diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml new file mode 100644 index 0000000..c5e0426 --- /dev/null +++ b/.github/workflows/docs.yml @@ -0,0 +1,36 @@ +name: Build & Deploy Docs +on: + push: + branches: [main] + +permissions: + contents: write + pages: write + id-token: write + +jobs: + build: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 + with: + python-version: "3.10" + - name: Install depdencies + run: | + python -m pip install -U pip + pip install . + pip install sphinx sphinx-rtd-theme + + - name: Build Docs + run: sphinx-build -b html docs/source docs/_build/html + - name: add .nojekyll + run: touch docs/_build/html/.nojekyll + + - name: Deploy to GitHub Pages + uses: peaceiris/actions-gh-pages@v4 + with: + github_token: ${{ secrets.GITHUB_TOKEN }} + publish_dir: docs/_build/html + publish_branch: gh-pages + force_orphan: true diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml new file mode 100644 index 0000000..53ce844 --- /dev/null +++ b/.github/workflows/tests.yml @@ -0,0 +1,34 @@ +name: Test CI + +on: + push: + branches: [main] + pull_request: + branches: [main] + +jobs: + test: + runs-on: ubuntu-latest + strategy: + matrix: + python-version: ["3.10", "3.11", "3.12"] + + steps: + - uses: actions/checkout@v4 + + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v5 + with: + python-version: ${{ matrix.python-version }} + cache: "pip" + + - name: Install dependencies + run: | + python -m pip install --upgrade pip + python -c "import duckdb; duckdb.install_extension('spatial')" + pip install coverage + + - name: Run unittests with coverage + run: | + coverage run -m tests + coverage report -m --omit="tests/*" diff --git a/README.md b/README.md index cbdafca..126d93e 100644 --- a/README.md +++ b/README.md @@ -1 +1,51 @@ -# SafeRoads \ No newline at end of file +# SafeRoad + +## About + +**SafeRoad** is an open-source Python library designed to monitor the structural condition of the road network by providing tools for data analysis and visualisation. +It aims to assist researchers, policymakers, and developers in understanding and mitigating structural safety issues of road networks. +**SafeRoad** is built with a focus on usability, performance, and extensibility. It uses modern Python libraries such as [duckdb](https://duckdb.org/), +[shapely](https://shapely.readthedocs.io/en/stable/), [matplotlib](https://matplotlib.org/) and [contextily](https://contextily.readthedocs.io/en/latest/) to provide a robust framework for road network safety analyses. + +You can access the more detailed description of the methodology from the [paper](https://doi.org/10.1177/14759217211045912). + +The library is modular, user-friendly, and well-documented, making it suitable for both beginners and experts in the field of structural safety. + +## Installation + +1. Create a virtual environment and activate it + +```bash +# conda +conda create --name saferoad_env python=3.10 +# activate environment +conda activate saferoad_env +``` + +2. Clone the reposity to your current directory + +```bash +git clone https://github.com/SafeStruct/SafeRoad.git +``` + +3. Install the library from directory using pip + +```bash +pip install -e ./SafeRoad +``` + +4. Testing the installation using tutorial + +```bash +# before testing one extension needs to be installed +python -c "import duckdb; duckdb.install_extension('spatial')" +# change the directory to examples and run the tutorial +cd SafeRoad/examples +python tutorial.py +``` + +If everything works correctly, you should see a folder called **`SafeRoadDB`** inside the example folder. Inside that folder, you will find the generated **PDF** report for the entire dataset provided. + +# Disclaimer + +The Multi-Temporal SAR data provided in the repository is not real; it is synthetic. It is produced to demonstrate how the tool works and what kind of report it generates. diff --git a/docs/Makefile b/docs/Makefile new file mode 100644 index 0000000..d0c3cbf --- /dev/null +++ b/docs/Makefile @@ -0,0 +1,20 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line, and also +# from the environment for the first two. +SPHINXOPTS ?= +SPHINXBUILD ?= sphinx-build +SOURCEDIR = source +BUILDDIR = build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/build/doctrees/api/modules.doctree b/docs/build/doctrees/api/modules.doctree new file mode 100644 index 0000000..8707dcb Binary files /dev/null and b/docs/build/doctrees/api/modules.doctree differ diff --git a/docs/build/doctrees/api/saferoad.database.doctree b/docs/build/doctrees/api/saferoad.database.doctree new file mode 100644 index 0000000..dda63ac Binary files /dev/null and b/docs/build/doctrees/api/saferoad.database.doctree differ diff --git a/docs/build/doctrees/api/saferoad.doctree b/docs/build/doctrees/api/saferoad.doctree new file mode 100644 index 0000000..b952a35 Binary files /dev/null and b/docs/build/doctrees/api/saferoad.doctree differ diff --git a/docs/build/doctrees/api/saferoad.pipeline.doctree b/docs/build/doctrees/api/saferoad.pipeline.doctree new file mode 100644 index 0000000..4f27ec9 Binary files /dev/null and b/docs/build/doctrees/api/saferoad.pipeline.doctree differ diff --git a/docs/build/doctrees/api/saferoad.plotter.doctree b/docs/build/doctrees/api/saferoad.plotter.doctree new file mode 100644 index 0000000..94d147a Binary files /dev/null and b/docs/build/doctrees/api/saferoad.plotter.doctree differ diff --git a/docs/build/doctrees/api/saferoad.saferoad.doctree b/docs/build/doctrees/api/saferoad.saferoad.doctree new file mode 100644 index 0000000..5fc9bdc Binary files /dev/null and b/docs/build/doctrees/api/saferoad.saferoad.doctree differ diff --git a/docs/build/doctrees/api/saferoad.utils.doctree b/docs/build/doctrees/api/saferoad.utils.doctree new file mode 100644 index 0000000..abbe299 Binary files /dev/null and b/docs/build/doctrees/api/saferoad.utils.doctree differ diff --git a/docs/build/doctrees/environment.pickle b/docs/build/doctrees/environment.pickle new file mode 100644 index 0000000..9b2b77a Binary files /dev/null and b/docs/build/doctrees/environment.pickle differ diff --git a/docs/build/doctrees/index.doctree b/docs/build/doctrees/index.doctree new file mode 100644 index 0000000..1f549d1 Binary files /dev/null and b/docs/build/doctrees/index.doctree differ diff --git a/docs/build/html/.buildinfo b/docs/build/html/.buildinfo new file mode 100644 index 0000000..96a2b95 --- /dev/null +++ b/docs/build/html/.buildinfo @@ -0,0 +1,4 @@ +# Sphinx build info version 1 +# This file records the configuration used when building these files. When it is not found, a full rebuild will be done. +config: ae51672f3ba48d0bc0d242e97b3d37a6 +tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/docs/build/html/_modules/index.html b/docs/build/html/_modules/index.html new file mode 100644 index 0000000..a087049 --- /dev/null +++ b/docs/build/html/_modules/index.html @@ -0,0 +1,105 @@ + + + + + + + + Overview: module code — saferoad 0.1 documentation + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Overview: module code
    • +
  • +
    • +
+
+
+
+
+ +

All modules for which code is available

+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/build/html/_modules/saferoad/database.html b/docs/build/html/_modules/saferoad/database.html new file mode 100644 index 0000000..b890359 --- /dev/null +++ b/docs/build/html/_modules/saferoad/database.html @@ -0,0 +1,178 @@ + + + + + + + + saferoad.database — saferoad 0.1 documentation + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for saferoad.database

+import os
+import duckdb
+from pathlib import Path
+from datetime import datetime
+
+
+

+[docs]
+class DataBase:
+    """Class for managing a DuckDB database connection.
+
+    This class provides methods to set up a DuckDB database connection, and run SQL queries.
+    """
+
+    def __init__(self):
+        self.connection = None
+        self.db_path = None
+
+

+[docs]
+    def setup(self):
+        """Setup the DuckDB database connection.
+        This method initializes the database path and establishes a connection
+        to the DuckDB database. It also loads the spatial extension if available.
+
+        :raises AssertionError: If the database connection or path is already established.
+        """
+        assert self.connection is None, "Database connection already established."
+        assert self.db_path is None, "Database path already initialized."
+
+        self.db_path = self.init_database_path()
+        self.connection = duckdb.connect(self.db_path)
+        # Load the spatial extension if available
+        self.connection.install_extension("spatial")
+        self.connection.load_extension("spatial")

+
+
+

+[docs]
+    def init_database_path(self):
+        """Initialize the database path.
+
+        This method creates a directory for the database if it does not exist
+        and generates a filename based on the current date and time.
+
+        :return: The path to the DuckDB database file.
+        :rtype: str
+        """
+        assert self.connection is None, "Database connection already established."
+        assert self.db_path is None, "Database path already initialized."
+
+        # Create a folder for the database if it does not exist
+        dbFolder = "SafeRoadDB"
+        os.makedirs(dbFolder, exist_ok=True)
+        filename = os.path.join(
+            dbFolder, f"{datetime.now().strftime('%Y%m%d%H%M')}.duckdb"
+        )
+        return filename

+
+
+

+[docs]
+    def run(self, query: str):
+        """Run a SQL query on the database.
+
+        This method executes the provided SQL query on the connected database.
+
+        :param query: The SQL query to execute.
+        :type query: str
+        :raises AssertionError: If the database connection is not established.
+        """
+        if self.connection is None:
+            raise AssertionError("Database connection is not established.")
+
+        self.connection.execute(query)

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/build/html/_modules/saferoad/pipeline.html b/docs/build/html/_modules/saferoad/pipeline.html new file mode 100644 index 0000000..f7ef736 --- /dev/null +++ b/docs/build/html/_modules/saferoad/pipeline.html @@ -0,0 +1,1761 @@ + + + + + + + + saferoad.pipeline — saferoad 0.1 documentation + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for saferoad.pipeline

+from pathlib import Path
+from numpy import where as npwhere
+
+
+

+[docs]
+class Pipeline:
+

+[docs]
+    class Processing:
+        """Processing class for processing road data.
+
+        This class provides methods to load files into a database, dissolve features,
+        split lines into segments, generate patches, and save tables as GeoJSON files.
+        It assumes that the database connection is already established and that the
+        necessary SQL functions for spatial operations are available.
+        The methods in this class generate SQL queries that can be executed against
+        a spatial database, such as DuckDB.
+
+        The class does not handle database connections or query execution directly;
+        it only generates the SQL queries as strings.
+        The methods are static and can be called without instantiating the class.
+        The class is designed to be used in a pipeline for processing road data,
+        where each method can be chained together to perform a series of operations
+        on the road data.
+
+        The methods assume that the input data is already in a suitable format for
+        processing, such as a GIS vector file for loading into a database or a table
+        containing road geometries for dissolving and segmenting.
+        The class is intended to be used in conjunction with a spatial database
+        that supports SQL queries for spatial operations, such as DuckDB with
+        the Spatial extension or PostGIS.
+        The methods are designed to be flexible and can be adapted to different
+        use cases by modifying the parameters passed to them.
+        """
+
+

+[docs]
+        @staticmethod
+        def load_file(file_path: str, table_name: str) -> str:
+            """Load a file into a database table.
+
+            Method validates the provided file path and determines if existing
+            and determines the appropriate loading method based on the file type.
+            It generates an SQL query to create or replace a table in the database
+            with the contents of the file. The method supports CSV files and other
+            GIS vector files, using different loading methods for each type.
+
+            :param file_path: The path to the file to be loaded.
+            :type file_path: str
+            :param table_name: The name of the table to create or replace in the database.
+            :type table_name: str
+            :raises AssertionError: If the file does not exist.
+            :return: SQL query string to create or replace the table with the file data.
+            :rtype: str
+            """
+
+            assert Path(file_path).exists(), f"File {file_path} does not exist."
+
+            loader_method = (
+                "read_csv_auto" if Path(file_path).suffix == ".csv" else "ST_Read"
+            )
+            query = f"""
+            CREATE OR REPLACE TABLE '{table_name}' AS SELECT * FROM {loader_method}('{file_path}');
+            CREATE OR REPLACE SEQUENCE {table_name}_id;
+            ALTER TABLE '{table_name}' ADD COLUMN uid INTEGER DEFAULT nextval('{table_name}_id');
+            """
+            return query

+
+
+

+[docs]
+        @staticmethod
+        def dissolve_features(attribute: str, table_name: str) -> str:
+            """Dissolve features in a table based on a specified attribute.
+
+            This method generates an SQL query to dissolve features in a table
+            based on the provided attribute. It assumes the table is already loaded
+            into the database.
+
+            :param attribute: The attribute to dissolve features by.
+            :type attribute: str
+            :param table_name: The name of the table containing the features, default is "road".
+            :type table_name: str
+
+            :return: SQL query string to dissolve features.
+            :rtype: str
+            """
+
+            group_expression = f"GROUP BY {attribute}, " if attribute else ""
+
+            return f"""
+                CREATE OR REPLACE TABLE dissolved AS
+                SELECT UNNEST(ST_Dump(ST_LineMerge(ST_Union_Agg(geom))), recursive := true) as geom
+                FROM {table_name} {group_expression}
+                """

+
+
+

+[docs]
+        @staticmethod
+        def split_to_segments(
+            segment_length: float, table_name: str = "dissolved"
+        ) -> str:
+            """Split lines into segments of a specified length.
+
+            This method generates an SQL query to split lines in a table into segments
+            of a specified length. It transforms the geometries to a computational
+            coordinate reference system (CRS) and calculates the lengths in meters.
+            The segments are created by generating windows of fixed length along the lines.
+            The resulting segments are stored in a new table named "segments".
+            The method assumes that the input table is already dissolved and contains
+            geometries in a suitable format.
+
+            The segments are created by cutting the lines into fixed-length segments
+            and filtering out any empty geometries.
+            This method is useful for preparing road segments for further analysis or processing.
+            It is particularly useful in road network analysis, where roads need to be
+            divided into manageable segments for tasks such as traffic analysis, road condition
+            assessment, or infrastructure planning.
+
+            :param segment_length: The length of each segment in meters.
+            :type segment_length: float
+            :param table_name: The name of the table containing the lines, default is "dissolved".
+            :type table_name: str
+            :return: SQL query string to create or replace the segments table.
+            :rtype: str
+            """
+            computational_crs = None
+            query = f"""
+            CREATE OR REPLACE TABLE segments AS
+            -- transform to local projection system
+            -- and calculate length in meters
+            WITH measured AS (
+            SELECT  
+                -- ST_Transform(geom, 'EPSG:4326', '{computational_crs}', always_xy := true) AS geom_m,
+                -- ST_Length(ST_Transform(geom, 'EPSG:4326', '{computational_crs}', always_xy := true)) AS len_m
+                geom,
+                ST_Length(geom) AS len_m
+            FROM {table_name}
+            ),
+            -- generate windows of fixed length
+            windows AS (
+            SELECT
+                
+                m.geom,
+                m.len_m,
+                -- start distance (meters)
+                (i * CAST({segment_length} AS DOUBLE)) AS s,
+                -- end distance (meters), capped at total length
+                LEAST((i + 1) * CAST({segment_length} AS DOUBLE), m.len_m) AS e
+            FROM measured AS m
+            JOIN LATERAL generate_series(
+                0,
+                CAST(CEIL(m.len_m / CAST({segment_length} AS DOUBLE)) AS BIGINT) - 1
+            ) AS g(i) ON TRUE
+            WHERE m.len_m > 0
+            ),
+            -- cut the lines into segments
+            cut AS (
+            SELECT
+                ST_LineSubstring(
+                    geom,
+                    s / NULLIF(len_m, 0),
+                    e / NULLIF(len_m, 0)
+                ) AS geom
+            FROM windows
+            WHERE len_m > 0
+            )
+            -- filter out empty geometries
+            SELECT geom
+            FROM cut
+            WHERE NOT ST_IsEmpty(geom);
+            """
+            return query

+
+
+

+[docs]
+        @staticmethod
+        def generate_patches(
+            width: float, length: float, table_name: str = "segments"
+        ) -> str:
+            """Generate patches from segments.
+
+            This method generates patches from segments by creating a rectangular
+            envelope around each segment, rotated to align with the segment's direction.
+            The patches are created by translating and rotating the envelope to the
+            start point of the segment, ensuring that each patch is oriented correctly
+            along the segment's direction.
+
+            :param width: The width of the patches in meters.
+            :type width: float
+            :param length: The length of the patches in meters.
+            :type length: float
+            :param table_name: The name of the table containing the segments, default is "segments".
+            :type table_name: str
+            :return: SQL query string to create or replace the patches table.
+            :rtype: str
+            """
+            assert width > 0, "Width must be greater than 0."
+            assert length > 0, "Length must be greater than 0."
+            query = f"""
+            CREATE OR REPLACE TABLE patches AS
+            SELECT 
+                ST_Translate(
+                ST_RotateZ(
+                    ST_MakeEnvelope(-{width} / 2, 0, {width} / 2, {length}),
+                    -ST_Azimuth(ST_StartPoint(geom), ST_EndPoint(geom))
+                ),
+                ST_X(ST_StartPoint(geom)), ST_Y(ST_StartPoint(geom))
+                ) AS geom
+            FROM {table_name};
+            CREATE OR REPLACE SEQUENCE patches_id;
+            ALTER TABLE patches ADD COLUMN uid INTEGER DEFAULT nextval('patches_id');
+            """
+            return query

+
+
+        # @staticmethod
+        # def save_table_as_geojson(table_name: str, output_path: str) -> str:
+        #     """Save a table as a GeoJSON file.
+        #     This method generates an SQL query to save the contents of a table
+        #     as a GeoJSON file. It assumes that the table contains geometries in a
+        #     format compatible with GeoJSON and that the output path is a valid file path.
+
+        #     :param table_name: The name of the table to save as GeoJSON.
+        #     :type table_name: str
+        #     :param output_path: The path where the GeoJSON file will be saved.
+        #     :type output_path: str
+        #     :raises AssertionError: If the output path does not exist.
+        #     :return: SQL query string to save the table as a GeoJSON file.
+        #     :rtype: str
+        #     """
+        #     assert Path(
+        #         output_path
+        #     ).parent.exists(), f"Directory {Path(output_path).parent} does not exist."
+
+        #     return f"""
+        #     COPY (SELECT * FROM {table_name}) TO '{output_path}' (FORMAT GDAL, DRIVER 'GeoJSON', OVERWRITE TRUE);
+        #     """
+
+

+[docs]
+        @staticmethod
+        def spatial_tranformation(
+            source_epsg: str, target_epsg: str, table_name: str
+        ) -> str:
+            """Transform geometries from one EPSG code to another.
+
+            This method generates an SQL query to transform geometries in a table
+            from one EPSG code to another. It assumes that the input table contains
+            geometries in the source EPSG code and that the target EPSG code is valid.
+
+            :param source_epsg: The source EPSG code of the geometries.
+            :type source_epsg: str
+            :param target_epsg: The target EPSG code to transform the geometries to.
+            :type target_epsg: str
+            :param table_name: The name of the table containing the geometries, default is "patches".
+            :type table_name: str
+
+            :return: SQL query string to transform the geometries.
+            :rtype: str
+            """
+
+            return f"""
+            UPDATE '{table_name}' SET geom = ST_Transform(geom, '{source_epsg}', '{target_epsg}', always_xy := true);
+            """

+
+
+

+[docs]
+        @staticmethod
+        def build_geometry(latitude: str, longitude: str, table_name: str) -> str:
+            """Build a geometry from latitude and longitude coordinates.
+
+            This method generates an SQL query to build a geometry column in a table
+            from latitude and longitude coordinates. It assumes that the input table
+            contains columns for latitude and longitude.
+
+            :param latitude: The name of the column containing latitude values.
+            :type latitude: str
+            :param longitude: The name of the column containing longitude values.
+            :type longitude: str
+            :param table_name: The name of the table to add the geometry column to.
+            :type table_name: str
+            :return: SQL query string to build the geometry column.
+            :rtype: str
+            """
+            return f"""
+            ALTER TABLE {table_name} ADD COLUMN geom GEOMETRY;
+            UPDATE {table_name} SET geom = ST_Point({longitude}, {latitude});    
+            """

+
+
+

+[docs]
+        @staticmethod
+        def relate_point_patches(point_name: str, patch_name: str = "patches") -> str:
+            """Relate points to patches by assigning patch IDs to points.
+
+            This method generates an SQL query to relate points in one table to patches
+            in another table by assigning patch IDs to points based on their spatial
+            relationship. It assumes that the point table contains a geometry column
+            and that the patch table contains a geometry column and a unique identifier
+            for each patch.
+
+            :param point_name: The name of the table containing the points.
+            :type point_name: str
+            :param patch_name: The name of the table containing the patches, default is "patches".
+            :type patch_name: str
+            :return: SQL query string to relate points to patches.
+            :rtype: str
+            """
+            return f"""
+            ALTER TABLE {point_name} ADD COLUMN patch_id INTEGER;
+            UPDATE {point_name}
+            SET patch_id = subquery.uid
+            FROM (
+                SELECT first.uid AS point_uid, second.uid AS uid
+                FROM {point_name} AS first
+                JOIN {patch_name} AS second
+                ON ST_Within(first.geom, second.geom)
+            ) AS subquery
+            WHERE {point_name}.uid = subquery.point_uid;
+            """

+
+
+

+[docs]
+        @staticmethod
+        def calc_point_density(point_name: str, patch_name: str = "patches") -> str:
+            """Calculate point density per patch.
+
+            This method generates an SQL query to calculate the point density per patch
+            by counting the number of points within each patch and dividing by the area
+            of the patch in square kilometers. It assumes that the point table contains
+            a geometry column and that the patch table contains a geometry column and
+            a unique identifier for each patch.
+
+            :param point_name: The name of the table containing the points.
+            :type point_name: str
+            :param patch_name: The name of the table containing the patches, default is "patches".
+            :type patch_name: str
+            :return: SQL query string to calculate point density per patch.
+            :rtype: str
+            """
+            return f"""
+            -- calculate the point count and density per patch
+            ALTER TABLE {patch_name} ADD COLUMN point_count INTEGER;
+            ALTER TABLE {patch_name} ADD COLUMN ps_density FLOAT DEFAULT 0.0;
+
+            UPDATE {patch_name}
+            SET point_count = subquery.point_count,
+                ps_density = subquery.point_count / (ST_Area(subquery.geom) / 1_000_000.0)
+            FROM (
+                SELECT second.uid AS patch_id, COUNT(first.uid) AS point_count, second.geom
+                FROM {point_name} AS first
+                JOIN {patch_name} AS second
+                ON ST_Within(first.geom, second.geom)
+                GROUP BY second.uid, second.geom
+            ) AS subquery
+            WHERE {patch_name}.uid = subquery.patch_id;
+            """

+
+
+

+[docs]
+        @staticmethod
+        def get_column_names(table_name: str) -> str:
+            """Get column names from a table.
+
+            This method generates an SQL query to retrieve the column names from a specified table.
+
+            :param table_name: The name of the table to retrieve column names from.
+            :type table_name: str
+            :return: SQL query string to get the column names.
+            :rtype: str
+            """
+            return f"""
+            SELECT column_name FROM (DESCRIBE {table_name});
+            """

+
+
+

+[docs]
+        @staticmethod
+        def calc_cum_disp(dates: list[str], table_name: str = "pspoint") -> str:
+            """Calculate cumulative displacement.
+
+            This method generates an SQL query to calculate the cumulative displacement
+            for each point in a table based on the provided date fields. It assumes that
+            the input table contains columns for displacement values at different dates.
+
+            :param dates: List of column names representing dates.
+            :type dates: list[str]
+            :param table_name: The name of the table containing the points, default is "pspoint".
+            :type table_name: str
+            :return: SQL query string to calculate cumulative displacement.
+            :rtype: str
+            """
+            return f"""
+            ALTER TABLE {table_name} ADD COLUMN displacement FLOAT;
+            UPDATE {table_name}
+            SET displacement = first.disp
+            FROM (
+                SELECT uid, {dates[-1]} - {dates[0]} as disp 
+                FROM {table_name}
+            ) as first
+            JOIN {table_name} as second
+            ON first.uid = second.uid
+            WHERE {table_name}.uid = first.uid;            
+            """

+
+
+

+[docs]
+        @staticmethod
+        def calc_avg_cum_disp() -> str:
+            """Calculate average cumulative displacement per patch.
+
+            This method generates an SQL query to calculate the average cumulative
+            displacement for each patch by averaging the displacement values of points
+            within each patch. It assumes that the point table contains a geometry column,
+            a displacement column, and that the patch table contains a geometry column
+            and a unique identifier for each patch.
+
+            :return: SQL query string to calculate average cumulative displacement per patch.
+            :rtype: str
+            """
+            return f"""
+            ALTER TABLE patches ADD COLUMN avg_cum_disp DOUBLE DEFAULT 0.0;
+            UPDATE patches
+            SET avg_cum_disp = (
+                SELECT AVG(displacement)
+                FROM pspoint
+                WHERE patch_id = patches.uid
+            );
+            """

+
+
+

+[docs]
+        @staticmethod
+        def calc_avg_velocity(
+            time_vector: list[float],
+            field_names: list[str],
+            table_name: str = "pspoint",
+        ) -> str:
+            """Calculate average velocity using linear regression.
+
+            This method generates an SQL query to calculate the average velocity for
+            each point in a table using linear regression on displacement values over time.
+            It assumes that the input table contains columns for displacement values at different dates and that the time vector is provided in days.
+
+            :param time_vector: List of time differences in days from the first date.
+            :type time_vector: list[float]
+            :param field_names: List of column names representing displacement values at different dates.
+            :type field_names: list[str]
+            :param table_name: The name of the table containing the points, default is "pspoint".
+            :type table_name: str
+            :return: SQL query string to calculate average velocity.
+            :rtype: str
+            """
+            x_vals_sql = "[" + ", ".join(map(str, time_vector)) + "]::DOUBLE[]"
+
+            y_fields_sql = (
+                "[" + ", ".join(f"CAST({c} AS DOUBLE)" for c in field_names) + "]"
+            )
+
+            return f"""
+            ALTER TABLE {table_name} ADD COLUMN avg_velocity_gcp DOUBLE;
+            WITH pairs AS (
+                SELECT 
+                    uid,
+                    list_element({y_fields_sql}, idx) as y,
+                    list_element({x_vals_sql}, idx) as x,
+                    patch_id
+                FROM {table_name}
+                CROSS JOIN UNNEST(range(1, {len(field_names)} + 1)) AS u(idx)
+                WHERE patch_id IS NOT NULL
+            ),
+            slopes AS (
+                SELECT 
+                    uid,
+                    regr_slope(y, x) AS slope
+                FROM pairs
+                WHERE y IS NOT NULL AND x IS NOT NULL
+                GROUP BY uid
+            )
+            UPDATE {table_name} AS t
+            SET avg_velocity_gcp = s.slope * 365.25 -- convert from per day to per year
+            FROM slopes AS s
+            WHERE t.uid = s.uid;
+            """

+
+
+

+[docs]
+        @staticmethod
+        def calc_avg_disp_ts_gcp(date_fields: list[str]):
+            """Calculate average displacement time series for patches (Average Local Displacement (ALD)).
+
+            This method generates an SQL query to calculate the average displacement
+            time series for each patch by averaging the displacement values of points within each patch
+            for the specified date fields. It assumes that the point table contains a geometry column,
+            displacement columns for each date field, and that the patch table contains a geometry column
+            and a unique identifier for each patch.
+
+            :param date_fields: List of column names representing displacement values at different dates.
+            :type date_fields: list[str]
+            :return: SQL query string to calculate average displacement time series per patch.
+            :rtype: str
+            """
+            expression = ", ".join([f"AVG({i})" for i in (date_fields)])
+
+            return f"""
+            ALTER TABLE patches ADD COLUMN avg_disp_ts_gcp DOUBLE[];
+            UPDATE patches
+            SET avg_disp_ts_gcp = subquery.disp_ts
+            FROM (
+                SELECT 
+                    patch_id,
+                    ARRAY[{expression}] AS disp_ts
+                FROM pspoint
+                WHERE patch_id IS NOT NULL
+                GROUP BY patch_id
+            ) AS subquery
+            WHERE patches.uid = subquery.patch_id;
+            """

+
+
+

+[docs]
+        @staticmethod
+        def calc_std_disp_ts_gcp(date_fields: list[str]):
+            """Calculate standard deviation of displacement time series for patches based on global control point (GCP).
+
+            This method generates an SQL query to calculate the standard deviation
+            of displacement time series for each patch by computing the standard deviation of displacement values of points within each patch for the specified date fields.
+
+            :param date_fields: List of column names representing displacement values at different dates.
+            :type date_fields: list[str]
+            :return: SQL query string to calculate standard deviation of displacement time series per patch.
+            :rtype: str
+            """
+
+            expression = ", ".join([f"stddev_pop({i})" for i in (date_fields)])
+
+            return f"""
+            ALTER TABLE patches ADD COLUMN std_disp_ts_gcp DOUBLE[];
+            UPDATE patches
+            SET std_disp_ts_gcp = subquery.disp_ts
+            FROM (
+                SELECT 
+                    patch_id,
+                    ARRAY[{expression}] AS disp_ts
+                FROM pspoint
+                WHERE patch_id IS NOT NULL
+                GROUP BY patch_id
+            ) AS subquery
+            WHERE patches.uid = subquery.patch_id;
+            """

+
+
+

+[docs]
+        @staticmethod
+        def select_lcps(table_name: str = "pspoint") -> str:
+            """Select linear control points (LCPs) for patches.
+
+            This method generates an SQL query to select local control points (LCPs) for each patch by identifying the point with the minimum absolute average velocity within each patch.
+
+            :param table_name: The name of the table containing the points, default is "pspoint".
+            :type table_name: str
+            :return: SQL query string to select LCPs for patches.
+            :rtype: str
+            """
+
+            return f"""
+            ALTER TABLE patches ADD COLUMN lcp_uid INTEGER;
+            WITH min_velocities AS (
+                SELECT 
+                    patch_id,
+                    MIN(ABS(avg_velocity_gcp)) AS min_velocity
+                FROM {table_name}
+                WHERE patch_id IS NOT NULL
+                GROUP BY patch_id
+            )
+            UPDATE patches
+            SET lcp_uid = subquery.uid
+            FROM (
+                SELECT
+                    p.patch_id,
+                    p.uid
+                FROM {table_name} AS p
+                JOIN min_velocities AS mv 
+                ON p.patch_id = mv.patch_id 
+                AND ABS(p.avg_velocity_gcp) = mv.min_velocity
+            ) AS subquery
+            WHERE patches.uid = subquery.patch_id;
+            """

+
+
+

+[docs]
+        @staticmethod
+        def calc_lcp_velocity() -> str:
+            """Calculate LCP velocity for patches.
+
+            This method generates an SQL query to calculate the velocity of the linear control point (LCP)
+            for each patch by retrieving the average velocity of the point identified as the LCP.
+
+            :return: SQL query string to calculate LCP velocity for patches.
+            :rtype: str
+            """
+            return f""" 
+            ALTER TABLE patches ADD COLUMN lcp_velocity FLOAT;
+            WITH subquery AS (
+                SELECT patch_id, avg_velocity_gcp
+                FROM pspoint
+                WHERE uid IN (
+                    SELECT lcp_uid 
+                    FROM patches 
+                    WHERE lcp_uid IS NOT NULL
+                )
+            )
+            UPDATE patches
+            SET lcp_velocity = subquery.avg_velocity_gcp
+            FROM subquery
+            WHERE patches.uid = subquery.patch_id;
+            """

+
+
+

+[docs]
+        @staticmethod
+        def calc_avg_velocity_gcp(table_name: str = "pspoint") -> str:
+            """Calculate average velocity based on ground control points (GCPs) for patches.
+
+            This method generates an SQL query to calculate the average velocity for each patch based on the average velocities of points within each patch.
+
+            :param table_name: The name of the table containing the points, default is "pspoint".
+            :type table_name: str
+            :return: SQL query string to calculate average velocity based on GCPs for patches.
+            :rtype: str
+            """
+
+            return f""" 
+            ALTER TABLE patches ADD COLUMN avg_gcp_velocity FLOAT;
+            UPDATE patches
+            SET avg_gcp_velocity = subquery.avg_vel 
+            FROM (
+                SELECT
+                    patch_id,
+                    AVG(avg_velocity_gcp) AS avg_vel
+                FROM {table_name}
+                WHERE patch_id IS NOT NULL
+                GROUP BY patch_id
+            ) AS subquery
+            WHERE patches.uid = subquery.patch_id 
+            """

+
+
+

+[docs]
+        @staticmethod
+        def calc_avg_std_disp_ts_lcp(
+            date_fields: list[str], table_name: str = "pspoint"
+        ) -> str:
+            """Calculate average and standard deviation of displacement time series based on LCPs.
+
+            This method generates an SQL query to calculate the average and standard
+            deviation of displacement time series for each patch based on the displacement
+            values of points within each patch relative to the local control point (LCP).
+            It assumes that the point table contains columns for displacement values at
+            different dates and that the patch table contains a geometry column and a unique
+            identifier for each patch.
+
+            :param date_fields: List of column names representing displacement values at different dates.
+            :type date_fields: list[str]
+            :param table_name: The name of the table containing the points, default is "pspoint".
+            :type table_name: str
+            :return: SQL query string to calculate average and standard deviation of displacement time series based on LCPs.
+            :rtype: str
+            """
+            expression_to_select_date_fields = (
+                "[" + ", ".join([i for i in date_fields]) + "]"
+            )
+            indices = range(1, len(date_fields) + 1)
+            expression_to_explode = ",".join(
+                f"list_extract(displacement, {i}) as {date_fields[i-1]}"
+                for i in indices
+            )
+            expresion_for_avg_per_date = ", ".join([f"AVG({i})" for i in date_fields])
+            expresion_for_std_per_date = ", ".join(
+                [f"stddev_pop({i})" for i in date_fields]
+            )
+            return f"""
+            ALTER TABLE patches ADD COLUMN avg_disp_ts_lcp DOUBLE[];
+            ALTER TABLE patches ADD COLUMN std_disp_ts_lcp DOUBLE[];
+            WITH y as (
+                SELECT patch_id, {expression_to_select_date_fields} as y
+                FROM {table_name}
+                WHERE patch_id IS NOT NULL
+            ),
+            x as (
+                SELECT patch_id, {expression_to_select_date_fields} as x
+                FROM {table_name}
+                WHERE uid IN (SELECT lcp_uid FROM patches WHERE lcp_uid IS NOT NULL)
+            ),
+            process as (
+                SELECT
+                    x.patch_id,
+                    list_transform(list_zip(y.y, x.x), arr -> arr[1] - arr[2]) as displacement
+                FROM x
+                JOIN y
+                ON x.patch_id = y.patch_id
+            ),
+            exploded AS (
+                SELECT
+                    patch_id,
+                    {expression_to_explode}
+                FROM process
+            ),
+            final AS (
+                SELECT 
+                    patch_id,
+                    ARRAY[{expresion_for_avg_per_date}] AS avg_disp_ts_lcp,
+                    ARRAY[{expresion_for_std_per_date}] AS std_disp_ts_lcp
+                FROM exploded
+                GROUP BY patch_id
+            )
+            UPDATE patches
+            SET avg_disp_ts_lcp = final.avg_disp_ts_lcp,
+                std_disp_ts_lcp = final.std_disp_ts_lcp
+            FROM final
+            WHERE patches.uid = final.patch_id;
+            """

+
+
+

+[docs]
+        @staticmethod
+        def calc_med_std_disp_ts_lcp(
+            date_fields: list[str], table_name: str = "pspoint"
+        ) -> str:
+            """Calculate average and standard deviation of displacement time series based on LCPs.
+
+            This method generates an SQL query to calculate the average and standard
+            deviation of displacement time series for each patch based on the displacement
+            values of points within each patch relative to the local control point (LCP).
+            It assumes that the point table contains columns for displacement values at
+            different dates and that the patch table contains a geometry column and a unique
+            identifier for each patch.
+
+            :param date_fields: List of column names representing displacement values at different dates.
+            :type date_fields: list[str]
+            :param table_name: The name of the table containing the points, default is "pspoint".
+            :type table_name: str
+            :return: SQL query string to calculate average and standard deviation of displacement time series based on LCPs.
+            :rtype: str
+            """
+            expression_to_select_date_fields = (
+                "[" + ", ".join([i for i in date_fields]) + "]"
+            )
+            indices = range(1, len(date_fields) + 1)
+            expression_to_explode = ",".join(
+                f"list_extract(displacement, {i}) as {date_fields[i-1]}"
+                for i in indices
+            )
+            expresion_for_avg_per_date = ", ".join([f"AVG({i})" for i in date_fields])
+            expresion_for_std_per_date = ", ".join(
+                [f"stddev_pop({i})" for i in date_fields]
+            )
+            return f"""
+            ALTER TABLE patches ADD COLUMN med_disp_ts_lcp DOUBLE[];
+            ALTER TABLE patches ADD COLUMN med_std_disp_ts_lcp DOUBLE[];
+            WITH y as (
+                SELECT patch_id, {expression_to_select_date_fields} as y
+                FROM {table_name}
+                WHERE patch_id IS NOT NULL
+            ),
+            x as (
+                SELECT patch_id, {expression_to_select_date_fields} as x
+                FROM {table_name}
+                WHERE uid IN (SELECT lcp_uid FROM patches WHERE lcp_uid IS NOT NULL)
+            ),
+            process as (
+                SELECT
+                    x.patch_id,
+                    list_transform(list_zip(y.y, x.x), arr -> arr[1] - arr[2]) as displacement
+                FROM x
+                JOIN y
+                ON x.patch_id = y.patch_id
+            ),
+            exploded AS (
+                SELECT
+                    patch_id,
+                    {expression_to_explode}
+                FROM process
+            ),
+            final AS (
+                SELECT 
+                    patch_id,
+                    ARRAY[{expresion_for_avg_per_date}] AS med_disp_ts_lcp,
+                    ARRAY[{expresion_for_std_per_date}] AS med_std_disp_ts_lcp
+                FROM exploded
+                GROUP BY patch_id
+            )
+            UPDATE patches
+            SET med_disp_ts_lcp = final.med_disp_ts_lcp,
+                med_std_disp_ts_lcp = final.med_std_disp_ts_lcp
+            FROM final
+            WHERE patches.uid = final.patch_id;
+            """

+
+
+

+[docs]
+        @staticmethod
+        def calc_avg_velocity_lcp(table_name: str = "pspoint") -> str:
+            """Calculate average velocity based on local control points (LCPs) for points.
+
+            This method generates an SQL query to calculate the average velocity for each point
+            based on the average velocity of the local control point (LCP) associated with the patch
+            to which the point belongs. It assumes that the point table contains a geometry column,
+            an average velocity column based on ground control points (GCPs), and that the patch table
+            contains a geometry column, a unique identifier for each patch, and the average velocity
+            of the LCP.
+
+            :param table_name: The name of the table containing the points, default is "pspoint".
+            :type table_name: str
+            :return: SQL query string to calculate average velocity based on LCPs for points.
+            :rtype: str
+            """
+            return f"""
+            ALTER TABLE {table_name} ADD COLUMN avg_velocity_lcp DOUBLE;
+            WITH y AS (
+                SELECT uid, patch_id, avg_velocity_gcp
+                FROM {table_name}
+                WHERE patch_id IS NOT NULL
+            ),
+            x AS (
+                SELECT uid, lcp_velocity, lcp_uid
+                FROM patches
+                WHERE lcp_uid IS NOT NULL
+            )
+            UPDATE {table_name}
+            SET avg_velocity_lcp = subquery.avg_velocity_lcp
+            FROM (
+                SELECT 
+                    y.avg_velocity_gcp - x.lcp_velocity AS avg_velocity_lcp, 
+                    y.uid as uid
+                FROM x
+                JOIN y ON x.uid = y.patch_id
+            ) AS subquery
+            WHERE {table_name}.uid = subquery.uid;
+            """

+
+
+

+[docs]
+        @staticmethod
+        def calc_avg_lcp_velocity(table_name: str = "pspoint") -> str:
+            """Calculate average LCP velocity per patch.
+
+            This method generates an SQL query to calculate the average velocity of
+            local control points (LCPs) for each patch by averaging the average velocities
+            of points within each patch based on their LCP velocities.
+
+            :param table_name: The name of the table containing the points, default is "pspoint".
+            :type table_name: str
+            :return: SQL query string to calculate average LCP velocity per patch.
+            :rtype: str
+            """
+            return f"""
+            ALTER TABLE patches ADD COLUMN avg_lcp_velocity FLOAT;
+            UPDATE patches
+            SET avg_lcp_velocity = subquery.avg_vel
+            FROM (
+                SELECT
+                    patch_id,
+                    AVG(avg_velocity_lcp) as avg_vel
+                FROM {table_name}
+                WHERE patch_id IS NOT NULL
+                GROUP BY patch_id
+            ) AS subquery
+            WHERE patches.uid = subquery.patch_id
+            """

+
+
+

+[docs]
+        @staticmethod
+        def calc_outliers(scaling_factor: float, table_name: str = "pspoint") -> str:
+            """Identify outlier points based on absolute average velocity.
+
+            This method generates an SQL query to identify outlier points based on their
+            average velocity relative to local control points (LCPs). Points with an absolute
+            average LCP velocity exceeding a specified threshold (5 mm/year) are
+            marked as outliers by assigning their unique identifier to a new column in the
+            patches table. The unit parameter allows for specifying the measurement unit
+            (meters, centimeters, or millimeters) to appropriately scale the threshold.
+
+            :param scaling_factor: The factor to scale the velocity threshold based on the unit.
+            :type scaling_factor: float
+            :param table_name: The name of the table containing the points, default is "pspoint".
+            :type table_name: str
+            :return: SQL query string to identify outlier points.
+            :rtype: str
+            """
+
+            return f""" 
+            CREATE OR REPLACE TABLE outliers AS
+                SELECT patch_id, uid
+                FROM {table_name}
+                WHERE ABS(avg_velocity_lcp) > 5 / {scaling_factor}
+                AND patch_id IS NOT NULL;
+            ALTER TABLE outliers ADD COLUMN criteria VARCHAR;
+            UPDATE outliers SET criteria = 'threshold';
+            """

+
+
+

+[docs]
+        @staticmethod
+        def calc_outliers_avg_lcd(
+            table_name: str = "pspoint", date_vector=list[int], date_names=list[str]
+        ) -> str:
+            """Calculate outliers based on average local control displacement (LCD).
+
+            This method generates an SQL query to identify outlier points based on their
+            displacement time series relative to the average local control displacement (LCD)
+            of their associated patches. Points with displacement values exceeding the average
+            LCD by more than standard deviations at any time point are marked as outliers
+            by inserting their unique identifier and patch ID into a new outliers table.
+
+            :param table_name: The name of the table containing the points, default is "pspoint".
+            :type table_name: str
+            :param date_vector: List of time differences in days from the first date.
+            :type date_vector: list[int]
+            :param date_names: List of column names representing displacement values at different dates.
+            :type date_names: list[str]
+            :return: SQL query string to identify outlier points based on average LCD.
+            :rtype: str
+            """
+
+            if date_vector.max() < 730:
+                return ""
+
+            first_index = npwhere(date_vector >= date_vector - 730)[0][0]
+            column_select = ", ".join(date_names[first_index:])
+
+            return f"""
+            with underthreshold as (
+                SELECT 
+                    uid, 
+                    patch_id,
+                    ARRAY[{column_select}] as disp_ts 
+                FROM {table_name}
+                WHERE uid NOT IN (SELECT uid FROM outliers)
+                AND patch_id IS NOT NULL
+            ),
+            sel_ts as (
+                SELECT 
+                    uid, 
+                    avg_disp_ts_lcp[{first_index}:] as avg,
+                    std_disp_ts_lcp[{first_index}:] as std,
+                FROM patches
+                WHERE patches.uid IN (SELECT patch_id FROM underthreshold)  
+            ),
+            joined as (
+                SELECT * from underthreshold
+                JOIN sel_ts on sel_ts.uid = underthreshold.patch_id
+            ),
+            ald_points as (
+                SELECT
+                    uid,
+                    patch_id,
+                    list_min(
+                        list_apply(
+                            array_zip(disp_ts, avg, std),
+                            x -> abs(x[1]-x[2]) > x[3]
+                        )
+                    ) as all_greater
+                FROM joined
+                WHERE all_greater = true
+            )
+            INSERT INTO outliers (uid, patch_id, criteria)
+            SELECT uid, patch_id, 'ald' FROM ald_points
+            """

+
+
+

+[docs]
+        @staticmethod
+        def calc_outliers_med_lcd(
+            table_name: str = "pspoint", date_vector=list[int], date_names=list[str]
+        ) -> str:
+            """Calculate outliers based on median local control displacement (LCD).
+
+            This method generates an SQL query to identify outlier points based on their
+            displacement time series relative to the median local control displacement (LCD)
+            of their associated patches. Points with displacement values exceeding the median
+            LCD by more than standard deviations at any time point are marked as outliers
+            by inserting their unique identifier and patch ID into a new outliers table.
+
+            :param table_name: The name of the table containing the points, default is "pspoint".
+            :type table_name: str
+            :param date_vector: List of time differences in days from the first date.
+            :type date_vector: list[int]
+            :param date_names: List of column names representing displacement values at different dates.
+            :type date_names: list[str]
+            :return: SQL query string to identify outlier points based on median LCD.
+            :rtype: str
+            """
+
+            if date_vector.max() < 730:
+                return ""
+
+            first_index = npwhere(date_vector >= date_vector - 730)[0][0]
+            column_select = ", ".join(date_names[first_index:])
+
+            return f"""
+            with underthreshold as (
+                SELECT 
+                    uid, 
+                    patch_id,
+                    ARRAY[{column_select}] as disp_ts 
+                FROM {table_name}
+                WHERE uid NOT IN (SELECT uid FROM outliers)
+                AND patch_id IS NOT NULL
+            ),
+            sel_ts as (
+                SELECT 
+                    uid, 
+                    med_disp_ts_lcp[{first_index}:] as med,
+                    med_std_disp_ts_lcp[{first_index}:] as std,
+                FROM patches
+                WHERE patches.uid IN (SELECT uid FROM underthreshold)  
+            ),
+            joined as (
+                SELECT * from underthreshold
+                JOIN sel_ts on sel_ts.uid = underthreshold.patch_id
+            ),
+            med_points as (
+                SELECT
+                    uid,
+                    patch_id,
+                    list_min(
+                        list_apply(
+                            array_zip(disp_ts, med, std),
+                            x -> abs(x[1]-x[2]) > x[3]
+                        )
+                    ) as all_greater
+                FROM joined
+                WHERE all_greater = true
+            )
+            INSERT INTO outliers (uid, patch_id, criteria)
+            SELECT uid, patch_id, 'med' FROM med_points
+            """

+
+
+

+[docs]
+        @staticmethod
+        def lcp_ts_patch(date_fields: list[str], table_name: str = "pspoint"):
+            """Get Local Control Point's displacement time series for patches.
+
+            This method generates an SQL query to retrieve the displacement time series
+            of the local control point (LCP) for each patch based on specified date fields.
+
+            :param date_fields: List of column names representing displacement values at different dates.
+            :type date_fields: list[str]
+            :param table_name: The name of the table containing the points, default is "pspoint".
+            :type table_name: str
+            :return: SQL query string to get LCP displacement time series for patches.
+            :rtype: str
+
+            """
+            date_fields_str = "[" + ", ".join(date_fields) + "]"
+            return f"""
+            ALTER TABLE patches ADD COLUMN lcp_disp_ts DOUBLE[];
+            UPDATE patches
+            SET lcp_disp_ts = subquery.disp_ts
+            FROM (
+                SELECT 
+                    p.patch_id,
+                    {date_fields_str} AS disp_ts
+                FROM {table_name} AS p
+                JOIN patches AS pa
+                ON p.uid = pa.lcp_uid
+                WHERE p.patch_id IS NOT NULL
+                -- GROUP BY p.patch_id
+            ) AS subquery
+            WHERE patches.uid = subquery.patch_id;
+            """

+
+
+

+[docs]
+        def outlier_ts_patch(date_fields: list[str], table_name: str = "pspoint"):
+            """Get outlier displacement time series for patches.
+
+            This method generates an SQL query to retrieve the displacement time series
+            of outlier points based on specified date fields.
+
+            :param date_fields: List of column names representing displacement values at different dates.
+            :type date_fields: list[str]
+            :param table_name: The name of the table containing the points, default is "pspoint".
+            :type table_name: str
+            :return: SQL query string to get outlier displacement time series for patches.
+            :rtype: str
+            """
+
+            date_fields_str = "[" + ", ".join(date_fields) + "]"
+            return f"""
+            ALTER TABLE outliers ADD COLUMN disp_ts DOUBLE[];
+            UPDATE outliers
+            SET disp_ts = subquery.disp_ts
+            FROM (
+                SELECT 
+                    p.uid,
+                    {date_fields_str} AS disp_ts
+                FROM {table_name} AS p
+                WHERE p.uid IN (SELECT uid FROM outliers)
+            ) AS subquery
+            WHERE outliers.uid = subquery.uid;
+            """

