From 38dc11e78b405bbdd85af43af7a63256bc17b996 Mon Sep 17 00:00:00 2001 From: beckynevin Date: Tue, 6 May 2025 18:11:42 +0000 Subject: [PATCH 01/13] adding data science notebook --- ...tion_to_Data_Science_with_Rubin_Data.ipynb | 2910 +++++++++++++++++ 1 file changed, 2910 insertions(+) create mode 100644 DP0.2/20_Introduction_to_Data_Science_with_Rubin_Data.ipynb diff --git a/DP0.2/20_Introduction_to_Data_Science_with_Rubin_Data.ipynb b/DP0.2/20_Introduction_to_Data_Science_with_Rubin_Data.ipynb new file mode 100644 index 00000000..6232a288 --- /dev/null +++ b/DP0.2/20_Introduction_to_Data_Science_with_Rubin_Data.ipynb @@ -0,0 +1,2910 @@ +{ + "cells": [ + { + "attachments": { + "90083a24-00a4-4a6f-a1c3-b9b4c6b0de9e.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "id": "6c298cbb-eb18-4cbc-b064-709a7c2b9b4c", + "metadata": {}, + "source": [ + "# XXX.X. How to use `pandas` and `scikit-learn` to do data science / machine learning with the TAP service\n", + "\n", + "
\n", + "\n", + "
\n", + "For the Rubin Science Platform at data.lsst.cloud.
\n", + "Data Release: DPX or DRX
\n", + "Container Size: small
\n", + "LSST Science Pipelines version: Weekly 2024_16
\n", + "Last verified to run: 2024-07-09
\n", + "Repository: github.com/lsst/tutorial-notebooks
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4a4e60f3-a825-4dcc-8cd5-37ed43b7373b", + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext pycodestyle_magic\n", + "%flake8_on\n", + "import logging\n", + "logging.getLogger(\"flake8\").setLevel(logging.FATAL)" + ] + }, + { + "cell_type": "markdown", + "id": "25e71641-fbfb-4470-8049-03ba631dbef1", + "metadata": {}, + "source": [ + "**Learning objective:** This notebook guides a PI through the process of using python's data science and machine learning libraries to explore data from complex ADQL queries with the TAP service. The goal is to build a predictive model to estimate missing $r-$band Kron Flux values when the other bands are available, and visualize the results and quantify the model performance.\n", + "\n", + "**LSST data products:** Object, Forcedsource, and CcdVisit tables.\n", + "\n", + "**Packages:** lsst.rsp, pandas, scikit-learn, seaborn\n", + "\n", + "**Credit:**\n", + "Based on notebooks developed by Leanne Guy (TAP query) and Alex Drlica-Wagner and Melissa Graham (Butler query).\n", + "Please consider acknowledging them if this notebook is used for the preparation of journal articles, software releases, or other notebooks.\n", + "\n", + "**Get Support:**\n", + "Everyone is encouraged to ask questions or raise issues in the \n", + "Support Category \n", + "of the Rubin Community Forum.\n", + "Rubin staff will respond to all questions posted there." + ] + }, + { + "cell_type": "markdown", + "id": "ca5378d1-00fe-44ad-be88-5f1a0a85b404", + "metadata": {}, + "source": [ + "## 1. Introduction\n", + "\n", + "This notebook provides an intermediate-level demonstration of how to use the Table Access Protocol (TAP) server and ADQL (Astronomy Data Query Language) to query and retrieve data from the DP0.2 catalogs.\n", + "\n", + "TAP provides standardized access to catalog data for discovery, search, and retrieval.\n", + "Full documentation for TAP is provided by the International Virtual Observatory Alliance (IVOA).\n", + "ADQL is similar to SQL (Structured Query Langage).\n", + "The documentation for ADQL includes more information about syntax and keywords.\n", + "Note that not all ADQL functionality is supported yet in the DP0-era RSP.\n", + "\n", + "**See the recommendations for TAP queries in DP0.2 tutorial 02a \"Introduction to the TAP Service\".**\n", + "\n", + "The [documentation for Data Preview 0.2](https://dp0-2.lsst.io/) includes definitions\n", + "of the data products, descriptions of catalog contents, and ADQL recipes.\n", + "\n", + "### 1.1. Package imports\n", + "\n", + "Import general python packages, the Rubin TAP service utilities, and various scikit-learn utilities." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "3f4900a4-3358-472a-b9ba-c42e3f2f0771", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T18:16:10.447704Z", + "iopub.status.busy": "2024-12-03T18:16:10.447270Z", + "iopub.status.idle": "2024-12-03T18:16:11.741424Z", + "shell.execute_reply": "2024-12-03T18:16:11.740792Z", + "shell.execute_reply.started": "2024-12-03T18:16:10.447676Z" + } + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import pandas\n", + "\n", + "from astropy import units as u\n", + "from astropy.coordinates import SkyCoord\n", + "\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.metrics import mean_squared_error\n", + "from sklearn.ensemble import RandomForestRegressor\n", + "\n", + "from lsst.rsp import get_tap_service, retrieve_query" + ] + }, + { + "cell_type": "markdown", + "id": "90251edc-e77a-4f2c-aef3-935381faebc9", + "metadata": {}, + "source": [ + "Set up seaborn to use 538's aesthetics. This is probably not what we want to the rtn-045 default plotting settings though..." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "94acc9f6-2033-4ace-aefd-d036a35f4221", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T18:18:25.800083Z", + "iopub.status.busy": "2024-12-03T18:18:25.799778Z", + "iopub.status.idle": "2024-12-03T18:18:25.803998Z", + "shell.execute_reply": "2024-12-03T18:18:25.803416Z", + "shell.execute_reply.started": "2024-12-03T18:18:25.800052Z" + } + }, + "outputs": [], + "source": [ + "sns.set_style('whitegrid')\n", + "plt.style.use('fivethirtyeight')\n", + "palette = sns.color_palette(\"muted\") # Choose a desired palette\n", + "sns.set_palette(palette)" + ] + }, + { + "cell_type": "markdown", + "id": "ca1e28f4-805e-4480-a7c6-0473b7e2b088", + "metadata": {}, + "source": [ + "### 1.2. Define functions and parameters\n", + "\n", + "Instantiate the TAP service." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "caf56589-100a-4481-8f24-5f5058b6671f", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T18:18:31.983738Z", + "iopub.status.busy": "2024-12-03T18:18:31.983012Z", + "iopub.status.idle": "2024-12-03T18:18:32.026282Z", + "shell.execute_reply": "2024-12-03T18:18:32.025768Z", + "shell.execute_reply.started": "2024-12-03T18:18:31.983710Z" + } + }, + "outputs": [], + "source": [ + "service = get_tap_service(\"tap\")\n", + "assert service is not None" + ] + }, + { + "cell_type": "markdown", + "id": "9eb0f20e-6c28-404f-8032-acc00f73405a", + "metadata": {}, + "source": [ + "Set the maximum number of rows to display from pandas." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "2b7b6002-2457-4c20-a03e-6bfa24a0aa27", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T18:18:32.908017Z", + "iopub.status.busy": "2024-12-03T18:18:32.907315Z", + "iopub.status.idle": "2024-12-03T18:18:32.910925Z", + "shell.execute_reply": "2024-12-03T18:18:32.910377Z", + "shell.execute_reply.started": "2024-12-03T18:18:32.907992Z" + } + }, + "outputs": [], + "source": [ + "pandas.set_option('display.max_rows', 6)" + ] + }, + { + "cell_type": "markdown", + "id": "cd325fe4-6c7c-4803-ad79-f30d7edc23e3", + "metadata": {}, + "source": [ + "## 2. Query for Kron fluxes around extended (galaxy) objects.\n", + "I forget why I chose this specific statistic for the demo.\n", + "\n", + "Kron radius: A radius that is calculated using the light profile of the object, typically as the first moment (i.e., a weighted average of radius with brightness) of the light distribution.\n", + "\n", + "Kron flux: The total flux measured within a certain multiple (often 2.5×) of the Kron radius, typically capturing about 90–95% of the total light for extended sources like galaxies." + ] + }, + { + "cell_type": "markdown", + "id": "6b4f495d-1215-421d-bdb0-bc32fec92c25", + "metadata": {}, + "source": [ + "Define the coordinates and radius to use for the example queries in Sections 2 and 3." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "7ddd0344-b354-45a0-9e5a-755149c9bc54", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T18:18:35.079591Z", + "iopub.status.busy": "2024-12-03T18:18:35.078853Z", + "iopub.status.idle": "2024-12-03T18:18:35.082610Z", + "shell.execute_reply": "2024-12-03T18:18:35.082033Z", + "shell.execute_reply.started": "2024-12-03T18:18:35.079566Z" + } + }, + "outputs": [], + "source": [ + "center_ra = 62\n", + "center_dec = -37\n", + "radius = 0.1\n", + "\n", + "str_center_coords = str(center_ra) + \", \" + str(center_dec)\n", + "str_radius = str(radius)" + ] + }, + { + "cell_type": "markdown", + "id": "dd80babb-ee05-49e9-9f9c-923d5c0cee31", + "metadata": {}, + "source": [ + "Start with the same query as used in the beginner TAP tutorial notebook 02a." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "985e3b62-8065-42ec-a40c-1232c4c45f17", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T18:19:13.171378Z", + "iopub.status.busy": "2024-12-03T18:19:13.170670Z", + "iopub.status.idle": "2024-12-03T18:19:13.174956Z", + "shell.execute_reply": "2024-12-03T18:19:13.174323Z", + "shell.execute_reply.started": "2024-12-03T18:19:13.171349Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SELECT coord_ra, coord_dec, g_kronFlux, g_kronFlux_flag, r_kronFlux, r_kronFlux_flag, i_kronFlux, i_kronFlux_flag FROM dp02_dc2_catalogs.Object WHERE CONTAINS(POINT('ICRS', coord_ra, coord_dec), CIRCLE('ICRS', 62, -37, 0.1)) = 1 AND detect_isPrimary = 1 AND g_extendedness = 1\n" + ] + } + ], + "source": [ + "query = \"SELECT coord_ra, coord_dec, g_kronFlux, g_kronFlux_flag, \"\\\n", + " \"r_kronFlux, r_kronFlux_flag, i_kronFlux, i_kronFlux_flag \"\\\n", + " \"FROM dp02_dc2_catalogs.Object \"\\\n", + " \"WHERE CONTAINS(POINT('ICRS', coord_ra, coord_dec), \"\\\n", + " \"CIRCLE('ICRS', \" + str_center_coords + \", \" + str_radius + \")) = 1 \"\\\n", + " \"AND detect_isPrimary = 1 AND g_extendedness = 1\"\n", + "print(query)" + ] + }, + { + "cell_type": "markdown", + "id": "f024085b-0f7f-45c6-8184-41b528c15396", + "metadata": {}, + "source": [ + "Run the query job asynchronously." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "c02adc91-5f5e-418b-87a3-cba8beba7dd2", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T18:19:14.234845Z", + "iopub.status.busy": "2024-12-03T18:19:14.234550Z", + "iopub.status.idle": "2024-12-03T18:19:21.521239Z", + "shell.execute_reply": "2024-12-03T18:19:21.520430Z", + "shell.execute_reply.started": "2024-12-03T18:19:14.234825Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Job phase is COMPLETED\n" + ] + } + ], + "source": [ + "job = service.submit_job(query)\n", + "job.run()\n", + "job.wait(phases=['COMPLETED', 'ERROR'])\n", + "print('Job phase is', job.phase)" + ] + }, + { + "cell_type": "markdown", + "id": "80b28a39-bd12-49d6-9cce-f8ddfc31296c", + "metadata": {}, + "source": [ + "## 3. Explore the data using a `pandas` DataFrame object.\n", + "DEFINE WHAT A DATAFRAME IS." + ] + }, + { + "cell_type": "markdown", + "id": "07d1cfb1-589b-402b-8b8f-c2c70652b6c6", + "metadata": {}, + "source": [ + "Return the results as a `pandas` dataframe, and then delete the query to save space." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "8cd2f538-c2d7-44ca-ab4d-825120b8f2e7", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T18:19:21.522743Z", + "iopub.status.busy": "2024-12-03T18:19:21.522437Z", + "iopub.status.idle": "2024-12-03T18:19:21.848448Z", + "shell.execute_reply": "2024-12-03T18:19:21.847864Z", + "shell.execute_reply.started": "2024-12-03T18:19:21.522716Z" + } + }, + "outputs": [], + "source": [ + "results = job.fetch_result().to_table().to_pandas()\n", + "job.delete()\n", + "del query" + ] + }, + { + "cell_type": "markdown", + "id": "27888222-8fca-4620-a838-9260b0f5f47f", + "metadata": {}, + "source": [ + "Display `results`." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "ee4d121e-6b4d-4371-afae-4f7587b95d51", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T18:18:54.059294Z", + "iopub.status.busy": "2024-12-03T18:18:54.058632Z", + "iopub.status.idle": "2024-12-03T18:18:54.074345Z", + "shell.execute_reply": "2024-12-03T18:18:54.073785Z", + "shell.execute_reply.started": "2024-12-03T18:18:54.059270Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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coord_racoord_decg_kronFluxg_kronFlux_flagr_kronFluxr_kronFlux_flagi_kronFluxi_kronFlux_flag
062.018897-37.09567171.568352True91.185588True624.454022True
162.020999-37.093227174.729861False110.922305False52.040203True
262.000430-37.093196131.680920False137.655812False136.174616True
...........................
1156161.950427-36.94658651.054369True175.646973False123.073904True
1156261.976752-36.904225199.039503False187.972452False115.825734False
1156361.932319-36.941077266.123377False218.853195False481.650950True
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11564 rows × 8 columns

\n", + "
" + ], + "text/plain": [ + " coord_ra coord_dec g_kronFlux g_kronFlux_flag r_kronFlux \\\n", + "0 62.018897 -37.095671 71.568352 True 91.185588 \n", + "1 62.020999 -37.093227 174.729861 False 110.922305 \n", + "2 62.000430 -37.093196 131.680920 False 137.655812 \n", + "... ... ... ... ... ... \n", + "11561 61.950427 -36.946586 51.054369 True 175.646973 \n", + "11562 61.976752 -36.904225 199.039503 False 187.972452 \n", + "11563 61.932319 -36.941077 266.123377 False 218.853195 \n", + "\n", + " r_kronFlux_flag i_kronFlux i_kronFlux_flag \n", + "0 True 624.454022 True \n", + "1 False 52.040203 True \n", + "2 False 136.174616 True \n", + "... ... ... ... \n", + "11561 False 123.073904 True \n", + "11562 False 115.825734 False \n", + "11563 False 481.650950 True \n", + "\n", + "[11564 rows x 8 columns]" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results" + ] + }, + { + "cell_type": "markdown", + "id": "1d493b9b-0aba-4586-bcd0-3e1ce1f09c16", + "metadata": {}, + "source": [ + "`results` is a `pandas` DataFrame object (see below). There's lots you can do with this type of object..." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "db2168fe-593a-423d-b2f4-26ac0db60e8c", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T18:19:48.071264Z", + "iopub.status.busy": "2024-12-03T18:19:48.070609Z", + "iopub.status.idle": "2024-12-03T18:19:48.074799Z", + "shell.execute_reply": "2024-12-03T18:19:48.074312Z", + "shell.execute_reply.started": "2024-12-03T18:19:48.071232Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "pandas.core.frame.DataFrame" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(results)" + ] + }, + { + "cell_type": "markdown", + "id": "9c59c4e8-90bd-4aa8-8ce2-9a14c09988a0", + "metadata": {}, + "source": [ + "Some options are inspection- and summary-oriented, such as the `.head()`, `.tail()`, and `.describe()` attributes. Let's check these out now. `.head()` and `.tail()` give you the first and last five rows, respectively, but can be modified to print out a different number of rows. `.describe()` will provide statistics of the distribution of values in each column, including the mean and standard deviation." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "eec25f58-d3f3-4ef4-b3e2-ab105c4718fd", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T18:23:39.207146Z", + "iopub.status.busy": "2024-12-03T18:23:39.206862Z", + "iopub.status.idle": "2024-12-03T18:23:39.230317Z", + "shell.execute_reply": "2024-12-03T18:23:39.229617Z", + "shell.execute_reply.started": "2024-12-03T18:23:39.207125Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " coord_ra coord_dec g_kronFlux g_kronFlux_flag r_kronFlux \\\n", + "0 62.018897 -37.095671 71.568352 True 91.185588 \n", + "1 62.020999 -37.093227 174.729861 False 110.922305 \n", + "2 62.000430 -37.093196 131.680920 False 137.655812 \n", + "3 62.015568 -37.092868 372.665560 False 171.582869 \n", + "4 62.002969 -37.092762 247.219720 False 153.138653 \n", + "\n", + " r_kronFlux_flag i_kronFlux i_kronFlux_flag \n", + "0 True 624.454022 True \n", + "1 False 52.040203 True \n", + "2 False 136.174616 True \n", + "3 False 211.338418 True \n", + "4 True 184.829166 True \n", + " coord_ra coord_dec g_kronFlux g_kronFlux_flag r_kronFlux \\\n", + "11554 61.913511 -36.960012 158.939682 False NaN \n", + "11555 61.986424 -36.950292 125.535210 False 71.749436 \n", + "11556 61.942562 -36.951137 108.966860 False 135.301872 \n", + "... ... ... ... ... ... \n", + "11561 61.950427 -36.946586 51.054369 True 175.646973 \n", + "11562 61.976752 -36.904225 199.039503 False 187.972452 \n", + "11563 61.932319 -36.941077 266.123377 False 218.853195 \n", + "\n", + " r_kronFlux_flag i_kronFlux i_kronFlux_flag \n", + "11554 True 102.094474 True \n", + "11555 True NaN True \n", + "11556 False 191.964068 True \n", + "... ... ... ... \n", + "11561 False 123.073904 True \n", + "11562 False 115.825734 False \n", + "11563 False 481.650950 True \n", + "\n", + "[10 rows x 8 columns]\n", + " coord_ra coord_dec g_kronFlux r_kronFlux i_kronFlux\n", + "count 11564.000000 11564.000000 11447.000000 1.145300e+04 1.129200e+04\n", + "mean 61.999517 -37.001530 761.468963 1.368137e+03 2.071233e+03\n", + "std 0.062390 0.050868 13194.742739 2.395294e+04 3.258561e+04\n", + "... ... ... ... ... ...\n", + "50% 61.999039 -37.001694 182.963268 2.409297e+02 3.517374e+02\n", + "75% 62.049891 -36.960187 340.536311 4.811457e+02 7.569082e+02\n", + "max 62.124349 -36.900195 782163.585831 1.957761e+06 2.870998e+06\n", + "\n", + "[8 rows x 5 columns]\n" + ] + } + ], + "source": [ + "print(results.head())\n", + "print(results.tail(10))\n", + "print(results.describe())" + ] + }, + { + "cell_type": "markdown", + "id": "1b37fc18-e00b-4ef2-8d18-85d823d60d9e", + "metadata": {}, + "source": [ + "## 4. Visualize using `seaborn`\n", + "Let's look further into visualizing these statistics using `seaborn`'s boxplot tool." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "4fc9b578-2be4-4fb2-8d74-ebca809ea99f", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T18:55:30.244276Z", + "iopub.status.busy": "2024-12-03T18:55:30.243648Z", + "iopub.status.idle": "2024-12-03T18:55:30.835103Z", + "shell.execute_reply": "2024-12-03T18:55:30.834453Z", + "shell.execute_reply.started": "2024-12-03T18:55:30.244249Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8, 6))\n", + "sns.boxplot(data=results)\n", + "plt.title('Box Plot of Data Distributions')\n", + "plt.xlabel('Feature')\n", + "plt.ylabel('Value')\n", + "plt.xticks(rotation=90)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "ef5f20de-0fd6-4ba1-9cab-9d59cd05df99", + "metadata": {}, + "source": [ + "It looks like outliers are dominant in the visualization. Hide these and also only plot the kron Flux values." + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "bacf5114-6a64-4100-8eb6-f1d9ddc36f89", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T18:59:02.839663Z", + "iopub.status.busy": "2024-12-03T18:59:02.839254Z", + "iopub.status.idle": "2024-12-03T18:59:03.091807Z", + "shell.execute_reply": "2024-12-03T18:59:03.091120Z", + "shell.execute_reply.started": "2024-12-03T18:59:02.839637Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8, 6))\n", + "sns.boxplot(data=results[['g_kronFlux','r_kronFlux','i_kronFlux']], showfliers=False)\n", + "plt.title('Box Plot of Data Distributions')\n", + "plt.xlabel('Feature')\n", + "plt.ylabel('Value')\n", + "plt.xticks(rotation=90)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "75d6336b-1068-46cf-8105-b945fd18020e", + "metadata": {}, + "source": [ + "Boxplots show a box and whiskers.\n", + "- The \"box\" is the interquartile range (IQR), which is the 25th percentile of the distribution of a value to the 75th percentile.\n", + "- The horizontal line inside the box is the median of the distribution.\n", + "- The whisker extends from the IQR to 1.5*IQR away from the edge of the box.\n", + "- Points outside the whisker are considered outliers (hidden here).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "39521ac6-0bec-42e7-9062-8fc9ce5edc55", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T19:11:16.903575Z", + "iopub.status.busy": "2024-12-03T19:11:16.902993Z", + "iopub.status.idle": "2024-12-03T19:11:17.202209Z", + "shell.execute_reply": "2024-12-03T19:11:17.201547Z", + "shell.execute_reply.started": "2024-12-03T19:11:16.903550Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_638/2438356921.py:3: FutureWarning: \n", + "\n", + "The `bw` parameter is deprecated in favor of `bw_method`/`bw_adjust`.\n", + "Setting `bw_method=0.2`, but please see docs for the new parameters\n", + "and update your code. This will become an error in seaborn v0.15.0.\n", + "\n", + " sns.violinplot(data=filtered_results,\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8, 6))\n", + "filtered_results = results[['g_kronFlux', 'r_kronFlux', 'i_kronFlux']].apply(lambda x: x[(x > x.quantile(0.05)) & (x < x.quantile(0.95))])\n", + "sns.violinplot(data=filtered_results,\n", + " cut=0,\n", + " bw=0.2)\n", + "plt.title('Box Plot of Data Distributions')\n", + "plt.xlabel('Feature')\n", + "plt.ylabel('Value')\n", + "plt.xticks(rotation=90)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "4f2c50a8-098e-4230-b122-3880c8bb1883", + "metadata": {}, + "source": [ + "A violinplot gives a lot of the same information as a boxplot, in fact, there are little boxplots within the violinplot; the horizontal white line is the median, the thicker grey box is the IQR and the thin line shows the 1.5*IQR span. A violinplot also uses a kernel density extimator to visualize the distribution of each feature. Here we see that most of the data are clustered around relatively low values for all of the kron fluxes." + ] + }, + { + "cell_type": "markdown", + "id": "3ec470a9-8db0-403a-89ba-32e0dd9bef15", + "metadata": {}, + "source": [ + "## 5. Investigate the fluxes and associated flags" + ] + }, + { + "cell_type": "markdown", + "id": "18dba188-ea4c-47e9-8faf-62e3552add1e", + "metadata": {}, + "source": [ + "Use `pandas` to investigate if there are any flags on the `kronFlux` measurement. The `.value_counts()` method will show the number of True and False columns, where True are rows for which the `g_kronFlux` measurement was flagged for a variety of reasons. There are many other columns that investigate specific reasons why this measurement is untrustworthy; the `g_kronFlux_flag` is a way to combine all of the individual flags." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "0be4535d-cc89-45ef-98e9-591b9f459fae", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T00:04:41.789910Z", + "iopub.status.busy": "2024-12-03T00:04:41.789726Z", + "iopub.status.idle": "2024-12-03T00:04:41.794781Z", + "shell.execute_reply": "2024-12-03T00:04:41.794289Z", + "shell.execute_reply.started": "2024-12-03T00:04:41.789894Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "r_kronFlux_flag\n", + "False 10723\n", + "True 841\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results['r_kronFlux_flag'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "id": "af623422-c6cf-4c21-8971-5599c60ea8d7", + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-15T18:24:36.531474Z", + "iopub.status.busy": "2024-11-15T18:24:36.531192Z", + "iopub.status.idle": "2024-11-15T18:24:36.547424Z", + "shell.execute_reply": "2024-11-15T18:24:36.546685Z", + "shell.execute_reply.started": "2024-11-15T18:24:36.531443Z" + } + }, + "source": [ + "Let's compare the value of the `g_kronFlux` between flagged and unflagged cases." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "dddfd1e7-3faa-465f-ade4-dc136ae39262", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T00:04:41.795547Z", + "iopub.status.busy": "2024-12-03T00:04:41.795330Z", + "iopub.status.idle": "2024-12-03T00:04:41.924873Z", + "shell.execute_reply": "2024-12-03T00:04:41.924253Z", + "shell.execute_reply.started": "2024-12-03T00:04:41.795530Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.clf()\n", + "plt.hist(results[results['r_kronFlux_flag']]['r_kronFlux'], label='Flagged', density=True)\n", + "#plt.hist(results[results['r_kronFlux_flag']==False]['r_kronFlux'], label='Not flagged', density=True)\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "24e94c30-66b3-4b9c-b79b-bcf024d5f214", + "metadata": {}, + "source": [ + "Okay what about the `r_kronFlux` measurement?" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "0e66ccb2-3922-471b-8c15-7fb055d02a10", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T00:04:41.925819Z", + "iopub.status.busy": "2024-12-03T00:04:41.925606Z", + "iopub.status.idle": "2024-12-03T00:04:41.931416Z", + "shell.execute_reply": "2024-12-03T00:04:41.930843Z", + "shell.execute_reply.started": "2024-12-03T00:04:41.925802Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "r_kronFlux_flag\n", + "False 10723\n", + "True 841\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results['r_kronFlux_flag'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "id": "fa7a4ce1-7bd4-47f8-a480-188b2c70579a", + "metadata": {}, + "source": [ + "Perform an intersection to see if the flagged entries overlap between these two photometric bands." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "06786c33-2563-4237-9d0f-22d6308c0d7b", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T00:04:41.932301Z", + "iopub.status.busy": "2024-12-03T00:04:41.932089Z", + "iopub.status.idle": "2024-12-03T00:04:41.970411Z", + "shell.execute_reply": "2024-12-03T00:04:41.969900Z", + "shell.execute_reply.started": "2024-12-03T00:04:41.932283Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " coord_ra coord_dec g_kronFlux g_kronFlux_flag r_kronFlux \\\n", + "1749 61.916451 -37.018987 232.455339 True 380.415747 \n", + "1758 61.910063 -37.017256 115.486047 True 105.318235 \n", + "1774 61.950787 -37.015252 132.207788 True 193.917364 \n", + "... ... ... ... ... ... \n", + "11466 61.956032 -37.074942 59.901381 True 315.077832 \n", + "11471 61.942023 -37.073313 145.759753 True 120.304211 \n", + "11494 61.924542 -37.071842 248.013148 True 273.729756 \n", + "\n", + " r_kronFlux_flag i_kronFlux i_kronFlux_flag \n", + "1749 True 562.754481 False \n", + "1758 True 218.924537 True \n", + "1774 True 522.751057 False \n", + "... ... ... ... \n", + "11466 True NaN True \n", + "11471 True 243.456290 True \n", + "11494 True 257.108970 True \n", + "\n", + "[328 rows x 8 columns]\n" + ] + } + ], + "source": [ + "# get the unique values (which will be True or False)\n", + "r_values = set(results['r_kronFlux_flag'].unique())\n", + "g_values = set(results['g_kronFlux_flag'].unique())\n", + "\n", + "# find the intersection\n", + "overlap = r_values & g_values\n", + "\n", + "overlap_true_rows = results[\n", + " (results['r_kronFlux_flag'].isin(overlap)) & \n", + " (results['g_kronFlux_flag'].isin(overlap)) & \n", + " (results['r_kronFlux_flag'] == True) & \n", + " (results['g_kronFlux_flag'] == True)\n", + "]\n", + "\n", + "print(overlap_true_rows)" + ] + }, + { + "cell_type": "markdown", + "id": "ec13b104-ad8d-4bd6-8a93-b6d1d57b921e", + "metadata": {}, + "source": [ + "There are six overlapping true rows, meaning that in six cases, we cannot use the $r-$band kronFlux to predict the $g-$band kronFlux." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "d29499cf-cab9-4a5c-8811-ffd1e5c9d82f", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T00:04:41.971139Z", + "iopub.status.busy": "2024-12-03T00:04:41.970965Z", + "iopub.status.idle": "2024-12-03T00:04:41.978973Z", + "shell.execute_reply": "2024-12-03T00:04:41.978503Z", + "shell.execute_reply.started": "2024-12-03T00:04:41.971124Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " coord_ra coord_dec g_kronFlux g_kronFlux_flag r_kronFlux \\\n", + "46 62.036008 -36.904244 60.546756 False 0.103155 \n", + "347 62.072778 -36.964144 15.582472 False 0.000000 \n", + "700 62.067443 -36.954921 94.684525 False NaN \n", + "... ... ... ... ... ... \n", + "11477 61.984400 -37.069348 63.584932 False 85.069624 \n", + "11483 61.911589 -37.067708 159.077697 False 139.900920 \n", + "11493 61.957278 -37.071416 165.461776 False 182.058161 \n", + "\n", + " r_kronFlux_flag i_kronFlux i_kronFlux_flag \n", + "46 True 68.234239 False \n", + "347 True 100.430859 False \n", + "700 True 259.766837 False \n", + "... ... ... ... \n", + "11477 True 100.221708 True \n", + "11483 True 279.320760 True \n", + "11493 True 349.606659 True \n", + "\n", + "[513 rows x 8 columns]\n" + ] + } + ], + "source": [ + "g_false_r_true = results[\n", + " (results['r_kronFlux_flag'] == True) & \n", + " (results['g_kronFlux_flag'] == False)\n", + "]\n", + "\n", + "print(g_false_r_true)" + ] + }, + { + "cell_type": "markdown", + "id": "69b67af5-de80-414c-985d-8f6cdccfc3a3", + "metadata": {}, + "source": [ + "For the unflagged values, let's look at the relationship between these fluxes." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "e6294681-9c60-4ec6-805c-d378300acaa3", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T00:04:41.979744Z", + "iopub.status.busy": "2024-12-03T00:04:41.979551Z", + "iopub.status.idle": "2024-12-03T00:04:42.213955Z", + "shell.execute_reply": "2024-12-03T00:04:42.213438Z", + "shell.execute_reply.started": "2024-12-03T00:04:41.979728Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "clean = results[\n", + " (results['r_kronFlux_flag'] == False) & \n", + " (results['g_kronFlux_flag'] == False) &\n", + " (results['i_kronFlux_flag'] == False)\n", + "]\n", + "\n", + "plt.scatter(clean['g_kronFlux'], clean['r_kronFlux'])\n", + "plt.xlabel(r'$g-$band kronFlux [nJy]')\n", + "plt.ylabel(r'$r-$band kronFlux [nJy]');" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "5afedb17-6478-4f2b-bdfc-38e73cd4a65e", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T00:04:42.215968Z", + "iopub.status.busy": "2024-12-03T00:04:42.215730Z", + "iopub.status.idle": "2024-12-03T00:04:42.382817Z", + "shell.execute_reply": "2024-12-03T00:04:42.382256Z", + "shell.execute_reply.started": "2024-12-03T00:04:42.215949Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(clean['g_kronFlux'], clean['r_kronFlux'])\n", + "plt.xlabel(r'$g-$band kronFlux [nJy]')\n", + "plt.ylabel(r'$r-$band kronFlux [nJy]')\n", + "plt.xlim([0,30000])\n", + "plt.ylim([0,0.5e5]);" + ] + }, + { + "cell_type": "markdown", + "id": "2f503394-3816-4d31-9cf0-9de88e229f87", + "metadata": {}, + "source": [ + "Zooming in on this relationship, it looks roughly linear, so we should be able to do some predictive work here." + ] + }, + { + "cell_type": "markdown", + "id": "4704605a-4665-4ccc-bd7e-cefaf5e09828", + "metadata": {}, + "source": [ + "## 6. Prepare the training and test sets" + ] + }, + { + "cell_type": "markdown", + "id": "0b3c8d4e-0541-49c7-9fa1-f9b3b5d29aac", + "metadata": {}, + "source": [ + "The first step is to define the training and validation data." + ] + }, + { + "cell_type": "markdown", + "id": "1ab2c517-125b-4c70-ad2d-b447f7e6721e", + "metadata": {}, + "source": [ + "The `.to_frame()` argument is required to input the X data as a 2D shape, as expected by scikit-learn." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "fc90feca-ede1-44b0-929b-2fec1ddf5ad4", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T00:04:42.383760Z", + "iopub.status.busy": "2024-12-03T00:04:42.383543Z", + "iopub.status.idle": "2024-12-03T00:04:42.389713Z", + "shell.execute_reply": "2024-12-03T00:04:42.389149Z", + "shell.execute_reply.started": "2024-12-03T00:04:42.383742Z" + } + }, + "outputs": [], + "source": [ + "X_train, X_test, y_train, y_test = train_test_split(\n", + " clean['g_kronFlux'].to_frame(), clean['r_kronFlux'].to_frame(), test_size=0.2, random_state=42)" + ] + }, + { + "cell_type": "markdown", + "id": "ecb487df-ce82-4d2d-9821-64620e1e922b", + "metadata": {}, + "source": [ + "Good practice to use a scaler." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "8e675e27-74f0-43e8-91af-8652e2710609", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T00:04:42.390439Z", + "iopub.status.busy": "2024-12-03T00:04:42.390274Z", + "iopub.status.idle": "2024-12-03T00:04:42.404155Z", + "shell.execute_reply": "2024-12-03T00:04:42.403690Z", + "shell.execute_reply.started": "2024-12-03T00:04:42.390426Z" + } + }, + "outputs": [], + "source": [ + "scaler = StandardScaler()\n", + "X_train = scaler.fit_transform(X_train)\n", + "X_test = scaler.transform(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "4771e145-2649-4eda-8891-987c7e9c9009", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T00:04:42.404900Z", + "iopub.status.busy": "2024-12-03T00:04:42.404711Z", + "iopub.status.idle": "2024-12-03T00:04:42.413985Z", + "shell.execute_reply": "2024-12-03T00:04:42.413340Z", + "shell.execute_reply.started": "2024-12-03T00:04:42.404885Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-0.05664546],\n", + " [ 0.0240961 ],\n", + " [-0.06126054],\n", + " ...,\n", + " [ 0.51047933],\n", + " [-0.01411049],\n", + " [-0.03585177]])" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_test" + ] + }, + { + "cell_type": "markdown", + "id": "6011b2e6-5197-4475-8eb4-3a8841b3bd28", + "metadata": {}, + "source": [ + "## 7. Model (using `scikit-learn`)" + ] + }, + { + "cell_type": "markdown", + "id": "48c8d648-288f-4635-a961-248f127fe524", + "metadata": {}, + "source": [ + "## 7.1 Start with a linear regression" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "e02ca479-6105-442b-9879-2eb215dc4d66", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T00:04:42.414746Z", + "iopub.status.busy": "2024-12-03T00:04:42.414553Z", + "iopub.status.idle": "2024-12-03T00:04:42.430795Z", + "shell.execute_reply": "2024-12-03T00:04:42.430269Z", + "shell.execute_reply.started": "2024-12-03T00:04:42.414730Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
LinearRegression()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "LinearRegression()" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model = LinearRegression()\n", + "model.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "aca8064a-c94f-4792-86b3-62ec2130471b", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T00:04:42.431529Z", + "iopub.status.busy": "2024-12-03T00:04:42.431336Z", + "iopub.status.idle": "2024-12-03T00:04:42.438736Z", + "shell.execute_reply": "2024-12-03T00:04:42.438086Z", + "shell.execute_reply.started": "2024-12-03T00:04:42.431514Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE: 81831676.75317723\n" + ] + } + ], + "source": [ + "y_pred = model.predict(X_test)\n", + "mse = mean_squared_error(y_test, y_pred)\n", + "print(\"MSE:\", mse)" + ] + }, + { + "cell_type": "markdown", + "id": "c9086695-43c6-49e5-a751-bcabc275172d", + "metadata": {}, + "source": [ + "Plot how the predicted values compare to the true values." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "ee8bd887-928e-4a41-bd77-149b344ab238", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T00:04:42.439562Z", + "iopub.status.busy": "2024-12-03T00:04:42.439353Z", + "iopub.status.idle": "2024-12-03T00:04:42.606543Z", + "shell.execute_reply": "2024-12-03T00:04:42.605896Z", + "shell.execute_reply.started": "2024-12-03T00:04:42.439546Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.clf()\n", + "plt.scatter(y_test, y_pred)\n", + "plt.plot([0,1e6],[0,1e6], color='black', ls='--')\n", + "plt.xlabel('True')\n", + "plt.ylabel('Predicted')\n", + "plt.xlim([0,2e4])\n", + "plt.ylim([0,2e4]);" + ] + }, + { + "cell_type": "markdown", + "id": "1de26eb9-e7cf-4e94-bf14-0fda5d8efe18", + "metadata": {}, + "source": [ + "## 7.2 Improve the model with more features\n", + "Let's see if this will improve with more predictive values, this time including the i band information." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "d80e56eb-6e3d-4110-bee6-3681ee4a923b", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T00:04:43.535589Z", + "iopub.status.busy": "2024-12-03T00:04:43.535315Z", + "iopub.status.idle": "2024-12-03T00:04:43.657195Z", + "shell.execute_reply": "2024-12-03T00:04:43.656652Z", + "shell.execute_reply.started": "2024-12-03T00:04:43.535570Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE: 409962.71379404626\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Train-test split\n", + "X_train, X_test, y_train, y_test = train_test_split(\n", + " clean[['g_kronFlux', 'i_kronFlux']], # Use these two features\n", + " clean['r_kronFlux'], # Target variable\n", + " test_size=0.2,\n", + " random_state=42\n", + ")\n", + "\n", + "# Standardize the features\n", + "scaler = StandardScaler()\n", + "X_train = scaler.fit_transform(X_train)\n", + "X_test = scaler.transform(X_test)\n", + "\n", + "# Train the model\n", + "model = LinearRegression()\n", + "model.fit(X_train, y_train)\n", + "\n", + "# Make predictions\n", + "y_pred = model.predict(X_test)\n", + "\n", + "# Evaluate the model\n", + "mse = mean_squared_error(y_test, y_pred)\n", + "print(\"MSE:\", mse)\n", + "\n", + "# Scatter plot: True vs Predicted\n", + "plt.clf()\n", + "plt.scatter(y_test, y_pred)\n", + "plt.plot([0, 1e6], [0, 1e6], color='black', ls='--')\n", + "plt.xlabel('True')\n", + "plt.ylabel('Predicted')\n", + "plt.xlim([0, 2e4])\n", + "plt.ylim([0, 2e4])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "30cd4c78-35e5-4459-84bf-9642068da002", + "metadata": {}, + "source": [ + "Test for the reader: try to improve this further by including more features." + ] + }, + { + "cell_type": "markdown", + "id": "89e92033-ff41-4662-b5aa-ece41c216a07", + "metadata": {}, + "source": [ + "## 7.3 Random forest regressor\n", + "These are great" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "efb56f9d-6487-444d-b283-6d60c1694948", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T00:04:45.724451Z", + "iopub.status.busy": "2024-12-03T00:04:45.723900Z", + "iopub.status.idle": "2024-12-03T00:04:48.635415Z", + "shell.execute_reply": "2024-12-03T00:04:48.634885Z", + "shell.execute_reply.started": "2024-12-03T00:04:45.724427Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE: 33565886.06951628\n" + ] + }, + { + "data": { + "image/png": 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nB8899xwiIyMB3K1qsGnTJjRv3lzmyBo5IaMff/xRACh3mzJlitRmzZo1wsnJSWRnZ5d7/rVr10SfPn2Eu7u7cHBwEG3atBFz5swRGRkZBu0yMjLExIkThYuLi3BxcRETJ04UWVlZBm2uXr0qhg0bJpycnIS7u7uYNWuWKCwsrNH1aDQaAUBoNJoaPY+IiKiuxcfHiwceeEAAELa2tuLdd98VOp1O7rAsgtzf3wohhJAvFWxYcnJyoFKpoNFo2KNGREQWZciQITh48CBatWqFyMhIaaoOyf/9bRVzxoiIiKh21q9fjylTpiAxMZGJmIVhMkZERNQAnTp1CsuXL5fut2jRAhs3boS7u7uMUVFFrKbOGBEREVVPCIF///vfeOWVV1BcXIwOHTogLCxM7rCoCkzGiIiIGojMzExMmzYN3377LQBg9OjR+Mc//iFzVFQdDlMSERE1ALGxsejcuTO+/fZbODg4YOXKlfjmm2/QrFkzuUOjarBnjIiIyMp99tlnmDt3LkpKStCmTRvs2LFD2hKQLB97xoiIiKycWq1GSUkJxo4da7A3M1kH9owRERFZodzcXDRt2hQA8MQTT+Do0aPo1asXFAqFzJFRTbFnjIiIyIro9XosX74cDz74IP766y/peO/evZmIWSkmY0RERFYiPT0dQ4cOxeuvv47U1FRs3rxZ7pDIDDhMSUREZAV++uknTJgwAampqXBycsLKlSsxbdo0ucMiM2DPGBERkQXT6XRYunQpBg4ciNTUVAQEBODUqVN4+umnOSzZQDAZIyIismAff/wxlixZAr1ej2nTpuHkyZN4+OGH5Q6LzIjJGBERkQWbOXMmunXrhi+//BLr16+Hs7Nznb6eTi8QezkDexNvIPZyBnR6UaevR5wzRkREZFFKSkqwdetWhIeHw8bGBs7Ozjh+/DhsbOq+/yQqKRVL9yUjVVMoHfNROWJxWABCA33q/PUbK/aMERERWYgbN25g4MCBmDp1Kj744APpeH0lYjO3xBskYgCQpinEzC3xiEpKrfMYGismY0RERBYgKioKnTp1ws8//4ymTZuidevW9fbaOr3A0n3JqGhAsvTY0n3JHLKsI0zGiIiIZFRcXIyFCxdi6NChuH37Njp37oz4+HiMGzeu3mI4mZJZrkfsXgJAqqYQJ1My6y2mxoRzxoiIiGRy7do1jB8/HjExMQCAF154AR988AEcHR3rNY70O5UnYqa0o5phMkZERCST9PR0nDp1Cq6urli3bh2efPJJWeLwcjEu+TO2HdUMkzEiIqJ6JISQirV27doVW7ZsQdeuXXH//ffLFlN3f3f4qByRpimscN6YAoBa5Yju/u71HVqjwDljRERE9SQlJQV9+/ZFfHy8dGzMmDGyJmIAYGujwOKwAAB3E697ld5fHBYAWxtW/K8LTMaIiIjqwa5du9C5c2ccPXoUM2bMgBCWtTIxNNAHqyd1gVplOBSpVjli9aQurDNWhzhMSUREVIe0Wi0WLFiAlStXAgCCg4MRGRlpkftKhgb64NEANU6mZCL9TiG8XO4OTbJHrG4xGSMiIqojly5dwtixY6VhyZdffhnLli2Dvb29zJFVztZGgZA2HnKH0agwGSMiIqoDycnJCA4Oxp07d+Dh4YFNmzZh2LBhcodFFojJGBERUR146KGHEBISgvz8fGzbtg0tW7aUOySyUEzGiIiIzOTixYto0aIFmjRpAhsbG2zfvh1NmzaFnR2/bqlyXE1JRERkBlu3bkXnzp3x4osvSseaNWvGRIyqxWSMiIioFvLz8/HMM89g0qRJyMvLw6VLl1BYyG2DyHhMxoiIiEyUnJyM7t27Y926dVAoFPjXv/6FQ4cO1fvekmTd2HdKRERkgo0bN+KFF15Afn4+vL298dVXX2HAgAFyh0VWiD1jRERENZSZmYkFCxYgPz8fgwYNwm+//cZEjEzGnjEiomro9IIVycmAu7s7Nm/ejLi4OCxatAi2trZyh0RWTCEsbXMsK5aTkwOVSgWNRgNXV1e5wyEiM4hKSsXSfclI1fw9IdtH5YjFYQHcq68REUJg3bp1aN68OUaOHCl3OGRmcn9/yzpM+fPPPyMsLAy+vr5QKBTYs2ePweNTp06FQqEwuAUHBxu00Wq1mD17Njw9PeHs7IwRI0bgzz//NGiTlZWF8PBwqFQqqFQqhIeHIzs726DNtWvXEBYWBmdnZ3h6emLOnDkoKiqqi8smIisRlZSKmVviDRIxAEjTFGLmlnhEJaXKFBnVpzt37mDixImIiIjA1KlT8ddff8kdEjUwsiZjeXl56Nixo7R5akVCQ0ORmpoq3Q4cOGDw+Ny5c7F7925ERkbi2LFjyM3NxfDhw6HT6aQ2EyZMQGJiIqKiohAVFYXExESEh4dLj+t0OgwbNgx5eXk4duwYIiMjsXPnTsyfP9/8F01EVkGnF1i6LxkVDR2UHlu6Lxk6PQcXGrKEhAR06dIF27Ztg62tLRYtWgS1Wi13WNTAyDpnbOjQoRg6dGiVbZRKZaW/+BqNBuvWrcPmzZsxaNAgAMCWLVvQqlUrHDp0CEOGDMG5c+cQFRWF48ePo0ePHgCAtWvXIiQkBBcuXEC7du1w8OBBJCcn4/r16/D19QUAfPjhh5g6dSqWLVvGIUeiRuhkSma5HrF7CQCpmkKcTMnkpsoNkBACq1evxksvvYSioiK0atUKkZGR6Nmzp9yhUQNk8aspf/rpJ3h5eeHBBx9EREQE0tPTpcfi4uJQXFyMwYMHS8d8fX0RGBiImJgYAEBsbCxUKpWUiAFAcHAwVCqVQZvAwEApEQOAIUOGQKvVIi4urq4vkYgsUPod44p2GtuOrEdJSQnGjh2LF154AUVFRQgLC0NCQkKNEzGdXiD2cgb2Jt5A7OUM9qJSpSx6NeXQoUPx1FNPwc/PDykpKXjjjTcwYMAAxMXFQalUIi0tDQ4ODnBzczN4nre3N9LS0gAAaWlp8PLyKnduLy8vgzbe3t4Gj7u5ucHBwUFqUxGtVgutVivdz8nJMflaiciyeLkYV7TT2HZkPezs7NC8eXPY2dnhvffew9y5c6FQ1Gz1LBd+UE1YdM/Y2LFjMWzYMAQGBiIsLAzff/89fv/9d+zfv7/K5wkhDP5wKvojMqVNWStWrJAWBahUKrRq1cqYyyIiK9Dd3x0+KkdU9i+AAne/XLv7u9dnWFRHhBC4c+eOdP/DDz/EiRMn8NJLL5mUiHHhB9WERSdjZfn4+MDPzw8XL14EAKjVahQVFSErK8ugXXp6utTTpVarcfPmzXLnunXrlkGbsj1gWVlZKC4uLtdjdq9FixZBo9FIt+vXr9fq+ojIctjaKLA4LAAAyiVkpfcXhwWw3lgDkJWVhdGjR2PkyJHS4i9HR0d06dKlxufiwg8yhVUlYxkZGbh+/Tp8fO528QYFBcHe3h7R0dFSm9TUVCQlJUlj+yEhIdBoNDh58qTU5sSJE9BoNAZtkpKSkJr69/9WDh48CKVSiaCgoErjUSqVcHV1NbgRUcMRGuiD1ZO6QK0yHIpUqxyxelIXDjc1AMePH0fnzp2xZ88e/PLLL/j1119rdb6aLPwgKiXrnLHc3FxcunRJup+SkoLExES4u7vD3d0dS5YswRNPPAEfHx9cuXIFr732Gjw9PfH4448DAFQqFaZPn4758+fDw8MD7u7uWLBgATp06CCtrmzfvj1CQ0MRERGBNWvWAACeffZZDB8+HO3atQMADB48GAEBAQgPD8f7778vbXMRERHBBIuokQsN9MGjAWpW4G9g9Ho9PvroIyxatAglJSW4//77sWPHjir/A24MS174wZ0kLJesydivv/6K/v37S/fnzZsHAJgyZQpWr16NM2fO4Msvv0R2djZ8fHzQv39/bN++HS4uLtJzPv74Y9jZ2WHMmDEoKCjAwIEDsXHjRoOtKbZu3Yo5c+ZIqy5HjBhhUNvM1tYW+/fvx/PPP49evXrByckJEyZMwAcffFDXbwERWQFbGwXLVzQgGRkZmDJlijT/eMyYMfjiiy+gUqlqfW5LXfjBBQWWjdshmZHc2ykQEVH1hgwZIk1F+eSTT/Dcc8/VeJJ+ZXR6gd7vHkaaprDCeWMK3B3mPvbqgHrrlSpdUFA2ntJX55C7/N/fVjVnjIiIqLY+/PBDdOzYEcePH8eMGTPMlogBlrfwgwsKrAOTMSIiatDS09Oxc+dO6X5gYCASEhLQqVOnOnk9S1r4wQUF1sGii74SERHVxpEjRzB+/HjcunULP//8M0JCQgBUXFvSnCxl4YclLyigvzEZIyKiBken02HZsmVYunQp9Ho92rdvb9RcIHOuOLSEhR+WuqCADDEZIyKiBiUtLQ2TJk3CDz/8AACYOnUqVq5cCWdn5yqf1xBXHJbuJFHdggLuJCEvzhkjIqIG44cffkCnTp3www8/oEmTJti0aRM2bNhgVCLWELcwsrQFBVQxJmNERNRgnDlzBjdv3kRgYCDi4uIwefLkap/T0FccWtKCAqoYhymJiMiqCSGkCfkvvvgiHBwcMG3aNDg5ORn1/JqsOJR7DpipLGVBAVWMPWNERGS1oqKi0Lt3b9y5cwfA3VWSzz//vNGJGNB4VhyWLigY2akFQtp4MBGzIEzGiIjI6hQXF2PRokUYOnQoYmJi8M4775h8Lq44JLlxmJKIiKzK9evXMW7cOMTExAAAnn/+ebzxxhsmn48rDklu7BkjIiKr8d1336FTp06IiYmBq6srduzYgc8++wyOjqb3WnHFIcmNyRgREVmFdevWISwsDJmZmejatSsSEhLw1FNPmeXcXHFIclIIIaxzra4FknvXdyKihuzmzZvo3LkzxowZg3fffRdKpdLsr2HOCvxkPeT+/uacMSIislgJCQno3LkzAMDb2xtJSUlwd6+7uVuWsIURNT4cpiQiIouj1WoxZ84cdOnSBdu2bZOO12UiRiQX9owREZFFuXz5MsaOHYu4uDgAwPnz52WOiKhuMRkjIiKL8fXXX+OZZ55BTk4O3N3dsWnTJgwfPlzusIjqFIcpiYhIdoWFhXj++ecxZswY5OTkoFevXkhMTGQiRo0CkzEiIpJdTEwMVq9eDQBYtGgRfvzxR7Rq1UrmqIjqB4cpiYhIdgMGDMDbb7+Nrl27YsiQIXKHQ1Sv2DNGRET1Lj8/H3PnzsXVq1elY6+//joTMWqU2DNGRET16ty5cxgzZgySkpLw66+/4ujRo1AoWFiVGi/2jBERUb3ZtGkTunbtiqSkJHh7e2Pp0qVMxKjRYzJGRER1Li8vD1OmTMHUqVORn5+PgQMHIjExEQMHDpQ7NCLZcZiSiIjq1NWrVxEaGorz58/DxsYGS5cuxaJFi2Brayt3aEQWgckYERHVKW9vbyiVSvj6+uKrr75C37595Q6JyKIwGSMiIrPLzc2Fk5MTbG1t4ejoiN27d6Np06Zo3ry53KERWRzOGSMiIrNKTExEly5dsGzZMumYv78/EzGiSjAZIyIisxBCYPXq1QgODsbFixexfv165OXlyR0WkcVjMkZERLWm0WgwduxYPP/889BqtRg+fDji4uLg7Owsd2hEFo/JGBER1cqvv/6KLl264Ouvv4adnR0++OADfPvtt/Dw8JA7NCKrwAn8RERkMo1Gg0GDBkGj0cDPzw+RkZEIDg6WOywiq8KeMSIiMplKpcL777+PUaNGISEhgYkYkQkUQgghdxANRU5ODlQqFTQaDVxdXeUOh4ioTpw4cQI2Njbo1q0bgLsT9wFwWyOyWnJ/f7NnjIiIjCKEwIcffojevXvjqaeeQlZWFoC7SRgTMSLTyZqM/fzzzwgLC4Ovry8UCgX27NkjPVZcXIxXX30VHTp0gLOzM3x9fTF58mT89ddfBufo16+f9A9B6W3cuHEGbbKyshAeHg6VSgWVSoXw8HBkZ2cbtLl27RrCwsLg7OwMT09PzJkzB0VFRXV16UREViUjIwMjRozAggULUFJSgu7du8PGhv+fJzIHWf+S8vLy0LFjR6xcubLcY/n5+YiPj8cbb7yB+Ph47Nq1C7///jtGjBhRrm1ERARSU1Ol25o1awwenzBhAhITExEVFYWoqCgkJiYiPDxcelyn02HYsGHIy8vDsWPHEBkZiZ07d2L+/Pnmv2giIivzyy+/oHPnzvjuu++gVCqxevVqbN++HSqVSu7QiBoGYSEAiN27d1fZ5uTJkwKAuHr1qnSsb9++4sUXX6z0OcnJyQKAOH78uHQsNjZWABDnz58XQghx4MABYWNjI27cuCG12bZtm1AqlUKj0Rh9DRqNRgCo0XOIiCyVTqcTK1asELa2tgKAaNu2rUhISJA7LCKzk/v726r6mDUaDRQKBZo1a2ZwfOvWrfD09MTDDz+MBQsW4M6dO9JjsbGxUKlU6NGjh3QsODgYKpUKMTExUpvAwED4+vpKbYYMGQKtVou4uLhK49FqtcjJyTG4ERE1FAqFArGxsdDpdJgwYQLi4uLQqVMnucMianCsps5YYWEhFi5ciAkTJhisdJg4cSL8/f2hVquRlJSERYsW4bfffkN0dDQAIC0tDV5eXuXO5+XlhbS0NKmNt7e3weNubm5wcHCQ2lRkxYoVWLp0qTkuj4jIYgghpDm4GzZswP79+zFp0iRO0ieqI1aRjBUXF2PcuHHQ6/VYtWqVwWMRERHSz4GBgWjbti26du2K+Ph4dOnSBUDFy61L/7EpZUybshYtWoR58+ZJ93NyctCqVSvjL4yIyILodDosX74cly5dwsaNG6FQKODu7m4wx5aIzM/ik7Hi4mKMGTMGKSkpOHz4cLX1P7p06QJ7e3tcvHgRXbp0gVqtxs2bN8u1u3XrltQbplarceLECYPHs7KyUFxcXK7H7F5KpRJKpdKEqyIisixpaWmYNGkSfvjhBwDAtGnT0K9fP3mDImokLHrOWGkidvHiRRw6dMiofc7Onj2L4uJi+Pj4AABCQkKg0Whw8uRJqc2JEyeg0WjQs2dPqU1SUhJSU1OlNgcPHoRSqURQUJCZr4qIyLL88MMP6NSpE3744Qc0adIEGzduZCJGVI9k7RnLzc3FpUuXpPspKSlITEyEu7s7fH198eSTTyI+Ph7fffcddDqdNH/L3d0dDg4OuHz5MrZu3YrHHnsMnp6eSE5Oxvz589G5c2f06tULANC+fXuEhoYiIiJCKnnx7LPPYvjw4WjXrh0AYPDgwQgICEB4eDjef/99ZGZmYsGCBYiIiGAlfSJqsHQ6Hd5880289dZbEEIgMDAQ27dvR0BAgNyhETUusqzh/J8ff/xRACh3mzJlikhJSanwMQDixx9/FEIIce3aNdGnTx/h7u4uHBwcRJs2bcScOXNERkaGwetkZGSIiRMnChcXF+Hi4iImTpwosrKyDNpcvXpVDBs2TDg5OQl3d3cxa9YsUVhYWKPrkXtpLBFRTYwbN076d/WZZ54ReXl5codEJAu5v7+5N6UZyb23FRFRTfzwww8YPXo0Vq9ejQkTJsgdDpFs5P7+tvgJ/EREZB4lJSVISkqSaoUNHDgQV69eLVe7kYjql0VP4CciIvO4fv06+vXrhz59+hjM1WUiRiQ/JmNERA3c/v370alTJ/zyyy8AgMuXL8scERHdi8kYEVEDVVxcjAULFmD48OHIzMxEUFAQEhISMGTIELlDI6J7cM4YEVEDdOXKFYwbN04qaD1nzhy89957LFRNZIGYjBERNUBr167FiRMn0KxZM6xfvx6PP/643CERUSWYjBERNUCLFy9GRkYGFi5ciPvuu0/ucIioCpwzRkTUAPzxxx+YMWMGiouLAQAODg74/PPPmYgRWQH2jBERWblvvvkG06dPR05ODry8vPDmm2/KHRIR1QB7xoiIrFRhYSFeeOEFPPXUU8jJyUGvXr0QEREhd1hEVENG94ydPn3a6JM+8sgjJgVDRETGuXjxIsaMGYPExEQAwMKFC/Hmm2/C3t5e3sCIqMaMTsY6deoEhUIBIQQUCkWVbXU6Xa0DIyKiih04cABjx45Fbm4uPD09sXnzZoSGhsodFhGZyOhhypSUFPzxxx9ISUnBzp074e/vj1WrViEhIQEJCQlYtWoV2rRpg507d9ZlvEREjV6bNm0ghECfPn2QmJjIRIzIyimEEKKmT+revTuWLFmCxx57zOD4gQMH8MYbbyAuLs5sAVoTuXd9J6KGKzs722AfycTERAQGBsLOjuuwiGpL7u9vkybwnzlzBv7+/uWO+/v7Izk5udZBERHR37788kv4+fnhyJEj0rFOnToxESNqIExKxtq3b4+3334bhYWF0jGtVou3334b7du3N1twRESNWV5eHqZNm4YpU6YgJycH//nPf+QOiYjqgEn/rfr8888RFhaGVq1aoWPHjgCA3377DQqFAt99951ZAyQiaoySkpIwZswYnDt3DjY2NliyZAlee+01ucMiojpg0pwxAMjPz8eWLVtw/vx5CCEQEBCACRMmwNnZ2dwxWg25x5yJyPoJIbB+/XrMnj0bBQUF8PHxwbZt29C3b1+5QyNqsOT+/jY5GaPy5P4wicj6/fDDDxg0aBAAYPDgwdi8eTO8vLxkjoqoYZP7+9vkCvybN29G79694evri6tXrwIAPv74Y+zdu9dswRFRw6TTC8RezsDexBuIvZwBnZ7/Jyw1YMAATJw4EcuXL8f333/PRIyoETBpztjq1avxr3/9C3PnzsXbb78tFXl1c3PDJ598gpEjR5o1SCKqfzq9wMmUTKTfKYSXiyO6+7vD1qbqgs/GiEpKxdJ9yUjV/L0AyEfliMVhAQgN9Kn1+a2NEAJffvklRowYATc3NygUCmzevLna4tpE1HCYNEwZEBCA5cuXY9SoUXBxccFvv/2G+++/H0lJSejXrx9u375dF7FaPLm7OYnMpa4SpqikVMzcEo+y/+iUph2rJ3VpVAlZTk4OIiIisGPHDjz++OPYuXMnkzAiGcj9/W3SMGVKSgo6d+5c7rhSqUReXl6tgyIi+ZQmTPcmYgCQpinEzC3xiEpKNem8Or3A0n3J5RIxANKxpfuSG82QZVxcHLp06YIdO3bAzs4OvXv3ljskIpKJScmYv7+/tDntvb7//nsEBATUNiYikkldJkwnUzLLJXhlz5+qKcTJlMwan9uaCCHw6aefomfPnrh8+TL8/Pxw7NgxzJs3j71iRI2USXPGXn75ZbzwwgsoLCyEEAInT57Etm3bsGLFChYlJLJiNUmYQtp41Ojc6XcqP68p7axRdnY2pk+fjl27dgEARo0ahfXr18PNzU3myIhITiYlY9OmTUNJSQleeeUV5OfnY8KECWjRogX+/e9/Y9y4ceaOkYjqSV0mTF4ujmZtZ410Oh1OnToFe3t7fPDBB5g9ezZ7w4jItGQMACIiIhAREYHbt29Dr9dz+TVRA1CXCVN3f3f4qByRpimscBhUAUCturtqsyEpXSOlUCjg4eGBr7/+GjY2NujWrZvMkRGRpTBpztiAAQOQnZ0NAPD09JQSsZycHAwYMMBswRFR/SpNmCrrq1Hg7qpKUxImWxsFFocFSOcpe14AWBwWYJbyGZYiMzMTI0eOxMaNG6VjPXr0YCJGRAZMSsZ++uknFBUVlTteWFiIo0eP1jooIpJHXSdMoYE+WD2pC9Qqw541tcqxwZW1iImJQadOnbBv3z7Mnz8fd+7ckTskIrJQNRqmPH36tPRzcnIy0tLSpPs6nQ5RUVFo0aKF+aIjonpXmjCVrTOmNlNh1tBAHzwaoK6TgrKWQK/X4/3338frr78OnU6Htm3bYseOHXBxcZE7NCKyUDUq+mpjYyNNNq3oaU5OTvj000/x9NNPmy9CKyJ30Tgic6qrCvwN2a1btzBlyhR8//33AIDx48djzZo1VpGI8fOmxkzu7+8a9YylpKRACIH7778fJ0+eRPPmzaXHHBwc4OXlBVtbW7MHSUT1z9ZGUePyFY1Zbm4ugoKCcP36dTg6OuLTTz/F9OnTrWK1JLeoIpJXjZIxPz8/AHe74YmI6G9NmzbFlClT8M0332DHjh3o0KGD3CEZpbItqkp3XGhoc/mILJFJe1OuWLEC3t7e5YYj169fj1u3buHVV181W4DWRO5uTiKqXzdv3kRBQQHuu+8+AEBJSQm0Wi2cnZ3lDcxIOr1A73cPV1rot7TcyLFXB3DIkho0ub+/TVpNuWbNGjz00EPljj/88MP4/PPPax0UEZGlO3z4MDp16oQnnngCWq0WAGBnZ2c1iRjALaqILIVJyVhaWhp8fMp3Wzdv3hypqcZvIvzzzz8jLCwMvr6+UCgU2LNnj8HjQggsWbIEvr6+cHJyQr9+/XD27FmDNlqtFrNnz4anpyecnZ0xYsQI/PnnnwZtsrKyEB4eDpVKBZVKhfDwcKlOWqlr164hLCwMzs7O8PT0xJw5cyos30FEjZtOp8PixYsxaNAgpKWloaioCOnp6XKHZRJuUUVkGUxKxlq1aoVffvml3PFffvkFvr6+Rp8nLy8PHTt2xMqVKyt8/L333sNHH32ElStX4tSpU1Cr1Xj00UcN6vXMnTsXu3fvRmRkJI4dO4bc3FwMHz4cOp1OajNhwgQkJiYiKioKUVFRSExMRHh4uPS4TqfDsGHDkJeXh2PHjiEyMhI7d+7E/Pnzjb4WImr4/vrrLwwaNAhvvvkmhBB45plncOLECbRq1Uru0EzCLaqILIQwwTvvvCM8PDzE+vXrxZUrV8SVK1fEunXrhIeHh1i+fLkppxQAxO7du6X7er1eqNVq8c4770jHCgsLhUqlEp9//rkQQojs7Gxhb28vIiMjpTY3btwQNjY2IioqSgghRHJysgAgjh8/LrWJjY0VAMT58+eFEEIcOHBA2NjYiBs3bkhttm3bJpRKpdBoNEZfg0ajEQBq9Bwisg7//e9/RfPmzQUA0bRpU7F161a5Q6q1Ep1eBC8/JO579TvhV8Htvle/E8HLD4kSnV7uUInqlNzf3yb1jL3yyiuYPn06nn/+edx///24//77MXv2bMyZMweLFi0yS5KYkpKCtLQ0DB48WDqmVCrRt29fxMTEAADi4uJQXFxs0MbX1xeBgYFSm9jYWKhUKvTo0UNqExwcDJVKZdAmMDDQoFdvyJAh0Gq1iIuLqzRGrVaLnJwcgxsR1Q+dXiD2cgb2Jt5A7OUM6PQ1XotkNL1ej8WLF+PWrVvo2LEj4uLiMGHChDp7vfrSGLeoIrJEJm0UrlAo8O677+KNN97AuXPn4OTkhLZt20KpVJotsNLq/t7e3gbHvb29cfXqVamNg4MD3NzcyrUpfX5aWlqFm5h7eXkZtCn7Om5ubnBwcDDYZaCsFStWYOnSpTW8MiKqrfqui2VjY4OvvvoKn376KZYvXw5Hx4YzbFfXOy4QUfVMSsZKNW3atM43vC1bMFEIUW0RxbJtKmpvSpuyFi1ahHnz5kn3c3JyrHbuCJG1qK+6WAcOHMBvv/0m9fb7+/vjo48+qvV5LVFD36KKyNIZnYyNHj0aGzduhKurK0aPHl1l2127dtU6MLVaDaD8ys309HSpF0utVqOoqAhZWVkGvWPp6eno2bOn1ObmzZvlzn/r1i2D85w4ccLg8aysLBQXF5frMbuXUqk0a28gEVVNpxdYui+5XCIG3C3DoACwdF8yHg1Qm5xIFBcX4/XXX8f7778PAOjZsyf69u1rcsyWpKotj7jjApF8jJ4zplKppF6i0hIRld3Mwd/fH2q1GtHR0dKxoqIiHDlyREq0goKCYG9vb9AmNTUVSUlJUpuQkBBoNBqcPHlSanPixAloNBqDNklJSQZlOQ4ePAilUomgoCCzXA8R1V5d18W6evUq+vTpIyVis2fPRnBwsEnnqiumzpWLSkpF73cPY/za43gxMhHj1x5H73cPIyrJ+HJERFQ3jO4Z27BhQ4U/10Zubi4uXbok3U9JSUFiYiLc3d3RunVrzJ07F8uXL0fbtm3Rtm1bLF++HE2aNJEmzqpUKkyfPh3z58+Hh4cH3N3dsWDBAnTo0AGDBg0CALRv3x6hoaGIiIjAmjVrAADPPvsshg8fjnbt2gEABg8ejICAAISHh+P9999HZmYmFixYgIiICFbSJ7IgdVkXa+/evZg2bRqysrLQrFkzrF+/Ho8//niNz1OXTJ0rxy2PiCybSaspzeXXX39F586d0blzZwDAvHnz0LlzZ/zrX/8CcHfV5ty5c/H888+ja9euuHHjBg4ePAgXFxfpHB9//DFGjRqFMWPGoFevXmjSpAn27dtnsGH51q1b0aFDBwwePBiDBw/GI488gs2bN0uP29raYv/+/XB0dESvXr0wZswYjBo1Ch988EE9vRNEZIy6qov1+uuvY9SoUcjKykL37t2RkJBgkYnYzC3x5XoGSxOqynq4qhvaBe4O7dblalQiqprRe1N27ty52onzpeLj42sVlLWSe28rooaudC/FNE1hhcmFqXspbt68GZMnT8b8+fOxfPlyODg4mC1mc6jNHpKxlzMwfu3xal9jW0Qw54xRoyX397fRw5SjRo2Sfi4sLMSqVasQEBCAkJAQAMDx48dx9uxZPP/882YPkogI+Lsu1swt8VAABglZTetiZWZmwt3dHQAQHh6ODh06oFOnTuYOuUYqm2Bfk7lyZRMqbnlEZPmMTsYWL14s/fzMM89gzpw5eOutt8q1uX79uvmiIyIqo7Z1sQoLCzF//nzs3bsXCQkJaN68OQDInohVNR9MW6I36hwVJVTc8ojI8plUZ+zrr7/Gr7/+Wu74pEmT0LVrV6xfv77WgRERVcbUulgXL17E2LFjkZCQAAD4/vvvMXny5PoIuUrVTbCfO6itUeepKKHKyiuCjQKobEpY6RBnd3/3mgVNRGZjUjLm5OSEY8eOoW1bw38gjh071qAqUxOR5appXazIyEhEREQgNzcXnp6e2Lx5M0JDQ+swQuMYUztt28lrULs64mZO1XPlyiZUUUmpeOGr8kleWdzyiEheJiVjc+fOxcyZMxEXFyfV4Dl+/DjWr18vrYQkIrIEBQUFmDt3Lr744gsAQJ8+ffDVV1+hRYsWMkd2lzHzwdJytHhp0IP45NDvRs+VqyrJK2WjAFaO78yyFkQyMykZW7hwIe6//378+9//xldffQXgbj2vjRs3YsyYMWYNkIioNpYuXYovvvgCCoUCr7/+OhYvXgw7u1rtBGdWxk6cv8+zSY3mylWX5AF3hy7dnLmLCJHcTP4XacyYMUy8iMjivfbaazh27BiWLFkiFYOuTlXbBplbTSbYh7TxMHquHFdRElkPk5Ox7OxsfPPNN/jjjz+wYMECuLu7Iz4+Ht7e3hbT/U9EjU9eXh42bdqEmTNnQqFQwNXVFUePHjW6TqKpVe5N1d3fHT4qx2prp5XOBzN2rhxXURJZD5Mq8J8+fRoPPvgg3n33Xbz//vvIzs4GAOzevRuLFi0yZ3xEREY7e/YsunfvjhdeeAGrVq2SjtckETOlyn1tlNZOA/6e/1WqprXT7lWa5FX2LAXuJplcRUkkP5OSsXnz5mHq1Km4ePGiwerJoUOH4ueffzZbcERExhBCYP369ejWrRuSk5Ph4+ODhx9+uEbnkHPboNLaaWqVYS+VWuVo8r6RdZXkEZH5mTRMeerUKWnT7Xu1aNECaWlptQ6KiMhYubm5mDFjBrZu3QoAGDx4MDZv3gwvL68anac2Ve7NwdTaadWdszYFcomofpiUjDk6OiInJ6fc8QsXLkjVrImI6trp06fx1FNP4ffff4etrS3efvttvPLKK7CxqXmnvyVMeK9p7TRj1EWSR0TmZVIyNnLkSLz55pvYsWMHgLvzMa5du4aFCxfiiSeeMGuARESVycnJweXLl9GyZUts27YNvXv3NvlcDXnCe10keURkPibNGfvggw9w69YteHl5oaCgAH379sUDDzwAFxcXLFu2zNwxEhFJhPh7zlbv3r2xfft2JCQk1CoRAzjhnYjkoxD3/stWQ4cPH0Z8fDz0ej26dOlidA2fhionJwcqlQoajQaurq5yh0PU4MTHx2PatGnYtm0bAgICzH7+0tWUQMVV7k2dTE9Elk3u7+8aJ2MlJSVwdHREYmIiAgMD6youqyT3h0nUUAkh8Nlnn2H+/PkoKirCY489hv3799fJa9V3nTEikp/c3981njNmZ2cHPz8/6HS6uoiHiMhAdnY2pk+fjl27dgG4O2d1w4YNdfZ6nPBORPXNpDlj//znP7Fo0SJkZmaaOx4iIsnJkyfRuXNn7Nq1C/b29vjkk0+we/duuLm51enrlk54H9mpBULaeDARI6I6ZdJqyv/7v//DpUuX4OvrCz8/Pzg7Oxs8Hh8fb5bgiMh49bmfYn2IiYlBv379UFxcDH9/f2zfvh3dunWTOywiIrMzKRkbNWoUFAoFajH3n4jMqCHOc+revTuCg4Ph7e2N//znP1CpVHKHRERUJ2o0gT8/Px8vv/wy9uzZg+LiYgwcOBCffvopPD096zJGqyH3BEBqnEpXAJb9Q7bGFYBxcXEIDAyEUqkEANy5cwdNmzY1em9JIiJTyP39XaM5Y4sXL8bGjRsxbNgwjB8/HocOHcLMmTPrKjYiqoac+ymak16vx3vvvYcePXrglVdekY67uLgwESOiBq9Gw5S7du3CunXrMG7cOADAxIkT0atXL+h0Otja2tZJgERUObn3UzSH27dvY/Lkyfj++++l+/w3hYgakxr1jF2/fh3/+Mc/pPvdu3eHnZ0d/vrrL7MHRkTVs4T9FGvj6NGj6NSpE77//ns4Ojriiy++wJYtW5iIEVGjUqOeMZ1OBwcHB8MT2NmhpKTErEERNWTmXPVorfsp6vV6rFixAv/617+g1+vx0EMPYceOHejQoYPcoRER1bsaJWNCCEydOlWaXAsAhYWFmDFjhkF5i9LijERkyNyrHkv3U0zTFFY4b0wBQG2B+yn+9ddfeO+996DX6zF58mR89tlnaNq0qdxhERHJokarKadNm2ZUu7qsjm3J5F6NQZatrlY9Wut+irt374ZGo8HUqVPlDoWIGjm5v79rtVE4GZL7wyTLpdML9H73cKWT7Ut7sI69OsCkIUtLrzOm0+nw9ttvIzg4GEOGDJEnhgZWFJeIzEfu72+Tir4SUc3U9apHS95PMTU1FRMnTsSPP/6IZu6e+HTXT9DaOMG9qRJq1/qJ09KTVSJq3JiMEdWD+lj1WLqfoiWJjo7GpEmTkJ6eDhsHR9j2nIJ/fn/FoE1dJ0WVDQ+naQoxc0u8xQ7jElHjYdJG4URUM9a66tFUJSUl+Oc//4khQ4YgPT0d9s3vg3ryJ2j6cP9ybVP/lxRFJaWaPY6GUhSXiBo29owR1QNrXfVoivz8fISGhuLo0aMAAK/uw6HsPQ029soqn7d0XzIeDVAbDFnWdp5XQyiKS0QNH5Mxonpga6PA4rAAzNwSDwUqXvW4OCzAIuZ41VaTJk3Qpk0bJCYm4pW3P8Z//lJX+5yKkiJzzPOy9qK4RNQ4cJiSqJ6EBvpg9aQuUKsMhyLVKkern7dUXFwMjUYj3V+5ciUSEhLQoU9ojc5TmhSVzvMq26uVVsMhzcY2PExE1ok9Y0T1yJJXPZrq2rVrGDduHNzc3LBv3z7Y2NjA2dkZbdq0QfrljBqdy8vFsdp5XgpUPKRZkcY0PExE1svie8buu+8+KBSKcrcXXngBADB16tRyjwUHBxucQ6vVYvbs2fD09ISzszNGjBiBP//806BNVlYWwsPDoVKpoFKpEB4ejuzs7Pq6TGpESlc9juzUAiFtPKw6Efv222/RqVMnxMbG4pdffsHFixcNHi9NhqqjwN0hyO7+7jWa51Wd0uHh0tco+5pAwxkeJiLrZfHJ2KlTp5CamirdoqOjAQBPPfWU1CY0NNSgzYEDBwzOMXfuXOzevRuRkZE4duwYcnNzMXz4cOh0OqnNhAkTkJiYiKioKERFRSExMRHh4eH1c5FEVqaoqAgvvfQSRo4ciaysLHTr1g0JCQlo166dQbvSZMiYVKc0KTL3PK+GPDxMRA2DxQ9TNm/e3OD+O++8gzZt2qBv377SMaVSCbW64knCGo0G69atw+bNmzFo0CAAwJYtW9CqVSscOnQIQ4YMwblz5xAVFYXjx4+jR48eAIC1a9ciJCQEFy5cKPcFQ9SYpaSkYOzYsTh16hQAYN68eVixYgUcHBwqbF+aDJWdjF+q7KT8upjn1RCHh4mo4bD4ZOxeRUVF2LJlC+bNmweF4u9/RH/66Sd4eXmhWbNm6Nu3L5YtWwYvLy8AQFxcHIqLizF48GCpva+vLwIDAxETE4MhQ4YgNjYWKpVKSsQAIDg4GCqVCjExMZUmY1qtFlqtVrqfk5Nj7ksmsihCCDz55JOIj4+Hm5sbNm3ahLCwsGqfd28ylKYpQGZeUaUV+OtqnpclFsUlIgKsLBnbs2cPsrOzDTYWHjp0KJ566in4+fkhJSUFb7zxBgYMGIC4uDgolUqkpaXBwcEBbm5uBufy9vZGWloaACAtLU1K3u7l5eUltanIihUrsHTpUvNcHJEVUCgUWLNmDV5++WVs2rQJrVu3Nvq5xiZDjakMCBERYAVzxu61bt06DB06FL6+vtKxsWPHYtiwYQgMDERYWBi+//57/P7779i/f3+V5xJCGPSu3ftzZW3KWrRoETQajXS7fv26CVdFZNkuXbqEb775RrrftWtXHD58uEaJWE1xnhcRNSZW0zN29epVHDp0CLt27aqynY+PD/z8/KRVXWq1GkVFRcjKyjLoHUtPT0fPnj2lNjdv3ix3rlu3bsHb27vS11IqlVAqq64qTmTNtm/fjoiICGi1WrRp0wadO3cGUPF/XsyN87yIqLGwmp6xDRs2wMvLC8OGDauyXUZGBq5fvw4fn7v/cw4KCoK9vb20ChMAUlNTkZSUJCVjISEh0Gg0OHnypNTmxIkT0Gg0UhuixqSgoAAzZszAuHHjcOfOHfTo0aPcYpr60JDKgBARVcYqesb0ej02bNiAKVOmwM7u75Bzc3OxZMkSPPHEE/Dx8cGVK1fw2muvwdPTE48//jgAQKVSYfr06Zg/fz48PDzg7u6OBQsWoEOHDtLqyvbt2yM0NBQRERFYs2YNAODZZ5/F8OHDuZKSGp0LFy5gzJgxOH36NBQKBV5//XUsXrzY4G+PiIjMxyr+dT106BCuXbuGp59+2uC4ra0tzpw5gy+//BLZ2dnw8fFB//79sX37dri4uEjtPv74Y9jZ2WHMmDEoKCjAwIEDsXHjRtja2kpttm7dijlz5kirLkeMGIGVK1fWzwUSWYivvvoKzz77LPLy8uDl5YUtW7bg0UcflTssIqIGTSGEqGj1OJkgJycHKpUKGo0Grq6ucodDZqbTC4uYv1SXcSxfvhyvv/46+vfvj61bt0rD/UREDZnc399W0TNGJLeopNRyRUvLFiu11jj0ej1sbO5OH124cCFatmyJiRMnGvQcExFR3bGaCfxEcolKSsXMLfHlqsenaQoxc0s8opJSrTIOIQTWr1+PkJAQ5OfnAwBsbGwwefJkJmJERPWIyRhRFXR6gaX7kiusBF96bOm+ZOj0dTvab+44cnNzMXnyZEyfPh0nT57EF198YbZYiYioZpiMEVXhZEpmhfsplhIAUjWFOJmSaTVxnD59Gl27dsWWLVtgY2ODZcuWYc6cOWaMloiIaoJzxoiqkH6n8gTIlHZyxiGEwNq1azFnzhxotVq0aNEC27Ztwz/+8Q9zhUlERCZgzxhRFbxcHKtvVIN2csaxYsUKPPfcc9BqtXjssceQmJjIRIyIyAIwGSOqQnd/d/ioHFFZ4QgF7q5m7O7vbvFxTJ48GWq1Gu+99x727dsHT0/POomViIhqhskYURVsbRRYHBYAAOUSodL7i8MC6rzemClxCCFw9OhR6X7Lli1x6dIlvPzyy1IpCyIikh//RSaqRmigD1ZP6gK1ynAIUK1yxOpJXeqtzlhN4sjOzsZTTz2FPn36YM+ePdJxZ2fneomViIiMxwn8REYIDfTBowFq2SvwVxYHAMRezkD6nULcTknGsvnPISUlBfb29khPT6/XGImIqGaYjBEZydZGgZA2HvX+uhVtf3RvHKVV+f/KLsCdX79F1k8bAH0J1C1a49vd36Bbt271HjMRERmPyRiRBatu+6PSqvwlBXeQceATFFw6AQBo8mBP2A+dgwynluXOaSl7bBIR0V1MxogsVGmiVbamfun2R59N6Iy39p+DAKC9nnQ3EbO1g/uAZ9C08zDYKBRYui8ZjwaopWTLUvbYJCKivymEEHW7j0sjIveu79Rw6PQCvd89XGnVfQUAN2d7ZOYVS8c0MdvheH8QlOoHDNpuiwhGSBuPSpO70j6x+lyMQERkSeT+/uZqSiLcTX5iL2dgb+INxF7OqPO9JqtT3fZHJfka/L79HZTk/r39karn2HKJGHC3Kr+l7LFJRETlcZiSGj1LHLqralujwutJuP3t+9DlZkBXkAPvp5ZWeS4vF8ca7W0pxyIFIqLGjD1j1KiVDt2VTVRK52VFJaXKEldF2xoJoYcmdgdubnsNutwM2Lm3xH2hEUZV5beUPTaJiKg8JmPUaFny0F3Z7Y90edlI37EY2T9/CQg9nB/uj86zV+PD50YAqL4qv6XssUlEROUxGaNGqyZDd/Xt3u2Pim9dQerGOSi8kgCFnRIeQ1+E57B5ePPJIDz2iHFV+S1lj00iIiqPc8ao0bL0obvS7Y/+9Y1AukMT2Hs2hefIhfBr86DBfDZjdgcoTe5mbomHAjDoDazPPTaJiKg8JmPUaBk7JHf7jhY6vajzROXeYqz2Rbl4tMsD/0u0hmFPv73QKV3RysutwiKtxuwOUJrclV2soGadMSIiWbHOmBnJXaeEaqa0lleaprDCeWP3quvVlfeu6CxIicft7z5Ey75jsea9JWZ/TVbgJyIyJPf3N+eMUaN177ys6lKRulxdWbqi86+sPGT9/CXSdyyGPl+DtITDmPHlKbO/Zmkv2shOLRDSxoOJGBGRzJiMUaNWOnRXdgJ8WXW1urJ0RWdxzm3c3PYacmJ3ABBo2ikU3hPehcLGlsVYiYgaOCZj1OiFBvrg2KsD8Maw9lW2q4vVlSdTMvFH/FGkbpwD7Z9noXBwgmfYy/AYMgs29kpZV3QSEVH94AR+anQqmzPl6aI06vnmXF258+hppO9eBuhK4ODdBp4jX4W9m2+dviYREVkWJmPUqFS19VF9F0bV6QUOXy+BW79pKMn6C279p0Nh51Cnr0lERJaHyRg1GqUT5cvOviqdnP/ZhM7wUTlWurpSgbtlIGpbGPXbb79F69atUeDSCpl5RXDtOrLK9u7O9izGSkTUgHHOGDUKxmx99Nb+c3hjWMWrK81RGLWoqAjz5s3DyJEjMWbMGFxNu23U8x7v1IIrHomIGjAmY9QoGLv1kZuzg1HbC9VUSkoKevfujY8//hgAMGzYMPi6G1fLZlCA2qTXJCIi68BhSmoUarL10chOLQy2F/JsqgQEcDtPi9jLGTUukrpr1y48/fTT0Gg0cHNzw8aNGzFixAjo9KJehkXNgYViiYjqDpMxahRqOjm/tDBqVFIqFnz9W4UT/qvrJSsqKsL8+fOxcuVKAEBISAi2bdsGPz8/6TWsYb/IqhY9cAslIqLa4zAlNQrd/d3ho3KstNK+AncTjHt7oUon/Jcd3jS2Gr+dnR0uXLgAAHjllVdw5MgRKRErVVnR2doOi5pLbd8DIiKqHvemNCO597aiqpUmFkDFvVD3Jj+l+1ZWNs+sdAjx2KsDyvVc6fV62Njc/X/OzZs3kZCQgNDQ0Cpjs8RhwNq8B0RE1kTu72/2jFGjUZNeKGMn/N9bGb+goADPPfccZsyYIR3z9vauNhEDLHO/SFPeAyIiqjmLTsaWLFkChUJhcFOr/15ZJoTAkiVL4OvrCycnJ/Tr1w9nz541OIdWq8Xs2bPh6ekJZ2dnjBgxAn/++adBm6ysLISHh0OlUkGlUiE8PBzZ2dn1cYlkBjq9QOzlDOxNvIHYyxlV7uNYuvXRtohg/HtcJ2yLCMaxVweUGw6syYR/ALhw4QKCg4PxxRdf4D//+Q/OnDlj+gVZiJq+B0REZBqLn8D/8MMP49ChQ9J9W1tb6ef33nsPH330ETZu3IgHH3wQb7/9Nh599FFcuHABLi4uAIC5c+di3759iIyMhIeHB+bPn4/hw4cjLi5OOteECRPw559/IioqCgDw7LPPIjw8HPv27avHKyVTmDK5vLQXqio1mfC/ZcsWzJgxA3l5eWjevDm2bt2KDh06GH8RFqq+dyQgImqsLHrO2JIlS7Bnzx4kJiaWe0wIAV9fX8ydOxevvvoqgLu9YN7e3nj33Xfx3HPPQaPRoHnz5ti8eTPGjh0LAPjrr7/QqlUrHDhwAEOGDMG5c+cQEBCA48ePo0ePHgCA48ePIyQkBOfPn0e7du2MjlfuMefGprKK+hXNAaup0vlSVZWdaO4EPHzlG2zcuBEA0K9/f8xb9in0Ts3MOu9LrvlkxrwHnDNGRA2B3N/fFj1MCQAXL16Er68v/P39MW7cOPzxxx8A7hbRTEtLw+DBg6W2SqUSffv2RUxMDAAgLi4OxcXFBm18fX0RGBgotYmNjYVKpZISMQAIDg6GSqWS2lRGq9UiJyfH4Eb1w5iK+kv3JVc5ZFmV0rITQMXV+IUQKPxuGTZu3AiFQoGJM+ehYOBCzN57BS9GJmL82uPo/e7hWq82jEpKRe93D2P82uNmPa8xqnsPAMsovUFEZO0sOhnr0aMHvvzyS/z3v//F2rVrkZaWhp49eyIjIwNpaWkA7k6Qvpe3t7f0WFpaGhwcHODm5lZlGy8vr3Kv7eXlJbWpzIoVK6R5ZiqVCq1atTL5Wqlm6mNyeVUT/j8PD8KKJa/Dx8cHy9duxy+uA5B2p9igXaqmEDNqUf7BEspKWHrpDSKihsCi54wNHTpU+rlDhw4ICQlBmzZtsGnTJgQHBwMAFArD/5ULIcodK6tsm4raG3OeRYsWYd68edL9nJwcJmT1xNyTyysbCnw0QA0XR3vEXs6AtiAX3rpMTHv8f8NygcNw4feLGPzpcQhU/joLd53BowHqGvUgVdfzp8Ddnr+antcUoYE+BjsSWErpDSKihsKik7GynJ2d0aFDB1y8eBGjRo0CcLdny8fn7/+dp6enS71larUaRUVFyMrKMugdS09PR8+ePaU2N2/eLPdat27dKtfrVpZSqYRSqaztZZEJzDm5vLJFACM6+uDb31KRqilEUXoKbu19F6JAA/s9P2DK4G4AgKSbhVX20AFAdn4xVh6+hBcHtTUqZqBmPX/VLUYwB2MWPRARkWksepiyLK1Wi3PnzsHHxwf+/v5Qq9WIjo6WHi8qKsKRI0ekRCsoKAj29vYGbVJTU5GUlCS1CQkJgUajwcmTJ6U2J06cgEajkdqQ5alJRf2qSl9UNhSYqinEmp9T8Fd2Ae4kRiFt83yUZP4J2Dpg0dZfpCFCY3veNsSk1Gj+GstKEBE1HhbdM7ZgwQKEhYWhdevWSE9Px9tvv42cnBxMmTIFCoUCc+fOxfLly9G2bVu0bdsWy5cvR5MmTTBhwgQAgEqlwvTp0zF//nx4eHjA3d0dCxYsQIcOHTBo0CAAQPv27REaGoqIiAisWbMGwN3SFsOHD6/RSkoyv6pWERq7r2N0clqlpS8eDVBXOhQIAHptPjL+uxL5534GADjeHwTPYfNg10QlDREa20OXnV9co14slpUgImo8LDoZ+/PPPzF+/Hjcvn0bzZs3R3BwMI4fPy7t7/fKK6+goKAAzz//PLKystCjRw8cPHhQqjEGAB9//DHs7OwwZswYFBQUYODAgdi4caNBvbKtW7dizpw50qrLESNGSJs7kzyMqR9WOrm8bDv1/9oBqLD0RekE+LmDHqx0KLDo5mXc2vsOSrJSAYUNmvWdAtfuj0OhsDEYIuzu745mTvbILiiu8Dz3qkkvVmnPX3VlJe7dS5OIiKyTRdcZszZy1ylpKGpaP6yiHjQA1e6rqGpij+z8ipOozOjPcSf+O9i6NEfzka9A2aJ9uTb/HtcJIzu1wL8P/Y6PD12s9rq2RQTXaN5VTfbSJCIi08n9/W3RPWPUeJQmVGmaAry1/1yNVhFWNLk89nJGtRPgK0vEAMCt/9OArR1UIWNh6+RSYZvSIcJZA9piQ8yVSs9nai9WdT1/TMSIiBoGJmMku4qGJCtT0SrCinrGjB0SbOZkD01BMQpTf8edhAPwCJ0NhY0tFHYOcB/wTIXPKZtc2doo8M7oDlX25plaHJVlJYiIGj4mYySryoYkq1OabFU2t2xct9ZGnWdySGu8/e5HyPppA6AvgYOnH1y7P15p+8qSq7rsxWJZCSKiho3JGMlCpxc4/kcGFu48U+NEDLg7RHjg9F94/quEco+laQrxyaHf0ayJPTT5xZWeX1dwB2/PnY6sc3e3vWryYE80feRRAOXrjJWqKrm6t0AsIBByvyeC23iwF4uIiKrEZIzqXU2GJcsqHSK8navFi5HlEzHg77llZUte3Et74zxuffsudDm3AFs7zHh5CcKnP4dbuVqDocBXQtsbNURY0TXtjL/BuV1ERFQtrqY0I7lXY1gDU4clgb+HCJ/5hz/WHk0xOYbcM4eQEfUpoNfBrpkPmo98FX7tAnHs1QEm9WLVdPUnERFZFrm/v9kzRvWmqv0WjeHu7IBRnX2x7pjpiRgAOHi3gcLGFk4P9oRH6GzYKJtUuyggyM8NcVezyvWQWdIekkREZJ2YjFG9qW6/xYrcO9SYkVeEdceumPTaurws2Drf3Z/UwcsfPlP/DTv3lgabwVe1KMBGAdy7m1FpAVqVk4NF7SFJRETWx6r2piTrZso+irUdQxdCD03sDtz4fDq0f12Qjtt7tDJIxIC7iwIq26uy7LaSpVX8DyWnGRUH95AkIqLKsGeM6k1N9lEs2xNlCl1eNm5/9yEKr9yd6J//ewyUvhXvN9pUaYdOrZphwIc/GZUAlg5B7k68YVQs3EOSiIgqw54xqjel+y1WNXOqiYMtJvZoVatEzEYBFF47jdSNc1B4JQEKOyU8hs5Bs75TK31OrrYEvd79oUbDqAJAZl4x3J0dKr0mBe4OaXIPSSIiqgyTMao3tjYKaQPvypKX/CIdtp64bvJrCL0Owdk/4mbkP6HLzYS9R2uoJ3+Epo8MLjcsWVZmXvWbfVdkVCdfAOWvqbbV94mIqHFgMkb1qrRSvVpl/mE7GwUw3vMvbPv8Q0Do0bTDIPhM/ggOzf3M/lr3ejRAXeE1qVWOLGtBRETV4pwxqnel+y0e/yMDL2yNR3aBaT1SZa0c3xlDOzyG678dRWhoKJp3HmRycVlj3LtHpa2NgntIEhGRSZiMUb2oaDNvG4Wi1omY0OugO70f/7d0AR575O5w4datW6XH702QPJsq8culW1j10x+1ek2g4iFI7iFJRESmYDJGda6yzbwfC1TX6rwld27j9r4PoL2ehF2eeRjdY0u5NmUTJBuFwqhkzN3ZAZl5Rfc8z3B1pzk2ACciIgKYjFEdq2yroDRNIdb9csXk8xb8EYfb330IfUEOmjg7Y/jw4eXaVNQbV7qiM01TWGEJi9KhxyMv9zeouF9ZBX4iIqLa4t6UZiT33laWpqhEj+AVh6pcpWijAIQwvrir0JUg++gW5Jz4BgDgqG6DDV9uhVPzlnB3csD5m3dwPSsf+doSHL14Czfv/N27VVo1HwBmbom/e74KXmPVhM7SkCcRETV8cn9/MxkzI7k/TEsSlZSK13YnGQz1VeXebY8qU3LnNm7vfRfaG+cAAC5dhsF3yLMo1Nsa/RrA3Y27AVQ6ud+HQ5BERI2K3N/fLG1BZlc6NGlsIvZ0r/vg5uxQbTuFjS2Ks1OhcGgCz5EL4f7oTKMTMeDvZK904+43hgVU2K50q6OopFSjz01ERGQqzhkjs9LpBZbuS67RnpKujvbQFpdU+JjQ66CwuZtw2Tq7wevx12Hj7Ab7ZqZN/i/duPv4Hxl4a39ypW0U+Dtp49wwIiKqS+wZI7M6mZJZ47pen/xwEXlF+nLHi7PTkLblZeSd+1k6pmzR3uRE7F6xlzOqjLM0aTuZklnr1yIiIqoKkzEyq/Q75imwmn8hBqkbX0RR6u/I+mkjhM48hWH/Zlzfnbmuh4iIqDJMxsisvFxqt82RKClGZvTnuLVnOYQ2Dw6+7aCe8A4UtvZmia904+6Q+z2Nal/b6yEiIqoO54yR0Sqq21V2PlVpHS9TtiAqzvoLt/e+i6KblwEArt1Ho1mfyVDYmvfXdHFYAILbeBhVb6y7v7tZX5uIiKgsJmNklKikVCz59izScrTSMbWrEktGPGxQAsLWRoERHX2w5ueUGp1fl5eN1I1zIYryYePkCo9hL6FJm25mix8A3J3tsfzxDlK8i8MCMHNLfLmyGhVtdURERFRXOExJ1YpKSsWMLfEGiRgApOVoMaNMCQidXiDy5LUav4atczM07TgYypYB8Jn6f2ZPxADgjeGGiWNooA9WT+oCtcpwKFKtcsTqSV1YZ4yIiOoFe8aoSjq9wMJdZ6pss3DXGakExJxtCdAU6ow6d3HmDSjs7GHn6gUAcOs7FVAopFIWxlAAcHG0Q05hxaUx7qV2LT//KzTQx2AzcW51RERE9Y3JGFXp+OUMZOdXvZIxO78Yxy9n4OeL6dh/xrhCqblnf0Tmfz+Dg5c/vMevgMLWrsZzw0rTpeWjAjFne6LBRt5l2SiAID+3Ch8ru5k4ERFRfeIwJVVpy4krRrX75dJtrD1a/TwxfXEhMr7/P2R89yFEcSFgawd9UYFRr1G2s6p0ONHDxbHKRAwA9AKIu5pl1OsQERHVJ/aMUaV0eoGff79tVNvEP7OqTYiKbl/D7b3voPj2NQAKqHqNg6rnOKOHJVeO7ww3Z2W54cSdcX8a9fy0HNYMIyIiy8NkjABUXLbiZEom8oqMm/+Vciu3ysdzzxxC5sHVECVa2Dq7wSNsAZz8Ohp17mZN7PHO6A4VTqiPSkrFkn1njTpPZq62+kZERET1jMkYISopFUv3JRvUBvNROeKxQOO3HUrNqXxTcKErRs6pPRAlWjj6dYJn2HzYOlc8f+tero62mN67DWYNeKDCCfWlG5Ibuw+muxGbkRMREdU3JmONXGUJTZqmEOt+uWKW11DY2qP5yIXI/z0Grj2eMHpYcvWkruj1QMWV8k3ZkFytcqpBayIiovrBCfyNWFUJTU2SnHLPFQJ3EqOgOfGNdMzeoyVUIWOgsLFFEwfjkrHbVQwr1nRDch9W0yciIgtl0cnYihUr0K1bN7i4uMDLywujRo3ChQsXDNpMnToVCoXC4BYcHGzQRqvVYvbs2fD09ISzszNGjBiBP/80nPSdlZWF8PBwqFQqqFQqhIeHIzs7u64vUVY1TWiModfm4/a+D5D535XIPvKltLVRkF8zNHO6u79kvpHz0KraF7ImG3grwGr6RERkuSw6GTty5AheeOEFHD9+HNHR0SgpKcHgwYORl5dn0C40NBSpqanS7cCBAwaPz507F7t370ZkZCSOHTuG3NxcDB8+HDrd30nBhAkTkJiYiKioKERFRSExMRHh4eH1cp1yqUlCY4yim5eRuulF5J87Aihs0KzPZNh7+cPR3gZxV7ORXVB1vbJSpZt5V9WTZewG3u7O9qymT0REFs2i54xFRUUZ3N+wYQO8vLwQFxeHPn36SMeVSiXU6oonm2s0Gqxbtw6bN2/GoEGDAABbtmxBq1atcOjQIQwZMgTnzp1DVFQUjh8/jh49egAA1q5di5CQEFy4cAHt2rWroyuUl7EJTXWEEMhNOIDMw/8BdMWwdWkOzxGvwLFlewBAYbHe6HMZuy9k6YbklW30DQAezg6IXTQQDnYW/X8OIiJq5KzqW0qj0QAA3N0Ne0x++ukneHl54cEHH0RERATS09Olx+Li4lBcXIzBgwdLx3x9fREYGIiYmBgAQGxsLFQqlZSIAUBwcDBUKpXUpiEqTWhqO3iXceBjZEavBnTFcHqgO3ym/VtKxGrK2H0hbW0UWBwWAADl4lf877bs8UAmYkREZPGs5ptKCIF58+ahd+/eCAwMlI4PHToUW7duxeHDh/Hhhx/i1KlTGDBgALTau5O/09LS4ODgADc3w1IK3t7eSEtLk9p4eXmVe00vLy+pTUW0Wi1ycnIMbtbk3oSmNpQt2gM2tnDrPx3NR78BWyfXGp9jcogftkUE49irA4weUuRG30RE1BBY9DDlvWbNmoXTp0/j2LFjBsfHjh0r/RwYGIiuXbvCz88P+/fvx+jRoys9nxACCsXffSr3/lxZm7JWrFiBpUuX1uQyLE5ooA+e7eOPtUdTqq2gX0oIAV1eFuya3u2hbNoxFI6tH4G9ewuT4xga6GPS/pDc6JuIiKydVfSMzZ49G99++y1+/PFHtGzZssq2Pj4+8PPzw8WLFwEAarUaRUVFyMoy3JcwPT0d3t7eUpubN2+WO9etW7ekNhVZtGgRNBqNdLt+/XpNL012UUmp+OJn4xMxXWEubu1ehrQtL0NfeLfqvkKhMDkRM2ayfnVKN/oe2akFQtp4MBEjIiKrYtHJmBACs2bNwq5du3D48GH4+/tX+5yMjAxcv34dPj53h6iCgoJgb2+P6OhoqU1qaiqSkpLQs2dPAEBISAg0Gg1OnjwptTlx4gQ0Go3UpiJKpRKurq4GN0uk0wvEXs7A3sQbiL2cAd3/Mq+iEj1e233G6Jpi2hvnkbphDgouHocuNwPaG+drHZsAy04QEVHjphBC1Ka+Z516/vnn8dVXX2Hv3r0GKxpVKhWcnJyQm5uLJUuW4IknnoCPjw+uXLmC1157DdeuXcO5c+fg4uICAJg5cya+++47bNy4Ee7u7liwYAEyMjIQFxcHW9u7BUiHDh2Kv/76C2vWrAEAPPvss/Dz88O+ffuMjjcnJwcqlQrRCSnIg70sQ2Zl95jMyivCW/sNtzpSuzrC36MJ4q9nQ1tS/UpHIfTIObkH2T9vAvQ62DXzgefIV6FUP1DreN2a2OPXfz7KZIyIiGRT+v2t0Whk6Vix6GSssvlaGzZswNSpU1FQUIBRo0YhISEB2dnZ8PHxQf/+/fHWW2+hVatWUvvCwkK8/PLL+Oqrr1BQUICBAwdi1apVBm0yMzMxZ84cfPvttwCAESNGYOXKlWjWrJnR8ZZ+mK3m7oCNsgmAu0Nwi8MC6mUyeUV7TNaWriAHGfs/RsHlUwCAJg/9Ax6hs2CjdDbba8zq3wa9HmjOuV5ERCQLJmMNSEXJWGlqUder+2q6abaxMr7/P+SePgjY2sN9YASadhpa5aKG2qjPxJWIiKiU3MmYRc8ZawhKk6Ol+5KluVrmZsqm2cZq1m8qHP06wif8Q7h0fqzOEjHg7ubkM7fEIyoptc5eg4iIyNIwGasHAkCqphAnUzLr5Pzm3GNSl5eNnFN7UNphauvkCu9xy+Dgfb9Zzl+V+khciYiILI3V1BlrCMy9F6S5z1t47Qxu73sfutxM2Cid0fSRR81y3pq4N3E1pe4YERGRtWEyVo/MtRekuc8r9DpoYndA88s2QOhh79EKDj5tzRSdaeoqcSUiIrI0TMbqgQJ3t+ipTWHTqhizaXZldLlZuP3dByi8+hsAwDlwENwfnQEbB/MljrP6P4CQ+z0ABRB7OQMrf7xU7XPqKnElIiKyNEzG6ljpdPe6KGx6b02xcd1a4eNDF2v0/MKrp3Fr33vQ52VDYa+E++Dn0TRwoNniK01CX3r0Qenag+/3wM74PytNHOs6cSUiIrI0TMbqmLqOyjVEJaViybfJSMv5ezivWRN75BWWoNjIye9C6KHP08De0w/NRy2EvUer6p9kpMqS0NLNyWduiYcCMEjI6jJxJSIislSsM2ZG9VWBPyopFTO2xJv0XKHXQWFjK93Pv3gCjvd1go290uR4OrZ0xdWMAmQXFEvHqqsZVlGBWtYZIyIiOchdZ4zJmBnVx4ep0wsEvR2N7Pzi6huXUfBHHDKjP4fXmDdh72a+hOeNYe0xtZe/wTZMxiShZbduYgV+IiKSg9zJGIcprYhOL7D+WEqNEzGhK0H20S3IOfENAEATEwnPYS+ZJSYbBRAech9sbRQ1LkVhynOIiIgaGiZjVkCnF/j0h9+x9lgK8rS6Gj23JOcWbn/7HrQ3zgEAmnYeBvcB080WW8Q//OFgx9rBREREpmIyZuGiklIxf8dvyCuqWRIGAPmXTiBj/yfQF96BwqEJPIbOgfNDvc0SlwLAs338seixALOcj4iIqLFiMmbBajNRP//iCdza9RYAwEH9ADxHvGrWeWJfPt0d/3iwudnOR0RE1FgxGasnNZ2srtMLLNx1xuTXc7q/Cxx8HoTS9yG49ZsGhZ29yee6V2kdsJ4PeJrlfERERI0dk7F6YEoZh5hLt2s8Ub/g6m9wbPkwFLZ2UNjaQz3hHSjsHEyOm3XAiIiI6h5nXtexqKRUzNwSb5CIAXc3w565JR5RSakVPmfG1jijX0OUFCPz0BqkR76O7GNbpOOmJmI+KkesmtAFapXhlkRqlSNWT+rCOmBERERmxJ6xOqTTCyzdl1zpfpECwNJ9yRjwkDfirmYh/U4hrtzOxyeHfjd6j8nirFTc/vZdFKX9b79HvR5CCCgUpvdclfbYDQlUsw4YERFRHWMyVodOpmSW6xErK1VTiOAVPyAzr6jG5887fwwZ3/8fRFE+bBxd4DHsJTR5oLup4QIAXhrUVur5Yh0wIiKiusdkrA6laQqMalfTREyUFCHz8H+Qm3AAAKBsEQDPES/DzrV2qxt9VI6YNaBtrc5BRERENcNkrA6Z0ttljJKcW8hLOgwAcA1+Cs3+Mclgv0lTcWI+ERFR/WMyVofcm5q++XZV7N1bwGPoi7BRNoHT/UG1Pp+NAlg5nhPziYiI5MDVlHVEpxfIzNWa5Vz64kJkRK1E4fUk6Zhz+3+YJREDgJXjO+OxR5iIERERyYE9Y3UgOjkN7x8+hbScqifvG6P49nXc2vsOim9fRcEfcWjx7Jpa1Q67V3W1zoiIiKjuMRmrAy9t/w02yia1Pk/umR+QGb0KolgLG+dm8HjsxVolYj4qR3zwZEfcztOyVAUREZGFYDJmgfRFhciMXo28pB8AAI5+HeE5fAFsm7rV6ryLwwLQqy23MSIiIrIkTMYsjK4gB2lbXkFJ5p+AwgaqXuOhChlTq9WSbk3ssWJ0Bw5HEhERWSAmYxbGxtEFDs3vgyjKh2fYy3Bs3cHkc3Xza4a5j7ZD8P0eHI4kIiKyUEzGLIBemw9AwEbpDIVCAY+hsyFKimHr3MzkczZxsEXkcz2ZhBEREVk4lraQWdHNP5D65UvIOPBvCHF3R0obpXOtEjEA+GhMRyZiREREVoA9YzIRQiA38Xtk/rAW0BVDFGuhy8uCXVP3Wp3XvYk9RnVuAZWTA3R6wYSMiIjIwjEZk4Fem4eM7z9F/oVjAACnNt3gMewl2Dq5mnxOOxsF7GwVyMwvxvpfrmD9L1dYR4yIiMgKMBmrZ9rUi7j97bsoyU4DbGzh1ncqXLqNgkJRux6sEr1AiV4YHEvVFGLGlnhM73UfBgWoWVeMiIjIAjEZq0dCr5MSMVtXLzQf+SqUvu3q/HXX/XIF69hTRkREZJE4gb8eKWxs4THsJTRp1ws+0/6vXhKxe6VpCjFzSzyiklLr9XWJiIiockzG6pj2rwvIO39Muu/Y8mE0H7UIto5N6z2W0kHMpfuSoSszpElERETy4DBlHRFC4M6p3cg6sgkKWzs4ePrB3rOV3GFB4O5cspMpmQhp4yF3OERERI0ee8bKWLVqFfz9/eHo6IigoCAcPXq0xufQFeTg1s43kfXjekCvg9P93WDrUruSFeaWfqdQ7hCIiIgITMYMbN++HXPnzsXrr7+OhIQE/OMf/8DQoUNx7dq1Gp0nbesrKLh8CrC1h/vg5+E58lXYKJ3rKGrTeLk4yh0CERERAVCI0rLvhB49eqBLly5YvXq1dKx9+/YYNWoUVqxYUe3zc3JyoFKpAAB2br5oPnIhHLzvr7N4TaEAoFY54tirA1jmgoiICH9/f2s0Gri6ml7z01ScM/Y/RUVFiIuLw8KFCw2ODx48GDExMRU+R6vVQqvVSvc1Gg0AwPGBHvB4dCZslE3+t++kPBT4e9L+vfcX9G+LvNw78gRFRERkYXJycgAAcvVPMRn7n9u3b0On08Hb29vguLe3N9LS0ip8zooVK7B06dJyxwsvncCNSyfqJE5zePITuSMgIiKyPBkZGdIIV31iMlZG2Ur4QohKq+MvWrQI8+bNk+5nZ2fDz88P165dk+XDlEtOTg5atWqF69evy9K9KxdeN6+7MeB187obA41Gg9atW8PdXZ7FdkzG/sfT0xO2trblesHS09PL9ZaVUiqVUCqV5Y6rVKpG9UtcytXVldfdiPC6Gxded+PSWK/bxkaedY1cTfk/Dg4OCAoKQnR0tMHx6Oho9OzZU6aoiIiIqKFjz9g95s2bh/DwcHTt2hUhISH44osvcO3aNcyYMUPu0IiIiKiBYjJ2j7FjxyIjIwNvvvkmUlNTERgYiAMHDsDPz8+o5yuVSixevLjCocuGjNfN624MeN287saA1y3PdbPOGBEREZGMOGeMiIiISEZMxoiIiIhkxGSMiIiISEZMxoiIiIhkxGTMTFatWgV/f384OjoiKCgIR48elTsko61YsQLdunWDi4sLvLy8MGrUKFy4cMGgzdSpU6FQKAxuwcHBBm20Wi1mz54NT09PODs7Y8SIEfjzzz8N2mRlZSE8PBwqlQoqlQrh4eHIzs6u60us0JIlS8pdk1qtlh4XQmDJkiXw9fWFk5MT+vXrh7Nnzxqcw9quGQDuu+++ctetUCjwwgsvAGg4n/XPP/+MsLAw+Pr6QqFQYM+ePQaP1+fne+3aNYSFhcHZ2Rmenp6YM2cOioqK6uKyq7zu4uJivPrqq+jQoQOcnZ3h6+uLyZMn46+//jI4R79+/cr9DowbN85qrxuo399rS7ruiv7WFQoF3n//famNtX3exnxnWd3ft6Bai4yMFPb29mLt2rUiOTlZvPjii8LZ2VlcvXpV7tCMMmTIELFhwwaRlJQkEhMTxbBhw0Tr1q1Fbm6u1GbKlCkiNDRUpKamSreMjAyD88yYMUO0aNFCREdHi/j4eNG/f3/RsWNHUVJSIrUJDQ0VgYGBIiYmRsTExIjAwEAxfPjwervWey1evFg8/PDDBteUnp4uPf7OO+8IFxcXsXPnTnHmzBkxduxY4ePjI3JycqQ21nbNQgiRnp5ucM3R0dECgPjxxx+FEA3nsz5w4IB4/fXXxc6dOwUAsXv3boPH6+vzLSkpEYGBgaJ///4iPj5eREdHC19fXzFr1qx6v+7s7GwxaNAgsX37dnH+/HkRGxsrevToIYKCggzO0bdvXxEREWHwO5CdnW3QxpquW4j6+722tOu+93pTU1PF+vXrhUKhEJcvX5baWNvnbcx3lrX9fTMZM4Pu3buLGTNmGBx76KGHxMKFC2WKqHbS09MFAHHkyBHp2JQpU8TIkSMrfU52drawt7cXkZGR0rEbN24IGxsbERUVJYQQIjk5WQAQx48fl9rExsYKAOL8+fPmv5BqLF68WHTs2LHCx/R6vVCr1eKdd96RjhUWFgqVSiU+//xzIYR1XnNFXnzxRdGmTRuh1+uFEA3zsy77JVWfn++BAweEjY2NuHHjhtRm27ZtQqlUCo1GUyfXW6qiL+eyTp48KQAY/Oexb9++4sUXX6z0OdZ43fX1e21p113WyJEjxYABAwyOWfvnXfY7yxr/vjlMWUtFRUWIi4vD4MGDDY4PHjwYMTExMkVVOxqNBgDKbZj6008/wcvLCw8++CAiIiKQnp4uPRYXF4fi4mKD98HX1xeBgYHS+xAbGwuVSoUePXpIbYKDg6FSqWR7ry5evAhfX1/4+/tj3Lhx+OOPPwAAKSkpSEtLM7gepVKJvn37SrFa6zXfq6ioCFu2bMHTTz8NhUIhHW+In/W96vPzjY2NRWBgIHx9faU2Q4YMgVarRVxcXJ1epzE0Gg0UCgWaNWtmcHzr1q3w9PTEww8/jAULFuDOnTvSY9Z63fXxe22J113q5s2b2L9/P6ZPn17uMWv+vMt+Z1nj3zcr8NfS7du3odPpym0m7u3tXW7TcWsghMC8efPQu3dvBAYGSseHDh2Kp556Cn5+fkhJScEbb7yBAQMGIC4uDkqlEmlpaXBwcICbm5vB+e59H9LS0uDl5VXuNb28vGR5r3r06IEvv/wSDz74IG7evIm3334bPXv2xNmzZ6V4Kvpcr169CgBWec1l7dmzB9nZ2Zg6dap0rCF+1mXV5+eblpZW7nXc3Nzg4OAg+3tRWFiIhQsXYsKECQabQk+cOBH+/v5Qq9VISkrCokWL8Ntvv0l791rjddfX77WlXfe9Nm3aBBcXF4wePdrguDV/3hV9Z1nj3zeTMTO5t1cBuPsLUvaYNZg1axZOnz6NY8eOGRwfO3as9HNgYCC6du0KPz8/7N+/v9wf9r3Kvg8VvSdyvVdDhw6Vfu7QoQNCQkLQpk0bbNq0SZrYa8rnasnXXNa6deswdOhQg//VNcTPujL19fla4ntRXFyMcePGQa/XY9WqVQaPRURESD8HBgaibdu26Nq1K+Lj49GlSxcA1nfd9fl7bUnXfa/169dj4sSJcHR0NDhuzZ93Zd9ZFcVjyX/fHKasJU9PT9ja2pbLgNPT08tly5Zu9uzZ+Pbbb/Hjjz+iZcuWVbb18fGBn58fLl68CABQq9UoKipCVlaWQbt73we1Wo2bN2+WO9etW7cs4r1ydnZGhw4dcPHiRWlVZVWfq7Vf89WrV3Ho0CE888wzVbZriJ91fX6+arW63OtkZWWhuLhYtveiuLgYY8aMQUpKCqKjow16xSrSpUsX2NvbG/wOWON136uufq8t9bqPHj2KCxcuVPv3DljP513Zd5Y1/n0zGaslBwcHBAUFSd25paKjo9GzZ0+ZoqoZIQRmzZqFXbt24fDhw/D396/2ORkZGbh+/Tp8fHwAAEFBQbC3tzd4H1JTU5GUlCS9DyEhIdBoNDh58qTU5sSJE9BoNBbxXmm1Wpw7dw4+Pj5Sl/2911NUVIQjR45IsVr7NW/YsAFeXl4YNmxYle0a4mddn59vSEgIkpKSkJqaKrU5ePAglEolgoKC6vQ6K1KaiF28eBGHDh2Ch4dHtc85e/YsiouLpd8Ba7zusurq99pSr3vdunUICgpCx44dq21r6Z93dd9ZVvn3bfRUf6pUaWmLdevWieTkZDF37lzh7Owsrly5IndoRpk5c6ZQqVTip59+MljanJ+fL4QQ4s6dO2L+/PkiJiZGpKSkiB9//FGEhISIFi1alFsm3LJlS3Ho0CERHx8vBgwYUOEy4UceeUTExsaK2NhY0aFDB9nKPMyfP1/89NNP4o8//hDHjx8Xw4cPFy4uLtLn9s477wiVSiV27dolzpw5I8aPH1/h0mhruuZSOp1OtG7dWrz66qsGxxvSZ33nzh2RkJAgEhISBADx0UcfiYSEBGnVYH19vqVL3wcOHCji4+PFoUOHRMuWLeus1EFV111cXCxGjBghWrZsKRITEw3+3rVarRBCiEuXLomlS5eKU6dOiZSUFLF//37x0EMPic6dO1vtddfn77UlXXcpjUYjmjRpIlavXl3u+db4eVf3nSWE9f19Mxkzk88++0z4+fkJBwcH0aVLF4OyEJYOQIW3DRs2CCGEyM/PF4MHDxbNmzcX9vb2onXr1mLKlCni2rVrBucpKCgQs2bNEu7u7sLJyUkMHz68XJuMjAwxceJE4eLiIlxcXMTEiRNFVlZWPV2podK6M/b29sLX11eMHj1anD17Vnpcr9eLxYsXC7VaLZRKpejTp484c+aMwTms7ZpL/fe//xUAxIULFwyON6TP+scff6zw93rKlClCiPr9fK9evSqGDRsmnJychLu7u5g1a5YoLCys9+tOSUmp9O+9tM7ctWvXRJ8+fYS7u7twcHAQbdq0EXPmzClXk8uarru+f68t5bpLrVmzRjg5OZWrHSaEdX7e1X1nCWF9f9+K/10YEREREcmAc8aIiIiIZMRkjIiIiEhGTMaIiIiIZMRkjIiIiEhGTMaIiIiIZMRkjIiIiEhGTMaIiIiIZMRkjIiIiEhGTMaIqFFRKBRV3qZOnSp3iETUyNjJHQARUX26d0Pf7du341//+hcuXLggHXNycjJoX1xcDHt7+3qLj4gaH/aMEVGjolarpZtKpYJCoZDuFxYWolmzZtixYwf69esHR0dHbNmyBUuWLEGnTp0MzvPJJ5/gvvvuMzi2YcMGtG/fHo6OjnjooYewatWq+rswIrJaTMaIiMp49dVXMWfOHJw7dw5Dhgwx6jlr167F66+/jmXLluHcuXNYvnw53njjDWzatKmOoyUia8dhSiKiMubOnYvRo0fX6DlvvfUWPvzwQ+l5/v7+SE5Oxpo1azBlypS6CJOIGggmY0REZXTt2rVG7W/duoXr169j+vTpiIiIkI6XlJRApVKZOzwiamCYjBERleHs7Gxw38bGBkIIg2PFxcXSz3q9HsDdocoePXoYtLO1ta2jKImooWAyRkRUjebNmyMtLQ1CCCgUCgBAYmKi9Li3tzdatGiBP/74AxMnTpQpSiKyVkzGiIiq0a9fP9y6dQvvvfcennzySURFReH777+Hq6ur1GbJkiWYM2cOXF1dMXToUGi1Wvz666/IysrCvHnzZIyeiCwdV1MSEVWjffv2WLVqFT777DN07NgRJ0+exIIFCwzaPPPMM/jPf/6DjRs3okOHDujbty82btwIf39/maImImuhEGUnQhARERFRvWHPGBEREZGMmIwRERERyYjJGBEREZGMmIwRERERyYjJGBEREZGMmIwRERERyYjJGBEREZGMmIwRERERyYjJGBEREZGMmIwRERERyYjJGBEREZGMmIwRERERyej/AeidAR0rkoh1AAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model = RandomForestRegressor()\n", + "model.fit(X_train, y_train)\n", + "\n", + "# Make predictions\n", + "y_pred = model.predict(X_test)\n", + "\n", + "# Evaluate the model\n", + "mse = mean_squared_error(y_test, y_pred)\n", + "print(\"MSE:\", mse)\n", + "\n", + "# Scatter plot: True vs Predicted\n", + "plt.clf()\n", + "plt.scatter(y_test, y_pred)\n", + "plt.plot([0, 1e6], [0, 1e6], color='black', ls='--')\n", + "plt.xlabel('True')\n", + "plt.ylabel('Predicted')\n", + "plt.xlim([0, 2e4])\n", + "plt.ylim([0, 2e4])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "4ed0b4e6-904a-4af8-8bfe-cad402de09a4", + "metadata": {}, + "source": [ + "## 8. Hyperparameter tuning\n", + "With any `scikit-learn` model, it's possible to tune the hyperparameters to achieve better performance." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e6420904-44bb-40cf-8ce0-92b22a802e59", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T17:29:45.803252Z", + "iopub.status.busy": "2024-12-03T17:29:45.802663Z" + } + }, + "outputs": [], + "source": [ + "from sklearn.model_selection import GridSearchCV\n", + "param_grid = {'n_estimators': [1, 10, 50, 100, 200, 1000, 10000]} # default 100 for n_estimators\n", + "\n", + "# Create GridSearchCV object\n", + "grid = GridSearchCV(model, param_grid, cv=5)\n", + "\n", + "grid.fit(X_train, y_train)\n", + "\n", + "# Get the best parameters\n", + "print(grid.best_params_)" + ] + }, + { + "cell_type": "markdown", + "id": "8445ad22-4275-4a56-ace7-ce1a678dd900", + "metadata": {}, + "source": [ + "### 8.2 Now retrieve the best fit model" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "89167a5d-e835-4f8d-8d57-ac82f0df6239", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T00:19:21.191881Z", + "iopub.status.busy": "2024-12-03T00:19:21.191156Z", + "iopub.status.idle": "2024-12-03T00:19:21.944107Z", + "shell.execute_reply": "2024-12-03T00:19:21.943414Z", + "shell.execute_reply.started": "2024-12-03T00:19:21.191857Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best Model: RandomForestRegressor(n_estimators=1000)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE: 19817809.291009396\n" + ] + } + ], + "source": [ + "best_model = grid.best_estimator_\n", + "print(\"Best Model:\", best_model)\n", + "y_pred = best_model.predict(X_test)\n", + "plt.clf()\n", + "plt.scatter(y_test, y_pred)\n", + "plt.plot([0, 1e6], [0, 1e6], color='black', ls='--')\n", + "plt.xlabel('True')\n", + "plt.ylabel('Predicted')\n", + "plt.xlim([0, 2e4])\n", + "plt.ylim([0, 2e4])\n", + "plt.show()\n", + "\n", + "mse = mean_squared_error(y_test, y_pred)\n", + "print(\"MSE:\", mse)" + ] + }, + { + "cell_type": "markdown", + "id": "d5c33bd9-b95e-4ec8-a44d-cf3f219339e8", + "metadata": {}, + "source": [ + "## 9. Other available `scikit-learn` choices\n", + "The below two cells explore available options from `scikit-learn` for regression metrics and regression models, respectively. The metric cell is truncated with a `break` statement to only print details of the first metric. The model cell demonstrates printing the class information for the `RandomForestRegressor` class." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "5adca7ed-eb36-4e8b-99e3-3b66833fb3e3", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T17:56:30.644538Z", + "iopub.status.busy": "2024-12-03T17:56:30.643733Z", + "iopub.status.idle": "2024-12-03T17:56:30.650634Z", + "shell.execute_reply": "2024-12-03T17:56:30.649954Z", + "shell.execute_reply.started": "2024-12-03T17:56:30.644507Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['brier_score_loss', 'check_scoring', 'coverage_error', 'd2_absolute_error_score', 'd2_pinball_score', 'd2_tweedie_score', 'explained_variance_score', 'label_ranking_loss', 'log_loss', 'max_error', 'mean_absolute_error', 'mean_absolute_percentage_error', 'mean_gamma_deviance', 'mean_pinball_loss', 'mean_poisson_deviance', 'mean_squared_error', 'mean_squared_log_error', 'mean_tweedie_deviance', 'median_absolute_error', 'pairwise_distances', 'r2_score', 'root_mean_squared_error', 'root_mean_squared_log_error']\n", + "--- mean_tweedie_deviance ---\n", + "Help on function mean_tweedie_deviance in module sklearn.metrics._regression:\n", + "\n", + "mean_tweedie_deviance(y_true, y_pred, *, sample_weight=None, power=0)\n", + " Mean Tweedie deviance regression loss.\n", + " \n", + " Read more in the :ref:`User Guide `.\n", + " \n", + " Parameters\n", + " ----------\n", + " y_true : array-like of shape (n_samples,)\n", + " Ground truth (correct) target values.\n", + " \n", + " y_pred : array-like of shape (n_samples,)\n", + " Estimated target values.\n", + " \n", + " sample_weight : array-like of shape (n_samples,), default=None\n", + " Sample weights.\n", + " \n", + " power : float, default=0\n", + " Tweedie power parameter. Either power <= 0 or power >= 1.\n", + " \n", + " The higher `p` the less weight is given to extreme\n", + " deviations between true and predicted targets.\n", + " \n", + " - power < 0: Extreme stable distribution. Requires: y_pred > 0.\n", + " - power = 0 : Normal distribution, output corresponds to\n", + " mean_squared_error. y_true and y_pred can be any real numbers.\n", + " - power = 1 : Poisson distribution. Requires: y_true >= 0 and\n", + " y_pred > 0.\n", + " - 1 < p < 2 : Compound Poisson distribution. Requires: y_true >= 0\n", + " and y_pred > 0.\n", + " - power = 2 : Gamma distribution. Requires: y_true > 0 and y_pred > 0.\n", + " - power = 3 : Inverse Gaussian distribution. Requires: y_true > 0\n", + " and y_pred > 0.\n", + " - otherwise : Positive stable distribution. Requires: y_true > 0\n", + " and y_pred > 0.\n", + " \n", + " Returns\n", + " -------\n", + " loss : float\n", + " A non-negative floating point value (the best value is 0.0).\n", + " \n", + " Examples\n", + " --------\n", + " >>> from sklearn.metrics import mean_tweedie_deviance\n", + " >>> y_true = [2, 0, 1, 4]\n", + " >>> y_pred = [0.5, 0.5, 2., 2.]\n", + " >>> mean_tweedie_deviance(y_true, y_pred, power=1)\n", + " 1.4260...\n", + "\n", + "================================================================================\n" + ] + } + ], + "source": [ + "import sklearn.metrics as metrics\n", + "import inspect\n", + "regression_metrics = [\n", + " name for name, obj in inspect.getmembers(metrics)\n", + " if inspect.isfunction(obj)\n", + " and ('regression' in (obj.__doc__ or '').lower() or 'error' in (obj.__doc__ or '').lower())\n", + " and 'classification' not in (obj.__doc__ or '').lower()\n", + "]\n", + "print(regression_metrics)\n", + "\n", + "\n", + "# Print the filtered metrics and their documentation\n", + "for metric in regression_metrics:\n", + " metric_func = getattr(metrics, metric)\n", + " if metric == \"mean_tweedie_deviance\":\n", + " print(f\"--- {metric} ---\")\n", + " help(metric_func)\n", + " print(\"=\"*80)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "de4a41f7-9f4f-4662-9302-2da7d68df434", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T17:40:53.931835Z", + "iopub.status.busy": "2024-12-03T17:40:53.931115Z", + "iopub.status.idle": "2024-12-03T17:40:54.007554Z", + "shell.execute_reply": "2024-12-03T17:40:54.007006Z", + "shell.execute_reply.started": "2024-12-03T17:40:53.931811Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ARDRegression\n", + "AdaBoostRegressor\n", + "BaggingRegressor\n", + "BayesianRidge\n", + "CCA\n", + "DecisionTreeRegressor\n", + "DummyRegressor\n", + "ElasticNet\n", + "ElasticNetCV\n", + "ExtraTreeRegressor\n", + "ExtraTreesRegressor\n", + "GammaRegressor\n", + "GaussianProcessRegressor\n", + "GradientBoostingRegressor\n", + "HistGradientBoostingRegressor\n", + "HuberRegressor\n", + "IsotonicRegression\n", + "KNeighborsRegressor\n", + "KernelRidge\n", + "Lars\n", + "LarsCV\n", + "Lasso\n", + "LassoCV\n", + "LassoLars\n", + "LassoLarsCV\n", + "LassoLarsIC\n", + "LinearRegression\n", + "LinearSVR\n", + "MLPRegressor\n", + "MultiOutputRegressor\n", + "MultiTaskElasticNet\n", + "MultiTaskElasticNetCV\n", + "MultiTaskLasso\n", + "MultiTaskLassoCV\n", + "NuSVR\n", + "OrthogonalMatchingPursuit\n", + "OrthogonalMatchingPursuitCV\n", + "PLSCanonical\n", + "PLSRegression\n", + "PassiveAggressiveRegressor\n", + "PoissonRegressor\n", + "QuantileRegressor\n", + "RANSACRegressor\n", + "RadiusNeighborsRegressor\n", + "RandomForestRegressor\n", + "Help on class RandomForestRegressor in module sklearn.ensemble._forest:\n", + "\n", + "class RandomForestRegressor(ForestRegressor)\n", + " | RandomForestRegressor(n_estimators=100, *, criterion='squared_error', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=1.0, max_leaf_nodes=None, min_impurity_decrease=0.0, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, ccp_alpha=0.0, max_samples=None, monotonic_cst=None)\n", + " | \n", + " | A random forest regressor.\n", + " | \n", + " | A random forest is a meta estimator that fits a number of decision tree\n", + " | regressors on various sub-samples of the dataset and uses averaging to\n", + " | improve the predictive accuracy and control over-fitting.\n", + " | Trees in the forest use the best split strategy, i.e. equivalent to passing\n", + " | `splitter=\"best\"` to the underlying :class:`~sklearn.tree.DecisionTreeRegressor`.\n", + " | The sub-sample size is controlled with the `max_samples` parameter if\n", + " | `bootstrap=True` (default), otherwise the whole dataset is used to build\n", + " | each tree.\n", + " | \n", + " | For a comparison between tree-based ensemble models see the example\n", + " | :ref:`sphx_glr_auto_examples_ensemble_plot_forest_hist_grad_boosting_comparison.py`.\n", + " | \n", + " | Read more in the :ref:`User Guide `.\n", + " | \n", + " | Parameters\n", + " | ----------\n", + " | n_estimators : int, default=100\n", + " | The number of trees in the forest.\n", + " | \n", + " | .. versionchanged:: 0.22\n", + " | The default value of ``n_estimators`` changed from 10 to 100\n", + " | in 0.22.\n", + " | \n", + " | criterion : {\"squared_error\", \"absolute_error\", \"friedman_mse\", \"poisson\"}, default=\"squared_error\"\n", + " | The function to measure the quality of a split. Supported criteria\n", + " | are \"squared_error\" for the mean squared error, which is equal to\n", + " | variance reduction as feature selection criterion and minimizes the L2\n", + " | loss using the mean of each terminal node, \"friedman_mse\", which uses\n", + " | mean squared error with Friedman's improvement score for potential\n", + " | splits, \"absolute_error\" for the mean absolute error, which minimizes\n", + " | the L1 loss using the median of each terminal node, and \"poisson\" which\n", + " | uses reduction in Poisson deviance to find splits.\n", + " | Training using \"absolute_error\" is significantly slower\n", + " | than when using \"squared_error\".\n", + " | \n", + " | .. versionadded:: 0.18\n", + " | Mean Absolute Error (MAE) criterion.\n", + " | \n", + " | .. versionadded:: 1.0\n", + " | Poisson criterion.\n", + " | \n", + " | max_depth : int, default=None\n", + " | The maximum depth of the tree. If None, then nodes are expanded until\n", + " | all leaves are pure or until all leaves contain less than\n", + " | min_samples_split samples.\n", + " | \n", + " | min_samples_split : int or float, default=2\n", + " | The minimum number of samples required to split an internal node:\n", + " | \n", + " | - If int, then consider `min_samples_split` as the minimum number.\n", + " | - If float, then `min_samples_split` is a fraction and\n", + " | `ceil(min_samples_split * n_samples)` are the minimum\n", + " | number of samples for each split.\n", + " | \n", + " | .. versionchanged:: 0.18\n", + " | Added float values for fractions.\n", + " | \n", + " | min_samples_leaf : int or float, default=1\n", + " | The minimum number of samples required to be at a leaf node.\n", + " | A split point at any depth will only be considered if it leaves at\n", + " | least ``min_samples_leaf`` training samples in each of the left and\n", + " | right branches. This may have the effect of smoothing the model,\n", + " | especially in regression.\n", + " | \n", + " | - If int, then consider `min_samples_leaf` as the minimum number.\n", + " | - If float, then `min_samples_leaf` is a fraction and\n", + " | `ceil(min_samples_leaf * n_samples)` are the minimum\n", + " | number of samples for each node.\n", + " | \n", + " | .. versionchanged:: 0.18\n", + " | Added float values for fractions.\n", + " | \n", + " | min_weight_fraction_leaf : float, default=0.0\n", + " | The minimum weighted fraction of the sum total of weights (of all\n", + " | the input samples) required to be at a leaf node. Samples have\n", + " | equal weight when sample_weight is not provided.\n", + " | \n", + " | max_features : {\"sqrt\", \"log2\", None}, int or float, default=1.0\n", + " | The number of features to consider when looking for the best split:\n", + " | \n", + " | - If int, then consider `max_features` features at each split.\n", + " | - If float, then `max_features` is a fraction and\n", + " | `max(1, int(max_features * n_features_in_))` features are considered at each\n", + " | split.\n", + " | - If \"sqrt\", then `max_features=sqrt(n_features)`.\n", + " | - If \"log2\", then `max_features=log2(n_features)`.\n", + " | - If None or 1.0, then `max_features=n_features`.\n", + " | \n", + " | .. note::\n", + " | The default of 1.0 is equivalent to bagged trees and more\n", + " | randomness can be achieved by setting smaller values, e.g. 0.3.\n", + " | \n", + " | .. versionchanged:: 1.1\n", + " | The default of `max_features` changed from `\"auto\"` to 1.0.\n", + " | \n", + " | Note: the search for a split does not stop until at least one\n", + " | valid partition of the node samples is found, even if it requires to\n", + " | effectively inspect more than ``max_features`` features.\n", + " | \n", + " | max_leaf_nodes : int, default=None\n", + " | Grow trees with ``max_leaf_nodes`` in best-first fashion.\n", + " | Best nodes are defined as relative reduction in impurity.\n", + " | If None then unlimited number of leaf nodes.\n", + " | \n", + " | min_impurity_decrease : float, default=0.0\n", + " | A node will be split if this split induces a decrease of the impurity\n", + " | greater than or equal to this value.\n", + " | \n", + " | The weighted impurity decrease equation is the following::\n", + " | \n", + " | N_t / N * (impurity - N_t_R / N_t * right_impurity\n", + " | - N_t_L / N_t * left_impurity)\n", + " | \n", + " | where ``N`` is the total number of samples, ``N_t`` is the number of\n", + " | samples at the current node, ``N_t_L`` is the number of samples in the\n", + " | left child, and ``N_t_R`` is the number of samples in the right child.\n", + " | \n", + " | ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,\n", + " | if ``sample_weight`` is passed.\n", + " | \n", + " | .. versionadded:: 0.19\n", + " | \n", + " | bootstrap : bool, default=True\n", + " | Whether bootstrap samples are used when building trees. If False, the\n", + " | whole dataset is used to build each tree.\n", + " | \n", + " | oob_score : bool or callable, default=False\n", + " | Whether to use out-of-bag samples to estimate the generalization score.\n", + " | By default, :func:`~sklearn.metrics.r2_score` is used.\n", + " | Provide a callable with signature `metric(y_true, y_pred)` to use a\n", + " | custom metric. Only available if `bootstrap=True`.\n", + " | \n", + " | n_jobs : int, default=None\n", + " | The number of jobs to run in parallel. :meth:`fit`, :meth:`predict`,\n", + " | :meth:`decision_path` and :meth:`apply` are all parallelized over the\n", + " | trees. ``None`` means 1 unless in a :obj:`joblib.parallel_backend`\n", + " | context. ``-1`` means using all processors. See :term:`Glossary\n", + " | ` for more details.\n", + " | \n", + " | random_state : int, RandomState instance or None, default=None\n", + " | Controls both the randomness of the bootstrapping of the samples used\n", + " | when building trees (if ``bootstrap=True``) and the sampling of the\n", + " | features to consider when looking for the best split at each node\n", + " | (if ``max_features < n_features``).\n", + " | See :term:`Glossary ` for details.\n", + " | \n", + " | verbose : int, default=0\n", + " | Controls the verbosity when fitting and predicting.\n", + " | \n", + " | warm_start : bool, default=False\n", + " | When set to ``True``, reuse the solution of the previous call to fit\n", + " | and add more estimators to the ensemble, otherwise, just fit a whole\n", + " | new forest. See :term:`Glossary ` and\n", + " | :ref:`tree_ensemble_warm_start` for details.\n", + " | \n", + " | ccp_alpha : non-negative float, default=0.0\n", + " | Complexity parameter used for Minimal Cost-Complexity Pruning. The\n", + " | subtree with the largest cost complexity that is smaller than\n", + " | ``ccp_alpha`` will be chosen. By default, no pruning is performed. See\n", + " | :ref:`minimal_cost_complexity_pruning` for details.\n", + " | \n", + " | .. versionadded:: 0.22\n", + " | \n", + " | max_samples : int or float, default=None\n", + " | If bootstrap is True, the number of samples to draw from X\n", + " | to train each base estimator.\n", + " | \n", + " | - If None (default), then draw `X.shape[0]` samples.\n", + " | - If int, then draw `max_samples` samples.\n", + " | - If float, then draw `max(round(n_samples * max_samples), 1)` samples. Thus,\n", + " | `max_samples` should be in the interval `(0.0, 1.0]`.\n", + " | \n", + " | .. versionadded:: 0.22\n", + " | \n", + " | monotonic_cst : array-like of int of shape (n_features), default=None\n", + " | Indicates the monotonicity constraint to enforce on each feature.\n", + " | - 1: monotonically increasing\n", + " | - 0: no constraint\n", + " | - -1: monotonically decreasing\n", + " | \n", + " | If monotonic_cst is None, no constraints are applied.\n", + " | \n", + " | Monotonicity constraints are not supported for:\n", + " | - multioutput regressions (i.e. when `n_outputs_ > 1`),\n", + " | - regressions trained on data with missing values.\n", + " | \n", + " | Read more in the :ref:`User Guide `.\n", + " | \n", + " | .. versionadded:: 1.4\n", + " | \n", + " | Attributes\n", + " | ----------\n", + " | estimator_ : :class:`~sklearn.tree.DecisionTreeRegressor`\n", + " | The child estimator template used to create the collection of fitted\n", + " | sub-estimators.\n", + " | \n", + " | .. versionadded:: 1.2\n", + " | `base_estimator_` was renamed to `estimator_`.\n", + " | \n", + " | estimators_ : list of DecisionTreeRegressor\n", + " | The collection of fitted sub-estimators.\n", + " | \n", + " | feature_importances_ : ndarray of shape (n_features,)\n", + " | The impurity-based feature importances.\n", + " | The higher, the more important the feature.\n", + " | The importance of a feature is computed as the (normalized)\n", + " | total reduction of the criterion brought by that feature. It is also\n", + " | known as the Gini importance.\n", + " | \n", + " | Warning: impurity-based feature importances can be misleading for\n", + " | high cardinality features (many unique values). See\n", + " | :func:`sklearn.inspection.permutation_importance` as an alternative.\n", + " | \n", + " | n_features_in_ : int\n", + " | Number of features seen during :term:`fit`.\n", + " | \n", + " | .. versionadded:: 0.24\n", + " | \n", + " | feature_names_in_ : ndarray of shape (`n_features_in_`,)\n", + " | Names of features seen during :term:`fit`. Defined only when `X`\n", + " | has feature names that are all strings.\n", + " | \n", + " | .. versionadded:: 1.0\n", + " | \n", + " | n_outputs_ : int\n", + " | The number of outputs when ``fit`` is performed.\n", + " | \n", + " | oob_score_ : float\n", + " | Score of the training dataset obtained using an out-of-bag estimate.\n", + " | This attribute exists only when ``oob_score`` is True.\n", + " | \n", + " | oob_prediction_ : ndarray of shape (n_samples,) or (n_samples, n_outputs)\n", + " | Prediction computed with out-of-bag estimate on the training set.\n", + " | This attribute exists only when ``oob_score`` is True.\n", + " | \n", + " | estimators_samples_ : list of arrays\n", + " | The subset of drawn samples (i.e., the in-bag samples) for each base\n", + " | estimator. Each subset is defined by an array of the indices selected.\n", + " | \n", + " | .. versionadded:: 1.4\n", + " | \n", + " | See Also\n", + " | --------\n", + " | sklearn.tree.DecisionTreeRegressor : A decision tree regressor.\n", + " | sklearn.ensemble.ExtraTreesRegressor : Ensemble of extremely randomized\n", + " | tree regressors.\n", + " | sklearn.ensemble.HistGradientBoostingRegressor : A Histogram-based Gradient\n", + " | Boosting Regression Tree, very fast for big datasets (n_samples >=\n", + " | 10_000).\n", + " | \n", + " | Notes\n", + " | -----\n", + " | The default values for the parameters controlling the size of the trees\n", + " | (e.g. ``max_depth``, ``min_samples_leaf``, etc.) lead to fully grown and\n", + " | unpruned trees which can potentially be very large on some data sets. To\n", + " | reduce memory consumption, the complexity and size of the trees should be\n", + " | controlled by setting those parameter values.\n", + " | \n", + " | The features are always randomly permuted at each split. Therefore,\n", + " | the best found split may vary, even with the same training data,\n", + " | ``max_features=n_features`` and ``bootstrap=False``, if the improvement\n", + " | of the criterion is identical for several splits enumerated during the\n", + " | search of the best split. To obtain a deterministic behaviour during\n", + " | fitting, ``random_state`` has to be fixed.\n", + " | \n", + " | The default value ``max_features=1.0`` uses ``n_features``\n", + " | rather than ``n_features / 3``. The latter was originally suggested in\n", + " | [1], whereas the former was more recently justified empirically in [2].\n", + " | \n", + " | References\n", + " | ----------\n", + " | .. [1] L. Breiman, \"Random Forests\", Machine Learning, 45(1), 5-32, 2001.\n", + " | \n", + " | .. [2] P. Geurts, D. Ernst., and L. Wehenkel, \"Extremely randomized\n", + " | trees\", Machine Learning, 63(1), 3-42, 2006.\n", + " | \n", + " | Examples\n", + " | --------\n", + " | >>> from sklearn.ensemble import RandomForestRegressor\n", + " | >>> from sklearn.datasets import make_regression\n", + " | >>> X, y = make_regression(n_features=4, n_informative=2,\n", + " | ... random_state=0, shuffle=False)\n", + " | >>> regr = RandomForestRegressor(max_depth=2, random_state=0)\n", + " | >>> regr.fit(X, y)\n", + " | RandomForestRegressor(...)\n", + " | >>> print(regr.predict([[0, 0, 0, 0]]))\n", + " | [-8.32987858]\n", + " | \n", + " | Method resolution order:\n", + " | RandomForestRegressor\n", + " | ForestRegressor\n", + " | sklearn.base.RegressorMixin\n", + " | BaseForest\n", + " | sklearn.base.MultiOutputMixin\n", + " | sklearn.ensemble._base.BaseEnsemble\n", + " | sklearn.base.MetaEstimatorMixin\n", + " | sklearn.base.BaseEstimator\n", + " | sklearn.utils._estimator_html_repr._HTMLDocumentationLinkMixin\n", + " | sklearn.utils._metadata_requests._MetadataRequester\n", + " | builtins.object\n", + " | \n", + " | Methods defined here:\n", + " | \n", + " | __init__(self, n_estimators=100, *, criterion='squared_error', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=1.0, max_leaf_nodes=None, min_impurity_decrease=0.0, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, ccp_alpha=0.0, max_samples=None, monotonic_cst=None)\n", + " | Initialize self. See help(type(self)) for accurate signature.\n", + " | \n", + " | set_fit_request(self: sklearn.ensemble._forest.RandomForestRegressor, *, sample_weight: Union[bool, NoneType, str] = '$UNCHANGED$') -> sklearn.ensemble._forest.RandomForestRegressor from sklearn.utils._metadata_requests.RequestMethod.__get__.\n", + " | Request metadata passed to the ``fit`` method.\n", + " | \n", + " | Note that this method is only relevant if\n", + " | ``enable_metadata_routing=True`` (see :func:`sklearn.set_config`).\n", + " | Please see :ref:`User Guide ` on how the routing\n", + " | mechanism works.\n", + " | \n", + " | The options for each parameter are:\n", + " | \n", + " | - ``True``: metadata is requested, and passed to ``fit`` if provided. The request is ignored if metadata is not provided.\n", + " | \n", + " | - ``False``: metadata is not requested and the meta-estimator will not pass it to ``fit``.\n", + " | \n", + " | - ``None``: metadata is not requested, and the meta-estimator will raise an error if the user provides it.\n", + " | \n", + " | - ``str``: metadata should be passed to the meta-estimator with this given alias instead of the original name.\n", + " | \n", + " | The default (``sklearn.utils.metadata_routing.UNCHANGED``) retains the\n", + " | existing request. This allows you to change the request for some\n", + " | parameters and not others.\n", + " | \n", + " | .. versionadded:: 1.3\n", + " | \n", + " | .. note::\n", + " | This method is only relevant if this estimator is used as a\n", + " | sub-estimator of a meta-estimator, e.g. used inside a\n", + " | :class:`~sklearn.pipeline.Pipeline`. Otherwise it has no effect.\n", + " | \n", + " | Parameters\n", + " | ----------\n", + " | sample_weight : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED\n", + " | Metadata routing for ``sample_weight`` parameter in ``fit``.\n", + " | \n", + " | Returns\n", + " | -------\n", + " | self : object\n", + " | The updated object.\n", + " | \n", + " | set_score_request(self: sklearn.ensemble._forest.RandomForestRegressor, *, sample_weight: Union[bool, NoneType, str] = '$UNCHANGED$') -> sklearn.ensemble._forest.RandomForestRegressor from sklearn.utils._metadata_requests.RequestMethod.__get__.\n", + " | Request metadata passed to the ``score`` method.\n", + " | \n", + " | Note that this method is only relevant if\n", + " | ``enable_metadata_routing=True`` (see :func:`sklearn.set_config`).\n", + " | Please see :ref:`User Guide ` on how the routing\n", + " | mechanism works.\n", + " | \n", + " | The options for each parameter are:\n", + " | \n", + " | - ``True``: metadata is requested, and passed to ``score`` if provided. The request is ignored if metadata is not provided.\n", + " | \n", + " | - ``False``: metadata is not requested and the meta-estimator will not pass it to ``score``.\n", + " | \n", + " | - ``None``: metadata is not requested, and the meta-estimator will raise an error if the user provides it.\n", + " | \n", + " | - ``str``: metadata should be passed to the meta-estimator with this given alias instead of the original name.\n", + " | \n", + " | The default (``sklearn.utils.metadata_routing.UNCHANGED``) retains the\n", + " | existing request. This allows you to change the request for some\n", + " | parameters and not others.\n", + " | \n", + " | .. versionadded:: 1.3\n", + " | \n", + " | .. note::\n", + " | This method is only relevant if this estimator is used as a\n", + " | sub-estimator of a meta-estimator, e.g. used inside a\n", + " | :class:`~sklearn.pipeline.Pipeline`. Otherwise it has no effect.\n", + " | \n", + " | Parameters\n", + " | ----------\n", + " | sample_weight : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED\n", + " | Metadata routing for ``sample_weight`` parameter in ``score``.\n", + " | \n", + " | Returns\n", + " | -------\n", + " | self : object\n", + " | The updated object.\n", + " | \n", + " | ----------------------------------------------------------------------\n", + " | Data and other attributes defined here:\n", + " | \n", + " | __abstractmethods__ = frozenset()\n", + " | \n", + " | __annotations__ = {'_parameter_constraints': }\n", + " | \n", + " | ----------------------------------------------------------------------\n", + " | Methods inherited from ForestRegressor:\n", + " | \n", + " | predict(self, X)\n", + " | Predict regression target for X.\n", + " | \n", + " | The predicted regression target of an input sample is computed as the\n", + " | mean predicted regression targets of the trees in the forest.\n", + " | \n", + " | Parameters\n", + " | ----------\n", + " | X : {array-like, sparse matrix} of shape (n_samples, n_features)\n", + " | The input samples. Internally, its dtype will be converted to\n", + " | ``dtype=np.float32``. If a sparse matrix is provided, it will be\n", + " | converted into a sparse ``csr_matrix``.\n", + " | \n", + " | Returns\n", + " | -------\n", + " | y : ndarray of shape (n_samples,) or (n_samples, n_outputs)\n", + " | The predicted values.\n", + " | \n", + " | ----------------------------------------------------------------------\n", + " | Methods inherited from sklearn.base.RegressorMixin:\n", + " | \n", + " | score(self, X, y, sample_weight=None)\n", + " | Return the coefficient of determination of the prediction.\n", + " | \n", + " | The coefficient of determination :math:`R^2` is defined as\n", + " | :math:`(1 - \\frac{u}{v})`, where :math:`u` is the residual\n", + " | sum of squares ``((y_true - y_pred)** 2).sum()`` and :math:`v`\n", + " | is the total sum of squares ``((y_true - y_true.mean()) ** 2).sum()``.\n", + " | The best possible score is 1.0 and it can be negative (because the\n", + " | model can be arbitrarily worse). A constant model that always predicts\n", + " | the expected value of `y`, disregarding the input features, would get\n", + " | a :math:`R^2` score of 0.0.\n", + " | \n", + " | Parameters\n", + " | ----------\n", + " | X : array-like of shape (n_samples, n_features)\n", + " | Test samples. For some estimators this may be a precomputed\n", + " | kernel matrix or a list of generic objects instead with shape\n", + " | ``(n_samples, n_samples_fitted)``, where ``n_samples_fitted``\n", + " | is the number of samples used in the fitting for the estimator.\n", + " | \n", + " | y : array-like of shape (n_samples,) or (n_samples, n_outputs)\n", + " | True values for `X`.\n", + " | \n", + " | sample_weight : array-like of shape (n_samples,), default=None\n", + " | Sample weights.\n", + " | \n", + " | Returns\n", + " | -------\n", + " | score : float\n", + " | :math:`R^2` of ``self.predict(X)`` w.r.t. `y`.\n", + " | \n", + " | Notes\n", + " | -----\n", + " | The :math:`R^2` score used when calling ``score`` on a regressor uses\n", + " | ``multioutput='uniform_average'`` from version 0.23 to keep consistent\n", + " | with default value of :func:`~sklearn.metrics.r2_score`.\n", + " | This influences the ``score`` method of all the multioutput\n", + " | regressors (except for\n", + " | :class:`~sklearn.multioutput.MultiOutputRegressor`).\n", + " | \n", + " | ----------------------------------------------------------------------\n", + " | Data descriptors inherited from sklearn.base.RegressorMixin:\n", + " | \n", + " | __dict__\n", + " | dictionary for instance variables\n", + " | \n", + " | __weakref__\n", + " | list of weak references to the object\n", + " | \n", + " | ----------------------------------------------------------------------\n", + " | Methods inherited from BaseForest:\n", + " | \n", + " | apply(self, X)\n", + " | Apply trees in the forest to X, return leaf indices.\n", + " | \n", + " | Parameters\n", + " | ----------\n", + " | X : {array-like, sparse matrix} of shape (n_samples, n_features)\n", + " | The input samples. Internally, its dtype will be converted to\n", + " | ``dtype=np.float32``. If a sparse matrix is provided, it will be\n", + " | converted into a sparse ``csr_matrix``.\n", + " | \n", + " | Returns\n", + " | -------\n", + " | X_leaves : ndarray of shape (n_samples, n_estimators)\n", + " | For each datapoint x in X and for each tree in the forest,\n", + " | return the index of the leaf x ends up in.\n", + " | \n", + " | decision_path(self, X)\n", + " | Return the decision path in the forest.\n", + " | \n", + " | .. versionadded:: 0.18\n", + " | \n", + " | Parameters\n", + " | ----------\n", + " | X : {array-like, sparse matrix} of shape (n_samples, n_features)\n", + " | The input samples. Internally, its dtype will be converted to\n", + " | ``dtype=np.float32``. If a sparse matrix is provided, it will be\n", + " | converted into a sparse ``csr_matrix``.\n", + " | \n", + " | Returns\n", + " | -------\n", + " | indicator : sparse matrix of shape (n_samples, n_nodes)\n", + " | Return a node indicator matrix where non zero elements indicates\n", + " | that the samples goes through the nodes. The matrix is of CSR\n", + " | format.\n", + " | \n", + " | n_nodes_ptr : ndarray of shape (n_estimators + 1,)\n", + " | The columns from indicator[n_nodes_ptr[i]:n_nodes_ptr[i+1]]\n", + " | gives the indicator value for the i-th estimator.\n", + " | \n", + " | fit(self, X, y, sample_weight=None)\n", + " | Build a forest of trees from the training set (X, y).\n", + " | \n", + " | Parameters\n", + " | ----------\n", + " | X : {array-like, sparse matrix} of shape (n_samples, n_features)\n", + " | The training input samples. Internally, its dtype will be converted\n", + " | to ``dtype=np.float32``. If a sparse matrix is provided, it will be\n", + " | converted into a sparse ``csc_matrix``.\n", + " | \n", + " | y : array-like of shape (n_samples,) or (n_samples, n_outputs)\n", + " | The target values (class labels in classification, real numbers in\n", + " | regression).\n", + " | \n", + " | sample_weight : array-like of shape (n_samples,), default=None\n", + " | Sample weights. If None, then samples are equally weighted. Splits\n", + " | that would create child nodes with net zero or negative weight are\n", + " | ignored while searching for a split in each node. In the case of\n", + " | classification, splits are also ignored if they would result in any\n", + " | single class carrying a negative weight in either child node.\n", + " | \n", + " | Returns\n", + " | -------\n", + " | self : object\n", + " | Fitted estimator.\n", + " | \n", + " | ----------------------------------------------------------------------\n", + " | Readonly properties inherited from BaseForest:\n", + " | \n", + " | estimators_samples_\n", + " | The subset of drawn samples for each base estimator.\n", + " | \n", + " | Returns a dynamically generated list of indices identifying\n", + " | the samples used for fitting each member of the ensemble, i.e.,\n", + " | the in-bag samples.\n", + " | \n", + " | Note: the list is re-created at each call to the property in order\n", + " | to reduce the object memory footprint by not storing the sampling\n", + " | data. Thus fetching the property may be slower than expected.\n", + " | \n", + " | feature_importances_\n", + " | The impurity-based feature importances.\n", + " | \n", + " | The higher, the more important the feature.\n", + " | The importance of a feature is computed as the (normalized)\n", + " | total reduction of the criterion brought by that feature. It is also\n", + " | known as the Gini importance.\n", + " | \n", + " | Warning: impurity-based feature importances can be misleading for\n", + " | high cardinality features (many unique values). See\n", + " | :func:`sklearn.inspection.permutation_importance` as an alternative.\n", + " | \n", + " | Returns\n", + " | -------\n", + " | feature_importances_ : ndarray of shape (n_features,)\n", + " | The values of this array sum to 1, unless all trees are single node\n", + " | trees consisting of only the root node, in which case it will be an\n", + " | array of zeros.\n", + " | \n", + " | ----------------------------------------------------------------------\n", + " | Methods inherited from sklearn.ensemble._base.BaseEnsemble:\n", + " | \n", + " | __getitem__(self, index)\n", + " | Return the index'th estimator in the ensemble.\n", + " | \n", + " | __iter__(self)\n", + " | Return iterator over estimators in the ensemble.\n", + " | \n", + " | __len__(self)\n", + " | Return the number of estimators in the ensemble.\n", + " | \n", + " | ----------------------------------------------------------------------\n", + " | Methods inherited from sklearn.base.BaseEstimator:\n", + " | \n", + " | __getstate__(self)\n", + " | Helper for pickle.\n", + " | \n", + " | __repr__(self, N_CHAR_MAX=700)\n", + " | Return repr(self).\n", + " | \n", + " | __setstate__(self, state)\n", + " | \n", + " | __sklearn_clone__(self)\n", + " | \n", + " | get_params(self, deep=True)\n", + " | Get parameters for this estimator.\n", + " | \n", + " | Parameters\n", + " | ----------\n", + " | deep : bool, default=True\n", + " | If True, will return the parameters for this estimator and\n", + " | contained subobjects that are estimators.\n", + " | \n", + " | Returns\n", + " | -------\n", + " | params : dict\n", + " | Parameter names mapped to their values.\n", + " | \n", + " | set_params(self, **params)\n", + " | Set the parameters of this estimator.\n", + " | \n", + " | The method works on simple estimators as well as on nested objects\n", + " | (such as :class:`~sklearn.pipeline.Pipeline`). The latter have\n", + " | parameters of the form ``__`` so that it's\n", + " | possible to update each component of a nested object.\n", + " | \n", + " | Parameters\n", + " | ----------\n", + " | **params : dict\n", + " | Estimator parameters.\n", + " | \n", + " | Returns\n", + " | -------\n", + " | self : estimator instance\n", + " | Estimator instance.\n", + " | \n", + " | ----------------------------------------------------------------------\n", + " | Methods inherited from sklearn.utils._metadata_requests._MetadataRequester:\n", + " | \n", + " | get_metadata_routing(self)\n", + " | Get metadata routing of this object.\n", + " | \n", + " | Please check :ref:`User Guide ` on how the routing\n", + " | mechanism works.\n", + " | \n", + " | Returns\n", + " | -------\n", + " | routing : MetadataRequest\n", + " | A :class:`~sklearn.utils.metadata_routing.MetadataRequest` encapsulating\n", + " | routing information.\n", + " | \n", + " | ----------------------------------------------------------------------\n", + " | Class methods inherited from sklearn.utils._metadata_requests._MetadataRequester:\n", + " | \n", + " | __init_subclass__(**kwargs)\n", + " | Set the ``set_{method}_request`` methods.\n", + " | \n", + " | This uses PEP-487 [1]_ to set the ``set_{method}_request`` methods. It\n", + " | looks for the information available in the set default values which are\n", + " | set using ``__metadata_request__*`` class attributes, or inferred\n", + " | from method signatures.\n", + " | \n", + " | The ``__metadata_request__*`` class attributes are used when a method\n", + " | does not explicitly accept a metadata through its arguments or if the\n", + " | developer would like to specify a request value for those metadata\n", + " | which are different from the default ``None``.\n", + " | \n", + " | References\n", + " | ----------\n", + " | .. [1] https://www.python.org/dev/peps/pep-0487\n", + "\n", + "None\n", + "RegressorChain\n", + "Ridge\n", + "RidgeCV\n", + "SGDRegressor\n", + "SVR\n", + "StackingRegressor\n", + "TheilSenRegressor\n", + "TransformedTargetRegressor\n", + "TweedieRegressor\n", + "VotingRegressor\n" + ] + } + ], + "source": [ + "from sklearn.utils import all_estimators\n", + "\n", + "# Get all regression models\n", + "regressors = all_estimators(type_filter='regressor')\n", + "\n", + "# Print the names of all available regression models\n", + "for name, estimator in regressors:\n", + " print(name)\n", + "\n", + "for name, estimator in regressors:\n", + " if name == \"RandomForestRegressor\":\n", + " print(help(estimator))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "be44f0da-e766-4bd0-9312-4b1977397c8d", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "LSST", + "language": "python", + "name": "lsst" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 0d79f19ef2d048a15cd03ea6721c2d5efa62c875 Mon Sep 17 00:00:00 2001 From: beckynevin Date: Tue, 6 May 2025 18:14:58 +0000 Subject: [PATCH 02/13] renaming --- DP0.2/20_Introduction_to_Data_Science.ipynb | 2910 +++++++++++++++++++ 1 file changed, 2910 insertions(+) create mode 100644 DP0.2/20_Introduction_to_Data_Science.ipynb diff --git a/DP0.2/20_Introduction_to_Data_Science.ipynb b/DP0.2/20_Introduction_to_Data_Science.ipynb new file mode 100644 index 00000000..ff91598b --- /dev/null +++ b/DP0.2/20_Introduction_to_Data_Science.ipynb @@ -0,0 +1,2910 @@ +{ + "cells": [ + { + "attachments": { + "90083a24-00a4-4a6f-a1c3-b9b4c6b0de9e.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "id": "6c298cbb-eb18-4cbc-b064-709a7c2b9b4c", + "metadata": {}, + "source": [ + "# Introduction to Data Science\n", + "\n", + "
\n", + "\n", + "
\n", + "For the Rubin Science Platform at data.lsst.cloud.
\n", + "Data Release: DPX or DRX
\n", + "Container Size: small
\n", + "LSST Science Pipelines version: Weekly 2024_16
\n", + "Last verified to run: 2024-07-09
\n", + "Repository: github.com/lsst/tutorial-notebooks
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4a4e60f3-a825-4dcc-8cd5-37ed43b7373b", + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext pycodestyle_magic\n", + "%flake8_on\n", + "import logging\n", + "logging.getLogger(\"flake8\").setLevel(logging.FATAL)" + ] + }, + { + "cell_type": "markdown", + "id": "25e71641-fbfb-4470-8049-03ba631dbef1", + "metadata": {}, + "source": [ + "**Learning objective:** This notebook guides a PI through the process of using python's data science and machine learning libraries to explore data from complex ADQL queries with the TAP service. The goal is to build a predictive model to estimate missing $r-$band Kron Flux values when the other bands are available, and visualize the results and quantify the model performance.\n", + "\n", + "**LSST data products:** Object, Forcedsource, and CcdVisit tables.\n", + "\n", + "**Packages:** lsst.rsp, pandas, scikit-learn, seaborn\n", + "\n", + "**Credit:**\n", + "Based on notebooks developed by Leanne Guy (TAP query) and Alex Drlica-Wagner and Melissa Graham (Butler query).\n", + "Please consider acknowledging them if this notebook is used for the preparation of journal articles, software releases, or other notebooks.\n", + "\n", + "**Get Support:**\n", + "Everyone is encouraged to ask questions or raise issues in the \n", + "Support Category \n", + "of the Rubin Community Forum.\n", + "Rubin staff will respond to all questions posted there." + ] + }, + { + "cell_type": "markdown", + "id": "ca5378d1-00fe-44ad-be88-5f1a0a85b404", + "metadata": {}, + "source": [ + "## 1. Introduction\n", + "\n", + "This notebook provides an intermediate-level demonstration of how to use the Table Access Protocol (TAP) server and ADQL (Astronomy Data Query Language) to query and retrieve data from the DP0.2 catalogs.\n", + "\n", + "TAP provides standardized access to catalog data for discovery, search, and retrieval.\n", + "Full documentation for TAP is provided by the International Virtual Observatory Alliance (IVOA).\n", + "ADQL is similar to SQL (Structured Query Langage).\n", + "The documentation for ADQL includes more information about syntax and keywords.\n", + "Note that not all ADQL functionality is supported yet in the DP0-era RSP.\n", + "\n", + "**See the recommendations for TAP queries in DP0.2 tutorial 02a \"Introduction to the TAP Service\".**\n", + "\n", + "The [documentation for Data Preview 0.2](https://dp0-2.lsst.io/) includes definitions\n", + "of the data products, descriptions of catalog contents, and ADQL recipes.\n", + "\n", + "### 1.1. Package imports\n", + "\n", + "Import general python packages, the Rubin TAP service utilities, and various scikit-learn utilities." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "3f4900a4-3358-472a-b9ba-c42e3f2f0771", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T18:16:10.447704Z", + "iopub.status.busy": "2024-12-03T18:16:10.447270Z", + "iopub.status.idle": "2024-12-03T18:16:11.741424Z", + "shell.execute_reply": "2024-12-03T18:16:11.740792Z", + "shell.execute_reply.started": "2024-12-03T18:16:10.447676Z" + } + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import pandas\n", + "\n", + "from astropy import units as u\n", + "from astropy.coordinates import SkyCoord\n", + "\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.metrics import mean_squared_error\n", + "from sklearn.ensemble import RandomForestRegressor\n", + "\n", + "from lsst.rsp import get_tap_service, retrieve_query" + ] + }, + { + "cell_type": "markdown", + "id": "90251edc-e77a-4f2c-aef3-935381faebc9", + "metadata": {}, + "source": [ + "Set up seaborn to use 538's aesthetics. This is probably not what we want to the rtn-045 default plotting settings though..." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "94acc9f6-2033-4ace-aefd-d036a35f4221", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T18:18:25.800083Z", + "iopub.status.busy": "2024-12-03T18:18:25.799778Z", + "iopub.status.idle": "2024-12-03T18:18:25.803998Z", + "shell.execute_reply": "2024-12-03T18:18:25.803416Z", + "shell.execute_reply.started": "2024-12-03T18:18:25.800052Z" + } + }, + "outputs": [], + "source": [ + "sns.set_style('whitegrid')\n", + "plt.style.use('fivethirtyeight')\n", + "palette = sns.color_palette(\"muted\") # Choose a desired palette\n", + "sns.set_palette(palette)" + ] + }, + { + "cell_type": "markdown", + "id": "ca1e28f4-805e-4480-a7c6-0473b7e2b088", + "metadata": {}, + "source": [ + "### 1.2. Define functions and parameters\n", + "\n", + "Instantiate the TAP service." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "caf56589-100a-4481-8f24-5f5058b6671f", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T18:18:31.983738Z", + "iopub.status.busy": "2024-12-03T18:18:31.983012Z", + "iopub.status.idle": "2024-12-03T18:18:32.026282Z", + "shell.execute_reply": "2024-12-03T18:18:32.025768Z", + "shell.execute_reply.started": "2024-12-03T18:18:31.983710Z" + } + }, + "outputs": [], + "source": [ + "service = get_tap_service(\"tap\")\n", + "assert service is not None" + ] + }, + { + "cell_type": "markdown", + "id": "9eb0f20e-6c28-404f-8032-acc00f73405a", + "metadata": {}, + "source": [ + "Set the maximum number of rows to display from pandas." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "2b7b6002-2457-4c20-a03e-6bfa24a0aa27", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T18:18:32.908017Z", + "iopub.status.busy": "2024-12-03T18:18:32.907315Z", + "iopub.status.idle": "2024-12-03T18:18:32.910925Z", + "shell.execute_reply": "2024-12-03T18:18:32.910377Z", + "shell.execute_reply.started": "2024-12-03T18:18:32.907992Z" + } + }, + "outputs": [], + "source": [ + "pandas.set_option('display.max_rows', 6)" + ] + }, + { + "cell_type": "markdown", + "id": "cd325fe4-6c7c-4803-ad79-f30d7edc23e3", + "metadata": {}, + "source": [ + "## 2. Query for Kron fluxes around extended (galaxy) objects.\n", + "I forget why I chose this specific statistic for the demo.\n", + "\n", + "Kron radius: A radius that is calculated using the light profile of the object, typically as the first moment (i.e., a weighted average of radius with brightness) of the light distribution.\n", + "\n", + "Kron flux: The total flux measured within a certain multiple (often 2.5×) of the Kron radius, typically capturing about 90–95% of the total light for extended sources like galaxies." + ] + }, + { + "cell_type": "markdown", + "id": "6b4f495d-1215-421d-bdb0-bc32fec92c25", + "metadata": {}, + "source": [ + "Define the coordinates and radius to use for the example queries in Sections 2 and 3." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "7ddd0344-b354-45a0-9e5a-755149c9bc54", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T18:18:35.079591Z", + "iopub.status.busy": "2024-12-03T18:18:35.078853Z", + "iopub.status.idle": "2024-12-03T18:18:35.082610Z", + "shell.execute_reply": "2024-12-03T18:18:35.082033Z", + "shell.execute_reply.started": "2024-12-03T18:18:35.079566Z" + } + }, + "outputs": [], + "source": [ + "center_ra = 62\n", + "center_dec = -37\n", + "radius = 0.1\n", + "\n", + "str_center_coords = str(center_ra) + \", \" + str(center_dec)\n", + "str_radius = str(radius)" + ] + }, + { + "cell_type": "markdown", + "id": "dd80babb-ee05-49e9-9f9c-923d5c0cee31", + "metadata": {}, + "source": [ + "Start with the same query as used in the beginner TAP tutorial notebook 02a." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "985e3b62-8065-42ec-a40c-1232c4c45f17", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T18:19:13.171378Z", + "iopub.status.busy": "2024-12-03T18:19:13.170670Z", + "iopub.status.idle": "2024-12-03T18:19:13.174956Z", + "shell.execute_reply": "2024-12-03T18:19:13.174323Z", + "shell.execute_reply.started": "2024-12-03T18:19:13.171349Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SELECT coord_ra, coord_dec, g_kronFlux, g_kronFlux_flag, r_kronFlux, r_kronFlux_flag, i_kronFlux, i_kronFlux_flag FROM dp02_dc2_catalogs.Object WHERE CONTAINS(POINT('ICRS', coord_ra, coord_dec), CIRCLE('ICRS', 62, -37, 0.1)) = 1 AND detect_isPrimary = 1 AND g_extendedness = 1\n" + ] + } + ], + "source": [ + "query = \"SELECT coord_ra, coord_dec, g_kronFlux, g_kronFlux_flag, \"\\\n", + " \"r_kronFlux, r_kronFlux_flag, i_kronFlux, i_kronFlux_flag \"\\\n", + " \"FROM dp02_dc2_catalogs.Object \"\\\n", + " \"WHERE CONTAINS(POINT('ICRS', coord_ra, coord_dec), \"\\\n", + " \"CIRCLE('ICRS', \" + str_center_coords + \", \" + str_radius + \")) = 1 \"\\\n", + " \"AND detect_isPrimary = 1 AND g_extendedness = 1\"\n", + "print(query)" + ] + }, + { + "cell_type": "markdown", + "id": "f024085b-0f7f-45c6-8184-41b528c15396", + "metadata": {}, + "source": [ + "Run the query job asynchronously." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "c02adc91-5f5e-418b-87a3-cba8beba7dd2", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T18:19:14.234845Z", + "iopub.status.busy": "2024-12-03T18:19:14.234550Z", + "iopub.status.idle": "2024-12-03T18:19:21.521239Z", + "shell.execute_reply": "2024-12-03T18:19:21.520430Z", + "shell.execute_reply.started": "2024-12-03T18:19:14.234825Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Job phase is COMPLETED\n" + ] + } + ], + "source": [ + "job = service.submit_job(query)\n", + "job.run()\n", + "job.wait(phases=['COMPLETED', 'ERROR'])\n", + "print('Job phase is', job.phase)" + ] + }, + { + "cell_type": "markdown", + "id": "80b28a39-bd12-49d6-9cce-f8ddfc31296c", + "metadata": {}, + "source": [ + "## 3. Explore the data using a `pandas` DataFrame object.\n", + "DEFINE WHAT A DATAFRAME IS." + ] + }, + { + "cell_type": "markdown", + "id": "07d1cfb1-589b-402b-8b8f-c2c70652b6c6", + "metadata": {}, + "source": [ + "Return the results as a `pandas` dataframe, and then delete the query to save space." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "8cd2f538-c2d7-44ca-ab4d-825120b8f2e7", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T18:19:21.522743Z", + "iopub.status.busy": "2024-12-03T18:19:21.522437Z", + "iopub.status.idle": "2024-12-03T18:19:21.848448Z", + "shell.execute_reply": "2024-12-03T18:19:21.847864Z", + "shell.execute_reply.started": "2024-12-03T18:19:21.522716Z" + } + }, + "outputs": [], + "source": [ + "results = job.fetch_result().to_table().to_pandas()\n", + "job.delete()\n", + "del query" + ] + }, + { + "cell_type": "markdown", + "id": "27888222-8fca-4620-a838-9260b0f5f47f", + "metadata": {}, + "source": [ + "Display `results`." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "ee4d121e-6b4d-4371-afae-4f7587b95d51", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T18:18:54.059294Z", + "iopub.status.busy": "2024-12-03T18:18:54.058632Z", + "iopub.status.idle": "2024-12-03T18:18:54.074345Z", + "shell.execute_reply": "2024-12-03T18:18:54.073785Z", + "shell.execute_reply.started": "2024-12-03T18:18:54.059270Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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coord_racoord_decg_kronFluxg_kronFlux_flagr_kronFluxr_kronFlux_flagi_kronFluxi_kronFlux_flag
