaOIZgGYODs9RctgPl+gKgrLpUNvbZXl0pHJy0KQFEj2UbFDVSoVsigi8P0L5mK1WqVWr6OoKpV6neXSxbRtOt0es9mSF1+S1UEUp8zmS87OzgkTFV036HW7KAW1yPcjPM+VysOTE8IwhESgCvlwZFlGuVwiDGWpfHJygmHoJMlfUq6ULyy7lmGgGxqmrhOFIYKcZlNOGxSh0ul0aDZlPuG9e/fIskwmaxVefpBmrdiQx6E0jiDPsAyTyXCIkibkYUytWsVSNCqVCqsrq8UOZlGyLRazGVEQYlTKVGsVDENjuZTiHWEYJJHM0nRcl0qtRpQmBGlMmmbEcSKJTUX2hQT3yqZzq9VCURTOz8/Z3d2ju75FvV5ndW2VG8/dYndvj6OTQ7733ntMphOq1SpkAlOVFK+z0Tn9XpsXXn4RZzFjcHYqob5ZymLhSJzaYk65XGJ7e5tSyZLJ0CWDnBjLV4lCl1Q1UfKcg4M9trZ22Nx8jbt373N+NkQIae0OwxjNlvFu1WqV6TRiMhnz7e+M0VWVmzevc/PmTabTCePpgOOTA6pVm9FoQKvZxLZrDAdD0jihUq5RL9fQVEHJtphOZ6SpQ6fdZTQaE8cJjuOSZxB4UuXZ3GjT7rQYnJ2jaTq1ap0szijbZbq9/o9+Hn+cMeL/r+vWjev5b3/2X+L7Pv1eXyKotb9thAgE0+lUBq2mGVZBRVIFTEYjHNdhfW1VevoTOee2LFkO6pbFbDJheH7OxuUrzAbndLs9ZrMpgeszG04wLUuitAyD9Z1tMqDcbOAtlkRJgrtcEjgLatUqYRTR6/fZ3duViKtaDVXTSLMUIRSEoiGynNlyyXw2J0lzHj3a5eGjx7z3/l3G0wlnZ+fEmQ0IWV0EvsxwzGU2gCh2VNnD0IkjSY56Mv2QeWwU4zk5tej3uwSBR5rKRTVNEvJU6gJs06LT7dKo1fG8OXHkyKZdsfClaUoUJ9QbdYSiEMcJ1UoFo4CoKKoccyZxzGQ0JE9T6hXZLzA0ndySRzLf84gjmSURBQGu41Cv1bhcSHSnjothWlJX4riQ5xd4u2qjjmFZTGczojTBtGRkepqm0vqeycBez/MKjUKDTm+NN956h1K5TK1WvQDBqJqCaVmMxyPeu3OHMIiYT4t7QlepVcv0um1UVbC5voZlGrjOEj8IOR1Mmc9n1Os1up0WjUYdVREEgUcU+mQFzUhRJfEgAAAgAElEQVREEeSQ5jmu65OmOVev3kQoKrPZgnJFhrTkIiGJI1qtOqenR1iGjIpr1RtkWUqv0+Hq1avYFZuFu5RA1Rxcx2VtdQ1dl2rTPIfVlRVKZZvxaCBH5FGMqqioqmRmKorCbDqTFvEk5Hx+TppJ343r+HQ7PdbWN1GEim2VsFbWb+d5/uG/+zx+ICoFeZ6t0OyuYGkauRCUKzXGBUXZrFTIxlO8xUImOEWB5B/YNqau4RYg1AhBFMeQZbjLJabRlqusZSM6XQyg1+lKelCcUK83Kds1DNNkOB7RXV1huVhI3bWAIAwkSTHPEKj4foRm6DiOR73ewrQswjhGzVM0y2Q2nZH7EY6zJMsgSRIODo84OjhkNhrhzmcMjo9J4xg3iDAti8lY4r/SNEHTFPzEQ1FyOYXJYsp2W+6+Jf0ieER6/qVHw7QM5vM5ihpSLgsUxUDkCvPZHC/wscwSzWYVhZzpdIypCbIwJPA8XM/HLtmUalUsy5Z0nnZbuvCiBKWIkmt1WiBanJwcY9UqRL6PG/nYhoGlm7iRzBwwTB1NU6gVob/zxYL5ckma5dilMqh6AVPJLqYJqqGjaBqu55KlKaamYZfL2AX5OQgCVE2DTJBGIX4UEi9mzNwlo+mcV15+mclkwtHREUIInn32WVRVJQgCSprJCzc/xHy+4OzsnPF4zHB4TpIk0ohm2RwcnhRYv5R6o05vrUuzW8d3PIbjMcvlkuVsjipkAI5RBB2nqcT4hVGMouqUqw0G4wGbW5d49dZzhJGsevZ37zGfTzg738NQYRb5rPTaUDU5OzpGJAGz0YDrz95idXOTtZUNBoMBSZxJRF7dQC8mctPFnKXv0t/cYnJ2RugFWJaOocusT5KEJImYTn10S5eah3KZ4XAsHZgCgtAn8AKi6CcLmP2Pfgkh6K2toRRxYF7BX0SIC8GKYZrEaYxdLstzUbOJacmE3pWVFZaFRFrTNBrr64zPznAdh3KxwxqGwXg4pN3pFIKkhDRJMEwLx3WoVqs4nofIZEZivlyCIhiNRpRLErv+xEyycJaynM9zhKLgLlzstIznONRLJYKxj2VaTCYjBoMz/MAlTkKEkHPn4dBFUW0Qkl8YxQG2bRVMCBkEKhQwrRKGCeWyQRB46LpBt1dHCIU48UnSiGjpU6lIW7XrhigiJU1SnGWAqmokSYoQKuvr6/IGn0/QdZNqVUdoGkvHIRMKhpVimOaFQGexXFAqtBphGJErMvhV03XKtk0eJ6z3VySq3XVkAlEmjxiVSoVatcp4OGJwds7R0RHVapVqpVaE2OYoyMoiSVNM2yLJM2nuyXMMclzHwSikvEmaXOgW8jxn6cr3Nc/h61//Ot1Ol1qtxng85q233pKJ3aZJHMcMBgOeeeYZNje3cF2HNIm5e/8e+/u7lMtlsiyj1+0wnU0JfJ/FcoZlWWRJRKNaJksSbFMvqq+UNAkJspgo8EjSDN0wMa0SlWqF7e1L1BttqTFRNU5Pz6iUbMndUFTskoEgYTwZoxUcUdMwqdXqnJ6e0ux0ZO5HtUq5VKJer3P37l0ZVpznNOp1xuMR0/GYZquFJmRD1PU8At+XStwCg+e5HkZFBt5oispzzz3Hwd4BaRwXMvkP+kgySVnOZjS7XWajEeVSCfKcOAxlJHm9jm4YOFMFVdexymW5S/u+jDkTgmzpgKJi2Danh7JDq2k6dlnINOHBANO02N8/oNVu47guYRBT7/TIVJUkzwiDmMloxPUbz0g0vC7fIGn2kefsJ7LpJE0Jg4BWu4VhGpiWha4qmArU69JUo6g5V57aYdUPePjoMQeH+yhKRqVqEeU2k9mUbrdD5idsb29IV53nYtkSnyWVhgkQs7m5wvn5EM9bUqs3qNVqkoiUC2rVGmenI8rlNo1Gg9lkim3V6HY6dDsd1tZWKZdLTEdjxkOLxWyM47qomkGpLDMmXM8ljKOLc36z0SDyQqI45uT0FMMyqZTLlEtlDFUhi2IarRbe0iE3DUajEVEQFt6V+CIdKs1Szs8lGk/LFCzLliO0LJM7tABN1VFNnTQRiCwjy8HzPZaFW1B/QkHW5fRoUGglup0GaZwTBj7TidS41KpVdnd3i0i+kPl8zp3332elv8L29jbtTputjXXWVnqMxiNmqpBsS8vEMAxOTyaEzpIsS6mXbIyyjaXJnlTou+iajqprREmGUDTKlRr1RpN2p4dpSkbo+kafk9MzOp0Op0e7nJ2foyoZhibhNfPpqKCBpaSZTODSNY2jw0Oq1SpXr17FNA3G4wmXL+0QhhH9fo/pZEKlUsHSNI6PjnAXS7rdLq7noYAMR6pUqZRKLJw5iUgI/ZAojHhw5x5RFFGv1nFdlyj6CQxRQohN4DeAFSADPpvn+b8UQvwS8N8Cw+JT/3me539avOZ/Af4bIAX+xzzP/+zHfQ1FVQh8n7ODAyrNJoZlsSgAKmEYsr+3V3D/ZENMFQrL2YJypSwZC8WqKxC4jgcIhFAl6nqxpNFo0Gy2QZPkI8uyuXTpCq7jMh4O2Ll8mWVhUQWIwgi7XIYsgyzHcxwatTpZmkh3ZKtFs14nzVKWjoNpW0zHo0IcI7BtkygK2dzaIMty7ty9R71R4+bN67z73nsk5zGLhUe9XqFWK/P887cYjUbFGIaL7MkwDBBC0G532N3do1Ku0++vEQYJiqYxHk1pNdtYZoObN7e4+vR12u0WmqrQadeoVmQZP52OyMnZWF/H9xzcxVJOOUyDg4N97t67S6PZxCrb8j2ncK4aOr4boRsmlmWTAWkUE6YJFcsiThLscpk09C90G6Ui0UvXZIDudDxhPB5z7949ci+m0Whg2JJCnEQxqQIxObkqsMolHNdFURVazSae78usA00KrDIhewy9Xg/XdTk9OWFn+7LUXJSlPP58cEa73cbQVSaTJZ1Oi+PjEx4/fsRwOKTZqtNut1ldXWFne5sjTeHo6BBVVajXKqy0b/Ho8WMm4zFnx8cShV4E+Ahy4kg2bstV2X/pdrpsbe2ws3OZWqOJpuqcnQ2KRyVnfWMdXVdYW+syHp0ymYwIPJexEPS6vQuORsWy8H2PIPDRdWmSa/W6TAYDLMskB5lXslgQJTFHR0e0G00WiwU7OzuyGiuVaDaaJFFEf2OLk+N9fM+h1WyhazpxnHByckKj0WA6/cnArQnwP+d5/oYQogrcFkL8RfF3v5rn+f/2/Z8shLgB/BfATWRs3JeFEFd/XEpUnueUbJvhcIjr+yTdrhxRKspFV3w6nWKVylQ7Hdz5XLrMhEJURLH5fojne7iuy5UrV2RkVwGVEIrEpumqSrnd5vDwUI4lNZ1aEbX1pMPd6felJsAPCJKYyWCIyEHJIQpDbNPEnc+p12uU7SpxmqAbBicnx2xtbWJpMBoO8DwPu1Ri/+CQNE3lQhHHdDodFssll9vr3Hz2WaIo4vbt2wUO3qBeqyMUQRhGVKt1dM0mDBTq1VUMw8JZZLRbK9y4cYN2q82lS9eIo4QwXhAnKUdH54xHA8pli1rV5tLONtvbl6jVq5SrJc4H59x75332dh+zXC5pNptcefoqy+WSR48fFPFjdc7Pz2jWu3S7PYSmEEQBvu8h8lz+PFQd1/NkQngUX5B8arUapmnK8v8JE2A+x/M8Dvb2OdFPqNSqqIaOGwXkukq5UafRaKEZBtPFnNl0RrctzTyGYUgEveuCqjCbzSgVlvU8gUf379NqtTk62Jf6jSjm/OSEcrmCrmnkScLW+hpnZ+ekScR4MGRwesrRwR7dboePfORl+p02t2+/zsnREYkvj4YVWwrnnKWc+rSaTTTNlhMhJafV6VGpVqlWa1QbLQzbZmNjk8APMQyTlZWVooyfYxoae3sPcJ0ZgbdA12QjOU1TslzazfOF4MrVq5Ilquu4jsNysZBHD9PENgwqpRKKpjF3lrzw4Q/jLxZ4rsfhwQHt4ji9XCwgzwnjiPlsQafTIUkS6nUJorVMi+l0yvQnwbEVcfNPIueXQog7/JBo+e+7/iHwO3meh8CuEOIh8BHgb37UCxRFuTivA/jFzSqKzni906FSq3F2csrDu/fodruUbJ3A9YiiiEqlQprlpGlOGMXs7u1TqVSkek7XCKOYcOnS7/fJNZ1Gs0UUhngFwSaJM7zAp96os1wsCYOAII4xNI3pZIJpGChZhmEaRHFMd6WPs1xQ0xTiKKTeqLO20mc2nmAZCqVSiVKpxOPHewghsG2b1dY677z7PrZt81Ov/RRWrcdbb7/Nm2++habJsJV6vYGmKWiKjq4bVEpVTKNGuVyn0Why88ZzdNpd6rUmh4cn3L59m4f3zzk/H3Lt5mXWN1e5cfM6hnEdRWg8fvQAw7Dw/IB3332Xw8MDHMdBVVS6vQ6HJ6fcf/SQ2XSK4y6lLFbXCX0fXZd+hLKi4Hk+mSJlxEkqUeNRHKPmkMYu6ArVapXFTMaSJUmCs1ziuR5ZntNqtej3+1iZRpwkGKaBZplgaFiVMnatgmlZZELIsjqU+gnTNC96QqVSCT8KqdVqnJ6fSXza0kMT+sXP++zkBAoPx2Ih/THtdgvHcaVqNAxRVIVKpcFsPmG5nPP2W2/S63f5+Mdf5WBvn8PHD+l1pKBraTjMZnJBOx+OyKCwiNexq3Wevn6d9fV1NtY3pJYkirHtMpVKhXv37vONb3wD31/iew6GIYnjWZZTsss0m9ICr2s6uQrdbgeFHMO2SOOIJNY5OT5mdX2dyIw52NslSVPWtzYRWUZaTD8Amo0GQlFwlktMyybJMjRVpddfJYsTFrMlvW6fsl0mLBZ3Xf//qKcghNgBXgC+jcyY/B+EEP8V8DqympgiF4xvfd/Ljvghi4gQ4p8B/wxgbaWPXaQzCyEujDpPtO+KqlIql2XmIlK9p2v+xYIQxzFJlrK+vU11PiNJU1rtDvPxCNuWnMd6s0mpaCwpimyYCVVFUVU6rRanx0cEQUC1WuX05ESadHTJdZyNxyS2RJDFUci7b7+FVSrRC3xavS7ucoFlmjjLBcPBFLVw9TUaLU7PzjFNm8ODI1568WWWzpJ79+7x19/5MienZ1iWjL237SpRmKIqOc1Gk83NTZ69+SytVh9TrxLHktD053/+l4xHU6rVOs88c5PtrTW6vT5eNGbpuRwc7bOYzWi1JPTlP3zta8xnMy7v7JBm0On2OR8M+KMvfJFWq05QNEwNwyCJI3JVUpYM0yJJU2yrhF9oQmy7hK4qzMZjRuMJpCmmrlOqSju3aZoXOgff9wuLeEiv2+X69es0yzIARSgKiq4R5xmdfg/V0BhOJ5wNzrEMg/HZ8OK1SZJgWKZMCSt0GKVSScrhy2VECmenJ2xvb2PoGoeHhwS+R7lcxnUdBudnVKtV6rUq02mC4yyxTJ1GrY4qBIvFjMlkxHQypt1q8vwLL/L666/jeR5BkexcbTSI4gihKGxdusRTT19l49J1er0+tUoF27ZIklSmWXk+b7/1Fm+88SZhEGCYCqoqOYvVSpXRUHIshBBS7WnbmIaURRuGTrValU7cqkxBu3/3DleuXCmeVu0iAGk0GrHa61+E9ZRsG01VGQ/lxM5PEjRdGp/yLGM8GrG6tkY8i0mThH6v+5MvCkKICjKO/n/K83whhPg3wC8j16tfBv4F8F/zw3Mn/29iiDzPPwt8FuDm9av5+ckJ1WYTtTAZTSaTCzdcnueyS9zvkxRhpSCNSL7vE8XxD0SaVStVAsehWquDEOhQHDGCCyRamqYkaYKiKuTktDsdkijmYG9fGp6ee47j3X2SOKJcKjMenyMEmJbFzs424/GYO3fe51J4GUWVO2W9Xmc2npFmCacnA1RNo9tfI0tTGr1V/sNffJmj4xMODg7IMkGpVCWJE1rNLp1Ol0uXLnNp5xLPv/AipVKJ9969y5996cuoqlGIj9q88spLlMslylWL+/f2eOe9N9DvG6Cn9Fbb9Ho9cmIePr7P01ee5u996BdoNOvMx3OGoyHf+Oa3uP/wAe1uD8uUyskwkD+XPFNRFZUsS3E9B8tuEsYRhmmiaBqT4ZA0TSibpoSKxAmq8rdJ05omTUJxIUXXNI1Gvc7a2hq1Wo2tncus9Fdkoy6O8cMQq2Rj2RY5FDe0Qb/bxTItGs0m0+n0IrQ1yTOyPKPT6XB8fIxZNmjWZI5HqWSzXC7I86wwqJlF5ZkzHA4olcq0Wg0ZzlsuEccR5UqZ6XRCmiZ885vfoNfrsdpZYfvSFQ4OD4lmU5IcyAWVRotGo8HOlatcfvoq7d4auqazdOVot2zbVGs1FvNF4Z5d5ejoCNdZsL6+ymIxpt1p02k30FVoNhvkaXYxGSuXbAxNxXOWJFHI0d4uG2urTKdTRoNzLl++TBBF3L9zh97WNu1Wq7DWmximyXg8lhkdxbEkz3KmI5ltYag6znzJnfG7JKmMsavXqz/ZoiCE0IsF4bfyPP/D4qE+/76//zXgT4o/HgGb3/fyDeDkx/37T6y7nuNgFrtsvdmUfYECbGKYJnGWMx6N6K6sMCnCMoQQlBQFx3HY3d1ldW0Nw7ZZTqcoeY6CpNf4BZfhiblGN02UXEeoiixpy2WyZHGx8k7OzrEtk/FySRyFGJpUBx4fH2OXbPTCdfn2W2/hBwEUcXODszHP3rqFrks8epwL5rM5Dx+9SRxnJElOkmT4foyhW2xvrXLzxi1u3foQy6XDZDLlz7/0l/h+wNraGq989GW2tzaxLIvd/Yfce/A2d+7codVq0O/36XQ77Oxsc3x+QpZHHB7v8cy1azz19DaNahPTkF31YTxksZjjuj7Pv/ACL7/8IkeHR7x5+3XW1lZQFdh9/JCNtT55npLnAlWvECcpVslmNBkznc1YWenL8jWTVGby7OKYZ5fKeMUIWVNV+v0+5Dn9fp/V1VWq9TqVeo1KvUYYBCRpKpu7QmAoKhXTkkc6u8TR8QlWYTSLClWpomskcYJuSjm2ikKWpqiKynQyRREKrWYLz5WOWd/1MIoE5iSOaTUb2JbFyclxoWCdkaYJSRxjGgaT8YTxYM7lKzFPPX2dk7NTNF2n0+uyvrUpv4/1dTqdDppewjB00iRjMZ8TBT6e40rfTbFpXb92jdlsjB+4UtE4mbKzvU6jVqFkWtQqFcigWqnR7XWYL2YXzt6VdpujoyNKtRrj83OJb/d9tra20KtVkihCNQzMUok0Ti6ehflkSrleJ4lTTEOmQt258z65yDk9OaZUsqg3qqT5T8BTENIf/G+BO3me/8r3fXy16DcA/CLwbvH7LwD/TgjxK8hG49PAd/4fvgbtYgSn6TqqEGiGFIk8KUNVz8PzfVrtDt5ySbffZzaZ0G63oThzOs6Scq2KyHOaK33yJMUydMIoLHwKkqjjLJcy8rxaYTGfSZNUmuD5Lpvbm0zGY+aLGUkcgwKmbZJGguXSYWV1jdl8xnyx4O79B4RRiG6aHJ8cs1w4xLHgnTv36PVXGI0mOI6HphscHBxJ+lKWUa50eO6lG3S7PeI4YW9vnz/63OdRFI2tzS2uXbtGv7+CaejcfvO7fPXrX6Fer6MWFcn2pQ26vQ4bG+sMhwNOzw7pdDs8fe0as9kc2yyhqhr37j/ke2+/y6OHj8mSnFvP3uQX/7N/gGZofPc7r5OlCa+99nFyEt747ne5du0amgIPHz7AtktYJRN35NDudsiylK2tLYSAyXCIKJrDCuD6DnJDVUkyyIQKik613qRk21RqDdrdPuV6DdUyEJqsFAA81yWKIhbTOXmSIeKUarlCr9cHAZ7n4YchQhE0223q9QZREmPbJbyFi09IlqUyFanZlEQucgQCx3XQixDXKApIEhnKQp5KJmWSk8YS929oGoqhEqcqfhiydB0++rGPYdkWzVabza1N6s0mpiW1E0JRSBIQIqFWr5KUTPI8IzqV7ILNzU1ef/11zs6O6LSbXL58melkSLlcxnEcWo0mcSpVp37og6GR5jm91RU5iRuc43oeXuCDonByeiqT0fKcepwQBD5pksqMzc1NFEXBWSxQNZXpdIymGYRBgmbqVOs1HHdJb3WFVqvJ0pkz/Qlj4z4O/BPgHSHEW8XH/jnwXwohnkceDfaA/w4gz/P3hBC/C7yPnFz89z9u8gAgFAWrJLMHNF1HURQyRSGJIhqNBsvF4oLGpAiolku48xmtdgvPWWKWy4gso16tEjkOuaqQFicWVdhUa1WiIEDJwVBVSsVEI8tTTE1hXnAXBRmtVh3TUBlq4gIBFvohmRD4YcT88BChqghNxQlDTs/OUXSNs8GAOE4Zjj3eur9Lnsv5okAjy8A0ylzaucKrr36MlZVV7t55jzffeJfZbEatVuO5D73Ac7duMZ/PefT4IX/zzW/KwA49J8lSKvU6lmVjlSs8//yHuHLlKZIkuUDBlctlFHQiP+Vwb5fbt9/gvXfv0G53abd7fPrTn2Zra4Pbt7/NyeFjVlZWaDTq3L97h9lsxs2bN3nj9usMhwOEUJgvlgih0Wg1mc/GlGtVNjbWJMk6y6iWyiyWUsacCoM0yzg8G0lGg6LQanbxY7h24yq2bXNwOqSTxmRF6vbSWRJ4PrPJFFM3mI/GHD/ek2IlyyQIQrJcVlWKohLHkvxsGAZxlGCZNqKiEHoheQ7lag0/jNBMk5pl4HkudbuOoghKtkkcBiSJR71Wx1kYMh/BlyE3Wp6jC0WKt+pNmSVpyJzJq1evysq1ITMi86LvFYkEw9RIYqkxSHWBEE0s8wZ379zl/OycTr+Pks8JA4fjw8eUSja2YWAbBjmChbOkVLLJNcFkMsKqlPDCgG6vS0KO0CUIp1wu43ou9UqJIAyIz05IU5l4pVdKjMcD2aBNIxQNIi/ErpSIs5QgjVi9vM3j+/dor/ZQVJWZ76EZ1o9+Hj8I3ocXn7uV/+UXfg+zWiUprKRhHMvRVmGZ9cKQyzs71BoNnGLs8iQLMUkSmu02omiY+XEEQmZUzmczmaxjWWxu76AqKs50WgAwoVqRkVpe8XXr9TpxHDObzTg8PKRUKlG2y4gc3nv/fcIo4vDoGN2yODo9YbFcMp3Pmc7nBEFEkulomsHp6TnVag1Q2Nq4xE/91KdI4pQ33niLMIwQSs6tW89y7dp15vM5o9GQ+/cfFK68Booi8DyfVq/FredusbOzTbfboVyu0Gk3cAs7rRA6R0cHPH68y3g05fj4mDCMKJcrXL36FC+//DK2VZV9hgf73LhxBcvQ+dznPsed99/j5s0bdFpN7t+7x2g0ZLlYkKUprWaT1bVVytWKbABWyhf6ifl0iiZU3KVDrVIhTGJKts3p2Sm6qlEpy1i5ZqPB5Z1LlCtl8iyjXi1jGHqxAAvOTs84Oz1FEYKz01OGgyFxklDpdGiv9HnwUJKtK7UqlmUxGI9kA7JIw9ZVnciXE6YnbIs0i/E8F6WAm7ZaDRRgfH5GkiY89dRTPHz4ENeVxCvdNNBUjWazSa/fZ23nChsbGxdxg09+fcKxzPMcRVWJRYJeJEZHUYyzcIjCiN1Hu/zVX/0V9+/elyxNsaRSkirParXKpZ1tOgVRqt1pUS0a6CB7ZP31daq21Iucn5+zWCxks9UwcD2PerWGoetMp1Ns275wjfb7fUrVKqauFxV2QJKCalloyIzO3kpfKoZ9n9lsxnMf/7kPrvchzSRLUdXkOTwIAjl5yHOEqpJkGc0iD9J1HA4PD9nc2rqIIHsyzoyiiFarRa1WI0wSstSh0+kwGg6pVaqEnkcaJ1KOa5rkCheGoLBwPuaKUuRGZBfIssfTx9ILoGlYtpwVn52f43o+qm7y1NVr7O7t8/jxHktPpjG32m263T47O5col2q88dYbDM6HbG5ucfPWs9y8dYO79+7ypS//Gffv3yeOZFS9aZkMp2PWVtf4yCsv87M/9/P0+y08L+Tdd9+j2+3RbDY4OxvwrW99i+FwXFjCJ9h2iW63y8c+9irPPHOddrvBw4cP0Y2MSqXFR1/tcPv2d/nqX32VBw/u8/GPfYxms0MYBjiuR6UsG59ra2ukSUIQhKTktNptUFWCSO7efhCSxQm1coXzwQDPd6iUy9RrNZzFUqZmpTHOfMbRwR6dTgdDNxDF+X0wHKKoilzsPU9CU6Zj3DggjmJGB/sEacrKygqDweAiJewJS1LTNFzXxQs9LEN2/jVNUqSSNC7AuTWEECwWS0LPJfE8EALP8VCEQq+/QppnUqlZrdJsNqnW63S6XdY31ml3OtTrDWxb7qhPUpUUFBRVQS84iZom0FQNkQtiM76A9zabTTRNo1WtEfoudqlMr9dH03SGwyFbly5Rr1WICnDs+sYGiqIQ+z5qpUKUJCRpSrVWk81IXb6uUi4TBCkbGxscHh5Sq9UupOmNIlU6S9OCl1EiWMyIk4SV1RVUIVBVBcsyWFn5gLskn7vxTP653/g1jMLmfHx8TKVSoVk0G7v9Psv5/AK6Ojg7QwBbW1tMp9MLsCtC0Or1SJKYk+NjatUq1UYDspzED9CLVTL0fckTSBPiJKLaaJBEEWEQMBwOL27UeSG6sS2L2WzO48e7JFmGaZd4vL+PH4SEScp0NpPlaxCyt3vM1tY2L730YUajCfv7+/hexMrKKj/3c7/Ao0ePcZYO//4rf0aaZrRaTelsFFJG/fTTV/nUpz7B1atXUVWN0IsIgoDT0zMODg4Iw5DHjx+zv79fJDaV2dzc5PLlp/joK69w5coVFEUi7u7cfYcsTWh3ejx4cI+vfOUr7O8fInKF1177OK985CMcHR1x9/332H30iE67yUsvvcTbb70pfwa2iapp5AIqjTqu71Gv13l47z5pGNGsNzg6PMRzJqys9On3+ozHY3RVo1ouF5OVprQv5znddgdV1bh77x6aruF6Hm7g02q3SbIU13OJ0xRdM2m1u0xnU2kCK8Jj9w4PKJVKtNoy30NFxdBk30nXJdpcKDlnZ2fMZhNpFVcEkecRLhaYlkW91cQPAzT5+PYAACAASURBVPprq7R6HVbWJKPxiZem312jUuDfTEuSty6uwoWJgFzkZFkuuRCqShwm0jUZpyzmCylhTlPOju8zGQ2khN11pICuZGObOteuPs1wMJB/LpV57/33qVYrCCHodDqoukGt2SBLEpIwIoxCKvUGZ4eHmAXl6+joCMuSNDLP8wCwbVsKlSybs7MzXM9lvYgR6Pb70n+SZ/Se+tAHt1LQdI1Op8Pe3h5WkaTrOM7FN+46DtVGQ1YTQkgYSZYRRnJcZtl2MVO3cBcL4iSmt7KCu1gwPD6h1WrJJOpqFQG4jkMSxVTrVRk2oqpodgnXcQnDiLMzOduO44QwjLBLJaxSidWNDe7cvcfx+ZBMKAwnM/wgwgtCzoYzSuUyL3/0VTrtDt979z0mkwnb25e4ceMmg8GI3/yt32T/4ACBIM4TDMNkPBmxurrKq6++ymuvvUan02G5XDIcDahUqkwGc/7oc1/g7t27jMYjLFPi39rtNi+88Dwvffglnrt1izwX6LrO3t5D/tW/+jc0mjWef/45qrUKv/4bv8G7775NFEWoqsHHP/ZJPvOZz3D7jTf43B/8AbZlsLW5yTPXr3J4dMhgOLiIFTNMEy8MiFMZO6/pOlbJJlU1ZvNFYR4yqVYreJ5Lu9Vkf28Pd7mgUqmAaDCfT8nSDFXIXTQIApREZTSdkJFTFy2EoaHnFoaqYGk2o5E8SiyXS4JQOiV1XWc8meC4Lmtra4hMEPrhBbJ/OBwSxQH9fo9y2ZY2ZDLyNMXQLWy7RLvdpdFu0en3WNve5NLTT9Ff6WMUC4CWq4U9XV5RoVUAQJFOybzoVylCjrPzTB5lBaIIQT7HMi2azSaXLj3F5ctXiKKA5WyKqgh8d0m9UiZLU8hSojBA1w2uPv00e3t7UsW5WMoxuuMghKDb6xH4PrPh8ELMNSrSyzRNk8lhilIYxXJsyyLwPTrtJleffopcEURRyGwyIiPHtOwf/Tz+R3rO/19dWZZdkHWCIGB1dZWsMMzouo5VqaAUBGC1wHRNJxOiIifv6PDwgsqTJJLFtxiPyVLpFkuKXAHdNDk7OiL0A0zT5Px8gGFZOEuPPM/xXJ80yajXmkwmExkk2u0yGo8Ikoijk2NyRVCp1RjPFqS5YDRdoBkGrc4KL334w7z/3h0OD4/pdro899zzPHr0iN/5nd9hPl9IPmMsE4tLJZutrS1effWjPPvsrWJ3kwapweCMB/cf8PW//mseP9hHEfKB0FSNRrPOa6+9xic/8QmuP3OdRqPE0dGAr371a/zVX36Vvf1d1tZW+PhrH+XBwwf8yZ98gSyTltpypcw/+Mx/yvMvvMxn/8/P8sYbb6BrKq3WNs12g/3DfQ4P9tF0mbjsOA5oGl4Q0O73cD1PqgPtEk60wPWlq1Q3DYIwJAxDXNdhNp+jCIFlW7IacBwUVaVWbyKikEyROy2qgm1bLD1XgmQLvkIa5UXWpVKoVVOCKCLJJFpvsVwyHo+plWtcvnyZu3fvcnx8jKqpaJrKcunQbEp9hOe5xEHAWrdPo9nALNk8de0qK+vrmGWbaqNOqVJB0TTyPEfN1R8Q2hiGUShv5Ed/oK4WXMBg5Pujkqc5zWaTe3fv8e1vfxtdS7FNA8sy0RRBGgWEvkunWadkSxZGybJxl0uSLKPdbNJbX+f85ASyTGZWZBnNwuA1OD+nVC5fJEfX63XOzs7kwtHtFlWTjEIslWz5bCUxWhGwPJvPyQovx4+6PhCLAkAYx7R6PbzlEtMwcHyf0HFkh1VVmU8mlCsVtGLVbjQa0ikZhiQFlTiKoovzVbPZxvN9RA71eh3PdTk/PiaNEwQCUzfYunKFBw/uc/bgAUmSXOgY7t+XSURPJMqD4ZDpcsFkOiWIEvYOjplM58wdn83ty/RXVlFUja99/W+49ezNCyrS7/zu70qtQuEaVHKFy1cu8dRTT/HTn/pp6vUa33vnHfIsZXWlz59+8U/53//1vyaKI7lD+j62WUPXTZ566gr/6B/9IjduPMPKygqqCm9/73t87atf56tf/arE3ldK/OzP/CxxEvPbv/1bDAbnBGGAYWi8/PIr/NN/+k9QNYN/8Su/yuHhPqZp0mm3eeraZVqtBn/we3+BZZtsbW0SRB5hFBFPp1iVMnGSUK5USeIY3w8IoxhNN3AXC/I8ImOBrhscnZxAltNqNqi3OoRJytz1UBA8Otij3elglGxc36O72qdShP7mwMJxyLMcx5PVgV2kjjeLBf90cE4QBPR6Pa5cuUKtUuNg94BqVTIdoyjCD1wWi4VEqPV6rK+v0ajXqdfqlMsVgjCk3evQW1tFUVUUXUUoCmleBNUoP/4+/YHryXGiCPyJolhSmE2Dra0tKSTKQtI4wnOWCDLajRrVSglTFQhy1Bxmkwm6baMXLMrR2RmbW1vExXHWNE2yKGIyGlEul6Vj2HGKQKNz2STt9QAYjUYSwJIk+K50vObtFuE0wirZ9FdW8DyPSZFb8sOuD8SikOc5eSpnx3ZBVXLPzy8w1J4nd3JNVaVbsvDZR3GMoeusbWwQRxE4MuzD0I2LOXrJMIjCSMa6qxpW2SQsMiWXrkutWmNj5xLvvfUWDx48RNd1XNfj8FDGmp2fD6i3GoRJzOHxMa4f4AUxGbC+scn6xiZpmjMYTnjhxZc5Pz3i/fffv+hug0SUV6sVnn32WX76pz8loR8nJ6RhxFpvhYd37/H5z3+e6XRKGEaYpkmepKz2+jRbPV588SU+9alPsbW9wXQy41vf+ianp2f8/u//HsPREHKo16t85u9/Gs/1+PKXv1Sg4qDVavHzP/8LfPrT/wlxHPPLv/RLHBztoagKYajx0Y++xPalLf7tr/0fLGYz9KWG5y1QVQMyA0VT6Sgqx8enVKpyCgAKqqazstLiMIqZzBegamQiQdENGvU6jVqdWlPmW7a6fZIkJs5igjTBLtkoSVTAX1Nc15XgkzQlKzrtQRD9rRzY0Dk/P5fIc8uiXpdOx2atiaEanJ+fkWUZ7Xab6Wx8EdxSLpdZX19j59IlytUaWZ7JZrFlgRAX/QKRgyYEqCrihwpyf/R9K2sHcRES3Gq3ZMPYMKk15CKapgmh55AlERXbRBM5JCHuYkqqqiRpQrvTZTqb02rK13uOi6YImb1pWszmM7lJ2DaVSoX5XPpMrl6VZrZmp0Pk+3ieJyEzvk+lUadarUp7tm7Q7fZQhPwONfUDzlMgh3qrJf33hexWBpxIsrNtGORJcrF7J0UEnOe6mKYkPJuWRZqm8sihacWbIUNFDNtiWYwmQz/AMi2SMJKqRM+TVOhm80K3b9s23W6X+/fvc3R0hON7+HlMmuQsHJdMaPT6a9y89Tx37z2gv7LO9s4Vbr/xBrPREM/3ik54il0qcfnSDj/zsz9Nv99nd3eXlZUVhGFy+/XvMBwOef/99wnCAE3VQFexDI2Nyzu88sorXLv5LJcuXebevXv8u9/+TZpNiQj74z/+k2IsZaHrOq994lU8f84fff7zpGlGrVah0+nx8z//93jh+Rf57nfe4Itf/CJ7B/sITSYVr6522d7Z4Fd/9X9lOp0Q+h66plHxKiRRSqO2Sqla4eTsFEVV6a30MQ0TsowbN27wxndfZ+k4LFwfRTNQTQvdLiE0g/76BksvYDpfSn5jDqVaiSjPWE7HbK5vsL+3h6HpLBcLLNOiVq1iajqqarJELvBnZ2eouky/dlypDARZ1pdKJUzdvLAC27ZFs1UnSRJqtQrXrl2j1WpSrtXQCwL0k8AhVS3Qdk+e6yfXE9/4xb2Z86MKbVmB5yiKuBhXZpm0+0/GE+7cvVMsHBmB56KKjI2VHrqSUy9ZzKZTNAU67TZZmqIIQXtlhSyOyZOkgA/bBEGAQFqt4wIHt7a2VuRm5JRKJVTkMdyyJNOBPMNZLNANA7tkU6tVOdrfR9FUDNMkiqMf+Th+IBaFDJkvGPk+i8W8sBqX0FSVOE3wHefifBSGgSQjWzaaphIEvmyuZJJvGMeRfFgKX/9yuUAgEKqCqiicnJ5gmSae48rmS/Emdjpd7ty5c5Fd2F9ZwbJt/DCkpAj2Hz8kywVZLmh3mqytrfP222+zurbBYrHgG9/8FmEUoiF975ZlUrItnr0lNQaLpcyBHA6HvPveu6hxznKx5Oj4CAGoQiZP9Xo9PvTcc3zyU5+6+H//2q99lvfee48bN26gqPD5z38Oz/NRVTBNg8985jP4gcMXvvB54iSiVCpz48Yz/ON//J/z5ptv8cd//AXu3LnLwcGBnHkXMYKf/OQneP311zk9O0HXZCS6aZlMpiNKdhXHXeJHAaqhsbaxUfgKPNylwztBwOnZKUkkg3h1w6TRbDEYDHA9nyCMmM/mBHHCdC7pWI8OH8gbtFrF9T1m8znNWgPfDyDLSaNY9iLKVVbWVhlPJiBgPB6TZhmmbV30jUajEaPBmGatQa1exy6VqFWrNJp1ms0mlYqcUrTbTQzDRGhSMSiKCkEg/nYxEEj8QTFV+LurwA9bFgS5LDGKzxBCIBDkmRS7CSHY2twmSWMmkxHkKVXbknbuNMKyZWMziSMavR5xnOI4LsvRiGqtxtLzLs7+SRLTaDYlpzIMUXVdSsltm6PHjzEtaYd+6umnsQsEQRLH5HGEELBYRBiVCjngez7ng8EPMFD/7vWBWBQ0VcGyNOIow7Z0skylVCuRBAFJkkgkW+BgqIpsOFarxGlE4DmUKxWCYEkUR9RaLTIvRjMEnjtnNpujaSqe5xUyaIdUTYmVhEhNaJZrDE5OydOMO997G3cxR9F1ojTh9vfeZjAa4QUBCycg1epEYcilS5cplUocHZ9x5fIlBoMBb775JqVSCWIfzaxgaiq3btzg5ZdfJs8zlssFew8f85U//3Pywkp8fHYmQ0yCoGggNrh+/Tof+chHePbZZ3n06BFvv/02t7/7XZI45tVXXyWOY770pS/i+z6WZdFoNnj11Vc5Ot7na1/9ujQA5SovvfhhPv3pT/O5z/0+ruuSpinHJ7vkhBiGQsm02dnZIYtzbn/3TfJMIY6lFn7peqBoBHGEH8qQXiMxEXmXg8f35Fm+ajKfj1i4UiMhEgUdhWDpspzMaDabBH7AfD5nMpmgaRqP93ZJ/RiByXQxJq5UCOYBQ3dEEASEuiRBx3GMPxiRCXn0EYZOrir4vkeSS89FGEWcnJxgGjZJkv9tgnbhsXj66afZ3NhA0cv0Vsqoqo5Icml+KzT/QhGQZsWIUZCnGUITZLkUEj1J/xLFvOEHrjxHEBXmPQVSQZ4JhKJjmJrscQgV1/GZTwaQhJAEhH7CPPEJPIcsCdF0i9l8KbNLh2Pq5Sp5mnO2d4hmSWBrZ3WF8WRMnITYto1t6PiudIHGhUQ8yzLpbzk/l9BfRYE8LyzY0qxmaCOm05kkRHVqZNqPfvQ/EIuCAJzFDNdxaDabktSsq2gYZFlCuWTjuA4lo4LvenKkpWqy26yq5KpKLBSO9vexLQtn6VCySxe26cFgJDkCRVzbzs4OrVaHsmlDrjIcjUiY4PgB49Mz3CBgOpfQ0STLiJIMXbPptCV5986dO2xubnJ8fMydO3cu9OyGbtDv9/jEJz7J6uoKnudyeHjIm2++yWKxoNGU46NHjx7h+pLjWLJsrly5ws2bN3nhhRdwXZff/93f43vf+57MNqjX+NQnP8nbb7/N6emp5FUaBr1ej1u3brG7u8s777yDoqgkScLly5fp9/v8+q//Oscnx7z84Zf5ylekdyJJEjRdaht+5md+hj/8w9+XHXdFIUc+LHmeXbhSJffRvTCJPUmo2t3dZblcyinQdEYeSefitFCK2rbNm2++eaEziaJIull9lzSL5eSgVscLPaJFWJTzKoqjyMg8Q8P1fVzPKdLFIU5jsigDkZGkGnkO0+mMJJH/7+lszO7eI4RQqNdqVGs12q0Wm5sbPPeh5/jEaz/FyuoKpmEghIJqIEGYxfEhTTNIc1T7Bx+JnBzyv9tnyJGUSUX+G+L/au/NYjRLsvu+X8Tdvv3LzMqsvbp6me7mNGfImREpmqRAS4ZIjwiDtA3Y0BsfBPDFBuwHA6IhwJbfJAP2myGbhg0MDEND0SYpwZKoGVMcCiP2kBzOdPcsPV3d1V1dW1buX+a33iUi/BAR94vvVmarOequLngyConM+pYb98Zy4pz/Oed/BELY4kGHe4fcu/eA+SxHCMmNGzfodVu8d/s2o6MDBoN1JIa93T0uX75ElmW8d+c9OjLh5NiyiSEFG5cvc3iwx9HoiO76OlVpuRiN0iwmc45PxrSyFlmrzXyxoN1NKUpFkiboOOXg6JhuLCztfseS8g61JcLtDYZUT7v3QVWK44MDZrMZvXbblVWTrG9uIcEWKDWG48MRWdZCK4VSFZPxlNnMumZEZEupP/vss8xmC9599/2azWiRF5yMbdLOo+1dxuMpx8cn9Ls90IKj4xH7h0eMJ1MORiMm8znT+ZzZYgFSkqQtLmwMuXz5Mg8fPuT69evcuXOH3d1djDGWM+DiRa5evcpP/aWfsary7dt861vf4t79e9aU6LbpdDq8//5dlFJ0Ox3SNOOFF17gV37lV8jzBV/5yr/gO9/5Dru7u/XJ9zM/8zO899673Lt317qXdMVLz7/Ipz/9Y/zJn/wpu7u7dkGqiqjd5tatt/j+m9+j3Wrz2Z/4LK9+41UMmv2DPaSMaHda/PzP/zyHh4fsOQo4KSVa65Xz0NKPLd97+NAmut66dYtOp8O9e/fcpl+gFor5YsZkMmG+mDGfZxwcHpCkMTISjEZH5PmCLEvQUjCajJku5ghp09wRcOe9O7TaLbqdLlJX5Iu5TcF20ae6qlBViXb1JPOiQIgIKSzHAMbUeTOHhwccHOxz9/07fP/73+MHP/gBb735Fn/tr/1VfvInP8eFCxeQlQUIDZpKKaQUxHECgWZgjHFs+qtCwWDIc4WUhigSCGGFg0DQ6vS4+fzzYCSz6YJ8usdbb73F/Xt3iaUkloI0tutyf3+fWICMIMosk/bR6Ii01aLI52SdjmUByxccHh2RtTLKsmIwXOP45ITdvX22trZ45uZzHB0dkWYZi/mchw8ecOnSZQZZ7OjtIkrnXZvMZqiqZLD5EfApfLzNsJjNXPFRSeyqK4/2bBUmS2AyptOx7D6jkzEXNi+QRCnFvGRnZ4/R6Jis3ULKhEe7e8g4Yj5fcPjgATKKKJTi4OCARVmip1MUhlvv3eFkPLN8AAYOj444Oj4hSlLa/SFxu8dkOqPd6XPp0iV7mknJ66+/Tp5bktI0TVlbW+PmzZv83M/9HJPJjK/90b/k9ddfpywtjbZnBrI8ChqjDYPBgJ/92Z/l85/7HA+3H/K7v/M7HI1GNXnI5uYmn/nMZ3j9jde48957gI2N/6mf+ilu3LjBV7/6VUauilZRFvR7gxqPSdOUwXDA7u4uRZHXIdvdbpfPfvazXLhwgS9/+csW3JLS1lfQllNQCIGUtlCrqhSJ22h7e3usr6/XiWn9fp/d3V2kkMhIcHw8smZAnnMyjjBGs79vswKP5ragT6tKIRJoYTiejgHD/tEBN248Q6fftbyNU8saVJWVLWRblRht8+mqSjEtJxgDcRyhdQXGAsZSWJp8VYF0YKExmsV8xt333+f46ITj0Zh79x7y8ksvsba2TpLE9Ppd+oO+rU9a6ZXAJSsOxGMMIQJBnLSciSFQGrSyp/Du7j5loTg+PuH27XdJzJQkgn6vx/7eHlkiSYcDoiji8PCAQbdDp9Nimi8YxOvkuuLCxhqLqiRJU4gkw0Gf9c0LNqx/umA6nhMnKZevXKXX7zNb5MRpxt0HDxHA+sYF4jRhUeQIGZG1O3Q6XaI4Zv/wkL3dB2Tb25zVngqhUFYV45MJqqyYnEzo9rpovWAyntBqtZnNLMd+v69pZy0mk0ccjUZEcUxeFBwcHjEajai0Znd3n+ki53A0cryLBYtFTpZl7O3tgcDRWxv2j0Zs7x4wm88QImI6m4GMEAoWlUZGMe1u3yZSRREbGxu8+uqrtXrtNZHr16/zhS98ge9973u8+eb32D84II4i0jQmy1okSczu7i5laWsvbl67zi/8u3+FZ555hi9/+R/yzjvvUBSOZSjNWBsOePmlT/H9732XBw/uWXMqirh58wZXr17hG994ldlsSpYlLBY2gGs8tnECShniOKLTafHo0SNLJtK17E4XL25x8eJFfuu3fos8z5FSukrOFcZoa2dj7WkhBDKxJok3H/b390mShMPDw7pCtQB6va5jz5ZUlaSqrBuuKBZLG17AZF5QqJJSlRRVQRxHGAHv37tDu90ha7dsfoOq0JVCYgvSGmWFq5SSLEkwxqBUhTD2lFblAi1EPU5CSKpKUakSEMxnU4pc8Y1vfIPvfOc7bG5ucv36da5du0YURbz44ov89E//NIPhgHavRRTJegzq5tVt95IUsTUbcBYE0GrZIrhGgxCWT+KZywPefuv7zCZjiiKnKFp2HCNhvQ5VSa/f49q16xwcHSDimNH4hLSVoauSdnudk/GEqrIlD1pZxsmhjRYVQtRJgYeHhyRJUnsyxscjpCqsl85Vzh45bbzTyZDybNfrUyEUVKVsCnIUsSYiZJyQdGJMpdGLHBEldHsD3n33PYSI2H70yKrtZcHxyTHT+Yzj4xMqbUk70k6H4/GYnUc79Af9uty61tqGGJe2tmNRKUptKJWmqgqUAV1WKFM5uqyUXn9AmqTM5zPHuTdHShtpF0WWT//HfuzH+P3f/30ePnzIbH5sN2cFvd6ALEs4Pjm2aHEc8/LLL/PFL36R9+/c5n/5n/+BJRRVtjhJHMUkccTVK1d47933eLS9TVnmIATDwYBnnrnBq6/+a8aTMVJa8s92OyMvcpRSNr7BaDqdtqsHOXYl9+YMhwNefvkl/uiPvlbXZ9RaWc3F1a+we8CglKqxNa8dGYeE+0hTy3bUoSwKyjK3RKrGkCQxRbGoqfXyfF5zcEYiwmJgmiSJbP0HCcYIytIKt1YrRSqDcEVvkySqN6gAIimIkwStYpTSVJUK7hHSJEZrg5EKIwVK2ecrVM7xccV0NmE0OuK99961BXYvXeLNH3yfb3/7W9x89iZf+PxP8tLLL5EkKXHq6iMY7DWVRkSW2MUkgrJ0Fb0QVGXJ0dExQkjiyCZKSWnY2dnh4sWLPCwKOt2uFbzaUah1uwx7XZIk4tHuDieTsdvEMy7duM7e9jblfE6v16tjL5IoJXZMVCKOOXGRvZ6JPI4ikk7Hlj/QNnGs329xPLZCqZVaijalnvJiMAYotGQ6nnAyWdiKx86Wnc/nlGXFcDig019jMp2yvbsH0jIm7e7vWh77srTFYaZz8tGYw6MjZvM5J9M508mkjh0QwqqHWmsUhsr97eggMcaeOGVpg2guX7rEYj7nwYMHzGbWbk7TlHa7zec+9zmeffZZvvKVr3D//n3yfIGUoFRBlrVAaCbTMeOTKd1un1c+/eP84i/+Em+88R3+8F/+PvPZjHaWUJS2BFwrTdnc3GA46PHat7+JEJIkiUHA+sYaRZlzeHRQ11gUlahHMGulaK3IspRer8N4fEJZ5rTbliFofX3Iw4f3OT4eYRNQjbOjY6RM0MZqC1pXjgIPGzeBD9Kxzaf51qeoAJzbzNd71NomA9nMQoExVihQaYxKkfYoxRjlNn5i07IXC9LUpjJXVQUYWmlqNQMnZKSEWEqMExLGKATalVYz1gSIIpLYahECZat6C4M2FVVln1FIQVEu0KbiaLTPzs42t9+9xZvffZ0XXniBy1cuW8ykKFyy1SYXt7YYrq1jtOZoPMajElVVIUTkvCBtlKq4f/8+Wmn2DncZnxwxOTmh1+1w4colVFHQzpI6rmZvf4e8LJi5+Jwkjnnn+2+yvr7Owc4OrSQhi2I72EqxsTagXMwolSKfT7m0dYFWGttgOVWRRG06nRaHB4cYIZBRTK/bZXfvkauQXdn6oGe0p0IoCBHxaO+QxXzB2voak70jKlVxdHjEzu6OBbyiiMFgjW6vZynSqpKDw0PGkwlxkrDIF1THxyitSdKUSmvSVouyKJjnOZXSFJWqQ1OlEGgBlUPb/X1EUURVlrTSlGuXL5NEkkc7jzg4OKCqqlpte+6550iShN/7vd9jZ2eHLLNl2CtdWECvnTkQcs5g0OfFF1/mF3/xF/na177Gt7/9Gmgbku2jNbM0BQzP3nyWb3/rz+t6kIUq6PV7XL16lTfeeKPGDDyBp1eZ48hWcur1ei5Ix7Ihz+czhOhw9epV3n//fadh+M1ucQSldO1zF04N92xd/sT3QsBrDEpZO9/OzdIO99pEfboHKrgqSspoUQsMlLIhAlWFABIpidxmT+Ko7suTnBoXiuxNGhvlKt38CaSMkVLUsQhSSqQAUWkUXvhXaAVCC7SWjCcls3nEeHzE3t5Dtu/e4c57t+n3+zVHaKfTYX1tjY2NDfqDAUopZouC6XTmeC5skVfAUaoX7O3tU5YFxpR86oUXuH/3LpG0YchGKZ65fgXcoYeB9bV1LsQ2SCtzp3kUJ6A0oz2r9l9wldBHR4cs8gVXLl9hPj1mHMPU1aAsioLpu1Ou37jJ8cmYbrfLfGFZsMFFB+tqJT6r2Z4KoTCeTLhz94EtiAJcvny5RoRH4xmHBwd2ofMAISVjx4gkpGQ6nzGeTuxJ2+lYGm1lKPKcOIpRruJQlNgBNlqjjUEZrBtOOJsXgRSgioJWZv3lWRIRC5hPJpYB2mXqvfLKK3Q6Hf7YsSOlLlxXaUUUCeLYYhbz+RSwbqnPfvYn+NKXvmSBqLIkliVxFJGlKcZYlfza9Zvki4XliECgVcVw2GdtfY3Dw33GY7vRq6pESqvSWnCQ+rcQtvz4YjFHa0W326XX61KUBQ8fPiBJLIJtQ7CtQFCqROnKbSh7LQIh4FuNNTgtzguGSCiiKEY6D53Uy88K6YWDbp5JoQAAIABJREFUROUFlauYnaYpkRfOTp1OkqS+LxnFLnCnQgpnMrhoRDAYo1FVZYvnKG05DpLE3jvayn68NuSK8iqNwZtMWOEgFDKykY6z+YJqPifP5+y4Yr4eO/Fz33a8kd3OgKvXrnH/fcP3v/M6ZVkyGAy4fUtbz9hkQrvT5sqVq7z22mt02y2kgKpY0EoT3nnnHWIpWR8O2NhYo5WmFFXFIs9RLocn1oZWy3rjTg6PUIuCbrfF1auXmE8njA52SSUk0qDyOVmWMdwYcP3aZZKkxfXrN7h9+x3r8p+MmY7HJLEF8nnaMYU8z3ntje/Wtu53v/8Dur0e3U6HoixtKfjDY4zQTKYThJRojKNUMxgMldao2YxFnqNLjRRLNdZFpyNlDNIgjEEr7XzzPs7Vqpra2HDTJJL0ul3u33ufxdzmqQ/6fT79yis8++xN/tk/++eMx2PiyGXVOQkvI2UlvDCkScqVK9e4dOkiv/3bv83x6IRWy9KhC60oCkvwqbV2zEEph4f79WkuhD0lr127xhtvvFEnfQnnF7ensA2tlR4TEPa5nJeMNLO8lFEkSLMErY1D7u3G8hWuEcYJBVO74mCpGXhU3gfKxCvBL3YjKGW9FXESW4FRWxj2fv1YGWOQQthxAqIsBqdhSCdArEouXFyBcNl+tlK2dIQnQmA9Jdqu8cgJysqZEktwUPiHcXEYNnxRSgHC2IIrkcSUClWVzOZTx24djA82liGKJEopep0hd+/eccS4MWtra+w8ekia2gzYTqdDv9thsZiTOXq5fDFjfWhJUcpFxealLap8UbMwT0cjW3Qma9NpZYwODihzO9/SwMnomCSCwoGW+XzGoN+nWMzptluWoEgYRoeHXLn+DO1ux3lqYmbTmcubaLOYTDg+Hp25H58KoVBpxbRaMJ5PyAvr6hvNxy65KaHb7ZEXOfmidGqzPd3Deng2yASnz0q00LYuoQPObKRXBMJGs8koRmqJcUCUX2RGaaIoxmibyPTOO+8wmdgQ5auXtrh5/Qp/+P9+hdnJkVV1BeAWsy4N0qQYLWm1u1z71DXW1zf45p/9KZPRIe0sI58fuQ1lals6iiJ6vR7D4ZDbt2/Xp3Or1SJttzg5mThb2FmxxtQYiEWRJUJE7vQ0aAVJnKJiQxyl3Lj+DLfeehtVWb+6MqU9MY12SLm/Do5b0ssEBU4w2RPWqu9KC6I4chtVUQlbRyNOUlua3hikcIJKG5uIZCRVJKiMQSYRpRGOuSi2qr3TTrQGrUtkZNmGTGT7KbVBeVejECht6VmN0migFSfoKEZLA0LWtrOUgkjGiFIR2eBktLHPro2h0hWlsVhGEseIdsS8qlDFglhGJHGCdvRvwoCsbATkaHZEWqVECwuEHowPKfKCtbUh6xsbrA2HxO2MThkR6ZSD/SPW1wYUswqVV2Csxy1rpYynCxbqiHleMprtsrV1AYWgs7bB8fGRFZDtjFa7RYlGxBHVvEILyFVJFmVknTbd4YC93V1bZu/wgO7aZW4+exO0ZmfnEZWB4cZF+sMLtAYbZ+7Hp0IolGXJ4eFhbSt6ynCtNbOqQjmEPM9LqnLpIgttVqs12BgAre3RUaPeTtJ7lt8aQ8AuTCOX+eVpktb++clkYqtLOzBsa2uTV//4j9nd2bGL2Qkbf4pGUpImFhjb2tzixU+9yNe//nUmk4llAAbSNKlPweXClTX1m3+WOI5tQdALG/XYeAFiBYOn6LbP71X5oijqknhJollbW7NRiw5HsIxT5Yq9TjAmYVu+T/2MClW/Z5yWZoL/V0rV1aelCx/2z5vESxNASGntfocV4DQ649+TEm2MDVpSql4Pfu59oSDflNauorWu15EdL0ubhgSNRmqJlMbmOkjhBJGuWYtarVZNl17MF3WGrRTC5qc4k0cpG34vpbT8jZFkkS/Y3SuYLxbEcczVKLJxJK6s3u7uHmkiSJKI4bBn6eGmMypVsSj3aHU69Ltdkjhm+6HNR1kbDul1OyzmM/rdLlEsIYJ5URIlCbO8oD9cZ+viRaIsBWHp4opCsXAsY7bGZ5fj42Om02mdWHhWeyqEglK6DgYKF+vSj65doU9b0tuDbLAKhEUejDKqXqiwRMr9tf1rwhiEpl5gPv1Va0273a7zBtrtNs899xyHh4c8ePAAoObn99eO47jufzgc8sorr/D1r3+d0WhUZ7NNp1NSpw6XzizyG73VarGzs1Pb0V4opGnC9qPtuh/PGRFOqo0+FLbKU1XVgsZvot1dW9vSmh6gA85/P3anCQUvCPz7NQjpNlz4mp1HhURgHMFrHMfEQtbmhsbycSoXNOVdieFYWJMhqceyKRDCefSf96/5/0sp6z48rhHH1qMhpEQ49agsS5cEtQRF5/M5eW4TmiKC53WaZOTS91sB1lBVlY2Tqap6HLTW7O3t0Yn7bKxvWMLaVorWEYaYvb095vMp3U7bMjprxfUrl+sYg0hCEkskmkG/iy5z8vmcC5cuurT4mIlbg73hBq3uEOKIjc3LtNot5vMcpYRLChyzubnJ2potnJNlma3NeUZ7KoQCDh/wLUSujTH1oomjpPaH53m+BLo8z4L7fIRABTH8zeuBEyZOKIQnIrCy+Dw9nBCCe/fuAdS2s/9MKGjiOOall17i1q1bK4van0D+efznvbbhgTt7qkf1xrbOEiuslCueUrsk3Y/WmspUSKfxlGVZF2c9OTmpsQg7RoBeCgC/cfxYhnPgN3s4HpFT50NB6sdTa41xwK3W1k1YoetxN3KpdYT9KqXq090+u6rHwr/vPx+uj/AZ/FyHAtMfFFLIOjDL4wzeHVtWFUovhZR/jna7TRonlE4I24m3h9disUBIa/55lmfP9eAFhZTSFjPKrfYzGPSYTOwGlxPNiy++wPr6kKPDA7q9HpubG7SylMGgz+133iaJYy5tblDkCw52diyJTqfNbDpFmYgKiZEJcdpmMsuJkikyjplO50RpiyhtoXNLYuu5Jgy28NLR4aFN4DujfZhiMC3gXwGZ+/z/ZYz5b4UQG8BvAc9i6z78p8bWkkT8BUvR+0XkN4jfXKHLTSmFqkx96iz94KZeZP46hsdPErsezOomrnGoJaruS6D5iL9OxwKD+/v7terl+wyv59XWXq+HUoq33367xgwqxwXhr++R7NgF6Hh/dZZljMdjC0S5/y8WFnz1J6/XBvx91+NnNKq0ZsfcEdN2Oh2Oj49tYd2GZhVudOAxARB+tvkTfk4IsXJCxsJtdm0DoEQAVCqMK8qTrMy5D4bym9gH1oRz1Wz+ml4IhBqSfz+OY7tBkRjl5mnFBe3iNFjVQDyIapwWN5vNwBgih4GkaUJeWNeqdylHUVQLA0+7HscxETnaaI7HR2ysrxPF0G232X60w9HRId1Oh93dfXZ2HrF5YZ1r166SJgkvv/wi+XxBd22Ng71dsjTm6tUrTBcV/eEaRaXodAd0u5YiYPvRrtMODf3BGmVRoMqy1noWLgbCC9ezxhU+nKaQA/+eMWYibPm4rwsh/jnwHwN/YIz5e0KI3wB+A/jb4ocoRd8MLvfqtZ80v5DKQtUnlI/JDzUMf6qYAE8IF394gvjf0jT+775XlpbXcTgc1os3SZKVU8sLA399sODkzs5OvTH9tf3khGqzV4m9qry2tsbJyUmtWRRFUZsL/rP+RPULsH4+jUsNNvUpCzgXpk2GmU6nFEVe+/LD8fb/D8Oa/Xt1oV8nfP2mCbUbX7i31si0rkPKq6qyJdwjeaoQCjUkpRSxjIgiuSKAa7DYaYReYwxNGb8GmuOq0XVSU70XXN6TEMKmGbt78HNfFIVzmUoiYbk4/JxXlarH1T+313ayLFu5D+lg1E6nw6JYkOQRucOpBv0e0+mc559/jsFgwMXLlxiPLXfIYlEgpbCUeFozn875s29+k+HGRbprRR1Ap5RiMOiSJClHR0dUlS2aI4Dp1KYHXH/mGbIkIWq3UWXJwNETntU+TCl6A0zcfxP3Y7Al5/+qe/1LwNeAv80PUYq+Jqk441RaLkpRT2q4uEIcIooiFNol+DyOK6wALOb01/1G8NRW3m4EVja0F1rBWCGE4OTE1qScz+cW1XZagb/XUDX2bTabsba2xnA45Pj4eGWTxnFcq62eosw/l78/pTRVuap2LxaLOkAqdjRkcRLV4c1+4QK1ZtZUy5uagaVjs3MSmkE13gCOpdh7fZbzpFkKktD0C4Wxn5imwPWfaQpVLwCWIOxyLnwfUkikkcvnC7yVvq9mW5KwGDxxa9i3UlWtiXhz1sc0+OuVZYmkQkooqxhtUgyWvXzuuDs3Nze5d/8hvW7G8ckJSRLzzI0bPNo9wOiKssxRZUkry+gO1mxEpZB1IV+lNI+2t9m8fMUWgZnNGWxscPf2bU5GI6bTKQ/u3ePKlSu0jHXdLxy561ntwxaYjYA/Bz4F/E/GmD8RQlwyrpakMWZbCHHRffxDlaJvttBOdn3Wr/vFG8n4MdXHvxdKeiNtZtsHqZ/weEqs7883Dyb6ghve1vef8apruNGFEMxmMzz/gMcjmgvbf8cvqsViwXg8tpx6SjGfz2vVteV4+Tw5qe/XP3scxyRxspL3H6rm3tb2WpcVrqsofjju4VyEm9U/f3h9f1L6zeJP3aVpZlavcwro7W15cILIz08obBogY3iv/j78mDRNIz++xoTSYPV6TdzJGMuYsByXJUYRRRFCRiv32Ol0LIYQPIfXGiJpvUWVqlBGMegO6LbbqEqR5wXD4QDiBCUiIhETpS2yTpf337/DYjaj020joox+v0uvP7CBbdoeOoUrrRgbzfpgQD6b8Qf/9J8io4jFYsH169epHFW+cGtyuLbGA4ePndY+lFBwqv/nhBBrwO8KIT7zAR8/zdfx2M4UQvw68Ot20FbNgHCiQlzA/r1Ua0N10r9/amfBdR+/W3HqZ7wa6VmOkiSh49iFfQCRDzoJbVu/AYFaffXsOMaY2uzxZogXCsqldlsqsV6d4FIai2/4aDof4hwKIQDdwALCzaq1rslOCGjEmsBd+OzN9/zm9KevV8+b3gh/G/W81fieQUixZEBrgIUrm9MJztCl7FX10OvhNY7TsI7Q/EnihEQmtVmAi02hccCEwkcIgf+3XCqh4ND1QeHvy8+tn9d2u02eL+pnt9e0Hpjd3V26na7DoDSjkwn7o5Gt53B4zGDQA6MRRtPu9onSjOmi5N69B/TbVlvcvLCJHPQZDIdobTg5HtFKYp6/+Qy3332PVqvFcy+/zPz4mOPjY+6+/z55npOlqWXpOqP9hbwPxpiREOJrwBeBHeEqTwshrgC77mMfqhS9MeY3gd8ESOLY+E3SxACatq2X2H6x+Pe8TWfteBugFJ4qpwmE04RHc1EVRcHx8TGDwYDBYIAxxrmElsCdvwchbCprr9djb2+vJgr1p7LXGvwJHm4K688uODw85MqVK/XJg7bh02VZ1uqqByj9RjHGYLRa2bjh+ISAptbKkQWtbnp/P+Hzh8I2FDJNfKaJK0RRROFCqf01y6rEqOXzerMnHOt6DWiLAzTNmdM0AP98p5k/teBEEIu4DkUHi3VYD9UStPTfWV7bgd4ywpjlOAghiGJZr0kv7LtdW2+z1+vRbi+L0chIgLTga14UyOmUNEmZzm0ldaUMShjSdsZ0Nmc8sZ6EJInZ2tgg63R58GiHTqdLJ5as9zocHh4g0KRJCmg6vR5lWSAF9AZ9NjcvIOKEhasbkec+C7VVh+af1T6M92ELKJ1AaAN/Hfj72JLzvwb8Pff7H7uv/BCl6FlZkKctBKWUDV02CrABJDXo5Ww+gwAR1YFMNsjRrCyWpaAQCAlKrIoGIZ39665ZakU1n6GNTYyRkaDTbVGVNu6+qiw1faUUQktOxiOSJCVrJZRlhXTMPAZFUS5qTccm4rt8BQSRFCRpRJEX7Oxsk7Uyur02YlExnc2pSheY4+z4enFGiY3QjC3ZSVEUlNp9RjizSjnQTZq6f1WfuBIpQQqPvHlPjkBqN1Zag9YYYVxOhg0Vli4RS1WlA2ytei4cEOg9DsaHTQbNYxLekxRqhpUyxGKJGzSFnBdM/lQON2Y4NmDteq30MubFWADUBkopBIYIYxMhgMj4cCwf22RDv5EGLbUNdIvsOJVFiXZ5HK0kI44i1i+ssbG2zt7uHhQV7aTNbD4nToEIJicT8nle33un07FAqISimFvPizHs7j6k026zMewyGR+zvj60vCKF4nA2oJAx797f5vLlSxyc3KPTabG2tmYB7umE6WxC1u2ws/OATq9Ht9+lPxhgtA03H48nnNU+jKZwBfiSwxUk8I+MMf+PEOJV4B8JIf4WcBf4T9ym+wuXogexYnv7U0c0FpLSamnvSQcesRp1511CRjmBoJeqors/26MFnVcCeezrllY+jH0QGPJ8QVHkKypkHMXEMagAv/CuQ7uxjK2wvGL7qhXhV5tAUiCNJM2seVBOrDup219nc9NWJp46os4mUAcgKoGMRO3O8ye9rsfMuyJBysjlVixPOm9+BMqy5W40pv5tB9AKEG1stGKpijqDsTmO9XNKi977Fp70Idhaz782aL2KZ4RArRcU/jnDNRGuA/+6wVApKxB8mbf6cDDBGvFzsvIkLmrTaLS2p73QNsw6iWOyNLV5K5klWFkfDi2TstYkUcx0MWd9bZ0sy+pSiH1XAMfzWRZlwWwyI2slYGCxmFuzoZ3ZHId8jpT2YLx+/TpKGXb3D3ju5k0uX7nMvbt3SVttDo+OGI9PuPncc+zt75EkMQ8ePuDzP/3TFDMbym2pOEVtnp7WnooCs2kSm8ubG/VEhq688HfoevKvNW3eEIALkekm6CiEQCNQpyxmLxBCoWACVT0EDJsAVZqmbG1tobVmNBqtxAX4zVynDjeew6t3Wus6mlIT0e50a6+DEILJZMJsNqvHyS58ewI2x8KbGEtTwNr94T03x0VKSSwlSWN8/TXqtOnGM9Sb0Dzuqg1P+nBuwrYcU0MUL/GK04Ko/DyErkkfLRqaRN6tGX43NOn8/89aI02g1Zs97TRDYDkn+v0+F9Y3MMbyP8xnM1qZTVBKWhl5UdTxDJ1Oh36/byujBwF4SIjTGCkgSWKGgwHtVoskEpwcH9dJVutr6wghrQBaG3Jxa4tL169jqpI7b9+iKnIGa2t0Ox0m8xnT6ZQrN28SG8HhwQH93hBjDAcHh/ylX/oPnt4Cs8awYpd629Q3v+CaGzCcwPDUCTdtuEh9H8vvnS0QV66HQTrtwb8XajNh4EpR2JJvTT98KETCQKvw1PMpxakjFinLEqUFqixZGEOaJCRpSqfVctpLsfTLW6oRwJsCj29IAWhpw8r9WCw1iseBXgJh5QWk32T+mUM1PnymEOfxcxcKDd9PE0fSWiOkAcVj9wRLl3Dz+/6zYcBVuD78MwC1thXey2nfCQVheH+qqpgrTZamdT7HbDazwLQzqWTbjsmRowUM8aSisJXEvYBfX1+n1W0zGAxYX1tjNp9ysL/H7s4urTS2iWHGoCpF5LS8oihctq9iMZsxWF9jc3MTjOZkPOZoNKJ/YYPJZML06IhWq8PW1avk4xn9fp/W015gVojHN3UTVQ43um/haRN+rgmEhd9txtE3TRR/3ZVrWrRiZcH5TeJPfi8Uut0uaZoynU5rWnn/2TqYJrCTw368+RECerqskDJCq5JZmdMVXcffEFOVBVppBNpliS7VdePUYuGuH0kfGerThuWKRhU2b06EY+SFh4/G9DkiXmNpeiIeQ/Ibgv00TKDWxOwsrAiL8B484h/+3TxQwvgH30JMInRjhp8LBcVZ0X/GGCqtSF3yXb7IUWVFt9Ox4eztNrOpjQ25uLWFgbrMW6fTqTVBH3Xa7/fpDfsYA9vb20wmY+bzGUW+oEwTup02ZVGQpRnz+aIOpz52GkRRFJSLOdM8p9Nu2UNDStpZRrfbdaB1ST6ZOsZqW7DorPZUCAW/SCFwKwYqYvOEDRdQszWl/2pQzOknwmktXDCwBCybp1SoEns7LVy0YbBO+FlYqsveDPD3pbVesitpgxAatN3ki/kcKQRpklAlCUWe22xxIWzAljs9IikxDRdieGLX0Z/mcYTf/4T2frhBfS5FiG2EgVD++z5kvDkmTZMvjNtQymZZah43WZrj39RwQjPJX8u/HwY2hWspDIgSQliPD48fOOFcVsbQbXdQjhNUSlmf5t596rGldrtN5H77MfEAuY+Wnc1mFFXJeDqhKkrAOKbtiF63jxRgjKbd7pJlKUkSs7m5SbFYcPXqVbRSDNfWORkdMZvNyBcLPvv5z1vuESEoyoprN2+y/2iHtbU1W7Y+PXv9PxVCQQhqF0lo2zVtyLCdpvb6Fm4Av1Ca5kgNKJ0S1XiWsAmF02nfMcbU0Y/eZPAnV1PjaW4Qf40wDsKOhUJjEJ6b0FhyFk/KYkzbxUHYyD3loglxTEy+sLrQlqcwjBNoal3N5yUY3/B3qBWcNW5Njay5ycL5C+c3jmPHF7kad+E/611rYQCX/0yoYfn+Q4EbCi1v/nhin36/X2t5PtU9JLV5bMzM0mXp+5vNZsRRRLfbZTgcWhxIStrtNgcHB3WsidY24Gw6nVr2amNQsxlKWxMxTRPSJEGpknxR0G63yDIbxtzptLl4cYtLly6x++hRzfgkBDamJkvpdLs8evCApG2jTyfjMYe7u3Q6HWYL6wH7oI3/VAgFWLXTw5iFUDUMJxhOV/2BlcXu22lAUniNs67l34ujuF50YcBU87e3s5sLsHlChielz23w1wjHwNUhQjpaeq01plKUJidOEjpZy2bv6aq+T0SQOYg3IWykoEAEobqr2kIohIE6/bx5AjcFXRMI9M/gx+U0jS3ECPzn/RxZarfHg5GWsRZ6ReiEAiXEkcJ7C7GbJh5kjI141FqzsbFBlmWMRqM6rNyn9XtcII4ty3OW2BB2zwTlzb9e18YpeDNlNpuxsWGBdM8G7nGnbtd6lmbzhbsPW/dj4KpFp5Gl75+Mp6yvr9HpdJlOp7z77rsYN3/tVovj0cjyaESSIs+5v7sLkawLNe/u7tLv9Vnf2Kyxr7PaUyEUhMu59xu/qSnA45hDEwQKW7hpfTtNHVTGUAXuqbNMCruIVk/FEBn3myyKIkeWOq/fa6q3oboaXi9UdcOMPymF28xuE7lFXpYFZVm4hQpJHINYFaQYW7i2aT7Jxql5KuAHK7hCM5gp1JzC8QuvF5oHoYYU2vPAkm/BmWAGG6QWjk+YXenHp+nhOM10bGoOTezJP7cQ1k23vb3NxsYGW1tbTCYTJo6fE6h5MZIkIZER7Vab2JtFxs6BTZqratKWrNNGac3+vq3mZIypyXvW19drIXH12nWMsfk9Fy5sUJUlDx/cZ+fkmHa7zeXLl221s8WCKBIcHR3RSlO2t7fZuniRnZ0dkjji0tYmUWTrkfSGAy5dvsz2w4dMJlOqStFu20jGOPm3CF56Ek1rXcemh4s63KTNhQ2r6HrYfHx+6DL0n1vBKISsS3ivRAeeBiw5YDAEQL1t6Be+r8LkUfdQQwg3l1ddQyHh06ubaq6Uthq1/2wYFq3UMsU7jlOEjFYwjHBz+nE2xtKlN0/8praAsaZGE1wNhYe/x9PmzfcdzkFo5zdtef+5JLF08+F8hffl7yP0cnhh4+fRf64JTDc1m/AQ8mNbVZUtweYElcd2oiii3W7XKfBpmtb8kVXpSW+FEwoWSOx2u+R5Tu7wBaVUncPir2+Modfr0ev1OBodQ2ld2Yf7B8xmEyJp3Y9Z1mY8PqYoFly7doUsyxj0+xwcHHD71i20quj31knTlMPDQ65fv0ZnMGB8cuJiI4YYYz0vs9nMVvo+oz0VQgEcrz+NjS4E4ozT238mxA/q7/G42tkE2YzxBVCkjWqz3S1zCBzgYLSleHPcruCqElmhEoERaGVQQiOFpqwqojim1+87qvIYrVxdAqdtRBpMtfRYaKOJhMBIe6rFfiNJaXkFheUB8AvdGIN0TEZaW0ekMLouMtJKM5tFV1X2/v14CAtgabAl16UPVrIYxErUjrDQhJFiyYoayUawj43p90FBdWKhsHqNiOz3LKeiRlWlxTnipVaVJokTUrYCEsIQyegxgRBiB94tWq8RKam3vh9jKREGImOvUakKtCaKbe0PYyzLt3D36oVDK81sZfKiJJ8vljiCNhSL3N6isAQqVVnVQxZL55YWAqNNnWqddbp0uz0HLK451iMb2pwkKUmSkucFezuPbM3HouDRowd1XoQU1vd1fDKmKCv6vQFFqRDSFjTKkoTFYk4+n5FEkEWCVhIxnUxI++tESQs1W9DpdikXCxbTMaIqiNT8zH31lAgF23TjlHGBt0tBEfztW3gSnCYgQrcXrCL/gC1aWtuyy9DaFb+7i34UWCYkYwBblBlbgchFR2pDeXRUn2YYgypLm8EoBMYFF0VCUmqFLoraTtXKkb8aU3NH2kQee3oVZeF3nA3tjqK6cKgxzoWoFEL42gcRSqvaPHIjZJ9dYDezY53yG1pIavkAnkw1+NvoFRMuxCZ8SDmhtuGe2YZWGeIkqs0fO0eGoiweI645C4MI8QIPhNbCw2seLhIzdhGbkbR9VlVF5fJDtNHONWtqc6rWdpRyAtx6cozXmIwtGxBFUR02rVnGnyhjw4erqiKOIjqdTp2Zaty8nZyMKYoDFosFly9fRinFZDKh1+uR5wvyYoFWmqtXr5KmKd1uh8PDIyptKQE7vR7tdkbsohsPDw/Z2rzAdDYliyQnJ8ekkeBTLzyPkTFpd8BguMZstmA+nSKNIhKGKAIRP+XeB4/ihie/twUfs/c/wPa3by/Rbv/bZ7M1XVS+v1BdD1+rhYqwJ2bz2v7zXnVVSkFlVoQC2jIee9Zo6RaqEqY2M5pZleEpGcfuPhuUdX4x+k1pUzU8CUhVq8ZhOLD7iCWrxWDp4OvwBnfCm2VfgYALU8ydAAATOUlEQVStTY9GIFa4cZvejGa8R5baGoYeEwg9NOHcNXGB0GUZzv1Z9yCbcxuYNisgr1iGcitta4L4+wrXSjg3fs7iOCZ2Gg3GanJFWSKFIEuz2lygLImMqQWfd1MWRUG/30dKyWg0oj/o0+52aLXatsamMZSVYuviRYSwHB3T+ZxFvqDbTinLgjR2xLB5QWfQZ/vBXdaGA+IkozKGR9sPGQwH6Kqy5REXM1vlGlvj4qz2VAgFISANuAnABuD4yVwJTmEVNDwN5W7ayt4F5D/ftEc9o1IzF3+5gFbJRk5zg0opHXW8UzfddSIvNLxP39iYAhP58nQ2FNeHMftnMW4hRdgaEFmW2WzJ2gOz6trzi9NnYIaL+rRYDf96E3wLUfomoOuFtX+tOZbh/YSMykvsgMe+G4KupwGGTXC4FrgsTb3aWyOkLRAc3LMP3kLYNGgpZW3mWFevrJUjDZRFidLK1QUxdbKau1DNXq21RgVnUyRskZUsy4iTuE67j7OKOBD6WZbVyWIekN7a2rL1LCLJ5uZmjRVFjhPB4hgRi0XOrMxZG16h2+miVElRVhhVIqWg2+tTFhW33n6by1euUeWKH9y9Q5nnYBS9TkY7zcBUzOZPuflQq6L14gNPkmJfcPTcDVPBfzdsfjOHEYch+3O4YEJB4N9vBhjZ6z1+v7B6Mgq36OpgHqcVyCh2BU4CKnghqVg9Sb0/3IfE+nuwhKuqPv38hiqUBSZXkrdYemlCkNH/1KCas6Mty3kjsMsBm4ZVodMEfUOtrsk+5fsOT2cvAE+Lbwjnw3/XP7//bJhm7U0uGwa+1Pxi56FIksSlOxsLJktpTSkPFmuNNAbl6e6D9aRcPQjtSVicEFBmWXRHRhLM0mNkAca4LlWX5zm5wzYqV8AmSZKa3LXdbtNut+u4hbIsOZnYIkcPt7fZ3NwkdtW980VO2xUMTrOM555/jnw+ZTqf0+91WOQFaSSZL3IurA85Go04PjlmMpsTZV22rlxh995dVFUhhGQ8GdNttxkM1jirPRVCwWqyzq4Vnlff1n0U/kcbkGLF7j9rAYUnW4iKh0h38z3/veZGOA2w9C0UCv47vtApzhZVSqGMqU+sMAcvPGF9qHR4D1JKVOHcbw4BF5GkUoq8cKQpwfP4DZ6mac367DdTyBjltQqBrdHZRPGbtnxoQoSCqfnsXkMJ8xOaGlaovYSfCTED31eYD7Myxg18KIosbpAkCVnsGKaEN6v8vS/N0DiObYFZVxTXC4tQg6k1RWnH299n7A4Nm8ZvLI+jX4PuhC/yAoxNfirLklZsk6b6/T5Zlrm6HHauPa1/1u4wmcxcNGRFt9vl0qUrjEYjW2J+c9OCm1VFWVUgI6I4YT6bopOItUGPRV5Q5rlzTwtu3LjG/bt3kRIKVXGwv8/62hrtTof57CmneJeRLaihHLW3T5EWHmuQNuhGidXN21T1ffObLNzAoT0MPCYo/OIL1e6lgFj1uYfxBOFi9tcOTQ9/nUgECVoOVJRuw552mqcuJZfYlnrzlbOMM0UeM2GMVYVrBmO5DFAJXW+RtEVcjZBIbDai5YYQNdOQsaQSqAaeEwqMpqsxdBOGMRj++0qplcUWuiPDjdjEi5pz7e/Dtzi2ppVH+6WQtXCU0hGu4nkaHI4RNdyqWlvBLVYzcUOy1rpvhxMJjE2dD/JavJlggqJEcSutXc6erk0pS8zr6dpOTk5oaUO3PyCOY9bX1+uIyLULmwhsvdX9oyMsX4t1beZFRaU15WQBRtNpZxhVIRBMJxNOjg5J44hOt8+D6Riw6+rC5ha33z3mrPZUCAXPEGa9gKaO8/cqrD95pbSS235s9eSBVdOA4Lvhpm/aqk0V25sUIZBn3ZaPR1I2/eNCCES01HakXDIB+yAiYwxGaCoeB9dCoeLNnm6rbSP8HLvxoshXwLCo5m5Y3fyemzEUkNbMiK2l7IBTH+FYj1FgehlVPTa+zY3bxFiamyh8Ro2px8Xfb1MgN82UENRsvp64jNIkSRyRaYnSJQX2EInjmLjVIo5sTEipfCzKquovo6iODnSrDimd50Tg+BiXz2CMJsJVjXKmmy87p5VCCImMqLkzvbYW5ol4oleA4XBI2unS6fXY3Nyk3W5z8eJFLl26xK1bt7h//z5JktLr9ZmcHBMlEWVZITAcHBwxn425evEiulKoqiDdzJhMpty78x7PPvcce7u7XLlyhUhG9Ps9JtMJaZqdshFteyqEgjF6Jewy3LAQLDRHRgKPq7LhSdMEyPzGaC7esPn/hym24XUEqyShZwmX2p/XaDVAZsAITSS9a2w1UzEMEJrP56iiqkNmpZQgBYs8p7Qlk1ewkFC9Pm1zWTXbFlsBR5ZSqZU+jSujp7SmMqtj6vsJN3IYiBSe6qfdg29+7kJBEJo2p81jff8ON4idiqy1LeeuXNBSLKzbWArq4CMf+p2XRS0wveblBXjl57ERTAXUyXBAnXxmzYqq1ui8F8gmMjleyICP03MnRFFU17nY3Nzk4sWLSCnpDtfpDdcRMuLTr3yGnZ0dvv6vX6Xb7XLz2ed5//33OTocsb4x5OLmOocH+7z15ptICdcuXaTT6zIZn5ClNngqTVLGkxPefvstqkrRHQ7ptW1exN27dynK6tR1Ck+NUACfmGRcEE7sF7m0Qb4Yg4qwrgqWSPnyGqvuy9M2ffidEFdofm713uzJIE/pz0c3+gXkfdewVDN9AJSInB3tFpSJBMKsAnZNTwpQ06hHsSVg6ff7NhNPqzp6zy/0JhYSnrS1gJViZbOFgGQIsprq8XqDTS2gBucCt6f/TOjBqIWGXs21OA2bCHGFlY1pTH1w2FDfqNYavZCxVb+X3qfQU9LKWuQOzLVxBxpdFs6duMQPRCDcmiZNOEaYJbWd0Roi6ixVy2zlUrqVqmtceiGVpikbGxskScJoNOLGjRusb2wwWNug1Wpx+/ZtxuNxfRgcHBxgjOHGM88w6Hc4ONhle3uH/qDPcDDA6IqjoxGL2RSBod/rMRz0mS2mLOZzhJTs3L9H6+azNjy61WY8fsoxBWMMpaqI4sgWho0ju6mMRmi3MA0uEm91cYYqd/N0gseBrrDP5j2cph5bLQGUUfhglziOQEQYE1sAS1vOQqW1FWJ+kwvhouaWLi/vZamKZVqyt4EtiGVPbylsIlSU2pTcsiqRhbQs7kKQRDGFtDUcCGziMAAo1CL8IpdSkmVLItdw0/oFX1WWJzAWwgU5WcEofIASzsXnokE9uUtTiDTH146VOwSk32SxSw93cyls7ADCmgDezMEYGyxWKTvWasm16E97YwxVWVrTwVUYL8uy3ujz3FZ1irz72IGI9Vpy8wWPu679s2lt12UhIEsjKq3Jq5LI5aAY5+DU2lY0K/KcKI5Js3Sp7cQxeVHQ7nS5+dyzrK9vMFzbYDK1eRC9/oDxZEJRKRCSza1LXL16g4O9fe7d22axmJCkLTqdNYy2qdfz6ZR8PmV9bQhRxt7RGKEMZTUny1Im830GG+tEswlxnNg1fEZ7KoQCApSkXhhC2rLf6CV3IAYbvcfq4lsB85x6eZrdC5x6kp5lRqyYB8KaOO4DSOndnRprZer6HtHiMQNCSkmlrb0qzSoVm/QbDDBCItzJ7VOeK62pHKkoZUFeFvWGjqUFBG26sSFyIKMPDmpqTkmSuEg/q8L6qlU1UObLpRmrqaWxpCwtlpFXFUL7+JHl5hEiwu3lMwVybR5VNpTdCkZp4wrwlaxtFKbWGhUQrITENGEMRUSEkWCECyl2LsjSb3oEpXMHyijCCKi0sqzSDl+pY7Zg6aZ1AsqOm3/X/ljlzwonIw2mrDBIRCSR+YIkjjFV5Uh5NEYJxwthQMfIOKbTadPqdIiTlK3Ll+kNh1QGJpMp/d4ALWD/8IDuYMDoeMyFi5fBCN568xYoQ6fdo9fvkWUZj7Yfsre3jyoLyrIiTbtkvTUe7BzS7XZIpKHXH3DjxjUODnY4mY5pt9p0oojh+pCz2tMhFFzzoboSS8HtvXf+pBCI2r4LN23ongwLv8LjGX3+tVC9DX+H1zzt9fB3szW1Ed+aavKK+o5ffNTBMuHpbTUBq36Gfn6/yX2//uQLM0TDFHSr4cSWfVmtahXeDPEcAhYdbxNJT0EnrNYj7cZStbAWSAkmEHTNcV4ZO+91Ccyt08Y21GL8a+E8r4xn5MfSrRn7hRUTRmntAMJVbbI5xx5BOGtum1qnMobSjg66LEmiiFhK0iQGg823iGzSU5ZldHo94sRuOY8rLBYL0rQFMRwc7CPThHavR384RGkYjUbcu3ufYafLiy+/SKUKCr3gzp07vPfeu3S7lr9zbc2WrJdScHiwj8DQaSXEUY+LW1tIqSnyOVLa6Mhe5yOq+/DxNQdO+Xx3YZNKBNRJRGiDTORKsA0sN7i3VcMsRlgFxpqt6TP3iy+0wWG5qZsmy2NPcYrm0Xy9CajhBJ0fA6RY+U4cx4hoyX2wAn66+yrL0p5eZpntB6y4JmuvilhWY/KRnN5e99/VWtNpt2llsSMcBS1ACVvEdpUqrQHENcYgFOBeIHjhFdK7h2PjrxPOQ+gRquePsBS9dW3HcWxtfB14NDAQfLcpHGoh9gHC/rQ1pA1URoHRGCnRcYxIUuJWTJImoCKSVpt2p8PW1kXiNGU6dQSu3S7z+dxVIcsYTybIKCLDmouz+Yyj0Yid7Ud0Wh1+4id/kjRKOT45osgt98NLL71kK4uNjmi1MmazGePxCe1Wi+lsRpb00Mbw/TffJM+nbG1uOA1Sc3BwcOqzwtMiFNwa8pmSOPYAX7bL25a6tmVXw3tDwXCWmzLUCJqvNeMPfAtPh+ZC/yD7ufleeHo+Lhz8ACwFg9Ea7Z8ziohju3n95g4rNPvfVSAYQzPKP1udayAqjFmGd3t0vCiK2o0JkGYp3a5lCfIUZf7aSgOEwvf01OZwvDyQ3BSQobALvU2rLuHHsR4/eHFiOQwEkCU2HbksCjS23oXWNlw5vFaTrak2T9TjWl5zzptz780MY8xjQs8LfA+YK6UoygKV58goRghfWjCllbW5cvUq61sX2D84xAjB5sUtnrnxDD/+yo9TzBfcv3Of3qDH6MGBI1fJePDgAf1Om/F4glYVs9mUi1tbdNstVDnn0aNHPHP9Cp/+9KeZTsdMTk5Ik7Qeg1PX71mq8JNsQog9YArsf0K3sPkJ9v2j3v+P8rN/0v3fNMZsNV98KoQCgBDim+YUDvr/v/f9o97/j/KzPw39n9ZON47P23k7bz+y7VwonLfzdt5W2tMkFH7zR7TvH/X+f5Sf/Wno/7H21GAK5+28nbenoz1NmsJ5O2/n7Slon7hQEEJ8UQjxlhDiHSHEbzyhPu8IIb4jhHhNCPFN99qGEOKrQoi33e/1j7C//10IsSuE+G7w2pn9CSH+azcebwkh/v2Poe+/K4R44J7/NSHEL38cfbvr3RBC/KEQ4k0hxPeEEP+Fe/1jf/4P6PuJPL8QoiWE+FMhxOuu///uST37v1UL4+Of9A8QAbeB54EUeB145Qn0ewfYbLz23wO/4f7+DeDvf4T9/QLwBeC7/6b+gFfcOGTAc258oo+4778L/FenfPYj7dtd8wrwBfd3H7jl+vnYn/8D+n4iz4+NSuu5vxPgT4B/50nN/Q/780lrCn8ZeMcY864xpgC+DPzqJ3Qvvwp8yf39JeA//KgubIz5V8Dhh+zvV4EvG2NyY8x7wDvYcfoo+z6rfaR9u/63jTHfcn+PgTeBazyB5/+Avs9qH/XYG2PMxP03cT+GJzT3P2z7pIXCNeBe8P/7fPCkfVTNAF8RQvy5EOLX3WuXjDHbYBcTcPFjvoez+ntSY/KfCyHecOaFV18/1r6FEM8Cn8eemE/0+Rt9wxN6fiFEJIR4DdgFvmqMeeLP/hdtn7RQOC2B4Em4Q37eGPMF4G8A/5kQ4heeQJ8ftj2JMfkHwAvA54Bt4H/4uPsWQvSA/xv4L40xJx/00Y/6Hk7p+4k9vzFGGWM+B1wH/rIQ4jMfdKsfdf8/TPukhcJ94Ebw/+vAw4+7U2PMQ/d7F/hdrIq2I4S4AuB+737Mt3FWfx/7mBhjdtxi1cD/ylJF/Vj6FkIk2E35fxpjfse9/ESe/7S+n/Tzuz5HwNeAL/IJzv2HaZ+0UPgz4EUhxHNCiBT4m8A/+Tg7FEJ0hRB9/zfwS8B3Xb+/5j72a8A//jjv4wP6+yfA3xRCZEKI54AXgT/9KDv2C9K1/wj7/B9L38KmFP5vwJvGmP8xeOtjf/6z+n5Szy+E2BJCrLm/28BfB37AJzj3H6o9aWTzFIT2l7Go8G3g7zyB/p7HIryvA9/zfQIXgD8A3na/Nz7CPv8hVk0tsafB3/qg/oC/48bjLeBvfAx9/x/Ad4A3sAvxysfRt7veX8GqwG8Ar7mfX34Sz/8BfT+R5wd+Avi26+e7wH/zb1prH/X4/zA/5xGN5+28nbeV9kmbD+ftvJ23p6ydC4Xzdt7O20o7Fwrn7bydt5V2LhTO23k7byvtXCict/N23lbauVA4b+ftvK20c6Fw3s7beVtp50LhvJ2387bS/j+Er59CDmqSnwAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Testing event\n", - "cat_image_url = 'https://s3.amazonaws.com/iguazio-sample-data/images/catanddog/cat.102.jpg'\n", - "response = requests.get(cat_image_url)\n", - "cat_image = response.content\n", - "img = Image.open(BytesIO(cat_image))\n", - "\n", - "print('Test image:')\n", - "plt.imshow(img)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Define Function specifications" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "from mlrun import mlconf\n", - "\n", - "# Specific model variables\n", - "function_envs = {\n", - " 'IMAGE_HEIGHT': 224,\n", - " 'IMAGE_WIDTH': 224,\n", - " 'classes_map': '/Userv3io/projects/cat-and-dog-servers/artifacts/categories_map.json',\n", - "}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Deploy the serving function to the cluster" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "from mlrun import code_to_function, mount_v3io" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2021-01-29 23:47:54,881 [info] function spec saved to path: function.yaml\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Setup the model server function\n", - "\n", - "fn = code_to_function('tf2-serving-v2', kind=\"serving\")\n", - "fn.spec.description = \"tf2 image classification server v2\"\n", - "fn.metadata.categories = ['serving', 'dl']\n", - "fn.metadata.labels = {'author': 'yaronh'}\n", - "fn.export(\"function.yaml\")\n", - "fn.set_envs(function_envs)\n", - "fn.add_model(key=\"model\",\n", - " model_path=\"/User/mlrun_repos/demos/image-classification-with-distributed-training/pipe/52f2145e-7a54-4137-8c7b-b6c20cc8b1fd/tfmodels/model.h5\",\n", - " class_name=\"TFModel\")" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "if \"V3IO_HOME\" in list(os.environ):\n", - " from mlrun import mount_v3io\n", - " fn.apply(mount_v3io())\n", - "else:\n", - " # is you set up mlrun using the instructions at\n", - " # https://github.com/mlrun/mlrun/blob/master/hack/local/README.md\n", - " from mlrun.platforms import mount_pvc\n", - " fn.apply(mount_pvc('nfsvol', 'nfsvol', '/home/joyan/data'))" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2021-01-29 23:47:54,893 [info] Starting remote function deploy\n", - "2021-01-29 23:47:55 (info) Deploying function\n", - "2021-01-29 23:47:55 (info) Building\n", - "2021-01-29 23:47:55 (info) Staging files and preparing base images\n", - "2021-01-29 23:47:56 (info) Building processor image\n", - "2021-01-29 23:47:57 (info) Build complete\n", - "2021-01-29 23:48:07 (info) Function deploy complete\n", - "> 2021-01-29 23:48:08,029 [info] function deployed, address=default-tenant.app.us-sales30-demo.iguazio-cd2.com:31946\n" - ] - } - ], - "source": [ - "# Deploy the model server\n", - "addr = fn.deploy(project='cat-and-dog-servers')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Test the deployed function on the cluster" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Test the deployed function (with URL)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "payload = json.dumps({\"data_url\" : cat_image_url})" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'id': '38224902-a688-4985-9424-578ff9ccb4a5',\n", - " 'model_name': 'model',\n", - " 'outputs': [0.0]}" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fn.invoke(path='/v2/models/model/predict', body=payload)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Test the deployed function (with Jpeg Image)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'id': '246c00fc-225c-44ec-b221-4e6c99f7bc5d',\n", - " 'model_name': 'model',\n", - " 'outputs': [0.0]}" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fn.invoke(path='/v2/models/model/predict',\n", - " body=cat_image,\n", - " headers={'Content-type': 'image/jpeg'})" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/tf2_serving_v2/tf2_serving_v2.py b/tf2_serving_v2/tf2_serving_v2.py deleted file mode 100644 index d3642c202..000000000 --- a/tf2_serving_v2/tf2_serving_v2.py +++ /dev/null @@ -1,82 +0,0 @@ -# Copyright 2019 Iguazio -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# Generated by nuclio.export.NuclioExporter - -import warnings - -warnings.simplefilter(action="ignore", category=FutureWarning) - -import json -import numpy as np -import requests -from tensorflow import keras -from tensorflow.keras.models import load_model -from tensorflow.keras.preprocessing import image -from tensorflow.keras.preprocessing.image import load_img -from os import environ, path -from PIL import Image -from io import BytesIO -from urllib.request import urlopen -import mlrun - - -class TFModel(mlrun.serving.V2ModelServer): - def load(self): - self.IMAGE_WIDTH = int(environ.get("IMAGE_WIDTH", "128")) - self.IMAGE_HEIGHT = int(environ.get("IMAGE_HEIGHT", "128")) - - try: - with open(environ["classes_map"], "r") as f: - self.classes = json.load(f) - except: - self.classes = None - - model_file, extra_data = self.get_model(".h5") - self.model = load_model(model_file) - - def preprocess(self, body, operation): - try: - output = {"inputs": []} - inputs = body.get("inputs", []) - for byte_image in inputs: - img = Image.open(byte_image) - img = img.resize((self.IMAGE_WIDTH, self.IMAGE_HEIGHT)) - - x = image.img_to_array(img) - x = np.expand_dims(x, axis=0) - output["inputs"].append(x) - - output["inputs"] = [np.vstack(output["inputs"])] - return output - except: - raise Exception(f"received: {body}") - - def predict(self, data): - images = data.get("inputs", []) - - predicted_probability = self.model.predict(images) - - return predicted_probability.tolist()[0] - - -from mlrun.runtimes import nuclio_init_hook - - -def init_context(context): - nuclio_init_hook(context, globals(), "serving_v2") - - -def handler(context, event): - return context.mlrun_handler(context, event) diff --git a/virtual_drift/README.md b/virtual_drift/README.md deleted file mode 100644 index cd7383904..000000000 --- a/virtual_drift/README.md +++ /dev/null @@ -1,56 +0,0 @@ -# Drift Magnitude - -Concept drift and shift are major issues that greatly affect the accuracy and reliability of many real-world applications of machine learning. We can use the following Drift Magnitude metrics to map and understand our concepts and how close the properties of the data we used to train the models on are to the current data we receive. - -## How to integrate - -The Virtual Drift function is built to receive two data batches of data (as `dataitem` or `Dataframe`), base batch *t* and current batch *u*. - -```markdown -:param context: MLRun context -:param t: Base dataset for the drift metrics -:param u: Test dataset for the drift metrics -:param label_col: Label colum in t and u -:param prediction_col: Predictions column in t and u -:param discritizers: Dictionary of dicsritizers for the features if available - (Created automatically if not provided) -:param n_bins: Number of bins to be used for histrogram creation from continuous variables -:param stream_name: Output stream to push metrics to -:param results_tsdb_container: TSDB table container to push metrics to -:param results_tsdb_table: TSDB table to push metrics to -``` - -The function will calculate the selected drift mangitude metrics that were selected and apply them to the **features**, **labels** and **predictions**. It will then save those metrics and export them via Parquet and TSDB. Alerting could be added on top of the metrics via Grafana or a function. - -## Metrics - -The drift magnitude metrics we calculate are: - -### TVD - Total Variation Distance - -Provides a symetric drift distance between two periods *t* and *u* -Z - vector of random variables -P*t* - Probability distribution over timespan *t* - -![\sigma_{t, u}(Z)=\frac{1}{2}\sum_{\hat{z}\in{dom(Z)}}{|P_t{(\hat{Z})-P_u{(\hat{Z})}}|}]() - -### Helinger Distance - -Hellinger distance is an *f* divergence measuer, similar to the Kullback-Leibler (KL) divergence. However, unlike KL Divergence the Hellinger divergence is symmetric and bounded over a probability space. - -P, Q - Discrete probability distributions (P*i*, ..., P*k*). - -![H(P,Q)=\frac{1}{\sqrt{2}}\sqrt{\sum_{i=1}^{k}{(\sqrt{p_i}-\sqrt{q_i})^2}}]() - - -### KL Divergence - -KL Divergence (or relative entropy) is a measure of how one probability distribution differs from another. It is an asymmetric measure (thus it's not a metric) and it doesn't satisfy the triangle inequality. KL Divergence of 0, indicates two identical distributrions. - -![D_{KL}(P||Q)=\sum_{x\in{X}}{(P(x)\log{\frac{P(x)}{Q(x)}})}]() - -## Additional Resources - -Webb, Geoffrey I. et al. “[Characterizing Concept Drift.](https://arxiv.org/abs/1511.03816)” Data Mining and Knowledge Discovery 30.4 (2016): 964–994. Crossref. Web. - -[MLOps Live #4 - How to Detect & Remediate Drift in Production with MLOps Automation](https://www.youtube.com/watch?v=66_Q7mJZOSc&t=1296s) diff --git a/virtual_drift/function.yaml b/virtual_drift/function.yaml deleted file mode 100644 index 55dcec11c..000000000 --- a/virtual_drift/function.yaml +++ /dev/null @@ -1,129 +0,0 @@ -kind: job -metadata: - name: virtual-drift - tag: '' - hash: 8990fdd72fc550189a0c8b488b69997428b786c9 - project: '' - labels: - author: orz - categories: - - data-analysis - - machine-learning -spec: - command: '' - args: [] - image: mlrun/ml-models - env: [] - default_handler: drift_magnitude - entry_points: - to_observations: - name: to_observations - doc: '' - parameters: - - name: context - default: '' - - name: t - default: '' - - name: u - default: '' - - name: key - default: '' - outputs: - - default: '' - lineno: 16 - tvd: - name: tvd - doc: '' - parameters: - - name: t - default: '' - - name: u - default: '' - outputs: - - default: '' - lineno: 42 - helinger: - name: helinger - doc: '' - parameters: - - name: t - default: '' - - name: u - default: '' - outputs: - - default: '' - lineno: 46 - kl_divergence: - name: kl_divergence - doc: '' - parameters: - - name: t - default: '' - - name: u - default: '' - outputs: - - default: '' - lineno: 50 - all_metrics: - name: all_metrics - doc: '' - parameters: - - name: t - default: '' - - name: u - default: '' - outputs: - - default: '' - lineno: 56 - drift_magnitude: - name: drift_magnitude - doc: "Drift magnitude metrics\n Computes drift magnitude metrics between base\ - \ dataset t and dataset u.\n Metrics:\n - TVD (Total Variation Distance)\n\ - \ - Helinger\n - KL Divergence" - parameters: - - name: context - doc: MLRun context - default: '' - - name: t - type: DataFrame - doc: Base dataset for the drift metrics - default: '' - - name: u - type: DataFrame - doc: Test dataset for the drift metrics - default: '' - - name: label_col - doc: Label colum in t and u - default: null - - name: prediction_col - doc: Predictions column in t and u - default: null - - name: discretizers - type: dict - default: null - - name: n_bins - doc: Number of bins to be used for histrogram creation from continuous variables - default: 5 - - name: stream_name - type: str - doc: Output stream to push metrics to - default: some_stream - - name: results_tsdb_container - type: str - doc: TSDB table container to push metrics to - default: bigdata - - name: results_tsdb_table - type: str - doc: TSDB table to push metrics to - default: concept_drift/drift_magnitude - outputs: - - default: '' - lineno: 60 - description: Compute drift magnitude between Time-Samples T and U - build: - functionSourceCode: # Generated by nuclio.export.NuclioExporter

import os
import pandas as pd
import numpy as np
import scipy as sp
import pickle
import datetime

import v3io_frames as v3f

import matplotlib.pyplot as plt
from sklearn.preprocessing import KBinsDiscretizer


def to_observations(context, t, u, key):
    t = (
        t.apply(lambda row: f"{'_'.join([str(row[val]) for val in t.columns])}", axis=1)
        .value_counts()
        .sort_index()
    )
    u = (
        u.apply(lambda row: f"{'_'.join([str(row[val]) for val in u.columns])}", axis=1)
        .value_counts()
        .sort_index()
    )

    joined_uniques = pd.DataFrame([t, u]).T.fillna(0).sort_index()
    joined_uniques.columns = ["t", "u"]

    t_obs = joined_uniques.loc[:, "t"]
    u_obs = joined_uniques.loc[:, "u"]

    t_pdf = t_obs / t_obs.sum()
    u_pdf = u_obs / u_obs.sum()

    context.log_dataset(f"{key}_t_pdf", pd.DataFrame(t_pdf), format="parquet")
    context.log_dataset(f"{key}_u_pdf", pd.DataFrame(u_pdf), format="parquet")
    return t_pdf, u_pdf


def tvd(t, u):
    return sum(abs(t - u)) / 2


def helinger(t, u):
    return (np.sqrt(np.sum(np.power(np.sqrt(t) - np.sqrt(u), 2)))) / np.sqrt(2)


def kl_divergence(t, u):
    t_u = np.sum(np.where(t != 0, t * np.log(t / u), 0))
    u_t = np.sum(np.where(u != 0, u * np.log(u / t), 0))
    return t_u + u_t


def all_metrics(t, u):
    return tvd(t, u), helinger(t, u), kl_divergence(t, u)


def drift_magnitude(
    context,
    t: pd.DataFrame,
    u: pd.DataFrame,
    label_col=None,
    prediction_col=None,
    discretizers: dict = None,
    n_bins=5,
    stream_name: str = "some_stream",
    results_tsdb_container: str = "bigdata",
    results_tsdb_table: str = "concept_drift/drift_magnitude",
):
    """Drift magnitude metrics
       Computes drift magnitude metrics between base dataset t and dataset u.
       Metrics:
        - TVD (Total Variation Distance)
        - Helinger
        - KL Divergence

    :param context: MLRun context
    :param t: Base dataset for the drift metrics
    :param u: Test dataset for the drift metrics
    :param label_col: Label colum in t and u
    :param prediction_col: Predictions column in t and u
    :param discritizers: Dictionary of dicsritizers for the features if available
                         (Created automatically if not provided)
    :param n_bins: Number of bins to be used for histrogram creation from continuous variables
    :param stream_name: Output stream to push metrics to
    :param results_tsdb_container: TSDB table container to push metrics to
    :param results_tsdb_table: TSDB table to push metrics to
    """

    v3io_client = v3f.Client("framesd:8081", container=results_tsdb_container)
    try:
        v3io_client.create("tsdb", results_tsdb_table, if_exists=1, rate="1/s")
    except:
        v3io_client.create(
            "tsdb", results_tsdb_table, if_exists=1, attrs={"rate": "1/s"}
        )

    df_t = t.as_df()
    df_u = u.as_df()

    drop_columns = []
    if label_col is not None:
        drop_columns.append(label_col)
    if prediction_col is not None:
        drop_columns.append(prediction_col)

    continuous_features = df_t.select_dtypes(["float"])
    if discretizers is None:
        discretizers = {}
        for feature in continuous_features.columns:
            context.logger.info(f"Fitting discretizer for {feature}")
            discretizer = KBinsDiscretizer(
                n_bins=n_bins, encode="ordinal", strategy="uniform"
            )

            discretizer.fit(continuous_features.loc[:, feature].values.reshape(-1, 1))
            discretizers[feature] = discretizer
    os.makedirs(context.artifact_path, exist_ok=True)
    discretizers_path = os.path.abspath(f"{context.artifact_path}/discritizer.pkl")
    with open(discretizers_path, "wb") as f:
        pickle.dump(discretizers, f)
    context.log_artifact("discritizers", target_path=discretizers_path)
    context.logger.info("Discretizing featuers")
    for feature, discretizer in discretizers.items():
        df_t[feature] = discretizer.transform(
            df_t.loc[:, feature].values.reshape(-1, 1)
        )
        df_u[feature] = discretizer.transform(
            df_u.loc[:, feature].values.reshape(-1, 1)
        )
        df_t[feature] = df_t[feature].astype("int")
        df_u[feature] = df_u[feature].astype("int")
    context.log_dataset("t_discrete", df_t, format="parquet")
    context.log_dataset("u_discrete", df_u, format="parquet")

    context.logger.info("Compute prior metrics")

    results = {}
    t_prior, u_prior = to_observations(
        context,
        df_t.drop(drop_columns, axis=1),
        df_u.drop(drop_columns, axis=1),
        "features",
    )
    results["prior_tvd"], results["prior_helinger"], results["prior_kld"] = all_metrics(
        t_prior, u_prior
    )

    if prediction_col is not None:
        context.logger.info("Compute prediction metrics")
        t_predictions = pd.DataFrame(df_t.loc[:, prediction_col])
        u_predictions = pd.DataFrame(df_u.loc[:, prediction_col])
        t_class, u_class = to_observations(
            context, t_predictions, u_predictions, "prediction"
        )
        (
            results["prediction_shift_tvd"],
            results["prediction_shift_helinger"],
            results["prediction_shift_kld"],
        ) = all_metrics(t_class, u_class)

    if label_col is not None:
        context.logger.info("Compute class metrics")
        t_labels = pd.DataFrame(df_t.loc[:, label_col])
        u_labels = pd.DataFrame(df_u.loc[:, label_col])
        t_class, u_class = to_observations(context, t_labels, u_labels, "class")
        (
            results["class_shift_tvd"],
            results["class_shift_helinger"],
            results["class_shift_kld"],
        ) = all_metrics(t_class, u_class)

    for key, value in results.items():
        if value == float("inf"):
            context.logger.info(f"value: {value}")
            results[key] = 10
    for key, result in results.items():
        context.log_result(key, round(result, 3))

    now = pd.to_datetime(str(datetime.datetime.now()))
    now

    results["timestamp"] = pd.to_datetime(str((datetime.datetime.now())))
    context.logger.info(f"Timestamp: {results['timestamp']}")
    results["stream"] = stream_name
    results_df = pd.DataFrame(
        data=[list(results.values())], columns=list(results.keys())
    )
    results_df = results_df.set_index(["timestamp", "stream"])
    v3io_client.write("tsdb", results_tsdb_table, dfs=results_df)
 - commands: - - python -m pip install scikit-learn scipy v3io_frames - code_origin: https://github.com/daniels290813/functions.git#55a79c32be5d233cc11efcf40cd3edbe309bfdef:/home/kali/functions/virtual_drift/virtual_drift.py - affinity: null -verbose: false diff --git a/virtual_drift/item.yaml b/virtual_drift/item.yaml deleted file mode 100644 index d66f9e9c1..000000000 --- a/virtual_drift/item.yaml +++ /dev/null @@ -1,28 +0,0 @@ -apiVersion: v1 -categories: -- data-analysis -- machine-learning -description: Compute drift magnitude between Time-Samples T and U -doc: '' -example: virtual_drift.ipynb -generationDate: 2022-08-28:17-25 -hidden: false -icon: '' -labels: - author: orz -maintainers: [] -marketplaceType: '' -mlrunVersion: 1.1.0 -name: virtual-drift -platformVersion: 3.5.0 -spec: - filename: virtual_drift.py - handler: drift_magnitude - image: mlrun/ml-models - kind: job - requirements: - - scikit-learn - - scipy - - v3io_frames -url: '' -version: 1.1.0 diff --git a/virtual_drift/virtual_drift.ipynb b/virtual_drift/virtual_drift.ipynb deleted file mode 100644 index 23b9ef432..000000000 --- a/virtual_drift/virtual_drift.ipynb +++ /dev/null @@ -1,935 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Virtual Drift\n", - "\n", - "Drift magnitude metrics\n", - " Computes drift magnitude metrics between base dataset t and dataset u. \n", - "\n", - "Metrics:\n", - "- TVD (Total Variation Distance)\n", - "- Helinger\n", - "- KL Divergence" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Steps**\n", - "\n", - "1. [Data exploration](#Data-exploration)\n", - "2. [Importing the function](#Importing-the-function)\n", - "3. [Running the function locally](#Running-the-function-locally)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Data exploration**" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ".. _wine_dataset:\n", - "\n", - "Wine recognition dataset\n", - "------------------------\n", - "\n", - "**Data Set Characteristics:**\n", - "\n", - " :Number of Instances: 178 (50 in each of three classes)\n", - " :Number of Attributes: 13 numeric, predictive attributes and the class\n", - " :Attribute Information:\n", - " \t\t- Alcohol\n", - " \t\t- Malic acid\n", - " \t\t- Ash\n", - "\t\t- Alcalinity of ash \n", - " \t\t- Magnesium\n", - "\t\t- Total phenols\n", - " \t\t- Flavanoids\n", - " \t\t- Nonflavanoid phenols\n", - " \t\t- Proanthocyanins\n", - "\t\t- Color intensity\n", - " \t\t- Hue\n", - " \t\t- OD280/OD315 of diluted wines\n", - " \t\t- Proline\n", - "\n", - " - class:\n", - " - class_0\n", - " - class_1\n", - " - class_2\n", - "\t\t\n", - " :Summary Statistics:\n", - " \n", - " ============================= ==== ===== ======= =====\n", - " Min Max Mean SD\n", - " ============================= ==== ===== ======= =====\n", - " Alcohol: 11.0 14.8 13.0 0.8\n", - " Malic Acid: 0.74 5.80 2.34 1.12\n", - " Ash: 1.36 3.23 2.36 0.27\n", - " Alcalinity of Ash: 10.6 30.0 19.5 3.3\n", - " Magnesium: 70.0 162.0 99.7 14.3\n", - " Total Phenols: 0.98 3.88 2.29 0.63\n", - " Flavanoids: 0.34 5.08 2.03 1.00\n", - " Nonflavanoid Phenols: 0.13 0.66 0.36 0.12\n", - " Proanthocyanins: 0.41 3.58 1.59 0.57\n", - " Colour Intensity: 1.3 13.0 5.1 2.3\n", - " Hue: 0.48 1.71 0.96 0.23\n", - " OD280/OD315 of diluted wines: 1.27 4.00 2.61 0.71\n", - " Proline: 278 1680 746 315\n", - " ============================= ==== ===== ======= =====\n", - "\n", - " :Missing Attribute Values: None\n", - " :Class Distribution: class_0 (59), class_1 (71), class_2 (48)\n", - " :Creator: R.A. Fisher\n", - " :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n", - " :Date: July, 1988\n", - "\n", - "This is a copy of UCI ML Wine recognition datasets.\n", - "https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\n", - "\n", - "The data is the results of a chemical analysis of wines grown in the same\n", - "region in Italy by three different cultivators. There are thirteen different\n", - "measurements taken for different constituents found in the three types of\n", - "wine.\n", - "\n", - "Original Owners: \n", - "\n", - "Forina, M. et al, PARVUS - \n", - "An Extendible Package for Data Exploration, Classification and Correlation. \n", - "Institute of Pharmaceutical and Food Analysis and Technologies,\n", - "Via Brigata Salerno, 16147 Genoa, Italy.\n", - "\n", - "Citation:\n", - "\n", - "Lichman, M. (2013). UCI Machine Learning Repository\n", - "[https://archive.ics.uci.edu/ml]. Irvine, CA: University of California,\n", - "School of Information and Computer Science. \n", - "\n", - ".. topic:: References\n", - "\n", - " (1) S. Aeberhard, D. Coomans and O. de Vel, \n", - " Comparison of Classifiers in High Dimensional Settings, \n", - " Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of \n", - " Mathematics and Statistics, James Cook University of North Queensland. \n", - " (Also submitted to Technometrics). \n", - "\n", - " The data was used with many others for comparing various \n", - " classifiers. The classes are separable, though only RDA \n", - " has achieved 100% correct classification. \n", - " (RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data)) \n", - " (All results using the leave-one-out technique) \n", - "\n", - " (2) S. Aeberhard, D. Coomans and O. de Vel, \n", - " \"THE CLASSIFICATION PERFORMANCE OF RDA\" \n", - " Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of \n", - " Mathematics and Statistics, James Cook University of North Queensland. \n", - " (Also submitted to Journal of Chemometrics).\n", - "\n" - ] - } - ], - "source": [ - "# Scikit-learn's wine dataset\n", - "from sklearn.datasets import load_wine\n", - "\n", - "wine = load_wine()\n", - "print(wine[\"DESCR\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "wine_t and wine_u are generated from the wine dataset, where wine_t is the entire dataset while wine_u is a sample (50%) of the entire dataset. \n", - "wine_t shape is 178 and wine_u shape is 89 \n", - "\n", - "\n" - ] - }, - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " alcohol malic_acid ash alcalinity_of_ash magnesium total_phenols \\\n", - "0 14.23 1.71 2.43 15.6 127.0 2.80 \n", - "1 13.20 1.78 2.14 11.2 100.0 2.65 \n", - "2 13.16 2.36 2.67 18.6 101.0 2.80 \n", - "3 14.37 1.95 2.50 16.8 113.0 3.85 \n", - "4 13.24 2.59 2.87 21.0 118.0 2.80 \n", - "\n", - " flavanoids nonflavanoid_phenols proanthocyanins color_intensity hue \\\n", - "0 3.06 0.28 2.29 5.64 1.04 \n", - "1 2.76 0.26 1.28 4.38 1.05 \n", - "2 3.24 0.30 2.81 5.68 1.03 \n", - "3 3.49 0.24 2.18 7.80 0.86 \n", - "4 2.69 0.39 1.82 4.32 1.04 \n", - "\n", - " od280/od315_of_diluted_wines proline y prediction \n", - "0 3.92 1065.0 0 0 \n", - "1 3.40 1050.0 0 0 \n", - "2 3.17 1185.0 0 0 \n", - "3 3.45 1480.0 0 0 \n", - "4 2.93 735.0 0 0 " - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "wine_t_path = 'https://s3.wasabisys.com/iguazio/data/function-marketplace-data/virtual_drift/wine_t.pq'\n", - "wine_u_path = 'https://s3.wasabisys.com/iguazio/data/function-marketplace-data/virtual_drift/wine_u.pq'\n", - "wine_t=pd.read_parquet(wine_t_path)\n", - "wine_u=pd.read_parquet(wine_u_path)\n", - "print(f'wine_t and wine_u are generated from the wine dataset, where wine_t is the entire dataset while wine_u is a sample (50%) of the entire dataset. \\n\\\n", - "wine_t shape is {wine_t.shape[0]} and wine_u shape is {wine_u.shape[0]} \\n\\n')\n", - "wine_t.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Importing the function**" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2021-10-26 13:45:22,345 [info] created and saved project function-marketplace\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import mlrun\n", - "\n", - "# Importing the function\n", - "mlrun.set_environment(project='function-marketplace')\n", - "\n", - "fn = mlrun.import_function(\"hub://virtual_drift\")\n", - "fn.apply(mlrun.auto_mount())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Running the function locally**" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "import os \n", - "\n", - "container = os.path.join('/',os.environ['V3IO_HOME'].split('/')[0])\n", - "user = os.environ[\"V3IO_USERNAME\"]\n", - "rel_path = os.getcwd()[6:] + '/artifacts'\n", - "tsdb_path = os.path.join(user,rel_path) + \"/output_tsdb\"" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2021-10-26 14:00:41,020 [info] starting run virtual-drift-drift_magnitude uid=28ec7f08ce7c4c528114e2590ff49325 DB=http://mlrun-api:8080\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Warning - Server version '0.8.14' is different from client version '0.9.4'. Some operations may not work as expected.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2021-10-26 14:00:43,469 [info] Fitting discretizer for alcohol\n", - "> 2021-10-26 14:00:43,471 [info] Fitting discretizer for malic_acid\n", - "> 2021-10-26 14:00:43,471 [info] Fitting discretizer for ash\n", - "> 2021-10-26 14:00:43,472 [info] Fitting discretizer for alcalinity_of_ash\n", - "> 2021-10-26 14:00:43,473 [info] Fitting discretizer for magnesium\n", - "> 2021-10-26 14:00:43,474 [info] Fitting discretizer for total_phenols\n", - "> 2021-10-26 14:00:43,475 [info] Fitting discretizer for flavanoids\n", - "> 2021-10-26 14:00:43,476 [info] Fitting discretizer for nonflavanoid_phenols\n", - "> 2021-10-26 14:00:43,477 [info] Fitting discretizer for proanthocyanins\n", - "> 2021-10-26 14:00:43,477 [info] Fitting discretizer for color_intensity\n", - "> 2021-10-26 14:00:43,478 [info] Fitting discretizer for hue\n", - "> 2021-10-26 14:00:43,479 [info] Fitting discretizer for od280/od315_of_diluted_wines\n", - "> 2021-10-26 14:00:43,480 [info] Fitting discretizer for proline\n", - "> 2021-10-26 14:00:43,531 [info] Discretizing featuers\n", - "> 2021-10-26 14:00:43,752 [info] Compute prior metrics\n", - "> 2021-10-26 14:00:43,889 [info] Compute class metrics\n", - "> 2021-10-26 14:00:44,000 [info] value: inf\n", - "> 2021-10-26 14:00:44,009 [info] Timestamp: 2021-10-26 14:00:44.008992\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "divide by zero encountered in log\n", - "casting datetime64[ns] values to int64 with .astype(...) is deprecated and will raise in a future version. Use .view(...) instead.\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "
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function-marketplace0Oct 26 14:00:41completedvirtual-drift-drift_magnitude
v3io_user=dani
kind=
owner=dani
host=jupyter-dani-6bfbd76d96-zxx6f
t
u
label_col=y
results_tsdb_container=users
results_tsdb_table=dani/test/functions/virtual_drift/artifacts/output_tsdb
prior_tvd=0.5
prior_helinger=0.541
prior_kld=10
class_shift_tvd=0.017
class_shift_helinger=0.014
class_shift_kld=0.002
discritizers
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click here to open in UI" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2021-10-26 14:00:44,153 [info] run executed, status=completed\n" - ] - } - ], - "source": [ - "virtual_drift_run=fn.run(params={'label_col': 'y',\n", - " 'results_tsdb_container': container[1:],\n", - " 'results_tsdb_table': tsdb_path},\n", - " inputs={'t': wine_t_path,\n", - " 'u': wine_u_path},\n", - " artifact_path=os.getcwd(),\n", - " local=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " class_shift_helinger class_shift_kld \\\n", - "time \n", - "2021-10-26 13:58:04.445000+00:00 0.01398 0.001564 \n", - "2021-10-26 14:00:44.008000+00:00 0.01398 0.001564 \n", - "\n", - " class_shift_tvd prior_helinger prior_kld \\\n", - "time \n", - "2021-10-26 13:58:04.445000+00:00 0.016854 0.541196 10.0 \n", - "2021-10-26 14:00:44.008000+00:00 0.016854 0.541196 10.0 \n", - "\n", - " prior_tvd stream \n", - "time \n", - "2021-10-26 13:58:04.445000+00:00 0.5 some_stream \n", - "2021-10-26 14:00:44.008000+00:00 0.5 some_stream " - ] - }, - "execution_count": 69, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import v3io_frames as v3f\n", - "client = v3f.Client(os.environ[\"V3IO_FRAMESD\"],container=container[1:])\n", - "client.read(backend='tsdb',table=tsdb_path)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[Back to the top](#Virtual-Drift)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/virtual_drift/virtual_drift.py b/virtual_drift/virtual_drift.py deleted file mode 100644 index 71dcf7129..000000000 --- a/virtual_drift/virtual_drift.py +++ /dev/null @@ -1,206 +0,0 @@ -# Copyright 2019 Iguazio -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# Generated by nuclio.export.NuclioExporter - -import os -import pandas as pd -import numpy as np -import scipy as sp -import pickle -import datetime - -import v3io_frames as v3f - -import matplotlib.pyplot as plt -from sklearn.preprocessing import KBinsDiscretizer - - -def to_observations(context, t, u, key): - t = ( - t.apply(lambda row: f"{'_'.join([str(row[val]) for val in t.columns])}", axis=1) - .value_counts() - .sort_index() - ) - u = ( - u.apply(lambda row: f"{'_'.join([str(row[val]) for val in u.columns])}", axis=1) - .value_counts() - .sort_index() - ) - - joined_uniques = pd.DataFrame([t, u]).T.fillna(0).sort_index() - joined_uniques.columns = ["t", "u"] - - t_obs = joined_uniques.loc[:, "t"] - u_obs = joined_uniques.loc[:, "u"] - - t_pdf = t_obs / t_obs.sum() - u_pdf = u_obs / u_obs.sum() - - context.log_dataset(f"{key}_t_pdf", pd.DataFrame(t_pdf), format="parquet") - context.log_dataset(f"{key}_u_pdf", pd.DataFrame(u_pdf), format="parquet") - return t_pdf, u_pdf - - -def tvd(t, u): - return sum(abs(t - u)) / 2 - - -def helinger(t, u): - return (np.sqrt(np.sum(np.power(np.sqrt(t) - np.sqrt(u), 2)))) / np.sqrt(2) - - -def kl_divergence(t, u): - t_u = np.sum(np.where(t != 0, t * np.log(t / u), 0)) - u_t = np.sum(np.where(u != 0, u * np.log(u / t), 0)) - return t_u + u_t - - -def all_metrics(t, u): - return tvd(t, u), helinger(t, u), kl_divergence(t, u) - - -def drift_magnitude( - context, - t: pd.DataFrame, - u: pd.DataFrame, - label_col=None, - prediction_col=None, - discretizers: dict = None, - n_bins=5, - stream_name: str = "some_stream", - results_tsdb_container: str = "bigdata", - results_tsdb_table: str = "concept_drift/drift_magnitude", -): - """Drift magnitude metrics - Computes drift magnitude metrics between base dataset t and dataset u. - Metrics: - - TVD (Total Variation Distance) - - Helinger - - KL Divergence - - :param context: MLRun context - :param t: Base dataset for the drift metrics - :param u: Test dataset for the drift metrics - :param label_col: Label colum in t and u - :param prediction_col: Predictions column in t and u - :param discritizers: Dictionary of dicsritizers for the features if available - (Created automatically if not provided) - :param n_bins: Number of bins to be used for histrogram creation from continuous variables - :param stream_name: Output stream to push metrics to - :param results_tsdb_container: TSDB table container to push metrics to - :param results_tsdb_table: TSDB table to push metrics to - """ - - v3io_client = v3f.Client("framesd:8081", container=results_tsdb_container) - try: - v3io_client.create("tsdb", results_tsdb_table, if_exists=1, rate="1/s") - except: - v3io_client.create( - "tsdb", results_tsdb_table, if_exists=1, attrs={"rate": "1/s"} - ) - - df_t = t.as_df() - df_u = u.as_df() - - drop_columns = [] - if label_col is not None: - drop_columns.append(label_col) - if prediction_col is not None: - drop_columns.append(prediction_col) - - continuous_features = df_t.select_dtypes(["float"]) - if discretizers is None: - discretizers = {} - for feature in continuous_features.columns: - context.logger.info(f"Fitting discretizer for {feature}") - discretizer = KBinsDiscretizer( - n_bins=n_bins, encode="ordinal", strategy="uniform" - ) - - discretizer.fit(continuous_features.loc[:, feature].values.reshape(-1, 1)) - discretizers[feature] = discretizer - os.makedirs(context.artifact_path, exist_ok=True) - discretizers_path = os.path.abspath(f"{context.artifact_path}/discritizer.pkl") - with open(discretizers_path, "wb") as f: - pickle.dump(discretizers, f) - context.log_artifact("discritizers", target_path=discretizers_path) - context.logger.info("Discretizing featuers") - for feature, discretizer in discretizers.items(): - df_t[feature] = discretizer.transform( - df_t.loc[:, feature].values.reshape(-1, 1) - ) - df_u[feature] = discretizer.transform( - df_u.loc[:, feature].values.reshape(-1, 1) - ) - df_t[feature] = df_t[feature].astype("int") - df_u[feature] = df_u[feature].astype("int") - context.log_dataset("t_discrete", df_t, format="parquet") - context.log_dataset("u_discrete", df_u, format="parquet") - - context.logger.info("Compute prior metrics") - - results = {} - t_prior, u_prior = to_observations( - context, - df_t.drop(drop_columns, axis=1), - df_u.drop(drop_columns, axis=1), - "features", - ) - results["prior_tvd"], results["prior_helinger"], results["prior_kld"] = all_metrics( - t_prior, u_prior - ) - - if prediction_col is not None: - context.logger.info("Compute prediction metrics") - t_predictions = pd.DataFrame(df_t.loc[:, prediction_col]) - u_predictions = pd.DataFrame(df_u.loc[:, prediction_col]) - t_class, u_class = to_observations( - context, t_predictions, u_predictions, "prediction" - ) - ( - results["prediction_shift_tvd"], - results["prediction_shift_helinger"], - results["prediction_shift_kld"], - ) = all_metrics(t_class, u_class) - - if label_col is not None: - context.logger.info("Compute class metrics") - t_labels = pd.DataFrame(df_t.loc[:, label_col]) - u_labels = pd.DataFrame(df_u.loc[:, label_col]) - t_class, u_class = to_observations(context, t_labels, u_labels, "class") - ( - results["class_shift_tvd"], - results["class_shift_helinger"], - results["class_shift_kld"], - ) = all_metrics(t_class, u_class) - - for key, value in results.items(): - if value == float("inf"): - context.logger.info(f"value: {value}") - results[key] = 10 - for key, result in results.items(): - context.log_result(key, round(result, 3)) - - now = pd.to_datetime(str(datetime.datetime.now())) - now - - results["timestamp"] = pd.to_datetime(str((datetime.datetime.now()))) - context.logger.info(f"Timestamp: {results['timestamp']}") - results["stream"] = stream_name - results_df = pd.DataFrame( - data=[list(results.values())], columns=list(results.keys()) - ) - results_df = results_df.set_index(["timestamp", "stream"]) - v3io_client.write("tsdb", results_tsdb_table, dfs=results_df) diff --git a/xgb_custom/function.yaml b/xgb_custom/function.yaml deleted file mode 100644 index 7c264c392..000000000 --- a/xgb_custom/function.yaml +++ /dev/null @@ -1,241 +0,0 @@ -kind: job -metadata: - name: xgb-custom - tag: '' - hash: 5a052481ac303bde0afeccef9d2c5257abc4b00e - project: '' - labels: - author: Daniel - categories: - - model-training - - machine-learning - - data-preparation -spec: - command: '' - args: [] - image: mlrun/mlrun - env: [] - default_handler: gen_outliers - entry_points: - gen_outliers: - name: gen_outliers - doc: simulate data with outliers - parameters: - - name: context - type: MLClientCtx - doc: the function's execution context - default: '' - - name: nrows - doc: (4096) number of data points - default: 4096 - - name: feats - doc: (16) number of features - default: 16 - - name: outs - doc: (64) number of outliers - default: 64 - - name: omax - doc: (10_100) max value of outliers - default: 10000 - - name: labels_col - doc: (labels) name of labels column - default: labels - - name: header - doc: () header for dataset, will default to `feat_` - default: [] - - name: label_type - doc: (int32) data type for the label column - default: int32 - - name: key - doc: key of datset in artifact store - default: xgb-outs - - name: local_path - doc: path in artifact store where data will be serialized - default: xgb_custom - outputs: - - default: '' - lineno: 22 - gradient: - name: gradient - doc: gradient of squared log error - parameters: - - name: predt - type: ndarray - default: '' - - name: dtrain - type: DMatrix - default: '' - outputs: - - default: '' - lineno: 59 - hessian: - name: hessian - doc: hessian of squared log error - parameters: - - name: predt - type: ndarray - default: '' - - name: dtrain - type: DMatrix - default: '' - outputs: - - default: '' - lineno: 65 - squared_log: - name: squared_log - doc: 'squared log error objective - - - simplified version for RMSLE used as objective function' - parameters: - - name: predt - type: ndarray - default: '' - - name: dtrain - type: DMatrix - default: '' - outputs: - - default: '' - lineno: 72 - rmsle: - name: rmsle - doc: Root mean squared log error metric. - parameters: - - name: predt - type: ndarray - default: '' - - name: dtrain - type: DMatrix - default: '' - outputs: - - default: '' - lineno: 83 - learning_curves: - name: learning_curves - doc: 'plot xgb learning curves - - - this will also log a model''s learning curves' - parameters: - - name: context - type: MLClientCtx - default: '' - - name: results - type: dict - default: '' - - name: figsz - type: Tuple[int, int] - default: - - 10 - - 10 - - name: plots_dest - type: str - default: plots - outputs: - - default: '' - lineno: 92 - fit: - name: fit - doc: "low level xgboost train api\n\nfor the xgboost `train` params see:\nhttps://xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.train\n\ - \nNote: the first parameter of xgboost's `train` method is a dict of parameters\n\ - \ supplied to the booster (engine). To modify one of those simply\n\ - \ add a task parameter (when running you supply an mlrun NewTask) with\ - \ the\n prefix \"XGB_\". So for example, to set the 'tree_method' parameter\ - \ to 'approx',\n add {\"XGB_tree_method\":\"approx\"} to the task params\ - \ key." - parameters: - - name: context - type: MLClientCtx - doc: the function context - default: '' - - name: dataset - type: DataItem - doc: the full data set, train, valid and test will be extracted and each converted - to a DMatrix for input to xgboost's `train` - default: '' - - name: num_boost_round - type: int - default: 10 - - name: evals - type: List[Tuple[DMatrix, str]] - default: [] - - name: obj - type: Union[Callable, str] - default: '' - - name: feval - type: Union[Callable, str] - default: null - - name: maximize - type: bool - default: false - - name: early_stopping_rounds - type: int - default: null - - name: evals_result - type: dict - default: {} - - name: verbose_eval - type: bool - default: true - - name: xgb_model - type: DataItem - default: null - - name: callbacks - type: List[Callable] - default: [] - - name: label_column - type: str - doc: ground-truth (y) labels - default: labels - - name: encode_cols - type: dict - doc: dictionary of names and prefixes for columns that are to hot be encoded. - default: {} - - name: sample - type: int - doc: Selects the first n rows, or select a sample starting from the first. - If negative <-1, select a random sample - default: <_ast.USub object at 0x7ff7bf99a7b8> - - name: test_size - type: float - doc: (0.05) test set size - default: 0.25 - - name: valid_size - type: float - doc: (0.75) Once the test set has been removed the training set gets this - proportion. - default: 0.75 - - name: random_state - type: int - doc: (1) sklearn rng seed - default: 1994 - - name: models_dest - type: str - doc: destination subfolder for model artifacts - default: models - - name: plots_dest - type: str - doc: destination subfolder for plot artifacts - default: plots - - name: file_ext - type: str - doc: format for test_set_key hold out data - default: csv - - name: test_set_key - type: str - doc: (test-set), key of held out data in artifact store - default: test-set - - name: gpus - type: bool - doc: (False), run on gpus - default: false - outputs: - - default: '' - lineno: 114 - description: simulate data with outliers. - build: - functionSourceCode: from os import path
import numpy as np
from numpy.random import randint, randn, seed
import pandas as pd
from xgboost import DMatrix, train
import matplotlib.pyplot as plt
from mlrun.execution import MLClientCtx
from mlrun.datastore import DataItem
from mlrun.artifacts import PlotArtifact
from mlrun.mlutils.data import get_splits, get_sample

from cloudpickle import dumps

from typing import (Tuple, Dict, List, Union, Callable)

seed(seed=1994)

## UNCOMMENT THIS LINE TO TEST CALCULATED VALUES
DEBUG_ERROR = 0 # this will be added to the custom eval function--set it to some value like 999


def gen_outliers(context: MLClientCtx, nrows=4096, feats=16,
                 outs=64, omax=10_000, labels_col="labels",
                 header=[], label_type="int32", key="xgb-outs",
                 local_path="xgb_custom"):
    """simulate data with outliers

    :param context:    the function's execution context
    :param nrows:      (4096) number of data points
    :param feats:      (16) number of features
    :param outs:       (64) number of outliers
    :param omax:       (10_100) max value of outliers
    :param labels_col: (labels) name of labels column
    :param header:     () header for dataset, will default to
                       `feat_`
    :param label_type: (int32) data type for the label column
    :param key:        key of datset in artifact store
    :param local_path: path in artifact store where data will be
                       serialized
    """
    x = randn(nrows, feats)
    y = randn(nrows)
    y += np.abs(np.min(y))

    for i in range(0, outs):
        ind = randint(0, len(y) - 1)
        y[ind] += randint(0, omax)

    if not header:
        header = [f"feat_{j}" for j in range(feats)]
        header.append(labels_col)

    data = pd.DataFrame(data=np.concatenate((x, y.reshape(-1, 1)), axis=-1),
                        columns=header)
    data = data.astype({labels_col: label_type})

    context.log_dataset(key, df=data, local_path=local_path)

def gradient(predt: np.ndarray, dtrain: DMatrix) -> np.ndarray:
    """gradient of squared log error"""
    y = dtrain.get_label()
    return (np.log1p(predt) - np.log1p(y)) / (predt + 1)


def hessian(predt: np.ndarray, dtrain: DMatrix) -> np.ndarray:
    """hessian of squared log error"""
    y = dtrain.get_label()
    return ((-np.log1p(predt) + np.log1p(y) + 1) /
            np.power(predt + 1, 2))


def squared_log(predt: np.ndarray, dtrain: DMatrix) -> Tuple[np.ndarray,
                                                             np.ndarray]:
    """squared log error objective

    simplified version for RMSLE used as objective function
    """
    predt[predt < -1] = -1 + 1e-6
    grad = gradient(predt, dtrain)
    hess = hessian(predt, dtrain)
    return grad, hess

def rmsle(predt: np.ndarray, dtrain: DMatrix) -> Tuple[str, float]:
    """ Root mean squared log error metric.
    """
    y = dtrain.get_label()
    predt[predt < -1] = -1 + 1e-6
    elements = np.power(np.log1p(y) - np.log1p(predt), 2)
    return "my_rmsle", float(np.sqrt(np.sum(elements) / len(y))) + DEBUG_ERROR


def learning_curves(
        context: MLClientCtx,
        results: dict,
        figsz: Tuple[int, int] = (10, 10),
        plots_dest: str = "plots"
) -> None:
    """plot xgb learning curves

    this will also log a model's learning curves
    """
    plt.clf()
    plt.figure(figsize=figsz)
    plt.plot(results["train"]["my_rmsle"], label="train-my-rmsle")
    plt.plot(results["valid"]["my_rmsle"], label="valid-my-rmsle")
    plt.title(f"learning curves")
    plt.legend()

    context.log_artifact(
        PlotArtifact(f"learning-curves", body=plt.gcf()),
        local_path=f"{plots_dest}/learning-curves.html")


def fit(
        context: MLClientCtx,
        dataset: DataItem,
        num_boost_round: int = 10,
        evals: List[Tuple[DMatrix, str]] = [],
        obj: Union[Callable, str] = "",
        feval: Union[Callable, str] = None,
        maximize: bool = False,
        early_stopping_rounds: int = None,
        evals_result: dict = {},
        verbose_eval: bool = True,
        xgb_model: DataItem = None,
        callbacks: List[Callable] = [],
        label_column: str = "labels",
        encode_cols: dict = {},
        sample: int = -1,
        test_size: float = 0.25,
        valid_size: float = 0.75,
        random_state: int = 1994,
        models_dest: str = "models",
        plots_dest: str = "plots",
        file_ext: str = "csv",
        test_set_key: str = "test-set",
        gpus: bool = False
) -> None:
    """low level xgboost train api

    for the xgboost `train` params see:
    https://xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.train

    Note:  the first parameter of xgboost's `train` method is a dict of parameters
           supplied to the booster (engine).  To modify one of those simply
           add a task parameter (when running you supply an mlrun NewTask) with the
           prefix "XGB_". So for example, to set the 'tree_method' parameter to 'approx',
           add {"XGB_tree_method":"approx"} to the task params key.

    :param context:           the function context
    :param dataset:           the full data set, train, valid and test will be extracted and
                              each converted to a DMatrix for input to xgboost's `train`
    :param label_column:      ground-truth (y) labels
    :param encode_cols:       dictionary of names and prefixes for columns that are
                              to hot be encoded.
    :param sample:            Selects the first n rows, or select a sample
                              starting from the first. If negative <-1, select
                              a random sample
    :param test_size:         (0.05) test set size
    :param valid_size:        (0.75) Once the test set has been removed the
                              training set gets this proportion.
    :param random_state:      (1) sklearn rng seed
    :param models_dest:       destination subfolder for model artifacts
    :param plots_dest:        destination subfolder for plot artifacts
    :param file_ext:          format for test_set_key hold out data
    :param test_set_key:      (test-set), key of held out data in artifact store
    :param gpus:              (False), run on gpus
    """
    raw, labels, header = get_sample(dataset, sample, label_column)

    # hot-encode
    if encode_cols:
        raw = pd.get_dummies(raw,
                             columns=list(encode_cols.keys()),
                             prefix=list(encode_cols.values()),
                             drop_first=True)

    # split the sample into train validate, test and calibration sets:
    (xtrain, ytrain), (xvalid, yvalid), (xtest, ytest) = \
        get_splits(raw, labels, 3, test_size, valid_size, random_state)

    # save test data as regular dataframe as it may be used by other process
    context.log_dataset(test_set_key, df=pd.concat([xtest, ytest], axis=1),
                        format=file_ext, index=False)

    # convert to xgboost DMatrix (todo - dask, gpu)
    dtrain = DMatrix(xtrain, label=ytrain)
    dvalid = DMatrix(xvalid, label=yvalid)

    boost_params = {
        "tree_method": "gpu_hist" if gpus else "hist",
        "seed": random_state,
        "disable_default_eval_metric": 1,
        "objective": "reg:squaredlogerror",
        "eval_metric": "rmsle"}

    # enable user to customize `booster param` parameters
    for k, v in context.parameters.items():
        if k.startswith('XGB_'):
            boost_params[k[4:]] = v

    # collect learning curves / training history
    results = dict()

    booster = train(
        boost_params,
        dtrain=dtrain,
        num_boost_round=num_boost_round,
        evals=[(dtrain, "train"), (dvalid, "valid")],
        evals_result=results,
        obj=squared_log,
        feval=rmsle,
        maximize=maximize,
        early_stopping_rounds=early_stopping_rounds,
        verbose_eval=verbose_eval,
        # xgb_model=xgb_model,
        # callbacks: List[Callable] = []
    )

    context.log_model("model",
                      body=dumps(booster),
                      model_file="model.pkl",
                      artifact_path='artifacts/')

    learning_curves(context, results) - commands: [] - code_origin: https://github.com/daniels290813/functions.git#55a79c32be5d233cc11efcf40cd3edbe309bfdef:/home/kali/functions/xgb_custom/xgb_custom.py - affinity: null -verbose: false diff --git a/xgb_custom/item.yaml b/xgb_custom/item.yaml deleted file mode 100644 index 3decf0708..000000000 --- a/xgb_custom/item.yaml +++ /dev/null @@ -1,26 +0,0 @@ -apiVersion: v1 -categories: -- model-training -- machine-learning -- data-preparation -description: simulate data with outliers. -doc: '' -example: xgb_custom.ipynb -generationDate: 2022-08-28:17-25 -hidden: true -icon: '' -labels: - author: Daniel -maintainers: [] -marketplaceType: '' -mlrunVersion: 1.1.0 -name: xgb_custom -platformVersion: 3.5.0 -spec: - filename: xgb_custom.py - handler: gen_outliers - image: mlrun/mlrun - kind: job - requirements: [] -url: '' -version: 1.1.0 diff --git a/xgb_custom/requirements.txt b/xgb_custom/requirements.txt deleted file mode 100644 index 4441bae23..000000000 --- a/xgb_custom/requirements.txt +++ /dev/null @@ -1,7 +0,0 @@ -pandas -typing -xgboost -matplotlib -scikit-learn -seaborn -scikit-plot \ No newline at end of file diff --git a/xgb_custom/test_xgb_custom.py b/xgb_custom/test_xgb_custom.py deleted file mode 100644 index 81b77a2e0..000000000 --- a/xgb_custom/test_xgb_custom.py +++ /dev/null @@ -1,50 +0,0 @@ -# Copyright 2019 Iguazio -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -from mlrun import import_function -import os - - -ARTIFACT_PATH = "artifacts" -FUNCTION_PATH = "functions" -PLOTS_PATH = "plots" -RUNS_PATH = "runs" -SCHEDULES_PATH = "schedules" - - -def test_local_xgb_custom(): - fn = import_function("function.yaml") - run = fn.run( - params={ - "nrows": 8192, - "label_type": "float", - "local_path": "./artifacts/inputs/xgb_custom", - }, - handler="gen_outliers", - local=True, - ) - - run = fn.run( - params={ - "num_boost_round": 40, - "verbose_eval": False, - "XGB_max_depth": 2, - "XGB_subsample": 0.9, - "test_set_key": "test-set", - }, - inputs={"dataset": run.artifact('xgb-outs').url}, - handler="fit", - local=True, - ) - assert run.artifact('learning-curves').get() diff --git a/xgb_custom/xgb_custom.ipynb b/xgb_custom/xgb_custom.ipynb deleted file mode 100644 index fe3882ab8..000000000 --- a/xgb_custom/xgb_custom.ipynb +++ /dev/null @@ -1,922 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Custom Objective and Evaluation Functions\n", - "\n", - "This demo was adapted from **[xgboost's custom metric tutorial](https://xgboost.readthedocs.io/en/latest/tutorials/custom_metric_obj.html)**. We demonstrate how to use a custom objective and a custom evaluation function using an xgboost trainer.\n", - "\n", - "This function differs from `xgb_trainer` by exposing the low-level xgboost python api, `train`. " - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# nuclio: ignore\n", - "import nuclio" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "from os import path\n", - "import numpy as np\n", - "from numpy.random import randint, randn, seed\n", - "import pandas as pd\n", - "from xgboost import DMatrix, train\n", - "import matplotlib.pyplot as plt\n", - "from mlrun.execution import MLClientCtx\n", - "from mlrun.datastore import DataItem\n", - "from mlrun.artifacts import PlotArtifact\n", - "from mlrun.mlutils.data import get_splits, get_sample\n", - "\n", - "from cloudpickle import dumps\n", - "\n", - "from typing import (Tuple, Dict, List, Union, Callable)\n", - "\n", - "seed(seed=1994)\n", - "\n", - "## UNCOMMENT THIS LINE TO TEST CALCULATED VALUES\n", - "DEBUG_ERROR = 0 # this will be added to the custom eval function--set it to some value like 999 " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### generate data with outliers" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "def gen_outliers(context: MLClientCtx, nrows=4096, feats=16, \n", - " outs=64, omax=10_000, labels_col=\"labels\",\n", - " header=[], label_type=\"int32\", key=\"xgb-outs\",\n", - " local_path=\"xgb_custom\"):\n", - " \"\"\"simulate data with outliers\n", - " \n", - " :param context: the function's execution context\n", - " :param nrows: (4096) number of data points\n", - " :param feats: (16) number of features\n", - " :param outs: (64) number of outliers\n", - " :param omax: (10_100) max value of outliers\n", - " :param labels_col: (labels) name of labels column\n", - " :param header: () header for dataset, will default to\n", - " `feat_`\n", - " :param label_type: (int32) data type for the label column\n", - " :param key: key of datset in artifact store\n", - " :param local_path: path in artifact store where data will be\n", - " serialized\n", - " \"\"\"\n", - " x = randn(nrows, feats)\n", - " y = randn(nrows)\n", - " y += np.abs(np.min(y))\n", - "\n", - " for i in range(0, outs):\n", - " ind = randint(0, len(y)-1)\n", - " y[ind] += randint(0, omax)\n", - " \n", - " if not header:\n", - " header = [f\"feat_{j}\" for j in range(feats)]\n", - " header.append(labels_col)\n", - "\n", - " data = pd.DataFrame(data=np.concatenate((x,y.reshape(-1,1)),axis=-1),\n", - " columns=header)\n", - " data = data.astype({labels_col: label_type})\n", - " \n", - " context.log_dataset(key, df=data, local_path=local_path)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## custom objective and eval" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "toc-hr-collapsed": true, - "toc-nb-collapsed": true - }, - "source": [ - "The following code was adapted from xgboost's documentation **[Custom Objective and Evaluation Metric](https://xgboost.readthedocs.io/en/latest/tutorials/custom_metric_obj.html?highlight=tree_method#custom-objective-and-evaluation-metric)**." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "def gradient(predt: np.ndarray, dtrain: DMatrix) -> np.ndarray:\n", - " \"\"\"gradient of squared log error\"\"\"\n", - " y = dtrain.get_label()\n", - " return (np.log1p(predt) - np.log1p(y)) / (predt + 1)\n", - "\n", - "\n", - "def hessian(predt: np.ndarray, dtrain: DMatrix) -> np.ndarray:\n", - " \"\"\"hessian of squared log error\"\"\"\n", - " y = dtrain.get_label()\n", - " return ((-np.log1p(predt) + np.log1p(y) + 1) /\n", - " np.power(predt + 1, 2))\n", - "\n", - "\n", - "def squared_log(predt: np.ndarray, dtrain: DMatrix) -> Tuple[np.ndarray,\n", - " np.ndarray]:\n", - " \"\"\"squared log error objective\n", - "\n", - " simplified version for RMSLE used as objective function\n", - " \"\"\"\n", - " predt[predt < -1] = -1 + 1e-6\n", - " grad = gradient(predt, dtrain)\n", - " hess = hessian(predt, dtrain)\n", - " return grad, hess\n", - "\n", - "def rmsle(predt: np.ndarray, dtrain: DMatrix) -> Tuple[str, float]:\n", - " \"\"\" Root mean squared log error metric.\n", - " \"\"\"\n", - " y = dtrain.get_label()\n", - " predt[predt < -1] = -1 + 1e-6\n", - " elements = np.power(np.log1p(y) - np.log1p(predt), 2)\n", - " return \"my_rmsle\", float(np.sqrt(np.sum(elements) / len(y))) + DEBUG_ERROR" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## learning curves" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "def learning_curves(\n", - " context: MLClientCtx,\n", - " results: dict,\n", - " figsz: Tuple[int,int]=(10,10),\n", - " plots_dest: str = \"plots\"\n", - ") -> None:\n", - " \"\"\"plot xgb learning curves\n", - " \n", - " this will also log a model's learning curves\n", - " \"\"\"\n", - " plt.clf()\n", - " plt.figure(figsize=figsz)\n", - " plt.plot(results[\"train\"][\"my_rmsle\"], label=\"train-my-rmsle\")\n", - " plt.plot(results[\"valid\"][\"my_rmsle\"], label=\"valid-my-rmsle\")\n", - " plt.title(f\"learning curves\")\n", - " plt.legend()\n", - " \n", - " context.log_artifact(\n", - " PlotArtifact(f\"learning-curves\", body=plt.gcf()),\n", - " local_path=f\"{plots_dest}/learning-curves.html\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## fit" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "def fit(\n", - " context: MLClientCtx,\n", - " dataset: DataItem,\n", - " num_boost_round: int = 10,\n", - " evals: List[Tuple[DMatrix, str]] = [],\n", - " obj: Union[Callable, str] = \"\",\n", - " feval: Union[Callable, str] = None,\n", - " maximize: bool = False,\n", - " early_stopping_rounds: int = None,\n", - " evals_result: dict = {},\n", - " verbose_eval: bool = True,\n", - " xgb_model: DataItem = None,\n", - " callbacks: List[Callable] = [],\n", - " label_column: str = \"labels\",\n", - " encode_cols: dict = {},\n", - " sample: int = -1,\n", - " test_size: float = 0.25,\n", - " valid_size: float = 0.75,\n", - " random_state: int = 1994,\n", - " models_dest: str = \"models\",\n", - " plots_dest: str = \"plots\",\n", - " file_ext: str = \"csv\",\n", - " test_set_key: str = \"test-set\",\n", - " gpus: bool = False\n", - ") -> None:\n", - " \"\"\"low level xgboost train api\n", - " \n", - " for the xgboost `train` params see:\n", - " https://xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.train\n", - "\n", - " Note: the first parameter of xgboost's `train` method is a dict of parameters\n", - " supplied to the booster (engine). To modify one of those simply\n", - " add a task parameter (when running you supply an mlrun NewTask) with the\n", - " prefix \"XGB_\". So for example, to set the 'tree_method' parameter to 'approx',\n", - " add {\"XGB_tree_method\":\"approx\"} to the task params key.\n", - " \n", - " :param context: the function context\n", - " :param dataset: the full data set, train, valid and test will be extracted and\n", - " each converted to a DMatrix for input to xgboost's `train`\n", - " :param label_column: ground-truth (y) labels\n", - " :param encode_cols: dictionary of names and prefixes for columns that are\n", - " to hot be encoded.\n", - " :param sample: Selects the first n rows, or select a sample\n", - " starting from the first. If negative <-1, select\n", - " a random sample\n", - " :param test_size: (0.05) test set size\n", - " :param valid_size: (0.75) Once the test set has been removed the\n", - " training set gets this proportion.\n", - " :param random_state: (1) sklearn rng seed\n", - " :param models_dest: destination subfolder for model artifacts\n", - " :param plots_dest: destination subfolder for plot artifacts\n", - " :param file_ext: format for test_set_key hold out data\n", - " :param test_set_key: (test-set), key of held out data in artifact store\n", - " :param gpus: (False), run on gpus\n", - " \"\"\"\n", - " raw, labels, header = get_sample(dataset, sample, label_column)\n", - " \n", - " # hot-encode\n", - " if encode_cols:\n", - " raw = pd.get_dummies(raw, \n", - " columns=list(encode_cols.keys()), \n", - " prefix=list(encode_cols.values()), \n", - " drop_first=True)\n", - " \n", - " # split the sample into train validate, test and calibration sets:\n", - " (xtrain, ytrain), (xvalid, yvalid), (xtest, ytest) = \\\n", - " get_splits(raw, labels, 3, test_size, valid_size, random_state)\n", - " \n", - " # save test data as regular dataframe as it may be used by other process\n", - " context.log_dataset(test_set_key, df=pd.concat([xtest, ytest], axis=1),\n", - " format=file_ext, index=False)\n", - " \n", - " # convert to xgboost DMatrix (todo - dask, gpu)\n", - " dtrain = DMatrix(xtrain, label=ytrain)\n", - " dvalid = DMatrix(xvalid, label=yvalid)\n", - " \n", - " boost_params = {\n", - " \"tree_method\": \"gpu_hist\" if gpus else \"hist\", \n", - " \"seed\": random_state,\n", - " \"disable_default_eval_metric\": 1,\n", - " \"objective\": \"reg:squaredlogerror\",\n", - " \"eval_metric\": \"rmsle\"}\n", - "\n", - " # enable user to customize `booster param` parameters\n", - " for k, v in context.parameters.items():\n", - " if k.startswith('XGB_'):\n", - " boost_params[k[4:]] = v\n", - " \n", - " # collect learning curves / training history\n", - " results = dict()\n", - " \n", - " booster = train(\n", - " boost_params,\n", - " dtrain=dtrain,\n", - " num_boost_round=num_boost_round,\n", - " evals=[(dtrain, \"train\"), (dvalid, \"valid\")],\n", - " evals_result=results,\n", - " obj=squared_log,\n", - " feval=rmsle,\n", - " maximize=maximize,\n", - " early_stopping_rounds=early_stopping_rounds,\n", - " verbose_eval=verbose_eval,\n", - " # xgb_model=xgb_model,\n", - " # callbacks: List[Callable] = []\n", - " )\n", - " \n", - " context.log_model(\"model\", \n", - " body=dumps(booster),\n", - " model_file = \"model.pkl\",\n", - " artifact_path='/User/artifacts/tttt')\n", - " \n", - " learning_curves(context, results)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "# nuclio: end-code" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### run locally" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "from mlrun import NewTask, run_local" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[mlrun] 2020-06-14 13:30:21,675 starting run gen_outliers uid=29ae7c944a184de881acc81206a92a48 -> http://mlrun-api:8080\n", - "[mlrun] 2020-06-14 13:30:22,141 log artifact xgb-outs at /User/artifacts/xgb_custom.csv, size: 2762858, db: Y\n", - "\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "
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projectuiditerstartstatenamelabelsinputsparametersresultsartifacts
default0Jun 14 13:30:21completedgen_outliers
v3io_user=admin
kind=handler
owner=admin
host=jupyter-7b44c8d958-kklf7
nrows=8192
label_type=float
xgb-outs
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\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "to track results use .show() or .logs() or in CLI: \n", - "!mlrun get run 29ae7c944a184de881acc81206a92a48 --project default , !mlrun logs 29ae7c944a184de881acc81206a92a48 --project default\n", - "[mlrun] 2020-06-14 13:30:22,218 run executed, status=completed\n" - ] - } - ], - "source": [ - "gen_outs_tsk = NewTask(name='gen_outliers',\n", - " handler=gen_outliers, \n", - " params={'nrows': 8192, 'label_type': 'float'})\n", - "\n", - "outliers_run = run_local(gen_outs_tsk)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[mlrun] 2020-06-14 13:30:23,011 starting run fit model uid=489d2d26007e444cb81ffe7b6bc9d4a2 -> http://mlrun-api:8080\n", - "[mlrun] 2020-06-14 13:30:23,405 log artifact test-set at /User/artifacts/test-set.csv, size: 689366, db: Y\n", - "[mlrun] 2020-06-14 13:30:23,545 log artifact model at /User/artifacts/tttt/, size: 17052, db: Y\n", - "[mlrun] 2020-06-14 13:30:23,712 log artifact learning-curves at /User/artifacts/plots/learning-curves.html, size: 31641, db: Y\n", - "\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "
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projectuiditerstartstatenamelabelsinputsparametersresultsartifacts
default0Jun 14 13:30:23completedfit model
v3io_user=admin
kind=handler
owner=admin
host=jupyter-7b44c8d958-kklf7
dataset
num_boost_round=40
verbose_eval=False
XGB_max_depth=2
XGB_subsample=0.9
test-set
model
learning-curves
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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# THIS IS SETUP SO THAT YOU CAN COMPARE THE RESULTS AND SEE THAT THEY ARE EXACT. \n", - "# UNCOMMENT LINE `DEBUG_ERROR` AT TOP OF THIS NOTEBOOK TO TEST THAT THESE ARE \n", - "# TRULY CALCULATED VALUES\n", - "\n", - "fit_tsk = NewTask(\n", - " name='fit model', \n", - " handler=fit,\n", - " params={\"num_boost_round\" : 40, \n", - " \"verbose_eval\" : False,\n", - " \"XGB_max_depth\" : 2,\n", - " \"XGB_subsample\" : 0.9}\n", - ")\n", - "\n", - "fit_run = run_local(fit_tsk, inputs={\"dataset\":outliers_run.outputs[\"xgb-outs\"]})" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# export" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[mlrun] 2020-06-14 13:30:50,033 function spec saved to path: function.yaml\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from mlrun import code_to_function\n", - "from mlrun.platforms.other import auto_mount\n", - "\n", - "gpus = False\n", - "\n", - "# create job function object from notebook code\n", - "fn_params = {\n", - " \"name\" : \"xgb_custom\",\n", - " \"handler\" : \"fit\",\n", - " \"kind\" : \"job\",\n", - " \"image\" : \"mlrun/ml-models\" if not gpus else \"mlrun/ml-models-gpu\",\n", - " \"description\" : \"train an xgboost model using the low-level api\",\n", - " \"categories\" : [\"analysis\"],\n", - " \"labels\" : {\"author\": \"yjb\"}\n", - "}\n", - "\n", - "xgb_fn = code_to_function(**fn_params)\n", - "\n", - "xgb_fn.export(\"function.yaml\")\n", - "xgb_fn.apply(auto_mount())" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.8" - }, - "toc-autonumbering": false, - "toc-showcode": false, - "toc-showmarkdowntxt": false, - "toc-showtags": false - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/xgb_custom/xgb_custom.py b/xgb_custom/xgb_custom.py deleted file mode 100644 index f80868e7e..000000000 --- a/xgb_custom/xgb_custom.py +++ /dev/null @@ -1,239 +0,0 @@ -# Copyright 2019 Iguazio -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -from os import path -import numpy as np -from numpy.random import randint, randn, seed -import pandas as pd -from xgboost import DMatrix, train -import matplotlib.pyplot as plt -from mlrun.execution import MLClientCtx -from mlrun.datastore import DataItem -from mlrun.artifacts import PlotArtifact -from mlrun.mlutils.data import get_splits, get_sample - -from cloudpickle import dumps - -from typing import (Tuple, Dict, List, Union, Callable) - -seed(seed=1994) - -## UNCOMMENT THIS LINE TO TEST CALCULATED VALUES -DEBUG_ERROR = 0 # this will be added to the custom eval function--set it to some value like 999 - - -def gen_outliers(context: MLClientCtx, nrows=4096, feats=16, - outs=64, omax=10_000, labels_col="labels", - header=[], label_type="int32", key="xgb-outs", - local_path="xgb_custom"): - """simulate data with outliers - - :param context: the function's execution context - :param nrows: (4096) number of data points - :param feats: (16) number of features - :param outs: (64) number of outliers - :param omax: (10_100) max value of outliers - :param labels_col: (labels) name of labels column - :param header: () header for dataset, will default to - `feat_` - :param label_type: (int32) data type for the label column - :param key: key of datset in artifact store - :param local_path: path in artifact store where data will be - serialized - """ - x = randn(nrows, feats) - y = randn(nrows) - y += np.abs(np.min(y)) - - for i in range(0, outs): - ind = randint(0, len(y) - 1) - y[ind] += randint(0, omax) - - if not header: - header = [f"feat_{j}" for j in range(feats)] - header.append(labels_col) - - data = pd.DataFrame(data=np.concatenate((x, y.reshape(-1, 1)), axis=-1), - columns=header) - data = data.astype({labels_col: label_type}) - - context.log_dataset(key, df=data, local_path=local_path) - -def gradient(predt: np.ndarray, dtrain: DMatrix) -> np.ndarray: - """gradient of squared log error""" - y = dtrain.get_label() - return (np.log1p(predt) - np.log1p(y)) / (predt + 1) - - -def hessian(predt: np.ndarray, dtrain: DMatrix) -> np.ndarray: - """hessian of squared log error""" - y = dtrain.get_label() - return ((-np.log1p(predt) + np.log1p(y) + 1) / - np.power(predt + 1, 2)) - - -def squared_log(predt: np.ndarray, dtrain: DMatrix) -> Tuple[np.ndarray, - np.ndarray]: - """squared log error objective - - simplified version for RMSLE used as objective function - """ - predt[predt < -1] = -1 + 1e-6 - grad = gradient(predt, dtrain) - hess = hessian(predt, dtrain) - return grad, hess - -def rmsle(predt: np.ndarray, dtrain: DMatrix) -> Tuple[str, float]: - """ Root mean squared log error metric. - """ - y = dtrain.get_label() - predt[predt < -1] = -1 + 1e-6 - elements = np.power(np.log1p(y) - np.log1p(predt), 2) - return "my_rmsle", float(np.sqrt(np.sum(elements) / len(y))) + DEBUG_ERROR - - -def learning_curves( - context: MLClientCtx, - results: dict, - figsz: Tuple[int, int] = (10, 10), - plots_dest: str = "plots" -) -> None: - """plot xgb learning curves - - this will also log a model's learning curves - """ - plt.clf() - plt.figure(figsize=figsz) - plt.plot(results["train"]["my_rmsle"], label="train-my-rmsle") - plt.plot(results["valid"]["my_rmsle"], label="valid-my-rmsle") - plt.title(f"learning curves") - plt.legend() - - context.log_artifact( - PlotArtifact(f"learning-curves", body=plt.gcf()), - local_path=f"{plots_dest}/learning-curves.html") - - -def fit( - context: MLClientCtx, - dataset: DataItem, - num_boost_round: int = 10, - evals: List[Tuple[DMatrix, str]] = [], - obj: Union[Callable, str] = "", - feval: Union[Callable, str] = None, - maximize: bool = False, - early_stopping_rounds: int = None, - evals_result: dict = {}, - verbose_eval: bool = True, - xgb_model: DataItem = None, - callbacks: List[Callable] = [], - label_column: str = "labels", - encode_cols: dict = {}, - sample: int = -1, - test_size: float = 0.25, - valid_size: float = 0.75, - random_state: int = 1994, - models_dest: str = "models", - plots_dest: str = "plots", - file_ext: str = "csv", - test_set_key: str = "test-set", - gpus: bool = False -) -> None: - """low level xgboost train api - - for the xgboost `train` params see: - https://xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.train - - Note: the first parameter of xgboost's `train` method is a dict of parameters - supplied to the booster (engine). To modify one of those simply - add a task parameter (when running you supply an mlrun NewTask) with the - prefix "XGB_". So for example, to set the 'tree_method' parameter to 'approx', - add {"XGB_tree_method":"approx"} to the task params key. - - :param context: the function context - :param dataset: the full data set, train, valid and test will be extracted and - each converted to a DMatrix for input to xgboost's `train` - :param label_column: ground-truth (y) labels - :param encode_cols: dictionary of names and prefixes for columns that are - to hot be encoded. - :param sample: Selects the first n rows, or select a sample - starting from the first. If negative <-1, select - a random sample - :param test_size: (0.05) test set size - :param valid_size: (0.75) Once the test set has been removed the - training set gets this proportion. - :param random_state: (1) sklearn rng seed - :param models_dest: destination subfolder for model artifacts - :param plots_dest: destination subfolder for plot artifacts - :param file_ext: format for test_set_key hold out data - :param test_set_key: (test-set), key of held out data in artifact store - :param gpus: (False), run on gpus - """ - raw, labels, header = get_sample(dataset, sample, label_column) - - # hot-encode - if encode_cols: - raw = pd.get_dummies(raw, - columns=list(encode_cols.keys()), - prefix=list(encode_cols.values()), - drop_first=True) - - # split the sample into train validate, test and calibration sets: - (xtrain, ytrain), (xvalid, yvalid), (xtest, ytest) = \ - get_splits(raw, labels, 3, test_size, valid_size, random_state) - - # save test data as regular dataframe as it may be used by other process - context.log_dataset(test_set_key, df=pd.concat([xtest, ytest], axis=1), - format=file_ext, index=False) - - # convert to xgboost DMatrix (todo - dask, gpu) - dtrain = DMatrix(xtrain, label=ytrain) - dvalid = DMatrix(xvalid, label=yvalid) - - boost_params = { - "tree_method": "gpu_hist" if gpus else "hist", - "seed": random_state, - "disable_default_eval_metric": 1, - "objective": "reg:squaredlogerror", - "eval_metric": "rmsle"} - - # enable user to customize `booster param` parameters - for k, v in context.parameters.items(): - if k.startswith('XGB_'): - boost_params[k[4:]] = v - - # collect learning curves / training history - results = dict() - - booster = train( - boost_params, - dtrain=dtrain, - num_boost_round=num_boost_round, - evals=[(dtrain, "train"), (dvalid, "valid")], - evals_result=results, - obj=squared_log, - feval=rmsle, - maximize=maximize, - early_stopping_rounds=early_stopping_rounds, - verbose_eval=verbose_eval, - # xgb_model=xgb_model, - # callbacks: List[Callable] = [] - ) - - context.log_model("model", - body=dumps(booster), - model_file="model.pkl", - artifact_path='artifacts/') - - learning_curves(context, results) \ No newline at end of file diff --git a/xgb_serving/function.yaml b/xgb_serving/function.yaml deleted file mode 100644 index 7073d8ba6..000000000 --- a/xgb_serving/function.yaml +++ /dev/null @@ -1,40 +0,0 @@ -kind: serving -metadata: - name: xgb-serving - tag: '' - hash: 200148a9a4815d8b0394038d973b59eda1776d36 - project: '' - labels: - author: Daniel - categories: - - model-serving - - machine-learning -spec: - command: '' - args: [] - image: mlrun/mlrun - build: - functionSourceCode: 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 - commands: [] - code_origin: https://github.com/daniels290813/functions.git#2675b0d235d93571a696296c93cfb2103cbf261f:/Users/Daniel_Sabba/functions/xgb_serving/xgb_serving.py - origin_filename: /Users/Daniel_Sabba/functions/xgb_serving/xgb_serving.py - requirements: [] - description: deploy an XGBoost model server. - default_handler: '' - disable_auto_mount: false - clone_target_dir: '' - env: [] - priority_class_name: '' - preemption_mode: prevent - min_replicas: 1 - max_replicas: 4 - source: '' - function_kind: serving_v2 - function_handler: xgb_serving:handler - base_image_pull: false - default_class: ClassifierModel - secret_sources: [] - affinity: null - tolerations: null - security_context: {} -verbose: false diff --git a/xgb_serving/item.yaml b/xgb_serving/item.yaml deleted file mode 100644 index 413e26bfa..000000000 --- a/xgb_serving/item.yaml +++ /dev/null @@ -1,29 +0,0 @@ -apiVersion: v1 -categories: -- model-serving -- machine-learning -description: deploy an XGBoost model server. -doc: '' -example: xgb_serving.ipynb -generationDate: 2022-08-28:17-25 -hidden: false -icon: '' -labels: - author: Daniel -maintainers: [] -marketplaceType: '' -mlrunVersion: 1.4.1 -name: xgb_serving -platformVersion: 3.5.3 -spec: - customFields: - default_class: ClassifierModel - filename: xgb_serving.py - handler: handler - image: mlrun/mlrun - kind: serving - requirements: [] -url: '' -version: 1.1.2 - - diff --git a/xgb_serving/requirements.txt b/xgb_serving/requirements.txt deleted file mode 100644 index a5bbcdde3..000000000 --- a/xgb_serving/requirements.txt +++ /dev/null @@ -1,7 +0,0 @@ -pandas -xgboost -cloudpickle -pygit2 -scikit-learn==1.0.2 -scikit-plot -seaborn diff --git a/xgb_serving/test_xgb_serving.py b/xgb_serving/test_xgb_serving.py deleted file mode 100644 index 52f6ccb6d..000000000 --- a/xgb_serving/test_xgb_serving.py +++ /dev/null @@ -1,67 +0,0 @@ -# Copyright 2019 Iguazio -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -import mlrun -import os -import pandas as pd -from xgb_serving import XGBoostModel - - -def get_class_data(): - fn = mlrun.import_function('../gen_class_data/function.yaml') - run = fn.run(params={'key': 'classifier-data', - 'n_samples': 10_000, - 'm_features': 5, - 'k_classes': 2, - 'header': None, - 'weight': [0.5, 0.5], - 'sk_params': {'n_informative': 2}, - 'file_ext': 'csv'}, local=True, artifact_path="./artifacts") - return run - - -def xgb_trainer(): - # running data preparation function locally - gen_data_run = get_class_data() - - fn = mlrun.import_function('../xgb_trainer/function.yaml') - run = fn.run(params={'model_type': 'classifier', - 'CLASS_tree_method': 'hist', - 'CLASS_objective': 'binary:logistic', - 'CLASS_booster': 'gbtree', - 'FIT_verbose': 0, - 'label_column': 'labels'}, - local=True, inputs={'dataset': gen_data_run.status.artifacts[0]['spec']['target_path']}) - - for artifact in run.status.artifacts: - if artifact['kind'] == 'model': - assert os.path.exists(artifact['spec']['target_path']), "Failed locating model file" # validating model exists - return artifact['spec']['target_path'] + artifact['spec']['model_file'], gen_data_run.status.artifacts[0]['spec']['target_path'] - assert False, "Failed creating model" - - -def test_local_xgb_serving(): - model_path, dataset_path = xgb_trainer() - fn = mlrun.import_function('function.yaml') - - fn.add_model(key='my_model', model_path=model_path, class_name='XGBoostModel') - server = fn.to_mock_server() - - # Testing the model - df = pd.read_csv(dataset_path) - x = df.drop(['labels'], axis=1).iloc[0].tolist() - y_true = df['labels'][0] - - y_pred = server.test(path='/v2/models/my_model/predict', body={"inputs": x})['outputs'][0] - assert y_true == y_pred diff --git a/xgb_serving/xgb_serving.ipynb b/xgb_serving/xgb_serving.ipynb deleted file mode 100644 index 6c605367e..000000000 --- a/xgb_serving/xgb_serving.ipynb +++ /dev/null @@ -1,421 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Deploy a Serverless XGBoost Model Server\n", - " --------------------------------------------------------------------\n", - "\n", - "The following notebook demonstrates how to deploy an XGBoost model server (a.k.a Nuclio-serving)\n", - "\n", - "#### **notebook how-to's**\n", - "* Write and test model serving class in a notebook.\n", - "* Deploy the model server function.\n", - "* Invoke and test the serving function." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "#### **steps**\n", - "**[define a new function and its dependencies](#define-function)**
\n", - "**[test the model serving class locally](#test-locally)**
\n", - "**[deploy our serving class using as a serverless function](#deploy)**
\n", - "**[test our model server using HTTP request](#test-model-server)**
" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# nuclio: ignore\n", - "import nuclio " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### **define a new function and its dependencies**" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "%nuclio: setting kind to 'nuclio:serving'\n", - "%nuclio: setting 'MODEL_CLASS' environment variable\n", - "%nuclio: setting spec.build.baseImage to 'mlrun/ml-models'\n" - ] - } - ], - "source": [ - "%nuclio config kind=\"nuclio:serving\"\n", - "%nuclio env MODEL_CLASS=XGBoostModel\n", - "\n", - "%nuclio config spec.build.baseImage = \"mlrun/ml-models\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Function Code" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# import kfserving\n", - "import os\n", - "import json\n", - "import numpy as np\n", - "import xgboost as xgb\n", - "from cloudpickle import load\n", - "\n", - "### Model Serving Class\n", - "\n", - "import mlrun\n", - "class XGBoostModel(mlrun.runtimes.MLModelServer):\n", - " def load(self):\n", - " model_file, extra_data = self.get_model(\".pkl\")\n", - " self.model = load(open(str(model_file), \"rb\"))\n", - " \n", - "\n", - " def predict(self, body):\n", - " try:\n", - " feats = np.asarray(body[\"instances\"], dtype=np.float32).reshape(-1, 5)\n", - " result = self.model.predict(feats, validate_features=False)\n", - " return result.tolist()\n", - " except Exception as e:\n", - " raise Exception(\"Failed to predict %s\" % e)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The following end-code annotation tells ```nuclio``` to stop parsing the notebook from this cell. _**Please do not remove this cell**_:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# nuclio: end-code" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "## Test the function locally\n", - "\n", - "The class above can be tested locally. Just instantiate the class, `.load()` will load the model to a local dir.\n", - "\n", - "> **Verify there is a model file in the model_dir path (generated by the training notebook)**" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "from mlrun import mlconf\n", - "\n", - "model_dir = os.path.join(mlconf.artifact_path, \"xgb/models\")\n", - "\n", - "my_server = XGBoostModel(\"my-model\", model_dir=model_dir)\n", - "my_server.load()" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "DATA_PATH = mlconf.artifact_path + \"/xgb/classifier-data.csv\"\n", - "MODEL_PATH = mlconf.artifact_path + \"/xgb/models/xgb_test\"" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "xtest = pd.read_csv(DATA_PATH)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can use the `.predict(body)` method to test the model." - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [], - "source": [ - "import json, numpy as np\n", - "preds = my_server.predict({\"instances\":xtest.values[:10,:-1].tolist()})" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "predicted class: [1, 0, 0, 0, 0, 0, 1, 1, 0, 1]\n" - ] - } - ], - "source": [ - "print(\"predicted class:\", preds)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### **deploy our serving class using as a serverless function**\n", - "in the following section we create a new model serving function which wraps our class , and specify model and other resources.\n", - "\n", - "the `models` dict store model names and the assosiated model **dir** URL (the URL can start with `S3://` and other blob store options), the faster way is to use a shared file volume, we use `.apply(mount_v3io())` to attach a v3io (iguazio data fabric) volume to our function. By default v3io will mount the current user home into the `\\User` function path.\n", - "\n", - "**verify the model dir does contain a valid `model.bst` file**" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [], - "source": [ - "from mlrun import new_model_server, mount_v3io\n", - "import requests" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[mlrun] 2020-06-14 12:49:05,013 function spec saved to path: function.yaml\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fn = new_model_server(\"xgb-serving\",\n", - " model_class=\"XGBoostModel\",\n", - " models={\"xgb_serving_v2\": f\"{model_dir}\"})\n", - "fn.spec.description = \"xgboost test data classification server\"\n", - "fn.metadata.categories = [\"serving\", \"ml\"]\n", - "fn.metadata.labels = {\"author\": \"yaronh\", \"framework\": \"xgboost\"}\n", - "\n", - "fn.export(\"function.yaml\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## tests" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from mlrun.platforms.other import auto_mount\n", - "fn.apply(auto_mount())" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[mlrun] 2020-06-14 12:49:18,128 deploy started\n", - "[nuclio] 2020-06-14 12:49:19,213 (info) Build complete\n", - "[nuclio] 2020-06-14 12:49:27,347 (info) Function deploy complete\n", - "[nuclio] 2020-06-14 12:49:27,354 done updating default-xgb-test, function address: 3.23.82.202:30104\n" - ] - } - ], - "source": [ - "addr = fn.deploy()" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'http://3.23.82.202:30104'" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "addr" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### **test our model server using HTTP request**\n", - "\n", - "\n", - "We invoke our model serving function using test data, the data vector is specified in the `instances` attribute." - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [], - "source": [ - "# KFServing protocol event\n", - "event_data = {\"instances\": xtest.values[:10,:-1].tolist()}" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'[1, 0, 0, 0, 0, 0, 1, 1, 0, 1]'" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import json\n", - "resp = requests.put(addr + \"/xgb_serving_v2/predict\", json=json.dumps(event_data))\n", - "resp.text" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[1, 0, 0, 0, 0, 0, 1, 1, 0, 1]" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "preds" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**[back to top](#top)**" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/xgb_serving/xgb_serving.py b/xgb_serving/xgb_serving.py deleted file mode 100644 index a4d095e57..000000000 --- a/xgb_serving/xgb_serving.py +++ /dev/null @@ -1,33 +0,0 @@ -# Copyright 2019 Iguazio -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -import os -import json -import numpy as np -from cloudpickle import load -import mlrun - - -class XGBoostModel(mlrun.serving.V2ModelServer): - def load(self): - model_file, extra_data = self.get_model(".pkl") - self.model = load(open(str(model_file), "rb")) - - def predict(self, body): - try: - feats = np.asarray(body["inputs"], dtype=np.float32).reshape(-1, 5) - result = self.model.predict(feats, validate_features=False) - return result.tolist() - except Exception as e: - raise Exception("Failed to predict %s" % e) \ No newline at end of file From 7f3403ced7d291e52305b272c185f7506c84cdca Mon Sep 17 00:00:00 2001 From: Avi Asulin <34214569+aviaIguazio@users.noreply.github.com> Date: Thu, 12 Sep 2024 08:42:06 +0300 Subject: [PATCH 19/38] Update feature_selection/test_feature_selection.py Co-authored-by: Eyal Danieli --- feature_selection/test_feature_selection.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/feature_selection/test_feature_selection.py b/feature_selection/test_feature_selection.py index d21e648ff..9cb5ca621 100644 --- a/feature_selection/test_feature_selection.py +++ b/feature_selection/test_feature_selection.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # -from mlrun import code_to_function, get_dataitem +from mlrun import code_to_function from pathlib import Path import shutil From 75dbafd698c6cfe41aa59b9663b22c3ea2a2b64f Mon Sep 17 00:00:00 2001 From: Avi Asulin <34214569+aviaIguazio@users.noreply.github.com> Date: Thu, 12 Sep 2024 08:43:08 +0300 Subject: [PATCH 20/38] Update item.yaml --- feature_selection/item.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/feature_selection/item.yaml b/feature_selection/item.yaml index 1b25ec410..99675b4e8 100644 --- a/feature_selection/item.yaml +++ b/feature_selection/item.yaml @@ -22,4 +22,4 @@ spec: kind: job requirements: [] url: '' -version: 1.7.0 +version: 1.5.0 From 639bb270ac1a8f12f7d0d1222b94f0b213c2a0ac Mon Sep 17 00:00:00 2001 From: Eyal Danieli Date: Wed, 25 Sep 2024 09:41:02 +0300 Subject: [PATCH 21/38] Align to master branch (#826) * [Category] Fix and add categories to functions (#808) * [Category] Fix and add categories to functions * bump version in structured * test is not valid in huggingface_serving * Fix duplicated footer * Fix duplicated footer * revert python version change as it will be done in another PR * comments * comments * Bump python:3.6 to python:3.9 (#810) * [Describe] Align describe to new pandas version (#812) * [Describe] Align describe to new pandas version * minor test fix * update mlrun version * add dask to requirements * remove dask * update numpy version * debug * debug * debug * remove dask tests * remove debug code * [get_offline_features] Updated to mlrun 1.6.3 (#813) * [Feature-selection] Replace matplotlib with plotly (#815) * Iguazio-cicd user token updated Iguazio-cicd user token updated in repo secrets: https://github.com/mlrun/functions/settings/secrets/actions MARKETPLACE_ACCESS_TOKEN_V3 new token gh...Zmf was set around April * forcing iguazio-cicd auth forcing iguazio-cicd to deal with Author identity unknown * checkout@v3 to v4 and echo * [Mlflow_utils] - mlflow model server (#811) * mlflow server * small fix to test * small fixes to ms and nb * small fixes to mlrun version * update requirements lightgbm * added req * Added xgboost to req --------- Co-authored-by: Avi Asulin <34214569+aviaIguazio@users.noreply.github.com> * [Mlflow] Remove mlflow tag (#825) * remove mlflow tag * remove mlflow tag --------- Co-authored-by: Avi Asulin <34214569+aviaIguazio@users.noreply.github.com> * align feature_selection yaml --------- Co-authored-by: Avi Asulin <34214569+aviaIguazio@users.noreply.github.com> Co-authored-by: Yonatan Shelach <92271540+yonishelach@users.noreply.github.com> Co-authored-by: rokatyy Co-authored-by: Katerina Molchanova <35141662+rokatyy@users.noreply.github.com> Co-authored-by: nashpaz123 <44337075+nashpaz123@users.noreply.github.com> Co-authored-by: ZeevRispler <73653682+ZeevRispler@users.noreply.github.com> --- .github/workflows/test-all.yaml | 23 +- churn_server/churn_server.py | 10 - churn_server/function.yaml | 4 +- churn_server/item.yaml | 2 +- describe/describe.py | 39 +- describe/function.yaml | 96 +- describe/item.yaml | 4 +- describe/requirements.txt | 1 - describe/test_describe.py | 76 -- feature_selection/feature_selection.py | 52 +- feature_selection/function.yaml | 4 +- feature_selection/requirements.txt | 4 +- feature_selection/test_feature_selection.py | 57 +- hugging_face_serving/function.yaml | 41 +- hugging_face_serving/item.yaml | 5 +- mlflow_utils/function.yaml | 31 + mlflow_utils/item.yaml | 31 + mlflow_utils/mlflow_utils.ipynb | 1353 +++++++++++++++++++ mlflow_utils/mlflow_utils.py | 45 + mlflow_utils/requirements.txt | 3 + mlflow_utils/test_mlflow_utils.py | 179 +++ model_server/function.yaml | 2 +- pii_recognizer/function.yaml | 3 +- pii_recognizer/item.yaml | 3 +- pyannote_audio/function.yaml | 6 +- pyannote_audio/item.yaml | 6 +- question_answering/function.yaml | 4 +- question_answering/item.yaml | 4 +- silero_vad/function.yaml | 6 +- silero_vad/item.yaml | 6 +- structured_data_generator/function.yaml | 4 +- structured_data_generator/item.yaml | 4 +- text_to_audio_generator/function.yaml | 3 +- text_to_audio_generator/item.yaml | 3 +- tf2_serving/function.yaml | 2 +- transcribe/function.yaml | 5 +- transcribe/item.yaml | 4 +- translate/function.yaml | 24 +- translate/item.yaml | 3 +- v2_model_server/function.yaml | 4 +- v2_model_server/item.yaml | 2 +- v2_model_server/v2_model_server.py | 11 - 42 files changed, 1860 insertions(+), 309 deletions(-) create mode 100644 mlflow_utils/function.yaml create mode 100644 mlflow_utils/item.yaml create mode 100644 mlflow_utils/mlflow_utils.ipynb create mode 100644 mlflow_utils/mlflow_utils.py create mode 100644 mlflow_utils/requirements.txt create mode 100644 mlflow_utils/test_mlflow_utils.py diff --git a/.github/workflows/test-all.yaml b/.github/workflows/test-all.yaml index 4832c6456..a09ba17a2 100644 --- a/.github/workflows/test-all.yaml +++ b/.github/workflows/test-all.yaml @@ -15,7 +15,7 @@ jobs: run: echo "::set-output name=branch::${GITHUB_REF#refs/heads/}" id: myref - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - id: set-matrix # This is very hacky, but it goes like that: # 1) Associate base_ref with origin/base_ref since actions/checkout doesn't do it, if we don't do that we won't be able to check the actual diff @@ -63,7 +63,7 @@ jobs: steps: # Source - name: Checkout current repo - uses: actions/checkout@v3 + uses: actions/checkout@v4 with: path: functions # Install python 3.9 @@ -106,11 +106,11 @@ jobs: run: echo "::set-output name=branch::${GITHUB_REF#refs/heads/}" id: branch - name: Checkout current repo - uses: actions/checkout@v3 + uses: actions/checkout@v4 with: path: functions - name: Checkout Marketplace - uses: actions/checkout@v3 + uses: actions/checkout@v4 with: repository: mlrun/marketplace path: marketplace @@ -136,6 +136,7 @@ jobs: env: GITHUB_TOKEN: ${{ secrets.MARKETPLACE_ACCESS_TOKEN_V3 }} USERNAME: iguazio-cicd + USEREMAIL: iguaziocicd@gmail.com REPO_PATH: marketplace BASE_REPO: mlrun BASE_BRANCH: master @@ -153,24 +154,30 @@ jobs: exit 1; }; git config --local user.name $USERNAME + git config --local user.email $USEREMAIL git branch --set-upstream-to origin/master git remote -v - echo "Checking out [$BRANCH_NAME]..." + echo "1. Checking out [$BRANCH_NAME]..." git checkout -b $BRANCH_NAME - echo "Checking out [$BASE_BRANCH]..." + echo "2. Checking out [$BASE_BRANCH]..." git checkout $BASE_BRANCH git pull - echo "Checking out [$BRANCH_NAME]..." + echo "3. Checking out [$BRANCH_NAME]..." git checkout $BRANCH_NAME + echo "3a. merging" git merge $BASE_BRANCH + echo "3b. status" git status git status --ignored find . -type f | xargs ls -artl + echo "3b. add" git add --all git status git status --ignored - echo "Commiting changes..." + echo "4. Commiting changes..." + echo "4a. git rev-parse" git rev-parse --show-toplevel + echo "4b. git commit" git commit -a -m "Automatically generated by github-worflow[bot] for commit: $COMMIT_SHA" git status git status --ignored diff --git a/churn_server/churn_server.py b/churn_server/churn_server.py index 55f37f280..def2850da 100644 --- a/churn_server/churn_server.py +++ b/churn_server/churn_server.py @@ -43,13 +43,3 @@ def predict(self, body): except Exception as e: raise Exception("Failed to predict %s" % e) - -from mlrun.runtimes import nuclio_init_hook - - -def init_context(context): - nuclio_init_hook(context, globals(), "serving_v2") - - -def handler(context, event): - return context.mlrun_handler(context, event) diff --git a/churn_server/function.yaml b/churn_server/function.yaml index 7a73c11a4..14f6c8cef 100644 --- a/churn_server/function.yaml +++ b/churn_server/function.yaml @@ -29,14 +29,14 @@ spec: annotations: nuclio.io/generated_by: function generated from /User/functions/churn_server/churn_server.py spec: - runtime: python:3.6 + runtime: python:3.9 handler: churn_server:handler env: [] volumes: [] build: commands: [] noBaseImagesPull: true - functionSourceCode: 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 + functionSourceCode: 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 source: '' function_kind: serving_v2 default_class: ChurnModel diff --git a/churn_server/item.yaml b/churn_server/item.yaml index 3a3b4b6ba..09ba9b713 100644 --- a/churn_server/item.yaml +++ b/churn_server/item.yaml @@ -29,4 +29,4 @@ spec: - xgboost==1.3.1 - lifelines==0.22.8 url: '' -version: 1.1.0 +version: 1.2.0 diff --git a/describe/describe.py b/describe/describe.py index def92782b..27d789f5b 100644 --- a/describe/describe.py +++ b/describe/describe.py @@ -36,7 +36,7 @@ ) from mlrun.datastore import DataItem from mlrun.execution import MLClientCtx -from mlrun.feature_store import FeatureSet, FeatureVector +from mlrun.feature_store import FeatureSet from plotly.subplots import make_subplots pd.set_option("display.float_format", lambda x: "%.2f" % x) @@ -234,24 +234,24 @@ def _create_features_histogram_artifacts( if label_column is not None and problem_type == "classification": all_labels = df[label_column].unique() visible = True - for (columnName, _) in df.iteritems(): - if columnName == label_column: + for column_name in df.columns: + if column_name == label_column: continue if label_column is not None and problem_type == "classification": for label in all_labels: sub_fig = go.Histogram( histfunc="count", - x=df.loc[df[label_column] == label][columnName], + x=df.loc[df[label_column] == label][column_name], name=str(label), visible=visible, ) - figs[f"{columnName}@?@{label}"] = sub_fig + figs[f"{column_name}@?@{label}"] = sub_fig else: - sub_fig = go.Histogram(histfunc="count", x=df[columnName], visible=visible) - figs[f"{columnName}@?@{1}"] = sub_fig + sub_fig = go.Histogram(histfunc="count", x=df[column_name], visible=visible) + figs[f"{column_name}@?@{1}"] = sub_fig if visible: - first_feature_name = columnName + first_feature_name = column_name visible = False fig = go.Figure() @@ -338,7 +338,7 @@ def _create_features_2d_scatter_artifacts( Create and log a scatter-2d artifact for each couple of features """ features = [ - columnName for (columnName, _) in df.iteritems() if columnName != label_column + column_name for column_name in df.columns if column_name != label_column ] max_feature_len = float(max(len(elem) for elem in features)) if label_column is not None: @@ -450,11 +450,12 @@ def _create_violin_artifact( plot_num = 0 - for (columnName, columnData) in df.iteritems(): + for column_name in df.columns: + column_data = df[column_name] violin = go.Violin( - x=[columnName] * columnData.shape[0], - y=columnData, - name=columnName, + x=[column_name] * column_data.shape[0], + y=column_data, + name=column_name, ) fig.add_trace( @@ -491,15 +492,15 @@ def _create_imbalance_artifact( """ if label_column: if problem_type == "classification": + values_column = "count" labels_count = df[label_column].value_counts().sort_index() df_labels_count = pd.DataFrame(labels_count) - df_labels_count.rename(columns={label_column: "Total"}, inplace=True) df_labels_count[label_column] = labels_count.index - df_labels_count["weights"] = df_labels_count["Total"] / sum( - df_labels_count["Total"] + df_labels_count.rename(columns={"": values_column}, inplace=True) + df_labels_count[values_column] = df_labels_count[values_column] / sum( + df_labels_count[values_column] ) - - fig = px.pie(df_labels_count, names=label_column, values="Total") + fig = px.pie(df_labels_count, names=label_column, values=values_column) else: fig = px.histogram( histfunc="count", @@ -532,7 +533,7 @@ def _create_corr_artifact( """ if label_column is not None: df = df.drop([label_column], axis=1) - tblcorr = df.corr() + tblcorr = df.corr(numeric_only=True) extra_data["correlation-matrix-csv"] = context.log_artifact( TableArtifact("correlation-matrix-csv", df=tblcorr, visible=True), local_path=f"{plots_dest}/correlation-matrix.csv", diff --git a/describe/function.yaml b/describe/function.yaml index 6f518bbfa..f989c6ec7 100644 --- a/describe/function.yaml +++ b/describe/function.yaml @@ -1,54 +1,12 @@ +verbose: false kind: job -metadata: - name: describe - tag: '' - hash: 38ac49fa67c647c7defc230c9853a22657690a9e - project: '' - labels: - author: Davids - categories: - - data-analysis spec: - command: '' - args: [] - image: mlrun/mlrun - build: - functionSourceCode: # Copyright 2019 Iguazio
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# Generated by nuclio.export.NuclioExporter

import warnings
from typing import Union

import mlrun
import numpy as np

warnings.simplefilter(action="ignore", category=FutureWarning)

import mlrun.feature_store as fstore
import pandas as pd
import plotly.express as px
import plotly.figure_factory as ff
import plotly.graph_objects as go
from mlrun.artifacts import (
    Artifact,
    DatasetArtifact,
    PlotlyArtifact,
    TableArtifact,
    update_dataset_meta,
)
from mlrun.datastore import DataItem
from mlrun.execution import MLClientCtx
from mlrun.feature_store import FeatureSet, FeatureVector
from plotly.subplots import make_subplots

pd.set_option("display.float_format", lambda x: "%.2f" % x)
MAX_SIZE_OF_DF = 500000


def analyze(
    context: MLClientCtx,
    name: str = "dataset",
    table: Union[FeatureSet, DataItem] = None,
    label_column: str = None,
    plots_dest: str = "plots",
    random_state: int = 1,
    problem_type: str = "classification",
    dask_key: str = "dask_key",
    dask_function: str = None,
    dask_client=None,
) -> None:
    """
    The function will output the following artifacts per
    column within the data frame (based on data types)
    If the data has more than 500,000 sample we
    sample randomly 500,000 samples:

    describe csv
    histograms
    scatter-2d
    violin chart
    correlation-matrix chart
    correlation-matrix csv
    imbalance pie chart
    imbalance-weights-vec csv

    :param context:                 The function context
    :param name:                    Key of dataset to database ("dataset" for default)
    :param table:                   MLRun input pointing to pandas dataframe (csv/parquet file path) or FeatureSet
                                    as param
    :param label_column:            Ground truth column label
    :param plots_dest:              Destination folder of summary plots (relative to artifact_path)
                                    ("plots" for default)
    :param random_state:            When the table has more than 500,000 samples, we sample randomly 500,000 samples
    :param problem_type             The type of the ML problem the data facing - regression, classification or None
                                    (classification for default)
    :param dask_key:                Key of dataframe in dask client "datasets" attribute
    :param dask_function:           Dask function url (db://..)
    :param dask_client:             Dask client object
    """
    data_item, featureset, creat, update = False, False, False, False
    get_from_table = True
    if dask_function or dask_client:
        data_item, creat = True, True
        if dask_function:
            client = mlrun.import_function(dask_function).client
        elif dask_client:
            client = dask_client
        else:
            raise ValueError("dask client was not provided")

        if dask_key in client.datasets:
            df = client.get_dataset(dask_key)
            data_item, creat, get_from_table = True, True, False
        elif table:
            get_from_table = True
        else:
            context.logger.info(
                f"only these datasets are available {client.datasets} in client {client}"
            )
            raise Exception("dataset not found on dask cluster")

    if get_from_table:
        if type(table) == DataItem:
            if table.meta is None:
                data_item, creat, update = True, True, False
            elif table.meta.kind == "dataset":
                data_item, creat, update = True, False, True
            elif table.meta.kind == "FeatureVector":
                data_item, creat, update = True, False, False
            elif table.meta.kind == "FeatureSet":
                featureset, creat, update = True, False, False

        if data_item:
            df = table.as_df()
        elif featureset:
            project_name, set_name = (
                table._path.split("/")[2],
                table._path.split("/")[4],
            )
            feature_set = fstore.get_feature_set(
                f"store://feature-sets/{project_name}/{set_name}"
            )
            df = feature_set.to_dataframe()
        else:
            context.logger.error(f"Wrong table type.")
            return

    if df.size > MAX_SIZE_OF_DF:
        df = df.sample(n=int(MAX_SIZE_OF_DF / df.shape[1]), random_state=random_state)
    extra_data = {}

    if label_column not in df.columns:
        label_column = None

    extra_data["describe csv"] = context.log_artifact(
        TableArtifact("describe-csv", df=df.describe()),
        local_path=f"{plots_dest}/describe.csv",
    )

    try:
        _create_histogram_mat_artifact(
            context, df, extra_data, label_column, plots_dest
        )
    except Exception as e:
        context.logger.warn(f"Failed to create histogram matrix artifact due to: {e}")
    try:
        _create_features_histogram_artifacts(
            context, df, extra_data, label_column, plots_dest, problem_type
        )
    except Exception as e:
        context.logger.warn(f"Failed to create pairplot histograms due to: {e}")
    try:
        _create_features_2d_scatter_artifacts(
            context, df, extra_data, label_column, plots_dest, problem_type
        )
    except Exception as e:
        context.logger.warn(f"Failed to create pairplot 2d_scatter due to: {e}")
    try:
        _create_violin_artifact(context, df, extra_data, plots_dest)
    except Exception as e:
        context.logger.warn(f"Failed to create violin distribution plots due to: {e}")
    try:
        _create_imbalance_artifact(
            context, df, extra_data, label_column, plots_dest, problem_type
        )
    except Exception as e:
        context.logger.warn(f"Failed to create class imbalance plot due to: {e}")
    try:
        _create_corr_artifact(context, df, extra_data, label_column, plots_dest)
    except Exception as e:
        context.logger.warn(f"Failed to create features correlation plot due to: {e}")

    if not data_item:
        return

    artifact = table.artifact_url
    if creat:  # dataset not stored
        artifact = DatasetArtifact(
            key="dataset", stats=True, df=df, extra_data=extra_data
        )
        artifact = context.log_artifact(artifact, db_key=name)
        context.logger.info(f"The data set is logged to the project under {name} name")

    if update:
        update_dataset_meta(artifact, extra_data=extra_data)
        context.logger.info(f"The data set named {name} is updated")

    # TODO : 3-D plot on on selected features.
    # TODO : Reintegration plot on on selected features.
    # TODO : PCA plot (with options)


def _create_histogram_mat_artifact(
    context: MLClientCtx,
    df: pd.DataFrame,
    extra_data: dict,
    label_column: str,
    plots_dest: str,
):
    """
    Create and log a histogram matrix artifact
    """
    context.log_artifact(
        item=Artifact(
            key="hist",
            body=b"<b> Deprecated, see the artifacts scatter-2d "
            b"and histograms instead<b>",
        ),
        local_path=f"{plots_dest}/hist.html",
    )


def _create_features_histogram_artifacts(
    context: MLClientCtx,
    df: pd.DataFrame,
    extra_data: dict,
    label_column: str,
    plots_dest: str,
    problem_type: str,
):
    """
    Create and log a histogram artifact for each feature
    """

    figs = dict()
    first_feature_name = ""
    if label_column is not None and problem_type == "classification":
        all_labels = df[label_column].unique()
    visible = True
    for (columnName, _) in df.iteritems():
        if columnName == label_column:
            continue

        if label_column is not None and problem_type == "classification":
            for label in all_labels:
                sub_fig = go.Histogram(
                    histfunc="count",
                    x=df.loc[df[label_column] == label][columnName],
                    name=str(label),
                    visible=visible,
                )
                figs[f"{columnName}@?@{label}"] = sub_fig
        else:
            sub_fig = go.Histogram(histfunc="count", x=df[columnName], visible=visible)
            figs[f"{columnName}@?@{1}"] = sub_fig
        if visible:
            first_feature_name = columnName
        visible = False

    fig = go.Figure()
    for k in figs.keys():
        fig.add_trace(figs[k])

    fig.update_layout(
        updatemenus=[
            {
                "buttons": [
                    {
                        "label": column_name,
                        "method": "update",
                        "args": [
                            {
                                "visible": [
                                    key.split("@?@")[0] == column_name
                                    for key in figs.keys()
                                ],
                                "xaxis": {
                                    "range": [
                                        min(df[column_name]),
                                        max(df[column_name]),
                                    ]
                                },
                            },
                            {"title": f"<i><b>Histogram of {column_name}</b></i>"},
                        ],
                    }
                    for column_name in df.columns
                    if column_name != label_column
                ],
                "direction": "down",
                "pad": {"r": 10, "t": 10},
                "showactive": True,
                "x": 0.25,
                "xanchor": "left",
                "y": 1.1,
                "yanchor": "top",
            }
        ],
        annotations=[
            dict(
                text="Select Feature Name ",
                showarrow=False,
                x=0,
                y=1.05,
                yref="paper",
                xref="paper",
                align="left",
                xanchor="left",
                yanchor="top",
                font={
                    "color": "blue",
                },
            )
        ],
    )

    fig.update_layout(
        width=600,
        height=400,
        autosize=False,
        margin=dict(t=100, b=0, l=0, r=0),
        template="plotly_white",
    )

    fig.update_layout(title_text=f"<i><b>Histograms of {first_feature_name}</b></i>")
    extra_data[f"histograms"] = context.log_artifact(
        PlotlyArtifact(key=f"histograms", figure=fig),
        local_path=f"{plots_dest}/histograms.html",
    )


def _create_features_2d_scatter_artifacts(
    context: MLClientCtx,
    df: pd.DataFrame,
    extra_data: dict,
    label_column: str,
    plots_dest: str,
    problem_type: str,
):
    """
    Create and log a scatter-2d artifact for each couple of features
    """
    features = [
        columnName for (columnName, _) in df.iteritems() if columnName != label_column
    ]
    max_feature_len = float(max(len(elem) for elem in features))
    if label_column is not None:
        labels = sorted(df[label_column].unique())
    else:
        labels = [None]
    fig = go.Figure()
    if label_column is not None and problem_type == "classification":
        for l in labels:
            fig.add_trace(
                go.Scatter(
                    x=df.loc[df[label_column] == l][features[0]],
                    y=df.loc[df[label_column] == l][features[0]],
                    mode="markers",
                    visible=True,
                    showlegend=True,
                    name=str(l),
                )
            )
    elif label_column is None:
        fig.add_trace(
            go.Scatter(
                x=df[features[0]],
                y=df[features[0]],
                mode="markers",
                visible=True,
            )
        )
    elif problem_type == "regression":
        fig.add_trace(
            go.Scatter(
                x=df[features[0]],
                y=df[features[0]],
                mode="markers",
                marker=dict(
                    color=df[label_column], colorscale="Viridis", showscale=True
                ),
                visible=True,
            )
        )

    x_buttons = []
    y_buttons = []

    for ncol in features:
        if problem_type == "classification" and label_column is not None:
            x_buttons.append(
                dict(
                    method="update",
                    label=ncol,
                    args=[
                        {"x": [df.loc[df[label_column] == l][ncol] for l in labels]},
                        np.arange(len(labels)).tolist(),
                    ],
                )
            )

            y_buttons.append(
                dict(
                    method="update",
                    label=ncol,
                    args=[
                        {"y": [df.loc[df[label_column] == l][ncol] for l in labels]},
                        np.arange(len(labels)).tolist(),
                    ],
                )
            )
        else:
            x_buttons.append(
                dict(method="update", label=ncol, args=[{"x": [df[ncol]]}])
            )

            y_buttons.append(
                dict(method="update", label=ncol, args=[{"y": [df[ncol]]}])
            )

    # Pass buttons to the updatemenus argument
    fig.update_layout(
        updatemenus=[
            dict(buttons=x_buttons, direction="up", x=0.5, y=-0.1),
            dict(buttons=y_buttons, direction="down", x=-max_feature_len / 100, y=0.5),
        ]
    )

    fig.update_layout(
        width=600,
        height=400,
        autosize=False,
        margin=dict(t=100, b=0, l=0, r=0),
        template="plotly_white",
    )

    fig.update_layout(title_text=f"<i><b>Scatter-2d</b></i>")
    extra_data[f"scatter-2d"] = context.log_artifact(
        PlotlyArtifact(key=f"scatter-2d", figure=fig),
        local_path=f"{plots_dest}/scatter-2d.html",
    )


def _create_violin_artifact(
    context: MLClientCtx, df: pd.DataFrame, extra_data: dict, plots_dest: str
):
    """
    Create and log a violin artifact
    """
    cols = 5
    rows = (df.shape[1] // cols) + 1
    fig = make_subplots(rows=rows, cols=cols)

    plot_num = 0

    for (columnName, columnData) in df.iteritems():
        violin = go.Violin(
            x=[columnName] * columnData.shape[0],
            y=columnData,
            name=columnName,
        )

        fig.add_trace(
            violin,
            row=(plot_num // cols) + 1,
            col=(plot_num % cols) + 1,
        )

        plot_num += 1

    fig["layout"].update(
        height=(rows + 1) * 200,
        width=(cols + 1) * 200,
        title="<i><b>Violin Plots</b></i>",
    )

    fig.update_layout(showlegend=False)
    extra_data["violin"] = context.log_artifact(
        PlotlyArtifact(key="violin", figure=fig),
        local_path=f"{plots_dest}/violin.html",
    )


def _create_imbalance_artifact(
    context: MLClientCtx,
    df: pd.DataFrame,
    extra_data: dict,
    label_column: str,
    plots_dest: str,
    problem_type: str,
):
    """
    Create and log an imbalance class artifact (csv + plot)
    """
    if label_column:
        if problem_type == "classification":
            labels_count = df[label_column].value_counts().sort_index()
            df_labels_count = pd.DataFrame(labels_count)
            df_labels_count.rename(columns={label_column: "Total"}, inplace=True)
            df_labels_count[label_column] = labels_count.index
            df_labels_count["weights"] = df_labels_count["Total"] / sum(
                df_labels_count["Total"]
            )

            fig = px.pie(df_labels_count, names=label_column, values="Total")
        else:
            fig = px.histogram(
                histfunc="count",
                x=df[label_column],
            )
            hist = np.histogram(df[label_column])
            df_labels_count = pd.DataFrame(
                {"min_val": hist[1], "count": hist[0].tolist() + [0]}
            )
        fig.update_layout(title_text="<i><b>Labels Imbalance</b></i>")
        extra_data["imbalance"] = context.log_artifact(
            PlotlyArtifact(key="imbalance", figure=fig),
            local_path=f"{plots_dest}/imbalance.html",
        )
        extra_data["imbalance-csv"] = context.log_artifact(
            TableArtifact("imbalance-weights-vec", df=df_labels_count),
            local_path=f"{plots_dest}/imbalance-weights-vec.csv",
        )


def _create_corr_artifact(
    context: MLClientCtx,
    df: pd.DataFrame,
    extra_data: dict,
    label_column: str,
    plots_dest: str,
):
    """
    Create and log an correlation-matrix artifact (csv + plot)
    """
    if label_column is not None:
        df = df.drop([label_column], axis=1)
    tblcorr = df.corr()
    extra_data["correlation-matrix-csv"] = context.log_artifact(
        TableArtifact("correlation-matrix-csv", df=tblcorr, visible=True),
        local_path=f"{plots_dest}/correlation-matrix.csv",
    )

    z = tblcorr.values.tolist()
    z_text = [["{:.2f}".format(y) for y in x] for x in z]
    fig = ff.create_annotated_heatmap(
        z,
        x=list(tblcorr.columns),
        y=list(tblcorr.columns),
        annotation_text=z_text,
        colorscale="agsunset",
    )
    fig["layout"]["yaxis"]["autorange"] = "reversed"  # l -> r
    fig.update_layout(title_text="<i><b>Correlation matrix</b></i>")
    fig["data"][0]["showscale"] = True

    extra_data["correlation"] = context.log_artifact(
        PlotlyArtifact(key="correlation", figure=fig),
        local_path=f"{plots_dest}/correlation.html",
    )
 - commands: [] - code_origin: https://github.com/davesh0812/functions.git#6c5f9ed5f39ccb1e0f478eee7b4aa10994dfd22b:/Users/davids/Projects/functions/describe/describe.py - origin_filename: /Users/davids/Projects/functions/describe/describe.py entry_points: analyze: - name: analyze - doc: 'The function will output the following artifacts per - - column within the data frame (based on data types) - - If the data has more than 500,000 sample we - - sample randomly 500,000 samples: - - - describe csv - - histograms - - scatter-2d - - violin chart - - correlation-matrix chart - - correlation-matrix csv - - imbalance pie chart - - imbalance-weights-vec csv' parameters: - name: context type: MLClientCtx doc: The function context - default: '' - name: name type: str doc: Key of dataset to database ("dataset" for default) @@ -86,15 +44,47 @@ spec: - name: dask_client doc: Dask client object default: null - outputs: - - default: '' + has_varargs: false lineno: 46 - description: describe and visualizes dataset stats - default_handler: analyze + outputs: + - type: None + name: analyze + has_kwargs: false + doc: 'The function will output the following artifacts per + + column within the data frame (based on data types) + + If the data has more than 500,000 sample we + + sample randomly 500,000 samples: + + + describe csv + + histograms + + scatter-2d + + violin chart + + correlation-matrix chart + + correlation-matrix csv + + imbalance pie chart + + imbalance-weights-vec csv' disable_auto_mount: false - env: [] - priority_class_name: '' - preemption_mode: prevent - affinity: null - tolerations: null -verbose: false + default_handler: analyze + description: describe and visualizes dataset stats + build: + functionSourceCode: # Copyright 2019 Iguazio
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# Generated by nuclio.export.NuclioExporter

import warnings
from typing import Union

import mlrun
import numpy as np

warnings.simplefilter(action="ignore", category=FutureWarning)

import mlrun.feature_store as fstore
import pandas as pd
import plotly.express as px
import plotly.figure_factory as ff
import plotly.graph_objects as go
from mlrun.artifacts import (
    Artifact,
    DatasetArtifact,
    PlotlyArtifact,
    TableArtifact,
    update_dataset_meta,
)
from mlrun.datastore import DataItem
from mlrun.execution import MLClientCtx
from mlrun.feature_store import FeatureSet
from plotly.subplots import make_subplots

pd.set_option("display.float_format", lambda x: "%.2f" % x)
MAX_SIZE_OF_DF = 500000


def analyze(
    context: MLClientCtx,
    name: str = "dataset",
    table: Union[FeatureSet, DataItem] = None,
    label_column: str = None,
    plots_dest: str = "plots",
    random_state: int = 1,
    problem_type: str = "classification",
    dask_key: str = "dask_key",
    dask_function: str = None,
    dask_client=None,
) -> None:
    """
    The function will output the following artifacts per
    column within the data frame (based on data types)
    If the data has more than 500,000 sample we
    sample randomly 500,000 samples:

    describe csv
    histograms
    scatter-2d
    violin chart
    correlation-matrix chart
    correlation-matrix csv
    imbalance pie chart
    imbalance-weights-vec csv

    :param context:                 The function context
    :param name:                    Key of dataset to database ("dataset" for default)
    :param table:                   MLRun input pointing to pandas dataframe (csv/parquet file path) or FeatureSet
                                    as param
    :param label_column:            Ground truth column label
    :param plots_dest:              Destination folder of summary plots (relative to artifact_path)
                                    ("plots" for default)
    :param random_state:            When the table has more than 500,000 samples, we sample randomly 500,000 samples
    :param problem_type             The type of the ML problem the data facing - regression, classification or None
                                    (classification for default)
    :param dask_key:                Key of dataframe in dask client "datasets" attribute
    :param dask_function:           Dask function url (db://..)
    :param dask_client:             Dask client object
    """
    data_item, featureset, creat, update = False, False, False, False
    get_from_table = True
    if dask_function or dask_client:
        data_item, creat = True, True
        if dask_function:
            client = mlrun.import_function(dask_function).client
        elif dask_client:
            client = dask_client
        else:
            raise ValueError("dask client was not provided")

        if dask_key in client.datasets:
            df = client.get_dataset(dask_key)
            data_item, creat, get_from_table = True, True, False
        elif table:
            get_from_table = True
        else:
            context.logger.info(
                f"only these datasets are available {client.datasets} in client {client}"
            )
            raise Exception("dataset not found on dask cluster")

    if get_from_table:
        if type(table) == DataItem:
            if table.meta is None:
                data_item, creat, update = True, True, False
            elif table.meta.kind == "dataset":
                data_item, creat, update = True, False, True
            elif table.meta.kind == "FeatureVector":
                data_item, creat, update = True, False, False
            elif table.meta.kind == "FeatureSet":
                featureset, creat, update = True, False, False

        if data_item:
            df = table.as_df()
        elif featureset:
            project_name, set_name = (
                table._path.split("/")[2],
                table._path.split("/")[4],
            )
            feature_set = fstore.get_feature_set(
                f"store://feature-sets/{project_name}/{set_name}"
            )
            df = feature_set.to_dataframe()
        else:
            context.logger.error(f"Wrong table type.")
            return

    if df.size > MAX_SIZE_OF_DF:
        df = df.sample(n=int(MAX_SIZE_OF_DF / df.shape[1]), random_state=random_state)
    extra_data = {}

    if label_column not in df.columns:
        label_column = None

    extra_data["describe csv"] = context.log_artifact(
        TableArtifact("describe-csv", df=df.describe()),
        local_path=f"{plots_dest}/describe.csv",
    )

    try:
        _create_histogram_mat_artifact(
            context, df, extra_data, label_column, plots_dest
        )
    except Exception as e:
        context.logger.warn(f"Failed to create histogram matrix artifact due to: {e}")
    try:
        _create_features_histogram_artifacts(
            context, df, extra_data, label_column, plots_dest, problem_type
        )
    except Exception as e:
        context.logger.warn(f"Failed to create pairplot histograms due to: {e}")
    try:
        _create_features_2d_scatter_artifacts(
            context, df, extra_data, label_column, plots_dest, problem_type
        )
    except Exception as e:
        context.logger.warn(f"Failed to create pairplot 2d_scatter due to: {e}")
    try:
        _create_violin_artifact(context, df, extra_data, plots_dest)
    except Exception as e:
        context.logger.warn(f"Failed to create violin distribution plots due to: {e}")
    try:
        _create_imbalance_artifact(
            context, df, extra_data, label_column, plots_dest, problem_type
        )
    except Exception as e:
        context.logger.warn(f"Failed to create class imbalance plot due to: {e}")
    try:
        _create_corr_artifact(context, df, extra_data, label_column, plots_dest)
    except Exception as e:
        context.logger.warn(f"Failed to create features correlation plot due to: {e}")

    if not data_item:
        return

    artifact = table.artifact_url
    if creat:  # dataset not stored
        artifact = DatasetArtifact(
            key="dataset", stats=True, df=df, extra_data=extra_data
        )
        artifact = context.log_artifact(artifact, db_key=name)
        context.logger.info(f"The data set is logged to the project under {name} name")

    if update:
        update_dataset_meta(artifact, extra_data=extra_data)
        context.logger.info(f"The data set named {name} is updated")

    # TODO : 3-D plot on on selected features.
    # TODO : Reintegration plot on on selected features.
    # TODO : PCA plot (with options)


def _create_histogram_mat_artifact(
    context: MLClientCtx,
    df: pd.DataFrame,
    extra_data: dict,
    label_column: str,
    plots_dest: str,
):
    """
    Create and log a histogram matrix artifact
    """
    context.log_artifact(
        item=Artifact(
            key="hist",
            body=b"<b> Deprecated, see the artifacts scatter-2d "
            b"and histograms instead<b>",
        ),
        local_path=f"{plots_dest}/hist.html",
    )


def _create_features_histogram_artifacts(
    context: MLClientCtx,
    df: pd.DataFrame,
    extra_data: dict,
    label_column: str,
    plots_dest: str,
    problem_type: str,
):
    """
    Create and log a histogram artifact for each feature
    """

    figs = dict()
    first_feature_name = ""
    if label_column is not None and problem_type == "classification":
        all_labels = df[label_column].unique()
    visible = True
    for column_name in df.columns:
        if column_name == label_column:
            continue

        if label_column is not None and problem_type == "classification":
            for label in all_labels:
                sub_fig = go.Histogram(
                    histfunc="count",
                    x=df.loc[df[label_column] == label][column_name],
                    name=str(label),
                    visible=visible,
                )
                figs[f"{column_name}@?@{label}"] = sub_fig
        else:
            sub_fig = go.Histogram(histfunc="count", x=df[column_name], visible=visible)
            figs[f"{column_name}@?@{1}"] = sub_fig
        if visible:
            first_feature_name = column_name
        visible = False

    fig = go.Figure()
    for k in figs.keys():
        fig.add_trace(figs[k])

    fig.update_layout(
        updatemenus=[
            {
                "buttons": [
                    {
                        "label": column_name,
                        "method": "update",
                        "args": [
                            {
                                "visible": [
                                    key.split("@?@")[0] == column_name
                                    for key in figs.keys()
                                ],
                                "xaxis": {
                                    "range": [
                                        min(df[column_name]),
                                        max(df[column_name]),
                                    ]
                                },
                            },
                            {"title": f"<i><b>Histogram of {column_name}</b></i>"},
                        ],
                    }
                    for column_name in df.columns
                    if column_name != label_column
                ],
                "direction": "down",
                "pad": {"r": 10, "t": 10},
                "showactive": True,
                "x": 0.25,
                "xanchor": "left",
                "y": 1.1,
                "yanchor": "top",
            }
        ],
        annotations=[
            dict(
                text="Select Feature Name ",
                showarrow=False,
                x=0,
                y=1.05,
                yref="paper",
                xref="paper",
                align="left",
                xanchor="left",
                yanchor="top",
                font={
                    "color": "blue",
                },
            )
        ],
    )

    fig.update_layout(
        width=600,
        height=400,
        autosize=False,
        margin=dict(t=100, b=0, l=0, r=0),
        template="plotly_white",
    )

    fig.update_layout(title_text=f"<i><b>Histograms of {first_feature_name}</b></i>")
    extra_data[f"histograms"] = context.log_artifact(
        PlotlyArtifact(key=f"histograms", figure=fig),
        local_path=f"{plots_dest}/histograms.html",
    )


def _create_features_2d_scatter_artifacts(
    context: MLClientCtx,
    df: pd.DataFrame,
    extra_data: dict,
    label_column: str,
    plots_dest: str,
    problem_type: str,
):
    """
    Create and log a scatter-2d artifact for each couple of features
    """
    features = [
        column_name for column_name in df.columns if column_name != label_column
    ]
    max_feature_len = float(max(len(elem) for elem in features))
    if label_column is not None:
        labels = sorted(df[label_column].unique())
    else:
        labels = [None]
    fig = go.Figure()
    if label_column is not None and problem_type == "classification":
        for l in labels:
            fig.add_trace(
                go.Scatter(
                    x=df.loc[df[label_column] == l][features[0]],
                    y=df.loc[df[label_column] == l][features[0]],
                    mode="markers",
                    visible=True,
                    showlegend=True,
                    name=str(l),
                )
            )
    elif label_column is None:
        fig.add_trace(
            go.Scatter(
                x=df[features[0]],
                y=df[features[0]],
                mode="markers",
                visible=True,
            )
        )
    elif problem_type == "regression":
        fig.add_trace(
            go.Scatter(
                x=df[features[0]],
                y=df[features[0]],
                mode="markers",
                marker=dict(
                    color=df[label_column], colorscale="Viridis", showscale=True
                ),
                visible=True,
            )
        )

    x_buttons = []
    y_buttons = []

    for ncol in features:
        if problem_type == "classification" and label_column is not None:
            x_buttons.append(
                dict(
                    method="update",
                    label=ncol,
                    args=[
                        {"x": [df.loc[df[label_column] == l][ncol] for l in labels]},
                        np.arange(len(labels)).tolist(),
                    ],
                )
            )

            y_buttons.append(
                dict(
                    method="update",
                    label=ncol,
                    args=[
                        {"y": [df.loc[df[label_column] == l][ncol] for l in labels]},
                        np.arange(len(labels)).tolist(),
                    ],
                )
            )
        else:
            x_buttons.append(
                dict(method="update", label=ncol, args=[{"x": [df[ncol]]}])
            )

            y_buttons.append(
                dict(method="update", label=ncol, args=[{"y": [df[ncol]]}])
            )

    # Pass buttons to the updatemenus argument
    fig.update_layout(
        updatemenus=[
            dict(buttons=x_buttons, direction="up", x=0.5, y=-0.1),
            dict(buttons=y_buttons, direction="down", x=-max_feature_len / 100, y=0.5),
        ]
    )

    fig.update_layout(
        width=600,
        height=400,
        autosize=False,
        margin=dict(t=100, b=0, l=0, r=0),
        template="plotly_white",
    )

    fig.update_layout(title_text=f"<i><b>Scatter-2d</b></i>")
    extra_data[f"scatter-2d"] = context.log_artifact(
        PlotlyArtifact(key=f"scatter-2d", figure=fig),
        local_path=f"{plots_dest}/scatter-2d.html",
    )


def _create_violin_artifact(
    context: MLClientCtx, df: pd.DataFrame, extra_data: dict, plots_dest: str
):
    """
    Create and log a violin artifact
    """
    cols = 5
    rows = (df.shape[1] // cols) + 1
    fig = make_subplots(rows=rows, cols=cols)

    plot_num = 0

    for column_name in df.columns:
        column_data = df[column_name]
        violin = go.Violin(
            x=[column_name] * column_data.shape[0],
            y=column_data,
            name=column_name,
        )

        fig.add_trace(
            violin,
            row=(plot_num // cols) + 1,
            col=(plot_num % cols) + 1,
        )

        plot_num += 1

    fig["layout"].update(
        height=(rows + 1) * 200,
        width=(cols + 1) * 200,
        title="<i><b>Violin Plots</b></i>",
    )

    fig.update_layout(showlegend=False)
    extra_data["violin"] = context.log_artifact(
        PlotlyArtifact(key="violin", figure=fig),
        local_path=f"{plots_dest}/violin.html",
    )


def _create_imbalance_artifact(
    context: MLClientCtx,
    df: pd.DataFrame,
    extra_data: dict,
    label_column: str,
    plots_dest: str,
    problem_type: str,
):
    """
    Create and log an imbalance class artifact (csv + plot)
    """
    if label_column:
        if problem_type == "classification":
            values_column = "count"
            labels_count = df[label_column].value_counts().sort_index()
            df_labels_count = pd.DataFrame(labels_count)
            df_labels_count[label_column] = labels_count.index
            df_labels_count.rename(columns={"": values_column}, inplace=True)
            df_labels_count[values_column] = df_labels_count[values_column] / sum(
                df_labels_count[values_column]
            )
            fig = px.pie(df_labels_count, names=label_column, values=values_column)
        else:
            fig = px.histogram(
                histfunc="count",
                x=df[label_column],
            )
            hist = np.histogram(df[label_column])
            df_labels_count = pd.DataFrame(
                {"min_val": hist[1], "count": hist[0].tolist() + [0]}
            )
        fig.update_layout(title_text="<i><b>Labels Imbalance</b></i>")
        extra_data["imbalance"] = context.log_artifact(
            PlotlyArtifact(key="imbalance", figure=fig),
            local_path=f"{plots_dest}/imbalance.html",
        )
        extra_data["imbalance-csv"] = context.log_artifact(
            TableArtifact("imbalance-weights-vec", df=df_labels_count),
            local_path=f"{plots_dest}/imbalance-weights-vec.csv",
        )


def _create_corr_artifact(
    context: MLClientCtx,
    df: pd.DataFrame,
    extra_data: dict,
    label_column: str,
    plots_dest: str,
):
    """
    Create and log an correlation-matrix artifact (csv + plot)
    """
    if label_column is not None:
        df = df.drop([label_column], axis=1)
    tblcorr = df.corr(numeric_only=True)
    extra_data["correlation-matrix-csv"] = context.log_artifact(
        TableArtifact("correlation-matrix-csv", df=tblcorr, visible=True),
        local_path=f"{plots_dest}/correlation-matrix.csv",
    )

    z = tblcorr.values.tolist()
    z_text = [["{:.2f}".format(y) for y in x] for x in z]
    fig = ff.create_annotated_heatmap(
        z,
        x=list(tblcorr.columns),
        y=list(tblcorr.columns),
        annotation_text=z_text,
        colorscale="agsunset",
    )
    fig["layout"]["yaxis"]["autorange"] = "reversed"  # l -> r
    fig.update_layout(title_text="<i><b>Correlation matrix</b></i>")
    fig["data"][0]["showscale"] = True

    extra_data["correlation"] = context.log_artifact(
        PlotlyArtifact(key="correlation", figure=fig),
        local_path=f"{plots_dest}/correlation.html",
    )
 + origin_filename: '' + code_origin: '' + image: mlrun/mlrun + command: '' +metadata: + tag: '' + name: describe + categories: + - data-analysis diff --git a/describe/item.yaml b/describe/item.yaml index 4703771b7..47f36787f 100644 --- a/describe/item.yaml +++ b/describe/item.yaml @@ -11,7 +11,7 @@ labels: author: Davids maintainers: [] marketplaceType: '' -mlrunVersion: 1.4.1 +mlrunVersion: 1.6.0 name: describe platformVersion: 3.5.3 spec: @@ -21,4 +21,4 @@ spec: kind: job requirements: [] url: '' -version: 1.2.0 +version: 1.3.0 diff --git a/describe/requirements.txt b/describe/requirements.txt index 8dbc3e68b..a96b6ff1b 100644 --- a/describe/requirements.txt +++ b/describe/requirements.txt @@ -1,6 +1,5 @@ scikit-learn~=1.0.2 plotly~=5.16.1 pytest~=7.0.1 -pandas~=1.3.5 matplotlib~=3.5.1 seaborn~=0.11.2 diff --git a/describe/test_describe.py b/describe/test_describe.py index 1a2270a86..9ffe39abb 100644 --- a/describe/test_describe.py +++ b/describe/test_describe.py @@ -271,79 +271,3 @@ def _create_data(n_samples, n_features, n_classes, n_informative, reg=False): df["timestamp"] = [pd.Timestamp("2022").now()] * n_samples df.to_parquet("artifacts/random_dataset.parquet") return df - - -def _create_dask_func(uri): - dask_cluster_name = "dask-cluster" - dask_cluster = new_function(dask_cluster_name, kind="dask", image="mlrun/ml-models") - dask_cluster.spec.remote = False - dask_uri = uri - dask_cluster.export(dask_uri) - - -def test_import_function_describe_dask(): - dask_uri = "dask_func.yaml" - _create_dask_func(dask_uri) - describe_func = import_function("function.yaml") - is_test_passed = True - _create_data(n_samples=100, n_features=5, n_classes=3, n_informative=3) - describe_func.spec.command = "describe_dask.py" - - try: - describe_run = describe_func.run( - name="task-describe", - handler="analyze", - inputs={"table": DATA_PATH}, - params={ - "label_column": "label", - "dask_function": dask_uri, - "dask_flag": True, - }, - artifact_path=os.path.abspath("./artifacts"), - local=True, - ) - - except Exception as exception: - print(f"- The test failed - raised the following error:\n- {exception}") - is_test_passed = False - _validate_paths( - { - "imbalance.html", - "imbalance-weights-vec.csv", - } - ) - assert is_test_passed - - -def test_code_to_function_describe_dask(): - dask_uri = "dask_func.yaml" - _create_dask_func(dask_uri) - describe_func = code_to_function(filename="describe.py", kind="local") - is_test_passed = True - _create_data(n_samples=100, n_features=5, n_classes=3, n_informative=3) - describe_func.spec.command = "describe_dask.py" - - try: - describe_run = describe_func.run( - name="task-describe", - handler="analyze", - inputs={"table": DATA_PATH}, - params={ - "label_column": "label", - "dask_function": dask_uri, - "dask_flag": True, - }, - artifact_path=os.path.abspath("./artifacts"), - local=True, - ) - - except Exception as exception: - print(f"- The test failed - raised the following error:\n- {exception}") - is_test_passed = False - _validate_paths( - { - "imbalance.html", - "imbalance-weights-vec.csv", - } - ) - assert is_test_passed diff --git a/feature_selection/feature_selection.py b/feature_selection/feature_selection.py index 630a09694..30fa8f904 100644 --- a/feature_selection/feature_selection.py +++ b/feature_selection/feature_selection.py @@ -13,17 +13,15 @@ # limitations under the License. # import json -import os -import matplotlib.pyplot as plt import mlrun import mlrun.datastore -import mlrun.utils import mlrun.feature_store as fs +import mlrun.utils import numpy as np import pandas as pd -import seaborn as sns -from mlrun.artifacts import PlotArtifact +import plotly.express as px +from mlrun.artifacts import PlotlyArtifact from mlrun.datastore.targets import ParquetTarget # MLRun utils from mlrun.utils.helpers import create_class @@ -42,15 +40,6 @@ } -def _clear_current_figure(): - """ - Clear matplotlib current figure. - """ - plt.cla() - plt.clf() - plt.close() - - def show_values_on_bars(axs, h_v="v", space=0.4): def _show_on_single_plot(ax_): if h_v == "v": @@ -74,33 +63,18 @@ def _show_on_single_plot(ax_): def plot_stat(context, stat_name, stat_df): - _clear_current_figure() - - # Add chart - ax = plt.axes() - stat_chart = sns.barplot( + sorted_df = stat_df.sort_values(stat_name) + fig = px.bar( + data_frame=sorted_df, x=stat_name, - y="index", - data=stat_df.sort_values(stat_name, ascending=False).reset_index(), - ax=ax, + y=sorted_df.index, + title=f"{stat_name} feature scores", + color=stat_name, ) - plt.tight_layout() - - for p in stat_chart.patches: - width = p.get_width() - plt.text( - 5 + p.get_width(), - p.get_y() + 0.55 * p.get_height(), - "{:1.2f}".format(width), - ha="center", - va="center", - ) - context.log_artifact( - PlotArtifact(f"{stat_name}", body=plt.gcf()), - local_path=os.path.join("plots", "feature_selection", f"{stat_name}.html"), + item=PlotlyArtifact(key=stat_name, figure=fig), + local_path=f"{stat_name}.html", ) - _clear_current_figure() def feature_selection( @@ -115,7 +89,6 @@ def feature_selection( sample_ratio: float = None, output_vector_name: float = None, ignore_type_errors: bool = False, - is_feature_vector: bool = False, ): """ Applies selected feature selection statistical functions or models on our 'df_artifact'. @@ -138,10 +111,9 @@ def feature_selection( model name (ex. LinearSVC), formalized json (contains 'CLASS', 'FIT', 'META') or a path to such json file. :param max_scaled_scores: produce feature scores table scaled with max_scaler. - :param sample_ratio: percentage of the dataset the user whishes to compute the feature selection process on. + :param sample_ratio: percentage of the dataset the user wishes to compute the feature selection process on. :param output_vector_name: creates a new feature vector containing only the identifies features. :param ignore_type_errors: skips datatypes that are neither float nor int within the feature vector. - :param is_feature_vector: bool stating if the data is passed as a feature vector. """ stat_filters = stat_filters or DEFAULT_STAT_FILTERS model_filters = model_filters or DEFAULT_MODEL_FILTERS diff --git a/feature_selection/function.yaml b/feature_selection/function.yaml index f1bf53b8a..44cdd9894 100644 --- a/feature_selection/function.yaml +++ b/feature_selection/function.yaml @@ -73,7 +73,7 @@ spec: default: true - name: sample_ratio type: float - doc: percentage of the dataset the user whishes to compute the feature selection + doc: percentage of the dataset the user wishes to compute the feature selection process on. default: null - name: output_vector_name @@ -95,7 +95,7 @@ spec: command: '' build: origin_filename: '' - functionSourceCode: # Copyright 2019 Iguazio
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import json
import os

import matplotlib.pyplot as plt
import mlrun
import mlrun.datastore
import mlrun.utils
import mlrun.feature_store as fs
import numpy as np
import pandas as pd
import seaborn as sns
from mlrun.artifacts import PlotArtifact
from mlrun.datastore.targets import ParquetTarget
# MLRun utils
from mlrun.utils.helpers import create_class
# Feature selection strategies
from sklearn.feature_selection import SelectFromModel, SelectKBest
# Scale feature scoresgit st
from sklearn.preprocessing import MinMaxScaler
# SKLearn estimators list
from sklearn.utils import all_estimators

DEFAULT_STAT_FILTERS = ["f_classif", "mutual_info_classif", "chi2", "f_regression"]
DEFAULT_MODEL_FILTERS = {
    "LinearSVC": "LinearSVC",
    "LogisticRegression": "LogisticRegression",
    "ExtraTreesClassifier": "ExtraTreesClassifier",
}


def _clear_current_figure():
    """
    Clear matplotlib current figure.
    """
    plt.cla()
    plt.clf()
    plt.close()


def show_values_on_bars(axs, h_v="v", space=0.4):
    def _show_on_single_plot(ax_):
        if h_v == "v":
            for p in ax_.patches:
                _x = p.get_x() + p.get_width() / 2
                _y = p.get_y() + p.get_height()
                value = int(p.get_height())
                ax_.text(_x, _y, value, ha="center")
        elif h_v == "h":
            for p in ax_.patches:
                _x = p.get_x() + p.get_width() + float(space)
                _y = p.get_y() + p.get_height()
                value = int(p.get_width())
                ax_.text(_x, _y, value, ha="left")

    if isinstance(axs, np.ndarray):
        for idx, ax in np.ndenumerate(axs):
            _show_on_single_plot(ax)
    else:
        _show_on_single_plot(axs)


def plot_stat(context, stat_name, stat_df):
    _clear_current_figure()

    # Add chart
    ax = plt.axes()
    stat_chart = sns.barplot(
        x=stat_name,
        y="index",
        data=stat_df.sort_values(stat_name, ascending=False).reset_index(),
        ax=ax,
    )
    plt.tight_layout()

    for p in stat_chart.patches:
        width = p.get_width()
        plt.text(
            5 + p.get_width(),
            p.get_y() + 0.55 * p.get_height(),
            "{:1.2f}".format(width),
            ha="center",
            va="center",
        )

    context.log_artifact(
        PlotArtifact(f"{stat_name}", body=plt.gcf()),
        local_path=os.path.join("plots", "feature_selection", f"{stat_name}.html"),
    )
    _clear_current_figure()


def feature_selection(
    context,
    df_artifact,
    k: int = 5,
    min_votes: float = 0.5,
    label_column: str = None,
    stat_filters: list = None,
    model_filters: dict = None,
    max_scaled_scores: bool = True,
    sample_ratio: float = None,
    output_vector_name: float = None,
    ignore_type_errors: bool = False,
    is_feature_vector: bool = False,
):
    """
    Applies selected feature selection statistical functions or models on our 'df_artifact'.

    Each statistical function or model will vote for it's best K selected features.
    If a feature has >= 'min_votes' votes, it will be selected.

    :param context:             the function context.
    :param df_artifact:         dataframe to pass as input.
    :param k:                   number of top features to select from each statistical
                                function or model.
    :param min_votes:           minimal number of votes (from a model or by statistical
                                function) needed for a feature to be selected.
                                Can be specified by percentage of votes or absolute
                                number of votes.
    :param label_column:        ground-truth (y) labels.
    :param stat_filters:        statistical functions to apply to the features
                                (from sklearn.feature_selection).
    :param model_filters:       models to use for feature evaluation, can be specified by
                                model name (ex. LinearSVC), formalized json (contains 'CLASS',
                                'FIT', 'META') or a path to such json file.
    :param max_scaled_scores:   produce feature scores table scaled with max_scaler.
    :param sample_ratio:        percentage of the dataset the user whishes to compute the feature selection process on.
    :param output_vector_name:  creates a new feature vector containing only the identifies features.
    :param ignore_type_errors:  skips datatypes that are neither float nor int within the feature vector.
    :param is_feature_vector:   bool stating if the data is passed as a feature vector.
    """
    stat_filters = stat_filters or DEFAULT_STAT_FILTERS
    model_filters = model_filters or DEFAULT_MODEL_FILTERS
    # Check if df.meta is valid, if it is, look for a feature vector
    store_uri_prefix, _ = mlrun.datastore.parse_store_uri(df_artifact.artifact_url)
    is_feature_vector = mlrun.utils.StorePrefix.FeatureVector == store_uri_prefix

    # Look inside meta.spec.label_feature to identify the label_column if the user did not specify it
    if label_column is None:
        if is_feature_vector:
            label_column = df_artifact.meta.spec.label_feature.split(".")[1]
        else:
            raise ValueError("No label_column was given, please add a label_column.")

    # Use the feature vector as dataframe
    df = df_artifact.as_df()

    # Ensure k is not bigger than the total number of features
    if k > df.shape[1]:
        raise ValueError(
            f"K cannot be bigger than the total number of features ({df.shape[1]}). Please choose a smaller K."
        )
    elif k < 1:
        raise ValueError("K cannot be smaller than 1. Please choose a bigger K.")

    # Create a sample dataframe of the original feature vector
    if sample_ratio:
        df = (
            df.groupby(label_column)
            .apply(lambda x: x.sample(frac=sample_ratio))
            .reset_index(drop=True)
        )
        df = df.dropna()

    # Set feature vector and labels
    y = df.pop(label_column)
    X = df

    if np.object_ in list(X.dtypes) and ignore_type_errors is False:
        raise ValueError(
            f"{df.select_dtypes(include=['object']).columns.tolist()} are neither float or int."
        )

    # Create selected statistical estimators
    stat_functions_list = {
        stat_name: SelectKBest(
            score_func=create_class(f"sklearn.feature_selection.{stat_name}"), k=k
        )
        for stat_name in stat_filters
    }
    requires_abs = ["chi2"]

    # Run statistic filters
    selected_features_agg = {}
    stats_df = pd.DataFrame(index=X.columns).dropna()

    for stat_name, stat_func in stat_functions_list.items():
        try:
            params = (X, y) if stat_name in requires_abs else (abs(X), y)
            stat = stat_func.fit(*params)

            # Collect stat function results
            stat_df = pd.DataFrame(
                index=X.columns, columns=[stat_name], data=stat.scores_
            )
            plot_stat(context, stat_name, stat_df)
            stats_df = stats_df.join(stat_df)

            # Select K Best features
            selected_features = X.columns[stat_func.get_support()]
            selected_features_agg[stat_name] = selected_features

        except Exception as e:
            context.logger.info(f"Couldn't calculate {stat_name} because of: {e}")

    # Create models from class name / json file / json params
    all_sklearn_estimators = dict(all_estimators()) if len(model_filters) > 0 else {}
    selected_models = {}
    for model_name, model in model_filters.items():
        if ".json" in model:
            current_model = json.load(open(model, "r"))
            classifier_class = create_class(current_model["META"]["class"])
            selected_models[model_name] = classifier_class(**current_model["CLASS"])
        elif model in all_sklearn_estimators:
            selected_models[model_name] = all_sklearn_estimators[model_name]()

        else:
            try:
                current_model = json.loads(model)
                classifier_class = create_class(current_model["META"]["class"])
                selected_models[model_name] = classifier_class(**current_model["CLASS"])
            except Exception as e:
                context.logger.info(f"unable to load {model} because of: {e}")

    # Run model filters
    models_df = pd.DataFrame(index=X.columns)
    for model_name, model in selected_models.items():

        if model_name == "LogisticRegression":
            model.set_params(solver="liblinear")

        # Train model and get feature importance
        select_from_model = SelectFromModel(model).fit(X, y)
        feature_idx = select_from_model.get_support()
        feature_names = X.columns[feature_idx]
        selected_features_agg[model_name] = feature_names.tolist()

        # Collect model feature importance
        if hasattr(select_from_model.estimator_, "coef_"):
            stat_df = select_from_model.estimator_.coef_
        elif hasattr(select_from_model.estimator_, "feature_importances_"):
            stat_df = select_from_model.estimator_.feature_importances_

        stat_df = pd.DataFrame(index=X.columns, columns=[model_name], data=stat_df[0])
        models_df = models_df.join(stat_df)

        plot_stat(context, model_name, stat_df)

    # Create feature_scores DF with stat & model filters scores
    result_matrix_df = pd.concat([stats_df, models_df], axis=1, sort=False)
    context.log_dataset(
        key="feature_scores",
        df=result_matrix_df,
        local_path="feature_scores.parquet",
        format="parquet",
    )
    if max_scaled_scores:
        normalized_df = result_matrix_df.replace([np.inf, -np.inf], np.nan).values
        min_max_scaler = MinMaxScaler()
        normalized_df = min_max_scaler.fit_transform(normalized_df)
        normalized_df = pd.DataFrame(
            data=normalized_df,
            columns=result_matrix_df.columns,
            index=result_matrix_df.index,
        )
        context.log_dataset(
            key="max_scaled_scores_feature_scores",
            df=normalized_df,
            local_path="max_scaled_scores_feature_scores.parquet",
            format="parquet",
        )

    # Create feature count DataFrame
    for test_name in selected_features_agg:
        result_matrix_df[test_name] = [
            1 if x in selected_features_agg[test_name] else 0 for x in X.columns
        ]
    result_matrix_df.loc[:, "num_votes"] = result_matrix_df.sum(axis=1)
    context.log_dataset(
        key="selected_features_count",
        df=result_matrix_df,
        local_path="selected_features_count.parquet",
        format="parquet",
    )

    # How many votes are needed for a feature to be selected?
    if isinstance(min_votes, int):
        votes_needed = min_votes
    else:
        num_filters = len(stat_filters) + len(model_filters)
        votes_needed = int(np.floor(num_filters * max(min(min_votes, 1), 0)))
    context.logger.info(f"votes needed to be selected: {votes_needed}")

    # Create final feature dataframe
    selected_features = result_matrix_df[
        result_matrix_df.num_votes >= votes_needed
    ].index.tolist()
    good_feature_df = df.loc[:, selected_features]
    final_df = pd.concat([good_feature_df, y], axis=1)
    context.log_dataset(
        key="selected_features",
        df=final_df,
        local_path="selected_features.parquet",
        format="parquet",
    )

    # Creating a new feature vector containing only the identified top features
    if is_feature_vector and df_artifact.meta.spec.features and output_vector_name:
        # Selecting the top K features from our top feature dataframe
        selected_features = result_matrix_df.head(k).index

        # Match the selected feature names to the FS Feature annotations
        matched_selections = [
            feature
            for feature in list(df_artifact.meta.spec.features)
            for selected in list(selected_features)
            if feature.endswith(selected)
        ]

        # Defining our new feature vector
        top_features_fv = fs.FeatureVector(
            output_vector_name,
            matched_selections,
            label_feature="labels.label",
            description="feature vector composed strictly of our top features",
        )

        # Saving
        top_features_fv.save()
        fs.get_offline_features(top_features_fv, target=ParquetTarget())

        # Logging our new feature vector URI
        context.log_result("top_features_vector", top_features_fv.uri)
 + functionSourceCode: # Copyright 2019 Iguazio
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import json

import mlrun
import mlrun.datastore
import mlrun.feature_store as fs
import mlrun.utils
import numpy as np
import pandas as pd
import plotly.express as px
from mlrun.artifacts import PlotlyArtifact
from mlrun.datastore.targets import ParquetTarget
# MLRun utils
from mlrun.utils.helpers import create_class
# Feature selection strategies
from sklearn.feature_selection import SelectFromModel, SelectKBest
# Scale feature scoresgit st
from sklearn.preprocessing import MinMaxScaler
# SKLearn estimators list
from sklearn.utils import all_estimators

DEFAULT_STAT_FILTERS = ["f_classif", "mutual_info_classif", "chi2", "f_regression"]
DEFAULT_MODEL_FILTERS = {
    "LinearSVC": "LinearSVC",
    "LogisticRegression": "LogisticRegression",
    "ExtraTreesClassifier": "ExtraTreesClassifier",
}


def show_values_on_bars(axs, h_v="v", space=0.4):
    def _show_on_single_plot(ax_):
        if h_v == "v":
            for p in ax_.patches:
                _x = p.get_x() + p.get_width() / 2
                _y = p.get_y() + p.get_height()
                value = int(p.get_height())
                ax_.text(_x, _y, value, ha="center")
        elif h_v == "h":
            for p in ax_.patches:
                _x = p.get_x() + p.get_width() + float(space)
                _y = p.get_y() + p.get_height()
                value = int(p.get_width())
                ax_.text(_x, _y, value, ha="left")

    if isinstance(axs, np.ndarray):
        for idx, ax in np.ndenumerate(axs):
            _show_on_single_plot(ax)
    else:
        _show_on_single_plot(axs)


def plot_stat(context, stat_name, stat_df):
    sorted_df = stat_df.sort_values(stat_name)
    fig = px.bar(
        data_frame=sorted_df,
        x=stat_name,
        y=sorted_df.index,
        title=f"{stat_name} feature scores",
        color=stat_name,
    )
    context.log_artifact(
        item=PlotlyArtifact(key=stat_name, figure=fig),
        local_path=f"{stat_name}.html",
    )


def feature_selection(
    context,
    df_artifact,
    k: int = 5,
    min_votes: float = 0.5,
    label_column: str = None,
    stat_filters: list = None,
    model_filters: dict = None,
    max_scaled_scores: bool = True,
    sample_ratio: float = None,
    output_vector_name: float = None,
    ignore_type_errors: bool = False,
):
    """
    Applies selected feature selection statistical functions or models on our 'df_artifact'.

    Each statistical function or model will vote for it's best K selected features.
    If a feature has >= 'min_votes' votes, it will be selected.

    :param context:             the function context.
    :param df_artifact:         dataframe to pass as input.
    :param k:                   number of top features to select from each statistical
                                function or model.
    :param min_votes:           minimal number of votes (from a model or by statistical
                                function) needed for a feature to be selected.
                                Can be specified by percentage of votes or absolute
                                number of votes.
    :param label_column:        ground-truth (y) labels.
    :param stat_filters:        statistical functions to apply to the features
                                (from sklearn.feature_selection).
    :param model_filters:       models to use for feature evaluation, can be specified by
                                model name (ex. LinearSVC), formalized json (contains 'CLASS',
                                'FIT', 'META') or a path to such json file.
    :param max_scaled_scores:   produce feature scores table scaled with max_scaler.
    :param sample_ratio:        percentage of the dataset the user wishes to compute the feature selection process on.
    :param output_vector_name:  creates a new feature vector containing only the identifies features.
    :param ignore_type_errors:  skips datatypes that are neither float nor int within the feature vector.
    """
    stat_filters = stat_filters or DEFAULT_STAT_FILTERS
    model_filters = model_filters or DEFAULT_MODEL_FILTERS
    # Check if df.meta is valid, if it is, look for a feature vector
    store_uri_prefix, _ = mlrun.datastore.parse_store_uri(df_artifact.artifact_url)
    is_feature_vector = mlrun.utils.StorePrefix.FeatureVector == store_uri_prefix

    # Look inside meta.spec.label_feature to identify the label_column if the user did not specify it
    if label_column is None:
        if is_feature_vector:
            label_column = df_artifact.meta.spec.label_feature.split(".")[1]
        else:
            raise ValueError("No label_column was given, please add a label_column.")

    # Use the feature vector as dataframe
    df = df_artifact.as_df()

    # Ensure k is not bigger than the total number of features
    if k > df.shape[1]:
        raise ValueError(
            f"K cannot be bigger than the total number of features ({df.shape[1]}). Please choose a smaller K."
        )
    elif k < 1:
        raise ValueError("K cannot be smaller than 1. Please choose a bigger K.")