+
+
+

+[docs]
+        @staticmethod
+        def outlier_rel_ts_patch():
+            """Calculate outlier displacement time series relative to LCP for patches.
+
+            This method generates an SQL query to calculate the displacement time series
+            of outlier points relative to the displacement time series of their associated
+            local control points (LCPs). It assumes that the outliers table contains a
+            geometry column, a displacement time series column, and a patch ID column,
+            and that the patches table contains a geometry column, a unique identifier
+            for each patch, and a displacement time series column for the LCP.
+
+            :return: SQL query string to calculate outlier displacement time series relative to LCP for patches.
+            :rtype: str
+            """
+            return f"""
+            ALTER TABLE outliers ADD COLUMN rel_ts DOUBLE[];
+            with p_lcp as (
+                SELECT uid, lcp_disp_ts
+                FROM patches
+                WHERE lcp_uid IS NOT NULL
+            ),
+            p_out as (
+                SELECT o.uid, o.patch_id, o.disp_ts, p_lcp.lcp_disp_ts
+                FROM outliers AS o
+                JOIN p_lcp
+                ON o.patch_id = p_lcp.uid
+            )
+            UPDATE outliers
+            SET rel_ts = list_transform(
+                p_out.disp_ts, 
+                (value, idx) -> value - list_element(p_out.lcp_disp_ts, idx)
+            )
+            FROM p_out
+            WHERE outliers.uid = p_out.uid
+            """

+

+
+
+

+[docs]
+    class Visualisation:
+        """SQL queries for visualisation of results.
+
+        2D visualisation is done in Web Mercator (EPSG:3857).
+        """
+
+        projection = "EPSG:3857"
+
+

+[docs]
+        @staticmethod
+        def get_table_bbox(table_name: str, source_crs: str) -> str:
+            """Get the bounding box of a table in a specified projection.
+
+            This method generates an SQL query to retrieve the bounding box of geometries
+            from a specified table, transforming the geometries from a source CRS to
+            a target projection defined in the Visualisation class.
+
+            :param table_name: The name of the table to get the bounding box from.
+            :type table_name: str
+            :param source_crs: The source coordinate reference system of the geometries.
+            :type source_crs: str
+            :return: SQL query string to get the bounding box of the table.
+            :rtype: str"""
+            return f"""
+            with transformed as (
+                SELECT 
+                ST_TRANSFORM(geom, '{source_crs}', '{Pipeline.Visualisation.projection}', always_xy := true) as geom 
+                FROM {table_name}
+            )
+            SELECT
+                MIN(ST_XMIN(geom)) AS min_x,
+                MIN(ST_YMIN(geom)) AS min_y,
+                MAX(ST_XMAX(geom)) AS max_x,
+                MAX(ST_YMAX(geom)) AS max_y
+            FROM transformed;
+            """

+
+
+

+[docs]
+        @staticmethod
+        def get_ps_density(source_crs: str) -> str:
+            """Get point density per patch.
+
+            This method generates an SQL query to retrieve the point density for each patch,
+            transforming the geometries from a source CRS to a target projection defined
+            in the Visualisation class.
+
+            :param source_crs: The source coordinate reference system of the geometries.
+            :type source_crs: str
+            :return: SQL query string to get the point density per patch.
+            :rtype: str
+            """
+            return f"""
+            SELECT
+                ST_AsWKB(ST_TRANSFORM(geom, '{source_crs}', '{Pipeline.Visualisation.projection}', always_xy := true)) AS geom,
+                ps_density
+            FROM patches
+            """

+
+
+

+[docs]
+        @staticmethod
+        def get_outliers_map(source_crs: str, scaling_factor: float) -> str:
+            """Get outlier points based on absolute average LCP velocity.
+
+            This method generates an SQL query to retrieve outlier points based on their
+            average velocity relative to local control points (LCPs). Points with an absolute
+            average LCP velocity exceeding a specified threshold (5 mm/year) are
+            marked as outliers by assigning their unique identifier to a new column in the
+            patches table. The unit parameter allows for specifying the measurement unit
+            (meters, centimeters, or millimeters) to appropriately scale the threshold.
+
+            :param source_crs: The source coordinate reference system of the geometries.
+            :type source_crs: str
+            :param scaling_factor: The factor to scale the velocity threshold based on the unit.
+            :type scaling_factor: float
+            :return: SQL query string to identify outlier points.
+            :rtype: str
+            """
+            return f"""
+            SELECT
+                ST_AsWKB(ST_TRANSFORM(geom, '{source_crs}', '{Pipeline.Visualisation.projection}', always_xy := true)) AS geom,
+                ABS(avg_velocity_lcp) * {scaling_factor} AS velocity,
+            FROM pspoint
+            WHERE uid IN (SELECT uid FROM outliers)
+            ORDER BY velocity ASC;
+            """

+
+
+

+[docs]
+        @staticmethod
+        def get_cum_disp_map(source_crs: str, scaling_factor: float) -> str:
+            """Get cumulative displacement per patch.
+
+            This method generates an SQL query to retrieve the cumulative displacement
+            for each patch, transforming the geometries from a source CRS to a target
+            projection defined in the Visualisation class.
+
+            :param source_crs: The source coordinate reference system of the geometries.
+            :type source_crs: str
+            :param unit: The measurement unit for displacement ("m", "cm", or "mm").
+            :type unit: str
+            :return: SQL query string to get the cumulative displacement per patch.
+            :rtype: str
+            """
+            # scaling_factor = [1, 100, 1000][["m", "cm", "mm"].index(unit)]
+            return f"""
+            SELECT
+                ST_AsWKB(ST_TRANSFORM(geom, '{source_crs}', '{Pipeline.Visualisation.projection}', always_xy := true)) AS geom,
+                avg_cum_disp * {scaling_factor} AS cum_disp
+            FROM patches
+            WHERE avg_cum_disp IS NOT NULL
+            ORDER BY cum_disp ASC;
+            """

+
+
+

+[docs]
+        @staticmethod
+        def get_ps_vel_gcp(source_crs: str, scaling_factor: float) -> str:
+            """Get average velocity based on ground control points (GCPs).
+            This method generates an SQL query to retrieve the average velocity for
+            each point based on ground control points (GCPs), transforming the geometries
+            from a source CRS to a target projection defined in the Visualisation class.
+
+            :param source_crs: The source coordinate reference system of the geometries.
+            :type source_crs: str
+            :param unit: The measurement unit for velocity ("m", "cm", or "mm").
+            :type unit: str
+            :return: SQL query string to get the average velocity based on GCPs.
+            :rtype: str
+            """
+            return f"""
+            SELECT
+                ST_AsWKB(ST_TRANSFORM(geom, '{source_crs}', '{Pipeline.Visualisation.projection}', always_xy := true)) AS geom,
+                avg_velocity_gcp * {scaling_factor} AS velocity
+            FROM pspoint
+            WHERE patch_id IS NOT NULL
+            ORDER BY velocity ASC;
+            """

+
+
+

+[docs]
+        @staticmethod
+        def get_ps_vel_lcp(source_crs: str, scaling_factor: float) -> str:
+            """Get average velocity based on local control points (LCPs).
+
+            This method generates an SQL query to retrieve the average velocity for
+            each point based on local control points (LCPs), transforming the geometries
+            from a source CRS to a target projection defined in the Visualisation class.
+
+            :param source_crs: The source coordinate reference system of the geometries.
+            :type source_crs: str
+            :param unit: The measurement unit for velocity ("m", "cm", or "mm").
+            :type unit: str
+            :return: SQL query string to get the average velocity based on LCPs.
+            :rtype: str
+            """
+            return f"""
+            SELECT
+                ST_AsWKB(ST_TRANSFORM(geom, '{source_crs}', '{Pipeline.Visualisation.projection}', always_xy := true)) AS geom,
+                ABS(avg_velocity_lcp) * {scaling_factor} AS velocity
+            FROM pspoint
+            WHERE patch_id IS NOT NULL
+            ORDER BY velocity ASC;
+            """

+
+
+

+[docs]
+        @staticmethod
+        def get_center(point_uid: float, source_crs: str) -> str:
+            """Get the center point of the outlier within a specific patch.
+
+            This method generates an SQL query to retrieve the center point of the outlier
+            within a specific patch, transforming the geometry from a source CRS to a target
+            projection defined in the Visualisation class.
+
+            :param point_uid: The unique identifier of the outlier point.
+            :type point_uid: float
+            :param source_crs: The source coordinate reference system of the geometries.
+            :type source_crs: str
+            :return: SQL query string to get the center point of the outlier within the specified patch.
+            """
+            return f"""
+            SELECT
+                ST_AsWKB(ST_TRANSFORM(ST_Centroid(geom), '{source_crs}', '{Pipeline.Visualisation.projection}', always_xy := true)) AS geom
+            FROM pspoint
+            WHERE uid = {point_uid}
+            """

+
+
+

+[docs]
+        @staticmethod
+        def get_patch_center(patch_id: float, source_crs: str) -> str:
+            """Get the center point of a specific patch.
+
+            This method generates an SQL query to retrieve the center point of a specific
+            patch, transforming the geometry from a source CRS to a target projection defined in the Visualisation class.
+
+            :param patch_id: The unique identifier of the patch.
+            :type patch_id: float
+            :param source_crs: The source coordinate reference system of the geometries.
+            :type source_crs: str
+            :return: SQL query string to get the center point of the specified patch.
+            """
+            return f"""
+            SELECT
+                ST_AsWKB(ST_TRANSFORM(ST_Centroid(geom), '{source_crs}', '{Pipeline.Visualisation.projection}', always_xy := true)) AS geom
+            FROM patches
+            WHERE uid = {patch_id}
+            """

+
+
+

+[docs]
+        @staticmethod
+        def get_ps_points(patch_id: float, source_crs: str, table_name: str) -> str:
+            """Get points within a specific patch.
+
+            This method generates an SQL query to retrieve points within a specific patch,
+            transforming the geometries from a source CRS to a target projection defined
+            in the Visualisation class.
+
+            :param patch_id: The unique identifier of the patch.
+            :type patch_id: float
+            :param source_crs: The source coordinate reference system of the geometries.
+            :type source_crs: str
+            :param table_name: The name of the table containing the points.
+            :type table_name: str
+            :return: SQL query string to get the points within the specified patch.
+            :rtype: str
+            """
+            return f"""
+            SELECT
+                ST_AsWKB(ST_TRANSFORM(geom, '{source_crs}', '{Pipeline.Visualisation.projection}', always_xy := true)) AS geom,
+            FROM {table_name}
+            WHERE patch_id = {patch_id}
+            """

+
+
+

+[docs]
+        @staticmethod
+        def get_lcp_point(patch_id: float, source_crs: str) -> str:
+            """Get the local control point (LCP) within a specific patch.
+
+            This method generates an SQL query to retrieve the local control point (LCP)
+            within a specific patch, transforming the geometry from a source CRS to a target
+            projection defined in the Visualisation class.
+
+            :param patch_id: The unique identifier of the patch.
+            :type patch_id: float
+            :param source_crs: The source coordinate reference system of the geometries.
+            :type source_crs: str
+            :return: SQL query string to get the LCP within the specified patch.
+            """
+            return f"""
+            SELECT
+                ST_AsWKB(ST_TRANSFORM(geom, '{source_crs}', '{Pipeline.Visualisation.projection}', always_xy := true)) AS geom
+            FROM pspoint
+            WHERE uid = (
+                SELECT lcp_uid
+                FROM patches
+                WHERE uid = {patch_id}
+            )
+            """

+
+
+

+[docs]
+        @staticmethod
+        def get_outlier_points(patch_id: float, source_crs: str) -> str:
+            """Get the outlier points within a specific patch.
+
+            This method generates an SQL query to retrieve the outlier point within a specific patch,
+            transforming the geometry from a source CRS to a target projection defined in the Visualisation class.
+
+            :param patch_id: The unique identifier of the patch.
+            :type patch_id: float
+            :param source_crs: The source coordinate reference system of the geometries.
+            :type source_crs: str
+            :return: SQL query string to get the outlier point within the specified patch.
+            """
+            return f"""
+            SELECT
+                uid,
+                ST_AsWKB(ST_TRANSFORM(geom, '{source_crs}', '{Pipeline.Visualisation.projection}', always_xy := true)) AS geom
+            FROM pspoint
+            WHERE uid IN (
+                SELECT uid
+                FROM outliers
+                WHERE patch_id = {patch_id}
+            )
+            """

+
+
+

+[docs]
+        @staticmethod
+        def get_cum_disp_graph(outlier_id: float, scaling_factor: float) -> str:
+            """Get outlier displacement time series.
+
+            This method generates an SQL query to retrieve the outlier displacement time series
+            for a specific patch, scaling the displacement values by a specified factor.
+
+            :param outlier_id: The unique identifier of the patch.
+            :type outlier_id: float
+            :param scaling_factor: The factor to scale the displacement values.
+            :type scaling_factor: float
+            :return: SQL query string to get the outlier displacement time series for the specified patch.
+            """
+            return f"""
+            SELECT
+                list_transform(disp_ts, x -> x * {scaling_factor})
+            FROM outliers
+            WHERE uid = {outlier_id}
+            """

+
+
+

+[docs]
+        @staticmethod
+        def get_avg_cum_disp_graph(patch_id: float, scaling_factor: float) -> str:
+            """Get average cumulative displacement time series.
+
+            This method generates an SQL query to retrieve the average cumulative displacement
+            time series for a specific patch, scaling the displacement values by a specified factor.
+
+            :param patch_id: The unique identifier of the patch.
+            :type patch_id: float
+            :param scaling_factor: The factor to scale the displacement values.
+            :type scaling_factor: float
+            :return: SQL query string to get the average cumulative displacement time series for the specified patch.
+            """
+            return f"""
+            SELECT
+                list_transform(avg_disp_ts_gcp, x -> x * {scaling_factor})
+            FROM patches
+            WHERE uid = {patch_id}
+            """

+
+
+

+[docs]
+        @staticmethod
+        def get_std_cum_disp_graph(patch_id: float, scaling_factor: float) -> str:
+            """Get standard deviation of cumulative displacement time series.
+
+            This method generates an SQL query to retrieve the standard deviation of cumulative
+            displacement time series for a specific patch, scaling the displacement values by a specified factor.
+
+            :param patch_id: The unique identifier of the patch.
+            :type patch_id: float
+            :param scaling_factor: The factor to scale the displacement values.
+            :type scaling_factor: float
+            :return: SQL query string to get the standard deviation of cumulative displacement time series for the specified patch.
+            """
+            return f"""
+            SELECT
+                list_transform(std_disp_ts_gcp, x -> x * {scaling_factor})
+            FROM patches
+            WHERE uid = {patch_id}
+            """

+
+
+

+[docs]
+        def get_outlier_rel_ts_graph(outlier_id: float, scaling_factor: float) -> str:
+            # TODO: check the logic here
+            """Get outlier relative displacement time series.
+
+            This method generates an SQL query to retrieve the outlier relative displacement
+            time series for a specific patch, scaling the displacement values by a specified factor.
+
+            :param outlier_id: The unique identifier of the patch.
+            :type outlier_id: float
+            :param scaling_factor: The factor to scale the displacement values.
+            :type scaling_factor: float
+            :return: SQL query string to get the outlier relative displacement time series for the specified patch.
+            """
+            return f"""
+            SELECT
+                list_transform(rel_ts, x -> x * {scaling_factor})
+            FROM outliers
+            WHERE uid = {outlier_id}
+            """

+
+
+

+[docs]
+        @staticmethod
+        def get_lcp_disp_graph(patch_id: float, scaling_factor: float) -> str:
+            """Get local control point (LCP) displacement time series.
+
+            This method generates an SQL query to retrieve the local control point (LCP)
+            displacement time series for a specific patch, scaling the displacement values by a specified factor.
+
+            :param patch_id: The unique identifier of the patch.
+            :type patch_id: float
+            :param scaling_factor: The factor to scale the displacement values.
+            :type scaling_factor: float
+            :return: SQL query string to get the LCP displacement time series for the specified patch.
+            """
+            return f"""
+            SELECT
+                list_transform(lcp_disp_ts, x -> x * {scaling_factor})
+            FROM patches
+            WHERE uid = {patch_id}
+            """

+
+
+

+[docs]
+        @staticmethod
+        def get_avg_disp_lcp_graph(patch_id: float, scaling_factor: float) -> str:
+            """Get average displacement time series based on local control points (LCPs).
+
+            This method generates an SQL query to retrieve the average displacement time series
+            based on local control points (LCPs) for a specific patch, scaling the displacement
+            values by a specified factor.
+
+            :param patch_id: The unique identifier of the patch.
+            :type patch_id: float
+            :param scaling_factor: The factor to scale the displacement values.
+            :type scaling_factor: float
+            :return: SQL query string to get the average displacement time series based on LCPs for the specified patch.
+            """
+            return f"""
+            SELECT
+                list_transform(avg_disp_ts_lcp, x -> x * {scaling_factor})
+            FROM patches
+            WHERE uid = {patch_id}
+            """

+
+
+

+[docs]
+        @staticmethod
+        def get_std_disp_lcp_graph(patch_id: float, scaling_factor: float) -> str:
+            """Get standard deviation of displacement time series based on local control points (LCPs).
+
+            This method generates an SQL query to retrieve the standard deviation of displacement
+            time series based on local control points (LCPs) for a specific patch, scaling the
+            displacement values by a specified factor.
+
+            :param patch_id: The unique identifier of the patch.
+            :type patch_id: float
+            :param scaling_factor: The factor to scale the displacement values.
+            :type scaling_factor: float
+            :return: SQL query string to get the standard deviation of displacement time series based on LCPs for the specified patch.
+            """
+            return f"""
+            SELECT
+                list_transform(std_disp_ts_lcp, x -> x * {scaling_factor})
+            FROM patches
+            WHERE uid = {patch_id}
+            """

+
+
+

+[docs]
+        @staticmethod
+        def patch_uids() -> str:
+            """Get all patch UIDs that have outliers.
+
+            This method generates an SQL query to retrieve all unique identifiers (UIDs)
+            of patches that have been identified as containing outliers.
+
+            :return: SQL query string to get all patch UIDs with outliers.
+            :rtype: str
+            """
+            return """
+            SELECT patch_id
+            FROM outliers
+            GROUP BY patch_id;
+            """

+

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/build/html/_modules/saferoad/plotter.html b/docs/build/html/_modules/saferoad/plotter.html new file mode 100644 index 0000000..78482da --- /dev/null +++ b/docs/build/html/_modules/saferoad/plotter.html @@ -0,0 +1,1099 @@ + + + + + + + + saferoad.plotter — saferoad 0.1 documentation + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for saferoad.plotter

+from matplotlib import pyplot as plt
+import contextily as ctx
+from shapely.wkb import loads as load_wkb
+from matplotlib.lines import Line2D
+from datetime import datetime
+import matplotlib.dates as mdates
+
+
+

+[docs]
+class Plotter:
+    """Class to create and manage plots for geospatial data analysis."""
+
+

+[docs]
+    class MapPane:
+        """
+        Class to create and manage a multi-panel map figure.
+
+        Panels include:
+        - PS Density
+        - Outliers
+        - Cumulative Displacement
+        - PS Velocity based on GCP
+        - PS Velocity based on LCP
+        """
+
+        def __init__(self):
+            self.generate_figure()
+
+

+[docs]
+        def generate_figure(self):
+            """
+            Generates a multi-panel figure with specified layout and titles.
+            """
+            fig = plt.figure(figsize=(18, 12))
+            layout = [
+                ["A", "A", "B", "B", "C", "C"],  # top row: 3 panels, equal widths
+                [".", "D", "D", "E", "E", "."],  # bottom row: 2 panels centered
+            ]
+            axs = fig.subplot_mosaic(layout, empty_sentinel=".")
+
+            # Optional spacing (no constrained_layout -> safer with contextily)
+            fig.subplots_adjust(wspace=0.25, hspace=0.35)
+            axs["A"].set_title("PS Density", fontsize=10)
+            axs["B"].set_title("Outliers", fontsize=10)
+            axs["C"].set_title("Cumulative Displacement", fontsize=10)
+            axs["D"].set_title("PS Velocity based on GCP", fontsize=10)
+            axs["E"].set_title("PS Velocity based on LCP", fontsize=10)
+            self.fig = fig
+            self.axs = [axs["A"], axs["B"], axs["C"], axs["D"], axs["E"]]

+
+
+

+[docs]
+        def plot_basemap(self, bbox: tuple[float, float, float, float]):
+            """
+            Plots a basemap on all axes using the provided bounding box.
+
+            :param bbox: A tuple representing the bounding box (minx, miny, maxx, maxy).
+            """
+            bbox = [i for i in bbox[0]]
+
+            for ax in self.axs:
+                ax.set_xlim(bbox[0] - 2000, bbox[2] + 2000)
+                ax.set_ylim(bbox[1] - 2000, bbox[3] + 4000)
+                ctx.add_basemap(
+                    ax,
+                    source=ctx.providers.CartoDB.Positron,
+                    crs="EPSG:3857",
+                )

+
+
+

+[docs]
+        def plot_ps_density(self, plot_data: list[tuple]) -> None:
+            """
+            Plots PS Density data on the first axis.
+
+            :param plot_data: List of tuples containing geometry and PS density value.
+            """
+            label_store = []
+            for geom, psdensity in plot_data:
+                geom = load_wkb(geom)
+                color, label = self.ps_density_color_label(psdensity)
+                if label not in label_store:
+                    label_store.append(label)
+                else:
+                    label = None
+
+                self.axs[0].fill(
+                    *geom.exterior.xy,
+                    fc=color,
+                    ec=color,
+                    label=label,
+                )
+            self.axs[0].legend(
+                title="PS Density",
+                loc="upper right",
+                fontsize=6,
+                title_fontsize=6,
+                frameon=True,
+                facecolor="white",
+                edgecolor="black",
+            )

+
+
+

+[docs]
+        def plot_outliers(self, plot_data: list[tuple]) -> None:
+            """
+            Plots outlier data on the second axis.
+
+            :param plot_data: List of tuples containing geometry and outlier velocity value.
+            """
+            label_store = []
+            for geom, vel in plot_data:
+                geom = load_wkb(geom)
+                color, label = self.outlier_color_label(vel)
+                if label not in label_store:
+                    label_store.append(label)
+                else:
+                    label = None
+
+                self.axs[1].scatter(
+                    *geom.xy,
+                    s=10,
+                    c=color,
+                    label=label,
+                )
+            self.axs[1].legend(
+                title="Outlier",
+                loc="upper right",
+                fontsize=6,
+                title_fontsize=6,
+                frameon=True,
+                facecolor="white",
+                edgecolor="black",
+            )

+
+
+

+[docs]
+        def plot_cum_disp(self, plot_data: list[tuple]) -> None:
+            """
+            Plots cumulative displacement data on the third axis.
+
+            :param plot_data: List of tuples containing geometry and cumulative displacement value.
+            """
+            label_store = []
+            for geom, disp in plot_data:
+                geom = load_wkb(geom)
+                color, label = self.cum_disp_color_label(disp)
+                if label not in label_store:
+                    label_store.append(label)
+                else:
+                    label = None
+
+                self.axs[2].fill(
+                    *geom.exterior.xy,
+                    fc=color,
+                    ec=color,
+                    label=label,
+                )
+            self.axs[2].legend(
+                title="Cumulative\nDisplacement",
+                loc="upper right",
+                fontsize=6,
+                title_fontsize=6,
+                frameon=True,
+                facecolor="white",
+                edgecolor="black",
+            )

+
+
+

+[docs]
+        def plot_gcp_velocity(self, plot_data: list[tuple]) -> None:
+            """Plots GCP velocity data on the fourth axis.
+
+            :param plot_data: List of tuples containing geometry and GCP velocity value.
+            """
+            points, colors = [], []
+            for geom, vel in plot_data:
+                geom = load_wkb(geom)
+                points.append(geom.xy)
+                colors.append(self.gcp_vel_color(vel))
+
+            x_coords = [p[0][0] for p in points]
+            y_coords = [p[1][0] for p in points]
+
+            self.axs[3].scatter(
+                x_coords,
+                y_coords,
+                s=4,
+                c=colors,
+                alpha=0.7,
+            )
+            legend_elements = [
+                Line2D(
+                    [0],
+                    [0],
+                    marker="o",
+                    color="w",
+                    markerfacecolor="red",
+                    markersize=6,
+                    label="(-15,-6) mm",
+                ),
+                Line2D(
+                    [0],
+                    [0],
+                    marker="o",
+                    color="w",
+                    markerfacecolor="orange",
+                    markersize=6,
+                    label="(-6,-4) mm",
+                ),
+                Line2D(
+                    [0],
+                    [0],
+                    marker="o",
+                    color="w",
+                    markerfacecolor="yellow",
+                    markersize=6,
+                    label="(-4,-2) mm",
+                ),
+                Line2D(
+                    [0],
+                    [0],
+                    marker="o",
+                    color="w",
+                    markerfacecolor="lightgreen",
+                    markersize=6,
+                    label="(-2,2) mm",
+                ),
+                Line2D(
+                    [0],
+                    [0],
+                    marker="o",
+                    color="w",
+                    markerfacecolor="cyan",
+                    markersize=6,
+                    label="(2,5) mm",
+                ),
+                Line2D(
+                    [0],
+                    [0],
+                    marker="o",
+                    color="w",
+                    markerfacecolor="blue",
+                    markersize=6,
+                    label="(5,15) mm",
+                ),
+            ]
+
+            self.axs[3].legend(
+                handles=legend_elements,
+                title="PS Velocity GCP",
+                loc="upper right",
+                fontsize=6,
+                title_fontsize=6,
+                frameon=True,
+                facecolor="white",
+                edgecolor="black",
+            )

+
+
+

+[docs]
+        def plot_lcp_velocity(self, plot_data: list[tuple]) -> None:
+            """Plots LCP velocity data on the fifth axis.
+
+            :param plot_data: List of tuples containing geometry and LCP velocity value.
+            """
+            points, colors = [], []
+            for geom, vel in plot_data:
+                geom = load_wkb(geom)
+                points.append(geom.xy)
+                colors.append(self.lcp_vel_color(vel))
+            x_coords = [p[0][0] for p in points]
+            y_coords = [p[1][0] for p in points]
+
+            self.axs[4].scatter(
+                x_coords,
+                y_coords,
+                s=2,
+                c=colors,
+                alpha=0.6,
+            )
+
+            legend_elements = [
+                Line2D(
+                    [0],
+                    [0],
+                    marker="o",
+                    color="w",
+                    markerfacecolor="green",
+                    markersize=6,
+                    label="(0,2) mm",
+                ),
+                Line2D(
+                    [0],
+                    [0],
+                    marker="o",
+                    color="w",
+                    markerfacecolor="red",
+                    markersize=6,
+                    label="(2,12) mm",
+                ),
+            ]
+            self.axs[4].legend(
+                handles=legend_elements,
+                title="PS Velocity LCP",
+                loc="upper right",
+                fontsize=6,
+                title_fontsize=6,
+                frameon=True,
+                facecolor="white",
+                edgecolor="black",
+            )

+
+
+

+[docs]
+        def post_process(self):
+            """
+            Post-processes the figure for better aesthetics.
+
+            """
+            for i in [1, 2, 4]:
+                self.axs[i].get_yaxis().set_visible(False)
+                xticks = self.axs[i].get_xticks()
+                self.axs[i].set_xticks(xticks[::2])  # Keep every other tick
+                self.axs[i].set_xticklabels(self.axs[i].get_xticks())
+            for i in [0, 3]:
+                yticks = self.axs[i].get_yticks()
+                self.axs[i].set_yticks(yticks[::3])  # Keep every other tick
+                self.axs[i].set_yticklabels(
+                    self.axs[i].get_yticks(),
+                    horizontalalignment="center",
+                    verticalalignment="center",
+                )
+                xticks = self.axs[i].get_xticks()
+                self.axs[i].set_xticks(xticks[::2])  # Keep every other tick
+                self.axs[i].set_xticklabels(self.axs[i].get_xticks())

+
+
+            # self.fig.tight_layout()
+
+

+[docs]
+        def get_figure(self) -> tuple[plt.Figure, list[plt.Axes]]:
+            """
+            Returns the figure and axes.
+
+            :return: Tuple containing the figure and axes.
+            :rtype: tuple[plt.Figure, list[plt.Axes]]
+            """
+            return self.fig, self.axs

+
+
+

+[docs]
+        def outlier_color_label(self, value: float) -> tuple[str, str]:
+            """Returns color and label based on outlier velocity value.
+
+            :param value: Outlier velocity value.
+            :type value: float
+            :return: Tuple containing color and label.
+            :rtype: tuple[str, str]
+            """
+            if 5 <= value <= 7:
+                return "yellow", "5<|v|<7 mm/yr"
+            elif 7 < value <= 9:
+                return "orange", "7<|v|<9 mm/yr"
+            else:
+                return "red", "9<|v|<12 mm/yr"

+
+
+

+[docs]
+        def ps_density_color_label(self, value: float) -> tuple[str, str]:
+            """Returns color and label based on PS density value.
+
+            :param value: PS density value.
+            :type value: float
+            :return: Tuple containing color and label.
+            :rtype: tuple[str, str]
+            """
+            if value < 100:
+                return "red", "Low"
+            if 100 <= value < 500:
+                return "yellow", "Medium"
+            else:
+                return "green", "High"

+
+
+

+[docs]
+        def cum_disp_color_label(self, value: float) -> str:
+            """Returns color and label based on cumulative displacement value.
+
+            :param value: Cumulative displacement value.
+            :type value: float
+            :return: Tuple containing color and label.
+            :rtype: tuple[str, str]
+            """
+            if value < -20:
+                return "red", "(-60,-20) mm"
+            elif value < -15:
+                return "orange", "(-20,-15) mm"
+            elif value < -5:
+                return "yellow", "(-15,-5) mm"
+            elif value < 5:
+                return "lightgreen", "(-5,5) mm"
+            elif value < 15:
+                return "cyan", "(5,15) mm"
+            else:
+                return "blue", "(15,60) mm"

+
+
+

+[docs]
+        def gcp_vel_color(self, value: float) -> str:
+            """Returns color based on GCP velocity value.
+
+            :param value: GCP velocity value.
+            :type value: float
+            :return: Color string.
+            :rtype: str
+            """
+            if value <= -6:
+                return "red"
+            elif value <= -4:
+                return "orange"
+            elif value <= -2:
+                return "yellow"
+            elif value <= 2:
+                return "lightgreen"
+            elif value <= 5:
+                return "cyan"
+            else:
+                return "blue"

+
+
+

+[docs]
+        def lcp_vel_color(self, value: float) -> str:
+            """Returns color based on LCP velocity value.
+
+            :param value: LCP velocity value.
+            :type value: float
+            :return: Color string.
+            :rtype: str
+            """
+            if value <= 2:
+                return "green"
+            else:
+                return "red"

+

+
+
+

+[docs]
+    class TimeSeriesPane:
+        """Class to create and manage a multi-panel time series figure.
+        Panels include:
+        - Basemap with Outliers
+        - Basemap with PS Points
+        - Cumulative Displacement
+        - Relative Displacement
+        - Local Control Point Displacement
+        """
+
+        def __init__(self):
+            self.generate_figure()
+
+

+[docs]
+        def generate_figure(self):
+            """Generates a multi-panel figure with specified layout and titles."""
+            fig = plt.figure(figsize=(12, 12), dpi=150)
+            layout = [
+                3 * ["A"] + 3 * ["C"],  # ["A", "A", "A", "C", "C", "C"],
+                3 * ["A"] + 3 * ["C"],  # ["A", "A", "A", "C", "C", "C"],
+                3 * ["A"] + 3 * ["D"],  # ["A", "A", "A", "D", "D", "D"],
+                3 * ["B"] + 3 * ["D"],  # ["B", "B", "B", "D", "D", "D"],
+                3 * ["B"] + 3 * ["E"],  # ["B", "B", "B", "E", "E", "E"],
+                3 * ["B"] + 3 * ["E"],  # ["B", "B", "B", "E", "E", "E"],
+            ]
+            axs = fig.subplot_mosaic(layout, empty_sentinel=".")
+            fig.subplots_adjust(wspace=1, hspace=1)
+            axs["C"].set_title("Cumulative Displacement", fontsize=10)
+            axs["C"].set_ylabel("[mm]", fontsize=8, labelpad=2)
+            axs["D"].set_title("Relative Displacement", fontsize=10)
+            axs["D"].set_ylabel("[mm]", fontsize=8, labelpad=2)
+            axs["E"].set_title("Local Control Point Displacement", fontsize=10)
+            axs["E"].set_ylabel("[mm]", fontsize=8, labelpad=2)
+            self.fig = fig
+            self.axs = [axs["A"], axs["B"], axs["C"], axs["D"], axs["E"]]

+
+
+

+[docs]
+        def plot_basemap_outlier(self, center: tuple[float, float]):
+            """
+            Plots a basemap on the first axis using the provided center point.
+
+            :param center: A tuple representing the center point (x, y).
+            :type center: tuple[float, float]
+            """
+            self.axs[0].set_xlim(center[0][0] - 2000, center[0][0] + 2000)
+            self.axs[0].set_ylim(center[1][0] - 2000, center[1][0] + 4000)
+            ctx.add_basemap(
+                self.axs[0],
+                source=ctx.providers.CartoDB.Positron,
+                crs="EPSG:3857",
+            )

+
+
+

+[docs]
+        def plot_basemap_pspoint(self, center: tuple[float, float]):
+            """
+            Plots a basemap on the second axis using the provided center point.
+
+            :param center: A tuple representing the center point (x, y).
+            :type center: tuple[float, float]
+            """
+            self.axs[1].set_xlim(center[0][0] - 1000, center[0][0] + 1000)
+            self.axs[1].set_ylim(center[1][0] - 1000, center[1][0] + 1000)
+            ctx.add_basemap(
+                self.axs[1],
+                source=ctx.providers.CartoDB.Positron,
+                crs="EPSG:3857",
+            )

+
+
+

+[docs]
+        def plot_all_outliers(self, plot_data: list[tuple]) -> None:
+            """Plots all outlier data on the first axis.
+
+            :param plot_data: List of tuples containing geometry and outlier velocity value.
+            :type plot_data: list[tuple]
+            """
+            label_store = []
+            for geom, vel in plot_data:
+                geom = load_wkb(geom)
+                color, label = self.outlier_color_label(vel)
+                if label not in label_store:
+                    label_store.append(label)
+                else:
+                    label = None
+
+                self.axs[0].scatter(
+                    *geom.xy,
+                    s=10,
+                    c=color,
+                    label=label,
+                )
+            self.axs[0].legend(
+                title="Outlier",
+                loc="upper right",
+                fontsize=6,
+                title_fontsize=6,
+                frameon=True,
+                facecolor="white",
+                edgecolor="black",
+            )

+
+
+

+[docs]
+        def plot_square(self, plot_data: list[tuple]) -> None:
+            """Plots a square on the first axis to indicate zoom area.
+
+            :param plot_data: List of tuples containing geometry and outlier velocity value.
+            :type plot_data: list[tuple]
+            """
+            label_store = []
+            x, y = plot_data[0][0], plot_data[1][0]
+            square = [
+                (x - 75, y - 75),
+                (x + 75, y - 75),
+                (x + 75, y + 75),
+                (x - 75, y + 75),
+                (x - 75, y - 75),  # Close the square
+            ]
+            square_x, square_y = zip(*square)
+            self.axs[0].fill(
+                square_x,
+                square_y,
+                fc="none",
+                ec="black",
+                label="Zoom Area",
+            )
+            self.axs[0].legend(
+                title="Outliers",
+                loc="upper right",
+                fontsize=6,
+                title_fontsize=6,
+                frameon=True,
+                facecolor="white",
+                edgecolor="black",
+            )

+
+
+

+[docs]
+        def plot_pspoint_dist(self, plot_data: list[tuple]) -> None:
+            """Plots PS Points on the second axis.
+
+            :param plot_data: List of tuples containing geometry.
+            :type plot_data: list[tuple]
+            """
+            points = []
+            for geom in plot_data:
+                if isinstance(geom, tuple) and len(geom) > 0:
+                    geom = load_wkb(geom[0])
+                    points.append(geom.xy)
+            x_coords = [p[0][0] for p in points]
+            y_coords = [p[1][0] for p in points]
+
+            self.axs[1].scatter(
+                x_coords,
+                y_coords,
+                s=6,
+                c="black",
+                label="PS Points",
+            )
+            self.axs[1].legend(
+                title="Legend",
+                loc="upper right",
+                fontsize=6,
+                title_fontsize=6,
+                frameon=True,
+                facecolor="white",
+                edgecolor="black",
+            )

+
+
+

+[docs]
+        def plot_outlier(self, plot_data: list[tuple]) -> None:
+            """Plots outlier data on the second axis.
+
+            :param plot_data: List of tuples containing geometry and outlier velocity value.
+            :type plot_data: list[tuple]
+            """
+            label_store = []
+            for geom, vel in plot_data:
+                geom = load_wkb(geom)
+                color, label = self.outlier_color_label(vel)
+                if label not in label_store:
+                    label_store.append(label)
+                else:
+                    label = None
+
+                self.axs[1].scatter(
+                    *geom.xy,
+                    s=16,
+                    c=color,
+                    label=label,
+                )
+            self.axs[1].legend(
+                title="Legend",
+                loc="upper right",
+                fontsize=6,
+                title_fontsize=6,
+                frameon=True,
+                facecolor="white",
+                edgecolor="black",
+            )

+
+
+

+[docs]
+        def plot_lcp(self, plot_data: list[tuple]) -> None:
+            """Plots LCP data on the second axis.
+
+            :param plot_data: List of tuples containing geometry.
+            :type plot_data: list[tuple]
+            """
+            geom = load_wkb(plot_data[0])
+            self.axs[1].scatter(
+                *geom.xy,
+                s=20,
+                marker="^",
+                c="lightblue",
+                label="LCP",
+            )
+            self.axs[1].legend(
+                title="Legend",
+                loc="upper right",
+                fontsize=6,
+                title_fontsize=6,
+                frameon=True,
+                facecolor="white",
+                edgecolor="black",
+            )

+
+
+

+[docs]
+        def plot_cum_disp_graph(
+            self, plot_data: list[tuple], dates: list[datetime]
+        ) -> None:
+            """Plots cumulative displacement data on the third axis.
+
+            :param plot_data: List of tuples containing cumulative displacement values.
+            :type plot_data: list[tuple]
+            :param dates: List of datetime objects corresponding to the displacement values.
+            :type dates: list[datetime]
+            """
+            y = plot_data[0][0]
+            x = dates
+            self.axs[2].scatter(
+                x,
+                y,
+                s=6,
+                color="black",
+            )

+
+
+

+[docs]
+        def plot_avg_disp_graph(
+            self, plot_data: list[tuple], dates: list[datetime]
+        ) -> None:
+            """Plots average cumulative displacement data on the third axis.
+
+            :param plot_data: List of tuples containing average cumulative displacement values.
+            :type plot_data: list[tuple]
+            :param dates: List of datetime objects corresponding to the displacement values.
+            :type dates: list[datetime]
+            """
+
+            y = plot_data[0][0]
+            x = dates
+            self.axs[2].plot(
+                x,
+                y,
+                color="red",
+                linewidth=1,
+            )

+
+
+

+[docs]
+        def plot_std_disp_graph(
+            self, avg_data: list[tuple], std_data: list[tuple], dates: list[datetime]
+        ):
+            """Plots standard deviation of cumulative displacement data on the third axis.
+
+            :param avg_data: List of tuples containing average cumulative displacement values.
+            :type avg_data: list[tuple]
+            :param std_data: List of tuples containing standard deviation of cumulative displacement values.
+            :type std_data: list[tuple]
+            :param dates: List of datetime objects corresponding to the displacement values.
+            :type dates: list[datetime]
+            """
+            y_avg = avg_data[0][0]
+            y_std = std_data[0][0]
+            x = dates
+            self.axs[2].fill_between(
+                x,
+                [y_avg[i] - 2 * y_std[i] for i in range(len(y_avg))],
+                [y_avg[i] + 2 * y_std[i] for i in range(len(y_avg))],
+                color="dimgray",
+                alpha=0.5,
+                # label="±2 Std Dev",
+            )
+            self.axs[2].fill_between(
+                x,
+                [y_avg[i] - y_std[i] for i in range(len(y_avg))],
+                [y_avg[i] + y_std[i] for i in range(len(y_avg))],
+                color="whitesmoke",
+                alpha=0.5,
+                # label="±1 Std Dev",
+            )

+
+
+

+[docs]
+        def plot_lcp_disp_graph(
+            self, plot_data: list[tuple], dates: list[datetime]
+        ) -> None:
+            """Plots LCP displacement data on the fifth axis.
+
+            :param plot_data: List of tuples containing LCP displacement values.
+            :type plot_data: list[tuple]
+            :param dates: List of datetime objects corresponding to the displacement values.
+            :type dates: list[datetime]
+            """
+            y = plot_data[0][0]
+            x = dates
+            self.axs[4].scatter(
+                x,
+                y,
+                s=6,
+                color="black",
+            )

+
+
+

+[docs]
+        def plot_avg_lcp_disp_graph(
+            self, plot_data: list[tuple], dates: list[datetime]
+        ) -> None:
+            """Plots average LCP displacement data on the fifth axis.
+
+            :param plot_data: List of tuples containing average LCP displacement values.
+            :type plot_data: list[tuple]
+            :param dates: List of datetime objects corresponding to the displacement values.
+            :type dates: list[datetime]
+            """
+            y = plot_data[0][0]
+            x = dates
+            self.axs[4].plot(
+                x,
+                y,
+                color="red",
+                linewidth=1,
+            )

+
+
+

+[docs]
+        def plot_std_lcp_disp_graph(
+            self, avg_data: list[tuple], std_data: list[tuple], dates: list[datetime]
+        ):
+            """Plots standard deviation of LCP displacement data on the fifth axis.
+
+            :param avg_data: List of tuples containing average LCP displacement values.
+            :type avg_data: list[tuple]
+            :param std_data: List of tuples containing standard deviation of LCP displacement values.
+            :type std_data: list[tuple]
+            :param dates: List of datetime objects corresponding to the displacement values.
+            :type dates: list[datetime]
+            """
+            y_avg = avg_data[0][0]
+            y_std = std_data[0][0]
+            x = dates
+            self.axs[4].fill_between(
+                x,
+                [y_avg[i] - 2 * y_std[i] for i in range(len(y_avg))],
+                [y_avg[i] + 2 * y_std[i] for i in range(len(y_avg))],
+                color="dimgray",
+                alpha=0.5,
+                # label="±2 Std Dev",
+            )
+            self.axs[4].fill_between(
+                x,
+                [y_avg[i] - y_std[i] for i in range(len(y_avg))],
+                [y_avg[i] + y_std[i] for i in range(len(y_avg))],
+                color="whitesmoke",
+                alpha=0.5,
+                # label="±1 Std Dev",
+            )

+
+
+

+[docs]
+        def plot_rel_disp_graph(
+            self, plot_data: list[tuple], dates: list[datetime]
+        ) -> None:
+            """Plots relative displacement data on the fourth axis.
+
+            :param plot_data: List of tuples containing relative displacement values.
+            :type plot_data: list[tuple]
+            :param dates: List of datetime objects corresponding to the displacement values.
+            :type dates: list[datetime]
+            """
+            y = plot_data[0][0]
+            x = dates
+            self.axs[3].scatter(
+                x,
+                y,
+                s=6,
+                color="black",
+            )

+
+
+

+[docs]
+        def plot_avg_rel_disp_graph(
+            self, plot_data: list[tuple], dates: list[datetime]
+        ) -> None:
+            """Plots average relative displacement data on the fourth axis.
+
+            :param plot_data: List of tuples containing average relative displacement values.
+            :type plot_data: list[tuple]
+            :param dates: List of datetime objects corresponding to the displacement values.
+            :type dates: list[datetime]
+            """
+            y = plot_data[0][0]
+            x = dates
+            self.axs[3].plot(
+                x,
+                y,
+                color="red",
+                linewidth=1,
+            )

+
+
+

+[docs]
+        def plot_std_rel_disp_graph(
+            self, avg_data: list[tuple], std_data: list[tuple], dates: list[datetime]
+        ):
+            """Plots standard deviation of relative displacement data on the fourth axis.
+
+            :param avg_data: List of tuples containing average relative displacement values.
+            :type avg_data: list[tuple]
+            :param std_data: List of tuples containing standard deviation of relative displacement values.
+            :type std_data: list[tuple]
+            :param dates: List of datetime objects corresponding to the displacement values.
+            :type dates: list[datetime]
+            """
+            y_avg = avg_data[0][0]
+            y_std = std_data[0][0]
+            x = dates
+            self.axs[3].fill_between(
+                x,
+                [y_avg[i] - 2 * y_std[i] for i in range(len(y_avg))],
+                [y_avg[i] + 2 * y_std[i] for i in range(len(y_avg))],
+                color="dimgray",
+                alpha=0.5,
+                # label="±2 Std Dev",
+            )
+            self.axs[3].fill_between(
+                x,
+                [y_avg[i] - y_std[i] for i in range(len(y_avg))],
+                [y_avg[i] + y_std[i] for i in range(len(y_avg))],
+                color="whitesmoke",
+                alpha=0.5,
+                # label="±1 Std Dev",
+            )

+
+
+

+[docs]
+        def outlier_color_label(self, value: float) -> tuple[str, str]:
+            """Returns color and label based on outlier velocity value.
+
+            :param value: Outlier velocity value.
+            :type value: float
+            :return: Tuple containing color and label.
+            :rtype: tuple[str, str]
+            """
+            if value <= 7:
+                return "yellow", "5<|v|<7 mm/yr"
+            elif value <= 9:
+                return "orange", "7<|v|<9 mm/yr"
+            else:
+                return "red", "9<|v|<12 mm/yr"

+
+
+

+[docs]
+        def post_process(self):
+            """Post-processes the figure for better aesthetics."""
+            for i in [0, 1]:
+                xticks = self.axs[i].get_xticks()
+                yticks = self.axs[i].get_yticks()
+                self.axs[i].set_xticks(xticks[::3])  # Keep every other tick
+                self.axs[i].set_xticklabels(self.axs[i].get_xticks())
+                self.axs[i].set_yticks(yticks[::3])  # Keep every other tick
+                self.axs[i].set_yticklabels(self.axs[i].get_yticks())
+
+            ymins, ymaxs = [], []
+
+            for ax in [self.axs[2], self.axs[3], self.axs[4]]:
+                ymin, ymax = ax.get_ylim()
+                ymins.append(ymin)
+                ymaxs.append(ymax)
+
+            for ax in [self.axs[2], self.axs[3], self.axs[4]]:
+                ax.set_ylim(min(ymins), max(ymaxs))
+                ax.xaxis.set_major_locator(mdates.YearLocator())
+                ax.xaxis.set_major_formatter(mdates.DateFormatter("%Y"))
+                ax.xaxis.set_minor_locator(mdates.MonthLocator())

+
+
+

+[docs]
+        def get_figure(self) -> tuple[plt.Figure, list[plt.Axes]]:
+            """Returns the figure and axes.
+
+            :return: Tuple containing the figure and axes.
+            :rtype: tuple[plt.Figure, list[plt.Axes]]
+            """
+            return self.fig, self.axs

+

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/build/html/_modules/saferoad/saferoad.html b/docs/build/html/_modules/saferoad/saferoad.html new file mode 100644 index 0000000..65875a8 --- /dev/null +++ b/docs/build/html/_modules/saferoad/saferoad.html @@ -0,0 +1,785 @@ + + + + + + + + saferoad.saferoad — saferoad 0.1 documentation + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for saferoad.saferoad

+# standard modules
+import io
+from datetime import datetime
+from joblib import Parallel, delayed
+from shapely.wkb import loads as wkb_loads
+from dataclasses import dataclass
+from typing import Literal
+from tqdm import tqdm
+
+# plotting modules
+import matplotlib
+from matplotlib.backends.backend_pdf import PdfPages
+from matplotlib import pyplot as plt
+
+matplotlib.use("Agg")
+# saferoad modules
+from .plotter import Plotter
+from .database import DataBase
+from .pipeline import Pipeline
+from .utils import extract_dates, time_vector, Timer
+
+
+

+[docs]
+@dataclass
+class PsData:
+    """Class for storing point source data information.
+
+    :param filepath: Path to the point source data file.
+    :type filepath: str
+    :param latitude: Field name of the latitude field in the data file.
+    :type latitude: str
+    :param longitude: Field name of the longitude field in the data file.
+    :type longitude: str
+    :param unit: Unit of measurement for the coordinates. Options are "m", "cm", or "mm". Default is "m".
+    :type unit: Literal["m", "cm", "mm"]
+    :param crs_code: Coordinate Reference System code. Default is "EPSG:4326".
+    :type crs_code: str
+    :param name: Name identifier for the point source data. Default is "pspoint".
+    :type name: str
+    """
+
+    filepath: str
+    latitude: str
+    longitude: str
+    unit: Literal["m", "cm", "mm"] = "m"
+    crs_code: str = "EPSG:4326"
+    name: str = "pspoint"
+
+    def __post_init__(self):
+        assert self.filepath, "File path must be provided."
+        assert isinstance(self.filepath, str), "File path must be a string."
+        assert self.latitude, "Latitude field name must be provided."
+        assert isinstance(self.latitude, str), "Latitude field name must be a string."
+        assert self.longitude, "Longitude field name must be provided."
+        assert isinstance(self.longitude, str), "Longitude field name must be a string."
+        assert self.unit in ["m", "cm", "mm"], "Unit must be one of 'm', 'cm', or 'mm'."
+        assert self.crs_code, "CRS code must be provided."
+        assert isinstance(self.crs_code, str), "CRS code must be a string."
+        assert self.name, "Name must be provided."
+        assert isinstance(self.name, str), "Name must be a string."