062.018897-37.09567171.568352True91.185588True624.454022True
162.020999-37.093227174.729861False110.922305False52.040203True
262.000430-37.093196131.680920False137.655812False136.174616True
...........................
1156161.950427-36.94658651.054369True175.646973False123.073904True
1156261.976752-36.904225199.039503False187.972452False115.825734False
1156361.932319-36.941077266.123377False218.853195False481.650950True
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11564 rows × 8 columns

\n", + "
" + ], + "text/plain": [ + " coord_ra coord_dec g_kronFlux g_kronFlux_flag r_kronFlux \\\n", + "0 62.018897 -37.095671 71.568352 True 91.185588 \n", + "1 62.020999 -37.093227 174.729861 False 110.922305 \n", + "2 62.000430 -37.093196 131.680920 False 137.655812 \n", + "... ... ... ... ... ... \n", + "11561 61.950427 -36.946586 51.054369 True 175.646973 \n", + "11562 61.976752 -36.904225 199.039503 False 187.972452 \n", + "11563 61.932319 -36.941077 266.123377 False 218.853195 \n", + "\n", + " r_kronFlux_flag i_kronFlux i_kronFlux_flag \n", + "0 True 624.454022 True \n", + "1 False 52.040203 True \n", + "2 False 136.174616 True \n", + "... ... ... ... \n", + "11561 False 123.073904 True \n", + "11562 False 115.825734 False \n", + "11563 False 481.650950 True \n", + "\n", + "[11564 rows x 8 columns]" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results" + ] + }, + { + "cell_type": "markdown", + "id": "1d493b9b-0aba-4586-bcd0-3e1ce1f09c16", + "metadata": {}, + "source": [ + "`results` is a `pandas` DataFrame object (see below). There's lots you can do with this type of object..." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "db2168fe-593a-423d-b2f4-26ac0db60e8c", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T18:19:48.071264Z", + "iopub.status.busy": "2024-12-03T18:19:48.070609Z", + "iopub.status.idle": "2024-12-03T18:19:48.074799Z", + "shell.execute_reply": "2024-12-03T18:19:48.074312Z", + "shell.execute_reply.started": "2024-12-03T18:19:48.071232Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "pandas.core.frame.DataFrame" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(results)" + ] + }, + { + "cell_type": "markdown", + "id": "9c59c4e8-90bd-4aa8-8ce2-9a14c09988a0", + "metadata": {}, + "source": [ + "Some options are inspection- and summary-oriented, such as the `.head()`, `.tail()`, and `.describe()` attributes. Let's check these out now. `.head()` and `.tail()` give you the first and last five rows, respectively, but can be modified to print out a different number of rows. `.describe()` will provide statistics of the distribution of values in each column, including the mean and standard deviation." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "eec25f58-d3f3-4ef4-b3e2-ab105c4718fd", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T18:23:39.207146Z", + "iopub.status.busy": "2024-12-03T18:23:39.206862Z", + "iopub.status.idle": "2024-12-03T18:23:39.230317Z", + "shell.execute_reply": "2024-12-03T18:23:39.229617Z", + "shell.execute_reply.started": "2024-12-03T18:23:39.207125Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " coord_ra coord_dec g_kronFlux g_kronFlux_flag r_kronFlux \\\n", + "0 62.018897 -37.095671 71.568352 True 91.185588 \n", + "1 62.020999 -37.093227 174.729861 False 110.922305 \n", + "2 62.000430 -37.093196 131.680920 False 137.655812 \n", + "3 62.015568 -37.092868 372.665560 False 171.582869 \n", + "4 62.002969 -37.092762 247.219720 False 153.138653 \n", + "\n", + " r_kronFlux_flag i_kronFlux i_kronFlux_flag \n", + "0 True 624.454022 True \n", + "1 False 52.040203 True \n", + "2 False 136.174616 True \n", + "3 False 211.338418 True \n", + "4 True 184.829166 True \n", + " coord_ra coord_dec g_kronFlux g_kronFlux_flag r_kronFlux \\\n", + "11554 61.913511 -36.960012 158.939682 False NaN \n", + "11555 61.986424 -36.950292 125.535210 False 71.749436 \n", + "11556 61.942562 -36.951137 108.966860 False 135.301872 \n", + "... ... ... ... ... ... \n", + "11561 61.950427 -36.946586 51.054369 True 175.646973 \n", + "11562 61.976752 -36.904225 199.039503 False 187.972452 \n", + "11563 61.932319 -36.941077 266.123377 False 218.853195 \n", + "\n", + " r_kronFlux_flag i_kronFlux i_kronFlux_flag \n", + "11554 True 102.094474 True \n", + "11555 True NaN True \n", + "11556 False 191.964068 True \n", + "... ... ... ... \n", + "11561 False 123.073904 True \n", + "11562 False 115.825734 False \n", + "11563 False 481.650950 True \n", + "\n", + "[10 rows x 8 columns]\n", + " coord_ra coord_dec g_kronFlux r_kronFlux i_kronFlux\n", + "count 11564.000000 11564.000000 11447.000000 1.145300e+04 1.129200e+04\n", + "mean 61.999517 -37.001530 761.468963 1.368137e+03 2.071233e+03\n", + "std 0.062390 0.050868 13194.742739 2.395294e+04 3.258561e+04\n", + "... ... ... ... ... ...\n", + "50% 61.999039 -37.001694 182.963268 2.409297e+02 3.517374e+02\n", + "75% 62.049891 -36.960187 340.536311 4.811457e+02 7.569082e+02\n", + "max 62.124349 -36.900195 782163.585831 1.957761e+06 2.870998e+06\n", + "\n", + "[8 rows x 5 columns]\n" + ] + } + ], + "source": [ + "print(results.head())\n", + "print(results.tail(10))\n", + "print(results.describe())" + ] + }, + { + "cell_type": "markdown", + "id": "1b37fc18-e00b-4ef2-8d18-85d823d60d9e", + "metadata": {}, + "source": [ + "## 4. Visualize using `seaborn`\n", + "Let's look further into visualizing these statistics using `seaborn`'s boxplot tool." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "4fc9b578-2be4-4fb2-8d74-ebca809ea99f", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T18:55:30.244276Z", + "iopub.status.busy": "2024-12-03T18:55:30.243648Z", + "iopub.status.idle": "2024-12-03T18:55:30.835103Z", + "shell.execute_reply": "2024-12-03T18:55:30.834453Z", + "shell.execute_reply.started": "2024-12-03T18:55:30.244249Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8, 6))\n", + "sns.boxplot(data=results)\n", + "plt.title('Box Plot of Data Distributions')\n", + "plt.xlabel('Feature')\n", + "plt.ylabel('Value')\n", + "plt.xticks(rotation=90)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "ef5f20de-0fd6-4ba1-9cab-9d59cd05df99", + "metadata": {}, + "source": [ + "It looks like outliers are dominant in the visualization. Hide these and also only plot the kron Flux values." + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "bacf5114-6a64-4100-8eb6-f1d9ddc36f89", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T18:59:02.839663Z", + "iopub.status.busy": "2024-12-03T18:59:02.839254Z", + "iopub.status.idle": "2024-12-03T18:59:03.091807Z", + "shell.execute_reply": "2024-12-03T18:59:03.091120Z", + "shell.execute_reply.started": "2024-12-03T18:59:02.839637Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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79+7ddvPSPxPS9dCSk7ycxQLygiAClCJ//PGHFi9enOUBTWpqqkJDQyVd/yOV1cF3uscff9z2eMeOHZnm+/n5adiwYZKufxs4atQoff3115Kuf6uZ053gb9W4cePUokUL2782bdpoyJAhtoNwLy8vffLJJ9l+u5iV9E6orq6u6tChQ7bt2rdvb+swW5gdvQvyvTFbxstorly5ctP2Fy9eVHh4uI4dO2b7V7FiRUn2l94VBKvVqgsXLujUqVN260u/dKWg15cbGTvl5+b1Spc+NPfmzZvtznAUpo4dOxbYstq1a5ftJVeOjo7q3LmzpOtnLw8fPlxg6y0M6fthtWrVcvwCJuPZvZz23YcfftjuksSM3N3dbeH6xr46GS9PXblypaxW682LBwoZl2YBJUj//v01cOBAu2lXr15VZGSkfvvtN82ZM0crVqzQgQMHNHnyZHl5ednanT17VklJSZKuXwaTk7p168rR0VGpqamZLgVL161bN23btk0bNmzQ77//Lun69fnjx483ZOjYGjVqqF27durVq9ctf+ubvk133XVXtn/wJcnJyUl169ZVaGio/vnnn/yUm6OCfm/MlPFgOrtRj/bs2aOFCxfqzz//zPKsSbqCOsDetGmTfvrpJ+3Zs0cJCQnZtjPjcpWM9WT3emUlMDBQYWFhOn36tB5//HG1bdtWLVq00D333FNooyEV5L1OGjRokOv5R48evWl7syQnJ+v06dOSbr7vVqpUSb6+voqMjMxx373jjjtyXE76Ja83BteqVavK399fYWFhmj9/vrZv3662bdvK399fDRs2zHIkOqCwEUSAEs7FxUW33367XnzxRTVo0EBDhw7VsWPH9MUXX9jdXT3jAV/GgJIVR0dHeXh4KDo6OscDxREjRmjTpk22SycGDx6c47CSeXHjndXLli0rT0/PWzpou1H6NqV/856T9H4h8fHxslqthXL39sJ4b8ySMTxk1UfoxqFTc3L16tV81WK1WjVhwgStWLEiV+3Tw6CR0sOPg4PDLQeRiIgIzZ49WwkJCVq5cqVWrlwp6fqliq1atdITTzxRoP2E0vtUFISb7XsZ94Oi3J8h4z6Y2/9PIiMjc9ymmw10kX7JVlYjBU6YMEHvvPOO9uzZoxMnTujEiRP69ttv5eDgoHr16umRRx5R165d8/X/J3ArCCJAKRIQEKC77rpLR48e1S+//KK33347yz9quTmYzs1p/SVLlthdv71jxw67UbQKQlZ3Vi8oBfU6FKSiWNOtyHgZzY2hdOfOnbYQUq1aNT377LNq0qSJqlSpIldXVzk4OEiSvvnmG82cOTPftSxfvtwWQurUqaOePXuqYcOG8vHxUdmyZW3rGzNmjP73v//le323KiYmRufPn5eU+bXKjYEDB6pr165as2aNdu3apf379+vKlSuKjIzU4sWL9eOPP6pfv36ZzqLmVfrrVRAKI9CbrShsU6VKlTR9+nSFhoZqw4YNCgsL0/Hjx5WWlqa//vpLf/31l+bOnauPP/74pmdwgIJAEAFKmdtvv11Hjx5VamqqTp48aRvFKOO30xk7NWYlNTXV9k1fdiNf7d+/X99++62k65eUJCQk2O4h0r1794LYlEJToUIFXbhwQTExMTdtm97G3d290A40Cvq9MdP27dttj2+8z0F6h/8KFSro22+/zfYb5II605O+vho1aigkJCTbb5rNOrOU02uVW7fddpv69OmjPn36KC0tTYcOHdKGDRu0ZMkSJSQkKCQkRHXr1tVDDz1UQFUXjJvtexnn39ixOqczAhmlD0hRmDLug7n5/yR9/y7szuLNmjWz3cDx8uXL2rVrl1auXKlNmzYpOjpaI0aM0E8//ZTjpalAQaCzOlDKZLw5X8azFdWqVbMdiP399985LuPw4cO252Z1NuLKlSsaPXq00tLSVL58eX333Xe66667JElffvmljh8/nu/tKEzp23T06NEc70CfkpJi+4Y/fYjiwlCQ742ZYmJibEMQlytXLtNQuOmfi2bNmuV4GUv6EKT5lb6+Bx98MNsQYrVaTekMbbVatXDhQtvvbdu2zfcyHRwc1KBBAw0ZMkRffPGFbfqNN8gsCt/c3+xznj4inpR530vv63CzUcZOnTqV4/yCeB2cnZ1tg0tkrDkr0dHRtptrGrnvli9fXm3bttWnn35qG+zi/PnzRXLUPZQ8BBGgFLFarXYHcbfddpvtsaOjo+0bsrCwsBzvjrx06VLb44w3XUv38ccf254/YsQI1ahRQ+PHj5eLi4uuXr2qUaNGKSUlJb+bU2jSD5ATExOzvDN1ul9//dV2sJPV/SXSv03M77YW5HtjlrS0NI0dO9bWr+Pxxx/P9K1vekjO6Zvqw4cP66+//spxXbl93XOzvo0bN+rChQs5LqcwzJo1y7av1q1bt8Dfy8aNG9vC142d/jOOMJdTEC9Mv/76a7Z9ctLS0myXynl4eNjdVFX6v/vJJCQk6OTJk1kuw2q1au3atTnWkP465Pc1SH/vTp8+rbCwsGzbZRwC3Kx9N+OoXkaNtobSjSAClCI//vij7Ru3u+++2zbEZ7r0S6bS0tI0fvz4LP8Ab9myxXZdfd26dXXPPffYzf/111+1atUqSVKnTp1sw9/WqlXLdt+No0ePasqUKQW4ZQUrKCjINnTo119/rYiIiExtIiIibN8qu7i42A29mS69I/uZM2fyXVNBvDdmiYiI0JAhQ7Rt2zZJ10f96devX6Z26d8c79271zbSUEYXL17UmDFjbrq+3L7u6evbvHlzlp2Dz5w5o48//vim6ytI8fHxmjRpku0u7q6urho5cuQtL2f16tU53l9jz549tgP9qlWr2s3LOMxrTqG3MMXExGjSpElZzpsxY4btbEa3bt3k5ORkNz/jPTLmzp2b5TK+/fZbHTp0KMca0l+Hixcv5jia2s08+eSTtsvFPvzwwywv9Tt06JDmzJljW+/DDz+c5/Vl58iRIzc9u5dxGPIbPxdAYaCPCFCCZHVn9eTkZEVERGjDhg22y2LKlCmjIUOGZHr+Aw88oA4dOmjdunUKCwtTnz599Oyzz6p27dpKSEiw3Yfk2rVrcnJy0rvvvmv3/H///Vfvv/++pOsj87zxxht287t3766tW7dqy5Ytmjdvnu6//35D71SdW56envrPf/6j999/X9HR0erTp4+ef/5523X6e/fu1Zw5c2wHr6+++qrt4Dejxo0bKzQ0VAcOHNCcOXN0//332wKOi4uLKleunOua8vveFKYbP3dJSUmKi4vT8ePHFRYWpq1bt9rOPtxxxx2aNGmS3N3dMy2nc+fO2rRpkxITExUcHKznn3/edmfuffv2ad68eYqOjlajRo20f//+bOvJ7eveuXNnffnllzp//rz69eun559/XrVr19bVq1e1a9cuLViwQCkpKapbt+5ND1pzKzEx0e61SklJ0eXLl3X27Fnt379fv/32m+2gt3z58powYUKehsV977339OWXX6p169Zq3LixatSoIRcXF128eFG7d+/Wjz/+KOn65VrdunWze26VKlVsd3T//vvvVblyZfn5+dkOpr28vAp9VKX69etr6dKlioiI0JNPPilfX19duHBBK1as0IYNGyRd/z+mb9++mZ5bp04d3XPPPdq7d69WrFihlJQUBQYGqkKFCoqIiNCqVau0adMmW5vsNG7cWNL1viYffPCBevToYTcUeE7388modu3aev755zV79mydOHFCvXv3Vu/evVW/fn0lJydrx44d+uGHH5SUlCSLxaJ33nnnlu57lFtHjhzRuHHjbHeor1u3rry9vWW1WnXu3DmtXbvWNtR63bp1i+yQyChZCCJACbJkyRItWbIkxzZubm566623sj31P3r0aF27dk3r16/XsWPHNG7cuExtypcvr/fff9/ukgir1ar33ntPcXFxcnBw0Lhx47I82Bw1apR69eqlmJgYjRs3Tj/88EORvIvv448/rvj4eE2ZMkWXLl3SV199lamNg4ODgoOD9dRTT2W5jCeffFJLlixRXFycJk+erMmTJ9vm+fv72771zq28vjeFLTefu/Lly6tbt24aMGBAtv0x2rVrp6CgIK1YsULnz5/Xp59+ajffwcFBw4YNU1xcXI5BJLeve8+ePbVjxw7t2LFD4eHhmjBhgt1yXFxcNGbMGG3ZsqXAgsjBgwf1zDPP5NjG0dFRrVu31n/+8x+7yydvVUxMjH7++Wf9/PPPWc53cXHRu+++axuwIqMXXnhBH330kSIiIjLd4Xz06NEKDAzMc125ERwcrHnz5mn79u1Z3iz0tttu01dffZXtvS9GjRqll156SdHR0VqzZo3WrFljN79jx44KCgrSyy+/nG0NzZs3V8OGDfXXX39p7dq1mS7lupWbmA4aNEhJSUlasGCBIiMj9dFHH2Vq4+LionfeeUetWrXK9XLz4tChQzl+nmvXrq2PPvqoSPQVQslHEAFKOEdHR1WoUEG33367AgICFBQUZHfpxY2cnZ313//+V0FBQVq+fLn279+vixcvysXFRdWqVVPLli3Vs2fPTDcJ/P777213/37hhReyvSzIy8tLI0eO1GuvvaaoqCi9//77+uCDDwpsewtS79691apVKy1cuFC7du1SVFSUpOt3rW7evLl69OiRY6fSypUra/bs2Zo9e7bCwsJ0/vz5fN37Iq/vjZHKlCmjcuXKyc3NTZUrV1bdunXVuHFjtW7d+qb3P5CuH0A2b95cP//8s44ePaqUlBR5e3urSZMm6tGjhxo0aKDp06fnuIzcvu6Ojo767LPPtGTJEq1evVonTpyQ1WpV5cqVde+996pnz566/fbbtWXLljy/Hjfj6uoqNzc3VaxYUXXq1FGDBg3Upk2bHPfR3Fi8eLF27typP//8U+Hh4YqJidHly5fl6uqqGjVqqEWLFnryySdVpUqVLJ//1FNPydvbWz/99JOOHDmiuLg4u4EuCpuTk5M+//xzLV26VKtXr9bJkyd19epVVa1aVW3bttVzzz2X5Rcd6WrWrKm5c+dq9uzZ2rJli6KiolSuXDndddddevzxx9W+fXvb/1fZKVOmjL766ivNnTtXmzZt0tmzZ5WYmJin4bEtFotee+01tW/fXkuWLNHu3bsVExMjBwcHValSRQEBAXrmmWeyfT8KwqOPPipfX1/t3LlTe/bsUVRUlGJiYpSamioPDw/VqVNHbdu2VZcuXQy56SwgSZbY2NiiO+A8AAAAgBKJzuoAAAAADEcQAQAAAGA4gggAAAAAwxFEAAAAABiOIAIAAADAcAQRAAAAAIYjiAAAAAAwHEEEAAAAgOEIIgAAAAAMRxABAAAAYDiCCAAAAADDEUQAAAAAGI4gAgAAAMBwBBEAAAAAhiOIAAAAADAcQQQAAACA4QgiAAAAAAxHEAEAAABgOIIIAAAAAMMRRAAAAAAYjiACAAAAwHAEEQAAAACGI4gAAAAAMBxBBAAAAIDhCCIAAAAADEcQAQAAAGA4gggAAAAAwxFEAAAAABjO0ewCAAAAUDBiY2N16dIls8soVTw8POTp6Wl2GcUSQQQAAKCE2Lhxo1asWGF2GaVKUFCQunbtanYZxZIlNjbWanYRAAAAyL/iekYkMjJSISEh6t+/v3x9fc0u55ZwRiTvOCMCAABQQnh6ehbrg2JfX1/5+fmZXQYMQmd1AAAAAIYjiAAAAAAwHEEEAAAAgOEIIgAAAAAMRxABAAAAYDiCCAAAAADDEUQAAAAAGI4gAgAAAMBwBBEAAAAAhiOIAAAAADAcQQQAAACA4QgiAAAAAAxHEAEAAABgOEezC8iL//3vf9qzZ48OHjyoY8eOKSUlRaNHj1ZgYGCmttOnT1dISEiWy3F2dtbmzZuznLdmzRotWLBAx48fl5OTkxo1aqSBAweqfv36WbYPDw/X1KlTFRoaqsTERNWoUUPdunXTU089pTJlyHsAAABARsUyiEybNk2RkZHy9PRUpUqVFBkZedPndOnSRb6+vnbTHBwcsmw7a9YsTZ06VVWqVNETTzyhxMRErVu3TgMGDNCXX36pZs2a2bU/fvy4+vfvr6SkJD3yyCPy8fHRtm3b9Mknn+iff/7RO++8k/eNBQAAAEqgYhlE3n33XdWoUUO+vr6aM2eOJk+efNPnBAYGZgoQWQkPD9f06dNVs2ZNzZ49W+7u7pKkHj16qG/fvpo4caIWLVokR8f/e+k+/PBDxcfH67PPPlPLli0lSYMGDdKrr76qpUuXqkOHDmrevHketxYAAAAoeYrlNUMtWrTIdHajoKxcuVJpaWnq27evLYRIUu3atdW5c2edOXNGu3btsk0/deqUdu/erWbNmtlCiCQ5Ojpq0KBBkqSlS5cWSq0AAABAcVUsg0he7NmzR999951++OEHbd68WcnJyVm2Cw0NlSQFBARkmnffffdJksLCwmzT0h+nz8uoQYMGKl++vHbv3p3v+gEAAICSpFhempUX33zzjd3vlSpV0pgxYzIFjtOnT6tcuXKqVKlSpmXUqFHD1iZj+4zzMrJYLKpevboOHjyopKQklS1bNscak5KScrcxAAAAJUj6F8TJyckcDxVzNzvezajEB5E6depozJgx8vf3l5eXl6KiorRu3TrNnj1bw4cP18yZM1WnTh1b+/j4eHl5eWW5rPRLteLj4+3aZ5x3Izc3N1u7m70xERERSktLy/3GAQAAlADnzp2z+4niycHBQbVq1cp1+xIfRNq0aWP3e40aNdSvXz95e3vrv//9r7799lt98MEH5hR3g6pVq5pdAgAAgGmqVKmS5VUmKJlKfBDJTpcuXfThhx9q3759dtPd3d3tznhklNXZj6zOkmSUkJAg6f/OjOTkVk5lAQAAlBTOzs62nxwPlR6lprP6jZycnOTm5pbpOsQaNWroypUrunDhQqbnZNUfJKt+I+msVqvOnDkjHx8fubq6FmT5AAAAQLFWaoNIeHi44uLiMg0D7O/vL0nasWNHpuds377drk3Gx+nzMvr77791+fJlNW3atMDqBgAAAEqCEh1EEhISdPTo0UzT4+LiNGHCBElShw4d7OYFBgbKwcFBs2bNsrvc6tixY1q9erWqV69ud3NCPz8/NW3aVKGhodqyZYttempqqqZNmyZJ6tatW0FuFgAAAFDsFcs+IkuXLtXevXslXQ8IkrRs2TLbPUBat26tNm3a6NKlS3r22WdVr1493XnnnapYsaLOnz+vrVu36tKlSwoICFCvXr3slu3n56cBAwZo2rRp6tWrlx5++GElJiZq3bp1Sk1N1TvvvGN3V3VJGjFihPr3768333xTjzzyiCpVqqRt27bpn3/+UdeuXbmrOgAAAHCDYhlE9u7dq1WrVmWalh5OfH191aZNG1WoUEHdu3fX/v37tWnTJl2+fFmurq6qXbu2OnXqpK5du8rBwSHT8l988UVVrVpV8+fP15IlS+Tk5KTGjRvrpZdeUv369TO1r1WrlmbNmqWpU6dq69atSkxMVPXq1fX666+re/fuhfMiAAAAAMWYJTY21mp2EQAAACi9Tp06pfHjx2vUqFHy8/MzuxwYpET3EQEAAABQNBFEAAAAABiOIAIAAADAcAQRAAAAAIYjiAAAAAAwHEEEAAAAgOEIIgAAAAAMRxABAAAAYDiCCAAAAADDEUQAAAAAGI4gAgAAAMBwBBEAAAAAhiOIAAAAADAcQQQAAACA4QgiAAAAAAxHEAEAAABgOIIIAAAAAMMRRAAAAAAYjiACAAAAwHAEEQAAAACGI4gAAAAAMBxBBAAAAIDhCCIAAAAADEcQAQAAAGA4gggAAAAAwxFEAAAAABiOIAIAAADAcAQRAAAAAIYjiAAAAAAwHEEEAAAAgOEIIgAAAAAMRxABAAAAYDiCCAAAAADDEUQAAAAAGI4gAgAAAMBwBBEAAAAAhiOIAAAAADAcQQQAAACA4QgiAAAAAAxHEAEAAABgOIIIAAAAAMMRRAAAAAAYjiACAAAAwHAEEQAAAACGI4gAAAAAMBxBBAAAAIDhCCIAAAAADEcQAQAAAGA4gggAAAAAwxFEAAAAABiOIAIAAADAcAQRAAAAAIYjiAAAAAAwHEEEAAAAgOEIIgAAAAAMRxABAAAAYDiCCAAAAADDEUQAAAAAGI4gAgAAAMBwBBEAAAAAhiOIAAAAADAcQQQAAACA4QgiAAAAAAxHEAEAAABgOIIIAAAAAMMRRAAAAAAYjiACAAAAwHAEEQAAAACGI4gAAAAAMBxBBAAAAIDhCCIAAAAADEcQAQAAAGA4gggAAAAAwxFEAAAAABiOIAIAAADAcAQRAAAAAIYjiAAAAAAwHEEEAAAAgOEIIgAAAAAMRxABAAAAYDiCCAAAAADDEUQAAAAAGI4gAgAAAMBwBBEAAAAAhiOIAAAAADAcQQQAAACA4QgiAAAAAAxHEAEAAABgOIIIAAAAAMMRRAAAAAAYjiACAAAAwHAEEQAAAACGczS7gLz43//+pz179ujgwYM6duyYUlJSNHr0aAUGBmbZPj4+XjNmzNCGDRsUHR0tb29vtW3bVgMGDJC7u3uWz1mzZo0WLFig48ePy8nJSY0aNdLAgQNVv379LNuHh4dr6tSpCg0NVWJiomrUqKFu3brpqaeeUpky5D0AAAAgo2J5hDxt2jT9/PPPOnfunCpVqpRj28TERAUHB2v+/Pny8/PTM888ozvuuEPz589XcHCwEhMTMz1n1qxZGj16tGJiYvTEE0/okUce0d69ezVgwACFhoZman/8+HG98MIL2rhxo+677z716NFDkvTJJ5/ogw8+KJiNBgAAAEqQYnlG5N1331WNGjXk6+urOXPmaPLkydm2nTt3ro4cOaLevXtr6NChtunTp09XSEiI5s6dq4EDB9qmh4eHa/r06apZs6Zmz55tO2PSo0cP9e3bVxMnTtSiRYvk6Ph/L92HH36o+Ph4ffbZZ2rZsqUkadCgQXr11Ve1dOlSdejQQc2bNy/olwEAAAAotorlGZEWLVrI19f3pu2sVquWLVumcuXKqX///nbz+vTpowoVKmj58uWyWq226StXrlRaWpr69u1rd9lW7dq11blzZ505c0a7du2yTT916pR2796tZs2a2UKIJDk6OmrQoEGSpKVLl+Z1UwEAAIASqVgGkdwKDw/X+fPn1bhxY7m6utrNc3FxUZMmTRQVFaXTp0/bpqdfehUQEJBpeffdd58kKSwszDYt/XH6vIwaNGig8uXLa/fu3fnfGAAAAKAEKZaXZuVWesCoUaNGlvNr1qxpa5fxcbly5bLse5K+nIzBJad1WCwWVa9eXQcPHlRSUpLKli2bY71JSUk32yQAMN2lS5cUFxdndhmlSoUKFeTh4WF2GUChSU5Otv3keKh4u9nxbkYlOojEx8dLUrYjY7m5udm1S3/s5eWVZfv05dzYPrfruNkbExERobS0tBzbAIDZNm/erC1btphdRqnSsmVLtWrVyuwygEJz7tw5u58onhwcHFSrVq1cty/RQaS4qVq1qtklAMBNde7cuVgeFJ87d05z5sxRnz59VKVKFbPLuSWcEUFpUaVKlWyvZEHJU6KDSFZnMDJKSEiwa5f+OLv2WZ39yO060s+M5ORWTmUBgFnKli2r2267zewybpmzs7Ok65fl+vn5mVwNgIzS909nZ2eOh0qREt1ZPas+HRmFh4fbtUt/fOXKFV24cCFT+6z6g+S0DqvVqjNnzsjHxydTZ3kAAACgNCvRQaRmzZry8fHRvn37Mt248OrVq9qzZ498fHzsgoW/v78kaceOHZmWt337drs2GR+nz8vo77//1uXLl9W0adP8bwwAAABQgpToIGKxWNS1a1dduXJFISEhdvPmzJmjuLg4de3aVRaLxTY9MDBQDg4OmjVrlt3lVseOHdPq1atVvXp1u5sT+vn5qWnTpgoNDbXrvJmamqpp06ZJkrp161ZIWwgAAAAUT8Wyj8jSpUu1d+9eSdcDgiQtW7bMdg+Q1q1bq02bNpKk3r17648//rDdYb1u3bo6evSotm7dqjp16qh37952y/bz89OAAQM0bdo09erVSw8//LASExO1bt06paam6p133rG7q7okjRgxQv3799ebb76pRx55RJUqVdK2bdv0zz//qGvXrtxVHQAAALhBsQwie/fu1apVqzJNSw8nvr6+tiDi6uqqadOmacaMGfrtt98UGhoqb29vPfPMMxowYECWfTdefPFFVa1aVfPnz9eSJUvk5OSkxo0b66WXXlL9+vUzta9Vq5ZmzZqlqVOnauvWrUpMTFT16tX1+uuvq3v37gX/AgAAAADFnCU2NtZqdhEAABS2U6dOafz48Ro1ahSjZgFFDPtn6VSi+4gAAAAAKJoIIgAAAAAMRxABAAAAYDiCCAAAAADDEUQAAAAAGI4gAgAAAMBwBBEAAAAAhiOIAAAAADAcQQQAAACA4QgiAAAAAAxHEAEAAABgOIIIAAAAAMMRRAAAAAAYjiACAAAAwHAEEQAAAACGI4gAAAAAMBxBBAAAAIDhCCIAAAAADEcQAQAAAGA4gggAAAAAwxFEAAAAABiOIAIAAADAcAQRAAAAAIYjiAAAAAAwHEEEAAAAgOEIIgAAAAAMRxABAAAAYDiCCAAAAADDEUQAAAAAGI4gAgAAAMBwBBEAAAAAhiOIAAAAADAcQQQAAACA4QgiAAAAAAxHEAEAAABgOIIIAAAAAMMRRAAAAAAYjiACAAAAwHAEEQAAAACGI4gAAAAAMBxBBAAAAIDhHM0uAAAAoKiJjo5WfHy82WWUGpGRkXY/YQx3d3d5e3ubtn6CCAAAQAbR0dEaOWqkUpJTzC6l1AkJCTG7hFLFydlJE8ZPMC2MEEQAAAAyiI+PV0pyinwe8ZKTl5PZ5QCFIiUmRefXxyg+Pp4gAgAAUJQ4eTnJxcfZ7DKAEovO6gAAAAAMRxABAAAAYDiCCAAAAADDEUQAAAAAGI4gAgAAAMBwBBEAAAAAhiOIAAAAADAcQQQAAACA4QgiAAAAAAyX7zurh4eHa/78+dq1a5eioqKUnJysbdu22eYvX75cUVFR6tWrl8qVK5ff1QEAAAAoAfIVRNauXasJEyYoJSVFVqtVkmSxWOzaxMXFKSQkRLfffrseeeSR/KwOpUhsbKwuXbpkdhmlioeHhzw9Pc0uAwAAlBJ5DiJHjhzR2LFjZbVa1b17d7Vt21ZffPGFDh8+bNeuXbt2+uqrr/THH38QRJBrGzdu1IoVK8wuo1QJCgpS165dzS4DAACUEnkOIt99952uXbumYcOG6emnn5YkOTs7Z2rn6+srLy8v/fPPP3mvEqVO69at1aRJE7PLuGWRkZEKCQlR//795evra3Y5t8TDw8PsEgAAQCmS5yCyZ88eubm52UJITipXrqzIyMi8rgqlkKenZ7G+TMjX11d+fn5mlwEAAFBk5XnUrIsXL6patWq5W0mZMkpMTMzrqgAAAACUMHkOIu7u7oqOjs5V2zNnznDZBwAAAACbPAeROnXqKDo6OlPn9Btt2rRJcXFxatiwYV5XBQAAAKCEyXMQ6dy5s6xWq/773/9me2bk+PHj+vDDD2WxWBQYGJjnIgEAAACULHnurN6xY0etWrVKf/75p5555hk9+OCDioqKkiQtXLhQ+/bt08aNG5WSkqKHHnpIrVq1KrCiAQAAABRveT4jYrFY9NFHH6ldu3a6dOmSVq5cqXPnzslqteqzzz7T+vXrlZKSonbt2mn8+PEFWTMAAACAYi5fd1YvV66c/vvf/+rAgQNav369jh49qsuXL8vV1VV33nmnHnnkEd1zzz0FVSsAAACAEiJfQSRd/fr1Vb9+/YJYFAAAAIBSIM+XZgEAAABAXhFEAAAAABguz5dmDRo06JbaWywWTZkyJa+rAwAAAFCC5DmIhIWF3bSNxWKRJFmtVttjAAAAAMhzEBk1alS285KSkhQeHq5169YpPj5e/fv3V6VKlfK6KgAAAAAlTJ6DSG7ulD5w4ECNHDlSP//8s+bOnZvXVQEAAAAoYQq1s7q7u7tGjhyp8+fPa8aMGYW5KgAAAADFSKGPmlWpUiXVqlVLf/zxR2GvCgAAAEAxYcjwvcnJyYqOjjZiVQAAAACKgUIPIv/8849Onz4tT0/Pwl4VAAAAgGIiz53Vz507l+08q9WqmJgY7d+/X99//72sVqtatmyZ11UBAAAAKGHyHES6deuWq3ZWq1XVqlXTSy+9lNdVAQAAAChh8hxErFZrjvNdXV1Vo0YNPfjgg+rVq5fc3d3zuioAAAAAJUyeg8iOHTsKsg4AAAAApYgho2YBAAAAQEYEEQAAAACGI4gAAAAAMFyu+ojkdoSsnFgsFv3888/5Xg4AAACA4i9XQSQyMjLfK7JYLPleBgAAAICSIVdBZOrUqYVdBwAAAIBSJFdBxN/fv7DrAAAAAFCK0FkdAAAAgOEIIgAAAAAMl+c7q2d08eJFHT58WJcuXVJqamq27bp06VIQqwMAAABQzOUriJw7d04fffSRtm3bJqvVetP2ZgWRrl27Zjvy1+OPP663337bblp8fLxmzJihDRs2KDo6Wt7e3mrbtq0GDBggd3f3LJezZs0aLViwQMePH5eTk5MaNWqkgQMHqn79+gW+PQAAAEBxl+cgEhsbqwEDBigqKko+Pj66cuWKrly5onvuuUeXLl3SqVOndO3aNbm4uKhBgwYFWXOeuLu7q2fPnpmm16tXz+73xMREBQcH68iRIwoICFCHDh109OhRzZ8/X6GhoZoxY4ZcXV3tnjNr1ixNnTpVVapU0RNPPKHExEStW7dOAwYM0JdffqlmzZoV6rYBAAAAxU2eg8j333+vqKgodevWTW+//bYGDBig/fv365tvvpEkXbp0SfPmzdN3332nmjVrZjrrYLTy5ctr4MCBN203d+5cHTlyRL1799bQoUNt06dPn66QkBDNnTvXbjnh4eGaPn26atasqdmzZ9vOmPTo0UN9+/bVxIkTtWjRIjk6FshVcAAAAECJkOfO6lu2bJGTk5MGDx6c5XwPDw8NGjRI//nPf7Rs2TKtXLkyz0UaxWq1atmyZSpXrpz69+9vN69Pnz6qUKGCli9fbncZ2sqVK5WWlqa+ffvaXbZVu3Ztde7cWWfOnNGuXbsM2wYAAACgOMhzEImIiJCvr688PDwk/d+d02/srN6jRw95eHho6dKlea+yACQnJ2vlypWaNWuWfvzxRx05ciRTm/DwcJ0/f16NGzfOdPmVi4uLmjRpoqioKJ0+fdo2PTQ0VJIUEBCQaXn33XefJCksLKwgNwUAAAAo9vJ1vVDGMwDpB+6xsbGqVKmSbbrFYpGvr69OnDiRn1XlW3R0tMaNG2c37f7779fYsWPl6ekpSbaAUaNGjSyXUbNmTVu7jI/LlStnt83p0peTMbjkJCkpKVftUHQlJyfbfvJ+AkUL+ydyK/2zApQGBf1/YtmyZXPdNs9BxMfHRzExMbbfq1SpIkk6fPiw3UH5tWvXFBkZaepOHRQUJH9/f9WqVUtOTk46ceKEQkJCtHXrVr3++usKCQmRxWJRfHy8JGU7Mpabm5sk2dqlP/by8sqyffpyMrbPSUREhNLS0nK9XSh6zp07Z/cTQNHB/onc4jOC0qQgP+8ODg6qVatWrtvnOYjccccd2r59u1JTU+Xo6Ch/f38tXbpUM2bMUKNGjVShQgVJ0rRp0xQbG6u77747r6vKtxv7ezRs2FCTJk3SSy+9pL1792rLli1q1aqVSdX9n6pVq5pdAgpIlSpVsj2zBsBc7J8A8H/M/D8xz0GkZcuW+uOPP/Tnn3/q/vvvV9u2beXr66tDhw4pKChIt99+u6Kjo3XhwgVZLBZ17969IOvOtzJlyigoKEh79+7Vvn371KpVq5uewUhISJBkf8bE3d092/Y3O8Nyo1s5lYWiydnZ2faT9xMoWtg/kVvpnxWgNDDz/8RcB5FJkyYpKChId911lySpTZs2SklJsXVWd3Z21meffaa33npLJ0+e1KFDh66vwNFRffr0UVBQUCGUnz/pfUPSr4u7WZ+O8PBwu3bpj/fv368LFy5k6idysz4nAEq36OjoXF+6ifxLv7Ftdje4ReFwd3eXt7e32WUAKIJyHUQWLlyoRYsWqU6dOgoKCtKjjz6a6SzHHXfcoQULFujvv/9WRESEypYtq0aNGqlixYoFXnhB+OuvvyRJvr6+kq53Rvfx8dG+ffuUmJhoN3LW1atXtWfPHvn4+NgFC39/f+3fv187duzIdOf47du329oAQEbR0dEaNXKkklNSzC6l1AkJCTG7hFLF2clJ4ydMIIwAyCTXQeTOO+/UP//8o8OHD+vIkSP68ssv1bp1awUGBiogIMA2fK/FYlHDhg3VsGHDQiv6Vhw/flw+Pj4qX7683fQ9e/Zo/vz5cnZ2Vtu2bSVdr71r164KCQlRSEiI3Q0N58yZo7i4OPXv39+2rZIUGBio77//XrNmzVLr1q1tl2EdO3ZMq1evVvXq1dW8eXMDthRAcRIfH6/klBR1r+0iH1fLzZ8AFEPnE61afOyq4uPjCSIAMsl1EPnhhx905MgRLV++XOvWrdOlS5f0yy+/aP369fLx8VFgYKC6dOmi6tWrF2a9t2z9+vWaO3eu7r33Xvn6+srZ2VnHjh3Tjh07VKZMGY0YMcI24pck9e7dW3/88YftDut169bV0aNHtXXrVtWpU0e9e/e2W76fn58GDBigadOmqVevXnr44YeVmJiodevWKTU1Ve+88w53VQeQLR9Xi6q5OZhdBlBIGAkSQPZu6Qi5Tp06Gj58uP7zn/9o06ZNWr58ubZv366oqCjNmjVLs2bNUtOmTRUUFKSHH364SHQGbN68uU6ePKnDhw9r9+7dunr1qry8vNS+fXs988wzatCggV17V1dXTZs2TTNmzNBvv/2m0NBQeXt765lnntGAAQMy3ehQkl588UVVrVpV8+fP15IlS+Tk5KTGjRvrpZdeUv369Y3aVAAAAKDYyNNX9Y6Ojmrbtq3atm2r6OhorV69WqtWrdKJEycUFham3bt36+OPP1b79u0VGBioxo0bF3Tduebv73/LfTTc3d01bNgwDRs2LNfP6dixozp27Hir5QEAAAClUpn8LsDb21u9e/fWggUL9O233+rxxx+Xu7u7rly5omXLlmngwIHq0aOH5s6dWxD1AgAAACgB8h1EMmrQoIHeeustrV69WuPHj1eLFi1ksVh06tQpTZ48uSBXBQAAAKAYK9Agks7JyUnly5dXhQoV6KgNAAAAIJMCTQmnTp3SypUr9b///U8XLlyQJFmtVlWuXFmdO3cuyFUBAAAAKMbyHUTi4+P1yy+/aMWKFTpw4ICk6+HD2dlZDz74oIKCgnTffffZ3XsDAAAAQOmWpyBitVq1Y8cOrVy5Un/88YeSk5NltVolyXbn9Y4dO6pChQoFWiwAAACAkuGWgsipU6e0atUqrV692u7SKw8PDz366KMKCgpSnTp1CqVQAAAAACVHroNI//799ddff0m6Hj7KlCmjgIAABQYGqk2bNnRKBwAAAJBruU4P+/fvlyRVr15dgYGBCgwMlI+PT6EVBgAAAKDkynUQ6dKli4KCgtS0adPCrAcAAABAKZDrIDJ69OjCrAMAAABAKVIoNzQEAAAAgJwQRAAAAAAYjiACAAAAwHAEEQAAAACGI4gAAAAAMBxBBAAAAIDhCCIAAAAADEcQAQAAAGA4gggAAAAAwxFEAAAAABiOIAIAAADAcI5mFwAAAFAUJV9MMbsEoNAUhc83QQQAACALF36JMbsEoEQjiJRw0dHRio+PN7uMUiMyMtLuJ4zh7u4ub29vs8sAUMJUau8l54pOZpcBFIrkiymmh22CSAkWHR2tkSNHKSUl2exSSp2QkBCzSyhVnJycNWHCeMIIgALlXNFJLj7OZpcBlFgEkRIsPj5eKSnJcqj6iCzOXmaXAxQKa3KMUiLWKz4+niACAEAxQhApBSzOXrK4+phdBgAAAGDD8L0AAAAADEcQAQAAAGA4gggAAAAAwxFEAAAAABiOIAIAAADAcAQRAAAAAIYjiAAAAAAwHEEEAAAAgOEIIgAAAAAMRxABAAAAYDiCCAAAAADDEUQAAAAAGI4gAgAAAMBwBBEAAAAAhiOIAAAAADAcQQQAAACA4QgiAAAAAAznaHYBAFCanU+8ZnYJQKHh8w0gJwQRADDR4mPJZpcAAIApCCIAYKLutZ3l48pVsiiZzideI2wDyBZBBABM5ONaRtXcHMwuAwAAw/E1HAAAAADDEUQAAAAAGI4gAgAAAMBwBBEAAAAAhiOIAAAAADAcQQQAAACA4QgiAAAAAAzHfUQAAACykBKTYnYJQKEpCp9vgggAAEAG7u7ucnJ20vn1MWaXAhQqJ2cnubu7m7Z+gkgpYL160ewSgELD5xtAQfP29taE8RMUHx9vdimlRmRkpEJCQtS/f3/5+vqaXU6p4e7uLm9vb9PWTxApBdIifzG7BAAAihVvb29TD9BKK19fX/n5+ZldBgxCECkFHHzby+JS0ewygEJhvXqRsA0AQDFEECkFLC4VZXH1MbsMAAAAwIbhewEAAAAYjiACAAAAwHAEEQAAAACGI4gAAAAAMBxBBAAAAIDhGDULAEx0PtEqKc3sMoBCcf3zDQBZI4gAgAnc3d3l7OSkxceuml0KUKicnZzk7u5udhkAiiCCCACYwNvbW+MnTFB8fLzZpZQakZGRCgkJUf/+/eXr62t2OaWGu7s7dygHkCWCCACYxNvbmwM0E/j6+srPz8/sMgCg1KOzOgAAAADDEUQAAAAAGI4gAgAAAMBw9BEpBazJMWaXABQaPt8AABRPBJESzN3dXU5OzkqJWG92KUChcnJyZnhQAACKGYJICebt7a0JE8YzPKiBGB7UHAwPCgBA8UMQKeEYHtQcDA8KAACQMzqrAwAAADAcQQQAAACA4QgiAAAAAAxHEAEAAABgOIIIAAAAAMMRRAAAAAAYjiACAAAAwHAEEQAAAACGI4gAAAAAMBxBBAAAAIDhCCIAAAAADEcQAQAAAGA4gggAAAAAwxFEAAAAABjO0ewCSpIDBw5o+vTp2r9/v1JSUlSrVi317NlTHTt2NLs0AAAAoEghiBSQ0NBQvfLKK3JyclL79u3l7u6uDRs2aPTo0YqMjFTfvn3NLhEAAAAoMggiBSA1NVUTJ06UxWLRN998o7vvvluS1L9/f/Xr10/Tp09Xu3btVLNmTZMrBQAAAIoG+ogUgF27dunMmTN69NFHbSFEktzc3NSvXz+lpaVp5cqVJlYIAAAAFC0EkQIQFhYmSQoICMg0L31aehsAAAAAXJpVIMLDwyVJNWrUyDSvQoUK8vT01OnTp2+6nKSkpAKvDcZKTk62/eT9BIoW9k+g6GL/LDnKli2b67YEkQKQkJAgSXJ3d89yvpubm6Kiom66nIiICKWlpRVobTDWuXPn7H4CKDrYP4Gii/2zZHBwcFCtWrVy3Z4gUoRUrVrV7BJQQKpUqZLlGTIA5mP/BIou9s/ShSBSANzc3CRJ8fHxWc5PSEjI9mxJRrdyKgtFk7Ozs+0n7ydQtLB/AkUX+2fpRGf1ApA+LG9W/UDi4uIUGxtLugcAAAAyIIgUgKZNm0qSduzYkWle+jR/f39DawIAAACKMoJIAbj33ntVrVo1rV27VkeOHLFNT0hI0MyZM+Xg4KAuXbqYWCEAAABQtNBHpAA4Ojrq3Xff1SuvvKKBAweqQ4cOcnNz04YNGxQREaHg4GD5+fmZXSYAAABQZBBECkjz5s01Y8YMTZ8+XevXr1dKSopq1aql4OBgdezY0ezyAAAAgCKFIFKAGjRooC+++MLsMgAAAIAijz4iAAAAAAxHEAEAAABgOIIIAAAAAMMRRAAAAAAYjiACAAAAwHAEEQAAAACGI4gAAAAAMBxBBAAAAIDhCCIAAAAADEcQAQAAAGA4gggAAAAAwxFEAAAAABiOIAIAAADAcAQRAAAAAIYjiAAAAAAwHEEEAAAAgOEIIgAAAAAMRxABAAAAYDiCCAAAAADDEUQAAAAAGI4gAgAAAMBwBBEAAAAAhiOIAAAAADAcQQQAAACA4RzNLgAAULzExsbq0qVLZpdxyyIjI+1+FiceHh7y9PQ0uwwAKFAEEQDALdm4caNWrFhhdhl5FhISYnYJtywoKEhdu3Y1uwwAKFAEEQDALWndurWaNGlidhmlioeHh9klAECBI4gAAG6Jp6cnlwkBAPKNzuoAAAAADMcZERRJdIY1Hp1hAQCAkQgiKJLoDGs8OsMCAAAjEURQJNEZ1nh0hgUAAEYiiKBIojMsAABAyUZndQAAAACGI4gAAAAAMBxBBAAAAIDhCCIAAAAADEcQAQAAAGA4gggAAAAAwxFEAAAAABiOIAIAAADAcAQRAAAAAIYjiAAAAAAwHEEEAAAAgOEIIgAAAAAMRxABAAAAYDiCCAAAAADDEUQAAAAAGI4gAgAAAMBwBBEAAAAAhiOIAAAAADAcQQQAAACA4QgiAAAAAAxHEAEAAABgOIIIAAAAAMMRRAAAAAAYjiACAAAAwHAEEQAAAACGI4gAAAAAMBxBBAAAAIDhCCIAAAAADEcQAQAAAGA4gggAAAAAwxFEAAAAABiOIAIAAADAcAQRAAAAAIYjiAAAAAAwHEEEAAAAgOEIIgAAAAAMRxABAAAAYDiCCAAAAADDEUQAAAAAGI4gAgAAAMBwBBEAAAAAhiOIAAAAADAcQQQAAACA4QgiAAAAAAxHEAEAAABgOIIIAAAAAMMRRAAAAAAYjiACAAAAwHAEEQAAAACGI4gAAAAAMBxBBAAAAIDhCCIAAAAADEcQAQAAAGA4gggAAAAAwxFEAAAAABiOIAIAAADAcAQRAAAAAIZzNLsAAAAAFIzY2FhdunTJ7DJuWWRkpN3P4sTDw0Oenp5ml1EsWWJjY61mF1GYxo4dq1WrVmU5z8/PT4sXL840/dq1a/rxxx+1dOlSnT59Wq6urmrWrJkGDRqkmjVrZrmsAwcOaPr06dq/f79SUlJUq1Yt9ezZUx07dizQ7QEAAMjOsmXLtGLFCrPLKFWCgoLUtWtXs8solkrNGZGePXvK3d3dblp26fWDDz7Q0qVLdccdd6h79+6KiYnR+vXrtWPHDoWEhKhWrVp27UNDQ/XKK6/IyclJ7du3l7u7uzZs2KDRo0crMjJSffv2LazNAgAAsGndurWaNGlidhmlioeHh9klFFul5ozI0qVLVbVq1Zu237VrlwYPHqwmTZro66+/lrOzsyRp586dGjp0qJo0aaJvvvnG1j41NVU9evRQVFSUZs6cqbvvvluSlJCQoH79+unUqVNauHBhtmdSAAAAgNKIzuo3WLp0qSQpODjYFkIkqUWLFrrvvvu0e/dunTp1yjZ9165dOnPmjB599FFbCJEkNzc39evXT2lpaVq5cqVh9QMAAADFQam5NGvLli26cuWKnJycdOedd6pZs2ZycHDI1C4sLEyurq665557Ms277777tG3bNu3evVt+fn629pIUEBCQqX36tPQ2AAAAAK4rNUHk448/tvu9Zs2amjBhgurWrWublpiYqAsXLqh27dpZhpQaNWpIksLDw23T0h+nz8uoQoUK8vT01OnTp3NVY1JSUq7aAQAAAEVR2bJlc922xAcRf39/PfTQQ6pfv748PT0VGRmpn376SYsXL9bQoUM1b948+fj4SJLi4+MlKVOn9nRubm6Srvf/SJf+OKfnREVF5arWiIgIpaWl5W7DAAAAgCLEwcEh06BOOSkWQaR9+/a3NCb21KlT1axZM0nXh1TL6Pbbb9drr72msmXLavbs2Zo/f75eeeWVAq03r3LTmR4AAAAoCYpFEOnQoYOuXLmS6/be3t43bdO1a1fNnj1be/futU1LP6uRfmbkRulnP9LPjGR8nNNzsjtbcqNbOZUFAAAAFGfFIoi88cYbBb7M9DGfM/bLcHV1VaVKlWyXSN3YTyS9r0fGoXjTH58+fVr16tWzax8XF6fY2Fg1bty4wOsHAAAAirNSO3zv33//LUny9fW1m+7v76/ExES7MyXptm/fLklq2rSpbVr64x07dmRqnz7N39+/YIoGAAAASogSHUQuXLigM2fOZJoeFRWlTz/9VJL06KOP2s3r1q2bJGnatGlKSUmxTd+5c6e2b9+upk2b2obulaR7771X1apV09q1a3XkyBHb9ISEBM2cOVMODg7q0qVLQW4WAAAAUOyV6Durh4aGavDgwbrnnnt0++23q0KFCoqMjNTmzZuVmJioLl26aPTo0bJYLHbPmzhxopYtW6Y77rhDLVu2VExMjNavXy9nZ2eFhIRkGg1g165deuWVV+Ts7KwOHTrIzc1NGzZsUEREhIKDg/Xiiy8audkAAABAkVeig8i///6rkJAQ/f3334qKirJ1HK9bt64ee+wxtW/fPsvnXbt2TYsXL9bPP/+sM2fOyNXVVc2aNdOgQYPszoZk9Pfff2v69Onav3+/UlJSVKtWLT3zzDPq2LFjYW4iAAAAUCyV6CACAAAAoGgq0X1EAAAAABRNBBEAAAAAhiOIAAAAADAcQQQAAACA4QgiAAAAAAxHEAEAAABgOIIIAAAAAMMRRAAAAAAYjiACAAAAwHAEEQAAAACGI4gAAAAAMBxBBAAAAIDhCCIAAAAADEcQAQAAAGA4gggAAAAAwxFEgEKWlJRkdgkAABQpaWlpuWoXExNTyJXATAQRII9GjBihy5cv59jm8OHD6tOnj0EVAcho4cKFN22TkJCgMWPGGFANgIwGDBigs2fP5thm8+bN6tWrl0EVwQwEESCPfv/9d/Xq1UthYWFZzv/hhx/Uv39/RUREGFwZAEmaNGmShg0bposXL2Y5/++//9bzzz+vtWvXGlwZgAMHDqh3795avXp1pnkpKSn65JNPNHz48FyfOUHxRBAB8mjUqFGKj4/Xyy+/rClTptj+s4yOjtbQoUP11VdfydfXVzNnzjS5UqB06ty5s7Zu3apnn31W27Zts5s3a9YsDRw4UFFRURo+fLhJFQKl19dff61y5cpp3LhxGjlypOLj4yVJx44dU58+fbR48WI1bdpU33//vcmVojBZYmNjrWYXARRXp0+f1qhRo3Tw4EE1aNBAXbt21dSpU3Xx4kV169ZNw4YNU9myZc0uEyi1fvnlF33wwQdKSEjQ008/raeeekoTJ07U7t27Vbt2bU2YMEG1atUyu0ygVIqLi9OECRO0ceNG+fr6qmPHjpo3b57S0tI0cOBAPf/887JYLGaXiUJEEAHyKS0tTV999ZXmz58vi8Uid3d3jR49Wg899JDZpQGQdO7cOY0ePVr79u2TJFksFnXv3l1Dhw6Vk5OTydUBmDVrlqZNmyaLxSIPDw99/vnnqlevntllwQBcmgXk08mTJ7Vjxw7b71euXNHRo0dltZLxgaLA09NTNWrUkNVqldVqVfny5dW6dWtCCFAE7Ny5Uz/++KMkydXVVZcuXdKSJUsYcbKUIIgA+fDjjz+qb9++OnXqlIKDgzV//nzVrl1bM2bM0EsvvaR///3X7BKBUu3IkSN6/vnntWrVKgUEBOjNN99USkqKhgwZYte3C4CxUlNT9eWXX+rVV19VYmKixo0bp59//ln333+/VqxYoeeee04HDx40u0wUMi7NAvJo+PDh2rx5s6pWrarx48erQYMGkq7/5/rVV19p4cKFcnNz04gRI9ShQweTqwVKn3nz5mnq1KmyWq0aNGiQnn32WUnSmTNnNHr0aP3999+qX7++xo8fr+rVq5tcLVC69OnTR4cOHVLDhg01fvx4Va1a1TZv4cKF+vrrr3Xt2jUNHDiQYfBLMIIIkEcBAQHq2LGjRowYoXLlymWav337do0dO1YXL17U9u3bTagQKN0CAgLk5+enCRMmqE6dOnbz0tLS9M0332ju3LkqW7asNmzYYFKVQOl0//3364UXXlD//v3l4OCQaf4///yjUaNG6cSJE/wNLcEIIkAerVmzRh07dsyxzcWLFzVhwgR9+umnBlUFIN3EiRP1+uuv5zhyXVhYmN577z0tX77cwMoAhIWFyd/fP8c2ycnJ+uKLL/TGG28YVBWMRhABAJRqly9fVvny5c0uAwBKHTqrAwBKNUIIAJjD0ewCgOJq/PjxuW47atSoQqwEQFZWrVqV67ZdunQpxEoA3GjQoEG5amexWDRlypRCrgZm4dIsII8CAgJynG+xWGS1WmWxWOhoB5ggICDgpndlZh8FzMHfUEgEESDPIiMjs5weHx+vw4cPa9asWapTp46GDh1qNywhAGOsXLkyy+kJCQk6dOiQ1q5dq4ceekitWrVSYGCgwdUByEr639ApU6bIx8dHEydOzHJULZQMBBGgkERHR6tXr17q37+/unfvbnY5AG6wb98+vfzyy/r000/VokULs8sBkEFCQoJ69eqloKAg9e/f3+xyUEjorA4UEm9vb7Vq1UqLFy82uxQAWWjcuLEefPBBTZ8+3exSANzAzc3Ndpd1lFwEEaAQubm5ZXsJFwDzValSRUePHjW7DABZKFOmjKKjo80uA4WIIAIUksuXL2vjxo3y8vIyuxQAWbBardqzZ49cXFzMLgXADc6ePatff/1VVapUMbsUFCKG7wXyKCQkJMvpaWlpioqK0qZNmxQXF6d+/foZXBkA6fqdm7OSlpam8+fPa/Xq1Tpw4IA6depkcGUAshsCPzU1VefPn9fevXuVmpqqAQMGGFwZjERndSCPbjb0YLly5dS9e3cNGjTopkOIAih4Nxu+12q1qlGjRvr000/l4eFhYGUAbvY3tGbNmurVq5cef/xxgyqCGQgiQB5l922rxWJRhQoV5OfnJ0dHTjoCZpk+fXqWQaRMmTIqX7686tWrp0aNGplQGYDs+k+WKVNG7u7ucnNzM7gimIEgAgAAAMBwdFYHAAAAYDiuGwFyKbtLsXLD39+/ACsBAKB4WbVqVZ6f26VLlwKsBEUJl2YBuXSzjq852b59ewFXA+BGed1HLRaLtm3bVggVAUiXl/3TarXKYrHwN7QE44wIkEv9+vVj9CugCGvatCn7KFBEjRo1yuwSUARxRgQAAACA4eisDgAAAMBwBBHgFoSEhOSr0zqAwhUWFqZz586ZXQaALIwfP15//PGH3bSUlBTFx8ebVBHMRhABbsGMGTMyBZE5c+bokUceMakiABkNHjxYK1eutJv2yy+/6M033zSpIgDpVq5cqSNHjthNmz17Nn9DSzGCCJBPycnJfJsDFBFWa+ZujydPnsz0LSwAwHwEEQAAAACGI4gAAAAAMBxBBAAAAIDhuKEhcIuioqL0999/2/0uSQcOHMjy+nRJatCggSG1ARA3NQSKsGPHjumXX36x+12S1q9fn+3f0Pbt2xtSG4zHDQ2BWxAQEJDlQY7Vas3x4Gf79u2FWRaA/y8gIEAODg5ycHCwTUtLS9O1a9fk5OSU5XMsFgud2QEDZPU3ND185PS3lb+hJRdnRIBb0KVLF7NLAJCDKlWqmF0CgGz079/f7BJQxHBGBAAAAIDh6KwOAAAAwHAEEQAAAACGo48IkA/Hjx/X4sWLdeDAAcXHxystLS1TG4vFop9//tmE6gCkpKTo999/18GDB3X58mVdu3Yty3ajRo0yuDIAO3fu1Lx583TgwAFdvnw5y1GzLBaLtm3bZkJ1MAJBBMijsLAwvfrqq0pOTpaDg4O8vLzsRupJl91whAAKV2RkpIYMGaKzZ8/muB9aLBaCCGCw3377Te+++66uXbumKlWqyM/PT46OHJaWNrzjQB59/fXXSk1N1bvvvqsuXbpkGUIAmOezzz7TmTNn1KlTJz322GOqXLky+ylQRISEhMjFxUUff/yx7r33XrPLgUkIIkAeHT16VB06dNBjjz1mdikAsrBr1y7de++9eu+998wuBcANwsPD1alTJ0JIKUdndSCP3NzcVLFiRbPLAJANq9WqOnXqmF0GgCx4enqqbNmyZpcBkxFEgDxq2bKl9uzZY3YZALLRsGFDnTx50uwyAGShXbt22rlzp1JTU80uBSYiiAB5NHToUMXHx+uTTz5RUlKS2eUAuMGQIUMUGhqqX3/91exSANxg0KBBqlChgt59912dO3fO7HJgEu6sDuTRoEGDFB8fr6NHj8rV1VU1atSQm5tbpnYWi0VTpkwxoUKgdAsJCdGBAwe0detWNW3aVHfffbfc3d0ztbNYLOrXr58JFQKlV7du3ZSamqoLFy5Iktzd3bPdPxkCv+QiiAB5FBAQkKt2FotF27dvL+RqANyIfRQourp27ZrrtsuWLSvESmAmgggAoEQKCwvLdVt/f/9CrAQAkBWCCAAAAADDcR8RoIAkJiYqISFBbm5ucnV1NbscAACKhdTUVIWHhys+Pl5ubm7cZb0U4V0G8iE1NVVz587VypUrdfbsWdv0atWqKTAwUM8995ycnJxMrBDAvn37tHLlSh05csR2oHP33Xerc+fOatKkidnlAaVWXFycvv76a61du1ZXr161TXdxcdGjjz6qwYMHy9PT07wCUei4NAvIo6SkJA0dOlT79+9XmTJlVL16dXl7eysmJkZnzpxRWlqaGjRooMmTJ3PTJsAkX3zxhebPny+r9fqfujJlyujatWuSrndSf/rppzVs2DAzSwRKpbi4OPXr10/h4eHy8PBQvXr1bH9DDx48qNjYWNWoUUMzZ86Uh4eH2eWikHBGBMijuXPnat++fXr00Uf18ssv67bbbrPNO3/+vL7++mutWbNGc+fO1YABA0ysFCidVq1apXnz5un2229X//795e/vbzvQCQ0NVUhIiBYuXKg6deqoS5cuZpcLlCozZ85UeHi4+vTpoxdffNHuC7ukpCTNnj1bs2bN0rfffsuXBSUYZ0SAPOrRo4fKlSun2bNnZ9vmhRde0JUrV7Ro0SLjCgMgSXrxxRd14cIFzZ8/P8t7/MTHx6tXr16qVKmSvv32WxMqBEqvbt26qWrVqjneZ+vll1/W2bNntXTpUuMKg6G4szqQR5GRkWrRokWObe69915FRkYaVBGAjI4fP662bdtmGUKk6zdQa9OmjY4fP25wZQAuXLighg0b5timQYMGthseomQiiAB55OLioosXL+bY5uLFi3JxcTGoIgA3Su8bkh2LxWJQJQAycnd317lz53Jsc+7cuSzvto6SgyAC5FGjRo30yy+/6NixY1nOP378uNavX69GjRoZXBkASapVq5Y2bNigK1euZDk/ISFBGzZsUK1atQyuDIC/v79+/fVX7dy5M8v5O3fu1K+//srNRks4+ogAebRv3z4FBwfLwcFBjz32mPz9/eXl5aWYmBiFhYVpxYoVSk1N1dSpU3XPPfeYXS5Q6qxcuVLjx49XrVq1NGDAAPn7+8vT01OxsbG2zuonTpzQyJEjFRgYaHa5QKly/Phx9e3bV1evXtUDDzxg9zc0NDRU27ZtU9myZTVz5kzVrl3b7HJRSAgiQD789ttvmjhxouLj4+0u8bBarXJ3d9c777yjdu3amVghULpNmjRJCxcutO2fFovFdrmW1WpVjx499Prrr5tZIlBq7du3T2PHjtWZM2ck2e+f1atX1+jRo/kir4QjiAD5dOXKFW3cuFGHDx+23Vn97rvv1kMPPZRtJ1kAxtmzZ49WrFiho0eP2vbR9CF7mzZtanZ5QKlmtVq1d+/eTH9D77nnHvpwlQIEESCPQkJCVK1aNXXq1MnsUgBkISwsTO7u7qpTp47ZpQC4wfjx43XnnXfqmWeeMbsUmIjO6kAeffvtt/rnn3/MLgNANgYPHsz9B4Aiau3atYqJiTG7DJiMIALkka+vr+Li4swuA0A2KlasKEdHR7PLAJCF6tWrc48QEESAvOrQoYO2b9+u+Ph4s0sBkIX77rtPu3fvvum9RAAY77HHHtOWLVsUFRVldikwEX1EgDxKSUnRm2++qejoaA0cOFD169eXl5eX2WUB+P/Onz+vfv36KSAgQEOGDJGHh4fZJQH4/yIiIvTxxx/r2LFj6t27t+1vaFYd1KtUqWJChTACQQTIo/vuu0/S9RE/chrZw2KxaNu2bUaVBeD/GzRokC5duqTjx4/LyclJVatWzfLLAovFoilTpphQIVB6BQQE2Ibr5W9o6cXFs0AeNWnShKEFgSIsLCzM9jg5OVknT57UyZMnM7VjPwaM17lzZ/Y9cEYEAAAAgPHorA7k0bVr13LVjuEJgaItt/sygIKTlJSUq3anTp0q5EpgJoIIkEcTJ068aZuYmBgNHjzYgGoA3Cg39xBJS0vTqFGjCr8YAHbeeustpaWl5djm1KlTevnllw2qCGYgiAB5tHLlSk2ePDnb+bGxsRo0aJDCw8MNrApAug8//FAbN27Mdr7VatXo0aP166+/GlgVAEnatm2bxo0bl+3806dPa/Dgwbp8+bKBVcFoBBEgj7p37665c+dq/vz5meZlDCGjR482oToADRs21MiRI7V79+5M86xWq0aNGqX169friSeeMKE6oHQbMmSI1qxZo88//zzTvDNnzmjQoEG6fPmyPv30U+OLg2EIIkAeDR8+XI888oi+/PJLrVmzxjY9PYScPHlSY8aMUceOHU2sEii9PvvsM1WvXl3Dhw/XP//8Y5tutVo1ZswY/fLLL3r88cf15ptvmlglUDr17t1bvXr10oIFCzRnzhzb9LNnzyo4OFhxcXGaNGmSmjdvbmKVKGyMmgXkQ2pqqoYNG6awsDB98sknqlevngYPHqwTJ05o9OjR6tSpk9klAqXa+fPn1b9/f6WmpmrGjBny9fXV6NGjtW7dOnXr1k1vv/222SUCpdqYMWO0du1ajRw5Uk2bNtVLL72kS5cu6dNPP1WLFi3MLg+FjCAC5FNiYqKCg4N16tQp3XbbbQoPD9fIkSPVpUsXs0sDoOsdXgcMGKDy5curbt26Wr9+vbp27ap33nnH7NKAUi8tLU3Dhw/Xjh075OHhofj4eH3yyScKCAgwuzQYgCACFIDY2FgNGDBAZ86c0bvvvqvAwECzSwKQwYEDB/Tyyy8rMTFRQUFBevfdd80uCcD/l5SUpJdffllHjhwhhJQyBBEglwYNGpTj/JiYGEVHR+uuu+6ym26xWDRlypTCLA2ApJCQkBzn7969W0eOHFH37t1Vpsz/dZG0WCzq169fYZcHlGrdunXLcf7Vq1d15coVVaxY0W66xWLRzz//XIiVwUwEESCX8voNjcVi0fbt2wu4GgA3Yh8Fiq6uXbvm+bnLli0rwEpQlBBEAAAlQlhYWJ6f6+/vX4CVAABygyACGOzo0aM6cuQIndmBIio+Pl7x8fGqUqWK2aUAuEFYWJjCwsLUv39/s0tBAeA+IoDBfv/9d40fP97sMgBkY/78+Te9nh2AOUJDQ2/aHwzFB0EEAAAAgOEIIgAAAAAMRxABAAAAYDiCCAAAAADDEUQAAAAAGI4gAgAAAMBwBBEAAAAAhiOIAAAAADAcQQQwmK+vr5o2bWp2GUCJd99992nUqFG3/Dyr1Sqr1VoIFQHIrzp16qhz585ml4ECYomNjeV/WyAPcnN3dIvFIjc3N/n5+alVq1aqXLmyAZUBkKR27drp8ccf15AhQ8wuBSj10v9mDh48WN7e3rn6G5ouL18ooHggiAB5FBAQIIvFIklZfntqsVjspjs4OKhfv37q16+fYTUCpdnQoUNVpkwZffHFF2aXApR66X8zFy5cKD8/PwUEBOTqeRaLRdu3by/k6mAWggiQR2fPntVnn32mAwcO6Omnn1bjxo3l5eWlmJgY7du3TwsXLlT9+vX14osv6siRI5o1a5b+/fdfjR8/Xu3btze7fKDE279/v4KDg/X2228rMDDQ7HKAUi0yMlKS5OPjI0dHR9vvueHr61tYZcFkBBEgj+bMmaMFCxbohx9+kJeXV6b5Fy5c0HPPPadevXrp+eefV1RUlJ5++mnVqVNH33zzjQkVA6VLSEiI9u7dqz///FN16tRRgwYN5OXlZTuTmc5isXCmEgBM4Gh2AUBxtXz5crVr1y7LECJJlSpVUrt27bRs2TI9//zzqly5slq1aqUtW7YYXClQOs2YMcP2+PDhwzp8+HCW7QgiAGAOggiQR1FRUXJ2ds6xjYuLi6Kiomy/V6lSRcnJyYVdGgBJU6dONbsEAEAOCCJAHvn4+Gjjxo0KDg7OMpAkJydr48aN8vHxsU2LiYlR+fLljSwTKLX8/f3NLgEAkAPuIwLk0WOPPaYzZ84oODhYmzdv1qVLlyRJly5d0qZNm/TSSy/p7NmzCgoKsj1nz549uuuuu8wqGQAAoMjgjAiQR71799aJEye0Zs0aDR8+XJL9kL1Wq1UdO3ZUnz59JEnR0dFq2bKl7r//ftNqBgAAKCoYNQvIp507d2rNmjX6559/lJCQIDc3N91111169NFH1aJFC7PLAwAAKJIIIgAAAAAMRx8RAAAAAIYjiAAAAAAwHEEEAAAAgOEIIgAAAAAMRxABAAAAYDjuIwIAyLfg4GCFhYXdtN3OnTsNqMbe5cuXNX/+fEnSwIEDDV8/ACBrBBEAQIG57bbbVKVKFbPLsHP58mWFhIRIIogAQFFCEAEAFJigoCAO9gEAuUIfEQAAAACG44wIAMAUe/bs0eLFi7V3715dvHhRrq6uqlu3rp544gk9/PDDmdqnpKRo8+bN2rx5sw4cOKDz588rKSlJ3t7eatq0qXr37q3atWvbPWfs2LFatWqV7fcWLVrYzR89erQCAwMVERGhbt26Scq+H8v06dMVEhKiLl26aMyYMbbpNz538+bNWrhwoQ4dOqRLly7po48+Ups2bSRJ165d09q1a7V69WodPnxY8fHxqlixoq3+u++++1ZfRgAotggiAADDff311/ruu+8kSe7u7rrjjjsUHR2tnTt3aufOnXriiSf01ltv2T0nPDxcI0aMUJkyZVSxYkX5+voqJSVF586d0+rVq7V+/Xp98MEHatWqle05NWvWVL169XTw4EFJ0j333GO3TC8vrwLdrh9++EFffPGFPDw8VK1aNZUtW9Y2LyEhQSNGjLAFHW9vb9WuXVtnzpzRunXr9Ouvv2rMmDHq2LFjgdYEAEUVQQQAYKgff/xR3333nTw9PfXGG2+offv2tnk7duzQmDFj9NNPP6lhw4YKDAy0zfP09NTYsWP1wAMPyMPDwzY9OTlZy5Yt06RJkzRu3DgtX77cFgD69u2rRx991HbGYsaMGYW6bZMnT9Zrr72m7t27y8HBQZJ09epVSdLEiRO1c+dO3X333Xr77bdVv359SdfPkixatEiff/65JkyYoHr16snPz69Q6wSAooA+IgCAAhMSEqIWLVpk+e/3339XUlKSpk+fLun6ZVMZQ4gkBQQEaMSIEZKkOXPm2M3z9vZWp06d7EKIJDk7O6t79+5q3769YmNjtWnTpkLcwpw99thj6tmzpy2ESJKLi4v+/vtvrV+/XhUqVNCkSZNsIUSSypQpo549e+qpp55ScnKy5s2bZ0bpAGA4zogAAApMTsP3enh4aNeuXYqNjZWvr6/uv//+LNs9+OCDcnR01KlTp3T+/Hn5+PjYzd+5c6e2bt2q8PBwJSQk6Nq1a5Kkc+fOSZIOHz6cKeAY5bHHHsty+q+//irp+rbduD3pHn74YS1atEi7du0qtPoAoCghiAAACszNhu+dNWuWJCk+Pl4DBgzItp3FYpEkRUVF2Q7cr1y5ohEjRmjHjh051nDp0qVbLbvA3HHHHVlOP3r0qCQpLCws2+1Ov4QrKiqqcIoDgCKGIAIAMMzly5dtP/fu3XvT9klJSbbHX3zxhXbs2CFPT0+9/PLLatasmSpVqmTrD/LNN99o5syZSk1NLZzic8HV1TXL6XFxcZKkyMhIRUZG5riM9EACACUdQQQAYJj0A/XWrVvr448/zvXzUlNTtXbtWknSmDFj1LJly0xt8nMmJP0MjCRZrVa739NlDEW3qly5cpKk1157TT179szzcgCgJKGzOgDAMHfeeack6a+//rL17ciN2NhYXblyRZLUpEmTLNvs27cvy+lZhYobZTyTER0dnWWb8PDwmy4nO+n3N8nNWSAAKC0IIgAAw7Ro0ULly5dXdHS0li5dmuvnZbwfx4ULFzLN37lzp44cOXLT52Z3VsPT01MVKlSQJO3fvz/T/LNnz2r79u25rvdGjzzyiCRp48aNOnbsWJ6XAwAlCUEEAGAYNzc3DRo0SJL06aefat68eZnCQVxcnFavXq0vv/zSNs3d3V133XWX7XnpfS4kadeuXRo5cqRcXFyyXKenp6fc3d1tbbOTfiPEadOm2fXjOHPmjN55551bOoNzoyZNmqhdu3ZKTU3VK6+8ok2bNslqtdq1iYiI0Ny5c7Vs2bI8rwcAihP6iAAADPXUU0/p0qVLmj59uj7//HNNnTpVfn5+cnJy0sWLFxUZGSmr1Sp/f3+75w0dOlTDhg3T9u3bFRQUpJo1a+ry5cuKiIhQnTp11KJFC33//feZ1mexWNSpUyctXrxYw4cPV61atWxnP/r06WMbRnjgwIHasmWLTpw4oSeffFJ+fn66du2aTp48qbvuuks9evTI1z0+xowZo5SUFP3xxx96/fXXVaFCBVWvXl3Xrl1TVFSUYmJiJEn9+/fP8zoAoDjhjAgAwHD9+vXT3Llz1bVrV1WuXFmnTp3S8ePH5ejoqPvvv1/Dhw/X2LFj7Z5z3333acqUKWrRooUsFotOnjwpZ2dnvfjiiwoJCbG7BOtGr7zyivr27auaNWvq9OnTCgsLU1hYmF1/kKpVq2rmzJlq37693N3dFR4erpSUFPXp00chISG2Dud5VbZsWX388cf69NNP1aZNG7m4uOjo0aOKiIhQxYoV1aFDB02YMEG9evXK13oAoLiwxMbGWm/eDAAAAAAKDmdEAAAAABiOIAIAAADAcAQRAAAAAIYjiAAAAAAwHEEEAAAAgOEIIgAAAAAMRxABAAAAYDiCCAAAAADDEUQAAAAAGI4gAgAAAMBwBBEAAAAAhiOIAAAAADAcQQQAAACA4f4fY/O9dMwRJX8AAAAASUVORK5CYII=", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8, 6))\n", + "sns.boxplot(data=results[['g_kronFlux','r_kronFlux','i_kronFlux']], showfliers=False)\n", + "plt.title('Box Plot of Data Distributions')\n", + "plt.xlabel('Feature')\n", + "plt.ylabel('Value')\n", + "plt.xticks(rotation=90)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "75d6336b-1068-46cf-8105-b945fd18020e", + "metadata": {}, + "source": [ + "Boxplots show a box and whiskers.\n", + "- The \"box\" is the interquartile range (IQR), which is the 25th percentile of the distribution of a value to the 75th percentile.\n", + "- The horizontal line inside the box is the median of the distribution.\n", + "- The whisker extends from the IQR to 1.5*IQR away from the edge of the box.\n", + "- Points outside the whisker are considered outliers (hidden here).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "39521ac6-0bec-42e7-9062-8fc9ce5edc55", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T19:11:16.903575Z", + "iopub.status.busy": "2024-12-03T19:11:16.902993Z", + "iopub.status.idle": "2024-12-03T19:11:17.202209Z", + "shell.execute_reply": "2024-12-03T19:11:17.201547Z", + "shell.execute_reply.started": "2024-12-03T19:11:16.903550Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_638/2438356921.py:3: FutureWarning: \n", + "\n", + "The `bw` parameter is deprecated in favor of `bw_method`/`bw_adjust`.\n", + "Setting `bw_method=0.2`, but please see docs for the new parameters\n", + "and update your code. This will become an error in seaborn v0.15.0.\n", + "\n", + " sns.violinplot(data=filtered_results,\n" + ] + }, + { + "data": { + "image/png": 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qpNSlMb1URA3F4EEU4LZu3Ypvv/3W5QmMJElITk4GUPVHydXJtt0dd9zhuL179+4ajyclJeGpp54CUHW17/nnn8eHH34IoOqqZV0rrTfUSy+9hMGDBzv+GzlyJB5//HHHSXdMTAz+85//1Hr10BV70ajBYMCNN95Y63Y33HCDo8DVk4XZ7vzZ+Fr1YTGVlZVX3L6oqAgZGRk4c+aM47/o6GgAzkPp3EFRFOTn5+PcuXNOx7MPRXH38eqjehF9fV4vO/tU2b///rtTD4Yn3XzzzW7b15gxY2odQqVWq3HrrbcCqOqdPHnypNuO6wn292Hbtm3rvOBSvfeurvfu6NGjnYYYVhcWFuYI05fX2lQfbrpmzRooinLlxhN5EIdaETVz06dPx6OPPup0n9lsRnZ2Nn799Vd8/vnnWL16NY4dO4aPPvoIMTExju0uXLgAk8kEoGpYS126du0KtVoNSZJqDO2ymzhxInbu3InNmzfjt99+A1A1vv7ll1/2ylSu7dq1w5gxY3Dfffc1+Kqu/TldffXVtf6BBwCNRoOuXbsiOTkZp0+fbkpz6+Tun40vVT95rm1WogMHDuCbb77B3r17XfaK2LnrhHrbtm34/vvvceDAAVRUVNS6nS+Gn1RvT22vlyvjxo1DSkoKMjMzcccdd2DUqFEYPHgw+vTp47HZity51kiPHj3q/fipU6euuL2vWCwWZGZmArjye7dFixZo06YNsrOz63zvdujQoc792IewXh5U4+Pj0b9/f6SkpGDp0qXYtWsXRo0ahf79+6Nnz54uZ4oj8iQGD6IApNPp0L59ezz88MPo0aMH/vrXv+LMmTN4//33nVYvr36CVz2QuKJWqxEZGYmCgoI6TwyffvppbNu2zTEU4s9//nOd0zw2xuUrl+v1ekRFRTXoJO1y9udkv7JeF3tdR3l5ORRF8cjq6J742fhK9bDgqsbn8qlM62I2m5vUFkVR8Morr2D16tX12t4e/rzJHnZUKlWDg0dWVhYWL16MiooKrFmzBmvWrAFQNfTw2muvxZ133unWOh97TYQ7XOm9V/194M/1CNXfg/X9PMnOzq7zOV1pYgr7ECxXM/m98soreOaZZ3DgwAGkp6cjPT0dn332GVQqFbp164axY8diwoQJTfr8JKovBg+iADdkyBBcffXVOHXqFH7++Wf8v//3/1z+EavPyXN9uulXrFjhNP569+7dTrNcuYOrlcvdxV2vgzv5Y5saovqwmMtD6J49exyho23btrj//vvRt29ftG7dGgaDASqVCgDwySef4NNPP21yW1atWuUIHZ07d8a9996Lnj17Ii4uDnq93nG8OXPmYP369U0+XkMVFhYiLy8PQM3Xqj4effRRTJgwARs2bMC+fftw+PBhVFZWIjs7G99++y2+++47PPLIIzV6SRvL/nq5gycCvK/5w3Nq0aIF5s+fj+TkZGzevBkpKSlIS0uDzWbDkSNHcOTIESxZsgRvv/32FXtoiJqKwYMoCLRv3x6nTp2CJEk4e/asY5ah6lefqxchuiJJkuNKXm0zUx0+fBifffYZgKohIhUVFY41PO6++253PBWPiYiIQH5+PgoLC6+4rX2bsLAwj51YuPtn40u7du1y3L58nQF7gX5ERAQ+++yzWq8Qu6snx368du3aYeHChbVeSfZVz1Fdr1V9tWrVCtOmTcO0adNgs9lw4sQJbN68GStWrEBFRQUWLlyIrl274vrrr3dTq93jSu+96o9fXghd1xX/6uwTSHhS9fdgfT5P7O9vTxd3DxgwwLFgYllZGfbt24c1a9Zg27ZtKCgowNNPP43vv/++zqGmRE3F4nKiIFB9MbzqvRFt27Z1nHgdPXq0zn2cPHnS8b2uehsqKysxe/Zs2Gw2hIeH44svvsDVV18NAPjggw+QlpbW5OfhSfbndOrUqTpXeLdarY4r+PYpgz3BnT8bXyosLHRMCRwSElJjalr778WAAQPqHJZinxK0qezHu+6662oNHYqi+KR4WVEUfPPNN46vR40a1eR9qlQq9OjRA48//jjef/99x/2XL0jpD1fmr/R7bp+xDqj53rPXKlxpFrBz587V+bg7XgetVuuYDKJ6m10pKChwLGbpzfdueHg4Ro0ahf/+97+OySny8vL8clY8CiwMHkQBTlEUp5O2Vq1aOW6r1WrHFbCUlJQ6Vx9euXKl43b1Rc7s3n77bcf3P/3002jXrh1efvll6HQ6mM1mPP/887BarU19Oh5jPyE2Go0uV362++WXXxwnN67Wd7BfLWzqc3Xnz8ZXbDYbXnzxRUddxh133FHjqq49FNd1JfrkyZM4cuRInceq7+ten+Nt2bIF+fn5de7HExYtWuR4r3bt2tXtP8vevXs7wtblRfrVZ4CrK3h70i+//FJrTY3NZnMMfYuMjHRaxBT4Yz2XiooKnD171uU+FEXBTz/9VGcb7K9DU18D+88uMzMTKSkptW5XfUpuX713q8+65a3Z0Ch4MXgQBbjvvvvOcUWtS5cujik37exDoGw2G15++WWXf3C3b9/uGBfftWtX9OnTx+nxX375BWvXrgUA3HLLLY7paDt27OhY9+LUqVP4+OOP3fjM3Gv8+PGOqTw//PBDZGVl1dgmKyvLcdVYp9M5TYVpZy88P3/+fJPb5I6fja9kZWXh8ccfx86dOwFUzcrzyCOP1NjOfmX44MGDjpmAqisqKsKcOXOueLz6vu724/3+++8ui3nPnz+Pt99++4rHc6fy8nK88847jlXSDQYDnnvuuQbvZ926dXWub3HgwAHHiX18fLzTY9WnXa0r5HpSYWEh3nnnHZePLViwwNFbMXHiRGg0GqfHq69RsWTJEpf7+Oyzz3DixIk622B/HYqKiuqc7exK7rrrLsfwrzfffNPl0L0TJ07g888/dxx39OjRjT5ebVJTU6/Ye1d9WvDLfy+I3I01HkTNnKuVyy0WC7KysrB582bHMBdRFPH444/X+P5rrrkGN954IzZu3IiUlBRMmzYN999/Pzp16oSKigrHOiCyLEOj0eDZZ591+v6LFy/i9ddfB1A1c84///lPp8fvvvtu7NixA9u3b8fXX3+NYcOGeXUl6PqKiorC//3f/+H1119HQUEBpk2bhj/96U+OcfYHDx7E559/7jhZffLJJx0nu9X17t0bycnJOHbsGD7//HMMGzbMEWh0Oh1atmxZ7zY19WfjSZf/3plMJpSWliItLQ0pKSnYsWOHo3ehQ4cOeOeddxAWFlZjP7feeiu2bdsGo9GImTNn4k9/+pNj5etDhw7h66+/RkFBAXr16oXDhw/X2p76vu633norPvjgA+Tl5eGRRx7Bn/70J3Tq1Almsxn79u3DsmXLYLVa0bVr1yuepNaX0Wh0eq2sVivKyspw4cIFHD58GL/++qvjJDc8PByvvPJKo6apfeGFF/DBBx9gxIgR6N27N9q1awedToeioiLs378f3333HYCq4VcTJ050+t7WrVs7Vkz/8ssv0bJlSyQlJTlOnmNiYjw+61H37t2xcuVKZGVl4a677kKbNm2Qn5+P1atXY/PmzQCqPmMeeuihGt/buXNn9OnTBwcPHsTq1athtVoxbtw4REREICsrC2vXrsW2bdsc29Smd+/eAKpqRd544w1MnjzZaWruutbTqa5Tp07405/+hMWLFyM9PR1Tp07F1KlT0b17d1gsFuzevRtfffUVTCYTBEHAM88806B1h+orNTUVL730kmMF+K5duyI2NhaKoiAnJwc//fSTY+rzrl27+u0UxRQ4GDyImrkVK1ZgxYoVdW4TGhqKf//737V25c+ePRuyLGPTpk04c+YMXnrppRrbhIeH4/XXX3ca4qAoCl544QWUlpZCpVLhpZdecnly+fzzz+O+++5DYWEhXnrpJXz11Vd+uUruHXfcgfLycnz88ccoKSnB//73vxrbqFQqzJw5E5MmTXK5j7vuugsrVqxAaWkpPvroI3z00UeOx/r37++4ql1fjf3ZeFp9fu/Cw8MxceJEzJgxo9Z6ijFjxmD8+PFYvXo18vLy8N///tfpcZVKhaeeegqlpaV1Bo/6vu733nsvdu/ejd27dyMjIwOvvPKK0350Oh3mzJmD7du3uy14HD9+HFOmTKlzG7VajREjRuD//u//nIZDNlRhYSF++OEH/PDDDy4f1+l0ePbZZx0TTFT34IMP4q233kJWVlaNFcRnz56NcePGNbpd9TFz5kx8/fXX2LVrl8vFOVu1aoX//e9/ta498fzzz+Oxxx5DQUEBNmzYgA0bNjg9fvPNN2P8+PH4y1/+UmsbBg4ciJ49e+LIkSP46aefagzNasiiobNmzYLJZMKyZcuQnZ2Nt956q8Y2Op0OzzzzDK699tp677cxTpw4Uefvc6dOnfDWW2/5Ra0PBTYGD6IApFarERERgfbt22PIkCEYP36801CKy2m1Wrz22msYP348Vq1ahcOHD6OoqAg6nQ5t27bF8OHDce+999ZYlO/LL790rK794IMP1jrMJyYmBs899xz+9re/ITc3F6+//jreeOMNtz1fd5o6dSquvfZafPPNN9i3bx9yc3MBVK0KPXDgQEyePLnOItCWLVti8eLFWLx4MVJSUpCXl9ektSca+7PxJlEUERISgtDQULRs2RJdu3ZF7969MWLEiCuuPwBUnTAOHDgQP/zwA06dOgWr1YrY2Fj07dsXkydPRo8ePTB//vw691Hf112tVuPdd9/FihUrsG7dOqSnp0NRFLRs2RKDBg3Cvffei/bt22P79u2Nfj2uxGAwIDQ0FNHR0ejcuTN69OiBkSNH1vkerY9vv/0We/bswd69e5GRkYHCwkKUlZXBYDCgXbt2GDx4MO666y60bt3a5fdPmjQJsbGx+P7775GamorS0lKniSk8TaPR4L333sPKlSuxbt06nD17FmazGfHx8Rg1ahQeeOABlxc27BITE7FkyRIsXrwY27dvR25uLkJCQnD11VfjjjvuwA033OD4vKqNKIr43//+hyVLlmDbtm24cOECjEZjo6arFgQBf/vb33DDDTdgxYoV2L9/PwoLC6FSqdC6dWsMGTIEU6ZMqfXn4Q433XQT2rRpgz179uDAgQPIzc1FYWEhJElCZGQkOnfujFGjRuG2227zyiKvREJxcbH/Tv5OREREREQBgcXlRERERETkcQweRERERETkcQweRERERETkcQweRERERETkcQweRERERETkcQweRERERETkcQweRERERETkcQweRERERETkcQweRERERETkcQweRERERETkcQweRERERETkcQweRERERETkcQweRERERETkcQweRERERETkcQweRERERETkcQweRERERETkcQweRERERETkcQweRERERETkcQweRERERETkcQweRERERETkcQweRERERETkcQweRERERETkcQweRERERETkcQweRERERETkcQweRERERETkcQweRERERETkcQweRERERETkcQweRERERETkcQweRERERORVsiyjoKAAiqL4uinkRQweRERERORVhw4dwtNPP428vDxfN4W8iMGDiIiIiLzKYrE4/UvBgcGDiIiIiLxKkiQAgM1m83FLyJsYPIiIiIjIq+yBwx5AKDgweBARERGRV7HHIzgxeBARERGRV9kDB4NHcGHwICIiIiKvskpWABxqFWwYPIiIiIjIq6xWK0SNyOARZBg8iIiIiMirLJIFokbgUKsgw+BBRERERF5llawQ1OzxCDYMHkRERETkVTZJYo9HEGLwICIiIiKvskoSBI3AHo8gw+BBRERERF4lXQoe7PEILgweRERERORVkq0qeFisFl83hbyIwYOIiIiIvEq6VOPBoVbBhcGDiIiIiLxKskkQ1AIkG4NHMGHwICIiIiKvsl0KHvYVzCk4MHgQERERkVdJkq1q5XL2eAQVBg8iIiIi8iqbzQZBLcDGGo+gwuBBRERERF4ly5eCh8zpdIMJgwcREREReZXNJkNQcR2PYMPgQUREREReJdtsEFQCJAaPoMLgQUREREReZZPlqqFWDB5BhcGDiIiIiLxKkRUIKkCWZV83hbyIwYOIiIiIvEpWZAiiwOARZBg8iIiIiMirZPlScTlntQoqDB5ERERE5FWKrAAcahV0GDyIiIiIyKsURYEgClAUBo9gwuBBRERERF6lyMqlGg/F100hL2LwICIiIiKvUhQFEC79S0GDwYOIiIiIvKpqqBVrPIINgwcREREReZUCBRAE9ngEGQYPIiIiIvIq4dL/GDyCC4MHEREREfkEg0dwYfAgIiIiIq8TBAAMHkGFwYOIiIiIvMoeN2QGj6DC4EFERERE3lfV5eHrVpAXMXgQERERkU+wwyO4MHgQERERkXcpqJraiskjqDB4EBEREZFPMHYEFwYPIiIiIvIq5VLk4HS6wYXBg4iIiIi8SsCl2nIKKgweREREROQb7PEIKgweREREROR9Ams8gg2DBxERERF5lT1wsMYjuDB4EBEREZH3scgj6DB4EBEREZFXCfbQwR6PoMLgQUREREReplT7PwULBg8iIiIiIvI4ta8b0BhffPEFPvzwQwDAp59+il69etXYpry8HAsWLMDmzZtRUFCA2NhYjBo1CjNmzEBYWJjL/W7YsAHLli1DWloaNBoNevXqhUcffRTdu3d3uX1GRgbmzp2L5ORkGI1GtGvXDhMnTsSkSZMgisx0RERERK4oyuU3KBg0u7Pj9PR0zJ8/HwaDodZtjEYjZs6ciaVLlyIpKQlTpkxBhw4dsHTpUsycORNGo7HG9yxatAizZ89GYWEh7rzzTowdOxYHDx7EjBkzkJycXGP7tLQ0PPjgg9iyZQuGDh2KyZMnAwD+85//4I033nDfEyYiIiIKQAKn0w06zarHw2az4cUXX8TVV1+NxMRErF+/3uV2S5YsQWpqKqZOnYq//vWvjvvnz5+PhQsXYsmSJXj00Ucd92dkZGD+/PlITEzE4sWLHT0ikydPxkMPPYRXX30Vy5cvh1r9x8v15ptvory8HO+++y6GDx8OAJg1axaefPJJrFy5EjfeeCMGDhzoiZeBiIiIiKjZaVY9Hl988QVOnTqF5557rtahTIqi4Mcff0RISAimT5/u9Ni0adMQERGBVatWOc0bvWbNGthsNjz00ENOw7A6deqEW2+9FefPn8e+ffsc9587dw779+/HgAEDHKEDANRqNWbNmgUAWLlypTueMhEREVHA4joewaXZBI8zZ85g4cKFePjhh9GpU6dat8vIyEBeXh569+5dYziWTqdD3759kZubi8zMTMf99qFUQ4YMqbG/oUOHAgBSUlIc99lv2x+rrkePHggPD8f+/fsb8OyIiIiIggyX8Qg6zWKolSRJePHFF9G+fXtMmzatzm3tgaJdu3YuH09MTHRsV/12SEgIWrRoUWN7+36qB5W6jiEIAhISEnD8+HGYTCbo9fpa22oymep8LkRERESB6dJ0urLC86Fmrq5z3cs1i+CxePFinDp1CosWLXKqs3ClvLwcAGqduSo0NNRpO/vtmJgYl9vb93P59vU9Rl0/jKysLNhstlofJyIiIgpEslwVPCRJcrq4S82LSqVCx44d67293weP1NRUfPbZZ3jggQfQtWtXXzfHreLj433dBCIiIiKvE8WqcVZqtbrWUSoUePw+eLz44otISEjAjBkz6rW9qx6K6ioqKpy2s9+ubXtXvRv1PYa956M2DemaIiIiIgocguMfng8FD78PHqdOnQIAXHvttS4ff+SRRwAAb731FkaOHOmyJqO6jIwMAM71Ge3atcPhw4eRn59fo87DVT1HXcdQFAXnz59HXFxcnWuNEBEREQU7QWCFeTDx++Bx++23u7x///79yMzMxPXXX4+oqCjHsKXExETExcXh0KFDMBqNTif/ZrMZBw4cQFxcnFOQ6N+/Pw4fPozdu3fjtttuczrOrl27HNtU397+2OXF7kePHkVZWRmGDRvWhGdNRERERBRY/D54PPfccy7vf/HFF5GZmYlp06ahV69ejvsFQcCECROwcOFCLFy40GkBwc8//xylpaWYPn26U8IeN24cvvzySyxatAgjRoxwDKU6c+YM1q1bh4SEBKfFAJOSktCvXz8kJydj+/btjrU8JEnCvHnzAAATJ05022tARERERNTc+X3waIypU6di69atjhXMu3btilOnTmHHjh3o3Lkzpk6d6rR9UlISZsyYgXnz5uG+++7D6NGjYTQasXHjRkiShGeeeabGbFpPP/00pk+fjn/9618YO3YsWrRogZ07d+L06dOYMGECVy0nIiIiIqomIIOHwWDAvHnzsGDBAvz6669ITk5GbGwspkyZghkzZrisvXj44YcRHx+PpUuXYsWKFdBoNOjduzcee+wxdO/evcb2HTt2xKJFizB37lzs2LEDRqMRCQkJ+Pvf/467777bG0+TiIiIiKjZEIqLi7lWPRERERF5zf/980nE3BEJ8882vDrnVV83h7xE9HUDiIiIiCgI8dJ30GHwICIiIiKf4HS6wYXBg4iIiIiIPI7Bg4iIiIh8gv0dwYXBg4iIiIiIPI7Bg4iIiIiIPI7Bg4iIiIiIPI7Bg4iIiIiIPI7Bg4iIiIiIPI7Bg4iIiIiIPI7Bg4iIiIiIPI7Bg4iIiIh8QvF1A8irGDyIiIiIiMjjGDyIiIiIyCcUhX0ewYTBg4iIiIi8T/B1A8jbGDyIiIiIiMjjGDyIiIiIyCc41Cq4MHgQERERkVcpQNVQK+aOoMLgQUREREQ+oTB5BBUGDyIiIiLyAYE9HkGGwYOIiIiIvEvBpdzB5BFMGDyIiIiIyLuES7Ppsrg8qDB4EBEREZF3OXo8KJgweBARERGRTygyo0cwYfAgIiIiIi9Tqno8ONQqqDB4EBEREZHXOMKG4Nt2kPcxeBARERGR1yiKAggCBEFgj0eQYfAgIiIiIq9RFAWC8MdtCh4MHkRERETkNfYeDwjgtFZBhsGDiIiIiLzG0eMhAIoi+7o55EUMHkRERETkNbIswz7WiiOtgguDBxERERF5lSAAgsBprYINgwcREREReU1VQbk9dLDLI5gweBARERGR11QNtaq6zdgRXBg8iIiIiMhrqk+nS8GFwYOIiIiIvMYxnS4FHQYPIiIiIvIaWZaZO4IUgwcREREReQ17PIIXgwcREREReU1V8Kh2m4IGgwcREREReQ2HWgUvBg8iIiIi8hoOtQpeDB5ERERE5DXs8QheDB5ERERE5DXVFxDkEoLBhcGDiIiIiLymenF5tRsUBBg8iIiIiMhrZFmGINoDB3s8ggmDBxERERF5zeVT6HJK3eDB4EFEREREXiPLMmDv8RAEBo8gwuBBRERERF4jyzLsQ6wEUbj0NQUDBg8iIiIi8hpFURw1HoLAoVbBhMGDiIiIiLzGaTpdgT0ewYTBg4iIiIi8pvp0ugJrPIIKgwcREREReY1zjwfY4xFEGDyIiIiIyGucejxYXB5UGDyIiIiIyGtkWXZauJxDrYIHgwcREREReY1Tjwc41CqYMHgQERERkddULSBYdVsQWVweTBg8iIiIiMhrFEVB9ajBHo/gweBBRERERF4jyzIEe5EHezyCCoMHEREREXkNp9MNXgweREREROQ1zgsIclarYMLgQUREREReI8syFPZ4BCUGDyIiIiLyGuceD9Z4BBMGDyIiIiLyGqegwaFWQYXBg4iIiIi8pnqPB4daBRcGDyIiIiLymqrgcamXg8EjqDB4EBEREZHXOE2nS0GFwYOIiIiIvKZ6TQeLy4MLgwcRERERec3lNR4MHsGDwYOIiIiIvEZRFChQnL6m4MDgQURERERewx6P4MXgQURERERec3nQYPAIHgweREREROQb7PEIKgweREREROQ1Tut4gMEjmDB4EBEREZHXVAWNS0Ue7PEIKgweRERERORdXEAwKDF4EBEREZHXXD6dLgUPBg8iIiIi8q5quYNDrYIHgwcREREReZVQbR0PCh4MHkRERETkNVVDrZy/puDA4EFERERERB7H4EFERERERB7H4EFERERE3iU4/UNBgsGDiIiIiLymek2HAtZ4BBO1rxtwJWVlZfjkk09w7NgxZGVloaysDFFRUUhMTMTdd9+NUaNGQRCc83J5eTkWLFiAzZs3o6CgALGxsRg1ahRmzJiBsLAwl8fZsGEDli1bhrS0NGg0GvTq1QuPPvoounfv7nL7jIwMzJ07F8nJyTAajWjXrh0mTpyISZMmQRSZ54iIiIhccQoa7PIIKn5/hlxcXIzVq1fDYDBgxIgRuP/++zFs2DCkp6fj3//+N15//XWn7Y1GI2bOnImlS5ciKSkJU6ZMQYcOHbB06VLMnDkTRqOxxjEWLVqE2bNno7CwEHfeeSfGjh2LgwcPYsaMGUhOTq6xfVpaGh588EFs2bIFQ4cOxeTJkwEA//nPf/DGG2945oUgIiIiChQcahWU/L7HIz4+Hr/88gvUauemVlRU4OGHH8bKlStxzz33oFOnTgCAJUuWIDU1FVOnTsVf//pXx/bz58/HwoULsWTJEjz66KOO+zMyMjB//nwkJiZi8eLFjh6RyZMn46GHHsKrr76K5cuXOx3/zTffRHl5Od59910MHz4cADBr1iw8+eSTWLlyJW688UYMHDjQY68JERERUXPlNNRK4FCrYOL3PR4qlapG6ACA0NBQDB06FABw/vx5AFW/uD/++CNCQkIwffp0p+2nTZuGiIgIrFq1yukXfM2aNbDZbHjooYechmF16tQJt956K86fP499+/Y57j937hz279+PAQMGOEIHAKjVasyaNQsAsHLlyqY/cSIiIqIAxx6P4OL3waM2ZrMZ+/btgyAI6NChA4Cq3ou8vDz07t0bBoPBaXudToe+ffsiNzcXmZmZjvvtQ6mGDBlS4xj2YJOSkuK4z37b/lh1PXr0QHh4OPbv39/EZ0dEREQUmFhcHrz8fqiVXVlZGZYuXQpFUVBYWIgdO3bg4sWLmD59OhITEwHAESjatWvnch/Vt6t+OyQkBC1atKixvX0/1YNKXccQBAEJCQk4fvw4TCYT9Hp9nc/JZDLV+TgRERFRoLFYLBDsl76FqovJPCdqvq50vltdswoeCxcudHytVqvxxBNP4P7773fcV15eDgC1zlwVGhrqtJ39dkxMjMvt7fu5fPv6HuNKP4isrCzYbLY6tyEiIiIKJIVFhcClGUllRUZubi7Cw8N93CpqDJVKhY4dO9Z7+2YTPOLj47Fnzx7YbDZcvHgRP//8M+bOnYtDhw7htddec1kH4u/i4+N93QQiIiIirzp67ChQWnVbFEW0aNGi1tEqFFia3dm6SqVCfHw8pk2bBlEU8b///Q8rV67EpEmTXPZQVFdRUQHAubciLCys1u1d9W7U9xj2no+6NKRrioiIiCgQqNSqP6rKhapRLDwnCg7Ntrgc+KMg3F7w7aomo7qMjAyn7ey3KysrkZ+fX2N7V/UcdR1DURScP38ecXFxNYrbiYiIiAiw2WwQqgUPFpcHj2YdPOxhQaVSAagqHo+Li8OhQ4dqLBRoNptx4MABxMXFOQWJ/v37AwB2795dY/+7du1y2qb6bftj1R09ehRlZWXo169fU54WERERUcCSZdlR4wFBqfqagoLfB4/U1FSXw5pKSkrw8ccfAwCuueYaAFWzSk2YMAGVlZVOhegA8Pnnn6O0tBQTJkyAIPwxa/S4ceOgUqmwaNEip+OcOXMG69atQ0JCgtNigElJSejXrx+Sk5Oxfft2x/2SJGHevHkAgIkTJzb9iRMREREFIJts++MMVBAYPIKI39d4rFmzBj/++CMGDBiANm3aQK/XIycnB9u3b0dlZSVGjx6Nm266ybH91KlTsXXrVscK5l27dsWpU6ewY8cOdO7cGVOnTnXaf1JSEmbMmIF58+bhvvvuw+jRo2E0GrFx40ZIkoRnnnmmRuH6008/jenTp+Nf//oXxo4dixYtWmDnzp04ffo0JkyYwFXLiYiIiGrhNNRKZI9HMBGKi4v9emDdgQMHsGrVKhw5cgR5eXkwmUyIjIxEly5dcOutt+LGG2906sEAqgq/FyxYgF9//RUFBQWIjY3F6NGjMWPGjFqnwd2wYQOWLl2KtLQ0aDQa9OrVC4899hi6d+/ucvtz585h7ty5SE5OhtFoREJCAu644w7cfffdEEW/70giIiIi8omvl3+NwziIsKtDULa3AhN63YFhw4b5ulnkBX4fPIiIiIgocCxZugTHNUcQ2ikEpfvKMb7bBFx77bW+bhZ5AS/NExEREZHXSJIEQbQXl4OLKQcRBg8iIiIi8hqn4nLWeAQVBg8iIiIi8prqPR6KWPU1BQcGDyIiIiLyGpvtjx4PQQQkG4NHsGDwICIiIiKvkWzVajxEgcEjiDB4EBEREZHX2Gw2CNV7PDjUKmgweBARERGR11QNtarq8RBEAVar1cctIm9h8CAiIiIir5EkCYLqUvBQCZBk9ngECwYPIiIiIvKa6kOtIII9HkGEwYOIiIiIvMapx0MUWOMRRBg8iIiIiMhrnGo8VJzVKpgweBARERGR1zgNtVIBVolDrYIFgwcREREReY3NZnMaamXjUKugweBBRERERF5jk5yDB4daBQ8GDyIiIiLyGtlmc5yBCqpLNR8UFBg8iIiIiMhrZEWBIFT1eEAUIEkMHsGCwYOIiIiIvEf546YgCrBxqFXQYPAgIiIiIi/6I3kIYlXNBwUHBg8iIiIi8h6h2m1RYI1HEGHwICIiIiKvqTbS6lJxueyztpB3MXgQERERkfco1YdaCbDJ7PEIFgweREREROQVsiwDQrWxViIgs8cjaDB4EBEREZFXyLIMQfwjeAiCwOARRBg8iIiIiMgrbDabU/CAeKkXhIICgwcREREReYUsy38sHoiqHg9FUer4DgokDB5ERERE5BU2mw2CSnC+U3C9LQUeBg8iIiIi8oqqHo/L7mSHR9Bg8CAiooCUnp6OTz78AEVFRb5uChFdIssyILLHI1gxeBARUUBKT0/H3gOHcPHiRV83hYgusdlsNXo8WOMRPBg8iIgoIFktFgCAJEk+bgkR2bnq8agx9IoClrqpO8jIyMDSpUuxb98+5ObmwmKxYOfOnY7HV61ahdzcXNx3330ICQlp6uGIiIjqxWIxQ6sSYLkUQIjI91zVeLDDI3g0KXj89NNPeOWVV2C1Wh3dZMJlv02lpaVYuHAh2rdvj7FjxzblcERERPUmWcwwqAX2eBD5EUVRWNMRxBo91Co1NRUvvvgiJEnC3Xffjblz56Jr1641thszZgwURcHWrVub1FAiIqKGsFos0KkEWK1WXzeFiC65fB0PCi6N7vH44osvIMsynnrqKdxzzz0AAK1WW2O7Nm3aICYmBqdPn258K4mIiBrIarVAJ1YVsxKRf5BluWaPh1B1vyiy9DjQNfonfODAAYSGhjpCR11atmyJ/Pz8xh6KiIiowWySBJ2KxeVE/kRRFJfF5ZzZKjg0OngUFRWhbdu29TuIKMJoNDb2UERERA0mWa3QiuBQKyI/UhUwLgsZgsDgESQaHTzCwsJQUFBQr23Pnz+PyMjIxh6KiIiowSRJglYlwMbgQeQ3ZFmGwB6PoNXo4NG5c2cUFBTg5MmTdW63bds2lJaWomfPno09FBERUYNJNulSj4fZ100hoktkWa55pyC4vp8CTqODx6233gpFUfDaa6/V2vORlpaGN998E4IgYNy4cY1uJBERUUNJVgk6lQAbazyI/EZVjYfzfezxCB6NntXq5ptvxtq1a7F3715MmTIF1113HXJzcwEA33zzDQ4dOoQtW7bAarXi+uuvx7XXXuu2RhMREV2JZC8u51ArIr/BHo/g1ugeD0EQ8NZbb2HMmDEoKSnBmjVrkJOTA0VR8O6772LTpk2wWq0YM2YMXn75ZXe2mYiI6IokSYJWFGC1cuVyIn/hagFBQaglkFDAadLK5SEhIXjttddw7NgxbNq0CadOnUJZWRkMBgOuuuoqjB07Fn369HFXW4mIiOrNJknQaYByDrUi8huu1/HgrFbBoknBw6579+7o3r27O3ZFRETkFlabBJ2eK5cT+RP2eAQ3LhFJREQBSZJs0LPGg8ivyLIMgT0eQYvBg4iIApIkSdCrWeNB5E8URYHCHo+g1eihVrNmzWrQ9oIg4OOPP27s4YiIiBpEstmgEwVYjOzxIPIXLns8RE6nGywaHTxSUlKuuI1w6TdLURTHbSIiIq9QALUISBKDB5G/cFVcLggCbDabbxpEXtXo4PH888/X+pjJZEJGRgY2btyI8vJyTJ8+HS1atGjsoYiIiBpBgVoEi8uJ/IgsyzWGWoFDrYJGo4NHfVYif/TRR/Hcc8/hhx9+wJIlSxp7KCIiokbRiAKsVk6nS+QvZFmuuXK5yAUEg4VHi8vDwsLw3HPPIS8vDwsWLPDkoYiIiBxkWYZg7/HgOh5EfqNqqNVl9RwCazyChcdntWrRogU6duyIrVu3evpQREREAKqGV6lFEWqhalpdIvIPLhcQFMEajyDhlgUEr8RisaCgoMAbhyIiIoIkSdCohEuz5/BKKpG/sNlsUC7r8eBQq+Dh8R6P06dPIzMzE1FRUZ4+FBEREYCqHg+VfTZFDuEg8hs2m63m2afAHo9g0egej5ycnFofUxQFhYWFOHz4ML788ksoioLhw4c39lBEREQNIkkS1JdObhg7iPxHbUOt2OMRHBodPCZOnFiv7RRFQdu2bfHYY4819lBEREQNIkkSVCLXjyLyN1XT6V421Io9HkGj0cHjSrMPGAwGtGvXDtdddx3uu+8+hIWFNfZQREREDWKz2aCy5w5F4UK2RH5CkqSaQ61YXB40Gh08du/e7c52EBERuU314KESq1ZFVqu9Mp8KEdVBskkQXAQPDrUKDh4vLiciIvI2m80G+0grUeCMOUT+oqrHw7n3UeFQq6DB4EFERAFHlmXHHzhR4NVUIn9hk22oMeqRQ62CBoMHEREFHFmWHSc3Ans8iPyGZJPwRwHWJYLC4BEk6jXgtb4zWNVFEAT88MMPTd4PERHRlSiK4pixU+QwDiK/Idkklz0evDgQHOoVPLKzs5t8IM4mQkRE3lJ9qJVQY9EAIvIVm80GoUaPBy8OBIt6BY+5c+d6uh1ERERuUzXle9W07wJrPIj8hs1mAzSXFZeLHGoVLOoVPPr37+/pdhAREbmNoiiO1ZEFXHntKSLyDpuL6XQZPIIHi8uJiCjgKIryx1ArjrQi8hs2m63m2SeHWgUNBg8iIgo4l/dwcKgVkX+wyTYIl63jweLy4OGWZVyLiopw8uRJlJSUVC0MU4vbbrvNHYcjIiK6IvZ4EPkfyUWPhyAKVdPsUsBrUvDIycnBW2+9hZ07d9Zr/CyDBxEReUPV1dNLxeVgjQeRv7DZbDVnOhWqekIo8DU6eBQXF2PGjBnIzc1FXFwcKisrUVlZiT59+qCkpATnzp2DLMvQ6XTo0aOHO9tMRERUp+rreAjgAoJE/kK2yTWKywURdY6YocDR6BqPL7/8Erm5uZg4cSLWrFmDTp06AQA++eQTLFu2DBs2bMCDDz4Iq9WKxMRETslLRERe47xyOXs8iPyFTbZVrepZnSiwxyNINLrHY/v27dBoNPjzn//s8vHIyEjMmjULMTExePfdd9GrVy+MGzeu0Q0lIiKqr6oej6qwIYKFq0T+QpZtNXs8OKtV0Gh0j0dWVhbatGmDyMhIAH+sTH55V9nkyZMRGRmJlStXNr6VREREDeC0cjl7PIj8hs0m15jxQWCPR9Bo0nS6YWFhjtsGgwFAVe1HdYIgoE2bNkhPT2/KoYiIiOqtevBgjweR/5BtNXs8ILLHI1g0OnjExcWhsLDQ8XXr1q0BACdPnnTaTpZlZGdnw2KxNPZQREREDaIoCgTh0qxWAoMHkb+w2Ww1ezwEgcXlQaLRwaNDhw4oLCx0/KL0798fiqJgwYIFKC0tdWw3b948FBcXo0OHDk1vLRERUT3IsuyY1UrkUCsivyHLNWe1gsjpdINFo4vLhw8fjq1bt2Lv3r0YNmwYRo0ahTZt2uDEiRMYP3482rdvj4KCAuTn50MQBNx9993ubDcREVGtqoKHgqrJdNnjQeQvbLIMteiixoNDrYJCvXs83nnnHZw6dcrx9ciRI/GPf/zDUVyu1Wrx7rvvIikpCSaTCSdOnEBeXh5UKhUefvhhjB8/3v2tJyIicsF5HQ/2eBD5i1p7PBg8gkK9ezy++eYbLF++HJ07d8b48eNx00031ejF6NChA5YtW4ajR48iKysLer0evXr1QnR0tNsbTkREVBdH8OBQKyK/YbPZANH59FMQ2OMRLOodPK666iqcPn0aJ0+eRGpqKj744AOMGDEC48aNw5AhQxzT6QqCgJ49e6Jnz54eazQREVFdqq/jYf+aiHzP1crlVT0eLC4PBvUOHl999RVSU1OxatUqbNy4ESUlJfj555+xadMmxMXFYdy4cbjtttuQkJDgyfYSERE1iHDlTYjIS6qGWnEdj2DVoFmtOnfujH/84x9Yt24d3njjDVxzzTUQBAG5ublYtGgRJk2ahFmzZmHdunUwmUyeajMREdEVKZf9S0S+J9vkGmefAms8gkajZrVSq9UYNWoURo0ahYKCAqxbtw5r165Feno6UlJSsH//frz99tu44YYbMG7cOPTu3dvd7SYiIqpV1fBf4bKvicjXqtbYuez9KAqwSQwewaBJK5cDQGxsLKZOnYply5bhs88+wx133IGwsDBUVlbixx9/xKOPPorJkydjyZIl7mgvERHRFQmC4NTjIYpN/nNHRG6guOiDFERAYo9HUGj0Oh6u9OjRAz169MDf/vY3/Pbbb1i9ejX27duHc+fO4aOPPsLUqVMbvM/c3Fz88ssv2LFjB86ePYuCggJERESgT58+mDp1qssi9vLycixYsACbN29GQUEBYmNjMWrUKMyYMQNhYWEuj7NhwwYsW7YMaWlp0Gg06NWrFx599FF0797d5fYZGRmYO3cukpOTYTQa0a5dO0ycOBGTJk3iHzgiIh8TBAH2JQRlhT0eRH7D1dhHUYDM4BEUPHKGrNFoEB4ejoiICKjVTcs2y5cvx7vvvosLFy5g8ODBuP/++9G3b19s3boV06dPx88//+y0vdFoxMyZM7F06VIkJSVhypQp6NChA5YuXYqZM2fCaDTWOMaiRYswe/ZsFBYW4s4778TYsWNx8OBBzJgxA8nJyTW2T0tLw4MPPogtW7Zg6NChmDx5MgDgP//5D954440mPV8iImo6lUr1R4+Hwh4PIn/hssdDxel0g4VbezzOnTuHNWvWYP369cjPzwdQNZavZcuWuPXWWxu1zx49euCTTz5Bv379nO7fv38//vKXv+DNN9/EiBEjoNVqAQBLlixBamoqpk6dir/+9a+O7efPn4+FCxdiyZIlePTRRx33Z2RkYP78+UhMTMTixYsdPSKTJ0/GQw89hFdffRXLly93ClBvvvkmysvL8e6772L48OEAgFmzZuHJJ5/EypUrceONN2LgwIGNer5ERNR0oihCVv7o8WDwIPJfgli1ojkFviZ/EpeXl+OHH37Aww8/jHvuuQdLlixBXl4eNBoNRo8ejffeew+rVq3CrFmzGrX/UaNG1QgdANCvXz8MGDAApaWlOH36NICqkPPjjz8iJCQE06dPd9p+2rRpiIiIwKpVq5zmc1+zZg1sNhseeughp2FYnTp1wq233orz589j3759jvvPnTuH/fv3Y8CAAY7QAVQV3Nuf48qVKxv1XImIyD1EUXQMtbIpVT0gROR7rms8BNgkruMRDBrV46EoCnbv3o01a9Zg69atsFgsjpN5+8rmN998MyIiItza2MvZeyHs/2ZkZCAvLw9Dhw6FwWBw2lan0zmGaGVmZiIxMREAHEOphgwZUmP/Q4cOxffff4+UlBQMHToUAJCSkuJ47HI9evRAeHg49u/f76ZnSEREjaFSqWC/fiqDwYPIH8iy7HpdHRVgs7HHIxg0KHicO3cOa9euxbp165yGUkVGRuKmm27C+PHj0blzZ4809HI5OTnYu3cvYmNj0alTJwBAZmYmAKBdu3Yuv8ceNqoHj8zMTISEhKBFixY1trfvx77fKx1DEAQkJCTg+PHjMJlM0Ov1dT4HrnVCROQZNpsNtksXVm2yAqvVys9cIh+zWq0QVDWjh73Hg+/R5ulK57vV1Tt4TJ8+HUeOHAFQFTZEUcSQIUMwbtw4jBw5sslF5A0hSRLmzJkDi8WCv/71r44rWeXl5QBQ68xVoaGhTtvZb8fExLjc3r6fy7ev7zGu9IPIyspiMRURkQcUFBRAupQ8JJsNFy9eREVFhY9bRRTczGYzFBddHoIoQJJsThd6qXlQqVTo2LFjvbevd1o4fPgwACAhIQHjxo3DuHHjEBcX1/AWNpEsy3j55Zexf/9+TJw4sdFF6/4gPj7e100gIgpIRqMRZ0QRgAwIIhISEhAeHu7rZhEFtfLycqjVroc9iqJY64gVChz1Dh633XYbxo8f77LQ21sURcGrr76K9evX45ZbbsG///1vp8dd9VBUZ7/aVb23IiwsrNbtXfVu1PcY9p6PujSka4qIiOpPr9dDuTSaXAFgMBj4mUvkYyaTCYK6tnmNFL5Hg0C9Z7WaPXu2T0OHLMt45ZVXsHr1atx4442YPXt2jekRXdVkVJeRkeG0nf12ZWWlo2alOlf1HHUdQ1EUnD9/HnFxcTWK24mIyHtEUYR9AkNOp0vkH2qr8QAA11XnFGiaxSexLMt49dVXsXr1atxwww148cUXXc5QkpiYiLi4OBw6dKjGQoFmsxkHDhxAXFycU5Do378/AGD37t019rdr1y6nbarftj9W3dGjR1FWVubTgEZERPaVy6vIl+oSici3JEmCWEvwUFytaE4Bx+8/iav3dIwZM6bW0AFU/aGZMGECKisrsXDhQqfHPv/8c5SWlmLChAkQhD9+6ceNGweVSoVFixY5DZ86c+YM1q1bh4SEBKfFAJOSktCvXz8kJydj+/btjvslScK8efMAABMnTnTHUyciokaq/jlPRP5BkiSgtpmthapzPgps3puKqpEWLlyINWvWICQkBImJifjss89qbDNy5EjHNL5Tp07F1q1bHSuYd+3aFadOncKOHTvQuXNnTJ061el7k5KSMGPGDMybNw/33XcfRo8eDaPRiI0bN0KSJDzzzDM1Zux6+umnMX36dPzrX//C2LFj0aJFC+zcuROnT5/GhAkTuGo5EZEfqH4BlUGEyPesVisE0fV7UVSLsFqt0Ol0Xm4VeZPfB4/s7GwAQGVlJRYtWuRym/j4eEfwMBgMmDdvHhYsWIBff/0VycnJiI2NxZQpUzBjxgyXtRcPP/ww4uPjsXTpUqxYsQIajQa9e/fGY489hu7du9fYvmPHjli0aBHmzp2LHTt2wGg0IiEhAX//+99x9913u/HZExFRY1U/vVE4joPI5yRJqvXMk8EjOAjFxcX8NCYiooBy5swZrP/8I9zdzoYPUwU8+/IbnDGHyMeOHDmCr35dgrDrQmo8VvxjGZ558llER0f7oGXkLX5f40FERNRQ1Xs4BHDsOJE/sFqttdZ4CGqh6nEKaAweREQUcGRZhn3yHJFFq0R+wWq1QhFdD7QRVAwewYDBg4iIAo7NZoNwqbxcEKq+JiLfslgstdZ4CGqh6nEKaAweREQUcGw2G+yT56gEgT0eRH7AarVCUdXyXlSDwSMIMHgQEVHAsdlsUF3q8VCJAns8iPyAyWyqo8eDwSMYMHgQEVHAkSQJonApeFz6moh8y2Q2QVDXsnK5isEjGDB4EBFRwLHZbLCf36hE1ngQ+QOj2QhBXcupp1ph8AgCDB5ERBRwJEmCClVjydUCezyI/IHJZIKoqa3HQ4HZbPZyi8jbGDyIiCjgVAWPS0OtGDyI/ILRbIRQS/CARoHRZPRug8jrGDyIiCjgSJIE9aUeD5WgcH0AIj9gNpkgalyfegpqkcEjCDB4EBFRwLFarY4FBDnUisg/mMzmWovLRY2ASmOll1tE3sbgQUREAUeymqG69BdOBYXBg8gPmEwmiFrXwUPQCjCaGDwCHYMHEREFHKvF4pjVSi0weBD5A7PJDFHr+tRT1IqoNHKoVaBj8CAiooAjWSxQCVXJgzUeRP7BYrbUWlwualjjEQwYPIiIKOBIkhXqakOtuI4Hke8pigxBqCV4aAWYTCYvt4i8jcGDiIgCTvXichVk9ngQ+ZiiKJcmuHZN1IowcahVwGPwICKigGOzSlCLl4ZasceDyOcsFkutU+kCVcXlZjNXLg90DB5ERBRwrFK1Hg+xquaDiHzHaDRCpVPV+rggCFBk2YstIl9g8CAiooBjk5zX8bBaGTyIfKmyshIqXd2nnQoAmeEjoDF4EBFRwJEk2x/reAhVQYSIfMdoNEKoZQ0PO5VWxQLzAMfgQUREAUeSpD+m0xUFFpcT+VhFRQWgrXsblV6EkQXmAY3Bg4iIAo7NJjkWEGSPB5HvVQWPuua1AkSdWLUdBSwGDyIiCji2y4ZaSVauXE7kSxUVFZC1dddvCFqBwSPAMXgQEVHAqRpqVXVbLQqQONSKyKdKy0sh6uqu8YBOQWVlpXcaRD7B4EFERAHHapOgttd4CFUrmROR71QFjyvMaqVV2OMR4Bg8iIgo4Fw+qxWDB5FvlZWXQaWvfR0PAICuKqBQ4GLwICKigCNJtmpDrQArazyIfKq8vAyivu7TTpVeRElpiZdaRL7A4EFERAHHJlcLHoLAHg8iH6uoqIDqCsFD1Ivs8QhwDB5ERBR4FAWCYx2PqmJzIvKdykrjFWs8RL2IsvIyL7WIfIHBg4iIAppa5HS6RL4m22wQVFdYuVzPdTwCHYMHEREFNLUgwMoeDyKfkSQJyhVm0gUAUa9CRTmDRyBj8CAiooCiKAqAP1ZIVotg8CDyoYqKCmgM6ituJ6oF9k4GOAYPIiIKKJIkQS3+8edNzRoPIp8qLy+/8lS6lyiCAlmue4Vzar4YPIiIKKBYrVaoxT/GdagEATaeyBD5TFlZGQR9PcZaAVDrVazzCGAMHkREFFCsVis0lxWx1u+Uh4g8oby8HDDUb1u1QY2yMs5sFagYPIiIKKBYLBaoBeeoUVX3QUS+UFpaCllnq9e2gl6oCioUkBg8iIgooFitVmhq/HVTGD6IfKS4tBhiPYdaQa+wxyOAMXgQEVFAsVgsNYJH1erlLDAn8oXi0mKoDPUsLtcpKC3l6uWBisGDiIgCitVqhfqyi6tqlQCr1eqbBhEFuargUb9TTsEAFJUUebhF5CsMHkREFFCqgofzsCqNKMJisfioRUTBrbS0FGI9ezxUBhWDRwBj8CAiooBSNdTqsuDBHg8inykvK4cqpH6nnKoQFYpLiz3bIPIZBg8iIgooVqsValze4wH2eBD5iGS1QlTXM3gYRNZ4BDAGDyIiCihV0+k6LxioFsAeDyIfsFqtgKr+K+moQlQoL+V0uoGKwYOIiAKK1WqFBpcFD5HBg8gXSktLoQlR13t7QSVAsnEGukDF4EHUBD9v2oz/99yLOHr0qK+bQkSXWMxmaC4vLhcUDrUi8oHi4mKoQutXWG4nakSYTCYPtYh8icGDqAmOHD2O/Eo9zpxJ93VTiOgSs9mEy4eTqxk8iHyipKQEMDRs8U5NqLrq+yjgMHgQNUFpWRkEfRwKivgBSeQvrGYTtKLzmHKtIHOoFZEPFBcXQzbIV96wGjFERHFxsWcaRD7F4EHUBOVlZRB0sSgqZvAg8hdmk6nmyuVgjweRL+QW5EIV2sDTzRCFwSNAMXgQNYHZbIKgjeTUf0R+xGIy1ggeGtg4ZpzIB/KL8htc46GEyCgoLPBQi8iXGDyIGkmW5aqVAlQGVJRz6j8if2E2m6G5bPpOrVgVSIjIu4qKCqEOqzmrlc1oQ9GeEtiMthqPqULVyC3I9UbzyMsYPIgaqbKyEqJaD4gamM28kkrkL8xmM7SX/XXTqgSYGTyIvK6kuNTlUCubSUbx3lLYTDXrP9RhKuQX5nujeeRlDB5EjVReXg5BbYAgCFAUQFEaNmsHEXmGxWyuUVyuEQGTkcGDyJsURYG1AauW26nDVCgqKvJQq8iXGDyIGqmiogJQ6QEAoloLI09qiPyC2WKG9rIh5RxqReR95eXlUBsaVt8BAKJBREUZhzAHIgYPokaqqKiADC0AQFDpGTyI/ITZbKk5na5K4JBIIi8rLHRd33ElgiAAKoFTYAcgBg+iRjIajY7gAZUWlZWVvm0QEQEALFYrNJddZNWIVbUfROQ9BQUFEMOEK2/ogjZcg8LCQje3iHyNwYOokUwmE2y4dHYjaDhVJ5GfsNlkqAQXPR4mBg8ib8rLz4MtpOasVfUhhonIz2eBeaBh8CBqJIvFAhuqupAVQc0uYSK/UXOiB60ILiBI5GVZF7Ogjmj4UCsAUMJkBo8AxOBB1EgWixUQqno8FIiQJMnHLSKiqtnlXAUPAWYGDyKvuph3EZrIhheXA4AYLiDrYpabW0S+xuBB1Eg2WYGAS8M5BBE2W+O6k4nIfWw2W41hVgCgUVXVfhCR9xTkF0Ad3rgeD3WEGjl5OW5uEfkagwdRI8myDNiDB9fxIPILFosFWlXNP20qQbj0niUib5BlGRaLGeLlq3nWkyZCjbzcPDe3inyNwYOoKapdWBVcXGUlIu+yWCzQqGp5L/LiAJHXFBcXQxOmbfT3iwYRFeUVvKgXYBg8iIgoYJjNZmhE18GD5y9E3pObmwt1I+s7gKqLeeoQFcrLuZBgIGHwIGo0xVUNKxH5kMVigaaWv2yCoHC4FZGXXLx4EUp4095vmkgNLl686KYWkT9g8CBqCo6uIvIrVqu11uChEUVOqUvkJZnZmRAjmniaGaEgJ4cF5oGEwYOokWRZgT15KGDhKpE/sFgs0AiuuyI1KoHr7RB5yYXsC9BEN25GKzshoirAUOBg8CBqJJvtj1mtFAicTpfID9Q11EqjEtjjQeQluRdzoYlqWvDQxGiQeYHBI5AweBA1klWSAKHqLaSA63gQ+QOLxQINXPc+akUBZrPZyy0iCj4WiwU2xQZR3bTTTE2UGrkXc93UKvIHDB5EjWSxWgGx6mqOAhVXLifyAyaTCRrBdfDQiGDwIPKCixcvQhfd+Kl07USNCMlm5RDJAMLgQdRIZpMZEKqmCrSBRatE/sBsNtfR46EweBB5QVZWFoRI9+xLG61jgXkAYfAgaiST2QyIGgCAImhgNPGEhsjXTMYK6FSui8t1ogKTyeTlFhEFnzPnzkCIcc++hGggM5N1HoGCwYOokUwmM4RLwUMQNais5AkNka8ZKyqgq2UBQa0gs8eDyAvSz6VDG9v0oVYAIMYISDuX5pZ9ke8xeBA1kslkcvR4QNSgotLo2wYREYwV5dDVsliyDjZUVlZ6t0FEQSgvN6/JU+naaVtokJ6R7pZ9ke8xeBA1UlXwuHRFR9QyeBD5gcqKChjUrns89CoFlRVlXm4RUXApLS2FSidCqKXnsaE0kWoU5OVDUVwPoaTmhcGDqJEsZrNT8DAaGTyIfK2ysgJ6lesTHoNKQEVpqZdbRBRczp49C02cxm37E0QB6jANCgsL3bZP8h0GD6JGkiSrY1YrQdRUzXJFRD5VUVGJkFpGeBjUAirKGDyIPCn1TCrQwvXMco2ljhORlsY6j0DA4EHUSIqiQBAuXVkV1TBbGDyIfK3SaKx1qFWIGigv51ArIk9KPZMKXSv3FJbbCXECTp4+6dZ9km8weBA1ktNoU0HNBY6I/IAkSVDXMrY8RC2gvLzcyy0iCh6KouBizkVoot031AoAdK20OJV2yq37JN9g8CBqBEVRnJOHwJXLiXxNkiTUVc4aqhFQVl7htfYQBZucnBxoIzVuKyy3U4erUFxUzAt8AYDBg6gRFEUBhGofrIIIRbb5rkFEhPLycoRqa5lLF4BGFGDlBQIijzlx8gTE1u4NHQAgCAL0rXQ4e/as2/dN3sXgQdQIsixDEKq9fQQBsuzeYjoiapiysjKEaeo+6VEB7J0k8pBDxw5CE++e9TtqaKXg6PGjntk3eQ2DB5FbuP8KDxE1TGlpKcLUdc/1H6pVsc6DyAMURcHZ9LPQtdR5ZP/6tjocOX7YI/sm72HwICKigFBaWopQse4hj2EaAaVcy4PI7XJycqAOV0OsZVa5ptJEq5GXl886j2bOQ/1h7rV+/XocOHAAx48fx5kzZ2C1WjF79myMGzfO5fbl5eVYsGABNm/ejIKCAsTGxmLUqFGYMWMGwsLCXH7Phg0bsGzZMqSlpUGj0aBXr1549NFH0b17d5fbZ2RkYO7cuUhOTobRaES7du0wceJETJo0CaLIPBcMFKfqcq6oSuRrJcVFCBMkALXPqBOmVhg8iDzg4KGDUCV47vxHEATo2+iQmpqKHj16eOw45FnN4gx53rx5+OGHH5CTk4MWLVrUua3RaMTMmTOxdOlSJCUlYcqUKejQoQOWLl2KmTNnulxdetGiRZg9ezYKCwtx5513YuzYsTh48CBmzJiB5OTkGtunpaXhwQcfxJYtWzB06FBMnjwZAPCf//wHb7zxhnueNPk1QRBqZA1B4HArIl8qLshHmLbu92GYKKG4uNg7DSIKIvsO7oMu0b3T6F5ObCcg+WDN8zJqPppFj8ezzz6Ldu3aoU2bNvj888/x0Ucf1brtkiVLkJqaiqlTp+Kvf/2r4/758+dj4cKFWLJkCR599FHH/RkZGZg/fz4SExOxePFiR4/I5MmT8dBDD+HVV1/F8uXLoVb/8VK9+eabKC8vx7vvvovhw4cDAGbNmoUnn3wSK1euxI033oiBAwe6+2UgP1I1q5WL+4jIZ4oLC9DxCsXlYSobigsLvNQiouBgMpmQn5+HFjHRHj2OoZ0Oh1ezzqM5axY9HoMHD0abNm2uuJ2iKPjxxx8REhKC6dOnOz02bdo0REREYNWqVU4niGvWrIHNZsNDDz3kNAyrU6dOuPXWW3H+/Hns27fPcf+5c+ewf/9+DBgwwBE6AECtVmPWrFkAgJUrVzb2qVIzUbVqefW3j8jgQeRjJSUlCL9C8AjXCCguyPdSi4iCw9GjR6FP0Hm851+lV0HRKrh48aJHj0Oe0yyCR31lZGQgLy8PvXv3hsFgcHpMp9Ohb9++yM3NRWZmpuN++1CqIUOG1Njf0KFDAQApKSmO++y37Y9V16NHD4SHh2P//v1NfzLk1yRJcgoegiAweBD5WElpKcKvMNQqXCOguKjQSy0iCg67kndB1c47p5TqRAH7kvddeUPyS81iqFV92QNFu3btXD6emJjo2K767ZCQEJe1I/b9VA8qdR1DEAQkJCTg+PHjMJlM0Ov1dbbXZDJd6SmRnyorKwME57ePrCj8mRL5kNVqheYKk3tEaAUUXyzie5XITaxWK1JTUxHX37PDrOz0nfTY/ut2jBk9xivHoyu70vludQEVPOxzs9c2c1VoaKjTdvbbMTExLre37+fy7et7jCv9ILKysmCzcbXr5qioqAiK4LxCsmyTnUIqEXmPxWJBfWbxDNMIKCkt43uVyE1SU1OhbaOBoPLOBCuaCDVKpDIcOnQI0dHeCTtUO5VKhY4dO9Z7+4AKHs1NfHy8r5tAjSTLMkS183A+Ua1Bq1atoNVqfdQqouB18eJFRBnUAOQ6txMFAQIUtG3bllOfE7nBmo1roL3Ks7NZXU53tQaZFzLRu3dvrx6Xmi6ggoerHorqKioqnLaz365te1e9G/U9hr3noy4N6Zoi/yJJEhTReXVWlcYAm83GnyuRDxiNRkTW89wnVKuCJEmIiIjwbKOIApzJZMLZs2cRN9S7PQ+GTnrs3bgHd068k1PZNzMBdbnHVU1GdRkZGU7b2W9XVlYiP7/mLCeu6jnqOoaiKDh//jzi4uJqFLdTYCkvL4cNzsFDUOlrDaRE5FlFRUUIV9Vv6GqEVuRaHkRusHffXujb6yCI3j35V4epYdPZkJ6e7tXjUtMFVPBITExEXFwcDh06VGOhQLPZjAMHDiAuLs4pSPTv3x8AsHv37hr727Vrl9M21W/bH6vu6NGjKCsrQ79+/Zr+ZMivlZWVwXpZ8FBEnaPHi4i8q6ggHxGiVK9tI9QyioqKPNwiosC36bdN0HfxzfBidWcVftnyi0+OTY0XUMFDEARMmDABlZWVWLhwodNjn3/+OUpLSzFhwgSnbrlx48ZBpVJh0aJFTlerz5w5g3Xr1iEhIcFpMcCkpCT069cPycnJ2L59u+N+SZIwb948AMDEiRM99AzJXxQVlwGi85AqG/RVs10RkdcV5F1ExBWm0rULF60oLOSUukRNkZWVhXJrObSx3q3vsAvtaMDRY0dhNpt9cnxqnGZR47Fy5UocPHgQQFUgAIAff/zRsQbHiBEjMHLkSADA1KlTsXXrVscK5l27dsWpU6ewY8cOdO7cGVOnTnXad1JSEmbMmIF58+bhvvvuw+jRo2E0GrFx40ZIkoRnnnnGadVyAHj66acxffp0/Otf/8LYsWPRokUL7Ny5E6dPn8aECRO4ankQKCktA1ThTvdZoeVQKyIfKcrPR696Bo9IDVCYxwXIiJpi89bN0HRWXXlDDxFUAnTttdi7dy+uvfZan7WDGqZZBI+DBw9i7dq1Ne6zh5E2bdo4gofBYMC8efOwYMEC/Prrr0hOTkZsbCymTJmCGTNmuKy9ePjhhxEfH4+lS5dixYoV0Gg06N27Nx577DF07969xvYdO3bEokWLMHfuXOzYsQNGoxEJCQn4+9//jrvvvtv9LwD5nbLyCkDlvPaLIuhQVsahVkS+UFhUhKh6LmAWpRNwLpfBg6ixzGYz9ibvReykSJ+2Q99Ni/Wb1mP48OEsMm8mmkXwmDNnDubMmVPv7cPCwvDUU0/hqaeeqvf33Hzzzbj55pvrvX1SUhLeeOONem9PgaWiogKCyrnGAyodSssZPIh8oaLSCH09L75GaQUUFBR4tkFEAez37b9D30ELUevbEfvaaA0qtWU4deoUOnfu7NO2UP0EVI0HkbeYTCZAvKygTtSioqLSNw0iCmKVlZXQq4V6X/GM0AooLinxcKuIApMsy/jpl59g6Km78sZeoOupweoNq33dDKonBg+iRrCYzYB4WUGdqEGl0eSbBhEFsfz8fETXt7sDVYsIilBgtVo92CqiwHTkyBEgUoE63D8GzegTdDifcx55eXm+bgrVA4MHUSNYrRZAdP7QFUQ1Z9cg8oG8vDxEa+q3hoddlF7N4VZEjbBy7UroevlmJitXBEGAvocWq9ez16M5YPAgagRZliEIl11hFTWwWCy+aRBREMvJvoBYVc3eiwqrgl/OW1BhVWo81kIrIycnxxvNIwoYJ06cQKmtFPrW/jHMyi60iwGHjh7i+jzNAIMHUSMoSs0TGQhqBg8iH8jOzEALfc0/Z5WSgl8vWFEp1Xy/xqgsyMnK8kbziALGtyu/haG/bxYMrIugEmDopcXKNSt93RS6AgYPokZwlTsgqquGYBGRV124cAGtQhr256yVQcT5c2keahFR4Dlx4gRKpBLo2/hXb4ddaNcQHDxykL0efo7Bg6iBJEkCRBdvHVFTVXRORF4jyzLKy8sRpmnYHP6tQkRcOH/eQ60iCiyKouDblcv9srfDjr0ezQODB1EDGY1GiJev4QFAEFSw2RpW4EpETZOTk4MWIQ2fXSdMI6CisrLqQgIR1engwYMolUv9trfDLrRrCA4dO8j6LT/G4EHUQJWVlTUXD7xEUWqp/yAij0hPT0dbXeMCf+tQNc6z14OoTpIk4evlXyNkqN7XTbkiQSUgZJAeXyz9wtdNoVoweBA1UGlpKaAyuHxMVOtgNBq93CKi4JWWehxt9Y0LHm21FqSnsc6DqC4///IzhPiqVcKbA0N7PXLLL1atN0J+h8GDqIGKi4shCSEuHxM0oSxsI/KiU6mpSAqv/+KB1SWFAiePHHRzi4gCR1lZGX765SeEDnB9sc0fCYKA0GF6fPnNlxz+7IcYPIgaKDc3DxaEunxMVoVxUTIiLykvL4dsNSO0gYXldglhItLPnuXwSKJafPXNVzD00kHlYrpqf6aN1UKJs2HDzxt83RS6TPP6TSLyAxnnsyFoI10+ZkY4srNZ1EbkDceOHUOHsMZ/v0YUEKUTkZ2d7b5GEQWIw4cP49SFVIT2aD69HdWFDQnBz5t/xsWLF33dFKqGwYOogS5kZUHQRbt+UBuN9HMXvNsgoiB1OGUvrg5t2qxUV4dYcfjgAfc0iChAGI1GLP5qMcKvD4EgNK5H0ddErYjQYXp88uk8yLLs6+bQJQweRA0gyzJKiosBtevLrIIuFpmZmd5tFFEQkmUZqamp6BjRuPoOu84RAg4m73VTq4gCw1fffAVNFxU0Uc2joLw2IUkGlOvLsenXTb5uCl3C4EHUAPn5+VDpImu/AqQORWlpKa+uEHnYmTNn0CZEhEZs2tXYlgYBJUWFKCsrc1PLiJq3o0eP4ljaUYT1dj2JSnMTNiwE6zau45ArP8HgQdQA586dg6RpUevjgiBApY/mmHEiD9u7czu6h1qavB9BENAtQsH+lBQ3tIqoeSssLMSnX3yKiFGhEJoY6v2FSi8i7FoD3v/4fZjNZl83J+gxeBA1wMlTabCqYuvcxqqOxdmzZ73TIKIgZLPZcPDAAXSJatowK7teUQp2bfvNLfsiaq4kScIHH38Aw2Btsx9idTlDOz3kRAmfffEZZ7HzMQYPogY4fSYdgqFlndtImjicSOWiZESecvToUbQLBfRq91yRbR0iorykkFNhU1D76puvYIytREjH5jmL1ZWE9QvBmbzT+G3rb75uSlBj8CCqJ1mWUZCfB2hcT6VrJ+jjkJZ+1juNIgpC23/7BX0jrG7dZ59IG7bzhISC1O49u3Ho9EGEDw6Mug5XBEFAxKgw/LjuR45K8CEGD6J6ysvLg0ofdeWpBdVhKC0p4YqpRB5QUlKCzLPp6BTpnmFWdv1iROzc/jvftxR0Tp06haXfL0XEmLCAqeuojUovImJMKP4373/Iz8/3dXOCEoMHUT2dO3cONnXd9R3AHwXmOTlcSJDI3bb+thn9ohWIbl5bIFQjICFEwcGDB926XyJ/duHCBXy88GNE3hgGdah7w7y/0sVpYbhGi7fffRulpaW+bk7QYfAgqqfUU2mwXKGw3M6qboH09HQPt4gouEiShO1bt2BArGeuyg6NlvDzutUe2TeRvykoKMA7/3sH4aMM0EZ7t5j87CfnceHrqotzWcu9P82toZ0eqj4C3n73bZhMJq8fP5gxeBDVU+qpMxAMreq1raSJw/GTZzzcIqLgsmvXTlwVJiNU45ngkRCmgq28iOO/KeCVlZXhrXffgn6YFrpWOl83xydCrzZA6mDBOx+8A6vVvTVjVDsGD6J6kCQJRcVFgCaiXtsLhtY4dfq0h1tFFDxkWcbGtWtwTQvPLs45PMaKdT9+79FjEPlSWVkZ3nznTah6CQhJ1Pu6OT4V1isEpZHF+N/c/zF8eAmDB1E9pKWlQWVodeXC8ksEtQEms8TVkIncZN/evWilNiNW79k/W12iVMg9fw7nz5/36HGIfKGwsBCvvvkq5K4SQjsH5rS5DRU2KAS5+hy8/e5bHHblBQweRPWw/+BhGFXxDfoe2dAWR48e9VCLiIKHLMtY/cMKjGzp+RmnBEHAyDgJK79d5vFjEXlTdnY2Xnv7NagGgqGjGkEQED4oFGWtS/HaW6/xgqGHMXgQ1UNy8gEIYUkN+h6rLgk796R4qEVEweP3bdvQVmtGCw/3dth1jVKhKDsTZ86wTosCQ3p6Ot5+7y0YrtMiJImhw5WwXiGQupjx2luvorCw0NfNCVgMHkRXkJWVBbNNBUET2qDvE0LikZaWDrPZ7KGWEQU+k8mEdat+wJhWnq3tqE4QBNzc2oZlSxZDlr13XCJPOHbsGD6Y9z7Cx4ZA3yY4C8nrK7RzCMT+wGtvv4asrCxfNycgMXgQXcHWbdth1ndy+ZgiGWHL2wNFMtZ4TBBEICQRKSns9SBqrFU/rED/KBsitN79c5UYrkKkrQw7d2z36nGJ3EVRFKxZvwYLvlqAyFvCoW2h9XWTmoWQ9gYYrtPi7ffewt59e33dnIDD4EFUB5vNhl279wLhV9eygQly/l7A5rogzRrSBRs3bfFgC4kC17lz53A4eTeGt/TNaso3tbZh9Q8ruMgYNTtGoxHvffQeth79DTETIqCJVPu6Sc2Kvo0OUbdHYNmapfhq2Vew2TxfXxYsGDyI6nDw4EHIutYQVI3rnhYMcSgoqeAq5kQNJEkSFi+Yh9vjZahF3wSPCK2I61tI+GrxZz45PlFjZGVl4YVXX0BuRA4iRodB1PBUrzHUoSpEj4vAofyDeP3t11BSUuLrJgUE/jYS1WHthk0wh3Zr0j4shq74aeOvbmoRUXD44bvlSFJXIjFc5dN29I8VUZ6djh3bf/dpO4jqY/ee3Xj7/begvUaFsF4h9Z4CnlwTVAIihoeismMFXnr9JZzm+lxNxuBBVIucnBzkFZZB0NdvtfJaRVyN5P37WWROVE+HDh7E8eRduKGN4uumQBAE3NlOweoV3yI7O9vXzSFyyWg0YsGiBVi+/htEjY9gEbmbhV5tQNhYAz767EP88OMPkCTJ101qthg8iGqxfsMmmEO6NfmKkSBqIId0wPbtO9zUMqLAlZ+fj6+/WIS7ExWfDbG6XJhGwIS2Nnzy4ftcYIz8zuHDh/H8S88hTTyN6HERUIf6tpcwUGljNYiZGIld53dizitzkJmZ6esmNUsMHkQumEwm7D9wEEJtReUNJIV1x08//wpF8f0VXCJ/VVlZif+98zbGtbYixktrdtRXhwgV+oZUYO4H77HQlPxCZWUl5n/2CRavWISwGw0I6x0CwU/CeqASNSIirgmFOFjBOx//FytWrmDvRwP51yc7kZ/YvmMnbCEdIYjumQlE0EbCJBtw6tQpt+yPKNBIkoSP3nsHg8IrcXWUf87AM6yliKjKbHz1+We8iEA+dejQITz/0vNIV6ch+vYIaKI0vm5SUNG30SHmzkjsydqNOS/PQUZGhq+b1GwweBBdRlEUbPx5M2zhTSsqv5zJ0A3rNmxy6z6JAoEkSfjkww/QRsrFoBb+/Wfp5ngFhWeO4Pvl3zB8kNeVlZVh3sJ5+PyHxQi/ycACch8S1SIihoVCHKrg3bnvYPmK5azlrAf//oQn8oHTp0/DJOshaCPdul8hLBFp6ec4JR9RNfbQEV5yFmNb+7o1VyYKAu5OVHDu4E6GD/IaSZKw/qf1eP7l53FOn4bo8ezl8Bf61lW9HykF+/DMnGewa/cufi7UgcGD6DJr1/8Mk8G9vR1A1UrmUmgX/LqZCwoSAYDVasW8D99HeMlZ3NgGzebKrVoUcE9SVfj47pulPMkgjzp8+DCefeFZ/HZqM2LuiERYj9Bm814JFqJaRPigUITfEoLvt3yHl157EefOnfN1s/wSgwdRNYWFhUhLz4AQluSR/cvh3bBl63YWo1HQKy8vx3/eeBUtys41q9BhZw8fuUd2Yf7HH8Jqtfq6SRRgcnJy8NY7b+HzHxdDP0qDiOGhUPnZpAvkTBOhRuQN4bD2NeP9Be9h/mfzUVpa6utm+RX+BhNVs2btT7CG9fDYSZCg1sOmT8D2HZxal4JXXl4e3nz1JfTT5GNUa6HZhQ47tSjgrkQgrOAU3nnrdVRUVPi6SRQAKisr8eWyL/Hme2+gpEMhom4NhzaGw6qaE0O8HjF3RCJddwZzXpmNtRvW8oLjJQweRJcUFRVhX8oBKBFdPXoca0QfrFq9nldIKSgdPnQI77zxKm6JrUS/2Oa/3oAgCBjbRkBPMQ9vvPwC5/anRrNYLFi7YS2efeFZHDUeQuykKIS0NzTbYB7sBFFAWPcQRN8Zia1ntuDfz/8b23dshyzLvm6aT/nnnIVEPvD1sm9hCe/jtil0ayNowmHWJmDDTz9j/LhbPXosIn8hyzJ+/P47HNr9O6Z1UBCta/6ho7qBLUS00hsx973/4NYJd+La60f4uknUTEiShK3btmLthjXQtFcj+o5wqPSB9f4IZipd1dof1lIJq3avxOr1q3H3xLvRv3//oAyVDB5EAI4dO4aTZy4ArSd45XhS5ED8/MsKDB0yCHFxcV45JpGvFBUVYeG8jxBlysP0qwC1GJid7e3CVJjRScF367/HyePHcP+0h6DX633dLPJTsixj9+7d+H719xDbCIgYH8ZVxwOYJkINzcgwWIqs+OaXZVi5diXuvetedO/ePagCSGB++hM1QHl5ORZ89gUsMSO89uYXVFqYo67BBx/O47hPCliKomD777/jzZdfQD8xB7cnVNVFBLIQjYAHOiiIyjuGl2c/i2PHjvm6SeRnFEXB/v378eycZ7By5/cIvUmPiGtDGTqChDZag8gbwiAMlbF45SK88uYrOHPmjK+b5TVCcXEx5wGkoCVJEt546x1cMLcDGlHboZiLIKV9DXXH+yDoohv8/arCXeh3VSgeeehPDf5eIn9WXFyMzz+dD7nwAm5vqyBM493A8eLeClguDaXWiMALg0K9enwAyDPK+P68iA7de+Pue+9n70eQUxQFR48exfIflsOorUTIQH3QFo2f/eQ8FKnq9FNQC2j/WIKPW+Q7xiwTjHvNaBXZGvfedS8SExN93SSP4lArClqKouDTRV8gpzwUiPVsQXltpOjBOHB8A9at/wm33nKTT9pA5E6SJGHzL5vwy0/rMbqlDb2TxKAaRlBdnEHE9KsUbD9/AC89fxQT774HgwYNDtrXI1gpioLjx49j+Q/LUSGUwzBYh6iW4b5uFvkJQ7we+tt1KMkoxHsL3kPbuLaYfMdktGvXztdN8wgGDwpKiqLgy6+W4ciZQkgtRsNXpwGCIMIaNxbrf1kHg8GAUSOv91FLiJru2LFj+ObLz5GoNeGxqxWEqDl0RCUIuL6VgF5RVmz48Wts3vgT7n/wYSQkBO8V3mBy4sQJLP9hOcrkUuj7axHVmoGDahIEASFJBhgS9Sg8l4d357+LxFaJuPuOu9G2bVtfN8+tGDwo6MiyjMVffIX9x7NhjRvt86uPgqiFteXN+H71elisFtx0w1iftoeoobKzs7Him69RlpOBO9vIaB0iAj6L8/4pWidiSnvgTEkuPnnvLXTu2Qe33zEJkZGRvm4aecCpU6ew/PtvUGQpgmGADlFtGDjoygRBQEh7AwxJeuSm5+C/H/8H7RM64O6Jd6NNmza+bp5bMHhQUDGbzfh43kKcybFCihsDQfCP+RUElR7WVrdhzcafUFBQhHsn3wUxQGf+ocBx8eJFrFrxLTLTTmFknA3dO4p+857yV50iVZgVrmDfhf1448WD6NN/EG69fQIiIiJ83TRyg7S0NCxbsQyFxgIYBugQHc+fKzWcIAgI7WhASAc9ctIu4O3/vYVOSZ0waeLdaNWqla+b1yQsLqegkZeXh/c++BhFaAc5sq9bejqaWlxeY3+KDeqC35HUQsCfZ05HaKj3C2KJriQvLw+rVnyL9FMnMCLOhp4xKoh+VrfgD8XlV2KVFezLV7CrQED/QUNwy7jbER7OK+PN0cWLF/H18q9xPj8ThoE6GBI4kUBdWFzeMIqioOK0Eab9FvTu3ht3Tbir2V6sYPCgoLBr1x58/c13MEddAzEsyW37dXfwAKo+YITSYzBUHsXMRx9C586d3bJfoqY6c+YMNq5dhQvn0nFdnA29/TBw2DWH4GFnlRXsyZOxp0BEj959cMMtt6F169a+bhbVQ1lZGb5b+R0OHTsEw0AtQjpwpfH6YPBoHMWmoOJ4JYyHLRh57UjcctMt0Ol0vm5WgzB4UEArLCzEZ4u/REZOWdU6HRr3nnx4Ing49m0qgLbwN/Tt1QVT7pkEg8Hg1v0T1YckSUjetw8/r18DjaUcQ6Ot6Bzlv4HDrjkFDztJVnC40IadhWpExbXGjbfdHnSLizUXZrMZGzZuwOZtm2HopUVotxAIKv6c6ovBo2lki4yKg0ZYz0i4/bYJuO7a65rN8GwGDwpIZrMZG376Gb9s3gpLxEAg/CqP/PH2ZPAAAEWRIZYcgq7yBCZOuA3XXXtts/lwoeatuLgY27duwe9bf0P7UAVDY2yXisabh+YYPOwURcHZMhk7C9UoknUYdcNNGDJ0KEJCQnzdtKAnyzK2/b4Nq9b+CE0nNUL7GCBqm8/7wl8weLiHVGFD5T4jxAIVptx9H3r16uX3FyoYPCigWK1WbPt9O1avWQ+LviNskX0hqLQeO56ng4fjONZyaEqSEYICTJ40Ef369WMAIbeTJAkHDhzAtl9/Rv7FbPSNUjAgVkColxf/c4fmHDyqKzTJ2Fso4Fgx0PGqq3Dd6BvQpUsXvv99IDMzE3MXzoUt2oqQgQauNN4EHVM7Y/TI0VVfCMAheT/2ZO/2baOaMUuBFZV7TYjRxGLmIzMRHe2585GmYvCggGAymfDLr7/h5182w6ZrB2tEX7cPq3LFW8Hjj+MVQFt2AHqlGLePuwVDhw6BWs3J6ahpMjMzsW3zrzh4IAUdwgT0j5KQGNa8F/4LlOBhZ1MUnCq2YX+pFrlmAUOGDcfw60egRYsWvm5awFMUBRs3bcT6X9YjfEQI9K2b15h6f8QeD8+oOGOEcY8ZU6dMRf9+/X3dHJcYPKhZy87OxoaffsH+gwdhC7kKtoheENTeq4XwdvBwHNdSAk3ZQahMF3Dt8GEYO2YUoqKivHZ8av4uXryIvbt3Yd+uHdDKZvSLsKJnjAraABmnHmjBo7oKq4KDhTIOlKigD4/CkGuuw4BBg7gmiAeUlpZi3sK5yJfzEH5dKIdVuQmDh+dIZRJKf61Ajw498cCUB6DVem7UR2MweFCzYzabkZKSgo2/bEVhcQVMId0ghF8FQfTulX/riU8ARar6QlBD0/Uxrx4fABSbCULZSWjKTyAxsS1uGH09evbsCZWKQwCopvz8fOzZvQt7d26HYDGiZ4SEHlEConSBdzIVyMGjutxKGYdLgGMlIiKiYzB4+PUYMHAgp+V1gyNHjuCzJZ9B30+D0C6sr3EnBg/PUmQFFfuNwDkRf3nsL0hI8J/Xl8GDmgVZlnHmzBn8snkbjh07DiUkCdaQzhAMcT5rkz8EDztFUaBUXoDOeAqCOQeDBvTHyBHX+tWHDflGfn4+kvftxd4dv0MyVaBnuISe0QKiAzBsVBcswcNOURRcNCo4XCzgWCkQExuHwcOvR99+/ZrtfP++Issyln67FMlH9yFidBg0kRzO6m4MHt5hyjGjfEslbrtxHMaOHuvr5gDgyuXkxxRFQUZGBrb9vhP7UvZD0UTDpLsKQtt7IIgqBMaAEPcQBAFCaAKsoQlQbBZsO3Eaew9+Bq1owTVDB2P4NUPRsmVLXzeTvEBRFJw/fx779+1Byr69EKwmdAuXcGecgFi9CIC9YYFIEAS0DhHQOgQY20ZBVuVFHP1tBTau+h6hkZEYMHgY+g8cxJqQetiybQv2n01BzO2RnCKXmjV9ax20EzVYt3YtEhMS/WJdMPZ4kF9RFAWZmZnYuWsP9u5LgVUIgUnXEUJYRwgq/yro86cej9oo1goI5aehNaUhRC/imqGDMGTwIIaQAGPvEUzZuwsH9+9HhFpBt1ALukWJiAzwno3aBFuPR13yjDKOlSg4XqaGotGj/8BB6D9oCNq2bdusJxDwhJKSErzw6hxET4yAysCQ7ins8fAuc54Flm02vDLnFZ9PSMMeD/I5WZaRnp6OXXuSkZKyHzYxFCZteyB2HAS1AcF52uQegiYUiO4DC/rAbCnFul1p2LTlIxg0wNAhAzF40AC0adOGJx/NkCRJOHHiBJJ3bcexY8fQOkREtxAzZnRUI0QtgB/vZBdnEDHCAIxoraDEXIFjRzfjyz2/o1QS0KdffwwYPBQdO3bkFL0AlixbAn1fHUMHBRRdnBbm1hVYu2EtJoyb4NO28C8T+YQkSTh58iR27NqHI0ePQtBGw6hJgtDidghqPYdReYCgjQC0fWFBX5itZfhpbzp+274QapjQv19fDBk8AB06dODJhx8zmUw4fPgwkndtR1paGpLCRXQLtWJ0ZxW0KhmAxtdNJD8XqRMxrBUwDDIqrQpOnN2J1UeTkWNU0L1HdwwYcg26du3q86uivnD06FGk56QhehBrYijwhA00YPP3mzFs8DCfjnoIvk8W8pny8nIcPnwYO3en4OzZdAiG1jBpkyC0uQuCqGXPhhcJmnAgujfM6A2TZMSWY2ex5+ByKJYidO3SFUMH90P37t2h0/nX8LZgVFlZiZTkZOzb9TtysrJxVYSIXuESbu8qQiUq4Mc4NVaIRkD/ODX6x8mw2BScyj+ILcuP44syGzp16oSBw65F7969odEER6D9cd2PCBmoZw8wBSRRK0LXU4OfNv2EqfdN9Vk7+BeLPEZRFGRlZWH/gYPYm3wQRUUlkEPawapLgpAwBIKgYtjwA4LaACGqG8zoBkW24kD2eRz/dguUyq8RH98GQwb2Q+/evViU6kVWq7UqpG/5FZmZGegWCVwbIaNtNxGioIAF4lc2cvRojBg1BgAgCEB42u8oPbzVx63yX1qVgB4xavSIkWGTgfSyU0helY7lX8ro2q0bhl0/Cp07dw7oHtE+PfpgW9YW6ON5wYUC1EWgz419fNoEFpeTWxmNRpw4cQJ7kw/i2PHjgDoMJk0CEJIEaKMC6kpScygubwpFUaCYcqEyZkJjyYRWlNGnd08M6N8HV111VdBcBfUWWZZx+vRp7Ni6GcePHkWHcBF9IiW0DxchBtD7xltYXO4ekqwgtdiGg2Va5BiB/gMHYti116Ndu3a+bprblZaWYvars9HinsD6W+WPWFzufTajDSU/VuDt19/26QUE9nhQk8iyjLNnz+LgoSNIOXAYxSUlEAxtYdYmQGh1JwSVlvUazZQgCBAMraAYWsGCgTBLRmw7mYG9R9dBrshGq9atMbBfL/Tq1RPx8fH8Q91IVqsVv2/dgp83rEOcTkHvcAvGdlVBI7Jng3xPLQroHqNG9xgZRknB0fQd+PLQPlhUetw24U70HzAgYHpBIiIi0D6xPfIycxCSaPB1c4jcqvKkCddec63P368MHtQgsiwjKysLR48ex4FDR3H+wnmoDHEwqeKB0GuAtpEQBIFDqAKQoDZAiOwCC7pAiVFw3lyAC79nYsPmvVAsJejQsSP69+mBbt26IS7Odws7Nhf2wLFx/VpcHSbjofYyIrQi+LFM/sqgFjAwTo2BcTJyjeX47cevsGblCoy74y707x8YAeSm0Tfh0xULYWir5xoeFDBkswzTSQtGPj3S103hUCuqm71O48TJkzh46DjOnk2HqIuGWdMasr4tBH0cBKH5/7FpjEAfatUQiixBMeZAZc6G1pIFQTbiqquuRt/e3dClSxfWh1SjKAq2/vYbNqxdhavDZFwXZw8c5E4cauUduUYZv+WqUKDoccfke9GnT19fN6lJ7KuW7zu0D+EjQqCL0/q6SQHLUmTFha9z0Pa+1tBGc+iup1SeNaJilwnjbvKP1ct5aY2cyLKMCxcu4MSJkzhw+DgyM85B1EXBom4Nm769oygcAHs1yEEQ1RBCE6CEJsAMQJEtOJSbg+PrDkO1ciME2YSOHTuib69u6Nq1K+Li4oJ2aNb237dh508/4OEOyqXAwXcSNV8tDSImJynINVZg+RefIXTWE7jqqqt83axGE0UR999zP4YMGIKFixfCklCBsIEh7P2gZsdmsqF8hxHh1gg8+fe/+c3CwezxCHKyLOP8+fM4dvwEDh4+jgvnM6t6NNStIevjIRjiHEGDnF0Xn4oxo0de+krAb8dk/JaS7csm+S1FtkIx5kBtzobGmg1IlejQoaOjR6RVq1ZBEURycnLw/luvY8ZVCkI1gf98fYk9Ht6XUylj+QUtnn3hZYSEhPi6OU1mtVqxYuUK7N6/C2HXhUDfmrNduRN7PDynIq0Sxj1m3HbTOIweOdqvhkEyeAQZRVGQm5uLI0eOYt/+w1VBQx9zKWi0uTR0ikGjPjjUqvGchmZZc6BI5ejYsSMG9OuFnj16ICoqytdN9Ij3//sWrjafxYA4/pH1NAYP31ifKSGs53WYNPleXzfFbc6ePYv5n30CubUNoYMMENX+cxLXnDF4uJ/NaEPZ9krEIAaPPvwYYmNjfd2kGjjUKgjY1wTYvfcAUlNPQlGFwKKNh03fGULCtY4aDX6UkrfUHJol4VhhNlLXHYL6h/XQqoAePbphyKD+6NKli19drWmK2ybcic8+/gCdImRE6QLjORHZZZbbkGrU4e9jbvB1U9yqffv2eGn2y1i5eiV+X74N+o466LvpoInkKRT5B3OuBeYTVpjPWzBx3ESMuH6E344iYI9HgJIkCceOHcOWbTuReuoUhJAEmLVJEELiIahYLOcO7PHwHEUyQqnIhN56DjDno0/vXrhu+FB06tSp2YeQgwcO4Lsln+GBDgrDhwexx8O7sipsWJ6pwuN/+xfatm3r6+Z4jNFoxI6dO/DLlk2wqq3QdFUhpIOBNSCNwB6PppEtMipPGWE5KSEqNBo3jb4J/fr18/s1thg8AtChw4fx+Rdfw6qOg1nfAUJoOw6f8gAGD+9QZAuUsrPQW9IQprXisekPIjEx0dfNapJ9e3bjx++/Q4QooW+EBd2iVdCIPHFxtzyjjPcOGfF/vQ2IMzDkuZtRUnC40IYDpRqIhnDc88C0Zl1Y3hCKouDs2bPY+OtGHD9xDPpOOui7shekIRg8GsecZ4H5eFXvxqABgzBm5Bi0bt3a182qNwaPAPPFl0ux70AqLDHXQdBF+7o5AY3Bw/vkyizoirZj4vibMHrUCF83p0kURUF6ejp+/+0XHD18GFdFCOgXZUNCqOi3XeTNDYOH+8mKgjMlNuwv1eBCJTBo8FAMHzGyWZ34uJtTL4jGCk0X9oLUB4NH/TXX3g1XGM0DTH5BEcwR/SAydFAAEkPiYTZ1RklJia+b0mSCIKBjx47o2LEjLBYL9u9PwdbNv6DwfC46hgNJeglJ4SLCucYH+ZCiKCgyKzhbZkOGWYv0MgXtO1yNUbeMRffu3Zv90Ed3MBgMGDN6DEaPGv1HL8g3x6DvoIOmgxq6VlpeTKAGU2wKjBdMkNJlWC5YMHDAIIz5S/Pq3XCFPR4B5vjxE1j42ReQVJEwGbpeGmbFDzxPUcxFkNK+hrrjfexh8iBFtkEpS4PedAKhOgVPPj4TrVq18nWzPKKkpASnTp1C6tHDSE09CYuxEu3CVUjSmZEUrkKsTuB7up7Y49FwsqLgYqWMs+UKMsxanC+XEBUZhc7duqNztx646qqrAmKqXE8zGo1ITk7Gzn07cP7CBejb6aBpr4I+XseekEvY41GTbJVhzDDBdk6BOduMq66+GsMGDkPv3r2bZe+GKwweAUiWZZw8eRLrN/6K9PSzUIW0hEmMhaJtWTVdLovL3YbBwzMUyQTFdBEqSx60tgLYTPno1bMnbrphFJKSknzdPK8yGo04c+YMUo8fRerxYygsLETbcDUStRYkhgloFSKyPqQWDB5XZrYpyK6Qca4CyDBrcLFCQpvWrdG5e0907toNHTp0CJgTHl8xmUw4cuQIduzdgTNnzkAfr4MqSYQhURfUU/MyeFSxmWRUnjNCPqfAWiChR/ceGDZoGLp06QK1OvAGJjF4BDiz2YyMjAykpaXj+MkzyMjMgGQDBH1LmIRoQBMJQRsFaMNZgN4IDB5No8gSYCmBYimBIBVDrxTCZsyDQadD+/bt0a1LJ3Ts2AFt27YNyA/gxrBarTh37hxST57A6eNHkZ2TAzVktAkR0VpjQZsQAW1CRBjUDCMMHs7KLDKyKmXkGAXkWLXIqZAgqDVol5CAq7r3ROfOXZCQkMDhUx5ktVpx8uRJ7NizA8eOH4MuTgsxETC0N0AVZLPcBXPwkCpsMKYbYTsHyOUy+vXthyEDhgTEzI1XwuARhCoqKnDu3DlkZGYi43wOsrNzUFRYAFkRoNJFwqaKgBnhgDYSgjYS0DCU1IbB48oUWQKspVAsxYC1BDqUQZRKYTOXQKPWILZFC8S3aY3EhDZITGyHxMRE6HRcIbghysvLkZmZiYxz53DuTCoyMzNhNZvROlSN1lorWutlxIeIiNAG1zCtYA0e8qW6jKwKGdlmFS5a1MitsCIsLAztEhORdFUXJCUloW3bttDr9b5ubtCSZRmnT5/Grn27cPDgQYgRIlRJArQtNUAQvE2lUgm56wvQ8pZYqCOC4MKSDFguWCFl2KC2aTCw/0AMGTgECQkJQfW5zOBBDlarFXl5ebh48SKysy8i43w2ci5eRElxESTJBpU2FNCEQxJCYUEoBE0YBE04oAkHxOAsngv24KEoCmAzQbGWAdYyQCqHFhVQyRVQrGWQrUZotRpEx8SiTZtWSExog9atWqFly5Zo0aIFVCoGWk+xWCy4cOECMjIycO5MKjLOnUVJSSliDOqgGZolyTKKKi2IDtFCHeBXEe1MNgUlZhviYmPQLqkDkjpdjaSkJLRq1Yq9hn5MURRkZGRg977dOJt51tfN8QqbZENRQRGiY6OhUgf+3wJRFND1qm4YNGBQwNYo1geDRxMcO3YM8+fPx+HDh2G1WtGxY0fce++9uPnmm33dNLeTZRmlpaUoKChAYWEhcvPykX0xH3l5+SgqLITRZIQgaiBqw6GoQmFRQiCrQgFNGAR1GKAJhSAGXm1JIAcPRVEA2QxYK6BI5YC1HCqlEhpUQJAqIFvKAMWG0NAwRMfEolXLWLRuFYe4FrGIiYlBbGwswsLCgjKQ+itZllFQUABJknzdFPIQnU6H6Ohovu+IyC8xeDRScnIynnjiCWg0Gtxwww0ICwvD5s2bkZWVhVmzZuGhhx7ydRO9zmQyoaCgAEVFRSgsLERefiEu5hagoLAQJcXFMFvMEEQNVNpwyKpQWGGATQwFRB2aa7+yYi2HnLsNYsvrIGjCfN2cRlKg2IzQKJXQoBKoFir0hhBERUWhRWwMWsbFomVcLKKjoxEdHY2YmBhotYEXJomIiMgzGDwaQZIkTJ48Gbm5ufj000/RpUsXAFW1E4888gjOnTuHb775ptmvruwJJpMJhYWFKCoqQlFREXLzClBWXunrZjWa1WpB1oXziG+bAI2m+Z6ER0dFIK5FjCNUREdHM1QQERGRW3HAZyPs27cP58+fx/jx4x2hAwBCQ0PxyCOP4Nlnn8WaNWvw5z//2Yet9E96vR7x8fGIj4/3dVOIiIiIyIuCo9rOzVJSUgAAQ4YMqfGY/T77NkRERERExB6PRsnIyAAAtGvXrsZjERERiIqKQmZm5hX3YzKZ3N42IiIiIiJvaci03AwejVBRUQEACAtzXUwcGhqK3NzcK+4nKysLNpvNrW0jIiIiIvIGlUqFjh071nt7Bg8fYp0DEREREQULBo9GCA0NBVC1WrArFRUVtfaGVMcVY4mIiIgoWLC4vBHs0+S6quMoLS1FcXGxy/oPIiIiIqJgxeDRCP369QMA7N69u8Zj9vv69+/v1TYREREREfkzBo9GGDRoENq2bYuffvoJqampjvsrKirw6aefQqVS4bbbbvNhC4mIiIiI/AtXLm+kffv24YknnoBWq8WNN96I0NBQbN68GVlZWZg5cyYefvhhXzeRiIiIiMhvMHg0wdGjRzF//nwcPnwYVqsVHTt2xJQpU3DzzTf7umlE9P/bu//4HuvF/+PPa2jY2BBtEicy+VXsfNoo/TjJ7591ks4KsZEZdVOLim0xbrfO8SNKFJOkSDqnFG6EIsrs8MY6Jr9nbWjsB9uY/Xp//+h4f4335s1xva+xx/1265Zdr9e1PVe3t+v9fF/X67oAAECFQvEAAAAAYDrWeAAAAAAwHcUDAAAAgOkoHgAAAABMR/EAAAAAYDqKBwAAAADTUTwAAAAAmI7iAQAAAMB0FA8AAAAApqN4AAAAADAdxQMAAACA6SgeAAAAAExH8QAAAABgOooHAAAAANNRPAAAAACYjuIBAAAAwHQUDwAAAACmo3gAAAAAMB3FA7jB8vPzrY4AAECFUlxc7NK8zMxMk5PAShQPwEXjx49XTk5OuXP279+vIUOGuCkRgEstX778qnPy8vIUExPjhjQALjV8+HClpaWVO2fr1q0KCQlxUyJYgeIBuGjTpk0KCQmRzWZzOv7ZZ58pLCxMx48fd3MyAJI0c+ZMjR07VllZWU7H9+7dq8GDB2vdunVuTgYgKSlJgwYN0po1a64YKyws1PTp0xUZGenymRHcnCgegIuioqKUm5uriIgIzZ071/GXY0ZGhsaMGaP33ntP/v7+WrhwocVJgcqpZ8+e+vnnn/Xcc89p27ZtpcYWLVqkESNGKD09XZGRkRYlBCqvOXPmqGbNmpo8ebImTpyo3NxcSdLhw4c1ZMgQrVixQu3bt9enn35qcVKYycjOzrZbHQK4Wfz222+KiorSvn371Lp1a/Xr10/z5s1TVlaW+vfvr7Fjx6p69epWxwQqrfXr1+vtt99WXl6eBg4cqKefflpTp07Vrl271KxZM02ZMkVNmza1OiZQKZ09e1ZTpkzR5s2b5e/vr+7du2vp0qUqLi7WiBEjNHjwYBmGYXVMmIjiAVyj4uJivffee1q2bJkMw5C3t7eio6P1yCOPWB0NgKSTJ08qOjpaiYmJkiTDMDRgwACNGTNG1apVszgdgEWLFumDDz6QYRjy8fHRrFmz1LJlS6tjwQ241Aq4RsnJydq+fbvj63PnzungwYOy2+nwQEXg6+uru+66S3a7XXa7XbVq1dKjjz5K6QAqgISEBH355ZeSpBo1aujMmTP65z//yR0hKwmKB3ANvvzySw0dOlTHjh3TyJEjtWzZMjVr1kwLFizQiy++qN9//93qiEClduDAAQ0ePFirV69WcHCwxo0bp8LCQo0ePbrU2iwA7lVUVKR3331XL7/8ss6fP6/Jkyfrq6++UseOHfXtt9/q+eef1759+6yOCZNxqRXgosjISG3dulUNGzZUbGysWrduLemPv0zfe+89LV++XF5eXho/fry6du1qcVqg8lm6dKnmzZsnu92u8PBwPffcc5Kk1NRURUdHa+/evWrVqpViY2PVqFEji9MClcuQIUP066+/qk2bNoqNjVXDhg0dY8uXL9ecOXNUUlKiESNGcFv6WxjFA3BRcHCwunfvrvHjx6tmzZpXjMfHx2vSpEnKyspSfHy8BQmByi04OFhNmjTRlClTFBAQUGqsuLhYH374oZYsWaLq1avrhx9+sCglUDl17NhRL7zwgsLCwlSlSpUrxg8dOqSoqCgdPXqUY+gtjOIBuGjt2rXq3r17uXOysrI0ZcoUzZgxw02pAFw0depUvfrqq+XeWc5ms+mtt97SN99848ZkAGw2mwIDA8udU1BQoNmzZ+u1115zUyq4G8UDAFCp5OTkqFatWlbHAIBKh8XlAIBKhdIBANaoanUA4GYRGxvr8tyoqCgTkwBwZvXq1S7P7dWrl4lJAFwuPDzcpXmGYWju3Lkmp4FVuNQKcFFwcHC544ZhyG63yzAMFsYBFggODr7qU495jQLW4BgKieIBuOzEiRNOt+fm5mr//v1atGiRAgICNGbMmFK3CQTgHqtWrXK6PS8vT7/++qvWrVunRx55RJ06dVLv3r3dnA6AMxePoXPnzlX9+vU1depUp3e9wq2B4gHcIBkZGQoJCVFYWJgGDBhgdRwAl0lMTFRERIRmzJihoKAgq+MAuEReXp5CQkLUp08fhYWFWR0HJmFxOXCD1KtXT506ddKKFSusjgLAifvuu08PP/yw5s+fb3UUAJfx8vJyPMUcty6KB3ADeXl5lXlJFgDr+fn56eDBg1bHAOCEh4eHMjIyrI4BE1E8gBskJydHmzdvVt26da2OAsAJu92u3bt3y9PT0+ooAC6TlpamjRs3ys/Pz+ooMBG30wVcFBcX53R7cXGx0tPTtWXLFp09e1ahoaFuTgZA+uPJyM4UFxfr1KlTWrNmjZKSktSjRw83JwNQ1i3pi4qKdOrUKe3Zs0dFRUUaPny4m5PBnVhcDrjoarcCrFmzpgYMGKDw8PCr3tITwI13tdvp2u12tW3bVjNmzJCPj48bkwG42jG0cePGCgkJ0ZNPPummRLACxQNwUVmfphqGodq1a6tJkyaqWpWTiIBV5s+f77R4eHh4qFatWmrZsqXatm1rQTIAZa1/9PDwkLe3t7y8vNycCFageAAAAAAwHYvLAQAAAJiO60KAMpR1aZUrAgMDb2ASAABuLqtXr77ufXv16nUDk6Ai4VIroAxXW6hanvj4+BucBsDlrvc1ahiGtm3bZkIiABddz+vTbrfLMAyOobcwzngAZQgNDeXuVEAF1r59e16jQAUVFRVldQRUQJzxAAAAAGA6FpcDAAAAMB3FAyhHXFzc/7TIHIC5bDabTp48aXUMAE7Exsbqxx9/LLWtsLBQubm5FiWC1SgeQDkWLFhwRfFYvHixnnjiCYsSAbjUqFGjtGrVqlLb1q9fr3HjxlmUCMBFq1at0oEDB0pt+/jjjzmGVmIUD+AaFRQU8GkNUEHY7VcuU0xOTr7iU1YAgPUoHgAAAABMR/EAAAAAYDqKBwAAAADT8QBB4CrS09O1d+/eUl9LUlJSktPryyWpdevWbskGQDxEEKjADh8+rPXr15f6WpI2bNhQ5jG0S5cubskG9+MBgkA5goODnb6psdvt5b7ZiY+PNzMWgP8KDg5WlSpVVKVKFce24uJilZSUqFq1ak73MQyDxeeAGzg7hl4sG+UdWzmG3ro44wGUo1evXlZHAFAOPz8/qyMAKENYWJjVEVDBcMYDAAAAgOlYXA4AAADAdBQPAAAAAKZjjQdwDY4cOaIVK1YoKSlJubm5Ki4uvmKOYRj66quvLEgHoLCwUJs2bdK+ffuUk5OjkpISp/OioqLcnAxAQkKCli5dqqSkJOXk5Di9q5VhGNq2bZsF6eAOFA/ARTabTS+//LIKCgpUpUoV1a1bt9SddC4q6/aAAMx14sQJjR49WmlpaeW+Dg3DoHgAbvb9999rwoQJKikpkZ+fn5o0aaKqVXkbWtnwfxxw0Zw5c1RUVKQJEyaoV69eTksHAOu88847Sk1NVY8ePdS3b181aNCA1ylQQcTFxcnT01PTpk3TAw88YHUcWITiAbjo4MGD6tq1q/r27Wt1FABO7NixQw888IDeeustq6MAuExKSop69OhB6ajkWFwOuMjLy0t16tSxOgaAMtjtdgUEBFgdA4ATvr6+ql69utUxYDGKB+Cihx56SLt377Y6BoAytGnTRsnJyVbHAOBE586dlZCQoKKiIqujwEIUD8BFY8aMUW5urqZPn678/Hyr4wC4zOjRo7Vz505t3LjR6igALhMeHq7atWtrwoQJOnnypNVxYBGeXA64KDw8XLm5uTp48KBq1Kihu+66S15eXlfMMwxDc+fOtSAhULnFxcUpKSlJP//8s9q3b68WLVrI29v7inmGYSg0NNSChEDl1b9/fxUVFen06dOSJG9v7zJfn9yS/tZF8QBcFBwc7NI8wzAUHx9vchoAl+M1ClRc/fr1c3nuypUrTUwCK1E8AAC3BJvN5vLcwMBAE5MAAJyheAAAAAAwHc/xAK7T+fPnlZeXJy8vL9WoUcPqOAAA3BSKioqUkpKi3NxceXl58RTzSoT/y8A1KCoq0pIlS7Rq1SqlpaU5tt95553q3bu3nn/+eVWrVs3ChAASExO1atUqHThwwPHGpkWLFurZs6fatWtndTyg0jp79qzmzJmjdevW6cKFC47tnp6e6tatm0aNGiVfX1/rAsJ0XGoFuCg/P19jxozRL7/8Ig8PDzVq1Ej16tVTZmamUlNTVVxcrNatW+v999/nIUmARWbPnq1ly5bJbv/j0Obh4aGSkhJJfywqHzhwoMaOHWtlRKBSOnv2rEJDQ5WSkiIfHx+1bNnScQzdt2+fsrOzddddd2nhwoXy8fGxOi5MwhkPwEVLlixRYmKiunXrpoiICN1xxx2OsVOnTmnOnDlau3atlixZouHDh1uYFKicVq9eraVLl+pPf/qTwsLCFBgY6Hhjs3PnTsXFxWn58uUKCAhQr169rI4LVCoLFy5USkqKhgwZomHDhpX6gC4/P18ff/yxFi1apI8++ogPB25hnPEAXPTMM8+oZs2a+vjjj8uc88ILL+jcuXP64osv3BcMgCRp2LBhOn36tJYtW+b0GTu5ubkKCQnR7bffro8++siChEDl1b9/fzVs2LDc51xFREQoLS1NX3/9tfuCwa14cjngohMnTigoKKjcOQ888IBOnDjhpkQALnXkyBH95S9/cVo6pD8eWPbYY4/pyJEjbk4G4PTp02rTpk25c1q3bu14wCBuTRQPwEWenp7Kysoqd05WVpY8PT3dlAjA5S6u7SiLYRhuSgLgUt7e3jp58mS5c06ePOn0aea4dVA8ABe1bdtW69ev1+HDh52OHzlyRBs2bFDbtm3dnAyAJDVt2lQ//PCDzp0753Q8Ly9PP/zwg5o2bermZAACAwO1ceNGJSQkOB1PSEjQxo0bebjnLY41HoCLEhMTNXLkSFWpUkV9+/ZVYGCg6tatq8zMTNlsNn377bcqKirSvHnzdP/991sdF6h0Vq1apdjYWDVt2lTDhw9XYGCgfH19lZ2d7VhcfvToUU2cOFG9e/e2Oi5QqRw5ckRDhw7VhQsX9OCDD5Y6hu7cuVPbtm1T9erVtXDhQjVr1szquDAJxQO4Bt9//72mTp2q3NzcUpds2O12eXt7680331Tnzp0tTAhUbjNnztTy5csdr0/DMByXX9ntdj3zzDN69dVXrYwIVFqJiYmaNGmSUlNTJZV+fTZq1EjR0dF8cHeLo3gA1+jcuXPavHmz9u/f73hyeYsWLfTII4+UuagVgPvs3r1b3377rQ4ePOh4jV68hW779u2tjgdUana7XXv27LniGHr//fezBqsSoHgALoqLi9Odd96pHj16WB0FgBM2m03e3t4KCAiwOgqAy8TGxuqee+7R3/72N6ujwEIsLgdc9NFHH+nQoUNWxwBQhlGjRnH/f6CCWrdunTIzM62OAYtRPAAX+fv76+zZs1bHAFCGOnXqqGrVqlbHAOBEo0aNeEYHKB6Aq7p27ar4+Hjl5uZaHQWAEx06dNCuXbuu+iwPAO7Xt29f/fTTT0pPT7c6CizEGg/ARYWFhRo3bpwyMjI0YsQItWrVSnXr1rU6FoD/OnXqlEJDQxUcHKzRo0fLx8fH6kgA/uv48eOaNm2aDh8+rEGDBjmOoc4WlPv5+VmQEO5A8QBc1KFDB0l/3JGjvDtvGIahbdu2uSsWgP8KDw/XmTNndOTIEVWrVk0NGzZ0+uGAYRiaO3euBQmByis4ONhx+1yOoZUXF8MCLmrXrh23+gMqMJvN5vhzQUGBkpOTlZycfMU8XseA+/Xs2ZPXHjjjAQAAAMB8LC4HXFRSUuLSPG4XCFRsrr6WAdw4+fn5Ls07duyYyUlgJYoH4KKpU6dedU5mZqZGjRrlhjQALufKMzyKi4sVFRVlfhgApbz++usqLi4ud86xY8cUERHhpkSwAsUDcNGqVav0/vvvlzmenZ2t8PBwpaSkuDEVgIv+/ve/a/PmzWWO2+12RUdHa+PGjW5MBUCStm3bpsmTJ5c5/ttvv2nUqFHKyclxYyq4G8UDcNGAAQO0ZMkSLVu27IqxS0tHdHS0BekAtGnTRhMnTtSuXbuuGLPb7YqKitKGDRv01FNPWZAOqNxGjx6ttWvXatasWVeMpaamKjw8XDk5OZoxY4b7w8FtKB6AiyIjI/XEE0/o3Xff1dq1ax3bL5aO5ORkxcTEqHv37hamBCqvd955R40aNVJkZKQOHTrk2G632xUTE6P169frySef1Lhx4yxMCVROgwYNUkhIiD7//HMtXrzYsT0tLU0jR47U2bNnNXPmTP3f//2fhSlhNu5qBVyDoqIijR07VjabTdOnT1fLli01atQoHT16VNHR0erRo4fVEYFK7dSpUwoLC1NRUZEWLFggf39/RUdH67vvvlP//v31xhtvWB0RqNRiYmK0bt06TZw4Ue3bt9eLL76oM2fOaMaMGQoKCrI6HkxG8QCu0fnz5zVy5EgdO3ZMd9xxh1JSUjRx4kT16tXL6mgA9McC1eHDh6tWrVq69957tWHDBvXr109vvvmm1dGASq+4uFiRkZHavn27fHx8lJubq+nTpys4ONjqaHADigdwHbKzszV8+HClpqZqwoQJ6t27t9WRAFwiKSlJEREROn/+vPr06aMJEyZYHQnAf+Xn5ysiIkIHDhygdFQyFA+gDOHh4eWOZ2ZmKiMjQ82bNy+13TAMzZ0718xoACTFxcWVO75r1y4dOHBAAwYMkIfH/1/SaBiGQkNDzY4HVGr9+/cvd/zChQs6d+6c6tSpU2q7YRj66quvTEwGK1E8gDJc7ycwhmEoPj7+BqcBcDleo0DF1a9fv+ved+XKlTcwCSoSigcA4KZks9mue9/AwMAbmAQA4AqKB2CygwcP6sCBAyw+Byqo3Nxc5ebmys/Pz+ooAC5js9lks9kUFhZmdRTcADzHAzDZpk2bFBsba3UMAGVYtmzZVa9HB2CNnTt3XnU9F24eFA8AAAAApqN4AAAAADAdxQMAAACA6SgeAAAAAExH8QAAAABgOooHAAAAANNRPAAAAACYjuIBAAAAwHQUD8Bk/v7+at++vdUxgFtehw4dFBUVdc372e122e12ExIB+F8FBASoZ8+eVsfADWJkZ2fzty3gAleePm4Yhry8vNSkSRN16tRJDRo0cEMyAJLUuXNnPfnkkxo9erTVUYBK7+Ixc9SoUapXr55Lx9CLrucDBNwcKB6Ai4KDg2UYhiQ5/XTUMIxS26tUqaLQ0FCFhoa6LSNQmY0ZM0YeHh6aPXu21VGASu/iMXP58uVq0qSJgoODXdrPMAzFx8ebnA5WoXgALkpLS9M777yjpKQkDRw4UPfdd5/q1q2rzMxMJSYmavny5WrVqpWGDRumAwcOaNGiRfr9998VGxurLl26WB0fuOX98ssvGjlypN544w317t3b6jhApXbixAlJUv369VW1alXH167w9/c3KxYsRvEAXLR48WJ9/vnn+uyzz1S3bt0rxk+fPq3nn39eISEhGjx4sNLT0zVw4EAFBAToww8/tCAxULnExcVpz549+ve//62AgAC1bt1adevWdZypvMgwDM5EAoAFqlodALhZfPPNN+rcubPT0iFJt99+uzp37qyVK1dq8ODBatCggTp16qSffvrJzUmBymnBggWOP+/fv1/79+93Oo/iAQDWoHgALkpPT9dtt91W7hxPT0+lp6c7vvbz81NBQYHZ0QBImjdvntURAADloHgALqpfv742b96skSNHOi0gBQUF2rx5s+rXr+/YlpmZqVq1arkzJlBpBQYGWh0BAFAOnuMBuKhv375KTU3VyJEjtXXrVp05c0aSdObMGW3ZskUvvvii0tLS1KdPH8c+u3fvVvPmza2KDAAAUGFwxgNw0aBBg3T06FGtXbtWkZGRkkrfQtdut6t79+4aMmSIJCkjI0MPPfSQOnbsaFlmAACAioK7WgHXKCEhQWvXrtWhQ4eUl5cnLy8vNW/eXN26dVNQUJDV8QAAACokigcAAAAA07HGAwAAAIDpKB4AAAAATEfxAAAAAGA6igcAAAAA01E8AAAAAJiO53gAAK7ZyJEjZbPZrjovISHBDWlKy8nJ0bJlyyRJI0aMcPvPBwA4R/EAAFy3O+64Q35+flbHKCUnJ0dxcXGSKB4AUJFQPAAA161Pnz68uQcAuIQ1HgAAAABMxxkPAIBb7N69WytWrNCePXuUlZWlGjVq6N5779VTTz2lxx9//Ir5hYWF2rp1q7Zu3aqkpCSdOnVK+fn5qlevntq3b69BgwapWbNmpfaZNGmSVq9e7fg6KCio1Hh0dLR69+6t48ePq3///pLKXocyf/58xcXFqVevXoqJiXFsv3zfrVu3avny5fr111915swZ/eMf/9Bjjz0mSSopKdG6deu0Zs0a7d+/X7m5uapTp44jf4sWLa71PyMA3LQoHgAA082ZM0effPKJJMnb21t33323MjIylJCQoISEBD311FN6/fXXS+2TkpKi8ePHy8PDQ3Xq1JG/v78KCwt18uRJrVmzRhs2bNDbb7+tTp06OfZp3LixWrZsqX379kmS7r///lLfs27dujf09/rss880e/Zs+fj46M4771T16tUdY3l5eRo/fryj2NSrV0/NmjVTamqqvvvuO23cuFExMTHq3r37Dc0EABUVxQMAYKovv/xSn3zyiXx9ffXaa6+pS5cujrHt27crJiZG//rXv9SmTRv17t3bMebr66tJkybpwQcflI+Pj2N7QUGBVq5cqZkzZ2ry5Mn65ptvHG/4hw4dqm7dujnOSCxYsMDU3+3999/XK6+8ogEDBqhKlSqSpAsXLkiSpk6dqoSEBLVo0UJvvPGGWrVqJemPsyBffPGFZs2apSlTpqhly5Zq0qSJqTkBoCJgjQcA4LrFxcUpKCjI6T+bNm1Sfn6+5s+fL+mPy6AuLR2SFBwcrPHjx0uSFi9eXGqsXr166tGjR6nSIUm33XabBgwYoC5duig7O1tbtmwx8TcsX9++ffXss886SockeXp6au/evdqwYYNq166tmTNnOkqHJHl4eOjZZ5/V008/rYKCAi1dutSK6ADgdpzxAABct/Jup+vj46MdO3YoOztb/v7+6tixo9N5Dz/8sKpWrapjx47p1KlTql+/fqnxhIQE/fzzz0pJSVFeXp5KSkokSSdPnpQk7d+//4pC4y59+/Z1un3jxo2S/vjdLv99Lnr88cf1xRdfaMeOHablA4CKhOIBALhuV7ud7qJFiyRJubm5Gj58eJnzDMOQJKWnpzveqJ87d07jx4/X9u3by81w5syZa419w9x9991Otx88eFCSZLPZyvy9L16SlZ6ebk44AKhgKB4AANPk5OQ4/r1nz56rzs/Pz3f8efbs2dq+fbt8fX0VERGhP//5z7r99tsd6zk+/PBDLVy4UEVFReaEd0GNGjWcbj979qwk6cSJEzpx4kS53+NiAQGAWx3FAwBgmotvzB999FFNmzbN5f2Kioq0bt06SVJMTIweeuihK+b8L2c6Lp5hkSS73V7q64suLUHXqmbNmpKkV155Rc8+++x1fx8AuJWwuBwAYJp77rlHkvSf//zHsTbDFdnZ2Tp37pwkqV27dk7nJCYmOt3urERc7tIzFRkZGU7npKSkXPX7lOXi80VcOcsDAJUFxQMAYJqgoCDVqlVLGRkZ+vrrr13e79LnYZw+ffqK8YSEBB04cOCq+5Z11sLX11e1a9eWJP3yyy9XjKelpSk+Pt7lvJd74oknJEmbN2/W4cOHr/v7AMCthOIBADCNl5eXwsPDJUkzZszQ0qVLrygDZ8+e1Zo1a/Tuu+86tnl7e6t58+aO/S6umZCkHTt2aOLEifL09HT6M319feXt7e2YW5aLDx784IMPSq3DSE1N1ZtvvnlNZ2gu165dO3Xu3FlFRUV66aWXtGXLFtnt9lJzjh8/riVLlmjlypXX/XMA4GbCGg8AgKmefvppnTlzRvPnz9esWbM0b948NWnSRNWqVVNWVpZOnDghu92uwMDAUvuNGTNGY8eOVXx8vPr06aPGjRsrJydHx48fV0BAgIKCgvTpp59e8fMMw1CPHj20YsUKRUZGqmnTpo6zG0OGDHHc1nfEiBH66aefdPToUf31r39VkyZNVFJSouTkZDVv3lzPPPPM//SMjZiYGBUWFurHH3/Uq6++qtq1a6tRo0YqKSlRenq6MjMzJUlhYWHX/TMA4GbCGQ8AgOlCQ0O1ZMkS9evXTw0aNNCxY8d05MgRVa1aVR07dlRkZKQmTZpUap8OHTpo7ty5CgoKkmEYSk5O1m233aZhw4YpLi6u1CVVl3vppZc0dOhQNW7cWL/99ptsNptsNlup9RwNGzbUwoUL1aVLF3l7eyslJUWFhYUaMmSI4uLiHAvEr1f16tU1bdo0zZgxQ4899pg8PT118OBBHT9+XHXq1FHXrl01ZcoUhYSE/E8/BwBuFkZ2drb96tMAAAAA4PpxxgMAAACA6SgeAAAAAExH8QAAAABgOooHAAAAANNRPAAAAACYjuIBAAAAwHQUDwAAAACmo3gAAAAAMB3FAwAAAIDpKB4AAAAATEfxAAAAAGA6igcAAAAA01E8AAAAAJju/wGqWvimKvm1aAAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8, 6))\n", + "filtered_results = results[['g_kronFlux', 'r_kronFlux', 'i_kronFlux']].apply(lambda x: x[(x > x.quantile(0.05)) & (x < x.quantile(0.95))])\n", + "sns.violinplot(data=filtered_results,\n", + " cut=0,\n", + " bw=0.2)\n", + "plt.title('Box Plot of Data Distributions')\n", + "plt.xlabel('Feature')\n", + "plt.ylabel('Value')\n", + "plt.xticks(rotation=90)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "4f2c50a8-098e-4230-b122-3880c8bb1883", + "metadata": {}, + "source": [ + "A violinplot gives a lot of the same information as a boxplot, in fact, there are little boxplots within the violinplot; the horizontal white line is the median, the thicker grey box is the IQR and the thin line shows the 1.5*IQR span. A violinplot also uses a kernel density extimator to visualize the distribution of each feature. Here we see that most of the data are clustered around relatively low values for all of the kron fluxes." + ] + }, + { + "cell_type": "markdown", + "id": "3ec470a9-8db0-403a-89ba-32e0dd9bef15", + "metadata": {}, + "source": [ + "## 5. Investigate the fluxes and associated flags" + ] + }, + { + "cell_type": "markdown", + "id": "18dba188-ea4c-47e9-8faf-62e3552add1e", + "metadata": {}, + "source": [ + "Use `pandas` to investigate if there are any flags on the `kronFlux` measurement. The `.value_counts()` method will show the number of True and False columns, where True are rows for which the `g_kronFlux` measurement was flagged for a variety of reasons. There are many other columns that investigate specific reasons why this measurement is untrustworthy; the `g_kronFlux_flag` is a way to combine all of the individual flags." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "0be4535d-cc89-45ef-98e9-591b9f459fae", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T00:04:41.789910Z", + "iopub.status.busy": "2024-12-03T00:04:41.789726Z", + "iopub.status.idle": "2024-12-03T00:04:41.794781Z", + "shell.execute_reply": "2024-12-03T00:04:41.794289Z", + "shell.execute_reply.started": "2024-12-03T00:04:41.789894Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "r_kronFlux_flag\n", + "False 10723\n", + "True 841\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results['r_kronFlux_flag'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "id": "af623422-c6cf-4c21-8971-5599c60ea8d7", + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-15T18:24:36.531474Z", + "iopub.status.busy": "2024-11-15T18:24:36.531192Z", + "iopub.status.idle": "2024-11-15T18:24:36.547424Z", + "shell.execute_reply": "2024-11-15T18:24:36.546685Z", + "shell.execute_reply.started": "2024-11-15T18:24:36.531443Z" + } + }, + "source": [ + "Let's compare the value of the `g_kronFlux` between flagged and unflagged cases." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "dddfd1e7-3faa-465f-ade4-dc136ae39262", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T00:04:41.795547Z", + "iopub.status.busy": "2024-12-03T00:04:41.795330Z", + "iopub.status.idle": "2024-12-03T00:04:41.924873Z", + "shell.execute_reply": "2024-12-03T00:04:41.924253Z", + "shell.execute_reply.started": "2024-12-03T00:04:41.795530Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.clf()\n", + "plt.hist(results[results['r_kronFlux_flag']]['r_kronFlux'], label='Flagged', density=True)\n", + "#plt.hist(results[results['r_kronFlux_flag']==False]['r_kronFlux'], label='Not flagged', density=True)\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "24e94c30-66b3-4b9c-b79b-bcf024d5f214", + "metadata": {}, + "source": [ + "Okay what about the `r_kronFlux` measurement?" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "0e66ccb2-3922-471b-8c15-7fb055d02a10", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T00:04:41.925819Z", + "iopub.status.busy": "2024-12-03T00:04:41.925606Z", + "iopub.status.idle": "2024-12-03T00:04:41.931416Z", + "shell.execute_reply": "2024-12-03T00:04:41.930843Z", + "shell.execute_reply.started": "2024-12-03T00:04:41.925802Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "r_kronFlux_flag\n", + "False 10723\n", + "True 841\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results['r_kronFlux_flag'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "id": "fa7a4ce1-7bd4-47f8-a480-188b2c70579a", + "metadata": {}, + "source": [ + "Perform an intersection to see if the flagged entries overlap between these two photometric bands." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "06786c33-2563-4237-9d0f-22d6308c0d7b", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T00:04:41.932301Z", + "iopub.status.busy": "2024-12-03T00:04:41.932089Z", + "iopub.status.idle": "2024-12-03T00:04:41.970411Z", + "shell.execute_reply": "2024-12-03T00:04:41.969900Z", + "shell.execute_reply.started": "2024-12-03T00:04:41.932283Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " coord_ra coord_dec g_kronFlux g_kronFlux_flag r_kronFlux \\\n", + "1749 61.916451 -37.018987 232.455339 True 380.415747 \n", + "1758 61.910063 -37.017256 115.486047 True 105.318235 \n", + "1774 61.950787 -37.015252 132.207788 True 193.917364 \n", + "... ... ... ... ... ... \n", + "11466 61.956032 -37.074942 59.901381 True 315.077832 \n", + "11471 61.942023 -37.073313 145.759753 True 120.304211 \n", + "11494 61.924542 -37.071842 248.013148 True 273.729756 \n", + "\n", + " r_kronFlux_flag i_kronFlux i_kronFlux_flag \n", + "1749 True 562.754481 False \n", + "1758 True 218.924537 True \n", + "1774 True 522.751057 False \n", + "... ... ... ... \n", + "11466 True NaN True \n", + "11471 True 243.456290 True \n", + "11494 True 257.108970 True \n", + "\n", + "[328 rows x 8 columns]\n" + ] + } + ], + "source": [ + "# get the unique values (which will be True or False)\n", + "r_values = set(results['r_kronFlux_flag'].unique())\n", + "g_values = set(results['g_kronFlux_flag'].unique())\n", + "\n", + "# find the intersection\n", + "overlap = r_values & g_values\n", + "\n", + "overlap_true_rows = results[\n", + " (results['r_kronFlux_flag'].isin(overlap)) & \n", + " (results['g_kronFlux_flag'].isin(overlap)) & \n", + " (results['r_kronFlux_flag'] == True) & \n", + " (results['g_kronFlux_flag'] == True)\n", + "]\n", + "\n", + "print(overlap_true_rows)" + ] + }, + { + "cell_type": "markdown", + "id": "ec13b104-ad8d-4bd6-8a93-b6d1d57b921e", + "metadata": {}, + "source": [ + "There are six overlapping true rows, meaning that in six cases, we cannot use the $r-$band kronFlux to predict the $g-$band kronFlux." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "d29499cf-cab9-4a5c-8811-ffd1e5c9d82f", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T00:04:41.971139Z", + "iopub.status.busy": "2024-12-03T00:04:41.970965Z", + "iopub.status.idle": "2024-12-03T00:04:41.978973Z", + "shell.execute_reply": "2024-12-03T00:04:41.978503Z", + "shell.execute_reply.started": "2024-12-03T00:04:41.971124Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " coord_ra coord_dec g_kronFlux g_kronFlux_flag r_kronFlux \\\n", + "46 62.036008 -36.904244 60.546756 False 0.103155 \n", + "347 62.072778 -36.964144 15.582472 False 0.000000 \n", + "700 62.067443 -36.954921 94.684525 False NaN \n", + "... ... ... ... ... ... \n", + "11477 61.984400 -37.069348 63.584932 False 85.069624 \n", + "11483 61.911589 -37.067708 159.077697 False 139.900920 \n", + "11493 61.957278 -37.071416 165.461776 False 182.058161 \n", + "\n", + " r_kronFlux_flag i_kronFlux i_kronFlux_flag \n", + "46 True 68.234239 False \n", + "347 True 100.430859 False \n", + "700 True 259.766837 False \n", + "... ... ... ... \n", + "11477 True 100.221708 True \n", + "11483 True 279.320760 True \n", + "11493 True 349.606659 True \n", + "\n", + "[513 rows x 8 columns]\n" + ] + } + ], + "source": [ + "g_false_r_true = results[\n", + " (results['r_kronFlux_flag'] == True) & \n", + " (results['g_kronFlux_flag'] == False)\n", + "]\n", + "\n", + "print(g_false_r_true)" + ] + }, + { + "cell_type": "markdown", + "id": "69b67af5-de80-414c-985d-8f6cdccfc3a3", + "metadata": {}, + "source": [ + "For the unflagged values, let's look at the relationship between these fluxes." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "e6294681-9c60-4ec6-805c-d378300acaa3", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T00:04:41.979744Z", + "iopub.status.busy": "2024-12-03T00:04:41.979551Z", + "iopub.status.idle": "2024-12-03T00:04:42.213955Z", + "shell.execute_reply": "2024-12-03T00:04:42.213438Z", + "shell.execute_reply.started": "2024-12-03T00:04:41.979728Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "clean = results[\n", + " (results['r_kronFlux_flag'] == False) & \n", + " (results['g_kronFlux_flag'] == False) &\n", + " (results['i_kronFlux_flag'] == False)\n", + "]\n", + "\n", + "plt.scatter(clean['g_kronFlux'], clean['r_kronFlux'])\n", + "plt.xlabel(r'$g-$band kronFlux [nJy]')\n", + "plt.ylabel(r'$r-$band kronFlux [nJy]');" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "5afedb17-6478-4f2b-bdfc-38e73cd4a65e", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T00:04:42.215968Z", + "iopub.status.busy": "2024-12-03T00:04:42.215730Z", + "iopub.status.idle": "2024-12-03T00:04:42.382817Z", + "shell.execute_reply": "2024-12-03T00:04:42.382256Z", + "shell.execute_reply.started": "2024-12-03T00:04:42.215949Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(clean['g_kronFlux'], clean['r_kronFlux'])\n", + "plt.xlabel(r'$g-$band kronFlux [nJy]')\n", + "plt.ylabel(r'$r-$band kronFlux [nJy]')\n", + "plt.xlim([0,30000])\n", + "plt.ylim([0,0.5e5]);" + ] + }, + { + "cell_type": "markdown", + "id": "2f503394-3816-4d31-9cf0-9de88e229f87", + "metadata": {}, + "source": [ + "Zooming in on this relationship, it looks roughly linear, so we should be able to do some predictive work here." + ] + }, + { + "cell_type": "markdown", + "id": "4704605a-4665-4ccc-bd7e-cefaf5e09828", + "metadata": {}, + "source": [ + "## 6. Prepare the training and test sets" + ] + }, + { + "cell_type": "markdown", + "id": "0b3c8d4e-0541-49c7-9fa1-f9b3b5d29aac", + "metadata": {}, + "source": [ + "The first step is to define the training and validation data." + ] + }, + { + "cell_type": "markdown", + "id": "1ab2c517-125b-4c70-ad2d-b447f7e6721e", + "metadata": {}, + "source": [ + "The `.to_frame()` argument is required to input the X data as a 2D shape, as expected by scikit-learn." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "fc90feca-ede1-44b0-929b-2fec1ddf5ad4", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T00:04:42.383760Z", + "iopub.status.busy": "2024-12-03T00:04:42.383543Z", + "iopub.status.idle": "2024-12-03T00:04:42.389713Z", + "shell.execute_reply": "2024-12-03T00:04:42.389149Z", + "shell.execute_reply.started": "2024-12-03T00:04:42.383742Z" + } + }, + "outputs": [], + "source": [ + "X_train, X_test, y_train, y_test = train_test_split(\n", + " clean['g_kronFlux'].to_frame(), clean['r_kronFlux'].to_frame(), test_size=0.2, random_state=42)" + ] + }, + { + "cell_type": "markdown", + "id": "ecb487df-ce82-4d2d-9821-64620e1e922b", + "metadata": {}, + "source": [ + "Good practice to use a scaler." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "8e675e27-74f0-43e8-91af-8652e2710609", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T00:04:42.390439Z", + "iopub.status.busy": "2024-12-03T00:04:42.390274Z", + "iopub.status.idle": "2024-12-03T00:04:42.404155Z", + "shell.execute_reply": "2024-12-03T00:04:42.403690Z", + "shell.execute_reply.started": "2024-12-03T00:04:42.390426Z" + } + }, + "outputs": [], + "source": [ + "scaler = StandardScaler()\n", + "X_train = scaler.fit_transform(X_train)\n", + "X_test = scaler.transform(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "4771e145-2649-4eda-8891-987c7e9c9009", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T00:04:42.404900Z", + "iopub.status.busy": "2024-12-03T00:04:42.404711Z", + "iopub.status.idle": "2024-12-03T00:04:42.413985Z", + "shell.execute_reply": "2024-12-03T00:04:42.413340Z", + "shell.execute_reply.started": "2024-12-03T00:04:42.404885Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-0.05664546],\n", + " [ 0.0240961 ],\n", + " [-0.06126054],\n", + " ...,\n", + " [ 0.51047933],\n", + " [-0.01411049],\n", + " [-0.03585177]])" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_test" + ] + }, + { + "cell_type": "markdown", + "id": "6011b2e6-5197-4475-8eb4-3a8841b3bd28", + "metadata": {}, + "source": [ + "## 7. Model (using `scikit-learn`)" + ] + }, + { + "cell_type": "markdown", + "id": "48c8d648-288f-4635-a961-248f127fe524", + "metadata": {}, + "source": [ + "## 7.1 Start with a linear regression" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "e02ca479-6105-442b-9879-2eb215dc4d66", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T00:04:42.414746Z", + "iopub.status.busy": "2024-12-03T00:04:42.414553Z", + "iopub.status.idle": "2024-12-03T00:04:42.430795Z", + "shell.execute_reply": "2024-12-03T00:04:42.430269Z", + "shell.execute_reply.started": "2024-12-03T00:04:42.414730Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
LinearRegression()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "LinearRegression()" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model = LinearRegression()\n", + "model.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "aca8064a-c94f-4792-86b3-62ec2130471b", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T00:04:42.431529Z", + "iopub.status.busy": "2024-12-03T00:04:42.431336Z", + "iopub.status.idle": "2024-12-03T00:04:42.438736Z", + "shell.execute_reply": "2024-12-03T00:04:42.438086Z", + "shell.execute_reply.started": "2024-12-03T00:04:42.431514Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE: 81831676.75317723\n" + ] + } + ], + "source": [ + "y_pred = model.predict(X_test)\n", + "mse = mean_squared_error(y_test, y_pred)\n", + "print(\"MSE:\", mse)" + ] + }, + { + "cell_type": "markdown", + "id": "c9086695-43c6-49e5-a751-bcabc275172d", + "metadata": {}, + "source": [ + "Plot how the predicted values compare to the true values." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "ee8bd887-928e-4a41-bd77-149b344ab238", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T00:04:42.439562Z", + "iopub.status.busy": "2024-12-03T00:04:42.439353Z", + "iopub.status.idle": "2024-12-03T00:04:42.606543Z", + "shell.execute_reply": "2024-12-03T00:04:42.605896Z", + "shell.execute_reply.started": "2024-12-03T00:04:42.439546Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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RUaORnZ2NZ555BmFhYQBKqxps27YNrVq1kjmyJk7I6PDhwwJAha/p06dLbTZs2CCcnZ1FVlZWhedfvXpVPPjgg8LDw0M4OjqK9u3bi4ULF4r09HSjdunp6WLq1KnC1dVVuLq6iqlTp4rMzEyjNleuXBGjRo0Szs7OwsPDQ8yfP18UFBTU6nq0Wq0AILRaba2eR0RE1NDi4uLEPffcIwAIOzs78fbbbwudTid3WFZB7vu3Qggh5EsFG5fs7Gyo1WpotVr2qBERkVUZPnw4Dhw4gLZt2yIsLEyaqkPy379tYs4YERER1c/mzZsxffp0JCQkMBGzMkzGiIiIGqFTp05h9erV0s+tW7fG1q1b4eHhIWNUVBmbqTNGRERENRNC4OOPP8ZLL72E4uJidOnSBaNHj5Y7LKoGkzEiIqJGIiMjAzNnzsSePXsAAOPHj8cDDzwgc1RUEw5TEhERNQJRUVHo0aMH9uzZA0dHR6xbtw7/+te/0KJFC7lDoxqwZ4yIiMjGffrpp1i0aBFKSkrQvn17fPfdd9KWgGT92DNGRERk4zQaDUpKSjBx4kSjvZnJNrBnjIiIyAbl5OSgefPmAIDHH38cx44dw4ABA6BQKGSOjGqLPWNEREQ2RK/XY/Xq1bjvvvtw48YN6fjAgQOZiNkoJmNEREQ2Ii0tDSNHjsSrr76KlJQUfP3113KHRGbAYUoiIiIbcOTIEUyZMgUpKSlwdnbGunXrMHPmTLnDIjNgzxgREZEV0+l0WLVqFR5++GGkpKSgU6dOOHXqFGbNmsVhyUaCyRgREZEV+/DDD7Fy5Uro9XrMnDkTMTEx6Ny5s9xhkRkxGSMiIrJizz77LHr37o2vvvoKmzdvhouLi9whkZlxzhgREZEVKSkpwY4dOxASEgKlUgkXFxdER0dDqWT/SWPFd5aIiMhKXL9+HQ8//DBmzJiB9957TzrORKxx47tLRERkBcLDw9G9e3f8+uuvaN68Odq1ayd3SGQhTMaIiIhkVFxcjGXLlmHkyJG4ffs2evTogbi4OEyaNEnu0MhCOGeMiIhIJlevXsXkyZMRGRkJAJg3bx7ee+89ODk5yRwZWRKTMSIiIpmkpaXh1KlTcHNzw6ZNm/DEE0/IHRLJgMkYERGRBQkhpGKtvXr1wvbt29GrVy/cfffdMkdGcuGcMSIiIgtJTk7GoEGDEBcXJx2bMGECE7EmjskYERGRBezatQs9evTAsWPHMHfuXAgh5A6JrASTMSIiogZUWFiIBQsW4PHHH4dWq0W/fv3w/fffc19JkjAZIyIiaiAXL15E//79sW7dOgDAiy++iF9//RV+fn4yR0bWhBP4iYiIGkBSUhL69euHO3fuoGXLlti2bRtGjRold1hkhZiMERERNYD7778fQUFByMvLw86dO9GmTRu5QyIrxWSMiIjITC5cuIDWrVujWbNmUCqV+Pbbb9G8eXPY2/N2S1XjnDEiIiIz2LFjB3r06IHnn39eOtaiRQsmYlQjJmNERET1kJeXhzlz5mDatGnIzc3FxYsXUVBQIHdYZEOYjBEREdVRUlIS+vTpg02bNkGhUOCf//wnDh48yL0lqVbYd0pERFQHW7duxbx585CXlwdvb2988803GDJkiNxhkQ1izxgREVEtZWRkYOnSpcjLy8PQoUPx22+/MRGjOmPPGBERUS15eHjg66+/RmxsLJYvXw47Ozu5QyIbphDcHMtssrOzoVarodVq4ebmJnc4RERkJkIIbNq0Ca1atcLYsWPlDofMTO77t6zDlL/++itGjx4NX19fKBQK/Pjjj0aPz5gxAwqFwuirX79+Rm0Me355enrCxcUFY8aMwZ9//mnUJjMzEyEhIVCr1VCr1QgJCUFWVpZRm6tXr2L06NFwcXGBp6cnFi5ciKKiooa4bCIisiF37tzB1KlTERoaihkzZuDGjRtyh0SNjKzJWG5uLrp16ybt2VWZESNGICUlRfrav3+/0eOLFi3C7t27ERYWhuPHjyMnJwfBwcHQ6XRSmylTpiAhIQHh4eEIDw9HQkICQkJCpMd1Oh1GjRqF3NxcHD9+HGFhYfjhhx+wZMkS8180ERHZjPj4ePTs2RM7d+6EnZ0dli9fDo1GI3dY1NgIKwFA7N692+jY9OnTxdixY6t8TlZWlnBwcBBhYWHSsevXrwulUinCw8OFEEIkJSUJACI6OlpqExUVJQCI33//XQghxP79+4VSqRTXr1+X2uzcuVOoVCqh1WpNvgatVisA1Oo5RERkffR6vfj000+Fo6OjACDatm0rTpw4IXdY1EDkvn9b/WrKI0eOwMvLC/fddx9CQ0ORlpYmPRYbG4vi4mIMGzZMOubr64uAgABERkYCAKKioqBWq9G3b1+pTb9+/aBWq43aBAQEwNfXV2ozfPhwFBYWIjY2tqEvkYiIrEhJSQkmTpyIefPmoaioCKNHj0Z8fDz69+8vd2jUSFn1asqRI0fiySefhJ+fH5KTk/Haa69hyJAhiI2NhUqlQmpqKhwdHeHu7m70PG9vb6SmpgIAUlNT4eXlVeHcXl5eRm28vb2NHnd3d4ejo6PUpjKFhYUoLCyUfs7Ozq7ztRIRkXWwt7dHq1atYG9vj3feeQeLFi2CQqGQOyxqxKw6GZs4caL0fUBAAHr16gU/Pz/s27cP48ePr/J5QgijP5zK/ojq0qa8NWvWYNWqVTVeBxERWTchBHJycuDq6goAeP/99zF79mz07NlT5sioKbD6YcqyfHx84OfnhwsXLgAANBoNioqKkJmZadQuLS1N6unSaDS4efNmhXPdunXLqE35HrDMzEwUFxdX6DEra/ny5dBqtdLXtWvX6nV9RERkeZmZmRg/fjzGjh0rLf5ycnJiIkYWY1PJWHp6Oq5duwYfHx8AQGBgIBwcHBARESG1SUlJQWJiojS2HxQUBK1Wi5iYGKnNyZMnodVqjdokJiYiJSVFanPgwAGoVCoEBgZWGY9KpYKbm5vRFxER2Y7o6Gj06NEDP/74I06cOIH//ve/codETZCsw5Q5OTm4ePGi9HNycjISEhLg4eEBDw8PrFy5Eo8//jh8fHxw+fJlvPLKK/D09MRjjz0GAFCr1Zg9ezaWLFmCli1bwsPDA0uXLkWXLl0wdOhQAEDHjh0xYsQIhIaGYsOGDQCAp59+GsHBwejQoQMAYNiwYejUqRNCQkLw7rvvSttchIaGMsEiImqE9Ho9PvjgAyxfvhwlJSW4++678d1331X7H3CiBiPLGs6/HD58WACo8DV9+nSRl5cnhg0bJlq1aiUcHBxEu3btxPTp08XVq1eNzpGfny/mz58vPDw8hLOzswgODq7QJj09XUydOlW4uroKV1dXMXXqVJGZmWnU5sqVK2LUqFHC2dlZeHh4iPnz54uCgoJaXY/cS2OJiKhmt2/fFqNGjZLuORMmTBBZWVlyh0Uykvv+ze2QzEju7RSIiKhmw4cPl6aifPTRR3jmmWe4WrKJk/v+bVNzxoiIiOrr/fffR7du3RAdHY25c+cyESPZMRkjIqJGLS0tDT/88IP0c0BAAOLj49G9e3f5giIqg8kYERE1WkePHkX37t0xadIkREVFScfZG0bWhMkYERE1OjqdDq+//jqGDBmClJQU3HvvvZzLS1bLqivwExER1VZqaiqmTZuG//znPwCAGTNmYN26dXBxcZE5MqLKMRkjIqJG4z//+Q+mTp2KmzdvolmzZvjss8/w1FNPyR0WUbWYjBERUaNx5swZ3Lx5EwEBAfj+++9x//33yx0SUY2YjBERkU0TQkgT8p9//nk4Ojpi5syZcHZ2ljkyItNwAj8REdms8PBwDBw4EHfu3AFQukryueeeYyJGNoXJGBER2Zzi4mIsX74cI0eORGRkJNauXSt3SER1xmFKIiKyKdeuXcOkSZMQGRkJAHjuuefw2muvyRwVUd0xGSMiIpvx888/Y/r06cjIyICbmxu+/PJLPPnkk3KHRVQvTMaIiMgmbNq0CXPmzAEA9OrVC99++y3uvvtumaMiqj8mY0RE5ej0AjHJGUi7UwAvVyf08feAnZLb58gtODgYPj4+mDBhAt5++22oVCq5QyIyCyZjRERlhCemYNXeJKRoC6RjPmonrBjdCSMCfGSMrGmKj49Hjx49AADe3t5ITEyEh4eHzFERmRdXUxIR/SU8MQXPbo8zSsQAIFVbgGe3xyE8MUWmyJqewsJCLFy4ED179sTOnTul40zEqDFiMkZEhNKhyVV7kyAqecxwbNXeJOj0lbUgc7p06RIGDBiATz75BADw+++/yxwRUcNiMkZEBCAmOaNCj1hZAkCKtgAxyRmWC6oJ+v7779GzZ0/ExsbCw8MDe/fuxapVq+QOi6hBMRkjIgKQdqfqRKwu7ah2CgoK8Nxzz2HChAnIzs7GgAEDkJCQgODgYLlDI2pwTMaIiAB4uTqZtR3VTmRkJD777DMAwPLly3H48GG0bdtW5qjqR6cXiLqUjp8SriPqUjqHuKlKXE1JRASgj78HfNROSNUWVDpvTAFAoy4tc0HmN2TIELz55pvo1asXhg8fLnc49cZVuVQb7BkjIgJgp1RgxehOAEoTr7IMP68Y3Yn1xswkLy8PixYtwpUrV6Rjr776aqNJxLgql2qDyRgR0V9GBPjgs2k9oVEbD0Vq1E74bFpP9miYyblz59C3b198/PHHmDp1KoRoPMN3XJVLdcFhSiKiMkYE+OCRThpW4G8g27Ztw3PPPYe8vDx4e3tj1apVUCgaz++2Nqtyg9q3tFxgZNWYjBERlWOnVPBGaWa5ubl47rnn8NVXXwEAHn74YWzfvh0ajUbmyMyLq3KpLpiMERFRg7py5QpGjBiB33//HUqlEqtWrcLy5cthZ2cnd2hmx1W5VBdMxoiIqEF5e3tDpVLB19cX33zzDQYNGiR3SA2Gq3KpLjiBn4iIzC4nJwc6nQ4A4OTkhN27dyMhIaFRJ2IAV+VS3TAZIyIis0pISEDPnj3x1ltvScf8/f3RqlUrGaOyHK7KpdpSiMa0plhm2dnZUKvV0Gq1cHNzkzscIiKLEkLg888/xwsvvIDCwkL4+fnh7NmzcHFxkTs0Wej0gqtybYTc92/OGSMionrTarUIDQ3F999/DwAIDg7G1q1bm2wiBnBVLpmOw5RERFQv//3vf9GzZ098//33sLe3x3vvvYc9e/agZUsmIkSmYM8YERHVmVarxdChQ6HVauHn54ewsDD069dP7rCIbAp7xoiIqM7UajXeffddjBs3DvHx8UzEiOqAE/jNSO4JgERElnDy5EkolUr07t0bAKS9JRvTtkbUtMh9/2bPGBERmUQIgffffx8DBw7Ek08+iczMTAClSRgTMaK6kzUZ+/XXXzF69Gj4+vpCoVDgxx9/lB4rLi7Gyy+/jC5dusDFxQW+vr546qmncOPGDaNzDB48WPqHwPA1adIkozaZmZkICQmBWq2GWq1GSEgIsrKyjNpcvXoVo0ePhouLCzw9PbFw4UIUFRU11KUTEdmU9PR0jBkzBkuXLkVJSQn69OkDpZL/nycyB1n/knJzc9GtWzesW7euwmN5eXmIi4vDa6+9hri4OOzatQv/+9//MGbMmAptQ0NDkZKSIn1t2LDB6PEpU6YgISEB4eHhCA8PR0JCAkJCQqTHdTodRo0ahdzcXBw/fhxhYWH44YcfsGTJEvNfNBGRjTlx4gR69OiBn3/+GSqVCp999hm+/fZbqNVquUMjahyElQAgdu/eXW2bmJgYAUBcuXJFOjZo0CDx/PPPV/mcpKQkAUBER0dLx6KiogQA8fvvvwshhNi/f79QKpXi+vXrUpudO3cKlUoltFqtydeg1WoFgFo9h4jIUkp0ehF58bb4Mf5PEXnxtijR6attr9PpxJo1a4SdnZ0AIO69914RHx9vmWCJLEju+7dN9TFrtVooFAq0aNHC6PiOHTvg6emJzp07Y+nSpbhz5470WFRUFNRqNfr27Ssd69evH9RqNSIjI6U2AQEB8PX1ldoMHz4chYWFiI2NrTKewsJCZGdnG30REVmj8MQUDHz7ECZvjMbzYQmYvDEaA98+hPDElCqfo1AoEBUVBZ1OhylTpiA2Nhbdu3e3XNBETYTN1BkrKCjAsmXLMGXKFKOVDlOnToW/vz80Gg0SExOxfPly/Pbbb4iIiAAApKamwsvLq8L5vLy8kJqaKrXx9vY2etzd3R2Ojo5Sm8qsWbMGq1atMsflERE1mPDEFDy7PQ7ll86nagvw7Pa4CvslCiGkObhbtmzBvn37MG3aNE7SJ2ogNpGMFRcXY9KkSdDr9Vi/fr3RY6GhodL3AQEBuPfee9GrVy/ExcWhZ8+eACpfbm34x8bAlDblLV++HIsXL5Z+zs7ORtu2bU2/MCKiBqbTC6zam1QhEQMAAUABYNXeJDzSSQMIPVavXo2LFy9i69atUCgU8PDwMJpjS0TmZ/XDlMXFxZgwYQKSk5MRERFRY/2Pnj17wsHBARcuXAAAaDQa3Lx5s0K7W7duSb1hGo2mQg9YZmYmiouLK/SYlaVSqeDm5mb0RURkTWKSM5CiLajycQEgRVuAX2LOYfjw4fjnP/+Jr776CkePHrVckERNnFUnY4ZE7MKFCzh48KBJ+5ydPXsWxcXF8PEp7XIPCgqCVqtFTEyM1ObkyZPQarXo37+/1CYxMREpKX/PnThw4ABUKhUCAwPNfFVERJaTdqfqRMwg/3ICQoIH4z//+Q+aNWuGrVu3YvDgwQ0fHBEBkHmYMicnBxcvXpR+Tk5ORkJCAjw8PODr64snnngCcXFx+Pnnn6HT6aTeKw8PDzg6OuLSpUvYsWMHHn30UXh6eiIpKQlLlixBjx49MGDAAABAx44dMWLECISGhkolL55++mkEBwejQ4cOAIBhw4ahU6dOCAkJwbvvvouMjAwsXboUoaGh7O0iIpvm5epU5WNCr4P2RBi0kWEABAICAvDtt9+iU6dOlguQiOTdDunIkSN46KGHKhyfPn06Vq5cCX9//0qfd/jwYQwePBjXrl3DtGnTkJiYiJycHLRt2xajRo3CihUr4OHhIbXPyMjAwoULsWfPHgDAmDFjsG7dOqNVmVevXsVzzz2HQ4cOwdnZGVOmTMF7770HlUpl8vXIvZ0CEVF5Or3AwLcPIVVbUGHe2K097yDv3K8AgFmzZmPaCyuQXaKEl6sT+vh7wE7JCfvUNMh9/+belGYk95tJRFQZw2pKAEYJWcHlBKTtXo0nn1+Bq+6BRnPLfNROWDG6k9EqS6LGSu77t1XPGSMiovobEeCDz6b1hHdzBxTd/EM67t+tH/7x9X8Qo+xcYZK/oexFdXXIiMg8bKK0BRER1U9ndQnswl+HNuE3vP/NL+jWuSMC/dwx6N3DJpW94JAlUcNhzxgRUSO3b98+dO/eHZEnTsBOqYC/KhdB7Vsi9kqmSWUvYpIzLBcsURPEZIyIqJEqLi7G0qVLERwcjIyMDAQGBiI+Ph7Dhw8HYFrZi9q0I6K64TAlEVEjdPnyZUyaNAknT54EACxcuBDvvPOO0Qrx6spelGVqOyKqGyZjRESN0MaNG3Hy5Em0aNECmzdvxmOPPVahTR9/D/ionSotewGUzhnTqEvLXBBRw+EwJRFRI7RixQo888wziI+PrzQRAwA7pQIrRpcWeC0/Pd/w84rRnTh5n6iBMRkjImoE/vjjD8ydOxfFxcUAAEdHR3z++ee46667qn2eoeyFRm08FKlRO+GzaT1ZZ4zIAjhMSURk4/71r39h9uzZyM7OhpeXF15//fVaPX9EgA8e6aRBTHIG0u4UsAJ/I6XTC77HVorJGBGRjSooKMCSJUuwfv16AMCAAQMQGhpap3PZKRUIat/SnOGRFQlPTMGqvUncZcFKmZyMnT592uSTdu3atU7BEBGRaS5cuIAJEyYgISEBALBs2TK8/vrrcHBwkDcwsjqG7bDKL9Iw7LLA4Wj5mZyMde/eHQqFAkIIKBTVd2vqdLp6B0ZERJXbv38/Jk6ciJycHHh6euLrr7/GiBEj5A6LrJBOL7BqbxJ3WbByJk/gT05Oxh9//IHk5GT88MMP8Pf3x/r16xEfH4/4+HisX78e7du3xw8//NCQ8RIRNXnt27eHEAIPPvggEhISmIhRlWKSM8y6y4JOLxB1KR0/JVxH1KV06PSVpXlUWyb3jPn5+UnfP/nkk/i///s/PProo9Kxrl27om3btnjttdcwbtw4swZJRNTUZWVloUWLFgCADh064Pjx4wgICIC9Paf+UtXMucsC5501nDqVtjhz5gz8/f0rHPf390dSUlK9gyIior999dVX8PPzw9GjR6Vj3bt3NzkRY29G02WuXRYM887K97IZ5p2FJ6bUOUaqYzLWsWNHvPnmmygo+PtNKSwsxJtvvomOHTuaLTgioqYsNzcXM2fOxPTp05GdnY0vv/yy1ucIT0zBwLcPYfLGaDwfloDJG6Mx8O1DvHk2EYZdFqqaDaZAae9Wdbss1DTvDCidd8Ykv+7qlIx9/vnnOHjwINq2bYuhQ4di6NChaNOmDSIiIvD555+bO0YioiYnMTERvXv3xtatW6FUKvH6669j69attToHezPIHLssmHveGVVUp2SsT58+SE5OxltvvYWuXbuiS5cuWL16NZKTk9GnTx9zx0hE1GQIIbBp0yb06dMH586dg4+PDw4dOoTXXnsNdnZ2Jp+HvRlkUN9dFsw574wqV+eZn82aNcPTTz9tzliIiJq8Q4cOYc6cOQCAYcOG4euvv4aXl1etz1Ob3gwWe2386rPLgrnmnVHV6rw35ddff42BAwfC19cXV65cAQB8+OGH+Omnn8wWHBFRUzNkyBBMnToVq1evxi+//FKnRAxgbwZVZNhlYWz31ghq39LkumLmmHdG1atTMvbZZ59h8eLFGDlyJDIzM6Uir+7u7vjoo4/MGR8RUaMmhMC2bduQmZkJAFAoFPj666+xfPlyKJV1/v8yezPIbMwx74yqV6e/9E8++QQbN27Eq6++arS0ulevXjhz5ozZgiMiasyys7MxadIkzJgxA7Nnz0aJTo+oS+nY89uNepegYG8GmVN9551R9eo0Zyw5ORk9evSocFylUiE3N7feQRERNXaxsbGYOHEiLl26BHt7e3i274qBbx9Canah1KY+BTUNvRnPbo+DAjCayM/eDKqL+sw7o+rVqWfM399f2py2rF9++QWdOnWqb0xERI2WEAKffPIJ+vfvj0uXLsHPzw/vbvsREXa9jRIxoP4lKNibQeZW13lnVL069Yy9+OKLmDdvHgoKCiCEQExMDHbu3Ik1a9bUqSghEVFTkJWVhdmzZ2PXrl0AgHHjxmHjl5sw+ot4CFScSG+OjZzZm0Fk/eqUjM2cORMlJSV46aWXkJeXhylTpqB169b4+OOPMWnSJHPHSETUKOh0Opw6dQoODg547733sGDBAkT/0fAlKAy9GURknepcZyw0NBShoaG4ffs29Hp9nZdfExE1ZkKUztZSKBRo2bIlvv/+eyiVSvTu3RsAS1AQUR3njA0ZMgRZWVkAAE9PTykRy87OxpAhQ8wWHBGRLcvIyMDYsWONtjHq27evlIgBLEFBRHVMxo4cOYKioqIKxwsKCnDs2LF6B0VEZOsiIyPRvXt37N27F0uWLMGdO3cqbccSFERUq2HK06dPS98nJSUhNTVV+lmn0yE8PBytW7c2X3RERDZGr9fj3XffxauvvgqdTod7770X3333HVxdXSttzxIURFSrZKx79+5QKBRQKBSVDkc6Ozvjk08+MVtwRES25NatW5g+fTp++eUXAMDkyZOxYcOGKhMxA0MJilV7k4wm82vqUWeMiGxHrZKx5ORkCCFw9913IyYmBq1atZIec3R0hJeXF+zs7MweJBGRtcvJyUFgYCCuXbsGJycnfPLJJ5g9ezYUCtN6tFiCgqjpqlUy5ufnB6C0G56IiP7WvHlzTJ8+Hf/617/w3XffoUuXLrU+B0tQEDVNCmFYd10La9asgbe3N2bNmmV0fPPmzbh16xZefvllswVoS7Kzs6FWq6HVauHm5iZ3OETUwG7evIn8/HzcddddAICSkhIUFhbCxcVF3sCIqFbkvn/XaTXlhg0bcP/991c43rlzZ3z++ef1DoqIyNodOnQI3bt3x+OPP47CwtJtjOzt7ZmIEVGt1SkZS01NhY9PxQmlrVq1QkqK6Xuo/frrrxg9ejR8fX2hUCjw448/Gj0uhMDKlSvh6+sLZ2dnDB48GGfPnjVqU1hYiAULFsDT0xMuLi4YM2YM/vzzT6M2mZmZCAkJgVqthlqtRkhIiFQnzeDq1asYPXo0XFxc4OnpiYULF1ZavoOImjadTocVK1Zg6NChSE1NRVFREdLS0uQOi4hsWJ2SsbZt2+LEiRMVjp84cQK+vr4mnyc3NxfdunXDunXrKn38nXfewQcffIB169bh1KlT0Gg0eOSRR4zq9SxatAi7d+9GWFgYjh8/jpycHAQHB0On00ltpkyZgoSEBISHhyM8PBwJCQkICQmRHtfpdBg1ahRyc3Nx/PhxhIWF4YcffsCSJUtMvhYiavxu3LiBoUOH4vXXX4cQAnPmzMHJkyfRtm1buUMjIlsm6mDt2rWiZcuWYvPmzeLy5cvi8uXLYtOmTaJly5Zi9erVdTmlACB2794t/azX64VGoxFr166VjhUUFAi1Wi0+//xzIYQQWVlZwsHBQYSFhUltrl+/LpRKpQgPDxdCCJGUlCQAiOjoaKlNVFSUACB+//13IYQQ+/fvF0qlUly/fl1qs3PnTqFSqYRWqzX5GrRarQBQq+cQkW3497//LVq1aiUAiObNm4sdO3bIHRIRmYnc9+867U350ksvISMjA88995w0lOfk5ISXX34Zy5cvN0uSmJycjNTUVAwbNkw6plKpMGjQIERGRuKZZ55BbGwsiouLjdr4+voiICAAkZGRGD58OKKioqBWq9G3b1+pTb9+/aBWqxEZGYkOHTogKioKAQEBRr16w4cPR2FhIWJjY/HQQw9VGmNhYaE0VwQonQBIRI2PXq/HihUrcOvWLXTr1g3fffcd7rvvPrnDIqJGok7DlAqFAm+//TZu3bqF6Oho/Pbbb8jIyMA///lPswVmqO7v7e1tdNzb21t6LDU1FY6OjnB3d6+2TWWbmHt5eRm1Kf867u7ucHR0NNploLw1a9ZI89DUajWHKogaKaVSiW+++QYvvPACoqOjmYgRkVnVKRkzaN68OXr37o2AgACoVCpzxWSkfMFEIUSNRRTLt6msfV3alLd8+XJotVrp69q1a9XGRUS2Y//+/VizZo30s7+/Pz744AM4OXHDbiIyL5OHKcePH4+tW7fCzc0N48ePr7btrl276h2YRqMBUHHlZlpamtSLpdFoUFRUhMzMTKPesbS0NPTv319qc/PmzQrnv3XrltF5Tp48afR4ZmYmiouLK/SYlaVSqRosCSUieRQXF+PVV1/Fu+++CwDo378/Bg0aJHNURNSYmdwzplarpV6iskNzlX2Zg7+/PzQaDSIiIqRjRUVFOHr0qJRoBQYGwsHBwahNSkoKEhMTpTZBQUHQarWIiYmR2pw8eRJardaoTWJiolFZjgMHDkClUiEwMNAs10NE1u/KlSt48MEHpURswYIF6Nevn8xREVGjJ8uygb/cuXNHxMfHi/j4eAFAfPDBByI+Pl5cuXJFCFG6alOtVotdu3aJM2fOiMmTJwsfHx+RnZ0tnWPu3LmiTZs24uDBgyIuLk4MGTJEdOvWTZSUlEhtRowYIbp27SqioqJEVFSU6NKliwgODpYeLykpEQEBAeLhhx8WcXFx4uDBg6JNmzZi/vz5tboeuVdjEFHd/fjjj8Ld3V0AEC1atBC7du2SOyQishC579+yJmOHDx8WACp8TZ8+XQhRWt5ixYoVQqPRCJVKJR588EFx5swZo3Pk5+eL+fPnCw8PD+Hs7CyCg4PF1atXjdqkp6eLqVOnCldXV+Hq6iqmTp0qMjMzjdpcuXJFjBo1Sjg7OwsPDw8xf/58UVBQUKvrkfvNJKK6eeWVV6R/f/r06SOSk5PlDomILEju+7fJe1P26NGjxonzBnFxcbXtoGsU5N7biojq5uuvv8ZTTz2FJUuWYPXq1XB0dJQ7JCKyILnv3yZP4B83bpz0fUFBAdavX49OnTohKCgIABAdHY2zZ8/iueeeM3uQRETmlpGRAQ8PDwBASEgIunTpgu7du8sbFBE1SSb3jJU1Z84c+Pj44I033jA6vmLFCly7dg2bN282W4C2RO7MmohqVlBQgCVLluCnn35CfHw8WrVqJXdIRCQzue/fdaoz9v333+Opp56qcHzatGn44Ycf6h0UEVFDuHDhAvr374/169fj+vXr+OWXX+QOiYiobsmYs7Mzjh8/XuH48ePHWRCRiKxSWFgYevbsifj4eHh6euKXX36p9D+VRESWVqe9KRctWoRnn30WsbGxUg2e6OhobN682axbIhER1Vd+fj4WLVqEL774AgDw4IMP4ptvvkHr1q1ljoyIqFSdkrFly5bh7rvvxscff4xvvvkGANCxY0ds3boVEyZMMGuARET1sWrVKnzxxRdQKBR49dVXsWLFCtjb1+mfPiKiBlGnCfxUObknABJRRdnZ2Xj00UexcuVKDB06VO5wiMgKyX3/rvNG4VlZWfjyyy/xyiuvICMjA0BpfbHr16+bLTgiotrKzc3F+vXrYfh/ppubG44dO8ZEjIisVp366k+fPo2hQ4dCrVbj8uXLmDNnDjw8PLB7925cuXIFX331lbnjJJKFTi8Qk5yBtDsF8HJ1Qh9/D9gpTSt+TJZ39uxZTJgwAUlJSRBCYN68eQBgcsFqW8DPJFHjU6dkbPHixZgxYwbeeecduLq6SsdHjhyJKVOmmC04IjmFJ6Zg1d4kpGgLpGM+aiesGN0JIwJ8ZIyMyhNCYMuWLZg/fz7y8/Ph4+ODzp07yx2W2fEzSdQ41WmY8tSpU3jmmWcqHG/dujVSU1PrHRSR3MITU/Ds9jijmx4ApGoL8Oz2OIQnpsgUGZWXk5ODkJAQzJ49G/n5+Rg2bBgSEhIwePBguUMzK34miRqvOiVjTk5OyM7OrnD8/PnzrGZNNk+nF1i1NwmVrWwxHFu1Nwk6Pde+yO306dMIDAzEjh07YGdnhzVr1uCXX36Bl5eX3KGZFT+TRI1bnZKxsWPH4vXXX0dxcTGA0vkYV69exbJly/D444+bNUAiS4tJzqjQ+1CWAJCiLUBMcoblgqJKZWdn49KlS2jTpg2OHDmCZcuWQams87okq8XPJFHjVqd/td577z3cunULXl5eyM/Px6BBg3DPPffA1dUVb731lrljJLKotDtV3/Tq0o7Mq2w1noEDB+Lbb79FfHw8Bg4cKGNUDYufSaLGrU4T+N3c3HD8+HEcOnQIcXFx0Ov16NmzJ5eOU6Pg5Wrall6mtiPziYuLw8yZM7Fz50506tQJAJpEbzw/k0SNW62TsZKSEjg5OSEhIQFDhgzBkCFDGiIuItn08feAj9oJqdqCSufoKABo1KUlBcgyhBD49NNPsWTJEhQVFeHFF1/Evn375A7LYviZJGrcaj1MaW9vDz8/P+h0uoaIh0h2dkoFVowu7XUpX73J8POK0Z1Y28lCsrKy8MQTT2DBggUoKirC2LFjsX37drnDsih+JsmSdHqBqEvp+CnhOqIupXNhiAXUaTukLVu24Pvvv8f27dvh4cH/iRnIvZ0CmRdrOskvJiYGEydOxOXLl+Hg4IB3330XCxcubFRFXGuDn0lqaE31Myb3/btOyViPHj1w8eJFFBcXw8/PDy4uLkaPx8XFmS1AWyL3m0nmx2rn8omMjMTgwYNRXFwMf39/fPvtt+jdu7fcYcmOn0lqKIZaduWTAsOn67NpPRttQib3/btOE/jHjRsHhUIB7jFOjZ2dUoGg9i3lDqNJ6tOnD/r16wdvb298+eWXUKvVcodkFfiZpIZQUy07BUpr2T3SScPkvwHUKhnLy8vDiy++iB9//BHFxcV4+OGH8cknn8DT07Oh4iOiJiQ2NhYBAQFQqVSwt7fHvn370Lx58yY7LEmW11R7HmtTy47/GTC/WiVjK1aswNatWzF16lQ4Ozvjm2++wbPPPovvv/++oeIjoiZAr9fjvffewyuvvIJ58+bh448/BgCjvW+JGlpTnS8FsJad3GqVjO3atQubNm3CpEmTAABTp07FgAEDoNPpYGdn1yABEpE8LNVDcPv2bTz11FP45ZdfpJ/5bwpZWlXzpQx7fzbm+VIAa9nJrVbJ2LVr1/DAAw9IP/fp0wf29va4ceMG2rZta/bgiEgeluohOHbsGCZPnozr16/DyckJ//d//4c5c+ZwWJIsivOlWMtObrWqM6bT6eDo6Gh0zN7eHiUlJWYNiojkY+ghKD9/xNBDEJ6YUu/X0Ov1eOuttzB48GBcv34d999/P2JiYhAaGspEjCyOe3+ylp3catUzJoTAjBkzoFKppGMFBQWYO3euUXmLXbt2mS9CIrIYS/UQ3LhxA++88w70ej2eeuopfPrpp2jevHmdz0dUH5wvVWpEgA8+m9azQq+4ponMm5NTrZKx6dOnVzg2bdo0swVDRPKy1IqqNm3aYOvWrdBqtZgxY0adz0NkDpwv9bcRAT54pJOmSa4olVOtkrEtW7Y0VBxEZAUaqodAp9PhzTffRL9+/TB8+HAAwGOPPVbr+IgaAudLGWMtO8ur9d6URNR4NUQPQUpKCh555BGsXLkSISEhyMrKqmN0RA2D86VIbkzGiEhi6CGo6pajQOmqSlN7CCIiItC9e3ccPnwYLi4u+PDDD9GiRQtzhUtkNob5Uhq18X80NGqnRl/WguRXp+2QiKhxMvQQPLs9DgrAaMimNj0EJSUlWLlyJVavXg0hBLp27YrvvvsOHTp0aKjQa9RUK6uT6ThfiuRSp43CqXJybzRKZC71qTOWl5eHESNG4NixYwCAuXPn4oMPPoCzs3ODxlydplxZnYhqJvf9mz1jZPXYo2F59ekhaNasGdq3b4+EhAR8+eWXmDBhggUirlpTr6xORNaPPWNmJHdm3RixR8M2FBcXIy8vD2q1GgCQm5uL1NRUtG/fXta4dHqBgW8fqrJch2GV3PGXhzDBJ2rC5L5/cwI/WS1LVIKn+rt69SoGDRqEKVOmQK/XAwBcXFxkT8QAVlYnIttg9cnYXXfdBYVCUeFr3rx5AIAZM2ZUeKxfv35G5ygsLMSCBQvg6ekJFxcXjBkzBn/++adRm8zMTISEhECtVkOtVnMJvsxqqgQPlFaC1+nZsSunPXv2oHv37oiKisKJEydw4cIFuUMywsrqRGQLrD4ZO3XqFFJSUqSviIgIAMCTTz4ptRkxYoRRm/379xudY9GiRdi9ezfCwsJw/Phx5OTkIDg4GDqdTmozZcoUJCQkIDw8HOHh4UhISEBISIhlLpIqYI+GdSsqKsILL7yAsWPHIjMzE71790Z8fLysqyUrw8rqRGQLrH4Cf6tWrYx+Xrt2Ldq3b49BgwZJx1QqFTQaTaXP12q12LRpE77++msMHToUALB9+3a0bdsWBw8exPDhw3Hu3DmEh4cjOjoaffv2BQBs3LgRQUFBOH/+vNXdYJoC9mhYr+TkZEycOBGnTp0CACxevBhr1qyBo6OjzJFVxMrqRGQLrL5nrKyioiJs374ds2bNgkLx92TbI0eOwMvLC/fddx9CQ0ORlpYmPRYbG4vi4mIMGzZMOubr64uAgABERkYCAKKioqBWq6VEDAD69esHtVottalMYWEhsrOzjb7IPNijYZ2EEHjiiSdw6tQpuLu7Y8+ePXj//fetMhEDWFmdiGyDTSVjP/74I7Kysow2Fh45ciR27NiBQ4cO4f3338epU6cwZMgQFBYWAgBSU1Ph6OgId3d3o3N5e3sjNTVVauPl5VXh9by8vKQ2lVmzZo00x0ytVqNt27ZmuEoCzF8JnsxDoVBgw4YNGDx4MBISEjB69Gi5Q6oRK6sTkbWz+mHKsjZt2oSRI0fC19dXOjZx4kTp+4CAAPTq1Qt+fn7Yt28fxo8fX+W5hBBGvWtlv6+qTXnLly/H4sWLpZ+zs7OZkJmJuSrBG7BWWd1dvHgRCQkJeOKJJwAAvXr1wqFDh6r927A2rKxORNbMZpKxK1eu4ODBg9i1a1e17Xx8fODn5yet6tJoNCgqKkJmZqZR71haWhr69+8vtbl582aFc926dQve3t5VvpZKpYJKparL5ZAJDD0a5euMaWpZZ4y1yuru22+/RWhoKAoLC9G+fXv06NEDQOX/ebF2dkoFgtq3lDsMIqIKbCYZ27JlC7y8vDBq1Khq26Wnp+PatWvw8Sm9yQYGBsLBwQERERFSJfCUlBQkJibinXfeAQAEBQVBq9UiJiYGffr0AQCcPHkSWq1WSthIHvXt0TBn9fWm1LuWn5+PF154ARs2bAAAPPDAAxUW0xARkXnYRAV+vV4Pf39/TJ48GWvXrpWO5+TkYOXKlXj88cfh4+ODy5cv45VXXsHVq1dx7tw5uLq6AgCeffZZ/Pzzz9i6dSs8PDywdOlSpKenIzY2FnZ2dgBK557duHFDuvk8/fTT8PPzw969e02OU+4KvmTMnNXXm1Lv2vnz5zFhwgScPn0aCoUCr776KlasWAF7e5v5vxsRUa3Iff+2iQn8Bw8exNWrVzFr1iyj43Z2djhz5gzGjh2L++67D9OnT8d9992HqKgoKREDgA8//BDjxo3DhAkTMGDAADRr1gx79+6VEjEA2LFjB7p06YJhw4Zh2LBh6Nq1K77++muLXSOZn7lqlTWlnQC++eYbBAYG4vTp0/Dy8sK///1vvPHGG0zEiIgakE38Czts2DBU1oHn7OyMf//73zU+38nJCZ988gk++eSTKtt4eHhg+/bt9YqTrIs5apXVtBOAAqU7ATzSSdMohiwvX76M3NxcPPTQQ9ixY4c03E9ERA3HJpIxapwaeg6WOWqV1aZ3zVYnh+v1eiiVpZ3ky5YtQ5s2bTB16lSjnmMiImo4TMZIFpaYg1VT9XXDa1ZXq6wx7wQghMCWLVuwYcMGHD58GM2aNYNSqcRTTz0ld2hERE2KTcwZo8alqjlYKdoCzN0eh48P/g9FJXpEXUrHTwnXEXUpvU4bgldXfd0gv1iHiKSqC/s21p0AcnJy8NRTT2H27NmIiYnBF198IXdIRERNlk2sprQVcq/GsAU1rXA0UCqAsvlXfXrNwhNTsGzXGWTlFVd4zJCkVVXiwhBvTXsbmrIi01zqO7wbn/Abxj3+BK7+cRFKpRKrXn8dryxfLg1VEhE1NXLfv/mvL1lUTXOwDMp3hNVn5eIjnTRwsq/8o254mVV7kyrtfbO2vQ3DE1Mw8O1DmLwxGs+HJWDyxmgMfPuQSb8XIQQWrngXgb174+ofF2HXvCVaTVqNfcp+OJBUsegxERFZBpMxsqi6zq2qKWmqTkxyBlKzC6s9d3UlLqxlb8P6ltiYuegVfPL6SxAlxXC+uxd8Zv4fnNoGNMoSHUREtoQT+Mmi6jO3qq4rF80xCV/uvQ1NKbGxcs9ZuDo54HZOYYX4dHqB087dYOfiDtfe4+DW5zEoFEqj5zemEh1ERLaEyRhZlCkrHGtS2941c0zCl3srJFNKbKRmF2LqlyelYxo3FZ5onYOl08chJjkDGXCF79MboXSseJ2NoUQHEZGtYjJGFmWYg/Xs9rg6n6O2vWs1JYCGSfhVlbiwhq2QapuA6gtycGb3apz8XySyszYhcNBwAKg0EavP6xARUf1xzhhZnDQHy01V6+fWVBesMmUn4VdGABjTzafSni5r2QqpNgloYcr/kLL1eeT9LxJQ2mP7kTPwdDHtd21rJTqIiBoDJmMkixEBPjix7GG8MPS+Wj3vtVEd6zQ8OCLAB08/6F/l41/8mlwhsappnhZQtwUFdWHo3avuyoUQyD71E1K3v4QS7U3Yq72hmfYO9B2GAgpU+3wF6pboEhFR/TEZI9nYKRV4fui9+HxaT3i4OJj0HHcTe3jK0+kF9vxWfS9W+cTKXBuNm0NNBWx1+Xdwa9ebyDy0EdCXoNl9/eEz42OofEqT3ds5hVZVooOIiP7GZIxkNyLAB68FdzapbV3nNNUlsbK2rZCqKrEBAIXXEpF/8SRgZw+PR+bCc9xyKJ2aS497uTpZTYkOIiIyxgn8ZBU0bg277VBdEitr3AqpfIkNTxcVlnz/G27eF4TiB0LgdHcgVJp7pPblFyfIXaKDiIgqYs8YWYWa5kTVd05TXRKrho6pruyUCtyrFvjX+8twd/NirBxTOvzYov/ECokYUHH40U6pQFD7lhjbvTWC2rdkIkZEJDMmY2QVGnrbobokVta2FZLBsWPH0L17d2zfvh2zZ8/m8CMRkY3jRuFmJPdGo41BQ9b0MpSpAGC0QrKmzcKtoc4YAOj1eqxduxb//Oc/odPp0KFDB3z33Xfo2rUrAPkL0xIR2Sq5799MxsxI7jezsWjIpKKuiZXciU5aWhqmTZuGiIgIAEBISAjWr1+P5s2b1/BM2yb3752Imga5799MxsxI7jeTTGNrN/gzZ85g+PDhSElJgbOzMz799FPMmDEDCoX1xmwO1tIjSUSNn9z3byZjZiT3m9mY2VoCZU45OTno3bs37Ozs8N1336FTp6p3E2gsDEPK5f9xqmlImYioLuS+f7O0BVm9pthDcuvWLbRs2RJKpRLNmzfH/v374e3tjWbNmskdWoOraecDBUoL9D7SSdNkEnIiaty4mpIalE4vEHUpHT8lXEfUpfRabx1kDXtD1vcaauvAgQPo3LkzPvjgA+mYv79/k0jEAOva+YCIyBLYM0YNxtQeraqGIK2hh8SSvXIlJSVYsWIF1qxZAyEEwsLCsGjRItjbN60/U2vb+YCIqKE1rX/lyWKqmvNj6NEyzPmpLtlROzua3EMS1L6lbNdgDn/++SemTJmCY8eOAQCeeeYZfPjhh00uEQOsc+cDIqKGxGFKMruaerSA0h6t/advVDsEeTAp1aTXa4geElOvwRxDlvv370f37t1x7NgxuLq6YufOnfj888/h7Oxc73PbImvd+YCIqKEwGSOzM3XOzz9+Sqw22dmdcN2k12uIHhJLzVu6ceMGHnvsMaSnp6NHjx6Ii4vDpEmT6nVOW2etOx8QETUUJmNkdqb2VGXkFlf5mPjrcQ8XR1l6SCw1b8nX1xfvvPMO5s2bh8jISNxzzz01P6kJ4BZPRNSUNL0JKdTgzNlTNa67L7acuAwFKt/CqKF6SBpy3tKePXvQrl07dO/eHQDw/PPP1/ocTcGIAB880knTZOvLEVHTwZ4xMjtT5vy0dHE06VyPdNLI0kPSEPOWioqKsHjxYowdOxYTJkzAnTt3zBJrY2anVCCofUuM7d4aQe1bMhEjokaJPWPUICb1bocPD/6vwnHDrfSNsQF4Y18SUrUFlc4bU6A04TL0hFi6h8Qwb+nZ7XFm6ZVLTk7GxIkTcerUKQDAqFGjoFKpzBozERHZJiZjZFaVlaooS1OmRpdSCZOTHUMPSV3UdSslw7yl8tejqWWdsV27dmHWrFnQarVwd3fH1q1bMWbMmDpdCxERNT7cm9KM5N7bSm5V1eUyeGHovZg/5F6jRKihi6qa4/x1TeaKioqwZMkSrFu3DgAQFBSEnTt3ws/Pr24XQ0REDULu+zeTMTOS+82Uk04vMPDtQ1X2iBmGHY+/PKRCItNQm4DLvdm0Xq/HiBEjEBERgZdeeglvvvkmHBwcGuz1iIiobuS+f3OYksyiNnW5yg831mcIsipybqWk1+uhVCqhVCrx9ddfIz4+HiNGjDDraxARUePB1ZRkFg1Rl6s+G3TLsdl0fn4+nnnmGcydO1c65u3tzUSMiIiqZdXJ2MqVK6FQKIy+NBqN9LgQAitXroSvry+cnZ0xePBgnD171ugchYWFWLBgATw9PeHi4oIxY8bgzz//NGqTmZmJkJAQqNVqqNVqhISEICsryxKX2GiYuy5XeGIKBr59CJM3RuP5sARM3hiNgW8fQnhiiknPt/Rm0+fPn0e/fv3wxRdf4Msvv8SZM2fMcl4iImr8rDoZA4DOnTsjJSVF+ip7k3vnnXfwwQcfYN26dTh16hQ0Gg0eeeQRo/pNixYtwu7duxEWFobjx48jJycHwcHB0Ol0UpspU6YgISEB4eHhCA8PR0JCAkJCQix6nbbOnHW5DHO9qtqz0pSEzJKbTW/fvh2BgYE4ffo0Wnh4YsX6Hchp5muWfSuJiKjxs/pkzN7eHhqNRvpq1aoVgNJesY8++givvvoqxo8fj4CAAGzbtg15eXn45ptvAABarRabNm3C+++/j6FDh6JHjx7Yvn07zpw5g4MHDwIAzp07h/DwcHz55ZcICgpCUFAQNm7ciJ9//hnnz5+X7bptjZ1SgddGdayyZhhgWl0uc23QbYnNpvPy8jBr1iyEhIQgNzcXbnd3h8vkD7Dlslute/KIiKjpsvpk7MKFC/D19YW/vz8mTZqEP/74A0BpEc3U1FQMGzZMaqtSqTBo0CBERkYCAGJjY1FcXGzUxtfXFwEBAVKbqKgoqNVq9O3bV2rTr18/qNVqqU1VCgsLkZ2dbfTVVIUnpuCNfecqfaw21fLNNderoTebFkLg0UcfxZYtW6BQKKAeMBktHl8F++Z/J3e16ckjIqKmy6qTsb59++Krr77Cv//9b2zcuBGpqano378/0tPTkZqaCqB0gnRZ3t7e0mOpqalwdHSEu7t7tW28vLwqvLaXl5fUpipr1qyR5pmp1Wq0bdu2ztdqy6oaVjR4bZTpNb3MOderITebVigUePHFF+Hj44P7Z72DFgOnQqG0M2pTm548IiJquqy6tMXIkSOl77t06YKgoCC0b98e27ZtQ79+/QCU3hTLEkJUOFZe+TaVtTflPMuXL8fixYuln7Ozs5tcQlbdsCJQ2gv1xr4kDA8wrYSEued61XWz6cpqn+Xn5eLcuXPo3bs3gNItjXYcOImZ209XeZ7qSnoQEREBVp6Mlefi4oIuXbrgwoULGDduHIDSni0fn797ONLS0qTeMo1Gg6KiImRmZhr1jqWlpaF///5Sm5s3b1Z4rVu3blXodStPpVI1+f0FTR1W3HoiGTMG+NeYBBnmepmyZ6WpalvHrLKq/W55N5D18zvIyUpHfHy8VEU/u8S0zmVzrdokIqLGx6qHKcsrLCzEuXPn4OPjA39/f2g0GkREREiPFxUV4ejRo1KiFRgYCAcHB6M2KSkpSExMlNoEBQVBq9UiJiZGanPy5ElotVqpDVUtNdu0JOONfefQ+60IvLH3bLU1wxp6rldNyg+5CiFwJyEciZ/Nx7Xki1A6OOHWrVtSe0uu2iQiosbJqnvGli5ditGjR6Ndu3ZIS0vDm2++iezsbEyfPh0KhQKLFi3C6tWrce+99+Lee+/F6tWr0axZM0yZMgUAoFarMXv2bCxZsgQtW7aEh4cHli5dii5dumDo0KEAgI4dO2LEiBEIDQ3Fhg0bAABPP/00goOD0aFDB9mu3RaEJ6bgjZ/P1tzwLxm5xdh04jI2nbhc7f6Q5tqguzZ0eoHoS+lY9sMZqUdOX5iH9H+vQ965XwEAzncH4q7Jr6BHz0DpeQ3Rk2eNGmrLKlvR1K+fiBqWVSdjf/75JyZPnozbt2+jVatW6NevH6Kjo6Uhopdeegn5+fl47rnnkJmZib59++LAgQNwdXWVzvHhhx/C3t4eEyZMQH5+Ph5++GFs3boVdnZ/T7besWMHFi5cKK26HDNmjLS5M1Wupk3Ba2JYaVjVRPq6zvWqi8qGJYtuXsKtn9aiJDMFUCjRYtB0uPV5DLdLlEbzvww9ec9uj4MCMPp9WKInzxIaejN3a9fUr5+IGh43CjcjuTcatZSaNgU3VXWbh1tKVUllRsTnuBP3M+xcW6HV2Jegat1ReuzjSd0xtnvrCudpjDdsuTdbl1tTv36ipkLu+7dV94yRdapp0r6p5F5pWN1KUPeHZgF29lAHTYSds6vRY5XN/2rInjy5hsjk3GzdGjT16yciy2EyRrVm7pWBcq00LJtUFqb8D3fi96PliAVQKO2gsHeEx5A5Ru1rmv9V21WbppCzx602BXgbY9mOpn79RGQ5TMaoUtX1xph7ZWB9z1fXnqO0OwWlqyX/uweZR7YA+hI4evrBrc9jFdrKMf+rqiGymubbmYulN1u3Nk39+onIcpiMNWFVJTE19cbUtIKwNlo4O0AvBHR6Uackpz49R066fNza/RbyL0QDAJrd1x/Nuz5SaduGXMlZGWsYImvqZTua+vUTkeUwGWuiqkpixnTzwRe/JtfYG1PVCsKylAqgpl2AsvKLMfXLk3UaeqtPz1F0dDSenjgR+VevAnb2cH9oNlx7BlfYdaGFswM+ndoT/e5uadF5QdYwRNZUynZUpalfPxFZjk0VfSXzqGovyVRtATZUkogBFfdZrGrfRw8XB8wecBdeGHovWjV3NDmmVG0B5m6Pw+s1FIU1qKnnqGys5W3duhUPPPAArl69Cp+2d8Fn2ntQB4423iLrr6+1j3fBgHs8LT5B2xqGyOQuwCu3pn79RGQ5TMaaGFOSmKqU7Y0BSlcQHn95CHaG9sPHk7pjZ2g/nHr1EfT298BHBy/g5p0ik+MyvPbmE5cxeWM0Br59COGJKVW2r03PUXk9e/aEvb09Jk6ciN8Tf8PmpRMaZDPxquj0AlGX0vFTwvUqE09rGSJryM3WbUFTv34isgwOUzYx5ihLUbY3pvwKwpo2DjdVTUONte05unnzprTXaNeuXREfH48OHTpAoVBgRICbrAVmKxuitaYhsoYo22GOch2WKvlhyQLERNQ0MRlrYswxrFVdb4w5a5BVN0nd1B4hTxdHrFmzBq+//jqOHDmCvn37AgDuv/9+o3YNUZaivJrmuH06pSfcXRylG/5rozph3jfWUdnfnL+f/adv4B8/JSIjt1g6Vts5g5Yu+WGJzwcRNV1MxpqY+gxrmdIbY845TGWHGvv4exj1TAT6udfYc9TSLh+r5k1DRMQBAMCuXbukZMzSTBkenr8zzmjBg4/aCU8/6I89v6VYbI/OhrZmfxI2/Jpc4XhKLcp1yF3yg4jI3JiMNTH1LUtRU29MQ8xhikhKxeLvEqpc+VlZz1H+1dNIPvgxYm/dhLOzM9atW4eZM2eaPTZTmdJjWH7qWKq2AF/8moxPp/SAu4vK5ofI9p9OqTQRMxCouVyHNZT8ICIyN07gb2KqWyFWk0VD76uxx8GQ7NV07tq89uYTlytd+fnFr8l4+kF/o8nVQq9DyX+/w61v/4GMWzfRqVMnnDp1CrNmzapQtsKS6tJjaEg43th3Dn38PTC2e2sEtbdsiQ1z0ekF/vFTYo3tqlp0YVCfhRtERNaKyVgTVNUKsZrc5dmsxjY1lQNQAHimXAJVnaryDkOisue3FBx98SFpReecNmn48z9fQa/XY+bMmYiJiUHnzp2rfQ1TVjfWV117DBtLchGTnIGMXNNW11aXuFpDyQ8iInPjMGUTVXaF2ImLt7Hu8MUan2NqQmFI9spPsC471+mlER0Rk5yBg0mp2HTicoVzGIYeq8uLDInKqeQMDLjXEwAwptts/H7yMEaMGIGQkJAaY7XURPD6Dg/benJRm/ir+5xZS8kPIiJzYjLWhBlWiPXx98APcX/WOKcp86+eDVNKCtRUDsDw2kHtW6K3v0elidujAZpKE7WyhF6HCfNfxSevL8X4vvdCoVBgx44dJl2/JSeCG3oMa9q1oCq2nlxcvp1rUjsPF4dqF4hYU8kPIiJzYTJGsFMqENxVg43HLlfb7o19SQAEXv85CanZhdJxjZsKK8d0rpC4mFoOoKrELfpSerXJWMmd27i99z0UXkvEU3/+D82+/cbk5EmOieBV9RhWt21UY0guwhNT8OHBCya1fXNsQLW/7+qSWlbFJyJbpRBCmH+CTBOVnZ0NtVoNrVYLNzc3ucMxmU4v0PutCKO6T3XxuRl7ksITU7ByTxJSsyvvrcv/Ixa3f34f+vxsKByd0XL4fNwTNBzHXx4i3Yir68GLupSOyRuja4xjZ2g/s9eXKh9XZm4h5n0TD6Dy5MKWSzXo9AID3z5kUu25Zx70x/JHO5l0XkvXGSOixk3u+zd7xuivydX1S8QAYNmuM2bpSapq+BAAhK4EWce2I/vkvwAADl53o9XYl+Hg0dpo4+yabtZyTgSvrMfwM6Wi2jl2tsrUIsCLHr4Xix65z+Tzsio+ETUmTMbIbAlHVl4xoi+lS5Pp66K64cOSO7dx+6e3UXj9HADAtecouD80Gwr7vzckP3HxdpWLAsrOBTN1Dtbl23l1uYxaa6zJhamfLf9WLrU+N6viE1FjwWSMzDo5POqP2/VKxqrrSVEo7VCclQKFYzO0HLkQLvcPrNCmulWhZeeCHX3xIWjcVEZz3yoTduoq5g+5xyJJUWNMLrj6kYioZqwzRtIKNfOoX9JSvidF6HXS93Yu7vB67FX4zPy/ShMxUxjKYcReycTkPu1qbN8YanzJqaYiwAqUDh/b8gIFIqL6YjJGsFMqMKZb9fOSRnXRmHSu+vbslO0hKc5KRer2F5F77lfpmKp1Rzi0MC2W6qTdKcBdnqYNjdl6jS851VQEGODqRyIiJmONmCmV5XV6gY8PXqh2z8DQB+7C/03uiRbNHKp9PfdmDuh3d/2SMUNPSt75SKRsfR5FKf9D5pGtELq/FxiY477t5erUqIbQLLGLQF1VteODRu1k0ytFiYjMhXPGGqnKVhO2cHbAzAF3Yf6Qe2GnVPxVPuJstfOmFAB+Pp2KF4d3xMz+/vjw4P+qbCtQuql3fW6uJcVFaHVmB6J/3AIAcPTtgFZjXobCzkHqSVk3uQfUzo6Y900csvJrtwq0fN2uxlBA1BbKPDTWBQpERObAOmNmJHedEoPqSkMAQItmDpjYqw2++DXZ5ErwHi4ONZa/qG9drIsXL2LixImIi4sDAPg8MAEOfadAYVf6f4ayCYapdcIqi7FsfIbfFWCbNb6qeq9tJX4iImsg9/2bPWONTHWlIQyy8oqrHZasjCl1yGqqXF9dEda0tDQEBgYiOzsbLVu2xLZt2zBi5KNVtk/V5tcqfqA0oVz9WBej5KSqqvjebipM7tMOhSV6RF1Kl7UXp6rfmxy7CBARkfkxGWtkTC2y2VAMqxUNxVcNahpK8/Lywpw5cxATE4OdO3eiTZs2AKpeEJDx1z6Zpmquskf08qFwtK84TbL8ENrl23nYGXPVaAsfuYb9qvu9qZ0dq32vq3oviIjIunACfyMTkZQqdwgAjFcgGobSyicO15IvIXR9OMITUwAAa9euxeHDh6VErDoezVW1isfBTlHjnodB7VtCZa/ERwf/V2EbJkPBWEOsllDV780Qy0ET32uuBiUism5MxhoRnV7gx4QbcocB4O8ViFUNpeWcPYwbW5/HrT3vYsWPp6HTCzg4OMDe3riztqpVghq32q1wzMwrrrFeWE3DfkDpsJ8lViqaEsvuhOsmncsWVoMSETVlHKZsRNYdulDr4bvqeLg41vp85Vcglh821RcXIPPgF8g5faD0gJ09btzKrHQorbohukc6aeCjdqrVkGxNPUQ1DfFactjPlFgycovh4eKIzNwim14NSkTU1LFnrJEIT0wxmuNUX80clXh6oH+tnlO+iKdOL3Di4i3p8aLbV5H61eK/EjEF1AMmw3vim7Bzdq2QKNU0RBeRlIoVozvVqt5/TT1Ecm4eXr4H0NQFCuO6+wJgQVUiIlvGnrFGwDCkZU55RXqs/ff5Wj1HU2aSe/lerZwzB5Fx4DOIkkLYubij5eilcPbrJj33ws0cadUiAJNWCR5/eQg+m9YTK/ckVZjjVZapPURyFYGtrAfQw8Wxmmf87ZFOGvTx96jwfI2V1RkjIqKqMRlrBKL/SJd1BSUAvDaqI2YM8JeKyZatfSV0xcg+9SNESSGc/LrDc/QS2Lm4Gz1/3eGLWHf4InzUTpjUu53Jw4WGlZDrDl2otGewNj1Ehur/liwCW1WdsMwahofLxmKnVLCgKhGRDWMy1sCqq61lDuGJKXj5X6fNdr668FE7SYlYZRPPFXYOaDV2GfL+Fwm3vo9DobSr8lyp2oJqq/yXZRgutFMq8PzQ+9BB41qvHiLDPorPbo+DApUXgTXnsJ8pk/QrU1kshtWgRERke5iMNaCG3qYmPDEFc/+qHi+nsklBTHIGbmTlI+e3f0NfmAN13ycAAA4t20AdNKHGc9VmnWL54UJzbLlTVRFYU5O62iTfptaEK7/7AYcgiYgaF6tOxtasWYNdu3bh999/h7OzM/r374+3334bHTp0kNrMmDED27ZtM3pe3759ER3991Y5hYWFWLp0KXbu3In8/Hw8/PDDWL9+vVE9q8zMTCxcuBB79uwBAIwZMwaffPIJWrRoUafYqxp+MkxAr+82NTq9wLJdZ+r8fHNwb+aANeONK9pfSb2N23vfQ965o4BCCee7esDRu71ZX7e64UJz9BDVNanbfzoF//gp0WgFanXJt6kLAV4L7gyNmxOHIImIGimrTsaOHj2KefPmoXfv3igpKcGrr76KYcOGISkpCS4uLlK7ESNGYMuWLdLPjo7Gk58XLVqEvXv3IiwsDC1btsSSJUsQHByM2NhY2NmVDplNmTIFf/75J8LDwwEATz/9NEJCQrB3795ax22JbWqiL6UjK692m2Sby7juvniyV1v0u7ulUfzx8fFYMOEx5N24AiiUaPHgU3Dwqt2KzJpYapVgbZO6NfuTKt1iKqWa5NvUhQAaNycOQRIRNWJWnYwZEiODLVu2wMvLC7GxsXjwwQel4yqVChqNptJzaLVabNq0CV9//TWGDh0KANi+fTvatm2LgwcPYvjw4Th37hzCw8MRHR2Nvn37AgA2btyIoKAgnD9/3qgnzhSxlzMbvF5V1B+36/S8+jD0SL0/obuUCOn0Aif/SMe2zV9g83urUFJcBDvXVvAc8xKc2nQ0ewzWOES3//SNavf6FKg8+ZZjwQAREVkfm6ozptVqAQAeHsY3pyNHjsDLywv33XcfQkNDkZaWJj0WGxuL4uJiDBs2TDrm6+uLgIAAREZGAgCioqKgVqulRAwA+vXrB7VaLbWpjVs5DV+vSjR8EfiKrwlgTDcfKaEIT0zBwLcPYdhjE/HFmldRUlwE53v6wGfmx2ZPxOY/dA92hvbD8ZeHWFUiptML/OOnxBrbGZLvsgwLBgDWCSMiasqsumesLCEEFi9ejIEDByIgIEA6PnLkSDz55JPw8/NDcnIyXnvtNQwZMgSxsbFQqVRITU2Fo6Mj3N2NSyl4e3sjNbV0b7/U1FR4eXlVeE0vLy+pTWUKCwtRWFgo/ZydnQ0AaNW84epV6fQC6w5dwNbIy7V+rjl88Wsymjk6QJtfhM0nSmNQte6I3KSjcB80A669x0GhMH/ykF9UYpVDdTHJGUaT66tTWfJd3wUDRERk+2wmGZs/fz5Onz6N48ePGx2fOHGi9H1AQAB69eoFPz8/7Nu3D+PHj6/yfEIIo6ShsgSifJvy1qxZg1WrVlU4HniXe4MMP4UnpmDZrjOyzRUDSnvHPog4D11uJuybl8bfvNsIOLXrCgeP1iado/zqQFNsOnEZvf096pycNFSJkdr0blaVfJtjFSgREdkumximXLBgAfbs2YPDhw8brYCsjI+PD/z8/HDhQmkBUI1Gg6KiImRmZhq1S0tLg7e3t9Tm5s2bFc5169YtqU1lli9fDq1WK31du3YNQMMMPxlWZ8qZiAGAriAHt3a/hdTtL0JfkAOgNJE1NREDgDfHBsBH7VSrrYyAipt0V7WJeHmG4dTJG6PxfFgCJm+MxsC3DyE8MaWWEVRkau9mSxfHapNvw4KBsd1bI6h9SyZiRERNiFUnY0IIzJ8/H7t27cKhQ4fg71/zyrz09HRcu3YNPj6lPSiBgYFwcHBARESE1CYlJQWJiYno378/ACAoKAharRYxMTFSm5MnT0Kr1UptKqNSqeDm5mb0ZWAYftKojW/W6mYOWDT0PjzSqfIFB5WpbnWmJRVe/x0pWxYi/0I0dDnpKLz+e62er1QA66f0xKNdfatMVqtTdt6VqQlWTXtc1jchM0zCr8kbYwOYYBERUaUUQsgxFdw0zz33HL755hv89NNPRisa1Wo1nJ2dkZOTg5UrV+Lxxx+Hj48PLl++jFdeeQVXr17FuXPn4OrqCgB49tln8fPPP2Pr1q3w8PDA0qVLkZ6eblTaYuTIkbhx4wY2bNgAoLS0hZ+fX61KW2RnZ0OtVkOr1cKluStikjOQqs3HiYu3EXHuJrT5JVJbU4q/GobWTly8jXWHL9bqd2dOQuiRHfMjsn7dBuh1sG/hA8+xL0OluadW51k/pQce7eor/VxZUdyafDypO1T2ykpruBlSHUMZCZ1eYODbh6o8v2G4+PjLQ+qVKFVVU87gmQf9sfzRTnU+PxERNayy9++yHSuWYtXJWFXztbZs2YIZM2YgPz8f48aNQ3x8PLKysuDj44OHHnoIb7zxBtq2bSu1LygowIsvvohvvvnGqOhr2TYZGRkVir6uW7euVkVfDW/mv6LO473D16pNMsonDuXVJVGpq/Jb/5Sly89G+r4PkX/pFACg2f0PoOWI+VCqXKp4RkUaNxVWjulc6XXWNuHcMbsvlv7rN5MSrJjkDEzeGF1pu7J2hvar9+KAyjf7dsCbYwOMElAiIrI+TMYaEcOb2W7Rd1ComtXYvqqemZp6WszN0EsHoEJCkf7L/yHn9AHAzgEeD4eiefeRtV4tuWNOXwy4x7PaNoZerJoWPbz3ZDdM/fJkja/52qiO8HBxxAvf/VZj20H3eWLuoHvqPWm+ofchJSKihiF3MmYzqyltiaHKvintyhd/teT8sAfu9cRzg42TEMOqvn+fTcG2qCtoMXgGSrQ34f7QbDh6312n17mdU1hjG1M36TblXADwxr5z8HBxrLkhgKP/u42j/7td731DuVk3ERHVhVVP4G8qypZHMHXzaHMYfF+rCiv30m/fQvSerzCskwZCAHbObvCe9FadEzHAtBWHOr2A2tkRswbcBXcXB6PHNGonaTi3NrXZyu4RaQpzTeonIiKqDfaMWYGyCUZ9qvLXhlIBhATdZXTs6NGjmDx5MlJSUjA6OQdo1rPer+PezKHKkg6GYb2IpFT8mHDDKHnycHHEuO6+eKSTxqjnro+/BzxcHGudaJnCXPuGEhER1QaTMZkpFUCg39+7A9SlKn9dzBzgj9grmUi7U4CWzRxwcOdneOP116HX6+HQsi3+m+sBx5qnvdUoM68YEUmpFYb+alqgkJlbhC0nLleYd2WnVGBcd1+p+r+5mWPfUCIiotpgMtYAatOfohdA7JVM6cbfx98DzVV2yCnUNUxwf9lyIhmbjidDl5OJ2z+/h4IrpRPdXQKGwuORuVA6mp4UquwUKNRVPsutsp4mUxYoVNdL9UgnTYMlYwaW6qEkIiLinLEGUpuErPyNv5NPw6/k0Aug4Mpp3Ni6AAVXfoPCQYWWo16A56hFtUrEAFSZiAHGPU06vcCJC7ex7IczJi1QKPvcskwttFofluqhJCIiYjLWAD6Y2K1C5f3qGG784YkpCHwzAjGXM2t4hnkIoYc+VwsHTz/4TP8IzQMebrDXOpiUioFvH8LUTSeRlV+7LZ1SswuMtj0CSldWNsSMLgVKS33Udt9QIiKiumKdMTMy1CnJyMxC0u1inLh4G19FXa52yFGpANZN7gGlUoG52+MaPEah10GhtJN+zrtwEk53dYfSQdXgr11X7s0ckFlmT84Wzg6YOeAu3OvVHG/sO2e21ac1FeIlIqLGSe46Y0zGzMjwZvb650+4VWhX8xPKaNHMocE3Ac//IxYZEZ/Da8LrcHC3TLKhAKBQlA6LmluLZg5YPa4LUrT5eGPfOZPjAYCnH/THnt9SjBK5+tYZIyIi2yR3MsYJ/A3gZnYhlCZU4C+rIRMxoStB1rHtyD75LwCANjIMnqNeaLDXM3ptAA2V7mflFWPeN3H4dEpP+KidTOoh05RJuF4a0ZEV84mISHZMxhq5kuxbuL3nHRReL+05at5jFDyGzLbY66vslSgs0TfY+QWAN/Yl4bVRHTHvm3jpmIGhmv+sAXdVqFnGivlERGQNmIw1YnkXTyJ930fQF9yBwrEZWo5cCJf7B1o0hoZMxAxStAVwd1Hhs2k9K9Qu03DokYiIrByTsUYq78JJ3Nr1BgDAUXMPPMe8bLF5YrWldrLHtCA/fHr4Up3PkXanAGO7t5b21uTQIxER2QomY42U89094ehzH1S+98N98Ewo7B1qfpJMZg30Rx//lvVKxgzlQTj0SEREtobJWCOSf+U3OLXpDIWdPRR2DtBMWQuFvaPcYdXoLk8XqZBrqrbApIKwBgqUDkWyLhgREdkqFn1tBERJMTIObkBa2KvIOr5dOm4LiRhQ2qtlp1RgxehOAEzfvcDQbsXoThyKJCIim8VkzMYVZ6YgdceLuBO7t/SAXg9rKh3XoplDlclV+Wr3IwJ88Nm0nhV2L/BROyG4qw9aOBsPtWrUTizQSkRENo/DlDYs9/fjSP/l/yCK8qB0ckXLUS+g2T195A4LAKBxU2HlmM4AgGe3x0klJgyq6tUaEeBT5SR8nV5wcj4RETU6TMZskCgpQsahL5ETvx8AoGrdCZ5jXoS9WyuZIyvtCft0ck/0a99SSpRqW3Kiqkn4nJxPRESNEZMxG1SSfQu5iYcAAG79nkSLB6YZ7TcpB0P/1NrxXTDgXk+jx6rr7SIiImrqmIzZIAeP1mg58nkoVc3gfHeg3OEAqLm4Knu1iIiIKsdkzAboiwuQ+Z8v4dJ5MJzaBgAAXDo+IHNUpSobliQiIiLTcTWllSu+fQ2pXy1Bzm/huL33fYiSIrlDkijw97AkEzEiIqK6Yc+YFcs58x9kRKyHKC6E0qUFWj76vNXUDmvp4oi3HgtgWQkiIqJ6YjJmhfRFBciI+Ay5if8BADj5dYNn8FLYNXeXObJSHi4OiFr+MBzt2bFKRERUX0zGrIwuPxup219CScafgEIJ9YDJUAdNkH21ZFmrH+vCRIyIiMhMmIxZGaWTKxxb3QVRlAfP0S/CqV0XuUOStGjmgLXju3BokoiIyIyYjFkBfWEeAAGlygUKhQItRy6AKCmGnUsLWeLxcHFERu7fCwVaNHPAzP7+mD/kHk7UJyIiMjMmYzIruvkHbu15G46efvActxwKhQJKlQugkieeFs4OiF7+MGKvZLJAKxERkQUwGZOJEAI5Cb8g4z8bAV0xRHEhdLmZsG/uIWtcMwfcBUd7JQu0EhERWQiTMRnoC3OR/ssnyDt/HADg3L43Wo56AXbObrLG5d7MAfOH3CtrDERERE0NkzELK0y5gNt73kZJViqgtIP7oBlw7T0OCoW8w4AKAGvGd+FwJBERkYUxGbMgoddJiZidmxdajX0ZKt8OcocFnxr2lSQiIqKGw2TMghRKO7Qc9QLu/HcPPEYsgJ1Tc7O/hqOdAgqFAoUl+spjAODtpsL7E7rjdk4hJ+gTERHJjMlYAyu8cR4l2bfgcv9AAIBTm85watO5wV6vSCcACACliZco85gh3Vo5pjMG3OPZYDEQERGR6ZiMNRAhBO6c2o3Mo9ugsLOHo6cfHDzbWuz13Zs5QGWvRGp2oXRMw+FIIiIiq8NkrJz169fj3XffRUpKCjp37oyPPvoIDzzwQK3OocvPRubed5F/6RQAwPneINi5WrZkRWZeMXbM6QulQsF6YURERFaMyVgZ3377LRYtWoT169djwIAB2LBhA0aOHImkpCS0a9fO5POk7ngJ+pwMwM4BHg+Honn3kbKslrydU4ix3Vtb/HWJiIjIdNztuYwPPvgAs2fPxpw5c9CxY0d89NFHaNu2LT777LNanUefkwF7d1/4hLwP1x6Pyla2wsvVSZbXJSIiItOxZ+wvRUVFiI2NxbJly4yODxs2DJGRkZU+p7CwEIWFf8/J0mq1AACne/qi5SPPQqlq9te+k+bhaK9EURWrJMtSAPByU+H+lvbIzs422+sTERE1RoZ7pRCihpYNg8nYX27fvg2dTgdvb2+j497e3khNTa30OWvWrMGqVasqHC+4eBLXL55skDhNdRWAx+uyhkBERGRT0tPToVarLf66TMbKKT+kKISocphx+fLlWLx4sfRzVlYW/Pz8cPXqVVneTLlkZ2ejbdu2uHbtGtzc5N3SyZJ43bzupoDXzetuCrRaLdq1awcPD3n2h2Yy9hdPT0/Y2dlV6AVLS0ur0FtmoFKpoFKpKhxXq9VN6kNs4ObmxutuQnjdTQuvu2lpqtetVMozlZ4T+P/i6OiIwMBAREREGB2PiIhA//79ZYqKiIiIGjv2jJWxePFihISEoFevXggKCsIXX3yBq1evYu7cuXKHRkRERI0Uk7EyJk6ciPT0dLz++utISUlBQEAA9u/fDz8/P5Oer1KpsGLFikqHLhszXjevuyngdfO6mwJetzzXrRByreMkIiIiIs4ZIyIiIpITkzEiIiIiGTEZIyIiIpIRkzEiIiIiGTEZM5P169fD398fTk5OCAwMxLFjx+QOyWRr1qxB79694erqCi8vL4wbNw7nz583ajNjxgwoFAqjr379+hm1KSwsxIIFC+Dp6QkXFxeMGTMGf/75p1GbzMxMhISEQK1WQ61WIyQkBFlZWQ19iZVauXJlhWvSaDTS40IIrFy5Er6+vnB2dsbgwYNx9uxZo3PY2jUDwF133VXhuhUKBebNmweg8bzXv/76K0aPHg1fX18oFAr8+OOPRo9b8v29evUqRo8eDRcXF3h6emLhwoUoKipqiMuu9rqLi4vx8ssvo0uXLnBxcYGvry+eeuop3Lhxw+gcgwcPrvAZmDRpks1eN2DZz7U1XXdlf+sKhQLvvvuu1MbW3m9T7lk29/ctqN7CwsKEg4OD2Lhxo0hKShLPP/+8cHFxEVeuXJE7NJMMHz5cbNmyRSQmJoqEhAQxatQo0a5dO5GTkyO1mT59uhgxYoRISUmRvtLT043OM3fuXNG6dWsREREh4uLixEMPPSS6desmSkpKpDYjRowQAQEBIjIyUkRGRoqAgAARHBxssWsta8WKFaJz585G15SWliY9vnbtWuHq6ip++OEHcebMGTFx4kTh4+MjsrOzpTa2ds1CCJGWlmZ0zREREQKAOHz4sBCi8bzX+/fvF6+++qr44YcfBACxe/duo8ct9f6WlJSIgIAA8dBDD4m4uDgREREhfH19xfz58y1+3VlZWWLo0KHi22+/Fb///ruIiooSffv2FYGBgUbnGDRokAgNDTX6DGRlZRm1saXrFsJyn2tru+6y15uSkiI2b94sFAqFuHTpktTG1t5vU+5Ztvb3zWTMDPr06SPmzp1rdOz+++8Xy5Ytkymi+klLSxMAxNGjR6Vj06dPF2PHjq3yOVlZWcLBwUGEhYVJx65fvy6USqUIDw8XQgiRlJQkAIjo6GipTVRUlAAgfv/9d/NfSA1WrFghunXrVuljer1eaDQasXbtWulYQUGBUKvV4vPPPxdC2OY1V+b5558X7du3F3q9XgjRON/r8jcpS76/+/fvF0qlUly/fl1qs3PnTqFSqYRWq22Q6zWo7OZcXkxMjABg9J/HQYMGieeff77K59jidVvqc21t113e2LFjxZAhQ4yO2fr7Xf6eZYt/3xymrKeioiLExsZi2LBhRseHDRuGyMhImaKqH61WCwAVNkw9cuQIvLy8cN999yE0NBRpaWnSY7GxsSguLjb6Pfj6+iIgIED6PURFRUGtVqNv375Sm379+kGtVsv2u7pw4QJ8fX3h7++PSZMm4Y8//gAAJCcnIzU11eh6VCoVBg0aJMVqq9dcVlFREbZv345Zs2ZBoVBIxxvje12WJd/fqKgoBAQEwNfXV2ozfPhwFBYWIjY2tkGv0xRarRYKhQItWrQwOr5jxw54enqic+fOWLp0Ke7cuSM9ZqvXbYnPtTVet8HNmzexb98+zJ49u8Jjtvx+l79n2eLfNyvw19Pt27eh0+kqbCbu7e1dYdNxWyCEwOLFizFw4EAEBARIx0eOHIknn3wSfn5+SE5OxmuvvYYhQ4YgNjYWKpUKqampcHR0hLu7u9H5yv4eUlNT4eXlVeE1vby8ZPld9e3bF1999RXuu+8+3Lx5E2+++Sb69++Ps2fPSvFU9r5euXIFAGzymsv78ccfkZWVhRkzZkjHGuN7XZ4l39/U1NQKr+Pu7g5HR0fZfxcFBQVYtmwZpkyZYrQp9NSpU+Hv7w+NRoPExEQsX74cv/32m7R3ry1et6U+19Z23WVt27YNrq6uGD9+vNFxW36/K7tn2eLfN5MxMynbqwCUfkDKH7MF8+fPx+nTp3H8+HGj4xMnTpS+DwgIQK9eveDn54d9+/ZV+MMuq/zvobLfiVy/q5EjR0rfd+nSBUFBQWjfvj22bdsmTeyty/tqzddc3qZNmzBy5Eij/9U1xve6KpZ6f63xd1FcXIxJkyZBr9dj/fr1Ro+FhoZK3wcEBODee+9Fr169EBcXh549ewKwveu25Ofamq67rM2bN2Pq1KlwcnIyOm7L73dV96zK4rHmv28OU9aTp6cn7OzsKmTAaWlpFbJla7dgwQLs2bMHhw8fRps2bapt6+PjAz8/P1y4cAEAoNFoUFRUhMzMTKN2ZX8PGo0GN2/erHCuW7duWcXvysXFBV26dMGFCxekVZXVva+2fs1XrlzBwYMHMWfOnGrbNcb32pLvr0ajqfA6mZmZKC4ulu13UVxcjAkTJiA5ORkRERFGvWKV6dmzJxwcHIw+A7Z43WU11OfaWq/72LFjOH/+fI1/74DtvN9V3bNs8e+byVg9OTo6IjAwUOrONYiIiED//v1liqp2hBCYP38+du3ahUOHDsHf37/G56Snp+PatWvw8fEBAAQGBsLBwcHo95CSkoLExETp9xAUFAStVouYmBipzcmTJ6HVaq3id1VYWIhz587Bx8dH6rIvez1FRUU4evSoFKutX/OWLVvg5eWFUaNGVduuMb7Xlnx/g4KCkJiYiJSUFKnNgQMHoFKpEBgY2KDXWRlDInbhwgUcPHgQLVu2rPE5Z8+eRXFxsfQZsMXrLq+hPtfWet2bNm1CYGAgunXrVmNba3+/a7pn2eTft8lT/alKhtIWmzZtEklJSWLRokXCxcVFXL58We7QTPLss88KtVotjhw5YrS0OS8vTwghxJ07d8SSJUtEZGSkSE5OFocPHxZBQUGidevWFZYJt2nTRhw8eFDExcWJIUOGVLpMuGvXriIqKkpERUWJLl26yFbmYcmSJeLIkSPijz/+ENHR0SI4OFi4urpK79vatWuFWq0Wu3btEmfOnBGTJ0+udGm0LV2zgU6nE+3atRMvv/yy0fHG9F7fuXNHxMfHi/j4eAFAfPDBByI+Pl5aNWip99ew9P3hhx8WcXFx4uDBg6JNmzYNVuqguusuLi4WY8aMEW3atBEJCQlGf++FhYVCCCEuXrwoVq1aJU6dOiWSk5PFvn37xP333y969Ohhs9dtyc+1NV23gVarFc2aNROfffZZhefb4vtd0z1LCNv7+2YyZiaffvqp8PPzE46OjqJnz55GZSGsHYBKv7Zs2SKEECIvL08MGzZMtGrVSjg4OIh27dqJ6dOni6tXrxqdJz8/X8yfP194eHgIZ2dnERwcXKFNenq6mDp1qnB1dRWurq5i6tSpIjMz00JXasxQd8bBwUH4+vqK8ePHi7Nnz0qP6/V6sWLFCqHRaIRKpRIPPvigOHPmjNE5bO2aDf79738LAOL8+fNGxxvTe3348OFKP9fTp08XQlj2/b1y5YoYNWqUcHZ2Fh4eHmL+/PmioKDA4tednJxc5d+7oc7c1atXxYMPPig8PDyEo6OjaN++vVi4cGGFmly2dN2W/lxby3UbbNiwQTg7O1eoHSaEbb7fNd2zhLC9v2/FXxdGRERERDLgnDEiIiIiGTEZIyIiIpIRkzEiIiIiGTEZIyIiIpIRkzEiIiIiGTEZIyIiIpIRkzEiIiIiGTEZIyIiIpIRkzEialIUCkW1XzNmzJA7RCJqYuzlDoCIyJLKbuj77bff4p///CfOnz8vHXN2djZqX1xcDAcHB4vFR0RND3vGiKhJ0Wg00pdarYZCoZB+LigoQIsWLfDdd99h8ODBcHJywvbt27Fy5Up0797d6DwfffQR7rrrLqNjW7ZsQceOHeHk5IT7778f69evt9yFEZHNYjJGRFTOyy+/jIULF+LcuXMYPny4Sc/ZuHEjXn31Vbz11ls4d+4cVq9ejddeew3btm1r4GiJyNZxmJKIqJxFixZh/PjxtXrOG2+8gffff196nr+/P5KSkrBhwwZMnz69IcIkokaCyRgRUTm9evWqVftbt27h2rVrmD17NkJDQ6XjJSUlUKvV5g6PiBoZJmNEROW4uLgY/axUKiGEMDpWXFwsfa/X6wGUDlX27dvXqJ2dnV0DRUlEjQWTMSKiGrRq1QqpqakQQkChUAAAEhISpMe9vb3RunVr/PHHH5g6dapMURKRrWIyRkRUg8GDB+PWrVt455138MQTTyA8PBy//PIL3NzcpDYrV67EwoUL4ebmhpEjR6KwsBD//e9/kZmZicWLF8sYPRFZO66mJCKqQceOHbF+/Xp8+umn6NatG2JiYrB06VKjNnPmzMGXX36JrVu3okuXLhg0aBC2bt0Kf39/maImIluhEOUnQhARERGRxbBnjIiIiEhGTMaIiIiIZMRkjIiIiEhGTMaIiIiIZMRkjIiIiEhGTMaIiIiIZMRkjIiIiEhGTMaIiIiIZMRkjIiIiEhGTMaIiIiIZMRkjIiIiEhGTMaIiIiIZPT/SA7Wq+7J0LsAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.clf()\n", + "plt.scatter(y_test, y_pred)\n", + "plt.plot([0,1e6],[0,1e6], color='black', ls='--')\n", + "plt.xlabel('True')\n", + "plt.ylabel('Predicted')\n", + "plt.xlim([0,2e4])\n", + "plt.ylim([0,2e4]);" + ] + }, + { + "cell_type": "markdown", + "id": "1de26eb9-e7cf-4e94-bf14-0fda5d8efe18", + "metadata": {}, + "source": [ + "## 7.2 Improve the model with more features\n", + "Let's see if this will improve with more predictive values, this time including the i band information." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "d80e56eb-6e3d-4110-bee6-3681ee4a923b", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T00:04:43.535589Z", + "iopub.status.busy": "2024-12-03T00:04:43.535315Z", + "iopub.status.idle": "2024-12-03T00:04:43.657195Z", + "shell.execute_reply": "2024-12-03T00:04:43.656652Z", + "shell.execute_reply.started": "2024-12-03T00:04:43.535570Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE: 409962.71379404626\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Train-test split\n", + "X_train, X_test, y_train, y_test = train_test_split(\n", + " clean[['g_kronFlux', 'i_kronFlux']], # Use these two features\n", + " clean['r_kronFlux'], # Target variable\n", + " test_size=0.2,\n", + " random_state=42\n", + ")\n", + "\n", + "# Standardize the features\n", + "scaler = StandardScaler()\n", + "X_train = scaler.fit_transform(X_train)\n", + "X_test = scaler.transform(X_test)\n", + "\n", + "# Train the model\n", + "model = LinearRegression()\n", + "model.fit(X_train, y_train)\n", + "\n", + "# Make predictions\n", + "y_pred = model.predict(X_test)\n", + "\n", + "# Evaluate the model\n", + "mse = mean_squared_error(y_test, y_pred)\n", + "print(\"MSE:\", mse)\n", + "\n", + "# Scatter plot: True vs Predicted\n", + "plt.clf()\n", + "plt.scatter(y_test, y_pred)\n", + "plt.plot([0, 1e6], [0, 1e6], color='black', ls='--')\n", + "plt.xlabel('True')\n", + "plt.ylabel('Predicted')\n", + "plt.xlim([0, 2e4])\n", + "plt.ylim([0, 2e4])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "30cd4c78-35e5-4459-84bf-9642068da002", + "metadata": {}, + "source": [ + "Test for the reader: try to improve this further by including more features." + ] + }, + { + "cell_type": "markdown", + "id": "89e92033-ff41-4662-b5aa-ece41c216a07", + "metadata": {}, + "source": [ + "## 7.3 Random forest regressor\n", + "These are great" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "efb56f9d-6487-444d-b283-6d60c1694948", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T00:04:45.724451Z", + "iopub.status.busy": "2024-12-03T00:04:45.723900Z", + "iopub.status.idle": "2024-12-03T00:04:48.635415Z", + "shell.execute_reply": "2024-12-03T00:04:48.634885Z", + "shell.execute_reply.started": "2024-12-03T00:04:45.724427Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE: 33565886.06951628\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model = RandomForestRegressor()\n", + "model.fit(X_train, y_train)\n", + "\n", + "# Make predictions\n", + "y_pred = model.predict(X_test)\n", + "\n", + "# Evaluate the model\n", + "mse = mean_squared_error(y_test, y_pred)\n", + "print(\"MSE:\", mse)\n", + "\n", + "# Scatter plot: True vs Predicted\n", + "plt.clf()\n", + "plt.scatter(y_test, y_pred)\n", + "plt.plot([0, 1e6], [0, 1e6], color='black', ls='--')\n", + "plt.xlabel('True')\n", + "plt.ylabel('Predicted')\n", + "plt.xlim([0, 2e4])\n", + "plt.ylim([0, 2e4])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "4ed0b4e6-904a-4af8-8bfe-cad402de09a4", + "metadata": {}, + "source": [ + "## 8. Hyperparameter tuning\n", + "With any `scikit-learn` model, it's possible to tune the hyperparameters to achieve better performance." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e6420904-44bb-40cf-8ce0-92b22a802e59", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T17:29:45.803252Z", + "iopub.status.busy": "2024-12-03T17:29:45.802663Z" + } + }, + "outputs": [], + "source": [ + "from sklearn.model_selection import GridSearchCV\n", + "param_grid = {'n_estimators': [1, 10, 50, 100, 200, 1000, 10000]} # default 100 for n_estimators\n", + "\n", + "# Create GridSearchCV object\n", + "grid = GridSearchCV(model, param_grid, cv=5)\n", + "\n", + "grid.fit(X_train, y_train)\n", + "\n", + "# Get the best parameters\n", + "print(grid.best_params_)" + ] + }, + { + "cell_type": "markdown", + "id": "8445ad22-4275-4a56-ace7-ce1a678dd900", + "metadata": {}, + "source": [ + "### 8.2 Now retrieve the best fit model" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "89167a5d-e835-4f8d-8d57-ac82f0df6239", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T00:19:21.191881Z", + "iopub.status.busy": "2024-12-03T00:19:21.191156Z", + "iopub.status.idle": "2024-12-03T00:19:21.944107Z", + "shell.execute_reply": "2024-12-03T00:19:21.943414Z", + "shell.execute_reply.started": "2024-12-03T00:19:21.191857Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best Model: RandomForestRegressor(n_estimators=1000)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE: 19817809.291009396\n" + ] + } + ], + "source": [ + "best_model = grid.best_estimator_\n", + "print(\"Best Model:\", best_model)\n", + "y_pred = best_model.predict(X_test)\n", + "plt.clf()\n", + "plt.scatter(y_test, y_pred)\n", + "plt.plot([0, 1e6], [0, 1e6], color='black', ls='--')\n", + "plt.xlabel('True')\n", + "plt.ylabel('Predicted')\n", + "plt.xlim([0, 2e4])\n", + "plt.ylim([0, 2e4])\n", + "plt.show()\n", + "\n", + "mse = mean_squared_error(y_test, y_pred)\n", + "print(\"MSE:\", mse)" + ] + }, + { + "cell_type": "markdown", + "id": "d5c33bd9-b95e-4ec8-a44d-cf3f219339e8", + "metadata": {}, + "source": [ + "## 9. Other available `scikit-learn` choices\n", + "The below two cells explore available options from `scikit-learn` for regression metrics and regression models, respectively. The metric cell is truncated with a `break` statement to only print details of the first metric. The model cell demonstrates printing the class information for the `RandomForestRegressor` class." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "5adca7ed-eb36-4e8b-99e3-3b66833fb3e3", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T17:56:30.644538Z", + "iopub.status.busy": "2024-12-03T17:56:30.643733Z", + "iopub.status.idle": "2024-12-03T17:56:30.650634Z", + "shell.execute_reply": "2024-12-03T17:56:30.649954Z", + "shell.execute_reply.started": "2024-12-03T17:56:30.644507Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['brier_score_loss', 'check_scoring', 'coverage_error', 'd2_absolute_error_score', 'd2_pinball_score', 'd2_tweedie_score', 'explained_variance_score', 'label_ranking_loss', 'log_loss', 'max_error', 'mean_absolute_error', 'mean_absolute_percentage_error', 'mean_gamma_deviance', 'mean_pinball_loss', 'mean_poisson_deviance', 'mean_squared_error', 'mean_squared_log_error', 'mean_tweedie_deviance', 'median_absolute_error', 'pairwise_distances', 'r2_score', 'root_mean_squared_error', 'root_mean_squared_log_error']\n", + "--- mean_tweedie_deviance ---\n", + "Help on function mean_tweedie_deviance in module sklearn.metrics._regression:\n", + "\n", + "mean_tweedie_deviance(y_true, y_pred, *, sample_weight=None, power=0)\n", + " Mean Tweedie deviance regression loss.\n", + " \n", + " Read more in the :ref:`User Guide `.\n", + " \n", + " Parameters\n", + " ----------\n", + " y_true : array-like of shape (n_samples,)\n", + " Ground truth (correct) target values.\n", + " \n", + " y_pred : array-like of shape (n_samples,)\n", + " Estimated target values.\n", + " \n", + " sample_weight : array-like of shape (n_samples,), default=None\n", + " Sample weights.\n", + " \n", + " power : float, default=0\n", + " Tweedie power parameter. Either power <= 0 or power >= 1.\n", + " \n", + " The higher `p` the less weight is given to extreme\n", + " deviations between true and predicted targets.\n", + " \n", + " - power < 0: Extreme stable distribution. Requires: y_pred > 0.\n", + " - power = 0 : Normal distribution, output corresponds to\n", + " mean_squared_error. y_true and y_pred can be any real numbers.\n", + " - power = 1 : Poisson distribution. Requires: y_true >= 0 and\n", + " y_pred > 0.\n", + " - 1 < p < 2 : Compound Poisson distribution. Requires: y_true >= 0\n", + " and y_pred > 0.\n", + " - power = 2 : Gamma distribution. Requires: y_true > 0 and y_pred > 0.\n", + " - power = 3 : Inverse Gaussian distribution. Requires: y_true > 0\n", + " and y_pred > 0.\n", + " - otherwise : Positive stable distribution. Requires: y_true > 0\n", + " and y_pred > 0.\n", + " \n", + " Returns\n", + " -------\n", + " loss : float\n", + " A non-negative floating point value (the best value is 0.0).\n", + " \n", + " Examples\n", + " --------\n", + " >>> from sklearn.metrics import mean_tweedie_deviance\n", + " >>> y_true = [2, 0, 1, 4]\n", + " >>> y_pred = [0.5, 0.5, 2., 2.]\n", + " >>> mean_tweedie_deviance(y_true, y_pred, power=1)\n", + " 1.4260...\n", + "\n", + "================================================================================\n" + ] + } + ], + "source": [ + "import sklearn.metrics as metrics\n", + "import inspect\n", + "regression_metrics = [\n", + " name for name, obj in inspect.getmembers(metrics)\n", + " if inspect.isfunction(obj)\n", + " and ('regression' in (obj.__doc__ or '').lower() or 'error' in (obj.__doc__ or '').lower())\n", + " and 'classification' not in (obj.__doc__ or '').lower()\n", + "]\n", + "print(regression_metrics)\n", + "\n", + "\n", + "# Print the filtered metrics and their documentation\n", + "for metric in regression_metrics:\n", + " metric_func = getattr(metrics, metric)\n", + " if metric == \"mean_tweedie_deviance\":\n", + " print(f\"--- {metric} ---\")\n", + " help(metric_func)\n", + " print(\"=\"*80)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "de4a41f7-9f4f-4662-9302-2da7d68df434", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-03T17:40:53.931835Z", + "iopub.status.busy": "2024-12-03T17:40:53.931115Z", + "iopub.status.idle": "2024-12-03T17:40:54.007554Z", + "shell.execute_reply": "2024-12-03T17:40:54.007006Z", + "shell.execute_reply.started": "2024-12-03T17:40:53.931811Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ARDRegression\n", + "AdaBoostRegressor\n", + "BaggingRegressor\n", + "BayesianRidge\n", + "CCA\n", + "DecisionTreeRegressor\n", + "DummyRegressor\n", + "ElasticNet\n", + "ElasticNetCV\n", + "ExtraTreeRegressor\n", + "ExtraTreesRegressor\n", + "GammaRegressor\n", + "GaussianProcessRegressor\n", + "GradientBoostingRegressor\n", + "HistGradientBoostingRegressor\n", + "HuberRegressor\n", + "IsotonicRegression\n", + "KNeighborsRegressor\n", + "KernelRidge\n", + "Lars\n", + "LarsCV\n", + "Lasso\n", + "LassoCV\n", + "LassoLars\n", + "LassoLarsCV\n", + "LassoLarsIC\n", + "LinearRegression\n", + "LinearSVR\n", + "MLPRegressor\n", + "MultiOutputRegressor\n", + "MultiTaskElasticNet\n", + "MultiTaskElasticNetCV\n", + "MultiTaskLasso\n", + "MultiTaskLassoCV\n", + "NuSVR\n", + "OrthogonalMatchingPursuit\n", + "OrthogonalMatchingPursuitCV\n", + "PLSCanonical\n", + "PLSRegression\n", + "PassiveAggressiveRegressor\n", + "PoissonRegressor\n", + "QuantileRegressor\n", + "RANSACRegressor\n", + "RadiusNeighborsRegressor\n", + "RandomForestRegressor\n", + "Help on class RandomForestRegressor in module sklearn.ensemble._forest:\n", + "\n", + "class RandomForestRegressor(ForestRegressor)\n", + " | RandomForestRegressor(n_estimators=100, *, criterion='squared_error', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=1.0, max_leaf_nodes=None, min_impurity_decrease=0.0, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, ccp_alpha=0.0, max_samples=None, monotonic_cst=None)\n", + " | \n", + " | A random forest regressor.\n", + " | \n", + " | A random forest is a meta estimator that fits a number of decision tree\n", + " | regressors on various sub-samples of the dataset and uses averaging to\n", + " | improve the predictive accuracy and control over-fitting.\n", + " | Trees in the forest use the best split strategy, i.e. equivalent to passing\n", + " | `splitter=\"best\"` to the underlying :class:`~sklearn.tree.DecisionTreeRegressor`.\n", + " | The sub-sample size is controlled with the `max_samples` parameter if\n", + " | `bootstrap=True` (default), otherwise the whole dataset is used to build\n", + " | each tree.\n", + " | \n", + " | For a comparison between tree-based ensemble models see the example\n", + " | :ref:`sphx_glr_auto_examples_ensemble_plot_forest_hist_grad_boosting_comparison.py`.\n", + " | \n", + " | Read more in the :ref:`User Guide `.\n", + " | \n", + " | Parameters\n", + " | ----------\n", + " | n_estimators : int, default=100\n", + " | The number of trees in the forest.\n", + " | \n", + " | .. versionchanged:: 0.22\n", + " | The default value of ``n_estimators`` changed from 10 to 100\n", + " | in 0.22.\n", + " | \n", + " | criterion : {\"squared_error\", \"absolute_error\", \"friedman_mse\", \"poisson\"}, default=\"squared_error\"\n", + " | The function to measure the quality of a split. Supported criteria\n", + " | are \"squared_error\" for the mean squared error, which is equal to\n", + " | variance reduction as feature selection criterion and minimizes the L2\n", + " | loss using the mean of each terminal node, \"friedman_mse\", which uses\n", + " | mean squared error with Friedman's improvement score for potential\n", + " | splits, \"absolute_error\" for the mean absolute error, which minimizes\n", + " | the L1 loss using the median of each terminal node, and \"poisson\" which\n", + " | uses reduction in Poisson deviance to find splits.\n", + " | Training using \"absolute_error\" is significantly slower\n", + " | than when using \"squared_error\".\n", + " | \n", + " | .. versionadded:: 0.18\n", + " | Mean Absolute Error (MAE) criterion.\n", + " | \n", + " | .. versionadded:: 1.0\n", + " | Poisson criterion.\n", + " | \n", + " | max_depth : int, default=None\n", + " | The maximum depth of the tree. If None, then nodes are expanded until\n", + " | all leaves are pure or until all leaves contain less than\n", + " | min_samples_split samples.\n", + " | \n", + " | min_samples_split : int or float, default=2\n", + " | The minimum number of samples required to split an internal node:\n", + " | \n", + " | - If int, then consider `min_samples_split` as the minimum number.\n", + " | - If float, then `min_samples_split` is a fraction and\n", + " | `ceil(min_samples_split * n_samples)` are the minimum\n", + " | number of samples for each split.\n", + " | \n", + " | .. versionchanged:: 0.18\n", + " | Added float values for fractions.\n", + " | \n", + " | min_samples_leaf : int or float, default=1\n", + " | The minimum number of samples required to be at a leaf node.\n", + " | A split point at any depth will only be considered if it leaves at\n", + " | least ``min_samples_leaf`` training samples in each of the left and\n", + " | right branches. This may have the effect of smoothing the model,\n", + " | especially in regression.\n", + " | \n", + " | - If int, then consider `min_samples_leaf` as the minimum number.\n", + " | - If float, then `min_samples_leaf` is a fraction and\n", + " | `ceil(min_samples_leaf * n_samples)` are the minimum\n", + " | number of samples for each node.\n", + " | \n", + " | .. versionchanged:: 0.18\n", + " | Added float values for fractions.\n", + " | \n", + " | min_weight_fraction_leaf : float, default=0.0\n", + " | The minimum weighted fraction of the sum total of weights (of all\n", + " | the input samples) required to be at a leaf node. Samples have\n", + " | equal weight when sample_weight is not provided.\n", + " | \n", + " | max_features : {\"sqrt\", \"log2\", None}, int or float, default=1.0\n", + " | The number of features to consider when looking for the best split:\n", + " | \n", + " | - If int, then consider `max_features` features at each split.\n", + " | - If float, then `max_features` is a fraction and\n", + " | `max(1, int(max_features * n_features_in_))` features are considered at each\n", + " | split.\n", + " | - If \"sqrt\", then `max_features=sqrt(n_features)`.\n", + " | - If \"log2\", then `max_features=log2(n_features)`.\n", + " | - If None or 1.0, then `max_features=n_features`.\n", + " | \n", + " | .. note::\n", + " | The default of 1.0 is equivalent to bagged trees and more\n", + " | randomness can be achieved by setting smaller values, e.g. 0.3.\n", + " | \n", + " | .. versionchanged:: 1.1\n", + " | The default of `max_features` changed from `\"auto\"` to 1.0.\n", + " | \n", + " | Note: the search for a split does not stop until at least one\n", + " | valid partition of the node samples is found, even if it requires to\n", + " | effectively inspect more than ``max_features`` features.\n", + " | \n", + " | max_leaf_nodes : int, default=None\n", + " | Grow trees with ``max_leaf_nodes`` in best-first fashion.\n", + " | Best nodes are defined as relative reduction in impurity.\n", + " | If None then unlimited number of leaf nodes.\n", + " | \n", + " | min_impurity_decrease : float, default=0.0\n", + " | A node will be split if this split induces a decrease of the impurity\n", + " | greater than or equal to this value.\n", + " | \n", + " | The weighted impurity decrease equation is the following::\n", + " | \n", + " | N_t / N * (impurity - N_t_R / N_t * right_impurity\n", + " | - N_t_L / N_t * left_impurity)\n", + " | \n", + " | where ``N`` is the total number of samples, ``N_t`` is the number of\n", + " | samples at the current node, ``N_t_L`` is the number of samples in the\n", + " | left child, and ``N_t_R`` is the number of samples in the right child.\n", + " | \n", + " | ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,\n", + " | if ``sample_weight`` is passed.\n", + " | \n", + " | .. versionadded:: 0.19\n", + " | \n", + " | bootstrap : bool, default=True\n", + " | Whether bootstrap samples are used when building trees. If False, the\n", + " | whole dataset is used to build each tree.\n", + " | \n", + " | oob_score : bool or callable, default=False\n", + " | Whether to use out-of-bag samples to estimate the generalization score.\n", + " | By default, :func:`~sklearn.metrics.r2_score` is used.\n", + " | Provide a callable with signature `metric(y_true, y_pred)` to use a\n", + " | custom metric. Only available if `bootstrap=True`.\n", + " | \n", + " | n_jobs : int, default=None\n", + " | The number of jobs to run in parallel. :meth:`fit`, :meth:`predict`,\n", + " | :meth:`decision_path` and :meth:`apply` are all parallelized over the\n", + " | trees. ``None`` means 1 unless in a :obj:`joblib.parallel_backend`\n", + " | context. ``-1`` means using all processors. See :term:`Glossary\n", + " | ` for more details.\n", + " | \n", + " | random_state : int, RandomState instance or None, default=None\n", + " | Controls both the randomness of the bootstrapping of the samples used\n", + " | when building trees (if ``bootstrap=True``) and the sampling of the\n", + " | features to consider when looking for the best split at each node\n", + " | (if ``max_features < n_features``).\n", + " | See :term:`Glossary ` for details.\n", + " | \n", + " | verbose : int, default=0\n", + " | Controls the verbosity when fitting and predicting.\n", + " | \n", + " | warm_start : bool, default=False\n", + " | When set to ``True``, reuse the solution of the previous call to fit\n", + " | and add more estimators to the ensemble, otherwise, just fit a whole\n", + " | new forest. See :term:`Glossary ` and\n", + " | :ref:`tree_ensemble_warm_start` for details.\n", + " | \n", + " | ccp_alpha : non-negative float, default=0.0\n", + " | Complexity parameter used for Minimal Cost-Complexity Pruning. The\n", + " | subtree with the largest cost complexity that is smaller than\n", + " | ``ccp_alpha`` will be chosen. By default, no pruning is performed. See\n", + " | :ref:`minimal_cost_complexity_pruning` for details.\n", + " | \n", + " | .. versionadded:: 0.22\n", + " | \n", + " | max_samples : int or float, default=None\n", + " | If bootstrap is True, the number of samples to draw from X\n", + " | to train each base estimator.\n", + " | \n", + " | - If None (default), then draw `X.shape[0]` samples.\n", + " | - If int, then draw `max_samples` samples.\n", + " | - If float, then draw `max(round(n_samples * max_samples), 1)` samples. Thus,\n", + " | `max_samples` should be in the interval `(0.0, 1.0]`.\n", + " | \n", + " | .. versionadded:: 0.22\n", + " | \n", + " | monotonic_cst : array-like of int of shape (n_features), default=None\n", + " | Indicates the monotonicity constraint to enforce on each feature.\n", + " | - 1: monotonically increasing\n", + " | - 0: no constraint\n", + " | - -1: monotonically decreasing\n", + " | \n", + " | If monotonic_cst is None, no constraints are applied.\n", + " | \n", + " | Monotonicity constraints are not supported for:\n", + " | - multioutput regressions (i.e. when `n_outputs_ > 1`),\n", + " | - regressions trained on data with missing values.\n", + " | \n", + " | Read more in the :ref:`User Guide `.\n", + " | \n", + " | .. versionadded:: 1.4\n", + " | \n", + " | Attributes\n", + " | ----------\n", + " | estimator_ : :class:`~sklearn.tree.DecisionTreeRegressor`\n", + " | The child estimator template used to create the collection of fitted\n", + " | sub-estimators.\n", + " | \n", + " | .. versionadded:: 1.2\n", + " | `base_estimator_` was renamed to `estimator_`.\n", + " | \n", + " | estimators_ : list of DecisionTreeRegressor\n", + " | The collection of fitted sub-estimators.\n", + " | \n", + " | feature_importances_ : ndarray of shape (n_features,)\n", + " | The impurity-based feature importances.\n", + " | The higher, the more important the feature.\n", + " | The importance of a feature is computed as the (normalized)\n", + " | total reduction of the criterion brought by that feature. It is also\n", + " | known as the Gini importance.\n", + " | \n", + " | Warning: impurity-based feature importances can be misleading for\n", + " | high cardinality features (many unique values). See\n", + " | :func:`sklearn.inspection.permutation_importance` as an alternative.\n", + " | \n", + " | n_features_in_ : int\n", + " | Number of features seen during :term:`fit`.\n", + " | \n", + " | .. versionadded:: 0.24\n", + " | \n", + " | feature_names_in_ : ndarray of shape (`n_features_in_`,)\n", + " | Names of features seen during :term:`fit`. Defined only when `X`\n", + " | has feature names that are all strings.\n", + " | \n", + " | .. versionadded:: 1.0\n", + " | \n", + " | n_outputs_ : int\n", + " | The number of outputs when ``fit`` is performed.\n", + " | \n", + " | oob_score_ : float\n", + " | Score of the training dataset obtained using an out-of-bag estimate.\n", + " | This attribute exists only when ``oob_score`` is True.\n", + " | \n", + " | oob_prediction_ : ndarray of shape (n_samples,) or (n_samples, n_outputs)\n", + " | Prediction computed with out-of-bag estimate on the training set.\n", + " | This attribute exists only when ``oob_score`` is True.\n", + " | \n", + " | estimators_samples_ : list of arrays\n", + " | The subset of drawn samples (i.e., the in-bag samples) for each base\n", + " | estimator. Each subset is defined by an array of the indices selected.\n", + " | \n", + " | .. versionadded:: 1.4\n", + " | \n", + " | See Also\n", + " | --------\n", + " | sklearn.tree.DecisionTreeRegressor : A decision tree regressor.\n", + " | sklearn.ensemble.ExtraTreesRegressor : Ensemble of extremely randomized\n", + " | tree regressors.\n", + " | sklearn.ensemble.HistGradientBoostingRegressor : A Histogram-based Gradient\n", + " | Boosting Regression Tree, very fast for big datasets (n_samples >=\n", + " | 10_000).\n", + " | \n", + " | Notes\n", + " | -----\n", + " | The default values for the parameters controlling the size of the trees\n", + " | (e.g. ``max_depth``, ``min_samples_leaf``, etc.) lead to fully grown and\n", + " | unpruned trees which can potentially be very large on some data sets. To\n", + " | reduce memory consumption, the complexity and size of the trees should be\n", + " | controlled by setting those parameter values.\n", + " | \n", + " | The features are always randomly permuted at each split. Therefore,\n", + " | the best found split may vary, even with the same training data,\n", + " | ``max_features=n_features`` and ``bootstrap=False``, if the improvement\n", + " | of the criterion is identical for several splits enumerated during the\n", + " | search of the best split. To obtain a deterministic behaviour during\n", + " | fitting, ``random_state`` has to be fixed.\n", + " | \n", + " | The default value ``max_features=1.0`` uses ``n_features``\n", + " | rather than ``n_features / 3``. The latter was originally suggested in\n", + " | [1], whereas the former was more recently justified empirically in [2].\n", + " | \n", + " | References\n", + " | ----------\n", + " | .. [1] L. Breiman, \"Random Forests\", Machine Learning, 45(1), 5-32, 2001.\n", + " | \n", + " | .. [2] P. Geurts, D. Ernst., and L. Wehenkel, \"Extremely randomized\n", + " | trees\", Machine Learning, 63(1), 3-42, 2006.\n", + " | \n", + " | Examples\n", + " | --------\n", + " | >>> from sklearn.ensemble import RandomForestRegressor\n", + " | >>> from sklearn.datasets import make_regression\n", + " | >>> X, y = make_regression(n_features=4, n_informative=2,\n", + " | ... random_state=0, shuffle=False)\n", + " | >>> regr = RandomForestRegressor(max_depth=2, random_state=0)\n", + " | >>> regr.fit(X, y)\n", + " | RandomForestRegressor(...)\n", + " | >>> print(regr.predict([[0, 0, 0, 0]]))\n", + " | [-8.32987858]\n", + " | \n", + " | Method resolution order:\n", + " | RandomForestRegressor\n", + " | ForestRegressor\n", + " | sklearn.base.RegressorMixin\n", + " | BaseForest\n", + " | sklearn.base.MultiOutputMixin\n", + " | sklearn.ensemble._base.BaseEnsemble\n", + " | sklearn.base.MetaEstimatorMixin\n", + " | sklearn.base.BaseEstimator\n", + " | sklearn.utils._estimator_html_repr._HTMLDocumentationLinkMixin\n", + " | sklearn.utils._metadata_requests._MetadataRequester\n", + " | builtins.object\n", + " | \n", + " | Methods defined here:\n", + " | \n", + " | __init__(self, n_estimators=100, *, criterion='squared_error', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=1.0, max_leaf_nodes=None, min_impurity_decrease=0.0, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, ccp_alpha=0.0, max_samples=None, monotonic_cst=None)\n", + " | Initialize self. See help(type(self)) for accurate signature.\n", + " | \n", + " | set_fit_request(self: sklearn.ensemble._forest.RandomForestRegressor, *, sample_weight: Union[bool, NoneType, str] = '$UNCHANGED$') -> sklearn.ensemble._forest.RandomForestRegressor from sklearn.utils._metadata_requests.RequestMethod.__get__.\n", + " | Request metadata passed to the ``fit`` method.\n", + " | \n", + " | Note that this method is only relevant if\n", + " | ``enable_metadata_routing=True`` (see :func:`sklearn.set_config`).\n", + " | Please see :ref:`User Guide ` on how the routing\n", + " | mechanism works.\n", + " | \n", + " | The options for each parameter are:\n", + " | \n", + " | - ``True``: metadata is requested, and passed to ``fit`` if provided. The request is ignored if metadata is not provided.\n", + " | \n", + " | - ``False``: metadata is not requested and the meta-estimator will not pass it to ``fit``.\n", + " | \n", + " | - ``None``: metadata is not requested, and the meta-estimator will raise an error if the user provides it.\n", + " | \n", + " | - ``str``: metadata should be passed to the meta-estimator with this given alias instead of the original name.\n", + " | \n", + " | The default (``sklearn.utils.metadata_routing.UNCHANGED``) retains the\n", + " | existing request. This allows you to change the request for some\n", + " | parameters and not others.\n", + " | \n", + " | .. versionadded:: 1.3\n", + " | \n", + " | .. note::\n", + " | This method is only relevant if this estimator is used as a\n", + " | sub-estimator of a meta-estimator, e.g. used inside a\n", + " | :class:`~sklearn.pipeline.Pipeline`. Otherwise it has no effect.\n", + " | \n", + " | Parameters\n", + " | ----------\n", + " | sample_weight : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED\n", + " | Metadata routing for ``sample_weight`` parameter in ``fit``.\n", + " | \n", + " | Returns\n", + " | -------\n", + " | self : object\n", + " | The updated object.\n", + " | \n", + " | set_score_request(self: sklearn.ensemble._forest.RandomForestRegressor, *, sample_weight: Union[bool, NoneType, str] = '$UNCHANGED$') -> sklearn.ensemble._forest.RandomForestRegressor from sklearn.utils._metadata_requests.RequestMethod.__get__.\n", + " | Request metadata passed to the ``score`` method.\n", + " | \n", + " | Note that this method is only relevant if\n", + " | ``enable_metadata_routing=True`` (see :func:`sklearn.set_config`).\n", + " | Please see :ref:`User Guide ` on how the routing\n", + " | mechanism works.\n", + " | \n", + " | The options for each parameter are:\n", + " | \n", + " | - ``True``: metadata is requested, and passed to ``score`` if provided. The request is ignored if metadata is not provided.\n", + " | \n", + " | - ``False``: metadata is not requested and the meta-estimator will not pass it to ``score``.\n", + " | \n", + " | - ``None``: metadata is not requested, and the meta-estimator will raise an error if the user provides it.\n", + " | \n", + " | - ``str``: metadata should be passed to the meta-estimator with this given alias instead of the original name.\n", + " | \n", + " | The default (``sklearn.utils.metadata_routing.UNCHANGED``) retains the\n", + " | existing request. This allows you to change the request for some\n", + " | parameters and not others.\n", + " | \n", + " | .. versionadded:: 1.3\n", + " | \n", + " | .. note::\n", + " | This method is only relevant if this estimator is used as a\n", + " | sub-estimator of a meta-estimator, e.g. used inside a\n", + " | :class:`~sklearn.pipeline.Pipeline`. Otherwise it has no effect.\n", + " | \n", + " | Parameters\n", + " | ----------\n", + " | sample_weight : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED\n", + " | Metadata routing for ``sample_weight`` parameter in ``score``.\n", + " | \n", + " | Returns\n", + " | -------\n", + " | self : object\n", + " | The updated object.\n", + " | \n", + " | ----------------------------------------------------------------------\n", + " | Data and other attributes defined here:\n", + " | \n", + " | __abstractmethods__ = frozenset()\n", + " | \n", + " | __annotations__ = {'_parameter_constraints': }\n", + " | \n", + " | ----------------------------------------------------------------------\n", + " | Methods inherited from ForestRegressor:\n", + " | \n", + " | predict(self, X)\n", + " | Predict regression target for X.\n", + " | \n", + " | The predicted regression target of an input sample is computed as the\n", + " | mean predicted regression targets of the trees in the forest.\n", + " | \n", + " | Parameters\n", + " | ----------\n", + " | X : {array-like, sparse matrix} of shape (n_samples, n_features)\n", + " | The input samples. Internally, its dtype will be converted to\n", + " | ``dtype=np.float32``. If a sparse matrix is provided, it will be\n", + " | converted into a sparse ``csr_matrix``.\n", + " | \n", + " | Returns\n", + " | -------\n", + " | y : ndarray of shape (n_samples,) or (n_samples, n_outputs)\n", + " | The predicted values.\n", + " | \n", + " | ----------------------------------------------------------------------\n", + " | Methods inherited from sklearn.base.RegressorMixin:\n", + " | \n", + " | score(self, X, y, sample_weight=None)\n", + " | Return the coefficient of determination of the prediction.\n", + " | \n", + " | The coefficient of determination :math:`R^2` is defined as\n", + " | :math:`(1 - \\frac{u}{v})`, where :math:`u` is the residual\n", + " | sum of squares ``((y_true - y_pred)** 2).sum()`` and :math:`v`\n", + " | is the total sum of squares ``((y_true - y_true.mean()) ** 2).sum()``.\n", + " | The best possible score is 1.0 and it can be negative (because the\n", + " | model can be arbitrarily worse). A constant model that always predicts\n", + " | the expected value of `y`, disregarding the input features, would get\n", + " | a :math:`R^2` score of 0.0.\n", + " | \n", + " | Parameters\n", + " | ----------\n", + " | X : array-like of shape (n_samples, n_features)\n", + " | Test samples. For some estimators this may be a precomputed\n", + " | kernel matrix or a list of generic objects instead with shape\n", + " | ``(n_samples, n_samples_fitted)``, where ``n_samples_fitted``\n", + " | is the number of samples used in the fitting for the estimator.\n", + " | \n", + " | y : array-like of shape (n_samples,) or (n_samples, n_outputs)\n", + " | True values for `X`.\n", + " | \n", + " | sample_weight : array-like of shape (n_samples,), default=None\n", + " | Sample weights.\n", + " | \n", + " | Returns\n", + " | -------\n", + " | score : float\n", + " | :math:`R^2` of ``self.predict(X)`` w.r.t. `y`.\n", + " | \n", + " | Notes\n", + " | -----\n", + " | The :math:`R^2` score used when calling ``score`` on a regressor uses\n", + " | ``multioutput='uniform_average'`` from version 0.23 to keep consistent\n", + " | with default value of :func:`~sklearn.metrics.r2_score`.\n", + " | This influences the ``score`` method of all the multioutput\n", + " | regressors (except for\n", + " | :class:`~sklearn.multioutput.MultiOutputRegressor`).\n", + " | \n", + " | ----------------------------------------------------------------------\n", + " | Data descriptors inherited from sklearn.base.RegressorMixin:\n", + " | \n", + " | __dict__\n", + " | dictionary for instance variables\n", + " | \n", + " | __weakref__\n", + " | list of weak references to the object\n", + " | \n", + " | ----------------------------------------------------------------------\n", + " | Methods inherited from BaseForest:\n", + " | \n", + " | apply(self, X)\n", + " | Apply trees in the forest to X, return leaf indices.\n", + " | \n", + " | Parameters\n", + " | ----------\n", + " | X : {array-like, sparse matrix} of shape (n_samples, n_features)\n", + " | The input samples. Internally, its dtype will be converted to\n", + " | ``dtype=np.float32``. If a sparse matrix is provided, it will be\n", + " | converted into a sparse ``csr_matrix``.\n", + " | \n", + " | Returns\n", + " | -------\n", + " | X_leaves : ndarray of shape (n_samples, n_estimators)\n", + " | For each datapoint x in X and for each tree in the forest,\n", + " | return the index of the leaf x ends up in.\n", + " | \n", + " | decision_path(self, X)\n", + " | Return the decision path in the forest.\n", + " | \n", + " | .. versionadded:: 0.18\n", + " | \n", + " | Parameters\n", + " | ----------\n", + " | X : {array-like, sparse matrix} of shape (n_samples, n_features)\n", + " | The input samples. Internally, its dtype will be converted to\n", + " | ``dtype=np.float32``. If a sparse matrix is provided, it will be\n", + " | converted into a sparse ``csr_matrix``.\n", + " | \n", + " | Returns\n", + " | -------\n", + " | indicator : sparse matrix of shape (n_samples, n_nodes)\n", + " | Return a node indicator matrix where non zero elements indicates\n", + " | that the samples goes through the nodes. The matrix is of CSR\n", + " | format.\n", + " | \n", + " | n_nodes_ptr : ndarray of shape (n_estimators + 1,)\n", + " | The columns from indicator[n_nodes_ptr[i]:n_nodes_ptr[i+1]]\n", + " | gives the indicator value for the i-th estimator.\n", + " | \n", + " | fit(self, X, y, sample_weight=None)\n", + " | Build a forest of trees from the training set (X, y).\n", + " | \n", + " | Parameters\n", + " | ----------\n", + " | X : {array-like, sparse matrix} of shape (n_samples, n_features)\n", + " | The training input samples. Internally, its dtype will be converted\n", + " | to ``dtype=np.float32``. If a sparse matrix is provided, it will be\n", + " | converted into a sparse ``csc_matrix``.\n", + " | \n", + " | y : array-like of shape (n_samples,) or (n_samples, n_outputs)\n", + " | The target values (class labels in classification, real numbers in\n", + " | regression).\n", + " | \n", + " | sample_weight : array-like of shape (n_samples,), default=None\n", + " | Sample weights. If None, then samples are equally weighted. Splits\n", + " | that would create child nodes with net zero or negative weight are\n", + " | ignored while searching for a split in each node. In the case of\n", + " | classification, splits are also ignored if they would result in any\n", + " | single class carrying a negative weight in either child node.\n", + " | \n", + " | Returns\n", + " | -------\n", + " | self : object\n", + " | Fitted estimator.\n", + " | \n", + " | ----------------------------------------------------------------------\n", + " | Readonly properties inherited from BaseForest:\n", + " | \n", + " | estimators_samples_\n", + " | The subset of drawn samples for each base estimator.\n", + " | \n", + " | Returns a dynamically generated list of indices identifying\n", + " | the samples used for fitting each member of the ensemble, i.e.,\n", + " | the in-bag samples.\n", + " | \n", + " | Note: the list is re-created at each call to the property in order\n", + " | to reduce the object memory footprint by not storing the sampling\n", + " | data. Thus fetching the property may be slower than expected.\n", + " | \n", + " | feature_importances_\n", + " | The impurity-based feature importances.\n", + " | \n", + " | The higher, the more important the feature.\n", + " | The importance of a feature is computed as the (normalized)\n", + " | total reduction of the criterion brought by that feature. It is also\n", + " | known as the Gini importance.\n", + " | \n", + " | Warning: impurity-based feature importances can be misleading for\n", + " | high cardinality features (many unique values). See\n", + " | :func:`sklearn.inspection.permutation_importance` as an alternative.\n", + " | \n", + " | Returns\n", + " | -------\n", + " | feature_importances_ : ndarray of shape (n_features,)\n", + " | The values of this array sum to 1, unless all trees are single node\n", + " | trees consisting of only the root node, in which case it will be an\n", + " | array of zeros.\n", + " | \n", + " | ----------------------------------------------------------------------\n", + " | Methods inherited from sklearn.ensemble._base.BaseEnsemble:\n", + " | \n", + " | __getitem__(self, index)\n", + " | Return the index'th estimator in the ensemble.\n", + " | \n", + " | __iter__(self)\n", + " | Return iterator over estimators in the ensemble.\n", + " | \n", + " | __len__(self)\n", + " | Return the number of estimators in the ensemble.\n", + " | \n", + " | ----------------------------------------------------------------------\n", + " | Methods inherited from sklearn.base.BaseEstimator:\n", + " | \n", + " | __getstate__(self)\n", + " | Helper for pickle.\n", + " | \n", + " | __repr__(self, N_CHAR_MAX=700)\n", + " | Return repr(self).\n", + " | \n", + " | __setstate__(self, state)\n", + " | \n", + " | __sklearn_clone__(self)\n", + " | \n", + " | get_params(self, deep=True)\n", + " | Get parameters for this estimator.\n", + " | \n", + " | Parameters\n", + " | ----------\n", + " | deep : bool, default=True\n", + " | If True, will return the parameters for this estimator and\n", + " | contained subobjects that are estimators.\n", + " | \n", + " | Returns\n", + " | -------\n", + " | params : dict\n", + " | Parameter names mapped to their values.\n", + " | \n", + " | set_params(self, **params)\n", + " | Set the parameters of this estimator.\n", + " | \n", + " | The method works on simple estimators as well as on nested objects\n", + " | (such as :class:`~sklearn.pipeline.Pipeline`). The latter have\n", + " | parameters of the form ``__`` so that it's\n", + " | possible to update each component of a nested object.\n", + " | \n", + " | Parameters\n", + " | ----------\n", + " | **params : dict\n", + " | Estimator parameters.\n", + " | \n", + " | Returns\n", + " | -------\n", + " | self : estimator instance\n", + " | Estimator instance.\n", + " | \n", + " | ----------------------------------------------------------------------\n", + " | Methods inherited from sklearn.utils._metadata_requests._MetadataRequester:\n", + " | \n", + " | get_metadata_routing(self)\n", + " | Get metadata routing of this object.\n", + " | \n", + " | Please check :ref:`User Guide ` on how the routing\n", + " | mechanism works.\n", + " | \n", + " | Returns\n", + " | -------\n", + " | routing : MetadataRequest\n", + " | A :class:`~sklearn.utils.metadata_routing.MetadataRequest` encapsulating\n", + " | routing information.\n", + " | \n", + " | ----------------------------------------------------------------------\n", + " | Class methods inherited from sklearn.utils._metadata_requests._MetadataRequester:\n", + " | \n", + " | __init_subclass__(**kwargs)\n", + " | Set the ``set_{method}_request`` methods.\n", + " | \n", + " | This uses PEP-487 [1]_ to set the ``set_{method}_request`` methods. It\n", + " | looks for the information available in the set default values which are\n", + " | set using ``__metadata_request__*`` class attributes, or inferred\n", + " | from method signatures.\n", + " | \n", + " | The ``__metadata_request__*`` class attributes are used when a method\n", + " | does not explicitly accept a metadata through its arguments or if the\n", + " | developer would like to specify a request value for those metadata\n", + " | which are different from the default ``None``.\n", + " | \n", + " | References\n", + " | ----------\n", + " | .. [1] https://www.python.org/dev/peps/pep-0487\n", + "\n", + "None\n", + "RegressorChain\n", + "Ridge\n", + "RidgeCV\n", + "SGDRegressor\n", + "SVR\n", + "StackingRegressor\n", + "TheilSenRegressor\n", + "TransformedTargetRegressor\n", + "TweedieRegressor\n", + "VotingRegressor\n" + ] + } + ], + "source": [ + "from sklearn.utils import all_estimators\n", + "\n", + "# Get all regression models\n", + "regressors = all_estimators(type_filter='regressor')\n", + "\n", + "# Print the names of all available regression models\n", + "for name, estimator in regressors:\n", + " print(name)\n", + "\n", + "for name, estimator in regressors:\n", + " if name == \"RandomForestRegressor\":\n", + " print(help(estimator))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "be44f0da-e766-4bd0-9312-4b1977397c8d", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "LSST", + "language": "python", + "name": "lsst" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 40984ec644cfce5405485baa8fc3fd6bf38a0d75 Mon Sep 17 00:00:00 2001 From: beckynevin Date: Tue, 6 May 2025 18:20:47 +0000 Subject: [PATCH 03/13] updating intro to be dp0.2 style --- DP0.2/20_Introduction_to_Data_Science.ipynb | 86 +++++++++++++-------- 1 file changed, 54 insertions(+), 32 deletions(-) diff --git a/DP0.2/20_Introduction_to_Data_Science.ipynb b/DP0.2/20_Introduction_to_Data_Science.ipynb index ff91598b..29611e4f 100644 --- a/DP0.2/20_Introduction_to_Data_Science.ipynb +++ b/DP0.2/20_Introduction_to_Data_Science.ipynb @@ -1,26 +1,18 @@ { "cells": [ { - "attachments": { - "90083a24-00a4-4a6f-a1c3-b9b4c6b0de9e.png": { - "image/png": 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" - } - }, "cell_type": "markdown", - "id": "6c298cbb-eb18-4cbc-b064-709a7c2b9b4c", + "id": "5eb2606b-2724-43be-83bb-fb4df67d0b35", "metadata": {}, "source": [ "# Introduction to Data Science\n", - "\n", - "
\n", - "\n", - "
\n", - "For the Rubin Science Platform at data.lsst.cloud.