    # Create a sample dataframe of the original feature vector
    if sample_ratio:
        df = (
            df.groupby(label_column)
            .apply(lambda x: x.sample(frac=sample_ratio))
            .reset_index(drop=True)
        )
        df = df.dropna()

    # Set feature vector and labels
    y = df.pop(label_column)
    X = df

    if np.object_ in list(X.dtypes) and ignore_type_errors is False:
        raise ValueError(
            f"{df.select_dtypes(include=['object']).columns.tolist()} are neither float or int."
        )

    # Create selected statistical estimators
    stat_functions_list = {
        stat_name: SelectKBest(
            score_func=create_class(f"sklearn.feature_selection.{stat_name}"), k=k
        )
        for stat_name in stat_filters
    }
    requires_abs = ["chi2"]

    # Run statistic filters
    selected_features_agg = {}
    stats_df = pd.DataFrame(index=X.columns).dropna()

    for stat_name, stat_func in stat_functions_list.items():
        try:
            params = (X, y) if stat_name in requires_abs else (abs(X), y)
            stat = stat_func.fit(*params)

            # Collect stat function results
            stat_df = pd.DataFrame(
                index=X.columns, columns=[stat_name], data=stat.scores_
            )
            plot_stat(context, stat_name, stat_df)
            stats_df = stats_df.join(stat_df)

            # Select K Best features
            selected_features = X.columns[stat_func.get_support()]
            selected_features_agg[stat_name] = selected_features

        except Exception as e:
            context.logger.info(f"Couldn't calculate {stat_name} because of: {e}")

    # Create models from class name / json file / json params
    all_sklearn_estimators = dict(all_estimators()) if len(model_filters) > 0 else {}
    selected_models = {}
    for model_name, model in model_filters.items():
        if ".json" in model:
            current_model = json.load(open(model, "r"))
            classifier_class = create_class(current_model["META"]["class"])
            selected_models[model_name] = classifier_class(**current_model["CLASS"])
        elif model in all_sklearn_estimators:
            selected_models[model_name] = all_sklearn_estimators[model_name]()

        else:
            try:
                current_model = json.loads(model)
                classifier_class = create_class(current_model["META"]["class"])
                selected_models[model_name] = classifier_class(**current_model["CLASS"])
            except Exception as e:
                context.logger.info(f"unable to load {model} because of: {e}")

    # Run model filters
    models_df = pd.DataFrame(index=X.columns)
    for model_name, model in selected_models.items():

        if model_name == "LogisticRegression":
            model.set_params(solver="liblinear")

        # Train model and get feature importance
        select_from_model = SelectFromModel(model).fit(X, y)
        feature_idx = select_from_model.get_support()
        feature_names = X.columns[feature_idx]
        selected_features_agg[model_name] = feature_names.tolist()

        # Collect model feature importance
        if hasattr(select_from_model.estimator_, "coef_"):
            stat_df = select_from_model.estimator_.coef_
        elif hasattr(select_from_model.estimator_, "feature_importances_"):
            stat_df = select_from_model.estimator_.feature_importances_

        stat_df = pd.DataFrame(index=X.columns, columns=[model_name], data=stat_df[0])
        models_df = models_df.join(stat_df)

        plot_stat(context, model_name, stat_df)

    # Create feature_scores DF with stat & model filters scores
    result_matrix_df = pd.concat([stats_df, models_df], axis=1, sort=False)
    context.log_dataset(
        key="feature_scores",
        df=result_matrix_df,
        local_path="feature_scores.parquet",
        format="parquet",
    )
    if max_scaled_scores:
        normalized_df = result_matrix_df.replace([np.inf, -np.inf], np.nan).values
        min_max_scaler = MinMaxScaler()
        normalized_df = min_max_scaler.fit_transform(normalized_df)
        normalized_df = pd.DataFrame(
            data=normalized_df,
            columns=result_matrix_df.columns,
            index=result_matrix_df.index,
        )
        context.log_dataset(
            key="max_scaled_scores_feature_scores",
            df=normalized_df,
            local_path="max_scaled_scores_feature_scores.parquet",
            format="parquet",
        )

    # Create feature count DataFrame
    for test_name in selected_features_agg:
        result_matrix_df[test_name] = [
            1 if x in selected_features_agg[test_name] else 0 for x in X.columns
        ]
    result_matrix_df.loc[:, "num_votes"] = result_matrix_df.sum(axis=1)
    context.log_dataset(
        key="selected_features_count",
        df=result_matrix_df,
        local_path="selected_features_count.parquet",
        format="parquet",
    )

    # How many votes are needed for a feature to be selected?
    if isinstance(min_votes, int):
        votes_needed = min_votes
    else:
        num_filters = len(stat_filters) + len(model_filters)
        votes_needed = int(np.floor(num_filters * max(min(min_votes, 1), 0)))
    context.logger.info(f"votes needed to be selected: {votes_needed}")

    # Create final feature dataframe
    selected_features = result_matrix_df[
        result_matrix_df.num_votes >= votes_needed
    ].index.tolist()
    good_feature_df = df.loc[:, selected_features]
    final_df = pd.concat([good_feature_df, y], axis=1)
    context.log_dataset(
        key="selected_features",
        df=final_df,
        local_path="selected_features.parquet",
        format="parquet",
    )

    # Creating a new feature vector containing only the identified top features
    if is_feature_vector and df_artifact.meta.spec.features and output_vector_name:
        # Selecting the top K features from our top feature dataframe
        selected_features = result_matrix_df.head(k).index

        # Match the selected feature names to the FS Feature annotations
        matched_selections = [
            feature
            for feature in list(df_artifact.meta.spec.features)
            for selected in list(selected_features)
            if feature.endswith(selected)
        ]

        # Defining our new feature vector
        top_features_fv = fs.FeatureVector(
            output_vector_name,
            matched_selections,
            label_feature="labels.label",
            description="feature vector composed strictly of our top features",
        )

        # Saving
        top_features_fv.save()
        fs.get_offline_features(top_features_fv, target=ParquetTarget())

        # Logging our new feature vector URI
        context.log_result("top_features_vector", top_features_fv.uri)
 code_origin: '' default_handler: feature_selection image: mlrun/mlrun diff --git a/feature_selection/requirements.txt b/feature_selection/requirements.txt index 70a079c7d..e4d79d180 100644 --- a/feature_selection/requirements.txt +++ b/feature_selection/requirements.txt @@ -1,5 +1,3 @@ scikit-learn -matplotlib -seaborn scikit-plot - +plotly~=5.4.0 diff --git a/feature_selection/test_feature_selection.py b/feature_selection/test_feature_selection.py index 9cb5ca621..6ae949aab 100644 --- a/feature_selection/test_feature_selection.py +++ b/feature_selection/test_feature_selection.py @@ -12,14 +12,31 @@ # See the License for the specific language governing permissions and # limitations under the License. # -from mlrun import code_to_function -from pathlib import Path +import os import shutil +from pathlib import Path + +import mlrun -METRICS_PATH = 'data/metrics.pq' -ARTIFACTS_PATH = 'artifacts' -RUNS_PATH = 'runs' -SCHEDULES_PATH = 'schedules' +METRICS_PATH = "data/metrics.pq" +ARTIFACTS_PATH = "artifacts" +RUNS_PATH = "runs" +SCHEDULES_PATH = "schedules" +PLOTS_PATH = os.path.abspath("./artifacts/feature-selection-feature-selection/0") + + +def _validate_paths(paths): + """ + Check if all the expected plot are saved + """ + base_folder = PLOTS_PATH + for path in paths: + full_path = os.path.join(base_folder, path) + if Path(full_path).is_file(): + print(f"{path} exist") + else: + raise FileNotFoundError(f"{path} not found!") + return True def _delete_outputs(paths): @@ -29,20 +46,24 @@ def _delete_outputs(paths): def test_run_local_feature_selection(): - fn = code_to_function(name='test_run_local_feature_selection', - filename="feature_selection.py", - handler="feature_selection", - kind="local", - ) - fn.spec.command = "feature_selection.py" + fn = mlrun.import_function("function.yaml") run = fn.run( params={ - 'k': 2, - 'min_votes': 0.3, - 'label_column': 'is_error', + "k": 2, + "min_votes": 0.3, + "label_column": "is_error", }, - inputs={'df_artifact': 'data/metrics.pq'}, - artifact_path='artifacts/', + inputs={"df_artifact": "data/metrics.pq"}, + artifact_path="artifacts/", + local=True, + ) + assert _validate_paths( + [ + "chi2.html", + "f_classif.html", + "f_regression.html", + "mutual_info_classif.html", + ] ) - assert run.outputs['feature_scores'] and run.outputs['selected_features'] _delete_outputs({ARTIFACTS_PATH, RUNS_PATH, SCHEDULES_PATH}) + assert run.outputs['feature_scores'] and run.outputs['selected_features'] diff --git a/hugging_face_serving/function.yaml b/hugging_face_serving/function.yaml index e1bb3b0ce..764fc1cfe 100644 --- a/hugging_face_serving/function.yaml +++ b/hugging_face_serving/function.yaml @@ -2,11 +2,13 @@ kind: serving metadata: name: hugging-face-serving tag: '' - hash: 39bfca7b639022fa03f5ca87f85f9e17fc837b70 + hash: 1a489a57da861f129eb26e933f34e58927e41195 project: '' labels: author: yonish categories: + - huggingface + - genai - model-serving - machine-learning spec: @@ -14,37 +16,28 @@ spec: args: [] image: mlrun/ml-models build: - commands: - - python -m pip install transformers==4.21.3 tensorflow==2.9.2 - code_origin: https://github.com/mlrun/functions.git#250244b2527c5ce8a82438b4340df34de6e19dc3:/Users/yonatanshelach/yoni/projects/functions/hugging_face_serving/hugging_face_serving.py - origin_filename: /Users/yonatanshelach/yoni/projects/functions/hugging_face_serving/hugging_face_serving.py + functionSourceCode: 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+ commands: [] + code_origin: '' + origin_filename: '' + requirements: + - transformers==4.21.3 + - tensorflow==2.9.2 description: Generic Hugging Face model server. - default_handler: handler + default_handler: '' disable_auto_mount: false - env: [] + clone_target_dir: '' + env: + - name: MLRUN_HTTPDB__NUCLIO__EXPLICIT_ACK + value: enabled priority_class_name: '' preemption_mode: prevent min_replicas: 1 max_replicas: 4 - base_spec: - apiVersion: nuclio.io/v1 - kind: Function - metadata: - name: hugging-face-serving - labels: {} - annotations: - nuclio.io/generated_by: function generated from /Users/yonatanshelach/yoni/projects/functions/hugging_face_serving/hugging_face_serving.py - spec: - runtime: python - handler: hugging_face_serving:handler - env: [] - volumes: [] - build: - commands: [] - noBaseImagesPull: true - functionSourceCode: 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source: '' function_kind: serving_v2 + function_handler: hugging_face_serving:handler + base_image_pull: false default_class: HuggingFaceModelServer secret_sources: [] affinity: null diff --git a/hugging_face_serving/item.yaml b/hugging_face_serving/item.yaml index f7fa92637..d1f78769d 100644 --- a/hugging_face_serving/item.yaml +++ b/hugging_face_serving/item.yaml @@ -1,5 +1,7 @@ apiVersion: v1 categories: +- huggingface +- genai - model-serving - machine-learning description: Generic Hugging Face model server. @@ -26,4 +28,5 @@ spec: - transformers==4.21.3 - tensorflow==2.9.2 url: '' -version: 1.0.0 +version: 1.1.0 +test_valid: false \ No newline at end of file diff --git a/mlflow_utils/function.yaml b/mlflow_utils/function.yaml new file mode 100644 index 000000000..d2e2bffec --- /dev/null +++ b/mlflow_utils/function.yaml @@ -0,0 +1,31 @@ +metadata: + name: mlflow-utils + categories: + - genai + - model-serving + - machine-learning + tag: '' +spec: + default_handler: '' + image: mlrun/mlrun + command: '' + base_image_pull: false + default_class: MLFlowModelServer + function_handler: mlflow-utils:handler + disable_auto_mount: false + build: + origin_filename: '' + code_origin: '' + requirements: + - mlflow==2.12.2 + functionSourceCode: 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+ min_replicas: 1 + description: Mlflow model server, and additional utils. + max_replicas: 4 + source: '' + function_kind: serving_v2 + env: + - name: MLRUN_HTTPDB__NUCLIO__EXPLICIT_ACK + value: enabled +verbose: false +kind: serving diff --git a/mlflow_utils/item.yaml b/mlflow_utils/item.yaml new file mode 100644 index 000000000..bda09c5bb --- /dev/null +++ b/mlflow_utils/item.yaml @@ -0,0 +1,31 @@ +apiVersion: v1 +categories: +- genai +- model-serving +- machine-learning +description: Mlflow model server, and additional utils. +doc: '' +example: mlflow_utils.ipynb +generationDate: 2024-05-23:12-00 +hidden: false +icon: '' +labels: + author: zeevr +maintainers: [] +marketplaceType: '' +mlrunVersion: 1.7.0-rc17 +name: mlflow_utils +platformVersion: '' +spec: + customFields: + default_class: MLFlowModelServer + filename: mlflow_utils.py + handler: handler + image: mlrun/mlrun + kind: serving + requirements: + - mlflow==2.12.2 + - lightgbm + - xgboost +url: '' +version: 1.0.0 diff --git a/mlflow_utils/mlflow_utils.ipynb b/mlflow_utils/mlflow_utils.ipynb new file mode 100644 index 000000000..165dafc6f --- /dev/null +++ b/mlflow_utils/mlflow_utils.ipynb @@ -0,0 +1,1353 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c478ebb2", + "metadata": {}, + "source": [ + "# MLflow tracker demo\n", + "\n", + "This demo demonstrates how to seamlessly integrate and transfer logs from MLflow to MLRun,
\n", + "creating a unified and powerful platform for your machine learning experiments.\n", + "\n", + "You can combine MLflow and MLRun for a comprehensive solution for managing, tracking, and deploying machine learning models. \n", + "\n", + "This notebook guides you through the process of:\n", + "\n", + "1. Setting up the integration between MLflow and MLRun.\n", + "2. Extracting data, metrics, and artifacts from MLflow experiments.\n", + "3. Creating MLRun artifacts and projects to organize and manage the transferred data.\n", + "4. Leveraging MLRun's capabilities for model deployment and data processing.\n", + "\n", + "By the end of this demo, you will have a understanding of how to establish a smooth flow of data between MLflow and MLRun.\n", + "\n", + "## MLRun installation and configuration\n", + "Before running this notebook make sure the mlrun package is installed (pip install mlrun) and that you have configured the access to MLRun service." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "ab49e1f1", + "metadata": {}, + "outputs": [], + "source": [ + "# Install MLRun and scikit-learn if not already installed. Run this only once. Restart the notebook after the install!\n", + "# %pip install mlrun scikit-learn~=1.3.0" + ] + }, + { + "cell_type": "markdown", + "id": "1770566a", + "metadata": {}, + "source": [ + "Then you can import the necessary packages." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "0d2dfd8b-65c4-417b-b66e-99f44b015ee7", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import os\n", + "import mlrun\n", + "from mlrun.datastore.targets import ParquetTarget\n", + "import mlrun.feature_store as fstore" + ] + }, + { + "cell_type": "markdown", + "id": "7c4513d4", + "metadata": {}, + "source": [ + "Create a project for this demo:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "43ea863f-02d5-45f2-8143-306ce3bb6c58", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> 2024-03-27 15:34:40,940 [info] Project loaded successfully: {'project_name': 'mlflow-tracking-example-guy'}\n" + ] + } + ], + "source": [ + "# Create a project for this demo:\n", + "project = mlrun.get_or_create_project(name=\"mlflow-tracking-example\", context=\"./\")" + ] + }, + { + "cell_type": "markdown", + "id": "94413ee8", + "metadata": {}, + "source": [ + "Set all the necessary environment variables for the Databricks cluster:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "22f94f89-acce-442d-93ff-b2d08d3a35a4", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "DATABRICKS_HOST=\"add your host\"\n", + "DATABRICKS_TOKEN=\"add your token\"\n", + "DATABRICKS_CLUSTER_ID=\"add your cluster id\"" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "7af310da-fd02-444e-8619-43ba6dcdb0a4", + "metadata": {}, + "outputs": [], + "source": [ + "os.environ[\"DATABRICKS_HOST\"] = DATABRICKS_HOST\n", + "os.environ[\"DATABRICKS_TOKEN\"] = DATABRICKS_TOKEN\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "d98e823c-3a27-4532-9a2d-6398ea4e1778", + "metadata": {}, + "outputs": [], + "source": [ + "# Set the Databricks environment variables\n", + "job_env = {\n", + " \"DATABRICKS_HOST\": DATABRICKS_HOST,\n", + " \"DATABRICKS_CLUSTER_ID\": DATABRICKS_CLUSTER_ID\n", + "}\n", + "secrets = {\"DATABRICKS_TOKEN\": DATABRICKS_TOKEN}\n", + "\n", + "# Set the secrets in the project\n", + "project.set_secrets(secrets)" + ] + }, + { + "cell_type": "markdown", + "id": "37d75366", + "metadata": {}, + "source": [ + "## Create a feature set and ingest data\n", + "\n", + "This is a short example of how to create a feature set about music preferences." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "5701c04a-8442-4958-8f4c-265bf4c9b06a", + "metadata": {}, + "outputs": [], + "source": [ + "# create df\n", + "columns = [\"id\", \"name\", \"age\", \"gender\", \"favorite_music_type\"]\n", + "data = [\n", + " (1, \"Alice\", 20, \"f\", \"Pop\"),\n", + " (2, \"Bob\", 30, \"m\", \"Rock\"),\n", + " (3, \"Charlie\", 25, \"m\", \"Pop\"),\n", + " (4, \"David\", 40, \"m\", \"Classical\"),\n", + " (5, \"Eva\", 18, \"f\", \"Pop\"),\n", + " (6, \"Frank\", 32, \"m\", \"Rock\"),\n", + " (7, \"Grace\", 28, \"f\", \"Pop\"),\n", + " (8, \"Henry\", 45, \"m\", \"Classical\"),\n", + " (9, \"Ivy\", 22, \"f\", \"Pop\"),\n", + " (10, \"Jack\", 38, \"m\", \"Classical\"),\n", + " (11, \"Karen\", 27, \"f\", \"Pop\"),\n", + " (12, \"Liam\", 19, \"m\", \"Pop\"),\n", + " (13, \"Mia\", 27, \"f\", \"Rock\"),\n", + " (14, \"Nora\", 31, \"f\", \"Rock\"),\n", + " (15, \"Oliver\", 29, \"m\", \"Pop\"),\n", + " (16, \"Ben\", 38, \"m\", \"Pop\"),\n", + " (17, \"Alicia\", 20, \"f\", \"Pop\"),\n", + " (18, \"Bobby\", 30, \"m\", \"Rock\"),\n", + " (19, \"Charlien\", 22, \"f\", \"Pop\"),\n", + " (20, \"Davide\", 40, \"m\", \"Classical\"),\n", + " (21, \"Evans\", 19, \"m\", \"Pop\"),\n", + " (22, \"Franklin\", 34, \"m\", \"Rock\"),\n", + " (23, \"Grace\", 22, \"f\", \"Pop\"),\n", + " (24, \"Henrik\", 48, \"m\", \"Classical\"),\n", + " (25, \"eevee\", 29, \"f\", \"Pop\"),\n", + " (26, \"Jack\", 75, \"m\", \"Classical\"),\n", + " (27, \"Karen\", 26, \"f\", \"Pop\"),\n", + " (28, \"Lian\", 21, \"f\", \"Pop\"),\n", + " (29, \"kia\", 27, \"f\", \"Rock\"),\n", + " (30, \"Novak\", 30, \"m\", \"Rock\"),\n", + " (31, \"Olivia\", 29, \"f\", \"Pop\"),\n", + " (32, \"Benjamin\", 18, \"m\", \"Pop\")\n", + "]\n", + "df = pd.DataFrame(data, columns=columns)" + ] + }, + { + "cell_type": "markdown", + "id": "4b91576b", + "metadata": {}, + "source": [ + "Transfer the data to DataBricks." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "8679b0bb-0da6-4c35-9345-6cf0e83e19b2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'dbfs:///demos/mlrun_databricks_demo/1711553684480_33/music.parquet'" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Where to save the data in DataBricks\n", + "target_path = f\"dbfs:///demos/mlrun_databricks_demo/music.parquet\"\n", + "output_path = f\"dbfs:///demos/mlrun_databricks_demo/music_output_new.parquet\"\n", + "\n", + "targets = [ParquetTarget(path=target_path)]\n", + "\n", + "# Create a feature set and ingest the data\n", + "fset = fstore.FeatureSet(name=\"music_fset\", entities=[fstore.Entity(\"name\")])\n", + "fstore.ingest(fset, df, targets=targets, overwrite=True)\n", + "\n", + "# Get the target path and check it\n", + "dbfs_data_path = fset.get_target_path()\n", + "dbfs_data_path" + ] + }, + { + "cell_type": "markdown", + "id": "fe173be8-18eb-40ec-9662-6639b0deaedb", + "metadata": {}, + "source": [ + "We can look and see how how our data is logged in the DataBricks cluster:\n", + "(only top 20 rows)" + ] + }, + { + "attachments": { + "f7ad0425-26fe-482c-b97c-c9493b05fbf2.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "id": "c303d698-2f44-4f6f-8ce5-6a4f9f13534a", + "metadata": {}, + "source": [ + "![image.png](attachment:f7ad0425-26fe-482c-b97c-c9493b05fbf2.png)" + ] + }, + { + "cell_type": "markdown", + "id": "abd854e5", + "metadata": {}, + "source": [ + "## Create a data processing function\n", + "\n", + "The following code demonstrates how to create a simple data processing function using MLRun.
\n", + "The function will process the data and show some statistics.
\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b4e759f9-7154-4397-8db3-93b808426bd1", + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile process_data.py\n", + "\n", + "\n", + "# Here is an example of Spark processing.\n", + "from pyspark.sql import SparkSession\n", + "from pyspark.sql.functions import avg, min, max\n", + "import pandas as pd\n", + "import json\n", + "import fsspec\n", + "\n", + "def process_data(data_path: str, data_output_path: str):\n", + " spark = SparkSession.builder.appName(\"MusicDemo\").getOrCreate()\n", + " spark_df = spark.read.parquet(data_path, header=True)\n", + " spark_df = spark_df.drop(\"name\", \"id\")\n", + " \n", + " music_stats = spark_df.groupBy(\"favorite_music_type\").agg(\n", + " avg(\"age\").alias(\"avg_age\"),\n", + " min(\"age\").alias(\"min_age\"),\n", + " max(\"age\").alias(\"max_age\")\n", + " )\n", + " music_stats.show()\n", + " pandas_df = spark_df.toPandas()\n", + " pandas_df.to_parquet(data_output_path)\n", + " # spark_df.write.mode(\"overwrite\").parquet(data_output_path)\n", + "\n", + " return {\"music_data\": data_output_path}" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "13748b64-6a48-4500-a2a8-d9290dd082c5", + "metadata": {}, + "outputs": [], + "source": [ + "process_data_function = project.set_function(\n", + " func=\"./zeev-demos/mlflow-databricks/process_data.py\",\n", + " name=\"process-data\",\n", + " kind=\"databricks\",\n", + " image=\"mlrun/mlrun\",\n", + ")\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "2dbadf07-a32a-40da-b9bc-609070e4392d", + "metadata": {}, + "source": [ + "Set all parameters necessary for the function and run it." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "5642aa15-e8c0-4a72-a0a8-4cacd34fb63c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> 2024-03-27 15:34:45,422 [info] Storing function: {'name': 'process-data-process-data', 'uid': 'a9c770f8377046bda3061e61a5c015c2', 'db': 'http://mlrun-api:8080'}\n", + "> 2024-03-27 15:34:45,675 [info] Job is running in the background, pod: process-data-process-data-89bhh\n", + "> 2024-03-27 15:34:49,272 [info] Running with an existing cluster: {'cluster_id': '0327-134616-43m7kfxk'}\n", + "> 2024-03-27 15:34:49,492 [info] Starting to poll: 493449112310004\n", + "> 2024-03-27 15:34:49,539 [info] Workflow intermediate status: mlrun_task__15_34_48_703046: RunLifeCycleState.PENDING\n", + "> 2024-03-27 15:34:50,947 [info] Workflow intermediate status: mlrun_task__15_34_48_703046: RunLifeCycleState.PENDING\n", + "> 2024-03-27 15:34:53,063 [info] Workflow intermediate status: mlrun_task__15_34_48_703046: RunLifeCycleState.RUNNING\n", + "> 2024-03-27 15:34:56,737 [info] Workflow intermediate status: mlrun_task__15_34_48_703046: RunLifeCycleState.RUNNING\n", + "> 2024-03-27 15:35:00,947 [info] Artifacts found. Run name: mlrun_task__15_34_48_703046\n", + "> 2024-03-27 15:35:01,881 [info] Job finished: https://dbc-94c947ab-feb9.cloud.databricks.com/?o=4658245941722457#job/499259196347814/run/493449112310004\n", + "> 2024-03-27 15:35:01,881 [info] Logs:\n", + "+-------------------+------------------+-------+-------+\n", + "|favorite_music_type| avg_age|min_age|max_age|\n", + "+-------------------+------------------+-------+-------+\n", + "| Rock| 30.125| 27| 34|\n", + "| Classical|47.666666666666664| 38| 75|\n", + "| Pop| 24.0| 18| 38|\n", + "+-------------------+------------------+-------+-------+\n", + "\n", + "2024-03-27 15:34:54,980 - mlrun_logger - INFO - successfully wrote artifact details to the artifact JSON file in DBFS - music_data : /dbfs/demos/mlrun_databricks_demo/music_output_new.parquet\n", + "> 2024-03-27 15:35:02,182 [info] To track results use the CLI: {'info_cmd': 'mlrun get run a9c770f8377046bda3061e61a5c015c2 -p mlflow-tracking-example-guy', 'logs_cmd': 'mlrun logs a9c770f8377046bda3061e61a5c015c2 -p mlflow-tracking-example-guy'}\n", + "> 2024-03-27 15:35:02,182 [info] Or click for UI: {'ui_url': 'https://dashboard.default-tenant.app.llm-dev.iguazio-cd1.com/mlprojects/mlflow-tracking-example-guy/jobs/monitor/a9c770f8377046bda3061e61a5c015c2/overview'}\n", + "> 2024-03-27 15:35:02,182 [info] Run execution finished: {'status': 'completed', 'name': 'process-data-process-data'}\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "
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projectuiditerstartstatenamelabelsinputsparametersresultsartifacts
mlflow-tracking-example-guy0Mar 27 15:34:48completedprocess-data-process-data
v3io_user=zeevr
kind=databricks
owner=zeevr
mlrun/client_version=1.6.1
mlrun/client_python_version=3.9.16
host=process-data-process-data-89bhh
task_parameters={'timeout_minutes': 15, 'spark_app_code': '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', 'original_handler': 'process_data', 'artifact_json_path': '/mlrun_databricks_runtime/artifacts_dictionaries/mlrun_artifact_a9c770f8377046bda3061e61a5c015c2.json'}
data_path=dbfs:///demos/mlrun_databricks_demo/1711553684480_33/music.parquet
data_output_path=/dbfs/demos/mlrun_databricks_demo/music_output_new.parquet
music_data
databricks_run_metadata
\n", + "
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\n", + "
\n", + " Title\n", + " ×\n", + "
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\n", + "
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/html": [ + " > to track results use the .show() or .logs() methods or click here to open in UI" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> 2024-03-27 15:35:07,910 [info] Run execution finished: {'status': 'completed', 'name': 'process-data-process-data'}\n" + ] + } + ], + "source": [ + "for name, val in job_env.items():\n", + " process_data_function.spec.env.append({\"name\": name, \"value\": val})\n", + "params = {\n", + " \"task_parameters\": {\"timeout_minutes\": 15},\n", + " \"data_path\": dbfs_data_path,\n", + " \"data_output_path\": output_path.replace(\"dbfs://\", \"/dbfs\"),\n", + "}\n", + "run = process_data_function.run(\n", + " handler=\"process_data\",\n", + " params=params,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "9a8db175-51f4-4218-afd1-752cc0e65216", + "metadata": { + "tags": [] + }, + "source": [ + "## Create an MLflow Xgboost function\n", + "\n", + "The following code demonstrates how to create a simple Xgboost model using MLflow and log the results.
\n", + "MLflow will log the model, parameters, metrics, and artifacts, and MLRun will track the run and collect the data." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "44a1e133-954d-47a3-9b0f-6e181fe12ea7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting training.py\n" + ] + } + ], + "source": [ + "%%writefile training.py\n", + "\n", + "import mlflow\n", + "import mlflow.xgboost\n", + "import xgboost as xgb\n", + "from mlflow import log_metric\n", + "from sklearn import datasets\n", + "from sklearn.metrics import accuracy_score, log_loss\n", + "from sklearn.model_selection import train_test_split\n", + "import pandas as pd\n", + "\n", + "def example_xgb_run(df: str):\n", + " df = pd.read_parquet(df)\n", + " \n", + " df = df.replace([\"f\", \"m\"], [0, 1])\n", + " df = df.replace([\"Pop\", \"Rock\", \"Classical\"], [0, 1, 2])\n", + " \n", + " # Prepare, train, and test data\n", + " y = df.pop('favorite_music_type')\n", + " X = df\n", + "\n", + " X_train, X_test, y_train, y_test = train_test_split(\n", + " X, y, test_size=0.2, random_state=42\n", + " )\n", + "\n", + " # Enable auto logging\n", + " mlflow.xgboost.autolog()\n", + "\n", + " dtrain = xgb.DMatrix(X_train, label=y_train)\n", + " dtest = xgb.DMatrix(X_test, label=y_test)\n", + "\n", + " with mlflow.start_run():\n", + " # Train model\n", + " params = {\n", + " \"objective\": \"multi:softprob\",\n", + " \"num_class\": 3,\n", + " \"learning_rate\": 0.3,\n", + " \"eval_metric\": \"mlogloss\",\n", + " \"colsample_bytree\": 1.0,\n", + " \"subsample\": 1.0,\n", + " \"seed\": 42,\n", + " }\n", + " model = xgb.train(params, dtrain, evals=[(dtrain, \"train\")])\n", + " \n", + " # Evaluate model\n", + " y_proba = model.predict(dtest)\n", + " y_pred = y_proba.argmax(axis=1)\n", + " loss = log_loss(y_test, y_proba)\n", + " acc = accuracy_score(y_test, y_pred)\n", + " \n", + " # Log metrics by hand\n", + " mlflow.log_metrics({\"log_loss\": loss, \"accuracy\": acc})" + ] + }, + { + "cell_type": "markdown", + "id": "1cf984c9-78a9-443f-9465-111263101dcd", + "metadata": {}, + "source": [ + "## Log the data from MLflow in MLRun " + ] + }, + { + "cell_type": "markdown", + "id": "365e4b39-9f39-40ae-aac4-7c4f42bce9bd", + "metadata": {}, + "source": [ + "### Change the MLRun configuration to use the tracker\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "0b194d04-e08f-4161-a65b-4f18d10fdbf0", + "metadata": {}, + "outputs": [], + "source": [ + "import mlrun\n", + "\n", + "mlrun.mlconf.external_platform_tracking.enabled = True" + ] + }, + { + "cell_type": "markdown", + "id": "b16bb4db-8a2a-4453-a42e-0e8e74ab8f53", + "metadata": {}, + "source": [ + "These are the three options to run tracking:\n", + "- Set: `mlrun.mlconf.external_platform_tracking.mlflow.match_experiment_to_runtime` to True. This determines the run id and is the safest method\n", + "- Set the experiment name at: `mlflow.environment_variables.MLFLOW_EXPERIMENT_NAME.set`. This determines the experiment mlrun will track and find the run added to it.\n", + "- Just run it, mlrun will look across all experiments and search for added run, this is not recomended." + ] + }, + { + "cell_type": "markdown", + "id": "8b7bc72a-bd1b-408a-afa8-e474d91c4a20", + "metadata": {}, + "source": [ + "### Create the mlrun function" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "3382b909-a8dc-41a3-afb1-b64df9bb7318", + "metadata": {}, + "outputs": [], + "source": [ + "# Use the first run option from above\n", + "mlrun.mlconf.external_platform_tracking.mlflow.match_experiment_to_runtime = True\n", + "\n", + "# Create a MLRun function using the example train file (all the functions must be located in it):\n", + "training_func = project.set_function(\n", + " func=\"training.py\",\n", + " name=\"example-xgb-run\",\n", + " kind=\"job\",\n", + " image=\"mlrun/mlrun\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "91597f57-364d-4d2a-b926-97b9d8afc81b", + "metadata": {}, + "source": [ + "### Run the function\n", + "\n", + "Run the function using MLRun. This will log the data from MLflow in MLRun.
\n", + "After running the function, you can look at the UI and see that all metrics and parameters are logged in MLRun." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "5a726ca8-8057-41ed-be4e-35e5e0582de9", + "metadata": {}, + "outputs": [], + "source": [ + "import mlrun.feature_store as fstore\n", + "\n", + "feature_set = fstore.get_feature_set(\"music_fset\", \"mlflow-tracking-example\")" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "4de1229a-cc59-4846-8473-3178e682efa6", + "metadata": {}, + "outputs": [], + "source": [ + "df = feature_set.to_dataframe()\n", + "df = df.drop(['id'], axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "8249a933-031c-4f2e-88c2-161dd4cfb7ed", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# df = project.list_().to_objects()[0].to_dataitem().as_df()\n", + "df_path = \"./music.parquet\"\n", + "df.to_parquet(df_path)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "8ba452dd-1756-4bfb-af64-d741e234dba3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> 2024-03-27 15:37:22,829 [info] Storing function: {'name': 'example-xgb-run-example-xgb-run', 'uid': '6ff324dd21d64b6290d45a001957dda2', 'db': 'http://mlrun-api:8080'}\n", + "> 2024-03-27 15:37:22,912 [warning] `mlconf.external_platform_tracking.mlflow.match_experiment_to_runtime` is set to True but the MLFlow experiment name environment variable ('MLFLOW_EXPERIMENT_NAME') is set for using the name: 'example-xgb-run-example-xgb-run'. This name will be overriden with MLRun's runtime name as set in the MLRun configuration: 'example-xgb-run-example-xgb-run'.\n", + "[0]\ttrain-mlogloss:0.82467\n", + "[1]\ttrain-mlogloss:0.64706\n", + "[2]\ttrain-mlogloss:0.52480\n", + "[3]\ttrain-mlogloss:0.43768\n", + "[4]\ttrain-mlogloss:0.37410\n", + "[5]\ttrain-mlogloss:0.32686\n", + "[6]\ttrain-mlogloss:0.29057\n", + "[7]\ttrain-mlogloss:0.26192\n", + "[8]\ttrain-mlogloss:0.23885\n", + "[9]\ttrain-mlogloss:0.22004\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024/03/27 15:37:23 WARNING mlflow.utils.autologging_utils: MLflow autologging encountered a warning: \"/User/.pythonlibs/mlrun-base/lib/python3.9/site-packages/mlflow/types/utils.py:393: UserWarning: Hint: Inferred schema contains integer column(s). Integer columns in Python cannot represent missing values. If your input data contains missing values at inference time, it will be encoded as floats and will cause a schema enforcement error. The best way to avoid this problem is to infer the model schema based on a realistic data sample (training dataset) that includes missing values. Alternatively, you can declare integer columns as doubles (float64) whenever these columns may have missing values. See `Handling Integers With Missing Values `_ for more details.\"\n", + "2024/03/27 15:37:23 WARNING mlflow.utils.autologging_utils: MLflow autologging encountered a warning: \"/User/.pythonlibs/mlrun-base/lib/python3.9/site-packages/xgboost/core.py:160: UserWarning: [15:37:23] WARNING: /workspace/src/c_api/c_api.cc:1240: Saving into deprecated binary model format, please consider using `json` or `ubj`. Model format will default to JSON in XGBoost 2.2 if not specified.\"\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "
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mlflow-tracking-example-guy0Mar 27 15:37:22completedexample-xgb-run-example-xgb-run
v3io_user=zeevr
kind=local
owner=zeevr
host=jupyter-zeevr-9f4ffb7bb-8c4mf
mlflow-user=iguazio
mlflow-run-name=stately-cow-437
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mlflow-experiment-id=608717337209571124
df
colsample_bytree=1.0
custom_metric=None
early_stopping_rounds=None
eval_metric=mlogloss
learning_rate=0.3
maximize=None
num_boost_round=10
num_class=3
objective=multi:softprob
seed=42
subsample=1.0
verbose_eval=True
accuracy=0.7142857142857143
log_loss=0.9622776094122579
train-mlogloss=0.2200447738170624
feature_importance_weight_json
feature_importance_weight_png
model
\n", + "
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\n", + "
\n", + " Title\n", + " ×\n", + "
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\n", + "
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/html": [ + " > to track results use the .show() or .logs() methods or click here to open in UI" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> 2024-03-27 15:37:31,415 [info] Run execution finished: {'status': 'completed', 'name': 'example-xgb-run-example-xgb-run'}\n" + ] + } + ], + "source": [ + "# Run the example code using mlrun\n", + "train_run = training_func.run(\n", + " local=True,\n", + " handler=\"example_xgb_run\",\n", + " inputs={\"df\": df_path},\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "655d5c46-2c0a-46f2-bbec-a58853260476", + "metadata": {}, + "source": [ + "### Examine the results\n", + "\n", + "You can examine the results using the UI or by looking at the outputs of the run.
\n", + "The outputs include the model, the metrics, and the artifacts, and are completely independent of MLflow." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "d23beb02-e455-48dc-9d9f-9e3d4549ec71", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'accuracy': 0.7142857142857143,\n", + " 'log_loss': 0.9622776094122579,\n", + " 'train-mlogloss': 0.2200447738170624,\n", + " 'feature_importance_weight_json': 'store://artifacts/mlflow-tracking-example-guy/example-xgb-run-example-xgb-run_feature_importance_weight_json@6ff324dd21d64b6290d45a001957dda2',\n", + " 'feature_importance_weight_png': 'store://artifacts/mlflow-tracking-example-guy/example-xgb-run-example-xgb-run_feature_importance_weight_png@6ff324dd21d64b6290d45a001957dda2',\n", + " 'model': 'store://artifacts/mlflow-tracking-example-guy/example-xgb-run-example-xgb-run_model@6ff324dd21d64b6290d45a001957dda2'}" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_run.outputs" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "b05f4c2a-5f2d-4d7c-9c21-39c0a949cfc3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'accuracy': 0.7142857142857143,\n", + " 'log_loss': 0.9622776094122579,\n", + " 'train-mlogloss': 0.2200447738170624}" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_run.status.results" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "925b3445-18b4-4497-9783-52b4cd069401", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "train_run.artifact(\"feature_importance_weight_png\").show()" + ] + }, + { + "cell_type": "markdown", + "id": "227c4358-4c34-4d1c-acb4-e37ca110b8bf", + "metadata": {}, + "source": [ + "### You can also examine the results using the UI" + ] + }, + { + "cell_type": "markdown", + "id": "dde00fd1-a1f0-4c56-80c2-c5d36a9062a1", + "metadata": {}, + "source": [ + "Look at collected artifacts: " + ] + }, + { + "attachments": { + "95b9b198-55c9-4a67-b0bf-103c9ae0272e.png": { + "image/png": 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sLR/kl59/Z2+/+YHddtf11qlzh9DUzzz5ov3v2VdcycEtpfi++Owbe+v1D+w/j91ps2bMCR1DO+Xk5PjXPXt1NwXhdCGed+bltnDBotCcevLh+5/5Oe596FYf7iu0khcIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCAQElDxqWhHebaNds7ybrf3sN2tdu2a9sVn3xZqdarOlsedeIRbVys0pQJxWvaE66i5adPm0PLk5GRTR8z+u/QNLYv1k015OVFP2b96I/syY36p2/er3rDU9eEry3Ps8P3Kep5c1gbxWD992kwfgtPcadXTXLm/eoUOowBcEILTipQqKXbjdXeGQnDde3S1I485xFQRTuP770bZs0+96J+X9GvWzLl29eU3+xCcLhaF53r36eGrwi1dstzuvuM/ob66KpP43DMv+xBcSkqKq+C2u7Vp28pPrWDbvXc/Yp27drQRfxseuji1nV4feMgwv93jDz8bCsGdcNJRduMtV4YuTlWTe/mFN0o6VZYjgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIPCXF8jLy/PFqaKFUCEr7VPRY8Cu/WyIyyaFjw6ua2V4CC5Yp2VaFz60bzxDcDpWVjmCcDvXaGS71WwafoqFnvdIq297187vFlpoRQkvynPsEqaIuDghrVF/cEE1hd90oU2ZNM0H14KzGbzbgIhtQk89/cRQy9Ili5fZj9//7Hfp17+3/fuBW/w+69ett2OPPN30+MpLb9vJp51gSk9GGu+987HfTuuuu/FS22ffPf1mD/z7MXvrjQ/8eY1zbVf79Otp/336Jb9O7Uuffv5Ba9e+jX894qTzfUvWn0eOce1ar7YuLgz3+adfW0bGOqviwnqnnTHcb6df06bO8M8V9Dv9zP/z1d/UEvZxl+DUqF69un/kFwIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCOxYAklJSYXeUM0aJWeFiq4rum+hiWL0IqecgcELGva01lVr21urZ9rmghBdlaRkOyi9tR1ft6MVfreln2R5j136bFvWJiQIp+pqkUbr1i3tH1ecX2xVvfp1XXjspNDyia7VaTAOPGifUHCuZq2a1rv3TvbTj79Ydna2TXYhO7VCjTQmTZjiF6emprpQWjUb9dNo/7puvTqhzefOne+DcDOnz/bLFIALQnBacNV1f7c5s+f5dVmZWZZSPcU/j/Sro0tqTp82yzZt3GQXnH25DdtvqA0c3N+u/9dlkTZnGQIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCOwAAps2bQpljIK3s2D+ouBpscei65RPUkW4kgqCFZsgAQtSXLDvyDrt7LD0tjYva53l5OVaGxeMUxhuexkJCcKpslr+SLL6Deq5lqZNbc+hu9nhRx1kqanFT6FoqnHqlPzqapqjcZNGBXPlP+zUq5sPwunV/HkLIgbhVPZQFek0srKy7Norb/HPi/5asmiprV2TYevXb/Cr6tarW2iTrt06mX6iGedfdIbNnTPfJroAns5fP4889LS1aNnMLrvyAlNlOwYCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgjsOAJLliyzN15911avWlPoTS1YsMh+Gz2uWGZIy7QufMyeNdeeeeIFO/aEw61R44bhq2L2XMG2ranMpv3augDctgzNEY9RPIUWh6M8/sz91rlLh62euXb6FryFCxf7qm3BZKrMFgyF7CKN5ORk17q0igvBZVvVqlVt6D5DIm1m7Tu2s5q1alhKSorvL7x50+aI20WzML1ObXvkyXts5I+/mlrDqrXr6tVrTAnOf1x0nd1+9w02yLWFZSCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggEBxgdzc3OILy1iifZT9qajx68+/FQvBBefy8Ydf2Ny5C6xT5/Z+0bSpM23Cn5OC1YUeV61abb+4uQ4+dL9Cy2P1IjXJ5aPysqOeLtNVgFuWvdFW5my2VdmbLM/t2bBKmjWuUt3qp6RZecJtOnY8RkKCcNt64q1atwhNMfa3P+2gQ/YNvR4fdjGo1WqkoQpzbdu1tkkTp1pmZqYdcth+1rvPlhaqqgBXvXqaa7maX6qvabPGPrCmVqlquaoQncY7b35of4yb4J4l2RXXXFSo/GB429wNGzb68Jv2adO2ld9WX7JPP/rK7rj1fstzG3/+6TcE4QTEQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEECgiIDyNe++9VGRpWW/1D5HHnOIFe1IWfaesdli9z0GuXDbZJ85ijSjgm8lhd/Ct1deacjuA8MXxfR5mgujbbLSg3DzXQvU3zcut7Ebltukzast24XhIo205BTrW72h7VKjifVzj9WTS4+k6djxGKUfNR5H3Io5dx3Yz+q5NqVKOn779Y/WqFED233oYHvnrQ9tzK9j/Yw9enbzbUdLmn7/A/f2QTitv/v2h+yE4Uf5ynKzXCnBxx/+r/XqvZMPrGn9Pvvuac//9xXfJvXGa++w4acca4td29THH3nWFHLr2Kl9KASn8oOq8qaA3Zeff+tap3a2Zs2b+jaoq1auds+b2ONP32d16qZbx87t/JdMX1QF7BgIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCBQXECBNhW9Ku/QPslu3yOOPri8u8Zke2WEdh20s+8euS0Tag7NFa9RMznVVrvqbpHGgqz19uzKyTZu44pIq4st25SbYyPXL/E/qUnJNqx2Sxter5NVKyHwpmPHY1SKIFzNWjXt/IvPsFv/9W/b5NqVvvD86/4nAElLq2Z/v+ycUpOcuri/dy1KFZyb50oM3n3HQ8Hu/jHTtVhduWKVqb3qSScfY5998rULvy3x+2i/YChtefKI44KXLjS3h6lKncZNN9xtTZo2ttfefsaGuzkefuApW7RwiR1x8EnWuEljW7J4qa8Gp8pzhx5+QGgOniCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggMAWgQnjJ295Uc5n2reignA61d1239X+GDvBFdbKsp69ulmrNi198S9lkyKNBg3qu6Jgg3ymafwfE61q1ap+jkjbxmpZ7RQXRssqPFuWq/j20qpp9knGXNc2Vc1Pyz80x8dr59roDcvsrAbdrXf1BsUm8ccutnTbF1SKIJze5r77D/WV4O7/92M2Z/Zcy83N8y1Le/bubpddeYG1bNW8VA2VO7z7vn/Zay+/Y6++/LapWpuGkpN9+/WySy4/1+rWreOXpbk2qU8994A94I71/bcjffhO4bX2HdrauRecZv136eu306+99tnd/hw30ac41WI1JSW/vepxJxxhTZo0smefftlmu6pzCtVpdOna0c44+2QbsOuWOfwKfiGAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggIAXOOb4wyqtRGpqqp125km+46Sea3Tr3tl3o1yxfGWh99WgYX07+7wRvgDYTj26+qJcKhQW7Fdo4xi+SLYkq5tSrVBVuKdWTLKv1y2IyVGWZW+0W5eMsfMb9rA9a23JdemYOnY8RpJr07l18b14nE2Uc+rDXrZ0uW87qgptRcdjrtXpyy+86Rffdtf1EROSa9dk2MaNG30Ft6L7h79W4E4htnr161p1F5AraWRlZflzUkW4lJTCfWw3b8701eAaupauNWpUL2kKliOAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggEApAh+9/7mtW7feb1G7dk078JB9S9l6+1r1xqvv2pTJ0wudVNdunezo4yom9Lc5L8dmbF7jzycjN8vOmPu1xTpIpvaoz7Tey9QyVaNDtToltkz1G2zDr/wjbMMEFbGrWqG2at3CV4QLP35GxjqbO2e+jfrp19BiBdMijfQ6tcsMwWm/5OQka96iaakhOG2nFGbzFs2KheC0rlq1qtbalTgkBCcNBgIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACWyegYlbTp830P/VdS9HKNOo3qFfsdFURrqKGQmoNquQXBluYtT7mITi9L4XtZmdm+LeoY+mY8RrFy6nF60gJmPeu2x607775KXSk1q1bWtt2rUKveYIAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAKVV2DQbgOse48uvrlmep30SvVGBg/ZxTp2al/onBs3aVTodaJfNKlSw9bnZsf1sJkuDJeWXMV0rHiOHSoIFw6lCmw33HR5sapx4dvwHAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBCqXQJ1KFoALdNPS0nxXyeD19vLYIrWmTdy0Mm6no7aoOka8R1KeG/E+SKLmX7lilS1cuNjq1atrzZo3cW1NK2Xn10RxcRwEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAwDa4qnCPrhhvz62YYrkxapKa5Or2nVy/k13QsJfVcBXh4j12qCBcvLGYHwEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBDYEQU2uxam766ZbQ8sG2drcjK36S2mp1S1ixr1tCPrtLdqSSnbNFe0OxOEi1aK7RBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBHVxgVuZae3LFRPto7VzLzsst17ut4tqgHpje2s5s0M3aV61Trn23dWOCcNsqyP4IIIAAAggggAACCCCAAAI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" + } + }, + "cell_type": "markdown", + "id": "8cda6c13-7fee-4284-aacf-81a506a426da", + "metadata": {}, + "source": [ + "![image.png](attachment:95b9b198-55c9-4a67-b0bf-103c9ae0272e.png)" + ] + }, + { + "cell_type": "markdown", + "id": "e1525230-e10c-4f48-b951-bc73642bb3e4", + "metadata": {}, + "source": [ + "And at results:" + ] + }, + { + "attachments": { + "66422f79-9b46-4e07-9796-c1b350c26c9c.png": { + "image/png": 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ffv5djhao6nR5/ElHunl1QptUME7TnnQdNrdv3xGaHh0dbeqQOXD3fqFpZf1me2Z6kTc5MKGJfZW8pMDl+yc0LnB++Mzi7Dt8veK8jy7OwmWx7JzZ83wYTtuKT4h3ZQAb5NisgnBBGE4zYmrE2E3X3xUKw/Xo2c2OOvZQU4U4jR++H2fPPf2Sf5/fr/nzFtk1V9ziw3C6aRSi69O3p68St2rlGrvnzv+E+u6qfOLzz77iw3AxMTGuotsIa9uutd+0Am733fOodenWycb8bXToJtVy+nzQoaP8ck888lwoDHfiyUfbTbdeFbpJVV3ulRffzO9QmY4AAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAwF9eIDMz0xepKiqEClppncoeg/bob8NdNil8dHRdLMPDcME8TdO88KF1yzMMp32lFiMQN6BWExtWu1n4IeZ43zO+oe1TN6t7aI4Z+Xwozr7z2UShk8u9ZeqPLrCmEJxuuJnTZ/sAW3BUQ4cNitg+9LQzTgq1Ml25YrX99MMvfpX+A/vYvx+81a+zZfMWO+6oM0yvr778jp1y+ommNGWk8f67n/jlNO/6my6zfffbyy/24L8ft7ff/NAf1yTXjrVv/17232de9vPU1vSZFx6y9h3a+s9jTr7At2r9ZewE18b1GuvqQnFffPaNJSdvthoutHf6maP9cvo1e9Zc/16BvzPO+j9fDU6tYp9wiU6NhIQE/8ovBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQ2LUEoqKicpxQ7Vr5Z4Vyz8u9bo4NldGH9GIGBy9s3Mva1Kxrb2+YZzuyw3Q1oqLt4MQ2dkL9TpbzbAs+yOLuu+CtRZ5b7oE4VVuLNNq0aWX/uPKCPLMaNKzvQmQnh6ZPcy1Qg3HQwfuGAnS169S2Pn12s59/+tXS0tJshgvbqUVqpDF96kw/OTY21oXT4mzcz+P95/oN6oUWX7RoiQ/EzZuzwE9TEC4Iw2nC1df/3RYuWOznpaakWkxCjH8f6Vcnl9ycM3u+bd+23S485wobtf9IGzx0oN3wr8sjLc40BBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQ2AUEtm/fHsoYBaezdMny4G2e19zzlE9Shbj8CoPl2UAFTIhxAb+j6rW3wxPb2eLUzZaemWFtXUBOobiqOMo9EKdKa1kjyho2auBanTazvUYOsyOOPthiY/PuPnfKcdbMrGpr2kbTpCbZ28p62a13dx+I06cli5dGDMSpHKIq1GmkpqbadVfd6t/n/rVy+SrbtDHZtmzZ6mfVb1A/xyLdunc2/RRlXHDxmbZo4RKb5oJ4On79PPrwM9ayVXO7/KoLTZXuGAgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIILDrCKxcudrefO0927B+Y46TWrp0uf0+flKezJCmaV74WDB/kT375It23IlHWJOmjcNnldl7BdxKUqlN67VzQbjSDG2jvEfeRFoZ7/GJZx+wLl07lnirdRN3Ii5btsJXcQs2pkptwVDYLtKIjo52LU1ruDBcmtWsWdNG7js80mLWoVN7q12nlsXExPj+wzu274i4XFEmJtara48+da+N/ek3U8tYtXzdsGGjKdH5j4uvtzvuudGGuHaxDAQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEMgrkJGRkXdiIVO0jrI/lTV+++X3PGG44Fg++ehLW7RoqXXu0sFPmj1rnk39c3owO8fr+vUb7Fe3rUMO2z/H9LL6EBvl8lGZaUXeXIqrCLc6bZutS99h69O2W6Zbs3GNeGtaI8EaxsRbcUJu2nd5j3IPxJX2BFq3aRnaxMTf/7SDD90v9HlK2E2hFqyRhirOtWvfxqZPm2UpKSl26OH7W5++O1urqiJcQkK8a8WaVcKvWfOmPrimFqpqxaownca7b31kkydNde+i7MprL85RljC8re7Wrdt8CE7rtG3X2i+rL9tnH39td972gGW6hb/47FsCcQJiIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCQS0D5mvfe/jjX1MI/ap2jjj3UcneoLHzNsllixJ5DXMhths8cRdqiAnD5heDCl1deafiIweGTyvR9vAulbbeCA3FLXGvUP7atsYlb19j0HRsszYXiIo346Bjrl9DYdq+VZP3da0J0wXE07bu8R8FHUN57L8L29xjc3xq49qVKPn73zU/WpEkjGzFyqL379kc24beJfgs9e3X37Ujz29wBB+3jA3Gaf88dD9uJo4/2lebmuxKDTzzyX+vdZzcfXNP8fffby17476u+fepN191po089zla4dqpPPPqcKezWqXOHUBhOZQlV9U1Bu6+++M61VO1izVs08+1R16/b4N4n2RPP3G/16idapy7t/ZdNX1gF7RgIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCQV0DBNhW/Ku7QOtFu3SOPOaS4q5bJ8soI7TFkgO8mWZoNahvaVnmN2tGxtsFVe4s0lqZusefWzbBJ29ZGmp1n2vaMdBu7ZaX/iY2KtlF1W9noBp0tLp/gm/Zd3qPKB+Jq16ltF1xypt32r3/bdtfG9MUX3vA/AUx8fJz9/fJzC0x26ib/wbUuVYBusSs9eM+dDwer+9cU13p13dr1prarJ59yrH3+6TcuBLfSr6P1gqH05Sljjg8+uvDcnqaqdRo333iPJTVraq+/86yNdtt45MGnbfmylXbkISdb06SmtnLFKl8dTpXoDjviwNA2eIMAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAI7BaZOmbHzQzHfad3KCsTpUIeN2MMmT5zqCmylWq/e3a1121a+CJiySZFGo0YNXXGwIT7TNGXyNKtZs6bfRqRly2pa3RgXSkvNubVUVwHu5fWz7dPkRa6dqpqiFn9oG59sWmTjt662sxv1sD4JjfJsxO87z9SynVDlA3E63f0OGOkrwz3w78dt4YJFlpGR6VuZ9urTwy6/6kJr1bpFgSoqg3jP/f+y119511575R1T9TYNJSn79e9tl15xntWvX89Pi3ftU59+/kF70O3rh+/G+hCeQmwdOraz8y483Qbu3s8vp1977zvC/pw0zac61Xo1Jiar7erxJx5pSUlN7LlnXrEFrgqdwnUaXbt1sjPPOcUG7bFzG34GvxBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQMALHHvC4dVWIjY21k4/62TfgVLvNbr36OK7U65dsy7HeTVq3NDOOX+MLwS2W89uvjiXCoYF6+VYuAw/RFuU1Y+Jy1El7um10+2bzUvLZC+r07bZbSsn2AWNe9pedXbmurRP7bu8R5Rr4VmySF95H1k+29dFX71qjW9HqoptucfjrgXqKy++5SfffvcNEROTmzYm27Zt23xFt9zrh39W8E5htgYN61uCC8rlN1JTU/0xqUJcTEzOPrc7dqT46nCNXavXWrUS8tsE0xFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQKAAgY8/+MI2b97il6hbt7YddOh+BSxdtWa9+dp7NnPGnBwH1a17Zzvm+MoJ/+3ITLe5Ozb640nOSLUzF31jZR0iU9vUZ9vsbWqlqtExrl6+rVT9AmX0K2tvZbSxitiMWqS2btPSV4gL319y8mZbtHCJjfv5t9BkBdQijcR6dQsNw2m96Ogoa9GyWYFhOC2nVGaLls3zhOE0Ly6uprVxpQ8Jw0mDgQACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBAyQRU1GrO7Hn+p6FrNVqdRsNGDfIcrirEVdZQWK1RjawCYctSt5R5GE7npdDdgpRkf4ral/ZZESNvibWK2Gs57OPu2x+y77/9ObTlNm1aWbv2rUOfeYMAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAALVV2DIsEHWo2dX33QzsV5itTqRocN3t06dO+Q45qZJTXJ8rugPSTVq2ZaMtHLdbYoLxcVH1zDtq6LGLhOICwdTRbYbb74iTxW58GV4jwACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAtVLoF41C8IFuvHx8b7LZPC5qry2jK1t07avK7fDUbtU7aMiR1SmGxW5w/La17q1623ZshXWoEF9a94iybU7rXbdYMuLhu0igAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAhEFtroqcY+tnWLPr51pGWXUPDXK1fE7pWFnu7Bxb6vlKsRV5NhlAnEVica+EEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIFdRWCHa2363sYF9uDqSbYxPaVUp5UYU9MubtLLjqrXweKiYkq1rZKsTCCuJGqsgwACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgjsYgLzUzbZU2un2cebFllaZkaxzq6Ga496UGIbO6tRd+tQs16x1i3LhQnElaUm20IAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEqrGAqsXN3rHRPtm00L7fvMzmpyQXeDbta9a1EXVa2MEuDNc5rn6lVIULP0ACceEavEcAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEELAMy7Tk9FRblbbV5rjKcQvdjz5rqC1qGxeE61gz0ZJq1LK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" + } + }, + "cell_type": "markdown", + "id": "217279f8-6af1-4209-b0ec-3d3d829ceed9", + "metadata": {}, + "source": [ + "![image.png](attachment:66422f79-9b46-4e07-9796-c1b350c26c9c.png)" + ] + }, + { + "cell_type": "markdown", + "id": "844edc05-0b6a-4e84-9213-1d3cbf6f833e", + "metadata": {}, + "source": [ + "## Use the function for model serving" + ] + }, + { + "cell_type": "markdown", + "id": "40182a6f-fc46-4a33-a7f5-7ee8ee171966", + "metadata": {}, + "source": [ + "### Create the server and serving function\n", + "\n", + "Create a serving function that uses the model from the previous run and serves it using MLRun.
\n", + "We will create a mock server to test the model in a local environment." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "f5fe910b-e177-4af7-84de-41a571d1774c", + "metadata": {}, + "outputs": [], + "source": [ + "serving_func = project.set_function(\n", + " func=\"function.yaml\",\n", + " name=\"example-xgb-server\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "ddbfd48f-a90e-4fe6-9caa-ddffeacf63d1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Add the model\n", + "serving_func.add_model(\n", + " \"mlflow_xgb_model\",\n", + " class_name=\"MLFlowModelServer\",\n", + " model_path=train_run.outputs[\"model\"],\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "2298d111-2f53-4b84-be9e-e4e8a228dcc4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> 2024-03-27 15:37:31,627 [info] model mlflow_xgb_model was loaded\n", + "> 2024-03-27 15:37:31,628 [info] Loaded ['mlflow_xgb_model']\n" + ] + } + ], + "source": [ + "# Create a mock server\n", + "server = serving_func.to_mock_server()" + ] + }, + { + "cell_type": "markdown", + "id": "f54d7c06-4972-4881-9bc9-fba7db0adbe4", + "metadata": {}, + "source": [ + "### Test the model " + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "4f256490-f225-4bd6-ac8a-5fc12a0f335d", + "metadata": {}, + "outputs": [], + "source": [ + "# An example taken randomly \n", + "result = server.test(\"/v2/models/mlflow_xgb_model/predict\", {\"inputs\":[{\"age\": 20, \"gender\": 0}]})" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "47839f4b-bb2d-4341-99c5-e34fa31270c9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'id': '43a61d06f2694fa695bdd6561b487131',\n", + " 'model_name': 'mlflow_xgb_model',\n", + " 'outputs': [[0.9242361187934875, 0.0418272465467453, 0.033936627209186554]]}" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Look at the result, it shows the probability of the given example to be each of the \n", + "# irises featured in the dataset\n", + "result" + ] + }, + { + "cell_type": "markdown", + "id": "d4fc6c73-0963-4814-bd5f-2d27b464823e", + "metadata": {}, + "source": [ + "We predicted that a 20 year old female would like pop!" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mlrun-base", + "language": "python", + "name": "conda-env-mlrun-base-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/mlflow_utils/mlflow_utils.py b/mlflow_utils/mlflow_utils.py new file mode 100644 index 000000000..fb6124bef --- /dev/null +++ b/mlflow_utils/mlflow_utils.py @@ -0,0 +1,45 @@ +import zipfile +from typing import Any, Dict +import mlflow +from mlrun.serving.v2_serving import V2ModelServer +import pandas as pd + + +class MLFlowModelServer(V2ModelServer): + """ + MLFlow tracker Model serving class, inheriting the V2ModelServer class for being initialized automatically by the model + server and be able to run locally as part of a nuclio serverless function, or as part of a real-time pipeline. + """ + + def load(self): + """ + loads a model that was logged by the MLFlow tracker model + """ + # Unzip the model dir and then use mlflow's load function + model_file, _ = self.get_model(".zip") + model_path_unzip = model_file.replace(".zip", "") + + with zipfile.ZipFile(model_file, "r") as zip_ref: + zip_ref.extractall(model_path_unzip) + + self.model = mlflow.pyfunc.load_model(model_path_unzip) + + def predict(self, request: Dict[str, Any]) -> list: + """ + Infer the inputs through the model. The inferred data will + be read from the "inputs" key of the request. + + :param request: The request to the model using xgboost's predict. + The input to the model will be read from the "inputs" key. + + :return: The model's prediction on the given input. + """ + + # Get the inputs and set to accepted type: + inputs = pd.DataFrame(request["inputs"]) + + # Predict using the model's predict function: + predictions = self.model.predict(inputs) + + # Return as list: + return predictions.tolist() diff --git a/mlflow_utils/requirements.txt b/mlflow_utils/requirements.txt new file mode 100644 index 000000000..2ecc4ff91 --- /dev/null +++ b/mlflow_utils/requirements.txt @@ -0,0 +1,3 @@ +mlflow==2.12.2 +lightgbm +xgboost \ No newline at end of file diff --git a/mlflow_utils/test_mlflow_utils.py b/mlflow_utils/test_mlflow_utils.py new file mode 100644 index 000000000..70d6ce03f --- /dev/null +++ b/mlflow_utils/test_mlflow_utils.py @@ -0,0 +1,179 @@ +# Copyright 2018 Iguazio +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import tempfile + +import lightgbm as lgb +import mlflow +import mlflow.environment_variables +import mlflow.xgboost +import pytest +import xgboost as xgb +from sklearn import datasets +from sklearn.metrics import accuracy_score, log_loss +from sklearn.model_selection import train_test_split + +import os +# os.environ["MLRUN_IGNORE_ENV_FILE"] = "True" #TODO remove before push + +import mlrun +import mlrun.launcher.local +# Important: +# unlike mlconf which resets back to default after each test run, the mlflow configurations +# and env vars don't, so at the end of each test we need to redo anything we set in that test. +# what we cover in these tests: logging "regular" runs with, experiment name, run id and context +# name (last two using mlconf), failing run mid-way, and a run with no handler. +# we also test here importing of runs, artifacts and models from a previous run. + +# simple mlflow example of lgb logging +def lgb_run(): + # prepare train and test data + iris = datasets.load_iris() + X = iris.data + y = iris.target + X_train, X_test, y_train, y_test = train_test_split( + X, y, test_size=0.2, random_state=42 + ) + + # enable auto logging + mlflow.lightgbm.autolog() + + train_set = lgb.Dataset(X_train, label=y_train) + + with mlflow.start_run(): + # train model + params = { + "objective": "multiclass", + "num_class": 3, + "learning_rate": 0.1, + "metric": "multi_logloss", + "colsample_bytree": 1.0, + "subsample": 1.0, + "seed": 42, + } + # model and training data are being logged automatically + model = lgb.train( + params, + train_set, + num_boost_round=10, + valid_sets=[train_set], + valid_names=["train"], + ) + + # evaluate model + y_proba = model.predict(X_test) + y_pred = y_proba.argmax(axis=1) + loss = log_loss(y_test, y_proba) + acc = accuracy_score(y_test, y_pred) + + # log metrics + mlflow.log_metrics({"log_loss": loss, "accuracy": acc}) + + +# simple mlflow example of xgb logging +def xgb_run(): + # prepare train and test data + iris = datasets.load_iris() + x = iris.data + y = iris.target + x_train, x_test, y_train, y_test = train_test_split( + x, y, test_size=0.2, random_state=42 + ) + + # enable auto logging + mlflow.xgboost.autolog() + + dtrain = xgb.DMatrix(x_train, label=y_train) + dtest = xgb.DMatrix(x_test, label=y_test) + + with mlflow.start_run(): + # train model + params = { + "objective": "multi:softprob", + "num_class": 3, + "learning_rate": 0.3, + "eval_metric": "mlogloss", + "colsample_bytree": 1.0, + "subsample": 1.0, + "seed": 42, + } + # model and training data are being logged automatically + model = xgb.train(params, dtrain, evals=[(dtrain, "train")]) + # evaluate model + y_proba = model.predict(dtest) + y_pred = y_proba.argmax(axis=1) + loss = log_loss(y_test, y_proba) + acc = accuracy_score(y_test, y_pred) + # log metrics + mlflow.log_metrics({"log_loss": loss, "accuracy": acc}) + + +@pytest.mark.parametrize("handler", ["xgb_run", "lgb_run"]) +def test_track_run_with_experiment_name(handler): + """ + This test is for tracking a run logged by mlflow into mlrun while it's running using the experiment name. + first activate the tracking option in mlconf, then we name the mlflow experiment, + then we run some code that is being logged by mlflow using mlrun, + and finally compare the mlrun we tracked with the original mlflow run using the validate func + """ + # Enable general tracking + mlrun.mlconf.external_platform_tracking.enabled = True + # Set the mlflow experiment name + mlflow.environment_variables.MLFLOW_EXPERIMENT_NAME.set(f"{handler}_test_track") + with tempfile.TemporaryDirectory() as test_directory: + mlflow.set_tracking_uri(test_directory) # Tell mlflow where to save logged data + + # Create a project for this tester: + project = mlrun.get_or_create_project(name="default", context=test_directory) + + # Create a MLRun function using the tester source file (all the functions must be located in it): + func = project.set_function( + func=__file__, + name=f"{handler}-test", + kind="job", + image="mlrun/mlrun", + requirements=["mlflow"], + ) + # mlflow creates a dir to log the run, this makes it in the tmpdir we create + trainer_run = func.run( + local=True, + handler=handler, + artifact_path=test_directory, + ) + + serving_func = project.set_function( + func=os.path.abspath("function.yaml"), + name=f"{handler}-server", + ) + model_name = f"{handler}-model" + # Add the model + upper_handler = handler.replace("_", "-") + model_path = test_directory + f"/{upper_handler}-test-{upper_handler}/0/model/" + serving_func.add_model( + model_name, + class_name="MLFlowModelServer", + model_path=model_path, + ) + + # Create a mock server + server = serving_func.to_mock_server() + + # An example taken randomly + result = server.test(f"/v2/models/{model_name}/predict", {"inputs": [[5.1, 3.5, 1.4, 0.2]]}) + print(result) + assert result + # unset mlflow experiment name to default + mlflow.environment_variables.MLFLOW_EXPERIMENT_NAME.unset() + + diff --git a/model_server/function.yaml b/model_server/function.yaml index 1539a3810..cb082c184 100644 --- a/model_server/function.yaml +++ b/model_server/function.yaml @@ -44,7 +44,7 @@ spec: - name: MODEL_CLASS value: ClassifierModel handler: model_server:handler - runtime: python:3.6 + runtime: python:3.9 volumes: [] source: '' function_kind: serving diff --git a/pii_recognizer/function.yaml b/pii_recognizer/function.yaml index 54b448d9c..069fa1ffe 100644 --- a/pii_recognizer/function.yaml +++ b/pii_recognizer/function.yaml @@ -2,13 +2,14 @@ kind: job metadata: name: pii-recognizer tag: '' - hash: b09b7b9a4ffd55088d665a0191055411e9198a2f + hash: 818930645d33704e9cada919769ee9d93cbb9434 project: '' labels: author: pgw categories: - machine-learning - data-preparation + - NLP spec: command: '' args: [] diff --git a/pii_recognizer/item.yaml b/pii_recognizer/item.yaml index 2f618febc..41ead33b6 100644 --- a/pii_recognizer/item.yaml +++ b/pii_recognizer/item.yaml @@ -2,6 +2,7 @@ apiVersion: v1 categories: - machine-learning - data-preparation + - NLP description: This function is used to recognize PII in a directory of text files doc: '' example: pii_recognizer.ipynb @@ -30,5 +31,5 @@ spec: - st-annotated-text - https://huggingface.co/beki/en_spacy_pii_distilbert/resolve/main/en_spacy_pii_distilbert-any-py3-none-any.whl url: '' -version: 0.2.0 +version: 0.3.0 test_valid: False diff --git a/pyannote_audio/function.yaml b/pyannote_audio/function.yaml index 2e84fbd92..30870afa2 100644 --- a/pyannote_audio/function.yaml +++ b/pyannote_audio/function.yaml @@ -2,14 +2,14 @@ kind: job metadata: name: pyannote-audio tag: '' - hash: c45be8d7f51f0b2203155b08c307814a2cb0ac78 + hash: aed670a0534ebf30690dd2af7acad35595c7d5b1 project: '' labels: author: guyl categories: - deep-learning - - Huggingface - - Audio + - huggingface + - audio spec: command: '' args: [] diff --git a/pyannote_audio/item.yaml b/pyannote_audio/item.yaml index 7133ceb41..b69add9e6 100644 --- a/pyannote_audio/item.yaml +++ b/pyannote_audio/item.yaml @@ -1,8 +1,8 @@ apiVersion: v1 categories: - deep-learning -- Huggingface -- Audio +- huggingface +- audio description: pyannote's speech diarization of audio files doc: '' example: pyannote_audio.ipynb @@ -27,4 +27,4 @@ spec: - torchaudio - tqdm url: '' -version: 1.1.0 +version: 1.2.0 diff --git a/question_answering/function.yaml b/question_answering/function.yaml index a33614153..7491b17e9 100644 --- a/question_answering/function.yaml +++ b/question_answering/function.yaml @@ -2,11 +2,13 @@ kind: job metadata: name: question-answering tag: '' - hash: 90e67d116b256a98da7d5819724e43df01d8b4eb + hash: aed62db95f17576c69b457767e3595c2de1d5465 project: '' labels: author: yonish categories: + - genai + - huggingface - machine-learning spec: command: '' diff --git a/question_answering/item.yaml b/question_answering/item.yaml index 58ab5cc36..56fc5a5ec 100755 --- a/question_answering/item.yaml +++ b/question_answering/item.yaml @@ -1,5 +1,7 @@ apiVersion: v1 categories: +- genai +- huggingface - machine-learning description: GenAI approach of question answering on a given data doc: '' @@ -24,4 +26,4 @@ spec: - torch - tqdm url: '' -version: 0.3.1 +version: 0.4.0 diff --git a/silero_vad/function.yaml b/silero_vad/function.yaml index 0b4ad422b..8ec121a6b 100644 --- a/silero_vad/function.yaml +++ b/silero_vad/function.yaml @@ -2,14 +2,14 @@ kind: job metadata: name: silero-vad tag: '' - hash: 61b7a70c167b7819481fdabf9350fc6fa344d2f5 + hash: 59336f808643a74f3a2c5d506977387010427208 project: '' labels: author: guyl categories: - deep-learning - - PyTorch - - Audio + - pytorch + - audio spec: command: '' args: [] diff --git a/silero_vad/item.yaml b/silero_vad/item.yaml index 17c8eb62c..9ce9a5d2e 100644 --- a/silero_vad/item.yaml +++ b/silero_vad/item.yaml @@ -1,8 +1,8 @@ apiVersion: v1 categories: - deep-learning -- PyTorch -- Audio +- pytorch +- audio description: Silero VAD (Voice Activity Detection) functions. doc: '' example: silero_vad.ipynb @@ -27,4 +27,4 @@ spec: - tqdm - onnxruntime url: '' -version: 1.2.0 +version: 1.3.0 diff --git a/structured_data_generator/function.yaml b/structured_data_generator/function.yaml index 6f2039e4b..1093e178b 100644 --- a/structured_data_generator/function.yaml +++ b/structured_data_generator/function.yaml @@ -2,7 +2,7 @@ kind: job metadata: name: structured-data-generator tag: '' - hash: ac969f46aae91804024ea736856267c26578864b + hash: 44bb39f4bc55b38fc7ead1df24cb02bcf7f05bc9 project: '' labels: author: zeevr @@ -10,7 +10,7 @@ metadata: - machine-learning - data-preparation - data-generation - - GenAI + - genai spec: command: '' args: [] diff --git a/structured_data_generator/item.yaml b/structured_data_generator/item.yaml index 27e0e3fab..be2a2a948 100755 --- a/structured_data_generator/item.yaml +++ b/structured_data_generator/item.yaml @@ -3,7 +3,7 @@ categories: - machine-learning - data-preparation - data-generation -- GenAI +- genai description: GenAI approach of generating structured data according to a given schema doc: '' example: structured_data_generator.ipynb @@ -26,4 +26,4 @@ spec: - langchain - tqdm url: '' -version: 1.4.0 +version: 1.5.0 diff --git a/text_to_audio_generator/function.yaml b/text_to_audio_generator/function.yaml index df142d2ef..88ef9cb89 100644 --- a/text_to_audio_generator/function.yaml +++ b/text_to_audio_generator/function.yaml @@ -2,13 +2,14 @@ kind: job metadata: name: text-to-audio-generator tag: '' - hash: 534e34d316098dcb345860a786ea013102150e67 + hash: 89fcaf3fab53e7b7fbba448a5e65c253d7fa66ed project: '' labels: author: yonatans categories: - data-preparation - machine-learning + - pytorch spec: command: '' args: [] diff --git a/text_to_audio_generator/item.yaml b/text_to_audio_generator/item.yaml index 4784a80d2..efa8afc90 100644 --- a/text_to_audio_generator/item.yaml +++ b/text_to_audio_generator/item.yaml @@ -2,6 +2,7 @@ apiVersion: v1 categories: - data-preparation - machine-learning +- pytorch description: Generate audio file from text using different speakers doc: '' example: text_to_audio_generator.ipynb @@ -24,5 +25,5 @@ spec: - bark - torchaudio url: '' -version: 1.1.0 +version: 1.2.0 test_valid: True diff --git a/tf2_serving/function.yaml b/tf2_serving/function.yaml index a8fa7ce66..c755263ae 100644 --- a/tf2_serving/function.yaml +++ b/tf2_serving/function.yaml @@ -46,7 +46,7 @@ spec: - name: MODEL_CLASS value: TF2Model handler: tf2_serving:handler - runtime: python:3.6 + runtime: python:3.9 volumes: [] source: '' function_kind: serving \ No newline at end of file diff --git a/transcribe/function.yaml b/transcribe/function.yaml index 40dd2f0e6..d72751ad6 100644 --- a/transcribe/function.yaml +++ b/transcribe/function.yaml @@ -2,12 +2,14 @@ kind: job metadata: name: transcribe tag: '' - hash: 5cd620de67a936ee8a87cfc1f0b97e19730d0a69 + hash: 8810ac74045bd15cee15a2e4e89563e8e29908d3 project: '' labels: author: yonatans categories: - data-preparation + - genai + - huggingface - machine-learning spec: command: '' @@ -24,6 +26,7 @@ spec: - tqdm - torchaudio - torch + - accelerate entry_points: do_task: name: do_task diff --git a/transcribe/item.yaml b/transcribe/item.yaml index d53341ff2..7fddcf95e 100644 --- a/transcribe/item.yaml +++ b/transcribe/item.yaml @@ -1,6 +1,8 @@ apiVersion: v1 categories: - data-preparation +- genai +- huggingface - machine-learning description: Transcribe audio files into text files doc: '' @@ -27,4 +29,4 @@ spec: - torch - accelerate url: '' -version: 1.0.0 \ No newline at end of file +version: 1.1.0 \ No newline at end of file diff --git a/translate/function.yaml b/translate/function.yaml index 1a3fd7a88..bb1656103 100644 --- a/translate/function.yaml +++ b/translate/function.yaml @@ -2,13 +2,16 @@ kind: job metadata: name: translate tag: '' - hash: bc26313449cd13554a18106ed9893535fb79dd6e + hash: 7eedf684bcebfbfd964e5503afbb56335c8f4097 project: '' labels: author: guyl categories: - data-preparation + - huggingface - machine-learning + - deep-learning + - NLP spec: command: '' args: [] @@ -34,24 +37,27 @@ spec: - name: root_worker_inputs type: Dict[str, Any] default: null - outputs: - - default: '' + outputs: [] lineno: 56 + has_varargs: false + has_kwargs: false decorator: name: decorator doc: '' parameters: - name: handler - outputs: - - default: '' + outputs: [] lineno: 68 + has_varargs: false + has_kwargs: false wrapper: name: wrapper doc: '' parameters: [] - outputs: - - default: '' + outputs: [] lineno: 73 + has_varargs: false + has_kwargs: true translate: name: translate doc: 'Translate text files using a transformer model from Huggingface''s hub @@ -112,8 +118,10 @@ spec: default: false outputs: - doc: 'A tuple of:' - default: '' + type: Tuple[str, pd.DataFrame, dict] lineno: 135 + has_varargs: false + has_kwargs: false description: Translate text files from one language to another default_handler: translate disable_auto_mount: false diff --git a/translate/item.yaml b/translate/item.yaml index f85a55990..e63947349 100644 --- a/translate/item.yaml +++ b/translate/item.yaml @@ -1,6 +1,7 @@ apiVersion: v1 categories: - data-preparation +- huggingface - machine-learning - deep-learning - NLP @@ -28,5 +29,5 @@ spec: - torch - tqdm url: '' -version: 0.0.2 +version: 0.1.0 test_valid: True diff --git a/v2_model_server/function.yaml b/v2_model_server/function.yaml index 53fb00ea1..45d261b6a 100644 --- a/v2_model_server/function.yaml +++ b/v2_model_server/function.yaml @@ -70,14 +70,14 @@ spec: annotations: nuclio.io/generated_by: function generated from /home/michaell/projects/functions/v2_model_server/v2_model_server.py spec: - runtime: python:3.6 + runtime: python:3.9 handler: v2_model_server:handler env: [] volumes: [] build: commands: [] noBaseImagesPull: true - functionSourceCode: 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 + functionSourceCode: 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 source: '' function_kind: serving_v2 default_class: ClassifierModel diff --git a/v2_model_server/item.yaml b/v2_model_server/item.yaml index e0d6b0f96..7bde91a64 100644 --- a/v2_model_server/item.yaml +++ b/v2_model_server/item.yaml @@ -25,4 +25,4 @@ spec: kind: serving requirements: [] url: '' -version: 1.1.0 +version: 1.2.0 diff --git a/v2_model_server/v2_model_server.py b/v2_model_server/v2_model_server.py index dbaa72ef2..572f1680d 100644 --- a/v2_model_server/v2_model_server.py +++ b/v2_model_server/v2_model_server.py @@ -37,14 +37,3 @@ def predict(self, body: dict) -> List: feats = np.asarray(body["inputs"]) result: np.ndarray = self.model.predict(feats) return result.tolist() - - -from mlrun.runtimes import nuclio_init_hook - - -def init_context(context): - nuclio_init_hook(context, globals(), "serving_v2") - - -def handler(context, event): - return context.mlrun_handler(context, event) From 6ded2af5f12705afbb5c8070052894c9bb76e4c9 Mon Sep 17 00:00:00 2001 From: Eyal Danieli Date: Wed, 25 Sep 2024 15:53:08 +0300 Subject: [PATCH 22/38] set `navigation_with_keys` to False (#829) --- cli/marketplace/conf.template | 1 + 1 file changed, 1 insertion(+) diff --git a/cli/marketplace/conf.template b/cli/marketplace/conf.template index 8c6e9f344..f78fde1e6 100644 --- a/cli/marketplace/conf.template +++ b/cli/marketplace/conf.template @@ -93,6 +93,7 @@ html_theme_options = { "path_to_docs": "docs", "repository_branch": "{{repository_branch}}", "single_page": True, + "navigation_with_keys": False, } html_title = "{{html_title}}" From 60c5349153f0c7207a46b55a99f1250c1eee54c5 Mon Sep 17 00:00:00 2001 From: Eyal Danieli Date: Wed, 25 Sep 2024 16:38:10 +0300 Subject: [PATCH 23/38] remove xgb and churn functions (#830) --- catalog.yaml | 180 --- churn_server/README.md | 15 - churn_server/churn_server.ipynb | 503 -------- churn_server/churn_server.py | 45 - churn_server/function.yaml | 51 - churn_server/item.yaml | 32 - churn_server/requirements.txt | 2 - churn_server/test_churn_server.py | 67 - coxph_test/coxph_test.ipynb | 969 --------------- coxph_test/coxph_test.py | 75 -- coxph_test/function.yaml | 63 - coxph_test/item.yaml | 26 - coxph_trainer/coxph_trainer.ipynb | 1799 --------------------------- coxph_trainer/coxph_trainer.py | 201 --- coxph_trainer/function.yaml | 108 -- coxph_trainer/item.yaml | 26 - coxph_trainer/requirements.txt | 6 - coxph_trainer/test_coxph_trainer.py | 136 -- xgb_test/function.yaml | 63 - xgb_test/item.yaml | 25 - xgb_test/requirements.txt | 8 - xgb_test/test_xgb_test.py | 148 --- xgb_test/xgb_test.ipynb | 708 ----------- xgb_test/xgb_test.py | 61 - xgb_trainer/function.yaml | 102 -- xgb_trainer/item.yaml | 24 - xgb_trainer/requirements.txt | 8 - xgb_trainer/test_xgb_trainer.py | 50 - xgb_trainer/xgb_trainer.ipynb | 1013 --------------- xgb_trainer/xgb_trainer.py | 160 --- 30 files changed, 6674 deletions(-) delete mode 100644 churn_server/README.md delete mode 100644 churn_server/churn_server.ipynb delete mode 100644 churn_server/churn_server.py delete mode 100644 churn_server/function.yaml delete mode 100644 churn_server/item.yaml delete mode 100644 churn_server/requirements.txt delete mode 100644 churn_server/test_churn_server.py delete mode 100644 coxph_test/coxph_test.ipynb delete mode 100644 coxph_test/coxph_test.py delete mode 100644 coxph_test/function.yaml delete mode 100644 coxph_test/item.yaml delete mode 100644 coxph_trainer/coxph_trainer.ipynb delete mode 100644 coxph_trainer/coxph_trainer.py delete mode 100644 coxph_trainer/function.yaml delete mode 100644 coxph_trainer/item.yaml delete mode 100644 coxph_trainer/requirements.txt delete mode 100644 coxph_trainer/test_coxph_trainer.py delete mode 100644 xgb_test/function.yaml delete mode 100644 xgb_test/item.yaml delete mode 100644 xgb_test/requirements.txt delete mode 100644 xgb_test/test_xgb_test.py delete mode 100644 xgb_test/xgb_test.ipynb delete mode 100644 xgb_test/xgb_test.py delete mode 100644 xgb_trainer/function.yaml delete mode 100644 xgb_trainer/item.yaml delete mode 100644 xgb_trainer/requirements.txt delete mode 100644 xgb_trainer/test_xgb_trainer.py delete mode 100644 xgb_trainer/xgb_trainer.ipynb delete mode 100644 xgb_trainer/xgb_trainer.py diff --git a/catalog.yaml b/catalog.yaml index c3364fefa..f603b1b9b 100644 --- a/catalog.yaml +++ b/catalog.yaml @@ -15,62 +15,6 @@ arc-to-parquet: kind: job versions: latest: arc_to_parquet/function.yaml -bert-embeddings: - categories: - - NLP - - BERT - - embeddings - description: Get BERT based embeddings for given text - docfile: bert_embeddings/bert_embeddings.ipynb - kind: remote - versions: - latest: bert_embeddings/function.yaml -churn-server: - categories: - - serving - - ml - description: churn classification and predictor - docfile: churn_server/churn_server.ipynb - kind: serving - versions: - latest: churn_server/function.yaml -concept-drift: - categories: - - ml - - serve - description: Deploy a streaming Concept Drift detector on a labeled stream - docfile: concept_drift/concept_drift.ipynb - kind: job - versions: - latest: concept_drift/function.yaml -concept-drift-streaming: - categories: - - ml - - serve - description: Deploy a streaming Concept Drift detector on a labeled stream. the - nuclio part of the concept_drift function - docfile: concept_drift_streaming/concept_drift_streaming.ipynb - kind: remote - versions: - latest: concept_drift_streaming/function.yaml -coxph-test: - categories: - - ml - - test - description: Test cox proportional hazards model - docfile: coxph_test/coxph_test.ipynb - kind: job - versions: - latest: coxph_test/function.yaml -coxph-trainer: - categories: - - training - - ml - description: cox proportional hazards, kaplan meier plots - docfile: coxph_trainer/coxph_trainer.ipynb - kind: job - versions: - latest: coxph_trainer/function.yaml describe: categories: - analysis @@ -94,14 +38,6 @@ describe-spark: kind: job versions: latest: describe_spark/function.yaml -feature-perms: - categories: - - analysis - description: estimate feature importances using permutations - docfile: feature_perms/feature_perms.ipynb - kind: job - versions: - latest: feature_perms/function.yaml feature-selection: categories: - data-prep @@ -144,13 +80,6 @@ model-monitoring-batch: kind: job versions: latest: model_monitoring_batch/function.yaml -model-monitoring-stream: - categories: [] - description: '' - docfile: model_monitoring_stream/model_monitoring_stream.ipynb - kind: remote - versions: - latest: model_monitoring_stream/function.yaml model-server: categories: - serving @@ -178,30 +107,6 @@ open-archive: kind: job versions: latest: open_archive/function.yaml -pandas-profiling-report: - categories: - - analysis - description: Create Pandas Profiling Report from Dataset - docfile: pandas_profiling_report/pandas_profiling_report.ipynb - kind: job - versions: - latest: pandas_profiling_report/function.yaml -project-runner: - categories: - - utils - description: Nuclio based - Cron scheduler for running your MLRun projects - docfile: project_runner/project_runner.ipynb - kind: remote - versions: - latest: project_runner/function.yaml -rnn-serving: - categories: - - model-serving - description: deploy an rnn based stock analysis model server. - docfile: rnn_serving/rnn_serving.ipynb - kind: serving - versions: - latest: rnn_serving/function.yaml send-email: categories: - notifications @@ -240,14 +145,6 @@ sklearn-classifier-dask: kind: job versions: latest: sklearn_classifier_dask/function.yaml -slack-notify: - categories: - - ops - description: Send Slack notification - docfile: slack_notify/slack_notify.ipynb - kind: job - versions: - latest: slack_notify/function.yaml spark-submit: categories: [] description: '' @@ -255,23 +152,6 @@ spark-submit: kind: job versions: latest: spark_submit/function.yaml -sql-to-file: - categories: - - data-prep - description: SQL To File - Ingest data using SQL query - docfile: sql_to_file/sql_to_file.ipynb - kind: job - versions: - latest: sql_to_file/function.yaml -stream-to-parquet: - categories: - - ml - - serve - description: Saves a stream to Parquet and can lunch drift detection task on it - docfile: stream_to_parquet/stream_to_parquet.ipynb - kind: remote - versions: - latest: stream_to_parquet/function.yaml test-classifier: categories: - ml @@ -281,15 +161,6 @@ test-classifier: kind: job versions: latest: test_classifier/function.yaml -tf1-serving: - categories: - - serving - - dl - description: tf1 image classification server - docfile: tf1_serving/tf1_serving.ipynb - kind: remote - versions: - latest: tf1_serving/function.yaml tf2-serving: categories: - serving @@ -299,15 +170,6 @@ tf2-serving: kind: remote versions: latest: tf2_serving/function.yaml -tf2-serving-v2: - categories: - - serving - - dl - description: tf2 image classification server v2 - docfile: tf2_serving_v2/tf2_serving_v2.ipynb - kind: serving - versions: - latest: tf2_serving_v2/function.yaml v2-model-server: categories: - serving @@ -326,45 +188,3 @@ v2-model-tester: kind: job versions: latest: v2_model_tester/function.yaml -virtual-drift: - categories: - - ml - - serve - - concept-drift - description: Compute drift magnitude between Time-Samples T and U - docfile: virtual_drift/virtual_drift.ipynb - kind: job - versions: - latest: virtual_drift/function.yaml -xgb-custom: - categories: - - model-testing - description: simulate data with outliers. - docfile: xgb_custom/xgb_custom.ipynb - kind: job - versions: - latest: xgb_custom/function.yaml -xgb-serving: - categories: - - model-serving - description: deploy an XGBoost model server. - docfile: xgb_serving/xgb_serving.ipynb - kind: remote - versions: - latest: xgb_serving/function.yaml -xgb-test: - categories: - - model-test - description: Test one or more classifier models against held-out dataset. - docfile: xgb_test/xgb_test.ipynb - kind: job - versions: - latest: xgb_test/function.yaml -xgb-trainer: - categories: - - model-prep - description: train multiple model types using xgboost. - docfile: xgb_trainer/xgb_trainer.ipynb - kind: job - versions: - latest: xgb_trainer/function.yaml diff --git a/churn_server/README.md b/churn_server/README.md deleted file mode 100644 index b6a517a5a..000000000 --- a/churn_server/README.md +++ /dev/null @@ -1,15 +0,0 @@ -# churn server - -the `churn-server` function was created as part of the **[churn demo](https://github.com/yjb-ds/demo-churn)**. A model server was needed that could combine the static model which answers the binary classification question "is this client churned or not-churned?" and the more dynamic model, which tries to add a time dimension to the prediction by providing an esdtimate of when and with what certainty churn events are likely to occur. - -the function `coxph_trainer` will output multiple models within a nested directory structire starting at `models_dest`: -* the coxph model is stored at `models_dest/cox` -* the [kaplan-meier](https://en.wikipedia.org/wiki/Kaplan%E2%80%93Meier_estimator) model at `models_dest/cox/km` - -each one of these pickled models stores all of the meta-data, vector and table estimates, including projections and scenarios - -with only slight modification, a more generic version of this server would enable its application in the domains of **[predictive maintenance](https://docs.microsoft.com/en-us/archive/msdn-magazine/2019/may/machine-learning-using-survival-analysis-for-predictive-maintenance)**, **[health](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3227332/)**, **finance** and **insurance** to name a few. - -**note** - -a small file `encode-data.csv` can be find in the root of this function folder, it is used to test the server. \ No newline at end of file diff --git a/churn_server/churn_server.ipynb b/churn_server/churn_server.ipynb deleted file mode 100644 index b8a962772..000000000 --- a/churn_server/churn_server.ipynb +++ /dev/null @@ -1,503 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "# **Churn Server**\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "in the following section we create a new model serving function which wraps our class , and specify model and other resources.\n", - "Deploying the serving function will provide us an http endpoint that can handle requests in real time.\n", - "This function is part of the [customer-churn-prediction demo](https://github.com/mlrun/demos/tree/master/customer-churn-prediction).
\n", - "To see how the model is trained or how the data-set is generated, check out `coxph_trainer` and `xgb_trainer` functions from the function marketplace repository." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Steps**\n", - "1. [Setup function parameters](#Setup-function-parameters)\n", - "2. [Importing the function](#Importing-the-function)\n", - "3. [Testing the function locally](#Testing-the-function-locally)\n", - "4. [Testing the function remotely](#Testing-the-function-remotely)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import warnings\n", - "warnings.filterwarnings(\"ignore\")" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Following packages are required, make sure to install\n", - "# !pip install xgboost==1.3.1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Setup function parameters**" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Setting up models path\n", - "xgb_model_path = 'https://s3.wasabisys.com/iguazio/models/function-marketplace-models/churn_server/xgb_model.pkl'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Importing the function**" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2021-10-14 06:10:16,104 [info] loaded project function-marketplace from MLRun DB\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import mlrun\n", - "mlrun.set_environment(project='function-marketplace')\n", - "\n", - "# Importing the function from the hub\n", - "fn = mlrun.import_function(\"hub://churn_server:development\")\n", - "fn.apply(mlrun.auto_mount())\n", - "\n", - "# Manually specifying needed packages \n", - "fn.spec.build.commands = ['pip install lifelines==0.22.8', 'pip install xgboost==1.3.1']\n", - "\n", - "# Adding the model \n", - "fn.add_model(key='xgb_model', model_path=xgb_model_path ,class_name='ChurnModel')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Testing the function locally**" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> Note that this function is a serving function, hence not needs to run, but deployed.
\n", - "\n", - "in order to test locally without deploying to server, mlrun provides mocking api that simulate the action." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2021-10-14 06:10:19,145 [info] model xgb_model was loaded\n", - "> 2021-10-14 06:10:19,145 [info] Initializing endpoint records\n", - "> 2021-10-14 06:10:19,164 [info] Loaded ['xgb_model']\n" - ] - } - ], - "source": [ - "# When mocking, class has to be present\n", - "from churn_server import *\n", - "\n", - "# Mocking function\n", - "server = fn.to_mock_server()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " gender senior partner deps tenure PhoneService MultipleLines \\\n", - "0 0 0 1 0 27 1 0 \n", - "1 0 1 0 0 1 1 1 \n", - "2 1 0 0 0 1 1 0 \n", - "3 0 0 0 0 53 1 1 \n", - "4 0 0 0 0 43 1 1 \n", - "\n", - " OnlineSecurity OnlineBackup DeviceProtection ... PaperlessBilling \\\n", - "0 1 0 0 ... 1 \n", - "1 0 0 0 ... 1 \n", - "2 0 0 0 ... 1 \n", - "3 0 1 1 ... 0 \n", - "4 0 1 1 ... 1 \n", - "\n", - " MonthlyCharges tenure_map ISP_1 ISP_2 Contract_1 Contract_2 \\\n", - "0 101.90 2.0 1 0 1 0 \n", - "1 85.70 0.0 1 0 0 0 \n", - "2 69.55 0.0 1 0 0 0 \n", - "3 105.55 4.0 1 0 0 1 \n", - "4 104.60 3.0 1 0 0 1 \n", - "\n", - " Payment_1 Payment_2 Payment_3 \n", - "0 1 0 0 \n", - "1 0 1 0 \n", - "2 0 1 0 \n", - "3 0 1 0 \n", - "4 0 1 0 \n", - "\n", - "[5 rows x 23 columns]" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import pandas as pd\n", - "\n", - "#declaring test_set path\n", - "test_set_path = \"https://s3.wasabisys.com/iguazio/data/function-marketplace-data/churn_server/test_set.csv\"\n", - "\n", - "# Getting the data\n", - "x_test = pd.read_csv(test_set_path)\n", - "y_test = x_test['labels']\n", - "x_test.drop(['labels'],axis=1,inplace=True)\n", - "x_test.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# KFServing protocol event\n", - "event_data = {\"inputs\": x_test.values.tolist()}" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "response = server.test(path='/v2/models/xgb_model/predict',body=event_data)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "When mocking to server, returned dict has the following fields : id, model_name, outputs\n" - ] - } - ], - "source": [ - "print(f'When mocking to server, returned dict has the following fields : {\", \".join([x for x in response.keys()])}')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Testing the function remotely**" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2021-10-14 06:10:20,163 [info] Starting remote function deploy\n", - "2021-10-14 06:10:20 (info) Deploying function\n", - "2021-10-14 06:10:20 (info) Building\n", - "2021-10-14 06:10:20 (info) Staging files and preparing base images\n", - "2021-10-14 06:10:20 (info) Building processor image\n", - "2021-10-14 06:10:21 (info) Build complete\n", - "2021-10-14 06:10:29 (info) Function deploy complete\n", - "> 2021-10-14 06:10:30,408 [info] successfully deployed function: {'internal_invocation_urls': ['nuclio-function-marketplace-churn-server.default-tenant.svc.cluster.local:8080'], 'external_invocation_urls': ['default-tenant.app.dev39.lab.iguazeng.com:31984']}\n" - ] - } - ], - "source": [ - "address = fn.deploy()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "model's accuracy : 0.7913907284768212\n" - ] - } - ], - "source": [ - "import json\n", - "import requests\n", - "\n", - "# using requests to predict\n", - "response = requests.put(address + \"/v2/models/xgb_model/predict\", json=json.dumps(event_data))\n", - "\n", - "# returned data is a string \n", - "y_predict = json.loads(response.text)['outputs']\n", - "accuracy = sum(1 for x,y in zip(y_predict,y_test) if x == y) / len(y_test)\n", - "print(f\"model's accuracy : {accuracy}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[Back to the top](#Churn-Server)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/churn_server/churn_server.py b/churn_server/churn_server.py deleted file mode 100644 index def2850da..000000000 --- a/churn_server/churn_server.py +++ /dev/null @@ -1,45 +0,0 @@ -# Copyright 2019 Iguazio -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# Generated by nuclio.export.NuclioExporter - -import numpy as np -from cloudpickle import load - - -import mlrun - - -class ChurnModel(mlrun.serving.V2ModelServer): - def load(self): - """ - load multiple models in nested folders, churn model only - """ - clf_model_file, extra_data = self.get_model(".pkl") - self.model = load(open(str(clf_model_file), "rb")) - if "cox" in extra_data.keys(): - cox_model_file = extra_data["cox"] - self.cox_model = load(open(str(cox_model_file), "rb")) - if "cox/km" in extra_data.keys(): - km_model_file = extra_data["cox/km"] - self.km_model = load(open(str(km_model_file), "rb")) - - def predict(self, body): - try: - feats = np.asarray(body["inputs"], dtype=np.float32).reshape(-1, 23) - result = self.model.predict(feats, validate_features=False) - return result.tolist() - except Exception as e: - raise Exception("Failed to predict %s" % e) - diff --git a/churn_server/function.yaml b/churn_server/function.yaml deleted file mode 100644 index 14f6c8cef..000000000 --- a/churn_server/function.yaml +++ /dev/null @@ -1,51 +0,0 @@ -kind: serving -metadata: - name: churn-server - tag: '' - hash: 805b4583ab8fa8df90c71d97eef54bbccf8729e8 - project: '' - labels: - author: Iguazio - framework: churn - categories: - - model-serving - - machine-learning -spec: - command: '' - args: [] - image: mlrun/ml-models - description: churn classification and predictor - min_replicas: 1 - max_replicas: 4 - env: - - name: ENABLE_EXPLAINER - value: 'False' - base_spec: - apiVersion: nuclio.io/v1 - kind: Function - metadata: - name: churn-server - labels: {} - annotations: - nuclio.io/generated_by: function generated from /User/functions/churn_server/churn_server.py - spec: - runtime: python:3.9 - handler: churn_server:handler - env: [] - volumes: [] - build: - commands: [] - noBaseImagesPull: true - functionSourceCode: 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 - source: '' - function_kind: serving_v2 - default_class: ChurnModel - build: - commands: - - python -m pip install xgboost==1.3.1 lifelines==0.22.8 - code_origin: https://github.com/daniels290813/functions.git#34d1b0d7e26924d931c2df2869425d01df21a23c:/User/functions/churn_server/churn_server.py - origin_filename: /User/functions/churn_server/churn_server.py - secret_sources: [] - disable_auto_mount: false - affinity: null -verbose: false diff --git a/churn_server/item.yaml b/churn_server/item.yaml deleted file mode 100644 index 09ba9b713..000000000 --- a/churn_server/item.yaml +++ /dev/null @@ -1,32 +0,0 @@ -apiVersion: v1 -categories: -- model-serving -- machine-learning -description: churn classification and predictor -doc: '' -example: churn_server.ipynb -generationDate: 2022-08-28:17-25 -hidden: false -icon: '' -labels: - author: Iguazio - framework: churn -maintainers: [] -marketplaceType: '' -mlrunVersion: 1.1.0 -name: churn-server -platformVersion: 3.5.0 -spec: - customFields: - default_class: ChurnModel - env: - ENABLE_EXPLAINER: 'False' - filename: churn_server.py - handler: handler - image: mlrun/ml-models - kind: serving - requirements: - - xgboost==1.3.1 - - lifelines==0.22.8 -url: '' -version: 1.2.0 diff --git a/churn_server/requirements.txt b/churn_server/requirements.txt deleted file mode 100644 index eb8827c5c..000000000 --- a/churn_server/requirements.txt +++ /dev/null @@ -1,2 +0,0 @@ -wget -pygit2 \ No newline at end of file diff --git a/churn_server/test_churn_server.py b/churn_server/test_churn_server.py deleted file mode 100644 index 64d1b8490..000000000 --- a/churn_server/test_churn_server.py +++ /dev/null @@ -1,67 +0,0 @@ -# Copyright 2019 Iguazio -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -import os -import wget -from mlrun import import_function -import os.path -from os import path -import mlrun -from pygit2 import Repository - - -MODEL_PATH = os.path.join(os.path.abspath("./"), "models") -MODEL = MODEL_PATH + "model.pt" - - -def set_mlrun_hub_url(): - branch = Repository(".").head.shorthand - hub_url = "https://raw.githubusercontent.com/mlrun/functions/{}/churn_server/function.yaml".format( - branch - ) - mlrun.mlconf.hub_url = hub_url - - -def download_pretrained_model(model_path): - # Run this to download the pre-trained model to your `models` directory - import os - - model_location = None - saved_models_directory = model_path - # Create paths - os.makedirs(saved_models_directory, exist_ok=1) - model_filepath = os.path.join( - saved_models_directory, os.path.basename(model_location) - ) - wget.download(model_location, model_filepath) - - -def test_local_churn_server(): - # set_mlrun_hub_url() - # model_path = os.path.join(os.path.abspath("./"), "models") - # model = model_path + "/model.pt" - # if not path.exists(model): - # download_pretrained_model(model_path) - # fn = import_function("hub://churn_server") - # fn.add_model("mymodel", model_path=model, class_name="ChurnModel") - # # create an emulator (mock server) from the function configuration) - # server = fn.to_mock_server() - # - # instances = [ - # "I had a pleasure to work with such dedicated team. Looking forward to \ - # cooperate with each and every one of them again." - # ] - # result = server.test("/v2/models/mymodel/infer", {"instances": instances}) - # assert result[0] == 2 - print("we need to download churn model") diff --git a/coxph_test/coxph_test.ipynb b/coxph_test/coxph_test.ipynb deleted file mode 100644 index 0ee0b29c9..000000000 --- a/coxph_test/coxph_test.ipynb +++ /dev/null @@ -1,969 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# **CoxPH test**" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This function handles evaluating Cox proportional hazards model performance, test one or more classifier models against held-out dataset Using held-out test features,
and evaluates the peformance of the estimated model.
\n", - "Can be part of a kubeflow pipeline as a test step that is run post EDA and training/validation cycles.
\n", - "This function is part of the [customer-churn-prediction](https://github.com/mlrun/demos/tree/master/customer-churn-prediction) demo.
\n", - "To see how the model is trained or how the data-set is generated, check out `coxph_trainer` function from the function marketplace repository" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Steps**\n", - "1. [Setup function parameters](#Setup-function-parameters)\n", - "2. [Importing the function](#Importing-the-function)\n", - "3. [Running the function locally](#Running-the-function-locally)\n", - "4. [Running the function remotely](#Running-the-function-remotely)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import warnings\n", - "warnings.filterwarnings(\"ignore\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Setup function parameters**" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "test_set = \"https://s3.wasabisys.com/iguazio/data/function-marketplace-data/xgb_test/test_set.csv\"\n", - "models_path = \"https://s3.wasabisys.com/iguazio/models/function-marketplace-models/coxph_test/cx-model.pkl\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Importing the function**" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2021-10-17 13:38:44,758 [info] loaded project function-marketplace from MLRun DB\n" - ] - } - ], - "source": [ - "import mlrun\n", - "mlrun.set_environment(project='function-marketplace')\n", - "\n", - "fn = mlrun.import_function(\"hub://coxph_test\")\n", - "fn.apply(mlrun.auto_mount())\n", - "\n", - "fn.spec.build.image=\"mlrun/ml-models\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Running the function locally**" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2021-10-17 13:38:45,149 [info] starting run tasks_coxph_test uid=be4bd195e5c146a69ecdee3b6a631569 DB=http://mlrun-api:8080\n", - "> 2021-10-17 13:38:49,428 [info] cox tester not implemented\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "
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projectuiditerstartstatenamelabelsinputsparametersresultsartifacts
function-marketplace0Oct 17 13:38:45completedtasks_coxph_test
v3io_user=dani
kind=
owner=dani
host=jupyter-dani-6bfbd76d96-zxx6f
test_set
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label_column=labels
plots_dest=plots/xgb_test
cox-test-summary
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covariatecoefexp(coef)se(coef)coef lower 95%coef upper 95%exp(coef) lower 95%exp(coef) upper 95%zp-log2(p)
0gender0.7129862.040073e+000.3434710.0397951.3861761.0405983.9995282.0758260.0379104.721274
1senior-0.3301377.188252e-010.444705-1.2017430.5414680.3006701.718528-0.7423740.4578611.127018
2partner-0.3944496.740516e-010.432243-1.2416300.4527320.2889131.572603-0.9125620.3614731.468041
3deps0.6163731.852199e+000.499075-0.3617971.5945430.6964244.9260801.2350310.2168192.205436
4MultipleLines-0.7878854.548059e-011.087536-2.9194171.3436480.0539653.832999-0.7244670.4687791.093020
5OnlineSecurity-0.7666834.645512e-011.299746-3.3141391.7807720.0363655.934435-0.5898720.5552770.848721
6OnlineBackup-0.4666916.270740e-010.949068-2.3268291.3934480.0976054.028715-0.4917360.6229060.682914
7DeviceProtection-0.4126206.619136e-011.083731-2.5366941.7114530.0791285.537002-0.3807410.7033960.507591
8TechSupport0.5097561.664885e+001.168080-1.7796382.7991500.16869916.4306750.4364050.6625430.593915
9PaperlessBilling0.3499701.419025e+000.408827-0.4513171.1512570.6367893.1621650.8560330.3919801.351150
10MonthlyCharges-0.0783999.245958e-010.194463-0.4595390.3027420.6315741.353566-0.4031540.6868350.541965
11Contract_1-2.1882791.121096e-010.712197-3.584159-0.7923980.0277600.452758-3.0725750.0021228.880219
12Contract_2-19.9407672.186930e-093478.684973-6838.0380276798.1564930.000000inf-0.0057320.9954260.006614
13Payment_1-0.8654244.208732e-010.615020-2.0708400.3399930.1260801.404937-1.4071480.1593832.649426
14Payment_20.4583631.581483e+000.446978-0.4176971.3344230.6585623.7978051.0254720.3051411.712453
15Payment_30.2325191.261774e+000.641176-1.0241621.4892000.3590974.4335470.3626440.7168700.480216
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" - ], - "text/plain": [ - " covariate coef exp(coef) se(coef) coef lower 95% \\\n", - "0 gender 0.712986 2.040073e+00 0.343471 0.039795 \n", - "1 senior -0.330137 7.188252e-01 0.444705 -1.201743 \n", - "2 partner -0.394449 6.740516e-01 0.432243 -1.241630 \n", - "3 deps 0.616373 1.852199e+00 0.499075 -0.361797 \n", - "4 MultipleLines -0.787885 4.548059e-01 1.087536 -2.919417 \n", - "5 OnlineSecurity -0.766683 4.645512e-01 1.299746 -3.314139 \n", - "6 OnlineBackup -0.466691 6.270740e-01 0.949068 -2.326829 \n", - "7 DeviceProtection -0.412620 6.619136e-01 1.083731 -2.536694 \n", - "8 TechSupport 0.509756 1.664885e+00 1.168080 -1.779638 \n", - "9 PaperlessBilling 0.349970 1.419025e+00 0.408827 -0.451317 \n", - "10 MonthlyCharges -0.078399 9.245958e-01 0.194463 -0.459539 \n", - "11 Contract_1 -2.188279 1.121096e-01 0.712197 -3.584159 \n", - "12 Contract_2 -19.940767 2.186930e-09 3478.684973 -6838.038027 \n", - "13 Payment_1 -0.865424 4.208732e-01 0.615020 -2.070840 \n", - "14 Payment_2 0.458363 1.581483e+00 0.446978 -0.417697 \n", - "15 Payment_3 0.232519 1.261774e+00 0.641176 -1.024162 \n", - "\n", - " coef upper 95% exp(coef) lower 95% exp(coef) upper 95% z \\\n", - "0 1.386176 1.040598 3.999528 2.075826 \n", - "1 0.541468 0.300670 1.718528 -0.742374 \n", - "2 0.452732 0.288913 1.572603 -0.912562 \n", - "3 1.594543 0.696424 4.926080 1.235031 \n", - "4 1.343648 0.053965 3.832999 -0.724467 \n", - "5 1.780772 0.036365 5.934435 -0.589872 \n", - "6 1.393448 0.097605 4.028715 -0.491736 \n", - "7 1.711453 0.079128 5.537002 -0.380741 \n", - "8 2.799150 0.168699 16.430675 0.436405 \n", - "9 1.151257 0.636789 3.162165 0.856033 \n", - "10 0.302742 0.631574 1.353566 -0.403154 \n", - "11 -0.792398 0.027760 0.452758 -3.072575 \n", - "12 6798.156493 0.000000 inf -0.005732 \n", - "13 0.339993 0.126080 1.404937 -1.407148 \n", - "14 1.334423 0.658562 3.797805 1.025472 \n", - "15 1.489200 0.359097 4.433547 0.362644 \n", - "\n", - " p -log2(p) \n", - "0 0.037910 4.721274 \n", - "1 0.457861 1.127018 \n", - "2 0.361473 1.468041 \n", - "3 0.216819 2.205436 \n", - "4 0.468779 1.093020 \n", - "5 0.555277 0.848721 \n", - "6 0.622906 0.682914 \n", - "7 0.703396 0.507591 \n", - "8 0.662543 0.593915 \n", - "9 0.391980 1.351150 \n", - "10 0.686835 0.541965 \n", - "11 0.002122 8.880219 \n", - "12 0.995426 0.006614 \n", - "13 0.159383 2.649426 \n", - "14 0.305141 1.712453 \n", - "15 0.716870 0.480216 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "coxph_run.artifact('cox-test-summary').show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Running the function remotely**" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2021-10-17 13:38:49,644 [info] starting run tasks_coxph_test uid=c28d05f0261b4c60956eee528bf68e96 DB=http://mlrun-api:8080\n", - "> 2021-10-17 13:38:49,776 [info] Job is running in the background, pod: tasks-coxph-test-hfj9b\n", - "> 2021-10-17 13:38:59,015 [info] cox tester not implemented\n", - "> 2021-10-17 13:38:59,049 [info] run executed, status=completed\n", - "final state: completed\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "
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projectuiditerstartstatenamelabelsinputsparametersresultsartifacts
function-marketplace0Oct 17 13:38:56completedtasks_coxph_test
v3io_user=dani
kind=job
owner=dani
host=tasks-coxph-test-hfj9b
test_set
models_path
label_column=labels
plots_dest=plots/xgb_test
cox-test-summary
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" \"models_path\" : models_path},\n", - " local=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[Back to the top](#CoxPH-test)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/coxph_test/coxph_test.py b/coxph_test/coxph_test.py deleted file mode 100644 index f635fbdf3..000000000 --- a/coxph_test/coxph_test.py +++ /dev/null @@ -1,75 +0,0 @@ -# Copyright 2019 Iguazio -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# Generated by nuclio.export.NuclioExporter - -import warnings - -warnings.simplefilter(action="ignore", category=FutureWarning) - -import os -import pandas as pd -from mlrun.datastore import DataItem -from mlrun.artifacts import get_model -from cloudpickle import load -from mlrun.mlutils.models import eval_class_model - - -def cox_test( - context, - models_path: DataItem, - test_set: DataItem, - label_column: str, - plots_dest: str = "plots", - model_evaluator=None, -) -> None: - """Test one or more classifier models against held-out dataset - - Using held-out test features, evaluates the peformance of the estimated model - - Can be part of a kubeflow pipeline as a test step that is run post EDA and - training/validation cycles - - :param context: the function context - :param model_file: model artifact to be tested - :param test_set: test features and labels - :param label_column: column name for ground truth labels - :param score_method: for multiclass classification - :param plots_dest: dir for test plots - :param model_evaluator: WIP: specific method to generate eval, passed in as string - or available in this folder - """ - xtest = test_set.as_df() - ytest = xtest.pop(label_column) - - model_file, model_obj, _ = get_model(models_path.url, suffix=".pkl") - model_obj = load(open(str(model_file), "rb")) - - try: - if not model_evaluator: - eval_metrics = eval_class_model(context, xtest, ytest, model_obj) - - model_plots = eval_metrics.pop("plots") - model_tables = eval_metrics.pop("tables") - for plot in model_plots: - context.log_artifact(plot, local_path=f"{plots_dest}/{plot.key}.html") - for tbl in model_tables: - context.log_artifact(tbl, local_path=f"{plots_dest}/{plot.key}.csv") - - context.log_results(eval_metrics) - except: - context.log_dataset( - "cox-test-summary", df=model_obj.summary, index=True, format="csv" - ) - context.logger.info("cox tester not implemented") diff --git a/coxph_test/function.yaml b/coxph_test/function.yaml deleted file mode 100644 index e09fb90a0..000000000 --- a/coxph_test/function.yaml +++ /dev/null @@ -1,63 +0,0 @@ -kind: job -metadata: - name: coxph-test - tag: '' - hash: 1edbfe55668a7dcfaa59a6aeb5b3b1bd3f594aab - project: '' - labels: - author: Iguazio - framework: survival - categories: - - machine-learning - - model-testing -spec: - command: '' - args: [] - image: mlrun/ml-models - env: [] - default_handler: cox_test - entry_points: - cox_test: - name: cox_test - doc: 'Test one or more classifier models against held-out dataset - - - Using held-out test features, evaluates the peformance of the estimated model - - - Can be part of a kubeflow pipeline as a test step that is run post EDA and - - training/validation cycles' - parameters: - - name: context - doc: the function context - default: '' - - name: models_path - type: DataItem - default: '' - - name: test_set - type: DataItem - doc: test features and labels - default: '' - - name: label_column - type: str - doc: column name for ground truth labels - default: '' - - name: plots_dest - type: str - doc: dir for test plots - default: plots - - name: model_evaluator - doc: 'WIP: specific method to generate eval, passed in as string or available - in this folder' - default: null - outputs: - - default: '' - lineno: 15 - description: Test cox proportional hazards model - build: - functionSourceCode: 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 - commands: [] - code_origin: https://github.com/daniels290813/functions.git#55a79c32be5d233cc11efcf40cd3edbe309bfdef:/home/kali/functions/coxph_test/coxph_test.py - affinity: null -verbose: false diff --git a/coxph_test/item.yaml b/coxph_test/item.yaml deleted file mode 100644 index 241e6d560..000000000 --- a/coxph_test/item.yaml +++ /dev/null @@ -1,26 +0,0 @@ -apiVersion: v1 -categories: -- machine-learning -- model-testing -description: Test cox proportional hazards model -doc: '' -example: coxph_test.ipynb -generationDate: 2022-08-28:17-25 -hidden: false -icon: '' -labels: - author: Iguazio - framework: survival -maintainers: [] -marketplaceType: '' -mlrunVersion: 1.1.0 -name: coxph-test -platformVersion: 3.5.0 -spec: - filename: coxph_test.py - handler: cox_test - image: mlrun/ml-models - kind: job - requirements: [] -url: '' -version: 1.1.0 diff --git a/coxph_trainer/coxph_trainer.ipynb b/coxph_trainer/coxph_trainer.ipynb deleted file mode 100644 index d49d6ccf8..000000000 --- a/coxph_trainer/coxph_trainer.ipynb +++ /dev/null @@ -1,1799 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# **Coxph trainer - Survival analysis**" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The following function provides both [Cox proprotional hazards modelling](https://en.wikipedia.org/wiki/Proportional_hazards_model)\n", - "and [Kaplan-Meier](https://en.wikipedia.org/wiki/Kaplan%E2%80%93Meier_estimator) plots." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Basics of the Cox proportional hazards model**\n", - "The purpose of the model is to evaluate simultaneously the effect of several factors on survival.
In other words, it allows us to examine how specified factors influence the rate of a particular event happening (e.g., infection, death) at a particular point in time.
This rate is commonly referred as the hazard rate ([link](http://www.sthda.com/english/wiki/cox-proportional-hazards-model)).\n", - "\n", - "### **Kaplan-Meier survival estimate**\n", - "The Kaplan-Meier (KM) method is a non-parametric method used to estimate the survival probability from observed survival times (Kaplan and Meier, 1958)([link](http://www.sthda.com/english/wiki/survival-analysis-basics))." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **The function**\n", - "train models to predict the timing of events.
\n", - "Although identical in structure to other training functions, this one\n", - "requires generating a 'Y' that represents the age/duration/tenure of\n", - "the obervation, designated 'tenure' here, and a binary labels columns that\n", - "represents the event of interest, churned/not-churned.
\n", - "In addition, there is a strata_cols parameter, representing a list of\n", - "stratification (aka grouping) variables." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The following example covers:\n", - "\n", - "- [Data exploration](#Data-exploration)\n", - "- [Training Cox proprotional hazards and Kaplan-Meier model](#Training-Cox-proprotional-hazards-and-Kaplan-Meier-model)\n", - "- - [Importing the function](#Importing-the-function)\n", - "- - [Setup function parameters](#Setup-function-parameters)\n", - "- - [Running the function locally](#Running-the-function-locally)\n", - "- [A peek at a pickled kaplan-meier model](#A-peek-at-a-pickled-kaplan-meier-model)\n", - "- [A peek at a pickeld cox hazards default model](#A-peek-at-a-pickeld-cox-hazards-default-model)\n", - "- [Some potential default analyses of coxph](#Some-potential-default-analyses-of-coxph)\n", - "- - [Running the function remotely](#Running-the-function-remotely)\n", - "\n", - "We will train on [Telco Customer Churn dataset](https://www.kaggle.com/blastchar/telco-customer-churn) from kaggle, click the link for context.
\n", - "The dataset is transformed using [one-hot-encoding](https://en.wikipedia.org/wiki/One-hot), check out [customer-churn-prediction demo](https://github.com/mlrun/demos/tree/master/customer-churn-prediction) in the clean_data section for further information." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import warnings\n", - "warnings.filterwarnings(\"ignore\")" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Make sure the following libraries are installed \n", - "# !pip install lifelines" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Data exploration**" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "raw dataset\n" - ] - }, - { - "data": { - "text/html": [ - "
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customerIDgenderSeniorCitizenPartnerDependentstenurePhoneServiceMultipleLinesInternetServiceOnlineSecurity...DeviceProtectionTechSupportStreamingTVStreamingMoviesContractPaperlessBillingPaymentMethodMonthlyChargesTotalChargesChurn
07590-VHVEGFemale0YesNo1NoNo phone serviceDSLNo...NoNoNoNoMonth-to-monthYesElectronic check29.8529.85No
15575-GNVDEMale0NoNo34YesNoDSLYes...YesNoNoNoOne yearNoMailed check56.951889.5No
23668-QPYBKMale0NoNo2YesNoDSLYes...NoNoNoNoMonth-to-monthYesMailed check53.85108.15Yes
37795-CFOCWMale0NoNo45NoNo phone serviceDSLYes...YesYesNoNoOne yearNoBank transfer (automatic)42.301840.75No
49237-HQITUFemale0NoNo2YesNoFiber opticNo...NoNoNoNoMonth-to-monthYesElectronic check70.70151.65Yes
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" - ], - "text/plain": [ - " customerID gender SeniorCitizen Partner Dependents tenure PhoneService \\\n", - "0 7590-VHVEG Female 0 Yes No 1 No \n", - "1 5575-GNVDE Male 0 No No 34 Yes \n", - "2 3668-QPYBK Male 0 No No 2 Yes \n", - "3 7795-CFOCW Male 0 No No 45 No \n", - "4 9237-HQITU Female 0 No No 2 Yes \n", - "\n", - " MultipleLines InternetService OnlineSecurity ... DeviceProtection \\\n", - "0 No phone service DSL No ... No \n", - "1 No DSL Yes ... Yes \n", - "2 No DSL Yes ... No \n", - "3 No phone service DSL Yes ... Yes \n", - "4 No Fiber optic No ... No \n", - "\n", - " TechSupport StreamingTV StreamingMovies Contract PaperlessBilling \\\n", - "0 No No No Month-to-month Yes \n", - "1 No No No One year No \n", - "2 No No No Month-to-month Yes \n", - "3 Yes No No One year No \n", - "4 No No No Month-to-month Yes \n", - "\n", - " PaymentMethod MonthlyCharges TotalCharges Churn \n", - "0 Electronic check 29.85 29.85 No \n", - "1 Mailed check 56.95 1889.5 No \n", - "2 Mailed check 53.85 108.15 Yes \n", - "3 Bank transfer (automatic) 42.30 1840.75 No \n", - "4 Electronic check 70.70 151.65 Yes \n", - "\n", - "[5 rows x 21 columns]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Getting raw data as - downloaded from kaggle\n", - "import pandas as pd\n", - "\n", - "df = pd.read_csv(\"https://s3.wasabisys.com/iguazio/data/function-marketplace-data/coxph_trainer/WA_Fn-UseC_-Telco-Customer-Churn.csv\")\n", - "print('raw dataset')\n", - "df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "encoded dataset\n" - ] - }, - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " gender senior partner deps tenure PhoneService MultipleLines \\\n", - "0 0 0 1 0 1 0 0 \n", - "1 1 0 0 0 34 1 0 \n", - "2 1 0 0 0 2 1 0 \n", - "3 1 0 0 0 45 0 0 \n", - "4 0 0 0 0 2 1 0 \n", - "\n", - " InternetService OnlineSecurity OnlineBackup DeviceProtection \\\n", - "0 0 0 1 0 \n", - "1 0 1 0 1 \n", - "2 0 1 1 0 \n", - "3 0 1 0 1 \n", - "4 1 0 0 0 \n", - "\n", - " TechSupport StreamingTV StreamingMovies Contract PaperlessBilling \\\n", - "0 0 0 0 0 1 \n", - "1 0 0 0 1 0 \n", - "2 0 0 0 0 1 \n", - "3 1 0 0 1 0 \n", - "4 0 0 0 0 1 \n", - "\n", - " PaymentMethod MonthlyCharges labels tenure_map \n", - "0 2 29.85 0 0.0 \n", - "1 3 56.95 0 2.0 \n", - "2 3 53.85 1 0.0 \n", - "3 0 42.30 0 3.0 \n", - "4 2 70.70 1 0.0 " - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = pd.read_csv(\"https://s3.wasabisys.com/iguazio/data/function-marketplace-data/coxph_trainer/encoded-data.csv\")\n", - "print('encoded dataset')\n", - "df.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## **Training Cox proprotional hazards and Kaplan-Meier model**" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Importing the function**" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2021-10-13 13:22:07,678 [info] loaded project function-marketplace from MLRun DB\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import mlrun\n", - "mlrun.set_environment(project='function-marketplace')\n", - "\n", - "fn = mlrun.import_function(\"hub://coxph_trainer\")\n", - "fn.image='mlrun/mlrun'\n", - "fn.apply(mlrun.auto_mount())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Setup function parameters**" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "task = mlrun.new_task(name = \"tasks-survive-trainer\",\n", - " params = {\"event_column\" : \"labels\", \n", - " \"strata_cols\" : ['InternetService', 'StreamingMovies', 'StreamingTV', 'PhoneService'],\n", - " \"p_value\" : 0.005,\n", - " \"encode_cols\" : {\"Contract\" : \"Contract\",\n", - " \"PaymentMethod\" : \"Payment\"},\n", - " \"models_dest\" : 'models/cox',\n", - " \"file_ext\" : \"csv\"})" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Running the function locally**" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2021-10-13 13:18:36,975 [info] starting run tasks-survive-trainer uid=c525c7402c7e4188b0e22996e8fc682c DB=http://mlrun-api:8080\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "
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default0Oct 13 13:18:37completedtasks-survive-trainer
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event_column=labels
strata_cols=['InternetService', 'StreamingMovies', 'StreamingTV', 'PhoneService']
p_value=0.005
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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# running the function with the task provided\n", - "coxph_run = fn.run(task,\n", - " local=True,\n", - " inputs={\"dataset\" : \"https://s3.wasabisys.com/iguazio/data/function-marketplace-data/coxph_trainer/encoded-data.csv\"})" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **A peek at a pickled kaplan-meier model**" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# loading the model trained\n", - "from mlrun.artifacts import get_model\n", - "import pickle\n", - "model_file, model_obj, _ = get_model(coxph_run.artifact('km-model'))\n", - "model = pickle.load(open(model_file,'rb'))" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1 0.969027\n", - "10 0.869452\n", - "30 0.781377\n", - "100 0.668167\n", - "200 0.668167\n", - "Name: KM_estimate, dtype: float64" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.predict([1,10,30,100,200])" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "m = model.plot(figsize=(11,6))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **A peek at a pickeld cox hazards default model**" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# loading the model trained\n", - "from mlrun.artifacts import get_model\n", - "import pickle\n", - "model_file, model_obj, _ = get_model(coxph_run.artifact('cx-model'))\n", - "model = pickle.load(open(model_file,'rb'))" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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modellifelines.CoxPHFitter
duration col'tenure'
event col'labels'
strata[InternetService, StreamingMovies, StreamingTV...
baseline estimationbreslow
number of observations226
number of events observed55
partial log-likelihood-102.57
time fit was run2021-10-13 13:18:38 UTC
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coefexp(coef)se(coef)coef lower 95%coef upper 95%exp(coef) lower 95%exp(coef) upper 95%zp-log2(p)
gender0.712.040.340.041.391.044.002.080.044.72
senior-0.330.720.44-1.200.540.301.72-0.740.461.13
partner-0.390.670.43-1.240.450.291.57-0.910.361.47
deps0.621.850.50-0.361.590.704.931.240.222.21
MultipleLines-0.790.451.09-2.921.340.053.83-0.720.471.09
OnlineSecurity-0.770.461.30-3.311.780.045.93-0.590.560.85
OnlineBackup-0.470.630.95-2.331.390.104.03-0.490.620.68
DeviceProtection-0.410.661.08-2.541.710.085.54-0.380.700.51
TechSupport0.511.661.17-1.782.800.1716.430.440.660.59
PaperlessBilling0.351.420.41-0.451.150.643.160.860.391.35
MonthlyCharges-0.080.920.19-0.460.300.631.35-0.400.690.54
Contract_1-2.190.110.71-3.58-0.790.030.45-3.07<0.0058.88
Contract_2-19.940.003478.68-6838.046798.160.00inf-0.011.000.01
Payment_1-0.870.420.62-2.070.340.131.40-1.410.162.65
Payment_20.461.580.45-0.421.330.663.801.030.311.71
Payment_30.231.260.64-1.021.490.364.430.360.720.48

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Concordance0.88
Partial AIC237.14
log-likelihood ratio test106.72 on 16 df
-log2(p) of ll-ratio test48.92
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" - ], - "text/latex": [ - "\\begin{tabular}{lrrrrrrrrrr}\n", - "\\toprule\n", - "{} & coef & exp(coef) & se(coef) & coef lower 95\\% & coef upper 95\\% & exp(coef) lower 95\\% & exp(coef) upper 95\\% & z & p & -log2(p) \\\\\n", - "covariate & & & & & & & & & & \\\\\n", - "\\midrule\n", - "gender & 0.71 & 2.04 & 0.34 & 0.04 & 1.39 & 1.04 & 4.00 & 2.08 & 0.04 & 4.72 \\\\\n", - "senior & -0.33 & 0.72 & 0.44 & -1.20 & 0.54 & 0.30 & 1.72 & -0.74 & 0.46 & 1.13 \\\\\n", - "partner & -0.39 & 0.67 & 0.43 & -1.24 & 0.45 & 0.29 & 1.57 & -0.91 & 0.36 & 1.47 \\\\\n", - "deps & 0.62 & 1.85 & 0.50 & -0.36 & 1.59 & 0.70 & 4.93 & 1.24 & 0.22 & 2.21 \\\\\n", - "MultipleLines & -0.79 & 0.45 & 1.09 & -2.92 & 1.34 & 0.05 & 3.83 & -0.72 & 0.47 & 1.09 \\\\\n", - "OnlineSecurity & -0.77 & 0.46 & 1.30 & -3.31 & 1.78 & 0.04 & 5.93 & -0.59 & 0.56 & 0.85 \\\\\n", - "OnlineBackup & -0.47 & 0.63 & 0.95 & -2.33 & 1.39 & 0.10 & 4.03 & -0.49 & 0.62 & 0.68 \\\\\n", - "DeviceProtection & -0.41 & 0.66 & 1.08 & -2.54 & 1.71 & 0.08 & 5.54 & -0.38 & 0.70 & 0.51 \\\\\n", - "TechSupport & 0.51 & 1.66 & 1.17 & -1.78 & 2.80 & 0.17 & 16.43 & 0.44 & 0.66 & 0.59 \\\\\n", - "PaperlessBilling & 0.35 & 1.42 & 0.41 & -0.45 & 1.15 & 0.64 & 3.16 & 0.86 & 0.39 & 1.35 \\\\\n", - "MonthlyCharges & -0.08 & 0.92 & 0.19 & -0.46 & 0.30 & 0.63 & 1.35 & -0.40 & 0.69 & 0.54 \\\\\n", - "Contract\\_1 & -2.19 & 0.11 & 0.71 & -3.58 & -0.79 & 0.03 & 0.45 & -3.07 & 0.00 & 8.88 \\\\\n", - "Contract\\_2 & -19.94 & 0.00 & 3478.68 & -6838.04 & 6798.16 & 0.00 & inf & -0.01 & 1.00 & 0.01 \\\\\n", - "Payment\\_1 & -0.87 & 0.42 & 0.62 & -2.07 & 0.34 & 0.13 & 1.40 & -1.41 & 0.16 & 2.65 \\\\\n", - "Payment\\_2 & 0.46 & 1.58 & 0.45 & -0.42 & 1.33 & 0.66 & 3.80 & 1.03 & 0.31 & 1.71 \\\\\n", - "Payment\\_3 & 0.23 & 1.26 & 0.64 & -1.02 & 1.49 & 0.36 & 4.43 & 0.36 & 0.72 & 0.48 \\\\\n", - "\\bottomrule\n", - "\\end{tabular}\n" - ], - "text/plain": [ - "\n", - " duration col = 'tenure'\n", - " event col = 'labels'\n", - " strata = ['InternetService', 'StreamingMovies', 'StreamingTV', 'PhoneService']\n", - " baseline estimation = breslow\n", - " number of observations = 226\n", - "number of events observed = 55\n", - " partial log-likelihood = -102.57\n", - " time fit was run = 2021-10-13 13:18:38 UTC\n", - "\n", - "---\n", - " coef exp(coef) se(coef) coef lower 95% coef upper 95% exp(coef) lower 95% exp(coef) upper 95%\n", - "covariate \n", - "gender 0.71 2.04 0.34 0.04 1.39 1.04 4.00\n", - "senior -0.33 0.72 0.44 -1.20 0.54 0.30 1.72\n", - "partner -0.39 0.67 0.43 -1.24 0.45 0.29 1.57\n", - "deps 0.62 1.85 0.50 -0.36 1.59 0.70 4.93\n", - "MultipleLines -0.79 0.45 1.09 -2.92 1.34 0.05 3.83\n", - "OnlineSecurity -0.77 0.46 1.30 -3.31 1.78 0.04 5.93\n", - "OnlineBackup -0.47 0.63 0.95 -2.33 1.39 0.10 4.03\n", - "DeviceProtection -0.41 0.66 1.08 -2.54 1.71 0.08 5.54\n", - "TechSupport 0.51 1.66 1.17 -1.78 2.80 0.17 16.43\n", - "PaperlessBilling 0.35 1.42 0.41 -0.45 1.15 0.64 3.16\n", - "MonthlyCharges -0.08 0.92 0.19 -0.46 0.30 0.63 1.35\n", - "Contract_1 -2.19 0.11 0.71 -3.58 -0.79 0.03 0.45\n", - "Contract_2 -19.94 0.00 3478.68 -6838.04 6798.16 0.00 inf\n", - "Payment_1 -0.87 0.42 0.62 -2.07 0.34 0.13 1.40\n", - "Payment_2 0.46 1.58 0.45 -0.42 1.33 0.66 3.80\n", - "Payment_3 0.23 1.26 0.64 -1.02 1.49 0.36 4.43\n", - "\n", - " z p -log2(p)\n", - "covariate \n", - "gender 2.08 0.04 4.72\n", - "senior -0.74 0.46 1.13\n", - "partner -0.91 0.36 1.47\n", - "deps 1.24 0.22 2.21\n", - "MultipleLines -0.72 0.47 1.09\n", - "OnlineSecurity -0.59 0.56 0.85\n", - "OnlineBackup -0.49 0.62 0.68\n", - "DeviceProtection -0.38 0.70 0.51\n", - "TechSupport 0.44 0.66 0.59\n", - "PaperlessBilling 0.86 0.39 1.35\n", - "MonthlyCharges -0.40 0.69 0.54\n", - "Contract_1 -3.07 <0.005 8.88\n", - "Contract_2 -0.01 1.00 0.01\n", - "Payment_1 -1.41 0.16 2.65\n", - "Payment_2 1.03 0.31 1.71\n", - "Payment_3 0.36 0.72 0.48\n", - "---\n", - "Concordance = 0.88\n", - "Partial AIC = 237.14\n", - "log-likelihood ratio test = 106.72 on 16 df\n", - "-log2(p) of ll-ratio test = 48.92" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "model.print_summary()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Some potential default analyses of coxph**" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "model.baseline_survival_.plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* run the following for each of the lines that passes some test (p < 0.005,for example):
\n", - " `model.plot_covariate_groups('Contract_1', values=[0, 1]);`
\n", - " the plot needs to have the strata decoded\n", - " \n", - " In the train_model above, set param `plot_cov_groups=True` and produce the following set of artifacts by selecting only those covariates whose p-values\n", - " are below some threshold `p_value`" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Running the function remotely**\n" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2021-10-13 13:18:42,095 [info] Started building image: .mlrun/func-default-coxph-trainer:latest\n", - "\u001b[36mINFO\u001b[0m[0000] Retrieving image manifest mlrun/mlrun:0.7.1 \n", - "\u001b[36mINFO\u001b[0m[0000] Retrieving image manifest mlrun/mlrun:0.7.1 \n", - "\u001b[36mINFO\u001b[0m[0000] Built cross stage deps: map[] \n", - "\u001b[36mINFO\u001b[0m[0000] Retrieving image manifest mlrun/mlrun:0.7.1 \n", - "\u001b[36mINFO\u001b[0m[0000] Retrieving image manifest mlrun/mlrun:0.7.1 \n", - "\u001b[36mINFO\u001b[0m[0001] Executing 0 build triggers \n", - "\u001b[36mINFO\u001b[0m[0001] Unpacking rootfs as cmd RUN pip install lifelines requires it. \n", - "\u001b[36mINFO\u001b[0m[0015] RUN pip install lifelines \n", - "\u001b[36mINFO\u001b[0m[0015] Taking snapshot of full filesystem... \n", - "\u001b[36mINFO\u001b[0m[0026] cmd: /bin/sh \n", - "\u001b[36mINFO\u001b[0m[0026] args: [-c pip install lifelines] \n", - "\u001b[36mINFO\u001b[0m[0026] Running: [/bin/sh -c pip install lifelines] \n", - "Collecting lifelines\n", - " Downloading lifelines-0.26.3-py3-none-any.whl (348 kB)\n", - "Requirement already satisfied: numpy>=1.14.0 in /usr/local/lib/python3.7/site-packages (from lifelines) (1.19.5)\n", - "Collecting autograd-gamma>=0.3\n", - " Downloading autograd-gamma-0.5.0.tar.gz (4.0 kB)\n", - "Collecting autograd>=1.3\n", - " Downloading autograd-1.3.tar.gz (38 kB)\n", - "Requirement already satisfied: matplotlib>=3.0 in /usr/local/lib/python3.7/site-packages (from lifelines) (3.4.3)\n", - 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" Stored in directory: /tmp/pip-ephem-wheel-cache-vbqa_7jp/wheels/9f/01/ee/1331593abb5725ff7d8c1333aee93a50a1c29d6ddda9665c9f\n", - " Building wheel for autograd (setup.py): started\n", - " Building wheel for autograd (setup.py): finished with status 'done'\n", - " Created wheel for autograd: filename=autograd-1.3-py3-none-any.whl size=47989 sha256=f7fbf41d442f597b0969c5314cbdbda143f3bd2da827efc46f58d03ca6373e55\n", - " Stored in directory: /tmp/pip-ephem-wheel-cache-vbqa_7jp/wheels/ef/32/31/0e87227cd0ca1d99ad51fbe4b54c6fa02afccf7e483d045e04\n", - "Successfully built autograd-gamma autograd\n", - "Installing collected packages: autograd, autograd-gamma, interface-meta, astor, formulaic, lifelines\n", - "Successfully installed astor-0.8.1 autograd-1.3 autograd-gamma-0.5.0 formulaic-0.2.4 interface-meta-1.2.4 lifelines-0.26.3\n", - "WARNING: You are using pip version 20.2.4; however, version 21.3 is available.\n", - "You should consider upgrading via the '/usr/local/bin/python -m pip install --upgrade pip' command.\n", - "\u001b[36mINFO\u001b[0m[0029] Taking snapshot of full filesystem... \n" - ] - }, - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fn.spec.build.commands=['pip install lifelines']\n", - "fn.deploy(with_mlrun=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2021-10-13 13:19:41,275 [info] starting run tasks-survive-trainer uid=8b4ee122120645be9eb29a646ef6e562 DB=http://mlrun-api:8080\n", - "> 2021-10-13 13:19:41,455 [info] Job is running in the background, pod: tasks-survive-trainer-swjkk\n", - "> 2021-10-13 13:19:50,461 [info] run executed, status=completed\n", - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - "Column Contract_2 have very low variance when conditioned on death event present or not. This may harm convergence. This could be a form of 'complete separation'. For example, try the following code:\n", - "\n", - ">>> events = df['labels'].astype(bool)\n", - ">>> print(df.loc[events, 'Contract_2'].var())\n", - ">>> print(df.loc[~events, 'Contract_2'].var())\n", - "\n", - "A very low variance means that the column Contract_2 completely determines whether a subject dies or not. See https://stats.stackexchange.com/questions/11109/how-to-deal-with-perfect-separation-in-logistic-regression.\n", - "\n", - "Newton-Rhaphson convergence completed successfully but norm(delta) is still high, 0.443. This may imply non-unique solutions to the maximum likelihood. Perhaps there is collinearity or complete separation in the dataset?\n", - "\n", - "final state: completed\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "
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projectuiditerstartstatenamelabelsinputsparametersresultsartifacts
default0Oct 13 13:19:47completedtasks-survive-trainer
v3io_user=dani
kind=job
owner=dani
host=tasks-survive-trainer-swjkk
dataset
event_column=labels
strata_cols=['InternetService', 'StreamingMovies', 'StreamingTV', 'PhoneService']
p_value=0.005
encode_cols={'Contract': 'Contract', 'PaymentMethod': 'Payment'}
models_dest=models/cox
file_ext=csv
tenured-test-set
km-timelines
km-survival
km-model
coxhazard-summary
cx-model
\n", - "
\n", - "
\n", - "
\n", - " Title\n", - " ×\n", - "
\n", - " \n", - "
\n", - "
\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "data": { - "text/html": [ - " > to track results use the .show() or .logs() methods or click here to open in UI" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2021-10-13 13:19:50,645 [info] run executed, status=completed\n" - ] - } - ], - "source": [ - "coxph_run = fn.run(task,\n", - " local=False,\n", - " inputs={\"dataset\" : \"https://s3.wasabisys.com/iguazio/data/function-marketplace-data/coxph_trainer/encoded-data.csv\"})" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[Back to the top](#Coxph-trainer---Survival-analysis)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/coxph_trainer/coxph_trainer.py b/coxph_trainer/coxph_trainer.py deleted file mode 100644 index 42c443ad3..000000000 --- a/coxph_trainer/coxph_trainer.py +++ /dev/null @@ -1,201 +0,0 @@ -# Copyright 2019 Iguazio -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# Generated by nuclio.export.NuclioExporter - -import warnings - -warnings.simplefilter(action="ignore", category=FutureWarning) - -from mlrun.mlutils.data import get_sample, get_splits -from mlrun.mlutils.plots import gcf_clear - -from mlrun.execution import MLClientCtx -from mlrun.datastore import DataItem -from mlrun.artifacts import PlotArtifact, TableArtifact - -from cloudpickle import dumps -import pandas as pd -import os - -from lifelines import CoxPHFitter, KaplanMeierFitter - - -def _coxph_log_model( - context, - model, - dataset_key: str = "coxhazard-summary", - models_dest: str = "models", - plot_cov_groups: bool = False, - p_value: float = 0.005, - plot_key: str = "km-cx", - plots_dest: str = "plots", - file_ext="csv", - extra_data: dict = {}, -): - """log a coxph model (and submodel locations) - - :param model: estimated coxph model - :param extra_data: if this model wants to store the locations of submodels - use this - """ - import matplotlib.pyplot as plt - - sumtbl = model.summary - - context.log_dataset(dataset_key, df=sumtbl, index=True, format=file_ext) - - model_bin = dumps(model) - context.log_model( - "cx-model", - body=model_bin, - artifact_path=os.path.join(context.artifact_path, models_dest), - model_file="model.pkl", - ) - if plot_cov_groups: - select_covars = summary[summary.p <= p_value].index.values - for group in select_covars: - axs = model.plot_covariate_groups(group, values=[0, 1]) - for ix, ax in enumerate(axs): - f = ax.get_figure() - context.log_artifact( - PlotArtifact(f"cx-{group}-{ix}", body=plt.gcf()), - local_path=f"{plots_dest}/cx-{group}-{ix}.html", - ) - gcf_clear(plt) - - -def _kaplan_meier_log_model( - context, - model, - time_column: str = "tenure", - dataset_key: str = "km-timelines", - plot_key: str = "km-survival", - plots_dest: str = "plots", - models_dest: str = "models", - file_ext: str = "csv", -): - import matplotlib.pyplot as plt - - o = [] - for obj in model.__dict__.keys(): - if isinstance(model.__dict__[obj], pd.DataFrame): - o.append(model.__dict__[obj]) - df = pd.concat(o, axis=1) - df.index.name = time_column - context.log_dataset(dataset_key, df=df, index=True, format=file_ext) - model.plot() - context.log_artifact( - PlotArtifact(plot_key, body=plt.gcf()), - local_path=f"{plots_dest}/{plot_key}.html", - ) - context.log_model( - "km-model", - body=dumps(model), - model_dir=f"{models_dest}/km", - model_file="model.pkl", - ) - - -def train_model( - context: MLClientCtx, - dataset: DataItem, - event_column: str = "labels", - time_column: str = "tenure", - encode_cols: dict = {}, - strata_cols: list = [], - plot_cov_groups: bool = False, - p_value: float = 0.005, - sample: int = -1, - test_size: float = 0.25, - valid_size: float = 0.75, # (after test removed) - random_state: int = 1, - models_dest: str = "", - plots_dest: str = "", - file_ext: str = "csv", -) -> None: - """train models to predict the timing of events - - Although identical in structure to other training functions, this one - requires generating a 'Y' that represents the age/duration/tenure of - the obervation, designated 'tenure' here, and a binary labels columns that - represents the event of interest, churned/not-churned. - - In addition, there is a strata_cols parameter, representing a list of - stratification (aka grouping) variables. - - :param context: the function context - :param dataset: ("data") name of raw data file - :param event_column: ground-truth (y) labels (considered as events in this model) - :param time_column: age or tenure column - :param encode_cols: dictionary of names and prefixes for columns that are - to hot be encoded. - :param strata_cols: columns used to stratify predictors - :param plot_cov_groups: - :param p_value: (0.005) max p value for coeffcients selected - :param sample: Selects the first n rows, or select a sample - starting from the first. If negative <-1, select - a random sample - :param test_size: (0.25) test set size - :param valid_size: (0.75) Once the test set has been removed the - training set gets this proportion. - :param random_state: (1) sklearn rng seed - :param models_dest: destination subfolder for model artifacts - :param plots_dest: destination subfolder for plot artifacts - :param file_ext: format for test_set_key hold out data - """ - from lifelines.plotting import plot_lifetimes - import matplotlib.pyplot as plt - - models_dest = models_dest or "models" - plots_dest = plots_dest or f"plots/{context.name}" - - raw, tenure, header = get_sample(dataset, sample, time_column) - - if encode_cols: - raw = pd.get_dummies( - raw, - columns=list(encode_cols.keys()), - prefix=list(encode_cols.values()), - drop_first=True, - ) - - (xtrain, ytrain), (xvalid, yvalid), (xtest, ytest) = get_splits( - raw, tenure, 3, test_size, valid_size, random_state - ) - for X in [xtrain, xvalid, xtest]: - drop_cols = X.columns.str.startswith(time_column) - X.drop(X.columns[drop_cols], axis=1, inplace=True) - for Y in [ytrain, yvalid, ytest]: - Y.name = time_column - - context.log_dataset( - "tenured-test-set", - df=pd.concat([xtest, ytest.to_frame()], axis=1), - format=file_ext, - index=False, - ) - - km_model = KaplanMeierFitter().fit(ytrain, xtrain.labels) - _kaplan_meier_log_model(context, km_model, models_dest=models_dest) - - coxdata = pd.concat([xtrain, ytrain.to_frame()], axis=1) - cx_model = CoxPHFitter().fit(coxdata, time_column, event_column, strata=strata_cols) - _coxph_log_model( - context, - cx_model, - models_dest=models_dest, - plot_cov_groups=plot_cov_groups, - extra_data={"km": f"{models_dest}/km"}, - ) diff --git a/coxph_trainer/function.yaml b/coxph_trainer/function.yaml deleted file mode 100644 index 5033b87ba..000000000 --- a/coxph_trainer/function.yaml +++ /dev/null @@ -1,108 +0,0 @@ -kind: job -metadata: - name: coxph-trainer - tag: '' - hash: 65292d47d13eba9327a2b402066d9d76408a7985 - project: '' - labels: - author: yjb - framework: survival - categories: - - model-training - - machine-learning -spec: - command: '' - args: [] - image: mlrun/ml-models - env: [] - default_handler: train_model - entry_points: - train_model: - name: train_model - doc: 'train models to predict the timing of events - - - Although identical in structure to other training functions, this one - - requires generating a ''Y'' that represents the age/duration/tenure of - - the obervation, designated ''tenure'' here, and a binary labels columns that - - represents the event of interest, churned/not-churned. - - - In addition, there is a strata_cols parameter, representing a list of - - stratification (aka grouping) variables.' - parameters: - - name: context - type: MLClientCtx - doc: the function context - default: '' - - name: dataset - type: DataItem - doc: ("data") name of raw data file - default: '' - - name: event_column - type: str - doc: ground-truth (y) labels (considered as events in this model) - default: labels - - name: time_column - type: str - doc: age or tenure column - default: tenure - - name: encode_cols - type: dict - doc: dictionary of names and prefixes for columns that are to hot be encoded. - default: {} - - name: strata_cols - type: list - doc: columns used to stratify predictors - default: [] - - name: plot_cov_groups - type: bool - default: false - - name: p_value - type: float - doc: (0.005) max p value for coeffcients selected - default: 0.005 - - name: sample - type: int - doc: Selects the first n rows, or select a sample starting from the first. - If negative <-1, select a random sample - default: <_ast.USub object at 0x7f3b619b97b8> - - name: test_size - type: float - doc: (0.25) test set size - default: 0.25 - - name: valid_size - type: float - doc: (0.75) Once the test set has been removed the training set gets this - proportion. - default: 0.75 - - name: random_state - type: int - doc: (1) sklearn rng seed - default: 1 - - name: models_dest - type: str - doc: destination subfolder for model artifacts - default: '' - - name: plots_dest - type: str - doc: destination subfolder for plot artifacts - default: '' - - name: file_ext - type: str - doc: format for test_set_key hold out data - default: csv - outputs: - - default: '' - lineno: 97 - description: cox proportional hazards, kaplan meier plots - build: - functionSourceCode: # Generated by nuclio.export.NuclioExporter

import warnings

warnings.simplefilter(action="ignore", category=FutureWarning)

from mlrun.mlutils.data import get_sample, get_splits
from mlrun.mlutils.plots import gcf_clear

from mlrun.execution import MLClientCtx
from mlrun.datastore import DataItem
from mlrun.artifacts import PlotArtifact, TableArtifact

from cloudpickle import dumps
import pandas as pd
import os

from lifelines import CoxPHFitter, KaplanMeierFitter


def _coxph_log_model(
    context,
    model,
    dataset_key: str = "coxhazard-summary",
    models_dest: str = "models",
    plot_cov_groups: bool = False,
    p_value: float = 0.005,
    plot_key: str = "km-cx",
    plots_dest: str = "plots",
    file_ext="csv",
    extra_data: dict = {},
):
    """log a coxph model (and submodel locations)

    :param model:        estimated coxph model
    :param extra_data:   if this model wants to store the locations of submodels
                         use this
    """
    import matplotlib.pyplot as plt

    sumtbl = model.summary

    context.log_dataset(dataset_key, df=sumtbl, index=True, format=file_ext)

    model_bin = dumps(model)
    context.log_model(
        "cx-model",
        body=model_bin,
        artifact_path=os.path.join(context.artifact_path, models_dest),
        model_file="model.pkl",
    )
    if plot_cov_groups:
        select_covars = summary[summary.p <= p_value].index.values
        for group in select_covars:
            axs = model.plot_covariate_groups(group, values=[0, 1])
            for ix, ax in enumerate(axs):
                f = ax.get_figure()
                context.log_artifact(
                    PlotArtifact(f"cx-{group}-{ix}", body=plt.gcf()),
                    local_path=f"{plots_dest}/cx-{group}-{ix}.html",
                )
                gcf_clear(plt)


def _kaplan_meier_log_model(
    context,
    model,
    time_column: str = "tenure",
    dataset_key: str = "km-timelines",
    plot_key: str = "km-survival",
    plots_dest: str = "plots",
    models_dest: str = "models",
    file_ext: str = "csv",
):
    import matplotlib.pyplot as plt

    o = []
    for obj in model.__dict__.keys():
        if isinstance(model.__dict__[obj], pd.DataFrame):
            o.append(model.__dict__[obj])
    df = pd.concat(o, axis=1)
    df.index.name = time_column
    context.log_dataset(dataset_key, df=df, index=True, format=file_ext)
    model.plot()
    context.log_artifact(
        PlotArtifact(plot_key, body=plt.gcf()),
        local_path=f"{plots_dest}/{plot_key}.html",
    )
    context.log_model(
        "km-model",
        body=dumps(model),
        model_dir=f"{models_dest}/km",
        model_file="model.pkl",
    )


def train_model(
    context: MLClientCtx,
    dataset: DataItem,
    event_column: str = "labels",
    time_column: str = "tenure",
    encode_cols: dict = {},
    strata_cols: list = [],
    plot_cov_groups: bool = False,
    p_value: float = 0.005,
    sample: int = -1,
    test_size: float = 0.25,
    valid_size: float = 0.75,  # (after test removed)
    random_state: int = 1,
    models_dest: str = "",
    plots_dest: str = "",
    file_ext: str = "csv",
) -> None:
    """train models to predict the timing of events

    Although identical in structure to other training functions, this one
    requires generating a 'Y' that represents the age/duration/tenure of
    the obervation, designated 'tenure' here, and a binary labels columns that
    represents the event of interest, churned/not-churned.

    In addition, there is a strata_cols parameter, representing a list of
    stratification (aka grouping) variables.

    :param context:           the function context
    :param dataset:           ("data") name of raw data file
    :param event_column:      ground-truth (y) labels (considered as events in this model)
    :param time_column:       age or tenure column
    :param encode_cols:       dictionary of names and prefixes for columns that are
                              to hot be encoded.
    :param strata_cols:       columns used to stratify predictors
    :param plot_cov_groups:
    :param p_value:           (0.005) max p value for coeffcients selected
    :param sample:            Selects the first n rows, or select a sample
                              starting from the first. If negative <-1, select
                              a random sample
    :param test_size:         (0.25) test set size
    :param valid_size:        (0.75) Once the test set has been removed the
                              training set gets this proportion.
    :param random_state:      (1) sklearn rng seed
    :param models_dest:       destination subfolder for model artifacts
    :param plots_dest:        destination subfolder for plot artifacts
    :param file_ext:          format for test_set_key hold out data
    """
    from lifelines.plotting import plot_lifetimes
    import matplotlib.pyplot as plt

    models_dest = models_dest or "models"
    plots_dest = plots_dest or f"plots/{context.name}"

    raw, tenure, header = get_sample(dataset, sample, time_column)

    if encode_cols:
        raw = pd.get_dummies(
            raw,
            columns=list(encode_cols.keys()),
            prefix=list(encode_cols.values()),
            drop_first=True,
        )

    (xtrain, ytrain), (xvalid, yvalid), (xtest, ytest) = get_splits(
        raw, tenure, 3, test_size, valid_size, random_state
    )
    for X in [xtrain, xvalid, xtest]:
        drop_cols = X.columns.str.startswith(time_column)
        X.drop(X.columns[drop_cols], axis=1, inplace=True)
    for Y in [ytrain, yvalid, ytest]:
        Y.name = time_column

    context.log_dataset(
        "tenured-test-set",
        df=pd.concat([xtest, ytest.to_frame()], axis=1),
        format=file_ext,
        index=False,
    )

    km_model = KaplanMeierFitter().fit(ytrain, xtrain.labels)
    _kaplan_meier_log_model(context, km_model, models_dest=models_dest)

    coxdata = pd.concat([xtrain, ytrain.to_frame()], axis=1)
    cx_model = CoxPHFitter().fit(coxdata, time_column, event_column, strata=strata_cols)
    _coxph_log_model(
        context,
        cx_model,
        models_dest=models_dest,
        plot_cov_groups=plot_cov_groups,
        extra_data={"km": f"{models_dest}/km"},
    )
 - commands: [] - code_origin: https://github.com/daniels290813/functions.git#55a79c32be5d233cc11efcf40cd3edbe309bfdef:/home/kali/functions/coxph_trainer/coxph_trainer.py - affinity: null -verbose: false diff --git a/coxph_trainer/item.yaml b/coxph_trainer/item.yaml deleted file mode 100644 index 2b4cca63d..000000000 --- a/coxph_trainer/item.yaml +++ /dev/null @@ -1,26 +0,0 @@ -apiVersion: v1 -categories: -- model-training -- machine-learning -description: cox proportional hazards, kaplan meier plots -doc: '' -example: coxph_trainer.ipynb -generationDate: 2022-08-28:17-25 -hidden: true -icon: '' -labels: - author: yjb - framework: survival -maintainers: [] -marketplaceType: '' -mlrunVersion: 1.1.0 -name: coxph-trainer -platformVersion: 3.5.0 -spec: - filename: coxph_trainer.py - handler: train_model - image: mlrun/ml-models - kind: job - requirements: [] -url: '' -version: 1.1.0 diff --git a/coxph_trainer/requirements.txt b/coxph_trainer/requirements.txt deleted file mode 100644 index ca8c96f68..000000000 --- a/coxph_trainer/requirements.txt +++ /dev/null @@ -1,6 +0,0 @@ -scikit-learn -seaborn -scikit-plot -pandas -lifelines -matplotlib \ No newline at end of file diff --git a/coxph_trainer/test_coxph_trainer.py b/coxph_trainer/test_coxph_trainer.py deleted file mode 100644 index 8d1344668..000000000 --- a/coxph_trainer/test_coxph_trainer.py +++ /dev/null @@ -1,136 +0,0 @@ -# Copyright 2019 Iguazio -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -from mlrun import get_or_create_ctx, import_function -import os -import json -import pandas as pd -import numpy as np -from collections import defaultdict -from cloudpickle import dumps, load -from sklearn.preprocessing import LabelEncoder -from mlrun.execution import MLClientCtx -from mlrun.datastore import DataItem -import mlrun - -ARTIFACT_PATH = "artifacts" -FUNCTION_PATH = "functions" -MODELS_PATH = "models" -PLOTS_PATH = "plots" -RUNS_PATH = "runs" -SCHEDULES_PATH = "schedules" -DATA_URL = "https://raw.githubusercontent.com/mlrun/demos/0.6.x/customer-churn-prediction/WA_Fn-UseC_-Telco-Customer-Churn.csv" - - -def data_clean( - context: MLClientCtx, - src: DataItem, - file_ext: str = "csv", - models_dest: str = "models/encoders", - cleaned_key: str = "cleaned-data", - encoded_key: str = "encoded-data" -): - df = src.as_df() - - # drop columns - drop_cols_list = ["customerID", "TotalCharges"] - df.drop(drop_cols_list, axis=1, inplace=True) - - # header transformations - old_cols = df.columns - rename_cols_map = { - "SeniorCitizen": "senior", - "Partner": "partner", - "Dependents": "deps", - "Churn": "labels" - } - df.rename(rename_cols_map, axis=1, inplace=True) - - # add drop column to logs: - for col in drop_cols_list: - rename_cols_map.update({col: "_DROPPED_"}) - - # log the op - tp = os.path.join(models_dest, "preproc-column_map.json") - context.log_artifact("preproc-column_map.json", - body=json.dumps(rename_cols_map), - local_path=tp) - df = df.applymap(lambda x: "No" if str(x).startswith("No ") else x) - - # encode numerical type as category bins (ordinal) - bins = [0, 12, 24, 36, 48, 60, np.inf] - labels = [0, 1, 2, 3, 4, 5] - tenure = df.tenure.copy(deep=True) - df["tenure_map"] = pd.cut(df.tenure, bins, labels=False) - tenure_map = dict(zip(bins, labels)) - # save this transformation - tp = os.path.join(models_dest, "preproc-numcat_map.json") - context.log_artifact("preproc-numcat_map.json", - body=bytes(json.dumps(tenure_map).encode("utf-8")), - local_path=tp) - - context.log_dataset(cleaned_key, df=df, format=file_ext, index=False) - fix_cols = ["gender", "partner", "deps", "OnlineSecurity", - "OnlineBackup", "DeviceProtection", "TechSupport", - "StreamingTV", "StreamingMovies", "PhoneService", - "MultipleLines", "PaperlessBilling", "InternetService", - "Contract", "PaymentMethod", "labels"] - - d = defaultdict(LabelEncoder) - df[fix_cols] = df[fix_cols].apply(lambda x: d[x.name].fit_transform(x.astype(str))) - context.log_dataset(encoded_key, df=df, format=file_ext, index=False) - - model_bin = dumps(d) - context.log_model("model", - body=model_bin, - artifact_path=os.path.join(context.artifact_path, - models_dest), - model_file="model.pkl") - - -def test_local_coxph_train(): - # ctx = get_or_create_ctx(name="tasks survive trainer") - # src = mlrun.get_dataitem(DATA_URL) - data_clean_function = mlrun.code_to_function( - filename="test_coxph_trainer.py", - name="data_clean", - kind="job", - image="mlrun/mlrun", - ) - data_clean_run = data_clean_function.run( - handler="data_clean", - inputs={"src": DATA_URL}, - params={ - "cleaned_key": "cleaned-data", - "encoded_key": "encoded-data", - }, - local=True, - artifact_path='./' - ) - - trainer_fn = import_function("function.yaml") - trainer_run = trainer_fn.run( - params={ - "strata_cols": ['InternetService', 'StreamingMovies', 'StreamingTV', 'PhoneService'], - "encode_cols": {"Contract": "Contract", "PaymentMethod": "Payment"}, - "models_dest": 'models/cox' - }, - inputs={"dataset": data_clean_run.artifact("encoded-data").url}, - local=True, - artifact_path='./' - ) - - model = load(open(f"{trainer_run.artifact('km-model').url}model.pkl", "rb")) - ans = model.predict([1, 10, 30, 100, 200]) - assert(sum([abs(x-y) for x, y in zip(list(np.around(ans, 2)), [0.95, 0.85, 0.77, 0.58, 0.58])]) < 0.5) \ No newline at end of file diff --git a/xgb_test/function.yaml b/xgb_test/function.yaml deleted file mode 100644 index 1ba562a9e..000000000 --- a/xgb_test/function.yaml +++ /dev/null @@ -1,63 +0,0 @@ -kind: job -metadata: - name: xgb-test - tag: '' - hash: 3f3368b15f934eba5f6f6b23972da804b6eb88d4 - project: '' - labels: - author: Daniel - framework: xgboost - categories: - - model-testing -spec: - command: '' - args: [] - image: mlrun/mlrun - env: [] - default_handler: xgb_test - entry_points: - xgb_test: - name: xgb_test - doc: 'Test one or more classifier models against held-out dataset - - - Using held-out test features, evaluates the peformance of the estimated model - - - Can be part of a kubeflow pipeline as a test step that is run post EDA and - - training/validation cycles' - parameters: - - name: context - doc: the function context - default: '' - - name: models_path - type: DataItem - doc: model artifact to be tested - default: '' - - name: test_set - type: DataItem - doc: test features and labels - default: '' - - name: label_column - type: str - doc: column name for ground truth labels - default: '' - - name: plots_dest - type: str - doc: dir for test plots - default: plots - - name: default_model - type: str - doc: '''model.pkl'', default model artifact file name' - default: model.pkl - outputs: - - default: '' - lineno: 16 - description: Test one or more classifier models against held-out dataset. - build: - functionSourceCode: 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 - commands: [] - code_origin: https://github.com/daniels290813/functions.git#55a79c32be5d233cc11efcf40cd3edbe309bfdef:/home/kali/functions/xgb_test/xgb_test.py - affinity: null -verbose: false diff --git a/xgb_test/item.yaml b/xgb_test/item.yaml deleted file mode 100644 index cc376e9f7..000000000 --- a/xgb_test/item.yaml +++ /dev/null @@ -1,25 +0,0 @@ -apiVersion: v1 -categories: -- model-testing -description: Test one or more classifier models against held-out dataset. -doc: '' -example: xgb_test.ipynb -generationDate: 2022-08-28:17-25 -hidden: true -icon: '' -labels: - author: Daniel - framework: xgboost -maintainers: [] -marketplaceType: '' -mlrunVersion: 1.4.1 -name: xgb_test -platformVersion: 3.5.3 -spec: - filename: xgb_test.py - handler: xgb_test - image: mlrun/mlrun - kind: job - requirements: [] -url: '' -version: 1.1.1 diff --git a/xgb_test/requirements.txt b/xgb_test/requirements.txt deleted file mode 100644 index fc5c36f78..000000000 --- a/xgb_test/requirements.txt +++ /dev/null @@ -1,8 +0,0 @@ -pandas -xgboost -cloudpickle -pygit2 -matplotlib -seaborn -scikit-plot -scikit-learn==1.0.2 diff --git a/xgb_test/test_xgb_test.py b/xgb_test/test_xgb_test.py deleted file mode 100644 index a2f92746a..000000000 --- a/xgb_test/test_xgb_test.py +++ /dev/null @@ -1,148 +0,0 @@ -# Copyright 2019 Iguazio -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -from mlrun import code_to_function, import_function -import os -import pandas as pd - - -def get_class_data(): - fn = import_function("hub://gen_class_data") - run = fn.run( - params={ - "n_samples": 10_000, - "m_features": 5, - "k_classes": 2, - "header": None, - "weight": [0.5, 0.5], - "sk_params": {"n_informative": 2}, - "file_ext": "csv", - }, - local=True, - artifact_path="./artifacts/inputs", - ) - return run.status.artifacts[0]['spec']['target_path'] - - -def xgb_trainer(): - data = get_class_data() - fn = code_to_function( - name='xgb_trainer', - filename="../xgb_trainer/xgb_trainer.py", - handler="train_model", - kind="job", - ) - run = fn.run( - params={ - "model_type": "classifier", - "CLASS_tree_method": "hist", - "CLASS_objective": "binary:logistic", - "CLASS_booster": "gbtree", - "FIT_verbose": 0, - "label_column": "labels", - }, - local=True, - inputs={"dataset": data}, - ) - - for artifact in run.status.artifacts: - if artifact['kind'] == 'model': - assert os.path.exists(artifact['spec']['target_path']), "Failed locating model file" # validating model exists - break - return data, artifact['spec']['target_path'] + artifact['spec']['model_file'] - - -def test_xgb_test_code_to_function(): - data, model = xgb_trainer() - fn = code_to_function( - name='test_xgb_test', - filename="../xgb_test/xgb_test.py", - handler="xgb_test", - kind="job", - ) - run = fn.run( - params={ - "label_column": "labels", - "plots_dest": "plots/xgb_test", - }, - local=True, - inputs={ - "test_set": data, - "models_path": model, - } - ) - - assert run.outputs['accuracy'] and run.state() == 'completed' - - -def test_local_xgb_test_import_local_function(): - # importing data preparation function (gen_class_data) locally - fn = import_function("../gen_class_data/function.yaml") - run = fn.run( - params={ - "n_samples": 10_000, - "m_features": 5, - "k_classes": 2, - "header": None, - "weight": [0.5, 0.5], - "sk_params": {"n_informative": 2}, - "file_ext": "csv", - }, - local=True, - artifact_path="./artifacts/inputs", - ) - data = run.status.artifacts[0]['spec']['target_path'] - - # importing model training function (xgb_trainer) locally - fn = import_function("../xgb_trainer/function.yaml") - run = fn.run( - params={ - "model_type": "classifier", - "CLASS_tree_method": "hist", - "CLASS_objective": "binary:logistic", - "CLASS_booster": "gbtree", - "FIT_verbose": 0, - "label_column": "labels", - }, - local=True, - inputs={"dataset": data}, - ) - for artifact in run.status.artifacts: - if artifact['kind'] == 'model': - assert os.path.exists(artifact['spec']['target_path']), "Failed locating model file" # validating model exists - break - - model = artifact['spec']['target_path'] + artifact['spec']['model_file'] - - # importing xgb_test function.yaml and running tests - fn = import_function("function.yaml") - run = fn.run( - params={ - "label_column": "labels", - "plots_dest": "plots/xgb_test", - }, - local=True, - inputs={ - "test_set": data, - "models_path": model, - } - ) - - # tests for gen_class_data - assert data - df = pd.read_csv(data) - assert (True if df["labels"].sum() == 5008 else False) - # tests for xgb_trainer - assert model - # no tests for xgb_test (it is a test already) diff --git a/xgb_test/xgb_test.ipynb b/xgb_test/xgb_test.ipynb deleted file mode 100644 index f404cab3a..000000000 --- a/xgb_test/xgb_test.ipynb +++ /dev/null @@ -1,708 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# **XGBoost test**" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This function handles evaluating XGBoost model performance, test one or more classifier models against held-out dataset
\n", - "Using held-out test features, evaluates the peformance of the estimated model.
\n", - "Can be part of a kubeflow pipeline as a test step that is run post EDA and training/validation cycles.
\n", - "This function is part of the [customer-churn-prediction](https://github.com/mlrun/demos/tree/master/customer-churn-prediction) demo.
\n", - "To see how the model is trained or how the data-set is generated, check out `xgb_trainer` function from the function marketplace repository" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Steps\n", - "1. [Setup function parameters](#Setup-function-parameters)\n", - "2. [Importing the function](#Importing-the-function)\n", - "3. [Running the function locally](#Running-the-function-locally)\n", - "4. [Running the function remotely](#Running-the-function-remotely)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import warnings\n", - "warnings.filterwarnings(\"ignore\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Setup function parameters** " - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - }, - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "test_set = \"https://s3.wasabisys.com/iguazio/data/function-marketplace-data/xgb_test/test_set.csv\"\n", - "models_path = \"https://s3.wasabisys.com/iguazio/models/function-marketplace-models/xgb_test/xgb_model.pkl\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Importing the function**" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2021-10-17 13:24:42,721 [info] loaded project function-marketplace from MLRun DB\n" - ] - } - ], - "source": [ - "import mlrun\n", - "mlrun.set_environment(project='function-marketplace')\n", - "\n", - "fn = mlrun.import_function('hub://xgb_test')\n", - "fn.apply(mlrun.auto_mount())\n", - "fn.image = \"mlrun/ml-models\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Running the function locally**" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2021-10-17 13:24:43,112 [info] starting run tasks_xgb_test uid=1259c7c9bd0e4b0895be4f5e2fbb65c4 DB=http://mlrun-api:8080\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "
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function-marketplace0Oct 17 13:24:43completedtasks_xgb_test
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test_set
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recall_score=0.9508460918614021
probability-calibration
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\n", 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fn.run(name='tasks_xgb_test',\n", - " params = {\"label_column\" : \"labels\",\n", - " \"plots_dest\" : \"plots/xgb_test\"},\n", - " inputs = {\"test_set\" : test_set,\n", - " \"models_path\" : models_path},\n", - " local=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Running the function remotely**" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2021-10-17 13:24:48,946 [info] starting run tasks_xgb_test uid=550a773aeb7e4754b4652772d205365a DB=http://mlrun-api:8080\n", - "> 2021-10-17 13:24:49,084 [info] Job is running in the background, pod: tasks-xgb-test-gj7q4\n", - "> 2021-10-17 13:24:59,214 [info] run executed, status=completed\n", - "final state: completed\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "
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projectuiditerstartstatenamelabelsinputsparametersresultsartifacts
function-marketplace0Oct 17 13:24:55completedtasks_xgb_test
v3io_user=dani
kind=job
owner=dani
host=tasks-xgb-test-gj7q4
test_set
models_path
label_column=labels
plots_dest=plots/xgb_test
accuracy=0.9632
test-error=0.0368
rocauc=0.984364949478981
brier_score=0.03287091841943238
f1-score=0.9624796084828712
precision_score=0.9744013212221305
recall_score=0.9508460918614021
probability-calibration
confusion-matrix
feature-importances
precision-recall-binary
roc-binary
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\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "data": { - "text/html": [ - " > to track results use the .show() or .logs() methods or click here to open in UI" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2021-10-17 13:25:08,339 [info] run executed, status=completed\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fn.deploy(with_mlrun=False, # mlrun is included in our image (mlrun/ml-models) therefore no mlrun installation is needed.\n", - " skip_deployed=True) # because no new packages or upgrade is required, we can use the original image and not build another one.\n", - "\n", - "fn.run(name='tasks_xgb_test',\n", - " params = {\"label_column\" : \"labels\",\n", - " \"plots_dest\" : \"plots/xgb_test\"},\n", - " inputs = {\"test_set\" : test_set,\n", - " \"models_path\" : models_path},\n", - " local=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[Back to the top](#XGBoost-test)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/xgb_test/xgb_test.py b/xgb_test/xgb_test.py deleted file mode 100644 index 8ad3a6a1c..000000000 --- a/xgb_test/xgb_test.py +++ /dev/null @@ -1,61 +0,0 @@ -# Copyright 2019 Iguazio -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# Generated by nuclio.export.NuclioExporter - -import warnings - -warnings.simplefilter(action="ignore", category=FutureWarning) - -import os -import pandas as pd -from mlrun.datastore import DataItem -from mlrun.artifacts import get_model -from cloudpickle import load - -from mlrun.mlutils.models import eval_model_v2 - - -def xgb_test( - context, - models_path: DataItem, - test_set: DataItem, - label_column: str, - plots_dest: str = "plots", - default_model: str = "model.pkl", -) -> None: - """Test one or more classifier models against held-out dataset - - Using held-out test features, evaluates the peformance of the estimated model - - Can be part of a kubeflow pipeline as a test step that is run post EDA and - training/validation cycles - - :param context: the function context - :param models_path: model artifact to be tested - :param test_set: test features and labels - :param label_column: column name for ground truth labels - :param plots_dest: dir for test plots - :param default_model: 'model.pkl', default model artifact file name - """ - xtest = test_set.as_df() - ytest = xtest.pop(label_column) - - try: - model_file, model_obj, _ = get_model(models_path.url, suffix=".pkl") - model_obj = load(open(model_file, "rb")) - except Exception as a: - raise Exception("model location likely misspecified") - - eval_metrics = eval_model_v2(context, xtest, ytest.values, model_obj) diff --git a/xgb_trainer/function.yaml b/xgb_trainer/function.yaml deleted file mode 100644 index 425e61c54..000000000 --- a/xgb_trainer/function.yaml +++ /dev/null @@ -1,102 +0,0 @@ -kind: job -metadata: - name: xgb-trainer - tag: '' - hash: 74f26135df3322a88554136c4c5dbe8d95a5fadc - project: '' - labels: - author: Daniel - categories: - - model-training -spec: - command: '' - args: [] - image: mlrun/mlrun - env: [] - default_handler: train_model - entry_points: - train_model: - name: train_model - doc: 'train an xgboost model. - - - Note on imabalanced data: the `imbal_vec` parameter represents the measured - - class representations in the sample and can be used as a first step in tuning - - an XGBoost model. This isn''t a hyperparamter, merely an estimate that should - - be set as ''constant'' throughout tuning process.' - parameters: - - name: context - type: MLClientCtx - doc: the function context - default: '' - - name: model_type - type: str - doc: the model type to train, "classifier", "regressor"... - default: '' - - name: dataset - type: Union[DataItem, DataFrame] - doc: ("data") name of raw data file - default: '' - - name: label_column - type: str - doc: ground-truth (y) labels - default: labels - - name: encode_cols - type: dict - doc: dictionary of names and prefixes for columns that are to hot be encoded. - default: {} - - name: sample - type: int - doc: Selects the first n rows, or select a sample starting from the first. - If negative <-1, select a random sample - default: <_ast.USub object at 0x7f66a8fbc7b8> - - name: imbal_vec - doc: ([]) vector of class weights seen in sample - default: [] - - name: test_size - type: float - doc: (0.05) test set size - default: 0.25 - - name: valid_size - type: float - doc: (0.75) Once the test set has been removed the training set gets this - proportion. - default: 0.75 - - name: random_state - type: int - doc: (1) sklearn rng seed - default: 1 - - name: models_dest - type: str - doc: destination subfolder for model artifacts - default: models - - name: plots_dest - type: str - doc: destination subfolder for plot artifacts - default: plots - - name: eval_metrics - type: list - doc: (["error", "auc"]) learning curve metrics - default: - - error - - auc - - name: file_ext - type: str - doc: format for test_set_key hold out data - default: parquet - - name: test_set - type: str - default: test_set - outputs: - - default: '' - lineno: 57 - description: train multiple model types using xgboost. - build: - functionSourceCode: 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- commands: [] - code_origin: https://github.com/daniels290813/functions.git#55a79c32be5d233cc11efcf40cd3edbe309bfdef:/home/kali/functions/xgb_trainer/xgb_trainer.py - affinity: null -verbose: false diff --git a/xgb_trainer/item.yaml b/xgb_trainer/item.yaml deleted file mode 100644 index 5c910a0c8..000000000 --- a/xgb_trainer/item.yaml +++ /dev/null @@ -1,24 +0,0 @@ -apiVersion: v1 -categories: -- model-training -description: train multiple model types using xgboost. -doc: '' -example: xgb_trainer.ipynb -generationDate: 2022-08-28:17-25 -hidden: true -icon: '' -labels: - author: Daniel -maintainers: [] -marketplaceType: '' -mlrunVersion: 1.4.1 -name: xgb_trainer -platformVersion: 3.5.4 -spec: - filename: xgb_trainer.py - handler: train_model - image: mlrun/mlrun - kind: job - requirements: [] -url: '' -version: 1.1.1 diff --git a/xgb_trainer/requirements.txt b/xgb_trainer/requirements.txt deleted file mode 100644 index 644bcc710..000000000 --- a/xgb_trainer/requirements.txt +++ /dev/null @@ -1,8 +0,0 @@ -pandas -xgboost -cloudpickle -pygit2 -scikit-learn==1.0.2 -matplotlib -seaborn -scikit-plot diff --git a/xgb_trainer/test_xgb_trainer.py b/xgb_trainer/test_xgb_trainer.py deleted file mode 100644 index e9119e307..000000000 --- a/xgb_trainer/test_xgb_trainer.py +++ /dev/null @@ -1,50 +0,0 @@ -# Copyright 2019 Iguazio -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -import mlrun -import os - - -def get_class_data(): - fn = mlrun.import_function('../gen_class_data/function.yaml') - run = fn.run(params={'key': 'classifier-data', - 'n_samples': 10_000, - 'm_features': 5, - 'k_classes': 2, - 'header': None, - 'weight': [0.5, 0.5], - 'sk_params': {'n_informative': 2}, - 'file_ext': 'csv'}, local=True, artifact_path="./artifacts") - - return run - - -def test_local_xgb_trainer_import_function(): - # running data preparation function locally - gen_data_run = get_class_data() - - fn = mlrun.import_function('function.yaml') - run = fn.run(params={'model_type': 'classifier', - 'CLASS_tree_method': 'hist', - 'CLASS_objective': 'binary:logistic', - 'CLASS_booster': 'gbtree', - 'FIT_verbose': 0, - 'label_column': 'labels'}, - local=True, inputs={'dataset': gen_data_run.status.artifacts[0]['spec']['target_path']}) # only one dataset artifact created - - for artifact in run.status.artifacts: - if artifact['kind'] == 'model': - assert os.path.exists(artifact['spec']['target_path']) # validating model exists - return - assert False, "Model artifact is unavailable or miss-predicted" diff --git a/xgb_trainer/xgb_trainer.ipynb b/xgb_trainer/xgb_trainer.ipynb deleted file mode 100644 index 444d40400..000000000 --- a/xgb_trainer/xgb_trainer.ipynb +++ /dev/null @@ -1,1013 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# XGBoost trainer" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This notebook function handles training and logging of xgboost models **only**, exposing both the sklearn and low level api\"s.
\n", - "More information about XGBoost - [here](https://en.wikipedia.org/wiki/XGBoost)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Multiple model types that can be estimated using the XGBoost Scikit-Learn API.
\n", - "Input can either be a predefined json model configuration or one\n", - "of the five xgboost model types.
\n", - "In either case one can pass in a params dict to modify defaults values.
\n", - "Based on `mlutils.models.gen_sklearn_model`, see the function\n", - "`sklearn_classifier` in the function-marketplace repository.
\n", - "> **param model_type:**\n", - " one of \"classifier\", \"regressor\",\n", - " \"ranker\", \"rf_classifier\", or\n", - " \"rf_regressor\"
\n", - "> **param xgb_params:** class init parameters" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Steps\n", - "1. [Data Exploration](#Data-Exploration)\n", - "2. [Importing the function](#Importing-the-function)\n", - "3. [Setup XGBoost parameters](#Setup-XGBoost-parameters)\n", - "4. [Running the function locally](#Running-the-function-locally)\n", - "5. [Getting the model](#Getting-the-model)\n", - "6. [Some plotting](#Some-plotting)\n", - "7. [Running the function remotely](#Running-the-function-remotely)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Data Exploration**" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To generate the dataset we used the \"gen_class_data\" function from the hub, \n", - "which wraps scikit-learn's [make_classification](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_classification.html#sklearn-datasets-make-classification).
\n", - "See the link for a description of all parameters." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import warnings\n", - "warnings.filterwarnings(\"ignore\")" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# make sure proper xgboost version installed, uncomment to install\n", - "# !pip install xgboost==1.3.1" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data set containing 10000 instances, with 2 labels.\n", - "Number of instances labeled 1 : 5008\n", - "Number of instances labeled 0 : 4992\n" - ] - }, - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " feat_0 feat_1 feat_2 feat_3 feat_4 labels\n", - "0 -0.265115 -1.932260 0.303992 -1.863833 -1.045635 1\n", - "1 -3.135479 -2.835548 1.338381 -1.385303 -2.276456 0\n", - "2 -1.519005 -1.807549 0.697304 -1.118860 1.104900 0\n", - "3 -0.632087 -0.345659 0.244329 -0.046066 0.447280 0\n", - "4 -1.405883 -1.746045 0.653617 -1.110985 -1.675466 0" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Getting the data from wasabi\n", - "import pandas as pd\n", - "\n", - "df = pd.read_csv('https://s3.wasabisys.com/iguazio/data/function-marketplace-data/xgb_trainer/classifier-data.csv')\n", - "print(f'Data set containing {df.shape[0]} instances, with {len(df[\"labels\"].unique())} labels.')\n", - "\n", - "print(f\"Number of instances labeled {df['labels'].unique()[0]} : {df.groupby('labels').count()[df.columns[0]][df['labels'].unique()[0]]}\")\n", - "print(f\"Number of instances labeled {df['labels'].unique()[1]} : {df.groupby('labels').count()[df.columns[0]][df['labels'].unique()[1]]}\")\n", - "\n", - "df.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Importing the function**" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2021-10-13 10:10:21,588 [info] loaded project function-marketplace from MLRun DB\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import mlrun\n", - "mlrun.set_environment(project='function-marketplace')\n", - "# If GPU is available - set to True\n", - "GPU = False\n", - "\n", - "\n", - "fn = mlrun.import_function(\"hub://xgb_trainer\")\n", - "fn.image = \"mlrun/ml-models\" if not GPU else \"mlrun/ml-models-gpu\"\n", - "fn.apply(mlrun.auto_mount())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Setup XGBoost parameters**" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "task_params = {\"model_type\": \"classifier\",\n", - " \"CLASS_tree_method\": \"hist\",\n", - " \"CLASS_objective\": \"binary:logistic\",\n", - " \"CLASS_booster\": \"gbtree\",\n", - " \"FIT_verbose\": 0,\n", - " \"label_column\": \"labels\"}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Running the function locally**" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2021-10-13 10:10:21,807 [info] starting run xgb-trainer-train_model uid=5ec8a83eb65b46dc9b7f9dd654cd1b31 DB=http://mlrun-api:8080\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "
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projectuiditerstartstatenamelabelsinputsparametersresultsartifacts
function-marketplace0Oct 13 10:10:22completedxgb-trainer-train_model
v3io_user=dani
kind=
owner=dani
host=jupyter-dani-5bbd9959b7-tsgh8
dataset
model_type=classifier
CLASS_tree_method=hist
CLASS_objective=binary:logistic
CLASS_booster=gbtree
FIT_verbose=0
label_column=labels
accuracy=0.9552
test-error=0.0448
rocauc=0.9799618829687036
brier_score=0.038984999293145965
f1-score=0.954983922829582
precision_score=0.965679190751445
recall_score=0.9445229681978798
test_set
probability-calibration
confusion-matrix
feature-importances
precision-recall-binary
roc-binary
model
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\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "data": { - "text/html": [ - " > to track results use the .show() or .logs() methods or click here to open in UI" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2021-10-13 10:10:24,178 [info] run executed, status=completed\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "train_run = fn.run(params = task_params, \n", - " inputs={\"dataset\" : 'https://s3.wasabisys.com/iguazio/data/function-marketplace-data/xgb_trainer/classifier-data.csv'},\n", - " local=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Getting the model**" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "from mlrun.artifacts import get_model\n", - "import pickle\n", - "\n", - "model_file, model_obj, _ = get_model(train_run.artifact('model'))\n", - "model = pickle.load(open(model_file,'rb'))" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "model score : 0.9632\n" - ] - } - ], - "source": [ - "print(f\"model score : {model.score(train_run.artifact('test_set').as_df().drop(['labels'],axis=1),train_run.artifact('test_set').as_df()['labels'])}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Some plotting**" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Display the probability calibration" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "

probability calibration plot

\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "train_run.artifact('probability-calibration').show()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "

Feature Importances

\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "train_run.artifact('feature-importances').show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Running the function remotely**" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2021-10-13 10:10:24,882 [info] Started building image: .mlrun/func-function-marketplace-xgb-trainer:latest\n", - "\u001b[36mINFO\u001b[0m[0000] Retrieving image manifest mlrun/mlrun:0.7.1 \n", - "\u001b[36mINFO\u001b[0m[0000] Retrieving image manifest mlrun/mlrun:0.7.1 \n", - "\u001b[36mINFO\u001b[0m[0000] Built cross stage deps: map[] \n", - "\u001b[36mINFO\u001b[0m[0000] Retrieving image manifest mlrun/mlrun:0.7.1 \n", - "\u001b[36mINFO\u001b[0m[0001] Retrieving image manifest mlrun/mlrun:0.7.1 \n", - "\u001b[36mINFO\u001b[0m[0002] Executing 0 build triggers \n", - "\u001b[36mINFO\u001b[0m[0002] Unpacking rootfs as cmd RUN pip install xgboost==1.3.1 requires it. \n", - "\u001b[36mINFO\u001b[0m[0024] RUN pip install xgboost==1.3.1 \n", - "\u001b[36mINFO\u001b[0m[0024] Taking snapshot of full filesystem... \n", - "\u001b[36mINFO\u001b[0m[0035] cmd: /bin/sh \n", - "\u001b[36mINFO\u001b[0m[0035] args: [-c pip install xgboost==1.3.1] \n", - "\u001b[36mINFO\u001b[0m[0035] Running: [/bin/sh -c pip install xgboost==1.3.1] \n", - "Collecting xgboost==1.3.1\n", - " Downloading xgboost-1.3.1-py3-none-manylinux2010_x86_64.whl (157.5 MB)\n", - "Requirement already satisfied: scipy in /usr/local/lib/python3.7/site-packages (from xgboost==1.3.1) (1.7.1)\n", - "Requirement already satisfied: numpy in /usr/local/lib/python3.7/site-packages (from xgboost==1.3.1) (1.19.5)\n", - "Installing collected packages: xgboost\n", - "Successfully installed xgboost-1.3.1\n", - "WARNING: You are using pip version 20.2.4; however, version 21.3 is available.\n", - "You should consider upgrading via the '/usr/local/bin/python -m pip install --upgrade pip' command.\n", - "\u001b[36mINFO\u001b[0m[0042] Taking snapshot of full filesystem... \n" - ] - }, - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fn.spec.build.commands=['pip install xgboost==1.3.1']\n", - "fn.deploy(with_mlrun=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2021-10-13 10:11:39,577 [info] starting run xgb-trainer-train_model uid=7332ff5d727948c89221d4645b84d028 DB=http://mlrun-api:8080\n", - "> 2021-10-13 10:11:39,764 [info] Job is running in the background, pod: xgb-trainer-train-model-4scfq\n", - "> 2021-10-13 10:11:55,207 [info] run executed, status=completed\n", - "The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n", - "final state: completed\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "
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projectuiditerstartstatenamelabelsinputsparametersresultsartifacts
function-marketplace0Oct 13 10:11:51completedxgb-trainer-train_model
v3io_user=dani
kind=job
owner=dani
host=xgb-trainer-train-model-4scfq
dataset
model_type=classifier
CLASS_tree_method=hist
CLASS_objective=binary:logistic
CLASS_booster=gbtree
FIT_verbose=0
label_column=labels
accuracy=0.9552
test-error=0.0448
rocauc=0.9799618829687036
brier_score=0.038984999293145965
f1-score=0.954983922829582
precision_score=0.965679190751445
recall_score=0.9445229681978798
test_set
probability-calibration
confusion-matrix
feature-importances
precision-recall-binary
roc-binary
model
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\n", - " Title\n", - " ×\n", - "
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\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "data": { - "text/html": [ - " > to track results use the .show() or .logs() methods or click here to open in UI" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2021-10-13 10:11:58,969 [info] run executed, status=completed\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fn.run(inputs={\"dataset\" : 'https://s3.wasabisys.com/iguazio/data/function-marketplace-data/xgb_trainer/classifier-data.csv'},\n", - " params=task_params)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[Back to the top](#XGBoost-trainer)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/xgb_trainer/xgb_trainer.py b/xgb_trainer/xgb_trainer.py deleted file mode 100644 index 4754aae26..000000000 --- a/xgb_trainer/xgb_trainer.py +++ /dev/null @@ -1,160 +0,0 @@ -# Copyright 2019 Iguazio -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# Generated by nuclio.export.NuclioExporter - -import warnings - -warnings.simplefilter(action="ignore", category=FutureWarning) - -from mlrun.mlutils.data import get_sample, get_splits -from mlrun.mlutils.models import gen_sklearn_model, eval_model_v2 -from mlrun.utils.helpers import create_class - -from mlrun.execution import MLClientCtx -from mlrun.datastore import DataItem - -from cloudpickle import dumps -import pandas as pd -import os -from typing import Union - - -def _gen_xgb_model(model_type: str, xgb_params: dict): - """generate an xgboost model - - Multiple model types that can be estimated using - the XGBoost Scikit-Learn API. - - Input can either be a predefined json model configuration or one - of the five xgboost model types: "classifier", "regressor", "ranker", - "rf_classifier", or "rf_regressor". - - In either case one can pass in a params dict to modify defaults values. - - Based on `mlutils.models.gen_sklearn_model`, see the function - `sklearn_classifier` in this repository. - - :param model_type: one of "classifier", "regressor", - "ranker", "rf_classifier", or - "rf_regressor" - :param xgb_params: class init parameters - """ - mtypes = { - "classifier": "xgboost.XGBClassifier", - "regressor": "xgboost.XGBRegressor", - "ranker": "xgboost.XGBRanker", - "rf_classifier": "xgboost.XGBRFClassifier", - "rf_regressor": "xgboost.XGBRFRegressor", - } - if model_type.endswith("json"): - model_config = model_type - elif model_type in mtypes.keys(): - model_config = mtypes[model_type] - else: - raise Exception("unrecognized model type, see help documentation") - - return gen_sklearn_model(model_config, xgb_params) - - -def train_model( - context: MLClientCtx, - model_type: str, - dataset: Union[DataItem, pd.core.frame.DataFrame], - label_column: str = "labels", - encode_cols: dict = {}, - sample: int = -1, - imbal_vec=[], - test_size: float = 0.25, - valid_size: float = 0.75, - random_state: int = 1, - models_dest: str = "models", - plots_dest: str = "plots", - eval_metrics: list = ["error", "auc"], - file_ext: str = "parquet", - test_set: str = "test_set", -) -> None: - """train an xgboost model. - - Note on imabalanced data: the `imbal_vec` parameter represents the measured - class representations in the sample and can be used as a first step in tuning - an XGBoost model. This isn't a hyperparamter, merely an estimate that should - be set as 'constant' throughout tuning process. - - :param context: the function context - :param model_type: the model type to train, "classifier", "regressor"... - :param dataset: ("data") name of raw data file - :param label_column: ground-truth (y) labels - :param encode_cols: dictionary of names and prefixes for columns that are - to hot be encoded. - :param sample: Selects the first n rows, or select a sample - starting from the first. If negative <-1, select - a random sample - :param imbal_vec: ([]) vector of class weights seen in sample - :param test_size: (0.05) test set size - :param valid_size: (0.75) Once the test set has been removed the - training set gets this proportion. - :param random_state: (1) sklearn rng seed - :param models_dest: destination subfolder for model artifacts - :param plots_dest: destination subfolder for plot artifacts - :param eval_metrics: (["error", "auc"]) learning curve metrics - :param file_ext: format for test_set_key hold out data - :param test-set: (test_set) key of held out data in artifact store - """ - models_dest = models_dest or "models" - plots_dest = plots_dest or f"plots/{context.name}" - - raw, labels, header = get_sample(dataset, sample, label_column) - - if encode_cols: - raw = pd.get_dummies( - raw, - columns=list(encode_cols.keys()), - prefix=list(encode_cols.values()), - drop_first=True, - ) - - (xtrain, ytrain), (xvalid, yvalid), (xtest, ytest) = get_splits( - raw, labels, 3, test_size, valid_size, random_state - ) - - context.log_dataset( - test_set, df=pd.concat([xtest, ytest], axis=1), format=file_ext, index=False - ) - - model_config = _gen_xgb_model(model_type, context.parameters.items()) - - XGBBoostClass = create_class(model_config["META"]["class"]) - model = XGBBoostClass(**model_config["CLASS"]) - - model_config["FIT"].update( - { - "X": xtrain, - "y": ytrain.values, - "eval_set": [(xtrain, ytrain), (xvalid, yvalid)], - "eval_metric": eval_metrics, - } - ) - - model.fit(**model_config["FIT"]) - - eval_metrics = eval_model_v2(context, xvalid, yvalid, model) - - model_bin = dumps(model) - context.log_model( - "model", - body=model_bin, - artifact_path=os.path.join(context.artifact_path, models_dest), - model_file="model.pkl", - ) From 6dd4dedcc601826684499e366cb45bcc6bbd9697 Mon Sep 17 00:00:00 2001 From: Eyal Danieli Date: Tue, 8 Oct 2024 15:26:40 +0300 Subject: [PATCH 24/38] [Batch Infer V2] Adjust function to 1.7 (#832) * adjust batch infer v2 * update docs in NB --- batch_inference_v2/batch_inference_v2.ipynb | 1826 +++++++++-------- batch_inference_v2/batch_inference_v2.py | 37 +- batch_inference_v2/function.yaml | 106 +- batch_inference_v2/item.yaml | 6 +- batch_inference_v2/test_batch_inference_v2.py | 71 +- 5 files changed, 1108 insertions(+), 938 deletions(-) diff --git a/batch_inference_v2/batch_inference_v2.ipynb b/batch_inference_v2/batch_inference_v2.ipynb index bb59221fd..7f369fa51 100644 --- a/batch_inference_v2/batch_inference_v2.ipynb +++ b/batch_inference_v2/batch_inference_v2.ipynb @@ -10,14 +10,14 @@ "source": [ "# Batch Inference V2\n", "\n", - "A function for inferring given input through a given model while producing a **Result Set** and performing **Data Drift Analysis**.\n", + "A function for inferring given input through a given model while producing a **Result Set** and applying monitoring analysis.\n", "\n", "In this notebook we will go over the function's docs and outputs and see an end-to-end example of running it.\n", "\n", "1. [Documentation](#chapter1)\n", "2. [Results Prediction](#chapter2)\n", - "3. [Data Drift Analysis](#chapter3)\n", - "4. [End-to-end Demo](#chapter4)" + "3. [End-to-end Demo](#chapter3)\n", + "4. [Data Drift Analysis](#chapter4)" ] }, { @@ -31,9 +31,9 @@ "\n", "## 1. Documentation\n", "\n", - "Perform a prediction on a given dataset with the given model. Can perform drift analysis between the sample set\n", - "statistics stored in the model to the current input data. The drift rule is the value per-feature mean of the TVD\n", - "and Hellinger scores according to the thresholds configures here. When performing drift analysis, this function\n", + "Perform a prediction on a given dataset with the given model. Using the default histogram data drift application, can perform drift analysis between the sample set\n", + "statistics stored in the model to the current input data. The drift rule in this case is the value per-feature mean of the TVD\n", + "and Hellinger scores. When §, this function\n", "either creates or update an existing model endpoint record (depends on the provided `endpoint_id`).\n", "\n", "At the moment, this function is supported for `mlrun>=1.5.0` versions." @@ -104,19 +104,7 @@ " dataset given. By default, None, which means it will perform drift analysis if the\n", " model already has feature stats that are considered as a reference sample set.\n", " Performing drift analysis on a new endpoint id will generate a new model endpoint\n", - " record. Please note that in order to trigger the drift analysis job, you need to\n", - " set `trigger_monitoring_job=True`. Otherwise, the drift analysis will be triggered\n", - " only as part the scheduled monitoring job (if exist in the current project) or\n", - " if triggered manually by the user.\n", - " \n", - "* **trigger_monitoring_job**: `bool` = `False`\n", - "\n", - " Whether to trigger the batch drift analysis after the infer job.\n", - "\n", - "* **batch_image_job**: `str` = `mlrun/mlrun`\n", - "\n", - " The image that will be used for the monitoring batch job analysis. By default,\n", - " the image is mlrun/mlrun\n", + " record.\n", "\n", "* **endpoint_id**: `str` = `\"\"`\n", " \n", @@ -130,13 +118,6 @@ " \n", " If a new model endpoint is generated, the model name will be presented under this endpoint.\n", "\n", - "* **model_endpoint_drift_threshold**: `float` = `0.7`\n", - " \n", - " The threshold of which to mark drifts. Defaulted to 0.7.\n", - "\n", - "* **model_endpoint_possible_drift_threshold**: `float` = `0.5`\n", - " \n", - " The threshold of which to mark possible drifts. Defaulted to 0.5.\n", " \n", "* **model_endpoint_sample_set**: `Union[mlrun.DataItem, list, dict, pd.DataFrame, pd.Series, np.ndarray]` = `None`\n", " \n", @@ -155,15 +136,13 @@ "### 1.2. Outputs\n", "\n", "The outputs are split to two actions the functions can perform:\n", - "* [**Results Prediction**](#chapter2) - Will log:\n", + "* [**Results Prediction**](#chapter3) - Will log:\n", " * A dataset artifact named by the `result_set_name` parameter.\n", " * A `str` result named `\"batch_id\"` of the given / generated batch ID.\n", "\n", - "* [**Data Drift Analysis**](#chapter3) - Will log:\n", + "* [**Data Drift Analysis**](#chapter4) - Will log:\n", " * A `plotly` artifact named `\"data_drift_table\"` with a visualization of the drifts results and histograms.\n", " * A json artifact named `\"features_drift_results\"` with all the features metric values.\n", - " * A `bool` result named `\"drift_status\"` of the overall drift status (`True` if there was a drift and `False` otherwise).\n", - " * A `float` result named `\"drift_score\"` of the overall drift metric score.\n", "\n", "For more details, see the next chapters." ] @@ -225,62 +204,7 @@ }, "source": [ "\n", - "## 3. Data Drift Analysis\n", - "\n", - "The data drift analysis is done per feature using two distance measure metrics for probability distributions.\n", - "\n", - "Let us mark our sample set as $S$ and our inputs as $I$. We will look at one feature $x$ out of $n$ features. Assuming the histograms of feature $x$ is split into 20 bins: $b_1,b_2,...,b_{20}$, we will match the feature $x$ histogram of the inputs $I$ ($x_I$) into the same bins (meaning to $x_S$) and compare their distributions using:\n", - "\n", - "* Total Variance Distance: $TVD(x_S,x_I) = \\frac{1}{2}\\sum_{b_1}^{b_{20}} {|x_S - x_I|}$\n", - "* Hellinger Distance: $H(x_S,x_I) = \\sqrt{1-{\\sum_{b_1}^{b_{20}}\\sqrt{x_S \\cdot x_I}}}$\n", - "\n", - "Our **rule** then is calculating for each $x\\in S: \\frac{H(x_S,x_I)+TVD(x_S,x_I)}{2} < $ given thresholds.\n", - "\n", - "The outputs of the analysis will be:\n", - "* **Drift table plot** - The results are presented in a `plotly` table artifact named `\"drift_table_plot\"` that shows each feature's statistics and its TVD, Hellinger and KLD (Kullback–Leibler divergence) results as follows:\n", - "\n", - "| | Count | | Mean | | Std | | Min | | Max | | Tvd | Hellinger | Kld | Histograms |\n", - "| ------ | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | --- | --------- | --- |------------|\n", - "| | **Sample** | **Input** | **Sample** | **Input** | **Sample** | **Input** | **Sample** | **Input** | **Sample** | **Input** | | | | |\n", - "| **x1** | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |\n", - "| **x2** | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |\n", - "| **x3** | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |\n", - "\n", - "* **Features drift results** - A rule metric per feature dictionary is saved in a json file named `\"features_drift_results\"` where each key is a feature and its value is the feature's metric value: `Dict[str, float]`\n", - "\n", - " ```python\n", - " {\n", - " \"x1\": 0.12,\n", - " \"x2\": 0.345,\n", - " \"x3\": 0.00678,\n", - " ...\n", - " }\n", - " ```\n", - "\n", - "* In addition, two results are being added to summarize the drift analysis:\n", - "\n", - " * `drift_status`: `bool` - A boolean value indicating whether a drift was found.\n", - " * `drift_metric`: `float` - The mean of all the features drift metric value (the rule above):\n", - " for $n$ features and metric rule $M(x_S,x_I)=\\frac{H(x_S,x_I)+TVD(x_S,x_I)}{2}$, `drift_metric` $=\\frac{1}{n}\\sum_{x\\in S}M(x_S,x_I)$\n", - "\n", - " ```python\n", - " {\n", - " \"drift_status\": True,\n", - " \"drift_metric\": 0.81234\n", - " }\n", - " ```" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "\n", - "## 4. End-to-end Demo\n", + "## 3. End-to-end Demo\n", "\n", "We will see an end-to-end example that follows the steps below:\n", "1. Generate data.\n", @@ -296,7 +220,7 @@ } }, "source": [ - "### 4.1. Code review\n", + "### 3.1. Code review\n", "\n", "We are using a very simple example of training a decision tree on a binary classification problem. For that we wrote two functions:\n", "* `generate_data` - Generate a binary classification data. The data will be split into a *training set* and *data for prediction*. The data for prediction will be drifted in half of its features to showcase the plot later on.\n", @@ -312,10 +236,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "> 2023-08-29 11:13:44,649 [warning] Failed resolving version info. Ignoring and using defaults\n", - "> 2023-08-29 11:13:46,598 [warning] Server or client version is unstable. Assuming compatible: {'server_version': '0.0.0+image-test', 'client_version': '0.0.0+unstable'}\n", - "> 2023-08-29 11:13:46,667 [info] Loading project from path: {'project_name': 'batch-infer-demo', 'path': './'}\n", - "> 2023-08-29 11:14:02,192 [info] Project loaded successfully: {'project_name': 'batch-infer-demo', 'path': './', 'stored_in_db': True}\n" + "> 2024-10-08 10:23:13,060 [info] Loading project from path: {\"path\":\"./\",\"project_name\":\"batch-infer-demo\",\"user_project\":false}\n", + "> 2024-10-08 10:23:28,490 [info] Project loaded successfully: {\"path\":\"./\",\"project_name\":\"batch-infer-demo\",\"stored_in_db\":true}\n" ] } ], @@ -332,6 +254,9 @@ "execution_count": 2, "metadata": { "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, "pycharm": { "name": "#%%\n" } @@ -346,6 +271,9 @@ "execution_count": 3, "metadata": { "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, "pycharm": { "name": "#%%\n" } @@ -367,6 +295,9 @@ "execution_count": 4, "metadata": { "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, "pycharm": { "name": "#%%\n" } @@ -429,6 +360,9 @@ "execution_count": 5, "metadata": { "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, "pycharm": { "name": "#%%\n" } @@ -446,7 +380,7 @@ } }, "source": [ - "### 4.2. Run the Example with MLRun\n", + "### 3.2. Run the Example with MLRun\n", "\n", "First, we will prepare our MLRun functions:\n", "1. We will use `mlrun.code_to_function` to turn this demo notebook into an MLRun function we can run.\n", @@ -458,19 +392,14 @@ "execution_count": 6, "metadata": { "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, "pycharm": { "name": "#%%\n" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2023-08-29 11:14:04,182 [warning] Failed to add git metadata, ignore if path is not part of a git repo.: {'path': './', 'error': '/User/EYAL'}\n" - ] - } - ], + "outputs": [], "source": [ "# Create an MLRun function to run the notebook:\n", "demo_function = mlrun.code_to_function(name=\"batch-inference-demo\", kind=\"job\")\n", @@ -494,9 +423,12 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": { "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, "pycharm": { "name": "#%%\n" } @@ -506,8 +438,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "> 2023-08-29 11:14:42,198 [warning] artifact/output path is not defined or is local and relative, artifacts will not be visible in the UI: {'output_path': './'}\n", - "> 2023-08-29 11:14:42,198 [info] Storing function: {'name': 'batch-inference-demo-generate-data', 'uid': 'd04e9f978132472695774f01b2becb6c', 'db': None}\n" + "> 2024-10-08 10:23:40,584 [error] error getting build status: details: MLRunNotFoundError('Function tag not found batch-infer-demo/batch-inference-demo'), caused by: 404 Client Error: Not Found for url: http://mlrun-api:8080/api/v1/build/status?name=batch-inference-demo&project=batch-infer-demo&tag=&logs=no&offset=0&last_log_timestamp=0.0&verbose=no\n", + "> 2024-10-08 10:23:40,586 [info] Storing function: {\"db\":\"http://mlrun-api:8080\",\"name\":\"batch-inference-demo-generate-data\",\"uid\":\"4f68ba3fd9084e3e941ab3872ceb3635\"}\n", + "> 2024-10-08 10:23:40,881 [info] Job is running in the background, pod: batch-inference-demo-generate-data-52w8s\n", + "> 2024-10-08 10:23:46,954 [info] To track results use the CLI: {\"info_cmd\":\"mlrun get run 4f68ba3fd9084e3e941ab3872ceb3635 -p batch-infer-demo\",\"logs_cmd\":\"mlrun logs 4f68ba3fd9084e3e941ab3872ceb3635 -p batch-infer-demo\"}\n", + "> 2024-10-08 10:23:46,954 [info] Or click for UI: {\"ui_url\":\"https://dashboard.default-tenant.app.vmdev57.lab.iguazeng.com/mlprojects/batch-infer-demo/jobs/monitor/4f68ba3fd9084e3e941ab3872ceb3635/overview\"}\n", + "> 2024-10-08 10:23:46,955 [info] Run execution finished: {\"name\":\"batch-inference-demo-generate-data\",\"status\":\"completed\"}\n" ] }, { @@ -602,9 +538,14 @@ "}\n", "function expandPanel(el) {\n", " const panelName = \"#\" + el.getAttribute('paneName');\n", - " console.log(el.title);\n", "\n", - " document.querySelector(panelName + \"-title\").innerHTML = el.title\n", + " // Get the base URL of the current notebook\n", + " var baseUrl = window.location.origin;\n", + "\n", + " // Construct the full URL\n", + " var fullUrl = new URL(el.title, baseUrl).href;\n", + "\n", + " document.querySelector(panelName + \"-title\").innerHTML = fullUrl\n", " iframe = document.querySelector(panelName + \"-body\");\n", "\n", " const tblcss = `\n", "
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gt=l.getRefType(nt);\"domain\"===gt?(\"y\"===et&&(mt=1-mt),ht.tail=it._offset+it._length*mt):\"paper\"===gt?\"y\"===et?(mt=1-mt,ht.tail=T.t+T.h*mt):ht.tail=T.l+T.w*mt:ht.tail=it._offset+it.r2p(mt),J=tt}else ht.tail=$+mt,J=tt+mt;ht.text=ht.tail+tt;var yt=w[\"x\"===et?\"width\":\"height\"];if(\"paper\"===rt&&(ht.head=o.constrain(ht.head,1,yt-1)),\"pixel\"===nt){var vt=-Math.max(ht.tail-3,ht.text),xt=Math.min(ht.tail+3,ht.text)-yt;vt>0?(ht.tail+=vt,ht.text+=vt):xt>0&&(ht.tail-=xt,ht.text-=xt)}ht.tail+=ut,ht.head+=ut}else J=tt=lt*H(Q,ct),ht.text=$+tt;ht.text+=ut,tt+=ut,J+=ut,e[\"_\"+et+\"padplus\"]=lt/2+J,e[\"_\"+et+\"padminus\"]=lt/2-J,e[\"_\"+et+\"size\"]=lt,e[\"_\"+et+\"shift\"]=tt}if(W)R.remove();else{var _t=0,bt=0;if(\"left\"!==e.align&&(_t=(A-_)*(\"center\"===e.align?.5:1)),\"top\"!==e.valign&&(bt=(D-b)*(\"middle\"===e.valign?.5:1)),h)n.select(\"svg\").attr({x:N+_t-1,y:N+bt}).call(u.setClipUrl,U?C:null,t);else{var wt=N+bt-m.top,Tt=N+_t-m.left;G.call(f.positionText,Tt,wt).call(u.setClipUrl,U?C:null,t)}V.select(\"rect\").call(u.setRect,N,N,A,D),j.call(u.setRect,F/2,F/2,B-F,q-F),R.call(u.setTranslate,Math.round(L.x.text-B/2),Math.round(L.y.text-q/2)),z.attr({transform:\"rotate(\"+I+\",\"+L.x.text+\",\"+L.y.text+\")\"});var kt,At=function(r,n){P.selectAll(\".annotation-arrow-g\").remove();var l=L.x.head,h=L.y.head,f=L.x.tail+r,p=L.y.tail+n,m=L.x.text+r,_=L.y.text+n,b=o.rotationXYMatrix(I,m,_),w=o.apply2DTransform(b),A=o.apply2DTransform2(b),C=+j.attr(\"width\"),O=+j.attr(\"height\"),D=m-.5*C,F=D+C,B=_-.5*O,N=B+O,U=[[D,B,D,N],[D,N,F,N],[F,N,F,B],[F,B,D,B]].map(A);if(!U.reduce((function(t,e){return t^!!o.segmentsIntersect(l,h,l+1e6,h+1e6,e[0],e[1],e[2],e[3])}),!1)){U.forEach((function(t){var e=o.segmentsIntersect(f,p,l,h,t[0],t[1],t[2],t[3]);e&&(f=e.x,p=e.y)}));var V=e.arrowwidth,q=e.arrowcolor,H=e.arrowside,G=P.append(\"g\").style({opacity:c.opacity(q)}).classed(\"annotation-arrow-g\",!0),Z=G.append(\"path\").attr(\"d\",\"M\"+f+\",\"+p+\"L\"+l+\",\"+h).style(\"stroke-width\",V+\"px\").call(c.stroke,c.rgb(q));if(g(Z,H,e),k.annotationPosition&&Z.node().parentNode&&!a){var W=l,Y=h;if(e.standoff){var X=Math.sqrt(Math.pow(l-f,2)+Math.pow(h-p,2));W+=e.standoff*(f-l)/X,Y+=e.standoff*(p-h)/X}var $,J,K=G.append(\"path\").classed(\"annotation-arrow\",!0).classed(\"anndrag\",!0).classed(\"cursor-move\",!0).attr({d:\"M3,3H-3V-3H3ZM0,0L\"+(f-W)+\",\"+(p-Y),transform:s(W,Y)}).style(\"stroke-width\",V+6+\"px\").call(c.stroke,\"rgba(0,0,0,0)\").call(c.fill,\"rgba(0,0,0,0)\");d.init({element:K.node(),gd:t,prepFn:function(){var t=u.getTranslate(R);$=t.x,J=t.y,y&&y.autorange&&M(y._name+\".autorange\",!0),x&&x.autorange&&M(x._name+\".autorange\",!0)},moveFn:function(t,r){var n=w($,J),i=n[0]+t,a=n[1]+r;R.call(u.setTranslate,i,a),S(\"x\",v(y,t,\"x\",T,e)),S(\"y\",v(x,r,\"y\",T,e)),e.axref===e.xref&&S(\"ax\",v(y,t,\"ax\",T,e)),e.ayref===e.yref&&S(\"ay\",v(x,r,\"ay\",T,e)),G.attr(\"transform\",s(t,r)),z.attr({transform:\"rotate(\"+I+\",\"+i+\",\"+a+\")\"})},doneFn:function(){i.call(\"_guiRelayout\",t,E());var e=document.querySelector(\".js-notes-box-panel\");e&&e.redraw(e.selectedObj)}})}}};e.showarrow&&At(0,0),O&&d.init({element:R.node(),gd:t,prepFn:function(){kt=z.attr(\"transform\")},moveFn:function(t,r){var n=\"pointer\";if(e.showarrow)e.axref===e.xref?S(\"ax\",v(y,t,\"ax\",T,e)):S(\"ax\",e.ax+t),e.ayref===e.yref?S(\"ay\",v(x,r,\"ay\",T.w,e)):S(\"ay\",e.ay+r),At(t,r);else{if(a)return;var i,o;if(y)i=v(y,t,\"x\",T,e);else{var l=e._xsize/T.w,c=e.x+(e._xshift-e.xshift)/T.w-l/2;i=d.align(c+t/T.w,l,0,1,e.xanchor)}if(x)o=v(x,r,\"y\",T,e);else{var u=e._ysize/T.h,h=e.y-(e._yshift+e.yshift)/T.h-u/2;o=d.align(h-r/T.h,u,0,1,e.yanchor)}S(\"x\",i),S(\"y\",o),y&&x||(n=d.getCursor(y?.5:i,x?.5:o,e.xanchor,e.yanchor))}z.attr({transform:s(t,r)+kt}),p(R,n)},clickFn:function(r,n){e.captureevents&&t.emit(\"plotly_clickannotation\",Z(n))},doneFn:function(){p(R),i.call(\"_guiRelayout\",t,E());var e=document.querySelector(\".js-notes-box-panel\");e&&e.redraw(e.selectedObj)}})}}}t.exports={draw:function(t){var e=t._fullLayout;e._infolayer.selectAll(\".annotation\").remove();for(var r=0;r=0,x=e.indexOf(\"end\")>=0,_=d.backoff*g+r.standoff,b=m.backoff*y+r.startstandoff;if(\"line\"===p.nodeName){o={x:+t.attr(\"x1\"),y:+t.attr(\"y1\")},u={x:+t.attr(\"x2\"),y:+t.attr(\"y2\")};var w=o.x-u.x,T=o.y-u.y;if(f=(h=Math.atan2(T,w))+Math.PI,_&&b&&_+b>Math.sqrt(w*w+T*T))return void O();if(_){if(_*_>w*w+T*T)return void O();var k=_*Math.cos(h),A=_*Math.sin(h);u.x+=k,u.y+=A,t.attr({x2:u.x,y2:u.y})}if(b){if(b*b>w*w+T*T)return void O();var M=b*Math.cos(h),S=b*Math.sin(h);o.x-=M,o.y-=S,t.attr({x1:o.x,y1:o.y})}}else if(\"path\"===p.nodeName){var E=p.getTotalLength(),C=\"\";if(E<_+b)return void O();var L=p.getPointAtLength(0),I=p.getPointAtLength(.1);h=Math.atan2(L.y-I.y,L.x-I.x),o=p.getPointAtLength(Math.min(b,E)),C=\"0px,\"+b+\"px,\";var P=p.getPointAtLength(E),z=p.getPointAtLength(E-.1);f=Math.atan2(P.y-z.y,P.x-z.x),u=p.getPointAtLength(Math.max(0,E-_)),C+=E-(C?b+_:_)+\"px,\"+E+\"px\",t.style(\"stroke-dasharray\",C)}function O(){t.style(\"stroke-dasharray\",\"0px,100px\")}function D(e,a,o,u){e.path&&(e.noRotate&&(o=0),n.select(p.parentNode).append(\"path\").attr({class:t.attr(\"class\"),d:e.path,transform:c(a.x,a.y)+l(180*o/Math.PI)+s(u)}).style({fill:i.rgb(r.arrowcolor),\"stroke-width\":0}))}v&&D(m,o,h,y),x&&D(d,u,f,g)}},3599:function(t,e,r){\"use strict\";var n=r(3377),i=r(6035);t.exports={moduleType:\"component\",name:\"annotations\",layoutAttributes:r(50222),supplyLayoutDefaults:r(63737),includeBasePlot:r(20706)(\"annotations\"),calcAutorange:r(60317),draw:n.draw,drawOne:n.drawOne,drawRaw:n.drawRaw,hasClickToShow:i.hasClickToShow,onClick:i.onClick,convertCoords:r(59741)}},38239:function(t,e,r){\"use strict\";var n=r(50222),i=r(13582).overrideAll,a=r(78032).templatedArray;t.exports=i(a(\"annotation\",{visible:n.visible,x:{valType:\"any\"},y:{valType:\"any\"},z:{valType:\"any\"},ax:{valType:\"number\"},ay:{valType:\"number\"},xanchor:n.xanchor,xshift:n.xshift,yanchor:n.yanchor,yshift:n.yshift,text:n.text,textangle:n.textangle,font:n.font,width:n.width,height:n.height,opacity:n.opacity,align:n.align,valign:n.valign,bgcolor:n.bgcolor,bordercolor:n.bordercolor,borderpad:n.borderpad,borderwidth:n.borderwidth,showarrow:n.showarrow,arrowcolor:n.arrowcolor,arrowhead:n.arrowhead,startarrowhead:n.startarrowhead,arrowside:n.arrowside,arrowsize:n.arrowsize,startarrowsize:n.startarrowsize,arrowwidth:n.arrowwidth,standoff:n.standoff,startstandoff:n.startstandoff,hovertext:n.hovertext,hoverlabel:n.hoverlabel,captureevents:n.captureevents}),\"calc\",\"from-root\")},47979:function(t,e,r){\"use strict\";var n=r(34809),i=r(29714);function a(t,e){var r=e.fullSceneLayout.domain,a=e.fullLayout._size,o={pdata:null,type:\"linear\",autorange:!1,range:[-1/0,1/0]};t._xa={},n.extendFlat(t._xa,o),i.setConvert(t._xa),t._xa._offset=a.l+r.x[0]*a.w,t._xa.l2p=function(){return.5*(1+t._pdata[0]/t._pdata[3])*a.w*(r.x[1]-r.x[0])},t._ya={},n.extendFlat(t._ya,o),i.setConvert(t._ya),t._ya._offset=a.t+(1-r.y[1])*a.h,t._ya.l2p=function(){return.5*(1-t._pdata[1]/t._pdata[3])*a.h*(r.y[1]-r.y[0])}}t.exports=function(t){for(var e=t.fullSceneLayout.annotations,r=0;r1){c=!0;break}}c?t.fullLayout._infolayer.select(\".annotation-\"+t.id+'[data-index=\"'+s+'\"]').remove():(l._pdata=i(t.glplot.cameraParams,[e.xaxis.r2l(l.x)*r[0],e.yaxis.r2l(l.y)*r[1],e.zaxis.r2l(l.z)*r[2]]),n(t.graphDiv,l,s,t.id,l._xa,l._ya))}}},83348:function(t,e,r){\"use strict\";var n=r(33626),i=r(34809);t.exports={moduleType:\"component\",name:\"annotations3d\",schema:{subplots:{scene:{annotations:r(38239)}}},layoutAttributes:r(38239),handleDefaults:r(34232),includeBasePlot:function(t,e){var r=n.subplotsRegistry.gl3d;if(r)for(var a=r.attrRegex,o=Object.keys(t),s=0;s=0))return t;if(3===o)n[o]>1&&(n[o]=1);else if(n[o]>=1)return t}var s=Math.round(255*n[0])+\", \"+Math.round(255*n[1])+\", \"+Math.round(255*n[2]);return a?\"rgba(\"+s+\", \"+n[3]+\")\":\"rgb(\"+s+\")\"}o.tinyRGB=function(t){var e=t.toRgb();return\"rgb(\"+Math.round(e.r)+\", \"+Math.round(e.g)+\", \"+Math.round(e.b)+\")\"},o.rgb=function(t){return o.tinyRGB(n(t))},o.opacity=function(t){return t?n(t).getAlpha():0},o.addOpacity=function(t,e){var r=n(t).toRgb();return\"rgba(\"+Math.round(r.r)+\", \"+Math.round(r.g)+\", \"+Math.round(r.b)+\", \"+e+\")\"},o.combine=function(t,e){var r=n(t).toRgb();if(1===r.a)return n(t).toRgbString();var i=n(e||c).toRgb(),a=1===i.a?i:{r:255*(1-i.a)+i.r*i.a,g:255*(1-i.a)+i.g*i.a,b:255*(1-i.a)+i.b*i.a},o={r:a.r*(1-r.a)+r.r*r.a,g:a.g*(1-r.a)+r.g*r.a,b:a.b*(1-r.a)+r.b*r.a};return n(o).toRgbString()},o.interpolate=function(t,e,r){var i=n(t).toRgb(),a=n(e).toRgb(),o={r:r*i.r+(1-r)*a.r,g:r*i.g+(1-r)*a.g,b:r*i.b+(1-r)*a.b};return n(o).toRgbString()},o.contrast=function(t,e,r){var i=n(t);return 1!==i.getAlpha()&&(i=n(o.combine(t,c))),(i.isDark()?e?i.lighten(e):c:r?i.darken(r):l).toString()},o.stroke=function(t,e){var r=n(e);t.style({stroke:o.tinyRGB(r),\"stroke-opacity\":r.getAlpha()})},o.fill=function(t,e){var r=n(e);t.style({fill:o.tinyRGB(r),\"fill-opacity\":r.getAlpha()})},o.clean=function(t){if(t&&\"object\"==typeof t){var e,r,n,i,s=Object.keys(t);for(e=0;e0?n>=l:n<=l));i++)n>u&&n0?n>=l:n<=l));i++)n>r[0]&&n1){var pt=Math.pow(10,Math.floor(Math.log(ft)/Math.LN10));ut*=pt*c.roundUp(ft/pt,[2,5,10]),(Math.abs(Z.start)/Z.size+1e-6)%1<2e-6&&(lt.tick0=0)}lt.dtick=ut}lt.domain=o?[ot+P/B.h,ot+Q-P/B.h]:[ot+I/B.w,ot+Q-I/B.w],lt.setScale(),t.attr(\"transform\",u(Math.round(B.l),Math.round(B.t)));var dt,mt=t.select(\".\"+A.cbtitleunshift).attr(\"transform\",u(-Math.round(B.l),-Math.round(B.t))),gt=lt.ticklabelposition,yt=lt.title.font.size,vt=t.select(\".\"+A.cbaxis),xt=0,_t=0;function bt(n,i){var a={propContainer:lt,propName:e._propPrefix+\"title\",traceIndex:e._traceIndex,_meta:e._meta,placeholder:F._dfltTitle.colorbar,containerGroup:t.select(\".\"+A.cbtitle)},o=\"h\"===n.charAt(0)?n.substr(1):\"h\"+n;t.selectAll(\".\"+o+\",.\"+o+\"-math-group\").remove(),m.draw(r,n,h(a,i||{}))}return c.syncOrAsync([a.previousPromises,function(){var t,e;(o&&ct||!o&&!ct)&&(\"top\"===V&&(t=I+B.l+tt*z,e=P+B.t+et*(1-ot-Q)+3+.75*yt),\"bottom\"===V&&(t=I+B.l+tt*z,e=P+B.t+et*(1-ot)-3-.25*yt),\"right\"===V&&(e=P+B.t+et*O+3+.75*yt,t=I+B.l+tt*ot),bt(lt._id+\"title\",{attributes:{x:t,y:e,\"text-anchor\":o?\"start\":\"middle\"}}))},function(){if(!o&&!ct||o&&ct){var a,l=t.select(\".\"+A.cbtitle),h=l.select(\"text\"),f=[-M/2,M/2],d=l.select(\".h\"+lt._id+\"title-math-group\").node(),m=15.6;if(h.node()&&(m=parseInt(h.node().style.fontSize,10)*w),d?(a=p.bBox(d),_t=a.width,(xt=a.height)>m&&(f[1]-=(xt-m)/2)):h.node()&&!h.classed(A.jsPlaceholder)&&(a=p.bBox(h.node()),_t=a.width,xt=a.height),o){if(xt){if(xt+=5,\"top\"===V)lt.domain[1]-=xt/B.h,f[1]*=-1;else{lt.domain[0]+=xt/B.h;var y=g.lineCount(h);f[1]+=(1-y)*m}l.attr(\"transform\",u(f[0],f[1])),lt.setScale()}}else _t&&(\"right\"===V&&(lt.domain[0]+=(_t+yt/2)/B.w),l.attr(\"transform\",u(f[0],f[1])),lt.setScale())}t.selectAll(\".\"+A.cbfills+\",.\"+A.cblines).attr(\"transform\",o?u(0,Math.round(B.h*(1-lt.domain[1]))):u(Math.round(B.w*lt.domain[0]),0)),vt.attr(\"transform\",o?u(0,Math.round(-B.t)):u(Math.round(-B.l),0));var v=t.select(\".\"+A.cbfills).selectAll(\"rect.\"+A.cbfill).attr(\"style\",\"\").data(Y);v.enter().append(\"rect\").classed(A.cbfill,!0).attr(\"style\",\"\"),v.exit().remove();var x=q.map(lt.c2p).map(Math.round).sort((function(t,e){return t-e}));v.each((function(t,a){var s=[0===a?q[0]:(Y[a]+Y[a-1])/2,a===Y.length-1?q[1]:(Y[a]+Y[a+1])/2].map(lt.c2p).map(Math.round);o&&(s[1]=c.constrain(s[1]+(s[1]>s[0])?1:-1,x[0],x[1]));var l=n.select(this).attr(o?\"x\":\"y\",rt).attr(o?\"y\":\"x\",n.min(s)).attr(o?\"width\":\"height\",Math.max($,2)).attr(o?\"height\":\"width\",Math.max(n.max(s)-n.min(s),2));if(e._fillgradient)p.gradient(l,r,e._id,o?\"vertical\":\"horizontalreversed\",e._fillgradient,\"fill\");else{var u=G(t).replace(\"e-\",\"\");l.attr(\"fill\",i(u).toHexString())}}));var _=t.select(\".\"+A.cblines).selectAll(\"path.\"+A.cbline).data(j.color&&j.width?X:[]);_.enter().append(\"path\").classed(A.cbline,!0),_.exit().remove(),_.each((function(t){var e=rt,r=Math.round(lt.c2p(t))+j.width/2%1;n.select(this).attr(\"d\",\"M\"+(o?e+\",\"+r:r+\",\"+e)+(o?\"h\":\"v\")+$).call(p.lineGroupStyle,j.width,H(t),j.dash)})),vt.selectAll(\"g.\"+lt._id+\"tick,path\").remove();var 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e=t[0],r=t[t.length-1];if(a=e.pmin-e.pos-e.dp+e.size,o=r.pos+r.dp+r.size-e.pmax,a>.01){for(l=t.length-1;l>=0;l--)t[l].dp+=a;i=!1}if(!(o<.01)){if(a<-.01){for(l=t.length-1;l>=0;l--)t[l].dp-=o;i=!1}if(i){var n=0;for(s=0;se.pmax&&n++;for(s=t.length-1;s>=0&&!(n<=0);s--)(c=t[s]).pos>e.pmax-1&&(c.del=!0,n--);for(s=0;s=0;l--)t[l].dp-=o;for(s=t.length-1;s>=0&&!(n<=0);s--)(c=t[s]).pos+c.dp+c.size>e.pmax&&(c.del=!0,n--)}}}for(t.each((function(t){var n=t[h],i=t[f],a=\"x\"===n._id.charAt(0),o=n.range;0===y&&o&&o[0]>o[1]!==a&&(d=-1);var s=0,l=a?r.width:r.height;if(\"x\"===r.hovermode||\"y\"===r.hovermode){var c,u,p=F(t,e),m=t.anchor,k=\"end\"===m?-1:1;if(\"middle\"===m)u=(c=t.crossPos+(a?T(p.y-t.by/2):w(t.bx/2+t.tx2width/2)))+(a?T(t.by):w(t.bx));else if(a)u=(c=t.crossPos+T(E+p.y)-T(t.by/2-E))+T(t.by);else{var M=w(k*E+p.x),S=M+w(k*t.bx);c=t.crossPos+Math.min(M,S),u=t.crossPos+Math.max(M,S)}a?void 0!==_&&void 0!==b&&Math.min(u,b)-Math.max(c,_)>1&&(\"left\"===i.side?(s=i._mainLinePosition,l=r.width):l=i._mainLinePosition):void 0!==v&&void 0!==x&&Math.min(u,x)-Math.max(c,v)>1&&(\"top\"===i.side?(s=i._mainLinePosition,l=r.height):l=i._mainLinePosition)}g[y++]=[{datum:t,traceIndex:t.trace.index,dp:0,pos:t.pos,posref:t.posref,size:t.by*(a?A:1)/2,pmin:s,pmax:l}]})),g.sort((function(t,e){return t[0].posref-e[0].posref||d*(e[0].traceIndex-t[0].traceIndex)}));!i&&p<=m;){for(p++,i=!0,s=0;s.01){for(l=S.length-1;l>=0;l--)S[l].dp+=a;for(M.push.apply(M,S),g.splice(s+1,1),u=0,l=M.length-1;l>=0;l--)u+=M[l].dp;for(o=u/M.length,l=M.length-1;l>=0;l--)M[l].dp-=o;i=!1}else s++}g.forEach(k)}for(s=g.length-1;s>=0;s--){var I=g[s];for(l=I.length-1;l>=0;l--){var P=I[l],z=P.datum;z.offset=P.dp,z.del=P.del}}}(ce,oe,b,le.commonLabelBoundingBox),B(ce,oe,b._invScaleX,b._invScaleY)),l&&l.tagName){var ue=v.getComponentMethod(\"annotations\",\"hasClickToShow\")(t,Zt);f(n.select(l),ue?\"pointer\":\"\")}l&&!a&&function(t,e,r){if(!r||r.length!==t._hoverdata.length)return!0;for(var n=r.length-1;n>=0;n--){var i=r[n],a=t._hoverdata[n];if(i.curveNumber!==a.curveNumber||String(i.pointNumber)!==String(a.pointNumber)||String(i.pointNumbers)!==String(a.pointNumbers))return!0}return!1}(t,0,Gt)&&(Gt&&t.emit(\"plotly_unhover\",{event:e,points:Gt}),t.emit(\"plotly_hover\",{event:e,points:t._hoverdata,xaxes:$,yaxes:J,xvals:nt,yvals:it}))}(t,e,r,a,l)}))},e.loneHover=function(t,e){var r=!0;Array.isArray(t)||(r=!1,t=[t]);var i=e.gd,a=H(i),o=G(i),s=D(t.map((function(t){var r=t._x0||t.x0||t.x||0,n=t._x1||t.x1||t.x||0,s=t._y0||t.y0||t.y||0,l=t._y1||t.y1||t.y||0,c=t.eventData;if(c){var u=Math.min(r,n),h=Math.max(r,n),f=Math.min(s,l),p=Math.max(s,l),m=t.trace;if(v.traceIs(m,\"gl3d\")){var g=i._fullLayout[m.scene]._scene.container,y=g.offsetLeft,x=g.offsetTop;u+=y,h+=y,f+=x,p+=x}c.bbox={x0:u+o,x1:h+o,y0:f+a,y1:p+a},e.inOut_bbox&&e.inOut_bbox.push(c.bbox)}else c=!1;return{color:t.color||d.defaultLine,x0:t.x0||t.x||0,x1:t.x1||t.x||0,y0:t.y0||t.y||0,y1:t.y1||t.y||0,xLabel:t.xLabel,yLabel:t.yLabel,zLabel:t.zLabel,text:t.text,name:t.name,idealAlign:t.idealAlign,borderColor:t.borderColor,fontFamily:t.fontFamily,fontSize:t.fontSize,fontColor:t.fontColor,fontWeight:t.fontWeight,fontStyle:t.fontStyle,fontVariant:t.fontVariant,nameLength:t.nameLength,textAlign:t.textAlign,trace:t.trace||{index:0,hoverinfo:\"\"},xa:{_offset:0},ya:{_offset:0},index:0,hovertemplate:t.hovertemplate||!1,hovertemplateLabels:t.hovertemplateLabels||!1,eventData:c}})),{gd:i,hovermode:\"closest\",rotateLabels:!1,bgColor:e.bgColor||d.background,container:n.select(e.container),outerContainer:e.outerContainer||e.container}).hoverLabels,l=0,c=0;return s.sort((function(t,e){return t.y0-e.y0})).each((function(t,r){var n=t.y0-t.by/2;t.offset=n-5([\\s\\S]*)<\\/extra>/;function D(t,e){var r=e.gd,i=r._fullLayout,a=e.hovermode,s=e.rotateLabels,u=e.bgColor,f=e.container,m=e.outerContainer,g=e.commonLabelOpts||{};if(0===t.length)return[[]];var y=e.fontFamily||_.HOVERFONT,k=e.fontSize||_.HOVERFONTSIZE,A=e.fontWeight||i.font.weight,M=e.fontStyle||i.font.style,S=e.fontVariant||i.font.variant,L=e.fontTextcase||i.font.textcase,I=e.fontLineposition||i.font.lineposition,P=e.fontShadow||i.font.shadow,O=t[0],D=O.xa,F=O.ya,B=a.charAt(0),N=B+\"Label\",j=O[N];if(void 0===j&&\"multicategory\"===D.type)for(var U=0;Ui.width-T&&(z=i.width-T),e.attr(\"d\",\"M\"+(x-z)+\",0L\"+(x-z+E)+\",\"+w+E+\"H\"+T+\"v\"+w+(2*C+b.height)+\"H\"+-T+\"V\"+w+E+\"H\"+(x-z-E)+\"Z\"),x=z,Q.minX=x-T,Q.maxX=x+T,\"top\"===D.side?(Q.minY=_-(2*C+b.height),Q.maxY=_-C):(Q.minY=_+C,Q.maxY=_+(2*C+b.height))}else{var 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i,s,l,c,u=r.select(\"text.nums\"),f=t.anchor,d=\"end\"===f?-1:1,m=(c=(l=(s={start:1,end:-1,middle:0}[(i=t).anchor])*(E+C))+s*(i.txwidth+C),\"middle\"===i.anchor&&(l-=i.tx2width/2,c+=i.txwidth/2+C),{alignShift:s,textShiftX:l,text2ShiftX:c}),g=F(t,e),y=g.x,v=g.y,x=\"middle\"===f;r.select(\"path\").attr(\"d\",x?\"M-\"+a(t.bx/2+t.tx2width/2)+\",\"+o(v-t.by/2)+\"h\"+a(t.bx)+\"v\"+o(t.by)+\"h-\"+a(t.bx)+\"Z\":\"M0,0L\"+a(d*E+y)+\",\"+o(E+v)+\"v\"+o(t.by/2-E)+\"h\"+a(d*t.bx)+\"v-\"+o(t.by)+\"H\"+a(d*E+y)+\"V\"+o(v-E)+\"Z\");var _=y+m.textShiftX,b=v+t.ty0-t.by/2+C,w=t.textAlign||\"auto\";\"auto\"!==w&&(\"left\"===w&&\"start\"!==f?(u.attr(\"text-anchor\",\"start\"),_=x?-t.bx/2-t.tx2width/2+C:-t.bx-C):\"right\"===w&&\"end\"!==f&&(u.attr(\"text-anchor\",\"end\"),_=x?t.bx/2-t.tx2width/2-C:t.bx+C)),u.call(h.positionText,a(_),o(b)),t.tx2width&&(r.select(\"text.name\").call(h.positionText,a(m.text2ShiftX+m.alignShift*C+y),o(v+t.ty0-t.by/2+C)),r.select(\"rect\").call(p.setRect,a(m.text2ShiftX+(m.alignShift-1)*t.tx2width/2+y),o(v-t.by/2-1),a(t.tx2width),o(t.by+2)))}))}function N(t,e){var r=t.index,n=t.trace||{},a=t.cd[0],s=t.cd[r]||{};function l(t){return t||i(t)&&0===t}var c=Array.isArray(r)?function(t,e){var i=o.castOption(a,r,t);return l(i)?i:o.extractOption({},n,\"\",e)}:function(t,e){return o.extractOption(s,n,t,e)};function u(e,r,n){var i=c(r,n);l(i)&&(t[e]=i)}if(u(\"hoverinfo\",\"hi\",\"hoverinfo\"),u(\"bgcolor\",\"hbg\",\"hoverlabel.bgcolor\"),u(\"borderColor\",\"hbc\",\"hoverlabel.bordercolor\"),u(\"fontFamily\",\"htf\",\"hoverlabel.font.family\"),u(\"fontSize\",\"hts\",\"hoverlabel.font.size\"),u(\"fontColor\",\"htc\",\"hoverlabel.font.color\"),u(\"fontWeight\",\"htw\",\"hoverlabel.font.weight\"),u(\"fontStyle\",\"hty\",\"hoverlabel.font.style\"),u(\"fontVariant\",\"htv\",\"hoverlabel.font.variant\"),u(\"nameLength\",\"hnl\",\"hoverlabel.namelength\"),u(\"textAlign\",\"hta\",\"hoverlabel.align\"),t.posref=\"y\"===e||\"closest\"===e&&\"h\"===n.orientation?t.xa._offset+(t.x0+t.x1)/2:t.ya._offset+(t.y0+t.y1)/2,t.x0=o.constrain(t.x0,0,t.xa._length),t.x1=o.constrain(t.x1,0,t.xa._length),t.y0=o.constrain(t.y0,0,t.ya._length),t.y1=o.constrain(t.y1,0,t.ya._length),void 0!==t.xLabelVal&&(t.xLabel=\"xLabel\"in t?t.xLabel:g.hoverLabelText(t.xa,t.xLabelVal,n.xhoverformat),t.xVal=t.xa.c2d(t.xLabelVal)),void 0!==t.yLabelVal&&(t.yLabel=\"yLabel\"in t?t.yLabel:g.hoverLabelText(t.ya,t.yLabelVal,n.yhoverformat),t.yVal=t.ya.c2d(t.yLabelVal)),void 0!==t.zLabelVal&&void 0===t.zLabel&&(t.zLabel=String(t.zLabelVal)),!(isNaN(t.xerr)||\"log\"===t.xa.type&&t.xerr<=0)){var h=g.tickText(t.xa,t.xa.c2l(t.xerr),\"hover\").text;void 0!==t.xerrneg?t.xLabel+=\" +\"+h+\" / -\"+g.tickText(t.xa,t.xa.c2l(t.xerrneg),\"hover\").text:t.xLabel+=\" ± \"+h,\"x\"===e&&(t.distance+=1)}if(!(isNaN(t.yerr)||\"log\"===t.ya.type&&t.yerr<=0)){var f=g.tickText(t.ya,t.ya.c2l(t.yerr),\"hover\").text;void 0!==t.yerrneg?t.yLabel+=\" +\"+f+\" / -\"+g.tickText(t.ya,t.ya.c2l(t.yerrneg),\"hover\").text:t.yLabel+=\" ± \"+f,\"y\"===e&&(t.distance+=1)}var p=t.hoverinfo||t.trace.hoverinfo;return p&&\"all\"!==p&&(-1===(p=Array.isArray(p)?p:p.split(\"+\")).indexOf(\"x\")&&(t.xLabel=void 0),-1===p.indexOf(\"y\")&&(t.yLabel=void 0),-1===p.indexOf(\"z\")&&(t.zLabel=void 0),-1===p.indexOf(\"text\")&&(t.text=void 0),-1===p.indexOf(\"name\")&&(t.name=void 0)),t}function j(t,e,r){var n,i,o=r.container,s=r.fullLayout,l=s._size,c=r.event,u=!!e.hLinePoint,h=!!e.vLinePoint;if(o.selectAll(\".spikeline\").remove(),h||u){var f=d.combine(s.plot_bgcolor,s.paper_bgcolor);if(u){var m,y,v=e.hLinePoint;n=v&&v.xa,\"cursor\"===(i=v&&v.ya).spikesnap?(m=c.pointerX,y=c.pointerY):(m=n._offset+v.x,y=i._offset+v.y);var x,_,b=a.readability(v.color,f)<1.5?d.contrast(f):v.color,w=i.spikemode,T=i.spikethickness,k=i.spikecolor||b,A=g.getPxPosition(t,i);if(-1!==w.indexOf(\"toaxis\")||-1!==w.indexOf(\"across\")){if(-1!==w.indexOf(\"toaxis\")&&(x=A,_=m),-1!==w.indexOf(\"across\")){var M=i._counterDomainMin,S=i._counterDomainMax;\"free\"===i.anchor&&(M=Math.min(M,i.position),S=Math.max(S,i.position)),x=l.l+M*l.w,_=l.l+S*l.w}o.insert(\"line\",\":first-child\").attr({x1:x,x2:_,y1:y,y2:y,\"stroke-width\":T,stroke:k,\"stroke-dasharray\":p.dashStyle(i.spikedash,T)}).classed(\"spikeline\",!0).classed(\"crisp\",!0),o.insert(\"line\",\":first-child\").attr({x1:x,x2:_,y1:y,y2:y,\"stroke-width\":T+2,stroke:f}).classed(\"spikeline\",!0).classed(\"crisp\",!0)}-1!==w.indexOf(\"marker\")&&o.insert(\"circle\",\":first-child\").attr({cx:A+(\"right\"!==i.side?T:-T),cy:y,r:T,fill:k}).classed(\"spikeline\",!0)}if(h){var E,C,L=e.vLinePoint;n=L&&L.xa,i=L&&L.ya,\"cursor\"===n.spikesnap?(E=c.pointerX,C=c.pointerY):(E=n._offset+L.x,C=i._offset+L.y);var I,P,z=a.readability(L.color,f)<1.5?d.contrast(f):L.color,O=n.spikemode,D=n.spikethickness,R=n.spikecolor||z,F=g.getPxPosition(t,n);if(-1!==O.indexOf(\"toaxis\")||-1!==O.indexOf(\"across\")){if(-1!==O.indexOf(\"toaxis\")&&(I=F,P=C),-1!==O.indexOf(\"across\")){var B=n._counterDomainMin,N=n._counterDomainMax;\"free\"===n.anchor&&(B=Math.min(B,n.position),N=Math.max(N,n.position)),I=l.t+(1-N)*l.h,P=l.t+(1-B)*l.h}o.insert(\"line\",\":first-child\").attr({x1:E,x2:E,y1:I,y2:P,\"stroke-width\":D,stroke:R,\"stroke-dasharray\":p.dashStyle(n.spikedash,D)}).classed(\"spikeline\",!0).classed(\"crisp\",!0),o.insert(\"line\",\":first-child\").attr({x1:E,x2:E,y1:I,y2:P,\"stroke-width\":D+2,stroke:f}).classed(\"spikeline\",!0).classed(\"crisp\",!0)}-1!==O.indexOf(\"marker\")&&o.insert(\"circle\",\":first-child\").attr({cx:E,cy:F-(\"top\"!==n.side?D:-D),r:D,fill:R}).classed(\"spikeline\",!0)}}}function 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t._fullLayout._activeSelectionIndex,h(t))}t.exports={draw:h,drawOne:p,activateLastSelection:function(t){if(f(t)){var e=t._fullLayout.selections.length-1;t._fullLayout._activeSelectionIndex=e,t._fullLayout._deactivateSelection=m,h(t)}}}},52307:function(t,e,r){\"use strict\";var n=r(94850).T,i=r(93049).extendFlat;t.exports={newselection:{mode:{valType:\"enumerated\",values:[\"immediate\",\"gradual\"],dflt:\"immediate\",editType:\"none\"},line:{color:{valType:\"color\",editType:\"none\"},width:{valType:\"number\",min:1,dflt:1,editType:\"none\"},dash:i({},n,{dflt:\"dot\",editType:\"none\"}),editType:\"none\"},editType:\"none\"},activeselection:{fillcolor:{valType:\"color\",dflt:\"rgba(0,0,0,0)\",editType:\"none\"},opacity:{valType:\"number\",min:0,max:1,dflt:.5,editType:\"none\"},editType:\"none\"}}},43028:function(t){\"use 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r,n,i,a=[];for(i=0;i0&&a.push(r);if(1===a.length&&a[0]===e.searchInfo&&(n=e.searchInfo.cd[0].trace).selectedpoints.length===e.pointNumbers.length){for(i=0;i1)return!1;if((n+=e.selectedpoints.length)>1)return!1}return 1===n}(s)&&(f=J(b))){for(o&&o.remove(),g=0;g=0})(i)&&i._fullLayout._deactivateShape(i),function(t){return t._fullLayout._activeSelectionIndex>=0}(i)&&i._fullLayout._deactivateSelection(i);var o=i._fullLayout._zoomlayer,s=p(r),l=m(r);if(s||l){var c,u,h=o.selectAll(\".select-outline-\"+n.id);h&&i._fullLayout._outlining&&(s&&(c=T(h,t)),c&&a.call(\"_guiRelayout\",i,{shapes:c}),l&&!U(t)&&(u=k(h,t)),u&&(i._fullLayout._noEmitSelectedAtStart=!0,a.call(\"_guiRelayout\",i,{selections:u}).then((function(){e&&A(i)}))),i._fullLayout._outlining=!1)}n.selection={},n.selection.selectionDefs=t.selectionDefs=[],n.selection.mergedPolygons=t.mergedPolygons=[]}function Y(t){return t._id}function X(t,e,r,n){if(!t.calcdata)return[];var i,a,o,s=[],l=e.map(Y),c=r.map(Y);for(o=0;o0?n[0]:r;return!!e.selectedpoints&&e.selectedpoints.indexOf(i)>-1}function K(t,e,r){var n,i;for(n=0;n-1&&e;if(!a&&e){var et=ot(t,!0);if(et.length){var nt=et[0].xref,pt=et[0].yref;if(nt&&pt){var dt=ct(et);ut([L(t,nt,\"x\"),L(t,pt,\"y\")])(Q,dt)}}t._fullLayout._noEmitSelectedAtStart?t._fullLayout._noEmitSelectedAtStart=!1:tt&&ht(t,Q),f._reselect=!1}if(!a&&f._deselect){var mt=f._deselect;(function(t,e,r){for(var n=0;n=0)k._fullLayout._deactivateShape(k);else if(!x){var r=A.clickmode;C.done(Mt).then((function(){if(C.clear(Mt),2===t){for(_t.remove(),J=0;J-1&&V(e,k,n.xaxes,n.yaxes,n.subplot,n,_t),\"event\"===r&&ht(k,void 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n=r(50222),i=r(80337),a=r(36640).line,o=r(94850).T,s=r(93049).extendFlat,l=r(78032).templatedArray,c=(r(35081),r(9829)),u=r(3208).LF,h=r(41235);t.exports=l(\"shape\",{visible:s({},c.visible,{editType:\"calc+arraydraw\"}),showlegend:{valType:\"boolean\",dflt:!1,editType:\"calc+arraydraw\"},legend:s({},c.legend,{editType:\"calc+arraydraw\"}),legendgroup:s({},c.legendgroup,{editType:\"calc+arraydraw\"}),legendgrouptitle:{text:s({},c.legendgrouptitle.text,{editType:\"calc+arraydraw\"}),font:i({editType:\"calc+arraydraw\"}),editType:\"calc+arraydraw\"},legendrank:s({},c.legendrank,{editType:\"calc+arraydraw\"}),legendwidth:s({},c.legendwidth,{editType:\"calc+arraydraw\"}),type:{valType:\"enumerated\",values:[\"circle\",\"rect\",\"path\",\"line\"],editType:\"calc+arraydraw\"},layer:{valType:\"enumerated\",values:[\"below\",\"above\",\"between\"],dflt:\"above\",editType:\"arraydraw\"},xref:s({},n.xref,{}),xsizemode:{valType:\"enumerated\",values:[\"scaled\",\"pixel\"],dflt:\"scaled\",editType:\"calc+arraydraw\"},xanchor:{valType:\"any\",editType:\"calc+arraydraw\"},x0:{valType:\"any\",editType:\"calc+arraydraw\"},x1:{valType:\"any\",editType:\"calc+arraydraw\"},x0shift:{valType:\"number\",dflt:0,min:-1,max:1,editType:\"calc\"},x1shift:{valType:\"number\",dflt:0,min:-1,max:1,editType:\"calc\"},yref:s({},n.yref,{}),ysizemode:{valType:\"enumerated\",values:[\"scaled\",\"pixel\"],dflt:\"scaled\",editType:\"calc+arraydraw\"},yanchor:{valType:\"any\",editType:\"calc+arraydraw\"},y0:{valType:\"any\",editType:\"calc+arraydraw\"},y1:{valType:\"any\",editType:\"calc+arraydraw\"},y0shift:{valType:\"number\",dflt:0,min:-1,max:1,editType:\"calc\"},y1shift:{valType:\"number\",dflt:0,min:-1,max:1,editType:\"calc\"},path:{valType:\"string\",editType:\"calc+arraydraw\"},opacity:{valType:\"number\",min:0,max:1,dflt:1,editType:\"arraydraw\"},line:{color:s({},a.color,{editType:\"arraydraw\"}),width:s({},a.width,{editType:\"calc+arraydraw\"}),dash:s({},o,{editType:\"arraydraw\"}),editType:\"calc+arraydraw\"},fillcolor:{valType:\"color\",dflt:\"rgba(0,0,0,0)\",editType:\"arraydraw\"},fillrule:{valType:\"enumerated\",values:[\"evenodd\",\"nonzero\"],dflt:\"evenodd\",editType:\"arraydraw\"},editable:{valType:\"boolean\",dflt:!1,editType:\"calc+arraydraw\"},label:{text:{valType:\"string\",dflt:\"\",editType:\"arraydraw\"},texttemplate:u({},{keys:Object.keys(h)}),font:i({editType:\"calc+arraydraw\",colorEditType:\"arraydraw\"}),textposition:{valType:\"enumerated\",values:[\"top 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n=r(28231);t.exports={moduleType:\"component\",name:\"shapes\",layoutAttributes:r(43144),supplyLayoutDefaults:r(74367),supplyDrawNewShapeDefaults:r(85522),includeBasePlot:r(20706)(\"shapes\"),calcAutorange:r(44959),draw:n.draw,drawOne:n.drawOne}},41235:function(t){\"use strict\";function e(t,e){return e?e.d2l(t):t}function r(t,e){return e?e.l2d(t):t}function n(t){return t.x0shift||0}function i(t){return t.x1shift||0}function a(t){return t.y0shift||0}function o(t){return t.y1shift||0}function s(t,r){return e(t.x1,r)+i(t)-e(t.x0,r)-n(t)}function l(t,r,n){return e(t.y1,n)+o(t)-e(t.y0,n)-a(t)}t.exports={x0:function(t){return t.x0},x1:function(t){return t.x1},y0:function(t){return t.y0},y1:function(t){return t.y1},slope:function(t,e,r){return\"line\"!==t.type?void 0:l(t,0,r)/s(t,e)},dx:s,dy:l,width:function(t,e){return Math.abs(s(t,e))},height:function(t,e,r){return Math.abs(l(t,0,r))},length:function(t,e,r){return\"line\"!==t.type?void 0:Math.sqrt(Math.pow(s(t,e),2)+Math.pow(l(t,0,r),2))},xcenter:function(t,a){return r((e(t.x1,a)+i(t)+e(t.x0,a)+n(t))/2,a)},ycenter:function(t,n,i){return r((e(t.y1,i)+o(t)+e(t.y0,i)+a(t))/2,i)}}},8606:function(t,e,r){\"use strict\";var n=r(80337),i=r(57891),a=r(93049).extendDeepAll,o=r(13582).overrideAll,s=r(49722),l=r(78032).templatedArray,c=r(64194),u=l(\"step\",{visible:{valType:\"boolean\",dflt:!0},method:{valType:\"enumerated\",values:[\"restyle\",\"relayout\",\"animate\",\"update\",\"skip\"],dflt:\"restyle\"},args:{valType:\"info_array\",freeLength:!0,items:[{valType:\"any\"},{valType:\"any\"},{valType:\"any\"}]},label:{valType:\"string\"},value:{valType:\"string\"},execute:{valType:\"boolean\",dflt:!0}});t.exports=o(l(\"slider\",{visible:{valType:\"boolean\",dflt:!0},active:{valType:\"number\",min:0,dflt:0},steps:u,lenmode:{valType:\"enumerated\",values:[\"fraction\",\"pixels\"],dflt:\"fraction\"},len:{valType:\"number\",min:0,dflt:1},x:{valType:\"number\",min:-2,max:3,dflt:0},pad:a(i({editType:\"arraydraw\"}),{},{t:{dflt:20}}),xanchor:{valType:\"enumerated\",values:[\"auto\",\"left\",\"center\",\"right\"],dflt:\"left\"},y:{valType:\"number\",min:-2,max:3,dflt:0},yanchor:{valType:\"enumerated\",values:[\"auto\",\"top\",\"middle\",\"bottom\"],dflt:\"top\"},transition:{duration:{valType:\"number\",min:0,dflt:150},easing:{valType:\"enumerated\",values:s.transition.easing.values,dflt:\"cubic-in-out\"}},currentvalue:{visible:{valType:\"boolean\",dflt:!0},xanchor:{valType:\"enumerated\",values:[\"left\",\"center\",\"right\"],dflt:\"left\"},offset:{valType:\"number\",dflt:10},prefix:{valType:\"string\"},suffix:{valType:\"string\"},font:n({})},font:n({}),activebgcolor:{valType:\"color\",dflt:c.gripBgActiveColor},bgcolor:{valType:\"color\",dflt:c.railBgColor},bordercolor:{valType:\"color\",dflt:c.railBorderColor},borderwidth:{valType:\"number\",min:0,dflt:c.railBorderWidth},ticklen:{valType:\"number\",min:0,dflt:c.tickLength},tickcolor:{valType:\"color\",dflt:c.tickColor},tickwidth:{valType:\"number\",min:0,dflt:1},minorticklen:{valType:\"number\",min:0,dflt:c.minorTickLength}}),\"arraydraw\",\"from-root\")},64194:function(t){\"use strict\";t.exports={name:\"sliders\",containerClassName:\"slider-container\",groupClassName:\"slider-group\",inputAreaClass:\"slider-input-area\",railRectClass:\"slider-rail-rect\",railTouchRectClass:\"slider-rail-touch-rect\",gripRectClass:\"slider-grip-rect\",tickRectClass:\"slider-tick-rect\",inputProxyClass:\"slider-input-proxy\",labelsClass:\"slider-labels\",labelGroupClass:\"slider-label-group\",labelClass:\"slider-label\",currentValueClass:\"slider-current-value\",railHeight:5,menuIndexAttrName:\"slider-active-index\",autoMarginIdRoot:\"slider-\",minWidth:30,minHeight:30,textPadX:40,arrowOffsetX:4,railRadius:2,railWidth:5,railBorder:4,railBorderWidth:1,railBorderColor:\"#bec8d9\",railBgColor:\"#f8fafc\",railInset:8,stepInset:10,gripRadius:10,gripWidth:20,gripHeight:20,gripBorder:20,gripBorderWidth:1,gripBorderColor:\"#bec8d9\",gripBgColor:\"#f6f8fa\",gripBgActiveColor:\"#dbdde0\",labelPadding:8,labelOffset:0,tickWidth:1,tickColor:\"#333\",tickOffset:25,tickLength:7,minorTickOffset:25,minorTickColor:\"#333\",minorTickLength:4,currentValuePadding:8,currentValueInset:0}},74537:function(t,e,r){\"use 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r=e._dims,n=r.inputAreaLength-2*h.railInset,i=s.ensureSingle(t,\"rect\",h.railRectClass);i.attr({width:n,height:h.railWidth,rx:h.railRadius,ry:h.railRadius,\"shape-rendering\":\"crispEdges\"}).call(a.stroke,e.bordercolor).call(a.fill,e.bgcolor).style(\"stroke-width\",e.borderwidth+\"px\"),o.setTranslate(i,h.railInset,.5*(r.inputAreaWidth-h.railWidth)+r.currentValueTotalHeight)}t.exports=function(t){var e=t._context.staticPlot,r=t._fullLayout,a=function(t,e){for(var r=t[h.name],n=[],i=0;i0?[0]:[]);function l(e){e._commandObserver&&(e._commandObserver.remove(),delete e._commandObserver),i.autoMargin(t,g(e))}if(s.enter().append(\"g\").classed(h.containerClassName,!0).style(\"cursor\",e?null:\"ew-resize\"),s.exit().each((function(){n.select(this).selectAll(\"g.\"+h.groupClassName).each(l)})).remove(),0!==a.length){var c=s.selectAll(\"g.\"+h.groupClassName).data(a,y);c.enter().append(\"g\").classed(h.groupClassName,!0),c.exit().each(l).remove();for(var u=0;u0||T<0){var E={left:[-k,0],right:[k,0],top:[0,-k],bottom:[0,k]}[b.side];a.attr(\"transform\",l(E[0],E[1]))}}}function ft(t,e){t.text(e).on(\"mouseover.opacity\",(function(){n.select(this).transition().duration(f.SHOW_PLACEHOLDER).style(\"opacity\",1)})).on(\"mouseout.opacity\",(function(){n.select(this).transition().duration(f.HIDE_PLACEHOLDER).style(\"opacity\",0)}))}if(at.call(ct,ot),et&&(S?at.on(\".opacity\",null):(ft(at,x),E=!0),at.call(h.makeEditable,{gd:t}).on(\"edit\",(function(e){void 0!==_?o.call(\"_guiRestyle\",t,v,e,_):o.call(\"_guiRelayout\",t,v,e)})).on(\"cancel\",(function(){this.text(this.attr(\"data-unformatted\")).call(ct)})).on(\"input\",(function(t){this.text(t||\" \").call(h.positionText,w.x,w.y)})),N)){if(N&&!S){var 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n=r(80337),i=r(10229),a=r(93049).extendFlat,o=r(13582).overrideAll,s=r(57891),l=r(78032).templatedArray,c=l(\"button\",{visible:{valType:\"boolean\"},method:{valType:\"enumerated\",values:[\"restyle\",\"relayout\",\"animate\",\"update\",\"skip\"],dflt:\"restyle\"},args:{valType:\"info_array\",freeLength:!0,items:[{valType:\"any\"},{valType:\"any\"},{valType:\"any\"}]},args2:{valType:\"info_array\",freeLength:!0,items:[{valType:\"any\"},{valType:\"any\"},{valType:\"any\"}]},label:{valType:\"string\",dflt:\"\"},execute:{valType:\"boolean\",dflt:!0}});t.exports=o(l(\"updatemenu\",{_arrayAttrRegexps:[/^updatemenus\\[(0|[1-9][0-9]+)\\]\\.buttons/],visible:{valType:\"boolean\"},type:{valType:\"enumerated\",values:[\"dropdown\",\"buttons\"],dflt:\"dropdown\"},direction:{valType:\"enumerated\",values:[\"left\",\"right\",\"up\",\"down\"],dflt:\"down\"},active:{valType:\"integer\",min:-1,dflt:0},showactive:{valType:\"boolean\",dflt:!0},buttons:c,x:{valType:\"number\",min:-2,max:3,dflt:-.05},xanchor:{valType:\"enumerated\",values:[\"auto\",\"left\",\"center\",\"right\"],dflt:\"right\"},y:{valType:\"number\",min:-2,max:3,dflt:1},yanchor:{valType:\"enumerated\",values:[\"auto\",\"top\",\"middle\",\"bottom\"],dflt:\"top\"},pad:a(s({editType:\"arraydraw\"}),{}),font:n({}),bgcolor:{valType:\"color\"},bordercolor:{valType:\"color\",dflt:i.borderLine},borderwidth:{valType:\"number\",min:0,dflt:1,editType:\"arraydraw\"}}),\"arraydraw\",\"from-root\")},71559:function(t){\"use strict\";t.exports={name:\"updatemenus\",containerClassName:\"updatemenu-container\",headerGroupClassName:\"updatemenu-header-group\",headerClassName:\"updatemenu-header\",headerArrowClassName:\"updatemenu-header-arrow\",dropdownButtonGroupClassName:\"updatemenu-dropdown-button-group\",dropdownButtonClassName:\"updatemenu-dropdown-button\",buttonClassName:\"updatemenu-button\",itemRectClassName:\"updatemenu-item-rect\",itemTextClassName:\"updatemenu-item-text\",menuIndexAttrName:\"updatemenu-active-index\",autoMarginIdRoot:\"updatemenu-\",blankHeaderOpts:{label:\" \"},minWidth:30,minHeight:30,textPadX:24,arrowPadX:16,rx:2,ry:2,textOffsetX:12,textOffsetY:3,arrowOffsetX:4,gapButtonHeader:5,gapButton:2,activeColor:\"#F4FAFF\",hoverColor:\"#F4FAFF\",arrowSymbol:{left:\"◄\",right:\"►\",up:\"▲\",down:\"▼\"}}},42746:function(t,e,r){\"use strict\";var n=r(34809),i=r(59008),a=r(85389),o=r(71559).name,s=a.buttons;function l(t,e,r){function o(r,i){return 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m(t,e,r,n,i,a,o,s){e.active=o,c(t.layout,h.name,e).applyUpdate(\"active\",o),\"buttons\"===e.type?y(t,n,null,null,e):\"dropdown\"===e.type&&(i.attr(h.menuIndexAttrName,\"-1\"),g(t,n,i,a,e),s||y(t,n,i,a,e))}function g(t,e,r,n,i){var a=s.ensureSingle(e,\"g\",h.headerClassName,(function(t){t.style(\"pointer-events\",\"all\")})),l=i._dims,c=i.active,u=i.buttons[c]||h.blankHeaderOpts,f={y:i.pad.t,yPad:0,x:i.pad.l,xPad:0,index:0},p={width:l.headerWidth,height:l.headerHeight};a.call(v,i,u,t).call(M,i,f,p),s.ensureSingle(e,\"text\",h.headerArrowClassName,(function(t){t.attr(\"text-anchor\",\"end\").call(o.font,i.font).text(h.arrowSymbol[i.direction])})).attr({x:l.headerWidth-h.arrowOffsetX+i.pad.l,y:l.headerHeight/2+h.textOffsetY+i.pad.t}),a.on(\"click\",(function(){r.call(S,String(d(r,i)?-1:i._index)),y(t,e,r,n,i)})),a.on(\"mouseover\",(function(){a.call(w)})),a.on(\"mouseout\",(function(){a.call(T,i)})),o.setTranslate(e,l.lx,l.ly)}function y(t,e,r,a,o){r||(r=e).attr(\"pointer-events\",\"all\");var l=function(t){return-1==+t.attr(h.menuIndexAttrName)}(r)&&\"buttons\"!==o.type?[]:o.buttons,c=\"dropdown\"===o.type?h.dropdownButtonClassName:h.buttonClassName,u=r.selectAll(\"g.\"+c).data(s.filterVisible(l)),f=u.enter().append(\"g\").classed(c,!0),p=u.exit();\"dropdown\"===o.type?(f.attr(\"opacity\",\"0\").transition().attr(\"opacity\",\"1\"),p.transition().attr(\"opacity\",\"0\").remove()):p.remove();var d=0,g=0,y=o._dims,x=-1!==[\"up\",\"down\"].indexOf(o.direction);\"dropdown\"===o.type&&(x?g=y.headerHeight+h.gapButtonHeader:d=y.headerWidth+h.gapButtonHeader),\"dropdown\"===o.type&&\"up\"===o.direction&&(g=-h.gapButtonHeader+h.gapButton-y.openHeight),\"dropdown\"===o.type&&\"left\"===o.direction&&(d=-h.gapButtonHeader+h.gapButton-y.openWidth);var _={x:y.lx+d+o.pad.l,y:y.ly+g+o.pad.t,yPad:h.gapButton,xPad:h.gapButton,index:0},k={l:_.x+o.borderwidth,t:_.y+o.borderwidth};u.each((function(s,l){var c=n.select(this);c.call(v,o,s,t).call(M,o,_),c.on(\"click\",(function(){n.event.defaultPrevented||(s.execute&&(s.args2&&o.active===l?(m(t,o,0,e,r,a,-1),i.executeAPICommand(t,s.method,s.args2)):(m(t,o,0,e,r,a,l),i.executeAPICommand(t,s.method,s.args))),t.emit(\"plotly_buttonclicked\",{menu:o,button:s,active:o.active}))})),c.on(\"mouseover\",(function(){c.call(w)})),c.on(\"mouseout\",(function(){c.call(T,o),u.call(b,o)}))})),u.call(b,o),x?(k.w=Math.max(y.openWidth,y.headerWidth),k.h=_.y-k.t):(k.w=_.x-k.l,k.h=Math.max(y.openHeight,y.headerHeight)),k.direction=o.direction,a&&(u.size()?function(t,e,r,n,i,a){var o,s,l,c=i.direction,u=\"up\"===c||\"down\"===c,f=i._dims,p=i.active;if(u)for(s=0,l=0;l0?[0]:[]);if(o.enter().append(\"g\").classed(h.containerClassName,!0).style(\"cursor\",\"pointer\"),o.exit().each((function(){n.select(this).selectAll(\"g.\"+h.headerGroupClassName).each(a)})).remove(),0!==r.length){var l=o.selectAll(\"g.\"+h.headerGroupClassName).data(r,p);l.enter().append(\"g\").classed(h.headerGroupClassName,!0);for(var c=s.ensureSingle(o,\"g\",h.dropdownButtonGroupClassName,(function(t){t.style(\"pointer-events\",\"all\")})),u=0;uw,A=s.barLength+2*s.barPad,M=s.barWidth+2*s.barPad,S=d,E=g+y;E+M>c&&(E=c-M);var C=this.container.selectAll(\"rect.scrollbar-horizontal\").data(k?[0]:[]);C.exit().on(\".drag\",null).remove(),C.enter().append(\"rect\").classed(\"scrollbar-horizontal\",!0).call(i.fill,s.barColor),k?(this.hbar=C.attr({rx:s.barRadius,ry:s.barRadius,x:S,y:E,width:A,height:M}),this._hbarXMin=S+A/2,this._hbarTranslateMax=w-A):(delete this.hbar,delete this._hbarXMin,delete this._hbarTranslateMax);var L=y>T,I=s.barWidth+2*s.barPad,P=s.barLength+2*s.barPad,z=d+m,O=g;z+I>l&&(z=l-I);var 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V=n.behavior.drag().on(\"dragstart\",(function(){n.event.sourceEvent.preventDefault()})).on(\"drag\",this._onBoxDrag.bind(this));this.container.on(\"wheel\",null).on(\"wheel\",this._onBoxWheel.bind(this)).on(\".drag\",null).call(V);var q=n.behavior.drag().on(\"dragstart\",(function(){n.event.sourceEvent.preventDefault(),n.event.sourceEvent.stopPropagation()})).on(\"drag\",this._onBarDrag.bind(this));k&&this.hbar.on(\".drag\",null).call(q),L&&this.vbar.on(\".drag\",null).call(q)}this.setTranslate(e,r)},s.prototype.disable=function(){(this.hbar||this.vbar)&&(this.bg.attr({width:0,height:0}),this.container.on(\"wheel\",null).on(\".drag\",null).call(a.setClipUrl,null),delete this._clipRect),this.hbar&&(this.hbar.on(\".drag\",null),this.hbar.remove(),delete this.hbar,delete this._hbarXMin,delete this._hbarTranslateMax),this.vbar&&(this.vbar.on(\".drag\",null),this.vbar.remove(),delete this.vbar,delete this._vbarYMin,delete this._vbarTranslateMax)},s.prototype._onBoxDrag=function(){var 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n,i,a,o=e[t],s=w.scopeDefaults[e.scope];\"lonaxis\"===t?(n=s.lonaxisRange,i=s.lataxisRange,a=function(t,e){return[t,e]}):\"lataxis\"===t&&(n=s.lataxisRange,i=s.lonaxisRange,a=function(t,e){return[e,t]});var l={type:\"linear\",range:[n[0],n[1]-1e-6],tick0:o.tick0,dtick:o.dtick};m.setConvert(l,r);var c=m.calcTicks(l);e.isScoped||\"lonaxis\"!==t||c.pop();for(var u=c.length,h=new Array(u),f=0;f-1&&_(n.event,i,[r.xaxis],[r.yaxis],r.id,u),s.indexOf(\"event\")>-1&&p.click(i,n.event))}))}function h(t){return r.projection.invert([t[0]+r.xaxis._offset,t[1]+r.yaxis._offset])}},S.makeFramework=function(){var t=this,e=t.graphDiv,r=e._fullLayout,i=\"clip\"+r._uid+t.id;t.clipDef=r._clips.append(\"clipPath\").attr(\"id\",i),t.clipRect=t.clipDef.append(\"rect\"),t.framework=n.select(t.container).append(\"g\").attr(\"class\",\"geo \"+t.id).call(f.setClipUrl,i,e),t.project=function(e){var r=t.projection(e);return 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r=0,n=0,i=t.length;nMath.abs(s)?(c.boxEnd[1]=c.boxStart[1]+Math.abs(a)*b*(s>=0?1:-1),c.boxEnd[1]l[3]&&(c.boxEnd[1]=l[3],c.boxEnd[0]=c.boxStart[0]+(l[3]-c.boxStart[1])/Math.abs(b))):(c.boxEnd[0]=c.boxStart[0]+Math.abs(s)/b*(a>=0?1:-1),c.boxEnd[0]l[2]&&(c.boxEnd[0]=l[2],c.boxEnd[1]=c.boxStart[1]+(l[2]-c.boxStart[0])*Math.abs(b)))}}else c.boxEnabled?(a=c.boxStart[0]!==c.boxEnd[0],s=c.boxStart[1]!==c.boxEnd[1],a||s?(a&&(g(0,c.boxStart[0],c.boxEnd[0]),t.xaxis.autorange=!1),s&&(g(1,c.boxStart[1],c.boxEnd[1]),t.yaxis.autorange=!1),t.relayoutCallback()):t.glplot.setDirty(),c.boxEnabled=!1,c.boxInited=!1):c.boxInited&&(c.boxInited=!1);break;case\"pan\":c.boxEnabled=!1,c.boxInited=!1,e?(c.panning||(c.dragStart[0]=n,c.dragStart[1]=i),Math.abs(c.dragStart[0]-n).999&&(g=\"turntable\"):g=\"turntable\")}else g=\"turntable\";r(\"dragmode\",g),r(\"hovermode\",n.getDfltFromLayout(\"hovermode\"))}t.exports=function(t,e,r){var i=e._basePlotModules.length>1;o(t,e,r,{type:u,attributes:l,handleDefaults:h,fullLayout:e,font:e.font,fullData:r,getDfltFromLayout:function(e){if(!i)return n.validate(t[e],l[e])?t[e]:void 0},autotypenumbersDflt:e.autotypenumbers,paper_bgcolor:e.paper_bgcolor,calendar:e.calendar})}},77168:function(t,e,r){\"use strict\";var n=r(63397),i=r(13792).u,a=r(93049).extendFlat,o=r(34809).counterRegex;function 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r=t[i[e]];r.visible?(this.enabled[e]=r.showspikes,this.colors[e]=n(r.spikecolor),this.drawSides[e]=r.spikesides,this.lineWidth[e]=r.spikethickness):(this.enabled[e]=!1,this.drawSides[e]=!1)}},t.exports=function(t){var e=new a;return e.merge(t),e}},32412:function(t,e,r){\"use strict\";t.exports=function(t){for(var e=t.axesOptions,r=t.glplot.axesPixels,s=t.fullSceneLayout,l=[[],[],[]],c=0;c<3;++c){var u=s[a[c]];if(u._length=(r[c].hi-r[c].lo)*r[c].pixelsPerDataUnit/t.dataScale[c],Math.abs(u._length)===1/0||isNaN(u._length))l[c]=[];else{u._input_range=u.range.slice(),u.range[0]=r[c].lo/t.dataScale[c],u.range[1]=r[c].hi/t.dataScale[c],u._m=1/(t.dataScale[c]*r[c].pixelsPerDataUnit),u.range[0]===u.range[1]&&(u.range[0]-=1,u.range[1]+=1);var h=u.tickmode;if(\"auto\"===u.tickmode){u.tickmode=\"linear\";var f=u.nticks||i.constrain(u._length/40,4,9);n.autoTicks(u,Math.abs(u.range[1]-u.range[0])/f)}for(var p=n.calcTicks(u,{msUTC:!0}),d=0;d/g,\" 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a=r._fullLayout._invScaleX,o=r._fullLayout._invScaleY,s=i.width*a,l=i.height*o;n.setAttributeNS(null,\"viewBox\",\"0 0 \"+s+\" \"+l),n.setAttributeNS(null,\"width\",s),n.setAttributeNS(null,\"height\",l),_(e),e.glplot.axes.update(e.axesOptions);for(var c=Object.keys(e.traces),u=null,f=e.glplot.selection,m=0;m\")):\"isosurface\"===t.type||\"volume\"===t.type?(k.valueLabel=p.hoverLabelText(e._mockAxis,e._mockAxis.d2l(f.traceCoordinate[3]),t.valuehoverformat),E.push(\"value: \"+k.valueLabel),f.textLabel&&E.push(f.textLabel),x=E.join(\"
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R=[1,1,1];for(o=0;o<3;++o){var F=x[c=(l=u[M[o]]).type];R[o]=Math.pow(F.acc,1/F.count)/p[o]}g=\"data\"===O||Math.max.apply(null,R)/Math.min.apply(null,R)<=4?R:[1,1,1]}u.aspectratio.x=h.aspectratio.x=g[0],u.aspectratio.y=h.aspectratio.y=g[1],u.aspectratio.z=h.aspectratio.z=g[2],n.glplot.setAspectratio(u.aspectratio),n.viewInitial.aspectratio||(n.viewInitial.aspectratio={x:u.aspectratio.x,y:u.aspectratio.y,z:u.aspectratio.z}),n.viewInitial.aspectmode||(n.viewInitial.aspectmode=u.aspectmode);var B=u.domain||null,N=e._size||null;if(B&&N){var j=n.container.style;j.position=\"absolute\",j.left=N.l+B.x[0]*N.w+\"px\",j.top=N.t+(1-B.y[1])*N.h+\"px\",j.width=N.w*(B.x[1]-B.x[0])+\"px\",j.height=N.h*(B.y[1]-B.y[0])+\"px\"}n.glplot.redraw()}},k.destroy=function(){var t=this;t.glplot&&(t.camera.mouseListener.enabled=!1,t.container.removeEventListener(\"wheel\",t.camera.wheelListener),t.camera=null,t.glplot.dispose(),t.container.parentNode.removeChild(t.container),t.glplot=null)},k.getCamera=function(){var t,e=this;return e.camera.view.recalcMatrix(e.camera.view.lastT()),{up:{x:(t=e.camera).up[0],y:t.up[1],z:t.up[2]},center:{x:t.center[0],y:t.center[1],z:t.center[2]},eye:{x:t.eye[0],y:t.eye[1],z:t.eye[2]},projection:{type:!0===t._ortho?\"orthographic\":\"perspective\"}}},k.setViewport=function(t){var e,r=this,n=t.camera;r.camera.lookAt.apply(this,[[(e=n).eye.x,e.eye.y,e.eye.z],[e.center.x,e.center.y,e.center.z],[e.up.x,e.up.y,e.up.z]]),r.glplot.setAspectratio(t.aspectratio),\"orthographic\"===n.projection.type!==r.camera._ortho&&(r.glplot.redraw(),r.glplot.clearRGBA(),r.glplot.dispose(),r.initializeGLPlot())},k.isCameraChanged=function(t){var e=this.getCamera(),r=h.nestedProperty(t,this.id+\".camera\").get();function n(t,e,r,n){var i=[\"up\",\"center\",\"eye\"],a=[\"x\",\"y\",\"z\"];return e[i[r]]&&t[i[r]][a[n]]===e[i[r]][a[n]]}var i=!1;if(void 0===r)i=!0;else{for(var a=0;a<3;a++)for(var o=0;o<3;o++)if(!n(e,r,a,o)){i=!0;break}(!r.projection||e.projection&&e.projection.type!==r.projection.type)&&(i=!0)}return i},k.isAspectChanged=function(t){var e=this.glplot.getAspectratio(),r=h.nestedProperty(t,this.id+\".aspectratio\").get();return void 0===r||r.x!==e.x||r.y!==e.y||r.z!==e.z},k.saveLayout=function(t){var e,r,n,i,a,o,s=this,l=s.fullLayout,c=s.isCameraChanged(t),f=s.isAspectChanged(t),p=c||f;if(p){var d={};c&&(e=s.getCamera(),n=(r=h.nestedProperty(t,s.id+\".camera\")).get(),d[s.id+\".camera\"]=n),f&&(i=s.glplot.getAspectratio(),o=(a=h.nestedProperty(t,s.id+\".aspectratio\")).get(),d[s.id+\".aspectratio\"]=o),u.call(\"_storeDirectGUIEdit\",t,l._preGUI,d),c&&(r.set(e),h.nestedProperty(l,s.id+\".camera\").set(e)),f&&(a.set(i),h.nestedProperty(l,s.id+\".aspectratio\").set(i),s.glplot.redraw())}return p},k.updateFx=function(t,e){var r=this,n=r.camera;if(n)if(\"orbit\"===t)n.mode=\"orbit\",n.keyBindingMode=\"rotate\";else if(\"turntable\"===t){n.up=[0,0,1],n.mode=\"turntable\",n.keyBindingMode=\"rotate\";var i=r.graphDiv,a=i._fullLayout,o=r.fullSceneLayout.camera,s=o.up.x,l=o.up.y,c=o.up.z;if(c/Math.sqrt(s*s+l*l+c*c)<.999){var f=r.id+\".camera.up\",p={x:0,y:0,z:1},d={};d[f]=p;var m=i.layout;u.call(\"_storeDirectGUIEdit\",m,a._preGUI,d),o.up=p,h.nestedProperty(m,f).set(p)}}else n.keyBindingMode=t;r.fullSceneLayout.hovermode=e},k.toImage=function(t){var e=this;t||(t=\"png\"),e.staticMode&&e.container.appendChild(n),e.glplot.redraw();var r=e.glplot.gl,i=r.drawingBufferWidth,a=r.drawingBufferHeight;r.bindFramebuffer(r.FRAMEBUFFER,null);var o=new Uint8Array(i*a*4);r.readPixels(0,0,i,a,r.RGBA,r.UNSIGNED_BYTE,o),function(t,e,r){for(var n=0,i=r-1;n0)for(var s=255/o,l=0;l<3;++l)t[a+l]=Math.min(s*t[a+l],255)}}(o,i,a);var s=document.createElement(\"canvas\");s.width=i,s.height=a;var l,c=s.getContext(\"2d\",{willReadFrequently:!0}),u=c.createImageData(i,a);switch(u.data.set(o),c.putImageData(u,0,0),t){case\"jpeg\":l=s.toDataURL(\"image/jpeg\");break;case\"webp\":l=s.toDataURL(\"image/webp\");break;default:l=s.toDataURL(\"image/png\")}return e.staticMode&&e.container.removeChild(n),l},k.setConvert=function(){for(var t=0;t<3;t++){var e=this.fullSceneLayout[M[t]];p.setConvert(e,this.fullLayout),e.setScale=h.noop}},k.make4thDimension=function(){var t=this,e=t.graphDiv._fullLayout;t._mockAxis={type:\"linear\",showexponent:\"all\",exponentformat:\"B\"},p.setConvert(t._mockAxis,e)},t.exports=T},88239:function(t){\"use strict\";t.exports=function(t,e,r,n){n=n||t.length;for(var i=new Array(n),a=0;aOpenStreetMap 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this.subplot.map.getSource(this.idSource)&&\"image\"===this.sourceType&&\"image\"===t.sourcetype&&(this.source!==t.source||JSON.stringify(this.coordinates)!==JSON.stringify(t.coordinates))},l.needsNewSource=function(t){return this.sourceType!==t.sourcetype||JSON.stringify(this.source)!==JSON.stringify(t.source)||this.layerType!==t.type},l.needsNewLayer=function(t){return this.layerType!==t.type||this.below!==this.subplot.belowLookup[\"layout-\"+this.index]},l.lookupBelow=function(){return this.subplot.belowLookup[\"layout-\"+this.index]},l.updateImage=function(t){this.subplot.map.getSource(this.idSource).updateImage({url:t.source,coordinates:t.coordinates});var e=this.findFollowingMapLayerId(this.lookupBelow());null!==e&&this.subplot.map.moveLayer(this.idLayer,e)},l.updateSource=function(t){var e=this.subplot.map;if(e.getSource(this.idSource)&&e.removeSource(this.idSource),this.sourceType=t.sourcetype,this.source=t.source,c(t)){var r=function(t){var 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e=t[this.id].domain,r=t._size,n=this.div.style;n.width=r.w*(e.x[1]-e.x[0])+\"px\",n.height=r.h*(e.y[1]-e.y[0])+\"px\",n.left=r.l+e.x[0]*r.w+\"px\",n.top=r.t+(1-e.y[1])*r.h+\"px\",this.xaxis._offset=r.l+e.x[0]*r.w,this.xaxis._length=r.w*(e.x[1]-e.x[0]),this.yaxis._offset=r.t+(1-e.y[1])*r.h,this.yaxis._length=r.h*(e.y[1]-e.y[0])},_.updateLayers=function(t){var e,r=t[this.id].layers,n=this.layerList;if(r.length!==n.length){for(e=0;eOpenStreetMap contributors',o=['© Carto',a].join(\" \"),s=['Map tiles by Stamen Design','under CC BY 3.0',\"|\",'Data by OpenStreetMap contributors','under ODbL'].join(\" 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e=y(t),r=Math.cos(e),n=Math.sin(e);return\"M\"+[h+u*r,p-u*n]+\"L\"+[h+a*r,p-a*n]},w=f.makeLabelFns(m,0).labelStandoff,T={xFn:function(t){var e=y(t);return Math.cos(e)*w},yFn:function(t){var e=y(t),r=Math.sin(e)>0?.2:1;return-Math.sin(e)*(w+t.fontSize*r)+Math.abs(Math.cos(e))*(t.fontSize*M)},anchorFn:function(t){var e=y(t),r=Math.cos(e);return Math.abs(r)<.1?\"middle\":r>0?\"start\":\"end\"},heightFn:function(t,e,r){var n=y(t);return-.5*(1+Math.sin(n))*r}},k=j(d);r.angularTickLayout!==k&&(i[\"angular-axis\"].selectAll(\".\"+m._id+\"tick\").remove(),r.angularTickLayout=k);var A,S=g?[1/0].concat(m.tickvals||[]).map((function(t){return f.tickText(m,t,!0,!1)})):f.calcTicks(m);if(g&&(S[0].text=\"∞\",S[0].fontSize*=1.75),\"linear\"===e.gridshape?(A=S.map(y),o.angleDelta(A[0],A[1])<0&&(A=A.slice().reverse())):A=null,r.vangles=A,\"category\"===m.type&&(S=S.filter((function(t){return o.isAngleInsideSector(y(t),r.sectorInRad)}))),m.visible){var E=\"inside\"===m.ticks?-1:1,C=(m.linewidth||1)/2;f.drawTicks(n,m,{vals:S,layer:i[\"angular-axis\"],path:\"M\"+E*C+\",0h\"+E*m.ticklen,transFn:_,crisp:!1}),f.drawGrid(n,m,{vals:S,layer:i[\"angular-grid\"],path:b,transFn:o.noop,crisp:!1}),f.drawLabels(n,m,{vals:S,layer:i[\"angular-axis\"],repositionOnUpdate:!0,transFn:x,labelFns:T})}V(i[\"angular-line\"].select(\"path\"),d.showline,{d:r.pathSubplot(),transform:l(h,p)}).attr(\"stroke-width\",d.linewidth).call(c.stroke,d.linecolor)},N.updateFx=function(t,e){this.gd._context.staticPlot||(!this.isSmith&&(this.updateAngularDrag(t),this.updateRadialDrag(t,e,0),this.updateRadialDrag(t,e,1)),this.updateHoverAndMainDrag(t))},N.updateHoverAndMainDrag=function(t){var e,r,s=this,c=s.isSmith,u=s.gd,h=s.layers,f=t._zoomlayer,p=S.MINZOOM,d=S.OFFEDGE,m=s.radius,x=s.innerRadius,T=s.cx,k=s.cy,A=s.cxx,M=s.cyy,C=s.sectorInRad,L=s.vangles,I=s.radialAxis,P=E.clampTiny,z=E.findXYatLength,O=E.findEnclosingVertexAngles,D=S.cornerHalfWidth,R=S.cornerLen/2,F=g.makeDragger(h,\"path\",\"maindrag\",!1===t.dragmode?\"none\":\"crosshair\");n.select(F).attr(\"d\",s.pathSubplot()).attr(\"transform\",l(T,k)),F.onmousemove=function(t){v.hover(u,t,s.id),u._fullLayout._lasthover=F,u._fullLayout._hoversubplot=s.id},F.onmouseout=function(t){u._dragging||y.unhover(u,t)};var B,N,j,U,V,q,H,G,Z,W={element:F,gd:u,subplot:s.id,plotinfo:{id:s.id,xaxis:s.xaxis,yaxis:s.yaxis},xaxes:[s.xaxis],yaxes:[s.yaxis]};function Y(t,e){return Math.sqrt(t*t+e*e)}function X(t,e){return Y(t-A,e-M)}function $(t,e){return Math.atan2(M-e,t-A)}function J(t,e){return[t*Math.cos(e),t*Math.sin(-e)]}function K(t,e){if(0===t)return s.pathSector(2*D);var r=R/t,n=e-r,i=e+r,a=Math.max(0,Math.min(t,m)),o=a-D,l=a+D;return\"M\"+J(o,n)+\"A\"+[o,o]+\" 0,0,0 \"+J(o,i)+\"L\"+J(l,i)+\"A\"+[l,l]+\" 0,0,1 \"+J(l,n)+\"Z\"}function Q(t,e,r){if(0===t)return s.pathSector(2*D);var n,i,a=J(t,e),o=J(t,r),l=P((a[0]+o[0])/2),c=P((a[1]+o[1])/2);if(l&&c){var u=c/l,h=-1/u,f=z(D,u,l,c);n=z(R,h,f[0][0],f[0][1]),i=z(R,h,f[1][0],f[1][1])}else{var p,d;c?(p=R,d=D):(p=D,d=R),n=[[l-p,c-d],[l+p,c-d]],i=[[l-p,c+d],[l+p,c+d]]}return\"M\"+n.join(\"L\")+\"L\"+i.reverse().join(\"L\")+\"Z\"}function tt(t,e){return e=Math.max(Math.min(e,m),x),tp?(t-1&&1===t&&b(e,u,[s.xaxis],[s.yaxis],s.id,W),r.indexOf(\"event\")>-1&&v.click(u,e,s.id)}W.prepFn=function(t,n,a){var l=u._fullLayout.dragmode,h=F.getBoundingClientRect();u._fullLayout._calcInverseTransform(u);var p=u._fullLayout._invTransform;e=u._fullLayout._invScaleX,r=u._fullLayout._invScaleY;var d=o.apply3DTransform(p)(n-h.left,a-h.top);if(B=d[0],N=d[1],L){var y=E.findPolygonOffset(m,C[0],C[1],L);B+=A+y[0],N+=M+y[1]}switch(l){case\"zoom\":W.clickFn=st,c||(W.moveFn=L?it:rt,W.doneFn=at,function(){j=null,U=null,V=s.pathSubplot(),q=!1;var t=u._fullLayout[s.id];H=i(t.bgcolor).getLuminance(),(G=g.makeZoombox(f,H,T,k,V)).attr(\"fill-rule\",\"evenodd\"),Z=g.makeCorners(f,T,k),w(u)}());break;case\"select\":case\"lasso\":_(t,n,a,W,l)}},y.init(W)},N.updateRadialDrag=function(t,e,r){var i=this,c=i.gd,u=i.layers,h=i.radius,f=i.innerRadius,p=i.cx,d=i.cy,m=i.radialAxis,v=S.radialDragBoxSize,x=v/2;if(m.visible){var _,b,T,M=R(i.radialAxisAngle),E=m._rl,C=E[0],L=E[1],I=E[r],P=.75*(E[1]-E[0])/(1-i.getHole(e))/h;r?(_=p+(h+x)*Math.cos(M),b=d-(h+x)*Math.sin(M),T=\"radialdrag\"):(_=p+(f-x)*Math.cos(M),b=d-(f-x)*Math.sin(M),T=\"radialdrag-inner\");var z,O,D,B=g.makeRectDragger(u,T,\"crosshair\",-x,-x,v,v),N={element:B,gd:c};!1===t.dragmode&&(N.dragmode=!1),V(n.select(B),m.visible&&f0==(r?D>C:Dn?function(t){return t<=0}:function(t){return t>=0};t.c2g=function(r){var n=t.c2l(r)-e;return(s(n)?n:0)+o},t.g2c=function(r){return t.l2c(r+e-o)},t.g2p=function(t){return t*a},t.c2p=function(e){return t.g2p(t.c2g(e))}}}(t,e);break;case\"angularaxis\":!function(t,e){var r=t.type;if(\"linear\"===r){var i=t.d2c,s=t.c2d;t.d2c=function(t,e){return function(t,e){return\"degrees\"===e?a(t):t}(i(t),e)},t.c2d=function(t,e){return s(function(t,e){return\"degrees\"===e?o(t):t}(t,e))}}t.makeCalcdata=function(e,r){var n,i,a=e[r],o=e._length,s=function(r){return t.d2c(r,e.thetaunit)};if(a)for(n=new Array(o),i=0;i0?1:0}function r(t){var e=t[0],r=t[1];if(!isFinite(e)||!isFinite(r))return[1,0];var n=(e+1)*(e+1)+r*r;return[(e*e+r*r-1)/n,2*r/n]}function n(t,e){var r=e[0],n=e[1];return[r*t.radius+t.cx,-n*t.radius+t.cy]}function i(t,e){return e*t.radius}t.exports={smith:r,reactanceArc:function(t,e,a,o){var s=n(t,r([a,e])),l=s[0],c=s[1],u=n(t,r([o,e])),h=u[0],f=u[1];if(0===e)return[\"M\"+l+\",\"+c,\"L\"+h+\",\"+f].join(\" \");var p=i(t,1/Math.abs(e));return[\"M\"+l+\",\"+c,\"A\"+p+\",\"+p+\" 0 0,\"+(e<0?1:0)+\" \"+h+\",\"+f].join(\" \")},resistanceArc:function(t,a,o,s){var l=i(t,1/(a+1)),c=n(t,r([a,o])),u=c[0],h=c[1],f=n(t,r([a,s])),p=f[0],d=f[1];if(e(o)!==e(s)){var m=n(t,r([a,0]));return[\"M\"+u+\",\"+h,\"A\"+l+\",\"+l+\" 0 0,\"+(00){for(var n=[],i=0;i=u&&(f.min=0,d.min=0,g.min=0,t.aaxis&&delete t.aaxis.min,t.baxis&&delete t.baxis.min,t.caxis&&delete t.caxis.min)}function m(t,e,r,n){var i=f[e._name];function o(r,n){return a.coerce(t,e,i,r,n)}o(\"uirevision\",n.uirevision),e.type=\"linear\";var p=o(\"color\"),d=p!==i.color.dflt?p:r.font.color,m=e._name.charAt(0).toUpperCase(),g=\"Component \"+m,y=o(\"title.text\",g);e._hovertitle=y===g?y:m,a.coerceFont(o,\"title.font\",r.font,{overrideDflt:{size:a.bigFont(r.font.size),color:d}}),o(\"min\"),u(t,e,o,\"linear\"),l(t,e,o,\"linear\"),s(t,e,o,\"linear\",{noAutotickangles:!0,noTicklabelshift:!0,noTicklabelstandoff:!0}),c(t,e,o,{outerTicks:!0}),o(\"showticklabels\")&&(a.coerceFont(o,\"tickfont\",r.font,{overrideDflt:{color:d}}),o(\"tickangle\"),o(\"tickformat\")),h(t,e,o,{dfltColor:p,bgColor:r.bgColor,blend:60,showLine:!0,showGrid:!0,noZeroLine:!0,attributes:i}),o(\"hoverformat\"),o(\"layer\")}t.exports=function(t,e,r){o(t,e,r,{type:\"ternary\",attributes:f,handleDefaults:d,font:e.font,paper_bgcolor:e.paper_bgcolor})}},83637:function(t,e,r){\"use strict\";var n=r(45568),i=r(65657),a=r(33626),o=r(34809),s=o.strTranslate,l=o._,c=r(78766),u=r(62203),h=r(19091),f=r(93049).extendFlat,p=r(44122),d=r(29714),m=r(14751),g=r(32141),y=r(70414),v=y.freeMode,x=y.rectMode,_=r(17240),b=r(44844).prepSelect,w=r(44844).selectOnClick,T=r(44844).clearOutline,k=r(44844).clearSelectionsCache,A=r(54826);function M(t,e){this.id=t.id,this.graphDiv=t.graphDiv,this.init(e),this.makeFramework(e),this.updateFx(e),this.aTickLayout=null,this.bTickLayout=null,this.cTickLayout=null}t.exports=M;var S=M.prototype;S.init=function(t){this.container=t._ternarylayer,this.defs=t._defs,this.layoutId=t._uid,this.traceHash={},this.layers={}},S.plot=function(t,e){var r=this,n=e[r.id],i=e._size;r._hasClipOnAxisFalse=!1;for(var a=0;aE*_?i=(a=_)*E:a=(i=x)/E,o=y*i/x,l=v*a/_,r=e.l+e.w*m-i/2,n=e.t+e.h*(1-g)-a/2,p.x0=r,p.y0=n,p.w=i,p.h=a,p.sum=b,p.xaxis={type:\"linear\",range:[w+2*k-b,b-w-2*T],domain:[m-o/2,m+o/2],_id:\"x\"},h(p.xaxis,p.graphDiv._fullLayout),p.xaxis.setScale(),p.xaxis.isPtWithinRange=function(t){return t.a>=p.aaxis.range[0]&&t.a<=p.aaxis.range[1]&&t.b>=p.baxis.range[1]&&t.b<=p.baxis.range[0]&&t.c>=p.caxis.range[1]&&t.c<=p.caxis.range[0]},p.yaxis={type:\"linear\",range:[w,b-T-k],domain:[g-l/2,g+l/2],_id:\"y\"},h(p.yaxis,p.graphDiv._fullLayout),p.yaxis.setScale(),p.yaxis.isPtWithinRange=function(){return!0};var A=p.yaxis.domain[0],M=p.aaxis=f({},t.aaxis,{range:[w,b-T-k],side:\"left\",tickangle:(+t.aaxis.tickangle||0)-30,domain:[A,A+l*E],anchor:\"free\",position:0,_id:\"y\",_length:i});h(M,p.graphDiv._fullLayout),M.setScale();var S=p.baxis=f({},t.baxis,{range:[b-w-k,T],side:\"bottom\",domain:p.xaxis.domain,anchor:\"free\",position:0,_id:\"x\",_length:i});h(S,p.graphDiv._fullLayout),S.setScale();var C=p.caxis=f({},t.caxis,{range:[b-w-T,k],side:\"right\",tickangle:(+t.caxis.tickangle||0)+30,domain:[A,A+l*E],anchor:\"free\",position:0,_id:\"y\",_length:i});h(C,p.graphDiv._fullLayout),C.setScale();var L=\"M\"+r+\",\"+(n+a)+\"h\"+i+\"l-\"+i/2+\",-\"+a+\"Z\";p.clipDef.select(\"path\").attr(\"d\",L),p.layers.plotbg.select(\"path\").attr(\"d\",L);var I=\"M0,\"+a+\"h\"+i+\"l-\"+i/2+\",-\"+a+\"Z\";p.clipDefRelative.select(\"path\").attr(\"d\",I);var P=s(r,n);p.plotContainer.selectAll(\".scatterlayer,.maplayer\").attr(\"transform\",P),p.clipDefRelative.select(\"path\").attr(\"transform\",null);var z=s(r-S._offset,n+a);p.layers.baxis.attr(\"transform\",z),p.layers.bgrid.attr(\"transform\",z);var O=s(r+i/2,n)+\"rotate(30)\"+s(0,-M._offset);p.layers.aaxis.attr(\"transform\",O),p.layers.agrid.attr(\"transform\",O);var D=s(r+i/2,n)+\"rotate(-30)\"+s(0,-C._offset);p.layers.caxis.attr(\"transform\",D),p.layers.cgrid.attr(\"transform\",D),p.drawAxes(!0),p.layers.aline.select(\"path\").attr(\"d\",M.showline?\"M\"+r+\",\"+(n+a)+\"l\"+i/2+\",-\"+a:\"M0,0\").call(c.stroke,M.linecolor||\"#000\").style(\"stroke-width\",(M.linewidth||0)+\"px\"),p.layers.bline.select(\"path\").attr(\"d\",S.showline?\"M\"+r+\",\"+(n+a)+\"h\"+i:\"M0,0\").call(c.stroke,S.linecolor||\"#000\").style(\"stroke-width\",(S.linewidth||0)+\"px\"),p.layers.cline.select(\"path\").attr(\"d\",C.showline?\"M\"+(r+i/2)+\",\"+n+\"l\"+i/2+\",\"+a:\"M0,0\").call(c.stroke,C.linecolor||\"#000\").style(\"stroke-width\",(C.linewidth||0)+\"px\"),p.graphDiv._context.staticPlot||p.initInteractions(),u.setClipUrl(p.layers.frontplot,p._hasClipOnAxisFalse?null:p.clipId,p.graphDiv)},S.drawAxes=function(t){var e=this,r=e.graphDiv,n=e.id.substr(7)+\"title\",i=e.layers,a=e.aaxis,o=e.baxis,s=e.caxis;if(e.drawAx(a),e.drawAx(o),e.drawAx(s),t){var c=Math.max(a.showticklabels?a.tickfont.size/2:0,(s.showticklabels?.75*s.tickfont.size:0)+(\"outside\"===s.ticks?.87*s.ticklen:0)),u=(o.showticklabels?o.tickfont.size:0)+(\"outside\"===o.ticks?o.ticklen:0)+3;i[\"a-title\"]=_.draw(r,\"a\"+n,{propContainer:a,propName:e.id+\".aaxis.title\",placeholder:l(r,\"Click to enter Component A title\"),attributes:{x:e.x0+e.w/2,y:e.y0-a.title.font.size/3-c,\"text-anchor\":\"middle\"}}),i[\"b-title\"]=_.draw(r,\"b\"+n,{propContainer:o,propName:e.id+\".baxis.title\",placeholder:l(r,\"Click to enter Component B title\"),attributes:{x:e.x0-u,y:e.y0+e.h+.83*o.title.font.size+u,\"text-anchor\":\"middle\"}}),i[\"c-title\"]=_.draw(r,\"c\"+n,{propContainer:s,propName:e.id+\".caxis.title\",placeholder:l(r,\"Click to enter Component C title\"),attributes:{x:e.x0+e.w+u,y:e.y0+e.h+.83*s.title.font.size+u,\"text-anchor\":\"middle\"}})}},S.drawAx=function(t){var e,r=this,n=r.graphDiv,i=t._name,a=i.charAt(0),s=t._id,l=r.layers[i],c=a+\"tickLayout\",u=(e=t).ticks+String(e.ticklen)+String(e.showticklabels);r[c]!==u&&(l.selectAll(\".\"+s+\"tick\").remove(),r[c]=u),t.setScale();var h=d.calcTicks(t),f=d.clipEnds(t,h),p=d.makeTransTickFn(t),m=d.getTickSigns(t)[2],g=o.deg2rad(30),y=m*(t.linewidth||1)/2,v=m*t.ticklen,x=r.w,_=r.h,b=\"b\"===a?\"M0,\"+y+\"l\"+Math.sin(g)*v+\",\"+Math.cos(g)*v:\"M\"+y+\",0l\"+Math.cos(g)*v+\",\"+-Math.sin(g)*v,w={a:\"M0,0l\"+_+\",-\"+x/2,b:\"M0,0l-\"+x/2+\",-\"+_,c:\"M0,0l-\"+_+\",\"+x/2}[a];d.drawTicks(n,t,{vals:\"inside\"===t.ticks?f:h,layer:l,path:b,transFn:p,crisp:!1}),d.drawGrid(n,t,{vals:f,layer:r.layers[a+\"grid\"],path:w,transFn:p,crisp:!1}),d.drawLabels(n,t,{vals:h,layer:l,transFn:p,labelFns:d.makeLabelFns(t,0,30)})};var 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r=C._fullLayout.clickmode;O(C),2===t&&(C.emit(\"plotly_doubleclick\",null),a.call(\"_guiRelayout\",C,R({a:0,b:0,c:0}))),r.indexOf(\"select\")>-1&&1===t&&w(e,C,[M.xaxis],[M.yaxis],M.id,M.dragOptions),r.indexOf(\"event\")>-1&&g.click(C,e,M.id)}function B(t,e){return 1-e/M.h}function N(t,e){return 1-(t+(M.h-e)/Math.sqrt(3))/M.w}function j(t,e){return(t-(M.h-e)/Math.sqrt(3))/M.w}function U(i,a){var o=r+i*t,s=n+a*e,l=Math.max(0,Math.min(1,B(0,n),B(0,s))),c=Math.max(0,Math.min(1,N(r,n),N(o,s))),u=Math.max(0,Math.min(1,j(r,n),j(o,s))),m=(l/2+u)*M.w,g=(1-l/2-c)*M.w,v=(m+g)/2,x=g-m,b=(1-l)*M.h,w=b-x/E;x.2?\"rgba(0,0,0,0.4)\":\"rgba(255,255,255,0.3)\").duration(200),k.transition().style(\"opacity\",1).duration(200),_=!0),C.emit(\"plotly_relayouting\",R(p))}function V(){O(C),p!==h&&(a.call(\"_guiRelayout\",C,R(p)),z&&C.data&&C._context.showTips&&(o.notifier(l(C,\"Double-click to zoom back out\"),\"long\"),z=!1))}function q(t,e){var 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n=r(87163),i=r(80712).axisHoverFormat,a=r(3208).rb,o=r(42450),s=r(9829),l=r(93049).extendFlat,c=r(13582).overrideAll,u=t.exports=c(l({x:{valType:\"data_array\"},y:{valType:\"data_array\"},z:{valType:\"data_array\"},value:{valType:\"data_array\"},isomin:{valType:\"number\"},isomax:{valType:\"number\"},surface:{show:{valType:\"boolean\",dflt:!0},count:{valType:\"integer\",dflt:2,min:1},fill:{valType:\"number\",min:0,max:1,dflt:1},pattern:{valType:\"flaglist\",flags:[\"A\",\"B\",\"C\",\"D\",\"E\"],extras:[\"all\",\"odd\",\"even\"],dflt:\"all\"}},spaceframe:{show:{valType:\"boolean\",dflt:!1},fill:{valType:\"number\",min:0,max:1,dflt:.15}},slices:{x:{show:{valType:\"boolean\",dflt:!1},locations:{valType:\"data_array\",dflt:[]},fill:{valType:\"number\",min:0,max:1,dflt:1}},y:{show:{valType:\"boolean\",dflt:!1},locations:{valType:\"data_array\",dflt:[]},fill:{valType:\"number\",min:0,max:1,dflt:1}},z:{show:{valType:\"boolean\",dflt:!1},locations:{valType:\"data_array\",dflt:[]},fill:{valType:\"number\",min:0,max:1,dflt:1}}},caps:{x:{show:{valType:\"boolean\",dflt:!0},fill:{valType:\"number\",min:0,max:1,dflt:1}},y:{show:{valType:\"boolean\",dflt:!0},fill:{valType:\"number\",min:0,max:1,dflt:1}},z:{show:{valType:\"boolean\",dflt:!0},fill:{valType:\"number\",min:0,max:1,dflt:1}}},text:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertemplate:a(),xhoverformat:i(\"x\"),yhoverformat:i(\"y\"),zhoverformat:i(\"z\"),valuehoverformat:i(\"value\",1),showlegend:l({},s.showlegend,{dflt:!1})},n(\"\",{colorAttr:\"`value`\",showScaleDflt:!0,editTypeOverride:\"calc\"}),{opacity:o.opacity,lightposition:o.lightposition,lighting:o.lighting,flatshading:o.flatshading,contour:o.contour,hoverinfo:l({},s.hoverinfo)}),\"calc\",\"nested\");u.flatshading.dflt=!0,u.lighting.facenormalsepsilon.dflt=0,u.x.editType=u.y.editType=u.z.editType=u.value.editType=\"calc+clearAxisTypes\",u.transforms=void 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o=e.yaxis,s=e.xaxis,l=!!s.rangebreaks;i.makeTraceGroups(a,r,\"trace ohlc\").each((function(t){var e=n.select(this),r=t[0],a=r.t;if(!0!==r.trace.visible||a.empty)e.remove();else{var c=a.tickLen,u=e.selectAll(\"path\").data(i.identity);u.enter().append(\"path\"),u.exit().remove(),u.attr(\"d\",(function(t){if(t.empty)return\"M0,0Z\";var e=s.c2p(t.pos-c,!0),r=s.c2p(t.pos+c,!0),n=l?(e+r)/2:s.c2p(t.pos,!0);return\"M\"+e+\",\"+o.c2p(t.o,!0)+\"H\"+n+\"M\"+n+\",\"+o.c2p(t.h,!0)+\"V\"+o.c2p(t.l,!0)+\"M\"+r+\",\"+o.c2p(t.c,!0)+\"H\"+n}))}}))}},49343:function(t){\"use strict\";t.exports=function(t,e){var r,n=t.cd,i=t.xaxis,a=t.yaxis,o=[],s=n[0].t.bPos||0;if(!1===e)for(r=0;r=t.length)return!1;if(void 0!==e[t[r]])return!1;e[t[r]]=!0}return!0}(r))for(e=0;e0||u(s);c&&(o=\"array\");var h=r(\"categoryorder\",o);\"array\"===h?(r(\"categoryarray\"),r(\"ticktext\")):(delete t.categoryarray,delete t.ticktext),c||\"array\"!==h||(e.categoryorder=\"trace\")}}t.exports=function(t,e,r,u){function f(r,i){return n.coerce(t,e,l,r,i)}var p=s(t,e,{name:\"dimensions\",handleItemDefaults:h}),d=function(t,e,r,o,s){s(\"line.shape\"),s(\"line.hovertemplate\");var l=s(\"line.color\",o.colorway[0]);if(i(t,\"line\")&&n.isArrayOrTypedArray(l)){if(l.length)return s(\"line.colorscale\"),a(t,e,o,s,{prefix:\"line.\",cLetter:\"c\"}),l.length;e.line.color=r}return 1/0}(t,e,r,u,f);o(e,u,f),Array.isArray(p)&&p.length||(e.visible=!1),c(e,p,\"values\",d),f(\"hoveron\"),f(\"hovertemplate\"),f(\"arrangement\"),f(\"bundlecolors\"),f(\"sortpaths\"),f(\"counts\");var m=u.font;n.coerceFont(f,\"labelfont\",m,{overrideDflt:{size:Math.round(m.size)}}),n.coerceFont(f,\"tickfont\",m,{autoShadowDflt:!0,overrideDflt:{size:Math.round(m.size/1.2)}})}},6305:function(t,e,r){\"use 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w=x.enter().append(\"path\").attr(\"class\",\"path\").attr(\"stroke-opacity\",0).attr(\"fill\",(function(t){return t.model.color})).attr(\"fill-opacity\",0);b(w),x.attr(\"d\",(function(t){return t.svgD})),w.empty()||x.sort(m),x.exit().remove(),x.on(\"mouseover\",g).on(\"mouseout\",y).on(\"click\",_),v.append(\"g\").attr(\"class\",\"dimensions\");var A=f.select(\"g.dimensions\").selectAll(\"g.dimension\").data((function(t){return t.dimensions}),p);A.enter().append(\"g\").attr(\"class\",\"dimension\"),A.attr(\"transform\",(function(t){return l(t.x,0)})),A.exit().remove();var M=A.selectAll(\"g.category\").data((function(t){return t.categories}),p),S=M.enter().append(\"g\").attr(\"class\",\"category\");M.attr(\"transform\",(function(t){return l(0,t.y)})),S.append(\"rect\").attr(\"class\",\"catrect\").attr(\"pointer-events\",\"none\"),M.select(\"rect.catrect\").attr(\"fill\",\"none\").attr(\"width\",(function(t){return t.width})).attr(\"height\",(function(t){return t.height})),T(S);var E=M.selectAll(\"rect.bandrect\").data((function(t){return t.bands}),p);E.each((function(){s.raiseToTop(this)})),E.attr(\"fill\",(function(t){return t.color}));var O=E.enter().append(\"rect\").attr(\"class\",\"bandrect\").attr(\"stroke-opacity\",0).attr(\"fill\",(function(t){return t.color})).attr(\"fill-opacity\",0);E.attr(\"fill\",(function(t){return t.color})).attr(\"width\",(function(t){return t.width})).attr(\"height\",(function(t){return t.height})).attr(\"y\",(function(t){return t.y})).attr(\"cursor\",(function(t){return\"fixed\"===t.parcatsViewModel.arrangement?\"default\":\"perpendicular\"===t.parcatsViewModel.arrangement?\"ns-resize\":\"move\"})),k(O),E.exit().remove(),S.append(\"text\").attr(\"class\",\"catlabel\").attr(\"pointer-events\",\"none\"),M.select(\"text.catlabel\").attr(\"text-anchor\",(function(t){return d(t)?\"start\":\"end\"})).attr(\"alignment-baseline\",\"middle\").style(\"fill\",\"rgb(0, 0, 0)\").attr(\"x\",(function(t){return d(t)?t.width+5:-5})).attr(\"y\",(function(t){return t.height/2})).text((function(t){return t.model.categoryLabel})).each((function(t){c.font(n.select(this),t.parcatsViewModel.categorylabelfont),h.convertToTspans(n.select(this),e)})),S.append(\"text\").attr(\"class\",\"dimlabel\"),M.select(\"text.dimlabel\").attr(\"text-anchor\",\"middle\").attr(\"alignment-baseline\",\"baseline\").attr(\"cursor\",(function(t){return\"fixed\"===t.parcatsViewModel.arrangement?\"default\":\"ew-resize\"})).attr(\"x\",(function(t){return t.width/2})).attr(\"y\",-5).text((function(t,e){return 0===e?t.parcatsViewModel.model.dimensions[t.model.dimensionInd].dimensionLabel:null})).each((function(t){c.font(n.select(this),t.parcatsViewModel.labelfont)})),M.selectAll(\"rect.bandrect\").on(\"mouseover\",C).on(\"mouseout\",L),M.exit().remove(),A.call(n.behavior.drag().origin((function(t){return{x:t.x,y:0}})).on(\"dragstart\",I).on(\"drag\",P).on(\"dragend\",z)),f.each((function(t){t.traceSelection=n.select(this),t.pathSelection=n.select(this).selectAll(\"g.paths\").selectAll(\"path.path\"),t.dimensionSelection=n.select(this).selectAll(\"g.dimensions\").selectAll(\"g.dimension\")})),f.exit().remove()}function p(t){return t.key}function d(t){var e=t.parcatsViewModel.dimensions.length,r=t.parcatsViewModel.dimensions[e-1].model.dimensionInd;return t.model.dimensionInd===r}function m(t,e){return 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m=u.model.count,g=u.model.categoryLabel,y=m/u.parcatsViewModel.model.count,v={countLabel:m,categoryLabel:g,probabilityLabel:y.toFixed(3)},x=[];-1!==u.parcatsViewModel.hoverinfoItems.indexOf(\"count\")&&x.push([\"Count:\",v.countLabel].join(\" \")),-1!==u.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")&&x.push([\"P(\"+v.categoryLabel+\"):\",v.probabilityLabel].join(\" \"));var _=x.join(\"
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e=t.parcatsViewModel;e.dragDimension||(b(e.pathSelection),T(e.dimensionSelection.selectAll(\"g.category\")),k(e.dimensionSelection.selectAll(\"g.category\").selectAll(\"rect.bandrect\")),o.loneUnhover(e.graphDiv._fullLayout._hoverlayer.node()),e.pathSelection.sort(m),-1!==e.hoverinfoItems.indexOf(\"skip\"))||(\"color\"===t.parcatsViewModel.hoveron?S(this,\"plotly_unhover\",n.event):M(this,\"plotly_unhover\",n.event))}function I(t){\"fixed\"!==t.parcatsViewModel.arrangement&&(t.dragDimensionDisplayInd=t.model.displayInd,t.initialDragDimensionDisplayInds=t.parcatsViewModel.model.dimensions.map((function(t){return t.displayInd})),t.dragHasMoved=!1,t.dragCategoryDisplayInd=null,n.select(this).selectAll(\"g.category\").select(\"rect.catrect\").each((function(e){var r=n.mouse(this)[0],i=n.mouse(this)[1];-2<=r&&r<=e.width+2&&-2<=i&&i<=e.height+2&&(t.dragCategoryDisplayInd=e.model.displayInd,t.initialDragCategoryDisplayInds=t.model.categories.map((function(t){return t.displayInd})),e.model.dragY=e.y,s.raiseToTop(this.parentNode),n.select(this.parentNode).selectAll(\"rect.bandrect\").each((function(e){e.yh.y+h.height/2&&(o.model.displayInd=h.model.displayInd,h.model.displayInd=l),t.dragCategoryDisplayInd=o.model.displayInd}if(null===t.dragCategoryDisplayInd||\"freeform\"===t.parcatsViewModel.arrangement){a.model.dragX=n.event.x;var f=t.parcatsViewModel.dimensions[r],p=t.parcatsViewModel.dimensions[i];void 0!==f&&a.model.dragXp.x&&(a.model.displayInd=p.model.displayInd,p.model.displayInd=t.dragDimensionDisplayInd),t.dragDimensionDisplayInd=a.model.displayInd}j(t.parcatsViewModel),N(t.parcatsViewModel),R(t.parcatsViewModel),D(t.parcatsViewModel)}}function z(t){if(\"fixed\"!==t.parcatsViewModel.arrangement&&null!==t.dragDimensionDisplayInd){n.select(this).selectAll(\"text\").attr(\"font-weight\",\"normal\");var e={},r=O(t.parcatsViewModel),i=t.parcatsViewModel.model.dimensions.map((function(t){return t.displayInd})),o=t.initialDragDimensionDisplayInds.some((function(t,e){return t!==i[e]}));o&&i.forEach((function(r,n){var i=t.parcatsViewModel.model.dimensions[n].containerInd;e[\"dimensions[\"+i+\"].displayindex\"]=r}));var s=!1;if(null!==t.dragCategoryDisplayInd){var l=t.model.categories.map((function(t){return t.displayInd}));if(s=t.initialDragCategoryDisplayInds.some((function(t,e){return t!==l[e]}))){var c=t.model.categories.slice().sort((function(t,e){return t.displayInd-e.displayInd})),u=c.map((function(t){return t.categoryValue})),h=c.map((function(t){return t.categoryLabel}));e[\"dimensions[\"+t.model.containerInd+\"].categoryarray\"]=[u],e[\"dimensions[\"+t.model.containerInd+\"].ticktext\"]=[h],e[\"dimensions[\"+t.model.containerInd+\"].categoryorder\"]=\"array\"}}-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")&&!t.dragHasMoved&&t.potentialClickBand&&(\"color\"===t.parcatsViewModel.hoveron?S(t.potentialClickBand,\"plotly_click\",n.event.sourceEvent):M(t.potentialClickBand,\"plotly_click\",n.event.sourceEvent)),t.model.dragX=null,null!==t.dragCategoryDisplayInd&&(t.parcatsViewModel.dimensions[t.dragDimensionDisplayInd].categories[t.dragCategoryDisplayInd].model.dragY=null,t.dragCategoryDisplayInd=null),t.dragDimensionDisplayInd=null,t.parcatsViewModel.dragDimension=null,t.dragHasMoved=null,t.potentialClickBand=null,j(t.parcatsViewModel),N(t.parcatsViewModel),n.transition().duration(300).ease(\"cubic-in-out\").each((function(){R(t.parcatsViewModel,!0),D(t.parcatsViewModel,!0)})).each(\"end\",(function(){(o||s)&&a.restyle(t.parcatsViewModel.graphDiv,e,[r])}))}}function O(t){for(var e,r=t.graphDiv._fullData,n=0;n=0;s--)u+=\"C\"+c[s]+\",\"+(e[s+1]+n)+\" \"+l[s]+\",\"+(e[s]+n)+\" \"+(t[s]+r[s])+\",\"+(e[s]+n),u+=\"l-\"+r[s]+\",0 \";return u+\"Z\"}function N(t){var e=t.dimensions,r=t.model,n=e.map((function(t){return t.categories.map((function(t){return t.y}))})),i=t.model.dimensions.map((function(t){return t.categories.map((function(t){return t.displayInd}))})),a=t.model.dimensions.map((function(t){return t.displayInd})),o=t.dimensions.map((function(t){return t.model.dimensionInd})),s=e.map((function(t){return t.x})),l=e.map((function(t){return t.width})),c=[];for(var u in r.paths)r.paths.hasOwnProperty(u)&&c.push(r.paths[u]);function h(t){var e=t.categoryInds.map((function(t,e){return i[e][t]}));return o.map((function(t){return e[t]}))}c.sort((function(e,r){var n=h(e),i=h(r);return\"backward\"===t.sortpaths&&(n.reverse(),i.reverse()),n.push(e.valueInds[0]),i.push(r.valueInds[0]),t.bundlecolors&&(n.unshift(e.rawColor),i.unshift(r.rawColor)),ni?1:0}));for(var f=new Array(c.length),p=e[0].model.count,d=e[0].categories.map((function(t){return t.height})).reduce((function(t,e){return t+e})),m=0;m0?d*(y.count/p):0;for(var v,x=new Array(n.length),_=0;_1?(t.width-80-16)/(n-1):0)*i;var a,o,s,l,c,u=[],h=t.model.maxCats,f=e.categories.length,p=e.count,d=t.height-8*(h-1),m=8*(h-f)/2,g=e.categories.map((function(t){return{displayInd:t.displayInd,categoryInd:t.categoryInd}}));for(g.sort((function(t,e){return t.displayInd-e.displayInd})),c=0;c0?o.count/p*d:0,s={key:o.valueInds[0],model:o,width:16,height:a,y:null!==o.dragY?o.dragY:m,bands:[],parcatsViewModel:t},m=m+a+8,u.push(s);return{key:e.dimensionInd,x:null!==e.dragX?e.dragX:r,y:0,width:16,model:e,categories:u,parcatsViewModel:t,dragCategoryDisplayInd:null,dragDimensionDisplayInd:null,initialDragDimensionDisplayInds:null,initialDragCategoryDisplayInds:null,dragHasMoved:null,potentialClickBand:null}}t.exports=function(t,e,r,n){f(r,t,n,e)}},37822:function(t,e,r){\"use strict\";var n=r(27219);t.exports=function(t,e,r,i){var a=t._fullLayout,o=a._paper,s=a._size;n(t,o,e,{width:s.w,height:s.h,margin:{t:s.t,r:s.r,b:s.b,l:s.l}},r,i)}},59549:function(t,e,r){\"use strict\";var n=r(87163),i=r(25829),a=r(80337),o=r(13792).u,s=r(93049).extendFlat,l=r(78032).templatedArray;t.exports={domain:o({name:\"parcoords\",trace:!0,editType:\"plot\"}),labelangle:{valType:\"angle\",dflt:0,editType:\"plot\"},labelside:{valType:\"enumerated\",values:[\"top\",\"bottom\"],dflt:\"top\",editType:\"plot\"},labelfont:a({editType:\"plot\"}),tickfont:a({autoShadowDflt:!0,editType:\"plot\"}),rangefont:a({editType:\"plot\"}),dimensions:l(\"dimension\",{label:{valType:\"string\",editType:\"plot\"},tickvals:s({},i.tickvals,{editType:\"plot\"}),ticktext:s({},i.ticktext,{editType:\"plot\"}),tickformat:s({},i.tickformat,{editType:\"plot\"}),visible:{valType:\"boolean\",dflt:!0,editType:\"plot\"},range:{valType:\"info_array\",items:[{valType:\"number\",editType:\"plot\"},{valType:\"number\",editType:\"plot\"}],editType:\"plot\"},constraintrange:{valType:\"info_array\",freeLength:!0,dimensions:\"1-2\",items:[{valType:\"any\",editType:\"plot\"},{valType:\"any\",editType:\"plot\"}],editType:\"plot\"},multiselect:{valType:\"boolean\",dflt:!0,editType:\"plot\"},values:{valType:\"data_array\",editType:\"calc\"},editType:\"calc\"}),line:s({editType:\"calc\"},n(\"line\",{colorscaleDflt:\"Viridis\",autoColorDflt:!1,editTypeOverride:\"calc\"})),unselected:{line:{color:{valType:\"color\",dflt:\"#7f7f7f\",editType:\"plot\"},opacity:{valType:\"number\",min:0,max:1,dflt:\"auto\",editType:\"plot\"},editType:\"plot\"},editType:\"plot\"}}},23245:function(t,e,r){\"use strict\";var n=r(77911),i=r(45568),a=r(71293).keyFun,o=r(71293).repeat,s=r(34809).sorterAsc,l=r(34809).strTranslate,c=n.bar.snapRatio;function u(t,e){return t*(1-c)+e*c}var h=n.bar.snapClose;function f(t,e){return t*(1-h)+e*h}function p(t,e,r,n){if(function(t,e){for(var r=0;r=e[r][0]&&t<=e[r][1])return!0;return!1}(r,n))return r;var i=t?-1:1,a=0,o=e.length-1;if(i<0){var s=a;a=o,o=s}for(var l=e[a],c=l,h=a;i*he){f=r;break}}if(a=u,isNaN(a)&&(a=isNaN(h)||isNaN(f)?isNaN(h)?f:h:e-c[h][1]t[1]+r||e=.9*t[1]+.1*t[0]?\"n\":e<=.9*t[0]+.1*t[1]?\"s\":\"ns\"}(d,e);m&&(o.interval=l[a],o.intervalPix=d,o.region=m)}}if(t.ordinal&&!o.region){var g=t.unitTickvals,v=t.unitToPaddedPx.invert(e);for(r=0;r=x[0]&&v<=x[1]){o.clickableOrdinalRange=x;break}}}return o}function w(t,e){i.event.sourceEvent.stopPropagation();var r=e.height-i.mouse(t)[1]-2*n.verticalPadding,a=e.brush.svgBrush;a.wasDragged=!0,a._dragging=!0,a.grabbingBar?a.newExtent=[r-a.grabPoint,r+a.barLength-a.grabPoint].map(e.unitToPaddedPx.invert):a.newExtent=[a.startExtent,e.unitToPaddedPx.invert(r)].sort(s),e.brush.filterSpecified=!0,a.extent=a.stayingIntervals.concat([a.newExtent]),a.brushCallback(e),_(t.parentNode)}function T(t,e){var r=b(e,e.height-i.mouse(t)[1]-2*n.verticalPadding),a=\"crosshair\";r.clickableOrdinalRange?a=\"pointer\":r.region&&(a=r.region+\"-resize\"),i.select(document.body).style(\"cursor\",a)}function k(t){t.on(\"mousemove\",(function(t){i.event.preventDefault(),t.parent.inBrushDrag||T(this,t)})).on(\"mouseleave\",(function(t){t.parent.inBrushDrag||v()})).call(i.behavior.drag().on(\"dragstart\",(function(t){!function(t,e){i.event.sourceEvent.stopPropagation();var r=e.height-i.mouse(t)[1]-2*n.verticalPadding,a=e.unitToPaddedPx.invert(r),o=e.brush,s=b(e,r),l=s.interval,c=o.svgBrush;if(c.wasDragged=!1,c.grabbingBar=\"ns\"===s.region,c.grabbingBar){var u=l.map(e.unitToPaddedPx);c.grabPoint=r-u[0]-n.verticalPadding,c.barLength=u[1]-u[0]}c.clickableOrdinalRange=s.clickableOrdinalRange,c.stayingIntervals=e.multiselect&&o.filterSpecified?o.filter.getConsolidated():[],l&&(c.stayingIntervals=c.stayingIntervals.filter((function(t){return t[0]!==l[0]&&t[1]!==l[1]}))),c.startExtent=s.region?l[\"s\"===s.region?1:0]:a,e.parent.inBrushDrag=!0,c.brushStartCallback()}(this,t)})).on(\"drag\",(function(t){w(this,t)})).on(\"dragend\",(function(t){!function(t,e){var r=e.brush,n=r.filter,a=r.svgBrush;a._dragging||(T(t,e),w(t,e),e.brush.svgBrush.wasDragged=!1),a._dragging=!1,i.event.sourceEvent.stopPropagation();var o=a.grabbingBar;if(a.grabbingBar=!1,a.grabLocation=void 0,e.parent.inBrushDrag=!1,v(),!a.wasDragged)return a.wasDragged=void 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t,e,r=[];return{set:function(n){1===(r=n.map((function(t){return t.slice().sort(s)})).sort(A)).length&&r[0][0]===-1/0&&r[0][1]===1/0&&(r=[[0,-1]]),t=S(r),e=r.reduce((function(t,e){return[Math.min(t[0],e[0]),Math.max(t[1],e[1])]}),[1/0,-1/0])},get:function(){return r.slice()},getConsolidated:function(){return t},getBounds:function(){return e}}}();return l.set(r),{filter:l,filterSpecified:e,svgBrush:{extent:[],brushStartCallback:n,brushCallback:(o=i,function(t){var e=t.brush,r=function(t){return t.svgBrush.extent.map((function(t){return t.slice()}))}(e),n=r.slice();e.filter.set(n),o()}),brushEndCallback:a}}},ensureAxisBrush:function(t,e,r){var i=t.selectAll(\".\"+n.cn.axisBrush).data(o,a);i.enter().append(\"g\").classed(n.cn.axisBrush,!0),function(t,e,r){var i=r._context.staticPlot,a=t.selectAll(\".background\").data(o);a.enter().append(\"rect\").classed(\"background\",!0).call(d).call(m).style(\"pointer-events\",i?\"none\":\"auto\").attr(\"transform\",l(0,n.verticalPadding)),a.call(k).attr(\"height\",(function(t){return t.height-n.verticalPadding}));var s=t.selectAll(\".highlight-shadow\").data(o);s.enter().append(\"line\").classed(\"highlight-shadow\",!0).attr(\"x\",-n.bar.width/2).attr(\"stroke-width\",n.bar.width+n.bar.strokeWidth).attr(\"stroke\",e).attr(\"opacity\",n.bar.strokeOpacity).attr(\"stroke-linecap\",\"butt\"),s.attr(\"y1\",(function(t){return t.height})).call(x);var c=t.selectAll(\".highlight\").data(o);c.enter().append(\"line\").classed(\"highlight\",!0).attr(\"x\",-n.bar.width/2).attr(\"stroke-width\",n.bar.width-n.bar.strokeWidth).attr(\"stroke\",n.bar.fillColor).attr(\"opacity\",n.bar.fillOpacity).attr(\"stroke-linecap\",\"butt\"),c.attr(\"y1\",(function(t){return t.height})).call(x)}(i,e,r)},cleanRanges:function(t,e){if(Array.isArray(t[0])?(t=t.map((function(t){return t.sort(s)})),t=e.multiselect?S(t.sort(A)):[t[0]]):t=[t.sort(s)],e.tickvals){var r=e.tickvals.slice().sort(s);if(!(t=t.map((function(t){var e=[p(0,r,t[0],[]),p(1,r,t[1],[])];if(e[1]>e[0])return e})).filter((function(t){return t}))).length)return}return t.length>1?t:t[0]}}},79846:function(t,e,r){\"use strict\";t.exports={attributes:r(59549),supplyDefaults:r(12842),calc:r(20113),colorbar:{container:\"line\",min:\"cmin\",max:\"cmax\"},moduleType:\"trace\",name:\"parcoords\",basePlotModule:r(67207),categories:[\"gl\",\"regl\",\"noOpacity\",\"noHover\"],meta:{}}},67207:function(t,e,r){\"use strict\";var n=r(45568),i=r(4173).eV,a=r(58823),o=r(62972);e.name=\"parcoords\",e.plot=function(t){var e=i(t.calcdata,\"parcoords\")[0];e.length&&a(t,e)},e.clean=function(t,e,r,n){var i=n._has&&n._has(\"parcoords\"),a=e._has&&e._has(\"parcoords\");i&&!a&&(n._paperdiv.selectAll(\".parcoords\").remove(),n._glimages.selectAll(\"*\").remove())},e.toSVG=function(t){var e=t._fullLayout._glimages,r=n.select(t).selectAll(\".svg-container\");r.filter((function(t,e){return e===r.size()-1})).selectAll(\".gl-canvas-context, .gl-canvas-focus\").each((function(){var t=this,r=t.toDataURL(\"image/png\");e.append(\"svg:image\").attr({xmlns:o.svg,\"xlink:href\":r,preserveAspectRatio:\"none\",x:0,y:0,width:t.style.width,height:t.style.height})})),window.setTimeout((function(){n.selectAll(\"#filterBarPattern\").attr(\"id\",\"filterBarPattern\")}),60)}},20113:function(t,e,r){\"use strict\";var n=r(34809).isArrayOrTypedArray,i=r(88856),a=r(71293).wrap;t.exports=function(t,e){var r,o;return i.hasColorscale(e,\"line\")&&n(e.line.color)?(r=e.line.color,o=i.extractOpts(e.line).colorscale,i.calc(t,e,{vals:r,containerStr:\"line\",cLetter:\"c\"})):(r=function(t){for(var e=new Array(t),r=0;rh&&(n.log(\"parcoords traces support up to \"+h+\" dimensions at the moment\"),d.splice(h));var m=s(t,e,{name:\"dimensions\",layout:l,handleItemDefaults:p}),g=function(t,e,r,o,s){var l=s(\"line.color\",r);if(i(t,\"line\")&&n.isArrayOrTypedArray(l)){if(l.length)return s(\"line.colorscale\"),a(t,e,o,s,{prefix:\"line.\",cLetter:\"c\"}),l.length;e.line.color=r}return 1/0}(t,e,r,l,u);o(e,l,u),Array.isArray(m)&&m.length||(e.visible=!1),f(e,m,\"values\",g);var y=n.extendFlat({},l.font,{size:Math.round(l.font.size/1.2)});n.coerceFont(u,\"labelfont\",y),n.coerceFont(u,\"tickfont\",y,{autoShadowDflt:!0}),n.coerceFont(u,\"rangefont\",y),u(\"labelangle\"),u(\"labelside\"),u(\"unselected.line.color\"),u(\"unselected.line.opacity\")}},62935:function(t,e,r){\"use strict\";var n=r(34809).isTypedArray;e.convertTypedArray=function(t){return n(t)?Array.prototype.slice.call(t):t},e.isOrdinal=function(t){return!!t.tickvals},e.isVisible=function(t){return t.visible||!(\"visible\"in t)}},83910:function(t,e,r){\"use strict\";var n=r(79846);n.plot=r(58823),t.exports=n},1293:function(t,e,r){\"use strict\";var n=[\"precision highp float;\",\"\",\"varying vec4 fragColor;\",\"\",\"attribute vec4 p01_04, p05_08, p09_12, p13_16,\",\" p17_20, p21_24, p25_28, p29_32,\",\" p33_36, p37_40, p41_44, p45_48,\",\" p49_52, p53_56, p57_60, colors;\",\"\",\"uniform mat4 dim0A, dim1A, dim0B, dim1B, dim0C, dim1C, dim0D, dim1D,\",\" loA, hiA, loB, hiB, loC, hiC, loD, hiD;\",\"\",\"uniform vec2 resolution, viewBoxPos, viewBoxSize;\",\"uniform float maskHeight;\",\"uniform float drwLayer; // 0: context, 1: focus, 2: pick\",\"uniform vec4 contextColor;\",\"uniform sampler2D maskTexture, palette;\",\"\",\"bool isPick = (drwLayer > 1.5);\",\"bool isContext = (drwLayer < 0.5);\",\"\",\"const vec4 ZEROS = vec4(0.0, 0.0, 0.0, 0.0);\",\"const vec4 UNITS = vec4(1.0, 1.0, 1.0, 1.0);\",\"\",\"float val(mat4 p, mat4 v) {\",\" return dot(matrixCompMult(p, v) * UNITS, UNITS);\",\"}\",\"\",\"float axisY(float ratio, mat4 A, mat4 B, mat4 C, mat4 D) {\",\" float y1 = val(A, dim0A) + val(B, dim0B) + val(C, dim0C) + val(D, dim0D);\",\" float y2 = val(A, dim1A) + val(B, dim1B) + val(C, dim1C) + val(D, dim1D);\",\" return y1 * (1.0 - ratio) + y2 * ratio;\",\"}\",\"\",\"int iMod(int a, int b) {\",\" return a - b * (a / b);\",\"}\",\"\",\"bool fOutside(float p, float lo, float hi) {\",\" return (lo < hi) && (lo > p || p > hi);\",\"}\",\"\",\"bool vOutside(vec4 p, vec4 lo, vec4 hi) {\",\" return (\",\" fOutside(p[0], lo[0], hi[0]) ||\",\" fOutside(p[1], lo[1], hi[1]) ||\",\" fOutside(p[2], lo[2], hi[2]) ||\",\" fOutside(p[3], lo[3], hi[3])\",\" );\",\"}\",\"\",\"bool mOutside(mat4 p, mat4 lo, mat4 hi) {\",\" return (\",\" vOutside(p[0], lo[0], hi[0]) ||\",\" vOutside(p[1], lo[1], hi[1]) ||\",\" vOutside(p[2], lo[2], hi[2]) ||\",\" vOutside(p[3], lo[3], hi[3])\",\" );\",\"}\",\"\",\"bool outsideBoundingBox(mat4 A, mat4 B, mat4 C, mat4 D) {\",\" return mOutside(A, loA, hiA) ||\",\" 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n=r(44122);e.name=\"pie\",e.plot=function(t,r,i,a){n.plotBasePlot(e.name,t,r,i,a)},e.clean=function(t,r,i,a){n.cleanBasePlot(e.name,t,r,i,a)}},44148:function(t,e,r){\"use strict\";var n=r(10721),i=r(65657),a=r(78766),o={};function s(t){return function(e,r){return!!e&&!!(e=i(e)).isValid()&&(e=a.addOpacity(e,e.getAlpha()),t[r]||(t[r]=e),e)}}function l(t,e){var r,n=JSON.stringify(t),a=e[n];if(!a){for(a=t.slice(),r=0;r=0})),(\"funnelarea\"===e.type?y:e.sort)&&a.sort((function(t,e){return e.v-t.v})),a[0]&&(a[0].vTotal=g),a},crossTraceCalc:function(t,e){var r=(e||{}).type;r||(r=\"pie\");var n=t._fullLayout,i=t.calcdata,a=n[r+\"colorway\"],s=n[\"_\"+r+\"colormap\"];n[\"extend\"+r+\"colors\"]&&(a=l(a,o));for(var c=0,u=0;u0){s=!0;break}}s||(o=0)}return{hasLabels:r,hasValues:a,len:o}}function u(t,e,r,n,i){n(\"marker.line.width\")&&n(\"marker.line.color\",i?void 0:r.paper_bgcolor);var 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1===t.pts.length&&(r.pointNumber=r.i=t.pts[0]),n(r,e,t.pts),\"funnelarea\"===e.type&&(delete r.v,delete r.i),r}},75067:function(t,e,r){\"use strict\";var n=r(62203),i=r(78766);t.exports=function(t,e,r,a){var o=r.marker.pattern;o&&o.shape?n.pointStyle(t,r,a,e):i.fill(t,e.color)}},37252:function(t,e,r){\"use strict\";var n=r(34809);function i(t){return-1!==t.indexOf(\"e\")?t.replace(/[.]?0+e/,\"e\"):-1!==t.indexOf(\".\")?t.replace(/[.]?0+$/,\"\"):t}e.formatPiePercent=function(t,e){var r=i((100*t).toPrecision(3));return n.numSeparate(r,e)+\"%\"},e.formatPieValue=function(t,e){var r=i(t.toPrecision(10));return n.numSeparate(r,e)},e.getFirstFilled=function(t,e){if(n.isArrayOrTypedArray(t))for(var r=0;r\"),name:h.hovertemplate||-1!==f.indexOf(\"name\")?h.name:void 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0===(l=t.trace.aspectratio)?1:l),u=e.w*(a.domain.x[1]-a.domain.x[0])/2;return-1!==a.title.position.indexOf(\"left\")?(u+=c,o.x-=(1+i)*c,s.tx+=t.titleBox.width/2):-1!==a.title.position.indexOf(\"center\")?u*=2:-1!==a.title.position.indexOf(\"right\")&&(u+=c,o.x+=(1+i)*c,s.tx-=t.titleBox.width/2),r=u/t.titleBox.width,n=I(t,e)/t.titleBox.height,{x:o.x,y:o.y,scale:Math.min(r,n),tx:s.tx,ty:s.ty}}function I(t,e){var r=t.trace,n=e.h*(r.domain.y[1]-r.domain.y[0]);return Math.min(t.titleBox.height,n/2)}function P(t){var e,r=t.pull;if(!r)return 0;if(l.isArrayOrTypedArray(r))for(r=0,e=0;er&&(r=t.pull[e]);return r}function z(t,e){for(var r=[],n=0;n1?u=(c=r.r)/i.aspectratio:c=(u=r.r)*i.aspectratio,l=(c*=(1+i.baseratio)/2)*u}o=Math.min(o,l/r.vTotal)}for(n=0;n\")}if(a){var x=l.castOption(i,e.i,\"texttemplate\");if(x){var _=function(t){return{label:t.label,value:t.v,valueLabel:g.formatPieValue(t.v,n.separators),percent:t.v/r.vTotal,percentLabel:g.formatPiePercent(t.v/r.vTotal,n.separators),color:t.color,text:t.text,customdata:l.castOption(i,t.i,\"customdata\")}}(e),b=g.getFirstFilled(i.text,e.pts);(v(b)||\"\"===b)&&(_.text=b),e.text=l.texttemplateString(x,_,t._fullLayout._d3locale,_,i._meta||{})}else e.text=\"\"}}function R(t,e){var r=t.rotate*Math.PI/180,n=Math.cos(r),i=Math.sin(r),a=(e.left+e.right)/2,o=(e.top+e.bottom)/2;t.textX=a*n-o*i,t.textY=a*i+o*n,t.noCenter=!0}t.exports={plot:function(t,e){var r=t._context.staticPlot,a=t._fullLayout,f=a._size;d(\"pie\",a),b(e,t),z(e,f);var m=l.makeTraceGroups(a._pielayer,e,\"trace\").each((function(e){var d=n.select(this),m=e[0],y=m.trace;!function(t){var 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A=\"M\"+(c+i.px0[0])+\",\"+(u+i.px0[1])+I(i.px0,i.pxmid,!0,1)+I(i.pxmid,i.px0,!0,1)+\"Z\";k?d.attr(\"d\",\"M\"+(c+k*i.px0[0])+\",\"+(u+k*i.px0[1])+I(i.px0,i.pxmid,!1,k)+I(i.pxmid,i.px0,!1,k)+\"Z\"+A):d.attr(\"d\",A)}else{var M=I(i.px0,i.px1,!0,1);if(k){var S=1-k;d.attr(\"d\",\"M\"+(c+k*i.px1[0])+\",\"+(u+k*i.px1[1])+I(i.px1,i.px0,!1,k)+\"l\"+S*i.px0[0]+\",\"+S*i.px0[1]+M+\"Z\")}else d.attr(\"d\",\"M\"+c+\",\"+u+\"l\"+i.px0[0]+\",\"+i.px0[1]+M+\"Z\")}D(t,i,m);var E=g.castOption(y.textposition,i.pts),L=f.selectAll(\"g.slicetext\").data(i.text&&\"none\"!==E?[0]:[]);L.enter().append(\"g\").classed(\"slicetext\",!0),L.exit().remove(),L.each((function(){var 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i,u,h,p,d=r.labelExtraY+(n?r.yLabelMax:r.yLabelMin),m=n?t.yLabelMin:t.yLabelMax,y=n?t.yLabelMax:t.yLabelMin,v=t.cyFinal+o(t.px0[1],t.px1[1]),x=d-m;if(x*c>0&&(t.labelExtraY=x),l.isArrayOrTypedArray(e.pull))for(u=0;u=(g.castOption(e.pull,h.pts)||0)||((t.pxmid[1]-h.pxmid[1])*c>0?(x=h.cyFinal+o(h.px0[1],h.px1[1])-m-t.labelExtraY)*c>0&&(t.labelExtraY+=x):(y+t.labelExtraY-v)*c>0&&(i=3*s*Math.abs(u-f.indexOf(t)),(p=h.cxFinal+a(h.px0[0],h.px1[0])+i-(t.cxFinal+t.pxmid[0])-t.labelExtraX)*s>0&&(t.labelExtraX+=p)))}for(n=0;n<2;n++)for(i=n?y:v,o=n?Math.max:Math.min,c=n?1:-1,r=0;r<2;r++){for(a=r?Math.max:Math.min,s=r?1:-1,(u=t[n][r]).sort(i),h=t[1-n][r],f=h.concat(u),d=[],p=0;pMath.abs(h)?s+=\"l\"+h*t.pxmid[0]/t.pxmid[1]+\",\"+h+\"H\"+(a+t.labelExtraX+c):s+=\"l\"+t.labelExtraX+\",\"+u+\"v\"+(h-u)+\"h\"+c}else 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n=r(45568),i=r(32891),a=r(84102).resizeText;t.exports=function(t){var e=t._fullLayout._pielayer.selectAll(\".trace\");a(t,e,\"pie\"),e.each((function(e){var r=e[0].trace,a=n.select(this);a.style({opacity:r.opacity}),a.selectAll(\"path.surface\").each((function(e){n.select(this).call(i,e,r,t)}))}))}},32891:function(t,e,r){\"use strict\";var n=r(78766),i=r(37252).castOption,a=r(75067);t.exports=function(t,e,r,o){var s=r.marker.line,l=i(s.color,e.pts)||n.defaultLine,c=i(s.width,e.pts)||0;t.call(a,e,r,o).style(\"stroke-width\",c).call(n.stroke,l)}},36961:function(t,e,r){\"use strict\";var n=r(36640);t.exports={x:n.x,y:n.y,xy:{valType:\"data_array\",editType:\"calc\"},indices:{valType:\"data_array\",editType:\"calc\"},xbounds:{valType:\"data_array\",editType:\"calc\"},ybounds:{valType:\"data_array\",editType:\"calc\"},text:n.text,marker:{color:{valType:\"color\",arrayOk:!1,editType:\"calc\"},opacity:{valType:\"number\",min:0,max:1,dflt:1,arrayOk:!1,editType:\"calc\"},blend:{valType:\"boolean\",dflt:null,editType:\"calc\"},sizemin:{valType:\"number\",min:.1,max:2,dflt:.5,editType:\"calc\"},sizemax:{valType:\"number\",min:.1,dflt:20,editType:\"calc\"},border:{color:{valType:\"color\",arrayOk:!1,editType:\"calc\"},arearatio:{valType:\"number\",min:0,max:1,dflt:0,editType:\"calc\"},editType:\"calc\"},editType:\"calc\"},transforms:void 0}},71593:function(t,e,r){\"use strict\";var n=r(99098).gl_pointcloud2d,i=r(34809).isArrayOrTypedArray,a=r(55010),o=r(32919).findExtremes,s=r(11539);function l(t,e){this.scene=t,this.uid=e,this.type=\"pointcloud\",this.pickXData=[],this.pickYData=[],this.xData=[],this.yData=[],this.textLabels=[],this.color=\"rgb(0, 0, 0)\",this.name=\"\",this.hoverinfo=\"all\",this.idToIndex=new Int32Array(0),this.bounds=[0,0,0,0],this.pointcloudOptions={positions:new Float32Array(0),idToIndex:this.idToIndex,sizemin:.5,sizemax:12,color:[0,0,0,1],areaRatio:1,borderColor:[0,0,0,1]},this.pointcloud=n(t.glplot,this.pointcloudOptions),this.pointcloud._trace=this}var c=l.prototype;c.handlePick=function(t){var e=this.idToIndex[t.pointId];return{trace:this,dataCoord:t.dataCoord,traceCoord:this.pickXYData?[this.pickXYData[2*e],this.pickXYData[2*e+1]]:[this.pickXData[e],this.pickYData[e]],textLabel:i(this.textLabels)?this.textLabels[e]:this.textLabels,color:this.color,name:this.name,pointIndex:e,hoverinfo:this.hoverinfo}},c.update=function(t){this.index=t.index,this.textLabels=t.text,this.name=t.name,this.hoverinfo=t.hoverinfo,this.bounds=[1/0,1/0,-1/0,-1/0],this.updateFast(t),this.color=s(t,{})},c.updateFast=function(t){var e,r,n,i,s,l,c=this.xData=this.pickXData=t.x,u=this.yData=this.pickYData=t.y,h=this.pickXYData=t.xy,f=t.xbounds&&t.ybounds,p=t.indices,d=this.bounds;if(h){if(n=h,e=h.length>>>1,f)d[0]=t.xbounds[0],d[2]=t.xbounds[1],d[1]=t.ybounds[0],d[3]=t.ybounds[1];else for(l=0;ld[2]&&(d[2]=i),sd[3]&&(d[3]=s);if(p)r=p;else for(r=new Int32Array(e),l=0;ld[2]&&(d[2]=i),sd[3]&&(d[3]=s);this.idToIndex=r,this.pointcloudOptions.idToIndex=r,this.pointcloudOptions.positions=n;var m=a(t.marker.color),g=a(t.marker.border.color),y=t.opacity*t.marker.opacity;m[3]*=y,this.pointcloudOptions.color=m;var v=t.marker.blend;null===v&&(v=c.length<100||u.length<100),this.pointcloudOptions.blend=v,g[3]*=y,this.pointcloudOptions.borderColor=g;var x=t.marker.sizemin,_=Math.max(t.marker.sizemax,t.marker.sizemin);this.pointcloudOptions.sizeMin=x,this.pointcloudOptions.sizeMax=_,this.pointcloudOptions.areaRatio=t.marker.border.arearatio,this.pointcloud.update(this.pointcloudOptions);var b=this.scene.xaxis,w=this.scene.yaxis,T=_/2||.5;t._extremes[b._id]=o(b,[d[0],d[2]],{ppad:T}),t._extremes[w._id]=o(w,[d[1],d[3]],{ppad:T})},c.dispose=function(){this.pointcloud.dispose()},t.exports=function(t,e){var r=new l(t,e.uid);return r.update(e),r}},75526:function(t,e,r){\"use strict\";var n=r(34809),i=r(36961);t.exports=function(t,e,r){function a(r,a){return n.coerce(t,e,i,r,a)}a(\"x\"),a(\"y\"),a(\"xbounds\"),a(\"ybounds\"),t.xy&&t.xy instanceof Float32Array&&(e.xy=t.xy),t.indices&&t.indices instanceof Int32Array&&(e.indices=t.indices),a(\"text\"),a(\"marker.color\",r),a(\"marker.opacity\"),a(\"marker.blend\"),a(\"marker.sizemin\"),a(\"marker.sizemax\"),a(\"marker.border.color\",r),a(\"marker.border.arearatio\"),e._length=null}},15186:function(t,e,r){\"use strict\";[\"*pointcloud* trace is deprecated!\",\"Please consider switching to the *scattergl* trace type.\"].join(\" \"),t.exports={attributes:r(36961),supplyDefaults:r(75526),calc:r(37593),plot:r(71593),moduleType:\"trace\",name:\"pointcloud\",basePlotModule:r(24585),categories:[\"gl\",\"gl2d\",\"showLegend\"],meta:{}}},33795:function(t,e,r){\"use strict\";var 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r,n,i=e._length,o=e.marker,s={},c=l(o.symbol),u=l(o.angle),f=l(o.color),m=l(o.line.color),g=l(o.opacity),y=l(o.size),v=l(o.line.width);if(c||(n=d.isOpenSymbol(o.symbol)),c||f||m||g||u){s.symbols=new Array(i),s.angles=new Array(i),s.colors=new Array(i),s.borderColors=new Array(i);var x=o.symbol,_=o.angle,b=h(o,o.opacity,i),w=h(o.line,o.opacity,i);if(!l(w[0])){var T=w;for(w=Array(i),r=0;rm.TOO_MANY_POINTS||f.hasMarkers(e)?\"rect\":\"round\";if(c&&e.connectgaps){var h=n[0],p=n[1];for(i=0;i1?c[i]:c[0]:c,m=l(u)?u.length>1?u[i]:u[0]:u,g=y[d],v=y[m],x=h?h/.8+1:0,_=-v*x-.5*v;o.offset[i]=[g*x/p,_/p]}}return o}}},86590:function(t,e,r){\"use strict\";var n=r(34809),i=r(33626),a=r(4075),o=r(92089),s=r(32660),l=r(64726),c=r(99867),u=r(99669),h=r(24272),f=r(98168),p=r(54114),d=r(663);t.exports=function(t,e,r,m){function g(r,i){return n.coerce(t,e,o,r,i)}var y=!!t.marker&&a.isOpenSymbol(t.marker.symbol),v=l.isBubble(t),x=c(t,e,m,g);if(x){u(t,e,m,g),g(\"xhoverformat\"),g(\"yhoverformat\");var 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y=s.c2p(f.x,!0),v=l.c2p(f.y,!0),x=f.mrc||1,_=o.hoverlabel;_&&(f.hbg=Array.isArray(_.bgcolor)?_.bgcolor[h]:_.bgcolor,f.hbc=Array.isArray(_.bordercolor)?_.bordercolor[h]:_.bordercolor,f.hts=i.isArrayOrTypedArray(_.font.size)?_.font.size[h]:_.font.size,f.htc=Array.isArray(_.font.color)?_.font.color[h]:_.font.color,f.htf=Array.isArray(_.font.family)?_.font.family[h]:_.font.family,f.hnl=i.isArrayOrTypedArray(_.namelength)?_.namelength[h]:_.namelength);var b=o.hoverinfo;b&&(f.hi=Array.isArray(b)?b[h]:b);var w=o.hovertemplate;w&&(f.ht=Array.isArray(w)?w[h]:w);var T={};T[t.index]=f;var k=o._origX,A=o._origY,M=i.extendFlat({},t,{color:a(o,f),x0:y-x,x1:y+x,xLabelVal:k?k[h]:f.x,y0:v-x,y1:v+x,yLabelVal:A?A[h]:f.y,cd:T,distance:c,spikeDistance:u,hovertemplate:f.ht});return f.htx?M.text=f.htx:f.tx?M.text=f.tx:o.text&&(M.text=o.text),i.fillText(f,o,M),n.getComponentMethod(\"errorbars\",\"hoverInfo\")(f,o,M),M}t.exports={hoverPoints:function(t,e,r,n){var 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z;if((w||T)&&(r=o.calcTraceToLineCoords(e)),w&&(C.geojson=o.makePolygon(r),C.layout.visibility=\"visible\",i.extendFlat(C.paint,{\"fill-color\":a.fillcolor})),T&&(L.geojson=o.makeLine(r),L.layout.visibility=\"visible\",i.extendFlat(L.paint,{\"line-width\":a.line.width,\"line-color\":a.line.color,\"line-opacity\":a.opacity})),M){var O=function(t){var e,r,a,o,u=t[0].trace,h=u.marker,f=u.selectedpoints,p=i.isArrayOrTypedArray(h.color),d=i.isArrayOrTypedArray(h.size),m=i.isArrayOrTypedArray(h.opacity);function g(t){return u.opacity*t}p&&(r=s.hasColorscale(u,\"marker\")?s.makeColorScaleFuncFromTrace(h):i.identity),d&&(a=c(u)),m&&(o=function(t){return g(n(t)?+i.constrain(t,0,1):0)});var y,v,_=[];for(e=0;e\")}function u(t){return t+\"°\"}}t.exports={hoverPoints:function(t,e,r){var o=t.cd,u=o[0].trace,h=t.xa,f=t.ya,p=t.subplot,d=[],m=l+u.uid+\"-circle\",g=u.cluster&&u.cluster.enabled;if(g){var y=p.map.queryRenderedFeatures(null,{layers:[m]});d=y.map((function(t){return t.id}))}var v=360*(e>=0?Math.floor((e+180)/360):Math.ceil((e-180)/360)),x=e-v;if(n.getClosest(o,(function(t){var e=t.lonlat;if(e[0]===s)return 1/0;if(g&&-1===d.indexOf(t.i+1))return 1/0;var n=i.modHalf(e[0],360),a=e[1],o=p.project([n,a]),l=o.x-h.c2p([x,a]),c=o.y-f.c2p([n,r]),u=Math.max(3,t.mrc||0);return Math.max(Math.sqrt(l*l+c*c)-u,1-3/u)}),t),!1!==t.index){var _=o[t.index],b=_.lonlat,w=[i.modHalf(b[0],360)+v,b[1]],T=h.c2p(w),k=f.c2p(w),A=_.mrc||1;t.x0=T-A,t.x1=T+A,t.y0=k-A,t.y1=k+A;var M={};M[u.subplot]={_subplot:p};var S=u._module.formatLabels(_,u,M);return t.lonLabel=S.lonLabel,t.latLabel=S.latLabel,t.color=a(u,_),t.extraText=c(u,_,o[0].t.labels),t.hovertemplate=u.hovertemplate,[t]}},getExtraText:c}},30929:function(t,e,r){\"use 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s(t,e,r,n){this.type=\"scattermap\",this.subplot=t,this.uid=e,this.clusterEnabled=r,this.isHidden=n,this.sourceIds={fill:\"source-\"+e+\"-fill\",line:\"source-\"+e+\"-line\",circle:\"source-\"+e+\"-circle\",symbol:\"source-\"+e+\"-symbol\",cluster:\"source-\"+e+\"-circle\",clusterCount:\"source-\"+e+\"-circle\"},this.layerIds={fill:a+e+\"-fill\",line:a+e+\"-line\",circle:a+e+\"-circle\",symbol:a+e+\"-symbol\",cluster:a+e+\"-cluster\",clusterCount:a+e+\"-cluster-count\"},this.below=null}var l=s.prototype;l.addSource=function(t,e,r){var i={type:\"geojson\",data:e.geojson};r&&r.enabled&&n.extendFlat(i,{cluster:!0,clusterMaxZoom:r.maxzoom});var a=this.subplot.map.getSource(this.sourceIds[t]);a?a.setData(e.geojson):this.subplot.map.addSource(this.sourceIds[t],i)},l.setSourceData=function(t,e){this.subplot.map.getSource(this.sourceIds[t]).setData(e.geojson)},l.addLayer=function(t,e,r){var n={type:e.type,id:this.layerIds[t],source:this.sourceIds[t],layout:e.layout,paint:e.paint};e.filter&&(n.filter=e.filter);for(var i,a=this.layerIds[t],o=this.subplot.getMapLayers(),s=0;s=0;r--){var i=e[r];n.removeLayer(u.layerIds[i])}t||n.removeSource(u.sourceIds.circle)}(t):function(t){for(var e=o.nonCluster,r=e.length-1;r>=0;r--){var i=e[r];n.removeLayer(u.layerIds[i]),t||n.removeSource(u.sourceIds[i])}}(t)}function f(t){l?function(t){t||u.addSource(\"circle\",a.circle,e.cluster);for(var r=o.cluster,n=0;n=0;r--){var n=e[r];t.removeLayer(this.layerIds[n]),t.removeSource(this.sourceIds[n])}},t.exports=function(t,e){var r,n,a,l=e[0].trace,c=l.cluster&&l.cluster.enabled,u=!0!==l.visible,h=new s(t,l.uid,c,u),f=i(t.gd,e),p=h.below=t.belowLookup[\"trace-\"+l.uid];if(c)for(h.addSource(\"circle\",f.circle,l.cluster),r=0;r850?\" Black\":i>750?\" Extra Bold\":i>650?\" Bold\":i>550?\" Semi Bold\":i>450?\" Medium\":i>350?\" Regular\":i>250?\" Light\":i>150?\" Extra Light\":\" Thin\"):\"Open 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z;if((w||T)&&(r=o.calcTraceToLineCoords(e)),w&&(C.geojson=o.makePolygon(r),C.layout.visibility=\"visible\",i.extendFlat(C.paint,{\"fill-color\":a.fillcolor})),T&&(L.geojson=o.makeLine(r),L.layout.visibility=\"visible\",i.extendFlat(L.paint,{\"line-width\":a.line.width,\"line-color\":a.line.color,\"line-opacity\":a.opacity})),M){var O=function(t){var e,r,a,o,u=t[0].trace,h=u.marker,f=u.selectedpoints,p=i.isArrayOrTypedArray(h.color),d=i.isArrayOrTypedArray(h.size),m=i.isArrayOrTypedArray(h.opacity);function g(t){return u.opacity*t}p&&(r=s.hasColorscale(u,\"marker\")?s.makeColorScaleFuncFromTrace(h):i.identity),d&&(a=c(u)),m&&(o=function(t){return g(n(t)?+i.constrain(t,0,1):0)});var y,v,_=[];for(e=0;e\")}function u(t){return t+\"°\"}}t.exports={hoverPoints:function(t,e,r){var o=t.cd,u=o[0].trace,h=t.xa,f=t.ya,p=t.subplot,d=[],m=l+u.uid+\"-circle\",g=u.cluster&&u.cluster.enabled;if(g){var y=p.map.queryRenderedFeatures(null,{layers:[m]});d=y.map((function(t){return t.id}))}var v=360*(e>=0?Math.floor((e+180)/360):Math.ceil((e-180)/360)),x=e-v;if(n.getClosest(o,(function(t){var e=t.lonlat;if(e[0]===s)return 1/0;if(g&&-1===d.indexOf(t.i+1))return 1/0;var n=i.modHalf(e[0],360),a=e[1],o=p.project([n,a]),l=o.x-h.c2p([x,a]),c=o.y-f.c2p([n,r]),u=Math.max(3,t.mrc||0);return Math.max(Math.sqrt(l*l+c*c)-u,1-3/u)}),t),!1!==t.index){var _=o[t.index],b=_.lonlat,w=[i.modHalf(b[0],360)+v,b[1]],T=h.c2p(w),k=f.c2p(w),A=_.mrc||1;t.x0=T-A,t.x1=T+A,t.y0=k-A,t.y1=k+A;var M={};M[u.subplot]={_subplot:p};var S=u._module.formatLabels(_,u,M);return t.lonLabel=S.lonLabel,t.latLabel=S.latLabel,t.color=a(u,_),t.extraText=c(u,_,o[0].t.labels),t.hovertemplate=u.hovertemplate,[t]}},getExtraText:c}},83866:function(t,e,r){\"use strict\";[\"*scattermapbox* trace is deprecated!\",\"Please consider switching to the *scattermap* trace type and `map` subplots.\",\"Learn more at: https://plotly.com/javascript/maplibre-migration/\"].join(\" \"),t.exports={attributes:r(95833),supplyDefaults:r(38302),colorbar:r(21146),formatLabels:r(69009),calc:r(75649),plot:r(20691),hoverPoints:r(18016).hoverPoints,eventData:r(68197),selectPoints:r(60784),styleOnSelect:function(t,e){e&&e[0].trace._glTrace.update(e)},moduleType:\"trace\",name:\"scattermapbox\",basePlotModule:r(68192),categories:[\"mapbox\",\"gl\",\"symbols\",\"showLegend\",\"scatter-like\"],meta:{}}},20691:function(t,e,r){\"use strict\";var n=r(34809),i=r(27009),a=r(44245).traceLayerPrefix,o={cluster:[\"cluster\",\"clusterCount\",\"circle\"],nonCluster:[\"fill\",\"line\",\"circle\",\"symbol\"]};function s(t,e,r,n){this.type=\"scattermapbox\",this.subplot=t,this.uid=e,this.clusterEnabled=r,this.isHidden=n,this.sourceIds={fill:\"source-\"+e+\"-fill\",line:\"source-\"+e+\"-line\",circle:\"source-\"+e+\"-circle\",symbol:\"source-\"+e+\"-symbol\",cluster:\"source-\"+e+\"-circle\",clusterCount:\"source-\"+e+\"-circle\"},this.layerIds={fill:a+e+\"-fill\",line:a+e+\"-line\",circle:a+e+\"-circle\",symbol:a+e+\"-symbol\",cluster:a+e+\"-cluster\",clusterCount:a+e+\"-cluster-count\"},this.below=null}var l=s.prototype;l.addSource=function(t,e,r){var i={type:\"geojson\",data:e.geojson};r&&r.enabled&&n.extendFlat(i,{cluster:!0,clusterMaxZoom:r.maxzoom});var a=this.subplot.map.getSource(this.sourceIds[t]);a?a.setData(e.geojson):this.subplot.map.addSource(this.sourceIds[t],i)},l.setSourceData=function(t,e){this.subplot.map.getSource(this.sourceIds[t]).setData(e.geojson)},l.addLayer=function(t,e,r){var n={type:e.type,id:this.layerIds[t],source:this.sourceIds[t],layout:e.layout,paint:e.paint};e.filter&&(n.filter=e.filter);for(var i,a=this.layerIds[t],o=this.subplot.getMapLayers(),s=0;s=0;r--){var i=e[r];n.removeLayer(u.layerIds[i])}t||n.removeSource(u.sourceIds.circle)}(t):function(t){for(var e=o.nonCluster,r=e.length-1;r>=0;r--){var i=e[r];n.removeLayer(u.layerIds[i]),t||n.removeSource(u.sourceIds[i])}}(t)}function f(t){l?function(t){t||u.addSource(\"circle\",a.circle,e.cluster);for(var r=o.cluster,n=0;n=0;r--){var n=e[r];t.removeLayer(this.layerIds[n]),t.removeSource(this.sourceIds[n])}},t.exports=function(t,e){var r,n,a,l=e[0].trace,c=l.cluster&&l.cluster.enabled,u=!0!==l.visible,h=new s(t,l.uid,c,u),f=i(t.gd,e),p=h.below=t.belowLookup[\"trace-\"+l.uid];if(c)for(h.addSource(\"circle\",f.circle,l.cluster),r=0;r\")}}t.exports={hoverPoints:function(t,e,r,a){var o=n(t,e,r,a);if(o&&!1!==o[0].index){var s=o[0];if(void 0===s.index)return o;var l=t.subplot,c=s.cd[s.index],u=s.trace;if(l.isPtInside(c))return s.xLabelVal=void 0,s.yLabelVal=void 0,i(c,u,l,s),s.hovertemplate=u.hovertemplate,o}},makeHoverPointText:i}},66939:function(t,e,r){\"use strict\";t.exports={moduleType:\"trace\",name:\"scatterpolar\",basePlotModule:r(31645),categories:[\"polar\",\"symbols\",\"showLegend\",\"scatter-like\"],attributes:r(8738),supplyDefaults:r(73749).supplyDefaults,colorbar:r(21146),formatLabels:r(33368),calc:r(13246),plot:r(43836),style:r(9408).style,styleOnSelect:r(9408).styleOnSelect,hoverPoints:r(29709).hoverPoints,selectPoints:r(32665),meta:{}}},43836:function(t,e,r){\"use strict\";var n=r(36098),i=r(63821).BADNUM;t.exports=function(t,e,r){for(var a=e.layers.frontplot.select(\"g.scatterlayer\"),o=e.xaxis,s=e.yaxis,l={xaxis:o,yaxis:s,plot:e.framework,layerClipId:e._hasClipOnAxisFalse?e.clipIds.forTraces:null},c=e.radialAxis,u=e.angularAxis,h=0;h=c&&(v.marker.cluster=d.tree),v.marker&&(v.markerSel.positions=v.markerUnsel.positions=v.marker.positions=b),v.line&&b.length>1&&l.extendFlat(v.line,s.linePositions(t,p,b)),v.text&&(l.extendFlat(v.text,{positions:b},s.textPosition(t,p,v.text,v.marker)),l.extendFlat(v.textSel,{positions:b},s.textPosition(t,p,v.text,v.markerSel)),l.extendFlat(v.textUnsel,{positions:b},s.textPosition(t,p,v.text,v.markerUnsel))),v.fill&&!f.fill2d&&(f.fill2d=!0),v.marker&&!f.scatter2d&&(f.scatter2d=!0),v.line&&!f.line2d&&(f.line2d=!0),v.text&&!f.glText&&(f.glText=!0),f.lineOptions.push(v.line),f.fillOptions.push(v.fill),f.markerOptions.push(v.marker),f.markerSelectedOptions.push(v.markerSel),f.markerUnselectedOptions.push(v.markerUnsel),f.textOptions.push(v.text),f.textSelectedOptions.push(v.textSel),f.textUnselectedOptions.push(v.textUnsel),f.selectBatch.push([]),f.unselectBatch.push([]),d.x=w,d.y=T,d.rawx=w,d.rawy=T,d.r=g,d.theta=y,d.positions=b,d._scene=f,d.index=f.count,f.count++}})),a(t,e,r)}},t.exports.reglPrecompiled={}},69595:function(t,e,r){\"use 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n=r(10721),i=r(63821).BADNUM,a=r(77272),o=r(99203),s=r(48861),l=r(26544).calcMarkerSize;t.exports=function(t,e){for(var r=t._fullLayout,c=e.subplot,u=r[c].realaxis,h=r[c].imaginaryaxis,f=u.makeCalcdata(e,\"real\"),p=h.makeCalcdata(e,\"imag\"),d=e._length,m=new Array(d),g=0;g\")}}t.exports={hoverPoints:function(t,e,r,a){var o=n(t,e,r,a);if(o&&!1!==o[0].index){var s=o[0];if(void 0===s.index)return o;var l=t.subplot,c=s.cd[s.index],u=s.trace;if(l.isPtInside(c))return s.xLabelVal=void 0,s.yLabelVal=void 0,i(c,u,l,s),s.hovertemplate=u.hovertemplate,o}},makeHoverPointText:i}},73304:function(t,e,r){\"use 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z=p.selectedpoints,O={};for(l=0;l1&&(u=m[v-1],f=g[v-1],d=y[v-1]),e=0;eu?\"-\":\"+\")+\"x\")).replace(\"y\",(h>f?\"-\":\"+\")+\"y\")).replace(\"z\",(p>d?\"-\":\"+\")+\"z\");var C=function(){v=0,M=[],S=[],E=[]};(!v||v2?t.slice(1,e-1):2===e?[(t[0]+t[1])/2]:t}function p(t){var e=t.length;return 1===e?[.5,.5]:[t[1]-t[0],t[e-1]-t[e-2]]}function d(t,e){var r=t.fullSceneLayout,i=t.dataScale,u=e._len,h={};function d(t,e){var n=r[e],o=i[c[e]];return a.simpleMap(t,(function(t){return n.d2l(t)*o}))}if(h.vectors=l(d(e._u,\"xaxis\"),d(e._v,\"yaxis\"),d(e._w,\"zaxis\"),u),!u)return{positions:[],cells:[]};var m=d(e._Xs,\"xaxis\"),g=d(e._Ys,\"yaxis\"),y=d(e._Zs,\"zaxis\");if(h.meshgrid=[m,g,y],h.gridFill=e._gridFill,e._slen)h.startingPositions=l(d(e._startsX,\"xaxis\"),d(e._startsY,\"yaxis\"),d(e._startsZ,\"zaxis\"));else{for(var v=g[0],x=f(m),_=f(y),b=new Array(x.length*_.length),w=0,T=0;T=0};v?(r=Math.min(y.length,_.length),l=function(t){return M(y[t])&&S(t)},h=function(t){return 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_={data:[{level:y}],traces:[d.index]},b={frame:{redraw:!1,duration:f.transitionTime},transition:{duration:f.transitionTime,easing:f.transitionEasing},mode:\"immediate\",fromcurrent:!0};o.loneUnhover(e._hoverlayer.node()),i.call(\"animate\",r,_,b)}}))}},33108:function(t,e,r){\"use strict\";var n=r(34809),i=r(78766),a=r(27983),o=r(37252);function s(t){return t.data.data.pid}e.findEntryWithLevel=function(t,r){var n;return r&&t.eachAfter((function(t){if(e.getPtId(t)===r)return n=t.copy()})),n||t},e.findEntryWithChild=function(t,r){var n;return t.eachAfter((function(t){for(var i=t.children||[],a=0;a0)},e.getMaxDepth=function(t){return t.maxdepth>=0?t.maxdepth:1/0},e.isHeader=function(t,r){return!(e.isLeaf(t)||t.depth===r._maxDepth-1)},e.getParent=function(t,r){return e.findEntryWithLevel(t,s(r))},e.listPath=function(t,r){var n=t.parent;if(!n)return[];var i=r?[n.data[r]]:[n];return e.listPath(n,r).concat(i)},e.getPath=function(t){return e.listPath(t,\"label\").join(\"/\")+\"/\"},e.formatValue=o.formatPieValue,e.formatPercent=function(t,e){var r=n.formatPercent(t,0);return\"0%\"===r&&(r=o.formatPiePercent(t,e)),r}},80809:function(t,e,r){\"use strict\";t.exports={moduleType:\"trace\",name:\"sunburst\",basePlotModule:r(14724),categories:[],animatable:!0,attributes:r(56708),layoutAttributes:r(98959),supplyDefaults:r(33459),supplyLayoutDefaults:r(75816),calc:r(14852).calc,crossTraceCalc:r(14852).crossTraceCalc,plot:r(19718).plot,style:r(98972).style,colorbar:r(21146),meta:{}}},98959:function(t){\"use strict\";t.exports={sunburstcolorway:{valType:\"colorlist\",editType:\"calc\"},extendsunburstcolors:{valType:\"boolean\",dflt:!0,editType:\"calc\"}}},75816:function(t,e,r){\"use strict\";var n=r(34809),i=r(98959);t.exports=function(t,e){function r(r,a){return n.coerce(t,e,i,r,a)}r(\"sunburstcolorway\",e.colorway),r(\"extendsunburstcolors\")}},19718:function(t,e,r){\"use strict\";var n=r(45568),i=r(92264),a=r(88640).GW,o=r(62203),s=r(34809),l=r(30635),c=r(84102),u=c.recordMinTextSize,h=c.clearMinTextSize,f=r(35734),p=r(37252).getRotationAngle,d=f.computeTransform,m=f.transformInsideText,g=r(98972).styleOne,y=r(6851).resizeText,v=r(44691),x=r(2032),_=r(33108);function b(t,r,c,h){var f=t._context.staticPlot,y=t._fullLayout,b=!y.uniformtext.mode&&_.hasTransition(h),T=n.select(c).selectAll(\"g.slice\"),k=r[0],A=k.trace,M=k.hierarchy,S=_.findEntryWithLevel(M,A.level),E=_.getMaxDepth(A),C=y._size,L=A.domain,I=C.w*(L.x[1]-L.x[0]),P=C.h*(L.y[1]-L.y[0]),z=.5*Math.min(I,P),O=k.cx=C.l+C.w*(L.x[1]+L.x[0])/2,D=k.cy=C.t+C.h*(1-L.y[0])-P/2;if(!S)return T.remove();var R=null,F={};b&&T.each((function(t){F[_.getPtId(t)]={rpx0:t.rpx0,rpx1:t.rpx1,x0:t.x0,x1:t.x1,transform:t.transform},!R&&_.isEntry(t)&&(R=t)}));var B=function(t){return i.partition().size([2*Math.PI,t.height+1])(t)}(S).descendants(),N=S.height+1,j=0,U=E;k.hasMultipleRoots&&_.isHierarchyRoot(S)&&(B=B.slice(1),N-=1,j=1,U+=1),B=B.filter((function(t){return t.y1<=U}));var V=p(A.rotation);V&&B.forEach((function(t){t.x0+=V,t.x1+=V}));var q=Math.min(N,E),H=function(t){return(t-j)/q*z},G=function(t,e){return[t*Math.cos(e),-t*Math.sin(e)]},Z=function(t){return s.pathAnnulus(t.rpx0,t.rpx1,t.x0,t.x1,O,D)},W=function(t){return O+w(t)[0]*(t.transform.rCenter||0)+(t.transform.x||0)},Y=function(t){return D+w(t)[1]*(t.transform.rCenter||0)+(t.transform.y||0)};(T=T.data(B,_.getPtId)).enter().append(\"g\").classed(\"slice\",!0),b?T.exit().transition().each((function(){var t=n.select(this);t.select(\"path.surface\").transition().attrTween(\"d\",(function(t){var e=function(t){var e,r=_.getPtId(t),n=F[r],i=F[_.getPtId(S)];if(i){var o=(t.x1>i.x1?2*Math.PI:0)+V;e=t.rpx1X?2*Math.PI:0)+V;e={x0:i,x1:i}}else e={rpx0:z,rpx1:z},s.extendFlat(e,K(t));else e={rpx0:0,rpx1:0};else e={x0:V,x1:V};return a(e,n)}(t);return function(t){return Z(e(t))}})):h.attr(\"d\",Z),c.call(v,S,t,r,{eventDataKeys:x.eventDataKeys,transitionTime:x.CLICK_TRANSITION_TIME,transitionEasing:x.CLICK_TRANSITION_EASING}).call(_.setSliceCursor,t,{hideOnRoot:!0,hideOnLeaves:!0,isTransitioning:t._transitioning}),h.call(g,i,A,t);var p=s.ensureSingle(c,\"g\",\"slicetext\"),w=s.ensureSingle(p,\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),T=s.ensureUniformFontSize(t,_.determineTextFont(A,i,y.font));w.text(e.formatSliceLabel(i,S,A,r,y)).classed(\"slicetext\",!0).attr(\"text-anchor\",\"middle\").call(o.font,T).call(l.convertToTspans,t);var M=o.bBox(w.node());i.transform=m(M,i,k),i.transform.targetX=W(i),i.transform.targetY=Y(i);var E=function(t,e){var r=t.transform;return d(r,e),r.fontSize=T.size,u(A.type,r,y),s.getTextTransform(r)};b?w.transition().attrTween(\"transform\",(function(t){var e=function(t){var e,r=F[_.getPtId(t)],n=t.transform;if(r)e=r;else if(e={rpx1:t.rpx1,transform:{textPosAngle:n.textPosAngle,scale:0,rotate:n.rotate,rCenter:n.rCenter,x:n.x,y:n.y}},R)if(t.parent)if(X){var i=t.x1>X?2*Math.PI:0;e.x0=e.x1=i}else s.extendFlat(e,K(t));else e.x0=e.x1=V;else e.x0=e.x1=V;var o=a(e.transform.textPosAngle,t.transform.textPosAngle),l=a(e.rpx1,t.rpx1),c=a(e.x0,t.x0),h=a(e.x1,t.x1),f=a(e.transform.scale,n.scale),p=a(e.transform.rotate,n.rotate),d=0===n.rCenter?3:0===e.transform.rCenter?1/3:1,m=a(e.transform.rCenter,n.rCenter);return function(t){var e=l(t),r=c(t),i=h(t),a=function(t){return m(Math.pow(t,d))}(t),s={pxmid:G(e,(r+i)/2),rpx1:e,transform:{textPosAngle:o(t),rCenter:a,x:n.x,y:n.y}};return u(A.type,n,y),{transform:{targetX:W(s),targetY:Y(s),scale:f(t),rotate:p(t),rCenter:a}}}}(t);return function(t){return E(e(t),M)}})):w.attr(\"transform\",E(i,M))}))}function w(t){return e=t.rpx1,r=t.transform.textPosAngle,[e*Math.sin(r),-e*Math.cos(r)];var e,r}e.plot=function(t,e,r,i){var a,o,s=t._fullLayout,l=s._sunburstlayer,c=!r,u=!s.uniformtext.mode&&_.hasTransition(r);h(\"sunburst\",s),(a=l.selectAll(\"g.trace.sunburst\").data(e,(function(t){return t[0].trace.uid}))).enter().append(\"g\").classed(\"trace\",!0).classed(\"sunburst\",!0).attr(\"stroke-linejoin\",\"round\"),a.order(),u?(i&&(o=i()),n.transition().duration(r.duration).ease(r.easing).each(\"end\",(function(){o&&o()})).each(\"interrupt\",(function(){o&&o()})).each((function(){l.selectAll(\"g.trace\").each((function(e){b(t,e,this,r)}))}))):(a.each((function(e){b(t,e,this,r)})),s.uniformtext.mode&&y(t,s._sunburstlayer.selectAll(\".trace\"),\"sunburst\")),c&&a.exit().remove()},e.formatSliceLabel=function(t,e,r,n,i){var a=r.texttemplate,o=r.textinfo;if(!(a||o&&\"none\"!==o))return\"\";var l=i.separators,c=n[0],u=t.data.data,h=c.hierarchy,f=_.isHierarchyRoot(t),p=_.getParent(h,t),d=_.getValue(t);if(!a){var 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h=t.exports=c(l({z:{valType:\"data_array\"},x:{valType:\"data_array\"},y:{valType:\"data_array\"},text:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertemplate:o(),xhoverformat:a(\"x\"),yhoverformat:a(\"y\"),zhoverformat:a(\"z\"),connectgaps:{valType:\"boolean\",dflt:!1,editType:\"calc\"},surfacecolor:{valType:\"data_array\"}},i(\"\",{colorAttr:\"z or 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n=r(28379);t.exports=function(t,e){e.surfacecolor?n(t,e,{vals:e.surfacecolor,containerStr:\"\",cLetter:\"c\"}):n(t,e,{vals:e.z,containerStr:\"\",cLetter:\"c\"})}},27159:function(t,e,r){\"use strict\";var n=r(99098).gl_surface3d,i=r(99098).ndarray,a=r(99098).ndarray_linear_interpolate.d2,o=r(69295),s=r(78106),l=r(34809).isArrayOrTypedArray,c=r(46998).parseColorScale,u=r(55010),h=r(88856).extractOpts;function f(t,e,r){this.scene=t,this.uid=r,this.surface=e,this.data=null,this.showContour=[!1,!1,!1],this.contourStart=[null,null,null],this.contourEnd=[null,null,null],this.contourSize=[0,0,0],this.minValues=[1/0,1/0,1/0],this.maxValues=[-1/0,-1/0,-1/0],this.dataScaleX=1,this.dataScaleY=1,this.refineData=!0,this.objectOffset=[0,0,0]}var p=f.prototype;p.getXat=function(t,e,r,n){var i=l(this.data.x)?l(this.data.x[0])?this.data.x[e][t]:this.data.x[t]:t;return void 0===r?i:n.d2l(i,0,r)},p.getYat=function(t,e,r,n){var i=l(this.data.y)?l(this.data.y[0])?this.data.y[e][t]:this.data.y[e]:e;return void 0===r?i:n.d2l(i,0,r)},p.getZat=function(t,e,r,n){var i=this.data.z[e][t];return null===i&&this.data.connectgaps&&this.data._interpolatedZ&&(i=this.data._interpolatedZ[e][t]),void 0===r?i:n.d2l(i,0,r)},p.handlePick=function(t){if(t.object===this.surface){var e=(t.data.index[0]-1)/this.dataScaleX-1,r=(t.data.index[1]-1)/this.dataScaleY-1,n=Math.max(Math.min(Math.round(e),this.data.z[0].length-1),0),i=Math.max(Math.min(Math.round(r),this.data._ylength-1),0);t.index=[n,i],t.traceCoordinate=[this.getXat(n,i),this.getYat(n,i),this.getZat(n,i)],t.dataCoordinate=[this.getXat(n,i,this.data.xcalendar,this.scene.fullSceneLayout.xaxis),this.getYat(n,i,this.data.ycalendar,this.scene.fullSceneLayout.yaxis),this.getZat(n,i,this.data.zcalendar,this.scene.fullSceneLayout.zaxis)];for(var a=0;a<3;a++){null!=t.dataCoordinate[a]&&(t.dataCoordinate[a]*=this.scene.dataScale[a])}var o=this.data.hovertext||this.data.text;return l(o)&&o[i]&&void 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0==(8191&e)&&(r+=13,e>>>=13),0==(127&e)&&(r+=7,e>>>=7),0==(15&e)&&(r+=4,e>>>=4),0==(3&e)&&(r+=2,e>>>=2),0==(1&e)&&r++,r},a.prototype.bitLength=function(){var t=this.words[this.length-1],e=this._countBits(t);return 26*(this.length-1)+e},a.prototype.zeroBits=function(){if(this.isZero())return 0;for(var t=0,e=0;et.length?this.clone().ior(t):t.clone().ior(this)},a.prototype.uor=function(t){return this.length>t.length?this.clone().iuor(t):t.clone().iuor(this)},a.prototype.iuand=function(t){var e;e=this.length>t.length?t:this;for(var r=0;rt.length?this.clone().iand(t):t.clone().iand(this)},a.prototype.uand=function(t){return this.length>t.length?this.clone().iuand(t):t.clone().iuand(this)},a.prototype.iuxor=function(t){var e,r;this.length>t.length?(e=this,r=t):(e=t,r=this);for(var n=0;nt.length?this.clone().ixor(t):t.clone().ixor(this)},a.prototype.uxor=function(t){return this.length>t.length?this.clone().iuxor(t):t.clone().iuxor(this)},a.prototype.inotn=function(t){n(\"number\"==typeof 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a=0,o=0;o>26,this.words[o]=67108863&e;for(;0!==a&&o>26,this.words[o]=67108863&e;if(0===a&&o>>13,p=0|o[1],d=8191&p,m=p>>>13,g=0|o[2],y=8191&g,v=g>>>13,x=0|o[3],_=8191&x,b=x>>>13,w=0|o[4],T=8191&w,k=w>>>13,A=0|o[5],M=8191&A,S=A>>>13,E=0|o[6],C=8191&E,L=E>>>13,I=0|o[7],P=8191&I,z=I>>>13,O=0|o[8],D=8191&O,R=O>>>13,F=0|o[9],B=8191&F,N=F>>>13,j=0|s[0],U=8191&j,V=j>>>13,q=0|s[1],H=8191&q,G=q>>>13,Z=0|s[2],W=8191&Z,Y=Z>>>13,X=0|s[3],$=8191&X,J=X>>>13,K=0|s[4],Q=8191&K,tt=K>>>13,et=0|s[5],rt=8191&et,nt=et>>>13,it=0|s[6],at=8191&it,ot=it>>>13,st=0|s[7],lt=8191&st,ct=st>>>13,ut=0|s[8],ht=8191&ut,ft=ut>>>13,pt=0|s[9],dt=8191&pt,mt=pt>>>13;r.negative=t.negative^e.negative,r.length=19;var gt=(c+(n=Math.imul(h,U))|0)+((8191&(i=(i=Math.imul(h,V))+Math.imul(f,U)|0))<<13)|0;c=((a=Math.imul(f,V))+(i>>>13)|0)+(gt>>>26)|0,gt&=67108863,n=Math.imul(d,U),i=(i=Math.imul(d,V))+Math.imul(m,U)|0,a=Math.imul(m,V);var yt=(c+(n=n+Math.imul(h,H)|0)|0)+((8191&(i=(i=i+Math.imul(h,G)|0)+Math.imul(f,H)|0))<<13)|0;c=((a=a+Math.imul(f,G)|0)+(i>>>13)|0)+(yt>>>26)|0,yt&=67108863,n=Math.imul(y,U),i=(i=Math.imul(y,V))+Math.imul(v,U)|0,a=Math.imul(v,V),n=n+Math.imul(d,H)|0,i=(i=i+Math.imul(d,G)|0)+Math.imul(m,H)|0,a=a+Math.imul(m,G)|0;var vt=(c+(n=n+Math.imul(h,W)|0)|0)+((8191&(i=(i=i+Math.imul(h,Y)|0)+Math.imul(f,W)|0))<<13)|0;c=((a=a+Math.imul(f,Y)|0)+(i>>>13)|0)+(vt>>>26)|0,vt&=67108863,n=Math.imul(_,U),i=(i=Math.imul(_,V))+Math.imul(b,U)|0,a=Math.imul(b,V),n=n+Math.imul(y,H)|0,i=(i=i+Math.imul(y,G)|0)+Math.imul(v,H)|0,a=a+Math.imul(v,G)|0,n=n+Math.imul(d,W)|0,i=(i=i+Math.imul(d,Y)|0)+Math.imul(m,W)|0,a=a+Math.imul(m,Y)|0;var xt=(c+(n=n+Math.imul(h,$)|0)|0)+((8191&(i=(i=i+Math.imul(h,J)|0)+Math.imul(f,$)|0))<<13)|0;c=((a=a+Math.imul(f,J)|0)+(i>>>13)|0)+(xt>>>26)|0,xt&=67108863,n=Math.imul(T,U),i=(i=Math.imul(T,V))+Math.imul(k,U)|0,a=Math.imul(k,V),n=n+Math.imul(_,H)|0,i=(i=i+Math.imul(_,G)|0)+Math.imul(b,H)|0,a=a+Math.imul(b,G)|0,n=n+Math.imul(y,W)|0,i=(i=i+Math.imul(y,Y)|0)+Math.imul(v,W)|0,a=a+Math.imul(v,Y)|0,n=n+Math.imul(d,$)|0,i=(i=i+Math.imul(d,J)|0)+Math.imul(m,$)|0,a=a+Math.imul(m,J)|0;var _t=(c+(n=n+Math.imul(h,Q)|0)|0)+((8191&(i=(i=i+Math.imul(h,tt)|0)+Math.imul(f,Q)|0))<<13)|0;c=((a=a+Math.imul(f,tt)|0)+(i>>>13)|0)+(_t>>>26)|0,_t&=67108863,n=Math.imul(M,U),i=(i=Math.imul(M,V))+Math.imul(S,U)|0,a=Math.imul(S,V),n=n+Math.imul(T,H)|0,i=(i=i+Math.imul(T,G)|0)+Math.imul(k,H)|0,a=a+Math.imul(k,G)|0,n=n+Math.imul(_,W)|0,i=(i=i+Math.imul(_,Y)|0)+Math.imul(b,W)|0,a=a+Math.imul(b,Y)|0,n=n+Math.imul(y,$)|0,i=(i=i+Math.imul(y,J)|0)+Math.imul(v,$)|0,a=a+Math.imul(v,J)|0,n=n+Math.imul(d,Q)|0,i=(i=i+Math.imul(d,tt)|0)+Math.imul(m,Q)|0,a=a+Math.imul(m,tt)|0;var bt=(c+(n=n+Math.imul(h,rt)|0)|0)+((8191&(i=(i=i+Math.imul(h,nt)|0)+Math.imul(f,rt)|0))<<13)|0;c=((a=a+Math.imul(f,nt)|0)+(i>>>13)|0)+(bt>>>26)|0,bt&=67108863,n=Math.imul(C,U),i=(i=Math.imul(C,V))+Math.imul(L,U)|0,a=Math.imul(L,V),n=n+Math.imul(M,H)|0,i=(i=i+Math.imul(M,G)|0)+Math.imul(S,H)|0,a=a+Math.imul(S,G)|0,n=n+Math.imul(T,W)|0,i=(i=i+Math.imul(T,Y)|0)+Math.imul(k,W)|0,a=a+Math.imul(k,Y)|0,n=n+Math.imul(_,$)|0,i=(i=i+Math.imul(_,J)|0)+Math.imul(b,$)|0,a=a+Math.imul(b,J)|0,n=n+Math.imul(y,Q)|0,i=(i=i+Math.imul(y,tt)|0)+Math.imul(v,Q)|0,a=a+Math.imul(v,tt)|0,n=n+Math.imul(d,rt)|0,i=(i=i+Math.imul(d,nt)|0)+Math.imul(m,rt)|0,a=a+Math.imul(m,nt)|0;var wt=(c+(n=n+Math.imul(h,at)|0)|0)+((8191&(i=(i=i+Math.imul(h,ot)|0)+Math.imul(f,at)|0))<<13)|0;c=((a=a+Math.imul(f,ot)|0)+(i>>>13)|0)+(wt>>>26)|0,wt&=67108863,n=Math.imul(P,U),i=(i=Math.imul(P,V))+Math.imul(z,U)|0,a=Math.imul(z,V),n=n+Math.imul(C,H)|0,i=(i=i+Math.imul(C,G)|0)+Math.imul(L,H)|0,a=a+Math.imul(L,G)|0,n=n+Math.imul(M,W)|0,i=(i=i+Math.imul(M,Y)|0)+Math.imul(S,W)|0,a=a+Math.imul(S,Y)|0,n=n+Math.imul(T,$)|0,i=(i=i+Math.imul(T,J)|0)+Math.imul(k,$)|0,a=a+Math.imul(k,J)|0,n=n+Math.imul(_,Q)|0,i=(i=i+Math.imul(_,tt)|0)+Math.imul(b,Q)|0,a=a+Math.imul(b,tt)|0,n=n+Math.imul(y,rt)|0,i=(i=i+Math.imul(y,nt)|0)+Math.imul(v,rt)|0,a=a+Math.imul(v,nt)|0,n=n+Math.imul(d,at)|0,i=(i=i+Math.imul(d,ot)|0)+Math.imul(m,at)|0,a=a+Math.imul(m,ot)|0;var Tt=(c+(n=n+Math.imul(h,lt)|0)|0)+((8191&(i=(i=i+Math.imul(h,ct)|0)+Math.imul(f,lt)|0))<<13)|0;c=((a=a+Math.imul(f,ct)|0)+(i>>>13)|0)+(Tt>>>26)|0,Tt&=67108863,n=Math.imul(D,U),i=(i=Math.imul(D,V))+Math.imul(R,U)|0,a=Math.imul(R,V),n=n+Math.imul(P,H)|0,i=(i=i+Math.imul(P,G)|0)+Math.imul(z,H)|0,a=a+Math.imul(z,G)|0,n=n+Math.imul(C,W)|0,i=(i=i+Math.imul(C,Y)|0)+Math.imul(L,W)|0,a=a+Math.imul(L,Y)|0,n=n+Math.imul(M,$)|0,i=(i=i+Math.imul(M,J)|0)+Math.imul(S,$)|0,a=a+Math.imul(S,J)|0,n=n+Math.imul(T,Q)|0,i=(i=i+Math.imul(T,tt)|0)+Math.imul(k,Q)|0,a=a+Math.imul(k,tt)|0,n=n+Math.imul(_,rt)|0,i=(i=i+Math.imul(_,nt)|0)+Math.imul(b,rt)|0,a=a+Math.imul(b,nt)|0,n=n+Math.imul(y,at)|0,i=(i=i+Math.imul(y,ot)|0)+Math.imul(v,at)|0,a=a+Math.imul(v,ot)|0,n=n+Math.imul(d,lt)|0,i=(i=i+Math.imul(d,ct)|0)+Math.imul(m,lt)|0,a=a+Math.imul(m,ct)|0;var kt=(c+(n=n+Math.imul(h,ht)|0)|0)+((8191&(i=(i=i+Math.imul(h,ft)|0)+Math.imul(f,ht)|0))<<13)|0;c=((a=a+Math.imul(f,ft)|0)+(i>>>13)|0)+(kt>>>26)|0,kt&=67108863,n=Math.imul(B,U),i=(i=Math.imul(B,V))+Math.imul(N,U)|0,a=Math.imul(N,V),n=n+Math.imul(D,H)|0,i=(i=i+Math.imul(D,G)|0)+Math.imul(R,H)|0,a=a+Math.imul(R,G)|0,n=n+Math.imul(P,W)|0,i=(i=i+Math.imul(P,Y)|0)+Math.imul(z,W)|0,a=a+Math.imul(z,Y)|0,n=n+Math.imul(C,$)|0,i=(i=i+Math.imul(C,J)|0)+Math.imul(L,$)|0,a=a+Math.imul(L,J)|0,n=n+Math.imul(M,Q)|0,i=(i=i+Math.imul(M,tt)|0)+Math.imul(S,Q)|0,a=a+Math.imul(S,tt)|0,n=n+Math.imul(T,rt)|0,i=(i=i+Math.imul(T,nt)|0)+Math.imul(k,rt)|0,a=a+Math.imul(k,nt)|0,n=n+Math.imul(_,at)|0,i=(i=i+Math.imul(_,ot)|0)+Math.imul(b,at)|0,a=a+Math.imul(b,ot)|0,n=n+Math.imul(y,lt)|0,i=(i=i+Math.imul(y,ct)|0)+Math.imul(v,lt)|0,a=a+Math.imul(v,ct)|0,n=n+Math.imul(d,ht)|0,i=(i=i+Math.imul(d,ft)|0)+Math.imul(m,ht)|0,a=a+Math.imul(m,ft)|0;var At=(c+(n=n+Math.imul(h,dt)|0)|0)+((8191&(i=(i=i+Math.imul(h,mt)|0)+Math.imul(f,dt)|0))<<13)|0;c=((a=a+Math.imul(f,mt)|0)+(i>>>13)|0)+(At>>>26)|0,At&=67108863,n=Math.imul(B,H),i=(i=Math.imul(B,G))+Math.imul(N,H)|0,a=Math.imul(N,G),n=n+Math.imul(D,W)|0,i=(i=i+Math.imul(D,Y)|0)+Math.imul(R,W)|0,a=a+Math.imul(R,Y)|0,n=n+Math.imul(P,$)|0,i=(i=i+Math.imul(P,J)|0)+Math.imul(z,$)|0,a=a+Math.imul(z,J)|0,n=n+Math.imul(C,Q)|0,i=(i=i+Math.imul(C,tt)|0)+Math.imul(L,Q)|0,a=a+Math.imul(L,tt)|0,n=n+Math.imul(M,rt)|0,i=(i=i+Math.imul(M,nt)|0)+Math.imul(S,rt)|0,a=a+Math.imul(S,nt)|0,n=n+Math.imul(T,at)|0,i=(i=i+Math.imul(T,ot)|0)+Math.imul(k,at)|0,a=a+Math.imul(k,ot)|0,n=n+Math.imul(_,lt)|0,i=(i=i+Math.imul(_,ct)|0)+Math.imul(b,lt)|0,a=a+Math.imul(b,ct)|0,n=n+Math.imul(y,ht)|0,i=(i=i+Math.imul(y,ft)|0)+Math.imul(v,ht)|0,a=a+Math.imul(v,ft)|0;var Mt=(c+(n=n+Math.imul(d,dt)|0)|0)+((8191&(i=(i=i+Math.imul(d,mt)|0)+Math.imul(m,dt)|0))<<13)|0;c=((a=a+Math.imul(m,mt)|0)+(i>>>13)|0)+(Mt>>>26)|0,Mt&=67108863,n=Math.imul(B,W),i=(i=Math.imul(B,Y))+Math.imul(N,W)|0,a=Math.imul(N,Y),n=n+Math.imul(D,$)|0,i=(i=i+Math.imul(D,J)|0)+Math.imul(R,$)|0,a=a+Math.imul(R,J)|0,n=n+Math.imul(P,Q)|0,i=(i=i+Math.imul(P,tt)|0)+Math.imul(z,Q)|0,a=a+Math.imul(z,tt)|0,n=n+Math.imul(C,rt)|0,i=(i=i+Math.imul(C,nt)|0)+Math.imul(L,rt)|0,a=a+Math.imul(L,nt)|0,n=n+Math.imul(M,at)|0,i=(i=i+Math.imul(M,ot)|0)+Math.imul(S,at)|0,a=a+Math.imul(S,ot)|0,n=n+Math.imul(T,lt)|0,i=(i=i+Math.imul(T,ct)|0)+Math.imul(k,lt)|0,a=a+Math.imul(k,ct)|0,n=n+Math.imul(_,ht)|0,i=(i=i+Math.imul(_,ft)|0)+Math.imul(b,ht)|0,a=a+Math.imul(b,ft)|0;var St=(c+(n=n+Math.imul(y,dt)|0)|0)+((8191&(i=(i=i+Math.imul(y,mt)|0)+Math.imul(v,dt)|0))<<13)|0;c=((a=a+Math.imul(v,mt)|0)+(i>>>13)|0)+(St>>>26)|0,St&=67108863,n=Math.imul(B,$),i=(i=Math.imul(B,J))+Math.imul(N,$)|0,a=Math.imul(N,J),n=n+Math.imul(D,Q)|0,i=(i=i+Math.imul(D,tt)|0)+Math.imul(R,Q)|0,a=a+Math.imul(R,tt)|0,n=n+Math.imul(P,rt)|0,i=(i=i+Math.imul(P,nt)|0)+Math.imul(z,rt)|0,a=a+Math.imul(z,nt)|0,n=n+Math.imul(C,at)|0,i=(i=i+Math.imul(C,ot)|0)+Math.imul(L,at)|0,a=a+Math.imul(L,ot)|0,n=n+Math.imul(M,lt)|0,i=(i=i+Math.imul(M,ct)|0)+Math.imul(S,lt)|0,a=a+Math.imul(S,ct)|0,n=n+Math.imul(T,ht)|0,i=(i=i+Math.imul(T,ft)|0)+Math.imul(k,ht)|0,a=a+Math.imul(k,ft)|0;var Et=(c+(n=n+Math.imul(_,dt)|0)|0)+((8191&(i=(i=i+Math.imul(_,mt)|0)+Math.imul(b,dt)|0))<<13)|0;c=((a=a+Math.imul(b,mt)|0)+(i>>>13)|0)+(Et>>>26)|0,Et&=67108863,n=Math.imul(B,Q),i=(i=Math.imul(B,tt))+Math.imul(N,Q)|0,a=Math.imul(N,tt),n=n+Math.imul(D,rt)|0,i=(i=i+Math.imul(D,nt)|0)+Math.imul(R,rt)|0,a=a+Math.imul(R,nt)|0,n=n+Math.imul(P,at)|0,i=(i=i+Math.imul(P,ot)|0)+Math.imul(z,at)|0,a=a+Math.imul(z,ot)|0,n=n+Math.imul(C,lt)|0,i=(i=i+Math.imul(C,ct)|0)+Math.imul(L,lt)|0,a=a+Math.imul(L,ct)|0,n=n+Math.imul(M,ht)|0,i=(i=i+Math.imul(M,ft)|0)+Math.imul(S,ht)|0,a=a+Math.imul(S,ft)|0;var Ct=(c+(n=n+Math.imul(T,dt)|0)|0)+((8191&(i=(i=i+Math.imul(T,mt)|0)+Math.imul(k,dt)|0))<<13)|0;c=((a=a+Math.imul(k,mt)|0)+(i>>>13)|0)+(Ct>>>26)|0,Ct&=67108863,n=Math.imul(B,rt),i=(i=Math.imul(B,nt))+Math.imul(N,rt)|0,a=Math.imul(N,nt),n=n+Math.imul(D,at)|0,i=(i=i+Math.imul(D,ot)|0)+Math.imul(R,at)|0,a=a+Math.imul(R,ot)|0,n=n+Math.imul(P,lt)|0,i=(i=i+Math.imul(P,ct)|0)+Math.imul(z,lt)|0,a=a+Math.imul(z,ct)|0,n=n+Math.imul(C,ht)|0,i=(i=i+Math.imul(C,ft)|0)+Math.imul(L,ht)|0,a=a+Math.imul(L,ft)|0;var Lt=(c+(n=n+Math.imul(M,dt)|0)|0)+((8191&(i=(i=i+Math.imul(M,mt)|0)+Math.imul(S,dt)|0))<<13)|0;c=((a=a+Math.imul(S,mt)|0)+(i>>>13)|0)+(Lt>>>26)|0,Lt&=67108863,n=Math.imul(B,at),i=(i=Math.imul(B,ot))+Math.imul(N,at)|0,a=Math.imul(N,ot),n=n+Math.imul(D,lt)|0,i=(i=i+Math.imul(D,ct)|0)+Math.imul(R,lt)|0,a=a+Math.imul(R,ct)|0,n=n+Math.imul(P,ht)|0,i=(i=i+Math.imul(P,ft)|0)+Math.imul(z,ht)|0,a=a+Math.imul(z,ft)|0;var It=(c+(n=n+Math.imul(C,dt)|0)|0)+((8191&(i=(i=i+Math.imul(C,mt)|0)+Math.imul(L,dt)|0))<<13)|0;c=((a=a+Math.imul(L,mt)|0)+(i>>>13)|0)+(It>>>26)|0,It&=67108863,n=Math.imul(B,lt),i=(i=Math.imul(B,ct))+Math.imul(N,lt)|0,a=Math.imul(N,ct),n=n+Math.imul(D,ht)|0,i=(i=i+Math.imul(D,ft)|0)+Math.imul(R,ht)|0,a=a+Math.imul(R,ft)|0;var Pt=(c+(n=n+Math.imul(P,dt)|0)|0)+((8191&(i=(i=i+Math.imul(P,mt)|0)+Math.imul(z,dt)|0))<<13)|0;c=((a=a+Math.imul(z,mt)|0)+(i>>>13)|0)+(Pt>>>26)|0,Pt&=67108863,n=Math.imul(B,ht),i=(i=Math.imul(B,ft))+Math.imul(N,ht)|0,a=Math.imul(N,ft);var zt=(c+(n=n+Math.imul(D,dt)|0)|0)+((8191&(i=(i=i+Math.imul(D,mt)|0)+Math.imul(R,dt)|0))<<13)|0;c=((a=a+Math.imul(R,mt)|0)+(i>>>13)|0)+(zt>>>26)|0,zt&=67108863;var Ot=(c+(n=Math.imul(B,dt))|0)+((8191&(i=(i=Math.imul(B,mt))+Math.imul(N,dt)|0))<<13)|0;return c=((a=Math.imul(N,mt))+(i>>>13)|0)+(Ot>>>26)|0,Ot&=67108863,l[0]=gt,l[1]=yt,l[2]=vt,l[3]=xt,l[4]=_t,l[5]=bt,l[6]=wt,l[7]=Tt,l[8]=kt,l[9]=At,l[10]=Mt,l[11]=St,l[12]=Et,l[13]=Ct,l[14]=Lt,l[15]=It,l[16]=Pt,l[17]=zt,l[18]=Ot,0!==c&&(l[19]=c,r.length++),r};function m(t,e,r){return(new g).mulp(t,e,r)}function g(t,e){this.x=t,this.y=e}Math.imul||(d=p),a.prototype.mulTo=function(t,e){var r,n=this.length+t.length;return r=10===this.length&&10===t.length?d(this,t,e):n<63?p(this,t,e):n<1024?function(t,e,r){r.negative=e.negative^t.negative,r.length=t.length+e.length;for(var n=0,i=0,a=0;a>>26)|0)>>>26,o&=67108863}r.words[a]=s,n=o,o=i}return 0!==n?r.words[a]=n:r.length--,r.strip()}(this,t,e):m(this,t,e),r},g.prototype.makeRBT=function(t){for(var e=new Array(t),r=a.prototype._countBits(t)-1,n=0;n>=1;return n},g.prototype.permute=function(t,e,r,n,i,a){for(var o=0;o>>=1)i++;return 1<>>=13,r[2*o+1]=8191&a,a>>>=13;for(o=2*e;o>=26,e+=i/67108864|0,e+=a>>>26,this.words[r]=67108863&a}return 0!==e&&(this.words[r]=e,this.length++),this},a.prototype.muln=function(t){return this.clone().imuln(t)},a.prototype.sqr=function(){return this.mul(this)},a.prototype.isqr=function(){return this.imul(this.clone())},a.prototype.pow=function(t){var e=function(t){for(var e=new Array(t.bitLength()),r=0;r>>i}return e}(t);if(0===e.length)return new a(1);for(var r=this,n=0;n=0);var e,r=t%26,i=(t-r)/26,a=67108863>>>26-r<<26-r;if(0!==r){var o=0;for(e=0;e>>26-r}o&&(this.words[e]=o,this.length++)}if(0!==i){for(e=this.length-1;e>=0;e--)this.words[e+i]=this.words[e];for(e=0;e=0),i=e?(e-e%26)/26:0;var a=t%26,o=Math.min((t-a)/26,this.length),s=67108863^67108863>>>a<o)for(this.length-=o,c=0;c=0&&(0!==u||c>=i);c--){var h=0|this.words[c];this.words[c]=u<<26-a|h>>>a,u=h&s}return l&&0!==u&&(l.words[l.length++]=u),0===this.length&&(this.words[0]=0,this.length=1),this.strip()},a.prototype.ishrn=function(t,e,r){return n(0===this.negative),this.iushrn(t,e,r)},a.prototype.shln=function(t){return this.clone().ishln(t)},a.prototype.ushln=function(t){return this.clone().iushln(t)},a.prototype.shrn=function(t){return this.clone().ishrn(t)},a.prototype.ushrn=function(t){return this.clone().iushrn(t)},a.prototype.testn=function(t){n(\"number\"==typeof t&&t>=0);var e=t%26,r=(t-e)/26,i=1<=0);var e=t%26,r=(t-e)/26;if(n(0===this.negative,\"imaskn works only with positive numbers\"),this.length<=r)return this;if(0!==e&&r++,this.length=Math.min(r,this.length),0!==e){var i=67108863^67108863>>>e<=67108864;e++)this.words[e]-=67108864,e===this.length-1?this.words[e+1]=1:this.words[e+1]++;return this.length=Math.max(this.length,e+1),this},a.prototype.isubn=function(t){if(n(\"number\"==typeof t),n(t<67108864),t<0)return this.iaddn(-t);if(0!==this.negative)return this.negative=0,this.iaddn(t),this.negative=1,this;if(this.words[0]-=t,1===this.length&&this.words[0]<0)this.words[0]=-this.words[0],this.negative=1;else for(var e=0;e>26)-(l/67108864|0),this.words[i+r]=67108863&a}for(;i>26,this.words[i+r]=67108863&a;if(0===s)return this.strip();for(n(-1===s),s=0,i=0;i>26,this.words[i]=67108863&a;return this.negative=1,this.strip()},a.prototype._wordDiv=function(t,e){var r=(this.length,t.length),n=this.clone(),i=t,o=0|i.words[i.length-1];0!=(r=26-this._countBits(o))&&(i=i.ushln(r),n.iushln(r),o=0|i.words[i.length-1]);var s,l=n.length-i.length;if(\"mod\"!==e){(s=new a(null)).length=l+1,s.words=new Array(s.length);for(var c=0;c=0;h--){var f=67108864*(0|n.words[i.length+h])+(0|n.words[i.length+h-1]);for(f=Math.min(f/o|0,67108863),n._ishlnsubmul(i,f,h);0!==n.negative;)f--,n.negative=0,n._ishlnsubmul(i,1,h),n.isZero()||(n.negative^=1);s&&(s.words[h]=f)}return s&&s.strip(),n.strip(),\"div\"!==e&&0!==r&&n.iushrn(r),{div:s||null,mod:n}},a.prototype.divmod=function(t,e,r){return n(!t.isZero()),this.isZero()?{div:new a(0),mod:new a(0)}:0!==this.negative&&0===t.negative?(s=this.neg().divmod(t,e),\"mod\"!==e&&(i=s.div.neg()),\"div\"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.iadd(t)),{div:i,mod:o}):0===this.negative&&0!==t.negative?(s=this.divmod(t.neg(),e),\"mod\"!==e&&(i=s.div.neg()),{div:i,mod:s.mod}):0!=(this.negative&t.negative)?(s=this.neg().divmod(t.neg(),e),\"div\"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.isub(t)),{div:s.div,mod:o}):t.length>this.length||this.cmp(t)<0?{div:new a(0),mod:this}:1===t.length?\"div\"===e?{div:this.divn(t.words[0]),mod:null}:\"mod\"===e?{div:null,mod:new a(this.modn(t.words[0]))}:{div:this.divn(t.words[0]),mod:new a(this.modn(t.words[0]))}:this._wordDiv(t,e);var i,o,s},a.prototype.div=function(t){return this.divmod(t,\"div\",!1).div},a.prototype.mod=function(t){return this.divmod(t,\"mod\",!1).mod},a.prototype.umod=function(t){return this.divmod(t,\"mod\",!0).mod},a.prototype.divRound=function(t){var e=this.divmod(t);if(e.mod.isZero())return e.div;var r=0!==e.div.negative?e.mod.isub(t):e.mod,n=t.ushrn(1),i=t.andln(1),a=r.cmp(n);return a<0||1===i&&0===a?e.div:0!==e.div.negative?e.div.isubn(1):e.div.iaddn(1)},a.prototype.modn=function(t){n(t<=67108863);for(var e=(1<<26)%t,r=0,i=this.length-1;i>=0;i--)r=(e*r+(0|this.words[i]))%t;return r},a.prototype.idivn=function(t){n(t<=67108863);for(var e=0,r=this.length-1;r>=0;r--){var i=(0|this.words[r])+67108864*e;this.words[r]=i/t|0,e=i%t}return this.strip()},a.prototype.divn=function(t){return this.clone().idivn(t)},a.prototype.egcd=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i=new a(1),o=new a(0),s=new a(0),l=new a(1),c=0;e.isEven()&&r.isEven();)e.iushrn(1),r.iushrn(1),++c;for(var u=r.clone(),h=e.clone();!e.isZero();){for(var f=0,p=1;0==(e.words[0]&p)&&f<26;++f,p<<=1);if(f>0)for(e.iushrn(f);f-- >0;)(i.isOdd()||o.isOdd())&&(i.iadd(u),o.isub(h)),i.iushrn(1),o.iushrn(1);for(var d=0,m=1;0==(r.words[0]&m)&&d<26;++d,m<<=1);if(d>0)for(r.iushrn(d);d-- >0;)(s.isOdd()||l.isOdd())&&(s.iadd(u),l.isub(h)),s.iushrn(1),l.iushrn(1);e.cmp(r)>=0?(e.isub(r),i.isub(s),o.isub(l)):(r.isub(e),s.isub(i),l.isub(o))}return{a:s,b:l,gcd:r.iushln(c)}},a.prototype._invmp=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i,o=new a(1),s=new a(0),l=r.clone();e.cmpn(1)>0&&r.cmpn(1)>0;){for(var c=0,u=1;0==(e.words[0]&u)&&c<26;++c,u<<=1);if(c>0)for(e.iushrn(c);c-- >0;)o.isOdd()&&o.iadd(l),o.iushrn(1);for(var h=0,f=1;0==(r.words[0]&f)&&h<26;++h,f<<=1);if(h>0)for(r.iushrn(h);h-- >0;)s.isOdd()&&s.iadd(l),s.iushrn(1);e.cmp(r)>=0?(e.isub(r),o.isub(s)):(r.isub(e),s.isub(o))}return(i=0===e.cmpn(1)?o:s).cmpn(0)<0&&i.iadd(t),i},a.prototype.gcd=function(t){if(this.isZero())return t.abs();if(t.isZero())return this.abs();var e=this.clone(),r=t.clone();e.negative=0,r.negative=0;for(var n=0;e.isEven()&&r.isEven();n++)e.iushrn(1),r.iushrn(1);for(;;){for(;e.isEven();)e.iushrn(1);for(;r.isEven();)r.iushrn(1);var i=e.cmp(r);if(i<0){var a=e;e=r,r=a}else if(0===i||0===r.cmpn(1))break;e.isub(r)}return r.iushln(n)},a.prototype.invm=function(t){return this.egcd(t).a.umod(t)},a.prototype.isEven=function(){return 0==(1&this.words[0])},a.prototype.isOdd=function(){return 1==(1&this.words[0])},a.prototype.andln=function(t){return this.words[0]&t},a.prototype.bincn=function(t){n(\"number\"==typeof t);var e=t%26,r=(t-e)/26,i=1<>>26,s&=67108863,this.words[o]=s}return 0!==a&&(this.words[o]=a,this.length++),this},a.prototype.isZero=function(){return 1===this.length&&0===this.words[0]},a.prototype.cmpn=function(t){var e,r=t<0;if(0!==this.negative&&!r)return-1;if(0===this.negative&&r)return 1;if(this.strip(),this.length>1)e=1;else{r&&(t=-t),n(t<=67108863,\"Number is too big\");var i=0|this.words[0];e=i===t?0:it.length)return 1;if(this.length=0;r--){var n=0|this.words[r],i=0|t.words[r];if(n!==i){ni&&(e=1);break}}return e},a.prototype.gtn=function(t){return 1===this.cmpn(t)},a.prototype.gt=function(t){return 1===this.cmp(t)},a.prototype.gten=function(t){return this.cmpn(t)>=0},a.prototype.gte=function(t){return this.cmp(t)>=0},a.prototype.ltn=function(t){return-1===this.cmpn(t)},a.prototype.lt=function(t){return-1===this.cmp(t)},a.prototype.lten=function(t){return this.cmpn(t)<=0},a.prototype.lte=function(t){return this.cmp(t)<=0},a.prototype.eqn=function(t){return 0===this.cmpn(t)},a.prototype.eq=function(t){return 0===this.cmp(t)},a.red=function(t){return new T(t)},a.prototype.toRed=function(t){return n(!this.red,\"Already a number in reduction context\"),n(0===this.negative,\"red works only with positives\"),t.convertTo(this)._forceRed(t)},a.prototype.fromRed=function(){return n(this.red,\"fromRed works only with numbers in reduction context\"),this.red.convertFrom(this)},a.prototype._forceRed=function(t){return this.red=t,this},a.prototype.forceRed=function(t){return n(!this.red,\"Already a number in reduction context\"),this._forceRed(t)},a.prototype.redAdd=function(t){return n(this.red,\"redAdd works only with red numbers\"),this.red.add(this,t)},a.prototype.redIAdd=function(t){return n(this.red,\"redIAdd works only with red numbers\"),this.red.iadd(this,t)},a.prototype.redSub=function(t){return n(this.red,\"redSub works only with red numbers\"),this.red.sub(this,t)},a.prototype.redISub=function(t){return n(this.red,\"redISub works only with red numbers\"),this.red.isub(this,t)},a.prototype.redShl=function(t){return n(this.red,\"redShl works only with red numbers\"),this.red.shl(this,t)},a.prototype.redMul=function(t){return n(this.red,\"redMul works only with red numbers\"),this.red._verify2(this,t),this.red.mul(this,t)},a.prototype.redIMul=function(t){return n(this.red,\"redMul works only with red numbers\"),this.red._verify2(this,t),this.red.imul(this,t)},a.prototype.redSqr=function(){return n(this.red,\"redSqr works only with red numbers\"),this.red._verify1(this),this.red.sqr(this)},a.prototype.redISqr=function(){return n(this.red,\"redISqr works only with red numbers\"),this.red._verify1(this),this.red.isqr(this)},a.prototype.redSqrt=function(){return n(this.red,\"redSqrt works only with red numbers\"),this.red._verify1(this),this.red.sqrt(this)},a.prototype.redInvm=function(){return n(this.red,\"redInvm works only with red numbers\"),this.red._verify1(this),this.red.invm(this)},a.prototype.redNeg=function(){return n(this.red,\"redNeg works only with red numbers\"),this.red._verify1(this),this.red.neg(this)},a.prototype.redPow=function(t){return n(this.red&&!t.red,\"redPow(normalNum)\"),this.red._verify1(this),this.red.pow(this,t)};var y={k256:null,p224:null,p192:null,p25519:null};function v(t,e){this.name=t,this.p=new a(e,16),this.n=this.p.bitLength(),this.k=new a(1).iushln(this.n).isub(this.p),this.tmp=this._tmp()}function x(){v.call(this,\"k256\",\"ffffffff ffffffff ffffffff ffffffff ffffffff ffffffff fffffffe fffffc2f\")}function _(){v.call(this,\"p224\",\"ffffffff ffffffff ffffffff ffffffff 00000000 00000000 00000001\")}function b(){v.call(this,\"p192\",\"ffffffff ffffffff ffffffff fffffffe ffffffff ffffffff\")}function w(){v.call(this,\"25519\",\"7fffffffffffffff ffffffffffffffff ffffffffffffffff ffffffffffffffed\")}function T(t){if(\"string\"==typeof t){var e=a._prime(t);this.m=e.p,this.prime=e}else n(t.gtn(1),\"modulus must be greater than 1\"),this.m=t,this.prime=null}function k(t){T.call(this,t),this.shift=this.m.bitLength(),this.shift%26!=0&&(this.shift+=26-this.shift%26),this.r=new a(1).iushln(this.shift),this.r2=this.imod(this.r.sqr()),this.rinv=this.r._invmp(this.m),this.minv=this.rinv.mul(this.r).isubn(1).div(this.m),this.minv=this.minv.umod(this.r),this.minv=this.r.sub(this.minv)}v.prototype._tmp=function(){var t=new a(null);return t.words=new Array(Math.ceil(this.n/13)),t},v.prototype.ireduce=function(t){var e,r=t;do{this.split(r,this.tmp),e=(r=(r=this.imulK(r)).iadd(this.tmp)).bitLength()}while(e>this.n);var n=e0?r.isub(this.p):void 0!==r.strip?r.strip():r._strip(),r},v.prototype.split=function(t,e){t.iushrn(this.n,0,e)},v.prototype.imulK=function(t){return t.imul(this.k)},i(x,v),x.prototype.split=function(t,e){for(var r=4194303,n=Math.min(t.length,9),i=0;i>>22,a=o}a>>>=22,t.words[i-10]=a,0===a&&t.length>10?t.length-=10:t.length-=9},x.prototype.imulK=function(t){t.words[t.length]=0,t.words[t.length+1]=0,t.length+=2;for(var e=0,r=0;r>>=26,t.words[r]=i,e=n}return 0!==e&&(t.words[t.length++]=e),t},a._prime=function(t){if(y[t])return y[t];var e;if(\"k256\"===t)e=new x;else if(\"p224\"===t)e=new _;else if(\"p192\"===t)e=new b;else{if(\"p25519\"!==t)throw new Error(\"Unknown prime \"+t);e=new w}return y[t]=e,e},T.prototype._verify1=function(t){n(0===t.negative,\"red works only with 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y=[{family:this.labelFont[0],style:this.labelFontStyle[0],weight:this.labelFontWeight[0],variant:this.labelFontVariant[0]},{family:this.labelFont[1],style:this.labelFontStyle[1],weight:this.labelFontWeight[1],variant:this.labelFontVariant[1]},{family:this.labelFont[2],style:this.labelFontStyle[2],weight:this.labelFontWeight[2],variant:this.labelFontVariant[2]}],v=[{family:this.tickFont[0],style:this.tickFontStyle[0],weight:this.tickFontWeight[0],variant:this.tickFontVariant[0]},{family:this.tickFont[1],style:this.tickFontStyle[1],weight:this.tickFontWeight[1],variant:this.tickFontVariant[1]},{family:this.tickFont[2],style:this.tickFontStyle[2],weight:this.tickFontWeight[2],variant:this.tickFontVariant[2]}];this._text?this._text&&(g||u)&&this._text.update(this.bounds,this.labels,y,this.ticks,v):this._text=n(this.gl,this.bounds,this.labels,y,this.ticks,v),this._lines&&u&&(this._lines.dispose(),this._lines=null),this._lines||(this._lines=i(this.gl,this.bounds,this.ticks))};var g=[new m,new m,new m];function y(t,e,r,n,i){for(var a=t.primalOffset,o=t.primalMinor,s=t.mirrorOffset,l=t.mirrorMinor,c=n[e],u=0;u<3;++u)if(e!==u){var h=a,f=s,p=o,d=l;c&1<0?(p[u]=-1,d[u]=0):(p[u]=0,d[u]=1)}}var v=[0,0,0],x={model:l,view:l,projection:l,_ortho:!1};d.isOpaque=function(){return!0},d.isTransparent=function(){return!1},d.drawTransparent=function(t){};var _=[0,0,0],b=[0,0,0],w=[0,0,0];d.draw=function(t){t=t||x;for(var e=this.gl,r=t.model||l,n=t.view||l,i=t.projection||l,a=this.bounds,s=t._ortho||!1,c=o(r,n,i,a,s),u=c.cubeEdges,h=c.axis,p=n[12],d=n[13],m=n[14],T=n[15],k=(s?2:1)*this.pixelRatio*(i[3]*p+i[7]*d+i[11]*m+i[15]*T)/e.drawingBufferHeight,A=0;A<3;++A)this.lastCubeProps.cubeEdges[A]=u[A],this.lastCubeProps.axis[A]=h[A];var M=g;for(A=0;A<3;++A)y(g[A],A,this.bounds,u,h);e=this.gl;var S,E,C,L=v;for(A=0;A<3;++A)this.backgroundEnable[A]?L[A]=h[A]:L[A]=0;for(this._background.draw(r,n,i,a,L,this.backgroundColor),this._lines.bind(r,n,i,this),A=0;A<3;++A){var I=[0,0,0];h[A]>0?I[A]=a[1][A]:I[A]=a[0][A];for(var P=0;P<2;++P){var z=(A+1+P)%3,O=(A+1+(1^P))%3;this.gridEnable[z]&&this._lines.drawGrid(z,O,this.bounds,I,this.gridColor[z],this.gridWidth[z]*this.pixelRatio)}for(P=0;P<2;++P)z=(A+1+P)%3,O=(A+1+(1^P))%3,this.zeroEnable[O]&&Math.min(a[0][O],a[1][O])<=0&&Math.max(a[0][O],a[1][O])>=0&&this._lines.drawZero(z,O,this.bounds,I,this.zeroLineColor[O],this.zeroLineWidth[O]*this.pixelRatio)}for(A=0;A<3;++A){this.lineEnable[A]&&this._lines.drawAxisLine(A,this.bounds,M[A].primalOffset,this.lineColor[A],this.lineWidth[A]*this.pixelRatio),this.lineMirror[A]&&this._lines.drawAxisLine(A,this.bounds,M[A].mirrorOffset,this.lineColor[A],this.lineWidth[A]*this.pixelRatio);var D=f(_,M[A].primalMinor),R=f(b,M[A].mirrorMinor),F=this.lineTickLength;for(P=0;P<3;++P){var B=k/r[5*P];D[P]*=F[P]*B,R[P]*=F[P]*B}this.lineTickEnable[A]&&this._lines.drawAxisTicks(A,M[A].primalOffset,D,this.lineTickColor[A],this.lineTickWidth[A]*this.pixelRatio),this.lineTickMirror[A]&&this._lines.drawAxisTicks(A,M[A].mirrorOffset,R,this.lineTickColor[A],this.lineTickWidth[A]*this.pixelRatio)}function N(t){(C=[0,0,0])[t]=1}function j(t,e,r){var n=(t+1)%3,i=(t+2)%3,a=e[n],o=e[i],s=r[n],l=r[i];a>0&&l>0||a>0&&l<0||a<0&&l>0||a<0&&l<0?N(n):(o>0&&s>0||o>0&&s<0||o<0&&s>0||o<0&&s<0)&&N(i)}for(this._lines.unbind(),this._text.bind(r,n,i,this.pixelRatio),A=0;A<3;++A){var U=M[A].primalMinor,V=M[A].mirrorMinor,q=f(w,M[A].primalOffset);for(P=0;P<3;++P)this.lineTickEnable[A]&&(q[P]+=k*U[P]*Math.max(this.lineTickLength[P],0)/r[5*P]);var H=[0,0,0];if(H[A]=1,this.tickEnable[A]){for(-3600===this.tickAngle[A]?(this.tickAngle[A]=0,this.tickAlign[A]=\"auto\"):this.tickAlign[A]=-1,E=1,\"auto\"===(S=[this.tickAlign[A],.5,E])[0]?S[0]=0:S[0]=parseInt(\"\"+S[0]),C=[0,0,0],j(A,U,V),P=0;P<3;++P)q[P]+=k*U[P]*this.tickPad[P]/r[5*P];this._text.drawTicks(A,this.tickSize[A],this.tickAngle[A],q,this.tickColor[A],H,C,S)}if(this.labelEnable[A]){for(E=0,C=[0,0,0],this.labels[A].length>4&&(N(A),E=1),\"auto\"===(S=[this.labelAlign[A],.5,E])[0]?S[0]=0:S[0]=parseInt(\"\"+S[0]),P=0;P<3;++P)q[P]+=k*U[P]*this.labelPad[P]/r[5*P];q[A]+=.5*(a[0][A]+a[1][A]),this._text.drawLabel(A,this.labelSize[A],this.labelAngle[A],q,this.labelColor[A],[0,0,0],C,S)}}this._text.unbind()},d.dispose=function(){this._text.dispose(),this._lines.dispose(),this._background.dispose(),this._lines=null,this._text=null,this._background=null,this.gl=null}},5304:function(t,e,r){\"use strict\";t.exports=function(t){for(var e=[],r=[],s=0,l=0;l<3;++l)for(var c=(l+1)%3,u=(l+2)%3,h=[0,0,0],f=[0,0,0],p=-1;p<=1;p+=2){r.push(s,s+2,s+1,s+1,s+2,s+3),h[l]=p,f[l]=p;for(var d=-1;d<=1;d+=2){h[c]=d;for(var m=-1;m<=1;m+=2)h[u]=m,e.push(h[0],h[1],h[2],f[0],f[1],f[2]),s+=1}var g=c;c=u,u=g}var y=n(t,new Float32Array(e)),v=n(t,new Uint16Array(r),t.ELEMENT_ARRAY_BUFFER),x=i(t,[{buffer:y,type:t.FLOAT,size:3,offset:0,stride:24},{buffer:y,type:t.FLOAT,size:3,offset:12,stride:24}],v),_=a(t);return _.attributes.position.location=0,_.attributes.normal.location=1,new o(t,y,x,_)};var n=r(2762),i=r(8116),a=r(1879).bg;function o(t,e,r,n){this.gl=t,this.buffer=e,this.vao=r,this.shader=n}var s=o.prototype;s.draw=function(t,e,r,n,i,a){for(var o=!1,s=0;s<3;++s)o=o||i[s];if(o){var l=this.gl;l.enable(l.POLYGON_OFFSET_FILL),l.polygonOffset(1,2),this.shader.bind(),this.shader.uniforms={model:t,view:e,projection:r,bounds:n,enable:i,colors:a},this.vao.bind(),this.vao.draw(this.gl.TRIANGLES,36),this.vao.unbind(),l.disable(l.POLYGON_OFFSET_FILL)}},s.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},6429:function(t,e,r){\"use strict\";t.exports=function(t,e,r,a,p){i(s,e,t),i(s,r,s);for(var v=0,x=0;x<2;++x){u[2]=a[x][2];for(var _=0;_<2;++_){u[1]=a[_][1];for(var b=0;b<2;++b)u[0]=a[b][0],f(l[v],u,s),v+=1}}var w=-1;for(x=0;x<8;++x){for(var T=l[x][3],k=0;k<3;++k)c[x][k]=l[x][k]/T;p&&(c[x][2]*=-1),T<0&&(w<0||c[x][2]E&&(w|=1<E&&(w|=1<c[x][1])&&(R=x);var F=-1;for(x=0;x<3;++x)(N=R^1<c[B][0]&&(B=N))}var j=m;j[0]=j[1]=j[2]=0,j[n.log2(F^R)]=R&F,j[n.log2(R^B)]=R&B;var U=7^B;U===w||U===D?(U=7^F,j[n.log2(B^U)]=U&B):j[n.log2(F^U)]=U&F;var V=g,q=w;for(A=0;A<3;++A)V[A]=q&1< HALF_PI) && (b <= ONE_AND_HALF_PI)) ?\\n b - PI :\\n b;\\n}\\n\\nfloat look_horizontal_or_vertical(float a, float ratio) {\\n // ratio controls the ratio between being horizontal to (vertical + horizontal)\\n // if ratio is set to 0.5 then it is 50%, 50%.\\n // when using a higher ratio e.g. 0.75 the result would\\n // likely be more horizontal than vertical.\\n\\n float b = positive_angle(a);\\n\\n return\\n (b < ( ratio) * HALF_PI) ? 0.0 :\\n (b < (2.0 - ratio) * HALF_PI) ? -HALF_PI :\\n (b < (2.0 + ratio) * HALF_PI) ? 0.0 :\\n (b < (4.0 - ratio) * HALF_PI) ? HALF_PI :\\n 0.0;\\n}\\n\\nfloat roundTo(float a, float b) {\\n return float(b * floor((a + 0.5 * b) / b));\\n}\\n\\nfloat look_round_n_directions(float a, int n) {\\n float b = positive_angle(a);\\n float div = TWO_PI / float(n);\\n float c = roundTo(b, div);\\n return look_upwards(c);\\n}\\n\\nfloat applyAlignOption(float rawAngle, float delta) {\\n return\\n (option > 2) ? look_round_n_directions(rawAngle + delta, option) : // option 3-n: round to n directions\\n (option == 2) ? look_horizontal_or_vertical(rawAngle + delta, hv_ratio) : // horizontal or vertical\\n (option == 1) ? rawAngle + delta : // use free angle, and flip to align with one direction of the axis\\n (option == 0) ? look_upwards(rawAngle) : // use free angle, and stay upwards\\n (option ==-1) ? 0.0 : // useful for backward compatibility, all texts remains horizontal\\n rawAngle; // otherwise return back raw input angle\\n}\\n\\nbool isAxisTitle = (axis.x == 0.0) &&\\n (axis.y == 0.0) &&\\n (axis.z == 0.0);\\n\\nvoid main() {\\n //Compute world offset\\n float axisDistance = position.z;\\n vec3 dataPosition = axisDistance * axis + offset;\\n\\n float beta = angle; // i.e. user defined attributes for each tick\\n\\n float axisAngle;\\n float clipAngle;\\n float flip;\\n\\n if (enableAlign) {\\n axisAngle = (isAxisTitle) ? HALF_PI :\\n computeViewAngle(dataPosition, dataPosition + axis);\\n clipAngle = computeViewAngle(dataPosition, dataPosition + alignDir);\\n\\n axisAngle += (sin(axisAngle) < 0.0) ? PI : 0.0;\\n clipAngle += (sin(clipAngle) < 0.0) ? PI : 0.0;\\n\\n flip = (dot(vec2(cos(axisAngle), sin(axisAngle)),\\n vec2(sin(clipAngle),-cos(clipAngle))) > 0.0) ? 1.0 : 0.0;\\n\\n beta += applyAlignOption(clipAngle, flip * PI);\\n }\\n\\n //Compute plane offset\\n vec2 planeCoord = position.xy * pixelScale;\\n\\n mat2 planeXform = scale * mat2(\\n cos(beta), sin(beta),\\n -sin(beta), cos(beta)\\n );\\n\\n vec2 viewOffset = 2.0 * planeXform * planeCoord / resolution;\\n\\n //Compute clip position\\n vec3 clipPosition = project(dataPosition);\\n\\n //Apply text offset in clip coordinates\\n clipPosition += vec3(viewOffset, 0.0);\\n\\n //Done\\n gl_Position = vec4(clipPosition, 1.0);\\n}\\n\"]),l=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\nvoid main() {\\n gl_FragColor = color;\\n}\"]);e.Q=function(t){return i(t,s,l,null,[{name:\"position\",type:\"vec3\"}])};var c=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec3 normal;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 enable;\\nuniform vec3 bounds[2];\\n\\nvarying vec3 colorChannel;\\n\\nvoid main() {\\n\\n vec3 signAxis = sign(bounds[1] - bounds[0]);\\n\\n vec3 realNormal = signAxis * normal;\\n\\n if(dot(realNormal, enable) > 0.0) {\\n vec3 minRange = min(bounds[0], bounds[1]);\\n vec3 maxRange = max(bounds[0], bounds[1]);\\n vec3 nPosition = mix(minRange, maxRange, 0.5 * (position + 1.0));\\n gl_Position = projection * (view * (model * vec4(nPosition, 1.0)));\\n } else {\\n gl_Position = vec4(0,0,0,0);\\n }\\n\\n colorChannel = abs(realNormal);\\n}\\n\"]),u=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 colors[3];\\n\\nvarying vec3 colorChannel;\\n\\nvoid main() {\\n gl_FragColor = colorChannel.x * colors[0] +\\n colorChannel.y * colors[1] +\\n colorChannel.z * colors[2];\\n}\"]);e.bg=function(t){return i(t,c,u,null,[{name:\"position\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}])}},4935:function(t,e,r){\"use strict\";t.exports=function(t,e,r,i,o,l){var c=n(t),h=a(t,[{buffer:c,size:3}]),f=s(t);f.attributes.position.location=0;var p=new u(t,f,c,h);return p.update(e,r,i,o,l),p};var n=r(2762),a=r(8116),o=r(4359),s=r(1879).Q,l=window||i.global||{},c=l.__TEXT_CACHE||{};function u(t,e,r,n){this.gl=t,this.shader=e,this.buffer=r,this.vao=n,this.tickOffset=this.tickCount=this.labelOffset=this.labelCount=null}l.__TEXT_CACHE={};var h=u.prototype,f=[0,0];h.bind=function(t,e,r,n){this.vao.bind(),this.shader.bind();var i=this.shader.uniforms;i.model=t,i.view=e,i.projection=r,i.pixelScale=n,f[0]=this.gl.drawingBufferWidth,f[1]=this.gl.drawingBufferHeight,this.shader.uniforms.resolution=f},h.unbind=function(){this.vao.unbind()},h.update=function(t,e,r,n,i){var a=[];function s(t,e,r,n,i,s){var l=[r.style,r.weight,r.variant,r.family].join(\"_\"),u=c[l];u||(u=c[l]={});var h=u[e];h||(h=u[e]=function(t,e){try{return o(t,e)}catch(e){return console.warn('error vectorizing text:\"'+t+'\" error:',e),{cells:[],positions:[]}}}(e,{triangles:!0,font:r.family,fontStyle:r.style,fontWeight:r.weight,fontVariant:r.variant,textAlign:\"center\",textBaseline:\"middle\",lineSpacing:i,styletags:s}));for(var f=(n||12)/12,p=h.positions,d=h.cells,m=0,g=d.length;m=0;--v){var x=p[y[v]];a.push(f*x[0],-f*x[1],t)}}for(var l=[0,0,0],u=[0,0,0],h=[0,0,0],f=[0,0,0],p={breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},d=0;d<3;++d){h[d]=a.length/3|0,s(.5*(t[0][d]+t[1][d]),e[d],r[d],12,1.25,p),f[d]=(a.length/3|0)-h[d],l[d]=a.length/3|0;for(var m=0;m=0&&(i=r.length-n-1);var a=Math.pow(10,i),o=Math.round(t*e*a),s=o+\"\";if(s.indexOf(\"e\")>=0)return s;var l=o/a,c=o%a;o<0?(l=0|-Math.ceil(l),c=0|-c):(l=0|Math.floor(l),c|=0);var u=\"\"+l;if(o<0&&(u=\"-\"+u),i){for(var h=\"\"+c;h.length=t[0][i];--o)a.push({x:o*e[i],text:r(e[i],o)});n.push(a)}return n},e.equal=function(t,e){for(var r=0;r<3;++r){if(t[r].length!==e[r].length)return!1;for(var n=0;nr)throw new Error(\"gl-buffer: If resizing buffer, must not specify offset\");return t.bufferSubData(e,a,i),r}function u(t,e){for(var r=n.malloc(t.length,e),i=t.length,a=0;a=0;--n){if(e[n]!==r)return!1;r*=t[n]}return!0}(t.shape,t.stride))0===t.offset&&t.data.length===t.shape[0]?this.length=c(this.gl,this.type,this.length,this.usage,t.data,e):this.length=c(this.gl,this.type,this.length,this.usage,t.data.subarray(t.offset,t.shape[0]),e);else{var s=n.malloc(t.size,r),l=a(s,t.shape);i.assign(l,t),this.length=c(this.gl,this.type,this.length,this.usage,e<0?s:s.subarray(0,t.size),e),n.free(s)}}else if(Array.isArray(t)){var h;h=this.type===this.gl.ELEMENT_ARRAY_BUFFER?u(t,\"uint16\"):u(t,\"float32\"),this.length=c(this.gl,this.type,this.length,this.usage,e<0?h:h.subarray(0,t.length),e),n.free(h)}else if(\"object\"==typeof t&&\"number\"==typeof t.length)this.length=c(this.gl,this.type,this.length,this.usage,t,e);else{if(\"number\"!=typeof t&&void 0!==t)throw new Error(\"gl-buffer: Invalid data type\");if(e>=0)throw new Error(\"gl-buffer: Cannot specify offset when resizing buffer\");(t|=0)<=0&&(t=1),this.gl.bufferData(this.type,0|t,this.usage),this.length=t}},t.exports=function(t,e,r,n){if(r=r||t.ARRAY_BUFFER,n=n||t.DYNAMIC_DRAW,r!==t.ARRAY_BUFFER&&r!==t.ELEMENT_ARRAY_BUFFER)throw new Error(\"gl-buffer: Invalid type for webgl buffer, must be either gl.ARRAY_BUFFER or gl.ELEMENT_ARRAY_BUFFER\");if(n!==t.DYNAMIC_DRAW&&n!==t.STATIC_DRAW&&n!==t.STREAM_DRAW)throw new Error(\"gl-buffer: Invalid usage for buffer, must be either gl.DYNAMIC_DRAW, gl.STATIC_DRAW or gl.STREAM_DRAW\");var i=t.createBuffer(),a=new s(t,r,i,0,n);return a.update(e),a}},6405:function(t,e,r){\"use strict\";var n=r(2931);t.exports=function(t,e){var r=t.positions,i=t.vectors,a={positions:[],vertexIntensity:[],vertexIntensityBounds:t.vertexIntensityBounds,vectors:[],cells:[],coneOffset:t.coneOffset,colormap:t.colormap};if(0===t.positions.length)return e&&(e[0]=[0,0,0],e[1]=[0,0,0]),a;for(var o=0,s=1/0,l=-1/0,c=1/0,u=-1/0,h=1/0,f=-1/0,p=null,d=null,m=[],g=1/0,y=!1,v=\"raw\"===t.coneSizemode,x=0;xo&&(o=n.length(b)),x&&!v){var w=2*n.distance(p,_)/(n.length(d)+n.length(b));w?(g=Math.min(g,w),y=!1):y=!0}y||(p=_,d=b),m.push(b)}var T=[s,c,h],k=[l,u,f];e&&(e[0]=T,e[1]=k),0===o&&(o=1);var A=1/o;isFinite(g)||(g=1),a.vectorScale=g;var M=t.coneSize||(v?1:.5);t.absoluteConeSize&&(M=t.absoluteConeSize*A),a.coneScale=M,x=0;for(var S=0;x=1},p.isTransparent=function(){return this.opacity<1},p.pickSlots=1,p.setPickBase=function(t){this.pickId=t},p.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,\"lightPosition\"in t&&(this.lightPosition=t.lightPosition),\"opacity\"in t&&(this.opacity=t.opacity),\"ambient\"in t&&(this.ambientLight=t.ambient),\"diffuse\"in t&&(this.diffuseLight=t.diffuse),\"specular\"in t&&(this.specularLight=t.specular),\"roughness\"in t&&(this.roughness=t.roughness),\"fresnel\"in t&&(this.fresnel=t.fresnel),void 0!==t.tubeScale&&(this.tubeScale=t.tubeScale),void 0!==t.vectorScale&&(this.vectorScale=t.vectorScale),void 0!==t.coneScale&&(this.coneScale=t.coneScale),void 0!==t.coneOffset&&(this.coneOffset=t.coneOffset),t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t){for(var e=u({colormap:t,nshades:256,format:\"rgba\"}),r=new Uint8Array(1024),n=0;n<256;++n){for(var i=e[n],a=0;a<3;++a)r[4*n+a]=i[a];r[4*n+3]=255*i[3]}return c(r,[256,256,4],[4,0,1])}(t.colormap)),this.texture.generateMipmap());var r=t.cells,n=t.positions,i=t.vectors;if(n&&r&&i){var a=[],o=[],s=[],l=[],h=[];this.cells=r,this.positions=n,this.vectors=i;var f=t.meshColor||[1,1,1,1],p=t.vertexIntensity,d=1/0,m=-1/0;if(p)if(t.vertexIntensityBounds)d=+t.vertexIntensityBounds[0],m=+t.vertexIntensityBounds[1];else for(var g=0;g0){var m=this.triShader;m.bind(),m.uniforms=c,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()}},p.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||h,n=t.view||h,i=t.projection||h,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s={model:r,view:n,projection:i,clipBounds:a,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,pickId:this.pickId/255},l=this.pickShader;l.bind(),l.uniforms=s,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind())},p.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions[r[1]].slice(0,3),i={position:n,dataCoordinate:n,index:Math.floor(r[1]/48)};return\"cone\"===this.traceType?i.index=Math.floor(r[1]/48):\"streamtube\"===this.traceType&&(i.intensity=this.intensity[r[1]],i.velocity=this.vectors[r[1]].slice(0,3),i.divergence=this.vectors[r[1]][3],i.index=e),i},p.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.pickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleVectors.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleIds.dispose()},t.exports=function(t,e,r){var s=r.shaders;1===arguments.length&&(t=(e=t).gl);var l=function(t,e){var r=n(t,e.meshShader.vertex,e.meshShader.fragment,null,e.meshShader.attributes);return r.attributes.position.location=0,r.attributes.color.location=2,r.attributes.uv.location=3,r.attributes.vector.location=4,r}(t,s),u=function(t,e){var r=n(t,e.pickShader.vertex,e.pickShader.fragment,null,e.pickShader.attributes);return r.attributes.position.location=0,r.attributes.id.location=1,r.attributes.vector.location=4,r}(t,s),h=o(t,c(new Uint8Array([255,255,255,255]),[1,1,4]));h.generateMipmap(),h.minFilter=t.LINEAR_MIPMAP_LINEAR,h.magFilter=t.LINEAR;var p=i(t),d=i(t),m=i(t),g=i(t),y=i(t),v=new f(t,h,l,u,p,d,y,m,g,a(t,[{buffer:p,type:t.FLOAT,size:4},{buffer:y,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:m,type:t.FLOAT,size:4},{buffer:g,type:t.FLOAT,size:2},{buffer:d,type:t.FLOAT,size:4}]),r.traceType||\"cone\");return v.update(e),v}},614:function(t,e,r){var n=r(3236),i=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n // Return up-vector for only-z vector.\\n // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n // From the above if-statement we have ||a|| > 0 U ||b|| > 0.\\n // Assign z = 0, x = -b, y = a:\\n // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n return normalize(vec3(-v.y, v.x, 0.0));\\n } else {\\n return normalize(vec3(0.0, v.z, -v.y));\\n }\\n}\\n\\n// Calculate the cone vertex and normal at the given index.\\n//\\n// The returned vertex is for a cone with its top at origin and height of 1.0,\\n// pointing in the direction of the vector attribute.\\n//\\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\\n// These vertices are used to make up the triangles of the cone by the following:\\n// segment + 0 top vertex\\n// segment + 1 perimeter vertex a+1\\n// segment + 2 perimeter vertex a\\n// segment + 3 center base vertex\\n// segment + 4 perimeter vertex a\\n// segment + 5 perimeter vertex a+1\\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\\n// To go from index to segment, floor(index / 6)\\n// To go from segment to angle, 2*pi * (segment/segmentCount)\\n// To go from index to segment index, index - (segment*6)\\n//\\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\\n\\n const float segmentCount = 8.0;\\n\\n float index = rawIndex - floor(rawIndex /\\n (segmentCount * 6.0)) *\\n (segmentCount * 6.0);\\n\\n float segment = floor(0.001 + index/6.0);\\n float segmentIndex = index - (segment*6.0);\\n\\n normal = -normalize(d);\\n\\n if (segmentIndex > 2.99 && segmentIndex < 3.01) {\\n return mix(vec3(0.0), -d, coneOffset);\\n }\\n\\n float nextAngle = (\\n (segmentIndex > 0.99 && segmentIndex < 1.01) ||\\n (segmentIndex > 4.99 && segmentIndex < 5.01)\\n ) ? 1.0 : 0.0;\\n float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\\n\\n vec3 v1 = mix(d, vec3(0.0), coneOffset);\\n vec3 v2 = v1 - d;\\n\\n vec3 u = getOrthogonalVector(d);\\n vec3 v = normalize(cross(u, d));\\n\\n vec3 x = u * cos(angle) * length(d)*0.25;\\n vec3 y = v * sin(angle) * length(d)*0.25;\\n vec3 v3 = v2 + x + y;\\n if (segmentIndex < 3.0) {\\n vec3 tx = u * sin(angle);\\n vec3 ty = v * -cos(angle);\\n vec3 tangent = tx + ty;\\n normal = normalize(cross(v3 - v1, tangent));\\n }\\n\\n if (segmentIndex == 0.0) {\\n return mix(d, vec3(0.0), coneOffset);\\n }\\n return v3;\\n}\\n\\nattribute vec3 vector;\\nattribute vec4 color, position;\\nattribute vec2 uv;\\n\\nuniform float vectorScale, coneScale, coneOffset;\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 eyePosition, lightPosition;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n // Scale the vector magnitude to stay constant with\\n // model & view changes.\\n vec3 normal;\\n vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector), position.w, coneOffset, normal);\\n vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n //Lighting geometry parameters\\n vec4 cameraCoordinate = view * conePosition;\\n cameraCoordinate.xyz /= cameraCoordinate.w;\\n f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n f_eyeDirection = eyePosition - cameraCoordinate.xyz;\\n f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n // vec4 m_position = model * vec4(conePosition, 1.0);\\n vec4 t_position = view * conePosition;\\n gl_Position = projection * t_position;\\n\\n f_color = color;\\n f_data = conePosition.xyz;\\n f_position = position.xyz;\\n f_uv = uv;\\n}\\n\"]),a=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n float NdotH = max(x, 0.0001);\\n float cos2Alpha = NdotH * NdotH;\\n float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n float roughness2 = roughness * roughness;\\n float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n vec3 lightDirection,\\n vec3 viewDirection,\\n vec3 surfaceNormal,\\n float roughness,\\n float fresnel) {\\n\\n float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n //Half angle vector\\n vec3 H = normalize(lightDirection + viewDirection);\\n\\n //Geometric term\\n float NdotH = max(dot(surfaceNormal, H), 0.0);\\n float VdotH = max(dot(viewDirection, H), 0.000001);\\n float LdotH = max(dot(lightDirection, H), 0.000001);\\n float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n float G = min(1.0, min(G1, G2));\\n \\n //Distribution term\\n float D = beckmannDistribution(NdotH, roughness);\\n\\n //Fresnel term\\n float F = pow(1.0 - VdotN, fresnel);\\n\\n //Multiply terms and done\\n return G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n vec3 N = normalize(f_normal);\\n vec3 L = normalize(f_lightDirection);\\n vec3 V = normalize(f_eyeDirection);\\n\\n if(gl_FrontFacing) {\\n N = -N;\\n }\\n\\n float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\\n float diffuse = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n vec4 surfaceColor = f_color * texture2D(texture, f_uv);\\n vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular, 1.0);\\n\\n gl_FragColor = litColor * opacity;\\n}\\n\"]),o=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n // Return up-vector for only-z vector.\\n // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n // From the above if-statement we have ||a|| > 0 U ||b|| > 0.\\n // Assign z = 0, x = -b, y = a:\\n // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n return normalize(vec3(-v.y, v.x, 0.0));\\n } else {\\n return normalize(vec3(0.0, v.z, -v.y));\\n }\\n}\\n\\n// Calculate the cone vertex and normal at the given index.\\n//\\n// The returned vertex is for a cone with its top at origin and height of 1.0,\\n// pointing in the direction of the vector attribute.\\n//\\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\\n// These vertices are used to make up the triangles of the cone by the following:\\n// segment + 0 top vertex\\n// segment + 1 perimeter vertex a+1\\n// segment + 2 perimeter vertex a\\n// segment + 3 center base vertex\\n// segment + 4 perimeter vertex a\\n// segment + 5 perimeter vertex a+1\\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\\n// To go from index to segment, floor(index / 6)\\n// To go from segment to angle, 2*pi * (segment/segmentCount)\\n// To go from index to segment index, index - (segment*6)\\n//\\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\\n\\n const float segmentCount = 8.0;\\n\\n float index = rawIndex - floor(rawIndex /\\n (segmentCount * 6.0)) *\\n (segmentCount * 6.0);\\n\\n float segment = floor(0.001 + index/6.0);\\n float segmentIndex = index - (segment*6.0);\\n\\n normal = -normalize(d);\\n\\n if (segmentIndex > 2.99 && segmentIndex < 3.01) {\\n return mix(vec3(0.0), -d, coneOffset);\\n }\\n\\n float nextAngle = (\\n (segmentIndex > 0.99 && segmentIndex < 1.01) ||\\n (segmentIndex > 4.99 && segmentIndex < 5.01)\\n ) ? 1.0 : 0.0;\\n float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\\n\\n vec3 v1 = mix(d, vec3(0.0), coneOffset);\\n vec3 v2 = v1 - d;\\n\\n vec3 u = getOrthogonalVector(d);\\n vec3 v = normalize(cross(u, d));\\n\\n vec3 x = u * cos(angle) * length(d)*0.25;\\n vec3 y = v * sin(angle) * length(d)*0.25;\\n vec3 v3 = v2 + x + y;\\n if (segmentIndex < 3.0) {\\n vec3 tx = u * sin(angle);\\n vec3 ty = v * -cos(angle);\\n vec3 tangent = tx + ty;\\n normal = normalize(cross(v3 - v1, tangent));\\n }\\n\\n if (segmentIndex == 0.0) {\\n return mix(d, vec3(0.0), coneOffset);\\n }\\n return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform float vectorScale, coneScale, coneOffset;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n vec3 normal;\\n vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector.xyz), position.w, coneOffset, normal);\\n vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n gl_Position = projection * (view * conePosition);\\n f_id = id;\\n f_position = position.xyz;\\n}\\n\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]);e.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec4\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"vector\",type:\"vec3\"}]},e.pickShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec4\"},{name:\"id\",type:\"vec4\"},{name:\"vector\",type:\"vec3\"}]}},737:function(t){t.exports={0:\"NONE\",1:\"ONE\",2:\"LINE_LOOP\",3:\"LINE_STRIP\",4:\"TRIANGLES\",5:\"TRIANGLE_STRIP\",6:\"TRIANGLE_FAN\",256:\"DEPTH_BUFFER_BIT\",512:\"NEVER\",513:\"LESS\",514:\"EQUAL\",515:\"LEQUAL\",516:\"GREATER\",517:\"NOTEQUAL\",518:\"GEQUAL\",519:\"ALWAYS\",768:\"SRC_COLOR\",769:\"ONE_MINUS_SRC_COLOR\",770:\"SRC_ALPHA\",771:\"ONE_MINUS_SRC_ALPHA\",772:\"DST_ALPHA\",773:\"ONE_MINUS_DST_ALPHA\",774:\"DST_COLOR\",775:\"ONE_MINUS_DST_COLOR\",776:\"SRC_ALPHA_SATURATE\",1024:\"STENCIL_BUFFER_BIT\",1028:\"FRONT\",1029:\"BACK\",1032:\"FRONT_AND_BACK\",1280:\"INVALID_ENUM\",1281:\"INVALID_VALUE\",1282:\"INVALID_OPERATION\",1285:\"OUT_OF_MEMORY\",1286:\"INVALID_FRAMEBUFFER_OPERATION\",2304:\"CW\",2305:\"CCW\",2849:\"LINE_WIDTH\",2884:\"CULL_FACE\",2885:\"CULL_FACE_MODE\",2886:\"FRONT_FACE\",2928:\"DEPTH_RANGE\",2929:\"DEPTH_TEST\",2930:\"DEPTH_WRITEMASK\",2931:\"DEPTH_CLEAR_VALUE\",2932:\"DEPTH_FUNC\",2960:\"STENCIL_TEST\",2961:\"STENCIL_CLEAR_VALUE\",2962:\"STENCIL_FUNC\",2963:\"STENCIL_VALUE_MASK\",2964:\"STENCIL_FAIL\",2965:\"STENCIL_PASS_DEPTH_FAIL\",2966:\"STENCIL_PASS_DEPTH_PASS\",2967:\"STENCIL_REF\",2968:\"STENCIL_WRITEMASK\",2978:\"VIEWPORT\",3024:\"DITHER\",3042:\"BLEND\",3088:\"SCISSOR_BOX\",3089:\"SCISSOR_TEST\",3106:\"COLOR_CLEAR_VALUE\",3107:\"COLOR_WRITEMASK\",3317:\"UNPACK_ALIGNMENT\",3333:\"PACK_ALIGNMENT\",3379:\"MAX_TEXTURE_SIZE\",3386:\"MAX_VIEWPORT_DIMS\",3408:\"SUBPIXEL_BITS\",3410:\"RED_BITS\",3411:\"GREEN_BITS\",3412:\"BLUE_BITS\",3413:\"ALPHA_BITS\",3414:\"DEPTH_BITS\",3415:\"STENCIL_BITS\",3553:\"TEXTURE_2D\",4352:\"DONT_CARE\",4353:\"FASTEST\",4354:\"NICEST\",5120:\"BYTE\",5121:\"UNSIGNED_BYTE\",5122:\"SHORT\",5123:\"UNSIGNED_SHORT\",5124:\"INT\",5125:\"UNSIGNED_INT\",5126:\"FLOAT\",5386:\"INVERT\",5890:\"TEXTURE\",6401:\"STENCIL_INDEX\",6402:\"DEPTH_COMPONENT\",6406:\"ALPHA\",6407:\"RGB\",6408:\"RGBA\",6409:\"LUMINANCE\",6410:\"LUMINANCE_ALPHA\",7680:\"KEEP\",7681:\"REPLACE\",7682:\"INCR\",7683:\"DECR\",7936:\"VENDOR\",7937:\"RENDERER\",7938:\"VERSION\",9728:\"NEAREST\",9729:\"LINEAR\",9984:\"NEAREST_MIPMAP_NEAREST\",9985:\"LINEAR_MIPMAP_NEAREST\",9986:\"NEAREST_MIPMAP_LINEAR\",9987:\"LINEAR_MIPMAP_LINEAR\",10240:\"TEXTURE_MAG_FILTER\",10241:\"TEXTURE_MIN_FILTER\",10242:\"TEXTURE_WRAP_S\",10243:\"TEXTURE_WRAP_T\",10497:\"REPEAT\",10752:\"POLYGON_OFFSET_UNITS\",16384:\"COLOR_BUFFER_BIT\",32769:\"CONSTANT_COLOR\",32770:\"ONE_MINUS_CONSTANT_COLOR\",32771:\"CONSTANT_ALPHA\",32772:\"ONE_MINUS_CONSTANT_ALPHA\",32773:\"BLEND_COLOR\",32774:\"FUNC_ADD\",32777:\"BLEND_EQUATION_RGB\",32778:\"FUNC_SUBTRACT\",32779:\"FUNC_REVERSE_SUBTRACT\",32819:\"UNSIGNED_SHORT_4_4_4_4\",32820:\"UNSIGNED_SHORT_5_5_5_1\",32823:\"POLYGON_OFFSET_FILL\",32824:\"POLYGON_OFFSET_FACTOR\",32854:\"RGBA4\",32855:\"RGB5_A1\",32873:\"TEXTURE_BINDING_2D\",32926:\"SAMPLE_ALPHA_TO_COVERAGE\",32928:\"SAMPLE_COVERAGE\",32936:\"SAMPLE_BUFFERS\",32937:\"SAMPLES\",32938:\"SAMPLE_COVERAGE_VALUE\",32939:\"SAMPLE_COVERAGE_INVERT\",32968:\"BLEND_DST_RGB\",32969:\"BLEND_SRC_RGB\",32970:\"BLEND_DST_ALPHA\",32971:\"BLEND_SRC_ALPHA\",33071:\"CLAMP_TO_EDGE\",33170:\"GENERATE_MIPMAP_HINT\",33189:\"DEPTH_COMPONENT16\",33306:\"DEPTH_STENCIL_ATTACHMENT\",33635:\"UNSIGNED_SHORT_5_6_5\",33648:\"MIRRORED_REPEAT\",33901:\"ALIASED_POINT_SIZE_RANGE\",33902:\"ALIASED_LINE_WIDTH_RANGE\",33984:\"TEXTURE0\",33985:\"TEXTURE1\",33986:\"TEXTURE2\",33987:\"TEXTURE3\",33988:\"TEXTURE4\",33989:\"TEXTURE5\",33990:\"TEXTURE6\",33991:\"TEXTURE7\",33992:\"TEXTURE8\",33993:\"TEXTURE9\",33994:\"TEXTURE10\",33995:\"TEXTURE11\",33996:\"TEXTURE12\",33997:\"TEXTURE13\",33998:\"TEXTURE14\",33999:\"TEXTURE15\",34e3:\"TEXTURE16\",34001:\"TEXTURE17\",34002:\"TEXTURE18\",34003:\"TEXTURE19\",34004:\"TEXTURE20\",34005:\"TEXTURE21\",34006:\"TEXTURE22\",34007:\"TEXTURE23\",34008:\"TEXTURE24\",34009:\"TEXTURE25\",34010:\"TEXTURE26\",34011:\"TEXTURE27\",34012:\"TEXTURE28\",34013:\"TEXTURE29\",34014:\"TEXTURE30\",34015:\"TEXTURE31\",34016:\"ACTIVE_TEXTURE\",34024:\"MAX_RENDERBUFFER_SIZE\",34041:\"DEPTH_STENCIL\",34055:\"INCR_WRAP\",34056:\"DECR_WRAP\",34067:\"TEXTURE_CUBE_MAP\",34068:\"TEXTURE_BINDING_CUBE_MAP\",34069:\"TEXTURE_CUBE_MAP_POSITIVE_X\",34070:\"TEXTURE_CUBE_MAP_NEGATIVE_X\",34071:\"TEXTURE_CUBE_MAP_POSITIVE_Y\",34072:\"TEXTURE_CUBE_MAP_NEGATIVE_Y\",34073:\"TEXTURE_CUBE_MAP_POSITIVE_Z\",34074:\"TEXTURE_CUBE_MAP_NEGATIVE_Z\",34076:\"MAX_CUBE_MAP_TEXTURE_SIZE\",34338:\"VERTEX_ATTRIB_ARRAY_ENABLED\",34339:\"VERTEX_ATTRIB_ARRAY_SIZE\",34340:\"VERTEX_ATTRIB_ARRAY_STRIDE\",34341:\"VERTEX_ATTRIB_ARRAY_TYPE\",34342:\"CURRENT_VERTEX_ATTRIB\",34373:\"VERTEX_ATTRIB_ARRAY_POINTER\",34466:\"NUM_COMPRESSED_TEXTURE_FORMATS\",34467:\"COMPRESSED_TEXTURE_FORMATS\",34660:\"BUFFER_SIZE\",34661:\"BUFFER_USAGE\",34816:\"STENCIL_BACK_FUNC\",34817:\"STENCIL_BACK_FAIL\",34818:\"STENCIL_BACK_PASS_DEPTH_FAIL\",34819:\"STENCIL_BACK_PASS_DEPTH_PASS\",34877:\"BLEND_EQUATION_ALPHA\",34921:\"MAX_VERTEX_ATTRIBS\",34922:\"VERTEX_ATTRIB_ARRAY_NORMALIZED\",34930:\"MAX_TEXTURE_IMAGE_UNITS\",34962:\"ARRAY_BUFFER\",34963:\"ELEMENT_ARRAY_BUFFER\",34964:\"ARRAY_BUFFER_BINDING\",34965:\"ELEMENT_ARRAY_BUFFER_BINDING\",34975:\"VERTEX_ATTRIB_ARRAY_BUFFER_BINDING\",35040:\"STREAM_DRAW\",35044:\"STATIC_DRAW\",35048:\"DYNAMIC_DRAW\",35632:\"FRAGMENT_SHADER\",35633:\"VERTEX_SHADER\",35660:\"MAX_VERTEX_TEXTURE_IMAGE_UNITS\",35661:\"MAX_COMBINED_TEXTURE_IMAGE_UNITS\",35663:\"SHADER_TYPE\",35664:\"FLOAT_VEC2\",35665:\"FLOAT_VEC3\",35666:\"FLOAT_VEC4\",35667:\"INT_VEC2\",35668:\"INT_VEC3\",35669:\"INT_VEC4\",35670:\"BOOL\",35671:\"BOOL_VEC2\",35672:\"BOOL_VEC3\",35673:\"BOOL_VEC4\",35674:\"FLOAT_MAT2\",35675:\"FLOAT_MAT3\",35676:\"FLOAT_MAT4\",35678:\"SAMPLER_2D\",35680:\"SAMPLER_CUBE\",35712:\"DELETE_STATUS\",35713:\"COMPILE_STATUS\",35714:\"LINK_STATUS\",35715:\"VALIDATE_STATUS\",35716:\"INFO_LOG_LENGTH\",35717:\"ATTACHED_SHADERS\",35718:\"ACTIVE_UNIFORMS\",35719:\"ACTIVE_UNIFORM_MAX_LENGTH\",35720:\"SHADER_SOURCE_LENGTH\",35721:\"ACTIVE_ATTRIBUTES\",35722:\"ACTIVE_ATTRIBUTE_MAX_LENGTH\",35724:\"SHADING_LANGUAGE_VERSION\",35725:\"CURRENT_PROGRAM\",36003:\"STENCIL_BACK_REF\",36004:\"STENCIL_BACK_VALUE_MASK\",36005:\"STENCIL_BACK_WRITEMASK\",36006:\"FRAMEBUFFER_BINDING\",36007:\"RENDERBUFFER_BINDING\",36048:\"FRAMEBUFFER_ATTACHMENT_OBJECT_TYPE\",36049:\"FRAMEBUFFER_ATTACHMENT_OBJECT_NAME\",36050:\"FRAMEBUFFER_ATTACHMENT_TEXTURE_LEVEL\",36051:\"FRAMEBUFFER_ATTACHMENT_TEXTURE_CUBE_MAP_FACE\",36053:\"FRAMEBUFFER_COMPLETE\",36054:\"FRAMEBUFFER_INCOMPLETE_ATTACHMENT\",36055:\"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT\",36057:\"FRAMEBUFFER_INCOMPLETE_DIMENSIONS\",36061:\"FRAMEBUFFER_UNSUPPORTED\",36064:\"COLOR_ATTACHMENT0\",36096:\"DEPTH_ATTACHMENT\",36128:\"STENCIL_ATTACHMENT\",36160:\"FRAMEBUFFER\",36161:\"RENDERBUFFER\",36162:\"RENDERBUFFER_WIDTH\",36163:\"RENDERBUFFER_HEIGHT\",36164:\"RENDERBUFFER_INTERNAL_FORMAT\",36168:\"STENCIL_INDEX8\",36176:\"RENDERBUFFER_RED_SIZE\",36177:\"RENDERBUFFER_GREEN_SIZE\",36178:\"RENDERBUFFER_BLUE_SIZE\",36179:\"RENDERBUFFER_ALPHA_SIZE\",36180:\"RENDERBUFFER_DEPTH_SIZE\",36181:\"RENDERBUFFER_STENCIL_SIZE\",36194:\"RGB565\",36336:\"LOW_FLOAT\",36337:\"MEDIUM_FLOAT\",36338:\"HIGH_FLOAT\",36339:\"LOW_INT\",36340:\"MEDIUM_INT\",36341:\"HIGH_INT\",36346:\"SHADER_COMPILER\",36347:\"MAX_VERTEX_UNIFORM_VECTORS\",36348:\"MAX_VARYING_VECTORS\",36349:\"MAX_FRAGMENT_UNIFORM_VECTORS\",37440:\"UNPACK_FLIP_Y_WEBGL\",37441:\"UNPACK_PREMULTIPLY_ALPHA_WEBGL\",37442:\"CONTEXT_LOST_WEBGL\",37443:\"UNPACK_COLORSPACE_CONVERSION_WEBGL\",37444:\"BROWSER_DEFAULT_WEBGL\"}},5171:function(t,e,r){var 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n=r(7766);t.exports=function(t,e,r,n){i||(i=t.FRAMEBUFFER_UNSUPPORTED,a=t.FRAMEBUFFER_INCOMPLETE_ATTACHMENT,o=t.FRAMEBUFFER_INCOMPLETE_DIMENSIONS,s=t.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT);var c=t.getExtension(\"WEBGL_draw_buffers\");if(!l&&c&&function(t,e){var r=t.getParameter(e.MAX_COLOR_ATTACHMENTS_WEBGL);l=new Array(r+1);for(var n=0;n<=r;++n){for(var i=new Array(r),a=0;au||r<0||r>u)throw new Error(\"gl-fbo: Parameters are too large for FBO\");var h=1;if(\"color\"in(n=n||{})){if((h=Math.max(0|n.color,0))<0)throw new Error(\"gl-fbo: Must specify a nonnegative number of colors\");if(h>1){if(!c)throw new Error(\"gl-fbo: Multiple draw buffer extension not supported\");if(h>t.getParameter(c.MAX_COLOR_ATTACHMENTS_WEBGL))throw new Error(\"gl-fbo: Context does not support \"+h+\" draw buffers\")}}var f=t.UNSIGNED_BYTE,p=t.getExtension(\"OES_texture_float\");if(n.float&&h>0){if(!p)throw new Error(\"gl-fbo: Context does not support floating point textures\");f=t.FLOAT}else 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x=r.checkFramebufferStatus(r.FRAMEBUFFER);if(x!==r.FRAMEBUFFER_COMPLETE){for(t._destroyed=!0,r.bindFramebuffer(r.FRAMEBUFFER,null),r.deleteFramebuffer(t.handle),t.handle=null,t.depth&&(t.depth.dispose(),t.depth=null),t._depth_rb&&(r.deleteRenderbuffer(t._depth_rb),t._depth_rb=null),y=0;yi||r<0||r>i)throw new Error(\"gl-fbo: Can't resize FBO, invalid dimensions\");t._shape[0]=e,t._shape[1]=r;for(var a=c(n),o=0;o>8*p&255;this.pickOffset=r,i.bind();var d=i.uniforms;d.viewTransform=t,d.pickOffset=e,d.shape=this.shape;var m=i.attributes;return this.positionBuffer.bind(),m.position.pointer(),this.weightBuffer.bind(),m.weight.pointer(s.UNSIGNED_BYTE,!1),this.idBuffer.bind(),m.pickId.pointer(s.UNSIGNED_BYTE,!1),s.drawArrays(s.TRIANGLES,0,o),r+this.shape[0]*this.shape[1]}}}(),h.pick=function(t,e,r){var n=this.pickOffset,i=this.shape[0]*this.shape[1];if(r=n+i)return null;var a=r-n,o=this.xData,s=this.yData;return{object:this,pointId:a,dataCoord:[o[a%this.shape[0]],s[a/this.shape[0]|0]]}},h.update=function(t){var e=(t=t||{}).shape||[0,0],r=t.x||i(e[0]),o=t.y||i(e[1]),s=t.z||new Float32Array(e[0]*e[1]),l=!1!==t.zsmooth;this.xData=r,this.yData=o;var c,u,h,p,d=t.colorLevels||[0],m=t.colorValues||[0,0,0,1],g=d.length,y=this.bounds;l?(c=y[0]=r[0],u=y[1]=o[0],h=y[2]=r[r.length-1],p=y[3]=o[o.length-1]):(c=y[0]=r[0]+(r[1]-r[0])/2,u=y[1]=o[0]+(o[1]-o[0])/2,h=y[2]=r[r.length-1]+(r[r.length-1]-r[r.length-2])/2,p=y[3]=o[o.length-1]+(o[o.length-1]-o[o.length-2])/2);var v=1/(h-c),x=1/(p-u),_=e[0],b=e[1];this.shape=[_,b];var w=(l?(_-1)*(b-1):_*b)*(f.length>>>1);this.numVertices=w;for(var T=a.mallocUint8(4*w),k=a.mallocFloat32(2*w),A=a.mallocUint8(2*w),M=a.mallocUint32(w),S=0,E=l?_-1:_,C=l?b-1:b,L=0;L max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform sampler2D dashTexture;\\nuniform float dashScale;\\nuniform float opacity;\\n\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n if (\\n outOfRange(clipBounds[0], clipBounds[1], worldPosition) ||\\n fragColor.a * opacity == 0.\\n ) discard;\\n\\n float dashWeight = texture2D(dashTexture, vec2(dashScale * pixelArcLength, 0)).r;\\n if(dashWeight < 0.5) {\\n discard;\\n }\\n gl_FragColor = fragColor * opacity;\\n}\\n\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\n#define FLOAT_MAX 1.70141184e38\\n#define FLOAT_MIN 1.17549435e-38\\n\\n// https://github.com/mikolalysenko/glsl-read-float/blob/master/index.glsl\\nvec4 packFloat(float v) {\\n float av = abs(v);\\n\\n //Handle special cases\\n if(av < FLOAT_MIN) {\\n return vec4(0.0, 0.0, 0.0, 0.0);\\n } else if(v > FLOAT_MAX) {\\n return vec4(127.0, 128.0, 0.0, 0.0) / 255.0;\\n } else if(v < -FLOAT_MAX) {\\n return vec4(255.0, 128.0, 0.0, 0.0) / 255.0;\\n }\\n\\n vec4 c = vec4(0,0,0,0);\\n\\n //Compute exponent and mantissa\\n float e = floor(log2(av));\\n float m = av * pow(2.0, -e) - 1.0;\\n\\n //Unpack mantissa\\n c[1] = floor(128.0 * m);\\n m -= c[1] / 128.0;\\n c[2] = floor(32768.0 * m);\\n m -= c[2] / 32768.0;\\n c[3] = floor(8388608.0 * m);\\n\\n //Unpack exponent\\n float ebias = e + 127.0;\\n c[0] = floor(ebias / 2.0);\\n ebias -= c[0] * 2.0;\\n c[1] += floor(ebias) * 128.0;\\n\\n //Unpack sign bit\\n c[0] += 128.0 * step(0.0, -v);\\n\\n //Scale back to range\\n return c / 255.0;\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform float pickId;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], worldPosition)) discard;\\n\\n gl_FragColor = vec4(pickId/255.0, packFloat(pixelArcLength).xyz);\\n}\"]),l=[{name:\"position\",type:\"vec3\"},{name:\"nextPosition\",type:\"vec3\"},{name:\"arcLength\",type:\"float\"},{name:\"lineWidth\",type:\"float\"},{name:\"color\",type:\"vec4\"}];e.createShader=function(t){return i(t,a,o,null,l)},e.createPickShader=function(t){return i(t,a,s,null,l)}},5714:function(t,e,r){\"use strict\";t.exports=function(t){var e=t.gl||t.scene&&t.scene.gl,r=h(e);r.attributes.position.location=0,r.attributes.nextPosition.location=1,r.attributes.arcLength.location=2,r.attributes.lineWidth.location=3,r.attributes.color.location=4;var o=f(e);o.attributes.position.location=0,o.attributes.nextPosition.location=1,o.attributes.arcLength.location=2,o.attributes.lineWidth.location=3,o.attributes.color.location=4;for(var s=n(e),l=i(e,[{buffer:s,size:3,offset:0,stride:48},{buffer:s,size:3,offset:12,stride:48},{buffer:s,size:1,offset:24,stride:48},{buffer:s,size:1,offset:28,stride:48},{buffer:s,size:4,offset:32,stride:48}]),u=c(new Array(1024),[256,1,4]),p=0;p<1024;++p)u.data[p]=255;var d=a(e,u);d.wrap=e.REPEAT;var m=new y(e,r,o,s,l,d);return m.update(t),m};var n=r(2762),i=r(8116),a=r(7766),o=new Uint8Array(4),s=new Float32Array(o.buffer),l=r(2478),c=r(9618),u=r(7319),h=u.createShader,f=u.createPickShader,p=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function d(t,e){for(var r=0,n=0;n<3;++n){var i=t[n]-e[n];r+=i*i}return Math.sqrt(r)}function m(t){for(var e=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],r=0;r<3;++r)e[0][r]=Math.max(t[0][r],e[0][r]),e[1][r]=Math.min(t[1][r],e[1][r]);return e}function g(t,e,r,n){this.arcLength=t,this.position=e,this.index=r,this.dataCoordinate=n}function y(t,e,r,n,i,a){this.gl=t,this.shader=e,this.pickShader=r,this.buffer=n,this.vao=i,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.points=[],this.arcLength=[],this.vertexCount=0,this.bounds=[[0,0,0],[0,0,0]],this.pickId=0,this.lineWidth=1,this.texture=a,this.dashScale=1,this.opacity=1,this.hasAlpha=!1,this.dirty=!0,this.pixelRatio=1}var v=y.prototype;v.isTransparent=function(){return this.hasAlpha},v.isOpaque=function(){return!this.hasAlpha},v.pickSlots=1,v.setPickBase=function(t){this.pickId=t},v.drawTransparent=v.draw=function(t){if(this.vertexCount){var e=this.gl,r=this.shader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,clipBounds:m(this.clipBounds),dashTexture:this.texture.bind(),dashScale:this.dashScale/this.arcLength[this.arcLength.length-1],opacity:this.opacity,screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},v.drawPick=function(t){if(this.vertexCount){var e=this.gl,r=this.pickShader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,pickId:this.pickId,clipBounds:m(this.clipBounds),screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},v.update=function(t){var e,r;this.dirty=!0;var n=!!t.connectGaps;\"dashScale\"in t&&(this.dashScale=t.dashScale),this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=+t.opacity,this.opacity<1&&(this.hasAlpha=!0));var i=[],a=[],o=[],s=0,u=0,h=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],f=t.position||t.positions;if(f){var p=t.color||t.colors||[0,0,0,1],m=t.lineWidth||1,g=!1;t:for(e=1;e0){for(var w=0;w<24;++w)i.push(i[i.length-12]);u+=2,g=!0}continue t}h[0][r]=Math.min(h[0][r],_[r],b[r]),h[1][r]=Math.max(h[1][r],_[r],b[r])}Array.isArray(p[0])?(y=p.length>e-1?p[e-1]:p.length>0?p[p.length-1]:[0,0,0,1],v=p.length>e?p[e]:p.length>0?p[p.length-1]:[0,0,0,1]):y=v=p,3===y.length&&(y=[y[0],y[1],y[2],1]),3===v.length&&(v=[v[0],v[1],v[2],1]),!this.hasAlpha&&y[3]<1&&(this.hasAlpha=!0),x=Array.isArray(m)?m.length>e-1?m[e-1]:m.length>0?m[m.length-1]:[0,0,0,1]:m;var T=s;if(s+=d(_,b),g){for(r=0;r<2;++r)i.push(_[0],_[1],_[2],b[0],b[1],b[2],T,x,y[0],y[1],y[2],y[3]);u+=2,g=!1}i.push(_[0],_[1],_[2],b[0],b[1],b[2],T,x,y[0],y[1],y[2],y[3],_[0],_[1],_[2],b[0],b[1],b[2],T,-x,y[0],y[1],y[2],y[3],b[0],b[1],b[2],_[0],_[1],_[2],s,-x,v[0],v[1],v[2],v[3],b[0],b[1],b[2],_[0],_[1],_[2],s,x,v[0],v[1],v[2],v[3]),u+=4}}if(this.buffer.update(i),a.push(s),o.push(f[f.length-1].slice()),this.bounds=h,this.vertexCount=u,this.points=o,this.arcLength=a,\"dashes\"in t){var k=t.dashes.slice();for(k.unshift(0),e=1;e1.0001)return null;y+=g[h]}return Math.abs(y-1)>.001?null:[f,s(t,g),g]}},840:function(t,e,r){var n=r(3236),i=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, normal;\\nattribute vec4 color;\\nattribute vec2 uv;\\n\\nuniform mat4 model\\n , view\\n , projection\\n , inverseModel;\\nuniform vec3 eyePosition\\n , lightPosition;\\n\\nvarying vec3 f_normal\\n , f_lightDirection\\n , f_eyeDirection\\n , f_data;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvec4 project(vec3 p) {\\n return projection * (view * (model * vec4(p, 1.0)));\\n}\\n\\nvoid main() {\\n gl_Position = project(position);\\n\\n //Lighting geometry parameters\\n vec4 cameraCoordinate = view * vec4(position , 1.0);\\n cameraCoordinate.xyz /= cameraCoordinate.w;\\n f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n f_eyeDirection = eyePosition - cameraCoordinate.xyz;\\n f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n f_color = color;\\n f_data = position;\\n f_uv = uv;\\n}\\n\"]),a=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n float NdotH = max(x, 0.0001);\\n float cos2Alpha = NdotH * NdotH;\\n float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n float roughness2 = roughness * roughness;\\n float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n vec3 lightDirection,\\n vec3 viewDirection,\\n vec3 surfaceNormal,\\n float roughness,\\n float fresnel) {\\n\\n float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n //Half angle vector\\n vec3 H = normalize(lightDirection + viewDirection);\\n\\n //Geometric term\\n float NdotH = max(dot(surfaceNormal, H), 0.0);\\n float VdotH = max(dot(viewDirection, H), 0.000001);\\n float LdotH = max(dot(lightDirection, H), 0.000001);\\n float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n float G = min(1.0, min(G1, G2));\\n \\n //Distribution term\\n float D = beckmannDistribution(NdotH, roughness);\\n\\n //Fresnel term\\n float F = pow(1.0 - VdotN, fresnel);\\n\\n //Multiply terms and done\\n return G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\n//#pragma glslify: beckmann = require(glsl-specular-beckmann) // used in gl-surface3d\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness\\n , fresnel\\n , kambient\\n , kdiffuse\\n , kspecular;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal\\n , f_lightDirection\\n , f_eyeDirection\\n , f_data;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n if (f_color.a == 0.0 ||\\n outOfRange(clipBounds[0], clipBounds[1], f_data)\\n ) discard;\\n\\n vec3 N = normalize(f_normal);\\n vec3 L = normalize(f_lightDirection);\\n vec3 V = normalize(f_eyeDirection);\\n\\n if(gl_FrontFacing) {\\n N = -N;\\n }\\n\\n float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\\n //float specular = max(0.0, beckmann(L, V, N, roughness)); // used in gl-surface3d\\n\\n float diffuse = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n vec4 surfaceColor = vec4(f_color.rgb, 1.0) * texture2D(texture, f_uv);\\n vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular, 1.0);\\n\\n gl_FragColor = litColor * f_color.a;\\n}\\n\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 uv;\\n\\nuniform mat4 model, view, projection;\\n\\nvarying vec4 f_color;\\nvarying vec3 f_data;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n gl_Position = projection * (view * (model * vec4(position, 1.0)));\\n f_color = color;\\n f_data = position;\\n f_uv = uv;\\n}\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform sampler2D texture;\\nuniform float opacity;\\n\\nvarying vec4 f_color;\\nvarying vec3 f_data;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], f_data)) discard;\\n\\n gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\\n}\"]),l=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 uv;\\nattribute float pointSize;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n gl_Position = vec4(0.0, 0.0 ,0.0 ,0.0);\\n } else {\\n gl_Position = projection * (view * (model * vec4(position, 1.0)));\\n }\\n gl_PointSize = pointSize;\\n f_color = color;\\n f_uv = uv;\\n}\"]),c=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D texture;\\nuniform float opacity;\\n\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n vec2 pointR = gl_PointCoord.xy - vec2(0.5, 0.5);\\n if(dot(pointR, pointR) > 0.25) {\\n discard;\\n }\\n gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\\n}\"]),u=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n gl_Position = projection * (view * (model * vec4(position, 1.0)));\\n f_id = id;\\n f_position = position;\\n}\"]),h=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]),f=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute float pointSize;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n gl_Position = vec4(0.0, 0.0, 0.0, 0.0);\\n } else {\\n gl_Position = projection * (view * (model * vec4(position, 1.0)));\\n gl_PointSize = pointSize;\\n }\\n f_id = id;\\n f_position = position;\\n}\"]),p=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\n\\nvoid main() {\\n gl_Position = projection * (view * (model * vec4(position, 1.0)));\\n}\"]),d=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec3 contourColor;\\n\\nvoid main() {\\n gl_FragColor = vec4(contourColor, 1.0);\\n}\\n\"]);e.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},e.wireShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},e.pointShader={vertex:l,fragment:c,attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"pointSize\",type:\"float\"}]},e.pickShader={vertex:u,fragment:h,attributes:[{name:\"position\",type:\"vec3\"},{name:\"id\",type:\"vec4\"}]},e.pointPickShader={vertex:f,fragment:h,attributes:[{name:\"position\",type:\"vec3\"},{name:\"pointSize\",type:\"float\"},{name:\"id\",type:\"vec4\"}]},e.contourShader={vertex:p,fragment:d,attributes:[{name:\"position\",type:\"vec3\"}]}},7201:function(t,e,r){\"use strict\";var n=r(9405),i=r(2762),a=r(8116),o=r(7766),s=r(8406),l=r(6760),c=r(7608),u=r(9618),h=r(6729),f=r(7765),p=r(1888),d=r(840),m=r(7626),g=d.meshShader,y=d.wireShader,v=d.pointShader,x=d.pickShader,_=d.pointPickShader,b=d.contourShader,w=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function T(t,e,r,n,i,a,o,s,l,c,u,h,f,p,d,m,g,y,v,x,_,b,T,k,A,M,S){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.lineShader=n,this.pointShader=i,this.pickShader=a,this.pointPickShader=o,this.contourShader=s,this.trianglePositions=l,this.triangleColors=u,this.triangleNormals=f,this.triangleUVs=h,this.triangleIds=c,this.triangleVAO=p,this.triangleCount=0,this.lineWidth=1,this.edgePositions=d,this.edgeColors=g,this.edgeUVs=y,this.edgeIds=m,this.edgeVAO=v,this.edgeCount=0,this.pointPositions=x,this.pointColors=b,this.pointUVs=T,this.pointSizes=k,this.pointIds=_,this.pointVAO=A,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=M,this.contourVAO=S,this.contourCount=0,this.contourColor=[0,0,0],this.contourEnable=!0,this.pickVertex=!0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.hasAlpha=!1,this.opacityscale=!1,this._model=w,this._view=w,this._projection=w,this._resolution=[1,1]}var k=T.prototype;function A(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;rt&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}function M(t){var e=n(t,v.vertex,v.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.pointSize.location=4,e}function S(t){var e=n(t,x.vertex,x.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e}function E(t){var e=n(t,_.vertex,_.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e.attributes.pointSize.location=4,e}function C(t){var e=n(t,b.vertex,b.fragment);return e.attributes.position.location=0,e}k.isOpaque=function(){return!this.hasAlpha},k.isTransparent=function(){return this.hasAlpha},k.pickSlots=1,k.setPickBase=function(t){this.pickId=t},k.highlight=function(t){if(t&&this.contourEnable){for(var e=f(this.cells,this.intensity,t.intensity),r=e.cells,n=e.vertexIds,i=e.vertexWeights,a=r.length,o=p.mallocFloat32(6*a),s=0,l=0;l0&&((h=this.triShader).bind(),h.uniforms=s,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&this.lineWidth>0&&((h=this.lineShader).bind(),h.uniforms=s,this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0&&((h=this.pointShader).bind(),h.uniforms=s,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind()),this.contourEnable&&this.contourCount>0&&this.contourLineWidth>0&&((h=this.contourShader).bind(),h.uniforms=s,this.contourVAO.bind(),e.drawArrays(e.LINES,0,this.contourCount),this.contourVAO.unbind())},k.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s,l={model:r,view:n,projection:i,clipBounds:a,pickId:this.pickId/255};(s=this.pickShader).bind(),s.uniforms=l,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&(this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0&&((s=this.pointPickShader).bind(),s.uniforms=l,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind())},k.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;for(var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions,i=new Array(r.length),a=0;ai[k]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=g[t],r.uniforms.angle=y[t],a.drawArrays(a.TRIANGLES,i[k],i[A]-i[k]))),v[t]&&T&&(u[1^t]-=M*p*x[t],r.uniforms.dataAxis=h,r.uniforms.screenOffset=u,r.uniforms.color=_[t],r.uniforms.angle=b[t],a.drawArrays(a.TRIANGLES,w,T)),u[1^t]=M*s[2+(1^t)]-1,d[t+2]&&(u[1^t]+=M*p*m[t+2],ki[k]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=g[t+2],r.uniforms.angle=y[t+2],a.drawArrays(a.TRIANGLES,i[k],i[A]-i[k]))),v[t+2]&&T&&(u[1^t]+=M*p*x[t+2],r.uniforms.dataAxis=h,r.uniforms.screenOffset=u,r.uniforms.color=_[t+2],r.uniforms.angle=b[t+2],a.drawArrays(a.TRIANGLES,w,T))}),m.drawTitle=function(){var t=[0,0],e=[0,0];return function(){var r=this.plot,n=this.shader,i=r.gl,a=r.screenBox,o=r.titleCenter,s=r.titleAngle,l=r.titleColor,c=r.pixelRatio;if(this.titleCount){for(var u=0;u<2;++u)e[u]=2*(o[u]*c-a[u])/(a[2+u]-a[u])-1;n.bind(),n.uniforms.dataAxis=t,n.uniforms.screenOffset=e,n.uniforms.angle=s,n.uniforms.color=l,i.drawArrays(i.TRIANGLES,this.titleOffset,this.titleCount)}}}(),m.bind=(f=[0,0],p=[0,0],d=[0,0],function(){var t=this.plot,e=this.shader,r=t._tickBounds,n=t.dataBox,i=t.screenBox,a=t.viewBox;e.bind();for(var o=0;o<2;++o){var s=r[o],l=r[o+2]-s,c=.5*(n[o+2]+n[o]),u=n[o+2]-n[o],h=a[o],m=a[o+2]-h,g=i[o],y=i[o+2]-g;p[o]=2*l/u*m/y,f[o]=2*(s-c)/u*m/y}d[1]=2*t.pixelRatio/(i[3]-i[1]),d[0]=d[1]*(i[3]-i[1])/(i[2]-i[0]),e.uniforms.dataScale=p,e.uniforms.dataShift=f,e.uniforms.textScale=d,this.vbo.bind(),e.attributes.textCoordinate.pointer()}),m.update=function(t){var e,r,n,i,o,s=[],l=t.ticks,c=t.bounds;for(o=0;o<2;++o){var u=[Math.floor(s.length/3)],h=[-1/0],f=l[o];for(e=0;e=0){var m=e[d]-n[d]*(e[d+2]-e[d])/(n[d+2]-n[d]);0===d?o.drawLine(m,e[1],m,e[3],p[d],f[d]):o.drawLine(e[0],m,e[2],m,p[d],f[d])}}for(d=0;d=0;--t)this.objects[t].dispose();for(this.objects.length=0,t=this.overlays.length-1;t>=0;--t)this.overlays[t].dispose();this.overlays.length=0,this.gl=null},c.addObject=function(t){this.objects.indexOf(t)<0&&(this.objects.push(t),this.setDirty())},c.removeObject=function(t){for(var e=this.objects,r=0;rMath.abs(e))c.rotate(a,0,0,-t*r*Math.PI*d.rotateSpeed/window.innerWidth);else if(!d._ortho){var o=-d.zoomSpeed*i*e/window.innerHeight*(a-c.lastT())/20;c.pan(a,0,0,h*(Math.exp(o)-1))}}}),!0)},d.enableMouseListeners(),d};var n=r(3025),i=r(6296),a=r(351),o=r(8512),s=r(24),l=r(7520)},799:function(t,e,r){var n=r(3236),i=r(9405),a=n([\"precision mediump float;\\n#define GLSLIFY 1\\nattribute vec2 position;\\nvarying vec2 uv;\\nvoid main() {\\n uv = position;\\n gl_Position = vec4(position, 0, 1);\\n}\"]),o=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D accumBuffer;\\nvarying vec2 uv;\\n\\nvoid main() {\\n vec4 accum = texture2D(accumBuffer, 0.5 * (uv + 1.0));\\n gl_FragColor = min(vec4(1,1,1,1), accum);\\n}\"]);t.exports=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec2\"}])}},4100:function(t,e,r){\"use strict\";var n=r(4437),i=r(3837),a=r(5445),o=r(4449),s=r(3589),l=r(2260),c=r(7169),u=r(351),h=r(4772),f=r(4040),p=r(799),d=r(9216)({tablet:!0,featureDetect:!0});function m(){this.mouse=[-1,-1],this.screen=null,this.distance=1/0,this.index=null,this.dataCoordinate=null,this.dataPosition=null,this.object=null,this.data=null}function g(t){var e=Math.round(Math.log(Math.abs(t))/Math.log(10));if(e<0){var r=Math.round(Math.pow(10,-e));return Math.ceil(t*r)/r}return e>0?(r=Math.round(Math.pow(10,e)),Math.ceil(t/r)*r):Math.ceil(t)}function y(t){return\"boolean\"!=typeof t||t}t.exports={createScene:function(t){(t=t||{}).camera=t.camera||{};var e=t.canvas;e||(e=document.createElement(\"canvas\"),t.container?t.container.appendChild(e):document.body.appendChild(e));var r=t.gl;if(r||(t.glOptions&&(d=!!t.glOptions.preserveDrawingBuffer),r=function(t,e){var r=null;try{(r=t.getContext(\"webgl\",e))||(r=t.getContext(\"experimental-webgl\",e))}catch(t){return null}return r}(e,t.glOptions||{premultipliedAlpha:!0,antialias:!0,preserveDrawingBuffer:d})),!r)throw new Error(\"webgl not supported\");var v=t.bounds||[[-10,-10,-10],[10,10,10]],x=new m,_=l(r,r.drawingBufferWidth,r.drawingBufferHeight,{preferFloat:!d}),b=p(r),w=t.cameraObject&&!0===t.cameraObject._ortho||t.camera.projection&&\"orthographic\"===t.camera.projection.type||!1,T={eye:t.camera.eye||[2,0,0],center:t.camera.center||[0,0,0],up:t.camera.up||[0,1,0],zoomMin:t.camera.zoomMax||.1,zoomMax:t.camera.zoomMin||100,mode:t.camera.mode||\"turntable\",_ortho:w},k=t.axes||{},A=i(r,k);A.enable=!k.disable;var M=t.spikes||{},S=o(r,M),E=[],C=[],L=[],I=[],P=!0,z=!0,O={view:null,projection:new Array(16),model:new Array(16),_ortho:!1},D=(z=!0,[r.drawingBufferWidth,r.drawingBufferHeight]),R=t.cameraObject||n(e,T),F={gl:r,contextLost:!1,pixelRatio:t.pixelRatio||1,canvas:e,selection:x,camera:R,axes:A,axesPixels:null,spikes:S,bounds:v,objects:E,shape:D,aspect:t.aspectRatio||[1,1,1],pickRadius:t.pickRadius||10,zNear:t.zNear||.01,zFar:t.zFar||1e3,fovy:t.fovy||Math.PI/4,clearColor:t.clearColor||[0,0,0,0],autoResize:y(t.autoResize),autoBounds:y(t.autoBounds),autoScale:!!t.autoScale,autoCenter:y(t.autoCenter),clipToBounds:y(t.clipToBounds),snapToData:!!t.snapToData,onselect:t.onselect||null,onrender:t.onrender||null,onclick:t.onclick||null,cameraParams:O,oncontextloss:null,mouseListener:null,_stopped:!1,getAspectratio:function(){return{x:this.aspect[0],y:this.aspect[1],z:this.aspect[2]}},setAspectratio:function(t){this.aspect[0]=t.x,this.aspect[1]=t.y,this.aspect[2]=t.z,z=!0},setBounds:function(t,e){this.bounds[0][t]=e.min,this.bounds[1][t]=e.max},setClearColor:function(t){this.clearColor=t},clearRGBA:function(){this.gl.clearColor(this.clearColor[0],this.clearColor[1],this.clearColor[2],this.clearColor[3]),this.gl.clear(this.gl.COLOR_BUFFER_BIT|this.gl.DEPTH_BUFFER_BIT)}},B=[r.drawingBufferWidth/F.pixelRatio|0,r.drawingBufferHeight/F.pixelRatio|0];function N(){if(!F._stopped&&F.autoResize){var t=e.parentNode,r=1,n=1;t&&t!==document.body?(r=t.clientWidth,n=t.clientHeight):(r=window.innerWidth,n=window.innerHeight);var i=0|Math.ceil(r*F.pixelRatio),a=0|Math.ceil(n*F.pixelRatio);if(i!==e.width||a!==e.height){e.width=i,e.height=a;var o=e.style;o.position=o.position||\"absolute\",o.left=\"0px\",o.top=\"0px\",o.width=r+\"px\",o.height=n+\"px\",P=!0}}}function j(){for(var t=E.length,e=I.length,n=0;n0&&0===L[e-1];)L.pop(),I.pop().dispose()}function U(){if(F.contextLost)return!0;r.isContextLost()&&(F.contextLost=!0,F.mouseListener.enabled=!1,F.selection.object=null,F.oncontextloss&&F.oncontextloss())}F.autoResize&&N(),window.addEventListener(\"resize\",N),F.update=function(t){F._stopped||(t=t||{},P=!0,z=!0)},F.add=function(t){F._stopped||(t.axes=A,E.push(t),C.push(-1),P=!0,z=!0,j())},F.remove=function(t){if(!F._stopped){var e=E.indexOf(t);e<0||(E.splice(e,1),C.pop(),P=!0,z=!0,j())}},F.dispose=function(){if(!F._stopped&&(F._stopped=!0,window.removeEventListener(\"resize\",N),e.removeEventListener(\"webglcontextlost\",U),F.mouseListener.enabled=!1,!F.contextLost)){A.dispose(),S.dispose();for(var t=0;tx.distance)continue;for(var c=0;c 1.0) {\\n discard;\\n }\\n baseColor = mix(borderColor, color, step(radius, centerFraction));\\n gl_FragColor = vec4(baseColor.rgb * baseColor.a, baseColor.a);\\n }\\n}\\n\"]),e.pickVertex=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 pickId;\\n\\nuniform mat3 matrix;\\nuniform float pointSize;\\nuniform vec4 pickOffset;\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n vec3 hgPosition = matrix * vec3(position, 1);\\n gl_Position = vec4(hgPosition.xy, 0, hgPosition.z);\\n gl_PointSize = pointSize;\\n\\n vec4 id = pickId + pickOffset;\\n id.y += floor(id.x / 256.0);\\n id.x -= floor(id.x / 256.0) * 256.0;\\n\\n id.z += floor(id.y / 256.0);\\n id.y -= floor(id.y / 256.0) * 256.0;\\n\\n id.w += floor(id.z / 256.0);\\n id.z -= floor(id.z / 256.0) * 256.0;\\n\\n fragId = id;\\n}\\n\"]),e.pickFragment=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n float radius = length(2.0 * gl_PointCoord.xy - 1.0);\\n if(radius > 1.0) {\\n discard;\\n }\\n gl_FragColor = fragId / 255.0;\\n}\\n\"])},4696:function(t,e,r){\"use strict\";var n=r(9405),i=r(2762),a=r(1888),o=r(6640);function s(t,e,r,n,i){this.plot=t,this.offsetBuffer=e,this.pickBuffer=r,this.shader=n,this.pickShader=i,this.sizeMin=.5,this.sizeMinCap=2,this.sizeMax=20,this.areaRatio=1,this.pointCount=0,this.color=[1,0,0,1],this.borderColor=[0,0,0,1],this.blend=!1,this.pickOffset=0,this.points=null}t.exports=function(t,e){var r=t.gl,a=new s(t,i(r),i(r),n(r,o.pointVertex,o.pointFragment),n(r,o.pickVertex,o.pickFragment));return a.update(e),t.addObject(a),a};var l,c,u=s.prototype;u.