+
+
+
+

+[docs]
+@dataclass
+class Road:
+    """Class for storing road data information.
+
+    :param filepath: Path to the road data file.
+    :type filepath: str
+    :param crs_code: Coordinate Reference System code. Default is "EPSG:4326".
+    :type crs_code: str
+    :param name: Name identifier for the road data. Default is "road".
+    :type name: str
+    """
+
+    filepath: str
+    crs_code: str = "EPSG:4326"
+    name: str = "road"
+
+    def __post_init__(self):
+        assert self.filepath, "File path must be provided."
+        assert isinstance(self.filepath, str), "File path must be a string."
+        assert self.name, "Name must be provided."
+        assert isinstance(self.name, str), "Name must be a string."

+
+
+
+

+[docs]
+class SafeRoad:
+    """
+    A class to perform road deformation analysis using MT-InSAR data.
+
+    :param road: An instance of the Road class containing road data information.
+    :type road: Road
+    :param ps_data: An instance of the PsData class containing MT-InSAR data information.
+    :type ps_data: PsData
+    :param computational_crs: A string representing the EPSG code for the computational CRS.
+    :type computational_crs: str
+    """
+
+    def __init__(self, road: Road, ps_data: PsData, computational_crs: str):
+
+        assert isinstance(road, Road), "road must be an instance of Road class"
+        assert isinstance(
+            ps_data, PsData
+        ), "ps_data must be an instance of PsData class"
+        assert isinstance(
+            computational_crs, str
+        ), "computational_crs must be a string representing the EPSG code"
+
+        self.ps_data = ps_data
+        self.road_data = road
+        self.database = DataBase()
+        # self.scaling_factor = self._set_scaling_factor_()
+        self.computational_crs = computational_crs
+
+    @property
+    def _scaling_factor_(self):
+        # determine the scaling factor based on the unit
+        units = ["m", "cm", "mm"]
+        sf = [1000, 10, 1]
+        return sf[units.index(self.ps_data.unit)]
+
+

+[docs]
+    def load_files(self):
+        """
+        Load the road and MT-InSAR data files into the database.
+        """
+
+        # setup the db if loading
+        with Timer("Setting up the database"):
+            self.database.setup()
+        # road_query
+        with Timer("Loading road data"):
+            self.database.run(
+                Pipeline.Processing.load_file(
+                    self.road_data.filepath, self.road_data.name
+                )
+            )
+        # ps_query
+        with Timer("Loading ps data"):
+            self.database.run(
+                Pipeline.Processing.load_file(self.ps_data.filepath, self.ps_data.name)
+            )

+
+
+

+[docs]
+    def preprocess(self, attribute: str = None):
+        """
+        Preprocess the road and MT-InSAR data.
+
+        This includes transforming the data to the computational CRS, dissolving road features,
+        and building geometries for the MT-InSAR points.
+        1. Transform the road and MT-InSAR data to the computational CRS.
+        2. Dissolve road features based on the provided attribute if given, otherwise merge all features.
+        3. Build geometries for the MT-InSAR points from the latitude and longitude columns.
+        4. Transform the MT-InSAR data to the computational CRS.
+
+        :param attribute: An optional string representing the attribute to dissolve road features.
+                          If None, all features will be merged. Default is None.
+        :type attribute: str, optional
+        """
+        # Transform the road projection
+        with Timer("Transforming road data to computational CRS"):
+            self.database.run(
+                Pipeline.Processing.spatial_tranformation(
+                    source_epsg=self.road_data.crs_code,
+                    target_epsg=self.computational_crs,
+                    table_name=self.road_data.name,
+                )
+            )
+
+        # Dissolve the road features based on the provided attribute if given if not merge everything
+        with Timer("Dissolving road features"):
+            self.database.run(
+                Pipeline.Processing.dissolve_features(
+                    attribute=attribute, table_name=self.road_data.name
+                )
+            )
+        # build the geometry for the psoints from csv headers
+        with Timer("Building geometry for PS data"):
+            self.database.run(
+                Pipeline.Processing.build_geometry(
+                    latitude=self.ps_data.latitude,
+                    longitude=self.ps_data.longitude,
+                    table_name=self.ps_data.name,
+                )
+            )
+
+        # Transform the ps data to the computational CRS
+        with Timer("Transforming PS data to computational CRS"):
+            self.database.run(
+                Pipeline.Processing.spatial_tranformation(
+                    source_epsg=self.ps_data.crs_code,
+                    target_epsg=self.computational_crs,
+                    table_name=self.ps_data.name,
+                )
+            )

+
+
+

+[docs]
+    def generate_rectangles(self, road_width: float, segment_length: float):
+        """
+        Generate road segments and patches.
+
+        This involves:
+        1. Splitting the road into segments of equal length.
+        2. Generating rectangles along the road segments with a specified width.
+        2. Rotating the generated rectangles int the same direction along the road.
+
+        :param road_width: A float representing the width of the road segments in meters.
+        :param segment_length: A float representing the length of each road segment in meters.
+        """
+
+        with Timer("Generating road segments and patches"):
+            self.database.run(
+                Pipeline.Processing.split_to_segments(segment_length=segment_length)
+            )
+            self.database.run(
+                Pipeline.Processing.generate_patches(
+                    width=road_width, length=segment_length
+                )
+            )

+
+
+

+[docs]
+    def run_analysis(self):
+        """
+        Run the deformation analysis on the MT-InSAR data for road network.
+
+        This includes calculating cumulative displacement, average velocity, point density,
+        local control points, and outliers.
+        """
+        # calculate displacement per points
+        # get the column names from the ps_data
+        fields = [
+            i[0]
+            for i in self.database.connection.sql(
+                Pipeline.Processing.get_column_names(self.ps_data.name)
+            ).fetchall()
+        ]
+
+        # extract dates from the column names and generate time vector
+        names, dates = extract_dates(column_names=fields)
+        date_vector = time_vector(dates=dates)
+
+        with Timer("Calculating cumulative displacement per point"):
+            self.database.run(
+                Pipeline.Processing.calc_cum_disp(
+                    dates=names, table_name=self.ps_data.name
+                )
+            )
+
+        # build spatial relation with
+        # assign points to patches generate patch_id field on pspoint table
+        with Timer("Relating points to patches"):
+            self.database.run(
+                Pipeline.Processing.relate_point_patches(self.ps_data.name)
+            )
+
+        # calculate average velocity per point needs patch_id in pspoint table for optimization purposes
+        with Timer("Calculating average velocity per point"):
+            self.database.run(
+                Pipeline.Processing.calc_avg_velocity(
+                    time_vector=date_vector,
+                    field_names=names,
+                    table_name=self.ps_data.name,
+                )
+            )
+
+        # calculate point count per patch and point density
+        with Timer("Calculating point density per patch"):
+            self.database.run(Pipeline.Processing.calc_point_density(self.ps_data.name))
+        # calculate average cumulative displacement per patch
+        with Timer("Calculating average cumulative displacement per patch"):
+            self.database.run(Pipeline.Processing.calc_avg_cum_disp())
+        # calculate average displacement time series per patch based on gcp
+        with Timer(
+            "Calculating average displacement time series per patch based on GCP"
+        ):
+            self.database.run(
+                Pipeline.Processing.calc_avg_disp_ts_gcp(date_fields=names)
+            )
+        # calculate standard deviation of displacement time series per patch based on gcp
+        with Timer(
+            "Calculating standard deviation of displacement time series per patch based on GCP"
+        ):
+            self.database.run(
+                Pipeline.Processing.calc_std_disp_ts_gcp(date_fields=names)
+            )
+
+        # calculate average velocity per patch based on gcp
+        with Timer("Calculating average velocity per patch based on GCP"):
+            self.database.run(
+                Pipeline.Processing.calc_avg_velocity_gcp(table_name=self.ps_data.name)
+            )
+
+        # calculate the local control point per patch
+        with Timer("Calculating local control points per patch"):
+            self.database.run(Pipeline.Processing.select_lcps(self.ps_data.name))
+
+        # calculate the local control point avg velocity per patch
+        with Timer("Calculating local control points average velocity per patch"):
+            self.database.run(Pipeline.Processing.calc_lcp_velocity())
+
+        # calculate the average and std of the displacement time series per patch based on lcp
+        with Timer(
+            "Calculating average and std of displacement time series per patch based on LCP"
+        ):
+            self.database.run(
+                Pipeline.Processing.calc_avg_std_disp_ts_lcp(
+                    date_fields=names, table_name=self.ps_data.name
+                )
+            )
+
+        # calculate the median and std of the displacement time series per patch based on lcp
+        with Timer(
+            "Calculating median and std of displacement time series per patch based on LCP"
+        ):
+            self.database.run(
+                Pipeline.Processing.calc_med_std_disp_ts_lcp(
+                    date_fields=names, table_name=self.ps_data.name
+                )
+            )
+
+        # calculate the average velocity per patch based on lcp
+        with Timer("Calculating average velocity per patch based on LCP"):
+            self.database.run(
+                Pipeline.Processing.calc_avg_velocity_lcp(table_name=self.ps_data.name)
+            )
+
+        # calculate the average relative velocity per patch based on lcp
+        with Timer("Calculating average relative velocity per patch based on LCP"):
+            self.database.run(
+                Pipeline.Processing.calc_avg_lcp_velocity(self.ps_data.name)
+            )
+
+        # calulate the outliers per patch based
+        with Timer("Calculating outliers per patch"):
+            self.database.run(
+                Pipeline.Processing.calc_outliers(
+                    self._scaling_factor_, self.ps_data.name
+                )
+            )
+
+            threshold_count = self.database.connection.execute(
+                "Select count(*) from outliers;"
+            ).fetchone()[0]
+            print("%s outliers detected by threshold method" % threshold_count)
+
+            self.database.run(
+                Pipeline.Processing.calc_outliers_avg_lcd(
+                    self.ps_data.name, date_vector=date_vector, date_names=names
+                )
+            )
+            ald_count = (
+                self.database.connection.execute(
+                    "Select count(*) from outliers;"
+                ).fetchone()[0]
+                - threshold_count
+            )
+            print("%s outliers detected by avg local disp method" % (ald_count))
+
+            self.database.run(
+                Pipeline.Processing.calc_outliers_med_lcd(
+                    self.ps_data.name, date_vector=date_vector, date_names=names
+                )
+            )
+            med_count = (
+                self.database.connection.execute(
+                    "select count(*) from outliers;"
+                ).fetchone()[0]
+                - ald_count
+                - threshold_count
+            )
+            print("%s outliers detected by median local disp method" % (med_count))
+
+        with Timer("Collecting and patching the LCP time series"):
+            self.database.run(
+                Pipeline.Processing.lcp_ts_patch(
+                    date_fields=names, table_name=self.ps_data.name
+                )
+            )
+        with Timer("Collecting outlier time series"):
+            self.database.run(
+                Pipeline.Processing.outlier_ts_patch(
+                    date_fields=names, table_name=self.ps_data.name
+                )
+            )
+        with Timer("collecting differential displacement time series"):
+            self.database.run(Pipeline.Processing.outlier_rel_ts_patch())

+
+
+

+[docs]
+    def generate_report(self, output_name: str):
+        """
+        Generate a PDF report containing maps and time series plots.
+
+        :param output_name: A string representing the name of the output PDF file endswith `.pdf` extension.
+        :type output_name: str
+        """
+
+        def render_ts_plots_workers(
+            patch_uid: float,
+            outliers: list[tuple],
+            dates: list[datetime],
+            outlier_center: tuple[float, float],  #
+            patch_center: tuple,
+            pspoints: list[tuple],
+            lcp_point: list[tuple],
+            cum_disp: list[tuple],
+            avg_cum_disp: list[tuple],
+            std_cum_disp: list[tuple],
+            outlier_rel_ts: list[tuple],
+            avg_lcp_disp: list[tuple],
+            std_lcp_disp: list[tuple],
+            lcp_disp: list[tuple],
+        ) -> bytes:
+
+            # plotting
+            tsplotter = Plotter.TimeSeriesPane()
+            # 0
+            tsplotter.plot_basemap_outlier(wkb_loads(outlier_center[0]).xy)
+            tsplotter.plot_all_outliers(outliers)
+            tsplotter.plot_square(wkb_loads(outlier_center[0]).xy)
+            tsplotter.plot_basemap_pspoint(wkb_loads(patch_center[0]).xy)
+            # 1
+            tsplotter.plot_pspoint_dist(pspoints)
+            tsplotter.plot_lcp(lcp_point)
+            tsplotter.plot_outlier(outliers)
+            # 2
+            tsplotter.plot_std_disp_graph(avg_cum_disp, std_cum_disp, dates)
+            tsplotter.plot_avg_disp_graph(avg_cum_disp, dates)
+            tsplotter.plot_cum_disp_graph(cum_disp, dates)
+
+            # 3
+            tsplotter.plot_std_rel_disp_graph(avg_lcp_disp, std_lcp_disp, dates)
+            tsplotter.plot_avg_rel_disp_graph(avg_lcp_disp, dates)
+            tsplotter.plot_rel_disp_graph(outlier_rel_ts, dates)
+
+            # 4
+            tsplotter.plot_std_lcp_disp_graph(avg_cum_disp, std_cum_disp, dates)
+            tsplotter.plot_avg_lcp_disp_graph(avg_cum_disp, dates)
+            tsplotter.plot_lcp_disp_graph(lcp_disp, dates)
+            tsplotter.post_process()
+
+            fig, _ = tsplotter.get_figure()
+
+            buf = io.BytesIO()
+            fig.savefig(buf, format="png")
+            plt.close(fig)
+            return buf.getvalue()
+
+        # export the results
+        with Timer("Generating and saving the report"):
+            with PdfPages(output_name) as pdf:
+                # bbox of the patches
+
+                bbox = self.database.connection.execute(
+                    Pipeline.Visualisation.get_table_bbox(
+                        "patches", self.computational_crs
+                    )
+                ).fetchall()
+                # ps density and patch geometries
+                ps_density = self.database.connection.execute(
+                    Pipeline.Visualisation.get_ps_density(self.computational_crs)
+                ).fetchall()
+                # outliers and coordinates
+                outliers = self.database.connection.execute(
+                    Pipeline.Visualisation.get_outliers_map(
+                        self.computational_crs, self._scaling_factor_
+                    )
+                ).fetchall()
+                # cumulative displacement per point
+                cum_disp = self.database.connection.execute(
+                    Pipeline.Visualisation.get_cum_disp_map(
+                        self.computational_crs, self._scaling_factor_
+                    )
+                ).fetchall()
+                # gcp average velocity per point
+                gcp_vel = self.database.connection.execute(
+                    Pipeline.Visualisation.get_ps_vel_gcp(
+                        self.computational_crs, self._scaling_factor_
+                    )
+                ).fetchall()
+                # lcp average velocity per point
+                lcp_vel = self.database.connection.execute(
+                    Pipeline.Visualisation.get_ps_vel_lcp(
+                        self.computational_crs, self._scaling_factor_
+                    )
+                ).fetchall()
+
+                # plotting the first page -- maps
+                print("Starting to render the map page")
+                map_plotter = Plotter.MapPane()
+                map_plotter.plot_basemap(bbox=bbox)
+                map_plotter.plot_ps_density(ps_density)
+                map_plotter.plot_outliers(outliers)
+                map_plotter.plot_cum_disp(cum_disp)
+                map_plotter.plot_gcp_velocity(gcp_vel)
+                map_plotter.plot_lcp_velocity(lcp_vel)
+                map_plotter.post_process()
+                fig, _ = map_plotter.get_figure()
+                plt.close(fig)
+                pdf.savefig(fig)
+                print("Map page has been rendered")
+
+                print("Intiating time series rendering")
+                # collect the patch_ids which has outliers
+                puids = [
+                    i[0]
+                    for i in self.database.connection.execute(
+                        Pipeline.Visualisation.patch_uids()
+                    ).fetchall()
+                ]
+                _, dates = extract_dates(
+                    [
+                        i[0]
+                        for i in self.database.connection.execute(
+                            Pipeline.Processing.get_column_names(self.ps_data.name)
+                        ).fetchall()
+                    ]
+                )
+                outliers_map = self.database.connection.execute(
+                    Pipeline.Visualisation.get_outliers_map(
+                        self.computational_crs, self._scaling_factor_
+                    )
+                ).fetchall()
+
+                # prepare the data for parallel processing
+                data_ready = []
+                for uid in puids:
+                    outliers = self.database.connection.execute(
+                        Pipeline.Visualisation.get_outlier_points(
+                            patch_id=uid, source_crs=self.computational_crs
+                        )
+                    ).fetchall()
+                    patch_center = self.database.connection.execute(
+                        Pipeline.Visualisation.get_patch_center(
+                            patch_id=uid, source_crs=self.computational_crs
+                        )
+                    ).fetchone()
+
+                    pspoints = self.database.connection.execute(
+                        Pipeline.Visualisation.get_ps_points(
+                            patch_id=uid,
+                            source_crs=self.computational_crs,
+                            table_name=self.ps_data.name,
+                        )
+                    ).fetchall()  # pspoints
+
+                    lcp_point = self.database.connection.execute(
+                        Pipeline.Visualisation.get_lcp_point(
+                            patch_id=uid, source_crs=self.computational_crs
+                        )
+                    ).fetchone()  # lcp point
+
+                    avg_cum_disp = self.database.connection.execute(
+                        Pipeline.Visualisation.get_avg_cum_disp_graph(
+                            patch_id=uid, scaling_factor=self._scaling_factor_
+                        )
+                    ).fetchall()  # avg_cum_disp
+
+                    std_cum_disp = self.database.connection.execute(
+                        Pipeline.Visualisation.get_std_cum_disp_graph(
+                            patch_id=uid, scaling_factor=self._scaling_factor_
+                        )
+                    ).fetchall()  # std_cum_disp
+
+                    avg_lcp_disp = self.database.connection.execute(
+                        Pipeline.Visualisation.get_avg_disp_lcp_graph(
+                            patch_id=uid, scaling_factor=self._scaling_factor_
+                        )
+                    ).fetchall()  # avg_lcp_disp
+
+                    std_lcp_disp = self.database.connection.execute(
+                        Pipeline.Visualisation.get_std_disp_lcp_graph(
+                            patch_id=uid, scaling_factor=self._scaling_factor_
+                        )
+                    ).fetchall()  # std_lcp_disp
+
+                    lcp_disp = self.database.connection.execute(
+                        Pipeline.Visualisation.get_lcp_disp_graph(
+                            patch_id=uid, scaling_factor=self._scaling_factor_
+                        )
+                    ).fetchall()  # lcp_disp
+
+                    for outlier in outliers:
+                        o_id = outlier[0]
+                        center = self.database.connection.execute(
+                            Pipeline.Visualisation.get_center(
+                                point_uid=o_id, source_crs=self.computational_crs
+                            )
+                        ).fetchone()
+                        cum_disp = self.database.connection.execute(
+                            Pipeline.Visualisation.get_cum_disp_graph(
+                                outlier_id=o_id, scaling_factor=self._scaling_factor_
+                            )
+                        ).fetchall()  # cum_disp
+
+                        outlier_rel_ts = self.database.connection.execute(
+                            Pipeline.Visualisation.get_outlier_rel_ts_graph(
+                                outlier_id=o_id, scaling_factor=self._scaling_factor_
+                            )
+                        ).fetchall()  # outlier_rel_ts
+                        data_ready.append(
+                            (
+                                uid,
+                                outliers_map,
+                                dates,
+                                center,
+                                patch_center,
+                                pspoints,
+                                lcp_point,
+                                cum_disp,
+                                avg_cum_disp,
+                                std_cum_disp,
+                                outlier_rel_ts,
+                                avg_lcp_disp,
+                                std_lcp_disp,
+                                lcp_disp,
+                            )
+                        )
+
+                print(f"Total pages to render for outliers: {len(data_ready)}")
+
+                png_pages = Parallel(n_jobs=-1, backend="loky")(
+                    delayed(render_ts_plots_workers)(
+                        patch_uid,
+                        outliers,
+                        dates,
+                        outlier_center,
+                        patch_center,
+                        pspoints,
+                        lcp_point,
+                        cum_disp,
+                        avg_cum_disp,
+                        std_cum_disp,
+                        outlier_rel_ts,
+                        avg_lcp_disp,
+                        std_lcp_disp,
+                        lcp_disp,
+                    )
+                    for (
+                        patch_uid,
+                        outliers,
+                        dates,
+                        outlier_center,
+                        patch_center,
+                        pspoints,
+                        lcp_point,
+                        cum_disp,
+                        avg_cum_disp,
+                        std_cum_disp,
+                        outlier_rel_ts,
+                        avg_lcp_disp,
+                        std_lcp_disp,
+                        lcp_disp,
+                    ) in tqdm(data_ready)
+                )
+                print("All plots rendered, now saving to pdf")
+                for png in png_pages:
+                    img = plt.imread(io.BytesIO(png), format="png")
+                    fig, ax = plt.subplots(figsize=(12, 12), dpi=80)
+                    # ax = fig.add_subplot(111)
+                    ax.imshow(img)
+                    ax.axis("off")
+                    ax.set_xticks([])
+                    ax.set_yticks([])
+                    ax.set_frame_on(False)
+                    pdf.savefig(fig)
+                    plt.close(fig)
+                print("All pages saved to pdf")

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/build/html/_modules/saferoad/utils.html b/docs/build/html/_modules/saferoad/utils.html new file mode 100644 index 0000000..d86ee4d --- /dev/null +++ b/docs/build/html/_modules/saferoad/utils.html @@ -0,0 +1,165 @@ + + + + + + + + saferoad.utils — saferoad 0.1 documentation + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for saferoad.utils

+import re
+import time
+from datetime import datetime
+from dateutil import parser as dsparser
+from numpy import array as nparray
+
+
+

+[docs]
+def extract_dates(column_names: list[str]) -> tuple[list[str], list[datetime]]:
+    """Extracts date fields from a list of column names.
+
+    :param column_names: List of column names to search for dates.
+    :type column_names: list[str]
+    :return: A tuple containing two numpy arrays: one with the column names and another with the parsed date objects.
+    :rtype: tuple[ndarray, ndarray]
+    """
+    assert isinstance(column_names, list), "column_names must be a list of strings"
+    assert all(
+        isinstance(name, str) for name in column_names
+    ), "All items in column_names must be strings"
+    assert len(column_names) > 0, "column_names must not be empty"
+
+    nameFields: list = []
+    dateFields: list = []
+    for date in column_names:
+        dateSearch = re.search(r"\d{8}", date)
+        if dateSearch:
+            pdate: datetime = dsparser.parse(dateSearch.group(), fuzzy=True).date()
+            dateFields.append(pdate)
+            nameFields.append(date)
+    return nameFields, dateFields

+
+
+
+

+[docs]
+def time_vector(dates: list[datetime]) -> nparray:
+    """Generates a time vector in days from a list of datetime objects.
+
+    :param dates: List of datetime objects.
+    :type dates: list[datetime]
+    :return: List of time differences in days from the first date.
+    :rtype: list[float]
+    """
+    return nparray([(i - dates[0]).days for i in dates])

+
+
+
+

+[docs]
+class Timer(object):
+    """Context manager for timing code execution."""
+
+    def __init__(self, name=None):
+        self.name = name
+
+    def __enter__(self):
+        self.tstart = time.time()
+
+    def __exit__(self, type, value, traceback):
+        if self.name:
+            print("%s took %.2f seconds" % (self.name, time.time() - self.tstart))

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/build/html/_sources/api/modules.rst.txt b/docs/build/html/_sources/api/modules.rst.txt new file mode 100644 index 0000000..75f2a20 --- /dev/null +++ b/docs/build/html/_sources/api/modules.rst.txt @@ -0,0 +1,7 @@ +saferoad +======== + +.. toctree:: + :maxdepth: 4 + + saferoad diff --git a/docs/build/html/_sources/api/saferoad.database.rst.txt b/docs/build/html/_sources/api/saferoad.database.rst.txt new file mode 100644 index 0000000..0e0c25b --- /dev/null +++ b/docs/build/html/_sources/api/saferoad.database.rst.txt @@ -0,0 +1,7 @@ +saferoad.database module +======================== + +.. automodule:: saferoad.database + :members: + :show-inheritance: + :undoc-members: diff --git a/docs/build/html/_sources/api/saferoad.pipeline.rst.txt b/docs/build/html/_sources/api/saferoad.pipeline.rst.txt new file mode 100644 index 0000000..8973f90 --- /dev/null +++ b/docs/build/html/_sources/api/saferoad.pipeline.rst.txt @@ -0,0 +1,7 @@ +saferoad.pipeline module +======================== + +.. automodule:: saferoad.pipeline + :members: + :show-inheritance: + :undoc-members: diff --git a/docs/build/html/_sources/api/saferoad.plotter.rst.txt b/docs/build/html/_sources/api/saferoad.plotter.rst.txt new file mode 100644 index 0000000..147c4b7 --- /dev/null +++ b/docs/build/html/_sources/api/saferoad.plotter.rst.txt @@ -0,0 +1,7 @@ +saferoad.plotter module +======================= + +.. automodule:: saferoad.plotter + :members: + :show-inheritance: + :undoc-members: diff --git a/docs/build/html/_sources/api/saferoad.rst.txt b/docs/build/html/_sources/api/saferoad.rst.txt new file mode 100644 index 0000000..85870b1 --- /dev/null +++ b/docs/build/html/_sources/api/saferoad.rst.txt @@ -0,0 +1,22 @@ +saferoad package +================ + +Submodules +---------- + +.. toctree:: + :maxdepth: 4 + + saferoad.database + saferoad.pipeline + saferoad.plotter + saferoad.saferoad + saferoad.utils + +Module contents +--------------- + +.. automodule:: saferoad + :members: + :show-inheritance: + :undoc-members: diff --git a/docs/build/html/_sources/api/saferoad.saferoad.rst.txt b/docs/build/html/_sources/api/saferoad.saferoad.rst.txt new file mode 100644 index 0000000..e29e335 --- /dev/null +++ b/docs/build/html/_sources/api/saferoad.saferoad.rst.txt @@ -0,0 +1,7 @@ +saferoad.saferoad module +======================== + +.. automodule:: saferoad.saferoad + :members: + :show-inheritance: + :undoc-members: diff --git a/docs/build/html/_sources/api/saferoad.utils.rst.txt b/docs/build/html/_sources/api/saferoad.utils.rst.txt new file mode 100644 index 0000000..8c742b5 --- /dev/null +++ b/docs/build/html/_sources/api/saferoad.utils.rst.txt @@ -0,0 +1,7 @@ +saferoad.utils module +===================== + +.. automodule:: saferoad.utils + :members: + :show-inheritance: + :undoc-members: diff --git a/docs/build/html/_sources/index.rst.txt b/docs/build/html/_sources/index.rst.txt new file mode 100644 index 0000000..2f021a4 --- /dev/null +++ b/docs/build/html/_sources/index.rst.txt @@ -0,0 +1,39 @@ +.. project documentation master file, created by + sphinx-quickstart on Mon Sep 1 09:55:46 2025. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +Welcome to SafeRoad's Documentation +====================================== + +**SafeRoad** is an open-source Python library designed to monitor the structural condition of the road network by providing tools for data analysis and visualisation. +It aims to assist researchers, policymakers, and developers in understanding and mitigating structural safety issues of road networks. +**SafeRoad** is built with a focus on usability, performance, and extensibility. It uses modern Python libraries such as `duckdb`_, +`shapely`_, `matplotlib`_ and `contextily`_ to provide a robust framework for road network safety analysis. + +You can access the relavant paper from `here`_. + +.. _here: https://doi.org/10.1177/14759217211045912 +.. _duckdb: https://duckdb.org/ +.. _shapely: https://shapely.readthedocs.io/en/stable/ +.. _matplotlib: https://matplotlib.org/ +.. _contextily: https://contextily.readthedocs.io/en/latest/ + +Summary +------- +SafeRoad offers a comprehensive suite of features, including: + +- Data preprocessing tools for cleaning and organizing road and Multi-Temporal dataset. +- Visualisation utilities to generate insightful charts and graphs. +- Statistical analysis methods to identify trends and patterns. +- Extensible APIs for integrating with other applications. + +The library is modular, user-friendly, and well-documented, making it suitable for both beginners and experts in the field of structural safety. + + + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + + api/modules \ No newline at end of file diff --git a/docs/build/html/_static/_sphinx_javascript_frameworks_compat.js b/docs/build/html/_static/_sphinx_javascript_frameworks_compat.js new file mode 100644 index 0000000..8141580 --- /dev/null +++ b/docs/build/html/_static/_sphinx_javascript_frameworks_compat.js @@ -0,0 +1,123 @@ +/* Compatability shim for jQuery and underscores.js. + * + * Copyright Sphinx contributors + * Released under the two clause BSD licence + */ + +/** + * small helper function to urldecode strings + * + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL + */ +jQuery.urldecode = function(x) { + if (!x) { + return x + } + return decodeURIComponent(x.replace(/\+/g, ' ')); +}; + +/** + * small helper function to urlencode strings + */ +jQuery.urlencode = encodeURIComponent; + +/** + * This function returns the parsed url parameters of the + * current request. Multiple values per key are supported, + * it will always return arrays of strings for the value parts. + */ +jQuery.getQueryParameters = function(s) { + if (typeof s === 'undefined') + s = document.location.search; + var parts = s.substr(s.indexOf('?') + 1).split('&'); + var result = {}; + for (var i = 0; i < parts.length; i++) { + var tmp = parts[i].split('=', 2); + var key = jQuery.urldecode(tmp[0]); + var value = jQuery.urldecode(tmp[1]); + if (key in result) + result[key].push(value); + else + result[key] = [value]; + } + return result; +}; + +/** + * highlight a given string on a jquery object by wrapping it in + * span elements with the given class name. + */ +jQuery.fn.highlightText = function(text, className) { + function highlight(node, addItems) { + if (node.nodeType === 3) { + var val = node.nodeValue; + var pos = val.toLowerCase().indexOf(text); + if (pos >= 0 && + !jQuery(node.parentNode).hasClass(className) && + !jQuery(node.parentNode).hasClass("nohighlight")) { + var span; + var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.className = className; + } + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + node.parentNode.insertBefore(span, node.parentNode.insertBefore( + document.createTextNode(val.substr(pos + text.length)), + node.nextSibling)); + node.nodeValue = val.substr(0, pos); + if (isInSVG) { + var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect"); + var bbox = node.parentElement.getBBox(); + rect.x.baseVal.value = bbox.x; + rect.y.baseVal.value = bbox.y; + rect.width.baseVal.value = bbox.width; + rect.height.baseVal.value = bbox.height; + rect.setAttribute('class', className); + addItems.push({ + "parent": node.parentNode, + "target": rect}); + } + } + } + else if (!jQuery(node).is("button, select, textarea")) { + jQuery.each(node.childNodes, function() { + highlight(this, addItems); + }); + } + } + var addItems = []; + var result = this.each(function() { + highlight(this, addItems); + }); + for (var i = 0; i < addItems.length; ++i) { + jQuery(addItems[i].parent).before(addItems[i].target); + } + return result; +}; + +/* + * backward compatibility for jQuery.browser + * This will be supported until firefox bug is fixed. + */ +if (!jQuery.browser) { + jQuery.uaMatch = function(ua) { + ua = ua.toLowerCase(); + + var match = /(chrome)[ \/]([\w.]+)/.exec(ua) || + /(webkit)[ \/]([\w.]+)/.exec(ua) || + /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) || + /(msie) ([\w.]+)/.exec(ua) || + ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) || + []; + + return { + browser: match[ 1 ] || "", + version: match[ 2 ] || "0" + }; + }; + jQuery.browser = {}; + jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true; +} diff --git a/docs/build/html/_static/basic.css b/docs/build/html/_static/basic.css new file mode 100644 index 0000000..4738b2e --- /dev/null +++ b/docs/build/html/_static/basic.css @@ -0,0 +1,906 @@ +/* + * Sphinx stylesheet -- basic theme. + */ + +/* -- main layout ----------------------------------------------------------- */ + +div.clearer { + clear: both; +} + +div.section::after { + display: block; + content: ''; + clear: left; +} + +/* -- relbar ---------------------------------------------------------------- */ + +div.related { + width: 100%; + font-size: 90%; +} + +div.related h3 { + display: none; +} + +div.related ul { + margin: 0; + padding: 0 0 0 10px; + list-style: none; +} + +div.related li { + display: inline; +} + +div.related li.right { + float: right; + margin-right: 5px; +} + +/* -- sidebar --------------------------------------------------------------- */ + +div.sphinxsidebarwrapper { + padding: 10px 5px 0 10px; +} + +div.sphinxsidebar { + float: left; + width: 230px; + margin-left: -100%; + font-size: 90%; + word-wrap: break-word; + overflow-wrap : break-word; +} + +div.sphinxsidebar ul { + list-style: none; +} + +div.sphinxsidebar ul ul, +div.sphinxsidebar ul.want-points { + margin-left: 20px; + list-style: square; +} + +div.sphinxsidebar ul ul { + margin-top: 0; + margin-bottom: 0; +} + +div.sphinxsidebar form { + margin-top: 10px; +} + +div.sphinxsidebar input { + border: 1px solid #98dbcc; + font-family: sans-serif; + font-size: 1em; +} + +div.sphinxsidebar #searchbox form.search { + overflow: hidden; +} + +div.sphinxsidebar #searchbox input[type="text"] { + float: left; + width: 80%; + padding: 0.25em; + box-sizing: border-box; +} + +div.sphinxsidebar #searchbox input[type="submit"] { + float: left; + width: 20%; + border-left: none; + padding: 0.25em; + box-sizing: border-box; +} + + +img { + border: 0; + max-width: 100%; +} + +/* -- search page ----------------------------------------------------------- */ + +ul.search { + margin-top: 10px; +} + +ul.search li { + padding: 5px 0; +} + +ul.search li a { + font-weight: bold; +} + +ul.search li p.context { + color: #888; + margin: 2px 0 0 30px; + text-align: left; +} + +ul.keywordmatches li.goodmatch a { + font-weight: bold; +} + +/* -- index page ------------------------------------------------------------ */ + +table.contentstable { + width: 90%; + margin-left: auto; + margin-right: auto; +} + +table.contentstable p.biglink { + line-height: 150%; +} + +a.biglink { + font-size: 1.3em; +} + +span.linkdescr { + font-style: italic; + padding-top: 5px; + font-size: 90%; +} + +/* -- general index --------------------------------------------------------- */ + +table.indextable { + width: 100%; +} + +table.indextable td { + text-align: left; + vertical-align: top; +} + +table.indextable ul { + margin-top: 0; + margin-bottom: 0; + list-style-type: none; +} + +table.indextable > tbody > tr > td > ul { + padding-left: 0em; +} + +table.indextable tr.pcap { + height: 10px; +} + +table.indextable tr.cap { + margin-top: 10px; + background-color: #f2f2f2; +} + +img.toggler { + margin-right: 3px; + margin-top: 3px; + cursor: pointer; +} + +div.modindex-jumpbox { + border-top: 1px solid #ddd; + border-bottom: 1px solid #ddd; + margin: 1em 0 1em 0; + padding: 0.4em; +} + +div.genindex-jumpbox { + border-top: 1px solid #ddd; + border-bottom: 1px solid #ddd; + margin: 1em 0 1em 0; + padding: 0.4em; +} + +/* -- domain module index --------------------------------------------------- */ + +table.modindextable td { + padding: 2px; + border-collapse: collapse; +} + +/* -- general body styles --------------------------------------------------- */ + +div.body { + min-width: 360px; + max-width: 800px; +} + +div.body p, div.body dd, div.body li, div.body blockquote { + -moz-hyphens: auto; + -ms-hyphens: auto; + -webkit-hyphens: auto; + hyphens: auto; +} + +a.headerlink { + visibility: hidden; +} + +a:visited { + color: #551A8B; +} + +h1:hover > a.headerlink, +h2:hover > a.headerlink, +h3:hover > a.headerlink, +h4:hover > a.headerlink, +h5:hover > a.headerlink, +h6:hover > a.headerlink, +dt:hover > a.headerlink, +caption:hover > a.headerlink, +p.caption:hover > a.headerlink, +div.code-block-caption:hover > a.headerlink { + visibility: visible; +} + +div.body p.caption { + text-align: inherit; +} + +div.body td { + text-align: left; +} + +.first { + margin-top: 0 !important; +} + +p.rubric { + margin-top: 30px; + font-weight: bold; +} + +img.align-left, figure.align-left, .figure.align-left, object.align-left { + clear: left; + float: left; + margin-right: 1em; +} + +img.align-right, figure.align-right, .figure.align-right, object.align-right { + clear: right; + float: right; + margin-left: 1em; +} + +img.align-center, figure.align-center, .figure.align-center, object.align-center { + display: block; + margin-left: auto; + margin-right: auto; +} + +img.align-default, figure.align-default, .figure.align-default { + display: block; + margin-left: auto; + margin-right: auto; +} + +.align-left { + text-align: left; +} + +.align-center { + text-align: center; +} + +.align-default { + text-align: center; +} + +.align-right { + text-align: right; +} + +/* -- sidebars -------------------------------------------------------------- */ + +div.sidebar, +aside.sidebar { + margin: 0 0 0.5em 1em; + border: 1px solid #ddb; + padding: 7px; + background-color: #ffe; + width: 40%; + float: right; + clear: right; + overflow-x: auto; +} + +p.sidebar-title { + font-weight: bold; +} + +nav.contents, +aside.topic, +div.admonition, div.topic, blockquote { + clear: left; +} + +/* -- topics ---------------------------------------------------------------- */ + +nav.contents, +aside.topic, +div.topic { + border: 1px solid #ccc; + padding: 7px; + margin: 10px 0 10px 0; +} + +p.topic-title { + font-size: 1.1em; + font-weight: bold; + margin-top: 10px; +} + +/* -- admonitions ----------------------------------------------------------- */ + +div.admonition { + margin-top: 10px; + margin-bottom: 10px; + padding: 7px; +} + +div.admonition dt { + font-weight: bold; +} + +p.admonition-title { + margin: 0px 10px 5px 0px; + font-weight: bold; +} + +div.body p.centered { + text-align: center; + margin-top: 25px; +} + +/* -- content of sidebars/topics/admonitions -------------------------------- */ + +div.sidebar > :last-child, +aside.sidebar > :last-child, +nav.contents > :last-child, +aside.topic > :last-child, +div.topic > :last-child, +div.admonition > :last-child { + margin-bottom: 0; +} + +div.sidebar::after, +aside.sidebar::after, +nav.contents::after, +aside.topic::after, +div.topic::after, +div.admonition::after, +blockquote::after { + display: block; + content: ''; + clear: both; +} + +/* -- tables ---------------------------------------------------------------- */ + +table.docutils { + margin-top: 10px; + margin-bottom: 10px; + border: 0; + border-collapse: collapse; +} + +table.align-center { + margin-left: auto; + margin-right: auto; +} + +table.align-default { + margin-left: auto; + margin-right: auto; +} + +table caption span.caption-number { + font-style: italic; +} + +table caption span.caption-text { +} + +table.docutils td, table.docutils th { + padding: 1px 8px 1px 5px; + border-top: 0; + border-left: 0; + border-right: 0; + border-bottom: 1px solid #aaa; +} + +th { + text-align: left; + padding-right: 5px; +} + +table.citation { + border-left: solid 1px gray; + margin-left: 1px; +} + +table.citation td { + border-bottom: none; +} + +th > :first-child, +td > :first-child { + margin-top: 0px; +} + +th > :last-child, +td > :last-child { + margin-bottom: 0px; +} + +/* -- figures --------------------------------------------------------------- */ + +div.figure, figure { + margin: 0.5em; + padding: 0.5em; +} + +div.figure p.caption, figcaption { + padding: 0.3em; +} + +div.figure p.caption span.caption-number, +figcaption span.caption-number { + font-style: italic; +} + +div.figure p.caption span.caption-text, +figcaption span.caption-text { +} + +/* -- 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+ document.dispatchEvent(event); + }); +}); \ No newline at end of file diff --git a/docs/build/html/_static/language_data.js b/docs/build/html/_static/language_data.js new file mode 100644 index 0000000..c7fe6c6 --- /dev/null +++ b/docs/build/html/_static/language_data.js @@ -0,0 +1,192 @@ +/* + * This script contains the language-specific data used by searchtools.js, + * namely the list of stopwords, stemmer, scorer and splitter. + */ + +var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"]; + + +/* Non-minified version is copied as a separate JS file, if available */ + +/** + * Porter Stemmer + */ +var Stemmer = function() { + + var step2list = { + ational: 'ate', + tional: 'tion', + enci: 'ence', + anci: 'ance', + izer: 'ize', + bli: 'ble', + alli: 'al', + entli: 'ent', + eli: 'e', + ousli: 'ous', + ization: 'ize', + ation: 'ate', + ator: 'ate', + alism: 'al', + iveness: 'ive', + fulness: 'ful', + ousness: 'ous', + aliti: 'al', + iviti: 'ive', + biliti: 'ble', + logi: 'log' + }; + + var step3list = { + icate: 'ic', + ative: '', + alize: 'al', + iciti: 'ic', + ical: 'ic', + ful: '', + ness: '' + }; + + var c = "[^aeiou]"; // consonant + var v = "[aeiouy]"; // vowel + var C = c + "[^aeiouy]*"; // consonant sequence + var V = v + "[aeiou]*"; // vowel sequence + + var mgr0 = "^(" + C + ")?" + V + C; // [C]VC... is m>0 + var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$"; // [C]VC[V] is m=1 + var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1 + var s_v = "^(" + C + ")?" + v; // vowel in stem + + this.stemWord = function (w) { + var stem; + var suffix; + var firstch; + var origword = w; + + if (w.length < 3) + return w; + + var re; + var re2; + var re3; + var re4; + + firstch = w.substr(0,1); + if (firstch == "y") + w = firstch.toUpperCase() + w.substr(1); + + // Step 1a + re = /^(.+?)(ss|i)es$/; + re2 = /^(.+?)([^s])s$/; + + if (re.test(w)) + w = w.replace(re,"$1$2"); + else if (re2.test(w)) + w = w.replace(re2,"$1$2"); + + // Step 1b + re = /^(.+?)eed$/; + re2 = /^(.+?)(ed|ing)$/; + if (re.test(w)) { + var fp = re.exec(w); + re = new RegExp(mgr0); + if (re.test(fp[1])) { + re = /.$/; + w = w.replace(re,""); + } + } + else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1]; + re2 = new RegExp(s_v); + if (re2.test(stem)) { + w = stem; + re2 = /(at|bl|iz)$/; + re3 = new RegExp("([^aeiouylsz])\\1$"); + re4 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + if (re2.test(w)) + w = w + "e"; + else if (re3.test(w)) { + re = /.$/; + w = w.replace(re,""); + } + else if (re4.test(w)) + w = w + "e"; + } + } + + // Step 1c + re = /^(.+?)y$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(s_v); + if (re.test(stem)) + w = stem + "i"; + } + + // Step 2 + re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = new RegExp(mgr0); + if (re.test(stem)) + w = stem + step2list[suffix]; + } + + // Step 3 + re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = new RegExp(mgr0); + if (re.test(stem)) + w = stem + step3list[suffix]; + } + + // Step 4 + re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/; + re2 = /^(.+?)(s|t)(ion)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(mgr1); + if (re.test(stem)) + w = stem; + } + else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1] + fp[2]; + re2 = new RegExp(mgr1); + if (re2.test(stem)) + w = stem; + } + + // Step 5 + re = /^(.+?)e$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(mgr1); + re2 = new RegExp(meq1); + re3 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) + w = stem; + } + re = /ll$/; + re2 = new RegExp(mgr1); + if (re.test(w) && re2.test(w)) { + re = /.$/; + w = w.replace(re,""); 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+ +/** + * Simple result scoring code. + */ +if (typeof Scorer === "undefined") { + var Scorer = { + // Implement the following function to further tweak the score for each result + // The function takes a result array [docname, title, anchor, descr, score, filename] + // and returns the new score. + /* + score: result => { + const [docname, title, anchor, descr, score, filename, kind] = result + return score + }, + */ + + // query matches the full name of an object + objNameMatch: 11, + // or matches in the last dotted part of the object name + objPartialMatch: 6, + // Additive scores depending on the priority of the object + objPrio: { + 0: 15, // used to be importantResults + 1: 5, // used to be objectResults + 2: -5, // used to be unimportantResults + }, + // Used when the priority is not in the mapping. + objPrioDefault: 0, + + // query found in title + title: 15, + partialTitle: 7, + // query found in terms + term: 5, + partialTerm: 2, + }; +} + +// Global search result kind enum, used by themes to style search results. +class SearchResultKind { + static get index() { return "index"; } + static get object() { return "object"; } + static get text() { return "text"; } + static get title() { return "title"; } +} + +const _removeChildren = (element) => { + while (element && element.lastChild) element.removeChild(element.lastChild); +}; + +/** + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping + */ +const _escapeRegExp = (string) => + string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string + +const _displayItem = (item, searchTerms, highlightTerms) => { + const docBuilder = DOCUMENTATION_OPTIONS.BUILDER; + const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX; + const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX; + const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY; + const contentRoot = document.documentElement.dataset.content_root; + + const [docName, title, anchor, descr, score, _filename, kind] = item; + + let listItem = document.createElement("li"); + // Add a class representing the item's type: + // can be used by a theme's CSS selector for styling + // See SearchResultKind for the class names. + listItem.classList.add(`kind-${kind}`); + let requestUrl; + let linkUrl; + if (docBuilder === "dirhtml") { + // dirhtml builder + let dirname = docName + "/"; + if (dirname.match(/\/index\/$/)) + dirname = dirname.substring(0, dirname.length - 6); + else if (dirname === "index/") dirname = ""; + requestUrl = contentRoot + dirname; + linkUrl = requestUrl; + } else { + // normal html builders + requestUrl = contentRoot + docName + docFileSuffix; + linkUrl = docName + docLinkSuffix; + } + let linkEl = listItem.appendChild(document.createElement("a")); + linkEl.href = linkUrl + anchor; + linkEl.dataset.score = score; + linkEl.innerHTML = title; + if (descr) { + listItem.appendChild(document.createElement("span")).innerHTML = + " (" + descr + ")"; + // highlight search terms in the description + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); + } + else if (showSearchSummary) + fetch(requestUrl) + .then((responseData) => responseData.text()) + .then((data) => { + if (data) + listItem.appendChild( + Search.makeSearchSummary(data, searchTerms, anchor) + ); + // highlight search terms in the summary + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); + }); + Search.output.appendChild(listItem); +}; +const _finishSearch = (resultCount) => { + Search.stopPulse(); + Search.title.innerText = _("Search Results"); + if (!resultCount) + Search.status.innerText = Documentation.gettext( + "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories." + ); + else + Search.status.innerText = Documentation.ngettext( + "Search finished, found one page matching the search query.", + "Search finished, found ${resultCount} pages matching the search query.", + resultCount, + ).replace('${resultCount}', resultCount); +}; +const _displayNextItem = ( + results, + resultCount, + searchTerms, + highlightTerms, +) => { + // results left, load the summary and display it + // this is intended to be dynamic (don't sub resultsCount) + if (results.length) { + _displayItem(results.pop(), searchTerms, highlightTerms); + setTimeout( + () => _displayNextItem(results, resultCount, searchTerms, highlightTerms), + 5 + ); + } + // search finished, update title and status message + else _finishSearch(resultCount); +}; +// Helper function used by query() to order search results. +// Each input is an array of [docname, title, anchor, descr, score, filename, kind]. +// Order the results by score (in opposite order of appearance, since the +// `_displayNextItem` function uses pop() to retrieve items) and then alphabetically. +const _orderResultsByScoreThenName = (a, b) => { + const leftScore = a[4]; + const rightScore = b[4]; + if (leftScore === rightScore) { + // same score: sort alphabetically + const leftTitle = a[1].toLowerCase(); + const rightTitle = b[1].toLowerCase(); + if (leftTitle === rightTitle) return 0; + return leftTitle > rightTitle ? -1 : 1; // inverted is intentional + } + return leftScore > rightScore ? 1 : -1; +}; + +/** + * Default splitQuery function. Can be overridden in ``sphinx.search`` with a + * custom function per language. + * + * The regular expression works by splitting the string on consecutive characters + * that are not Unicode letters, numbers, underscores, or emoji characters. + * This is the same as ``\W+`` in Python, preserving the surrogate pair area. + */ +if (typeof splitQuery === "undefined") { + var splitQuery = (query) => query + .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu) + .filter(term => term) // remove remaining empty strings +} + +/** + * Search Module + */ +const Search = { + _index: null, + _queued_query: null, + _pulse_status: -1, + + htmlToText: (htmlString, anchor) => { + const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html'); + for (const removalQuery of [".headerlink", "script", "style"]) { + htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() }); + } + if (anchor) { + const anchorContent = htmlElement.querySelector(`[role="main"] ${anchor}`); + if (anchorContent) return anchorContent.textContent; + + console.warn( + `Anchored content block not found. Sphinx search tries to obtain it via DOM query '[role=main] ${anchor}'. Check your theme or template.` + ); + } + + // if anchor not specified or not found, fall back to main content + const docContent = htmlElement.querySelector('[role="main"]'); + if (docContent) return docContent.textContent; + + console.warn( + "Content block not found. Sphinx search tries to obtain it via DOM query '[role=main]'. Check your theme or template." + ); + return ""; + }, + + init: () => { + const query = new URLSearchParams(window.location.search).get("q"); + document + .querySelectorAll('input[name="q"]') + .forEach((el) => (el.value = query)); + if (query) Search.performSearch(query); + }, + + loadIndex: (url) => + (document.body.appendChild(document.createElement("script")).src = url), + + setIndex: (index) => { + Search._index = index; + if (Search._queued_query !== null) { + const query = Search._queued_query; + Search._queued_query = null; + Search.query(query); + } + }, + + hasIndex: () => Search._index !== null, + + deferQuery: (query) => (Search._queued_query = query), + + stopPulse: () => (Search._pulse_status = -1), + + startPulse: () => { + if (Search._pulse_status >= 0) return; + + const pulse = () => { + Search._pulse_status = (Search._pulse_status + 1) % 4; + Search.dots.innerText = ".".repeat(Search._pulse_status); + if (Search._pulse_status >= 0) window.setTimeout(pulse, 500); + }; + pulse(); + }, + + /** + * perform a search for something (or wait until index is loaded) + */ + performSearch: (query) => { + // create the required interface elements + const searchText = document.createElement("h2"); + searchText.textContent = _("Searching"); + const searchSummary = document.createElement("p"); + searchSummary.classList.add("search-summary"); + searchSummary.innerText = ""; + const searchList = document.createElement("ul"); + searchList.setAttribute("role", "list"); + searchList.classList.add("search"); + + const out = document.getElementById("search-results"); + Search.title = out.appendChild(searchText); + Search.dots = Search.title.appendChild(document.createElement("span")); + Search.status = out.appendChild(searchSummary); + Search.output = out.appendChild(searchList); + + const searchProgress = document.getElementById("search-progress"); + // Some themes don't use the search progress node + if (searchProgress) { + searchProgress.innerText = _("Preparing search..."); + } + Search.startPulse(); + + // index already loaded, the browser was quick! + if (Search.hasIndex()) Search.query(query); + else Search.deferQuery(query); + }, + + _parseQuery: (query) => { + // stem the search terms and add them to the correct list + const stemmer = new Stemmer(); + const searchTerms = new Set(); + const excludedTerms = new Set(); + const highlightTerms = new Set(); + const objectTerms = new Set(splitQuery(query.toLowerCase().trim())); + splitQuery(query.trim()).forEach((queryTerm) => { + const queryTermLower = queryTerm.toLowerCase(); + + // maybe skip this "word" + // stopwords array is from language_data.js + if ( + stopwords.indexOf(queryTermLower) !== -1 || + queryTerm.match(/^\d+$/) + ) + return; + + // stem the word + let word = stemmer.stemWord(queryTermLower); + // select the correct list + if (word[0] === "-") excludedTerms.add(word.substr(1)); + else { + searchTerms.add(word); + highlightTerms.add(queryTermLower); + } + }); + + if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js + localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" ")) + } + + // console.debug("SEARCH: searching for:"); + // console.info("required: ", [...searchTerms]); + // console.info("excluded: ", [...excludedTerms]); + + return [query, searchTerms, excludedTerms, highlightTerms, objectTerms]; + }, + + /** + * execute search (requires search index to be loaded) + */ + _performSearch: (query, searchTerms, excludedTerms, highlightTerms, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + const allTitles = Search._index.alltitles; + const indexEntries = Search._index.indexentries; + + // Collect multiple result groups to be sorted separately and then ordered. + // Each is an array of [docname, title, anchor, descr, score, filename, kind]. + const normalResults = []; + const nonMainIndexResults = []; + + _removeChildren(document.getElementById("search-progress")); + + const queryLower = query.toLowerCase().trim(); + for (const [title, foundTitles] of Object.entries(allTitles)) { + if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) { + for (const [file, id] of foundTitles) { + const score = Math.round(Scorer.title * queryLower.length / title.length); + const boost = titles[file] === title ? 1 : 0; // add a boost for document titles + normalResults.push([ + docNames[file], + titles[file] !== title ? `${titles[file]} > ${title}` : title, + id !== null ? "#" + id : "", + null, + score + boost, + filenames[file], + SearchResultKind.title, + ]); + } + } + } + + // search for explicit entries in index directives + for (const [entry, foundEntries] of Object.entries(indexEntries)) { + if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) { + for (const [file, id, isMain] of foundEntries) { + const score = Math.round(100 * queryLower.length / entry.length); + const result = [ + docNames[file], + titles[file], + id ? "#" + id : "", + null, + score, + filenames[file], + SearchResultKind.index, + ]; + if (isMain) { + normalResults.push(result); + } else { + nonMainIndexResults.push(result); + } + } + } + } + + // lookup as object + objectTerms.forEach((term) => + normalResults.push(...Search.performObjectSearch(term, objectTerms)) + ); + + // lookup as search terms in fulltext + normalResults.push(...Search.performTermsSearch(searchTerms, excludedTerms)); + + // let the scorer override scores with a custom scoring function + if (Scorer.score) { + normalResults.forEach((item) => (item[4] = Scorer.score(item))); + nonMainIndexResults.forEach((item) => (item[4] = Scorer.score(item))); + } + + // Sort each group of results by score and then alphabetically by name. + normalResults.sort(_orderResultsByScoreThenName); + nonMainIndexResults.sort(_orderResultsByScoreThenName); + + // Combine the result groups in (reverse) order. + // Non-main index entries are typically arbitrary cross-references, + // so display them after other results. + let results = [...nonMainIndexResults, ...normalResults]; + + // remove duplicate search results + // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept + let seen = new Set(); + results = results.reverse().reduce((acc, result) => { + let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(','); + if (!seen.has(resultStr)) { + acc.push(result); + seen.add(resultStr); + } + return acc; + }, []); + + return results.reverse(); + }, + + query: (query) => { + const [searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms] = Search._parseQuery(query); + const results = Search._performSearch(searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms); + + // for debugging + //Search.lastresults = results.slice(); // a copy + // console.info("search results:", Search.lastresults); + + // print the results + _displayNextItem(results, results.length, searchTerms, highlightTerms); + }, + + /** + * search for object names + */ + performObjectSearch: (object, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const objects = Search._index.objects; + const objNames = Search._index.objnames; + const titles = Search._index.titles; + + const results = []; + + const objectSearchCallback = (prefix, match) => { + const name = match[4] + const fullname = (prefix ? prefix + "." : "") + name; + const fullnameLower = fullname.toLowerCase(); + if (fullnameLower.indexOf(object) < 0) return; + + let score = 0; + const parts = fullnameLower.split("."); + + // check for different match types: exact matches of full name or + // "last name" (i.e. last dotted part) + if (fullnameLower === object || parts.slice(-1)[0] === object) + score += Scorer.objNameMatch; + else if (parts.slice(-1)[0].indexOf(object) > -1) + score += Scorer.objPartialMatch; // matches in last name + + const objName = objNames[match[1]][2]; + const title = titles[match[0]]; + + // If more than one term searched for, we require other words to be + // found in the name/title/description + const otherTerms = new Set(objectTerms); + otherTerms.delete(object); + if (otherTerms.size > 0) { + const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase(); + if ( + [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0) + ) + return; + } + + let anchor = match[3]; + if (anchor === "") anchor = fullname; + else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname; + + const descr = objName + _(", in ") + title; + + // add custom score for some objects according to scorer + if (Scorer.objPrio.hasOwnProperty(match[2])) + score += Scorer.objPrio[match[2]]; + else score += Scorer.objPrioDefault; + + results.push([ + docNames[match[0]], + fullname, + "#" + anchor, + descr, + score, + filenames[match[0]], + SearchResultKind.object, + ]); + }; + Object.keys(objects).forEach((prefix) => + objects[prefix].forEach((array) => + objectSearchCallback(prefix, array) + ) + ); + return results; + }, + + /** + * search for full-text terms in the index + */ + performTermsSearch: (searchTerms, excludedTerms) => { + // prepare search + const terms = Search._index.terms; + const titleTerms = Search._index.titleterms; + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + + const scoreMap = new Map(); + const fileMap = new Map(); + + // perform the search on the required terms + searchTerms.forEach((word) => { + const files = []; + // find documents, if any, containing the query word in their text/title term indices + // use Object.hasOwnProperty to avoid mismatching against prototype properties + const arr = [ + { files: terms.hasOwnProperty(word) ? terms[word] : undefined, score: Scorer.term }, + { files: titleTerms.hasOwnProperty(word) ? titleTerms[word] : undefined, score: Scorer.title }, + ]; + // add support for partial matches + if (word.length > 2) { + const escapedWord = _escapeRegExp(word); + if (!terms.hasOwnProperty(word)) { + Object.keys(terms).forEach((term) => { + if (term.match(escapedWord)) + arr.push({ files: terms[term], score: Scorer.partialTerm }); + }); + } + if (!titleTerms.hasOwnProperty(word)) { + Object.keys(titleTerms).forEach((term) => { + if (term.match(escapedWord)) + arr.push({ files: titleTerms[term], score: Scorer.partialTitle }); + }); + } + } + + // no match but word was a required one + if (arr.every((record) => record.files === undefined)) return; + + // found search word in contents + arr.forEach((record) => { + if (record.files === undefined) return; + + let recordFiles = record.files; + if (recordFiles.length === undefined) recordFiles = [recordFiles]; + files.push(...recordFiles); + + // set score for the word in each file + recordFiles.forEach((file) => { + if (!scoreMap.has(file)) scoreMap.set(file, new Map()); + const fileScores = scoreMap.get(file); + fileScores.set(word, record.score); + }); + }); + + // create the mapping + files.forEach((file) => { + if (!fileMap.has(file)) fileMap.set(file, [word]); + else if (fileMap.get(file).indexOf(word) === -1) fileMap.get(file).push(word); + }); + }); + + // now check if the files don't contain excluded terms + const results = []; + for (const [file, wordList] of fileMap) { + // check if all requirements are matched + + // as search terms with length < 3 are discarded + const filteredTermCount = [...searchTerms].filter( + (term) => term.length > 2 + ).length; + if ( + wordList.length !== searchTerms.size && + wordList.length !== filteredTermCount + ) + continue; + + // ensure that none of the excluded terms is in the search result + if ( + [...excludedTerms].some( + (term) => + terms[term] === file || + titleTerms[term] === file || + (terms[term] || []).includes(file) || + (titleTerms[term] || []).includes(file) + ) + ) + break; + + // select one (max) score for the file. + const score = Math.max(...wordList.map((w) => scoreMap.get(file).get(w))); + // add result to the result list + results.push([ + docNames[file], + titles[file], + "", + null, + score, + filenames[file], + SearchResultKind.text, + ]); + } + return results; + }, + + /** + * helper function to return a node containing the + * search summary for a given text. keywords is a list + * of stemmed words. + */ + makeSearchSummary: (htmlText, keywords, anchor) => { + const text = Search.htmlToText(htmlText, anchor); + if (text === "") return null; + + const textLower = text.toLowerCase(); + const actualStartPosition = [...keywords] + .map((k) => textLower.indexOf(k.toLowerCase())) + .filter((i) => i > -1) + .slice(-1)[0]; + const startWithContext = Math.max(actualStartPosition - 120, 0); + + const top = startWithContext === 0 ? "" : "..."; + const tail = startWithContext + 240 < text.length ? "..." : ""; + + let summary = document.createElement("p"); + summary.classList.add("context"); + summary.textContent = top + text.substr(startWithContext, 240).trim() + tail; + + return summary; + }, +}; + +_ready(Search.init); diff --git a/docs/build/html/_static/sphinx_highlight.js b/docs/build/html/_static/sphinx_highlight.js new file mode 100644 index 0000000..8a96c69 --- /dev/null +++ b/docs/build/html/_static/sphinx_highlight.js @@ -0,0 +1,154 @@ +/* Highlighting utilities for Sphinx HTML documentation. */ +"use strict"; + +const SPHINX_HIGHLIGHT_ENABLED = true + +/** + * highlight a given string on a node by wrapping it in + * span elements with the given class name. + */ +const _highlight = (node, addItems, text, className) => { + if (node.nodeType === Node.TEXT_NODE) { + const val = node.nodeValue; + const parent = node.parentNode; + const pos = val.toLowerCase().indexOf(text); + if ( + pos >= 0 && + !parent.classList.contains(className) && + !parent.classList.contains("nohighlight") + ) { + let span; + + const closestNode = parent.closest("body, svg, foreignObject"); + const isInSVG = closestNode && closestNode.matches("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.classList.add(className); + } + + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + const rest = document.createTextNode(val.substr(pos + text.length)); + parent.insertBefore( + span, + parent.insertBefore( + rest, + node.nextSibling + ) + ); + node.nodeValue = val.substr(0, pos); + /* There may be more occurrences of search term in this node. So call this + * function recursively on the remaining fragment. + */ + _highlight(rest, addItems, text, className); + + if (isInSVG) { + const rect = document.createElementNS( + "http://www.w3.org/2000/svg", + "rect" + ); + const bbox = parent.getBBox(); + rect.x.baseVal.value = bbox.x; + rect.y.baseVal.value = bbox.y; + rect.width.baseVal.value = bbox.width; + rect.height.baseVal.value = bbox.height; + rect.setAttribute("class", className); + addItems.push({ parent: parent, target: rect }); + } + } + } else if (node.matches && !node.matches("button, select, textarea")) { + node.childNodes.forEach((el) => _highlight(el, addItems, text, className)); + } +}; +const _highlightText = (thisNode, text, className) => { + let addItems = []; + _highlight(thisNode, addItems, text, className); + addItems.forEach((obj) => + obj.parent.insertAdjacentElement("beforebegin", obj.target) + ); +}; + +/** + * Small JavaScript module for the documentation. + */ +const SphinxHighlight = { + + /** + * highlight the search words provided in localstorage in the text + */ + highlightSearchWords: () => { + if (!SPHINX_HIGHLIGHT_ENABLED) return; // bail if no highlight + + // get and clear terms from localstorage + const url = new URL(window.location); + const highlight = + localStorage.getItem("sphinx_highlight_terms") + || url.searchParams.get("highlight") + || ""; + localStorage.removeItem("sphinx_highlight_terms") + url.searchParams.delete("highlight"); + window.history.replaceState({}, "", url); + + // get individual terms from highlight string + const terms = highlight.toLowerCase().split(/\s+/).filter(x => x); + if (terms.length === 0) return; // nothing to do + + // There should never be more than one element matching "div.body" + const divBody = document.querySelectorAll("div.body"); + const body = divBody.length ? divBody[0] : document.querySelector("body"); + window.setTimeout(() => { + terms.forEach((term) => _highlightText(body, term, "highlighted")); + }, 10); + + const searchBox = document.getElementById("searchbox"); + if (searchBox === null) return; + searchBox.appendChild( + document + .createRange() + .createContextualFragment( + '