\n", - "Data Release: DPX or DRX
\n", - "Container Size: small
\n", - "LSST Science Pipelines version: Weekly 2024_16
\n", - "Last verified to run: 2024-07-09
\n", - "Repository: github.com/lsst/tutorial-notebooks
" + " \n", + "
\n", + "Contact author(s): Becky Nevin, Brian Nord
\n", + "Last verified to run: 2025-05-06
\n", + "LSST Science Pipelines version: Weekly 2025_09
\n", + "Container size: small
\n", + "Targeted learning level: intermediate
" ] }, { @@ -38,24 +30,52 @@ }, { "cell_type": "markdown", - "id": "25e71641-fbfb-4470-8049-03ba631dbef1", + "id": "05e4d2c6-9774-48db-930e-58536bd69de0", + "metadata": {}, + "source": [ + "**Description:** This notebook guides a PI through the process of using python's data science and machine learning libraries to explore data from complex ADQL queries with the TAP service. The goal is to build a predictive model to estimate missing $r-$band Kron Flux values when the other bands are available, and visualize the results and quantify the model performance." + ] + }, + { + "cell_type": "markdown", + "id": "242eb338-4d88-4a17-8e35-20d80774ec1b", + "metadata": {}, + "source": [ + "**Skills:** Use of data science and machine learning tools such as scikit-learn, pandas, and seaborn." + ] + }, + { + "cell_type": "markdown", + "id": "76ffcb7f-a070-47b7-8545-f10813ff78ae", + "metadata": {}, + "source": [ + "**LSST Data Products:** Object, Forcedsource, and CcdVisit tables." + ] + }, + { + "cell_type": "markdown", + "id": "bf4d0080-92f9-4a67-a829-5a772e428ac4", + "metadata": {}, + "source": [ + "**Packages:** lsst.rsp, pandas, scikit-learn, seaborn" + ] + }, + { + "cell_type": "markdown", + "id": "e354c692-b8b4-425f-bcbe-a35221a9993a", + "metadata": {}, + "source": [ + "**Credits:** Developed by Becky Nevin in collaboration with Melissa Graham, Brian Nord, and the Rubin Community Science Team for DP0.2. Based on notebooks developed by Leanne Guy (TAP query) and Alex Drlica-Wagner and Melissa Graham (Butler query).\n", + "Please consider acknowledging them if this notebook is used for the preparation of journal articles, software releases, or other notebooks." + ] + }, + { + "cell_type": "markdown", + "id": "2e3e438c-a028-417f-a21f-1d31c5946376", "metadata": {}, "source": [ - "**Learning objective:** This notebook guides a PI through the process of using python's data science and machine learning libraries to explore data from complex ADQL queries with the TAP service. The goal is to build a predictive model to estimate missing $r-$band Kron Flux values when the other bands are available, and visualize the results and quantify the model performance.\n", - "\n", - "**LSST data products:** Object, Forcedsource, and CcdVisit tables.\n", - "\n", - "**Packages:** lsst.rsp, pandas, scikit-learn, seaborn\n", - "\n", - "**Credit:**\n", - "Based on notebooks developed by Leanne Guy (TAP query) and Alex Drlica-Wagner and Melissa Graham (Butler query).\n", - "Please consider acknowledging them if this notebook is used for the preparation of journal articles, software releases, or other notebooks.\n", - "\n", "**Get Support:**\n", - "Everyone is encouraged to ask questions or raise issues in the \n", - "Support Category \n", - "of the Rubin Community Forum.\n", - "Rubin staff will respond to all questions posted there." + "Find DP0-related documentation and resources at dp0.lsst.io. Questions are welcome as new topics in the Support - Data Preview 0 Category of the Rubin Community Forum. Rubin staff will respond to all questions posted there." ] }, { @@ -120,7 +140,9 @@ "id": "90251edc-e77a-4f2c-aef3-935381faebc9", "metadata": {}, "source": [ - "Set up seaborn to use 538's aesthetics. This is probably not what we want to the rtn-045 default plotting settings though..." + "Set up seaborn to use 538's aesthetics.\n", + "\n", + "**This is probably not what we want to the rtn-045 default plotting settings though...**" ] }, { From 3a459018216d55274907b9fb07ef9a29ee06f3b8 Mon Sep 17 00:00:00 2001 From: beckynevin Date: Tue, 6 May 2025 18:47:48 +0000 Subject: [PATCH 04/13] working on outliers in histogram --- DP0.2/20_Introduction_to_Data_Science.ipynb | 480 +++++++++++--------- 1 file changed, 263 insertions(+), 217 deletions(-) diff --git a/DP0.2/20_Introduction_to_Data_Science.ipynb b/DP0.2/20_Introduction_to_Data_Science.ipynb index 29611e4f..dffa3604 100644 --- a/DP0.2/20_Introduction_to_Data_Science.ipynb +++ b/DP0.2/20_Introduction_to_Data_Science.ipynb @@ -105,15 +105,15 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 4, "id": "3f4900a4-3358-472a-b9ba-c42e3f2f0771", "metadata": { "execution": { - "iopub.execute_input": "2024-12-03T18:16:10.447704Z", - "iopub.status.busy": "2024-12-03T18:16:10.447270Z", - "iopub.status.idle": "2024-12-03T18:16:11.741424Z", - "shell.execute_reply": "2024-12-03T18:16:11.740792Z", - "shell.execute_reply.started": "2024-12-03T18:16:10.447676Z" + "iopub.execute_input": "2025-05-06T18:35:45.512630Z", + "iopub.status.busy": "2025-05-06T18:35:45.512130Z", + "iopub.status.idle": "2025-05-06T18:35:45.518945Z", + "shell.execute_reply": "2025-05-06T18:35:45.517910Z", + "shell.execute_reply.started": "2025-05-06T18:35:45.512594Z" } }, "outputs": [], @@ -147,15 +147,15 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 5, "id": "94acc9f6-2033-4ace-aefd-d036a35f4221", "metadata": { "execution": { - "iopub.execute_input": "2024-12-03T18:18:25.800083Z", - "iopub.status.busy": "2024-12-03T18:18:25.799778Z", - "iopub.status.idle": "2024-12-03T18:18:25.803998Z", - "shell.execute_reply": "2024-12-03T18:18:25.803416Z", - "shell.execute_reply.started": "2024-12-03T18:18:25.800052Z" + "iopub.execute_input": "2025-05-06T18:35:46.040743Z", + "iopub.status.busy": "2025-05-06T18:35:46.039669Z", + "iopub.status.idle": "2025-05-06T18:35:46.046641Z", + "shell.execute_reply": "2025-05-06T18:35:46.045592Z", + "shell.execute_reply.started": "2025-05-06T18:35:46.040696Z" } }, "outputs": [], @@ -178,15 +178,15 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 6, "id": "caf56589-100a-4481-8f24-5f5058b6671f", "metadata": { "execution": { - "iopub.execute_input": "2024-12-03T18:18:31.983738Z", - "iopub.status.busy": "2024-12-03T18:18:31.983012Z", - "iopub.status.idle": "2024-12-03T18:18:32.026282Z", - "shell.execute_reply": "2024-12-03T18:18:32.025768Z", - "shell.execute_reply.started": "2024-12-03T18:18:31.983710Z" + "iopub.execute_input": "2025-05-06T18:35:48.293168Z", + "iopub.status.busy": "2025-05-06T18:35:48.292706Z", + "iopub.status.idle": "2025-05-06T18:35:48.342930Z", + "shell.execute_reply": "2025-05-06T18:35:48.341849Z", + "shell.execute_reply.started": "2025-05-06T18:35:48.293110Z" } }, "outputs": [], @@ -205,15 +205,15 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 7, "id": "2b7b6002-2457-4c20-a03e-6bfa24a0aa27", "metadata": { "execution": { - "iopub.execute_input": "2024-12-03T18:18:32.908017Z", - "iopub.status.busy": "2024-12-03T18:18:32.907315Z", - "iopub.status.idle": "2024-12-03T18:18:32.910925Z", - "shell.execute_reply": "2024-12-03T18:18:32.910377Z", - "shell.execute_reply.started": "2024-12-03T18:18:32.907992Z" + "iopub.execute_input": "2025-05-06T18:35:49.263865Z", + "iopub.status.busy": "2025-05-06T18:35:49.263456Z", + "iopub.status.idle": "2025-05-06T18:35:49.268590Z", + "shell.execute_reply": "2025-05-06T18:35:49.267634Z", + "shell.execute_reply.started": "2025-05-06T18:35:49.263831Z" } }, "outputs": [], @@ -244,15 +244,15 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 8, "id": "7ddd0344-b354-45a0-9e5a-755149c9bc54", "metadata": { "execution": { - "iopub.execute_input": "2024-12-03T18:18:35.079591Z", - "iopub.status.busy": "2024-12-03T18:18:35.078853Z", - "iopub.status.idle": "2024-12-03T18:18:35.082610Z", - "shell.execute_reply": "2024-12-03T18:18:35.082033Z", - "shell.execute_reply.started": "2024-12-03T18:18:35.079566Z" + "iopub.execute_input": "2025-05-06T18:35:50.088253Z", + "iopub.status.busy": "2025-05-06T18:35:50.087804Z", + "iopub.status.idle": "2025-05-06T18:35:50.093373Z", + "shell.execute_reply": "2025-05-06T18:35:50.092357Z", + "shell.execute_reply.started": "2025-05-06T18:35:50.088219Z" } }, "outputs": [], @@ -275,15 +275,15 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 9, "id": "985e3b62-8065-42ec-a40c-1232c4c45f17", "metadata": { "execution": { - "iopub.execute_input": "2024-12-03T18:19:13.171378Z", - "iopub.status.busy": "2024-12-03T18:19:13.170670Z", - "iopub.status.idle": "2024-12-03T18:19:13.174956Z", - "shell.execute_reply": "2024-12-03T18:19:13.174323Z", - "shell.execute_reply.started": "2024-12-03T18:19:13.171349Z" + "iopub.execute_input": "2025-05-06T18:35:50.657816Z", + "iopub.status.busy": "2025-05-06T18:35:50.656791Z", + "iopub.status.idle": "2025-05-06T18:35:50.662469Z", + "shell.execute_reply": "2025-05-06T18:35:50.661520Z", + "shell.execute_reply.started": "2025-05-06T18:35:50.657774Z" } }, "outputs": [ @@ -315,15 +315,15 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 10, "id": "c02adc91-5f5e-418b-87a3-cba8beba7dd2", "metadata": { "execution": { - "iopub.execute_input": "2024-12-03T18:19:14.234845Z", - "iopub.status.busy": "2024-12-03T18:19:14.234550Z", - "iopub.status.idle": "2024-12-03T18:19:21.521239Z", - "shell.execute_reply": "2024-12-03T18:19:21.520430Z", - "shell.execute_reply.started": "2024-12-03T18:19:14.234825Z" + "iopub.execute_input": "2025-05-06T18:35:51.312186Z", + "iopub.status.busy": "2025-05-06T18:35:51.311703Z", + "iopub.status.idle": "2025-05-06T18:35:58.580103Z", + "shell.execute_reply": "2025-05-06T18:35:58.578888Z", + "shell.execute_reply.started": "2025-05-06T18:35:51.312127Z" } }, "outputs": [ @@ -348,7 +348,8 @@ "metadata": {}, "source": [ "## 3. Explore the data using a `pandas` DataFrame object.\n", - "DEFINE WHAT A DATAFRAME IS." + "From the `pandas` docs:\n", + "> A DataFrame is a two-dimensional, size-mutable, heterogeneous tabular data structure with labeled axes (rows and columns)." ] }, { @@ -361,15 +362,15 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 11, "id": "8cd2f538-c2d7-44ca-ab4d-825120b8f2e7", "metadata": { "execution": { - "iopub.execute_input": "2024-12-03T18:19:21.522743Z", - "iopub.status.busy": "2024-12-03T18:19:21.522437Z", - "iopub.status.idle": "2024-12-03T18:19:21.848448Z", - "shell.execute_reply": "2024-12-03T18:19:21.847864Z", - "shell.execute_reply.started": "2024-12-03T18:19:21.522716Z" + "iopub.execute_input": "2025-05-06T18:35:58.582107Z", + "iopub.status.busy": "2025-05-06T18:35:58.581718Z", + "iopub.status.idle": "2025-05-06T18:35:59.419620Z", + "shell.execute_reply": "2025-05-06T18:35:59.418605Z", + "shell.execute_reply.started": "2025-05-06T18:35:58.582071Z" } }, "outputs": [], @@ -389,15 +390,15 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 12, "id": "ee4d121e-6b4d-4371-afae-4f7587b95d51", "metadata": { "execution": { - "iopub.execute_input": "2024-12-03T18:18:54.059294Z", - "iopub.status.busy": "2024-12-03T18:18:54.058632Z", - "iopub.status.idle": "2024-12-03T18:18:54.074345Z", - "shell.execute_reply": "2024-12-03T18:18:54.073785Z", - "shell.execute_reply.started": "2024-12-03T18:18:54.059270Z" + "iopub.execute_input": "2025-05-06T18:35:59.421059Z", + "iopub.status.busy": "2025-05-06T18:35:59.420677Z", + "iopub.status.idle": "2025-05-06T18:35:59.443855Z", + "shell.execute_reply": "2025-05-06T18:35:59.442812Z", + "shell.execute_reply.started": "2025-05-06T18:35:59.421027Z" } }, "outputs": [ @@ -435,36 +436,36 @@ " \n", " \n", " 0\n", - " 62.018897\n", - " -37.095671\n", - " 71.568352\n", - " True\n", - " 91.185588\n", - " True\n", - " 624.454022\n", - " True\n", + " 62.006797\n", + " -36.902082\n", + " 1071.714409\n", + " False\n", + " 4245.695538\n", + " False\n", + " 10922.722964\n", + " False\n", " \n", " \n", " 1\n", - " 62.020999\n", - " -37.093227\n", - " 174.729861\n", + " 62.003768\n", + " -36.902438\n", + " 566.877720\n", " False\n", - " 110.922305\n", + " 787.147920\n", + " False\n", + " 1182.026506\n", " False\n", - " 52.040203\n", - " True\n", " \n", " \n", " 2\n", - " 62.000430\n", - " -37.093196\n", - " 131.680920\n", + " 62.008572\n", + " -36.902418\n", + " 174.862476\n", + " False\n", + " 520.394736\n", " False\n", - " 137.655812\n", + " 1266.903601\n", " False\n", - " 136.174616\n", - " True\n", " \n", " \n", " ...\n", @@ -479,36 +480,36 @@ " \n", " \n", " 11561\n", - " 61.950427\n", - " -36.946586\n", - " 51.054369\n", - " True\n", - " 175.646973\n", + " 61.991017\n", + " -37.082362\n", + " 493.321470\n", + " False\n", + " 648.269883\n", + " False\n", + " 908.325375\n", " False\n", - " 123.073904\n", - " True\n", " \n", " \n", " 11562\n", - " 61.976752\n", - " -36.904225\n", - " 199.039503\n", + " 61.950309\n", + " -37.085180\n", + " 116.922498\n", " False\n", - " 187.972452\n", + " 117.440266\n", " False\n", - " 115.825734\n", + " 54.748117\n", " False\n", " \n", " \n", " 11563\n", - " 61.932319\n", - " -36.941077\n", - " 266.123377\n", + " 61.951738\n", + " -37.085393\n", + " 146.265527\n", + " False\n", + " 260.318371\n", " False\n", - " 218.853195\n", + " 208.302958\n", " False\n", - " 481.650950\n", - " True\n", " \n", " \n", "\n", @@ -516,28 +517,28 @@ "" ], "text/plain": [ - " coord_ra coord_dec g_kronFlux g_kronFlux_flag r_kronFlux \\\n", - "0 62.018897 -37.095671 71.568352 True 91.185588 \n", - "1 62.020999 -37.093227 174.729861 False 110.922305 \n", - "2 62.000430 -37.093196 131.680920 False 137.655812 \n", - "... ... ... ... ... ... \n", - "11561 61.950427 -36.946586 51.054369 True 175.646973 \n", - "11562 61.976752 -36.904225 199.039503 False 187.972452 \n", - "11563 61.932319 -36.941077 266.123377 False 218.853195 \n", - "\n", - " r_kronFlux_flag i_kronFlux i_kronFlux_flag \n", - "0 True 624.454022 True \n", - "1 False 52.040203 True \n", - "2 False 136.174616 True \n", - "... ... ... ... \n", - "11561 False 123.073904 True \n", - "11562 False 115.825734 False \n", - "11563 False 481.650950 True \n", + " coord_ra coord_dec g_kronFlux g_kronFlux_flag r_kronFlux \\\n", + "0 62.006797 -36.902082 1071.714409 False 4245.695538 \n", + "1 62.003768 -36.902438 566.877720 False 787.147920 \n", + "2 62.008572 -36.902418 174.862476 False 520.394736 \n", + "... ... ... ... ... ... \n", + "11561 61.991017 -37.082362 493.321470 False 648.269883 \n", + "11562 61.950309 -37.085180 116.922498 False 117.440266 \n", + "11563 61.951738 -37.085393 146.265527 False 260.318371 \n", + "\n", + " r_kronFlux_flag i_kronFlux i_kronFlux_flag \n", + "0 False 10922.722964 False \n", + "1 False 1182.026506 False \n", + "2 False 1266.903601 False \n", + "... ... ... ... \n", + "11561 False 908.325375 False \n", + "11562 False 54.748117 False \n", + "11563 False 208.302958 False \n", "\n", "[11564 rows x 8 columns]" ] }, - "execution_count": 31, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -546,25 +547,17 @@ "results" ] }, - { - "cell_type": "markdown", - "id": "1d493b9b-0aba-4586-bcd0-3e1ce1f09c16", - "metadata": {}, - "source": [ - "`results` is a `pandas` DataFrame object (see below). There's lots you can do with this type of object..." - ] - }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 13, "id": "db2168fe-593a-423d-b2f4-26ac0db60e8c", "metadata": { "execution": { - "iopub.execute_input": "2024-12-03T18:19:48.071264Z", - "iopub.status.busy": "2024-12-03T18:19:48.070609Z", - "iopub.status.idle": "2024-12-03T18:19:48.074799Z", - "shell.execute_reply": "2024-12-03T18:19:48.074312Z", - "shell.execute_reply.started": "2024-12-03T18:19:48.071232Z" + "iopub.execute_input": "2025-05-06T18:35:59.446322Z", + "iopub.status.busy": "2025-05-06T18:35:59.445927Z", + "iopub.status.idle": "2025-05-06T18:35:59.452311Z", + "shell.execute_reply": "2025-05-06T18:35:59.451395Z", + "shell.execute_reply.started": "2025-05-06T18:35:59.446285Z" } }, "outputs": [ @@ -574,7 +567,7 @@ "pandas.core.frame.DataFrame" ] }, - "execution_count": 37, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -588,20 +581,22 @@ "id": "9c59c4e8-90bd-4aa8-8ce2-9a14c09988a0", "metadata": {}, "source": [ - "Some options are inspection- and summary-oriented, such as the `.head()`, `.tail()`, and `.describe()` attributes. Let's check these out now. `.head()` and `.tail()` give you the first and last five rows, respectively, but can be modified to print out a different number of rows. `.describe()` will provide statistics of the distribution of values in each column, including the mean and standard deviation." + "There's a lot of options for investigating DataFrame objects. Some options are inspection- and summary-oriented, such as the `.head()`, `.tail()`, and `.describe()` attributes.\n", + "\n", + "Check these out now. `.head()` and `.tail()` show the first and last five rows, respectively, but can be modified to print out a different number of rows. `.describe()` provides statistics of the distribution of values in each column, including the mean and standard deviation." ] }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 14, "id": "eec25f58-d3f3-4ef4-b3e2-ab105c4718fd", "metadata": { "execution": { - "iopub.execute_input": "2024-12-03T18:23:39.207146Z", - "iopub.status.busy": "2024-12-03T18:23:39.206862Z", - "iopub.status.idle": "2024-12-03T18:23:39.230317Z", - "shell.execute_reply": "2024-12-03T18:23:39.229617Z", - "shell.execute_reply.started": "2024-12-03T18:23:39.207125Z" + "iopub.execute_input": "2025-05-06T18:35:59.453572Z", + "iopub.status.busy": "2025-05-06T18:35:59.453238Z", + "iopub.status.idle": "2025-05-06T18:35:59.498466Z", + "shell.execute_reply": "2025-05-06T18:35:59.497510Z", + "shell.execute_reply.started": "2025-05-06T18:35:59.453542Z" } }, "outputs": [ @@ -609,36 +604,36 @@ "name": "stdout", "output_type": "stream", "text": [ - " coord_ra coord_dec g_kronFlux g_kronFlux_flag r_kronFlux \\\n", - "0 62.018897 -37.095671 71.568352 True 91.185588 \n", - "1 62.020999 -37.093227 174.729861 False 110.922305 \n", - "2 62.000430 -37.093196 131.680920 False 137.655812 \n", - "3 62.015568 -37.092868 372.665560 False 171.582869 \n", - "4 62.002969 -37.092762 247.219720 False 153.138653 \n", + " coord_ra coord_dec g_kronFlux g_kronFlux_flag r_kronFlux \\\n", + "0 62.006797 -36.902082 1071.714409 False 4245.695538 \n", + "1 62.003768 -36.902438 566.877720 False 787.147920 \n", + "2 62.008572 -36.902418 174.862476 False 520.394736 \n", + "3 62.008889 -36.902797 549.459095 False 504.405489 \n", + "4 62.005342 -36.903529 191.838094 False 511.872678 \n", "\n", - " r_kronFlux_flag i_kronFlux i_kronFlux_flag \n", - "0 True 624.454022 True \n", - "1 False 52.040203 True \n", - "2 False 136.174616 True \n", - "3 False 211.338418 True \n", - "4 True 184.829166 True \n", - " coord_ra coord_dec g_kronFlux g_kronFlux_flag r_kronFlux \\\n", - "11554 61.913511 -36.960012 158.939682 False NaN \n", - "11555 61.986424 -36.950292 125.535210 False 71.749436 \n", - "11556 61.942562 -36.951137 108.966860 False 135.301872 \n", - "... ... ... ... ... ... \n", - "11561 61.950427 -36.946586 51.054369 True 175.646973 \n", - "11562 61.976752 -36.904225 199.039503 False 187.972452 \n", - "11563 61.932319 -36.941077 266.123377 False 218.853195 \n", + " r_kronFlux_flag i_kronFlux i_kronFlux_flag \n", + "0 False 10922.722964 False \n", + "1 False 1182.026506 False \n", + "2 False 1266.903601 False \n", + "3 False 553.084499 False \n", + "4 False 909.234224 False \n", + " coord_ra coord_dec g_kronFlux g_kronFlux_flag r_kronFlux \\\n", + "11554 61.986579 -37.083734 484.161319 False 1139.300737 \n", + "11555 61.988051 -37.085378 108.308183 False 634.252111 \n", + "11556 61.985760 -37.082797 203.076232 False 714.794069 \n", + "... ... ... ... ... ... \n", + "11561 61.991017 -37.082362 493.321470 False 648.269883 \n", + "11562 61.950309 -37.085180 116.922498 False 117.440266 \n", + "11563 61.951738 -37.085393 146.265527 False 260.318371 \n", "\n", - " r_kronFlux_flag i_kronFlux i_kronFlux_flag \n", - "11554 True 102.094474 True \n", - "11555 True NaN True \n", - "11556 False 191.964068 True \n", - "... ... ... ... \n", - "11561 False 123.073904 True \n", - "11562 False 115.825734 False \n", - "11563 False 481.650950 True \n", + " r_kronFlux_flag i_kronFlux i_kronFlux_flag \n", + "11554 False 2663.259351 False \n", + "11555 False 1487.625610 False \n", + "11556 False 1734.816947 False \n", + "... ... ... ... \n", + "11561 False 908.325375 False \n", + "11562 False 54.748117 False \n", + "11563 False 208.302958 False \n", "\n", "[10 rows x 8 columns]\n", " coord_ra coord_dec g_kronFlux r_kronFlux i_kronFlux\n", @@ -666,26 +661,26 @@ "metadata": {}, "source": [ "## 4. Visualize using `seaborn`\n", - "Let's look further into visualizing these statistics using `seaborn`'s boxplot tool." + "Use the boxplot tool from `seaborn` to visualize the distribution of the values in each column of the DataFrame." ] }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 15, "id": "4fc9b578-2be4-4fb2-8d74-ebca809ea99f", "metadata": { "execution": { - "iopub.execute_input": "2024-12-03T18:55:30.244276Z", - "iopub.status.busy": "2024-12-03T18:55:30.243648Z", - "iopub.status.idle": "2024-12-03T18:55:30.835103Z", - "shell.execute_reply": "2024-12-03T18:55:30.834453Z", - "shell.execute_reply.started": "2024-12-03T18:55:30.244249Z" + "iopub.execute_input": "2025-05-06T18:35:59.499899Z", + "iopub.status.busy": "2025-05-06T18:35:59.499560Z", + "iopub.status.idle": "2025-05-06T18:36:00.808262Z", + "shell.execute_reply": "2025-05-06T18:36:00.807256Z", + "shell.execute_reply.started": "2025-05-06T18:35:59.499865Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -709,26 +704,26 @@ "id": "ef5f20de-0fd6-4ba1-9cab-9d59cd05df99", "metadata": {}, "source": [ - "It looks like outliers are dominant in the visualization. Hide these and also only plot the kron Flux values." + "The outliers are dominant in the visualization. Hide these and also only plot the kron Flux values." ] }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 16, "id": "bacf5114-6a64-4100-8eb6-f1d9ddc36f89", "metadata": { "execution": { - "iopub.execute_input": "2024-12-03T18:59:02.839663Z", - "iopub.status.busy": "2024-12-03T18:59:02.839254Z", - "iopub.status.idle": "2024-12-03T18:59:03.091807Z", - "shell.execute_reply": "2024-12-03T18:59:03.091120Z", - "shell.execute_reply.started": "2024-12-03T18:59:02.839637Z" + "iopub.execute_input": "2025-05-06T18:36:00.809919Z", + "iopub.status.busy": "2025-05-06T18:36:00.809540Z", + "iopub.status.idle": "2025-05-06T18:36:01.278574Z", + "shell.execute_reply": "2025-05-06T18:36:01.277570Z", + "shell.execute_reply.started": "2025-05-06T18:36:00.809879Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -759,36 +754,31 @@ "- Points outside the whisker are considered outliers (hidden here).\n" ] }, + { + "cell_type": "markdown", + "id": "448bec6a-15e1-49b5-a52e-bdd743ff207a", + "metadata": {}, + "source": [ + "Use `seaborn`'s violinplot tool to visualize the distribution for these same Kron flux values." + ] + }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 17, "id": "39521ac6-0bec-42e7-9062-8fc9ce5edc55", "metadata": { "execution": { - "iopub.execute_input": "2024-12-03T19:11:16.903575Z", - "iopub.status.busy": "2024-12-03T19:11:16.902993Z", - "iopub.status.idle": "2024-12-03T19:11:17.202209Z", - "shell.execute_reply": "2024-12-03T19:11:17.201547Z", - "shell.execute_reply.started": "2024-12-03T19:11:16.903550Z" + "iopub.execute_input": "2025-05-06T18:36:16.376938Z", + "iopub.status.busy": "2025-05-06T18:36:16.376088Z", + "iopub.status.idle": "2025-05-06T18:36:16.912995Z", + "shell.execute_reply": "2025-05-06T18:36:16.911929Z", + "shell.execute_reply.started": "2025-05-06T18:36:16.376896Z" } }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_638/2438356921.py:3: FutureWarning: \n", - "\n", - "The `bw` parameter is deprecated in favor of `bw_method`/`bw_adjust`.\n", - "Setting `bw_method=0.2`, but please see docs for the new parameters\n", - "and update your code. This will become an error in seaborn v0.15.0.\n", - "\n", - " sns.violinplot(data=filtered_results,\n" - ] - }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -801,8 +791,7 @@ "plt.figure(figsize=(8, 6))\n", "filtered_results = results[['g_kronFlux', 'r_kronFlux', 'i_kronFlux']].apply(lambda x: x[(x > x.quantile(0.05)) & (x < x.quantile(0.95))])\n", "sns.violinplot(data=filtered_results,\n", - " cut=0,\n", - " bw=0.2)\n", + " cut=0)\n", "plt.title('Box Plot of Data Distributions')\n", "plt.xlabel('Feature')\n", "plt.ylabel('Value')\n", @@ -815,7 +804,7 @@ "id": "4f2c50a8-098e-4230-b122-3880c8bb1883", "metadata": {}, "source": [ - "A violinplot gives a lot of the same information as a boxplot, in fact, there are little boxplots within the violinplot; the horizontal white line is the median, the thicker grey box is the IQR and the thin line shows the 1.5*IQR span. A violinplot also uses a kernel density extimator to visualize the distribution of each feature. Here we see that most of the data are clustered around relatively low values for all of the kron fluxes." + "A violinplot gives a lot of the same information as a boxplot, in fact, there are little boxplots within the violinplot; the horizontal white line is the median, the thicker grey box is the IQR and the thin line shows the 1.5*IQR span. A violinplot also uses a kernel density extimator to visualize the distribution of each feature. These plots reveal that most of the data are clustered around relatively low values for all of the Kron fluxes." ] }, { @@ -836,15 +825,15 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 18, "id": "0be4535d-cc89-45ef-98e9-591b9f459fae", "metadata": { "execution": { - "iopub.execute_input": "2024-12-03T00:04:41.789910Z", - "iopub.status.busy": "2024-12-03T00:04:41.789726Z", - "iopub.status.idle": "2024-12-03T00:04:41.794781Z", - "shell.execute_reply": "2024-12-03T00:04:41.794289Z", - "shell.execute_reply.started": "2024-12-03T00:04:41.789894Z" + "iopub.execute_input": "2025-05-06T18:36:23.116189Z", + "iopub.status.busy": "2025-05-06T18:36:23.115686Z", + "iopub.status.idle": "2025-05-06T18:36:23.129177Z", + "shell.execute_reply": "2025-05-06T18:36:23.128278Z", + "shell.execute_reply.started": "2025-05-06T18:36:23.116116Z" } }, "outputs": [ @@ -857,7 +846,7 @@ "Name: count, dtype: int64" ] }, - "execution_count": 13, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -879,26 +868,78 @@ } }, "source": [ - "Let's compare the value of the `g_kronFlux` between flagged and unflagged cases." + "Compare the value of the `r_kronFlux` between flagged and unflagged cases. First use `numpy`'s histogram function to look at the values of the flagged and unflagged $r-$band Kron fluxes." ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 33, + "id": "ecea878f-83be-4a6c-acad-65272567ef33", + "metadata": { + "execution": { + "iopub.execute_input": "2025-05-06T18:46:55.661296Z", + "iopub.status.busy": "2025-05-06T18:46:55.660249Z", + "iopub.status.idle": "2025-05-06T18:46:55.675021Z", + "shell.execute_reply": "2025-05-06T18:46:55.674137Z", + "shell.execute_reply.started": "2025-05-06T18:46:55.661251Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "unflagged values [10716 0 2 3 1 0 0 0 0 1] unflagged bins [-1.60122808e+02 1.95632031e+05 3.91424186e+05 5.87216340e+05\n", + " 7.83008494e+05 9.78800648e+05 1.17459280e+06 1.37038496e+06\n", + " 1.56617711e+06 1.76196926e+06 1.95776142e+06]\n", + "flagged values [ 2 28 185 284 125 56 35 11 3 1] unflagged bins [-158.2223317 -74.04158638 10.13915894 94.31990426 178.50064958\n", + " 262.6813949 346.86214022 431.04288554 515.22363086 599.40437618\n", + " 683.5851215 ]\n" + ] + } + ], + "source": [ + "r_kronFlux_unflagged = results[\n", + " (results['r_kronFlux_flag'] == False) & \n", + " (results['r_kronFlux'].notna())\n", + "]['r_kronFlux']\n", + "r_kronFlux_flagged = results[\n", + " (results['r_kronFlux_flag'] == True) & \n", + " (results['r_kronFlux'].notna())\n", + "]['r_kronFlux']\n", + "values, bins = np.histogram(r_kronFlux_unflagged)\n", + "print('unflagged values', values, 'unflagged bins', bins)\n", + "values, bins = np.histogram(r_kronFlux_flagged)\n", + "print('flagged values', values, 'unflagged bins', bins)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 31, "id": "dddfd1e7-3faa-465f-ade4-dc136ae39262", "metadata": { "execution": { - "iopub.execute_input": "2024-12-03T00:04:41.795547Z", - "iopub.status.busy": "2024-12-03T00:04:41.795330Z", - "iopub.status.idle": "2024-12-03T00:04:41.924873Z", - "shell.execute_reply": "2024-12-03T00:04:41.924253Z", - "shell.execute_reply.started": "2024-12-03T00:04:41.795530Z" + "iopub.execute_input": "2025-05-06T18:41:12.884727Z", + "iopub.status.busy": "2025-05-06T18:41:12.884243Z", + "iopub.status.idle": "2025-05-06T18:41:13.111692Z", + "shell.execute_reply": "2025-05-06T18:41:13.110702Z", + "shell.execute_reply.started": "2025-05-06T18:41:12.884686Z" } }, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[10716 0 2 3 1 0 0 0 0 1] [-1.60122808e+02 1.95632031e+05 3.91424186e+05 5.87216340e+05\n", + " 7.83008494e+05 9.78800648e+05 1.17459280e+06 1.37038496e+06\n", + " 1.56617711e+06 1.76196926e+06 1.95776142e+06]\n", + "-160.1228083 1957761.4190416\n" + ] + }, { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -909,8 +950,13 @@ ], "source": [ "plt.clf()\n", - "plt.hist(results[results['r_kronFlux_flag']]['r_kronFlux'], label='Flagged', density=True)\n", - "#plt.hist(results[results['r_kronFlux_flag']==False]['r_kronFlux'], label='Not flagged', density=True)\n", + "print(bins, values)\n", + "plt.hist(results[results['r_kronFlux_flag']]['r_kronFlux'],\n", + " label='Flagged', density=True, alpha=0.5)\n", + "plt.hist(results[(results['r_kronFlux_flag']==False) & (results['r_kronFlux']<4000)]['r_kronFlux'],\n", + " label='Not flagged', density=True, alpha=0.5)\n", + "print(np.min(results[results['r_kronFlux_flag']==False]['r_kronFlux']),\n", + " np.max(results[results['r_kronFlux_flag']==False]['r_kronFlux']))\n", "plt.legend()\n", "plt.show()" ] From a8096279f9dc8621a975e718bed587418eea3b01 Mon Sep 17 00:00:00 2001 From: beckynevin Date: Tue, 6 May 2025 21:55:02 +0000 Subject: [PATCH 05/13] facelift to text in intersection section --- DP0.2/20_Introduction_to_Data_Science.ipynb | 504 ++++++++------------ 1 file changed, 187 insertions(+), 317 deletions(-) diff --git a/DP0.2/20_Introduction_to_Data_Science.ipynb b/DP0.2/20_Introduction_to_Data_Science.ipynb index dffa3604..f866da5e 100644 --- a/DP0.2/20_Introduction_to_Data_Science.ipynb +++ b/DP0.2/20_Introduction_to_Data_Science.ipynb @@ -105,15 +105,15 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 34, "id": "3f4900a4-3358-472a-b9ba-c42e3f2f0771", "metadata": { "execution": { - "iopub.execute_input": "2025-05-06T18:35:45.512630Z", - "iopub.status.busy": "2025-05-06T18:35:45.512130Z", - "iopub.status.idle": "2025-05-06T18:35:45.518945Z", - "shell.execute_reply": "2025-05-06T18:35:45.517910Z", - "shell.execute_reply.started": "2025-05-06T18:35:45.512594Z" + "iopub.execute_input": "2025-05-06T21:09:53.536477Z", + "iopub.status.busy": "2025-05-06T21:09:53.536025Z", + "iopub.status.idle": "2025-05-06T21:09:53.542205Z", + "shell.execute_reply": "2025-05-06T21:09:53.541204Z", + "shell.execute_reply.started": "2025-05-06T21:09:53.536441Z" } }, "outputs": [], @@ -147,15 +147,15 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 35, "id": "94acc9f6-2033-4ace-aefd-d036a35f4221", "metadata": { "execution": { - "iopub.execute_input": "2025-05-06T18:35:46.040743Z", - "iopub.status.busy": "2025-05-06T18:35:46.039669Z", - "iopub.status.idle": "2025-05-06T18:35:46.046641Z", - "shell.execute_reply": "2025-05-06T18:35:46.045592Z", - "shell.execute_reply.started": "2025-05-06T18:35:46.040696Z" + "iopub.execute_input": "2025-05-06T21:09:55.564309Z", + "iopub.status.busy": "2025-05-06T21:09:55.563848Z", + "iopub.status.idle": "2025-05-06T21:09:55.569994Z", + "shell.execute_reply": "2025-05-06T21:09:55.569076Z", + "shell.execute_reply.started": "2025-05-06T21:09:55.564270Z" } }, "outputs": [], @@ -178,15 +178,15 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 36, "id": "caf56589-100a-4481-8f24-5f5058b6671f", "metadata": { "execution": { - "iopub.execute_input": "2025-05-06T18:35:48.293168Z", - "iopub.status.busy": "2025-05-06T18:35:48.292706Z", - "iopub.status.idle": "2025-05-06T18:35:48.342930Z", - "shell.execute_reply": "2025-05-06T18:35:48.341849Z", - "shell.execute_reply.started": "2025-05-06T18:35:48.293110Z" + "iopub.execute_input": "2025-05-06T21:09:56.608239Z", + "iopub.status.busy": "2025-05-06T21:09:56.607764Z", + "iopub.status.idle": "2025-05-06T21:09:56.655843Z", + "shell.execute_reply": "2025-05-06T21:09:56.654831Z", + "shell.execute_reply.started": "2025-05-06T21:09:56.608198Z" } }, "outputs": [], @@ -205,15 +205,15 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 37, "id": "2b7b6002-2457-4c20-a03e-6bfa24a0aa27", "metadata": { "execution": { - "iopub.execute_input": "2025-05-06T18:35:49.263865Z", - "iopub.status.busy": "2025-05-06T18:35:49.263456Z", - "iopub.status.idle": "2025-05-06T18:35:49.268590Z", - "shell.execute_reply": "2025-05-06T18:35:49.267634Z", - "shell.execute_reply.started": "2025-05-06T18:35:49.263831Z" + "iopub.execute_input": "2025-05-06T21:09:57.967997Z", + "iopub.status.busy": "2025-05-06T21:09:57.967578Z", + "iopub.status.idle": "2025-05-06T21:09:57.972601Z", + "shell.execute_reply": "2025-05-06T21:09:57.971653Z", + "shell.execute_reply.started": "2025-05-06T21:09:57.967961Z" } }, "outputs": [], @@ -244,15 +244,15 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 38, "id": "7ddd0344-b354-45a0-9e5a-755149c9bc54", "metadata": { "execution": { - "iopub.execute_input": "2025-05-06T18:35:50.088253Z", - "iopub.status.busy": "2025-05-06T18:35:50.087804Z", - "iopub.status.idle": "2025-05-06T18:35:50.093373Z", - "shell.execute_reply": "2025-05-06T18:35:50.092357Z", - "shell.execute_reply.started": "2025-05-06T18:35:50.088219Z" + "iopub.execute_input": "2025-05-06T21:10:01.100216Z", + "iopub.status.busy": "2025-05-06T21:10:01.099744Z", + "iopub.status.idle": "2025-05-06T21:10:01.105192Z", + "shell.execute_reply": "2025-05-06T21:10:01.104220Z", + "shell.execute_reply.started": "2025-05-06T21:10:01.100180Z" } }, "outputs": [], @@ -275,15 +275,15 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 39, "id": "985e3b62-8065-42ec-a40c-1232c4c45f17", "metadata": { "execution": { - "iopub.execute_input": "2025-05-06T18:35:50.657816Z", - "iopub.status.busy": "2025-05-06T18:35:50.656791Z", - "iopub.status.idle": "2025-05-06T18:35:50.662469Z", - "shell.execute_reply": "2025-05-06T18:35:50.661520Z", - "shell.execute_reply.started": "2025-05-06T18:35:50.657774Z" + "iopub.execute_input": "2025-05-06T21:10:01.968430Z", + "iopub.status.busy": "2025-05-06T21:10:01.967916Z", + "iopub.status.idle": "2025-05-06T21:10:01.973604Z", + "shell.execute_reply": "2025-05-06T21:10:01.972718Z", + "shell.execute_reply.started": "2025-05-06T21:10:01.968391Z" } }, "outputs": [ @@ -315,15 +315,15 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 40, "id": "c02adc91-5f5e-418b-87a3-cba8beba7dd2", "metadata": { "execution": { - "iopub.execute_input": "2025-05-06T18:35:51.312186Z", - "iopub.status.busy": "2025-05-06T18:35:51.311703Z", - "iopub.status.idle": "2025-05-06T18:35:58.580103Z", - "shell.execute_reply": "2025-05-06T18:35:58.578888Z", - "shell.execute_reply.started": "2025-05-06T18:35:51.312127Z" + "iopub.execute_input": "2025-05-06T21:10:03.104109Z", + "iopub.status.busy": "2025-05-06T21:10:03.103631Z", + "iopub.status.idle": "2025-05-06T21:10:04.286634Z", + "shell.execute_reply": "2025-05-06T21:10:04.285654Z", + "shell.execute_reply.started": "2025-05-06T21:10:03.104058Z" } }, "outputs": [ @@ -362,15 +362,15 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 41, "id": "8cd2f538-c2d7-44ca-ab4d-825120b8f2e7", "metadata": { "execution": { - "iopub.execute_input": "2025-05-06T18:35:58.582107Z", - "iopub.status.busy": "2025-05-06T18:35:58.581718Z", - "iopub.status.idle": "2025-05-06T18:35:59.419620Z", - "shell.execute_reply": "2025-05-06T18:35:59.418605Z", - "shell.execute_reply.started": "2025-05-06T18:35:58.582071Z" + "iopub.execute_input": "2025-05-06T21:10:06.496317Z", + "iopub.status.busy": "2025-05-06T21:10:06.495845Z", + "iopub.status.idle": "2025-05-06T21:10:07.174905Z", + "shell.execute_reply": "2025-05-06T21:10:07.173813Z", + "shell.execute_reply.started": "2025-05-06T21:10:06.496280Z" } }, "outputs": [], @@ -390,15 +390,15 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 42, "id": "ee4d121e-6b4d-4371-afae-4f7587b95d51", "metadata": { "execution": { - "iopub.execute_input": "2025-05-06T18:35:59.421059Z", - "iopub.status.busy": "2025-05-06T18:35:59.420677Z", - "iopub.status.idle": "2025-05-06T18:35:59.443855Z", - "shell.execute_reply": "2025-05-06T18:35:59.442812Z", - "shell.execute_reply.started": "2025-05-06T18:35:59.421027Z" + "iopub.execute_input": "2025-05-06T21:10:07.484290Z", + "iopub.status.busy": "2025-05-06T21:10:07.483831Z", + "iopub.status.idle": "2025-05-06T21:10:07.502940Z", + "shell.execute_reply": "2025-05-06T21:10:07.501991Z", + "shell.execute_reply.started": "2025-05-06T21:10:07.484255Z" } }, "outputs": [ @@ -538,7 +538,7 @@ "[11564 rows x 8 columns]" ] }, - "execution_count": 12, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -549,15 +549,15 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 43, "id": "db2168fe-593a-423d-b2f4-26ac0db60e8c", "metadata": { "execution": { - "iopub.execute_input": "2025-05-06T18:35:59.446322Z", - "iopub.status.busy": "2025-05-06T18:35:59.445927Z", - "iopub.status.idle": "2025-05-06T18:35:59.452311Z", - "shell.execute_reply": "2025-05-06T18:35:59.451395Z", - "shell.execute_reply.started": "2025-05-06T18:35:59.446285Z" + "iopub.execute_input": "2025-05-06T21:10:08.004249Z", + "iopub.status.busy": "2025-05-06T21:10:08.003782Z", + "iopub.status.idle": "2025-05-06T21:10:08.010260Z", + "shell.execute_reply": "2025-05-06T21:10:08.009349Z", + "shell.execute_reply.started": "2025-05-06T21:10:08.004212Z" } }, "outputs": [ @@ -567,7 +567,7 @@ "pandas.core.frame.DataFrame" ] }, - "execution_count": 13, + "execution_count": 43, "metadata": {}, "output_type": "execute_result" } @@ -588,15 +588,15 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 44, "id": "eec25f58-d3f3-4ef4-b3e2-ab105c4718fd", "metadata": { "execution": { - "iopub.execute_input": "2025-05-06T18:35:59.453572Z", - "iopub.status.busy": "2025-05-06T18:35:59.453238Z", - "iopub.status.idle": "2025-05-06T18:35:59.498466Z", - "shell.execute_reply": "2025-05-06T18:35:59.497510Z", - "shell.execute_reply.started": "2025-05-06T18:35:59.453542Z" + "iopub.execute_input": "2025-05-06T21:10:09.180104Z", + "iopub.status.busy": "2025-05-06T21:10:09.179663Z", + "iopub.status.idle": "2025-05-06T21:10:09.219045Z", + "shell.execute_reply": "2025-05-06T21:10:09.218100Z", + "shell.execute_reply.started": "2025-05-06T21:10:09.180067Z" } }, "outputs": [ @@ -666,15 +666,15 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 45, "id": "4fc9b578-2be4-4fb2-8d74-ebca809ea99f", "metadata": { "execution": { - "iopub.execute_input": "2025-05-06T18:35:59.499899Z", - "iopub.status.busy": "2025-05-06T18:35:59.499560Z", - "iopub.status.idle": "2025-05-06T18:36:00.808262Z", - "shell.execute_reply": "2025-05-06T18:36:00.807256Z", - "shell.execute_reply.started": "2025-05-06T18:35:59.499865Z" + "iopub.execute_input": "2025-05-06T21:10:10.448369Z", + "iopub.status.busy": "2025-05-06T21:10:10.447861Z", + "iopub.status.idle": "2025-05-06T21:10:11.456905Z", + "shell.execute_reply": "2025-05-06T21:10:11.455964Z", + "shell.execute_reply.started": "2025-05-06T21:10:10.448332Z" } }, "outputs": [ @@ -709,15 +709,15 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 46, "id": "bacf5114-6a64-4100-8eb6-f1d9ddc36f89", "metadata": { "execution": { - "iopub.execute_input": "2025-05-06T18:36:00.809919Z", - "iopub.status.busy": "2025-05-06T18:36:00.809540Z", - "iopub.status.idle": "2025-05-06T18:36:01.278574Z", - "shell.execute_reply": "2025-05-06T18:36:01.277570Z", - "shell.execute_reply.started": "2025-05-06T18:36:00.809879Z" + "iopub.execute_input": "2025-05-06T21:10:12.524316Z", + "iopub.status.busy": "2025-05-06T21:10:12.523846Z", + "iopub.status.idle": "2025-05-06T21:10:12.965104Z", + "shell.execute_reply": "2025-05-06T21:10:12.964215Z", + "shell.execute_reply.started": "2025-05-06T21:10:12.524278Z" } }, "outputs": [ @@ -764,15 +764,15 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 47, "id": "39521ac6-0bec-42e7-9062-8fc9ce5edc55", "metadata": { "execution": { - "iopub.execute_input": "2025-05-06T18:36:16.376938Z", - "iopub.status.busy": "2025-05-06T18:36:16.376088Z", - "iopub.status.idle": "2025-05-06T18:36:16.912995Z", - "shell.execute_reply": "2025-05-06T18:36:16.911929Z", - "shell.execute_reply.started": "2025-05-06T18:36:16.376896Z" + "iopub.execute_input": "2025-05-06T21:10:14.842455Z", + "iopub.status.busy": "2025-05-06T21:10:14.842007Z", + "iopub.status.idle": "2025-05-06T21:10:15.365278Z", + "shell.execute_reply": "2025-05-06T21:10:15.364282Z", + "shell.execute_reply.started": "2025-05-06T21:10:14.842420Z" } }, "outputs": [ @@ -825,140 +825,34 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 51, "id": "0be4535d-cc89-45ef-98e9-591b9f459fae", "metadata": { "execution": { - "iopub.execute_input": "2025-05-06T18:36:23.116189Z", - "iopub.status.busy": "2025-05-06T18:36:23.115686Z", - "iopub.status.idle": "2025-05-06T18:36:23.129177Z", - "shell.execute_reply": "2025-05-06T18:36:23.128278Z", - "shell.execute_reply.started": "2025-05-06T18:36:23.116116Z" + "iopub.execute_input": "2025-05-06T21:16:28.653250Z", + "iopub.status.busy": "2025-05-06T21:16:28.652714Z", + "iopub.status.idle": "2025-05-06T21:16:28.662789Z", + "shell.execute_reply": "2025-05-06T21:16:28.661890Z", + "shell.execute_reply.started": "2025-05-06T21:16:28.653208Z" } }, "outputs": [ { "data": { "text/plain": [ - "r_kronFlux_flag\n", - "False 10723\n", - "True 841\n", + "g_kronFlux_flag\n", + "False 10728\n", + "True 836\n", "Name: count, dtype: int64" ] }, - "execution_count": 18, + "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "results['r_kronFlux_flag'].value_counts()" - ] - }, - { - "cell_type": "markdown", - "id": "af623422-c6cf-4c21-8971-5599c60ea8d7", - "metadata": { - "execution": { - "iopub.execute_input": "2024-11-15T18:24:36.531474Z", - "iopub.status.busy": "2024-11-15T18:24:36.531192Z", - "iopub.status.idle": "2024-11-15T18:24:36.547424Z", - "shell.execute_reply": "2024-11-15T18:24:36.546685Z", - "shell.execute_reply.started": "2024-11-15T18:24:36.531443Z" - } - }, - "source": [ - "Compare the value of the `r_kronFlux` between flagged and unflagged cases. First use `numpy`'s histogram function to look at the values of the flagged and unflagged $r-$band Kron fluxes." - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "ecea878f-83be-4a6c-acad-65272567ef33", - "metadata": { - "execution": { - "iopub.execute_input": "2025-05-06T18:46:55.661296Z", - "iopub.status.busy": "2025-05-06T18:46:55.660249Z", - "iopub.status.idle": "2025-05-06T18:46:55.675021Z", - "shell.execute_reply": "2025-05-06T18:46:55.674137Z", - "shell.execute_reply.started": "2025-05-06T18:46:55.661251Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "unflagged values [10716 0 2 3 1 0 0 0 0 1] unflagged bins [-1.60122808e+02 1.95632031e+05 3.91424186e+05 5.87216340e+05\n", - " 7.83008494e+05 9.78800648e+05 1.17459280e+06 1.37038496e+06\n", - " 1.56617711e+06 1.76196926e+06 1.95776142e+06]\n", - "flagged values [ 2 28 185 284 125 56 35 11 3 1] unflagged bins [-158.2223317 -74.04158638 10.13915894 94.31990426 178.50064958\n", - " 262.6813949 346.86214022 431.04288554 515.22363086 599.40437618\n", - " 683.5851215 ]\n" - ] - } - ], - "source": [ - "r_kronFlux_unflagged = results[\n", - " (results['r_kronFlux_flag'] == False) & \n", - " (results['r_kronFlux'].notna())\n", - "]['r_kronFlux']\n", - "r_kronFlux_flagged = results[\n", - " (results['r_kronFlux_flag'] == True) & \n", - " (results['r_kronFlux'].notna())\n", - "]['r_kronFlux']\n", - "values, bins = np.histogram(r_kronFlux_unflagged)\n", - "print('unflagged values', values, 'unflagged bins', bins)\n", - "values, bins = np.histogram(r_kronFlux_flagged)\n", - "print('flagged values', values, 'unflagged bins', bins)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "dddfd1e7-3faa-465f-ade4-dc136ae39262", - "metadata": { - "execution": { - "iopub.execute_input": "2025-05-06T18:41:12.884727Z", - "iopub.status.busy": "2025-05-06T18:41:12.884243Z", - "iopub.status.idle": "2025-05-06T18:41:13.111692Z", - "shell.execute_reply": "2025-05-06T18:41:13.110702Z", - "shell.execute_reply.started": "2025-05-06T18:41:12.884686Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[10716 0 2 3 1 0 0 0 0 1] [-1.60122808e+02 1.95632031e+05 3.91424186e+05 5.87216340e+05\n", - " 7.83008494e+05 9.78800648e+05 1.17459280e+06 1.37038496e+06\n", - " 1.56617711e+06 1.76196926e+06 1.95776142e+06]\n", - "-160.1228083 1957761.4190416\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.clf()\n", - "print(bins, values)\n", - "plt.hist(results[results['r_kronFlux_flag']]['r_kronFlux'],\n", - " label='Flagged', density=True, alpha=0.5)\n", - "plt.hist(results[(results['r_kronFlux_flag']==False) & (results['r_kronFlux']<4000)]['r_kronFlux'],\n", - " label='Not flagged', density=True, alpha=0.5)\n", - "print(np.min(results[results['r_kronFlux_flag']==False]['r_kronFlux']),\n", - " np.max(results[results['r_kronFlux_flag']==False]['r_kronFlux']))\n", - "plt.legend()\n", - "plt.show()" + "results['g_kronFlux_flag'].value_counts()" ] }, { @@ -1050,11 +944,9 @@ } ], "source": [ - "# get the unique values (which will be True or False)\n", "r_values = set(results['r_kronFlux_flag'].unique())\n", "g_values = set(results['g_kronFlux_flag'].unique())\n", "\n", - "# find the intersection\n", "overlap = r_values & g_values\n", "\n", "overlap_true_rows = results[\n", @@ -1072,83 +964,56 @@ "id": "ec13b104-ad8d-4bd6-8a93-b6d1d57b921e", "metadata": {}, "source": [ - "There are six overlapping true rows, meaning that in six cases, we cannot use the $r-$band kronFlux to predict the $g-$band kronFlux." + "There are six overlapping rows, meaning that in six cases, both photometric bands are flagged. Since the task at hand is a prediction one between three bands ($g$, $r$, and $i$) the bigger concern is the cases where any of these three Kron fluxes are flagged. Exclude rows where this is the case and build a clean DataFrame." ] }, { "cell_type": "code", - "execution_count": 17, - "id": "d29499cf-cab9-4a5c-8811-ffd1e5c9d82f", + "execution_count": 54, + "id": "e6294681-9c60-4ec6-805c-d378300acaa3", "metadata": { "execution": { - "iopub.execute_input": "2024-12-03T00:04:41.971139Z", - "iopub.status.busy": "2024-12-03T00:04:41.970965Z", - "iopub.status.idle": "2024-12-03T00:04:41.978973Z", - "shell.execute_reply": "2024-12-03T00:04:41.978503Z", - "shell.execute_reply.started": "2024-12-03T00:04:41.971124Z" + "iopub.execute_input": "2025-05-06T21:24:53.420771Z", + "iopub.status.busy": "2025-05-06T21:24:53.420270Z", + "iopub.status.idle": "2025-05-06T21:24:53.428818Z", + "shell.execute_reply": "2025-05-06T21:24:53.427832Z", + "shell.execute_reply.started": "2025-05-06T21:24:53.420733Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " coord_ra coord_dec g_kronFlux g_kronFlux_flag r_kronFlux \\\n", - "46 62.036008 -36.904244 60.546756 False 0.103155 \n", - "347 62.072778 -36.964144 15.582472 False 0.000000 \n", - "700 62.067443 -36.954921 94.684525 False NaN \n", - "... ... ... ... ... ... \n", - "11477 61.984400 -37.069348 63.584932 False 85.069624 \n", - "11483 61.911589 -37.067708 159.077697 False 139.900920 \n", - "11493 61.957278 -37.071416 165.461776 False 182.058161 \n", - "\n", - " r_kronFlux_flag i_kronFlux i_kronFlux_flag \n", - "46 True 68.234239 False \n", - "347 True 100.430859 False \n", - "700 True 259.766837 False \n", - "... ... ... ... \n", - "11477 True 100.221708 True \n", - "11483 True 279.320760 True \n", - "11493 True 349.606659 True \n", - "\n", - "[513 rows x 8 columns]\n" - ] - } - ], + "outputs": [], "source": [ - "g_false_r_true = results[\n", - " (results['r_kronFlux_flag'] == True) & \n", - " (results['g_kronFlux_flag'] == False)\n", - "]\n", - "\n", - "print(g_false_r_true)" + "clean = results[\n", + " (results['r_kronFlux_flag'] == False) & \n", + " (results['g_kronFlux_flag'] == False) &\n", + " (results['i_kronFlux_flag'] == False)\n", + "]" ] }, { "cell_type": "markdown", - "id": "69b67af5-de80-414c-985d-8f6cdccfc3a3", + "id": "38a84cda-eedb-4238-9d27-f91cb9f56531", "metadata": {}, "source": [ - "For the unflagged values, let's look at the relationship between these fluxes." + "Visualize the relationship between $g$ and $r$ in this clean DataFrame." ] }, { "cell_type": "code", - "execution_count": 18, - "id": "e6294681-9c60-4ec6-805c-d378300acaa3", + "execution_count": 55, + "id": "61a66274-c3e1-4e41-b743-649fc00d69b7", "metadata": { "execution": { - "iopub.execute_input": "2024-12-03T00:04:41.979744Z", - "iopub.status.busy": "2024-12-03T00:04:41.979551Z", - "iopub.status.idle": "2024-12-03T00:04:42.213955Z", - "shell.execute_reply": "2024-12-03T00:04:42.213438Z", - "shell.execute_reply.started": "2024-12-03T00:04:41.979728Z" + "iopub.execute_input": "2025-05-06T21:25:25.068316Z", + "iopub.status.busy": "2025-05-06T21:25:25.067860Z", + "iopub.status.idle": "2025-05-06T21:25:25.500866Z", + "shell.execute_reply": "2025-05-06T21:25:25.499927Z", + "shell.execute_reply.started": "2025-05-06T21:25:25.068285Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -1158,34 +1023,36 @@ } ], "source": [ - "clean = results[\n", - " (results['r_kronFlux_flag'] == False) & \n", - " (results['g_kronFlux_flag'] == False) &\n", - " (results['i_kronFlux_flag'] == False)\n", - "]\n", - "\n", "plt.scatter(clean['g_kronFlux'], clean['r_kronFlux'])\n", "plt.xlabel(r'$g-$band kronFlux [nJy]')\n", "plt.ylabel(r'$r-$band kronFlux [nJy]');" ] }, + { + "cell_type": "markdown", + "id": "1b76242e-6a0d-4f0c-ac35-fa27d4fef24a", + "metadata": {}, + "source": [ + "Zoom in." + ] + }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 56, "id": "5afedb17-6478-4f2b-bdfc-38e73cd4a65e", "metadata": { "execution": { - "iopub.execute_input": "2024-12-03T00:04:42.215968Z", - "iopub.status.busy": "2024-12-03T00:04:42.215730Z", - "iopub.status.idle": "2024-12-03T00:04:42.382817Z", - "shell.execute_reply": "2024-12-03T00:04:42.382256Z", - "shell.execute_reply.started": "2024-12-03T00:04:42.215949Z" + "iopub.execute_input": "2025-05-06T21:25:44.109359Z", + "iopub.status.busy": "2025-05-06T21:25:44.108890Z", + "iopub.status.idle": "2025-05-06T21:25:44.400604Z", + "shell.execute_reply": "2025-05-06T21:25:44.399632Z", + "shell.execute_reply.started": "2025-05-06T21:25:44.109321Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -1207,7 +1074,7 @@ "id": "2f503394-3816-4d31-9cf0-9de88e229f87", "metadata": {}, "source": [ - "Zooming in on this relationship, it looks roughly linear, so we should be able to do some predictive work here." + "The relationship between $g-$ and $r-$band Kron fluxes looks roughly linear, so we should be able to do some predictive work here." ] }, { @@ -1215,36 +1082,21 @@ "id": "4704605a-4665-4ccc-bd7e-cefaf5e09828", "metadata": {}, "source": [ - "## 6. Prepare the training and test sets" - ] - }, - { - "cell_type": "markdown", - "id": "0b3c8d4e-0541-49c7-9fa1-f9b3b5d29aac", - "metadata": {}, - "source": [ - "The first step is to define the training and validation data." - ] - }, - { - "cell_type": "markdown", - "id": "1ab2c517-125b-4c70-ad2d-b447f7e6721e", - "metadata": {}, - "source": [ - "The `.to_frame()` argument is required to input the X data as a 2D shape, as expected by scikit-learn." + "## 6. Prepare the training and test sets\n", + "The goal is to predict the $r-$band Kron flux using the $g-$band Kron flux. The first step is to define the training and validation data." ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 58, "id": "fc90feca-ede1-44b0-929b-2fec1ddf5ad4", "metadata": { "execution": { - "iopub.execute_input": "2024-12-03T00:04:42.383760Z", - "iopub.status.busy": "2024-12-03T00:04:42.383543Z", - "iopub.status.idle": "2024-12-03T00:04:42.389713Z", - "shell.execute_reply": "2024-12-03T00:04:42.389149Z", - "shell.execute_reply.started": "2024-12-03T00:04:42.383742Z" + "iopub.execute_input": "2025-05-06T21:48:57.144027Z", + "iopub.status.busy": "2025-05-06T21:48:57.143589Z", + "iopub.status.idle": "2025-05-06T21:48:57.153923Z", + "shell.execute_reply": "2025-05-06T21:48:57.152971Z", + "shell.execute_reply.started": "2025-05-06T21:48:57.143991Z" } }, "outputs": [], @@ -1253,25 +1105,33 @@ " clean['g_kronFlux'].to_frame(), clean['r_kronFlux'].to_frame(), test_size=0.2, random_state=42)" ] }, + { + "cell_type": "markdown", + "id": "37d77db2-1a91-4696-831f-d426f3ca7539", + "metadata": {}, + "source": [ + "The `.to_frame()` argument is required to input the X data as a 2D shape, as expected by `scikit-learn`." + ] + }, { "cell_type": "markdown", "id": "ecb487df-ce82-4d2d-9821-64620e1e922b", "metadata": {}, "source": [ - "Good practice to use a scaler." + "It's practice to use a standard scaler when training machine learning models. Transform the training and test data." ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 66, "id": "8e675e27-74f0-43e8-91af-8652e2710609", "metadata": { "execution": { - "iopub.execute_input": "2024-12-03T00:04:42.390439Z", - "iopub.status.busy": "2024-12-03T00:04:42.390274Z", - "iopub.status.idle": "2024-12-03T00:04:42.404155Z", - "shell.execute_reply": "2024-12-03T00:04:42.403690Z", - "shell.execute_reply.started": "2024-12-03T00:04:42.390426Z" + "iopub.execute_input": "2025-05-06T21:50:59.656100Z", + "iopub.status.busy": "2025-05-06T21:50:59.655655Z", + "iopub.status.idle": "2025-05-06T21:50:59.663576Z", + "shell.execute_reply": "2025-05-06T21:50:59.662567Z", + "shell.execute_reply.started": "2025-05-06T21:50:59.656063Z" } }, "outputs": [], @@ -1281,39 +1141,49 @@ "X_test = scaler.transform(X_test)" ] }, + { + "cell_type": "markdown", + "id": "f27ee6f3-713e-4b60-a905-5d04dc7950d8", + "metadata": {}, + "source": [ + "The values of the training and test set should be scaled between values of 0 and 1 by default. Check this using a `seaborn` histogram." + ] + }, { "cell_type": "code", - "execution_count": 22, - "id": "4771e145-2649-4eda-8891-987c7e9c9009", + "execution_count": 73, + "id": "3ee4e732-8688-47fa-b62f-27cb4b9ee9ca", "metadata": { "execution": { - "iopub.execute_input": "2024-12-03T00:04:42.404900Z", - "iopub.status.busy": "2024-12-03T00:04:42.404711Z", - "iopub.status.idle": "2024-12-03T00:04:42.413985Z", - "shell.execute_reply": "2024-12-03T00:04:42.413340Z", - "shell.execute_reply.started": "2024-12-03T00:04:42.404885Z" + "iopub.execute_input": "2025-05-06T21:52:40.892238Z", + "iopub.status.busy": "2025-05-06T21:52:40.891747Z", + "iopub.status.idle": "2025-05-06T21:52:41.109899Z", + "shell.execute_reply": "2025-05-06T21:52:41.108965Z", + "shell.execute_reply.started": "2025-05-06T21:52:40.892199Z" } }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_22441/4138889604.py:1: RuntimeWarning: invalid value encountered in log\n", + " plt.hist(np.log(X_train.flatten()));\n" + ] + }, { "data": { + "image/png": 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AgKEIagAAAIYiqAEAABiKoAYAAGAoghoAAIChCGoAAACGIqgBAAAYiqAGAABgKIIaAACAoQhqAAAAhiKoAQAAGIqgBgAAYCiCGgAAgKEIagAAAIYiqAEAABiKoAYAAGAoghoAAIChCGoAAACGIqgBAAAYiqAGAABgKIIaAACAoQhqAAAAhiKoAQAAGIqgBgAAYCiCGgAAgKEIagAAAIYiqAEAABiKoAYAAGAoghoAAIChCGoAAACGIqgBAAAYiqAGAABgKIIaAACAoQhqAAAAhiKoAQAAGIqgBgAAYCiCGgAAgKEIagAAAIYK8nSBffv2qaCgQCdPntSpU6fU0NCgFStWaPr06S5tMzMzlZWV1eZ6bDabPv300zbnvf/++3r77bd1+vRpWa1WDR06VIsXL9bgwYM9LRcAAKDL8jioZWRk6Ny5c4qIiFCvXr107ty5Gy4zbdo0RUdHO00LDAxss212drbeeust9enTR7Nnz1ZdXZ3279+v1NRUvfHGGxo5cqSnJQMAAHRJHge1F198UbGxsYqOjtbmzZu1bt26Gy4zffp0twJWSUmJMjMzFRcXp5ycHNntdknSnDlzlJKSovT0dG3fvl1BQR6XDQAA0OV4fI7a6NGjXUbHOkpubq6ampqUkpLSGtIkacCAAZo6darOnDmjo0ePdsprAwAAmMYrFxMUFBRoy5Yt+vWvf61PP/1U9fX1bbY7duyYJGnMmDEu88aOHStJOn78eOcVCgAAYBCvHENcv3690++9evVSWlqaSyArLS1VaGioevXq5bKO2NjY1jbuaGhouMlqu4+WwNxecMZf0Ffuu7avHM09fFxNx2j2dQEdyNHcrMuXL/u6jJvC36H76CvPeLu/goOD3W7bqUEtPj5eaWlpSkhIUM+ePVVeXq79+/crJydHP/vZz7Rx40bFx8e3tq+urlbPnj3bXFfLodDq6mq3XruiokJNTU23vhHdQFlZma9L6DLoK/eVlZXJao/xdRkdw4+SWmNDo0pLz/q6jFvC36H76CvPeKO/AgMD1b9/f7fbd2pQS0xMdPo9NjZWCxcuVGRkpNasWaNNmzbplVde6ZTXjoqKktVq7ZR1+4v6+nqVlZWpd+/estlsvi7HaPSV+67tq6p6P7nwx+LrAjpOkDVIff7/CEVXw9+h++grz5jcXz7Zi06bNk2vvvqqTpw44TTdbre3O2LWMv3aiwyux2q1ejS02J3ZbDb6yk30lftsNpsCGvwj4fjHVlwVYLEoOLhrH5Lm79B99JVnTOwvnzyZwGq1KiwszOU8idjYWNXW1qqystJlmZZz02K76DdBAAAAT/kkqJWUlKiqqsrlNh8JCQmSpCNHjrgsc/jwYac2AAAA/q7TglpNTY2Ki4tdpldVVWn16tWSpKSkJKd506dPV2BgoLKzs50OgZ46dUp79+5V3759NWrUqM4qGQAAwCgen6O2Y8cOFRYWSroaoCRp586drfdAGz9+vBITE3Xp0iU9/vjjuv/++zVw4EDdcccdqqioUF5eni5duqQxY8Zo3rx5Tuvu16+fUlNTlZGRoXnz5mnixImtj5BqbGzUCy+8wFMJAABAt+Fx6iksLNSePXtcprWEt+joaCUmJio8PFyPPvqoPv/8c33yySf67rvvFBISogEDBmjKlCmaOXNmm8/7XLBggWJiYrRt2za9++67slqtGjZsmJ588kkeyg4AALoVj4NaWlqa0tLSbtjObrfrueeeu6miJk+erMmTJ9/UsgAAAP7CJxcTAAAA4MYIagAAAIYiqAEAABiKoAYAAGAoghoAAIChCGoAAACGIqgBAAAYiqAGAABgKIIaAACAoQhqAAAAhuIJ54BhLlY3qvaKw9dl3DRHs0VWe4wqqy1qbvZ1NQDQtRHUAMPUXnHomQ1nfV1Gh/jVgmhflwAAXRqHPgEAAAxFUAMAADAUQQ0AAMBQBDUAAABDEdQAAAAMRVADAAAwFEENAADAUAQ1AAAAQxHUAAAADMWTCQCgGwkMsOjshXpfl3FTrn08WUBNvUJ7BCjCzscY/BvvcADoRq40OPRPm875uowO8VpqjCLsvq4C6Fwc+gQAADAUQQ0AAMBQBDUAAABDEdQAAAAMRVADAAAwFEENAADAUAQ1AAAAQxHUAAAADEVQAwAAMBRBDQAAwFAENQAAAEMR1AAAAAxFUAMAADAUQQ0AAMBQBDUAAABDEdQAAAAMRVADAAAwFEENAADAUAQ1AAAAQxHUAAAADEVQAwAAMBRBDQAAwFAENQAAAEMR1AAAAAxFUAMAADAUQQ0AAMBQBDUAAABDEdQAAAAMRVADAAAwFEENAADAUAQ1AAAAQwV5usC+fftUUFCgkydP6tSpU2poaNCKFSs0ffr0NttXV1drw4YNOnjwoC5cuKDIyEhNmDBBqampstvtbS7z/vvv6+2339bp06dltVo1dOhQLV68WIMHD/a0XAAAgC7L4xG1jIwMvffeezp//rx69ep13bZ1dXVasmSJtm3bpn79+mnu3Lm65557tG3bNi1ZskR1dXUuy2RnZ2vFihX69ttvNXv2bD3yyCMqLCxUamqqjh075mm5AAAAXZbHI2ovvviiYmNjFR0drc2bN2vdunXttt26dauKioqUnJyspUuXtk7PzMxUVlaWtm7dqsWLF7dOLykpUWZmpuLi4pSTk9M64jZnzhylpKQoPT1d27dvV1CQx2UDAAB0OR6PqI0ePVrR0dE3bNfc3KydO3cqNDRUixYtcpo3f/58hYeHa9euXWpubm6dnpubq6amJqWkpDgdFh0wYICmTp2qM2fO6OjRo56WDAAA0CV12sUEJSUlqqio0LBhwxQSEuI0r0ePHho+fLjKy8tVWlraOr3l0OaYMWNc1jd27FhJ0vHjxzurZAAAAKN02jHElgAWGxvb5vy4uLjWdtf+HBoa2ua5by3ruTbYXU9DQ4PHNXc39fX1Tv+ifd7sK0ezpdNfw1uab9ykS/CX7ZD8a1sczc26fPmyr8swEvt3z3i7v4KDg91u22lBrbq6WpLavbIzLCzMqV3Lzz179myzfct6rm1/PRUVFWpqanK73u6srKzM1yV0Gd7oK6s9ptNfw2v8JRX4y3ZIfrUtjQ2NKi096+syjMb+3TPe6K/AwED179/f7fZ+e1Z+VFSUrFarr8swWn19vcrKytS7d2/ZbDZfl2M0b/ZVZbX/jKjJXzbFX7ZD8qttCbIGqU87R226O/bvnjG5vzotqN1oBKympsapXcvP7bW/0Qjd91mtVo+GFrszm81GX7nJG30VUOM/hyr8JRP4y3ZI/rUtARaLgoN7+LoMo7F/94yJ/dVpFxPc6JyykpISp3YtP9fW1qqystKl/Y3OeQMAAPA3nRbU4uLiFBUVpRMnTrjc2PbKlSsqKChQVFSUU/BKSEiQJB05csRlfYcPH3ZqAwAA4O86LahZLBbNnDlTtbW1ysrKcpq3efNmVVVVaebMmbJY/jIQP336dAUGBio7O9vpEOipU6e0d+9e9e3bV6NGjeqskgEAAIzi8TlqO3bsUGFhoaSrAUqSdu7c2XoPtPHjxysxMVGSlJycrEOHDrU+oWDQoEEqLi5WXl6e4uPjlZyc7LTufv36KTU1VRkZGZo3b54mTpyouro67d+/X42NjXrhhRd4KgEAAOg2PE49hYWF2rNnj8u0lvAWHR3dGtRCQkKUkZGhDRs26MCBAzp27JgiIyM1d+5cpaamutwIV5IWLFigmJgYbdu2Te+++66sVquGDRumJ598koeyAwCAbsXjoJaWlqa0tDS329vtdi1btkzLli1ze5nJkydr8uTJnpYGAADgVzrtHDUAAADcGoIaAACAoQhqAAAAhiKoAQAAGIqgBgAAYCiCGgAAgKEIagAAAIYiqAEAABiKoAYAAGAoghoAAIChCGoAAACGIqgBAAAYiqAGAABgKIIaAACAoQhqAAAAhiKoAQAAGIqgBgAAYCiCGgAAgKEIagAAAIYiqAEAABiKoAYAAGAoghoAAIChCGoAAACGIqgBAAAYiqAGAABgKIIaAACAoQhqAAAAhiKoAQAAGIqgBgAAYCiCGgAAgKEIagAAAIYiqAEAABiKoAYAAGAoghoAAIChCGoAAACGIqgBAAAYiqAGAABgKIIaAACAoQhqAAAAhiKoAQAAGIqgBgAAYCiCGgAAgKEIagAAAIYiqAEAABiKoAYAAGAoghoAAIChCGoAAACGIqgBAAAYiqAGAABgKIIaAACAoQhqAAAAhiKoAQAAGIqgBgAAYKggb7zIzJkzde7cuTbn/ehHP9Lzzz/vNK26ulobNmzQwYMHdeHCBUVGRmrChAlKTU2V3W73RskAAAA+55WgJkl2u12PPfaYy/T777/f6fe6ujotWbJERUVFGjNmjJKSklRcXKxt27bp2LFj2rBhg0JCQrxVNgAAgM94LajddtttWrx48Q3bbd26VUVFRUpOTtbSpUtbp2dmZiorK0tbt251az0AAABdnVHnqDU3N2vnzp0KDQ3VokWLnObNnz9f4eHh2rVrl5qbm31UIQAAgPd4bUStvr5eubm5qqio0G233aZhw4YpPj7eqU1JSYkqKio0duxYl8ObPXr00PDhw3Xo0CGVlpYqLi7OW6UDAAD4hNeC2oULF/Tyyy87Tfurv/orrVy5UhEREZKk0tJSSVJsbGyb62gJZ+4EtYaGhlus2P/V19c7/Yv2ebOvHM2WTn8Nb/GXsW9/2Q7Jv7bF0dysy5cv+7oMI7F/94y3+ys4ONjttl4JajNmzFBCQoL69+8vq9Wqr7/+WllZWcrLy9Ozzz6rrKwsWSwWVVdXS1K7V3aGhYVJUmu766moqFBTU1PHbYQfKysr83UJXYY3+spqj+n01/Aaf0kF/rIdkl9tS2NDo0pLz/q6DKOxf/eMN/orMDBQ/fv3d7u9V4La9883GzJkiF577TU9+eSTKiws1O9+9zuNGzeuQ18zKipKVqu1Q9fpb+rr61VWVqbevXvLZrP5uhyjebOvKqv9Z0RN/rIp/rIdkl9tS5A1SH3aOQLT3bF/94zJ/eW1Q5/fFxAQoBkzZqiwsFAnTpzQuHHjWkfS2hsxq6mpkdT+iNu1rFarR0OL3ZnNZqOv3OSNvgqo8Z9DFf6SCfxlOyT/2pYAi0XBwT18XYbR2L97xsT+8ulVny3nprWcY9ByblrLuWrfV1JS4tQOAADAn/k0qP3hD3+QJEVHR0u6erFAVFSUTpw4obq6Oqe2V65cUUFBgaKioghqAACgW+j0oHb69Gl99913LtMLCgq0bds22Ww2TZgwQZJksVg0c+ZM1dbWKisry6n95s2bVVVVpZkzZ8pi8afBewAAgLZ1+jlqH3zwgbZu3aqHHnpI0dHRstlsOnXqlI4cOaKAgAD9/Oc/V58+fVrbJycn69ChQ61PKBg0aJCKi4uVl5en+Ph4JScnd3bJAAAARuj0oDZq1Ch98803+uMf/6j8/HxduXJFPXv21KRJkzR37lw98MADTu1DQkKUkZGhDRs26MCBAzp27JgiIyM1d+5cpaam8pxPAADQbXR6UEtISFBCQoJHy9jtdi1btkzLli3rpKoAAADMZ9SzPgEAAPAXBDUAAABDEdQAAAAMRVADAAAwFEENAADAUAQ1AAAAQxHUAAAADNXp91EDvOVidaNqrzg6Zd2OZous9hhVVlsUUFPfKa/RoqlzNgEA0AUR1OA3aq849MyGs74u45b9akG0r0sAABiCQ58AAACGIqgBAAAYiqAGAABgKIIaAACAoQhqAAAAhiKoAQAAGIqgBgAAYCiCGgAAgKEIagAAAIYiqAEAABiKoAYAAGAoghoAAIChCGoAAACGIqgBAAAYiqAGAABgKIIaAACAoQhqAAAAhiKoAQAAGIqgBgAAYCiCGgAAgKEIagAAAIYiqAEAABiKoAYAAGAoghoAAIChCGoAAACGIqgBAAAYiqAGAABgKIIaAACAoQhqAAAAhiKoAQAAGIqgBgAAYCiCGgAAgKGCfF0AAAA3IzDAorMX6n1dxi0L7RGgCDsfx2gb7wwAQJd0pcGhf9p0ztdl3LLXUmMUYfd1FTAVQa2bC7u9tyqrLQqo6frfSpscvq4AAICORVDr5uqbArXcD76RStKvFkT7ugQAADoUFxMAAAAYiqAGAABgKIIaAACAoQhqAAAAhiKoAQAAGIqgBgAAYCiCGgAAgKEIagAAAIYy9oa3X3zxhTIzM/X555+roaFB/fv312OPPabJkyf7ujQAAACvMDKoHTt2TE8//bSsVqsmTZoku92ugwcPasWKFTp37pxSUlJ8XSIAAPiei9WNqr3S9Z7n52i2yGqPaX2kYmiPAEXYzYhIZlRxjcbGRqWnp8tisWj9+vW67777JEmLFi3SwoULlZmZqYcfflhxcXE+rhQAAFyr9opDz2w46+sybtlrqTGKsPu6iquMC2pHjx7VmTNnNGPGjNaQJklhYWFauHChXnzxReXm5uqnP/2pz2q8VN2ouvqu943h+xzNFgUFBeq2EP84VTHAIr/YFn/ZDsl/tsVftkNiW0wUYOmc9QYGBnbOiq+D/5OOZ7l48WKzr4u41ptvvqmcnBytXr1aSUlJTvOqqqr0yCOPaNiwYcrKyvJRhQAAAN5hXOwtKSmRJMXGxrrMCw8PV0REhEpLS71dFgAAgNcZF9RqamokSXZ72weHw8LCVF1d7c2SAAAAfMK4oAYAAICrjAtqYWFhktTuqFlNTU27o20AAAD+xLig1nLbjbbOQ6uqqtLFixfbPH8NAADA3xgX1EaMGCFJOnLkiMu8lmkJCQlerQkAAMAXjAtqDz30kO666y799re/VVFRUev0mpoabdy4UYGBgZo2bZoPKwQAAPAO4+6jJl296e3TTz8tm82mpKQkhYWF6eDBgzp79qyWLFmiBQsW+LpEAACATmdkUJOk//mf/3F5KPvcuXPbfCj78ePH9cknn+jLL7/Ul19+qZqaGk2bNk1paWntrt/hcOg3v/mNduzYodLSUoWEhGjkyJH6yU9+0q0fT/Xll18qJydHJ0+e1Lfffqs77rhD8fHxmjdvHoec21BUVKQtW7YoPz9fFy9e1O233657771XycnJGjVqlK/LM9Lnn3+u1NRUORwOPfXUU5o/f76vSzJCXV2dPvroIx06dEhFRUUqLy+X1WrVvffeq9mzZ+uHP/yhr0v0mS+++MLl8+Cxxx5r8/OguyovL9eHH36ovLw8ffPNN7pw4YLCw8P14IMPKjk5WUOGDPF1iUbbsmWL1q5dK0nauHGjhg4d6uOK/sK4R0i1eOCBB/Tv//7vbrXdvXu39uzZo+DgYPXp06f1XmzX88orr2jHjh2655579Oijj+rbb7/VBx98oCNHjigrK0v9+/e/1U3ocj766CMtX75cVqtViYmJ6t27t8rKylo/PFasWKHp06f7ukxj7NmzR6tXr1ZYWJjGjRunO++8UxcvXtTJkyd14sQJglobLl++rJdfflk9evRQXV2dr8sxSkFBgdLS0nT77bfroYce0sSJE/Xtt9/qo48+0ksvvaQTJ07oueee83WZXnfs2DE9/fTTslqtmjRpkux2uw4ePKgVK1bo3LlzSklJ8XWJRti+fbu2bNmivn37avTo0erZs6dKS0v18ccf6+OPP9aqVas0adIkX5dppK+//lqZmZkKCQkxcr9k7IiaJ7744gv16NFDd999t7744gstXLjwuiNqR48e1U9/+lMNHz5ca9eulc1mkyT9/ve/19KlSzV8+HCtX7/em5tghB//+Mf65ptvtHXrVsXHx7dO//LLLzV//nxFR0drx44dvivQICdPntSCBQs0ePBgvf766woPD3ea39jYqKAgY78H+czrr7+uXbt26YknntD69esZUbtGUVGRTp8+rUceecTpvXPhwgUtWLBA586dU05OjgYPHuzDKr2rsbFRc+bMUXl5uTZu3Nj6/OeamhotXLhQf/rTn/TOO+9066MgLQ4ePKiIiIjWC/Ja5Ofn66mnnlJoaKj27t3b+nmHq5qamrRw4UJZLBbFxcVp3759xo2oGXcxwc0YPHiwBgwY4PYDaFvCxpIlS5zetKNHj9bYsWOVn5+vP/3pT51RqtH+/Oc/KyoqyimkSdKgQYPUq1cv/e///q+PKjNPRkaGHA6HfvnLX7qENEmEtDYUFhbqnXfe0T/8wz+od+/evi7HOPHx8Zo8ebLLeycyMlI/+tGPJF09zaM7OXr0qM6cOaMf/vCHrSFNunq/zYULF6qpqUm5ubk+rNAcEyZMcAlp0tU7KYwcOVJVVVX66quvfFCZ2bZs2aLi4mL94he/UECAmZHIzKo62fHjxxUSEqIHH3zQZd7YsWMlXf0W0t30799flZWVKi4udppeVFSkyspKDuX9v++++05HjhzRfffdp9jYWB0/flxbt27Vf/7nf+rEiRO+Ls9ILYc8ExISNGvWLF+X0+W0hDd3v4z6i5ZgOmbMGJd5LdO6W3i9GS3vH75AOjt16pSysrK0YMECDRgwwNfltKvb/a/V1dWpsrKy3RG4lpvptjwcvjv5x3/8Rz3zzDNatGiREhMTdeedd6q8vFwHDx7U8OHD9fzzz/u6RCN8+eWXcjgcuvPOO/Xss8/qk08+cZo/ZswY/fM//zNP0LjG2rVrVVlZqTfeeMPXpXQ5TU1N2rt3rywWi0aPHu3rcryqZT/c1k3Ow8PDFRER0ebN0fEX58+f12effabIyEijw4i3NTY2auXKlbr77ruNP/2i2wW1lkdTXe+h75LcuiDB3yQkJGj9+vV6/vnntW/fvtbpvXv31owZM9SrVy8fVmeOlkPAv/vd73T77bfrX/7lXzRy5EhVVlZq7dq1OnTokNasWaM1a9b4uFIzHD9+XP/1X/+lZ555RnfddZevy+ly1q9fr6+++kozZszodh+0Lfvh6+2vy8vLvVlSl9LY2Ki0tDTV19dr6dKl3W5E9npycnJUXFys7Oxs40cajalu0qRJunTpktvt33rrLY0cObITK+qabqUf8/Ly9OKLL2rcuHH613/9V0VHR+v8+fPKzs7Wyy+/rOLiYi1btqyzSve6m+0rh8Mh6epIx/LlyzV+/HhJVz9M1qxZo7/7u7/Thx9+qLKyMr85F+tm+6qurk6rVq3S0KFD9eijj3ZiheboyH3Ze++9p5ycHN1333169tlnO6pEdAMOh0OrVq1Sfn6+Zs2apalTp/q6JGMUFRVp06ZNeuKJJzRo0CBfl3NDxgS1pKQk1dbWut0+MjLypl6n5ZvZ9R76Lv1lZK2rudl+vHTpkl566SXFxsZq5cqVrSdV3n333frlL3+p0tJSvfPOO3r00UfVt2/fTqnd2262r1reQ4GBgRo3bpxTG5vNpjFjxmjnzp06efKk3wS1m+2rt956S5WVlfq3f/s3Y0/U7WgdtS/bvXu3XnnlFQ0cOFD/8R//odDQ0I4qscto2Q9fb3/NKQaumpublZ6ern379mnKlClavny5r0syysqVK9W3b1+lpqb6uhS3GBPUvHV/oJCQEPXq1Utnz55VU1OTy1Bwy/kOXfVy75vtxxMnTui7775TQkKCyweqxWLRqFGj9Ic//EF//OMf/Sao3Wxf9evXT5IUHBzc5pD5bbfdJkm6cuXKzRdnmJvtq6KiIl25cqXd0bR169Zp3bp1euyxx/TMM8/cSonG6Ih92a5du7RmzRrdc889WrdunSIiIm69sC6oZT9cWlqq+++/32leVVWVLl68qGHDhvmiNGM5HA6lp6dr9+7dSkpK0ooVK7rNlyR3tVww9/0v2i0WLlwoSfrVr36lxMREb5XVLmOCmjclJCRo//79KiwsdLnb/uHDhyWpzcuc/VlDQ4MktXsLjpbp3INH6tu3r/r06aPz58+3eXjz66+/liRFR0f7ojyj/OAHP2gz2JeWlio/P1+DBw/WwIEDjbpnka/t2rVL6enpuvvuu/Xmm2/qjjvu8HVJPjNixAjl5OToyJEjSkpKcpp35Mg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"text/plain": [ - "array([[-0.05664546],\n", - " [ 0.0240961 ],\n", - " [-0.06126054],\n", - " ...,\n", - " [ 0.51047933],\n", - " [-0.01411049],\n", - " [-0.03585177]])" + "