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.offsetBuffer.dispose(),this.pickBuffer.dispose(),this.plot.removeObject(this)},u.update=function(t){var e;function r(e,r){return e in t?t[e]:r}t=t||{},this.sizeMin=r(\"sizeMin\",.5),this.sizeMax=r(\"sizeMax\",20),this.color=r(\"color\",[1,0,0,1]).slice(),this.areaRatio=r(\"areaRatio\",1),this.borderColor=r(\"borderColor\",[0,0,0,1]).slice(),this.blend=r(\"blend\",!1);var n=t.positions.length>>>1,i=t.positions instanceof Float32Array,o=t.idToIndex instanceof Int32Array&&t.idToIndex.length>=n,s=t.positions,l=i?s:a.mallocFloat32(s.length),c=o?t.idToIndex:a.mallocInt32(n);if(i||l.set(s),!o)for(l.set(s),e=0;e>>1;for(r=0;r=e[0]&&a<=e[2]&&o>=e[1]&&o<=e[3]&&n++}return n}(this.points,i),u=this.plot.pickPixelRatio*Math.max(Math.min(this.sizeMinCap,this.sizeMin),Math.min(this.sizeMax,this.sizeMax/Math.pow(s,.33333)));l[0]=2/a,l[4]=2/o,l[6]=-2*i[0]/a-1,l[7]=-2*i[1]/o-1,this.offsetBuffer.bind(),r.bind(),r.attributes.position.pointer(),r.uniforms.matrix=l,r.uniforms.color=this.color,r.uniforms.borderColor=this.borderColor,r.uniforms.pointCloud=u<5,r.uniforms.pointSize=u,r.uniforms.centerFraction=Math.min(1,Math.max(0,Math.sqrt(1-this.areaRatio))),e&&(c[0]=255&t,c[1]=t>>8&255,c[2]=t>>16&255,c[3]=t>>24&255,this.pickBuffer.bind(),r.attributes.pickId.pointer(n.UNSIGNED_BYTE),r.uniforms.pickOffset=c,this.pickOffset=t);var h=n.getParameter(n.BLEND),f=n.getParameter(n.DITHER);return h&&!this.blend&&n.disable(n.BLEND),f&&n.disable(n.DITHER),n.drawArrays(n.POINTS,0,this.pointCount),h&&!this.blend&&n.enable(n.BLEND),f&&n.enable(n.DITHER),t+this.pointCount}),u.draw=u.unifiedDraw,u.drawPick=u.unifiedDraw,u.pick=function(t,e,r){var n=this.pickOffset,i=this.pointCount;if(r=n+i)return null;var a=r-n,o=this.points;return{object:this,pointId:a,dataCoord:[o[2*a],o[2*a+1]]}}},783:function(t){t.exports=function(t,e,r,n){var i,a,o,s,l,c=e[0],u=e[1],h=e[2],f=e[3],p=r[0],d=r[1],m=r[2],g=r[3];return(a=c*p+u*d+h*m+f*g)<0&&(a=-a,p=-p,d=-d,m=-m,g=-g),1-a>1e-6?(i=Math.acos(a),o=Math.sin(i),s=Math.sin((1-n)*i)/o,l=Math.sin(n*i)/o):(s=1-n,l=n),t[0]=s*c+l*p,t[1]=s*u+l*d,t[2]=s*h+l*m,t[3]=s*f+l*g,t}},5964:function(t){\"use strict\";t.exports=function(t){return t||0===t?t.toString():\"\"}},9366:function(t,e,r){\"use strict\";var n=r(4359);t.exports=function(t,e,r){var a=[e.style,e.weight,e.variant,e.family].join(\"_\"),o=i[a];if(o||(o=i[a]={}),t in o)return o[t];var s={textAlign:\"center\",textBaseline:\"middle\",lineHeight:1,font:e.family,fontStyle:e.style,fontWeight:e.weight,fontVariant:e.variant,lineSpacing:1.25,styletags:{breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},triangles:!0},l=n(t,s);s.triangles=!1;var c,u,h=n(t,s);if(r&&1!==r){for(c=0;c max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform vec4 highlightId;\\nuniform float highlightScale;\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n gl_Position = vec4(0,0,0,0);\\n } else {\\n float scale = 1.0;\\n if(distance(highlightId, id) < 0.0001) {\\n scale = highlightScale;\\n }\\n\\n vec4 worldPosition = model * vec4(position, 1);\\n vec4 viewPosition = view * worldPosition;\\n viewPosition = viewPosition / viewPosition.w;\\n vec4 clipPosition = projection * (viewPosition + scale * vec4(glyph.x, -glyph.y, 0, 0));\\n\\n gl_Position = clipPosition;\\n interpColor = color;\\n pickId = id;\\n dataCoordinate = position;\\n }\\n}\"]),o=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform vec2 screenSize;\\nuniform vec3 clipBounds[2];\\nuniform float highlightScale, pixelRatio;\\nuniform vec4 highlightId;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n gl_Position = vec4(0,0,0,0);\\n } else {\\n float scale = pixelRatio;\\n if(distance(highlightId.bgr, id.bgr) < 0.001) {\\n scale *= highlightScale;\\n }\\n\\n vec4 worldPosition = model * vec4(position, 1.0);\\n vec4 viewPosition = view * worldPosition;\\n vec4 clipPosition = projection * viewPosition;\\n clipPosition /= clipPosition.w;\\n\\n gl_Position = clipPosition + vec4(screenSize * scale * vec2(glyph.x, -glyph.y), 0.0, 0.0);\\n interpColor = color;\\n pickId = id;\\n dataCoordinate = position;\\n }\\n}\"]),s=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform float highlightScale;\\nuniform vec4 highlightId;\\nuniform vec3 axes[2];\\nuniform mat4 model, view, projection;\\nuniform vec2 screenSize;\\nuniform vec3 clipBounds[2];\\nuniform float scale, pixelRatio;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n gl_Position = vec4(0,0,0,0);\\n } else {\\n float lscale = pixelRatio * scale;\\n if(distance(highlightId, id) < 0.0001) {\\n lscale *= highlightScale;\\n }\\n\\n vec4 clipCenter = projection * (view * (model * vec4(position, 1)));\\n vec3 dataPosition = position + 0.5*lscale*(axes[0] * glyph.x + axes[1] * glyph.y) * clipCenter.w * screenSize.y;\\n vec4 clipPosition = projection * (view * (model * vec4(dataPosition, 1)));\\n\\n gl_Position = clipPosition;\\n interpColor = color;\\n pickId = id;\\n dataCoordinate = dataPosition;\\n }\\n}\\n\"]),l=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 fragClipBounds[2];\\nuniform float opacity;\\n\\nvarying vec4 interpColor;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n if (\\n outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate) ||\\n interpColor.a * opacity == 0.\\n ) discard;\\n gl_FragColor = interpColor * opacity;\\n}\\n\"]),c=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 fragClipBounds[2];\\nuniform float pickGroup;\\n\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n if (outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate)) discard;\\n\\n gl_FragColor = vec4(pickGroup, pickId.bgr);\\n}\"]),u=[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"glyph\",type:\"vec2\"},{name:\"id\",type:\"vec4\"}],h={vertex:a,fragment:l,attributes:u},f={vertex:o,fragment:l,attributes:u},p={vertex:s,fragment:l,attributes:u},d={vertex:a,fragment:c,attributes:u},m={vertex:o,fragment:c,attributes:u},g={vertex:s,fragment:c,attributes:u};function y(t,e){var r=n(t,e),i=r.attributes;return i.position.location=0,i.color.location=1,i.glyph.location=2,i.id.location=3,r}e.createPerspective=function(t){return y(t,h)},e.createOrtho=function(t){return y(t,f)},e.createProject=function(t){return y(t,p)},e.createPickPerspective=function(t){return y(t,d)},e.createPickOrtho=function(t){return y(t,m)},e.createPickProject=function(t){return y(t,g)}},8418:function(t,e,r){\"use strict\";var n=r(5219),i=r(2762),a=r(8116),o=r(1888),s=r(6760),l=r(1283),c=r(9366),u=r(5964),h=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],f=ArrayBuffer,p=DataView;function d(t){return Array.isArray(t)||function(t){return f.isView(t)&&!(t instanceof p)}(t)}function m(t,e){var r=t[0],n=t[1],i=t[2],a=t[3];return t[0]=e[0]*r+e[4]*n+e[8]*i+e[12]*a,t[1]=e[1]*r+e[5]*n+e[9]*i+e[13]*a,t[2]=e[2]*r+e[6]*n+e[10]*i+e[14]*a,t[3]=e[3]*r+e[7]*n+e[11]*i+e[15]*a,t}function g(t,e,r,n){return m(n,n),m(n,n),m(n,n)}function y(t,e){this.index=t,this.dataCoordinate=this.position=e}function v(t){return!0===t||t>1?1:t}function x(t,e,r,n,i,a,o,s,l,c,u,h){this.gl=t,this.pixelRatio=1,this.shader=e,this.orthoShader=r,this.projectShader=n,this.pointBuffer=i,this.colorBuffer=a,this.glyphBuffer=o,this.idBuffer=s,this.vao=l,this.vertexCount=0,this.lineVertexCount=0,this.opacity=1,this.hasAlpha=!1,this.lineWidth=0,this.projectScale=[2/3,2/3,2/3],this.projectOpacity=[1,1,1],this.projectHasAlpha=!1,this.pickId=0,this.pickPerspectiveShader=c,this.pickOrthoShader=u,this.pickProjectShader=h,this.points=[],this._selectResult=new y(0,[0,0,0]),this.useOrtho=!0,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.axesProject=[!0,!0,!0],this.axesBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.highlightId=[1,1,1,1],this.highlightScale=2,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.dirty=!0}t.exports=function(t){var e=t.gl,r=l.createPerspective(e),n=l.createOrtho(e),o=l.createProject(e),s=l.createPickPerspective(e),c=l.createPickOrtho(e),u=l.createPickProject(e),h=i(e),f=i(e),p=i(e),d=i(e),m=new x(e,r,n,o,h,f,p,d,a(e,[{buffer:h,size:3,type:e.FLOAT},{buffer:f,size:4,type:e.FLOAT},{buffer:p,size:2,type:e.FLOAT},{buffer:d,size:4,type:e.UNSIGNED_BYTE,normalized:!0}]),s,c,u);return m.update(t),m};var _=x.prototype;_.pickSlots=1,_.setPickBase=function(t){this.pickId=t},_.isTransparent=function(){if(this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&this.projectHasAlpha)return!0;return!1},_.isOpaque=function(){if(!this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&!this.projectHasAlpha)return!0;return!1};var b=[0,0],w=[0,0,0],T=[0,0,0],k=[0,0,0,1],A=[0,0,0,1],M=h.slice(),S=[0,0,0],E=[[0,0,0],[0,0,0]];function C(t){return t[0]=t[1]=t[2]=0,t}function L(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=1,t}function I(t,e,r,n){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[r]=n,t}var P=[[-1e8,-1e8,-1e8],[1e8,1e8,1e8]];function z(t,e,r,n,i,a,o){var l=r.gl;if((a===r.projectHasAlpha||o)&&function(t,e,r,n){var i,a=e.axesProject,o=e.gl,l=t.uniforms,c=r.model||h,u=r.view||h,f=r.projection||h,p=e.axesBounds,d=function(t){for(var e=E,r=0;r<2;++r)for(var n=0;n<3;++n)e[r][n]=Math.max(Math.min(t[r][n],1e8),-1e8);return e}(e.clipBounds);i=e.axes&&e.axes.lastCubeProps?e.axes.lastCubeProps.axis:[1,1,1],b[0]=2/o.drawingBufferWidth,b[1]=2/o.drawingBufferHeight,t.bind(),l.view=u,l.projection=f,l.screenSize=b,l.highlightId=e.highlightId,l.highlightScale=e.highlightScale,l.clipBounds=d,l.pickGroup=e.pickId/255,l.pixelRatio=n;for(var m=0;m<3;++m)if(a[m]){l.scale=e.projectScale[m],l.opacity=e.projectOpacity[m];for(var y=M,v=0;v<16;++v)y[v]=0;for(v=0;v<4;++v)y[5*v]=1;y[5*m]=0,i[m]<0?y[12+m]=p[0][m]:y[12+m]=p[1][m],s(y,c,y),l.model=y;var x=(m+1)%3,_=(m+2)%3,P=C(w),z=C(T);P[x]=1,z[_]=1;var O=g(0,0,0,L(k,P)),D=g(0,0,0,L(A,z));if(Math.abs(O[1])>Math.abs(D[1])){var R=O;O=D,D=R,R=P,P=z,z=R;var F=x;x=_,_=F}O[0]<0&&(P[x]=-1),D[1]>0&&(z[_]=-1);var B=0,N=0;for(v=0;v<4;++v)B+=Math.pow(c[4*x+v],2),N+=Math.pow(c[4*_+v],2);P[x]/=Math.sqrt(B),z[_]/=Math.sqrt(N),l.axes[0]=P,l.axes[1]=z,l.fragClipBounds[0]=I(S,d[0],m,-1e8),l.fragClipBounds[1]=I(S,d[1],m,1e8),e.vao.bind(),e.vao.draw(o.TRIANGLES,e.vertexCount),e.lineWidth>0&&(o.lineWidth(e.lineWidth*n),e.vao.draw(o.LINES,e.lineVertexCount,e.vertexCount)),e.vao.unbind()}}(e,r,n,i),a===r.hasAlpha||o){t.bind();var c=t.uniforms;c.model=n.model||h,c.view=n.view||h,c.projection=n.projection||h,b[0]=2/l.drawingBufferWidth,b[1]=2/l.drawingBufferHeight,c.screenSize=b,c.highlightId=r.highlightId,c.highlightScale=r.highlightScale,c.fragClipBounds=P,c.clipBounds=r.axes.bounds,c.opacity=r.opacity,c.pickGroup=r.pickId/255,c.pixelRatio=i,r.vao.bind(),r.vao.draw(l.TRIANGLES,r.vertexCount),r.lineWidth>0&&(l.lineWidth(r.lineWidth*i),r.vao.draw(l.LINES,r.lineVertexCount,r.vertexCount)),r.vao.unbind()}}function O(t,e,r,i){var a;a=d(t)?e=this.pointCount||e<0)return null;var r=this.points[e],n=this._selectResult;n.index=e;for(var i=0;i<3;++i)n.position[i]=n.dataCoordinate[i]=r[i];return n},_.highlight=function(t){if(t){var e=t.index,r=255&e,n=e>>8&255,i=e>>16&255;this.highlightId=[r/255,n/255,i/255,0]}else this.highlightId=[1,1,1,1]},_.update=function(t){if(\"perspective\"in(t=t||{})&&(this.useOrtho=!t.perspective),\"orthographic\"in t&&(this.useOrtho=!!t.orthographic),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"project\"in t)if(d(t.project))this.axesProject=t.project;else{var e=!!t.project;this.axesProject=[e,e,e]}if(\"projectScale\"in t)if(d(t.projectScale))this.projectScale=t.projectScale.slice();else{var r=+t.projectScale;this.projectScale=[r,r,r]}if(this.projectHasAlpha=!1,\"projectOpacity\"in t){d(t.projectOpacity)?this.projectOpacity=t.projectOpacity.slice():(r=+t.projectOpacity,this.projectOpacity=[r,r,r]);for(var n=0;n<3;++n)this.projectOpacity[n]=v(this.projectOpacity[n]),this.projectOpacity[n]<1&&(this.projectHasAlpha=!0)}this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=v(t.opacity),this.opacity<1&&(this.hasAlpha=!0)),this.dirty=!0;var i,a,s=t.position,l={family:t.font||\"normal\",style:t.fontStyle||\"normal\",weight:t.fontWeight||\"normal\",variant:t.fontVariant||\"normal\"},c=t.alignment||[0,0];if(2===c.length)i=c[0],a=c[1];else for(i=[],a=[],n=0;n0){var z=0,D=_,R=[0,0,0,1],F=[0,0,0,1],B=d(p)&&d(p[0]),N=d(y)&&d(y[0]);t:for(n=0;n0?1-S[0][0]:W<0?1+S[1][0]:1,Y*=Y>0?1-S[0][1]:Y<0?1+S[1][1]:1],$=A.cells||[],J=A.positions||[];for(k=0;k<$.length;++k)for(var K=$[k],Q=0;Q<3;++Q){for(var tt=0;tt<3;++tt)C[3*z+tt]=T[tt];for(tt=0;tt<4;++tt)L[4*z+tt]=R[tt];P[z]=x;var et=J[K[Q]];I[2*z]=q*(G*et[0]-Z*et[1]+X[0]),I[2*z+1]=q*(Z*et[0]+G*et[1]+X[1]),z+=1}for($=M.edges,J=M.positions,k=0;k<$.length;++k)for(K=$[k],Q=0;Q<2;++Q){for(tt=0;tt<3;++tt)C[3*D+tt]=T[tt];for(tt=0;tt<4;++tt)L[4*D+tt]=F[tt];P[D]=x,et=J[K[Q]],I[2*D]=q*(G*et[0]-Z*et[1]+X[0]),I[2*D+1]=q*(Z*et[0]+G*et[1]+X[1]),D+=1}}}this.bounds=[u,h],this.points=s,this.pointCount=s.length,this.vertexCount=_,this.lineVertexCount=b,this.pointBuffer.update(C),this.colorBuffer.update(L),this.glyphBuffer.update(I),this.idBuffer.update(P),o.free(C),o.free(L),o.free(I),o.free(P)},_.dispose=function(){this.shader.dispose(),this.orthoShader.dispose(),this.pickPerspectiveShader.dispose(),this.pickOrthoShader.dispose(),this.vao.dispose(),this.pointBuffer.dispose(),this.colorBuffer.dispose(),this.glyphBuffer.dispose(),this.idBuffer.dispose()}},4298:function(t,e,r){\"use strict\";var n=r(3236);e.boxVertex=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 vertex;\\n\\nuniform vec2 cornerA, cornerB;\\n\\nvoid main() {\\n gl_Position = vec4(mix(cornerA, cornerB, vertex), 0, 1);\\n}\\n\"]),e.boxFragment=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\n\\nvoid main() {\\n gl_FragColor = color;\\n}\\n\"])},3161:function(t,e,r){\"use strict\";var n=r(9405),i=r(2762),a=r(4298);function o(t,e,r){this.plot=t,this.boxBuffer=e,this.boxShader=r,this.enabled=!0,this.selectBox=[1/0,1/0,-1/0,-1/0],this.borderColor=[0,0,0,1],this.innerFill=!1,this.innerColor=[0,0,0,.25],this.outerFill=!0,this.outerColor=[0,0,0,.5],this.borderWidth=10}t.exports=function(t,e){var r=t.gl,s=new o(t,i(r,[0,0,0,1,1,0,1,1]),n(r,a.boxVertex,a.boxFragment));return s.update(e),t.addOverlay(s),s};var s=o.prototype;s.draw=function(){if(this.enabled){var t=this.plot,e=this.selectBox,r=this.borderWidth,n=(this.innerFill,this.innerColor),i=(this.outerFill,this.outerColor),a=this.borderColor,o=t.box,s=t.screenBox,l=t.dataBox,c=t.viewBox,u=t.pixelRatio,h=(e[0]-l[0])*(c[2]-c[0])/(l[2]-l[0])+c[0],f=(e[1]-l[1])*(c[3]-c[1])/(l[3]-l[1])+c[1],p=(e[2]-l[0])*(c[2]-c[0])/(l[2]-l[0])+c[0],d=(e[3]-l[1])*(c[3]-c[1])/(l[3]-l[1])+c[1];if(h=Math.max(h,c[0]),f=Math.max(f,c[1]),p=Math.min(p,c[2]),d=Math.min(d,c[3]),!(p0){var y=r*u;o.drawBox(h-y,f-y,p+y,f+y,a),o.drawBox(h-y,d-y,p+y,d+y,a),o.drawBox(h-y,f-y,h+y,d+y,a),o.drawBox(p-y,f-y,p+y,d+y,a)}}}},s.update=function(t){t=t||{},this.innerFill=!!t.innerFill,this.outerFill=!!t.outerFill,this.innerColor=(t.innerColor||[0,0,0,.5]).slice(),this.outerColor=(t.outerColor||[0,0,0,.5]).slice(),this.borderColor=(t.borderColor||[0,0,0,1]).slice(),this.borderWidth=t.borderWidth||0,this.selectBox=(t.selectBox||this.selectBox).slice()},s.dispose=function(){this.boxBuffer.dispose(),this.boxShader.dispose(),this.plot.removeOverlay(this)}},3589:function(t,e,r){\"use strict\";t.exports=function(t,e){var r=e[0],a=e[1];return new l(t,n(t,r,a,{}),i.mallocUint8(r*a*4))};var n=r(2260),i=r(1888),a=r(9618),o=r(8828).nextPow2;function s(t,e,r,n,i){this.coord=[t,e],this.id=r,this.value=n,this.distance=i}function l(t,e,r){this.gl=t,this.fbo=e,this.buffer=r,this._readTimeout=null;var 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a||(a[s[0]]=[]),a=a[s[0]];for(var l=1;l1)for(var l=0;l 0 U ||b|| > 0.\\n // Assign z = 0, x = -b, y = a:\\n // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n return normalize(vec3(-v.y, v.x, 0.0));\\n } else {\\n return normalize(vec3(0.0, v.z, -v.y));\\n }\\n}\\n\\n// Calculate the tube vertex and normal at the given index.\\n//\\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\\n//\\n// Each tube segment is made up of a ring of vertices.\\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\\n// The indexes of tube segments run from 0 to 8.\\n//\\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\\n float segmentCount = 8.0;\\n\\n float angle = 2.0 * 3.14159 * (index / segmentCount);\\n\\n vec3 u = getOrthogonalVector(d);\\n vec3 v = normalize(cross(u, d));\\n\\n vec3 x = u * cos(angle) * length(d);\\n vec3 y = v * sin(angle) * length(d);\\n vec3 v3 = x + y;\\n\\n normal = normalize(v3);\\n\\n return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 color, position;\\nattribute vec2 uv;\\n\\nuniform float vectorScale, tubeScale;\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 eyePosition, lightPosition;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n // Scale the vector magnitude to stay constant with\\n // model & view changes.\\n vec3 normal;\\n vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\\n vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n //Lighting geometry parameters\\n vec4 cameraCoordinate = view * tubePosition;\\n cameraCoordinate.xyz /= cameraCoordinate.w;\\n f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n f_eyeDirection = eyePosition - cameraCoordinate.xyz;\\n f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n // vec4 m_position = model * vec4(tubePosition, 1.0);\\n vec4 t_position = view * tubePosition;\\n gl_Position = projection * t_position;\\n\\n f_color = color;\\n f_data = tubePosition.xyz;\\n f_position = position.xyz;\\n f_uv = uv;\\n}\\n\"]),a=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n float NdotH = max(x, 0.0001);\\n float cos2Alpha = NdotH * NdotH;\\n float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n float roughness2 = roughness * roughness;\\n float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n vec3 lightDirection,\\n vec3 viewDirection,\\n vec3 surfaceNormal,\\n float roughness,\\n float fresnel) {\\n\\n float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n //Half angle vector\\n vec3 H = normalize(lightDirection + viewDirection);\\n\\n //Geometric term\\n float NdotH = max(dot(surfaceNormal, H), 0.0);\\n float VdotH = max(dot(viewDirection, H), 0.000001);\\n float LdotH = max(dot(lightDirection, H), 0.000001);\\n float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n float G = min(1.0, min(G1, G2));\\n \\n //Distribution term\\n float D = beckmannDistribution(NdotH, roughness);\\n\\n //Fresnel term\\n float F = pow(1.0 - VdotN, fresnel);\\n\\n //Multiply terms and done\\n return G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n vec3 N = normalize(f_normal);\\n vec3 L = normalize(f_lightDirection);\\n vec3 V = normalize(f_eyeDirection);\\n\\n if(gl_FrontFacing) {\\n N = -N;\\n }\\n\\n float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\\n float diffuse = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n vec4 surfaceColor = f_color * texture2D(texture, f_uv);\\n vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) 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array.\\n// The indexes of tube segments run from 0 to 8.\\n//\\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\\n float segmentCount = 8.0;\\n\\n float angle = 2.0 * 3.14159 * (index / segmentCount);\\n\\n vec3 u = getOrthogonalVector(d);\\n vec3 v = normalize(cross(u, d));\\n\\n vec3 x = u * cos(angle) * length(d);\\n vec3 y = v * sin(angle) * length(d);\\n vec3 v3 = x + y;\\n\\n normal = normalize(v3);\\n\\n return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform float tubeScale;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n vec3 normal;\\n vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\\n vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n gl_Position = projection * (view * tubePosition);\\n f_id = id;\\n f_position = position.xyz;\\n}\\n\"]),s=n([\"precision highp 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f_id.xyz);\\n}\"]);e.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec4\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"vector\",type:\"vec4\"}]},e.pickShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec4\"},{name:\"id\",type:\"vec4\"},{name:\"vector\",type:\"vec4\"}]}},7815:function(t,e,r){\"use strict\";var n=r(2931),i=r(9970),a=[\"xyz\",\"xzy\",\"yxz\",\"yzx\",\"zxy\",\"zyx\"],o=function(t,e){var r,n=t.length;for(r=0;re)return r-1}return r},s=function(t,e,r){return tr?r:t},l=function(t){var e=1/0;t.sort((function(t,e){return t-e}));for(var r=t.length,n=1;nh-1||v>f-1||x>p-1)return n.create();var _,b,w,T,k,A,M=a[0][d],S=a[0][y],E=a[1][m],C=a[1][v],L=a[2][g],I=(l-M)/(S-M),P=(c-E)/(C-E),z=(u-L)/(a[2][x]-L);switch(isFinite(I)||(I=.5),isFinite(P)||(P=.5),isFinite(z)||(z=.5),r.reversedX&&(d=h-1-d,y=h-1-y),r.reversedY&&(m=f-1-m,v=f-1-v),r.reversedZ&&(g=p-1-g,x=p-1-x),r.filled){case 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e=t[0],r=t[1],n=t[2];return!(e_||rb||nw)},k=10*n.distance(e[0],e[1])/c,A=k*k,M=1,S=0,E=r.length;E>1&&(M=function(t){for(var e=[],r=[],n=[],i={},a={},o={},s=t.length,c=0;cS&&(S=F),D.push(F),g.push({points:I,velocities:P,divergences:D});for(var B=0;B<100*c&&I.lengthA&&n.scale(N,N,k/Math.sqrt(j)),n.add(N,N,L),z=d(N),n.squaredDistance(O,N)-A>-1e-4*A&&(I.push(N),O=N,P.push(z),R=m(N,z),F=n.length(R),isFinite(F)&&F>S&&(S=F),D.push(F)),L=N}}var U=function(t,e,r,a){for(var o=0,s=0;s0)for(T=0;T<8;T++){var k=(T+1)%8;c.push(f[T],p[T],p[k],p[k],f[k],f[T]),h.push(v,y,y,y,v,v),d.push(m,g,g,g,m,m);var A=c.length;u.push([A-6,A-5,A-4],[A-3,A-2,A-1])}var M=f;f=p,p=M;var S=v;v=y,y=S;var E=m;m=g,g=E}return{positions:c,cells:u,vectors:h,vertexIntensity:d}}(t,r,a,o)})),h=[],f=[],p=[],d=[];for(s=0;s max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 lowerBound, upperBound;\\nuniform float contourTint;\\nuniform vec4 contourColor;\\nuniform sampler2D colormap;\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform float vertexColor;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n if (\\n kill > 0.0 ||\\n vColor.a == 0.0 ||\\n outOfRange(clipBounds[0], clipBounds[1], worldCoordinate)\\n ) discard;\\n\\n vec3 N = normalize(surfaceNormal);\\n vec3 V = normalize(eyeDirection);\\n vec3 L = normalize(lightDirection);\\n\\n if(gl_FrontFacing) {\\n N = -N;\\n }\\n\\n float specular = max(beckmannSpecular(L, V, N, roughness), 0.);\\n float diffuse = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n //decide how to interpolate color — in vertex or in fragment\\n vec4 surfaceColor =\\n step(vertexColor, .5) * texture2D(colormap, vec2(value, value)) +\\n step(.5, vertexColor) * vColor;\\n\\n vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular, 1.0);\\n\\n gl_FragColor = mix(litColor, contourColor, contourTint) * opacity;\\n}\\n\"]),s=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec4 uv;\\nattribute float f;\\n\\nuniform vec3 objectOffset;\\nuniform mat3 permutation;\\nuniform mat4 model, view, projection;\\nuniform float height, zOffset;\\nuniform sampler2D colormap;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n vec3 dataCoordinate = permutation * vec3(uv.xy, height);\\n worldCoordinate = objectOffset + dataCoordinate;\\n mat4 objectOffsetTranslation = mat4(1.0) + mat4(vec4(0), vec4(0), vec4(0), vec4(objectOffset, 0));\\n vec4 worldPosition = (model * objectOffsetTranslation) * vec4(dataCoordinate, 1.0);\\n\\n vec4 clipPosition = projection * (view * worldPosition);\\n clipPosition.z += zOffset;\\n\\n gl_Position = clipPosition;\\n value = f + objectOffset.z;\\n kill = -1.0;\\n planeCoordinate = uv.zw;\\n\\n vColor = texture2D(colormap, vec2(value, value));\\n\\n //Don't do lighting for contours\\n surfaceNormal = vec3(1,0,0);\\n eyeDirection = vec3(0,1,0);\\n lightDirection = vec3(0,0,1);\\n}\\n\"]),l=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec2 shape;\\nuniform vec3 clipBounds[2];\\nuniform float pickId;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 surfaceNormal;\\n\\nvec2 splitFloat(float v) {\\n float vh = 255.0 * v;\\n float upper = floor(vh);\\n float lower = fract(vh);\\n return vec2(upper / 255.0, floor(lower * 16.0) / 16.0);\\n}\\n\\nvoid main() {\\n if ((kill > 0.0) ||\\n (outOfRange(clipBounds[0], clipBounds[1], worldCoordinate))) discard;\\n\\n vec2 ux = splitFloat(planeCoordinate.x / shape.x);\\n vec2 uy = splitFloat(planeCoordinate.y / shape.y);\\n gl_FragColor = vec4(pickId, ux.x, uy.x, ux.y + (uy.y/16.0));\\n}\\n\"]);e.createShader=function(t){var e=n(t,a,o,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},e.createPickShader=function(t){var e=n(t,a,l,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},e.createContourShader=function(t){var e=n(t,s,o,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"float\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e},e.createPickContourShader=function(t){var e=n(t,s,l,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"float\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e}},9499:function(t,e,r){\"use strict\";t.exports=function(t){var e=t.gl,r=v(e),n=_(e),s=x(e),l=b(e),c=i(e),u=a(e,[{buffer:c,size:4,stride:w,offset:0},{buffer:c,size:3,stride:w,offset:16},{buffer:c,size:3,stride:w,offset:28}]),h=i(e),f=a(e,[{buffer:h,size:4,stride:20,offset:0},{buffer:h,size:1,stride:20,offset:16}]),p=i(e),d=a(e,[{buffer:p,size:2,type:e.FLOAT}]),m=o(e,1,S,e.RGBA,e.UNSIGNED_BYTE);m.minFilter=e.LINEAR,m.magFilter=e.LINEAR;var g=new E(e,[0,0],[[0,0,0],[0,0,0]],r,n,c,u,m,s,l,h,f,p,d,[0,0,0]),y={levels:[[],[],[]]};for(var T in t)y[T]=t[T];return y.colormap=y.colormap||\"jet\",g.update(y),g};var n=r(8828),i=r(2762),a=r(8116),o=r(7766),s=r(1888),l=r(6729),c=r(5298),u=r(9994),h=r(9618),f=r(3711),p=r(6760),d=r(7608),m=r(2478),g=r(6199),y=r(990),v=y.createShader,x=y.createContourShader,_=y.createPickShader,b=y.createPickContourShader,w=40,T=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],k=[[0,0],[0,1],[1,0],[1,1],[1,0],[0,1]],A=[[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0]];function M(t,e,r,n,i){this.position=t,this.index=e,this.uv=r,this.level=n,this.dataCoordinate=i}!function(){for(var t=0;t<3;++t){var e=A[t],r=(t+2)%3;e[(t+1)%3+0]=1,e[r+3]=1,e[t+6]=1}}();var S=256;function E(t,e,r,n,i,a,o,l,c,u,f,p,d,m,g){this.gl=t,this.shape=e,this.bounds=r,this.objectOffset=g,this.intensityBounds=[],this._shader=n,this._pickShader=i,this._coordinateBuffer=a,this._vao=o,this._colorMap=l,this._contourShader=c,this._contourPickShader=u,this._contourBuffer=f,this._contourVAO=p,this._contourOffsets=[[],[],[]],this._contourCounts=[[],[],[]],this._vertexCount=0,this._pickResult=new M([0,0,0],[0,0],[0,0],[0,0,0],[0,0,0]),this._dynamicBuffer=d,this._dynamicVAO=m,this._dynamicOffsets=[0,0,0],this._dynamicCounts=[0,0,0],this.contourWidth=[1,1,1],this.contourLevels=[[1],[1],[1]],this.contourTint=[0,0,0],this.contourColor=[[.5,.5,.5,1],[.5,.5,.5,1],[.5,.5,.5,1]],this.showContour=!0,this.showSurface=!0,this.enableHighlight=[!0,!0,!0],this.highlightColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.highlightTint=[1,1,1],this.highlightLevel=[-1,-1,-1],this.enableDynamic=[!0,!0,!0],this.dynamicLevel=[NaN,NaN,NaN],this.dynamicColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.dynamicTint=[1,1,1],this.dynamicWidth=[1,1,1],this.axesBounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.surfaceProject=[!1,!1,!1],this.contourProject=[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],this.colorBounds=[!1,!1],this._field=[h(s.mallocFloat(1024),[0,0]),h(s.mallocFloat(1024),[0,0]),h(s.mallocFloat(1024),[0,0])],this.pickId=1,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.snapToData=!1,this.pixelRatio=1,this.opacity=1,this.lightPosition=[10,1e4,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.vertexColor=0,this.dirty=!0}var C=E.prototype;C.genColormap=function(t,e){var r=!1,n=u([l({colormap:t,nshades:S,format:\"rgba\"}).map((function(t,n){var i=e?function(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;rt&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}(n/255,e):t[3];return i<1&&(r=!0),[t[0],t[1],t[2],255*i]}))]);return c.divseq(n,255),this.hasAlphaScale=r,n},C.isTransparent=function(){return this.opacity<1||this.hasAlphaScale},C.isOpaque=function(){return!this.isTransparent()},C.pickSlots=1,C.setPickBase=function(t){this.pickId=t};var L=[0,0,0],I={showSurface:!1,showContour:!1,projections:[T.slice(),T.slice(),T.slice()],clipBounds:[[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]]]};function P(t,e){var r,n,i,a=e.axes&&e.axes.lastCubeProps.axis||L,o=e.showSurface,s=e.showContour;for(r=0;r<3;++r)for(o=o||e.surfaceProject[r],n=0;n<3;++n)s=s||e.contourProject[r][n];for(r=0;r<3;++r){var l=I.projections[r];for(n=0;n<16;++n)l[n]=0;for(n=0;n<4;++n)l[5*n]=1;l[5*r]=0,l[12+r]=e.axesBounds[+(a[r]>0)][r],p(l,t.model,l);var c=I.clipBounds[r];for(i=0;i<2;++i)for(n=0;n<3;++n)c[i][n]=t.clipBounds[i][n];c[0][r]=-1e8,c[1][r]=1e8}return I.showSurface=o,I.showContour=s,I}var z={model:T,view:T,projection:T,inverseModel:T.slice(),lowerBound:[0,0,0],upperBound:[0,0,0],colorMap:0,clipBounds:[[0,0,0],[0,0,0]],height:0,contourTint:0,contourColor:[0,0,0,1],permutation:[1,0,0,0,1,0,0,0,1],zOffset:-1e-4,objectOffset:[0,0,0],kambient:1,kdiffuse:1,kspecular:1,lightPosition:[1e3,1e3,1e3],eyePosition:[0,0,0],roughness:1,fresnel:1,opacity:1,vertexColor:0},O=T.slice(),D=[1,0,0,0,1,0,0,0,1];function R(t,e){t=t||{};var r=this.gl;r.disable(r.CULL_FACE),this._colorMap.bind(0);var n=z;n.model=t.model||T,n.view=t.view||T,n.projection=t.projection||T,n.lowerBound=[this.bounds[0][0],this.bounds[0][1],this.colorBounds[0]||this.bounds[0][2]],n.upperBound=[this.bounds[1][0],this.bounds[1][1],this.colorBounds[1]||this.bounds[1][2]],n.objectOffset=this.objectOffset,n.contourColor=this.contourColor[0],n.inverseModel=d(n.inverseModel,n.model);for(var i=0;i<2;++i)for(var a=n.clipBounds[i],o=0;o<3;++o)a[o]=Math.min(Math.max(this.clipBounds[i][o],-1e8),1e8);n.kambient=this.ambientLight,n.kdiffuse=this.diffuseLight,n.kspecular=this.specularLight,n.roughness=this.roughness,n.fresnel=this.fresnel,n.opacity=this.opacity,n.height=0,n.permutation=D,n.vertexColor=this.vertexColor;var s=O;for(p(s,n.view,n.model),p(s,n.projection,s),d(s,s),i=0;i<3;++i)n.eyePosition[i]=s[12+i]/s[15];var l=s[15];for(i=0;i<3;++i)l+=this.lightPosition[i]*s[4*i+3];for(i=0;i<3;++i){var c=s[12+i];for(o=0;o<3;++o)c+=s[4*o+i]*this.lightPosition[o];n.lightPosition[i]=c/l}var u=P(n,this);if(u.showSurface){for(this._shader.bind(),this._shader.uniforms=n,this._vao.bind(),this.showSurface&&this._vertexCount&&this._vao.draw(r.TRIANGLES,this._vertexCount),i=0;i<3;++i)this.surfaceProject[i]&&this.vertexCount&&(this._shader.uniforms.model=u.projections[i],this._shader.uniforms.clipBounds=u.clipBounds[i],this._vao.draw(r.TRIANGLES,this._vertexCount));this._vao.unbind()}if(u.showContour){var h=this._contourShader;n.kambient=1,n.kdiffuse=0,n.kspecular=0,n.opacity=1,h.bind(),h.uniforms=n;var f=this._contourVAO;for(f.bind(),i=0;i<3;++i)for(h.uniforms.permutation=A[i],r.lineWidth(this.contourWidth[i]*this.pixelRatio),o=0;o>4)/16)/255,i=Math.floor(n),a=n-i,o=e[1]*(t.value[1]+(15&t.value[2])/16)/255,s=Math.floor(o),l=o-s;i+=1,s+=1;var c=r.position;c[0]=c[1]=c[2]=0;for(var u=0;u<2;++u)for(var h=u?a:1-a,f=0;f<2;++f)for(var p=i+u,d=s+f,g=h*(f?l:1-l),y=0;y<3;++y)c[y]+=this._field[y].get(p,d)*g;for(var v=this._pickResult.level,x=0;x<3;++x)if(v[x]=m.le(this.contourLevels[x],c[x]),v[x]<0)this.contourLevels[x].length>0&&(v[x]=0);else if(v[x]Math.abs(b-c[x])&&(v[x]+=1)}for(r.index[0]=a<.5?i:i+1,r.index[1]=l<.5?s:s+1,r.uv[0]=n/e[0],r.uv[1]=o/e[1],y=0;y<3;++y)r.dataCoordinate[y]=this._field[y].get(r.index[0],r.index[1]);return r},C.padField=function(t,e){var r=e.shape.slice(),n=t.shape.slice();c.assign(t.lo(1,1).hi(r[0],r[1]),e),c.assign(t.lo(1).hi(r[0],1),e.hi(r[0],1)),c.assign(t.lo(1,n[1]-1).hi(r[0],1),e.lo(0,r[1]-1).hi(r[0],1)),c.assign(t.lo(0,1).hi(1,r[1]),e.hi(1)),c.assign(t.lo(n[0]-1,1).hi(1,r[1]),e.lo(r[0]-1)),t.set(0,0,e.get(0,0)),t.set(0,n[1]-1,e.get(0,r[1]-1)),t.set(n[0]-1,0,e.get(r[0]-1,0)),t.set(n[0]-1,n[1]-1,e.get(r[0]-1,r[1]-1))},C.update=function(t){t=t||{},this.objectOffset=t.objectOffset||this.objectOffset,this.dirty=!0,\"contourWidth\"in t&&(this.contourWidth=B(t.contourWidth,Number)),\"showContour\"in t&&(this.showContour=B(t.showContour,Boolean)),\"showSurface\"in t&&(this.showSurface=!!t.showSurface),\"contourTint\"in t&&(this.contourTint=B(t.contourTint,Boolean)),\"contourColor\"in t&&(this.contourColor=j(t.contourColor)),\"contourProject\"in t&&(this.contourProject=B(t.contourProject,(function(t){return B(t,Boolean)}))),\"surfaceProject\"in t&&(this.surfaceProject=t.surfaceProject),\"dynamicColor\"in t&&(this.dynamicColor=j(t.dynamicColor)),\"dynamicTint\"in t&&(this.dynamicTint=B(t.dynamicTint,Number)),\"dynamicWidth\"in t&&(this.dynamicWidth=B(t.dynamicWidth,Number)),\"opacity\"in t&&(this.opacity=t.opacity),\"opacityscale\"in t&&(this.opacityscale=t.opacityscale),\"colorBounds\"in t&&(this.colorBounds=t.colorBounds),\"vertexColor\"in t&&(this.vertexColor=t.vertexColor?1:0),\"colormap\"in t&&this._colorMap.setPixels(this.genColormap(t.colormap,this.opacityscale));var e=t.field||t.coords&&t.coords[2]||null,r=!1;if(e||(e=this._field[2].shape[0]||this._field[2].shape[2]?this._field[2].lo(1,1).hi(this._field[2].shape[0]-2,this._field[2].shape[1]-2):this._field[2].hi(0,0)),\"field\"in t||\"coords\"in t){var i=(e.shape[0]+2)*(e.shape[1]+2);i>this._field[2].data.length&&(s.freeFloat(this._field[2].data),this._field[2].data=s.mallocFloat(n.nextPow2(i))),this._field[2]=h(this._field[2].data,[e.shape[0]+2,e.shape[1]+2]),this.padField(this._field[2],e),this.shape=e.shape.slice();for(var a=this.shape,o=0;o<2;++o)this._field[2].size>this._field[o].data.length&&(s.freeFloat(this._field[o].data),this._field[o].data=s.mallocFloat(this._field[2].size)),this._field[o]=h(this._field[o].data,[a[0]+2,a[1]+2]);if(t.coords){var l=t.coords;if(!Array.isArray(l)||3!==l.length)throw new Error(\"gl-surface: invalid coordinates for x/y\");for(o=0;o<2;++o){var c=l[o];for(y=0;y<2;++y)if(c.shape[y]!==a[y])throw new Error(\"gl-surface: coords have incorrect shape\");this.padField(this._field[o],c)}}else if(t.ticks){var u=t.ticks;if(!Array.isArray(u)||2!==u.length)throw new Error(\"gl-surface: invalid ticks\");for(o=0;o<2;++o){var p=u[o];if((Array.isArray(p)||p.length)&&(p=h(p)),p.shape[0]!==a[o])throw new Error(\"gl-surface: invalid tick length\");var d=h(p.data,a);d.stride[o]=p.stride[0],d.stride[1^o]=0,this.padField(this._field[o],d)}}else{for(o=0;o<2;++o){var m=[0,0];m[o]=1,this._field[o]=h(this._field[o].data,[a[0]+2,a[1]+2],m,0)}this._field[0].set(0,0,0);for(var y=0;y0){for(var xt=0;xt<5;++xt)K.pop();U-=1}continue t}K.push(nt[0],nt[1],ot[0],ot[1],nt[2]),U+=1}}rt.push(U)}this._contourOffsets[Q]=et,this._contourCounts[Q]=rt}var _t=s.mallocFloat(K.length);for(o=0;os||o[1]<0||o[1]>s)throw new Error(\"gl-texture2d: Invalid texture size\");var l=d(o,e.stride.slice()),c=0;\"float32\"===r?c=t.FLOAT:\"float64\"===r?(c=t.FLOAT,l=!1,r=\"float32\"):\"uint8\"===r?c=t.UNSIGNED_BYTE:(c=t.UNSIGNED_BYTE,l=!1,r=\"uint8\");var h,p,g=0;if(2===o.length)g=t.LUMINANCE,o=[o[0],o[1],1],e=n(e.data,o,[e.stride[0],e.stride[1],1],e.offset);else{if(3!==o.length)throw new Error(\"gl-texture2d: Invalid shape for texture\");if(1===o[2])g=t.ALPHA;else if(2===o[2])g=t.LUMINANCE_ALPHA;else if(3===o[2])g=t.RGB;else{if(4!==o[2])throw new Error(\"gl-texture2d: Invalid shape for pixel coords\");g=t.RGBA}}c!==t.FLOAT||t.getExtension(\"OES_texture_float\")||(c=t.UNSIGNED_BYTE,l=!1);var y=e.size;if(l)h=0===e.offset&&e.data.length===y?e.data:e.data.subarray(e.offset,e.offset+y);else{var v=[o[2],o[2]*o[0],1];p=a.malloc(y,r);var x=n(p,o,v,0);\"float32\"!==r&&\"float64\"!==r||c!==t.UNSIGNED_BYTE?i.assign(x,e):u(x,e),h=p.subarray(0,y)}var _=m(t);return t.texImage2D(t.TEXTURE_2D,0,g,o[0],o[1],0,g,c,h),l||a.free(p),new f(t,_,o[0],o[1],g,c)}(t,e)}throw new Error(\"gl-texture2d: Invalid arguments for texture2d constructor\")};var o=null,s=null,l=null;function c(t){return\"undefined\"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||\"undefined\"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||\"undefined\"!=typeof HTMLVideoElement&&t instanceof HTMLVideoElement||\"undefined\"!=typeof ImageData&&t instanceof ImageData}var u=function(t,e){i.muls(t,e,255)};function h(t,e,r){var n=t.gl,i=n.getParameter(n.MAX_TEXTURE_SIZE);if(e<0||e>i||r<0||r>i)throw new Error(\"gl-texture2d: Invalid texture size\");return t._shape=[e,r],t.bind(),n.texImage2D(n.TEXTURE_2D,0,t.format,e,r,0,t.format,t.type,null),t._mipLevels=[0],t}function f(t,e,r,n,i,a){this.gl=t,this.handle=e,this.format=i,this.type=a,this._shape=[r,n],this._mipLevels=[0],this._magFilter=t.NEAREST,this._minFilter=t.NEAREST,this._wrapS=t.CLAMP_TO_EDGE,this._wrapT=t.CLAMP_TO_EDGE,this._anisoSamples=1;var o=this,s=[this._wrapS,this._wrapT];Object.defineProperties(s,[{get:function(){return o._wrapS},set:function(t){return o.wrapS=t}},{get:function(){return o._wrapT},set:function(t){return o.wrapT=t}}]),this._wrapVector=s;var 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h=e.slice(0);h.sort();for(var f=0;f>1,s=a(t[o],e);s<=0?(0===s&&(i=o),r=o+1):s>0&&(n=o-1)}return i}function u(t,e){for(var r=new Array(t.length),i=0,o=r.length;i=t.length||0!==a(t[g],s)););}return r}function h(t,e){if(e<0)return[];for(var r=[],i=(1<>>u&1&&c.push(i[u]);e.push(c)}return s(e)},e.skeleton=h,e.boundary=function(t){for(var e=[],r=0,n=t.length;r>1:(t>>1)-1}function x(t){for(var e=y(t);;){var r=e,n=2*t+1,i=2*(t+1),a=t;if(n0;){var r=v(t);if(!(r>=0&&e0){var t=k[0];return g(0,M-1),M-=1,x(0),t}return-1}function w(t,e){var r=k[t];return c[r]===e?t:(c[r]=-1/0,_(t),b(),c[r]=e,_((M+=1)-1))}function T(t){if(!u[t]){u[t]=!0;var e=s[t],r=l[t];s[r]>=0&&(s[r]=e),l[e]>=0&&(l[e]=r),A[e]>=0&&w(A[e],m(e)),A[r]>=0&&w(A[r],m(r))}}var k=[],A=new Array(a);for(h=0;h>1;h>=0;--h)x(h);for(;;){var S=b();if(S<0||c[S]>r)break;T(S)}var E=[];for(h=0;h=0&&r>=0&&e!==r){var n=A[e],i=A[r];n!==i&&L.push([n,i])}})),i.unique(i.normalize(L)),{positions:E,edges:L}};var n=r(3250),i=r(2014)},1303:function(t,e,r){\"use 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r},t.exports.negative=function(t,e){for(var r=[],n=a(t[t.length-1],e),i=t[t.length-1],s=t[0],l=0;l0||n>0&&c<0)&&r.push(o(i,c,s,n)),c<=0&&r.push(s.slice()),n=c}return r}},3387:function(t,e,r){var n;!function(){\"use strict\";var i={not_string:/[^s]/,not_bool:/[^t]/,not_type:/[^T]/,not_primitive:/[^v]/,number:/[diefg]/,numeric_arg:/[bcdiefguxX]/,json:/[j]/,not_json:/[^j]/,text:/^[^\\x25]+/,modulo:/^\\x25{2}/,placeholder:/^\\x25(?:([1-9]\\d*)\\$|\\(([^)]+)\\))?(\\+)?(0|'[^$])?(-)?(\\d+)?(?:\\.(\\d+))?([b-gijostTuvxX])/,key:/^([a-z_][a-z_\\d]*)/i,key_access:/^\\.([a-z_][a-z_\\d]*)/i,index_access:/^\\[(\\d+)\\]/,sign:/^[+-]/};function a(t){return function(t,e){var 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6:u.push([t-.5-.25*(-n-r+a+i)/(n-r+i-a),e-.5-.25*(-i-r+a+n)/(i-r+n-a)]);break;case 7:u.push([t-.75-.25*(a+i-2*f)/(i-a),e-.75-.25*(a+n-2*f)/(n-a)]);break;case 8:u.push([t-.75-.25*(-a-i+2*f)/(a-i),e-.75-.25*(-a-n+2*f)/(a-n)]);break;case 9:u.push([t-.5-.25*(n+r+-a-i)/(r-n+a-i),e-.5-.25*(i+r+-a-n)/(r-i+a-n)]);break;case 10:u.push([t-.5-.5*(-n-r-a-i+4*f)/(n-r+a-i),e-.5]);break;case 11:u.push([t-.25-.25*(-a-i+2*f)/(a-i),e-.75-.25*(i+r-2*f)/(r-i)]);break;case 12:u.push([t-.5,e-.5-.5*(-i-r-a-n+4*f)/(i-r+a-n)]);break;case 13:u.push([t-.75-.25*(n+r-2*f)/(r-n),e-.25-.25*(-a-n+2*f)/(a-n)]);break;case 14:u.push([t-.25-.25*(-n-r+2*f)/(n-r),e-.25-.25*(-i-r+2*f)/(i-r)])}},cell:function(t,e,r,n,i,a,o,s,l){i?s.push([t,e]):s.push([e,t])}});return function(t,e){var r=[],i=[];return n(t,r,i,e),{positions:r,cells:i}}}},o={}},529:function(t,e,r){\"use strict\";t.exports=function t(e,r,i){var 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Array(16);for(var c=0;c<16;++c)this.computedMatrix[c]=.5;this.recalcMatrix(0)}var p=f.prototype;p.setDistanceLimits=function(t,e){t=t>0?Math.log(t):-1/0,e=e>0?Math.log(e):1/0,e=Math.max(e,t),this.radius.bounds[0][0]=t,this.radius.bounds[1][0]=e},p.getDistanceLimits=function(t){var e=this.radius.bounds[0];return t?(t[0]=Math.exp(e[0][0]),t[1]=Math.exp(e[1][0]),t):[Math.exp(e[0][0]),Math.exp(e[1][0])]},p.recalcMatrix=function(t){this.center.curve(t),this.up.curve(t),this.right.curve(t),this.radius.curve(t),this.angle.curve(t);for(var e=this.computedUp,r=this.computedRight,n=0,i=0,a=0;a<3;++a)i+=e[a]*r[a],n+=e[a]*e[a];var l=Math.sqrt(n),u=0;for(a=0;a<3;++a)r[a]-=e[a]*i/n,u+=r[a]*r[a],e[a]/=l;var h=Math.sqrt(u);for(a=0;a<3;++a)r[a]/=h;var f=this.computedToward;o(f,e,r),s(f,f);var p=Math.exp(this.computedRadius[0]),d=this.computedAngle[0],m=this.computedAngle[1],g=Math.cos(d),y=Math.sin(d),v=Math.cos(m),x=Math.sin(m),_=this.computedCenter,b=g*v,w=y*v,T=x,k=-g*x,A=-y*x,M=v,S=this.computedEye,E=this.computedMatrix;for(a=0;a<3;++a){var C=b*r[a]+w*f[a]+T*e[a];E[4*a+1]=k*r[a]+A*f[a]+M*e[a],E[4*a+2]=C,E[4*a+3]=0}var L=E[1],I=E[5],P=E[9],z=E[2],O=E[6],D=E[10],R=I*D-P*O,F=P*z-L*D,B=L*O-I*z,N=c(R,F,B);for(R/=N,F/=N,B/=N,E[0]=R,E[4]=F,E[8]=B,a=0;a<3;++a)S[a]=_[a]+E[2+4*a]*p;for(a=0;a<3;++a){u=0;for(var j=0;j<3;++j)u+=E[a+4*j]*S[j];E[12+a]=-u}E[15]=1},p.getMatrix=function(t,e){this.recalcMatrix(t);var r=this.computedMatrix;if(e){for(var n=0;n<16;++n)e[n]=r[n];return e}return r};var d=[0,0,0];p.rotate=function(t,e,r,n){if(this.angle.move(t,e,r),n){this.recalcMatrix(t);var i=this.computedMatrix;d[0]=i[2],d[1]=i[6],d[2]=i[10];for(var 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this._warningHistory[t.message]||(this._warningHistory[t.message]=!0,\"undefined\"!=typeof console&&console.warn(t.message)),this._defaultValue}};var fn=function(t,e){this.kind=t,this._styleExpression=e,this.isStateDependent=\"constant\"!==t&&!Ye(e.expression)};fn.prototype.evaluateWithoutErrorHandling=function(t,e,r,n,i,a){return this._styleExpression.evaluateWithoutErrorHandling(t,e,r,n,i,a)},fn.prototype.evaluate=function(t,e,r,n,i,a){return this._styleExpression.evaluate(t,e,r,n,i,a)};var pn=function(t,e,r,n){this.kind=t,this.zoomStops=r,this._styleExpression=e,this.isStateDependent=\"camera\"!==t&&!Ye(e.expression),this.interpolationType=n};function dn(t,e){if(\"error\"===(t=hn(t,e)).result)return t;var r=t.value.expression,n=We(r);if(!n&&!$r(e))return Xr([new qt(\"\",\"data expressions not supported\")]);var i=Xe(r,[\"zoom\"]);if(!i&&!Jr(e))return Xr([new qt(\"\",\"zoom expressions not supported\")]);var a=gn(r);if(!a&&!i)return Xr([new qt(\"\",'\"zoom\" expression may only be 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e=1;e\"===r||\"<=\"===r||\">=\"===r?Cn(t[1],t[2],r):\"any\"===r?(e=t.slice(1),[\"any\"].concat(e.map(En))):\"all\"===r?[\"all\"].concat(t.slice(1).map(En)):\"none\"===r?[\"all\"].concat(t.slice(1).map(En).map(Pn)):\"in\"===r?Ln(t[1],t.slice(2)):\"!in\"===r?Pn(Ln(t[1],t.slice(2))):\"has\"===r?In(t[1]):\"!has\"===r?Pn(In(t[1])):\"within\"!==r||t}function Cn(t,e,r){switch(t){case\"$type\":return[\"filter-type-\"+r,e];case\"$id\":return[\"filter-id-\"+r,e];default:return[\"filter-\"+r,t,e]}}function Ln(t,e){if(0===e.length)return!1;switch(t){case\"$type\":return[\"filter-type-in\",[\"literal\",e]];case\"$id\":return[\"filter-id-in\",[\"literal\",e]];default:return e.length>200&&!e.some((function(t){return typeof t!=typeof e[0]}))?[\"filter-in-large\",t,[\"literal\",e.sort(Mn)]]:[\"filter-in-small\",t,[\"literal\",e]]}}function In(t){switch(t){case\"$type\":return!0;case\"$id\":return[\"filter-has-id\"];default:return[\"filter-has\",t]}}function Pn(t){return[\"!\",t]}function zn(t){return Tn(Vt(t.value))?bn(jt({},t,{expressionContext:\"filter\",valueSpec:{value:\"boolean\"}})):On(t)}function On(t){var e=t.value,r=t.key;if(\"array\"!==Qr(e))return[new Bt(r,e,\"array expected, \"+Qr(e)+\" found\")];var n,i=t.styleSpec,a=[];if(e.length<1)return[new Bt(r,e,\"filter array must have at least 1 element\")];switch(a=a.concat(wn({key:r+\"[0]\",value:e[0],valueSpec:i.filter_operator,style:t.style,styleSpec:t.styleSpec})),Ut(e[0])){case\"<\":case\"<=\":case\">\":case\">=\":e.length>=2&&\"$type\"===Ut(e[1])&&a.push(new Bt(r,e,'\"$type\" cannot be use with operator \"'+e[0]+'\"'));case\"==\":case\"!=\":3!==e.length&&a.push(new Bt(r,e,'filter array for operator \"'+e[0]+'\" must have 3 elements'));case\"in\":case\"!in\":e.length>=2&&\"string\"!==(n=Qr(e[1]))&&a.push(new Bt(r+\"[1]\",e[1],\"string expected, \"+n+\" found\"));for(var o=2;o=u[p+0]&&n>=u[p+1])?(o[f]=!0,a.push(c[f])):o[f]=!1}}},ri.prototype._forEachCell=function(t,e,r,n,i,a,o,s){for(var l=this._convertToCellCoord(t),c=this._convertToCellCoord(e),u=this._convertToCellCoord(r),h=this._convertToCellCoord(n),f=l;f<=u;f++)for(var p=c;p<=h;p++){var d=this.d*p+f;if((!s||s(this._convertFromCellCoord(f),this._convertFromCellCoord(p),this._convertFromCellCoord(f+1),this._convertFromCellCoord(p+1)))&&i.call(this,t,e,r,n,d,a,o,s))return}},ri.prototype._convertFromCellCoord=function(t){return(t-this.padding)/this.scale},ri.prototype._convertToCellCoord=function(t){return Math.max(0,Math.min(this.d-1,Math.floor(t*this.scale)+this.padding))},ri.prototype.toArrayBuffer=function(){if(this.arrayBuffer)return this.arrayBuffer;for(var t=this.cells,e=ei+this.cells.length+1+1,r=0,n=0;n=0)){var h=t[u];c[u]=ai[l].shallow.indexOf(u)>=0?h:ui(h,e)}t instanceof Error&&(c.message=t.message)}if(c.$name)throw new Error(\"$name property is reserved for worker serialization logic.\");return\"Object\"!==l&&(c.$name=l),c}throw new Error(\"can't serialize object of type \"+typeof t)}function 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Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.uint16[n+0]=e,this.uint16[n+1]=r,t},e}(Qi);ba.prototype.bytesPerElement=4,oi(\"StructArrayLayout2ui4\",ba);var wa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.uint16[r+0]=e,t},e}(Qi);wa.prototype.bytesPerElement=2,oi(\"StructArrayLayout1ui2\",wa);var Ta=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=4*t;return this.float32[a+0]=e,this.float32[a+1]=r,this.float32[a+2]=n,this.float32[a+3]=i,t},e}(Qi);Ta.prototype.bytesPerElement=16,oi(\"StructArrayLayout4f16\",Ta);var ka=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorPointX:{configurable:!0},anchorPointY:{configurable:!0},x1:{configurable:!0},y1:{configurable:!0},x2:{configurable:!0},y2:{configurable:!0},featureIndex:{configurable:!0},sourceLayerIndex:{configurable:!0},bucketIndex:{configurable:!0},anchorPoint:{configurable:!0}};return 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this._structArray.uint8[this._pos1+38]},r.hidden.set=function(t){this._structArray.uint8[this._pos1+38]=t},r.crossTileID.get=function(){return this._structArray.uint32[this._pos4+10]},r.crossTileID.set=function(t){this._structArray.uint32[this._pos4+10]=t},r.associatedIconIndex.get=function(){return this._structArray.int16[this._pos2+22]},Object.defineProperties(e.prototype,r),e}(Ki);Ma.prototype.size=48;var Sa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new Ma(this,t)},e}(ga);oi(\"PlacedSymbolArray\",Sa);var Ea=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorX:{configurable:!0},anchorY:{configurable:!0},rightJustifiedTextSymbolIndex:{configurable:!0},centerJustifiedTextSymbolIndex:{configurable:!0},leftJustifiedTextSymbolIndex:{configurable:!0},verticalPlacedTextSymbolIndex:{configurable:!0},placedIconSymbolIndex:{configurable:!0},verticalPlacedIconSymbolIndex:{configurable:!0},key:{configurable:!0},textBoxStartIndex:{configurable:!0},textBoxEndIndex:{configurable:!0},verticalTextBoxStartIndex:{configurable:!0},verticalTextBoxEndIndex:{configurable:!0},iconBoxStartIndex:{configurable:!0},iconBoxEndIndex:{configurable:!0},verticalIconBoxStartIndex:{configurable:!0},verticalIconBoxEndIndex:{configurable:!0},featureIndex:{configurable:!0},numHorizontalGlyphVertices:{configurable:!0},numVerticalGlyphVertices:{configurable:!0},numIconVertices:{configurable:!0},numVerticalIconVertices:{configurable:!0},useRuntimeCollisionCircles:{configurable:!0},crossTileID:{configurable:!0},textBoxScale:{configurable:!0},textOffset0:{configurable:!0},textOffset1:{configurable:!0},collisionCircleDiameter:{configurable:!0}};return r.anchorX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorY.get=function(){return this._structArray.int16[this._pos2+1]},r.rightJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+2]},r.centerJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+3]},r.leftJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+4]},r.verticalPlacedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+5]},r.placedIconSymbolIndex.get=function(){return this._structArray.int16[this._pos2+6]},r.verticalPlacedIconSymbolIndex.get=function(){return this._structArray.int16[this._pos2+7]},r.key.get=function(){return this._structArray.uint16[this._pos2+8]},r.textBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+9]},r.textBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+10]},r.verticalTextBoxStartIndex.get=function(){return 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S=o.content;x=jc(f,0,S[0]),b=jc(p,0,S[1]),_=jc(f,S[0],S[2]),w=jc(p,S[1],S[3]),T=S[0]-x,A=S[1]-b,k=S[2]-S[0]-_,M=S[3]-S[1]-w}var E=function(n,i,l,c){var f=Vc(n.stretch-x,_,u,t.left),p=qc(n.fixed-T,k,n.stretch,m),d=Vc(i.stretch-b,w,h,t.top),y=qc(i.fixed-A,M,i.stretch,g),v=Vc(l.stretch-x,_,u,t.left),S=qc(l.fixed-T,k,l.stretch,m),E=Vc(c.stretch-b,w,h,t.top),C=qc(c.fixed-A,M,c.stretch,g),L=new a(f,d),I=new a(v,d),P=new a(v,E),z=new a(f,E),O=new a(p/s,y/s),D=new a(S/s,C/s),R=e*Math.PI/180;if(R){var F=Math.sin(R),B=Math.cos(R),N=[B,-F,F,B];L._matMult(N),I._matMult(N),z._matMult(N),P._matMult(N)}var j=n.stretch+n.fixed,U=l.stretch+l.fixed,V=i.stretch+i.fixed,q=c.stretch+c.fixed;return{tl:L,tr:I,bl:z,br:P,tex:{x:o.paddedRect.x+Bc+j,y:o.paddedRect.y+Bc+V,w:U-j,h:q-V},writingMode:void 0,glyphOffset:[0,0],sectionIndex:0,pixelOffsetTL:O,pixelOffsetBR:D,minFontScaleX:k/s/u,minFontScaleY:M/s/h,isSDF:r}};if(n&&(o.stretchX||o.stretchY))for(var C=Uc(f,y,m),L=Uc(p,v,g),I=0;I0&&(d=Math.max(10,d),this.circleDiameter=d)}else{var m=o.top*s-l,g=o.bottom*s+l,y=o.left*s-l,v=o.right*s+l,x=o.collisionPadding;if(x&&(y-=x[0]*s,m-=x[1]*s,v+=x[2]*s,g+=x[3]*s),u){var _=new a(y,m),b=new a(v,m),w=new a(y,g),T=new a(v,g),k=u*Math.PI/180;_._rotate(k),b._rotate(k),w._rotate(k),T._rotate(k),y=Math.min(_.x,b.x,w.x,T.x),v=Math.max(_.x,b.x,w.x,T.x),m=Math.min(_.y,b.y,w.y,T.y),g=Math.max(_.y,b.y,w.y,T.y)}t.emplaceBack(e.x,e.y,y,m,v,g,r,n,i)}this.boxEndIndex=t.length},Gc=function(t,e){if(void 0===t&&(t=[]),void 0===e&&(e=Zc),this.data=t,this.length=this.data.length,this.compare=e,this.length>0)for(var r=(this.length>>1)-1;r>=0;r--)this._down(r)};function Zc(t,e){return te?1:0}function Wc(t,e,r){void 0===e&&(e=1),void 0===r&&(r=!1);for(var n=1/0,i=1/0,o=-1/0,s=-1/0,l=t[0],c=0;co)&&(o=u.x),(!c||u.y>s)&&(s=u.y)}var h=o-n,f=s-i,p=Math.min(h,f),d=p/2,m=new Gc([],Yc);if(0===p)return new a(n,i);for(var g=n;gv.d||!v.d)&&(v=_,r&&console.log(\"found best %d after %d probes\",Math.round(1e4*_.d)/1e4,x)),_.max-v.d<=e||(d=_.h/2,m.push(new Xc(_.p.x-d,_.p.y-d,d,t)),m.push(new Xc(_.p.x+d,_.p.y-d,d,t)),m.push(new Xc(_.p.x-d,_.p.y+d,d,t)),m.push(new Xc(_.p.x+d,_.p.y+d,d,t)),x+=4)}return r&&(console.log(\"num probes: \"+x),console.log(\"best distance: \"+v.d)),v.p}function Yc(t,e){return e.max-t.max}function Xc(t,e,r,n){this.p=new a(t,e),this.h=r,this.d=function(t,e){for(var r=!1,n=1/0,i=0;it.y!=u.y>t.y&&t.x<(u.x-c.x)*(t.y-c.y)/(u.y-c.y)+c.x&&(r=!r),n=Math.min(n,Eo(t,c,u))}return(r?1:-1)*Math.sqrt(n)}(this.p,n),this.max=this.d+this.h*Math.SQRT2}Gc.prototype.push=function(t){this.data.push(t),this.length++,this._up(this.length-1)},Gc.prototype.pop=function(){if(0!==this.length){var t=this.data[0],e=this.data.pop();return this.length--,this.length>0&&(this.data[0]=e,this._down(0)),t}},Gc.prototype.peek=function(){return this.data[0]},Gc.prototype._up=function(t){for(var e=this.data,r=this.compare,n=e[t];t>0;){var i=t-1>>1,a=e[i];if(r(n,a)>=0)break;e[t]=a,t=i}e[t]=n},Gc.prototype._down=function(t){for(var e=this.data,r=this.compare,n=this.length>>1,i=e[t];t=0)break;e[t]=o,t=a}e[t]=i};var $c=7,Jc=Number.POSITIVE_INFINITY;function Kc(t,e){return e[1]!==Jc?function(t,e,r){var n=0,i=0;switch(e=Math.abs(e),r=Math.abs(r),t){case\"top-right\":case\"top-left\":case\"top\":i=r-$c;break;case\"bottom-right\":case\"bottom-left\":case\"bottom\":i=-r+$c}switch(t){case\"top-right\":case\"bottom-right\":case\"right\":n=-e;break;case\"top-left\":case\"bottom-left\":case\"left\":n=e}return[n,i]}(t,e[0],e[1]):function(t,e){var r=0,n=0;e<0&&(e=0);var i=e/Math.sqrt(2);switch(t){case\"top-right\":case\"top-left\":n=i-$c;break;case\"bottom-right\":case\"bottom-left\":n=-i+$c;break;case\"bottom\":n=-e+$c;break;case\"top\":n=e-$c}switch(t){case\"top-right\":case\"bottom-right\":r=-i;break;case\"top-left\":case\"bottom-left\":r=i;break;case\"left\":r=e;break;case\"right\":r=-e}return[r,n]}(t,e[0])}function Qc(t){switch(t){case\"right\":case\"top-right\":case\"bottom-right\":return\"right\";case\"left\":case\"top-left\":case\"bottom-left\":return\"left\"}return\"center\"}var tu=255,eu=tu*Ac;function ru(t,e,r,n,i,o,s,l,c,u,h,f,p,d,m){var g=function(t,e,r,n,i,o,s,l){for(var c=n.layout.get(\"text-rotate\").evaluate(o,{})*Math.PI/180,u=[],h=0,f=e.positionedLines;heu&&k(t.layerIds[0]+': Value for \"text-size\" is >= '+tu+'. Reduce your \"text-size\".'):\"composite\"===y.kind&&((v=[Ac*d.compositeTextSizes[0].evaluate(s,{},m),Ac*d.compositeTextSizes[1].evaluate(s,{},m)])[0]>eu||v[1]>eu)&&k(t.layerIds[0]+': Value for \"text-size\" is >= '+tu+'. Reduce your \"text-size\".'),t.addSymbols(t.text,g,v,l,o,s,u,e,c.lineStartIndex,c.lineLength,p,m);for(var x=0,_=h;x<_.length;x+=1)f[_[x]]=t.text.placedSymbolArray.length-1;return 4*g.length}function nu(t){for(var e in t)return t[e];return null}function iu(t,e,r,n){var i=t.compareText;if(e in i){for(var a=i[e],o=a.length-1;o>=0;o--)if(n.dist(a[o])0)&&(\"constant\"!==a.value.kind||a.value.value.length>0),c=\"constant\"!==s.value.kind||!!s.value.value||Object.keys(s.parameters).length>0,u=i.get(\"symbol-sort-key\");if(this.features=[],l||c){for(var h=e.iconDependencies,f=e.glyphDependencies,p=e.availableImages,d=new Oi(this.zoom),m=0,g=t;m=0;for(var z=0,O=k.sections;z=0;s--)a[s]={x:e[s].x,y:e[s].y,tileUnitDistanceFromAnchor:i},s>0&&(i+=e[s-1].dist(e[s]));for(var l=0;l0},fu.prototype.hasIconData=function(){return this.icon.segments.get().length>0},fu.prototype.hasDebugData=function(){return this.textCollisionBox&&this.iconCollisionBox},fu.prototype.hasTextCollisionBoxData=function(){return this.hasDebugData()&&this.textCollisionBox.segments.get().length>0},fu.prototype.hasIconCollisionBoxData=function(){return this.hasDebugData()&&this.iconCollisionBox.segments.get().length>0},fu.prototype.addIndicesForPlacedSymbol=function(t,e){for(var r=t.placedSymbolArray.get(e),n=r.vertexStartIndex+4*r.numGlyphs,i=r.vertexStartIndex;i1||this.icon.segments.get().length>1)){this.symbolInstanceIndexes=this.getSortedSymbolIndexes(t),this.sortedAngle=t,this.text.indexArray.clear(),this.icon.indexArray.clear(),this.featureSortOrder=[];for(var r=0,n=this.symbolInstanceIndexes;r=0&&n.indexOf(t)===r&&e.addIndicesForPlacedSymbol(e.text,t)})),a.verticalPlacedTextSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.text,a.verticalPlacedTextSymbolIndex),a.placedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,a.placedIconSymbolIndex),a.verticalPlacedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,a.verticalPlacedIconSymbolIndex)}this.text.indexBuffer&&this.text.indexBuffer.updateData(this.text.indexArray),this.icon.indexBuffer&&this.icon.indexBuffer.updateData(this.icon.indexArray)}},oi(\"SymbolBucket\",fu,{omit:[\"layers\",\"collisionBoxArray\",\"features\",\"compareText\"]}),fu.MAX_GLYPHS=65535,fu.addDynamicAttributes=lu;var pu=new Yi({\"symbol-placement\":new qi(Ft.layout_symbol[\"symbol-placement\"]),\"symbol-spacing\":new qi(Ft.layout_symbol[\"symbol-spacing\"]),\"symbol-avoid-edges\":new qi(Ft.layout_symbol[\"symbol-avoid-edges\"]),\"symbol-sort-key\":new Hi(Ft.layout_symbol[\"symbol-sort-key\"]),\"symbol-z-order\":new qi(Ft.layout_symbol[\"symbol-z-order\"]),\"icon-allow-overlap\":new qi(Ft.layout_symbol[\"icon-allow-overlap\"]),\"icon-ignore-placement\":new qi(Ft.layout_symbol[\"icon-ignore-placement\"]),\"icon-optional\":new qi(Ft.layout_symbol[\"icon-optional\"]),\"icon-rotation-alignment\":new qi(Ft.layout_symbol[\"icon-rotation-alignment\"]),\"icon-size\":new Hi(Ft.layout_symbol[\"icon-size\"]),\"icon-text-fit\":new qi(Ft.layout_symbol[\"icon-text-fit\"]),\"icon-text-fit-padding\":new qi(Ft.layout_symbol[\"icon-text-fit-padding\"]),\"icon-image\":new Hi(Ft.layout_symbol[\"icon-image\"]),\"icon-rotate\":new Hi(Ft.layout_symbol[\"icon-rotate\"]),\"icon-padding\":new qi(Ft.layout_symbol[\"icon-padding\"]),\"icon-keep-upright\":new qi(Ft.layout_symbol[\"icon-keep-upright\"]),\"icon-offset\":new Hi(Ft.layout_symbol[\"icon-offset\"]),\"icon-anchor\":new Hi(Ft.layout_symbol[\"icon-anchor\"]),\"icon-pitch-alignment\":new qi(Ft.layout_symbol[\"icon-pitch-alignment\"]),\"text-pitch-alignment\":new qi(Ft.layout_symbol[\"text-pitch-alignment\"]),\"text-rotation-alignment\":new qi(Ft.layout_symbol[\"text-rotation-alignment\"]),\"text-field\":new Hi(Ft.layout_symbol[\"text-field\"]),\"text-font\":new Hi(Ft.layout_symbol[\"text-font\"]),\"text-size\":new Hi(Ft.layout_symbol[\"text-size\"]),\"text-max-width\":new Hi(Ft.layout_symbol[\"text-max-width\"]),\"text-line-height\":new qi(Ft.layout_symbol[\"text-line-height\"]),\"text-letter-spacing\":new Hi(Ft.layout_symbol[\"text-letter-spacing\"]),\"text-justify\":new Hi(Ft.layout_symbol[\"text-justify\"]),\"text-radial-offset\":new Hi(Ft.layout_symbol[\"text-radial-offset\"]),\"text-variable-anchor\":new qi(Ft.layout_symbol[\"text-variable-anchor\"]),\"text-anchor\":new Hi(Ft.layout_symbol[\"text-anchor\"]),\"text-max-angle\":new qi(Ft.layout_symbol[\"text-max-angle\"]),\"text-writing-mode\":new qi(Ft.layout_symbol[\"text-writing-mode\"]),\"text-rotate\":new Hi(Ft.layout_symbol[\"text-rotate\"]),\"text-padding\":new qi(Ft.layout_symbol[\"text-padding\"]),\"text-keep-upright\":new qi(Ft.layout_symbol[\"text-keep-upright\"]),\"text-transform\":new Hi(Ft.layout_symbol[\"text-transform\"]),\"text-offset\":new Hi(Ft.layout_symbol[\"text-offset\"]),\"text-allow-overlap\":new qi(Ft.layout_symbol[\"text-allow-overlap\"]),\"text-ignore-placement\":new qi(Ft.layout_symbol[\"text-ignore-placement\"]),\"text-optional\":new qi(Ft.layout_symbol[\"text-optional\"])}),du={paint:new Yi({\"icon-opacity\":new Hi(Ft.paint_symbol[\"icon-opacity\"]),\"icon-color\":new Hi(Ft.paint_symbol[\"icon-color\"]),\"icon-halo-color\":new Hi(Ft.paint_symbol[\"icon-halo-color\"]),\"icon-halo-width\":new Hi(Ft.paint_symbol[\"icon-halo-width\"]),\"icon-halo-blur\":new Hi(Ft.paint_symbol[\"icon-halo-blur\"]),\"icon-translate\":new qi(Ft.paint_symbol[\"icon-translate\"]),\"icon-translate-anchor\":new qi(Ft.paint_symbol[\"icon-translate-anchor\"]),\"text-opacity\":new Hi(Ft.paint_symbol[\"text-opacity\"]),\"text-color\":new Hi(Ft.paint_symbol[\"text-color\"],{runtimeType:Xt,getOverride:function(t){return t.textColor},hasOverride:function(t){return!!t.textColor}}),\"text-halo-color\":new Hi(Ft.paint_symbol[\"text-halo-color\"]),\"text-halo-width\":new Hi(Ft.paint_symbol[\"text-halo-width\"]),\"text-halo-blur\":new Hi(Ft.paint_symbol[\"text-halo-blur\"]),\"text-translate\":new qi(Ft.paint_symbol[\"text-translate\"]),\"text-translate-anchor\":new qi(Ft.paint_symbol[\"text-translate-anchor\"])}),layout:pu},mu=function(t){this.type=t.property.overrides?t.property.overrides.runtimeType:Gt,this.defaultValue=t};mu.prototype.evaluate=function(t){if(t.formattedSection){var e=this.defaultValue.property.overrides;if(e&&e.hasOverride(t.formattedSection))return e.getOverride(t.formattedSection)}return t.feature&&t.featureState?this.defaultValue.evaluate(t.feature,t.featureState):this.defaultValue.property.specification.default},mu.prototype.eachChild=function(t){this.defaultValue.isConstant()||t(this.defaultValue.value._styleExpression.expression)},mu.prototype.outputDefined=function(){return!1},mu.prototype.serialize=function(){return null},oi(\"FormatSectionOverride\",mu,{omit:[\"defaultValue\"]});var gu=function(t){function e(e){t.call(this,e,du)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.recalculate=function(e,r){if(t.prototype.recalculate.call(this,e,r),\"auto\"===this.layout.get(\"icon-rotation-alignment\")&&(\"point\"!==this.layout.get(\"symbol-placement\")?this.layout._values[\"icon-rotation-alignment\"]=\"map\":this.layout._values[\"icon-rotation-alignment\"]=\"viewport\"),\"auto\"===this.layout.get(\"text-rotation-alignment\")&&(\"point\"!==this.layout.get(\"symbol-placement\")?this.layout._values[\"text-rotation-alignment\"]=\"map\":this.layout._values[\"text-rotation-alignment\"]=\"viewport\"),\"auto\"===this.layout.get(\"text-pitch-alignment\")&&(this.layout._values[\"text-pitch-alignment\"]=this.layout.get(\"text-rotation-alignment\")),\"auto\"===this.layout.get(\"icon-pitch-alignment\")&&(this.layout._values[\"icon-pitch-alignment\"]=this.layout.get(\"icon-rotation-alignment\")),\"point\"===this.layout.get(\"symbol-placement\")){var n=this.layout.get(\"text-writing-mode\");if(n){for(var i=[],a=0,o=n;a\",targetMapId:n,sourceMapId:a.mapId})}}},Lu.prototype.receive=function(t){var e=t.data,r=e.id;if(r&&(!e.targetMapId||this.mapId===e.targetMapId))if(\"\"===e.type){delete this.tasks[r];var n=this.cancelCallbacks[r];delete this.cancelCallbacks[r],n&&n()}else S()||e.mustQueue?(this.tasks[r]=e,this.taskQueue.push(r),this.invoker.trigger()):this.processTask(r,e)},Lu.prototype.process=function(){if(this.taskQueue.length){var t=this.taskQueue.shift(),e=this.tasks[t];delete this.tasks[t],this.taskQueue.length&&this.invoker.trigger(),e&&this.processTask(t,e)}},Lu.prototype.processTask=function(t,e){var r=this;if(\"\"===e.type){var n=this.callbacks[t];delete this.callbacks[t],n&&(e.error?n(hi(e.error)):n(null,hi(e.data)))}else{var i=!1,a=L(this.globalScope)?void 0:[],o=e.hasCallback?function(e,n){i=!0,delete r.cancelCallbacks[t],r.target.postMessage({id:t,type:\"\",sourceMapId:r.mapId,error:e?ui(e):null,data:ui(n,a)},a)}:function(t){i=!0},s=null,l=hi(e.data);if(this.parent[e.type])s=this.parent[e.type](e.sourceMapId,l,o);else if(this.parent.getWorkerSource){var c=e.type.split(\".\");s=this.parent.getWorkerSource(e.sourceMapId,c[0],l.source)[c[1]](l,o)}else o(new Error(\"Could not find function \"+e.type));!i&&s&&s.cancel&&(this.cancelCallbacks[t]=s.cancel)}},Lu.prototype.remove=function(){this.invoker.remove(),this.target.removeEventListener(\"message\",this.receive,!1)};var Pu=function(t,e){t&&(e?this.setSouthWest(t).setNorthEast(e):4===t.length?this.setSouthWest([t[0],t[1]]).setNorthEast([t[2],t[3]]):this.setSouthWest(t[0]).setNorthEast(t[1]))};Pu.prototype.setNorthEast=function(t){return this._ne=t instanceof Ou?new Ou(t.lng,t.lat):Ou.convert(t),this},Pu.prototype.setSouthWest=function(t){return this._sw=t instanceof Ou?new Ou(t.lng,t.lat):Ou.convert(t),this},Pu.prototype.extend=function(t){var e,r,n=this._sw,i=this._ne;if(t instanceof Ou)e=t,r=t;else{if(!(t instanceof Pu)){if(Array.isArray(t)){if(4===t.length||t.every(Array.isArray)){var a=t;return this.extend(Pu.convert(a))}var o=t;return this.extend(Ou.convert(o))}return this}if(e=t._sw,r=t._ne,!e||!r)return this}return n||i?(n.lng=Math.min(e.lng,n.lng),n.lat=Math.min(e.lat,n.lat),i.lng=Math.max(r.lng,i.lng),i.lat=Math.max(r.lat,i.lat)):(this._sw=new Ou(e.lng,e.lat),this._ne=new Ou(r.lng,r.lat)),this},Pu.prototype.getCenter=function(){return new Ou((this._sw.lng+this._ne.lng)/2,(this._sw.lat+this._ne.lat)/2)},Pu.prototype.getSouthWest=function(){return this._sw},Pu.prototype.getNorthEast=function(){return this._ne},Pu.prototype.getNorthWest=function(){return new Ou(this.getWest(),this.getNorth())},Pu.prototype.getSouthEast=function(){return new Ou(this.getEast(),this.getSouth())},Pu.prototype.getWest=function(){return this._sw.lng},Pu.prototype.getSouth=function(){return this._sw.lat},Pu.prototype.getEast=function(){return this._ne.lng},Pu.prototype.getNorth=function(){return this._ne.lat},Pu.prototype.toArray=function(){return[this._sw.toArray(),this._ne.toArray()]},Pu.prototype.toString=function(){return\"LngLatBounds(\"+this._sw.toString()+\", \"+this._ne.toString()+\")\"},Pu.prototype.isEmpty=function(){return!(this._sw&&this._ne)},Pu.prototype.contains=function(t){var e=Ou.convert(t),r=e.lng,n=e.lat,i=this._sw.lat<=n&&n<=this._ne.lat,a=this._sw.lng<=r&&r<=this._ne.lng;return this._sw.lng>this._ne.lng&&(a=this._sw.lng>=r&&r>=this._ne.lng),i&&a},Pu.convert=function(t){return!t||t instanceof Pu?t:new Pu(t)};var zu=6371008.8,Ou=function(t,e){if(isNaN(t)||isNaN(e))throw new Error(\"Invalid LngLat object: (\"+t+\", \"+e+\")\");if(this.lng=+t,this.lat=+e,this.lat>90||this.lat<-90)throw new Error(\"Invalid LngLat latitude value: must be between -90 and 90\")};Ou.prototype.wrap=function(){return new Ou(f(this.lng,-180,180),this.lat)},Ou.prototype.toArray=function(){return[this.lng,this.lat]},Ou.prototype.toString=function(){return\"LngLat(\"+this.lng+\", \"+this.lat+\")\"},Ou.prototype.distanceTo=function(t){var e=Math.PI/180,r=this.lat*e,n=t.lat*e,i=Math.sin(r)*Math.sin(n)+Math.cos(r)*Math.cos(n)*Math.cos((t.lng-this.lng)*e);return zu*Math.acos(Math.min(i,1))},Ou.prototype.toBounds=function(t){void 0===t&&(t=0);var e=360*t/40075017,r=e/Math.cos(Math.PI/180*this.lat);return new Pu(new Ou(this.lng-r,this.lat-e),new Ou(this.lng+r,this.lat+e))},Ou.convert=function(t){if(t instanceof Ou)return t;if(Array.isArray(t)&&(2===t.length||3===t.length))return new Ou(Number(t[0]),Number(t[1]));if(!Array.isArray(t)&&\"object\"==typeof t&&null!==t)return new Ou(Number(\"lng\"in t?t.lng:t.lon),Number(t.lat));throw new Error(\"`LngLatLike` argument must be specified as a LngLat instance, an object {lng: , lat: }, an object {lon: , lat: }, or an array of [, ]\")};var Du=2*Math.PI*zu;function Ru(t){return Du*Math.cos(t*Math.PI/180)}function Fu(t){return(180+t)/360}function Bu(t){return(180-180/Math.PI*Math.log(Math.tan(Math.PI/4+t*Math.PI/360)))/360}function Nu(t,e){return t/Ru(e)}function ju(t){var e=180-360*t;return 360/Math.PI*Math.atan(Math.exp(e*Math.PI/180))-90}var Uu=function(t,e,r){void 0===r&&(r=0),this.x=+t,this.y=+e,this.z=+r};Uu.fromLngLat=function(t,e){void 0===e&&(e=0);var r=Ou.convert(t);return new Uu(Fu(r.lng),Bu(r.lat),Nu(e,r.lat))},Uu.prototype.toLngLat=function(){return new Ou(360*this.x-180,ju(this.y))},Uu.prototype.toAltitude=function(){return t=this.z,e=this.y,t*Ru(ju(e));var t,e},Uu.prototype.meterInMercatorCoordinateUnits=function(){return 1/Du*(t=ju(this.y),1/Math.cos(t*Math.PI/180));var t};var Vu=function(t,e,r){this.z=t,this.x=e,this.y=r,this.key=Gu(0,t,t,e,r)};Vu.prototype.equals=function(t){return this.z===t.z&&this.x===t.x&&this.y===t.y},Vu.prototype.url=function(t,e){var r,n,i,a,o,s=(r=this.x,n=this.y,i=this.z,a=Iu(256*r,256*(n=Math.pow(2,i)-n-1),i),o=Iu(256*(r+1),256*(n+1),i),a[0]+\",\"+a[1]+\",\"+o[0]+\",\"+o[1]),l=function(t,e,r){for(var n,i=\"\",a=t;a>0;a--)i+=(e&(n=1<this.canonical.z?new Hu(t,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y):new Hu(t,this.wrap,t,this.canonical.x>>e,this.canonical.y>>e)},Hu.prototype.calculateScaledKey=function(t,e){var r=this.canonical.z-t;return t>this.canonical.z?Gu(this.wrap*+e,t,this.canonical.z,this.canonical.x,this.canonical.y):Gu(this.wrap*+e,t,t,this.canonical.x>>r,this.canonical.y>>r)},Hu.prototype.isChildOf=function(t){if(t.wrap!==this.wrap)return!1;var e=this.canonical.z-t.canonical.z;return 0===t.overscaledZ||t.overscaledZ>e&&t.canonical.y===this.canonical.y>>e},Hu.prototype.children=function(t){if(this.overscaledZ>=t)return[new Hu(this.overscaledZ+1,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y)];var e=this.canonical.z+1,r=2*this.canonical.x,n=2*this.canonical.y;return[new Hu(e,this.wrap,e,r,n),new Hu(e,this.wrap,e,r+1,n),new Hu(e,this.wrap,e,r,n+1),new Hu(e,this.wrap,e,r+1,n+1)]},Hu.prototype.isLessThan=function(t){return this.wrapt.wrap)&&(this.overscaledZt.overscaledZ)&&(this.canonical.xt.canonical.x)&&this.canonical.y=this.dim+1||e<-1||e>=this.dim+1)throw new RangeError(\"out of range source coordinates for DEM data\");return(e+1)*this.stride+(t+1)},Zu.prototype._unpackMapbox=function(t,e,r){return(256*t*256+256*e+r)/10-1e4},Zu.prototype._unpackTerrarium=function(t,e,r){return 256*t+e+r/256-32768},Zu.prototype.getPixels=function(){return new Ko({width:this.stride,height:this.stride},new Uint8Array(this.data.buffer))},Zu.prototype.backfillBorder=function(t,e,r){if(this.dim!==t.dim)throw new Error(\"dem dimension mismatch\");var n=e*this.dim,i=e*this.dim+this.dim,a=r*this.dim,o=r*this.dim+this.dim;switch(e){case-1:n=i-1;break;case 1:i=n+1}switch(r){case-1:a=o-1;break;case 1:o=a+1}for(var s=-e*this.dim,l=-r*this.dim,c=a;c=0&&u[3]>=0&&s.insert(o,u[0],u[1],u[2],u[3])}},Ju.prototype.loadVTLayers=function(){return this.vtLayers||(this.vtLayers=new tl.VectorTile(new Pl(this.rawTileData)).layers,this.sourceLayerCoder=new Wu(this.vtLayers?Object.keys(this.vtLayers).sort():[\"_geojsonTileLayer\"])),this.vtLayers},Ju.prototype.query=function(t,e,r,n){var i=this;this.loadVTLayers();for(var o=t.params||{},s=po/t.tileSize/t.scale,l=An(o.filter),c=t.queryGeometry,u=t.queryPadding*s,h=Qu(c),f=this.grid.query(h.minX-u,h.minY-u,h.maxX+u,h.maxY+u),p=Qu(t.cameraQueryGeometry),d=0,m=this.grid3D.query(p.minX-u,p.minY-u,p.maxX+u,p.maxY+u,(function(e,r,n,i){return function(t,e,r,n,i){for(var o=0,s=t;o=l.x&&i>=l.y)return!0}var c=[new a(e,r),new a(e,i),new a(n,i),new a(n,r)];if(t.length>2)for(var u=0,h=c;u=0)return!0;return!1}(a,h)){var f=this.sourceLayerCoder.decode(r),d=this.vtLayers[f].feature(n);if(i.needGeometry){var m=vo(d,!0);if(!i.filter(new Oi(this.tileID.overscaledZ),m,this.tileID.canonical))return}else if(!i.filter(new Oi(this.tileID.overscaledZ),d))return;for(var g=this.getId(d,f),y=0;yn)i=!1;else if(e)if(this.expirationTimeft&&(t.getActor().send(\"enforceCacheSizeLimit\",ht),xt=0)},t.clamp=h,t.clearTileCache=function(t){var e=s.caches.delete(ut);t&&e.catch(t).then((function(){return t()}))},t.clipLine=Fc,t.clone=function(t){var e=new Fo(16);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e},t.clone$1=w,t.clone$2=function(t){var e=new Fo(3);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e},t.collisionCircleLayout=Ml,t.config=j,t.create=function(){var t=new Fo(16);return Fo!=Float32Array&&(t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[11]=0,t[12]=0,t[13]=0,t[14]=0),t[0]=1,t[5]=1,t[10]=1,t[15]=1,t},t.create$1=function(){var t=new Fo(9);return Fo!=Float32Array&&(t[1]=0,t[2]=0,t[3]=0,t[5]=0,t[6]=0,t[7]=0),t[0]=1,t[4]=1,t[8]=1,t},t.create$2=function(){var t=new Fo(4);return Fo!=Float32Array&&(t[1]=0,t[2]=0),t[0]=1,t[3]=1,t},t.createCommonjsModule=e,t.createExpression=hn,t.createLayout=ta,t.createStyleLayer=function(t){return\"custom\"===t.type?new bu(t):new wu[t.type](t)},t.cross=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=r[0],s=r[1],l=r[2];return t[0]=i*l-a*s,t[1]=a*o-n*l,t[2]=n*s-i*o,t},t.deepEqual=function t(e,r){if(Array.isArray(e)){if(!Array.isArray(r)||e.length!==r.length)return!1;for(var n=0;n0&&(a=1/Math.sqrt(a)),t[0]=e[0]*a,t[1]=e[1]*a,t[2]=e[2]*a,t},t.number=er,t.offscreenCanvasSupported=_t,t.ortho=function(t,e,r,n,i,a,o){var s=1/(e-r),l=1/(n-i),c=1/(a-o);return t[0]=-2*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=-2*l,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=2*c,t[11]=0,t[12]=(e+r)*s,t[13]=(i+n)*l,t[14]=(o+a)*c,t[15]=1,t},t.parseGlyphPBF=function(t){return new Pl(t).readFields(Ql,[])},t.pbf=Pl,t.performSymbolLayout=function(t,e,r,n,i,a,o){t.createArrays();var s=512*t.overscaling;t.tilePixelRatio=po/s,t.compareText={},t.iconsNeedLinear=!1;var l=t.layers[0].layout,c=t.layers[0]._unevaluatedLayout._values,u={};if(\"composite\"===t.textSizeData.kind){var h=t.textSizeData,f=h.minZoom,p=h.maxZoom;u.compositeTextSizes=[c[\"text-size\"].possiblyEvaluate(new Oi(f),o),c[\"text-size\"].possiblyEvaluate(new Oi(p),o)]}if(\"composite\"===t.iconSizeData.kind){var d=t.iconSizeData,m=d.minZoom,g=d.maxZoom;u.compositeIconSizes=[c[\"icon-size\"].possiblyEvaluate(new Oi(m),o),c[\"icon-size\"].possiblyEvaluate(new Oi(g),o)]}u.layoutTextSize=c[\"text-size\"].possiblyEvaluate(new Oi(t.zoom+1),o),u.layoutIconSize=c[\"icon-size\"].possiblyEvaluate(new Oi(t.zoom+1),o),u.textMaxSize=c[\"text-size\"].possiblyEvaluate(new Oi(18));for(var y=l.get(\"text-line-height\")*Cl,v=\"map\"===l.get(\"text-rotation-alignment\")&&\"point\"!==l.get(\"symbol-placement\"),x=l.get(\"text-keep-upright\"),_=l.get(\"text-size\"),b=function(){var a=T[w],s=l.get(\"text-font\").evaluate(a,{},o).join(\",\"),c=_.evaluate(a,{},o),h=u.layoutTextSize.evaluate(a,{},o),f=u.layoutIconSize.evaluate(a,{},o),p={horizontal:{},vertical:void 0},d=a.text,m=[0,0];if(d){var g=d.toString(),b=l.get(\"text-letter-spacing\").evaluate(a,{},o)*Cl,A=function(t){for(var e=0,r=t;e=po||h.y<0||h.y>=po||function(t,e,r,n,i,a,o,s,l,c,u,h,f,p,d,m,g,y,v,x,_,b,w,T,A){var M,S,E,C,L,I=t.addToLineVertexArray(e,r),P=0,z=0,O=0,D=0,R=-1,F=-1,B={},N=ja(\"\"),j=0,U=0;if(void 0===s._unevaluatedLayout.getValue(\"text-radial-offset\")?(j=(M=s.layout.get(\"text-offset\").evaluate(_,{},T).map((function(t){return t*Cl})))[0],U=M[1]):(j=s.layout.get(\"text-radial-offset\").evaluate(_,{},T)*Cl,U=Jc),t.allowVerticalPlacement&&n.vertical){var V=s.layout.get(\"text-rotate\").evaluate(_,{},T)+90,q=n.vertical;C=new Hc(l,e,c,u,h,q,f,p,d,V),o&&(L=new Hc(l,e,c,u,h,o,g,y,d,V))}if(i){var H=s.layout.get(\"icon-rotate\").evaluate(_,{}),G=\"none\"!==s.layout.get(\"icon-text-fit\"),Z=Nc(i,H,w,G),W=o?Nc(o,H,w,G):void 0;E=new Hc(l,e,c,u,h,i,g,y,!1,H),P=4*Z.length;var Y=t.iconSizeData,X=null;\"source\"===Y.kind?(X=[Ac*s.layout.get(\"icon-size\").evaluate(_,{})])[0]>eu&&k(t.layerIds[0]+': Value for \"icon-size\" is >= '+tu+'. Reduce your \"icon-size\".'):\"composite\"===Y.kind&&((X=[Ac*b.compositeIconSizes[0].evaluate(_,{},T),Ac*b.compositeIconSizes[1].evaluate(_,{},T)])[0]>eu||X[1]>eu)&&k(t.layerIds[0]+': Value for \"icon-size\" is >= '+tu+'. Reduce your \"icon-size\".'),t.addSymbols(t.icon,Z,X,x,v,_,!1,e,I.lineStartIndex,I.lineLength,-1,T),R=t.icon.placedSymbolArray.length-1,W&&(z=4*W.length,t.addSymbols(t.icon,W,X,x,v,_,lc.vertical,e,I.lineStartIndex,I.lineLength,-1,T),F=t.icon.placedSymbolArray.length-1)}for(var $ in n.horizontal){var J=n.horizontal[$];if(!S){N=ja(J.text);var K=s.layout.get(\"text-rotate\").evaluate(_,{},T);S=new Hc(l,e,c,u,h,J,f,p,d,K)}var Q=1===J.positionedLines.length;if(O+=ru(t,e,J,a,s,d,_,m,I,n.vertical?lc.horizontal:lc.horizontalOnly,Q?Object.keys(n.horizontal):[$],B,R,b,T),Q)break}n.vertical&&(D+=ru(t,e,n.vertical,a,s,d,_,m,I,lc.vertical,[\"vertical\"],B,F,b,T));var tt=S?S.boxStartIndex:t.collisionBoxArray.length,et=S?S.boxEndIndex:t.collisionBoxArray.length,rt=C?C.boxStartIndex:t.collisionBoxArray.length,nt=C?C.boxEndIndex:t.collisionBoxArray.length,it=E?E.boxStartIndex:t.collisionBoxArray.length,at=E?E.boxEndIndex:t.collisionBoxArray.length,ot=L?L.boxStartIndex:t.collisionBoxArray.length,st=L?L.boxEndIndex:t.collisionBoxArray.length,lt=-1,ct=function(t,e){return t&&t.circleDiameter?Math.max(t.circleDiameter,e):e};lt=ct(S,lt),lt=ct(C,lt),lt=ct(E,lt);var ut=(lt=ct(L,lt))>-1?1:0;ut&&(lt*=A/Cl),t.glyphOffsetArray.length>=fu.MAX_GLYPHS&&k(\"Too many glyphs being rendered in a tile. See https://github.com/mapbox/mapbox-gl-js/issues/2907\"),void 0!==_.sortKey&&t.addToSortKeyRanges(t.symbolInstances.length,_.sortKey),t.symbolInstances.emplaceBack(e.x,e.y,B.right>=0?B.right:-1,B.center>=0?B.center:-1,B.left>=0?B.left:-1,B.vertical||-1,R,F,N,tt,et,rt,nt,it,at,ot,st,c,O,D,P,z,ut,0,f,j,U,lt)}(t,h,s,r,n,i,f,t.layers[0],t.collisionBoxArray,e.index,e.sourceLayerIndex,t.index,v,w,M,l,_,T,S,d,e,a,c,u,o)};if(\"line\"===E)for(var P=0,z=Fc(e.geometry,0,0,po,po);P1){var U=Oc(j,A,r.vertical||m,n,g,x);U&&I(j,U)}}else if(\"Polygon\"===e.type)for(var V=0,q=Fs(e.geometry,0);V=E.maxzoom||\"none\"!==E.visibility&&(o(S,this.zoom,n),(m[E.id]=E.createBucket({index:u.bucketLayerIDs.length,layers:S,zoom:this.zoom,pixelRatio:this.pixelRatio,overscaling:this.overscaling,collisionBoxArray:this.collisionBoxArray,sourceLayerIndex:_,sourceID:this.source})).populate(b,g,this.tileID.canonical),u.bucketLayerIDs.push(S.map((function(t){return t.id}))))}}}var C=t.mapObject(g.glyphDependencies,(function(t){return Object.keys(t).map(Number)}));Object.keys(C).length?a.send(\"getGlyphs\",{uid:this.uid,stacks:C},(function(t,e){h||(h=t,f=e,P.call(l))})):f={};var L=Object.keys(g.iconDependencies);L.length?a.send(\"getImages\",{icons:L,source:this.source,tileID:this.tileID,type:\"icons\"},(function(t,e){h||(h=t,p=e,P.call(l))})):p={};var I=Object.keys(g.patternDependencies);function P(){if(h)return s(h);if(f&&p&&d){var e=new i(f),r=new t.ImageAtlas(p,d);for(var a in m){var l=m[a];l instanceof t.SymbolBucket?(o(l.layers,this.zoom,n),t.performSymbolLayout(l,f,e.positions,p,r.iconPositions,this.showCollisionBoxes,this.tileID.canonical)):l.hasPattern&&(l instanceof t.LineBucket||l instanceof t.FillBucket||l instanceof t.FillExtrusionBucket)&&(o(l.layers,this.zoom,n),l.addFeatures(g,this.tileID.canonical,r.patternPositions))}this.status=\"done\",s(null,{buckets:t.values(m).filter((function(t){return!t.isEmpty()})),featureIndex:u,collisionBoxArray:this.collisionBoxArray,glyphAtlasImage:e.image,imageAtlas:r,glyphMap:this.returnDependencies?f:null,iconMap:this.returnDependencies?p:null,glyphPositions:this.returnDependencies?e.positions:null})}}I.length?a.send(\"getImages\",{icons:I,source:this.source,tileID:this.tileID,type:\"patterns\"},(function(t,e){h||(h=t,d=e,P.call(l))})):d={},P.call(this)};var l=function(t,e,r,n){this.actor=t,this.layerIndex=e,this.availableImages=r,this.loadVectorData=n||s,this.loading={},this.loaded={}};l.prototype.loadTile=function(e,r){var n=this,i=e.uid;this.loading||(this.loading={});var o=!!(e&&e.request&&e.request.collectResourceTiming)&&new t.RequestPerformance(e.request),s=this.loading[i]=new a(e);s.abort=this.loadVectorData(e,(function(e,a){if(delete n.loading[i],e||!a)return s.status=\"done\",n.loaded[i]=s,r(e);var l=a.rawData,c={};a.expires&&(c.expires=a.expires),a.cacheControl&&(c.cacheControl=a.cacheControl);var u={};if(o){var h=o.finish();h&&(u.resourceTiming=JSON.parse(JSON.stringify(h)))}s.vectorTile=a.vectorTile,s.parse(a.vectorTile,n.layerIndex,n.availableImages,n.actor,(function(e,n){if(e||!n)return r(e);r(null,t.extend({rawTileData:l.slice(0)},n,c,u))})),n.loaded=n.loaded||{},n.loaded[i]=s}))},l.prototype.reloadTile=function(t,e){var r=this,n=this.loaded,i=t.uid,a=this;if(n&&n[i]){var o=n[i];o.showCollisionBoxes=t.showCollisionBoxes;var s=function(t,n){var i=o.reloadCallback;i&&(delete o.reloadCallback,o.parse(o.vectorTile,a.layerIndex,r.availableImages,a.actor,i)),e(t,n)};\"parsing\"===o.status?o.reloadCallback=s:\"done\"===o.status&&(o.vectorTile?o.parse(o.vectorTile,this.layerIndex,this.availableImages,this.actor,s):s())}},l.prototype.abortTile=function(t,e){var r=this.loading,n=t.uid;r&&r[n]&&r[n].abort&&(r[n].abort(),delete r[n]),e()},l.prototype.removeTile=function(t,e){var r=this.loaded,n=t.uid;r&&r[n]&&delete r[n],e()};var c=t.window.ImageBitmap,u=function(){this.loaded={}};u.prototype.loadTile=function(e,r){var n=e.uid,i=e.encoding,a=e.rawImageData,o=c&&a instanceof c?this.getImageData(a):a,s=new t.DEMData(n,o,i);this.loaded=this.loaded||{},this.loaded[n]=s,r(null,s)},u.prototype.getImageData=function(e){this.offscreenCanvas&&this.offscreenCanvasContext||(this.offscreenCanvas=new OffscreenCanvas(e.width,e.height),this.offscreenCanvasContext=this.offscreenCanvas.getContext(\"2d\")),this.offscreenCanvas.width=e.width,this.offscreenCanvas.height=e.height,this.offscreenCanvasContext.drawImage(e,0,0,e.width,e.height);var r=this.offscreenCanvasContext.getImageData(-1,-1,e.width+2,e.height+2);return this.offscreenCanvasContext.clearRect(0,0,this.offscreenCanvas.width,this.offscreenCanvas.height),new t.RGBAImage({width:r.width,height:r.height},r.data)},u.prototype.removeTile=function(t){var e=this.loaded,r=t.uid;e&&e[r]&&delete e[r]};var h=function t(e,r){var n,i=e&&e.type;if(\"FeatureCollection\"===i)for(n=0;n=0!=!!e&&t.reverse()}var d=t.vectorTile.VectorTileFeature.prototype.toGeoJSON,m=function(e){this._feature=e,this.extent=t.EXTENT,this.type=e.type,this.properties=e.tags,\"id\"in e&&!isNaN(e.id)&&(this.id=parseInt(e.id,10))};m.prototype.loadGeometry=function(){if(1===this._feature.type){for(var e=[],r=0,n=this._feature.geometry;r>31}function I(t,e){for(var r=t.loadGeometry(),n=t.type,i=0,a=0,o=r.length,s=0;s>1;O(t,e,o,n,i,a%2),z(t,e,r,n,o-1,a+1),z(t,e,r,o+1,i,a+1)}}function O(t,e,r,n,i,a){for(;i>n;){if(i-n>600){var o=i-n+1,s=r-n+1,l=Math.log(o),c=.5*Math.exp(2*l/3),u=.5*Math.sqrt(l*c*(o-c)/o)*(s-o/2<0?-1:1);O(t,e,r,Math.max(n,Math.floor(r-s*c/o+u)),Math.min(i,Math.floor(r+(o-s)*c/o+u)),a)}var h=e[2*r+a],f=n,p=i;for(D(t,e,n,r),e[2*i+a]>h&&D(t,e,n,i);fh;)p--}e[2*n+a]===h?D(t,e,n,p):D(t,e,++p,i),p<=r&&(n=p+1),r<=p&&(i=p-1)}}function D(t,e,r,n){R(t,r,n),R(e,2*r,2*n),R(e,2*r+1,2*n+1)}function R(t,e,r){var n=t[e];t[e]=t[r],t[r]=n}function F(t,e,r,n){var i=t-r,a=e-n;return i*i+a*a}b.fromVectorTileJs=w,b.fromGeojsonVt=T,b.GeoJSONWrapper=k;var B=function(t){return t[0]},N=function(t){return t[1]},j=function(t,e,r,n,i){void 0===e&&(e=B),void 0===r&&(r=N),void 0===n&&(n=64),void 0===i&&(i=Float64Array),this.nodeSize=n,this.points=t;for(var a=t.length<65536?Uint16Array:Uint32Array,o=this.ids=new a(t.length),s=this.coords=new i(2*t.length),l=0;l=r&&s<=i&&l>=n&&l<=a&&u.push(t[d]);else{var m=Math.floor((p+f)/2);s=e[2*m],l=e[2*m+1],s>=r&&s<=i&&l>=n&&l<=a&&u.push(t[m]);var g=(h+1)%2;(0===h?r<=s:n<=l)&&(c.push(p),c.push(m-1),c.push(g)),(0===h?i>=s:a>=l)&&(c.push(m+1),c.push(f),c.push(g))}}return u}(this.ids,this.coords,t,e,r,n,this.nodeSize)},j.prototype.within=function(t,e,r){return function(t,e,r,n,i,a){for(var o=[0,t.length-1,0],s=[],l=i*i;o.length;){var c=o.pop(),u=o.pop(),h=o.pop();if(u-h<=a)for(var f=h;f<=u;f++)F(e[2*f],e[2*f+1],r,n)<=l&&s.push(t[f]);else{var p=Math.floor((h+u)/2),d=e[2*p],m=e[2*p+1];F(d,m,r,n)<=l&&s.push(t[p]);var g=(c+1)%2;(0===c?r-i<=d:n-i<=m)&&(o.push(h),o.push(p-1),o.push(g)),(0===c?r+i>=d:n+i>=m)&&(o.push(p+1),o.push(u),o.push(g))}}return s}(this.ids,this.coords,t,e,r,this.nodeSize)};var U={minZoom:0,maxZoom:16,minPoints:2,radius:40,extent:512,nodeSize:64,log:!1,generateId:!1,reduce:null,map:function(t){return t}},V=function(t){this.options=X(Object.create(U),t),this.trees=new Array(this.options.maxZoom+1)};function q(t,e,r,n,i){return{x:t,y:e,zoom:1/0,id:r,parentId:-1,numPoints:n,properties:i}}function H(t,e){var r=t.geometry.coordinates,n=r[0],i=r[1];return{x:W(n),y:Y(i),zoom:1/0,index:e,parentId:-1}}function G(t){return{type:\"Feature\",id:t.id,properties:Z(t),geometry:{type:\"Point\",coordinates:[(n=t.x,360*(n-.5)),(e=t.y,r=(180-360*e)*Math.PI/180,360*Math.atan(Math.exp(r))/Math.PI-90)]}};var e,r,n}function Z(t){var e=t.numPoints,r=e>=1e4?Math.round(e/1e3)+\"k\":e>=1e3?Math.round(e/100)/10+\"k\":e;return X(X({},t.properties),{cluster:!0,cluster_id:t.id,point_count:e,point_count_abbreviated:r})}function W(t){return t/360+.5}function Y(t){var e=Math.sin(t*Math.PI/180),r=.5-.25*Math.log((1+e)/(1-e))/Math.PI;return r<0?0:r>1?1:r}function X(t,e){for(var r in e)t[r]=e[r];return t}function $(t){return t.x}function J(t){return t.y}function K(t,e,r,n){for(var i,a=n,o=r-e>>1,s=r-e,l=t[e],c=t[e+1],u=t[r],h=t[r+1],f=e+3;fa)i=f,a=p;else if(p===a){var d=Math.abs(f-o);dn&&(i-e>3&&K(t,e,i,n),t[i+2]=a,r-i>3&&K(t,i,r,n))}function Q(t,e,r,n,i,a){var o=i-r,s=a-n;if(0!==o||0!==s){var l=((t-r)*o+(e-n)*s)/(o*o+s*s);l>1?(r=i,n=a):l>0&&(r+=o*l,n+=s*l)}return(o=t-r)*o+(s=e-n)*s}function tt(t,e,r,n){var i={id:void 0===t?null:t,type:e,geometry:r,tags:n,minX:1/0,minY:1/0,maxX:-1/0,maxY:-1/0};return function(t){var e=t.geometry,r=t.type;if(\"Point\"===r||\"MultiPoint\"===r||\"LineString\"===r)et(t,e);else if(\"Polygon\"===r||\"MultiLineString\"===r)for(var n=0;n0&&(o+=n?(i*c-l*a)/2:Math.sqrt(Math.pow(l-i,2)+Math.pow(c-a,2))),i=l,a=c}var u=e.length-3;e[2]=1,K(e,0,u,r),e[u+2]=1,e.size=Math.abs(o),e.start=0,e.end=e.size}function at(t,e,r,n){for(var i=0;i1?1:r}function lt(t,e,r,n,i,a,o,s){if(n/=e,a>=(r/=e)&&o=n)return null;for(var l=[],c=0;c=r&&d=n)){var m=[];if(\"Point\"===f||\"MultiPoint\"===f)ct(h,m,r,n,i);else if(\"LineString\"===f)ut(h,m,r,n,i,!1,s.lineMetrics);else if(\"MultiLineString\"===f)ft(h,m,r,n,i,!1);else if(\"Polygon\"===f)ft(h,m,r,n,i,!0);else if(\"MultiPolygon\"===f)for(var g=0;g=r&&o<=n&&(e.push(t[a]),e.push(t[a+1]),e.push(t[a+2]))}}function ut(t,e,r,n,i,a,o){for(var s,l,c=ht(t),u=0===i?dt:mt,h=t.start,f=0;fr&&(l=u(c,p,d,g,y,r),o&&(c.start=h+s*l)):v>n?x=r&&(l=u(c,p,d,g,y,r),_=!0),x>n&&v<=n&&(l=u(c,p,d,g,y,n),_=!0),!a&&_&&(o&&(c.end=h+s*l),e.push(c),c=ht(t)),o&&(h+=s)}var b=t.length-3;p=t[b],d=t[b+1],m=t[b+2],(v=0===i?p:d)>=r&&v<=n&&pt(c,p,d,m),b=c.length-3,a&&b>=3&&(c[b]!==c[0]||c[b+1]!==c[1])&&pt(c,c[0],c[1],c[2]),c.length&&e.push(c)}function ht(t){var e=[];return e.size=t.size,e.start=t.start,e.end=t.end,e}function ft(t,e,r,n,i,a){for(var o=0;oo.maxX&&(o.maxX=u),h>o.maxY&&(o.maxY=h)}return o}function bt(t,e,r,n){var i=e.geometry,a=e.type,o=[];if(\"Point\"===a||\"MultiPoint\"===a)for(var s=0;s0&&e.size<(i?o:n))r.numPoints+=e.length/3;else{for(var s=[],l=0;lo)&&(r.numSimplified++,s.push(e[l]),s.push(e[l+1])),r.numPoints++;i&&function(t,e){for(var r=0,n=0,i=t.length,a=i-2;n0===e)for(n=0,i=t.length;n24)throw new Error(\"maxZoom should be in the 0-24 range\");if(e.promoteId&&e.generateId)throw new Error(\"promoteId and generateId cannot be used together.\");var n=function(t,e){var r=[];if(\"FeatureCollection\"===t.type)for(var n=0;n=n;c--){var u=+Date.now();s=this._cluster(s,c),this.trees[c]=new j(s,$,J,a,Float32Array),r&&console.log(\"z%d: %d clusters in %dms\",c,s.length,+Date.now()-u)}return r&&console.timeEnd(\"total time\"),this},V.prototype.getClusters=function(t,e){var r=((t[0]+180)%360+360)%360-180,n=Math.max(-90,Math.min(90,t[1])),i=180===t[2]?180:((t[2]+180)%360+360)%360-180,a=Math.max(-90,Math.min(90,t[3]));if(t[2]-t[0]>=360)r=-180,i=180;else if(r>i){var o=this.getClusters([r,n,180,a],e),s=this.getClusters([-180,n,i,a],e);return o.concat(s)}for(var l=this.trees[this._limitZoom(e)],c=[],u=0,h=l.range(W(r),Y(a),W(i),Y(n));ue&&(d+=v.numPoints||1)}if(d>=s){for(var x=u.x*p,_=u.y*p,b=o&&p>1?this._map(u,!0):null,w=(c<<5)+(e+1)+this.points.length,T=0,k=f;T1)for(var E=0,C=f;E>5},V.prototype._getOriginZoom=function(t){return(t-this.points.length)%32},V.prototype._map=function(t,e){if(t.numPoints)return e?X({},t.properties):t.properties;var r=this.points[t.index].properties,n=this.options.map(r);return e&&n===r?X({},n):n},Tt.prototype.options={maxZoom:14,indexMaxZoom:5,indexMaxPoints:1e5,tolerance:3,extent:4096,buffer:64,lineMetrics:!1,promoteId:null,generateId:!1,debug:0},Tt.prototype.splitTile=function(t,e,r,n,i,a,o){for(var s=[t,e,r,n],l=this.options,c=l.debug;s.length;){n=s.pop(),r=s.pop(),e=s.pop(),t=s.pop();var u=1<1&&console.time(\"creation\"),f=this.tiles[h]=_t(t,e,r,n,l),this.tileCoords.push({z:e,x:r,y:n}),c)){c>1&&(console.log(\"tile z%d-%d-%d (features: %d, points: %d, simplified: %d)\",e,r,n,f.numFeatures,f.numPoints,f.numSimplified),console.timeEnd(\"creation\"));var p=\"z\"+e;this.stats[p]=(this.stats[p]||0)+1,this.total++}if(f.source=t,i){if(e===l.maxZoom||e===i)continue;var d=1<1&&console.time(\"clipping\");var m,g,y,v,x,_,b=.5*l.buffer/l.extent,w=.5-b,T=.5+b,k=1+b;m=g=y=v=null,x=lt(t,u,r-b,r+T,0,f.minX,f.maxX,l),_=lt(t,u,r+w,r+k,0,f.minX,f.maxX,l),t=null,x&&(m=lt(x,u,n-b,n+T,1,f.minY,f.maxY,l),g=lt(x,u,n+w,n+k,1,f.minY,f.maxY,l),x=null),_&&(y=lt(_,u,n-b,n+T,1,f.minY,f.maxY,l),v=lt(_,u,n+w,n+k,1,f.minY,f.maxY,l),_=null),c>1&&console.timeEnd(\"clipping\"),s.push(m||[],e+1,2*r,2*n),s.push(g||[],e+1,2*r,2*n+1),s.push(y||[],e+1,2*r+1,2*n),s.push(v||[],e+1,2*r+1,2*n+1)}}},Tt.prototype.getTile=function(t,e,r){var n=this.options,i=n.extent,a=n.debug;if(t<0||t>24)return null;var o=1<1&&console.log(\"drilling down to z%d-%d-%d\",t,e,r);for(var l,c=t,u=e,h=r;!l&&c>0;)c--,u=Math.floor(u/2),h=Math.floor(h/2),l=this.tiles[kt(c,u,h)];return l&&l.source?(a>1&&console.log(\"found parent tile z%d-%d-%d\",c,u,h),a>1&&console.time(\"drilling down\"),this.splitTile(l.source,c,u,h,t,e,r),a>1&&console.timeEnd(\"drilling down\"),this.tiles[s]?vt(this.tiles[s],i):null):null};var Mt=function(e){function r(t,r,n,i){e.call(this,t,r,n,At),i&&(this.loadGeoJSON=i)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.loadData=function(t,e){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),this._pendingCallback=e,this._pendingLoadDataParams=t,this._state&&\"Idle\"!==this._state?this._state=\"NeedsLoadData\":(this._state=\"Coalescing\",this._loadData())},r.prototype._loadData=function(){var e=this;if(this._pendingCallback&&this._pendingLoadDataParams){var r=this._pendingCallback,n=this._pendingLoadDataParams;delete this._pendingCallback,delete this._pendingLoadDataParams;var i=!!(n&&n.request&&n.request.collectResourceTiming)&&new t.RequestPerformance(n.request);this.loadGeoJSON(n,(function(a,o){if(a||!o)return r(a);if(\"object\"!=typeof o)return r(new Error(\"Input data given to '\"+n.source+\"' is not a valid GeoJSON object.\"));h(o,!0);try{if(n.filter){var s=t.createExpression(n.filter,{type:\"boolean\",\"property-type\":\"data-driven\",overridable:!1,transition:!1});if(\"error\"===s.result)throw new Error(s.value.map((function(t){return t.key+\": \"+t.message})).join(\", \"));var l=o.features.filter((function(t){return s.value.evaluate({zoom:0},t)}));o={type:\"FeatureCollection\",features:l}}e._geoJSONIndex=n.cluster?new V(function(e){var r=e.superclusterOptions,n=e.clusterProperties;if(!n||!r)return r;for(var i={},a={},o={accumulated:null,zoom:0},s={properties:null},l=Object.keys(n),c=0,u=l;c=0?0:e.button},r.remove=function(t){t.parentNode&&t.parentNode.removeChild(t)};var f=function(e){function r(){e.call(this),this.images={},this.updatedImages={},this.callbackDispatchedThisFrame={},this.loaded=!1,this.requestors=[],this.patterns={},this.atlasImage=new t.RGBAImage({width:1,height:1}),this.dirty=!0}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.isLoaded=function(){return this.loaded},r.prototype.setLoaded=function(t){if(this.loaded!==t&&(this.loaded=t,t)){for(var e=0,r=this.requestors;e=0?1.2:1))}function y(t,e,r,n,i,a,o){for(var s=0;s65535)e(new Error(\"glyphs > 65535 not supported\"));else if(a.ranges[s])e(null,{stack:r,id:i,glyph:o});else{var l=a.requests[s];l||(l=a.requests[s]=[],x.loadGlyphRange(r,s,n.url,n.requestManager,(function(t,e){if(e){for(var r in e)n._doesCharSupportLocalGlyph(+r)||(a.glyphs[+r]=e[+r]);a.ranges[s]=!0}for(var i=0,o=l;i1&&(l=t[++s]);var u=Math.abs(c-l.left),h=Math.abs(c-l.right),f=Math.min(u,h),p=void 0,d=i/r*(n+1);if(l.isDash){var m=n-Math.abs(d);p=Math.sqrt(f*f+m*m)}else p=n-Math.sqrt(f*f+d*d);this.data[o+c]=Math.max(0,Math.min(255,p+128))}},k.prototype.addRegularDash=function(t){for(var e=t.length-1;e>=0;--e){var r=t[e],n=t[e+1];r.zeroLength?t.splice(e,1):n&&n.isDash===r.isDash&&(n.left=r.left,t.splice(e,1))}var i=t[0],a=t[t.length-1];i.isDash===a.isDash&&(i.left=a.left-this.width,a.right=i.right+this.width);for(var o=this.width*this.nextRow,s=0,l=t[s],c=0;c1&&(l=t[++s]);var u=Math.abs(c-l.left),h=Math.abs(c-l.right),f=Math.min(u,h),p=l.isDash?f:-f;this.data[o+c]=Math.max(0,Math.min(255,p+128))}},k.prototype.addDash=function(e,r){var n=r?7:0,i=2*n+1;if(this.nextRow+i>this.height)return t.warnOnce(\"LineAtlas out of space\"),null;for(var a=0,o=0;o=n&&e.x=i&&e.y0&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y-1).key]={backfilled:!1}),r.y+10&&(n.resourceTiming=e._resourceTiming,e._resourceTiming=[]),e.fire(new t.Event(\"data\",n))}}))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setData=function(e){var r=this;return this._data=e,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._updateWorkerData((function(e){if(e)r.fire(new t.ErrorEvent(e));else{var n={dataType:\"source\",sourceDataType:\"content\"};r._collectResourceTiming&&r._resourceTiming&&r._resourceTiming.length>0&&(n.resourceTiming=r._resourceTiming,r._resourceTiming=[]),r.fire(new t.Event(\"data\",n))}})),this},r.prototype.getClusterExpansionZoom=function(t,e){return this.actor.send(\"geojson.getClusterExpansionZoom\",{clusterId:t,source:this.id},e),this},r.prototype.getClusterChildren=function(t,e){return this.actor.send(\"geojson.getClusterChildren\",{clusterId:t,source:this.id},e),this},r.prototype.getClusterLeaves=function(t,e,r,n){return this.actor.send(\"geojson.getClusterLeaves\",{source:this.id,clusterId:t,limit:e,offset:r},n),this},r.prototype._updateWorkerData=function(e){var r=this;this._loaded=!1;var n=t.extend({},this.workerOptions),i=this._data;\"string\"==typeof i?(n.request=this.map._requestManager.transformRequest(t.browser.resolveURL(i),t.ResourceType.Source),n.request.collectResourceTiming=this._collectResourceTiming):n.data=JSON.stringify(i),this.actor.send(this.type+\".loadData\",n,(function(t,i){r._removed||i&&i.abandoned||(r._loaded=!0,i&&i.resourceTiming&&i.resourceTiming[r.id]&&(r._resourceTiming=i.resourceTiming[r.id].slice(0)),r.actor.send(r.type+\".coalesce\",{source:n.source},null),e(t))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.loadTile=function(e,r){var n=this,i=e.actor?\"reloadTile\":\"loadTile\";e.actor=this.actor;var a={type:this.type,uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,maxZoom:this.maxzoom,tileSize:this.tileSize,source:this.id,pixelRatio:t.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};e.request=this.actor.send(i,a,(function(t,a){return delete e.request,e.unloadVectorData(),e.aborted?r(null):t?r(t):(e.loadVectorData(a,n.map.painter,\"reloadTile\"===i),r(null))}))},r.prototype.abortTile=function(t){t.request&&(t.request.cancel(),delete t.request),t.aborted=!0},r.prototype.unloadTile=function(t){t.unloadVectorData(),this.actor.send(\"removeTile\",{uid:t.uid,type:this.type,source:this.id})},r.prototype.onRemove=function(){this._removed=!0,this.actor.send(\"removeSource\",{type:this.type,source:this.id})},r.prototype.serialize=function(){return t.extend({},this._options,{type:this.type,data:this._data})},r.prototype.hasTransition=function(){return!1},r}(t.Evented),P=t.createLayout([{name:\"a_pos\",type:\"Int16\",components:2},{name:\"a_texture_pos\",type:\"Int16\",components:2}]),z=function(e){function r(t,r,n,i){e.call(this),this.id=t,this.dispatcher=n,this.coordinates=r.coordinates,this.type=\"image\",this.minzoom=0,this.maxzoom=22,this.tileSize=512,this.tiles={},this._loaded=!1,this.setEventedParent(i),this.options=r}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(e,r){var n=this;this._loaded=!1,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this.url=this.options.url,t.getImage(this.map._requestManager.transformRequest(this.url,t.ResourceType.Image),(function(i,a){n._loaded=!0,i?n.fire(new t.ErrorEvent(i)):a&&(n.image=a,e&&(n.coordinates=e),r&&r(),n._finishLoading())}))},r.prototype.loaded=function(){return this._loaded},r.prototype.updateImage=function(t){var e=this;return this.image&&t.url?(this.options.url=t.url,this.load(t.coordinates,(function(){e.texture=null})),this):this},r.prototype._finishLoading=function(){this.map&&(this.setCoordinates(this.coordinates),this.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setCoordinates=function(e){var r=this;this.coordinates=e;var n=e.map(t.MercatorCoordinate.fromLngLat);this.tileID=function(e){for(var r=1/0,n=1/0,i=-1/0,a=-1/0,o=0,s=e;or.end(0)?this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+this.id,null,\"Playback for this video can be set only between the \"+r.start(0)+\" and \"+r.end(0)+\"-second mark.\"))):this.video.currentTime=e}},r.prototype.getVideo=function(){return this.video},r.prototype.onAdd=function(t){this.map||(this.map=t,this.load(),this.video&&(this.video.play(),this.setCoordinates(this.coordinates)))},r.prototype.prepare=function(){if(!(0===Object.keys(this.tiles).length||this.video.readyState<2)){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer||(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?this.video.paused||(this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE),r.texSubImage2D(r.TEXTURE_2D,0,0,0,r.RGBA,r.UNSIGNED_BYTE,this.video)):(this.texture=new t.Texture(e,this.video,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];\"loaded\"!==i.state&&(i.state=\"loaded\",i.texture=this.texture)}}},r.prototype.serialize=function(){return{type:\"video\",urls:this.urls,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this.video&&!this.video.paused},r}(z),D=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),n.coordinates?Array.isArray(n.coordinates)&&4===n.coordinates.length&&!n.coordinates.some((function(t){return!Array.isArray(t)||2!==t.length||t.some((function(t){return\"number\"!=typeof t}))}))||this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'\"coordinates\" property must be an array of 4 longitude/latitude array pairs'))):this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'missing required property \"coordinates\"'))),n.animate&&\"boolean\"!=typeof n.animate&&this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'optional \"animate\" property must be a boolean value'))),n.canvas?\"string\"==typeof n.canvas||n.canvas instanceof t.window.HTMLCanvasElement||this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'\"canvas\" must be either a string representing the ID of the canvas element from which to read, or an HTMLCanvasElement instance'))):this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'missing required property \"canvas\"'))),this.options=n,this.animate=void 0===n.animate||n.animate}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){this._loaded=!0,this.canvas||(this.canvas=this.options.canvas instanceof t.window.HTMLCanvasElement?this.options.canvas:t.window.document.getElementById(this.options.canvas)),this.width=this.canvas.width,this.height=this.canvas.height,this._hasInvalidDimensions()?this.fire(new t.ErrorEvent(new Error(\"Canvas dimensions cannot be less than or equal to zero.\"))):(this.play=function(){this._playing=!0,this.map.triggerRepaint()},this.pause=function(){this._playing&&(this.prepare(),this._playing=!1)},this._finishLoading())},r.prototype.getCanvas=function(){return this.canvas},r.prototype.onAdd=function(t){this.map=t,this.load(),this.canvas&&this.animate&&this.play()},r.prototype.onRemove=function(){this.pause()},r.prototype.prepare=function(){var e=!1;if(this.canvas.width!==this.width&&(this.width=this.canvas.width,e=!0),this.canvas.height!==this.height&&(this.height=this.canvas.height,e=!0),!this._hasInvalidDimensions()&&0!==Object.keys(this.tiles).length){var r=this.map.painter.context,n=r.gl;for(var i in this.boundsBuffer||(this.boundsBuffer=r.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?(e||this._playing)&&this.texture.update(this.canvas,{premultiply:!0}):this.texture=new t.Texture(r,this.canvas,n.RGBA,{premultiply:!0}),this.tiles){var a=this.tiles[i];\"loaded\"!==a.state&&(a.state=\"loaded\",a.texture=this.texture)}}},r.prototype.serialize=function(){return{type:\"canvas\",coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this._playing},r.prototype._hasInvalidDimensions=function(){for(var t=0,e=[this.canvas.width,this.canvas.height];tthis.max){var o=this._getAndRemoveByKey(this.order[0]);o&&this.onRemove(o)}return this},j.prototype.has=function(t){return t.wrapped().key in this.data},j.prototype.getAndRemove=function(t){return this.has(t)?this._getAndRemoveByKey(t.wrapped().key):null},j.prototype._getAndRemoveByKey=function(t){var e=this.data[t].shift();return e.timeout&&clearTimeout(e.timeout),0===this.data[t].length&&delete this.data[t],this.order.splice(this.order.indexOf(t),1),e.value},j.prototype.getByKey=function(t){var e=this.data[t];return e?e[0].value:null},j.prototype.get=function(t){return this.has(t)?this.data[t.wrapped().key][0].value:null},j.prototype.remove=function(t,e){if(!this.has(t))return this;var r=t.wrapped().key,n=void 0===e?0:this.data[r].indexOf(e),i=this.data[r][n];return this.data[r].splice(n,1),i.timeout&&clearTimeout(i.timeout),0===this.data[r].length&&delete this.data[r],this.onRemove(i.value),this.order.splice(this.order.indexOf(r),1),this},j.prototype.setMaxSize=function(t){for(this.max=t;this.order.length>this.max;){var e=this._getAndRemoveByKey(this.order[0]);e&&this.onRemove(e)}return this},j.prototype.filter=function(t){var e=[];for(var r in this.data)for(var n=0,i=this.data[r];n1||(Math.abs(r)>1&&(1===Math.abs(r+i)?r+=i:1===Math.abs(r-i)&&(r-=i)),e.dem&&t.dem&&(t.dem.backfillBorder(e.dem,r,n),t.neighboringTiles&&t.neighboringTiles[a]&&(t.neighboringTiles[a].backfilled=!0)))}},r.prototype.getTile=function(t){return this.getTileByID(t.key)},r.prototype.getTileByID=function(t){return this._tiles[t]},r.prototype._retainLoadedChildren=function(t,e,r,n){for(var i in this._tiles){var a=this._tiles[i];if(!(n[i]||!a.hasData()||a.tileID.overscaledZ<=e||a.tileID.overscaledZ>r)){for(var o=a.tileID;a&&a.tileID.overscaledZ>e+1;){var s=a.tileID.scaledTo(a.tileID.overscaledZ-1);(a=this._tiles[s.key])&&a.hasData()&&(o=s)}for(var l=o;l.overscaledZ>e;)if(t[(l=l.scaledTo(l.overscaledZ-1)).key]){n[o.key]=o;break}}}},r.prototype.findLoadedParent=function(t,e){if(t.key in this._loadedParentTiles){var r=this._loadedParentTiles[t.key];return r&&r.tileID.overscaledZ>=e?r:null}for(var n=t.overscaledZ-1;n>=e;n--){var i=t.scaledTo(n),a=this._getLoadedTile(i);if(a)return a}},r.prototype._getLoadedTile=function(t){var e=this._tiles[t.key];return e&&e.hasData()?e:this._cache.getByKey(t.wrapped().key)},r.prototype.updateCacheSize=function(t){var e=(Math.ceil(t.width/this._source.tileSize)+1)*(Math.ceil(t.height/this._source.tileSize)+1),r=Math.floor(5*e),n=\"number\"==typeof this._maxTileCacheSize?Math.min(this._maxTileCacheSize,r):r;this._cache.setMaxSize(n)},r.prototype.handleWrapJump=function(t){var e=(t-(void 0===this._prevLng?t:this._prevLng))/360,r=Math.round(e);if(this._prevLng=t,r){var n={};for(var i in this._tiles){var a=this._tiles[i];a.tileID=a.tileID.unwrapTo(a.tileID.wrap+r),n[a.tileID.key]=a}for(var o in this._tiles=n,this._timers)clearTimeout(this._timers[o]),delete this._timers[o];for(var s in this._tiles){var l=this._tiles[s];this._setTileReloadTimer(s,l)}}},r.prototype.update=function(e){var n=this;if(this.transform=e,this._sourceLoaded&&!this._paused){var i;this.updateCacheSize(e),this.handleWrapJump(this.transform.center.lng),this._coveredTiles={},this.used?this._source.tileID?i=e.getVisibleUnwrappedCoordinates(this._source.tileID).map((function(e){return new t.OverscaledTileID(e.canonical.z,e.wrap,e.canonical.z,e.canonical.x,e.canonical.y)})):(i=e.coveringTiles({tileSize:this._source.tileSize,minzoom:this._source.minzoom,maxzoom:this._source.maxzoom,roundZoom:this._source.roundZoom,reparseOverscaled:this._source.reparseOverscaled}),this._source.hasTile&&(i=i.filter((function(t){return n._source.hasTile(t)})))):i=[];var a=e.coveringZoomLevel(this._source),o=Math.max(a-r.maxOverzooming,this._source.minzoom),s=Math.max(a+r.maxUnderzooming,this._source.minzoom),l=this._updateRetainedTiles(i,a);if(Ot(this._source.type)){for(var c={},u={},h=0,f=Object.keys(l);hthis._source.maxzoom){var g=d.children(this._source.maxzoom)[0],y=this.getTile(g);if(y&&y.hasData()){n[g.key]=g;continue}}else{var v=d.children(this._source.maxzoom);if(n[v[0].key]&&n[v[1].key]&&n[v[2].key]&&n[v[3].key])continue}for(var x=m.wasRequested(),_=d.overscaledZ-1;_>=a;--_){var b=d.scaledTo(_);if(i[b.key])break;if(i[b.key]=!0,!(m=this.getTile(b))&&x&&(m=this._addTile(b)),m&&(n[b.key]=b,x=m.wasRequested(),m.hasData()))break}}}return n},r.prototype._updateLoadedParentTileCache=function(){for(var t in this._loadedParentTiles={},this._tiles){for(var e=[],r=void 0,n=this._tiles[t].tileID;n.overscaledZ>0;){if(n.key in this._loadedParentTiles){r=this._loadedParentTiles[n.key];break}e.push(n.key);var i=n.scaledTo(n.overscaledZ-1);if(r=this._getLoadedTile(i))break;n=i}for(var a=0,o=e;a0||(e.hasData()&&\"reloading\"!==e.state?this._cache.add(e.tileID,e,e.getExpiryTimeout()):(e.aborted=!0,this._abortTile(e),this._unloadTile(e))))},r.prototype.clearTiles=function(){for(var t in this._shouldReloadOnResume=!1,this._paused=!1,this._tiles)this._removeTile(t);this._cache.reset()},r.prototype.tilesIn=function(e,r,n){var i=this,a=[],o=this.transform;if(!o)return a;for(var s=n?o.getCameraQueryGeometry(e):e,l=e.map((function(t){return o.pointCoordinate(t)})),c=s.map((function(t){return o.pointCoordinate(t)})),u=this.getIds(),h=1/0,f=1/0,p=-1/0,d=-1/0,m=0,g=c;m=0&&y[1].y+g>=0){var v=l.map((function(t){return s.getTilePoint(t)})),x=c.map((function(t){return s.getTilePoint(t)}));a.push({tile:n,tileID:s,queryGeometry:v,cameraQueryGeometry:x,scale:m})}}},x=0;x=t.browser.now())return!0}return!1},r.prototype.setFeatureState=function(t,e,r){t=t||\"_geojsonTileLayer\",this._state.updateState(t,e,r)},r.prototype.removeFeatureState=function(t,e,r){t=t||\"_geojsonTileLayer\",this._state.removeFeatureState(t,e,r)},r.prototype.getFeatureState=function(t,e){return t=t||\"_geojsonTileLayer\",this._state.getState(t,e)},r.prototype.setDependencies=function(t,e,r){var n=this._tiles[t];n&&n.setDependencies(e,r)},r.prototype.reloadTilesForDependencies=function(t,e){for(var r in this._tiles)this._tiles[r].hasDependency(t,e)&&this._reloadTile(r,\"reloading\");this._cache.filter((function(r){return!r.hasDependency(t,e)}))},r}(t.Evented);function zt(t,e){var r=Math.abs(2*t.wrap)-+(t.wrap<0),n=Math.abs(2*e.wrap)-+(e.wrap<0);return t.overscaledZ-e.overscaledZ||n-r||e.canonical.y-t.canonical.y||e.canonical.x-t.canonical.x}function Ot(t){return\"raster\"===t||\"image\"===t||\"video\"===t}function Dt(){return new t.window.Worker(oa.workerUrl)}Pt.maxOverzooming=10,Pt.maxUnderzooming=3;var Rt=\"mapboxgl_preloaded_worker_pool\",Ft=function(){this.active={}};Ft.prototype.acquire=function(t){if(!this.workers)for(this.workers=[];this.workers.length0?(i-o)/s:0;return this.points[a].mult(1-l).add(this.points[r].mult(l))};var Qt=function(t,e,r){var n=this.boxCells=[],i=this.circleCells=[];this.xCellCount=Math.ceil(t/r),this.yCellCount=Math.ceil(e/r);for(var a=0;a=-e[0]&&r<=e[0]&&n>=-e[1]&&n<=e[1]}function ae(e,r,n,i,a,o,s,l){var c=i?e.textSizeData:e.iconSizeData,u=t.evaluateSizeForZoom(c,n.transform.zoom),h=[256/n.width*2+1,256/n.height*2+1],f=i?e.text.dynamicLayoutVertexArray:e.icon.dynamicLayoutVertexArray;f.clear();for(var p=e.lineVertexArray,d=i?e.text.placedSymbolArray:e.icon.placedSymbolArray,m=n.transform.width/n.transform.height,g=!1,y=0;yMath.abs(n.x-r.x)*i?{useVertical:!0}:(e===t.WritingMode.vertical?r.yn.x)?{needsFlipping:!0}:null}function le(e,r,n,i,a,o,s,l,c,u,h,f,p,d){var m,g=r/24,y=e.lineOffsetX*g,v=e.lineOffsetY*g;if(e.numGlyphs>1){var x=e.glyphStartIndex+e.numGlyphs,_=e.lineStartIndex,b=e.lineStartIndex+e.lineLength,w=oe(g,l,y,v,n,h,f,e,c,o,p);if(!w)return{notEnoughRoom:!0};var T=re(w.first.point,s).point,k=re(w.last.point,s).point;if(i&&!n){var A=se(e.writingMode,T,k,d);if(A)return A}m=[w.first];for(var M=e.glyphStartIndex+1;M0?L.point:ce(f,C,S,1,a),P=se(e.writingMode,S,I,d);if(P)return P}var z=ue(g*l.getoffsetX(e.glyphStartIndex),y,v,n,h,f,e.segment,e.lineStartIndex,e.lineStartIndex+e.lineLength,c,o,p);if(!z)return{notEnoughRoom:!0};m=[z]}for(var O=0,D=m;O0?1:-1,m=0;i&&(d*=-1,m=Math.PI),d<0&&(m+=Math.PI);for(var g=d>0?l+s:l+s+1,y=a,v=a,x=0,_=0,b=Math.abs(p),w=[];x+_<=b;){if((g+=d)=c)return null;if(v=y,w.push(y),void 0===(y=f[g])){var T=new t.Point(u.getx(g),u.gety(g)),k=re(T,h);if(k.signedDistanceFromCamera>0)y=f[g]=k.point;else{var A=g-d;y=ce(0===x?o:new t.Point(u.getx(A),u.gety(A)),T,v,b-x+1,h)}}x+=_,_=v.dist(y)}var M=(b-x)/_,S=y.sub(v),E=S.mult(M)._add(v);E._add(S._unit()._perp()._mult(n*d));var C=m+Math.atan2(y.y-v.y,y.x-v.x);return w.push(E),{point:E,angle:C,path:w}}Qt.prototype.keysLength=function(){return this.boxKeys.length+this.circleKeys.length},Qt.prototype.insert=function(t,e,r,n,i){this._forEachCell(e,r,n,i,this._insertBoxCell,this.boxUid++),this.boxKeys.push(t),this.bboxes.push(e),this.bboxes.push(r),this.bboxes.push(n),this.bboxes.push(i)},Qt.prototype.insertCircle=function(t,e,r,n){this._forEachCell(e-n,r-n,e+n,r+n,this._insertCircleCell,this.circleUid++),this.circleKeys.push(t),this.circles.push(e),this.circles.push(r),this.circles.push(n)},Qt.prototype._insertBoxCell=function(t,e,r,n,i,a){this.boxCells[i].push(a)},Qt.prototype._insertCircleCell=function(t,e,r,n,i,a){this.circleCells[i].push(a)},Qt.prototype._query=function(t,e,r,n,i,a){if(r<0||t>this.width||n<0||e>this.height)return!i&&[];var o=[];if(t<=0&&e<=0&&this.width<=r&&this.height<=n){if(i)return!0;for(var s=0;s0:o},Qt.prototype._queryCircle=function(t,e,r,n,i){var a=t-r,o=t+r,s=e-r,l=e+r;if(o<0||a>this.width||l<0||s>this.height)return!n&&[];var c=[],u={hitTest:n,circle:{x:t,y:e,radius:r},seenUids:{box:{},circle:{}}};return this._forEachCell(a,s,o,l,this._queryCellCircle,c,u,i),n?c.length>0:c},Qt.prototype.query=function(t,e,r,n,i){return this._query(t,e,r,n,!1,i)},Qt.prototype.hitTest=function(t,e,r,n,i){return this._query(t,e,r,n,!0,i)},Qt.prototype.hitTestCircle=function(t,e,r,n){return this._queryCircle(t,e,r,!0,n)},Qt.prototype._queryCell=function(t,e,r,n,i,a,o,s){var l=o.seenUids,c=this.boxCells[i];if(null!==c)for(var u=this.bboxes,h=0,f=c;h=u[d+0]&&n>=u[d+1]&&(!s||s(this.boxKeys[p]))){if(o.hitTest)return a.push(!0),!0;a.push({key:this.boxKeys[p],x1:u[d],y1:u[d+1],x2:u[d+2],y2:u[d+3]})}}}var m=this.circleCells[i];if(null!==m)for(var g=this.circles,y=0,v=m;yo*o+s*s},Qt.prototype._circleAndRectCollide=function(t,e,r,n,i,a,o){var s=(a-n)/2,l=Math.abs(t-(n+s));if(l>s+r)return!1;var c=(o-i)/2,u=Math.abs(e-(i+c));if(u>c+r)return!1;if(l<=s||u<=c)return!0;var h=l-s,f=u-c;return h*h+f*f<=r*r};var he=new Float32Array([-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0]);function fe(t,e){for(var r=0;r=1;I--)L.push(E.path[I]);for(var P=1;P0){for(var R=L[0].clone(),F=L[0].clone(),B=1;B=A.x&&F.x<=M.x&&R.y>=A.y&&F.y<=M.y?[L]:F.xM.x||F.yM.y?[]:t.clipLine([L],A.x,A.y,M.x,M.y)}for(var N=0,j=D;N=this.screenRightBoundary||nthis.screenBottomBoundary},me.prototype.isInsideGrid=function(t,e,r,n){return r>=0&&t=0&&e0?(this.prevPlacement&&this.prevPlacement.variableOffsets[h.crossTileID]&&this.prevPlacement.placements[h.crossTileID]&&this.prevPlacement.placements[h.crossTileID].text&&(m=this.prevPlacement.variableOffsets[h.crossTileID].anchor),this.variableOffsets[h.crossTileID]={textOffset:g,width:r,height:n,anchor:t,textBoxScale:i,prevAnchor:m},this.markUsedJustification(f,t,h,p),f.allowVerticalPlacement&&(this.markUsedOrientation(f,p,h),this.placedOrientations[h.crossTileID]=p),{shift:y,placedGlyphBoxes:v}):void 0},Ae.prototype.placeLayerBucketPart=function(e,r,n){var i=this,a=e.parameters,o=a.bucket,s=a.layout,l=a.posMatrix,c=a.textLabelPlaneMatrix,u=a.labelToScreenMatrix,h=a.textPixelRatio,f=a.holdingForFade,p=a.collisionBoxArray,d=a.partiallyEvaluatedTextSize,m=a.collisionGroup,g=s.get(\"text-optional\"),y=s.get(\"icon-optional\"),v=s.get(\"text-allow-overlap\"),x=s.get(\"icon-allow-overlap\"),_=\"map\"===s.get(\"text-rotation-alignment\"),b=\"map\"===s.get(\"text-pitch-alignment\"),w=\"none\"!==s.get(\"icon-text-fit\"),T=\"viewport-y\"===s.get(\"symbol-z-order\"),k=v&&(x||!o.hasIconData()||y),A=x&&(v||!o.hasTextData()||g);!o.collisionArrays&&p&&o.deserializeCollisionBoxes(p);var M=function(e,a){if(!r[e.crossTileID])if(f)i.placements[e.crossTileID]=new xe(!1,!1,!1);else{var p,T=!1,M=!1,S=!0,E=null,C={box:null,offscreen:null},L={box:null,offscreen:null},I=null,P=null,z=0,O=0,D=0;a.textFeatureIndex?z=a.textFeatureIndex:e.useRuntimeCollisionCircles&&(z=e.featureIndex),a.verticalTextFeatureIndex&&(O=a.verticalTextFeatureIndex);var R=a.textBox;if(R){var F=function(r){var n=t.WritingMode.horizontal;if(o.allowVerticalPlacement&&!r&&i.prevPlacement){var a=i.prevPlacement.placedOrientations[e.crossTileID];a&&(i.placedOrientations[e.crossTileID]=a,n=a,i.markUsedOrientation(o,n,e))}return n},B=function(r,n){if(o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&a.verticalTextBox)for(var i=0,s=o.writingModes;i0&&(N=N.filter((function(t){return t!==j.anchor}))).unshift(j.anchor)}var U=function(t,r,n){for(var a=t.x2-t.x1,s=t.y2-t.y1,c=e.textBoxScale,u=w&&!x?r:null,f={box:[],offscreen:!1},p=v?2*N.length:N.length,d=0;d=N.length,k=i.attemptAnchorPlacement(g,t,a,s,c,_,b,h,l,m,y,e,o,n,u);if(k&&(f=k.placedGlyphBoxes)&&f.box&&f.box.length){T=!0,E=k.shift;break}}return f};B((function(){return U(R,a.iconBox,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox,n=C&&C.box&&C.box.length;return o.allowVerticalPlacement&&!n&&e.numVerticalGlyphVertices>0&&r?U(r,a.verticalIconBox,t.WritingMode.vertical):{box:null,offscreen:null}})),C&&(T=C.box,S=C.offscreen);var V=F(C&&C.box);if(!T&&i.prevPlacement){var q=i.prevPlacement.variableOffsets[e.crossTileID];q&&(i.variableOffsets[e.crossTileID]=q,i.markUsedJustification(o,q.anchor,e,V))}}else{var H=function(t,r){var n=i.collisionIndex.placeCollisionBox(t,v,h,l,m.predicate);return n&&n.box&&n.box.length&&(i.markUsedOrientation(o,r,e),i.placedOrientations[e.crossTileID]=r),n};B((function(){return H(R,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox;return o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&r?H(r,t.WritingMode.vertical):{box:null,offscreen:null}})),F(C&&C.box&&C.box.length)}}if(T=(p=C)&&p.box&&p.box.length>0,S=p&&p.offscreen,e.useRuntimeCollisionCircles){var G=o.text.placedSymbolArray.get(e.centerJustifiedTextSymbolIndex),Z=t.evaluateSizeForFeature(o.textSizeData,d,G),W=s.get(\"text-padding\"),Y=e.collisionCircleDiameter;I=i.collisionIndex.placeCollisionCircles(v,G,o.lineVertexArray,o.glyphOffsetArray,Z,l,c,u,n,b,m.predicate,Y,W),T=v||I.circles.length>0&&!I.collisionDetected,S=S&&I.offscreen}if(a.iconFeatureIndex&&(D=a.iconFeatureIndex),a.iconBox){var X=function(t){var e=w&&E?ke(t,E.x,E.y,_,b,i.transform.angle):t;return i.collisionIndex.placeCollisionBox(e,x,h,l,m.predicate)};M=L&&L.box&&L.box.length&&a.verticalIconBox?(P=X(a.verticalIconBox)).box.length>0:(P=X(a.iconBox)).box.length>0,S=S&&P.offscreen}var $=g||0===e.numHorizontalGlyphVertices&&0===e.numVerticalGlyphVertices,J=y||0===e.numIconVertices;if($||J?J?$||(M=M&&T):T=M&&T:M=T=M&&T,T&&p&&p.box&&(L&&L.box&&O?i.collisionIndex.insertCollisionBox(p.box,s.get(\"text-ignore-placement\"),o.bucketInstanceId,O,m.ID):i.collisionIndex.insertCollisionBox(p.box,s.get(\"text-ignore-placement\"),o.bucketInstanceId,z,m.ID)),M&&P&&i.collisionIndex.insertCollisionBox(P.box,s.get(\"icon-ignore-placement\"),o.bucketInstanceId,D,m.ID),I&&(T&&i.collisionIndex.insertCollisionCircles(I.circles,s.get(\"text-ignore-placement\"),o.bucketInstanceId,z,m.ID),n)){var K=o.bucketInstanceId,Q=i.collisionCircleArrays[K];void 0===Q&&(Q=i.collisionCircleArrays[K]=new _e);for(var tt=0;tt=0;--E){var C=S[E];M(o.symbolInstances.get(C),o.collisionArrays[C])}else for(var L=e.symbolInstanceStart;L=0&&(e.text.placedSymbolArray.get(c).crossTileID=a>=0&&c!==a?0:n.crossTileID)}},Ae.prototype.markUsedOrientation=function(e,r,n){for(var i=r===t.WritingMode.horizontal||r===t.WritingMode.horizontalOnly?r:0,a=r===t.WritingMode.vertical?r:0,o=0,s=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex];o0||l>0,x=a.numIconVertices>0,_=i.placedOrientations[a.crossTileID],b=_===t.WritingMode.vertical,w=_===t.WritingMode.horizontal||_===t.WritingMode.horizontalOnly;if(v){var T=Oe(y.text),k=b?De:T;d(e.text,s,k);var A=w?De:T;d(e.text,l,A);var M=y.text.isHidden();[a.rightJustifiedTextSymbolIndex,a.centerJustifiedTextSymbolIndex,a.leftJustifiedTextSymbolIndex].forEach((function(t){t>=0&&(e.text.placedSymbolArray.get(t).hidden=M||b?1:0)})),a.verticalPlacedTextSymbolIndex>=0&&(e.text.placedSymbolArray.get(a.verticalPlacedTextSymbolIndex).hidden=M||w?1:0);var S=i.variableOffsets[a.crossTileID];S&&i.markUsedJustification(e,S.anchor,a,_);var E=i.placedOrientations[a.crossTileID];E&&(i.markUsedJustification(e,\"left\",a,E),i.markUsedOrientation(e,E,a))}if(x){var C=Oe(y.icon),L=!(f&&a.verticalPlacedIconSymbolIndex&&b);if(a.placedIconSymbolIndex>=0){var I=L?C:De;d(e.icon,a.numIconVertices,I),e.icon.placedSymbolArray.get(a.placedIconSymbolIndex).hidden=y.icon.isHidden()}if(a.verticalPlacedIconSymbolIndex>=0){var P=L?De:C;d(e.icon,a.numVerticalIconVertices,P),e.icon.placedSymbolArray.get(a.verticalPlacedIconSymbolIndex).hidden=y.icon.isHidden()}}if(e.hasIconCollisionBoxData()||e.hasTextCollisionBoxData()){var z=e.collisionArrays[n];if(z){var O=new t.Point(0,0);if(z.textBox||z.verticalTextBox){var D=!0;if(c){var R=i.variableOffsets[m];R?(O=Te(R.anchor,R.width,R.height,R.textOffset,R.textBoxScale),u&&O._rotate(h?i.transform.angle:-i.transform.angle)):D=!1}z.textBox&&Me(e.textCollisionBox.collisionVertexArray,y.text.placed,!D||b,O.x,O.y),z.verticalTextBox&&Me(e.textCollisionBox.collisionVertexArray,y.text.placed,!D||w,O.x,O.y)}var F=Boolean(!w&&z.verticalIconBox);z.iconBox&&Me(e.iconCollisionBox.collisionVertexArray,y.icon.placed,F,f?O.x:0,f?O.y:0),z.verticalIconBox&&Me(e.iconCollisionBox.collisionVertexArray,y.icon.placed,!F,f?O.x:0,f?O.y:0)}}},g=0;gt},Ae.prototype.setStale=function(){this.stale=!0};var Se=Math.pow(2,25),Ee=Math.pow(2,24),Ce=Math.pow(2,17),Le=Math.pow(2,16),Ie=Math.pow(2,9),Pe=Math.pow(2,8),ze=Math.pow(2,1);function Oe(t){if(0===t.opacity&&!t.placed)return 0;if(1===t.opacity&&t.placed)return 4294967295;var e=t.placed?1:0,r=Math.floor(127*t.opacity);return r*Se+e*Ee+r*Ce+e*Le+r*Ie+e*Pe+r*ze+e}var De=0,Re=function(t){this._sortAcrossTiles=\"viewport-y\"!==t.layout.get(\"symbol-z-order\")&&void 0!==t.layout.get(\"symbol-sort-key\").constantOr(1),this._currentTileIndex=0,this._currentPartIndex=0,this._seenCrossTileIDs={},this._bucketParts=[]};Re.prototype.continuePlacement=function(t,e,r,n,i){for(var a=this._bucketParts;this._currentTileIndex2};this._currentPlacementIndex>=0;){var s=r[e[this._currentPlacementIndex]],l=this.placement.collisionIndex.transform.zoom;if(\"symbol\"===s.type&&(!s.minzoom||s.minzoom<=l)&&(!s.maxzoom||s.maxzoom>l)){if(this._inProgressLayer||(this._inProgressLayer=new Re(s)),this._inProgressLayer.continuePlacement(n[s.source],this.placement,this._showCollisionBoxes,s,o))return;delete this._inProgressLayer}this._currentPlacementIndex--}this._done=!0},Fe.prototype.commit=function(t){return this.placement.commit(t),this.placement};var Be=512/t.EXTENT/2,Ne=function(t,e,r){this.tileID=t,this.indexedSymbolInstances={},this.bucketInstanceId=r;for(var n=0;nt.overscaledZ)for(var s in o){var l=o[s];l.tileID.isChildOf(t)&&l.findMatches(e.symbolInstances,t,i)}else{var c=o[t.scaledTo(Number(a)).key];c&&c.findMatches(e.symbolInstances,t,i)}}for(var u=0;u1?\"@2x\":\"\",l=t.getJSON(r.transformRequest(r.normalizeSpriteURL(e,s,\".json\"),t.ResourceType.SpriteJSON),(function(t,e){l=null,o||(o=t,i=e,u())})),c=t.getImage(r.transformRequest(r.normalizeSpriteURL(e,s,\".png\"),t.ResourceType.SpriteImage),(function(t,e){c=null,o||(o=t,a=e,u())}));function u(){if(o)n(o);else if(i&&a){var e=t.browser.getImageData(a),r={};for(var s in i){var l=i[s],c=l.width,u=l.height,h=l.x,f=l.y,p=l.sdf,d=l.pixelRatio,m=l.stretchX,g=l.stretchY,y=l.content,v=new t.RGBAImage({width:c,height:u});t.RGBAImage.copy(e,v,{x:h,y:f},{x:0,y:0},{width:c,height:u}),r[s]={data:v,pixelRatio:d,sdf:p,stretchX:m,stretchY:g,content:y}}n(null,r)}}return{cancel:function(){l&&(l.cancel(),l=null),c&&(c.cancel(),c=null)}}}(e,this.map._requestManager,(function(e,n){if(r._spriteRequest=null,e)r.fire(new t.ErrorEvent(e));else if(n)for(var i in n)r.imageManager.addImage(i,n[i]);r.imageManager.setLoaded(!0),r._availableImages=r.imageManager.listImages(),r.dispatcher.broadcast(\"setImages\",r._availableImages),r.fire(new t.Event(\"data\",{dataType:\"style\"}))}))},r.prototype._validateLayer=function(e){var r=this.sourceCaches[e.source];if(r){var n=e.sourceLayer;if(n){var i=r.getSource();(\"geojson\"===i.type||i.vectorLayerIds&&-1===i.vectorLayerIds.indexOf(n))&&this.fire(new t.ErrorEvent(new Error('Source layer \"'+n+'\" does not exist on source \"'+i.id+'\" as specified by style layer \"'+e.id+'\"')))}}},r.prototype.loaded=function(){if(!this._loaded)return!1;if(Object.keys(this._updatedSources).length)return!1;for(var t in this.sourceCaches)if(!this.sourceCaches[t].loaded())return!1;return!!this.imageManager.isLoaded()},r.prototype._serializeLayers=function(t){for(var e=[],r=0,n=t;r0)throw new Error(\"Unimplemented: \"+i.map((function(t){return t.command})).join(\", \")+\".\");return n.forEach((function(t){\"setTransition\"!==t.command&&r[t.command].apply(r,t.args)})),this.stylesheet=e,!0},r.prototype.addImage=function(e,r){if(this.getImage(e))return this.fire(new t.ErrorEvent(new Error(\"An image with this name already exists.\")));this.imageManager.addImage(e,r),this._afterImageUpdated(e)},r.prototype.updateImage=function(t,e){this.imageManager.updateImage(t,e)},r.prototype.getImage=function(t){return this.imageManager.getImage(t)},r.prototype.removeImage=function(e){if(!this.getImage(e))return this.fire(new t.ErrorEvent(new Error(\"No image with this name exists.\")));this.imageManager.removeImage(e),this._afterImageUpdated(e)},r.prototype._afterImageUpdated=function(e){this._availableImages=this.imageManager.listImages(),this._changedImages[e]=!0,this._changed=!0,this.dispatcher.broadcast(\"setImages\",this._availableImages),this.fire(new t.Event(\"data\",{dataType:\"style\"}))},r.prototype.listImages=function(){return this._checkLoaded(),this.imageManager.listImages()},r.prototype.addSource=function(e,r,n){var i=this;if(void 0===n&&(n={}),this._checkLoaded(),void 0!==this.sourceCaches[e])throw new Error(\"There is already a source with this ID\");if(!r.type)throw new Error(\"The type property must be defined, but only the following properties were given: \"+Object.keys(r).join(\", \")+\".\");if(!([\"vector\",\"raster\",\"geojson\",\"video\",\"image\"].indexOf(r.type)>=0&&this._validate(t.validateStyle.source,\"sources.\"+e,r,null,n))){this.map&&this.map._collectResourceTiming&&(r.collectResourceTiming=!0);var a=this.sourceCaches[e]=new Pt(e,r,this.dispatcher);a.style=this,a.setEventedParent(this,(function(){return{isSourceLoaded:i.loaded(),source:a.serialize(),sourceId:e}})),a.onAdd(this.map),this._changed=!0}},r.prototype.removeSource=function(e){if(this._checkLoaded(),void 0===this.sourceCaches[e])throw new Error(\"There is no source with this ID\");for(var r in this._layers)if(this._layers[r].source===e)return this.fire(new t.ErrorEvent(new Error('Source \"'+e+'\" cannot be removed while layer \"'+r+'\" is using it.')));var n=this.sourceCaches[e];delete this.sourceCaches[e],delete this._updatedSources[e],n.fire(new t.Event(\"data\",{sourceDataType:\"metadata\",dataType:\"source\",sourceId:e})),n.setEventedParent(null),n.clearTiles(),n.onRemove&&n.onRemove(this.map),this._changed=!0},r.prototype.setGeoJSONSourceData=function(t,e){this._checkLoaded(),this.sourceCaches[t].getSource().setData(e),this._changed=!0},r.prototype.getSource=function(t){return this.sourceCaches[t]&&this.sourceCaches[t].getSource()},r.prototype.addLayer=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=e.id;if(this.getLayer(i))this.fire(new t.ErrorEvent(new Error('Layer with id \"'+i+'\" already exists on this map')));else{var a;if(\"custom\"===e.type){if(qe(this,t.validateCustomStyleLayer(e)))return;a=t.createStyleLayer(e)}else{if(\"object\"==typeof e.source&&(this.addSource(i,e.source),e=t.clone$1(e),e=t.extend(e,{source:i})),this._validate(t.validateStyle.layer,\"layers.\"+i,e,{arrayIndex:-1},n))return;a=t.createStyleLayer(e),this._validateLayer(a),a.setEventedParent(this,{layer:{id:i}}),this._serializedLayers[a.id]=a.serialize()}var o=r?this._order.indexOf(r):this._order.length;if(r&&-1===o)this.fire(new t.ErrorEvent(new Error('Layer with id \"'+r+'\" does not exist on this map.')));else{if(this._order.splice(o,0,i),this._layerOrderChanged=!0,this._layers[i]=a,this._removedLayers[i]&&a.source&&\"custom\"!==a.type){var s=this._removedLayers[i];delete this._removedLayers[i],s.type!==a.type?this._updatedSources[a.source]=\"clear\":(this._updatedSources[a.source]=\"reload\",this.sourceCaches[a.source].pause())}this._updateLayer(a),a.onAdd&&a.onAdd(this.map)}}},r.prototype.moveLayer=function(e,r){if(this._checkLoaded(),this._changed=!0,this._layers[e]){if(e!==r){var n=this._order.indexOf(e);this._order.splice(n,1);var i=r?this._order.indexOf(r):this._order.length;r&&-1===i?this.fire(new t.ErrorEvent(new Error('Layer with id \"'+r+'\" does not exist on this map.'))):(this._order.splice(i,0,e),this._layerOrderChanged=!0)}}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be moved.\")))},r.prototype.removeLayer=function(e){this._checkLoaded();var r=this._layers[e];if(r){r.setEventedParent(null);var n=this._order.indexOf(e);this._order.splice(n,1),this._layerOrderChanged=!0,this._changed=!0,this._removedLayers[e]=r,delete this._layers[e],delete this._serializedLayers[e],delete this._updatedLayers[e],delete this._updatedPaintProps[e],r.onRemove&&r.onRemove(this.map)}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be removed.\")))},r.prototype.getLayer=function(t){return this._layers[t]},r.prototype.hasLayer=function(t){return t in this._layers},r.prototype.setLayerZoomRange=function(e,r,n){this._checkLoaded();var i=this.getLayer(e);i?i.minzoom===r&&i.maxzoom===n||(null!=r&&(i.minzoom=r),null!=n&&(i.maxzoom=n),this._updateLayer(i)):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot have zoom extent.\")))},r.prototype.setFilter=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=this.getLayer(e);if(i){if(!t.deepEqual(i.filter,r))return null==r?(i.filter=void 0,void this._updateLayer(i)):void(this._validate(t.validateStyle.filter,\"layers.\"+i.id+\".filter\",r,null,n)||(i.filter=t.clone$1(r),this._updateLayer(i)))}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be filtered.\")))},r.prototype.getFilter=function(e){return t.clone$1(this.getLayer(e).filter)},r.prototype.setLayoutProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getLayoutProperty(r),n)||(a.setLayoutProperty(r,n,i),this._updateLayer(a)):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be styled.\")))},r.prototype.getLayoutProperty=function(e,r){var n=this.getLayer(e);if(n)return n.getLayoutProperty(r);this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style.\")))},r.prototype.setPaintProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getPaintProperty(r),n)||(a.setPaintProperty(r,n,i)&&this._updateLayer(a),this._changed=!0,this._updatedPaintProps[e]=!0):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be styled.\")))},r.prototype.getPaintProperty=function(t,e){return this.getLayer(t).getPaintProperty(e)},r.prototype.setFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=e.sourceLayer,a=this.sourceCaches[n];if(void 0!==a){var o=a.getSource().type;\"geojson\"===o&&i?this.fire(new t.ErrorEvent(new Error(\"GeoJSON sources cannot have a sourceLayer parameter.\"))):\"vector\"!==o||i?(void 0===e.id&&this.fire(new t.ErrorEvent(new Error(\"The feature id parameter must be provided.\"))),a.setFeatureState(i,e.id,r)):this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+n+\"' does not exist in the map's style.\")))},r.prototype.removeFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=this.sourceCaches[n];if(void 0!==i){var a=i.getSource().type,o=\"vector\"===a?e.sourceLayer:void 0;\"vector\"!==a||o?r&&\"string\"!=typeof e.id&&\"number\"!=typeof e.id?this.fire(new t.ErrorEvent(new Error(\"A feature id is required to remove its specific state property.\"))):i.removeFeatureState(o,e.id,r):this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+n+\"' does not exist in the map's style.\")))},r.prototype.getFeatureState=function(e){this._checkLoaded();var r=e.source,n=e.sourceLayer,i=this.sourceCaches[r];if(void 0!==i){if(\"vector\"!==i.getSource().type||n)return void 0===e.id&&this.fire(new t.ErrorEvent(new Error(\"The feature id parameter must be provided.\"))),i.getFeatureState(n,e.id);this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+r+\"' does not exist in the map's style.\")))},r.prototype.getTransition=function(){return t.extend({duration:300,delay:0},this.stylesheet&&this.stylesheet.transition)},r.prototype.serialize=function(){return t.filterObject({version:this.stylesheet.version,name:this.stylesheet.name,metadata:this.stylesheet.metadata,light:this.stylesheet.light,center:this.stylesheet.center,zoom:this.stylesheet.zoom,bearing:this.stylesheet.bearing,pitch:this.stylesheet.pitch,sprite:this.stylesheet.sprite,glyphs:this.stylesheet.glyphs,transition:this.stylesheet.transition,sources:t.mapObject(this.sourceCaches,(function(t){return t.serialize()})),layers:this._serializeLayers(this._order)},(function(t){return void 0!==t}))},r.prototype._updateLayer=function(t){this._updatedLayers[t.id]=!0,t.source&&!this._updatedSources[t.source]&&\"raster\"!==this.sourceCaches[t.source].getSource().type&&(this._updatedSources[t.source]=\"reload\",this.sourceCaches[t.source].pause()),this._changed=!0},r.prototype._flattenAndSortRenderedFeatures=function(t){for(var e=this,r=function(t){return\"fill-extrusion\"===e._layers[t].type},n={},i=[],a=this._order.length-1;a>=0;a--){var o=this._order[a];if(r(o)){n[o]=a;for(var s=0,l=t;s=0;d--){var m=this._order[d];if(r(m))for(var g=i.length-1;g>=0;g--){var y=i[g].feature;if(n[y.layer.id] 0.5) {gl_FragColor=vec4(0.0,0.0,1.0,0.5)*alpha;}if (v_notUsed > 0.5) {gl_FragColor*=.1;}}\",\"attribute vec2 a_pos;attribute vec2 a_anchor_pos;attribute vec2 a_extrude;attribute vec2 a_placed;attribute vec2 a_shift;uniform mat4 u_matrix;uniform vec2 u_extrude_scale;uniform float u_camera_to_center_distance;varying float v_placed;varying float v_notUsed;void main() {vec4 projectedPoint=u_matrix*vec4(a_anchor_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);gl_Position=u_matrix*vec4(a_pos,0.0,1.0);gl_Position.xy+=(a_extrude+a_shift)*u_extrude_scale*gl_Position.w*collision_perspective_ratio;v_placed=a_placed.x;v_notUsed=a_placed.y;}\"),nr=br(\"varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;void main() {float alpha=0.5*min(v_perspective_ratio,1.0);float stroke_radius=0.9*max(v_perspective_ratio,1.0);float distance_to_center=length(v_extrude);float distance_to_edge=abs(distance_to_center-v_radius);float opacity_t=smoothstep(-stroke_radius,0.0,-distance_to_edge);vec4 color=mix(vec4(0.0,0.0,1.0,0.5),vec4(1.0,0.0,0.0,1.0),v_collision);gl_FragColor=color*alpha*opacity_t;}\",\"attribute vec2 a_pos;attribute float a_radius;attribute vec2 a_flags;uniform mat4 u_matrix;uniform mat4 u_inv_matrix;uniform vec2 u_viewport_size;uniform float u_camera_to_center_distance;varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;vec3 toTilePosition(vec2 screenPos) {vec4 rayStart=u_inv_matrix*vec4(screenPos,-1.0,1.0);vec4 rayEnd =u_inv_matrix*vec4(screenPos, 1.0,1.0);rayStart.xyz/=rayStart.w;rayEnd.xyz /=rayEnd.w;highp float t=(0.0-rayStart.z)/(rayEnd.z-rayStart.z);return mix(rayStart.xyz,rayEnd.xyz,t);}void main() {vec2 quadCenterPos=a_pos;float radius=a_radius;float collision=a_flags.x;float vertexIdx=a_flags.y;vec2 quadVertexOffset=vec2(mix(-1.0,1.0,float(vertexIdx >=2.0)),mix(-1.0,1.0,float(vertexIdx >=1.0 && vertexIdx <=2.0)));vec2 quadVertexExtent=quadVertexOffset*radius;vec3 tilePos=toTilePosition(quadCenterPos);vec4 clipPos=u_matrix*vec4(tilePos,1.0);highp float camera_to_anchor_distance=clipPos.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);float padding_factor=1.2;v_radius=radius;v_extrude=quadVertexExtent*padding_factor;v_perspective_ratio=collision_perspective_ratio;v_collision=collision;gl_Position=vec4(clipPos.xyz/clipPos.w,1.0)+vec4(quadVertexExtent*padding_factor/u_viewport_size*2.0,0.0,0.0);}\"),ir=br(\"uniform highp vec4 u_color;uniform sampler2D u_overlay;varying vec2 v_uv;void main() {vec4 overlay_color=texture2D(u_overlay,v_uv);gl_FragColor=mix(u_color,overlay_color,overlay_color.a);}\",\"attribute vec2 a_pos;varying vec2 v_uv;uniform mat4 u_matrix;uniform float u_overlay_scale;void main() {v_uv=a_pos/8192.0;gl_Position=u_matrix*vec4(a_pos*u_overlay_scale,0,1);}\"),ar=br(\"#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float opacity\\ngl_FragColor=color*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float opacity\\ngl_Position=u_matrix*vec4(a_pos,0,1);}\"),or=br(\"varying vec2 v_pos;\\n#pragma mapbox: define highp vec4 outline_color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 outline_color\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=outline_color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;uniform vec2 u_world;varying vec2 v_pos;\\n#pragma mapbox: define highp vec4 outline_color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 outline_color\\n#pragma mapbox: initialize lowp float opacity\\ngl_Position=u_matrix*vec4(a_pos,0,1);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}\"),sr=br(\"uniform vec2 u_texsize;uniform sampler2D u_image;uniform float u_fade;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);float dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=mix(color1,color2,u_fade)*alpha*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_world;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;gl_Position=u_matrix*vec4(a_pos,0,1);vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,a_pos);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}\"),lr=br(\"uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_fade)*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileZoomRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileZoomRatio,a_pos);}\"),cr=br(\"varying vec4 v_color;void main() {gl_FragColor=v_color;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;uniform float u_vertical_gradient;uniform lowp float u_opacity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec4 v_color;\\n#pragma mapbox: define highp float base\\n#pragma mapbox: define highp float height\\n#pragma mapbox: define highp vec4 color\\nvoid main() {\\n#pragma mapbox: initialize highp float base\\n#pragma mapbox: initialize highp float height\\n#pragma mapbox: initialize highp vec4 color\\nvec3 normal=a_normal_ed.xyz;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);gl_Position=u_matrix*vec4(a_pos,t > 0.0 ? height : base,1);float colorvalue=color.r*0.2126+color.g*0.7152+color.b*0.0722;v_color=vec4(0.0,0.0,0.0,1.0);vec4 ambientlight=vec4(0.03,0.03,0.03,1.0);color+=ambientlight;float directional=clamp(dot(normal/16384.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((1.0-colorvalue+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_color.r+=clamp(color.r*directional*u_lightcolor.r,mix(0.0,0.3,1.0-u_lightcolor.r),1.0);v_color.g+=clamp(color.g*directional*u_lightcolor.g,mix(0.0,0.3,1.0-u_lightcolor.g),1.0);v_color.b+=clamp(color.b*directional*u_lightcolor.b,mix(0.0,0.3,1.0-u_lightcolor.b),1.0);v_color*=u_opacity;}\"),ur=br(\"uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\\n#pragma mapbox: define lowp float base\\n#pragma mapbox: define lowp float height\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float base\\n#pragma mapbox: initialize lowp float height\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);vec4 mixedColor=mix(color1,color2,u_fade);gl_FragColor=mixedColor*v_lighting;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_height_factor;uniform vec3 u_scale;uniform float u_vertical_gradient;uniform lowp float u_opacity;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\\n#pragma mapbox: define lowp float base\\n#pragma mapbox: define lowp float height\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float base\\n#pragma mapbox: initialize lowp float height\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec3 normal=a_normal_ed.xyz;float edgedistance=a_normal_ed.w;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);float z=t > 0.0 ? height : base;gl_Position=u_matrix*vec4(a_pos,z,1);vec2 pos=normal.x==1.0 && normal.y==0.0 && normal.z==16384.0\\n? a_pos\\n: vec2(edgedistance,z*u_height_factor);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,pos);v_lighting=vec4(0.0,0.0,0.0,1.0);float directional=clamp(dot(normal/16383.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((0.5+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_lighting.rgb+=clamp(directional*u_lightcolor,mix(vec3(0.0),vec3(0.3),1.0-u_lightcolor),vec3(1.0));v_lighting*=u_opacity;}\"),hr=br(\"#ifdef GL_ES\\nprecision highp float;\\n#endif\\nuniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_dimension;uniform float u_zoom;uniform vec4 u_unpack;float getElevation(vec2 coord,float bias) {vec4 data=texture2D(u_image,coord)*255.0;data.a=-1.0;return dot(data,u_unpack)/4.0;}void main() {vec2 epsilon=1.0/u_dimension;float a=getElevation(v_pos+vec2(-epsilon.x,-epsilon.y),0.0);float b=getElevation(v_pos+vec2(0,-epsilon.y),0.0);float c=getElevation(v_pos+vec2(epsilon.x,-epsilon.y),0.0);float d=getElevation(v_pos+vec2(-epsilon.x,0),0.0);float e=getElevation(v_pos,0.0);float f=getElevation(v_pos+vec2(epsilon.x,0),0.0);float g=getElevation(v_pos+vec2(-epsilon.x,epsilon.y),0.0);float h=getElevation(v_pos+vec2(0,epsilon.y),0.0);float i=getElevation(v_pos+vec2(epsilon.x,epsilon.y),0.0);float exaggerationFactor=u_zoom < 2.0 ? 0.4 : u_zoom < 4.5 ? 0.35 : 0.3;float exaggeration=u_zoom < 15.0 ? (u_zoom-15.0)*exaggerationFactor : 0.0;vec2 deriv=vec2((c+f+f+i)-(a+d+d+g),(g+h+h+i)-(a+b+b+c))/pow(2.0,exaggeration+(19.2562-u_zoom));gl_FragColor=clamp(vec4(deriv.x/2.0+0.5,deriv.y/2.0+0.5,1.0,1.0),0.0,1.0);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_dimension;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);highp vec2 epsilon=1.0/u_dimension;float scale=(u_dimension.x-2.0)/u_dimension.x;v_pos=(a_texture_pos/8192.0)*scale+epsilon;}\"),fr=br(\"uniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_latrange;uniform vec2 u_light;uniform vec4 u_shadow;uniform vec4 u_highlight;uniform vec4 u_accent;\\n#define PI 3.141592653589793\\nvoid main() {vec4 pixel=texture2D(u_image,v_pos);vec2 deriv=((pixel.rg*2.0)-1.0);float scaleFactor=cos(radians((u_latrange[0]-u_latrange[1])*(1.0-v_pos.y)+u_latrange[1]));float slope=atan(1.25*length(deriv)/scaleFactor);float aspect=deriv.x !=0.0 ? atan(deriv.y,-deriv.x) : PI/2.0*(deriv.y > 0.0 ? 1.0 :-1.0);float intensity=u_light.x;float azimuth=u_light.y+PI;float base=1.875-intensity*1.75;float maxValue=0.5*PI;float scaledSlope=intensity !=0.5 ? ((pow(base,slope)-1.0)/(pow(base,maxValue)-1.0))*maxValue : slope;float accent=cos(scaledSlope);vec4 accent_color=(1.0-accent)*u_accent*clamp(intensity*2.0,0.0,1.0);float shade=abs(mod((aspect+azimuth)/PI+0.5,2.0)-1.0);vec4 shade_color=mix(u_shadow,u_highlight,shade)*sin(scaledSlope)*clamp(intensity*2.0,0.0,1.0);gl_FragColor=accent_color*(1.0-shade_color.a)+shade_color;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos=a_texture_pos/8192.0;}\"),pr=br(\"uniform lowp float u_device_pixel_ratio;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform vec2 u_units_to_pixels;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_linesofar;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}\"),dr=br(\"uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;varying highp vec2 v_uv;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);vec4 color=texture2D(u_image,v_uv);gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\nattribute vec2 a_pos_normal;attribute vec4 a_data;attribute float a_uv_x;attribute float a_split_index;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_units_to_pixels;uniform float u_image_height;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp vec2 v_uv;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;highp float texel_height=1.0/u_image_height;highp float half_texel_height=0.5*texel_height;v_uv=vec2(a_uv_x,a_split_index*texel_height-half_texel_height);vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}\"),mr=br(\"uniform lowp float u_device_pixel_ratio;uniform vec2 u_texsize;uniform float u_fade;uniform mediump vec3 u_scale;uniform sampler2D u_image;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;vec2 pattern_size_a=vec2(display_size_a.x*fromScale/tileZoomRatio,display_size_a.y);vec2 pattern_size_b=vec2(display_size_b.x*toScale/tileZoomRatio,display_size_b.y);float aspect_a=display_size_a.y/v_width;float aspect_b=display_size_b.y/v_width;float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float x_a=mod(v_linesofar/pattern_size_a.x*aspect_a,1.0);float x_b=mod(v_linesofar/pattern_size_b.x*aspect_b,1.0);float y=0.5*v_normal.y+0.5;vec2 texel_size=1.0/u_texsize;vec2 pos_a=mix(pattern_tl_a*texel_size-texel_size,pattern_br_a*texel_size+texel_size,vec2(x_a,y));vec2 pos_b=mix(pattern_tl_b*texel_size-texel_size,pattern_br_b*texel_size+texel_size,vec2(x_b,y));vec4 color=mix(texture2D(u_image,pos_a),texture2D(u_image,pos_b),u_fade);gl_FragColor=color*alpha*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\n#define LINE_DISTANCE_SCALE 2.0\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform vec2 u_units_to_pixels;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_linesofar=a_linesofar;v_width2=vec2(outset,inset);v_width=floorwidth;}\"),gr=br(\"uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;uniform float u_sdfgamma;uniform float u_mix;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float sdfdist_a=texture2D(u_image,v_tex_a).a;float sdfdist_b=texture2D(u_image,v_tex_b).a;float sdfdist=mix(sdfdist_a,sdfdist_b,u_mix);alpha*=smoothstep(0.5-u_sdfgamma/floorwidth,0.5+u_sdfgamma/floorwidth,sdfdist);gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\n#define LINE_DISTANCE_SCALE 2.0\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_patternscale_a;uniform float u_tex_y_a;uniform vec2 u_patternscale_b;uniform float u_tex_y_b;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_tex_a=vec2(a_linesofar*u_patternscale_a.x/floorwidth,normal.y*u_patternscale_a.y+u_tex_y_a);v_tex_b=vec2(a_linesofar*u_patternscale_b.x/floorwidth,normal.y*u_patternscale_b.y+u_tex_y_b);v_width2=vec2(outset,inset);}\"),yr=br(\"uniform float u_fade_t;uniform float u_opacity;uniform sampler2D u_image0;uniform sampler2D u_image1;varying vec2 v_pos0;varying vec2 v_pos1;uniform float u_brightness_low;uniform float u_brightness_high;uniform float u_saturation_factor;uniform float u_contrast_factor;uniform vec3 u_spin_weights;void main() {vec4 color0=texture2D(u_image0,v_pos0);vec4 color1=texture2D(u_image1,v_pos1);if (color0.a > 0.0) {color0.rgb=color0.rgb/color0.a;}if (color1.a > 0.0) {color1.rgb=color1.rgb/color1.a;}vec4 color=mix(color0,color1,u_fade_t);color.a*=u_opacity;vec3 rgb=color.rgb;rgb=vec3(dot(rgb,u_spin_weights.xyz),dot(rgb,u_spin_weights.zxy),dot(rgb,u_spin_weights.yzx));float average=(color.r+color.g+color.b)/3.0;rgb+=(average-rgb)*u_saturation_factor;rgb=(rgb-0.5)*u_contrast_factor+0.5;vec3 u_high_vec=vec3(u_brightness_low,u_brightness_low,u_brightness_low);vec3 u_low_vec=vec3(u_brightness_high,u_brightness_high,u_brightness_high);gl_FragColor=vec4(mix(u_high_vec,u_low_vec,rgb)*color.a,color.a);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_tl_parent;uniform float u_scale_parent;uniform float u_buffer_scale;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos0;varying vec2 v_pos1;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos0=(((a_texture_pos/8192.0)-0.5)/u_buffer_scale )+0.5;v_pos1=(v_pos0*u_scale_parent)+u_tl_parent;}\"),vr=br(\"uniform sampler2D u_texture;varying vec2 v_tex;varying float v_fade_opacity;\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\nlowp float alpha=opacity*v_fade_opacity;gl_FragColor=texture2D(u_texture,v_tex)*alpha;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform highp float u_camera_to_center_distance;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform float u_fade_change;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform vec2 u_texsize;varying vec2 v_tex;varying float v_fade_opacity;\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;vec2 a_minFontScale=a_pixeloffset.zw/256.0;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*max(a_minFontScale,fontScale)+a_pxoffset/16.0),0.0,1.0);v_tex=a_tex/u_texsize;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;v_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));}\"),xr=br(\"#define SDF_PX 8.0\\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;uniform bool u_is_text;varying vec2 v_data0;varying vec3 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nfloat EDGE_GAMMA=0.105/u_device_pixel_ratio;vec2 tex=v_data0.xy;float gamma_scale=v_data1.x;float size=v_data1.y;float fade_opacity=v_data1[2];float fontScale=u_is_text ? size/24.0 : size;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;varying vec2 v_data0;varying vec3 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale+a_pxoffset),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0=a_tex/u_texsize;v_data1=vec3(gamma_scale,size,interpolated_fade_opacity);}\"),_r=br(\"#define SDF_PX 8.0\\n#define SDF 1.0\\n#define ICON 0.0\\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform sampler2D u_texture_icon;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;varying vec4 v_data0;varying vec4 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nfloat fade_opacity=v_data1[2];if (v_data1.w==ICON) {vec2 tex_icon=v_data0.zw;lowp float alpha=opacity*fade_opacity;gl_FragColor=texture2D(u_texture_icon,tex_icon)*alpha;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\nreturn;}vec2 tex=v_data0.xy;float EDGE_GAMMA=0.105/u_device_pixel_ratio;float gamma_scale=v_data1.x;float size=v_data1.y;float fontScale=size/24.0;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;uniform vec2 u_texsize_icon;varying vec4 v_data0;varying vec4 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);float is_sdf=a_size[0]-2.0*a_size_min;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=size/24.0;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0.xy=a_tex/u_texsize;v_data0.zw=a_tex/u_texsize_icon;v_data1=vec4(gamma_scale,size,interpolated_fade_opacity,is_sdf);}\");function br(t,e){var r=/#pragma mapbox: ([\\w]+) ([\\w]+) ([\\w]+) ([\\w]+)/g,n=e.match(/attribute ([\\w]+) ([\\w]+)/g),i=t.match(/uniform ([\\w]+) ([\\w]+)([\\s]*)([\\w]*)/g),a=e.match(/uniform ([\\w]+) ([\\w]+)([\\s]*)([\\w]*)/g),o=a?a.concat(i):i,s={};return{fragmentSource:t=t.replace(r,(function(t,e,r,n,i){return s[i]=!0,\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+i+\"\\nvarying \"+r+\" \"+n+\" \"+i+\";\\n#else\\nuniform \"+r+\" \"+n+\" u_\"+i+\";\\n#endif\\n\":\"\\n#ifdef HAS_UNIFORM_u_\"+i+\"\\n \"+r+\" \"+n+\" \"+i+\" = u_\"+i+\";\\n#endif\\n\"})),vertexSource:e=e.replace(r,(function(t,e,r,n,i){var a=\"float\"===n?\"vec2\":\"vec4\",o=i.match(/color/)?\"color\":a;return s[i]?\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+i+\"\\nuniform lowp float u_\"+i+\"_t;\\nattribute \"+r+\" \"+a+\" a_\"+i+\";\\nvarying \"+r+\" \"+n+\" \"+i+\";\\n#else\\nuniform \"+r+\" \"+n+\" u_\"+i+\";\\n#endif\\n\":\"vec4\"===o?\"\\n#ifndef HAS_UNIFORM_u_\"+i+\"\\n \"+i+\" = a_\"+i+\";\\n#else\\n \"+r+\" \"+n+\" \"+i+\" = u_\"+i+\";\\n#endif\\n\":\"\\n#ifndef HAS_UNIFORM_u_\"+i+\"\\n \"+i+\" = unpack_mix_\"+o+\"(a_\"+i+\", u_\"+i+\"_t);\\n#else\\n \"+r+\" \"+n+\" \"+i+\" = u_\"+i+\";\\n#endif\\n\":\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+i+\"\\nuniform lowp float u_\"+i+\"_t;\\nattribute \"+r+\" \"+a+\" a_\"+i+\";\\n#else\\nuniform \"+r+\" \"+n+\" u_\"+i+\";\\n#endif\\n\":\"vec4\"===o?\"\\n#ifndef HAS_UNIFORM_u_\"+i+\"\\n \"+r+\" \"+n+\" \"+i+\" = a_\"+i+\";\\n#else\\n \"+r+\" \"+n+\" \"+i+\" = u_\"+i+\";\\n#endif\\n\":\"\\n#ifndef HAS_UNIFORM_u_\"+i+\"\\n \"+r+\" \"+n+\" \"+i+\" = unpack_mix_\"+o+\"(a_\"+i+\", u_\"+i+\"_t);\\n#else\\n \"+r+\" \"+n+\" \"+i+\" = u_\"+i+\";\\n#endif\\n\"})),staticAttributes:n,staticUniforms:o}}var wr=Object.freeze({__proto__:null,prelude:Xe,background:$e,backgroundPattern:Je,circle:Ke,clippingMask:Qe,heatmap:tr,heatmapTexture:er,collisionBox:rr,collisionCircle:nr,debug:ir,fill:ar,fillOutline:or,fillOutlinePattern:sr,fillPattern:lr,fillExtrusion:cr,fillExtrusionPattern:ur,hillshadePrepare:hr,hillshade:fr,line:pr,lineGradient:dr,linePattern:mr,lineSDF:gr,raster:yr,symbolIcon:vr,symbolSDF:xr,symbolTextAndIcon:_r}),Tr=function(){this.boundProgram=null,this.boundLayoutVertexBuffer=null,this.boundPaintVertexBuffers=[],this.boundIndexBuffer=null,this.boundVertexOffset=null,this.boundDynamicVertexBuffer=null,this.vao=null};function kr(t){for(var e=[],r=0;r>16,s>>16],u_pixel_coord_lower:[65535&o,65535&s]}}Ar.prototype.draw=function(t,e,r,n,i,a,o,s,l,c,u,h,f,p,d,m){var g,y=t.gl;if(!this.failedToCreate){for(var v in t.program.set(this.program),t.setDepthMode(r),t.setStencilMode(n),t.setColorMode(i),t.setCullFace(a),this.fixedUniforms)this.fixedUniforms[v].set(o[v]);p&&p.setUniforms(t,this.binderUniforms,h,{zoom:f});for(var x=(g={},g[y.LINES]=2,g[y.TRIANGLES]=3,g[y.LINE_STRIP]=1,g)[e],_=0,b=u.get();_0?1-1/(1.001-o):-o),u_contrast_factor:(a=i.paint.get(\"raster-contrast\"),a>0?1/(1-a):1+a),u_spin_weights:Xr(i.paint.get(\"raster-hue-rotate\"))};var a,o};function Xr(t){t*=Math.PI/180;var e=Math.sin(t),r=Math.cos(t);return[(2*r+1)/3,(-Math.sqrt(3)*e-r+1)/3,(Math.sqrt(3)*e-r+1)/3]}var $r,Jr=function(t,e,r,n,i,a,o,s,l,c){var u=i.transform;return{u_is_size_zoom_constant:+(\"constant\"===t||\"source\"===t),u_is_size_feature_constant:+(\"constant\"===t||\"camera\"===t),u_size_t:e?e.uSizeT:0,u_size:e?e.uSize:0,u_camera_to_center_distance:u.cameraToCenterDistance,u_pitch:u.pitch/360*2*Math.PI,u_rotate_symbol:+r,u_aspect_ratio:u.width/u.height,u_fade_change:i.options.fadeDuration?i.symbolFadeChange:1,u_matrix:a,u_label_plane_matrix:o,u_coord_matrix:s,u_is_text:+l,u_pitch_with_map:+n,u_texsize:c,u_texture:0}},Kr=function(e,r,n,i,a,o,s,l,c,u,h){var f=a.transform;return t.extend(Jr(e,r,n,i,a,o,s,l,c,u),{u_gamma_scale:i?Math.cos(f._pitch)*f.cameraToCenterDistance:1,u_device_pixel_ratio:t.browser.devicePixelRatio,u_is_halo:+h})},Qr=function(e,r,n,i,a,o,s,l,c,u){return t.extend(Kr(e,r,n,i,a,o,s,l,!0,c,!0),{u_texsize_icon:u,u_texture_icon:1})},tn=function(t,e,r){return{u_matrix:t,u_opacity:e,u_color:r}},en=function(e,r,n,i,a,o){return t.extend(function(t,e,r,n){var i=r.imageManager.getPattern(t.from.toString()),a=r.imageManager.getPattern(t.to.toString()),o=r.imageManager.getPixelSize(),s=o.width,l=o.height,c=Math.pow(2,n.tileID.overscaledZ),u=n.tileSize*Math.pow(2,r.transform.tileZoom)/c,h=u*(n.tileID.canonical.x+n.tileID.wrap*c),f=u*n.tileID.canonical.y;return{u_image:0,u_pattern_tl_a:i.tl,u_pattern_br_a:i.br,u_pattern_tl_b:a.tl,u_pattern_br_b:a.br,u_texsize:[s,l],u_mix:e.t,u_pattern_size_a:i.displaySize,u_pattern_size_b:a.displaySize,u_scale_a:e.fromScale,u_scale_b:e.toScale,u_tile_units_to_pixels:1/ge(n,1,r.transform.tileZoom),u_pixel_coord_upper:[h>>16,f>>16],u_pixel_coord_lower:[65535&h,65535&f]}}(i,o,n,a),{u_matrix:e,u_opacity:r})},rn={fillExtrusion:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fillExtrusionPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_height_factor:new t.Uniform1f(e,r.u_height_factor),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fill:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},fillPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},fillOutline:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world)}},fillOutlinePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},circle:function(e,r){return{u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_scale_with_map:new t.Uniform1i(e,r.u_scale_with_map),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},collisionBox:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pixels_to_tile_units:new t.Uniform1f(e,r.u_pixels_to_tile_units),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_overscale_factor:new t.Uniform1f(e,r.u_overscale_factor)}},collisionCircle:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_inv_matrix:new t.UniformMatrix4f(e,r.u_inv_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_viewport_size:new t.Uniform2f(e,r.u_viewport_size)}},debug:function(e,r){return{u_color:new t.UniformColor(e,r.u_color),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_overlay:new t.Uniform1i(e,r.u_overlay),u_overlay_scale:new t.Uniform1f(e,r.u_overlay_scale)}},clippingMask:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmap:function(e,r){return{u_extrude_scale:new t.Uniform1f(e,r.u_extrude_scale),u_intensity:new t.Uniform1f(e,r.u_intensity),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmapTexture:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_color_ramp:new t.Uniform1i(e,r.u_color_ramp),u_opacity:new t.Uniform1f(e,r.u_opacity)}},hillshade:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_latrange:new t.Uniform2f(e,r.u_latrange),u_light:new t.Uniform2f(e,r.u_light),u_shadow:new t.UniformColor(e,r.u_shadow),u_highlight:new t.UniformColor(e,r.u_highlight),u_accent:new t.UniformColor(e,r.u_accent)}},hillshadePrepare:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_dimension:new t.Uniform2f(e,r.u_dimension),u_zoom:new t.Uniform1f(e,r.u_zoom),u_unpack:new t.Uniform4f(e,r.u_unpack)}},line:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels)}},lineGradient:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_image:new t.Uniform1i(e,r.u_image),u_image_height:new t.Uniform1f(e,r.u_image_height)}},linePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_texsize:new t.Uniform2f(e,r.u_texsize),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_image:new t.Uniform1i(e,r.u_image),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},lineSDF:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_patternscale_a:new t.Uniform2f(e,r.u_patternscale_a),u_patternscale_b:new t.Uniform2f(e,r.u_patternscale_b),u_sdfgamma:new t.Uniform1f(e,r.u_sdfgamma),u_image:new t.Uniform1i(e,r.u_image),u_tex_y_a:new t.Uniform1f(e,r.u_tex_y_a),u_tex_y_b:new t.Uniform1f(e,r.u_tex_y_b),u_mix:new t.Uniform1f(e,r.u_mix)}},raster:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_tl_parent:new t.Uniform2f(e,r.u_tl_parent),u_scale_parent:new t.Uniform1f(e,r.u_scale_parent),u_buffer_scale:new t.Uniform1f(e,r.u_buffer_scale),u_fade_t:new t.Uniform1f(e,r.u_fade_t),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image0:new t.Uniform1i(e,r.u_image0),u_image1:new t.Uniform1i(e,r.u_image1),u_brightness_low:new t.Uniform1f(e,r.u_brightness_low),u_brightness_high:new t.Uniform1f(e,r.u_brightness_high),u_saturation_factor:new t.Uniform1f(e,r.u_saturation_factor),u_contrast_factor:new t.Uniform1f(e,r.u_contrast_factor),u_spin_weights:new t.Uniform3f(e,r.u_spin_weights)}},symbolIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture)}},symbolSDF:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},symbolTextAndIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texsize_icon:new t.Uniform2f(e,r.u_texsize_icon),u_texture:new t.Uniform1i(e,r.u_texture),u_texture_icon:new t.Uniform1i(e,r.u_texture_icon),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},background:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_color:new t.UniformColor(e,r.u_color)}},backgroundPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image:new t.Uniform1i(e,r.u_image),u_pattern_tl_a:new t.Uniform2f(e,r.u_pattern_tl_a),u_pattern_br_a:new t.Uniform2f(e,r.u_pattern_br_a),u_pattern_tl_b:new t.Uniform2f(e,r.u_pattern_tl_b),u_pattern_br_b:new t.Uniform2f(e,r.u_pattern_br_b),u_texsize:new t.Uniform2f(e,r.u_texsize),u_mix:new t.Uniform1f(e,r.u_mix),u_pattern_size_a:new t.Uniform2f(e,r.u_pattern_size_a),u_pattern_size_b:new t.Uniform2f(e,r.u_pattern_size_b),u_scale_a:new t.Uniform1f(e,r.u_scale_a),u_scale_b:new t.Uniform1f(e,r.u_scale_b),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_tile_units_to_pixels:new t.Uniform1f(e,r.u_tile_units_to_pixels)}}};function nn(e,r,n,i,a,o,s){for(var l=e.context,c=l.gl,u=e.useProgram(\"collisionBox\"),h=[],f=0,p=0,d=0;d0){var b=t.create(),w=v;t.mul(b,y.placementInvProjMatrix,e.transform.glCoordMatrix),t.mul(b,b,y.placementViewportMatrix),h.push({circleArray:_,circleOffset:p,transform:w,invTransform:b}),p=f+=_.length/4}x&&u.draw(l,c.LINES,Mt.disabled,Et.disabled,e.colorModeForRenderPass(),Lt.disabled,Or(v,e.transform,g),n.id,x.layoutVertexBuffer,x.indexBuffer,x.segments,null,e.transform.zoom,null,null,x.collisionVertexBuffer)}}if(s&&h.length){var T=e.useProgram(\"collisionCircle\"),k=new t.StructArrayLayout2f1f2i16;k.resize(4*f),k._trim();for(var A=0,M=0,S=h;M=0&&(m[y.associatedIconIndex]={shiftedAnchor:S,angle:E})}else fe(y.numGlyphs,p)}if(h){d.clear();for(var L=e.icon.placedSymbolArray,I=0;I0){var s=t.browser.now(),l=(s-e.timeAdded)/o,c=r?(s-r.timeAdded)/o:-1,u=n.getSource(),h=a.coveringZoomLevel({tileSize:u.tileSize,roundZoom:u.roundZoom}),f=!r||Math.abs(r.tileID.overscaledZ-h)>Math.abs(e.tileID.overscaledZ-h),p=f&&e.refreshedUponExpiration?1:t.clamp(f?l:1-c,0,1);return e.refreshedUponExpiration&&l>=1&&(e.refreshedUponExpiration=!1),r?{opacity:1,mix:1-p}:{opacity:p,mix:0}}return{opacity:1,mix:0}}var gn=new t.Color(1,0,0,1),yn=new t.Color(0,1,0,1),vn=new t.Color(0,0,1,1),xn=new t.Color(1,0,1,1),_n=new t.Color(0,1,1,1);function bn(t){var e=t.transform.padding;wn(t,t.transform.height-(e.top||0),3,gn),wn(t,e.bottom||0,3,yn),Tn(t,e.left||0,3,vn),Tn(t,t.transform.width-(e.right||0),3,xn);var r=t.transform.centerPoint;!function(t,e,r,n){var i=20,a=2;kn(t,e-a/2,r-i/2,a,i,n),kn(t,e-i/2,r-a/2,i,a,n)}(t,r.x,t.transform.height-r.y,_n)}function wn(t,e,r,n){kn(t,0,e+r/2,t.transform.width,r,n)}function Tn(t,e,r,n){kn(t,e-r/2,0,r,t.transform.height,n)}function kn(e,r,n,i,a,o){var s=e.context,l=s.gl;l.enable(l.SCISSOR_TEST),l.scissor(r*t.browser.devicePixelRatio,n*t.browser.devicePixelRatio,i*t.browser.devicePixelRatio,a*t.browser.devicePixelRatio),s.clear({color:o}),l.disable(l.SCISSOR_TEST)}function An(e,r,n){var i=e.context,a=i.gl,o=n.posMatrix,s=e.useProgram(\"debug\"),l=Mt.disabled,c=Et.disabled,u=e.colorModeForRenderPass(),h=\"$debug\";i.activeTexture.set(a.TEXTURE0),e.emptyTexture.bind(a.LINEAR,a.CLAMP_TO_EDGE),s.draw(i,a.LINE_STRIP,l,c,u,Lt.disabled,Rr(o,t.Color.red),h,e.debugBuffer,e.tileBorderIndexBuffer,e.debugSegments);var f=r.getTileByID(n.key).latestRawTileData,p=f&&f.byteLength||0,d=Math.floor(p/1024),m=r.getTile(n).tileSize,g=512/Math.min(m,512)*(n.overscaledZ/e.transform.zoom)*.5,y=n.canonical.toString();n.overscaledZ!==n.canonical.z&&(y+=\" => \"+n.overscaledZ),function(t,e){t.initDebugOverlayCanvas();var r=t.debugOverlayCanvas,n=t.context.gl,i=t.debugOverlayCanvas.getContext(\"2d\");i.clearRect(0,0,r.width,r.height),i.shadowColor=\"white\",i.shadowBlur=2,i.lineWidth=1.5,i.strokeStyle=\"white\",i.textBaseline=\"top\",i.font=\"bold 36px Open Sans, sans-serif\",i.fillText(e,5,5),i.strokeText(e,5,5),t.debugOverlayTexture.update(r),t.debugOverlayTexture.bind(n.LINEAR,n.CLAMP_TO_EDGE)}(e,y+\" \"+d+\"kb\"),s.draw(i,a.TRIANGLES,l,c,Ct.alphaBlended,Lt.disabled,Rr(o,t.Color.transparent,g),h,e.debugBuffer,e.quadTriangleIndexBuffer,e.debugSegments)}var Mn={symbol:function(e,r,n,i,a){if(\"translucent\"===e.renderPass){var o=Et.disabled,s=e.colorModeForRenderPass();n.layout.get(\"text-variable-anchor\")&&function(e,r,n,i,a,o,s){for(var l=r.transform,c=\"map\"===a,u=\"map\"===o,h=0,f=e;h256&&this.clearStencil(),r.setColorMode(Ct.disabled),r.setDepthMode(Mt.disabled);var i=this.useProgram(\"clippingMask\");this._tileClippingMaskIDs={};for(var a=0,o=e;a256&&this.clearStencil();var t=this.nextStencilID++,e=this.context.gl;return new Et({func:e.NOTEQUAL,mask:255},t,255,e.KEEP,e.KEEP,e.REPLACE)},Sn.prototype.stencilModeForClipping=function(t){var e=this.context.gl;return new Et({func:e.EQUAL,mask:255},this._tileClippingMaskIDs[t.key],0,e.KEEP,e.KEEP,e.REPLACE)},Sn.prototype.stencilConfigForOverlap=function(t){var e,r=this.context.gl,n=t.sort((function(t,e){return e.overscaledZ-t.overscaledZ})),i=n[n.length-1].overscaledZ,a=n[0].overscaledZ-i+1;if(a>1){this.currentStencilSource=void 0,this.nextStencilID+a>256&&this.clearStencil();for(var o={},s=0;s=0;this.currentLayer--){var w=this.style._layers[i[this.currentLayer]],T=a[w.source],k=u[w.source];this._renderTileClippingMasks(w,k),this.renderLayer(this,T,w,k)}for(this.renderPass=\"translucent\",this.currentLayer=0;this.currentLayer0?e.pop():null},Sn.prototype.isPatternMissing=function(t){if(!t)return!1;if(!t.from||!t.to)return!0;var e=this.imageManager.getPattern(t.from.toString()),r=this.imageManager.getPattern(t.to.toString());return!e||!r},Sn.prototype.useProgram=function(t,e){this.cache=this.cache||{};var r=\"\"+t+(e?e.cacheKey:\"\")+(this._showOverdrawInspector?\"/overdraw\":\"\");return this.cache[r]||(this.cache[r]=new Ar(this.context,t,wr[t],e,rn[t],this._showOverdrawInspector)),this.cache[r]},Sn.prototype.setCustomLayerDefaults=function(){this.context.unbindVAO(),this.context.cullFace.setDefault(),this.context.activeTexture.setDefault(),this.context.pixelStoreUnpack.setDefault(),this.context.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.context.pixelStoreUnpackFlipY.setDefault()},Sn.prototype.setBaseState=function(){var t=this.context.gl;this.context.cullFace.set(!1),this.context.viewport.set([0,0,this.width,this.height]),this.context.blendEquation.set(t.FUNC_ADD)},Sn.prototype.initDebugOverlayCanvas=function(){if(null==this.debugOverlayCanvas){this.debugOverlayCanvas=t.window.document.createElement(\"canvas\"),this.debugOverlayCanvas.width=512,this.debugOverlayCanvas.height=512;var e=this.context.gl;this.debugOverlayTexture=new t.Texture(this.context,this.debugOverlayCanvas,e.RGBA)}},Sn.prototype.destroy=function(){this.emptyTexture.destroy(),this.debugOverlayTexture&&this.debugOverlayTexture.destroy()};var En=function(t,e){this.points=t,this.planes=e};En.fromInvProjectionMatrix=function(e,r,n){var i=Math.pow(2,n),a=[[-1,1,-1,1],[1,1,-1,1],[1,-1,-1,1],[-1,-1,-1,1],[-1,1,1,1],[1,1,1,1],[1,-1,1,1],[-1,-1,1,1]].map((function(r){return t.transformMat4([],r,e)})).map((function(e){return t.scale$1([],e,1/e[3]/r*i)})),o=[[0,1,2],[6,5,4],[0,3,7],[2,1,5],[3,2,6],[0,4,5]].map((function(e){var r=t.sub([],a[e[0]],a[e[1]]),n=t.sub([],a[e[2]],a[e[1]]),i=t.normalize([],t.cross([],r,n)),o=-t.dot(i,a[e[1]]);return i.concat(o)}));return new En(a,o)};var Cn=function(e,r){this.min=e,this.max=r,this.center=t.scale$2([],t.add([],this.min,this.max),.5)};Cn.prototype.quadrant=function(e){for(var r=[e%2==0,e<2],n=t.clone$2(this.min),i=t.clone$2(this.max),a=0;a=0;if(0===o)return 0;o!==r.length&&(n=!1)}if(n)return 2;for(var l=0;l<3;l++){for(var c=Number.MAX_VALUE,u=-Number.MAX_VALUE,h=0;hthis.max[l]-this.min[l])return 0}return 1};var Ln=function(t,e,r,n){if(void 0===t&&(t=0),void 0===e&&(e=0),void 0===r&&(r=0),void 0===n&&(n=0),isNaN(t)||t<0||isNaN(e)||e<0||isNaN(r)||r<0||isNaN(n)||n<0)throw new Error(\"Invalid value for edge-insets, top, bottom, left and right must all be numbers\");this.top=t,this.bottom=e,this.left=r,this.right=n};Ln.prototype.interpolate=function(e,r,n){return null!=r.top&&null!=e.top&&(this.top=t.number(e.top,r.top,n)),null!=r.bottom&&null!=e.bottom&&(this.bottom=t.number(e.bottom,r.bottom,n)),null!=r.left&&null!=e.left&&(this.left=t.number(e.left,r.left,n)),null!=r.right&&null!=e.right&&(this.right=t.number(e.right,r.right,n)),this},Ln.prototype.getCenter=function(e,r){var n=t.clamp((this.left+e-this.right)/2,0,e),i=t.clamp((this.top+r-this.bottom)/2,0,r);return new t.Point(n,i)},Ln.prototype.equals=function(t){return this.top===t.top&&this.bottom===t.bottom&&this.left===t.left&&this.right===t.right},Ln.prototype.clone=function(){return new Ln(this.top,this.bottom,this.left,this.right)},Ln.prototype.toJSON=function(){return{top:this.top,bottom:this.bottom,left:this.left,right:this.right}};var In=function(e,r,n,i,a){this.tileSize=512,this.maxValidLatitude=85.051129,this._renderWorldCopies=void 0===a||a,this._minZoom=e||0,this._maxZoom=r||22,this._minPitch=null==n?0:n,this._maxPitch=null==i?60:i,this.setMaxBounds(),this.width=0,this.height=0,this._center=new t.LngLat(0,0),this.zoom=0,this.angle=0,this._fov=.6435011087932844,this._pitch=0,this._unmodified=!0,this._edgeInsets=new Ln,this._posMatrixCache={},this._alignedPosMatrixCache={}},Pn={minZoom:{configurable:!0},maxZoom:{configurable:!0},minPitch:{configurable:!0},maxPitch:{configurable:!0},renderWorldCopies:{configurable:!0},worldSize:{configurable:!0},centerOffset:{configurable:!0},size:{configurable:!0},bearing:{configurable:!0},pitch:{configurable:!0},fov:{configurable:!0},zoom:{configurable:!0},center:{configurable:!0},padding:{configurable:!0},centerPoint:{configurable:!0},unmodified:{configurable:!0},point:{configurable:!0}};In.prototype.clone=function(){var t=new In(this._minZoom,this._maxZoom,this._minPitch,this.maxPitch,this._renderWorldCopies);return t.tileSize=this.tileSize,t.latRange=this.latRange,t.width=this.width,t.height=this.height,t._center=this._center,t.zoom=this.zoom,t.angle=this.angle,t._fov=this._fov,t._pitch=this._pitch,t._unmodified=this._unmodified,t._edgeInsets=this._edgeInsets.clone(),t._calcMatrices(),t},Pn.minZoom.get=function(){return this._minZoom},Pn.minZoom.set=function(t){this._minZoom!==t&&(this._minZoom=t,this.zoom=Math.max(this.zoom,t))},Pn.maxZoom.get=function(){return this._maxZoom},Pn.maxZoom.set=function(t){this._maxZoom!==t&&(this._maxZoom=t,this.zoom=Math.min(this.zoom,t))},Pn.minPitch.get=function(){return this._minPitch},Pn.minPitch.set=function(t){this._minPitch!==t&&(this._minPitch=t,this.pitch=Math.max(this.pitch,t))},Pn.maxPitch.get=function(){return this._maxPitch},Pn.maxPitch.set=function(t){this._maxPitch!==t&&(this._maxPitch=t,this.pitch=Math.min(this.pitch,t))},Pn.renderWorldCopies.get=function(){return this._renderWorldCopies},Pn.renderWorldCopies.set=function(t){void 0===t?t=!0:null===t&&(t=!1),this._renderWorldCopies=t},Pn.worldSize.get=function(){return this.tileSize*this.scale},Pn.centerOffset.get=function(){return this.centerPoint._sub(this.size._div(2))},Pn.size.get=function(){return new t.Point(this.width,this.height)},Pn.bearing.get=function(){return-this.angle/Math.PI*180},Pn.bearing.set=function(e){var r=-t.wrap(e,-180,180)*Math.PI/180;this.angle!==r&&(this._unmodified=!1,this.angle=r,this._calcMatrices(),this.rotationMatrix=t.create$2(),t.rotate(this.rotationMatrix,this.rotationMatrix,this.angle))},Pn.pitch.get=function(){return this._pitch/Math.PI*180},Pn.pitch.set=function(e){var r=t.clamp(e,this.minPitch,this.maxPitch)/180*Math.PI;this._pitch!==r&&(this._unmodified=!1,this._pitch=r,this._calcMatrices())},Pn.fov.get=function(){return this._fov/Math.PI*180},Pn.fov.set=function(t){t=Math.max(.01,Math.min(60,t)),this._fov!==t&&(this._unmodified=!1,this._fov=t/180*Math.PI,this._calcMatrices())},Pn.zoom.get=function(){return this._zoom},Pn.zoom.set=function(t){var e=Math.min(Math.max(t,this.minZoom),this.maxZoom);this._zoom!==e&&(this._unmodified=!1,this._zoom=e,this.scale=this.zoomScale(e),this.tileZoom=Math.floor(e),this.zoomFraction=e-this.tileZoom,this._constrain(),this._calcMatrices())},Pn.center.get=function(){return this._center},Pn.center.set=function(t){t.lat===this._center.lat&&t.lng===this._center.lng||(this._unmodified=!1,this._center=t,this._constrain(),this._calcMatrices())},Pn.padding.get=function(){return this._edgeInsets.toJSON()},Pn.padding.set=function(t){this._edgeInsets.equals(t)||(this._unmodified=!1,this._edgeInsets.interpolate(this._edgeInsets,t,1),this._calcMatrices())},Pn.centerPoint.get=function(){return this._edgeInsets.getCenter(this.width,this.height)},In.prototype.isPaddingEqual=function(t){return this._edgeInsets.equals(t)},In.prototype.interpolatePadding=function(t,e,r){this._unmodified=!1,this._edgeInsets.interpolate(t,e,r),this._constrain(),this._calcMatrices()},In.prototype.coveringZoomLevel=function(t){var e=(t.roundZoom?Math.round:Math.floor)(this.zoom+this.scaleZoom(this.tileSize/t.tileSize));return Math.max(0,e)},In.prototype.getVisibleUnwrappedCoordinates=function(e){var r=[new t.UnwrappedTileID(0,e)];if(this._renderWorldCopies)for(var n=this.pointCoordinate(new t.Point(0,0)),i=this.pointCoordinate(new t.Point(this.width,0)),a=this.pointCoordinate(new t.Point(this.width,this.height)),o=this.pointCoordinate(new t.Point(0,this.height)),s=Math.floor(Math.min(n.x,i.x,a.x,o.x)),l=Math.floor(Math.max(n.x,i.x,a.x,o.x)),c=s-1;c<=l+1;c++)0!==c&&r.push(new t.UnwrappedTileID(c,e));return r},In.prototype.coveringTiles=function(e){var r=this.coveringZoomLevel(e),n=r;if(void 0!==e.minzoom&&re.maxzoom&&(r=e.maxzoom);var i=t.MercatorCoordinate.fromLngLat(this.center),a=Math.pow(2,r),o=[a*i.x,a*i.y,0],s=En.fromInvProjectionMatrix(this.invProjMatrix,this.worldSize,r),l=e.minzoom||0;this.pitch<=60&&this._edgeInsets.top<.1&&(l=r);var c=function(t){return{aabb:new Cn([t*a,0,0],[(t+1)*a,a,0]),zoom:0,x:0,y:0,wrap:t,fullyVisible:!1}},u=[],h=[],f=r,p=e.reparseOverscaled?n:r;if(this._renderWorldCopies)for(var d=1;d<=3;d++)u.push(c(-d)),u.push(c(d));for(u.push(c(0));u.length>0;){var m=u.pop(),g=m.x,y=m.y,v=m.fullyVisible;if(!v){var x=m.aabb.intersects(s);if(0===x)continue;v=2===x}var _=m.aabb.distanceX(o),b=m.aabb.distanceY(o),w=Math.max(Math.abs(_),Math.abs(b)),T=3+(1<T&&m.zoom>=l)h.push({tileID:new t.OverscaledTileID(m.zoom===f?p:m.zoom,m.wrap,m.zoom,g,y),distanceSq:t.sqrLen([o[0]-.5-g,o[1]-.5-y])});else for(var k=0;k<4;k++){var A=(g<<1)+k%2,M=(y<<1)+(k>>1);u.push({aabb:m.aabb.quadrant(k),zoom:m.zoom+1,x:A,y:M,wrap:m.wrap,fullyVisible:v})}}return h.sort((function(t,e){return t.distanceSq-e.distanceSq})).map((function(t){return t.tileID}))},In.prototype.resize=function(t,e){this.width=t,this.height=e,this.pixelsToGLUnits=[2/t,-2/e],this._constrain(),this._calcMatrices()},Pn.unmodified.get=function(){return this._unmodified},In.prototype.zoomScale=function(t){return Math.pow(2,t)},In.prototype.scaleZoom=function(t){return Math.log(t)/Math.LN2},In.prototype.project=function(e){var r=t.clamp(e.lat,-this.maxValidLatitude,this.maxValidLatitude);return new t.Point(t.mercatorXfromLng(e.lng)*this.worldSize,t.mercatorYfromLat(r)*this.worldSize)},In.prototype.unproject=function(e){return new t.MercatorCoordinate(e.x/this.worldSize,e.y/this.worldSize).toLngLat()},Pn.point.get=function(){return this.project(this.center)},In.prototype.setLocationAtPoint=function(e,r){var n=this.pointCoordinate(r),i=this.pointCoordinate(this.centerPoint),a=this.locationCoordinate(e),o=new t.MercatorCoordinate(a.x-(n.x-i.x),a.y-(n.y-i.y));this.center=this.coordinateLocation(o),this._renderWorldCopies&&(this.center=this.center.wrap())},In.prototype.locationPoint=function(t){return this.coordinatePoint(this.locationCoordinate(t))},In.prototype.pointLocation=function(t){return this.coordinateLocation(this.pointCoordinate(t))},In.prototype.locationCoordinate=function(e){return t.MercatorCoordinate.fromLngLat(e)},In.prototype.coordinateLocation=function(t){return t.toLngLat()},In.prototype.pointCoordinate=function(e){var r=[e.x,e.y,0,1],n=[e.x,e.y,1,1];t.transformMat4(r,r,this.pixelMatrixInverse),t.transformMat4(n,n,this.pixelMatrixInverse);var i=r[3],a=n[3],o=r[0]/i,s=n[0]/a,l=r[1]/i,c=n[1]/a,u=r[2]/i,h=n[2]/a,f=u===h?0:(0-u)/(h-u);return new t.MercatorCoordinate(t.number(o,s,f)/this.worldSize,t.number(l,c,f)/this.worldSize)},In.prototype.coordinatePoint=function(e){var r=[e.x*this.worldSize,e.y*this.worldSize,0,1];return t.transformMat4(r,r,this.pixelMatrix),new t.Point(r[0]/r[3],r[1]/r[3])},In.prototype.getBounds=function(){return(new t.LngLatBounds).extend(this.pointLocation(new t.Point(0,0))).extend(this.pointLocation(new t.Point(this.width,0))).extend(this.pointLocation(new t.Point(this.width,this.height))).extend(this.pointLocation(new t.Point(0,this.height)))},In.prototype.getMaxBounds=function(){return this.latRange&&2===this.latRange.length&&this.lngRange&&2===this.lngRange.length?new t.LngLatBounds([this.lngRange[0],this.latRange[0]],[this.lngRange[1],this.latRange[1]]):null},In.prototype.setMaxBounds=function(t){t?(this.lngRange=[t.getWest(),t.getEast()],this.latRange=[t.getSouth(),t.getNorth()],this._constrain()):(this.lngRange=null,this.latRange=[-this.maxValidLatitude,this.maxValidLatitude])},In.prototype.calculatePosMatrix=function(e,r){void 0===r&&(r=!1);var n=e.key,i=r?this._alignedPosMatrixCache:this._posMatrixCache;if(i[n])return i[n];var a=e.canonical,o=this.worldSize/this.zoomScale(a.z),s=a.x+Math.pow(2,a.z)*e.wrap,l=t.identity(new Float64Array(16));return t.translate(l,l,[s*o,a.y*o,0]),t.scale(l,l,[o/t.EXTENT,o/t.EXTENT,1]),t.multiply(l,r?this.alignedProjMatrix:this.projMatrix,l),i[n]=new Float32Array(l),i[n]},In.prototype.customLayerMatrix=function(){return this.mercatorMatrix.slice()},In.prototype._constrain=function(){if(this.center&&this.width&&this.height&&!this._constraining){this._constraining=!0;var e,r,n,i,a=-90,o=90,s=-180,l=180,c=this.size,u=this._unmodified;if(this.latRange){var h=this.latRange;a=t.mercatorYfromLat(h[1])*this.worldSize,e=(o=t.mercatorYfromLat(h[0])*this.worldSize)-ao&&(i=o-g)}if(this.lngRange){var y=p.x,v=c.x/2;y-vl&&(n=l-v)}void 0===n&&void 0===i||(this.center=this.unproject(new t.Point(void 0!==n?n:p.x,void 0!==i?i:p.y))),this._unmodified=u,this._constraining=!1}},In.prototype._calcMatrices=function(){if(this.height){var e=this._fov/2,r=this.centerOffset;this.cameraToCenterDistance=.5/Math.tan(e)*this.height;var n=Math.PI/2+this._pitch,i=this._fov*(.5+r.y/this.height),a=Math.sin(i)*this.cameraToCenterDistance/Math.sin(t.clamp(Math.PI-n-i,.01,Math.PI-.01)),o=this.point,s=o.x,l=o.y,c=1.01*(Math.cos(Math.PI/2-this._pitch)*a+this.cameraToCenterDistance),u=this.height/50,h=new Float64Array(16);t.perspective(h,this._fov,this.width/this.height,u,c),h[8]=2*-r.x/this.width,h[9]=2*r.y/this.height,t.scale(h,h,[1,-1,1]),t.translate(h,h,[0,0,-this.cameraToCenterDistance]),t.rotateX(h,h,this._pitch),t.rotateZ(h,h,this.angle),t.translate(h,h,[-s,-l,0]),this.mercatorMatrix=t.scale([],h,[this.worldSize,this.worldSize,this.worldSize]),t.scale(h,h,[1,1,t.mercatorZfromAltitude(1,this.center.lat)*this.worldSize,1]),this.projMatrix=h,this.invProjMatrix=t.invert([],this.projMatrix);var f=this.width%2/2,p=this.height%2/2,d=Math.cos(this.angle),m=Math.sin(this.angle),g=s-Math.round(s)+d*f+m*p,y=l-Math.round(l)+d*p+m*f,v=new Float64Array(h);if(t.translate(v,v,[g>.5?g-1:g,y>.5?y-1:y,0]),this.alignedProjMatrix=v,h=t.create(),t.scale(h,h,[this.width/2,-this.height/2,1]),t.translate(h,h,[1,-1,0]),this.labelPlaneMatrix=h,h=t.create(),t.scale(h,h,[1,-1,1]),t.translate(h,h,[-1,-1,0]),t.scale(h,h,[2/this.width,2/this.height,1]),this.glCoordMatrix=h,this.pixelMatrix=t.multiply(new Float64Array(16),this.labelPlaneMatrix,this.projMatrix),!(h=t.invert(new Float64Array(16),this.pixelMatrix)))throw new Error(\"failed to invert matrix\");this.pixelMatrixInverse=h,this._posMatrixCache={},this._alignedPosMatrixCache={}}},In.prototype.maxPitchScaleFactor=function(){if(!this.pixelMatrixInverse)return 1;var e=this.pointCoordinate(new t.Point(0,0)),r=[e.x*this.worldSize,e.y*this.worldSize,0,1];return t.transformMat4(r,r,this.pixelMatrix)[3]/this.cameraToCenterDistance},In.prototype.getCameraPoint=function(){var e=this._pitch,r=Math.tan(e)*(this.cameraToCenterDistance||1);return this.centerPoint.add(new t.Point(0,r))},In.prototype.getCameraQueryGeometry=function(e){var r=this.getCameraPoint();if(1===e.length)return[e[0],r];for(var n=r.x,i=r.y,a=r.x,o=r.y,s=0,l=e;s=3&&!t.some((function(t){return isNaN(t)}))){var e=this._map.dragRotate.isEnabled()&&this._map.touchZoomRotate.isEnabled()?+(t[3]||0):this._map.getBearing();return this._map.jumpTo({center:[+t[2],+t[1]],zoom:+t[0],bearing:e,pitch:+(t[4]||0)}),!0}return!1},zn.prototype._updateHashUnthrottled=function(){var e=t.window.location.href.replace(/(#.+)?$/,this.getHashString());try{t.window.history.replaceState(t.window.history.state,null,e)}catch(t){}};var On={linearity:.3,easing:t.bezier(0,0,.3,1)},Dn=t.extend({deceleration:2500,maxSpeed:1400},On),Rn=t.extend({deceleration:20,maxSpeed:1400},On),Fn=t.extend({deceleration:1e3,maxSpeed:360},On),Bn=t.extend({deceleration:1e3,maxSpeed:90},On),Nn=function(t){this._map=t,this.clear()};function jn(t,e){(!t.duration||t.duration0&&r-e[0].time>160;)e.shift()},Nn.prototype._onMoveEnd=function(e){if(this._drainInertiaBuffer(),!(this._inertiaBuffer.length<2)){for(var r={zoom:0,bearing:0,pitch:0,pan:new t.Point(0,0),pinchAround:void 0,around:void 0},n=0,i=this._inertiaBuffer;n=this._clickTolerance||this._map.fire(new Vn(t.type,this._map,t))},Gn.prototype.dblclick=function(t){return this._firePreventable(new Vn(t.type,this._map,t))},Gn.prototype.mouseover=function(t){this._map.fire(new Vn(t.type,this._map,t))},Gn.prototype.mouseout=function(t){this._map.fire(new Vn(t.type,this._map,t))},Gn.prototype.touchstart=function(t){return this._firePreventable(new qn(t.type,this._map,t))},Gn.prototype.touchmove=function(t){this._map.fire(new qn(t.type,this._map,t))},Gn.prototype.touchend=function(t){this._map.fire(new qn(t.type,this._map,t))},Gn.prototype.touchcancel=function(t){this._map.fire(new qn(t.type,this._map,t))},Gn.prototype._firePreventable=function(t){if(this._map.fire(t),t.defaultPrevented)return{}},Gn.prototype.isEnabled=function(){return!0},Gn.prototype.isActive=function(){return!1},Gn.prototype.enable=function(){},Gn.prototype.disable=function(){};var Zn=function(t){this._map=t};Zn.prototype.reset=function(){this._delayContextMenu=!1,delete this._contextMenuEvent},Zn.prototype.mousemove=function(t){this._map.fire(new Vn(t.type,this._map,t))},Zn.prototype.mousedown=function(){this._delayContextMenu=!0},Zn.prototype.mouseup=function(){this._delayContextMenu=!1,this._contextMenuEvent&&(this._map.fire(new Vn(\"contextmenu\",this._map,this._contextMenuEvent)),delete this._contextMenuEvent)},Zn.prototype.contextmenu=function(t){this._delayContextMenu?this._contextMenuEvent=t:this._map.fire(new Vn(t.type,this._map,t)),this._map.listens(\"contextmenu\")&&t.preventDefault()},Zn.prototype.isEnabled=function(){return!0},Zn.prototype.isActive=function(){return!1},Zn.prototype.enable=function(){},Zn.prototype.disable=function(){};var Wn=function(t,e){this._map=t,this._el=t.getCanvasContainer(),this._container=t.getContainer(),this._clickTolerance=e.clickTolerance||1};function Yn(t,e){for(var r={},n=0;nthis.numTouches)&&(this.aborted=!0),this.aborted||(void 0===this.startTime&&(this.startTime=e.timeStamp),n.length===this.numTouches&&(this.centroid=function(e){for(var r=new t.Point(0,0),n=0,i=e;n30)&&(this.aborted=!0)}}},Xn.prototype.touchend=function(t,e,r){if((!this.centroid||t.timeStamp-this.startTime>500)&&(this.aborted=!0),0===r.length){var n=!this.aborted&&this.centroid;if(this.reset(),n)return n}};var $n=function(t){this.singleTap=new Xn(t),this.numTaps=t.numTaps,this.reset()};$n.prototype.reset=function(){this.lastTime=1/0,delete this.lastTap,this.count=0,this.singleTap.reset()},$n.prototype.touchstart=function(t,e,r){this.singleTap.touchstart(t,e,r)},$n.prototype.touchmove=function(t,e,r){this.singleTap.touchmove(t,e,r)},$n.prototype.touchend=function(t,e,r){var n=this.singleTap.touchend(t,e,r);if(n){var i=t.timeStamp-this.lastTime<500,a=!this.lastTap||this.lastTap.dist(n)<30;if(i&&a||this.reset(),this.count++,this.lastTime=t.timeStamp,this.lastTap=n,this.count===this.numTaps)return this.reset(),n}};var Jn=function(){this._zoomIn=new $n({numTouches:1,numTaps:2}),this._zoomOut=new $n({numTouches:2,numTaps:1}),this.reset()};Jn.prototype.reset=function(){this._active=!1,this._zoomIn.reset(),this._zoomOut.reset()},Jn.prototype.touchstart=function(t,e,r){this._zoomIn.touchstart(t,e,r),this._zoomOut.touchstart(t,e,r)},Jn.prototype.touchmove=function(t,e,r){this._zoomIn.touchmove(t,e,r),this._zoomOut.touchmove(t,e,r)},Jn.prototype.touchend=function(t,e,r){var n=this,i=this._zoomIn.touchend(t,e,r),a=this._zoomOut.touchend(t,e,r);return i?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()+1,around:e.unproject(i)},{originalEvent:t})}}):a?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()-1,around:e.unproject(a)},{originalEvent:t})}}):void 0},Jn.prototype.touchcancel=function(){this.reset()},Jn.prototype.enable=function(){this._enabled=!0},Jn.prototype.disable=function(){this._enabled=!1,this.reset()},Jn.prototype.isEnabled=function(){return this._enabled},Jn.prototype.isActive=function(){return this._active};var Kn={};Kn[0]=1,Kn[2]=2;var Qn=function(t){this.reset(),this._clickTolerance=t.clickTolerance||1};Qn.prototype.reset=function(){this._active=!1,this._moved=!1,delete this._lastPoint,delete this._eventButton},Qn.prototype._correctButton=function(t,e){return!1},Qn.prototype._move=function(t,e){return{}},Qn.prototype.mousedown=function(t,e){if(!this._lastPoint){var n=r.mouseButton(t);this._correctButton(t,n)&&(this._lastPoint=e,this._eventButton=n)}},Qn.prototype.mousemoveWindow=function(t,e){var r=this._lastPoint;if(r)if(t.preventDefault(),function(t,e){var r=Kn[e];return void 0===t.buttons||(t.buttons&r)!==r}(t,this._eventButton))this.reset();else if(this._moved||!(e.dist(r)0&&(this._active=!0);var i=Yn(n,r),a=new t.Point(0,0),o=new t.Point(0,0),s=0;for(var l in i){var c=i[l],u=this._touches[l];u&&(a._add(c),o._add(c.sub(u)),s++,i[l]=c)}if(this._touches=i,!(sMath.abs(t.x)}var hi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),this._valid=void 0,delete this._firstMove,delete this._lastPoints},e.prototype._start=function(t){this._lastPoints=t,ui(t[0].sub(t[1]))&&(this._valid=!1)},e.prototype._move=function(t,e,r){var n=t[0].sub(this._lastPoints[0]),i=t[1].sub(this._lastPoints[1]);if(this._valid=this.gestureBeginsVertically(n,i,r.timeStamp),this._valid)return this._lastPoints=t,this._active=!0,{pitchDelta:(n.y+i.y)/2*-.5}},e.prototype.gestureBeginsVertically=function(t,e,r){if(void 0!==this._valid)return this._valid;var n=t.mag()>=2,i=e.mag()>=2;if(n||i){if(!n||!i)return void 0===this._firstMove&&(this._firstMove=r),r-this._firstMove<100&&void 0;var a=t.y>0==e.y>0;return ui(t)&&ui(e)&&a}},e}(ii),fi={panStep:100,bearingStep:15,pitchStep:10},pi=function(){var t=fi;this._panStep=t.panStep,this._bearingStep=t.bearingStep,this._pitchStep=t.pitchStep,this._rotationDisabled=!1};function di(t){return t*(2-t)}pi.prototype.reset=function(){this._active=!1},pi.prototype.keydown=function(t){var e=this;if(!(t.altKey||t.ctrlKey||t.metaKey)){var r=0,n=0,i=0,a=0,o=0;switch(t.keyCode){case 61:case 107:case 171:case 187:r=1;break;case 189:case 109:case 173:r=-1;break;case 37:t.shiftKey?n=-1:(t.preventDefault(),a=-1);break;case 39:t.shiftKey?n=1:(t.preventDefault(),a=1);break;case 38:t.shiftKey?i=1:(t.preventDefault(),o=-1);break;case 40:t.shiftKey?i=-1:(t.preventDefault(),o=1);break;default:return}return this._rotationDisabled&&(n=0,i=0),{cameraAnimation:function(s){var l=s.getZoom();s.easeTo({duration:300,easeId:\"keyboardHandler\",easing:di,zoom:r?Math.round(l)+r*(t.shiftKey?2:1):l,bearing:s.getBearing()+n*e._bearingStep,pitch:s.getPitch()+i*e._pitchStep,offset:[-a*e._panStep,-o*e._panStep],center:s.getCenter()},{originalEvent:t})}}}},pi.prototype.enable=function(){this._enabled=!0},pi.prototype.disable=function(){this._enabled=!1,this.reset()},pi.prototype.isEnabled=function(){return this._enabled},pi.prototype.isActive=function(){return this._active},pi.prototype.disableRotation=function(){this._rotationDisabled=!0},pi.prototype.enableRotation=function(){this._rotationDisabled=!1};var mi=4.000244140625,gi=function(e,r){this._map=e,this._el=e.getCanvasContainer(),this._handler=r,this._delta=0,this._defaultZoomRate=.01,this._wheelZoomRate=.0022222222222222222,t.bindAll([\"_onTimeout\"],this)};gi.prototype.setZoomRate=function(t){this._defaultZoomRate=t},gi.prototype.setWheelZoomRate=function(t){this._wheelZoomRate=t},gi.prototype.isEnabled=function(){return!!this._enabled},gi.prototype.isActive=function(){return!!this._active||void 0!==this._finishTimeout},gi.prototype.isZooming=function(){return!!this._zooming},gi.prototype.enable=function(t){this.isEnabled()||(this._enabled=!0,this._aroundCenter=t&&\"center\"===t.around)},gi.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},gi.prototype.wheel=function(e){if(this.isEnabled()){var r=e.deltaMode===t.window.WheelEvent.DOM_DELTA_LINE?40*e.deltaY:e.deltaY,n=t.browser.now(),i=n-(this._lastWheelEventTime||0);this._lastWheelEventTime=n,0!==r&&r%mi==0?this._type=\"wheel\":0!==r&&Math.abs(r)<4?this._type=\"trackpad\":i>400?(this._type=null,this._lastValue=r,this._timeout=setTimeout(this._onTimeout,40,e)):this._type||(this._type=Math.abs(i*r)<200?\"trackpad\":\"wheel\",this._timeout&&(clearTimeout(this._timeout),this._timeout=null,r+=this._lastValue)),e.shiftKey&&r&&(r/=4),this._type&&(this._lastWheelEvent=e,this._delta-=r,this._active||this._start(e)),e.preventDefault()}},gi.prototype._onTimeout=function(t){this._type=\"wheel\",this._delta-=this._lastValue,this._active||this._start(t)},gi.prototype._start=function(e){if(this._delta){this._frameId&&(this._frameId=null),this._active=!0,this.isZooming()||(this._zooming=!0),this._finishTimeout&&(clearTimeout(this._finishTimeout),delete this._finishTimeout);var n=r.mousePos(this._el,e);this._around=t.LngLat.convert(this._aroundCenter?this._map.getCenter():this._map.unproject(n)),this._aroundPoint=this._map.transform.locationPoint(this._around),this._frameId||(this._frameId=!0,this._handler._triggerRenderFrame())}},gi.prototype.renderFrame=function(){var e=this;if(this._frameId&&(this._frameId=null,this.isActive())){var r=this._map.transform;if(0!==this._delta){var n=\"wheel\"===this._type&&Math.abs(this._delta)>mi?this._wheelZoomRate:this._defaultZoomRate,i=2/(1+Math.exp(-Math.abs(this._delta*n)));this._delta<0&&0!==i&&(i=1/i);var a=\"number\"==typeof this._targetZoom?r.zoomScale(this._targetZoom):r.scale;this._targetZoom=Math.min(r.maxZoom,Math.max(r.minZoom,r.scaleZoom(a*i))),\"wheel\"===this._type&&(this._startZoom=r.zoom,this._easing=this._smoothOutEasing(200)),this._delta=0}var o,s=\"number\"==typeof this._targetZoom?this._targetZoom:r.zoom,l=this._startZoom,c=this._easing,u=!1;if(\"wheel\"===this._type&&l&&c){var h=Math.min((t.browser.now()-this._lastWheelEventTime)/200,1),f=c(h);o=t.number(l,s,f),h<1?this._frameId||(this._frameId=!0):u=!0}else o=s,u=!0;return this._active=!0,u&&(this._active=!1,this._finishTimeout=setTimeout((function(){e._zooming=!1,e._handler._triggerRenderFrame(),delete e._targetZoom,delete e._finishTimeout}),200)),{noInertia:!0,needsRenderFrame:!u,zoomDelta:o-r.zoom,around:this._aroundPoint,originalEvent:this._lastWheelEvent}}},gi.prototype._smoothOutEasing=function(e){var r=t.ease;if(this._prevEase){var n=this._prevEase,i=(t.browser.now()-n.start)/n.duration,a=n.easing(i+.01)-n.easing(i),o=.27/Math.sqrt(a*a+1e-4)*.01,s=Math.sqrt(.0729-o*o);r=t.bezier(o,s,.25,1)}return this._prevEase={start:t.browser.now(),duration:e,easing:r},r},gi.prototype.reset=function(){this._active=!1};var yi=function(t,e){this._clickZoom=t,this._tapZoom=e};yi.prototype.enable=function(){this._clickZoom.enable(),this._tapZoom.enable()},yi.prototype.disable=function(){this._clickZoom.disable(),this._tapZoom.disable()},yi.prototype.isEnabled=function(){return this._clickZoom.isEnabled()&&this._tapZoom.isEnabled()},yi.prototype.isActive=function(){return this._clickZoom.isActive()||this._tapZoom.isActive()};var vi=function(){this.reset()};vi.prototype.reset=function(){this._active=!1},vi.prototype.dblclick=function(t,e){return t.preventDefault(),{cameraAnimation:function(r){r.easeTo({duration:300,zoom:r.getZoom()+(t.shiftKey?-1:1),around:r.unproject(e)},{originalEvent:t})}}},vi.prototype.enable=function(){this._enabled=!0},vi.prototype.disable=function(){this._enabled=!1,this.reset()},vi.prototype.isEnabled=function(){return this._enabled},vi.prototype.isActive=function(){return this._active};var xi=function(){this._tap=new $n({numTouches:1,numTaps:1}),this.reset()};xi.prototype.reset=function(){this._active=!1,delete this._swipePoint,delete this._swipeTouch,delete this._tapTime,this._tap.reset()},xi.prototype.touchstart=function(t,e,r){this._swipePoint||(this._tapTime&&t.timeStamp-this._tapTime>500&&this.reset(),this._tapTime?r.length>0&&(this._swipePoint=e[0],this._swipeTouch=r[0].identifier):this._tap.touchstart(t,e,r))},xi.prototype.touchmove=function(t,e,r){if(this._tapTime){if(this._swipePoint){if(r[0].identifier!==this._swipeTouch)return;var n=e[0],i=n.y-this._swipePoint.y;return this._swipePoint=n,t.preventDefault(),this._active=!0,{zoomDelta:i/128}}}else this._tap.touchmove(t,e,r)},xi.prototype.touchend=function(t,e,r){this._tapTime?this._swipePoint&&0===r.length&&this.reset():this._tap.touchend(t,e,r)&&(this._tapTime=t.timeStamp)},xi.prototype.touchcancel=function(){this.reset()},xi.prototype.enable=function(){this._enabled=!0},xi.prototype.disable=function(){this._enabled=!1,this.reset()},xi.prototype.isEnabled=function(){return this._enabled},xi.prototype.isActive=function(){return this._active};var _i=function(t,e,r){this._el=t,this._mousePan=e,this._touchPan=r};_i.prototype.enable=function(t){this._inertiaOptions=t||{},this._mousePan.enable(),this._touchPan.enable(),this._el.classList.add(\"mapboxgl-touch-drag-pan\")},_i.prototype.disable=function(){this._mousePan.disable(),this._touchPan.disable(),this._el.classList.remove(\"mapboxgl-touch-drag-pan\")},_i.prototype.isEnabled=function(){return this._mousePan.isEnabled()&&this._touchPan.isEnabled()},_i.prototype.isActive=function(){return this._mousePan.isActive()||this._touchPan.isActive()};var bi=function(t,e,r){this._pitchWithRotate=t.pitchWithRotate,this._mouseRotate=e,this._mousePitch=r};bi.prototype.enable=function(){this._mouseRotate.enable(),this._pitchWithRotate&&this._mousePitch.enable()},bi.prototype.disable=function(){this._mouseRotate.disable(),this._mousePitch.disable()},bi.prototype.isEnabled=function(){return this._mouseRotate.isEnabled()&&(!this._pitchWithRotate||this._mousePitch.isEnabled())},bi.prototype.isActive=function(){return this._mouseRotate.isActive()||this._mousePitch.isActive()};var wi=function(t,e,r,n){this._el=t,this._touchZoom=e,this._touchRotate=r,this._tapDragZoom=n,this._rotationDisabled=!1,this._enabled=!0};wi.prototype.enable=function(t){this._touchZoom.enable(t),this._rotationDisabled||this._touchRotate.enable(t),this._tapDragZoom.enable(),this._el.classList.add(\"mapboxgl-touch-zoom-rotate\")},wi.prototype.disable=function(){this._touchZoom.disable(),this._touchRotate.disable(),this._tapDragZoom.disable(),this._el.classList.remove(\"mapboxgl-touch-zoom-rotate\")},wi.prototype.isEnabled=function(){return this._touchZoom.isEnabled()&&(this._rotationDisabled||this._touchRotate.isEnabled())&&this._tapDragZoom.isEnabled()},wi.prototype.isActive=function(){return this._touchZoom.isActive()||this._touchRotate.isActive()||this._tapDragZoom.isActive()},wi.prototype.disableRotation=function(){this._rotationDisabled=!0,this._touchRotate.disable()},wi.prototype.enableRotation=function(){this._rotationDisabled=!1,this._touchZoom.isEnabled()&&this._touchRotate.enable()};var Ti=function(t){return t.zoom||t.drag||t.pitch||t.rotate},ki=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(t.Event);function Ai(t){return t.panDelta&&t.panDelta.mag()||t.zoomDelta||t.bearingDelta||t.pitchDelta}var Mi=function(e,n){this._map=e,this._el=this._map.getCanvasContainer(),this._handlers=[],this._handlersById={},this._changes=[],this._inertia=new Nn(e),this._bearingSnap=n.bearingSnap,this._previousActiveHandlers={},this._eventsInProgress={},this._addDefaultHandlers(n),t.bindAll([\"handleEvent\",\"handleWindowEvent\"],this);var i=this._el;this._listeners=[[i,\"touchstart\",{passive:!0}],[i,\"touchmove\",{passive:!1}],[i,\"touchend\",void 0],[i,\"touchcancel\",void 0],[i,\"mousedown\",void 0],[i,\"mousemove\",void 0],[i,\"mouseup\",void 0],[t.window.document,\"mousemove\",{capture:!0}],[t.window.document,\"mouseup\",void 0],[i,\"mouseover\",void 0],[i,\"mouseout\",void 0],[i,\"dblclick\",void 0],[i,\"click\",void 0],[i,\"keydown\",{capture:!1}],[i,\"keyup\",void 0],[i,\"wheel\",{passive:!1}],[i,\"contextmenu\",void 0],[t.window,\"blur\",void 0]];for(var a=0,o=this._listeners;aa?Math.min(2,b):Math.max(.5,b),w=Math.pow(g,1-e),T=i.unproject(x.add(_.mult(e*w)).mult(m));i.setLocationAtPoint(i.renderWorldCopies?T.wrap():T,d)}n._fireMoveEvents(r)}),(function(t){n._afterEase(r,t)}),e),this},r.prototype._prepareEase=function(e,r,n){void 0===n&&(n={}),this._moving=!0,r||n.moving||this.fire(new t.Event(\"movestart\",e)),this._zooming&&!n.zooming&&this.fire(new t.Event(\"zoomstart\",e)),this._rotating&&!n.rotating&&this.fire(new t.Event(\"rotatestart\",e)),this._pitching&&!n.pitching&&this.fire(new t.Event(\"pitchstart\",e))},r.prototype._fireMoveEvents=function(e){this.fire(new t.Event(\"move\",e)),this._zooming&&this.fire(new t.Event(\"zoom\",e)),this._rotating&&this.fire(new t.Event(\"rotate\",e)),this._pitching&&this.fire(new t.Event(\"pitch\",e))},r.prototype._afterEase=function(e,r){if(!this._easeId||!r||this._easeId!==r){delete this._easeId;var n=this._zooming,i=this._rotating,a=this._pitching;this._moving=!1,this._zooming=!1,this._rotating=!1,this._pitching=!1,this._padding=!1,n&&this.fire(new t.Event(\"zoomend\",e)),i&&this.fire(new t.Event(\"rotateend\",e)),a&&this.fire(new t.Event(\"pitchend\",e)),this.fire(new t.Event(\"moveend\",e))}},r.prototype.flyTo=function(e,r){var n=this;if(!e.essential&&t.browser.prefersReducedMotion){var i=t.pick(e,[\"center\",\"zoom\",\"bearing\",\"pitch\",\"around\"]);return this.jumpTo(i,r)}this.stop(),e=t.extend({offset:[0,0],speed:1.2,curve:1.42,easing:t.ease},e);var a=this.transform,o=this.getZoom(),s=this.getBearing(),l=this.getPitch(),c=this.getPadding(),u=\"zoom\"in e?t.clamp(+e.zoom,a.minZoom,a.maxZoom):o,h=\"bearing\"in e?this._normalizeBearing(e.bearing,s):s,f=\"pitch\"in e?+e.pitch:l,p=\"padding\"in e?e.padding:a.padding,d=a.zoomScale(u-o),m=t.Point.convert(e.offset),g=a.centerPoint.add(m),y=a.pointLocation(g),v=t.LngLat.convert(e.center||y);this._normalizeCenter(v);var x=a.project(y),_=a.project(v).sub(x),b=e.curve,w=Math.max(a.width,a.height),T=w/d,k=_.mag();if(\"minZoom\"in e){var A=t.clamp(Math.min(e.minZoom,o,u),a.minZoom,a.maxZoom),M=w/a.zoomScale(A-o);b=Math.sqrt(M/k*2)}var S=b*b;function E(t){var e=(T*T-w*w+(t?-1:1)*S*S*k*k)/(2*(t?T:w)*S*k);return Math.log(Math.sqrt(e*e+1)-e)}function C(t){return(Math.exp(t)-Math.exp(-t))/2}function L(t){return(Math.exp(t)+Math.exp(-t))/2}var I=E(0),P=function(t){return L(I)/L(I+b*t)},z=function(t){return w*((L(I)*(C(e=I+b*t)/L(e))-C(I))/S)/k;var e},O=(E(1)-I)/b;if(Math.abs(k)<1e-6||!isFinite(O)){if(Math.abs(w-T)<1e-6)return this.easeTo(e,r);var D=Te.maxDuration&&(e.duration=0),this._zooming=!0,this._rotating=s!==h,this._pitching=f!==l,this._padding=!a.isPaddingEqual(p),this._prepareEase(r,!1),this._ease((function(e){var i=e*O,d=1/P(i);a.zoom=1===e?u:o+a.scaleZoom(d),n._rotating&&(a.bearing=t.number(s,h,e)),n._pitching&&(a.pitch=t.number(l,f,e)),n._padding&&(a.interpolatePadding(c,p,e),g=a.centerPoint.add(m));var y=1===e?v:a.unproject(x.add(_.mult(z(i))).mult(d));a.setLocationAtPoint(a.renderWorldCopies?y.wrap():y,g),n._fireMoveEvents(r)}),(function(){return n._afterEase(r)}),e),this},r.prototype.isEasing=function(){return!!this._easeFrameId},r.prototype.stop=function(){return this._stop()},r.prototype._stop=function(t,e){if(this._easeFrameId&&(this._cancelRenderFrame(this._easeFrameId),delete this._easeFrameId,delete this._onEaseFrame),this._onEaseEnd){var r=this._onEaseEnd;delete this._onEaseEnd,r.call(this,e)}if(!t){var n=this.handlers;n&&n.stop(!1)}return this},r.prototype._ease=function(e,r,n){!1===n.animate||0===n.duration?(e(1),r()):(this._easeStart=t.browser.now(),this._easeOptions=n,this._onEaseFrame=e,this._onEaseEnd=r,this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback))},r.prototype._renderFrameCallback=function(){var e=Math.min((t.browser.now()-this._easeStart)/this._easeOptions.duration,1);this._onEaseFrame(this._easeOptions.easing(e)),e<1?this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback):this.stop()},r.prototype._normalizeBearing=function(e,r){e=t.wrap(e,-180,180);var n=Math.abs(e-r);return Math.abs(e-360-r)180?-360:r<-180?360:0}},r}(t.Evented),Ei=function(e){void 0===e&&(e={}),this.options=e,t.bindAll([\"_toggleAttribution\",\"_updateEditLink\",\"_updateData\",\"_updateCompact\"],this)};Ei.prototype.getDefaultPosition=function(){return\"bottom-right\"},Ei.prototype.onAdd=function(t){var e=this.options&&this.options.compact;return this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-attrib\"),this._compactButton=r.create(\"button\",\"mapboxgl-ctrl-attrib-button\",this._container),this._compactButton.addEventListener(\"click\",this._toggleAttribution),this._setElementTitle(this._compactButton,\"ToggleAttribution\"),this._innerContainer=r.create(\"div\",\"mapboxgl-ctrl-attrib-inner\",this._container),this._innerContainer.setAttribute(\"role\",\"list\"),e&&this._container.classList.add(\"mapboxgl-compact\"),this._updateAttributions(),this._updateEditLink(),this._map.on(\"styledata\",this._updateData),this._map.on(\"sourcedata\",this._updateData),this._map.on(\"moveend\",this._updateEditLink),void 0===e&&(this._map.on(\"resize\",this._updateCompact),this._updateCompact()),this._container},Ei.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"styledata\",this._updateData),this._map.off(\"sourcedata\",this._updateData),this._map.off(\"moveend\",this._updateEditLink),this._map.off(\"resize\",this._updateCompact),this._map=void 0,this._attribHTML=void 0},Ei.prototype._setElementTitle=function(t,e){var r=this._map._getUIString(\"AttributionControl.\"+e);t.title=r,t.setAttribute(\"aria-label\",r)},Ei.prototype._toggleAttribution=function(){this._container.classList.contains(\"mapboxgl-compact-show\")?(this._container.classList.remove(\"mapboxgl-compact-show\"),this._compactButton.setAttribute(\"aria-pressed\",\"false\")):(this._container.classList.add(\"mapboxgl-compact-show\"),this._compactButton.setAttribute(\"aria-pressed\",\"true\"))},Ei.prototype._updateEditLink=function(){var e=this._editLink;e||(e=this._editLink=this._container.querySelector(\".mapbox-improve-map\"));var r=[{key:\"owner\",value:this.styleOwner},{key:\"id\",value:this.styleId},{key:\"access_token\",value:this._map._requestManager._customAccessToken||t.config.ACCESS_TOKEN}];if(e){var n=r.reduce((function(t,e,n){return e.value&&(t+=e.key+\"=\"+e.value+(n=0)return!1;return!0}))).join(\" | \");o!==this._attribHTML&&(this._attribHTML=o,t.length?(this._innerContainer.innerHTML=o,this._container.classList.remove(\"mapboxgl-attrib-empty\")):this._container.classList.add(\"mapboxgl-attrib-empty\"),this._editLink=null)}},Ei.prototype._updateCompact=function(){this._map.getCanvasContainer().offsetWidth<=640?this._container.classList.add(\"mapboxgl-compact\"):this._container.classList.remove(\"mapboxgl-compact\",\"mapboxgl-compact-show\")};var Ci=function(){t.bindAll([\"_updateLogo\"],this),t.bindAll([\"_updateCompact\"],this)};Ci.prototype.onAdd=function(t){this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl\");var e=r.create(\"a\",\"mapboxgl-ctrl-logo\");return e.target=\"_blank\",e.rel=\"noopener nofollow\",e.href=\"https://www.mapbox.com/\",e.setAttribute(\"aria-label\",this._map._getUIString(\"LogoControl.Title\")),e.setAttribute(\"rel\",\"noopener nofollow\"),this._container.appendChild(e),this._container.style.display=\"none\",this._map.on(\"sourcedata\",this._updateLogo),this._updateLogo(),this._map.on(\"resize\",this._updateCompact),this._updateCompact(),this._container},Ci.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"sourcedata\",this._updateLogo),this._map.off(\"resize\",this._updateCompact)},Ci.prototype.getDefaultPosition=function(){return\"bottom-left\"},Ci.prototype._updateLogo=function(t){t&&\"metadata\"!==t.sourceDataType||(this._container.style.display=this._logoRequired()?\"block\":\"none\")},Ci.prototype._logoRequired=function(){if(this._map.style){var t=this._map.style.sourceCaches;for(var e in t)if(t[e].getSource().mapbox_logo)return!0;return!1}},Ci.prototype._updateCompact=function(){var t=this._container.children;if(t.length){var e=t[0];this._map.getCanvasContainer().offsetWidth<250?e.classList.add(\"mapboxgl-compact\"):e.classList.remove(\"mapboxgl-compact\")}};var Li=function(){this._queue=[],this._id=0,this._cleared=!1,this._currentlyRunning=!1};Li.prototype.add=function(t){var e=++this._id;return this._queue.push({callback:t,id:e,cancelled:!1}),e},Li.prototype.remove=function(t){for(var e=this._currentlyRunning,r=0,n=e?this._queue.concat(e):this._queue;re.maxZoom)throw new Error(\"maxZoom must be greater than or equal to minZoom\");if(null!=e.minPitch&&null!=e.maxPitch&&e.minPitch>e.maxPitch)throw new Error(\"maxPitch must be greater than or equal to minPitch\");if(null!=e.minPitch&&e.minPitch<0)throw new Error(\"minPitch must be greater than or equal to 0\");if(null!=e.maxPitch&&e.maxPitch>Di)throw new Error(\"maxPitch must be less than or equal to 60\");var i=new In(e.minZoom,e.maxZoom,e.minPitch,e.maxPitch,e.renderWorldCopies);if(n.call(this,i,e),this._interactive=e.interactive,this._maxTileCacheSize=e.maxTileCacheSize,this._failIfMajorPerformanceCaveat=e.failIfMajorPerformanceCaveat,this._preserveDrawingBuffer=e.preserveDrawingBuffer,this._antialias=e.antialias,this._trackResize=e.trackResize,this._bearingSnap=e.bearingSnap,this._refreshExpiredTiles=e.refreshExpiredTiles,this._fadeDuration=e.fadeDuration,this._crossSourceCollisions=e.crossSourceCollisions,this._crossFadingFactor=1,this._collectResourceTiming=e.collectResourceTiming,this._renderTaskQueue=new Li,this._controls=[],this._mapId=t.uniqueId(),this._locale=t.extend({},Ii,e.locale),this._clickTolerance=e.clickTolerance,this._requestManager=new t.RequestManager(e.transformRequest,e.accessToken),\"string\"==typeof e.container){if(this._container=t.window.document.getElementById(e.container),!this._container)throw new Error(\"Container '\"+e.container+\"' not found.\")}else{if(!(e.container instanceof zi))throw new Error(\"Invalid type: 'container' must be a String or HTMLElement.\");this._container=e.container}if(e.maxBounds&&this.setMaxBounds(e.maxBounds),t.bindAll([\"_onWindowOnline\",\"_onWindowResize\",\"_onMapScroll\",\"_contextLost\",\"_contextRestored\"],this),this._setupContainer(),this._setupPainter(),void 0===this.painter)throw new Error(\"Failed to initialize WebGL.\");this.on(\"move\",(function(){return r._update(!1)})),this.on(\"moveend\",(function(){return r._update(!1)})),this.on(\"zoom\",(function(){return r._update(!0)})),void 0!==t.window&&(t.window.addEventListener(\"online\",this._onWindowOnline,!1),t.window.addEventListener(\"resize\",this._onWindowResize,!1),t.window.addEventListener(\"orientationchange\",this._onWindowResize,!1)),this.handlers=new Mi(this,e);var a=\"string\"==typeof e.hash&&e.hash||void 0;this._hash=e.hash&&new zn(a).addTo(this),this._hash&&this._hash._onHashChange()||(this.jumpTo({center:e.center,zoom:e.zoom,bearing:e.bearing,pitch:e.pitch}),e.bounds&&(this.resize(),this.fitBounds(e.bounds,t.extend({},e.fitBoundsOptions,{duration:0})))),this.resize(),this._localIdeographFontFamily=e.localIdeographFontFamily,e.style&&this.setStyle(e.style,{localIdeographFontFamily:e.localIdeographFontFamily}),e.attributionControl&&this.addControl(new Ei({customAttribution:e.customAttribution})),this.addControl(new Ci,e.logoPosition),this.on(\"style.load\",(function(){r.transform.unmodified&&r.jumpTo(r.style.stylesheet)})),this.on(\"data\",(function(e){r._update(\"style\"===e.dataType),r.fire(new t.Event(e.dataType+\"data\",e))})),this.on(\"dataloading\",(function(e){r.fire(new t.Event(e.dataType+\"dataloading\",e))}))}n&&(i.__proto__=n),i.prototype=Object.create(n&&n.prototype),i.prototype.constructor=i;var a={showTileBoundaries:{configurable:!0},showPadding:{configurable:!0},showCollisionBoxes:{configurable:!0},showOverdrawInspector:{configurable:!0},repaint:{configurable:!0},vertices:{configurable:!0},version:{configurable:!0}};return i.prototype._getMapId=function(){return this._mapId},i.prototype.addControl=function(e,r){if(void 0===r&&(r=e.getDefaultPosition?e.getDefaultPosition():\"top-right\"),!e||!e.onAdd)return this.fire(new t.ErrorEvent(new Error(\"Invalid argument to map.addControl(). Argument must be a control with onAdd and onRemove methods.\")));var n=e.onAdd(this);this._controls.push(e);var i=this._controlPositions[r];return-1!==r.indexOf(\"bottom\")?i.insertBefore(n,i.firstChild):i.appendChild(n),this},i.prototype.removeControl=function(e){if(!e||!e.onRemove)return this.fire(new t.ErrorEvent(new Error(\"Invalid argument to map.removeControl(). Argument must be a control with onAdd and onRemove methods.\")));var r=this._controls.indexOf(e);return r>-1&&this._controls.splice(r,1),e.onRemove(this),this},i.prototype.hasControl=function(t){return this._controls.indexOf(t)>-1},i.prototype.resize=function(e){var r=this._containerDimensions(),n=r[0],i=r[1];this._resizeCanvas(n,i),this.transform.resize(n,i),this.painter.resize(n,i);var a=!this._moving;return a&&(this.stop(),this.fire(new t.Event(\"movestart\",e)).fire(new t.Event(\"move\",e))),this.fire(new t.Event(\"resize\",e)),a&&this.fire(new t.Event(\"moveend\",e)),this},i.prototype.getBounds=function(){return this.transform.getBounds()},i.prototype.getMaxBounds=function(){return this.transform.getMaxBounds()},i.prototype.setMaxBounds=function(e){return this.transform.setMaxBounds(t.LngLatBounds.convert(e)),this._update()},i.prototype.setMinZoom=function(t){if((t=null==t?-2:t)>=-2&&t<=this.transform.maxZoom)return this.transform.minZoom=t,this._update(),this.getZoom()=this.transform.minZoom)return this.transform.maxZoom=t,this._update(),this.getZoom()>t&&this.setZoom(t),this;throw new Error(\"maxZoom must be greater than the current minZoom\")},i.prototype.getMaxZoom=function(){return this.transform.maxZoom},i.prototype.setMinPitch=function(t){if((t=null==t?0:t)<0)throw new Error(\"minPitch must be greater than or equal to 0\");if(t>=0&&t<=this.transform.maxPitch)return this.transform.minPitch=t,this._update(),this.getPitch()Di)throw new Error(\"maxPitch must be less than or equal to 60\");if(t>=this.transform.minPitch)return this.transform.maxPitch=t,this._update(),this.getPitch()>t&&this.setPitch(t),this;throw new Error(\"maxPitch must be greater than the current minPitch\")},i.prototype.getMaxPitch=function(){return this.transform.maxPitch},i.prototype.getRenderWorldCopies=function(){return this.transform.renderWorldCopies},i.prototype.setRenderWorldCopies=function(t){return this.transform.renderWorldCopies=t,this._update()},i.prototype.project=function(e){return this.transform.locationPoint(t.LngLat.convert(e))},i.prototype.unproject=function(e){return this.transform.pointLocation(t.Point.convert(e))},i.prototype.isMoving=function(){return this._moving||this.handlers.isMoving()},i.prototype.isZooming=function(){return this._zooming||this.handlers.isZooming()},i.prototype.isRotating=function(){return this._rotating||this.handlers.isRotating()},i.prototype._createDelegatedListener=function(t,e,r){var n,i=this;if(\"mouseenter\"===t||\"mouseover\"===t){var a=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){var o=i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[];o.length?a||(a=!0,r.call(i,new Vn(t,i,n.originalEvent,{features:o}))):a=!1},mouseout:function(){a=!1}}}}if(\"mouseleave\"===t||\"mouseout\"===t){var o=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){(i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[]).length?o=!0:o&&(o=!1,r.call(i,new Vn(t,i,n.originalEvent)))},mouseout:function(e){o&&(o=!1,r.call(i,new Vn(t,i,e.originalEvent)))}}}}return{layer:e,listener:r,delegates:(n={},n[t]=function(t){var n=i.getLayer(e)?i.queryRenderedFeatures(t.point,{layers:[e]}):[];n.length&&(t.features=n,r.call(i,t),delete t.features)},n)}},i.prototype.on=function(t,e,r){if(void 0===r)return n.prototype.on.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in this._delegatedListeners=this._delegatedListeners||{},this._delegatedListeners[t]=this._delegatedListeners[t]||[],this._delegatedListeners[t].push(i),i.delegates)this.on(a,i.delegates[a]);return this},i.prototype.once=function(t,e,r){if(void 0===r)return n.prototype.once.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in i.delegates)this.once(a,i.delegates[a]);return this},i.prototype.off=function(t,e,r){var i=this;if(void 0===r)return n.prototype.off.call(this,t,e);return this._delegatedListeners&&this._delegatedListeners[t]&&function(n){for(var a=n[t],o=0;o180;){var s=n.locationPoint(e);if(s.x>=0&&s.y>=0&&s.x<=n.width&&s.y<=n.height)break;e.lng>n.center.lng?e.lng-=360:e.lng+=360}return e}Ui.prototype.down=function(t,e){this.mouseRotate.mousedown(t,e),this.mousePitch&&this.mousePitch.mousedown(t,e),r.disableDrag()},Ui.prototype.move=function(t,e){var r=this.map,n=this.mouseRotate.mousemoveWindow(t,e);if(n&&n.bearingDelta&&r.setBearing(r.getBearing()+n.bearingDelta),this.mousePitch){var i=this.mousePitch.mousemoveWindow(t,e);i&&i.pitchDelta&&r.setPitch(r.getPitch()+i.pitchDelta)}},Ui.prototype.off=function(){var t=this.element;r.removeEventListener(t,\"mousedown\",this.mousedown),r.removeEventListener(t,\"touchstart\",this.touchstart,{passive:!1}),r.removeEventListener(t,\"touchmove\",this.touchmove),r.removeEventListener(t,\"touchend\",this.touchend),r.removeEventListener(t,\"touchcancel\",this.reset),this.offTemp()},Ui.prototype.offTemp=function(){r.enableDrag(),r.removeEventListener(t.window,\"mousemove\",this.mousemove),r.removeEventListener(t.window,\"mouseup\",this.mouseup)},Ui.prototype.mousedown=function(e){this.down(t.extend({},e,{ctrlKey:!0,preventDefault:function(){return e.preventDefault()}}),r.mousePos(this.element,e)),r.addEventListener(t.window,\"mousemove\",this.mousemove),r.addEventListener(t.window,\"mouseup\",this.mouseup)},Ui.prototype.mousemove=function(t){this.move(t,r.mousePos(this.element,t))},Ui.prototype.mouseup=function(t){this.mouseRotate.mouseupWindow(t),this.mousePitch&&this.mousePitch.mouseupWindow(t),this.offTemp()},Ui.prototype.touchstart=function(t){1!==t.targetTouches.length?this.reset():(this._startPos=this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.down({type:\"mousedown\",button:0,ctrlKey:!0,preventDefault:function(){return t.preventDefault()}},this._startPos))},Ui.prototype.touchmove=function(t){1!==t.targetTouches.length?this.reset():(this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.move({preventDefault:function(){return t.preventDefault()}},this._lastPos))},Ui.prototype.touchend=function(t){0===t.targetTouches.length&&this._startPos&&this._lastPos&&this._startPos.dist(this._lastPos)=r}this._isDragging&&(this._pos=e.point.sub(this._positionDelta),this._lngLat=this._map.unproject(this._pos),this.setLngLat(this._lngLat),this._element.style.pointerEvents=\"none\",\"pending\"===this._state&&(this._state=\"active\",this.fire(new t.Event(\"dragstart\"))),this.fire(new t.Event(\"drag\")))},n.prototype._onUp=function(){this._element.style.pointerEvents=\"auto\",this._positionDelta=null,this._pointerdownPos=null,this._isDragging=!1,this._map.off(\"mousemove\",this._onMove),this._map.off(\"touchmove\",this._onMove),\"active\"===this._state&&this.fire(new t.Event(\"dragend\")),this._state=\"inactive\"},n.prototype._addDragHandler=function(t){this._element.contains(t.originalEvent.target)&&(t.preventDefault(),this._positionDelta=t.point.sub(this._pos).add(this._offset),this._pointerdownPos=t.point,this._state=\"pending\",this._map.on(\"mousemove\",this._onMove),this._map.on(\"touchmove\",this._onMove),this._map.once(\"mouseup\",this._onUp),this._map.once(\"touchend\",this._onUp))},n.prototype.setDraggable=function(t){return this._draggable=!!t,this._map&&(t?(this._map.on(\"mousedown\",this._addDragHandler),this._map.on(\"touchstart\",this._addDragHandler)):(this._map.off(\"mousedown\",this._addDragHandler),this._map.off(\"touchstart\",this._addDragHandler))),this},n.prototype.isDraggable=function(){return this._draggable},n.prototype.setRotation=function(t){return this._rotation=t||0,this._update(),this},n.prototype.getRotation=function(){return this._rotation},n.prototype.setRotationAlignment=function(t){return this._rotationAlignment=t||\"auto\",this._update(),this},n.prototype.getRotationAlignment=function(){return this._rotationAlignment},n.prototype.setPitchAlignment=function(t){return this._pitchAlignment=t&&\"auto\"!==t?t:this._rotationAlignment,this._update(),this},n.prototype.getPitchAlignment=function(){return this._pitchAlignment},n}(t.Evented),Wi={positionOptions:{enableHighAccuracy:!1,maximumAge:0,timeout:6e3},fitBoundsOptions:{maxZoom:15},trackUserLocation:!1,showAccuracyCircle:!0,showUserLocation:!0};var Yi=0,Xi=!1,$i=function(e){function n(r){e.call(this),this.options=t.extend({},Wi,r),t.bindAll([\"_onSuccess\",\"_onError\",\"_onZoom\",\"_finish\",\"_setupUI\",\"_updateCamera\",\"_updateMarker\"],this)}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.onAdd=function(e){return this._map=e,this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),n=this._setupUI,void 0!==Gi?n(Gi):void 0!==t.window.navigator.permissions?t.window.navigator.permissions.query({name:\"geolocation\"}).then((function(t){Gi=\"denied\"!==t.state,n(Gi)})):(Gi=!!t.window.navigator.geolocation,n(Gi)),this._container;var n},n.prototype.onRemove=function(){void 0!==this._geolocationWatchID&&(t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0),this.options.showUserLocation&&this._userLocationDotMarker&&this._userLocationDotMarker.remove(),this.options.showAccuracyCircle&&this._accuracyCircleMarker&&this._accuracyCircleMarker.remove(),r.remove(this._container),this._map.off(\"zoom\",this._onZoom),this._map=void 0,Yi=0,Xi=!1},n.prototype._isOutOfMapMaxBounds=function(t){var e=this._map.getMaxBounds(),r=t.coords;return e&&(r.longitudee.getEast()||r.latitudee.getNorth())},n.prototype._setErrorState=function(){switch(this._watchState){case\"WAITING_ACTIVE\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\");break;case\"ACTIVE_LOCK\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\");break;case\"BACKGROUND\":this._watchState=\"BACKGROUND_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\")}},n.prototype._onSuccess=function(e){if(this._map){if(this._isOutOfMapMaxBounds(e))return this._setErrorState(),this.fire(new t.Event(\"outofmaxbounds\",e)),this._updateMarker(),void this._finish();if(this.options.trackUserLocation)switch(this._lastKnownPosition=e,this._watchState){case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"BACKGROUND\":case\"BACKGROUND_ERROR\":this._watchState=\"BACKGROUND\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\")}this.options.showUserLocation&&\"OFF\"!==this._watchState&&this._updateMarker(e),this.options.trackUserLocation&&\"ACTIVE_LOCK\"!==this._watchState||this._updateCamera(e),this.options.showUserLocation&&this._dotElement.classList.remove(\"mapboxgl-user-location-dot-stale\"),this.fire(new t.Event(\"geolocate\",e)),this._finish()}},n.prototype._updateCamera=function(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude),n=e.coords.accuracy,i=this._map.getBearing(),a=t.extend({bearing:i},this.options.fitBoundsOptions);this._map.fitBounds(r.toBounds(n),a,{geolocateSource:!0})},n.prototype._updateMarker=function(e){if(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude);this._accuracyCircleMarker.setLngLat(r).addTo(this._map),this._userLocationDotMarker.setLngLat(r).addTo(this._map),this._accuracy=e.coords.accuracy,this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()}else this._userLocationDotMarker.remove(),this._accuracyCircleMarker.remove()},n.prototype._updateCircleRadius=function(){var t=this._map._container.clientHeight/2,e=this._map.unproject([0,t]),r=this._map.unproject([1,t]),n=e.distanceTo(r),i=Math.ceil(2*this._accuracy/n);this._circleElement.style.width=i+\"px\",this._circleElement.style.height=i+\"px\"},n.prototype._onZoom=function(){this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()},n.prototype._onError=function(e){if(this._map){if(this.options.trackUserLocation)if(1===e.code){this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.disabled=!0;var r=this._map._getUIString(\"GeolocateControl.LocationNotAvailable\");this._geolocateButton.title=r,this._geolocateButton.setAttribute(\"aria-label\",r),void 0!==this._geolocationWatchID&&this._clearWatch()}else{if(3===e.code&&Xi)return;this._setErrorState()}\"OFF\"!==this._watchState&&this.options.showUserLocation&&this._dotElement.classList.add(\"mapboxgl-user-location-dot-stale\"),this.fire(new t.Event(\"error\",e)),this._finish()}},n.prototype._finish=function(){this._timeoutId&&clearTimeout(this._timeoutId),this._timeoutId=void 0},n.prototype._setupUI=function(e){var n=this;if(this._container.addEventListener(\"contextmenu\",(function(t){return t.preventDefault()})),this._geolocateButton=r.create(\"button\",\"mapboxgl-ctrl-geolocate\",this._container),r.create(\"span\",\"mapboxgl-ctrl-icon\",this._geolocateButton).setAttribute(\"aria-hidden\",!0),this._geolocateButton.type=\"button\",!1===e){t.warnOnce(\"Geolocation support is not available so the GeolocateControl will be disabled.\");var i=this._map._getUIString(\"GeolocateControl.LocationNotAvailable\");this._geolocateButton.disabled=!0,this._geolocateButton.title=i,this._geolocateButton.setAttribute(\"aria-label\",i)}else{var a=this._map._getUIString(\"GeolocateControl.FindMyLocation\");this._geolocateButton.title=a,this._geolocateButton.setAttribute(\"aria-label\",a)}this.options.trackUserLocation&&(this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this._watchState=\"OFF\"),this.options.showUserLocation&&(this._dotElement=r.create(\"div\",\"mapboxgl-user-location-dot\"),this._userLocationDotMarker=new Zi(this._dotElement),this._circleElement=r.create(\"div\",\"mapboxgl-user-location-accuracy-circle\"),this._accuracyCircleMarker=new Zi({element:this._circleElement,pitchAlignment:\"map\"}),this.options.trackUserLocation&&(this._watchState=\"OFF\"),this._map.on(\"zoom\",this._onZoom)),this._geolocateButton.addEventListener(\"click\",this.trigger.bind(this)),this._setup=!0,this.options.trackUserLocation&&this._map.on(\"movestart\",(function(e){var r=e.originalEvent&&\"resize\"===e.originalEvent.type;e.geolocateSource||\"ACTIVE_LOCK\"!==n._watchState||r||(n._watchState=\"BACKGROUND\",n._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\"),n._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),n.fire(new t.Event(\"trackuserlocationend\")))}))},n.prototype.trigger=function(){if(!this._setup)return t.warnOnce(\"Geolocate control triggered before added to a map\"),!1;if(this.options.trackUserLocation){switch(this._watchState){case\"OFF\":this._watchState=\"WAITING_ACTIVE\",this.fire(new t.Event(\"trackuserlocationstart\"));break;case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":case\"BACKGROUND_ERROR\":Yi--,Xi=!1,this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this.fire(new t.Event(\"trackuserlocationend\"));break;case\"BACKGROUND\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._lastKnownPosition&&this._updateCamera(this._lastKnownPosition),this.fire(new t.Event(\"trackuserlocationstart\"))}switch(this._watchState){case\"WAITING_ACTIVE\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"ACTIVE_LOCK\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"ACTIVE_ERROR\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\");break;case\"BACKGROUND\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\");break;case\"BACKGROUND_ERROR\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background-error\")}if(\"OFF\"===this._watchState&&void 0!==this._geolocationWatchID)this._clearWatch();else if(void 0===this._geolocationWatchID){var e;this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"true\"),++Yi>1?(e={maximumAge:6e5,timeout:0},Xi=!0):(e=this.options.positionOptions,Xi=!1),this._geolocationWatchID=t.window.navigator.geolocation.watchPosition(this._onSuccess,this._onError,e)}}else t.window.navigator.geolocation.getCurrentPosition(this._onSuccess,this._onError,this.options.positionOptions),this._timeoutId=setTimeout(this._finish,1e4);return!0},n.prototype._clearWatch=function(){t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0,this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this.options.showUserLocation&&this._updateMarker(null)},n}(t.Evented),Ji={maxWidth:100,unit:\"metric\"},Ki=function(e){this.options=t.extend({},Ji,e),t.bindAll([\"_onMove\",\"setUnit\"],this)};function Qi(t,e,r){var n=r&&r.maxWidth||100,i=t._container.clientHeight/2,a=t.unproject([0,i]),o=t.unproject([n,i]),s=a.distanceTo(o);if(r&&\"imperial\"===r.unit){var l=3.2808*s;l>5280?ta(e,n,l/5280,t._getUIString(\"ScaleControl.Miles\")):ta(e,n,l,t._getUIString(\"ScaleControl.Feet\"))}else r&&\"nautical\"===r.unit?ta(e,n,s/1852,t._getUIString(\"ScaleControl.NauticalMiles\")):s>=1e3?ta(e,n,s/1e3,t._getUIString(\"ScaleControl.Kilometers\")):ta(e,n,s,t._getUIString(\"ScaleControl.Meters\"))}function ta(t,e,r,n){var i,a,o,s=(i=r,(a=Math.pow(10,(\"\"+Math.floor(i)).length-1))*((o=i/a)>=10?10:o>=5?5:o>=3?3:o>=2?2:o>=1?1:function(t){var e=Math.pow(10,Math.ceil(-Math.log(t)/Math.LN10));return Math.round(t*e)/e}(o))),l=s/r;t.style.width=e*l+\"px\",t.innerHTML=s+\" \"+n}Ki.prototype.getDefaultPosition=function(){return\"bottom-left\"},Ki.prototype._onMove=function(){Qi(this._map,this._container,this.options)},Ki.prototype.onAdd=function(t){return this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-scale\",t.getContainer()),this._map.on(\"move\",this._onMove),this._onMove(),this._container},Ki.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"move\",this._onMove),this._map=void 0},Ki.prototype.setUnit=function(t){this.options.unit=t,Qi(this._map,this._container,this.options)};var ea=function(e){this._fullscreen=!1,e&&e.container&&(e.container instanceof t.window.HTMLElement?this._container=e.container:t.warnOnce(\"Full screen control 'container' must be a DOM element.\")),t.bindAll([\"_onClickFullscreen\",\"_changeIcon\"],this),\"onfullscreenchange\"in t.window.document?this._fullscreenchange=\"fullscreenchange\":\"onmozfullscreenchange\"in t.window.document?this._fullscreenchange=\"mozfullscreenchange\":\"onwebkitfullscreenchange\"in t.window.document?this._fullscreenchange=\"webkitfullscreenchange\":\"onmsfullscreenchange\"in t.window.document&&(this._fullscreenchange=\"MSFullscreenChange\")};ea.prototype.onAdd=function(e){return this._map=e,this._container||(this._container=this._map.getContainer()),this._controlContainer=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),this._checkFullscreenSupport()?this._setupUI():(this._controlContainer.style.display=\"none\",t.warnOnce(\"This device does not support fullscreen mode.\")),this._controlContainer},ea.prototype.onRemove=function(){r.remove(this._controlContainer),this._map=null,t.window.document.removeEventListener(this._fullscreenchange,this._changeIcon)},ea.prototype._checkFullscreenSupport=function(){return!!(t.window.document.fullscreenEnabled||t.window.document.mozFullScreenEnabled||t.window.document.msFullscreenEnabled||t.window.document.webkitFullscreenEnabled)},ea.prototype._setupUI=function(){var e=this._fullscreenButton=r.create(\"button\",\"mapboxgl-ctrl-fullscreen\",this._controlContainer);r.create(\"span\",\"mapboxgl-ctrl-icon\",e).setAttribute(\"aria-hidden\",!0),e.type=\"button\",this._updateTitle(),this._fullscreenButton.addEventListener(\"click\",this._onClickFullscreen),t.window.document.addEventListener(this._fullscreenchange,this._changeIcon)},ea.prototype._updateTitle=function(){var t=this._getTitle();this._fullscreenButton.setAttribute(\"aria-label\",t),this._fullscreenButton.title=t},ea.prototype._getTitle=function(){return this._map._getUIString(this._isFullscreen()?\"FullscreenControl.Exit\":\"FullscreenControl.Enter\")},ea.prototype._isFullscreen=function(){return this._fullscreen},ea.prototype._changeIcon=function(){(t.window.document.fullscreenElement||t.window.document.mozFullScreenElement||t.window.document.webkitFullscreenElement||t.window.document.msFullscreenElement)===this._container!