' + + '' + + _("Hide Search Matches") + + "

" + ) + ); + }, + + /** + * helper function to hide the search marks again + */ + hideSearchWords: () => { + document + .querySelectorAll("#searchbox .highlight-link") + .forEach((el) => el.remove()); + document + .querySelectorAll("span.highlighted") + .forEach((el) => el.classList.remove("highlighted")); + localStorage.removeItem("sphinx_highlight_terms") + }, + + initEscapeListener: () => { + // only install a listener if it is really needed + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return; + if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) { + SphinxHighlight.hideSearchWords(); + event.preventDefault(); + } + }); + }, +}; + +_ready(() => { + /* Do not call highlightSearchWords() when we are on the search page. + * It will highlight words from the *previous* search query. + */ + if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords(); + SphinxHighlight.initEscapeListener(); +}); diff --git a/docs/build/html/api/modules.html b/docs/build/html/api/modules.html new file mode 100644 index 0000000..f46a829 --- /dev/null +++ b/docs/build/html/api/modules.html @@ -0,0 +1,183 @@ + + + + + + + + + saferoad — saferoad 0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+ +
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/build/html/api/saferoad.database.html b/docs/build/html/api/saferoad.database.html new file mode 100644 index 0000000..d51daf7 --- /dev/null +++ b/docs/build/html/api/saferoad.database.html @@ -0,0 +1,177 @@ + + + + + + + + + saferoad.database module — saferoad 0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

saferoad.database module

+
+
+class saferoad.database.DataBase[source]
+

Bases: object

+

Class for managing a DuckDB database connection.

+

This class provides methods to set up a DuckDB database connection, and run SQL queries.

+
+
+init_database_path()[source]
+

Initialize the database path.

+

This method creates a directory for the database if it does not exist +and generates a filename based on the current date and time.

+
+
Returns:
+

The path to the DuckDB database file.

+
+
Return type:
+

str

+
+
+
+ +
+
+run(query: str)[source]
+

Run a SQL query on the database.

+

This method executes the provided SQL query on the connected database.

+
+
Parameters:
+

query (str) – The SQL query to execute.

+
+
Raises:
+

AssertionError – If the database connection is not established.

+
+
+
+ +
+
+setup()[source]
+

Setup the DuckDB database connection. +This method initializes the database path and establishes a connection +to the DuckDB database. It also loads the spatial extension if available.

+
+
Raises:
+

AssertionError – If the database connection or path is already established.

+
+
+
+ +
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/build/html/api/saferoad.html b/docs/build/html/api/saferoad.html new file mode 100644 index 0000000..246d27b --- /dev/null +++ b/docs/build/html/api/saferoad.html @@ -0,0 +1,1555 @@ + + + + + + + + + saferoad package — saferoad 0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

saferoad package

+
+

Submodules

+
+ +
+
+
+

Module contents

+
+
+class saferoad.DataBase[source]
+

Bases: object

+

Class for managing a DuckDB database connection.

+

This class provides methods to set up a DuckDB database connection, and run SQL queries.

+
+
+init_database_path()[source]
+

Initialize the database path.

+

This method creates a directory for the database if it does not exist +and generates a filename based on the current date and time.

+
+
Returns:
+

The path to the DuckDB database file.

+
+
Return type:
+

str

+
+
+
+ +
+
+run(query: str)[source]
+

Run a SQL query on the database.

+

This method executes the provided SQL query on the connected database.

+
+
Parameters:
+

query (str) – The SQL query to execute.

+
+
Raises:
+

AssertionError – If the database connection is not established.

+
+
+
+ +
+
+setup()[source]
+

Setup the DuckDB database connection. +This method initializes the database path and establishes a connection +to the DuckDB database. It also loads the spatial extension if available.

+
+
Raises:
+

AssertionError – If the database connection or path is already established.

+
+
+
+ +
+ +
+
+class saferoad.Pipeline[source]
+

Bases: object

+
+
+class Processing[source]
+

Bases: object

+

Processing class for processing road data.

+

This class provides methods to load files into a database, dissolve features, +split lines into segments, generate patches, and save tables as GeoJSON files. +It assumes that the database connection is already established and that the +necessary SQL functions for spatial operations are available. +The methods in this class generate SQL queries that can be executed against +a spatial database, such as DuckDB.

+

The class does not handle database connections or query execution directly; +it only generates the SQL queries as strings. +The methods are static and can be called without instantiating the class. +The class is designed to be used in a pipeline for processing road data, +where each method can be chained together to perform a series of operations +on the road data.

+

The methods assume that the input data is already in a suitable format for +processing, such as a GIS vector file for loading into a database or a table +containing road geometries for dissolving and segmenting. +The class is intended to be used in conjunction with a spatial database +that supports SQL queries for spatial operations, such as DuckDB with +the Spatial extension or PostGIS. +The methods are designed to be flexible and can be adapted to different +use cases by modifying the parameters passed to them.

+
+
+static build_geometry(latitude: str, longitude: str, table_name: str) → str[source]
+

Build a geometry from latitude and longitude coordinates.

+

This method generates an SQL query to build a geometry column in a table +from latitude and longitude coordinates. It assumes that the input table +contains columns for latitude and longitude.

+
+
Parameters:
+
    +

  • latitude (str) – The name of the column containing latitude values.

    • +

  • longitude (str) – The name of the column containing longitude values.

    • +

  • table_name (str) – The name of the table to add the geometry column to.

    • +
+
+
Returns:
+

SQL query string to build the geometry column.

+
+
Return type:
+

str

+
+
+
+ +
+
+static calc_avg_cum_disp() → str[source]
+

Calculate average cumulative displacement per patch.

+

This method generates an SQL query to calculate the average cumulative +displacement for each patch by averaging the displacement values of points +within each patch. It assumes that the point table contains a geometry column, +a displacement column, and that the patch table contains a geometry column +and a unique identifier for each patch.

+
+
Returns:
+

SQL query string to calculate average cumulative displacement per patch.

+
+
Return type:
+

str

+
+
+
+ +
+
+static calc_avg_disp_ts_gcp(date_fields: list[str])[source]
+

Calculate average displacement time series for patches (Average Local Displacement (ALD)).

+

This method generates an SQL query to calculate the average displacement +time series for each patch by averaging the displacement values of points within each patch +for the specified date fields. It assumes that the point table contains a geometry column, +displacement columns for each date field, and that the patch table contains a geometry column +and a unique identifier for each patch.

+
+
Parameters:
+

date_fields (list[str]) – List of column names representing displacement values at different dates.

+
+
Returns:
+

SQL query string to calculate average displacement time series per patch.

+
+
Return type:
+

str

+
+
+
+ +
+
+static calc_avg_lcp_velocity(table_name: str = 'pspoint') → str[source]
+

Calculate average LCP velocity per patch.

+

This method generates an SQL query to calculate the average velocity of +local control points (LCPs) for each patch by averaging the average velocities +of points within each patch based on their LCP velocities.

+
+
Parameters:
+

table_name (str) – The name of the table containing the points, default is “pspoint”.

+
+
Returns:
+

SQL query string to calculate average LCP velocity per patch.

+
+
Return type:
+

str

+
+
+
+ +
+
+static calc_avg_std_disp_ts_lcp(date_fields: list[str], table_name: str = 'pspoint') → str[source]
+

Calculate average and standard deviation of displacement time series based on LCPs.

+

This method generates an SQL query to calculate the average and standard +deviation of displacement time series for each patch based on the displacement +values of points within each patch relative to the local control point (LCP). +It assumes that the point table contains columns for displacement values at +different dates and that the patch table contains a geometry column and a unique +identifier for each patch.

+
+
Parameters:
+
    +

  • date_fields (list[str]) – List of column names representing displacement values at different dates.

    • +

  • table_name (str) – The name of the table containing the points, default is “pspoint”.

    • +
+
+
Returns:
+

SQL query string to calculate average and standard deviation of displacement time series based on LCPs.

+
+
Return type:
+

str

+
+
+
+ +
+
+static calc_avg_velocity(time_vector: list[float], field_names: list[str], table_name: str = 'pspoint') → str[source]
+

Calculate average velocity using linear regression.

+

This method generates an SQL query to calculate the average velocity for +each point in a table using linear regression on displacement values over time. +It assumes that the input table contains columns for displacement values at different dates and that the time vector is provided in days.

+
+
Parameters:
+
    +

  • time_vector (list[float]) – List of time differences in days from the first date.

    • +

  • field_names (list[str]) – List of column names representing displacement values at different dates.

    • +

  • table_name (str) – The name of the table containing the points, default is “pspoint”.

    • +
+
+
Returns:
+

SQL query string to calculate average velocity.

+
+
Return type:
+

str

+
+
+
+ +
+
+static calc_avg_velocity_gcp(table_name: str = 'pspoint') → str[source]
+

Calculate average velocity based on ground control points (GCPs) for patches.

+

This method generates an SQL query to calculate the average velocity for each patch based on the average velocities of points within each patch.

+
+
Parameters:
+

table_name (str) – The name of the table containing the points, default is “pspoint”.

+
+
Returns:
+

SQL query string to calculate average velocity based on GCPs for patches.

+
+
Return type:
+

str

+
+
+
+ +
+
+static calc_avg_velocity_lcp(table_name: str = 'pspoint') → str[source]
+

Calculate average velocity based on local control points (LCPs) for points.

+

This method generates an SQL query to calculate the average velocity for each point +based on the average velocity of the local control point (LCP) associated with the patch +to which the point belongs. It assumes that the point table contains a geometry column, +an average velocity column based on ground control points (GCPs), and that the patch table +contains a geometry column, a unique identifier for each patch, and the average velocity +of the LCP.

+
+
Parameters:
+

table_name (str) – The name of the table containing the points, default is “pspoint”.

+
+
Returns:
+

SQL query string to calculate average velocity based on LCPs for points.

+
+
Return type:
+

str

+
+
+
+ +
+
+static calc_cum_disp(dates: list[str], table_name: str = 'pspoint') → str[source]
+

Calculate cumulative displacement.

+

This method generates an SQL query to calculate the cumulative displacement +for each point in a table based on the provided date fields. It assumes that +the input table contains columns for displacement values at different dates.

+
+
Parameters:
+
    +

  • dates (list[str]) – List of column names representing dates.

    • +

  • table_name (str) – The name of the table containing the points, default is “pspoint”.

    • +
+
+
Returns:
+

SQL query string to calculate cumulative displacement.

+
+
Return type:
+

str

+
+
+
+ +
+
+static calc_lcp_velocity() → str[source]
+

Calculate LCP velocity for patches.

+

This method generates an SQL query to calculate the velocity of the linear control point (LCP) +for each patch by retrieving the average velocity of the point identified as the LCP.

+
+
Returns:
+

SQL query string to calculate LCP velocity for patches.

+
+
Return type:
+

str

+
+
+
+ +
+
+static calc_med_std_disp_ts_lcp(date_fields: list[str], table_name: str = 'pspoint') → str[source]
+

Calculate average and standard deviation of displacement time series based on LCPs.

+

This method generates an SQL query to calculate the average and standard +deviation of displacement time series for each patch based on the displacement +values of points within each patch relative to the local control point (LCP). +It assumes that the point table contains columns for displacement values at +different dates and that the patch table contains a geometry column and a unique +identifier for each patch.

+
+
Parameters:
+
    +

  • date_fields (list[str]) – List of column names representing displacement values at different dates.

    • +

  • table_name (str) – The name of the table containing the points, default is “pspoint”.

    • +
+
+
Returns:
+

SQL query string to calculate average and standard deviation of displacement time series based on LCPs.

+
+
Return type:
+

str

+
+
+
+ +
+
+static calc_outliers(scaling_factor: float, table_name: str = 'pspoint') → str[source]
+

Identify outlier points based on absolute average velocity.

+

This method generates an SQL query to identify outlier points based on their +average velocity relative to local control points (LCPs). Points with an absolute +average LCP velocity exceeding a specified threshold (5 mm/year) are +marked as outliers by assigning their unique identifier to a new column in the +patches table. The unit parameter allows for specifying the measurement unit +(meters, centimeters, or millimeters) to appropriately scale the threshold.

+
+
Parameters:
+
    +

  • scaling_factor (float) – The factor to scale the velocity threshold based on the unit.

    • +

  • table_name (str) – The name of the table containing the points, default is “pspoint”.

    • +
+
+
Returns:
+

SQL query string to identify outlier points.

+
+
Return type:
+

str

+
+
+
+ +
+
+static calc_outliers_avg_lcd(table_name: str = 'pspoint', date_vector=list[int], date_names=list[str]) → str[source]
+

Calculate outliers based on average local control displacement (LCD).

+

This method generates an SQL query to identify outlier points based on their +displacement time series relative to the average local control displacement (LCD) +of their associated patches. Points with displacement values exceeding the average +LCD by more than standard deviations at any time point are marked as outliers +by inserting their unique identifier and patch ID into a new outliers table.

+
+
Parameters:
+
    +

  • table_name (str) – The name of the table containing the points, default is “pspoint”.

    • +

  • date_vector (list[int]) – List of time differences in days from the first date.

    • +

  • date_names (list[str]) – List of column names representing displacement values at different dates.

    • +
+
+
Returns:
+

SQL query string to identify outlier points based on average LCD.

+
+
Return type:
+

str

+
+
+
+ +
+
+static calc_outliers_med_lcd(table_name: str = 'pspoint', date_vector=list[int], date_names=list[str]) → str[source]
+

Calculate outliers based on median local control displacement (LCD).

+

This method generates an SQL query to identify outlier points based on their +displacement time series relative to the median local control displacement (LCD) +of their associated patches. Points with displacement values exceeding the median +LCD by more than standard deviations at any time point are marked as outliers +by inserting their unique identifier and patch ID into a new outliers table.

+
+
Parameters:
+
    +

  • table_name (str) – The name of the table containing the points, default is “pspoint”.

    • +

  • date_vector (list[int]) – List of time differences in days from the first date.

    • +

  • date_names (list[str]) – List of column names representing displacement values at different dates.

    • +
+
+
Returns:
+

SQL query string to identify outlier points based on median LCD.

+
+
Return type:
+

str

+
+
+
+ +
+
+static calc_point_density(point_name: str, patch_name: str = 'patches') → str[source]
+

Calculate point density per patch.

+

This method generates an SQL query to calculate the point density per patch +by counting the number of points within each patch and dividing by the area +of the patch in square kilometers. It assumes that the point table contains +a geometry column and that the patch table contains a geometry column and +a unique identifier for each patch.

+
+
Parameters:
+
    +

  • point_name (str) – The name of the table containing the points.

    • +

  • patch_name (str) – The name of the table containing the patches, default is “patches”.

    • +
+
+
Returns:
+

SQL query string to calculate point density per patch.

+
+
Return type:
+

str

+
+
+
+ +
+
+static calc_std_disp_ts_gcp(date_fields: list[str])[source]
+

Calculate standard deviation of displacement time series for patches based on global control point (GCP).

+

This method generates an SQL query to calculate the standard deviation +of displacement time series for each patch by computing the standard deviation of displacement values of points within each patch for the specified date fields.

+
+
Parameters:
+

date_fields (list[str]) – List of column names representing displacement values at different dates.

+
+
Returns:
+

SQL query string to calculate standard deviation of displacement time series per patch.

+
+
Return type:
+

str

+
+
+
+ +
+
+static dissolve_features(attribute: str, table_name: str) → str[source]
+

Dissolve features in a table based on a specified attribute.

+

This method generates an SQL query to dissolve features in a table +based on the provided attribute. It assumes the table is already loaded +into the database.

+
+
Parameters:
+
    +

  • attribute (str) – The attribute to dissolve features by.

    • +

  • table_name (str) – The name of the table containing the features, default is “road”.

    • +
+
+
Returns:
+

SQL query string to dissolve features.

+
+
Return type:
+

str

+
+
+
+ +
+
+static generate_patches(width: float, length: float, table_name: str = 'segments') → str[source]
+

Generate patches from segments.

+

This method generates patches from segments by creating a rectangular +envelope around each segment, rotated to align with the segment’s direction. +The patches are created by translating and rotating the envelope to the +start point of the segment, ensuring that each patch is oriented correctly +along the segment’s direction.

+
+
Parameters:
+
    +

  • width (float) – The width of the patches in meters.

    • +

  • length (float) – The length of the patches in meters.

    • +

  • table_name (str) – The name of the table containing the segments, default is “segments”.

    • +
+
+
Returns:
+

SQL query string to create or replace the patches table.

+
+
Return type:
+

str

+
+
+
+ +
+
+static get_column_names(table_name: str) → str[source]
+

Get column names from a table.

+

This method generates an SQL query to retrieve the column names from a specified table.

+
+
Parameters:
+

table_name (str) – The name of the table to retrieve column names from.

+
+
Returns:
+

SQL query string to get the column names.

+
+
Return type:
+

str

+
+
+
+ +
+
+static lcp_ts_patch(date_fields: list[str], table_name: str = 'pspoint')[source]
+

Get Local Control Point’s displacement time series for patches.

+

This method generates an SQL query to retrieve the displacement time series +of the local control point (LCP) for each patch based on specified date fields.

+
+
Parameters:
+
    +

  • date_fields (list[str]) – List of column names representing displacement values at different dates.

    • +

  • table_name (str) – The name of the table containing the points, default is “pspoint”.

    • +
+
+
Returns:
+

SQL query string to get LCP displacement time series for patches.

+
+
Return type:
+

str

+
+
+
+ +
+
+static load_file(file_path: str, table_name: str) → str[source]
+

Load a file into a database table.

+

Method validates the provided file path and determines if existing +and determines the appropriate loading method based on the file type. +It generates an SQL query to create or replace a table in the database +with the contents of the file. The method supports CSV files and other +GIS vector files, using different loading methods for each type.

+
+
Parameters:
+
    +

  • file_path (str) – The path to the file to be loaded.

    • +

  • table_name (str) – The name of the table to create or replace in the database.

    • +
+
+
Raises:
+

AssertionError – If the file does not exist.

+
+
Returns:
+

SQL query string to create or replace the table with the file data.

+
+
Return type:
+

str

+
+
+
+ +
+
+static outlier_rel_ts_patch()[source]
+

Calculate outlier displacement time series relative to LCP for patches.

+

This method generates an SQL query to calculate the displacement time series +of outlier points relative to the displacement time series of their associated +local control points (LCPs). It assumes that the outliers table contains a +geometry column, a displacement time series column, and a patch ID column, +and that the patches table contains a geometry column, a unique identifier +for each patch, and a displacement time series column for the LCP.

+
+
Returns:
+

SQL query string to calculate outlier displacement time series relative to LCP for patches.

+
+
Return type:
+

str

+
+
+
+ +
+
+outlier_ts_patch(table_name: str = 'pspoint')[source]
+

Get outlier displacement time series for patches.

+

This method generates an SQL query to retrieve the displacement time series +of outlier points based on specified date fields.

+
+
Parameters:
+
    +

  • date_fields (list[str]) – List of column names representing displacement values at different dates.

    • +

  • table_name (str) – The name of the table containing the points, default is “pspoint”.

    • +
+
+
Returns:
+

SQL query string to get outlier displacement time series for patches.

+
+
Return type:
+

str

+
+
+
+ +
+
+static relate_point_patches(point_name: str, patch_name: str = 'patches') → str[source]
+

Relate points to patches by assigning patch IDs to points.

+

This method generates an SQL query to relate points in one table to patches +in another table by assigning patch IDs to points based on their spatial +relationship. It assumes that the point table contains a geometry column +and that the patch table contains a geometry column and a unique identifier +for each patch.

+
+
Parameters:
+
    +

  • point_name (str) – The name of the table containing the points.

    • +

  • patch_name (str) – The name of the table containing the patches, default is “patches”.

    • +
+
+
Returns:
+

SQL query string to relate points to patches.

+
+
Return type:
+

str

+
+
+
+ +
+
+static select_lcps(table_name: str = 'pspoint') → str[source]
+

Select linear control points (LCPs) for patches.

+

This method generates an SQL query to select local control points (LCPs) for each patch by identifying the point with the minimum absolute average velocity within each patch.

+
+
Parameters:
+

table_name (str) – The name of the table containing the points, default is “pspoint”.

+
+
Returns:
+

SQL query string to select LCPs for patches.

+
+
Return type:
+

str

+
+
+
+ +
+
+static spatial_tranformation(source_epsg: str, target_epsg: str, table_name: str) → str[source]
+

Transform geometries from one EPSG code to another.

+

This method generates an SQL query to transform geometries in a table +from one EPSG code to another. It assumes that the input table contains +geometries in the source EPSG code and that the target EPSG code is valid.

+
+
Parameters:
+
    +

  • source_epsg (str) – The source EPSG code of the geometries.

    • +

  • target_epsg (str) – The target EPSG code to transform the geometries to.

    • +

  • table_name (str) – The name of the table containing the geometries, default is “patches”.

    • +
+
+
Returns:
+

SQL query string to transform the geometries.

+
+
Return type:
+

str

+
+
+
+ +
+
+static split_to_segments(segment_length: float, table_name: str = 'dissolved') → str[source]
+

Split lines into segments of a specified length.

+

This method generates an SQL query to split lines in a table into segments +of a specified length. It transforms the geometries to a computational +coordinate reference system (CRS) and calculates the lengths in meters. +The segments are created by generating windows of fixed length along the lines. +The resulting segments are stored in a new table named “segments”. +The method assumes that the input table is already dissolved and contains +geometries in a suitable format.

+

The segments are created by cutting the lines into fixed-length segments +and filtering out any empty geometries. +This method is useful for preparing road segments for further analysis or processing. +It is particularly useful in road network analysis, where roads need to be +divided into manageable segments for tasks such as traffic analysis, road condition +assessment, or infrastructure planning.

+
+
Parameters:
+
    +

  • segment_length (float) – The length of each segment in meters.

    • +

  • table_name (str) – The name of the table containing the lines, default is “dissolved”.

    • +
+
+
Returns:
+

SQL query string to create or replace the segments table.

+
+
Return type:
+

str

+
+
+
+ +
+ +
+
+class Visualisation[source]
+

Bases: object

+

SQL queries for visualisation of results.

+

2D visualisation is done in Web Mercator (EPSG:3857).

+
+
+static get_avg_cum_disp_graph(patch_id: float, scaling_factor: float) → str[source]
+

Get average cumulative displacement time series.

+

This method generates an SQL query to retrieve the average cumulative displacement +time series for a specific patch, scaling the displacement values by a specified factor.

+
+
Parameters:
+
    +

  • patch_id (float) – The unique identifier of the patch.

    • +

  • scaling_factor (float) – The factor to scale the displacement values.

    • +
+
+
Returns:
+

SQL query string to get the average cumulative displacement time series for the specified patch.

+
+
+
+ +
+
+static get_avg_disp_lcp_graph(patch_id: float, scaling_factor: float) → str[source]
+

Get average displacement time series based on local control points (LCPs).

+

This method generates an SQL query to retrieve the average displacement time series +based on local control points (LCPs) for a specific patch, scaling the displacement +values by a specified factor.

+
+
Parameters:
+
    +

  • patch_id (float) – The unique identifier of the patch.

    • +

  • scaling_factor (float) – The factor to scale the displacement values.

    • +
+
+
Returns:
+

SQL query string to get the average displacement time series based on LCPs for the specified patch.

+
+
+
+ +
+
+static get_center(point_uid: float, source_crs: str) → str[source]
+

Get the center point of the outlier within a specific patch.

+

This method generates an SQL query to retrieve the center point of the outlier +within a specific patch, transforming the geometry from a source CRS to a target +projection defined in the Visualisation class.

+
+
Parameters:
+
    +

  • point_uid (float) – The unique identifier of the outlier point.

    • +

  • source_crs (str) – The source coordinate reference system of the geometries.

    • +
+
+
Returns:
+

SQL query string to get the center point of the outlier within the specified patch.

+
+
+
+ +
+
+static get_cum_disp_graph(outlier_id: float, scaling_factor: float) → str[source]
+

Get outlier displacement time series.

+

This method generates an SQL query to retrieve the outlier displacement time series +for a specific patch, scaling the displacement values by a specified factor.

+
+
Parameters:
+
    +

  • outlier_id (float) – The unique identifier of the patch.

    • +

  • scaling_factor (float) – The factor to scale the displacement values.

    • +
+
+
Returns:
+

SQL query string to get the outlier displacement time series for the specified patch.

+
+
+
+ +
+
+static get_cum_disp_map(source_crs: str, scaling_factor: float) → str[source]
+

Get cumulative displacement per patch.

+

This method generates an SQL query to retrieve the cumulative displacement +for each patch, transforming the geometries from a source CRS to a target +projection defined in the Visualisation class.

+
+
Parameters:
+
    +

  • source_crs (str) – The source coordinate reference system of the geometries.

    • +

  • unit (str) – The measurement unit for displacement (“m”, “cm”, or “mm”).

    • +
+
+
Returns:
+

SQL query string to get the cumulative displacement per patch.

+
+
Return type:
+

str

+
+
+
+ +
+
+static get_lcp_disp_graph(patch_id: float, scaling_factor: float) → str[source]
+

Get local control point (LCP) displacement time series.

+

This method generates an SQL query to retrieve the local control point (LCP) +displacement time series for a specific patch, scaling the displacement values by a specified factor.

+
+
Parameters:
+
    +

  • patch_id (float) – The unique identifier of the patch.

    • +

  • scaling_factor (float) – The factor to scale the displacement values.

    • +
+
+
Returns:
+

SQL query string to get the LCP displacement time series for the specified patch.

+
+
+
+ +
+
+static get_lcp_point(patch_id: float, source_crs: str) → str[source]
+

Get the local control point (LCP) within a specific patch.

+

This method generates an SQL query to retrieve the local control point (LCP) +within a specific patch, transforming the geometry from a source CRS to a target +projection defined in the Visualisation class.

+
+
Parameters:
+
    +

  • patch_id (float) – The unique identifier of the patch.

    • +

  • source_crs (str) – The source coordinate reference system of the geometries.

    • +
+
+
Returns:
+

SQL query string to get the LCP within the specified patch.

+
+
+
+ +
+
+static get_outlier_points(patch_id: float, source_crs: str) → str[source]
+

Get the outlier points within a specific patch.

+

This method generates an SQL query to retrieve the outlier point within a specific patch, +transforming the geometry from a source CRS to a target projection defined in the Visualisation class.

+
+
Parameters:
+
    +

  • patch_id (float) – The unique identifier of the patch.

    • +

  • source_crs (str) – The source coordinate reference system of the geometries.

    • +
+
+
Returns:
+

SQL query string to get the outlier point within the specified patch.

+
+
+
+ +
+
+get_outlier_rel_ts_graph(scaling_factor: float) → str[source]
+

Get outlier relative displacement time series.

+

This method generates an SQL query to retrieve the outlier relative displacement +time series for a specific patch, scaling the displacement values by a specified factor.

+
+
Parameters:
+
    +

  • outlier_id (float) – The unique identifier of the patch.

    • +

  • scaling_factor (float) – The factor to scale the displacement values.

    • +
+
+
Returns:
+

SQL query string to get the outlier relative displacement time series for the specified patch.

+
+
+
+ +
+
+static get_outliers_map(source_crs: str, scaling_factor: float) → str[source]
+

Get outlier points based on absolute average LCP velocity.

+

This method generates an SQL query to retrieve outlier points based on their +average velocity relative to local control points (LCPs). Points with an absolute +average LCP velocity exceeding a specified threshold (5 mm/year) are +marked as outliers by assigning their unique identifier to a new column in the +patches table. The unit parameter allows for specifying the measurement unit +(meters, centimeters, or millimeters) to appropriately scale the threshold.

+
+
Parameters:
+
    +

  • source_crs (str) – The source coordinate reference system of the geometries.

    • +

  • scaling_factor (float) – The factor to scale the velocity threshold based on the unit.

    • +
+
+
Returns:
+

SQL query string to identify outlier points.

+
+
Return type:
+

str

+
+
+
+ +
+
+static get_patch_center(patch_id: float, source_crs: str) → str[source]
+

Get the center point of a specific patch.

+

This method generates an SQL query to retrieve the center point of a specific +patch, transforming the geometry from a source CRS to a target projection defined in the Visualisation class.

+
+
Parameters:
+
    +

  • patch_id (float) – The unique identifier of the patch.

    • +

  • source_crs (str) – The source coordinate reference system of the geometries.

    • +
+
+
Returns:
+

SQL query string to get the center point of the specified patch.

+
+
+
+ +
+
+static get_ps_density(source_crs: str) → str[source]
+

Get point density per patch.

+

This method generates an SQL query to retrieve the point density for each patch, +transforming the geometries from a source CRS to a target projection defined +in the Visualisation class.

+
+
Parameters:
+

source_crs (str) – The source coordinate reference system of the geometries.

+
+
Returns:
+

SQL query string to get the point density per patch.

+
+
Return type:
+

str

+
+
+
+ +
+
+static get_ps_points(patch_id: float, source_crs: str, table_name: str) → str[source]
+

Get points within a specific patch.

+

This method generates an SQL query to retrieve points within a specific patch, +transforming the geometries from a source CRS to a target projection defined +in the Visualisation class.

+
+
Parameters:
+
    +

  • patch_id (float) – The unique identifier of the patch.

    • +

  • source_crs (str) – The source coordinate reference system of the geometries.

    • +

  • table_name (str) – The name of the table containing the points.

    • +
+
+
Returns:
+

SQL query string to get the points within the specified patch.

+
+
Return type:
+

str

+
+
+
+ +
+
+static get_ps_vel_gcp(source_crs: str, scaling_factor: float) → str[source]
+

Get average velocity based on ground control points (GCPs). +This method generates an SQL query to retrieve the average velocity for +each point based on ground control points (GCPs), transforming the geometries +from a source CRS to a target projection defined in the Visualisation class.

+
+
Parameters:
+
    +

  • source_crs (str) – The source coordinate reference system of the geometries.

    • +

  • unit (str) – The measurement unit for velocity (“m”, “cm”, or “mm”).

    • +
+
+
Returns:
+

SQL query string to get the average velocity based on GCPs.

+
+
Return type:
+

str

+
+
+
+ +
+
+static get_ps_vel_lcp(source_crs: str, scaling_factor: float) → str[source]
+

Get average velocity based on local control points (LCPs).

+

This method generates an SQL query to retrieve the average velocity for +each point based on local control points (LCPs), transforming the geometries +from a source CRS to a target projection defined in the Visualisation class.

+
+
Parameters:
+
    +

  • source_crs (str) – The source coordinate reference system of the geometries.

    • +

  • unit (str) – The measurement unit for velocity (“m”, “cm”, or “mm”).

    • +
+
+
Returns:
+

SQL query string to get the average velocity based on LCPs.

+
+
Return type:
+

str

+
+
+
+ +
+
+static get_std_cum_disp_graph(patch_id: float, scaling_factor: float) → str[source]
+

Get standard deviation of cumulative displacement time series.

+

This method generates an SQL query to retrieve the standard deviation of cumulative +displacement time series for a specific patch, scaling the displacement values by a specified factor.

+
+
Parameters:
+
    +

  • patch_id (float) – The unique identifier of the patch.

    • +

  • scaling_factor (float) – The factor to scale the displacement values.

    • +
+
+
Returns:
+

SQL query string to get the standard deviation of cumulative displacement time series for the specified patch.

+
+
+
+ +
+
+static get_std_disp_lcp_graph(patch_id: float, scaling_factor: float) → str[source]
+

Get standard deviation of displacement time series based on local control points (LCPs).

+

This method generates an SQL query to retrieve the standard deviation of displacement +time series based on local control points (LCPs) for a specific patch, scaling the +displacement values by a specified factor.

+
+
Parameters:
+
    +

  • patch_id (float) – The unique identifier of the patch.