" ] }, - "execution_count": 22, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "X_test" + "plt.hist(np.log(X_train.flatten()));" ] }, { From 44f6cdd1f6b7d108361df389170d9a0d861a63ba Mon Sep 17 00:00:00 2001 From: beckynevin Date: Wed, 7 May 2025 21:59:11 +0000 Subject: [PATCH 06/13] correcting palette --- DP0.2/20_Introduction_to_Data_Science.ipynb | 339 ++++++++++---------- 1 file changed, 168 insertions(+), 171 deletions(-) diff --git a/DP0.2/20_Introduction_to_Data_Science.ipynb b/DP0.2/20_Introduction_to_Data_Science.ipynb index f866da5e..c4e5a2c9 100644 --- a/DP0.2/20_Introduction_to_Data_Science.ipynb +++ b/DP0.2/20_Introduction_to_Data_Science.ipynb @@ -140,29 +140,26 @@ "id": "90251edc-e77a-4f2c-aef3-935381faebc9", "metadata": {}, "source": [ - "Set up seaborn to use 538's aesthetics.\n", - "\n", - "**This is probably not what we want to the rtn-045 default plotting settings though...**" + "Set up seaborn to use a friendly palette." ] }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 105, "id": "94acc9f6-2033-4ace-aefd-d036a35f4221", "metadata": { "execution": { - "iopub.execute_input": "2025-05-06T21:09:55.564309Z", - "iopub.status.busy": "2025-05-06T21:09:55.563848Z", - "iopub.status.idle": "2025-05-06T21:09:55.569994Z", - "shell.execute_reply": "2025-05-06T21:09:55.569076Z", - "shell.execute_reply.started": "2025-05-06T21:09:55.564270Z" + "iopub.execute_input": "2025-05-07T21:58:23.430410Z", + "iopub.status.busy": "2025-05-07T21:58:23.429958Z", + "iopub.status.idle": "2025-05-07T21:58:23.436042Z", + "shell.execute_reply": "2025-05-07T21:58:23.435036Z", + "shell.execute_reply.started": "2025-05-07T21:58:23.430373Z" } }, "outputs": [], "source": [ "sns.set_style('whitegrid')\n", - "plt.style.use('fivethirtyeight')\n", - "palette = sns.color_palette(\"muted\") # Choose a desired palette\n", + "palette = sns.color_palette(\"colorblind\")\n", "sns.set_palette(palette)" ] }, @@ -178,15 +175,15 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 87, "id": "caf56589-100a-4481-8f24-5f5058b6671f", "metadata": { "execution": { - "iopub.execute_input": "2025-05-06T21:09:56.608239Z", - "iopub.status.busy": "2025-05-06T21:09:56.607764Z", - "iopub.status.idle": "2025-05-06T21:09:56.655843Z", - "shell.execute_reply": "2025-05-06T21:09:56.654831Z", - "shell.execute_reply.started": "2025-05-06T21:09:56.608198Z" + "iopub.execute_input": "2025-05-07T21:56:34.234843Z", + "iopub.status.busy": "2025-05-07T21:56:34.234421Z", + "iopub.status.idle": "2025-05-07T21:56:34.290044Z", + "shell.execute_reply": "2025-05-07T21:56:34.288999Z", + "shell.execute_reply.started": "2025-05-07T21:56:34.234805Z" } }, "outputs": [], @@ -205,15 +202,15 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 88, "id": "2b7b6002-2457-4c20-a03e-6bfa24a0aa27", "metadata": { "execution": { - "iopub.execute_input": "2025-05-06T21:09:57.967997Z", - "iopub.status.busy": "2025-05-06T21:09:57.967578Z", - "iopub.status.idle": "2025-05-06T21:09:57.972601Z", - "shell.execute_reply": "2025-05-06T21:09:57.971653Z", - "shell.execute_reply.started": "2025-05-06T21:09:57.967961Z" + "iopub.execute_input": "2025-05-07T21:56:34.832667Z", + "iopub.status.busy": "2025-05-07T21:56:34.832208Z", + "iopub.status.idle": "2025-05-07T21:56:34.837480Z", + "shell.execute_reply": "2025-05-07T21:56:34.836547Z", + "shell.execute_reply.started": "2025-05-07T21:56:34.832633Z" } }, "outputs": [], @@ -244,15 +241,15 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 89, "id": "7ddd0344-b354-45a0-9e5a-755149c9bc54", "metadata": { "execution": { - "iopub.execute_input": "2025-05-06T21:10:01.100216Z", - "iopub.status.busy": "2025-05-06T21:10:01.099744Z", - "iopub.status.idle": "2025-05-06T21:10:01.105192Z", - "shell.execute_reply": "2025-05-06T21:10:01.104220Z", - "shell.execute_reply.started": "2025-05-06T21:10:01.100180Z" + "iopub.execute_input": "2025-05-07T21:56:35.718993Z", + "iopub.status.busy": "2025-05-07T21:56:35.718008Z", + "iopub.status.idle": "2025-05-07T21:56:35.723540Z", + "shell.execute_reply": "2025-05-07T21:56:35.722563Z", + "shell.execute_reply.started": "2025-05-07T21:56:35.718954Z" } }, "outputs": [], @@ -275,15 +272,15 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 90, "id": "985e3b62-8065-42ec-a40c-1232c4c45f17", "metadata": { "execution": { - "iopub.execute_input": "2025-05-06T21:10:01.968430Z", - "iopub.status.busy": "2025-05-06T21:10:01.967916Z", - "iopub.status.idle": "2025-05-06T21:10:01.973604Z", - "shell.execute_reply": "2025-05-06T21:10:01.972718Z", - "shell.execute_reply.started": "2025-05-06T21:10:01.968391Z" + "iopub.execute_input": "2025-05-07T21:56:36.240704Z", + "iopub.status.busy": "2025-05-07T21:56:36.240263Z", + "iopub.status.idle": "2025-05-07T21:56:36.246092Z", + "shell.execute_reply": "2025-05-07T21:56:36.245171Z", + "shell.execute_reply.started": "2025-05-07T21:56:36.240669Z" } }, "outputs": [ @@ -315,15 +312,15 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 91, "id": "c02adc91-5f5e-418b-87a3-cba8beba7dd2", "metadata": { "execution": { - "iopub.execute_input": "2025-05-06T21:10:03.104109Z", - "iopub.status.busy": "2025-05-06T21:10:03.103631Z", - "iopub.status.idle": "2025-05-06T21:10:04.286634Z", - "shell.execute_reply": "2025-05-06T21:10:04.285654Z", - "shell.execute_reply.started": "2025-05-06T21:10:03.104058Z" + "iopub.execute_input": "2025-05-07T21:56:36.777972Z", + "iopub.status.busy": "2025-05-07T21:56:36.777542Z", + "iopub.status.idle": "2025-05-07T21:56:37.966174Z", + "shell.execute_reply": "2025-05-07T21:56:37.965219Z", + "shell.execute_reply.started": "2025-05-07T21:56:36.777935Z" } }, "outputs": [ @@ -362,15 +359,15 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 92, "id": "8cd2f538-c2d7-44ca-ab4d-825120b8f2e7", "metadata": { "execution": { - "iopub.execute_input": "2025-05-06T21:10:06.496317Z", - "iopub.status.busy": "2025-05-06T21:10:06.495845Z", - "iopub.status.idle": "2025-05-06T21:10:07.174905Z", - "shell.execute_reply": "2025-05-06T21:10:07.173813Z", - "shell.execute_reply.started": "2025-05-06T21:10:06.496280Z" + "iopub.execute_input": "2025-05-07T21:56:37.967978Z", + "iopub.status.busy": "2025-05-07T21:56:37.967634Z", + "iopub.status.idle": "2025-05-07T21:56:38.578202Z", + "shell.execute_reply": "2025-05-07T21:56:38.575023Z", + "shell.execute_reply.started": "2025-05-07T21:56:37.967945Z" } }, "outputs": [], @@ -390,15 +387,15 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 93, "id": "ee4d121e-6b4d-4371-afae-4f7587b95d51", "metadata": { "execution": { - "iopub.execute_input": "2025-05-06T21:10:07.484290Z", - "iopub.status.busy": "2025-05-06T21:10:07.483831Z", - "iopub.status.idle": "2025-05-06T21:10:07.502940Z", - "shell.execute_reply": "2025-05-06T21:10:07.501991Z", - "shell.execute_reply.started": "2025-05-06T21:10:07.484255Z" + "iopub.execute_input": "2025-05-07T21:56:38.580173Z", + "iopub.status.busy": "2025-05-07T21:56:38.579767Z", + "iopub.status.idle": "2025-05-07T21:56:38.598960Z", + "shell.execute_reply": "2025-05-07T21:56:38.598034Z", + "shell.execute_reply.started": "2025-05-07T21:56:38.580121Z" } }, "outputs": [ @@ -436,36 +433,36 @@ " \n", " \n", " 0\n", - " 62.006797\n", - " -36.902082\n", - " 1071.714409\n", - " False\n", - " 4245.695538\n", - " False\n", - " 10922.722964\n", - " False\n", + " 62.018897\n", + " -37.095671\n", + " 71.568352\n", + " True\n", + " 91.185588\n", + " True\n", + " 624.454022\n", + " True\n", " \n", " \n", " 1\n", - " 62.003768\n", - " -36.902438\n", - " 566.877720\n", + " 62.020999\n", + " -37.093227\n", + " 174.729861\n", " False\n", - " 787.147920\n", - " False\n", - " 1182.026506\n", + " 110.922305\n", " False\n", + " 52.040203\n", + " True\n", " \n", " \n", " 2\n", - " 62.008572\n", - " -36.902418\n", - " 174.862476\n", - " False\n", - " 520.394736\n", + " 62.000430\n", + " -37.093196\n", + " 131.680920\n", " False\n", - " 1266.903601\n", + " 137.655812\n", " False\n", + " 136.174616\n", + " True\n", " \n", " \n", " ...\n", @@ -480,36 +477,36 @@ " \n", " \n", " 11561\n", - " 61.991017\n", - " -37.082362\n", - " 493.321470\n", - " False\n", - " 648.269883\n", - " False\n", - " 908.325375\n", + " 61.950427\n", + " -36.946586\n", + " 51.054369\n", + " True\n", + " 175.646973\n", " False\n", + " 123.073904\n", + " True\n", " \n", " \n", " 11562\n", - " 61.950309\n", - " -37.085180\n", - " 116.922498\n", + " 61.976752\n", + " -36.904225\n", + " 199.039503\n", " False\n", - " 117.440266\n", + " 187.972452\n", " False\n", - " 54.748117\n", + " 115.825734\n", " False\n", " \n", " \n", " 11563\n", - " 61.951738\n", - " -37.085393\n", - " 146.265527\n", - " False\n", - " 260.318371\n", + " 61.932319\n", + " -36.941077\n", + " 266.123377\n", " False\n", - " 208.302958\n", + " 218.853195\n", " False\n", + " 481.650950\n", + " True\n", " \n", " \n", "\n", @@ -517,28 +514,28 @@ "" ], "text/plain": [ - " coord_ra coord_dec g_kronFlux g_kronFlux_flag r_kronFlux \\\n", - "0 62.006797 -36.902082 1071.714409 False 4245.695538 \n", - "1 62.003768 -36.902438 566.877720 False 787.147920 \n", - "2 62.008572 -36.902418 174.862476 False 520.394736 \n", - "... ... ... ... ... ... \n", - "11561 61.991017 -37.082362 493.321470 False 648.269883 \n", - "11562 61.950309 -37.085180 116.922498 False 117.440266 \n", - "11563 61.951738 -37.085393 146.265527 False 260.318371 \n", - "\n", - " r_kronFlux_flag i_kronFlux i_kronFlux_flag \n", - "0 False 10922.722964 False \n", - "1 False 1182.026506 False \n", - "2 False 1266.903601 False \n", - "... ... ... ... \n", - "11561 False 908.325375 False \n", - "11562 False 54.748117 False \n", - "11563 False 208.302958 False \n", + " coord_ra coord_dec g_kronFlux g_kronFlux_flag r_kronFlux \\\n", + "0 62.018897 -37.095671 71.568352 True 91.185588 \n", + "1 62.020999 -37.093227 174.729861 False 110.922305 \n", + "2 62.000430 -37.093196 131.680920 False 137.655812 \n", + "... ... ... ... ... ... \n", + "11561 61.950427 -36.946586 51.054369 True 175.646973 \n", + "11562 61.976752 -36.904225 199.039503 False 187.972452 \n", + "11563 61.932319 -36.941077 266.123377 False 218.853195 \n", + "\n", + " r_kronFlux_flag i_kronFlux i_kronFlux_flag \n", + "0 True 624.454022 True \n", + "1 False 52.040203 True \n", + "2 False 136.174616 True \n", + "... ... ... ... \n", + "11561 False 123.073904 True \n", + "11562 False 115.825734 False \n", + "11563 False 481.650950 True \n", "\n", "[11564 rows x 8 columns]" ] }, - "execution_count": 42, + "execution_count": 93, "metadata": {}, "output_type": "execute_result" } @@ -549,15 +546,15 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 94, "id": "db2168fe-593a-423d-b2f4-26ac0db60e8c", "metadata": { "execution": { - "iopub.execute_input": "2025-05-06T21:10:08.004249Z", - "iopub.status.busy": "2025-05-06T21:10:08.003782Z", - "iopub.status.idle": "2025-05-06T21:10:08.010260Z", - "shell.execute_reply": "2025-05-06T21:10:08.009349Z", - "shell.execute_reply.started": "2025-05-06T21:10:08.004212Z" + "iopub.execute_input": "2025-05-07T21:56:39.172485Z", + "iopub.status.busy": "2025-05-07T21:56:39.172006Z", + "iopub.status.idle": "2025-05-07T21:56:39.178527Z", + "shell.execute_reply": "2025-05-07T21:56:39.177605Z", + "shell.execute_reply.started": "2025-05-07T21:56:39.172448Z" } }, "outputs": [ @@ -567,7 +564,7 @@ "pandas.core.frame.DataFrame" ] }, - "execution_count": 43, + "execution_count": 94, "metadata": {}, "output_type": "execute_result" } @@ -588,15 +585,15 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 95, "id": "eec25f58-d3f3-4ef4-b3e2-ab105c4718fd", "metadata": { "execution": { - "iopub.execute_input": "2025-05-06T21:10:09.180104Z", - "iopub.status.busy": "2025-05-06T21:10:09.179663Z", - "iopub.status.idle": 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91.185588 \n", + "1 62.020999 -37.093227 174.729861 False 110.922305 \n", + "2 62.000430 -37.093196 131.680920 False 137.655812 \n", + "3 62.015568 -37.092868 372.665560 False 171.582869 \n", + "4 62.002969 -37.092762 247.219720 False 153.138653 \n", "\n", - " r_kronFlux_flag i_kronFlux i_kronFlux_flag \n", - "0 False 10922.722964 False \n", - "1 False 1182.026506 False \n", - "2 False 1266.903601 False \n", - "3 False 553.084499 False \n", - "4 False 909.234224 False \n", - " coord_ra coord_dec g_kronFlux g_kronFlux_flag r_kronFlux \\\n", - "11554 61.986579 -37.083734 484.161319 False 1139.300737 \n", - "11555 61.988051 -37.085378 108.308183 False 634.252111 \n", - "11556 61.985760 -37.082797 203.076232 False 714.794069 \n", - "... ... ... ... ... ... \n", - "11561 61.991017 -37.082362 493.321470 False 648.269883 \n", - "11562 61.950309 -37.085180 116.922498 False 117.440266 \n", - "11563 61.951738 -37.085393 146.265527 False 260.318371 \n", + " r_kronFlux_flag i_kronFlux i_kronFlux_flag \n", + "0 True 624.454022 True \n", + "1 False 52.040203 True \n", + "2 False 136.174616 True \n", + "3 False 211.338418 True \n", + "4 True 184.829166 True \n", + " coord_ra coord_dec g_kronFlux g_kronFlux_flag r_kronFlux \\\n", + "11554 61.913511 -36.960012 158.939682 False NaN \n", + "11555 61.986424 -36.950292 125.535210 False 71.749436 \n", + "11556 61.942562 -36.951137 108.966860 False 135.301872 \n", + "... ... ... ... ... ... \n", + "11561 61.950427 -36.946586 51.054369 True 175.646973 \n", + "11562 61.976752 -36.904225 199.039503 False 187.972452 \n", + "11563 61.932319 -36.941077 266.123377 False 218.853195 \n", "\n", - " r_kronFlux_flag i_kronFlux i_kronFlux_flag \n", - "11554 False 2663.259351 False \n", - "11555 False 1487.625610 False \n", - "11556 False 1734.816947 False \n", - "... ... ... ... \n", - "11561 False 908.325375 False \n", - "11562 False 54.748117 False \n", - "11563 False 208.302958 False \n", + " r_kronFlux_flag i_kronFlux i_kronFlux_flag \n", + "11554 True 102.094474 True \n", + "11555 True NaN True \n", + "11556 False 191.964068 True \n", + "... ... ... ... \n", + "11561 False 123.073904 True \n", + "11562 False 115.825734 False \n", + "11563 False 481.650950 True \n", "\n", "[10 rows x 8 columns]\n", " coord_ra coord_dec g_kronFlux r_kronFlux i_kronFlux\n", @@ -666,21 +663,21 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 96, "id": "4fc9b578-2be4-4fb2-8d74-ebca809ea99f", "metadata": { "execution": { - "iopub.execute_input": "2025-05-06T21:10:10.448369Z", - "iopub.status.busy": "2025-05-06T21:10:10.447861Z", - "iopub.status.idle": "2025-05-06T21:10:11.456905Z", - "shell.execute_reply": "2025-05-06T21:10:11.455964Z", - "shell.execute_reply.started": "2025-05-06T21:10:10.448332Z" + "iopub.execute_input": "2025-05-07T21:56:40.857458Z", + "iopub.status.busy": "2025-05-07T21:56:40.856953Z", + "iopub.status.idle": "2025-05-07T21:56:41.863203Z", + "shell.execute_reply": "2025-05-07T21:56:41.862312Z", + "shell.execute_reply.started": "2025-05-07T21:56:40.857421Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -709,21 +706,21 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 106, "id": "bacf5114-6a64-4100-8eb6-f1d9ddc36f89", "metadata": { "execution": { - "iopub.execute_input": "2025-05-06T21:10:12.524316Z", - "iopub.status.busy": "2025-05-06T21:10:12.523846Z", - "iopub.status.idle": "2025-05-06T21:10:12.965104Z", - "shell.execute_reply": "2025-05-06T21:10:12.964215Z", - "shell.execute_reply.started": "2025-05-06T21:10:12.524278Z" + "iopub.execute_input": "2025-05-07T21:58:27.791613Z", + "iopub.status.busy": "2025-05-07T21:58:27.791201Z", + "iopub.status.idle": "2025-05-07T21:58:28.222964Z", + "shell.execute_reply": "2025-05-07T21:58:28.220553Z", + "shell.execute_reply.started": "2025-05-07T21:58:27.791579Z" } }, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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" ] @@ -764,21 +761,21 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 104, "id": "39521ac6-0bec-42e7-9062-8fc9ce5edc55", "metadata": { "execution": { - "iopub.execute_input": "2025-05-06T21:10:14.842455Z", - "iopub.status.busy": "2025-05-06T21:10:14.842007Z", - "iopub.status.idle": "2025-05-06T21:10:15.365278Z", - "shell.execute_reply": "2025-05-06T21:10:15.364282Z", - "shell.execute_reply.started": "2025-05-06T21:10:14.842420Z" + "iopub.execute_input": "2025-05-07T21:58:06.568327Z", + "iopub.status.busy": "2025-05-07T21:58:06.567858Z", + "iopub.status.idle": "2025-05-07T21:58:07.237288Z", + "shell.execute_reply": "2025-05-07T21:58:07.236304Z", + "shell.execute_reply.started": "2025-05-07T21:58:06.568289Z" } }, "outputs": [ { "data": { - "image/png": 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", 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KPV6jRo0wb948m9BhMWXKFGvwjoyMtLkvPT3d+rWl97Y81emlIqoqBg8iD7dr1y589913yM/PL3WfJEnWsfV9+vQp82LbYvz48davLftClBQWFmYdqnHx4kX861//wkcffQSg+FPLG4e/1MScOXPQoUMH6/969uyJKVOm4MSJEwCK96v43//+V6XJwZbnFBAQUGooUEn33nuvdYJrWa+Dvdjze+NqJYfFFBYW3rR9VlYW4uPjcfHiRev/LBduJYcJ2YOqqkhPT0dcXJzN+SzD9Ox9vsqo6utlYal5z549yMrKsntdZRk9erTdjjVq1Khyh1DpdDrrPIjs7GxERUXZ7byOYHkftmjRosIPXB599NFSjynLiBEjbIYYlhQYGGgNZTfOtSk53HTTpk1QVfWmtRM5EodaEdVy06dPx0svvWRzm8lkQlJSEnbs2IHly5dj48aNOHv2LFauXImQkBBru8TERBiNRgBA9+7dKzxPp06doNfrIYpiqaFdFuPGjcO+ffuwY8cO/PHHHwCKx9d//PHHTlnKtVWrVhg5ciSefPLJKn+qa3lOHTp0KPcPPAD4+PigU6dOOHr0aLmvgz3Y+3vjSiUvnstblejYsWNYvXo1jhw5UuGQj5JDgmpiz549WLNmDY4dO1bhxb29zlcVJespayWv8jz88MM4evQoEhIScM8992D48OG44447cPvttztstaKyVn6qrhvnldwoPDzc+nV0dLTNv92J2Wy2DiksucpeWRo1aoRmzZohOTm5wvfurbfeWuFxLENYb/xZbt68Ofr06YOjR49i5cqV2L9/P4YPH44+ffqgW7duVfr5IrIHBg8iD+Tr64s2bdrghRdeQHh4OJ566ilcvHgRH3zwgc3u5SWHvZQ1FKAkvV6PevXqIT09vcILw3nz5mHPnj3WoRAzZ8686R/Nqiq5c7kgCPDz80P9+vVrtDmd5bUoGczKY5mYmZeXB1VVHbI7uiO+N65S8uK9rDk+Ny5lWpGioqIa1aKqKt5880389NNPTjlfdVheL51OV6Wf6TFjxiApKQnLly9HQUEBNm7caJ3z06xZM9x1112YMGGCXef52HPO1s3ee5b5VIBrAmFllXzvVvb3SXJycoXv3ZstTGEZgmUZ3lXSJ598gpkzZ+LYsWO4dOkSli5diqVLl0Kn06FLly4YNWoUxo4d65Wbe5LzcagVkYcbMGAAbrvtNgDFk08tn6LfqDIXz5Xppl+zZo3N+Gt7LdtZkmXn8vbt26Ndu3Zo0aKF3f5o2ut1sCd3rKkqzp8/b/36xr0GDh8+bA0dLVq0wNtvv40tW7bg2LFjOH/+PKKjoxEdHY0XX3zRLrVs2LDBGjo6duyI999/H7/++iuOHz9uc77KLG/qCJmZmdad5ival6E8M2bMwB9//IF//etfGDBggHXoUnJyMr7//nuMHj0aixYtslu9Wq3Wbse62c+5O/+Ml8cd3ruhoaH4/vvv8e2332Ly5Mno0KEDNBoNJEnCqVOnsGDBAowcORJ//fWXQ+sgAtjjQeQVbr31Vly4cAGiKOLy5cvWVYZKflqZmZlZ4TFEUbR+klfeJMRTp05h6dKlAIqH1BQUFFj38Ci59Kw7qlu3LtLT060TdCtiaRMcHOyQ3g5LPRb2+N640oEDB6xf37jPwLp16wAUP99169aV27tT1qT06rAsCNCqVSv8+OOPZU7Yt+f5qmr//v3Wr2+//fZqHeOWW27Bs88+i2effRayLOPcuXPYsWMH1qxZg4KCAixevBidOnUqtXGgq93svVfyfXDjUErL+7CsT/xLKu+DF3sq+d6tyu8TR793+/bta13pLy8vDxEREfjpp5+wZ88epKenY/r06di5c2eFQ02Jaoo9HkReoORmeJIkWb9u0aKFtQv/Zp92RUVFWXsyyhqqUVhYiFmzZkGSJAQHB2Pjxo3WnpYPP/wQsbGxNX4ejmR5TtHR0RXu8G42m60TWx25NK09vzeulJmZiW3btgEonjR94yaHlp+Lvn37VjikzLIkaE3FxMQAKN5Nu7zQoaoqzp07Z5fzVYWqqjb7hgwfPrzGx9RqtQgPD8esWbOwYsUK6+03bkjpqABdFZYV6Spz/40/55a5Cnl5eRUe4/LlyxXeb4/XwcfHxzrZ+2bPKSMjw7qZpTPfu8HBwbjnnnuwbNky6+IUaWlpbrkqHnkWBg8iD6eqqs1FW8mNwHQ6Hfr06QMAOHr0aIW7D1s+mQZQ5g7Z8+fPtz5+3rx5aNWqFT755BP4+fmhqKgI//rXvyq8oHc1y3MyGAxl7vxs8dtvv1lXCCvrdbB8WljT52rP742ryLKM119/3TpPYsKECaU+1bUEYctSxmU5f/78TcNXZV93Swiv6Hy7du2yWYbUWZYtW2Z9r3bu3BkDBgyw6/F79OhhDbM3zpEo+Sm3q96nv/32W7k9ErIsWzdDrVevHjp27Ghzv2U/l8LCQly6dKnMY6iqat0XpzyWlfBq+hpY3ocJCQmllrgtqeSS3K5675Zcdcud586QZ2DwIPJwP/zwg/UTtU6dOqFx48Y291uGQMmyjDfeeKPMP7h79+61jovv3LlzqSEgv/32GzZt2gQAePDBB3HfffcBKN5p+7XXXgNQvCzpp59+asdnZl9jxoyxjof/+OOPkZSUVKpNUlKSdXK+n5+fzUZ2FpaJ51euXKlxTfb43rhKUlISnnzySevQobZt22LatGml2lmWCT5x4kSZG+ZlZWVZf4YqUtnX3XK+PXv2lDmZ98qVK3jnnXduej57KigowLvvvouFCxcCKO4ZKrmJXmX9/PPPFe5vcezYMeuFffPmzW3us7x+gH1+dqsjIyOj3Oe9aNEixMXFAShegvbGpbItIR2ATc9OSf/73/9u2pNleR0yMzOrtIfKjSZOnGid8P3222+XOXTv3LlzWL58ufW8I0eOrPb5yhMVFWUzx6osJZfxvfHngsjeOMeDqJYra+dys9lsXU7XMsxFo9Fg1qxZpR4/ePBg3H///fjll19w9OhRPPLII3jqqafQrl07FBQUYNeuXdbNsPR6Pf773//aPD4lJQVvvfUWgOKVcyxfWzz++OPYu3cv9u7di2+++QZ33nmnU3eCrqwGDRpg9uzZeOutt5Ceno5HHnkEzzzzjHVOwvHjx7F8+XLrxeprr71ms8qORY8ePRAREYEzZ85g+fLluPPOO62fMvv5+ZUKfhWp6ffGkW78uSsqKkJubi5iY2MRERGBffv2WXsX2rZti2XLlpW5AMBDDz2EPXv2wGAwYNKkSXj22Wetc5BOnjyJb775BhkZGejRowdOnjxZbj2Vfd0feughfPjhh0hLS8P48ePxzDPPoH379jCZTDhy5AhWrVoFs9mMzp072224lcFgsHmtRFFEXl4ekpKScOLECezYscN6kRscHIxPPvnEOkyxKl5//XV8+OGHGDZsGHr27ImWLVvCz88PWVlZiIyMxA8//ACguDet5P4RANC0aVPrjulff/01mjRpgrCwMOvk8ZCQEIevetS1a1esW7cOSUlJmDhxIpo2bYr09HT89NNP2LFjB4Di3zHPP/98qcd27NgRPXv2xIkTJ7Bx40aIoohHHnkEwcHBSEpKwqZNm7Bnzx5rm/L07NkTQPFckbfffhuTJ0+2mU9S0X46JbVr1w7PPPMMvvjiC1y6dAkPP/wwpk6dii5dusBsNuPgwYP45ptvYDQaIQgC/vvf/1Zp36HKioqKwpw5c6w7wHfq1AmNGjWCqqq4evUqfvnlF+vS5507d3bbJYrJczB4ENVya9aswZo1aypsU6dOHfznP/8pd+jGggULIMsytm/fjosXL2L27Nml2gQHB+Ozzz6z7ugMFA9deO2115CbmwutVouPPvqozIuTBQsW4IEHHkBmZiZef/11bNmyxS0nQY8fPx75+fn4v//7P+Tk5Fg3QCxJq9XilVdeKXey/MSJE/Hjjz8iJycHn3zyCT755BPrfX369LEZw18Z1f3eOFplfu6Cg4Px6KOP4qWXXip3PsXIkSMxZswYbNy4EWlpaaXCk1arxZw5c5CXl1dh8Kjs6/7EE0/g0KFDOHDgAOLj4/Hmm2/aHMfPzw8ffPAB9u7da7fgcfbsWTzwwAMVttHr9bj77rsxe/Zs3HLLLdU+V2ZmJtauXYu1a9eWeb+fnx/mz59vDXclPffcc3jnnXeQlJRUahWxBQsWYMyYMdWuqzJeeeUVfPPNNzhw4ECZm+ndcsst+Prrr8vde2LBggWYNGkS0tPTsXXrVmzdutXm/gceeACPPPIIpkyZUm4N/fr1Q/fu3XHq1Cn88ssvpYZmRUdHV/r5zJw5E0ajEd9++y2Sk5PL7Enz9fXF/Pnzcdddd1X6uNVx7ty5Cn+e27dvj8WLF7vFXB/ybAweRB5Ir9cjODgYbdq0wYABA/DII4/YDKW4kY+PDxYuXIgxY8bgp59+wqlTp5CZmQk/Pz+0aNECgwcPxhNPPFFq8u9XX32FiIgIAMUXLeUN8wkJCcF7772H5557DqmpqXjrrbfw+eef2+8J29HUqVMxZMgQfPvttzhy5AhSU1MBFC/h27dvX0yePBnt2rUr9/GNGzfG+vXr8cUXX+Do0aNITU2FyWSqdj3V/d44k0ajQZ06dRAYGIgmTZqgc+fO6NGjB4YNG1Zu4ChpwYIF6NevH9atW2edKN+oUSP06tULkyZNQnh4+E2XgK3s667X6/HFF19gzZo1+Pnnn3Hp0iWoqorGjRvjjjvuwBNPPIE2bdpg79691X49biYgIAB16tRBSEgIOnbsiK5du2L48OEVvkcr47fffsOhQ4dw+PBhxMfHIyMjA/n5+fD390erVq3Qv39/PPbYY2jatGmZj584cSIaNmyIH3/8EVFRUcjLy7NZjMLR9Ho9vvzyS6xbtw4///wz4uLiYDQa0bx5cwwfPhxTp06tsNeldevW2LhxI5YvX44///wTKSkpqFOnDm677TaMHz8e9957r/X3VXk0Gg2++uorrFixAnv27MGVK1dgNBqrteStIAh48803cd9992HNmjWIjIxERkYGtFotmjVrhgEDBuAf//hHud8Pe7j//vvRrFkzHDp0CMePH0dKSgoyMzMhiqJ1rszw4cPx0EMPOWWTVyJBrY0LYxMRERERUa3CyeVERERERORwDB5ERERERORwDB5ERERERORwDB5ERERERORwXNXKSRRFKbXCilar5dJ1RERERFQrqapq3bPJwtfX17qB5o0YPJzEZDLddPdQIiIiIqLarFOnTtYNXG/EoVZERERERORwDB5ERERERORwDB5ERERERORwnOPhJFqtttRtnTp1gk7HbwERERER1T6SJJWaw1zWNa8Fr3qdpKzVq3Q6HfR6vQuqISIiIiKyv4pWbOVQKyIiIiIicjgGDyIiIiIicjgGDyIiIiIicjgGDyIiIiIicjgGDyIiIiIicjgGDyIiIiIicjgGDyIiIiIicjgGDyIiIiIicjgGDyIiIiIicjgGDyIiIiIicjgGDyIiIiIicjgGDyIiIiIicjgGDyIiIiIicjgGDyIiIiIicjgGDyIiIiIicjgGDyIiIiIicjgGDyIiIiIicjgGDyIiIiJyKkVRkJ6e7uoyyMkYPIiIiIjIqU6fPo2JEyciOzvb1aWQEzF4EBEREZFTmUwmAIDZbHZxJeRMDB5ERERE5FSSJAEARFF0cSXkTAweRERERORUluAhy7KLKyFnYvAgIiIiIqdij4d3YvAgIiIiIqeyBA5LACHvwOBBRERERE5lCRwMHt6FwYOIiIiInMrS48GhVt6FwYOIiIiInMp0fRldBg/vwuBBRERERE5lEk2AXmDw8DIMHkRERETkVGbRBPhwjoe3YfAgIiIiIqcyiWaoeoHBw8sweBARERGRU5nMJqh6zvHwNgweRERERORUZtEMRa8yeHgZBg8iIiIicipRFKHoVQ618jIMHkRERETkVKIsQdUDZvZ4eBUGDyIiIiJyKlEUoeqvL6tLXoPBg4iIiIicyhI8RIk9Ht6EwYOIiIiInEqSJPZ4eCEGDyIiIiJyKlmWAS03EPQ2DB5ERERE5FSSLEHVCZBkBg9vwuBBRERERE4lyzJUHXs8vA2DBxERERE5lSxxqJU3YvAgIiIiIqdSFAWqFpAV2dWlkBMxeBARERGRU6mqAmgBRVVcXQo5EYMHERERETmVoihQNYAsM3h4EwYPIiIiInIqRVEBDYdaeRsGDyIiIiJyKlVRAM31/5LXYPAgIiIiIqdSLf9TVVeXQk7E4EFEREREzqWqgFAcPsh7MHgQERERkVOpKoqDB3s8vAqDBxERERE5mcrg4YUYPIiIiIiIyOEYPIiIiIiIyOEYPIiIiIiIyOEYPIiIiIjIqSwzOzjHw7sweBARERGR8wmuLoCcjcGDiIiIiIgcjsGDiIiIiIgcjsGDiIiIiIgcjsGDiIiIiJzLOqmck8u9CYMHERERETmVqoI7l3shBg8iIiIicjIVKle18joMHkRERETkVH/3eLi6EnImBg8iIiIicq7riYNDrbwLgwcREREROZUKABoGD2/D4EFEREREzqWqnFzuhRg8iIiIiMipVLV4cjmDh3dh8CAiIiIip7JMLldUxdWlkBPVyuDx5ZdfokOHDujQoQNOnTpVZpuCggIsWLAAQ4YMQZcuXTBkyBAsWLAABQUF5R5369atGDt2LLp3747evXvjmWeewZkzZ8ptHx8fj5dffhn9+vVDeHg4HnjgAXz33XdQFL6JiIiIiMplGWqlsMfDm9S64HHp0iV8/vnnCAgIKLeNwWDApEmTsHLlSoSFhWHKlClo06YNVq5ciUmTJsFgMJR6zLJlyzBr1ixkZmZiwoQJGDVqFE6cOIHHHnsMERERpdrHxsZi7Nix2LVrFwYOHIjJkycDAObPn4+3337bfk+YiIiIyMOouB48uHO5V9G5uoCqkGUZr7/+Om677Ta0bt0aW7ZsKbPdihUrEBUVhalTp+LVV1+13v75559jyZIlWLFiBWbMmGG9PT4+HosWLULr1q2xYcMGBAUFAQAmT56McePGYe7cudi+fTt0ur9frnnz5iE/Px/Lly/H4MGDAQCvvPIKnnnmGaxbtw733Xcf+vXr54iXgYiIiKj24xwPr1Orejy+/PJLXLhwAe+99x60Wm2ZbVRVxfr16xEQEIBp06bZ3Pfcc8+hbt262LBhg80P+saNGyFJEl544QVr6ACAdu3a4cEHH8SVK1dw5MgR6+1xcXGIjIxE3759raEDAPR6PWbOnAkAWL9+vV2eMxEREZFHEsAdBL1MrQkeFy9exOLFi/HCCy+gXbt25baLj49HWloaevbsWWo4lq+vL3r16oXU1FQkJCRYbz969CgAYMCAAaWON2jQIABAZGRkqfYDBw4s1T48PBzBwcHWNkRERERky5I3mDu8S60YaiVJEmbPno02bdrg2WefrbCtJVC0bt26zPtbtWplbWdpEx8fj4CAADRq1Kjc9vHx8dbbLF9b7itJEAS0bNkSZ8+ehdFohL+/f7m1Go1GiKJY4fMhIiIi8jzFczwURSlz7i3VDpIkVal9rQgey5YtQ3R0NNatWwe9Xl9h2/z8fABAYGBgmfdbbre0A4pXwGrQoEGF7UuuhmX5uuSwrPLOUVHwiImJKfc+IiIiIk8lyzKA4g9ho6KiXFwNOYvbB48LFy5g2bJleOqpp9C5c2dXl2NX7dq1s5mwTkREROQNtFotVAB+/n7o2LGjq8uhapIkqUofpLv9Ve/rr7+OFi1a4KWXXqpUe0svRHn7dZTVWxEYGGjTA1JW+5I9KGX1mtzsMWXx9/e/aQ8OERERkacRBMH634q2SCD3VtUpA24fPC5cuAAA6Nq1a5n3jx8/HgCwZMkSDBs2rMw5GSVZ5oCUnJ/RunVrnDx5Eunp6aXmeZQ1Z8TydckJ6haqquLKlSsIDQ3lG4mIiIioApYAQt7B7YPH2LFjy7z92LFjiI+Px9ChQ9GgQQM0a9YMQHEoCA0NxYkTJ2AwGGwu/k0mE44dO4bQ0FCb4NG7d2+cPHkSBw8exEMPPWRznv3791vbWPTp0wcAcODAgVKT3U+fPo28vDzceeed1X/SRERERB6OkcP7uH3wePfdd8u8ffbs2YiPj8dzzz2H7t27W28XBAHjxo3DkiVLsGTJEpsNBL/44gvk5uZi2rRpNgl7zJgx+Prrr7F06VLcfffd1mFYMTEx2Lx5M1q2bGmzGWBYWBh69+6NiIgI7N2717qXhyiKWLhwIQBg3Lhx9noJiIiIiDyPCgiMH17F7YNHdUydOhW7d++27mDeuXNnXLhwAfv27UPHjh0xdepUm/ZhYWGYPn06Fi5ciNGjR2PEiBEwGAzYtm0bJEnC/PnzS00CnzdvHiZMmIBp06Zh1KhRCA0Nxf79+xEdHY1x48Zx13IiIiKim+BIK+/ikcEjICAAq1evxuLFi/H777/j6NGjaNiwIaZMmYLp06eXOffihRdeQLNmzbBq1SqsWbMGer0ePXr0wIwZMxAeHl6qfdu2bbF+/Xp8+umn2LdvHwwGA1q1aoW5c+fi8ccfd8bTJCIiIqqd1Ov/Y/LwKoKqcs9IZxBFEadPn7a5LTw8nKtaERERkdd5+LExSBxZgH7nbsPiDz93dTlUTVW9vtU4oygiIiIior8JxXM82OHhVRg8iIiIiMipBIBDrbwQgwcREREROZWgsfR4MHh4EwYPIiIiInIyAYICaAReinoTfreJiIiIyKmsHR3s8PAqDB5ERERE5HxcV9XrMHgQERERkXMJAncu90IMHkRERETkVJZVrTi53LsweBARERGRcwkC53d4IQYPIiIiIiJyOAYPIiIiIiJyOAYPIiIiInIulUtaeSMGDyIiIiJyKhUABEBlAPEqDB5ERERE5FyqWhw8uJmHV2HwICIiIiKnsnZ0MHd4FQYPIiIiInI+DrXyOgweRERERORkKlQBUFTF1YWQEzF4EBEREZFTqUrxHA+ubuVdGDyIiIiIyKn+XtXK1ZWQMzF4EBEREZGTcVUrb8TgQURERETOdT1vqAqDhzdh8CAiIiIi5xLAVa28EIMHERERETkfh1p5HQYPIiIiInI6Fezx8DYMHkRERETkVKqqcqiVF2LwICIiIiInEwAweHgbBg8iIiIicj5uIOh1GDyIiIiIyMmKAwdzh3dh8CAiIiIi5+OqVl6HwYOIiIiInMoaN9jl4VUYPIiIiIjIqYTr/8fc4V0YPIiIiIjIqVTgevBQXF0KORGDBxERERG5hKKwy8ObMHgQERERkZOpULmBoNdh8CAiIiIi52Pw8DoMHkRERETkfAKgKpzj4U0YPIiIiIjIqSyTyxX2eHgVBg8iIiIicj72eHgdBg8iIiIici5VLe7x4KpWXoXBg4iIiIicxjKhXOU+Hl6HwYOIiIiInEZRFEAQrg+1Yo+HN2HwICIiIiKnURSl+AqUQ628DoMHERERETmNqqoQBAHQcHK5t2HwICIiIiKnkWUZEIq/VsEeD2/C4EFERERETqOqKqARiud5MHd4FQYPIiIiInKa4snlxV8zd3gXBg8iIiIichpFUSAIln8xengTBg8iIiIichpVVaFaejyYO7wKgwcREREROY11Hw+gRM8HeQMGDyIiIiJyGkVRIPAK1Cvx205ERERETqOqKieXeykGDyIiIiJyGsVm00BGD2/C4EFERERETlOyx4O5w7sweBARERGR0yiKAvX6rHLmDu/C4EFERERETqOqKlez8lIMHkRERETkNMU9Hq6uglyBwYOIiIiInKZ4H4/ir1XuIOhVGDyIiIiIyGlsJpez58OrMHgQERERkdOU7PEQwF4Pb8LgQUREREROUzzH43rY0Ag37OtBnozBg4iIiIicpmSPBwRAlmWX1kPOw+BBRERERE5TclUrQcNJHt6EwYOIiIiInMamxwPs8fAmDB5ERERE5DQ2q1ppBE4u9yIMHkRERETkNDaTywVwcrkXYfAgIiIiIqexWU6XwcOrMHgQERERkdOoqmqdXK4yeHgVBg8iIiIichpZlq1DrQTO8fAqDB5ERERE5DQ2k8u5j4dXYfAgIiIiIqdRFAWWPg5VAHs8vAiDBxERERE5l/D3fxg8vAeDBxERERE5TXGPx9/L6ZL3YPAgIiIiIpdQwVWtvAmDBxERERE5zY2TyznUynsweBARERGRS3CklXdh8CAiIiIiIodj8CAiIiIiIodj8CAiIiIiIodj8CAiIiIil+C8cu/C4EFERERETiMIAlAicGg0vBz1FvxOExEREZHTCILw92q66vUgQl6BwYOIiIiInKa4x4NhwxsxeBARERGRS6hgj4c30bm6gJvJy8vD559/jjNnziApKQm5ubmoX78+wsLC8Pjjj2P48OGlfmALCgqwaNEi7NixA+np6WjUqBGGDx+Ol156CYGBgWWeZ+vWrVi1ahViY2Oh1+vRvXt3zJgxA127di2zfXx8PD799FNERETAYDCgVatWGD9+PCZOnMixikRERETlKDnUChxq5VXc/go5OzsbP/30E/z9/XH33Xfjqaeewp133onY2FjMmDEDb731lk17g8GASZMmYeXKlQgLC8OUKVPQpk0brFy5EpMmTYLBYCh1jmXLlmHWrFnIzMzEhAkTMGrUKJw4cQKPPfYYIiIiSrWPjY3F2LFjsWvXLgwcOBCTJ08GAMyfPx9vv/22Y14IIiIiIg/DyOFd3L7Ho3nz5oiMjIROZ1tqQUEBxo8fj3Xr1uGJJ55Au3btAAArVqxAVFQUpk6dildffdXa/vPPP8eSJUuwYsUKzJgxw3p7fHw8Fi1ahNatW2PDhg0ICgoCAEyePBnjxo3D3LlzsX37dpvzz5s3D/n5+Vi+fDkGDx4MAHjllVfwzDPPYN26dbjvvvvQr18/h70mRERERLWVzapWXE7Xq7h9j4dWqy0VOgAgMDAQAwcOBAAkJCQAAFRVxfr16xEQEIBp06bZtH/uuedQt25dbNiwAWqJRaM3btwISZLwwgsvWEMHALRr1w4PPvggrly5giNHjlhvj4uLQ2RkJPr27WsNHQCg1+sxc+ZMAMD69evt8MyJiIiIPM+NQ6s41Mp7uH3wKI/JZMKRI0cgCALatm0LoLj3Ii0tDT179kRAQIBNe19fX/Tq1QupqanWoAIAR48eBQAMGDCg1DkGDRoEAIiMjCzV3hJ6SgoPD0dwcLC1DRERERERFXP7oVYWeXl5WLVqFRRFQWZmJvbt24dr165h+vTpaN26NYC/ez4s/75Rq1atrO0sbeLj4xEQEIBGjRqV2z4+Pt56m+Vry30lCYKAli1b4uzZszAajfD396/wORmNRoiiWGEbIiIiIk9SVFQE9foYKxUqDAbDTa+ZyD1JklSl9rUqeCxevNj6b71ej9deew1PPfWU9bb8/HwAKHflKsvtlnZA8VyRBg0aVNi+oKDApj0Am2FZ5Z3jZm+imJiYCu8nIiIi8jQJCQmQZQUAIMsyYmNjkZaW5uKqyBlqTfBo3rw5oqOjIcsyrl27hl9//RWffvopTp48iYULF5Y5D8TdtWvXrlbWTURERFRdRqMR2hPFo/21Wi3atm2LkJAQF1dF1SFJUpU+SK91V71arRbNmzfHs88+C41Gg48++gjr1q3DxIkTrb0QJXsoSiqrtyIwMNCmB6Ss9iV7UMrqNbnZY8rj7+8PvV5/03ZEREREnsLX1xe4PqFc0Ajw9fUtNTeXaoeqThmotZPLgb8neFsmc5c1J6MkyxyQkvMzWrduDYPBgPT09HLbl5wzcuN8kpJUVcWVK1cQGhrKNxARERFRGRRFgSpcn+MhwGa1UfJstTp4pKamAijuBQGKQ0FoaChOnDhRaqNAk8mEY8eOITQ01CZ49O7dGwBw8ODBUsffv3+/TRsA6NOnDwDgwIEDpdqfPn0aeXl51jZEREREZEuWZWvwgFAcRMg7uH3wiIqKKnNYU05ODj799FMAwJ133gmgeFWpcePGwWAwYMmSJTbtv/jiC+Tm5mLcuHE260WPGTMGOp0OS5cutTlPTEwMNm/ejJYtW9psBhgWFobevXsjIiICe/futd4uiiIWLlwIABg3blzNnzgRERGRByru8bj+D6E4iJB3cPs5Hhs3bsSGDRvQt29fNG3aFP7+/rh69Sr+/PNPGAwGjBgxAg888IC1/dSpU7F7927rDuadO3fGhQsXsG/fPnTs2BFTp061OX5YWBimT5+OhQsXYvTo0RgxYgQMBgO2bdsGSZIwf/78UhPA582bhwkTJmDatGkYNWoUQkNDsX//fkRHR2PcuHHctZyIiIioHCWHWkEjsMfDi7h98BgxYgQKCgpw6tQpREZGoqioCHXr1sXtt9+Ohx56CPfdd59ND0ZAQABWr16NxYsX4/fff8fRo0fRsGFDTJkyBdOnTy9z7sULL7yAZs2aYdWqVVizZg30ej169OiBGTNmIDw8vFT7tm3bYv369fj000+xb98+GAwGtGrVCnPnzsXjjz/u0NeDiIiIqDazHWqlssfDiwgqZ/Q4hSiKOH36tM1t4eHhXNWKiIiIvMrOnTvx3t6FyO4rosWOIPzfjPfRtm1bV5dF1VDV61u3n+NBRERERJ5DkiQoGsuqVuzx8CYMHkRERETkNLIsQxGK53WomuIgQt6BwYOIiIiInEaSJMgaS/BQGTy8CIMHERERETmNKIl/D7Vi8PAqDB5ERERE5DRm0Wy9AlUYPLwKgwcREREROY1JNEPVFn+taFSIoujagshpGDyIiIiIyGlMZlOJHg+FwcOLMHgQERERkdOYbXo8FA618iIMHkRERETkNCazGbgePGTO8fAqDB5ERERE5DSiaIZ6/QpUZo+HV2HwICIiIiKnMUtiiR4PzvHwJgweREREROQ0JXs8oAWKzEUurYech8GDiIiIiJzGLP7d46Fqrv+bvAKDBxERERE5jSiJ1lWtoAXMosml9ZDzMHgQERERkdPIkmS9AmWPh3dh8CAiIiIipxElyTrUChr2eHgTBg8iIiIichpZkmwml4tcTtdrMHgQERERkdNINwy1EiUOtfIWDB5ERERE5DSSJP/d46Fhj4c3YfAgIiIiIqeRZfnv5XS1gMQeD6/B4EFERERETiPL8t9XoOzx8CoMHkRERETkNIqs2Ay1kmXZpfWQ8zB4EBEREZHTKCV6PFTN9cnm5BUYPIiIiIjIaRRFAYTr/2CPh1dh8CAiIiIip1FVAML15KEBJJk9Ht6CwYOIiIiInEj9+yvheg8IeQUGDyIiIiJyDQ618ioMHkRE5JFiY2Px7ttvIDs729WlEFF52OPhVRg8iIjII0VHR+PPQ5FITk52dSlEVIJacqiVBpAl9nh4CwYPIiLySCaTCQBgNptdXAkRlUsAVJU9Ht5CV9MDxMfHY+XKlThy5AhSU1NhMplw/vx56/0bNmxASkoKnnzySdSpU6empyMiIqoUU5ERWkFl8CByZwKgKurN25FHqFHw+OWXX/Dmm2/CbDZDVYt/aATL8mjX5ebmYsmSJWjTpg1GjRpVk9MRERFVmmgywk8HiKLo6lKIqDwCoKgMHt6i2kOtoqKiMHv2bIiiiMcffxyrV69G586dS7UbOXIkVFXFrl27alQoERFRVYgmE/y0CoMHkZuxiRkCrB9ek+erdo/HihUrIMsy3njjDUyePBkA4OvrW6pds2bN0LBhQ1y8eLH6VRIREVWRJJnhq1G5VCeRu7khaHColfeodo9HZGQkAgMDraGjIo0bN0ZaWlp1T0VERFRlsiRBr1EgSdwVmcitCLZfq2Dw8BbVDh5ZWVlo0aJF5U6i0cBgMFT3VERERFUmywweRO5OBUr1gJDnqnbwCAoKQnp6eqXaXrlyBfXr16/uqYiIiKpMliToBJWbkxG5m5I5Q2Du8CbVDh4dO3ZERkaGzdK5ZdmzZw9yc3PRrVu36p6KiIioymRFgU5QGDyI3EipieScXO5Vqh08HnzwQaiqin//+9/IyMgos01sbCzefvttCIKAMWPGVLtIIiKiqiqe48HJ5URu54atF8h7VHtVq9GjR+Pnn3/G4cOHcf/992PIkCFISUkBAKxevRonTpzAzp07IYoi7r77btx11132qpmIiOimZFmCXlAhiyZXl0JE1xX3bqg33uiSWsj5qt3jIQgCFi9ejJEjRyInJwebNm3C1atXoaoq3nvvPWzfvh2iKGLkyJH45JNP7FkzERHRTVl6PLiPB5H7KA4etj0ejB3eo0Y7l9epUwcLFy7E6dOn8dtvvyE6Ohq5ubkICAhA+/btMWrUKNx+++32qpWIiKjSJEmCr1aBJJpdXQoRlcSRVl6rRsHDIjw8HOHh4fY4FBERkV2IkohgjQqJQ62I3JfADQS9SbWHWhEREbkz0SzCT6dANLPHg8hdKIpSagNBDrbyHgweRETkkcyiGf5aGWZTkatLIaLrVFWFcMOqVpxb7j2qPdTqiSeeqFJ7QRCwatWq6p6OiIioSkSzCH8fBRkmDrUicheqqpbq8eA+Ht6j2sHj6NGjN21jSbRlpVsiIiJHMoki6gQoMLHHg8htlNrQU+DW5d6k2sFjwYIF5d5nMBgQHx+Pbdu2IT8/H9OnT0doaGh1T0VERFRlZrOIOjoOtSJyJ4qilB7oz8+mvUa1g8fDDz980zYvv/wyZs6cibVr12LTpk3VPRUREVGVybICf50Ck4mTy4nchSzLgObGOR7s8fAWDp1cHhgYiPfeew9paWlYtGiRI09FRER0AxU+GhUmM+d4ELmLUqtakVdx+KpWjRo1Qtu2bbF7925Hn4qIiMhKVRX4aNjjQeROZFmGcGOPh4tqIedzynK6JpMJ6enpzjgVERFR8dANVYVeo8JsFl1dDhFdVzzU6oYbOdTKazg8eERHRyMhIQH169d39KmIiIgAAKIoQq8RioOHJLm6HCK6TpZlqDdcfTJ2eI9qTy6/evVqufepqorMzEycPHkSX331FVRVxV133VXdUxEREVWJKIrQaQGtACiy7OpyiOg6SZIA7Y23Mnp4i2oHj7vvvrtS7VRVRYsWLfDyyy9X91RERERVIooidE4ZTExEVSFJUqlVrQDu+eYtqh08brb0mb+/P1q3bo2hQ4fiySefRGBgYHVPRUREVCWSJFmDB5fqJHIfkiRB1dzwntQKkCQJer3eNUWR01Q7eFy4cMGedRAREdmNLMvQXv/wlJ+hErmP4uBhe5ugYfDwFuyIJiIijyPLMjQCezqI3E3x5PIb3ps6oXi1K/J4DB5ERORxFEWx9nRwqBWR+yirxwMaAaLIZa+9AYMHERF5nOKJqq6ugohuVPYcj+uTzsnjVWqOR2VXsKqIIAjYuXNnjY9DRERUGcwdRO5HlmUoNwyDFDTgUCsvUangkZycXOMTcYk0IiIiIu8mSRKUG3o8VIE9Ht6iUsHj22+/dXQdREREDsHPvYjcR5mTy9nj4TUqFTz69Onj6DqIiIjsynJpw7nlRO5DkqRSQ61UBg+vwcnlRETkcQRBsCYP9ngQuQ9ZlqEKiu2NDB5eg8GDiIg8Ejs6iNyPoihQbrj6VIXi28nzVXvn8pKysrJw/vx55OTkVDg56KGHHrLH6YiIiG6KHR1E7qd4VSv2eHirGgWPq1ev4j//+Q/27dtXqQ2aGDyIiIiIvJeiKFBvnOMhqOzx8BLVDh5ZWVmYOHEiUlJS0LhxYxQWFqKwsBC33347cnJyEBcXB1mW4efnh65du9qzZiIiogqVvIjh5HIi91HWPh6qwB4Pb1HtOR5ff/01UlJS8Oijj2Lv3r1o3749AOC7777DL7/8goMHD+K5556D2WxGWFgYVq9ebbeiiYiIKqIoCjTXL2445IrIfSiKAuXGGVic4+E1qt3jsXfvXuj1evzzn/8s8/569eph5syZaNiwId577z10794dY8aMqXahRERElaWqKjSWxHF9GIdGw/VUiFxNUZRSH3urglqpIftU+1X7t3BSUhKaNWuGevXqAfh7Z/IbJ5dPmjQJ9erVw7p166pfJRERURXIsgzt9R4PrSBwGAeRm5BkuVQ3JIdaeY8affwTFBRk/drf3x8AkJ2dbdNGEAQ0a9YMly5dqsmpiIiIKk2SJGiuD+fQcMUcIrchK1IZwYOTy71FtYNHaGgoMjMzrf9u2rQpAOD8+fM27RRFQXJyMsxmc3VPRUREVCW2PR4MHkTuQpJlqDdOvOIcD69R7eDRtm1bZGRkQBRFAECfPn2gqioWLVqE3Nxca7vPPvsM2dnZaNOmTc2rJSIiqgRJkqC5vleAVig9DJiIXENWSg+1Utjj4TWqPbl88ODB2LVrF44cOYJBgwZh+PDhaNasGc6dO4e77roLt956KzIyMpCWlgZBEPD444/bs24iIqJyybIM7fWhVloNgweRu1AUhUOtvFilezzeffddXLhwwfrve+65B//+97+tk8t9fHywfPlyhIWFwWg04ty5c0hNTYVWq8ULL7yARx55xO7FExERlUWWZetyuloO4yByG7Islb765HvUa1S6x2P16tX47rvv0LFjRzzyyCO4//77S/VitGnTBtu2bcPp06eRlJQEPz8/9OjRAw0aNLB74UREROVRFAUaFF/IaHhRQ+Q2pHKGWnEelneodPDo0KEDoqOjcf78eURFReGDDz7AsGHDMGbMGAwYMMC6nK4gCOjWrRu6devmsKKJiIgqIsuydVUrARzGQeQuJEmCWmofD4XBw0tUeqjV5s2bsWnTJkyaNAl169aF2WzGr7/+imeeeQZDhgzBZ599hitXrjiyViIioioTBHBzMiI3UfbkcvZKeosqrWrVsWNHzJ07F/v378fnn3+OwYMHQ6PRICUlBcuWLcOIESMwefJk/PzzzygqKnJUzURERDdV8tqGwYPIPUhS6TkeioY9Ht6iWqta6fV6DB8+HMOHD0dGRgZ+/vln/Pzzz4iNjUVkZCSOHTuG+fPn495778WYMWPQo0cPe9dNRERULkEQYIkaKgRoNDXaL5eI7ESSJag3XH1yjof3qPFv4oYNG2Lq1Kn45ZdfsG7dOowfPx5BQUEoLCzE+vXrMXHiRIwaNQorVqywR71EREQ3pdFooFzv81BUMHgQuQlZLmuoFXs8vEW19/EoS3h4OMLDw/Hmm2/ijz/+wMaNG3H48GHExcXhk08+wdSpU6t8zNTUVGzfvh379u3D5cuXkZGRgbp166Jnz56YOnVqmZPYCwoKsGjRIuzYsQPp6elo1KgRhg8fjpdeegmBgYFlnmfr1q1YtWoVYmNjodfr0b17d8yYMQNdu3Yts318fDw+/fRTREREwGAwoFWrVhg/fjwmTpzIP3BERC6m0WigXN8eWWXwIHIbYhlDrVTuteM1HPKbWK/XIzg4GMHBwdDpapZtVq9ejQULFiAxMRH9+/fHk08+idtvvx27du3ChAkT8Ouvv9q0NxgMmDRpElauXImwsDBMmTIFbdq0wcqVKzFp0iQYDIZS51i2bBlmzZqFzMxMTJgwAaNGjcKJEyfw2GOPISIiolT72NhYjB07Frt27cLAgQMxefJkAMD8+fPx9ttv1+j5EhFRzel0OsjQAgBkFTX+W0RE9iFJIlTtDTdqALNkdkk95Fx2/U18+fJlbNq0CVu2bEFaWhqA4gl9TZo0wYMPPlitY4aHh+P7779Hr169bG4/duwYpkyZgnfeeQfDhg2Dj48PAGDFihWIiorC1KlT8eqrr1rbf/7551iyZAlWrFiBGTNmWG+Pj4/HokWL0Lp1a2zYsAFBQUEAgMmTJ2PcuHGYO3cutm/fbvNHa968ecjPz8fy5csxePBgAMArr7yCZ555BuvWrcN9992Hfv36Vev5EhFRzRUHj+IeD0kBtNobr3SIyBXK6/Ewi6JrCiKnqnGPR0FBAdauXYvx48fjvvvuw4oVK5Camgq9Xo8RI0bgyy+/xJ49ezBz5sxqHX/48OGlQgcA9OrVC3379kVOTg6io6MBFIec9evXIyAgANOmTbNp/9xzz6Fu3brYsGGDzeomGzduhCRJeOGFF6yhAwDatWuHBx98EFeuXMGRI0est8fFxSEyMhJ9+/a1hg6guJfH8hzXr19fredKRET2odPpIF/fLEBSin9HE5HrSZJUao4HNIDIHg+vUK0eD1VVcfDgQWzatAm7du2CyWSyXsxbdjZ/4IEHULduXbsWeyNLL4Tlv/Hx8UhLS8PAgQMREBBg09bX1xe9evXCrl27kJCQgNatWwMAjh49CgAYMGBAqeMPGjQIP/74IyIjIzFw4ECb9pZ/lxQeHo7g4GBrGyIicg29Xg9Jud7jIcPaK05EriXJUplDrUSJPR7eoErBIy4uDps2bcLmzZtthlLVq1cP999/P8aOHYvbbrvNIYXe6OrVqzh06BAaNWqE9u3bAwASEhIAwBoqbtSqVStrO0ub+Ph4BAQEoFGjRuW2j4+Pt95m+dpyX0mCIKBly5Y4e/YsjEYj/P39K3wORqMRIrsWiYjsTpZliNeDh6yqMJlMLq6IiADAbDYDNwQPVQsYioxlzsMl91bVRQEqHTwmTJiAv/76C0Bx2NBoNBgwYADGjBmDYcOGObUbWxRFvPbaazCbzZg1a5Z17G5+fj4AlLtyleV2SzugeKhYgwYNKmxfUFBg0x6AzbCs8s5xs+ARExNT4f1ERFQ9mZmZMEnFy3PKsoKoqCgXV0REQPF1lHrjQH8NkJ6ezvepF6h08Dh16hSA4k/6H374YTz88MNo3Lixo+oql6IoeOONNxAZGYlHH30UDz30kNNrsJd27dpxpRUiIgfIyMjAZqH496tWq0XHjh1dXBERAdeHx5cxubxOYB2+T2shSZKq9EF6pa96H374YTzyyCNlTvR2FlVVMXfuXGzZsgWjR4/GO++8Y3O/pReiZA9FSWX1VgQGBtr0gJTVvmQPSlm9Jjd7THn8/f054ZGIyAGCgoKsGwgKGk2peX9E5BqqopSe46EFFFXl+7QWquqUgUqvarVgwQKXhg5LT8dPP/2E+++/H++//36pDaHKmpNRkmUOSMn5Ga1bt4bBYEB6enq57UvOGbF8bbmvJFVVceXKFYSGhvLNQ0TkQlqtFrJS/LVacVMiciJJlMqc4yGKXNXKG9SKrVwVRcGbb76JjRs34t5778WHH35Y5prsrVu3RmhoKE6cOFFqgpLJZMKxY8cQGhpqEzx69+4NADh48GCp4+3fv9+mDQD06dMHAHDgwIFS7U+fPo28vDxrGyIicg2tVgvFkjiYPIjchiRJZc7x4D4e3sHtg0fJ0DFy5Eh89NFH5W4EJQgCxo0bB4PBgCVLltjc98UXXyA3Nxfjxo2DIPy9gPSYMWOg0+mwdOlSm+FTMTEx2Lx5M1q2bGmzGWBYWBh69+6NiIgI7N2713q7KIpYuHAhAGDcuHH2eOpERFQDlrxR8nc+EbmWLMll93hwOV2v4PYzm5csWYKNGzciICAArVu3xtKlS0u1GTZsmHVC0tSpU7F7927rDuadO3fGhQsXsG/fPnTs2BFTp061eWxYWBimT5+OhQsXYvTo0RgxYgQMBgO2bdsGSZIwf/78UhPA582bhwkTJmDatGkYNWoUQkNDsX//fkRHR2PcuHHctZyIyMUEQYDKng4ityPLcumPvbVVnytAtZPbB4/k5GQAgMFgwLJly8ps06xZM2vwCAgIwOrVq7F48WL8/vvvOHr0KBo2bIgpU6Zg+vTpZc69eOGFF9CsWTOsWrUKa9asgV6vR48ePTBjxgyEh4eXat+2bVusX78en376Kfbt2weDwYBWrVph7ty5ePzxx+347ImIqDpUVQU7Oojcj6oCN745VQ0gMXh4BbcPHu+//z7ef//9Kj0mKCgIc+bMwZw5cyr9mNGjR2P06NGVbh8WFobPP/+8SnUREZFzMHgQuasyuiK1gChWbSM6qp3cfo4HERFRVSmKZTFdQOXsciK3Uda7UdWyx8NbMHgQEZHHkWUZWstfOOYOIrehljX5SguIEns8vAGDBxEReRxJkqCzDrVi8iByB4qiAGUMgVR1gMyhVl6BwYOIiDyOJEnQCtcDB5e3InILoihC0JWRPDjHw2sweBARkceRJAk6/oUjcitmsxkoK3gIAlRVcX5B5HT8tUxERB5HFEXorvd4CIJavHcAEbmU2Wwuu8cDHBDpLRg8iIjI4xQHj+JPUPUagZuTEbkBs9kMVVv2fezx8A4MHkRE5HFKBg8dd0UmcgsmkwnQl3+/ojB8eDoGDyIi8jhms7lEjweDB5E7MJlMUMrZulrQa4rngJBHY/AgIiKPY9PjweBB5BaKioqg6MqezSHoNcU9IuTRGDyIiMjjiKIIHYonlOsFlZ+kErkBk8kERVvOcCodGDy8AIMHERF5nOIej+J9AXQaFRJ3RSZyOaPRCElXdvBQ9cX3k2dj8CAiIo8jiiK013s8dILCHg8iN2A0GiHqyl7aWvEpHopFno3Bg4iIPI4kSdahVjpB5RwPIjdgNBph1pb9XlR0KgwGg5MrImdj8CAiIo9TPMejeHiVVlC4gSCRG8g3FEDxKfs+SSdzqJUXYPAgIiKPUzzUqngsuVbgHA8id5BXkAe1nH08RL3MHg8vwOBBREQeRxZN0ArFy3ZqoTB4ELmB3IJcqOX0eJh1IgoLC51bEDkdgwcREXkc0WwuETxkBg8iN5BfkA/Vt+z7JB8VOQW5zi2InI7Bg4iIPI4kmqG9/hdOK7DHg8gdFBQWlhs8VF8gN5/Bw9MxeBARkceRJclmqBUnlxO5XqGhsNyhVqoPg4c3YPAgIiKPI0l/D7XSMHgQuQVDQSGUCno88grynVsQOR2DBxEReRxJlKAVir/mHA8i92A2mYFyVrVS/YrngJBnY/AgIiKPY9vjweBB5A4URQEEoez7fIHCggInV0TOxuBBREQeRxRFa/DQqRJ3LidyMVEUoWrLv1/1BQwF3EDQ0zF4EBGRx5ElETrLUCuh+N9E5DoFBQUQ/CpIHloBssyeSU/H4EFERB7HpsdDo0I0m1xcEZF3y8/PB/zKHmZloUCFqqpOqohcgcGDiIg8jiRJ0GmuL6crqJBEs4srIvJu+fn5kH0rDhWCjwCDweCkisgVGDyIiMjjSKIInaXHQ1AhMngQuVR+fj4kn5ssa+2nKe4ZIY/F4EFERB5HFEVoNSWGWpmKXFwRkXfLz8+H2afiuVaqHxg8PByDBxEReRzxxh4PzvEgcqm8/DwU3SR4SL4Kg4eHY/AgIiKPYxM8NCrMZg61InKlzJyscncttxB9ZAYPD8fgQUREHkeUJOivD7XSc1UrIpfLys2C6ldxG5NeZPDwcAweRETkccyi+HfwEFSYTQweRK6Uk5978x4PXxlZudnOKYhcgsGDiIg8jija9niY2ONB5FJ5eblQ/Stuo/gV94yQ52LwICIij6MoCjTX9yrTc44Hkcvl5xfctMdD9QWy83KcUg+5BoMHERF5nJK7H+s1KswmBg8iVzIUGG46x0PxA3Lzcp1TELkEgwcREXkcocTXeo3CHg8iF5NlCdAKFbYp3scjz0kVkSsweBARkUdRVRVQFeu/fTQqTGLF+wcQkeMoigIFyk3bqX5AQX6BEyoiV2HwICIijyKKInQlPlnVa1SIouTCioi8W2FhIQRf7U3bqXrAVMSFIDwZgwcREXkUs9kMnxLXOBoBUBS1/AcQkUPl5eUB/pW45BQEKKpqM0eLPAuDBxEReRSz2QzdDX/dVPXmwzyIyDHy8vKg+FUuTAh6AUVFRQ6uiFyFwYOIiDxKWcGDiFwnLy8Poq9cqbaCv6a4h4Q8En81ExGRRxFL7FpuxaEbRC6Tl5cHk0/lFnhQ/IHcXC6p66kYPIiIyKOIogi9YBs0BEGFonC4FZErZGZlosivcsFD9FOQk5Pj2ILIZRg8iGrgl22/Y9I/XsSZM2dcXQoRXSeKInSCbcjQawSIXFKXyCVSs9Og+FeurcnXzB4PD8bgQVQDR4+dRlyqLy5ciHZ1KUR0nSiK0N4QPHRaMHgQuUhGdibUSgYPo6+IrOwsxxZELsPgQVQDmVk5MGlaICUtx9WlENF1ZrMZ+lI9HuDu5UQukpWdVekeDyUAuJaZ6tiCyGUYPIhqIC8vF6JwCzIyc1xdChFdVxw8bFfQYfAgcp2c7GwodSrXVgkA0jPTHFsQuQyDB1ENGArzIWoaIyeHS/8RuQuTyQQ9bHcq12sUmEzcEZnIFQz5Bqh+Zdwhll5tTq0DZGRlOr4ocgkGD6IakEQRslCXa44TuRGTyQS9YDufw1ejMngQuYCiKJChAIJgc7tQqCLkCxVCoW34UAKA3GxOLvdUDB5E1STLMhRVgCwEoqCAwYPIXRQVFcFXtR1W5SPI3A2ZyAVyc3Mh+Je+3NSYAUEu/m9Jqg9gNBidVB05G4MHUTUVFBQA2jpQ4QOziRc0RO7CaCiEr8Z2qJWvRmLwIHKBzMxMqIHCzRtaCAJUrco5WR6KwYOomnJycqBqggBBgKKCm5MRuQlDfi58tbbDN3wFEUYjP0UlcraMjAyYA+SbNyxBCNQiK4tL6noiBg+iasrPz4eE68t0aANgMBhcWxARAQCMhfnw1dh+EOADM9+jRC6Qnp6OQv+q9TbKgSoyMjIcVBG5EoMHUTXl5+fDbFkfUFMH+fn5ri2IiAAABkMh/LS2wcMXJvZ4ELlAUloyxDpVGxFQ5C8yeHgoBg+iajIYDDDLvgAABX4oLCx0cUVEBABFhsLSQ600CgwF/HCAyNmSUpKhBFbtMYUBJlxLueaYgsilGDyIqslkMkFS9AAABXpOhCNyE8YiI3xuGGrlq1FgMhS4qCIi75Wamgo5uGqPUYKAhJRExxRELsXgQVRNxcvpFr+FVGg5uZzITZiKTPDV2PZ4+GhVFBk5x4PI2bIzsyq9a7mFHARcTUl2TEHkUgweRHaiqqV3YCUi5zOZTPC5cY6HRoHRyOGQRM6kKApESQJ0VVhOF8U9HunpnOPhiRg8iKpJo9FAgCVsqNBo+HYicgdmUYT+hh4PnUaFmTuXEzlVdnY2hDpV/9uo+gKGAn5Q4Il4pURUTVqtFhqh+FNVjaBAq9W6uCIiAgBRlKAXbhhqpVFhNjN4EDlTSkpKled3AAAEAfAVuGiLB2LwIKqmksEDUNjjQeQmzKIE3Y09HoIKkcGDyKlSU1NhDBSr9+BgDVJTU+1bELkcr5SIqkkQBAiW4KEyeBC5C1VVoLlhSLlOo0IUJdcUROSlriQnorBO9QJ/UaCElJQUO1dErsYrJaJq0mg0EK5f3AhCcRAhItcra50HnaBCFKv5ySsRVUv81QQo1RlqBaCgThGSr161b0HkcgweRNWkqqrNBQ5XtSJyF6XfizqNCklijweRMyVfTYZct3qPlYJVXE66bN+CyOUYPIiqSVEUqNZ9PDTcx4PIbZQOHlqBwYPI2TIzMqEEVe+xcl3gSvIV+xZELsfgQVRNkiRBVotXslJVDWRZdnFFRKSqapljrfQaFWbO8SByGlEUIakSoK3eMGS5LpCWkmbnqsjVGDyIqkkURUiKDgCgQM/x40RuQJIk6G6cWY7rczzY40HkNKmpqRCCa7DMvF6ASTTzQz0Pw+BBVE3GIhNkVQ8AkFU9TNycjMjlzGYz9GVc62gFQJY5HJLIWZKTk2EKrlloEIK0yMjgDuaehMGDqJoKCoxQ4AsAkBQfFBUVubgiIioqKoJPGcFDEIqX2SUi50hITEB+kLFGxxDrKkhMTLRTReQOGDyIqik3rxCq4AcAMMu+MBgMLq6IiIqKiuCrKW+FOa48R+Qs0XEXIdav2XsuL8iIhEROMPckDB5E1ZSXXwhF8AcAFEl+KCgodHFFRGQ0GuGnLbtnQwtwZSsiJ4lPTIBcv2bHEOsruBh/0T4FkVtg8CCqpvz8QigoDh4K/JGTW+DiioiosLAQftqyw4W/HuyZJHKSzLTqL6VrIdcH4hLi7FMQuQUGD6JqKigshHJ9qJUi+CMnjz0eRK5WWFgIf6HsFeYC9CoKCvgBAZGj5eXlQfVRgTJWmKsKLqnreRg8iKrJaDT+3eMh+KOggJ+kErlafn4+AoSyF3qoo1WQn5/v5IqIvE9cXBykhjULHQAAjQDVV0Bubm7Nj0VugcGDqJpMRUYoQvGqVgp8GTyI3EBebg4CUPZKOgFaM/Ly8pxcEZH3uXT5MvLq2udvohRSHGTIMzB4EFWT2WyCCh8AgCr4wsjldIlcLjcrDYG6svcOqCMYGTyInOBMzFmYQuyzfHVuPQNiL1+yy7HI9Rg8iKpJURRAKN4wQIUOZrPZxRURUU5mBoL0ZQePQBiQk53t5IqIvE9sbAzkhvY5ljlEweno0/Y5GLkcgweRHajQQuGuyEQul5OdhcDygodOQk5mqpMrIvIuZrMZuXl5UALtMMcDgNQYuHiRS+p6CgYPIrsQoHBXZCKXy83NRXA5wSNILyM7M93JFRF5l7i4OKiNtHY7nuonwGAycilsD8HgQVRtqs3XAuzz6Q4RVV9OfkG5czyC9DJys7OcXBGRdzkfdR65IfYNCUoTDWJiYux6THINBg+iahKEv4OGABk6vc6F1RARAMiSBF05f9mC9TJycnKcWg+Rtzl25jiKQssO/9WV3aAQZ6LO2vWY5Bq1Inhs3rwZb731FsaMGYMuXbqgQ4cO2LhxY7ntCwoKsGDBAgwZMgRdunTBkCFDsGDBggo3jtq6dSvGjh2L7t27o3fv3njmmWdw5syZctvHx8fj5ZdfRr9+/RAeHo4HHngA3333XfGEY/IeqqXXQ4ZOy+BB5Epmsxk6QS33/kCdjLwCbvRJ5CiqqiLqwgWIt9j3uKZmKg4fP2Lfg5JL1Irg8dlnn2Ht2rW4evUqQkNDK2xrMBgwadIkrFy5EmFhYZgyZQratGmDlStXYtKkSWWOEVy2bBlmzZqFzMxMTJgwAaNGjcKJEyfw2GOPISIiolT72NhYjB07Frt27cLAgQMxefJkAMD8+fPx9ttv2+dJk9vTanUAij/VEVQZOh2DB5Er5ebmIsi3/Pt1GkAUy97VnIhqLiUlBbK/AujtO/RYbggkXkmEJEl2PS45X60IHv/973+xe/duHDlyBBMmTKiw7YoVKxAVFYWpU6fi66+/xqxZs7BixQpMmzYNUVFRWLFihU37+Ph4LFq0CK1bt8aWLVswe/Zs/Oc//8GPP/4InU6HuXPnlvpBnzdvHvLz87FkyRJ8/PHHePXVV7Fx40bccccdWLduHY4cYSr3BhqNFoIleECGVlsr3k5EHis7O7vcieUWeo3Kpa+JHOTEyRPIu8Vk/wMLAtRQLVe38gC14kqpf//+aNas2U3bqaqK9evXIyAgANOmTbO577nnnkPdunWxYcMGqOrfXfEbN26EJEl44YUXEBQUZL29Xbt2ePDBB3HlyhWbIBEXF4fIyEj07dsXgwcPtt6u1+sxc+ZMAMD69eur/Vyp9io554OInC8nJwdB2ooveoJ8wHkeRA6yP/IgCps6plcxq0kBjp085pBjk/PUiuBRWfHx8UhLS0PPnj0REBBgc5+vry969eqF1NRUJCQkWG8/evQoAGDAgAGljjdo0CAAQGRkZKn2AwcOLNU+PDwcwcHB1jbk2RRFhnr9LaRCA1m272Q6Iqqa7OxsBAoVr6YTrJeQzU0EiexOlmVciLL//A4LU0sVfx7e55iDk9N41KB0S6Bo3bp1mfe3atXK2s7SJj4+HgEBAWjUqFG57ePj4623Wb623FeSIAho2bIlzp49C6PRCH9//wrrNRqNHG9ci0mSCMtbSBV8YDQWcZ1xIhdKvZaMupqK34NBWhNSUlLQokULJ1VF5B2ioqIgNQSgc0zvv1xfQEZ2BlJSUhAcHOyQc1DVVXXejUcFj/z8fABAYGBgmfdbbre0A4pXwGrQoEGF7UuuhmX5uuSwrPLOcbPgwTWpazdJkgFN8S9YBX7Izc1BVFSUi6si8l5xMRcQfpM5HkHIx/lz58r9O0FE1fPzts3IbFkAOHBPq4IWIrZs2YLbb7/dYecgx/Ko4FHbtGvXjish1VKyLEPQ/P29U4QASJKIjh07urAqIu+2RTKjrr7iT9/qak0w6zV8rxLZ2QeffQjT/Y49R36YiKj4aEyaNMmxJ6JKkySpSh+ke9RVr6UXorz9OsrqrQgMDLTpASmrfclPxsrqNbnZY8rj7+8PvV5/03bkfrKzsyHoAoHr1ziq4AtRNJWaW0REzpOTk4O69Sru8ajnIyM2K53vVSI7SkxMRJGPCDXAsYusSLcAl/dcglarha9vBWtnk9NUdcqAR00uL2tORkmWOSAl52e0bt0aBoMB6enp5bYvOWfE8nXJCeoWqqriypUrCA0N5R81D5eTkwNFsB1jqqicYE7kSjl5+Qi+WY+Hj4TM9DQnVUTkHbb/sR2ZtzphjqNGQFFLldsW1GIeFTxat26N0NBQnDhxotQkX5PJhGPHjiE0NNQmePTu3RsAcPDgwVLH279/v00bAOjTpw8A4MCBA6Xanz59Gnl5edY25Lmys7MhqjfM89EGIi8vzzUFERFEUYT+Jn/VgvUyV7UisiNVVfHHnl0wtlWccr6sNkZs+m2zU85F9udRwUMQBIwbNw4GgwFLliyxue+LL75Abm4uxo0bZ7PfwpgxY6DT6bB06VKb4VMxMTHYvHkzWrZsiX79+llvDwsLQ+/evREREYG9e/dabxdFEQsXLgQAjBs3zkHPkNxFbm4ujLLtcDpFCOL+AEQuYjQa4aNRb9ouWC8jN7/s4bhEVHXR0dEoCnL8MCsLqSlwOeFyuUPeyb3Vijke69evx/HjxwHAumvl+vXrrftlDBs2DMOGDQMATJ06Fbt377buYN65c2dcuHAB+/btQ8eOHTF16lSbY4eFhWH69OlYuHAhRo8ejREjRsBgMGDbtm2QJAnz588vNQF83rx5mDBhAqZNm4ZRo0YhNDQU+/fvR3R0NMaNG2cTVMgz5eXlwSjWsXkHSajDHg8iF8nKykI9v5sHD41wfQ8eVeWmn0R2sHXHL0i/1bGrWdkQBBSGSfhz75944P4HnHNOsptaETyOHz+OTZs22dx24sQJnDhxAgDQrFkza/AICAjA6tWrsXjxYvz+++84evQoGjZsiClTpmD69Ollzr144YUX0KxZM6xatQpr1qyBXq9Hjx49MGPGDISHh5dq37ZtW6xfvx6ffvop9u3bB4PBgFatWmHu3Ll4/PHHHfAKkLvJzsmHpNr+LJmVgHIXNiAix8rOzkawrnKTHAP0xQuBlLcsOhFVjtlsxsFDB2F61LnnzW1vxk/bNjF41EK1Ini8//77eP/99yvdPigoCHPmzMGcOXMq/ZjRo0dj9OjRlW4fFhaGzz//vNLtybNk5xZCEUJsbiuS/FFYWOiiioi8W2ZmJoI1RZVqW9dHQVZWFoMHUQ0dOHgAxpYK4OPc3kM5RECWmIO4uDiEhYU59dxUMx41x4PIWfLzC6HAz+Y2k+TL4EHkIpkZGQgWKjfmu65ORGZmpoMrIvJ8azevR2YHo0vOnd6+EBu2/uSSc1P1MXgQVYPBUARV8LG5TYEvCgor94krEdlXRmoy6uorN9QqWChEVlaWgysi8mxXr17FtZwUyI1dM1eqqJ2Kg4cOwmw2u+T8VD0MHkTVYDKZoMB28yIVehiLTC6qiMi7packo4FP6T08THLpi6J6mgKkp15zRllEHmvDlp9c1tsBANALMLSSsXvPHtfVQFXG4EFUDWazCFXQ2tymCnoUFfGTFyJXSEtLRQNf2+CRY9bilcgw5Jht36shvhJSk0pvAktElWM0GrF7724YOjhn747yZHUuwvcbvoeq3nxFO3IPDB5E1SBJIm5cm0GFFqLIncuJXCEzKwf1bujxKJI1kFQNimTbP3UNfCWkXEt2ZnlEHmXHHztQECY5fVL5jeQGAnJ0+Th37pxL66DKY/AgqgZJkqFCe8OtWpjF0kM9iMixzGYzBFWGtpLXQA19RVxLTXNsUUQeSlVVrNm0Ftld3GNocWrnQny7brWry6BKYvAgqgZJkqCW2eNRucmtRGQ/V69eRWhA5Yd8+GpVSKIZssweSqKqOnbsGPIDjVCC3WMDTrEVcOFyNNLS+GFCbcDgQVQNomiGKuhtblMFTi4ncoUrV66gib5qS1k3DlCRnMzhVkRV9eV3XyEt3I2WjhcEZHYpwso1q1xdCVUCgwdRNZhMxlL7eCjwg8HgwhU+iLzUpejzaKqr2vK4TX3yEBcX56CKiDzT+fPncc2YCukW9+jtsDDcpuBgxEFkZ2e7uhS6CQYPomqQRBEQbIdaKUId5OXluagiIu8Ve+EsmgdUbUW5ZvocxESdcVBFRJ7pi1XLkdrdjXo7LHQCsjqZ8N26711dCd0EgwdRFYmiCKXUxHJAFoKRn5fj/IKIvJiqqoi/kohmAVUb5hgWWISos385qCoiz3Pp0iXEpSdAbOHqSspW0FnGzj07kZ+f7+pSqAIMHkRVlJGRAejqlbpdFXxgNpu5njiRE6WmpqKujwJdFf+ahfqJuHothRPMiSrpf18vRUr3QkBwr2FWVnoBOR3NWP0jV7hyZwweRFV07do1mNRGZd6naoORlVW1seZEVH1nzpxBG/+cKj9OIwDNAmXO8yCqhDNnzuBi6iWIYa6upGJ54RJ+3/NH8QeE5JYYPIiqKC4+AdmmxmXeZxaaIDEx0ckVEXmvk0f2oYN/9cJ+e980nDx+zM4VEXkWVVXxf0sXIqWfG/d2WOgFZHQ34n9fL3V1JVQOBg+iKjpz7hKMarMy78squgUxMZedXBGRd1JVFWfOnkXb4OqtJndbUD6OH9ln56qIPMu+/fuQqsuE1MTNQ8d1htsUHDt7HPHx8a4uhcrA4EFURRdjYmHWtizzPgNa4uTpaCdXROSd4uPjEeJjhp+2evOqmgeYkZSYiKKiIjtXRuQZzGYzlqz4H1L7GFxdSuVpBKT0NuCTJf/HOZduiMGDqArMZjPy8wshC8Fl3m/StEJMTIyTqyLyTkcO7kcX/9RqP14jAB3qGnHy5Ek7VkXkOVZ8+xUyWxZCqVc7ejssxNbAJcMV7Nqzy9Wl0A0YPIiqIDY2FpKu/LUEVSEARWYVhYVuuM45kYfZv2cHutXLrdExwgOuYd/OX+1UEZHnuHz5Mn7783dk9araHjluQRCQOsiAJSuWcn8tN8PgQVQF56MuIKOo7GFWFmZNK0RHc7gVkSNdvXoVijEHjfykGh2nc10DTp/+C2ZzLby4InIQWZbxnw/n49ogA6CrXb0dFkqggPRwAz5e9ImrS6ESGDyIquDkXxdhQKsK22QUtcCFaA63InKk3X/8jtsDU2p8HJ0GaB9UiMjISDtUReQZftywFleDMiE1q52hw6Kws4wTcX8h4miEq0uh6xg8iKogLi4OZk3zCtsUCc1x9jxXtiJyFFVVsWvHr+jTIMcux+sXdA2/b9lgl2MR1XYXL17E2i3rkNHPcYsu6FJUBP1SPPFbm+7ACeCCgGuDDfjgs4+4x5abYPAgqiRFUZCfnwdFCKqwnai5BQkJV5xUFZH3OXfuHBrqClDXxz67jrcLNiLhcixycnLscjyi2iovLw9v/vffuDrUANXXcb0ddfao0F3PAQFHHXYaAIBSX8C12/MxZ94bkGX7/M6g6mPwIKqkzMxMQFf/pu1kBCE/L4fL+BE5yLZN63BHoP026tQIQJ966fh9+za7HZOotlEUBW+9+zaudc6D3NixQ6x02X9/ra3Z+hCVUtQeuByQjCVf/s/xJ6MKMXgQVVJiYiJMaHLzhoIAVVeX3bpEDpCXl4ezp0+iWwP7rhw3ICQDv/3yMxRFsetxiWqLVd+vQpR4CYWdPLNXIOMOE3ZE7sK+A/tdXYpXY/AgqqSY2MvIMlUieAAwoSl3TSVygB3bf0WfepnQ2vkD2Xo+Mpro8nD8+HH7HpioFjh0+BA27tyMtEFFgFC7J5SXSyvg2j0GfPK//0NcXJyrq/FaDB5ElXTsZBSMN1nRyiKjqCVOn41ycEVE3kWWZfyyeQMGhaQ55PhD6l/BxjWrHHJsInf1119/4f3FH+LqKAOg99DQcZ0SKODq3YWY9e9Xce3aNVeX45UYPIgqQVVVxFy8AJPm1kq1N2ja49AR7oZMZE+HDx9GS99c1LPTpPIbtQsqQsbVeCQm2m/+CJE7i42NxdsfzEPySAOUQM8OHRZSYwGJA/LwyuyZxXM3yakYPIgq4eLFixCFJlAFfaXai8ItSElJ4w7mRHaiqirWrv4Kd9d3XCgQBODuBolY9/1Kh52DyF0kJSXhtbdfR9KwQij1vSN0WIgtBFzpmY2XX5+J/Px8V5fjVRg8iCphxx/7cLWoe5n3CaqpjBsF5KMrDhw45NjCiLzEX3/9BR9DCloFlvF+s6NeDfJw+vhRZGRkOPQ8RK6UlpaGmW/8C4l35kMO9a7QYVHUBojvkI6Zc/4Jo9Ho6nK8BoMH0U3Isow9f+5DgbZnqfu0Sg5aGV6BVskpdV+a1Bs/b/3DCRUSeb7VXy3FyJB4h59HpwGGhFzFj6u/cfi5iFwhISEBL/5rOq70yan1O5PXVGFHBRebJeOFmS9yHx8nYfAguolTp07BgJZQhDql7tOgCBpI0KD0Dq8mTWskXc1AWppjJsISeYvIyEio2QloF+y4nZRLGtgwC8cO7+PkU/I4586dwytzZiLhzhyYW7u6GveQ31XGxfbX8PwrL/A97wQMHkQ3sWbdL0gq6l/1BwoCUqU7sGXrb/YvishLKIqCFf9biIcbO2/5S50GuLdRAr5a+rnTzknkaIcOH8Kcd99E4sgCSLd4d0/HjYztVVzuk4npr76ES5cuubocj8bgQVSBrKwsRF+8DKO2Y7Ueny3cgV+3/wFZ9swNmYgcbevmTWiKa2hRx+zU8/ZqkI/kmL/w119/OfW8RI7wy/ZtWLD0QyQ/YIDcgKGjLGJLAQlD8vCvf8/CqVOnXF2Ox2LwIKrA5i2/Ik2+AxCq91ZRhEDkq7fiyJEjdq6MyPOlp6djww+r8EiTBKefWyMAjze9hM8+ehdms3NDD5G9yLKMxV8sxtINy5E82gAliKGjInJjAYn3FuKtj+fh562boaqqq0vyOAweROWQZRlbt+1AljCgRsdJNt+J79ZstlNVRN5BVVV8+uF/MTo0AYF6xSU1NK9jRjf/ZHzz5VKXnJ+oJjIyMvDizGnYHPc7ro42QvVj6KgMpZ6ApIcMWP7b13jr3be54pWdMXgQlePo0aMoUFpBEYJqdByT5lYkXs3mpDWiKtiw9gdo086iV4M8l9Zxb+OrOLn/d0REsNeSao8TJ07g2Zefw+lb45Ax0AxoGTqqQvUTcG2kEfvV43h62lQkJDi/19VTMXgQlePH9duQbB5U8wMJAlLE/tj48681PxaRFzh79ix+2/QDHm8eB8HF10t6DTC1ZQwWf/I+V6gjt6coClas+gpvf/YfJIzMR1FbV1dUiwkCcntIiOmbjhlzXsGOnTtcXZFHYPAgKkN2djYuxyWiSNPOLsfL1fTBzl1/cpI50U1cu3YNH/3333iqRSz8tO4xvrqhn4RxjWPx1ux/orCw0NXlEJUpIyMDL7/2Ctad2Yykhw1etxu5o0jNBCQ+VIjP1v8P//3oXRgMBleXVKsxeBCVYdeevchUboe9Pm5VhDowogXOnDljl+MReaLc3FzMfW0mxodGo1mAe03oDq9fiH6+0Zj3xqsQRdHV5RBZKYqCTVt+xtQZz+DYLdFIv8sE6Bg67EkNEHD1PgN2Fh3CE8/+A4ePHHZ1SbUWgwdRGbZu24UstY9dj5lc1Atbftll12MSeQqDwYC5r8/EPcHR6FTPPSdz3tUoA00LT+OD/85j7yW5hStXruC5l1/AF3u+wZWHC1HUwdUVeTBN8dCruJG5ePfrDzDnnTeRnZ3t6qpqHQYPohukpaUhK6cIoqaJXY9r1HbB8ePHIUmSXY9LVNsVFhbi9X9OR0/hDPqF5Li6nAo9dEsytIkHsOA//2b4IJcRRRFfrfoK0+fMwF+3xSH17iKo/uzlcAalnoDkBwzY538cT057Gtu2b+Oyu1XA4EF0g+2/70Kq2Nvux1UFHxg07XH06FG7H5uotsrPz8drr7yIPsJfGNIow9Xl3JQgABOaXYFP0gHMf2sOP0ggpzt//jz+8fyTWBP9MxLHGiC2YuBwOkGAoZOKKw8WYPFvy/HiP6cjOTnZ1VXVCgweRCXIsoytv/yOHNh3mJXFVXN//LB2q0OOTVTbpKam4p8vPYf++jMY1CjL1eVUmiAA45omom7aIbzx6succE5Oce3aNbw5fy5e//ANXOyfhsz+ZkDP0OFKah0BKfcU4fitMXjh1Wn4dMlC5OW5dglwd8fgQVTCnj/3IldqBVlTzyHHL9K0Q3xiFi5fvuyQ4xPVFhcuXMCrM57Hg8F/YUDD2jdOWhCAh29JRqeiCMyc9iyX2iWHyc3NxSeff4LnZr2A3QGRSHrEALkxA4c7EcMEJD5qwKbsHXjiuX9g1fffoqioyNVluSUGD6LrTCYTli3/FlfE+x13EkFAvPkBfPR/SzkmlLzWvr1/4r1/v4pnmp9Hl3qOWZrycr4v/hddPE8rsdDHIecAgLsaZeKB4L8wa8bzOH/+vMPOQ97HaDTim9Xf4Innp+Dn/J1IfNRQPHnc1ZvbUNl0AvK7y7gythDfRq3D409PwtZtWzkX7AYMHkTXLf7fClwz3Q5J08ih5zHquiD2qg5bf+GGguRdJEnC0kUL8eOyD/DPNlFoUcdxS+b+ENcI14y+AIBtSQ0cdh4A6FqvEM82O4cP572GzRs38EMFqhFZlrF562Y8PnUSVl/8CYljC1HQTeHu47WE6isgq5+I+AfysHjPl5g0dTIOHjrI3wvXMXgQAdh/4CB27TuLFHWUU84XL0/El1+v5ZAr8hqZmZmYNeN5FJzaiH+2uYB6Po79FDDF+HcvR7pJ79BzAUDzOma81jYKhzZ9gQXvzIXR6J5LApP7kmUZO3b+gcefnoQl+79C/Oh8ZPcVofoycNRGSqCAtLtMiBmSgfd++AhPT38GkZGRXh9AGDzI68XFxeGj//sCsfKzgKBzyjkVIQiXpCl4/Y35XAecPN6BA/vxyotPY5DmKMY1S4TOQ//yBOgUPN86FiHXdmL6s1MQFRXl6pKoFjCbzdi0eRMmTHkM//fbYsTcnYn0O01Q6zBweAK5gYCrI4040+MK5q16D088NwV79+712iFYzrnKInJTaWlpmPX6PMSKT0LW1nfquU3aWxFrGIWZs+Zi6eKP4e/v79TzEzlaYWEhFv/fB0i+cBSvtL6EEF/PX3pWEIARjdNwW0EuPp43C4PueQCTpkyFTsc/t2SrsLAQG7dswqatm5Dfyoyse01QggQADByeSGoi4OpIA1IzCvH+z58i+JtleGL8ZAwfdg/0esf3yroLD/3ciejmsrOzMWPmHFw0joVJG+aSGvI0fXAhowf++eq/YTY7brw7kbNFRkbixalPICRpO165NdorQkdJrQJNeL1tFNIP/4AZzz+FS5cuubokchM5OTlYumIZHp86Cd9ErUH8w/nIGGC+HjrI08kNBaTcbUTsPZn47M8vMGHKY1j70zqvGZ7Jj2DIK2VnZ2P6jNdxIX8UDNquLq0lXRiGv5JMmPXaW/jog3fg6+vr0nqIaiI9PR1LFn6IrPizeL7ZJdwSILq6JJfx0aoY1ywRF/PS8d4bM9Cj31148tkXUadOHVeXRi6QlpaGVT9+iwOHDyC7kwkF42SoPuzh8FZKXQHpg4qQUWjE8hPfYs2GH/HAyPvx6JhxCAoKcnV5DsPgQV4nLS0NL//zTUTljkS+9nZXlwMASMV9OBb3K17515