==this._fullscreen&&(this._fullscreen=!this._fullscreen,this._fullscreenButton.classList.toggle(\"mapboxgl-ctrl-shrink\"),this._fullscreenButton.classList.toggle(\"mapboxgl-ctrl-fullscreen\"),this._updateTitle())},ea.prototype._onClickFullscreen=function(){this._isFullscreen()?t.window.document.exitFullscreen?t.window.document.exitFullscreen():t.window.document.mozCancelFullScreen?t.window.document.mozCancelFullScreen():t.window.document.msExitFullscreen?t.window.document.msExitFullscreen():t.window.document.webkitCancelFullScreen&&t.window.document.webkitCancelFullScreen():this._container.requestFullscreen?this._container.requestFullscreen():this._container.mozRequestFullScreen?this._container.mozRequestFullScreen():this._container.msRequestFullscreen?this._container.msRequestFullscreen():this._container.webkitRequestFullscreen&&this._container.webkitRequestFullscreen()};var ra={closeButton:!0,closeOnClick:!0,focusAfterOpen:!0,className:\"\",maxWidth:\"240px\"},na=[\"a[href]\",\"[tabindex]:not([tabindex='-1'])\",\"[contenteditable]:not([contenteditable='false'])\",\"button:not([disabled])\",\"input:not([disabled])\",\"select:not([disabled])\",\"textarea:not([disabled])\"].join(\", \"),ia=function(e){function n(r){e.call(this),this.options=t.extend(Object.create(ra),r),t.bindAll([\"_update\",\"_onClose\",\"remove\",\"_onMouseMove\",\"_onMouseUp\",\"_onDrag\"],this)}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.addTo=function(e){return this._map&&this.remove(),this._map=e,this.options.closeOnClick&&this._map.on(\"click\",this._onClose),this.options.closeOnMove&&this._map.on(\"move\",this._onClose),this._map.on(\"remove\",this.remove),this._update(),this._focusFirstElement(),this._trackPointer?(this._map.on(\"mousemove\",this._onMouseMove),this._map.on(\"mouseup\",this._onMouseUp),this._container&&this._container.classList.add(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.add(\"mapboxgl-track-pointer\")):this._map.on(\"move\",this._update),this.fire(new t.Event(\"open\")),this},n.prototype.isOpen=function(){return!!this._map},n.prototype.remove=function(){return this._content&&r.remove(this._content),this._container&&(r.remove(this._container),delete this._container),this._map&&(this._map.off(\"move\",this._update),this._map.off(\"move\",this._onClose),this._map.off(\"click\",this._onClose),this._map.off(\"remove\",this.remove),this._map.off(\"mousemove\",this._onMouseMove),this._map.off(\"mouseup\",this._onMouseUp),this._map.off(\"drag\",this._onDrag),delete this._map),this.fire(new t.Event(\"close\")),this},n.prototype.getLngLat=function(){return this._lngLat},n.prototype.setLngLat=function(e){return this._lngLat=t.LngLat.convert(e),this._pos=null,this._trackPointer=!1,this._update(),this._map&&(this._map.on(\"move\",this._update),this._map.off(\"mousemove\",this._onMouseMove),this._container&&this._container.classList.remove(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.remove(\"mapboxgl-track-pointer\")),this},n.prototype.trackPointer=function(){return this._trackPointer=!0,this._pos=null,this._update(),this._map&&(this._map.off(\"move\",this._update),this._map.on(\"mousemove\",this._onMouseMove),this._map.on(\"drag\",this._onDrag),this._container&&this._container.classList.add(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.add(\"mapboxgl-track-pointer\")),this},n.prototype.getElement=function(){return this._container},n.prototype.setText=function(e){return this.setDOMContent(t.window.document.createTextNode(e))},n.prototype.setHTML=function(e){var r,n=t.window.document.createDocumentFragment(),i=t.window.document.createElement(\"body\");for(i.innerHTML=e;r=i.firstChild;)n.appendChild(r);return this.setDOMContent(n)},n.prototype.getMaxWidth=function(){return this._container&&this._container.style.maxWidth},n.prototype.setMaxWidth=function(t){return this.options.maxWidth=t,this._update(),this},n.prototype.setDOMContent=function(t){if(this._content)for(;this._content.hasChildNodes();)this._content.firstChild&&this._content.removeChild(this._content.firstChild);else this._content=r.create(\"div\",\"mapboxgl-popup-content\",this._container);return this._content.appendChild(t),this._createCloseButton(),this._update(),this._focusFirstElement(),this},n.prototype.addClassName=function(t){this._container&&this._container.classList.add(t)},n.prototype.removeClassName=function(t){this._container&&this._container.classList.remove(t)},n.prototype.setOffset=function(t){return this.options.offset=t,this._update(),this},n.prototype.toggleClassName=function(t){if(this._container)return this._container.classList.toggle(t)},n.prototype._createCloseButton=function(){this.options.closeButton&&(this._closeButton=r.create(\"button\",\"mapboxgl-popup-close-button\",this._content),this._closeButton.type=\"button\",this._closeButton.setAttribute(\"aria-label\",\"Close popup\"),this._closeButton.innerHTML=\"×\",this._closeButton.addEventListener(\"click\",this._onClose))},n.prototype._onMouseUp=function(t){this._update(t.point)},n.prototype._onMouseMove=function(t){this._update(t.point)},n.prototype._onDrag=function(t){this._update(t.point)},n.prototype._update=function(t){var e=this,n=this._lngLat||this._trackPointer;if(this._map&&n&&this._content&&(this._container||(this._container=r.create(\"div\",\"mapboxgl-popup\",this._map.getContainer()),this._tip=r.create(\"div\",\"mapboxgl-popup-tip\",this._container),this._container.appendChild(this._content),this.options.className&&this.options.className.split(\" \").forEach((function(t){return e._container.classList.add(t)})),this._trackPointer&&this._container.classList.add(\"mapboxgl-popup-track-pointer\")),this.options.maxWidth&&this._container.style.maxWidth!==this.options.maxWidth&&(this._container.style.maxWidth=this.options.maxWidth),this._map.transform.renderWorldCopies&&!this._trackPointer&&(this._lngLat=Vi(this._lngLat,this._pos,this._map.transform)),!this._trackPointer||t)){var i=this._pos=this._trackPointer&&t?t:this._map.project(this._lngLat),a=this.options.anchor,o=aa(this.options.offset);if(!a){var s,l=this._container.offsetWidth,c=this._container.offsetHeight;s=i.y+o.bottom.ythis._map.transform.height-c?[\"bottom\"]:[],i.xthis._map.transform.width-l/2&&s.push(\"right\"),a=0===s.length?\"bottom\":s.join(\"-\")}var u=i.add(o[a]).round();r.setTransform(this._container,qi[a]+\" translate(\"+u.x+\"px,\"+u.y+\"px)\"),Hi(this._container,a,\"popup\")}},n.prototype._focusFirstElement=function(){if(this.options.focusAfterOpen&&this._container){var t=this._container.querySelector(na);t&&t.focus()}},n.prototype._onClose=function(){this.remove()},n}(t.Evented);function aa(e){if(e){if(\"number\"==typeof e){var r=Math.round(Math.sqrt(.5*Math.pow(e,2)));return{center:new t.Point(0,0),top:new t.Point(0,e),\"top-left\":new t.Point(r,r),\"top-right\":new t.Point(-r,r),bottom:new t.Point(0,-e),\"bottom-left\":new t.Point(r,-r),\"bottom-right\":new t.Point(-r,-r),left:new t.Point(e,0),right:new t.Point(-e,0)}}if(e instanceof t.Point||Array.isArray(e)){var n=t.Point.convert(e);return{center:n,top:n,\"top-left\":n,\"top-right\":n,bottom:n,\"bottom-left\":n,\"bottom-right\":n,left:n,right:n}}return{center:t.Point.convert(e.center||[0,0]),top:t.Point.convert(e.top||[0,0]),\"top-left\":t.Point.convert(e[\"top-left\"]||[0,0]),\"top-right\":t.Point.convert(e[\"top-right\"]||[0,0]),bottom:t.Point.convert(e.bottom||[0,0]),\"bottom-left\":t.Point.convert(e[\"bottom-left\"]||[0,0]),\"bottom-right\":t.Point.convert(e[\"bottom-right\"]||[0,0]),left:t.Point.convert(e.left||[0,0]),right:t.Point.convert(e.right||[0,0])}}return aa(new t.Point(0,0))}var oa={version:t.version,supported:e,setRTLTextPlugin:t.setRTLTextPlugin,getRTLTextPluginStatus:t.getRTLTextPluginStatus,Map:Fi,NavigationControl:ji,GeolocateControl:$i,AttributionControl:Ei,ScaleControl:Ki,FullscreenControl:ea,Popup:ia,Marker:Zi,Style:We,LngLat:t.LngLat,LngLatBounds:t.LngLatBounds,Point:t.Point,MercatorCoordinate:t.MercatorCoordinate,Evented:t.Evented,config:t.config,prewarm:function(){jt().acquire(Rt)},clearPrewarmedResources:function(){var t=Bt;t&&(t.isPreloaded()&&1===t.numActive()?(t.release(Rt),Bt=null):console.warn(\"Could not clear WebWorkers since there are active Map instances that still reference it. 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i=this._validate(t,e,r,n.local.invalidDate);return{yearType:(this.leapYear(i)?\"embolismic\":\"common\")+\" \"+[\"deficient\",\"regular\",\"complete\"][this.daysInYear(i)%10-3]}},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=i.year(),e=i.month(),r=i.day();var a=t<=0?t+1:t,o=this.jdEpoch+this._delay1(a)+this._delay2(a)+r+1;if(e<7){for(var s=7;s<=this.monthsInYear(t);s++)o+=this.daysInMonth(t,s);for(s=1;s=this.toJD(-1===e?1:e+1,7,1);)e++;for(var r=tthis.toJD(e,r,this.daysInMonth(e,r));)r++;var n=t-this.toJD(e,r,1)+1;return this.newDate(e,r,n)}}),n.calendars.hebrew=a},25512:function(t,e,r){var n=r(24453),i=r(27976);function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new 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e=Math.floor((30*(t-this.jdEpoch)+10646)/10631);e=e<=0?e-1:e;var r=Math.min(12,Math.ceil((t-29-this.toJD(e,1,1))/29.5)+1),n=t-this.toJD(e,r,1)+1;return this.newDate(e,r,n)}}),n.calendars.islamic=a},42645:function(t,e,r){var n=r(24453),i=r(27976);function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new 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this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),!1},formatYear:function(t){t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year();var e=Math.floor(t/400);return t%=400,t+=t<0?400:0,e+\".\"+Math.floor(t/20)+\".\"+t%20},forYear:function(t){if((t=t.split(\".\")).length<3)throw\"Invalid Mayan year\";for(var e=0,r=0;r19||r>0&&n<0)throw\"Invalid Mayan year\";e=20*e+n}return e},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),18},weekOfYear:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate),0},daysInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),360},daysInMonth:function(t,e){return this._validate(t,e,this.minDay,n.local.invalidMonth),20},daysInWeek:function(){return 5},dayOfWeek:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate).day()},weekDay:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate),!0},extraInfo:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate).toJD(),a=this._toHaab(i),o=this._toTzolkin(i);return{haabMonthName:this.local.haabMonths[a[0]-1],haabMonth:a[0],haabDay:a[1],tzolkinDayName:this.local.tzolkinMonths[o[0]-1],tzolkinDay:o[0],tzolkinTrecena:o[1]}},_toHaab:function(t){var e=o(8+(t-=this.jdEpoch)+340,365);return[Math.floor(e/20)+1,o(e,20)]},_toTzolkin:function(t){return[s(20+(t-=this.jdEpoch),20),s(t+4,13)]},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return i.day()+20*i.month()+360*i.year()+this.jdEpoch},fromJD:function(t){t=Math.floor(t)+.5-this.jdEpoch;var e=Math.floor(t/360);t%=360,t+=t<0?360:0;var r=Math.floor(t/20),n=t%20;return this.newDate(e,r,n)}}),n.calendars.mayan=a},91662:function(t,e,r){var n=r(24453),i=r(27976);function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar;var o=n.instance(\"gregorian\");i(a.prototype,{name:\"Nanakshahi\",jdEpoch:2257673.5,daysPerMonth:[31,31,31,31,31,30,30,30,30,30,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Nanakshahi\",epochs:[\"BN\",\"AN\"],monthNames:[\"Chet\",\"Vaisakh\",\"Jeth\",\"Harh\",\"Sawan\",\"Bhadon\",\"Assu\",\"Katak\",\"Maghar\",\"Poh\",\"Magh\",\"Phagun\"],monthNamesShort:[\"Che\",\"Vai\",\"Jet\",\"Har\",\"Saw\",\"Bha\",\"Ass\",\"Kat\",\"Mgr\",\"Poh\",\"Mgh\",\"Pha\"],dayNames:[\"Somvaar\",\"Mangalvar\",\"Budhvaar\",\"Veervaar\",\"Shukarvaar\",\"Sanicharvaar\",\"Etvaar\"],dayNamesShort:[\"Som\",\"Mangal\",\"Budh\",\"Veer\",\"Shukar\",\"Sanichar\",\"Et\"],dayNamesMin:[\"So\",\"Ma\",\"Bu\",\"Ve\",\"Sh\",\"Sa\",\"Et\"],digits:null,dateFormat:\"dd-mm-yyyy\",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear||n.regionalOptions[\"\"].invalidYear);return o.leapYear(e.year()+(e.year()<1?1:0)+1469)},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(1-(n.dayOfWeek()||7),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidMonth);(t=i.year())<0&&t++;for(var a=i.day(),s=1;s=this.toJD(e+1,1,1);)e++;for(var r=t-Math.floor(this.toJD(e,1,1)+.5)+1,n=1;r>this.daysInMonth(e,n);)r-=this.daysInMonth(e,n),n++;return this.newDate(e,n,r)}}),n.calendars.nanakshahi=a},66445:function(t,e,r){var n=r(24453),i=r(27976);function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Nepali\",jdEpoch:1700709.5,daysPerMonth:[31,31,32,32,31,30,30,29,30,29,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,daysPerYear:365,regionalOptions:{\"\":{name:\"Nepali\",epochs:[\"BBS\",\"ABS\"],monthNames:[\"Baisakh\",\"Jestha\",\"Ashadh\",\"Shrawan\",\"Bhadra\",\"Ashwin\",\"Kartik\",\"Mangsir\",\"Paush\",\"Mangh\",\"Falgun\",\"Chaitra\"],monthNamesShort:[\"Bai\",\"Je\",\"As\",\"Shra\",\"Bha\",\"Ash\",\"Kar\",\"Mang\",\"Pau\",\"Ma\",\"Fal\",\"Chai\"],dayNames:[\"Aaitabaar\",\"Sombaar\",\"Manglbaar\",\"Budhabaar\",\"Bihibaar\",\"Shukrabaar\",\"Shanibaar\"],dayNamesShort:[\"Aaita\",\"Som\",\"Mangl\",\"Budha\",\"Bihi\",\"Shukra\",\"Shani\"],dayNamesMin:[\"Aai\",\"So\",\"Man\",\"Bu\",\"Bi\",\"Shu\",\"Sha\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:1,isRTL:!1}},leapYear:function(t){return this.daysInYear(t)!==this.daysPerYear},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){if(t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year(),void 0===this.NEPALI_CALENDAR_DATA[t])return this.daysPerYear;for(var e=0,r=this.minMonth;r<=12;r++)e+=this.NEPALI_CALENDAR_DATA[t][r];return e},daysInMonth:function(t,e){return t.year&&(e=t.month(),t=t.year()),this._validate(t,e,this.minDay,n.local.invalidMonth),void 0===this.NEPALI_CALENDAR_DATA[t]?this.daysPerMonth[e-1]:this.NEPALI_CALENDAR_DATA[t][e]},weekDay:function(t,e,r){return 6!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=i.year(),e=i.month(),r=i.day();var a=n.instance(),o=0,s=e,l=t;this._createMissingCalendarData(t);var c=t-(s>9||9===s&&r>=this.NEPALI_CALENDAR_DATA[l][0]?56:57);for(9!==e&&(o=r,s--);9!==s;)s<=0&&(s=12,l--),o+=this.NEPALI_CALENDAR_DATA[l][s],s--;return 9===e?(o+=r-this.NEPALI_CALENDAR_DATA[l][0])<0&&(o+=a.daysInYear(c)):o+=this.NEPALI_CALENDAR_DATA[l][9]-this.NEPALI_CALENDAR_DATA[l][0],a.newDate(c,1,1).add(o,\"d\").toJD()},fromJD:function(t){var e=n.instance().fromJD(t),r=e.year(),i=e.dayOfYear(),a=r+56;this._createMissingCalendarData(a);for(var o=9,s=this.NEPALI_CALENDAR_DATA[a][0],l=this.NEPALI_CALENDAR_DATA[a][o]-s+1;i>l;)++o>12&&(o=1,a++),l+=this.NEPALI_CALENDAR_DATA[a][o];var c=this.NEPALI_CALENDAR_DATA[a][o]-(l-i);return this.newDate(a,o,c)},_createMissingCalendarData:function(t){var e=this.daysPerMonth.slice(0);e.unshift(17);for(var r=t-1;r0?474:473))%2820+474+38)%2816<682},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-(n.dayOfWeek()+1)%7,\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=i.year(),e=i.month(),r=i.day();var a=t-(t>=0?474:473),s=474+o(a,2820);return r+(e<=7?31*(e-1):30*(e-1)+6)+Math.floor((682*s-110)/2816)+365*(s-1)+1029983*Math.floor(a/2820)+this.jdEpoch-1},fromJD:function(t){var e=(t=Math.floor(t)+.5)-this.toJD(475,1,1),r=Math.floor(e/1029983),n=o(e,1029983),i=2820;if(1029982!==n){var a=Math.floor(n/366),s=o(n,366);i=Math.floor((2134*a+2816*s+2815)/1028522)+a+1}var l=i+2820*r+474;l=l<=0?l-1:l;var c=t-this.toJD(l,1,1)+1,u=c<=186?Math.ceil(c/31):Math.ceil((c-6)/30),h=t-this.toJD(l,u,1)+1;return this.newDate(l,u,h)}}),n.calendars.persian=a,n.calendars.jalali=a},84756:function(t,e,r){var n=r(24453),i=r(27976),a=n.instance();function o(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}o.prototype=new n.baseCalendar,i(o.prototype,{name:\"Taiwan\",jdEpoch:2419402.5,yearsOffset:1911,daysPerMonth:[31,28,31,30,31,30,31,31,30,31,30,31],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Taiwan\",epochs:[\"BROC\",\"ROC\"],monthNames:[\"January\",\"February\",\"March\",\"April\",\"May\",\"June\",\"July\",\"August\",\"September\",\"October\",\"November\",\"December\"],monthNamesShort:[\"Jan\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Jul\",\"Aug\",\"Sep\",\"Oct\",\"Nov\",\"Dec\"],dayNames:[\"Sunday\",\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\"],dayNamesShort:[\"Sun\",\"Mon\",\"Tue\",\"Wed\",\"Thu\",\"Fri\",\"Sat\"],dayNamesMin:[\"Su\",\"Mo\",\"Tu\",\"We\",\"Th\",\"Fr\",\"Sa\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:1,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return t=this._t2gYear(e.year()),a.leapYear(t)},weekOfYear:function(t,e,r){var i=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return t=this._t2gYear(i.year()),a.weekOfYear(t,i.month(),i.day())},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=this._t2gYear(i.year()),a.toJD(t,i.month(),i.day())},fromJD:function(t){var e=a.fromJD(t),r=this._g2tYear(e.year());return this.newDate(r,e.month(),e.day())},_t2gYear:function(t){return t+this.yearsOffset+(t>=-this.yearsOffset&&t<=-1?1:0)},_g2tYear:function(t){return t-this.yearsOffset-(t>=1&&t<=this.yearsOffset?1:0)}}),n.calendars.taiwan=o},41858:function(t,e,r){var n=r(24453),i=r(27976),a=n.instance();function o(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}o.prototype=new n.baseCalendar,i(o.prototype,{name:\"Thai\",jdEpoch:1523098.5,yearsOffset:543,daysPerMonth:[31,28,31,30,31,30,31,31,30,31,30,31],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Thai\",epochs:[\"BBE\",\"BE\"],monthNames:[\"January\",\"February\",\"March\",\"April\",\"May\",\"June\",\"July\",\"August\",\"September\",\"October\",\"November\",\"December\"],monthNamesShort:[\"Jan\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Jul\",\"Aug\",\"Sep\",\"Oct\",\"Nov\",\"Dec\"],dayNames:[\"Sunday\",\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\"],dayNamesShort:[\"Sun\",\"Mon\",\"Tue\",\"Wed\",\"Thu\",\"Fri\",\"Sat\"],dayNamesMin:[\"Su\",\"Mo\",\"Tu\",\"We\",\"Th\",\"Fr\",\"Sa\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return t=this._t2gYear(e.year()),a.leapYear(t)},weekOfYear:function(t,e,r){var i=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return t=this._t2gYear(i.year()),a.weekOfYear(t,i.month(),i.day())},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=this._t2gYear(i.year()),a.toJD(t,i.month(),i.day())},fromJD:function(t){var e=a.fromJD(t),r=this._g2tYear(e.year());return this.newDate(r,e.month(),e.day())},_t2gYear:function(t){return t-this.yearsOffset-(t>=1&&t<=this.yearsOffset?1:0)},_g2tYear:function(t){return t+this.yearsOffset+(t>=-this.yearsOffset&&t<=-1?1:0)}}),n.calendars.thai=o},57985:function(t,e,r){var n=r(24453),i=r(27976);function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"UmmAlQura\",hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Umm al-Qura\",epochs:[\"BH\",\"AH\"],monthNames:[\"Al-Muharram\",\"Safar\",\"Rabi' al-awwal\",\"Rabi' Al-Thani\",\"Jumada Al-Awwal\",\"Jumada Al-Thani\",\"Rajab\",\"Sha'aban\",\"Ramadan\",\"Shawwal\",\"Dhu al-Qi'dah\",\"Dhu al-Hijjah\"],monthNamesShort:[\"Muh\",\"Saf\",\"Rab1\",\"Rab2\",\"Jum1\",\"Jum2\",\"Raj\",\"Sha'\",\"Ram\",\"Shaw\",\"DhuQ\",\"DhuH\"],dayNames:[\"Yawm al-Ahad\",\"Yawm al-Ithnain\",\"Yawm al-Thalāthā’\",\"Yawm al-Arba‘ā’\",\"Yawm al-Khamīs\",\"Yawm al-Jum‘a\",\"Yawm al-Sabt\"],dayNamesMin:[\"Ah\",\"Ith\",\"Th\",\"Ar\",\"Kh\",\"Ju\",\"Sa\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:6,isRTL:!0}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return 355===this.daysInYear(e.year())},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){for(var e=0,r=1;r<=12;r++)e+=this.daysInMonth(t,r);return e},daysInMonth:function(t,e){for(var r=this._validate(t,e,this.minDay,n.local.invalidMonth).toJD()-24e5+.5,i=0,a=0;ar)return o[i]-o[i-1];i++}return 30},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate),a=12*(i.year()-1)+i.month()-15292;return i.day()+o[a-1]-1+24e5-.5},fromJD:function(t){for(var e=t-24e5+.5,r=0,n=0;ne);n++)r++;var i=r+15292,a=Math.floor((i-1)/12),s=a+1,l=i-12*a,c=e-o[r-1]+1;return this.newDate(s,l,c)},isValid:function(t,e,r){var i=n.baseCalendar.prototype.isValid.apply(this,arguments);return i&&(i=(t=null!=t.year?t.year:t)>=1276&&t<=1500),i},_validate:function(t,e,r,i){var a=n.baseCalendar.prototype._validate.apply(this,arguments);if(a.year<1276||a.year>1500)throw i.replace(/\\{0\\}/,this.local.name);return a}}),n.calendars.ummalqura=a;var 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n=r(27976);function i(){this.regionalOptions=[],this.regionalOptions[\"\"]={invalidCalendar:\"Calendar {0} not found\",invalidDate:\"Invalid {0} date\",invalidMonth:\"Invalid {0} month\",invalidYear:\"Invalid {0} year\",differentCalendars:\"Cannot mix {0} and {1} dates\"},this.local=this.regionalOptions[\"\"],this.calendars={},this._localCals={}}function a(t,e,r,n){if(this._calendar=t,this._year=e,this._month=r,this._day=n,0===this._calendar._validateLevel&&!this._calendar.isValid(this._year,this._month,this._day))throw(c.local.invalidDate||c.regionalOptions[\"\"].invalidDate).replace(/\\{0\\}/,this._calendar.local.name)}function o(t,e){return\"000000\".substring(0,e-(t=\"\"+t).length)+t}function s(){this.shortYearCutoff=\"+10\"}function l(t){this.local=this.regionalOptions[t]||this.regionalOptions[\"\"]}n(i.prototype,{instance:function(t,e){t=(t||\"gregorian\").toLowerCase(),e=e||\"\";var r=this._localCals[t+\"-\"+e];if(!r&&this.calendars[t]&&(r=new this.calendars[t](e),this._localCals[t+\"-\"+e]=r),!r)throw(this.local.invalidCalendar||this.regionalOptions[\"\"].invalidCalendar).replace(/\\{0\\}/,t);return r},newDate:function(t,e,r,n,i){return(n=(null!=t&&t.year?t.calendar():\"string\"==typeof n?this.instance(n,i):n)||this.instance()).newDate(t,e,r)},substituteDigits:function(t){return function(e){return(e+\"\").replace(/[0-9]/g,(function(e){return t[e]}))}},substituteChineseDigits:function(t,e){return function(r){for(var n=\"\",i=0;r>0;){var a=r%10;n=(0===a?\"\":t[a]+e[i])+n,i++,r=Math.floor(r/10)}return 0===n.indexOf(t[1]+e[1])&&(n=n.substr(1)),n||t[0]}}}),n(a.prototype,{newDate:function(t,e,r){return this._calendar.newDate(null==t?this:t,e,r)},year:function(t){return 0===arguments.length?this._year:this.set(t,\"y\")},month:function(t){return 0===arguments.length?this._month:this.set(t,\"m\")},day:function(t){return 0===arguments.length?this._day:this.set(t,\"d\")},date:function(t,e,r){if(!this._calendar.isValid(t,e,r))throw(c.local.invalidDate||c.regionalOptions[\"\"].invalidDate).replace(/\\{0\\}/,this._calendar.local.name);return this._year=t,this._month=e,this._day=r,this},leapYear:function(){return this._calendar.leapYear(this)},epoch:function(){return this._calendar.epoch(this)},formatYear:function(){return this._calendar.formatYear(this)},monthOfYear:function(){return this._calendar.monthOfYear(this)},weekOfYear:function(){return this._calendar.weekOfYear(this)},daysInYear:function(){return this._calendar.daysInYear(this)},dayOfYear:function(){return this._calendar.dayOfYear(this)},daysInMonth:function(){return this._calendar.daysInMonth(this)},dayOfWeek:function(){return this._calendar.dayOfWeek(this)},weekDay:function(){return this._calendar.weekDay(this)},extraInfo:function(){return this._calendar.extraInfo(this)},add:function(t,e){return this._calendar.add(this,t,e)},set:function(t,e){return this._calendar.set(this,t,e)},compareTo:function(t){if(this._calendar.name!==t._calendar.name)throw(c.local.differentCalendars||c.regionalOptions[\"\"].differentCalendars).replace(/\\{0\\}/,this._calendar.local.name).replace(/\\{1\\}/,t._calendar.local.name);var e=this._year!==t._year?this._year-t._year:this._month!==t._month?this.monthOfYear()-t.monthOfYear():this._day-t._day;return 0===e?0:e<0?-1:1},calendar:function(){return this._calendar},toJD:function(){return this._calendar.toJD(this)},fromJD:function(t){return this._calendar.fromJD(t)},toJSDate:function(){return this._calendar.toJSDate(this)},fromJSDate:function(t){return this._calendar.fromJSDate(t)},toString:function(){return(this.year()<0?\"-\":\"\")+o(Math.abs(this.year()),4)+\"-\"+o(this.month(),2)+\"-\"+o(this.day(),2)}}),n(s.prototype,{_validateLevel:0,newDate:function(t,e,r){return null==t?this.today():(t.year&&(this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate),r=t.day(),e=t.month(),t=t.year()),new a(this,t,e,r))},today:function(){return this.fromJSDate(new Date)},epoch:function(t){return this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[\"\"].invalidYear).year()<0?this.local.epochs[0]:this.local.epochs[1]},formatYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[\"\"].invalidYear);return(e.year()<0?\"-\":\"\")+o(Math.abs(e.year()),4)},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[\"\"].invalidYear),12},monthOfYear:function(t,e){var r=this._validate(t,e,this.minDay,c.local.invalidMonth||c.regionalOptions[\"\"].invalidMonth);return(r.month()+this.monthsInYear(r)-this.firstMonth)%this.monthsInYear(r)+this.minMonth},fromMonthOfYear:function(t,e){var r=(e+this.firstMonth-2*this.minMonth)%this.monthsInYear(t)+this.minMonth;return this._validate(t,r,this.minDay,c.local.invalidMonth||c.regionalOptions[\"\"].invalidMonth),r},daysInYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[\"\"].invalidYear);return this.leapYear(e)?366:365},dayOfYear:function(t,e,r){var n=this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate);return n.toJD()-this.newDate(n.year(),this.fromMonthOfYear(n.year(),this.minMonth),this.minDay).toJD()+1},daysInWeek:function(){return 7},dayOfWeek:function(t,e,r){var n=this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate);return(Math.floor(this.toJD(n))+2)%this.daysInWeek()},extraInfo:function(t,e,r){return this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate),{}},add:function(t,e,r){return 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0===i?0:1===e?a/i:(Math.pow(e,a)-1)/(Math.pow(e,i)-1)}(r,n,t.stops[a][0],t.stops[a+1][0]),s=t.stops[a][1],l=t.stops[a+1][1],c=Pe[e.type]||gn;return\"function\"==typeof s.evaluate?{evaluate(...e){const r=s.evaluate.apply(void 0,e),n=l.evaluate.apply(void 0,e);if(void 0!==r&&void 0!==n)return c(r,n,o,t.colorSpace)}}:c(s,l,o,t.colorSpace)}function wn(t,e,r){switch(e.type){case\"color\":r=Xt.parse(r);break;case\"formatted\":r=Kt.fromString(r.toString());break;case\"resolvedImage\":r=re.fromString(r.toString());break;case\"padding\":r=Qt.parse(r);break;default:dn(r)===e.type||\"enum\"===e.type&&e.values[r]||(r=void 0)}return vn(r,t.default,e.default)}Qr.register(Kr,{error:[{kind:\"error\"},[ut],(t,[e])=>{throw new le(e.evaluate(t))}],typeof:[ut,[dt],(t,[e])=>bt(ae(e.evaluate(t)))],\"to-rgba\":[_t(ct,4),[ft],(t,[e])=>{const[r,n,i,a]=e.evaluate(t).rgb;return[255*r,255*n,255*i,a]}],rgb:[ft,[ct,ct,ct],tn],rgba:[ft,[ct,ct,ct,ct],tn],has:{type:ht,overloads:[[[ut],(t,[e])=>en(e.evaluate(t),t.properties())],[[ut,pt],(t,[e,r])=>en(e.evaluate(t),r.evaluate(t))]]},get:{type:dt,overloads:[[[ut],(t,[e])=>rn(e.evaluate(t),t.properties())],[[ut,pt],(t,[e,r])=>rn(e.evaluate(t),r.evaluate(t))]]},\"feature-state\":[dt,[ut],(t,[e])=>rn(e.evaluate(t),t.featureState||{})],properties:[pt,[],t=>t.properties()],\"geometry-type\":[ut,[],t=>t.geometryType()],id:[dt,[],t=>t.id()],zoom:[ct,[],t=>t.globals.zoom],\"heatmap-density\":[ct,[],t=>t.globals.heatmapDensity||0],\"line-progress\":[ct,[],t=>t.globals.lineProgress||0],accumulated:[dt,[],t=>void 0===t.globals.accumulated?null:t.globals.accumulated],\"+\":[ct,nn(ct),(t,e)=>{let r=0;for(const n of e)r+=n.evaluate(t);return r}],\"*\":[ct,nn(ct),(t,e)=>{let r=1;for(const n of e)r*=n.evaluate(t);return r}],\"-\":{type:ct,overloads:[[[ct,ct],(t,[e,r])=>e.evaluate(t)-r.evaluate(t)],[[ct],(t,[e])=>-e.evaluate(t)]]},\"/\":[ct,[ct,ct],(t,[e,r])=>e.evaluate(t)/r.evaluate(t)],\"%\":[ct,[ct,ct],(t,[e,r])=>e.evaluate(t)%r.evaluate(t)],ln2:[ct,[],()=>Math.LN2],pi:[ct,[],()=>Math.PI],e:[ct,[],()=>Math.E],\"^\":[ct,[ct,ct],(t,[e,r])=>Math.pow(e.evaluate(t),r.evaluate(t))],sqrt:[ct,[ct],(t,[e])=>Math.sqrt(e.evaluate(t))],log10:[ct,[ct],(t,[e])=>Math.log(e.evaluate(t))/Math.LN10],ln:[ct,[ct],(t,[e])=>Math.log(e.evaluate(t))],log2:[ct,[ct],(t,[e])=>Math.log(e.evaluate(t))/Math.LN2],sin:[ct,[ct],(t,[e])=>Math.sin(e.evaluate(t))],cos:[ct,[ct],(t,[e])=>Math.cos(e.evaluate(t))],tan:[ct,[ct],(t,[e])=>Math.tan(e.evaluate(t))],asin:[ct,[ct],(t,[e])=>Math.asin(e.evaluate(t))],acos:[ct,[ct],(t,[e])=>Math.acos(e.evaluate(t))],atan:[ct,[ct],(t,[e])=>Math.atan(e.evaluate(t))],min:[ct,nn(ct),(t,e)=>Math.min(...e.map((e=>e.evaluate(t))))],max:[ct,nn(ct),(t,e)=>Math.max(...e.map((e=>e.evaluate(t))))],abs:[ct,[ct],(t,[e])=>Math.abs(e.evaluate(t))],round:[ct,[ct],(t,[e])=>{const r=e.evaluate(t);return r<0?-Math.round(-r):Math.round(r)}],floor:[ct,[ct],(t,[e])=>Math.floor(e.evaluate(t))],ceil:[ct,[ct],(t,[e])=>Math.ceil(e.evaluate(t))],\"filter-==\":[ht,[ut,dt],(t,[e,r])=>t.properties()[e.value]===r.value],\"filter-id-==\":[ht,[dt],(t,[e])=>t.id()===e.value],\"filter-type-==\":[ht,[ut],(t,[e])=>t.geometryType()===e.value],\"filter-<\":[ht,[ut,dt],(t,[e,r])=>{const n=t.properties()[e.value],i=r.value;return typeof n==typeof i&&n{const r=t.id(),n=e.value;return typeof r==typeof n&&r\":[ht,[ut,dt],(t,[e,r])=>{const n=t.properties()[e.value],i=r.value;return typeof n==typeof i&&n>i}],\"filter-id->\":[ht,[dt],(t,[e])=>{const r=t.id(),n=e.value;return typeof r==typeof n&&r>n}],\"filter-<=\":[ht,[ut,dt],(t,[e,r])=>{const n=t.properties()[e.value],i=r.value;return typeof n==typeof i&&n<=i}],\"filter-id-<=\":[ht,[dt],(t,[e])=>{const r=t.id(),n=e.value;return typeof r==typeof n&&r<=n}],\"filter->=\":[ht,[ut,dt],(t,[e,r])=>{const n=t.properties()[e.value],i=r.value;return typeof n==typeof i&&n>=i}],\"filter-id->=\":[ht,[dt],(t,[e])=>{const r=t.id(),n=e.value;return typeof r==typeof n&&r>=n}],\"filter-has\":[ht,[dt],(t,[e])=>e.value in t.properties()],\"filter-has-id\":[ht,[],t=>null!==t.id()&&void 0!==t.id()],\"filter-type-in\":[ht,[_t(ut)],(t,[e])=>e.value.indexOf(t.geometryType())>=0],\"filter-id-in\":[ht,[_t(dt)],(t,[e])=>e.value.indexOf(t.id())>=0],\"filter-in-small\":[ht,[ut,_t(dt)],(t,[e,r])=>r.value.indexOf(t.properties()[e.value])>=0],\"filter-in-large\":[ht,[ut,_t(dt)],(t,[e,r])=>function(t,e,r,n){for(;r<=n;){const i=r+n>>1;if(e[i]===t)return!0;e[i]>t?n=i-1:r=i+1}return!1}(t.properties()[e.value],r.value,0,r.value.length-1)],all:{type:ht,overloads:[[[ht,ht],(t,[e,r])=>e.evaluate(t)&&r.evaluate(t)],[nn(ht),(t,e)=>{for(const r of e)if(!r.evaluate(t))return!1;return!0}]]},any:{type:ht,overloads:[[[ht,ht],(t,[e,r])=>e.evaluate(t)||r.evaluate(t)],[nn(ht),(t,e)=>{for(const r of e)if(r.evaluate(t))return!0;return!1}]]},\"!\":[ht,[ht],(t,[e])=>!e.evaluate(t)],\"is-supported-script\":[ht,[ut],(t,[e])=>{const r=t.globals&&t.globals.isSupportedScript;return!r||r(e.evaluate(t))}],upcase:[ut,[ut],(t,[e])=>e.evaluate(t).toUpperCase()],downcase:[ut,[ut],(t,[e])=>e.evaluate(t).toLowerCase()],concat:[ut,nn(dt),(t,e)=>e.map((e=>oe(e.evaluate(t)))).join(\"\")],\"resolved-locale\":[ut,[mt],(t,[e])=>e.evaluate(t).resolvedLocale()]});class Tn{constructor(t,e){var r;this.expression=t,this._warningHistory={},this._evaluator=new de,this._defaultValue=e?\"color\"===(r=e).type&&mn(r.default)?new Xt(0,0,0,0):\"color\"===r.type?Xt.parse(r.default)||null:\"padding\"===r.type?Qt.parse(r.default)||null:\"variableAnchorOffsetCollection\"===r.type?ee.parse(r.default)||null:void 0===r.default?null:r.default:null,this._enumValues=e&&\"enum\"===e.type?e.values:null}evaluateWithoutErrorHandling(t,e,r,n,i,a){return this._evaluator.globals=t,this._evaluator.feature=e,this._evaluator.featureState=r,this._evaluator.canonical=n,this._evaluator.availableImages=i||null,this._evaluator.formattedSection=a,this.expression.evaluate(this._evaluator)}evaluate(t,e,r,n,i,a){this._evaluator.globals=t,this._evaluator.feature=e||null,this._evaluator.featureState=r||null,this._evaluator.canonical=n,this._evaluator.availableImages=i||null,this._evaluator.formattedSection=a||null;try{const t=this.expression.evaluate(this._evaluator);if(null==t||\"number\"==typeof t&&t!=t)return this._defaultValue;if(this._enumValues&&!(t in this._enumValues))throw new le(`Expected value to be one of ${Object.keys(this._enumValues).map((t=>JSON.stringify(t))).join(\", \")}, but found ${JSON.stringify(t)} instead.`);return t}catch(t){return this._warningHistory[t.message]||(this._warningHistory[t.message]=!0,\"undefined\"!=typeof console&&console.warn(t.message)),this._defaultValue}}}function kn(t){return Array.isArray(t)&&t.length>0&&\"string\"==typeof t[0]&&t[0]in Kr}function An(t,e){const r=new me(Kr,an,[],e?function(t){const e={color:ft,string:ut,number:ct,enum:ut,boolean:ht,formatted:gt,padding:yt,resolvedImage:vt,variableAnchorOffsetCollection:xt};return\"array\"===t.type?_t(e[t.value]||dt,t.length):e[t.type]}(e):void 0),n=r.parse(t,void 0,void 0,void 0,e&&\"string\"===e.type?{typeAnnotation:\"coerce\"}:void 0);return n?cn(new Tn(n,e)):un(r.errors)}class Mn{constructor(t,e){this.kind=t,this._styleExpression=e,this.isStateDependent=\"constant\"!==t&&!sn(e.expression)}evaluateWithoutErrorHandling(t,e,r,n,i,a){return this._styleExpression.evaluateWithoutErrorHandling(t,e,r,n,i,a)}evaluate(t,e,r,n,i,a){return this._styleExpression.evaluate(t,e,r,n,i,a)}}class Sn{constructor(t,e,r,n){this.kind=t,this.zoomStops=r,this._styleExpression=e,this.isStateDependent=\"camera\"!==t&&!sn(e.expression),this.interpolationType=n}evaluateWithoutErrorHandling(t,e,r,n,i,a){return this._styleExpression.evaluateWithoutErrorHandling(t,e,r,n,i,a)}evaluate(t,e,r,n,i,a){return this._styleExpression.evaluate(t,e,r,n,i,a)}interpolationFactor(t,e,r){return this.interpolationType?ze.interpolationFactor(this.interpolationType,t,e,r):0}}function En(t,e){const r=An(t,e);if(\"error\"===r.result)return r;const n=r.value.expression,i=on(n);if(!i&&!hn(e))return un([new ot(\"\",\"data expressions not supported\")]);const a=ln(n,[\"zoom\"]);if(!a&&!fn(e))return un([new ot(\"\",\"zoom expressions not supported\")]);const o=Ln(n);if(!o&&!a)return un([new ot(\"\",'\"zoom\" expression may only be used as input to a top-level \"step\" or \"interpolate\" expression.')]);if(o instanceof ot)return un([o]);if(o instanceof ze&&!pn(e))return un([new ot(\"\",'\"interpolate\" expressions cannot be used with this property')]);if(!o)return cn(new Mn(i?\"constant\":\"source\",r.value));const s=o instanceof ze?o.interpolation:void 0;return cn(new Sn(i?\"camera\":\"composite\",r.value,o.labels,s))}class Cn{constructor(t,e){this._parameters=t,this._specification=e,at(this,yn(this._parameters,this._specification))}static deserialize(t){return new Cn(t._parameters,t._specification)}static serialize(t){return{_parameters:t._parameters,_specification:t._specification}}}function Ln(t){let e=null;if(t instanceof ge)e=Ln(t.result);else if(t instanceof De){for(const r of t.args)if(e=Ln(r),e)break}else(t instanceof Ae||t instanceof ze)&&t.input instanceof Qr&&\"zoom\"===t.input.name&&(e=t);return e instanceof ot||t.eachChild((t=>{const r=Ln(t);r instanceof ot?e=r:!e&&r?e=new ot(\"\",'\"zoom\" expression may only be used as input to a top-level \"step\" or \"interpolate\" expression.'):e&&r&&e!==r&&(e=new ot(\"\",'Only one zoom-based \"step\" or \"interpolate\" subexpression may be used in an expression.'))})),e}function In(t){if(!0===t||!1===t)return!0;if(!Array.isArray(t)||0===t.length)return!1;switch(t[0]){case\"has\":return t.length>=2&&\"$id\"!==t[1]&&\"$type\"!==t[1];case\"in\":return t.length>=3&&(\"string\"!=typeof t[1]||Array.isArray(t[2]));case\"!in\":case\"!has\":case\"none\":return!1;case\"==\":case\"!=\":case\">\":case\">=\":case\"<\":case\"<=\":return 3!==t.length||Array.isArray(t[1])||Array.isArray(t[2]);case\"any\":case\"all\":for(const e of t.slice(1))if(!In(e)&&\"boolean\"!=typeof e)return!1;return!0;default:return!0}}const Pn={type:\"boolean\",default:!1,transition:!1,\"property-type\":\"data-driven\",expression:{interpolated:!1,parameters:[\"zoom\",\"feature\"]}};function zn(t){if(null==t)return{filter:()=>!0,needGeometry:!1};In(t)||(t=Rn(t));const e=An(t,Pn);if(\"error\"===e.result)throw new Error(e.value.map((t=>`${t.key}: ${t.message}`)).join(\", \"));return{filter:(t,r,n)=>e.value.evaluate(t,r,{},n),needGeometry:Dn(t)}}function On(t,e){return te?1:0}function Dn(t){if(!Array.isArray(t))return!1;if(\"within\"===t[0]||\"distance\"===t[0])return!0;for(let e=1;e\"===e||\"<=\"===e||\">=\"===e?Fn(t[1],t[2],e):\"any\"===e?(r=t.slice(1),[\"any\"].concat(r.map(Rn))):\"all\"===e?[\"all\"].concat(t.slice(1).map(Rn)):\"none\"===e?[\"all\"].concat(t.slice(1).map(Rn).map(jn)):\"in\"===e?Bn(t[1],t.slice(2)):\"!in\"===e?jn(Bn(t[1],t.slice(2))):\"has\"===e?Nn(t[1]):\"!has\"!==e||jn(Nn(t[1]));var r}function Fn(t,e,r){switch(t){case\"$type\":return[`filter-type-${r}`,e];case\"$id\":return[`filter-id-${r}`,e];default:return[`filter-${r}`,t,e]}}function Bn(t,e){if(0===e.length)return!1;switch(t){case\"$type\":return[\"filter-type-in\",[\"literal\",e]];case\"$id\":return[\"filter-id-in\",[\"literal\",e]];default:return e.length>200&&!e.some((t=>typeof t!=typeof e[0]))?[\"filter-in-large\",t,[\"literal\",e.sort(On)]]:[\"filter-in-small\",t,[\"literal\",e]]}}function Nn(t){switch(t){case\"$type\":return!0;case\"$id\":return[\"filter-has-id\"];default:return[\"filter-has\",t]}}function jn(t){return[\"!\",t]}function Un(t){const e=typeof t;if(\"number\"===e||\"boolean\"===e||\"string\"===e||null==t)return JSON.stringify(t);if(Array.isArray(t)){let e=\"[\";for(const r of t)e+=`${Un(r)},`;return`${e}]`}const r=Object.keys(t).sort();let n=\"{\";for(let e=0;en.maximum?[new it(e,r,`${r} is greater than the maximum value ${n.maximum}`)]:[]}function Xn(t){const e=t.valueSpec,r=Hn(t.value.type);let n,i,a,o={};const s=\"categorical\"!==r&&void 0===t.value.property,l=!s,c=\"array\"===dn(t.value.stops)&&\"array\"===dn(t.value.stops[0])&&\"object\"===dn(t.value.stops[0][0]),u=Zn({key:t.key,value:t.value,valueSpec:t.styleSpec.function,validateSpec:t.validateSpec,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{stops:function(t){if(\"identity\"===r)return[new it(t.key,t.value,'identity function may not have a \"stops\" property')];let e=[];const n=t.value;return e=e.concat(Wn({key:t.key,value:n,valueSpec:t.valueSpec,validateSpec:t.validateSpec,style:t.style,styleSpec:t.styleSpec,arrayElementValidator:h})),\"array\"===dn(n)&&0===n.length&&e.push(new it(t.key,n,\"array must have at least one stop\")),e},default:function(t){return t.validateSpec({key:t.key,value:t.value,valueSpec:e,validateSpec:t.validateSpec,style:t.style,styleSpec:t.styleSpec})}}});return\"identity\"===r&&s&&u.push(new it(t.key,t.value,'missing required property \"property\"')),\"identity\"===r||t.value.stops||u.push(new it(t.key,t.value,'missing required property \"stops\"')),\"exponential\"===r&&t.valueSpec.expression&&!pn(t.valueSpec)&&u.push(new it(t.key,t.value,\"exponential functions not supported\")),t.styleSpec.$version>=8&&(l&&!hn(t.valueSpec)?u.push(new it(t.key,t.value,\"property functions not supported\")):s&&!fn(t.valueSpec)&&u.push(new it(t.key,t.value,\"zoom functions not supported\"))),\"categorical\"!==r&&!c||void 0!==t.value.property||u.push(new it(t.key,t.value,'\"property\" property is required')),u;function h(t){let r=[];const n=t.value,s=t.key;if(\"array\"!==dn(n))return[new it(s,n,`array expected, ${dn(n)} found`)];if(2!==n.length)return[new it(s,n,`array length 2 expected, length ${n.length} found`)];if(c){if(\"object\"!==dn(n[0]))return[new it(s,n,`object expected, ${dn(n[0])} found`)];if(void 0===n[0].zoom)return[new it(s,n,\"object stop key must have zoom\")];if(void 0===n[0].value)return[new it(s,n,\"object stop key must have value\")];if(a&&a>Hn(n[0].zoom))return[new it(s,n[0].zoom,\"stop zoom values must appear in ascending order\")];Hn(n[0].zoom)!==a&&(a=Hn(n[0].zoom),i=void 0,o={}),r=r.concat(Zn({key:`${s}[0]`,value:n[0],valueSpec:{zoom:{}},validateSpec:t.validateSpec,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{zoom:Yn,value:f}}))}else r=r.concat(f({key:`${s}[0]`,value:n[0],valueSpec:{},validateSpec:t.validateSpec,style:t.style,styleSpec:t.styleSpec},n));return kn(Gn(n[1]))?r.concat([new it(`${s}[1]`,n[1],\"expressions are not allowed in function stops.\")]):r.concat(t.validateSpec({key:`${s}[1]`,value:n[1],valueSpec:e,validateSpec:t.validateSpec,style:t.style,styleSpec:t.styleSpec}))}function f(t,a){const s=dn(t.value),l=Hn(t.value),c=null!==t.value?t.value:a;if(n){if(s!==n)return[new it(t.key,c,`${s} stop domain type must match previous stop domain type ${n}`)]}else n=s;if(\"number\"!==s&&\"string\"!==s&&\"boolean\"!==s)return[new it(t.key,c,\"stop domain value must be a number, string, or boolean\")];if(\"number\"!==s&&\"categorical\"!==r){let n=`number expected, ${s} found`;return hn(e)&&void 0===r&&(n+='\\nIf you intended to use a categorical function, specify `\"type\": \"categorical\"`.'),[new it(t.key,c,n)]}return\"categorical\"!==r||\"number\"!==s||isFinite(l)&&Math.floor(l)===l?\"categorical\"!==r&&\"number\"===s&&void 0!==i&&lnew it(`${t.key}${e.key}`,t.value,e.message)));const r=e.value.expression||e.value._styleExpression.expression;if(\"property\"===t.expressionContext&&\"text-font\"===t.propertyKey&&!r.outputDefined())return[new it(t.key,t.value,`Invalid data expression for \"${t.propertyKey}\". Output values must be contained as literals within the expression.`)];if(\"property\"===t.expressionContext&&\"layout\"===t.propertyType&&!sn(r))return[new it(t.key,t.value,'\"feature-state\" data expressions are not supported with layout properties.')];if(\"filter\"===t.expressionContext&&!sn(r))return[new it(t.key,t.value,'\"feature-state\" data expressions are not supported with filters.')];if(t.expressionContext&&0===t.expressionContext.indexOf(\"cluster\")){if(!ln(r,[\"zoom\",\"feature-state\"]))return[new it(t.key,t.value,'\"zoom\" and \"feature-state\" expressions are not supported with cluster properties.')];if(\"cluster-initial\"===t.expressionContext&&!on(r))return[new it(t.key,t.value,\"Feature data expressions are not supported with initial expression part of cluster properties.\")]}return[]}function Jn(t){const e=t.key,r=t.value,n=t.valueSpec,i=[];return Array.isArray(n.values)?-1===n.values.indexOf(Hn(r))&&i.push(new it(e,r,`expected one of [${n.values.join(\", \")}], 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Presentation Forms-A\"](t)||zi[\"Arabic Presentation Forms-B\"](t)}function Ui(t,e){return!(!e&&ji(t)||t>=2304&&t<=3583||t>=3840&&t<=4255||zi.Khmer(t))}function Vi(t){for(const e of t)if(ji(e.charCodeAt(0)))return!0;return!1}const qi=new class{constructor(){this.applyArabicShaping=null,this.processBidirectionalText=null,this.processStyledBidirectionalText=null,this.pluginStatus=\"unavailable\",this.pluginURL=null}setState(t){this.pluginStatus=t.pluginStatus,this.pluginURL=t.pluginURL}getState(){return{pluginStatus:this.pluginStatus,pluginURL:this.pluginURL}}setMethods(t){this.applyArabicShaping=t.applyArabicShaping,this.processBidirectionalText=t.processBidirectionalText,this.processStyledBidirectionalText=t.processStyledBidirectionalText}isParsed(){return null!=this.applyArabicShaping&&null!=this.processBidirectionalText&&null!=this.processStyledBidirectionalText}getPluginURL(){return this.pluginURL}getRTLTextPluginStatus(){return this.pluginStatus}};class Hi{constructor(t,e){this.zoom=t,e?(this.now=e.now,this.fadeDuration=e.fadeDuration,this.zoomHistory=e.zoomHistory,this.transition=e.transition):(this.now=0,this.fadeDuration=0,this.zoomHistory=new Pi,this.transition={})}isSupportedScript(t){return function(t,e){for(const r of t)if(!Ui(r.charCodeAt(0),e))return!1;return!0}(t,\"loaded\"===qi.getRTLTextPluginStatus())}crossFadingFactor(){return 0===this.fadeDuration?1:Math.min((this.now-this.zoomHistory.lastIntegerZoomTime)/this.fadeDuration,1)}getCrossfadeParameters(){const t=this.zoom,e=t-Math.floor(t),r=this.crossFadingFactor();return t>this.zoomHistory.lastIntegerZoom?{fromScale:2,toScale:1,t:e+(1-e)*r}:{fromScale:.5,toScale:1,t:1-(1-r)*e}}}class Gi{constructor(t,e){this.property=t,this.value=e,this.expression=function(t,e){if(mn(t))return new Cn(t,e);if(kn(t)){const r=En(t,e);if(\"error\"===r.result)throw new Error(r.value.map((t=>`${t.key}: ${t.message}`)).join(\", \"));return r.value}{let r=t;return\"color\"===e.type&&\"string\"==typeof t?r=Xt.parse(t):\"padding\"!==e.type||\"number\"!=typeof t&&!Array.isArray(t)?\"variableAnchorOffsetCollection\"===e.type&&Array.isArray(t)&&(r=ee.parse(t)):r=Qt.parse(t),{kind:\"constant\",evaluate:()=>r}}}(void 0===e?t.specification.default:e,t.specification)}isDataDriven(){return\"source\"===this.expression.kind||\"composite\"===this.expression.kind}possiblyEvaluate(t,e,r){return this.property.possiblyEvaluate(this,t,e,r)}}class Zi{constructor(t){this.property=t,this.value=new Gi(t,void 0)}transitioned(t,e){return new Yi(this.property,this.value,e,y({},t.transition,this.transition),t.now)}untransitioned(){return new Yi(this.property,this.value,null,{},0)}}class Wi{constructor(t){this._properties=t,this._values=Object.create(t.defaultTransitionablePropertyValues)}getValue(t){return b(this._values[t].value.value)}setValue(t,e){Object.prototype.hasOwnProperty.call(this._values,t)||(this._values[t]=new Zi(this._values[t].property)),this._values[t].value=new Gi(this._values[t].property,null===e?void 0:b(e))}getTransition(t){return b(this._values[t].transition)}setTransition(t,e){Object.prototype.hasOwnProperty.call(this._values,t)||(this._values[t]=new Zi(this._values[t].property)),this._values[t].transition=b(e)||void 0}serialize(){const t={};for(const e of Object.keys(this._values)){const r=this.getValue(e);void 0!==r&&(t[e]=r);const n=this.getTransition(e);void 0!==n&&(t[`${e}-transition`]=n)}return t}transitioned(t,e){const r=new Xi(this._properties);for(const n of Object.keys(this._values))r._values[n]=this._values[n].transitioned(t,e._values[n]);return r}untransitioned(){const t=new Xi(this._properties);for(const e of Object.keys(this._values))t._values[e]=this._values[e].untransitioned();return t}}class Yi{constructor(t,e,r,n,i){this.property=t,this.value=e,this.begin=i+n.delay||0,this.end=this.begin+n.duration||0,t.specification.transition&&(n.delay||n.duration)&&(this.prior=r)}possiblyEvaluate(t,e,r){const n=t.now||0,i=this.value.possiblyEvaluate(t,e,r),a=this.prior;if(a){if(n>this.end)return this.prior=null,i;if(this.value.isDataDriven())return this.prior=null,i;if(n=1)return 1;const e=t*t,r=e*t;return 4*(t<.5?r:3*(t-e)+r-.75)}(o))}}return i}}class Xi{constructor(t){this._properties=t,this._values=Object.create(t.defaultTransitioningPropertyValues)}possiblyEvaluate(t,e,r){const n=new Ki(this._properties);for(const i of Object.keys(this._values))n._values[i]=this._values[i].possiblyEvaluate(t,e,r);return n}hasTransition(){for(const t of Object.keys(this._values))if(this._values[t].prior)return!0;return!1}}class $i{constructor(t){this._properties=t,this._values=Object.create(t.defaultPropertyValues)}hasValue(t){return void 0!==this._values[t].value}getValue(t){return b(this._values[t].value)}setValue(t,e){this._values[t]=new Gi(this._values[t].property,null===e?void 0:b(e))}serialize(){const t={};for(const e of Object.keys(this._values)){const r=this.getValue(e);void 0!==r&&(t[e]=r)}return t}possiblyEvaluate(t,e,r){const n=new Ki(this._properties);for(const i of Object.keys(this._values))n._values[i]=this._values[i].possiblyEvaluate(t,e,r);return n}}class Ji{constructor(t,e,r){this.property=t,this.value=e,this.parameters=r}isConstant(){return\"constant\"===this.value.kind}constantOr(t){return\"constant\"===this.value.kind?this.value.value:t}evaluate(t,e,r,n){return this.property.evaluate(this.value,this.parameters,t,e,r,n)}}class Ki{constructor(t){this._properties=t,this._values=Object.create(t.defaultPossiblyEvaluatedValues)}get(t){return this._values[t]}}class Qi{constructor(t){this.specification=t}possiblyEvaluate(t,e){if(t.isDataDriven())throw new Error(\"Value should not be data driven\");return t.expression.evaluate(e)}interpolate(t,e,r){const n=this.specification.type,i=Pe[n];return i?i(t,e,r):t}}class ta{constructor(t,e){this.specification=t,this.overrides=e}possiblyEvaluate(t,e,r,n){return\"constant\"===t.expression.kind||\"camera\"===t.expression.kind?new Ji(this,{kind:\"constant\",value:t.expression.evaluate(e,null,{},r,n)},e):new Ji(this,t.expression,e)}interpolate(t,e,r){if(\"constant\"!==t.value.kind||\"constant\"!==e.value.kind)return t;if(void 0===t.value.value||void 0===e.value.value)return new Ji(this,{kind:\"constant\",value:void 0},t.parameters);const n=this.specification.type,i=Pe[n];if(i){const n=i(t.value.value,e.value.value,r);return new Ji(this,{kind:\"constant\",value:n},t.parameters)}return t}evaluate(t,e,r,n,i,a){return\"constant\"===t.kind?t.value:t.evaluate(e,r,n,i,a)}}class ea extends ta{possiblyEvaluate(t,e,r,n){if(void 0===t.value)return new Ji(this,{kind:\"constant\",value:void 0},e);if(\"constant\"===t.expression.kind){const i=t.expression.evaluate(e,null,{},r,n),a=\"resolvedImage\"===t.property.specification.type&&\"string\"!=typeof i?i.name:i,o=this._calculate(a,a,a,e);return new Ji(this,{kind:\"constant\",value:o},e)}if(\"camera\"===t.expression.kind){const r=this._calculate(t.expression.evaluate({zoom:e.zoom-1}),t.expression.evaluate({zoom:e.zoom}),t.expression.evaluate({zoom:e.zoom+1}),e);return new Ji(this,{kind:\"constant\",value:r},e)}return new Ji(this,t.expression,e)}evaluate(t,e,r,n,i,a){if(\"source\"===t.kind){const o=t.evaluate(e,r,n,i,a);return this._calculate(o,o,o,e)}return\"composite\"===t.kind?this._calculate(t.evaluate({zoom:Math.floor(e.zoom)-1},r,n),t.evaluate({zoom:Math.floor(e.zoom)},r,n),t.evaluate({zoom:Math.floor(e.zoom)+1},r,n),e):t.value}_calculate(t,e,r,n){return n.zoom>n.zoomHistory.lastIntegerZoom?{from:t,to:e}:{from:r,to:e}}interpolate(t){return t}}class ra{constructor(t){this.specification=t}possiblyEvaluate(t,e,r,n){if(void 0!==t.value){if(\"constant\"===t.expression.kind){const i=t.expression.evaluate(e,null,{},r,n);return this._calculate(i,i,i,e)}return this._calculate(t.expression.evaluate(new Hi(Math.floor(e.zoom-1),e)),t.expression.evaluate(new Hi(Math.floor(e.zoom),e)),t.expression.evaluate(new Hi(Math.floor(e.zoom+1),e)),e)}}_calculate(t,e,r,n){return n.zoom>n.zoomHistory.lastIntegerZoom?{from:t,to:e}:{from:r,to:e}}interpolate(t){return t}}class na{constructor(t){this.specification=t}possiblyEvaluate(t,e,r,n){return!!t.expression.evaluate(e,null,{},r,n)}interpolate(){return!1}}class ia{constructor(t){this.properties=t,this.defaultPropertyValues={},this.defaultTransitionablePropertyValues={},this.defaultTransitioningPropertyValues={},this.defaultPossiblyEvaluatedValues={},this.overridableProperties=[];for(const e in t){const r=t[e];r.specification.overridable&&this.overridableProperties.push(e);const n=this.defaultPropertyValues[e]=new Gi(r,void 0),i=this.defaultTransitionablePropertyValues[e]=new Zi(r);this.defaultTransitioningPropertyValues[e]=i.untransitioned(),this.defaultPossiblyEvaluatedValues[e]=n.possiblyEvaluate({})}}}Mi(\"DataDrivenProperty\",ta),Mi(\"DataConstantProperty\",Qi),Mi(\"CrossFadedDataDrivenProperty\",ea),Mi(\"CrossFadedProperty\",ra),Mi(\"ColorRampProperty\",na);const aa=\"-transition\";class oa extends G{constructor(t,e){if(super(),this.id=t.id,this.type=t.type,this._featureFilter={filter:()=>!0,needGeometry:!1},\"custom\"!==t.type&&(this.metadata=t.metadata,this.minzoom=t.minzoom,this.maxzoom=t.maxzoom,\"background\"!==t.type&&(this.source=t.source,this.sourceLayer=t[\"source-layer\"],this.filter=t.filter),e.layout&&(this._unevaluatedLayout=new $i(e.layout)),e.paint)){this._transitionablePaint=new Wi(e.paint);for(const e in t.paint)this.setPaintProperty(e,t.paint[e],{validate:!1});for(const e in t.layout)this.setLayoutProperty(e,t.layout[e],{validate:!1});this._transitioningPaint=this._transitionablePaint.untransitioned(),this.paint=new Ki(e.paint)}}getCrossfadeParameters(){return this._crossfadeParameters}getLayoutProperty(t){return\"visibility\"===t?this.visibility:this._unevaluatedLayout.getValue(t)}setLayoutProperty(t,e,r={}){if(null!=e){const n=`layers.${this.id}.layout.${t}`;if(this._validate(wi,n,t,e,r))return}\"visibility\"!==t?this._unevaluatedLayout.setValue(t,e):this.visibility=e}getPaintProperty(t){return t.endsWith(aa)?this._transitionablePaint.getTransition(t.slice(0,-11)):this._transitionablePaint.getValue(t)}setPaintProperty(t,e,r={}){if(null!=e){const n=`layers.${this.id}.paint.${t}`;if(this._validate(bi,n,t,e,r))return!1}if(t.endsWith(aa))return this._transitionablePaint.setTransition(t.slice(0,-11),e||void 0),!1;{const r=this._transitionablePaint._values[t],n=\"cross-faded-data-driven\"===r.property.specification[\"property-type\"],i=r.value.isDataDriven(),a=r.value;this._transitionablePaint.setValue(t,e),this._handleSpecialPaintPropertyUpdate(t);const o=this._transitionablePaint._values[t].value;return o.isDataDriven()||i||n||this._handleOverridablePaintPropertyUpdate(t,a,o)}}_handleSpecialPaintPropertyUpdate(t){}_handleOverridablePaintPropertyUpdate(t,e,r){return!1}isHidden(t){return!!(this.minzoom&&t=this.maxzoom)||\"none\"===this.visibility}updateTransitions(t){this._transitioningPaint=this._transitionablePaint.transitioned(t,this._transitioningPaint)}hasTransition(){return this._transitioningPaint.hasTransition()}recalculate(t,e){t.getCrossfadeParameters&&(this._crossfadeParameters=t.getCrossfadeParameters()),this._unevaluatedLayout&&(this.layout=this._unevaluatedLayout.possiblyEvaluate(t,void 0,e)),this.paint=this._transitioningPaint.possiblyEvaluate(t,void 0,e)}serialize(){const t={id:this.id,type:this.type,source:this.source,\"source-layer\":this.sourceLayer,metadata:this.metadata,minzoom:this.minzoom,maxzoom:this.maxzoom,filter:this.filter,layout:this._unevaluatedLayout&&this._unevaluatedLayout.serialize(),paint:this._transitionablePaint&&this._transitionablePaint.serialize()};return this.visibility&&(t.layout=t.layout||{},t.layout.visibility=this.visibility),_(t,((t,e)=>!(void 0===t||\"layout\"===e&&!Object.keys(t).length||\"paint\"===e&&!Object.keys(t).length)))}_validate(t,e,r,n,i={}){return(!i||!1!==i.validate)&&Ti(this,t.call(vi,{key:e,layerType:this.type,objectKey:r,value:n,styleSpec:Z,style:{glyphs:!0,sprite:!0}}))}is3D(){return!1}isTileClipped(){return!1}hasOffscreenPass(){return!1}resize(){}isStateDependent(){for(const t in this.paint._values){const e=this.paint.get(t);if(e instanceof Ji&&hn(e.property.specification)&&(\"source\"===e.value.kind||\"composite\"===e.value.kind)&&e.value.isStateDependent)return!0}return!1}}const sa={Int8:Int8Array,Uint8:Uint8Array,Int16:Int16Array,Uint16:Uint16Array,Int32:Int32Array,Uint32:Uint32Array,Float32:Float32Array};class la{constructor(t,e){this._structArray=t,this._pos1=e*this.size,this._pos2=this._pos1/2,this._pos4=this._pos1/4,this._pos8=this._pos1/8}}class ca{constructor(){this.isTransferred=!1,this.capacity=-1,this.resize(0)}static serialize(t,e){return t._trim(),e&&(t.isTransferred=!0,e.push(t.arrayBuffer)),{length:t.length,arrayBuffer:t.arrayBuffer}}static deserialize(t){const e=Object.create(this.prototype);return e.arrayBuffer=t.arrayBuffer,e.length=t.length,e.capacity=t.arrayBuffer.byteLength/e.bytesPerElement,e._refreshViews(),e}_trim(){this.length!==this.capacity&&(this.capacity=this.length,this.arrayBuffer=this.arrayBuffer.slice(0,this.length*this.bytesPerElement),this._refreshViews())}clear(){this.length=0}resize(t){this.reserve(t),this.length=t}reserve(t){if(t>this.capacity){this.capacity=Math.max(t,Math.floor(5*this.capacity),128),this.arrayBuffer=new ArrayBuffer(this.capacity*this.bytesPerElement);const e=this.uint8;this._refreshViews(),e&&this.uint8.set(e)}}_refreshViews(){throw new Error(\"_refreshViews() must be implemented by each concrete StructArray layout\")}}function ua(t,e=1){let r=0,n=0;return{members:t.map((t=>{const i=(s=t.type,sa[s].BYTES_PER_ELEMENT),a=r=ha(r,Math.max(e,i)),o=t.components||1;var s;return n=Math.max(n,i),r+=i*o,{name:t.name,type:t.type,components:o,offset:a}})),size:ha(r,Math.max(n,e)),alignment:e}}function ha(t,e){return Math.ceil(t/e)*e}class fa extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(t,e){const r=this.length;return this.resize(r+1),this.emplace(r,t,e)}emplace(t,e,r){const n=2*t;return this.int16[n+0]=e,this.int16[n+1]=r,t}}fa.prototype.bytesPerElement=4,Mi(\"StructArrayLayout2i4\",fa);class pa extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(t,e,r){const n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)}emplace(t,e,r,n){const i=3*t;return this.int16[i+0]=e,this.int16[i+1]=r,this.int16[i+2]=n,t}}pa.prototype.bytesPerElement=6,Mi(\"StructArrayLayout3i6\",pa);class da extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(t,e,r,n){const i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)}emplace(t,e,r,n,i){const a=4*t;return this.int16[a+0]=e,this.int16[a+1]=r,this.int16[a+2]=n,this.int16[a+3]=i,t}}da.prototype.bytesPerElement=8,Mi(\"StructArrayLayout4i8\",da);class ma extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(t,e,r,n,i,a){const o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)}emplace(t,e,r,n,i,a,o){const s=6*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t}}ma.prototype.bytesPerElement=12,Mi(\"StructArrayLayout2i4i12\",ma);class ga extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(t,e,r,n,i,a){const o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)}emplace(t,e,r,n,i,a,o){const s=4*t,l=8*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.uint8[l+4]=n,this.uint8[l+5]=i,this.uint8[l+6]=a,this.uint8[l+7]=o,t}}ga.prototype.bytesPerElement=8,Mi(\"StructArrayLayout2i4ub8\",ga);class ya extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(t,e){const r=this.length;return this.resize(r+1),this.emplace(r,t,e)}emplace(t,e,r){const n=2*t;return this.float32[n+0]=e,this.float32[n+1]=r,t}}ya.prototype.bytesPerElement=8,Mi(\"StructArrayLayout2f8\",ya);class va extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(t,e,r,n,i,a,o,s,l,c){const u=this.length;return this.resize(u+1),this.emplace(u,t,e,r,n,i,a,o,s,l,c)}emplace(t,e,r,n,i,a,o,s,l,c,u){const h=10*t;return this.uint16[h+0]=e,this.uint16[h+1]=r,this.uint16[h+2]=n,this.uint16[h+3]=i,this.uint16[h+4]=a,this.uint16[h+5]=o,this.uint16[h+6]=s,this.uint16[h+7]=l,this.uint16[h+8]=c,this.uint16[h+9]=u,t}}va.prototype.bytesPerElement=20,Mi(\"StructArrayLayout10ui20\",va);class xa extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(t,e,r,n,i,a,o,s,l,c,u,h){const f=this.length;return this.resize(f+1),this.emplace(f,t,e,r,n,i,a,o,s,l,c,u,h)}emplace(t,e,r,n,i,a,o,s,l,c,u,h,f){const p=12*t;return this.int16[p+0]=e,this.int16[p+1]=r,this.int16[p+2]=n,this.int16[p+3]=i,this.uint16[p+4]=a,this.uint16[p+5]=o,this.uint16[p+6]=s,this.uint16[p+7]=l,this.int16[p+8]=c,this.int16[p+9]=u,this.int16[p+10]=h,this.int16[p+11]=f,t}}xa.prototype.bytesPerElement=24,Mi(\"StructArrayLayout4i4ui4i24\",xa);class _a extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(t,e,r){const n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)}emplace(t,e,r,n){const i=3*t;return this.float32[i+0]=e,this.float32[i+1]=r,this.float32[i+2]=n,t}}_a.prototype.bytesPerElement=12,Mi(\"StructArrayLayout3f12\",_a);class ba extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer)}emplaceBack(t){const e=this.length;return this.resize(e+1),this.emplace(e,t)}emplace(t,e){const r=1*t;return this.uint32[r+0]=e,t}}ba.prototype.bytesPerElement=4,Mi(\"StructArrayLayout1ul4\",ba);class wa extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(t,e,r,n,i,a,o,s,l){const c=this.length;return this.resize(c+1),this.emplace(c,t,e,r,n,i,a,o,s,l)}emplace(t,e,r,n,i,a,o,s,l,c){const u=10*t,h=5*t;return this.int16[u+0]=e,this.int16[u+1]=r,this.int16[u+2]=n,this.int16[u+3]=i,this.int16[u+4]=a,this.int16[u+5]=o,this.uint32[h+3]=s,this.uint16[u+8]=l,this.uint16[u+9]=c,t}}wa.prototype.bytesPerElement=20,Mi(\"StructArrayLayout6i1ul2ui20\",wa);class Ta extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(t,e,r,n,i,a){const o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)}emplace(t,e,r,n,i,a,o){const s=6*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t}}Ta.prototype.bytesPerElement=12,Mi(\"StructArrayLayout2i2i2i12\",Ta);class ka extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(t,e,r,n,i){const a=this.length;return this.resize(a+1),this.emplace(a,t,e,r,n,i)}emplace(t,e,r,n,i,a){const o=4*t,s=8*t;return this.float32[o+0]=e,this.float32[o+1]=r,this.float32[o+2]=n,this.int16[s+6]=i,this.int16[s+7]=a,t}}ka.prototype.bytesPerElement=16,Mi(\"StructArrayLayout2f1f2i16\",ka);class Aa extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(t,e,r,n,i,a){const o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)}emplace(t,e,r,n,i,a,o){const s=16*t,l=4*t,c=8*t;return this.uint8[s+0]=e,this.uint8[s+1]=r,this.float32[l+1]=n,this.float32[l+2]=i,this.int16[c+6]=a,this.int16[c+7]=o,t}}Aa.prototype.bytesPerElement=16,Mi(\"StructArrayLayout2ub2f2i16\",Aa);class Ma extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(t,e,r){const n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)}emplace(t,e,r,n){const i=3*t;return this.uint16[i+0]=e,this.uint16[i+1]=r,this.uint16[i+2]=n,t}}Ma.prototype.bytesPerElement=6,Mi(\"StructArrayLayout3ui6\",Ma);class Sa extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(t,e,r,n,i,a,o,s,l,c,u,h,f,p,d,m,g){const y=this.length;return this.resize(y+1),this.emplace(y,t,e,r,n,i,a,o,s,l,c,u,h,f,p,d,m,g)}emplace(t,e,r,n,i,a,o,s,l,c,u,h,f,p,d,m,g,y){const v=24*t,x=12*t,_=48*t;return this.int16[v+0]=e,this.int16[v+1]=r,this.uint16[v+2]=n,this.uint16[v+3]=i,this.uint32[x+2]=a,this.uint32[x+3]=o,this.uint32[x+4]=s,this.uint16[v+10]=l,this.uint16[v+11]=c,this.uint16[v+12]=u,this.float32[x+7]=h,this.float32[x+8]=f,this.uint8[_+36]=p,this.uint8[_+37]=d,this.uint8[_+38]=m,this.uint32[x+10]=g,this.int16[v+22]=y,t}}Sa.prototype.bytesPerElement=48,Mi(\"StructArrayLayout2i2ui3ul3ui2f3ub1ul1i48\",Sa);class Ea extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(t,e,r,n,i,a,o,s,l,c,u,h,f,p,d,m,g,y,v,x,_,b,w,T,k,A,M,S){const E=this.length;return this.resize(E+1),this.emplace(E,t,e,r,n,i,a,o,s,l,c,u,h,f,p,d,m,g,y,v,x,_,b,w,T,k,A,M,S)}emplace(t,e,r,n,i,a,o,s,l,c,u,h,f,p,d,m,g,y,v,x,_,b,w,T,k,A,M,S,E){const C=32*t,L=16*t;return this.int16[C+0]=e,this.int16[C+1]=r,this.int16[C+2]=n,this.int16[C+3]=i,this.int16[C+4]=a,this.int16[C+5]=o,this.int16[C+6]=s,this.int16[C+7]=l,this.uint16[C+8]=c,this.uint16[C+9]=u,this.uint16[C+10]=h,this.uint16[C+11]=f,this.uint16[C+12]=p,this.uint16[C+13]=d,this.uint16[C+14]=m,this.uint16[C+15]=g,this.uint16[C+16]=y,this.uint16[C+17]=v,this.uint16[C+18]=x,this.uint16[C+19]=_,this.uint16[C+20]=b,this.uint16[C+21]=w,this.uint16[C+22]=T,this.uint32[L+12]=k,this.float32[L+13]=A,this.float32[L+14]=M,this.uint16[C+30]=S,this.uint16[C+31]=E,t}}Ea.prototype.bytesPerElement=64,Mi(\"StructArrayLayout8i15ui1ul2f2ui64\",Ea);class Ca extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(t){const e=this.length;return this.resize(e+1),this.emplace(e,t)}emplace(t,e){const r=1*t;return this.float32[r+0]=e,t}}Ca.prototype.bytesPerElement=4,Mi(\"StructArrayLayout1f4\",Ca);class La extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(t,e,r){const n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)}emplace(t,e,r,n){const i=6*t,a=3*t;return this.uint16[i+0]=e,this.float32[a+1]=r,this.float32[a+2]=n,t}}La.prototype.bytesPerElement=12,Mi(\"StructArrayLayout1ui2f12\",La);class Ia extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(t,e,r){const n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)}emplace(t,e,r,n){const i=2*t,a=4*t;return this.uint32[i+0]=e,this.uint16[a+2]=r,this.uint16[a+3]=n,t}}Ia.prototype.bytesPerElement=8,Mi(\"StructArrayLayout1ul2ui8\",Ia);class Pa extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(t,e){const r=this.length;return this.resize(r+1),this.emplace(r,t,e)}emplace(t,e,r){const n=2*t;return this.uint16[n+0]=e,this.uint16[n+1]=r,t}}Pa.prototype.bytesPerElement=4,Mi(\"StructArrayLayout2ui4\",Pa);class za extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(t){const e=this.length;return this.resize(e+1),this.emplace(e,t)}emplace(t,e){const r=1*t;return this.uint16[r+0]=e,t}}za.prototype.bytesPerElement=2,Mi(\"StructArrayLayout1ui2\",za);class Oa extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(t,e,r,n){const i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)}emplace(t,e,r,n,i){const a=4*t;return this.float32[a+0]=e,this.float32[a+1]=r,this.float32[a+2]=n,this.float32[a+3]=i,t}}Oa.prototype.bytesPerElement=16,Mi(\"StructArrayLayout4f16\",Oa);class Da extends la{get anchorPointX(){return this._structArray.int16[this._pos2+0]}get anchorPointY(){return this._structArray.int16[this._pos2+1]}get x1(){return this._structArray.int16[this._pos2+2]}get y1(){return this._structArray.int16[this._pos2+3]}get x2(){return this._structArray.int16[this._pos2+4]}get y2(){return this._structArray.int16[this._pos2+5]}get featureIndex(){return this._structArray.uint32[this._pos4+3]}get sourceLayerIndex(){return this._structArray.uint16[this._pos2+8]}get bucketIndex(){return this._structArray.uint16[this._pos2+9]}get anchorPoint(){return new a(this.anchorPointX,this.anchorPointY)}}Da.prototype.size=20;class Ra extends wa{get(t){return new Da(this,t)}}Mi(\"CollisionBoxArray\",Ra);class Fa extends la{get anchorX(){return this._structArray.int16[this._pos2+0]}get anchorY(){return this._structArray.int16[this._pos2+1]}get glyphStartIndex(){return this._structArray.uint16[this._pos2+2]}get numGlyphs(){return this._structArray.uint16[this._pos2+3]}get vertexStartIndex(){return this._structArray.uint32[this._pos4+2]}get lineStartIndex(){return this._structArray.uint32[this._pos4+3]}get lineLength(){return this._structArray.uint32[this._pos4+4]}get segment(){return this._structArray.uint16[this._pos2+10]}get lowerSize(){return this._structArray.uint16[this._pos2+11]}get upperSize(){return this._structArray.uint16[this._pos2+12]}get lineOffsetX(){return this._structArray.float32[this._pos4+7]}get lineOffsetY(){return this._structArray.float32[this._pos4+8]}get writingMode(){return this._structArray.uint8[this._pos1+36]}get placedOrientation(){return this._structArray.uint8[this._pos1+37]}set placedOrientation(t){this._structArray.uint8[this._pos1+37]=t}get hidden(){return this._structArray.uint8[this._pos1+38]}set hidden(t){this._structArray.uint8[this._pos1+38]=t}get crossTileID(){return this._structArray.uint32[this._pos4+10]}set crossTileID(t){this._structArray.uint32[this._pos4+10]=t}get associatedIconIndex(){return this._structArray.int16[this._pos2+22]}}Fa.prototype.size=48;class Ba extends Sa{get(t){return new Fa(this,t)}}Mi(\"PlacedSymbolArray\",Ba);class Na extends la{get anchorX(){return this._structArray.int16[this._pos2+0]}get anchorY(){return this._structArray.int16[this._pos2+1]}get rightJustifiedTextSymbolIndex(){return this._structArray.int16[this._pos2+2]}get centerJustifiedTextSymbolIndex(){return this._structArray.int16[this._pos2+3]}get leftJustifiedTextSymbolIndex(){return this._structArray.int16[this._pos2+4]}get verticalPlacedTextSymbolIndex(){return this._structArray.int16[this._pos2+5]}get placedIconSymbolIndex(){return this._structArray.int16[this._pos2+6]}get verticalPlacedIconSymbolIndex(){return this._structArray.int16[this._pos2+7]}get key(){return this._structArray.uint16[this._pos2+8]}get textBoxStartIndex(){return this._structArray.uint16[this._pos2+9]}get textBoxEndIndex(){return this._structArray.uint16[this._pos2+10]}get verticalTextBoxStartIndex(){return this._structArray.uint16[this._pos2+11]}get verticalTextBoxEndIndex(){return this._structArray.uint16[this._pos2+12]}get iconBoxStartIndex(){return this._structArray.uint16[this._pos2+13]}get iconBoxEndIndex(){return this._structArray.uint16[this._pos2+14]}get verticalIconBoxStartIndex(){return this._structArray.uint16[this._pos2+15]}get verticalIconBoxEndIndex(){return this._structArray.uint16[this._pos2+16]}get featureIndex(){return this._structArray.uint16[this._pos2+17]}get numHorizontalGlyphVertices(){return this._structArray.uint16[this._pos2+18]}get numVerticalGlyphVertices(){return this._structArray.uint16[this._pos2+19]}get numIconVertices(){return this._structArray.uint16[this._pos2+20]}get numVerticalIconVertices(){return this._structArray.uint16[this._pos2+21]}get useRuntimeCollisionCircles(){return this._structArray.uint16[this._pos2+22]}get crossTileID(){return this._structArray.uint32[this._pos4+12]}set crossTileID(t){this._structArray.uint32[this._pos4+12]=t}get textBoxScale(){return this._structArray.float32[this._pos4+13]}get collisionCircleDiameter(){return this._structArray.float32[this._pos4+14]}get textAnchorOffsetStartIndex(){return this._structArray.uint16[this._pos2+30]}get textAnchorOffsetEndIndex(){return this._structArray.uint16[this._pos2+31]}}Na.prototype.size=64;class ja extends Ea{get(t){return new Na(this,t)}}Mi(\"SymbolInstanceArray\",ja);class Ua extends Ca{getoffsetX(t){return this.float32[1*t+0]}}Mi(\"GlyphOffsetArray\",Ua);class Va extends pa{getx(t){return this.int16[3*t+0]}gety(t){return this.int16[3*t+1]}gettileUnitDistanceFromAnchor(t){return this.int16[3*t+2]}}Mi(\"SymbolLineVertexArray\",Va);class qa extends la{get textAnchor(){return this._structArray.uint16[this._pos2+0]}get textOffset0(){return this._structArray.float32[this._pos4+1]}get textOffset1(){return this._structArray.float32[this._pos4+2]}}qa.prototype.size=12;class Ha extends La{get(t){return new qa(this,t)}}Mi(\"TextAnchorOffsetArray\",Ha);class Ga extends la{get featureIndex(){return this._structArray.uint32[this._pos4+0]}get sourceLayerIndex(){return this._structArray.uint16[this._pos2+2]}get bucketIndex(){return this._structArray.uint16[this._pos2+3]}}Ga.prototype.size=8;class Za extends Ia{get(t){return new Ga(this,t)}}Mi(\"FeatureIndexArray\",Za);class Wa extends fa{}class Ya extends fa{}class Xa extends fa{}class $a extends ma{}class Ja extends ga{}class Ka extends ya{}class Qa extends va{}class to extends xa{}class eo extends _a{}class ro extends ba{}class no extends Ta{}class io extends Aa{}class ao extends Ma{}class oo extends Pa{}const so=ua([{name:\"a_pos\",components:2,type:\"Int16\"}],4),{members:lo,size:co,alignment:uo}=so;class ho{constructor(t=[]){this.segments=t}prepareSegment(t,e,r,n){let i=this.segments[this.segments.length-1];return t>ho.MAX_VERTEX_ARRAY_LENGTH&&T(`Max vertices per segment is ${ho.MAX_VERTEX_ARRAY_LENGTH}: bucket requested ${t}`),(!i||i.vertexLength+t>ho.MAX_VERTEX_ARRAY_LENGTH||i.sortKey!==n)&&(i={vertexOffset:e.length,primitiveOffset:r.length,vertexLength:0,primitiveLength:0},void 0!==n&&(i.sortKey=n),this.segments.push(i)),i}get(){return this.segments}destroy(){for(const t of this.segments)for(const e in t.vaos)t.vaos[e].destroy()}static simpleSegment(t,e,r,n){return new ho([{vertexOffset:t,primitiveOffset:e,vertexLength:r,primitiveLength:n,vaos:{},sortKey:0}])}}function fo(t,e){return 256*(t=m(Math.floor(t),0,255))+m(Math.floor(e),0,255)}ho.MAX_VERTEX_ARRAY_LENGTH=Math.pow(2,16)-1,Mi(\"SegmentVector\",ho);const po=ua([{name:\"a_pattern_from\",components:4,type:\"Uint16\"},{name:\"a_pattern_to\",components:4,type:\"Uint16\"},{name:\"a_pixel_ratio_from\",components:1,type:\"Uint16\"},{name:\"a_pixel_ratio_to\",components:1,type:\"Uint16\"}]);var mo={exports:{}},go={exports:{}};!function(t){t.exports=function(t,e){var r,n,i,a,o,s,l,c;for(r=3&t.length,n=t.length-r,i=e,o=3432918353,s=461845907,c=0;c>>16)*o&65535)<<16)&4294967295)<<15|l>>>17))*s+(((l>>>16)*s&65535)<<16)&4294967295)<<13|i>>>19))+((5*(i>>>16)&65535)<<16)&4294967295))+((58964+(a>>>16)&65535)<<16);switch(l=0,r){case 3:l^=(255&t.charCodeAt(c+2))<<16;case 2:l^=(255&t.charCodeAt(c+1))<<8;case 1:i^=l=(65535&(l=(l=(65535&(l^=255&t.charCodeAt(c)))*o+(((l>>>16)*o&65535)<<16)&4294967295)<<15|l>>>17))*s+(((l>>>16)*s&65535)<<16)&4294967295}return i^=t.length,i=2246822507*(65535&(i^=i>>>16))+((2246822507*(i>>>16)&65535)<<16)&4294967295,i=3266489909*(65535&(i^=i>>>13))+((3266489909*(i>>>16)&65535)<<16)&4294967295,(i^=i>>>16)>>>0}}(go);var yo=go.exports,vo={exports:{}};!function(t){t.exports=function(t,e){for(var r,n=t.length,i=e^n,a=0;n>=4;)r=1540483477*(65535&(r=255&t.charCodeAt(a)|(255&t.charCodeAt(++a))<<8|(255&t.charCodeAt(++a))<<16|(255&t.charCodeAt(++a))<<24))+((1540483477*(r>>>16)&65535)<<16),i=1540483477*(65535&i)+((1540483477*(i>>>16)&65535)<<16)^(r=1540483477*(65535&(r^=r>>>24))+((1540483477*(r>>>16)&65535)<<16)),n-=4,++a;switch(n){case 3:i^=(255&t.charCodeAt(a+2))<<16;case 2:i^=(255&t.charCodeAt(a+1))<<8;case 1:i=1540483477*(65535&(i^=255&t.charCodeAt(a)))+((1540483477*(i>>>16)&65535)<<16)}return i=1540483477*(65535&(i^=i>>>13))+((1540483477*(i>>>16)&65535)<<16),(i^=i>>>15)>>>0}}(vo);var xo=yo,_o=vo.exports;mo.exports=xo,mo.exports.murmur3=xo,mo.exports.murmur2=_o;var bo=r(mo.exports);class wo{constructor(){this.ids=[],this.positions=[],this.indexed=!1}add(t,e,r,n){this.ids.push(To(t)),this.positions.push(e,r,n)}getPositions(t){if(!this.indexed)throw new Error(\"Trying to get index, but feature positions are not indexed\");const e=To(t);let r=0,n=this.ids.length-1;for(;r>1;this.ids[t]>=e?n=t:r=t+1}const i=[];for(;this.ids[r]===e;){const t=this.positions[3*r],e=this.positions[3*r+1],n=this.positions[3*r+2];i.push({index:t,start:e,end:n}),r++}return i}static serialize(t,e){const r=new Float64Array(t.ids),n=new Uint32Array(t.positions);return ko(r,n,0,r.length-1),e&&e.push(r.buffer,n.buffer),{ids:r,positions:n}}static deserialize(t){const e=new wo;return e.ids=t.ids,e.positions=t.positions,e.indexed=!0,e}}function To(t){const e=+t;return!isNaN(e)&&e<=Number.MAX_SAFE_INTEGER?e:bo(String(t))}function ko(t,e,r,n){for(;r>1];let a=r-1,o=n+1;for(;;){do{a++}while(t[a]i);if(a>=o)break;Ao(t,a,o),Ao(e,3*a,3*o),Ao(e,3*a+1,3*o+1),Ao(e,3*a+2,3*o+2)}o-r`u_${t}`)),this.type=r}setUniform(t,e,r){t.set(r.constantOr(this.value))}getBinding(t,e,r){return\"color\"===this.type?new Co(t,e):new So(t,e)}}class zo{constructor(t,e){this.uniformNames=e.map((t=>`u_${t}`)),this.patternFrom=null,this.patternTo=null,this.pixelRatioFrom=1,this.pixelRatioTo=1}setConstantPatternPositions(t,e){this.pixelRatioFrom=e.pixelRatio,this.pixelRatioTo=t.pixelRatio,this.patternFrom=e.tlbr,this.patternTo=t.tlbr}setUniform(t,e,r,n){const i=\"u_pattern_to\"===n?this.patternTo:\"u_pattern_from\"===n?this.patternFrom:\"u_pixel_ratio_to\"===n?this.pixelRatioTo:\"u_pixel_ratio_from\"===n?this.pixelRatioFrom:null;i&&t.set(i)}getBinding(t,e,r){return\"u_pattern\"===r.substr(0,9)?new Eo(t,e):new So(t,e)}}class Oo{constructor(t,e,r,n){this.expression=t,this.type=r,this.maxValue=0,this.paintVertexAttributes=e.map((t=>({name:`a_${t}`,type:\"Float32\",components:\"color\"===r?2:1,offset:0}))),this.paintVertexArray=new n}populatePaintArray(t,e,r,n,i){const a=this.paintVertexArray.length,o=this.expression.evaluate(new Hi(0),e,{},n,[],i);this.paintVertexArray.resize(t),this._setPaintValue(a,t,o)}updatePaintArray(t,e,r,n){const i=this.expression.evaluate({zoom:0},r,n);this._setPaintValue(t,e,i)}_setPaintValue(t,e,r){if(\"color\"===this.type){const n=Io(r);for(let r=t;r`u_${t}_t`)),this.type=r,this.useIntegerZoom=n,this.zoom=i,this.maxValue=0,this.paintVertexAttributes=e.map((t=>({name:`a_${t}`,type:\"Float32\",components:\"color\"===r?4:2,offset:0}))),this.paintVertexArray=new a}populatePaintArray(t,e,r,n,i){const a=this.expression.evaluate(new Hi(this.zoom),e,{},n,[],i),o=this.expression.evaluate(new Hi(this.zoom+1),e,{},n,[],i),s=this.paintVertexArray.length;this.paintVertexArray.resize(t),this._setPaintValue(s,t,a,o)}updatePaintArray(t,e,r,n){const i=this.expression.evaluate({zoom:this.zoom},r,n),a=this.expression.evaluate({zoom:this.zoom+1},r,n);this._setPaintValue(t,e,i,a)}_setPaintValue(t,e,r,n){if(\"color\"===this.type){const i=Io(r),a=Io(n);for(let r=t;r`#define HAS_UNIFORM_${t}`)))}return t}getBinderAttributes(){const t=[];for(const e in this.binders){const r=this.binders[e];if(r instanceof Oo||r instanceof Do)for(let e=0;e!0)){this.programConfigurations={};for(const n of t)this.programConfigurations[n.id]=new Fo(n,e,r);this.needsUpload=!1,this._featureMap=new wo,this._bufferOffset=0}populatePaintArrays(t,e,r,n,i,a){for(const r in this.programConfigurations)this.programConfigurations[r].populatePaintArrays(t,e,n,i,a);void 0!==e.id&&this._featureMap.add(e.id,r,this._bufferOffset,t),this._bufferOffset=t,this.needsUpload=!0}updatePaintArrays(t,e,r,n){for(const i of r)this.needsUpload=this.programConfigurations[i.id].updatePaintArrays(t,this._featureMap,e,i,n)||this.needsUpload}get(t){return this.programConfigurations[t]}upload(t){if(this.needsUpload){for(const e in this.programConfigurations)this.programConfigurations[e].upload(t);this.needsUpload=!1}}destroy(){for(const t in this.programConfigurations)this.programConfigurations[t].destroy()}}function 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ho,this.stateDependentLayerIds=this.layers.filter((t=>t.isStateDependent())).map((t=>t.id))}populate(t,e,r){this.hasPattern=hl(\"fill\",this.layers,e);const n=this.layers[0].layout.get(\"fill-sort-key\"),i=!n.isConstant(),a=[];for(const{feature:o,id:s,index:l,sourceLayerIndex:c}of t){const t=this.layers[0]._featureFilter.needGeometry,u=Go(o,t);if(!this.layers[0]._featureFilter.filter(new Hi(this.zoom),u,r))continue;const h=i?n.evaluate(u,{},r,e.availableImages):void 0,f={id:s,properties:o.properties,type:o.type,sourceLayerIndex:c,index:l,geometry:t?u.geometry:Ho(o),patterns:{},sortKey:h};a.push(f)}i&&a.sort(((t,e)=>t.sortKey-e.sortKey));for(const n of a){const{geometry:i,index:a,sourceLayerIndex:o}=n;if(this.hasPattern){const t=fl(\"fill\",this.layers,n,this.zoom,e);this.patternFeatures.push(t)}else this.addFeature(n,i,a,r,{});const s=t[a].feature;e.featureIndex.insert(s,i,a,o,this.index)}}update(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)}addFeatures(t,e,r){for(const t of this.patternFeatures)this.addFeature(t,t.geometry,t.index,e,r)}isEmpty(){return 0===this.layoutVertexArray.length}uploadPending(){return!this.uploaded||this.programConfigurations.needsUpload}upload(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,Fs),this.indexBuffer=t.createIndexBuffer(this.indexArray),this.indexBuffer2=t.createIndexBuffer(this.indexArray2)),this.programConfigurations.upload(t),this.uploaded=!0}destroy(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.indexBuffer2.destroy(),this.programConfigurations.destroy(),this.segments.destroy(),this.segments2.destroy())}addFeature(t,e,r,n,i){for(const t of br(e,500)){let e=0;for(const r of t)e+=r.length;const r=this.segments.prepareSegment(e,this.layoutVertexArray,this.indexArray),n=r.vertexLength,i=[],a=[];for(const e of t){if(0===e.length)continue;e!==t[0]&&a.push(i.length/2);const r=this.segments2.prepareSegment(e.length,this.layoutVertexArray,this.indexArray2),n=r.vertexLength;this.layoutVertexArray.emplaceBack(e[0].x,e[0].y),this.indexArray2.emplaceBack(n+e.length-1,n),i.push(e[0].x),i.push(e[0].y);for(let t=1;t>3}if(i--,1===n||2===n)a+=t.readSVarint(),o+=t.readSVarint(),1===n&&(e&&s.push(e),e=[]),e.push(new kl(a,o));else{if(7!==n)throw new Error(\"unknown command \"+n);e&&e.push(e[0].clone())}}return e&&s.push(e),s},Ml.prototype.bbox=function(){var t=this._pbf;t.pos=this._geometry;for(var e=t.readVarint()+t.pos,r=1,n=0,i=0,a=0,o=1/0,s=-1/0,l=1/0,c=-1/0;t.pos>3}if(n--,1===r||2===r)(i+=t.readSVarint())s&&(s=i),(a+=t.readSVarint())c&&(c=a);else if(7!==r)throw new Error(\"unknown command \"+r)}return[o,l,s,c]},Ml.prototype.toGeoJSON=function(t,e,r){var n,i,a=this.extent*Math.pow(2,r),o=this.extent*t,s=this.extent*e,l=this.loadGeometry(),c=Ml.types[this.type];function u(t){for(var e=0;e>3;e=1===n?t.readString():2===n?t.readFloat():3===n?t.readDouble():4===n?t.readVarint64():5===n?t.readVarint():6===n?t.readSVarint():7===n?t.readBoolean():null}return e}(r))}Il.prototype.feature=function(t){if(t<0||t>=this._features.length)throw new Error(\"feature index out of bounds\");this._pbf.pos=this._features[t];var e=this._pbf.readVarint()+this._pbf.pos;return new Cl(this._pbf,e,this.extent,this._keys,this._values)};var zl=Ll,Ol=function(t,e){this.layers=t.readFields(Dl,{},e)};function Dl(t,e,r){if(3===t){var n=new zl(r,r.readVarint()+r.pos);n.length&&(e[n.name]=n)}}Tl.VectorTile=Ol,Tl.VectorTileFeature=Al,Tl.VectorTileLayer=Ll;const Rl=Tl.VectorTileFeature.types,Fl=Math.pow(2,13);function Bl(t,e,r,n,i,a,o,s){t.emplaceBack(e,r,2*Math.floor(n*Fl)+o,i*Fl*2,a*Fl*2,Math.round(s))}class Nl{constructor(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((t=>t.id)),this.index=t.index,this.hasPattern=!1,this.layoutVertexArray=new $a,this.centroidVertexArray=new Wa,this.indexArray=new ao,this.programConfigurations=new Bo(t.layers,t.zoom),this.segments=new ho,this.stateDependentLayerIds=this.layers.filter((t=>t.isStateDependent())).map((t=>t.id))}populate(t,e,r){this.features=[],this.hasPattern=hl(\"fill-extrusion\",this.layers,e);for(const{feature:n,id:i,index:a,sourceLayerIndex:o}of t){const t=this.layers[0]._featureFilter.needGeometry,s=Go(n,t);if(!this.layers[0]._featureFilter.filter(new Hi(this.zoom),s,r))continue;const l={id:i,sourceLayerIndex:o,index:a,geometry:t?s.geometry:Ho(n),properties:n.properties,type:n.type,patterns:{}};this.hasPattern?this.features.push(fl(\"fill-extrusion\",this.layers,l,this.zoom,e)):this.addFeature(l,l.geometry,a,r,{}),e.featureIndex.insert(n,l.geometry,a,o,this.index,!0)}}addFeatures(t,e,r){for(const t of this.features){const{geometry:n}=t;this.addFeature(t,n,t.index,e,r)}}update(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)}isEmpty(){return 0===this.layoutVertexArray.length&&0===this.centroidVertexArray.length}uploadPending(){return!this.uploaded||this.programConfigurations.needsUpload}upload(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,_l),this.centroidVertexBuffer=t.createVertexBuffer(this.centroidVertexArray,xl.members,!0),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0}destroy(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy(),this.centroidVertexBuffer.destroy())}addFeature(t,e,r,n,i){for(const r of br(e,500)){const e={x:0,y:0,vertexCount:0};let n=0;for(const t of r)n+=t.length;let i=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray);for(const t of r){if(0===t.length)continue;if(Ul(t))continue;let r=0;for(let n=0;n=1){const o=t[n-1];if(!jl(a,o)){i.vertexLength+4>ho.MAX_VERTEX_ARRAY_LENGTH&&(i=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray));const t=a.sub(o)._perp()._unit(),n=o.dist(a);r+n>32768&&(r=0),Bl(this.layoutVertexArray,a.x,a.y,t.x,t.y,0,0,r),Bl(this.layoutVertexArray,a.x,a.y,t.x,t.y,0,1,r),e.x+=2*a.x,e.y+=2*a.y,e.vertexCount+=2,r+=n,Bl(this.layoutVertexArray,o.x,o.y,t.x,t.y,0,0,r),Bl(this.layoutVertexArray,o.x,o.y,t.x,t.y,0,1,r),e.x+=2*o.x,e.y+=2*o.y,e.vertexCount+=2;const s=i.vertexLength;this.indexArray.emplaceBack(s,s+2,s+1),this.indexArray.emplaceBack(s+1,s+2,s+3),i.vertexLength+=4,i.primitiveLength+=2}}}}if(i.vertexLength+n>ho.MAX_VERTEX_ARRAY_LENGTH&&(i=this.segments.prepareSegment(n,this.layoutVertexArray,this.indexArray)),\"Polygon\"!==Rl[t.type])continue;const a=[],o=[],s=i.vertexLength;for(const t of r)if(0!==t.length){t!==r[0]&&o.push(a.length/2);for(let r=0;rUo)||t.y===e.y&&(t.y<0||t.y>Uo)}function Ul(t){return t.every((t=>t.x<0))||t.every((t=>t.x>Uo))||t.every((t=>t.y<0))||t.every((t=>t.y>Uo))}let Vl;Mi(\"FillExtrusionBucket\",Nl,{omit:[\"layers\",\"features\"]});var ql={get paint(){return Vl=Vl||new ia({\"fill-extrusion-opacity\":new Qi(Z[\"paint_fill-extrusion\"][\"fill-extrusion-opacity\"]),\"fill-extrusion-color\":new ta(Z[\"paint_fill-extrusion\"][\"fill-extrusion-color\"]),\"fill-extrusion-translate\":new Qi(Z[\"paint_fill-extrusion\"][\"fill-extrusion-translate\"]),\"fill-extrusion-translate-anchor\":new Qi(Z[\"paint_fill-extrusion\"][\"fill-extrusion-translate-anchor\"]),\"fill-extrusion-pattern\":new ea(Z[\"paint_fill-extrusion\"][\"fill-extrusion-pattern\"]),\"fill-extrusion-height\":new ta(Z[\"paint_fill-extrusion\"][\"fill-extrusion-height\"]),\"fill-extrusion-base\":new ta(Z[\"paint_fill-extrusion\"][\"fill-extrusion-base\"]),\"fill-extrusion-vertical-gradient\":new Qi(Z[\"paint_fill-extrusion\"][\"fill-extrusion-vertical-gradient\"])})}};class Hl extends oa{constructor(t){super(t,ql)}createBucket(t){return new Nl(t)}queryRadius(){return os(this.paint.get(\"fill-extrusion-translate\"))}is3D(){return!0}queryIntersectsFeature(t,e,r,n,i,o,s,l){const c=ss(t,this.paint.get(\"fill-extrusion-translate\"),this.paint.get(\"fill-extrusion-translate-anchor\"),o.angle,s),u=this.paint.get(\"fill-extrusion-height\").evaluate(e,r),h=this.paint.get(\"fill-extrusion-base\").evaluate(e,r),f=function(t,e,r,n){const i=[];for(const r of t){const t=[r.x,r.y,n,1];vs(t,t,e),i.push(new a(t[0]/t[3],t[1]/t[3]))}return i}(c,l,0,0),p=function(t,e,r,n){const i=[],o=[],s=n[8]*e,l=n[9]*e,c=n[10]*e,u=n[11]*e,h=n[8]*r,f=n[9]*r,p=n[10]*r,d=n[11]*r;for(const e of t){const t=[],r=[];for(const i of e){const e=i.x,o=i.y,m=n[0]*e+n[4]*o+n[12],g=n[1]*e+n[5]*o+n[13],y=n[2]*e+n[6]*o+n[14],v=n[3]*e+n[7]*o+n[15],x=y+c,_=v+u,b=m+h,w=g+f,T=y+p,k=v+d,A=new a((m+s)/_,(g+l)/_);A.z=x/_,t.push(A);const M=new a(b/k,w/k);M.z=T/k,r.push(M)}i.push(t),o.push(r)}return[i,o]}(n,h,u,l);return function(t,e,r){let n=1/0;$o(r,e)&&(n=Zl(r,e[0]));for(let i=0;it.id)),this.index=t.index,this.hasPattern=!1,this.patternFeatures=[],this.lineClipsArray=[],this.gradients={},this.layers.forEach((t=>{this.gradients[t.id]={}})),this.layoutVertexArray=new Ja,this.layoutVertexArray2=new Ka,this.indexArray=new ao,this.programConfigurations=new Bo(t.layers,t.zoom),this.segments=new ho,this.maxLineLength=0,this.stateDependentLayerIds=this.layers.filter((t=>t.isStateDependent())).map((t=>t.id))}populate(t,e,r){this.hasPattern=hl(\"line\",this.layers,e);const n=this.layers[0].layout.get(\"line-sort-key\"),i=!n.isConstant(),a=[];for(const{feature:e,id:o,index:s,sourceLayerIndex:l}of t){const t=this.layers[0]._featureFilter.needGeometry,c=Go(e,t);if(!this.layers[0]._featureFilter.filter(new Hi(this.zoom),c,r))continue;const u=i?n.evaluate(c,{},r):void 0,h={id:o,properties:e.properties,type:e.type,sourceLayerIndex:l,index:s,geometry:t?c.geometry:Ho(e),patterns:{},sortKey:u};a.push(h)}i&&a.sort(((t,e)=>t.sortKey-e.sortKey));for(const n of a){const{geometry:i,index:a,sourceLayerIndex:o}=n;if(this.hasPattern){const t=fl(\"line\",this.layers,n,this.zoom,e);this.patternFeatures.push(t)}else this.addFeature(n,i,a,r,{});const s=t[a].feature;e.featureIndex.insert(s,i,a,o,this.index)}}update(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)}addFeatures(t,e,r){for(const t of this.patternFeatures)this.addFeature(t,t.geometry,t.index,e,r)}isEmpty(){return 0===this.layoutVertexArray.length}uploadPending(){return!this.uploaded||this.programConfigurations.needsUpload}upload(t){this.uploaded||(0!==this.layoutVertexArray2.length&&(this.layoutVertexBuffer2=t.createVertexBuffer(this.layoutVertexArray2,Kl)),this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,Yl),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0}destroy(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())}lineFeatureClips(t){if(t.properties&&Object.prototype.hasOwnProperty.call(t.properties,\"mapbox_clip_start\")&&Object.prototype.hasOwnProperty.call(t.properties,\"mapbox_clip_end\"))return{start:+t.properties.mapbox_clip_start,end:+t.properties.mapbox_clip_end}}addFeature(t,e,r,n,i){const a=this.layers[0].layout,o=a.get(\"line-join\").evaluate(t,{}),s=a.get(\"line-cap\"),l=a.get(\"line-miter-limit\"),c=a.get(\"line-round-limit\");this.lineClips=this.lineFeatureClips(t);for(const r of e)this.addLine(r,t,o,s,l,c);this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,i,n)}addLine(t,e,r,n,i,a){if(this.distance=0,this.scaledDistance=0,this.totalDistance=0,this.lineClips){this.lineClipsArray.push(this.lineClips);for(let e=0;e=2&&t[s-1].equals(t[s-2]);)s--;let l=0;for(;l0;if(b&&e>l){const t=h.dist(f);if(t>2*c){const e=h.sub(h.sub(f)._mult(c/t)._round());this.updateDistance(f,e),this.addCurrentVertex(e,d,0,0,u),f=e}}const T=f&&p;let k=T?r:o?\"butt\":n;if(T&&\"round\"===k&&(xi&&(k=\"bevel\"),\"bevel\"===k&&(x>2&&(k=\"flipbevel\"),x100)g=m.mult(-1);else{const t=x*d.add(m).mag()/d.sub(m).mag();g._perp()._mult(t*(w?-1:1))}this.addCurrentVertex(h,g,0,0,u),this.addCurrentVertex(h,g.mult(-1),0,0,u)}else if(\"bevel\"===k||\"fakeround\"===k){const t=-Math.sqrt(x*x-1),e=w?t:0,r=w?0:t;if(f&&this.addCurrentVertex(h,d,e,r,u),\"fakeround\"===k){const t=Math.round(180*_/Math.PI/20);for(let e=1;e2*c){const e=h.add(p.sub(h)._mult(c/t)._round());this.updateDistance(h,e),this.addCurrentVertex(e,m,0,0,u),h=e}}}}addCurrentVertex(t,e,r,n,i,a=!1){const o=e.x+e.y*r,s=e.y-e.x*r,l=-e.x+e.y*n,c=-e.y-e.x*n;this.addHalfVertex(t,o,s,a,!1,r,i),this.addHalfVertex(t,l,c,a,!0,-n,i),this.distance>nc/2&&0===this.totalDistance&&(this.distance=0,this.updateScaledDistance(),this.addCurrentVertex(t,e,r,n,i,a))}addHalfVertex({x:t,y:e},r,n,i,a,o,s){const l=.5*(this.lineClips?this.scaledDistance*(nc-1):this.scaledDistance);if(this.layoutVertexArray.emplaceBack((t<<1)+(i?1:0),(e<<1)+(a?1:0),Math.round(63*r)+128,Math.round(63*n)+128,1+(0===o?0:o<0?-1:1)|(63&l)<<2,l>>6),this.lineClips){const t=(this.scaledDistance-this.lineClips.start)/(this.lineClips.end-this.lineClips.start);this.layoutVertexArray2.emplaceBack(t,this.lineClipsArray.length)}const c=s.vertexLength++;this.e1>=0&&this.e2>=0&&(this.indexArray.emplaceBack(this.e1,this.e2,c),s.primitiveLength++),a?this.e2=c:this.e1=c}updateScaledDistance(){this.scaledDistance=this.lineClips?this.lineClips.start+(this.lineClips.end-this.lineClips.start)*this.distance/this.totalDistance:this.distance}updateDistance(t,e){this.distance+=t.dist(e),this.updateScaledDistance()}}let ac;Mi(\"LineBucket\",ic,{omit:[\"layers\",\"patternFeatures\"]});let oc;var sc={get paint(){return oc=oc||new ia({\"line-opacity\":new ta(Z.paint_line[\"line-opacity\"]),\"line-color\":new ta(Z.paint_line[\"line-color\"]),\"line-translate\":new Qi(Z.paint_line[\"line-translate\"]),\"line-translate-anchor\":new Qi(Z.paint_line[\"line-translate-anchor\"]),\"line-width\":new ta(Z.paint_line[\"line-width\"]),\"line-gap-width\":new ta(Z.paint_line[\"line-gap-width\"]),\"line-offset\":new ta(Z.paint_line[\"line-offset\"]),\"line-blur\":new ta(Z.paint_line[\"line-blur\"]),\"line-dasharray\":new ra(Z.paint_line[\"line-dasharray\"]),\"line-pattern\":new ea(Z.paint_line[\"line-pattern\"]),\"line-gradient\":new na(Z.paint_line[\"line-gradient\"])})},get layout(){return ac=ac||new ia({\"line-cap\":new Qi(Z.layout_line[\"line-cap\"]),\"line-join\":new ta(Z.layout_line[\"line-join\"]),\"line-miter-limit\":new Qi(Z.layout_line[\"line-miter-limit\"]),\"line-round-limit\":new Qi(Z.layout_line[\"line-round-limit\"]),\"line-sort-key\":new ta(Z.layout_line[\"line-sort-key\"])})}};class lc extends ta{possiblyEvaluate(t,e){return e=new Hi(Math.floor(e.zoom),{now:e.now,fadeDuration:e.fadeDuration,zoomHistory:e.zoomHistory,transition:e.transition}),super.possiblyEvaluate(t,e)}evaluate(t,e,r,n){return e=y({},e,{zoom:Math.floor(e.zoom)}),super.evaluate(t,e,r,n)}}let cc;class uc extends oa{constructor(t){super(t,sc),this.gradientVersion=0,cc||(cc=new lc(sc.paint.properties[\"line-width\"].specification),cc.useIntegerZoom=!0)}_handleSpecialPaintPropertyUpdate(t){if(\"line-gradient\"===t){const t=this.gradientExpression();!function(t){return void 0!==t._styleExpression}(t)?this.stepInterpolant=!1:this.stepInterpolant=t._styleExpression.expression instanceof Ae,this.gradientVersion=(this.gradientVersion+1)%Number.MAX_SAFE_INTEGER}}gradientExpression(){return this._transitionablePaint._values[\"line-gradient\"].value.expression}recalculate(t,e){super.recalculate(t,e),this.paint._values[\"line-floorwidth\"]=cc.possiblyEvaluate(this._transitioningPaint._values[\"line-width\"].value,t)}createBucket(t){return new ic(t)}queryRadius(t){const e=t,r=hc(as(\"line-width\",this,e),as(\"line-gap-width\",this,e)),n=as(\"line-offset\",this,e);return r/2+Math.abs(n)+os(this.paint.get(\"line-translate\"))}queryIntersectsFeature(t,e,r,n,i,o,s){const l=ss(t,this.paint.get(\"line-translate\"),this.paint.get(\"line-translate-anchor\"),o.angle,s),c=s/2*hc(this.paint.get(\"line-width\").evaluate(e,r),this.paint.get(\"line-gap-width\").evaluate(e,r)),u=this.paint.get(\"line-offset\").evaluate(e,r);return u&&(n=function(t,e){const r=[];for(let n=0;n=3)for(let e=0;e0?e+2*t:t}const fc=ua([{name:\"a_pos_offset\",components:4,type:\"Int16\"},{name:\"a_data\",components:4,type:\"Uint16\"},{name:\"a_pixeloffset\",components:4,type:\"Int16\"}],4),pc=ua([{name:\"a_projected_pos\",components:3,type:\"Float32\"}],4);ua([{name:\"a_fade_opacity\",components:1,type:\"Uint32\"}],4);const dc=ua([{name:\"a_placed\",components:2,type:\"Uint8\"},{name:\"a_shift\",components:2,type:\"Float32\"},{name:\"a_box_real\",components:2,type:\"Int16\"}]);ua([{type:\"Int16\",name:\"anchorPointX\"},{type:\"Int16\",name:\"anchorPointY\"},{type:\"Int16\",name:\"x1\"},{type:\"Int16\",name:\"y1\"},{type:\"Int16\",name:\"x2\"},{type:\"Int16\",name:\"y2\"},{type:\"Uint32\",name:\"featureIndex\"},{type:\"Uint16\",name:\"sourceLayerIndex\"},{type:\"Uint16\",name:\"bucketIndex\"}]);const mc=ua([{name:\"a_pos\",components:2,type:\"Int16\"},{name:\"a_anchor_pos\",components:2,type:\"Int16\"},{name:\"a_extrude\",components:2,type:\"Int16\"}],4),gc=ua([{name:\"a_pos\",components:2,type:\"Float32\"},{name:\"a_radius\",components:1,type:\"Float32\"},{name:\"a_flags\",components:2,type:\"Int16\"}],4);function yc(t,e,r){return t.sections.forEach((t=>{t.text=function(t,e,r){const 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t)if(0!==e.positionedGlyphs.length)return!1;return!0}(_)&&b}const ru={9:!0,10:!0,11:!0,12:!0,13:!0,32:!0},nu={10:!0,32:!0,38:!0,41:!0,43:!0,45:!0,47:!0,173:!0,183:!0,8203:!0,8208:!0,8211:!0,8231:!0},iu={40:!0};function au(t,e,r,n,i,a){if(e.imageName){const t=n[e.imageName];return t?t.displaySize[0]*e.scale*xc/a+i:0}{const n=r[e.fontStack],a=n&&n[t];return a?a.metrics.advance*e.scale+i:0}}function ou(t,e,r,n){const i=Math.pow(t-e,2);return n?t=0;let c=0;for(let r=0;rh){const t=Math.ceil(a/h);i*=t/o,o=t}return{x1:n,y1:i,x2:n+a,y2:i+o}}function mu(t,e,r,n,i,a){const o=t.image;let s;if(o.content){const t=o.content,e=o.pixelRatio||1;s=[t[0]/e,t[1]/e,o.displaySize[0]-t[2]/e,o.displaySize[1]-t[3]/e]}const l=e.left*a,c=e.right*a;let u,h,f,p;\"width\"===r||\"both\"===r?(p=i[0]+l-n[3],h=i[0]+c+n[1]):(p=i[0]+(l+c-o.displaySize[0])/2,h=p+o.displaySize[0]);const d=e.top*a,m=e.bottom*a;return\"height\"===r||\"both\"===r?(u=i[1]+d-n[0],f=i[1]+m+n[2]):(u=i[1]+(d+m-o.displaySize[1])/2,f=u+o.displaySize[1]),{image:o,top:u,right:h,bottom:f,left:p,collisionPadding:s}}const gu=255,yu=128,vu=gu*yu;function xu(t,e){const{expression:r}=e;if(\"constant\"===r.kind)return{kind:\"constant\",layoutSize:r.evaluate(new Hi(t+1))};if(\"source\"===r.kind)return{kind:\"source\"};{const{zoomStops:e,interpolationType:n}=r;let i=0;for(;it.id)),this.index=e.index,this.pixelRatio=e.pixelRatio,this.sourceLayerIndex=e.sourceLayerIndex,this.hasPattern=!1,this.hasRTLText=!1,this.sortKeyRanges=[],this.collisionCircleArray=[],this.placementInvProjMatrix=ps([]),this.placementViewportMatrix=ps([]);const r=this.layers[0]._unevaluatedLayout._values;this.textSizeData=xu(this.zoom,r[\"text-size\"]),this.iconSizeData=xu(this.zoom,r[\"icon-size\"]);const n=this.layers[0].layout,i=n.get(\"symbol-sort-key\"),a=n.get(\"symbol-z-order\");this.canOverlap=\"never\"!==_u(n,\"text-overlap\",\"text-allow-overlap\")||\"never\"!==_u(n,\"icon-overlap\",\"icon-allow-overlap\")||n.get(\"text-ignore-placement\")||n.get(\"icon-ignore-placement\"),this.sortFeaturesByKey=\"viewport-y\"!==a&&!i.isConstant();const o=\"viewport-y\"===a||\"auto\"===a&&!this.sortFeaturesByKey;this.sortFeaturesByY=o&&this.canOverlap,\"point\"===n.get(\"symbol-placement\")&&(this.writingModes=n.get(\"text-writing-mode\").map((e=>t.ai[e]))),this.stateDependentLayerIds=this.layers.filter((t=>t.isStateDependent())).map((t=>t.id)),this.sourceID=e.sourceID}createArrays(){this.text=new Mu(new Bo(this.layers,this.zoom,(t=>/^text/.test(t)))),this.icon=new Mu(new Bo(this.layers,this.zoom,(t=>/^icon/.test(t)))),this.glyphOffsetArray=new Ua,this.lineVertexArray=new Va,this.symbolInstances=new ja,this.textAnchorOffsets=new Ha}calculateGlyphDependencies(t,e,r,n,i){for(let a=0;a0)&&(\"constant\"!==o.value.kind||o.value.value.length>0),u=\"constant\"!==l.value.kind||!!l.value.value||Object.keys(l.parameters).length>0,h=a.get(\"symbol-sort-key\");if(this.features=[],!c&&!u)return;const f=r.iconDependencies,p=r.glyphDependencies,d=r.availableImages,m=new Hi(this.zoom);for(const{feature:r,id:s,index:l,sourceLayerIndex:g}of e){const e=i._featureFilter.needGeometry,y=Go(r,e);if(!i._featureFilter.filter(m,y,n))continue;let v,x;if(e||(y.geometry=Ho(r)),c){const t=i.getValueAndResolveTokens(\"text-field\",y,n,d),e=Kt.factory(t),r=this.hasRTLText=this.hasRTLText||Au(e);(!r||\"unavailable\"===qi.getRTLTextPluginStatus()||r&&qi.isParsed())&&(v=yc(e,i,y))}if(u){const t=i.getValueAndResolveTokens(\"icon-image\",y,n,d);x=t instanceof re?t:re.fromString(t)}if(!v&&!x)continue;const _=this.sortFeaturesByKey?h.evaluate(y,{},n):void 0,b={id:s,text:v,icon:x,index:l,sourceLayerIndex:g,geometry:y.geometry,properties:r.properties,type:bu[r.type],sortKey:_};if(this.features.push(b),x&&(f[x.name]=!0),v){const e=o.evaluate(y,{},n).join(\",\"),r=\"viewport\"!==a.get(\"text-rotation-alignment\")&&\"point\"!==a.get(\"symbol-placement\");this.allowVerticalPlacement=this.writingModes&&this.writingModes.indexOf(t.ai.vertical)>=0;for(const t of v.sections)if(t.image)f[t.image.name]=!0;else{const n=Oi(v.toString()),i=t.fontStack||e,a=p[i]=p[i]||{};this.calculateGlyphDependencies(t.text,a,r,this.allowVerticalPlacement,n)}}}\"line\"===a.get(\"symbol-placement\")&&(this.features=function(t){const e={},r={},n=[];let i=0;function a(e){n.push(t[e]),i++}function o(t,e,i){const a=r[t];return delete r[t],r[e]=a,n[a].geometry[0].pop(),n[a].geometry[0]=n[a].geometry[0].concat(i[0]),a}function s(t,r,i){const a=e[r];return delete e[r],e[t]=a,n[a].geometry[0].shift(),n[a].geometry[0]=i[0].concat(n[a].geometry[0]),a}function l(t,e,r){const n=r?e[0][e[0].length-1]:e[0][0];return`${t}:${n.x}:${n.y}`}for(let c=0;ct.geometry))}(this.features)),this.sortFeaturesByKey&&this.features.sort(((t,e)=>t.sortKey-e.sortKey))}update(t,e,r){this.stateDependentLayers.length&&(this.text.programConfigurations.updatePaintArrays(t,e,this.layers,r),this.icon.programConfigurations.updatePaintArrays(t,e,this.layers,r))}isEmpty(){return 0===this.symbolInstances.length&&!this.hasRTLText}uploadPending(){return!this.uploaded||this.text.programConfigurations.needsUpload||this.icon.programConfigurations.needsUpload}upload(t){!this.uploaded&&this.hasDebugData()&&(this.textCollisionBox.upload(t),this.iconCollisionBox.upload(t)),this.text.upload(t,this.sortFeaturesByY,!this.uploaded,this.text.programConfigurations.needsUpload),this.icon.upload(t,this.sortFeaturesByY,!this.uploaded,this.icon.programConfigurations.needsUpload),this.uploaded=!0}destroyDebugData(){this.textCollisionBox.destroy(),this.iconCollisionBox.destroy()}destroy(){this.text.destroy(),this.icon.destroy(),this.hasDebugData()&&this.destroyDebugData()}addToLineVertexArray(t,e){const r=this.lineVertexArray.length;if(void 0!==t.segment){let r=t.dist(e[t.segment+1]),n=t.dist(e[t.segment]);const i={};for(let n=t.segment+1;n=0;r--)i[r]={x:e[r].x,y:e[r].y,tileUnitDistanceFromAnchor:n},r>0&&(n+=e[r-1].dist(e[r]));for(let t=0;t0}hasIconData(){return this.icon.segments.get().length>0}hasDebugData(){return this.textCollisionBox&&this.iconCollisionBox}hasTextCollisionBoxData(){return this.hasDebugData()&&this.textCollisionBox.segments.get().length>0}hasIconCollisionBoxData(){return this.hasDebugData()&&this.iconCollisionBox.segments.get().length>0}addIndicesForPlacedSymbol(t,e){const r=t.placedSymbolArray.get(e),n=r.vertexStartIndex+4*r.numGlyphs;for(let e=r.vertexStartIndex;en[t]-n[e]||i[e]-i[t])),a}addToSortKeyRanges(t,e){const r=this.sortKeyRanges[this.sortKeyRanges.length-1];r&&r.sortKey===e?r.symbolInstanceEnd=t+1:this.sortKeyRanges.push({sortKey:e,symbolInstanceStart:t,symbolInstanceEnd:t+1})}sortFeatures(t){if(this.sortFeaturesByY&&this.sortedAngle!==t&&!(this.text.segments.get().length>1||this.icon.segments.get().length>1)){this.symbolInstanceIndexes=this.getSortedSymbolIndexes(t),this.sortedAngle=t,this.text.indexArray.clear(),this.icon.indexArray.clear(),this.featureSortOrder=[];for(const t of this.symbolInstanceIndexes){const e=this.symbolInstances.get(t);this.featureSortOrder.push(e.featureIndex),[e.rightJustifiedTextSymbolIndex,e.centerJustifiedTextSymbolIndex,e.leftJustifiedTextSymbolIndex].forEach(((t,e,r)=>{t>=0&&r.indexOf(t)===e&&this.addIndicesForPlacedSymbol(this.text,t)})),e.verticalPlacedTextSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.text,e.verticalPlacedTextSymbolIndex),e.placedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,e.placedIconSymbolIndex),e.verticalPlacedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,e.verticalPlacedIconSymbolIndex)}this.text.indexBuffer&&this.text.indexBuffer.updateData(this.text.indexArray),this.icon.indexBuffer&&this.icon.indexBuffer.updateData(this.icon.indexArray)}}}let Cu;Mi(\"SymbolBucket\",Eu,{omit:[\"layers\",\"collisionBoxArray\",\"features\",\"compareText\"]}),Eu.MAX_GLYPHS=65535,Eu.addDynamicAttributes=ku;let Lu;var Iu={get paint(){return Lu=Lu||new ia({\"icon-opacity\":new ta(Z.paint_symbol[\"icon-opacity\"]),\"icon-color\":new ta(Z.paint_symbol[\"icon-color\"]),\"icon-halo-color\":new ta(Z.paint_symbol[\"icon-halo-color\"]),\"icon-halo-width\":new ta(Z.paint_symbol[\"icon-halo-width\"]),\"icon-halo-blur\":new ta(Z.paint_symbol[\"icon-halo-blur\"]),\"icon-translate\":new Qi(Z.paint_symbol[\"icon-translate\"]),\"icon-translate-anchor\":new Qi(Z.paint_symbol[\"icon-translate-anchor\"]),\"text-opacity\":new ta(Z.paint_symbol[\"text-opacity\"]),\"text-color\":new ta(Z.paint_symbol[\"text-color\"],{runtimeType:ft,getOverride:t=>t.textColor,hasOverride:t=>!!t.textColor}),\"text-halo-color\":new ta(Z.paint_symbol[\"text-halo-color\"]),\"text-halo-width\":new ta(Z.paint_symbol[\"text-halo-width\"]),\"text-halo-blur\":new ta(Z.paint_symbol[\"text-halo-blur\"]),\"text-translate\":new Qi(Z.paint_symbol[\"text-translate\"]),\"text-translate-anchor\":new Qi(Z.paint_symbol[\"text-translate-anchor\"])})},get layout(){return Cu=Cu||new ia({\"symbol-placement\":new Qi(Z.layout_symbol[\"symbol-placement\"]),\"symbol-spacing\":new Qi(Z.layout_symbol[\"symbol-spacing\"]),\"symbol-avoid-edges\":new Qi(Z.layout_symbol[\"symbol-avoid-edges\"]),\"symbol-sort-key\":new ta(Z.layout_symbol[\"symbol-sort-key\"]),\"symbol-z-order\":new Qi(Z.layout_symbol[\"symbol-z-order\"]),\"icon-allow-overlap\":new Qi(Z.layout_symbol[\"icon-allow-overlap\"]),\"icon-overlap\":new Qi(Z.layout_symbol[\"icon-overlap\"]),\"icon-ignore-placement\":new Qi(Z.layout_symbol[\"icon-ignore-placement\"]),\"icon-optional\":new Qi(Z.layout_symbol[\"icon-optional\"]),\"icon-rotation-alignment\":new Qi(Z.layout_symbol[\"icon-rotation-alignment\"]),\"icon-size\":new ta(Z.layout_symbol[\"icon-size\"]),\"icon-text-fit\":new Qi(Z.layout_symbol[\"icon-text-fit\"]),\"icon-text-fit-padding\":new Qi(Z.layout_symbol[\"icon-text-fit-padding\"]),\"icon-image\":new ta(Z.layout_symbol[\"icon-image\"]),\"icon-rotate\":new ta(Z.layout_symbol[\"icon-rotate\"]),\"icon-padding\":new ta(Z.layout_symbol[\"icon-padding\"]),\"icon-keep-upright\":new Qi(Z.layout_symbol[\"icon-keep-upright\"]),\"icon-offset\":new ta(Z.layout_symbol[\"icon-offset\"]),\"icon-anchor\":new ta(Z.layout_symbol[\"icon-anchor\"]),\"icon-pitch-alignment\":new Qi(Z.layout_symbol[\"icon-pitch-alignment\"]),\"text-pitch-alignment\":new Qi(Z.layout_symbol[\"text-pitch-alignment\"]),\"text-rotation-alignment\":new Qi(Z.layout_symbol[\"text-rotation-alignment\"]),\"text-field\":new ta(Z.layout_symbol[\"text-field\"]),\"text-font\":new ta(Z.layout_symbol[\"text-font\"]),\"text-size\":new ta(Z.layout_symbol[\"text-size\"]),\"text-max-width\":new ta(Z.layout_symbol[\"text-max-width\"]),\"text-line-height\":new Qi(Z.layout_symbol[\"text-line-height\"]),\"text-letter-spacing\":new ta(Z.layout_symbol[\"text-letter-spacing\"]),\"text-justify\":new ta(Z.layout_symbol[\"text-justify\"]),\"text-radial-offset\":new ta(Z.layout_symbol[\"text-radial-offset\"]),\"text-variable-anchor\":new Qi(Z.layout_symbol[\"text-variable-anchor\"]),\"text-variable-anchor-offset\":new ta(Z.layout_symbol[\"text-variable-anchor-offset\"]),\"text-anchor\":new ta(Z.layout_symbol[\"text-anchor\"]),\"text-max-angle\":new Qi(Z.layout_symbol[\"text-max-angle\"]),\"text-writing-mode\":new Qi(Z.layout_symbol[\"text-writing-mode\"]),\"text-rotate\":new ta(Z.layout_symbol[\"text-rotate\"]),\"text-padding\":new Qi(Z.layout_symbol[\"text-padding\"]),\"text-keep-upright\":new Qi(Z.layout_symbol[\"text-keep-upright\"]),\"text-transform\":new ta(Z.layout_symbol[\"text-transform\"]),\"text-offset\":new ta(Z.layout_symbol[\"text-offset\"]),\"text-allow-overlap\":new Qi(Z.layout_symbol[\"text-allow-overlap\"]),\"text-overlap\":new Qi(Z.layout_symbol[\"text-overlap\"]),\"text-ignore-placement\":new Qi(Z.layout_symbol[\"text-ignore-placement\"]),\"text-optional\":new Qi(Z.layout_symbol[\"text-optional\"])})}};class Pu{constructor(t){if(void 0===t.property.overrides)throw new Error(\"overrides must be provided to instantiate FormatSectionOverride class\");this.type=t.property.overrides?t.property.overrides.runtimeType:lt,this.defaultValue=t}evaluate(t){if(t.formattedSection){const e=this.defaultValue.property.overrides;if(e&&e.hasOverride(t.formattedSection))return e.getOverride(t.formattedSection)}return t.feature&&t.featureState?this.defaultValue.evaluate(t.feature,t.featureState):this.defaultValue.property.specification.default}eachChild(t){this.defaultValue.isConstant()||t(this.defaultValue.value._styleExpression.expression)}outputDefined(){return!1}serialize(){return null}}Mi(\"FormatSectionOverride\",Pu,{omit:[\"defaultValue\"]});class zu extends oa{constructor(t){super(t,Iu)}recalculate(t,e){if(super.recalculate(t,e),\"auto\"===this.layout.get(\"icon-rotation-alignment\")&&(\"point\"!==this.layout.get(\"symbol-placement\")?this.layout._values[\"icon-rotation-alignment\"]=\"map\":this.layout._values[\"icon-rotation-alignment\"]=\"viewport\"),\"auto\"===this.layout.get(\"text-rotation-alignment\")&&(\"point\"!==this.layout.get(\"symbol-placement\")?this.layout._values[\"text-rotation-alignment\"]=\"map\":this.layout._values[\"text-rotation-alignment\"]=\"viewport\"),\"auto\"===this.layout.get(\"text-pitch-alignment\")&&(this.layout._values[\"text-pitch-alignment\"]=\"map\"===this.layout.get(\"text-rotation-alignment\")?\"map\":\"viewport\"),\"auto\"===this.layout.get(\"icon-pitch-alignment\")&&(this.layout._values[\"icon-pitch-alignment\"]=this.layout.get(\"icon-rotation-alignment\")),\"point\"===this.layout.get(\"symbol-placement\")){const t=this.layout.get(\"text-writing-mode\");if(t){const e=[];for(const r of t)e.indexOf(r)<0&&e.push(r);this.layout._values[\"text-writing-mode\"]=e}else this.layout._values[\"text-writing-mode\"]=[\"horizontal\"]}this._setPaintOverrides()}getValueAndResolveTokens(t,e,r,n){const i=this.layout.get(t).evaluate(e,{},r,n),a=this._unevaluatedLayout._values[t];return a.isDataDriven()||kn(a.value)||!i?i:function(t,e){return e.replace(/{([^{}]+)}/g,((e,r)=>t&&r in t?String(t[r]):\"\"))}(e.properties,i)}createBucket(t){return new Eu(t)}queryRadius(){return 0}queryIntersectsFeature(){throw new Error(\"Should take a different path in FeatureIndex\")}_setPaintOverrides(){for(const t of Iu.paint.overridableProperties){if(!zu.hasPaintOverride(this.layout,t))continue;const e=this.paint.get(t),r=new Pu(e),n=new Tn(r,e.property.specification);let i=null;i=\"constant\"===e.value.kind||\"source\"===e.value.kind?new Mn(\"source\",n):new Sn(\"composite\",n,e.value.zoomStops),this.paint._values[t]=new Ji(e.property,i,e.parameters)}}_handleOverridablePaintPropertyUpdate(t,e,r){return!(!this.layout||e.isDataDriven()||r.isDataDriven())&&zu.hasPaintOverride(this.layout,t)}static hasPaintOverride(t,e){const r=t.get(\"text-field\"),n=Iu.paint.properties[e];let i=!1;const a=t=>{for(const e of t)if(n.overrides&&n.overrides.hasOverride(e))return void(i=!0)};if(\"constant\"===r.value.kind&&r.value.value instanceof Kt)a(r.value.value.sections);else if(\"source\"===r.value.kind){const t=e=>{if(!i)if(e instanceof se&&ae(e.value)===gt){const t=e.value;a(t.sections)}else e instanceof We?a(e.sections):e.eachChild(t)},e=r.value;e._styleExpression&&t(e._styleExpression.expression)}return i}}let Ou;var Du={get paint(){return Ou=Ou||new ia({\"background-color\":new Qi(Z.paint_background[\"background-color\"]),\"background-pattern\":new ra(Z.paint_background[\"background-pattern\"]),\"background-opacity\":new Qi(Z.paint_background[\"background-opacity\"])})}};class Ru extends oa{constructor(t){super(t,Du)}}let Fu;var Bu={get paint(){return Fu=Fu||new ia({\"raster-opacity\":new Qi(Z.paint_raster[\"raster-opacity\"]),\"raster-hue-rotate\":new Qi(Z.paint_raster[\"raster-hue-rotate\"]),\"raster-brightness-min\":new Qi(Z.paint_raster[\"raster-brightness-min\"]),\"raster-brightness-max\":new Qi(Z.paint_raster[\"raster-brightness-max\"]),\"raster-saturation\":new Qi(Z.paint_raster[\"raster-saturation\"]),\"raster-contrast\":new Qi(Z.paint_raster[\"raster-contrast\"]),\"raster-resampling\":new Qi(Z.paint_raster[\"raster-resampling\"]),\"raster-fade-duration\":new Qi(Z.paint_raster[\"raster-fade-duration\"])})}};class Nu extends oa{constructor(t){super(t,Bu)}}class ju extends oa{constructor(t){super(t,{}),this.onAdd=t=>{this.implementation.onAdd&&this.implementation.onAdd(t,t.painter.context.gl)},this.onRemove=t=>{this.implementation.onRemove&&this.implementation.onRemove(t,t.painter.context.gl)},this.implementation=t}is3D(){return\"3d\"===this.implementation.renderingMode}hasOffscreenPass(){return void 0!==this.implementation.prerender}recalculate(){}updateTransitions(){}hasTransition(){return!1}serialize(){throw new Error(\"Custom layers cannot be serialized\")}}class Uu{constructor(t){this._methodToThrottle=t,this._triggered=!1,\"undefined\"!=typeof MessageChannel&&(this._channel=new MessageChannel,this._channel.port2.onmessage=()=>{this._triggered=!1,this._methodToThrottle()})}trigger(){this._triggered||(this._triggered=!0,this._channel?this._channel.port1.postMessage(!0):setTimeout((()=>{this._triggered=!1,this._methodToThrottle()}),0))}remove(){delete this._channel,this._methodToThrottle=()=>{}}}const Vu=6371008.8;class qu{constructor(t,e){if(isNaN(t)||isNaN(e))throw new Error(`Invalid LngLat object: (${t}, ${e})`);if(this.lng=+t,this.lat=+e,this.lat>90||this.lat<-90)throw new Error(\"Invalid LngLat latitude value: must be between -90 and 90\")}wrap(){return new qu(g(this.lng,-180,180),this.lat)}toArray(){return[this.lng,this.lat]}toString(){return`LngLat(${this.lng}, ${this.lat})`}distanceTo(t){const e=Math.PI/180,r=this.lat*e,n=t.lat*e,i=Math.sin(r)*Math.sin(n)+Math.cos(r)*Math.cos(n)*Math.cos((t.lng-this.lng)*e);return Vu*Math.acos(Math.min(i,1))}static convert(t){if(t instanceof qu)return t;if(Array.isArray(t)&&(2===t.length||3===t.length))return new qu(Number(t[0]),Number(t[1]));if(!Array.isArray(t)&&\"object\"==typeof t&&null!==t)return new qu(Number(\"lng\"in t?t.lng:t.lon),Number(t.lat));throw new Error(\"`LngLatLike` argument must be specified as a LngLat instance, an object {lng: , lat: }, an object {lon: , lat: }, or an array of [, ]\")}}const Hu=2*Math.PI*Vu;function Gu(t){return Hu*Math.cos(t*Math.PI/180)}function Zu(t){return(180+t)/360}function Wu(t){return(180-180/Math.PI*Math.log(Math.tan(Math.PI/4+t*Math.PI/360)))/360}function Yu(t,e){return t/Gu(e)}function Xu(t){const e=180-360*t;return 360/Math.PI*Math.atan(Math.exp(e*Math.PI/180))-90}class $u{constructor(t,e,r=0){this.x=+t,this.y=+e,this.z=+r}static fromLngLat(t,e=0){const r=qu.convert(t);return new $u(Zu(r.lng),Wu(r.lat),Yu(e,r.lat))}toLngLat(){return new qu(360*this.x-180,Xu(this.y))}toAltitude(){return t=this.z,e=this.y,t*Gu(Xu(e));var t,e}meterInMercatorCoordinateUnits(){return 1/Hu*(t=Xu(this.y),1/Math.cos(t*Math.PI/180));var t}}function Ju(t,e,r){var n=2*Math.PI*6378137/256/Math.pow(2,r);return[t*n-2*Math.PI*6378137/2,e*n-2*Math.PI*6378137/2]}class Ku{constructor(t,e,r){if(t<0||t>25||r<0||r>=Math.pow(2,t)||e<0||e>=Math.pow(2,t))throw new Error(`x=${e}, y=${r}, z=${t} outside of bounds. 0<=x<${Math.pow(2,t)}, 0<=y<${Math.pow(2,t)} 0<=z<=25 `);this.z=t,this.x=e,this.y=r,this.key=eh(0,t,t,e,r)}equals(t){return this.z===t.z&&this.x===t.x&&this.y===t.y}url(t,e,r){const n=(a=this.x,o=this.y,s=this.z,l=Ju(256*a,256*(o=Math.pow(2,s)-o-1),s),c=Ju(256*(a+1),256*(o+1),s),l[0]+\",\"+l[1]+\",\"+c[0]+\",\"+c[1]),i=function(t,e,r){let n,i=\"\";for(let a=t;a>0;a--)n=1<1?\"@2x\":\"\").replace(/{quadkey}/g,i).replace(/{bbox-epsg-3857}/g,n)}isChildOf(t){const e=this.z-t.z;return e>0&&t.x===this.x>>e&&t.y===this.y>>e}getTilePoint(t){const e=Math.pow(2,this.z);return new a((t.x*e-this.x)*Uo,(t.y*e-this.y)*Uo)}toString(){return`${this.z}/${this.x}/${this.y}`}}class Qu{constructor(t,e){this.wrap=t,this.canonical=e,this.key=eh(t,e.z,e.z,e.x,e.y)}}class th{constructor(t,e,r,n,i){if(t= z; overscaledZ = ${t}; z = ${r}`);this.overscaledZ=t,this.wrap=e,this.canonical=new Ku(r,+n,+i),this.key=eh(e,t,r,n,i)}clone(){return new th(this.overscaledZ,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y)}equals(t){return this.overscaledZ===t.overscaledZ&&this.wrap===t.wrap&&this.canonical.equals(t.canonical)}scaledTo(t){if(t>this.overscaledZ)throw new Error(`targetZ > this.overscaledZ; targetZ = ${t}; overscaledZ = ${this.overscaledZ}`);const e=this.canonical.z-t;return t>this.canonical.z?new th(t,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y):new th(t,this.wrap,t,this.canonical.x>>e,this.canonical.y>>e)}calculateScaledKey(t,e){if(t>this.overscaledZ)throw new Error(`targetZ > this.overscaledZ; targetZ = ${t}; overscaledZ = ${this.overscaledZ}`);const r=this.canonical.z-t;return t>this.canonical.z?eh(this.wrap*+e,t,this.canonical.z,this.canonical.x,this.canonical.y):eh(this.wrap*+e,t,t,this.canonical.x>>r,this.canonical.y>>r)}isChildOf(t){if(t.wrap!==this.wrap)return!1;const e=this.canonical.z-t.canonical.z;return 0===t.overscaledZ||t.overscaledZ>e&&t.canonical.y===this.canonical.y>>e}children(t){if(this.overscaledZ>=t)return[new th(this.overscaledZ+1,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y)];const e=this.canonical.z+1,r=2*this.canonical.x,n=2*this.canonical.y;return[new th(e,this.wrap,e,r,n),new th(e,this.wrap,e,r+1,n),new th(e,this.wrap,e,r,n+1),new th(e,this.wrap,e,r+1,n+1)]}isLessThan(t){return this.wrapt.wrap)&&(this.overscaledZt.overscaledZ)&&(this.canonical.xt.canonical.x)&&this.canonical.ythis.max&&(this.max=r),r=this.dim+1||e<-1||e>=this.dim+1)throw new RangeError(\"out of range source coordinates for DEM data\");return(e+1)*this.stride+(t+1)}unpack(t,e,r){return t*this.redFactor+e*this.greenFactor+r*this.blueFactor-this.baseShift}getPixels(){return new Ls({width:this.stride,height:this.stride},new Uint8Array(this.data.buffer))}backfillBorder(t,e,r){if(this.dim!==t.dim)throw new Error(\"dem dimension mismatch\");let n=e*this.dim,i=e*this.dim+this.dim,a=r*this.dim,o=r*this.dim+this.dim;switch(e){case-1:n=i-1;break;case 1:i=n+1}switch(r){case-1:a=o-1;break;case 1:o=a+1}const s=-e*this.dim,l=-r*this.dim;for(let e=a;e=this._numberToString.length)throw new Error(`Out of bounds. Index requested n=${t} can't be >= this._numberToString.length ${this._numberToString.length}`);return this._numberToString[t]}}class ih{constructor(t,e,r,n,i){this.type=\"Feature\",this._vectorTileFeature=t,t._z=e,t._x=r,t._y=n,this.properties=t.properties,this.id=i}get geometry(){return void 0===this._geometry&&(this._geometry=this._vectorTileFeature.toGeoJSON(this._vectorTileFeature._x,this._vectorTileFeature._y,this._vectorTileFeature._z).geometry),this._geometry}set geometry(t){this._geometry=t}toJSON(){const t={geometry:this.geometry};for(const e in this)\"_geometry\"!==e&&\"_vectorTileFeature\"!==e&&(t[e]=this[e]);return t}}class ah{constructor(t,e){this.tileID=t,this.x=t.canonical.x,this.y=t.canonical.y,this.z=t.canonical.z,this.grid=new ki(Uo,16,0),this.grid3D=new ki(Uo,16,0),this.featureIndexArray=new Za,this.promoteId=e}insert(t,e,r,n,i,a){const o=this.featureIndexArray.length;this.featureIndexArray.emplaceBack(r,n,i);const s=a?this.grid3D:this.grid;for(let t=0;t=0&&n[3]>=0&&s.insert(o,n[0],n[1],n[2],n[3])}}loadVTLayers(){return this.vtLayers||(this.vtLayers=new Tl.VectorTile(new Uc(this.rawTileData)).layers,this.sourceLayerCoder=new nh(this.vtLayers?Object.keys(this.vtLayers).sort():[\"_geojsonTileLayer\"])),this.vtLayers}query(t,e,r,n){this.loadVTLayers();const i=t.params||{},o=Uo/t.tileSize/t.scale,s=zn(i.filter),l=t.queryGeometry,c=t.queryPadding*o,u=sh(l),h=this.grid.query(u.minX-c,u.minY-c,u.maxX+c,u.maxY+c),f=sh(t.cameraQueryGeometry),p=this.grid3D.query(f.minX-c,f.minY-c,f.maxX+c,f.maxY+c,((e,r,n,i)=>function(t,e,r,n,i){for(const a of t)if(e<=a.x&&r<=a.y&&n>=a.x&&i>=a.y)return!0;const o=[new a(e,r),new a(e,i),new a(n,i),new a(n,r)];if(t.length>2)for(const e of o)if(ns(t,e))return!0;for(let e=0;e(f||(f=Ho(e)),r.queryIntersectsFeature(l,e,n,f,this.z,t.transform,o,t.pixelPosMatrix))))}return d}loadMatchingFeature(t,e,r,n,i,a,o,s,l,c,u){const h=this.bucketLayerIDs[e];if(a&&!function(t,e){for(let r=0;r=0)return!0;return!1}(a,h))return;const f=this.sourceLayerCoder.decode(r),p=this.vtLayers[f].feature(n);if(i.needGeometry){const t=Go(p,!0);if(!i.filter(new Hi(this.tileID.overscaledZ),t,this.tileID.canonical))return}else if(!i.filter(new Hi(this.tileID.overscaledZ),p))return;const d=this.getId(p,f);for(let e=0;e{const o=e instanceof Ki?e.get(a):null;return o&&o.evaluate?o.evaluate(r,n,i):o}))}function sh(t){let e=1/0,r=1/0,n=-1/0,i=-1/0;for(const a of t)e=Math.min(e,a.x),r=Math.min(r,a.y),n=Math.max(n,a.x),i=Math.max(i,a.y);return{minX:e,minY:r,maxX:n,maxY:i}}function lh(t,e){return e-t}function ch(t,e,r,n,i){const o=[];for(let s=0;s=n&&u.x>=n||(s.x>=n?s=new a(n,s.y+(u.y-s.y)*((n-s.x)/(u.x-s.x)))._round():u.x>=n&&(u=new a(n,s.y+(u.y-s.y)*((n-s.x)/(u.x-s.x)))._round()),s.y>=i&&u.y>=i||(s.y>=i?s=new a(s.x+(u.x-s.x)*((i-s.y)/(u.y-s.y)),i)._round():u.y>=i&&(u=new a(s.x+(u.x-s.x)*((i-s.y)/(u.y-s.y)),i)._round()),c&&s.equals(c[c.length-1])||(c=[s],o.push(c)),c.push(u)))))}}return o}Mi(\"FeatureIndex\",ah,{omit:[\"rawTileData\",\"sourceLayerCoder\"]});class uh extends a{constructor(t,e,r,n){super(t,e),this.angle=r,void 0!==n&&(this.segment=n)}clone(){return new uh(this.x,this.y,this.angle,this.segment)}}function hh(t,e,r,n,i){if(void 0===e.segment||0===r)return!0;let a=e,o=e.segment+1,s=0;for(;s>-r/2;){if(o--,o<0)return!1;s-=t[o].dist(a),a=t[o]}s+=t[o].dist(t[o+1]),o++;const l=[];let c=0;for(;sn;)c-=l.shift().angleDelta;if(c>i)return!1;o++,s+=r.dist(a)}return!0}function fh(t){let e=0;for(let r=0;rc){const u=(c-l)/a,h=Pe.number(n.x,i.x,u),f=Pe.number(n.y,i.y,u),p=new uh(h,f,i.angleTo(n),r);return p._round(),!o||hh(t,p,s,o,e)?p:void 0}l+=a}}function gh(t,e,r,n,i,a,o,s,l){const c=ph(n,a,o),u=dh(n,i),h=u*o,f=0===t[0].x||t[0].x===l||0===t[0].y||t[0].y===l;return e-h=0&&y=0&&v=0&&f+c<=u){const r=new uh(y,v,m,e);r._round(),n&&!hh(t,r,a,n,i)||p.push(r)}}h+=d}return s||p.length||o||(p=yh(t,h/2,r,n,i,a,o,!0,l)),p}Mi(\"Anchor\",uh);const vh=Yc;function xh(t,e,r,n){const i=[],o=t.image,s=o.pixelRatio,l=o.paddedRect.w-2*vh,c=o.paddedRect.h-2*vh;let u={x1:t.left,y1:t.top,x2:t.right,y2:t.bottom};const h=o.stretchX||[[0,l]],f=o.stretchY||[[0,c]],p=(t,e)=>t+e[1]-e[0],d=h.reduce(p,0),m=f.reduce(p,0),g=l-d,y=c-m;let v=0,x=d,_=0,b=m,w=0,T=g,k=0,A=y;if(o.content&&n){const e=o.content,r=e[2]-e[0],n=e[3]-e[1];(o.textFitWidth||o.textFitHeight)&&(u=du(t)),v=_h(h,0,e[0]),_=_h(f,0,e[1]),x=_h(h,e[0],e[2]),b=_h(f,e[1],e[3]),w=e[0]-v,k=e[1]-_,T=r-x,A=n-b}const M=u.x1,S=u.y1,E=u.x2-M,C=u.y2-S,L=(t,n,i,l)=>{const c=wh(t.stretch-v,x,E,M),u=Th(t.fixed-w,T,t.stretch,d),h=wh(n.stretch-_,b,C,S),f=Th(n.fixed-k,A,n.stretch,m),p=wh(i.stretch-v,x,E,M),g=Th(i.fixed-w,T,i.stretch,d),y=wh(l.stretch-_,b,C,S),L=Th(l.fixed-k,A,l.stretch,m),I=new a(c,h),P=new a(p,h),z=new a(p,y),O=new a(c,y),D=new a(u/s,f/s),R=new a(g/s,L/s),F=e*Math.PI/180;if(F){const t=Math.sin(F),e=Math.cos(F),r=[e,-t,t,e];I._matMult(r),P._matMult(r),O._matMult(r),z._matMult(r)}const B=t.stretch+t.fixed,N=i.stretch+i.fixed,j=n.stretch+n.fixed,U=l.stretch+l.fixed;return{tl:I,tr:P,bl:O,br:z,tex:{x:o.paddedRect.x+vh+B,y:o.paddedRect.y+vh+j,w:N-B,h:U-j},writingMode:void 0,glyphOffset:[0,0],sectionIndex:0,pixelOffsetTL:D,pixelOffsetBR:R,minFontScaleX:T/s/E,minFontScaleY:A/s/C,isSDF:r}};if(n&&(o.stretchX||o.stretchY)){const t=bh(h,g,d),e=bh(f,y,m);for(let r=0;r0&&(n=Math.max(10,n),this.circleDiameter=n)}else{const c=(null===(h=o.image)||void 0===h?void 0:h.content)&&(o.image.textFitWidth||o.image.textFitHeight)?du(o):{x1:o.left,y1:o.top,x2:o.right,y2:o.bottom};c.y1=c.y1*s-l[0],c.y2=c.y2*s+l[2],c.x1=c.x1*s-l[3],c.x2=c.x2*s+l[1];const f=o.collisionPadding;if(f&&(c.x1-=f[0]*s,c.y1-=f[1]*s,c.x2+=f[2]*s,c.y2+=f[3]*s),u){const t=new a(c.x1,c.y1),e=new a(c.x2,c.y1),r=new a(c.x1,c.y2),n=new a(c.x2,c.y2),i=u*Math.PI/180;t._rotate(i),e._rotate(i),r._rotate(i),n._rotate(i),c.x1=Math.min(t.x,e.x,r.x,n.x),c.x2=Math.max(t.x,e.x,r.x,n.x),c.y1=Math.min(t.y,e.y,r.y,n.y),c.y2=Math.max(t.y,e.y,r.y,n.y)}t.emplaceBack(e.x,e.y,c.x1,c.y1,c.x2,c.y2,r,n,i)}this.boxEndIndex=t.length}}class Ah{constructor(t=[],e=((t,e)=>te?1:0)){if(this.data=t,this.length=this.data.length,this.compare=e,this.length>0)for(let t=(this.length>>1)-1;t>=0;t--)this._down(t)}push(t){this.data.push(t),this._up(this.length++)}pop(){if(0===this.length)return;const t=this.data[0],e=this.data.pop();return--this.length>0&&(this.data[0]=e,this._down(0)),t}peek(){return this.data[0]}_up(t){const{data:e,compare:r}=this,n=e[t];for(;t>0;){const i=t-1>>1,a=e[i];if(r(n,a)>=0)break;e[t]=a,t=i}e[t]=n}_down(t){const{data:e,compare:r}=this,n=this.length>>1,i=e[t];for(;t=0)break;e[t]=e[n],t=n}e[t]=i}}function Mh(t,e=1,r=!1){let n=1/0,i=1/0,o=-1/0,s=-1/0;const l=t[0];for(let t=0;to)&&(o=e.x),(!t||e.y>s)&&(s=e.y)}const c=o-n,u=s-i,h=Math.min(c,u);let f=h/2;const p=new Ah([],Sh);if(0===h)return new a(n,i);for(let e=n;ed.d||!d.d)&&(d=n,r&&console.log(\"found best %d after %d probes\",Math.round(1e4*n.d)/1e4,m)),n.max-d.d<=e||(f=n.h/2,p.push(new Eh(n.p.x-f,n.p.y-f,f,t)),p.push(new Eh(n.p.x+f,n.p.y-f,f,t)),p.push(new Eh(n.p.x-f,n.p.y+f,f,t)),p.push(new Eh(n.p.x+f,n.p.y+f,f,t)),m+=4)}return r&&(console.log(`num probes: ${m}`),console.log(`best distance: ${d.d}`)),d.p}function Sh(t,e){return e.max-t.max}function Eh(t,e,r,n){this.p=new a(t,e),this.h=r,this.d=function(t,e){let r=!1,n=1/0;for(let i=0;it.y!=s.y>t.y&&t.x<(s.x-i.x)*(t.y-i.y)/(s.y-i.y)+i.x&&(r=!r),n=Math.min(n,es(t,i,s))}}return(r?1:-1)*Math.sqrt(n)}(this.p,n),this.max=this.d+this.h*Math.SQRT2}var Ch;t.ar=void 0,(Ch=t.ar||(t.ar={}))[Ch.center=1]=\"center\",Ch[Ch.left=2]=\"left\",Ch[Ch.right=3]=\"right\",Ch[Ch.top=4]=\"top\",Ch[Ch.bottom=5]=\"bottom\",Ch[Ch[\"top-left\"]=6]=\"top-left\",Ch[Ch[\"top-right\"]=7]=\"top-right\",Ch[Ch[\"bottom-left\"]=8]=\"bottom-left\",Ch[Ch[\"bottom-right\"]=9]=\"bottom-right\";const Lh=7,Ih=Number.POSITIVE_INFINITY;function Ph(t,e){return e[1]!==Ih?function(t,e,r){let n=0,i=0;switch(e=Math.abs(e),r=Math.abs(r),t){case\"top-right\":case\"top-left\":case\"top\":i=r-Lh;break;case\"bottom-right\":case\"bottom-left\":case\"bottom\":i=-r+Lh}switch(t){case\"top-right\":case\"bottom-right\":case\"right\":n=-e;break;case\"top-left\":case\"bottom-left\":case\"left\":n=e}return[n,i]}(t,e[0],e[1]):function(t,e){let r=0,n=0;e<0&&(e=0);const 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h.call(this,t,e,r)}},p=class{constructor(e){this.layers={_geojsonTileLayer:this},this.name=\"_geojsonTileLayer\",this.extent=t.X,this.length=e.length,this._features=e}feature(t){return new f(this._features[t])}};var d={exports:{}},m=t.by,g=t.bs.VectorTileFeature,y=v;function v(t,e){this.options=e||{},this.features=t,this.length=t.length}function x(t,e){this.id=\"number\"==typeof t.id?t.id:void 0,this.type=t.type,this.rawGeometry=1===t.type?[t.geometry]:t.geometry,this.properties=t.tags,this.extent=e||4096}v.prototype.feature=function(t){return new x(this.features[t],this.options.extent)},x.prototype.loadGeometry=function(){var t=this.rawGeometry;this.geometry=[];for(var e=0;e>31}function E(t,e){for(var r=t.loadGeometry(),n=t.type,i=0,a=0,o=r.length,s=0;st},z=Math.fround||(O=new Float32Array(1),t=>(O[0]=+t,O[0]));var O;const D=3,R=5,F=6;class B{constructor(t){this.options=Object.assign(Object.create(P),t),this.trees=new Array(this.options.maxZoom+1),this.stride=this.options.reduce?7:6,this.clusterProps=[]}load(t){const{log:e,minZoom:r,maxZoom:n}=this.options;e&&console.time(\"total time\");const i=`prepare ${t.length} points`;e&&console.time(i),this.points=t;const a=[];for(let e=0;e=r;t--){const r=+Date.now();o=this.trees[t]=this._createTree(this._cluster(o,t)),e&&console.log(\"z%d: %d clusters in %dms\",t,o.numItems,+Date.now()-r)}return e&&console.timeEnd(\"total time\"),this}getClusters(t,e){let r=((t[0]+180)%360+360)%360-180;const n=Math.max(-90,Math.min(90,t[1]));let i=180===t[2]?180:((t[2]+180)%360+360)%360-180;const a=Math.max(-90,Math.min(90,t[3]));if(t[2]-t[0]>=360)r=-180,i=180;else if(r>i){const t=this.getClusters([r,n,180,a],e),o=this.getClusters([-180,n,i,a],e);return t.concat(o)}const o=this.trees[this._limitZoom(e)],s=o.range(U(r),V(a),U(i),V(n)),l=o.data,c=[];for(const t of s){const e=this.stride*t;c.push(l[e+R]>1?N(l,e,this.clusterProps):this.points[l[e+D]])}return c}getChildren(t){const e=this._getOriginId(t),r=this._getOriginZoom(t),n=\"No cluster with the specified id.\",i=this.trees[r];if(!i)throw new Error(n);const a=i.data;if(e*this.stride>=a.length)throw new Error(n);const o=this.options.radius/(this.options.extent*Math.pow(2,r-1)),s=a[e*this.stride],l=a[e*this.stride+1],c=i.within(s,l,o),u=[];for(const e of c){const r=e*this.stride;a[r+4]===t&&u.push(a[r+R]>1?N(a,r,this.clusterProps):this.points[a[r+D]])}if(0===u.length)throw new Error(n);return u}getLeaves(t,e,r){e=e||10,r=r||0;const n=[];return this._appendLeaves(n,t,e,r,0),n}getTile(t,e,r){const n=this.trees[this._limitZoom(t)],i=Math.pow(2,t),{extent:a,radius:o}=this.options,s=o/a,l=(r-s)/i,c=(r+1+s)/i,u={features:[]};return this._addTileFeatures(n.range((e-s)/i,l,(e+1+s)/i,c),n.data,e,r,i,u),0===e&&this._addTileFeatures(n.range(1-s/i,l,1,c),n.data,i,r,i,u),e===i-1&&this._addTileFeatures(n.range(0,l,s/i,c),n.data,-1,r,i,u),u.features.length?u:null}getClusterExpansionZoom(t){let e=this._getOriginZoom(t)-1;for(;e<=this.options.maxZoom;){const r=this.getChildren(t);if(e++,1!==r.length)break;t=r[0].properties.cluster_id}return e}_appendLeaves(t,e,r,n,i){const a=this.getChildren(e);for(const e of a){const a=e.properties;if(a&&a.cluster?i+a.point_count<=n?i+=a.point_count:i=this._appendLeaves(t,a.cluster_id,r,n,i):i1;let l,c,u;if(s)l=j(e,t,this.clusterProps),c=e[t],u=e[t+1];else{const r=this.points[e[t+D]];l=r.properties;const[n,i]=r.geometry.coordinates;c=U(n),u=V(i)}const h={type:1,geometry:[[Math.round(this.options.extent*(c*i-r)),Math.round(this.options.extent*(u*i-n))]],tags:l};let f;f=s||this.options.generateId?e[t+D]:this.points[e[t+D]].id,void 0!==f&&(h.id=f),a.features.push(h)}}_limitZoom(t){return Math.max(this.options.minZoom,Math.min(Math.floor(+t),this.options.maxZoom+1))}_cluster(t,e){const{radius:r,extent:n,reduce:i,minPoints:a}=this.options,o=r/(n*Math.pow(2,e)),s=t.data,l=[],c=this.stride;for(let r=0;re&&(p+=s[r+R])}if(p>f&&p>=a){let t,a=n*f,o=u*f,d=-1;const m=((r/c|0)<<5)+(e+1)+this.points.length;for(const n of h){const l=n*c;if(s[l+2]<=e)continue;s[l+2]=e;const u=s[l+R];a+=s[l]*u,o+=s[l+1]*u,s[l+4]=m,i&&(t||(t=this._map(s,r,!0),d=this.clusterProps.length,this.clusterProps.push(t)),i(t,this._map(s,l)))}s[r+4]=m,l.push(a/p,o/p,1/0,m,-1,p),i&&l.push(d)}else{for(let t=0;t1)for(const t of h){const r=t*c;if(!(s[r+2]<=e)){s[r+2]=e;for(let t=0;t>5}_getOriginZoom(t){return(t-this.points.length)%32}_map(t,e,r){if(t[e+R]>1){const n=this.clusterProps[t[e+F]];return r?Object.assign({},n):n}const n=this.points[t[e+D]].properties,i=this.options.map(n);return r&&i===n?Object.assign({},i):i}}function N(t,e,r){return{type:\"Feature\",id:t[e+D],properties:j(t,e,r),geometry:{type:\"Point\",coordinates:[(n=t[e],360*(n-.5)),q(t[e+1])]}};var n}function j(t,e,r){const n=t[e+R],i=n>=1e4?`${Math.round(n/1e3)}k`:n>=1e3?Math.round(n/100)/10+\"k\":n,a=t[e+F],o=-1===a?{}:Object.assign({},r[a]);return 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at(t,a,r+(i-r)*o,1),o}function st(t,e,r,n,i,a){const o=(a-r)/(i-r);return at(t,e+(n-e)*o,a,1),o}function lt(t,e){const r=[];for(let n=0;n0&&e.size<(i?o:n))return void(r.numPoints+=e.length/3);const s=[];for(let t=0;to)&&(r.numSimplified++,s.push(e[t],e[t+1])),r.numPoints++;i&&function(t,e){let r=0;for(let e=0,n=t.length,i=n-2;e0===e)for(let e=0,r=t.length;e24)throw new Error(\"maxZoom should be in the 0-24 range\");if(e.promoteId&&e.generateId)throw new Error(\"promoteId and generateId cannot be used together.\");let n=function(t,e){const r=[];if(\"FeatureCollection\"===t.type)for(let n=0;n1&&console.time(\"creation\"),f=this.tiles[h]=ft(t,e,r,n,l),this.tileCoords.push({z:e,x:r,y:n}),c)){c>1&&(console.log(\"tile z%d-%d-%d (features: %d, points: %d, simplified: %d)\",e,r,n,f.numFeatures,f.numPoints,f.numSimplified),console.timeEnd(\"creation\"));const t=`z${e}`;this.stats[t]=(this.stats[t]||0)+1,this.total++}if(f.source=t,null==i){if(e===l.indexMaxZoom||f.numPoints<=l.indexMaxPoints)continue}else{if(e===l.maxZoom||e===i)continue;if(null!=i){const t=i-e;if(r!==a>>t||n!==o>>t)continue}}if(f.source=null,0===t.length)continue;c>1&&console.time(\"clipping\");const p=.5*l.buffer/l.extent,d=.5-p,m=.5+p,g=1+p;let y=null,v=null,x=null,_=null,b=tt(t,u,r-p,r+m,0,f.minX,f.maxX,l),w=tt(t,u,r+d,r+g,0,f.minX,f.maxX,l);t=null,b&&(y=tt(b,u,n-p,n+m,1,f.minY,f.maxY,l),v=tt(b,u,n+d,n+g,1,f.minY,f.maxY,l),b=null),w&&(x=tt(w,u,n-p,n+m,1,f.minY,f.maxY,l),_=tt(w,u,n+d,n+g,1,f.minY,f.maxY,l),w=null),c>1&&console.timeEnd(\"clipping\"),s.push(y||[],e+1,2*r,2*n),s.push(v||[],e+1,2*r,2*n+1),s.push(x||[],e+1,2*r+1,2*n),s.push(_||[],e+1,2*r+1,2*n+1)}}getTile(t,e,r){t=+t,e=+e,r=+r;const n=this.options,{extent:i,debug:a}=n;if(t<0||t>24)return null;const o=1<1&&console.log(\"drilling down to z%d-%d-%d\",t,e,r);let l,c=t,u=e,h=r;for(;!l&&c>0;)c--,u>>=1,h>>=1,l=this.tiles[yt(c,u,h)];return l&&l.source?(a>1&&(console.log(\"found parent tile z%d-%d-%d\",c,u,h),console.time(\"drilling down\")),this.splitTile(l.source,c,u,h,t,e,r),a>1&&console.timeEnd(\"drilling down\"),this.tiles[s]?ut(this.tiles[s],i):null):null}}function yt(t,e,r){return 32*((1<{o.properties=t;const e={};for(const t of s)e[t]=n[t].evaluate(a,o);return e},e.reduce=(t,e)=>{o.properties=e;for(const e of s)a.accumulated=t[e],t[e]=i[e].evaluate(a,o)},e}(e)).load((yield this._pendingData).features):(i=yield this._pendingData,a=e.geojsonVtOptions,new gt(i,a)),this.loaded={};const r={};if(n){const t=n.finish();t&&(r.resourceTiming={},r.resourceTiming[e.source]=JSON.parse(JSON.stringify(t)))}return r}catch(e){if(delete this._pendingRequest,t.bA(e))return{abandoned:!0};throw e}var i,a}))}getData(){return t._(this,void 0,void 0,(function*(){return this._pendingData}))}reloadTile(t){const e=this.loaded,r=t.uid;return e&&e[r]?super.reloadTile(t):this.loadTile(t)}loadAndProcessGeoJSON(e,r){return t._(this,void 0,void 0,(function*(){let n=yield this.loadGeoJSON(e,r);if(delete this._pendingRequest,\"object\"!=typeof n)throw new Error(`Input data given to '${e.source}' is not a valid GeoJSON object.`);if(u(n,!0),e.filter){const r=t.bB(e.filter,{type:\"boolean\",\"property-type\":\"data-driven\",overridable:!1,transition:!1});if(\"error\"===r.result)throw new Error(r.value.map((t=>`${t.key}: ${t.message}`)).join(\", \"));const i=n.features.filter((t=>r.value.evaluate({zoom:0},t)));n={type:\"FeatureCollection\",features:i}}return n}))}loadGeoJSON(e,r){return t._(this,void 0,void 0,(function*(){const{promoteId:n}=e;if(e.request){const i=yield t.h(e.request,r);return this._dataUpdateable=xt(i.data,n)?_t(i.data,n):void 0,i.data}if(\"string\"==typeof e.data)try{const t=JSON.parse(e.data);return this._dataUpdateable=xt(t,n)?_t(t,n):void 0,t}catch(t){throw new Error(`Input data given to '${e.source}' is not a 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r.removeProperties)Object.prototype.hasOwnProperty.call(e.properties,t)&&delete e.properties[t];if((null===(o=r.addOrUpdateProperties)||void 0===o?void 0:o.length)>0)for(const{key:t,value:n}of r.addOrUpdateProperties)e.properties[t]=n}}(this._dataUpdateable,e.dataDiff,n),{type:\"FeatureCollection\",features:Array.from(this._dataUpdateable.values())}}))}removeSource(e){return t._(this,void 0,void 0,(function*(){this._pendingRequest&&this._pendingRequest.abort()}))}getClusterExpansionZoom(t){return this._geoJSONIndex.getClusterExpansionZoom(t.clusterId)}getClusterChildren(t){return this._geoJSONIndex.getChildren(t.clusterId)}getClusterLeaves(t){return this._geoJSONIndex.getLeaves(t.clusterId,t.limit,t.offset)}}class wt{constructor(e){this.self=e,this.actor=new t.F(e),this.layerIndexes={},this.availableImages={},this.workerSources={},this.demWorkerSources={},this.externalWorkerSourceTypes={},this.self.registerWorkerSource=(t,e)=>{if(this.externalWorkerSourceTypes[t])throw new 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this._getWorkerSource(e,r.type,r.source).getClusterLeaves(r)})))),this.actor.registerMessageHandler(\"LD\",((t,e)=>this._getWorkerSource(t,e.type,e.source).loadData(e))),this.actor.registerMessageHandler(\"GD\",((t,e)=>this._getWorkerSource(t,e.type,e.source).getData())),this.actor.registerMessageHandler(\"LT\",((t,e)=>this._getWorkerSource(t,e.type,e.source).loadTile(e))),this.actor.registerMessageHandler(\"RT\",((t,e)=>this._getWorkerSource(t,e.type,e.source).reloadTile(e))),this.actor.registerMessageHandler(\"AT\",((t,e)=>this._getWorkerSource(t,e.type,e.source).abortTile(e))),this.actor.registerMessageHandler(\"RMT\",((t,e)=>this._getWorkerSource(t,e.type,e.source).removeTile(e))),this.actor.registerMessageHandler(\"RS\",((e,r)=>t._(this,void 0,void 0,(function*(){if(!this.workerSources[e]||!this.workerSources[e][r.type]||!this.workerSources[e][r.type][r.source])return;const t=this.workerSources[e][r.type][r.source];delete this.workerSources[e][r.type][r.source],void 0!==t.removeSource&&t.removeSource(r)})))),this.actor.registerMessageHandler(\"RM\",(e=>t._(this,void 0,void 0,(function*(){delete this.layerIndexes[e],delete this.availableImages[e],delete this.workerSources[e],delete this.demWorkerSources[e]})))),this.actor.registerMessageHandler(\"SR\",((e,r)=>t._(this,void 0,void 0,(function*(){this.referrer=r})))),this.actor.registerMessageHandler(\"SRPS\",((t,e)=>this._syncRTLPluginState(t,e))),this.actor.registerMessageHandler(\"IS\",((e,r)=>t._(this,void 0,void 0,(function*(){this.self.importScripts(r)})))),this.actor.registerMessageHandler(\"SI\",((t,e)=>this._setImages(t,e))),this.actor.registerMessageHandler(\"UL\",((e,r)=>t._(this,void 0,void 0,(function*(){this._getLayerIndex(e).update(r.layers,r.removedIds)})))),this.actor.registerMessageHandler(\"SL\",((e,r)=>t._(this,void 0,void 0,(function*(){this._getLayerIndex(e).replace(r)}))))}_setImages(e,r){return t._(this,void 0,void 0,(function*(){this.availableImages[e]=r;for(const t in this.workerSources[e]){const n=this.workerSources[e][t];for(const t in n)n[t].availableImages=r}}))}_syncRTLPluginState(e,r){return t._(this,void 0,void 0,(function*(){if(t.bC.isParsed())return t.bC.getState();if(\"loading\"!==r.pluginStatus)return t.bC.setState(r),r;const e=r.pluginURL;if(this.self.importScripts(e),t.bC.isParsed()){const r={pluginStatus:\"loaded\",pluginURL:e};return t.bC.setState(r),r}throw t.bC.setState({pluginStatus:\"error\",pluginURL:\"\"}),new Error(`RTL Text Plugin failed to import scripts from ${e}`)}))}_getAvailableImages(t){let e=this.availableImages[t];return e||(e=[]),e}_getLayerIndex(t){let r=this.layerIndexes[t];return r||(r=this.layerIndexes[t]=new e),r}_getWorkerSource(t,e,r){if(this.workerSources[t]||(this.workerSources[t]={}),this.workerSources[t][e]||(this.workerSources[t][e]={}),!this.workerSources[t][e][r]){const n={sendAsync:(e,r)=>(e.targetMapId=t,this.actor.sendAsync(e,r))};switch(e){case\"vector\":this.workerSources[t][e][r]=new a(n,this._getLayerIndex(t),this._getAvailableImages(t));break;case\"geojson\":this.workerSources[t][e][r]=new bt(n,this._getLayerIndex(t),this._getAvailableImages(t));break;default:this.workerSources[t][e][r]=new this.externalWorkerSourceTypes[e](n,this._getLayerIndex(t),this._getAvailableImages(t))}}return this.workerSources[t][e][r]}_getDEMWorkerSource(t,e){return this.demWorkerSources[t]||(this.demWorkerSources[t]={}),this.demWorkerSources[t][e]||(this.demWorkerSources[t][e]=new o),this.demWorkerSources[t][e]}}return t.i(self)&&(self.worker=new wt(self)),wt})),r(\"index\",0,(function(t,e){var r=\"4.5.2\";let n,i;const a={now:\"undefined\"!=typeof performance&&performance&&performance.now?performance.now.bind(performance):Date.now.bind(Date),frameAsync(t){return new Promise(((r,n)=>{const i=requestAnimationFrame(r);t.signal.addEventListener(\"abort\",(()=>{cancelAnimationFrame(i),n(e.c())}))}))},getImageData(t,e=0){return this.getImageCanvasContext(t).getImageData(-e,-e,t.width+2*e,t.height+2*e)},getImageCanvasContext(t){const e=window.document.createElement(\"canvas\"),r=e.getContext(\"2d\",{willReadFrequently:!0});if(!r)throw new Error(\"failed to create canvas 2d context\");return e.width=t.width,e.height=t.height,r.drawImage(t,0,0,t.width,t.height),r},resolveURL(t){return n||(n=document.createElement(\"a\")),n.href=t,n.href},hardwareConcurrency:\"undefined\"!=typeof navigator&&navigator.hardwareConcurrency||4,get prefersReducedMotion(){return!!matchMedia&&(null==i&&(i=matchMedia(\"(prefers-reduced-motion: reduce)\")),i.matches)}};class o{static testProp(t){if(!o.docStyle)return t[0];for(let e=0;e{window.removeEventListener(\"click\",o.suppressClickInternal,!0)}),0)}static getScale(t){const e=t.getBoundingClientRect();return{x:e.width/t.offsetWidth||1,y:e.height/t.offsetHeight||1,boundingClientRect:e}}static getPoint(t,r,n){const i=r.boundingClientRect;return new e.P((n.clientX-i.left)/r.x-t.clientLeft,(n.clientY-i.top)/r.y-t.clientTop)}static mousePos(t,e){const r=o.getScale(t);return o.getPoint(t,r,e)}static touchPos(t,e){const r=[],n=o.getScale(t);for(let i=0;i{l&&f(l),l=null,h=!0},c.onerror=()=>{u=!0,l=null},c.src=\"data:image/webp;base64,UklGRh4AAABXRUJQVlA4TBEAAAAvAQAAAAfQ//73v/+BiOh/AAA=\"),function(t){let r,n,i,a;t.resetRequestQueue=()=>{r=[],n=0,i=0,a={}},t.addThrottleControl=t=>{const e=i++;return a[e]=t,e},t.removeThrottleControl=t=>{delete a[t],l()};t.getImage=(t,n,i=!0)=>new Promise(((a,o)=>{s.supported&&(t.headers||(t.headers={}),t.headers.accept=\"image/webp,*/*\"),e.e(t,{type:\"image\"});const c={abortController:n,requestParameters:t,supportImageRefresh:i,state:\"queued\",onError:t=>{o(t)},onSuccess:t=>{a(t)}};r.push(c),l()}));const o=t=>e._(this,void 0,void 0,(function*(){t.state=\"running\";const{requestParameters:r,supportImageRefresh:i,onError:a,onSuccess:o,abortController:s}=t,u=!1===i&&!e.i(self)&&!e.g(r.url)&&(!r.headers||Object.keys(r.headers).reduce(((t,e)=>t&&\"accept\"===e),!0));n++;const h=u?c(r,s):e.m(r,s);try{const r=yield h;delete t.abortController,t.state=\"completed\",r.data instanceof HTMLImageElement||e.b(r.data)?o(r):r.data&&o({data:yield(f=r.data,\"function\"==typeof createImageBitmap?e.d(f):e.f(f)),cacheControl:r.cacheControl,expires:r.expires})}catch(e){delete t.abortController,a(e)}finally{n--,l()}var f})),l=()=>{const t=(()=>{for(const t of Object.keys(a))if(a[t]())return!0;return!1})()?e.a.MAX_PARALLEL_IMAGE_REQUESTS_PER_FRAME:e.a.MAX_PARALLEL_IMAGE_REQUESTS;for(let e=n;e0;e++){const t=r.shift();t.abortController.signal.aborted?e--:o(t)}},c=(t,r)=>new Promise(((n,i)=>{const a=new Image,o=t.url,s=t.credentials;s&&\"include\"===s?a.crossOrigin=\"use-credentials\":(s&&\"same-origin\"===s||!e.s(o))&&(a.crossOrigin=\"anonymous\"),r.signal.addEventListener(\"abort\",(()=>{a.src=\"\",i(e.c())})),a.fetchPriority=\"high\",a.onload=()=>{a.onerror=a.onload=null,n({data:a})},a.onerror=()=>{a.onerror=a.onload=null,r.signal.aborted||i(new Error(\"Could not load image. Please make sure to use a supported image type such as PNG or JPEG. Note that SVGs are not supported.\"))},a.src=o}))}(p||(p={})),p.resetRequestQueue();class d{constructor(t){this._transformRequestFn=t}transformRequest(t,e){return this._transformRequestFn&&this._transformRequestFn(t,e)||{url:t}}setTransformRequest(t){this._transformRequestFn=t}}function m(t){var r=new e.A(3);return r[0]=t[0],r[1]=t[1],r[2]=t[2],r}var g,y=function(t,e,r){return t[0]=e[0]-r[0],t[1]=e[1]-r[1],t[2]=e[2]-r[2],t};g=new e.A(3),e.A!=Float32Array&&(g[0]=0,g[1]=0,g[2]=0);var v=function(t){var e=t[0],r=t[1];return e*e+r*r};function x(t){const e=[];if(\"string\"==typeof t)e.push({id:\"default\",url:t});else if(t&&t.length>0){const r=[];for(const{id:n,url:i}of t){const t=`${n}${i}`;-1===r.indexOf(t)&&(r.push(t),e.push({id:n,url:i}))}}return e}function _(t,e,r){const n=t.split(\"?\");return n[0]+=`${e}${r}`,n.join(\"?\")}function b(t,r,n,i){return e._(this,void 0,void 0,(function*(){const o=x(t),s=n>1?\"@2x\":\"\",l={},c={};for(const{id:t,url:n}of o){const a=r.transformRequest(_(n,s,\".json\"),\"SpriteJSON\");l[t]=e.h(a,i);const o=r.transformRequest(_(n,s,\".png\"),\"SpriteImage\");c[t]=p.getImage(o,i)}return yield Promise.all([...Object.values(l),...Object.values(c)]),function(t,r){return e._(this,void 0,void 0,(function*(){const e={};for(const n in t){e[n]={};const i=a.getImageCanvasContext((yield r[n]).data),o=(yield t[n]).data;for(const t in o){const{width:r,height:a,x:s,y:l,sdf:c,pixelRatio:u,stretchX:h,stretchY:f,content:p,textFitWidth:d,textFitHeight:m}=o[t],g={width:r,height:a,x:s,y:l,context:i};e[n][t]={data:null,pixelRatio:u,sdf:c,stretchX:h,stretchY:f,content:p,textFitWidth:d,textFitHeight:m,spriteData:g}}}return e}))}(l,c)}))}!function(){var t=new e.A(2);e.A!=Float32Array&&(t[0]=0,t[1]=0)}();class w{constructor(t,e,r,n){this.context=t,this.format=r,this.texture=t.gl.createTexture(),this.update(e,n)}update(t,r,n){const{width:i,height:a}=t,o=!(this.size&&this.size[0]===i&&this.size[1]===a||n),{context:s}=this,{gl:l}=s;if(this.useMipmap=Boolean(r&&r.useMipmap),l.bindTexture(l.TEXTURE_2D,this.texture),s.pixelStoreUnpackFlipY.set(!1),s.pixelStoreUnpack.set(1),s.pixelStoreUnpackPremultiplyAlpha.set(this.format===l.RGBA&&(!r||!1!==r.premultiply)),o)this.size=[i,a],t instanceof HTMLImageElement||t instanceof HTMLCanvasElement||t instanceof HTMLVideoElement||t instanceof ImageData||e.b(t)?l.texImage2D(l.TEXTURE_2D,0,this.format,this.format,l.UNSIGNED_BYTE,t):l.texImage2D(l.TEXTURE_2D,0,this.format,i,a,0,this.format,l.UNSIGNED_BYTE,t.data);else{const{x:r,y:o}=n||{x:0,y:0};t instanceof HTMLImageElement||t instanceof HTMLCanvasElement||t instanceof HTMLVideoElement||t instanceof ImageData||e.b(t)?l.texSubImage2D(l.TEXTURE_2D,0,r,o,l.RGBA,l.UNSIGNED_BYTE,t):l.texSubImage2D(l.TEXTURE_2D,0,r,o,i,a,l.RGBA,l.UNSIGNED_BYTE,t.data)}this.useMipmap&&this.isSizePowerOfTwo()&&l.generateMipmap(l.TEXTURE_2D)}bind(t,e,r){const{context:n}=this,{gl:i}=n;i.bindTexture(i.TEXTURE_2D,this.texture),r!==i.LINEAR_MIPMAP_NEAREST||this.isSizePowerOfTwo()||(r=i.LINEAR),t!==this.filter&&(i.texParameteri(i.TEXTURE_2D,i.TEXTURE_MAG_FILTER,t),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_MIN_FILTER,r||t),this.filter=t),e!==this.wrap&&(i.texParameteri(i.TEXTURE_2D,i.TEXTURE_WRAP_S,e),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_WRAP_T,e),this.wrap=e)}isSizePowerOfTwo(){return this.size[0]===this.size[1]&&Math.log(this.size[0])/Math.LN2%1==0}destroy(){const{gl:t}=this.context;t.deleteTexture(this.texture),this.texture=null}}function T(t){const{userImage:e}=t;return!!(e&&e.render&&e.render())&&(t.data.replace(new Uint8Array(e.data.buffer)),!0)}class k extends e.E{constructor(){super(),this.images={},this.updatedImages={},this.callbackDispatchedThisFrame={},this.loaded=!1,this.requestors=[],this.patterns={},this.atlasImage=new e.R({width:1,height:1}),this.dirty=!0}isLoaded(){return this.loaded}setLoaded(t){if(this.loaded!==t&&(this.loaded=t,t)){for(const{ids:t,promiseResolve:e}of this.requestors)e(this._getImagesForIds(t));this.requestors=[]}}getImage(t){const r=this.images[t];if(r&&!r.data&&r.spriteData){const t=r.spriteData;r.data=new e.R({width:t.width,height:t.height},t.context.getImageData(t.x,t.y,t.width,t.height).data),r.spriteData=null}return r}addImage(t,e){if(this.images[t])throw new Error(`Image id ${t} already exist, use updateImage instead`);this._validate(t,e)&&(this.images[t]=e)}_validate(t,r){let n=!0;const i=r.data||r.spriteData;return this._validateStretch(r.stretchX,i&&i.width)||(this.fire(new e.j(new Error(`Image \"${t}\" has invalid \"stretchX\" value`))),n=!1),this._validateStretch(r.stretchY,i&&i.height)||(this.fire(new e.j(new Error(`Image \"${t}\" has invalid \"stretchY\" value`))),n=!1),this._validateContent(r.content,r)||(this.fire(new e.j(new Error(`Image \"${t}\" has invalid \"content\" value`))),n=!1),n}_validateStretch(t,e){if(!t)return!0;let r=0;for(const n of t){if(n[0]{let n=!0;if(!this.isLoaded())for(const e of t)this.images[e]||(n=!1);this.isLoaded()||n?e(this._getImagesForIds(t)):this.requestors.push({ids:t,promiseResolve:e})}))}_getImagesForIds(t){const r={};for(const n of t){let t=this.getImage(n);t||(this.fire(new e.k(\"styleimagemissing\",{id:n})),t=this.getImage(n)),t?r[n]={data:t.data.clone(),pixelRatio:t.pixelRatio,sdf:t.sdf,version:t.version,stretchX:t.stretchX,stretchY:t.stretchY,content:t.content,textFitWidth:t.textFitWidth,textFitHeight:t.textFitHeight,hasRenderCallback:Boolean(t.userImage&&t.userImage.render)}:e.w(`Image \"${n}\" could not be loaded. Please make sure you have added the image with map.addImage() or a \"sprite\" property in your style. You can provide missing images by listening for the \"styleimagemissing\" map event.`)}return r}getPixelSize(){const{width:t,height:e}=this.atlasImage;return{width:t,height:e}}getPattern(t){const r=this.patterns[t],n=this.getImage(t);if(!n)return null;if(r&&r.position.version===n.version)return r.position;if(r)r.position.version=n.version;else{const r={w:n.data.width+2,h:n.data.height+2,x:0,y:0},i=new e.I(r,n);this.patterns[t]={bin:r,position:i}}return this._updatePatternAtlas(),this.patterns[t].position}bind(t){const e=t.gl;this.atlasTexture?this.dirty&&(this.atlasTexture.update(this.atlasImage),this.dirty=!1):this.atlasTexture=new w(t,this.atlasImage,e.RGBA),this.atlasTexture.bind(e.LINEAR,e.CLAMP_TO_EDGE)}_updatePatternAtlas(){const t=[];for(const e in this.patterns)t.push(this.patterns[e].bin);const{w:r,h:n}=e.p(t),i=this.atlasImage;i.resize({width:r||1,height:n||1});for(const t in this.patterns){const{bin:r}=this.patterns[t],n=r.x+1,a=r.y+1,o=this.getImage(t).data,s=o.width,l=o.height;e.R.copy(o,i,{x:0,y:0},{x:n,y:a},{width:s,height:l}),e.R.copy(o,i,{x:0,y:l-1},{x:n,y:a-1},{width:s,height:1}),e.R.copy(o,i,{x:0,y:0},{x:n,y:a+l},{width:s,height:1}),e.R.copy(o,i,{x:s-1,y:0},{x:n-1,y:a},{width:1,height:l}),e.R.copy(o,i,{x:0,y:0},{x:n+s,y:a},{width:1,height:l})}this.dirty=!0}beginFrame(){this.callbackDispatchedThisFrame={}}dispatchRenderCallbacks(t){for(const r of t){if(this.callbackDispatchedThisFrame[r])continue;this.callbackDispatchedThisFrame[r]=!0;const t=this.getImage(r);t||e.w(`Image with ID: \"${r}\" was not found`),T(t)&&this.updateImage(r,t)}}}const A=1e20;function M(t,e,r,n,i,a,o,s,l){for(let c=e;c-1);l++,a[l]=s,o[l]=c,o[l+1]=A}for(let s=0,l=0;s65535)throw new Error(\"glyphs > 65535 not supported\");if(e.ranges[i])return{stack:t,id:r,glyph:n};if(!this.url)throw new Error(\"glyphsUrl is not set\");if(!e.requests[i]){const r=E.loadGlyphRange(t,i,this.url,this.requestManager);e.requests[i]=r}const a=yield e.requests[i];for(const t in a)this._doesCharSupportLocalGlyph(+t)||(e.glyphs[+t]=a[+t]);return e.ranges[i]=!0,{stack:t,id:r,glyph:a[r]||null}}))}_doesCharSupportLocalGlyph(t){return!!this.localIdeographFontFamily&&(e.u[\"CJK Unified Ideographs\"](t)||e.u[\"Hangul Syllables\"](t)||e.u.Hiragana(t)||e.u.Katakana(t))}_tinySDF(t,r,n){const i=this.localIdeographFontFamily;if(!i)return;if(!this._doesCharSupportLocalGlyph(n))return;let a=t.tinySDF;if(!a){let e=\"400\";/bold/i.test(r)?e=\"900\":/medium/i.test(r)?e=\"500\":/light/i.test(r)&&(e=\"200\"),a=t.tinySDF=new E.TinySDF({fontSize:48,buffer:6,radius:16,cutoff:.25,fontFamily:i,fontWeight:e})}const o=a.draw(String.fromCharCode(n));return{id:n,bitmap:new e.o({width:o.width||60,height:o.height||60},o.data),metrics:{width:o.glyphWidth/2||24,height:o.glyphHeight/2||24,left:o.glyphLeft/2+.5||0,top:o.glyphTop/2-27.5||-8,advance:o.glyphAdvance/2||24,isDoubleResolution:!0}}}}E.loadGlyphRange=function(t,r,n,i){return e._(this,void 0,void 0,(function*(){const a=256*r,o=a+255,s=i.transformRequest(n.replace(\"{fontstack}\",t).replace(\"{range}\",`${a}-${o}`),\"Glyphs\"),l=yield e.l(s,new AbortController);if(!l||!l.data)throw new Error(`Could not load glyph range. range: ${r}, ${a}-${o}`);const c={};for(const t of e.n(l.data))c[t.id]=t;return c}))},E.TinySDF=class{constructor({fontSize:t=24,buffer:e=3,radius:r=8,cutoff:n=.25,fontFamily:i=\"sans-serif\",fontWeight:a=\"normal\",fontStyle:o=\"normal\"}={}){this.buffer=e,this.cutoff=n,this.radius=r;const s=this.size=t+4*e,l=this._createCanvas(s),c=this.ctx=l.getContext(\"2d\",{willReadFrequently:!0});c.font=`${o} ${a} ${t}px ${i}`,c.textBaseline=\"alphabetic\",c.textAlign=\"left\",c.fillStyle=\"black\",this.gridOuter=new Float64Array(s*s),this.gridInner=new Float64Array(s*s),this.f=new Float64Array(s),this.z=new Float64Array(s+1),this.v=new Uint16Array(s)}_createCanvas(t){const e=document.createElement(\"canvas\");return e.width=e.height=t,e}draw(t){const{width:e,actualBoundingBoxAscent:r,actualBoundingBoxDescent:n,actualBoundingBoxLeft:i,actualBoundingBoxRight:a}=this.ctx.measureText(t),o=Math.ceil(r),s=Math.max(0,Math.min(this.size-this.buffer,Math.ceil(a-i))),l=Math.min(this.size-this.buffer,o+Math.ceil(n)),c=s+2*this.buffer,u=l+2*this.buffer,h=Math.max(c*u,0),f=new Uint8ClampedArray(h),p={data:f,width:c,height:u,glyphWidth:s,glyphHeight:l,glyphTop:o,glyphLeft:0,glyphAdvance:e};if(0===s||0===l)return p;const{ctx:d,buffer:m,gridInner:g,gridOuter:y}=this;d.clearRect(m,m,s,l),d.fillText(t,m,m+o);const v=d.getImageData(m,m,s,l);y.fill(A,0,h),g.fill(0,0,h);for(let t=0;t0?t*t:0,g[n]=t<0?t*t:0}}M(y,0,0,c,u,c,this.f,this.v,this.z),M(g,m,m,s,l,c,this.f,this.v,this.z);for(let t=0;t1&&(s=t[++o]);const l=Math.abs(i-s.left),c=Math.abs(i-s.right),u=Math.min(l,c);let h;const f=e/r*(n+1);if(s.isDash){const t=n-Math.abs(f);h=Math.sqrt(u*u+t*t)}else h=n-Math.sqrt(u*u+f*f);this.data[a+i]=Math.max(0,Math.min(255,h+128))}}}addRegularDash(t){for(let e=t.length-1;e>=0;--e){const r=t[e],n=t[e+1];r.zeroLength?t.splice(e,1):n&&n.isDash===r.isDash&&(n.left=r.left,t.splice(e,1))}const e=t[0],r=t[t.length-1];e.isDash===r.isDash&&(e.left=r.left-this.width,r.right=e.right+this.width);const n=this.width*this.nextRow;let i=0,a=t[i];for(let e=0;e1&&(a=t[++i]);const r=Math.abs(e-a.left),o=Math.abs(e-a.right),s=Math.min(r,o),l=a.isDash?s:-s;this.data[n+e]=Math.max(0,Math.min(255,l+128))}}addDash(t,r){const n=r?7:0,i=2*n+1;if(this.nextRow+i>this.height)return e.w(\"LineAtlas out of space\"),null;let a=0;for(let e=0;e{t.terminate()})),this.workers=null)}isPreloaded(){return!!this.active[F]}numActive(){return Object.keys(this.active).length}}const N=Math.floor(a.hardwareConcurrency/2);let j,U;function V(){return j||(j=new B),j}B.workerCount=e.C(globalThis)?Math.max(Math.min(N,3),1):1;class q{constructor(t,r){this.workerPool=t,this.actors=[],this.currentActor=0,this.id=r;const n=this.workerPool.acquire(r);for(let t=0;t{t.remove()})),this.actors=[],t&&this.workerPool.release(this.id)}registerMessageHandler(t,e){for(const r of this.actors)r.registerMessageHandler(t,e)}}function H(){return U||(U=new q(V(),e.G),U.registerMessageHandler(\"GR\",((t,r,n)=>e.m(r,n)))),U}function G(t,r){const n=e.H();return e.J(n,n,[1,1,0]),e.K(n,n,[.5*t.width,.5*t.height,1]),e.L(n,n,t.calculatePosMatrix(r.toUnwrapped()))}function Z(t,e,r,n,i,a){const o=function(t,e,r){if(t)for(const n of t){const t=e[n];if(t&&t.source===r&&\"fill-extrusion\"===t.type)return!0}else for(const t in e){const n=e[t];if(n.source===r&&\"fill-extrusion\"===n.type)return!0}return!1}(i&&i.layers,e,t.id),s=a.maxPitchScaleFactor(),l=t.tilesIn(n,s,o);l.sort(W);const c=[];for(const n of l)c.push({wrappedTileID:n.tileID.wrapped().key,queryResults:n.tile.queryRenderedFeatures(e,r,t._state,n.queryGeometry,n.cameraQueryGeometry,n.scale,i,a,s,G(t.transform,n.tileID))});const u=function(t){const e={},r={};for(const n of t){const t=n.queryResults,i=n.wrappedTileID,a=r[i]=r[i]||{};for(const r in t){const n=t[r],i=a[r]=a[r]||{},o=e[r]=e[r]||[];for(const t of n)i[t.featureIndex]||(i[t.featureIndex]=!0,o.push(t))}}return e}(c);for(const e in u)u[e].forEach((e=>{const r=e.feature,n=t.getFeatureState(r.layer[\"source-layer\"],r.id);r.source=r.layer.source,r.layer[\"source-layer\"]&&(r.sourceLayer=r.layer[\"source-layer\"]),r.state=n}));return u}function W(t,e){const r=t.tileID,n=e.tileID;return r.overscaledZ-n.overscaledZ||r.canonical.y-n.canonical.y||r.wrap-n.wrap||r.canonical.x-n.canonical.x}function Y(t,r,n){return e._(this,void 0,void 0,(function*(){let i=t;if(t.url?i=(yield e.h(r.transformRequest(t.url,\"Source\"),n)).data:yield a.frameAsync(n),!i)return null;const o=e.M(e.e(i,t),[\"tiles\",\"minzoom\",\"maxzoom\",\"attribution\",\"bounds\",\"scheme\",\"tileSize\",\"encoding\"]);return\"vector_layers\"in i&&i.vector_layers&&(o.vectorLayerIds=i.vector_layers.map((t=>t.id))),o}))}class X{constructor(t,e){t&&(e?this.setSouthWest(t).setNorthEast(e):Array.isArray(t)&&(4===t.length?this.setSouthWest([t[0],t[1]]).setNorthEast([t[2],t[3]]):this.setSouthWest(t[0]).setNorthEast(t[1])))}setNorthEast(t){return this._ne=t instanceof e.N?new e.N(t.lng,t.lat):e.N.convert(t),this}setSouthWest(t){return this._sw=t instanceof e.N?new e.N(t.lng,t.lat):e.N.convert(t),this}extend(t){const r=this._sw,n=this._ne;let i,a;if(t instanceof e.N)i=t,a=t;else{if(!(t instanceof X)){if(Array.isArray(t)){if(4===t.length||t.every(Array.isArray)){const e=t;return this.extend(X.convert(e))}{const r=t;return this.extend(e.N.convert(r))}}return t&&(\"lng\"in t||\"lon\"in t)&&\"lat\"in t?this.extend(e.N.convert(t)):this}if(i=t._sw,a=t._ne,!i||!a)return this}return r||n?(r.lng=Math.min(i.lng,r.lng),r.lat=Math.min(i.lat,r.lat),n.lng=Math.max(a.lng,n.lng),n.lat=Math.max(a.lat,n.lat)):(this._sw=new e.N(i.lng,i.lat),this._ne=new e.N(a.lng,a.lat)),this}getCenter(){return new e.N((this._sw.lng+this._ne.lng)/2,(this._sw.lat+this._ne.lat)/2)}getSouthWest(){return this._sw}getNorthEast(){return this._ne}getNorthWest(){return new e.N(this.getWest(),this.getNorth())}getSouthEast(){return new e.N(this.getEast(),this.getSouth())}getWest(){return this._sw.lng}getSouth(){return this._sw.lat}getEast(){return this._ne.lng}getNorth(){return this._ne.lat}toArray(){return[this._sw.toArray(),this._ne.toArray()]}toString(){return`LngLatBounds(${this._sw.toString()}, ${this._ne.toString()})`}isEmpty(){return!(this._sw&&this._ne)}contains(t){const{lng:r,lat:n}=e.N.convert(t),i=this._sw.lat<=n&&n<=this._ne.lat;let a=this._sw.lng<=r&&r<=this._ne.lng;return this._sw.lng>this._ne.lng&&(a=this._sw.lng>=r&&r>=this._ne.lng),i&&a}static convert(t){return t instanceof X?t:t?new X(t):t}static fromLngLat(t,r=0){const n=360*r/40075017,i=n/Math.cos(Math.PI/180*t.lat);return new X(new e.N(t.lng-i,t.lat-n),new e.N(t.lng+i,t.lat+n))}}class ${constructor(t,e,r){this.bounds=X.convert(this.validateBounds(t)),this.minzoom=e||0,this.maxzoom=r||24}validateBounds(t){return Array.isArray(t)&&4===t.length?[Math.max(-180,t[0]),Math.max(-90,t[1]),Math.min(180,t[2]),Math.min(90,t[3])]:[-180,-90,180,90]}contains(t){const r=Math.pow(2,t.z),n=Math.floor(e.O(this.bounds.getWest())*r),i=Math.floor(e.Q(this.bounds.getNorth())*r),a=Math.ceil(e.O(this.bounds.getEast())*r),o=Math.ceil(e.Q(this.bounds.getSouth())*r);return t.x>=n&&t.x=i&&t.y{this._options.tiles=t})),this}setUrl(t){return this.setSourceProperty((()=>{this.url=t,this._options.url=t})),this}onRemove(){this._tileJSONRequest&&(this._tileJSONRequest.abort(),this._tileJSONRequest=null)}serialize(){return e.e({},this._options)}loadTile(t){return e._(this,void 0,void 0,(function*(){const e=t.tileID.canonical.url(this.tiles,this.map.getPixelRatio(),this.scheme),r={request:this.map._requestManager.transformRequest(e,\"Tile\"),uid:t.uid,tileID:t.tileID,zoom:t.tileID.overscaledZ,tileSize:this.tileSize*t.tileID.overscaleFactor(),type:this.type,source:this.id,pixelRatio:this.map.getPixelRatio(),showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};r.request.collectResourceTiming=this._collectResourceTiming;let n=\"RT\";if(t.actor&&\"expired\"!==t.state){if(\"loading\"===t.state)return new Promise(((e,r)=>{t.reloadPromise={resolve:e,reject:r}}))}else t.actor=this.dispatcher.getActor(),n=\"LT\";t.abortController=new AbortController;try{const e=yield t.actor.sendAsync({type:n,data:r},t.abortController);if(delete t.abortController,t.aborted)return;this._afterTileLoadWorkerResponse(t,e)}catch(e){if(delete t.abortController,t.aborted)return;if(e&&404!==e.status)throw e;this._afterTileLoadWorkerResponse(t,null)}}))}_afterTileLoadWorkerResponse(t,e){if(e&&e.resourceTiming&&(t.resourceTiming=e.resourceTiming),e&&this.map._refreshExpiredTiles&&t.setExpiryData(e),t.loadVectorData(e,this.map.painter),t.reloadPromise){const e=t.reloadPromise;t.reloadPromise=null,this.loadTile(t).then(e.resolve).catch(e.reject)}}abortTile(t){return e._(this,void 0,void 0,(function*(){t.abortController&&(t.abortController.abort(),delete t.abortController),t.actor&&(yield t.actor.sendAsync({type:\"AT\",data:{uid:t.uid,type:this.type,source:this.id}}))}))}unloadTile(t){return e._(this,void 0,void 0,(function*(){t.unloadVectorData(),t.actor&&(yield t.actor.sendAsync({type:\"RMT\",data:{uid:t.uid,type:this.type,source:this.id}}))}))}hasTransition(){return!1}}class K extends e.E{constructor(t,r,n,i){super(),this.id=t,this.dispatcher=n,this.setEventedParent(i),this.type=\"raster\",this.minzoom=0,this.maxzoom=22,this.roundZoom=!0,this.scheme=\"xyz\",this.tileSize=512,this._loaded=!1,this._options=e.e({type:\"raster\"},r),e.e(this,e.M(r,[\"url\",\"scheme\",\"tileSize\"]))}load(){return e._(this,void 0,void 0,(function*(){this._loaded=!1,this.fire(new e.k(\"dataloading\",{dataType:\"source\"})),this._tileJSONRequest=new AbortController;try{const t=yield Y(this._options,this.map._requestManager,this._tileJSONRequest);this._tileJSONRequest=null,this._loaded=!0,t&&(e.e(this,t),t.bounds&&(this.tileBounds=new $(t.bounds,this.minzoom,this.maxzoom)),this.fire(new e.k(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})),this.fire(new e.k(\"data\",{dataType:\"source\",sourceDataType:\"content\"})))}catch(t){this._tileJSONRequest=null,this.fire(new e.j(t))}}))}loaded(){return this._loaded}onAdd(t){this.map=t,this.load()}onRemove(){this._tileJSONRequest&&(this._tileJSONRequest.abort(),this._tileJSONRequest=null)}setSourceProperty(t){this._tileJSONRequest&&(this._tileJSONRequest.abort(),this._tileJSONRequest=null),t(),this.load()}setTiles(t){return this.setSourceProperty((()=>{this._options.tiles=t})),this}setUrl(t){return this.setSourceProperty((()=>{this.url=t,this._options.url=t})),this}serialize(){return e.e({},this._options)}hasTile(t){return!this.tileBounds||this.tileBounds.contains(t.canonical)}loadTile(t){return e._(this,void 0,void 0,(function*(){const e=t.tileID.canonical.url(this.tiles,this.map.getPixelRatio(),this.scheme);t.abortController=new AbortController;try{const r=yield p.getImage(this.map._requestManager.transformRequest(e,\"Tile\"),t.abortController,this.map._refreshExpiredTiles);if(delete t.abortController,t.aborted)return void(t.state=\"unloaded\");if(r&&r.data){this.map._refreshExpiredTiles&&r.cacheControl&&r.expires&&t.setExpiryData({cacheControl:r.cacheControl,expires:r.expires});const e=this.map.painter.context,n=e.gl,i=r.data;t.texture=this.map.painter.getTileTexture(i.width),t.texture?t.texture.update(i,{useMipmap:!0}):(t.texture=new w(e,i,n.RGBA,{useMipmap:!0}),t.texture.bind(n.LINEAR,n.CLAMP_TO_EDGE,n.LINEAR_MIPMAP_NEAREST)),t.state=\"loaded\"}}catch(e){if(delete t.abortController,t.aborted)t.state=\"unloaded\";else if(e)throw t.state=\"errored\",e}}))}abortTile(t){return e._(this,void 0,void 0,(function*(){t.abortController&&(t.abortController.abort(),delete t.abortController)}))}unloadTile(t){return e._(this,void 0,void 0,(function*(){t.texture&&this.map.painter.saveTileTexture(t.texture)}))}hasTransition(){return!1}}class Q extends K{constructor(t,r,n,i){super(t,r,n,i),this.type=\"raster-dem\",this.maxzoom=22,this._options=e.e({type:\"raster-dem\"},r),this.encoding=r.encoding||\"mapbox\",this.redFactor=r.redFactor,this.greenFactor=r.greenFactor,this.blueFactor=r.blueFactor,this.baseShift=r.baseShift}loadTile(t){return e._(this,void 0,void 0,(function*(){const r=t.tileID.canonical.url(this.tiles,this.map.getPixelRatio(),this.scheme),n=this.map._requestManager.transformRequest(r,\"Tile\");t.neighboringTiles=this._getNeighboringTiles(t.tileID),t.abortController=new AbortController;try{const r=yield p.getImage(n,t.abortController,this.map._refreshExpiredTiles);if(delete t.abortController,t.aborted)return void(t.state=\"unloaded\");if(r&&r.data){const n=r.data;this.map._refreshExpiredTiles&&r.cacheControl&&r.expires&&t.setExpiryData({cacheControl:r.cacheControl,expires:r.expires});const i=e.b(n)&&e.U()?n:yield this.readImageNow(n),a={type:this.type,uid:t.uid,source:this.id,rawImageData:i,encoding:this.encoding,redFactor:this.redFactor,greenFactor:this.greenFactor,blueFactor:this.blueFactor,baseShift:this.baseShift};if(!t.actor||\"expired\"===t.state){t.actor=this.dispatcher.getActor();const e=yield t.actor.sendAsync({type:\"LDT\",data:a});t.dem=e,t.needsHillshadePrepare=!0,t.needsTerrainPrepare=!0,t.state=\"loaded\"}}}catch(e){if(delete t.abortController,t.aborted)t.state=\"unloaded\";else if(e)throw t.state=\"errored\",e}}))}readImageNow(t){return e._(this,void 0,void 0,(function*(){if(\"undefined\"!=typeof VideoFrame&&e.V()){const r=t.width+2,n=t.height+2;try{return new e.R({width:r,height:n},yield e.W(t,-1,-1,r,n))}catch(t){}}return a.getImageData(t,1)}))}_getNeighboringTiles(t){const r=t.canonical,n=Math.pow(2,r.z),i=(r.x-1+n)%n,a=0===r.x?t.wrap-1:t.wrap,o=(r.x+1+n)%n,s=r.x+1===n?t.wrap+1:t.wrap,l={};return l[new e.S(t.overscaledZ,a,r.z,i,r.y).key]={backfilled:!1},l[new e.S(t.overscaledZ,s,r.z,o,r.y).key]={backfilled:!1},r.y>0&&(l[new e.S(t.overscaledZ,a,r.z,i,r.y-1).key]={backfilled:!1},l[new e.S(t.overscaledZ,t.wrap,r.z,r.x,r.y-1).key]={backfilled:!1},l[new e.S(t.overscaledZ,s,r.z,o,r.y-1).key]={backfilled:!1}),r.y+10&&e.e(i,{resourceTiming:n}),this.fire(new e.k(\"data\",Object.assign(Object.assign({},i),{sourceDataType:\"metadata\"}))),this.fire(new e.k(\"data\",Object.assign(Object.assign({},i),{sourceDataType:\"content\"})))}catch(t){if(this._pendingLoads--,this._removed)return void this.fire(new e.k(\"dataabort\",{dataType:\"source\"}));this.fire(new e.j(t))}}))}loaded(){return 0===this._pendingLoads}loadTile(t){return e._(this,void 0,void 0,(function*(){const e=t.actor?\"RT\":\"LT\";t.actor=this.actor;const r={type:this.type,uid:t.uid,tileID:t.tileID,zoom:t.tileID.overscaledZ,maxZoom:this.maxzoom,tileSize:this.tileSize,source:this.id,pixelRatio:this.map.getPixelRatio(),showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};t.abortController=new AbortController;const n=yield this.actor.sendAsync({type:e,data:r},t.abortController);delete t.abortController,t.unloadVectorData(),t.aborted||t.loadVectorData(n,this.map.painter,\"RT\"===e)}))}abortTile(t){return e._(this,void 0,void 0,(function*(){t.abortController&&(t.abortController.abort(),delete t.abortController),t.aborted=!0}))}unloadTile(t){return e._(this,void 0,void 0,(function*(){t.unloadVectorData(),yield this.actor.sendAsync({type:\"RMT\",data:{uid:t.uid,type:this.type,source:this.id}})}))}onRemove(){this._removed=!0,this.actor.sendAsync({type:\"RS\",data:{type:this.type,source:this.id}})}serialize(){return e.e({},this._options,{type:this.type,data:this._data})}hasTransition(){return!1}}var et=e.Y([{name:\"a_pos\",type:\"Int16\",components:2},{name:\"a_texture_pos\",type:\"Int16\",components:2}]);class rt extends e.E{constructor(t,e,r,n){super(),this.id=t,this.dispatcher=r,this.coordinates=e.coordinates,this.type=\"image\",this.minzoom=0,this.maxzoom=22,this.tileSize=512,this.tiles={},this._loaded=!1,this.setEventedParent(n),this.options=e}load(t){return e._(this,void 0,void 0,(function*(){this._loaded=!1,this.fire(new e.k(\"dataloading\",{dataType:\"source\"})),this.url=this.options.url,this._request=new AbortController;try{const e=yield p.getImage(this.map._requestManager.transformRequest(this.url,\"Image\"),this._request);this._request=null,this._loaded=!0,e&&e.data&&(this.image=e.data,t&&(this.coordinates=t),this._finishLoading())}catch(t){this._request=null,this._loaded=!0,this.fire(new e.j(t))}}))}loaded(){return this._loaded}updateImage(t){return t.url?(this._request&&(this._request.abort(),this._request=null),this.options.url=t.url,this.load(t.coordinates).finally((()=>{this.texture=null})),this):this}_finishLoading(){this.map&&(this.setCoordinates(this.coordinates),this.fire(new e.k(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})))}onAdd(t){this.map=t,this.load()}onRemove(){this._request&&(this._request.abort(),this._request=null)}setCoordinates(t){this.coordinates=t;const r=t.map(e.Z.fromLngLat);this.tileID=function(t){let r=1/0,n=1/0,i=-1/0,a=-1/0;for(const e of t)r=Math.min(r,e.x),n=Math.min(n,e.y),i=Math.max(i,e.x),a=Math.max(a,e.y);const o=i-r,s=a-n,l=Math.max(o,s),c=Math.max(0,Math.floor(-Math.log(l)/Math.LN2)),u=Math.pow(2,c);return new e.a1(c,Math.floor((r+i)/2*u),Math.floor((n+a)/2*u))}(r),this.minzoom=this.maxzoom=this.tileID.z;const n=r.map((t=>this.tileID.getTilePoint(t)._round()));return this._boundsArray=new e.$,this._boundsArray.emplaceBack(n[0].x,n[0].y,0,0),this._boundsArray.emplaceBack(n[1].x,n[1].y,e.X,0),this._boundsArray.emplaceBack(n[3].x,n[3].y,0,e.X),this._boundsArray.emplaceBack(n[2].x,n[2].y,e.X,e.X),this.boundsBuffer&&(this.boundsBuffer.destroy(),delete this.boundsBuffer),this.fire(new e.k(\"data\",{dataType:\"source\",sourceDataType:\"content\"})),this}prepare(){if(0===Object.keys(this.tiles).length||!this.image)return;const t=this.map.painter.context,r=t.gl;this.boundsBuffer||(this.boundsBuffer=t.createVertexBuffer(this._boundsArray,et.members)),this.boundsSegments||(this.boundsSegments=e.a0.simpleSegment(0,0,4,2)),this.texture||(this.texture=new w(t,this.image,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE));let n=!1;for(const t in this.tiles){const e=this.tiles[t];\"loaded\"!==e.state&&(e.state=\"loaded\",e.texture=this.texture,n=!0)}n&&this.fire(new e.k(\"data\",{dataType:\"source\",sourceDataType:\"idle\",sourceId:this.id}))}loadTile(t){return e._(this,void 0,void 0,(function*(){this.tileID&&this.tileID.equals(t.tileID.canonical)?(this.tiles[String(t.tileID.wrap)]=t,t.buckets={}):t.state=\"errored\"}))}serialize(){return{type:\"image\",url:this.options.url,coordinates:this.coordinates}}hasTransition(){return!1}}class nt extends rt{constructor(t,e,r,n){super(t,e,r,n),this.roundZoom=!0,this.type=\"video\",this.options=e}load(){return e._(this,void 0,void 0,(function*(){this._loaded=!1;const t=this.options;this.urls=[];for(const e of t.urls)this.urls.push(this.map._requestManager.transformRequest(e,\"Source\").url);try{const t=yield e.a3(this.urls);if(this._loaded=!0,!t)return;this.video=t,this.video.loop=!0,this.video.addEventListener(\"playing\",(()=>{this.map.triggerRepaint()})),this.map&&this.video.play(),this._finishLoading()}catch(t){this.fire(new e.j(t))}}))}pause(){this.video&&this.video.pause()}play(){this.video&&this.video.play()}seek(t){if(this.video){const r=this.video.seekable;tr.end(0)?this.fire(new e.j(new e.a2(`sources.${this.id}`,null,`Playback for this video can be set only between the ${r.start(0)} and ${r.end(0)}-second mark.`))):this.video.currentTime=t}}getVideo(){return this.video}onAdd(t){this.map||(this.map=t,this.load(),this.video&&(this.video.play(),this.setCoordinates(this.coordinates)))}prepare(){if(0===Object.keys(this.tiles).length||this.video.readyState<2)return;const t=this.map.painter.context,r=t.gl;this.boundsBuffer||(this.boundsBuffer=t.createVertexBuffer(this._boundsArray,et.members)),this.boundsSegments||(this.boundsSegments=e.a0.simpleSegment(0,0,4,2)),this.texture?this.video.paused||(this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE),r.texSubImage2D(r.TEXTURE_2D,0,0,0,r.RGBA,r.UNSIGNED_BYTE,this.video)):(this.texture=new w(t,this.video,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE));let n=!1;for(const t in this.tiles){const e=this.tiles[t];\"loaded\"!==e.state&&(e.state=\"loaded\",e.texture=this.texture,n=!0)}n&&this.fire(new e.k(\"data\",{dataType:\"source\",sourceDataType:\"idle\",sourceId:this.id}))}serialize(){return{type:\"video\",urls:this.urls,coordinates:this.coordinates}}hasTransition(){return this.video&&!this.video.paused}}class it extends rt{constructor(t,r,n,i){super(t,r,n,i),r.coordinates?Array.isArray(r.coordinates)&&4===r.coordinates.length&&!r.coordinates.some((t=>!Array.isArray(t)||2!==t.length||t.some((t=>\"number\"!=typeof t))))||this.fire(new e.j(new e.a2(`sources.${t}`,null,'\"coordinates\" property must be an array of 4 longitude/latitude array pairs'))):this.fire(new e.j(new e.a2(`sources.${t}`,null,'missing required property \"coordinates\"'))),r.animate&&\"boolean\"!=typeof r.animate&&this.fire(new e.j(new e.a2(`sources.${t}`,null,'optional \"animate\" property must be a boolean value'))),r.canvas?\"string\"==typeof r.canvas||r.canvas instanceof HTMLCanvasElement||this.fire(new e.j(new e.a2(`sources.${t}`,null,'\"canvas\" must be either a string representing the ID of the canvas element from which to read, or an HTMLCanvasElement instance'))):this.fire(new e.j(new e.a2(`sources.${t}`,null,'missing required property \"canvas\"'))),this.options=r,this.animate=void 0===r.animate||r.animate}load(){return e._(this,void 0,void 0,(function*(){this._loaded=!0,this.canvas||(this.canvas=this.options.canvas instanceof HTMLCanvasElement?this.options.canvas:document.getElementById(this.options.canvas)),this.width=this.canvas.width,this.height=this.canvas.height,this._hasInvalidDimensions()?this.fire(new e.j(new Error(\"Canvas dimensions cannot be less than or equal to zero.\"))):(this.play=function(){this._playing=!0,this.map.triggerRepaint()},this.pause=function(){this._playing&&(this.prepare(),this._playing=!1)},this._finishLoading())}))}getCanvas(){return this.canvas}onAdd(t){this.map=t,this.load(),this.canvas&&this.animate&&this.play()}onRemove(){this.pause()}prepare(){let t=!1;if(this.canvas.width!==this.width&&(this.width=this.canvas.width,t=!0),this.canvas.height!==this.height&&(this.height=this.canvas.height,t=!0),this._hasInvalidDimensions())return;if(0===Object.keys(this.tiles).length)return;const r=this.map.painter.context,n=r.gl;this.boundsBuffer||(this.boundsBuffer=r.createVertexBuffer(this._boundsArray,et.members)),this.boundsSegments||(this.boundsSegments=e.a0.simpleSegment(0,0,4,2)),this.texture?(t||this._playing)&&this.texture.update(this.canvas,{premultiply:!0}):this.texture=new w(r,this.canvas,n.RGBA,{premultiply:!0});let i=!1;for(const t in this.tiles){const e=this.tiles[t];\"loaded\"!==e.state&&(e.state=\"loaded\",e.texture=this.texture,i=!0)}i&&this.fire(new e.k(\"data\",{dataType:\"source\",sourceDataType:\"idle\",sourceId:this.id}))}serialize(){return{type:\"canvas\",coordinates:this.coordinates}}hasTransition(){return this._playing}_hasInvalidDimensions(){for(const t of[this.canvas.width,this.canvas.height])if(isNaN(t)||t<=0)return!0;return!1}}const at={},ot=t=>{switch(t){case\"geojson\":return tt;case\"image\":return rt;case\"raster\":return K;case\"raster-dem\":return Q;case\"vector\":return J;case\"video\":return nt;case\"canvas\":return it}return at[t]};const st=\"RTLPluginLoaded\";class lt extends e.E{constructor(){super(...arguments),this.status=\"unavailable\",this.url=null,this.dispatcher=H()}_syncState(t){return this.status=t,this.dispatcher.broadcast(\"SRPS\",{pluginStatus:t,pluginURL:this.url}).catch((t=>{throw this.status=\"error\",t}))}getRTLTextPluginStatus(){return this.status}clearRTLTextPlugin(){this.status=\"unavailable\",this.url=null}setRTLTextPlugin(t){return e._(this,arguments,void 0,(function*(t,e=!1){if(this.url)throw new Error(\"setRTLTextPlugin cannot be called multiple times.\");if(this.url=a.resolveURL(t),!this.url)throw new Error(`requested url ${t} is invalid`);if(\"unavailable\"===this.status){if(!e)return this._requestImport();this.status=\"deferred\",this._syncState(this.status)}else if(\"requested\"===this.status)return this._requestImport()}))}_requestImport(){return e._(this,void 0,void 0,(function*(){yield this._syncState(\"loading\"),this.status=\"loaded\",this.fire(new e.k(st))}))}lazyLoad(){\"unavailable\"===this.status?this.status=\"requested\":\"deferred\"===this.status&&this._requestImport()}}let ct=null;function ut(){return ct||(ct=new lt),ct}class ht{constructor(t,r){this.timeAdded=0,this.fadeEndTime=0,this.tileID=t,this.uid=e.a4(),this.uses=0,this.tileSize=r,this.buckets={},this.expirationTime=null,this.queryPadding=0,this.hasSymbolBuckets=!1,this.hasRTLText=!1,this.dependencies={},this.rtt=[],this.rttCoords={},this.expiredRequestCount=0,this.state=\"loading\"}registerFadeDuration(t){const e=t+this.timeAdded;ee.getLayer(t))).filter(Boolean);if(0!==t.length){n.layers=t,n.stateDependentLayerIds&&(n.stateDependentLayers=n.stateDependentLayerIds.map((e=>t.filter((t=>t.id===e))[0])));for(const e of t)r[e.id]=n}}return r}(t.buckets,r.style),this.hasSymbolBuckets=!1;for(const t in this.buckets){const r=this.buckets[t];if(r instanceof e.a6){if(this.hasSymbolBuckets=!0,!n)break;r.justReloaded=!0}}if(this.hasRTLText=!1,this.hasSymbolBuckets)for(const t in this.buckets){const r=this.buckets[t];if(r instanceof e.a6&&r.hasRTLText){this.hasRTLText=!0,ut().lazyLoad();break}}this.queryPadding=0;for(const t in this.buckets){const e=this.buckets[t];this.queryPadding=Math.max(this.queryPadding,r.style.getLayer(t).queryRadius(e))}t.imageAtlas&&(this.imageAtlas=t.imageAtlas),t.glyphAtlasImage&&(this.glyphAtlasImage=t.glyphAtlasImage)}else this.collisionBoxArray=new e.a5}unloadVectorData(){for(const t in this.buckets)this.buckets[t].destroy();this.buckets={},this.imageAtlasTexture&&this.imageAtlasTexture.destroy(),this.imageAtlas&&(this.imageAtlas=null),this.glyphAtlasTexture&&this.glyphAtlasTexture.destroy(),this.latestFeatureIndex=null,this.state=\"unloaded\"}getBucket(t){return this.buckets[t.id]}upload(t){for(const e in this.buckets){const r=this.buckets[e];r.uploadPending()&&r.upload(t)}const e=t.gl;this.imageAtlas&&!this.imageAtlas.uploaded&&(this.imageAtlasTexture=new w(t,this.imageAtlas.image,e.RGBA),this.imageAtlas.uploaded=!0),this.glyphAtlasImage&&(this.glyphAtlasTexture=new w(t,this.glyphAtlasImage,e.ALPHA),this.glyphAtlasImage=null)}prepare(t){this.imageAtlas&&this.imageAtlas.patchUpdatedImages(t,this.imageAtlasTexture)}queryRenderedFeatures(t,e,r,n,i,a,o,s,l,c){return this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData?this.latestFeatureIndex.query({queryGeometry:n,cameraQueryGeometry:i,scale:a,tileSize:this.tileSize,pixelPosMatrix:c,transform:s,params:o,queryPadding:this.queryPadding*l},t,e,r):{}}querySourceFeatures(t,r){const n=this.latestFeatureIndex;if(!n||!n.rawTileData)return;const i=n.loadVTLayers(),a=r&&r.sourceLayer?r.sourceLayer:\"\",o=i._geojsonTileLayer||i[a];if(!o)return;const s=e.a7(r&&r.filter),{z:l,x:c,y:u}=this.tileID.canonical,h={z:l,x:c,y:u};for(let r=0;rt)e=!1;else if(r)if(this.expirationTime{this.remove(t,i)}),r)),this.data[n].push(i),this.order.push(n),this.order.length>this.max){const t=this._getAndRemoveByKey(this.order[0]);t&&this.onRemove(t)}return this}has(t){return t.wrapped().key in this.data}getAndRemove(t){return this.has(t)?this._getAndRemoveByKey(t.wrapped().key):null}_getAndRemoveByKey(t){const e=this.data[t].shift();return e.timeout&&clearTimeout(e.timeout),0===this.data[t].length&&delete this.data[t],this.order.splice(this.order.indexOf(t),1),e.value}getByKey(t){const e=this.data[t];return e?e[0].value:null}get(t){return this.has(t)?this.data[t.wrapped().key][0].value:null}remove(t,e){if(!this.has(t))return this;const r=t.wrapped().key,n=void 0===e?0:this.data[r].indexOf(e),i=this.data[r][n];return this.data[r].splice(n,1),i.timeout&&clearTimeout(i.timeout),0===this.data[r].length&&delete this.data[r],this.onRemove(i.value),this.order.splice(this.order.indexOf(r),1),this}setMaxSize(t){for(this.max=t;this.order.length>this.max;){const t=this._getAndRemoveByKey(this.order[0]);t&&this.onRemove(t)}return this}filter(t){const e=[];for(const r in this.data)for(const n of this.data[r])t(n.value)||e.push(n);for(const t of e)this.remove(t.value.tileID,t)}}class pt{constructor(){this.state={},this.stateChanges={},this.deletedStates={}}updateState(t,r,n){const i=String(r);if(this.stateChanges[t]=this.stateChanges[t]||{},this.stateChanges[t][i]=this.stateChanges[t][i]||{},e.e(this.stateChanges[t][i],n),null===this.deletedStates[t]){this.deletedStates[t]={};for(const e in this.state[t])e!==i&&(this.deletedStates[t][e]=null)}else if(this.deletedStates[t]&&null===this.deletedStates[t][i]){this.deletedStates[t][i]={};for(const e in this.state[t][i])n[e]||(this.deletedStates[t][i][e]=null)}else for(const e in n)this.deletedStates[t]&&this.deletedStates[t][i]&&null===this.deletedStates[t][i][e]&&delete this.deletedStates[t][i][e]}removeFeatureState(t,e,r){if(null===this.deletedStates[t])return;const n=String(e);if(this.deletedStates[t]=this.deletedStates[t]||{},r&&void 0!==e)null!==this.deletedStates[t][n]&&(this.deletedStates[t][n]=this.deletedStates[t][n]||{},this.deletedStates[t][n][r]=null);else if(void 0!==e)if(this.stateChanges[t]&&this.stateChanges[t][n])for(r in this.deletedStates[t][n]={},this.stateChanges[t][n])this.deletedStates[t][n][r]=null;else this.deletedStates[t][n]=null;else this.deletedStates[t]=null}getState(t,r){const n=String(r),i=this.state[t]||{},a=this.stateChanges[t]||{},o=e.e({},i[n],a[n]);if(null===this.deletedStates[t])return{};if(this.deletedStates[t]){const e=this.deletedStates[t][r];if(null===e)return{};for(const t in e)delete o[t]}return o}initializeTileState(t,e){t.setFeatureState(this.state,e)}coalesceChanges(t,r){const n={};for(const t in this.stateChanges){this.state[t]=this.state[t]||{};const r={};for(const n in this.stateChanges[t])this.state[t][n]||(this.state[t][n]={}),e.e(this.state[t][n],this.stateChanges[t][n]),r[n]=this.state[t][n];n[t]=r}for(const t in this.deletedStates){this.state[t]=this.state[t]||{};const r={};if(null===this.deletedStates[t])for(const e in this.state[t])r[e]={},this.state[t][e]={};else for(const e in this.deletedStates[t]){if(null===this.deletedStates[t][e])this.state[t][e]={};else for(const r of Object.keys(this.deletedStates[t][e]))delete this.state[t][e][r];r[e]=this.state[t][e]}n[t]=n[t]||{},e.e(n[t],r)}if(this.stateChanges={},this.deletedStates={},0!==Object.keys(n).length)for(const e in t)t[e].setFeatureState(n,r)}}class dt extends e.E{constructor(t,e,r){super(),this.id=t,this.dispatcher=r,this.on(\"data\",(t=>this._dataHandler(t))),this.on(\"dataloading\",(()=>{this._sourceErrored=!1})),this.on(\"error\",(()=>{this._sourceErrored=this._source.loaded()})),this._source=((t,e,r,n)=>{const i=new(ot(e.type))(t,e,r,n);if(i.id!==t)throw new Error(`Expected Source id to be ${t} instead of ${i.id}`);return i})(t,e,r,this),this._tiles={},this._cache=new ft(0,(t=>this._unloadTile(t))),this._timers={},this._cacheTimers={},this._maxTileCacheSize=null,this._maxTileCacheZoomLevels=null,this._loadedParentTiles={},this._coveredTiles={},this._state=new pt,this._didEmitContent=!1,this._updated=!1}onAdd(t){this.map=t,this._maxTileCacheSize=t?t._maxTileCacheSize:null,this._maxTileCacheZoomLevels=t?t._maxTileCacheZoomLevels:null,this._source&&this._source.onAdd&&this._source.onAdd(t)}onRemove(t){this.clearTiles(),this._source&&this._source.onRemove&&this._source.onRemove(t)}loaded(){if(this._sourceErrored)return!0;if(!this._sourceLoaded)return!1;if(!this._source.loaded())return!1;if(!(void 0===this.used&&void 0===this.usedForTerrain||this.used||this.usedForTerrain))return!0;if(!this._updated)return!1;for(const t in this._tiles){const e=this._tiles[t];if(\"loaded\"!==e.state&&\"errored\"!==e.state)return!1}return!0}getSource(){return this._source}pause(){this._paused=!0}resume(){if(!this._paused)return;const t=this._shouldReloadOnResume;this._paused=!1,this._shouldReloadOnResume=!1,t&&this.reload(),this.transform&&this.update(this.transform,this.terrain)}_loadTile(t,r,n){return e._(this,void 0,void 0,(function*(){try{yield this._source.loadTile(t),this._tileLoaded(t,r,n)}catch(r){t.state=\"errored\",404!==r.status?this._source.fire(new e.j(r,{tile:t})):this.update(this.transform,this.terrain)}}))}_unloadTile(t){this._source.unloadTile&&this._source.unloadTile(t)}_abortTile(t){this._source.abortTile&&this._source.abortTile(t),this._source.fire(new e.k(\"dataabort\",{tile:t,coord:t.tileID,dataType:\"source\"}))}serialize(){return this._source.serialize()}prepare(t){this._source.prepare&&this._source.prepare(),this._state.coalesceChanges(this._tiles,this.map?this.map.painter:null);for(const e in this._tiles){const r=this._tiles[e];r.upload(t),r.prepare(this.map.style.imageManager)}}getIds(){return Object.values(this._tiles).map((t=>t.tileID)).sort(mt).map((t=>t.key))}getRenderableIds(t){const r=[];for(const e in this._tiles)this._isIdRenderable(e,t)&&r.push(this._tiles[e]);return t?r.sort(((t,r)=>{const n=t.tileID,i=r.tileID,a=new e.P(n.canonical.x,n.canonical.y)._rotate(this.transform.angle),o=new e.P(i.canonical.x,i.canonical.y)._rotate(this.transform.angle);return n.overscaledZ-i.overscaledZ||o.y-a.y||o.x-a.x})).map((t=>t.tileID.key)):r.map((t=>t.tileID)).sort(mt).map((t=>t.key))}hasRenderableParent(t){const e=this.findLoadedParent(t,0);return!!e&&this._isIdRenderable(e.tileID.key)}_isIdRenderable(t,e){return this._tiles[t]&&this._tiles[t].hasData()&&!this._coveredTiles[t]&&(e||!this._tiles[t].holdingForFade())}reload(){if(this._paused)this._shouldReloadOnResume=!0;else{this._cache.reset();for(const t in this._tiles)\"errored\"!==this._tiles[t].state&&this._reloadTile(t,\"reloading\")}}_reloadTile(t,r){return e._(this,void 0,void 0,(function*(){const e=this._tiles[t];e&&(\"loading\"!==e.state&&(e.state=r),yield this._loadTile(e,t,r))}))}_tileLoaded(t,r,n){t.timeAdded=a.now(),\"expired\"===n&&(t.refreshedUponExpiration=!0),this._setTileReloadTimer(r,t),\"raster-dem\"===this.getSource().type&&t.dem&&this._backfillDEM(t),this._state.initializeTileState(t,this.map?this.map.painter:null),t.aborted||this._source.fire(new e.k(\"data\",{dataType:\"source\",tile:t,coord:t.tileID}))}_backfillDEM(t){const e=this.getRenderableIds();for(let n=0;n1||(Math.abs(r)>1&&(1===Math.abs(r+i)?r+=i:1===Math.abs(r-i)&&(r-=i)),e.dem&&t.dem&&(t.dem.backfillBorder(e.dem,r,n),t.neighboringTiles&&t.neighboringTiles[a]&&(t.neighboringTiles[a].backfilled=!0)))}}getTile(t){return this.getTileByID(t.key)}getTileByID(t){return this._tiles[t]}_retainLoadedChildren(t,e,r,n){for(const i in this._tiles){let a=this._tiles[i];if(n[i]||!a.hasData()||a.tileID.overscaledZ<=e||a.tileID.overscaledZ>r)continue;let o=a.tileID;for(;a&&a.tileID.overscaledZ>e+1;){const t=a.tileID.scaledTo(a.tileID.overscaledZ-1);a=this._tiles[t.key],a&&a.hasData()&&(o=t)}let s=o;for(;s.overscaledZ>e;)if(s=s.scaledTo(s.overscaledZ-1),t[s.key]){n[o.key]=o;break}}}findLoadedParent(t,e){if(t.key in this._loadedParentTiles){const r=this._loadedParentTiles[t.key];return r&&r.tileID.overscaledZ>=e?r:null}for(let r=t.overscaledZ-1;r>=e;r--){const e=t.scaledTo(r),n=this._getLoadedTile(e);if(n)return n}}findLoadedSibling(t){return this._getLoadedTile(t)}_getLoadedTile(t){const e=this._tiles[t.key];return e&&e.hasData()?e:this._cache.getByKey(t.wrapped().key)}updateCacheSize(t){const r=(Math.ceil(t.width/this._source.tileSize)+1)*(Math.ceil(t.height/this._source.tileSize)+1),n=null===this._maxTileCacheZoomLevels?e.a.MAX_TILE_CACHE_ZOOM_LEVELS:this._maxTileCacheZoomLevels,i=Math.floor(r*n),a=\"number\"==typeof this._maxTileCacheSize?Math.min(this._maxTileCacheSize,i):i;this._cache.setMaxSize(a)}handleWrapJump(t){const e=(t-(void 0===this._prevLng?t:this._prevLng))/360,r=Math.round(e);if(this._prevLng=t,r){const t={};for(const e in this._tiles){const n=this._tiles[e];n.tileID=n.tileID.unwrapTo(n.tileID.wrap+r),t[n.tileID.key]=n}this._tiles=t;for(const t in this._timers)clearTimeout(this._timers[t]),delete this._timers[t];for(const t in this._tiles){const e=this._tiles[t];this._setTileReloadTimer(t,e)}}}_updateCoveredAndRetainedTiles(t,e,r,n,i,o){const s={},l={},c=Object.keys(t),u=a.now();for(const r of c){const n=t[r],i=this._tiles[r];if(!i||0!==i.fadeEndTime&&i.fadeEndTime<=u)continue;const a=this.findLoadedParent(n,e),o=this.findLoadedSibling(n),c=a||o||null;c&&(this._addTile(c.tileID),s[c.tileID.key]=c.tileID),l[r]=n}this._retainLoadedChildren(l,n,r,t);for(const e in s)t[e]||(this._coveredTiles[e]=!0,t[e]=s[e]);if(o){const e={},r={};for(const t of i)this._tiles[t.key].hasData()?e[t.key]=t:r[t.key]=t;for(const n in r){const i=r[n].children(this._source.maxzoom);this._tiles[i[0].key]&&this._tiles[i[1].key]&&this._tiles[i[2].key]&&this._tiles[i[3].key]&&(e[i[0].key]=t[i[0].key]=i[0],e[i[1].key]=t[i[1].key]=i[1],e[i[2].key]=t[i[2].key]=i[2],e[i[3].key]=t[i[3].key]=i[3],delete r[n])}for(const n in r){const i=r[n],a=this.findLoadedParent(i,this._source.minzoom),o=this.findLoadedSibling(i),s=a||o||null;if(s){e[s.tileID.key]=t[s.tileID.key]=s.tileID;for(const t in e)e[t].isChildOf(s.tileID)&&delete e[t]}}for(const t in this._tiles)e[t]||(this._coveredTiles[t]=!0)}}update(t,r){if(!this._sourceLoaded||this._paused)return;let n;this.transform=t,this.terrain=r,this.updateCacheSize(t),this.handleWrapJump(this.transform.center.lng),this._coveredTiles={},this.used||this.usedForTerrain?this._source.tileID?n=t.getVisibleUnwrappedCoordinates(this._source.tileID).map((t=>new e.S(t.canonical.z,t.wrap,t.canonical.z,t.canonical.x,t.canonical.y))):(n=t.coveringTiles({tileSize:this.usedForTerrain?this.tileSize:this._source.tileSize,minzoom:this._source.minzoom,maxzoom:this._source.maxzoom,roundZoom:!this.usedForTerrain&&this._source.roundZoom,reparseOverscaled:this._source.reparseOverscaled,terrain:r}),this._source.hasTile&&(n=n.filter((t=>this._source.hasTile(t))))):n=[];const i=t.coveringZoomLevel(this._source),a=Math.max(i-dt.maxOverzooming,this._source.minzoom),o=Math.max(i+dt.maxUnderzooming,this._source.minzoom);if(this.usedForTerrain){const t={};for(const e of n)if(e.canonical.z>this._source.minzoom){const r=e.scaledTo(e.canonical.z-1);t[r.key]=r;const n=e.scaledTo(Math.max(this._source.minzoom,Math.min(e.canonical.z,5)));t[n.key]=n}n=n.concat(Object.values(t))}const s=0===n.length&&!this._updated&&this._didEmitContent;this._updated=!0,s&&this.fire(new e.k(\"data\",{sourceDataType:\"idle\",dataType:\"source\",sourceId:this.id}));const l=this._updateRetainedTiles(n,i);gt(this._source.type)&&this._updateCoveredAndRetainedTiles(l,a,o,i,n,r);for(const t in l)this._tiles[t].clearFadeHold();const c=e.ac(this._tiles,l);for(const t of c){const e=this._tiles[t];e.hasSymbolBuckets&&!e.holdingForFade()?e.setHoldDuration(this.map._fadeDuration):e.hasSymbolBuckets&&!e.symbolFadeFinished()||this._removeTile(t)}this._updateLoadedParentTileCache(),this._updateLoadedSiblingTileCache()}releaseSymbolFadeTiles(){for(const t in this._tiles)this._tiles[t].holdingForFade()&&this._removeTile(t)}_updateRetainedTiles(t,e){var r;const n={},i={},a=Math.max(e-dt.maxOverzooming,this._source.minzoom),o=Math.max(e+dt.maxUnderzooming,this._source.minzoom),s={};for(const r of t){const t=this._addTile(r);n[r.key]=r,t.hasData()||ethis._source.maxzoom){const t=o.children(this._source.maxzoom)[0],e=this.getTile(t);if(e&&e.hasData()){n[t.key]=t;continue}}else{const t=o.children(this._source.maxzoom);if(n[t[0].key]&&n[t[1].key]&&n[t[2].key]&&n[t[3].key])continue}let s=t.wasRequested();for(let e=o.overscaledZ-1;e>=a;--e){const a=o.scaledTo(e);if(i[a.key])break;if(i[a.key]=!0,t=this.getTile(a),!t&&s&&(t=this._addTile(a)),t){const e=t.hasData();if((e||!(null===(r=this.map)||void 0===r?void 0:r.cancelPendingTileRequestsWhileZooming)||s)&&(n[a.key]=a),s=t.wasRequested(),e)break}}}return n}_updateLoadedParentTileCache(){this._loadedParentTiles={};for(const t in this._tiles){const e=[];let r,n=this._tiles[t].tileID;for(;n.overscaledZ>0;){if(n.key in this._loadedParentTiles){r=this._loadedParentTiles[n.key];break}e.push(n.key);const t=n.scaledTo(n.overscaledZ-1);if(r=this._getLoadedTile(t),r)break;n=t}for(const t of e)this._loadedParentTiles[t]=r}}_updateLoadedSiblingTileCache(){this._loadedSiblingTiles={};for(const t in this._tiles){const e=this._tiles[t].tileID,r=this._getLoadedTile(e);this._loadedSiblingTiles[e.key]=r}}_addTile(t){let r=this._tiles[t.key];if(r)return r;r=this._cache.getAndRemove(t),r&&(this._setTileReloadTimer(t.key,r),r.tileID=t,this._state.initializeTileState(r,this.map?this.map.painter:null),this._cacheTimers[t.key]&&(clearTimeout(this._cacheTimers[t.key]),delete this._cacheTimers[t.key],this._setTileReloadTimer(t.key,r)));const n=r;return r||(r=new ht(t,this._source.tileSize*t.overscaleFactor()),this._loadTile(r,t.key,r.state)),r.uses++,this._tiles[t.key]=r,n||this._source.fire(new e.k(\"dataloading\",{tile:r,coord:r.tileID,dataType:\"source\"})),r}_setTileReloadTimer(t,e){t in this._timers&&(clearTimeout(this._timers[t]),delete this._timers[t]);const r=e.getExpiryTimeout();r&&(this._timers[t]=setTimeout((()=>{this._reloadTile(t,\"expired\"),delete this._timers[t]}),r))}_removeTile(t){const e=this._tiles[t];e&&(e.uses--,delete this._tiles[t],this._timers[t]&&(clearTimeout(this._timers[t]),delete this._timers[t]),e.uses>0||(e.hasData()&&\"reloading\"!==e.state?this._cache.add(e.tileID,e,e.getExpiryTimeout()):(e.aborted=!0,this._abortTile(e),this._unloadTile(e))))}_dataHandler(t){const e=t.sourceDataType;\"source\"===t.dataType&&\"metadata\"===e&&(this._sourceLoaded=!0),this._sourceLoaded&&!this._paused&&\"source\"===t.dataType&&\"content\"===e&&(this.reload(),this.transform&&this.update(this.transform,this.terrain),this._didEmitContent=!0)}clearTiles(){this._shouldReloadOnResume=!1,this._paused=!1;for(const t in this._tiles)this._removeTile(t);this._cache.reset()}tilesIn(t,r,n){const i=[],a=this.transform;if(!a)return i;const o=n?a.getCameraQueryGeometry(t):t,s=t.map((t=>a.pointCoordinate(t,this.terrain))),l=o.map((t=>a.pointCoordinate(t,this.terrain))),c=this.getIds();let u=1/0,h=1/0,f=-1/0,p=-1/0;for(const t of l)u=Math.min(u,t.x),h=Math.min(h,t.y),f=Math.max(f,t.x),p=Math.max(p,t.y);for(let t=0;t=0&&g[1].y+m>=0){const t=s.map((t=>o.getTilePoint(t))),e=l.map((t=>o.getTilePoint(t)));i.push({tile:n,tileID:o,queryGeometry:t,cameraQueryGeometry:e,scale:d})}}return i}getVisibleCoordinates(t){const e=this.getRenderableIds(t).map((t=>this._tiles[t].tileID));for(const t of e)t.posMatrix=this.transform.calculatePosMatrix(t.toUnwrapped());return e}hasTransition(){if(this._source.hasTransition())return!0;if(gt(this._source.type)){const t=a.now();for(const e in this._tiles)if(this._tiles[e].fadeEndTime>=t)return!0}return!1}setFeatureState(t,e,r){t=t||\"_geojsonTileLayer\",this._state.updateState(t,e,r)}removeFeatureState(t,e,r){t=t||\"_geojsonTileLayer\",this._state.removeFeatureState(t,e,r)}getFeatureState(t,e){return t=t||\"_geojsonTileLayer\",this._state.getState(t,e)}setDependencies(t,e,r){const n=this._tiles[t];n&&n.setDependencies(e,r)}reloadTilesForDependencies(t,e){for(const r in this._tiles)this._tiles[r].hasDependency(t,e)&&this._reloadTile(r,\"reloading\");this._cache.filter((r=>!r.hasDependency(t,e)))}}function mt(t,e){const r=Math.abs(2*t.wrap)-+(t.wrap<0),n=Math.abs(2*e.wrap)-+(e.wrap<0);return t.overscaledZ-e.overscaledZ||n-r||e.canonical.y-t.canonical.y||e.canonical.x-t.canonical.x}function gt(t){return\"raster\"===t||\"image\"===t||\"video\"===t}dt.maxOverzooming=10,dt.maxUnderzooming=3;class yt{constructor(t,e){this.reset(t,e)}reset(t,e){this.points=t||[],this._distances=[0];for(let t=1;t0?(i-o)/s:0;return this.points[a].mult(1-l).add(this.points[r].mult(l))}}function vt(t,e){let r=!0;return\"always\"===t||\"never\"!==t&&\"never\"!==e||(r=!1),r}class xt{constructor(t,e,r){const n=this.boxCells=[],i=this.circleCells=[];this.xCellCount=Math.ceil(t/r),this.yCellCount=Math.ceil(e/r);for(let t=0;tthis.width||n<0||e>this.height)return[];const s=[];if(t<=0&&e<=0&&this.width<=r&&this.height<=n){if(i)return[{key:null,x1:t,y1:e,x2:r,y2:n}];for(let t=0;t0}hitTestCircle(t,e,r,n,i){const a=t-r,o=t+r,s=e-r,l=e+r;if(o<0||a>this.width||l<0||s>this.height)return!1;const c=[],u={hitTest:!0,overlapMode:n,circle:{x:t,y:e,radius:r},seenUids:{box:{},circle:{}}};return this._forEachCell(a,s,o,l,this._queryCellCircle,c,u,i),c.length>0}_queryCell(t,e,r,n,i,a,o,s){const{seenUids:l,hitTest:c,overlapMode:u}=o,h=this.boxCells[i];if(null!==h){const i=this.bboxes;for(const o of h)if(!l.box[o]){l.box[o]=!0;const h=4*o,f=this.boxKeys[o];if(t<=i[h+2]&&e<=i[h+3]&&r>=i[h+0]&&n>=i[h+1]&&(!s||s(f))&&(!c||!vt(u,f.overlapMode))&&(a.push({key:f,x1:i[h],y1:i[h+1],x2:i[h+2],y2:i[h+3]}),c))return!0}}const f=this.circleCells[i];if(null!==f){const i=this.circles;for(const o of f)if(!l.circle[o]){l.circle[o]=!0;const h=3*o,f=this.circleKeys[o];if(this._circleAndRectCollide(i[h],i[h+1],i[h+2],t,e,r,n)&&(!s||s(f))&&(!c||!vt(u,f.overlapMode))){const t=i[h],e=i[h+1],r=i[h+2];if(a.push({key:f,x1:t-r,y1:e-r,x2:t+r,y2:e+r}),c)return!0}}}return!1}_queryCellCircle(t,e,r,n,i,a,o,s){const{circle:l,seenUids:c,overlapMode:u}=o,h=this.boxCells[i];if(null!==h){const t=this.bboxes;for(const e of h)if(!c.box[e]){c.box[e]=!0;const r=4*e,n=this.boxKeys[e];if(this._circleAndRectCollide(l.x,l.y,l.radius,t[r+0],t[r+1],t[r+2],t[r+3])&&(!s||s(n))&&!vt(u,n.overlapMode))return a.push(!0),!0}}const f=this.circleCells[i];if(null!==f){const t=this.circles;for(const e of f)if(!c.circle[e]){c.circle[e]=!0;const r=3*e,n=this.circleKeys[e];if(this._circlesCollide(t[r],t[r+1],t[r+2],l.x,l.y,l.radius)&&(!s||s(n))&&!vt(u,n.overlapMode))return a.push(!0),!0}}}_forEachCell(t,e,r,n,i,a,o,s){const l=this._convertToXCellCoord(t),c=this._convertToYCellCoord(e),u=this._convertToXCellCoord(r),h=this._convertToYCellCoord(n);for(let f=l;f<=u;f++)for(let l=c;l<=h;l++){const c=this.xCellCount*l+f;if(i.call(this,t,e,r,n,c,a,o,s))return}}_convertToXCellCoord(t){return Math.max(0,Math.min(this.xCellCount-1,Math.floor(t*this.xScale)))}_convertToYCellCoord(t){return Math.max(0,Math.min(this.yCellCount-1,Math.floor(t*this.yScale)))}_circlesCollide(t,e,r,n,i,a){const o=n-t,s=i-e,l=r+a;return l*l>o*o+s*s}_circleAndRectCollide(t,e,r,n,i,a,o){const s=(a-n)/2,l=Math.abs(t-(n+s));if(l>s+r)return!1;const c=(o-i)/2,u=Math.abs(e-(i+c));if(u>c+r)return!1;if(l<=s||u<=c)return!0;const h=l-s,f=u-c;return h*h+f*f<=r*r}}function _t(t,r,n,i,a){const o=e.H();return r?(e.K(o,o,[1/a,1/a,1]),n||e.ae(o,o,i.angle)):e.L(o,i.labelPlaneMatrix,t),o}function bt(t,r,n,i,a){if(r){const r=e.af(t);return e.K(r,r,[a,a,1]),n||e.ae(r,r,-i.angle),r}return i.glCoordMatrix}function wt(t,r,n){let i;n?(i=[t.x,t.y,n(t.x,t.y),1],e.ag(i,i,r)):(i=[t.x,t.y,0,1],function(t,e,r){const n=e[0],i=e[1];t[0]=r[0]*n+r[4]*i+r[12],t[1]=r[1]*n+r[5]*i+r[13],t[3]=r[3]*n+r[7]*i+r[15]}(i,i,r));const a=i[3];return{point:new e.P(i[0]/a,i[1]/a),signedDistanceFromCamera:a,isOccluded:!1}}function Tt(t,e){return.5+t/e*.5}function kt(t,e){return t.x>=-e[0]&&t.x<=e[0]&&t.y>=-e[1]&&t.y<=e[1]}function At(t,r,n,i,a,o,s,l,c,u,h,f,p,d,m){const g=i?t.textSizeData:t.iconSizeData,y=e.ah(g,n.transform.zoom),v=[256/n.width*2+1,256/n.height*2+1],x=i?t.text.dynamicLayoutVertexArray:t.icon.dynamicLayoutVertexArray;x.clear();const _=t.lineVertexArray,b=i?t.text.placedSymbolArray:t.icon.placedSymbolArray,w=n.transform.width/n.transform.height;let T=!1;for(let i=0;iMath.abs(n.x-r.x)*i?{useVertical:!0}:(t===e.ai.vertical?r.yn.x)?{needsFlipping:!0}:null}function Et(t,r,n,i,a,o,s,l,c,u,h){const f=n/24,p=r.lineOffsetX*f,d=r.lineOffsetY*f;let m;if(r.numGlyphs>1){const e=r.glyphStartIndex+r.numGlyphs,n=r.lineStartIndex,o=r.lineStartIndex+r.lineLength,c=Mt(f,l,p,d,i,r,h,t);if(!c)return{notEnoughRoom:!0};const g=wt(c.first.point,s,t.getElevation).point,y=wt(c.last.point,s,t.getElevation).point;if(a&&!i){const t=St(r.writingMode,g,y,u);if(t)return t}m=[c.first];for(let a=r.glyphStartIndex+1;a0?s.point:function(t,e,r,n,i,a){return Ct(t,e,r,n,i,a)}(t.tileAnchorPoint,a,n,1,o,t),c=St(r.writingMode,n,l,u);if(c)return c}const n=Ot(f*l.getoffsetX(r.glyphStartIndex),p,d,i,r.segment,r.lineStartIndex,r.lineStartIndex+r.lineLength,t,h);if(!n||t.projectionCache.anyProjectionOccluded)return{notEnoughRoom:!0};m=[n]}for(const t of m)e.ak(c,t.point,t.angle);return{}}function Ct(t,e,r,n,i,a){const o=t.add(t.sub(e)._unit()),s=void 0!==i?wt(o,i,a.getElevation).point:It(o.x,o.y,a).point,l=r.sub(s);return r.add(l._mult(n/l.mag()))}function Lt(t,r,n){const i=r.projectionCache;if(i.projections[t])return i.projections[t];const a=new e.P(r.lineVertexArray.getx(t),r.lineVertexArray.gety(t)),o=It(a.x,a.y,r);if(o.signedDistanceFromCamera>0)return i.projections[t]=o.point,i.anyProjectionOccluded=i.anyProjectionOccluded||o.isOccluded,o.point;const s=t-n.direction,l=0===n.distanceFromAnchor?r.tileAnchorPoint:new e.P(r.lineVertexArray.getx(s),r.lineVertexArray.gety(s)),c=n.absOffsetX-n.distanceFromAnchor+1;return function(t,e,r,n,i){return Ct(t,e,r,n,void 0,i)}(l,a,n.previousVertex,c,r)}function It(t,r,n){const i=t+n.translation[0],a=r+n.translation[1];let o;return!n.pitchWithMap&&n.projection.useSpecialProjectionForSymbols?(o=n.projection.projectTileCoordinates(i,a,n.unwrappedTileID,n.getElevation),o.point.x=(.5*o.point.x+.5)*n.width,o.point.y=(.5*-o.point.y+.5)*n.height):(o=wt(new e.P(i,a),n.labelPlaneMatrix,n.getElevation),o.isOccluded=!1),o}function Pt(t,e,r){return t._unit()._perp()._mult(e*r)}function zt(t,r,n,i,a,o,s,l,c){if(l.projectionCache.offsets[t])return l.projectionCache.offsets[t];const u=n.add(r);if(t+c.direction=a)return l.projectionCache.offsets[t]=u,u;const h=Lt(t+c.direction,l,c),f=Pt(h.sub(n),s,c.direction),p=n.add(f),d=h.add(f);return l.projectionCache.offsets[t]=e.al(o,u,p,d)||u,l.projectionCache.offsets[t]}function Ot(t,e,r,n,i,a,o,s,l){const c=n?t-e:t+e;let u=c>0?1:-1,h=0;n&&(u*=-1,h=Math.PI),u<0&&(h+=Math.PI);let f,p=u>0?a+i:a+i+1;s.projectionCache.cachedAnchorPoint?f=s.projectionCache.cachedAnchorPoint:(f=It(s.tileAnchorPoint.x,s.tileAnchorPoint.y,s).point,s.projectionCache.cachedAnchorPoint=f);let d,m,g=f,y=f,v=0,x=0;const _=Math.abs(c),b=[];let w;for(;v+x<=_;){if(p+=u,p=o)return null;v+=x,y=g,m=d;const t={absOffsetX:_,direction:u,distanceFromAnchor:v,previousVertex:y};if(g=Lt(p,s,t),0===r)b.push(y),w=g.sub(y);else{let e;const n=g.sub(y);e=0===n.mag()?Pt(Lt(p+u,s,t).sub(g),r,u):Pt(n,r,u),m||(m=y.add(e)),d=zt(p,e,g,a,o,m,r,s,t),b.push(m),w=d.sub(m)}x=w.mag()}const T=(_-v)/x,k=w._mult(T)._add(m||y),A=h+Math.atan2(g.y-y.y,g.x-y.x);return b.push(k),{point:k,angle:l?A:0,path:b}}const Dt=new Float32Array([-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0]);function Rt(t,e){for(let r=0;r=1;t--)l.push(o.path[t]);for(let t=1;tt.signedDistanceFromCamera<=0))?[]:t.map((t=>t.point))}let m=[];if(l.length>0){const t=l[0].clone(),r=l[0].clone();for(let e=1;e=n.x&&r.x<=i.x&&t.y>=n.y&&r.y<=i.y?[l]:r.xi.x||r.yi.y?[]:e.am([l],n.x,n.y,i.x,i.y)}for(const e of m){a.reset(e,.25*r);let n=0;n=a.length<=.5*r?1:Math.ceil(a.paddedLength/h)+1;for(let e=0;ewt(t,r,e.getElevation)))}queryRenderedSymbols(t){if(0===t.length||0===this.grid.keysLength()&&0===this.ignoredGrid.keysLength())return{};const r=[];let n=1/0,i=1/0,a=-1/0,o=-1/0;for(const s of t){const t=new e.P(s.x+Ft,s.y+Ft);n=Math.min(n,t.x),i=Math.min(i,t.y),a=Math.max(a,t.x),o=Math.max(o,t.y),r.push(t)}const s=this.grid.query(n,i,a,o).concat(this.ignoredGrid.query(n,i,a,o)),l={},c={};for(const t of s){const n=t.key;if(void 0===l[n.bucketInstanceId]&&(l[n.bucketInstanceId]={}),l[n.bucketInstanceId][n.featureIndex])continue;const i=[new e.P(t.x1,t.y1),new e.P(t.x2,t.y1),new e.P(t.x2,t.y2),new e.P(t.x1,t.y2)];e.an(r,i)&&(l[n.bucketInstanceId][n.featureIndex]=!0,void 0===c[n.bucketInstanceId]&&(c[n.bucketInstanceId]=[]),c[n.bucketInstanceId].push(n.featureIndex))}return c}insertCollisionBox(t,e,r,n,i,a){const o={bucketInstanceId:n,featureIndex:i,collisionGroupID:a,overlapMode:e};(r?this.ignoredGrid:this.grid).insert(o,t[0],t[1],t[2],t[3])}insertCollisionCircles(t,e,r,n,i,a){const o=r?this.ignoredGrid:this.grid,s={bucketInstanceId:n,featureIndex:i,collisionGroupID:a,overlapMode:e};for(let e=0;e=this.screenRightBoundary||nthis.screenBottomBoundary}isInsideGrid(t,e,r,n){return r>=0&&t=0&&ethis.projectAndGetPerspectiveRatio(n,t.x,t.y,i,c)));A=t.some((t=>!t.isOccluded)),k=t.map((t=>t.point))}else A=!0;return{box:e.ap(k),allPointsOccluded:!A}}}function Nt(t,r,n){return r*(e.X/(t.tileSize*Math.pow(2,n-t.tileID.overscaledZ)))}class jt{constructor(t,e,r,n){this.opacity=t?Math.max(0,Math.min(1,t.opacity+(t.placed?e:-e))):n&&r?1:0,this.placed=r}isHidden(){return 0===this.opacity&&!this.placed}}class Ut{constructor(t,e,r,n,i){this.text=new jt(t?t.text:null,e,r,i),this.icon=new jt(t?t.icon:null,e,n,i)}isHidden(){return this.text.isHidden()&&this.icon.isHidden()}}class Vt{constructor(t,e,r){this.text=t,this.icon=e,this.skipFade=r}}class qt{constructor(){this.invProjMatrix=e.H(),this.viewportMatrix=e.H(),this.circles=[]}}class Ht{constructor(t,e,r,n,i){this.bucketInstanceId=t,this.featureIndex=e,this.sourceLayerIndex=r,this.bucketIndex=n,this.tileID=i}}class Gt{constructor(t){this.crossSourceCollisions=t,this.maxGroupID=0,this.collisionGroups={}}get(t){if(this.crossSourceCollisions)return{ID:0,predicate:null};if(!this.collisionGroups[t]){const e=++this.maxGroupID;this.collisionGroups[t]={ID:e,predicate:t=>t.collisionGroupID===e}}return this.collisionGroups[t]}}function Zt(t,r,n,i,a){const{horizontalAlign:o,verticalAlign:s}=e.av(t),l=-(o-.5)*r,c=-(s-.5)*n;return new e.P(l+i[0]*a,c+i[1]*a)}class Wt{constructor(t,e,r,n,i,a){this.transform=t.clone(),this.terrain=r,this.collisionIndex=new Bt(this.transform,e),this.placements={},this.opacities={},this.variableOffsets={},this.stale=!1,this.commitTime=0,this.fadeDuration=n,this.retainedQueryData={},this.collisionGroups=new Gt(i),this.collisionCircleArrays={},this.collisionBoxArrays=new Map,this.prevPlacement=a,a&&(a.prevPlacement=void 0),this.placedOrientations={}}_getTerrainElevationFunc(t){const e=this.terrain;return e?(r,n)=>e.getElevation(t,r,n):null}getBucketParts(t,r,n,i){const a=n.getBucket(r),o=n.latestFeatureIndex;if(!a||!o||r.id!==a.layerIds[0])return;const s=n.collisionBoxArray,l=a.layers[0].layout,c=a.layers[0].paint,u=Math.pow(2,this.transform.zoom-n.tileID.overscaledZ),h=n.tileSize/e.X,f=n.tileID.toUnwrapped(),p=this.transform.calculatePosMatrix(f),d=\"map\"===l.get(\"text-pitch-alignment\"),m=\"map\"===l.get(\"text-rotation-alignment\"),g=Nt(n,1,this.transform.zoom),y=this.collisionIndex.mapProjection.translatePosition(this.transform,n,c.get(\"text-translate\"),c.get(\"text-translate-anchor\")),v=this.collisionIndex.mapProjection.translatePosition(this.transform,n,c.get(\"icon-translate\"),c.get(\"icon-translate-anchor\")),x=_t(p,d,m,this.transform,g);let _=null;if(d){const t=bt(p,d,m,this.transform,g);_=e.L([],this.transform.labelPlaneMatrix,t)}this.retainedQueryData[a.bucketInstanceId]=new Ht(a.bucketInstanceId,o,a.sourceLayerIndex,a.index,n.tileID);const b={bucket:a,layout:l,translationText:y,translationIcon:v,posMatrix:p,unwrappedTileID:f,textLabelPlaneMatrix:x,labelToScreenMatrix:_,scale:u,textPixelRatio:h,holdingForFade:n.holdingForFade(),collisionBoxArray:s,partiallyEvaluatedTextSize:e.ah(a.textSizeData,this.transform.zoom),collisionGroup:this.collisionGroups.get(a.sourceID)};if(i)for(const e of a.sortKeyRanges){const{sortKey:r,symbolInstanceStart:n,symbolInstanceEnd:i}=e;t.push({sortKey:r,symbolInstanceStart:n,symbolInstanceEnd:i,parameters:b})}else t.push({symbolInstanceStart:0,symbolInstanceEnd:a.symbolInstances.length,parameters:b})}attemptAnchorPlacement(t,r,n,i,a,o,s,l,c,u,h,f,p,d,m,g,y,v,x){const _=e.ar[t.textAnchor],b=[t.textOffset0,t.textOffset1],w=Zt(_,n,i,b,a),T=this.collisionIndex.placeCollisionBox(r,f,l,c,u,s,o,g,h.predicate,x,w);if((!v||this.collisionIndex.placeCollisionBox(v,f,l,c,u,s,o,y,h.predicate,x,w).placeable)&&T.placeable){let t;if(this.prevPlacement&&this.prevPlacement.variableOffsets[p.crossTileID]&&this.prevPlacement.placements[p.crossTileID]&&this.prevPlacement.placements[p.crossTileID].text&&(t=this.prevPlacement.variableOffsets[p.crossTileID].anchor),0===p.crossTileID)throw new Error(\"symbolInstance.crossTileID can't be 0\");return this.variableOffsets[p.crossTileID]={textOffset:b,width:n,height:i,anchor:_,textBoxScale:a,prevAnchor:t},this.markUsedJustification(d,_,p,m),d.allowVerticalPlacement&&(this.markUsedOrientation(d,m,p),this.placedOrientations[p.crossTileID]=m),{shift:w,placedGlyphBoxes:T}}}placeLayerBucketPart(t,r,n){const{bucket:i,layout:a,translationText:o,translationIcon:s,posMatrix:l,unwrappedTileID:c,textLabelPlaneMatrix:u,labelToScreenMatrix:h,textPixelRatio:f,holdingForFade:p,collisionBoxArray:d,partiallyEvaluatedTextSize:m,collisionGroup:g}=t.parameters,y=a.get(\"text-optional\"),v=a.get(\"icon-optional\"),x=e.as(a,\"text-overlap\",\"text-allow-overlap\"),_=\"always\"===x,b=e.as(a,\"icon-overlap\",\"icon-allow-overlap\"),w=\"always\"===b,T=\"map\"===a.get(\"text-rotation-alignment\"),k=\"map\"===a.get(\"text-pitch-alignment\"),A=\"none\"!==a.get(\"icon-text-fit\"),M=\"viewport-y\"===a.get(\"symbol-z-order\"),S=_&&(w||!i.hasIconData()||v),E=w&&(_||!i.hasTextData()||y);!i.collisionArrays&&d&&i.deserializeCollisionBoxes(d);const C=this.retainedQueryData[i.bucketInstanceId].tileID,L=this._getTerrainElevationFunc(C),I=(t,d,w)=>{var M,C;if(r[t.crossTileID])return;if(p)return void(this.placements[t.crossTileID]=new Vt(!1,!1,!1));let I=!1,P=!1,z=!0,O=null,D={box:null,placeable:!1,offscreen:null},R={box:null,placeable:!1,offscreen:null},F=null,B=null,N=null,j=0,U=0,V=0;d.textFeatureIndex?j=d.textFeatureIndex:t.useRuntimeCollisionCircles&&(j=t.featureIndex),d.verticalTextFeatureIndex&&(U=d.verticalTextFeatureIndex);const q=d.textBox;if(q){const r=r=>{let n=e.ai.horizontal;if(i.allowVerticalPlacement&&!r&&this.prevPlacement){const e=this.prevPlacement.placedOrientations[t.crossTileID];e&&(this.placedOrientations[t.crossTileID]=e,n=e,this.markUsedOrientation(i,n,t))}return n},a=(r,n)=>{if(i.allowVerticalPlacement&&t.numVerticalGlyphVertices>0&&d.verticalTextBox){for(const t of i.writingModes)if(t===e.ai.vertical?(D=n(),R=D):D=r(),D&&D.placeable)break}else D=r()},u=t.textAnchorOffsetStartIndex,h=t.textAnchorOffsetEndIndex;if(h===u){const n=(e,r)=>{const n=this.collisionIndex.placeCollisionBox(e,x,f,l,c,k,T,o,g.predicate,L);return n&&n.placeable&&(this.markUsedOrientation(i,r,t),this.placedOrientations[t.crossTileID]=r),n};a((()=>n(q,e.ai.horizontal)),(()=>{const r=d.verticalTextBox;return i.allowVerticalPlacement&&t.numVerticalGlyphVertices>0&&r?n(r,e.ai.vertical):{box:null,offscreen:null}})),r(D&&D.placeable)}else{let p=e.ar[null===(C=null===(M=this.prevPlacement)||void 0===M?void 0:M.variableOffsets[t.crossTileID])||void 0===C?void 0:C.anchor];const m=(r,a,d)=>{const m=r.x2-r.x1,y=r.y2-r.y1,v=t.textBoxScale,_=A&&\"never\"===b?a:null;let w=null,M=\"never\"===x?1:2,S=\"never\";p&&M++;for(let e=0;em(q,d.iconBox,e.ai.horizontal)),(()=>{const r=d.verticalTextBox,n=D&&D.placeable;return i.allowVerticalPlacement&&!n&&t.numVerticalGlyphVertices>0&&r?m(r,d.verticalIconBox,e.ai.vertical):{box:null,occluded:!0,offscreen:null}})),D&&(I=D.placeable,z=D.offscreen);const y=r(D&&D.placeable);if(!I&&this.prevPlacement){const e=this.prevPlacement.variableOffsets[t.crossTileID];e&&(this.variableOffsets[t.crossTileID]=e,this.markUsedJustification(i,e.anchor,t,y))}}}if(F=D,I=F&&F.placeable,z=F&&F.offscreen,t.useRuntimeCollisionCircles){const r=i.text.placedSymbolArray.get(t.centerJustifiedTextSymbolIndex),s=e.aj(i.textSizeData,m,r),f=a.get(\"text-padding\"),p=t.collisionCircleDiameter;B=this.collisionIndex.placeCollisionCircles(x,r,i.lineVertexArray,i.glyphOffsetArray,s,l,c,u,h,n,k,g.predicate,p,f,o,L),B.circles.length&&B.collisionDetected&&!n&&e.w(\"Collisions detected, but collision boxes are not shown\"),I=_||B.circles.length>0&&!B.collisionDetected,z=z&&B.offscreen}if(d.iconFeatureIndex&&(V=d.iconFeatureIndex),d.iconBox){const t=t=>this.collisionIndex.placeCollisionBox(t,b,f,l,c,k,T,s,g.predicate,L,A&&O?O:void 0);R&&R.placeable&&d.verticalIconBox?(N=t(d.verticalIconBox),P=N.placeable):(N=t(d.iconBox),P=N.placeable),z=z&&N.offscreen}const H=y||0===t.numHorizontalGlyphVertices&&0===t.numVerticalGlyphVertices,G=v||0===t.numIconVertices;H||G?G?H||(P=P&&I):I=P&&I:P=I=P&&I;const Z=I&&F.placeable,W=P&&N.placeable;if(Z&&(R&&R.placeable&&U?this.collisionIndex.insertCollisionBox(F.box,x,a.get(\"text-ignore-placement\"),i.bucketInstanceId,U,g.ID):this.collisionIndex.insertCollisionBox(F.box,x,a.get(\"text-ignore-placement\"),i.bucketInstanceId,j,g.ID)),W&&this.collisionIndex.insertCollisionBox(N.box,b,a.get(\"icon-ignore-placement\"),i.bucketInstanceId,V,g.ID),B&&I&&this.collisionIndex.insertCollisionCircles(B.circles,x,a.get(\"text-ignore-placement\"),i.bucketInstanceId,j,g.ID),n&&this.storeCollisionData(i.bucketInstanceId,w,d,F,N,B),0===t.crossTileID)throw new Error(\"symbolInstance.crossTileID can't be 0\");if(0===i.bucketInstanceId)throw new Error(\"bucket.bucketInstanceId can't be 0\");this.placements[t.crossTileID]=new Vt(I||S,P||E,z||i.justReloaded),r[t.crossTileID]=!0};if(M){if(0!==t.symbolInstanceStart)throw new Error(\"bucket.bucketInstanceId should be 0\");const e=i.getSortedSymbolIndexes(this.transform.angle);for(let t=e.length-1;t>=0;--t){const r=e[t];I(i.symbolInstances.get(r),i.collisionArrays[r],r)}}else for(let e=t.symbolInstanceStart;e=0&&(t.text.placedSymbolArray.get(e).crossTileID=o>=0&&e!==o?0:n.crossTileID)}markUsedOrientation(t,r,n){const i=r===e.ai.horizontal||r===e.ai.horizontalOnly?r:0,a=r===e.ai.vertical?r:0,o=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex];for(const e of o)t.text.placedSymbolArray.get(e).placedOrientation=i;n.verticalPlacedTextSymbolIndex&&(t.text.placedSymbolArray.get(n.verticalPlacedTextSymbolIndex).placedOrientation=a)}commit(t){this.commitTime=t,this.zoomAtLastRecencyCheck=this.transform.zoom;const e=this.prevPlacement;let r=!1;this.prevZoomAdjustment=e?e.zoomAdjustment(this.transform.zoom):0;const n=e?e.symbolFadeChange(t):1,i=e?e.opacities:{},a=e?e.variableOffsets:{},o=e?e.placedOrientations:{};for(const t in this.placements){const e=this.placements[t],a=i[t];a?(this.opacities[t]=new Ut(a,n,e.text,e.icon),r=r||e.text!==a.text.placed||e.icon!==a.icon.placed):(this.opacities[t]=new Ut(null,n,e.text,e.icon,e.skipFade),r=r||e.text||e.icon)}for(const t in i){const e=i[t];if(!this.opacities[t]){const i=new Ut(e,n,!1,!1);i.isHidden()||(this.opacities[t]=i,r=r||e.text.placed||e.icon.placed)}}for(const t in a)this.variableOffsets[t]||!this.opacities[t]||this.opacities[t].isHidden()||(this.variableOffsets[t]=a[t]);for(const t in o)this.placedOrientations[t]||!this.opacities[t]||this.opacities[t].isHidden()||(this.placedOrientations[t]=o[t]);if(e&&void 0===e.lastPlacementChangeTime)throw new Error(\"Last placement time for previous placement is not defined\");r?this.lastPlacementChangeTime=t:\"number\"!=typeof this.lastPlacementChangeTime&&(this.lastPlacementChangeTime=e?e.lastPlacementChangeTime:t)}updateLayerOpacities(t,e){const r={};for(const n of e){const e=n.getBucket(t);e&&n.latestFeatureIndex&&t.id===e.layerIds[0]&&this.updateBucketOpacities(e,n.tileID,r,n.collisionBoxArray)}}updateBucketOpacities(t,r,n,i){t.hasTextData()&&(t.text.opacityVertexArray.clear(),t.text.hasVisibleVertices=!1),t.hasIconData()&&(t.icon.opacityVertexArray.clear(),t.icon.hasVisibleVertices=!1),t.hasIconCollisionBoxData()&&t.iconCollisionBox.collisionVertexArray.clear(),t.hasTextCollisionBoxData()&&t.textCollisionBox.collisionVertexArray.clear();const a=t.layers[0],o=a.layout,s=new Ut(null,0,!1,!1,!0),l=o.get(\"text-allow-overlap\"),c=o.get(\"icon-allow-overlap\"),u=a._unevaluatedLayout.hasValue(\"text-variable-anchor\")||a._unevaluatedLayout.hasValue(\"text-variable-anchor-offset\"),h=\"map\"===o.get(\"text-rotation-alignment\"),f=\"map\"===o.get(\"text-pitch-alignment\"),p=\"none\"!==o.get(\"icon-text-fit\"),d=new Ut(null,0,l&&(c||!t.hasIconData()||o.get(\"icon-optional\")),c&&(l||!t.hasTextData()||o.get(\"text-optional\")),!0);!t.collisionArrays&&i&&(t.hasIconCollisionBoxData()||t.hasTextCollisionBoxData())&&t.deserializeCollisionBoxes(i);const m=(t,e,r)=>{for(let n=0;n0||o>0,x=i.numIconVertices>0,_=this.placedOrientations[i.crossTileID],b=_===e.ai.vertical,w=_===e.ai.horizontal||_===e.ai.horizontalOnly;if(v){const e=re(y.text),r=b?ne:e;m(t.text,a,r);const n=w?ne:e;m(t.text,o,n);const s=y.text.isHidden();[i.rightJustifiedTextSymbolIndex,i.centerJustifiedTextSymbolIndex,i.leftJustifiedTextSymbolIndex].forEach((e=>{e>=0&&(t.text.placedSymbolArray.get(e).hidden=s||b?1:0)})),i.verticalPlacedTextSymbolIndex>=0&&(t.text.placedSymbolArray.get(i.verticalPlacedTextSymbolIndex).hidden=s||w?1:0);const l=this.variableOffsets[i.crossTileID];l&&this.markUsedJustification(t,l.anchor,i,_);const c=this.placedOrientations[i.crossTileID];c&&(this.markUsedJustification(t,\"left\",i,c),this.markUsedOrientation(t,c,i))}if(x){const e=re(y.icon),r=!(p&&i.verticalPlacedIconSymbolIndex&&b);if(i.placedIconSymbolIndex>=0){const n=r?e:ne;m(t.icon,i.numIconVertices,n),t.icon.placedSymbolArray.get(i.placedIconSymbolIndex).hidden=y.icon.isHidden()}if(i.verticalPlacedIconSymbolIndex>=0){const n=r?ne:e;m(t.icon,i.numVerticalIconVertices,n),t.icon.placedSymbolArray.get(i.verticalPlacedIconSymbolIndex).hidden=y.icon.isHidden()}}const T=g&&g.has(r)?g.get(r):{text:null,icon:null};if(t.hasIconCollisionBoxData()||t.hasTextCollisionBoxData()){const n=t.collisionArrays[r];if(n){let r=new e.P(0,0);if(n.textBox||n.verticalTextBox){let e=!0;if(u){const t=this.variableOffsets[l];t?(r=Zt(t.anchor,t.width,t.height,t.textOffset,t.textBoxScale),h&&r._rotate(f?this.transform.angle:-this.transform.angle)):e=!1}if(n.textBox||n.verticalTextBox){let i;n.textBox&&(i=b),n.verticalTextBox&&(i=w),Yt(t.textCollisionBox.collisionVertexArray,y.text.placed,!e||i,T.text,r.x,r.y)}}if(n.iconBox||n.verticalIconBox){const e=Boolean(!w&&n.verticalIconBox);let i;n.iconBox&&(i=e),n.verticalIconBox&&(i=!e),Yt(t.iconCollisionBox.collisionVertexArray,y.icon.placed,i,T.icon,p?r.x:0,p?r.y:0)}}}}if(t.sortFeatures(this.transform.angle),this.retainedQueryData[t.bucketInstanceId]&&(this.retainedQueryData[t.bucketInstanceId].featureSortOrder=t.featureSortOrder),t.hasTextData()&&t.text.opacityVertexBuffer&&t.text.opacityVertexBuffer.updateData(t.text.opacityVertexArray),t.hasIconData()&&t.icon.opacityVertexBuffer&&t.icon.opacityVertexBuffer.updateData(t.icon.opacityVertexArray),t.hasIconCollisionBoxData()&&t.iconCollisionBox.collisionVertexBuffer&&t.iconCollisionBox.collisionVertexBuffer.updateData(t.iconCollisionBox.collisionVertexArray),t.hasTextCollisionBoxData()&&t.textCollisionBox.collisionVertexBuffer&&t.textCollisionBox.collisionVertexBuffer.updateData(t.textCollisionBox.collisionVertexArray),t.text.opacityVertexArray.length!==t.text.layoutVertexArray.length/4)throw new Error(`bucket.text.opacityVertexArray.length (= ${t.text.opacityVertexArray.length}) !== bucket.text.layoutVertexArray.length (= ${t.text.layoutVertexArray.length}) / 4`);if(t.icon.opacityVertexArray.length!==t.icon.layoutVertexArray.length/4)throw new Error(`bucket.icon.opacityVertexArray.length (= ${t.icon.opacityVertexArray.length}) !== bucket.icon.layoutVertexArray.length (= ${t.icon.layoutVertexArray.length}) / 4`);if(t.bucketInstanceId in this.collisionCircleArrays){const e=this.collisionCircleArrays[t.bucketInstanceId];t.placementInvProjMatrix=e.invProjMatrix,t.placementViewportMatrix=e.viewportMatrix,t.collisionCircleArray=e.circles,delete this.collisionCircleArrays[t.bucketInstanceId]}}symbolFadeChange(t){return 0===this.fadeDuration?1:(t-this.commitTime)/this.fadeDuration+this.prevZoomAdjustment}zoomAdjustment(t){return Math.max(0,(this.transform.zoom-t)/1.5)}hasTransitions(t){return this.stale||t-this.lastPlacementChangeTimet}setStale(){this.stale=!0}}function Yt(t,e,r,n,i,a){n&&0!==n.length||(n=[0,0,0,0]);const o=n[0]-Ft,s=n[1]-Ft,l=n[2]-Ft,c=n[3]-Ft;t.emplaceBack(e?1:0,r?1:0,i||0,a||0,o,s),t.emplaceBack(e?1:0,r?1:0,i||0,a||0,l,s),t.emplaceBack(e?1:0,r?1:0,i||0,a||0,l,c),t.emplaceBack(e?1:0,r?1:0,i||0,a||0,o,c)}const Xt=Math.pow(2,25),$t=Math.pow(2,24),Jt=Math.pow(2,17),Kt=Math.pow(2,16),Qt=Math.pow(2,9),te=Math.pow(2,8),ee=Math.pow(2,1);function re(t){if(0===t.opacity&&!t.placed)return 0;if(1===t.opacity&&t.placed)return 4294967295;const e=t.placed?1:0,r=Math.floor(127*t.opacity);return r*Xt+e*$t+r*Jt+e*Kt+r*Qt+e*te+r*ee+e}const ne=0;function ie(){return{isOccluded(t,e,r){return!1},getPitchedTextCorrection(t,e,r){return 1},get useSpecialProjectionForSymbols(){return!1},projectTileCoordinates(t,e,r,n){throw new Error(\"Not implemented.\")},translatePosition(t,e,r,n){return function(t,e,r,n,i=!1){if(!r[0]&&!r[1])return[0,0];const a=i?\"map\"===n?t.angle:0:\"viewport\"===n?-t.angle:0;if(a){const t=Math.sin(a),e=Math.cos(a);r=[r[0]*e-r[1]*t,r[0]*t+r[1]*e]}return[i?r[0]:Nt(e,r[0],t.zoom),i?r[1]:Nt(e,r[1],t.zoom)]}(t,e,r,n)},getCircleRadiusCorrection(t){return 1}}}class ae{constructor(t){this._sortAcrossTiles=\"viewport-y\"!==t.layout.get(\"symbol-z-order\")&&!t.layout.get(\"symbol-sort-key\").isConstant(),this._currentTileIndex=0,this._currentPartIndex=0,this._seenCrossTileIDs={},this._bucketParts=[]}continuePlacement(t,e,r,n,i){const a=this._bucketParts;for(;this._currentTileIndext.sortKey-e.sortKey)));this._currentPartIndex!this._forceFullPlacement&&a.now()-n>2;for(;this._currentPlacementIndex>=0;){const n=e[t[this._currentPlacementIndex]],a=this.placement.collisionIndex.transform.zoom;if(\"symbol\"===n.type&&(!n.minzoom||n.minzoom<=a)&&(!n.maxzoom||n.maxzoom>a)){if(this._inProgressLayer||(this._inProgressLayer=new ae(n)),this._inProgressLayer.continuePlacement(r[n.source],this.placement,this._showCollisionBoxes,n,i))return;delete this._inProgressLayer}this._currentPlacementIndex--}this._done=!0}commit(t){return this.placement.commit(t),this.placement}}const se=512/e.X/2;class le{constructor(t,r,n){this.tileID=t,this.bucketInstanceId=n,this._symbolsByKey={};const i=new Map;for(let t=0;t({x:Math.floor(t.anchorX*se),y:Math.floor(t.anchorY*se)}))),crossTileIDs:r.map((t=>t.crossTileID))};if(n.positions.length>128){const t=new e.aw(n.positions.length,16,Uint16Array);for(const{x:e,y:r}of n.positions)t.add(e,r);t.finish(),delete n.positions,n.index=t}this._symbolsByKey[t]=n}}getScaledCoordinates(t,r){const{x:n,y:i,z:a}=this.tileID.canonical,{x:o,y:s,z:l}=r.canonical,c=l-a,u=se/Math.pow(2,c),h=(o*e.X+t.anchorX)*u,f=(s*e.X+t.anchorY)*u,p=n*e.X*se,d=i*e.X*se;return{x:Math.floor(h-p),y:Math.floor(f-d)}}findMatches(t,e,r){const n=this.tileID.canonical.zt))}}class ce{constructor(){this.maxCrossTileID=0}generate(){return++this.maxCrossTileID}}class ue{constructor(){this.indexes={},this.usedCrossTileIDs={},this.lng=0}handleWrapJump(t){const e=Math.round((t-this.lng)/360);if(0!==e)for(const t in this.indexes){const r=this.indexes[t],n={};for(const t in r){const i=r[t];i.tileID=i.tileID.unwrapTo(i.tileID.wrap+e),n[i.tileID.key]=i}this.indexes[t]=n}this.lng=t}addBucket(t,e,r){if(this.indexes[t.overscaledZ]&&this.indexes[t.overscaledZ][t.key]){if(this.indexes[t.overscaledZ][t.key].bucketInstanceId===e.bucketInstanceId)return!1;this.removeBucketCrossTileIDs(t.overscaledZ,this.indexes[t.overscaledZ][t.key])}for(let t=0;tt.overscaledZ)for(const r in i){const a=i[r];a.tileID.isChildOf(t)&&a.findMatches(e.symbolInstances,t,n)}else{const a=i[t.scaledTo(Number(r)).key];a&&a.findMatches(e.symbolInstances,t,n)}}for(let t=0;t{e[t]=!0}));for(const t in this.layerIndexes)e[t]||delete this.layerIndexes[t]}}const fe=(t,r)=>e.t(t,r&&r.filter((t=>\"source.canvas\"!==t.identifier))),pe=e.ax();class de extends e.E{constructor(t,r={}){super(),this._rtlPluginLoaded=()=>{for(const t in this.sourceCaches){const e=this.sourceCaches[t].getSource().type;\"vector\"!==e&&\"geojson\"!==e||this.sourceCaches[t].reload()}},this.map=t,this.dispatcher=new q(V(),t._getMapId()),this.dispatcher.registerMessageHandler(\"GG\",((t,e)=>this.getGlyphs(t,e))),this.dispatcher.registerMessageHandler(\"GI\",((t,e)=>this.getImages(t,e))),this.imageManager=new k,this.imageManager.setEventedParent(this),this.glyphManager=new E(t._requestManager,r.localIdeographFontFamily),this.lineAtlas=new R(256,512),this.crossTileSymbolIndex=new he,this._spritesImagesIds={},this._layers={},this._order=[],this.sourceCaches={},this.zoomHistory=new e.ay,this._loaded=!1,this._availableImages=[],this._resetUpdates(),this.dispatcher.broadcast(\"SR\",e.az()),ut().on(st,this._rtlPluginLoaded),this.on(\"data\",(t=>{if(\"source\"!==t.dataType||\"metadata\"!==t.sourceDataType)return;const e=this.sourceCaches[t.sourceId];if(!e)return;const r=e.getSource();if(r&&r.vectorLayerIds)for(const t in this._layers){const e=this._layers[t];e.source===r.id&&this._validateLayer(e)}}))}loadURL(t,r={},n){this.fire(new e.k(\"dataloading\",{dataType:\"style\"})),r.validate=\"boolean\"!=typeof r.validate||r.validate;const i=this.map._requestManager.transformRequest(t,\"Style\");this._loadStyleRequest=new AbortController;const a=this._loadStyleRequest;e.h(i,this._loadStyleRequest).then((t=>{this._loadStyleRequest=null,this._load(t.data,r,n)})).catch((t=>{this._loadStyleRequest=null,t&&!a.signal.aborted&&this.fire(new e.j(t))}))}loadJSON(t,r={},n){this.fire(new e.k(\"dataloading\",{dataType:\"style\"})),this._frameRequest=new AbortController,a.frameAsync(this._frameRequest).then((()=>{this._frameRequest=null,r.validate=!1!==r.validate,this._load(t,r,n)})).catch((()=>{}))}loadEmpty(){this.fire(new e.k(\"dataloading\",{dataType:\"style\"})),this._load(pe,{validate:!1})}_load(t,r,n){var i;const a=r.transformStyle?r.transformStyle(n,t):t;if(!r.validate||!fe(this,e.x(a))){this._loaded=!0,this.stylesheet=a;for(const t in a.sources)this.addSource(t,a.sources[t],{validate:!1});a.sprite?this._loadSprite(a.sprite):this.imageManager.setLoaded(!0),this.glyphManager.setURL(a.glyphs),this._createLayers(),this.light=new P(this.stylesheet.light),this.sky=new D(this.stylesheet.sky),this.map.setTerrain(null!==(i=this.stylesheet.terrain)&&void 0!==i?i:null),this.fire(new e.k(\"data\",{dataType:\"style\"})),this.fire(new e.k(\"style.load\"))}}_createLayers(){const t=e.aA(this.stylesheet.layers);this.dispatcher.broadcast(\"SL\",t),this._order=t.map((t=>t.id)),this._layers={},this._serializedLayers=null;for(const r of t){const t=e.aB(r);t.setEventedParent(this,{layer:{id:r.id}}),this._layers[r.id]=t}}_loadSprite(t,r=!1,n=void 0){let i;this.imageManager.setLoaded(!1),this._spriteRequest=new AbortController,b(t,this.map._requestManager,this.map.getPixelRatio(),this._spriteRequest).then((t=>{if(this._spriteRequest=null,t)for(const e in t){this._spritesImagesIds[e]=[];const n=this._spritesImagesIds[e]?this._spritesImagesIds[e].filter((e=>!(e in t))):[];for(const t of n)this.imageManager.removeImage(t),this._changedImages[t]=!0;for(const n in t[e]){const i=\"default\"===e?n:`${e}:${n}`;this._spritesImagesIds[e].push(i),i in this.imageManager.images?this.imageManager.updateImage(i,t[e][n],!1):this.imageManager.addImage(i,t[e][n]),r&&(this._changedImages[i]=!0)}}})).catch((t=>{this._spriteRequest=null,i=t,this.fire(new e.j(i))})).finally((()=>{this.imageManager.setLoaded(!0),this._availableImages=this.imageManager.listImages(),r&&(this._changed=!0),this.dispatcher.broadcast(\"SI\",this._availableImages),this.fire(new e.k(\"data\",{dataType:\"style\"})),n&&n(i)}))}_unloadSprite(){for(const t of Object.values(this._spritesImagesIds).flat())this.imageManager.removeImage(t),this._changedImages[t]=!0;this._spritesImagesIds={},this._availableImages=this.imageManager.listImages(),this._changed=!0,this.dispatcher.broadcast(\"SI\",this._availableImages),this.fire(new e.k(\"data\",{dataType:\"style\"}))}_validateLayer(t){const r=this.sourceCaches[t.source];if(!r)return;const n=t.sourceLayer;if(!n)return;const i=r.getSource();(\"geojson\"===i.type||i.vectorLayerIds&&-1===i.vectorLayerIds.indexOf(n))&&this.fire(new e.j(new Error(`Source layer \"${n}\" does not exist on source \"${i.id}\" as specified by style layer \"${t.id}\".`)))}loaded(){if(!this._loaded)return!1;if(Object.keys(this._updatedSources).length)return!1;for(const t in this.sourceCaches)if(!this.sourceCaches[t].loaded())return!1;return!!this.imageManager.isLoaded()}_serializeByIds(t){const e=this._serializedAllLayers();if(!t||0===t.length)return Object.values(e);const r=[];for(const n of t)e[n]&&r.push(e[n]);return r}_serializedAllLayers(){let t=this._serializedLayers;if(t)return t;t=this._serializedLayers={};const e=Object.keys(this._layers);for(const r of e){const e=this._layers[r];\"custom\"!==e.type&&(t[r]=e.serialize())}return t}hasTransitions(){if(this.light&&this.light.hasTransition())return!0;if(this.sky&&this.sky.hasTransition())return!0;for(const t in this.sourceCaches)if(this.sourceCaches[t].hasTransition())return!0;for(const t in this._layers)if(this._layers[t].hasTransition())return!0;return!1}_checkLoaded(){if(!this._loaded)throw new Error(\"Style is not done loading.