    • +

  • scaling_factor (float) – The factor to scale the displacement values.

    • +
+
+
Returns:
+

SQL query string to get the standard deviation of displacement time series based on LCPs for the specified patch.

+
+
+
+ +
+
+static get_table_bbox(table_name: str, source_crs: str) → str[source]
+

Get the bounding box of a table in a specified projection.

+

This method generates an SQL query to retrieve the bounding box of geometries +from a specified table, transforming the geometries from a source CRS to +a target projection defined in the Visualisation class.

+
+
Parameters:
+
    +

  • table_name (str) – The name of the table to get the bounding box from.

    • +

  • source_crs (str) – The source coordinate reference system of the geometries.

    • +
+
+
Returns:
+

SQL query string to get the bounding box of the table.

+
+
Return type:
+

str

+
+
+
+ +
+
+static patch_uids() → str[source]
+

Get all patch UIDs that have outliers.

+

This method generates an SQL query to retrieve all unique identifiers (UIDs) +of patches that have been identified as containing outliers.

+
+
Returns:
+

SQL query string to get all patch UIDs with outliers.

+
+
Return type:
+

str

+
+
+
+ +
+
+projection = 'EPSG:3857'
+
+ +
+ +
+ +
+
+class saferoad.PsData(filepath: str, latitude: str, longitude: str, unit: Literal['m', 'cm', 'mm'] = 'm', crs_code: str = 'EPSG:4326', name: str = 'pspoint')[source]
+

Bases: object

+

Class for storing point source data information.

+
+
Parameters:
+
    +

  • filepath (str) – Path to the point source data file.

    • +

  • latitude (str) – Field name of the latitude field in the data file.

    • +

  • longitude (str) – Field name of the longitude field in the data file.

    • +

  • unit (Literal["m", "cm", "mm"]) – Unit of measurement for the coordinates. Options are “m”, “cm”, or “mm”. Default is “m”.

    • +

  • crs_code (str) – Coordinate Reference System code. Default is “EPSG:4326”.

    • +

  • name (str) – Name identifier for the point source data. Default is “pspoint”.

    • +
+
+
+
+
+crs_code: str = 'EPSG:4326'
+
+ +
+
+filepath: str
+
+ +
+
+latitude: str
+
+ +
+
+longitude: str
+
+ +
+
+name: str = 'pspoint'
+
+ +
+
+unit: Literal['m', 'cm', 'mm'] = 'm'
+
+ +
+ +
+
+class saferoad.Road(filepath: str, crs_code: str = 'EPSG:4326', name: str = 'road')[source]
+

Bases: object

+

Class for storing road data information.

+
+
Parameters:
+
    +

  • filepath (str) – Path to the road data file.

    • +

  • crs_code (str) – Coordinate Reference System code. Default is “EPSG:4326”.

    • +

  • name (str) – Name identifier for the road data. Default is “road”.

    • +
+
+
+
+
+crs_code: str = 'EPSG:4326'
+
+ +
+
+filepath: str
+
+ +
+
+name: str = 'road'
+
+ +
+ +
+
+class saferoad.SafeRoad(road: Road, ps_data: PsData, computational_crs: str)[source]
+

Bases: object

+

A class to perform road deformation analysis using MT-InSAR data.

+
+
Parameters:
+
    +

  • road (Road) – An instance of the Road class containing road data information.

    • +

  • ps_data (PsData) – An instance of the PsData class containing MT-InSAR data information.

    • +

  • computational_crs (str) – A string representing the EPSG code for the computational CRS.

    • +
+
+
+
+
+generate_rectangles(road_width: float, segment_length: float)[source]
+

Generate road segments and patches.

+

This involves: +1. Splitting the road into segments of equal length. +2. Generating rectangles along the road segments with a specified width. +2. Rotating the generated rectangles int the same direction along the road.

+
+
Parameters:
+
    +

  • road_width – A float representing the width of the road segments in meters.

    • +

  • segment_length – A float representing the length of each road segment in meters.

    • +
+
+
+
+ +
+
+generate_report(output_name: str)[source]
+

Generate a PDF report containing maps and time series plots.

+
+
Parameters:
+

output_name (str) – A string representing the name of the output PDF file endswith .pdf extension.

+
+
+
+ +
+
+load_files()[source]
+

Load the road and MT-InSAR data files into the database.

+
+ +
+
+preprocess(attribute: str = None)[source]
+

Preprocess the road and MT-InSAR data.

+

This includes transforming the data to the computational CRS, dissolving road features, +and building geometries for the MT-InSAR points. +1. Transform the road and MT-InSAR data to the computational CRS. +2. Dissolve road features based on the provided attribute if given, otherwise merge all features. +3. Build geometries for the MT-InSAR points from the latitude and longitude columns. +4. Transform the MT-InSAR data to the computational CRS.

+
+
Parameters:
+

attribute (str, optional) – An optional string representing the attribute to dissolve road features. +If None, all features will be merged. Default is None.

+
+
+
+ +
+
+run_analysis()[source]
+

Run the deformation analysis on the MT-InSAR data for road network.

+

This includes calculating cumulative displacement, average velocity, point density, +local control points, and outliers.

+
+ +
+ +
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/build/html/api/saferoad.pipeline.html b/docs/build/html/api/saferoad.pipeline.html new file mode 100644 index 0000000..5da4497 --- /dev/null +++ b/docs/build/html/api/saferoad.pipeline.html @@ -0,0 +1,1184 @@ + + + + + + + + + saferoad.pipeline module — saferoad 0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

saferoad.pipeline module

+
+
+class saferoad.pipeline.Pipeline[source]
+

Bases: object

+
+
+class Processing[source]
+

Bases: object

+

Processing class for processing road data.

+

This class provides methods to load files into a database, dissolve features, +split lines into segments, generate patches, and save tables as GeoJSON files. +It assumes that the database connection is already established and that the +necessary SQL functions for spatial operations are available. +The methods in this class generate SQL queries that can be executed against +a spatial database, such as DuckDB.

+

The class does not handle database connections or query execution directly; +it only generates the SQL queries as strings. +The methods are static and can be called without instantiating the class. +The class is designed to be used in a pipeline for processing road data, +where each method can be chained together to perform a series of operations +on the road data.

+

The methods assume that the input data is already in a suitable format for +processing, such as a GIS vector file for loading into a database or a table +containing road geometries for dissolving and segmenting. +The class is intended to be used in conjunction with a spatial database +that supports SQL queries for spatial operations, such as DuckDB with +the Spatial extension or PostGIS. +The methods are designed to be flexible and can be adapted to different +use cases by modifying the parameters passed to them.

+
+
+static build_geometry(latitude: str, longitude: str, table_name: str) → str[source]
+

Build a geometry from latitude and longitude coordinates.

+

This method generates an SQL query to build a geometry column in a table +from latitude and longitude coordinates. It assumes that the input table +contains columns for latitude and longitude.

+
+
Parameters:
+
    +

  • latitude (str) – The name of the column containing latitude values.

    • +

  • longitude (str) – The name of the column containing longitude values.

    • +

  • table_name (str) – The name of the table to add the geometry column to.

    • +
+
+
Returns:
+

SQL query string to build the geometry column.

+
+
Return type:
+

str

+
+
+
+ +
+
+static calc_avg_cum_disp() → str[source]
+

Calculate average cumulative displacement per patch.

+

This method generates an SQL query to calculate the average cumulative +displacement for each patch by averaging the displacement values of points +within each patch. It assumes that the point table contains a geometry column, +a displacement column, and that the patch table contains a geometry column +and a unique identifier for each patch.

+
+
Returns:
+

SQL query string to calculate average cumulative displacement per patch.

+
+
Return type:
+

str

+
+
+
+ +
+
+static calc_avg_disp_ts_gcp(date_fields: list[str])[source]
+

Calculate average displacement time series for patches (Average Local Displacement (ALD)).

+

This method generates an SQL query to calculate the average displacement +time series for each patch by averaging the displacement values of points within each patch +for the specified date fields. It assumes that the point table contains a geometry column, +displacement columns for each date field, and that the patch table contains a geometry column +and a unique identifier for each patch.

+
+
Parameters:
+

date_fields (list[str]) – List of column names representing displacement values at different dates.

+
+
Returns:
+

SQL query string to calculate average displacement time series per patch.

+
+
Return type:
+

str

+
+
+
+ +
+
+static calc_avg_lcp_velocity(table_name: str = 'pspoint') → str[source]
+

Calculate average LCP velocity per patch.

+

This method generates an SQL query to calculate the average velocity of +local control points (LCPs) for each patch by averaging the average velocities +of points within each patch based on their LCP velocities.

+
+
Parameters:
+

table_name (str) – The name of the table containing the points, default is “pspoint”.

+
+
Returns:
+

SQL query string to calculate average LCP velocity per patch.

+
+
Return type:
+

str

+
+
+
+ +
+
+static calc_avg_std_disp_ts_lcp(date_fields: list[str], table_name: str = 'pspoint') → str[source]
+

Calculate average and standard deviation of displacement time series based on LCPs.

+

This method generates an SQL query to calculate the average and standard +deviation of displacement time series for each patch based on the displacement +values of points within each patch relative to the local control point (LCP). +It assumes that the point table contains columns for displacement values at +different dates and that the patch table contains a geometry column and a unique +identifier for each patch.

+
+
Parameters:
+
    +

  • date_fields (list[str]) – List of column names representing displacement values at different dates.

    • +

  • table_name (str) – The name of the table containing the points, default is “pspoint”.

    • +
+
+
Returns:
+

SQL query string to calculate average and standard deviation of displacement time series based on LCPs.

+
+
Return type:
+

str

+
+
+
+ +
+
+static calc_avg_velocity(time_vector: list[float], field_names: list[str], table_name: str = 'pspoint') → str[source]
+

Calculate average velocity using linear regression.

+

This method generates an SQL query to calculate the average velocity for +each point in a table using linear regression on displacement values over time. +It assumes that the input table contains columns for displacement values at different dates and that the time vector is provided in days.

+
+
Parameters:
+
    +

  • time_vector (list[float]) – List of time differences in days from the first date.

    • +

  • field_names (list[str]) – List of column names representing displacement values at different dates.

    • +

  • table_name (str) – The name of the table containing the points, default is “pspoint”.

    • +
+
+
Returns:
+

SQL query string to calculate average velocity.

+
+
Return type:
+

str

+
+
+
+ +
+
+static calc_avg_velocity_gcp(table_name: str = 'pspoint') → str[source]
+

Calculate average velocity based on ground control points (GCPs) for patches.

+

This method generates an SQL query to calculate the average velocity for each patch based on the average velocities of points within each patch.

+
+
Parameters:
+

table_name (str) – The name of the table containing the points, default is “pspoint”.

+
+
Returns:
+

SQL query string to calculate average velocity based on GCPs for patches.

+
+
Return type:
+

str

+
+
+
+ +
+
+static calc_avg_velocity_lcp(table_name: str = 'pspoint') → str[source]
+

Calculate average velocity based on local control points (LCPs) for points.

+

This method generates an SQL query to calculate the average velocity for each point +based on the average velocity of the local control point (LCP) associated with the patch +to which the point belongs. It assumes that the point table contains a geometry column, +an average velocity column based on ground control points (GCPs), and that the patch table +contains a geometry column, a unique identifier for each patch, and the average velocity +of the LCP.

+
+
Parameters:
+

table_name (str) – The name of the table containing the points, default is “pspoint”.

+
+
Returns:
+

SQL query string to calculate average velocity based on LCPs for points.

+
+
Return type:
+

str

+
+
+
+ +
+
+static calc_cum_disp(dates: list[str], table_name: str = 'pspoint') → str[source]
+

Calculate cumulative displacement.

+

This method generates an SQL query to calculate the cumulative displacement +for each point in a table based on the provided date fields. It assumes that +the input table contains columns for displacement values at different dates.

+
+
Parameters:
+
    +

  • dates (list[str]) – List of column names representing dates.

    • +

  • table_name (str) – The name of the table containing the points, default is “pspoint”.

    • +
+
+
Returns:
+

SQL query string to calculate cumulative displacement.

+
+
Return type:
+

str

+
+
+
+ +
+
+static calc_lcp_velocity() → str[source]
+

Calculate LCP velocity for patches.

+

This method generates an SQL query to calculate the velocity of the linear control point (LCP) +for each patch by retrieving the average velocity of the point identified as the LCP.

+
+
Returns:
+

SQL query string to calculate LCP velocity for patches.

+
+
Return type:
+

str

+
+
+
+ +
+
+static calc_med_std_disp_ts_lcp(date_fields: list[str], table_name: str = 'pspoint') → str[source]
+

Calculate average and standard deviation of displacement time series based on LCPs.

+

This method generates an SQL query to calculate the average and standard +deviation of displacement time series for each patch based on the displacement +values of points within each patch relative to the local control point (LCP). +It assumes that the point table contains columns for displacement values at +different dates and that the patch table contains a geometry column and a unique +identifier for each patch.

+
+
Parameters:
+
    +

  • date_fields (list[str]) – List of column names representing displacement values at different dates.

    • +

  • table_name (str) – The name of the table containing the points, default is “pspoint”.

    • +
+
+
Returns:
+

SQL query string to calculate average and standard deviation of displacement time series based on LCPs.

+
+
Return type:
+

str

+
+
+
+ +
+
+static calc_outliers(scaling_factor: float, table_name: str = 'pspoint') → str[source]
+

Identify outlier points based on absolute average velocity.

+

This method generates an SQL query to identify outlier points based on their +average velocity relative to local control points (LCPs). Points with an absolute +average LCP velocity exceeding a specified threshold (5 mm/year) are +marked as outliers by assigning their unique identifier to a new column in the +patches table. The unit parameter allows for specifying the measurement unit +(meters, centimeters, or millimeters) to appropriately scale the threshold.

+
+
Parameters:
+
    +

  • scaling_factor (float) – The factor to scale the velocity threshold based on the unit.

    • +

  • table_name (str) – The name of the table containing the points, default is “pspoint”.

    • +
+
+
Returns:
+

SQL query string to identify outlier points.

+
+
Return type:
+

str

+
+
+
+ +
+
+static calc_outliers_avg_lcd(table_name: str = 'pspoint', date_vector=list[int], date_names=list[str]) → str[source]
+

Calculate outliers based on average local control displacement (LCD).

+

This method generates an SQL query to identify outlier points based on their +displacement time series relative to the average local control displacement (LCD) +of their associated patches. Points with displacement values exceeding the average +LCD by more than standard deviations at any time point are marked as outliers +by inserting their unique identifier and patch ID into a new outliers table.

+
+
Parameters:
+
    +

  • table_name (str) – The name of the table containing the points, default is “pspoint”.

    • +

  • date_vector (list[int]) – List of time differences in days from the first date.

    • +

  • date_names (list[str]) – List of column names representing displacement values at different dates.

    • +
+
+
Returns:
+

SQL query string to identify outlier points based on average LCD.

+
+
Return type:
+

str

+
+
+
+ +
+
+static calc_outliers_med_lcd(table_name: str = 'pspoint', date_vector=list[int], date_names=list[str]) → str[source]
+

Calculate outliers based on median local control displacement (LCD).

+

This method generates an SQL query to identify outlier points based on their +displacement time series relative to the median local control displacement (LCD) +of their associated patches. Points with displacement values exceeding the median +LCD by more than standard deviations at any time point are marked as outliers +by inserting their unique identifier and patch ID into a new outliers table.

+
+
Parameters:
+
    +

  • table_name (str) – The name of the table containing the points, default is “pspoint”.

    • +

  • date_vector (list[int]) – List of time differences in days from the first date.

    • +

  • date_names (list[str]) – List of column names representing displacement values at different dates.

    • +
+
+
Returns:
+

SQL query string to identify outlier points based on median LCD.

+
+
Return type:
+

str

+
+
+
+ +
+
+static calc_point_density(point_name: str, patch_name: str = 'patches') → str[source]
+

Calculate point density per patch.

+

This method generates an SQL query to calculate the point density per patch +by counting the number of points within each patch and dividing by the area +of the patch in square kilometers. It assumes that the point table contains +a geometry column and that the patch table contains a geometry column and +a unique identifier for each patch.

+
+
Parameters:
+
    +

  • point_name (str) – The name of the table containing the points.

    • +

  • patch_name (str) – The name of the table containing the patches, default is “patches”.

    • +
+
+
Returns:
+

SQL query string to calculate point density per patch.

+
+
Return type:
+

str

+
+
+
+ +
+
+static calc_std_disp_ts_gcp(date_fields: list[str])[source]
+

Calculate standard deviation of displacement time series for patches based on global control point (GCP).

+

This method generates an SQL query to calculate the standard deviation +of displacement time series for each patch by computing the standard deviation of displacement values of points within each patch for the specified date fields.

+
+
Parameters:
+

date_fields (list[str]) – List of column names representing displacement values at different dates.

+
+
Returns:
+

SQL query string to calculate standard deviation of displacement time series per patch.

+
+
Return type:
+

str

+
+
+
+ +
+
+static dissolve_features(attribute: str, table_name: str) → str[source]
+

Dissolve features in a table based on a specified attribute.

+

This method generates an SQL query to dissolve features in a table +based on the provided attribute. It assumes the table is already loaded +into the database.

+
+
Parameters:
+
    +

  • attribute (str) – The attribute to dissolve features by.

    • +

  • table_name (str) – The name of the table containing the features, default is “road”.

    • +
+
+
Returns:
+

SQL query string to dissolve features.

+
+
Return type:
+

str

+
+
+
+ +
+
+static generate_patches(width: float, length: float, table_name: str = 'segments') → str[source]
+

Generate patches from segments.

+

This method generates patches from segments by creating a rectangular +envelope around each segment, rotated to align with the segment’s direction. +The patches are created by translating and rotating the envelope to the +start point of the segment, ensuring that each patch is oriented correctly +along the segment’s direction.

+
+
Parameters:
+
    +

  • width (float) – The width of the patches in meters.

    • +

  • length (float) – The length of the patches in meters.

    • +

  • table_name (str) – The name of the table containing the segments, default is “segments”.

    • +
+
+
Returns:
+

SQL query string to create or replace the patches table.

+
+
Return type:
+

str

+
+
+
+ +
+
+static get_column_names(table_name: str) → str[source]
+

Get column names from a table.

+

This method generates an SQL query to retrieve the column names from a specified table.

+
+
Parameters:
+

table_name (str) – The name of the table to retrieve column names from.

+
+
Returns:
+

SQL query string to get the column names.

+
+
Return type:
+

str

+
+
+
+ +
+
+static lcp_ts_patch(date_fields: list[str], table_name: str = 'pspoint')[source]
+

Get Local Control Point’s displacement time series for patches.

+

This method generates an SQL query to retrieve the displacement time series +of the local control point (LCP) for each patch based on specified date fields.

+
+
Parameters:
+
    +

  • date_fields (list[str]) – List of column names representing displacement values at different dates.

    • +

  • table_name (str) – The name of the table containing the points, default is “pspoint”.

    • +
+
+
Returns:
+

SQL query string to get LCP displacement time series for patches.

+
+
Return type:
+

str

+
+
+
+ +
+
+static load_file(file_path: str, table_name: str) → str[source]
+

Load a file into a database table.

+

Method validates the provided file path and determines if existing +and determines the appropriate loading method based on the file type. +It generates an SQL query to create or replace a table in the database +with the contents of the file. The method supports CSV files and other +GIS vector files, using different loading methods for each type.

+
+
Parameters:
+
    +

  • file_path (str) – The path to the file to be loaded.

    • +

  • table_name (str) – The name of the table to create or replace in the database.

    • +
+
+
Raises:
+

AssertionError – If the file does not exist.

+
+
Returns:
+

SQL query string to create or replace the table with the file data.

+
+
Return type:
+

str

+
+
+
+ +
+
+static outlier_rel_ts_patch()[source]
+

Calculate outlier displacement time series relative to LCP for patches.

+

This method generates an SQL query to calculate the displacement time series +of outlier points relative to the displacement time series of their associated +local control points (LCPs). It assumes that the outliers table contains a +geometry column, a displacement time series column, and a patch ID column, +and that the patches table contains a geometry column, a unique identifier +for each patch, and a displacement time series column for the LCP.

+
+
Returns:
+

SQL query string to calculate outlier displacement time series relative to LCP for patches.

+
+
Return type:
+

str

+
+
+
+ +
+
+outlier_ts_patch(table_name: str = 'pspoint')[source]
+

Get outlier displacement time series for patches.

+

This method generates an SQL query to retrieve the displacement time series +of outlier points based on specified date fields.

+
+
Parameters:
+
    +

  • date_fields (list[str]) – List of column names representing displacement values at different dates.

    • +

  • table_name (str) – The name of the table containing the points, default is “pspoint”.

    • +
+
+
Returns:
+

SQL query string to get outlier displacement time series for patches.

+
+
Return type:
+

str

+
+
+
+ +
+
+static relate_point_patches(point_name: str, patch_name: str = 'patches') → str[source]
+

Relate points to patches by assigning patch IDs to points.

+

This method generates an SQL query to relate points in one table to patches +in another table by assigning patch IDs to points based on their spatial +relationship. It assumes that the point table contains a geometry column +and that the patch table contains a geometry column and a unique identifier +for each patch.

+
+
Parameters:
+
    +

  • point_name (str) – The name of the table containing the points.

    • +

  • patch_name (str) – The name of the table containing the patches, default is “patches”.

    • +
+
+
Returns:
+

SQL query string to relate points to patches.

+
+
Return type:
+

str

+
+
+
+ +
+
+static select_lcps(table_name: str = 'pspoint') → str[source]
+

Select linear control points (LCPs) for patches.

+

This method generates an SQL query to select local control points (LCPs) for each patch by identifying the point with the minimum absolute average velocity within each patch.

+
+
Parameters:
+

table_name (str) – The name of the table containing the points, default is “pspoint”.

+
+
Returns:
+

SQL query string to select LCPs for patches.

+
+
Return type:
+

str

+
+
+
+ +
+
+static spatial_tranformation(source_epsg: str, target_epsg: str, table_name: str) → str[source]
+

Transform geometries from one EPSG code to another.

+

This method generates an SQL query to transform geometries in a table +from one EPSG code to another. It assumes that the input table contains +geometries in the source EPSG code and that the target EPSG code is valid.

+
+
Parameters:
+
    +

  • source_epsg (str) – The source EPSG code of the geometries.

    • +

  • target_epsg (str) – The target EPSG code to transform the geometries to.

    • +

  • table_name (str) – The name of the table containing the geometries, default is “patches”.

    • +
+
+
Returns:
+

SQL query string to transform the geometries.

+
+
Return type:
+

str

+
+
+
+ +
+
+static split_to_segments(segment_length: float, table_name: str = 'dissolved') → str[source]
+

Split lines into segments of a specified length.

+

This method generates an SQL query to split lines in a table into segments +of a specified length. It transforms the geometries to a computational +coordinate reference system (CRS) and calculates the lengths in meters. +The segments are created by generating windows of fixed length along the lines. +The resulting segments are stored in a new table named “segments”. +The method assumes that the input table is already dissolved and contains +geometries in a suitable format.

+

The segments are created by cutting the lines into fixed-length segments +and filtering out any empty geometries. +This method is useful for preparing road segments for further analysis or processing. +It is particularly useful in road network analysis, where roads need to be +divided into manageable segments for tasks such as traffic analysis, road condition +assessment, or infrastructure planning.

+
+
Parameters:
+
    +

  • segment_length (float) – The length of each segment in meters.

    • +

  • table_name (str) – The name of the table containing the lines, default is “dissolved”.

    • +
+
+
Returns:
+

SQL query string to create or replace the segments table.

+
+
Return type:
+

str

+
+
+
+ +
+ +
+
+class Visualisation[source]
+

Bases: object

+

SQL queries for visualisation of results.

+

2D visualisation is done in Web Mercator (EPSG:3857).

+
+
+static get_avg_cum_disp_graph(patch_id: float, scaling_factor: float) → str[source]
+

Get average cumulative displacement time series.

+

This method generates an SQL query to retrieve the average cumulative displacement +time series for a specific patch, scaling the displacement values by a specified factor.

+
+
Parameters:
+
    +

  • patch_id (float) – The unique identifier of the patch.

    • +

  • scaling_factor (float) – The factor to scale the displacement values.

    • +
+
+
Returns:
+

SQL query string to get the average cumulative displacement time series for the specified patch.

+
+
+
+ +
+
+static get_avg_disp_lcp_graph(patch_id: float, scaling_factor: float) → str[source]
+

Get average displacement time series based on local control points (LCPs).

+

This method generates an SQL query to retrieve the average displacement time series +based on local control points (LCPs) for a specific patch, scaling the displacement +values by a specified factor.

+
+
Parameters:
+
    +

  • patch_id (float) – The unique identifier of the patch.

    • +

  • scaling_factor (float) – The factor to scale the displacement values.

    • +
+
+
Returns:
+

SQL query string to get the average displacement time series based on LCPs for the specified patch.

+
+
+
+ +
+
+static get_center(point_uid: float, source_crs: str) → str[source]
+

Get the center point of the outlier within a specific patch.

+

This method generates an SQL query to retrieve the center point of the outlier +within a specific patch, transforming the geometry from a source CRS to a target +projection defined in the Visualisation class.

+
+
Parameters:
+
    +

  • point_uid (float) – The unique identifier of the outlier point.

    • +

  • source_crs (str) – The source coordinate reference system of the geometries.

    • +
+
+
Returns:
+

SQL query string to get the center point of the outlier within the specified patch.

+
+
+
+ +
+
+static get_cum_disp_graph(outlier_id: float, scaling_factor: float) → str[source]
+

Get outlier displacement time series.

+

This method generates an SQL query to retrieve the outlier displacement time series +for a specific patch, scaling the displacement values by a specified factor.

+
+
Parameters:
+
    +

  • outlier_id (float) – The unique identifier of the patch.

    • +

  • scaling_factor (float) – The factor to scale the displacement values.

    • +
+
+
Returns:
+

SQL query string to get the outlier displacement time series for the specified patch.

+
+
+
+ +
+
+static get_cum_disp_map(source_crs: str, scaling_factor: float) → str[source]
+

Get cumulative displacement per patch.

+

This method generates an SQL query to retrieve the cumulative displacement +for each patch, transforming the geometries from a source CRS to a target +projection defined in the Visualisation class.

+
+
Parameters:
+
    +

  • source_crs (str) – The source coordinate reference system of the geometries.

    • +

  • unit (str) – The measurement unit for displacement (“m”, “cm”, or “mm”).

    • +
+
+
Returns:
+

SQL query string to get the cumulative displacement per patch.

+
+
Return type:
+

str

+
+
+
+ +
+
+static get_lcp_disp_graph(patch_id: float, scaling_factor: float) → str[source]
+

Get local control point (LCP) displacement time series.

+

This method generates an SQL query to retrieve the local control point (LCP) +displacement time series for a specific patch, scaling the displacement values by a specified factor.

+
+
Parameters:
+
    +

  • patch_id (float) – The unique identifier of the patch.

    • +

  • scaling_factor (float) – The factor to scale the displacement values.

    • +
+
+
Returns:
+

SQL query string to get the LCP displacement time series for the specified patch.

+
+
+
+ +
+
+static get_lcp_point(patch_id: float, source_crs: str) → str[source]
+

Get the local control point (LCP) within a specific patch.

+

This method generates an SQL query to retrieve the local control point (LCP) +within a specific patch, transforming the geometry from a source CRS to a target +projection defined in the Visualisation class.

+
+
Parameters:
+
    +

  • patch_id (float) – The unique identifier of the patch.

    • +

  • source_crs (str) – The source coordinate reference system of the geometries.

    • +
+
+
Returns:
+

SQL query string to get the LCP within the specified patch.

+
+
+
+ +
+
+static get_outlier_points(patch_id: float, source_crs: str) → str[source]
+

Get the outlier points within a specific patch.

+

This method generates an SQL query to retrieve the outlier point within a specific patch, +transforming the geometry from a source CRS to a target projection defined in the Visualisation class.

+
+
Parameters:
+
    +

  • patch_id (float) – The unique identifier of the patch.

    • +

  • source_crs (str) – The source coordinate reference system of the geometries.

    • +
+
+
Returns:
+

SQL query string to get the outlier point within the specified patch.

+
+
+
+ +
+
+get_outlier_rel_ts_graph(scaling_factor: float) → str[source]
+

Get outlier relative displacement time series.

+

This method generates an SQL query to retrieve the outlier relative displacement +time series for a specific patch, scaling the displacement values by a specified factor.

+
+
Parameters:
+
    +

  • outlier_id (float) – The unique identifier of the patch.

    • +

  • scaling_factor (float) – The factor to scale the displacement values.

    • +
+
+
Returns:
+

SQL query string to get the outlier relative displacement time series for the specified patch.

+
+
+
+ +
+
+static get_outliers_map(source_crs: str, scaling_factor: float) → str[source]
+

Get outlier points based on absolute average LCP velocity.

+

This method generates an SQL query to retrieve outlier points based on their +average velocity relative to local control points (LCPs). Points with an absolute +average LCP velocity exceeding a specified threshold (5 mm/year) are +marked as outliers by assigning their unique identifier to a new column in the +patches table. The unit parameter allows for specifying the measurement unit +(meters, centimeters, or millimeters) to appropriately scale the threshold.

+
+
Parameters:
+
    +

  • source_crs (str) – The source coordinate reference system of the geometries.

    • +

  • scaling_factor (float) – The factor to scale the velocity threshold based on the unit.

    • +
+
+
Returns:
+

SQL query string to identify outlier points.

+
+
Return type:
+

str

+
+
+
+ +
+
+static get_patch_center(patch_id: float, source_crs: str) → str[source]
+

Get the center point of a specific patch.

+

This method generates an SQL query to retrieve the center point of a specific +patch, transforming the geometry from a source CRS to a target projection defined in the Visualisation class.

+
+
Parameters:
+
    +

  • patch_id (float) – The unique identifier of the patch.

    • +

  • source_crs (str) – The source coordinate reference system of the geometries.

    • +
+
+
Returns:
+

SQL query string to get the center point of the specified patch.

+
+
+
+ +
+
+static get_ps_density(source_crs: str) → str[source]
+

Get point density per patch.

+

This method generates an SQL query to retrieve the point density for each patch, +transforming the geometries from a source CRS to a target projection defined +in the Visualisation class.

+
+
Parameters:
+

source_crs (str) – The source coordinate reference system of the geometries.

+
+
Returns:
+

SQL query string to get the point density per patch.

+
+
Return type:
+

str

+
+
+
+ +
+
+static get_ps_points(patch_id: float, source_crs: str, table_name: str) → str[source]
+

Get points within a specific patch.

+

This method generates an SQL query to retrieve points within a specific patch, +transforming the geometries from a source CRS to a target projection defined +in the Visualisation class.

+
+
Parameters:
+
    +

  • patch_id (float) – The unique identifier of the patch.

    • +

  • source_crs (str) – The source coordinate reference system of the geometries.

    • +

  • table_name (str) – The name of the table containing the points.

    • +
+
+
Returns:
+

SQL query string to get the points within the specified patch.

+
+
Return type:
+

str

+
+
+
+ +
+
+static get_ps_vel_gcp(source_crs: str, scaling_factor: float) → str[source]
+

Get average velocity based on ground control points (GCPs). +This method generates an SQL query to retrieve the average velocity for +each point based on ground control points (GCPs), transforming the geometries +from a source CRS to a target projection defined in the Visualisation class.

+
+
Parameters:
+
    +

  • source_crs (str) – The source coordinate reference system of the geometries.

    • +

  • unit (str) – The measurement unit for velocity (“m”, “cm”, or “mm”).

    • +
+
+
Returns:
+

SQL query string to get the average velocity based on GCPs.

+
+
Return type:
+

str

+
+
+
+ +
+
+static get_ps_vel_lcp(source_crs: str, scaling_factor: float) → str[source]
+

Get average velocity based on local control points (LCPs).

+

This method generates an SQL query to retrieve the average velocity for +each point based on local control points (LCPs), transforming the geometries +from a source CRS to a target projection defined in the Visualisation class.

+
+
Parameters:
+
    +

  • source_crs (str) – The source coordinate reference system of the geometries.

    • +

  • unit (str) – The measurement unit for velocity (“m”, “cm”, or “mm”).

    • +
+
+
Returns:
+

SQL query string to get the average velocity based on LCPs.

+
+
Return type:
+

str

+
+
+
+ +
+
+static get_std_cum_disp_graph(patch_id: float, scaling_factor: float) → str[source]
+

Get standard deviation of cumulative displacement time series.

+

This method generates an SQL query to retrieve the standard deviation of cumulative +displacement time series for a specific patch, scaling the displacement values by a specified factor.

+
+
Parameters:
+
    +

  • patch_id (float) – The unique identifier of the patch.

    • +

  • scaling_factor (float) – The factor to scale the displacement values.

    • +
+
+
Returns:
+

SQL query string to get the standard deviation of cumulative displacement time series for the specified patch.

+
+
+
+ +
+
+static get_std_disp_lcp_graph(patch_id: float, scaling_factor: float) → str[source]
+

Get standard deviation of displacement time series based on local control points (LCPs).

+

This method generates an SQL query to retrieve the standard deviation of displacement +time series based on local control points (LCPs) for a specific patch, scaling the +displacement values by a specified factor.

+
+
Parameters:
+
    +

  • patch_id (float) – The unique identifier of the patch.

    • +

  • scaling_factor (float) – The factor to scale the displacement values.

    • +
+
+
Returns:
+

SQL query string to get the standard deviation of displacement time series based on LCPs for the specified patch.

+
+
+
+ +
+
+static get_table_bbox(table_name: str, source_crs: str) → str[source]
+

Get the bounding box of a table in a specified projection.

+

This method generates an SQL query to retrieve the bounding box of geometries +from a specified table, transforming the geometries from a source CRS to +a target projection defined in the Visualisation class.

+
+
Parameters:
+
    +

  • table_name (str) – The name of the table to get the bounding box from.

    • +

  • source_crs (str) – The source coordinate reference system of the geometries.

    • +
+
+
Returns:
+

SQL query string to get the bounding box of the table.

+
+
Return type:
+

str

+
+
+
+ +
+
+static patch_uids() → str[source]
+

Get all patch UIDs that have outliers.

+

This method generates an SQL query to retrieve all unique identifiers (UIDs) +of patches that have been identified as containing outliers.

+
+
Returns:
+

SQL query string to get all patch UIDs with outliers.

+
+
Return type:
+

str

+
+
+
+ +
+
+projection = 'EPSG:3857'
+
+ +
+ +
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/build/html/api/saferoad.plotter.html b/docs/build/html/api/saferoad.plotter.html new file mode 100644 index 0000000..4d798a6 --- /dev/null +++ b/docs/build/html/api/saferoad.plotter.html @@ -0,0 +1,584 @@ + + + + + + + + + saferoad.plotter module — saferoad 0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

saferoad.plotter module

+
+
+class saferoad.plotter.Plotter[source]
+

Bases: object

+

Class to create and manage plots for geospatial data analysis.

+
+
+class MapPane[source]
+

Bases: object

+

Class to create and manage a multi-panel map figure.

+

Panels include: +- PS Density +- Outliers +- Cumulative Displacement +- PS Velocity based on GCP +- PS Velocity based on LCP

+
+
+cum_disp_color_label(value: float) → str[source]
+

Returns color and label based on cumulative displacement value.

+
+
Parameters:
+

value (float) – Cumulative displacement value.

+
+
Returns:
+

Tuple containing color and label.

+
+
Return type:
+

tuple[str, str]

+
+
+
+ +
+
+gcp_vel_color(value: float) → str[source]
+

Returns color based on GCP velocity value.

+
+
Parameters:
+

value (float) – GCP velocity value.

+
+
Returns:
+

Color string.

+
+
Return type:
+

str

+
+
+
+ +
+
+generate_figure()[source]
+

Generates a multi-panel figure with specified layout and titles.

+
+ +
+
+get_figure() → tuple[Figure, list[Axes]][source]
+

Returns the figure and axes.

+
+
Returns:
+

Tuple containing the figure and axes.

+
+
Return type:
+

tuple[plt.Figure, list[plt.Axes]]

+
+
+
+ +
+
+lcp_vel_color(value: float) → str[source]
+

Returns color based on LCP velocity value.

+
+
Parameters:
+

value (float) – LCP velocity value.

+
+
Returns:
+

Color string.

+
+
Return type:
+

str

+
+
+
+ +
+
+outlier_color_label(value: float) → tuple[str, str][source]
+

Returns color and label based on outlier velocity value.

+
+
Parameters:
+

value (float) – Outlier velocity value.

+
+
Returns:
+

Tuple containing color and label.

+
+
Return type:
+

tuple[str, str]

+
+
+
+ +
+
+plot_basemap(bbox: tuple[float, float, float, float])[source]
+

Plots a basemap on all axes using the provided bounding box.

+
+
Parameters:
+

bbox – A tuple representing the bounding box (minx, miny, maxx, maxy).

+
+
+
+ +
+
+plot_cum_disp(plot_data: list[tuple]) → None[source]
+

Plots cumulative displacement data on the third axis.

+
+
Parameters:
+

plot_data – List of tuples containing geometry and cumulative displacement value.

+
+
+
+ +
+
+plot_gcp_velocity(plot_data: list[tuple]) → None[source]
+

Plots GCP velocity data on the fourth axis.

+
+
Parameters:
+

plot_data – List of tuples containing geometry and GCP velocity value.

+
+
+
+ +
+
+plot_lcp_velocity(plot_data: list[tuple]) → None[source]
+

Plots LCP velocity data on the fifth axis.

+
+
Parameters:
+

plot_data – List of tuples containing geometry and LCP velocity value.

+
+
+
+ +
+
+plot_outliers(plot_data: list[tuple]) → None[source]
+

Plots outlier data on the second axis.

+
+
Parameters:
+

plot_data – List of tuples containing geometry and outlier velocity value.

+
+
+
+ +
+
+plot_ps_density(plot_data: list[tuple]) → None[source]
+

Plots PS Density data on the first axis.

+
+
Parameters:
+

plot_data – List of tuples containing geometry and PS density value.

+
+
+
+ +
+
+post_process()[source]
+

Post-processes the figure for better aesthetics.

+
+ +
+
+ps_density_color_label(value: float) → tuple[str, str][source]
+

Returns color and label based on PS density value.

+
+
Parameters:
+

value (float) – PS density value.

+
+
Returns:
+

Tuple containing color and label.

+
+
Return type:
+

tuple[str, str]

+
+
+
+ +
+ +
+
+class TimeSeriesPane[source]
+

Bases: object

+

Class to create and manage a multi-panel time series figure. +Panels include: +- Basemap with Outliers +- Basemap with PS Points +- Cumulative Displacement +- Relative Displacement +- Local Control Point Displacement

+
+
+generate_figure()[source]
+

Generates a multi-panel figure with specified layout and titles.

+
+ +
+
+get_figure() → tuple[Figure, list[Axes]][source]
+

Returns the figure and axes.

+
+
Returns:
+

Tuple containing the figure and axes.

+
+
Return type:
+

tuple[plt.Figure, list[plt.Axes]]

+
+
+
+ +
+
+outlier_color_label(value: float) → tuple[str, str][source]
+

Returns color and label based on outlier velocity value.

+
+
Parameters:
+

value (float) – Outlier velocity value.

+
+
Returns:
+

Tuple containing color and label.

+
+
Return type:
+

tuple[str, str]

+
+
+
+ +
+
+plot_all_outliers(plot_data: list[tuple]) → None[source]
+

Plots all outlier data on the first axis.

+
+
Parameters:
+

plot_data (list[tuple]) – List of tuples containing geometry and outlier velocity value.

+
+
+
+ +
+
+plot_avg_disp_graph(plot_data: list[tuple], dates: list[datetime]) → None[source]
+

Plots average cumulative displacement data on the third axis.

+
+
Parameters:
+
    +

  • plot_data (list[tuple]) – List of tuples containing average cumulative displacement values.

    • +

  • dates (list[datetime]) – List of datetime objects corresponding to the displacement values.

    • +
+
+
+
+ +
+
+plot_avg_lcp_disp_graph(plot_data: list[tuple], dates: list[datetime]) → None[source]
+

Plots average LCP displacement data on the fifth axis.

+
+
Parameters:
+
    +

  • plot_data (list[tuple]) – List of tuples containing average LCP displacement values.

    • +

  • dates (list[datetime]) – List of datetime objects corresponding to the displacement values.

    • +
+
+
+
+ +
+
+plot_avg_rel_disp_graph(plot_data: list[tuple], dates: list[datetime]) → None[source]
+

Plots average relative displacement data on the fourth axis.

+
+
Parameters:
+
    +

  • plot_data (list[tuple]) – List of tuples containing average relative displacement values.

    • +

  • dates (list[datetime]) – List of datetime objects corresponding to the displacement values.

    • +
+
+
+
+ +
+
+plot_basemap_outlier(center: tuple[float, float])[source]
+

Plots a basemap on the first axis using the provided center point.

+
+
Parameters:
+

center (tuple[float, float]) – A tuple representing the center point (x, y).

+
+
+
+ +
+
+plot_basemap_pspoint(center: tuple[float, float])[source]
+

Plots a basemap on the second axis using the provided center point.

+
+
Parameters:
+

center (tuple[float, float]) – A tuple representing the center point (x, y).

+
+
+
+ +
+
+plot_cum_disp_graph(plot_data: list[tuple], dates: list[datetime]) → None[source]
+

Plots cumulative displacement data on the third axis.

+
+
Parameters:
+
    +

  • plot_data (list[tuple]) – List of tuples containing cumulative displacement values.

    • +

  • dates (list[datetime]) – List of datetime objects corresponding to the displacement values.

    • +
+
+
+
+ +
+
+plot_lcp(plot_data: list[tuple]) → None[source]
+

Plots LCP data on the second axis.

+
+
Parameters:
+

plot_data (list[tuple]) – List of tuples containing geometry.

+
+
+
+ +
+
+plot_lcp_disp_graph(plot_data: list[tuple], dates: list[datetime]) → None[source]
+

Plots LCP displacement data on the fifth axis.

+
+
Parameters:
+
    +

  • plot_data (list[tuple]) – List of tuples containing LCP displacement values.

    • +

  • dates (list[datetime]) – List of datetime objects corresponding to the displacement values.

    • +
+
+
+
+ +
+
+plot_outlier(plot_data: list[tuple]) → None[source]
+

Plots outlier data on the second axis.

+
+
Parameters:
+

plot_data (list[tuple]) – List of tuples containing geometry and outlier velocity value.

+
+
+
+ +
+
+plot_pspoint_dist(plot_data: list[tuple]) → None[source]
+

Plots PS Points on the second axis.

+
+
Parameters:
+

plot_data (list[tuple]) – List of tuples containing geometry.

+
+
+
+ +
+
+plot_rel_disp_graph(plot_data: list[tuple], dates: list[datetime]) → None[source]
+

Plots relative displacement data on the fourth axis.

+
+
Parameters:
+
    +

  • plot_data (list[tuple]) – List of tuples containing relative displacement values.

    • +

  • dates (list[datetime]) – List of datetime objects corresponding to the displacement values.

    • +
+
+
+
+ +
+
+plot_square(plot_data: list[tuple]) → None[source]
+

Plots a square on the first axis to indicate zoom area.

+
+
Parameters:
+

plot_data (list[tuple]) – List of tuples containing geometry and outlier velocity value.

+
+
+
+ +
+
+plot_std_disp_graph(avg_data: list[tuple], std_data: list[tuple], dates: list[datetime])[source]
+

Plots standard deviation of cumulative displacement data on the third axis.

+
+
Parameters:
+
    +

  • avg_data (list[tuple]) – List of tuples containing average cumulative displacement values.

    • +

  • std_data (list[tuple]) – List of tuples containing standard deviation of cumulative displacement values.

    • +

  • dates (list[datetime]) – List of datetime objects corresponding to the displacement values.

    • +
+
+
+
+ +
+
+plot_std_lcp_disp_graph(avg_data: list[tuple], std_data: list[tuple], dates: list[datetime])[source]
+

Plots standard deviation of LCP displacement data on the fifth axis.

+
+
Parameters:
+
    +

  • avg_data (list[tuple]) – List of tuples containing average LCP displacement values.

    • +

  • std_data (list[tuple]) – List of tuples containing standard deviation of LCP displacement values.

    • +

  • dates (list[datetime]) – List of datetime objects corresponding to the displacement values.

    • +
+
+
+
+ +
+
+plot_std_rel_disp_graph(avg_data: list[tuple], std_data: list[tuple], dates: list[datetime])[source]
+

Plots standard deviation of relative displacement data on the fourth axis.

+
+
Parameters:
+
    +

  • avg_data (list[tuple]) – List of tuples containing average relative displacement values.

    • +

  • std_data (list[tuple]) – List of tuples containing standard deviation of relative displacement values.

    • +

  • dates (list[datetime]) – List of datetime objects corresponding to the displacement values.

    • +
+
+
+
+ +
+
+post_process()[source]
+

Post-processes the figure for better aesthetics.

+
+ +
+ +
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/build/html/api/saferoad.saferoad.html b/docs/build/html/api/saferoad.saferoad.html new file mode 100644 index 0000000..b375416 --- /dev/null +++ b/docs/build/html/api/saferoad.saferoad.html @@ -0,0 +1,282 @@ + + + + + + + + + saferoad.saferoad module — saferoad 0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

saferoad.saferoad module

+
+
+class saferoad.saferoad.PsData(filepath: str, latitude: str, longitude: str, unit: Literal['m', 'cm', 'mm'] = 'm', crs_code: str = 'EPSG:4326', name: str = 'pspoint')[source]
+

Bases: object

+

Class for storing point source data information.

+
+
Parameters:
+
    +

  • filepath (str) – Path to the point source data file.

    • +

  • latitude (str) – Field name of the latitude field in the data file.

    • +

  • longitude (str) – Field name of the longitude field in the data file.

    • +

  • unit (Literal["m", "cm", "mm"]) – Unit of measurement for the coordinates. Options are “m”, “cm”, or “mm”. Default is “m”.

    • +

  • crs_code (str) – Coordinate Reference System code. Default is “EPSG:4326”.

    • +

  • name (str) – Name identifier for the point source data. Default is “pspoint”.

    • +
+
+
+
+
+crs_code: str = 'EPSG:4326'
+
+ +
+
+filepath: str
+
+ +
+
+latitude: str
+
+ +
+
+longitude: str
+
+ +
+
+name: str = 'pspoint'
+
+ +
+
+unit: Literal['m', 'cm', 'mm'] = 'm'
+
+ +
+ +
+
+class saferoad.saferoad.Road(filepath: str, crs_code: str = 'EPSG:4326', name: str = 'road')[source]
+

Bases: object

+

Class for storing road data information.

+
+
Parameters:
+
    +

  • filepath (str) – Path to the road data file.

    • +

  • crs_code (str) – Coordinate Reference System code. Default is “EPSG:4326”.

    • +

  • name (str) – Name identifier for the road data. Default is “road”.

    • +
+
+
+
+
+crs_code: str = 'EPSG:4326'
+
+ +
+
+filepath: str
+
+ +
+
+name: str = 'road'
+
+ +
+ +
+
+class saferoad.saferoad.SafeRoad(road: Road, ps_data: PsData, computational_crs: str)[source]
+

Bases: object

+

A class to perform road deformation analysis using MT-InSAR data.

+
+
Parameters:
+
    +

  • road (Road) – An instance of the Road class containing road data information.

    • +

  • ps_data (PsData) – An instance of the PsData class containing MT-InSAR data information.

    • +

  • computational_crs (str) – A string representing the EPSG code for the computational CRS.

    • +
+
+
+
+
+generate_rectangles(road_width: float, segment_length: float)[source]
+

Generate road segments and patches.

+

This involves: +1. Splitting the road into segments of equal length. +2. Generating rectangles along the road segments with a specified width. +2. Rotating the generated rectangles int the same direction along the road.

+
+
Parameters:
+
    +

  • road_width – A float representing the width of the road segments in meters.

    • +

  • segment_length – A float representing the length of each road segment in meters.

    • +
+
+
+
+ +
+
+generate_report(output_name: str)[source]
+

Generate a PDF report containing maps and time series plots.

+
+
Parameters:
+

output_name (str) – A string representing the name of the output PDF file endswith .pdf extension.

+
+
+
+ +
+
+load_files()[source]
+

Load the road and MT-InSAR data files into the database.

+
+ +
+
+preprocess(attribute: str = None)[source]
+

Preprocess the road and MT-InSAR data.