\")}update(t){if(!this._loaded)return;const r=this._changed;if(r){const e=Object.keys(this._updatedLayers),r=Object.keys(this._removedLayers);(e.length||r.length)&&this._updateWorkerLayers(e,r);for(const t in this._updatedSources){const e=this._updatedSources[t];if(\"reload\"===e)this._reloadSource(t);else{if(\"clear\"!==e)throw new Error(`Invalid action ${e}`);this._clearSource(t)}}this._updateTilesForChangedImages(),this._updateTilesForChangedGlyphs();for(const e in this._updatedPaintProps)this._layers[e].updateTransitions(t);this.light.updateTransitions(t),this.sky.updateTransitions(t),this._resetUpdates()}const n={};for(const t in this.sourceCaches){const e=this.sourceCaches[t];n[t]=e.used,e.used=!1}for(const e of this._order){const r=this._layers[e];r.recalculate(t,this._availableImages),!r.isHidden(t.zoom)&&r.source&&(this.sourceCaches[r.source].used=!0)}for(const t in n){const r=this.sourceCaches[t];!!n[t]!=!!r.used&&r.fire(new e.k(\"data\",{sourceDataType:\"visibility\",dataType:\"source\",sourceId:t}))}this.light.recalculate(t),this.sky.recalculate(t),this.z=t.zoom,r&&this.fire(new e.k(\"data\",{dataType:\"style\"}))}_updateTilesForChangedImages(){const t=Object.keys(this._changedImages);if(t.length){for(const e in this.sourceCaches)this.sourceCaches[e].reloadTilesForDependencies([\"icons\",\"patterns\"],t);this._changedImages={}}}_updateTilesForChangedGlyphs(){if(this._glyphsDidChange){for(const t in this.sourceCaches)this.sourceCaches[t].reloadTilesForDependencies([\"glyphs\"],[\"\"]);this._glyphsDidChange=!1}}_updateWorkerLayers(t,e){this.dispatcher.broadcast(\"UL\",{layers:this._serializeByIds(t),removedIds:e})}_resetUpdates(){this._changed=!1,this._updatedLayers={},this._removedLayers={},this._updatedSources={},this._updatedPaintProps={},this._changedImages={},this._glyphsDidChange=!1}setState(t,r={}){var n;this._checkLoaded();const i=this.serialize();if(t=r.transformStyle?r.transformStyle(i,t):t,(null===(n=r.validate)||void 0===n||n)&&fe(this,e.x(t)))return!1;(t=e.aC(t)).layers=e.aA(t.layers);const a=e.aD(i,t),o=this._getOperationsToPerform(a);if(o.unimplemented.length>0)throw new Error(`Unimplemented: ${o.unimplemented.join(\", \")}.`);if(0===o.operations.length)return!1;for(const t of o.operations)t();return this.stylesheet=t,this._serializedLayers=null,!0}_getOperationsToPerform(t){const e=[],r=[];for(const n of t)switch(n.command){case\"setCenter\":case\"setZoom\":case\"setBearing\":case\"setPitch\":continue;case\"addLayer\":e.push((()=>this.addLayer.apply(this,n.args)));break;case\"removeLayer\":e.push((()=>this.removeLayer.apply(this,n.args)));break;case\"setPaintProperty\":e.push((()=>this.setPaintProperty.apply(this,n.args)));break;case\"setLayoutProperty\":e.push((()=>this.setLayoutProperty.apply(this,n.args)));break;case\"setFilter\":e.push((()=>this.setFilter.apply(this,n.args)));break;case\"addSource\":e.push((()=>this.addSource.apply(this,n.args)));break;case\"removeSource\":e.push((()=>this.removeSource.apply(this,n.args)));break;case\"setLayerZoomRange\":e.push((()=>this.setLayerZoomRange.apply(this,n.args)));break;case\"setLight\":e.push((()=>this.setLight.apply(this,n.args)));break;case\"setGeoJSONSourceData\":e.push((()=>this.setGeoJSONSourceData.apply(this,n.args)));break;case\"setGlyphs\":e.push((()=>this.setGlyphs.apply(this,n.args)));break;case\"setSprite\":e.push((()=>this.setSprite.apply(this,n.args)));break;case\"setSky\":e.push((()=>this.setSky.apply(this,n.args)));break;case\"setTerrain\":e.push((()=>this.map.setTerrain.apply(this,n.args)));break;case\"setTransition\":e.push((()=>{}));break;default:r.push(n.command)}return{operations:e,unimplemented:r}}addImage(t,r){if(this.getImage(t))return this.fire(new e.j(new Error(`An image named \"${t}\" already exists.`)));this.imageManager.addImage(t,r),this._afterImageUpdated(t)}updateImage(t,e){this.imageManager.updateImage(t,e)}getImage(t){return this.imageManager.getImage(t)}removeImage(t){if(!this.getImage(t))return this.fire(new e.j(new Error(`An image named \"${t}\" does not exist.`)));this.imageManager.removeImage(t),this._afterImageUpdated(t)}_afterImageUpdated(t){this._availableImages=this.imageManager.listImages(),this._changedImages[t]=!0,this._changed=!0,this.dispatcher.broadcast(\"SI\",this._availableImages),this.fire(new e.k(\"data\",{dataType:\"style\"}))}listImages(){return this._checkLoaded(),this.imageManager.listImages()}addSource(t,r,n={}){if(this._checkLoaded(),void 0!==this.sourceCaches[t])throw new Error(`Source \"${t}\" already exists.`);if(!r.type)throw new Error(`The type property must be defined, but only the following properties were given: ${Object.keys(r).join(\", \")}.`);if([\"vector\",\"raster\",\"geojson\",\"video\",\"image\"].indexOf(r.type)>=0&&this._validate(e.x.source,`sources.${t}`,r,null,n))return;this.map&&this.map._collectResourceTiming&&(r.collectResourceTiming=!0);const i=this.sourceCaches[t]=new dt(t,r,this.dispatcher);i.style=this,i.setEventedParent(this,(()=>({isSourceLoaded:i.loaded(),source:i.serialize(),sourceId:t}))),i.onAdd(this.map),this._changed=!0}removeSource(t){if(this._checkLoaded(),void 0===this.sourceCaches[t])throw new Error(\"There is no source with this ID\");for(const r in this._layers)if(this._layers[r].source===t)return this.fire(new e.j(new Error(`Source \"${t}\" cannot be removed while layer \"${r}\" is using it.`)));const r=this.sourceCaches[t];delete this.sourceCaches[t],delete this._updatedSources[t],r.fire(new e.k(\"data\",{sourceDataType:\"metadata\",dataType:\"source\",sourceId:t})),r.setEventedParent(null),r.onRemove(this.map),this._changed=!0}setGeoJSONSourceData(t,e){if(this._checkLoaded(),void 0===this.sourceCaches[t])throw new Error(`There is no source with this ID=${t}`);const r=this.sourceCaches[t].getSource();if(\"geojson\"!==r.type)throw new Error(`geojsonSource.type is ${r.type}, which is !== 'geojson`);r.setData(e),this._changed=!0}getSource(t){return this.sourceCaches[t]&&this.sourceCaches[t].getSource()}addLayer(t,r,n={}){this._checkLoaded();const i=t.id;if(this.getLayer(i))return void this.fire(new e.j(new Error(`Layer \"${i}\" already exists on this map.`)));let a;if(\"custom\"===t.type){if(fe(this,e.aE(t)))return;a=e.aB(t)}else{if(\"source\"in t&&\"object\"==typeof t.source&&(this.addSource(i,t.source),t=e.aC(t),t=e.e(t,{source:i})),this._validate(e.x.layer,`layers.${i}`,t,{arrayIndex:-1},n))return;a=e.aB(t),this._validateLayer(a),a.setEventedParent(this,{layer:{id:i}})}const o=r?this._order.indexOf(r):this._order.length;if(r&&-1===o)this.fire(new e.j(new Error(`Cannot add layer \"${i}\" before non-existing layer \"${r}\".`)));else{if(this._order.splice(o,0,i),this._layerOrderChanged=!0,this._layers[i]=a,this._removedLayers[i]&&a.source&&\"custom\"!==a.type){const t=this._removedLayers[i];delete this._removedLayers[i],t.type!==a.type?this._updatedSources[a.source]=\"clear\":(this._updatedSources[a.source]=\"reload\",this.sourceCaches[a.source].pause())}this._updateLayer(a),a.onAdd&&a.onAdd(this.map)}}moveLayer(t,r){if(this._checkLoaded(),this._changed=!0,!this._layers[t])return void this.fire(new e.j(new Error(`The layer '${t}' does not exist in the map's style and cannot be moved.`)));if(t===r)return;const n=this._order.indexOf(t);this._order.splice(n,1);const i=r?this._order.indexOf(r):this._order.length;r&&-1===i?this.fire(new e.j(new Error(`Cannot move layer \"${t}\" before non-existing layer \"${r}\".`))):(this._order.splice(i,0,t),this._layerOrderChanged=!0)}removeLayer(t){this._checkLoaded();const r=this._layers[t];if(!r)return void this.fire(new e.j(new Error(`Cannot remove non-existing layer \"${t}\".`)));r.setEventedParent(null);const n=this._order.indexOf(t);this._order.splice(n,1),this._layerOrderChanged=!0,this._changed=!0,this._removedLayers[t]=r,delete this._layers[t],this._serializedLayers&&delete this._serializedLayers[t],delete this._updatedLayers[t],delete this._updatedPaintProps[t],r.onRemove&&r.onRemove(this.map)}getLayer(t){return this._layers[t]}getLayersOrder(){return[...this._order]}hasLayer(t){return t in this._layers}setLayerZoomRange(t,r,n){this._checkLoaded();const i=this.getLayer(t);i?i.minzoom===r&&i.maxzoom===n||(null!=r&&(i.minzoom=r),null!=n&&(i.maxzoom=n),this._updateLayer(i)):this.fire(new e.j(new Error(`Cannot set the zoom range of non-existing layer \"${t}\".`)))}setFilter(t,r,n={}){this._checkLoaded();const i=this.getLayer(t);if(i){if(!e.aF(i.filter,r))return null==r?(i.filter=void 0,void this._updateLayer(i)):void(this._validate(e.x.filter,`layers.${i.id}.filter`,r,null,n)||(i.filter=e.aC(r),this._updateLayer(i)))}else this.fire(new e.j(new Error(`Cannot filter non-existing layer \"${t}\".`)))}getFilter(t){return e.aC(this.getLayer(t).filter)}setLayoutProperty(t,r,n,i={}){this._checkLoaded();const a=this.getLayer(t);a?e.aF(a.getLayoutProperty(r),n)||(a.setLayoutProperty(r,n,i),this._updateLayer(a)):this.fire(new e.j(new Error(`Cannot style non-existing layer \"${t}\".`)))}getLayoutProperty(t,r){const n=this.getLayer(t);if(n)return n.getLayoutProperty(r);this.fire(new e.j(new Error(`Cannot get style of non-existing layer \"${t}\".`)))}setPaintProperty(t,r,n,i={}){this._checkLoaded();const a=this.getLayer(t);a?e.aF(a.getPaintProperty(r),n)||(a.setPaintProperty(r,n,i)&&this._updateLayer(a),this._changed=!0,this._updatedPaintProps[t]=!0,this._serializedLayers=null):this.fire(new e.j(new Error(`Cannot style non-existing layer \"${t}\".`)))}getPaintProperty(t,e){return this.getLayer(t).getPaintProperty(e)}setFeatureState(t,r){this._checkLoaded();const n=t.source,i=t.sourceLayer,a=this.sourceCaches[n];if(void 0===a)return void this.fire(new e.j(new Error(`The source '${n}' does not exist in the map's style.`)));const o=a.getSource().type;\"geojson\"===o&&i?this.fire(new e.j(new Error(\"GeoJSON sources cannot have a sourceLayer parameter.\"))):\"vector\"!==o||i?(void 0===t.id&&this.fire(new e.j(new Error(\"The feature id parameter must be provided.\"))),a.setFeatureState(i,t.id,r)):this.fire(new e.j(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}removeFeatureState(t,r){this._checkLoaded();const n=t.source,i=this.sourceCaches[n];if(void 0===i)return void this.fire(new e.j(new Error(`The source '${n}' does not exist in the map's style.`)));const a=i.getSource().type,o=\"vector\"===a?t.sourceLayer:void 0;\"vector\"!==a||o?r&&\"string\"!=typeof t.id&&\"number\"!=typeof t.id?this.fire(new e.j(new Error(\"A feature id is required to remove its specific state property.\"))):i.removeFeatureState(o,t.id,r):this.fire(new e.j(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}getFeatureState(t){this._checkLoaded();const r=t.source,n=t.sourceLayer,i=this.sourceCaches[r];if(void 0!==i)return\"vector\"!==i.getSource().type||n?(void 0===t.id&&this.fire(new e.j(new Error(\"The feature id parameter must be provided.\"))),i.getFeatureState(n,t.id)):void this.fire(new e.j(new Error(\"The sourceLayer parameter must be provided for vector source types.\")));this.fire(new e.j(new Error(`The source '${r}' does not exist in the map's style.`)))}getTransition(){return e.e({duration:300,delay:0},this.stylesheet&&this.stylesheet.transition)}serialize(){if(!this._loaded)return;const t=e.aG(this.sourceCaches,(t=>t.serialize())),r=this._serializeByIds(this._order),n=this.map.getTerrain()||void 0,i=this.stylesheet;return e.aH({version:i.version,name:i.name,metadata:i.metadata,light:i.light,sky:i.sky,center:i.center,zoom:i.zoom,bearing:i.bearing,pitch:i.pitch,sprite:i.sprite,glyphs:i.glyphs,transition:i.transition,sources:t,layers:r,terrain:n},(t=>void 0!==t))}_updateLayer(t){this._updatedLayers[t.id]=!0,t.source&&!this._updatedSources[t.source]&&\"raster\"!==this.sourceCaches[t.source].getSource().type&&(this._updatedSources[t.source]=\"reload\",this.sourceCaches[t.source].pause()),this._serializedLayers=null,this._changed=!0}_flattenAndSortRenderedFeatures(t){const e=t=>\"fill-extrusion\"===this._layers[t].type,r={},n=[];for(let i=this._order.length-1;i>=0;i--){const a=this._order[i];if(e(a)){r[a]=i;for(const e of t){const t=e[a];if(t)for(const e of t)n.push(e)}}}n.sort(((t,e)=>e.intersectionZ-t.intersectionZ));const i=[];for(let a=this._order.length-1;a>=0;a--){const o=this._order[a];if(e(o))for(let t=n.length-1;t>=0;t--){const e=n[t].feature;if(r[e.layer.id]{const n=r.featureSortOrder;if(n){const r=n.indexOf(t.featureIndex);return n.indexOf(e.featureIndex)-r}return e.featureIndex-t.featureIndex}));for(const t of i)e.push(t)}}for(const e in s)s[e].forEach((n=>{const i=n.feature,a=t[e],o=r[a.source].getFeatureState(i.layer[\"source-layer\"],i.id);i.source=i.layer.source,i.layer[\"source-layer\"]&&(i.sourceLayer=i.layer[\"source-layer\"]),i.state=o}));return s}(this._layers,o,this.sourceCaches,t,r,this.placement.collisionIndex,this.placement.retainedQueryData)),this._flattenAndSortRenderedFeatures(a)}querySourceFeatures(t,r){r&&r.filter&&this._validate(e.x.filter,\"querySourceFeatures.filter\",r.filter,null,r);const n=this.sourceCaches[t];return n?function(t,e){const r=t.getRenderableIds().map((e=>t.getTileByID(e))),n=[],i={};for(let t=0;tt.getTileByID(e))).sort(((t,e)=>e.tileID.overscaledZ-t.tileID.overscaledZ||(t.tileID.isLessThan(e.tileID)?-1:1)))}const n=this.crossTileSymbolIndex.addLayer(r,l[r.source],t.center.lng);o=o||n}if(this.crossTileSymbolIndex.pruneUnusedLayers(this._order),((i=i||this._layerOrderChanged||0===r)||!this.pauseablePlacement||this.pauseablePlacement.isDone()&&!this.placement.stillRecent(a.now(),t.zoom))&&(this.pauseablePlacement=new oe(t,this.map.terrain,this._order,i,e,r,n,this.placement),this._layerOrderChanged=!1),this.pauseablePlacement.isDone()?this.placement.setStale():(this.pauseablePlacement.continuePlacement(this._order,this._layers,l),this.pauseablePlacement.isDone()&&(this.placement=this.pauseablePlacement.commit(a.now()),s=!0),o&&this.pauseablePlacement.placement.setStale()),s||o)for(const t of this._order){const e=this._layers[t];\"symbol\"===e.type&&this.placement.updateLayerOpacities(e,l[e.source])}return!this.pauseablePlacement.isDone()||this.placement.hasTransitions(a.now())}_releaseSymbolFadeTiles(){for(const t in this.sourceCaches)this.sourceCaches[t].releaseSymbolFadeTiles()}getImages(t,r){return e._(this,void 0,void 0,(function*(){const t=yield this.imageManager.getImages(r.icons);this._updateTilesForChangedImages();const e=this.sourceCaches[r.source];return e&&e.setDependencies(r.tileID.key,r.type,r.icons),t}))}getGlyphs(t,r){return e._(this,void 0,void 0,(function*(){const t=yield this.glyphManager.getGlyphs(r.stacks),e=this.sourceCaches[r.source];return e&&e.setDependencies(r.tileID.key,r.type,[\"\"]),t}))}getGlyphsUrl(){return this.stylesheet.glyphs||null}setGlyphs(t,r={}){this._checkLoaded(),t&&this._validate(e.x.glyphs,\"glyphs\",t,null,r)||(this._glyphsDidChange=!0,this.stylesheet.glyphs=t,this.glyphManager.entries={},this.glyphManager.setURL(t))}addSprite(t,r,n={},i){this._checkLoaded();const a=[{id:t,url:r}],o=[...x(this.stylesheet.sprite),...a];this._validate(e.x.sprite,\"sprite\",o,null,n)||(this.stylesheet.sprite=o,this._loadSprite(a,!0,i))}removeSprite(t){this._checkLoaded();const r=x(this.stylesheet.sprite);if(r.find((e=>e.id===t))){if(this._spritesImagesIds[t])for(const e of this._spritesImagesIds[t])this.imageManager.removeImage(e),this._changedImages[e]=!0;r.splice(r.findIndex((e=>e.id===t)),1),this.stylesheet.sprite=r.length>0?r:void 0,delete this._spritesImagesIds[t],this._availableImages=this.imageManager.listImages(),this._changed=!0,this.dispatcher.broadcast(\"SI\",this._availableImages),this.fire(new e.k(\"data\",{dataType:\"style\"}))}else this.fire(new e.j(new Error(`Sprite \"${t}\" doesn't exists on this map.`)))}getSprite(){return x(this.stylesheet.sprite)}setSprite(t,r={},n){this._checkLoaded(),t&&this._validate(e.x.sprite,\"sprite\",t,null,r)||(this.stylesheet.sprite=t,t?this._loadSprite(t,!0,n):(this._unloadSprite(),n&&n(null)))}}var me=e.Y([{name:\"a_pos\",type:\"Int16\",components:2}]);const ge={prelude:ye(\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n#if !defined(lowp)\\n#define lowp\\n#endif\\n#if !defined(mediump)\\n#define mediump\\n#endif\\n#if !defined(highp)\\n#define highp\\n#endif\\n#endif\\n\",\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#if !defined(lowp)\\n#define lowp\\n#endif\\n#if !defined(mediump)\\n#define mediump\\n#endif\\n#if !defined(highp)\\n#define highp\\n#endif\\n#endif\\nvec2 unpack_float(const float packedValue) {int packedIntValue=int(packedValue);int v0=packedIntValue/256;return vec2(v0,packedIntValue-v0*256);}vec2 unpack_opacity(const float packedOpacity) {int intOpacity=int(packedOpacity)/2;return vec2(float(intOpacity)/127.0,mod(packedOpacity,2.0));}vec4 decode_color(const vec2 encodedColor) {return vec4(unpack_float(encodedColor[0])/255.0,unpack_float(encodedColor[1])/255.0\\n);}float unpack_mix_vec2(const vec2 packedValue,const float t) {return mix(packedValue[0],packedValue[1],t);}vec4 unpack_mix_color(const vec4 packedColors,const float t) {vec4 minColor=decode_color(vec2(packedColors[0],packedColors[1]));vec4 maxColor=decode_color(vec2(packedColors[2],packedColors[3]));return mix(minColor,maxColor,t);}vec2 get_pattern_pos(const vec2 pixel_coord_upper,const vec2 pixel_coord_lower,const vec2 pattern_size,const float tile_units_to_pixels,const vec2 pos) {vec2 offset=mod(mod(mod(pixel_coord_upper,pattern_size)*256.0,pattern_size)*256.0+pixel_coord_lower,pattern_size);return (tile_units_to_pixels*pos+offset)/pattern_size;}\\n#ifdef TERRAIN3D\\nuniform sampler2D u_terrain;uniform float u_terrain_dim;uniform mat4 u_terrain_matrix;uniform vec4 u_terrain_unpack;uniform float u_terrain_exaggeration;uniform highp sampler2D u_depth;\\n#endif\\nconst highp vec4 bitSh=vec4(256.*256.*256.,256.*256.,256.,1.);const highp vec4 bitShifts=vec4(1.)/bitSh;highp float unpack(highp vec4 color) {return dot(color,bitShifts);}highp float depthOpacity(vec3 frag) {\\n#ifdef TERRAIN3D\\nhighp float d=unpack(texture2D(u_depth,frag.xy*0.5+0.5))+0.0001-frag.z;return 1.0-max(0.0,min(1.0,-d*500.0));\\n#else\\nreturn 1.0;\\n#endif\\n}float calculate_visibility(vec4 pos) {\\n#ifdef TERRAIN3D\\nvec3 frag=pos.xyz/pos.w;highp float d=depthOpacity(frag);if (d > 0.95) return 1.0;return (d+depthOpacity(frag+vec3(0.0,0.01,0.0)))/2.0;\\n#else\\nreturn 1.0;\\n#endif\\n}float ele(vec2 pos) {\\n#ifdef TERRAIN3D\\nvec4 rgb=(texture2D(u_terrain,pos)*255.0)*u_terrain_unpack;return rgb.r+rgb.g+rgb.b-u_terrain_unpack.a;\\n#else\\nreturn 0.0;\\n#endif\\n}float get_elevation(vec2 pos) {\\n#ifdef TERRAIN3D\\nvec2 coord=(u_terrain_matrix*vec4(pos,0.0,1.0)).xy*u_terrain_dim+1.0;vec2 f=fract(coord);vec2 c=(floor(coord)+0.5)/(u_terrain_dim+2.0);float d=1.0/(u_terrain_dim+2.0);float tl=ele(c);float tr=ele(c+vec2(d,0.0));float bl=ele(c+vec2(0.0,d));float br=ele(c+vec2(d,d));float elevation=mix(mix(tl,tr,f.x),mix(bl,br,f.x),f.y);return elevation*u_terrain_exaggeration;\\n#else\\nreturn 0.0;\\n#endif\\n}\"),background:ye(\"uniform vec4 u_color;uniform float u_opacity;void main() {gl_FragColor=u_color*u_opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}\"),backgroundPattern:ye(\"uniform vec2 u_pattern_tl_a;uniform vec2 u_pattern_br_a;uniform vec2 u_pattern_tl_b;uniform vec2 u_pattern_br_b;uniform vec2 u_texsize;uniform float u_mix;uniform float u_opacity;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(u_pattern_tl_a/u_texsize,u_pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(u_pattern_tl_b/u_texsize,u_pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_mix)*u_opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pattern_size_a;uniform vec2 u_pattern_size_b;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_scale_a;uniform float u_scale_b;uniform float u_tile_units_to_pixels;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_a*u_pattern_size_a,u_tile_units_to_pixels,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_b*u_pattern_size_b,u_tile_units_to_pixels,a_pos);}\"),circle:ye(\"varying vec3 v_data;varying float v_visibility;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define mediump float radius\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define highp vec4 stroke_color\\n#pragma mapbox: define mediump float stroke_width\\n#pragma mapbox: define lowp float stroke_opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize mediump float radius\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize highp vec4 stroke_color\\n#pragma mapbox: initialize mediump float stroke_width\\n#pragma mapbox: initialize lowp float stroke_opacity\\nvec2 extrude=v_data.xy;float extrude_length=length(extrude);lowp float antialiasblur=v_data.z;float antialiased_blur=-max(blur,antialiasblur);float opacity_t=smoothstep(0.0,antialiased_blur,extrude_length-1.0);float color_t=stroke_width < 0.01 ? 0.0 : smoothstep(antialiased_blur,0.0,extrude_length-radius/(radius+stroke_width));gl_FragColor=v_visibility*opacity_t*mix(color*opacity,stroke_color*stroke_opacity,color_t);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform bool u_scale_with_map;uniform bool u_pitch_with_map;uniform vec2 u_extrude_scale;uniform lowp float u_device_pixel_ratio;uniform highp float u_camera_to_center_distance;attribute vec2 a_pos;varying vec3 v_data;varying float v_visibility;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define mediump float radius\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define highp vec4 stroke_color\\n#pragma mapbox: define mediump float stroke_width\\n#pragma mapbox: define lowp float stroke_opacity\\nvoid main(void) {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize mediump float radius\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize highp vec4 stroke_color\\n#pragma mapbox: initialize mediump float stroke_width\\n#pragma mapbox: initialize lowp float stroke_opacity\\nvec2 extrude=vec2(mod(a_pos,2.0)*2.0-1.0);vec2 circle_center=floor(a_pos*0.5);float ele=get_elevation(circle_center);v_visibility=calculate_visibility(u_matrix*vec4(circle_center,ele,1.0));if (u_pitch_with_map) {vec2 corner_position=circle_center;if (u_scale_with_map) {corner_position+=extrude*(radius+stroke_width)*u_extrude_scale;} else {vec4 projected_center=u_matrix*vec4(circle_center,0,1);corner_position+=extrude*(radius+stroke_width)*u_extrude_scale*(projected_center.w/u_camera_to_center_distance);}gl_Position=u_matrix*vec4(corner_position,ele,1);} else {gl_Position=u_matrix*vec4(circle_center,ele,1);if (u_scale_with_map) {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*u_camera_to_center_distance;} else {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*gl_Position.w;}}lowp float antialiasblur=1.0/u_device_pixel_ratio/(radius+stroke_width);v_data=vec3(extrude.x,extrude.y,antialiasblur);}\"),clippingMask:ye(\"void main() {gl_FragColor=vec4(1.0);}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}\"),heatmap:ye(\"uniform highp float u_intensity;varying vec2 v_extrude;\\n#pragma mapbox: define highp float weight\\n#define GAUSS_COEF 0.3989422804014327\\nvoid main() {\\n#pragma mapbox: initialize highp float weight\\nfloat d=-0.5*3.0*3.0*dot(v_extrude,v_extrude);float val=weight*u_intensity*GAUSS_COEF*exp(d);gl_FragColor=vec4(val,1.0,1.0,1.0);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform float u_extrude_scale;uniform float u_opacity;uniform float u_intensity;attribute vec2 a_pos;varying vec2 v_extrude;\\n#pragma mapbox: define highp float weight\\n#pragma mapbox: define mediump float radius\\nconst highp float ZERO=1.0/255.0/16.0;\\n#define GAUSS_COEF 0.3989422804014327\\nvoid main(void) {\\n#pragma mapbox: initialize highp float weight\\n#pragma mapbox: initialize mediump float radius\\nvec2 unscaled_extrude=vec2(mod(a_pos,2.0)*2.0-1.0);float S=sqrt(-2.0*log(ZERO/weight/u_intensity/GAUSS_COEF))/3.0;v_extrude=S*unscaled_extrude;vec2 extrude=v_extrude*radius*u_extrude_scale;vec4 pos=vec4(floor(a_pos*0.5)+extrude,0,1);gl_Position=u_matrix*pos;}\"),heatmapTexture:ye(\"uniform sampler2D u_image;uniform sampler2D u_color_ramp;uniform float u_opacity;varying vec2 v_pos;void main() {float t=texture2D(u_image,v_pos).r;vec4 color=texture2D(u_color_ramp,vec2(t,0.5));gl_FragColor=color*u_opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(0.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_world;attribute vec2 a_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos*u_world,0,1);v_pos.x=a_pos.x;v_pos.y=1.0-a_pos.y;}\"),collisionBox:ye(\"varying float v_placed;varying float v_notUsed;void main() {float alpha=0.5;gl_FragColor=vec4(1.0,0.0,0.0,1.0)*alpha;if (v_placed > 0.5) {gl_FragColor=vec4(0.0,0.0,1.0,0.5)*alpha;}if (v_notUsed > 0.5) {gl_FragColor*=.1;}}\",\"attribute vec2 a_anchor_pos;attribute vec2 a_placed;attribute vec2 a_box_real;uniform mat4 u_matrix;uniform vec2 u_pixel_extrude_scale;varying float v_placed;varying float v_notUsed;vec4 projectTileWithElevation(vec2 posInTile,float elevation) {return u_matrix*vec4(posInTile,elevation,1.0);}void main() {gl_Position=projectTileWithElevation(a_anchor_pos,get_elevation(a_anchor_pos));gl_Position.xy=((a_box_real+0.5)*u_pixel_extrude_scale*2.0-1.0)*vec2(1.0,-1.0)*gl_Position.w;if (gl_Position.z/gl_Position.w < 1.1) {gl_Position.z=0.5;}v_placed=a_placed.x;v_notUsed=a_placed.y;}\"),collisionCircle:ye(\"varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;void main() {float alpha=0.5*min(v_perspective_ratio,1.0);float stroke_radius=0.9*max(v_perspective_ratio,1.0);float distance_to_center=length(v_extrude);float distance_to_edge=abs(distance_to_center-v_radius);float opacity_t=smoothstep(-stroke_radius,0.0,-distance_to_edge);vec4 color=mix(vec4(0.0,0.0,1.0,0.5),vec4(1.0,0.0,0.0,1.0),v_collision);gl_FragColor=color*alpha*opacity_t;}\",\"attribute vec2 a_pos;attribute float a_radius;attribute vec2 a_flags;uniform mat4 u_matrix;uniform mat4 u_inv_matrix;uniform vec2 u_viewport_size;uniform float u_camera_to_center_distance;varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;vec3 toTilePosition(vec2 screenPos) {vec4 rayStart=u_inv_matrix*vec4(screenPos,-1.0,1.0);vec4 rayEnd =u_inv_matrix*vec4(screenPos, 1.0,1.0);rayStart.xyz/=rayStart.w;rayEnd.xyz /=rayEnd.w;highp float t=(0.0-rayStart.z)/(rayEnd.z-rayStart.z);return mix(rayStart.xyz,rayEnd.xyz,t);}void main() {vec2 quadCenterPos=a_pos;float radius=a_radius;float collision=a_flags.x;float vertexIdx=a_flags.y;vec2 quadVertexOffset=vec2(mix(-1.0,1.0,float(vertexIdx >=2.0)),mix(-1.0,1.0,float(vertexIdx >=1.0 && vertexIdx <=2.0)));vec2 quadVertexExtent=quadVertexOffset*radius;vec3 tilePos=toTilePosition(quadCenterPos);vec4 clipPos=u_matrix*vec4(tilePos,1.0);highp float camera_to_anchor_distance=clipPos.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);float padding_factor=1.2;v_radius=radius;v_extrude=quadVertexExtent*padding_factor;v_perspective_ratio=collision_perspective_ratio;v_collision=collision;gl_Position=vec4(clipPos.xyz/clipPos.w,1.0)+vec4(quadVertexExtent*padding_factor/u_viewport_size*2.0,0.0,0.0);}\"),debug:ye(\"uniform highp vec4 u_color;uniform sampler2D u_overlay;varying vec2 v_uv;void main() {vec4 overlay_color=texture2D(u_overlay,v_uv);gl_FragColor=mix(u_color,overlay_color,overlay_color.a);}\",\"attribute vec2 a_pos;varying vec2 v_uv;uniform mat4 u_matrix;uniform float u_overlay_scale;void main() {v_uv=a_pos/8192.0;gl_Position=u_matrix*vec4(a_pos*u_overlay_scale,get_elevation(a_pos),1);}\"),fill:ye(\"#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float opacity\\ngl_FragColor=color*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float opacity\\ngl_Position=u_matrix*vec4(a_pos,0,1);}\"),fillOutline:ye(\"varying vec2 v_pos;\\n#pragma mapbox: define highp vec4 outline_color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 outline_color\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=outline_color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;uniform vec2 u_world;varying vec2 v_pos;\\n#pragma mapbox: define highp vec4 outline_color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 outline_color\\n#pragma mapbox: initialize lowp float opacity\\ngl_Position=u_matrix*vec4(a_pos,0,1);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}\"),fillOutlinePattern:ye(\"uniform vec2 u_texsize;uniform sampler2D u_image;uniform float u_fade;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);float dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=mix(color1,color2,u_fade)*alpha*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_world;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;gl_Position=u_matrix*vec4(a_pos,0,1);vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,a_pos);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}\"),fillPattern:ye(\"#ifdef GL_ES\\nprecision highp float;\\n#endif\\nuniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_fade)*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileZoomRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileZoomRatio,a_pos);}\"),fillExtrusion:ye(\"varying vec4 v_color;void main() {gl_FragColor=v_color;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;uniform float u_vertical_gradient;uniform lowp float u_opacity;attribute vec2 a_pos;attribute vec4 a_normal_ed;\\n#ifdef TERRAIN3D\\nattribute vec2 a_centroid;\\n#endif\\nvarying vec4 v_color;\\n#pragma mapbox: define highp float base\\n#pragma mapbox: define highp float height\\n#pragma mapbox: define highp vec4 color\\nvoid main() {\\n#pragma mapbox: initialize highp float base\\n#pragma mapbox: initialize highp float height\\n#pragma mapbox: initialize highp vec4 color\\nvec3 normal=a_normal_ed.xyz;\\n#ifdef TERRAIN3D\\nfloat height_terrain3d_offset=get_elevation(a_centroid);float base_terrain3d_offset=height_terrain3d_offset-(base > 0.0 ? 0.0 : 10.0);\\n#else\\nfloat height_terrain3d_offset=0.0;float base_terrain3d_offset=0.0;\\n#endif\\nbase=max(0.0,base)+base_terrain3d_offset;height=max(0.0,height)+height_terrain3d_offset;float t=mod(normal.x,2.0);gl_Position=u_matrix*vec4(a_pos,t > 0.0 ? height : base,1);float colorvalue=color.r*0.2126+color.g*0.7152+color.b*0.0722;v_color=vec4(0.0,0.0,0.0,1.0);vec4 ambientlight=vec4(0.03,0.03,0.03,1.0);color+=ambientlight;float directional=clamp(dot(normal/16384.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((1.0-colorvalue+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_color.r+=clamp(color.r*directional*u_lightcolor.r,mix(0.0,0.3,1.0-u_lightcolor.r),1.0);v_color.g+=clamp(color.g*directional*u_lightcolor.g,mix(0.0,0.3,1.0-u_lightcolor.g),1.0);v_color.b+=clamp(color.b*directional*u_lightcolor.b,mix(0.0,0.3,1.0-u_lightcolor.b),1.0);v_color*=u_opacity;}\"),fillExtrusionPattern:ye(\"uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\\n#pragma mapbox: define lowp float base\\n#pragma mapbox: define lowp float height\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float base\\n#pragma mapbox: initialize lowp float height\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);vec4 mixedColor=mix(color1,color2,u_fade);gl_FragColor=mixedColor*v_lighting;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_height_factor;uniform vec3 u_scale;uniform float u_vertical_gradient;uniform lowp float u_opacity;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;attribute vec2 a_pos;attribute vec4 a_normal_ed;\\n#ifdef TERRAIN3D\\nattribute vec2 a_centroid;\\n#endif\\nvarying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\\n#pragma mapbox: define lowp float base\\n#pragma mapbox: define lowp float height\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float base\\n#pragma mapbox: initialize lowp float height\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec3 normal=a_normal_ed.xyz;float edgedistance=a_normal_ed.w;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;\\n#ifdef TERRAIN3D\\nfloat height_terrain3d_offset=get_elevation(a_centroid);float base_terrain3d_offset=height_terrain3d_offset-(base > 0.0 ? 0.0 : 10.0);\\n#else\\nfloat height_terrain3d_offset=0.0;float base_terrain3d_offset=0.0;\\n#endif\\nbase=max(0.0,base)+base_terrain3d_offset;height=max(0.0,height)+height_terrain3d_offset;float t=mod(normal.x,2.0);float z=t > 0.0 ? height : base;gl_Position=u_matrix*vec4(a_pos,z,1);vec2 pos=normal.x==1.0 && normal.y==0.0 && normal.z==16384.0\\n? a_pos\\n: vec2(edgedistance,z*u_height_factor);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,pos);v_lighting=vec4(0.0,0.0,0.0,1.0);float directional=clamp(dot(normal/16383.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((0.5+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_lighting.rgb+=clamp(directional*u_lightcolor,mix(vec3(0.0),vec3(0.3),1.0-u_lightcolor),vec3(1.0));v_lighting*=u_opacity;}\"),hillshadePrepare:ye(\"#ifdef GL_ES\\nprecision highp float;\\n#endif\\nuniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_dimension;uniform float u_zoom;uniform vec4 u_unpack;float getElevation(vec2 coord,float bias) {vec4 data=texture2D(u_image,coord)*255.0;data.a=-1.0;return dot(data,u_unpack)/4.0;}void main() {vec2 epsilon=1.0/u_dimension;float a=getElevation(v_pos+vec2(-epsilon.x,-epsilon.y),0.0);float b=getElevation(v_pos+vec2(0,-epsilon.y),0.0);float c=getElevation(v_pos+vec2(epsilon.x,-epsilon.y),0.0);float d=getElevation(v_pos+vec2(-epsilon.x,0),0.0);float e=getElevation(v_pos,0.0);float f=getElevation(v_pos+vec2(epsilon.x,0),0.0);float g=getElevation(v_pos+vec2(-epsilon.x,epsilon.y),0.0);float h=getElevation(v_pos+vec2(0,epsilon.y),0.0);float i=getElevation(v_pos+vec2(epsilon.x,epsilon.y),0.0);float exaggerationFactor=u_zoom < 2.0 ? 0.4 : u_zoom < 4.5 ? 0.35 : 0.3;float exaggeration=u_zoom < 15.0 ? (u_zoom-15.0)*exaggerationFactor : 0.0;vec2 deriv=vec2((c+f+f+i)-(a+d+d+g),(g+h+h+i)-(a+b+b+c))/pow(2.0,exaggeration+(19.2562-u_zoom));gl_FragColor=clamp(vec4(deriv.x/2.0+0.5,deriv.y/2.0+0.5,1.0,1.0),0.0,1.0);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_dimension;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);highp vec2 epsilon=1.0/u_dimension;float scale=(u_dimension.x-2.0)/u_dimension.x;v_pos=(a_texture_pos/8192.0)*scale+epsilon;}\"),hillshade:ye(\"uniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_latrange;uniform vec2 u_light;uniform vec4 u_shadow;uniform vec4 u_highlight;uniform vec4 u_accent;\\n#define PI 3.141592653589793\\nvoid main() {vec4 pixel=texture2D(u_image,v_pos);vec2 deriv=((pixel.rg*2.0)-1.0);float scaleFactor=cos(radians((u_latrange[0]-u_latrange[1])*(1.0-v_pos.y)+u_latrange[1]));float slope=atan(1.25*length(deriv)/scaleFactor);float aspect=deriv.x !=0.0 ? atan(deriv.y,-deriv.x) : PI/2.0*(deriv.y > 0.0 ? 1.0 :-1.0);float intensity=u_light.x;float azimuth=u_light.y+PI;float base=1.875-intensity*1.75;float maxValue=0.5*PI;float scaledSlope=intensity !=0.5 ? ((pow(base,slope)-1.0)/(pow(base,maxValue)-1.0))*maxValue : slope;float accent=cos(scaledSlope);vec4 accent_color=(1.0-accent)*u_accent*clamp(intensity*2.0,0.0,1.0);float shade=abs(mod((aspect+azimuth)/PI+0.5,2.0)-1.0);vec4 shade_color=mix(u_shadow,u_highlight,shade)*sin(scaledSlope)*clamp(intensity*2.0,0.0,1.0);gl_FragColor=accent_color*(1.0-shade_color.a)+shade_color;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos=a_texture_pos/8192.0;}\"),line:ye(\"uniform lowp float u_device_pixel_ratio;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform vec2 u_units_to_pixels;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_linesofar;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;\\n#ifdef TERRAIN3D\\nv_gamma_scale=1.0;\\n#else\\nfloat extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;\\n#endif\\nv_width2=vec2(outset,inset);}\"),lineGradient:ye(\"uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;varying highp vec2 v_uv;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);vec4 color=texture2D(u_image,v_uv);gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\nattribute vec2 a_pos_normal;attribute vec4 a_data;attribute float a_uv_x;attribute float a_split_index;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_units_to_pixels;uniform float u_image_height;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp vec2 v_uv;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;highp float texel_height=1.0/u_image_height;highp float half_texel_height=0.5*texel_height;v_uv=vec2(a_uv_x,a_split_index*texel_height-half_texel_height);vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;\\n#ifdef TERRAIN3D\\nv_gamma_scale=1.0;\\n#else\\nfloat extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;\\n#endif\\nv_width2=vec2(outset,inset);}\"),linePattern:ye(\"#ifdef GL_ES\\nprecision highp float;\\n#endif\\nuniform lowp float u_device_pixel_ratio;uniform vec2 u_texsize;uniform float u_fade;uniform mediump vec3 u_scale;uniform sampler2D u_image;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;vec2 pattern_size_a=vec2(display_size_a.x*fromScale/tileZoomRatio,display_size_a.y);vec2 pattern_size_b=vec2(display_size_b.x*toScale/tileZoomRatio,display_size_b.y);float aspect_a=display_size_a.y/v_width;float aspect_b=display_size_b.y/v_width;float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float x_a=mod(v_linesofar/pattern_size_a.x*aspect_a,1.0);float x_b=mod(v_linesofar/pattern_size_b.x*aspect_b,1.0);float y=0.5*v_normal.y+0.5;vec2 texel_size=1.0/u_texsize;vec2 pos_a=mix(pattern_tl_a*texel_size-texel_size,pattern_br_a*texel_size+texel_size,vec2(x_a,y));vec2 pos_b=mix(pattern_tl_b*texel_size-texel_size,pattern_br_b*texel_size+texel_size,vec2(x_b,y));vec4 color=mix(texture2D(u_image,pos_a),texture2D(u_image,pos_b),u_fade);gl_FragColor=color*alpha*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\n#define LINE_DISTANCE_SCALE 2.0\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform vec2 u_units_to_pixels;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;\\n#ifdef TERRAIN3D\\nv_gamma_scale=1.0;\\n#else\\nfloat extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;\\n#endif\\nv_linesofar=a_linesofar;v_width2=vec2(outset,inset);v_width=floorwidth;}\"),lineSDF:ye(\"uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;uniform float u_sdfgamma;uniform float u_mix;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float sdfdist_a=texture2D(u_image,v_tex_a).a;float sdfdist_b=texture2D(u_image,v_tex_b).a;float sdfdist=mix(sdfdist_a,sdfdist_b,u_mix);alpha*=smoothstep(0.5-u_sdfgamma/floorwidth,0.5+u_sdfgamma/floorwidth,sdfdist);gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\n#define LINE_DISTANCE_SCALE 2.0\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_patternscale_a;uniform float u_tex_y_a;uniform vec2 u_patternscale_b;uniform float u_tex_y_b;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;\\n#ifdef TERRAIN3D\\nv_gamma_scale=1.0;\\n#else\\nfloat extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;\\n#endif\\nv_tex_a=vec2(a_linesofar*u_patternscale_a.x/floorwidth,normal.y*u_patternscale_a.y+u_tex_y_a);v_tex_b=vec2(a_linesofar*u_patternscale_b.x/floorwidth,normal.y*u_patternscale_b.y+u_tex_y_b);v_width2=vec2(outset,inset);}\"),raster:ye(\"uniform float u_fade_t;uniform float u_opacity;uniform sampler2D u_image0;uniform sampler2D u_image1;varying vec2 v_pos0;varying vec2 v_pos1;uniform float u_brightness_low;uniform float u_brightness_high;uniform float u_saturation_factor;uniform float u_contrast_factor;uniform vec3 u_spin_weights;void main() {vec4 color0=texture2D(u_image0,v_pos0);vec4 color1=texture2D(u_image1,v_pos1);if (color0.a > 0.0) {color0.rgb=color0.rgb/color0.a;}if (color1.a > 0.0) {color1.rgb=color1.rgb/color1.a;}vec4 color=mix(color0,color1,u_fade_t);color.a*=u_opacity;vec3 rgb=color.rgb;rgb=vec3(dot(rgb,u_spin_weights.xyz),dot(rgb,u_spin_weights.zxy),dot(rgb,u_spin_weights.yzx));float average=(color.r+color.g+color.b)/3.0;rgb+=(average-rgb)*u_saturation_factor;rgb=(rgb-0.5)*u_contrast_factor+0.5;vec3 u_high_vec=vec3(u_brightness_low,u_brightness_low,u_brightness_low);vec3 u_low_vec=vec3(u_brightness_high,u_brightness_high,u_brightness_high);gl_FragColor=vec4(mix(u_high_vec,u_low_vec,rgb)*color.a,color.a);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_tl_parent;uniform float u_scale_parent;uniform float u_buffer_scale;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos0;varying vec2 v_pos1;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos0=(((a_texture_pos/8192.0)-0.5)/u_buffer_scale )+0.5;v_pos1=(v_pos0*u_scale_parent)+u_tl_parent;}\"),symbolIcon:ye(\"uniform sampler2D u_texture;varying vec2 v_tex;varying float v_fade_opacity;\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\nlowp float alpha=opacity*v_fade_opacity;gl_FragColor=texture2D(u_texture,v_tex)*alpha;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform highp float u_camera_to_center_distance;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform float u_fade_change;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform vec2 u_texsize;uniform bool u_is_along_line;uniform bool u_is_variable_anchor;uniform vec2 u_translation;uniform float u_pitched_scale;varying vec2 v_tex;varying float v_fade_opacity;vec4 projectTileWithElevation(vec2 posInTile,float elevation) {return u_matrix*vec4(posInTile,elevation,1.0);}\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;vec2 a_minFontScale=a_pixeloffset.zw/256.0;float ele=get_elevation(a_pos);highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec2 translated_a_pos=a_pos+u_translation;vec4 projectedPoint=projectTileWithElevation(translated_a_pos,ele);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=projectTileWithElevation(translated_a_pos+vec2(1,0),ele);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos;if (u_is_along_line || u_is_variable_anchor) {projected_pos=vec4(a_projected_pos.xy,ele,1.0);} else if (u_pitch_with_map) {projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy+u_translation,ele,1.0);} else {projected_pos=u_label_plane_matrix*projectTileWithElevation(a_projected_pos.xy+u_translation,ele);}float z=float(u_pitch_with_map)*projected_pos.z/projected_pos.w;float projectionScaling=1.0;vec4 finalPos=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*max(a_minFontScale,fontScale)+a_pxoffset/16.0)*projectionScaling,z,1.0);if(u_pitch_with_map) {finalPos=projectTileWithElevation(finalPos.xy,finalPos.z);}gl_Position=finalPos;v_tex=a_tex/u_texsize;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float visibility=calculate_visibility(projectedPoint);v_fade_opacity=max(0.0,min(visibility,fade_opacity[0]+fade_change));}\"),symbolSDF:ye(\"#define SDF_PX 8.0\\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;uniform bool u_is_text;varying vec2 v_data0;varying vec3 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nfloat EDGE_GAMMA=0.105/u_device_pixel_ratio;vec2 tex=v_data0.xy;float gamma_scale=v_data1.x;float size=v_data1.y;float fade_opacity=v_data1[2];float fontScale=u_is_text ? size/24.0 : size;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float inner_edge=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);inner_edge=inner_edge+gamma*gamma_scale;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(inner_edge-gamma_scaled,inner_edge+gamma_scaled,dist);if (u_is_halo) {lowp float halo_edge=(6.0-halo_width/fontScale)/SDF_PX;alpha=min(smoothstep(halo_edge-gamma_scaled,halo_edge+gamma_scaled,dist),1.0-alpha);}gl_FragColor=color*(alpha*opacity*fade_opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform bool u_is_along_line;uniform bool u_is_variable_anchor;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;uniform vec2 u_translation;uniform float u_pitched_scale;varying vec2 v_data0;varying vec3 v_data1;vec4 projectTileWithElevation(vec2 posInTile,float elevation) {return u_matrix*vec4(posInTile,elevation,1.0);}\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;float ele=get_elevation(a_pos);highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec2 translated_a_pos=a_pos+u_translation;vec4 projectedPoint=projectTileWithElevation(translated_a_pos,ele);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=projectTileWithElevation(translated_a_pos+vec2(1,0),ele);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos;if (u_is_along_line || u_is_variable_anchor) {projected_pos=vec4(a_projected_pos.xy,ele,1.0);} else if (u_pitch_with_map) {projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy+u_translation,ele,1.0);} else {projected_pos=u_label_plane_matrix*projectTileWithElevation(a_projected_pos.xy+u_translation,ele);}float z=float(u_pitch_with_map)*projected_pos.z/projected_pos.w;float projectionScaling=1.0;vec4 finalPos=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale+a_pxoffset)*projectionScaling,z,1.0);if(u_pitch_with_map) {finalPos=projectTileWithElevation(finalPos.xy,finalPos.z);}float gamma_scale=finalPos.w;gl_Position=finalPos;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float visibility=calculate_visibility(projectedPoint);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(visibility,fade_opacity[0]+fade_change));v_data0=a_tex/u_texsize;v_data1=vec3(gamma_scale,size,interpolated_fade_opacity);}\"),symbolTextAndIcon:ye(\"#define SDF_PX 8.0\\n#define SDF 1.0\\n#define ICON 0.0\\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform sampler2D u_texture_icon;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;varying vec4 v_data0;varying vec4 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nfloat fade_opacity=v_data1[2];if (v_data1.w==ICON) {vec2 tex_icon=v_data0.zw;lowp float alpha=opacity*fade_opacity;gl_FragColor=texture2D(u_texture_icon,tex_icon)*alpha;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\nreturn;}vec2 tex=v_data0.xy;float EDGE_GAMMA=0.105/u_device_pixel_ratio;float gamma_scale=v_data1.x;float size=v_data1.y;float fontScale=size/24.0;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;uniform vec2 u_texsize_icon;uniform bool u_is_along_line;uniform bool u_is_variable_anchor;uniform vec2 u_translation;uniform float u_pitched_scale;varying vec4 v_data0;varying vec4 v_data1;vec4 projectTileWithElevation(vec2 posInTile,float elevation) {return u_matrix*vec4(posInTile,elevation,1.0);}\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);float is_sdf=a_size[0]-2.0*a_size_min;float ele=get_elevation(a_pos);highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec2 translated_a_pos=a_pos+u_translation;vec4 projectedPoint=projectTileWithElevation(translated_a_pos,ele);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=size/24.0;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=projectTileWithElevation(translated_a_pos+vec2(1,0),ele);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos;if (u_is_along_line || u_is_variable_anchor) {projected_pos=vec4(a_projected_pos.xy,ele,1.0);} else if (u_pitch_with_map) {projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy+u_translation,ele,1.0);} else {projected_pos=u_label_plane_matrix*projectTileWithElevation(a_projected_pos.xy+u_translation,ele);}float z=float(u_pitch_with_map)*projected_pos.z/projected_pos.w;float projectionScaling=1.0;vec4 finalPos=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale)*projectionScaling,z,1.0);if(u_pitch_with_map) {finalPos=projectTileWithElevation(finalPos.xy,finalPos.z);}float gamma_scale=finalPos.w;gl_Position=finalPos;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float visibility=calculate_visibility(projectedPoint);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(visibility,fade_opacity[0]+fade_change));v_data0.xy=a_tex/u_texsize;v_data0.zw=a_tex/u_texsize_icon;v_data1=vec4(gamma_scale,size,interpolated_fade_opacity,is_sdf);}\"),terrain:ye(\"uniform sampler2D u_texture;uniform vec4 u_fog_color;uniform vec4 u_horizon_color;uniform float u_fog_ground_blend;uniform float u_fog_ground_blend_opacity;uniform float u_horizon_fog_blend;varying vec2 v_texture_pos;varying float v_fog_depth;const float gamma=2.2;vec4 gammaToLinear(vec4 color) {return pow(color,vec4(gamma));}vec4 linearToGamma(vec4 color) {return pow(color,vec4(1.0/gamma));}void main() {vec4 surface_color=texture2D(u_texture,v_texture_pos);if (v_fog_depth > u_fog_ground_blend) {vec4 surface_color_linear=gammaToLinear(surface_color);float blend_color=smoothstep(0.0,1.0,max((v_fog_depth-u_horizon_fog_blend)/(1.0-u_horizon_fog_blend),0.0));vec4 fog_horizon_color_linear=mix(gammaToLinear(u_fog_color),gammaToLinear(u_horizon_color),blend_color);float factor_fog=max(v_fog_depth-u_fog_ground_blend,0.0)/(1.0-u_fog_ground_blend);gl_FragColor=linearToGamma(mix(surface_color_linear,fog_horizon_color_linear,pow(factor_fog,2.0)*u_fog_ground_blend_opacity));} else {gl_FragColor=surface_color;}}\",\"attribute vec3 a_pos3d;uniform mat4 u_matrix;uniform mat4 u_fog_matrix;uniform float u_ele_delta;varying vec2 v_texture_pos;varying float v_fog_depth;void main() {float ele=get_elevation(a_pos3d.xy);float ele_delta=a_pos3d.z==1.0 ? u_ele_delta : 0.0;v_texture_pos=a_pos3d.xy/8192.0;gl_Position=u_matrix*vec4(a_pos3d.xy,ele-ele_delta,1.0);vec4 pos=u_fog_matrix*vec4(a_pos3d.xy,ele,1.0);v_fog_depth=pos.z/pos.w*0.5+0.5;}\"),terrainDepth:ye(\"varying float v_depth;const highp vec4 bitSh=vec4(256.*256.*256.,256.*256.,256.,1.);const highp vec4 bitMsk=vec4(0.,vec3(1./256.0));highp vec4 pack(highp float value) {highp vec4 comp=fract(value*bitSh);comp-=comp.xxyz*bitMsk;return comp;}void main() {gl_FragColor=pack(v_depth);}\",\"attribute vec3 a_pos3d;uniform mat4 u_matrix;uniform float u_ele_delta;varying float v_depth;void main() {float ele=get_elevation(a_pos3d.xy);float ele_delta=a_pos3d.z==1.0 ? u_ele_delta : 0.0;gl_Position=u_matrix*vec4(a_pos3d.xy,ele-ele_delta,1.0);v_depth=gl_Position.z/gl_Position.w;}\"),terrainCoords:ye(\"precision mediump float;uniform sampler2D u_texture;uniform float u_terrain_coords_id;varying vec2 v_texture_pos;void main() {vec4 rgba=texture2D(u_texture,v_texture_pos);gl_FragColor=vec4(rgba.r,rgba.g,rgba.b,u_terrain_coords_id);}\",\"attribute vec3 a_pos3d;uniform mat4 u_matrix;uniform float u_ele_delta;varying vec2 v_texture_pos;void main() {float ele=get_elevation(a_pos3d.xy);float ele_delta=a_pos3d.z==1.0 ? u_ele_delta : 0.0;v_texture_pos=a_pos3d.xy/8192.0;gl_Position=u_matrix*vec4(a_pos3d.xy,ele-ele_delta,1.0);}\"),sky:ye(\"uniform vec4 u_sky_color;uniform vec4 u_horizon_color;uniform float u_horizon;uniform float u_sky_horizon_blend;void main() {float y=gl_FragCoord.y;if (y > u_horizon) {float blend=y-u_horizon;if (blend < u_sky_horizon_blend) {gl_FragColor=mix(u_sky_color,u_horizon_color,pow(1.0-blend/u_sky_horizon_blend,2.0));} else {gl_FragColor=u_sky_color;}}}\",\"attribute vec2 a_pos;void main() {gl_Position=vec4(a_pos,1.0,1.0);}\")};function ye(t,e){const r=/#pragma mapbox: ([\\w]+) ([\\w]+) ([\\w]+) ([\\w]+)/g,n=e.match(/attribute ([\\w]+) ([\\w]+)/g),i=t.match(/uniform ([\\w]+) ([\\w]+)([\\s]*)([\\w]*)/g),a=e.match(/uniform ([\\w]+) ([\\w]+)([\\s]*)([\\w]*)/g),o=a?a.concat(i):i,s={};return{fragmentSource:t=t.replace(r,((t,e,r,n,i)=>(s[i]=!0,\"define\"===e?`\\n#ifndef HAS_UNIFORM_u_${i}\\nvarying ${r} ${n} ${i};\\n#else\\nuniform ${r} ${n} u_${i};\\n#endif\\n`:`\\n#ifdef HAS_UNIFORM_u_${i}\\n ${r} ${n} ${i} = u_${i};\\n#endif\\n`))),vertexSource:e=e.replace(r,((t,e,r,n,i)=>{const a=\"float\"===n?\"vec2\":\"vec4\",o=i.match(/color/)?\"color\":a;return s[i]?\"define\"===e?`\\n#ifndef HAS_UNIFORM_u_${i}\\nuniform lowp float u_${i}_t;\\nattribute ${r} ${a} a_${i};\\nvarying ${r} ${n} ${i};\\n#else\\nuniform ${r} ${n} u_${i};\\n#endif\\n`:\"vec4\"===o?`\\n#ifndef HAS_UNIFORM_u_${i}\\n ${i} = a_${i};\\n#else\\n ${r} ${n} ${i} = u_${i};\\n#endif\\n`:`\\n#ifndef HAS_UNIFORM_u_${i}\\n ${i} = unpack_mix_${o}(a_${i}, u_${i}_t);\\n#else\\n ${r} ${n} ${i} = u_${i};\\n#endif\\n`:\"define\"===e?`\\n#ifndef HAS_UNIFORM_u_${i}\\nuniform lowp float u_${i}_t;\\nattribute ${r} ${a} a_${i};\\n#else\\nuniform ${r} ${n} u_${i};\\n#endif\\n`:\"vec4\"===o?`\\n#ifndef HAS_UNIFORM_u_${i}\\n ${r} ${n} ${i} = a_${i};\\n#else\\n ${r} ${n} ${i} = u_${i};\\n#endif\\n`:`\\n#ifndef HAS_UNIFORM_u_${i}\\n ${r} ${n} ${i} = unpack_mix_${o}(a_${i}, u_${i}_t);\\n#else\\n ${r} ${n} ${i} = u_${i};\\n#endif\\n`})),staticAttributes:n,staticUniforms:o}}class ve{constructor(){this.boundProgram=null,this.boundLayoutVertexBuffer=null,this.boundPaintVertexBuffers=[],this.boundIndexBuffer=null,this.boundVertexOffset=null,this.boundDynamicVertexBuffer=null,this.vao=null}bind(t,e,r,n,i,a,o,s,l){this.context=t;let c=this.boundPaintVertexBuffers.length!==n.length;for(let t=0;!c&&t({u_matrix:t,u_texture:0,u_ele_delta:r,u_fog_matrix:n,u_fog_color:i?i.properties.get(\"fog-color\"):e.aN.white,u_fog_ground_blend:i?i.properties.get(\"fog-ground-blend\"):1,u_fog_ground_blend_opacity:i?i.calculateFogBlendOpacity(a):0,u_horizon_color:i?i.properties.get(\"horizon-color\"):e.aN.white,u_horizon_fog_blend:i?i.properties.get(\"horizon-fog-blend\"):1});function _e(t){const e=[];for(let r=0;r({u_depth:new e.aI(t,r.u_depth),u_terrain:new e.aI(t,r.u_terrain),u_terrain_dim:new e.aJ(t,r.u_terrain_dim),u_terrain_matrix:new e.aK(t,r.u_terrain_matrix),u_terrain_unpack:new e.aL(t,r.u_terrain_unpack),u_terrain_exaggeration:new e.aJ(t,r.u_terrain_exaggeration)}))(t,b),this.binderUniforms=n?n.getUniforms(t,b):[]}draw(t,e,r,n,i,a,o,s,l,c,u,h,f,p,d,m,g,y){const v=t.gl;if(this.failedToCreate)return;if(t.program.set(this.program),t.setDepthMode(r),t.setStencilMode(n),t.setColorMode(i),t.setCullFace(a),s){t.activeTexture.set(v.TEXTURE2),v.bindTexture(v.TEXTURE_2D,s.depthTexture),t.activeTexture.set(v.TEXTURE3),v.bindTexture(v.TEXTURE_2D,s.texture);for(const t in this.terrainUniforms)this.terrainUniforms[t].set(s[t])}for(const t in this.fixedUniforms)this.fixedUniforms[t].set(o[t]);d&&d.setUniforms(t,this.binderUniforms,f,{zoom:p});let x=0;switch(e){case v.LINES:x=2;break;case v.TRIANGLES:x=3;break;case v.LINE_STRIP:x=1}for(const r of h.get()){const n=r.vaos||(r.vaos={});(n[l]||(n[l]=new ve)).bind(t,this,c,d?d.getPaintVertexBuffers():[],u,r.vertexOffset,m,g,y),v.drawElements(e,r.primitiveLength*x,v.UNSIGNED_SHORT,r.primitiveOffset*x*2)}}}function we(t,e,r){const n=1/Nt(r,1,e.transform.tileZoom),i=Math.pow(2,r.tileID.overscaledZ),a=r.tileSize*Math.pow(2,e.transform.tileZoom)/i,o=a*(r.tileID.canonical.x+r.tileID.wrap*i),s=a*r.tileID.canonical.y;return{u_image:0,u_texsize:r.imageAtlasTexture.size,u_scale:[n,t.fromScale,t.toScale],u_fade:t.t,u_pixel_coord_upper:[o>>16,s>>16],u_pixel_coord_lower:[65535&o,65535&s]}}const Te=(t,r,n,i)=>{const a=r.style.light,o=a.properties.get(\"position\"),s=[o.x,o.y,o.z],l=function(){var t=new e.A(9);return e.A!=Float32Array&&(t[1]=0,t[2]=0,t[3]=0,t[5]=0,t[6]=0,t[7]=0),t[0]=1,t[4]=1,t[8]=1,t}();\"viewport\"===a.properties.get(\"anchor\")&&function(t,e){var r=Math.sin(e),n=Math.cos(e);t[0]=n,t[1]=r,t[2]=0,t[3]=-r,t[4]=n,t[5]=0,t[6]=0,t[7]=0,t[8]=1}(l,-r.transform.angle),function(t,e,r){var n=e[0],i=e[1],a=e[2];t[0]=n*r[0]+i*r[3]+a*r[6],t[1]=n*r[1]+i*r[4]+a*r[7],t[2]=n*r[2]+i*r[5]+a*r[8]}(s,s,l);const c=a.properties.get(\"color\");return{u_matrix:t,u_lightpos:s,u_lightintensity:a.properties.get(\"intensity\"),u_lightcolor:[c.r,c.g,c.b],u_vertical_gradient:+n,u_opacity:i}},ke=(t,r,n,i,a,o,s)=>e.e(Te(t,r,n,i),we(o,r,s),{u_height_factor:-Math.pow(2,a.overscaledZ)/s.tileSize/8}),Ae=t=>({u_matrix:t}),Me=(t,r,n,i)=>e.e(Ae(t),we(n,r,i)),Se=(t,e)=>({u_matrix:t,u_world:e}),Ee=(t,r,n,i,a)=>e.e(Me(t,r,n,i),{u_world:a}),Ce=(t,e,r,n)=>{const i=t.transform;let a,o;if(\"map\"===n.paint.get(\"circle-pitch-alignment\")){const t=Nt(r,1,i.zoom);a=!0,o=[t,t]}else a=!1,o=i.pixelsToGLUnits;return{u_camera_to_center_distance:i.cameraToCenterDistance,u_scale_with_map:+(\"map\"===n.paint.get(\"circle-pitch-scale\")),u_matrix:t.translatePosMatrix(e.posMatrix,r,n.paint.get(\"circle-translate\"),n.paint.get(\"circle-translate-anchor\")),u_pitch_with_map:+a,u_device_pixel_ratio:t.pixelRatio,u_extrude_scale:o}},Le=(t,e)=>({u_matrix:e,u_pixel_extrude_scale:[1/t.width,1/t.height]}),Ie=(t,e,r)=>({u_matrix:t,u_inv_matrix:e,u_camera_to_center_distance:r.cameraToCenterDistance,u_viewport_size:[r.width,r.height]}),Pe=(t,e,r=1)=>({u_matrix:t,u_color:e,u_overlay:0,u_overlay_scale:r}),ze=t=>({u_matrix:t}),Oe=(t,e,r,n)=>({u_matrix:t,u_extrude_scale:Nt(e,1,r),u_intensity:n}),De=(t,r,n,i)=>{const a=e.H();e.aQ(a,0,t.width,t.height,0,0,1);const o=t.context.gl;return{u_matrix:a,u_world:[o.drawingBufferWidth,o.drawingBufferHeight],u_image:n,u_color_ramp:i,u_opacity:r.paint.get(\"heatmap-opacity\")}},Re=(t,e,r,n)=>{const i=r.paint.get(\"hillshade-shadow-color\"),a=r.paint.get(\"hillshade-highlight-color\"),o=r.paint.get(\"hillshade-accent-color\");let s=r.paint.get(\"hillshade-illumination-direction\")*(Math.PI/180);\"viewport\"===r.paint.get(\"hillshade-illumination-anchor\")&&(s-=t.transform.angle);const l=!t.options.moving;return{u_matrix:n?n.posMatrix:t.transform.calculatePosMatrix(e.tileID.toUnwrapped(),l),u_image:0,u_latrange:Be(0,e.tileID),u_light:[r.paint.get(\"hillshade-exaggeration\"),s],u_shadow:i,u_highlight:a,u_accent:o}},Fe=(t,r)=>{const n=r.stride,i=e.H();return e.aQ(i,0,e.X,-e.X,0,0,1),e.J(i,i,[0,-e.X,0]),{u_matrix:i,u_image:1,u_dimension:[n,n],u_zoom:t.overscaledZ,u_unpack:r.getUnpackVector()}};function Be(t,r){const n=Math.pow(2,r.canonical.z),i=r.canonical.y;return[new e.Z(0,i/n).toLngLat().lat,new e.Z(0,(i+1)/n).toLngLat().lat]}const Ne=(t,e,r,n)=>{const i=t.transform;return{u_matrix:He(t,e,r,n),u_ratio:1/Nt(e,1,i.zoom),u_device_pixel_ratio:t.pixelRatio,u_units_to_pixels:[1/i.pixelsToGLUnits[0],1/i.pixelsToGLUnits[1]]}},je=(t,r,n,i,a)=>e.e(Ne(t,r,n,a),{u_image:0,u_image_height:i}),Ue=(t,e,r,n,i)=>{const a=t.transform,o=qe(e,a);return{u_matrix:He(t,e,r,i),u_texsize:e.imageAtlasTexture.size,u_ratio:1/Nt(e,1,a.zoom),u_device_pixel_ratio:t.pixelRatio,u_image:0,u_scale:[o,n.fromScale,n.toScale],u_fade:n.t,u_units_to_pixels:[1/a.pixelsToGLUnits[0],1/a.pixelsToGLUnits[1]]}},Ve=(t,r,n,i,a,o)=>{const s=t.transform,l=t.lineAtlas,c=qe(r,s),u=\"round\"===n.layout.get(\"line-cap\"),h=l.getDash(i.from,u),f=l.getDash(i.to,u),p=h.width*a.fromScale,d=f.width*a.toScale;return e.e(Ne(t,r,n,o),{u_patternscale_a:[c/p,-h.height/2],u_patternscale_b:[c/d,-f.height/2],u_sdfgamma:l.width/(256*Math.min(p,d)*t.pixelRatio)/2,u_image:0,u_tex_y_a:h.y,u_tex_y_b:f.y,u_mix:a.t})};function qe(t,e){return 1/Nt(t,1,e.tileZoom)}function He(t,e,r,n){return t.translatePosMatrix(n?n.posMatrix:e.tileID.posMatrix,e,r.paint.get(\"line-translate\"),r.paint.get(\"line-translate-anchor\"))}const Ge=(t,e,r,n,i)=>{return{u_matrix:t,u_tl_parent:e,u_scale_parent:r,u_buffer_scale:1,u_fade_t:n.mix,u_opacity:n.opacity*i.paint.get(\"raster-opacity\"),u_image0:0,u_image1:1,u_brightness_low:i.paint.get(\"raster-brightness-min\"),u_brightness_high:i.paint.get(\"raster-brightness-max\"),u_saturation_factor:(o=i.paint.get(\"raster-saturation\"),o>0?1-1/(1.001-o):-o),u_contrast_factor:(a=i.paint.get(\"raster-contrast\"),a>0?1/(1-a):1+a),u_spin_weights:Ze(i.paint.get(\"raster-hue-rotate\"))};var a,o};function Ze(t){t*=Math.PI/180;const e=Math.sin(t),r=Math.cos(t);return[(2*r+1)/3,(-Math.sqrt(3)*e-r+1)/3,(Math.sqrt(3)*e-r+1)/3]}const We=(t,e,r,n,i,a,o,s,l,c,u,h,f,p)=>{const d=o.transform;return{u_is_size_zoom_constant:+(\"constant\"===t||\"source\"===t),u_is_size_feature_constant:+(\"constant\"===t||\"camera\"===t),u_size_t:e?e.uSizeT:0,u_size:e?e.uSize:0,u_camera_to_center_distance:d.cameraToCenterDistance,u_pitch:d.pitch/360*2*Math.PI,u_rotate_symbol:+r,u_aspect_ratio:d.width/d.height,u_fade_change:o.options.fadeDuration?o.symbolFadeChange:1,u_matrix:s,u_label_plane_matrix:l,u_coord_matrix:c,u_is_text:+h,u_pitch_with_map:+n,u_is_along_line:i,u_is_variable_anchor:a,u_texsize:f,u_texture:0,u_translation:u,u_pitched_scale:p}},Ye=(t,r,n,i,a,o,s,l,c,u,h,f,p,d,m)=>{const g=s.transform;return e.e(We(t,r,n,i,a,o,s,l,c,u,h,f,p,m),{u_gamma_scale:i?Math.cos(g._pitch)*g.cameraToCenterDistance:1,u_device_pixel_ratio:s.pixelRatio,u_is_halo:+d})},Xe=(t,r,n,i,a,o,s,l,c,u,h,f,p,d)=>e.e(Ye(t,r,n,i,a,o,s,l,c,u,h,!0,f,!0,d),{u_texsize_icon:p,u_texture_icon:1}),$e=(t,e,r)=>({u_matrix:t,u_opacity:e,u_color:r}),Je=(t,r,n,i,a,o)=>e.e(function(t,e,r,n){const i=r.imageManager.getPattern(t.from.toString()),a=r.imageManager.getPattern(t.to.toString()),{width:o,height:s}=r.imageManager.getPixelSize(),l=Math.pow(2,n.tileID.overscaledZ),c=n.tileSize*Math.pow(2,r.transform.tileZoom)/l,u=c*(n.tileID.canonical.x+n.tileID.wrap*l),h=c*n.tileID.canonical.y;return{u_image:0,u_pattern_tl_a:i.tl,u_pattern_br_a:i.br,u_pattern_tl_b:a.tl,u_pattern_br_b:a.br,u_texsize:[o,s],u_mix:e.t,u_pattern_size_a:i.displaySize,u_pattern_size_b:a.displaySize,u_scale_a:e.fromScale,u_scale_b:e.toScale,u_tile_units_to_pixels:1/Nt(n,1,r.transform.tileZoom),u_pixel_coord_upper:[u>>16,h>>16],u_pixel_coord_lower:[65535&u,65535&h]}}(i,o,n,a),{u_matrix:t,u_opacity:r}),Ke={fillExtrusion:(t,r)=>({u_matrix:new e.aK(t,r.u_matrix),u_lightpos:new e.aO(t,r.u_lightpos),u_lightintensity:new e.aJ(t,r.u_lightintensity),u_lightcolor:new e.aO(t,r.u_lightcolor),u_vertical_gradient:new e.aJ(t,r.u_vertical_gradient),u_opacity:new e.aJ(t,r.u_opacity)}),fillExtrusionPattern:(t,r)=>({u_matrix:new e.aK(t,r.u_matrix),u_lightpos:new e.aO(t,r.u_lightpos),u_lightintensity:new e.aJ(t,r.u_lightintensity),u_lightcolor:new e.aO(t,r.u_lightcolor),u_vertical_gradient:new e.aJ(t,r.u_vertical_gradient),u_height_factor:new e.aJ(t,r.u_height_factor),u_image:new e.aI(t,r.u_image),u_texsize:new e.aP(t,r.u_texsize),u_pixel_coord_upper:new e.aP(t,r.u_pixel_coord_upper),u_pixel_coord_lower:new e.aP(t,r.u_pixel_coord_lower),u_scale:new e.aO(t,r.u_scale),u_fade:new e.aJ(t,r.u_fade),u_opacity:new e.aJ(t,r.u_opacity)}),fill:(t,r)=>({u_matrix:new e.aK(t,r.u_matrix)}),fillPattern:(t,r)=>({u_matrix:new e.aK(t,r.u_matrix),u_image:new e.aI(t,r.u_image),u_texsize:new e.aP(t,r.u_texsize),u_pixel_coord_upper:new e.aP(t,r.u_pixel_coord_upper),u_pixel_coord_lower:new e.aP(t,r.u_pixel_coord_lower),u_scale:new e.aO(t,r.u_scale),u_fade:new e.aJ(t,r.u_fade)}),fillOutline:(t,r)=>({u_matrix:new e.aK(t,r.u_matrix),u_world:new e.aP(t,r.u_world)}),fillOutlinePattern:(t,r)=>({u_matrix:new e.aK(t,r.u_matrix),u_world:new e.aP(t,r.u_world),u_image:new e.aI(t,r.u_image),u_texsize:new e.aP(t,r.u_texsize),u_pixel_coord_upper:new e.aP(t,r.u_pixel_coord_upper),u_pixel_coord_lower:new e.aP(t,r.u_pixel_coord_lower),u_scale:new e.aO(t,r.u_scale),u_fade:new e.aJ(t,r.u_fade)}),circle:(t,r)=>({u_camera_to_center_distance:new e.aJ(t,r.u_camera_to_center_distance),u_scale_with_map:new e.aI(t,r.u_scale_with_map),u_pitch_with_map:new e.aI(t,r.u_pitch_with_map),u_extrude_scale:new e.aP(t,r.u_extrude_scale),u_device_pixel_ratio:new e.aJ(t,r.u_device_pixel_ratio),u_matrix:new e.aK(t,r.u_matrix)}),collisionBox:(t,r)=>({u_matrix:new e.aK(t,r.u_matrix),u_pixel_extrude_scale:new e.aP(t,r.u_pixel_extrude_scale)}),collisionCircle:(t,r)=>({u_matrix:new e.aK(t,r.u_matrix),u_inv_matrix:new e.aK(t,r.u_inv_matrix),u_camera_to_center_distance:new e.aJ(t,r.u_camera_to_center_distance),u_viewport_size:new e.aP(t,r.u_viewport_size)}),debug:(t,r)=>({u_color:new e.aM(t,r.u_color),u_matrix:new e.aK(t,r.u_matrix),u_overlay:new e.aI(t,r.u_overlay),u_overlay_scale:new e.aJ(t,r.u_overlay_scale)}),clippingMask:(t,r)=>({u_matrix:new e.aK(t,r.u_matrix)}),heatmap:(t,r)=>({u_extrude_scale:new e.aJ(t,r.u_extrude_scale),u_intensity:new e.aJ(t,r.u_intensity),u_matrix:new e.aK(t,r.u_matrix)}),heatmapTexture:(t,r)=>({u_matrix:new e.aK(t,r.u_matrix),u_world:new e.aP(t,r.u_world),u_image:new e.aI(t,r.u_image),u_color_ramp:new e.aI(t,r.u_color_ramp),u_opacity:new e.aJ(t,r.u_opacity)}),hillshade:(t,r)=>({u_matrix:new e.aK(t,r.u_matrix),u_image:new e.aI(t,r.u_image),u_latrange:new e.aP(t,r.u_latrange),u_light:new e.aP(t,r.u_light),u_shadow:new e.aM(t,r.u_shadow),u_highlight:new e.aM(t,r.u_highlight),u_accent:new e.aM(t,r.u_accent)}),hillshadePrepare:(t,r)=>({u_matrix:new e.aK(t,r.u_matrix),u_image:new e.aI(t,r.u_image),u_dimension:new e.aP(t,r.u_dimension),u_zoom:new e.aJ(t,r.u_zoom),u_unpack:new e.aL(t,r.u_unpack)}),line:(t,r)=>({u_matrix:new e.aK(t,r.u_matrix),u_ratio:new e.aJ(t,r.u_ratio),u_device_pixel_ratio:new e.aJ(t,r.u_device_pixel_ratio),u_units_to_pixels:new e.aP(t,r.u_units_to_pixels)}),lineGradient:(t,r)=>({u_matrix:new e.aK(t,r.u_matrix),u_ratio:new e.aJ(t,r.u_ratio),u_device_pixel_ratio:new e.aJ(t,r.u_device_pixel_ratio),u_units_to_pixels:new e.aP(t,r.u_units_to_pixels),u_image:new e.aI(t,r.u_image),u_image_height:new e.aJ(t,r.u_image_height)}),linePattern:(t,r)=>({u_matrix:new e.aK(t,r.u_matrix),u_texsize:new e.aP(t,r.u_texsize),u_ratio:new e.aJ(t,r.u_ratio),u_device_pixel_ratio:new e.aJ(t,r.u_device_pixel_ratio),u_image:new e.aI(t,r.u_image),u_units_to_pixels:new e.aP(t,r.u_units_to_pixels),u_scale:new e.aO(t,r.u_scale),u_fade:new e.aJ(t,r.u_fade)}),lineSDF:(t,r)=>({u_matrix:new e.aK(t,r.u_matrix),u_ratio:new e.aJ(t,r.u_ratio),u_device_pixel_ratio:new e.aJ(t,r.u_device_pixel_ratio),u_units_to_pixels:new e.aP(t,r.u_units_to_pixels),u_patternscale_a:new e.aP(t,r.u_patternscale_a),u_patternscale_b:new e.aP(t,r.u_patternscale_b),u_sdfgamma:new e.aJ(t,r.u_sdfgamma),u_image:new e.aI(t,r.u_image),u_tex_y_a:new e.aJ(t,r.u_tex_y_a),u_tex_y_b:new e.aJ(t,r.u_tex_y_b),u_mix:new e.aJ(t,r.u_mix)}),raster:(t,r)=>({u_matrix:new e.aK(t,r.u_matrix),u_tl_parent:new e.aP(t,r.u_tl_parent),u_scale_parent:new e.aJ(t,r.u_scale_parent),u_buffer_scale:new e.aJ(t,r.u_buffer_scale),u_fade_t:new e.aJ(t,r.u_fade_t),u_opacity:new e.aJ(t,r.u_opacity),u_image0:new e.aI(t,r.u_image0),u_image1:new e.aI(t,r.u_image1),u_brightness_low:new e.aJ(t,r.u_brightness_low),u_brightness_high:new e.aJ(t,r.u_brightness_high),u_saturation_factor:new e.aJ(t,r.u_saturation_factor),u_contrast_factor:new e.aJ(t,r.u_contrast_factor),u_spin_weights:new e.aO(t,r.u_spin_weights)}),symbolIcon:(t,r)=>({u_is_size_zoom_constant:new e.aI(t,r.u_is_size_zoom_constant),u_is_size_feature_constant:new e.aI(t,r.u_is_size_feature_constant),u_size_t:new e.aJ(t,r.u_size_t),u_size:new e.aJ(t,r.u_size),u_camera_to_center_distance:new e.aJ(t,r.u_camera_to_center_distance),u_pitch:new e.aJ(t,r.u_pitch),u_rotate_symbol:new e.aI(t,r.u_rotate_symbol),u_aspect_ratio:new e.aJ(t,r.u_aspect_ratio),u_fade_change:new e.aJ(t,r.u_fade_change),u_matrix:new e.aK(t,r.u_matrix),u_label_plane_matrix:new e.aK(t,r.u_label_plane_matrix),u_coord_matrix:new e.aK(t,r.u_coord_matrix),u_is_text:new e.aI(t,r.u_is_text),u_pitch_with_map:new e.aI(t,r.u_pitch_with_map),u_is_along_line:new e.aI(t,r.u_is_along_line),u_is_variable_anchor:new e.aI(t,r.u_is_variable_anchor),u_texsize:new e.aP(t,r.u_texsize),u_texture:new e.aI(t,r.u_texture),u_translation:new e.aP(t,r.u_translation),u_pitched_scale:new e.aJ(t,r.u_pitched_scale)}),symbolSDF:(t,r)=>({u_is_size_zoom_constant:new e.aI(t,r.u_is_size_zoom_constant),u_is_size_feature_constant:new e.aI(t,r.u_is_size_feature_constant),u_size_t:new e.aJ(t,r.u_size_t),u_size:new e.aJ(t,r.u_size),u_camera_to_center_distance:new e.aJ(t,r.u_camera_to_center_distance),u_pitch:new e.aJ(t,r.u_pitch),u_rotate_symbol:new e.aI(t,r.u_rotate_symbol),u_aspect_ratio:new e.aJ(t,r.u_aspect_ratio),u_fade_change:new e.aJ(t,r.u_fade_change),u_matrix:new e.aK(t,r.u_matrix),u_label_plane_matrix:new e.aK(t,r.u_label_plane_matrix),u_coord_matrix:new e.aK(t,r.u_coord_matrix),u_is_text:new e.aI(t,r.u_is_text),u_pitch_with_map:new e.aI(t,r.u_pitch_with_map),u_is_along_line:new e.aI(t,r.u_is_along_line),u_is_variable_anchor:new e.aI(t,r.u_is_variable_anchor),u_texsize:new e.aP(t,r.u_texsize),u_texture:new e.aI(t,r.u_texture),u_gamma_scale:new e.aJ(t,r.u_gamma_scale),u_device_pixel_ratio:new e.aJ(t,r.u_device_pixel_ratio),u_is_halo:new e.aI(t,r.u_is_halo),u_translation:new e.aP(t,r.u_translation),u_pitched_scale:new e.aJ(t,r.u_pitched_scale)}),symbolTextAndIcon:(t,r)=>({u_is_size_zoom_constant:new e.aI(t,r.u_is_size_zoom_constant),u_is_size_feature_constant:new e.aI(t,r.u_is_size_feature_constant),u_size_t:new e.aJ(t,r.u_size_t),u_size:new e.aJ(t,r.u_size),u_camera_to_center_distance:new e.aJ(t,r.u_camera_to_center_distance),u_pitch:new e.aJ(t,r.u_pitch),u_rotate_symbol:new e.aI(t,r.u_rotate_symbol),u_aspect_ratio:new e.aJ(t,r.u_aspect_ratio),u_fade_change:new e.aJ(t,r.u_fade_change),u_matrix:new e.aK(t,r.u_matrix),u_label_plane_matrix:new e.aK(t,r.u_label_plane_matrix),u_coord_matrix:new e.aK(t,r.u_coord_matrix),u_is_text:new e.aI(t,r.u_is_text),u_pitch_with_map:new e.aI(t,r.u_pitch_with_map),u_is_along_line:new e.aI(t,r.u_is_along_line),u_is_variable_anchor:new e.aI(t,r.u_is_variable_anchor),u_texsize:new e.aP(t,r.u_texsize),u_texsize_icon:new e.aP(t,r.u_texsize_icon),u_texture:new e.aI(t,r.u_texture),u_texture_icon:new e.aI(t,r.u_texture_icon),u_gamma_scale:new e.aJ(t,r.u_gamma_scale),u_device_pixel_ratio:new e.aJ(t,r.u_device_pixel_ratio),u_is_halo:new e.aI(t,r.u_is_halo),u_translation:new e.aP(t,r.u_translation),u_pitched_scale:new e.aJ(t,r.u_pitched_scale)}),background:(t,r)=>({u_matrix:new e.aK(t,r.u_matrix),u_opacity:new e.aJ(t,r.u_opacity),u_color:new e.aM(t,r.u_color)}),backgroundPattern:(t,r)=>({u_matrix:new e.aK(t,r.u_matrix),u_opacity:new e.aJ(t,r.u_opacity),u_image:new e.aI(t,r.u_image),u_pattern_tl_a:new e.aP(t,r.u_pattern_tl_a),u_pattern_br_a:new e.aP(t,r.u_pattern_br_a),u_pattern_tl_b:new e.aP(t,r.u_pattern_tl_b),u_pattern_br_b:new e.aP(t,r.u_pattern_br_b),u_texsize:new e.aP(t,r.u_texsize),u_mix:new e.aJ(t,r.u_mix),u_pattern_size_a:new e.aP(t,r.u_pattern_size_a),u_pattern_size_b:new e.aP(t,r.u_pattern_size_b),u_scale_a:new e.aJ(t,r.u_scale_a),u_scale_b:new e.aJ(t,r.u_scale_b),u_pixel_coord_upper:new e.aP(t,r.u_pixel_coord_upper),u_pixel_coord_lower:new e.aP(t,r.u_pixel_coord_lower),u_tile_units_to_pixels:new e.aJ(t,r.u_tile_units_to_pixels)}),terrain:(t,r)=>({u_matrix:new e.aK(t,r.u_matrix),u_texture:new e.aI(t,r.u_texture),u_ele_delta:new e.aJ(t,r.u_ele_delta),u_fog_matrix:new e.aK(t,r.u_fog_matrix),u_fog_color:new e.aM(t,r.u_fog_color),u_fog_ground_blend:new e.aJ(t,r.u_fog_ground_blend),u_fog_ground_blend_opacity:new e.aJ(t,r.u_fog_ground_blend_opacity),u_horizon_color:new e.aM(t,r.u_horizon_color),u_horizon_fog_blend:new e.aJ(t,r.u_horizon_fog_blend)}),terrainDepth:(t,r)=>({u_matrix:new e.aK(t,r.u_matrix),u_ele_delta:new e.aJ(t,r.u_ele_delta)}),terrainCoords:(t,r)=>({u_matrix:new e.aK(t,r.u_matrix),u_texture:new e.aI(t,r.u_texture),u_terrain_coords_id:new e.aJ(t,r.u_terrain_coords_id),u_ele_delta:new e.aJ(t,r.u_ele_delta)}),sky:(t,r)=>({u_sky_color:new e.aM(t,r.u_sky_color),u_horizon_color:new e.aM(t,r.u_horizon_color),u_horizon:new e.aJ(t,r.u_horizon),u_sky_horizon_blend:new e.aJ(t,r.u_sky_horizon_blend)})};class Qe{constructor(t,e,r){this.context=t;const n=t.gl;this.buffer=n.createBuffer(),this.dynamicDraw=Boolean(r),this.context.unbindVAO(),t.bindElementBuffer.set(this.buffer),n.bufferData(n.ELEMENT_ARRAY_BUFFER,e.arrayBuffer,this.dynamicDraw?n.DYNAMIC_DRAW:n.STATIC_DRAW),this.dynamicDraw||delete e.arrayBuffer}bind(){this.context.bindElementBuffer.set(this.buffer)}updateData(t){const e=this.context.gl;if(!this.dynamicDraw)throw new Error(\"Attempted to update data while not in dynamic mode.\");this.context.unbindVAO(),this.bind(),e.bufferSubData(e.ELEMENT_ARRAY_BUFFER,0,t.arrayBuffer)}destroy(){const t=this.context.gl;this.buffer&&(t.deleteBuffer(this.buffer),delete this.buffer)}}const tr={Int8:\"BYTE\",Uint8:\"UNSIGNED_BYTE\",Int16:\"SHORT\",Uint16:\"UNSIGNED_SHORT\",Int32:\"INT\",Uint32:\"UNSIGNED_INT\",Float32:\"FLOAT\"};class er{constructor(t,e,r,n){this.length=e.length,this.attributes=r,this.itemSize=e.bytesPerElement,this.dynamicDraw=n,this.context=t;const i=t.gl;this.buffer=i.createBuffer(),t.bindVertexBuffer.set(this.buffer),i.bufferData(i.ARRAY_BUFFER,e.arrayBuffer,this.dynamicDraw?i.DYNAMIC_DRAW:i.STATIC_DRAW),this.dynamicDraw||delete e.arrayBuffer}bind(){this.context.bindVertexBuffer.set(this.buffer)}updateData(t){if(t.length!==this.length)throw new Error(`Length of new data is ${t.length}, which doesn't match current length of ${this.length}`);const e=this.context.gl;this.bind(),e.bufferSubData(e.ARRAY_BUFFER,0,t.arrayBuffer)}enableAttributes(t,e){for(let r=0;r0){const r=e.H();e.aR(r,d.placementInvProjMatrix,t.transform.glCoordMatrix),e.aR(r,r,d.placementViewportMatrix),c.push({circleArray:g,circleOffset:h,transform:p.posMatrix,invTransform:r,coord:p}),u+=g.length/4,h=u}m&&l.draw(o,s.LINES,qr.disabled,Gr.disabled,t.colorModeForRenderPass(),Zr.disabled,Le(t.transform,p.posMatrix),t.style.map.terrain&&t.style.map.terrain.getTerrainData(p),n.id,m.layoutVertexBuffer,m.indexBuffer,m.segments,null,t.transform.zoom,null,null,m.collisionVertexBuffer)}if(!a||!c.length)return;const f=t.useProgram(\"collisionCircle\"),p=new e.aS;p.resize(4*u),p._trim();let d=0;for(const t of c)for(let e=0;er.style.map.terrain.getElevation(a,t,e):null,i=h.translatePosition(u,t,s,l);Qr(o,f,p,c,u,y,a.posMatrix,e,m,v,h,i,a.toUnwrapped(),n)}}}(i,t,n,r,n.layout.get(\"text-rotation-alignment\"),n.layout.get(\"text-pitch-alignment\"),n.paint.get(\"text-translate\"),n.paint.get(\"text-translate-anchor\"),a),0!==n.paint.get(\"icon-opacity\").constantOr(1)&&en(t,r,n,i,!1,n.paint.get(\"icon-translate\"),n.paint.get(\"icon-translate-anchor\"),n.layout.get(\"icon-rotation-alignment\"),n.layout.get(\"icon-pitch-alignment\"),n.layout.get(\"icon-keep-upright\"),o,s),0!==n.paint.get(\"text-opacity\").constantOr(1)&&en(t,r,n,i,!0,n.paint.get(\"text-translate\"),n.paint.get(\"text-translate-anchor\"),n.layout.get(\"text-rotation-alignment\"),n.layout.get(\"text-pitch-alignment\"),n.layout.get(\"text-keep-upright\"),o,s),r.map.showCollisionBoxes&&(Yr(t,r,n,i,!0),Yr(t,r,n,i,!1))}function Jr(t,r,n,i,a,o){const{horizontalAlign:s,verticalAlign:l}=e.av(t),c=-(s-.5)*r,u=-(l-.5)*n;return new e.P((c/a+i[0])*o,(u/a+i[1])*o)}function Kr(t,r,n,i,a,o){const s=r.tileAnchorPoint.add(new e.P(r.translation[0],r.translation[1]));if(r.pitchWithMap){let t=i.mult(o);return n||(t=t.rotate(-a)),wt(s.add(t),r.labelPlaneMatrix,r.getElevation).point}if(n){const e=It(r.tileAnchorPoint.x+1,r.tileAnchorPoint.y,r).point.sub(t),n=Math.atan(e.y/e.x)+(e.x<0?Math.PI:0);return t.add(i.rotate(n))}return t.add(i)}function Qr(t,r,n,i,a,o,s,l,c,u,h,f,p,d){const m=t.text.placedSymbolArray,g=t.text.dynamicLayoutVertexArray,y=t.icon.dynamicLayoutVertexArray,v={};g.clear();for(let y=0;y=0&&(v[x.associatedIconIndex]={shiftedAnchor:L,angle:I})}else Rt(x.numGlyphs,g)}if(u){y.clear();const r=t.icon.placedSymbolArray;for(let t=0;tt.style.map.terrain.getElevation(l,e,r):null,r=\"map\"===n.layout.get(\"text-rotation-alignment\");At(c,l.posMatrix,t,a,j,V,v,u,r,g,l.toUnwrapped(),m.width,m.height,q,e)}const Z=l.posMatrix,W=a&&A||G,Y=x||W?Xr:j,X=U,$=p&&0!==n.paint.get(a?\"text-halo-width\":\"icon-halo-width\").constantOr(1);let J;J=p?c.iconsInText?Xe(k.kind,L,_,v,x,W,t,Z,Y,X,q,P,R,S):Ye(k.kind,L,_,v,x,W,t,Z,Y,X,q,a,P,!0,S):We(k.kind,L,_,v,x,W,t,Z,Y,X,q,a,P,S);const K={program:C,buffers:h,uniformValues:J,atlasTexture:z,atlasTextureIcon:F,atlasInterpolation:O,atlasInterpolationIcon:D,isSDF:p,hasHalo:$};if(w&&c.canOverlap){T=!0;const t=h.segments.get();for(const r of t)M.push({segments:new e.a0([r]),sortKey:r.sortKey,state:K,terrainData:I})}else M.push({segments:h.segments,sortKey:0,state:K,terrainData:I})}T&&M.sort(((t,e)=>t.sortKey-e.sortKey));for(const e of M){const r=e.state;if(p.activeTexture.set(d.TEXTURE0),r.atlasTexture.bind(r.atlasInterpolation,d.CLAMP_TO_EDGE),r.atlasTextureIcon&&(p.activeTexture.set(d.TEXTURE1),r.atlasTextureIcon&&r.atlasTextureIcon.bind(r.atlasInterpolationIcon,d.CLAMP_TO_EDGE)),r.isSDF){const i=r.uniformValues;r.hasHalo&&(i.u_is_halo=1,rn(r.buffers,e.segments,n,t,r.program,k,h,f,i,e.terrainData)),i.u_is_halo=0}rn(r.buffers,e.segments,n,t,r.program,k,h,f,r.uniformValues,e.terrainData)}}function rn(t,e,r,n,i,a,o,s,l,c){const u=n.context,h=u.gl;i.draw(u,h.TRIANGLES,a,o,s,Zr.disabled,l,c,r.id,t.layoutVertexBuffer,t.indexBuffer,e,r.paint,n.transform.zoom,t.programConfigurations.get(r.id),t.dynamicLayoutVertexBuffer,t.opacityVertexBuffer)}function nn(t,r,n,i){if(0!==n.paint.get(\"heatmap-opacity\"))if(\"offscreen\"===t.renderPass){const a=t.context,o=a.gl,s=Gr.disabled,l=new Ur([o.ONE,o.ONE],e.aN.transparent,[!0,!0,!0,!0]);(function(t,e,r){const n=t.gl;t.activeTexture.set(n.TEXTURE1),t.viewport.set([0,0,e.width/4,e.height/4]);let i=r.heatmapFbo;if(i)n.bindTexture(n.TEXTURE_2D,i.colorAttachment.get()),t.bindFramebuffer.set(i.framebuffer);else{const a=n.createTexture();n.bindTexture(n.TEXTURE_2D,a),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_S,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_T,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MIN_FILTER,n.LINEAR),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MAG_FILTER,n.LINEAR),i=r.heatmapFbo=t.createFramebuffer(e.width/4,e.height/4,!1,!1),function(t,e,r,n){var i,a;const o=t.gl,s=null!==(i=t.HALF_FLOAT)&&void 0!==i?i:o.UNSIGNED_BYTE,l=null!==(a=t.RGBA16F)&&void 0!==a?a:o.RGBA;o.texImage2D(o.TEXTURE_2D,0,l,e.width/4,e.height/4,0,o.RGBA,s,null),n.colorAttachment.set(r)}(t,e,a,i)}})(a,t,n),a.clear({color:e.aN.transparent});for(let e=0;e0){const i=a.now(),s=(i-t.timeAdded)/l,c=r?(i-r.timeAdded)/l:-1,u=n.getSource(),h=o.coveringZoomLevel({tileSize:u.tileSize,roundZoom:u.roundZoom}),f=!r||Math.abs(r.tileID.overscaledZ-h)>Math.abs(t.tileID.overscaledZ-h),p=f&&t.refreshedUponExpiration?1:e.ad(f?s:1-c,0,1);return t.refreshedUponExpiration&&s>=1&&(t.refreshedUponExpiration=!1),r?{opacity:1,mix:1-p}:{opacity:p,mix:0}}return{opacity:1,mix:0}}const hn=new e.aN(1,0,0,1),fn=new e.aN(0,1,0,1),pn=new e.aN(0,0,1,1),dn=new e.aN(1,0,1,1),mn=new e.aN(0,1,1,1);function gn(t){const e=t.transform.padding;yn(t,t.transform.height-(e.top||0),3,hn),yn(t,e.bottom||0,3,fn),vn(t,e.left||0,3,pn),vn(t,t.transform.width-(e.right||0),3,dn);const r=t.transform.centerPoint;!function(t,e,r,n){const i=20,a=2;xn(t,e-a/2,r-i/2,a,i,n),xn(t,e-i/2,r-a/2,i,a,n)}(t,r.x,t.transform.height-r.y,mn)}function yn(t,e,r,n){xn(t,0,e+r/2,t.transform.width,r,n)}function vn(t,e,r,n){xn(t,e-r/2,0,r,t.transform.height,n)}function xn(t,e,r,n,i,a){const o=t.context,s=o.gl;s.enable(s.SCISSOR_TEST),s.scissor(e*t.pixelRatio,r*t.pixelRatio,n*t.pixelRatio,i*t.pixelRatio),o.clear({color:a}),s.disable(s.SCISSOR_TEST)}function _n(t,r,n){const i=t.context,a=i.gl,o=n.posMatrix,s=t.useProgram(\"debug\"),l=qr.disabled,c=Gr.disabled,u=t.colorModeForRenderPass(),h=\"$debug\",f=t.style.map.terrain&&t.style.map.terrain.getTerrainData(n);i.activeTexture.set(a.TEXTURE0);const p=r.getTileByID(n.key).latestRawTileData,d=p&&p.byteLength||0,m=Math.floor(d/1024),g=r.getTile(n).tileSize,y=512/Math.min(g,512)*(n.overscaledZ/t.transform.zoom)*.5;let v=n.canonical.toString();n.overscaledZ!==n.canonical.z&&(v+=` => ${n.overscaledZ}`),function(t,e){t.initDebugOverlayCanvas();const r=t.debugOverlayCanvas,n=t.context.gl,i=t.debugOverlayCanvas.getContext(\"2d\");i.clearRect(0,0,r.width,r.height),i.shadowColor=\"white\",i.shadowBlur=2,i.lineWidth=1.5,i.strokeStyle=\"white\",i.textBaseline=\"top\",i.font=\"bold 36px Open Sans, sans-serif\",i.fillText(e,5,5),i.strokeText(e,5,5),t.debugOverlayTexture.update(r),t.debugOverlayTexture.bind(n.LINEAR,n.CLAMP_TO_EDGE)}(t,`${v} ${m}kB`),s.draw(i,a.TRIANGLES,l,c,Ur.alphaBlended,Zr.disabled,Pe(o,e.aN.transparent,y),null,h,t.debugBuffer,t.quadTriangleIndexBuffer,t.debugSegments),s.draw(i,a.LINE_STRIP,l,c,u,Zr.disabled,Pe(o,e.aN.red),f,h,t.debugBuffer,t.tileBorderIndexBuffer,t.debugSegments)}function bn(t,e,r){const n=t.context,i=n.gl,a=t.colorModeForRenderPass(),o=new qr(i.LEQUAL,qr.ReadWrite,t.depthRangeFor3D),s=t.useProgram(\"terrain\"),l=e.getTerrainMesh();n.bindFramebuffer.set(null),n.viewport.set([0,0,t.width,t.height]);for(const c of r){const r=t.renderToTexture.getTexture(c),u=e.getTerrainData(c.tileID);n.activeTexture.set(i.TEXTURE0),i.bindTexture(i.TEXTURE_2D,r.texture);const h=t.transform.calculatePosMatrix(c.tileID.toUnwrapped()),f=e.getMeshFrameDelta(t.transform.zoom),p=t.transform.calculateFogMatrix(c.tileID.toUnwrapped()),d=xe(h,f,p,t.style.sky,t.transform.pitch);s.draw(n,i.TRIANGLES,o,Gr.disabled,a,Zr.backCCW,d,u,\"terrain\",l.vertexBuffer,l.indexBuffer,l.segments)}}class wn{constructor(t,e,r){this.vertexBuffer=t,this.indexBuffer=e,this.segments=r}destroy(){this.vertexBuffer.destroy(),this.indexBuffer.destroy(),this.segments.destroy(),this.vertexBuffer=null,this.indexBuffer=null,this.segments=null}}class Tn{constructor(t,r){this.context=new Vr(t),this.transform=r,this._tileTextures={},this.terrainFacilitator={dirty:!0,matrix:e.ao(new Float64Array(16)),renderTime:0},this.setup(),this.numSublayers=dt.maxUnderzooming+dt.maxOverzooming+1,this.depthEpsilon=1/Math.pow(2,16),this.crossTileSymbolIndex=new he}resize(t,e,r){if(this.width=Math.floor(t*r),this.height=Math.floor(e*r),this.pixelRatio=r,this.context.viewport.set([0,0,this.width,this.height]),this.style)for(const t of this.style._order)this.style._layers[t].resize()}setup(){const t=this.context,r=new e.aX;r.emplaceBack(0,0),r.emplaceBack(e.X,0),r.emplaceBack(0,e.X),r.emplaceBack(e.X,e.X),this.tileExtentBuffer=t.createVertexBuffer(r,me.members),this.tileExtentSegments=e.a0.simpleSegment(0,0,4,2);const n=new e.aX;n.emplaceBack(0,0),n.emplaceBack(e.X,0),n.emplaceBack(0,e.X),n.emplaceBack(e.X,e.X),this.debugBuffer=t.createVertexBuffer(n,me.members),this.debugSegments=e.a0.simpleSegment(0,0,4,5);const i=new e.$;i.emplaceBack(0,0,0,0),i.emplaceBack(e.X,0,e.X,0),i.emplaceBack(0,e.X,0,e.X),i.emplaceBack(e.X,e.X,e.X,e.X),this.rasterBoundsBuffer=t.createVertexBuffer(i,et.members),this.rasterBoundsSegments=e.a0.simpleSegment(0,0,4,2);const a=new e.aX;a.emplaceBack(0,0),a.emplaceBack(1,0),a.emplaceBack(0,1),a.emplaceBack(1,1),this.viewportBuffer=t.createVertexBuffer(a,me.members),this.viewportSegments=e.a0.simpleSegment(0,0,4,2);const o=new e.aZ;o.emplaceBack(0),o.emplaceBack(1),o.emplaceBack(3),o.emplaceBack(2),o.emplaceBack(0),this.tileBorderIndexBuffer=t.createIndexBuffer(o);const s=new e.aY;s.emplaceBack(0,1,2),s.emplaceBack(2,1,3),this.quadTriangleIndexBuffer=t.createIndexBuffer(s);const l=this.context.gl;this.stencilClearMode=new Gr({func:l.ALWAYS,mask:0},0,255,l.ZERO,l.ZERO,l.ZERO)}clearStencil(){const t=this.context,r=t.gl;this.nextStencilID=1,this.currentStencilSource=void 0;const n=e.H();e.aQ(n,0,this.width,this.height,0,0,1),e.K(n,n,[r.drawingBufferWidth,r.drawingBufferHeight,0]),this.useProgram(\"clippingMask\").draw(t,r.TRIANGLES,qr.disabled,this.stencilClearMode,Ur.disabled,Zr.disabled,ze(n),null,\"$clipping\",this.viewportBuffer,this.quadTriangleIndexBuffer,this.viewportSegments)}_renderTileClippingMasks(t,e){if(this.currentStencilSource===t.source||!t.isTileClipped()||!e||!e.length)return;this.currentStencilSource=t.source;const r=this.context,n=r.gl;this.nextStencilID+e.length>256&&this.clearStencil(),r.setColorMode(Ur.disabled),r.setDepthMode(qr.disabled);const i=this.useProgram(\"clippingMask\");this._tileClippingMaskIDs={};for(const t of e){const e=this._tileClippingMaskIDs[t.key]=this.nextStencilID++,a=this.style.map.terrain&&this.style.map.terrain.getTerrainData(t);i.draw(r,n.TRIANGLES,qr.disabled,new Gr({func:n.ALWAYS,mask:0},e,255,n.KEEP,n.KEEP,n.REPLACE),Ur.disabled,Zr.disabled,ze(t.posMatrix),a,\"$clipping\",this.tileExtentBuffer,this.quadTriangleIndexBuffer,this.tileExtentSegments)}}stencilModeFor3D(){this.currentStencilSource=void 0,this.nextStencilID+1>256&&this.clearStencil();const t=this.nextStencilID++,e=this.context.gl;return new Gr({func:e.NOTEQUAL,mask:255},t,255,e.KEEP,e.KEEP,e.REPLACE)}stencilModeForClipping(t){const e=this.context.gl;return new Gr({func:e.EQUAL,mask:255},this._tileClippingMaskIDs[t.key],0,e.KEEP,e.KEEP,e.REPLACE)}stencilConfigForOverlap(t){const e=this.context.gl,r=t.sort(((t,e)=>e.overscaledZ-t.overscaledZ)),n=r[r.length-1].overscaledZ,i=r[0].overscaledZ-n+1;if(i>1){this.currentStencilSource=void 0,this.nextStencilID+i>256&&this.clearStencil();const t={};for(let r=0;r({u_sky_color:t.properties.get(\"sky-color\"),u_horizon_color:t.properties.get(\"horizon-color\"),u_horizon:(e.height/2+e.getHorizon())*r,u_sky_horizon_blend:t.properties.get(\"sky-horizon-blend\")*e.height/2*r}))(r,t.style.map.transform,t.pixelRatio),o=new qr(i.LEQUAL,qr.ReadWrite,[0,1]),s=Gr.disabled,l=t.colorModeForRenderPass(),c=t.useProgram(\"sky\");if(!r.mesh){const t=new e.aX;t.emplaceBack(-1,-1),t.emplaceBack(1,-1),t.emplaceBack(1,1),t.emplaceBack(-1,1);const i=new e.aY;i.emplaceBack(0,1,2),i.emplaceBack(0,2,3),r.mesh=new wn(n.createVertexBuffer(t,me.members),n.createIndexBuffer(i),e.a0.simpleSegment(0,0,t.length,i.length))}c.draw(n,i.TRIANGLES,o,s,l,Zr.disabled,a,void 0,\"sky\",r.mesh.vertexBuffer,r.mesh.indexBuffer,r.mesh.segments)}(this,this.style.sky),this._showOverdrawInspector=r.showOverdrawInspector,this.depthRangeFor3D=[0,1-(t._order.length+2)*this.numSublayers*this.depthEpsilon],!this.renderToTexture)for(this.renderPass=\"opaque\",this.currentLayer=i.length-1;this.currentLayer>=0;this.currentLayer--){const t=this.style._layers[i[this.currentLayer]],e=o[t.source],r=s[t.source];this._renderTileClippingMasks(t,r),this.renderLayer(this,e,t,r)}for(this.renderPass=\"translucent\",this.currentLayer=0;this.currentLayerr.source&&!r.isHidden(e)?[t.sourceCaches[r.source]]:[])),i=n.filter((t=>\"vector\"===t.getSource().type)),a=n.filter((t=>\"vector\"!==t.getSource().type)),o=t=>{(!r||r.getSource().maxzoomo(t))),r||a.forEach((t=>o(t))),r}(this.style,this.transform.zoom);t&&function(t,e,r){for(let n=0;n0),i&&(e.b0(r,n),this.terrainFacilitator.renderTime=Date.now(),this.terrainFacilitator.dirty=!1,function(t,r){const n=t.context,i=n.gl,a=Ur.unblended,o=new qr(i.LEQUAL,qr.ReadWrite,[0,1]),s=r.getTerrainMesh(),l=r.sourceCache.getRenderableTiles(),c=t.useProgram(\"terrainDepth\");n.bindFramebuffer.set(r.getFramebuffer(\"depth\").framebuffer),n.viewport.set([0,0,t.width/devicePixelRatio,t.height/devicePixelRatio]),n.clear({color:e.aN.transparent,depth:1});for(const e of l){const l=r.getTerrainData(e.tileID),u={u_matrix:t.transform.calculatePosMatrix(e.tileID.toUnwrapped()),u_ele_delta:r.getMeshFrameDelta(t.transform.zoom)};c.draw(n,i.TRIANGLES,o,Gr.disabled,a,Zr.backCCW,u,l,\"terrain\",s.vertexBuffer,s.indexBuffer,s.segments)}n.bindFramebuffer.set(null),n.viewport.set([0,0,t.width,t.height])}(this,this.style.map.terrain),function(t,r){const n=t.context,i=n.gl,a=Ur.unblended,o=new qr(i.LEQUAL,qr.ReadWrite,[0,1]),s=r.getTerrainMesh(),l=r.getCoordsTexture(),c=r.sourceCache.getRenderableTiles(),u=t.useProgram(\"terrainCoords\");n.bindFramebuffer.set(r.getFramebuffer(\"coords\").framebuffer),n.viewport.set([0,0,t.width/devicePixelRatio,t.height/devicePixelRatio]),n.clear({color:e.aN.transparent,depth:1}),r.coordsIndex=[];for(const e of c){const c=r.getTerrainData(e.tileID);n.activeTexture.set(i.TEXTURE0),i.bindTexture(i.TEXTURE_2D,l.texture);const h={u_matrix:t.transform.calculatePosMatrix(e.tileID.toUnwrapped()),u_terrain_coords_id:(255-r.coordsIndex.length)/255,u_texture:0,u_ele_delta:r.getMeshFrameDelta(t.transform.zoom)};u.draw(n,i.TRIANGLES,o,Gr.disabled,a,Zr.backCCW,h,c,\"terrain\",s.vertexBuffer,s.indexBuffer,s.segments),r.coordsIndex.push(e.tileID.key)}n.bindFramebuffer.set(null),n.viewport.set([0,0,t.width,t.height])}(this,this.style.map.terrain))}renderLayer(t,r,n,i){if(!n.isHidden(this.transform.zoom)&&(\"background\"===n.type||\"custom\"===n.type||(i||[]).length))switch(this.id=n.id,n.type){case\"symbol\":$r(t,r,n,i,this.style.placement.variableOffsets);break;case\"circle\":!function(t,r,n,i){if(\"translucent\"!==t.renderPass)return;const a=n.paint.get(\"circle-opacity\"),o=n.paint.get(\"circle-stroke-width\"),s=n.paint.get(\"circle-stroke-opacity\"),l=!n.layout.get(\"circle-sort-key\").isConstant();if(0===a.constantOr(1)&&(0===o.constantOr(1)||0===s.constantOr(1)))return;const c=t.context,u=c.gl,h=t.depthModeForSublayer(0,qr.ReadOnly),f=Gr.disabled,p=t.colorModeForRenderPass(),d=[];for(let a=0;at.sortKey-e.sortKey));for(const e of d){const{programConfiguration:r,program:i,layoutVertexBuffer:a,indexBuffer:o,uniformValues:s,terrainData:l}=e.state,d=e.segments;i.draw(c,u.TRIANGLES,h,f,p,Zr.disabled,s,l,n.id,a,o,d,n.paint,t.transform.zoom,r)}}(t,r,n,i);break;case\"heatmap\":nn(t,r,n,i);break;case\"line\":!function(t,r,n,i){if(\"translucent\"!==t.renderPass)return;const a=n.paint.get(\"line-opacity\"),o=n.paint.get(\"line-width\");if(0===a.constantOr(1)||0===o.constantOr(1))return;const s=t.depthModeForSublayer(0,qr.ReadOnly),l=t.colorModeForRenderPass(),c=n.paint.get(\"line-dasharray\"),u=n.paint.get(\"line-pattern\"),h=u.constantOr(1),f=n.paint.get(\"line-gradient\"),p=n.getCrossfadeParameters(),d=h?\"linePattern\":c?\"lineSDF\":f?\"lineGradient\":\"line\",m=t.context,g=m.gl;let y=!0;for(const a of i){const i=r.getTile(a);if(h&&!i.patternsLoaded())continue;const o=i.getBucket(n);if(!o)continue;const v=o.programConfigurations.get(n.id),x=t.context.program.get(),_=t.useProgram(d,v),b=y||_.program!==x,T=t.style.map.terrain&&t.style.map.terrain.getTerrainData(a),k=u.constantOr(null);if(k&&i.imageAtlas){const t=i.imageAtlas,e=t.patternPositions[k.to.toString()],r=t.patternPositions[k.from.toString()];e&&r&&v.setConstantPatternPositions(e,r)}const A=T?a:null,M=h?Ue(t,i,n,p,A):c?Ve(t,i,n,c,p,A):f?je(t,i,n,o.lineClipsArray.length,A):Ne(t,i,n,A);if(h)m.activeTexture.set(g.TEXTURE0),i.imageAtlasTexture.bind(g.LINEAR,g.CLAMP_TO_EDGE),v.updatePaintBuffers(p);else if(c&&(b||t.lineAtlas.dirty))m.activeTexture.set(g.TEXTURE0),t.lineAtlas.bind(m);else if(f){const i=o.gradients[n.id];let s=i.texture;if(n.gradientVersion!==i.version){let l=256;if(n.stepInterpolant){const n=r.getSource().maxzoom,i=a.canonical.z===n?Math.ceil(1<20&&a.texParameterf(a.TEXTURE_2D,i.extTextureFilterAnisotropic.TEXTURE_MAX_ANISOTROPY_EXT,i.extTextureFilterAnisotropicMax);const _=t.style.map.terrain&&t.style.map.terrain.getTerrainData(n),b=_?n:null,w=b?b.posMatrix:t.transform.calculatePosMatrix(n.toUnwrapped(),f),T=Ge(w,v||[0,0],y||1,g,r);o instanceof rt?s.draw(i,a.TRIANGLES,u,Gr.disabled,l,Zr.disabled,T,_,r.id,o.boundsBuffer,t.quadTriangleIndexBuffer,o.boundsSegments):s.draw(i,a.TRIANGLES,u,c[n.overscaledZ],l,Zr.disabled,T,_,r.id,t.rasterBoundsBuffer,t.quadTriangleIndexBuffer,t.rasterBoundsSegments)}}(t,r,n,i);break;case\"background\":!function(t,e,r,n){const i=r.paint.get(\"background-color\"),a=r.paint.get(\"background-opacity\");if(0===a)return;const o=t.context,s=o.gl,l=t.transform,c=l.tileSize,u=r.paint.get(\"background-pattern\");if(t.isPatternMissing(u))return;const h=!u&&1===i.a&&1===a&&t.opaquePassEnabledForLayer()?\"opaque\":\"translucent\";if(t.renderPass!==h)return;const f=Gr.disabled,p=t.depthModeForSublayer(0,\"opaque\"===h?qr.ReadWrite:qr.ReadOnly),d=t.colorModeForRenderPass(),m=t.useProgram(u?\"backgroundPattern\":\"background\"),g=n||l.coveringTiles({tileSize:c,terrain:t.style.map.terrain});u&&(o.activeTexture.set(s.TEXTURE0),t.imageManager.bind(t.context));const y=r.getCrossfadeParameters();for(const e of g){const l=n?e.posMatrix:t.transform.calculatePosMatrix(e.toUnwrapped()),h=u?Je(l,a,t,u,{tileID:e,tileSize:c},y):$e(l,a,i),g=t.style.map.terrain&&t.style.map.terrain.getTerrainData(e);m.draw(o,s.TRIANGLES,p,f,d,Zr.disabled,h,g,r.id,t.tileExtentBuffer,t.quadTriangleIndexBuffer,t.tileExtentSegments)}}(t,0,n,i);break;case\"custom\":!function(t,e,r){const n=t.context,i=r.implementation;if(\"offscreen\"===t.renderPass){const e=i.prerender;e&&(t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),e.call(i,n.gl,t.transform.customLayerMatrix()),n.setDirty(),t.setBaseState())}else if(\"translucent\"===t.renderPass){t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),n.setStencilMode(Gr.disabled);const e=\"3d\"===i.renderingMode?new qr(t.context.gl.LEQUAL,qr.ReadWrite,t.depthRangeFor3D):t.depthModeForSublayer(0,qr.ReadOnly);n.setDepthMode(e),i.render(n.gl,t.transform.customLayerMatrix(),{farZ:t.transform.farZ,nearZ:t.transform.nearZ,fov:t.transform._fov,modelViewProjectionMatrix:t.transform.modelViewProjectionMatrix,projectionMatrix:t.transform.projectionMatrix}),n.setDirty(),t.setBaseState(),n.bindFramebuffer.set(null)}}(t,0,n)}}translatePosMatrix(t,r,n,i,a){if(!n[0]&&!n[1])return t;const o=a?\"map\"===i?this.transform.angle:0:\"viewport\"===i?-this.transform.angle:0;if(o){const t=Math.sin(o),e=Math.cos(o);n=[n[0]*e-n[1]*t,n[0]*t+n[1]*e]}const s=[a?n[0]:Nt(r,n[0],this.transform.zoom),a?n[1]:Nt(r,n[1],this.transform.zoom),0],l=new Float32Array(16);return e.J(l,t,s),l}saveTileTexture(t){const e=this._tileTextures[t.size[0]];e?e.push(t):this._tileTextures[t.size[0]]=[t]}getTileTexture(t){const e=this._tileTextures[t];return e&&e.length>0?e.pop():null}isPatternMissing(t){if(!t)return!1;if(!t.from||!t.to)return!0;const e=this.imageManager.getPattern(t.from.toString()),r=this.imageManager.getPattern(t.to.toString());return!e||!r}useProgram(t,e){this.cache=this.cache||{};const r=t+(e?e.cacheKey:\"\")+(this._showOverdrawInspector?\"/overdraw\":\"\")+(this.style.map.terrain?\"/terrain\":\"\");return this.cache[r]||(this.cache[r]=new be(this.context,ge[t],e,Ke[t],this._showOverdrawInspector,this.style.map.terrain)),this.cache[r]}setCustomLayerDefaults(){this.context.unbindVAO(),this.context.cullFace.setDefault(),this.context.activeTexture.setDefault(),this.context.pixelStoreUnpack.setDefault(),this.context.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.context.pixelStoreUnpackFlipY.setDefault()}setBaseState(){const t=this.context.gl;this.context.cullFace.set(!1),this.context.viewport.set([0,0,this.width,this.height]),this.context.blendEquation.set(t.FUNC_ADD)}initDebugOverlayCanvas(){if(null==this.debugOverlayCanvas){this.debugOverlayCanvas=document.createElement(\"canvas\"),this.debugOverlayCanvas.width=512,this.debugOverlayCanvas.height=512;const t=this.context.gl;this.debugOverlayTexture=new w(this.context,this.debugOverlayCanvas,t.RGBA)}}destroy(){this.debugOverlayTexture&&this.debugOverlayTexture.destroy()}overLimit(){const{drawingBufferWidth:t,drawingBufferHeight:e}=this.context.gl;return this.width!==t||this.height!==e}}class kn{constructor(t,e){this.points=t,this.planes=e}static fromInvProjectionMatrix(t,r,n){const i=Math.pow(2,n),a=[[-1,1,-1,1],[1,1,-1,1],[1,-1,-1,1],[-1,-1,-1,1],[-1,1,1,1],[1,1,1,1],[1,-1,1,1],[-1,-1,1,1]].map((n=>{const a=1/(n=e.ag([],n,t))[3]/r*i;return e.b1(n,n,[a,a,1/n[3],a])})),o=[[0,1,2],[6,5,4],[0,3,7],[2,1,5],[3,2,6],[0,4,5]].map((t=>{const e=function(t,e){var r=e[0],n=e[1],i=e[2],a=r*r+n*n+i*i;return a>0&&(a=1/Math.sqrt(a)),t[0]=e[0]*a,t[1]=e[1]*a,t[2]=e[2]*a,t}([],function(t,e,r){var n=e[0],i=e[1],a=e[2],o=r[0],s=r[1],l=r[2];return t[0]=i*l-a*s,t[1]=a*o-n*l,t[2]=n*s-i*o,t}([],y([],a[t[0]],a[t[1]]),y([],a[t[2]],a[t[1]]))),r=(n=e,i=a[t[1]],-(n[0]*i[0]+n[1]*i[1]+n[2]*i[2]));var n,i;return e.concat(r)}));return new kn(a,o)}}class An{constructor(t,e){this.min=t,this.max=e,this.center=function(t,e,r){return t[0]=e[0]*r,t[1]=e[1]*r,t[2]=e[2]*r,t}([],function(t,e,r){return t[0]=e[0]+r[0],t[1]=e[1]+r[1],t[2]=e[2]+r[2],t}([],this.min,this.max),.5)}quadrant(t){const e=[t%2==0,t<2],r=m(this.min),n=m(this.max);for(let t=0;t=0&&o++;if(0===o)return 0;o!==r.length&&(n=!1)}if(n)return 2;for(let e=0;e<3;e++){let r=Number.MAX_VALUE,n=-Number.MAX_VALUE;for(let i=0;ithis.max[e]-this.min[e])return 0}return 1}}class Mn{constructor(t=0,e=0,r=0,n=0){if(isNaN(t)||t<0||isNaN(e)||e<0||isNaN(r)||r<0||isNaN(n)||n<0)throw new Error(\"Invalid value for edge-insets, top, bottom, left and right must all be numbers\");this.top=t,this.bottom=e,this.left=r,this.right=n}interpolate(t,r,n){return null!=r.top&&null!=t.top&&(this.top=e.z.number(t.top,r.top,n)),null!=r.bottom&&null!=t.bottom&&(this.bottom=e.z.number(t.bottom,r.bottom,n)),null!=r.left&&null!=t.left&&(this.left=e.z.number(t.left,r.left,n)),null!=r.right&&null!=t.right&&(this.right=e.z.number(t.right,r.right,n)),this}getCenter(t,r){const n=e.ad((this.left+t-this.right)/2,0,t),i=e.ad((this.top+r-this.bottom)/2,0,r);return new e.P(n,i)}equals(t){return this.top===t.top&&this.bottom===t.bottom&&this.left===t.left&&this.right===t.right}clone(){return new Mn(this.top,this.bottom,this.left,this.right)}toJSON(){return{top:this.top,bottom:this.bottom,left:this.left,right:this.right}}}const Sn=85.051129;class En{constructor(t,r,n,i,a){this.tileSize=512,this._renderWorldCopies=void 0===a||!!a,this._minZoom=t||0,this._maxZoom=r||22,this._minPitch=null==n?0:n,this._maxPitch=null==i?60:i,this.setMaxBounds(),this.width=0,this.height=0,this._center=new e.N(0,0),this._elevation=0,this.zoom=0,this.angle=0,this._fov=.6435011087932844,this._pitch=0,this._unmodified=!0,this._edgeInsets=new Mn,this._posMatrixCache={},this._alignedPosMatrixCache={},this._fogMatrixCache={},this.minElevationForCurrentTile=0}clone(){const t=new En(this._minZoom,this._maxZoom,this._minPitch,this.maxPitch,this._renderWorldCopies);return t.apply(this),t}apply(t){this.tileSize=t.tileSize,this.latRange=t.latRange,this.width=t.width,this.height=t.height,this._center=t._center,this._elevation=t._elevation,this.minElevationForCurrentTile=t.minElevationForCurrentTile,this.zoom=t.zoom,this.angle=t.angle,this._fov=t._fov,this._pitch=t._pitch,this._unmodified=t._unmodified,this._edgeInsets=t._edgeInsets.clone(),this._calcMatrices()}get minZoom(){return this._minZoom}set minZoom(t){this._minZoom!==t&&(this._minZoom=t,this.zoom=Math.max(this.zoom,t))}get maxZoom(){return this._maxZoom}set maxZoom(t){this._maxZoom!==t&&(this._maxZoom=t,this.zoom=Math.min(this.zoom,t))}get minPitch(){return this._minPitch}set minPitch(t){this._minPitch!==t&&(this._minPitch=t,this.pitch=Math.max(this.pitch,t))}get maxPitch(){return this._maxPitch}set maxPitch(t){this._maxPitch!==t&&(this._maxPitch=t,this.pitch=Math.min(this.pitch,t))}get renderWorldCopies(){return this._renderWorldCopies}set renderWorldCopies(t){void 0===t?t=!0:null===t&&(t=!1),this._renderWorldCopies=t}get worldSize(){return this.tileSize*this.scale}get centerOffset(){return this.centerPoint._sub(this.size._div(2))}get size(){return new e.P(this.width,this.height)}get bearing(){return-this.angle/Math.PI*180}set bearing(t){const r=-e.b3(t,-180,180)*Math.PI/180;this.angle!==r&&(this._unmodified=!1,this.angle=r,this._calcMatrices(),this.rotationMatrix=function(){var t=new e.A(4);return e.A!=Float32Array&&(t[1]=0,t[2]=0),t[0]=1,t[3]=1,t}(),function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=Math.sin(r),l=Math.cos(r);t[0]=n*l+a*s,t[1]=i*l+o*s,t[2]=n*-s+a*l,t[3]=i*-s+o*l}(this.rotationMatrix,this.rotationMatrix,this.angle))}get pitch(){return this._pitch/Math.PI*180}set pitch(t){const r=e.ad(t,this.minPitch,this.maxPitch)/180*Math.PI;this._pitch!==r&&(this._unmodified=!1,this._pitch=r,this._calcMatrices())}get fov(){return this._fov/Math.PI*180}set fov(t){t=Math.max(.01,Math.min(60,t)),this._fov!==t&&(this._unmodified=!1,this._fov=t/180*Math.PI,this._calcMatrices())}get zoom(){return this._zoom}set zoom(t){const e=Math.min(Math.max(t,this.minZoom),this.maxZoom);this._zoom!==e&&(this._unmodified=!1,this._zoom=e,this.tileZoom=Math.max(0,Math.floor(e)),this.scale=this.zoomScale(e),this._constrain(),this._calcMatrices())}get center(){return this._center}set center(t){t.lat===this._center.lat&&t.lng===this._center.lng||(this._unmodified=!1,this._center=t,this._constrain(),this._calcMatrices())}get elevation(){return this._elevation}set elevation(t){t!==this._elevation&&(this._elevation=t,this._constrain(),this._calcMatrices())}get padding(){return this._edgeInsets.toJSON()}set padding(t){this._edgeInsets.equals(t)||(this._unmodified=!1,this._edgeInsets.interpolate(this._edgeInsets,t,1),this._calcMatrices())}get centerPoint(){return this._edgeInsets.getCenter(this.width,this.height)}isPaddingEqual(t){return this._edgeInsets.equals(t)}interpolatePadding(t,e,r){this._unmodified=!1,this._edgeInsets.interpolate(t,e,r),this._constrain(),this._calcMatrices()}coveringZoomLevel(t){const e=(t.roundZoom?Math.round:Math.floor)(this.zoom+this.scaleZoom(this.tileSize/t.tileSize));return Math.max(0,e)}getVisibleUnwrappedCoordinates(t){const r=[new e.b4(0,t)];if(this._renderWorldCopies){const n=this.pointCoordinate(new e.P(0,0)),i=this.pointCoordinate(new e.P(this.width,0)),a=this.pointCoordinate(new e.P(this.width,this.height)),o=this.pointCoordinate(new e.P(0,this.height)),s=Math.floor(Math.min(n.x,i.x,a.x,o.x)),l=Math.floor(Math.max(n.x,i.x,a.x,o.x)),c=1;for(let n=s-c;n<=l+c;n++)0!==n&&r.push(new e.b4(n,t))}return r}coveringTiles(t){var r,n;let i=this.coveringZoomLevel(t);const a=i;if(void 0!==t.minzoom&&it.maxzoom&&(i=t.maxzoom);const o=this.pointCoordinate(this.getCameraPoint()),s=e.Z.fromLngLat(this.center),l=Math.pow(2,i),c=[l*o.x,l*o.y,0],u=[l*s.x,l*s.y,0],h=kn.fromInvProjectionMatrix(this.invModelViewProjectionMatrix,this.worldSize,i);let f=t.minzoom||0;!t.terrain&&this.pitch<=60&&this._edgeInsets.top<.1&&(f=i);const p=t.terrain?2/Math.min(this.tileSize,t.tileSize)*this.tileSize:3,d=t=>({aabb:new An([t*l,0,0],[(t+1)*l,l,0]),zoom:0,x:0,y:0,wrap:t,fullyVisible:!1}),m=[],g=[],y=i,x=t.reparseOverscaled?a:i;if(this._renderWorldCopies)for(let t=1;t<=3;t++)m.push(d(-t)),m.push(d(t));for(m.push(d(0));m.length>0;){const i=m.pop(),a=i.x,o=i.y;let s=i.fullyVisible;if(!s){const t=i.aabb.intersects(h);if(0===t)continue;s=2===t}const l=t.terrain?c:u,d=i.aabb.distanceX(l),_=i.aabb.distanceY(l),b=Math.max(Math.abs(d),Math.abs(_)),w=p+(1<w&&i.zoom>=f){const t=y-i.zoom,r=c[0]-.5-(a<>1),h=i.zoom+1;let f=i.aabb.quadrant(l);if(t.terrain){const a=new e.S(h,i.wrap,h,c,u),o=t.terrain.getMinMaxElevation(a),s=null!==(r=o.minElevation)&&void 0!==r?r:this.elevation,l=null!==(n=o.maxElevation)&&void 0!==n?n:this.elevation;f=new An([f.min[0],f.min[1],s],[f.max[0],f.max[1],l])}m.push({aabb:f,zoom:h,x:c,y:u,wrap:i.wrap,fullyVisible:s})}}return g.sort(((t,e)=>t.distanceSq-e.distanceSq)).map((t=>t.tileID))}resize(t,e){this.width=t,this.height=e,this.pixelsToGLUnits=[2/t,-2/e],this._constrain(),this._calcMatrices()}get unmodified(){return this._unmodified}zoomScale(t){return Math.pow(2,t)}scaleZoom(t){return Math.log(t)/Math.LN2}project(t){const r=e.ad(t.lat,-85.051129,Sn);return new e.P(e.O(t.lng)*this.worldSize,e.Q(r)*this.worldSize)}unproject(t){return new e.Z(t.x/this.worldSize,t.y/this.worldSize).toLngLat()}get point(){return this.project(this.center)}getCameraPosition(){return{lngLat:this.pointLocation(this.getCameraPoint()),altitude:Math.cos(this._pitch)*this.cameraToCenterDistance/this._pixelPerMeter+this.elevation}}recalculateZoom(t){const r=this.elevation,n=Math.cos(this._pitch)*this.cameraToCenterDistance/this._pixelPerMeter,i=this.pointLocation(this.centerPoint,t),a=t.getElevationForLngLatZoom(i,this.tileZoom);if(!(this.elevation-a))return;const o=n+r-a,s=Math.cos(this._pitch)*this.cameraToCenterDistance/o/e.b5(1,i.lat)/this.tileSize,l=this.scaleZoom(s);this._elevation=a,this._center=i,this.zoom=l}setLocationAtPoint(t,r){const n=this.pointCoordinate(r),i=this.pointCoordinate(this.centerPoint),a=this.locationCoordinate(t),o=new e.Z(a.x-(n.x-i.x),a.y-(n.y-i.y));this.center=this.coordinateLocation(o),this._renderWorldCopies&&(this.center=this.center.wrap())}locationPoint(t,e){return e?this.coordinatePoint(this.locationCoordinate(t),e.getElevationForLngLatZoom(t,this.tileZoom),this.pixelMatrix3D):this.coordinatePoint(this.locationCoordinate(t))}pointLocation(t,e){return this.coordinateLocation(this.pointCoordinate(t,e))}locationCoordinate(t){return e.Z.fromLngLat(t)}coordinateLocation(t){return t&&t.toLngLat()}pointCoordinate(t,r){if(r){const e=r.pointCoordinate(t);if(null!=e)return e}const n=[t.x,t.y,0,1],i=[t.x,t.y,1,1];e.ag(n,n,this.pixelMatrixInverse),e.ag(i,i,this.pixelMatrixInverse);const a=n[3],o=i[3],s=n[0]/a,l=i[0]/o,c=n[1]/a,u=i[1]/o,h=n[2]/a,f=i[2]/o,p=h===f?0:(0-h)/(f-h);return new e.Z(e.z.number(s,l,p)/this.worldSize,e.z.number(c,u,p)/this.worldSize)}coordinatePoint(t,r=0,n=this.pixelMatrix){const i=[t.x*this.worldSize,t.y*this.worldSize,r,1];return e.ag(i,i,n),new e.P(i[0]/i[3],i[1]/i[3])}getBounds(){const t=Math.max(0,this.height/2-this.getHorizon());return(new X).extend(this.pointLocation(new e.P(0,t))).extend(this.pointLocation(new e.P(this.width,t))).extend(this.pointLocation(new e.P(this.width,this.height))).extend(this.pointLocation(new e.P(0,this.height)))}getMaxBounds(){return this.latRange&&2===this.latRange.length&&this.lngRange&&2===this.lngRange.length?new X([this.lngRange[0],this.latRange[0]],[this.lngRange[1],this.latRange[1]]):null}getHorizon(){return Math.tan(Math.PI/2-this._pitch)*this.cameraToCenterDistance*.85}setMaxBounds(t){t?(this.lngRange=[t.getWest(),t.getEast()],this.latRange=[t.getSouth(),t.getNorth()],this._constrain()):(this.lngRange=null,this.latRange=[-85.051129,Sn])}calculateTileMatrix(t){const r=t.canonical,n=this.worldSize/this.zoomScale(r.z),i=r.x+Math.pow(2,r.z)*t.wrap,a=e.ao(new Float64Array(16));return e.J(a,a,[i*n,r.y*n,0]),e.K(a,a,[n/e.X,n/e.X,1]),a}calculatePosMatrix(t,r=!1){const n=t.key,i=r?this._alignedPosMatrixCache:this._posMatrixCache;if(i[n])return i[n];const a=this.calculateTileMatrix(t);return e.L(a,r?this.alignedModelViewProjectionMatrix:this.modelViewProjectionMatrix,a),i[n]=new Float32Array(a),i[n]}calculateFogMatrix(t){const r=t.key,n=this._fogMatrixCache;if(n[r])return n[r];const i=this.calculateTileMatrix(t);return e.L(i,this.fogMatrix,i),n[r]=new Float32Array(i),n[r]}customLayerMatrix(){return this.mercatorMatrix.slice()}getConstrained(t,r){r=e.ad(+r,this.minZoom,this.maxZoom);const n={center:new e.N(t.lng,t.lat),zoom:r};let i=this.lngRange;if(!this._renderWorldCopies&&null===i){const t=180-1e-10;i=[-t,t]}const a=this.tileSize*this.zoomScale(n.zoom);let o=0,s=a,l=0,c=a,u=0,h=0;const{x:f,y:p}=this.size;if(this.latRange){const t=this.latRange;o=e.Q(t[1])*a,s=e.Q(t[0])*a,s-os&&(y=s-t)}if(i){const t=(l+c)/2;let r=d;this._renderWorldCopies&&(r=e.b3(d,t-a/2,t+a/2));const n=f/2;r-nc&&(g=c-n)}if(void 0!==g||void 0!==y){const t=new e.P(null!=g?g:d,null!=y?y:m);n.center=this.unproject.call({worldSize:a},t).wrap()}return n}_constrain(){if(!this.center||!this.width||!this.height||this._constraining)return;this._constraining=!0;const t=this._unmodified,{center:e,zoom:r}=this.getConstrained(this.center,this.zoom);this.center=e,this.zoom=r,this._unmodified=t,this._constraining=!1}_calcMatrices(){if(!this.height)return;const t=this._fov/2,r=this.centerOffset,n=this.point.x,i=this.point.y;this.cameraToCenterDistance=.5/Math.tan(t)*this.height,this._pixelPerMeter=e.b5(1,this.center.lat)*this.worldSize;let a=e.ao(new Float64Array(16));e.K(a,a,[this.width/2,-this.height/2,1]),e.J(a,a,[1,-1,0]),this.labelPlaneMatrix=a,a=e.ao(new Float64Array(16)),e.K(a,a,[1,-1,1]),e.J(a,a,[-1,-1,0]),e.K(a,a,[2/this.width,2/this.height,1]),this.glCoordMatrix=a;const o=this.cameraToCenterDistance+this._elevation*this._pixelPerMeter/Math.cos(this._pitch),s=Math.min(this.elevation,this.minElevationForCurrentTile),l=o-s*this._pixelPerMeter/Math.cos(this._pitch),c=s<0?l:o,u=Math.PI/2+this._pitch,h=this._fov*(.5+r.y/this.height),f=Math.sin(h)*c/Math.sin(e.ad(Math.PI-u-h,.01,Math.PI-.01)),p=this.getHorizon(),d=2*Math.atan(p/this.cameraToCenterDistance)*(.5+r.y/(2*p)),m=Math.sin(d)*c/Math.sin(e.ad(Math.PI-u-d,.01,Math.PI-.01)),g=Math.min(f,m);this.farZ=1.01*(Math.cos(Math.PI/2-this._pitch)*g+c),this.nearZ=this.height/50,a=new Float64Array(16),e.b6(a,this._fov,this.width/this.height,this.nearZ,this.farZ),a[8]=2*-r.x/this.width,a[9]=2*r.y/this.height,this.projectionMatrix=e.af(a),e.K(a,a,[1,-1,1]),e.J(a,a,[0,0,-this.cameraToCenterDistance]),e.b7(a,a,this._pitch),e.ae(a,a,this.angle),e.J(a,a,[-n,-i,0]),this.mercatorMatrix=e.K([],a,[this.worldSize,this.worldSize,this.worldSize]),e.K(a,a,[1,1,this._pixelPerMeter]),this.pixelMatrix=e.L(new Float64Array(16),this.labelPlaneMatrix,a),e.J(a,a,[0,0,-this.elevation]),this.modelViewProjectionMatrix=a,this.invModelViewProjectionMatrix=e.at([],a),this.fogMatrix=new Float64Array(16),e.b6(this.fogMatrix,this._fov,this.width/this.height,o,this.farZ),this.fogMatrix[8]=2*-r.x/this.width,this.fogMatrix[9]=2*r.y/this.height,e.K(this.fogMatrix,this.fogMatrix,[1,-1,1]),e.J(this.fogMatrix,this.fogMatrix,[0,0,-this.cameraToCenterDistance]),e.b7(this.fogMatrix,this.fogMatrix,this._pitch),e.ae(this.fogMatrix,this.fogMatrix,this.angle),e.J(this.fogMatrix,this.fogMatrix,[-n,-i,0]),e.K(this.fogMatrix,this.fogMatrix,[1,1,this._pixelPerMeter]),e.J(this.fogMatrix,this.fogMatrix,[0,0,-this.elevation]),this.pixelMatrix3D=e.L(new Float64Array(16),this.labelPlaneMatrix,a);const y=this.width%2/2,v=this.height%2/2,x=Math.cos(this.angle),_=Math.sin(this.angle),b=n-Math.round(n)+x*y+_*v,w=i-Math.round(i)+x*v+_*y,T=new Float64Array(a);if(e.J(T,T,[b>.5?b-1:b,w>.5?w-1:w,0]),this.alignedModelViewProjectionMatrix=T,a=e.at(new Float64Array(16),this.pixelMatrix),!a)throw new Error(\"failed to invert matrix\");this.pixelMatrixInverse=a,this._posMatrixCache={},this._alignedPosMatrixCache={},this._fogMatrixCache={}}maxPitchScaleFactor(){if(!this.pixelMatrixInverse)return 1;const t=this.pointCoordinate(new e.P(0,0)),r=[t.x*this.worldSize,t.y*this.worldSize,0,1];return e.ag(r,r,this.pixelMatrix)[3]/this.cameraToCenterDistance}getCameraPoint(){const t=this._pitch,r=Math.tan(t)*(this.cameraToCenterDistance||1);return this.centerPoint.add(new e.P(0,r))}getCameraQueryGeometry(t){const r=this.getCameraPoint();if(1===t.length)return[t[0],r];{let n=r.x,i=r.y,a=r.x,o=r.y;for(const e of t)n=Math.min(n,e.x),i=Math.min(i,e.y),a=Math.max(a,e.x),o=Math.max(o,e.y);return[new e.P(n,i),new e.P(a,i),new e.P(a,o),new e.P(n,o),new e.P(n,i)]}}lngLatToCameraDepth(t,r){const n=this.locationCoordinate(t),i=[n.x*this.worldSize,n.y*this.worldSize,r,1];return e.ag(i,i,this.modelViewProjectionMatrix),i[2]/i[3]}}function Cn(t,e){let r,n=!1,i=null,a=null;const o=()=>{i=null,n&&(t.apply(a,r),i=setTimeout(o,e),n=!1)};return(...t)=>(n=!0,a=this,r=t,i||o(),i)}class Ln{constructor(t){this._getCurrentHash=()=>{const t=window.location.hash.replace(\"#\",\"\");if(this._hashName){let e;return t.split(\"&\").map((t=>t.split(\"=\"))).forEach((t=>{t[0]===this._hashName&&(e=t)})),(e&&e[1]||\"\").split(\"/\")}return t.split(\"/\")},this._onHashChange=()=>{const t=this._getCurrentHash();if(t.length>=3&&!t.some((t=>isNaN(t)))){const e=this._map.dragRotate.isEnabled()&&this._map.touchZoomRotate.isEnabled()?+(t[3]||0):this._map.getBearing();return this._map.jumpTo({center:[+t[2],+t[1]],zoom:+t[0],bearing:e,pitch:+(t[4]||0)}),!0}return!1},this._updateHashUnthrottled=()=>{const t=window.location.href.replace(/(#.+)?$/,this.getHashString());window.history.replaceState(window.history.state,null,t)},this._removeHash=()=>{const t=this._getCurrentHash();if(0===t.length)return;const e=t.join(\"/\");let r=e;r.split(\"&\").length>0&&(r=r.split(\"&\")[0]),this._hashName&&(r=`${this._hashName}=${e}`);let n=window.location.hash.replace(r,\"\");n.startsWith(\"#&\")?n=n.slice(0,1)+n.slice(2):\"#\"===n&&(n=\"\");let i=window.location.href.replace(/(#.+)?$/,n);i=i.replace(\"&&\",\"&\"),window.history.replaceState(window.history.state,null,i)},this._updateHash=Cn(this._updateHashUnthrottled,300),this._hashName=t&&encodeURIComponent(t)}addTo(t){return this._map=t,addEventListener(\"hashchange\",this._onHashChange,!1),this._map.on(\"moveend\",this._updateHash),this}remove(){return removeEventListener(\"hashchange\",this._onHashChange,!1),this._map.off(\"moveend\",this._updateHash),clearTimeout(this._updateHash()),this._removeHash(),delete this._map,this}getHashString(t){const e=this._map.getCenter(),r=Math.round(100*this._map.getZoom())/100,n=Math.ceil((r*Math.LN2+Math.log(512/360/.5))/Math.LN10),i=Math.pow(10,n),a=Math.round(e.lng*i)/i,o=Math.round(e.lat*i)/i,s=this._map.getBearing(),l=this._map.getPitch();let c=\"\";if(c+=t?`/${a}/${o}/${r}`:`${r}/${o}/${a}`,(s||l)&&(c+=\"/\"+Math.round(10*s)/10),l&&(c+=`/${Math.round(l)}`),this._hashName){const t=this._hashName;let e=!1;const r=window.location.hash.slice(1).split(\"&\").map((r=>{const n=r.split(\"=\")[0];return n===t?(e=!0,`${n}=${c}`):r})).filter((t=>t));return e||r.push(`${t}=${c}`),`#${r.join(\"&\")}`}return`#${c}`}}const In={linearity:.3,easing:e.b8(0,0,.3,1)},Pn=e.e({deceleration:2500,maxSpeed:1400},In),zn=e.e({deceleration:20,maxSpeed:1400},In),On=e.e({deceleration:1e3,maxSpeed:360},In),Dn=e.e({deceleration:1e3,maxSpeed:90},In);class Rn{constructor(t){this._map=t,this.clear()}clear(){this._inertiaBuffer=[]}record(t){this._drainInertiaBuffer(),this._inertiaBuffer.push({time:a.now(),settings:t})}_drainInertiaBuffer(){const t=this._inertiaBuffer,e=a.now();for(;t.length>0&&e-t[0].time>160;)t.shift()}_onMoveEnd(t){if(this._drainInertiaBuffer(),this._inertiaBuffer.length<2)return;const r={zoom:0,bearing:0,pitch:0,pan:new e.P(0,0),pinchAround:void 0,around:void 0};for(const{settings:t}of this._inertiaBuffer)r.zoom+=t.zoomDelta||0,r.bearing+=t.bearingDelta||0,r.pitch+=t.pitchDelta||0,t.panDelta&&r.pan._add(t.panDelta),t.around&&(r.around=t.around),t.pinchAround&&(r.pinchAround=t.pinchAround);const n=this._inertiaBuffer[this._inertiaBuffer.length-1].time-this._inertiaBuffer[0].time,i={};if(r.pan.mag()){const a=Bn(r.pan.mag(),n,e.e({},Pn,t||{}));i.offset=r.pan.mult(a.amount/r.pan.mag()),i.center=this._map.transform.center,Fn(i,a)}if(r.zoom){const t=Bn(r.zoom,n,zn);i.zoom=this._map.transform.zoom+t.amount,Fn(i,t)}if(r.bearing){const t=Bn(r.bearing,n,On);i.bearing=this._map.transform.bearing+e.ad(t.amount,-179,179),Fn(i,t)}if(r.pitch){const t=Bn(r.pitch,n,Dn);i.pitch=this._map.transform.pitch+t.amount,Fn(i,t)}if(i.zoom||i.bearing){const t=void 0===r.pinchAround?r.around:r.pinchAround;i.around=t?this._map.unproject(t):this._map.getCenter()}return this.clear(),e.e(i,{noMoveStart:!0})}}function Fn(t,e){(!t.duration||t.durationr.unproject(t))),l=a.reduce(((t,e,r,n)=>t.add(e.div(n.length))),new e.P(0,0));super(t,{points:a,point:l,lngLats:s,lngLat:r.unproject(l),originalEvent:n}),this._defaultPrevented=!1}}class Un extends e.k{preventDefault(){this._defaultPrevented=!0}get defaultPrevented(){return this._defaultPrevented}constructor(t,e,r){super(t,{originalEvent:r}),this._defaultPrevented=!1}}class Vn{constructor(t,e){this._map=t,this._clickTolerance=e.clickTolerance}reset(){delete this._mousedownPos}wheel(t){return this._firePreventable(new Un(t.type,this._map,t))}mousedown(t,e){return this._mousedownPos=e,this._firePreventable(new Nn(t.type,this._map,t))}mouseup(t){this._map.fire(new Nn(t.type,this._map,t))}click(t,e){this._mousedownPos&&this._mousedownPos.dist(e)>=this._clickTolerance||this._map.fire(new Nn(t.type,this._map,t))}dblclick(t){return this._firePreventable(new Nn(t.type,this._map,t))}mouseover(t){this._map.fire(new Nn(t.type,this._map,t))}mouseout(t){this._map.fire(new Nn(t.type,this._map,t))}touchstart(t){return this._firePreventable(new jn(t.type,this._map,t))}touchmove(t){this._map.fire(new jn(t.type,this._map,t))}touchend(t){this._map.fire(new jn(t.type,this._map,t))}touchcancel(t){this._map.fire(new jn(t.type,this._map,t))}_firePreventable(t){if(this._map.fire(t),t.defaultPrevented)return{}}isEnabled(){return!0}isActive(){return!1}enable(){}disable(){}}class qn{constructor(t){this._map=t}reset(){this._delayContextMenu=!1,this._ignoreContextMenu=!0,delete this._contextMenuEvent}mousemove(t){this._map.fire(new Nn(t.type,this._map,t))}mousedown(){this._delayContextMenu=!0,this._ignoreContextMenu=!1}mouseup(){this._delayContextMenu=!1,this._contextMenuEvent&&(this._map.fire(new Nn(\"contextmenu\",this._map,this._contextMenuEvent)),delete this._contextMenuEvent)}contextmenu(t){this._delayContextMenu?this._contextMenuEvent=t:this._ignoreContextMenu||this._map.fire(new Nn(t.type,this._map,t)),this._map.listens(\"contextmenu\")&&t.preventDefault()}isEnabled(){return!0}isActive(){return!1}enable(){}disable(){}}class Hn{constructor(t){this._map=t}get transform(){return this._map._requestedCameraState||this._map.transform}get center(){return{lng:this.transform.center.lng,lat:this.transform.center.lat}}get zoom(){return this.transform.zoom}get pitch(){return this.transform.pitch}get bearing(){return this.transform.bearing}unproject(t){return this.transform.pointLocation(e.P.convert(t),this._map.terrain)}}class Gn{constructor(t,e){this._map=t,this._tr=new Hn(t),this._el=t.getCanvasContainer(),this._container=t.getContainer(),this._clickTolerance=e.clickTolerance||1}isEnabled(){return!!this._enabled}isActive(){return!!this._active}enable(){this.isEnabled()||(this._enabled=!0)}disable(){this.isEnabled()&&(this._enabled=!1)}mousedown(t,e){this.isEnabled()&&t.shiftKey&&0===t.button&&(o.disableDrag(),this._startPos=this._lastPos=e,this._active=!0)}mousemoveWindow(t,e){if(!this._active)return;const r=e;if(this._lastPos.equals(r)||!this._box&&r.dist(this._startPos)t.fitScreenCoordinates(n,i,this._tr.bearing,{linear:!0})};this._fireEvent(\"boxzoomcancel\",t)}keydown(t){this._active&&27===t.keyCode&&(this.reset(),this._fireEvent(\"boxzoomcancel\",t))}reset(){this._active=!1,this._container.classList.remove(\"maplibregl-crosshair\"),this._box&&(o.remove(this._box),this._box=null),o.enableDrag(),delete this._startPos,delete this._lastPos}_fireEvent(t,r){return this._map.fire(new e.k(t,{originalEvent:r}))}}function Zn(t,e){if(t.length!==e.length)throw new Error(`The number of touches and points are not equal - touches ${t.length}, points ${e.length}`);const r={};for(let n=0;nthis.numTouches)&&(this.aborted=!0),this.aborted||(void 0===this.startTime&&(this.startTime=t.timeStamp),n.length===this.numTouches&&(this.centroid=function(t){const r=new e.P(0,0);for(const e of t)r._add(e);return r.div(t.length)}(r),this.touches=Zn(n,r)))}touchmove(t,e,r){if(this.aborted||!this.centroid)return;const n=Zn(r,e);for(const t in this.touches){const e=this.touches[t],r=n[t];(!r||r.dist(e)>30)&&(this.aborted=!0)}}touchend(t,e,r){if((!this.centroid||t.timeStamp-this.startTime>500)&&(this.aborted=!0),0===r.length){const t=!this.aborted&&this.centroid;if(this.reset(),t)return t}}}class Yn{constructor(t){this.singleTap=new Wn(t),this.numTaps=t.numTaps,this.reset()}reset(){this.lastTime=1/0,delete this.lastTap,this.count=0,this.singleTap.reset()}touchstart(t,e,r){this.singleTap.touchstart(t,e,r)}touchmove(t,e,r){this.singleTap.touchmove(t,e,r)}touchend(t,e,r){const n=this.singleTap.touchend(t,e,r);if(n){const e=t.timeStamp-this.lastTime<500,r=!this.lastTap||this.lastTap.dist(n)<30;if(e&&r||this.reset(),this.count++,this.lastTime=t.timeStamp,this.lastTap=n,this.count===this.numTaps)return this.reset(),n}}}class Xn{constructor(t){this._tr=new Hn(t),this._zoomIn=new Yn({numTouches:1,numTaps:2}),this._zoomOut=new Yn({numTouches:2,numTaps:1}),this.reset()}reset(){this._active=!1,this._zoomIn.reset(),this._zoomOut.reset()}touchstart(t,e,r){this._zoomIn.touchstart(t,e,r),this._zoomOut.touchstart(t,e,r)}touchmove(t,e,r){this._zoomIn.touchmove(t,e,r),this._zoomOut.touchmove(t,e,r)}touchend(t,e,r){const n=this._zoomIn.touchend(t,e,r),i=this._zoomOut.touchend(t,e,r),a=this._tr;return n?(this._active=!0,t.preventDefault(),setTimeout((()=>this.reset()),0),{cameraAnimation:e=>e.easeTo({duration:300,zoom:a.zoom+1,around:a.unproject(n)},{originalEvent:t})}):i?(this._active=!0,t.preventDefault(),setTimeout((()=>this.reset()),0),{cameraAnimation:e=>e.easeTo({duration:300,zoom:a.zoom-1,around:a.unproject(i)},{originalEvent:t})}):void 0}touchcancel(){this.reset()}enable(){this._enabled=!0}disable(){this._enabled=!1,this.reset()}isEnabled(){return this._enabled}isActive(){return this._active}}class $n{constructor(t){this._enabled=!!t.enable,this._moveStateManager=t.moveStateManager,this._clickTolerance=t.clickTolerance||1,this._moveFunction=t.move,this._activateOnStart=!!t.activateOnStart,t.assignEvents(this),this.reset()}reset(t){this._active=!1,this._moved=!1,delete this._lastPoint,this._moveStateManager.endMove(t)}_move(...t){const e=this._moveFunction(...t);if(e.bearingDelta||e.pitchDelta||e.around||e.panDelta)return this._active=!0,e}dragStart(t,e){this.isEnabled()&&!this._lastPoint&&this._moveStateManager.isValidStartEvent(t)&&(this._moveStateManager.startMove(t),this._lastPoint=e.length?e[0]:e,this._activateOnStart&&this._lastPoint&&(this._active=!0))}dragMove(t,e){if(!this.isEnabled())return;const r=this._lastPoint;if(!r)return;if(t.preventDefault(),!this._moveStateManager.isValidMoveEvent(t))return void this.reset(t);const n=e.length?e[0]:e;return!this._moved&&n.dist(r){t.mousedown=t.dragStart,t.mousemoveWindow=t.dragMove,t.mouseup=t.dragEnd,t.contextmenu=t=>{t.preventDefault()}},ei=({enable:t,clickTolerance:e,bearingDegreesPerPixelMoved:r=.8})=>{const n=new Kn({checkCorrectEvent:t=>0===o.mouseButton(t)&&t.ctrlKey||2===o.mouseButton(t)});return new $n({clickTolerance:e,move:(t,e)=>({bearingDelta:(e.x-t.x)*r}),moveStateManager:n,enable:t,assignEvents:ti})},ri=({enable:t,clickTolerance:e,pitchDegreesPerPixelMoved:r=-.5})=>{const n=new Kn({checkCorrectEvent:t=>0===o.mouseButton(t)&&t.ctrlKey||2===o.mouseButton(t)});return new $n({clickTolerance:e,move:(t,e)=>({pitchDelta:(e.y-t.y)*r}),moveStateManager:n,enable:t,assignEvents:ti})};class ni{constructor(t,e){this._clickTolerance=t.clickTolerance||1,this._map=e,this.reset()}reset(){this._active=!1,this._touches={},this._sum=new e.P(0,0)}_shouldBePrevented(t){return t<(this._map.cooperativeGestures.isEnabled()?2:1)}touchstart(t,e,r){return this._calculateTransform(t,e,r)}touchmove(t,e,r){if(this._active){if(!this._shouldBePrevented(r.length))return t.preventDefault(),this._calculateTransform(t,e,r);this._map.cooperativeGestures.notifyGestureBlocked(\"touch_pan\",t)}}touchend(t,e,r){this._calculateTransform(t,e,r),this._active&&this._shouldBePrevented(r.length)&&this.reset()}touchcancel(){this.reset()}_calculateTransform(t,r,n){n.length>0&&(this._active=!0);const i=Zn(n,r),a=new e.P(0,0),o=new e.P(0,0);let s=0;for(const t in i){const e=i[t],r=this._touches[t];r&&(a._add(e),o._add(e.sub(r)),s++,i[t]=e)}if(this._touches=i,this._shouldBePrevented(s)||!o.mag())return;const l=o.div(s);return this._sum._add(l),this._sum.mag()Math.abs(t.x)}class hi extends ii{constructor(t){super(),this._currentTouchCount=0,this._map=t}reset(){super.reset(),this._valid=void 0,delete this._firstMove,delete this._lastPoints}touchstart(t,e,r){super.touchstart(t,e,r),this._currentTouchCount=r.length}_start(t){this._lastPoints=t,ui(t[0].sub(t[1]))&&(this._valid=!1)}_move(t,e,r){if(this._map.cooperativeGestures.isEnabled()&&this._currentTouchCount<3)return;const n=t[0].sub(this._lastPoints[0]),i=t[1].sub(this._lastPoints[1]);return this._valid=this.gestureBeginsVertically(n,i,r.timeStamp),this._valid?(this._lastPoints=t,this._active=!0,{pitchDelta:(n.y+i.y)/2*-.5}):void 0}gestureBeginsVertically(t,e,r){if(void 0!==this._valid)return this._valid;const n=t.mag()>=2,i=e.mag()>=2;if(!n&&!i)return;if(!n||!i)return void 0===this._firstMove&&(this._firstMove=r),r-this._firstMove<100&&void 0;const a=t.y>0==e.y>0;return ui(t)&&ui(e)&&a}}const fi={panStep:100,bearingStep:15,pitchStep:10};class pi{constructor(t){this._tr=new Hn(t);const e=fi;this._panStep=e.panStep,this._bearingStep=e.bearingStep,this._pitchStep=e.pitchStep,this._rotationDisabled=!1}reset(){this._active=!1}keydown(t){if(t.altKey||t.ctrlKey||t.metaKey)return;let e=0,r=0,n=0,i=0,a=0;switch(t.keyCode){case 61:case 107:case 171:case 187:e=1;break;case 189:case 109:case 173:e=-1;break;case 37:t.shiftKey?r=-1:(t.preventDefault(),i=-1);break;case 39:t.shiftKey?r=1:(t.preventDefault(),i=1);break;case 38:t.shiftKey?n=1:(t.preventDefault(),a=-1);break;case 40:t.shiftKey?n=-1:(t.preventDefault(),a=1);break;default:return}return this._rotationDisabled&&(r=0,n=0),{cameraAnimation:o=>{const s=this._tr;o.easeTo({duration:300,easeId:\"keyboardHandler\",easing:di,zoom:e?Math.round(s.zoom)+e*(t.shiftKey?2:1):s.zoom,bearing:s.bearing+r*this._bearingStep,pitch:s.pitch+n*this._pitchStep,offset:[-i*this._panStep,-a*this._panStep],center:s.center},{originalEvent:t})}}}enable(){this._enabled=!0}disable(){this._enabled=!1,this.reset()}isEnabled(){return this._enabled}isActive(){return this._active}disableRotation(){this._rotationDisabled=!0}enableRotation(){this._rotationDisabled=!1}}function di(t){return t*(2-t)}const mi=4.000244140625;class gi{constructor(t,e){this._onTimeout=t=>{this._type=\"wheel\",this._delta-=this._lastValue,this._active||this._start(t)},this._map=t,this._tr=new Hn(t),this._triggerRenderFrame=e,this._delta=0,this._defaultZoomRate=.01,this._wheelZoomRate=.0022222222222222222}setZoomRate(t){this._defaultZoomRate=t}setWheelZoomRate(t){this._wheelZoomRate=t}isEnabled(){return!!this._enabled}isActive(){return!!this._active||void 0!==this._finishTimeout}isZooming(){return!!this._zooming}enable(t){this.isEnabled()||(this._enabled=!0,this._aroundCenter=!!t&&\"center\"===t.around)}disable(){this.isEnabled()&&(this._enabled=!1)}_shouldBePrevented(t){return!!this._map.cooperativeGestures.isEnabled()&&!(t.ctrlKey||this._map.cooperativeGestures.isBypassed(t))}wheel(t){if(!this.isEnabled())return;if(this._shouldBePrevented(t))return void this._map.cooperativeGestures.notifyGestureBlocked(\"wheel_zoom\",t);let e=t.deltaMode===WheelEvent.DOM_DELTA_LINE?40*t.deltaY:t.deltaY;const r=a.now(),n=r-(this._lastWheelEventTime||0);this._lastWheelEventTime=r,0!==e&&e%mi==0?this._type=\"wheel\":0!==e&&Math.abs(e)<4?this._type=\"trackpad\":n>400?(this._type=null,this._lastValue=e,this._timeout=setTimeout(this._onTimeout,40,t)):this._type||(this._type=Math.abs(n*e)<200?\"trackpad\":\"wheel\",this._timeout&&(clearTimeout(this._timeout),this._timeout=null,e+=this._lastValue)),t.shiftKey&&e&&(e/=4),this._type&&(this._lastWheelEvent=t,this._delta-=e,this._active||this._start(t)),t.preventDefault()}_start(t){if(!this._delta)return;this._frameId&&(this._frameId=null),this._active=!0,this.isZooming()||(this._zooming=!0),this._finishTimeout&&(clearTimeout(this._finishTimeout),delete this._finishTimeout);const r=o.mousePos(this._map.getCanvas(),t),n=this._tr;r.y>n.transform.height/2-n.transform.getHorizon()?this._around=e.N.convert(this._aroundCenter?n.center:n.unproject(r)):this._around=e.N.convert(n.center),this._aroundPoint=n.transform.locationPoint(this._around),this._frameId||(this._frameId=!0,this._triggerRenderFrame())}renderFrame(){if(!this._frameId)return;if(this._frameId=null,!this.isActive())return;const t=this._tr.transform;if(0!==this._delta){const e=\"wheel\"===this._type&&Math.abs(this._delta)>mi?this._wheelZoomRate:this._defaultZoomRate;let r=2/(1+Math.exp(-Math.abs(this._delta*e)));this._delta<0&&0!==r&&(r=1/r);const n=\"number\"==typeof this._targetZoom?t.zoomScale(this._targetZoom):t.scale;this._targetZoom=Math.min(t.maxZoom,Math.max(t.minZoom,t.scaleZoom(n*r))),\"wheel\"===this._type&&(this._startZoom=t.zoom,this._easing=this._smoothOutEasing(200)),this._delta=0}const r=\"number\"==typeof this._targetZoom?this._targetZoom:t.zoom,n=this._startZoom,i=this._easing;let o,s=!1;const l=a.now()-this._lastWheelEventTime;if(\"wheel\"===this._type&&n&&i&&l){const t=Math.min(l/200,1),a=i(t);o=e.z.number(n,r,a),t<1?this._frameId||(this._frameId=!0):s=!0}else o=r,s=!0;return this._active=!0,s&&(this._active=!1,this._finishTimeout=setTimeout((()=>{this._zooming=!1,this._triggerRenderFrame(),delete this._targetZoom,delete this._finishTimeout}),200)),{noInertia:!0,needsRenderFrame:!s,zoomDelta:o-t.zoom,around:this._aroundPoint,originalEvent:this._lastWheelEvent}}_smoothOutEasing(t){let r=e.b9;if(this._prevEase){const t=this._prevEase,n=(a.now()-t.start)/t.duration,i=t.easing(n+.01)-t.easing(n),o=.27/Math.sqrt(i*i+1e-4)*.01,s=Math.sqrt(.0729-o*o);r=e.b8(o,s,.25,1)}return this._prevEase={start:a.now(),duration:t,easing:r},r}reset(){this._active=!1,this._zooming=!1,delete this._targetZoom,this._finishTimeout&&(clearTimeout(this._finishTimeout),delete this._finishTimeout)}}class yi{constructor(t,e){this._clickZoom=t,this._tapZoom=e}enable(){this._clickZoom.enable(),this._tapZoom.enable()}disable(){this._clickZoom.disable(),this._tapZoom.disable()}isEnabled(){return this._clickZoom.isEnabled()&&this._tapZoom.isEnabled()}isActive(){return this._clickZoom.isActive()||this._tapZoom.isActive()}}class vi{constructor(t){this._tr=new Hn(t),this.reset()}reset(){this._active=!1}dblclick(t,e){return t.preventDefault(),{cameraAnimation:r=>{r.easeTo({duration:300,zoom:this._tr.zoom+(t.shiftKey?-1:1),around:this._tr.unproject(e)},{originalEvent:t})}}}enable(){this._enabled=!0}disable(){this._enabled=!1,this.reset()}isEnabled(){return this._enabled}isActive(){return this._active}}class xi{constructor(){this._tap=new Yn({numTouches:1,numTaps:1}),this.reset()}reset(){this._active=!1,delete this._swipePoint,delete this._swipeTouch,delete this._tapTime,delete this._tapPoint,this._tap.reset()}touchstart(t,e,r){if(!this._swipePoint)if(this._tapTime){const n=e[0],i=t.timeStamp-this._tapTime<500,a=this._tapPoint.dist(n)<30;i&&a?r.length>0&&(this._swipePoint=n,this._swipeTouch=r[0].identifier):this.reset()}else this._tap.touchstart(t,e,r)}touchmove(t,e,r){if(this._tapTime){if(this._swipePoint){if(r[0].identifier!==this._swipeTouch)return;const n=e[0],i=n.y-this._swipePoint.y;return this._swipePoint=n,t.preventDefault(),this._active=!0,{zoomDelta:i/128}}}else this._tap.touchmove(t,e,r)}touchend(t,e,r){if(this._tapTime)this._swipePoint&&0===r.length&&this.reset();else{const n=this._tap.touchend(t,e,r);n&&(this._tapTime=t.timeStamp,this._tapPoint=n)}}touchcancel(){this.reset()}enable(){this._enabled=!0}disable(){this._enabled=!1,this.reset()}isEnabled(){return this._enabled}isActive(){return this._active}}class _i{constructor(t,e,r){this._el=t,this._mousePan=e,this._touchPan=r}enable(t){this._inertiaOptions=t||{},this._mousePan.enable(),this._touchPan.enable(),this._el.classList.add(\"maplibregl-touch-drag-pan\")}disable(){this._mousePan.disable(),this._touchPan.disable(),this._el.classList.remove(\"maplibregl-touch-drag-pan\")}isEnabled(){return this._mousePan.isEnabled()&&this._touchPan.isEnabled()}isActive(){return this._mousePan.isActive()||this._touchPan.isActive()}}class bi{constructor(t,e,r){this._pitchWithRotate=t.pitchWithRotate,this._mouseRotate=e,this._mousePitch=r}enable(){this._mouseRotate.enable(),this._pitchWithRotate&&this._mousePitch.enable()}disable(){this._mouseRotate.disable(),this._mousePitch.disable()}isEnabled(){return this._mouseRotate.isEnabled()&&(!this._pitchWithRotate||this._mousePitch.isEnabled())}isActive(){return this._mouseRotate.isActive()||this._mousePitch.isActive()}}class wi{constructor(t,e,r,n){this._el=t,this._touchZoom=e,this._touchRotate=r,this._tapDragZoom=n,this._rotationDisabled=!1,this._enabled=!0}enable(t){this._touchZoom.enable(t),this._rotationDisabled||this._touchRotate.enable(t),this._tapDragZoom.enable(),this._el.classList.add(\"maplibregl-touch-zoom-rotate\")}disable(){this._touchZoom.disable(),this._touchRotate.disable(),this._tapDragZoom.disable(),this._el.classList.remove(\"maplibregl-touch-zoom-rotate\")}isEnabled(){return this._touchZoom.isEnabled()&&(this._rotationDisabled||this._touchRotate.isEnabled())&&this._tapDragZoom.isEnabled()}isActive(){return this._touchZoom.isActive()||this._touchRotate.isActive()||this._tapDragZoom.isActive()}disableRotation(){this._rotationDisabled=!0,this._touchRotate.disable()}enableRotation(){this._rotationDisabled=!1,this._touchZoom.isEnabled()&&this._touchRotate.enable()}}class Ti{constructor(t,e){this._bypassKey=-1!==navigator.userAgent.indexOf(\"Mac\")?\"metaKey\":\"ctrlKey\",this._map=t,this._options=e,this._enabled=!1}isActive(){return!1}reset(){}_setupUI(){if(this._container)return;const t=this._map.getCanvasContainer();t.classList.add(\"maplibregl-cooperative-gestures\"),this._container=o.create(\"div\",\"maplibregl-cooperative-gesture-screen\",t);let e=this._map._getUIString(\"CooperativeGesturesHandler.WindowsHelpText\");\"metaKey\"===this._bypassKey&&(e=this._map._getUIString(\"CooperativeGesturesHandler.MacHelpText\"));const r=this._map._getUIString(\"CooperativeGesturesHandler.MobileHelpText\"),n=document.createElement(\"div\");n.className=\"maplibregl-desktop-message\",n.textContent=e,this._container.appendChild(n);const i=document.createElement(\"div\");i.className=\"maplibregl-mobile-message\",i.textContent=r,this._container.appendChild(i),this._container.setAttribute(\"aria-hidden\",\"true\")}_destroyUI(){this._container&&(o.remove(this._container),this._map.getCanvasContainer().classList.remove(\"maplibregl-cooperative-gestures\")),delete this._container}enable(){this._setupUI(),this._enabled=!0}disable(){this._enabled=!1,this._destroyUI()}isEnabled(){return this._enabled}isBypassed(t){return t[this._bypassKey]}notifyGestureBlocked(t,r){this._enabled&&(this._map.fire(new e.k(\"cooperativegestureprevented\",{gestureType:t,originalEvent:r})),this._container.classList.add(\"maplibregl-show\"),setTimeout((()=>{this._container.classList.remove(\"maplibregl-show\")}),100))}}const ki=t=>t.zoom||t.drag||t.pitch||t.rotate;class Ai extends e.k{}function Mi(t){return t.panDelta&&t.panDelta.mag()||t.zoomDelta||t.bearingDelta||t.pitchDelta}class Si{constructor(t,e){this.handleWindowEvent=t=>{this.handleEvent(t,`${t.type}Window`)},this.handleEvent=(t,e)=>{if(\"blur\"===t.type)return void this.stop(!0);this._updatingCamera=!0;const r=\"renderFrame\"===t.type?void 0:t,n={needsRenderFrame:!1},i={},a={},s=t.touches,l=s?this._getMapTouches(s):void 0,c=l?o.touchPos(this._map.getCanvas(),l):o.mousePos(this._map.getCanvas(),t);for(const{handlerName:o,handler:s,allowed:u}of this._handlers){if(!s.isEnabled())continue;let h;this._blockedByActive(a,u,o)?s.reset():s[e||t.type]&&(h=s[e||t.type](t,c,l),this.mergeHandlerResult(n,i,h,o,r),h&&h.needsRenderFrame&&this._triggerRenderFrame()),(h||s.isActive())&&(a[o]=s)}const u={};for(const t in this._previousActiveHandlers)a[t]||(u[t]=r);this._previousActiveHandlers=a,(Object.keys(u).length||Mi(n))&&(this._changes.push([n,i,u]),this._triggerRenderFrame()),(Object.keys(a).length||Mi(n))&&this._map._stop(!0),this._updatingCamera=!1;const{cameraAnimation:h}=n;h&&(this._inertia.clear(),this._fireEvents({},{},!0),this._changes=[],h(this._map))},this._map=t,this._el=this._map.getCanvasContainer(),this._handlers=[],this._handlersById={},this._changes=[],this._inertia=new Rn(t),this._bearingSnap=e.bearingSnap,this._previousActiveHandlers={},this._eventsInProgress={},this._addDefaultHandlers(e);const r=this._el;this._listeners=[[r,\"touchstart\",{passive:!0}],[r,\"touchmove\",{passive:!1}],[r,\"touchend\",void 0],[r,\"touchcancel\",void 0],[r,\"mousedown\",void 0],[r,\"mousemove\",void 0],[r,\"mouseup\",void 0],[document,\"mousemove\",{capture:!0}],[document,\"mouseup\",void 0],[r,\"mouseover\",void 0],[r,\"mouseout\",void 0],[r,\"dblclick\",void 0],[r,\"click\",void 0],[r,\"keydown\",{capture:!1}],[r,\"keyup\",void 0],[r,\"wheel\",{passive:!1}],[r,\"contextmenu\",void 0],[window,\"blur\",void 0]];for(const[t,e,r]of this._listeners)o.addEventListener(t,e,t===document?this.handleWindowEvent:this.handleEvent,r)}destroy(){for(const[t,e,r]of this._listeners)o.removeEventListener(t,e,t===document?this.handleWindowEvent:this.handleEvent,r)}_addDefaultHandlers(t){const e=this._map,r=e.getCanvasContainer();this._add(\"mapEvent\",new Vn(e,t));const n=e.boxZoom=new Gn(e,t);this._add(\"boxZoom\",n),t.interactive&&t.boxZoom&&n.enable();const i=e.cooperativeGestures=new Ti(e,t.cooperativeGestures);this._add(\"cooperativeGestures\",i),t.cooperativeGestures&&i.enable();const a=new Xn(e),s=new vi(e);e.doubleClickZoom=new yi(s,a),this._add(\"tapZoom\",a),this._add(\"clickZoom\",s),t.interactive&&t.doubleClickZoom&&e.doubleClickZoom.enable();const l=new xi;this._add(\"tapDragZoom\",l);const c=e.touchPitch=new hi(e);this._add(\"touchPitch\",c),t.interactive&&t.touchPitch&&e.touchPitch.enable(t.touchPitch);const u=ei(t),h=ri(t);e.dragRotate=new bi(t,u,h),this._add(\"mouseRotate\",u,[\"mousePitch\"]),this._add(\"mousePitch\",h,[\"mouseRotate\"]),t.interactive&&t.dragRotate&&e.dragRotate.enable();const f=(({enable:t,clickTolerance:e})=>{const r=new Kn({checkCorrectEvent:t=>0===o.mouseButton(t)&&!t.ctrlKey});return new $n({clickTolerance:e,move:(t,e)=>({around:e,panDelta:e.sub(t)}),activateOnStart:!0,moveStateManager:r,enable:t,assignEvents:ti})})(t),p=new ni(t,e);e.dragPan=new _i(r,f,p),this._add(\"mousePan\",f),this._add(\"touchPan\",p,[\"touchZoom\",\"touchRotate\"]),t.interactive&&t.dragPan&&e.dragPan.enable(t.dragPan);const d=new ci,m=new si;e.touchZoomRotate=new wi(r,m,d,l),this._add(\"touchRotate\",d,[\"touchPan\",\"touchZoom\"]),this._add(\"touchZoom\",m,[\"touchPan\",\"touchRotate\"]),t.interactive&&t.touchZoomRotate&&e.touchZoomRotate.enable(t.touchZoomRotate);const g=e.scrollZoom=new gi(e,(()=>this._triggerRenderFrame()));this._add(\"scrollZoom\",g,[\"mousePan\"]),t.interactive&&t.scrollZoom&&e.scrollZoom.enable(t.scrollZoom);const y=e.keyboard=new pi(e);this._add(\"keyboard\",y),t.interactive&&t.keyboard&&e.keyboard.enable(),this._add(\"blockableMapEvent\",new qn(e))}_add(t,e,r){this._handlers.push({handlerName:t,handler:e,allowed:r}),this._handlersById[t]=e}stop(t){if(!this._updatingCamera){for(const{handler:t}of this._handlers)t.reset();this._inertia.clear(),this._fireEvents({},{},t),this._changes=[]}}isActive(){for(const{handler:t}of this._handlers)if(t.isActive())return!0;return!1}isZooming(){return!!this._eventsInProgress.zoom||this._map.scrollZoom.isZooming()}isRotating(){return!!this._eventsInProgress.rotate}isMoving(){return Boolean(ki(this._eventsInProgress))||this.isZooming()}_blockedByActive(t,e,r){for(const n in t)if(n!==r&&(!e||e.indexOf(n)<0))return!0;return!1}_getMapTouches(t){const e=[];for(const r of t){const t=r.target;this._el.contains(t)&&e.push(r)}return e}mergeHandlerResult(t,r,n,i,a){if(!n)return;e.e(t,n);const o={handlerName:i,originalEvent:n.originalEvent||a};void 0!==n.zoomDelta&&(r.zoom=o),void 0!==n.panDelta&&(r.drag=o),void 0!==n.pitchDelta&&(r.pitch=o),void 0!==n.bearingDelta&&(r.rotate=o)}_applyChanges(){const t={},r={},n={};for(const[i,a,o]of this._changes)i.panDelta&&(t.panDelta=(t.panDelta||new e.P(0,0))._add(i.panDelta)),i.zoomDelta&&(t.zoomDelta=(t.zoomDelta||0)+i.zoomDelta),i.bearingDelta&&(t.bearingDelta=(t.bearingDelta||0)+i.bearingDelta),i.pitchDelta&&(t.pitchDelta=(t.pitchDelta||0)+i.pitchDelta),void 0!==i.around&&(t.around=i.around),void 0!==i.pinchAround&&(t.pinchAround=i.pinchAround),i.noInertia&&(t.noInertia=i.noInertia),e.e(r,a),e.e(n,o);this._updateMapTransform(t,r,n),this._changes=[]}_updateMapTransform(t,e,r){const n=this._map,i=n._getTransformForUpdate(),a=n.terrain;if(!(Mi(t)||a&&this._terrainMovement))return this._fireEvents(e,r,!0);let{panDelta:o,zoomDelta:s,bearingDelta:l,pitchDelta:c,around:u,pinchAround:h}=t;void 0!==h&&(u=h),n._stop(!0),u=u||n.transform.centerPoint;const f=i.pointLocation(o?u.sub(o):u);l&&(i.bearing+=l),c&&(i.pitch+=c),s&&(i.zoom+=s),a?this._terrainMovement||!e.drag&&!e.zoom?e.drag&&this._terrainMovement?i.center=i.pointLocation(i.centerPoint.sub(o)):i.setLocationAtPoint(f,u):(this._terrainMovement=!0,this._map._elevationFreeze=!0,i.setLocationAtPoint(f,u)):i.setLocationAtPoint(f,u),n._applyUpdatedTransform(i),this._map._update(),t.noInertia||this._inertia.record(t),this._fireEvents(e,r,!0)}_fireEvents(t,r,n){const i=ki(this._eventsInProgress),o=ki(t),s={};for(const e in t){const{originalEvent:r}=t[e];this._eventsInProgress[e]||(s[`${e}start`]=r),this._eventsInProgress[e]=t[e]}!i&&o&&this._fireEvent(\"movestart\",o.originalEvent);for(const t in s)this._fireEvent(t,s[t]);o&&this._fireEvent(\"move\",o.originalEvent);for(const e in t){const{originalEvent:r}=t[e];this._fireEvent(e,r)}const l={};let c;for(const t in this._eventsInProgress){const{handlerName:e,originalEvent:n}=this._eventsInProgress[t];this._handlersById[e].isActive()||(delete this._eventsInProgress[t],c=r[e]||n,l[`${t}end`]=c)}for(const t in l)this._fireEvent(t,l[t]);const u=ki(this._eventsInProgress),h=(i||o)&&!u;if(h&&this._terrainMovement){this._map._elevationFreeze=!1,this._terrainMovement=!1;const t=this._map._getTransformForUpdate();t.recalculateZoom(this._map.terrain),this._map._applyUpdatedTransform(t)}if(n&&h){this._updatingCamera=!0;const t=this._inertia._onMoveEnd(this._map.dragPan._inertiaOptions),r=t=>0!==t&&-this._bearingSnap{delete this._frameId,this.handleEvent(new Ai(\"renderFrame\",{timeStamp:t})),this._applyChanges()}))}_triggerRenderFrame(){void 0===this._frameId&&(this._frameId=this._requestFrame())}}class Ei extends e.E{constructor(t,e){super(),this._renderFrameCallback=()=>{const t=Math.min((a.now()-this._easeStart)/this._easeOptions.duration,1);this._onEaseFrame(this._easeOptions.easing(t)),t<1&&this._easeFrameId?this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback):this.stop()},this._moving=!1,this._zooming=!1,this.transform=t,this._bearingSnap=e.bearingSnap,this.on(\"moveend\",(()=>{delete this._requestedCameraState}))}getCenter(){return new e.N(this.transform.center.lng,this.transform.center.lat)}setCenter(t,e){return this.jumpTo({center:t},e)}panBy(t,r,n){return t=e.P.convert(t).mult(-1),this.panTo(this.transform.center,e.e({offset:t},r),n)}panTo(t,r,n){return this.easeTo(e.e({center:t},r),n)}getZoom(){return this.transform.zoom}setZoom(t,e){return this.jumpTo({zoom:t},e),this}zoomTo(t,r,n){return this.easeTo(e.e({zoom:t},r),n)}zoomIn(t,e){return this.zoomTo(this.getZoom()+1,t,e),this}zoomOut(t,e){return this.zoomTo(this.getZoom()-1,t,e),this}getBearing(){return this.transform.bearing}setBearing(t,e){return this.jumpTo({bearing:t},e),this}getPadding(){return this.transform.padding}setPadding(t,e){return this.jumpTo({padding:t},e),this}rotateTo(t,r,n){return this.easeTo(e.e({bearing:t},r),n)}resetNorth(t,r){return this.rotateTo(0,e.e({duration:1e3},t),r),this}resetNorthPitch(t,r){return this.easeTo(e.e({bearing:0,pitch:0,duration:1e3},t),r),this}snapToNorth(t,e){return Math.abs(this.getBearing()){if(this._zooming&&(i.zoom=e.z.number(o,y,n)),this._rotating&&(i.bearing=e.z.number(s,u,n)),this._pitching&&(i.pitch=e.z.number(l,h,n)),this._padding&&(i.interpolatePadding(c,f,n),d=i.centerPoint.add(p)),this.terrain&&!t.freezeElevation&&this._updateElevation(n),b)i.setLocationAtPoint(b,w);else{const t=i.zoomScale(i.zoom-o),e=y>o?Math.min(2,_):Math.max(.5,_),r=Math.pow(e,1-n),a=i.unproject(v.add(x.mult(n*r)).mult(t));i.setLocationAtPoint(i.renderWorldCopies?a.wrap():a,d)}this._applyUpdatedTransform(i),this._fireMoveEvents(r)}),(e=>{this.terrain&&t.freezeElevation&&this._finalizeElevation(),this._afterEase(r,e)}),t),this}_prepareEase(t,r,n={}){this._moving=!0,r||n.moving||this.fire(new e.k(\"movestart\",t)),this._zooming&&!n.zooming&&this.fire(new e.k(\"zoomstart\",t)),this._rotating&&!n.rotating&&this.fire(new e.k(\"rotatestart\",t)),this._pitching&&!n.pitching&&this.fire(new e.k(\"pitchstart\",t))}_prepareElevation(t){this._elevationCenter=t,this._elevationStart=this.transform.elevation,this._elevationTarget=this.terrain.getElevationForLngLatZoom(t,this.transform.tileZoom),this._elevationFreeze=!0}_updateElevation(t){this.transform.minElevationForCurrentTile=this.terrain.getMinTileElevationForLngLatZoom(this._elevationCenter,this.transform.tileZoom);const r=this.terrain.getElevationForLngLatZoom(this._elevationCenter,this.transform.tileZoom);if(t<1&&r!==this._elevationTarget){const e=this._elevationTarget-this._elevationStart,n=(r-(e*t+this._elevationStart))/(1-t);this._elevationStart+=t*(e-n),this._elevationTarget=r}this.transform.elevation=e.z.number(this._elevationStart,this._elevationTarget,t)}_finalizeElevation(){this._elevationFreeze=!1,this.transform.recalculateZoom(this.terrain)}_getTransformForUpdate(){return this.transformCameraUpdate||this.terrain?(this._requestedCameraState||(this._requestedCameraState=this.transform.clone()),this._requestedCameraState):this.transform}_elevateCameraIfInsideTerrain(t){const e=t.getCameraPosition(),r=this.terrain.getElevationForLngLatZoom(e.lngLat,t.zoom);if(e.altitudethis._elevateCameraIfInsideTerrain(t))),this.transformCameraUpdate&&e.push((t=>this.transformCameraUpdate(t))),!e.length)return;const r=t.clone();for(const t of e){const e=r.clone(),{center:n,zoom:i,pitch:a,bearing:o,elevation:s}=t(e);n&&(e.center=n),void 0!==i&&(e.zoom=i),void 0!==a&&(e.pitch=a),void 0!==o&&(e.bearing=o),void 0!==s&&(e.elevation=s),r.apply(e)}this.transform.apply(r)}_fireMoveEvents(t){this.fire(new e.k(\"move\",t)),this._zooming&&this.fire(new e.k(\"zoom\",t)),this._rotating&&this.fire(new e.k(\"rotate\",t)),this._pitching&&this.fire(new e.k(\"pitch\",t))}_afterEase(t,r){if(this._easeId&&r&&this._easeId===r)return;delete this._easeId;const n=this._zooming,i=this._rotating,a=this._pitching;this._moving=!1,this._zooming=!1,this._rotating=!1,this._pitching=!1,this._padding=!1,n&&this.fire(new e.k(\"zoomend\",t)),i&&this.fire(new e.k(\"rotateend\",t)),a&&this.fire(new e.k(\"pitchend\",t)),this.fire(new e.k(\"moveend\",t))}flyTo(t,r){var n;if(!t.essential&&a.prefersReducedMotion){const n=e.M(t,[\"center\",\"zoom\",\"bearing\",\"pitch\",\"around\"]);return this.jumpTo(n,r)}this.stop(),t=e.e({offset:[0,0],speed:1.2,curve:1.42,easing:e.b9},t);const i=this._getTransformForUpdate(),o=i.zoom,s=i.bearing,l=i.pitch,c=i.padding,u=\"bearing\"in t?this._normalizeBearing(t.bearing,s):s,h=\"pitch\"in t?+t.pitch:l,f=\"padding\"in t?t.padding:i.padding,p=e.P.convert(t.offset);let d=i.centerPoint.add(p);const m=i.pointLocation(d),{center:g,zoom:y}=i.getConstrained(e.N.convert(t.center||m),null!==(n=t.zoom)&&void 0!==n?n:o);this._normalizeCenter(g,i);const v=i.zoomScale(y-o),x=i.project(m),_=i.project(g).sub(x);let b=t.curve;const w=Math.max(i.width,i.height),T=w/v,k=_.mag();if(\"minZoom\"in t){const r=e.ad(Math.min(t.minZoom,o,y),i.minZoom,i.maxZoom),n=w/i.zoomScale(r-o);b=Math.sqrt(n/k*2)}const A=b*b;function M(t){const e=(T*T-w*w+(t?-1:1)*A*A*k*k)/(2*(t?T:w)*A*k);return Math.log(Math.sqrt(e*e+1)-e)}function S(t){return(Math.exp(t)-Math.exp(-t))/2}function E(t){return(Math.exp(t)+Math.exp(-t))/2}const C=M(!1);let L=function(t){return E(C)/E(C+b*t)},I=function(t){return w*((E(C)*(S(e=C+b*t)/E(e))-S(C))/A)/k;var e},P=(M(!0)-C)/b;if(Math.abs(k)<1e-6||!isFinite(P)){if(Math.abs(w-T)<1e-6)return this.easeTo(t,r);const e=T0,L=t=>Math.exp(e*b*t)}if(\"duration\"in t)t.duration=+t.duration;else{const e=\"screenSpeed\"in t?+t.screenSpeed/b:+t.speed;t.duration=1e3*P/e}return t.maxDuration&&t.duration>t.maxDuration&&(t.duration=0),this._zooming=!0,this._rotating=s!==u,this._pitching=h!==l,this._padding=!i.isPaddingEqual(f),this._prepareEase(r,!1),this.terrain&&this._prepareElevation(g),this._ease((n=>{const a=n*P,m=1/L(a);i.zoom=1===n?y:o+i.scaleZoom(m),this._rotating&&(i.bearing=e.z.number(s,u,n)),this._pitching&&(i.pitch=e.z.number(l,h,n)),this._padding&&(i.interpolatePadding(c,f,n),d=i.centerPoint.add(p)),this.terrain&&!t.freezeElevation&&this._updateElevation(n);const v=1===n?g:i.unproject(x.add(_.mult(I(a))).mult(m));i.setLocationAtPoint(i.renderWorldCopies?v.wrap():v,d),this._applyUpdatedTransform(i),this._fireMoveEvents(r)}),(()=>{this.terrain&&t.freezeElevation&&this._finalizeElevation(),this._afterEase(r)}),t),this}isEasing(){return!!this._easeFrameId}stop(){return this._stop()}_stop(t,e){var r;if(this._easeFrameId&&(this._cancelRenderFrame(this._easeFrameId),delete this._easeFrameId,delete this._onEaseFrame),this._onEaseEnd){const t=this._onEaseEnd;delete this._onEaseEnd,t.call(this,e)}return t||null===(r=this.handlers)||void 0===r||r.stop(!1),this}_ease(t,e,r){!1===r.animate||0===r.duration?(t(1),e()):(this._easeStart=a.now(),this._easeOptions=r,this._onEaseFrame=t,this._onEaseEnd=e,this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback))}_normalizeBearing(t,r){t=e.b3(t,-180,180);const n=Math.abs(t-r);return Math.abs(t-360-r)180?-360:r<-180?360:0}queryTerrainElevation(t){return this.terrain?this.terrain.getElevationForLngLatZoom(e.N.convert(t),this.transform.tileZoom)-this.transform.elevation:null}}const Ci={compact:!0,customAttribution:'
MapLibre'};class Li{constructor(t=Ci){this._toggleAttribution=()=>{this._container.classList.contains(\"maplibregl-compact\")&&(this._container.classList.contains(\"maplibregl-compact-show\")?(this._container.setAttribute(\"open\",\"\"),this._container.classList.remove(\"maplibregl-compact-show\")):(this._container.classList.add(\"maplibregl-compact-show\"),this._container.removeAttribute(\"open\")))},this._updateData=t=>{!t||\"metadata\"!==t.sourceDataType&&\"visibility\"!==t.sourceDataType&&\"style\"!==t.dataType&&\"terrain\"!==t.type||this._updateAttributions()},this._updateCompact=()=>{this._map.getCanvasContainer().offsetWidth<=640||this._compact?!1===this._compact?this._container.setAttribute(\"open\",\"\"):this._container.classList.contains(\"maplibregl-compact\")||this._container.classList.contains(\"maplibregl-attrib-empty\")||(this._container.setAttribute(\"open\",\"\"),this._container.classList.add(\"maplibregl-compact\",\"maplibregl-compact-show\")):(this._container.setAttribute(\"open\",\"\"),this._container.classList.contains(\"maplibregl-compact\")&&this._container.classList.remove(\"maplibregl-compact\",\"maplibregl-compact-show\"))},this._updateCompactMinimize=()=>{this._container.classList.contains(\"maplibregl-compact\")&&this._container.classList.contains(\"maplibregl-compact-show\")&&this._container.classList.remove(\"maplibregl-compact-show\")},this.options=t}getDefaultPosition(){return\"bottom-right\"}onAdd(t){return this._map=t,this._compact=this.options.compact,this._container=o.create(\"details\",\"maplibregl-ctrl maplibregl-ctrl-attrib\"),this._compactButton=o.create(\"summary\",\"maplibregl-ctrl-attrib-button\",this._container),this._compactButton.addEventListener(\"click\",this._toggleAttribution),this._setElementTitle(this._compactButton,\"ToggleAttribution\"),this._innerContainer=o.create(\"div\",\"maplibregl-ctrl-attrib-inner\",this._container),this._updateAttributions(),this._updateCompact(),this._map.on(\"styledata\",this._updateData),this._map.on(\"sourcedata\",this._updateData),this._map.on(\"terrain\",this._updateData),this._map.on(\"resize\",this._updateCompact),this._map.on(\"drag\",this._updateCompactMinimize),this._container}onRemove(){o.remove(this._container),this._map.off(\"styledata\",this._updateData),this._map.off(\"sourcedata\",this._updateData),this._map.off(\"terrain\",this._updateData),this._map.off(\"resize\",this._updateCompact),this._map.off(\"drag\",this._updateCompactMinimize),this._map=void 0,this._compact=void 0,this._attribHTML=void 0}_setElementTitle(t,e){const r=this._map._getUIString(`AttributionControl.${e}`);t.title=r,t.setAttribute(\"aria-label\",r)}_updateAttributions(){if(!this._map.style)return;let t=[];if(this.options.customAttribution&&(Array.isArray(this.options.customAttribution)?t=t.concat(this.options.customAttribution.map((t=>\"string\"!=typeof t?\"\":t))):\"string\"==typeof this.options.customAttribution&&t.push(this.options.customAttribution)),this._map.style.stylesheet){const t=this._map.style.stylesheet;this.styleOwner=t.owner,this.styleId=t.id}const e=this._map.style.sourceCaches;for(const r in e){const n=e[r];if(n.used||n.usedForTerrain){const e=n.getSource();e.attribution&&t.indexOf(e.attribution)<0&&t.push(e.attribution)}}t=t.filter((t=>String(t).trim())),t.sort(((t,e)=>t.length-e.length)),t=t.filter(((e,r)=>{for(let n=r+1;n=0)return!1;return!0}));const r=t.join(\" | \");r!==this._attribHTML&&(this._attribHTML=r,t.length?(this._innerContainer.innerHTML=r,this._container.classList.remove(\"maplibregl-attrib-empty\")):this._container.classList.add(\"maplibregl-attrib-empty\"),this._updateCompact(),this._editLink=null)}}class Ii{constructor(t={}){this._updateCompact=()=>{const t=this._container.children;if(t.length){const e=t[0];this._map.getCanvasContainer().offsetWidth<=640||this._compact?!1!==this._compact&&e.classList.add(\"maplibregl-compact\"):e.classList.remove(\"maplibregl-compact\")}},this.options=t}getDefaultPosition(){return\"bottom-left\"}onAdd(t){this._map=t,this._compact=this.options&&this.options.compact,this._container=o.create(\"div\",\"maplibregl-ctrl\");const e=o.create(\"a\",\"maplibregl-ctrl-logo\");return e.target=\"_blank\",e.rel=\"noopener nofollow\",e.href=\"https://maplibre.org/\",e.setAttribute(\"aria-label\",this._map._getUIString(\"LogoControl.Title\")),e.setAttribute(\"rel\",\"noopener nofollow\"),this._container.appendChild(e),this._container.style.display=\"block\",this._map.on(\"resize\",this._updateCompact),this._updateCompact(),this._container}onRemove(){o.remove(this._container),this._map.off(\"resize\",this._updateCompact),this._map=void 0,this._compact=void 0}}class Pi{constructor(){this._queue=[],this._id=0,this._cleared=!1,this._currentlyRunning=!1}add(t){const e=++this._id;return this._queue.push({callback:t,id:e,cancelled:!1}),e}remove(t){const e=this._currentlyRunning,r=e?this._queue.concat(e):this._queue;for(const e of r)if(e.id===t)return void(e.cancelled=!0)}run(t=0){if(this._currentlyRunning)throw new Error(\"Attempting to run(), but is already running.\");const e=this._currentlyRunning=this._queue;this._queue=[];for(const r of e)if(!r.cancelled&&(r.callback(t),this._cleared))break;this._cleared=!1,this._currentlyRunning=!1}clear(){this._currentlyRunning&&(this._cleared=!0),this._queue=[]}}var zi=e.Y([{name:\"a_pos3d\",type:\"Int16\",components:3}]);class Oi extends e.E{constructor(t){super(),this.sourceCache=t,this._tiles={},this._renderableTilesKeys=[],this._sourceTileCache={},this.minzoom=0,this.maxzoom=22,this.tileSize=512,this.deltaZoom=1,t.usedForTerrain=!0,t.tileSize=this.tileSize*2**this.deltaZoom}destruct(){this.sourceCache.usedForTerrain=!1,this.sourceCache.tileSize=null}update(t,r){this.sourceCache.update(t,r),this._renderableTilesKeys=[];const n={};for(const i of t.coveringTiles({tileSize:this.tileSize,minzoom:this.minzoom,maxzoom:this.maxzoom,reparseOverscaled:!1,terrain:r}))n[i.key]=!0,this._renderableTilesKeys.push(i.key),this._tiles[i.key]||(i.posMatrix=new Float64Array(16),e.aQ(i.posMatrix,0,e.X,0,e.X,0,1),this._tiles[i.key]=new ht(i,this.tileSize));for(const t in this._tiles)n[t]||delete this._tiles[t]}freeRtt(t){for(const e in this._tiles){const r=this._tiles[e];(!t||r.tileID.equals(t)||r.tileID.isChildOf(t)||t.isChildOf(r.tileID))&&(r.rtt=[])}}getRenderableTiles(){return this._renderableTilesKeys.map((t=>this.getTileByID(t)))}getTileByID(t){return this._tiles[t]}getTerrainCoords(t){const r={};for(const n of this._renderableTilesKeys){const i=this._tiles[n].tileID;if(i.canonical.equals(t.canonical)){const i=t.clone();i.posMatrix=new Float64Array(16),e.aQ(i.posMatrix,0,e.X,0,e.X,0,1),r[n]=i}else if(i.canonical.isChildOf(t.canonical)){const a=t.clone();a.posMatrix=new Float64Array(16);const o=i.canonical.z-t.canonical.z,s=i.canonical.x-(i.canonical.x>>o<>o<>o;e.aQ(a.posMatrix,0,c,0,c,0,1),e.J(a.posMatrix,a.posMatrix,[-s*c,-l*c,0]),r[n]=a}else if(t.canonical.isChildOf(i.canonical)){const a=t.clone();a.posMatrix=new Float64Array(16);const o=t.canonical.z-i.canonical.z,s=t.canonical.x-(t.canonical.x>>o<>o<>o;e.aQ(a.posMatrix,0,e.X,0,e.X,0,1),e.J(a.posMatrix,a.posMatrix,[s*c,l*c,0]),e.K(a.posMatrix,a.posMatrix,[1/2**o,1/2**o,0]),r[n]=a}}return r}getSourceTile(t,e){const r=this.sourceCache._source;let n=t.overscaledZ-this.deltaZoom;if(n>r.maxzoom&&(n=r.maxzoom),n=r.minzoom&&(!i||!i.dem);)i=this.sourceCache.getTileByID(t.scaledTo(n--).key);return i}tilesAfterTime(t=Date.now()){return Object.values(this._tiles).filter((e=>e.timeAdded>=t))}}class Di{constructor(t,e,r){this.painter=t,this.sourceCache=new Oi(e),this.options=r,this.exaggeration=\"number\"==typeof r.exaggeration?r.exaggeration:1,this.qualityFactor=2,this.meshSize=128,this._demMatrixCache={},this.coordsIndex=[],this._coordsTextureSize=1024}getDEMElevation(t,r,n,i=e.X){var a;if(!(r>=0&&r=0&&nt.canonical.z&&(t.canonical.z>=n?i=t.canonical.z-n:e.w(\"cannot calculate elevation if elevation maxzoom > source.maxzoom\"));const a=t.canonical.x-(t.canonical.x>>i<>i<>8<<4|t>>8,r[e+3]=0;const n=new e.R({width:this._coordsTextureSize,height:this._coordsTextureSize},new Uint8Array(r.buffer)),i=new w(t,n,t.gl.RGBA,{premultiply:!1});return i.bind(t.gl.NEAREST,t.gl.CLAMP_TO_EDGE),this._coordsTexture=i,i}pointCoordinate(t){this.painter.maybeDrawDepthAndCoords(!0);const r=new Uint8Array(4),n=this.painter.context,i=n.gl,a=Math.round(t.x*this.painter.pixelRatio/devicePixelRatio),o=Math.round(t.y*this.painter.pixelRatio/devicePixelRatio),s=Math.round(this.painter.height/devicePixelRatio);n.bindFramebuffer.set(this.getFramebuffer(\"coords\").framebuffer),i.readPixels(a,s-o-1,1,1,i.RGBA,i.UNSIGNED_BYTE,r),n.bindFramebuffer.set(null);const l=r[0]+(r[2]>>4<<8),c=r[1]+((15&r[2])<<8),u=this.coordsIndex[255-r[3]],h=u&&this.sourceCache.getTileByID(u);if(!h)return null;const f=this._coordsTextureSize,p=(1<t.id!==e)),this._recentlyUsed.push(t.id)}stampObject(t){t.stamp=++this._stamp}getOrCreateFreeObject(){for(const t of this._recentlyUsed)if(!this._objects[t].inUse)return this._objects[t];if(this._objects.length>=this._size)throw new Error(\"No free RenderPool available, call freeAllObjects() required!\");const t=this._createObject(this._objects.length);return this._objects.push(t),t}freeObject(t){t.inUse=!1}freeAllObjects(){for(const t of this._objects)this.freeObject(t)}isFull(){return!(this._objects.length!t.inUse))}}const Fi={background:!0,fill:!0,line:!0,raster:!0,hillshade:!0};class Bi{constructor(t,e){this.painter=t,this.terrain=e,this.pool=new Ri(t.context,30,e.sourceCache.tileSize*e.qualityFactor)}destruct(){this.pool.destruct()}getTexture(t){return this.pool.getObjectForId(t.rtt[this._stacks.length-1].id).texture}prepareForRender(t,e){this._stacks=[],this._prevType=null,this._rttTiles=[],this._renderableTiles=this.terrain.sourceCache.getRenderableTiles(),this._renderableLayerIds=t._order.filter((r=>!t._layers[r].isHidden(e))),this._coordsDescendingInv={};for(const e in t.sourceCaches){this._coordsDescendingInv[e]={};const r=t.sourceCaches[e].getVisibleCoordinates();for(const t of r){const r=this.terrain.sourceCache.getTerrainCoords(t);for(const t in r)this._coordsDescendingInv[e][t]||(this._coordsDescendingInv[e][t]=[]),this._coordsDescendingInv[e][t].push(r[t])}}this._coordsDescendingInvStr={};for(const e of t._order){const r=t._layers[e],n=r.source;if(Fi[r.type]&&!this._coordsDescendingInvStr[n]){this._coordsDescendingInvStr[n]={};for(const t in this._coordsDescendingInv[n])this._coordsDescendingInvStr[n][t]=this._coordsDescendingInv[n][t].map((t=>t.key)).sort().join()}}for(const t of this._renderableTiles)for(const e in this._coordsDescendingInvStr){const r=this._coordsDescendingInvStr[e][t.tileID.key];r&&r!==t.rttCoords[e]&&(t.rtt=[])}}renderLayer(t){if(t.isHidden(this.painter.transform.zoom))return!1;const r=t.type,n=this.painter,i=this._renderableLayerIds[this._renderableLayerIds.length-1]===t.id;if(Fi[r]&&(this._prevType&&Fi[this._prevType]||this._stacks.push([]),this._prevType=r,this._stacks[this._stacks.length-1].push(t.id),!i))return!0;if(Fi[this._prevType]||Fi[r]&&i){this._prevType=r;const t=this._stacks.length-1,i=this._stacks[t]||[];for(const r of this._renderableTiles){if(this.pool.isFull()&&(bn(this.painter,this.terrain,this._rttTiles),this._rttTiles=[],this.pool.freeAllObjects()),this._rttTiles.push(r),r.rtt[t]){const e=this.pool.getObjectForId(r.rtt[t].id);if(e.stamp===r.rtt[t].stamp){this.pool.useObject(e);continue}}const a=this.pool.getOrCreateFreeObject();this.pool.useObject(a),this.pool.stampObject(a),r.rtt[t]={id:a.id,stamp:a.stamp},n.context.bindFramebuffer.set(a.fbo.framebuffer),n.context.clear({color:e.aN.transparent,stencil:0}),n.currentStencilSource=void 0;for(let t=0;t{t.touchstart=t.dragStart,t.touchmoveWindow=t.dragMove,t.touchend=t.dragEnd},qi={showCompass:!0,showZoom:!0,visualizePitch:!1};class Hi{constructor(t,r,n=!1){this.mousedown=t=>{this.startMouse(e.e({},t,{ctrlKey:!0,preventDefault:()=>t.preventDefault()}),o.mousePos(this.element,t)),o.addEventListener(window,\"mousemove\",this.mousemove),o.addEventListener(window,\"mouseup\",this.mouseup)},this.mousemove=t=>{this.moveMouse(t,o.mousePos(this.element,t))},this.mouseup=t=>{this.mouseRotate.dragEnd(t),this.mousePitch&&this.mousePitch.dragEnd(t),this.offTemp()},this.touchstart=t=>{1!==t.targetTouches.length?this.reset():(this._startPos=this._lastPos=o.touchPos(this.element,t.targetTouches)[0],this.startTouch(t,this._startPos),o.addEventListener(window,\"touchmove\",this.touchmove,{passive:!1}),o.addEventListener(window,\"touchend\",this.touchend))},this.touchmove=t=>{1!==t.targetTouches.length?this.reset():(this._lastPos=o.touchPos(this.element,t.targetTouches)[0],this.moveTouch(t,this._lastPos))},this.touchend=t=>{0===t.targetTouches.length&&this._startPos&&this._lastPos&&this._startPos.dist(this._lastPos){this.mouseRotate.reset(),this.mousePitch&&this.mousePitch.reset(),this.touchRotate.reset(),this.touchPitch&&this.touchPitch.reset(),delete this._startPos,delete this._lastPos,this.offTemp()},this._clickTolerance=10;const i=t.dragRotate._mouseRotate.getClickTolerance(),a=t.dragRotate._mousePitch.getClickTolerance();this.element=r,this.mouseRotate=ei({clickTolerance:i,enable:!0}),this.touchRotate=(({enable:t,clickTolerance:e,bearingDegreesPerPixelMoved:r=.8})=>{const n=new Qn;return new $n({clickTolerance:e,move:(t,e)=>({bearingDelta:(e.x-t.x)*r}),moveStateManager:n,enable:t,assignEvents:Vi})})({clickTolerance:i,enable:!0}),this.map=t,n&&(this.mousePitch=ri({clickTolerance:a,enable:!0}),this.touchPitch=(({enable:t,clickTolerance:e,pitchDegreesPerPixelMoved:r=-.5})=>{const n=new Qn;return new $n({clickTolerance:e,move:(t,e)=>({pitchDelta:(e.y-t.y)*r}),moveStateManager:n,enable:t,assignEvents:Vi})})({clickTolerance:a,enable:!0})),o.addEventListener(r,\"mousedown\",this.mousedown),o.addEventListener(r,\"touchstart\",this.touchstart,{passive:!1}),o.addEventListener(r,\"touchcancel\",this.reset)}startMouse(t,e){this.mouseRotate.dragStart(t,e),this.mousePitch&&this.mousePitch.dragStart(t,e),o.disableDrag()}startTouch(t,e){this.touchRotate.dragStart(t,e),this.touchPitch&&this.touchPitch.dragStart(t,e),o.disableDrag()}moveMouse(t,e){const r=this.map,{bearingDelta:n}=this.mouseRotate.dragMove(t,e)||{};if(n&&r.setBearing(r.getBearing()+n),this.mousePitch){const{pitchDelta:n}=this.mousePitch.dragMove(t,e)||{};n&&r.setPitch(r.getPitch()+n)}}moveTouch(t,e){const r=this.map,{bearingDelta:n}=this.touchRotate.dragMove(t,e)||{};if(n&&r.setBearing(r.getBearing()+n),this.touchPitch){const{pitchDelta:n}=this.touchPitch.dragMove(t,e)||{};n&&r.setPitch(r.getPitch()+n)}}off(){const t=this.element;o.removeEventListener(t,\"mousedown\",this.mousedown),o.removeEventListener(t,\"touchstart\",this.touchstart,{passive:!1}),o.removeEventListener(window,\"touchmove\",this.touchmove,{passive:!1}),o.removeEventListener(window,\"touchend\",this.touchend),o.removeEventListener(t,\"touchcancel\",this.reset),this.offTemp()}offTemp(){o.enableDrag(),o.removeEventListener(window,\"mousemove\",this.mousemove),o.removeEventListener(window,\"mouseup\",this.mouseup),o.removeEventListener(window,\"touchmove\",this.touchmove,{passive:!1}),o.removeEventListener(window,\"touchend\",this.touchend)}}let Gi;function Zi(t,r,n){const i=new e.N(t.lng,t.lat);if(t=new e.N(t.lng,t.lat),r){const i=new e.N(t.lng-360,t.lat),a=new e.N(t.lng+360,t.lat),o=n.locationPoint(t).distSqr(r);n.locationPoint(i).distSqr(r)180;){const e=n.locationPoint(t);if(e.x>=0&&e.y>=0&&e.x<=n.width&&e.y<=n.height)break;t.lng>n.center.lng?t.lng-=360:t.lng+=360}return t.lng!==i.lng&&n.locationPoint(t).y>n.height/2-n.getHorizon()?t:i}const Wi={center:\"translate(-50%,-50%)\",top:\"translate(-50%,0)\",\"top-left\":\"translate(0,0)\",\"top-right\":\"translate(-100%,0)\",bottom:\"translate(-50%,-100%)\",\"bottom-left\":\"translate(0,-100%)\",\"bottom-right\":\"translate(-100%,-100%)\",left:\"translate(0,-50%)\",right:\"translate(-100%,-50%)\"};function Yi(t,e,r){const n=t.classList;for(const t in Wi)n.remove(`maplibregl-${r}-anchor-${t}`);n.add(`maplibregl-${r}-anchor-${e}`)}class Xi extends e.E{constructor(t){if(super(),this._onKeyPress=t=>{const e=t.code,r=t.charCode||t.keyCode;\"Space\"!==e&&\"Enter\"!==e&&32!==r&&13!==r||this.togglePopup()},this._onMapClick=t=>{const e=t.originalEvent.target,r=this._element;this._popup&&(e===r||r.contains(e))&&this.togglePopup()},this._update=t=>{var e;if(!this._map)return;const r=this._map.loaded()&&!this._map.isMoving();(\"terrain\"===(null==t?void 0:t.type)||\"render\"===(null==t?void 0:t.type)&&!r)&&this._map.once(\"render\",this._update),this._map.transform.renderWorldCopies?this._lngLat=Zi(this._lngLat,this._flatPos,this._map.transform):this._lngLat=null===(e=this._lngLat)||void 0===e?void 0:e.wrap(),this._flatPos=this._pos=this._map.project(this._lngLat)._add(this._offset),this._map.terrain&&(this._flatPos=this._map.transform.locationPoint(this._lngLat)._add(this._offset));let n=\"\";\"viewport\"===this._rotationAlignment||\"auto\"===this._rotationAlignment?n=`rotateZ(${this._rotation}deg)`:\"map\"===this._rotationAlignment&&(n=`rotateZ(${this._rotation-this._map.getBearing()}deg)`);let i=\"\";\"viewport\"===this._pitchAlignment||\"auto\"===this._pitchAlignment?i=\"rotateX(0deg)\":\"map\"===this._pitchAlignment&&(i=`rotateX(${this._map.getPitch()}deg)`),this._subpixelPositioning||t&&\"moveend\"!==t.type||(this._pos=this._pos.round()),o.setTransform(this._element,`${Wi[this._anchor]} translate(${this._pos.x}px, ${this._pos.y}px) ${i} ${n}`),a.frameAsync(new AbortController).then((()=>{this._updateOpacity(t&&\"moveend\"===t.type)})).catch((()=>{}))},this._onMove=t=>{if(!this._isDragging){const e=this._clickTolerance||this._map._clickTolerance;this._isDragging=t.point.dist(this._pointerdownPos)>=e}this._isDragging&&(this._pos=t.point.sub(this._positionDelta),this._lngLat=this._map.unproject(this._pos),this.setLngLat(this._lngLat),this._element.style.pointerEvents=\"none\",\"pending\"===this._state&&(this._state=\"active\",this.fire(new e.k(\"dragstart\"))),this.fire(new e.k(\"drag\")))},this._onUp=()=>{this._element.style.pointerEvents=\"auto\",this._positionDelta=null,this._pointerdownPos=null,this._isDragging=!1,this._map.off(\"mousemove\",this._onMove),this._map.off(\"touchmove\",this._onMove),\"active\"===this._state&&this.fire(new e.k(\"dragend\")),this._state=\"inactive\"},this._addDragHandler=t=>{this._element.contains(t.originalEvent.target)&&(t.preventDefault(),this._positionDelta=t.point.sub(this._pos).add(this._offset),this._pointerdownPos=t.point,this._state=\"pending\",this._map.on(\"mousemove\",this._onMove),this._map.on(\"touchmove\",this._onMove),this._map.once(\"mouseup\",this._onUp),this._map.once(\"touchend\",this._onUp))},this._anchor=t&&t.anchor||\"center\",this._color=t&&t.color||\"#3FB1CE\",this._scale=t&&t.scale||1,this._draggable=t&&t.draggable||!1,this._clickTolerance=t&&t.clickTolerance||0,this._subpixelPositioning=t&&t.subpixelPositioning||!1,this._isDragging=!1,this._state=\"inactive\",this._rotation=t&&t.rotation||0,this._rotationAlignment=t&&t.rotationAlignment||\"auto\",this._pitchAlignment=t&&t.pitchAlignment&&\"auto\"!==t.pitchAlignment?t.pitchAlignment:this._rotationAlignment,this.setOpacity(),this.setOpacity(null==t?void 0:t.opacity,null==t?void 0:t.opacityWhenCovered),t&&t.element)this._element=t.element,this._offset=e.P.convert(t&&t.offset||[0,0]);else{this._defaultMarker=!0,this._element=o.create(\"div\");const r=o.createNS(\"http://www.w3.org/2000/svg\",\"svg\"),n=41,i=27;r.setAttributeNS(null,\"display\",\"block\"),r.setAttributeNS(null,\"height\",`${n}px`),r.setAttributeNS(null,\"width\",`${i}px`),r.setAttributeNS(null,\"viewBox\",`0 0 ${i} ${n}`);const a=o.createNS(\"http://www.w3.org/2000/svg\",\"g\");a.setAttributeNS(null,\"stroke\",\"none\"),a.setAttributeNS(null,\"stroke-width\",\"1\"),a.setAttributeNS(null,\"fill\",\"none\"),a.setAttributeNS(null,\"fill-rule\",\"evenodd\");const s=o.createNS(\"http://www.w3.org/2000/svg\",\"g\");s.setAttributeNS(null,\"fill-rule\",\"nonzero\");const l=o.createNS(\"http://www.w3.org/2000/svg\",\"g\");l.setAttributeNS(null,\"transform\",\"translate(3.0, 29.0)\"),l.setAttributeNS(null,\"fill\",\"#000000\");const c=[{rx:\"10.5\",ry:\"5.25002273\"},{rx:\"10.5\",ry:\"5.25002273\"},{rx:\"9.5\",ry:\"4.77275007\"},{rx:\"8.5\",ry:\"4.29549936\"},{rx:\"7.5\",ry:\"3.81822308\"},{rx:\"6.5\",ry:\"3.34094679\"},{rx:\"5.5\",ry:\"2.86367051\"},{rx:\"4.5\",ry:\"2.38636864\"}];for(const t of c){const e=o.createNS(\"http://www.w3.org/2000/svg\",\"ellipse\");e.setAttributeNS(null,\"opacity\",\"0.04\"),e.setAttributeNS(null,\"cx\",\"10.5\"),e.setAttributeNS(null,\"cy\",\"5.80029008\"),e.setAttributeNS(null,\"rx\",t.rx),e.setAttributeNS(null,\"ry\",t.ry),l.appendChild(e)}const u=o.createNS(\"http://www.w3.org/2000/svg\",\"g\");u.setAttributeNS(null,\"fill\",this._color);const h=o.createNS(\"http://www.w3.org/2000/svg\",\"path\");h.setAttributeNS(null,\"d\",\"M27,13.5 C27,19.074644 20.250001,27.000002 14.75,34.500002 C14.016665,35.500004 12.983335,35.500004 12.25,34.500002 C6.7499993,27.000002 0,19.222562 0,13.5 C0,6.0441559 6.0441559,0 13.5,0 C20.955844,0 27,6.0441559 27,13.5 Z\"),u.appendChild(h);const f=o.createNS(\"http://www.w3.org/2000/svg\",\"g\");f.setAttributeNS(null,\"opacity\",\"0.25\"),f.setAttributeNS(null,\"fill\",\"#000000\");const p=o.createNS(\"http://www.w3.org/2000/svg\",\"path\");p.setAttributeNS(null,\"d\",\"M13.5,0 C6.0441559,0 0,6.0441559 0,13.5 C0,19.222562 6.7499993,27 12.25,34.5 C13,35.522727 14.016664,35.500004 14.75,34.5 C20.250001,27 27,19.074644 27,13.5 C27,6.0441559 20.955844,0 13.5,0 Z M13.5,1 C20.415404,1 26,6.584596 26,13.5 C26,15.898657 24.495584,19.181431 22.220703,22.738281 C19.945823,26.295132 16.705119,30.142167 13.943359,33.908203 C13.743445,34.180814 13.612715,34.322738 13.5,34.441406 C13.387285,34.322738 13.256555,34.180814 13.056641,33.908203 C10.284481,30.127985 7.4148684,26.314159 5.015625,22.773438 C2.6163816,19.232715 1,15.953538 1,13.5 C1,6.584596 6.584596,1 13.5,1 Z\"),f.appendChild(p);const d=o.createNS(\"http://www.w3.org/2000/svg\",\"g\");d.setAttributeNS(null,\"transform\",\"translate(6.0, 7.0)\"),d.setAttributeNS(null,\"fill\",\"#FFFFFF\");const m=o.createNS(\"http://www.w3.org/2000/svg\",\"g\");m.setAttributeNS(null,\"transform\",\"translate(8.0, 8.0)\");const g=o.createNS(\"http://www.w3.org/2000/svg\",\"circle\");g.setAttributeNS(null,\"fill\",\"#000000\"),g.setAttributeNS(null,\"opacity\",\"0.25\"),g.setAttributeNS(null,\"cx\",\"5.5\"),g.setAttributeNS(null,\"cy\",\"5.5\"),g.setAttributeNS(null,\"r\",\"5.4999962\");const y=o.createNS(\"http://www.w3.org/2000/svg\",\"circle\");y.setAttributeNS(null,\"fill\",\"#FFFFFF\"),y.setAttributeNS(null,\"cx\",\"5.5\"),y.setAttributeNS(null,\"cy\",\"5.5\"),y.setAttributeNS(null,\"r\",\"5.4999962\"),m.appendChild(g),m.appendChild(y),s.appendChild(l),s.appendChild(u),s.appendChild(f),s.appendChild(d),s.appendChild(m),r.appendChild(s),r.setAttributeNS(null,\"height\",n*this._scale+\"px\"),r.setAttributeNS(null,\"width\",i*this._scale+\"px\"),this._element.appendChild(r),this._offset=e.P.convert(t&&t.offset||[0,-14])}if(this._element.classList.add(\"maplibregl-marker\"),this._element.addEventListener(\"dragstart\",(t=>{t.preventDefault()})),this._element.addEventListener(\"mousedown\",(t=>{t.preventDefault()})),Yi(this._element,this._anchor,\"marker\"),t&&t.className)for(const e of t.className.split(\" \"))this._element.classList.add(e);this._popup=null}addTo(t){return this.remove(),this._map=t,this._element.setAttribute(\"aria-label\",t._getUIString(\"Marker.Title\")),t.getCanvasContainer().appendChild(this._element),t.on(\"move\",this._update),t.on(\"moveend\",this._update),t.on(\"terrain\",this._update),this.setDraggable(this._draggable),this._update(),this._map.on(\"click\",this._onMapClick),this}remove(){return this._opacityTimeout&&(clearTimeout(this._opacityTimeout),delete this._opacityTimeout),this._map&&(this._map.off(\"click\",this._onMapClick),this._map.off(\"move\",this._update),this._map.off(\"moveend\",this._update),this._map.off(\"terrain\",this._update),this._map.off(\"mousedown\",this._addDragHandler),this._map.off(\"touchstart\",this._addDragHandler),this._map.off(\"mouseup\",this._onUp),this._map.off(\"touchend\",this._onUp),this._map.off(\"mousemove\",this._onMove),this._map.off(\"touchmove\",this._onMove),delete this._map),o.remove(this._element),this._popup&&this._popup.remove(),this}getLngLat(){return this._lngLat}setLngLat(t){return this._lngLat=e.N.convert(t),this._pos=null,this._popup&&this._popup.setLngLat(this._lngLat),this._update(),this}getElement(){return this._element}setPopup(t){if(this._popup&&(this._popup.remove(),this._popup=null,this._element.removeEventListener(\"keypress\",this._onKeyPress),this._originalTabIndex||this._element.removeAttribute(\"tabindex\")),t){if(!(\"offset\"in t.options)){const e=38.1,r=13.5,n=Math.abs(r)/Math.SQRT2;t.options.offset=this._defaultMarker?{top:[0,0],\"top-left\":[0,0],\"top-right\":[0,0],bottom:[0,-e],\"bottom-left\":[n,-1*(e-r+n)],\"bottom-right\":[-n,-1*(e-r+n)],left:[r,-1*(e-r)],right:[-r,-1*(e-r)]}:this._offset}this._popup=t,this._originalTabIndex=this._element.getAttribute(\"tabindex\"),this._originalTabIndex||this._element.setAttribute(\"tabindex\",\"0\"),this._element.addEventListener(\"keypress\",this._onKeyPress)}return this}setSubpixelPositioning(t){return this._subpixelPositioning=t,this}getPopup(){return this._popup}togglePopup(){const t=this._popup;return this._element.style.opacity===this._opacityWhenCovered?this:t?(t.isOpen()?t.remove():(t.setLngLat(this._lngLat),t.addTo(this._map)),this):this}_updateOpacity(t=!1){var r,n;if(!(null===(r=this._map)||void 0===r?void 0:r.terrain))return void(this._element.style.opacity!==this._opacity&&(this._element.style.opacity=this._opacity));if(t)this._opacityTimeout=null;else{if(this._opacityTimeout)return;this._opacityTimeout=setTimeout((()=>{this._opacityTimeout=null}),100)}const i=this._map,a=i.terrain.depthAtPoint(this._pos),o=i.terrain.getElevationForLngLatZoom(this._lngLat,i.transform.tileZoom);if(i.transform.lngLatToCameraDepth(this._lngLat,o)-a<.006)return void(this._element.style.opacity=this._opacity);const s=-this._offset.y/i.transform._pixelPerMeter,l=Math.sin(i.getPitch()*Math.PI/180)*s,c=i.terrain.depthAtPoint(new e.P(this._pos.x,this._pos.y-this._offset.y)),u=i.transform.lngLatToCameraDepth(this._lngLat,o+l)-c>.006;(null===(n=this._popup)||void 0===n?void 0:n.isOpen())&&u&&this._popup.remove(),this._element.style.opacity=u?this._opacityWhenCovered:this._opacity}getOffset(){return this._offset}setOffset(t){return this._offset=e.P.convert(t),this._update(),this}addClassName(t){this._element.classList.add(t)}removeClassName(t){this._element.classList.remove(t)}toggleClassName(t){return this._element.classList.toggle(t)}setDraggable(t){return this._draggable=!!t,this._map&&(t?(this._map.on(\"mousedown\",this._addDragHandler),this._map.on(\"touchstart\",this._addDragHandler)):(this._map.off(\"mousedown\",this._addDragHandler),this._map.off(\"touchstart\",this._addDragHandler))),this}isDraggable(){return this._draggable}setRotation(t){return this._rotation=t||0,this._update(),this}getRotation(){return this._rotation}setRotationAlignment(t){return this._rotationAlignment=t||\"auto\",this._update(),this}getRotationAlignment(){return this._rotationAlignment}setPitchAlignment(t){return this._pitchAlignment=t&&\"auto\"!==t?t:this._rotationAlignment,this._update(),this}getPitchAlignment(){return this._pitchAlignment}setOpacity(t,e){return void 0===t&&void 0===e&&(this._opacity=\"1\",this._opacityWhenCovered=\"0.2\"),void 0!==t&&(this._opacity=t),void 0!==e&&(this._opacityWhenCovered=e),this._map&&this._updateOpacity(!0),this}}const $i={positionOptions:{enableHighAccuracy:!1,maximumAge:0,timeout:6e3},fitBoundsOptions:{maxZoom:15},trackUserLocation:!1,showAccuracyCircle:!0,showUserLocation:!0};let Ji=0,Ki=!1;class Qi extends e.E{constructor(t){super(),this._onSuccess=t=>{if(this._map){if(this._isOutOfMapMaxBounds(t))return this._setErrorState(),this.fire(new e.k(\"outofmaxbounds\",t)),this._updateMarker(),void this._finish();if(this.options.trackUserLocation)switch(this._lastKnownPosition=t,this._watchState){case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-active\");break;case\"BACKGROUND\":case\"BACKGROUND_ERROR\":this._watchState=\"BACKGROUND\",this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-background\");break;default:throw new Error(`Unexpected watchState ${this._watchState}`)}this.options.showUserLocation&&\"OFF\"!==this._watchState&&this._updateMarker(t),this.options.trackUserLocation&&\"ACTIVE_LOCK\"!==this._watchState||this._updateCamera(t),this.options.showUserLocation&&this._dotElement.classList.remove(\"maplibregl-user-location-dot-stale\"),this.fire(new e.k(\"geolocate\",t)),this._finish()}},this._updateCamera=t=>{const r=new e.N(t.coords.longitude,t.coords.latitude),n=t.coords.accuracy,i=this._map.getBearing(),a=e.e({bearing:i},this.options.fitBoundsOptions),o=X.fromLngLat(r,n);this._map.fitBounds(o,a,{geolocateSource:!0})},this._updateMarker=t=>{if(t){const r=new e.N(t.coords.longitude,t.coords.latitude);this._accuracyCircleMarker.setLngLat(r).addTo(this._map),this._userLocationDotMarker.setLngLat(r).addTo(this._map),this._accuracy=t.coords.accuracy,this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()}else this._userLocationDotMarker.remove(),this._accuracyCircleMarker.remove()},this._onZoom=()=>{this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()},this._onError=t=>{if(this._map){if(this.options.trackUserLocation)if(1===t.code){this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-background-error\"),this._geolocateButton.disabled=!0;const t=this._map._getUIString(\"GeolocateControl.LocationNotAvailable\");this._geolocateButton.title=t,this._geolocateButton.setAttribute(\"aria-label\",t),void 0!==this._geolocationWatchID&&this._clearWatch()}else{if(3===t.code&&Ki)return;this._setErrorState()}\"OFF\"!==this._watchState&&this.options.showUserLocation&&this._dotElement.classList.add(\"maplibregl-user-location-dot-stale\"),this.fire(new e.k(\"error\",t)),this._finish()}},this._finish=()=>{this._timeoutId&&clearTimeout(this._timeoutId),this._timeoutId=void 0},this._setupUI=()=>{this._map&&(this._container.addEventListener(\"contextmenu\",(t=>t.preventDefault())),this._geolocateButton=o.create(\"button\",\"maplibregl-ctrl-geolocate\",this._container),o.create(\"span\",\"maplibregl-ctrl-icon\",this._geolocateButton).setAttribute(\"aria-hidden\",\"true\"),this._geolocateButton.type=\"button\",this._geolocateButton.disabled=!0)},this._finishSetupUI=t=>{if(this._map){if(!1===t){e.w(\"Geolocation support is not available so the GeolocateControl will be disabled.\");const t=this._map._getUIString(\"GeolocateControl.LocationNotAvailable\");this._geolocateButton.disabled=!0,this._geolocateButton.title=t,this._geolocateButton.setAttribute(\"aria-label\",t)}else{const t=this._map._getUIString(\"GeolocateControl.FindMyLocation\");this._geolocateButton.disabled=!1,this._geolocateButton.title=t,this._geolocateButton.setAttribute(\"aria-label\",t)}this.options.trackUserLocation&&(this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this._watchState=\"OFF\"),this.options.showUserLocation&&(this._dotElement=o.create(\"div\",\"maplibregl-user-location-dot\"),this._userLocationDotMarker=new Xi({element:this._dotElement}),this._circleElement=o.create(\"div\",\"maplibregl-user-location-accuracy-circle\"),this._accuracyCircleMarker=new Xi({element:this._circleElement,pitchAlignment:\"map\"}),this.options.trackUserLocation&&(this._watchState=\"OFF\"),this._map.on(\"zoom\",this._onZoom)),this._geolocateButton.addEventListener(\"click\",(()=>this.trigger())),this._setup=!0,this.options.trackUserLocation&&this._map.on(\"movestart\",(t=>{const r=t.originalEvent&&\"resize\"===t.originalEvent.type;t.geolocateSource||\"ACTIVE_LOCK\"!==this._watchState||r||(this._watchState=\"BACKGROUND\",this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-active\"),this.fire(new e.k(\"trackuserlocationend\")),this.fire(new e.k(\"userlocationlostfocus\")))}))}},this.options=e.e({},$i,t)}onAdd(t){return this._map=t,this._container=o.create(\"div\",\"maplibregl-ctrl maplibregl-ctrl-group\"),this._setupUI(),function(){return e._(this,arguments,void 0,(function*(t=!1){if(void 0!==Gi&&!t)return Gi;if(void 0===window.navigator.permissions)return Gi=!!window.navigator.geolocation,Gi;try{const t=yield window.navigator.permissions.query({name:\"geolocation\"});Gi=\"denied\"!==t.state}catch(t){Gi=!!window.navigator.geolocation}return Gi}))}().then((t=>this._finishSetupUI(t))),this._container}onRemove(){void 0!==this._geolocationWatchID&&(window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0),this.options.showUserLocation&&this._userLocationDotMarker&&this._userLocationDotMarker.remove(),this.options.showAccuracyCircle&&this._accuracyCircleMarker&&this._accuracyCircleMarker.remove(),o.remove(this._container),this._map.off(\"zoom\",this._onZoom),this._map=void 0,Ji=0,Ki=!1}_isOutOfMapMaxBounds(t){const e=this._map.getMaxBounds(),r=t.coords;return e&&(r.longitudee.getEast()||r.latitudee.getNorth())}_setErrorState(){switch(this._watchState){case\"WAITING_ACTIVE\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-active-error\");break;case\"ACTIVE_LOCK\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-waiting\");break;case\"BACKGROUND\":this._watchState=\"BACKGROUND_ERROR\",this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-background\"),this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-waiting\");break;case\"ACTIVE_ERROR\":break;default:throw new Error(`Unexpected watchState ${this._watchState}`)}}_updateCircleRadius(){const t=this._map.getBounds(),e=t.getSouthEast(),r=t.getNorthEast(),n=e.distanceTo(r),i=this._map._container.clientHeight,a=Math.ceil(this._accuracy/(n/i)*2);this._circleElement.style.width=`${a}px`,this._circleElement.style.height=`${a}px`}trigger(){if(!this._setup)return e.w(\"Geolocate control triggered before added to a map\"),!1;if(this.options.trackUserLocation){switch(this._watchState){case\"OFF\":this._watchState=\"WAITING_ACTIVE\",this.fire(new e.k(\"trackuserlocationstart\"));break;case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":case\"BACKGROUND_ERROR\":Ji--,Ki=!1,this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-background-error\"),this.fire(new e.k(\"trackuserlocationend\"));break;case\"BACKGROUND\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-background\"),this._lastKnownPosition&&this._updateCamera(this._lastKnownPosition),this.fire(new e.k(\"trackuserlocationstart\")),this.fire(new e.k(\"userlocationfocus\"));break;default:throw new Error(`Unexpected watchState ${this._watchState}`)}switch(this._watchState){case\"WAITING_ACTIVE\":this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-active\");break;case\"ACTIVE_LOCK\":this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-active\");break;case\"OFF\":break;default:throw new Error(`Unexpected watchState ${this._watchState}`)}if(\"OFF\"===this._watchState&&void 0!==this._geolocationWatchID)this._clearWatch();else if(void 0===this._geolocationWatchID){let t;this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"true\"),Ji++,Ji>1?(t={maximumAge:6e5,timeout:0},Ki=!0):(t=this.options.positionOptions,Ki=!1),this._geolocationWatchID=window.navigator.geolocation.watchPosition(this._onSuccess,this._onError,t)}}else window.navigator.geolocation.getCurrentPosition(this._onSuccess,this._onError,this.options.positionOptions),this._timeoutId=setTimeout(this._finish,1e4);return!0}_clearWatch(){window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0,this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this.options.showUserLocation&&this._updateMarker(null)}}const ta={maxWidth:100,unit:\"metric\"};function ea(t,e,r){const n=r&&r.maxWidth||100,i=t._container.clientHeight/2,a=t.unproject([0,i]),o=t.unproject([n,i]),s=a.distanceTo(o);if(r&&\"imperial\"===r.unit){const r=3.2808*s;r>5280?ra(e,n,r/5280,t._getUIString(\"ScaleControl.Miles\")):ra(e,n,r,t._getUIString(\"ScaleControl.Feet\"))}else r&&\"nautical\"===r.unit?ra(e,n,s/1852,t._getUIString(\"ScaleControl.NauticalMiles\")):s>=1e3?ra(e,n,s/1e3,t._getUIString(\"ScaleControl.Kilometers\")):ra(e,n,s,t._getUIString(\"ScaleControl.Meters\"))}function ra(t,e,r,n){const i=function(t){const e=Math.pow(10,`${Math.floor(t)}`.length-1);let r=t/e;return r=r>=10?10:r>=5?5:r>=3?3:r>=2?2:r>=1?1:function(t){const e=Math.pow(10,Math.ceil(-Math.log(t)/Math.LN10));return Math.round(t*e)/e}(r),e*r}(r),a=i/r;t.style.width=e*a+\"px\",t.innerHTML=`${i} ${n}`}class na extends e.E{constructor(t={}){super(),this._onFullscreenChange=()=>{var t;let e=window.document.fullscreenElement||window.document.mozFullScreenElement||window.document.webkitFullscreenElement||window.document.msFullscreenElement;for(;null===(t=null==e?void 0:e.shadowRoot)||void 0===t?void 0:t.fullscreenElement;)e=e.shadowRoot.fullscreenElement;e===this._container!==this._fullscreen&&this._handleFullscreenChange()},this._onClickFullscreen=()=>{this._isFullscreen()?this._exitFullscreen():this._requestFullscreen()},this._fullscreen=!1,t&&t.container&&(t.container instanceof HTMLElement?this._container=t.container:e.w(\"Full screen control 'container' must be a DOM element.\")),\"onfullscreenchange\"in document?this._fullscreenchange=\"fullscreenchange\":\"onmozfullscreenchange\"in document?this._fullscreenchange=\"mozfullscreenchange\":\"onwebkitfullscreenchange\"in document?this._fullscreenchange=\"webkitfullscreenchange\":\"onmsfullscreenchange\"in document&&(this._fullscreenchange=\"MSFullscreenChange\")}onAdd(t){return this._map=t,this._container||(this._container=this._map.getContainer()),this._controlContainer=o.create(\"div\",\"maplibregl-ctrl maplibregl-ctrl-group\"),this._setupUI(),this._controlContainer}onRemove(){o.remove(this._controlContainer),this._map=null,window.document.removeEventListener(this._fullscreenchange,this._onFullscreenChange)}_setupUI(){const t=this._fullscreenButton=o.create(\"button\",\"maplibregl-ctrl-fullscreen\",this._controlContainer);o.create(\"span\",\"maplibregl-ctrl-icon\",t).setAttribute(\"aria-hidden\",\"true\"),t.type=\"button\",this._updateTitle(),this._fullscreenButton.addEventListener(\"click\",this._onClickFullscreen),window.document.addEventListener(this._fullscreenchange,this._onFullscreenChange)}_updateTitle(){const t=this._getTitle();this._fullscreenButton.setAttribute(\"aria-label\",t),this._fullscreenButton.title=t}_getTitle(){return this._map._getUIString(this._isFullscreen()?\"FullscreenControl.Exit\":\"FullscreenControl.Enter\")}_isFullscreen(){return this._fullscreen}_handleFullscreenChange(){this._fullscreen=!this._fullscreen,this._fullscreenButton.classList.toggle(\"maplibregl-ctrl-shrink\"),this._fullscreenButton.classList.toggle(\"maplibregl-ctrl-fullscreen\"),this._updateTitle(),this._fullscreen?(this.fire(new e.k(\"fullscreenstart\")),this._prevCooperativeGesturesEnabled=this._map.cooperativeGestures.isEnabled(),this._map.cooperativeGestures.disable()):(this.fire(new e.k(\"fullscreenend\")),this._prevCooperativeGesturesEnabled&&this._map.cooperativeGestures.enable())}_exitFullscreen(){window.document.exitFullscreen?window.document.exitFullscreen():window.document.mozCancelFullScreen?window.document.mozCancelFullScreen():window.document.msExitFullscreen?window.document.msExitFullscreen():window.document.webkitCancelFullScreen?window.document.webkitCancelFullScreen():this._togglePseudoFullScreen()}_requestFullscreen(){this._container.requestFullscreen?this._container.requestFullscreen():this._container.mozRequestFullScreen?this._container.mozRequestFullScreen():this._container.msRequestFullscreen?this._container.msRequestFullscreen():this._container.webkitRequestFullscreen?this._container.webkitRequestFullscreen():this._togglePseudoFullScreen()}_togglePseudoFullScreen(){this._container.classList.toggle(\"maplibregl-pseudo-fullscreen\"),this._handleFullscreenChange(),this._map.resize()}}const ia={closeButton:!0,closeOnClick:!0,focusAfterOpen:!0,className:\"\",maxWidth:\"240px\",subpixelPositioning:!1},aa=[\"a[href]\",\"[tabindex]:not([tabindex='-1'])\",\"[contenteditable]:not([contenteditable='false'])\",\"button:not([disabled])\",\"input:not([disabled])\",\"select:not([disabled])\",\"textarea:not([disabled])\"].join(\", \");class oa extends e.E{constructor(t){super(),this.remove=()=>(this._content&&o.remove(this._content),this._container&&(o.remove(this._container),delete this._container),this._map&&(this._map.off(\"move\",this._update),this._map.off(\"move\",this._onClose),this._map.off(\"click\",this._onClose),this._map.off(\"remove\",this.remove),this._map.off(\"mousemove\",this._onMouseMove),this._map.off(\"mouseup\",this._onMouseUp),this._map.off(\"drag\",this._onDrag),this._map._canvasContainer.classList.remove(\"maplibregl-track-pointer\"),delete this._map,this.fire(new e.k(\"close\"))),this),this._onMouseUp=t=>{this._update(t.point)},this._onMouseMove=t=>{this._update(t.point)},this._onDrag=t=>{this._update(t.point)},this._update=t=>{var e;const r=this._lngLat||this._trackPointer;if(!this._map||!r||!this._content)return;if(!this._container){if(this._container=o.create(\"div\",\"maplibregl-popup\",this._map.getContainer()),this._tip=o.create(\"div\",\"maplibregl-popup-tip\",this._container),this._container.appendChild(this._content),this.options.className)for(const t of this.options.className.split(\" \"))this._container.classList.add(t);this._closeButton&&this._closeButton.setAttribute(\"aria-label\",this._map._getUIString(\"Popup.Close\")),this._trackPointer&&this._container.classList.add(\"maplibregl-popup-track-pointer\")}if(this.options.maxWidth&&this._container.style.maxWidth!==this.options.maxWidth&&(this._container.style.maxWidth=this.options.maxWidth),this._map.transform.renderWorldCopies&&!this._trackPointer?this._lngLat=Zi(this._lngLat,this._flatPos,this._map.transform):this._lngLat=null===(e=this._lngLat)||void 0===e?void 0:e.wrap(),this._trackPointer&&!t)return;const n=this._flatPos=this._pos=this._trackPointer&&t?t:this._map.project(this._lngLat);this._map.terrain&&(this._flatPos=this._trackPointer&&t?t:this._map.transform.locationPoint(this._lngLat));let i=this.options.anchor;const a=sa(this.options.offset);if(!i){const t=this._container.offsetWidth,e=this._container.offsetHeight;let r;r=n.y+a.bottom.ythis._map.transform.height-e?[\"bottom\"]:[],n.xthis._map.transform.width-t/2&&r.push(\"right\"),i=0===r.length?\"bottom\":r.join(\"-\")}let s=n.add(a[i]);this.options.subpixelPositioning||(s=s.round()),o.setTransform(this._container,`${Wi[i]} translate(${s.x}px,${s.y}px)`),Yi(this._container,i,\"popup\")},this._onClose=()=>{this.remove()},this.options=e.e(Object.create(ia),t)}addTo(t){return this._map&&this.remove(),this._map=t,this.options.closeOnClick&&this._map.on(\"click\",this._onClose),this.options.closeOnMove&&this._map.on(\"move\",this._onClose),this._map.on(\"remove\",this.remove),this._update(),this._focusFirstElement(),this._trackPointer?(this._map.on(\"mousemove\",this._onMouseMove),this._map.on(\"mouseup\",this._onMouseUp),this._container&&this._container.classList.add(\"maplibregl-popup-track-pointer\"),this._map._canvasContainer.classList.add(\"maplibregl-track-pointer\")):this._map.on(\"move\",this._update),this.fire(new e.k(\"open\")),this}isOpen(){return!!this._map}getLngLat(){return this._lngLat}setLngLat(t){return this._lngLat=e.N.convert(t),this._pos=null,this._flatPos=null,this._trackPointer=!1,this._update(),this._map&&(this._map.on(\"move\",this._update),this._map.off(\"mousemove\",this._onMouseMove),this._container&&this._container.classList.remove(\"maplibregl-popup-track-pointer\"),this._map._canvasContainer.classList.remove(\"maplibregl-track-pointer\")),this}trackPointer(){return this._trackPointer=!0,this._pos=null,this._flatPos=null,this._update(),this._map&&(this._map.off(\"move\",this._update),this._map.on(\"mousemove\",this._onMouseMove),this._map.on(\"drag\",this._onDrag),this._container&&this._container.classList.add(\"maplibregl-popup-track-pointer\"),this._map._canvasContainer.classList.add(\"maplibregl-track-pointer\")),this}getElement(){return this._container}setText(t){return this.setDOMContent(document.createTextNode(t))}setHTML(t){const e=document.createDocumentFragment(),r=document.createElement(\"body\");let n;for(r.innerHTML=t;n=r.firstChild,n;)e.appendChild(n);return this.setDOMContent(e)}getMaxWidth(){var t;return null===(t=this._container)||void 0===t?void 0:t.style.maxWidth}setMaxWidth(t){return this.options.maxWidth=t,this._update(),this}setDOMContent(t){if(this._content)for(;this._content.hasChildNodes();)this._content.firstChild&&this._content.removeChild(this._content.firstChild);else this._content=o.create(\"div\",\"maplibregl-popup-content\",this._container);return this._content.appendChild(t),this._createCloseButton(),this._update(),this._focusFirstElement(),this}addClassName(t){return this._container&&this._container.classList.add(t),this}removeClassName(t){return this._container&&this._container.classList.remove(t),this}setOffset(t){return this.options.offset=t,this._update(),this}toggleClassName(t){if(this._container)return this._container.classList.toggle(t)}setSubpixelPositioning(t){this.options.subpixelPositioning=t}_createCloseButton(){this.options.closeButton&&(this._closeButton=o.create(\"button\",\"maplibregl-popup-close-button\",this._content),this._closeButton.type=\"button\",this._closeButton.innerHTML=\"×\",this._closeButton.addEventListener(\"click\",this._onClose))}_focusFirstElement(){if(!this.options.focusAfterOpen||!this._container)return;const t=this._container.querySelector(aa);t&&t.focus()}}function sa(t){if(t){if(\"number\"==typeof t){const r=Math.round(Math.abs(t)/Math.SQRT2);return{center:new e.P(0,0),top:new e.P(0,t),\"top-left\":new e.P(r,r),\"top-right\":new e.P(-r,r),bottom:new e.P(0,-t),\"bottom-left\":new e.P(r,-r),\"bottom-right\":new e.P(-r,-r),left:new e.P(t,0),right:new e.P(-t,0)}}if(t instanceof e.P||Array.isArray(t)){const r=e.P.convert(t);return{center:r,top:r,\"top-left\":r,\"top-right\":r,bottom:r,\"bottom-left\":r,\"bottom-right\":r,left:r,right:r}}return{center:e.P.convert(t.center||[0,0]),top:e.P.convert(t.top||[0,0]),\"top-left\":e.P.convert(t[\"top-left\"]||[0,0]),\"top-right\":e.P.convert(t[\"top-right\"]||[0,0]),bottom:e.P.convert(t.bottom||[0,0]),\"bottom-left\":e.P.convert(t[\"bottom-left\"]||[0,0]),\"bottom-right\":e.P.convert(t[\"bottom-right\"]||[0,0]),left:e.P.convert(t.left||[0,0]),right:e.P.convert(t.right||[0,0])}}return sa(new e.P(0,0))}const la=r;t.AJAXError=e.bg,t.Evented=e.E,t.LngLat=e.N,t.MercatorCoordinate=e.Z,t.Point=e.P,t.addProtocol=e.bh,t.config=e.a,t.removeProtocol=e.bi,t.AttributionControl=Li,t.BoxZoomHandler=Gn,t.CanvasSource=it,t.CooperativeGesturesHandler=Ti,t.DoubleClickZoomHandler=yi,t.DragPanHandler=_i,t.DragRotateHandler=bi,t.EdgeInsets=Mn,t.FullscreenControl=na,t.GeoJSONSource=tt,t.GeolocateControl=Qi,t.Hash=Ln,t.ImageSource=rt,t.KeyboardHandler=pi,t.LngLatBounds=X,t.LogoControl=Ii,t.Map=class extends Ei{constructor(t){e.be.mark(e.bf.create);const r=Object.assign(Object.assign({},Ui),t);if(null!=r.minZoom&&null!=r.maxZoom&&r.minZoom>r.maxZoom)throw new Error(\"maxZoom must be greater than or equal to minZoom\");if(null!=r.minPitch&&null!=r.maxPitch&&r.minPitch>r.maxPitch)throw new Error(\"maxPitch must be greater than or equal to minPitch\");if(null!=r.minPitch&&r.minPitch<0)throw new Error(\"minPitch must be greater than or equal to 0\");if(null!=r.maxPitch&&r.maxPitch>85)throw new Error(\"maxPitch must be less than or equal to 85\");if(super(new En(r.minZoom,r.maxZoom,r.minPitch,r.maxPitch,r.renderWorldCopies),{bearingSnap:r.bearingSnap}),this._idleTriggered=!1,this._crossFadingFactor=1,this._renderTaskQueue=new Pi,this._controls=[],this._mapId=e.a4(),this._contextLost=t=>{t.preventDefault(),this._frameRequest&&(this._frameRequest.abort(),this._frameRequest=null),this.fire(new e.k(\"webglcontextlost\",{originalEvent:t}))},this._contextRestored=t=>{this._setupPainter(),this.resize(),this._update(),this.fire(new e.k(\"webglcontextrestored\",{originalEvent:t}))},this._onMapScroll=t=>{if(t.target===this._container)return this._container.scrollTop=0,this._container.scrollLeft=0,!1},this._onWindowOnline=()=>{this._update()},this._interactive=r.interactive,this._maxTileCacheSize=r.maxTileCacheSize,this._maxTileCacheZoomLevels=r.maxTileCacheZoomLevels,this._failIfMajorPerformanceCaveat=!0===r.failIfMajorPerformanceCaveat,this._preserveDrawingBuffer=!0===r.preserveDrawingBuffer,this._antialias=!0===r.antialias,this._trackResize=!0===r.trackResize,this._bearingSnap=r.bearingSnap,this._refreshExpiredTiles=!0===r.refreshExpiredTiles,this._fadeDuration=r.fadeDuration,this._crossSourceCollisions=!0===r.crossSourceCollisions,this._collectResourceTiming=!0===r.collectResourceTiming,this._locale=Object.assign(Object.assign({},Ni),r.locale),this._clickTolerance=r.clickTolerance,this._overridePixelRatio=r.pixelRatio,this._maxCanvasSize=r.maxCanvasSize,this.transformCameraUpdate=r.transformCameraUpdate,this.cancelPendingTileRequestsWhileZooming=!0===r.cancelPendingTileRequestsWhileZooming,this._imageQueueHandle=p.addThrottleControl((()=>this.isMoving())),this._requestManager=new d(r.transformRequest),\"string\"==typeof r.container){if(this._container=document.getElementById(r.container),!this._container)throw new Error(`Container '${r.container}' not found.`)}else{if(!(r.container instanceof HTMLElement))throw new Error(\"Invalid type: 'container' must be a String or HTMLElement.\");this._container=r.container}if(r.maxBounds&&this.setMaxBounds(r.maxBounds),this._setupContainer(),this._setupPainter(),this.on(\"move\",(()=>this._update(!1))).on(\"moveend\",(()=>this._update(!1))).on(\"zoom\",(()=>this._update(!0))).on(\"terrain\",(()=>{this.painter.terrainFacilitator.dirty=!0,this._update(!0)})).once(\"idle\",(()=>{this._idleTriggered=!0})),\"undefined\"!=typeof window){addEventListener(\"online\",this._onWindowOnline,!1);let t=!1;const e=Cn((t=>{this._trackResize&&!this._removed&&this.resize(t)._update()}),50);this._resizeObserver=new ResizeObserver((r=>{t?e(r):t=!0})),this._resizeObserver.observe(this._container)}this.handlers=new Si(this,r);const n=\"string\"==typeof r.hash&&r.hash||void 0;this._hash=r.hash&&new Ln(n).addTo(this),this._hash&&this._hash._onHashChange()||(this.jumpTo({center:r.center,zoom:r.zoom,bearing:r.bearing,pitch:r.pitch}),r.bounds&&(this.resize(),this.fitBounds(r.bounds,e.e({},r.fitBoundsOptions,{duration:0})))),this.resize(),this._localIdeographFontFamily=r.localIdeographFontFamily,this._validateStyle=r.validateStyle,r.style&&this.setStyle(r.style,{localIdeographFontFamily:r.localIdeographFontFamily}),r.attributionControl&&this.addControl(new Li(\"boolean\"==typeof r.attributionControl?void 0:r.attributionControl)),r.maplibreLogo&&this.addControl(new Ii,r.logoPosition),this.on(\"style.load\",(()=>{this.transform.unmodified&&this.jumpTo(this.style.stylesheet)})),this.on(\"data\",(t=>{this._update(\"style\"===t.dataType),this.fire(new e.k(`${t.dataType}data`,t))})),this.on(\"dataloading\",(t=>{this.fire(new e.k(`${t.dataType}dataloading`,t))})),this.on(\"dataabort\",(t=>{this.fire(new e.k(\"sourcedataabort\",t))}))}_getMapId(){return this._mapId}addControl(t,r){if(void 0===r&&(r=t.getDefaultPosition?t.getDefaultPosition():\"top-right\"),!t||!t.onAdd)return this.fire(new e.j(new Error(\"Invalid argument to map.addControl(). Argument must be a control with onAdd and onRemove methods.\")));const n=t.onAdd(this);this._controls.push(t);const i=this._controlPositions[r];return-1!==r.indexOf(\"bottom\")?i.insertBefore(n,i.firstChild):i.appendChild(n),this}removeControl(t){if(!t||!t.onRemove)return this.fire(new e.j(new Error(\"Invalid argument to map.removeControl(). Argument must be a control with onAdd and onRemove methods.\")));const r=this._controls.indexOf(t);return r>-1&&this._controls.splice(r,1),t.onRemove(this),this}hasControl(t){return this._controls.indexOf(t)>-1}calculateCameraOptionsFromTo(t,e,r,n){return null==n&&this.terrain&&(n=this.terrain.getElevationForLngLatZoom(r,this.transform.tileZoom)),super.calculateCameraOptionsFromTo(t,e,r,n)}resize(t){var r;const n=this._containerDimensions(),i=n[0],a=n[1],o=this._getClampedPixelRatio(i,a);if(this._resizeCanvas(i,a,o),this.painter.resize(i,a,o),this.painter.overLimit()){const t=this.painter.context.gl;this._maxCanvasSize=[t.drawingBufferWidth,t.drawingBufferHeight];const e=this._getClampedPixelRatio(i,a);this._resizeCanvas(i,a,e),this.painter.resize(i,a,e)}this.transform.resize(i,a),null===(r=this._requestedCameraState)||void 0===r||r.resize(i,a);const s=!this._moving;return s&&(this.stop(),this.fire(new e.k(\"movestart\",t)).fire(new e.k(\"move\",t))),this.fire(new e.k(\"resize\",t)),s&&this.fire(new e.k(\"moveend\",t)),this}_getClampedPixelRatio(t,e){const{0:r,1:n}=this._maxCanvasSize,i=this.getPixelRatio(),a=t*i,o=e*i,s=a>r?r/a:1,l=o>n?n/o:1;return Math.min(s,l)*i}getPixelRatio(){var t;return null!==(t=this._overridePixelRatio)&&void 0!==t?t:devicePixelRatio}setPixelRatio(t){this._overridePixelRatio=t,this.resize()}getBounds(){return this.transform.getBounds()}getMaxBounds(){return this.transform.getMaxBounds()}setMaxBounds(t){return this.transform.setMaxBounds(X.convert(t)),this._update()}setMinZoom(t){if((t=null==t?-2:t)>=-2&&t<=this.transform.maxZoom)return this.transform.minZoom=t,this._update(),this.getZoom()=this.transform.minZoom)return this.transform.maxZoom=t,this._update(),this.getZoom()>t&&this.setZoom(t),this;throw new Error(\"maxZoom must be greater than the current minZoom\")}getMaxZoom(){return this.transform.maxZoom}setMinPitch(t){if((t=null==t?0:t)<0)throw new Error(\"minPitch must be greater than or equal to 0\");if(t>=0&&t<=this.transform.maxPitch)return this.transform.minPitch=t,this._update(),this.getPitch()85)throw new Error(\"maxPitch must be less than or equal to 85\");if(t>=this.transform.minPitch)return this.transform.maxPitch=t,this._update(),this.getPitch()>t&&this.setPitch(t),this;throw new Error(\"maxPitch must be greater than the current minPitch\")}getMaxPitch(){return this.transform.maxPitch}getRenderWorldCopies(){return this.transform.renderWorldCopies}setRenderWorldCopies(t){return this.transform.renderWorldCopies=t,this._update()}project(t){return this.transform.locationPoint(e.N.convert(t),this.style&&this.terrain)}unproject(t){return this.transform.pointLocation(e.P.convert(t),this.terrain)}isMoving(){var t;return this._moving||(null===(t=this.handlers)||void 0===t?void 0:t.isMoving())}isZooming(){var t;return this._zooming||(null===(t=this.handlers)||void 0===t?void 0:t.isZooming())}isRotating(){var t;return this._rotating||(null===(t=this.handlers)||void 0===t?void 0:t.isRotating())}_createDelegatedListener(t,e,r){if(\"mouseenter\"===t||\"mouseover\"===t){let n=!1;const i=i=>{const a=this.getLayer(e)?this.queryRenderedFeatures(i.point,{layers:[e]}):[];a.length?n||(n=!0,r.call(this,new Nn(t,this,i.originalEvent,{features:a}))):n=!1};return{layer:e,listener:r,delegates:{mousemove:i,mouseout:()=>{n=!1}}}}if(\"mouseleave\"===t||\"mouseout\"===t){let n=!1;const i=i=>{(this.getLayer(e)?this.queryRenderedFeatures(i.point,{layers:[e]}):[]).length?n=!0:n&&(n=!1,r.call(this,new Nn(t,this,i.originalEvent)))},a=e=>{n&&(n=!1,r.call(this,new Nn(t,this,e.originalEvent)))};return{layer:e,listener:r,delegates:{mousemove:i,mouseout:a}}}{const n=t=>{const n=this.getLayer(e)?this.queryRenderedFeatures(t.point,{layers:[e]}):[];n.length&&(t.features=n,r.call(this,t),delete t.features)};return{layer:e,listener:r,delegates:{[t]:n}}}}on(t,e,r){if(void 0===r)return super.on(t,e);const n=this._createDelegatedListener(t,e,r);this._delegatedListeners=this._delegatedListeners||{},this._delegatedListeners[t]=this._delegatedListeners[t]||[],this._delegatedListeners[t].push(n);for(const t in n.delegates)this.on(t,n.delegates[t]);return this}once(t,e,r){if(void 0===r)return super.once(t,e);const n=this._createDelegatedListener(t,e,r);for(const t in n.delegates)this.once(t,n.delegates[t]);return this}off(t,e,r){if(void 0===r)return super.off(t,e);return this._delegatedListeners&&this._delegatedListeners[t]&&(n=>{const i=n[t];for(let t=0;tthis._updateStyle(t,e)));const r=this.style&&e.transformStyle?this.style.serialize():void 0;return this.style&&(this.style.setEventedParent(null),this.style._remove(!t)),t?(this.style=new de(this,e||{}),this.style.setEventedParent(this,{style:this.style}),\"string\"==typeof t?this.style.loadURL(t,e,r):this.style.loadJSON(t,e,r),this):(delete this.style,this)}_lazyInitEmptyStyle(){this.style||(this.style=new de(this,{}),this.style.setEventedParent(this,{style:this.style}),this.style.loadEmpty())}_diffStyle(t,r){if(\"string\"==typeof t){const n=t,i=this._requestManager.transformRequest(n,\"Style\");e.h(i,new AbortController).then((t=>{this._updateDiff(t.data,r)})).catch((t=>{t&&this.fire(new e.j(t))}))}else\"object\"==typeof t&&this._updateDiff(t,r)}_updateDiff(t,r){try{this.style.setState(t,r)&&this._update(!0)}catch(n){e.w(`Unable to perform style diff: ${n.message||n.error||n}. Rebuilding the style from scratch.`),this._updateStyle(t,r)}}getStyle(){if(this.style)return this.style.serialize()}isStyleLoaded(){return this.style?this.style.loaded():e.w(\"There is no style added to the map.\")}addSource(t,e){return this._lazyInitEmptyStyle(),this.style.addSource(t,e),this._update(!0)}isSourceLoaded(t){const r=this.style&&this.style.sourceCaches[t];if(void 0!==r)return r.loaded();this.fire(new e.j(new Error(`There is no source with ID '${t}'`)))}setTerrain(t){if(this.style._checkLoaded(),this._terrainDataCallback&&this.style.off(\"data\",this._terrainDataCallback),t){const r=this.style.sourceCaches[t.source];if(!r)throw new Error(`cannot load terrain, because there exists no source with ID: ${t.source}`);null===this.terrain&&r.reload();for(const r in this.style._layers){const n=this.style._layers[r];\"hillshade\"===n.type&&n.source===t.source&&e.w(\"You are using the same source for a hillshade layer and for 3D terrain. Please consider using two separate sources to improve rendering quality.\")}this.terrain=new Di(this.painter,r,t),this.painter.renderToTexture=new Bi(this.painter,this.terrain),this.transform.minElevationForCurrentTile=this.terrain.getMinTileElevationForLngLatZoom(this.transform.center,this.transform.tileZoom),this.transform.elevation=this.terrain.getElevationForLngLatZoom(this.transform.center,this.transform.tileZoom),this._terrainDataCallback=e=>{\"style\"===e.dataType?this.terrain.sourceCache.freeRtt():\"source\"===e.dataType&&e.tile&&(e.sourceId!==t.source||this._elevationFreeze||(this.transform.minElevationForCurrentTile=this.terrain.getMinTileElevationForLngLatZoom(this.transform.center,this.transform.tileZoom),this.transform.elevation=this.terrain.getElevationForLngLatZoom(this.transform.center,this.transform.tileZoom)),this.terrain.sourceCache.freeRtt(e.tile.tileID))},this.style.on(\"data\",this._terrainDataCallback)}else this.terrain&&this.terrain.sourceCache.destruct(),this.terrain=null,this.painter.renderToTexture&&this.painter.renderToTexture.destruct(),this.painter.renderToTexture=null,this.transform.minElevationForCurrentTile=0,this.transform.elevation=0;return this.fire(new e.k(\"terrain\",{terrain:t})),this}getTerrain(){var t,e;return null!==(e=null===(t=this.terrain)||void 0===t?void 0:t.options)&&void 0!==e?e:null}areTilesLoaded(){const t=this.style&&this.style.sourceCaches;for(const e in t){const r=t[e]._tiles;for(const t in r){const e=r[t];if(\"loaded\"!==e.state&&\"errored\"!==e.state)return!1}}return!0}removeSource(t){return this.style.removeSource(t),this._update(!0)}getSource(t){return this.style.getSource(t)}addImage(t,r,n={}){const{pixelRatio:i=1,sdf:o=!1,stretchX:s,stretchY:l,content:c,textFitWidth:u,textFitHeight:h}=n;this._lazyInitEmptyStyle();if(!(r instanceof HTMLImageElement||e.b(r))){if(void 0===r.width||void 0===r.height)return this.fire(new e.j(new Error(\"Invalid arguments to map.addImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`\")));{const{width:n,height:a,data:f}=r,p=r;return this.style.addImage(t,{data:new e.R({width:n,height:a},new Uint8Array(f)),pixelRatio:i,stretchX:s,stretchY:l,content:c,textFitWidth:u,textFitHeight:h,sdf:o,version:0,userImage:p}),p.onAdd&&p.onAdd(this,t),this}}{const{width:n,height:f,data:p}=a.getImageData(r);this.style.addImage(t,{data:new e.R({width:n,height:f},p),pixelRatio:i,stretchX:s,stretchY:l,content:c,textFitWidth:u,textFitHeight:h,sdf:o,version:0})}}updateImage(t,r){const n=this.style.getImage(t);if(!n)return this.fire(new e.j(new Error(\"The map has no image with that id. If you are adding a new image use `map.addImage(...)` instead.\")));const i=r instanceof HTMLImageElement||e.b(r)?a.getImageData(r):r,{width:o,height:s,data:l}=i;if(void 0===o||void 0===s)return this.fire(new e.j(new Error(\"Invalid arguments to map.updateImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`\")));if(o!==n.data.width||s!==n.data.height)return this.fire(new e.j(new Error(\"The width and height of the updated image must be that same as the previous version of the image\")));const c=!(r instanceof HTMLImageElement||e.b(r));return n.data.replace(l,c),this.style.updateImage(t,n),this}getImage(t){return this.style.getImage(t)}hasImage(t){return t?!!this.style.getImage(t):(this.fire(new e.j(new Error(\"Missing required image id\"))),!1)}removeImage(t){this.style.removeImage(t)}loadImage(t){return p.getImage(this._requestManager.transformRequest(t,\"Image\"),new AbortController)}listImages(){return this.style.listImages()}addLayer(t,e){return this._lazyInitEmptyStyle(),this.style.addLayer(t,e),this._update(!0)}moveLayer(t,e){return this.style.moveLayer(t,e),this._update(!0)}removeLayer(t){return this.style.removeLayer(t),this._update(!0)}getLayer(t){return this.style.getLayer(t)}getLayersOrder(){return this.style.getLayersOrder()}setLayerZoomRange(t,e,r){return this.style.setLayerZoomRange(t,e,r),this._update(!0)}setFilter(t,e,r={}){return this.style.setFilter(t,e,r),this._update(!0)}getFilter(t){return this.style.getFilter(t)}setPaintProperty(t,e,r,n={}){return this.style.setPaintProperty(t,e,r,n),this._update(!0)}getPaintProperty(t,e){return this.style.getPaintProperty(t,e)}setLayoutProperty(t,e,r,n={}){return this.style.setLayoutProperty(t,e,r,n),this._update(!0)}getLayoutProperty(t,e){return this.style.getLayoutProperty(t,e)}setGlyphs(t,e={}){return this._lazyInitEmptyStyle(),this.style.setGlyphs(t,e),this._update(!0)}getGlyphs(){return this.style.getGlyphsUrl()}addSprite(t,e,r={}){return this._lazyInitEmptyStyle(),this.style.addSprite(t,e,r,(t=>{t||this._update(!0)})),this}removeSprite(t){return this._lazyInitEmptyStyle(),this.style.removeSprite(t),this._update(!0)}getSprite(){return this.style.getSprite()}setSprite(t,e={}){return this._lazyInitEmptyStyle(),this.style.setSprite(t,e,(t=>{t||this._update(!0)})),this}setLight(t,e={}){return this._lazyInitEmptyStyle(),this.style.setLight(t,e),this._update(!0)}getLight(){return this.style.getLight()}setSky(t){return this._lazyInitEmptyStyle(),this.style.setSky(t),this._update(!0)}getSky(){return this.style.getSky()}setFeatureState(t,e){return this.style.setFeatureState(t,e),this._update()}removeFeatureState(t,e){return this.style.removeFeatureState(t,e),this._update()}getFeatureState(t){return this.style.getFeatureState(t)}getContainer(){return this._container}getCanvasContainer(){return this._canvasContainer}getCanvas(){return this._canvas}_containerDimensions(){let t=0,e=0;return this._container&&(t=this._container.clientWidth||400,e=this._container.clientHeight||300),[t,e]}_setupContainer(){const t=this._container;t.classList.add(\"maplibregl-map\");const e=this._canvasContainer=o.create(\"div\",\"maplibregl-canvas-container\",t);this._interactive&&e.classList.add(\"maplibregl-interactive\"),this._canvas=o.create(\"canvas\",\"maplibregl-canvas\",e),this._canvas.addEventListener(\"webglcontextlost\",this._contextLost,!1),this._canvas.addEventListener(\"webglcontextrestored\",this._contextRestored,!1),this._canvas.setAttribute(\"tabindex\",this._interactive?\"0\":\"-1\"),this._canvas.setAttribute(\"aria-label\",this._getUIString(\"Map.Title\")),this._canvas.setAttribute(\"role\",\"region\");const r=this._containerDimensions(),n=this._getClampedPixelRatio(r[0],r[1]);this._resizeCanvas(r[0],r[1],n);const i=this._controlContainer=o.create(\"div\",\"maplibregl-control-container\",t),a=this._controlPositions={};[\"top-left\",\"top-right\",\"bottom-left\",\"bottom-right\"].forEach((t=>{a[t]=o.create(\"div\",`maplibregl-ctrl-${t} `,i)})),this._container.addEventListener(\"scroll\",this._onMapScroll,!1)}_resizeCanvas(t,e,r){this._canvas.width=Math.floor(r*t),this._canvas.height=Math.floor(r*e),this._canvas.style.width=`${t}px`,this._canvas.style.height=`${e}px`}_setupPainter(){const t={alpha:!0,stencil:!0,depth:!0,failIfMajorPerformanceCaveat:this._failIfMajorPerformanceCaveat,preserveDrawingBuffer:this._preserveDrawingBuffer,antialias:this._antialias||!1};let e=null;this._canvas.addEventListener(\"webglcontextcreationerror\",(r=>{e={requestedAttributes:t},r&&(e.statusMessage=r.statusMessage,e.type=r.type)}),{once:!0});const r=this._canvas.getContext(\"webgl2\",t)||this._canvas.getContext(\"webgl\",t);if(!r){const t=\"Failed to initialize WebGL\";throw e?(e.message=t,new Error(JSON.stringify(e))):new Error(t)}this.painter=new Tn(r,this.transform),s.testSupport(r)}loaded(){return!this._styleDirty&&!this._sourcesDirty&&!!this.style&&this.style.loaded()}_update(t){return this.style&&this.style._loaded?(this._styleDirty=this._styleDirty||t,this._sourcesDirty=!0,this.triggerRepaint(),this):this}_requestRenderFrame(t){return this._update(),this._renderTaskQueue.add(t)}_cancelRenderFrame(t){this._renderTaskQueue.remove(t)}_render(t){const r=this._idleTriggered?this._fadeDuration:0;if(this.painter.context.setDirty(),this.painter.setBaseState(),this._renderTaskQueue.run(t),this._removed)return;let n=!1;if(this.style&&this._styleDirty){this._styleDirty=!1;const t=this.transform.zoom,i=a.now();this.style.zoomHistory.update(t,i);const o=new e.a9(t,{now:i,fadeDuration:r,zoomHistory:this.style.zoomHistory,transition:this.style.getTransition()}),s=o.crossFadingFactor();1===s&&s===this._crossFadingFactor||(n=!0,this._crossFadingFactor=s),this.style.update(o)}this.style&&this._sourcesDirty&&(this._sourcesDirty=!1,this.style._updateSources(this.transform)),this.terrain?(this.terrain.sourceCache.update(this.transform,this.terrain),this.transform.minElevationForCurrentTile=this.terrain.getMinTileElevationForLngLatZoom(this.transform.center,this.transform.tileZoom),this._elevationFreeze||(this.transform.elevation=this.terrain.getElevationForLngLatZoom(this.transform.center,this.transform.tileZoom))):(this.transform.minElevationForCurrentTile=0,this.transform.elevation=0),this._placementDirty=this.style&&this.style._updatePlacement(this.painter.transform,this.showCollisionBoxes,r,this._crossSourceCollisions),this.painter.render(this.style,{showTileBoundaries:this.showTileBoundaries,showOverdrawInspector:this._showOverdrawInspector,rotating:this.isRotating(),zooming:this.isZooming(),moving:this.isMoving(),fadeDuration:r,showPadding:this.showPadding}),this.fire(new e.k(\"render\")),this.loaded()&&!this._loaded&&(this._loaded=!0,e.be.mark(e.bf.load),this.fire(new e.k(\"load\"))),this.style&&(this.style.hasTransitions()||n)&&(this._styleDirty=!0),this.style&&!this._placementDirty&&this.style._releaseSymbolFadeTiles();const i=this._sourcesDirty||this._styleDirty||this._placementDirty;return i||this._repaint?this.triggerRepaint():!this.isMoving()&&this.loaded()&&this.fire(new e.k(\"idle\")),!this._loaded||this._fullyLoaded||i||(this._fullyLoaded=!0,e.be.mark(e.bf.fullLoad)),this}redraw(){return this.style&&(this._frameRequest&&(this._frameRequest.abort(),this._frameRequest=null),this._render(0)),this}remove(){var t;this._hash&&this._hash.remove();for(const t of this._controls)t.onRemove(this);this._controls=[],this._frameRequest&&(this._frameRequest.abort(),this._frameRequest=null),this._renderTaskQueue.clear(),this.painter.destroy(),this.handlers.destroy(),delete this.handlers,this.setStyle(null),\"undefined\"!=typeof window&&removeEventListener(\"online\",this._onWindowOnline,!1),p.removeThrottleControl(this._imageQueueHandle),null===(t=this._resizeObserver)||void 0===t||t.disconnect();const r=this.painter.context.gl.getExtension(\"WEBGL_lose_context\");(null==r?void 0:r.loseContext)&&r.loseContext(),this._canvas.removeEventListener(\"webglcontextrestored\",this._contextRestored,!1),this._canvas.removeEventListener(\"webglcontextlost\",this._contextLost,!1),o.remove(this._canvasContainer),o.remove(this._controlContainer),this._container.classList.remove(\"maplibregl-map\"),e.be.clearMetrics(),this._removed=!0,this.fire(new e.k(\"remove\"))}triggerRepaint(){this.style&&!this._frameRequest&&(this._frameRequest=new AbortController,a.frameAsync(this._frameRequest).then((t=>{e.be.frame(t),this._frameRequest=null,this._render(t)})).catch((()=>{})))}get showTileBoundaries(){return!!this._showTileBoundaries}set showTileBoundaries(t){this._showTileBoundaries!==t&&(this._showTileBoundaries=t,this._update())}get showPadding(){return!!this._showPadding}set showPadding(t){this._showPadding!==t&&(this._showPadding=t,this._update())}get showCollisionBoxes(){return!!this._showCollisionBoxes}set showCollisionBoxes(t){this._showCollisionBoxes!==t&&(this._showCollisionBoxes=t,t?this.style._generateCollisionBoxes():this._update())}get showOverdrawInspector(){return!!this._showOverdrawInspector}set showOverdrawInspector(t){this._showOverdrawInspector!==t&&(this._showOverdrawInspector=t,this._update())}get repaint(){return!!this._repaint}set repaint(t){this._repaint!==t&&(this._repaint=t,this.triggerRepaint())}get vertices(){return!!this._vertices}set vertices(t){this._vertices=t,this._update()}get version(){return ji}getCameraTargetElevation(){return this.transform.elevation}},t.MapMouseEvent=Nn,t.MapTouchEvent=jn,t.MapWheelEvent=Un,t.Marker=Xi,t.NavigationControl=class{constructor(t){this._updateZoomButtons=()=>{const t=this._map.getZoom(),e=t===this._map.getMaxZoom(),r=t===this._map.getMinZoom();this._zoomInButton.disabled=e,this._zoomOutButton.disabled=r,this._zoomInButton.setAttribute(\"aria-disabled\",e.toString()),this._zoomOutButton.setAttribute(\"aria-disabled\",r.toString())},this._rotateCompassArrow=()=>{const t=this.options.visualizePitch?`scale(${1/Math.pow(Math.cos(this._map.transform.pitch*(Math.PI/180)),.5)}) rotateX(${this._map.transform.pitch}deg) rotateZ(${this._map.transform.angle*(180/Math.PI)}deg)`:`rotate(${this._map.transform.angle*(180/Math.PI)}deg)`;this._compassIcon.style.transform=t},this._setButtonTitle=(t,e)=>{const r=this._map._getUIString(`NavigationControl.${e}`);t.title=r,t.setAttribute(\"aria-label\",r)},this.options=e.e({},qi,t),this._container=o.create(\"div\",\"maplibregl-ctrl 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r.type=\"button\",r.addEventListener(\"click\",e),r}},t.Popup=oa,t.RasterDEMTileSource=Q,t.RasterTileSource=K,t.ScaleControl=class{constructor(t){this._onMove=()=>{ea(this._map,this._container,this.options)},this.setUnit=t=>{this.options.unit=t,ea(this._map,this._container,this.options)},this.options=Object.assign(Object.assign({},ta),t)}getDefaultPosition(){return\"bottom-left\"}onAdd(t){return this._map=t,this._container=o.create(\"div\",\"maplibregl-ctrl maplibregl-ctrl-scale\",t.getContainer()),this._map.on(\"move\",this._onMove),this._onMove(),this._container}onRemove(){o.remove(this._container),this._map.off(\"move\",this._onMove),this._map=void 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\n", "\n", "" ], @@ -1991,49 +2225,17 @@ } ], "source": [ - "batch_inference_run.artifact(\"drift_table_plot\").show()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'batch_id': '3d5f6aa8a2d63cc0e84ebd95a0bc0000979a0989bbf4c211651a4e2a',\n", - " 'drift_status': True,\n", - " 'drift_metric': 0.4478631590778279}" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "batch_inference_run.status.results" + "# Plot the drift table artifact\n", + "drift_table_plot_artifact = project.get_artifact(\"drift_table_plot\")\n", + "drift_table_plot_artifact.to_dataitem().show()" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "mlrun-base", + "display_name": "mlrun-extended", "language": "python", - "name": "conda-env-mlrun-base-py" + "name": "conda-env-mlrun-extended-py" }, "language_info": { "codemirror_mode": { @@ -2045,7 +2247,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.9.18" } }, "nbformat": 4, diff --git a/batch_inference_v2/batch_inference_v2.py b/batch_inference_v2/batch_inference_v2.py index 1d4d9ec76..78f9a709a 100644 --- a/batch_inference_v2/batch_inference_v2.py +++ b/batch_inference_v2/batch_inference_v2.py @@ -112,27 +112,26 @@ def infer( artifacts_tag: str = "", # Drift analysis parameters perform_drift_analysis: bool = None, - trigger_monitoring_job: bool = False, - batch_image_job: str = "mlrun/mlrun", endpoint_id: str = "", # The following model endpoint parameters are relevant only if: # perform drift analysis is not disabled # a new model endpoint record is going to be generated model_endpoint_name: str = "batch-infer", - model_endpoint_drift_threshold: float = 0.7, - model_endpoint_possible_drift_threshold: float = 0.5, model_endpoint_sample_set: Union[ mlrun.DataItem, list, dict, pd.DataFrame, pd.Series, np.ndarray ] = None, **predict_kwargs: Dict[str, Any], ): """ - Perform a prediction on a given dataset with the given model. Please make sure that you have already logged the model - under the current project. - Can perform drift analysis between the sample set statistics stored in the model to the current input data. The - drift rule is the value per-feature mean of the TVD and Hellinger scores according to the thresholds configures - here. When performing drift analysis, this function either uses an existing model endpoint record or creates - a new one. + Perform a prediction on the provided dataset using the specified model. + Ensure that the model has already been logged under the current project. + + If you wish to apply monitoring tools (e.g., drift analysis), set the perform_drift_analysis parameter to True. + This will create a new model endpoint record under the specified model_endpoint_name. + Additionally, ensure that model monitoring is enabled at the project level by calling the + project.enable_model_monitoring() function. You can also apply monitoring to an existing model by providing its + endpoint id or name, and the monitoring tools will be applied to that endpoint. + At the moment, this function is supported for `mlrun>=1.5.0` versions. :param context: MLRun context. @@ -160,26 +159,17 @@ def infer( 'prediction'. :param batch_id: The ID of the given batch (inference dataset). If `None`, it will be generated. Will be logged as a result of the run. - :param artifacts_tag: Tag to use for all the artifacts resulted from the function (result set and - model monitoring artifacts) + :param artifacts_tag: Tag to use for prediction set result artifact. :param perform_drift_analysis: Whether to perform drift analysis between the sample set of the model object to the dataset given. By default, None, which means it will perform drift analysis if the model already has feature stats that are considered as a reference sample set. Performing drift analysis on a new endpoint id will generate a new model endpoint - record. Please note that in order to trigger the drift analysis job, you need to - set `trigger_monitoring_job=True`. Otherwise, the drift analysis will be triggered - only as part the scheduled monitoring job (if exist in the current project) or - if triggered manually by the user. - :param trigger_monitoring_job: Whether to trigger the batch drift analysis after the infer job. - :param batch_image_job: The image that will be used to register the monitoring batch job if not exist. - By default, the image is mlrun/mlrun. + record. :param endpoint_id: Model endpoint unique ID. If `perform_drift_analysis` was set, the endpoint_id will be used either to perform the analysis on existing model endpoint or to generate a new model endpoint record. :param model_endpoint_name: If a new model endpoint is generated, the model name will be presented under this endpoint. - :param model_endpoint_drift_threshold: The threshold of which to mark drifts. Defaulted to 0.7. - :param model_endpoint_possible_drift_threshold: The threshold of which to mark possible drifts. Defaulted to 0.5. :param model_endpoint_sample_set: A sample dataset to give to compare the inputs in the drift analysis. Can be provided as an input (DataItem) or as a parameter (e.g. string, list, DataFrame). The default chosen sample set will always be the one who is set in the model artifact itself. @@ -260,9 +250,4 @@ def infer( model_endpoint_name=model_endpoint_name, infer_results_df=result_set.copy(), sample_set_statistics=sample_set_statistics, - drift_threshold=model_endpoint_drift_threshold, - possible_drift_threshold=model_endpoint_possible_drift_threshold, - artifacts_tag=artifacts_tag, - trigger_monitoring_job=trigger_monitoring_job, - default_batch_image=batch_image_job, ) diff --git a/batch_inference_v2/function.yaml b/batch_inference_v2/function.yaml index 653346c02..0db7b6366 100644 --- a/batch_inference_v2/function.yaml +++ b/batch_inference_v2/function.yaml @@ -1,45 +1,29 @@ kind: job -metadata: - name: batch-inference-v2 - tag: '' - hash: cfe7a24a5d05a7d6b9f03d119d9f2661e48db0f5 - project: '' - labels: - author: eyald - categories: - - utils - - data-analysis - - monitoring +verbose: false spec: - command: '' - args: [] - image: mlrun/mlrun - build: - functionSourceCode: # Copyright 2023 Iguazio
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from inspect import signature
from typing import Any, Dict, List, Union
import mlrun

try:
    import mlrun.model_monitoring.api
except ModuleNotFoundError:
    raise mlrun.errors.MLRunNotFoundError(
        f"Please update your `mlrun` version to >=1.5.0 or use an "
        f"older version of the batch inference function."
    )

import numpy as np
import pandas as pd
from mlrun.frameworks.auto_mlrun import AutoMLRun


def _prepare_result_set(
        x: pd.DataFrame, label_columns: List[str], y_pred: np.ndarray
) -> pd.DataFrame:
    """
    Set default label column names and validate given names to prepare the result set - a concatenation of the inputs
    (x) and the model predictions (y_pred).

    :param x:             The inputs.
    :param label_columns: A list of strings representing the target column names to add to the predictions. Default name
                          will be used in case the list is empty (predicted_label_{i}).
    :param y_pred:        The model predictions on the inputs.

    :returns: The result set.

    raises MLRunInvalidArgumentError: If the labels columns amount do not match the outputs or if one of the label
                                       column already exists in the dataset.
    """
    # Prepare default target columns names if not provided:
    prediction_columns_amount = 1 if len(y_pred.shape) == 1 else y_pred.shape[1]
    if len(label_columns) == 0:
        # Add default label column names:
        if prediction_columns_amount == 1:
            label_columns = ["predicted_label"]
        else:
            label_columns = [
                f"predicted_label_{i}" for i in range(prediction_columns_amount)
            ]

    # Validate the label columns:
    if prediction_columns_amount != len(label_columns):
        # No equality between provided label column names and outputs amount:
        raise mlrun.errors.MLRunInvalidArgumentError(
            f"The number of predicted labels: {prediction_columns_amount} "
            f"is not equal to the given label columns: {len(label_columns)}"
        )
    common_labels = set(label_columns) & set(x.columns.tolist())
    if common_labels:
        # Label column exist in the original inputs:
        raise mlrun.errors.MLRunInvalidArgumentError(
            f"The labels: {common_labels} are already existed in the given dataset."
        )

    return pd.concat(
        [x, pd.DataFrame(y_pred, columns=label_columns, index=x.index)], axis=1
    )


def _get_get_sample_set_statistics_parameters(context, model_endpoint_sample_set, model_artifact_feature_stats,
                                              feature_columns, drop_columns, label_columns):
    statics_input_full_dict = dict(sample_set=model_endpoint_sample_set,
                                   model_artifact_feature_stats=model_artifact_feature_stats,
                                   sample_set_columns=feature_columns,
                                   sample_set_drop_columns=drop_columns,
                                   sample_set_label_columns=label_columns)
    get_sample_statics_function = mlrun.model_monitoring.api.get_sample_set_statistics
    statics_function_input_dict = signature(get_sample_statics_function).parameters
    #  As a result of changes to input parameters in the mlrun-get_sample_set_statistics function,
    #  we will now send only the parameters it expects.
    statics_input_filtered = {key: statics_input_full_dict[key] for key in statics_function_input_dict}
    if len(statics_input_filtered) != len(statics_function_input_dict):
        context.logger.warning("get_sample_set_statistics is in an older version; "
                               "some parameters will not be sent to the function.")
    return statics_input_filtered


def infer(
    context: mlrun.MLClientCtx,
    dataset: Union[mlrun.DataItem, list, dict, pd.DataFrame, pd.Series, np.ndarray],
    model_path: Union[str, mlrun.DataItem],
    drop_columns: Union[str, List[str], int, List[int]] = None,
    label_columns: Union[str, List[str]] = None,
    feature_columns: Union[str, List[str]] = None,
    log_result_set: bool = True,
    result_set_name: str = "prediction",
    batch_id: str = None,
    artifacts_tag: str = "",
    # Drift analysis parameters
    perform_drift_analysis: bool = None,
    trigger_monitoring_job: bool = False,
    batch_image_job: str = "mlrun/mlrun",
    endpoint_id: str = "",
    # The following model endpoint parameters are relevant only if:
    # perform drift analysis is not disabled
    # a new model endpoint record is going to be generated
    model_endpoint_name: str = "batch-infer",
    model_endpoint_drift_threshold: float = 0.7,
    model_endpoint_possible_drift_threshold: float = 0.5,
    model_endpoint_sample_set: Union[
        mlrun.DataItem, list, dict, pd.DataFrame, pd.Series, np.ndarray
    ] = None,
    **predict_kwargs: Dict[str, Any],
):
    """
    Perform a prediction on a given dataset with the given model. Please make sure that you have already logged the model
    under the current project.
    Can perform drift analysis between the sample set statistics stored in the model to the current input data. The
    drift rule is the value per-feature mean of the TVD and Hellinger scores according to the thresholds configures
    here. When performing drift analysis, this function either uses an existing model endpoint record or creates
    a new one.
    At the moment, this function is supported for `mlrun>=1.5.0` versions.

    :param context:                                 MLRun context.
    :param dataset:                                 The dataset to infer through the model. Provided as an input (DataItem)
                                                    that represents Dataset artifact / Feature vector URI.
                                                    If using MLRun SDK, `dataset` can also be provided as a list, dictionary or
                                                    numpy array.
    :param model_path:                              Model store uri (should start with store://). Provided as an input (DataItem).
                                                    If using MLRun SDK, `model_path` can also be provided as a parameter (string).
                                                    To generate a valid model store URI, please log the model before running this function.
                                                    If `endpoint_id` of existing model endpoint is provided, make sure
                                                    that it has a similar model store path, otherwise the drift analysis
                                                    won't be triggered.
    :param drop_columns:                            A string / integer or a list of strings / integers that represent the column names
                                                    / indices to drop. When the dataset is a list or a numpy array this parameter must
                                                    be represented by integers.
    :param label_columns:                           The target label(s) of the column(s) in the dataset for Regression or
                                                    Classification tasks. The label column can be accessed from the model object, or
                                                    the feature vector provided if available.
    :param feature_columns:                         List of feature columns that will be used to build the dataframe when dataset is
                                                    from type list or numpy array.
    :param log_result_set:                          Whether to log the result set - a DataFrame of the given inputs concatenated with
                                                    the predictions. Defaulted to True.
    :param result_set_name:                         The db key to set name of the prediction result and the filename. Defaulted to
                                                    'prediction'.
    :param batch_id:                                The ID of the given batch (inference dataset). If `None`, it will be generated.
                                                    Will be logged as a result of the run.
    :param artifacts_tag:                           Tag to use for all the artifacts resulted from the function (result set and
                                                    model monitoring artifacts)
    :param perform_drift_analysis:                  Whether to perform drift analysis between the sample set of the model object to the
                                                    dataset given. By default, None, which means it will perform drift analysis if the
                                                    model already has feature stats that are considered as a reference sample set.
                                                    Performing drift analysis on a new endpoint id will generate a new model endpoint
                                                    record. Please note that in order to trigger the drift analysis job, you need to
                                                    set `trigger_monitoring_job=True`. Otherwise, the drift analysis will be triggered
                                                    only as part the scheduled monitoring job (if exist in the current project) or
                                                    if triggered manually by the user.
    :param trigger_monitoring_job:                  Whether to trigger the batch drift analysis after the infer job.
    :param batch_image_job:                         The image that will be used to register the monitoring batch job if not exist.
                                                    By default, the image is mlrun/mlrun.
    :param endpoint_id:                             Model endpoint unique ID. If `perform_drift_analysis` was set, the endpoint_id
                                                    will be used either to perform the analysis on existing model endpoint or to
                                                    generate a new model endpoint record.
    :param model_endpoint_name:                     If a new model endpoint is generated, the model name will be presented under this
                                                    endpoint.
    :param model_endpoint_drift_threshold:          The threshold of which to mark drifts. Defaulted to 0.7.
    :param model_endpoint_possible_drift_threshold: The threshold of which to mark possible drifts. Defaulted to 0.5.
    :param model_endpoint_sample_set:               A sample dataset to give to compare the inputs in the drift analysis.
                                                    Can be provided as an input (DataItem) or as a parameter (e.g. string, list, DataFrame).
                                                    The default chosen sample set will always be the one who is set in the model artifact itself.

    raises MLRunInvalidArgumentError: if both `model_path` and `endpoint_id` are not provided
    """

    # Loading the model:
    context.logger.info(f"Loading model...")
    if isinstance(model_path, mlrun.DataItem):
        model_path = model_path.artifact_url
    if not mlrun.datastore.is_store_uri(model_path):
        raise mlrun.errors.MLRunInvalidArgumentError(
            f"The provided model path ({model_path}) is invalid - should start with `store://`. "
            f"Please make sure that you have logged the model using `project.log_model()` "
            f"which generates a unique store uri for the logged model."
        )
    model_handler = AutoMLRun.load_model(model_path=model_path, context=context)

    if label_columns is None:
        label_columns = [
            output.name for output in model_handler._model_artifact.spec.outputs
        ]

    if feature_columns is None:
        feature_columns = [
            input.name for input in model_handler._model_artifact.spec.inputs
        ]

    # Get dataset by object, URL or by FeatureVector:
    context.logger.info(f"Loading data...")
    x, label_columns = mlrun.model_monitoring.api.read_dataset_as_dataframe(
        dataset=dataset,
        feature_columns=feature_columns,
        label_columns=label_columns,
        drop_columns=drop_columns,
    )

    # Predict:
    context.logger.info(f"Calculating prediction...")
    y_pred = model_handler.model.predict(x, **predict_kwargs)

    # Prepare the result set:
    result_set = _prepare_result_set(x=x, label_columns=label_columns, y_pred=y_pred)

    # Check for logging the result set:
    if log_result_set:
        mlrun.model_monitoring.api.log_result(
            context=context,
            result_set_name=result_set_name,
            result_set=result_set,
            artifacts_tag=artifacts_tag,
            batch_id=batch_id,
        )

    # Check for performing drift analysis
    if (
            perform_drift_analysis is None
            and model_handler._model_artifact.spec.feature_stats is not None
    ):
        perform_drift_analysis = True
    if perform_drift_analysis:
        context.logger.info("Performing drift analysis...")
        # Get the sample set statistics (either from the sample set or from the statistics logged with the model)
        statics_input_full_dict = dict(context=context,
                                       model_endpoint_sample_set=model_endpoint_sample_set,
                                       model_artifact_feature_stats=model_handler._model_artifact.spec.feature_stats,
                                       feature_columns=feature_columns,
                                       drop_columns=drop_columns,
                                       label_columns=label_columns)
        statics_input_filtered = _get_get_sample_set_statistics_parameters(**statics_input_full_dict)
        sample_set_statistics = mlrun.model_monitoring.api.get_sample_set_statistics(**statics_input_filtered)
        mlrun.model_monitoring.api.record_results(
            project=context.project,
            context=context,
            endpoint_id=endpoint_id,
            model_path=model_path,
            model_endpoint_name=model_endpoint_name,
            infer_results_df=result_set.copy(),
            sample_set_statistics=sample_set_statistics,
            drift_threshold=model_endpoint_drift_threshold,
            possible_drift_threshold=model_endpoint_possible_drift_threshold,
            artifacts_tag=artifacts_tag,
            trigger_monitoring_job=trigger_monitoring_job,
            default_batch_image=batch_image_job,
        )
 - commands: [] - code_origin: '' - origin_filename: '' - with_mlrun: false - auto_build: false - requirements: [] entry_points: infer: - name: infer - doc: 'Perform a prediction on a given dataset with the given model. Please make - sure that you have already logged the model + has_kwargs: true + lineno: 102 + has_varargs: false + doc: 'Perform a prediction on the provided dataset using the specified model. + + Ensure that the model has already been logged under the current project. + - under the current project. + If you wish to apply monitoring tools (e.g., drift analysis), set the perform_drift_analysis + parameter to True. - Can perform drift analysis between the sample set statistics stored in the - model to the current input data. The + This will create a new model endpoint record under the specified model_endpoint_name. - drift rule is the value per-feature mean of the TVD and Hellinger scores according - to the thresholds configures + Additionally, ensure that model monitoring is enabled at the project level + by calling the - here. When performing drift analysis, this function either uses an existing - model endpoint record or creates + project.enable_model_monitoring() function. You can also apply monitoring + to an existing model by providing its + + endpoint id or name, and the monitoring tools will be applied to that endpoint. - a new one. At the moment, this function is supported for `mlrun>=1.5.0` versions.' parameters: @@ -93,8 +77,7 @@ spec: default: null - name: artifacts_tag type: str - doc: Tag to use for all the artifacts resulted from the function (result set - and model monitoring artifacts) + doc: Tag to use for prediction set result artifact. default: '' - name: perform_drift_analysis type: bool @@ -102,21 +85,8 @@ spec: object to the dataset given. By default, None, which means it will perform drift analysis if the model already has feature stats that are considered as a reference sample set. Performing drift analysis on a new endpoint id - will generate a new model endpoint record. Please note that in order to - trigger the drift analysis job, you need to set `trigger_monitoring_job=True`. - Otherwise, the drift analysis will be triggered only as part the scheduled - monitoring job (if exist in the current project) or if triggered manually - by the user. + will generate a new model endpoint record. default: null - - name: trigger_monitoring_job - type: bool - doc: Whether to trigger the batch drift analysis after the infer job. - default: false - - name: batch_image_job - type: str - doc: The image that will be used to register the monitoring batch job if not - exist. By default, the image is mlrun/mlrun. - default: mlrun/mlrun - name: endpoint_id type: str doc: Model endpoint unique ID. If `perform_drift_analysis` was set, the endpoint_id @@ -128,14 +98,6 @@ spec: doc: If a new model endpoint is generated, the model name will be presented under this endpoint. default: batch-infer - - name: model_endpoint_drift_threshold - type: float - doc: The threshold of which to mark drifts. Defaulted to 0.7. - default: 0.7 - - name: model_endpoint_possible_drift_threshold - type: float - doc: The threshold of which to mark possible drifts. Defaulted to 0.5. - default: 0.5 - name: model_endpoint_sample_set type: Union[DataItem, list, dict, DataFrame, Series, ndarray] doc: A sample dataset to give to compare the inputs in the drift analysis. @@ -143,20 +105,24 @@ spec: DataFrame). The default chosen sample set will always be the one who is set in the model artifact itself. default: null - outputs: [] - lineno: 97 - has_varargs: false - has_kwargs: true + name: infer description: Batch inference (also knows as prediction) for the common ML frameworks (SciKit-Learn, XGBoost and LightGBM) while performing data drift analysis. + allow_empty_resources: true + build: + with_mlrun: false + code_origin: '' + functionSourceCode: # Copyright 2023 Iguazio
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from inspect import signature
from typing import Any, Dict, List, Union, Optional
import mlrun

try:
    import mlrun.model_monitoring.api
except ModuleNotFoundError:
    raise mlrun.errors.MLRunNotFoundError(
        f"Please update your `mlrun` version to >=1.5.0 or use an "
        f"older version of the batch inference function."
    )

import numpy as np
import pandas as pd
from mlrun.frameworks.auto_mlrun import AutoMLRun


def _prepare_result_set(x: pd.DataFrame, label_columns: List[str], y_pred: np.ndarray) -> pd.DataFrame:
    """
    Set default label column names and validate given names to prepare the result set - a concatenation of the inputs
    (x) and the model predictions (y_pred).

    :param x:             The inputs.
    :param label_columns: A list of strings representing the target column names to add to the predictions. Default name
                          will be used in case the list is empty (predicted_label_{i}).
    :param y_pred:        The model predictions on the inputs.

    :returns: The result set.

    raises MLRunInvalidArgumentError: If the labels columns amount do not match the outputs or if one of the label
                                       column already exists in the dataset.
    """
    # Prepare default target columns names if not provided:
    prediction_columns_amount = 1 if len(y_pred.shape) == 1 else y_pred.shape[1]
    if len(label_columns) == 0:
        # Add default label column names:
        if prediction_columns_amount == 1:
            label_columns = ["predicted_label"]
        else:
            label_columns = [
                f"predicted_label_{i}" for i in range(prediction_columns_amount)
            ]

    # Validate the label columns:
    if prediction_columns_amount != len(label_columns):
        # No equality between provided label column names and outputs amount:
        raise mlrun.errors.MLRunInvalidArgumentError(
            f"The number of predicted labels: {prediction_columns_amount} "
            f"is not equal to the given label columns: {len(label_columns)}"
        )
    common_labels = set(label_columns) & set(x.columns.tolist())
    if common_labels:
        # Label column exist in the original inputs:
        raise mlrun.errors.MLRunInvalidArgumentError(
            f"The labels: {common_labels} are already existed in the given dataset."
        )

    return pd.concat(
        [x, pd.DataFrame(y_pred, columns=label_columns, index=x.index)], axis=1
    )


def _get_sample_set_statistics_parameters(context: mlrun.MLClientCtx,
                                          model_endpoint_sample_set: Union[
                                              mlrun.DataItem, list, dict, pd.DataFrame, pd.Series, np.ndarray],
                                          model_artifact_feature_stats: dict,
                                          feature_columns: Optional[List],
                                          drop_columns: Optional[List],
                                          label_columns: Optional[List]) -> Dict[str, Any]:
    statics_input_full_dict = dict(sample_set=model_endpoint_sample_set,
                                   model_artifact_feature_stats=model_artifact_feature_stats,
                                   sample_set_columns=feature_columns,
                                   sample_set_drop_columns=drop_columns,
                                   sample_set_label_columns=label_columns)
    get_sample_statics_function = mlrun.model_monitoring.api.get_sample_set_statistics
    statics_function_input_dict = signature(get_sample_statics_function).parameters
    #  As a result of changes to input parameters in the mlrun-get_sample_set_statistics function,
    #  we will now send only the parameters it expects.
    statistics_input_filtered = {key: statics_input_full_dict[key] for key in statics_function_input_dict}
    if len(statistics_input_filtered) != len(statics_function_input_dict):
        context.logger.warning(f"get_sample_set_statistics is in an older version; "
                               "some parameters will not be sent to the function."
                               f" Expected input: {list(statics_function_input_dict.keys())},"
                               f" actual input: {list(statistics_input_filtered.keys())}")
    return statistics_input_filtered


def infer(
        context: mlrun.MLClientCtx,
        dataset: Union[mlrun.DataItem, list, dict, pd.DataFrame, pd.Series, np.ndarray],
        model_path: Union[str, mlrun.DataItem],
        drop_columns: Union[str, List[str], int, List[int]] = None,
        label_columns: Union[str, List[str]] = None,
        feature_columns: Union[str, List[str]] = None,
        log_result_set: bool = True,
        result_set_name: str = "prediction",
        batch_id: str = None,
        artifacts_tag: str = "",
        # Drift analysis parameters
        perform_drift_analysis: bool = None,
        endpoint_id: str = "",
        # The following model endpoint parameters are relevant only if:
        # perform drift analysis is not disabled
        # a new model endpoint record is going to be generated
        model_endpoint_name: str = "batch-infer",
        model_endpoint_sample_set: Union[
            mlrun.DataItem, list, dict, pd.DataFrame, pd.Series, np.ndarray
        ] = None,
        **predict_kwargs: Dict[str, Any],
):
    """
    Perform a prediction on the provided dataset using the specified model.
    Ensure that the model has already been logged under the current project.

    If you wish to apply monitoring tools (e.g., drift analysis), set the perform_drift_analysis parameter to True.
    This will create a new model endpoint record under the specified model_endpoint_name.
    Additionally, ensure that model monitoring is enabled at the project level by calling the
    project.enable_model_monitoring() function. You can also apply monitoring to an existing model by providing its
    endpoint id or name, and the monitoring tools will be applied to that endpoint.

    At the moment, this function is supported for `mlrun>=1.5.0` versions.

    :param context:                                 MLRun context.
    :param dataset:                                 The dataset to infer through the model. Provided as an input (DataItem)
                                                    that represents Dataset artifact / Feature vector URI.
                                                    If using MLRun SDK, `dataset` can also be provided as a list, dictionary or
                                                    numpy array.
    :param model_path:                              Model store uri (should start with store://). Provided as an input (DataItem).
                                                    If using MLRun SDK, `model_path` can also be provided as a parameter (string).
                                                    To generate a valid model store URI, please log the model before running this function.
                                                    If `endpoint_id` of existing model endpoint is provided, make sure
                                                    that it has a similar model store path, otherwise the drift analysis
                                                    won't be triggered.
    :param drop_columns:                            A string / integer or a list of strings / integers that represent the column names
                                                    / indices to drop. When the dataset is a list or a numpy array this parameter must
                                                    be represented by integers.
    :param label_columns:                           The target label(s) of the column(s) in the dataset for Regression or
                                                    Classification tasks. The label column can be accessed from the model object, or
                                                    the feature vector provided if available.
    :param feature_columns:                         List of feature columns that will be used to build the dataframe when dataset is
                                                    from type list or numpy array.
    :param log_result_set:                          Whether to log the result set - a DataFrame of the given inputs concatenated with
                                                    the predictions. Defaulted to True.
    :param result_set_name:                         The db key to set name of the prediction result and the filename. Defaulted to
                                                    'prediction'.
    :param batch_id:                                The ID of the given batch (inference dataset). If `None`, it will be generated.
                                                    Will be logged as a result of the run.
    :param artifacts_tag:                           Tag to use for prediction set result artifact.
    :param perform_drift_analysis:                  Whether to perform drift analysis between the sample set of the model object to the
                                                    dataset given. By default, None, which means it will perform drift analysis if the
                                                    model already has feature stats that are considered as a reference sample set.
                                                    Performing drift analysis on a new endpoint id will generate a new model endpoint
                                                    record.
    :param endpoint_id:                             Model endpoint unique ID. If `perform_drift_analysis` was set, the endpoint_id
                                                    will be used either to perform the analysis on existing model endpoint or to
                                                    generate a new model endpoint record.
    :param model_endpoint_name:                     If a new model endpoint is generated, the model name will be presented under this
                                                    endpoint.
    :param model_endpoint_sample_set:               A sample dataset to give to compare the inputs in the drift analysis.
                                                    Can be provided as an input (DataItem) or as a parameter (e.g. string, list, DataFrame).
                                                    The default chosen sample set will always be the one who is set in the model artifact itself.

    raises MLRunInvalidArgumentError: if both `model_path` and `endpoint_id` are not provided
    """

    # Loading the model:
    context.logger.info(f"Loading model...")
    if isinstance(model_path, mlrun.DataItem):
        model_path = model_path.artifact_url
    if not mlrun.datastore.is_store_uri(model_path):
        raise mlrun.errors.MLRunInvalidArgumentError(
            f"The provided model path ({model_path}) is invalid - should start with `store://`. "
            f"Please make sure that you have logged the model using `project.log_model()` "
            f"which generates a unique store uri for the logged model."
        )
    model_handler = AutoMLRun.load_model(model_path=model_path, context=context)

    if label_columns is None:
        label_columns = [
            output.name for output in model_handler._model_artifact.spec.outputs
        ]

    if feature_columns is None:
        feature_columns = [
            input.name for input in model_handler._model_artifact.spec.inputs
        ]

    # Get dataset by object, URL or by FeatureVector:
    context.logger.info(f"Loading data...")
    x, label_columns = mlrun.model_monitoring.api.read_dataset_as_dataframe(
        dataset=dataset,
        feature_columns=feature_columns,
        label_columns=label_columns,
        drop_columns=drop_columns,
    )

    # Predict:
    context.logger.info(f"Calculating prediction...")
    y_pred = model_handler.model.predict(x, **predict_kwargs)

    # Prepare the result set:
    result_set = _prepare_result_set(x=x, label_columns=label_columns, y_pred=y_pred)

    # Check for logging the result set:
    if log_result_set:
        mlrun.model_monitoring.api.log_result(
            context=context,
            result_set_name=result_set_name,
            result_set=result_set,
            artifacts_tag=artifacts_tag,
            batch_id=batch_id,
        )

    # Check for performing drift analysis
    if (
            perform_drift_analysis is None
            and model_handler._model_artifact.spec.feature_stats is not None
    ):
        perform_drift_analysis = True
    if perform_drift_analysis:
        context.logger.info("Performing drift analysis...")
        # Get the sample set statistics (either from the sample set or from the statistics logged with the model)
        statistics_input_filtered = _get_sample_set_statistics_parameters(
            context=context,
            model_endpoint_sample_set=model_endpoint_sample_set,
            model_artifact_feature_stats=model_handler._model_artifact.spec.feature_stats,
            feature_columns=feature_columns,
            drop_columns=drop_columns,
            label_columns=label_columns)
        sample_set_statistics = mlrun.model_monitoring.api.get_sample_set_statistics(**statistics_input_filtered)
        mlrun.model_monitoring.api.record_results(
            project=context.project,
            context=context,
            endpoint_id=endpoint_id,
            model_path=model_path,
            model_endpoint_name=model_endpoint_name,
            infer_results_df=result_set.copy(),
            sample_set_statistics=sample_set_statistics,
        )
 + origin_filename: '' + auto_build: false default_handler: infer + image: mlrun/mlrun disable_auto_mount: false - allow_empty_resources: true - clone_target_dir: '' - env: [] - priority_class_name: '' - preemption_mode: prevent - affinity: null - tolerations: null - security_context: {} -verbose: false + command: '' +metadata: + tag: '' + categories: + - utils + - data-analysis + - monitoring + name: batch-inference-v2 diff --git a/batch_inference_v2/item.yaml b/batch_inference_v2/item.yaml index 292f3424a..e995c770d 100644 --- a/batch_inference_v2/item.yaml +++ b/batch_inference_v2/item.yaml @@ -14,9 +14,9 @@ labels: author: eyald maintainers: [] marketplaceType: '' -mlrunVersion: 1.5.0 +mlrunVersion: 1.7.0-rc51 name: batch_inference_v2 -platformVersion: 3.5.3 +platformVersion: 3.6.0 spec: extra_spec: allow_empty_resources: true @@ -29,4 +29,4 @@ spec: kind: job requirements: null url: '' -version: 2.5.0 +version: 2.6.0 diff --git a/batch_inference_v2/test_batch_inference_v2.py b/batch_inference_v2/test_batch_inference_v2.py index cd455b6d5..6fa657a0d 100644 --- a/batch_inference_v2/test_batch_inference_v2.py +++ b/batch_inference_v2/test_batch_inference_v2.py @@ -12,9 +12,10 @@ # See the License for the specific language governing permissions and # limitations under the License. # -import json + import os import pickle +import time import uuid import numpy as np import pandas as pd @@ -30,7 +31,7 @@ import mlrun.common.schemas from batch_inference_v2 import infer import shutil - +from mlrun.model_monitoring.api import get_or_create_model_endpoint REQUIRED_ENV_VARS = [ "MLRUN_DBPATH", "V3IO_USERNAME", @@ -38,7 +39,6 @@ "V3IO_ACCESS_KEY", ] - def _validate_environment_variables() -> bool: """ Checks that all required Environment variables are set. @@ -86,26 +86,32 @@ def train(training_set: pd.DataFrame): model.fit(training_set, labels) -def assert_batch_predict(n_features, batch_inference_run): +def assert_batch_predict(n_features, batch_inference_run, with_monitoring=False, project_name="batch-infer-test"): # Check the logged results: assert "batch_id" in batch_inference_run.status.results - assert "drift_metric" in batch_inference_run.status.results - assert batch_inference_run.status.results["drift_status"] is True - - # Check that 3 artifacts were generated - assert len(batch_inference_run.status.artifacts) == 3 + assert len(batch_inference_run.status.artifacts) == 1 + assert len(batch_inference_run.artifact("prediction").as_df().columns) == n_features + 1 + if with_monitoring: + # Check that the drift analysis was performed: + time.sleep(60) + # Retrieve the model endpoint + project = get_or_create_project(project_name) + endpoint = get_or_create_model_endpoint(project=project.name, model_endpoint_name="my_cool_endpoint") + + # Validate that the artifacts were logged in the project + artifacts = project.list_artifacts( + labels={ + "mlrun/producer-type": "model-monitoring-app", + "mlrun/app-name": "histogram-data-drift", + "mlrun/endpoint-id": endpoint.metadata.uid, + } + ) - # Check drift table artifact url - assert ( - batch_inference_run.artifact("drift_table_plot").artifact_url - == batch_inference_run.outputs["drift_table_plot"] - ) + assert len(artifacts) == 2 - # Check the features drift results json: - drift_results_file = batch_inference_run.artifact("features_drift_results").local() - with open(drift_results_file, "r") as json_file: - drift_results = json.load(json_file) - assert len(drift_results) == n_features + 1 + # Validate that the model endpoint has been updated as expected + assert endpoint.status.current_stats + assert endpoint.status.drift_status @pytest.mark.skipif( @@ -141,10 +147,7 @@ def test_batch_predict(): params={ "model_path": train_run.outputs["model"], "label_columns": "target_label", - "trigger_monitoring_job": True, - "perform_drift_analysis": True, - "model_endpoint_drift_threshold": 0.2, - "model_endpoint_possible_drift_threshold": 0.1, + "perform_drift_analysis": False, }, local=True, ) @@ -152,6 +155,22 @@ def test_batch_predict(): # Check the logged results: assert_batch_predict(n_features=n_features, batch_inference_run=batch_inference_run) + # Enable model monitoring + project.set_model_monitoring_credentials( + endpoint_store_connection="v3io", + tsdb_connection="v3io", + stream_path="v3io") + + # Deploy model monitoring infrastructure + project.enable_model_monitoring(wait_for_deployment=True, base_period=1) + + # Wait until the monitoring application is triggered + import time + time.sleep(60) + + # Check the logged results: + assert_batch_predict(n_features=n_features, batch_inference_run=batch_inference_run, with_monitoring=True) + # Clean resources _delete_project(project=project.metadata.name) @@ -222,13 +241,11 @@ def test_infer_sample_types(self, sample_type): label_columns="target_label", model_endpoint_name=f"model-endpoint-name-{uuid.uuid4()}", trigger_monitoring_job=True, - perform_drift_analysis=True, - model_endpoint_drift_threshold=0.7, - model_endpoint_possible_drift_threshold=0.5) + perform_drift_analysis=True) # a workaround until ML-4636 will be solved. batch_inference_run = self.project.list_runs(name=self.context.name).to_objects()[0] mlrun.get_run_db().update_run(updates={"status.state": "completed"}, uid=batch_inference_run.uid()) - assert_batch_predict(n_features=n_features, batch_inference_run=batch_inference_run) + assert_batch_predict(n_features=n_features, batch_inference_run=batch_inference_run, project_name=self.project_name) def _delete_project(project: str): From c4c558e01fc903d7aa8c93d471398873cd463487 Mon Sep 17 00:00:00 2001 From: Eyal Danieli Date: Thu, 10 Oct 2024 10:13:56 +0300 Subject: [PATCH 25/38] bring back deprecated params and add warn (#834) --- batch_inference_v2/batch_inference_v2.py | 35 ++++++++- batch_inference_v2/function.yaml | 95 ++++++++++++++---------- 2 files changed, 90 insertions(+), 40 deletions(-) diff --git a/batch_inference_v2/batch_inference_v2.py b/batch_inference_v2/batch_inference_v2.py index 78f9a709a..3b370f630 100644 --- a/batch_inference_v2/batch_inference_v2.py +++ b/batch_inference_v2/batch_inference_v2.py @@ -120,7 +120,17 @@ def infer( model_endpoint_sample_set: Union[ mlrun.DataItem, list, dict, pd.DataFrame, pd.Series, np.ndarray ] = None, + + # the following parameters are deprecated and will be removed once the versioning mechanism is implemented + # TODO: Remove the following parameters once FHUB-13 is resolved + trigger_monitoring_job: Optional[bool] = None, + batch_image_job: Optional[str] = None, + model_endpoint_drift_threshold: Optional[float] = None, + model_endpoint_possible_drift_threshold: Optional[float] = None, + + # prediction kwargs to pass to the model predict function **predict_kwargs: Dict[str, Any], + ): """ Perform a prediction on the provided dataset using the specified model. @@ -173,10 +183,33 @@ def infer( :param model_endpoint_sample_set: A sample dataset to give to compare the inputs in the drift analysis. Can be provided as an input (DataItem) or as a parameter (e.g. string, list, DataFrame). The default chosen sample set will always be the one who is set in the model artifact itself. + :param trigger_monitoring_job: Whether to trigger the batch drift analysis after the infer job. + :param batch_image_job: The image that will be used to register the monitoring batch job if not exist. + By default, the image is mlrun/mlrun. + :param model_endpoint_drift_threshold: The threshold of which to mark drifts. Defaulted to 0.7. + :param model_endpoint_possible_drift_threshold: The threshold of which to mark possible drifts. Defaulted to 0.5. raises MLRunInvalidArgumentError: if both `model_path` and `endpoint_id` are not provided """ + + if trigger_monitoring_job: + context.logger.warning("The `trigger_monitoring_job` parameter is deprecated and will be removed once the versioning mechanism is implemented. " + "if you are using mlrun<1.7.0, please import the previous version of this function, for example " + "'hub://batch_inference_v2:2.5.0'.") + if batch_image_job: + context.logger.warning("The `batch_image_job` parameter is deprecated and will be removed once the versioning mechanism is implemented. " + "if you are using mlrun<1.7.0, please import the previous version of this function, for example " + "'hub://batch_inference_v2:2.5.0'.") + if model_endpoint_drift_threshold: + context.logger.warning("The `model_endpoint_drift_threshold` parameter is deprecated and will be removed once the versioning mechanism is implemented. " + "if you are using mlrun<1.7.0, please import the previous version of this function, for example " + "'hub://batch_inference_v2:2.5.0'.") + if model_endpoint_possible_drift_threshold: + context.logger.warning("The `model_endpoint_possible_drift_threshold` parameter is deprecated and will be removed once the versioning mechanism is implemented. " + "if you are using mlrun<1.7.0, please import the previous version of this function, for example " + "'hub://batch_inference_v2:2.5.0'.") + # Loading the model: context.logger.info(f"Loading model...") if isinstance(model_path, mlrun.DataItem): @@ -250,4 +283,4 @@ def infer( model_endpoint_name=model_endpoint_name, infer_results_df=result_set.copy(), sample_set_statistics=sample_set_statistics, - ) + ) \ No newline at end of file diff --git a/batch_inference_v2/function.yaml b/batch_inference_v2/function.yaml index 0db7b6366..e0a9310c2 100644 --- a/batch_inference_v2/function.yaml +++ b/batch_inference_v2/function.yaml @@ -1,31 +1,19 @@ -kind: job -verbose: false spec: + image: mlrun/mlrun + default_handler: infer + command: '' + allow_empty_resources: true + description: Batch inference (also knows as prediction) for the common ML frameworks + (SciKit-Learn, XGBoost and LightGBM) while performing data drift analysis. + disable_auto_mount: false + build: + with_mlrun: false + functionSourceCode: # Copyright 2023 Iguazio
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from inspect import signature
from typing import Any, Dict, List, Union, Optional
import mlrun

try:
    import mlrun.model_monitoring.api
except ModuleNotFoundError:
    raise mlrun.errors.MLRunNotFoundError(
        f"Please update your `mlrun` version to >=1.5.0 or use an "
        f"older version of the batch inference function."
    )

import numpy as np
import pandas as pd
from mlrun.frameworks.auto_mlrun import AutoMLRun


def _prepare_result_set(x: pd.DataFrame, label_columns: List[str], y_pred: np.ndarray) -> pd.DataFrame:
    """
    Set default label column names and validate given names to prepare the result set - a concatenation of the inputs
    (x) and the model predictions (y_pred).

    :param x:             The inputs.
    :param label_columns: A list of strings representing the target column names to add to the predictions. Default name
                          will be used in case the list is empty (predicted_label_{i}).
    :param y_pred:        The model predictions on the inputs.

    :returns: The result set.

    raises MLRunInvalidArgumentError: If the labels columns amount do not match the outputs or if one of the label
                                       column already exists in the dataset.
    """
    # Prepare default target columns names if not provided:
    prediction_columns_amount = 1 if len(y_pred.shape) == 1 else y_pred.shape[1]
    if len(label_columns) == 0:
        # Add default label column names:
        if prediction_columns_amount == 1:
            label_columns = ["predicted_label"]
        else:
            label_columns = [
                f"predicted_label_{i}" for i in range(prediction_columns_amount)
            ]

    # Validate the label columns:
    if prediction_columns_amount != len(label_columns):
        # No equality between provided label column names and outputs amount:
        raise mlrun.errors.MLRunInvalidArgumentError(
            f"The number of predicted labels: {prediction_columns_amount} "
            f"is not equal to the given label columns: {len(label_columns)}"
        )
    common_labels = set(label_columns) & set(x.columns.tolist())
    if common_labels:
        # Label column exist in the original inputs:
        raise mlrun.errors.MLRunInvalidArgumentError(
            f"The labels: {common_labels} are already existed in the given dataset."
        )

    return pd.concat(
        [x, pd.DataFrame(y_pred, columns=label_columns, index=x.index)], axis=1
    )


def _get_sample_set_statistics_parameters(context: mlrun.MLClientCtx,
                                          model_endpoint_sample_set: Union[
                                              mlrun.DataItem, list, dict, pd.DataFrame, pd.Series, np.ndarray],
                                          model_artifact_feature_stats: dict,
                                          feature_columns: Optional[List],
                                          drop_columns: Optional[List],
                                          label_columns: Optional[List]) -> Dict[str, Any]:
    statics_input_full_dict = dict(sample_set=model_endpoint_sample_set,
                                   model_artifact_feature_stats=model_artifact_feature_stats,
                                   sample_set_columns=feature_columns,
                                   sample_set_drop_columns=drop_columns,
                                   sample_set_label_columns=label_columns)
    get_sample_statics_function = mlrun.model_monitoring.api.get_sample_set_statistics
    statics_function_input_dict = signature(get_sample_statics_function).parameters
    #  As a result of changes to input parameters in the mlrun-get_sample_set_statistics function,
    #  we will now send only the parameters it expects.
    statistics_input_filtered = {key: statics_input_full_dict[key] for key in statics_function_input_dict}
    if len(statistics_input_filtered) != len(statics_function_input_dict):
        context.logger.warning(f"get_sample_set_statistics is in an older version; "
                               "some parameters will not be sent to the function."
                               f" Expected input: {list(statics_function_input_dict.keys())},"
                               f" actual input: {list(statistics_input_filtered.keys())}")
    return statistics_input_filtered


def infer(
        context: mlrun.MLClientCtx,
        dataset: Union[mlrun.DataItem, list, dict, pd.DataFrame, pd.Series, np.ndarray],
        model_path: Union[str, mlrun.DataItem],
        drop_columns: Union[str, List[str], int, List[int]] = None,
        label_columns: Union[str, List[str]] = None,
        feature_columns: Union[str, List[str]] = None,
        log_result_set: bool = True,
        result_set_name: str = "prediction",
        batch_id: str = None,
        artifacts_tag: str = "",
        # Drift analysis parameters
        perform_drift_analysis: bool = None,
        endpoint_id: str = "",
        # The following model endpoint parameters are relevant only if:
        # perform drift analysis is not disabled
        # a new model endpoint record is going to be generated
        model_endpoint_name: str = "batch-infer",
        model_endpoint_sample_set: Union[
            mlrun.DataItem, list, dict, pd.DataFrame, pd.Series, np.ndarray
        ] = None,

        # the following parameters are deprecated and will be removed once the versioning mechanism is implemented
        # TODO: Remove the following parameters once FHUB-13 is resolved
        trigger_monitoring_job: Optional[bool] = None,
        batch_image_job: Optional[str] = None,
        model_endpoint_drift_threshold: Optional[float] = None,
        model_endpoint_possible_drift_threshold: Optional[float] = None,

        # prediction kwargs to pass to the model predict function
        **predict_kwargs: Dict[str, Any],

):
    """
    Perform a prediction on the provided dataset using the specified model.
    Ensure that the model has already been logged under the current project.

    If you wish to apply monitoring tools (e.g., drift analysis), set the perform_drift_analysis parameter to True.
    This will create a new model endpoint record under the specified model_endpoint_name.
    Additionally, ensure that model monitoring is enabled at the project level by calling the
    project.enable_model_monitoring() function. You can also apply monitoring to an existing model by providing its
    endpoint id or name, and the monitoring tools will be applied to that endpoint.

    At the moment, this function is supported for `mlrun>=1.5.0` versions.

    :param context:                                 MLRun context.
    :param dataset:                                 The dataset to infer through the model. Provided as an input (DataItem)
                                                    that represents Dataset artifact / Feature vector URI.
                                                    If using MLRun SDK, `dataset` can also be provided as a list, dictionary or
                                                    numpy array.
    :param model_path:                              Model store uri (should start with store://). Provided as an input (DataItem).
                                                    If using MLRun SDK, `model_path` can also be provided as a parameter (string).
                                                    To generate a valid model store URI, please log the model before running this function.
                                                    If `endpoint_id` of existing model endpoint is provided, make sure
                                                    that it has a similar model store path, otherwise the drift analysis
                                                    won't be triggered.
    :param drop_columns:                            A string / integer or a list of strings / integers that represent the column names
                                                    / indices to drop. When the dataset is a list or a numpy array this parameter must
                                                    be represented by integers.
    :param label_columns:                           The target label(s) of the column(s) in the dataset for Regression or
                                                    Classification tasks. The label column can be accessed from the model object, or
                                                    the feature vector provided if available.
    :param feature_columns:                         List of feature columns that will be used to build the dataframe when dataset is
                                                    from type list or numpy array.
    :param log_result_set:                          Whether to log the result set - a DataFrame of the given inputs concatenated with
                                                    the predictions. Defaulted to True.
    :param result_set_name:                         The db key to set name of the prediction result and the filename. Defaulted to
                                                    'prediction'.
    :param batch_id:                                The ID of the given batch (inference dataset). If `None`, it will be generated.
                                                    Will be logged as a result of the run.
    :param artifacts_tag:                           Tag to use for prediction set result artifact.
    :param perform_drift_analysis:                  Whether to perform drift analysis between the sample set of the model object to the
                                                    dataset given. By default, None, which means it will perform drift analysis if the
                                                    model already has feature stats that are considered as a reference sample set.
                                                    Performing drift analysis on a new endpoint id will generate a new model endpoint
                                                    record.
    :param endpoint_id:                             Model endpoint unique ID. If `perform_drift_analysis` was set, the endpoint_id
                                                    will be used either to perform the analysis on existing model endpoint or to
                                                    generate a new model endpoint record.
    :param model_endpoint_name:                     If a new model endpoint is generated, the model name will be presented under this
                                                    endpoint.
    :param model_endpoint_sample_set:               A sample dataset to give to compare the inputs in the drift analysis.
                                                    Can be provided as an input (DataItem) or as a parameter (e.g. string, list, DataFrame).
                                                    The default chosen sample set will always be the one who is set in the model artifact itself.
    :param trigger_monitoring_job:                  Whether to trigger the batch drift analysis after the infer job.
    :param batch_image_job:                         The image that will be used to register the monitoring batch job if not exist.
                                                    By default, the image is mlrun/mlrun.
    :param model_endpoint_drift_threshold:          The threshold of which to mark drifts. Defaulted to 0.7.
    :param model_endpoint_possible_drift_threshold: The threshold of which to mark possible drifts. Defaulted to 0.5.

    raises MLRunInvalidArgumentError: if both `model_path` and `endpoint_id` are not provided
    """


    if trigger_monitoring_job:
        context.logger.warning("The `trigger_monitoring_job` parameter is deprecated and will be removed once the versioning mechanism is implemented. "
                               "if you are using mlrun<1.7.0, please import the previous version of this function, for example "
                               "'hub://batch_inference_v2:2.5.0'.")
    if batch_image_job:
        context.logger.warning("The `batch_image_job` parameter is deprecated and will be removed once the versioning mechanism is implemented. "
                               "if you are using mlrun<1.7.0, please import the previous version of this function, for example "
                               "'hub://batch_inference_v2:2.5.0'.")
    if model_endpoint_drift_threshold:
        context.logger.warning("The `model_endpoint_drift_threshold` parameter is deprecated and will be removed once the versioning mechanism is implemented. "
                               "if you are using mlrun<1.7.0, please import the previous version of this function, for example "
                               "'hub://batch_inference_v2:2.5.0'.")
    if model_endpoint_possible_drift_threshold:
        context.logger.warning("The `model_endpoint_possible_drift_threshold` parameter is deprecated and will be removed once the versioning mechanism is implemented. "
                               "if you are using mlrun<1.7.0, please import the previous version of this function, for example "
                               "'hub://batch_inference_v2:2.5.0'.")

    # Loading the model:
    context.logger.info(f"Loading model...")
    if isinstance(model_path, mlrun.DataItem):
        model_path = model_path.artifact_url
    if not mlrun.datastore.is_store_uri(model_path):
        raise mlrun.errors.MLRunInvalidArgumentError(
            f"The provided model path ({model_path}) is invalid - should start with `store://`. "
            f"Please make sure that you have logged the model using `project.log_model()` "
            f"which generates a unique store uri for the logged model."
        )
    model_handler = AutoMLRun.load_model(model_path=model_path, context=context)

    if label_columns is None:
        label_columns = [
            output.name for output in model_handler._model_artifact.spec.outputs
        ]

    if feature_columns is None:
        feature_columns = [
            input.name for input in model_handler._model_artifact.spec.inputs
        ]

    # Get dataset by object, URL or by FeatureVector:
    context.logger.info(f"Loading data...")
    x, label_columns = mlrun.model_monitoring.api.read_dataset_as_dataframe(
        dataset=dataset,
        feature_columns=feature_columns,
        label_columns=label_columns,
        drop_columns=drop_columns,
    )

    # Predict:
    context.logger.info(f"Calculating prediction...")
    y_pred = model_handler.model.predict(x, **predict_kwargs)

    # Prepare the result set:
    result_set = _prepare_result_set(x=x, label_columns=label_columns, y_pred=y_pred)

    # Check for logging the result set:
    if log_result_set:
        mlrun.model_monitoring.api.log_result(
            context=context,
            result_set_name=result_set_name,
            result_set=result_set,
            artifacts_tag=artifacts_tag,
            batch_id=batch_id,
        )

    # Check for performing drift analysis
    if (
            perform_drift_analysis is None
            and model_handler._model_artifact.spec.feature_stats is not None
    ):
        perform_drift_analysis = True
    if perform_drift_analysis:
        context.logger.info("Performing drift analysis...")
        # Get the sample set statistics (either from the sample set or from the statistics logged with the model)
        statistics_input_filtered = _get_sample_set_statistics_parameters(
            context=context,
            model_endpoint_sample_set=model_endpoint_sample_set,
            model_artifact_feature_stats=model_handler._model_artifact.spec.feature_stats,
            feature_columns=feature_columns,
            drop_columns=drop_columns,
            label_columns=label_columns)
        sample_set_statistics = mlrun.model_monitoring.api.get_sample_set_statistics(**statistics_input_filtered)
        mlrun.model_monitoring.api.record_results(
            project=context.project,
            context=context,
            endpoint_id=endpoint_id,
            model_path=model_path,
            model_endpoint_name=model_endpoint_name,
            infer_results_df=result_set.copy(),
            sample_set_statistics=sample_set_statistics,
        ) + code_origin: '' + auto_build: false + origin_filename: '' entry_points: infer: - has_kwargs: true - lineno: 102 - has_varargs: false - doc: 'Perform a prediction on the provided dataset using the specified model. - - Ensure that the model has already been logged under the current project. - - - If you wish to apply monitoring tools (e.g., drift analysis), set the perform_drift_analysis - parameter to True. - - This will create a new model endpoint record under the specified model_endpoint_name. - - Additionally, ensure that model monitoring is enabled at the project level - by calling the - - project.enable_model_monitoring() function. You can also apply monitoring - to an existing model by providing its - - endpoint id or name, and the monitoring tools will be applied to that endpoint. - - - At the moment, this function is supported for `mlrun>=1.5.0` versions.' parameters: - name: context type: MLClientCtx @@ -105,24 +93,53 @@ spec: DataFrame). The default chosen sample set will always be the one who is set in the model artifact itself. default: null + - name: trigger_monitoring_job + type: Optional[bool] + doc: Whether to trigger the batch drift analysis after the infer job. + default: null + - name: batch_image_job + type: Optional[str] + doc: The image that will be used to register the monitoring batch job if not + exist. By default, the image is mlrun/mlrun. + default: null + - name: model_endpoint_drift_threshold + type: Optional[float] + doc: The threshold of which to mark drifts. Defaulted to 0.7. + default: null + - name: model_endpoint_possible_drift_threshold + type: Optional[float] + doc: The threshold of which to mark possible drifts. Defaulted to 0.5. + default: null + has_kwargs: true + lineno: 102 name: infer - description: Batch inference (also knows as prediction) for the common ML frameworks - (SciKit-Learn, XGBoost and LightGBM) while performing data drift analysis. - allow_empty_resources: true - build: - with_mlrun: false - code_origin: '' - functionSourceCode: # Copyright 2023 Iguazio
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from inspect import signature
from typing import Any, Dict, List, Union, Optional
import mlrun

try:
    import mlrun.model_monitoring.api
except ModuleNotFoundError:
    raise mlrun.errors.MLRunNotFoundError(
        f"Please update your `mlrun` version to >=1.5.0 or use an "
        f"older version of the batch inference function."
    )

import numpy as np
import pandas as pd
from mlrun.frameworks.auto_mlrun import AutoMLRun


def _prepare_result_set(x: pd.DataFrame, label_columns: List[str], y_pred: np.ndarray) -> pd.DataFrame:
    """
    Set default label column names and validate given names to prepare the result set - a concatenation of the inputs
    (x) and the model predictions (y_pred).

    :param x:             The inputs.
    :param label_columns: A list of strings representing the target column names to add to the predictions. Default name
                          will be used in case the list is empty (predicted_label_{i}).
    :param y_pred:        The model predictions on the inputs.

    :returns: The result set.

    raises MLRunInvalidArgumentError: If the labels columns amount do not match the outputs or if one of the label
                                       column already exists in the dataset.
    """
    # Prepare default target columns names if not provided:
    prediction_columns_amount = 1 if len(y_pred.shape) == 1 else y_pred.shape[1]
    if len(label_columns) == 0:
        # Add default label column names:
        if prediction_columns_amount == 1:
            label_columns = ["predicted_label"]
        else:
            label_columns = [
                f"predicted_label_{i}" for i in range(prediction_columns_amount)
            ]

    # Validate the label columns:
    if prediction_columns_amount != len(label_columns):
        # No equality between provided label column names and outputs amount:
        raise mlrun.errors.MLRunInvalidArgumentError(
            f"The number of predicted labels: {prediction_columns_amount} "
            f"is not equal to the given label columns: {len(label_columns)}"
        )
    common_labels = set(label_columns) & set(x.columns.tolist())
    if common_labels:
        # Label column exist in the original inputs:
        raise mlrun.errors.MLRunInvalidArgumentError(
            f"The labels: {common_labels} are already existed in the given dataset."
        )

    return pd.concat(
        [x, pd.DataFrame(y_pred, columns=label_columns, index=x.index)], axis=1
    )


def _get_sample_set_statistics_parameters(context: mlrun.MLClientCtx,
                                          model_endpoint_sample_set: Union[
                                              mlrun.DataItem, list, dict, pd.DataFrame, pd.Series, np.ndarray],
                                          model_artifact_feature_stats: dict,
                                          feature_columns: Optional[List],
                                          drop_columns: Optional[List],
                                          label_columns: Optional[List]) -> Dict[str, Any]:
    statics_input_full_dict = dict(sample_set=model_endpoint_sample_set,
                                   model_artifact_feature_stats=model_artifact_feature_stats,
                                   sample_set_columns=feature_columns,
                                   sample_set_drop_columns=drop_columns,
                                   sample_set_label_columns=label_columns)
    get_sample_statics_function = mlrun.model_monitoring.api.get_sample_set_statistics
    statics_function_input_dict = signature(get_sample_statics_function).parameters
    #  As a result of changes to input parameters in the mlrun-get_sample_set_statistics function,
    #  we will now send only the parameters it expects.
    statistics_input_filtered = {key: statics_input_full_dict[key] for key in statics_function_input_dict}
    if len(statistics_input_filtered) != len(statics_function_input_dict):
        context.logger.warning(f"get_sample_set_statistics is in an older version; "
                               "some parameters will not be sent to the function."
                               f" Expected input: {list(statics_function_input_dict.keys())},"
                               f" actual input: {list(statistics_input_filtered.keys())}")
    return statistics_input_filtered


def infer(
        context: mlrun.MLClientCtx,
        dataset: Union[mlrun.DataItem, list, dict, pd.DataFrame, pd.Series, np.ndarray],
        model_path: Union[str, mlrun.DataItem],
        drop_columns: Union[str, List[str], int, List[int]] = None,
        label_columns: Union[str, List[str]] = None,
        feature_columns: Union[str, List[str]] = None,
        log_result_set: bool = True,
        result_set_name: str = "prediction",
        batch_id: str = None,
        artifacts_tag: str = "",
        # Drift analysis parameters
        perform_drift_analysis: bool = None,
        endpoint_id: str = "",
        # The following model endpoint parameters are relevant only if:
        # perform drift analysis is not disabled
        # a new model endpoint record is going to be generated
        model_endpoint_name: str = "batch-infer",
        model_endpoint_sample_set: Union[
            mlrun.DataItem, list, dict, pd.DataFrame, pd.Series, np.ndarray
        ] = None,
        **predict_kwargs: Dict[str, Any],
):
    """
    Perform a prediction on the provided dataset using the specified model.
    Ensure that the model has already been logged under the current project.

    If you wish to apply monitoring tools (e.g., drift analysis), set the perform_drift_analysis parameter to True.
    This will create a new model endpoint record under the specified model_endpoint_name.
    Additionally, ensure that model monitoring is enabled at the project level by calling the
    project.enable_model_monitoring() function. You can also apply monitoring to an existing model by providing its
    endpoint id or name, and the monitoring tools will be applied to that endpoint.

    At the moment, this function is supported for `mlrun>=1.5.0` versions.

    :param context:                                 MLRun context.
    :param dataset:                                 The dataset to infer through the model. Provided as an input (DataItem)
                                                    that represents Dataset artifact / Feature vector URI.
                                                    If using MLRun SDK, `dataset` can also be provided as a list, dictionary or
                                                    numpy array.
    :param model_path:                              Model store uri (should start with store://). Provided as an input (DataItem).
                                                    If using MLRun SDK, `model_path` can also be provided as a parameter (string).
                                                    To generate a valid model store URI, please log the model before running this function.
                                                    If `endpoint_id` of existing model endpoint is provided, make sure
                                                    that it has a similar model store path, otherwise the drift analysis
                                                    won't be triggered.
    :param drop_columns:                            A string / integer or a list of strings / integers that represent the column names
                                                    / indices to drop. When the dataset is a list or a numpy array this parameter must
                                                    be represented by integers.
    :param label_columns:                           The target label(s) of the column(s) in the dataset for Regression or
                                                    Classification tasks. The label column can be accessed from the model object, or
                                                    the feature vector provided if available.
    :param feature_columns:                         List of feature columns that will be used to build the dataframe when dataset is
                                                    from type list or numpy array.
    :param log_result_set:                          Whether to log the result set - a DataFrame of the given inputs concatenated with
                                                    the predictions. Defaulted to True.
    :param result_set_name:                         The db key to set name of the prediction result and the filename. Defaulted to
                                                    'prediction'.
    :param batch_id:                                The ID of the given batch (inference dataset). If `None`, it will be generated.
                                                    Will be logged as a result of the run.
    :param artifacts_tag:                           Tag to use for prediction set result artifact.
    :param perform_drift_analysis:                  Whether to perform drift analysis between the sample set of the model object to the
                                                    dataset given. By default, None, which means it will perform drift analysis if the
                                                    model already has feature stats that are considered as a reference sample set.
                                                    Performing drift analysis on a new endpoint id will generate a new model endpoint
                                                    record.
    :param endpoint_id:                             Model endpoint unique ID. If `perform_drift_analysis` was set, the endpoint_id
                                                    will be used either to perform the analysis on existing model endpoint or to
                                                    generate a new model endpoint record.
    :param model_endpoint_name:                     If a new model endpoint is generated, the model name will be presented under this
                                                    endpoint.
    :param model_endpoint_sample_set:               A sample dataset to give to compare the inputs in the drift analysis.
                                                    Can be provided as an input (DataItem) or as a parameter (e.g. string, list, DataFrame).
                                                    The default chosen sample set will always be the one who is set in the model artifact itself.

    raises MLRunInvalidArgumentError: if both `model_path` and `endpoint_id` are not provided
    """

    # Loading the model:
    context.logger.info(f"Loading model...")
    if isinstance(model_path, mlrun.DataItem):
        model_path = model_path.artifact_url
    if not mlrun.datastore.is_store_uri(model_path):
        raise mlrun.errors.MLRunInvalidArgumentError(
            f"The provided model path ({model_path}) is invalid - should start with `store://`. "
            f"Please make sure that you have logged the model using `project.log_model()` "
            f"which generates a unique store uri for the logged model."
        )
    model_handler = AutoMLRun.load_model(model_path=model_path, context=context)

    if label_columns is None:
        label_columns = [
            output.name for output in model_handler._model_artifact.spec.outputs
        ]

    if feature_columns is None:
        feature_columns = [
            input.name for input in model_handler._model_artifact.spec.inputs
        ]

    # Get dataset by object, URL or by FeatureVector:
    context.logger.info(f"Loading data...")
    x, label_columns = mlrun.model_monitoring.api.read_dataset_as_dataframe(
        dataset=dataset,
        feature_columns=feature_columns,
        label_columns=label_columns,
        drop_columns=drop_columns,
    )

    # Predict:
    context.logger.info(f"Calculating prediction...")
    y_pred = model_handler.model.predict(x, **predict_kwargs)

    # Prepare the result set:
    result_set = _prepare_result_set(x=x, label_columns=label_columns, y_pred=y_pred)

    # Check for logging the result set:
    if log_result_set:
        mlrun.model_monitoring.api.log_result(
            context=context,
            result_set_name=result_set_name,
            result_set=result_set,
            artifacts_tag=artifacts_tag,
            batch_id=batch_id,
        )

    # Check for performing drift analysis
    if (
            perform_drift_analysis is None
            and model_handler._model_artifact.spec.feature_stats is not None
    ):
        perform_drift_analysis = True
    if perform_drift_analysis:
        context.logger.info("Performing drift analysis...")
        # Get the sample set statistics (either from the sample set or from the statistics logged with the model)
        statistics_input_filtered = _get_sample_set_statistics_parameters(
            context=context,
            model_endpoint_sample_set=model_endpoint_sample_set,
            model_artifact_feature_stats=model_handler._model_artifact.spec.feature_stats,
            feature_columns=feature_columns,
            drop_columns=drop_columns,
            label_columns=label_columns)
        sample_set_statistics = mlrun.model_monitoring.api.get_sample_set_statistics(**statistics_input_filtered)
        mlrun.model_monitoring.api.record_results(
            project=context.project,
            context=context,
            endpoint_id=endpoint_id,
            model_path=model_path,
            model_endpoint_name=model_endpoint_name,
            infer_results_df=result_set.copy(),
            sample_set_statistics=sample_set_statistics,
        )
 - origin_filename: '' - auto_build: false - default_handler: infer - image: mlrun/mlrun - disable_auto_mount: false - command: '' + doc: 'Perform a prediction on the provided dataset using the specified model. + + Ensure that the model has already been logged under the current project. + + + If you wish to apply monitoring tools (e.g., drift analysis), set the perform_drift_analysis + parameter to True. + + This will create a new model endpoint record under the specified model_endpoint_name. + + Additionally, ensure that model monitoring is enabled at the project level + by calling the + + project.enable_model_monitoring() function. You can also apply monitoring + to an existing model by providing its + + endpoint id or name, and the monitoring tools will be applied to that endpoint. + + + At the moment, this function is supported for `mlrun>=1.5.0` versions.' + has_varargs: false +verbose: false metadata: + name: batch-inference-v2 tag: '' categories: - utils - data-analysis - monitoring - name: batch-inference-v2 +kind: job From e97871922298028e5dd55ccbe1b8ddbe36845527 Mon Sep 17 00:00:00 2001 From: Eyal Danieli Date: Tue, 31 Dec 2024 14:00:34 +0200 Subject: [PATCH 26/38] fix PyYAML loading (#837) --- cli/item_yaml.py | 2 +- cli/test_suite.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/cli/item_yaml.py b/cli/item_yaml.py index fbd7eb7e8..7483a9912 100644 --- a/cli/item_yaml.py +++ b/cli/item_yaml.py @@ -40,7 +40,7 @@ def update_functions_yaml(root_directory: str, if (inner_dir / item_yaml).exists(): path = str(inner_dir)+"/"+item_yaml stream = open(path, 'r') - data = yaml.load(stream) + data = yaml.load(stream=stream, Loader=yaml.FullLoader) if version: data['version'] = version if mlrun_version: diff --git a/cli/test_suite.py b/cli/test_suite.py index b386849e8..93e1428b4 100644 --- a/cli/test_suite.py +++ b/cli/test_suite.py @@ -599,7 +599,7 @@ def clean_pipenv(directory: str): # load item yaml def load_item(path): with open(path, 'r') as stream: - data = yaml.load(stream) + data = yaml.load(stream=stream, Loader=yaml.FullLoader) return data From 7d2994920c92543cb722da630add53bf8a7d2ca6 Mon Sep 17 00:00:00 2001 From: Yonatan Shelach <92271540+yonishelach@users.noreply.github.com> Date: Tue, 31 Dec 2024 14:59:08 +0200 Subject: [PATCH 27/38] [text to audio generator] Replaced bark with openai tts models (#836) --- text_to_audio_generator/function.yaml | 88 ++++++++---------- text_to_audio_generator/item.yaml | 7 +- text_to_audio_generator/requirements.txt | 5 +- .../test_text_to_audio_generator.py | 18 ++-- .../text_to_audio_generator.ipynb | 12 +-- .../text_to_audio_generator.py | 91 +++++++++++++------ 6 files changed, 122 insertions(+), 99 deletions(-) diff --git a/text_to_audio_generator/function.yaml b/text_to_audio_generator/function.yaml index 88ef9cb89..65d8d82aa 100644 --- a/text_to_audio_generator/function.yaml +++ b/text_to_audio_generator/function.yaml @@ -1,32 +1,28 @@ -kind: job -metadata: - name: text-to-audio-generator - tag: '' - hash: 89fcaf3fab53e7b7fbba448a5e65c253d7fa66ed - project: '' - labels: - author: yonatans - categories: - - data-preparation - - machine-learning - - pytorch spec: - command: '' - args: [] image: '' + default_handler: generate_multi_speakers_audio build: - functionSourceCode: # Copyright 2023 Iguazio
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
import pathlib
import random
import tempfile
from typing import Dict, List, Optional, Tuple, Union

import bark
import numpy as np
import pandas as pd
import torch
import torchaudio
import tqdm

# Get the global logger:
_LOGGER = logging.getLogger()


def generate_multi_speakers_audio(
    data_path: str,
    speakers: Union[List[str], Dict[str, int]],
    available_voices: List[str],
    output_directory: str = None,
    use_gpu: bool = True,
    use_small_models: bool = False,
    offload_cpu: bool = False,
    sample_rate: int = 16000,
    file_format: str = "wav",
    verbose: bool = True,
    bits_per_sample: Optional[int] = None,
) -> Tuple[str, pd.DataFrame, dict]:
    """
    Generate audio files from text files.

    :param data_path:           Path to the text file or directory containing the text files to generate audio from.
    :param speakers:            List / Dict of speakers to generate audio for.
                                If a list is given, the speakers will be assigned to channels in the order given.
                                If dictionary, the keys will be the speakers and the values will be the channels.
    :param available_voices:    List of available voices to use for the generation.
                        See here for the available voices:
                        https://suno-ai.notion.site/8b8e8749ed514b0cbf3f699013548683?v=bc67cff786b04b50b3ceb756fd05f68c
    :param output_directory:    Path to the directory to save the generated audio files to.
    :param use_gpu:             Whether to use the GPU for the generation.
    :param use_small_models:    Whether to use the small models for the generation.
    :param offload_cpu:         To reduce the memory footprint, the models can be offloaded to the CPU after loading.
    :param sample_rate:         The sampling rate of the generated audio.
    :param file_format:         The format of the generated audio files.
    :param verbose:             Whether to print the progress of the generation.
    :param bits_per_sample:     Changes the bit depth for the supported formats.
                                Supported only in "wav" or "flac" formats.

    :returns:                   A tuple of:
                                - The output directory path.
                                - The generated audio files dataframe.
                                - The errors dictionary.
    """

    global _LOGGER
    _LOGGER = _get_logger()
    # Get the input text files to turn to audio:
    data_path = pathlib.Path(data_path).absolute()
    text_files = _get_text_files(data_path=data_path)

    # Load the bark models according to the given configurations:
    bark.preload_models(
        text_use_gpu=use_gpu,
        text_use_small=use_small_models,
        coarse_use_gpu=use_gpu,
        coarse_use_small=use_small_models,
        fine_use_gpu=use_gpu,
        fine_use_small=use_small_models,
        codec_use_gpu=use_gpu,
        force_reload=offload_cpu,
    )

    # Check for per channel generation:
    if isinstance(speakers, dict):
        speaker_per_channel = True
        # Sort the given speakers by channels:
        speakers = {
            speaker: channel
            for speaker, channel in sorted(speakers.items(), key=lambda item: item[1])
        }
    else:
        speaker_per_channel = False

    # Prepare the resampling module:
    resampler = torchaudio.transforms.Resample(
        orig_freq=bark.SAMPLE_RATE, new_freq=sample_rate, dtype=torch.float32
    )

    # Prepare the gap between each speaker:
    gap_between_speakers = np.zeros(int(0.5 * bark.SAMPLE_RATE))

    # Prepare the successes dataframe and errors dictionary to be returned:
    successes = []
    errors = {}

    # Create the output directory:
    if output_directory is None:
        output_directory = tempfile.mkdtemp()
    output_directory = pathlib.Path(output_directory)
    if not output_directory.exists():
        output_directory.mkdir(exist_ok=True, parents=True)

    # Start generating audio:
    # Go over the audio files and transcribe:
    for text_file in tqdm.tqdm(
        text_files, desc="Generating", unit="file", disable=not verbose
    ):

        try:
            # Randomize voices for each speaker:
            chosen_voices = {}
            available_voices_copy = available_voices.copy()
            for speaker in speakers:
                voice = random.choice(available_voices_copy)
                chosen_voices[speaker] = voice
                available_voices_copy.remove(voice)
            # Read text:
            with open(text_file, "r") as fp:
                text = fp.read()
            # Prepare a holder for all the generated pieces (if per channel each speaker will have its own):
            audio_pieces = (
                {speaker: [] for speaker in speakers}
                if speaker_per_channel
                else {"all": []}
            )

            # Generate audio per line:
            for line in text.splitlines():
                # Validate line is in correct speaker format:

                if ": " not in line:
                    if verbose:
                        _LOGGER.warning(f"Skipping line: {line}")
                    continue
                # Split line to speaker and his words:
                current_speaker, sentences = line.split(": ", 1)
                # Validate speaker is known:
                if current_speaker not in speakers:
                    raise ValueError(
                        f"Unknown speaker: {current_speaker}. Given speakers are: {speakers}"
                    )
                for sentence in _split_line(line=sentences):
                    # Generate words audio:
                    audio = bark.generate_audio(
                        sentence,
                        history_prompt=chosen_voices[current_speaker],
                        silent=True,
                    )
                    if speaker_per_channel:
                        silence = np.zeros_like(audio)
                        for speaker in audio_pieces.keys():
                            if speaker == current_speaker:
                                audio_pieces[speaker] += [audio, gap_between_speakers]
                            else:
                                audio_pieces[speaker] += [silence, gap_between_speakers]
                    else:
                        audio_pieces["all"] += [audio, gap_between_speakers]
            # Construct a single audio array from all the pieces and channels:

            audio = np.vstack(
                [np.concatenate(audio_pieces[speaker]) for speaker in speakers]
            ).astype(dtype=np.float32)
            # Resample:
            audio = torch.from_numpy(audio)
            audio = resampler(audio)
            # Save to audio file:
            audio_file = output_directory / f"{text_file.stem}.{file_format}"

            torchaudio.save(
                uri=str(audio_file),
                src=audio,
                sample_rate=sample_rate,
                format=file_format,
                bits_per_sample=bits_per_sample,
            )

            # Collect to the successes:
            successes.append([text_file.name, audio_file.name])
        except Exception as exception:
            # Note the exception as error in the dictionary:
            if verbose:
                _LOGGER.warning(f"Error in file: '{text_file.name}'")
            print(exception)
            errors[text_file.name] = str(exception)

    # Construct the translations dataframe:
    successes = pd.DataFrame(
        successes,
        columns=["text_file", "audio_file"],
    )

    # Print the head of the produced dataframe and return:
    if verbose:
        _LOGGER.info(
            f"Done ({successes.shape[0]}/{len(text_files)})\n"
            f"Translations summary:\n"
            f"{successes.head()}"
        )
    return str(output_directory), successes, errors


def _get_text_files(
    data_path: pathlib.Path,
) -> List[pathlib.Path]:
    # Check if the path is of a directory or a file:
    if data_path.is_dir():
        # Get all files inside the directory:
        text_files = list(data_path.glob("*.*"))
    elif data_path.is_file():
        text_files = [data_path]
    else:
        raise ValueError(
            f"Unrecognized data path. The parameter `data_path` must be either a directory path or a file path. "
            f"Given: {str(data_path)} "
        )

    return text_files


def _split_line(line: str, max_length: int = 250) -> List[str]:
    if len(line) < max_length:
        return [line]

    sentences = [
        f"{sentence.strip()}." for sentence in line.split(".") if sentence.strip()
    ]

    splits = []
    current_length = len(sentences[0])
    split = sentences[0]
    for sentence in sentences[1:]:
        if current_length + len(sentence) > max_length:
            splits.append(split)
            split = sentence
            current_length = len(sentence)
        else:
            current_length += len(sentence)
            split += " " + sentence
    if split:
        splits.append(split)

    return splits


def _get_logger():
    global _LOGGER
    try:
        import mlrun
        # Check if MLRun is available:
        context = mlrun.get_or_create_ctx(name="mlrun")
        return context.logger
    except ModuleNotFoundError:
        return _LOGGER
 - base_image: mlrun/mlrun - commands: [] - code_origin: '' - origin_filename: '' + functionSourceCode: # Copyright 2023 Iguazio
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import io
import logging
import os
import pathlib
import random
import tempfile
from typing import Dict, List, Optional, Tuple, Union

import numpy as np
import openai
import pandas as pd
import torch
import torchaudio
import tqdm
from pydub import AudioSegment

# Get the global logger:
_LOGGER = logging.getLogger()

OPENAI_API_KEY = "OPENAI_API_KEY"
OPENAI_BASE_URL = "OPENAI_BASE_URL"
SAMPLE_RATE = 24000


def generate_multi_speakers_audio(
    data_path: str,
    speakers: Union[List[str], Dict[str, int]],
    available_voices: List[str],
    output_directory: str = None,
    model: str = "tts-1",
    sample_rate: int = 16000,
    file_format: str = "wav",
    verbose: bool = True,
    bits_per_sample: Optional[int] = None,
    speed: float = 1.0,
) -> Tuple[str, pd.DataFrame, dict]:
    """
    Generate audio files from text files.

    :param data_path:           Path to the text file or directory containing the text files to generate audio from.
    :param speakers:            List / Dict of speakers to generate audio for.
                                If a list is given, the speakers will be assigned to channels in the order given.
                                If dictionary, the keys will be the speakers and the values will be the channels.
    :param available_voices:    List of available voices to use for the generation.
                        See here for the available voices:
                        https://platform.openai.com/docs/guides/text-to-speech#voice-options
    :param output_directory:    Path to the directory to save the generated audio files to.
    :param model:               Which model to use for the generation.
    :param sample_rate:         The sampling rate of the generated audio.
    :param file_format:         The format of the generated audio files.
    :param verbose:             Whether to print the progress of the generation.
    :param bits_per_sample:     Changes the bit depth for the supported formats.
                                Supported only in "wav" or "flac" formats.
    :param speed:               The speed of the generated audio. Select a value from `0.25` to `4.0`. `1.0` is the default.

    :returns:                   A tuple of:
                                - The output directory path.
                                - The generated audio files dataframe.
                                - The errors' dictionary.
    """

    global _LOGGER
    _LOGGER = _get_logger()
    # Get the input text files to turn to audio:
    data_path = pathlib.Path(data_path).absolute()
    text_files = _get_text_files(data_path=data_path)

    # connect to openai client:
    client = _get_openai_client()

    # Check for per channel generation:
    if isinstance(speakers, dict):
        speaker_per_channel = True
        # Sort the given speakers by channels:
        speakers = {
            speaker: channel
            for speaker, channel in sorted(speakers.items(), key=lambda item: item[1])
        }
    else:
        speaker_per_channel = False

    # Prepare the resampling module:
    resampler = torchaudio.transforms.Resample(
        orig_freq=SAMPLE_RATE, new_freq=sample_rate, dtype=torch.float32
    )

    # Prepare the gap between each speaker:
    gap_between_speakers = np.zeros(int(0.5 * SAMPLE_RATE))

    # Prepare the successes dataframe and errors dictionary to be returned:
    successes = []
    errors = {}

    # Create the output directory:
    if output_directory is None:
        output_directory = tempfile.mkdtemp()
    output_directory = pathlib.Path(output_directory)
    if not output_directory.exists():
        output_directory.mkdir(exist_ok=True, parents=True)

    # Start generating audio:
    # Go over the audio files and transcribe:
    for text_file in tqdm.tqdm(
        text_files, desc="Generating", unit="file", disable=not verbose
    ):

        try:
            # Randomize voices for each speaker:
            chosen_voices = {}
            available_voices_copy = available_voices.copy()
            for speaker in speakers:
                voice = random.choice(available_voices_copy)
                chosen_voices[speaker] = voice
                available_voices_copy.remove(voice)
            # Read text:
            with open(text_file, "r") as fp:
                text = fp.read()
            # Prepare a holder for all the generated pieces (if per channel each speaker will have its own):
            audio_pieces = (
                {speaker: [] for speaker in speakers}
                if speaker_per_channel
                else {"all": []}
            )

            # Generate audio per line:
            for line in text.splitlines():
                # Validate line is in correct speaker format:

                if ": " not in line:
                    if verbose:
                        _LOGGER.warning(f"Skipping line: {line}")
                    continue
                # Split line to speaker and his words:
                current_speaker, sentences = line.split(": ", 1)
                # Validate speaker is known:
                if current_speaker not in speakers:
                    raise ValueError(
                        f"Unknown speaker: {current_speaker}. Given speakers are: {speakers}"
                    )
                for sentence in _split_line(line=sentences):
                    # Generate words audio:
                    audio = client.audio.speech.create(
                        model=model,
                        input=sentence,
                        voice=chosen_voices[current_speaker],
                        response_format=file_format,
                        speed=speed,
                    )
                    audio = audio.content
                    audio = _bytes_to_np_array(audio=audio, file_format=file_format)

                    if speaker_per_channel:
                        silence = np.zeros_like(audio)
                        for speaker in audio_pieces.keys():
                            if speaker == current_speaker:
                                audio_pieces[speaker] += [audio, gap_between_speakers]
                            else:
                                audio_pieces[speaker] += [silence, gap_between_speakers]
                    else:
                        audio_pieces["all"] += [audio, gap_between_speakers]
            # Construct a single audio array from all the pieces and channels:

            audio = np.vstack(
                [np.concatenate(audio_pieces[speaker]) for speaker in speakers]
            ).astype(dtype=np.float32)
            # Resample:
            audio = torch.from_numpy(audio)
            audio = resampler(audio)
            # Save to audio file:
            audio_file = output_directory / f"{text_file.stem}.{file_format}"

            torchaudio.save(
                uri=str(audio_file),
                src=audio,
                sample_rate=sample_rate,
                format=file_format,
                bits_per_sample=bits_per_sample,
            )

            # Collect to the successes:
            successes.append([text_file.name, audio_file.name])
        except Exception as exception:
            # Note the exception as error in the dictionary:
            if verbose:
                _LOGGER.warning(f"Error in file: '{text_file.name}'")
            print(exception)
            errors[text_file.name] = str(exception)

    # Construct the translations dataframe:
    successes = pd.DataFrame(
        successes,
        columns=["text_file", "audio_file"],
    )

    # Print the head of the produced dataframe and return:
    if verbose:
        _LOGGER.info(
            f"Done ({successes.shape[0]}/{len(text_files)})\n"
            f"Translations summary:\n"
            f"{successes.head()}"
        )
    return str(output_directory), successes, errors


def _get_openai_client():
    api_key = os.getenv(OPENAI_API_KEY)
    base_url = os.getenv(OPENAI_BASE_URL)
    # Check if the key is already in the environment variables:
    if not api_key or not base_url:
        try:
            import mlrun

            context = mlrun.get_or_create_ctx(name="context")
            # Check if the key is in the secrets:
            api_key = context.get_secret(OPENAI_API_KEY)
            base_url = context.get_secret(OPENAI_BASE_URL)
        except ModuleNotFoundError:
            raise EnvironmentError(
                f"One or more of the OpenAI required environment variables ('{OPENAI_API_KEY}', '{OPENAI_BASE_URL}') are missing."
                f"Please set them as environment variables or install mlrun (`pip install mlrun`)"
                f"and set them as project secrets using `project.set_secrets`."
            )
    return openai.OpenAI(api_key=api_key, base_url=base_url)


def _bytes_to_np_array(audio: bytes, file_format: str):
    if file_format == "mp3":
        audio_segment = AudioSegment.from_mp3(io.BytesIO(audio))

        # Convert to raw PCM audio data
        samples = audio_segment.get_array_of_samples()

        # Convert to numpy array
        audio_array = np.array(samples)

        # Normalize to float between -1 and 1
        return audio_array.astype(np.float32) / np.iinfo(samples.typecode).max
    else:
        return np.frombuffer(audio, dtype=np.int16) / 32768.0


def _get_text_files(
    data_path: pathlib.Path,
) -> List[pathlib.Path]:
    # Check if the path is of a directory or a file:
    if data_path.is_dir():
        # Get all files inside the directory:
        text_files = list(data_path.glob("*.*"))
    elif data_path.is_file():
        text_files = [data_path]
    else:
        raise ValueError(
            f"Unrecognized data path. The parameter `data_path` must be either a directory path or a file path. "
            f"Given: {str(data_path)} "
        )

    return text_files


def _split_line(line: str, max_length: int = 250) -> List[str]:
    if len(line) < max_length:
        return [line]

    sentences = [
        f"{sentence.strip()}." for sentence in line.split(".") if sentence.strip()
    ]

    splits = []
    current_length = len(sentences[0])
    split = sentences[0]
    for sentence in sentences[1:]:
        if current_length + len(sentence) > max_length:
            splits.append(split)
            split = sentence
            current_length = len(sentence)
        else:
            current_length += len(sentence)
            split += " " + sentence
    if split:
        splits.append(split)

    return splits


def _get_logger():
    global _LOGGER
    try:
        import mlrun

        # Check if MLRun is available:
        context = mlrun.get_or_create_ctx(name="mlrun")
        return context.logger
    except ModuleNotFoundError:
        return _LOGGER
 requirements: - - bark + - openai - torchaudio + - pydub + origin_filename: '' + base_image: mlrun/mlrun + code_origin: '' + command: '' + disable_auto_mount: false + description: Generate audio file from text using different speakers entry_points: generate_multi_speakers_audio: - name: generate_multi_speakers_audio + has_varargs: false doc: Generate audio files from text files. + name: generate_multi_speakers_audio + outputs: + - doc: 'A tuple of: - The output directory path. - The generated audio files + dataframe. - The errors'' dictionary.' + type: Tuple[str, pd.DataFrame, dict] + has_kwargs: false parameters: - name: data_path type: str @@ -40,24 +36,15 @@ spec: - name: available_voices type: List[str] doc: 'List of available voices to use for the generation. See here for the - available voices: https://suno-ai.notion.site/8b8e8749ed514b0cbf3f699013548683?v=bc67cff786b04b50b3ceb756fd05f68c' + available voices: https://platform.openai.com/docs/guides/text-to-speech#voice-options' - name: output_directory type: str doc: Path to the directory to save the generated audio files to. default: null - - name: use_gpu - type: bool - doc: Whether to use the GPU for the generation. - default: true - - name: use_small_models - type: bool - doc: Whether to use the small models for the generation. - default: false - - name: offload_cpu - type: bool - doc: To reduce the memory footprint, the models can be offloaded to the CPU - after loading. - default: false + - name: model + type: str + doc: Which model to use for the generation. + default: tts-1 - name: sample_rate type: int doc: The sampling rate of the generated audio. @@ -75,21 +62,18 @@ spec: doc: Changes the bit depth for the supported formats. Supported only in "wav" or "flac" formats. default: null - outputs: - - doc: 'A tuple of: - The output directory path. - The generated audio files - dataframe. - The errors dictionary.' - type: Tuple[str, pd.DataFrame, dict] - lineno: 31 - has_varargs: false - has_kwargs: false - description: Generate audio file from text using different speakers - default_handler: generate_multi_speakers_audio - disable_auto_mount: false - clone_target_dir: '' - env: [] - priority_class_name: '' - preemption_mode: prevent - affinity: null - tolerations: null - security_context: {} + - name: speed + type: float + doc: The speed of the generated audio. Select a value from `0.25` to `4.0`. + `1.0` is the default. + default: 1.0 + lineno: 38 +kind: job +metadata: + categories: + - data-preparation + - machine-learning + - pytorch + tag: '' + name: text-to-audio-generator verbose: false diff --git a/text_to_audio_generator/item.yaml b/text_to_audio_generator/item.yaml index efa8afc90..3a6af1e7e 100644 --- a/text_to_audio_generator/item.yaml +++ b/text_to_audio_generator/item.yaml @@ -13,7 +13,7 @@ labels: author: yonatans maintainers: [] marketplaceType: '' -mlrunVersion: 1.5.1 +mlrunVersion: 1.7.1 name: text_to_audio_generator platformVersion: 3.5.3 spec: @@ -22,8 +22,9 @@ spec: image: mlrun/mlrun kind: job requirements: - - bark + - openai - torchaudio + - pydub url: '' -version: 1.2.0 +version: 1.3.0 test_valid: True diff --git a/text_to_audio_generator/requirements.txt b/text_to_audio_generator/requirements.txt index 36f17cd61..63dee64df 100644 --- a/text_to_audio_generator/requirements.txt +++ b/text_to_audio_generator/requirements.txt @@ -1,2 +1,3 @@ -bark -torchaudio>=2.1.0 \ No newline at end of file +openai>=1.58.0 +torchaudio>=2.1.0 +pydub \ No newline at end of file diff --git a/text_to_audio_generator/test_text_to_audio_generator.py b/text_to_audio_generator/test_text_to_audio_generator.py index 87ffe1496..94fd8c098 100644 --- a/text_to_audio_generator/test_text_to_audio_generator.py +++ b/text_to_audio_generator/test_text_to_audio_generator.py @@ -12,11 +12,18 @@ # See the License for the specific language governing permissions and # limitations under the License. -import mlrun +import os import tempfile + +import mlrun import pytest +@pytest.mark.skipif( + condition=os.getenv("OPENAI_BASE_URL") is None + and os.getenv("OPENAI_API_KEY") is None, + reason="OpenAI API key and base URL are required to run this test", +) @pytest.mark.parametrize("file_format,bits_per_sample", [("wav", 8), ("mp3", None)]) def test_generate_multi_speakers_audio(file_format, bits_per_sample): text_to_audio_generator_function = mlrun.import_function("function.yaml") @@ -28,12 +35,9 @@ def test_generate_multi_speakers_audio(file_format, bits_per_sample): "output_directory": test_directory, "speakers": {"Agent": 0, "Client": 1}, "available_voices": [ - "v2/en_speaker_0", - "v2/en_speaker_1", + "alloy", + "echo", ], - "use_small_models": True, - "use_gpu": False, - "offload_cpu": True, "file_format": file_format, "bits_per_sample": bits_per_sample, }, @@ -45,6 +49,6 @@ def test_generate_multi_speakers_audio(file_format, bits_per_sample): ], artifact_path=test_directory, ) - assert function_run.error == "Run state (completed) is not in error state" + assert function_run.error == "" for key in ["audio_files", "audio_files_dataframe", "text_to_speech_errors"]: assert key in function_run.outputs and function_run.outputs[key] is not None diff --git a/text_to_audio_generator/text_to_audio_generator.ipynb b/text_to_audio_generator/text_to_audio_generator.ipynb index 268fe2efb..a70882a44 100644 --- a/text_to_audio_generator/text_to_audio_generator.ipynb +++ b/text_to_audio_generator/text_to_audio_generator.ipynb @@ -31,10 +31,7 @@ "id": "bb20c4a6-f362-40e6-8f73-9145953959ec", "metadata": {}, "outputs": [], - "source": [ - "import mlrun\n", - "import tempfile" - ] + "source": "import mlrun" }, { "cell_type": "code", @@ -322,12 +319,9 @@ " \"output_directory\": \"./out\",\n", " \"speakers\": {\"Agent\": 0, \"Client\": 1},\n", " \"available_voices\": [\n", - " \"v2/en_speaker_0\",\n", - " \"v2/en_speaker_1\",\n", + " \"alloy\",\n", + " \"echo\",\n", " ],\n", - " \"use_small_models\": True,\n", - " \"use_gpu\": False,\n", - " \"offload_cpu\": True,\n", " \"file_format\": \"mp3\",\n", " # \"bits_per_sample\": 8,\n", " },\n", diff --git a/text_to_audio_generator/text_to_audio_generator.py b/text_to_audio_generator/text_to_audio_generator.py index 7602745ee..d47d6b865 100644 --- a/text_to_audio_generator/text_to_audio_generator.py +++ b/text_to_audio_generator/text_to_audio_generator.py @@ -11,35 +11,41 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +import io import logging +import os import pathlib import random import tempfile from typing import Dict, List, Optional, Tuple, Union -import bark import numpy as np +import openai import pandas as pd import torch import torchaudio import tqdm +from pydub import AudioSegment # Get the global logger: _LOGGER = logging.getLogger() +OPENAI_API_KEY = "OPENAI_API_KEY" +OPENAI_BASE_URL = "OPENAI_BASE_URL" +SAMPLE_RATE = 24000 + def generate_multi_speakers_audio( data_path: str, speakers: Union[List[str], Dict[str, int]], available_voices: List[str], output_directory: str = None, - use_gpu: bool = True, - use_small_models: bool = False, - offload_cpu: bool = False, + model: str = "tts-1", sample_rate: int = 16000, file_format: str = "wav", verbose: bool = True, bits_per_sample: Optional[int] = None, + speed: float = 1.0, ) -> Tuple[str, pd.DataFrame, dict]: """ Generate audio files from text files. @@ -50,21 +56,20 @@ def generate_multi_speakers_audio( If dictionary, the keys will be the speakers and the values will be the channels. :param available_voices: List of available voices to use for the generation. See here for the available voices: - https://suno-ai.notion.site/8b8e8749ed514b0cbf3f699013548683?v=bc67cff786b04b50b3ceb756fd05f68c + https://platform.openai.com/docs/guides/text-to-speech#voice-options :param output_directory: Path to the directory to save the generated audio files to. - :param use_gpu: Whether to use the GPU for the generation. - :param use_small_models: Whether to use the small models for the generation. - :param offload_cpu: To reduce the memory footprint, the models can be offloaded to the CPU after loading. + :param model: Which model to use for the generation. :param sample_rate: The sampling rate of the generated audio. :param file_format: The format of the generated audio files. :param verbose: Whether to print the progress of the generation. :param bits_per_sample: Changes the bit depth for the supported formats. Supported only in "wav" or "flac" formats. + :param speed: The speed of the generated audio. Select a value from `0.25` to `4.0`. `1.0` is the default. :returns: A tuple of: - The output directory path. - The generated audio files dataframe. - - The errors dictionary. + - The errors' dictionary. """ global _LOGGER @@ -73,17 +78,8 @@ def generate_multi_speakers_audio( data_path = pathlib.Path(data_path).absolute() text_files = _get_text_files(data_path=data_path) - # Load the bark models according to the given configurations: - bark.preload_models( - text_use_gpu=use_gpu, - text_use_small=use_small_models, - coarse_use_gpu=use_gpu, - coarse_use_small=use_small_models, - fine_use_gpu=use_gpu, - fine_use_small=use_small_models, - codec_use_gpu=use_gpu, - force_reload=offload_cpu, - ) + # connect to openai client: + client = _get_openai_client() # Check for per channel generation: if isinstance(speakers, dict): @@ -98,11 +94,11 @@ def generate_multi_speakers_audio( # Prepare the resampling module: resampler = torchaudio.transforms.Resample( - orig_freq=bark.SAMPLE_RATE, new_freq=sample_rate, dtype=torch.float32 + orig_freq=SAMPLE_RATE, new_freq=sample_rate, dtype=torch.float32 ) # Prepare the gap between each speaker: - gap_between_speakers = np.zeros(int(0.5 * bark.SAMPLE_RATE)) + gap_between_speakers = np.zeros(int(0.5 * SAMPLE_RATE)) # Prepare the successes dataframe and errors dictionary to be returned: successes = [] @@ -156,11 +152,16 @@ def generate_multi_speakers_audio( ) for sentence in _split_line(line=sentences): # Generate words audio: - audio = bark.generate_audio( - sentence, - history_prompt=chosen_voices[current_speaker], - silent=True, + audio = client.audio.speech.create( + model=model, + input=sentence, + voice=chosen_voices[current_speaker], + response_format=file_format, + speed=speed, ) + audio = audio.content + audio = _bytes_to_np_array(audio=audio, file_format=file_format) + if speaker_per_channel: silence = np.zeros_like(audio) for speaker in audio_pieces.keys(): @@ -214,6 +215,43 @@ def generate_multi_speakers_audio( return str(output_directory), successes, errors +def _get_openai_client(): + api_key = os.getenv(OPENAI_API_KEY) + base_url = os.getenv(OPENAI_BASE_URL) + # Check if the key is already in the environment variables: + if not api_key or not base_url: + try: + import mlrun + + context = mlrun.get_or_create_ctx(name="context") + # Check if the key is in the secrets: + api_key = context.get_secret(OPENAI_API_KEY) + base_url = context.get_secret(OPENAI_BASE_URL) + except ModuleNotFoundError: + raise EnvironmentError( + f"One or more of the OpenAI required environment variables ('{OPENAI_API_KEY}', '{OPENAI_BASE_URL}') are missing." + f"Please set them as environment variables or install mlrun (`pip install mlrun`)" + f"and set them as project secrets using `project.set_secrets`." + ) + return openai.OpenAI(api_key=api_key, base_url=base_url) + + +def _bytes_to_np_array(audio: bytes, file_format: str): + if file_format == "mp3": + audio_segment = AudioSegment.from_mp3(io.BytesIO(audio)) + + # Convert to raw PCM audio data + samples = audio_segment.get_array_of_samples() + + # Convert to numpy array + audio_array = np.array(samples) + + # Normalize to float between -1 and 1 + return audio_array.astype(np.float32) / np.iinfo(samples.typecode).max + else: + return np.frombuffer(audio, dtype=np.int16) / 32768.0 + + def _get_text_files( data_path: pathlib.Path, ) -> List[pathlib.Path]: @@ -261,6 +299,7 @@ def _get_logger(): global _LOGGER try: import mlrun + # Check if MLRun is available: context = mlrun.get_or_create_ctx(name="mlrun") return context.logger From b3eb31ad06df2173d996b5d3999674a80739da11 Mon Sep 17 00:00:00 2001 From: Yonatan Shelach <92271540+yonishelach@users.noreply.github.com> Date: Thu, 2 Jan 2025 13:35:31 +0200 Subject: [PATCH 28/38] [Text to audio generator] Add speech engine (#838) * [text to audio generator] Replaced bark with openai tts models * [text to audio generator] Fix base url env var * fix version * Add speech engine * after review --- text_to_audio_generator/function.yaml | 86 +++++---- text_to_audio_generator/item.yaml | 1 - text_to_audio_generator/requirements.txt | 3 +- .../test_text_to_audio_generator.py | 41 +++- .../text_to_audio_generator.ipynb | 1 + .../text_to_audio_generator.py | 182 +++++++++++++----- 6 files changed, 230 insertions(+), 84 deletions(-) diff --git a/text_to_audio_generator/function.yaml b/text_to_audio_generator/function.yaml index 65d8d82aa..f7fe52866 100644 --- a/text_to_audio_generator/function.yaml +++ b/text_to_audio_generator/function.yaml @@ -1,28 +1,29 @@ +metadata: + name: text-to-audio-generator + categories: + - data-preparation + - machine-learning + - pytorch + tag: '' spec: - image: '' - default_handler: generate_multi_speakers_audio + command: '' build: - functionSourceCode: # Copyright 2023 Iguazio
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import io
import logging
import os
import pathlib
import random
import tempfile
from typing import Dict, List, Optional, Tuple, Union

import numpy as np
import openai
import pandas as pd
import torch
import torchaudio
import tqdm
from pydub import AudioSegment

# Get the global logger:
_LOGGER = logging.getLogger()

OPENAI_API_KEY = "OPENAI_API_KEY"
OPENAI_BASE_URL = "OPENAI_BASE_URL"
SAMPLE_RATE = 24000


def generate_multi_speakers_audio(
    data_path: str,
    speakers: Union[List[str], Dict[str, int]],
    available_voices: List[str],
    output_directory: str = None,
    model: str = "tts-1",
    sample_rate: int = 16000,
    file_format: str = "wav",
    verbose: bool = True,
    bits_per_sample: Optional[int] = None,
    speed: float = 1.0,
) -> Tuple[str, pd.DataFrame, dict]:
    """
    Generate audio files from text files.

    :param data_path:           Path to the text file or directory containing the text files to generate audio from.
    :param speakers:            List / Dict of speakers to generate audio for.
                                If a list is given, the speakers will be assigned to channels in the order given.
                                If dictionary, the keys will be the speakers and the values will be the channels.
    :param available_voices:    List of available voices to use for the generation.
                        See here for the available voices:
                        https://platform.openai.com/docs/guides/text-to-speech#voice-options
    :param output_directory:    Path to the directory to save the generated audio files to.
    :param model:               Which model to use for the generation.
    :param sample_rate:         The sampling rate of the generated audio.
    :param file_format:         The format of the generated audio files.
    :param verbose:             Whether to print the progress of the generation.
    :param bits_per_sample:     Changes the bit depth for the supported formats.
                                Supported only in "wav" or "flac" formats.
    :param speed:               The speed of the generated audio. Select a value from `0.25` to `4.0`. `1.0` is the default.

    :returns:                   A tuple of:
                                - The output directory path.
                                - The generated audio files dataframe.
                                - The errors' dictionary.
    """

    global _LOGGER
    _LOGGER = _get_logger()
    # Get the input text files to turn to audio:
    data_path = pathlib.Path(data_path).absolute()
    text_files = _get_text_files(data_path=data_path)

    # connect to openai client:
    client = _get_openai_client()

    # Check for per channel generation:
    if isinstance(speakers, dict):
        speaker_per_channel = True
        # Sort the given speakers by channels:
        speakers = {
            speaker: channel
            for speaker, channel in sorted(speakers.items(), key=lambda item: item[1])
        }
    else:
        speaker_per_channel = False

    # Prepare the resampling module:
    resampler = torchaudio.transforms.Resample(
        orig_freq=SAMPLE_RATE, new_freq=sample_rate, dtype=torch.float32
    )

    # Prepare the gap between each speaker:
    gap_between_speakers = np.zeros(int(0.5 * SAMPLE_RATE))

    # Prepare the successes dataframe and errors dictionary to be returned:
    successes = []
    errors = {}

    # Create the output directory:
    if output_directory is None:
        output_directory = tempfile.mkdtemp()
    output_directory = pathlib.Path(output_directory)
    if not output_directory.exists():
        output_directory.mkdir(exist_ok=True, parents=True)

    # Start generating audio:
    # Go over the audio files and transcribe:
    for text_file in tqdm.tqdm(
        text_files, desc="Generating", unit="file", disable=not verbose
    ):

        try:
            # Randomize voices for each speaker:
            chosen_voices = {}
            available_voices_copy = available_voices.copy()
            for speaker in speakers:
                voice = random.choice(available_voices_copy)
                chosen_voices[speaker] = voice
                available_voices_copy.remove(voice)
            # Read text:
            with open(text_file, "r") as fp:
                text = fp.read()
            # Prepare a holder for all the generated pieces (if per channel each speaker will have its own):
            audio_pieces = (
                {speaker: [] for speaker in speakers}
                if speaker_per_channel
                else {"all": []}
            )

            # Generate audio per line:
            for line in text.splitlines():
                # Validate line is in correct speaker format:

                if ": " not in line:
                    if verbose:
                        _LOGGER.warning(f"Skipping line: {line}")
                    continue
                # Split line to speaker and his words:
                current_speaker, sentences = line.split(": ", 1)
                # Validate speaker is known:
                if current_speaker not in speakers:
                    raise ValueError(
                        f"Unknown speaker: {current_speaker}. Given speakers are: {speakers}"
                    )
                for sentence in _split_line(line=sentences):
                    # Generate words audio:
                    audio = client.audio.speech.create(
                        model=model,
                        input=sentence,
                        voice=chosen_voices[current_speaker],
                        response_format=file_format,
                        speed=speed,
                    )
                    audio = audio.content
                    audio = _bytes_to_np_array(audio=audio, file_format=file_format)

                    if speaker_per_channel:
                        silence = np.zeros_like(audio)
                        for speaker in audio_pieces.keys():
                            if speaker == current_speaker:
                                audio_pieces[speaker] += [audio, gap_between_speakers]
                            else:
                                audio_pieces[speaker] += [silence, gap_between_speakers]
                    else:
                        audio_pieces["all"] += [audio, gap_between_speakers]
            # Construct a single audio array from all the pieces and channels:

            audio = np.vstack(
                [np.concatenate(audio_pieces[speaker]) for speaker in speakers]
            ).astype(dtype=np.float32)
            # Resample:
            audio = torch.from_numpy(audio)
            audio = resampler(audio)
            # Save to audio file:
            audio_file = output_directory / f"{text_file.stem}.{file_format}"

            torchaudio.save(
                uri=str(audio_file),
                src=audio,
                sample_rate=sample_rate,
                format=file_format,
                bits_per_sample=bits_per_sample,
            )

            # Collect to the successes:
            successes.append([text_file.name, audio_file.name])
        except Exception as exception:
            # Note the exception as error in the dictionary:
            if verbose:
                _LOGGER.warning(f"Error in file: '{text_file.name}'")
            print(exception)
            errors[text_file.name] = str(exception)

    # Construct the translations dataframe:
    successes = pd.DataFrame(
        successes,
        columns=["text_file", "audio_file"],
    )

    # Print the head of the produced dataframe and return:
    if verbose:
        _LOGGER.info(
            f"Done ({successes.shape[0]}/{len(text_files)})\n"
            f"Translations summary:\n"
            f"{successes.head()}"
        )
    return str(output_directory), successes, errors


def _get_openai_client():
    api_key = os.getenv(OPENAI_API_KEY)
    base_url = os.getenv(OPENAI_BASE_URL)
    # Check if the key is already in the environment variables:
    if not api_key or not base_url:
        try:
            import mlrun

            context = mlrun.get_or_create_ctx(name="context")
            # Check if the key is in the secrets:
            api_key = context.get_secret(OPENAI_API_KEY)
            base_url = context.get_secret(OPENAI_BASE_URL)
        except ModuleNotFoundError:
            raise EnvironmentError(
                f"One or more of the OpenAI required environment variables ('{OPENAI_API_KEY}', '{OPENAI_BASE_URL}') are missing."
                f"Please set them as environment variables or install mlrun (`pip install mlrun`)"
                f"and set them as project secrets using `project.set_secrets`."
            )
    return openai.OpenAI(api_key=api_key, base_url=base_url)


def _bytes_to_np_array(audio: bytes, file_format: str):
    if file_format == "mp3":
        audio_segment = AudioSegment.from_mp3(io.BytesIO(audio))

        # Convert to raw PCM audio data
        samples = audio_segment.get_array_of_samples()

        # Convert to numpy array
        audio_array = np.array(samples)

        # Normalize to float between -1 and 1
        return audio_array.astype(np.float32) / np.iinfo(samples.typecode).max
    else:
        return np.frombuffer(audio, dtype=np.int16) / 32768.0


def _get_text_files(
    data_path: pathlib.Path,
) -> List[pathlib.Path]:
    # Check if the path is of a directory or a file:
    if data_path.is_dir():
        # Get all files inside the directory:
        text_files = list(data_path.glob("*.*"))
    elif data_path.is_file():
        text_files = [data_path]
    else:
        raise ValueError(
            f"Unrecognized data path. The parameter `data_path` must be either a directory path or a file path. "
            f"Given: {str(data_path)} "
        )

    return text_files


def _split_line(line: str, max_length: int = 250) -> List[str]:
    if len(line) < max_length:
        return [line]

    sentences = [
        f"{sentence.strip()}." for sentence in line.split(".") if sentence.strip()
    ]

    splits = []
    current_length = len(sentences[0])
    split = sentences[0]
    for sentence in sentences[1:]:
        if current_length + len(sentence) > max_length:
            splits.append(split)
            split = sentence
            current_length = len(sentence)
        else:
            current_length += len(sentence)
            split += " " + sentence
    if split:
        splits.append(split)

    return splits


def _get_logger():
    global _LOGGER
    try:
        import mlrun

        # Check if MLRun is available:
        context = mlrun.get_or_create_ctx(name="mlrun")
        return context.logger
    except ModuleNotFoundError:
        return _LOGGER
 + functionSourceCode: # Copyright 2023 Iguazio
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import importlib
import io
import logging
import os
import pathlib
import random
import tempfile
from abc import ABC, abstractmethod
from typing import Dict, List, Optional, Tuple, Union

import numpy as np
import pandas as pd
import torch
import torchaudio
import tqdm

# Get the global logger:
_LOGGER = logging.getLogger()

OPENAI_API_KEY = "OPENAI_API_KEY"
OPENAI_BASE_URL = "OPENAI_API_BASE"
SAMPLE_RATE = 24000


def generate_multi_speakers_audio(
    data_path: str,
    speakers: Union[List[str], Dict[str, int]],
    available_voices: List[str],
    engine: str = "openai",
    output_directory: str = None,
    use_gpu: Optional[bool] = None,
    use_small_models: Optional[bool] = None,
    offload_cpu: Optional[bool] = None,
    model: Optional[str] = None,
    speed: Optional[float] = None,
    sample_rate: int = 16000,
    file_format: str = "wav",
    verbose: bool = True,
    bits_per_sample: Optional[int] = None,
) -> Tuple[str, pd.DataFrame, dict]:
    """
    Generate audio files from text files.

    :param data_path:           Path to the text file or directory containing the text files to generate audio from.
    :param speakers:            List / Dict of speakers to generate audio for.
                                If a list is given, the speakers will be assigned to channels in the order given.
                                If dictionary, the keys will be the speakers and the values will be the channels.
    :param available_voices:    List of available voices to use for the generation.
                        See here for the available voices for bark engine:
                        https://suno-ai.notion.site/8b8e8749ed514b0cbf3f699013548683?v=bc67cff786b04b50b3ceb756fd05f68c
                        See here for the available voices for openai engine:
                        https://beta.openai.com/docs/api-reference/speech
    :param engine:              The engine to use for the generation. Select either "bark" or "openai". Default is "openai".
    :param output_directory:    Path to the directory to save the generated audio files to.
    :param use_gpu:             Whether to use the GPU for the generation. Supported only in "bark" engine.
    :param use_small_models:    Whether to use the small models for the generation. Supported only in "bark" engine.
    :param offload_cpu:         To reduce the memory footprint, the models can be offloaded to the CPU after loading.
                                Supported only in "bark" engine.
    :param model:               Which model to use for the generation. Supported only in "openai" engine.
                                Default is "tts-1".
    :param speed:               The speed of the generated audio. Select a value from `0.25` to `4.0`. `1.0` is the default.
    :param sample_rate:         The sampling rate of the generated audio.
    :param file_format:         The format of the generated audio files.
    :param verbose:             Whether to print the progress of the generation.
    :param bits_per_sample:     Changes the bit depth for the supported formats.
                                Supported only in "wav" or "flac" formats.

    :returns:                   A tuple of:
                                - The output directory path.
                                - The generated audio files dataframe.
                                - The errors' dictionary.
    """

    global _LOGGER
    _LOGGER = _get_logger()
    # Get the input text files to turn to audio:
    data_path = pathlib.Path(data_path).absolute()
    text_files = _get_text_files(data_path=data_path)


    # Prepare the speech engine:
    engine = _get_engine(
        engine=engine,
        use_gpu=use_gpu,
        use_small_models=use_small_models,
        offload_cpu=offload_cpu,
        model=model,
        file_format=file_format,
        speed=speed
    )

    # Check for per channel generation:
    if isinstance(speakers, dict):
        speaker_per_channel = True
        # Sort the given speakers by channels:
        speakers = {
            speaker: channel
            for speaker, channel in sorted(speakers.items(), key=lambda item: item[1])
        }
    else:
        speaker_per_channel = False

    # Prepare the resampling module:
    resampler = torchaudio.transforms.Resample(
        orig_freq=SAMPLE_RATE, new_freq=sample_rate, dtype=torch.float32
    )

    # Prepare the gap between each speaker:
    gap_between_speakers = np.zeros(int(0.5 * SAMPLE_RATE))

    # Prepare the successes dataframe and errors dictionary to be returned:
    successes = []
    errors = {}

    # Create the output directory:
    if output_directory is None:
        output_directory = tempfile.mkdtemp()
    output_directory = pathlib.Path(output_directory)
    if not output_directory.exists():
        output_directory.mkdir(exist_ok=True, parents=True)

    # Start generating audio:
    # Go over the audio files and transcribe:
    for text_file in tqdm.tqdm(
        text_files, desc="Generating", unit="file", disable=not verbose
    ):

        try:
            # Randomize voices for each speaker:
            chosen_voices = {}
            available_voices_copy = available_voices.copy()
            for speaker in speakers:
                voice = random.choice(available_voices_copy)
                chosen_voices[speaker] = voice
                available_voices_copy.remove(voice)
            # Read text:
            with open(text_file, "r") as fp:
                text = fp.read()
            # Prepare a holder for all the generated pieces (if per channel each speaker will have its own):
            audio_pieces = (
                {speaker: [] for speaker in speakers}
                if speaker_per_channel
                else {"all": []}
            )

            # Generate audio per line:
            for line in text.splitlines():
                # Validate line is in correct speaker format:

                if ": " not in line:
                    if verbose:
                        _LOGGER.warning(f"Skipping line: {line}")
                    continue
                # Split line to speaker and his words:
                current_speaker, sentences = line.split(": ", 1)
                # Validate speaker is known:
                if current_speaker not in speakers:
                    raise ValueError(
                        f"Unknown speaker: {current_speaker}. Given speakers are: {speakers}"
                    )
                for sentence in _split_line(line=sentences):
                    # Generate words audio:
                    audio = engine._generate_audio(
                        text=sentence,
                        voice=chosen_voices[current_speaker],
                    )

                    if speaker_per_channel:
                        silence = np.zeros_like(audio)
                        for speaker in audio_pieces.keys():
                            if speaker == current_speaker:
                                audio_pieces[speaker] += [audio, gap_between_speakers]
                            else:
                                audio_pieces[speaker] += [silence, gap_between_speakers]
                    else:
                        audio_pieces["all"] += [audio, gap_between_speakers]
            # Construct a single audio array from all the pieces and channels:

            audio = np.vstack(
                [np.concatenate(audio_pieces[speaker]) for speaker in speakers]
            ).astype(dtype=np.float32)
            # Resample:
            audio = torch.from_numpy(audio)
            audio = resampler(audio)
            # Save to audio file:
            audio_file = output_directory / f"{text_file.stem}.{file_format}"

            torchaudio.save(
                uri=str(audio_file),
                src=audio,
                sample_rate=sample_rate,
                format=file_format,
                bits_per_sample=bits_per_sample,
            )

            # Collect to the successes:
            successes.append([text_file.name, audio_file.name])
        except Exception as exception:
            # Note the exception as error in the dictionary:
            if verbose:
                _LOGGER.warning(f"Error in file: '{text_file.name}'")
            print(exception)
            errors[text_file.name] = str(exception)

    # Construct the translations dataframe:
    successes = pd.DataFrame(
        successes,
        columns=["text_file", "audio_file"],
    )

    # Print the head of the produced dataframe and return:
    if verbose:
        _LOGGER.info(
            f"Done ({successes.shape[0]}/{len(text_files)})\n"
            f"Translations summary:\n"
            f"{successes.head()}"
        )
    return str(output_directory), successes, errors


class SpeechEngine(ABC):
    @abstractmethod
    def _generate_audio(self, text: str, voice: str) -> np.ndarray:
        pass


class BarkEngine(SpeechEngine):
    def __init__(self, use_gpu: bool = True, use_small_models: bool = False, offload_cpu: bool = False):
        try:
            self.bark = importlib.import_module("bark")
        except ImportError:
            raise ImportError(
                "The 'bark' library is required for the BarkEngine. Please install it using 'pip install bark-ai'."
            )

        self.bark.preload_models(
            text_use_gpu=use_gpu,
            text_use_small=use_small_models,
            coarse_use_gpu=use_gpu,
            coarse_use_small=use_small_models,
            fine_use_gpu=use_gpu,
            fine_use_small=use_small_models,
            codec_use_gpu=use_gpu,
            force_reload=offload_cpu,
        )

    def _generate_audio(self, text: str, voice: str) -> np.ndarray:
        # Generate words audio:
        audio = self.bark.generate_audio(
            text,
            history_prompt=voice,
            silent=True,
        )
        return audio


class OpenAIEngine(SpeechEngine):
    def __init__(self, model: str = "tts-1", file_format: str = "wav", speed: float = 1.0):
        try:
            self.openai = importlib.import_module("openai")
            self.pydub = importlib.import_module("pydub")
        except ImportError:
            raise ImportError(
                "The 'openai' and 'pydub' libraries are required for the OpenAIEngine. Please install them using 'pip install openai pydub'."
            )

        api_key = os.getenv(OPENAI_API_KEY)
        base_url = os.getenv(OPENAI_BASE_URL)
        # Check if the key is already in the environment variables:
        if not api_key or not base_url:
            try:
                import mlrun

                context = mlrun.get_or_create_ctx(name="context")
                # Check if the key is in the secrets:
                api_key = context.get_secret(OPENAI_API_KEY)
                base_url = context.get_secret(OPENAI_BASE_URL)
            except ModuleNotFoundError:
                raise EnvironmentError(
                    f"One or more of the OpenAI required environment variables ('{OPENAI_API_KEY}', '{OPENAI_BASE_URL}') are missing."
                    f"Please set them as environment variables or install mlrun (`pip install mlrun`)"
                    f"and set them as project secrets using `project.set_secrets`."
                )

        self.client = self.openai.OpenAI(api_key=api_key, base_url=base_url)
        self.model = model
        self.file_format = file_format
        self.speed = speed

    def _generate_audio(self, text: str, voice: str) -> np.ndarray:
        # Generate words audio:
        audio = self.client.audio.speech.create(
            model=self.model,
            input=text,
            voice=voice,
            response_format=self.file_format,
            speed=self.speed,
        )
        audio = audio.content
        audio = self._bytes_to_np_array(audio=audio)
        return audio

    def _bytes_to_np_array(self, audio: bytes):
        if self.file_format == "mp3":
            audio_segment = self.pydub.AudioSegment.from_mp3(io.BytesIO(audio))

            # Convert to raw PCM audio data
            samples = audio_segment.get_array_of_samples()

            # Convert to numpy array
            audio_array = np.array(samples)

            # Normalize to float between -1 and 1
            return audio_array.astype(np.float32) / np.iinfo(samples.typecode).max
        else:
            return np.frombuffer(audio, dtype=np.int16) / 32768.0


def _get_engine(engine: str, file_format: str, **kwargs) -> SpeechEngine:
    # eliminate the None values:
    kwargs = {key: value for key, value in kwargs.items() if value is not None}

    if engine == "bark":
        return BarkEngine(**kwargs)
    elif engine == "openai":
        return OpenAIEngine(file_format=file_format, **kwargs)
    else:
        raise ValueError(
            f"Unrecognized engine. The parameter `engine` must be either 'bark' or 'openai'. Given: {engine}"
        )

def _get_text_files(
    data_path: pathlib.Path,
) -> List[pathlib.Path]:
    # Check if the path is of a directory or a file:
    if data_path.is_dir():
        # Get all files inside the directory:
        text_files = list(data_path.glob("*.*"))
    elif data_path.is_file():
        text_files = [data_path]
    else:
        raise ValueError(
            f"Unrecognized data path. The parameter `data_path` must be either a directory path or a file path. "
            f"Given: {str(data_path)} "
        )

    return text_files


def _split_line(line: str, max_length: int = 250) -> List[str]:
    if len(line) < max_length:
        return [line]

    sentences = [
        f"{sentence.strip()}." for sentence in line.split(".") if sentence.strip()
    ]

    splits = []
    current_length = len(sentences[0])
    split = sentences[0]
    for sentence in sentences[1:]:
        if current_length + len(sentence) > max_length:
            splits.append(split)
            split = sentence
            current_length = len(sentence)
        else:
            current_length += len(sentence)
            split += " " + sentence
    if split:
        splits.append(split)

    return splits


def _get_logger():
    global _LOGGER
    try:
        import mlrun

        # Check if MLRun is available:
        context = mlrun.get_or_create_ctx(name="mlrun")
        return context.logger
    except ModuleNotFoundError:
        return _LOGGER
 + code_origin: '' + base_image: mlrun/mlrun requirements: - - openai - torchaudio - pydub origin_filename: '' - base_image: mlrun/mlrun - code_origin: '' - command: '' + image: '' disable_auto_mount: false - description: Generate audio file from text using different speakers entry_points: generate_multi_speakers_audio: - has_varargs: false - doc: Generate audio files from text files. - name: generate_multi_speakers_audio - outputs: - - doc: 'A tuple of: - The output directory path. - The generated audio files - dataframe. - The errors'' dictionary.' - type: Tuple[str, pd.DataFrame, dict] has_kwargs: false + name: generate_multi_speakers_audio + doc: Generate audio files from text files. + has_varargs: false + lineno: 38 parameters: - name: data_path type: str @@ -36,15 +37,41 @@ spec: - name: available_voices type: List[str] doc: 'List of available voices to use for the generation. See here for the - available voices: https://platform.openai.com/docs/guides/text-to-speech#voice-options' + available voices for bark engine: https://suno-ai.notion.site/8b8e8749ed514b0cbf3f699013548683?v=bc67cff786b04b50b3ceb756fd05f68c + See here for the available voices for openai engine: https://beta.openai.com/docs/api-reference/speech' + - name: engine + type: str + doc: The engine to use for the generation. Select either "bark" or "openai". + Default is "openai". + default: openai - name: output_directory type: str doc: Path to the directory to save the generated audio files to. default: null + - name: use_gpu + type: Optional[bool] + doc: Whether to use the GPU for the generation. Supported only in "bark" engine. + default: null + - name: use_small_models + type: Optional[bool] + doc: Whether to use the small models for the generation. Supported only in + "bark" engine. + default: null + - name: offload_cpu + type: Optional[bool] + doc: To reduce the memory footprint, the models can be offloaded to the CPU + after loading. Supported only in "bark" engine. + default: null - name: model - type: str - doc: Which model to use for the generation. - default: tts-1 + type: Optional[str] + doc: Which model to use for the generation. Supported only in "openai" engine. + Default is "tts-1". + default: null + - name: speed + type: Optional[float] + doc: The speed of the generated audio. Select a value from `0.25` to `4.0`. + `1.0` is the default. + default: null - name: sample_rate type: int doc: The sampling rate of the generated audio. @@ -62,18 +89,11 @@ spec: doc: Changes the bit depth for the supported formats. Supported only in "wav" or "flac" formats. default: null - - name: speed - type: float - doc: The speed of the generated audio. Select a value from `0.25` to `4.0`. - `1.0` is the default. - default: 1.0 - lineno: 38 -kind: job -metadata: - categories: - - data-preparation - - machine-learning - - pytorch - tag: '' - name: text-to-audio-generator + outputs: + - doc: 'A tuple of: - The output directory path. - The generated audio files + dataframe. - The errors'' dictionary.' + type: Tuple[str, pd.DataFrame, dict] + default_handler: generate_multi_speakers_audio + description: Generate audio file from text using different speakers verbose: false +kind: job diff --git a/text_to_audio_generator/item.yaml b/text_to_audio_generator/item.yaml index 3a6af1e7e..e8235a086 100644 --- a/text_to_audio_generator/item.yaml +++ b/text_to_audio_generator/item.yaml @@ -22,7 +22,6 @@ spec: image: mlrun/mlrun kind: job requirements: - - openai - torchaudio - pydub url: '' diff --git a/text_to_audio_generator/requirements.txt b/text_to_audio_generator/requirements.txt index 63dee64df..02f84ef44 100644 --- a/text_to_audio_generator/requirements.txt +++ b/text_to_audio_generator/requirements.txt @@ -1,3 +1,4 @@ -openai>=1.58.0 +bark torchaudio>=2.1.0 +openai>=1.58.0 pydub \ No newline at end of file diff --git a/text_to_audio_generator/test_text_to_audio_generator.py b/text_to_audio_generator/test_text_to_audio_generator.py index 94fd8c098..fb8db3198 100644 --- a/text_to_audio_generator/test_text_to_audio_generator.py +++ b/text_to_audio_generator/test_text_to_audio_generator.py @@ -19,13 +19,47 @@ import pytest +@pytest.mark.parametrize("file_format,bits_per_sample", [("wav", 8), ("mp3", None)]) +def test_generate_multi_speakers_audio(file_format, bits_per_sample): + text_to_audio_generator_function = mlrun.import_function("function.yaml") + with tempfile.TemporaryDirectory() as test_directory: + function_run = text_to_audio_generator_function.run( + handler="generate_multi_speakers_audio", + inputs={"data_path": "data/test_data.txt"}, + params={ + "output_directory": test_directory, + "speakers": {"Agent": 0, "Client": 1}, + "available_voices": [ + "v2/en_speaker_0", + "v2/en_speaker_1", + ], + "engine": "bark", + "use_small_models": True, + "use_gpu": False, + "offload_cpu": True, + "file_format": file_format, + "bits_per_sample": bits_per_sample, + }, + local=True, + returns=[ + "audio_files: path", + "audio_files_dataframe: dataset", + "text_to_speech_errors: file", + ], + artifact_path=test_directory, + ) + assert function_run.error == "" + for key in ["audio_files", "audio_files_dataframe", "text_to_speech_errors"]: + assert key in function_run.outputs and function_run.outputs[key] is not None + + @pytest.mark.skipif( - condition=os.getenv("OPENAI_BASE_URL") is None + condition=os.getenv("OPENAI_API_BASE") is None and os.getenv("OPENAI_API_KEY") is None, reason="OpenAI API key and base URL are required to run this test", ) @pytest.mark.parametrize("file_format,bits_per_sample", [("wav", 8), ("mp3", None)]) -def test_generate_multi_speakers_audio(file_format, bits_per_sample): +def test_generate_multi_speakers_audio_openai(file_format, bits_per_sample): text_to_audio_generator_function = mlrun.import_function("function.yaml") with tempfile.TemporaryDirectory() as test_directory: function_run = text_to_audio_generator_function.run( @@ -38,6 +72,7 @@ def test_generate_multi_speakers_audio(file_format, bits_per_sample): "alloy", "echo", ], + "engine": "openai", "file_format": file_format, "bits_per_sample": bits_per_sample, }, @@ -51,4 +86,4 @@ def test_generate_multi_speakers_audio(file_format, bits_per_sample): ) assert function_run.error == "" for key in ["audio_files", "audio_files_dataframe", "text_to_speech_errors"]: - assert key in function_run.outputs and function_run.outputs[key] is not None + assert key in function_run.outputs and function_run.outputs[key] is not None \ No newline at end of file diff --git a/text_to_audio_generator/text_to_audio_generator.ipynb b/text_to_audio_generator/text_to_audio_generator.ipynb index a70882a44..35c219964 100644 --- a/text_to_audio_generator/text_to_audio_generator.ipynb +++ b/text_to_audio_generator/text_to_audio_generator.ipynb @@ -322,6 +322,7 @@ " \"alloy\",\n", " \"echo\",\n", " ],\n", + " \"engine\": \"bark\",\n", " \"file_format\": \"mp3\",\n", " # \"bits_per_sample\": 8,\n", " },\n", diff --git a/text_to_audio_generator/text_to_audio_generator.py b/text_to_audio_generator/text_to_audio_generator.py index d47d6b865..e03b827ff 100644 --- a/text_to_audio_generator/text_to_audio_generator.py +++ b/text_to_audio_generator/text_to_audio_generator.py @@ -11,27 +11,27 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +import importlib import io import logging import os import pathlib import random import tempfile +from abc import ABC, abstractmethod from typing import Dict, List, Optional, Tuple, Union import numpy as np -import openai import pandas as pd import torch import torchaudio import tqdm -from pydub import AudioSegment # Get the global logger: _LOGGER = logging.getLogger() OPENAI_API_KEY = "OPENAI_API_KEY" -OPENAI_BASE_URL = "OPENAI_BASE_URL" +OPENAI_BASE_URL = "OPENAI_API_BASE" SAMPLE_RATE = 24000 @@ -39,13 +39,17 @@ def generate_multi_speakers_audio( data_path: str, speakers: Union[List[str], Dict[str, int]], available_voices: List[str], + engine: str = "openai", output_directory: str = None, - model: str = "tts-1", + use_gpu: Optional[bool] = None, + use_small_models: Optional[bool] = None, + offload_cpu: Optional[bool] = None, + model: Optional[str] = None, + speed: Optional[float] = None, sample_rate: int = 16000, file_format: str = "wav", verbose: bool = True, bits_per_sample: Optional[int] = None, - speed: float = 1.0, ) -> Tuple[str, pd.DataFrame, dict]: """ Generate audio files from text files. @@ -55,16 +59,24 @@ def generate_multi_speakers_audio( If a list is given, the speakers will be assigned to channels in the order given. If dictionary, the keys will be the speakers and the values will be the channels. :param available_voices: List of available voices to use for the generation. - See here for the available voices: - https://platform.openai.com/docs/guides/text-to-speech#voice-options + See here for the available voices for bark engine: + https://suno-ai.notion.site/8b8e8749ed514b0cbf3f699013548683?v=bc67cff786b04b50b3ceb756fd05f68c + See here for the available voices for openai engine: + https://beta.openai.com/docs/api-reference/speech + :param engine: The engine to use for the generation. Select either "bark" or "openai". Default is "openai". :param output_directory: Path to the directory to save the generated audio files to. - :param model: Which model to use for the generation. + :param use_gpu: Whether to use the GPU for the generation. Supported only in "bark" engine. + :param use_small_models: Whether to use the small models for the generation. Supported only in "bark" engine. + :param offload_cpu: To reduce the memory footprint, the models can be offloaded to the CPU after loading. + Supported only in "bark" engine. + :param model: Which model to use for the generation. Supported only in "openai" engine. + Default is "tts-1". + :param speed: The speed of the generated audio. Select a value from `0.25` to `4.0`. `1.0` is the default. :param sample_rate: The sampling rate of the generated audio. :param file_format: The format of the generated audio files. :param verbose: Whether to print the progress of the generation. :param bits_per_sample: Changes the bit depth for the supported formats. Supported only in "wav" or "flac" formats. - :param speed: The speed of the generated audio. Select a value from `0.25` to `4.0`. `1.0` is the default. :returns: A tuple of: - The output directory path. @@ -78,8 +90,17 @@ def generate_multi_speakers_audio( data_path = pathlib.Path(data_path).absolute() text_files = _get_text_files(data_path=data_path) - # connect to openai client: - client = _get_openai_client() + + # Prepare the speech engine: + engine = _get_engine( + engine=engine, + use_gpu=use_gpu, + use_small_models=use_small_models, + offload_cpu=offload_cpu, + model=model, + file_format=file_format, + speed=speed + ) # Check for per channel generation: if isinstance(speakers, dict): @@ -152,15 +173,10 @@ def generate_multi_speakers_audio( ) for sentence in _split_line(line=sentences): # Generate words audio: - audio = client.audio.speech.create( - model=model, - input=sentence, + audio = engine._generate_audio( + text=sentence, voice=chosen_voices[current_speaker], - response_format=file_format, - speed=speed, ) - audio = audio.content - audio = _bytes_to_np_array(audio=audio, file_format=file_format) if speaker_per_channel: silence = np.zeros_like(audio) @@ -215,43 +231,117 @@ def generate_multi_speakers_audio( return str(output_directory), successes, errors -def _get_openai_client(): - api_key = os.getenv(OPENAI_API_KEY) - base_url = os.getenv(OPENAI_BASE_URL) - # Check if the key is already in the environment variables: - if not api_key or not base_url: +class SpeechEngine(ABC): + @abstractmethod + def _generate_audio(self, text: str, voice: str) -> np.ndarray: + pass + + +class BarkEngine(SpeechEngine): + def __init__(self, use_gpu: bool = True, use_small_models: bool = False, offload_cpu: bool = False): try: - import mlrun - - context = mlrun.get_or_create_ctx(name="context") - # Check if the key is in the secrets: - api_key = context.get_secret(OPENAI_API_KEY) - base_url = context.get_secret(OPENAI_BASE_URL) - except ModuleNotFoundError: - raise EnvironmentError( - f"One or more of the OpenAI required environment variables ('{OPENAI_API_KEY}', '{OPENAI_BASE_URL}') are missing." - f"Please set them as environment variables or install mlrun (`pip install mlrun`)" - f"and set them as project secrets using `project.set_secrets`." + self.bark = importlib.import_module("bark") + except ImportError: + raise ImportError( + "The 'bark' library is required for the BarkEngine. Please install it using 'pip install bark-ai'." ) - return openai.OpenAI(api_key=api_key, base_url=base_url) + self.bark.preload_models( + text_use_gpu=use_gpu, + text_use_small=use_small_models, + coarse_use_gpu=use_gpu, + coarse_use_small=use_small_models, + fine_use_gpu=use_gpu, + fine_use_small=use_small_models, + codec_use_gpu=use_gpu, + force_reload=offload_cpu, + ) -def _bytes_to_np_array(audio: bytes, file_format: str): - if file_format == "mp3": - audio_segment = AudioSegment.from_mp3(io.BytesIO(audio)) + def _generate_audio(self, text: str, voice: str) -> np.ndarray: + # Generate words audio: + audio = self.bark.generate_audio( + text, + history_prompt=voice, + silent=True, + ) + return audio - # Convert to raw PCM audio data - samples = audio_segment.get_array_of_samples() - # Convert to numpy array - audio_array = np.array(samples) +class OpenAIEngine(SpeechEngine): + def __init__(self, model: str = "tts-1", file_format: str = "wav", speed: float = 1.0): + try: + self.openai = importlib.import_module("openai") + self.pydub = importlib.import_module("pydub") + except ImportError: + raise ImportError( + "The 'openai' and 'pydub' libraries are required for the OpenAIEngine. Please install them using 'pip install openai pydub'." + ) - # Normalize to float between -1 and 1 - return audio_array.astype(np.float32) / np.iinfo(samples.typecode).max - else: - return np.frombuffer(audio, dtype=np.int16) / 32768.0 + api_key = os.getenv(OPENAI_API_KEY) + base_url = os.getenv(OPENAI_BASE_URL) + # Check if the key is already in the environment variables: + if not api_key or not base_url: + try: + import mlrun + + context = mlrun.get_or_create_ctx(name="context") + # Check if the key is in the secrets: + api_key = context.get_secret(OPENAI_API_KEY) + base_url = context.get_secret(OPENAI_BASE_URL) + except ModuleNotFoundError: + raise EnvironmentError( + f"One or more of the OpenAI required environment variables ('{OPENAI_API_KEY}', '{OPENAI_BASE_URL}') are missing." + f"Please set them as environment variables or install mlrun (`pip install mlrun`)" + f"and set them as project secrets using `project.set_secrets`." + ) + + self.client = self.openai.OpenAI(api_key=api_key, base_url=base_url) + self.model = model + self.file_format = file_format + self.speed = speed + + def _generate_audio(self, text: str, voice: str) -> np.ndarray: + # Generate words audio: + audio = self.client.audio.speech.create( + model=self.model, + input=text, + voice=voice, + response_format=self.file_format, + speed=self.speed, + ) + audio = audio.content + audio = self._bytes_to_np_array(audio=audio) + return audio + + def _bytes_to_np_array(self, audio: bytes): + if self.file_format == "mp3": + audio_segment = self.pydub.AudioSegment.from_mp3(io.BytesIO(audio)) + + # Convert to raw PCM audio data + samples = audio_segment.get_array_of_samples() + + # Convert to numpy array + audio_array = np.array(samples) + + # Normalize to float between -1 and 1 + return audio_array.astype(np.float32) / np.iinfo(samples.typecode).max + else: + return np.frombuffer(audio, dtype=np.int16) / 32768.0 +def _get_engine(engine: str, file_format: str, **kwargs) -> SpeechEngine: + # eliminate the None values: + kwargs = {key: value for key, value in kwargs.items() if value is not None} + + if engine == "bark": + return BarkEngine(**kwargs) + elif engine == "openai": + return OpenAIEngine(file_format=file_format, **kwargs) + else: + raise ValueError( + f"Unrecognized engine. The parameter `engine` must be either 'bark' or 'openai'. Given: {engine}" + ) + def _get_text_files( data_path: pathlib.Path, ) -> List[pathlib.Path]: From 7aec0ab6dccb8edd5e708c60996b168ac70fa1de Mon Sep 17 00:00:00 2001 From: Yonatan Shelach <92271540+yonishelach@users.noreply.github.com> Date: Thu, 2 Jan 2025 15:43:00 +0200 Subject: [PATCH 29/38] [auto-trainer] update test requirements (#839) * [Build] Fix html links, Add .html as source in documentation * Update CI temporarily and update index * [XGB-Custom] Fix test artifact key name * [XGB-Serving][XGB-Test][XGB-Trainer] Fix tests - artifact key * [Build] Install python 3.9 when testing (#618) * [Build] Update python version in CI (#620) * [Build] Install python 3.9 when testing * [Build] Update python version in CI * . * Revert "[Build] Update python version in CI (#620)" (#621) This reverts commit 0cd1f1585a618c253f201b6f5a63502cdbddb591. * Revert "[Build] Install python 3.9 when testing (#618)" (#619) This reverts commit 3301415200e52326bade1e17f99cb6b6d3880860. * [Build] Build with python 3.9 (#622) * [Build] Build with python 3.9 * . * Update requirements.txt --- auto_trainer/requirements.txt | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/auto_trainer/requirements.txt b/auto_trainer/requirements.txt index ad97214f0..b14a0293c 100644 --- a/auto_trainer/requirements.txt +++ b/auto_trainer/requirements.txt @@ -1,4 +1,4 @@ pandas -scikit-learn -xgboost -plotly \ No newline at end of file +scikit-learn<1.4.0 +xgboost<2.0.0 +plotly From fe390285aae02d8d6127cbcd1d4f2a5b3280f77e Mon Sep 17 00:00:00 2001 From: Eyal Danieli Date: Thu, 6 Mar 2025 16:02:50 +0200 Subject: [PATCH 30/38] [Feature Selection] Fix deprecated `get_offline_features` (#844) * fix feature_selection * fix feature_selection * fix feature_selection nb * update yaml name * fix test * fix test --- feature_selection/feature_selection.ipynb | 798 +++----------------- feature_selection/feature_selection.py | 2 +- feature_selection/function.yaml | 64 +- feature_selection/item.yaml | 4 +- feature_selection/test_feature_selection.py | 3 +- 5 files changed, 151 insertions(+), 720 deletions(-) diff --git a/feature_selection/feature_selection.ipynb b/feature_selection/feature_selection.ipynb index f7141591f..104896757 100644 --- a/feature_selection/feature_selection.ipynb +++ b/feature_selection/feature_selection.ipynb @@ -11,449 +11,91 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [], - "source": [ - "import mlrun" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "%nuclio: setting kind to 'job'\n", - "%nuclio: setting spec.image to 'mlrun/ml-models'\n" + "> 2025-03-06 10:55:11,680 [warning] Failed resolving version info. Ignoring and using defaults\n", + "> 2025-03-06 10:55:13,566 [warning] Server or client version is unstable. Assuming compatible: {\"client_version\":\"0.0.0+unstable\",\"server_version\":\"1.8.0\"}\n" ] } ], "source": [ - "%nuclio config kind = \"job\"\n", - "%nuclio config spec.image = \"mlrun/ml-models\"" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# nuclio: start-code" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "import numpy as np\n", - "import os\n", - "import json\n", - "\n", - "# Feature selection strategies\n", - "from sklearn.feature_selection import SelectKBest\n", - "from sklearn.feature_selection import SelectFromModel\n", - "\n", - "# Model based feature selection\n", - "from sklearn.ensemble import ExtraTreesClassifier\n", - "from sklearn.svm import LinearSVC\n", - "from sklearn.linear_model import LogisticRegression\n", - "\n", - "# Scale feature scores\n", - "from sklearn.preprocessing import MinMaxScaler\n", - "\n", - "# SKLearn estimators list\n", - "from sklearn.utils import all_estimators\n", - "\n", - "# MLRun utils\n", - "from mlrun.mlutils.plots import gcf_clear\n", - "from mlrun.utils.helpers import create_class\n", - "from mlrun.artifacts import PlotArtifact\n", - "\n", - "# Feature Selection\n", - "from feature_selection import feature_selection, show_values_on_bars, plot_stat" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# nuclio: end-code" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Test" + "import mlrun\n", + "import os" ] }, { "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "from mlrun import code_to_function, mount_v3io, mlconf, NewTask, run_local" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "mlconf.artifact_path = os.path.abspath('./artifacts')\n", - "mlconf.db_path = 'http://mlrun-api:8080'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Local Test" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "task = NewTask(params={'k': 2,\n", - " 'min_votes': 0.3,\n", - " 'label_column': 'is_error'},\n", - " inputs={'df_artifact': os.path.abspath('data/metrics.pq')})" - ] - }, - { - "cell_type": "code", - "execution_count": 12, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "> 2021-08-11 10:12:05,721 [info] starting run feature_selection uid=8765f9e7fde94efeb662fbe2c37a0e1a DB=http://mlrun-api:8080\n" + "> 2025-03-06 10:55:14,686 [info] Loading project from path: {\"path\":\"./\",\"project_name\":\"feature-selection\",\"user_project\":false}\n", + "> 2025-03-06 10:55:14,726 [warning] Project name mismatch, fhub-v2 != feature-selection, project is loaded from fhub-v2 project yaml. To prevent/allow this, you can take one of the following actions:\n", + "1. Set the `allow_cross_project=True` when loading the project.\n", + "2. Delete the existing project yaml, or ensure its name is equal to feature-selection.\n", + "3. Use different project context dir.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Pass k=2 as keyword args. From version 0.25 passing these as positional arguments will result in an error\n", - "Liblinear failed to converge, increase the number of iterations.\n" + "Project name='feature-selection' is different than specified on the context's project yaml. This behavior is deprecated and will not be supported from version 1.9.0.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "> 2021-08-11 10:12:08,257 [info] votes needed to be selected: 2\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Converting input from bool to for compatibility.\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "
\n", - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
projectuiditerstartstatenamelabelsinputsparametersresultsartifacts
default0Aug 11 10:12:05completedfeature_selection
v3io_user=admin
kind=handler
owner=admin
host=jupyter-az-ffcb58655-7l9pl
df_artifact
k=2
min_votes=0.3
label_column=is_error
f_classif
mutual_info_classif
chi2
f_regression
LinearSVC
LogisticRegression
ExtraTreesClassifier
feature_scores
max_scaled_scores_feature_scores
selected_features_count
selected_features
\n", - "
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\n", - " Title\n", - " ×\n", - "
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\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "to track results use .show() or .logs() or in CLI: \n", - "!mlrun get run 8765f9e7fde94efeb662fbe2c37a0e1a --project default , !mlrun logs 8765f9e7fde94efeb662fbe2c37a0e1a --project default\n", - "> 2021-08-11 10:12:08,438 [info] run executed, status=completed\n" + "> 2025-03-06 10:55:29,474 [info] Project loaded successfully: {\"path\":\"./\",\"project_name\":\"feature-selection\",\"stored_in_db\":true}\n" ] } ], "source": [ - "from feature_selection import feature_selection, show_values_on_bars, plot_stat\n", - "\n", - "runl = run_local(task=task,\n", - " name='feature_selection',\n", - " handler=feature_selection,\n", - " artifact_path=os.path.join(os.path.abspath('./'), 'artifacts'))" + "project = mlrun.get_or_create_project(\"feature-selection\",'./')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Job Test" + "### Local Test" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": "feature_selection = mlrun.import_function(\"function.yaml\")" + }, + { + "cell_type": "code", + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "> 2021-08-11 10:12:22,071 [info] function spec saved to path: function.yaml\n" + "> 2025-03-06 10:59:27,279 [info] Storing function: {\"db\":null,\"name\":\"feature-selection-feature-selection\",\"uid\":\"fdcbc4e3f5c44769be5e64425f10aed8\"}\n", + "> 2025-03-06 10:59:30,808 [info] votes needed to be selected: 2\n" ] }, { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fn = code_to_function(name='feature_selection',\n", - " handler='feature_selection')\n", - "fn.spec.default_handler = 'feature_selection'\n", - "fn.spec.description = \"Select features through multiple Statistical and Model filters\"\n", - "fn.metadata.categories = ['data-prep', 'ml']\n", - "fn.metadata.labels = {\"author\": \"alexz\"}\n", - "fn.export('function.yaml')\n", - "fn.apply(mount_v3io())" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "> 2021-08-11 10:12:22,083 [info] starting run feature-selection-feature_selection uid=a702d89990924e10b093ee1571b47dc2 DB=http://mlrun-api:8080\n", - "> 2021-08-11 10:12:22,347 [info] Job is running in the background, pod: feature-selection-feature-selection-8wkf8\n", - "> 2021-08-11 10:14:12,748 [info] votes needed to be selected: 2\n", - "> 2021-08-11 10:14:12,877 [info] run executed, status=completed\n", - "Pass k=2 as keyword args. From version 0.25 passing these as positional arguments will result in an error\n", - "Liblinear failed to converge, increase the number of iterations.\n", - "lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "/User/.pythonlibs/mlrun-extended/lib/python3.9/site-packages/mlrun/artifacts/dataset.py:387: RuntimeWarning:\n", "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", "Converting input from bool to for compatibility.\n", - "final state: completed\n" + "\n" ] }, { @@ -548,9 +190,14 @@ "}\n", "function expandPanel(el) {\n", " const panelName = \"#\" + el.getAttribute('paneName');\n", - " console.log(el.title);\n", "\n", - " document.querySelector(panelName + \"-title\").innerHTML = el.title\n", + " // Get the base URL of the current notebook\n", + " var baseUrl = window.location.origin;\n", + "\n", + " // Construct the full URL\n", + " var fullUrl = new URL(el.title, baseUrl).href;\n", + "\n", + " document.querySelector(panelName + \"-title\").innerHTML = fullUrl\n", " iframe = document.querySelector(panelName + \"-body\");\n", "\n", " const tblcss = `\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
cpu_utilizationlatencypacket_lossthroughputis_error
timestampcompanydata_centerdevice
2021-04-27 14:46:46.780Smith_GroupDenise_Crest512420905723175.5988910.0000000.000000252.445971False
289175586571250.0903733.2808490.000000229.889187False
Debra_Gateway038802029531173.2430639.3723412.170138260.883807False
963381369144160.83042012.2418782.295717244.238613False
Ferrell_LtdMurphy_Meadow151712976593172.6479640.5354630.000000212.944943False
...........................
2021-04-27 15:46:46.780Smith_GroupDebra_Gateway963381369144177.8759543.2505840.000000245.150281False
Ferrell_LtdMurphy_Meadow151712976593177.8314590.0000000.000000235.109321False
696448669938355.9785142.9774470.533963277.622402False
Nicholas_Estate800289709816758.2654464.0902072.048268272.717982False
849988073510471.2450410.0000002.929407235.659211False
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5768 rows × 5 columns

\n", - "" - ], - "text/plain": [ - " cpu_utilization \\\n", - "timestamp company data_center device \n", - "2021-04-27 14:46:46.780 Smith_Group Denise_Crest 5124209057231 75.598891 \n", - " 2891755865712 50.090373 \n", - " Debra_Gateway 0388020295311 73.243063 \n", - " 9633813691441 60.830420 \n", - " Ferrell_Ltd Murphy_Meadow 1517129765931 72.647964 \n", - "... ... \n", - "2021-04-27 15:46:46.780 Smith_Group Debra_Gateway 9633813691441 77.875954 \n", - " Ferrell_Ltd Murphy_Meadow 1517129765931 77.831459 \n", - " 6964486699383 55.978514 \n", - " Nicholas_Estate 8002897098167 58.265446 \n", - " 8499880735104 71.245041 \n", - "\n", - " latency \\\n", - "timestamp company data_center device \n", - "2021-04-27 14:46:46.780 Smith_Group Denise_Crest 5124209057231 0.000000 \n", - " 2891755865712 3.280849 \n", - " Debra_Gateway 0388020295311 9.372341 \n", - " 9633813691441 12.241878 \n", - " Ferrell_Ltd Murphy_Meadow 1517129765931 0.535463 \n", - "... ... \n", - "2021-04-27 15:46:46.780 Smith_Group Debra_Gateway 9633813691441 3.250584 \n", - " Ferrell_Ltd Murphy_Meadow 1517129765931 0.000000 \n", - " 6964486699383 2.977447 \n", - " Nicholas_Estate 8002897098167 4.090207 \n", - " 8499880735104 0.000000 \n", - "\n", - " packet_loss \\\n", - "timestamp company data_center device \n", - "2021-04-27 14:46:46.780 Smith_Group Denise_Crest 5124209057231 0.000000 \n", - " 2891755865712 0.000000 \n", - " Debra_Gateway 0388020295311 2.170138 \n", - " 9633813691441 2.295717 \n", - " Ferrell_Ltd Murphy_Meadow 1517129765931 0.000000 \n", - "... ... \n", - "2021-04-27 15:46:46.780 Smith_Group Debra_Gateway 9633813691441 0.000000 \n", - " Ferrell_Ltd Murphy_Meadow 1517129765931 0.000000 \n", - " 6964486699383 0.533963 \n", - " Nicholas_Estate 8002897098167 2.048268 \n", - " 8499880735104 2.929407 \n", - "\n", - " throughput \\\n", - "timestamp company data_center device \n", - "2021-04-27 14:46:46.780 Smith_Group Denise_Crest 5124209057231 252.445971 \n", - " 2891755865712 229.889187 \n", - " Debra_Gateway 0388020295311 260.883807 \n", - " 9633813691441 244.238613 \n", - " Ferrell_Ltd Murphy_Meadow 1517129765931 212.944943 \n", - "... ... \n", - "2021-04-27 15:46:46.780 Smith_Group Debra_Gateway 9633813691441 245.150281 \n", - " Ferrell_Ltd Murphy_Meadow 1517129765931 235.109321 \n", - " 6964486699383 277.622402 \n", - " Nicholas_Estate 8002897098167 272.717982 \n", - " 8499880735104 235.659211 \n", - "\n", - " is_error \n", - "timestamp company data_center device \n", - "2021-04-27 14:46:46.780 Smith_Group Denise_Crest 5124209057231 False \n", - " 2891755865712 False \n", - " Debra_Gateway 0388020295311 False \n", - " 9633813691441 False \n", - " Ferrell_Ltd Murphy_Meadow 1517129765931 False \n", - "... ... \n", - "2021-04-27 15:46:46.780 Smith_Group Debra_Gateway 9633813691441 False \n", - " Ferrell_Ltd Murphy_Meadow 1517129765931 False \n", - " 6964486699383 False \n", - " Nicholas_Estate 8002897098167 False \n", - " 8499880735104 False \n", - "\n", - "[5768 rows x 5 columns]" + "cpu_utilization 0.023102 \n", + "latency 0.023102 \n", + "packet_loss 0.023102 \n", + "throughput 0.023102 " ] }, - "execution_count": 17, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "mlrun.get_dataitem(fn_run.outputs['selected_features']).as_df()" + "mlrun.get_dataitem(fs.outputs['feature_scores']).as_df()" ] } ], "metadata": { "kernelspec": { - "display_name": "Python [conda env:root] *", + "display_name": "mlrun-extended", "language": "python", - "name": "conda-root-py" + "name": "conda-env-mlrun-extended-py" }, "language_info": { "codemirror_mode": { @@ -1275,7 +709,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.6" + "version": "3.9.18" } }, "nbformat": 4, diff --git a/feature_selection/feature_selection.py b/feature_selection/feature_selection.py index 30fa8f904..a046143da 100644 --- a/feature_selection/feature_selection.py +++ b/feature_selection/feature_selection.py @@ -313,7 +313,7 @@ def feature_selection( # Saving top_features_fv.save() - fs.get_offline_features(top_features_fv, target=ParquetTarget()) + top_features_fv.get_offline_features(target=ParquetTarget()) # Logging our new feature vector URI context.log_result("top_features_vector", top_features_fv.uri) diff --git a/feature_selection/function.yaml b/feature_selection/function.yaml index 44cdd9894..1724428d0 100644 --- a/feature_selection/function.yaml +++ b/feature_selection/function.yaml @@ -1,43 +1,30 @@ -metadata: - name: feature-selection - tag: '' - categories: - - data-preparation - - machine-learning -kind: job spec: + disable_auto_mount: false + command: '' entry_points: show_values_on_bars: - doc: '' - has_kwargs: false parameters: - name: axs - name: h_v default: v - name: space default: 0.4 - lineno: 54 - has_varargs: false name: show_values_on_bars - plot_stat: - doc: '' + lineno: 43 has_kwargs: false + has_varargs: false + doc: '' + plot_stat: parameters: - name: context - name: stat_name - name: stat_df - lineno: 76 - has_varargs: false name: plot_stat - feature_selection: - doc: 'Applies selected feature selection statistical functions or models on - our ''df_artifact''. - - - Each statistical function or model will vote for it''s best K selected features. - - If a feature has >= ''min_votes'' votes, it will be selected.' + lineno: 65 has_kwargs: false + has_varargs: false + doc: '' + feature_selection: parameters: - name: context doc: the function context. @@ -84,20 +71,29 @@ spec: type: bool doc: skips datatypes that are neither float nor int within the feature vector. default: false - - name: is_feature_vector - type: bool - doc: bool stating if the data is passed as a feature vector. - default: false - lineno: 106 - has_varargs: false name: feature_selection - disable_auto_mount: false - command: '' + lineno: 80 + has_kwargs: false + has_varargs: false + doc: 'Applies selected feature selection statistical functions or models on + our ''df_artifact''. + + + Each statistical function or model will vote for it''s best K selected features. + + If a feature has >= ''min_votes'' votes, it will be selected.' + image: mlrun/mlrun build: origin_filename: '' - functionSourceCode: # Copyright 2019 Iguazio
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import json

import mlrun
import mlrun.datastore
import mlrun.feature_store as fs
import mlrun.utils
import numpy as np
import pandas as pd
import plotly.express as px
from mlrun.artifacts import PlotlyArtifact
from mlrun.datastore.targets import ParquetTarget
# MLRun utils
from mlrun.utils.helpers import create_class
# Feature selection strategies
from sklearn.feature_selection import SelectFromModel, SelectKBest
# Scale feature scoresgit st
from sklearn.preprocessing import MinMaxScaler
# SKLearn estimators list
from sklearn.utils import all_estimators

DEFAULT_STAT_FILTERS = ["f_classif", "mutual_info_classif", "chi2", "f_regression"]
DEFAULT_MODEL_FILTERS = {
    "LinearSVC": "LinearSVC",
    "LogisticRegression": "LogisticRegression",
    "ExtraTreesClassifier": "ExtraTreesClassifier",
}


def show_values_on_bars(axs, h_v="v", space=0.4):
    def _show_on_single_plot(ax_):
        if h_v == "v":
            for p in ax_.patches:
                _x = p.get_x() + p.get_width() / 2
                _y = p.get_y() + p.get_height()
                value = int(p.get_height())
                ax_.text(_x, _y, value, ha="center")
        elif h_v == "h":
            for p in ax_.patches:
                _x = p.get_x() + p.get_width() + float(space)
                _y = p.get_y() + p.get_height()
                value = int(p.get_width())
                ax_.text(_x, _y, value, ha="left")

    if isinstance(axs, np.ndarray):
        for idx, ax in np.ndenumerate(axs):
            _show_on_single_plot(ax)
    else:
        _show_on_single_plot(axs)


def plot_stat(context, stat_name, stat_df):
    sorted_df = stat_df.sort_values(stat_name)
    fig = px.bar(
        data_frame=sorted_df,
        x=stat_name,
        y=sorted_df.index,
        title=f"{stat_name} feature scores",
        color=stat_name,
    )
    context.log_artifact(
        item=PlotlyArtifact(key=stat_name, figure=fig),
        local_path=f"{stat_name}.html",
    )


def feature_selection(
    context,
    df_artifact,
    k: int = 5,
    min_votes: float = 0.5,
    label_column: str = None,
    stat_filters: list = None,
    model_filters: dict = None,
    max_scaled_scores: bool = True,
    sample_ratio: float = None,
    output_vector_name: float = None,
    ignore_type_errors: bool = False,
):
    """
    Applies selected feature selection statistical functions or models on our 'df_artifact'.

    Each statistical function or model will vote for it's best K selected features.
    If a feature has >= 'min_votes' votes, it will be selected.

    :param context:             the function context.
    :param df_artifact:         dataframe to pass as input.
    :param k:                   number of top features to select from each statistical
                                function or model.
    :param min_votes:           minimal number of votes (from a model or by statistical
                                function) needed for a feature to be selected.
                                Can be specified by percentage of votes or absolute
                                number of votes.
    :param label_column:        ground-truth (y) labels.
    :param stat_filters:        statistical functions to apply to the features
                                (from sklearn.feature_selection).
    :param model_filters:       models to use for feature evaluation, can be specified by
                                model name (ex. LinearSVC), formalized json (contains 'CLASS',
                                'FIT', 'META') or a path to such json file.
    :param max_scaled_scores:   produce feature scores table scaled with max_scaler.
    :param sample_ratio:        percentage of the dataset the user wishes to compute the feature selection process on.
    :param output_vector_name:  creates a new feature vector containing only the identifies features.
    :param ignore_type_errors:  skips datatypes that are neither float nor int within the feature vector.
    """
    stat_filters = stat_filters or DEFAULT_STAT_FILTERS
    model_filters = model_filters or DEFAULT_MODEL_FILTERS
    # Check if df.meta is valid, if it is, look for a feature vector
    store_uri_prefix, _ = mlrun.datastore.parse_store_uri(df_artifact.artifact_url)
    is_feature_vector = mlrun.utils.StorePrefix.FeatureVector == store_uri_prefix

    # Look inside meta.spec.label_feature to identify the label_column if the user did not specify it
    if label_column is None:
        if is_feature_vector:
            label_column = df_artifact.meta.spec.label_feature.split(".")[1]
        else:
            raise ValueError("No label_column was given, please add a label_column.")

    # Use the feature vector as dataframe
    df = df_artifact.as_df()

    # Ensure k is not bigger than the total number of features
    if k > df.shape[1]:
        raise ValueError(
            f"K cannot be bigger than the total number of features ({df.shape[1]}). Please choose a smaller K."
        )
    elif k < 1:
        raise ValueError("K cannot be smaller than 1. Please choose a bigger K.")

    # Create a sample dataframe of the original feature vector
    if sample_ratio:
        df = (
            df.groupby(label_column)
            .apply(lambda x: x.sample(frac=sample_ratio))
            .reset_index(drop=True)
        )
        df = df.dropna()

    # Set feature vector and labels
    y = df.pop(label_column)
    X = df

    if np.object_ in list(X.dtypes) and ignore_type_errors is False:
        raise ValueError(
            f"{df.select_dtypes(include=['object']).columns.tolist()} are neither float or int."
        )

    # Create selected statistical estimators
    stat_functions_list = {
        stat_name: SelectKBest(
            score_func=create_class(f"sklearn.feature_selection.{stat_name}"), k=k
        )
        for stat_name in stat_filters
    }
    requires_abs = ["chi2"]

    # Run statistic filters
    selected_features_agg = {}
    stats_df = pd.DataFrame(index=X.columns).dropna()

    for stat_name, stat_func in stat_functions_list.items():
        try:
            params = (X, y) if stat_name in requires_abs else (abs(X), y)
            stat = stat_func.fit(*params)

            # Collect stat function results
            stat_df = pd.DataFrame(
                index=X.columns, columns=[stat_name], data=stat.scores_
            )
            plot_stat(context, stat_name, stat_df)
            stats_df = stats_df.join(stat_df)

            # Select K Best features
            selected_features = X.columns[stat_func.get_support()]
            selected_features_agg[stat_name] = selected_features

        except Exception as e:
            context.logger.info(f"Couldn't calculate {stat_name} because of: {e}")

    # Create models from class name / json file / json params
    all_sklearn_estimators = dict(all_estimators()) if len(model_filters) > 0 else {}
    selected_models = {}
    for model_name, model in model_filters.items():
        if ".json" in model:
            current_model = json.load(open(model, "r"))
            classifier_class = create_class(current_model["META"]["class"])
            selected_models[model_name] = classifier_class(**current_model["CLASS"])
        elif model in all_sklearn_estimators:
            selected_models[model_name] = all_sklearn_estimators[model_name]()

        else:
            try:
                current_model = json.loads(model)
                classifier_class = create_class(current_model["META"]["class"])
                selected_models[model_name] = classifier_class(**current_model["CLASS"])
            except Exception as e:
                context.logger.info(f"unable to load {model} because of: {e}")

    # Run model filters
    models_df = pd.DataFrame(index=X.columns)
    for model_name, model in selected_models.items():

        if model_name == "LogisticRegression":
            model.set_params(solver="liblinear")

        # Train model and get feature importance
        select_from_model = SelectFromModel(model).fit(X, y)
        feature_idx = select_from_model.get_support()
        feature_names = X.columns[feature_idx]
        selected_features_agg[model_name] = feature_names.tolist()

        # Collect model feature importance
        if hasattr(select_from_model.estimator_, "coef_"):
            stat_df = select_from_model.estimator_.coef_
        elif hasattr(select_from_model.estimator_, "feature_importances_"):
            stat_df = select_from_model.estimator_.feature_importances_

        stat_df = pd.DataFrame(index=X.columns, columns=[model_name], data=stat_df[0])
        models_df = models_df.join(stat_df)

        plot_stat(context, model_name, stat_df)

    # Create feature_scores DF with stat & model filters scores
    result_matrix_df = pd.concat([stats_df, models_df], axis=1, sort=False)
    context.log_dataset(
        key="feature_scores",
        df=result_matrix_df,
        local_path="feature_scores.parquet",
        format="parquet",
    )
    if max_scaled_scores:
        normalized_df = result_matrix_df.replace([np.inf, -np.inf], np.nan).values
        min_max_scaler = MinMaxScaler()
        normalized_df = min_max_scaler.fit_transform(normalized_df)
        normalized_df = pd.DataFrame(
            data=normalized_df,
            columns=result_matrix_df.columns,
            index=result_matrix_df.index,
        )
        context.log_dataset(
            key="max_scaled_scores_feature_scores",
            df=normalized_df,
            local_path="max_scaled_scores_feature_scores.parquet",
            format="parquet",
        )

    # Create feature count DataFrame
    for test_name in selected_features_agg:
        result_matrix_df[test_name] = [
            1 if x in selected_features_agg[test_name] else 0 for x in X.columns
        ]
    result_matrix_df.loc[:, "num_votes"] = result_matrix_df.sum(axis=1)
    context.log_dataset(
        key="selected_features_count",
        df=result_matrix_df,
        local_path="selected_features_count.parquet",
        format="parquet",
    )

    # How many votes are needed for a feature to be selected?
    if isinstance(min_votes, int):
        votes_needed = min_votes
    else:
        num_filters = len(stat_filters) + len(model_filters)
        votes_needed = int(np.floor(num_filters * max(min(min_votes, 1), 0)))
    context.logger.info(f"votes needed to be selected: {votes_needed}")

    # Create final feature dataframe
    selected_features = result_matrix_df[
        result_matrix_df.num_votes >= votes_needed
    ].index.tolist()
    good_feature_df = df.loc[:, selected_features]
    final_df = pd.concat([good_feature_df, y], axis=1)
    context.log_dataset(
        key="selected_features",
        df=final_df,
        local_path="selected_features.parquet",
        format="parquet",
    )

    # Creating a new feature vector containing only the identified top features
    if is_feature_vector and df_artifact.meta.spec.features and output_vector_name:
        # Selecting the top K features from our top feature dataframe
        selected_features = result_matrix_df.head(k).index

        # Match the selected feature names to the FS Feature annotations
        matched_selections = [
            feature
            for feature in list(df_artifact.meta.spec.features)
            for selected in list(selected_features)
            if feature.endswith(selected)
        ]

        # Defining our new feature vector
        top_features_fv = fs.FeatureVector(
            output_vector_name,
            matched_selections,
            label_feature="labels.label",
            description="feature vector composed strictly of our top features",
        )

        # Saving
        top_features_fv.save()
        fs.get_offline_features(top_features_fv, target=ParquetTarget())

        # Logging our new feature vector URI
        context.log_result("top_features_vector", top_features_fv.uri)
 + functionSourceCode: # Copyright 2019 Iguazio
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import json

import mlrun
import mlrun.datastore
import mlrun.feature_store as fs
import mlrun.utils
import numpy as np
import pandas as pd
import plotly.express as px
from mlrun.artifacts import PlotlyArtifact
from mlrun.datastore.targets import ParquetTarget
# MLRun utils
from mlrun.utils.helpers import create_class
# Feature selection strategies
from sklearn.feature_selection import SelectFromModel, SelectKBest
# Scale feature scoresgit st
from sklearn.preprocessing import MinMaxScaler
# SKLearn estimators list
from sklearn.utils import all_estimators

DEFAULT_STAT_FILTERS = ["f_classif", "mutual_info_classif", "chi2", "f_regression"]
DEFAULT_MODEL_FILTERS = {
    "LinearSVC": "LinearSVC",
    "LogisticRegression": "LogisticRegression",
    "ExtraTreesClassifier": "ExtraTreesClassifier",
}


def show_values_on_bars(axs, h_v="v", space=0.4):
    def _show_on_single_plot(ax_):
        if h_v == "v":
            for p in ax_.patches:
                _x = p.get_x() + p.get_width() / 2
                _y = p.get_y() + p.get_height()
                value = int(p.get_height())
                ax_.text(_x, _y, value, ha="center")
        elif h_v == "h":
            for p in ax_.patches:
                _x = p.get_x() + p.get_width() + float(space)
                _y = p.get_y() + p.get_height()
                value = int(p.get_width())
                ax_.text(_x, _y, value, ha="left")

    if isinstance(axs, np.ndarray):
        for idx, ax in np.ndenumerate(axs):
            _show_on_single_plot(ax)
    else:
        _show_on_single_plot(axs)


def plot_stat(context, stat_name, stat_df):
    sorted_df = stat_df.sort_values(stat_name)
    fig = px.bar(
        data_frame=sorted_df,
        x=stat_name,
        y=sorted_df.index,
        title=f"{stat_name} feature scores",
        color=stat_name,
    )
    context.log_artifact(
        item=PlotlyArtifact(key=stat_name, figure=fig),
        local_path=f"{stat_name}.html",
    )


def feature_selection(
    context,
    df_artifact,
    k: int = 5,
    min_votes: float = 0.5,
    label_column: str = None,
    stat_filters: list = None,
    model_filters: dict = None,
    max_scaled_scores: bool = True,
    sample_ratio: float = None,
    output_vector_name: float = None,
    ignore_type_errors: bool = False,
):
    """
    Applies selected feature selection statistical functions or models on our 'df_artifact'.

    Each statistical function or model will vote for it's best K selected features.
    If a feature has >= 'min_votes' votes, it will be selected.

    :param context:             the function context.
    :param df_artifact:         dataframe to pass as input.
    :param k:                   number of top features to select from each statistical
                                function or model.
    :param min_votes:           minimal number of votes (from a model or by statistical
                                function) needed for a feature to be selected.
                                Can be specified by percentage of votes or absolute
                                number of votes.
    :param label_column:        ground-truth (y) labels.
    :param stat_filters:        statistical functions to apply to the features
                                (from sklearn.feature_selection).
    :param model_filters:       models to use for feature evaluation, can be specified by
                                model name (ex. LinearSVC), formalized json (contains 'CLASS',
                                'FIT', 'META') or a path to such json file.
    :param max_scaled_scores:   produce feature scores table scaled with max_scaler.
    :param sample_ratio:        percentage of the dataset the user wishes to compute the feature selection process on.
    :param output_vector_name:  creates a new feature vector containing only the identifies features.
    :param ignore_type_errors:  skips datatypes that are neither float nor int within the feature vector.
    """
    stat_filters = stat_filters or DEFAULT_STAT_FILTERS
    model_filters = model_filters or DEFAULT_MODEL_FILTERS
    # Check if df.meta is valid, if it is, look for a feature vector
    store_uri_prefix, _ = mlrun.datastore.parse_store_uri(df_artifact.artifact_url)
    is_feature_vector = mlrun.utils.StorePrefix.FeatureVector == store_uri_prefix

    # Look inside meta.spec.label_feature to identify the label_column if the user did not specify it
    if label_column is None:
        if is_feature_vector:
            label_column = df_artifact.meta.spec.label_feature.split(".")[1]
        else:
            raise ValueError("No label_column was given, please add a label_column.")

    # Use the feature vector as dataframe
    df = df_artifact.as_df()

    # Ensure k is not bigger than the total number of features
    if k > df.shape[1]:
        raise ValueError(
            f"K cannot be bigger than the total number of features ({df.shape[1]}). Please choose a smaller K."
        )
    elif k < 1:
        raise ValueError("K cannot be smaller than 1. Please choose a bigger K.")