+

This includes transforming the data to the computational CRS, dissolving road features, +and building geometries for the MT-InSAR points. +1. Transform the road and MT-InSAR data to the computational CRS. +2. Dissolve road features based on the provided attribute if given, otherwise merge all features. +3. Build geometries for the MT-InSAR points from the latitude and longitude columns. +4. Transform the MT-InSAR data to the computational CRS.

+
+
Parameters:
+

attribute (str, optional) – An optional string representing the attribute to dissolve road features. +If None, all features will be merged. Default is None.

+
+
+
+ +
+
+run_analysis()[source]
+

Run the deformation analysis on the MT-InSAR data for road network.

+

This includes calculating cumulative displacement, average velocity, point density, +local control points, and outliers.

+
+ +
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/build/html/api/saferoad.utils.html b/docs/build/html/api/saferoad.utils.html new file mode 100644 index 0000000..ed98e63 --- /dev/null +++ b/docs/build/html/api/saferoad.utils.html @@ -0,0 +1,164 @@ + + + + + + + + + saferoad.utils module — saferoad 0.1 documentation + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

saferoad.utils module

+
+
+class saferoad.utils.Timer(name=None)[source]
+

Bases: object

+

Context manager for timing code execution.

+
+ +
+
+saferoad.utils.extract_dates(column_names: list[str]) → tuple[list[str], list[datetime]][source]
+

Extracts date fields from a list of column names.

+
+
Parameters:
+

column_names (list[str]) – List of column names to search for dates.

+
+
Returns:
+

A tuple containing two numpy arrays: one with the column names and another with the parsed date objects.

+
+
Return type:
+

tuple[ndarray, ndarray]

+
+
+
+ +
+
+saferoad.utils.time_vector(dates: list[datetime]) → array[source]
+

Generates a time vector in days from a list of datetime objects.

+
+
Parameters:
+

dates (list[datetime]) – List of datetime objects.

+
+
Returns:
+

List of time differences in days from the first date.

+
+
Return type:
+

list[float]

+
+
+
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/build/html/genindex.html b/docs/build/html/genindex.html new file mode 100644 index 0000000..0cf08e3 --- /dev/null +++ b/docs/build/html/genindex.html @@ -0,0 +1,802 @@ + + + + + + + + Index — saferoad 0.1 documentation + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Index
    • +
  • +
    • +
+
+
+
+
+ + +

Index

+ +
+ B + | C + | D + | E + | F + | G + | I + | L + | M + | N + | O + | P + | R + | S + | T + | U + +
+

B

+ + +
+ +

C

+ + + +
+ +

D

+ + + +
+ +

E

+ + +
+ +

F

+ + +
+ +

G

+ + + +
+ +

I

+ + +
+ +

L

+ + + +
+ +

M

+ + +
+ +

N

+ + +
+ +

O

+ + + +
+ +

P

+ + + +
+ +

R

+ + + +
+ +

S

+ + + +
+ +

T

+ + + +
+ +

U

+ + +
+ + + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/build/html/index.html b/docs/build/html/index.html new file mode 100644 index 0000000..5e41aff --- /dev/null +++ b/docs/build/html/index.html @@ -0,0 +1,132 @@ + + + + + + + + + Welcome to SafeRoad’s Documentation — saferoad 0.1 documentation + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Welcome to SafeRoad’s Documentation

+

SafeRoad is an open-source Python library designed to monitor the structural condition of the road network by providing tools for data analysis and visualisation. +It aims to assist researchers, policymakers, and developers in understanding and mitigating structural safety issues of road networks. +SafeRoad is built with a focus on usability, performance, and extensibility. It uses modern Python libraries such as duckdb, +shapely, matplotlib and contextily to provide a robust framework for road network safety analysis.

+

You can access the relavant paper from here.

+
+

Summary

+

SafeRoad offers a comprehensive suite of features, including:

+
    +

  • Data preprocessing tools for cleaning and organizing road and Multi-Temporal dataset.

    • +

  • Visualisation utilities to generate insightful charts and graphs.

    • +

  • Statistical analysis methods to identify trends and patterns.

    • +

  • Extensible APIs for integrating with other applications.

    • +
+

The library is modular, user-friendly, and well-documented, making it suitable for both beginners and experts in the field of structural safety.

+
+

Contents:

+ +
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/build/html/objects.inv b/docs/build/html/objects.inv new file mode 100644 index 0000000..faf4841 Binary files /dev/null and b/docs/build/html/objects.inv differ diff --git a/docs/build/html/py-modindex.html b/docs/build/html/py-modindex.html new file mode 100644 index 0000000..667b493 --- /dev/null +++ b/docs/build/html/py-modindex.html @@ -0,0 +1,145 @@ + + + + + + + + Python Module Index — saferoad 0.1 documentation + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Python Module Index
    • +
  • +
    • +
+
+
+
+
+ + +