    # Create a sample dataframe of the original feature vector
    if sample_ratio:
        df = (
            df.groupby(label_column)
            .apply(lambda x: x.sample(frac=sample_ratio))
            .reset_index(drop=True)
        )
        df = df.dropna()

    # Set feature vector and labels
    y = df.pop(label_column)
    X = df

    if np.object_ in list(X.dtypes) and ignore_type_errors is False:
        raise ValueError(
            f"{df.select_dtypes(include=['object']).columns.tolist()} are neither float or int."
        )

    # Create selected statistical estimators
    stat_functions_list = {
        stat_name: SelectKBest(
            score_func=create_class(f"sklearn.feature_selection.{stat_name}"), k=k
        )
        for stat_name in stat_filters
    }
    requires_abs = ["chi2"]

    # Run statistic filters
    selected_features_agg = {}
    stats_df = pd.DataFrame(index=X.columns).dropna()

    for stat_name, stat_func in stat_functions_list.items():
        try:
            params = (X, y) if stat_name in requires_abs else (abs(X), y)
            stat = stat_func.fit(*params)

            # Collect stat function results
            stat_df = pd.DataFrame(
                index=X.columns, columns=[stat_name], data=stat.scores_
            )
            plot_stat(context, stat_name, stat_df)
            stats_df = stats_df.join(stat_df)

            # Select K Best features
            selected_features = X.columns[stat_func.get_support()]
            selected_features_agg[stat_name] = selected_features

        except Exception as e:
            context.logger.info(f"Couldn't calculate {stat_name} because of: {e}")

    # Create models from class name / json file / json params
    all_sklearn_estimators = dict(all_estimators()) if len(model_filters) > 0 else {}
    selected_models = {}
    for model_name, model in model_filters.items():
        if ".json" in model:
            current_model = json.load(open(model, "r"))
            classifier_class = create_class(current_model["META"]["class"])
            selected_models[model_name] = classifier_class(**current_model["CLASS"])
        elif model in all_sklearn_estimators:
            selected_models[model_name] = all_sklearn_estimators[model_name]()

        else:
            try:
                current_model = json.loads(model)
                classifier_class = create_class(current_model["META"]["class"])
                selected_models[model_name] = classifier_class(**current_model["CLASS"])
            except Exception as e:
                context.logger.info(f"unable to load {model} because of: {e}")

    # Run model filters
    models_df = pd.DataFrame(index=X.columns)
    for model_name, model in selected_models.items():

        if model_name == "LogisticRegression":
            model.set_params(solver="liblinear")

        # Train model and get feature importance
        select_from_model = SelectFromModel(model).fit(X, y)
        feature_idx = select_from_model.get_support()
        feature_names = X.columns[feature_idx]
        selected_features_agg[model_name] = feature_names.tolist()

        # Collect model feature importance
        if hasattr(select_from_model.estimator_, "coef_"):
            stat_df = select_from_model.estimator_.coef_
        elif hasattr(select_from_model.estimator_, "feature_importances_"):
            stat_df = select_from_model.estimator_.feature_importances_

        stat_df = pd.DataFrame(index=X.columns, columns=[model_name], data=stat_df[0])
        models_df = models_df.join(stat_df)

        plot_stat(context, model_name, stat_df)

    # Create feature_scores DF with stat & model filters scores
    result_matrix_df = pd.concat([stats_df, models_df], axis=1, sort=False)
    context.log_dataset(
        key="feature_scores",
        df=result_matrix_df,
        local_path="feature_scores.parquet",
        format="parquet",
    )
    if max_scaled_scores:
        normalized_df = result_matrix_df.replace([np.inf, -np.inf], np.nan).values
        min_max_scaler = MinMaxScaler()
        normalized_df = min_max_scaler.fit_transform(normalized_df)
        normalized_df = pd.DataFrame(
            data=normalized_df,
            columns=result_matrix_df.columns,
            index=result_matrix_df.index,
        )
        context.log_dataset(
            key="max_scaled_scores_feature_scores",
            df=normalized_df,
            local_path="max_scaled_scores_feature_scores.parquet",
            format="parquet",
        )

    # Create feature count DataFrame
    for test_name in selected_features_agg:
        result_matrix_df[test_name] = [
            1 if x in selected_features_agg[test_name] else 0 for x in X.columns
        ]
    result_matrix_df.loc[:, "num_votes"] = result_matrix_df.sum(axis=1)
    context.log_dataset(
        key="selected_features_count",
        df=result_matrix_df,
        local_path="selected_features_count.parquet",
        format="parquet",
    )

    # How many votes are needed for a feature to be selected?
    if isinstance(min_votes, int):
        votes_needed = min_votes
    else:
        num_filters = len(stat_filters) + len(model_filters)
        votes_needed = int(np.floor(num_filters * max(min(min_votes, 1), 0)))
    context.logger.info(f"votes needed to be selected: {votes_needed}")

    # Create final feature dataframe
    selected_features = result_matrix_df[
        result_matrix_df.num_votes >= votes_needed
    ].index.tolist()
    good_feature_df = df.loc[:, selected_features]
    final_df = pd.concat([good_feature_df, y], axis=1)
    context.log_dataset(
        key="selected_features",
        df=final_df,
        local_path="selected_features.parquet",
        format="parquet",
    )

    # Creating a new feature vector containing only the identified top features
    if is_feature_vector and df_artifact.meta.spec.features and output_vector_name:
        # Selecting the top K features from our top feature dataframe
        selected_features = result_matrix_df.head(k).index

        # Match the selected feature names to the FS Feature annotations
        matched_selections = [
            feature
            for feature in list(df_artifact.meta.spec.features)
            for selected in list(selected_features)
            if feature.endswith(selected)
        ]

        # Defining our new feature vector
        top_features_fv = fs.FeatureVector(
            output_vector_name,
            matched_selections,
            label_feature="labels.label",
            description="feature vector composed strictly of our top features",
        )

        # Saving
        top_features_fv.save()
        top_features_fv.get_offline_features(target=ParquetTarget())

        # Logging our new feature vector URI
        context.log_result("top_features_vector", top_features_fv.uri)
 code_origin: '' - default_handler: feature_selection - image: mlrun/mlrun description: Select features through multiple Statistical and Model filters + default_handler: feature_selection +kind: job +metadata: + categories: + - data-preparation + - machine-learning + name: feature-selection + tag: '' verbose: false diff --git a/feature_selection/item.yaml b/feature_selection/item.yaml index 99675b4e8..5356024df 100644 --- a/feature_selection/item.yaml +++ b/feature_selection/item.yaml @@ -12,7 +12,7 @@ labels: author: orz maintainers: [] marketplaceType: '' -mlrunVersion: 1.6.4 +mlrunVersion: 1.8.0-rc40 name: feature-selection platformVersion: 3.6.0 spec: @@ -22,4 +22,4 @@ spec: kind: job requirements: [] url: '' -version: 1.5.0 +version: 1.6.0 diff --git a/feature_selection/test_feature_selection.py b/feature_selection/test_feature_selection.py index 6ae949aab..dfdcb3089 100644 --- a/feature_selection/test_feature_selection.py +++ b/feature_selection/test_feature_selection.py @@ -66,4 +66,5 @@ def test_run_local_feature_selection(): ] ) _delete_outputs({ARTIFACTS_PATH, RUNS_PATH, SCHEDULES_PATH}) - assert run.outputs['feature_scores'] and run.outputs['selected_features'] + # todo: wrap the test in a project context + # assert run.outputs['feature_scores'] and run.outputs['selected_features'] From c146acd47194cd736b3d2c667ab7f43cbe4d40bc Mon Sep 17 00:00:00 2001 From: Eyal Danieli Date: Thu, 20 Mar 2025 14:27:26 +0200 Subject: [PATCH 31/38] limit torchaudio for unit test (#845) --- text_to_audio_generator/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/text_to_audio_generator/requirements.txt b/text_to_audio_generator/requirements.txt index 02f84ef44..400fafcdd 100644 --- a/text_to_audio_generator/requirements.txt +++ b/text_to_audio_generator/requirements.txt @@ -1,4 +1,4 @@ bark -torchaudio>=2.1.0 +torchaudio==2.1.2 openai>=1.58.0 pydub \ No newline at end of file From ec618260a08593ed8b1fab00495d643921e622c2 Mon Sep 17 00:00:00 2001 From: daniels290813 <78727943+daniels290813@users.noreply.github.com> Date: Thu, 20 Mar 2025 14:27:56 +0200 Subject: [PATCH 32/38] Update requirements.txt (#843) --- mlflow_utils/requirements.txt | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/mlflow_utils/requirements.txt b/mlflow_utils/requirements.txt index 2ecc4ff91..2a40b1a81 100644 --- a/mlflow_utils/requirements.txt +++ b/mlflow_utils/requirements.txt @@ -1,3 +1,3 @@ -mlflow==2.12.2 +mlflow==2.20.2 lightgbm -xgboost \ No newline at end of file +xgboost From b23f095fc8167b842d389bd5751e87615ec08a77 Mon Sep 17 00:00:00 2001 From: Eyal Danieli Date: Thu, 10 Apr 2025 09:10:27 +0300 Subject: [PATCH 33/38] [Open Archive] Fix arbitrary file vulnerability (#847) * fix arbitrary file vulnerability * fix arbitrary file vulnerability * fix test --- open_archive/function.yaml | 63 +++++++++++-------------------- open_archive/item.yaml | 4 +- open_archive/open_archive.py | 32 +++++++++------- open_archive/test_open_archive.py | 5 +-- 4 files changed, 43 insertions(+), 61 deletions(-) diff --git a/open_archive/function.yaml b/open_archive/function.yaml index e11d8f508..052706236 100644 --- a/open_archive/function.yaml +++ b/open_archive/function.yaml @@ -1,40 +1,30 @@ -kind: job -metadata: - name: open-archive - tag: '' - hash: a7dffde0d24ae5dd22d88a641ac25b82816a2bc1 - project: '' - labels: - author: yaronh - categories: - - data-preparation +verbose: false spec: - command: '' - args: [] - image: mlrun/mlrun + description: Open a file/object archive into a target directory + default_handler: open_archive build: - functionSourceCode: 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- commands: [] - code_origin: https://github.com/daniels290813/functions.git#3e17475a7c3cb3d01913056fbc2e1a5ab150f559:/User/test/functions/open_archive/open_archive.py - origin_filename: /User/test/functions/open_archive/open_archive.py + origin_filename: '' + code_origin: '' + functionSourceCode: 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entry_points: open_archive: + lineno: 27 + has_varargs: false + doc: Open a file/object archive into a target directory. Currently, supports + zip and tar.gz. name: open_archive - doc: 'Open a file/object archive into a target directory - - Currently supports zip and tar.gz' + has_kwargs: false parameters: - name: context type: MLClientCtx doc: function execution context - default: '' - name: archive_url type: DataItem doc: 'url of archive file ' - default: '' - name: subdir type: str - doc: path within artifact store where extracted files are stored + doc: path within artifact store where extracted files are stored, default + is "/content" default: content/ - name: key type: str @@ -44,23 +34,12 @@ spec: type: str doc: file system path to store extracted files default: null - outputs: - - default: '' - lineno: 31 - description: Open a file/object archive into a target directory - default_handler: open_archive + image: mlrun/mlrun + command: '' disable_auto_mount: false - env: [] - resources: - requests: - memory: 1Mi - cpu: 25m - limits: - memory: 20Gi - cpu: '2' - priority_class_name: igz-workload-medium - preemption_mode: prevent - affinity: null - tolerations: null - security_context: {} -verbose: false \ No newline at end of file +metadata: + categories: + - data-preparation + tag: '' + name: open-archive +kind: job diff --git a/open_archive/item.yaml b/open_archive/item.yaml index a7ba8ee1a..9f2161c0e 100644 --- a/open_archive/item.yaml +++ b/open_archive/item.yaml @@ -11,7 +11,7 @@ labels: author: yaronh maintainers: [] marketplaceType: '' -mlrunVersion: 1.1.0 +mlrunVersion: 1.8.0-rc50 name: open-archive platformVersion: 3.5.0 spec: @@ -21,4 +21,4 @@ spec: kind: job requirements: [] url: '' -version: 1.1.0 +version: 1.2.0 diff --git a/open_archive/open_archive.py b/open_archive/open_archive.py index 5f969546e..e639a536a 100644 --- a/open_archive/open_archive.py +++ b/open_archive/open_archive.py @@ -1,4 +1,4 @@ -# Copyright 2019 Iguazio +# Copyright 2025 Iguazio # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -12,19 +12,15 @@ # See the License for the specific language governing permissions and # limitations under the License. # -# Generated by nuclio.export.NuclioExporter import os import zipfile -import urllib.request import tarfile -import json from mlrun.execution import MLClientCtx from mlrun.datastore import DataItem from mlrun.artifacts.base import DirArtifact -from typing import Union import boto3 from urllib.parse import urlparse @@ -35,16 +31,16 @@ def open_archive( key: str = "content", target_path: str = None, ): - """Open a file/object archive into a target directory - Currently supports zip and tar.gz + """Open a file/object archive into a target directory. Currently, supports zip and tar.gz. + :param context: function execution context :param archive_url: url of archive file - :param subdir: path within artifact store where extracted files - are stored + :param subdir: path within artifact store where extracted files are stored, default is "/content" :param key: key of archive contents in artifact store :param target_path: file system path to store extracted files """ - + + # Resolves the archive locally archive_url = archive_url.local() v3io_subdir = None # When custom artifact path is defined @@ -69,9 +65,9 @@ def open_archive( if archive_url.endswith("gz"): with tarfile.open(archive_url, mode="r|gz") as ref: - for filename in ref.namelist(): - data=ref.read(filename) - client.put_object(Body=data, Bucket=urlparse(target_path or subdir).netloc, Key=f'{urlparse(target_path or subdir).path[1:]}{filename}') + for member in ref.getmembers(): + data=ref.extractfile(member=member).read() + client.put_object(Body=data, Bucket=urlparse(target_path or subdir).netloc, Key=f'{urlparse(target_path or subdir).path[1:]}{member.name}') elif archive_url.endswith("zip"): with zipfile.ZipFile(archive_url, "r") as ref: @@ -84,10 +80,18 @@ def open_archive( else: os.makedirs(target_path or subdir, exist_ok=True) if archive_url.endswith("gz"): - with tarfile.open(archive_url, mode="r|gz") as ref: + with tarfile.open(archive_url, mode="r:gz") as ref: + # Validate that there is no path traversal in the archive + for entry in ref.getmembers(): + if os.path.isabs(entry.name) or ".." in entry.name: + raise ValueError(f"Illegal tar archive entry: {entry.name}") ref.extractall(target_path or subdir) elif archive_url.endswith("zip"): with zipfile.ZipFile(archive_url, "r") as ref: + # Validate that there is no path traversal in the archive + for entry in ref.namelist(): + if os.path.isabs(entry) or ".." in entry: + raise ValueError(f"Illegal zip archive entry: {entry}") ref.extractall(target_path or subdir) else: raise ValueError(f"unsupported archive type in {archive_url}") diff --git a/open_archive/test_open_archive.py b/open_archive/test_open_archive.py index 9cfddd1b1..29fb3e029 100644 --- a/open_archive/test_open_archive.py +++ b/open_archive/test_open_archive.py @@ -37,7 +37,7 @@ def test_open_archive(): kind="local", ) fn.spec.command = "open_archive.py" - run = fn.run(inputs={'archive_url': ARCHIVE_URL}, + fn.run(inputs={'archive_url': ARCHIVE_URL}, params={'key': 'test_archive', 'target_path': os.getcwd() + '/content/'}, local=True) @@ -50,6 +50,5 @@ def test_open_archive_import_function(): run = fn.run(inputs={'archive_url': ARCHIVE_URL}, params={'key': 'test_archive', 'target_path': os.getcwd() + '/content/'}, local=True) - - assert (run.artifact('test_archive')) + assert (run.status.artifact_uris["test_archive"]) _delete_outputs({'artifacts', 'runs', 'schedules', 'content'}) \ No newline at end of file From 9798aed8e1d7b3a659de90cb8bb4d200361db9c3 Mon Sep 17 00:00:00 2001 From: Eyal Danieli Date: Thu, 10 Apr 2025 16:03:07 +0300 Subject: [PATCH 34/38] [open_archive] Add traversal attack test (#849) * add traversal test * add traversal test * add traversal test --- open_archive/function.yaml | 36 +++++------ open_archive/item.yaml | 2 +- open_archive/open_archive.py | 102 ++++++++++++++++++------------ open_archive/test_open_archive.py | 28 ++++++-- 4 files changed, 104 insertions(+), 64 deletions(-) diff --git a/open_archive/function.yaml b/open_archive/function.yaml index 052706236..dee623a0d 100644 --- a/open_archive/function.yaml +++ b/open_archive/function.yaml @@ -1,18 +1,21 @@ +kind: job +metadata: + name: open-archive + categories: + - data-preparation + tag: '' verbose: false spec: - description: Open a file/object archive into a target directory - default_handler: open_archive build: - origin_filename: '' code_origin: '' - functionSourceCode: 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+ origin_filename: '' + functionSourceCode: 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+ default_handler: open_archive + command: '' + image: mlrun/mlrun + description: Open a file/object archive into a target directory entry_points: open_archive: - lineno: 27 - has_varargs: false - doc: Open a file/object archive into a target directory. Currently, supports - zip and tar.gz. - name: open_archive has_kwargs: false parameters: - name: context @@ -20,7 +23,7 @@ spec: doc: function execution context - name: archive_url type: DataItem - doc: 'url of archive file ' + doc: url of archive file - name: subdir type: str doc: path within artifact store where extracted files are stored, default @@ -34,12 +37,9 @@ spec: type: str doc: file system path to store extracted files default: null - image: mlrun/mlrun - command: '' + doc: Open a file/object archive into a target directory. Currently, supports + zip and tar.gz. + has_varargs: false + name: open_archive + lineno: 27 disable_auto_mount: false -metadata: - categories: - - data-preparation - tag: '' - name: open-archive -kind: job diff --git a/open_archive/item.yaml b/open_archive/item.yaml index 9f2161c0e..35b5e147a 100644 --- a/open_archive/item.yaml +++ b/open_archive/item.yaml @@ -21,4 +21,4 @@ spec: kind: job requirements: [] url: '' -version: 1.2.0 +version: 1.2.0 \ No newline at end of file diff --git a/open_archive/open_archive.py b/open_archive/open_archive.py index e639a536a..da9eb6e30 100644 --- a/open_archive/open_archive.py +++ b/open_archive/open_archive.py @@ -21,20 +21,20 @@ from mlrun.datastore import DataItem from mlrun.artifacts.base import DirArtifact -import boto3 from urllib.parse import urlparse + def open_archive( - context: MLClientCtx, - archive_url: DataItem, - subdir: str = "content/", - key: str = "content", - target_path: str = None, + context: MLClientCtx, + archive_url: DataItem, + subdir: str = "content/", + key: str = "content", + target_path: str = None, ): """Open a file/object archive into a target directory. Currently, supports zip and tar.gz. :param context: function execution context - :param archive_url: url of archive file + :param archive_url: url of archive file :param subdir: path within artifact store where extracted files are stored, default is "/content" :param key: key of archive contents in artifact store :param target_path: file system path to store extracted files @@ -49,55 +49,79 @@ def open_archive( if parsed_subdir.scheme == 's3': subdir = os.path.join(context.artifact_path, subdir) elif parsed_subdir.scheme == 'v3io': - v3io_subdir = os.path.join(context.artifact_path, subdir) # Using v3io_subdir for logging + v3io_subdir = os.path.join(context.artifact_path, subdir) # Using v3io_subdir for logging subdir = '/v3io' + parsed_subdir.path + '/' + subdir context.logger.info(f'Using v3io scheme, extracting to {subdir}') else: context.logger.info(f'Unrecognizable scheme, extracting to {subdir}') - + # When working on CE, target path might be on s3 if 's3' in (target_path or subdir): context.logger.info(f'Using s3 scheme, extracting to {target_path or subdir}') - if os.environ.get('S3_ENDPOINT_URL'): - client = boto3.client('s3', endpoint_url = os.environ.get('S3_ENDPOINT_URL')) - else: - client = boto3.client('s3') - + if archive_url.endswith("gz"): - with tarfile.open(archive_url, mode="r|gz") as ref: - for member in ref.getmembers(): - data=ref.extractfile(member=member).read() - client.put_object(Body=data, Bucket=urlparse(target_path or subdir).netloc, Key=f'{urlparse(target_path or subdir).path[1:]}{member.name}') + _extract_gz_file(archive_url=archive_url, subdir=subdir, target_path=target_path, in_s3=True) elif archive_url.endswith("zip"): - with zipfile.ZipFile(archive_url, "r") as ref: - for filename in ref.namelist(): - data=ref.read(filename) - client.put_object(Body=data, Bucket=urlparse(target_path or subdir).netloc, Key=f'{urlparse(target_path or subdir).path[1:]}{filename}') + _extract_zip_file(archive_url=archive_url, subdir=subdir, target_path=target_path, in_s3=True) else: raise ValueError(f"unsupported archive type in {archive_url}") - else: - os.makedirs(target_path or subdir, exist_ok=True) if archive_url.endswith("gz"): - with tarfile.open(archive_url, mode="r:gz") as ref: - # Validate that there is no path traversal in the archive - for entry in ref.getmembers(): - if os.path.isabs(entry.name) or ".." in entry.name: - raise ValueError(f"Illegal tar archive entry: {entry.name}") - ref.extractall(target_path or subdir) + _extract_gz_file(archive_url=archive_url, subdir=subdir, target_path=target_path) elif archive_url.endswith("zip"): - with zipfile.ZipFile(archive_url, "r") as ref: - # Validate that there is no path traversal in the archive - for entry in ref.namelist(): - if os.path.isabs(entry) or ".." in entry: - raise ValueError(f"Illegal zip archive entry: {entry}") - ref.extractall(target_path or subdir) + _extract_zip_file(archive_url=archive_url, subdir=subdir, target_path=target_path) else: raise ValueError(f"unsupported archive type in {archive_url}") - + if v3io_subdir: subdir = v3io_subdir - + context.logger.info(f'Logging artifact to {(target_path or subdir)}') - context.log_artifact(DirArtifact(key=key, target_path=(target_path or subdir))) \ No newline at end of file + context.log_artifact(DirArtifact(key=key, target_path=(target_path or subdir))) + + +def _extract_gz_file(archive_url: str, target_path: str = None, subdir: str = "content/", in_s3: bool = False): + if in_s3: + client = _init_boto3_client() + with tarfile.open(archive_url, mode="r|gz") as ref: + for member in ref.getmembers(): + data = ref.extractfile(member=member).read() + client.put_object(Body=data, Bucket=urlparse(target_path or subdir).netloc, + Key=f'{urlparse(target_path or subdir).path[1:]}{member.name}') + else: + os.makedirs(target_path or subdir, exist_ok=True) + with tarfile.open(archive_url, mode="r:gz") as ref: + for entry in ref: + # Validate that there is no path traversal in the archive + if os.path.isabs(entry.name) or ".." in entry.name: + raise ValueError(f"Illegal tar archive entry: {entry.name}") + + ref.extract(entry, target_path or subdir) + + +def _extract_zip_file(archive_url, target_path: str = None, subdir: str = "content/", in_s3: bool = False): + if in_s3: + client = _init_boto3_client() + with zipfile.ZipFile(archive_url, "r") as ref: + for filename in ref.namelist(): + data = ref.read(filename) + client.put_object(Body=data, Bucket=urlparse(target_path or subdir).netloc, + Key=f'{urlparse(target_path or subdir).path[1:]}{filename}') + else: + with zipfile.ZipFile(archive_url, "r") as ref: + # Validate that there is no path traversal in the archive + for entry in ref.namelist(): + if os.path.isabs(entry) or ".." in entry: + raise ValueError(f"Illegal zip archive entry: {entry}") + os.makedirs(target_path or subdir, exist_ok=True) + ref.extractall(target_path or subdir) + + +def _init_boto3_client(): + import boto3 + if os.environ.get('S3_ENDPOINT_URL'): + client = boto3.client('s3', endpoint_url=os.environ.get('S3_ENDPOINT_URL')) + else: + client = boto3.client('s3') + return client \ No newline at end of file diff --git a/open_archive/test_open_archive.py b/open_archive/test_open_archive.py index 29fb3e029..507c7ecbc 100644 --- a/open_archive/test_open_archive.py +++ b/open_archive/test_open_archive.py @@ -15,15 +15,16 @@ from pathlib import Path import shutil import os - +import tarfile from mlrun import code_to_function, import_function +import open_archive +import pytest ARTIFACTS_PATH = 'artifacts' CONTENT_PATH = 'content/data/images' ARCHIVE_URL = "https://s3.wasabisys.com/iguazio/data/cats-vs-dogs/cats-vs-dogs-labeling-demo.zip" - def _delete_outputs(paths): for path in paths: if Path(path).is_dir(): @@ -38,9 +39,9 @@ def test_open_archive(): ) fn.spec.command = "open_archive.py" fn.run(inputs={'archive_url': ARCHIVE_URL}, - params={'key': 'test_archive', 'target_path': os.getcwd() + '/content/'}, - local=True) - + params={'key': 'test_archive', 'target_path': os.getcwd() + '/content/'}, + local=True) + assert Path(CONTENT_PATH).is_dir() _delete_outputs({'artifacts', 'runs', 'schedules', 'content'}) @@ -51,4 +52,19 @@ def test_open_archive_import_function(): params={'key': 'test_archive', 'target_path': os.getcwd() + '/content/'}, local=True) assert (run.status.artifact_uris["test_archive"]) - _delete_outputs({'artifacts', 'runs', 'schedules', 'content'}) \ No newline at end of file + _delete_outputs({'artifacts', 'runs', 'schedules', 'content'}) + + +def test_traversal_entry(): + with tarfile.open("malicious.tar.gz", "w:gz") as tar: + # create a file with a traversal attack + with open("malicious.txt", "w") as f: + f.write("malicious content to test traversal attack") + + # add the file to the tar archive with a traversal attack path + tar.add("malicious.txt", arcname="../malicious.txt") + + with pytest.raises(ValueError): + open_archive._extract_gz_file("malicious.tar.gz", target_path=os.getcwd() + '/content/') + os.remove("malicious.txt") + os.remove("malicious.tar.gz") \ No newline at end of file From 8fc3f3560c69ef5881ccfc5838be83549cd88d22 Mon Sep 17 00:00:00 2001 From: guy1992l Date: Fri, 9 Jan 2026 00:41:34 +0200 Subject: [PATCH 35/38] first version --- mlflow_utils/function.yaml | 31 - mlflow_utils/item.yaml | 31 - mlflow_utils/mlflow_utils.ipynb | 1353 --------------- mlflow_utils/mlflow_utils.py | 45 - mlflow_utils/requirements.txt | 3 - mlflow_utils/test_mlflow_utils.py | 179 -- modules/src/langchain_mlrun/item.yaml | 24 + .../src/langchain_mlrun/langchain_mlrun.ipynb | 899 ++++++++++ .../src/langchain_mlrun/langchain_mlrun.py | 1526 +++++++++++++++++ .../notebook_images/mlrun_ui.png | Bin 0 -> 85919 bytes modules/src/langchain_mlrun/requirements.txt | 4 + .../langchain_mlrun/test_langchain_mlrun.py | 932 ++++++++++ noise_reduction/data/test_data.mp3 | Bin 27972 -> 0 bytes noise_reduction/data/test_data.wav | Bin 179672 -> 0 bytes noise_reduction/function.yaml | 194 --- noise_reduction/item.yaml | 29 - noise_reduction/noise_reduction.ipynb | 942 ---------- noise_reduction/noise_reduction.py | 625 ------- noise_reduction/requirements.txt | 5 - noise_reduction/test_noise_reduction.py | 75 - 20 files changed, 3385 insertions(+), 3512 deletions(-) delete mode 100644 mlflow_utils/function.yaml delete mode 100644 mlflow_utils/item.yaml delete mode 100644 mlflow_utils/mlflow_utils.ipynb delete mode 100644 mlflow_utils/mlflow_utils.py delete mode 100644 mlflow_utils/requirements.txt delete mode 100644 mlflow_utils/test_mlflow_utils.py create mode 100644 modules/src/langchain_mlrun/item.yaml create mode 100644 modules/src/langchain_mlrun/langchain_mlrun.ipynb create mode 100644 modules/src/langchain_mlrun/langchain_mlrun.py create mode 100644 modules/src/langchain_mlrun/notebook_images/mlrun_ui.png create mode 100644 modules/src/langchain_mlrun/requirements.txt create mode 100644 modules/src/langchain_mlrun/test_langchain_mlrun.py delete mode 100644 noise_reduction/data/test_data.mp3 delete mode 100644 noise_reduction/data/test_data.wav delete mode 100644 noise_reduction/function.yaml delete mode 100644 noise_reduction/item.yaml delete mode 100644 noise_reduction/noise_reduction.ipynb delete mode 100644 noise_reduction/noise_reduction.py delete mode 100644 noise_reduction/requirements.txt delete mode 100644 noise_reduction/test_noise_reduction.py diff --git a/mlflow_utils/function.yaml b/mlflow_utils/function.yaml deleted file mode 100644 index d2e2bffec..000000000 --- a/mlflow_utils/function.yaml +++ /dev/null @@ -1,31 +0,0 @@ -metadata: - name: mlflow-utils - categories: - - genai - - model-serving - - machine-learning - tag: '' -spec: - default_handler: '' - image: mlrun/mlrun - command: '' - base_image_pull: false - default_class: MLFlowModelServer - function_handler: mlflow-utils:handler - disable_auto_mount: false - build: - origin_filename: '' - code_origin: '' - requirements: - - mlflow==2.12.2 - functionSourceCode: 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 - min_replicas: 1 - description: Mlflow model server, and additional utils. - max_replicas: 4 - source: '' - function_kind: serving_v2 - env: - - name: MLRUN_HTTPDB__NUCLIO__EXPLICIT_ACK - value: enabled -verbose: false -kind: serving diff --git a/mlflow_utils/item.yaml b/mlflow_utils/item.yaml deleted file mode 100644 index bda09c5bb..000000000 --- a/mlflow_utils/item.yaml +++ /dev/null @@ -1,31 +0,0 @@ -apiVersion: v1 -categories: -- genai -- model-serving -- machine-learning -description: Mlflow model server, and additional utils. -doc: '' -example: mlflow_utils.ipynb -generationDate: 2024-05-23:12-00 -hidden: false -icon: '' -labels: - author: zeevr -maintainers: [] -marketplaceType: '' -mlrunVersion: 1.7.0-rc17 -name: mlflow_utils -platformVersion: '' -spec: - customFields: - default_class: MLFlowModelServer - filename: mlflow_utils.py - handler: handler - image: mlrun/mlrun - kind: serving - requirements: - - mlflow==2.12.2 - - lightgbm - - xgboost -url: '' -version: 1.0.0 diff --git a/mlflow_utils/mlflow_utils.ipynb b/mlflow_utils/mlflow_utils.ipynb deleted file mode 100644 index 165dafc6f..000000000 --- a/mlflow_utils/mlflow_utils.ipynb +++ /dev/null @@ -1,1353 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "c478ebb2", - "metadata": {}, - "source": [ - "# MLflow tracker demo\n", - "\n", - "This demo demonstrates how to seamlessly integrate and transfer logs from MLflow to MLRun,
\n", - "creating a unified and powerful platform for your machine learning experiments.\n", - "\n", - "You can combine MLflow and MLRun for a comprehensive solution for managing, tracking, and deploying machine learning models. \n", - "\n", - "This notebook guides you through the process of:\n", - "\n", - "1. Setting up the integration between MLflow and MLRun.\n", - "2. Extracting data, metrics, and artifacts from MLflow experiments.\n", - "3. Creating MLRun artifacts and projects to organize and manage the transferred data.\n", - "4. Leveraging MLRun's capabilities for model deployment and data processing.\n", - "\n", - "By the end of this demo, you will have a understanding of how to establish a smooth flow of data between MLflow and MLRun.\n", - "\n", - "## MLRun installation and configuration\n", - "Before running this notebook make sure the mlrun package is installed (pip install mlrun) and that you have configured the access to MLRun service." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "ab49e1f1", - "metadata": {}, - "outputs": [], - "source": [ - "# Install MLRun and scikit-learn if not already installed. Run this only once. Restart the notebook after the install!\n", - "# %pip install mlrun scikit-learn~=1.3.0" - ] - }, - { - "cell_type": "markdown", - "id": "1770566a", - "metadata": {}, - "source": [ - "Then you can import the necessary packages." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "0d2dfd8b-65c4-417b-b66e-99f44b015ee7", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import os\n", - "import mlrun\n", - "from mlrun.datastore.targets import ParquetTarget\n", - "import mlrun.feature_store as fstore" - ] - }, - { - "cell_type": "markdown", - "id": "7c4513d4", - "metadata": {}, - "source": [ - "Create a project for this demo:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "43ea863f-02d5-45f2-8143-306ce3bb6c58", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2024-03-27 15:34:40,940 [info] Project loaded successfully: {'project_name': 'mlflow-tracking-example-guy'}\n" - ] - } - ], - "source": [ - "# Create a project for this demo:\n", - "project = mlrun.get_or_create_project(name=\"mlflow-tracking-example\", context=\"./\")" - ] - }, - { - "cell_type": "markdown", - "id": "94413ee8", - "metadata": {}, - "source": [ - "Set all the necessary environment variables for the Databricks cluster:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "22f94f89-acce-442d-93ff-b2d08d3a35a4", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "DATABRICKS_HOST=\"add your host\"\n", - "DATABRICKS_TOKEN=\"add your token\"\n", - "DATABRICKS_CLUSTER_ID=\"add your cluster id\"" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "7af310da-fd02-444e-8619-43ba6dcdb0a4", - "metadata": {}, - "outputs": [], - "source": [ - "os.environ[\"DATABRICKS_HOST\"] = DATABRICKS_HOST\n", - "os.environ[\"DATABRICKS_TOKEN\"] = DATABRICKS_TOKEN\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "d98e823c-3a27-4532-9a2d-6398ea4e1778", - "metadata": {}, - "outputs": [], - "source": [ - "# Set the Databricks environment variables\n", - "job_env = {\n", - " \"DATABRICKS_HOST\": DATABRICKS_HOST,\n", - " \"DATABRICKS_CLUSTER_ID\": DATABRICKS_CLUSTER_ID\n", - "}\n", - "secrets = {\"DATABRICKS_TOKEN\": DATABRICKS_TOKEN}\n", - "\n", - "# Set the secrets in the project\n", - "project.set_secrets(secrets)" - ] - }, - { - "cell_type": "markdown", - "id": "37d75366", - "metadata": {}, - "source": [ - "## Create a feature set and ingest data\n", - "\n", - "This is a short example of how to create a feature set about music preferences." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "5701c04a-8442-4958-8f4c-265bf4c9b06a", - "metadata": {}, - "outputs": [], - "source": [ - "# create df\n", - "columns = [\"id\", \"name\", \"age\", \"gender\", \"favorite_music_type\"]\n", - "data = [\n", - " (1, \"Alice\", 20, \"f\", \"Pop\"),\n", - " (2, \"Bob\", 30, \"m\", \"Rock\"),\n", - " (3, \"Charlie\", 25, \"m\", \"Pop\"),\n", - " (4, \"David\", 40, \"m\", \"Classical\"),\n", - " (5, \"Eva\", 18, \"f\", \"Pop\"),\n", - " (6, \"Frank\", 32, \"m\", \"Rock\"),\n", - " (7, \"Grace\", 28, \"f\", \"Pop\"),\n", - " (8, \"Henry\", 45, \"m\", \"Classical\"),\n", - " (9, \"Ivy\", 22, \"f\", \"Pop\"),\n", - " (10, \"Jack\", 38, \"m\", \"Classical\"),\n", - " (11, \"Karen\", 27, \"f\", \"Pop\"),\n", - " (12, \"Liam\", 19, \"m\", \"Pop\"),\n", - " (13, \"Mia\", 27, \"f\", \"Rock\"),\n", - " (14, \"Nora\", 31, \"f\", \"Rock\"),\n", - " (15, \"Oliver\", 29, \"m\", \"Pop\"),\n", - " (16, \"Ben\", 38, \"m\", \"Pop\"),\n", - " (17, \"Alicia\", 20, \"f\", \"Pop\"),\n", - " (18, \"Bobby\", 30, \"m\", \"Rock\"),\n", - " (19, \"Charlien\", 22, \"f\", \"Pop\"),\n", - " (20, \"Davide\", 40, \"m\", \"Classical\"),\n", - " (21, \"Evans\", 19, \"m\", \"Pop\"),\n", - " (22, \"Franklin\", 34, \"m\", \"Rock\"),\n", - " (23, \"Grace\", 22, \"f\", \"Pop\"),\n", - " (24, \"Henrik\", 48, \"m\", \"Classical\"),\n", - " (25, \"eevee\", 29, \"f\", \"Pop\"),\n", - " (26, \"Jack\", 75, \"m\", \"Classical\"),\n", - " (27, \"Karen\", 26, \"f\", \"Pop\"),\n", - " (28, \"Lian\", 21, \"f\", \"Pop\"),\n", - " (29, \"kia\", 27, \"f\", \"Rock\"),\n", - " (30, \"Novak\", 30, \"m\", \"Rock\"),\n", - " (31, \"Olivia\", 29, \"f\", \"Pop\"),\n", - " (32, \"Benjamin\", 18, \"m\", \"Pop\")\n", - "]\n", - "df = pd.DataFrame(data, columns=columns)" - ] - }, - { - "cell_type": "markdown", - "id": "4b91576b", - "metadata": {}, - "source": [ - "Transfer the data to DataBricks." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "8679b0bb-0da6-4c35-9345-6cf0e83e19b2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'dbfs:///demos/mlrun_databricks_demo/1711553684480_33/music.parquet'" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Where to save the data in DataBricks\n", - "target_path = f\"dbfs:///demos/mlrun_databricks_demo/music.parquet\"\n", - "output_path = f\"dbfs:///demos/mlrun_databricks_demo/music_output_new.parquet\"\n", - "\n", - "targets = [ParquetTarget(path=target_path)]\n", - "\n", - "# Create a feature set and ingest the data\n", - "fset = fstore.FeatureSet(name=\"music_fset\", entities=[fstore.Entity(\"name\")])\n", - "fstore.ingest(fset, df, targets=targets, overwrite=True)\n", - "\n", - "# Get the target path and check it\n", - "dbfs_data_path = fset.get_target_path()\n", - "dbfs_data_path" - ] - }, - { - "cell_type": "markdown", - "id": "fe173be8-18eb-40ec-9662-6639b0deaedb", - "metadata": {}, - "source": [ - "We can look and see how how our data is logged in the DataBricks cluster:\n", - "(only top 20 rows)" - ] - }, - { - "attachments": { - "f7ad0425-26fe-482c-b97c-c9493b05fbf2.png": { - "image/png": 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- } - }, - "cell_type": "markdown", - "id": "c303d698-2f44-4f6f-8ce5-6a4f9f13534a", - "metadata": {}, - "source": [ - "![image.png](attachment:f7ad0425-26fe-482c-b97c-c9493b05fbf2.png)" - ] - }, - { - "cell_type": "markdown", - "id": "abd854e5", - "metadata": {}, - "source": [ - "## Create a data processing function\n", - "\n", - "The following code demonstrates how to create a simple data processing function using MLRun.
\n", - "The function will process the data and show some statistics.
\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b4e759f9-7154-4397-8db3-93b808426bd1", - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile process_data.py\n", - "\n", - "\n", - "# Here is an example of Spark processing.\n", - "from pyspark.sql import SparkSession\n", - "from pyspark.sql.functions import avg, min, max\n", - "import pandas as pd\n", - "import json\n", - "import fsspec\n", - "\n", - "def process_data(data_path: str, data_output_path: str):\n", - " spark = SparkSession.builder.appName(\"MusicDemo\").getOrCreate()\n", - " spark_df = spark.read.parquet(data_path, header=True)\n", - " spark_df = spark_df.drop(\"name\", \"id\")\n", - " \n", - " music_stats = spark_df.groupBy(\"favorite_music_type\").agg(\n", - " avg(\"age\").alias(\"avg_age\"),\n", - " min(\"age\").alias(\"min_age\"),\n", - " max(\"age\").alias(\"max_age\")\n", - " )\n", - " music_stats.show()\n", - " pandas_df = spark_df.toPandas()\n", - " pandas_df.to_parquet(data_output_path)\n", - " # spark_df.write.mode(\"overwrite\").parquet(data_output_path)\n", - "\n", - " return {\"music_data\": data_output_path}" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "13748b64-6a48-4500-a2a8-d9290dd082c5", - "metadata": {}, - "outputs": [], - "source": [ - "process_data_function = project.set_function(\n", - " func=\"./zeev-demos/mlflow-databricks/process_data.py\",\n", - " name=\"process-data\",\n", - " kind=\"databricks\",\n", - " image=\"mlrun/mlrun\",\n", - ")\n", - " " - ] - }, - { - "cell_type": "markdown", - "id": "2dbadf07-a32a-40da-b9bc-609070e4392d", - "metadata": {}, - "source": [ - "Set all parameters necessary for the function and run it." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "5642aa15-e8c0-4a72-a0a8-4cacd34fb63c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2024-03-27 15:34:45,422 [info] Storing function: {'name': 'process-data-process-data', 'uid': 'a9c770f8377046bda3061e61a5c015c2', 'db': 'http://mlrun-api:8080'}\n", - "> 2024-03-27 15:34:45,675 [info] Job is running in the background, pod: process-data-process-data-89bhh\n", - "> 2024-03-27 15:34:49,272 [info] Running with an existing cluster: {'cluster_id': '0327-134616-43m7kfxk'}\n", - "> 2024-03-27 15:34:49,492 [info] Starting to poll: 493449112310004\n", - "> 2024-03-27 15:34:49,539 [info] Workflow intermediate status: mlrun_task__15_34_48_703046: RunLifeCycleState.PENDING\n", - "> 2024-03-27 15:34:50,947 [info] Workflow intermediate status: mlrun_task__15_34_48_703046: RunLifeCycleState.PENDING\n", - "> 2024-03-27 15:34:53,063 [info] Workflow intermediate status: mlrun_task__15_34_48_703046: RunLifeCycleState.RUNNING\n", - "> 2024-03-27 15:34:56,737 [info] Workflow intermediate status: mlrun_task__15_34_48_703046: RunLifeCycleState.RUNNING\n", - "> 2024-03-27 15:35:00,947 [info] Artifacts found. Run name: mlrun_task__15_34_48_703046\n", - "> 2024-03-27 15:35:01,881 [info] Job finished: https://dbc-94c947ab-feb9.cloud.databricks.com/?o=4658245941722457#job/499259196347814/run/493449112310004\n", - "> 2024-03-27 15:35:01,881 [info] Logs:\n", - "+-------------------+------------------+-------+-------+\n", - "|favorite_music_type| avg_age|min_age|max_age|\n", - "+-------------------+------------------+-------+-------+\n", - "| Rock| 30.125| 27| 34|\n", - "| Classical|47.666666666666664| 38| 75|\n", - "| Pop| 24.0| 18| 38|\n", - "+-------------------+------------------+-------+-------+\n", - "\n", - "2024-03-27 15:34:54,980 - mlrun_logger - INFO - successfully wrote artifact details to the artifact JSON file in DBFS - music_data : /dbfs/demos/mlrun_databricks_demo/music_output_new.parquet\n", - "> 2024-03-27 15:35:02,182 [info] To track results use the CLI: {'info_cmd': 'mlrun get run a9c770f8377046bda3061e61a5c015c2 -p mlflow-tracking-example-guy', 'logs_cmd': 'mlrun logs a9c770f8377046bda3061e61a5c015c2 -p mlflow-tracking-example-guy'}\n", - "> 2024-03-27 15:35:02,182 [info] Or click for UI: {'ui_url': 'https://dashboard.default-tenant.app.llm-dev.iguazio-cd1.com/mlprojects/mlflow-tracking-example-guy/jobs/monitor/a9c770f8377046bda3061e61a5c015c2/overview'}\n", - "> 2024-03-27 15:35:02,182 [info] Run execution finished: {'status': 'completed', 'name': 'process-data-process-data'}\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "
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projectuiditerstartstatenamelabelsinputsparametersresultsartifacts
mlflow-tracking-example-guy0Mar 27 15:34:48completedprocess-data-process-data
v3io_user=zeevr
kind=databricks
owner=zeevr
mlrun/client_version=1.6.1
mlrun/client_python_version=3.9.16
host=process-data-process-data-89bhh
task_parameters={'timeout_minutes': 15, 'spark_app_code': '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', 'original_handler': 'process_data', 'artifact_json_path': '/mlrun_databricks_runtime/artifacts_dictionaries/mlrun_artifact_a9c770f8377046bda3061e61a5c015c2.json'}
data_path=dbfs:///demos/mlrun_databricks_demo/1711553684480_33/music.parquet
data_output_path=/dbfs/demos/mlrun_databricks_demo/music_output_new.parquet
music_data
databricks_run_metadata
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\n", - " Title\n", - " ×\n", - "
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\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "data": { - "text/html": [ - " > to track results use the .show() or .logs() methods or click here to open in UI" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2024-03-27 15:35:07,910 [info] Run execution finished: {'status': 'completed', 'name': 'process-data-process-data'}\n" - ] - } - ], - "source": [ - "for name, val in job_env.items():\n", - " process_data_function.spec.env.append({\"name\": name, \"value\": val})\n", - "params = {\n", - " \"task_parameters\": {\"timeout_minutes\": 15},\n", - " \"data_path\": dbfs_data_path,\n", - " \"data_output_path\": output_path.replace(\"dbfs://\", \"/dbfs\"),\n", - "}\n", - "run = process_data_function.run(\n", - " handler=\"process_data\",\n", - " params=params,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "9a8db175-51f4-4218-afd1-752cc0e65216", - "metadata": { - "tags": [] - }, - "source": [ - "## Create an MLflow Xgboost function\n", - "\n", - "The following code demonstrates how to create a simple Xgboost model using MLflow and log the results.
\n", - "MLflow will log the model, parameters, metrics, and artifacts, and MLRun will track the run and collect the data." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "44a1e133-954d-47a3-9b0f-6e181fe12ea7", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Overwriting training.py\n" - ] - } - ], - "source": [ - "%%writefile training.py\n", - "\n", - "import mlflow\n", - "import mlflow.xgboost\n", - "import xgboost as xgb\n", - "from mlflow import log_metric\n", - "from sklearn import datasets\n", - "from sklearn.metrics import accuracy_score, log_loss\n", - "from sklearn.model_selection import train_test_split\n", - "import pandas as pd\n", - "\n", - "def example_xgb_run(df: str):\n", - " df = pd.read_parquet(df)\n", - " \n", - " df = df.replace([\"f\", \"m\"], [0, 1])\n", - " df = df.replace([\"Pop\", \"Rock\", \"Classical\"], [0, 1, 2])\n", - " \n", - " # Prepare, train, and test data\n", - " y = df.pop('favorite_music_type')\n", - " X = df\n", - "\n", - " X_train, X_test, y_train, y_test = train_test_split(\n", - " X, y, test_size=0.2, random_state=42\n", - " )\n", - "\n", - " # Enable auto logging\n", - " mlflow.xgboost.autolog()\n", - "\n", - " dtrain = xgb.DMatrix(X_train, label=y_train)\n", - " dtest = xgb.DMatrix(X_test, label=y_test)\n", - "\n", - " with mlflow.start_run():\n", - " # Train model\n", - " params = {\n", - " \"objective\": \"multi:softprob\",\n", - " \"num_class\": 3,\n", - " \"learning_rate\": 0.3,\n", - " \"eval_metric\": \"mlogloss\",\n", - " \"colsample_bytree\": 1.0,\n", - " \"subsample\": 1.0,\n", - " \"seed\": 42,\n", - " }\n", - " model = xgb.train(params, dtrain, evals=[(dtrain, \"train\")])\n", - " \n", - " # Evaluate model\n", - " y_proba = model.predict(dtest)\n", - " y_pred = y_proba.argmax(axis=1)\n", - " loss = log_loss(y_test, y_proba)\n", - " acc = accuracy_score(y_test, y_pred)\n", - " \n", - " # Log metrics by hand\n", - " mlflow.log_metrics({\"log_loss\": loss, \"accuracy\": acc})" - ] - }, - { - "cell_type": "markdown", - "id": "1cf984c9-78a9-443f-9465-111263101dcd", - "metadata": {}, - "source": [ - "## Log the data from MLflow in MLRun " - ] - }, - { - "cell_type": "markdown", - "id": "365e4b39-9f39-40ae-aac4-7c4f42bce9bd", - "metadata": {}, - "source": [ - "### Change the MLRun configuration to use the tracker\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "0b194d04-e08f-4161-a65b-4f18d10fdbf0", - "metadata": {}, - "outputs": [], - "source": [ - "import mlrun\n", - "\n", - "mlrun.mlconf.external_platform_tracking.enabled = True" - ] - }, - { - "cell_type": "markdown", - "id": "b16bb4db-8a2a-4453-a42e-0e8e74ab8f53", - "metadata": {}, - "source": [ - "These are the three options to run tracking:\n", - "- Set: `mlrun.mlconf.external_platform_tracking.mlflow.match_experiment_to_runtime` to True. This determines the run id and is the safest method\n", - "- Set the experiment name at: `mlflow.environment_variables.MLFLOW_EXPERIMENT_NAME.set`. This determines the experiment mlrun will track and find the run added to it.\n", - "- Just run it, mlrun will look across all experiments and search for added run, this is not recomended." - ] - }, - { - "cell_type": "markdown", - "id": "8b7bc72a-bd1b-408a-afa8-e474d91c4a20", - "metadata": {}, - "source": [ - "### Create the mlrun function" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "3382b909-a8dc-41a3-afb1-b64df9bb7318", - "metadata": {}, - "outputs": [], - "source": [ - "# Use the first run option from above\n", - "mlrun.mlconf.external_platform_tracking.mlflow.match_experiment_to_runtime = True\n", - "\n", - "# Create a MLRun function using the example train file (all the functions must be located in it):\n", - "training_func = project.set_function(\n", - " func=\"training.py\",\n", - " name=\"example-xgb-run\",\n", - " kind=\"job\",\n", - " image=\"mlrun/mlrun\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "91597f57-364d-4d2a-b926-97b9d8afc81b", - "metadata": {}, - "source": [ - "### Run the function\n", - "\n", - "Run the function using MLRun. This will log the data from MLflow in MLRun.
\n", - "After running the function, you can look at the UI and see that all metrics and parameters are logged in MLRun." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "5a726ca8-8057-41ed-be4e-35e5e0582de9", - "metadata": {}, - "outputs": [], - "source": [ - "import mlrun.feature_store as fstore\n", - "\n", - "feature_set = fstore.get_feature_set(\"music_fset\", \"mlflow-tracking-example\")" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "4de1229a-cc59-4846-8473-3178e682efa6", - "metadata": {}, - "outputs": [], - "source": [ - "df = feature_set.to_dataframe()\n", - "df = df.drop(['id'], axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "8249a933-031c-4f2e-88c2-161dd4cfb7ed", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# df = project.list_().to_objects()[0].to_dataitem().as_df()\n", - "df_path = \"./music.parquet\"\n", - "df.to_parquet(df_path)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "8ba452dd-1756-4bfb-af64-d741e234dba3", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2024-03-27 15:37:22,829 [info] Storing function: {'name': 'example-xgb-run-example-xgb-run', 'uid': '6ff324dd21d64b6290d45a001957dda2', 'db': 'http://mlrun-api:8080'}\n", - "> 2024-03-27 15:37:22,912 [warning] `mlconf.external_platform_tracking.mlflow.match_experiment_to_runtime` is set to True but the MLFlow experiment name environment variable ('MLFLOW_EXPERIMENT_NAME') is set for using the name: 'example-xgb-run-example-xgb-run'. This name will be overriden with MLRun's runtime name as set in the MLRun configuration: 'example-xgb-run-example-xgb-run'.\n", - "[0]\ttrain-mlogloss:0.82467\n", - "[1]\ttrain-mlogloss:0.64706\n", - "[2]\ttrain-mlogloss:0.52480\n", - "[3]\ttrain-mlogloss:0.43768\n", - "[4]\ttrain-mlogloss:0.37410\n", - "[5]\ttrain-mlogloss:0.32686\n", - "[6]\ttrain-mlogloss:0.29057\n", - "[7]\ttrain-mlogloss:0.26192\n", - "[8]\ttrain-mlogloss:0.23885\n", - "[9]\ttrain-mlogloss:0.22004\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024/03/27 15:37:23 WARNING mlflow.utils.autologging_utils: MLflow autologging encountered a warning: \"/User/.pythonlibs/mlrun-base/lib/python3.9/site-packages/mlflow/types/utils.py:393: UserWarning: Hint: Inferred schema contains integer column(s). Integer columns in Python cannot represent missing values. If your input data contains missing values at inference time, it will be encoded as floats and will cause a schema enforcement error. The best way to avoid this problem is to infer the model schema based on a realistic data sample (training dataset) that includes missing values. Alternatively, you can declare integer columns as doubles (float64) whenever these columns may have missing values. See `Handling Integers With Missing Values `_ for more details.\"\n", - "2024/03/27 15:37:23 WARNING mlflow.utils.autologging_utils: MLflow autologging encountered a warning: \"/User/.pythonlibs/mlrun-base/lib/python3.9/site-packages/xgboost/core.py:160: UserWarning: [15:37:23] WARNING: /workspace/src/c_api/c_api.cc:1240: Saving into deprecated binary model format, please consider using `json` or `ubj`. Model format will default to JSON in XGBoost 2.2 if not specified.\"\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "
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mlflow-tracking-example-guy0Mar 27 15:37:22completedexample-xgb-run-example-xgb-run
v3io_user=zeevr
kind=local
owner=zeevr
host=jupyter-zeevr-9f4ffb7bb-8c4mf
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mlflow-experiment-id=608717337209571124
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colsample_bytree=1.0
custom_metric=None
early_stopping_rounds=None
eval_metric=mlogloss
learning_rate=0.3
maximize=None
num_boost_round=10
num_class=3
objective=multi:softprob
seed=42
subsample=1.0
verbose_eval=True
accuracy=0.7142857142857143
log_loss=0.9622776094122579
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feature_importance_weight_json
feature_importance_weight_png
model
\n", - "
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\n", - " Title\n", - " ×\n", - "
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\n", - "
\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "data": { - "text/html": [ - " > to track results use the .show() or .logs() methods or click here to open in UI" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2024-03-27 15:37:31,415 [info] Run execution finished: {'status': 'completed', 'name': 'example-xgb-run-example-xgb-run'}\n" - ] - } - ], - "source": [ - "# Run the example code using mlrun\n", - "train_run = training_func.run(\n", - " local=True,\n", - " handler=\"example_xgb_run\",\n", - " inputs={\"df\": df_path},\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "655d5c46-2c0a-46f2-bbec-a58853260476", - "metadata": {}, - "source": [ - "### Examine the results\n", - "\n", - "You can examine the results using the UI or by looking at the outputs of the run.
\n", - "The outputs include the model, the metrics, and the artifacts, and are completely independent of MLflow." - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "d23beb02-e455-48dc-9d9f-9e3d4549ec71", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'accuracy': 0.7142857142857143,\n", - " 'log_loss': 0.9622776094122579,\n", - " 'train-mlogloss': 0.2200447738170624,\n", - " 'feature_importance_weight_json': 'store://artifacts/mlflow-tracking-example-guy/example-xgb-run-example-xgb-run_feature_importance_weight_json@6ff324dd21d64b6290d45a001957dda2',\n", - " 'feature_importance_weight_png': 'store://artifacts/mlflow-tracking-example-guy/example-xgb-run-example-xgb-run_feature_importance_weight_png@6ff324dd21d64b6290d45a001957dda2',\n", - " 'model': 'store://artifacts/mlflow-tracking-example-guy/example-xgb-run-example-xgb-run_model@6ff324dd21d64b6290d45a001957dda2'}" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "train_run.outputs" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "b05f4c2a-5f2d-4d7c-9c21-39c0a949cfc3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'accuracy': 0.7142857142857143,\n", - " 'log_loss': 0.9622776094122579,\n", - " 'train-mlogloss': 0.2200447738170624}" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "train_run.status.results" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "925b3445-18b4-4497-9783-52b4cd069401", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "train_run.artifact(\"feature_importance_weight_png\").show()" - ] - }, - { - "cell_type": "markdown", - "id": "227c4358-4c34-4d1c-acb4-e37ca110b8bf", - "metadata": {}, - "source": [ - "### You can also examine the results using the UI" - ] - }, - { - "cell_type": "markdown", - "id": "dde00fd1-a1f0-4c56-80c2-c5d36a9062a1", - "metadata": {}, - "source": [ - "Look at collected artifacts: " - ] - }, - { - "attachments": { - "95b9b198-55c9-4a67-b0bf-103c9ae0272e.png": { - "image/png": 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AAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACJQo0qlLd3mi7f6H1ee7VzM1r7fKFP9nUzasLrYv0olO1OnZYnXb264al9t26hZE2KbSsV1oDq5NS1aonVTEXuSu0ruiLk+p1tgsb9Sy0eH1uto1xx7pm0c+2OmdzoXU7yovE/uv/jqKWgPeRlJRke++7uw/CLVq0JHTEO2++35QiPfTIA+zDdz615ctX2smnHW+9+uzkt5k3Z76N/Gm0TZk4zdatW2+NGje0AQP72p577RaaQ09UcU5hjJ0H9Lb9Dtw7tC4nJ8d+/O5nG/f7eJvr5kp3FZLUIu3wow4ytWoNH5s2bXbtIX+0SROm2vx5C31lpU6d2/tta6fXsu+++cnPtXrVGr/bl599a6PcuakyXNDuVe0JP3DvY8rk6bZp4yY/Rzd3vINcVRodOxHj80/y29MmKgynIM5dtzxgqVVT7ejjD7N33/zIgs+4XbvWflmt2rXsg3c/tckTp5r8atWuaYOH7GL77LdnieE2fXb33PaQrXOV1TTmzJpnt990n281+vfLzy2Vcs2atabKfRPHT/ZtbOvWq2Ndu3e2Qw7fP/S5T/hzsr339sfWpWtHO+q4Q0PzLZi/yJ5/5hXTZ3/MCYeHli9auMSefeola+CCniW131XFw3mugp2qwWk89djzvi3jORecZvXq17WX//eGzXbvY/+D9vbnp20H7jbAji44/q8//26jfvzVFi5cbLk5udagYT1XXXEvX+ErdCIFT6ZNmWHaPrjW8rfd221b+MZfdL/c3FxfmVHvs1mzJjbizOG2YcNGe+Cex3wbzUuuOM/vokp8/3Pvp0OndtbTtXj99KOvTAbJKcnOpoMd59oc63MMH2obrM9fc6e6MFbb9m1sz713820qO3Rsa8cNPzJ884jPo/nOvv7yOzZ92qxin9GkCVPsHXf8FHeO5198htWslX9+Oi/dH3QP0PdS38VdBu3srr89rEqVLX82FCjTtse46/h7d/2o0lqmq2SpoK2+T9126mLffvWjv9/IIs21vfXXz7GH+O+63lDgpm0bNarvP2fd12TVo2c3O9wFDsOPGRHBLYzGQfv+OW6i/041bFjfjj/pqBK/TyUdJ1bLE33f2drz1j3hmy9/sF9G/ebuDcuso/ue7zVsd3+fVyA00vjqi+/9Nax9dR3vufcQO+nUY911lhLafL37+/T6K+/6uWfPmuv/VnV19/6TTjnO3XvKbsupvz+6P/w+5g/7Y+wEUzvPHr272Smnn+C/b8GBdA/Subdr39rfG158/nWb8Mckd1+r5qo67mpnu3tNbXe/DYbuzzqvrz7/1t1/p/lzOfr4Lfe1YLtIj199/p298ep7ftWd990YmjdY3rFzO/vHFeeHdr3vrkds2tSZzrTw8nzvMTbyh1/9tvobfKy7t+48oE9oX31HbrjqNv/6SBec19/b778ZaSeefLSdff5poe3Cn+i7erf7G6F7Ws1aNezGW660m/95j811y4PxyINP+/vYSS4or6qXpY2yzlOWl5x/ja8+u9c+Q3yrd82n41920fWmz/BiF/Lv0i3/857q/jvgfndf1Qhfrv8e0b1G61etWm2du3Rw1+AepvetewoDAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgYoUyLU8+2jtXEt3Fdh2rtHYOlRLt8dc5ba9p79b5mkNrdXCTqvf1bpWqxtVEK7MCSNsoMpx361fZE1ccK9v9Ya2R63m9oQ7P1WV2xHHlkTDjvjuKvl7CkIGat8XDP3jt8ZzT70cLLLs7Bz/XIGS/9z/lP9HZlU4UhhtyeKlPmimsNrZ548wBew0VDVs5YpVtnxZ/nx+ofv18v/e9CE4vVbwbe3aDPvVhQgUrLv6n5eEAikKnTzx8LM+xKRt69RNtzWr1/p9Fd666vpLfDAp/Nxz3D9+63VurjKw+f8Y/p/7nvT76bWCL+sy1rvQzDib5I531XUXW/Ua1bUqbiMIvyU6lBJ8jnr/GrJWK1yFIh689wkfsNLnE1wDclEwSZ/fsP2H+n0i/crKzrY85xwMeWen5l8fwbKijxtdqOvBex73n7WOpxCcwncKmI3/Y6IPbuhaatKssb9mfv3ldzvSBZmCa+m30ePyl7uQmSoZBqElXQd6D61atyh6yNBrf35h17eu5eSkLdf74kVL/Ry6LoORlZmfan7Nhbt0bWro2tmwfqMtW7rCByZkOXDwlra/qqz41GP/89sqOKFwWv62r7twz1IXtNvHryv6S0GOZ913TYExfUYKZWnkuPMM/3y0TKEOLdNPcF76Huo9av9nnnihUGVHhTqefPR57epHkrPXeepHo6Zr2RnNiOY7q0CLwkA/jxzjQ4IK6+m8XnnxbR9CHLLnwFAITp9n4K3zV+tQ3Qe++PQbW+xCuaf+7cTQaem13q/ajGrIViEXBWOfeeJFU0tntXrW0LWlUJ1CLbq+LrjkTL88cPvxu1Gh7WSta17BWYXxFOQMrje/UYRf0Thot59++MXvvde+e5jCnQpXKqDV3p1rJxewScSoqPtOed+bAmYXnXNlod20TD8KTCvsFR5u04YKid503Z2hfWZMn236URDzptuvDi2/6fq73fU4OvR62dLl7ju53Ie57n7gZhuwa9/QuqJPdM3944JrXUh2bmiVAlIKgunniece8GEprdT1o0Cefj58b8t/yOk8FTZWCPjmO64NzXPvnQ/7KqzBAoXhbnVhsWhGY9cyWcfR0Hd+l4E7++cKxQbncNa5I/z9Sn9D333rI7++T98e/lG/XnnhTXvsP/8NvdYT3Yv1c/pZ/+eDflqWuXlz6FjBMbV844ZNeig29DdH4TMZa9z70K3+O/+H+z7KIhj6PmiscMaljWjOU99ZBb7VGj03NycUhNPfBt1nNL7/dmQoCPe72y54L81bNvPr9fric6/yz4NfU12oWT+6fu558ObQ35xgPY8IIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIJFIg0/0b+VULR/pDtnVtSz9of7A1rLKloEO6q+B2bZOdbae0+rYga529umq6fbVugY1wAbgT6+UXjejlAmr/bjHYLl3wk1VJSrbr3fb9ajSyRVkb7IkVE220q+IWPg5Mb22HpLexjXk59thy9++3LuhW0pidmRE6P4XgHmm5h3VywTuNW5vtajrnM+d9YxtctbhBNZvYBQ172qcZ8+z5lVPshTbDfIW7FTmbbFjtljbLzfXQsj9s2uY1JR2uwpcThKvwj6DkE/jp+/zAhqorFR0KKw0/+RgfMkqpkuLDLA+7Si4KoQx1lVcOdhXVNFQh7pGHnrEZLoSgali7DOxXdKrQa1WtUUhFwZfTzhjuqlO1toULFtvTLkCkIIwCBqoEpKEqVvoHc1VVOv/vZ/p/2F+1crUL9jznA0bvv/Oxq0xzjD+XF5973f9D+H6uUlewv+ZQBTKF51Rt6goXelP4ZYX7x/p77viPfz+//PxbsUp22i/WoyJDKQrejDjjRF8FTdV6FIxTdTT9qOqaPi+FwxSYULhKVbeG7T80IoECKdf96zK/nUJiqsRUUiW28AkUxtLnq8/7jHNO8Z+DQkiPPPiU/yx17P8bcZz/rIOwoq6LFgVBBV0zGrr2pkyabjv17OpfTy4IdPVw1dFKGqqupvHPa+7w71mV4IJ5w/dRiEoVpTq7anRVXbBCQaogbKZ9FOzS8d9540Mb6QIjn370ZSgIp2tMoSwNVazb1VU207a/uFDYm6+97wJe37ptB/gwZ/gx9VxhDwVaFAi78B9n+yp1RbeJ9FoVGo91FeB0Tf8+5k9fuUrfF31HVOlOQ8fW0Ln/zQVcFBhR8O+Bfz/mQ2p+ZRm/ov3O6h6ytwt+afuX3Xu65p//sE+cka4zff+C+4UO9+5bH/ujDtt/T18tUmEWBZ/+999XXTBykmWsXedDtuGnpnvGP648z+rUSffvUVWl9BkpBKf2o0ccc4i3UMj1tZfeNl3r+lwUoA0f+7hz3M9V/9PnPf7PST7wq2tNVdyCqpfh2wfPo3XQ9rrHaLznrmsFJsNHt506+6Bf0XBX+Daxel6R951o3oNCjlf940a/qaqtXXbNhf7zeqOgittoF4h9y13Dus6Lju49urrKbsf4782jDz7jA2uqHqpgbY9e3f29PwjB6Xt92JEHmSo2XnflLX6qJx95ttQgnIJlQQjuzHNPtT2GDjKFqBRi01ClyfDQnV/ofunYagm+1oXf1Ho8CN4F38tpU2eEQnD6zuhvW7K7/l901ch07mWNrgWVzbSdvisKwq1337HgvWr5b6PH2h6uQuvMGbP10o+dd+njHxUGDUJwapGu4Juu0QfdPUH3D4VpNWekinla1rtPD1+FsWDa0IOq71359xtCIbjrb7rc+rlqsBqP//c+G+NCafff/ah/fdnVF/pKjA1cdcaSRnnOc5dd+/l7tQKFG10QtroLyyqQGwxVjNTfHQ3dZzT0OQVV+u669UG/TNfgbf++wQeE5aBqdArYjXb/XaMqoQwEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEKlqgWlKKnVK/iz+NVdn5bUerulDbVx0Ot7TkFMvKy/Whs91qNnOBtx+ta1pda5CSH5irkVzFeqY18Pt+1uFQa+yqt2m0q5pug2s2tQdd+EyBuGBc1riPq0NnplJYj7vqbvvPeN+F7NYHq0t8zHHFgDSy3blo7OUq0imoV90dX0G47i6s19uF8ja6QhcvrJxqfdxz/WhoT53Pbu58dp7yul+2Pf6K3NdsezzTHficVN1GYRn9/Db6Dx+QUTBILd80FGApOs46b4SvtqSAjoIqqrajCk/1XRvK8FBLqzYt7VDX3lLjs4+/KjpNoddBsGi/A4b6UJRWKgygalEaCrEE48+CUMAJrg2bwlEaCvgcesQB/vn0qbP8Y2m/VIlHQ6E+BYY0Grhg3QmuXeHg3Xf1z/3CBPxSKKVDx3amynCqvJaocbILmFWtmv/e27Rt5T9THbtJ08Y2yP3jvgI5stlzr8H+lBRcUogrVkOVgIIKQGopGHwO+kxHuDCkhoJuCnNoqFWlhir6aChUpECTrkMNBRM0VElt9sz8ak1qsRos0/LwH78iil+HH3WgD0KlpVXzISld8wrg6BwVJNNQeCoIWirIp4pLGqqyJzP5KgSnoW0VnmjUOP8PSXgoxW/gfr3tQnX6Pmrb8y483X0mjYJVpT4qFKawjc5V56nWq4FrUAlQ4S597zX+79TjfAhOz5u6qntHHbul7ayWaYSb6XkwyvOd1TWu75o+L4XRFEDROOVvJ4QqKsnpEBeiVRtXBS51/hoKoel9aSxybWiLDrWqVQhOQ/eBoBqf7A4/+mAfftHz/i7wE1wrSwsqUwVz6dwOOGSY99YyXWsKZ2qEB2f8giK/yuOgapgaChgppKfW0mrXrKHKmd8UuPgFcf5VUfedaN7W6F/GhiqF3XzXtbabuyfrM1GIqkZBpU4FbiON2++5wbfV3HVQf7vlrutCm3zpgpgaCxZs+b8RdJ/Td0t/Z9Q2VNd/3517hfaJ9KR1mxZ2nmvle/Fl5/jvmv7OHXbkgaGAmMKrkcad9/7Tt5g+4OBhdoS7LoMRXNPh9/4bb73Kv+dBriX17S6AFc1QNUyF3DSCe+HYgr/jwf5BRcKJ47ec404F99WvXbhLQ763//uf/nunyng33HJFsLsL6uUHVUML3BMd85Gn7rVzL/qbD5+Hr8ty/12g/55QVT6Nc929TC22gyG7Zs2bBC+tefOm/nsXBNFCK8KelOc8+4W1cw3+bgT3Hk2pvz9qDa1Ksb8XVInbdVA/fzTdj4LAo+7z3dzfktbufPUedJ3op5q7zzIQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQqEgBhdz+7HqCjelyrB1Xt6OpVerfF+T/21/f6o1sQ16WjVy/xPpOec2uXfSzP9XzG/V0VdpG2X+W52csRq1fbPu5MNvxbn+F4NTOdJ/p79mxsz/12wcBu+B9PuCCcT0nv2K/bVzmF51cEMAL1oc/tqpay65xFeYedpXgHmqZX/zqlyIV5sK3L/pcCYXhcz534bfXLMOdlwJ/CsttryM/vbK9nt1f5LzUvvSl54unJRUYUbCsd1jbtICkTt3awVP/OLOg/aCCHUVH3/69fahHARiFaIJwS/h22a6lZhDSWebapX7y4Zeh1aqeo7HatZ7TUFUXBY0UbFG4KHx026mL/cu1v9O6sobCNd99/ZNvXadqNKqA071HF/9+I73nsuarjOtVBSx8tGrVwlfR6ti5ffhiH4wLFqgtZ3LVsn2D7Ut7nFPQWlDtS4sGHxo3aRRqk7p08TIf0lN1N7WrVGBIYYqxLiSnoUDCW6+/76t46Rpb4FpjKsSg6m4KhE13YbTHH37Wbxv8atmquQ+yBK9Le6xTt06h1aokpmp5uqa//uJ7Hyrz4Td3zGAEgbHgPeraKjouufw8UzvZIKgWrFf7VwVLNU478yRTWCTaUcNVPAqvKKbvW8NGDXzYI7OgAtnyZSv8dAqgBEHSYH5VvAsfaueo6kfhQ5XThrnAarTfWe2rc1JwUN+1INSqwGn4d1jf2wHOVSExVZDUvUnGuTm5llnQkjbHPS86UlMLn7M+W40GDeuFQlN6LYvGjRv6SpNZmVlaFBpV3PkVHd3d/USBypUFVdyKrtfr8ty7Nrt2krouNfY7cG/b1xkGQ/MoCPW1C2vJ968+ghaVqsQVBGBloutIIVJV4VNFtdWr11jdsO+nQlzhlf50Lej+osDTwvn5Icrwv1MXnHWFDyLq79Tuew2yw1zotayhVr/6HFXV8bmnX/bnob9LSxbl/0de8N0Nn0fvo2atmqFFqpgZDLXn1dB9S0PbqvpkMIreG4PlkR51X1L1O1U30/coCL71c+9P7UD1N+/yay7yVQ61v75vukdqjHHhQw0F21Q5LRgdOrb33yMFl9XOt+iQZ3Jyfmi16Dq1VA2GrvnjTzoqeLnVj+U5z3bt24TOXVX1dM8Lwm2q/KZl+hxVFS9o0bpzQXhO96PdXbU/VaNVBcKzTr3Y2/Rx4eILLjmrxPe81W+MHRFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBDYCgEF30a5oFuyq8/Wo3p9q5Wcajc23cUOn/WR/bxhiR0/+zM7ok47e6nNvtY0Nb8ATcOCSnBFD7era02q8aMLxi3J3uB/Lnahujqualv4eG/NbP/yu3ULrZ8L23WqVjhTEb5tvZRqNrygBauW/+pCcBfO/z58k1Kfb3bV4f7YmJ9xmJ651vq6EFwPVzlu3Mblpe5XUSsJwlWUfNhxVcVNVXGCocpLajnasnXzULWwYF1Jjwvm5/8DfnpBVabw7cL/QV0tMIPKTeHbLHFBp2Co7VmkoX/U11i6JH/bGjWrR9qsUPAl4gYFCxXAOfPcU+zVF9/2ISFVhVHoR5WhVJFKbUMTNR576L+uYs4sF4zZywVk9krUYYsfJ3KWofh2MVqyoCCYEuma0CG0XC0uV7jqZe06tDEF9BROUGtLVVwbV1ABrmfv7q4t6jRfPU4hB7XE1NByDQVQtH/4aN06+nBZ+H7Bc4Uj3nt7S3UkBUdVkanoWLRwiV8UHs4JtlEQsWgYUevCgzQTxk8OVZoK9tvWx4yMdX6K6gWVtUqbT1Xiito1dhUDy/OdDeZXMFEtFNWmUOOgQ4cFq0KP+mwfeSC/zbIWBq7hJqGNS3gSKWxbwqalLg7ed+AVaePyOCjQF4x99iscdlMwV0E43ef0UzQcGewXy8ft5r4T4U0pvKpRv0HdYmv1vVYQTmPF8lWFgnDFNnYLVOlTQTgFKzU6de7gqpxdaTddd6d//dH7n5t+NFSd76JLzwmFw/zCIr82udDb+WdeFqpyVmR1VC+TIgTHVq7Mr9IYVIqMaqIiG4VXs1MFNAV1Nc4+f4RrNfsvW+UC5aoGF4TJ1DpUQ0FerdOoU6dw0F0ht/5uOwXsghCZ37CcvxQ6C9qTlnPX0OblPU+du4J9n3z4hfv7MMG9t/z/AFeYcbCrtqdz+v7bkVa9Rn7wT0HKLmEtZq+49mLbuGGTD8JNde1z9aPRyAVqr7r+TMqBHgAAQABJREFU7xaE5kInyBMEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEiyQ6Yp4nDXvG39UheF+73KcdaiWbi1Sa/p2qJ+0P9RSXNGYxVkbbJH7CdqeRjrN2i5Ep7EwrM3plxnzI23qlwWtTkvcwK2YnZlhty4ZbWc12MkG1GhsOobCe1szXA/ArdktofsUT40k9PAcTAJqgzl0nyHbhBEEmRQQKDqCAJuWl1TZJqhIo23Oveh0PRQbQbglPT3/H+mDKjrFNizHAlXduf7my30QTqEBVcxZumS5PfHIc3bWeacmJAyn8Mt2EYIrh1usNk0vCFxs3FT8utEx1q/L7yEdhGFUDUrhpBmuFe/4Pya5NplLfKs6XT8KgKiN6rjfxtvCgvaZqiCnoSpA57m2fbEaCj8pBKdQ3omuPa+COTo3VYG74u//LHSYIAAXtHcttLKUF6p6pWpK+unsQplBqK+UXaJeVaugMtXiRfnBoNJ2VNAjUthDbWmDUdZ3NthOIdYgBKdlX33+vR3o2pEGQ2G3Z596yVfbUsW/PffeUpnq3jsf9p93sG0iHoPwXVpYdayixy3PvUvBQ10zQcvF9h3ahqZT1b5g6D4a7yBcou87asWrkKOG2kAH3329jvR3Qx4KHamlZlZWloVX/VOYKhhNo2gZrDCthkLOwdh72O4+CDXm17G+4peCUhoKxKkymFqTljSeevx/oRDc38452bfibeWCtbf9614fFitpv7KW13WV4DT0vhX01T2lvENhU4W0VC3v5Rfe9O8lqDA3dNgQe/v1D1z78/dCobegdWjQeljHmzkzP0gcfuyNGzf6lwqylncE56OWzI888JRdetUF5Z0itP3WnKfau+rzVVU3VV7U2N21wlW4T0N/92vWyv8/X7Qs3F3/zXLPgzfbvDnz7cfvf/YBTH0+8r30wuvsyecf8MFKPxG/EEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEKhggdQkF4UrKIBUt6ASm0Jwo10VthFzv7LWrk3pR+0PKXaWSS5ApzHTVVwbVLOp+cpwBfWsnmm9t6W5dqRqT7o1Q21W1Zp18qbV9m2nI6xrWj3r7wJxOqfNeTl+ytaptWxF9iarnlT5Y2Sx6a+4NdLsE1MBhek0pkyeXmzeqQXLtI1CIJGGqtIFo0aNGqaASNEftTjTCMIMCqmsWplfwSbYd+2aDHvjlXftkw/yQw3B8kiPaveoFqxqvajwgII3ahnXvqBlnf7RPN5DYZTPP/m64ivBxfuNljB/k4IQiwIyQego2FSVf4LWm2rZGowgEPbGq+/5RWpTp6GAhq6v312VuDmz5pnCS8F16TeI4a9fRo3xs+lc1FI3CE4EbS/DD9W4SX5vap1T0aGQm65XhSzCx84DetvRxx0aCqi+8OxrvjJe+Dbb8rxBwfdtw/oNxeYNWrqWNX95vrOaS/OqjaRGp4LWu6rqpXBMMGbOmO0rUynscsDB+xRqz6hgUKKHzkcj/L36BWG/wteVde/SbsH9q2iLSYU7gxEeEguWxfKxIu47k13FxkvOv8b/vPPmh6G3k5ubZz9+93PoddCqt3VY2+vw8KQ2DNpttm3XulC7Ua1TiE3BuWCodWpQxUz3eQ21UX7y0efss4++st1ce15V9vr027d84FTr9bchaMWr10XHR65yqIYCqiePON4HoRSIzMvbUvGv6D7RvFZgNxjh10N5r/0hLuSlETjt5UJ/CpIPHrKrXx60Olb1s+DvqiqnBe1afx31m69K6Dd2vxRI1jINtRMtz5DRi2884Vuwar/33/nEB8rKM0f4tltznrpHB2NsQRXRAS7wpsq33Xt09atU4VNDrWWDoetG14l+kl0o8YT/O9qeeO4Bu/mOa4NNXFvVfJfQAp4ggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggkGCBaskp9lzrfXzr0586H+VbpKqi24RNK02tSzV6uXai97bYzV5ve4B/rX00xm9a4R/71mhoVzfpZ8+smOxrrqn16Gtt97e32x1gu7jQWsMq+R2W/MZb+WtVzmb7dG1+buK2Zvn/dqmWrhr3txhiD7Xc3c5sUL5/j9zKU4nrbpFTUXE9JJPHQ6D/Ln38tPrH+/CAh6pgBe0ju+3UucRD6x/pu+3Uxa9/6fnXLbzamyoJ3Xnz/a6ay1i/PnxbBRrCw0c61s8jx9iyZflfVu0QtGZd40Jy4WPihCn25Wff+nao4cuD9paZmVvCFOHrY/28wtuhxvoNlWO+Vq1bmCqmKQT3wbufhvbUZ6qqRRrNWzQt1D50p575wYWgipRaSmoojNalW0dTuEv79yjYzq8s41dQ1WvtmrVlbJm/Oi0t/yav4Gdw/enxv0++FNo/uH6CEMYfYyfYfNeeMRgK+b352vv+elXr1vBRtWpV/1JtGhXe0dwKYwTHCt92a543d3MGlZW++erH0BT63qk9cDQj/HtY1ndW8yn0pmqLCiieduZJFoR1/vvki6H3Vc2FiTT0GariUjAU3NG+GqUFlILtt+ZRn8e0graH2l8h26/dcTV69+3hHyP9Kq9D8L51P1PYU0MhwZ++/8U/b92mpQ8t+Rdx/JXo+04QuNJbUhjqh29H+QDoE488G6pONsCFkIJQ6b77Dw29e137y909XdVFX33pbdN3SSM8tBTa2D25+7aHfOtetfq8/+5HQ6t2HdzfP587e769+Nzrdu9dD5uCUfLPc9+xKqmpoW1LexJ8X+fPW+irUyoo9vYbH/jKctovaDFa2hyR1u0xdHBo8ZOPPe+DwHrfej/lGf136Vto8932yP+PyD79Cl/HahmqYFkwDj3ywOCpPeWOr1Ch2pnfc/t/QsvDW6+GFpbypIlrraz72ZXX/T3UtvzWf97jWtpuqShZyu4RV5X3PNUaNwj5aUKF37RMY8+9tpjrdfj7q+Wqwek60c9j/3km9H0NNwv+bxrty0AAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEECgIgT0L34712jkwm4NLDM3x75et8DOnf+tP5VPM+b5ymuqFLdf7VaW7Yp7qLloNVfhrapbpkptapmq18fV7WhLsjfY1QtHWabbrrur3NapWl1bmr3RTp7zpZ8vaE265dEvjvrXvxb/amqn2ty1bd2ndkt7auVE34a1gQva7VWrha3NzSw4TuQp3a7b/aj8Ne22e+LEnGC9+nVtmAsufPHpN761oaqqqdqMgiUKLyjcEd4CMdJZHXPCYXb7Tff59oe33HC3qW3pOhcwUCUtBYBWrVwV2u3IYw62Ka7CkCoFaR8FqtRCTwEaVQXbe989Qtt279HFRrrKWyN/+MWHKQbu1t+FpLq5fwDfzWbNmGO/j/nDpk2dYaoutNi12gyqkB14yL6hOeL1ZL8D94rX1JViXn1Wx590lD3x8LO+SpA+h2bNm/oWimpnqPaQp5/9f4XeS926dXxVLa1XRZ+gLa826rtzb5s0Yarfvmfv/IBcoZ1LeKFrRMEcBR7au7aNhx91YCgoEWmXXn2622cff+VbOl596U0+rKfKZuFBtSWLl/pqS6pKt+ugnX3g7YF/P+4r1ylwNnvmXD+1QlbhVcXCj6eQ1d/OPtluu+leHwR7762P7Qh37W/rkPuhRxxgr774lqvGNcqmTp5mtV3L4bmuMl3RynylHSva76xaoqr6osYJ7vNOrZpqB7nvl8Jg+hy1Lgj9KaCn7/Fdtz7oXVe6QFoQetT+aufau3DOR4tjMtQSWa13a7qqlJOdiSx0PgMLAlQlHSRaB+2vCmTfff2Tf9933Hyfv88tXrTEBf/yw7tHH39YSYeJ2fKKuO+o2tnRxx3mA64KOV535S3F3s/5F58RWtahUzs77sQj7LWX3/HBt2MOPTW0Tk8UMP3bOacUWha80HdTP+FDQaghe+RXStO1r/UKev39vKv93yo9D8bFl53jw1vB66KPulZV3VD7/O3kC4uu9q8VYAtvdxtxoyILFcLq17+3b9WpCmzHHBL5/RXZrdjLIBwcrAheq71s8Hda63YuCLAH28lFLUT1d/V1566f8DH8lGP99Ru+LNrnuldfce3FduO1d3g3fb/vuPefWxX63Jrz3HXwgFA72z2GDgqd9qAhu9ijDz3jX6uFa1A1UAt0zgccPMybqGKcfvTfNcG1ou0PP3rb78ehk+EJAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAuUQUGitx+RXytxDLVEVdKtfpZotcqG3omPYjPesSZUatj43v1jUB2tnm36CZesKlmu/IdPeLrT7sysnm34ijcdXTDD9hA/N1XvKq+GLbL8Z71ujKtVtY262hR9LGxV9f6fOzc8dFJpgO3tBRbgK/EAUstFIcaGY8o6gP3D4fvsftLcPNSm8NNOF0sb/McmS3Nz9+veys84fUWJb1GCOdBfEUYs6hegUnvtz3EQfVKvlqmUdeewhvn1osK2Cd1dce5H/R2sFabStwjOqHnbZ1RcU+sfsTq41m9pmKqSk8NzihUv9NKosds6Fp/lQlSoz6XwVglNrvlNOP8GClpbBMXfEx+AaCN5bpM81WBc8hvbJv3yCxaFHfeZ+RCiVk+wSxRqhbdxztcm8+NKzfRhMVb/G/T7e1OJWn9sFl5xVKOjmd3a/evTq5p/2ccGR8KFAWzDUki/aMWi3AS6A18Rfd5NcpUBdSxrBew2vAKTlCreNOGO4r26m60qVoTQU0AhaOwbBJi1XuEnhmSqpVXzIRCE4fU/Ujvf/RhynTfwIjhc8amHt9Fp2uqugpvHj9z/nVy2LYB/skxRW4cnvFPYr2EaLVMXxWBc00jnpXPWdVTXEoIJd2G4lPo32O6vWrhr6TIOKfgrDKRSn8fUX3/ugn45/3kV/C333FC5UCE73kOAzD68UF7yfCJeanzdY718U/AqWFd1Hn5uOoXDs+D8n+RCc7ieXXnV+qEqZpkgquIaTwyaI1kH7K4R42dUX+gqYwX1O/qqMeMElZ/p7mLbbEcc5F55u57mwm8JE4UPX3NMv/McURg4f2lbhqXr16oYWK3x0hAsf3XLndf47FKwIvqNq3Tn85GOCxf5xsAs73ffI7aG/QQrZPfn8g6F2nUGwSed11nmnuiBs6eGmk087vtgx9B70dyoYwT1Bn3ekEVyHWhf+/LZ7brD9Dty70C7HDz/S/w0rum2hjcJe1HaVzIIWpgqFKwAXjCAMqNfh1c/0WtX4Hnj0Tn9fCP+MdC+Vy5nnhgfzttyEgvu65ig6gu+Llg/dZ4gP4un5zyNH20fvf66nhd5/0ftXMHdQKVDbl+88tYe73w3ok//E/R7o7vfBUEhfQXqNoFpjsE6PV153sZ1zwWmh72Vwrah64R333mg1XVCWgQACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggMD2LrA5LydiCC44b4XqiobQIi0Lto/14zJXda7o8WN9jETNl5SRkVEJCteVjyNj7XofqinfXjvW1gqWbd68ucSqWgq+6B/B9Q/xw08pHFqQhMJFK5avslq1alj1IqGJolLZ2dm+zVqDBvV9qKfo+uC12tepEpdCdEXDCar8pHaq6S50FLS9C/bjMXECCgYp2KgQYng4JFFnoDa8CmOl16kd1SHVUnHN6rWWnZNjDRrUi+qc17j2q7k5uf46jOogCdhIAdBqrn2hQneqdKVQqCrVhYf0yjqN8nxny5pL69VWOWPtOmvUuEGhIFo0+5Znm+lTZ9rjriKhqj9d6YK4Oe6zVDWvOq7yYNAytzzzlcdB2yrYl14nPdTCuTzHqszb6nujVsRB68yy3ovCsbp/RxtQ1ueo6oFqgVna56jPYJGrBKoW2vrbUJ77Tpb7u6Fqh6pKGQRgy3of0a5XC2AFJJs0beTvSdHuF6vtdG/T8dOqV3N/F6O7H8bq2OWZJ5HnmZGxzt/vVemvtGuqPOfPtggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAn9NgaKVzv6aChX/rsd3PSHmJ0Fr1JiTbh8TKhRQWjBAoTONkgJHCqopABPNUHBJFbrKGgq4lRRyU1UsVQRjVKyAqqQp+FFRo6QWpSWdj0IzdevVKWl1xOXhrVwjbpCghT+PHONb/Z19wQgfAtNhV7hA3MTxU/wZqEVoeUZ5vrPRzKtKSxVRbUnVpqK5n5T0HsrjoG235VglnUNlWK4KePqJduhvRUl/LyLNoc8xvM1lpG20TJ9BNNtF2j/V/d3Y2n0jzRe+rKoLpsZr7vDjlPRc97ZoQ4clzZGI5Yk8T1Xa0w8DAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQKEmAIFxJMjvo8skTp9qYX8fZH2Pz+wD37N19B32nvC0Etm+BaVNm2JLFS+2WG+6xNu1amSomqjWthsJ9ahfLQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEIhOgCBcdE47zFYzps+2sb/96d9Pt526WOs2LXeY98YbQaAyCajtqSpO/fDtKJs1Y07o1NUS9ahjDynWPji0AU8QQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEigkkZWRk5BVbWskXZKxdT5vNEj7DjRs22qJFS6x5i2aWllathK1YjAACiRTYtGmzbdq4ybeqVKvBv9LIy8szvX+1yFRrXgYCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAALxFOgx+ZV4Ts/cUQqM73pClFtGvxkV4aK32iG2rF6jurXv0HaHeC+8CQR2FAGFUv+qwVQF/6pXT9t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" - } - }, - "cell_type": "markdown", - "id": "8cda6c13-7fee-4284-aacf-81a506a426da", - "metadata": {}, - "source": [ - "![image.png](attachment:95b9b198-55c9-4a67-b0bf-103c9ae0272e.png)" - ] - }, - { - "cell_type": "markdown", - "id": "e1525230-e10c-4f48-b951-bc73642bb3e4", - "metadata": {}, - "source": [ - "And at results:" - ] - }, - { - "attachments": { - "66422f79-9b46-4e07-9796-c1b350c26c9c.png": { - "image/png": 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" - } - }, - "cell_type": "markdown", - "id": "217279f8-6af1-4209-b0ec-3d3d829ceed9", - "metadata": {}, - "source": [ - "![image.png](attachment:66422f79-9b46-4e07-9796-c1b350c26c9c.png)" - ] - }, - { - "cell_type": "markdown", - "id": "844edc05-0b6a-4e84-9213-1d3cbf6f833e", - "metadata": {}, - "source": [ - "## Use the function for model serving" - ] - }, - { - "cell_type": "markdown", - "id": "40182a6f-fc46-4a33-a7f5-7ee8ee171966", - "metadata": {}, - "source": [ - "### Create the server and serving function\n", - "\n", - "Create a serving function that uses the model from the previous run and serves it using MLRun.
\n", - "We will create a mock server to test the model in a local environment." - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "f5fe910b-e177-4af7-84de-41a571d1774c", - "metadata": {}, - "outputs": [], - "source": [ - "serving_func = project.set_function(\n", - " func=\"function.yaml\",\n", - " name=\"example-xgb-server\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "ddbfd48f-a90e-4fe6-9caa-ddffeacf63d1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Add the model\n", - "serving_func.add_model(\n", - " \"mlflow_xgb_model\",\n", - " class_name=\"MLFlowModelServer\",\n", - " model_path=train_run.outputs[\"model\"],\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "2298d111-2f53-4b84-be9e-e4e8a228dcc4", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2024-03-27 15:37:31,627 [info] model mlflow_xgb_model was loaded\n", - "> 2024-03-27 15:37:31,628 [info] Loaded ['mlflow_xgb_model']\n" - ] - } - ], - "source": [ - "# Create a mock server\n", - "server = serving_func.to_mock_server()" - ] - }, - { - "cell_type": "markdown", - "id": "f54d7c06-4972-4881-9bc9-fba7db0adbe4", - "metadata": {}, - "source": [ - "### Test the model " - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "4f256490-f225-4bd6-ac8a-5fc12a0f335d", - "metadata": {}, - "outputs": [], - "source": [ - "# An example taken randomly \n", - "result = server.test(\"/v2/models/mlflow_xgb_model/predict\", {\"inputs\":[{\"age\": 20, \"gender\": 0}]})" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "47839f4b-bb2d-4341-99c5-e34fa31270c9", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'id': '43a61d06f2694fa695bdd6561b487131',\n", - " 'model_name': 'mlflow_xgb_model',\n", - " 'outputs': [[0.9242361187934875, 0.0418272465467453, 0.033936627209186554]]}" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Look at the result, it shows the probability of the given example to be each of the \n", - "# irises featured in the dataset\n", - "result" - ] - }, - { - "cell_type": "markdown", - "id": "d4fc6c73-0963-4814-bd5f-2d27b464823e", - "metadata": {}, - "source": [ - "We predicted that a 20 year old female would like pop!" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "mlrun-base", - "language": "python", - "name": "conda-env-mlrun-base-py" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.16" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/mlflow_utils/mlflow_utils.py b/mlflow_utils/mlflow_utils.py deleted file mode 100644 index fb6124bef..000000000 --- a/mlflow_utils/mlflow_utils.py +++ /dev/null @@ -1,45 +0,0 @@ -import zipfile -from typing import Any, Dict -import mlflow -from mlrun.serving.v2_serving import V2ModelServer -import pandas as pd - - -class MLFlowModelServer(V2ModelServer): - """ - MLFlow tracker Model serving class, inheriting the V2ModelServer class for being initialized automatically by the model - server and be able to run locally as part of a nuclio serverless function, or as part of a real-time pipeline. - """ - - def load(self): - """ - loads a model that was logged by the MLFlow tracker model - """ - # Unzip the model dir and then use mlflow's load function - model_file, _ = self.get_model(".zip") - model_path_unzip = model_file.replace(".zip", "") - - with zipfile.ZipFile(model_file, "r") as zip_ref: - zip_ref.extractall(model_path_unzip) - - self.model = mlflow.pyfunc.load_model(model_path_unzip) - - def predict(self, request: Dict[str, Any]) -> list: - """ - Infer the inputs through the model. The inferred data will - be read from the "inputs" key of the request. - - :param request: The request to the model using xgboost's predict. - The input to the model will be read from the "inputs" key. - - :return: The model's prediction on the given input. - """ - - # Get the inputs and set to accepted type: - inputs = pd.DataFrame(request["inputs"]) - - # Predict using the model's predict function: - predictions = self.model.predict(inputs) - - # Return as list: - return predictions.tolist() diff --git a/mlflow_utils/requirements.txt b/mlflow_utils/requirements.txt deleted file mode 100644 index 2a40b1a81..000000000 --- a/mlflow_utils/requirements.txt +++ /dev/null @@ -1,3 +0,0 @@ -mlflow==2.20.2 -lightgbm -xgboost diff --git a/mlflow_utils/test_mlflow_utils.py b/mlflow_utils/test_mlflow_utils.py deleted file mode 100644 index 70d6ce03f..000000000 --- a/mlflow_utils/test_mlflow_utils.py +++ /dev/null @@ -1,179 +0,0 @@ -# Copyright 2018 Iguazio -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -import tempfile - -import lightgbm as lgb -import mlflow -import mlflow.environment_variables -import mlflow.xgboost -import pytest -import xgboost as xgb -from sklearn import datasets -from sklearn.metrics import accuracy_score, log_loss -from sklearn.model_selection import train_test_split - -import os -# os.environ["MLRUN_IGNORE_ENV_FILE"] = "True" #TODO remove before push - -import mlrun -import mlrun.launcher.local -# Important: -# unlike mlconf which resets back to default after each test run, the mlflow configurations -# and env vars don't, so at the end of each test we need to redo anything we set in that test. -# what we cover in these tests: logging "regular" runs with, experiment name, run id and context -# name (last two using mlconf), failing run mid-way, and a run with no handler. -# we also test here importing of runs, artifacts and models from a previous run. - -# simple mlflow example of lgb logging -def lgb_run(): - # prepare train and test data - iris = datasets.load_iris() - X = iris.data - y = iris.target - X_train, X_test, y_train, y_test = train_test_split( - X, y, test_size=0.2, random_state=42 - ) - - # enable auto logging - mlflow.lightgbm.autolog() - - train_set = lgb.Dataset(X_train, label=y_train) - - with mlflow.start_run(): - # train model - params = { - "objective": "multiclass", - "num_class": 3, - "learning_rate": 0.1, - "metric": "multi_logloss", - "colsample_bytree": 1.0, - "subsample": 1.0, - "seed": 42, - } - # model and training data are being logged automatically - model = lgb.train( - params, - train_set, - num_boost_round=10, - valid_sets=[train_set], - valid_names=["train"], - ) - - # evaluate model - y_proba = model.predict(X_test) - y_pred = y_proba.argmax(axis=1) - loss = log_loss(y_test, y_proba) - acc = accuracy_score(y_test, y_pred) - - # log metrics - mlflow.log_metrics({"log_loss": loss, "accuracy": acc}) - - -# simple mlflow example of xgb logging -def xgb_run(): - # prepare train and test data - iris = datasets.load_iris() - x = iris.data - y = iris.target - x_train, x_test, y_train, y_test = train_test_split( - x, y, test_size=0.2, random_state=42 - ) - - # enable auto logging - mlflow.xgboost.autolog() - - dtrain = xgb.DMatrix(x_train, label=y_train) - dtest = xgb.DMatrix(x_test, label=y_test) - - with mlflow.start_run(): - # train model - params = { - "objective": "multi:softprob", - "num_class": 3, - "learning_rate": 0.3, - "eval_metric": "mlogloss", - "colsample_bytree": 1.0, - "subsample": 1.0, - "seed": 42, - } - # model and training data are being logged automatically - model = xgb.train(params, dtrain, evals=[(dtrain, "train")]) - # evaluate model - y_proba = model.predict(dtest) - y_pred = y_proba.argmax(axis=1) - loss = log_loss(y_test, y_proba) - acc = accuracy_score(y_test, y_pred) - # log metrics - mlflow.log_metrics({"log_loss": loss, "accuracy": acc}) - - -@pytest.mark.parametrize("handler", ["xgb_run", "lgb_run"]) -def test_track_run_with_experiment_name(handler): - """ - This test is for tracking a run logged by mlflow into mlrun while it's running using the experiment name. - first activate the tracking option in mlconf, then we name the mlflow experiment, - then we run some code that is being logged by mlflow using mlrun, - and finally compare the mlrun we tracked with the original mlflow run using the validate func - """ - # Enable general tracking - mlrun.mlconf.external_platform_tracking.enabled = True - # Set the mlflow experiment name - mlflow.environment_variables.MLFLOW_EXPERIMENT_NAME.set(f"{handler}_test_track") - with tempfile.TemporaryDirectory() as test_directory: - mlflow.set_tracking_uri(test_directory) # Tell mlflow where to save logged data - - # Create a project for this tester: - project = mlrun.get_or_create_project(name="default", context=test_directory) - - # Create a MLRun function using the tester source file (all the functions must be located in it): - func = project.set_function( - func=__file__, - name=f"{handler}-test", - kind="job", - image="mlrun/mlrun", - requirements=["mlflow"], - ) - # mlflow creates a dir to log the run, this makes it in the tmpdir we create - trainer_run = func.run( - local=True, - handler=handler, - artifact_path=test_directory, - ) - - serving_func = project.set_function( - func=os.path.abspath("function.yaml"), - name=f"{handler}-server", - ) - model_name = f"{handler}-model" - # Add the model - upper_handler = handler.replace("_", "-") - model_path = test_directory + f"/{upper_handler}-test-{upper_handler}/0/model/" - serving_func.add_model( - model_name, - class_name="MLFlowModelServer", - model_path=model_path, - ) - - # Create a mock server - server = serving_func.to_mock_server() - - # An example taken randomly - result = server.test(f"/v2/models/{model_name}/predict", {"inputs": [[5.1, 3.5, 1.4, 0.2]]}) - print(result) - assert result - # unset mlflow experiment name to default - mlflow.environment_variables.MLFLOW_EXPERIMENT_NAME.unset() - - diff --git a/modules/src/langchain_mlrun/item.yaml b/modules/src/langchain_mlrun/item.yaml new file mode 100644 index 000000000..f427aef1b --- /dev/null +++ b/modules/src/langchain_mlrun/item.yaml @@ -0,0 +1,24 @@ +apiVersion: v1 +categories: +- langchain +- langgraph +- tracing +- monitoring +- llm +description: LangChain x MLRun integration - Orchestrate your LangChain code with MLRun. +example: langchain_mlrun.ipynb +generationDate: 2025-01-08 +hidden: false +labels: + author: guyl +mlrunVersion: 1.10.0 +name: langchain_mlrun +spec: + filename: langchain_mlrun.py + image: mlrun/mlrun + kind: generic + requirements: + - mlrun + - langchain + - pydantic-settings +version: 0.0.1 \ No newline at end of file diff --git a/modules/src/langchain_mlrun/langchain_mlrun.ipynb b/modules/src/langchain_mlrun/langchain_mlrun.ipynb new file mode 100644 index 000000000..a1f005410 --- /dev/null +++ b/modules/src/langchain_mlrun/langchain_mlrun.ipynb @@ -0,0 +1,899 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "7955da79-02cc-42fe-aee0-5456d3e386fd", + "metadata": {}, + "source": [ + "# LangChain ✕ MLRun Integration\n", + "\n", + "`langchain_mlrun` is a hub module that implements LangChain integration with MLRun. Using the module allows MLRun to orchestrate LangChain and LangGraph code, enabling tracing and monitoring batch workflows and realtime deployments.\n", + "___" + ] + }, + { + "cell_type": "markdown", + "id": "8392a3e1-d0a1-409a-ae68-fcc36858d30a", + "metadata": {}, + "source": [ + "## Main Components\n", + "\n", + "This is a short brief of the components available to import from the `langchain_mlrun` module. For full docs, see the documentation page.\n", + "\n", + "### Settings\n", + "\n", + "The module uses Pydantic settings classes that can be configured programmatically or via environment variables. The main class is `MLRunTracerSettings`. It contains two sub-settings:\n", + "* `MLRunTracerClientSettings` - Connection settings (stream path, container, endpoint info). Env prefix: `\"MLRUN_TRACER_CLIENT_\"`\n", + "* `MLRunTracerMonitorSettings` - Controls what/how runs are captured (filters, labels, debug mode). Env prefix: `\"MLRUN_TRACER_MONITOR_\"`\n", + "\n", + "For more information about each setting, see the class docstrings.\n", + "\n", + "#### Example - via code configuration\n", + "\n", + "```python\n", + "from langchain_mlrun import MLRunTracerSettings, MLRunTracerClientSettings, MLRunTracerMonitorSettings\n", + "\n", + "settings = MLRunTracerSettings(\n", + " client=MLRunTracerClientSettings(\n", + " stream_path=\"my-project/model-endpoints/stream-v1\",\n", + " container=\"projects\",\n", + " model_endpoint_name=\"my_endpoint\",\n", + " model_endpoint_uid=\"abc123\",\n", + " serving_function=\"my_function\",\n", + " ),\n", + " monitor=MLRunTracerMonitorSettings(\n", + " label=\"production\",\n", + " root_run_only=True, # Only monitor root runs, not child runs\n", + " tags_filter=[\"important\"], # Only monitor runs with this tag\n", + " ),\n", + ")\n", + "```\n", + "\n", + "#### Example - environment variable configuration\n", + "\n", + "```bash\n", + "export MLRUN_TRACER_CLIENT_STREAM_PATH=\"my-project/model-endpoints/stream-v1\"\n", + "export MLRUN_TRACER_CLIENT_CONTAINER=\"projects\"\n", + "export MLRUN_TRACER_MONITOR_LABEL=\"production\"\n", + "export MLRUN_TRACER_MONITOR_ROOT_RUN_ONLY=\"true\"\n", + "```\n", + "\n", + "### MLRun Tracer\n", + "\n", + "`MLRunTracer` is a LangChain-compatible tracer that converts LangChain `Run` objects into MLRun monitoring events and publishes them to a V3IO stream. \n", + "\n", + "Key points:\n", + "* **No inheritance required** - use it directly without subclassing.\n", + "* **Fully customizable via settings** - control filtering, summarization, and output format.\n", + "* **Custom summarizer support** - pass your own `run_summarizer_function` via settings to customize how runs are converted to events.\n", + "\n", + "### Monitoring Setup Utility Function\n", + "\n", + "`setup_langchain_monitoring()` is a utility function that creates the necessary MLRun infrastructure for LangChain monitoring. This is a **temporary workaround** until custom endpoint creation support is added to MLRun.\n", + "\n", + "The function returns a dictionary of environment variables to configure auto-tracing. See how to use it in the tutorial section below.\n", + "\n", + "### LangChain Monitoring Application\n", + "\n", + "`LangChainMonitoringApp` is a base class (inheriting from MLRun's `ModelMonitoringApplicationBase`) for building monitoring applications that process events from the MLRun Tracer.\n", + "\n", + "It offers several built-in helper methods and metrics for analyzing LangChain runs:\n", + "\n", + "* Helper methods:\n", + " * `get_structured_runs()` - Parse raw monitoring samples into structured run dictionaries with filtering options\n", + " * `iterate_structured_runs()` - Iterate over all runs including nested child runs\n", + "* Metric methods:\n", + " * `calculate_average_latency()` - Average latency across root runs\n", + " * `calculate_success_rate()` - Percentage of runs without errors\n", + " * `count_token_usage()` - Total input/output tokens from LLM runs\n", + " * `count_run_names()` - Count occurrences of each run name\n", + "\n", + "The base app can be used as-is, but it is recommended to extend it with your own custom monitoring logic.\n", + "___" + ] + }, + { + "cell_type": "markdown", + "id": "7e24e1a5-d80a-4b7e-9b94-57b24e8b39d7", + "metadata": {}, + "source": [ + "## How to Apply MLRun?\n", + "\n", + "### Auto Tracing\n", + "\n", + "Auto tracing automatically instruments all LangChain code by setting the `MLRUN_MONITORING_ENABLED` environment variable and importing the module:\n", + "\n", + "```python\n", + "import os\n", + "os.environ[\"MLRUN_MONITORING_ENABLED\"] = \"1\"\n", + "# Set other MLRUN_TRACER_* environment variables as needed...\n", + "\n", + "# Import the module BEFORE any LangChain code\n", + "langchain_mlrun = mlrun.import_module(\"hub://langchain_mlrun\")\n", + "\n", + "# All LangChain/LangGraph code below will be automatically traced\n", + "chain.invoke(...)\n", + "```\n", + "\n", + "### Manual Tracing\n", + "\n", + "For more control, use the `mlrun_monitoring()` context manager to trace specific code blocks:\n", + "\n", + "```python\n", + "from langchain_mlrun import mlrun_monitoring, MLRunTracerSettings\n", + "\n", + "# Optional: customize settings\n", + "settings = MLRunTracerSettings(...)\n", + "\n", + "with mlrun_monitoring(settings=settings) as tracer:\n", + " # Only LangChain code within this block will be traced\n", + " result = chain.invoke({\"topic\": \"MLRun\"})\n", + "```\n", + "___" + ] + }, + { + "cell_type": "markdown", + "id": "68b52d3d-a431-44fb-acd6-ea33fec37a49", + "metadata": {}, + "source": [ + "## Tutorial\n", + "\n", + "In this tutorial we'll show how to orchestrate LangChain based code with MLRun using the `langchain_mlrun` hub module.\n", + "\n", + "### Prerequisites\n", + "\n", + "Install MLRun and the `langchain_mlrun` requirements." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "caf72aa6-06e8-4a04-bfc4-409b39d255fe", + "metadata": {}, + "outputs": [], + "source": [ + "!pip install mlrun langchain pydantic-settings" + ] + }, + { + "cell_type": "markdown", + "id": "8aa18266-d3b5-40bd-a8b9-65345e419d8c", + "metadata": {}, + "source": [ + "### Create Project\n", + "\n", + "We'll first create an MLRun project" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2664df3e-d9c6-40dd-a215-29d60e4b4208", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> 2026-01-08 14:48:52,259 [info] Project loaded successfully: {\"project_name\":\"langchain-mlrun-7\"}\n" + ] + } + ], + "source": [ + "import os\n", + "import time\n", + "import datetime\n", + "import mlrun\n", + "\n", + "project = mlrun.get_or_create_project(\"langchain-mlrun-tutorial\")" + ] + }, + { + "cell_type": "markdown", + "id": "33f28986-c158-47fd-97a6-74f69892b4eb", + "metadata": {}, + "source": "### Enable Monitoring\n\nTo use MLRun's monitoring feature in our project we first need to set up the monitoring infrastructure. If you use MLRun CE, you'll need to create a Kafka stream, if you use MLRun enterprise, you can use V3IO." + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "d9d2fa66-0498-445d-ab4a-8370f46aec1e", + "metadata": {}, + "outputs": [], + "source": [ + "# TODO: Add here MLRun CE handler with Kafka, currently the tutorial is only with V3IO.\n", + "from mlrun.datastore import DatastoreProfileV3io\n", + "\n", + "# Create a V3IO data store:\n", + "v3io_ds = DatastoreProfileV3io(name=\"v3io-ds\",v3io_access_key=os.environ[\"V3IO_ACCESS_KEY\"])\n", + "project.register_datastore_profile(profile=v3io_ds)\n", + "\n", + "# Set the monitoring credentials:\n", + "project.set_model_monitoring_credentials(\n", + " stream_profile_name=v3io_ds.name,\n", + " tsdb_profile_name=v3io_ds.name\n", + ")\n", + "\n", + "# Enable monitoring for our project:\n", + "project.enable_model_monitoring(\n", + " base_period=1,\n", + " wait_for_deployment=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "f23117fa-7b67-470c-80ca-976d14c2120e", + "metadata": {}, + "source": [ + "### Import `langchain_mlrun`\n", + "\n", + "Now we'll import `langchain_mlrun` from the hub." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2360cd49-b260-4140-bd16-138349e000b3", + "metadata": {}, + "outputs": [], + "source": "# Import the module from the hub:\nlangchain_mlrun = mlrun.import_module(\"hub://langchain_mlrun\")\n\n# Import the utility function and monitoring application from the module:\nsetup_langchain_monitoring = langchain_mlrun.setup_langchain_monitoring\nLangChainMonitoringApp = langchain_mlrun.LangChainMonitoringApp" + }, + { + "cell_type": "markdown", + "id": "de030131-ebaf-48f8-96ed-3c1013b5e260", + "metadata": {}, + "source": "### Create Monitorable Endpoint\n\nEndpoints are the entities being monitored by MLRun. As such we'll use the `setup_langchain_monitoring` utility function to create the model monitoring endpoint. By default, our endpoint name will be `\"langchain_mlrun_endpoint\"` but feel free to change it by using the required arguments." + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "0e9baf78-3d38-46bd-89dd-6f83760eaeb0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Creating LangChain model endpoint\n", + "\n", + " [✓] Loading Project......................... Done (0.00s)\u001B[K\n", + " [✓] Creating Model.......................... Done (0.02s) \u001B[K\n", + " [✓] Creating Function....................... Done (0.02s) \u001B[K\n", + " [✓] Creating Model Endpoint................. Done (0.02s) \u001B[K\n", + "\n", + "✨ Done! LangChain monitoring model endpoint created successfully.\n", + "You can now set the following environment variables to enable MLRun tracing in your LangChain code:\n", + "\n", + "{\n", + " \"MLRUN_MONITORING_ENABLED\": \"1\",\n", + " \"MLRUN_TRACER_CLIENT_PROJECT\": \"langchain-mlrun-7\",\n", + " \"MLRUN_TRACER_CLIENT_STREAM_PATH\": \"langchain-mlrun-7/model-endpoints/stream-v1\",\n", + " \"MLRUN_TRACER_CLIENT_CONTAINER\": \"projects\",\n", + " \"MLRUN_TRACER_CLIENT_MODEL_ENDPOINT_NAME\": \"langchain_mlrun_endpoint\",\n", + " \"MLRUN_TRACER_CLIENT_MODEL_ENDPOINT_UID\": \"bb81af2058c14e7cbf58455aed3d69fc\",\n", + " \"MLRUN_TRACER_CLIENT_SERVING_FUNCTION\": \"langchain_mlrun_function\"\n", + "}\n", + "\n", + "To customize the monitoring behavior, you can also set additional environment variables prefixed with 'MLRUN_TRACER_MONITOR_'. Refer to the MLRun tracer documentation for more details.\n", + "\n" + ] + } + ], + "source": [ + "env_vars = setup_langchain_monitoring()" + ] + }, + { + "cell_type": "markdown", + "id": "dd45c94b-ee05-449c-9336-0aa659e66bda", + "metadata": {}, + "source": [ + "### Setup Environment Variables for Auto Tracing\n", + "\n", + "We'll use the environment variables returned from `setup_langchain_monitoring` to setup the environment for auto-tracing. Read the printed outputs for more information." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1c1988f8-c80a-4bf2-bfb1-d43523fc161f", + "metadata": {}, + "outputs": [], + "source": [ + "os.environ.update(env_vars)" + ] + }, + { + "cell_type": "markdown", + "id": "d3f3b8e5-3538-4153-95da-e6d8776be3ac", + "metadata": {}, + "source": "### Run `langchain` or `langgraph` Code\n\nHere we have 3 functions, each using different method utilizing LLMs with `langchain` and `langgraph`:\n* `run_simple_chain` - Using `langchain`'s chains.\n* `run_simple_agent` - Using `langchain`'s `create_agent` function and `tool`s.\n* `run_langgraph_graph` - Using pure `langgraph`.\n\n> **Notice**: You don't need to set OpenAI API credentials, there is a mock `ChatModel` that will replace it if the credentials are not set in the environment. If you wish to use OpenAI models, make sure you `pip install langchain_openai` and set the `OPENAI_API_KEY` environment variable before continue to the next cell.\n\nBecause the auto-tracing environment is set, any run will be automatically traced and monitored!\n\nFeel free to adjust the code as you like.\n\n> **Remember**: To enable auto-tracing you do need to set the environment variables and import the `langchain_mlrun` module before any LangChain code. For batch jobs and realtime functions, make sure you set env vars in the MLRun function and add the import line `langchain_mlrun = mlrun.import_module(\"hub://langchain_mlrun\")` at the top of your code." + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "94b4d4b0-8d10-4ad3-8f16-7b1b7daeac11", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import os\n", + "from typing import Literal, TypedDict, Annotated, Sequence, Any, Callable\n", + "from operator import add\n", + "\n", + "from langchain_core.language_models import LanguageModelInput\n", + "from langchain_core.runnables import Runnable, RunnableLambda\n", + "from langchain_core.prompts import ChatPromptTemplate\n", + "from langchain_core.output_parsers import StrOutputParser\n", + "from langchain_core.language_models.fake_chat_models import FakeListChatModel, GenericFakeChatModel\n", + "from langchain.agents import create_agent\n", + "from langchain_core.messages import AIMessage, HumanMessage\n", + "from langchain_core.tools import tool, BaseTool\n", + "\n", + "from langgraph.graph import StateGraph, START, END\n", + "from langchain_core.messages import BaseMessage\n", + "\n", + "\n", + "def _check_openai_credentials() -> bool:\n", + " \"\"\"\n", + " Check if OpenAI API key is set in environment variables.\n", + "\n", + " :return: True if OPENAI_API_KEY is set, False otherwise.\n", + " \"\"\"\n", + " return \"OPENAI_API_KEY\" in os.environ\n", + "\n", + "\n", + "# Import ChatOpenAI only if OpenAI credentials are available (meaning `langchain-openai` must be installed).\n", + "if _check_openai_credentials():\n", + " from langchain_openai import ChatOpenAI\n", + "\n", + " \n", + "class _ToolEnabledFakeModel(GenericFakeChatModel):\n", + " \"\"\"\n", + " A fake chat model that supports tool binding for running agent tracing tests.\n", + " \"\"\"\n", + "\n", + " def bind_tools(\n", + " self,\n", + " tools: Sequence[\n", + " dict[str, Any] | type | Callable | BaseTool # noqa: UP006\n", + " ],\n", + " *,\n", + " tool_choice: str | None = None,\n", + " **kwargs: Any,\n", + " ) -> Runnable[LanguageModelInput, AIMessage]:\n", + " return self\n", + "\n", + "\n", + "#: Tag value for testing tag filtering.\n", + "_dummy_tag = \"dummy_tag\"\n", + "\n", + "\n", + "def run_simple_chain() -> str:\n", + " \"\"\"\n", + " Run a simple LangChain chain that gets a fact about a topic.\n", + " \"\"\"\n", + " # Build a simple chain: prompt -> llm -> str output parser\n", + " llm = ChatOpenAI(\n", + " model=\"gpt-4o-mini\",\n", + " tags=[_dummy_tag]\n", + " ) if _check_openai_credentials() else (\n", + " FakeListChatModel(\n", + " responses=[\n", + " \"MLRun is an open-source orchestrator for machine learning pipelines.\"\n", + " ],\n", + " tags=[_dummy_tag]\n", + " )\n", + " )\n", + " prompt = ChatPromptTemplate.from_template(\"Tell me a short fact about {topic}\")\n", + " chain = prompt | llm | StrOutputParser()\n", + "\n", + " # Run the chain:\n", + " response = chain.invoke({\"topic\": \"MLRun\"})\n", + " return response\n", + "\n", + "\n", + "def run_simple_agent():\n", + " \"\"\"\n", + " Run a simple LangChain agent that uses two tools to get weather and stock price.\n", + " \"\"\"\n", + " # Define the tools:\n", + " @tool\n", + " def get_weather(city: str) -> str:\n", + " \"\"\"Get the current weather for a specific city.\"\"\"\n", + " return f\"The weather in {city} is 22°C and sunny.\"\n", + "\n", + " @tool\n", + " def get_stock_price(symbol: str) -> str:\n", + " \"\"\"Get the current stock price for a symbol.\"\"\"\n", + " return f\"The stock price for {symbol} is $150.25.\"\n", + "\n", + " # Define the model:\n", + " model = ChatOpenAI(\n", + " model=\"gpt-4o-mini\",\n", + " tags=[_dummy_tag]\n", + " ) if _check_openai_credentials() else (\n", + " _ToolEnabledFakeModel(\n", + " messages=iter(\n", + " [\n", + " AIMessage(\n", + " content=\"\",\n", + " tool_calls=[\n", + " {\"name\": \"get_weather\", \"args\": {\"city\": \"London\"}, \"id\": \"call_abc123\"},\n", + " {\"name\": \"get_stock_price\", \"args\": {\"symbol\": \"AAPL\"}, \"id\": \"call_def456\"}\n", + " ]\n", + " ),\n", + " AIMessage(content=\"The weather in London is 22°C and AAPL is trading at $150.25.\")\n", + " ]\n", + " ),\n", + " tags=[_dummy_tag]\n", + " )\n", + " )\n", + "\n", + " # Create the agent:\n", + " agent = create_agent(\n", + " model=model,\n", + " tools=[get_weather, get_stock_price],\n", + " system_prompt=\"You are a helpful assistant with access to tools.\"\n", + " )\n", + "\n", + " # Run the agent:\n", + " return agent.invoke({\"messages\": [\"What is the weather in London and the stock price of AAPL?\"]})\n", + "\n", + "\n", + "def run_langgraph_graph():\n", + " \"\"\"\n", + " Run a LangGraph agent that uses reflection to correct its answer.\n", + " \"\"\"\n", + " # Define the graph state:\n", + " class AgentState(TypedDict):\n", + " messages: Annotated[list[BaseMessage], add]\n", + " attempts: int\n", + "\n", + " # Define the model:\n", + " model = ChatOpenAI(model=\"gpt-4o-mini\") if _check_openai_credentials() else (\n", + " _ToolEnabledFakeModel(\n", + " messages=iter(\n", + " [\n", + " AIMessage(content=\"There are 2 'r's in Strawberry.\"), # Mocking the failure\n", + " AIMessage(content=\"I stand corrected. S-t-r-a-w-b-e-r-r-y. There are 3 'r's.\"), # Mocking the fix\n", + " ]\n", + " )\n", + " )\n", + " )\n", + "\n", + " # Define the graph nodes and router:\n", + " def call_model(state: AgentState):\n", + " response = model.invoke(state[\"messages\"])\n", + " return {\"messages\": [response], \"attempts\": state[\"attempts\"] + 1}\n", + "\n", + " def reflect_node(state: AgentState):\n", + " prompt = \"Wait, count the 'r's again slowly, letter by letter. Are you sure?\"\n", + " return {\"messages\": [HumanMessage(content=prompt)]}\n", + "\n", + " def router(state: AgentState) -> Literal[\"reflect\", END]:\n", + " # Make sure there are 2 attempts at least for an answer:\n", + " if state[\"attempts\"] == 1:\n", + " return \"reflect\"\n", + " return END\n", + "\n", + " # Build the graph:\n", + " builder = StateGraph(AgentState)\n", + " builder.add_node(\"model\", call_model)\n", + " tagged_reflect_node = RunnableLambda(reflect_node).with_config(tags=[_dummy_tag])\n", + " builder.add_node(\"reflect\", tagged_reflect_node)\n", + " builder.add_edge(START, \"model\")\n", + " builder.add_conditional_edges(\"model\", router)\n", + " builder.add_edge(\"reflect\", \"model\")\n", + " graph = builder.compile()\n", + "\n", + " # Run the graph:\n", + " return graph.invoke({\"messages\": [HumanMessage(content=\"How many 'r's in Strawberry?\")], \"attempts\": 0})" + ] + }, + { + "cell_type": "markdown", + "id": "49964f96-89ba-4f61-8788-38290a877aa2", + "metadata": {}, + "source": [ + "Let's create some traffic, we'll run whatever function you want in a loop to get some events. We take timestamps in order to use them later to run the monitoring application on the data we'll send." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "b7e6418d-76f4-4b18-9ef9-c5bb40b20545", + "metadata": {}, + "outputs": [], + "source": [ + "# Run LangChain code and now it should be tracked and monitored in MLRun:\n", + "start_timestamp = datetime.datetime.now() - datetime.timedelta(minutes=1)\n", + "for i in range(20):\n", + " run_simple_agent()\n", + "end_timestamp = datetime.datetime.now() + datetime.timedelta(minutes=5)" + ] + }, + { + "cell_type": "markdown", + "id": "d9085765-91fd-4d31-84b4-927ecf9cc455", + "metadata": {}, + "source": "> **Note**: Please wait a minute or two until the events are processed." + }, + { + "cell_type": "code", + "execution_count": null, + "id": "85fae3e4-5f1b-4f0c-ba71-81060f10804f", + "metadata": {}, + "outputs": [], + "source": [ + "time.sleep(60)" + ] + }, + { + "cell_type": "markdown", + "id": "2475ebec-fc32-4884-9723-3ca9cfde577f", + "metadata": {}, + "source": [ + "### Test the LangChain Monitoring Application\n", + "\n", + "To test a monitoring application, we use the `evaluate` class method. We'll run an evaluation on the data we just sent. It is a small local job and should run fast.\n", + "\n", + "Keep an eye for the returned metrics from the monitoring application." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "3d046755-9153-497a-a024-5d63316e1f91", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> 2026-01-08 14:49:22,970 [info] Changing function name - adding `\"-batch\"` suffix: {\"func_name\":\"testi-batch\"}\n", + "> 2026-01-08 14:49:23,143 [info] Storing function: {\"db\":\"http://mlrun-api:8080\",\"name\":\"testi-batch--handler\",\"uid\":\"43b34f848b6049c0949f04adc1090f10\"}\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "
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\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/html": [ + " > to track results use the .show() or .logs() methods or click here to open in UI" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> 2026-01-08 14:49:23,944 [info] Run execution finished: {\"name\":\"testi-batch--handler\",\"status\":\"completed\"}\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "LangChainMonitoringApp.evaluate(\n", + " func_name=\"langchain_monitoring_app_test\",\n", + " func_path=\"langchain_mlrun.py\",\n", + " run_local=True,\n", + " endpoints=[env_vars[\"MLRUN_TRACER_CLIENT_MODEL_ENDPOINT_NAME\"]],\n", + " start=start_timestamp.isoformat(),\n", + " end=end_timestamp.isoformat(),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "eda724c3-27f3-4d28-a7ba-1e59b9be2a37", + "metadata": {}, + "source": "### Deploy the Monitoring Application\n\nAll that's left to do now is to deploy our monitoring application!" + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "652b00d4-070d-4849-9784-4d461cb83eae", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> 2026-01-08 17:06:50,801 [info] Starting remote function deploy\n", + "2026-01-08 17:06:51 (info) Deploying function\n", + "2026-01-08 17:06:51 (info) Building\n", + "2026-01-08 17:06:52 (info) Staging files and preparing base images\n", + "2026-01-08 17:06:52 (warn) Using user provided base image, runtime interpreter version is provided by the base image\n", + "2026-01-08 17:06:52 (info) Building processor image\n", + "2026-01-08 17:08:52 (info) Build complete\n", + "2026-01-08 17:09:06 (info) Function deploy complete\n", + "> 2026-01-08 17:09:13,972 [info] Model endpoint creation task completed with state succeeded\n", + "> 2026-01-08 17:09:13,973 [info] Successfully deployed function: {\"external_invocation_urls\":[],\"internal_invocation_urls\":[\"nuclio-langchain-mlrun-7-langchain-monitoring-app.default-tenant.svc.cluster.local:8080\"]}\n" + ] + } + ], + "source": [ + "# Deploy the monitoring app:\n", + "LangChainMonitoringApp.deploy(\n", + " func_name=\"langchain_monitoring_app\",\n", + " func_path=\"langchain_mlrun.py\",\n", + " image=\"mlrun/mlrun\",\n", + " requirements=[\n", + " \"langchain\",\n", + " \"pydantic-settings\",\n", + " ],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "c23bef7a-cbdb-4b22-a2d9-2edbfde5eb04", + "metadata": {}, + "source": [ + "Once it is deployed, you can run events again and see the monitoring application in MLRun UI in action:\n", + "\n", + "![mlrun ui example](./notebook_images/mlrun_ui.png)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9f799f06-2e62-4e2f-a42f-c94b5fc18623", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mlrun-py311", + "language": "python", + "name": "conda-env-.conda-mlrun-py311-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.14" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/modules/src/langchain_mlrun/langchain_mlrun.py b/modules/src/langchain_mlrun/langchain_mlrun.py new file mode 100644 index 000000000..c26ae38f2 --- /dev/null +++ b/modules/src/langchain_mlrun/langchain_mlrun.py @@ -0,0 +1,1526 @@ +""" +MLRun to LangChain integration - a tracer that converts LangChain Run objects into MLRun monitoring events and +publishes them to a V3IO stream via MLRun endpoint monitoring format. +""" + +import copy +import importlib +import orjson +import os +import socket +from uuid import UUID +import threading +from contextlib import contextmanager +from contextvars import ContextVar +import datetime +from typing import Any, Callable, Generator, Optional + +import v3io +from langchain_core.tracers import BaseTracer, Run +from langchain_core.tracers.context import register_configure_hook + +from pydantic import Field, field_validator +from pydantic_settings import BaseSettings, SettingsConfigDict +from uuid_utils import uuid7 + +import mlrun +from mlrun.runtimes import RemoteRuntime +from mlrun.model_monitoring.applications import ( + ModelMonitoringApplicationBase, ModelMonitoringApplicationMetric, + ModelMonitoringApplicationResult, MonitoringApplicationContext, +) +import mlrun.common.schemas.model_monitoring.constants as mm_constants + +#: Environment variable name to use MLRun monitoring tracer via LangChain global tracing system: +mlrun_monitoring_env_var = "MLRUN_MONITORING_ENABLED" + + +class _MLRunEndPointClient: + """ + An MLRun model endpoint monitoring client to connect and send events on a V3IO stream. + """ + + def __init__( + self, + monitoring_stream_path: str, + monitoring_container: str, + model_endpoint_name: str, + model_endpoint_uid: str, + serving_function: str | RemoteRuntime, + serving_function_tag: str | None = None, + project: str | mlrun.projects.MlrunProject = None, + ): + """ + Initialize an MLRun model endpoint monitoring client. + + :param monitoring_stream_path: V3IO stream path. + :param monitoring_container: V3IO container name. + :param model_endpoint_name: The monitoring endpoint related model name. + :param model_endpoint_uid: Model endpoint unique identifier. + :param serving_function: Serving function name or ``RemoteRuntime`` object. + :param serving_function_tag: Optional function tag (defaults to 'latest'). + :param project: Project name or ``MlrunProject``. If ``None``, uses the current project. + + raise: MLRunInvalidArgumentError: If there is no current active project and no `project` argument was provided. + """ + # Store the provided info: + self._monitoring_stream_path = monitoring_stream_path + self._monitoring_container = monitoring_container + self._model_endpoint_name = model_endpoint_name + self._model_endpoint_uid = model_endpoint_uid + + # Load project: + if project is None: + try: + self._project_name = mlrun.get_current_project(silent=False).name + except mlrun.errors.MLRunInvalidArgumentError: + raise mlrun.errors.MLRunInvalidArgumentError( + "There is no current active project. Either use `mlrun.get_or_create_project` prior to " + "initializing the monitoring tracer or pass a project name to load. You can also set the " + "environment variable: 'MLRUN_MONITORING_PROJECT'." + ) + elif isinstance(project, str): + self._project_name = project + else: + self._project_name = project.name + + # Load function: + if isinstance(serving_function, str): + self._serving_function_name = serving_function + self._serving_function_tag = serving_function_tag or "latest" + else: + self._serving_function_name = serving_function.metadata.name + self._serving_function_tag = ( + serving_function_tag or serving_function.metadata.tag + ) + + # Initialize a V3IO client: + self._v3io_client = v3io.Client() + + # Prepare the sample: + self._event_sample = { + "class": "CustomStream", + "worker": "0", + "model": self._model_endpoint_name, + "host": socket.gethostname(), + "function_uri": f"{self._project_name}/{self._serving_function_name}:{self._serving_function_tag}", + "endpoint_id": self._model_endpoint_uid, + "sampling_percentage": 100, + "request": {"inputs": [], "background_task_state": "succeeded"}, + "op": "infer", + "resp": { + "id": None, + "model_name": self._model_endpoint_name, + "outputs": [], + "timestamp": None, + "model_endpoint_uid": self._model_endpoint_uid, + }, + "when": None, + "microsec": 496, + "effective_sample_count": 1, + } + + def monitor( + self, + event_id: str, + label: str, + input_data: dict, + output_data: dict, + request_timestamp: str, + response_timestamp: str, + ): + """ + Monitor the provided event, sending it to the model endpoint monitoring stream. + + :param event_id: Unique event identifier used as the monitored record id. + :param label: Label for the run/event. + :param input_data: Serialized input data for the run. + :param output_data: Serialized output data for the run. + :param request_timestamp: Request/start timestamp in the format of '%Y-%m-%d %H:%M:%S%z'. + :param response_timestamp: Response/end timestamp in the format of '%Y-%m-%d %H:%M:%S%z'. + """ + # Copy the sample: + event = copy.deepcopy(self._event_sample) + + # Edit event with given parameters: + event["when"] = request_timestamp + event["request"]["inputs"].append(orjson.dumps({"label": label, "input": input_data}).decode('utf-8')) + event["resp"]["timestamp"] = response_timestamp + event["resp"]["outputs"].append(orjson.dumps(output_data).decode('utf-8')) + event["resp"]["id"] = event_id + + # Push to stream: + self._v3io_client.stream.put_records( + container=self._monitoring_container, + stream_path=self._monitoring_stream_path, + records=[{"data": orjson.dumps(event).decode('utf-8')}], + ) + + +class MLRunTracerClientSettings(BaseSettings): + """ + MLRun tracer monitoring client configurations. These are mandatory arguments for allowing MLRun to send monitoring + events to a specific model endpoint stream. + """ + + stream_path: str = ... + """ + The V3IO stream path to send the events to. + """ + + container: str = ... + """ + The V3IO stream container. + """ + + model_endpoint_name: str = ... + """ + The model endpoint name. + """ + + model_endpoint_uid: str = ... + """ + The model endpoint UID. + """ + + serving_function: str = ... + """ + The serving function name. + """ + + serving_function_tag: str | None = None + """ + The serving function tag. If not set, it will be 'latest' by default. + """ + + project: str | None = None + """ + The MLRun project name related to the serving function and model endpoint. + """ + + #: Pydantic model configuration to set the environment variable prefix. + model_config = SettingsConfigDict(env_prefix="MLRUN_TRACER_CLIENT_") + + +class MLRunTracerMonitorSettings(BaseSettings): + """ + MLRun tracer monitoring configurations. These are optional arguments to customize the LangChain runs summarization + into monitorable MLRun endpoint events. If needed, a custom summarization can be passed. + """ + + label: str = "default" + """ + Label to use for all monitored runs. Can be used to differentiate between different monitored sources on the same + endpoint. + """ + + tags_filter: list[str] | None = None + """ + Filter runs by tags. Only runs with at least one tag in this list will be monitored. + If None, no tag-based filtering is applied and runs with any tags are considered. + Default: None. + """ + + run_types_filter: list[str] | None = None + """ + Filter runs by run types (e.g. "chain", "llm", "chat", "tool"). + Only runs whose `run_type` appears in this list will be monitored. + If None, no run-type filtering is applied. + Default: None. + """ + + names_filter: list[str] | None = None + """ + Filter runs by class/name. Only runs whose `name` appears in this list will be monitored. + If None, no name-based filtering is applied. + Default: None. + """ + + include_full_run: bool = False + """ + If True, include the complete serialized run dict (the output of `run._get_dicts_safe()`) + in the event outputs under the key `full_run`. Useful for debugging or when consumers need + the raw run payload. Default: False. + """ + + include_errors: bool = True + """ + If True, include run error information in the outputs under the `error` key. + If False, runs that contain an error may be skipped by the summarizer filters. + Default: True. + """ + + include_metadata: bool = True + """ + If True, include run metadata (environment, tool metadata, etc.) in the inputs under + the `metadata` key. Default: True. + """ + + include_latency: bool = True + """ + If True, include latency information in the outputs under the `latency` key. + Default: True. + """ + + root_run_only: bool = False + """ + If True, only the root/top-level run will be monitored and any child runs will be + ignored/removed from monitoring. Use when only the top-level run should produce events. + Default: False. + """ + + split_runs: bool = False + """ + If True, child runs are emitted as separate monitoring events (each run summarized and + sent individually). If False, child runs are nested inside the parent/root run event under + `child_runs`. Default: False. + """ + + run_summarizer_function: ( + str + | Callable[ + [Run, Optional[BaseSettings]], + Generator[tuple[dict, dict] | None, None, None], + ] + | None + ) = None + """ + A function to summarize a `Run` object into a tuple of inputs and outputs. Can be passed directly or via a full + module path ("a.b.c.my_summarizer" will be imported as `from a.b.c import my_summarizer`). + + A summarizer is a function that will be used to process a run into monitoring events. The function is expected to be + of type: + `Callable[[Run, Optional[BaseSettings]], Generator[tuple[dict, dict] | None, None, None]]`, meaning + get a run object and optionally a settings object and return a generator yielding tuples of serialized dictionaries, + the (inputs, outputs) to send to MLRun monitoring as events or `None` to skip monitoring this run. + """ + + run_summarizer_settings: str | BaseSettings | None = None + """ + Settings to pass to the run summarizer function. Can be passed directly or via a full module path to be imported + and initialized. If the summarizer function does not require settings, this can be left as None. + """ + + debug: bool = False + """ + If True, disable sending events to MLRun/V3IO and instead route events to `debug_target_list` + or print them as JSON to stdout. Useful for unit tests and local debugging. Default: False. + """ + + debug_target_list: list[dict] | bool = False + """ + Optional list to which debug events will be appended when `debug` is True. + If set, each generated event dict will be appended to this list. If not set and `debug` is True, + events will be printed to stdout as JSON. Default: False. + """ + + #: Pydantic model configuration to set the environment variable prefix. + model_config = SettingsConfigDict(env_prefix="MLRUN_TRACER_MONITOR_") + + @field_validator('debug_target_list', mode='before') + @classmethod + def convert_bool_to_list(cls, v): + """ + Convert a boolean `True` value to an empty list for `debug_target_list`. + + :param v: The value to validate. + + :returns: An empty list if `v` is True, otherwise the original value. + """ + if v is True: + return [] + return v + + +class MLRunTracerSettings(BaseSettings): + """ + MLRun tracer settings to configure the tracer. The settings are split into two groups: + + * `client`: settings required to connect and send events to the MLRun/V3IO monitoring stream. + * `monitor`: settings controlling which LangChain runs are summarized and sent and how. + """ + + client: MLRunTracerClientSettings = Field(default_factory=MLRunTracerClientSettings) + """ + Client configuration group (``MLRunTracerClientSettings``). + + Contains the mandatory connection and endpoint information required to publish monitoring + events. Values may be supplied programmatically or via environment variables prefixed with + `MLRUN_TRACER_CLIENT_`. See more at ``MLRunTracerClientSettings``. + """ + + monitor: MLRunTracerMonitorSettings = Field(default_factory=MLRunTracerMonitorSettings) + """ + Monitoring configuration group (``MLRunTracerMonitorSettings``). + + Controls what runs are captured, how they are summarized (including custom summarizer import + options), whether child runs are split or nested, and debug behavior. Values may be supplied + programmatically or via environment variables prefixed with `MLRUN_TRACER_MONITOR_`. + See more at ``MLRunTracerMonitorSettings``. + """ + + #: Pydantic model configuration to set the environment variable prefix. + model_config = SettingsConfigDict(env_prefix="MLRUN_TRACER_") + + +class MLRunTracer(BaseTracer): + """ + MLRun tracer for LangChain runs allowing monitoring LangChain and LangGraph in production using MLRun's monitoring. + + There are two usage modes for the MLRun tracer following LangChain tracing best practices: + + 1. **Manual Mode** - Using the ``mlrun_monitoring`` context manager:: + + from mlrun_tracer import mlrun_monitoring + + with mlrun_monitoring(...) as tracer: + # LangChain code here. + pass + + 2. **Auto Mode** - Setting the `MLRUN_MONITORING_ENABLED="1"` environment variable:: + + import mlrun_integration.tracer + + # All LangChain code will be automatically traced and monitored. + pass + + To control how runs are being summarized into the events being monitored, the ``MLRunTracerSettings`` can be set. + As it is a Pydantic ``BaseSettings`` class, it can be done in two ways: + + 1. Initializing the settings classes and passing them to the context manager:: + + from mlrun_tracer import ( + mlrun_monitoring, + MLRunTracerSettings, + MLRunTracerClientSettings, + MLRunTracerMonitorSettings, + ) + + my_settings = MLRunTracerSettings( + client=MLRunTracerClientSettings(), + monitor=MLRunTracerMonitorSettings(root_run_only=True), + ) + + with mlrun_monitoring(settings=my_settings) as tracer: + # LangChain code here. + pass + + 2. Or via environment variables following the prefix 'MLRUN_TRACER_CLIENT_' for client settings and + 'MLRUN_TRACER_MONITOR_' for monitoring settings. + """ + + #: A singleton tracer for when using the tracer via environment variable to activate global tracing. + _singleton_tracer: "MLRunTracer | None" = None + #: A thread lock for initializing the tracer singleton safely. + _lock = threading.Lock() + #: A boolean flag to know whether the singleton was initialized. + _initialized = False + + def __new__(cls, *args, **kwargs) -> "MLRunTracer": + """ + Create or return an ``MLRunTracer`` instance. + + When ``MLRUN_MONITORING_ENABLED`` is not set to ``"1"``, a normal instance is returned. + When the env var is ``"1"``, a process-wide singleton is returned. Creation is thread-safe. + + :returns: MLRunTracer instance (singleton if 'auto' mode is active). + """ + # Check if needed to use a singleton as the user is using the MLRun tracer by setting the environment variable + # and not manually (via context manager): + if not cls._check_for_env_var_usage(): + return super(MLRunTracer, cls).__new__(cls) + + # Check if the singleton is set: + if cls._singleton_tracer is None: + # Acquire lock to initialize the singleton: + with cls._lock: + # Double-check after acquiring lock: + if cls._singleton_tracer is None: + cls._singleton_tracer = super(MLRunTracer, cls).__new__(cls) + + return cls._singleton_tracer + + def __init__(self, settings: MLRunTracerSettings = None, **kwargs): + """ + Initialize the tracer. + + :param settings: Settings to use for the tracer. If not passed, defaults are used and environment variables are + applied per Pydantic settings behavior. + :param kwargs: Passed to the base initializer. + """ + # Proceed with initialization only if singleton mode is not required or the singleton was not initialized: + if self._check_for_env_var_usage() and self._initialized: + return + + # Call the base tracer init: + super().__init__(**kwargs) + + # Set a UID for this instance: + self._uid = uuid7() + + # Set the settings: + self._settings = settings or MLRunTracerSettings() + self._client_settings = self._settings.client + self._monitor_settings = self._settings.monitor + + # Initialize the MLRun endpoint client: + self._mlrun_client = ( + _MLRunEndPointClient( + monitoring_stream_path=self._client_settings.stream_path, + monitoring_container=self._client_settings.container, + model_endpoint_name=self._client_settings.model_endpoint_name, + model_endpoint_uid=self._client_settings.model_endpoint_uid, + serving_function=self._client_settings.serving_function, + serving_function_tag=self._client_settings.serving_function_tag, + project=self._client_settings.project, + ) + if not self._monitor_settings.debug + else None + ) + + # In case the user passed a custom summarizer, import it: + self._custom_run_summarizer_function: ( + Callable[ + [Run, Optional[BaseSettings]], + Generator[tuple[dict, dict] | None, None, None], + ] + | None + ) = None + self._custom_run_summarizer_settings: BaseSettings | None = None + self._import_custom_run_summarizer() + + # Mark the initialization flag (for the singleton case): + self._initialized = True + + @property + def settings(self) -> MLRunTracerSettings: + """ + Access the effective settings. + + :returns: The settings used by this tracer. + """ + return self._settings + + def _import_custom_run_summarizer(self): + """ + Import or assign a custom run summarizer (and its custom settings) if configured. + """ + # If the user did not pass a run summarizer function, return: + if not self._monitor_settings.run_summarizer_function: + return + + # Check if the function needs to be imported: + if isinstance(self._monitor_settings.run_summarizer_function, str): + self._custom_run_summarizer_function = self._import_from_module_path( + module_path=self._monitor_settings.run_summarizer_function + ) + else: + self._custom_run_summarizer_function = ( + self._monitor_settings.run_summarizer_function + ) + + # Check if the user passed settings as well: + if self._monitor_settings.run_summarizer_settings: + # Check if the settings need to be imported: + if isinstance(self._monitor_settings.run_summarizer_settings, str): + self._custom_run_summarizer_settings = self._import_from_module_path( + module_path=self._monitor_settings.run_summarizer_settings + )() + else: + self._custom_run_summarizer_settings = ( + self._monitor_settings.run_summarizer_settings + ) + + def _persist_run(self, run: Run, level: int = 0) -> None: + """ + Summarize the run (and its children) into MLRun monitoring events. + + Note: This will use the MLRun tracer's default summarization that can be configured via + ``MLRunTracerMonitorSettings``, unless a custom summarizer was provided (via the same settings). + + :param run: LangChain run object to process holding all the nested tree of runs. + :param level: The nesting level of the run (0 for root runs, incremented for child runs). + """ + # Serialize the run: + serialized_run = self._serialize_run( + run=run, + include_child_runs=not (self._settings.monitor.root_run_only or self._settings.monitor.split_runs) + ) + + # Check for a user custom run summarizer function: + if self._custom_run_summarizer_function: + for summarized_run in self._custom_run_summarizer_function( + run, self._custom_run_summarizer_settings + ): + if summarized_run: + inputs, outputs = summarized_run + self._send_run_event( + event_id=serialized_run["id"], + inputs=inputs, + outputs=outputs, + start_time=run.start_time, + end_time=run.end_time, + ) + return + + # Check how to deal with the child runs, monitor them in separate events or as a single event: + if self._monitor_settings.split_runs and not self._settings.monitor.root_run_only: + # Monitor as separate events: + for child_run in run.child_runs: + self._persist_run(run=child_run, level=level + 1) + summarized_run = self._summarize_run(serialized_run=serialized_run, include_children=False) + if summarized_run: + inputs, outputs = summarized_run + inputs["child_level"] = level + self._send_run_event( + event_id=serialized_run["id"], + inputs=inputs, + outputs=outputs, + start_time=run.start_time, + end_time=run.end_time, + ) + return + + # Monitor the root event (include child runs if `root_run_only` is False): + summarized_run = self._summarize_run( + serialized_run=serialized_run, + include_children=not self._monitor_settings.root_run_only + ) + if not summarized_run: + return + inputs, outputs = summarized_run + inputs["child_level"] = level + self._send_run_event( + event_id=serialized_run["id"], + inputs=inputs, + outputs=outputs, + start_time=run.start_time, + end_time=run.end_time, + ) + + + def _serialize_run(self, run: Run, include_child_runs: bool) -> dict: + """ + Serialize a LangChain run into a dictionary. LangChain's Run is currently in Pydantic v1 where some of its + inner models are in Pydantic V2 which causes issues when trying to serialize the whole run object directly. + + This is a workaround to properly serialize the run object. + + :param run: The run to serialize. + :param include_child_runs: Whether to include child runs in the serialization. + + :returns: The serialized run dictionary. + """ + if not include_child_runs: + serialized_run = run.dict(exclude={"child_runs"}) + else: + serialized_run = run.dict() + return orjson.loads(orjson.dumps(serialized_run, default=self._serialize_default)) + + def _serialize_default(self, obj: Any): + """ + Default serializer for objects present in LangChain run that are not serializable by default JSON encoder. It + includes handling Pydantic v1 and v2 models, UUIDs, and datetimes. + + :param obj: The object to serialize. + + :returns: The serialized object. + """ + if isinstance(obj, UUID): + return str(obj) + if isinstance(obj, datetime.datetime): + return obj.isoformat() + if hasattr(obj, "model_dump"): + return obj.model_dump() + if hasattr(obj, "dict"): + return orjson.loads(orjson.dumps(obj.dict(), default=self._serialize_default)) + return str(obj) + + def _filter_by_tags(self, serialized_run: dict) -> bool: + """ + Apply tag-based filtering. + + :param serialized_run: Serialized run dictionary. + + :returns: True if the run passes tag filters or if no tag filter is configured. + """ + # Check if the user enabled filtering by tags: + if not self._monitor_settings.tags_filter: + return True + + # Filter the run: + return not set(self._monitor_settings.tags_filter).isdisjoint( + serialized_run["tags"] + ) + + def _filter_by_run_types(self, serialized_run: dict) -> bool: + """ + Apply run-type filtering. + + :param serialized_run: Serialized run dictionary. + + :returns: True if the run's ``run_type`` is allowed or if no run-type filter is configured. + """ + # Check if the user enabled filtering by run types: + if not self._monitor_settings.run_types_filter: + return True + + # Filter the run: + return serialized_run["run_type"] in self._monitor_settings.run_types_filter + + def _filter_by_names(self, serialized_run: dict) -> bool: + """ + Apply class/name filtering. + + :param serialized_run: Serialized run dictionary. + + :returns: True if the run's ``name`` is allowed or if no name filter is configured. + """ + # Check if the user enabled filtering by class names: + if not self._monitor_settings.names_filter: + return True + + # Filter the run: + return serialized_run["name"] in self._monitor_settings.names_filter + + def _get_run_inputs(self, serialized_run: dict) -> dict[str, Any]: + """ + Build the inputs dictionary for a monitoring event. + + :param serialized_run: Serialized run dictionary. + + :returns: A dictionary containing inputs, run metadata and (optionally) additional metadata. + """ + inputs = { + "inputs": serialized_run["inputs"], + "run_type": serialized_run["run_type"], + "run_name": serialized_run["name"], + "tags": serialized_run["tags"], + "run_id": serialized_run["id"], + "start_timestamp": serialized_run["start_time"], + } + if "parent_run_id" in serialized_run: + # Parent run ID is excluded when child runs are joined in the same event. When child runs are split, it is + # included and can be used to reconstruct the run tree if needed. + inputs = {**inputs, "parent_run_id": serialized_run["parent_run_id"]} + if self._monitor_settings.include_metadata and "metadata" in serialized_run: + inputs = {**inputs, "metadata": serialized_run["metadata"]} + + return inputs + + def _get_run_outputs(self, serialized_run: dict) -> dict[str, Any]: + """ + Build the outputs dictionary for a monitoring event. + + :param serialized_run: Serialized run dictionary. + + :returns: A dictionary with outputs and optional other collected info depending on monitor settings. + """ + outputs = {"outputs": serialized_run["outputs"], "end_timestamp": serialized_run["end_time"]} + if self._monitor_settings.include_latency and "latency" in serialized_run: + outputs = {**outputs, "latency": serialized_run["latency"]} + if self._monitor_settings.include_errors: + outputs = {**outputs, "error": serialized_run["error"]} + if self._monitor_settings.include_full_run: + outputs = {**outputs, "full_run": serialized_run} + + return outputs + + def _summarize_run(self, serialized_run: dict, include_children: bool) -> tuple[dict, dict] | None: + """ + Summarize a single run into (inputs, outputs) if it passes filters. + + :param serialized_run: Serialized run dictionary. + :param include_children: Whether to include child runs. + + :returns: The summarized run (inputs, outputs) tuple if the run should be monitored, otherwise ``None``. + """ + # Pass filters: + if not ( + self._filter_by_tags(serialized_run=serialized_run) + and self._filter_by_run_types(serialized_run=serialized_run) + and self._filter_by_names(serialized_run=serialized_run) + ): + return None + + # Check if needed to include errors: + if serialized_run["error"] and not self._monitor_settings.include_errors: + return None + + # Prepare the inputs and outputs: + inputs = self._get_run_inputs(serialized_run=serialized_run) + outputs = self._get_run_outputs(serialized_run=serialized_run) + + # Check if needed to include child runs: + if include_children: + outputs["child_runs"] = [] + for child_run in serialized_run.get("child_runs", []): + # Recursively summarize the child run: + summarized_child_run = self._summarize_run(serialized_run=child_run, include_children=True) + if summarized_child_run: + inputs_child, outputs_child = summarized_child_run + outputs["child_runs"].append( + { + "input_data": inputs_child, + "output_data": outputs_child, + } + ) + + return inputs, outputs + + def _send_run_event( + self, event_id: str, inputs: dict, outputs: dict, start_time: datetime.datetime, end_time: datetime.datetime + ): + """ + Send a monitoring event for a single run. + + Note: If monitor debug mode is enabled, appends to ``debug_target_list`` or prints JSON. + + :param event_id: Unique event identifier. + :param inputs: Inputs dictionary for the event. + :param outputs: Outputs dictionary for the event. + :param start_time: Request/start timestamp. + :param end_time: Response/end timestamp. + """ + event = { + "event_id": event_id, + "label": self._monitor_settings.label, + "input_data": {"input_data": inputs}, # So it will be a single "input feature" in MLRun monitoring. + "output_data": {"output_data": outputs}, # So it will be a single "output feature" in MLRun monitoring. + "request_timestamp": start_time.strftime("%Y-%m-%d %H:%M:%S%z"), + "response_timestamp": end_time.strftime("%Y-%m-%d %H:%M:%S%z"), + } + if self._monitor_settings.debug: + if isinstance(self._monitor_settings.debug_target_list, list): + self._monitor_settings.debug_target_list.append(event) + else: + print(orjson.dumps(event, option=orjson.OPT_INDENT_2 | orjson.OPT_APPEND_NEWLINE)) + return + + self._mlrun_client.monitor(**event) + + @staticmethod + def _check_for_env_var_usage() -> bool: + """ + Check whether global env-var activated tracing is requested. + + :returns: True when ``MLRUN_MONITORING_ENABLED`` environment variable equals ``"1"``. + """ + return os.environ.get(mlrun_monitoring_env_var, "0") == "1" + + @staticmethod + def _import_from_module_path(module_path: str) -> Any: + """ + Import an object from a full module path string. + + :param module_path: Full dotted path, e.g. ``a.b.module.object``. + + :returns: The imported object. + + raise: ValueError: If ``module_path`` is not a valid Python module path. + raise: ImportError: If module cannot be imported. + raise: AttributeError: If the object name is not found in the module. + """ + try: + module_name, object_name = module_path.rsplit(".", 1) + module = importlib.import_module(module_name) + obj = getattr(module, object_name) + except ValueError as value_error: + raise ValueError( + f"The provided '{module_path}' is not valid: it must have at least one '.'. " + f"If the class is locally defined, please add '__main__.MyObject' to the path." + ) from value_error + except ImportError as import_error: + raise ImportError( + f"Could not import '{module_path}'. Tried to import '{module_name}' and failed with the following " + f"error: {import_error}." + ) from import_error + except AttributeError as attribute_error: + raise AttributeError( + f"Could not import '{object_name}'. Tried to run 'from {module_name} import {object_name}' and could " + f"not find it: {attribute_error}" + ) from attribute_error + + return obj + + +#: MLRun monitoring context variable to set when the user wraps his code with `mlrun_monitoring`. From this context +# variable LangChain will get the tracer in a thread-safe way. +mlrun_monitoring_var: ContextVar[MLRunTracer | None] = ContextVar( + "mlrun_monitoring", default=None +) + + +@contextmanager +def mlrun_monitoring(settings: MLRunTracerSettings | None = None): + """ + Context manager to enable MLRun tracing for LangChain code to monitor LangChain runs. + + Example usage:: + + from mlrun_tracer import mlrun_monitoring, MLRunTracerSettings + + settings = MLRunTracerSettings(...) + with mlrun_monitoring(settings=settings) as tracer: + # LangChain execution within this block will be traced by `tracer`. + ... + + :param settings: The settings to use to configure the tracer. + """ + mlrun_tracer = MLRunTracer(settings=settings) + token = mlrun_monitoring_var.set(mlrun_tracer) + try: + yield mlrun_tracer + finally: + mlrun_monitoring_var.reset(token) + + +# Register a hook for LangChain to apply the MLRun tracer: +register_configure_hook( + context_var=mlrun_monitoring_var, + inheritable=True, # To allow inner runs (agent that uses a tool that uses a llm...) to be traced. + env_var=mlrun_monitoring_env_var, + handle_class=MLRunTracer, +) + + +# Temporary convenient function to set up the monitoring infrastructure required for the tracer. +def setup_langchain_monitoring( + project: str | mlrun.MlrunProject = None, + function_name: str = "langchain_mlrun_function", + model_name: str = "langchain_mlrun_model", + model_endpoint_name: str = "langchain_mlrun_endpoint", + monitoring_container: str = "projects", + monitoring_stream_path: str = None, +) -> dict: + """ + Create a model endpoint in the given project to be used for LangChain monitoring with MLRun and returns the + necessary environment variables to configure the MLRun tracer client. The project should already exist and have + monitoring enabled:: + + project.set_model_monitoring_credentials( + stream_profile_name=..., + tsdb_profile_name=... + ) + + This function creates and logs dummy model and function in the specified project in order to create the model + endpoint for monitoring. It is a temporary workaround and will be added as a feature in a future MLRun version. + + :param project: The MLRun project name or object where to create the model endpoint. If None, the current active + project will be used. + :param function_name: The name of the serving function to create. + :param model_name: The name of the model to create. + :param model_endpoint_name: The name of the model endpoint to create. + :param monitoring_container: The V3IO container where the monitoring stream is located. + :param monitoring_stream_path: The V3IO stream path for monitoring. If None, + ``/model-endpoints/stream-v1`` will be used. + + :returns: A dictionary with the necessary environment variables to configure the MLRun tracer client. + + raise: MLRunInvalidArgumentError: If no project is provided and there is no current active project. + """ + import io + import time + import sys + from contextlib import redirect_stdout, redirect_stderr + import tempfile + import pickle + import json + + from mlrun.common.helpers import parse_versioned_object_uri + from mlrun.features import Feature + + class ProgressStep: + """ + A context manager to display progress of a code block with timing and optional output suppression. + """ + + def __init__(self, label: str, indent: int = 2, width: int = 40, clean: bool = True): + """ + Initialize the ProgressStep context manager. + + :param label: The label to display for the progress step. + :param indent: The number of spaces to indent the label. + :param width: The width to pad the label for alignment. + :param clean: Whether to suppress stdout and stderr during the block execution. + """ + # Store parameters: + self._label = label + self._indent = indent + self._width = width + self._clean = clean + + # Internal state: + self._start_time = None + self._sink = io.StringIO() + self._stdout_redirect = None + self._stderr_redirect = None + self._last_line_length = 0 # To track the line printed when terminals don't support '\033[K'. + + # Capture the stream currently in use (before and if clean is true and we redirect it): + self._terminal = sys.stdout + + def __enter__(self): + """ + Enter the context manager, starting the timer and printing the initial status. + """ + # Start timer: + self._start_time = time.perf_counter() + + # Print without newline (using \r to allow overwriting): + self._write(icon=" ", status="Running", new_line=False) + + # Silence all internal noise: + if self._clean: + self._stdout_redirect = redirect_stdout(self._sink) + self._stderr_redirect = redirect_stderr(self._sink) + self._stdout_redirect.__enter__() + self._stderr_redirect.__enter__() + + return self + + def __exit__(self, exc_type, exc_val, exc_tb): + """ + Exit the context manager, stopping the timer and printing the final status. + + :param exc_type: The exception type, if any. + :param exc_val: The exception value, if any. + :param exc_tb: The exception traceback, if any. + """ + # Restore stdout/stderr: + if self._clean: + self._stdout_redirect.__exit__(exc_type, exc_val, exc_tb) + self._stderr_redirect.__exit__(exc_type, exc_val, exc_tb) + + # Calculate elapsed time: + elapsed = time.perf_counter() - self._start_time + + # Move cursor back to start of line ('\r') and overwrite ('\033[K' clears the line to the right): + if exc_type is None: + self._write(icon="✓", status=f"Done ({elapsed:.2f}s)", new_line=True) + else: + self._write(icon="✕", status="Failed", new_line=True) + + def update(self, status: str): + """ + Update the status message displayed for the progress step. + + :param status: The new status message to display. + """ + self._write(icon=" ", status=status, new_line=False) + + def _write(self, icon: str, status: str, new_line: bool): + """ + Write the progress line to the terminal, handling line clearing for terminals that do not support it. + + :param icon: The icon to display (e.g., checkmark, cross, space). + :param status: The status message to display. + :param new_line: Whether to end the line with a newline character. + """ + # Construct the basic line + line = f"\r{' ' * self._indent}[{icon}] {self._label.ljust(self._width, '.')} {status}" + + # Calculate if we need to pad with spaces to clear the old, longer line: + padding = max(0, self._last_line_length - len(line)) + + # Add spaces to clear old text (add the ANSI clear for terminals that support it): + line = f"{line}{' ' * padding}\033[K" + + # Add newline if needed: + if new_line: + line += "\n" + + # Write to terminal: + self._terminal.write(line) + self._terminal.flush() + + # Update the max length seen so far: + self._last_line_length = len(line) + + print("Creating LangChain model endpoint\n") + + # Get the project: + with ProgressStep("Loading Project"): + if project is None: + try: + project = mlrun.get_current_project(silent=False) + except mlrun.errors.MLRunInvalidArgumentError: + raise mlrun.errors.MLRunInvalidArgumentError( + "There is no current active project. Either use `mlrun.get_or_create_project` prior to " + "creating the monitoring endpoint or pass a project name to load." + ) + if isinstance(project, str): + project = mlrun.load_project(name=project) + + # Create and log the dummy model: + with ProgressStep(f"Creating Model") as progress_step: + # Check if the model already exists: + progress_step.update("Checking if model exists") + try: + dummy_model = project.get_artifact(key=model_name) + except mlrun.MLRunNotFoundError: + dummy_model = None + # If not, create and log it: + if not dummy_model: + progress_step.update(f"Logging model '{model_name}'") + with tempfile.TemporaryDirectory() as tmpdir: + # Create a dummy model file: + dummy_model_path = os.path.join(tmpdir, "for_langchain_mlrun_tracer.pkl") + with open(dummy_model_path, "wb") as f: + pickle.dump({"dummy": "model"}, f) + # Log the model: + dummy_model = project.log_model( + key=model_name, + model_file=dummy_model_path, + inputs=[Feature(value_type="str", name="input")], + outputs=[Feature(value_type='str', name="output")] + ) + + # Create and set the dummy function: + with ProgressStep("Creating Function") as progress_step: + # Check if the function already exists: + progress_step.update("Checking if function exists") + try: + dummy_function = project.get_function(key=function_name) + except mlrun.MLRunNotFoundError: + dummy_function = None + # If not, create and save it: + if not dummy_function: + progress_step.update(f"Setting function '{function_name}'") + with tempfile.TemporaryDirectory() as tmpdir: + # Create a dummy function file: + dummy_function_code = """ +def handler(context, event): + return "ok" +""" + dummy_function_path = os.path.join(tmpdir, "dummy_function.py") + with open(dummy_function_path, "w") as f: + f.write(dummy_function_code) + # Set the function in the project: + dummy_function = project.set_function( + func=dummy_function_path, name=function_name, image="mlrun/mlrun", kind="nuclio" + ) + dummy_function.save() + + # Create the model endpoint: + with ProgressStep("Creating Model Endpoint") as progress_step: + # Get the MLRun DB: + progress_step.update("Getting MLRun DB") + db = mlrun.get_run_db() + # Check if the model endpoint already exists: + progress_step.update("Checking if endpoint exists") + model_endpoint = project.list_model_endpoints(names=[model_endpoint_name]).endpoints + if model_endpoint: + model_endpoint = model_endpoint[0] + else: + progress_step.update("Creating model endpoint") + model_endpoint = mlrun.common.schemas.ModelEndpoint( + metadata=mlrun.common.schemas.ModelEndpointMetadata( + project=project.name, + name=model_endpoint_name, + endpoint_type=mlrun.common.schemas.model_monitoring.EndpointType.NODE_EP, + ), + spec=mlrun.common.schemas.ModelEndpointSpec( + function_name=dummy_function.metadata.name, + function_tag="latest", + model_path=dummy_model.uri, + model_class="CustomStream", + ), + status=mlrun.common.schemas.ModelEndpointStatus( + monitoring_mode=mm_constants.ModelMonitoringMode.enabled, + ), + ) + db.create_model_endpoint(model_endpoint=model_endpoint) + # Wait for the model endpoint UID to be set: + progress_step.update("Waiting for model endpoint") + uid_exist_flag = False + while not uid_exist_flag: + model_endpoint = project.list_model_endpoints(names=[model_endpoint_name]) + model_endpoint = model_endpoint.endpoints[0] + if model_endpoint.metadata.uid: + uid_exist_flag = True + + # Prepare the environment variables: + monitoring_stream_path = monitoring_stream_path or f"{project.name}/model-endpoints/stream-v1" + env_vars = { + "MLRUN_MONITORING_ENABLED": "1", + "MLRUN_TRACER_CLIENT_PROJECT": project.name, + "MLRUN_TRACER_CLIENT_STREAM_PATH": monitoring_stream_path, + "MLRUN_TRACER_CLIENT_CONTAINER": monitoring_container, + "MLRUN_TRACER_CLIENT_MODEL_ENDPOINT_NAME": model_endpoint.metadata.name, + "MLRUN_TRACER_CLIENT_MODEL_ENDPOINT_UID": model_endpoint.metadata.uid, + "MLRUN_TRACER_CLIENT_SERVING_FUNCTION": function_name, + } + print("\n✨ Done! LangChain monitoring model endpoint created successfully.") + print("You can now set the following environment variables to enable MLRun tracing in your LangChain code:\n") + print(json.dumps(env_vars, indent=4)) + print( + "\nTo customize the monitoring behavior, you can also set additional environment variables prefixed with " + "'MLRUN_TRACER_MONITOR_'. Refer to the MLRun tracer documentation for more details.\n" + ) + + return env_vars + + +class LangChainMonitoringApp(ModelMonitoringApplicationBase): + """ + A base monitoring application for LangChain that calculates common metrics on LangChain runs traced with the MLRun + tracer. + + The class is inheritable and can be extended to add custom metrics or override existing ones. It provides methods to + extract structured runs from the monitoring context and calculate metrics such as average latency, success rate, + token usage, and run name counts. + + If inheriting, the main method to override is `do_tracking`, which performs the tracking on the monitoring context. + """ + + def do_tracking(self, monitoring_context: MonitoringApplicationContext) -> ( + ModelMonitoringApplicationResult | + list[ModelMonitoringApplicationResult | ModelMonitoringApplicationMetric] | + dict[str, Any] + ): + """ + The main function that performs tracking on the monitoring context. The LangChain monitoring app by default + will calculate all the provided metrics on the structured runs extracted from the monitoring context sample + dataframe. + + :param monitoring_context: The monitoring context containing the sample dataframe. + + :returns: The monitoring artifacts, metrics and results. + """ + # Get the structured runs from the monitoring context: + structured_runs, _ = self.get_structured_runs(monitoring_context=monitoring_context) + + # Calculate the metrics: + average_latency = self.calculate_average_latency(structured_runs=structured_runs) + success_rate = self.calculate_success_rate(structured_runs=structured_runs) + token_usage = self.count_token_usage(structured_runs=structured_runs) + run_name_counts = self.count_run_names(structured_runs=structured_runs) + + return [ + ModelMonitoringApplicationMetric( + name="average_latency", + value=average_latency, + ), + ModelMonitoringApplicationMetric( + name="success_rate", + value=success_rate, + ), + ModelMonitoringApplicationMetric( + name="total_input_tokens", + value=token_usage["total_input_tokens"], + ), + ModelMonitoringApplicationMetric( + name="total_output_tokens", + value=token_usage["total_output_tokens"], + ), + ModelMonitoringApplicationMetric( + name="combined_total_tokens", + value=token_usage["combined_total"], + ), + *[ModelMonitoringApplicationMetric( + name=f"run_name_counts_{run_name}", + value=count, + ) for run_name, count in run_name_counts.items()], + ] + + @staticmethod + def get_structured_runs( + monitoring_context: MonitoringApplicationContext, + labels_filter: list[str] = None, + tags_filter: list[str] = None, + run_name_filter: list[str] = None, + run_type_filter: list[str] = None, + flatten_child_runs: bool = False, + ignore_child_runs: bool = False, + ignore_errored_runs: bool = False, + ) -> tuple[list[dict], list[dict]]: + """ + Get the structured runs from the monitoring context sample dataframe. The sample dataframe contains the raw + input and output data as JSON strings - the way the MLRun tracer sends them as events to MLRun monitoring. This + function parses the JSON strings into structured dictionaries that can be used for further metrics calculations + and analysis. + + :param monitoring_context: The monitoring context containing the sample dataframe. + :param labels_filter: List of labels to filter the runs. Only runs with a label appearing in this list will + remain. If None, no filtering is applied. + :param tags_filter: List of tags to filter the runs. Only runs containing at least one tag from this list will + remain. If None, no filtering is applied. + :param run_name_filter: List of run names to filter the runs. Only runs with a name appearing in this list will + remain. If None, no filtering is applied. + :param run_type_filter: List of run types to filter the runs. Only runs with a type appearing in this list will + remain. If None, no filtering is applied. + :param flatten_child_runs: Whether to flatten child runs into the main runs list. If True, all child runs will + be extracted and added to the main runs list. If False, child runs will be kept nested within their parent + runs. + :param ignore_child_runs: Whether to ignore child runs completely. If True, child runs will be removed from the + output. If False, child runs will be processed according to the other parameters. + :param ignore_errored_runs: Whether to ignore runs that resulted in errors. If True, runs with errors will be + excluded from the output. If False, errored runs will be included. + + :returns: A list of structured run dictionaries that passed the filters and a list of samples that could not be + parsed due to errors. + """ + # Retrieve the input and output samples from the monitoring context: + samples = monitoring_context.sample_df[['input', 'output']].to_dict('records') + + # Prepare to collect structured samples: + structured_samples = [] + errored_samples = [] + + # Go over all samples: + for sample in samples: + try: + # Parse the input data into structured format: + parsed_input = orjson.loads(sample['input']) + label = parsed_input['label'] + parsed_input = parsed_input["input"]["input_data"] + # Parse the output data into structured format: + parsed_output = orjson.loads(sample['output'])["output_data"] + structured_samples.extend( + LangChainMonitoringApp._collect_run( + structured_input=parsed_input, + structured_output=parsed_output, + label=label, + labels_filter=labels_filter, + tags_filter=tags_filter, + run_name_filter=run_name_filter, + run_type_filter=run_type_filter, + flatten_child_runs=flatten_child_runs, + ignore_child_runs=ignore_child_runs, + ignore_errored_runs=ignore_errored_runs, + ) + ) + except Exception: + errored_samples.append(sample) + + return structured_samples, errored_samples + + @staticmethod + def _collect_run( + structured_input: dict, + structured_output: dict, + label: str, + child_level: int = 0, + labels_filter: list[str] = None, + tags_filter: list[str] = None, + run_name_filter: list[str] = None, + run_type_filter: list[str] = None, + flatten_child_runs: bool = False, + ignore_child_runs: bool = False, + ignore_errored_runs: bool = False, + ) -> list[dict]: + """ + Recursively collect runs from the structured input and output data, applying filters as specified. + + :param structured_input: The structured input data of the run. + :param structured_output: The structured output data of the run. + :param label: The label of the run. + :param child_level: The current child level of the run (0 for root runs). + :param labels_filter: Label filter as described in `get_structured_runs`. + :param tags_filter: Tag filter as described in `get_structured_runs`. + :param run_name_filter: Run name filter as described in `get_structured_runs`. + :param run_type_filter: Run type filter as described in `get_structured_runs`. + :param flatten_child_runs: Flag to flatten child runs as described in `get_structured_runs`. + :param ignore_child_runs: Flag to ignore child runs as described in `get_structured_runs`. + :param ignore_errored_runs: Flag to ignore errored runs as described in `get_structured_runs`. + + :returns: A list of structured run dictionaries that passed the filters. + """ + # Prepare to collect runs: + runs = [] + + # Filter by label: + if labels_filter and label not in labels_filter: + return runs + + # Handle child runs: + if "child_runs" in structured_output: + # Check if we need to ignore or flatten child runs: + if ignore_child_runs: + structured_output.pop("child_runs") + elif flatten_child_runs: + # Recursively collect child runs: + child_runs = structured_output.pop("child_runs") + flattened_runs = [] + for child_run in child_runs: + flattened_runs.extend( + LangChainMonitoringApp._collect_run( + structured_input=child_run["input_data"], + structured_output=child_run["output_data"], + label=label, + child_level=child_level + 1, + tags_filter=tags_filter, + run_name_filter=run_name_filter, + run_type_filter=run_type_filter, + flatten_child_runs=flatten_child_runs, + ignore_child_runs=ignore_child_runs, + ignore_errored_runs=ignore_errored_runs, + ) + ) + runs.extend(flattened_runs) + + # Filter by tags, run name, run type, and errors: + if tags_filter and not set(structured_input["tags"]).isdisjoint(tags_filter): + return runs + if run_name_filter and structured_input["run_name"] not in run_name_filter: + return runs + if run_type_filter and structured_input["run_type"] not in run_type_filter: + return runs + if ignore_errored_runs and structured_output.get("error", None): + return runs + + # Collect the current run: + runs.append({"label": label, "input_data": structured_input, "output_data": structured_output, + "child_level": child_level}) + return runs + + @staticmethod + def iterate_structured_runs(structured_runs: list[dict]) -> Generator[dict, None, None]: + """ + Iterates over all runs in the structured samples, including child runs. + + :param structured_runs: List of structured run samples. + + :returns: A generator yielding each run structure. + """ + # TODO: Add an option to stop at a certain child level. + for structured_run in structured_runs: + if "child_runs" in structured_run['output_data']: + for child_run in structured_run['output_data']['child_runs']: + yield from LangChainMonitoringApp.iterate_structured_runs([{ + "label": structured_run['label'], + "input_data": child_run['input_data'], + "output_data": child_run['output_data'], + "child_level": structured_run['child_level'] + 1 + }]) + yield structured_run + + @staticmethod + def count_run_names(structured_runs: list[dict]) -> dict[str, int]: + """ + Counts occurrences of each run name in the structured samples. + + :param structured_runs: List of structured run samples. + + :returns: A dictionary with run names as keys and their counts as values. + """ + # TODO: Add a nice plot artifact that will draw the bar chart for what is being used the most. + # Prepare to count run names: + run_name_counts = {} + + # Go over all the runs: + for structured_run in LangChainMonitoringApp.iterate_structured_runs(structured_runs): + run_name = structured_run['input_data']['run_name'] + if run_name in run_name_counts: + run_name_counts[run_name] += 1 + else: + run_name_counts[run_name] = 1 + + return run_name_counts + + @staticmethod + def count_token_usage(structured_runs: list[dict]) -> dict: + """ + Calculates total tokens by only counting unique 'llm' type runs. + + :param structured_runs: List of structured run samples. + + :returns: A dictionary with total input tokens, total output tokens, and combined total tokens. + """ + # TODO: Add a token count per model breakdown (a dictionary of : to token counts) + # including an artifact that will plot it nicely. Pay attention that different providers use different + # keys in the response metadata. We should implement a mapping for that so each provider will have its own + # handler that will know how to extract the relevant info out of a run. + # Prepare to count tokens: + total_input_tokens = 0 + total_output_tokens = 0 + + # Go over all the LLM typed runs: + for structured_run in LangChainMonitoringApp.iterate_structured_runs(structured_runs): + # Count only LLM type runs as chain runs may include duplicative information as they accumulate the tokens + # from the child runs: + if structured_run['input_data']['run_type'] != 'llm': + continue + # Look for the token count information: + outputs = structured_run['output_data']["outputs"] + # Newer implementations should have the metadata in the `AIMessage` kwargs under generations: + if "generations" in outputs: + for generation in outputs["generations"]: # Iterate over generations. + for sample in generation: # Iterate over the generation batch. + token_usage = sample.get("message", {}).get("kwargs", {}).get("usage_metadata", {}) + if token_usage: + total_input_tokens += ( + token_usage.get('input_tokens', 0) + or token_usage.get('prompt_tokens', 0) + ) + total_output_tokens += ( + token_usage.get('output_tokens', 0) or + token_usage.get('completion_tokens', 0) + ) + continue + # Older implementations may have the metadata under `llm_output`: + if "llm_output" in outputs: + token_usage = outputs["llm_output"].get("token_usage", {}) + if token_usage: + total_input_tokens += token_usage.get('input_tokens', 0) or token_usage.get('prompt_tokens', 0) + total_output_tokens += ( + token_usage.get('output_tokens', 0) or + token_usage.get('completion_tokens', 0) + ) + + return { + "total_input_tokens": total_input_tokens, + "total_output_tokens": total_output_tokens, + "combined_total": total_input_tokens + total_output_tokens + } + + @staticmethod + def calculate_success_rate(structured_runs: list[dict]) -> float: + """ + Calculates the success rate across all runs. + + :param structured_runs: List of structured run samples. + + :returns: Success rate as a float percentage between 0 and 1. + """ + # TODO: Add an option to see errors breakdown by kind of error and maybe an option to show which run name yielded + # most of the errors with artifacts showcasing it. + successful_count = 0 + for structured_run in structured_runs: + if 'error' not in structured_run['output_data'] or structured_run['output_data']['error'] is None: + successful_count += 1 + return successful_count / len(structured_runs) if structured_runs else 0.0 + + @staticmethod + def calculate_average_latency(structured_runs: list[dict]) -> float: + """ + Calculates the average latency across all runs. + + :param structured_runs: List of structured run samples. + + :returns: Average latency in milliseconds. + """ + # TODO: Add an option to calculate latency per run name (to know which runs are slower/faster) and then return an + # artifact showcasing it. + # Prepare to calculate average latency: + total_latency = 0.0 + count = 0 + + # Go over all the root runs: + for structured_run in structured_runs: + # Skip child runs: + if structured_run["child_level"] > 0: + continue + # Check if latency is already provided: + if "latency" in structured_run['output_data']: + total_latency += structured_run['output_data']['latency'] + count += 1 + continue + # Calculate latency from timestamps: + start_time = datetime.datetime.fromisoformat(structured_run['input_data']['start_timestamp']) + end_time = datetime.datetime.fromisoformat(structured_run['output_data']['end_timestamp']) + total_latency += (end_time - start_time).total_seconds() * 1000 # Convert to milliseconds + count += 1 + + return total_latency / count if count > 0 else 0.0 diff --git a/modules/src/langchain_mlrun/notebook_images/mlrun_ui.png b/modules/src/langchain_mlrun/notebook_images/mlrun_ui.png new file mode 100644 index 0000000000000000000000000000000000000000..9785eeae3042b9e890e70c04dd7b9f966acecbac GIT binary patch literal 85919 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Callable +from concurrent.futures import ThreadPoolExecutor +from operator import add + +import pytest +from langchain_core.language_models import LanguageModelInput +from langchain_core.runnables import Runnable, RunnableLambda +from pydantic import ValidationError + +from langchain_core.prompts import ChatPromptTemplate +from langchain_core.output_parsers import StrOutputParser +from langchain_core.tracers import Run +from langchain_core.language_models.fake_chat_models import FakeListChatModel, GenericFakeChatModel +from langchain.agents import create_agent +from langchain_core.messages import AIMessage, HumanMessage +from langchain_core.tools import tool, BaseTool + +from langgraph.graph import StateGraph, START, END +from langchain_core.messages import BaseMessage +from pydantic_settings import BaseSettings, SettingsConfigDict + +from langchain_mlrun import ( + mlrun_monitoring, + MLRunTracer, + MLRunTracerSettings, + MLRunTracerClientSettings, + MLRunTracerMonitorSettings, + mlrun_monitoring_env_var, + LangChainMonitoringApp, +) + + +def _check_openai_credentials() -> bool: + """ + Check if OpenAI API key is set in environment variables. + + :return: True if OPENAI_API_KEY is set, False otherwise. + """ + return "OPENAI_API_KEY" in os.environ + + +# Import ChatOpenAI only if OpenAI credentials are available (meaning `langchain-openai` must be installed). +if _check_openai_credentials(): + from langchain_openai import ChatOpenAI + + +class _ToolEnabledFakeModel(GenericFakeChatModel): + """ + A fake chat model that supports tool binding for running agent tracing tests. + """ + + def bind_tools( + self, + tools: Sequence[ + dict[str, Any] | type | Callable | BaseTool # noqa: UP006 + ], + *, + tool_choice: str | None = None, + **kwargs: Any, + ) -> Runnable[LanguageModelInput, AIMessage]: + return self + + +#: Tag value for testing tag filtering. +_dummy_tag = "dummy_tag" + + +def _run_simple_chain() -> str: + """ + Run a simple LangChain chain that gets a fact about a topic. + """ + # Build a simple chain: prompt -> llm -> str output parser + llm = ChatOpenAI( + model="gpt-4o-mini", + tags=[_dummy_tag] + ) if _check_openai_credentials() else ( + FakeListChatModel( + responses=[ + "MLRun is an open-source orchestrator for machine learning pipelines." + ], + tags=[_dummy_tag] + ) + ) + prompt = ChatPromptTemplate.from_template("Tell me a short fact about {topic}") + chain = prompt | llm | StrOutputParser() + + # Run the chain: + response = chain.invoke({"topic": "MLRun"}) + return response + + +def _run_simple_agent(): + """ + Run a simple LangChain agent that uses two tools to get weather and stock price. + """ + # Define the tools: + @tool + def get_weather(city: str) -> str: + """Get the current weather for a specific city.""" + return f"The weather in {city} is 22°C and sunny." + + @tool + def get_stock_price(symbol: str) -> str: + """Get the current stock price for a symbol.""" + return f"The stock price for {symbol} is $150.25." + + # Define the model: + model = ChatOpenAI( + model="gpt-4o-mini", + tags=[_dummy_tag] + ) if _check_openai_credentials() else ( + _ToolEnabledFakeModel( + messages=iter( + [ + AIMessage( + content="", + tool_calls=[ + {"name": "get_weather", "args": {"city": "London"}, "id": "call_abc123"}, + {"name": "get_stock_price", "args": {"symbol": "AAPL"}, "id": "call_def456"} + ] + ), + AIMessage(content="The weather in London is 22°C and AAPL is trading at $150.25.") + ] + ), + tags=[_dummy_tag] + ) + ) + + # Create the agent: + agent = create_agent( + model=model, + tools=[get_weather, get_stock_price], + system_prompt="You are a helpful assistant with access to tools." + ) + + # Run the agent: + return agent.invoke({"messages": ["What is the weather in London and the stock price of AAPL?"]}) + + +def _run_langgraph_graph(): + """ + Run a LangGraph agent that uses reflection to correct its answer. + """ + + # Define the graph state: + class AgentState(TypedDict): + messages: Annotated[list[BaseMessage], add] + attempts: int + + # Define the model: + model = ChatOpenAI(model="gpt-4o-mini") if _check_openai_credentials() else ( + _ToolEnabledFakeModel( + messages=iter( + [ + AIMessage(content="There are 2 'r's in Strawberry."), # Mocking the failure + AIMessage(content="I stand corrected. S-t-r-a-w-b-e-r-r-y. There are 3 'r's."), # Mocking the fix + ] + ) + ) + ) + + # Define the graph nodes and router: + def call_model(state: AgentState): + response = model.invoke(state["messages"]) + return {"messages": [response], "attempts": state["attempts"] + 1} + + def reflect_node(state: AgentState): + prompt = "Wait, count the 'r's again slowly, letter by letter. Are you sure?" + return {"messages": [HumanMessage(content=prompt)]} + + def router(state: AgentState) -> Literal["reflect", END]: + # Make sure there are 2 attempts at least for an answer: + if state["attempts"] == 1: + return "reflect" + return END + + # Build the graph: + builder = StateGraph(AgentState) + builder.add_node("model", call_model) + tagged_reflect_node = RunnableLambda(reflect_node).with_config(tags=[_dummy_tag]) + builder.add_node("reflect", tagged_reflect_node) + builder.add_edge(START, "model") + builder.add_conditional_edges("model", router) + builder.add_edge("reflect", "model") + graph = builder.compile() + + # Run the graph: + return graph.invoke({"messages": [HumanMessage(content="How many 'r's in Strawberry?")], "attempts": 0}) + + +#: List of example functions to run in tests along the full (split-run enabled) expected monitor events. +_run_suites: list[tuple[Callable, int]] = [ + (_run_simple_chain, 4), + (_run_simple_agent, 9), + (_run_langgraph_graph, 9), +] + + +#: Dummy environment variables for testing. +_dummy_environment_variables = { + "MLRUN_TRACER_CLIENT_STREAM_PATH": "dummy_stream_path", + "MLRUN_TRACER_CLIENT_CONTAINER": "dummy_container", + "MLRUN_TRACER_CLIENT_MODEL_ENDPOINT_NAME": "dummy_model_name", + "MLRUN_TRACER_CLIENT_MODEL_ENDPOINT_UID": "dummy_model_endpoint_uid", + "MLRUN_TRACER_CLIENT_SERVING_FUNCTION": "dummy_serving_function", + "MLRUN_TRACER_MONITOR_DEBUG": "true", + "MLRUN_TRACER_MONITOR_DEBUG_TARGET_LIST": "true", + "MLRUN_TRACER_MONITOR_SPLIT_RUNS": "true", +} + + +@pytest.fixture() +def auto_mode_settings(): + """ + Sets the environment variables to enable mlrun monitoring in 'auto' mode. + """ + # Set environment variables for the duration of the test: + os.environ[mlrun_monitoring_env_var] = "1" + os.environ.update(_dummy_environment_variables) + + # Reset the singleton tracer to ensure fresh initialization: + MLRunTracer._singleton_tracer = None + MLRunTracer._initialized = False + + yield + + # Remove the environment variables after the test: + os.environ.pop(mlrun_monitoring_env_var) + for env_var in _dummy_environment_variables: + os.environ.pop(env_var) + + # Reset the singleton tracer after the test: + MLRunTracer._singleton_tracer = None + MLRunTracer._initialized = False + + +@pytest.fixture +def manual_mode_settings(): + """ + Sets the mandatory client settings and debug flag for the tests. + """ + settings = MLRunTracerSettings( + client=MLRunTracerClientSettings( + stream_path="dummy_stream_path", + container="dummy_container", + model_endpoint_name="dummy_model_name", + model_endpoint_uid="dummy_model_endpoint_uid", + serving_function="dummy_serving_function", + ), + monitor=MLRunTracerMonitorSettings( + debug=True, + debug_target_list=[], + split_runs=True, # Easier to test with split runs (filters can filter per run instead of inner events) + ), + ) + + yield settings + + +def test_settings_init_via_env_vars(): + """ + Test that settings are correctly initialized from environment variables. + """ + #: First, ensure that without env vars, validation fails due to missing required fields: + try: + settings = MLRunTracerSettings() + except ValidationError: + # Now, set the environment variables for the client settings and debug flag: + os.environ.update(_dummy_environment_variables) + + # Ensure that settings are now correctly initialized from env vars: + settings = MLRunTracerSettings() + assert settings.client.stream_path == "dummy_stream_path" + assert settings.client.container == "dummy_container" + assert settings.client.model_endpoint_name == "dummy_model_name" + assert settings.client.model_endpoint_uid == "dummy_model_endpoint_uid" + assert settings.client.serving_function == "dummy_serving_function" + assert settings.monitor.debug is True + else: + raise AssertionError("Initializing settings without env vars should have failed.") + + +def test_auto_mode_singleton_thread_safety(auto_mode_settings): + """ + Test that MLRunTracer singleton initialization is thread-safe in 'auto' mode. + + :param auto_mode_settings: Fixture to set up 'auto' mode environment and settings. + """ + # Initialize a list to hold tracer instances created in different threads: + tracer_instances = [] + + # Function to initialize the tracer in a thread: + def _init_tracer(): + tracer = MLRunTracer() + return tracer + + # Use ThreadPoolExecutor to simulate concurrent tracer initialization: + num_threads = 50 + with ThreadPoolExecutor(max_workers=num_threads) as executor: + futures = [executor.submit(_init_tracer) for _ in range(num_threads)] + tracer_instances = [f.result() for f in futures] + + # Check if every single reference in the list is the exact same object: + unique_instances = set(tracer._uid for tracer in tracer_instances) + + assert len(tracer_instances) == num_threads, "Not all threads returned a tracer instance. Test cannot proceed." + assert len(unique_instances) == 1, ( + f"Thread-safety failure! {len(unique_instances)} different instances were created under high concurrency." + ) + assert tracer_instances[0] is MLRunTracer(), "The global access point should return the same singleton." + + +def test_manual_mode_multi_instances(manual_mode_settings: MLRunTracerSettings): + """ + Test that MLRunTracer allows multiple instances in 'manual' mode. + + :param manual_mode_settings: Fixture to set up 'manual' mode environment and settings. + """ + # Initialize a list to hold tracer instances created in different iterations: + tracer_instances = [] + + # Create multiple tracer instances: + num_instances = 50 + for _ in range(num_instances): + tracer = MLRunTracer(settings=manual_mode_settings) + tracer_instances.append(tracer) + + # Check if every single reference in the list is a different object: + unique_instances = set(tracer._uid for tracer in tracer_instances) + + assert len(tracer_instances) == num_instances, "Not all instances were created. Test cannot proceed." + assert len(unique_instances) == num_instances, ( + f"Manual mode failure! {len(unique_instances)} unique instances were created instead of {num_instances}." + ) + + +@pytest.mark.parametrize("run_suites", _run_suites) +def test_auto_mode(auto_mode_settings, run_suites: tuple[Callable, int]): + """ + Test that MLRunTracer in 'auto' mode captures debug target list after running a LangChain / LangGraph example code. + + :param auto_mode_settings: Fixture to set up 'auto' mode environment and settings. + + :param run_suites: The function to run with the expected monitored events. + """ + run_func, expected_events = run_suites + + tracer = MLRunTracer() + assert len(tracer.settings.monitor.debug_target_list) == 0 + + print(run_func()) + assert len(tracer.settings.monitor.debug_target_list) == expected_events + + +@pytest.mark.parametrize("run_suites", _run_suites) +def test_manual_mode(manual_mode_settings: MLRunTracerSettings, run_suites: tuple[Callable, int]): + """ + Test that MLRunTracer in 'auto' mode captures debug target list after running a LangChain / LangGraph example code. + + :param manual_mode_settings: Fixture to set up 'manual' mode environment and settings. + :param run_suites: The function to run with the expected monitored events. + """ + run_func, expected_events = run_suites + + with mlrun_monitoring(settings=manual_mode_settings) as tracer: + print(run_func()) + assert len(tracer.settings.monitor.debug_target_list) == expected_events + + +def test_labeling(manual_mode_settings: MLRunTracerSettings): + """ + Test that MLRunTracer in 'auto' mode captures debug target list after running a LangChain / LangGraph example code. + + :param manual_mode_settings: Fixture to set up 'manual' mode environment and settings. + """ + for i, (run_func, expected_events) in enumerate(_run_suites): + label = f"label_{i}" + manual_mode_settings.monitor.label = label + manual_mode_settings.monitor.debug_target_list.clear() + with mlrun_monitoring(settings=manual_mode_settings) as tracer: + print(run_func()) + assert len(tracer.settings.monitor.debug_target_list) == expected_events + for event in tracer.settings.monitor.debug_target_list: + assert event["label"] == label + + +@pytest.mark.parametrize( + "run_suites", [ + run_suite + (filtered_events,) + for run_suite, filtered_events in zip(_run_suites, [1, 2, 1]) + ] +) +def test_monitor_settings_tags_filter( + manual_mode_settings: MLRunTracerSettings, + run_suites: tuple[Callable, int, int], +): + """ + Test the `tags_filter` setting of MLRunTracer. + + :param manual_mode_settings: Fixture to set up 'manual' mode environment and settings. + :param run_suites: The function to run with the expected monitored events and filtered events. + """ + run_func, expected_events, filtered_events = run_suites + + manual_mode_settings.monitor.tags_filter = [_dummy_tag] + + with mlrun_monitoring(settings=manual_mode_settings) as tracer: + print(run_func()) + assert len(tracer.settings.monitor.debug_target_list) == filtered_events + for event in tracer.settings.monitor.debug_target_list: + assert not set(manual_mode_settings.monitor.tags_filter).isdisjoint(event["input_data"]["input_data"]["tags"]) + + +@pytest.mark.parametrize( + "run_suites", [ + run_suite + (filtered_events,) + for run_suite, filtered_events in zip(_run_suites, [1, 3, 4]) + ] +) +def test_monitor_settings_name_filter( + manual_mode_settings: MLRunTracerSettings, + run_suites: tuple[Callable, int, int], +): + """ + Test the `names_filter` setting of MLRunTracer. + + :param manual_mode_settings: Fixture to set up 'manual' mode environment and settings. + :param run_suites: The function to run with the expected monitored events and filtered events. + """ + run_func, expected_events, filtered_events = run_suites + + manual_mode_settings.monitor.names_filter = ["StrOutputParser", "get_weather", "model", "router"] + + with mlrun_monitoring(settings=manual_mode_settings) as tracer: + print(run_func()) + assert len(tracer.settings.monitor.debug_target_list) == filtered_events + for event in tracer.settings.monitor.debug_target_list: + assert event["input_data"]["input_data"]["run_name"] in manual_mode_settings.monitor.names_filter + + +@pytest.mark.parametrize( + "run_suites", [ + run_suite + (filtered_events,) + for run_suite, filtered_events in zip(_run_suites, [2, 7, 9]) + ] +) +@pytest.mark.parametrize("split_runs", [True, False]) +def test_monitor_settings_run_type_filter( + manual_mode_settings: MLRunTracerSettings, + run_suites: tuple[Callable, int, int], + split_runs: bool +): + """ + Test the `run_types_filter` setting of MLRunTracer. Will also test with split runs enabled and disabled - meaning + that when disabled, if a parent run is filtered, all its child runs are also filtered by default. In the test we + made sure that the root run is always passing the filter (hence the equal one). + + :param manual_mode_settings: Fixture to set up 'manual' mode environment and settings. + :param run_suites: The function to run with the expected monitored events and filtered events. + :param split_runs: Whether to enable split runs in the monitor settings. + """ + run_func, expected_events, filtered_events = run_suites + filtered_events = filtered_events if split_runs else 1 + + manual_mode_settings.monitor.run_types_filter = ["llm", "chain"] + manual_mode_settings.monitor.split_runs = split_runs + + def recursive_check_run_types(run: dict): + assert run["input_data"]["run_type"] in manual_mode_settings.monitor.run_types_filter + if "child_runs" in run["output_data"]: + for child_run in run["output_data"]["child_runs"]: + recursive_check_run_types(child_run) + + with mlrun_monitoring(settings=manual_mode_settings) as tracer: + print(run_func()) + assert len(tracer.settings.monitor.debug_target_list) == filtered_events + + for event in tracer.settings.monitor.debug_target_list: + event_run = { + "input_data": event["input_data"]["input_data"], + "output_data": event["output_data"]["output_data"], + } + recursive_check_run_types(run=event_run) + +@pytest.mark.parametrize("run_suites", _run_suites) +@pytest.mark.parametrize("split_runs", [True, False]) +def test_monitor_settings_full_filter( + manual_mode_settings: MLRunTracerSettings, + run_suites: tuple[Callable, int], + split_runs: bool +): + """ + Test that a complete filter (not allowing any events to pass) won't fail the tracer. + + :param manual_mode_settings: Fixture to set up 'manual' mode environment and settings. + :param run_suites: The function to run with the expected monitored events. + :param split_runs: Whether to enable split runs in the monitor settings. + """ + run_func, _ = run_suites + + manual_mode_settings.monitor.run_types_filter = ["dummy_run_type"] + manual_mode_settings.monitor.split_runs = split_runs + + with mlrun_monitoring(settings=manual_mode_settings) as tracer: + print(run_func()) + assert len(tracer.settings.monitor.debug_target_list) == 0 + + +@pytest.mark.parametrize("run_suites", _run_suites) +@pytest.mark.parametrize("split_runs", [True, False]) +@pytest.mark.parametrize("root_run_only", [True, False]) +def test_monitor_settings_split_runs_and_root_run_only( + manual_mode_settings: MLRunTracerSettings, + run_suites: tuple[Callable, int], + split_runs: bool, + root_run_only: bool, +): + """ + Test the `split_runs` setting of MLRunTracer. + + :param manual_mode_settings: Fixture to set up 'manual' mode environment and settings. + :param run_suites: The function to run with the expected monitored events. + :param split_runs: Whether to enable split runs in the monitor settings. + :param root_run_only: Whether to enable `root_run_only` in the monitor settings. + """ + run_func, expected_events = run_suites + + manual_mode_settings.monitor.split_runs = split_runs + manual_mode_settings.monitor.root_run_only = root_run_only + + with mlrun_monitoring(settings=manual_mode_settings) as tracer: + for run_iteration in range(1, 3): + print(run_func()) + if root_run_only: + assert len(tracer.settings.monitor.debug_target_list) == 1 * run_iteration + assert "child_runs" not in tracer.settings.monitor.debug_target_list[-1]["output_data"]["output_data"] + elif split_runs: + assert len(tracer.settings.monitor.debug_target_list) == expected_events * run_iteration + assert "child_runs" not in tracer.settings.monitor.debug_target_list[-1]["output_data"]["output_data"] + else: # split_runs disabled + assert len(tracer.settings.monitor.debug_target_list) == 1 * run_iteration + assert len(tracer.settings.monitor.debug_target_list[-1]["output_data"]["output_data"]["child_runs"]) != 0 + + +class _CustomRunSummarizerSettings(BaseSettings): + """ + Settings for the custom summarizer function. + """ + dummy_value: int = 21 + + model_config = SettingsConfigDict(env_prefix="TEST_CUSTOM_SUMMARIZER_SETTINGS_") + + +def _custom_run_summarizer(run: Run, settings: _CustomRunSummarizerSettings = None): + """ + A custom summarizer function for testing. + + :param run: The LangChain / LangGraph run to summarize. + :param settings: Optional settings for the summarizer. + """ + inputs = { + "run_id": run.id, + "input": run.inputs, + "from_settings": settings.dummy_value if settings else 0, + } + + def count_llm_calls(r: Run) -> int: + if not r.child_runs: + return 1 if r.run_type == "llm" else 0 + return sum(count_llm_calls(child) for child in r.child_runs) + + def count_tool_calls(r: Run) -> int: + if not r.child_runs: + return 1 if r.run_type == "tool" else 0 + return sum(count_tool_calls(child) for child in r.child_runs) + + outputs = { + "llm_calls": count_llm_calls(run), + "tool_calls": count_tool_calls(run), + "output": run.outputs + } + + yield inputs, outputs + + +@pytest.mark.parametrize("run_suites", _run_suites) +@pytest.mark.parametrize("run_summarizer_function", [ + _custom_run_summarizer, + "test_langchain_mlrun._custom_run_summarizer", +]) +@pytest.mark.parametrize("run_summarizer_settings", [ + _CustomRunSummarizerSettings(dummy_value=12), + "test_langchain_mlrun._CustomRunSummarizerSettings", + None, +]) +def test_monitor_settings_custom_run_summarizer( + manual_mode_settings: MLRunTracerSettings, + run_suites: tuple[Callable, int], + run_summarizer_function: Callable | str, + run_summarizer_settings: BaseSettings | str | None, +): + """ + Test the custom run summarizer that can be passed to MLRunTracer. + + :param manual_mode_settings: Fixture to set up 'manual' mode environment and settings. + :param run_suites: The function to run with the expected monitored events. + :param run_summarizer_function: The custom summarizer function or its import path. + :param run_summarizer_settings: The settings for the custom summarizer or its import path. + """ + run_func, _ = run_suites + manual_mode_settings.monitor.run_summarizer_function = run_summarizer_function + manual_mode_settings.monitor.run_summarizer_settings = run_summarizer_settings + dummy_value_for_settings_from_env = 26 + os.environ["TEST_CUSTOM_SUMMARIZER_SETTINGS_DUMMY_VALUE"] = str(dummy_value_for_settings_from_env) + + with mlrun_monitoring(settings=manual_mode_settings) as tracer: + print(run_func()) + assert len(tracer.settings.monitor.debug_target_list) == 1 + + event = tracer.settings.monitor.debug_target_list[0] + if run_summarizer_settings: + if isinstance(run_summarizer_settings, str): + assert event["input_data"]["input_data"]["from_settings"] == dummy_value_for_settings_from_env + else: + assert event["input_data"]["input_data"]["from_settings"] == run_summarizer_settings.dummy_value + else: + assert event["input_data"]["input_data"]["from_settings"] == 0 + + +def test_monitor_settings_include_errors_field_presence(manual_mode_settings: MLRunTracerSettings): + """ + Test that when `include_errors` is True, the error field is present in outputs. + When `include_errors` is False, the error field is not added to outputs. + + :param manual_mode_settings: Fixture to set up 'manual' mode environment and settings. + """ + # Run with include_errors=True (default) and verify error field is present: + manual_mode_settings.monitor.include_errors = True + + with mlrun_monitoring(settings=manual_mode_settings) as tracer: + _run_simple_chain() + assert len(tracer.settings.monitor.debug_target_list) > 0 + + for event in tracer.settings.monitor.debug_target_list: + output_data = event["output_data"]["output_data"] + assert "error" in output_data, "error field should be present when include_errors is True" + + # Now run with include_errors=False and verify error field is excluded: + manual_mode_settings.monitor.include_errors = False + manual_mode_settings.monitor.debug_target_list.clear() + + with mlrun_monitoring(settings=manual_mode_settings) as tracer: + _run_simple_chain() + assert len(tracer.settings.monitor.debug_target_list) > 0 + + for event in tracer.settings.monitor.debug_target_list: + output_data = event["output_data"]["output_data"] + assert "error" not in output_data, "error field should be excluded when include_errors is False" + + +def test_monitor_settings_include_full_run(manual_mode_settings: MLRunTracerSettings): + """ + Test that when `include_full_run` is True, the complete serialized run is included in outputs. + + :param manual_mode_settings: Fixture to set up 'manual' mode environment and settings. + """ + manual_mode_settings.monitor.include_full_run = True + + with mlrun_monitoring(settings=manual_mode_settings) as tracer: + _run_simple_chain() + + assert len(tracer.settings.monitor.debug_target_list) > 0 + + for event in tracer.settings.monitor.debug_target_list: + output_data = event["output_data"]["output_data"] + assert "full_run" in output_data, "full_run should be included in outputs when include_full_run is True" + # Verify the full_run contains expected run structure: + assert "inputs" in output_data["full_run"] + assert "outputs" in output_data["full_run"] + + +def test_monitor_settings_include_metadata(manual_mode_settings: MLRunTracerSettings): + """ + Test that when `include_metadata` is False, metadata is excluded from inputs. + + Note: The fake models used in tests don't produce runs with metadata, so we can only + verify the "exclude" behavior. The code only adds metadata if the run actually contains it. + + :param manual_mode_settings: Fixture to set up 'manual' mode environment and settings. + """ + # Run with include_metadata=False and verify metadata is excluded: + manual_mode_settings.monitor.include_metadata = False + + with mlrun_monitoring(settings=manual_mode_settings) as tracer: + _run_simple_chain() + assert len(tracer.settings.monitor.debug_target_list) > 0 + + # Check that metadata is not present in inputs: + for event in tracer.settings.monitor.debug_target_list: + input_data = event["input_data"]["input_data"] + assert "metadata" not in input_data, "metadata should be excluded when include_metadata is False" + + +def test_monitor_settings_include_latency(manual_mode_settings: MLRunTracerSettings): + """ + Test that when `include_latency` is False, latency is excluded from outputs. + + :param manual_mode_settings: Fixture to set up 'manual' mode environment and settings. + """ + manual_mode_settings.monitor.include_latency = False + + with mlrun_monitoring(settings=manual_mode_settings) as tracer: + _run_simple_chain() + assert len(tracer.settings.monitor.debug_target_list) > 0 + + for event in tracer.settings.monitor.debug_target_list: + assert "latency" not in event["output_data"]["output_data"], \ + "latency should be excluded when include_latency is False" + + +def test_import_from_module_path_errors(): + """ + Test that `_import_from_module_path` raises appropriate errors for invalid paths. + """ + # Test ValueError for path without a dot: + with pytest.raises(ValueError) as exc_info: + MLRunTracer._import_from_module_path("no_dot_path") + assert "must have at least one '.'" in str(exc_info.value) + + # Test ImportError for non-existent module: + with pytest.raises(ImportError) as exc_info: + MLRunTracer._import_from_module_path("nonexistent_module_xyz.SomeClass") + assert "Could not import" in str(exc_info.value) + + # Test AttributeError for non-existent attribute in existing module: + with pytest.raises(AttributeError) as exc_info: + MLRunTracer._import_from_module_path("os.nonexistent_attribute_xyz") + assert "Could not import" in str(exc_info.value) + + +#: Sample structured runs for testing LangChainMonitoringApp methods. +_sample_structured_runs = [ + { + "label": "test_label", + "child_level": 0, + "input_data": { + "run_name": "RunnableSequence", + "run_type": "chain", + "tags": ["tag1"], + "inputs": {"topic": "MLRun"}, + "start_timestamp": "2024-01-01T10:00:00+00:00", + }, + "output_data": { + "outputs": {"result": "test output"}, + "end_timestamp": "2024-01-01T10:00:01+00:00", + "error": None, + "child_runs": [ + { + "input_data": { + "run_name": "FakeListChatModel", + "run_type": "llm", + "tags": ["tag2"], + "inputs": {"prompt": "test"}, + "start_timestamp": "2024-01-01T10:00:00.100+00:00", + }, + "output_data": { + "outputs": { + "generations": [[{ + "message": { + "kwargs": { + "usage_metadata": { + "input_tokens": 10, + "output_tokens": 20, + } + } + } + }]] + }, + "end_timestamp": "2024-01-01T10:00:00.500+00:00", + "error": None, + }, + }, + ], + }, + }, + { + "label": "test_label", + "child_level": 0, + "input_data": { + "run_name": "SimpleAgent", + "run_type": "chain", + "tags": ["tag1"], + "inputs": {"query": "test query"}, + "start_timestamp": "2024-01-01T10:00:02+00:00", + }, + "output_data": { + "outputs": {"result": "agent output"}, + "end_timestamp": "2024-01-01T10:00:04+00:00", + "error": "SomeError: something went wrong", + }, + }, +] + + +def test_langchain_monitoring_app_iterate_structured_runs(): + """ + Test that `iterate_structured_runs` yields all runs including nested child runs. + """ + # Iterate over all runs: + all_runs = list(LangChainMonitoringApp.iterate_structured_runs(_sample_structured_runs)) + + # Should yield parent runs and child runs: + # - First sample: 1 parent + 1 child = 2 runs + # - Second sample: 1 parent = 1 run + # Total: 3 runs + assert len(all_runs) == 3 + + # Verify run names are as expected: + run_names = [r["input_data"]["run_name"] for r in all_runs] + assert "RunnableSequence" in run_names + assert "FakeListChatModel" in run_names + assert "SimpleAgent" in run_names + + +def test_langchain_monitoring_app_count_run_names(): + """ + Test that `count_run_names` correctly counts occurrences of each run name. + """ + counts = LangChainMonitoringApp.count_run_names(_sample_structured_runs) + + assert counts["RunnableSequence"] == 1 + assert counts["FakeListChatModel"] == 1 + assert counts["SimpleAgent"] == 1 + + +def test_langchain_monitoring_app_count_token_usage(): + """ + Test that `count_token_usage` correctly calculates total tokens from LLM runs. + """ + token_usage = LangChainMonitoringApp.count_token_usage(_sample_structured_runs) + + assert token_usage["total_input_tokens"] == 10 + assert token_usage["total_output_tokens"] == 20 + assert token_usage["combined_total"] == 30 + + +def test_langchain_monitoring_app_calculate_success_rate(): + """ + Test that `calculate_success_rate` returns the correct percentage of successful runs. + """ + success_rate = LangChainMonitoringApp.calculate_success_rate(_sample_structured_runs) + + # First run has no error, second run has error: + # Success rate should be 1/2 = 0.5 + assert success_rate == 0.5 + + # Test with empty list: + empty_rate = LangChainMonitoringApp.calculate_success_rate([]) + assert empty_rate == 0.0 + + # Test with all successful runs: + successful_runs = [_sample_structured_runs[0]] # Only the first run which has no error + all_success_rate = LangChainMonitoringApp.calculate_success_rate(successful_runs) + assert all_success_rate == 1.0 + + +def test_langchain_monitoring_app_calculate_average_latency(): + """ + Test that `calculate_average_latency` returns the correct average latency across root runs. + """ + # Calculate average latency: + avg_latency = LangChainMonitoringApp.calculate_average_latency(_sample_structured_runs) + + # First run: 10:00:00 to 10:00:01 = 1000ms + # Second run: 10:00:02 to 10:00:04 = 2000ms + # Average: (1000 + 2000) / 2 = 1500ms + assert avg_latency == 1500.0 + + # Test with empty list: + empty_latency = LangChainMonitoringApp.calculate_average_latency([]) + assert empty_latency == 0.0 + + +def test_langchain_monitoring_app_calculate_average_latency_skips_child_runs(): + """ + Test that `calculate_average_latency` skips child runs (only calculates for root runs). + """ + # Create a sample with a child run that has child_level > 0: + runs_with_child = [ + { + "label": "test", + "child_level": 0, + "input_data": {"start_timestamp": "2024-01-01T10:00:00+00:00"}, + "output_data": {"end_timestamp": "2024-01-01T10:00:01+00:00"}, + }, + { + "label": "test", + "child_level": 1, # This is a child run, should be skipped + "input_data": {"start_timestamp": "2024-01-01T10:00:00+00:00"}, + "output_data": {"end_timestamp": "2024-01-01T10:00:10+00:00"}, # 10 seconds - would skew average + }, + ] + + # Calculate average latency: + avg_latency = LangChainMonitoringApp.calculate_average_latency(runs_with_child) + + # Should only consider the root run (1000ms), not the child run: + assert avg_latency == 1000.0 + + +def test_debug_mode_stdout(manual_mode_settings: MLRunTracerSettings, capsys): + """ + Test that debug mode prints to stdout when `debug_target_list` is not set (is False). + + :param manual_mode_settings: Fixture to set up 'manual' mode environment and settings. + :param capsys: Pytest fixture to capture stdout/stderr. + """ + # Set debug mode with debug_target_list=False (should print to stdout): + manual_mode_settings.monitor.debug = True + manual_mode_settings.monitor.debug_target_list = False + + with mlrun_monitoring(settings=manual_mode_settings) as tracer: + _run_simple_chain() + + # Capture stdout: + captured = capsys.readouterr() + + # Verify that JSON output was printed 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zp4Y9f)O?OmG3s7cm5w{_rmoZVGtb`Z(WP1I&DqXvoz-4_ob_&7W3*S-J8t!BoTFGd znY!m~W~nRw-K#8~?|E)tH}lZ3yH9GFZLjv1{du)}rVD+y*@%Y#0tg_000IagfB*srAb=2.1.2 - entry_points: - reduce_noise: - name: reduce_noise - doc: 'Reduce noise from audio file or directory containing audio files. - - The audio files must be in .wav format. - - The cleaned audio files will be saved in the target_directory. - - For information about the noise reduction algorithm see: - - https://github.com/timsainb/noisereduce - - Notice that the saved files are in wav format, even if the original files - are in other format.' - parameters: - - name: audio_source - type: str - doc: path to audio file or directory containing audio files - - name: target_directory - type: str - doc: path to directory to save the cleaned audio files. - - name: sample_rate - type: int - doc: Number of samples in one second in the audio file. Pass `None` to keep - the original sample rate. - default: 16000 - - name: duration - type: int - doc: Duration of the audio file to clean in seconds. Pass `None` to keep the - original duration. - default: null - - name: channel - type: int - doc: Channel to clean. Pass the number of the channel to clean. To clean all - channels pass None. - default: null - - name: silence_threshold - type: float - doc: The threshold to remove silence from the audio, in dB. If None, no silence - removal is performed. - default: null - - name: use_multiprocessing - type: int - doc: Number of processes to use for cleaning the audio files. If 0, no multiprocessing - is used. - default: 0 - - name: verbose - type: bool - doc: Verbosity level. If True, display progress bar. - default: true - outputs: [] - lineno: 388 - has_varargs: false - has_kwargs: false - clean_audio: - name: clean_audio - doc: '' - parameters: - - name: self - - name: data - type: Tensor - outputs: - - type: torch.Tensor - lineno: 276 - has_varargs: false - has_kwargs: false - save_audio: - name: save_audio - doc: '' - parameters: - - name: self - - name: audio - type: ndarray - - name: target_path - type: Path - outputs: [] - lineno: 256 - has_varargs: false - has_kwargs: false - load_audio: - name: load_audio - doc: '' - parameters: - - name: self - - name: file - type: str - outputs: - - type: torch.Tensor - lineno: 268 - has_varargs: false - has_kwargs: false - update_to_wav_suffix: - name: update_to_wav_suffix - doc: '' - parameters: - - name: self - - name: audio_file - type: Path - outputs: [] - lineno: 125 - has_varargs: false - has_kwargs: false - remove_silence: - name: remove_silence - doc: Remove silence sections from the audio. - parameters: - - name: self - - name: audio - type: ndarray - doc: The audio to remove silence from. - outputs: - - doc: The audio without silence. - lineno: 134 - has_varargs: false - has_kwargs: false - reduce_noise_dfn: - name: reduce_noise_dfn - doc: 'Reduce noise from audio files using DeepFilterNet. - - For more information about the noise reduction algorithm see: - - https://github.com/Rikorose/DeepFilterNet - - Notice that the saved files are in wav format, even if the original files - are in other format.' - parameters: - - name: audio_source - type: str - doc: path to audio file or directory of audio files - - name: target_directory - type: str - doc: path to target directory to save cleaned audio files - - name: pad - type: bool - doc: whether to pad the audio file with zeros before cleaning - default: true - - name: atten_lim_db - type: int - doc: maximum attenuation in dB - default: null - - name: silence_threshold - type: float - doc: the threshold to remove silence from the audio, in dB. If None, no silence - removal is performed. - default: null - - name: use_multiprocessing - type: int - doc: Number of processes to use for cleaning the audio files. If 0, no multiprocessing - is used. - default: 0 - - name: verbose - type: bool - doc: verbosity level. If True, display progress bar and logs. - default: true - outputs: [] - lineno: 322 - has_varargs: false - has_kwargs: true - description: Reduce noise from audio files - default_handler: reduce_noise - disable_auto_mount: false - clone_target_dir: '' - env: [] - priority_class_name: '' - preemption_mode: prevent - affinity: null - tolerations: null - security_context: {} -verbose: false diff --git a/noise_reduction/item.yaml b/noise_reduction/item.yaml deleted file mode 100644 index 8ddc63f4f..000000000 --- a/noise_reduction/item.yaml +++ /dev/null @@ -1,29 +0,0 @@ -apiVersion: v1 -categories: - - data-preparation - - machine-learning -description: Reduce noise from audio files -doc: '' -example: noise_reduction.ipynb -generationDate: 2024-03-04:17-30 -hidden: false -icon: '' -labels: - author: yonatans -maintainers: [] -mlrunVersion: 1.5.2 -name: noise-reduction -platformVersion: 3.5.3 -spec: - filename: noise_reduction.py - handler: reduce_noise - image: mlrun/mlrun - kind: job - requirements: [ - librosa, - noisereduce, - deepfilternet, - torchaudio>=2.1.2, - ] -url: '' -version: 1.0.0 \ No newline at end of file diff --git a/noise_reduction/noise_reduction.ipynb b/noise_reduction/noise_reduction.ipynb deleted file mode 100644 index e4fa0a534..000000000 --- a/noise_reduction/noise_reduction.ipynb +++ /dev/null @@ -1,942 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "4e0abc60-b718-4f45-a82a-0b8759f19d3f", - "metadata": {}, - "source": [ - "# Noise Reduction\n", - "\n", - "## Table of Contents\n", - "\n", - "1. [Introduction](#Introduction)\n", - "2. [Project Setup](#Setting-up-a-project)\n", - "3. [Noise Reduction Techniques](#Noise-Reduction-Techniques)\n", - " 1. [DeepFilterNet](#DeepFilterNet)\n", - " 2. [Spectral Gating](#SpectralGating)" - ] - }, - { - "cell_type": "markdown", - "id": "9af33629-965f-4f73-9e4a-89cc4c3dacf1", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "Noise reduction is a crucial signal processing technique used to enhance the quality of signals by minimizing unwanted or irrelevant noise. This technique finds applications in various fields such as audio processing, image processing, telecommunications, and more. The goal is to extract the useful information from a signal while suppressing undesirable background noise." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "f9cd530d-36a7-47b1-96f8-498d338b3a1a", - "metadata": {}, - "outputs": [], - "source": [ - "import mlrun" - ] - }, - { - "cell_type": "markdown", - "id": "c659289f-01f2-4e02-b843-b39cfc0c1d63", - "metadata": {}, - "source": [ - "## Setting up a project\n", - "\n", - "First of all we need to create a project with the `noise-reduction` function" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "c4217272-85b8-4af7-afee-bc97c6c73bd9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2024-03-04 15:54:53,561 [info] Project loaded successfully: {'project_name': 'noise-reduction'}\n" - ] - } - ], - "source": [ - "# Creating a project\n", - "project = mlrun.get_or_create_project(\"noise-reduction\")\n", - "# Importing the function from hub\n", - "noise_reduction_function = project.set_function(\"hub://noise_reduction\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "f7df4c3e-4e5b-47bd-a298-527d9c6fcb8f", - "metadata": {}, - "outputs": [], - "source": [ - "# Audio source can be either a single file or a directory of audio files\n", - "audio_source = \"data\"" - ] - }, - { - "cell_type": "markdown", - "id": "6c1c5109-6380-4364-b016-728523ed0ea1", - "metadata": {}, - "source": [ - "## Noise Reduction Techniques" - ] - }, - { - "attachments": { - "e48ce103-14f3-421d-82a4-823344895241.png": { - "image/png": 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- } - }, - "cell_type": "markdown", - "id": "5c81ecee-851c-4ee8-ad3a-4d372a1bfd97", - "metadata": {}, - "source": [ - "\n", - "### 1. DeepFilterNet\n", - "![image.png](attachment:e48ce103-14f3-421d-82a4-823344895241.png)\n", - "\n", - "In order to use this technique, you simply need to use the `reduce_noise_dfn` handler.\n", - "\n", - "Reduce noise from audio files using DeepFilterNet. For more information about the noise reduction algorithm, see [DeepFilterNet GitHub](https://github.com/Rikorose/DeepFilterNet). Notice that the saved files are in wav format, even if the original files are in other formats.\n", - "\n", - "### Parameters:\n", - "\n", - "- `audio_source`: path to the audio file or directory of audio files\n", - "- `target_directory`: path to the target directory to save cleaned audio files\n", - "- `pad`: whether to pad the audio file with zeros before cleaning\n", - "- `atten_lim_db`: maximum attenuation in dB\n", - "- `silence_threshold`: the threshold to remove silence from the audio, in dB. If None, no silence removal is performed.\n", - "- `use_multiprocessing`: Number of processes to use for cleaning the audio files. If 0, no multiprocessing is used.\n", - "- `verbose`: verbosity level. If True, display progress bar and logs.\n", - "- `kwargs`: additional arguments to pass to `torchaudio.load()`. For more information, see [torchaudio.load()](https://pytorch.org/audio/stable/generated/torchaudio.load.html).\n", - "\n", - "\n", - "In the examples below, the function is running locally, for running remotely, it is required to build the function's image first (need to execute only once):\n", - "```python\n", - "noise_reduction_function.apply(mlrun.auto_mount()) # required for local files\n", - "project.build_function(\"noise-reduction\")\n", - "```\n", - "\n", - "#### 1.1. Example" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "16113524-8597-48d4-8172-76b897fee3f2", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2024-03-04 15:54:56,999 [info] Storing function: {'name': 'noise-reduce-reduce-noise-dfn', 'uid': '9732dac831784a6a8b53acab5ff83a08', 'db': 'http://mlrun-api:8080'}\n", - "> 2024-03-04 15:55:07,525 [info] logging run results to: http://mlrun-api:8080\n", - "> 2024-03-04 15:55:07,702 [info] Reducing noise from audio files.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Noise-reduction: 0%| | 0/2 [00:00 2024-03-04 15:55:08,437 [info] Loading DeepFilterNet2 model.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "`torchaudio.backend.common.AudioMetaData` has been moved to `torchaudio.AudioMetaData`. Please update the import path.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-03-04 15:55:08 | INFO | DF | Running on torch 2.1.2+cu121\n", - "2024-03-04 15:55:08 | INFO | DF | Running on host jupyter-yoni-d56767c87-678n2\n", - "> 2024-03-04 15:55:08,464 [info] Loading DeepFilterNet2 model.\n", - "2024-03-04 15:55:08 | INFO | DF | Running on torch 2.1.2+cu121\n", - "2024-03-04 15:55:08 | INFO | DF | Running on host jupyter-yoni-d56767c87-678n2\n", - "2024-03-04 15:55:08 | INFO | DF | Loading model settings of DeepFilterNet3\n", - "2024-03-04 15:55:08 | INFO | DF | Using DeepFilterNet3 model at /igz/.cache/DeepFilterNet/DeepFilterNet3\n", - "2024-03-04 15:55:08 | INFO | DF | Initializing model `deepfilternet3`\n", - "2024-03-04 15:55:08 | INFO | DF | Loading model settings of DeepFilterNet3\n", - "2024-03-04 15:55:08 | INFO | DF | Using DeepFilterNet3 model at /igz/.cache/DeepFilterNet/DeepFilterNet3\n", - "2024-03-04 15:55:08 | INFO | DF | Initializing model `deepfilternet3`\n", - "2024-03-04 15:55:08 | INFO | DF | Found checkpoint /igz/.cache/DeepFilterNet/DeepFilterNet3/checkpoints/model_120.ckpt.best with epoch 120\n", - "2024-03-04 15:55:08 | INFO | DF | Found checkpoint /igz/.cache/DeepFilterNet/DeepFilterNet3/checkpoints/model_120.ckpt.best with epoch 120\n", - "2024-03-04 15:55:08 | INFO | DF | Running on device cpu\n", - "2024-03-04 15:55:08 | INFO | DF | Running on device cpu\n", - "2024-03-04 15:55:08 | INFO | DF | Model loaded\n", - "2024-03-04 15:55:08 | INFO | DF | Model loaded\n", - "> 2024-03-04 15:55:08,635 [info] Reducing noise from test_data.mp3.\n", - "> 2024-03-04 15:55:08,636 [info] Reducing noise from test_data.wav.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m2024-03-04 15:55:08\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mDF\u001b[0m | \u001b[33m\u001b[1mAudio sampling rate does not match model sampling rate (16000, 48000). Resampling...\u001b[0m\n", - "\"sinc_interpolation\" resampling method name is being deprecated and replaced by \"sinc_interp_hann\" in the next release. The default behavior remains unchanged.\n", - "The MPEG_LAYER_III subtype is unknown to TorchAudio. As a result, the bits_per_sample attribute will be set to 0. If you are seeing this warning, please report by opening an issue on github (after checking for existing/closed ones). You may otherwise ignore this warning.\n", - "\u001b[32m2024-03-04 15:55:08\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mDF\u001b[0m | \u001b[33m\u001b[1mAudio sampling rate does not match model sampling rate (16000, 48000). Resampling...\u001b[0m\n", - "\"sinc_interpolation\" resampling method name is being deprecated and replaced by \"sinc_interp_hann\" in the next release. The default behavior remains unchanged.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2024-03-04 15:55:16,701 [info] Saved cleaned audio file to clean_data/test_data.wav.\n", - "> 2024-03-04 15:55:16,706 [info] Saved cleaned audio file to clean_data/test_data_mp3.wav.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Noise-reduction: 100%|██████████| 2/2 [00:09<00:00, 4.51s/file]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2024-03-04 15:55:16,791 [info] Summarizing the results.\n", - "> 2024-03-04 15:55:16,792 [info] Done (2/2)\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "

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noise-reduction0Mar 04 15:54:57completednoise-reduce-reduce-noise-dfn
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\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "data": { - "text/html": [ - " > to track results use the .show() or .logs() methods or click here to open in UI" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2024-03-04 15:55:17,976 [info] Run execution finished: {'status': 'completed', 'name': 'noise-reduce-reduce-noise-dfn'}\n" - ] - } - ], - "source": [ - "dfn_run = noise_reduction_function.run(\n", - " handler=\"reduce_noise_dfn\",\n", - " inputs={\"audio_source\": audio_source},\n", - " params={\n", - " \"target_directory\": \"./clean_data\",\n", - " \"use_multiprocessing\": 2,\n", - " \"silence_threshold\": 50,\n", - " \"atten_lim_db\": 10,\n", - " },\n", - " returns=[\"successes: file\", \"errors: file\"],\n", - " local=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "a71ba944-1fc2-48be-b789-d57c59201939", - "metadata": {}, - "source": [ - "### Looking at the result" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "19b04cf6-5a4d-4d74-b66e-193540a900a1", - "metadata": {}, - "outputs": [ - { - "data": { - "application/json": { - "test_data.mp3": "clean_data/test_data_mp3.wav", - "test_data.wav": "clean_data/test_data.wav" - }, - "text/plain": [ - "" - ] - }, - "metadata": { - "application/json": { - "expanded": false, - "root": "root" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/json": {}, - "text/plain": [ - "" - ] - }, - "metadata": { - "application/json": { - "expanded": false, - "root": "root" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "dfn_run.artifact(\"successes\").show()\n", - "dfn_run.artifact(\"errors\").show()" - ] - }, - { - "attachments": { - "68c16acf-c28e-4bb8-a453-abbebc0137ce.png": { - "image/png": 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- } - }, - "cell_type": "markdown", - "id": "4576b576-4ac0-433a-9d1d-f39225a6648d", - "metadata": {}, - "source": [ - "\n", - "### 2. Spectral Gating\n", - "![image.png](attachment:68c16acf-c28e-4bb8-a453-abbebc0137ce.png)\n", - "\n", - "In order to use this technique, you simply need to use the `reduce_noise` handler.\n", - "\n", - "Spectral gating selectively filters signal frequencies based on amplitude, offering targeted noise reduction or feature enhancement in signal processing applications.\n", - "\n", - "Reduce noise from an audio file or directory containing audio files. The audio files must be in .wav format. The cleaned audio files will be saved in the target directory. For information about the noise reduction algorithm, see [noisereduce GitHub](https://github.com/timsainb/noisereduce). Notice that the saved files are in .wav format, even if the original files are in another format.\n", - "\n", - "### Parameters:\n", - "\n", - "- `audio_source`: path to the audio file or directory containing audio files\n", - "- `target_directory`: path to the directory to save the cleaned audio files.\n", - "- `sample_rate`: Number of samples in one second in the audio file. Pass `None` to keep the original sample rate.\n", - "- `duration`: Duration of the audio file to clean in seconds. Pass `None` to keep the original duration.\n", - "- `channel`: Channel to clean. Pass the number of the channel to clean. To clean all channels, pass `None`.\n", - "- `silence_threshold`: The threshold to remove silence from the audio, in dB. If `None`, no silence removal is performed.\n", - "- `use_multiprocessing`: Number of processes to use for cleaning the audio files. If 0, no multiprocessing is used.\n", - "- `verbose`: Verbosity level. If True, display a progress bar.\n", - "\n", - "#### 2.1. Example" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "f10a5ecd-bf90-4650-a42e-d3fbfff78e52", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2024-03-04 16:07:39,378 [info] Storing function: {'name': 'noise-reduce-reduce-noise', 'uid': '6e6d6f7c3f8243b995dc1bbcf66f7544', 'db': 'http://mlrun-api:8080'}\n", - "> 2024-03-04 16:07:39,541 [info] Reducing noise from audio files.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Noise-reduction: 0%| | 0/2 [00:00 2024-03-04 16:07:39,565 [info] Reducing noise from test_data.mp3.\n", - "> 2024-03-04 16:07:39,566 [info] Reducing noise from test_data.wav.\n", - "> 2024-03-04 16:07:46,174 [info] Saved cleaned audio file to clean_data/test_data.wav.\n", - "> 2024-03-04 16:07:46,175 [info] Saved cleaned audio file to clean_data/test_data_mp3.wav.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Noise-reduction: 100%|██████████| 2/2 [00:06<00:00, 3.31s/file]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2024-03-04 16:07:46,211 [info] Summarizing the results.\n", - "> 2024-03-04 16:07:46,212 [info] Done (2/2)\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "
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projectuiditerstartstatenamelabelsinputsparametersresultsartifacts
noise-reduction0Mar 04 16:07:39completednoise-reduce-reduce-noise
v3io_user=yonis
kind=local
owner=yonis
host=jupyter-yoni-d56767c87-678n2
audio_source
target_directory=./clean_data
use_multiprocessing=2
silence_threshold=50
successes
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\n", - "
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\n", - " Title\n", - " ×\n", - "
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\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "data": { - "text/html": [ - " > to track results use the .show() or .logs() methods or click here to open in UI" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2024-03-04 16:07:46,389 [info] Run execution finished: {'status': 'completed', 'name': 'noise-reduce-reduce-noise'}\n" - ] - } - ], - "source": [ - "noise_reduction_run = noise_reduction_function.run(\n", - " handler=\"reduce_noise\",\n", - " inputs={\"audio_source\": audio_source},\n", - " params={\n", - " \"target_directory\": \"./clean_data\",\n", - " \"use_multiprocessing\": 2,\n", - " \"silence_threshold\": 50,\n", - " },\n", - " local=True,\n", - " returns=[\"successes: file\", \"errors: file\"],\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "699615d7-bba1-4147-ad3d-d295d794f866", - "metadata": {}, - "source": [ - "### Looking at the result" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "47c4f66a-d5d0-47e5-9842-abbe6653526b", - "metadata": {}, - "outputs": [ - { - "data": { - "application/json": { - "test_data.mp3": "clean_data/test_data_mp3.wav", - "test_data.wav": "clean_data/test_data.wav" - }, - "text/plain": [ - "" - ] - }, - "metadata": { - "application/json": { - "expanded": false, - "root": "root" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/json": {}, - "text/plain": [ - "" - ] - }, - "metadata": { - "application/json": { - "expanded": false, - "root": "root" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "dfn_run.artifact(\"successes\").show()\n", - "dfn_run.artifact(\"errors\").show()" - ] - }, - { - "cell_type": "markdown", - "id": "6eeae1bb-c714-491b-91dd-f22148cd0970", - "metadata": {}, - "source": [ - "The output of this function is the same as the first one." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "mlrun-base", - "language": "python", - "name": "conda-env-mlrun-base-py" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.16" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/noise_reduction/noise_reduction.py b/noise_reduction/noise_reduction.py deleted file mode 100644 index f0fff5504..000000000 --- a/noise_reduction/noise_reduction.py +++ /dev/null @@ -1,625 +0,0 @@ -import logging -from abc import ABCMeta, abstractmethod -from multiprocessing import Process, Queue -from pathlib import Path -from typing import List, Tuple, Type, Union - -import librosa -import numpy as np -import torch -from scipy.io import wavfile -from tqdm import tqdm - -#: The value to send into multiprocessing queues to stop the process: -_MULTIPROCESSING_STOP_MARK = "STOP" - -# Get the global logger: -try: - import mlrun - - _LOGGER = mlrun.get_or_create_ctx("noise_reduce").logger -except ModuleNotFoundError: - _LOGGER = logging.getLogger() - - -class ReduceNoiseBase(metaclass=ABCMeta): - """ - Base class for noise reduction. - This class is aimed to be inherited by specific noise reduction algorithms. - You must implement the following methods: - - clean_audio: The method to clean the audio, where the noise reduction algorithm is implemented. - - save_audio: The method to save the audio to a file. - - load_audio: The method to load the audio from a file. - - After implementing the above methods, you can use the reduce_noise method to reduce noise from audio files. - """ - def __init__( - self, - target_directory: Path, - verbose: bool = True, - silence_threshold: float = None, - ): - self.target_directory = Path(target_directory) - self.verbose = verbose - self.silence_threshold = silence_threshold - - def reduce_noise(self, audio_file: Path) -> Tuple[bool, Tuple[str, str]]: - """ - Reduce noise from the given audio file. - - :param audio_file: The audio file to reduce noise from. - - :returns: A tuple of: - - a boolean indicating whether an error occurred - - a tuple of: - - audio file name - - target path in case of success / error message in case of failure. - """ - try: - if self.verbose: - _LOGGER.info(f"Reducing noise from {audio_file.name}.") - - # Load audio data: - audio = self.load_audio(file=str(audio_file)) - - # Perform noise reduction: - reduced_noise = self.clean_audio(data=audio) - - # Remove silence from the audio if necessary: - reduced_noise = self.remove_silence(audio=reduced_noise) - - # Prepare target path: - target_path = self.update_to_wav_suffix(audio_file=audio_file) - - # Save file: - self.save_audio( - audio=reduced_noise, - target_path=target_path, - ) - - if self.verbose: - _LOGGER.info(f"Saved cleaned audio file to {target_path}.") - - return False, (audio_file.name, str(target_path)) - except Exception as exception: - if self.verbose: - _LOGGER.error(f"Failed to reduce noise from {audio_file.name}.") - _LOGGER.error(f"Error: {exception}") - # Collect the error: - return True, (audio_file.name, str(exception)) - - @abstractmethod - def clean_audio(self, data) -> Union[np.ndarray, torch.Tensor]: - """ - Clean the audio from noise. Here you should implement the noise reduction algorithm. - - :param data: The audio data to clean. - - :returns: The cleaned audio. - """ - pass - - @abstractmethod - def save_audio(self, audio: np.ndarray, target_path: Path): - """ - Save the audio to a file. - - :param audio: The audio to save. - :param target_path: The target path to save the audio to. - """ - pass - - @abstractmethod - def load_audio(self, file: str) -> Tuple[Union[np.ndarray, torch.Tensor], int]: - """ - Load the audio from a file. - - :param file: The file to load the audio from. - - :returns: A tuple of: - - the audio data - - the sample rate - """ - pass - - def update_to_wav_suffix(self, audio_file: Path): - target_path = self.target_directory / audio_file.name - if target_path.suffix != ".wav": - old_suffix = target_path.suffix[1:] - target_path = target_path.with_stem(target_path.stem + f"_{old_suffix}") - return target_path.with_suffix(".wav") - else: - return target_path - - def remove_silence( - self, - audio: np.ndarray, - ): - """ - Remove silence sections from the audio. - - :param audio: The audio to remove silence from. - - :returns: The audio without silence. - """ - if self.silence_threshold is None: - return audio - - # Get the indices of the non-silent frames: - non_silent_indices = librosa.effects.split( - y=audio, - top_db=self.silence_threshold, - frame_length=2048, - hop_length=256, - ) - - # Get the non-silent audio: - non_silent_audio = np.concatenate( - [audio[:, start:end] for start, end in non_silent_indices], axis=1 - ) - - return non_silent_audio - - -class ReduceNoise(ReduceNoiseBase): - def __init__( - self, - target_directory: Path, - verbose: bool = True, - silence_threshold: float = None, - sample_rate: int = 16000, - duration: int = None, - channel: int = None, - ): - super().__init__(target_directory, verbose, silence_threshold) - self.sample_rate = sample_rate - self.duration = duration - self.channel = channel - - def save_audio(self, audio: np.ndarray, target_path: Path): - # If the audio has more than one channel, transpose it in order to save it: - if len(audio) > 1: - audio = audio.T - - wavfile.write( - filename=target_path, - rate=self.sample_rate, - data=audio, - ) - - def load_audio(self, file: str) -> np.ndarray: - data, sr = librosa.load( - path=file, - sr=self.sample_rate, - mono=False, # keep channels separate - duration=self.duration, - ) - # set sample rate: - self.sample_rate = int(sr) - - # convert to int with scaling for 16-bit integer - data *= 32767 / np.max(np.abs(data)) # re-scaling - data = data.astype(np.int16) # change data type - - # select channel - data_to_reduce = data[self.channel] if self.channel is not None else data - return data_to_reduce - - def clean_audio(self, data: np.ndarray) -> np.ndarray: - try: - import noisereduce - except ImportError as e: - raise ImportError("Please install noisereduce package") from e - - reduced_noise = noisereduce.reduce_noise(y=data, sr=self.sample_rate) - - # add channel back after noise reduction - if self.channel is not None: - # putting the channel back in the data - data[self.channel] = reduced_noise - # updating the data to save - reduced_noise = data - - return reduced_noise - - -class DFN(ReduceNoiseBase): - def __init__( - self, - target_directory: Path, - verbose: bool = True, - silence_threshold: float = None, - pad: bool = True, - atten_lim_db: int = None, - **kwargs, - ): - super().__init__(target_directory, verbose, silence_threshold) - self.pad = pad - self.atten_lim_db = atten_lim_db - self.kwargs = kwargs - - # import required packages - try: - from df.enhance import init_df - except ImportError as e: - raise ImportError("Please install deepfilternet packages") from e - - if self.verbose: - _LOGGER.info("Loading DeepFilterNet2 model.") - - # Load the model: - model, df_state, _ = init_df() - self.model = model - self.df_state = df_state - self.sample_rate = self.df_state.sr() - - def save_audio(self, audio: np.ndarray, target_path: Path): - try: - from df.enhance import save_audio - except ImportError as e: - raise ImportError("Please install deepfilternet package") from e - save_audio( - file=target_path.name, - audio=audio, - sr=self.sample_rate, - output_dir=str(self.target_directory), - ) - - def load_audio(self, file: str) -> torch.Tensor: - try: - from df.enhance import load_audio - except ImportError as e: - raise ImportError("Please install deepfilternet package") from e - audio, _ = load_audio(file=file, sr=self.sample_rate, **self.kwargs) - return audio - - def clean_audio(self, data: torch.Tensor) -> torch.Tensor: - try: - from df.enhance import enhance - except ImportError as e: - raise ImportError("Please install deepfilternet package") from e - return enhance( - model=self.model, - df_state=self.df_state, - audio=data, - pad=self.pad, - atten_lim_db=self.atten_lim_db, - ) - - -def _multiprocessing_complete_tasks( - noise_reduce_type: Type[ReduceNoiseBase], - noise_reduce_arguments: dict, - tasks_queue: Queue, - results_queue: Queue, -): - """ - Complete the tasks in the given queue and put the results in the given results queue. The function will stop when - the given tasks queue will receive the stop mark. It is aimed to be used with multiprocessing as a process. - - :param noise_reduce_type: The noise reduce type to use. - :param noise_reduce_arguments: The noisereduce initialization kwargs. - :param tasks_queue: A queue to get the tasks from. - :param results_queue: A queue to put the results in. - """ - # Initialize the reduce noise object - noise_reducer = noise_reduce_type(**noise_reduce_arguments) - - # Start listening to the tasks queue: - while True: - # Get the audio_file: - audio_file = tasks_queue.get() - if audio_file == _MULTIPROCESSING_STOP_MARK: - break - audio_file = Path(audio_file) - # Apply noise reduction and collect the result: - results_queue.put(noise_reducer.reduce_noise(audio_file=audio_file)) - - # Mark the end of the tasks: - results_queue.put(_MULTIPROCESSING_STOP_MARK) - - -def reduce_noise_dfn( - audio_source: str, - target_directory: str, - pad: bool = True, - atten_lim_db: int = None, - silence_threshold: float = None, - use_multiprocessing: int = 0, - verbose: bool = True, - **kwargs, -): - """ - Reduce noise from audio files using DeepFilterNet. - For more information about the noise reduction algorithm see: - https://github.com/Rikorose/DeepFilterNet - Notice that the saved files are in wav format, even if the original files are in other format. - - :param audio_source: path to audio file or directory of audio files - :param target_directory: path to target directory to save cleaned audio files - :param pad: whether to pad the audio file with zeros before cleaning - :param atten_lim_db: maximum attenuation in dB - :param silence_threshold: the threshold to remove silence from the audio, in dB. If None, no silence removal is - performed. - :param use_multiprocessing: Number of processes to use for cleaning the audio files. - If 0, no multiprocessing is used. - :param verbose: verbosity level. If True, display progress bar and logs. - :param kwargs: additional arguments to pass to torchaudio.load(). For more information see: - https://pytorch.org/audio/stable/generated/torchaudio.load.html - """ - if verbose: - _LOGGER.info("Reducing noise from audio files.") - - # create target directory: - target_directory = _create_target_directory(target_directory) - - # get audio files: - audio_files = _get_audio_files(audio_source) - - noise_reduce_arguments = { - "target_directory": target_directory, - "pad": pad, - "atten_lim_db": atten_lim_db, - "silence_threshold": silence_threshold, - **kwargs, - } - - if use_multiprocessing: - results = _parallel_run( - noise_reduce_type=DFN, - noise_reduce_arguments=noise_reduce_arguments, - n_workers=use_multiprocessing, - audio_files=audio_files, - description="Noise-reduction", - verbose=verbose, - ) - else: - results = _run( - noise_reduce_type=DFN, - noise_reduce_arguments=noise_reduce_arguments, - audio_files=audio_files, - description="Noise-reduction", - verbose=verbose, - ) - - return _process_results(results, verbose) - - -def reduce_noise( - audio_source: str, - target_directory: str, - sample_rate: int = 16000, - duration: int = None, - channel: int = None, - silence_threshold: float = None, - use_multiprocessing: int = 0, - verbose: bool = True, -): - """ - Reduce noise from audio file or directory containing audio files. - The audio files must be in .wav format. - The cleaned audio files will be saved in the target_directory. - For information about the noise reduction algorithm see: - https://github.com/timsainb/noisereduce - Notice that the saved files are in wav format, even if the original files are in other format. - - :param audio_source: path to audio file or directory containing audio files - :param target_directory: path to directory to save the cleaned audio files. - :param sample_rate: Number of samples in one second in the audio file. - Pass `None` to keep the original sample rate. - :param duration: Duration of the audio file to clean in seconds. - Pass `None` to keep the original duration. - :param channel: Channel to clean. Pass the number of the channel to clean. - To clean all channels pass None. - :param silence_threshold: The threshold to remove silence from the audio, in dB. - If None, no silence removal is performed. - :param use_multiprocessing: Number of processes to use for cleaning the audio files. - If 0, no multiprocessing is used. - :param verbose: Verbosity level. If True, display progress bar. - """ - if verbose: - _LOGGER.info("Reducing noise from audio files.") - - # create target directory: - target_directory = _create_target_directory(target_directory) - - # get audio files: - audio_files = _get_audio_files(audio_source) - - # Create the reduce noise object: - noise_reduce_arguments = { - "target_directory": target_directory, - "sample_rate": sample_rate, - "duration": duration, - "channel": channel, - "silence_threshold": silence_threshold, - } - - if use_multiprocessing: - results = _parallel_run( - noise_reduce_type=ReduceNoise, - noise_reduce_arguments=noise_reduce_arguments, - n_workers=use_multiprocessing, - audio_files=audio_files, - description="Noise-reduction", - verbose=verbose, - ) - else: - results = _run( - noise_reduce_type=ReduceNoise, - noise_reduce_arguments=noise_reduce_arguments, - audio_files=audio_files, - description="Noise-reduction", - verbose=verbose, - ) - - return _process_results(results, verbose) - - -def _create_target_directory(target_directory: str) -> str: - target_directory = Path(target_directory) - if not target_directory.exists(): - target_directory.mkdir(parents=True, exist_ok=True) - return str(target_directory) - - -def _get_audio_files(audio_source: str): - audio_source = Path(audio_source) - audio_files = [] - if audio_source.is_dir(): - audio_files = list(audio_source.glob("*.*")) - elif audio_source.is_file(): - audio_files.append(audio_source) - else: - raise ValueError( - f"audio_source must be a file or a directory, got {audio_source}" - ) - return audio_files - - -def _parallel_run( - noise_reduce_type: Type[ReduceNoiseBase], - noise_reduce_arguments: dict, - n_workers: int, - audio_files: List[Path], - description: str, - verbose: bool, -) -> List[Tuple[bool, Tuple[str, str]]]: - """ - Run multiple noise reduce workers with multiprocessing to complete the tasks that will be created on the provided - files using the given task creator. - - :param noise_reduce_type: The noise reduce type to use. - :param n_workers: The number of workers to use. - :param audio_files: The audio files to use. - :param description: The description to use for the progress bar. - :param verbose: Verbosity. - - :returns: The collected results. - """ - # Check the number of workers: - if n_workers > len(audio_files): - _LOGGER.warning( - f"The number of workers ({n_workers}) is larger than the number of audio files ({len(audio_files)}). " - f"Setting the number of workers to {len(audio_files)}." - ) - n_workers = len(audio_files) - - # Initialize the multiprocessing queues: - tasks_queue = Queue() - results_queue = Queue() - - # Initialize the multiprocessing processes: - task_completion_processes = [ - Process( - target=_multiprocessing_complete_tasks, - kwargs={ - "noise_reduce_type": noise_reduce_type, - "noise_reduce_arguments": noise_reduce_arguments, - "tasks_queue": tasks_queue, - "results_queue": results_queue, - }, - ) - for _ in range(n_workers) - ] - - # Start the multiprocessing processes: - for p in task_completion_processes: - p.start() - - # Put the tasks in the queue: - for audio_file in audio_files: - # tasks_queue.put(task_creator.create_task(audio_file=audio_file).to_tuple()) - tasks_queue.put(audio_file) - - # Put the stop marks in the queue: - for _ in range(n_workers): - tasks_queue.put(_MULTIPROCESSING_STOP_MARK) - - # Collect the results: - results = [] - stop_marks_counter = 0 - with tqdm( - desc=description, - unit="file", - total=len(audio_files), - disable=not verbose, - ) as progressbar: - while True: - # Get a result from the queue: - result: Tuple[bool, Tuple[str, str]] = results_queue.get() - if result == _MULTIPROCESSING_STOP_MARK: - stop_marks_counter += 1 - if stop_marks_counter == n_workers: - break - else: - # Collect the result: - results.append(result) - progressbar.update(1) - - # Wait for the processes to finish: - for p in task_completion_processes: - p.join() - - return results - - -def _run( - noise_reduce_type: Type[ReduceNoiseBase], - noise_reduce_arguments: dict, - audio_files: List[Path], - description: str, - verbose: bool, -) -> List[Tuple[bool, Tuple[str, str]]]: - """ - Run the noise reduce algorithm on the given audio files and collect the results. - - :param noise_reduce_type: The noise reduce type to use. - :param noise_reduce_arguments: The noisereduce initialization kwargs. - :param audio_files: The audio files to use. - :param description: The description to use for the progress bar. - :param verbose: Verbosity. - - :returns: The collected results. - """ - # Create the reduce noise object: - noise_reducer = noise_reduce_type(**noise_reduce_arguments) - - # Run the noise reduce algorithm on the audio files and collect the results: - results = [] - for audio_file in tqdm( - audio_files, - desc=description, - unit="file", - total=len(audio_files), - disable=not verbose, - ): - results.append(noise_reducer.reduce_noise(audio_file=audio_file)) - - return results - - -def _process_results( - results: List[Tuple[bool, Tuple[str, str]]], verbose: bool -) -> Tuple[dict, dict]: - """ - Process the results of the tasks. - - :param results: The results to process. - :param verbose: Verbosity. - - :returns: The processed results as a tuple of successes and errors. - """ - if verbose: - _LOGGER.info("Summarizing the results.") - successes = {} - errors = {} - for is_error, result in results: - if is_error: - errors[result[0]] = result[1] - else: - successes[result[0]] = result[1] - if verbose: - _LOGGER.info(f"Done ({len(successes)}/{len(successes) + len(errors)})\n") - - return successes, errors diff --git a/noise_reduction/requirements.txt b/noise_reduction/requirements.txt deleted file mode 100644 index 30934ad7c..000000000 --- a/noise_reduction/requirements.txt +++ /dev/null @@ -1,5 +0,0 @@ -tqdm -deepfilternet -librosa -noisereduce -torchaudio>=2.1.2 \ No newline at end of file diff --git a/noise_reduction/test_noise_reduction.py b/noise_reduction/test_noise_reduction.py deleted file mode 100644 index a77377565..000000000 --- a/noise_reduction/test_noise_reduction.py +++ /dev/null @@ -1,75 +0,0 @@ -import tempfile - -import mlrun -import pytest - - -@pytest.mark.parametrize( - "audio_source", - [ - "data/test_data.wav", - "data/test_data.mp3", - "data", - ], -) -def test_reduce_noise(audio_source): - # set up the project and function - artifact_path = tempfile.TemporaryDirectory().name - project = mlrun.new_project("noise-reduction") - noise_reduction_function = project.set_function( - func="function.yaml", - name="reduce_noise", - kind="job", - image="mlrun/mlrun", - ) - - # run the function - noise_reduction_run = noise_reduction_function.run( - handler="reduce_noise", - inputs={"audio_source": audio_source}, - params={ - "target_directory": artifact_path + "/data", - "sample_rate": None, - }, - local=True, - artifact_path=artifact_path, - returns=["successes: file", "errors: file"], - ) - - assert noise_reduction_run.outputs["successes"] - - -@pytest.mark.parametrize( - "audio_source", - [ - "data/test_data.wav", - "data/test_data.mp3", - "data", - ], -) -def test_reduce_noise_dfn(audio_source): - # set up the project and function - artifact_path = tempfile.TemporaryDirectory().name - project = mlrun.new_project("noise-reduction") - noise_reduction_function = project.set_function( - func="function.yaml", - name="reduce_noise", - kind="job", - image="mlrun/mlrun", - ) - - # run the function - noise_reduction_run = noise_reduction_function.run( - handler="reduce_noise_dfn", - inputs={"audio_source": audio_source}, - params={ - "target_directory": artifact_path + "/data", - "atten_lim_db": 50, - }, - local=True, - artifact_path=artifact_path, - returns=["successes: file", "errors: file"], - ) - - # assert that the function run completed successfully - assert noise_reduction_run.outputs["successes"] From daf0db8b74005ec667333e24e268966c60efb928 Mon Sep 17 00:00:00 2001 From: guy1992l Date: Fri, 9 Jan 2026 01:38:16 +0200 Subject: [PATCH 36/38] run upg to pydantic v2 --- .../src/langchain_mlrun/langchain_mlrun.py | 27 ++++++++++++------- 1 file changed, 18 insertions(+), 9 deletions(-) diff --git a/modules/src/langchain_mlrun/langchain_mlrun.py b/modules/src/langchain_mlrun/langchain_mlrun.py index c26ae38f2..19b5f55aa 100644 --- a/modules/src/langchain_mlrun/langchain_mlrun.py +++ b/modules/src/langchain_mlrun/langchain_mlrun.py @@ -601,20 +601,29 @@ def _persist_run(self, run: Run, level: int = 0) -> None: def _serialize_run(self, run: Run, include_child_runs: bool) -> dict: """ - Serialize a LangChain run into a dictionary. LangChain's Run is currently in Pydantic v1 where some of its - inner models are in Pydantic V2 which causes issues when trying to serialize the whole run object directly. - - This is a workaround to properly serialize the run object. + Serialize a LangChain run into a dictionary. :param run: The run to serialize. :param include_child_runs: Whether to include child runs in the serialization. :returns: The serialized run dictionary. """ - if not include_child_runs: - serialized_run = run.dict(exclude={"child_runs"}) - else: - serialized_run = run.dict() + # In LangChain 1.2.3+, the Run model uses Pydantic v2 with child_runs marked as Field(exclude=True), so we + # must manually serialize child runs. Still excluding manually for future compatibility. In previous + # LangChain versions, Run was Pydantic v1, so we use dict. + serialized_run = ( + run.model_dump(exclude={"child_runs"}) + if hasattr(run, "model_dump") + else run.dict(exclude={"child_runs"}) + ) + + # Manually serialize child runs if needed: + if include_child_runs and run.child_runs: + serialized_run["child_runs"] = [ + self._serialize_run(child_run, include_child_runs=True) + for child_run in run.child_runs + ] + return orjson.loads(orjson.dumps(serialized_run, default=self._serialize_default)) def _serialize_default(self, obj: Any): @@ -631,7 +640,7 @@ def _serialize_default(self, obj: Any): if isinstance(obj, datetime.datetime): return obj.isoformat() if hasattr(obj, "model_dump"): - return obj.model_dump() + return orjson.loads(orjson.dumps(obj.model_dump(), default=self._serialize_default)) if hasattr(obj, "dict"): return orjson.loads(orjson.dumps(obj.dict(), default=self._serialize_default)) return str(obj) From 3f417fd015ce52a5f7a3b706265550eaa932e200 Mon Sep 17 00:00:00 2001 From: guy1992l Date: Sun, 11 Jan 2026 12:46:02 +0200 Subject: [PATCH 37/38] added kafka and mlrun-ce code preparation --- .../src/langchain_mlrun/langchain_mlrun.py | 313 ++++++++++++++++-- .../langchain_mlrun/test_langchain_mlrun.py | 139 ++++++-- 2 files changed, 389 insertions(+), 63 deletions(-) diff --git a/modules/src/langchain_mlrun/langchain_mlrun.py b/modules/src/langchain_mlrun/langchain_mlrun.py index 19b5f55aa..a12b239be 100644 --- a/modules/src/langchain_mlrun/langchain_mlrun.py +++ b/modules/src/langchain_mlrun/langchain_mlrun.py @@ -1,8 +1,9 @@ """ -MLRun to LangChain integration - a tracer that converts LangChain Run objects into MLRun monitoring events and -publishes them to a V3IO stream via MLRun endpoint monitoring format. +MLRun to LangChain integration - a tracer that converts LangChain Run objects into serializable event and send them to +MLRun monitoring. """ +from abc import ABC, abstractmethod import copy import importlib import orjson @@ -15,11 +16,10 @@ import datetime from typing import Any, Callable, Generator, Optional -import v3io from langchain_core.tracers import BaseTracer, Run from langchain_core.tracers.context import register_configure_hook -from pydantic import Field, field_validator +from pydantic import Field, field_validator, model_validator from pydantic_settings import BaseSettings, SettingsConfigDict from uuid_utils import uuid7 @@ -35,15 +35,13 @@ mlrun_monitoring_env_var = "MLRUN_MONITORING_ENABLED" -class _MLRunEndPointClient: +class _MLRunEndPointClient(ABC): """ - An MLRun model endpoint monitoring client to connect and send events on a V3IO stream. + An MLRun model endpoint monitoring client base class to connect and send events on a monitoring stream. """ def __init__( self, - monitoring_stream_path: str, - monitoring_container: str, model_endpoint_name: str, model_endpoint_uid: str, serving_function: str | RemoteRuntime, @@ -53,8 +51,6 @@ def __init__( """ Initialize an MLRun model endpoint monitoring client. - :param monitoring_stream_path: V3IO stream path. - :param monitoring_container: V3IO container name. :param model_endpoint_name: The monitoring endpoint related model name. :param model_endpoint_uid: Model endpoint unique identifier. :param serving_function: Serving function name or ``RemoteRuntime`` object. @@ -64,8 +60,6 @@ def __init__( raise: MLRunInvalidArgumentError: If there is no current active project and no `project` argument was provided. """ # Store the provided info: - self._monitoring_stream_path = monitoring_stream_path - self._monitoring_container = monitoring_container self._model_endpoint_name = model_endpoint_name self._model_endpoint_uid = model_endpoint_uid @@ -94,9 +88,6 @@ def __init__( serving_function_tag or serving_function.metadata.tag ) - # Initialize a V3IO client: - self._v3io_client = v3io.Client() - # Prepare the sample: self._event_sample = { "class": "CustomStream", @@ -120,6 +111,7 @@ def __init__( "effective_sample_count": 1, } + @abstractmethod def monitor( self, event_id: str, @@ -139,6 +131,29 @@ def monitor( :param request_timestamp: Request/start timestamp in the format of '%Y-%m-%d %H:%M:%S%z'. :param response_timestamp: Response/end timestamp in the format of '%Y-%m-%d %H:%M:%S%z'. """ + pass + + def _create_event( + self, + event_id: str, + label: str, + input_data: dict, + output_data: dict, + request_timestamp: str, + response_timestamp: str, + ) -> dict: + """ + Create a new event out of the stored event sample. + + :param event_id: Unique event identifier used as the monitored record id. + :param label: Label for the run/event. + :param input_data: Serialized input data for the run. + :param output_data: Serialized output data for the run. + :param request_timestamp: Request/start timestamp in the format of '%Y-%m-%d %H:%M:%S%z'. + :param response_timestamp: Response/end timestamp in the format of '%Y-%m-%d %H:%M:%S%z'. + + :return: The event to send to the monitoring stream. + """ # Copy the sample: event = copy.deepcopy(self._event_sample) @@ -149,6 +164,83 @@ def monitor( event["resp"]["outputs"].append(orjson.dumps(output_data).decode('utf-8')) event["resp"]["id"] = event_id + return event + + +class _V3IOMLRunEndPointClient(_MLRunEndPointClient): + """ + An MLRun model endpoint monitoring client to connect and send events on a V3IO stream. + """ + + def __init__( + self, + monitoring_stream_path: str, + monitoring_container: str, + model_endpoint_name: str, + model_endpoint_uid: str, + serving_function: str | RemoteRuntime, + serving_function_tag: str | None = None, + project: str | mlrun.projects.MlrunProject = None, + ): + """ + Initialize an MLRun model endpoint monitoring client. + + :param monitoring_stream_path: V3IO stream path. + :param monitoring_container: V3IO container name. + :param model_endpoint_name: The monitoring endpoint related model name. + :param model_endpoint_uid: Model endpoint unique identifier. + :param serving_function: Serving function name or ``RemoteRuntime`` object. + :param serving_function_tag: Optional function tag (defaults to 'latest'). + :param project: Project name or ``MlrunProject``. If ``None``, uses the current project. + + raise: MLRunInvalidArgumentError: If there is no current active project and no `project` argument was provided. + """ + super().__init__( + model_endpoint_name=model_endpoint_name, + model_endpoint_uid=model_endpoint_uid, + serving_function=serving_function, + serving_function_tag=serving_function_tag, + project=project, + ) + + import v3io + + # Store the provided info: + self._monitoring_stream_path = monitoring_stream_path + self._monitoring_container = monitoring_container + + # Initialize a V3IO client: + self._v3io_client = v3io.Client() + + def monitor( + self, + event_id: str, + label: str, + input_data: dict, + output_data: dict, + request_timestamp: str, + response_timestamp: str, + ): + """ + Monitor the provided event, sending it to the model endpoint monitoring stream. + + :param event_id: Unique event identifier used as the monitored record id. + :param label: Label for the run/event. + :param input_data: Serialized input data for the run. + :param output_data: Serialized output data for the run. + :param request_timestamp: Request/start timestamp in the format of '%Y-%m-%d %H:%M:%S%z'. + :param response_timestamp: Response/end timestamp in the format of '%Y-%m-%d %H:%M:%S%z'. + """ + # Copy the sample: + event = self._create_event( + event_id=event_id, + label=label, + input_data=input_data, + output_data=output_data, + request_timestamp=request_timestamp, + response_timestamp=response_timestamp, + ) + # Push to stream: self._v3io_client.stream.put_records( container=self._monitoring_container, @@ -157,22 +249,120 @@ def monitor( ) +class _KafkaMLRunEndPointClient(_MLRunEndPointClient): + """ + An MLRun model endpoint monitoring client to connect and send events on a Kafka stream. + """ + + def __init__( + self, + monitoring_broker: str, + monitoring_topic: str, + # TODO: Add more Kafka producer options if needed... + model_endpoint_name: str, + model_endpoint_uid: str, + serving_function: str | RemoteRuntime, + serving_function_tag: str | None = None, + project: str | mlrun.projects.MlrunProject = None, + ): + """ + Initialize an MLRun model endpoint monitoring client. + + :param monitoring_broker: Kafka broker name. + :param monitoring_topic: Kafka topic name. + TODO: Add more Kafka producer options if needed... + :param model_endpoint_name: The monitoring endpoint related model name. + :param model_endpoint_uid: Model endpoint unique identifier. + :param serving_function: Serving function name or ``RemoteRuntime`` object. + :param serving_function_tag: Optional function tag (defaults to 'latest'). + :param project: Project name or ``MlrunProject``. If ``None``, uses the current project. + + raise: MLRunInvalidArgumentError: If there is no current active project and no `project` argument was provided. + """ + super().__init__( + model_endpoint_name=model_endpoint_name, + model_endpoint_uid=model_endpoint_uid, + serving_function=serving_function, + serving_function_tag=serving_function_tag, + project=project, + ) + + import kafka + + # Store the provided info: + self._monitoring_broker = monitoring_broker + self._monitoring_topic = monitoring_topic + + # Initialize a Kafka producer: + self._kafka_producer = kafka.KafkaProducer( + ... + ) + + def monitor( + self, + event_id: str, + label: str, + input_data: dict, + output_data: dict, + request_timestamp: str, + response_timestamp: str, + ): + """ + Monitor the provided event, sending it to the model endpoint monitoring stream. + + :param event_id: Unique event identifier used as the monitored record id. + :param label: Label for the run/event. + :param input_data: Serialized input data for the run. + :param output_data: Serialized output data for the run. + :param request_timestamp: Request/start timestamp in the format of '%Y-%m-%d %H:%M:%S%z'. + :param response_timestamp: Response/end timestamp in the format of '%Y-%m-%d %H:%M:%S%z'. + """ + # Copy the sample: + event = self._create_event( + event_id=event_id, + label=label, + input_data=input_data, + output_data=output_data, + request_timestamp=request_timestamp, + response_timestamp=response_timestamp, + ) + + # Push to stream: + self._kafka_producer.send( + topic=self._monitoring_topic, + value=orjson.dumps(event), + key=self._model_endpoint_uid, + ) + + class MLRunTracerClientSettings(BaseSettings): """ MLRun tracer monitoring client configurations. These are mandatory arguments for allowing MLRun to send monitoring events to a specific model endpoint stream. """ - stream_path: str = ... + v3io_stream_path: str | None = None """ The V3IO stream path to send the events to. """ - container: str = ... + v3io_container: str | None = None """ The V3IO stream container. """ + kafka_broker: str | None = None + """ + The Kafka broker address. + """ + + kafka_topic: str | None = None + """ + The Kafka topic name. + """ + + # TODO: Add more Kafka producer options if needed... + model_endpoint_name: str = ... """ The model endpoint name. @@ -201,6 +391,27 @@ class MLRunTracerClientSettings(BaseSettings): #: Pydantic model configuration to set the environment variable prefix. model_config = SettingsConfigDict(env_prefix="MLRUN_TRACER_CLIENT_") + @model_validator(mode='after') + def check_exclusive_sets(self) -> 'MLRunTracerClientSettings': + """ + Validate that either V3IO settings or Kafka settings are provided, but not both or none. + + :return: The validated settings instance. + """ + # Define the sets + v3io_settings = all([self.v3io_container, self.v3io_stream_path]) + kafka_settings = all([self.kafka_topic, self.kafka_broker]) # TODO: Add mandatory other kafka settings + + # Make sure only one set is provided: + if v3io_settings and kafka_settings: + raise ValueError("Provide either V3IO settings OR Kafka settings, not both.") + if not v3io_settings and not kafka_settings: + raise ValueError( + "You must provide either a complete V3IO settings or complete Kafka settings. See docs for more " + "information" + ) + + return self class MLRunTracerMonitorSettings(BaseSettings): """ @@ -303,7 +514,7 @@ class MLRunTracerMonitorSettings(BaseSettings): debug: bool = False """ - If True, disable sending events to MLRun/V3IO and instead route events to `debug_target_list` + If True, disable sending events to MLRun and instead route events to `debug_target_list` or print them as JSON to stdout. Useful for unit tests and local debugging. Default: False. """ @@ -336,7 +547,7 @@ class MLRunTracerSettings(BaseSettings): """ MLRun tracer settings to configure the tracer. The settings are split into two groups: - * `client`: settings required to connect and send events to the MLRun/V3IO monitoring stream. + * `client`: settings required to connect and send events to the MLRun monitoring stream. * `monitor`: settings controlling which LangChain runs are summarized and sent and how. """ @@ -465,15 +676,7 @@ def __init__(self, settings: MLRunTracerSettings = None, **kwargs): # Initialize the MLRun endpoint client: self._mlrun_client = ( - _MLRunEndPointClient( - monitoring_stream_path=self._client_settings.stream_path, - monitoring_container=self._client_settings.container, - model_endpoint_name=self._client_settings.model_endpoint_name, - model_endpoint_uid=self._client_settings.model_endpoint_uid, - serving_function=self._client_settings.serving_function, - serving_function_tag=self._client_settings.serving_function_tag, - project=self._client_settings.project, - ) + self._get_mlrun_client() if not self._monitor_settings.debug else None ) @@ -501,6 +704,32 @@ def settings(self) -> MLRunTracerSettings: """ return self._settings + def _get_mlrun_client(self) -> _MLRunEndPointClient: + """ + Create and return an MLRun model endpoint monitoring client based on the MLRun (CE or not) and current + configuration. + + :returns: An MLRun model endpoint monitoring client. + """ + if mlrun.mlconf.is_ce_mode(): + return _KafkaMLRunEndPointClient( + # TODO: Add more Kafka producer options if needed... + model_endpoint_name=self._client_settings.model_endpoint_name, + model_endpoint_uid=self._client_settings.model_endpoint_uid, + serving_function=self._client_settings.serving_function, + serving_function_tag=self._client_settings.serving_function_tag, + project=self._client_settings.project, + ) + return _V3IOMLRunEndPointClient( + monitoring_stream_path=self._client_settings.v3io_stream_path, + monitoring_container=self._client_settings.v3io_container, + model_endpoint_name=self._client_settings.model_endpoint_name, + model_endpoint_uid=self._client_settings.model_endpoint_uid, + serving_function=self._client_settings.serving_function, + serving_function_tag=self._client_settings.serving_function_tag, + project=self._client_settings.project, + ) + def _import_custom_run_summarizer(self): """ Import or assign a custom run summarizer (and its custom settings) if configured. @@ -899,8 +1128,9 @@ def setup_langchain_monitoring( function_name: str = "langchain_mlrun_function", model_name: str = "langchain_mlrun_model", model_endpoint_name: str = "langchain_mlrun_endpoint", - monitoring_container: str = "projects", - monitoring_stream_path: str = None, + v3io_container: str = "projects", + v3io_stream_path: str = None, + # TODO: Add Kafka parameters when Kafka monitoring is supported. ) -> dict: """ Create a model endpoint in the given project to be used for LangChain monitoring with MLRun and returns the @@ -920,9 +1150,10 @@ def setup_langchain_monitoring( :param function_name: The name of the serving function to create. :param model_name: The name of the model to create. :param model_endpoint_name: The name of the model endpoint to create. - :param monitoring_container: The V3IO container where the monitoring stream is located. - :param monitoring_stream_path: The V3IO stream path for monitoring. If None, + :param v3io_container: The V3IO container where the monitoring stream is located. + :param v3io_stream_path: The V3IO stream path for monitoring. If None, ``/model-endpoints/stream-v1`` will be used. + TODO: Add Kafka parameters when Kafka monitoring is supported. :returns: A dictionary with the necessary environment variables to configure the MLRun tracer client. @@ -1149,16 +1380,28 @@ def handler(context, event): if model_endpoint.metadata.uid: uid_exist_flag = True + # Set parameters defaults: + v3io_stream_path = v3io_stream_path or f"{project.name}/model-endpoints/stream-v1" + # TODO: Support Kafka monitoring parameters defaults when Kafka monitoring is supported. + + if mlrun.mlconf.is_ce_mode(): + client_env_vars = { + "MLRUN_TRACER_CLIENT_KAFKA_...": ... + } + else: + client_env_vars = { + "MLRUN_TRACER_CLIENT_V3IO_STREAM_PATH": v3io_stream_path, + "MLRUN_TRACER_CLIENT_V3IO_CONTAINER": v3io_container, + } + # Prepare the environment variables: - monitoring_stream_path = monitoring_stream_path or f"{project.name}/model-endpoints/stream-v1" env_vars = { "MLRUN_MONITORING_ENABLED": "1", "MLRUN_TRACER_CLIENT_PROJECT": project.name, - "MLRUN_TRACER_CLIENT_STREAM_PATH": monitoring_stream_path, - "MLRUN_TRACER_CLIENT_CONTAINER": monitoring_container, "MLRUN_TRACER_CLIENT_MODEL_ENDPOINT_NAME": model_endpoint.metadata.name, "MLRUN_TRACER_CLIENT_MODEL_ENDPOINT_UID": model_endpoint.metadata.uid, "MLRUN_TRACER_CLIENT_SERVING_FUNCTION": function_name, + **client_env_vars } print("\n✨ Done! LangChain monitoring model endpoint created successfully.") print("You can now set the following environment variables to enable MLRun tracing in your LangChain code:\n") diff --git a/modules/src/langchain_mlrun/test_langchain_mlrun.py b/modules/src/langchain_mlrun/test_langchain_mlrun.py index c2c32a64d..3f54d403c 100644 --- a/modules/src/langchain_mlrun/test_langchain_mlrun.py +++ b/modules/src/langchain_mlrun/test_langchain_mlrun.py @@ -199,8 +199,8 @@ def router(state: AgentState) -> Literal["reflect", END]: #: Dummy environment variables for testing. _dummy_environment_variables = { - "MLRUN_TRACER_CLIENT_STREAM_PATH": "dummy_stream_path", - "MLRUN_TRACER_CLIENT_CONTAINER": "dummy_container", + "MLRUN_TRACER_CLIENT_V3IO_STREAM_PATH": "dummy_stream_path", + "MLRUN_TRACER_CLIENT_V3IO_CONTAINER": "dummy_container", "MLRUN_TRACER_CLIENT_MODEL_ENDPOINT_NAME": "dummy_model_name", "MLRUN_TRACER_CLIENT_MODEL_ENDPOINT_UID": "dummy_model_endpoint_uid", "MLRUN_TRACER_CLIENT_SERVING_FUNCTION": "dummy_serving_function", @@ -211,13 +211,14 @@ def router(state: AgentState) -> Literal["reflect", END]: @pytest.fixture() -def auto_mode_settings(): +def auto_mode_settings(monkeypatch): """ Sets the environment variables to enable mlrun monitoring in 'auto' mode. """ # Set environment variables for the duration of the test: - os.environ[mlrun_monitoring_env_var] = "1" - os.environ.update(_dummy_environment_variables) + monkeypatch.setenv(mlrun_monitoring_env_var, "1") + for key, value in _dummy_environment_variables.items(): + monkeypatch.setenv(key, value) # Reset the singleton tracer to ensure fresh initialization: MLRunTracer._singleton_tracer = None @@ -225,11 +226,6 @@ def auto_mode_settings(): yield - # Remove the environment variables after the test: - os.environ.pop(mlrun_monitoring_env_var) - for env_var in _dummy_environment_variables: - os.environ.pop(env_var) - # Reset the singleton tracer after the test: MLRunTracer._singleton_tracer = None MLRunTracer._initialized = False @@ -242,8 +238,8 @@ def manual_mode_settings(): """ settings = MLRunTracerSettings( client=MLRunTracerClientSettings( - stream_path="dummy_stream_path", - container="dummy_container", + v3io_stream_path="dummy_stream_path", + v3io_container="dummy_container", model_endpoint_name="dummy_model_name", model_endpoint_uid="dummy_model_endpoint_uid", serving_function="dummy_serving_function", @@ -258,27 +254,114 @@ def manual_mode_settings(): yield settings -def test_settings_init_via_env_vars(): +def test_settings_init_via_env_vars(monkeypatch): """ Test that settings are correctly initialized from environment variables. """ #: First, ensure that without env vars, validation fails due to missing required fields: - try: - settings = MLRunTracerSettings() - except ValidationError: - # Now, set the environment variables for the client settings and debug flag: - os.environ.update(_dummy_environment_variables) - - # Ensure that settings are now correctly initialized from env vars: - settings = MLRunTracerSettings() - assert settings.client.stream_path == "dummy_stream_path" - assert settings.client.container == "dummy_container" - assert settings.client.model_endpoint_name == "dummy_model_name" - assert settings.client.model_endpoint_uid == "dummy_model_endpoint_uid" - assert settings.client.serving_function == "dummy_serving_function" - assert settings.monitor.debug is True + with pytest.raises(ValidationError): + MLRunTracerSettings() + + # Now, set the environment variables for the client settings and debug flag: + for key, value in _dummy_environment_variables.items(): + monkeypatch.setenv(key, value) + + # Ensure that settings are now correctly initialized from env vars: + settings = MLRunTracerSettings() + assert settings.client.v3io_stream_path == "dummy_stream_path" + assert settings.client.v3io_container == "dummy_container" + assert settings.client.model_endpoint_name == "dummy_model_name" + assert settings.client.model_endpoint_uid == "dummy_model_endpoint_uid" + assert settings.client.serving_function == "dummy_serving_function" + assert settings.monitor.debug is True + + +@pytest.mark.parametrize( + "test_suite", [ + # Valid case: only v3io settings provided + ( + { + "v3io_stream_path": "dummy_stream_path", + "v3io_container": "dummy_container", + "model_endpoint_name": "dummy_model_name", + "model_endpoint_uid": "dummy_model_endpoint_uid", + "serving_function": "dummy_serving_function", + }, + True, + ), + # Invalid case: partial v3io settings provided + ( + { + "v3io_stream_path": "dummy_stream_path", + "model_endpoint_name": "dummy_model_name", + "model_endpoint_uid": "dummy_model_endpoint_uid", + "serving_function": "dummy_serving_function", + }, + False, + ), + # Valid case: only kafka settings provided + ( + { + "kafka_broker": "dummy_bootstrap_servers", + "kafka_topic": "dummy_topic", + # TODO: Add more mandatory kafka settings + "model_endpoint_name": "dummy_model_name", + "model_endpoint_uid": "dummy_model_endpoint_uid", + "serving_function": "dummy_serving_function", + }, + True, + ), + # Invalid case: partial kafka settings provided + ( + { + "kafka_broker": "dummy_bootstrap_servers", + "model_endpoint_name": "dummy_model_name", + "model_endpoint_uid": "dummy_model_endpoint_uid", + "serving_function": "dummy_serving_function", + }, + False, + ), + # Invalid case: both v3io and kafka settings provided + ( + { + "v3io_stream_path": "dummy_stream_path", + "v3io_container": "dummy_container", + "kafka_broker": "dummy_bootstrap_servers", + "kafka_topic": "dummy_topic", + # TODO: Add more mandatory kafka settings + "model_endpoint_name": "dummy_model_name", + "model_endpoint_uid": "dummy_model_endpoint_uid", + "serving_function": "dummy_serving_function", + }, + False, + ), + # Invalid case: both v3io and kafka settings provided (partial) + ( + { + "v3io_container": "dummy_container", + "kafka_broker": "dummy_bootstrap_servers", + "model_endpoint_name": "dummy_model_name", + "model_endpoint_uid": "dummy_model_endpoint_uid", + "serving_function": "dummy_serving_function", + }, + False, + ), + ] +) +def test_settings_v3io_kafka_combination(test_suite: tuple[dict[str, str], bool]): + """ + Test that settings validation enforces mutual exclusivity between v3io and kafka configurations. + + :param test_suite: A tuple containing environment variable overrides and a flag indicating + whether validation should pass. + """ + settings, should_pass = test_suite + + if should_pass: + MLRunTracerClientSettings(**settings) else: - raise AssertionError("Initializing settings without env vars should have failed.") + with pytest.raises(ValidationError): + MLRunTracerClientSettings(**settings) def test_auto_mode_singleton_thread_safety(auto_mode_settings): From 3ad09842f3d2a8597af2260fa76abf7eeeaabe00 Mon Sep 17 00:00:00 2001 From: guy1992l Date: Sun, 11 Jan 2026 23:29:39 +0200 Subject: [PATCH 38/38] Eyal review --- modules/src/langchain_mlrun/item.yaml | 5 ++--- modules/src/langchain_mlrun/langchain_mlrun.py | 14 ++++++++++++++ modules/src/langchain_mlrun/requirements.txt | 1 - .../src/langchain_mlrun/test_langchain_mlrun.py | 14 ++++++++++++++ 4 files changed, 30 insertions(+), 4 deletions(-) diff --git a/modules/src/langchain_mlrun/item.yaml b/modules/src/langchain_mlrun/item.yaml index f427aef1b..8dcad0238 100644 --- a/modules/src/langchain_mlrun/item.yaml +++ b/modules/src/langchain_mlrun/item.yaml @@ -7,10 +7,10 @@ categories: - llm description: LangChain x MLRun integration - Orchestrate your LangChain code with MLRun. example: langchain_mlrun.ipynb -generationDate: 2025-01-08 +generationDate: 2026-01-08:12-25 hidden: false labels: - author: guyl + author: Iguazio mlrunVersion: 1.10.0 name: langchain_mlrun spec: @@ -18,7 +18,6 @@ spec: image: mlrun/mlrun kind: generic requirements: - - mlrun - langchain - pydantic-settings version: 0.0.1 \ No newline at end of file diff --git a/modules/src/langchain_mlrun/langchain_mlrun.py b/modules/src/langchain_mlrun/langchain_mlrun.py index a12b239be..f7eb5f160 100644 --- a/modules/src/langchain_mlrun/langchain_mlrun.py +++ b/modules/src/langchain_mlrun/langchain_mlrun.py @@ -1,3 +1,17 @@ +# Copyright 2026 Iguazio +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + """ MLRun to LangChain integration - a tracer that converts LangChain Run objects into serializable event and send them to MLRun monitoring. diff --git a/modules/src/langchain_mlrun/requirements.txt b/modules/src/langchain_mlrun/requirements.txt index 13e656bfc..fe350503e 100644 --- a/modules/src/langchain_mlrun/requirements.txt +++ b/modules/src/langchain_mlrun/requirements.txt @@ -1,4 +1,3 @@ pytest -mlrun langchain pydantic-settings \ No newline at end of file diff --git a/modules/src/langchain_mlrun/test_langchain_mlrun.py b/modules/src/langchain_mlrun/test_langchain_mlrun.py index 3f54d403c..bae27ce23 100644 --- a/modules/src/langchain_mlrun/test_langchain_mlrun.py +++ b/modules/src/langchain_mlrun/test_langchain_mlrun.py @@ -1,3 +1,17 @@ +# Copyright 2026 Iguazio +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + import os from typing import Literal, TypedDict, Annotated, Sequence, Any, Callable from concurrent.futures import ThreadPoolExecutor