Python Module Index

+ +
+ s +
+ + + + + + + + + + + + + + + + + + + + + + +
 
+ s
+ saferoad +
    + saferoad.database +
    + saferoad.pipeline +
    + saferoad.plotter +
    + saferoad.saferoad +
    + saferoad.utils +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/build/html/search.html b/docs/build/html/search.html new file mode 100644 index 0000000..563484d --- /dev/null +++ b/docs/build/html/search.html @@ -0,0 +1,120 @@ + + + + + + + + Search — saferoad 0.1 documentation + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Search
    • +
  • +
    • +
+
+
+
+
+ + + + +
+ +
+ +
+
+ +
+
+
+
+ + + + + + + + + \ No newline at end of file diff --git a/docs/build/html/searchindex.js b/docs/build/html/searchindex.js new file mode 100644 index 0000000..bbaeb56 --- /dev/null +++ b/docs/build/html/searchindex.js @@ -0,0 +1 @@ +Search.setIndex({"alltitles":{"Contents:":[[7,null]],"Module contents":[[1,"module-saferoad"]],"Submodules":[[1,"submodules"]],"Summary":[[7,"summary"]],"Welcome to SafeRoad\u2019s Documentation":[[7,null]],"saferoad":[[0,null]],"saferoad package":[[1,null]],"saferoad.database module":[[2,null]],"saferoad.pipeline module":[[3,null]],"saferoad.plotter module":[[4,null]],"saferoad.saferoad module":[[5,null]],"saferoad.utils 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package","saferoad.database module","saferoad.pipeline module","saferoad.plotter module","saferoad.saferoad module","saferoad.utils module","Welcome to SafeRoad\u2019s Documentation"],"titleterms":{"":7,"content":[1,7],"databas":2,"document":7,"modul":[1,2,3,4,5,6],"packag":1,"pipelin":3,"plotter":4,"saferoad":[0,1,2,3,4,5,6,7],"submodul":1,"summari":7,"util":6,"welcom":7}}) \ No newline at end of file diff --git a/docs/make.bat b/docs/make.bat new file mode 100644 index 0000000..747ffb7 --- /dev/null +++ b/docs/make.bat @@ -0,0 +1,35 @@ +@ECHO OFF + +pushd %~dp0 + +REM Command file for Sphinx documentation + +if "%SPHINXBUILD%" == "" ( + set SPHINXBUILD=sphinx-build +) +set SOURCEDIR=source +set BUILDDIR=build + +%SPHINXBUILD% >NUL 2>NUL +if errorlevel 9009 ( + echo. + echo.The 'sphinx-build' command was not found. Make sure you have Sphinx + echo.installed, then set the SPHINXBUILD environment variable to point + echo.to the full path of the 'sphinx-build' executable. Alternatively you + echo.may add the Sphinx directory to PATH. + echo. + echo.If you don't have Sphinx installed, grab it from + echo.https://www.sphinx-doc.org/ + exit /b 1 +) + +if "%1" == "" goto help + +%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% +goto end + +:help +%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% + +:end +popd diff --git a/docs/requirements.txt b/docs/requirements.txt new file mode 100644 index 0000000..1b3a6e8 --- /dev/null +++ b/docs/requirements.txt @@ -0,0 +1 @@ +sphinx \ No newline at end of file diff --git a/docs/source/api/modules.rst b/docs/source/api/modules.rst new file mode 100644 index 0000000..75f2a20 --- /dev/null +++ b/docs/source/api/modules.rst @@ -0,0 +1,7 @@ +saferoad +======== + +.. toctree:: + :maxdepth: 4 + + saferoad diff --git a/docs/source/api/saferoad.database.rst b/docs/source/api/saferoad.database.rst new file mode 100644 index 0000000..0e0c25b --- /dev/null +++ b/docs/source/api/saferoad.database.rst @@ -0,0 +1,7 @@ +saferoad.database module +======================== + +.. automodule:: saferoad.database + :members: + :show-inheritance: + :undoc-members: diff --git a/docs/source/api/saferoad.pipeline.rst b/docs/source/api/saferoad.pipeline.rst new file mode 100644 index 0000000..8973f90 --- /dev/null +++ b/docs/source/api/saferoad.pipeline.rst @@ -0,0 +1,7 @@ +saferoad.pipeline module +======================== + +.. automodule:: saferoad.pipeline + :members: + :show-inheritance: + :undoc-members: diff --git a/docs/source/api/saferoad.plotter.rst b/docs/source/api/saferoad.plotter.rst new file mode 100644 index 0000000..147c4b7 --- /dev/null +++ b/docs/source/api/saferoad.plotter.rst @@ -0,0 +1,7 @@ +saferoad.plotter module +======================= + +.. automodule:: saferoad.plotter + :members: + :show-inheritance: + :undoc-members: diff --git a/docs/source/api/saferoad.rst b/docs/source/api/saferoad.rst new file mode 100644 index 0000000..85870b1 --- /dev/null +++ b/docs/source/api/saferoad.rst @@ -0,0 +1,22 @@ +saferoad package +================ + +Submodules +---------- + +.. toctree:: + :maxdepth: 4 + + saferoad.database + saferoad.pipeline + saferoad.plotter + saferoad.saferoad + saferoad.utils + +Module contents +--------------- + +.. automodule:: saferoad + :members: + :show-inheritance: + :undoc-members: diff --git a/docs/source/api/saferoad.saferoad.rst b/docs/source/api/saferoad.saferoad.rst new file mode 100644 index 0000000..e29e335 --- /dev/null +++ b/docs/source/api/saferoad.saferoad.rst @@ -0,0 +1,7 @@ +saferoad.saferoad module +======================== + +.. automodule:: saferoad.saferoad + :members: + :show-inheritance: + :undoc-members: diff --git a/docs/source/api/saferoad.utils.rst b/docs/source/api/saferoad.utils.rst new file mode 100644 index 0000000..8c742b5 --- /dev/null +++ b/docs/source/api/saferoad.utils.rst @@ -0,0 +1,7 @@ +saferoad.utils module +===================== + +.. automodule:: saferoad.utils + :members: + :show-inheritance: + :undoc-members: diff --git a/docs/source/conf.py b/docs/source/conf.py new file mode 100644 index 0000000..7de886e --- /dev/null +++ b/docs/source/conf.py @@ -0,0 +1,32 @@ +import os +import sys + +# Add your src/ directory so autodoc can import your package +sys.path.insert(0, os.path.abspath(os.path.join(__file__, "..", "..", "..", "src"))) + + +project = "saferoad" +copyright = "2025, SafeStruct" +author = "Mura Sahin" + +version = "0.1" +release = "0.1" + + +extensions = [ + "sphinx.ext.autodoc", + "sphinx.ext.autosummary", + "sphinx.ext.viewcode", +] + +# Autodoc / autosummary defaults +autosummary_generate = True +autodoc_default_options = { + "members": True, + "undoc-members": True, + "show-inheritance": True, +} + + +html_theme = "sphinx_rtd_theme" +html_static_path = ["_static"] diff --git a/docs/source/index.rst b/docs/source/index.rst new file mode 100644 index 0000000..2f021a4 --- /dev/null +++ b/docs/source/index.rst @@ -0,0 +1,39 @@ +.. project documentation master file, created by + sphinx-quickstart on Mon Sep 1 09:55:46 2025. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +Welcome to SafeRoad's Documentation +====================================== + +**SafeRoad** is an open-source Python library designed to monitor the structural condition of the road network by providing tools for data analysis and visualisation. +It aims to assist researchers, policymakers, and developers in understanding and mitigating structural safety issues of road networks. +**SafeRoad** is built with a focus on usability, performance, and extensibility. It uses modern Python libraries such as `duckdb`_, +`shapely`_, `matplotlib`_ and `contextily`_ to provide a robust framework for road network safety analysis. + +You can access the relavant paper from `here`_. + +.. _here: https://doi.org/10.1177/14759217211045912 +.. _duckdb: https://duckdb.org/ +.. _shapely: https://shapely.readthedocs.io/en/stable/ +.. _matplotlib: https://matplotlib.org/ +.. _contextily: https://contextily.readthedocs.io/en/latest/ + +Summary +------- +SafeRoad offers a comprehensive suite of features, including: + +- Data preprocessing tools for cleaning and organizing road and Multi-Temporal dataset. +- Visualisation utilities to generate insightful charts and graphs. +- Statistical analysis methods to identify trends and patterns. +- Extensible APIs for integrating with other applications. + +The library is modular, user-friendly, and well-documented, making it suitable for both beginners and experts in the field of structural safety. + + + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + + api/modules \ No newline at end of file diff --git a/examples/fixtures/A10_ams.geojson b/examples/fixtures/A10_ams.geojson new file mode 100644 index 0000000..cc835af --- /dev/null +++ b/examples/fixtures/A10_ams.geojson @@ -0,0 +1,17683 @@ +{ + "type": "FeatureCollection", + "generator": "overpass-turbo", + "copyright": "The data included in this document is from www.openstreetmap.org. 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diff --git a/examples/tutorial.py b/examples/tutorial.py new file mode 100644 index 0000000..983da48 --- /dev/null +++ b/examples/tutorial.py @@ -0,0 +1,30 @@ +from saferoad import SafeRoad, PsData, Road + +# define the SafeRoad object + +SR = SafeRoad( + road=Road(filepath="./fixtures/A10_ams.geojson", crs_code="EPSG:4326", name="road"), + ps_data=PsData( + filepath="./fixtures/psdata_synthetisch.csv", + latitude="pnt_lat", + longitude="pnt_lon", + unit="m", + crs_code="EPSG:4326", + ), + computational_crs="EPSG:28992", +) + +# laoding the data into database +SR.load_files() + +# applying preprocess steps +SR.preprocess() + +# generating patches +SR.generate_rectangles(road_width=8, segment_length=500) + +# run the analysis +SR.run_analysis() + +# generate the report +SR.generate_report(output_name="./SafeRoadDB/SafeRoadReport.pdf") diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..e53c73b --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,16 @@ +# pyproject.toml +[build-system] +requires = ["setuptools>=61.0"] +build-backend = "setuptools.build_meta" + +[project] +name = "saferoad" +version = "0.1.0" +description = "Road network safety analysis using Multi Temporal InSAR and road network data" +authors = [{ name = "Murat Sahin", email = "m.sahin@tudelft.nl" }] +readme = "README.md" +requires-python = ">=3.10" +dependencies = ["matplotlib", "duckdb", "shapely", "contextily", "tqdm"] + +[tool.setuptools.packages.find] +where = ["src"] diff --git a/src/saferoad/__init__.py b/src/saferoad/__init__.py new file mode 100644 index 0000000..7661727 --- /dev/null +++ b/src/saferoad/__init__.py @@ -0,0 +1,7 @@ +from .saferoad import SafeRoad, PsData, Road +from .database import DataBase +from .pipeline import Pipeline + +__version__ = "0.1.0" + +__all__ = ["SafeRoad", "PsData", "Road", "DataBase", "Pipeline", "__version__"] diff --git a/src/saferoad/database.py b/src/saferoad/database.py new file mode 100644 index 0000000..a066c06 --- /dev/null +++ b/src/saferoad/database.py @@ -0,0 +1,65 @@ +import os +import duckdb +from pathlib import Path +from datetime import datetime + + +class DataBase: + """Class for managing a DuckDB database connection. + + This class provides methods to set up a DuckDB database connection, and run SQL queries. + """ + + def __init__(self): + self.connection = None + self.db_path = None + + def setup(self): + """Setup the DuckDB database connection. + This method initializes the database path and establishes a connection + to the DuckDB database. It also loads the spatial extension if available. + + :raises AssertionError: If the database connection or path is already established. + """ + assert self.connection is None, "Database connection already established." + assert self.db_path is None, "Database path already initialized." + + self.db_path = self.init_database_path() + self.connection = duckdb.connect(self.db_path) + # Load the spatial extension if available + self.connection.install_extension("spatial") + self.connection.load_extension("spatial") + + def init_database_path(self): + """Initialize the database path. + + This method creates a directory for the database if it does not exist + and generates a filename based on the current date and time. + + :return: The path to the DuckDB database file. + :rtype: str + """ + assert self.connection is None, "Database connection already established." + assert self.db_path is None, "Database path already initialized." + + # Create a folder for the database if it does not exist + dbFolder = "SafeRoadDB" + os.makedirs(dbFolder, exist_ok=True) + filename = os.path.join( + dbFolder, f"{datetime.now().strftime('%Y%m%d%H%M')}.duckdb" + ) + return filename + + def run(self, query: str): + """Run a SQL query on the database. + + This method executes the provided SQL query on the connected database. + + :param query: The SQL query to execute. + :type query: str + :raises AssertionError: If the database connection is not established. + """ + if self.connection is None: + raise AssertionError("Database connection is not established.") + + self.connection.execute(query) diff --git a/src/saferoad/pipeline.py b/src/saferoad/pipeline.py new file mode 100644 index 0000000..034797d --- /dev/null +++ b/src/saferoad/pipeline.py @@ -0,0 +1,1513 @@ +from pathlib import Path +from numpy import where as npwhere + + +class Pipeline: + class Processing: + """Processing class for processing road data. + + This class provides methods to load files into a database, dissolve features, + split lines into segments, generate patches, and save tables as GeoJSON files. + It assumes that the database connection is already established and that the + necessary SQL functions for spatial operations are available. + The methods in this class generate SQL queries that can be executed against + a spatial database, such as DuckDB. + + The class does not handle database connections or query execution directly; + it only generates the SQL queries as strings. + The methods are static and can be called without instantiating the class. + The class is designed to be used in a pipeline for processing road data, + where each method can be chained together to perform a series of operations + on the road data. + + The methods assume that the input data is already in a suitable format for + processing, such as a GIS vector file for loading into a database or a table + containing road geometries for dissolving and segmenting. + The class is intended to be used in conjunction with a spatial database + that supports SQL queries for spatial operations, such as DuckDB with + the Spatial extension or PostGIS. + The methods are designed to be flexible and can be adapted to different + use cases by modifying the parameters passed to them. + """ + + @staticmethod + def load_file(file_path: str, table_name: str) -> str: + """Load a file into a database table. + + Method validates the provided file path and determines if existing + and determines the appropriate loading method based on the file type. + It generates an SQL query to create or replace a table in the database + with the contents of the file. The method supports CSV files and other + GIS vector files, using different loading methods for each type. + + :param file_path: The path to the file to be loaded. + :type file_path: str + :param table_name: The name of the table to create or replace in the database. + :type table_name: str + :raises AssertionError: If the file does not exist. + :return: SQL query string to create or replace the table with the file data. + :rtype: str + """ + + assert Path(file_path).exists(), f"File {file_path} does not exist." + + loader_method = ( + "read_csv_auto" if Path(file_path).suffix == ".csv" else "ST_Read" + ) + query = f""" + CREATE OR REPLACE TABLE '{table_name}' AS SELECT * FROM {loader_method}('{file_path}'); + CREATE OR REPLACE SEQUENCE {table_name}_id; + ALTER TABLE '{table_name}' ADD COLUMN uid INTEGER DEFAULT nextval('{table_name}_id'); + """ + return query + + @staticmethod + def dissolve_features(attribute: str, table_name: str) -> str: + """Dissolve features in a table based on a specified attribute. + + This method generates an SQL query to dissolve features in a table + based on the provided attribute. It assumes the table is already loaded + into the database. + + :param attribute: The attribute to dissolve features by. + :type attribute: str + :param table_name: The name of the table containing the features, default is "road". + :type table_name: str + + :return: SQL query string to dissolve features. + :rtype: str + """ + + group_expression = f"GROUP BY {attribute}, " if attribute else "" + + return f""" + CREATE OR REPLACE TABLE dissolved AS + SELECT UNNEST(ST_Dump(ST_LineMerge(ST_Union_Agg(geom))), recursive := true) as geom + FROM {table_name} {group_expression} + """ + + @staticmethod + def split_to_segments( + segment_length: float, table_name: str = "dissolved" + ) -> str: + """Split lines into segments of a specified length. + + This method generates an SQL query to split lines in a table into segments + of a specified length. It transforms the geometries to a computational + coordinate reference system (CRS) and calculates the lengths in meters. + The segments are created by generating windows of fixed length along the lines. + The resulting segments are stored in a new table named "segments". + The method assumes that the input table is already dissolved and contains + geometries in a suitable format. + + The segments are created by cutting the lines into fixed-length segments + and filtering out any empty geometries. + This method is useful for preparing road segments for further analysis or processing. + It is particularly useful in road network analysis, where roads need to be + divided into manageable segments for tasks such as traffic analysis, road condition + assessment, or infrastructure planning. + + :param segment_length: The length of each segment in meters. + :type segment_length: float + :param table_name: The name of the table containing the lines, default is "dissolved". + :type table_name: str + :return: SQL query string to create or replace the segments table. + :rtype: str + """ + computational_crs = None + query = f""" + CREATE OR REPLACE TABLE segments AS + -- transform to local projection system + -- and calculate length in meters + WITH measured AS ( + SELECT + -- ST_Transform(geom, 'EPSG:4326', '{computational_crs}', always_xy := true) AS geom_m, + -- ST_Length(ST_Transform(geom, 'EPSG:4326', '{computational_crs}', always_xy := true)) AS len_m + geom, + ST_Length(geom) AS len_m + FROM {table_name} + ), + -- generate windows of fixed length + windows AS ( + SELECT + + m.geom, + m.len_m, + -- start distance (meters) + (i * CAST({segment_length} AS DOUBLE)) AS s, + -- end distance (meters), capped at total length + LEAST((i + 1) * CAST({segment_length} AS DOUBLE), m.len_m) AS e + FROM measured AS m + JOIN LATERAL generate_series( + 0, + CAST(CEIL(m.len_m / CAST({segment_length} AS DOUBLE)) AS BIGINT) - 1 + ) AS g(i) ON TRUE + WHERE m.len_m > 0 + ), + -- cut the lines into segments + cut AS ( + SELECT + ST_LineSubstring( + geom, + s / NULLIF(len_m, 0), + e / NULLIF(len_m, 0) + ) AS geom + FROM windows + WHERE len_m > 0 + ) + -- filter out empty geometries + SELECT geom + FROM cut + WHERE NOT ST_IsEmpty(geom); + """ + return query + + @staticmethod + def generate_patches( + width: float, length: float, table_name: str = "segments" + ) -> str: + """Generate patches from segments. + + This method generates patches from segments by creating a rectangular + envelope around each segment, rotated to align with the segment's direction. + The patches are created by translating and rotating the envelope to the + start point of the segment, ensuring that each patch is oriented correctly + along the segment's direction. + + :param width: The width of the patches in meters. + :type width: float + :param length: The length of the patches in meters. + :type length: float + :param table_name: The name of the table containing the segments, default is "segments". + :type table_name: str + :return: SQL query string to create or replace the patches table. + :rtype: str + """ + assert width > 0, "Width must be greater than 0." + assert length > 0, "Length must be greater than 0." + query = f""" + CREATE OR REPLACE TABLE patches AS + SELECT + ST_Translate( + ST_RotateZ( + ST_MakeEnvelope(-{width} / 2, 0, {width} / 2, {length}), + -ST_Azimuth(ST_StartPoint(geom), ST_EndPoint(geom)) + ), + ST_X(ST_StartPoint(geom)), ST_Y(ST_StartPoint(geom)) + ) AS geom + FROM {table_name}; + CREATE OR REPLACE SEQUENCE patches_id; + ALTER TABLE patches ADD COLUMN uid INTEGER DEFAULT nextval('patches_id'); + """ + return query + + # @staticmethod + # def save_table_as_geojson(table_name: str, output_path: str) -> str: + # """Save a table as a GeoJSON file. + # This method generates an SQL query to save the contents of a table + # as a GeoJSON file. It assumes that the table contains geometries in a + # format compatible with GeoJSON and that the output path is a valid file path. + + # :param table_name: The name of the table to save as GeoJSON. + # :type table_name: str + # :param output_path: The path where the GeoJSON file will be saved. + # :type output_path: str + # :raises AssertionError: If the output path does not exist. + # :return: SQL query string to save the table as a GeoJSON file. + # :rtype: str + # """ + # assert Path( + # output_path + # ).parent.exists(), f"Directory {Path(output_path).parent} does not exist." + + # return f""" + # COPY (SELECT * FROM {table_name}) TO '{output_path}' (FORMAT GDAL, DRIVER 'GeoJSON', OVERWRITE TRUE); + # """ + + @staticmethod + def spatial_tranformation( + source_epsg: str, target_epsg: str, table_name: str + ) -> str: + """Transform geometries from one EPSG code to another. + + This method generates an SQL query to transform geometries in a table + from one EPSG code to another. It assumes that the input table contains + geometries in the source EPSG code and that the target EPSG code is valid. + + :param source_epsg: The source EPSG code of the geometries. + :type source_epsg: str + :param target_epsg: The target EPSG code to transform the geometries to. + :type target_epsg: str + :param table_name: The name of the table containing the geometries, default is "patches". + :type table_name: str + + :return: SQL query string to transform the geometries. + :rtype: str + """ + + return f""" + UPDATE '{table_name}' SET geom = ST_Transform(geom, '{source_epsg}', '{target_epsg}', always_xy := true); + """ + + @staticmethod + def build_geometry(latitude: str, longitude: str, table_name: str) -> str: + """Build a geometry from latitude and longitude coordinates. + + This method generates an SQL query to build a geometry column in a table + from latitude and longitude coordinates. It assumes that the input table + contains columns for latitude and longitude. + + :param latitude: The name of the column containing latitude values. + :type latitude: str + :param longitude: The name of the column containing longitude values. + :type longitude: str + :param table_name: The name of the table to add the geometry column to. + :type table_name: str + :return: SQL query string to build the geometry column. + :rtype: str + """ + return f""" + ALTER TABLE {table_name} ADD COLUMN geom GEOMETRY; + UPDATE {table_name} SET geom = ST_Point({longitude}, {latitude}); + """ + + @staticmethod + def relate_point_patches(point_name: str, patch_name: str = "patches") -> str: + """Relate points to patches by assigning patch IDs to points. + + This method generates an SQL query to relate points in one table to patches + in another table by assigning patch IDs to points based on their spatial + relationship. It assumes that the point table contains a geometry column + and that the patch table contains a geometry column and a unique identifier + for each patch. + + :param point_name: The name of the table containing the points. + :type point_name: str + :param patch_name: The name of the table containing the patches, default is "patches". + :type patch_name: str + :return: SQL query string to relate points to patches. + :rtype: str + """ + return f""" + ALTER TABLE {point_name} ADD COLUMN patch_id INTEGER; + UPDATE {point_name} + SET patch_id = subquery.uid + FROM ( + SELECT first.uid AS point_uid, second.uid AS uid + FROM {point_name} AS first + JOIN {patch_name} AS second + ON ST_Within(first.geom, second.geom) + ) AS subquery + WHERE {point_name}.uid = subquery.point_uid; + """ + + @staticmethod + def calc_point_density(point_name: str, patch_name: str = "patches") -> str: + """Calculate point density per patch. + + This method generates an SQL query to calculate the point density per patch + by counting the number of points within each patch and dividing by the area + of the patch in square kilometers. It assumes that the point table contains + a geometry column and that the patch table contains a geometry column and + a unique identifier for each patch. + + :param point_name: The name of the table containing the points. + :type point_name: str + :param patch_name: The name of the table containing the patches, default is "patches". + :type patch_name: str + :return: SQL query string to calculate point density per patch. + :rtype: str + """ + return f""" + -- calculate the point count and density per patch + ALTER TABLE {patch_name} ADD COLUMN point_count INTEGER; + ALTER TABLE {patch_name} ADD COLUMN ps_density FLOAT DEFAULT 0.0; + + UPDATE {patch_name} + SET point_count = subquery.point_count, + ps_density = subquery.point_count / (ST_Area(subquery.geom) / 1_000_000.0) + FROM ( + SELECT second.uid AS patch_id, COUNT(first.uid) AS point_count, second.geom + FROM {point_name} AS first + JOIN {patch_name} AS second + ON ST_Within(first.geom, second.geom) + GROUP BY second.uid, second.geom + ) AS subquery + WHERE {patch_name}.uid = subquery.patch_id; + """ + + @staticmethod + def get_column_names(table_name: str) -> str: + """Get column names from a table. + + This method generates an SQL query to retrieve the column names from a specified table. + + :param table_name: The name of the table to retrieve column names from. + :type table_name: str + :return: SQL query string to get the column names. + :rtype: str + """ + return f""" + SELECT column_name FROM (DESCRIBE {table_name}); + """ + + @staticmethod + def calc_cum_disp(dates: list[str], table_name: str = "pspoint") -> str: + """Calculate cumulative displacement. + + This method generates an SQL query to calculate the cumulative displacement + for each point in a table based on the provided date fields. It assumes that + the input table contains columns for displacement values at different dates. + + :param dates: List of column names representing dates. + :type dates: list[str] + :param table_name: The name of the table containing the points, default is "pspoint". + :type table_name: str + :return: SQL query string to calculate cumulative displacement. + :rtype: str + """ + return f""" + ALTER TABLE {table_name} ADD COLUMN displacement FLOAT; + UPDATE {table_name} + SET displacement = first.disp + FROM ( + SELECT uid, {dates[-1]} - {dates[0]} as disp + FROM {table_name} + ) as first + JOIN {table_name} as second + ON first.uid = second.uid + WHERE {table_name}.uid = first.uid; + """ + + @staticmethod + def calc_avg_cum_disp() -> str: + """Calculate average cumulative displacement per patch. + + This method generates an SQL query to calculate the average cumulative + displacement for each patch by averaging the displacement values of points + within each patch. It assumes that the point table contains a geometry column, + a displacement column, and that the patch table contains a geometry column + and a unique identifier for each patch. + + :return: SQL query string to calculate average cumulative displacement per patch. + :rtype: str + """ + return f""" + ALTER TABLE patches ADD COLUMN avg_cum_disp DOUBLE DEFAULT 0.0; + UPDATE patches + SET avg_cum_disp = ( + SELECT AVG(displacement) + FROM pspoint + WHERE patch_id = patches.uid + ); + """ + + @staticmethod + def calc_avg_velocity( + time_vector: list[float], + field_names: list[str], + table_name: str = "pspoint", + ) -> str: + """Calculate average velocity using linear regression. + + This method generates an SQL query to calculate the average velocity for + each point in a table using linear regression on displacement values over time. + It assumes that the input table contains columns for displacement values at different dates and that the time vector is provided in days. + + :param time_vector: List of time differences in days from the first date. + :type time_vector: list[float] + :param field_names: List of column names representing displacement values at different dates. + :type field_names: list[str] + :param table_name: The name of the table containing the points, default is "pspoint". + :type table_name: str + :return: SQL query string to calculate average velocity. + :rtype: str + """ + x_vals_sql = "[" + ", ".join(map(str, time_vector)) + "]::DOUBLE[]" + + y_fields_sql = ( + "[" + ", ".join(f"CAST({c} AS DOUBLE)" for c in field_names) + "]" + ) + + return f""" + ALTER TABLE {table_name} ADD COLUMN avg_velocity_gcp DOUBLE; + WITH pairs AS ( + SELECT + uid, + list_element({y_fields_sql}, idx) as y, + list_element({x_vals_sql}, idx) as x, + patch_id + FROM {table_name} + CROSS JOIN UNNEST(range(1, {len(field_names)} + 1)) AS u(idx) + WHERE patch_id IS NOT NULL + ), + slopes AS ( + SELECT + uid, + regr_slope(y, x) AS slope + FROM pairs + WHERE y IS NOT NULL AND x IS NOT NULL + GROUP BY uid + ) + UPDATE {table_name} AS t + SET avg_velocity_gcp = s.slope * 365.25 -- convert from per day to per year + FROM slopes AS s + WHERE t.uid = s.uid; + """ + + @staticmethod + def calc_avg_disp_ts_gcp(date_fields: list[str]): + """Calculate average displacement time series for patches (Average Local Displacement (ALD)). + + This method generates an SQL query to calculate the average displacement + time series for each patch by averaging the displacement values of points within each patch + for the specified date fields. It assumes that the point table contains a geometry column, + displacement columns for each date field, and that the patch table contains a geometry column + and a unique identifier for each patch. + + :param date_fields: List of column names representing displacement values at different dates. + :type date_fields: list[str] + :return: SQL query string to calculate average displacement time series per patch. + :rtype: str + """ + expression = ", ".join([f"AVG({i})" for i in (date_fields)]) + + return f""" + ALTER TABLE patches ADD COLUMN avg_disp_ts_gcp DOUBLE[]; + UPDATE patches + SET avg_disp_ts_gcp = subquery.disp_ts + FROM ( + SELECT + patch_id, + ARRAY[{expression}] AS disp_ts + FROM pspoint + WHERE patch_id IS NOT NULL + GROUP BY patch_id + ) AS subquery + WHERE patches.uid = subquery.patch_id; + """ + + @staticmethod + def calc_std_disp_ts_gcp(date_fields: list[str]): + """Calculate standard deviation of displacement time series for patches based on global control point (GCP). + + This method generates an SQL query to calculate the standard deviation + of displacement time series for each patch by computing the standard deviation of displacement values of points within each patch for the specified date fields. + + :param date_fields: List of column names representing displacement values at different dates. + :type date_fields: list[str] + :return: SQL query string to calculate standard deviation of displacement time series per patch. + :rtype: str + """ + + expression = ", ".join([f"stddev_pop({i})" for i in (date_fields)]) + + return f""" + ALTER TABLE patches ADD COLUMN std_disp_ts_gcp DOUBLE[]; + UPDATE patches + SET std_disp_ts_gcp = subquery.disp_ts + FROM ( + SELECT + patch_id, + ARRAY[{expression}] AS disp_ts + FROM pspoint + WHERE patch_id IS NOT NULL + GROUP BY patch_id + ) AS subquery + WHERE patches.uid = subquery.patch_id; + """ + + @staticmethod + def select_lcps(table_name: str = "pspoint") -> str: + """Select linear control points (LCPs) for patches. + + This method generates an SQL query to select local control points (LCPs) for each patch by identifying the point with the minimum absolute average velocity within each patch. + + :param table_name: The name of the table containing the points, default is "pspoint". + :type table_name: str + :return: SQL query string to select LCPs for patches. + :rtype: str + """ + + return f""" + ALTER TABLE patches ADD COLUMN lcp_uid INTEGER; + WITH min_velocities AS ( + SELECT + patch_id, + MIN(ABS(avg_velocity_gcp)) AS min_velocity + FROM {table_name} + WHERE patch_id IS NOT NULL + GROUP BY patch_id + ) + UPDATE patches + SET lcp_uid = subquery.uid + FROM ( + SELECT + p.patch_id, + p.uid + FROM {table_name} AS p + JOIN min_velocities AS mv + ON p.patch_id = mv.patch_id + AND ABS(p.avg_velocity_gcp) = mv.min_velocity + ) AS subquery + WHERE patches.uid = subquery.patch_id; + """ + + @staticmethod + def calc_lcp_velocity() -> str: + """Calculate LCP velocity for patches. + + This method generates an SQL query to calculate the velocity of the linear control point (LCP) + for each patch by retrieving the average velocity of the point identified as the LCP. + + :return: SQL query string to calculate LCP velocity for patches. + :rtype: str + """ + return f""" + ALTER TABLE patches ADD COLUMN lcp_velocity FLOAT; + WITH subquery AS ( + SELECT patch_id, avg_velocity_gcp + FROM pspoint + WHERE uid IN ( + SELECT lcp_uid + FROM patches + WHERE lcp_uid IS NOT NULL + ) + ) + UPDATE patches + SET lcp_velocity = subquery.avg_velocity_gcp + FROM subquery + WHERE patches.uid = subquery.patch_id; + """ + + @staticmethod + def calc_avg_velocity_gcp(table_name: str = "pspoint") -> str: + """Calculate average velocity based on ground control points (GCPs) for patches. + + This method generates an SQL query to calculate the average velocity for each patch based on the average velocities of points within each patch. + + :param table_name: The name of the table containing the points, default is "pspoint". + :type table_name: str + :return: SQL query string to calculate average velocity based on GCPs for patches. + :rtype: str + """ + + return f""" + ALTER TABLE patches ADD COLUMN avg_gcp_velocity FLOAT; + UPDATE patches + SET avg_gcp_velocity = subquery.avg_vel + FROM ( + SELECT + patch_id, + AVG(avg_velocity_gcp) AS avg_vel + FROM {table_name} + WHERE patch_id IS NOT NULL + GROUP BY patch_id + ) AS subquery + WHERE patches.uid = subquery.patch_id + """ + + @staticmethod + def calc_avg_std_disp_ts_lcp( + date_fields: list[str], table_name: str = "pspoint" + ) -> str: + """Calculate average and standard deviation of displacement time series based on LCPs. + + This method generates an SQL query to calculate the average and standard + deviation of displacement time series for each patch based on the displacement + values of points within each patch relative to the local control point (LCP). + It assumes that the point table contains columns for displacement values at + different dates and that the patch table contains a geometry column and a unique + identifier for each patch. + + :param date_fields: List of column names representing displacement values at different dates. + :type date_fields: list[str] + :param table_name: The name of the table containing the points, default is "pspoint". + :type table_name: str + :return: SQL query string to calculate average and standard deviation of displacement time series based on LCPs. + :rtype: str + """ + expression_to_select_date_fields = ( + "[" + ", ".join([i for i in date_fields]) + "]" + ) + indices = range(1, len(date_fields) + 1) + expression_to_explode = ",".join( + f"list_extract(displacement, {i}) as {date_fields[i-1]}" + for i in indices + ) + expresion_for_avg_per_date = ", ".join([f"AVG({i})" for i in date_fields]) + expresion_for_std_per_date = ", ".join( + [f"stddev_pop({i})" for i in date_fields] + ) + return f""" + ALTER TABLE patches ADD COLUMN avg_disp_ts_lcp DOUBLE[]; + ALTER TABLE patches ADD COLUMN std_disp_ts_lcp DOUBLE[]; + WITH y as ( + SELECT patch_id, {expression_to_select_date_fields} as y + FROM {table_name} + WHERE patch_id IS NOT NULL + ), + x as ( + SELECT patch_id, {expression_to_select_date_fields} as x + FROM {table_name} + WHERE uid IN (SELECT lcp_uid FROM patches WHERE lcp_uid IS NOT NULL) + ), + process as ( + SELECT + x.patch_id, + list_transform(list_zip(y.y, x.x), arr -> arr[1] - arr[2]) as displacement + FROM x + JOIN y + ON x.patch_id = y.patch_id + ), + exploded AS ( + SELECT + patch_id, + {expression_to_explode} + FROM process + ), + final AS ( + SELECT + patch_id, + ARRAY[{expresion_for_avg_per_date}] AS avg_disp_ts_lcp, + ARRAY[{expresion_for_std_per_date}] AS std_disp_ts_lcp + FROM exploded + GROUP BY patch_id + ) + UPDATE patches + SET avg_disp_ts_lcp = final.avg_disp_ts_lcp, + std_disp_ts_lcp = final.std_disp_ts_lcp + FROM final + WHERE patches.uid = final.patch_id; + """ + + @staticmethod + def calc_med_std_disp_ts_lcp( + date_fields: list[str], table_name: str = "pspoint" + ) -> str: + """Calculate average and standard deviation of displacement time series based on LCPs. + + This method generates an SQL query to calculate the average and standard + deviation of displacement time series for each patch based on the displacement + values of points within each patch relative to the local control point (LCP). + It assumes that the point table contains columns for displacement values at + different dates and that the patch table contains a geometry column and a unique + identifier for each patch. + + :param date_fields: List of column names representing displacement values at different dates. + :type date_fields: list[str] + :param table_name: The name of the table containing the points, default is "pspoint". + :type table_name: str + :return: SQL query string to calculate average and standard deviation of displacement time series based on LCPs. + :rtype: str + """ + expression_to_select_date_fields = ( + "[" + ", ".join([i for i in date_fields]) + "]" + ) + indices = range(1, len(date_fields) + 1) + expression_to_explode = ",".join( + f"list_extract(displacement, {i}) as {date_fields[i-1]}" + for i in indices + ) + expresion_for_avg_per_date = ", ".join([f"AVG({i})" for i in date_fields]) + expresion_for_std_per_date = ", ".join( + [f"stddev_pop({i})" for i in date_fields] + ) + return f""" + ALTER TABLE patches ADD COLUMN med_disp_ts_lcp DOUBLE[]; + ALTER TABLE patches ADD COLUMN med_std_disp_ts_lcp DOUBLE[]; + WITH y as ( + SELECT patch_id, {expression_to_select_date_fields} as y + FROM {table_name} + WHERE patch_id IS NOT NULL + ), + x as ( + SELECT patch_id, {expression_to_select_date_fields} as x + FROM {table_name} + WHERE uid IN (SELECT lcp_uid FROM patches WHERE lcp_uid IS NOT NULL) + ), + process as ( + SELECT + x.patch_id, + list_transform(list_zip(y.y, x.x), arr -> arr[1] - arr[2]) as displacement + FROM x + JOIN y + ON x.patch_id = y.patch_id + ), + exploded AS ( + SELECT + patch_id, + {expression_to_explode} + FROM process + ), + final AS ( + SELECT + patch_id, + ARRAY[{expresion_for_avg_per_date}] AS med_disp_ts_lcp, + ARRAY[{expresion_for_std_per_date}] AS med_std_disp_ts_lcp + FROM exploded + GROUP BY patch_id + ) + UPDATE patches + SET med_disp_ts_lcp = final.med_disp_ts_lcp, + med_std_disp_ts_lcp = final.med_std_disp_ts_lcp + FROM final + WHERE patches.uid = final.patch_id; + """ + + @staticmethod + def calc_avg_velocity_lcp(table_name: str = "pspoint") -> str: + """Calculate average velocity based on local control points (LCPs) for points. + + This method generates an SQL query to calculate the average velocity for each point + based on the average velocity of the local control point (LCP) associated with the patch + to which the point belongs. It assumes that the point table contains a geometry column, + an average velocity column based on ground control points (GCPs), and that the patch table + contains a geometry column, a unique identifier for each patch, and the average velocity + of the LCP. + + :param table_name: The name of the table containing the points, default is "pspoint". + :type table_name: str + :return: SQL query string to calculate average velocity based on LCPs for points. + :rtype: str + """ + return f""" + ALTER TABLE {table_name} ADD COLUMN avg_velocity_lcp DOUBLE; + WITH y AS ( + SELECT uid, patch_id, avg_velocity_gcp + FROM {table_name} + WHERE patch_id IS NOT NULL + ), + x AS ( + SELECT uid, lcp_velocity, lcp_uid + FROM patches + WHERE lcp_uid IS NOT NULL + ) + UPDATE {table_name} + SET avg_velocity_lcp = subquery.avg_velocity_lcp + FROM ( + SELECT + y.avg_velocity_gcp - x.lcp_velocity AS avg_velocity_lcp, + y.uid as uid + FROM x + JOIN y ON x.uid = y.patch_id + ) AS subquery + WHERE {table_name}.uid = subquery.uid; + """ + + @staticmethod + def calc_avg_lcp_velocity(table_name: str = "pspoint") -> str: + """Calculate average LCP velocity per patch. + + This method generates an SQL query to calculate the average velocity of + local control points (LCPs) for each patch by averaging the average velocities + of points within each patch based on their LCP velocities. + + :param table_name: The name of the table containing the points, default is "pspoint". + :type table_name: str + :return: SQL query string to calculate average LCP velocity per patch. + :rtype: str + """ + return f""" + ALTER TABLE patches ADD COLUMN avg_lcp_velocity FLOAT; + UPDATE patches + SET avg_lcp_velocity = subquery.avg_vel + FROM ( + SELECT + patch_id, + AVG(avg_velocity_lcp) as avg_vel + FROM {table_name} + WHERE patch_id IS NOT NULL + GROUP BY patch_id + ) AS subquery + WHERE patches.uid = subquery.patch_id + """ + + @staticmethod + def calc_outliers(scaling_factor: float, table_name: str = "pspoint") -> str: + """Identify outlier points based on absolute average velocity. + + This method generates an SQL query to identify outlier points based on their + average velocity relative to local control points (LCPs). Points with an absolute + average LCP velocity exceeding a specified threshold (5 mm/year) are + marked as outliers by assigning their unique identifier to a new column in the + patches table. The unit parameter allows for specifying the measurement unit + (meters, centimeters, or millimeters) to appropriately scale the threshold. + + :param scaling_factor: The factor to scale the velocity threshold based on the unit. + :type scaling_factor: float + :param table_name: The name of the table containing the points, default is "pspoint". + :type table_name: str + :return: SQL query string to identify outlier points. + :rtype: str + """ + + return f""" + CREATE OR REPLACE TABLE outliers AS + SELECT patch_id, uid + FROM {table_name} + WHERE ABS(avg_velocity_lcp) > 5 / {scaling_factor} + AND patch_id IS NOT NULL; + ALTER TABLE outliers ADD COLUMN criteria VARCHAR; + UPDATE outliers SET criteria = 'threshold'; + """ + + @staticmethod + def calc_outliers_avg_lcd( + table_name: str = "pspoint", date_vector=list[int], date_names=list[str] + ) -> str: + """Calculate outliers based on average local control displacement (LCD). + + This method generates an SQL query to identify outlier points based on their + displacement time series relative to the average local control displacement (LCD) + of their associated patches. Points with displacement values exceeding the average + LCD by more than standard deviations at any time point are marked as outliers + by inserting their unique identifier and patch ID into a new outliers table. + + :param table_name: The name of the table containing the points, default is "pspoint". + :type table_name: str + :param date_vector: List of time differences in days from the first date. + :type date_vector: list[int] + :param date_names: List of column names representing displacement values at different dates. + :type date_names: list[str] + :return: SQL query string to identify outlier points based on average LCD. + :rtype: str + """ + + if date_vector.max() < 730: + return "" + + first_index = npwhere(date_vector >= date_vector - 730)[0][0] + column_select = ", ".join(date_names[first_index:]) + + return f""" + with underthreshold as ( + SELECT + uid, + patch_id, + ARRAY[{column_select}] as disp_ts + FROM {table_name} + WHERE uid NOT IN (SELECT uid FROM outliers) + AND patch_id IS NOT NULL + ), + sel_ts as ( + SELECT + uid, + avg_disp_ts_lcp[{first_index}:] as avg, + std_disp_ts_lcp[{first_index}:] as std, + FROM patches + WHERE patches.uid IN (SELECT patch_id FROM underthreshold) + ), + joined as ( + SELECT * from underthreshold + JOIN sel_ts on sel_ts.uid = underthreshold.patch_id + ), + ald_points as ( + SELECT + uid, + patch_id, + list_min( + list_apply( + array_zip(disp_ts, avg, std), + x -> abs(x[1]-x[2]) > x[3] + ) + ) as all_greater + FROM joined + WHERE all_greater = true + ) + INSERT INTO outliers (uid, patch_id, criteria) + SELECT uid, patch_id, 'ald' FROM ald_points + """ + + @staticmethod + def calc_outliers_med_lcd( + table_name: str = "pspoint", date_vector=list[int], date_names=list[str] + ) -> str: + """Calculate outliers based on median local control displacement (LCD). + + This method generates an SQL query to identify outlier points based on their + displacement time series relative to the median local control displacement (LCD) + of their associated patches. Points with displacement values exceeding the median + LCD by more than standard deviations at any time point are marked as outliers + by inserting their unique identifier and patch ID into a new outliers table. + + :param table_name: The name of the table containing the points, default is "pspoint". + :type table_name: str + :param date_vector: List of time differences in days from the first date. + :type date_vector: list[int] + :param date_names: List of column names representing displacement values at different dates. + :type date_names: list[str] + :return: SQL query string to identify outlier points based on median LCD. + :rtype: str + """ + + if date_vector.max() < 730: + return "" + + first_index = npwhere(date_vector >= date_vector - 730)[0][0] + column_select = ", ".join(date_names[first_index:]) + + return f""" + with underthreshold as ( + SELECT + uid, + patch_id, + ARRAY[{column_select}] as disp_ts + FROM {table_name} + WHERE uid NOT IN (SELECT uid FROM outliers) + AND patch_id IS NOT NULL + ), + sel_ts as ( + SELECT + uid, + med_disp_ts_lcp[{first_index}:] as med, + med_std_disp_ts_lcp[{first_index}:] as std, + FROM patches + WHERE patches.uid IN (SELECT uid FROM underthreshold) + ), + joined as ( + SELECT * from underthreshold + JOIN sel_ts on sel_ts.uid = underthreshold.patch_id + ), + med_points as ( + SELECT + uid, + patch_id, + list_min( + list_apply( + array_zip(disp_ts, med, std), + x -> abs(x[1]-x[2]) > x[3] + ) + ) as all_greater + FROM joined + WHERE all_greater = true + ) + INSERT INTO outliers (uid, patch_id, criteria) + SELECT uid, patch_id, 'med' FROM med_points + """ + + @staticmethod + def lcp_ts_patch(date_fields: list[str], table_name: str = "pspoint"): + """Get Local Control Point's displacement time series for patches. + + This method generates an SQL query to retrieve the displacement time series + of the local control point (LCP) for each patch based on specified date fields. + + :param date_fields: List of column names representing displacement values at different dates. + :type date_fields: list[str] + :param table_name: The name of the table containing the points, default is "pspoint". + :type table_name: str + :return: SQL query string to get LCP displacement time series for patches. + :rtype: str + + """ + date_fields_str = "[" + ", ".join(date_fields) + "]" + return f""" + ALTER TABLE patches ADD COLUMN lcp_disp_ts DOUBLE[]; + UPDATE patches + SET lcp_disp_ts = subquery.disp_ts + FROM ( + SELECT + p.patch_id, + {date_fields_str} AS disp_ts + FROM {table_name} AS p + JOIN patches AS pa + ON p.uid = pa.lcp_uid + WHERE p.patch_id IS NOT NULL + -- GROUP BY p.patch_id + ) AS subquery + WHERE patches.uid = subquery.patch_id; + """ + + def outlier_ts_patch(date_fields: list[str], table_name: str = "pspoint"): + """Get outlier displacement time series for patches. + + This method generates an SQL query to retrieve the displacement time series + of outlier points based on specified date fields. + + :param date_fields: List of column names representing displacement values at different dates. + :type date_fields: list[str] + :param table_name: The name of the table containing the points, default is "pspoint". + :type table_name: str + :return: SQL query string to get outlier displacement time series for patches. + :rtype: str + """ + + date_fields_str = "[" + ", ".join(date_fields) + "]" + return f""" + ALTER TABLE outliers ADD COLUMN disp_ts DOUBLE[]; + UPDATE outliers + SET disp_ts = subquery.disp_ts + FROM ( + SELECT + p.uid, + {date_fields_str} AS disp_ts + FROM {table_name} AS p + WHERE p.uid IN (SELECT uid FROM outliers) + ) AS subquery + WHERE outliers.uid = subquery.uid; + """ + + @staticmethod + def outlier_rel_ts_patch(): + """Calculate outlier displacement time series relative to LCP for patches. + + This method generates an SQL query to calculate the displacement time series + of outlier points relative to the displacement time series of their associated + local control points (LCPs). It assumes that the outliers table contains a + geometry column, a displacement time series column, and a patch ID column, + and that the patches table contains a geometry column, a unique identifier + for each patch, and a displacement time series column for the LCP. + + :return: SQL query string to calculate outlier displacement time series relative to LCP for patches. + :rtype: str + """ + return f""" + ALTER TABLE outliers ADD COLUMN rel_ts DOUBLE[]; + with p_lcp as ( + SELECT uid, lcp_disp_ts + FROM patches + WHERE lcp_uid IS NOT NULL + ), + p_out as ( + SELECT o.uid, o.patch_id, o.disp_ts, p_lcp.lcp_disp_ts + FROM outliers AS o + JOIN p_lcp + ON o.patch_id = p_lcp.uid + ) + UPDATE outliers + SET rel_ts = list_transform( + p_out.disp_ts, + (value, idx) -> value - list_element(p_out.lcp_disp_ts, idx) + ) + FROM p_out + WHERE outliers.uid = p_out.uid + """ + + class Visualisation: + """SQL queries for visualisation of results. + + 2D visualisation is done in Web Mercator (EPSG:3857). + """ + + projection = "EPSG:3857" + + @staticmethod + def get_table_bbox(table_name: str, source_crs: str) -> str: + """Get the bounding box of a table in a specified projection. + + This method generates an SQL query to retrieve the bounding box of geometries + from a specified table, transforming the geometries from a source CRS to + a target projection defined in the Visualisation class. + + :param table_name: The name of the table to get the bounding box from. + :type table_name: str + :param source_crs: The source coordinate reference system of the geometries. + :type source_crs: str + :return: SQL query string to get the bounding box of the table. + :rtype: str""" + return f""" + with transformed as ( + SELECT + ST_TRANSFORM(geom, '{source_crs}', '{Pipeline.Visualisation.projection}', always_xy := true) as geom + FROM {table_name} + ) + SELECT + MIN(ST_XMIN(geom)) AS min_x, + MIN(ST_YMIN(geom)) AS min_y, + MAX(ST_XMAX(geom)) AS max_x, + MAX(ST_YMAX(geom)) AS max_y + FROM transformed; + """ + + @staticmethod + def get_ps_density(source_crs: str) -> str: + """Get point density per patch. + + This method generates an SQL query to retrieve the point density for each patch, + transforming the geometries from a source CRS to a target projection defined + in the Visualisation class. + + :param source_crs: The source coordinate reference system of the geometries. + :type source_crs: str + :return: SQL query string to get the point density per patch. + :rtype: str + """ + return f""" + SELECT + ST_AsWKB(ST_TRANSFORM(geom, '{source_crs}', '{Pipeline.Visualisation.projection}', always_xy := true)) AS geom, + ps_density + FROM patches + """ + + @staticmethod + def get_outliers_map(source_crs: str, scaling_factor: float) -> str: + """Get outlier points based on absolute average LCP velocity. + + This method generates an SQL query to retrieve outlier points based on their + average velocity relative to local control points (LCPs). Points with an absolute + average LCP velocity exceeding a specified threshold (5 mm/year) are + marked as outliers by assigning their unique identifier to a new column in the + patches table. The unit parameter allows for specifying the measurement unit + (meters, centimeters, or millimeters) to appropriately scale the threshold. + + :param source_crs: The source coordinate reference system of the geometries. + :type source_crs: str + :param scaling_factor: The factor to scale the velocity threshold based on the unit. + :type scaling_factor: float + :return: SQL query string to identify outlier points. + :rtype: str + """ + return f""" + SELECT + ST_AsWKB(ST_TRANSFORM(geom, '{source_crs}', '{Pipeline.Visualisation.projection}', always_xy := true)) AS geom, + ABS(avg_velocity_lcp) * {scaling_factor} AS velocity, + FROM pspoint + WHERE uid IN (SELECT uid FROM outliers) + ORDER BY velocity ASC; + """ + + @staticmethod + def get_cum_disp_map(source_crs: str, scaling_factor: float) -> str: + """Get cumulative displacement per patch. + + This method generates an SQL query to retrieve the cumulative displacement + for each patch, transforming the geometries from a source CRS to a target + projection defined in the Visualisation class. + + :param source_crs: The source coordinate reference system of the geometries. + :type source_crs: str + :param unit: The measurement unit for displacement ("m", "cm", or "mm"). + :type unit: str + :return: SQL query string to get the cumulative displacement per patch. + :rtype: str + """ + # scaling_factor = [1, 100, 1000][["m", "cm", "mm"].index(unit)] + return f""" + SELECT + ST_AsWKB(ST_TRANSFORM(geom, '{source_crs}', '{Pipeline.Visualisation.projection}', always_xy := true)) AS geom, + avg_cum_disp * {scaling_factor} AS cum_disp + FROM patches + WHERE avg_cum_disp IS NOT NULL + ORDER BY cum_disp ASC; + """ + + @staticmethod + def get_ps_vel_gcp(source_crs: str, scaling_factor: float) -> str: + """Get average velocity based on ground control points (GCPs). + This method generates an SQL query to retrieve the average velocity for + each point based on ground control points (GCPs), transforming the geometries + from a source CRS to a target projection defined in the Visualisation class. + + :param source_crs: The source coordinate reference system of the geometries. + :type source_crs: str + :param unit: The measurement unit for velocity ("m", "cm", or "mm"). + :type unit: str + :return: SQL query string to get the average velocity based on GCPs. + :rtype: str + """ + return f""" + SELECT + ST_AsWKB(ST_TRANSFORM(geom, '{source_crs}', '{Pipeline.Visualisation.projection}', always_xy := true)) AS geom, + avg_velocity_gcp * {scaling_factor} AS velocity + FROM pspoint + WHERE patch_id IS NOT NULL + ORDER BY velocity ASC; + """ + + @staticmethod + def get_ps_vel_lcp(source_crs: str, scaling_factor: float) -> str: + """Get average velocity based on local control points (LCPs). + + This method generates an SQL query to retrieve the average velocity for + each point based on local control points (LCPs), transforming the geometries + from a source CRS to a target projection defined in the Visualisation class. + + :param source_crs: The source coordinate reference system of the geometries. + :type source_crs: str + :param unit: The measurement unit for velocity ("m", "cm", or "mm"). + :type unit: str + :return: SQL query string to get the average velocity based on LCPs. + :rtype: str + """ + return f""" + SELECT + ST_AsWKB(ST_TRANSFORM(geom, '{source_crs}', '{Pipeline.Visualisation.projection}', always_xy := true)) AS geom, + ABS(avg_velocity_lcp) * {scaling_factor} AS velocity + FROM pspoint + WHERE patch_id IS NOT NULL + ORDER BY velocity ASC; + """ + + @staticmethod + def get_center(point_uid: float, source_crs: str) -> str: + """Get the center point of the outlier within a specific patch. + + This method generates an SQL query to retrieve the center point of the outlier + within a specific patch, transforming the geometry from a source CRS to a target + projection defined in the Visualisation class. + + :param point_uid: The unique identifier of the outlier point. + :type point_uid: float + :param source_crs: The source coordinate reference system of the geometries. + :type source_crs: str + :return: SQL query string to get the center point of the outlier within the specified patch. + """ + return f""" + SELECT + ST_AsWKB(ST_TRANSFORM(ST_Centroid(geom), '{source_crs}', '{Pipeline.Visualisation.projection}', always_xy := true)) AS geom + FROM pspoint + WHERE uid = {point_uid} + """ + + @staticmethod + def get_patch_center(patch_id: float, source_crs: str) -> str: + """Get the center point of a specific patch. + + This method generates an SQL query to retrieve the center point of a specific + patch, transforming the geometry from a source CRS to a target projection defined in the Visualisation class. + + :param patch_id: The unique identifier of the patch. + :type patch_id: float + :param source_crs: The source coordinate reference system of the geometries. + :type source_crs: str + :return: SQL query string to get the center point of the specified patch. + """ + return f""" + SELECT + ST_AsWKB(ST_TRANSFORM(ST_Centroid(geom), '{source_crs}', '{Pipeline.Visualisation.projection}', always_xy := true)) AS geom + FROM patches + WHERE uid = {patch_id} + """ + + @staticmethod + def get_ps_points(patch_id: float, source_crs: str, table_name: str) -> str: + """Get points within a specific patch. + + This method generates an SQL query to retrieve points within a specific patch, + transforming the geometries from a source CRS to a target projection defined + in the Visualisation class. + + :param patch_id: The unique identifier of the patch. + :type patch_id: float + :param source_crs: The source coordinate reference system of the geometries. + :type source_crs: str + :param table_name: The name of the table containing the points. + :type table_name: str + :return: SQL query string to get the points within the specified patch. + :rtype: str + """ + return f""" + SELECT + ST_AsWKB(ST_TRANSFORM(geom, '{source_crs}', '{Pipeline.Visualisation.projection}', always_xy := true)) AS geom, + FROM {table_name} + WHERE patch_id = {patch_id} + """ + + @staticmethod + def get_lcp_point(patch_id: float, source_crs: str) -> str: + """Get the local control point (LCP) within a specific patch. + + This method generates an SQL query to retrieve the local control point (LCP) + within a specific patch, transforming the geometry from a source CRS to a target + projection defined in the Visualisation class. + + :param patch_id: The unique identifier of the patch. + :type patch_id: float + :param source_crs: The source coordinate reference system of the geometries. + :type source_crs: str + :return: SQL query string to get the LCP within the specified patch. + """ + return f""" + SELECT + ST_AsWKB(ST_TRANSFORM(geom, '{source_crs}', '{Pipeline.Visualisation.projection}', always_xy := true)) AS geom + FROM pspoint + WHERE uid = ( + SELECT lcp_uid + FROM patches + WHERE uid = {patch_id} + ) + """ + + @staticmethod + def get_outlier_points(patch_id: float, source_crs: str) -> str: + """Get the outlier points within a specific patch. + + This method generates an SQL query to retrieve the outlier point within a specific patch, + transforming the geometry from a source CRS to a target projection defined in the Visualisation class. + + :param patch_id: The unique identifier of the patch. + :type patch_id: float + :param source_crs: The source coordinate reference system of the geometries. + :type source_crs: str + :return: SQL query string to get the outlier point within the specified patch. + """ + return f""" + SELECT + uid, + ST_AsWKB(ST_TRANSFORM(geom, '{source_crs}', '{Pipeline.Visualisation.projection}', always_xy := true)) AS geom + FROM pspoint + WHERE uid IN ( + SELECT uid + FROM outliers + WHERE patch_id = {patch_id} + ) + """ + + @staticmethod + def get_cum_disp_graph(outlier_id: float, scaling_factor: float) -> str: + """Get outlier displacement time series. + + This method generates an SQL query to retrieve the outlier displacement time series + for a specific patch, scaling the displacement values by a specified factor. + + :param outlier_id: The unique identifier of the patch. + :type outlier_id: float + :param scaling_factor: The factor to scale the displacement values. + :type scaling_factor: float + :return: SQL query string to get the outlier displacement time series for the specified patch. + """ + return f""" + SELECT + list_transform(disp_ts, x -> x * {scaling_factor}) + FROM outliers + WHERE uid = {outlier_id} + """ + + @staticmethod + def get_avg_cum_disp_graph(patch_id: float, scaling_factor: float) -> str: + """Get average cumulative displacement time series. + + This method generates an SQL query to retrieve the average cumulative displacement + time series for a specific patch, scaling the displacement values by a specified factor. + + :param patch_id: The unique identifier of the patch. + :type patch_id: float + :param scaling_factor: The factor to scale the displacement values. + :type scaling_factor: float + :return: SQL query string to get the average cumulative displacement time series for the specified patch. + """ + return f""" + SELECT + list_transform(avg_disp_ts_gcp, x -> x * {scaling_factor}) + FROM patches + WHERE uid = {patch_id} + """ + + @staticmethod + def get_std_cum_disp_graph(patch_id: float, scaling_factor: float) -> str: + """Get standard deviation of cumulative displacement time series. + + This method generates an SQL query to retrieve the standard deviation of cumulative + displacement time series for a specific patch, scaling the displacement values by a specified factor. + + :param patch_id: The unique identifier of the patch. + :type patch_id: float + :param scaling_factor: The factor to scale the displacement values. + :type scaling_factor: float + :return: SQL query string to get the standard deviation of cumulative displacement time series for the specified patch. + """ + return f""" + SELECT + list_transform(std_disp_ts_gcp, x -> x * {scaling_factor}) + FROM patches + WHERE uid = {patch_id} + """ + + def get_outlier_rel_ts_graph(outlier_id: float, scaling_factor: float) -> str: + # TODO: check the logic here + """Get outlier relative displacement time series. + + This method generates an SQL query to retrieve the outlier relative displacement + time series for a specific patch, scaling the displacement values by a specified factor. + + :param outlier_id: The unique identifier of the patch. + :type outlier_id: float + :param scaling_factor: The factor to scale the displacement values. + :type scaling_factor: float + :return: SQL query string to get the outlier relative displacement time series for the specified patch. + """ + return f""" + SELECT + list_transform(rel_ts, x -> x * {scaling_factor}) + FROM outliers + WHERE uid = {outlier_id} + """ + + @staticmethod + def get_lcp_disp_graph(patch_id: float, scaling_factor: float) -> str: + """Get local control point (LCP) displacement time series. + + This method generates an SQL query to retrieve the local control point (LCP) + displacement time series for a specific patch, scaling the displacement values by a specified factor. + + :param patch_id: The unique identifier of the patch. + :type patch_id: float + :param scaling_factor: The factor to scale the displacement values. + :type scaling_factor: float + :return: SQL query string to get the LCP displacement time series for the specified patch. + """ + return f""" + SELECT + list_transform(lcp_disp_ts, x -> x * {scaling_factor}) + FROM patches + WHERE uid = {patch_id} + """ + + @staticmethod + def get_avg_disp_lcp_graph(patch_id: float, scaling_factor: float) -> str: + """Get average displacement time series based on local control points (LCPs). + + This method generates an SQL query to retrieve the average displacement time series + based on local control points (LCPs) for a specific patch, scaling the displacement + values by a specified factor. + + :param patch_id: The unique identifier of the patch. + :type patch_id: float + :param scaling_factor: The factor to scale the displacement values. + :type scaling_factor: float + :return: SQL query string to get the average displacement time series based on LCPs for the specified patch. + """ + return f""" + SELECT + list_transform(avg_disp_ts_lcp, x -> x * {scaling_factor}) + FROM patches + WHERE uid = {patch_id} + """ + + @staticmethod + def get_std_disp_lcp_graph(patch_id: float, scaling_factor: float) -> str: + """Get standard deviation of displacement time series based on local control points (LCPs). + + This method generates an SQL query to retrieve the standard deviation of displacement + time series based on local control points (LCPs) for a specific patch, scaling the + displacement values by a specified factor. + + :param patch_id: The unique identifier of the patch. + :type patch_id: float + :param scaling_factor: The factor to scale the displacement values. + :type scaling_factor: float + :return: SQL query string to get the standard deviation of displacement time series based on LCPs for the specified patch. + """ + return f""" + SELECT + list_transform(std_disp_ts_lcp, x -> x * {scaling_factor}) + FROM patches + WHERE uid = {patch_id} + """ + + @staticmethod + def patch_uids() -> str: + """Get all patch UIDs that have outliers. + + This method generates an SQL query to retrieve all unique identifiers (UIDs) + of patches that have been identified as containing outliers. + + :return: SQL query string to get all patch UIDs with outliers. + :rtype: str + """ + return """ + SELECT patch_id + FROM outliers + GROUP BY patch_id; + """ diff --git a/src/saferoad/plotter.py b/src/saferoad/plotter.py new file mode 100644 index 0000000..a874fd7 --- /dev/null +++ b/src/saferoad/plotter.py @@ -0,0 +1,888 @@ +from matplotlib import pyplot as plt +import contextily as ctx +from shapely.wkb import loads as load_wkb +from matplotlib.lines import Line2D +from datetime import datetime +import matplotlib.dates as mdates + + +class Plotter: + """Class to create and manage plots for geospatial data analysis.""" + + class MapPane: + """ + Class to create and manage a multi-panel map figure. + + Panels include: + - PS Density + - Outliers + - Cumulative Displacement + - PS Velocity based on GCP + - PS Velocity based on LCP + """ + + def __init__(self): + self.generate_figure() + + def generate_figure(self): + """ + Generates a multi-panel figure with specified layout and titles. + """ + fig = plt.figure(figsize=(18, 12)) + layout = [ + ["A", "A", "B", "B", "C", "C"], # top row: 3 panels, equal widths + [".", "D", "D", "E", "E", "."], # bottom row: 2 panels centered + ] + axs = fig.subplot_mosaic(layout, empty_sentinel=".") + + # Optional spacing (no constrained_layout -> safer with contextily) + fig.subplots_adjust(wspace=0.25, hspace=0.35) + axs["A"].set_title("PS Density", fontsize=10) + axs["B"].set_title("Outliers", fontsize=10) + axs["C"].set_title("Cumulative Displacement", fontsize=10) + axs["D"].set_title("PS Velocity based on GCP", fontsize=10) + axs["E"].set_title("PS Velocity based on LCP", fontsize=10) + self.fig = fig + self.axs = [axs["A"], axs["B"], axs["C"], axs["D"], axs["E"]] + + def plot_basemap(self, bbox: tuple[float, float, float, float]): + """ + Plots a basemap on all axes using the provided bounding box. + + :param bbox: A tuple representing the bounding box (minx, miny, maxx, maxy). + """ + bbox = [i for i in bbox[0]] + + for ax in self.axs: + ax.set_xlim(bbox[0] - 2000, bbox[2] + 2000) + ax.set_ylim(bbox[1] - 2000, bbox[3] + 4000) + ctx.add_basemap( + ax, + source=ctx.providers.CartoDB.Positron, + crs="EPSG:3857", + ) + + def plot_ps_density(self, plot_data: list[tuple]) -> None: + """ + Plots PS Density data on the first axis. + + :param plot_data: List of tuples containing geometry and PS density value. + """ + label_store = [] + for geom, psdensity in plot_data: + geom = load_wkb(geom) + color, label = self.ps_density_color_label(psdensity) + if label not in label_store: + label_store.append(label) + else: + label = None + + self.axs[0].fill( + *geom.exterior.xy, + fc=color, + ec=color, + label=label, + ) + self.axs[0].legend( + title="PS Density", + loc="upper right", + fontsize=6, + title_fontsize=6, + frameon=True, + facecolor="white", + edgecolor="black", + ) + + def plot_outliers(self, plot_data: list[tuple]) -> None: + """ + Plots outlier data on the second axis. + + :param plot_data: List of tuples containing geometry and outlier velocity value. + """ + label_store = [] + for geom, vel in plot_data: + geom = load_wkb(geom) + color, label = self.outlier_color_label(vel) + if label not in label_store: + label_store.append(label) + else: + label = None + + self.axs[1].scatter( + *geom.xy, + s=10, + c=color, + label=label, + ) + self.axs[1].legend( + title="Outlier", + loc="upper right", + fontsize=6, + title_fontsize=6, + frameon=True, + facecolor="white", + edgecolor="black", + ) + + def plot_cum_disp(self, plot_data: list[tuple]) -> None: + """ + Plots cumulative displacement data on the third axis. + + :param plot_data: List of tuples containing geometry and cumulative displacement value. + """ + label_store = [] + for geom, disp in plot_data: + geom = load_wkb(geom) + color, label = self.cum_disp_color_label(disp) + if label not in label_store: + label_store.append(label) + else: + label = None + + self.axs[2].fill( + *geom.exterior.xy, + fc=color, + ec=color, + label=label, + ) + self.axs[2].legend( + title="Cumulative\nDisplacement", + loc="upper right", + fontsize=6, + title_fontsize=6, + frameon=True, + facecolor="white", + edgecolor="black", + ) + + def plot_gcp_velocity(self, plot_data: list[tuple]) -> None: + """Plots GCP velocity data on the fourth axis. + + :param plot_data: List of tuples containing geometry and GCP velocity value. + """ + points, colors = [], [] + for geom, vel in plot_data: + geom = load_wkb(geom) + points.append(geom.xy) + colors.append(self.gcp_vel_color(vel)) + + x_coords = [p[0][0] for p in points] + y_coords = [p[1][0] for p in points] + + self.axs[3].scatter( + x_coords, + y_coords, + s=4, + c=colors, + alpha=0.7, + ) + legend_elements = [ + Line2D( + [0], + [0], + marker="o", + color="w", + markerfacecolor="red", + markersize=6, + label="(-15,-6) mm", + ), + Line2D( + [0], + [0], + marker="o", + color="w", + markerfacecolor="orange", + markersize=6, + label="(-6,-4) mm", + ), + Line2D( + [0], + [0], + marker="o", + color="w", + markerfacecolor="yellow", + markersize=6, + label="(-4,-2) mm", + ), + Line2D( + [0], + [0], + marker="o", + color="w", + markerfacecolor="lightgreen", + markersize=6, + label="(-2,2) mm", + ), + Line2D( + [0], + [0], + marker="o", + color="w", + markerfacecolor="cyan", + markersize=6, + label="(2,5) mm", + ), + Line2D( + [0], + [0], + marker="o", + color="w", + markerfacecolor="blue", + markersize=6, + label="(5,15) mm", + ), + ] + + self.axs[3].legend( + handles=legend_elements, + title="PS Velocity GCP", + loc="upper right", + fontsize=6, + title_fontsize=6, + frameon=True, + facecolor="white", + edgecolor="black", + ) + + def plot_lcp_velocity(self, plot_data: list[tuple]) -> None: + """Plots LCP velocity data on the fifth axis. + + :param plot_data: List of tuples containing geometry and LCP velocity value. + """ + points, colors = [], [] + for geom, vel in plot_data: + geom = load_wkb(geom) + points.append(geom.xy) + colors.append(self.lcp_vel_color(vel)) + x_coords = [p[0][0] for p in points] + y_coords = [p[1][0] for p in points] + + self.axs[4].scatter( + x_coords, + y_coords, + s=2, + c=colors, + alpha=0.6, + ) + + legend_elements = [ + Line2D( + [0], + [0], + marker="o", + color="w", + markerfacecolor="green", + markersize=6, + label="(0,2) mm", + ), + Line2D( + [0], + [0], + marker="o", + color="w", + markerfacecolor="red", + markersize=6, + label="(2,12) mm", + ), + ] + self.axs[4].legend( + handles=legend_elements, + title="PS Velocity LCP", + loc="upper right", + fontsize=6, + title_fontsize=6, + frameon=True, + facecolor="white", + edgecolor="black", + ) + + def post_process(self): + """ + Post-processes the figure for better aesthetics. + + """ + for i in [1, 2, 4]: + self.axs[i].get_yaxis().set_visible(False) + xticks = self.axs[i].get_xticks() + self.axs[i].set_xticks(xticks[::2]) # Keep every other tick + self.axs[i].set_xticklabels(self.axs[i].get_xticks()) + for i in [0, 3]: + yticks = self.axs[i].get_yticks() + self.axs[i].set_yticks(yticks[::3]) # Keep every other tick + self.axs[i].set_yticklabels( + self.axs[i].get_yticks(), + horizontalalignment="center", + verticalalignment="center", + ) + xticks = self.axs[i].get_xticks() + self.axs[i].set_xticks(xticks[::2]) # Keep every other tick + self.axs[i].set_xticklabels(self.axs[i].get_xticks()) + + # self.fig.tight_layout() + + def get_figure(self) -> tuple[plt.Figure, list[plt.Axes]]: + """ + Returns the figure and axes. + + :return: Tuple containing the figure and axes. + :rtype: tuple[plt.Figure, list[plt.Axes]] + """ + return self.fig, self.axs + + def outlier_color_label(self, value: float) -> tuple[str, str]: + """Returns color and label based on outlier velocity value. + + :param value: Outlier velocity value. + :type value: float + :return: Tuple containing color and label. + :rtype: tuple[str, str] + """ + if 5 <= value <= 7: + return "yellow", "5<|v|<7 mm/yr" + elif 7 < value <= 9: + return "orange", "7<|v|<9 mm/yr" + else: + return "red", "9<|v|<12 mm/yr" + + def ps_density_color_label(self, value: float) -> tuple[str, str]: + """Returns color and label based on PS density value. + + :param value: PS density value. + :type value: float + :return: Tuple containing color and label. + :rtype: tuple[str, str] + """ + if value < 100: + return "red", "Low" + if 100 <= value < 500: + return "yellow", "Medium" + else: + return "green", "High" + + def cum_disp_color_label(self, value: float) -> str: + """Returns color and label based on cumulative displacement value. + + :param value: Cumulative displacement value. + :type value: float + :return: Tuple containing color and label. + :rtype: tuple[str, str] + """ + if value < -20: + return "red", "(-60,-20) mm" + elif value < -15: + return "orange", "(-20,-15) mm" + elif value < -5: + return "yellow", "(-15,-5) mm" + elif value < 5: + return "lightgreen", "(-5,5) mm" + elif value < 15: + return "cyan", "(5,15) mm" + else: + return "blue", "(15,60) mm" + + def gcp_vel_color(self, value: float) -> str: + """Returns color based on GCP velocity value. + + :param value: GCP velocity value. + :type value: float + :return: Color string. + :rtype: str + """ + if value <= -6: + return "red" + elif value <= -4: + return "orange" + elif value <= -2: + return "yellow" + elif value <= 2: + return "lightgreen" + elif value <= 5: + return "cyan" + else: + return "blue" + + def lcp_vel_color(self, value: float) -> str: + """Returns color based on LCP velocity value. + + :param value: LCP velocity value. + :type value: float + :return: Color string. + :rtype: str + """ + if value <= 2: + return "green" + else: + return "red" + + class TimeSeriesPane: + """Class to create and manage a multi-panel time series figure. + Panels include: + - Basemap with Outliers + - Basemap with PS Points + - Cumulative Displacement + - Relative Displacement + - Local Control Point Displacement + """ + + def __init__(self): + self.generate_figure() + + def generate_figure(self): + """Generates a multi-panel figure with specified layout and titles.""" + fig = plt.figure(figsize=(12, 12), dpi=150) + layout = [ + 3 * ["A"] + 3 * ["C"], # ["A", "A", "A", "C", "C", "C"], + 3 * ["A"] + 3 * ["C"], # ["A", "A", "A", "C", "C", "C"], + 3 * ["A"] + 3 * ["D"], # ["A", "A", "A", "D", "D", "D"], + 3 * ["B"] + 3 * ["D"], # ["B", "B", "B", "D", "D", "D"], + 3 * ["B"] + 3 * ["E"], # ["B", "B", "B", "E", "E", "E"], + 3 * ["B"] + 3 * ["E"], # ["B", "B", "B", "E", "E", "E"], + ] + axs = fig.subplot_mosaic(layout, empty_sentinel=".") + fig.subplots_adjust(wspace=1, hspace=1) + axs["C"].set_title("Cumulative Displacement", fontsize=10) + axs["C"].set_ylabel("[mm]", fontsize=8, labelpad=2) + axs["D"].set_title("Relative Displacement", fontsize=10) + axs["D"].set_ylabel("[mm]", fontsize=8, labelpad=2) + axs["E"].set_title("Local Control Point Displacement", fontsize=10) + axs["E"].set_ylabel("[mm]", fontsize=8, labelpad=2) + self.fig = fig + self.axs = [axs["A"], axs["B"], axs["C"], axs["D"], axs["E"]] + plt.close() + + def plot_basemap_outlier(self, center: tuple[float, float]): + """ + Plots a basemap on the first axis using the provided center point. + + :param center: A tuple representing the center point (x, y). + :type center: tuple[float, float] + """ + self.axs[0].set_xlim(center[0][0] - 2000, center[0][0] + 2000) + self.axs[0].set_ylim(center[1][0] - 2000, center[1][0] + 4000) + ctx.add_basemap( + self.axs[0], + source=ctx.providers.CartoDB.Positron, + crs="EPSG:3857", + ) + + def plot_basemap_pspoint(self, center: tuple[float, float]): + """ + Plots a basemap on the second axis using the provided center point. + + :param center: A tuple representing the center point (x, y). + :type center: tuple[float, float] + """ + self.axs[1].set_xlim(center[0][0] - 1000, center[0][0] + 1000) + self.axs[1].set_ylim(center[1][0] - 1000, center[1][0] + 1000) + ctx.add_basemap( + self.axs[1], + source=ctx.providers.CartoDB.Positron, + crs="EPSG:3857", + ) + + def plot_all_outliers(self, plot_data: list[tuple]) -> None: + """Plots all outlier data on the first axis. + + :param plot_data: List of tuples containing geometry and outlier velocity value. + :type plot_data: list[tuple] + """ + label_store = [] + for geom, vel in plot_data: + geom = load_wkb(geom) + color, label = self.outlier_color_label(vel) + if label not in label_store: + label_store.append(label) + else: + label = None + + self.axs[0].scatter( + *geom.xy, + s=10, + c=color, + label=label, + ) + self.axs[0].legend( + title="Outlier", + loc="upper right", + fontsize=6, + title_fontsize=6, + frameon=True, + facecolor="white", + edgecolor="black", + ) + + def plot_square(self, plot_data: list[tuple]) -> None: + """Plots a square on the first axis to indicate zoom area. + + :param plot_data: List of tuples containing geometry and outlier velocity value. + :type plot_data: list[tuple] + """ + label_store = [] + x, y = plot_data[0][0], plot_data[1][0] + square = [ + (x - 75, y - 75), + (x + 75, y - 75), + (x + 75, y + 75), + (x - 75, y + 75), + (x - 75, y - 75), # Close the square + ] + square_x, square_y = zip(*square) + self.axs[0].fill( + square_x, + square_y, + fc="none", + ec="black", + label="Zoom Area", + ) + self.axs[0].legend( + title="Outliers", + loc="upper right", + fontsize=6, + title_fontsize=6, + frameon=True, + facecolor="white", + edgecolor="black", + ) + + def plot_pspoint_dist(self, plot_data: list[tuple]) -> None: + """Plots PS Points on the second axis. + + :param plot_data: List of tuples containing geometry. + :type plot_data: list[tuple] + """ + points = [] + for geom in plot_data: + if isinstance(geom, tuple) and len(geom) > 0: + geom = load_wkb(geom[0]) + points.append(geom.xy) + x_coords = [p[0][0] for p in points] + y_coords = [p[1][0] for p in points] + + self.axs[1].scatter( + x_coords, + y_coords, + s=6, + c="black", + label="PS Points", + ) + self.axs[1].legend( + title="Legend", + loc="upper right", + fontsize=6, + title_fontsize=6, + frameon=True, + facecolor="white", + edgecolor="black", + ) + + def plot_outlier(self, plot_data: list[tuple]) -> None: + """Plots outlier data on the second axis. + + :param plot_data: List of tuples containing geometry and outlier velocity value. + :type plot_data: list[tuple] + """ + label_store = [] + for geom, vel in plot_data: + geom = load_wkb(geom) + color, label = self.outlier_color_label(vel) + if label not in label_store: + label_store.append(label) + else: + label = None + + self.axs[1].scatter( + *geom.xy, + s=16, + c=color, + label=label, + ) + self.axs[1].legend( + title="Legend", + loc="upper right", + fontsize=6, + title_fontsize=6, + frameon=True, + facecolor="white", + edgecolor="black", + ) + + def plot_lcp(self, plot_data: list[tuple]) -> None: + """Plots LCP data on the second axis. + + :param plot_data: List of tuples containing geometry. + :type plot_data: list[tuple] + """ + geom = load_wkb(plot_data[0]) + self.axs[1].scatter( + *geom.xy, + s=20, + marker="^", + c="lightblue", + label="LCP", + ) + self.axs[1].legend( + title="Legend", + loc="upper right", + fontsize=6, + title_fontsize=6, + frameon=True, + facecolor="white", + edgecolor="black", + ) + + def plot_cum_disp_graph( + self, plot_data: list[tuple], dates: list[datetime] + ) -> None: + """Plots cumulative displacement data on the third axis. + + :param plot_data: List of tuples containing cumulative displacement values. + :type plot_data: list[tuple] + :param dates: List of datetime objects corresponding to the displacement values. + :type dates: list[datetime] + """ + y = plot_data[0][0] + x = dates + self.axs[2].scatter( + x, + y, + s=6, + color="black", + ) + + def plot_avg_disp_graph( + self, plot_data: list[tuple], dates: list[datetime] + ) -> None: + """Plots average cumulative displacement data on the third axis. + + :param plot_data: List of tuples containing average cumulative displacement values. + :type plot_data: list[tuple] + :param dates: List of datetime objects corresponding to the displacement values. + :type dates: list[datetime] + """ + + y = plot_data[0][0] + x = dates + self.axs[2].plot( + x, + y, + color="red", + linewidth=1, + ) + + def plot_std_disp_graph( + self, avg_data: list[tuple], std_data: list[tuple], dates: list[datetime] + ): + """Plots standard deviation of cumulative displacement data on the third axis. + + :param avg_data: List of tuples containing average cumulative displacement values. + :type avg_data: list[tuple] + :param std_data: List of tuples containing standard deviation of cumulative displacement values. + :type std_data: list[tuple] + :param dates: List of datetime objects corresponding to the displacement values. + :type dates: list[datetime] + """ + y_avg = avg_data[0][0] + y_std = std_data[0][0] + x = dates + self.axs[2].fill_between( + x, + [y_avg[i] - 2 * y_std[i] for i in range(len(y_avg))], + [y_avg[i] + 2 * y_std[i] for i in range(len(y_avg))], + color="dimgray", + alpha=0.5, + # label="±2 Std Dev", + ) + self.axs[2].fill_between( + x, + [y_avg[i] - y_std[i] for i in range(len(y_avg))], + [y_avg[i] + y_std[i] for i in range(len(y_avg))], + color="whitesmoke", + alpha=0.5, + # label="±1 Std Dev", + ) + + def plot_lcp_disp_graph( + self, plot_data: list[tuple], dates: list[datetime] + ) -> None: + """Plots LCP displacement data on the fifth axis. + + :param plot_data: List of tuples containing LCP displacement values. + :type plot_data: list[tuple] + :param dates: List of datetime objects corresponding to the displacement values. + :type dates: list[datetime] + """ + y = plot_data[0][0] + x = dates + self.axs[4].scatter( + x, + y, + s=6, + color="black", + ) + + def plot_avg_lcp_disp_graph( + self, plot_data: list[tuple], dates: list[datetime] + ) -> None: + """Plots average LCP displacement data on the fifth axis. + + :param plot_data: List of tuples containing average LCP displacement values. + :type plot_data: list[tuple] + :param dates: List of datetime objects corresponding to the displacement values. + :type dates: list[datetime] + """ + y = plot_data[0][0] + x = dates + self.axs[4].plot( + x, + y, + color="red", + linewidth=1, + ) + + def plot_std_lcp_disp_graph( + self, avg_data: list[tuple], std_data: list[tuple], dates: list[datetime] + ): + """Plots standard deviation of LCP displacement data on the fifth axis. + + :param avg_data: List of tuples containing average LCP displacement values. + :type avg_data: list[tuple] + :param std_data: List of tuples containing standard deviation of LCP displacement values. + :type std_data: list[tuple] + :param dates: List of datetime objects corresponding to the displacement values. + :type dates: list[datetime] + """ + y_avg = avg_data[0][0] + y_std = std_data[0][0] + x = dates + self.axs[4].fill_between( + x, + [y_avg[i] - 2 * y_std[i] for i in range(len(y_avg))], + [y_avg[i] + 2 * y_std[i] for i in range(len(y_avg))], + color="dimgray", + alpha=0.5, + # label="±2 Std Dev", + ) + self.axs[4].fill_between( + x, + [y_avg[i] - y_std[i] for i in range(len(y_avg))], + [y_avg[i] + y_std[i] for i in range(len(y_avg))], + color="whitesmoke", + alpha=0.5, + # label="±1 Std Dev", + ) + + def plot_rel_disp_graph( + self, plot_data: list[tuple], dates: list[datetime] + ) -> None: + """Plots relative displacement data on the fourth axis. + + :param plot_data: List of tuples containing relative displacement values. + :type plot_data: list[tuple] + :param dates: List of datetime objects corresponding to the displacement values. + :type dates: list[datetime] + """ + y = plot_data[0][0] + x = dates + self.axs[3].scatter( + x, + y, + s=6, + color="black", + ) + + def plot_avg_rel_disp_graph( + self, plot_data: list[tuple], dates: list[datetime] + ) -> None: + """Plots average relative displacement data on the fourth axis. + + :param plot_data: List of tuples containing average relative displacement values. + :type plot_data: list[tuple] + :param dates: List of datetime objects corresponding to the displacement values. + :type dates: list[datetime] + """ + y = plot_data[0][0] + x = dates + self.axs[3].plot( + x, + y, + color="red", + linewidth=1, + ) + + def plot_std_rel_disp_graph( + self, avg_data: list[tuple], std_data: list[tuple], dates: list[datetime] + ): + """Plots standard deviation of relative displacement data on the fourth axis. + + :param avg_data: List of tuples containing average relative displacement values. + :type avg_data: list[tuple] + :param std_data: List of tuples containing standard deviation of relative displacement values. + :type std_data: list[tuple] + :param dates: List of datetime objects corresponding to the displacement values. + :type dates: list[datetime] + """ + y_avg = avg_data[0][0] + y_std = std_data[0][0] + x = dates + self.axs[3].fill_between( + x, + [y_avg[i] - 2 * y_std[i] for i in range(len(y_avg))], + [y_avg[i] + 2 * y_std[i] for i in range(len(y_avg))], + color="dimgray", + alpha=0.5, + # label="±2 Std Dev", + ) + self.axs[3].fill_between( + x, + [y_avg[i] - y_std[i] for i in range(len(y_avg))], + [y_avg[i] + y_std[i] for i in range(len(y_avg))], + color="whitesmoke", + alpha=0.5, + # label="±1 Std Dev", + ) + + def outlier_color_label(self, value: float) -> tuple[str, str]: + """Returns color and label based on outlier velocity value. + + :param value: Outlier velocity value. + :type value: float + :return: Tuple containing color and label. + :rtype: tuple[str, str] + """ + if value <= 7: + return "yellow", "5<|v|<7 mm/yr" + elif value <= 9: + return "orange", "7<|v|<9 mm/yr" + else: + return "red", "9<|v|<12 mm/yr" + + def post_process(self): + """Post-processes the figure for better aesthetics.""" + for i in [0, 1]: + xticks = self.axs[i].get_xticks() + yticks = self.axs[i].get_yticks() + self.axs[i].set_xticks(xticks[::3]) # Keep every other tick + self.axs[i].set_xticklabels(self.axs[i].get_xticks()) + self.axs[i].set_yticks(yticks[::3]) # Keep every other tick + self.axs[i].set_yticklabels(self.axs[i].get_yticks()) + + ymins, ymaxs = [], [] + + for ax in [self.axs[2], self.axs[3], self.axs[4]]: + ymin, ymax = ax.get_ylim() + ymins.append(ymin) + ymaxs.append(ymax) + + for ax in [self.axs[2], self.axs[3], self.axs[4]]: + ax.set_ylim(min(ymins), max(ymaxs)) + ax.xaxis.set_major_locator(mdates.YearLocator()) + ax.xaxis.set_major_formatter(mdates.DateFormatter("%Y")) + ax.xaxis.set_minor_locator(mdates.MonthLocator()) + + def get_figure(self) -> tuple[plt.Figure, list[plt.Axes]]: + """Returns the figure and axes. + + :return: Tuple containing the figure and axes. + :rtype: tuple[plt.Figure, list[plt.Axes]] + """ + return self.fig, self.axs diff --git a/src/saferoad/saferoad.py b/src/saferoad/saferoad.py new file mode 100644 index 0000000..565dcac --- /dev/null +++ b/src/saferoad/saferoad.py @@ -0,0 +1,660 @@ +# standard modules +import io +from datetime import datetime +from joblib import Parallel, delayed +from shapely.wkb import loads as wkb_loads +from dataclasses import dataclass +from typing import Literal +from tqdm import tqdm + +# plotting modules +import matplotlib +from matplotlib.backends.backend_pdf import PdfPages +from matplotlib import pyplot as plt + +matplotlib.use("Agg") +# saferoad modules +from .plotter import Plotter +from .database import DataBase +from .pipeline import Pipeline +from .utils import extract_dates, time_vector, Timer + + +@dataclass +class PsData: + """Class for storing point source data information. + + :param filepath: Path to the point source data file. + :type filepath: str + :param latitude: Field name of the latitude field in the data file. + :type latitude: str + :param longitude: Field name of the longitude field in the data file. + :type longitude: str + :param unit: Unit of measurement for the coordinates. Options are "m", "cm", or "mm". Default is "m". + :type unit: Literal["m", "cm", "mm"] + :param crs_code: Coordinate Reference System code. Default is "EPSG:4326". + :type crs_code: str + :param name: Name identifier for the point source data. Default is "pspoint". + :type name: str + """ + + filepath: str + latitude: str + longitude: str + unit: Literal["m", "cm", "mm"] = "m" + crs_code: str = "EPSG:4326" + name: str = "pspoint" + + def __post_init__(self): + assert self.filepath, "File path must be provided." + assert isinstance(self.filepath, str), "File path must be a string." + assert self.latitude, "Latitude field name must be provided." + assert isinstance(self.latitude, str), "Latitude field name must be a string." + assert self.longitude, "Longitude field name must be provided." + assert isinstance(self.longitude, str), "Longitude field name must be a string." + assert self.unit in ["m", "cm", "mm"], "Unit must be one of 'm', 'cm', or 'mm'." + assert self.crs_code, "CRS code must be provided." + assert isinstance(self.crs_code, str), "CRS code must be a string." + assert self.name, "Name must be provided." + assert isinstance(self.name, str), "Name must be a string." + + +@dataclass +class Road: + """Class for storing road data information. + + :param filepath: Path to the road data file. + :type filepath: str + :param crs_code: Coordinate Reference System code. Default is "EPSG:4326". + :type crs_code: str + :param name: Name identifier for the road data. Default is "road". + :type name: str + """ + + filepath: str + crs_code: str = "EPSG:4326" + name: str = "road" + + def __post_init__(self): + assert self.filepath, "File path must be provided." + assert isinstance(self.filepath, str), "File path must be a string." + assert self.name, "Name must be provided." + assert isinstance(self.name, str), "Name must be a string." + + +class SafeRoad: + """ + A class to perform road deformation analysis using MT-InSAR data. + + :param road: An instance of the Road class containing road data information. + :type road: Road + :param ps_data: An instance of the PsData class containing MT-InSAR data information. + :type ps_data: PsData + :param computational_crs: A string representing the EPSG code for the computational CRS. + :type computational_crs: str + """ + + def __init__(self, road: Road, ps_data: PsData, computational_crs: str): + + assert isinstance(road, Road), "road must be an instance of Road class" + assert isinstance( + ps_data, PsData + ), "ps_data must be an instance of PsData class" + assert isinstance( + computational_crs, str + ), "computational_crs must be a string representing the EPSG code" + + self.ps_data = ps_data + self.road_data = road + self.database = DataBase() + # self.scaling_factor = self._set_scaling_factor_() + self.computational_crs = computational_crs + + @property + def _scaling_factor_(self): + # determine the scaling factor based on the unit + units = ["m", "cm", "mm"] + sf = [1000, 10, 1] + return sf[units.index(self.ps_data.unit)] + + def load_files(self): + """ + Load the road and MT-InSAR data files into the database. + """ + + # setup the db if loading + with Timer("Setting up the database"): + self.database.setup() + # road_query + with Timer("Loading road data"): + self.database.run( + Pipeline.Processing.load_file( + self.road_data.filepath, self.road_data.name + ) + ) + # ps_query + with Timer("Loading ps data"): + self.database.run( + Pipeline.Processing.load_file(self.ps_data.filepath, self.ps_data.name) + ) + + def preprocess(self, attribute: str = None): + """ + Preprocess the road and MT-InSAR data. + + This includes transforming the data to the computational CRS, dissolving road features, + and building geometries for the MT-InSAR points. + 1. Transform the road and MT-InSAR data to the computational CRS. + 2. Dissolve road features based on the provided attribute if given, otherwise merge all features. + 3. Build geometries for the MT-InSAR points from the latitude and longitude columns. + 4. Transform the MT-InSAR data to the computational CRS. + + :param attribute: An optional string representing the attribute to dissolve road features. + If None, all features will be merged. Default is None. + :type attribute: str, optional + """ + # Transform the road projection + with Timer("Transforming road data to computational CRS"): + self.database.run( + Pipeline.Processing.spatial_tranformation( + source_epsg=self.road_data.crs_code, + target_epsg=self.computational_crs, + table_name=self.road_data.name, + ) + ) + + # Dissolve the road features based on the provided attribute if given if not merge everything + with Timer("Dissolving road features"): + self.database.run( + Pipeline.Processing.dissolve_features( + attribute=attribute, table_name=self.road_data.name + ) + ) + # build the geometry for the psoints from csv headers + with Timer("Building geometry for PS data"): + self.database.run( + Pipeline.Processing.build_geometry( + latitude=self.ps_data.latitude, + longitude=self.ps_data.longitude, + table_name=self.ps_data.name, + ) + ) + + # Transform the ps data to the computational CRS + with Timer("Transforming PS data to computational CRS"): + self.database.run( + Pipeline.Processing.spatial_tranformation( + source_epsg=self.ps_data.crs_code, + target_epsg=self.computational_crs, + table_name=self.ps_data.name, + ) + ) + + def generate_rectangles(self, road_width: float, segment_length: float): + """ + Generate road segments and patches. + + This involves: + 1. Splitting the road into segments of equal length. + 2. Generating rectangles along the road segments with a specified width. + 2. Rotating the generated rectangles int the same direction along the road. + + :param road_width: A float representing the width of the road segments in meters. + :param segment_length: A float representing the length of each road segment in meters. + """ + + with Timer("Generating road segments and patches"): + self.database.run( + Pipeline.Processing.split_to_segments(segment_length=segment_length) + ) + self.database.run( + Pipeline.Processing.generate_patches( + width=road_width, length=segment_length + ) + ) + + def run_analysis(self): + """ + Run the deformation analysis on the MT-InSAR data for road network. + + This includes calculating cumulative displacement, average velocity, point density, + local control points, and outliers. + """ + # calculate displacement per points + # get the column names from the ps_data + fields = [ + i[0] + for i in self.database.connection.sql( + Pipeline.Processing.get_column_names(self.ps_data.name) + ).fetchall() + ] + + # extract dates from the column names and generate time vector + names, dates = extract_dates(column_names=fields) + date_vector = time_vector(dates=dates) + + with Timer("Calculating cumulative displacement per point"): + self.database.run( + Pipeline.Processing.calc_cum_disp( + dates=names, table_name=self.ps_data.name + ) + ) + + # build spatial relation with + # assign points to patches generate patch_id field on pspoint table + with Timer("Relating points to patches"): + self.database.run( + Pipeline.Processing.relate_point_patches(self.ps_data.name) + ) + + # calculate average velocity per point needs patch_id in pspoint table for optimization purposes + with Timer("Calculating average velocity per point"): + self.database.run( + Pipeline.Processing.calc_avg_velocity( + time_vector=date_vector, + field_names=names, + table_name=self.ps_data.name, + ) + ) + + # calculate point count per patch and point density + with Timer("Calculating point density per patch"): + self.database.run(Pipeline.Processing.calc_point_density(self.ps_data.name)) + # calculate average cumulative displacement per patch + with Timer("Calculating average cumulative displacement per patch"): + self.database.run(Pipeline.Processing.calc_avg_cum_disp()) + # calculate average displacement time series per patch based on gcp + with Timer( + "Calculating average displacement time series per patch based on GCP" + ): + self.database.run( + Pipeline.Processing.calc_avg_disp_ts_gcp(date_fields=names) + ) + # calculate standard deviation of displacement time series per patch based on gcp + with Timer( + "Calculating standard deviation of displacement time series per patch based on GCP" + ): + self.database.run( + Pipeline.Processing.calc_std_disp_ts_gcp(date_fields=names) + ) + + # calculate average velocity per patch based on gcp + with Timer("Calculating average velocity per patch based on GCP"): + self.database.run( + Pipeline.Processing.calc_avg_velocity_gcp(table_name=self.ps_data.name) + ) + + # calculate the local control point per patch + with Timer("Calculating local control points per patch"): + self.database.run(Pipeline.Processing.select_lcps(self.ps_data.name)) + + # calculate the local control point avg velocity per patch + with Timer("Calculating local control points average velocity per patch"): + self.database.run(Pipeline.Processing.calc_lcp_velocity()) + + # calculate the average and std of the displacement time series per patch based on lcp + with Timer( + "Calculating average and std of displacement time series per patch based on LCP" + ): + self.database.run( + Pipeline.Processing.calc_avg_std_disp_ts_lcp( + date_fields=names, table_name=self.ps_data.name + ) + ) + + # calculate the median and std of the displacement time series per patch based on lcp + with Timer( + "Calculating median and std of displacement time series per patch based on LCP" + ): + self.database.run( + Pipeline.Processing.calc_med_std_disp_ts_lcp( + date_fields=names, table_name=self.ps_data.name + ) + ) + + # calculate the average velocity per patch based on lcp + with Timer("Calculating average velocity per patch based on LCP"): + self.database.run( + Pipeline.Processing.calc_avg_velocity_lcp(table_name=self.ps_data.name) + ) + + # calculate the average relative velocity per patch based on lcp + with Timer("Calculating average relative velocity per patch based on LCP"): + self.database.run( + Pipeline.Processing.calc_avg_lcp_velocity(self.ps_data.name) + ) + + # calulate the outliers per patch based + with Timer("Calculating outliers per patch"): + self.database.run( + Pipeline.Processing.calc_outliers( + self._scaling_factor_, self.ps_data.name + ) + ) + + threshold_count = self.database.connection.execute( + "Select count(*) from outliers;" + ).fetchone()[0] + print("%s outliers detected by threshold method" % threshold_count) + + self.database.run( + Pipeline.Processing.calc_outliers_avg_lcd( + self.ps_data.name, date_vector=date_vector, date_names=names + ) + ) + ald_count = ( + self.database.connection.execute( + "Select count(*) from outliers;" + ).fetchone()[0] + - threshold_count + ) + print("%s outliers detected by avg local disp method" % (ald_count)) + + self.database.run( + Pipeline.Processing.calc_outliers_med_lcd( + self.ps_data.name, date_vector=date_vector, date_names=names + ) + ) + med_count = ( + self.database.connection.execute( + "select count(*) from outliers;" + ).fetchone()[0] + - ald_count + - threshold_count + ) + print("%s outliers detected by median local disp method" % (med_count)) + + with Timer("Collecting and patching the LCP time series"): + self.database.run( + Pipeline.Processing.lcp_ts_patch( + date_fields=names, table_name=self.ps_data.name + ) + ) + with Timer("Collecting outlier time series"): + self.database.run( + Pipeline.Processing.outlier_ts_patch( + date_fields=names, table_name=self.ps_data.name + ) + ) + with Timer("collecting differential displacement time series"): + self.database.run(Pipeline.Processing.outlier_rel_ts_patch()) + + def generate_report(self, output_name: str): + """ + Generate a PDF report containing maps and time series plots. + + :param output_name: A string representing the name of the output PDF file endswith `.pdf` extension. + :type output_name: str + """ + + def render_ts_plots_workers( + patch_uid: float, + outliers: list[tuple], + dates: list[datetime], + outlier_center: tuple[float, float], # + patch_center: tuple, + pspoints: list[tuple], + lcp_point: list[tuple], + cum_disp: list[tuple], + avg_cum_disp: list[tuple], + std_cum_disp: list[tuple], + outlier_rel_ts: list[tuple], + avg_lcp_disp: list[tuple], + std_lcp_disp: list[tuple], + lcp_disp: list[tuple], + ) -> bytes: + + # plotting + tsplotter = Plotter.TimeSeriesPane() + # 0 + tsplotter.plot_basemap_outlier(wkb_loads(outlier_center[0]).xy) + tsplotter.plot_all_outliers(outliers) + tsplotter.plot_square(wkb_loads(outlier_center[0]).xy) + tsplotter.plot_basemap_pspoint(wkb_loads(patch_center[0]).xy) + # 1 + tsplotter.plot_pspoint_dist(pspoints) + tsplotter.plot_lcp(lcp_point) + tsplotter.plot_outlier(outliers) + # 2 + tsplotter.plot_std_disp_graph(avg_cum_disp, std_cum_disp, dates) + tsplotter.plot_avg_disp_graph(avg_cum_disp, dates) + tsplotter.plot_cum_disp_graph(cum_disp, dates) + + # 3 + tsplotter.plot_std_rel_disp_graph(avg_lcp_disp, std_lcp_disp, dates) + tsplotter.plot_avg_rel_disp_graph(avg_lcp_disp, dates) + tsplotter.plot_rel_disp_graph(outlier_rel_ts, dates) + + # 4 + tsplotter.plot_std_lcp_disp_graph(avg_cum_disp, std_cum_disp, dates) + tsplotter.plot_avg_lcp_disp_graph(avg_cum_disp, dates) + tsplotter.plot_lcp_disp_graph(lcp_disp, dates) + tsplotter.post_process() + + fig, _ = tsplotter.get_figure() + + buf = io.BytesIO() + fig.savefig(buf, format="png") + plt.close(fig) + return buf.getvalue() + + # export the results + with Timer("Generating and saving the report"): + with PdfPages(output_name) as pdf: + # bbox of the patches + + bbox = self.database.connection.execute( + Pipeline.Visualisation.get_table_bbox( + "patches", self.computational_crs + ) + ).fetchall() + # ps density and patch geometries + ps_density = self.database.connection.execute( + Pipeline.Visualisation.get_ps_density(self.computational_crs) + ).fetchall() + # outliers and coordinates + outliers = self.database.connection.execute( + Pipeline.Visualisation.get_outliers_map( + self.computational_crs, self._scaling_factor_ + ) + ).fetchall() + # cumulative displacement per point + cum_disp = self.database.connection.execute( + Pipeline.Visualisation.get_cum_disp_map( + self.computational_crs, self._scaling_factor_ + ) + ).fetchall() + # gcp average velocity per point + gcp_vel = self.database.connection.execute( + Pipeline.Visualisation.get_ps_vel_gcp( + self.computational_crs, self._scaling_factor_ + ) + ).fetchall() + # lcp average velocity per point + lcp_vel = self.database.connection.execute( + Pipeline.Visualisation.get_ps_vel_lcp( + self.computational_crs, self._scaling_factor_ + ) + ).fetchall() + + # plotting the first page -- maps + print("Starting to render the map page") + map_plotter = Plotter.MapPane() + map_plotter.plot_basemap(bbox=bbox) + map_plotter.plot_ps_density(ps_density) + map_plotter.plot_outliers(outliers) + map_plotter.plot_cum_disp(cum_disp) + map_plotter.plot_gcp_velocity(gcp_vel) + map_plotter.plot_lcp_velocity(lcp_vel) + map_plotter.post_process() + fig, _ = map_plotter.get_figure() + plt.close(fig) + pdf.savefig(fig) + print("Map page has been rendered") + + print("Intiating time series rendering") + # collect the patch_ids which has outliers + puids = [ + i[0] + for i in self.database.connection.execute( + Pipeline.Visualisation.patch_uids() + ).fetchall() + ] + _, dates = extract_dates( + [ + i[0] + for i in self.database.connection.execute( + Pipeline.Processing.get_column_names(self.ps_data.name) + ).fetchall() + ] + ) + outliers_map = self.database.connection.execute( + Pipeline.Visualisation.get_outliers_map( + self.computational_crs, self._scaling_factor_ + ) + ).fetchall() + + # prepare the data for parallel processing + data_ready = [] + for uid in puids: + outliers = self.database.connection.execute( + Pipeline.Visualisation.get_outlier_points( + patch_id=uid, source_crs=self.computational_crs + ) + ).fetchall() + patch_center = self.database.connection.execute( + Pipeline.Visualisation.get_patch_center( + patch_id=uid, source_crs=self.computational_crs + ) + ).fetchone() + + pspoints = self.database.connection.execute( + Pipeline.Visualisation.get_ps_points( + patch_id=uid, + source_crs=self.computational_crs, + table_name=self.ps_data.name, + ) + ).fetchall() # pspoints + + lcp_point = self.database.connection.execute( + Pipeline.Visualisation.get_lcp_point( + patch_id=uid, source_crs=self.computational_crs + ) + ).fetchone() # lcp point + + avg_cum_disp = self.database.connection.execute( + Pipeline.Visualisation.get_avg_cum_disp_graph( + patch_id=uid, scaling_factor=self._scaling_factor_ + ) + ).fetchall() # avg_cum_disp + + std_cum_disp = self.database.connection.execute( + Pipeline.Visualisation.get_std_cum_disp_graph( + patch_id=uid, scaling_factor=self._scaling_factor_ + ) + ).fetchall() # std_cum_disp + + avg_lcp_disp = self.database.connection.execute( + Pipeline.Visualisation.get_avg_disp_lcp_graph( + patch_id=uid, scaling_factor=self._scaling_factor_ + ) + ).fetchall() # avg_lcp_disp + + std_lcp_disp = self.database.connection.execute( + Pipeline.Visualisation.get_std_disp_lcp_graph( + patch_id=uid, scaling_factor=self._scaling_factor_ + ) + ).fetchall() # std_lcp_disp + + lcp_disp = self.database.connection.execute( + Pipeline.Visualisation.get_lcp_disp_graph( + patch_id=uid, scaling_factor=self._scaling_factor_ + ) + ).fetchall() # lcp_disp + + for outlier in outliers: + o_id = outlier[0] + center = self.database.connection.execute( + Pipeline.Visualisation.get_center( + point_uid=o_id, source_crs=self.computational_crs + ) + ).fetchone() + cum_disp = self.database.connection.execute( + Pipeline.Visualisation.get_cum_disp_graph( + outlier_id=o_id, scaling_factor=self._scaling_factor_ + ) + ).fetchall() # cum_disp + + outlier_rel_ts = self.database.connection.execute( + Pipeline.Visualisation.get_outlier_rel_ts_graph( + outlier_id=o_id, scaling_factor=self._scaling_factor_ + ) + ).fetchall() # outlier_rel_ts + data_ready.append( + ( + uid, + outliers_map, + dates, + center, + patch_center, + pspoints, + lcp_point, + cum_disp, + avg_cum_disp, + std_cum_disp, + outlier_rel_ts, + avg_lcp_disp, + std_lcp_disp, + lcp_disp, + ) + ) + + print(f"Total pages to render for outliers: {len(data_ready)}") + + png_pages = Parallel(n_jobs=-1, backend="loky")( + delayed(render_ts_plots_workers)( + patch_uid, + outliers, + dates, + outlier_center, + patch_center, + pspoints, + lcp_point, + cum_disp, + avg_cum_disp, + std_cum_disp, + outlier_rel_ts, + avg_lcp_disp, + std_lcp_disp, + lcp_disp, + ) + for ( + patch_uid, + outliers, + dates, + outlier_center, + patch_center, + pspoints, + lcp_point, + cum_disp, + avg_cum_disp, + std_cum_disp, + outlier_rel_ts, + avg_lcp_disp, + std_lcp_disp, + lcp_disp, + ) in tqdm(data_ready) + ) + print("All plots rendered, now saving to pdf") + for png in png_pages: + img = plt.imread(io.BytesIO(png), format="png") + fig, ax = plt.subplots(figsize=(12, 12), dpi=80) + # ax = fig.add_subplot(111) + ax.imshow(img) + ax.axis("off") + ax.set_xticks([]) + ax.set_yticks([]) + ax.set_frame_on(False) + pdf.savefig(fig) + plt.close(fig) + print("All pages saved to pdf") diff --git a/src/saferoad/utils.py b/src/saferoad/utils.py new file mode 100644 index 0000000..434865d --- /dev/null +++ b/src/saferoad/utils.py @@ -0,0 +1,55 @@ +import re +import time +from datetime import datetime +from dateutil import parser as dsparser +from numpy import array as nparray + + +def extract_dates(column_names: list[str]) -> tuple[list[str], list[datetime]]: + """Extracts date fields from a list of column names. + + :param column_names: List of column names to search for dates. + :type column_names: list[str] + :return: A tuple containing two numpy arrays: one with the column names and another with the parsed date objects. + :rtype: tuple[ndarray, ndarray] + """ + assert isinstance(column_names, list), "column_names must be a list of strings" + assert all( + isinstance(name, str) for name in column_names + ), "All items in column_names must be strings" + assert len(column_names) > 0, "column_names must not be empty" + + nameFields: list = [] + dateFields: list = [] + for date in column_names: + dateSearch = re.search(r"\d{8}", date) + if dateSearch: + pdate: datetime = dsparser.parse(dateSearch.group(), fuzzy=True).date() + dateFields.append(pdate) + nameFields.append(date) + return nameFields, dateFields + + +def time_vector(dates: list[datetime]) -> nparray: + """Generates a time vector in days from a list of datetime objects. + + :param dates: List of datetime objects. + :type dates: list[datetime] + :return: List of time differences in days from the first date. + :rtype: list[float] + """ + return nparray([(i - dates[0]).days for i in dates]) + + +class Timer(object): + """Context manager for timing code execution.""" + + def __init__(self, name=None): + self.name = name + + def __enter__(self): + self.tstart = time.time() + + def __exit__(self, type, value, traceback): + if self.name: + print("%s took %.2f seconds" % (self.name, time.time() - self.tstart)) diff --git a/tests/__main__.py b/tests/__main__.py new file mode 100644 index 0000000..bdbf88f --- /dev/null +++ b/tests/__main__.py @@ -0,0 +1,6 @@ +from .test_suite import suite +import unittest + +if __name__ == "__main__": + runner = unittest.TextTestRunner(verbosity=2) + runner.run(suite()) diff --git a/tests/test_database.py b/tests/test_database.py new file mode 100644 index 0000000..2c105e9 --- /dev/null +++ b/tests/test_database.py @@ -0,0 +1,56 @@ +import os +import shutil +import unittest +from src.saferoad.database import DataBase + + +class TestDataBase(unittest.TestCase): + def setUp(self): + self.db = DataBase() + + def tearDown(self): + if self.db.connection: + self.db.connection.close() + # Remove the database file if it exists + if self.db.db_path and os.path.exists(self.db.db_path): + os.remove(self.db.db_path) + # Remove the directory and all its contents if it exists + if os.path.exists("SafeRoadDB"): + shutil.rmtree("SafeRoadDB") + # os.rmdir("SafeRoadDB") + + def test_setup_creates_database_file(self): + self.db.setup() + + # Check if the database file is created + self.assertIsNotNone(self.db.db_path) + self.assertTrue(os.path.exists(self.db.db_path)) + + def test_setup_raises_error_if_already_initialized(self): + self.db.setup() + + with self.assertRaises(AssertionError) as context: + self.db.setup() + self.assertIn( + "Database connection already established.", str(context.exception) + ) + + def test_run_executes_query(self): + self.db.setup() + + # Create a table and insert data + self.db.run("CREATE TABLE test_table (id INTEGER, name STRING);") + self.db.run("INSERT INTO test_table VALUES (1, 'Test Name');") + + # Verify the table exists and data is inserted + result = self.db.connection.execute("SELECT * FROM test_table;").fetchall() + self.assertEqual(result, [(1, "Test Name")]) + + def test_run_raises_error_if_no_connection(self): + with self.assertRaises(AssertionError) as context: + self.db.run("SELECT 1;") + self.assertIn("Database connection is not established.", str(context.exception)) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_pipeline.py b/tests/test_pipeline.py new file mode 100644 index 0000000..658b7fe --- /dev/null +++ b/tests/test_pipeline.py @@ -0,0 +1,309 @@ +import unittest +from pathlib import Path +from unittest.mock import patch, mock_open +from saferoad.pipeline import Pipeline + + +class TestPipelineProcessing(unittest.TestCase): + + def test_load_file(self): + # Mock Path.exists to always return True + with patch("pathlib.Path.exists", return_value=True): + # Test CSV file + query = Pipeline.Processing.load_file("test.csv", "test_table") + self.assertIn("read_csv_auto", query) + self.assertIn("test_table", query) + + # Test non-CSV file + query = Pipeline.Processing.load_file("test.shp", "test_table") + self.assertIn("ST_Read", query) + self.assertIn("test_table", query) + + # Test file not found + with patch("pathlib.Path.exists", return_value=False): + with self.assertRaises(AssertionError): + Pipeline.Processing.load_file("nonexistent.csv", "test_table") + + def test_dissolve_features(self): + # Test with attribute + query = Pipeline.Processing.dissolve_features("test_attr", "test_table") + self.assertIn("GROUP BY test_attr", query) + self.assertIn("test_table", query) + + # Test without attribute + query = Pipeline.Processing.dissolve_features("", "test_table") + self.assertNotIn("GROUP BY", query) + self.assertIn("test_table", query) + + def test_split_to_segments(self): + query = Pipeline.Processing.split_to_segments(10.0, "test_table") + self.assertIn("10.0", query) + self.assertIn("test_table", query) + self.assertIn("CREATE OR REPLACE TABLE segments", query) + + def test_generate_patches(self): + # Test with valid parameters + query = Pipeline.Processing.generate_patches(5.0, 10.0, "test_segments") + self.assertIn("5.0", query) + self.assertIn("10.0", query) + self.assertIn("test_segments", query) + + # Test with invalid width + with self.assertRaises(AssertionError): + Pipeline.Processing.generate_patches(0, 10.0) + + # Test with invalid length + with self.assertRaises(AssertionError): + Pipeline.Processing.generate_patches(5.0, 0) + + def test_spatial_transformation(self): + query = Pipeline.Processing.spatial_tranformation( + "EPSG:4326", "EPSG:3857", "test_table" + ) + self.assertIn("EPSG:4326", query) + self.assertIn("EPSG:3857", query) + self.assertIn("test_table", query) + + def test_build_geometry(self): + query = Pipeline.Processing.build_geometry("lat", "lon", "test_table") + self.assertIn("lat", query) + self.assertIn("lon", query) + self.assertIn("test_table", query) + self.assertIn("ST_Point", query) + + def test_relate_point_patches(self): + query = Pipeline.Processing.relate_point_patches("test_points", "test_patches") + self.assertIn("test_points", query) + self.assertIn("test_patches", query) + self.assertIn("ST_Within", query) + + def test_calc_point_density(self): + query = Pipeline.Processing.calc_point_density("test_points", "test_patches") + self.assertIn("test_points", query) + self.assertIn("test_patches", query) + self.assertIn("ps_density", query) + + def test_get_column_names(self): + query = Pipeline.Processing.get_column_names("test_table") + self.assertIn("test_table", query) + self.assertIn("column_name", query) + + def test_calc_cum_disp(self): + dates = ["date1", "date2", "date3"] + query = Pipeline.Processing.calc_cum_disp(dates, "test_table") + self.assertIn("date1", query) + self.assertIn("date3", query) + self.assertIn("test_table", query) + + def test_calc_avg_cum_disp(self): + query = Pipeline.Processing.calc_avg_cum_disp() + self.assertIn("avg_cum_disp", query) + self.assertIn("AVG(displacement)", query) + + def test_calc_avg_velocity(self): + time_vector = [0, 10, 20] + fields = ["field1", "field2", "field3"] + query = Pipeline.Processing.calc_avg_velocity(time_vector, fields, "test_table") + for field in fields: + self.assertIn(field, query) + self.assertIn("test_table", query) + self.assertIn("avg_velocity_gcp", query) + + def test_calc_avg_disp_ts_gcp(self): + date_fields = ["date1", "date2", "date3"] + query = Pipeline.Processing.calc_avg_disp_ts_gcp(date_fields) + for date in date_fields: + self.assertIn(date, query) + self.assertIn("avg_disp_ts_gcp", query) + + def test_calc_std_disp_ts_gcp(self): + date_fields = ["date1", "date2", "date3"] + query = Pipeline.Processing.calc_std_disp_ts_gcp(date_fields) + for date in date_fields: + self.assertIn(date, query) + self.assertIn("std_disp_ts_gcp", query) + + def test_select_lcps(self): + query = Pipeline.Processing.select_lcps("test_table") + self.assertIn("test_table", query) + self.assertIn("lcp_uid", query) + + def test_calc_lcp_velocity(self): + query = Pipeline.Processing.calc_lcp_velocity() + self.assertIn("lcp_velocity", query) + self.assertIn("avg_velocity_gcp", query) + + def test_calc_avg_velocity_gcp(self): + query = Pipeline.Processing.calc_avg_velocity_gcp("test_table") + self.assertIn("test_table", query) + self.assertIn("avg_gcp_velocity", query) + + def test_calc_avg_std_disp_ts_lcp(self): + date_fields = ["date1", "date2", "date3"] + query = Pipeline.Processing.calc_avg_std_disp_ts_lcp(date_fields, "test_table") + for date in date_fields: + self.assertIn(date, query) + self.assertIn("test_table", query) + self.assertIn("avg_disp_ts_lcp", query) + self.assertIn("std_disp_ts_lcp", query) + + def test_calc_avg_velocity_lcp(self): + query = Pipeline.Processing.calc_avg_velocity_lcp("test_table") + self.assertIn("test_table", query) + self.assertIn("avg_velocity_lcp", query) + + def test_calc_avg_lcp_velocity(self): + query = Pipeline.Processing.calc_avg_lcp_velocity("test_table") + self.assertIn("test_table", query) + self.assertIn("avg_lcp_velocity", query) + + def test_calc_outliers(self): + query = Pipeline.Processing.calc_outliers(1000.0, "test_table") + self.assertIn("test_table", query) + self.assertIn("uid", query) + self.assertIn("1000.0", query) + + def test_lcp_ts_patch(self): + date_fields = ["date1", "date2", "date3"] + query = Pipeline.Processing.lcp_ts_patch(date_fields, "test_table") + for date in date_fields: + self.assertIn(date, query) + self.assertIn("test_table", query) + self.assertIn("lcp_disp_ts", query) + + def test_outlier_ts_patch(self): + date_fields = ["date1", "date2", "date3"] + query = Pipeline.Processing.outlier_ts_patch(date_fields, "test_table") + for date in date_fields: + self.assertIn(date, query) + self.assertIn("test_table", query) + self.assertIn("disp_ts", query) + + def test_outlier_rel_ts_patch(self): + query = Pipeline.Processing.outlier_rel_ts_patch() + self.assertIn("rel_ts", query) + self.assertIn("disp_ts", query) + self.assertIn("lcp_disp_ts", query) + + +class TestPipelineVisualisation(unittest.TestCase): + + def test_get_table_bbox(self): + query = Pipeline.Visualisation.get_table_bbox("test_table", "EPSG:4326") + self.assertIn("test_table", query) + self.assertIn("EPSG:4326", query) + self.assertIn("EPSG:3857", query) # Default projection + + def test_get_ps_density(self): + query = Pipeline.Visualisation.get_ps_density("EPSG:4326") + self.assertIn("EPSG:4326", query) + self.assertIn("EPSG:3857", query) # Default projection + self.assertIn("ps_density", query) + + def test_get_outliers_map(self): + query = Pipeline.Visualisation.get_outliers_map("EPSG:4326", 1000.0) + self.assertIn("EPSG:4326", query) + self.assertIn("EPSG:3857", query) # Default projection + self.assertIn("1000.0", query) + self.assertIn("avg_velocity_lcp", query) + + def test_get_cum_disp_map(self): + query = Pipeline.Visualisation.get_cum_disp_map("EPSG:4326", 1000.0) + self.assertIn("EPSG:4326", query) + self.assertIn("EPSG:3857", query) # Default projection + self.assertIn("1000.0", query) + self.assertIn("avg_cum_disp", query) + + def test_get_ps_vel_gcp(self): + query = Pipeline.Visualisation.get_ps_vel_gcp("EPSG:4326", 1000.0) + self.assertIn("EPSG:4326", query) + self.assertIn("EPSG:3857", query) # Default projection + self.assertIn("1000.0", query) + self.assertIn("avg_velocity_gcp", query) + + def test_get_ps_vel_lcp(self): + query = Pipeline.Visualisation.get_ps_vel_lcp("EPSG:4326", 1000.0) + self.assertIn("EPSG:4326", query) + self.assertIn("EPSG:3857", query) # Default projection + self.assertIn("1000.0", query) + self.assertIn("avg_velocity_lcp", query) + + def test_get_ps_points(self): + query = Pipeline.Visualisation.get_ps_points(123, "EPSG:4326", "test_table") + self.assertIn("EPSG:4326", query) + self.assertIn("EPSG:3857", query) # Default projection + self.assertIn("test_table", query) + self.assertIn("123", query) + + def test_get_center(self): + query = Pipeline.Visualisation.get_center(123, "EPSG:4326") + self.assertIn("EPSG:4326", query) + self.assertIn("EPSG:3857", query) # Default projection + self.assertIn("123", query) + self.assertIn("ST_Centroid", query) + + def test_get_patch_center(self): + query = Pipeline.Visualisation.get_patch_center(123, "EPSG:4326") + self.assertIn("EPSG:4326", query) + self.assertIn("EPSG:3857", query) # Default projection + self.assertIn("123", query) + self.assertIn("ST_Centroid", query) + + def test_get_lcp_point(self): + query = Pipeline.Visualisation.get_lcp_point(123, "EPSG:4326") + self.assertIn("EPSG:4326", query) + self.assertIn("EPSG:3857", query) # Default projection + self.assertIn("123", query) + self.assertIn("lcp_uid", query) + + def test_get_outlier_points(self): + query = Pipeline.Visualisation.get_outlier_points(123, "EPSG:4326") + self.assertIn("EPSG:4326", query) + self.assertIn("EPSG:3857", query) # Default projection + self.assertIn("123", query) + + def test_get_cum_disp_graph(self): + query = Pipeline.Visualisation.get_cum_disp_graph(123, 1000.0) + self.assertIn("123", query) + self.assertIn("1000.0", query) + self.assertIn("disp_ts", query) + + def test_get_avg_cum_disp_graph(self): + query = Pipeline.Visualisation.get_avg_cum_disp_graph(123, 1000.0) + self.assertIn("123", query) + self.assertIn("1000.0", query) + self.assertIn("avg_disp_ts_gcp", query) + + def test_get_std_cum_disp_graph(self): + query = Pipeline.Visualisation.get_std_cum_disp_graph(123, 1000.0) + self.assertIn("123", query) + self.assertIn("1000.0", query) + self.assertIn("std_disp_ts_gcp", query) + + def test_get_lcp_disp_graph(self): + query = Pipeline.Visualisation.get_lcp_disp_graph(123, 1000.0) + self.assertIn("123", query) + self.assertIn("1000.0", query) + self.assertIn("lcp_disp_ts", query) + + def test_get_avg_disp_lcp_graph(self): + query = Pipeline.Visualisation.get_avg_disp_lcp_graph(123, 1000.0) + self.assertIn("123", query) + self.assertIn("1000.0", query) + self.assertIn("avg_disp_ts_lcp", query) + + def test_get_std_disp_lcp_graph(self): + query = Pipeline.Visualisation.get_std_disp_lcp_graph(123, 1000.0) + self.assertIn("123", query) + self.assertIn("1000.0", query) + self.assertIn("std_disp_ts_lcp", query) + + def test_get_outlier_rel_ts_graph(self): + query = Pipeline.Visualisation.get_outlier_rel_ts_graph(123, 1000.0) + self.assertIn("123", query) + self.assertIn("1000.0", query) + + def test_patch_uids(self): + query = Pipeline.Visualisation.patch_uids() + self.assertIn("patch_id", query) + self.assertIn("outliers", query) diff --git a/tests/test_plotter.py b/tests/test_plotter.py new file mode 100644 index 0000000..459e52d --- /dev/null +++ b/tests/test_plotter.py @@ -0,0 +1,170 @@ +import unittest +from unittest.mock import patch +from shapely.geometry import Point, Polygon +from shapely.wkb import dumps as dump_wkb +import matplotlib.pyplot as plt +from datetime import datetime, timedelta + +from src.saferoad.plotter import Plotter # replace with actual import path + + +class TestMapPane(unittest.TestCase): + def setUp(self): + self.pane = Plotter.MapPane() + + def test_generate_figure(self): + fig, axs = self.pane.get_figure() + self.assertIsInstance(fig, plt.Figure) + self.assertEqual(len(axs), 5) + + @patch("contextily.add_basemap") + def test_plot_basemap(self, mock_basemap): + bbox = ([0, 0, 100, 100],) + self.pane.plot_basemap(bbox) + self.assertTrue(mock_basemap.called) + + def test_ps_density_color_label(self): + self.assertEqual(self.pane.ps_density_color_label(50), ("red", "Low")) + self.assertEqual(self.pane.ps_density_color_label(200), ("yellow", "Medium")) + self.assertEqual(self.pane.ps_density_color_label(600), ("green", "High")) + + def test_outlier_color_label(self): + self.assertEqual(self.pane.outlier_color_label(6), ("yellow", "5<|v|<7 mm/yr")) + self.assertEqual(self.pane.outlier_color_label(8), ("orange", "7<|v|<9 mm/yr")) + self.assertEqual(self.pane.outlier_color_label(12), ("red", "9<|v|<12 mm/yr")) + + def test_cum_disp_color_label(self): + self.assertEqual(self.pane.cum_disp_color_label(-30), ("red", "(-60,-20) mm")) + self.assertEqual( + self.pane.cum_disp_color_label(-17), ("orange", "(-20,-15) mm") + ) + self.assertEqual(self.pane.cum_disp_color_label(-10), ("yellow", "(-15,-5) mm")) + self.assertEqual(self.pane.cum_disp_color_label(0), ("lightgreen", "(-5,5) mm")) + self.assertEqual(self.pane.cum_disp_color_label(10), ("cyan", "(5,15) mm")) + self.assertEqual(self.pane.cum_disp_color_label(20), ("blue", "(15,60) mm")) + + def test_gcp_vel_color(self): + self.assertEqual(self.pane.gcp_vel_color(-7), "red") + self.assertEqual(self.pane.gcp_vel_color(-5), "orange") + self.assertEqual(self.pane.gcp_vel_color(-3), "yellow") + self.assertEqual(self.pane.gcp_vel_color(1), "lightgreen") + self.assertEqual(self.pane.gcp_vel_color(3), "cyan") + self.assertEqual(self.pane.gcp_vel_color(10), "blue") + + def test_lcp_vel_color(self): + self.assertEqual(self.pane.lcp_vel_color(1), "green") + self.assertEqual(self.pane.lcp_vel_color(5), "red") + + def test_plot_ps_density(self): + poly = Polygon([(0, 0), (1, 0), (1, 1), (0, 1)]) + data = [(dump_wkb(poly), 200)] + self.pane.plot_ps_density(data) + self.assertTrue(len(self.pane.axs[0].patches) > 0) + + def test_plot_outliers(self): + pt = Point(0, 0) + data = [(dump_wkb(pt), 8)] + self.pane.plot_outliers(data) + self.assertTrue(len(self.pane.axs[1].collections) > 0) + + def test_plot_cum_disp(self): + poly = Polygon([(0, 0), (1, 0), (1, 1), (0, 1)]) + data = [(dump_wkb(poly), -10)] + self.pane.plot_cum_disp(data) + self.assertTrue(len(self.pane.axs[2].patches) > 0) + + def test_plot_gcp_velocity(self): + pt = Point(0, 0) + data = [(dump_wkb(pt), -5)] + self.pane.plot_gcp_velocity(data) + self.assertTrue(len(self.pane.axs[3].collections) > 0) + + def test_plot_lcp_velocity(self): + pt = Point(0, 0) + data = [(dump_wkb(pt), 5)] + self.pane.plot_lcp_velocity(data) + self.assertTrue(len(self.pane.axs[4].collections) > 0) + + def test_post_process_runs(self): + self.pane.post_process() # should not raise + + +class TestTimeSeriesPane(unittest.TestCase): + def setUp(self): + self.pane = Plotter.TimeSeriesPane() + self.dates = [datetime(2020, 1, 1) + timedelta(days=i) for i in range(5)] + + @patch("contextily.add_basemap") + def test_plot_basemap_outlier(self, mock_basemap): + self.pane.plot_basemap_outlier(([100], [200])) + self.assertTrue(mock_basemap.called) + + @patch("contextily.add_basemap") + def test_plot_basemap_pspoint(self, mock_basemap): + self.pane.plot_basemap_pspoint(([100], [200])) + self.assertTrue(mock_basemap.called) + + def test_outlier_color_label(self): + self.assertEqual(self.pane.outlier_color_label(6), ("yellow", "5<|v|<7 mm/yr")) + self.assertEqual(self.pane.outlier_color_label(8), ("orange", "7<|v|<9 mm/yr")) + self.assertEqual(self.pane.outlier_color_label(12), ("red", "9<|v|<12 mm/yr")) + + def test_plot_all_outliers(self): + pt = Point(0, 0) + data = [(dump_wkb(pt), 10)] + self.pane.plot_all_outliers(data) + self.assertTrue(len(self.pane.axs[0].collections) > 0) + + def test_plot_square(self): + self.pane.plot_square([(0, 0), (10, 10)]) + self.assertTrue(len(self.pane.axs[0].patches) > 0) + + def test_plot_pspoint_dist(self): + pt = Point(0, 0) + data = [(dump_wkb(pt),)] + self.pane.plot_pspoint_dist(data) + self.assertTrue(len(self.pane.axs[1].collections) > 0) + + def test_plot_outlier(self): + pt = Point(0, 0) + data = [(dump_wkb(pt), 9)] + self.pane.plot_outlier(data) + self.assertTrue(len(self.pane.axs[1].collections) > 0) + + def test_plot_lcp(self): + pt = Point(0, 0) + data = [dump_wkb(pt)] + self.pane.plot_lcp(data) + self.assertTrue(len(self.pane.axs[1].collections) > 0) + + def test_cum_disp_graphs(self): + y = [([1, 2, 3, 4, 5],)] + std = [([1, 1, 1, 1, 1],)] + self.pane.plot_cum_disp_graph(y, self.dates) + self.pane.plot_avg_disp_graph(y, self.dates) + self.pane.plot_std_disp_graph(y, std, self.dates) + # scatter + 2x fill_between -> collections exist + self.assertGreater(len(self.pane.axs[2].collections), 0) + + def test_rel_disp_graphs(self): + y = [([1, 2, 3, 4, 5],)] + std = [([1, 1, 1, 1, 1],)] + self.pane.plot_rel_disp_graph(y, self.dates) + self.pane.plot_avg_rel_disp_graph(y, self.dates) + self.pane.plot_std_rel_disp_graph(y, std, self.dates) + self.assertTrue(len(self.pane.axs[3].collections) > 0) + + def test_lcp_disp_graphs(self): + y = [([1, 2, 3, 4, 5],)] + std = [([1, 1, 1, 1, 1],)] + self.pane.plot_lcp_disp_graph(y, self.dates) + self.pane.plot_avg_lcp_disp_graph(y, self.dates) + self.pane.plot_std_lcp_disp_graph(y, std, self.dates) + self.assertTrue(len(self.pane.axs[4].collections) > 0) + + def test_post_process_runs(self): + self.pane.post_process() # should not raise + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_saferoad.py b/tests/test_saferoad.py new file mode 100644 index 0000000..eb0bade --- /dev/null +++ b/tests/test_saferoad.py @@ -0,0 +1,203 @@ +import unittest +from src.saferoad.saferoad import SafeRoad, PsData, Road +import os + + +class TestSafeRoad(unittest.TestCase): + def setUp(self): + # Setup the test data + self.computational_crs = "EPSG:28992" + self.road = Road( + filepath="./examples/fixtures/A10_ams.geojson", + crs_code="EPSG:4326", + name="road", + ) + self.ps_data = PsData( + filepath="./examples/fixtures/psdata_mod.csv", + latitude="pnt_lat", + longitude="pnt_lon", + unit="m", + crs_code="EPSG:4326", + name="pspoint", + ) + self.saferoad = SafeRoad( + road=self.road, + ps_data=self.ps_data, + computational_crs=self.computational_crs, + ) + + def test_initialization(self): + # Test initialization of SafeRoad + self.assertEqual( + self.saferoad.road_data.filepath, "./examples/fixtures/A10_ams.geojson" + ) + self.assertEqual( + self.saferoad.ps_data.filepath, "./examples/fixtures/psdata_mod.csv" + ) + self.assertEqual(self.saferoad.computational_crs, "EPSG:28992") + + def test_load_files(self): + # Test loading files + try: + self.saferoad.load_files() + except Exception as e: + self.fail(f"load_files raised an exception: {e}") + + def test_preprocess(self): + # Test preprocessing + try: + self.saferoad.load_files() + self.saferoad.preprocess() + except Exception as e: + self.fail(f"preprocess raised an exception: {e}") + + def test_generate_rectangles(self): + # Test generating rectangles + try: + self.saferoad.load_files() + self.saferoad.preprocess() + self.saferoad.generate_rectangles(road_width=8, segment_length=500) + except Exception as e: + self.fail(f"generate_rectangles raised an exception: {e}") + + def test_run_analysis(self): + # Test running analysis + try: + self.saferoad.load_files() + self.saferoad.preprocess() + self.saferoad.generate_rectangles(road_width=8, segment_length=500) + self.saferoad.run_analysis() + except Exception as e: + self.fail(f"run_analysis raised an exception: {e}") + + def test_generate_report(self): + # Test generating report + try: + self.saferoad.load_files() + self.saferoad.preprocess() + self.saferoad.generate_rectangles(road_width=8, segment_length=500) + self.saferoad.run_analysis() + self.saferoad.generate_report("./test_report2.pdf") + except Exception as e: + self.fail(f"generate_report raised an exception: {e}") + + finally: + # Clean up the test file + if os.path.exists("./test_report2.pdf"): + os.remove("./test_report2.pdf") + + def test_scaling_factor(self): + # Test the _scaling_factor_ property + self.ps_data.unit = "m" + self.assertEqual(self.saferoad._scaling_factor_, 1000) + + self.ps_data.unit = "cm" + self.assertEqual(self.saferoad._scaling_factor_, 10) + + self.ps_data.unit = "mm" + self.assertEqual(self.saferoad._scaling_factor_, 1) + + def test_invalid_initialization(self): + # Test initialization with invalid inputs + with self.assertRaises(AssertionError): + SafeRoad( + road="not_a_road", + ps_data=self.ps_data, + computational_crs=self.computational_crs, + ) + with self.assertRaises(AssertionError): + SafeRoad( + road=self.road, + ps_data="not_ps_data", + computational_crs=self.computational_crs, + ) + with self.assertRaises(AssertionError): + SafeRoad(road=self.road, ps_data=self.ps_data, computational_crs=123) + + def test_generate_rectangles_different_values(self): + # Test generating rectangles with different values + try: + self.saferoad.load_files() + self.saferoad.preprocess() + # Test with smaller values + self.saferoad.generate_rectangles(road_width=5, segment_length=250) + # Test with larger values + self.saferoad.generate_rectangles(road_width=10, segment_length=1000) + except Exception as e: + self.fail( + f"generate_rectangles with different values raised an exception: {e}" + ) + + def test_ps_data_validation(self): + # Test PsData validation + with self.assertRaises(AssertionError): + PsData(filepath="", latitude="lat", longitude="lon") # Empty filepath + with self.assertRaises(AssertionError): + PsData(filepath="test.csv", latitude="", longitude="lon") # Empty latitude + with self.assertRaises(AssertionError): + PsData(filepath="test.csv", latitude="lat", longitude="") # Empty longitude + with self.assertRaises(AssertionError): + PsData( + filepath="test.csv", + latitude="lat", + longitude="lon", + unit="invalid", + ) # Invalid unit + + def test_road_validation(self): + # Test Road validation + with self.assertRaises(AssertionError): + Road(filepath="", name="road") # Empty filepath + with self.assertRaises(AssertionError): + Road(filepath=123, name="road") # Non-string filepath + with self.assertRaises(AssertionError): + Road(filepath="test.geojson", name="") # Empty name + + def test_generate_report_custom_output(self): + # Test generating report with custom output path + try: + self.saferoad.load_files() + self.saferoad.preprocess() + self.saferoad.generate_rectangles(road_width=8, segment_length=500) + self.saferoad.run_analysis() + self.saferoad.generate_report("./custom_report_output.pdf") + except Exception as e: + self.fail(f"generate_report with custom output raised an exception: {e}") + + finally: + # Clean up the test file + if os.path.exists("./custom_report_output.pdf"): + os.remove("./custom_report_output.pdf") + + def test_render_ts_plots_workers(self): + """Test that time series plots are correctly rendered in the report.""" + + # First, load files and run necessary preprocessing + self.saferoad.load_files() + self.saferoad.preprocess() + self.saferoad.generate_rectangles(road_width=8, segment_length=500) + self.saferoad.run_analysis() + + # Generate a report + report_path = "./test_ts_plots_report.pdf" + try: + self.saferoad.generate_report(report_path) + + # Check if the report file exists + self.assertTrue(os.path.exists(report_path)) + + # Check file size to ensure it contains plots (a PDF with time series plots + # should be reasonably large) + file_size = os.path.getsize(report_path) + self.assertGreater( + file_size, 10000 + ) # A reasonable minimum size for a PDF with plots + + finally: + # Clean up the test file + if os.path.exists(report_path): + os.remove(report_path) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_suite.py b/tests/test_suite.py new file mode 100644 index 0000000..a75d7ad --- /dev/null +++ b/tests/test_suite.py @@ -0,0 +1,22 @@ +import unittest + + +def suite(): + laoder = unittest.TestLoader() + suite = unittest.TestSuite() + + for module in [ + "test_utils", + "test_pipeline", + "test_database", + "test_plotter", + "test_saferoad", + ]: + suite.addTests(laoder.loadTestsFromName(f"tests.{module}")) + + return suite + + +if __name__ == "__main__": + runner = unittest.TextTestRunner(verbosity=2) + runner.run(suite()) diff --git a/tests/test_utils.py b/tests/test_utils.py new file mode 100644 index 0000000..f1a7fce --- /dev/null +++ b/tests/test_utils.py @@ -0,0 +1,134 @@ +import unittest +from datetime import datetime +from src.saferoad.utils import extract_dates, time_vector, Timer +import io +from contextlib import redirect_stdout +import time + + +class TestExtractDates(unittest.TestCase): + + def test_valid_dates(self): + column_names = ["data_20230101", "info_20221231", "value_20220101"] + expected_names = ["data_20230101", "info_20221231", "value_20220101"] + expected_dates = [ + datetime(2023, 1, 1).date(), + datetime(2022, 12, 31).date(), + datetime(2022, 1, 1).date(), + ] + result_names, result_dates = extract_dates(column_names) + self.assertEqual(result_names, expected_names) + self.assertEqual(result_dates, expected_dates) + + def test_no_dates(self): + column_names = ["data", "info", "value"] + expected_names = [] + expected_dates = [] + result_names, result_dates = extract_dates(column_names) + self.assertEqual(result_names, expected_names) + self.assertEqual(result_dates, expected_dates) + + def test_mixed_columns(self): + column_names = ["data_20230101", "info", "value_20220101"] + expected_names = ["data_20230101", "value_20220101"] + expected_dates = [ + datetime(2023, 1, 1).date(), + datetime(2022, 1, 1).date(), + ] + result_names, result_dates = extract_dates(column_names) + self.assertEqual(result_names, expected_names) + self.assertEqual(result_dates, expected_dates) + + def test_empty_list(self): + column_names = [] + with self.assertRaises(AssertionError): + extract_dates(column_names) + + def test_invalid_input_type(self): + column_names = "data_20230101" + with self.assertRaises(AssertionError): + extract_dates(column_names) + + def test_non_string_elements(self): + column_names = ["data_20230101", 123, "value_20220101"] + with self.assertRaises(AssertionError): + extract_dates(column_names) + + def test_time_vector(self): + dates = [ + datetime(2022, 1, 1), + datetime(2022, 1, 2), + datetime(2022, 1, 10), + datetime(2022, 1, 20), + ] + expected_vector = [0, 1, 9, 19] + result_vector = time_vector(dates) + self.assertEqual(list(result_vector), expected_vector) + + def test_time_vector_single_date(self): + dates = [datetime(2022, 1, 1)] + expected_vector = [0] + result_vector = time_vector(dates) + self.assertEqual(result_vector, expected_vector) + + def test_extract_date_with_list_numbers(self): + column_names = ["data_20230101", "info_20221231", "value_20220101", 123] + with self.assertRaises(AssertionError): + extract_dates(column_names) + + def test_extract_date_with_empty_list(self): + column_names = [] + with self.assertRaises(AssertionError): + extract_dates(column_names) + + +class TestTimer(unittest.TestCase): + + def test_timer_with_name(self): + """Test that Timer works correctly with a name parameter.""" + + # Capture stdout + f = io.StringIO() + with redirect_stdout(f): + with Timer(name="test_timer"): + time.sleep(0.1) # Sleep for a short time + + output = f.getvalue() + self.assertIn("test_timer took", output) + self.assertRegex(output, r"test_timer took \d+\.\d+ seconds") + + def test_timer_without_name(self): + """Test that Timer works correctly without a name parameter.""" + + # Capture stdout + f = io.StringIO() + with redirect_stdout(f): + with Timer(): + time.sleep(0.1) # Sleep for a short time + + # No output should be produced when name is None + output = f.getvalue() + self.assertEqual(output, "") + + def test_timer_accuracy(self): + """Test that Timer measures time with reasonable accuracy.""" + + sleep_time = 0.2 # seconds + timer = Timer("accuracy_test") + + # Measure the time manually + start_time = time.time() + with redirect_stdout(io.StringIO()): # Suppress output + with timer: + time.sleep(sleep_time) + end_time = time.time() + + elapsed_time = end_time - start_time + # Check that the elapsed time is reasonably close to sleep_time + # Allow for some overhead in timing operations + self.assertGreaterEqual(elapsed_time, sleep_time) + self.assertLess(elapsed_time, sleep_time + 0.1) # Allow 0.1s margin + + +if __name__ == "__main__": + unittest.main()