v4+IN3eGFCtY4oivhp3Rr8+vMG3BeagD635nHVz+vaBxdhTuB57L6QjuefOoQnnn4Ow+4ZAYEvkFe4ePEivlmzCudiziGzSxEM4xVAx8BBxdQ6ArLuEJHd04zVZ3/Clme3YEC/AZj86CTccsstri7P7hg8yKtER0djztz3EFM0DgZtF1eXYyNVuBfHEv/Esy/8E59+PB+hoaGuLonopmRZxu5dO/HDqhXo6JeKOe2S4K/z7lVbyqLTAMMbp6NXvSz89P0n+Hn9D5jy7Evo1asXA4gHUhQFERER+ObHlUgpTENq10KYx+P6Hhz8flNpqq+A3Nsl5HYTkXFxFw68dggdWrfDk49NQadOnVxdnt0weJBXUFUVGzZuxrff/YxYaSrM2hauLqlMWcJdOJ0dgmdf+Bde+9c09O/fz9UlEZVJURTs37cXq79ehhbaDLzY7Aoa+XnXPI7qaOAr45lWsUgoSMTaz/+N7wKb4annZ6Bbt26uLo3swGw24/cdv+O7DT8gv24R0rsWQmrCsEFVoBNg6KTC0LEQKQmncOGzN9FQ2wBPPTYF/fv3h1ardXWFNcLgQR4vNTUV733wGc7HqYhTXoOqDXB1SRUyaLsiSmyK/370FQb2PYwZ059BYGCgq8siAlDcw3Fg/378sHI5QpGOqY0T0MTfe+dxVFerQBOmB0bjUn4CvvnoKnT1W2LS0y+gW7du7AGphXJycrDh5w3YtuNXFLQUkTXcBCWYgYNqQBAgtgaSWhtwLb0Q7238BEFfLsb4h8fj3pGjau1KmILq7TuZOIkoijh9+rTNbeHh4V61hJqzmUwmrF2/ERt++hUJ0kMo0PSEvQed65UUtDD+G4n+8yFqmtj12FBlhOBPNNP/iWefnoThw4fV+k86qPbKzc3Fti2b8Nu2LWgTkIthDZLQLMB9V2KbHnErTErxwo0+GgVL+rr3Zp0X8/ywK7sVstR6eHDsY7j7nuG19sLCm2RkZOCr777BoaMHkXObGfmdJW74VwmTLo3AvcOvb9grAOtNv2FT4m+uLaoW0OSrqHfWB0GX9bh/xP14bNwEl88Jrer1LYOHkzB4OI/ZbMbWrb/iuzU/IUPujmvKKKiC/Xs5fOXLaGRaCR/1GlJ9n0WhrrfdzwEAWiULLXRb0cg/Ec9NnYzBgwdBo+FK2OQcsbGx2Pjjapz56wT610/DwJAMBOkVV5d1U7UteFikF+mwN6sJTuU2wMDBQ/DQ2Alo0sTOH2pQjVkCx8HIg8jsWgRDRwXQMnBUVsj/FAjXO0pVHZA5jX/TqkIwqQg+o0e9aF88MPI+TBjrugDC4OGmGDwcLzs7Gz9t2opt2/5AltIF15RRkIVgh52vqfG/8FMSAABmoRmSAuY57FzA9d4V3TbU903GhHEPYtQofiJKjpGdnY09u3Zi5/Yt0BRlYUi9BHRvUFirrqtqa/CwMMkCjmTWw77spmjQuBnuuW8MBg4ahIAA9x4q6ukyMjLw9fff4EDEQWSGM3BUF4OHffwdQHwwetRojH/kUacHEAYPN8Xg4RiqquLMmTNY99Ov+Ov0OaQrA5CJQVAEx7/xWhdOhwYmAIACH8TXWeLwcwKAVslEY+2faCCcxKCB/fDIw/chLMw1GyCS5zAajTh48CB2btuIlORE3F43A33rZSK0ls7fqO3Bw0JVgSuFvjiaF4q/cuqiQ8eOuOe+MejVqxd3Q3eijIwMfPPDSuw/cqC4h6MTA0dNMHjYV8kekAfvfQCPjnFeAGHwcFMMHvaVlpaGbb/+gW3b/0Ch3BhJRXcUbwQoOG8OhKuCh4WgmhCoHEczn8OoHyhhzEMjMezuIQgOdlwvD3kWk8mEkydPYvdvW3Du7Bl0rZuPvsEpaB1oqvV7cHhK8ChJVoELuf44mt8MsQUB6NO3H+6651506dKF878cJD8/H198sxz7j+xHZlcTCjvK1/fgoJpg8HAMwaQi+LQe9S764sH7RmPS+Mcdfp1Z1etbflxCtUZubi5279mLLb/sQka2Aalib+RgJmRNXa/8SVYFX+Rr++OC3B+67DTErzqEb77diFatmuHhB4ahf/87OBSLSsnLy8ORw4dxYPdviL0Ui9uCjehR5xoevc0AHf/2uzWtAHSuZ0TnerEwyQJOXb6MtZ/uxQcFvggP74qBQ0eiV69e8PPzc3WpHuHy5cuY884buNouBwWPctM/cn+qr4Dc3hLywkV8d2wDDs48iPfnLUDDhg1dXZqVF16uUW1SUFCA/fsPYuuve5CYdA1Zak9kKuMhapoCvEiykjShuIaHcE15EJcuXca5JUcRuPgr3NahA0bfNwR9+/aFj4+Pq8skF0lJScHB/ftwYM/vyMpIQ5fgHAwKzMCUjkXQ8DqqVvLVqujbsAB9EQ1REXAhPRZ/fnMISxfWQVhYGAYMHYk77uiPevXqubrUWmnn7p1YtGIxrg41cB8OqnVUXwFZA0QUxl7Bsy8/j//MmYcuXdxj02QGD3I7BoMBhw4dwuZf9iA+4QpyEY4MaQRMmtbFy+EycJRPEGDStsEVpQ2gjkPcuWiciPkTdZSl6NKlCx64dwh69erFIX4eTpIknDt3DhEH9yLyyCFopAJ0q5OOsXVz0LSDudYPoyJbeo2KrvUN6Fo/DkpTIK4gBn/9dBLrVtZDneD66DtgMPr2H4S2bdtyRbybkGUZi79Ygh0nduPagwaodfhmodrL1Ba4Uj8Pc96fiycf/QcefuAhl+8TxOBBbqGoqAhHjhzB5l92Izb2MvKFrkiThsCkuRUQNACHL1edoIVR2wnxcidAlXD5ZBQiz/8Of2URunfvjgfuHYIePXpwgqqHSE9Px9GICETs34nY2Eu4NVhEJ9+rmN7UgLo+sqvLIyfRCECbIBPaBCVhDJKQVqTD2cNnsXzPT7hm0KNrly7oM+hu9OrVi/PBbpCbm4s577yBGH0iMh4o4uRx8ghyiIDkhw1Y8dtKnI06i9kzX3fpCAhecZDLmM1mREREYMu2PYiOjkaB0Bmp4gAUaaYAYNiwK0EHo64rLstdAVVEXOR5HDmzFf7KQvTqdTvuHzUE4eHhnKBai9zYqyGIhehUJxN3BmbjH52KeM1EAIBQPwlD/bIxFNkwywIuZsYi8vuD+HZZEALrsjekpNffno0zTeNg6KSCQ6vIk6i+Aq6NMuLPQ0fg+7+FeP2V11xWC4MHOZXZbMaxY8fwy/Y/cfbsWRRqOiLV3AtGzePFK1LxutfxBD0Mum64JHWDoJpx+dBZHDyxHn7qJ+jbpxfuHTmEq+S4qYyMDBw9GoGIfbsQExOLsGAzezXKMXzkvbhn5L0AAAEqAqO/RcGJtS6uyrV8tCq61DOgS73i/YdK9YZ07Yp+dw7D7bffjqCgIBdX63x5Bfkw3qqAoYM8kiAgv7WI3LRcl5bB4EEOZzQacezYMWz7bR/Onz8Hg+Y2pJh6wqh9tHgdPV7fuowq+MCg64kYqScEtQiX95/B3si18FM+RJ8+vTFy+J0IDw/ncCwXkWUZUVFRiDi4FxGHDkA1F6BzYBYG1snCE+zVqNCO337F1l9/A+A5y+naW6nekPRYHFl9AN/8Lwj1GjRE3wF3oW//gbj11ltdPi7cGbRaLaC4ugoixxEUQKdz7RxPXk2QQ2RnZ+PQocP47Y+DiI+Ph0HTEanm7jBqxxeHDf7kuR1V8EOhrjdipN4QVBMuHziDvcc2wlf+GJ06dcLIewaid+/eTt8V1dtIkoSTJ09i1/YtOH36L7QKNKOz3zW8eEsh6rFXgxzER6uiS30DutQv7g1JNepxZv8ZLPljHdJNPujb7w4MHXE/Onbs6LEhRKvVQlAAbm5GHksB9C7+IJGXf2QXlk9m9x88ioOHjiE3vwjZSldkyXcXr0alaPjTVouogi8Kdb0QK/Uqnph++iIiog4jUP0KjUMbYfCg3rijX2+0adPGYy9CnEmWZZw+fRq7f9uK48ePo22QEbcHJmMs99YgF2nsL6KxfyaGIRNFsoBTF2Ox8tQepJj80X/gnRg6/F60a9fOo97/Wp0WggFAoKsrIXIMjQEuH8HAS0GqFkVRkJCQgJOnTmPfgeO4fDkWorY1rhV1hEHzNGRNSHFDDqOq/QQdjNpOuKJ0AlQVMdeu4a+15/Djz/+DXklFp85dMKh/D3Tv3g1NmjTxqAsRR7t06RK2bVqHiIgItA4swu0BSXiggwE+Wn7mSu7DT6uiX6MC9MMFFEoanDp7CcuO/oEsyR+DBg/F/Q89gsaNG7u6zBqb+vjT+Ojzj5HR0YD8cAnc5MZxpPqAPq34a7mua2vxCmYVIZG+aJQSjHFvjnVpKYKqqvwL5wRV3VLe3ciyjISEBJw4+RcOHjmFy5diIGubIMPUFnlqh+t7bHhXymhdOB0amAAACnwQX2eJiytyPkE1w0+JRT3tRdTXxUCHPHTs2AmD+ndHeHg4mjZtyiBShry8PKxYughRJw9jWIMEdKtfCD+GDbuaHnErTEpxdxHneDhGvqjB8ay62J3VDMNGjcb4x5+Ar6+vq8uqkYKCAiz+cgkO/nUYKQMtmweSvelSVAT+oUKXBeSNAszt2bXrKD6XgMYR/nj43ocwafzjdr/urOr1LYOHk9S24GE0GhEVFYWTp84i8vhZXL2aBFl3CzJMbZGvtodJ0woQvLvD7MU7t+DeUfcAAFQIWL2rIdbtLHBxVa4lqCb4KZdQVxuDBrpYaJQshIW1Qd9endGtW1e0bdvWq3dQl2UZv2zdjPU/rMLdDZJxZ6NMThB3EAYP5zHJAranNcWpwiZ45sVX0H/AgFr/gUNUVBTe+78FSG2Qg4w+RVB9a/fzcUfabBX1v1WR/YQAuT5fX3vT5KtofCAAt/q3wBv/nIOmTZs65DwMHm7KnYOHqqpITU3FuXPnEHn8HE6fOYf8AiPM2jCkFd0Ko6YtROGW4o38yIo9HpWgSvBVEhGAWDTyi4NOTEBISAh6dO+MXj27oFOnTqhfv76rq3Sad9+eA3NCBMbekoAgPZfPcSQGD+dLM+rxfXIY+owcj8efeMrV5dSYLMtY+9M6rP15HdJ6GVDUTgVqeaByJwweDqKoCDqtQ8Pz/nhx6ou4e8hQh34QUNXrW+/+yNpLybKMS5cu4cTJvxBx7CziLsdCFuohV74V2WIYTJrBkDV1AQn8CaGaEXQwacNgQhiyRQCqCl16Bk78fhmb/9yHOlgJH60Z7Tt0QL/eXdGjeze0aNGi1n9aWp7AoGA0r5PG0EEeKdRfRJs6eQiu18DVpdiFVqvFxEcfw7C77sZHiz5G9I8XUdhaQl6YGVJjMISQ+1BU6JOA+vH+8L0ioH+fOzB9+TQEBrrfSgm8rPQClqBx/MQpHDx8CleuxEPWNUdaUVsU4k6YNJMA4Xoy5U8EOZIgQBIaQdI0QqHct/gmqQgXTsXjz79iEeKzEzolA+3ad8DA/j08Loj0HzwMP5/Zgx4NYjhvlTyOqAg4nR+CCf36u7oUuwoNDcVH8z9Ebm4uDhw6iF93b0fi7kQYW8nICTNBugUMIeR8sgp9IhASHwCfROC2227DvfeNQp8+feDv7+/q6srFy0wPpSgKzp8/j19+3Y0jEUchapoiragtCjACZk0LQNRwxSlyC6rghyLtbSjCbUiTAKgiLp6Lx4GoiwjR74SfNhdDhwzCqBFDERYW5upya6Rbt27Y0fp2vHXOF93r5aBf3TQ0r2N2dVkeqYm/GQmFfgCARr6ii6vxXIoKxOb7ISKvKaLy6uCuIUMRGhrq6rIcom7durhv1L24b9S9KCgowOHDh7Ft96+4vCcORS1V5IQZITYFV8Mix5FU+FwBQuLrQH8V6NqlC+4dMwq9evWqNfMnOcfDSZw5x2PLL9uxctUaGNUmSC7qBYO2G1Shdq804o44x8M5NGo+gpUTuMX3GAJ9izBr5gvo1aunq8uqkcLCQuz980/8sW0j8rNS0Sc4FX0aZHODQDu6nO+LlZdCcc3oi2fbXUPvhoWuLsmjpBj1OJLdECdyQ9Cy9a0Y/sAj6NevX625+LEno9GIiIgIbNv9Ky5evAhTCyCrtQFic4CrR5SPczwqSVThEw80SgiELkVFzx49MWroSHTv3t0t5glzcrmbclbw+H3HLny2dD1ilGlQhCC7HptsMXg4n15JQTvtUrz/39fQuXNnV5djF6mpqdj5+6/YtWM76sCA1n65aOGTg1Z1TGjkJ/LD0xpIMerx71OtML97Apr4s9ejuiQFSDH6IKHQF4lSCC4X1oFPYAPcc99DGDJ0GIKDg11dotswmUyIjIzEr3u249y5cxCbCsi6pRBic0AJ4pu5JAaPcqgqtDmAT6KAkGt1oMsA+vTug5FDRiA8PBxarXsNV+Hkci936XICBIjwUZJQpO3o6nI8mlnTBH5KAgBAEhq5uBovoCrwV+MBKIhPSPKY4NG4cWM8/sSTmDh5CpKSkhATE4Poc6dwJOocrl1OR0N/BS398tFCn4VWdUxo7M8wQo4jKUCywRdXCn2RKDXEFWMA8kUNWjZvhna3d8XAjl3xdIcOaNSIv/PK4uvri4EDB2LgwIEwm804ffo0jhw/gqN/RiKnIBdiUyC7sQHmFoBah29kAqCq0OQCPklASGogNNcUNG4cijt69kPfB/vitttuc7uwURMMHh7mxeefwvBhd+Kj/1uKq6kbYRTCkGFsAZOmNcyapl63yZ8jZfpMRCPTSvio15Dtc5+ry/E4gmqGj5IIPzUBDf2uwFeOQ9fw9njlpU88cgy5IAho0aIFWrRogaFDhwIoXur62rVriImJwcXzp7Hx/BkkxaWgvp+Klv4FaKHLRIs6JjT2E+HLDQipigySBteMeiQa/IpDhsEfRlmLVi1boH3fbhjaqQvatm2LkJAQj1ngwZl8fHzQq1cv9OrVC0DxEMuzZ8/i0LHDOL7zOPKNBTA1VZHdpHhYlhrA19hbaPKuTwxPrQPtNRUNQxqiX8++6DuqDzp27OjRQxYZPDxQ27Zt8cX/PkFmZiZiYmJw+uwFnDr9K5KTrkDR1EWB0hLZpiaQhBBImoaQhBCoguf+kDuKSXsrUv1eRAvjv4sn7FO1CGoR9GoGdEoG9GoGQvyvwU9NgF5jRuvWt6Jn9w7o1HE02rVrh6Ag7xo+KAgCmjZtiqZNm2Lw4MEAisNIWlpacRiJOoPfo88jOe4aJLMJDf1VNPYxIFSbjcZ+ZjTxM6OBr8QeEi8mKUCGSY8Uox6pJn+kKfWRavJDlhEIqBOA5s2aoW2PrhjZsThkeNO+Os5Wp04d9O3bF337Fq/ol5+fjzNnzhQHkd9OwCAaUNRUQU4TY3EQ8ecb11No8ouXu22QUge6ayrq162Pvj37oN89fdGpUyf4+fm5ukSnYfDwYCEhIQgJCUG/fv0AFF+wZGRkIDo6GvEJiYhLiEVS8n5kZqTBZDIDurqQhBAUSCHIN4dAEkIgahpCFupxcjpVm6AWQadmQqdkQq9moK5vFgJ0WdDK6YBcAP+AADQKbYIWzZogrFUT3HprN7Rv3x5169Z1deluSRAENG7cGI0bN8bAgQOtt0uShJSUFCQmJiLx/9u78+ioygT948/NBoEkJARkCSDwAwkMgq0GBVoR6XZBtrA0KDGAoICAwxIcURydZleWaQmQQaZZxGbUGUB2aQRsIUqxiICEPSEQJZCFLJiQpFK/P+jUIWQrYqpuSL6fcziH3KXylJ6i6ql73/e9GKdT50/p75cu6lpSqmq656thrTzd556uBp4ZalgzRw28c1XLg/VEqgKbTcrIc79VLrK8dNVaR4l5Pkq84S6r4a6G99VX0/tbqNn/a6NHmt2vpk2bqn79+nJzY1FYM/n6+qpLly7q0uXW9MPp6en68ccftf/wdzq67QdlWbP1a2Or0htly1oFh9G4pd+6SuuWYru1blgV45EsBSTWkmeCVMe3jjo9HKLHn3pM7du3r9TT3Tobg8tdpDKvXC7dmn43NTVVV65cUWJioi5d/kWxF39RQsIvSk1NVk5Ozq2rIu51lKs6ysqro8xcP+WqjqyGv/KMOrIadWQzqk9rl24Ndm6a9a4uec9QrltDs+O4js0mN2XJ3ZYmd9t1edjS5GWkycczTTXd0+WhNCnvutxkVY2a3gqsV19Nghqqxf2N1CSokRo0aKCGDRvKz8+PWzhcIDMzU5cuXdLly5cVf+Gs4mPP6nJCgrKyslS3pqrsbVq5VptOJeYouIGXPKvo7EI3cg2l3ZTq+PmqSZMmatbyATVr0UpNmjRRUFBQtf6Ac69LTU3Vjz/+qH2HonU1+arZcSpcbk6uzh8/q//3YGt5elWOz0IVqUWT5uryaGe1b99etWvXNjuO0zCrlQsdO3ZMixcv1tGjR5Wbm6tWrVpp2LBh6t27d5FjK3vxcER2drZSUlKUkpKi5ORkJSUlK+GXJP1yJUlJySm6fj1FN7OzZZOn5OGnPNVRttVXN3L9dDPfT1aj4E8dWQ0fybj3L7hVueJhy5W7LV0etnS529LlbktTTfcM1fZMl5dbhjxsaVJemtyMfHnXqi3/gLqqXy9QjRoGqnHDeqpXL1B169ZVYGCgAgICqvR9qlWB1WpVUlKSbt68aXYUp7l586Zq1Ki6V2xr1arFGAzcs7KysijH9zhmtXKRAwcOaOTIkfL09NQLL7wgX19f7dy5UxEREUpISNCYMWPMjljhatasab/fvDQ5OTn2gpKamqqUlBQlXk3Rlavnde1ailJTU5SZkabcPKvkVlNy91Ou/OwlJSffV/n3yJUT9/xUSVLNvJ/k6fazyWkc4277VV5uGfLxSlcNt4x/Xp1Il6FceXp5ys/PXwEBAapfP1CNGgSowX0tFRAQoICAANWtW1cBAQHy8OCfjqrA3d1dDRo0MDsGgGqK0lH9cMWjHPLy8vT888/rypUr+uyzz9SuXTtJt25nGDJkiGJjY7V161Y1b97cfk5VuOLhDFlZWfaCUlBSfr6SrF9/vTe+gc3OztI/9n6lJ596VjVr3hv/gPrXqa0G99W1l4iCP1X5W2EAAFDxuOLhAt9//73i4+PVv39/e+mQJB8fH73++uuaNGmS1q9fr8mTJ5uY8t7g7e2toKAgBQUFmR2l3CZPHMu3NgAAAGVgSotysFgsklRoRpkCXbt2LXQMqj5KBwAAQNm44lEOcXFxkqT777+/yL46deooICBAFy9eLPNxsrKylJubW9HxAAAAAKfLy7u7uZApHuWQmZkpSSUuZubj46MrV66U+Thnz56t0FwAAABAZUXxMFHr1q2ZHQgAAAD3pLy8vLv6Ip1PveXg4+MjScrIyCh2f2ZmZolXQ27n7e1d7We1AgAAwL3pbocMMLi8HAqmyS1uHEdaWppSU1OLHf8BAAAAVFcUj3IICQmRJO3bt6/Ivv3790uSOnXq5NJMAAAAQGVG8SiHzp07q2nTptqyZYtiYmLs2zMzM7V06VJ5eHgoNDTUxIQAAABA5cIYj3Lw8PDQzJkzNWrUKL300kvq1auXfHx8tHPnTl2+fFkTJ05UixYtzI4JAAAAVBoUj3J6/PHH9be//U0fffSRtm/frtzcXLVq1Ur/+q//qj59+pgdDwAAAKhUKB6/QYcOHbRixQqzYwAAAACVHmM8AAAAADgdxQMAAACA01E8AAAAADgdxQMAAACA01E8AAAAADgdxQMAAACA0zGdrovYbLYi2/Ly8kxIAgAAAPx2xX2WLe4zbwGKh4tYrdYi206ePGlCEgAAAMA5ivvMW4BbrQAAAAA4HcUDAAAAgNNRPAAAAAA4nWErbQQIKkx+fr5u3rxZaJu7u7sMwzApEQAAAFB+NputyJiOGjVqyM2t+GsbFA8AAAAATsetVgAAAACcjuIBAAAAwOkoHgAAAACcjuIBAAAAwOkoHkAFy87ONjsCAACVSmmrWd8uOTnZyUlgJooH4KAJEyYoPT291GNOnjyp/v37uygRgNt98sknZR6TmZmpN9980wVpANzuxRdf1KVLl0o9Zu/everTp4+LEsEMFA/AQX//+9/Vp08fWSyWYvevXLlSQ4YM0eXLl12cDIAkzZo1S6NHj1ZKSkqx+48dO6b+/ftr8+bNLk4G4Pjx4woNDdXGjRuL7MvJydHMmTM1duxY5eXluT4cXIbiATho9uzZSk9P1/Dhw7Vw4UL7ZeOkpCSNHDlSH3zwgYKCgvTZZ5+ZnBSonvr166dvvvlGffr00bfffltoX1RUlF566SUlJiZq+vTpJiUEqq+VK1eqVq1amjZtmqZMmaLMzExJ0tmzZzVgwACtXbtWjz76qL788kuTk8KZWEAQuAsXL17UlClTdOLECXXo0EGDBg3SokWLlJKSoj/96U96++23VbNmTbNjAtXWtm3b9N577ykzM1Ph4eF66aWXNH36dB08eFAPPPCAFi5cqFatWpkdE6iW0tLS9M4772jXrl0KCgpS7969tWrVKuXl5emNN97Qq6++KsMwzI4JJ6J4AHfJarXqww8/1KpVq2QYhvz8/DR79mz16NHD7GgAJP3888+aOnWqjhw5IkkyDENhYWGKiIiQl5eXyekAREVF6T//8z9lGIb8/f318ccfq3379mbHggtwqxVwly5cuKD9+/fbf75x44ZOnz4tOjxQOdStW1fNmjWTzWaTzWaTn5+fevToQekAKoHo6Gh9+umnkiRvb29dv35d69atU1ZWlsnJ4AoUD+AufPrppxo0aJBiY2M1ceJEbdmyRa1bt9bixYsVFhamX375xeyIQLUWExNjH8DatWtXvffee8rNzdUrr7yiRYsWOTylJ4CKlZeXpw8++ECjRo1SVlaW5s+fr127dunJJ5/U//3f/6lfv346ceKE2THhZNxqBTho7Nix2rt3r5o0aaIFCxaoQ4cOkqTc3FzNnz9fa9askY+Pj95//3298MILJqcFqp9Vq1Zp4cKFstlsmjx5skaMGCFJio+PV0REhI4dO6YHH3xQCxYsULNmzUxOC1QvAwYM0E8//aSHHnpI8+fPV5MmTez71qxZowULFshqteqNN97Qa6+9ZmJSOBPFA3BQcHCw+vTpo/fee0+1a9cusn/fvn166623lJycrJiYGBMSAtVbcHCwWrRooYULF6pt27aF9lmtVn300Uf6+OOP5e3trcOHD5uUEqie2rVrp9GjR2v8+PFyd3cvsv/06dOKiIjQuXPneA+twigegIM2bdpU5sJGKSkpevvttxUVFeWiVAAKTJ8+Xe+88468vb1LPMZisejf/u3ftGfPHhcmA3Dw4EGFhISUekxOTo7mzZund99910Wp4GoUDwBAtZKeni4/Pz+zYwBAtUPxAAAAAOB0HmYHAO4V06ZNc+g4wzA0e/ZsJ6cBcKeNGzc6fGy/fv2clgNAUeHh4Q4dZxiGVq9e7eQ0MAtXPAAHBQcHl7rfMAzZbDYZhsHAOMAEwcHBZa56zGsUMAfvoZAoHoDDEhISit2ekZGhkydPKioqSm3bttXUqVMLTRMIwDU2bNhQ7PaC1+iWLVv09NNPq3v37goNDXVxOgDFyczM1E8//aRFixbpvvvu06JFi4qd9QpVA8UDqCBJSUnq3bu3xo8fr6FDh5odB8AdfvjhBw0fPlxRUVHq3Lmz2XEA3CYzM1N9+vRR//79NX78eLPjwElYuRyoIPXq1VP37t21du1as6MAKMbvfvc7de/eXX/5y1/MjgLgDj4+PnriiSe0fv16s6PAiSgeQAWqXbt2ibdkATBf48aNdfr0abNjACiGm5ubrl27ZnYMOBHFA6gg6enp+vrrr1WvXj2zowAohs1m06FDh1SjRg2zowC4w6VLl7Rjxw41btzY7ChwIqbTBRwUGRlZ7Har1arExETt3r1baWlpGjdunIuTAZBurYxcnLy8PCUmJurLL7/U8ePH1bdvXxcnA1DSlPQF76GHDx9WXl4e4zuqOAaXAw4qayrAWrVqKSwsTJMmTSpzSk8AFa+s6XRtNpseeughRUVFyd/f33XBAJT5Htq8eXONGDFCgwcPdlEimIHiATjIYrEUu93NzU1+fn5q0aKFPD09XZwKQIHFixcXWzwMw1CdOnXUvn17PfTQQ64PBqDE8Y9ubm7y9fWVj4+PixPBDBQPAAAAAE7H4HIAAAAATsfgcqAEJQ1UdURISEgFJgEA4N6ycePGcp/br1+/CsuByoVbrYASlDVQtTQxMTEVnAbAncr7GjUMQydPnnRCIgAFyvP6tNlsMgyD99AqjCseQAnGjRvH7FRAJcaVRaDymjNnjtkRUAlxxQMAAACA0zG4HAAAAIDTUTyAUkRGRv6mQeYAnOvgwYP6+eefzY4BoBjTpk3T119/XWhbTk6OMjMzTUoEs1E8gFJERkbqwIEDhbYtX75cjz32mEmJANwuPDxc69evL7Rt27ZtGj9+vEmJABTYsGFDkYHiy5cvZ3xWNUbxAO5STk6O0tPTzY4BQLdmwbnThQsXinzLCgAwH8UDAAAAgNNRPAAAAAA4HcUDAAAAgNOxgCBQhsTERB07dsz+85UrVySp0LY7dejQwem5ANzCQp9A5XX27Flt27bN/vOZM2ckSdu3by92jJYk9ezZ0yXZ4HosIAiUIjg4uNgPNTabrdQPO3fO4gHAOYKDg+Xh4SF3d3f7NqvVKqvVKi8vr2LPMQxDR48edVFCoPoq7j204GNnae+tvIdWXVzxAEoRGhpqdgQApWjcuLHZEQCUgGmtcSeueAAAAABwOgaXAwAAAHA6igcAAAAAp2OMB3AXzp07p7Vr1+r48ePKyMiQ1WotcoxhGNq1a5cJ6QDk5ORo165dZb5GZ8+ebUI6oHqLjo7WypUr7a/P/Pz8IscYhqGTJ0+akA6uQPEAHGSxWDRq1Cjl5OTIw8NDgYGBhWbSKcCwKcAcCQkJeuWVVxQfH1/q65DiAbjeV199pUmTJik/P1+NGzdWy5Yti30PRdVG8QActGDBAlmtVs2cOVOhoaH8gwlUMnPmzNHFixfVt29fDRgwQA0bNuR1ClQSS5YsUY0aNbR06VJ17tzZ7DgwCcUDcNCpU6fUs2dPDRw40OwoAIrx/fffq3Pnzpo3b57ZUQDcITY2Vn379qV0VHMMLgccVLt2bQUGBpodA0AJ8vPz1bZtW7NjAChGQECAatasaXYMmIziATioW7duOnTokNkxAJTgd7/7nS5cuGB2DADFeP755/Xdd98pLy/P7CgwEcUDcNDUqVOVkZGhmTNnKisry+w4AO4wZcoUHThwQDt27DA7CoA7TJw4UX5+fpo0aZJ+/vlns+PAJKxcDjgoPDxcGRkZOnXqlLy9vdW8eXP5+PgUOc4wDK1evdqEhED1FhkZqRMnTuibb75RSEiI2rVrV+JrdNy4cSYkBKqvHj16KC8vT1evXpUk+fn5lfj6ZEr6qoviATgoODjYoeMMw1BMTIyT0wC4E69RoPJ6+umnHT529+7dTkwCM1E8AABVgsVicfjYTp06OTEJAKA4FA8AAAAATsc6HkA5/frrr8rMzJSPj49q1apldhwAAO4JeXl5iouLU0ZGhnx8fNSiRQt5ePCRtDrg/zJwF3Jzc/Xf//3f2rBhg+Lj4+3bmzZtqv79++uVV16Rl5eXiQkBHDlyRBs2bFBMTIz9g027du3Ut29fPfroo2bHA6qttLQ0zZ8/X1u2bFF2drZ9e82aNdWrVy9NnjxZAQEBJiaEs3GrFeCg7OxsjRgxQkePHpW7u7uaNm2qevXqKTk5WfHx8bJarerYsaNWrVrFIkmASebNm6dVq1ap4K3Nzc1N+fn5km4NKg8PD9e0adPMjAhUS2lpaRo8eLDi4uLk7++v9u3bq379+kpKStKJEyeUmpqq+++/X5999pn8/f3Njgsn4YoH4KAVK1bohx9+UK9evRQREaGGDRva9yUmJmrBggXatGmTVqxYofHjx5uYFKieNm7cqJUrV6ply5YaP368OnXqZP9y4MCBA1qyZInWrFmjtm3bql+/fmbHBaqVpUuXKi4uTqNHj9aYMWPk7e1t35edna2oqChFRUVp2bJlfDlQhXHFA3DQ888/r9q1a+t///d/Szxm4MCBunHjhrZv3+7CZAAkafDgwbp69ao2b95c7PoAmZmZ6t27t+rXr6/PP//chIRA9dWjRw81adKk1HWuhg8frkuXLunrr792YTK4EiuXAw5KSEhQly5dSj2mc+fOSkhIcFEiALc7c+aMnnnmmWJLhyT5+Pjoj3/8o86ePeviZACuXr2qhx56qNRjOnbsaF9gEFUTxQNwkLe3t1JSUko9JiUlpdDlYwCuVdZFfMMwXJQEwO18fX3L/GIuISFBvr6+LkoEM1A8AAd17NhRW7duLfHb0nPnzmnbtm1lfqMDwDkeeOAB7dy5Uzdu3Ch2f2Zmpnbu3KnWrVu7OBmATp06aceOHYqOji52/3fffacdO3awuGcVxxgPwEFHjhxReHi43N3dNXDgQIWEhKhevXpKSkqSxWLR+vXrlZeXp9WrV+uRRx4xOy5Q7WzYsEHTpk1T69atNX78eIWEhKhu3bpKSUmRxWLRkiVLdO7cOc2ePVuhoaFmxwWqlXPnzmnQoEHKzs5Wt27dFBISosDAQCUnJ8tisegf//iHatasqc8//5wvB6owigdwF7766itNnz5dGRkZhW7ZsNls8vX11YwZM/Tcc8+ZmBCo3mbPnq01a9bYX5+3T6drs9kUFham6dOnmxkRqLaOHDmiadOm6eLFi5Ju3fpY8DG0WbNmmjNnDl/cVXEUD+Au3bhxQ7t27VJMTIx95fK2bduqR48eJQ5qBeA6hw4d0vr163Xq1KlCr9HQ0FAWEARMZrPZdPjw4SLvoY888ghjsKoBigfgoMjISDVt2lR9+/Y1OwqAYhw8eND+IQZA5TJt2jS1adNGw4cPNzsKTMTgcsBBUVFROnPmjNkxAJQgPDyc9TmASmrLli1KTk42OwZMRvEAHNS4cWOlpaWZHQNACQIDA+Xp6Wl2DADFuP/++3Xt2jWzY8BkFA/AQS+88IL27dunjIwMs6MAKMbvf/97HTx4sMy1PAC43sCBA7V3714lJiaaHQUmYowH4KCcnBxNmDBB165d0xtvvKEHH3xQgYGBZscC8E+JiYkaMmSIunbtqoiICPn7+5sdCcA/Xb58WTNmzNCZM2c0atQo+3tocQPKGzdubEJCuALFA3BQwYBVm81W6swbhmHo5MmTrooF4J/Cw8N1/fp1nT17Vp6enmrSpEmxH2wMw9Dq1atNSglUT8HBwfbpc3kPrb48zA4A3CuYhhOo3CwWi/3vOTk5unDhgi5cuFDkOKbsBFyvX79+vPbAFQ8AAAAAzsfgcsBBBasfl4XpAoHKzdHXMoCKk52d7dBxsbGxTk4CM1E8AAdNnz69zGOSk5M1bNgwF6QBcKcvvviizGOsVqumTJnigjQAbjdhwgRZrdZSj4mNjeU9tIqjeAAOWr9+vRYsWFDi/pSUFIWHhysuLs51oQDYvf/++9q1a1eJ+202myIiIrRjxw4XpgIgSd9++62mTZtW4v6LFy8qPDycKeurOIoH4KCwsDCtWLFCq1atKrIvJSVFw4YNU2xsrObMmeP6cADUsWNHTZkyRYcOHSqyr6B0bN++XUOGDDEhHVC9RUREaNOmTZo7d26RffHx8Xr55ZeVkZGhZcuWmZAOrkLxABw0ffp0Pf/88/rggw+0efNm+/bU1FQNGzZM58+f19y5c9W7d28TUwLV13/913+pWbNmGjt2rE6fPm3fbrPZNHXqVG3dulWDBw/We++9Z2JKoHoaNWqURowYodWrV2v58uX27ZcuXVJYWJjS09MVFRWlxx9/3MSUcDZmtQLuQm5urkaPHi2LxaJly5apffv2Cg8P1/nz5zVnzhz17dvX7IhAtZaYmKgXX3xRubm5WrdunYKCghQREaGtW7fqT3/6k/785z+bHRGo1t58801t3rxZs2bNUkhIiMLCwnT9+nVFRUWpc+fOZseDk1E8gLv066+/6uWXX9aFCxfUqFEjxcXFafbs2erXr5/Z0QDo1gDVF198UXXq1FG7du20fft2DRo0SDNmzDA7GlDtWa1WjR07VtHR0fL391dGRoaWLl2qrl27mh0NLkDxAMohJSVFL730kuLj4zVr1iyFhoaaHQnAbY4fP65hw4YpKytLAwYM0MyZM82OBOCfsrOzNXz4cMXExFA6qhmKB1CC8PDwUvcnJyfr2rVrCg4OLrTdMAytXr3amdEASIqMjCx1/6FDhxQTE6OwsLBCKyYbhqFx48Y5Ox5QrfXo0aPU/Tdv3tSNGzdUt27dQtsNwyh1djrc2ygeQAnuLBSOMgxDMTExFZwGwJ14jQKV19NPP13uc3fv3l2BSVCZUDwAAPcki8VS7nM7depUgUkAAI6geABOdurUKZ06dYrB50AllZmZqfT0dDVu3NjsKADuYLFYZLFYNH78eLOjoAKwjgfgZLt27Sp1tVYA5lq1alWZ96MDMIfFYtGSJUvMjoEKQvEAAAAA4HQUDwAAAABOR/EAAAAA4HQUDwAAAABOR/EAAAAA4HQUDwAAAABOR/EAAAAA4HQUDwAAAABOR/EAnCwoKEiPPvqo2TGAKq9t27aaMmXKXZ9ns9lks9mckAjAbxUcHKx+/fqZHQMVxLDxry3gEEdWH3dzc5OPj49atGih7t27q0GDBi5IBkCSQkJCNHjwYEVERJgdBaj2pk2bJsMwNHnyZNWrV8+h91BJMgxDs2fPdnI6mIXiATgoODhYhmFIUrHfjhqGUWi7h4eHXn/9db3++usuywhUZyNHjpRhGFqxYoXZUYBqr+A9c9u2bWrRooWCg4MdOs8wDMXExDg5HcxC8QAcdOnSJc2ePVvHjx9XeHi4Hn74YQUGBio5OVlHjhzRmjVr1KFDB40ZM0anT5/WsmXL9Msvv2jBggXq2bOn2fGBKu/o0aMKCwvTjBkzFBoaanYcoFpLSEiQJDVo0EAeHh72nx0RFBTkrFgwGcUDcNDy5cu1Zs0affnllwoMDCyy/9q1a+rXr5+GDx+uV199VYmJierZs6fatm2rtWvXmpAYqF4iIyP1ww8/KDo6Wm3btlWHDh1Ur169IscZhqFx48aZkBAAqjeKB+CgZ555Rk8++aSmT59e4jEzZszQt99+q507d0qSpkyZom+++UaHDh1yVUyg2uJWDgCo3DzMDgDcK65cuSJPT89Sj/Hy8tKVK1fsPzdu3Fg3b950djQAktasWWN2BABAKSgegIMaNGig3bt3a9KkSfLy8iqyPycnR7t37y40k1VycrLq1KnjyphAtdWpUyezIwAASsE6HoCDBg4cqIsXLyosLEx79+7V9evXJUnXr1/Xnj17NHToUMXHx2vAgAH2cw4fPqw2bdqYlBgAAKDy4IoH4KBRo0bp/Pnz2rRpk8aOHSvp1rod+fn5km5Nsdu7d2+99tprkqSkpCR169ZNTzzxhGmZAQAAKgsGlwN36bvvvtOmTZt0+vRpZWZmysfHR23atFGfPn3UuXNns+MBAABUShQPAAAAAE7HGA8AAAAATkfxAAAAAOB0FA8AAAAATkfxAAAAAOB0FA8AAAAATsc6HgCAu/byyy/LYrGUedzp06ddkKaw9PR0rV69WpI0YcIEl/9+AEDxKB4AgHJr1KiRGjVqZHaMQtLT0xUZGSmJ4gEAlQnFAwBQbgMGDODDPQDAIYzxAAAAAOB0XPEAALjEoUOH9Omnn+rw4cNKSUlRrVq19C//8i8aMmSInn322SLH5+TkaO/evdqzZ49OnDihxMREZWVlqX79+goJCdHIkSP1wAMPFDrnrbfe0oYNG+w/t2nTptD+OXPmqH///rp8+bJ69OghqeRxKIsXL1ZkZKRCQ0M1d+5c+/Y7z927d6/WrFmjn376SdevX9eSJUv0hz/8QZKUn5+vzZs368svv9TJkyeVmZmpgIAAhYSEaNSoUWrXrl05/ksCwL2J4gEAcLr58+fr448/liT5+vqqVatWSkpKUnR0tKKjozVkyBD9x3/8R6Fz4uLiNGHCBLm5uSkwMFBBQUHKzc1VQkKCNm7cqG3btmnx4sV66qmn7Oc0b95c7du314kTJyRJDz/8cKHHDAwMrNDn9de//lXz5s2Tv7+/mjZtKm9vb/u+zMxMTZgwQdHR0ZKk+vXrq3Xr1oqPj9fWrVv11Vdfac6cOerTp0+FZgKAyoriAQBwqr/97W/6+OOPFRAQoH//939Xz5497fv279+vqVOn6n/+53/UsWNH9e/f376vbt26+uCDD9StWzf5+/vbt+fk5OiLL77QrFmz9NZbb2nPnj32D/xjxoxRr1697Fck1q1b59TntnDhQr399tsKCwuTu7u7JOnmzZuSpOnTpys6Olrt2rXTn//8Zz344IOSbl0F+eSTTzR37ly98847at++vVq2bOnUnABQGTDGAwBQbpGRkWrTpk2xf3bt2qWsrCx99NFHkqQPP/ywUOmQpK5du+r999+XJC1fvrzQvnr16qlv376FSockeXl5aejQoerZs6dSU1O1Z88epz2/sgwYMEDDhg2zlw5JqlGjho4dO6bt27fL399fUVFR9tIhSW5ubho2bJiGDh2qnJwcrVq1yoTkAOB6XPEAAJRbadPp+vv768CBA0pNTVVQUJCeeOKJYo/r3r27PD09FRsbq8TERDVo0KDQ/u+++07ffPON4uLilJmZKavVKkn65ZdfJEknT54sUmhcZcCAAcVu3759u6Rbz+3O51PgmWee0SeffKIDBw44LR8AVCYUDwBAuZU1nW5UVJQkKSMjQy+++GKZj3d78bhx44YmTJig/fv3l3rO9evXHQ9cwVq1alXs9oIB6xaLpcTnXXBL1pUrV5wTDgAqGYoHAMBp0tLSJN1a1O/IkSNlHp+VlWX/+7x587R//34FBARoypQpeuyxx3TfffepZs2akqS//OUvWrp0qfLy8pwT3gG1atUqdnvB805ISFBCQkKpj5GdnV3huQCgMqJ4AACcpnbt2pKkP/zhD1qyZInD5+Xl5Wnz5s2SbhWQbt26FTnmt1zpMAzD/nebzVbo5wK//vpruR+/oJC8/fbbGjZsWLkfBwCqEgaXAwCcpmCdjR9//FH5+fkOn5eSkmL/4P/II48Ue8wPP/xQ7PbiSsSdbp/2Nikpqdhj4uLiynyckhSsH+LIVR4AqC4oHgAAp+nSpYv8/Px07do1ff755w6fd3sxuHr1apH90dHRiomJKfPckm5jqlu3rurUqSNJOnr0aJH9ly5d0r59+xzOe6fnnntOkvT111/rzJkz5X4cAKhKKB4AAKfx8fHRxIkTJUkzZ87UqlWripSBtLQ0bdy4UfPmzbNv8/X1VXBwsCRp1qxZ9jETkvT9999r8uTJqlGjRrG/MyAgQL6+vvZjS9K9e3dJ0qJFiwqNw4iPj9fEiRNls9nu4pkW9uijj+q5555Tbm6uRo4cqd27dxd5vMuXL2vFihX64osvyv17AOBewhgPAIBTDR06VNevX9fixYs1Z84cLVy4UC1btpSnp6dSUlKUkJAgm82mTp06FTpv6tSpeu2117Rv3z499dRTat68udLT03X58mW1bdtWnTt31l//+tciv88wDPXt21dr167V2LFj1bp1a/vVjVdffVVPPvmkJGnChAnau3evzp8/r2effVYtWrRQfn6+zp8/r+DgYIWFhWnlypXlft5z585VTk6Odu/erbFjx9pXN8/Pz1diYqL9Fq/x48eX+3cAwL2EKx4AAKcbN26cNm7cqEGDBqlhw4aKjY3VuXPn5OHhoSeeeELvvvuuPvzww0Ln/P73v9eaNWvUpUsXSdKFCxfk5eWlsWPHat26dSXOKCVJb775psaMGaPmzZsrLi5OFotFFoul0HiOJk2a6LPPPlPPnj3l4+Oj2NhY5eTkaPTo0Vq3bp19YHx5eXt7a+nSpVq2bJn++Mc/ysvLS6dOnVJCQoLq1q2rXr16aeHChRoxYsRv+j0AcK8wbL/lWjIAAAAAOIArHgAAAACcjuIBAAAAwOkoHgAAAACcjuIBAAAAwOkoHgAAAACcjuIBAAAAwOkoHgAAAACcjuIBAAAAwOkoHgAAAACcjuIBAAAAwOkoHgAAAACcjuIBAAAAwOkoHgAAAACc7v8Dx8TfMLuZBzwAAAAASUVORK5CYII=", "text/plain": [ "
" ] From e7d048dfb709490dad3f5995686984a8018c67b1 Mon Sep 17 00:00:00 2001 From: beckynevin Date: Wed, 7 May 2025 22:26:26 +0000 Subject: [PATCH 07/13] starting to change to log --- DP0.2/20_Introduction_to_Data_Science.ipynb | 1635 ++++++------------- 1 file changed, 522 insertions(+), 1113 deletions(-) diff --git a/DP0.2/20_Introduction_to_Data_Science.ipynb b/DP0.2/20_Introduction_to_Data_Science.ipynb index c4e5a2c9..aba78a7f 100644 --- a/DP0.2/20_Introduction_to_Data_Science.ipynb +++ b/DP0.2/20_Introduction_to_Data_Science.ipynb @@ -105,15 +105,15 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 1, "id": "3f4900a4-3358-472a-b9ba-c42e3f2f0771", "metadata": { "execution": { - "iopub.execute_input": "2025-05-06T21:09:53.536477Z", - "iopub.status.busy": "2025-05-06T21:09:53.536025Z", - "iopub.status.idle": "2025-05-06T21:09:53.542205Z", - "shell.execute_reply": "2025-05-06T21:09:53.541204Z", - "shell.execute_reply.started": "2025-05-06T21:09:53.536441Z" + "iopub.execute_input": "2025-05-07T22:05:03.838119Z", + "iopub.status.busy": "2025-05-07T22:05:03.837815Z", + "iopub.status.idle": "2025-05-07T22:05:07.659466Z", + "shell.execute_reply": "2025-05-07T22:05:07.658406Z", + "shell.execute_reply.started": "2025-05-07T22:05:03.838088Z" } }, "outputs": [], @@ -145,20 +145,20 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 2, "id": "94acc9f6-2033-4ace-aefd-d036a35f4221", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T21:58:23.430410Z", - "iopub.status.busy": "2025-05-07T21:58:23.429958Z", - "iopub.status.idle": "2025-05-07T21:58:23.436042Z", - "shell.execute_reply": "2025-05-07T21:58:23.435036Z", - "shell.execute_reply.started": "2025-05-07T21:58:23.430373Z" + "iopub.execute_input": "2025-05-07T22:05:07.664107Z", + "iopub.status.busy": "2025-05-07T22:05:07.663771Z", + "iopub.status.idle": "2025-05-07T22:05:07.669508Z", + "shell.execute_reply": "2025-05-07T22:05:07.668536Z", + "shell.execute_reply.started": "2025-05-07T22:05:07.664073Z" } }, "outputs": [], "source": [ - "sns.set_style('whitegrid')\n", + "sns.set_style(\"whitegrid\")\n", "palette = sns.color_palette(\"colorblind\")\n", "sns.set_palette(palette)" ] @@ -175,15 +175,15 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 3, "id": "caf56589-100a-4481-8f24-5f5058b6671f", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T21:56:34.234843Z", - "iopub.status.busy": "2025-05-07T21:56:34.234421Z", - "iopub.status.idle": "2025-05-07T21:56:34.290044Z", - "shell.execute_reply": "2025-05-07T21:56:34.288999Z", - "shell.execute_reply.started": "2025-05-07T21:56:34.234805Z" + "iopub.execute_input": "2025-05-07T22:05:07.673906Z", + "iopub.status.busy": "2025-05-07T22:05:07.673591Z", + "iopub.status.idle": "2025-05-07T22:05:07.724463Z", + "shell.execute_reply": "2025-05-07T22:05:07.723437Z", + "shell.execute_reply.started": "2025-05-07T22:05:07.673877Z" } }, "outputs": [], @@ -202,20 +202,20 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 4, "id": "2b7b6002-2457-4c20-a03e-6bfa24a0aa27", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T21:56:34.832667Z", - "iopub.status.busy": "2025-05-07T21:56:34.832208Z", - "iopub.status.idle": "2025-05-07T21:56:34.837480Z", - "shell.execute_reply": "2025-05-07T21:56:34.836547Z", - "shell.execute_reply.started": "2025-05-07T21:56:34.832633Z" + "iopub.execute_input": "2025-05-07T22:05:07.785841Z", + "iopub.status.busy": "2025-05-07T22:05:07.785517Z", + "iopub.status.idle": "2025-05-07T22:05:07.790060Z", + "shell.execute_reply": "2025-05-07T22:05:07.789215Z", + "shell.execute_reply.started": "2025-05-07T22:05:07.785810Z" } }, "outputs": [], "source": [ - "pandas.set_option('display.max_rows', 6)" + "pandas.set_option(\"display.max_rows\", 6)" ] }, { @@ -241,15 +241,15 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 5, "id": "7ddd0344-b354-45a0-9e5a-755149c9bc54", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T21:56:35.718993Z", - "iopub.status.busy": "2025-05-07T21:56:35.718008Z", - "iopub.status.idle": "2025-05-07T21:56:35.723540Z", - "shell.execute_reply": "2025-05-07T21:56:35.722563Z", - "shell.execute_reply.started": "2025-05-07T21:56:35.718954Z" + "iopub.execute_input": "2025-05-07T22:05:10.643647Z", + "iopub.status.busy": "2025-05-07T22:05:10.643208Z", + "iopub.status.idle": "2025-05-07T22:05:10.648607Z", + "shell.execute_reply": "2025-05-07T22:05:10.647556Z", + "shell.execute_reply.started": "2025-05-07T22:05:10.643609Z" } }, "outputs": [], @@ -272,15 +272,15 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 6, "id": "985e3b62-8065-42ec-a40c-1232c4c45f17", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T21:56:36.240704Z", - "iopub.status.busy": "2025-05-07T21:56:36.240263Z", - "iopub.status.idle": "2025-05-07T21:56:36.246092Z", - "shell.execute_reply": "2025-05-07T21:56:36.245171Z", - "shell.execute_reply.started": "2025-05-07T21:56:36.240669Z" + "iopub.execute_input": "2025-05-07T22:05:11.807873Z", + "iopub.status.busy": "2025-05-07T22:05:11.807426Z", + "iopub.status.idle": "2025-05-07T22:05:11.813478Z", + "shell.execute_reply": "2025-05-07T22:05:11.812520Z", + "shell.execute_reply.started": "2025-05-07T22:05:11.807835Z" } }, "outputs": [ @@ -312,15 +312,15 @@ }, { "cell_type": "code", - "execution_count": 91, + "execution_count": 7, "id": "c02adc91-5f5e-418b-87a3-cba8beba7dd2", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T21:56:36.777972Z", - "iopub.status.busy": "2025-05-07T21:56:36.777542Z", - "iopub.status.idle": "2025-05-07T21:56:37.966174Z", - "shell.execute_reply": "2025-05-07T21:56:37.965219Z", - "shell.execute_reply.started": "2025-05-07T21:56:36.777935Z" + "iopub.execute_input": "2025-05-07T22:05:12.604063Z", + "iopub.status.busy": "2025-05-07T22:05:12.603619Z", + "iopub.status.idle": "2025-05-07T22:05:13.791167Z", + "shell.execute_reply": "2025-05-07T22:05:13.790094Z", + "shell.execute_reply.started": "2025-05-07T22:05:12.604025Z" } }, "outputs": [ @@ -335,8 +335,8 @@ "source": [ "job = service.submit_job(query)\n", "job.run()\n", - "job.wait(phases=['COMPLETED', 'ERROR'])\n", - "print('Job phase is', job.phase)" + "job.wait(phases=[\"COMPLETED\", \"ERROR\"])\n", + "print(\"Job phase is\", job.phase)" ] }, { @@ -359,15 +359,15 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 8, "id": "8cd2f538-c2d7-44ca-ab4d-825120b8f2e7", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T21:56:37.967978Z", - "iopub.status.busy": "2025-05-07T21:56:37.967634Z", - "iopub.status.idle": "2025-05-07T21:56:38.578202Z", - "shell.execute_reply": "2025-05-07T21:56:38.575023Z", - "shell.execute_reply.started": "2025-05-07T21:56:37.967945Z" + "iopub.execute_input": "2025-05-07T22:05:14.347205Z", + "iopub.status.busy": "2025-05-07T22:05:14.346714Z", + "iopub.status.idle": "2025-05-07T22:05:15.261252Z", + "shell.execute_reply": "2025-05-07T22:05:15.260192Z", + "shell.execute_reply.started": "2025-05-07T22:05:14.347162Z" } }, "outputs": [], @@ -387,15 +387,15 @@ }, { "cell_type": "code", - "execution_count": 93, + "execution_count": 9, "id": "ee4d121e-6b4d-4371-afae-4f7587b95d51", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T21:56:38.580173Z", - "iopub.status.busy": "2025-05-07T21:56:38.579767Z", - "iopub.status.idle": "2025-05-07T21:56:38.598960Z", - "shell.execute_reply": "2025-05-07T21:56:38.598034Z", - "shell.execute_reply.started": "2025-05-07T21:56:38.580121Z" + "iopub.execute_input": "2025-05-07T22:05:15.389946Z", + "iopub.status.busy": "2025-05-07T22:05:15.389502Z", + "iopub.status.idle": "2025-05-07T22:05:15.413383Z", + "shell.execute_reply": "2025-05-07T22:05:15.412410Z", + "shell.execute_reply.started": "2025-05-07T22:05:15.389907Z" } }, "outputs": [ @@ -535,7 +535,7 @@ "[11564 rows x 8 columns]" ] }, - "execution_count": 93, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -546,15 +546,15 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 10, "id": "db2168fe-593a-423d-b2f4-26ac0db60e8c", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T21:56:39.172485Z", - "iopub.status.busy": "2025-05-07T21:56:39.172006Z", - "iopub.status.idle": "2025-05-07T21:56:39.178527Z", - "shell.execute_reply": "2025-05-07T21:56:39.177605Z", - "shell.execute_reply.started": "2025-05-07T21:56:39.172448Z" + "iopub.execute_input": "2025-05-07T22:05:15.818183Z", + "iopub.status.busy": "2025-05-07T22:05:15.817048Z", + "iopub.status.idle": "2025-05-07T22:05:15.823850Z", + "shell.execute_reply": "2025-05-07T22:05:15.822948Z", + "shell.execute_reply.started": "2025-05-07T22:05:15.818117Z" } }, "outputs": [ @@ -564,7 +564,7 @@ "pandas.core.frame.DataFrame" ] }, - "execution_count": 94, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -585,15 +585,15 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 11, "id": "eec25f58-d3f3-4ef4-b3e2-ab105c4718fd", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T21:56:40.017197Z", - "iopub.status.busy": "2025-05-07T21:56:40.016754Z", - "iopub.status.idle": "2025-05-07T21:56:40.056718Z", - "shell.execute_reply": "2025-05-07T21:56:40.055812Z", - "shell.execute_reply.started": "2025-05-07T21:56:40.017162Z" + "iopub.execute_input": "2025-05-07T22:05:17.628778Z", + "iopub.status.busy": "2025-05-07T22:05:17.627960Z", + "iopub.status.idle": "2025-05-07T22:05:17.667654Z", + "shell.execute_reply": "2025-05-07T22:05:17.666625Z", + "shell.execute_reply.started": "2025-05-07T22:05:17.628732Z" } }, "outputs": [ @@ -663,21 +663,21 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 12, "id": "4fc9b578-2be4-4fb2-8d74-ebca809ea99f", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T21:56:40.857458Z", - "iopub.status.busy": "2025-05-07T21:56:40.856953Z", - "iopub.status.idle": "2025-05-07T21:56:41.863203Z", - "shell.execute_reply": "2025-05-07T21:56:41.862312Z", - "shell.execute_reply.started": "2025-05-07T21:56:40.857421Z" + "iopub.execute_input": "2025-05-07T22:05:18.635581Z", + "iopub.status.busy": "2025-05-07T22:05:18.635131Z", + "iopub.status.idle": "2025-05-07T22:05:19.692427Z", + "shell.execute_reply": "2025-05-07T22:05:19.691484Z", + "shell.execute_reply.started": "2025-05-07T22:05:18.635542Z" } }, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -706,21 +706,21 @@ }, { "cell_type": "code", - "execution_count": 106, + "execution_count": 13, "id": "bacf5114-6a64-4100-8eb6-f1d9ddc36f89", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T21:58:27.791613Z", - "iopub.status.busy": "2025-05-07T21:58:27.791201Z", - "iopub.status.idle": "2025-05-07T21:58:28.222964Z", - "shell.execute_reply": "2025-05-07T21:58:28.220553Z", - "shell.execute_reply.started": "2025-05-07T21:58:27.791579Z" + "iopub.execute_input": "2025-05-07T22:05:34.059685Z", + "iopub.status.busy": "2025-05-07T22:05:34.059225Z", + "iopub.status.idle": "2025-05-07T22:05:34.518113Z", + "shell.execute_reply": "2025-05-07T22:05:34.517180Z", + "shell.execute_reply.started": "2025-05-07T22:05:34.059647Z" } }, "outputs": [ { "data": { - "image/png": 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NGC7pVmB85ZVXJEmXLl3SmjVrMjxemTJl9P777zuEEJv+/fvbg/iuXbsc9l26dMn+ve3qbkacuYoFOIMgAhQg69ev1w8//KCYmJg0+5KSkux985s0aZLum2+bHj162L+3rUuRmp+fn71rx/Hjx/XWW2/p008/lXTrU83bu8vci1GjRqlmzZr2fw899JD69++vvXv3Srq1XsZXX311V4ONbffJ3d09Tdeh1Nq3b28fMJve45BdsvN3Y7bU3WiuX79+x/ZXrlxRWFiYjh8/bv9neyOXultRdrBarbp06ZJCQ0Mdzmfr1pfd58uKu328bGw1b9iwQVeuXMn2utLTsWPHbDtWu3btMuxy5erqah9HcfXqVYWEhGTbeXOC7XVYqVKlTD+A+de//pXmNul55plnHLokpubh4WEPabeP1UndPTUoKEhWq/WOtQM5ja5ZQD4yePBgDRkyxGHbzZs3FR4errVr12rmzJlasmSJDh06pDlz5qhUqVL2dufOnVN8fLwkqUGDBpmep3bt2nJzc1NiYmKarmA23bt316ZNm7R27Vr98ccfkm71z//ss88MmTq2SpUqatu2rV588cW7/tTXdp9q1qyZ4R98SSpcuLBq166t4ODgDB+H7JDdvxszpX4zndGsR7t379b8+fO1Y8eOTLuIpO5CdC82bNigBQsWaPfu3Zm+2c+u892N1PWkN1NYRgICAhQcHKwzZ87o6aefVps2bdSsWTM9/PDDOTYbUnozSznr9nEpt6tXr579+2PHjjn8nJskJCTYuyCmnsUvPWXKlJGvr68iIiIyfe3ef//9mR7H1uX19udyxYoV1aRJEwUHB2vOnDnavHmz2rRpoyZNmqh+/fp39fwCsgtBBMjnihQpogceeEADBw5UvXr19NJLL+n48eOaNGmSw+rqqbvJpNd1IDU3Nzd5e3vr0qVLmb5RfP/997VhwwZ714nhw4ff8Y/o3Uq9srrFYlHRokXl4+NzT4vl2R6L1EEtI7aBntHR0bJarTmyentO/G7MkvrNfHpjhG6fOjUzN27cuKdarFarRo8erV9//dWQ8znD9ni5urre1XO6S5cuCg8P18yZMxUbG6slS5bYxwz5+vrqySefVM+ePbN1nFB2jvm602vPNh5LMicgZlXq125W/z+JiIjI9LV7p4kubF22bN3BUps8ebKGDx+u3bt369SpU/r666/19ddfy9XVVXXr1lW7du3UrVu3ArnYKMxB1yygAGnevLlq1aol6dZgVtun7LfLypvprFzWX7BggUP/7eyaJjQ128rqNWrUUPXq1VWpUqVs+yOaXY9DdsqNNd2NI0eO2L+/fa2D7du320NIpUqVNHbsWC1fvly7d+/WkSNHdOzYMR07dkxvvPFGttTyyy+/2EOIv7+/Pv74Y/3222/as2ePw/myMp1qToiMjNTFixclpX2ssmLo0KH6448/9NZbb6l58+b2rk4RERH68ccf1bFjR02bNi3b6nVxccm2Y93peZ6bn+MZyQ2v3bJly+rHH3/UvHnz1KdPH9WsWVOFChVSUlKS9u/fr4kTJ6pt27b63//+l6N1ADZcEQEKmPvvv19Hjx5VYmKiTp8+bZ/FKPWnmZGRkZkeIzEx0f5JX0aDGvfv36+vv/5a0q0uOLGxsfY1RFJPdZsblShRQpcuXbIP+M2MrY2Xl1eOXA2x1WOTHb8bM23ZssX+/e3rHCxatEjSrfu7aNGiDK/+pDfI3Rm2CQaqVKmin3/+Od0JALLzfHdr8+bN9u8ffvhhp45Rvnx5vfrqq3r11VeVnJysw4cPa+3atVqwYIFiY2M1ffp01a5dO81Chma702sv9evg9q6XttdhelcEUsvog5jslPq1ezf/n+T0a7dp06b2mQSjo6O1c+dO/frrr9qwYYMuXbqkwYMHa926dZl2TQWyA1dEgAIm9eJ8SUlJ9u8rVapkv+R/p0/DQkJC7Fc60uvacf36dY0YMUJJSUny8vLSkiVL7FdiPvnkE508efKe70dOst2nY8eOZboCfUJCgn2gbE5OhZudvxszRUZGatWqVZJuDcK+fdFF2/OiadOmmXZBs01Beq9OnDgh6dZq3xmFEKvVqsOHD2fL+e6G1Wp1WLekTZs293xMFxcX1atXTyNGjNCsWbPs229fIDOnAvXdsM14l5X9tz/PbWMdoqOjMz3G6dOnM92fHY9D4cKF7YPH73SfLl++bF9c08jXrpeXl55++mkFBgbaJ7u4ePFirpx1D/kPQQQoQKxWq8ObuNQLk7m6uqpJkyaSpODg4ExXR7Z9ci0p3RW8x40bZ7/9+++/rypVqmjy5MkqWrSobty4obfeeivTN/hms92nuLi4dFemtlmzZo19BrL0Hgfbp4n3el+z83djluTkZL377rv2cRY9e/ZM86mvLRjbpk5Oz5EjR+4YxrL6uNtCeWbnW79+vcO0p0YJDAy0v1br1Kmj5s2bZ+vxGzZsaA+3t4+xSP0puFmv0zVr1mR4xSI5Odm+OKu3t7f8/f0d9tvWk7l+/bpOnTqV7jGsVqt9XZ6M2Gbau9fHwPY6PHPmTJopdVNLPQW4Wa/d1LN65eaxN8g/CCJAAfLTTz/ZP3GrXbu2ypUr57Df1mUqOTlZ7733Xrp/gDdu3GjvV1+nTp00XUbWrFmjoKAgSVKnTp307LPPSrq1Evg777wj6dY0qFOmTMnGe5a9unTpYu9P/9lnnyk8PDxNm/DwcPtg/6JFizosrGdjG8h+9uzZe64pO343ZgkPD9eLL75o72pUrVo1DRo0KE0727TEe/fuTXcBvytXrtifQ5nJ6uNuO9+GDRvSHRx89uxZffDBB3c8X3aKjY3V+PHjNXXqVEm3rhylXtQvq5YuXZrp+hq7d++2v9GvWLGiwz7b4ydlz3PXGZcvX87wfk+bNk2hoaGSbk15e/vU3LbQLsnhyk9qX3311R2vdNkeh8jIyLtaw+V2vXr1sg8gHzt2bLpd/Q4fPqyZM2faz9u2bVunz5eRkJAQhzFa6Uk9bfDtzwsgJzBGBMhH0ltZPSEhwT59r61bTKFChTRixIg0t2/RooU6dOiglStXKjg4WF27dtVLL72k6tWrKzY2VuvXr7cvzuXm5qaPPvrI4fbnz5/Xf//7X0m3ZuaxfW/zwgsvaOPGjdq4caNmz56tJ554wtCVqrOqZMmSGjlypP773//q0qVL6tq1q1555RX7mIY9e/Zo5syZ9jev77zzjsMsPjYNGzbUzp07dfDgQc2cOVNPPPGE/VPookWLpgmCmbnX301Ouv15d+PGDV27dk0nT57Uzp07tWnTJvvVh2rVqikwMDDdCQU6d+6sDRs2KC4uTr1799arr75qH8O0b98+zZ49W5cvX1bDhg21b9++DOvJ6uPeuXNnffLJJ7p48aJ69OihV155RTVq1NDNmze1Y8cOzZ07VwkJCapTp062dc+Ki4tzeKwSExMVHR2t8PBw7d27V2vXrrW/6fXy8tLkyZPt3RrvxrvvvqtPPvlErVu31kMPPaTKlSuraNGiunLlinbt2qWffvpJ0q2rbanXr5CkChUq2Fd0//7773XffffJz8/PPhi9VKlSOT6r0oMPPqhFixYpPDxcvXr1UoUKFXTp0iX9+uuvWrt2raRb/8e8/vrraW7r7++vhx56SHv37tWSJUuUmJiorl27ysvLS+Hh4QoKCtKGDRvsbTLy0EMPSbo11mTs2LHq06ePw3iUzNbzSa169ep65ZVX9M033+jUqVMKCAjQgAEDVLduXSUkJGjr1q2aPXu24uPjZbFY9NFHH93VukdZFRISolGjRtlXqK9du7bKlCkjq9Wqv//+WytXrrRPtV6nTp1cOyUy8heCCJCPLFiwQAsWLMi0TfHixfXhhx9m2NVj4sSJSk5O1urVq3X8+HGNHDkyTRsvLy998cUX9hWnpVtdHd555x1du3ZNLi4u+vTTT9N9szJx4kQ999xzioyM1Lvvvqvly5fnykHVPXr0UExMjD7//HNFRUXZF2RMzcXFRcOGDctw8H2vXr30888/KyoqSpMnT9bkyZPt+5o0aeIwBiArnP3d5LSsPO+8vLz0r3/9S0OGDMlwPEbbtm3VpUsXLVmyRBcvXkwTplxcXDRq1ChFR0dnGkSy+rj37dtX27Zt05YtWxQWFqbRo0c7HKdo0aKaNGmSNm7cmG1B5NChQ3ruuecybePm5qZWrVpp5MiRKl++vNPnioyM1MKFC7Vw4cJ09xctWlTjxo2zh73UXnvtNX3wwQcKDw9PM0vZxIkT1aVLF6fryophw4Zp9uzZ2rJlS7qL+5UvX17ff/99hmtfTJw4Ub1799alS5e0YsUKrVixwmH/c889p65du6p///4Z1vDII4+oQYMG2r9/v1auXJmmK9exY8eyfH+GDx+u+Ph4zZs3TxEREeleaStSpIjGjRunJ598MsvHdcbhw4czfT7XqFFD06dPzxVjhZD/EUSAfM7NzU1eXl564IEH1Lx5c3Xt2tWh68XtChcurKlTp6pLly769ddftX//fkVGRqpo0aKqVKmSWrRoob59+6YZTPzdd99p586dkm69icmoW1CpUqU0YcIEvfbaa7pw4YL++9//6ssvv8y+O5yNBgwYoKeeekrz5s3Tjh07dOHCBUm3pgxu2rSp+vTpo+rVq2d4+3Llymnx4sX65ptvFBwcrAsXLujmzZtO1+Ps78ZIhQoVUvHixeXh4aH77rtPderUUcOGDdW6desMA0hqEydO1COPPKJFixbZB96XKVNGjRo1Uu/evVWvXr07Tjmb1cfdzc1N33zzjRYsWKClS5fq1KlTslqtKleunJo1a6a+ffvqgQce0MaNG51+PO7E3d1dxYsXV6lSpeTv768HH3xQbdq0yfQ1mhVr1qzRtm3btH37doWFheny5cuKiYlRsWLFVKVKFT366KN6/vnnVaFChXRv36tXL5UuXVo///yzQkJCFB0d7TC5RU5zc3PTt99+q0WLFmnp0qUKDQ1VfHy8KlasqDZt2mjAgAGZXpWpWrWqlixZopkzZ+qvv/7S+fPnVbx4cdWqVUs9evRQ+/bt7f9fZaRQoUL67rvvNGvWLG3YsEFnz55VfHy8U1PsWiwWjR49Ws8++6wWLFigXbt26fLly3JxcZGvr6+aN2+ufv36Zfj7yA4dOnSQr6+vtm3bpj179uj8+fOKjIxUYmKifaxNmzZt1LlzZ0MWnQUkyWLNi5NxAwAAAMjTGKwOAAAAwHAEEQAAAACGI4gAAAAAMBxBBAAAAIDhmDXLJCkpKWlmcXFxcWG6PAAAAORJVqvVvm6UTZEiReyLet6OIGKSmzdv3nGFUwAAACAvq127tn1R2dvRNQsAAACA4QgiAAAAAAxHEAEAAABgOMaImMTFxSXNttq1a8vVlV8JAAAA8p6kpKQ0Y6DTe89rw7tek6Q3O5arq6vc3NxMqAYAAADIfpnNCEvXLAAAAACGI4gAAAAAMBxBBAAAAIDhCCIAAAAADEcQAQAAAGA4gggAAAAAwxFEAAAAABiOIAIAAADAcAQRAAAAAIYjiAAAAAAwHEEEAAAAgOEIIgAAAAAMRxABAAAAYDiCCAAAAADDEUQAAAAAGI4gAgAAAMBwBBEAAAAAhnM1uwAAAABkj/DwcMXExJhdRoHi6empihUrml1GnkQQAQAAyAeuXr2qTp06KSUlxexSChQXFxf98ccf8vHxMbuUPIcgAgAAkA/4+Pho2bJlefKKSGhoqEaPHq3x48fLz8/P7HLuiqenJyHESQQRAACAfCKvdxHy8/OTv7+/2WXAIAxWBwAAAGA4gggAAAAAwxFEAAAAABiOIAIAAADAcAQRAAAAAIYjiAAAAAAwHEEEAAAAgOEIIgAAAAAMRxABAAAAYDiCCAAAAADDEUQAAAAAGI4gAgAAAMBwBBEAAAAAhnM1uwBnLFu2THv27NGhQ4d0/PhxJSYmauLEierSpUuattOmTdP06dPTPU7hwoV18ODBdPetWLFCc+fO1cmTJ+Xm5qYGDRpo6NChevDBB9NtHxYWpilTpmjnzp2Ki4tTlSpV1KNHD/Xq1UuFCpH3AAAAgNTyZBD54osvFBERIR8fH5UtW1YRERF3vE1AQIB8fX0dtrm4uKTbNjAwUFOmTFGFChXUs2dPxcXFadWqVXr++ef13XffqWnTpg7tT548qZ49e+rGjRtq27atypUrp02bNmncuHE6duyYxo0b5/ydBQAAAPKhPBlEPvroI1WpUkW+vr6aOXOmJk+efMfbBAQEpAkQ6QkLC9O0adNUtWpV/fLLL/L09JQk9enTR927d9eYMWO0evVqubr+30P3/vvvKyYmRjNnzlSLFi0kScOGDdMrr7yiRYsW6dlnn9Ujjzzi5L0FAAAA8p882Wfo0UcfTXN1I7ssWbJESUlJGjhwoD2ESFL16tXVqVMnnT17Vjt27LBvDw0N1a5du9S0aVN7CJEkNzc3DR8+XJK0ePHiHKkVAAAAyKvyZBBxxu7du/Xtt9/q+++/119//aWEhIR02wUHB0uSmjdvnmbf448/LknatWtXmvaPPfZYmvb16tWTl5eXvQ0AAACAW/Jk1yxnfPnllw4/lylTRpMmTUoTOMLCwuTu7q4yZcqkOUaVKlXsbVK3T70vNYvFosqVK+vQoUOKj49XsWLFMq0xPj5eiYmJWbk7AAAA+caNGzfsX+Pi4kyuBs5KSkq6q/b5Poj4+/tr0qRJaty4sUqXLq3z589r1apV+uabbzRw4EAtWrRItWrVsrePjY1VyZIl0z2Wh4eHvU3q9pIcunGld5uYmJg7BpETJ05k/Y4BAADkE2fOnJF0q8t7SkqKydXAKPk+iLRu3drh5ypVquiNN95Q6dKl9Z///EdfffVVmqslZqlevbrDIHgAAICCwLbUgZ+fn2rWrGlyNXBWUlLSXX2wXmDf9Xbu3FkffPCB9u7d67Ddw8NDMTEx6d7GdvXDdpUj9fd3c5uMFCtWTG5ubncuHgAAIB8pWrSo/au7u7vJ1cBZdzvEoMAMVr9d4cKFVbx4cXufRJuqVasqLi5Oly5dSnMb22XDqlWrOrRPvS81q9Wqs2fPqmzZsryoAAAAgFQKbBAJCwvTtWvX0kwD3LhxY0nS1q1b09xm8+bNDm0kqUmTJpKkLVu2pGl/4MABRUdH29sAAAAAuCVfB5HY2FgdPXo0zfZr165p9OjRkqRnn33WYV+XLl3k6uqqr7/+2qG71YkTJ7Rs2TJVrlzZYXFCPz8/NW7cWDt37tTGjRvt2xMTEzV16lRJUvfu3bPzbgEAAAB5Xp4cI7J48WLt2bNHknT8+HH7Ntt6Ha1bt1br1q0VFRWlTp06qW7duqpRo4ZKlSqlCxcuaNOmTYqKilLz5s3Vv39/h2P7+flp8ODBmjp1qjp27KhnnnlGcXFxWrVqlZKSkjRu3Lg0A8rff/999ezZU4MGDVK7du1UtmxZbd68WceOHVP37t1ZVR0AAAC4TZ4MInv27FFQUJDDtr1799oHnvv6+qp169by9vbWCy+8oP3792vDhg32KXRr1Kihjh07qnv37nJxcUlz/IEDB8rX11dz587VggUL5ObmpoYNG2ro0KGqV69emvbVqlXT4sWLNWXKFG3atElxcXGqUqWKxowZoxdeeCFnHgQAAAAgD7NYrVar2UUURImJiTpw4IDDtnr16jFrFgAAKHBCQkLUq1cv/fTTT/L39ze7HDjpbt/f5usxIgAAAAByJ4IIAAAAAMMRRAAAAAAYjiACAAAAwHAEEQAAAACGI4gAAAAAMBxBBAAAAIDhCCIAAAAADEcQAQAAAGA4gggAAAAAwxFEAAAAABiOIAIAAADAcAQRAAAAAIYjiAAAAAAwHEEEAAAAgOEIIgAAAAAMRxABAAAAYDiCCAAAAADDEUQAAAAAGI4gAgAAAMBwBBEAAAAAhiOIAAAAADAcQQQAAACA4QgiAAAAAAxHEAEAAABgOIIIAAAAAMMRRAAAAAAYjiACAAAAwHAEEQAAAACGI4gAAAAAMBxBBAAAAIDhCCIAAAAADEcQAQAAAGA4gggAAAAAwxFEAAAAABiOIAIAAADAcAQRAAAAAIYjiAAAAAAwHEEEAAAAgOEIIgAAAAAMRxABAAAAYDiCCAAAAADDEUQAAAAAGI4gAgAAAMBwBBEAAAAAhiOIAAAAADAcQQQAAACA4QgiAAAAAAxHEAEAAABgOIIIAAAAAMMRRAAAAAAYjiACAAAAwHAEEQAAAACGI4gAAAAAMBxBBAAAAIDhCCIAAAAADEcQAQAAAGA4gggAAAAAwxFEAAAAABiOIAIAAADAcAQRAAAAAIYjiAAAAAAwHEEEAAAAgOEIIgAAAAAMRxABAAAAYDiCCAAAAADDEUQAAAAAGI4gAgAAAMBwBBEAAAAAhiOIAAAAADAcQQQAAACA4QgiAAAAAAxHEAEAAABgOIIIAAAAAMMRRAAAAAAYjiACAAAAwHAEEQAAAACGI4gAAAAAMBxBBAAAAIDhCCIAAAAADEcQAQAAAGA4gggAAAAAwxFEAAAAABiOIAIAAADAcAQRAAAAAIYjiAAAAAAwHEEEAAAAgOEIIgAAAAAMRxABAAAAYDiCCAAAAADDEUQAAAAAGI4gAgAAAMBwrmYX4Ixly5Zpz549OnTokI4fP67ExERNnDhRXbp0Sbd9bGyspk2bprVr1+rSpUsqU6aM2rRpoyFDhsjDwyPd26xYsUJz587VyZMn5ebmpgYNGmjo0KF68MEH020fFhamKVOmaOfOnYqLi1OVKlXUo0cP9erVS4UKkfcAAACA1PLkO+QvvvhCCxcu1N9//62yZctm2jYuLk69e/fWnDlz5Ofnp/79++uBBx7QnDlz1Lt3b8XFxaW5TWBgoEaMGKHIyEj17NlT7dq10969e/X8889r586dadqfPHlS3bp10/r16/XYY4+pT58+kqRx48Zp7Nix2XOnAQAAgHwkT14R+eijj1SlShX5+vpq5syZmjx5coZtZ82apZCQEA0YMEBvv/22ffuXX36pGTNmaNasWRo6dKh9e1hYmKZNm6aqVavql19+kaenpySpT58+6t69u8aMGaPVq1fL1fX/Hrr3339fMTExmjlzplq0aCFJGjZsmF555RUtWrRIzz77rB555JHsfhgAAACAPCtPXhF59NFH5evre8d2VqtVixcvlru7uwYNGuSw77XXXlOJEiX0yy+/yGq12rcvWbJESUlJGjhwoD2ESFL16tXVqVMnnT17Vjt27LBvDw0N1a5du9S0aVN7CJEkNzc3DR8+XJK0ePFip+8rAAAAkB/lySCSVWFhYbp48aIeeughubu7O+wrUqSIGjVqpAsXLujMmTP27cHBwZKk5s2bpzne448/LknatWtXmvaPPfZYmvb16tWTl5eXvQ0AAACAW/Jk16yssgWMqlWrpru/SpUq9na2NmFhYXJ3d1eZMmUybB8WFmbfZvveti81i8WiypUr69ChQ4qPj1exYsUyrTc+Pl6JiYmZtgEAs0VERCg2NtbsMgoUDw+PLPUEAPKqGzdu2L+mN34XeUNSUtJdtc/XQSQmJkaSMpwZy7bd1k66NcNWyZIlM22f+g+w7fvU3bgyOsedgsiJEycy3Q8AZouJidGbb77p0KUVOa9QoUKaPHlyhn9rgLzO9uFxaGioUlJSTK4GRsnXQSSvqV69usMgeADIjRYuXJgnr4icOXNGH3zwgcaOHZvuVezcjCsiyO9sSx34+fmpZs2aJlcDZyUlJd3VB+v5+l2v7ZOjjP5gpnc1w8PDw+EKSXrtU19hSe+qyp1uk5FixYrJzc3tju0AwEzVq1c3uwSnFC1aVJJUs2ZN+fv7m1wNgNRsr8+iRYumGdeLvONuhxjk68Hq6Y3pSM12GTD1J2NVq1ZVXFycLl26lGH71GNObN+nHvBuY7VadfbsWZUtW5YXFQAAAJBKvg4iVatWVdmyZbV37940A59u3ryp3bt3q2zZsg5BpHHjxpKkrVu3pjne5s2bHdpIUpMmTSRJW7ZsSdP+wIEDio6OtrcBAAAAcEu+DiIWi0Xdu3dXXFycZsyY4bDvm2++0bVr19S9e3dZLBb79i5dusjV1VVff/21Q3erEydOaNmyZapcubLD4oR+fn5q3Lixdu7cqY0bN9q3JyYmaurUqZKk7t2759A9BAAAAPKmPDlGZPHixdqzZ48k6fjx4/ZttvU6WrdurdatW0uSBgwYoD///NO+wnqdOnV09OhRbdq0Sf7+/howYIDDsf38/DR48GBNnTpVHTt21DPPPKO4uDitWrVKSUlJGjduXJoB5e+//7569uypQYMGqV27dipbtqw2b96sY8eOqXv37qyqDgAAANwmTwaRPXv2KCgoyGHb3r17tXfvXkmSr6+vPYi4u7tr/vz5mj59un7//XcFBwerdOnS6t+/vwYPHpzu2I2BAwfK19dXc+fO1YIFC+Tm5qaGDRtq6NChqlevXpr21apV0+LFizVlyhRt2rRJcXFxqlKlisaMGaMXXnghBx4BAAAAIG+zWJkM3hSJiYk6cOCAw7Z69eoxaxYA5JCQkBD16tVLP/30E7NmAbkMr8/84W7f3+brMSIAAAAAcieCCAAAAADDEUQAAAAAGI4gAgAAAMBwBBEAAAAAhiOIAAAAADAcQQQAAACA4QgiAAAAAAxHEAEAAABgOIIIAAAAAMMRRAAAAAAYjiACAAAAwHAEEQAAAACGI4gAAAAAMBxBBAAAAIDhCCIAAAAADEcQAQAAAGA4gggAAAAAwxFEAAAAABiOIAIAAADAcAQRAAAAAIYjiAAAAAAwHEEEAAAAgOEIIgAAAAAMRxABAAAAYDiCCAAAAADDEUQAAAAAGI4gAgAAAMBwBBEAAAAAhiOIAAAAADAcQQQAAACA4QgiAAAAAAxHEAEAAABgOIIIAAAAAMMRRAAAAAAYjiACAAAAwHAEEQAAAACGI4gAAAAAMBxBBAAAAIDhCCIAAAAADEcQAQAAAGA4V7MLAAAAyG3++ecfRUVFmV1GgREaGurwFcbw9vZW+fLlTTs/QQQAACCVf/75R50DApRw86bZpRQ4o0ePNruEAqVwkSJaGhRkWhghiAAAAKQSFRWlhJs3deXR6koqUczscoAc4XotXiW3nVBUVBRBBAAAIDdJKlFMiSU9zC4DyLcYrA4AAADAcAQRAAAAAIYjiAAAAAAwHEEEAAAAgOEIIgAAAAAMRxABAAAAYDiCCAAAAADDEUQAAAAAGI4gAgAAAMBw97yyelhYmObMmaMdO3bowoULunnzpo4cOWLf/8svv+j8+fN68cUXVbx48Xs9HQAAAIB84J6CyMqVKzV69GglJCTIarVKkiwWi0Oba9euacaMGXrggQfUrl27ezkdCpDw8HDFxMSYXUaB4unpqYoVK5pdBgAAKCCcDiIhISEaOXKkUlJS9MILL+iZZ57Rxx9/7HA1RJLatm2rTz/9VOvXryeIIEuuXr2qTp06KSUlxexSChQXFxf98ccf8vHxMbsUAABQADgdRGbNmqXk5GS999576tOnjySpSJEiadr5+vqqdOnSOn78uPNVokDx8fHRsmXL8uQVkdDQUI0ePVrjx4+Xn5+f2eXcFU9PT0IIAAAwjNNBZNeuXfLw8LCHkMyUK1dOERERzp4KBVBe7yLk5+cnf39/s8sAAADItZyeNevKlSuqVKlS1k5SqJDi4uKcPRUAAACAfMbpIOLp6alLly5lqe3Zs2fp8gEAAADAzukg4u/vr8uXL6cZnH67DRs26Nq1a6pfv76zpwIAAACQzzgdRDp16iSr1ar//Oc/unz5crptTp48qbFjx8pisahLly5OFwkAAAAgf3F6sHrHjh21dOlSbd++XR06dNBTTz2l8+fPS5Lmz5+vvXv3at26dUpMTFSrVq305JNPZlfNAAAAAPI4p6+IWCwWTZ8+XW3btlVUVJSCgoL0999/y2q1asKECVq9erUSExPVtm1bTZ48OTtrBgAAAJDH3dPK6sWLF9fUqVN14MABrVmzRseOHdO1a9fk7u6uGjVqqF27dnr44Yezq1YAAAAA+cQ9BRGbevXqqV69etlxKAAAAAAFgNNdswAAAADAWQQRAAAAAIZzumtW375976q9xWLR3LlznT0dAAAAgHzE6SASHBx8xzYWi0WSZLVa7d8DAAAAgNNBZOLEiRnui4uLU1hYmFatWqWYmBgNHjxYZcuWdfZUAAAAAPIZp4NIQEDAHdv8+9//1vDhw7Vw4UIFBQU5eyoAAAAA+UyODlb38PDQhAkTdPHiRU2bNi0nTwUAAAAgD8nxWbPKlCmjatWq6c8//8zpUwEAAADIIwyZvvfmzZu6dOmSEacCAAAAkAfkeBA5duyYzpw5Ix8fn5w+FQAAAIA8wunB6n///XeG+6xWqyIjI7Vv3z599913slqtevLJJ509FQAAAIB8xukg0qpVqyy1s1qtqlSpkv797387eyoAAAAA+YzTQcRqtWa6v1ixYqpatapatmypF198UR4eHs6eCgAAAEA+43QQOXr0aHbWAQAAAKAAMWTWLAAAAABIjSACAAAAwHAEEQAAAACGy9IYkazOkJUZi8WidevW3fNxAAAAAOR9WQoiERER93wii8Vyz8cAAAAAkD9kKYjMmzcvp+sAAAAAUIBkKYg0adIkp+sAAAAAUIAwWB0AAACA4QgiAAAAAAzn9MrqqV25ckVHjhxRVFSUkpKSMmzXuXPn7DgdAAAAgDzunoLI33//rQ8//FCbNm2S1Wq9Y3uzgkjLli0znPmrR48e+vDDDx22xcbGatq0aVq7dq0uXbqkMmXKqE2bNhoyZIg8PDzSPc6KFSs0d+5cnTx5Um5ubmrQoIGGDh2qBx98MNvvDwAAAJDXOR1Erly5ol69eun8+fMqV66crl+/ruvXr+vhhx9WVFSUQkNDlZycrKJFi+aKN+Oenp7q169fmu1169Z1+DkuLk69e/dWSEiImjdvrmeffVZHjx7VnDlztHPnTv30009yd3d3uE1gYKCmTJmiChUqqGfPnoqLi9OqVav0/PPP67vvvlPTpk1z9L4BAAAAeY3TQeT777/X+fPn9a9//UsffvihevXqpX379umHH36QJEVFRWn27Nn69ttv5efnl+aqg9G8vLw0ZMiQO7abNWuWQkJCNGDAAL399tv27V9++aVmzJihWbNmaejQofbtYWFhmjZtmqpWrapffvlFnp6ekqQ+ffqoe/fuGjNmjFavXi1X12zpBQcAAADkC04PVt+4caPc3Nz05ptvprvf29tbw4cP16hRo7R48WItWbLE6SKNYrVatXjxYrm7u2vQoEEO+1577TWVKFFCv/zyi0M3tCVLligpKUkDBw60hxBJql69ujp16qSzZ89qx44dht0HAAAAIC9wOoiEh4fL19dX3t7ekv5v5fTbB6v37t1b3t7eWrRokfNVZoOEhAQFBQUpMDBQP/30k44ePZqmTVhYmC5evKiHHnooTferIkWKqFGjRrpw4YLOnDlj3x4cHCxJat68eZrjPf7445KkXbt2ZeddAQAAAPK8e+ovlPoKQLFixSRJV69eVZkyZezbLRaLfH19derUqXs51T27dOmSRo4c6bDt8ccf1yeffKKSJUtKkj1gVK1aNd1jVKlSxd7O1iYsLEzu7u4O9/n29mFhYVmqMT4+XomJiVlqi9zpxo0b9q9xcXEmVwMgNV6fyCrbcwUoCLLz/8TMZs9Nj9NBpGzZsoqMjLT/XKFCBUnSkSNH1KJFC/v2lJQURUREKCEhwdlT3bMuXbqoSZMmqlatmgoXLqxTp05p+vTp2rRpk9544w0tWLBAFotFMTExkpThzFi27bZ20q0ZtmxBJqP2sbGxWarzxIkTWb5PyJ1sYTY0NFQpKSkmVwMgNV6fyKrUPR+A/M7M/xOdDiLVqlXT5s2blZiYKDc3NzVp0kSLFi3StGnT1KBBA5UoUUKS9MUXX+jq1auqXbt2thV9twYPHuzwc/369fXNN9+od+/e2rNnjzZu3Kgnn3zSnOJSqV69OoPa87hChW71dvTz81PNmjVNrgZAarw+kVW25wpQEGTn/4lJSUl39cG60+96W7RoofXr12vHjh16/PHH1aZNG/n6+urw4cN68skndf/99+vy5cu6ePGiLBaLXnjhBWdPlSMKFSqkLl26aM+ePdq7d6+efPJJe1ezjK5g2Lan7pLm4eHhcIUkvfYZXWG5XbFixeTm5pbl+4Dcp2jRovavt48zAmAuXp/IKttzBSgIsvP/xLsdYpDlIDJ+/Hh17dpVtWrVkiQ9/fTTSkxMtA9WL1y4sGbOnKkhQ4bo9OnTOnz48K0TuLrq1VdfVdeuXe+qMCP4+PhIujU2Q7rzmA7bpVpbO+nWeJJ9+/bZFz5Mr31GY04AFGz//POPoqKizC6jwAgNDXX4CmN4e3urfPnyZpcBIBfKchCZP3++fvjhB/n7+6tr167q0KFDmqscDzzwgFatWqUDBw4oPDxcRYsWVcOGDTMcQ2G2AwcOSJJ8fX0l3QoMZcuW1d69exUXF+eQDm/evKndu3erbNmyDkGkcePG2rdvn7Zu3Zpm5fjNmzfb2wBAav/884+6BHTWjZvmjZ8rqEaPHm12CQVK0SKFtSRoKWEEQBpZDiI1a9bUsWPHdOTIEYWEhGjSpElq3bq1unTpoubNm9un77VYLKpfv77q16+fY0XfjZMnT6ps2bLy8vJy2L57927Nnj1bhQsXVps2bSTdqr179+6aMWOGZsyY4bCg4TfffKNr165p0KBB9vsq3RoI//333+vrr79Wq1at7N22Tpw4oWXLlqly5cp65JFHDLinAPKSqKgo3biZoL6PXNd9XgycRv50PrqQ5u249XwniAC4XZaDyLJlyxQSEqJff/1VK1euVFRUlH777TetXr1a5cqVU0BAgAICAlS5cuWcrPeurV69WrNmzVKzZs3k6+urwoUL6/jx49q6dasKFSqkDz74wD7jlyQNGDBAf/75p32F9Tp16ujo0aPatGmT/P39NWDAAIfj+/n5afDgwZo6dao6duyoZ555RnFxcVq1apWSkpI0btw4BqADyNB9XimqVDLZ7DIAADDcXb1D9vf315gxY/Tuu+9qw4YN+vXXX7VlyxadP39egYGBCgwMVKNGjdS1a1e1bds2Vwz2atq0qU6dOqUjR44oODhYCQkJKlWqlNq3b6/+/furXr16Du3d3d01f/58TZ8+Xb///ruCg4NVunRp9e/fX4MHD053MM/AgQPl6+uruXPnasGCBXJzc1PDhg01dOjQNMcHAAAA4OSsWW5ubmrTpo3atGmjy5cva+nSpVq6dKlOnjypXbt2affu3Ro3bpzat2+vLl26qGHDhtldd5Y1adJETZo0uavbeHp6atSoURo1alSWb9OxY0d17NjxbssDAAAACqR7nii7dOnSGjBggFauXKlFixapR48e8vT01PXr17V48WL16tVL7dq106xZs7KjXgAAAAD5QLau2FOvXj198MEH2rJliyZPnmwfxB4aGqrJkydn56kAAAAA5GE5snSom5ubvLy85OXlxUBtAAAAAGlka0o4ffq0goKCtHz5cl28eFGSZLVadd9996lTp07ZeSoAAAAAedg9B5HY2FitWrVKS5YssS8QaLVaVbhwYbVs2VJdu3bVY4895rD2BgAAAICCzakgYrVatXXrVgUFBWn9+vW6efOmrFarJNlXXn/uuedUokSJbC0WAAAAQP5wV0EkNDRUQUFBWrZsmUPXK29vb3Xo0EHdunVTrVq1cqRQAAAAAPlHloNIz5499b///U/SrfBRqFAhNW/eXF26dFHr1q3l5uaWY0UCAAAAyF+yHET2798vSapSpYoCAgIUEBCgcuXK5VRdAAAAAPKxLAeRgIAAde3aVY0aNcrJegAAAAAUAFkOIhMnTszJOgAAAAAUIDmyoCEAAAAAZIYgAgAAAMBwBBEAAAAAhiOIAAAAADAcQQQAAACA4QgiAAAAAAxHEAEAAABgOIIIAAAAAMMRRAAAAAAYjiACAAAAwHAEEQAAAACGczW7AAAAgNzI9Vqc2SUAOSY3PL8JIgAAAOkoue2k2SUA+RpBJJ/7559/FBUVZXYZBUZoaKjDVxjD29tb5cuXN7sMAPnMlUerKamEu9llADnC9Vqc6WGbIJKP/fPPP+ocEKCEmzfNLqXAGT16tNklFCiFixTR0qAgwgiAbJVUwl2JJT3MLgPItwgi+VhUVJQSbt7U1ZpPK8ndx+xygBzhGndVPsf+UFRUFEEEAIA8hCBSACS5+yjJo6zZZQAAAAB2TN8LAAAAwHAEEQAAAACGI4gAAAAAMBxBBAAAAIDhCCIAAAAADEcQAQAAAGA4gggAAAAAwxFEAAAAABiOIAIAAADAcAQRAAAAAIYjiAAAAAAwHEEEAAAAgOEIIgAAAAAMRxABAAAAYDiCCAAAAADDEUQAAAAAGI4gAgAAAMBwrmYXAAAF2floPg9C/sXzG0BmCCIAYKJ5O4qbXQIAAKYgiACAifo+cl33eaWYXQaQI85HFyJsA8gQQQQATHSfV4oqlUw2uwwAAAxH500AAAAAhiOIAAAAADAcQQQAAACA4QgiAAAAAAxHEAEAAABgOIIIAAAAAMMRRAAAAAAYjnVEAAAA0uF6Ld7sEoAckxue3wQRAACAVLy9vVW4SBGV3HbC7FKAHFW4SBF5e3ubdn6CSAHgGnfV7BKAHMPzG0B2K1++vJYGBSkqKsrsUgqM0NBQjR49WuPHj5efn5/Z5RQY3t7eKl++vGnnJ4gUAD7H/jC7BAAA8pTy5cub+gatoPLz85O/v7/ZZcAgBJEC4GrNp5Xk7mN2GUCOcI27StgGACAPIogUAEnuPkryKGt2GQAAAIAd0/cCAAAAMBxBBAAAAIDhCCIAAAAADEcQAQAAAGA4gggAAAAAwzFrFgCY6Hw0nwch/+L5DSAzBBEAMIG3t7eKFimseTvMrgTIWUWLFJa3t7fZZQDIhQgiAGCC8uXLa0nQUkVFRZldSoERGhqq0aNHa/z48fLz8zO7nALD29ubFcoBpIsgAgAmKV++PG/QTODn5yd/f3+zywCAAo/OmwAAAAAMRxABAAAAYDiCCAAAAADDMUakAHCNu2p2CUCO4fkNAEDeRBDJx7y9vVW4SBH5HPvD7FKAHFW4SBGmBwUAII8hiORj5cuX19KgIKYHNRDTg5qD6UEBAMh7CCL5HNODmoPpQQEAADLHYHUAAAAAhiOIAAAAADAcQQQAAACA4QgiAAAAAAxHEAEAAABgOIIIAAAAAMMRRAAAAAAYjiACAAAAwHAEEQAAAACGI4gAAAAAMBxBBAAAAIDhCCIAAAAADEcQAQAAAGA4gggAAAAAwxFEstGBAwf0yiuvqHHjxmrQoIG6deumFStWmF0WAAAAkOu4ml1AfrFz5069/PLLcnNz07PPPitPT0+tXbtWI0aMUEREhF5//XWzSwQAAAByDYJINkhKStKYMWNksVj0448/qnbt2pKkQYMGqWfPnpo2bZratm2rqlWrmlsoAAAAkEvQNSsb7NixQ2fPnlWHDh3sIUSSPDw89MYbbygpKUlLliwxsUIAAAAgdyGIZIPg4GBJ0mOPPZZmX/PmzR3aAAAAAKBrVrYICwuTJFWpUiXNvhIlSsjHx0dnzpy543Hi4+OVmJiY3eXBQDdu3LB/jYuLM7kaAKnx+gRyL16f+UNSUtJdtSeIZIPY2FhJkqenZ7r7PTw8dP78+Tse58SJE9laF4xnC5yhoaFKSUkxuRoAqfH6BHIvXp8FE0EkF6levbpcXfmV5GWFCt3q7ejn56eaNWuaXA2A1Hh9ArkXr8/8ISkp6a4+WOddbzbw8PCQJMXExKS7PzY2NsOrJakVK1ZMbm5u2VobjFW0aFH7V3d3d5OrAZAar08g9+L1mT/c7RADBqtnA9u0vOmNA7l27ZquXr2a7vgRAAAAoKAiiGSDxo0bS5K2bNmSZt/WrVslSU2aNDG0JgAAACA3I4hkg2bNmqlSpUpauXKlQkJC7NtjY2P11VdfydXVVQEBASZWCAAAAOQujBHJBq6urvroo480YMAA9erVSx06dJCHh4fWrl2r8PBwDRs2TH5+fmaXCQAAAOQaBJFs8sgjj+inn37Sl19+qdWrVysxMVHVqlXTv//9b3Xs2NHs8gAAAIBchSCSjerVq6dZs2aZXQYAAACQ6zFGBAAAAIDhCCIAAAAADEcQAQAAAGA4gggAAAAAwxFEAAAAABiOIAIAAADAcAQRAAAAAIYjiAAAAAAwHEEEAAAAgOEIIgAAAAAMRxABAAAAYDiCCAAAAADDEUQAAAAAGI4gAgAAAMBwBBEAAAAAhiOIAAAAADAcQQQAAACA4QgiAAAAAAxHEAEAAABgOIIIAAAAAMMRRAAAAAAYjiACAAAAwHAEEQAAAACGI4gAAAAAMJyr2QUAAPKW8PBwxcTEmF3GXQsNDXX4mpd4enqqYsWKZpcBANmKIAIAyLKrV6+qU6dOSklJMbsUp40ePdrsEu6ai4uL/vjjD/n4+JhdCgBkG4IIACDLfHx8tGzZsjx5RSQv8/T0JIQAyHcIIgCAu0IXIQBAdmCwOgAAAADDcUUEuRKDYY3HYFgAAGAkgghyHQbDmoPBsAAAwEgEEeQ6DIY1B4NhAQCAkQgiyJXoIgQAAJC/MVgdAAAAgOEIIgAAAAAMRxABAAAAYDiCCAAAAADDEUQAAAAAGI4gAgAAAMBwBBEAAAAAhiOIAAAAADAcQQQAAACA4QgiAAAAAAxHEAEAAABgOIIIAAAAAMMRRAAAAAAYjiACAAAAwHAEEQAAAACGI4gAAAAAMBxBBAAAAIDhCCIAAAAADEcQAQAAAGA4gggAAAAAwxFEAAAAABiOIAIAAADAcAQRAAAAAIYjiAAAAAAwHEEEAAAAgOEIIgAAAAAMRxABAAAAYDiCCAAAAADDEUQAAAAAGI4gAgAAAMBwBBEAAAAAhiOIAAAAADAcQQQAAACA4QgiAAAAAAxHEAEAAABgOIIIAAAAAMMRRAAAAAAYjiACAAAAwHAEEQAAAACGI4gAAAAAMBxBBAAAAIDhCCIAAAAADEcQAQAAAGA4gggAAAAAwxFEAAAAABiOIAIAAADAcAQRAAAAAIYjiAAAAAAwHEEEAAAAgOEIIgAAAAAMRxABAAAAYDiCCAAAAADDEUQAAAAAGI4gAgAAAMBwBBEAAAAAhiOIAAAAADAcQQQAAACA4VzNLgAAAADZIzw8XDExMWaXcddCQ0MdvuYlnp6eqlixotll5En5PoiMHDlSQUFB6e7z8/PTmjVr0mxPSUnRTz/9pIULF+rMmTNyd3dX06ZNNXz4cFWtWjXdYx04cEDTpk3T/v37lZiYqGrVqqlfv3567rnnsvPuAAAApOvq1avq1KmTUlJSzC7FaaNHjza7hLvm4uKiP/74Qz4+PmaXkufk+yBi07dvX3l5eTlsy+gJM3bsWC1atEjVqlVT7969FRkZqd9++01bt27Vzz//rGrVqjm037lzp15++WW5ubnp2Weflaenp9auXasRI0YoIiJCr7/+eo7dLwAAAOnW+5ply5blySsieZmnpychxEkFJoj069cvS5fNduzYoUWLFqlRo0aaPXu2ChcuLEnq3LmzXnzxRb3//vv64Ycf7O2TkpI0ZswYWSwW/fjjj6pdu7YkadCgQerZs6emTZumtm3bZnglBQAAILvQRQh5CYPVb7N48WJJ0rBhw+whRJKaNWumxx57TLt27XLov7hjxw6dPXtWHTp0sIcQSfLw8NAbb7yhpKQkLVmyxLg7AAAAAOQBBeaKyMaNG3X9+nUVLlxYNWvWVJMmTeTi4pKm3c6dO+Xu7q6HHnoozb7HHntMmzdv1q5du+Tn5ydJCg4Otu+7XfPmzR3aAAAAALilwASRDz/80OHnqlWr6vPPP1edOnXs2+Li4nTp0iXVqFEj3ZBi614VFhZm32b7vkqVKmnalyhRQj4+Pjpz5kyWaoyPj1diYmKW2gIAAAC5SVJS0l21z/dBpHHjxmrZsqXq1asnHx8fhYeHa+HChfrhhx/00ksvafny5SpXrpwk2Qd3eXh4pHss2/bY2Fj7Ntv3np6eGd7m/PnzWar1xIkTWbtTAAAAQB6XJ4JI06ZNFRUVleX28+bNU9OmTSVJXbt2ddj3wAMP6L333lOxYsUUGBioOXPm6N13383Ocp1WvXp1ubrmiV8JAAAA4CApKemuPljPE+96O3TooOvXr2e5fenSpe/Yplu3bgoMDNS+ffvs22xXNVJf8UjNtj31FRPb9xlNlRcbG5vh1ZLbFStWTG5ubllqCwAAAOQmdzvEIE8Ekf/85z/ZfkzbfM/x8fH2be7u7ipTpozCw8OVnJycZpyIbTxI6ql4bd+fOXNGdevWdWh/7do1Xb16VQ0bNsz2+gEAAIC8rMBO3/u///1PkuTr6+uwvUmTJoqLi9PevXvT3GbLli2Sbo07sbF9b9uX2tatW+3HBAAAAPB/8nUQuXTpks6ePZtm+4ULF/TRRx9JutXtK7V//etfkqSpU6cqISHBvn379u3asmWLGjdubJ+6V7q1vkilSpW0cuVKhYSE2LfHxsbqq6++kqurqwICArL1fgEAAAB5XZ7omuWs06dPq1+/fnr44Yd1//33q0SJEoqIiNBff/2luLg4BQQEqF27dg63eeSRR9S9e3ctXrxYAQEBatGihSIjI/Xbb7/Jw8ND77//vkN7V1dXffTRRxowYIB69eqlDh06yMPDQ2vXrlV4eLiGDRvmEFwAAAAASBar1Wo1u4ic8s8//2jGjBk6cOCAzp8/r+vXr8vDw0N16tRRt27d1L59+3Rvl5KSoh9//FELFy7UmTNn5O7urqZNm2r48OEZhooDBw7oyy+/1P79+5WYmKhq1aqpX79+6tixY7rtExMTdeDAAYdt9erVY7A6AAAA8qS7fX+br4NIbkYQAQAAQH5yt+9v8/UYEQAAAAC5E0EEAAAAgOEIIgAAAAAMRxABAAAAYLh8PX1vbpbeHAFJSUkmVAIAAADcu/Tey2Y2LxZBxCTJyclpth05csSESgAAAICckd57Xhu6ZgEAAAAwHEEEAAAAgOEIIgAAAAAMx8rqJklJSdHNmzcdtrm4uMhisZhUEQAAAOA8q9WaZkxIkSJFVKhQ+tc+CCIAAAAADEfXLAAAAACGI4gAAAAAMBxBBAAAAIDhCCIAAAAADEcQAXLYjRs3zC4BAIBcJbPVtlOLjIzM4UpgJoII4KQhQ4YoOjo60zZHjhxRly5dDKoIQGrz58+/Y5vY2Fi98847BlQDILXnn39e586dy7TNX3/9pY4dOxpUEcxAEAGc9Mcff6hjx44KDg5Od//s2bPVs2dPhYeHG1wZAEkaP368XnvtNV25ciXd/QcOHFCXLl20YsUKgysDcPDgQQUEBGjp0qVp9iUkJOijjz7SwIEDlZSUZHxxMAxBBHDShAkTFB0drf79++vzzz+3X2a+fPmyXn75ZX3yySfy9fXVwoULTa4UKJg6d+6sjRs3qmPHjtq8ebPDvsDAQPXq1UsXLlzQmDFjTKoQKLhmz54td3d3jRo1Sm+99ZZiY2MlSSdOnFDXrl31ww8/qFGjRlq2bJnJlSInsaAhcA/OnDmjt956S4cOHVK9evXUvXt3TZkyRVeuXNG//vUvvffeeypatKjZZQIF1m+//aaxY8cqNjZWffv2Va9evTRmzBjt2rVLNWrU0Oeff65q1aqZXSZQIF27dk2jR4/WunXr5Ovrq+eee05z5sxRUlKShg4dqldeeUUWi8XsMpGDCCLAPUpOTtann36qOXPmyGKxyMvLSxMmTFCrVq3MLg2ApL///ltvv/229u7dK0myWCzq3bu3RowYocKFC5tcHYDAwEBNnTpVFotF3t7e+vbbb1W3bl2zy4IB6JoF3KPTp09r69at9p+vX7+uY8eOiYwP5A4lS5ZU5cqVZbVaZbVa5eXlpVatWhFCgFxg27Zt+vHHHyVJxYoVU1RUlBYsWKD4+HiTK4MRCCLAPfjxxx/VvXt3hYaGatiwYVq5cqWqV6+uadOmqXfv3vrnn3/MLhEo0EJCQuwDYps3b66xY8cqMTFRL730kqZMmZLlKUQBZK+kpCR98sknGjBggOLj4/XZZ59p3bp1euKJJ/Trr7+qc+fOOnTokNllIofRNQtw0sCBA/XXX3+pYsWKmjx5surVqydJSkxM1GeffaZ58+bJw8ND77//vp599lmTqwUKnjlz5ujzzz+X1WrVm2++qRdffFGSdPbsWY0YMUIHDhzQgw8+qMmTJ6ty5comVwsULF27dtXhw4fVoEEDffbZZ6pYsaJ937x58zR58mQlJydr6NChevXVV02sFDmJIAI4qVatWurYsaPGjh2r4sWLp9m/ZcsWjRw5UpGRkQoJCTGhQqBgq1Wrlvz8/PT555/L39/fYV9ycrK+/PJLffvttypWrJj27NljUpVAwVS7dm299tprGjx4sFxcXNLsP3bsmEaMGKGTJ0/yNzQfI4gATlq+fPkdF1q6cuWK3nvvPQUGBhpUFQCbMWPGaPTo0SpWrFiGbYKDg/Xuu+9qw4YNBlYGYNeuXWrcuHGmbRISEjRp0iT95z//MagqGI0gAgAo0KKjo+Xl5WV2GQBQ4BBEAAAAABjO1ewCgLxq1KhRWWpnsVg0YcKEHK4GwO2WLl2a5badO3fOsToApNW3b98stbNYLJo7d24OVwOzcEUEcFKtWrUy3W+xWGS1WmWxWBhoB5igVq1ad1yVmdcoYA7+hkIiiABOi4iISHd7TEyMjhw5osDAQPn7++vtt992mJYQgDGCgoLS3W57ja5cuVItW7bUU089pYCAAIOrA5Ce2NhYHT58WFOmTFHZsmU1ZcqUdGfVQv5AEAFyyOXLl/Xcc89p8ODBeuGFF8wuB8Bt9u3bp/79+yswMFDNmjUzuxwAqcTGxqpjx47q0qWLBg8ebHY5yCGsrA7kkNKlS+upp57SDz/8YHYpANLRsGFDPfXUU/riiy/MLgXAbTw8PPT4449ryZIlZpeCHEQQAXJQ8eLFM+zCBcB8FSpU0LFjx8wuA0A6ChUqpEuXLpldBnIQQQTIIdHR0Vq/fr1Kly5tdikA0mG1WrV7924VKVLE7FIA3ObcuXNas2aNKlSoYHYpyEFM3ws4afr06eluT05O1oULF/Tnn3/q2rVrGjRokMGVAZBurdycnqSkJF24cEHLli3TwYMH1alTJ4MrA5DRFPi2v6F79uxRUlIS40PyOQarA06609SD7u7u6t27t4YPH37HKUQBZL87Td9rtVrVoEEDBQYGytvb27jCANzxb2jVqlX14osvqkePHgZVBDMQRAAnBQcHp7u9UKFC8vLykp+fn9zc3AyuCoDNtGnT0g0iFotFJUqUUN26ddWgQQPjCwOQ4fjJQoUKydPTUx4eHgZXBDMQRAAAAAAYjsHqAAAAAAzHYHUgizIa+JoVjRs3zsZKAADIW5YuXer0bTt37pxtdSB3oWsWkEV3GviamZCQkGyuBsDtnH2NWiwWHTlyJAcqAmDjzOvTarXKYrHwNzQf44oIkEWDBg1i9isgF+PKI5B7TZw40ewSkAtxRQQAAACA4RisDgAAAMBwBBHgLkyfPv2eBq0DyFm7du3S33//bXYZANIxatQorV+/3mFbQkKCYmNjTaoIZiOIAHdh+vTp2rlzp8O2mTNnqmnTpiZVBCC1vn37asmSJQ7bfvvtNw0ePNikigDYBAUFpRl4PnPmTMZ3FWAEEeAeJSQkKDo62uwyAOjWLDu3O336dJpPYQEA5iOIAAAAADAcQQQAAACA4QgiAAAAAAzHgobAXbpw4YIOHDhg//n8+fOS5LDtdvXq1cvxugDcwsKjQO514sQJ/fbbb/afjx8/LklavXp1umO8JKl9+/aG1AbjsaAhcBdq1aqV7pscq9Wa6Zuf22cJAZAzatWqJVdXV7m4uNi3JScnKzk5WYULF073NhaLRfv37zeoQqDgSu9vqO1taGZ/W/kbmn9xRQS4CwEBAWaXACATFSpUMLsEABlgGm3cjisiAAAAAAzHYHUAAAAAhiOIAAAAADAcY0SAe3Dy5En98MMPOnjwoGJiYpScnJymjcVi0bp160yoDkBCQoLWrVt3x9fohAkTTKgOKNi2bdum2bNn21+fKSkpadpYLBYdOXLEhOpgBIII4KTg4GANGDBACQkJcnV1ValSpRxm6rFhGBZgjoiICL300ks6e/Zspq9DgghgvN9//13Dhw9XSkqKKlSooPvvvz/dv6HI3wgigJMmT56s5ORkffTRRwoICOA/UCCXmThxos6cOaNOnTqpa9euuu+++3idArnEjBkzVKRIEX311Vdq1qyZ2eXAJAQRwElHjx5V+/bt1a1bN7NLAZCOHTt2qFmzZpo0aZLZpQC4TWhoqDp16kQIKeAYrA44qXjx4ipVqpTZZQDIQEpKivz9/c0uA0A6fHx8VLRoUbPLgMkIIoCTWrRood27d5tdBoAMNGzYUKdPnza7DADpaNeunbZv366kpCSzS4GJCCKAk95++23FxMToo48+Unx8vNnlALjNW2+9pZ07d2rNmjVmlwLgNsOGDZOXl5eGDx+uv//+2+xyYBJWVgec1LdvX8XExOjo0aMqVqyYqlatKg8PjzTtLBaL5s6da0KFQME2ffp0HTp0SBs3blTjxo1Vu3btDF+jgwYNMqFCoOBq1aqVkpKSdPHiRUmSl5dXhq9PpsDPvwgigJNq1aqVpXYWi0UhISE5XA2A2/EaBXKvli1bZrntn3/+mYOVwEwEEQBAvhQcHJzltk2aNMnBSgAA6SGIAAAAADAc64gA2SQuLk6xsbHy8PCQu7u72eUAAJAnJCUlKSwsTDExMfLw8JCfn59cXXmLWhDwWwbuQWJior777jsFBQXp7Nmz9u2VKlVSly5d9NJLL6lw4cImVghg7969CgoKUkhIiP2NTu3atdWpUyc1atTI7PKAAuvatWv67LPPtHLlSt24ccO+vWjRourQoYPefPNN+fj4mFghchpdswAn3bhxQy+++KL2798vFxcXVapUSaVLl1ZkZKTOnj2r5ORk1a9fX3PmzGHRJsAkkyZN0pw5c2T7U1eoUCGlpKRIujVIvW/fvho1apSZJQIF0rVr19SjRw+FhYXJ29tbdevWVZkyZXT58mUdOnRIV69eVZUqVbRw4UJ5e3ubXS5yCFdEACfNmjVL+/btU4cOHTRixAjdd9999n0XLlzQ5MmTtXz5cs2aNUuDBw82sVKgYFq6dKlmz56t+++/X4MHD1aTJk3sHxbs3LlTM2bM0Lx58+Tv76/OnTubXS5QoHz11VcKCwvTa6+9ptdff13FihWz77tx44YCAwMVGBior7/+mg8L8jGuiABOateunYoXL65ffvklwzbdunXT9evXtXr1agMrAyBJPXr00MWLF7VixYp01yeIjY3Vc889pzJlymjRokUmVAgUXK1atVLFihUzXWerf//+OnfunNavX29gZTASK6sDToqIiNCjjz6aaZtmzZopIiLCoIoApHb8+HG1adMm3RAiSR4eHnr66ad14sQJgysDcPHiRTVo0CDTNvXr17cveIj8iSACOKlYsWK6cuVKpm2uXLnicLkZgLHudNHfYrEYVAmA1Dw9Pe/4QV1ERIQ8PT0NqghmIIgATqpfv75WrVqV4aepJ0+e1G+//XbHT3wA5IwaNWpo7dq1un79err7Y2NjtXbtWlWvXt3gygA0adJEa9as0bZt29Ldv337dq1Zs4bFRvM5xogATtq7d6/69u0rFxcXdevWTY0bN1bp0qV1+fJlBQcHa8mSJUpKStLcuXP18MMPm10uUOAEBQVp1KhRql69ugYPHqzGjRurZMmSunLlioKDgzVjxgydPHlSEyZMUEBAgNnlAgXKyZMn1b17d924cUMtWrRQ48aNVapUKUVGRio4OFibNm1S0aJFtWjRIj4syMcIIsA9+P333zVmzBjFxMQ4dPGwWq3y9PTUuHHj1LZtWxMrBAq2CRMmaN68efbXZ+rpe61Wq3r37q0xY8aYWSJQYO3du1ejRo3SmTNnJN3qKml7W1q5cmVNnDiRD/LyOYIIcI+uX7+udevWKSQkxL6yur+/v1q1apXhIFkAxtm9e7eWLFmio0ePOrxGAwICWNAQMJnVatWePXvS/A19+OGHGcNVABBEACdNnz5dlSpVUqdOncwuBUA6du3aZX9TAyB3GTVqlGrWrKn+/fubXQpMxGB1wEmBgYE6fvy42WUAyEDfvn1ZHwTIpVauXKnIyEizy4DJCCKAkypUqKBr166ZXQaADJQqVUpubm5mlwEgHVWqVNGlS5fMLgMmI4gATnr22We1ZcsWxcTEmF0KgHQ89thj2rVr1x3XEgFgvG7duumvv/7ShQsXzC4FJmKMCOCkhIQEDRkyRJcuXdLQoUP14IMPqlSpUmaXBeD/u3Dhgnr27KnmzZtrxIgR8vb2NrskAP9feHi4xo0bp+PHj2vAgAH2v6HpDVCvUKGCCRXCCAQRwEm2AbBWqzXTmT0sFouOHDliVFkA/r++ffsqKipKJ06ckJubmypWrJjuGx2LxaK5c+eaVCVQMNWqVcs+XS9/QwsuV7MLAPIqpv0Ecrfg4GD79wkJCTp9+rROnz6dph1ThALG69y5M689cEUEAAAAgPEYrA44ybY6850wPSGQu2X1tQwg+9y4cSNL7UJDQ3O4EpiJIAI4acyYMXdsExkZqX79+hlQDYDbLV68+I5tkpOT9dZbbxlQDYDUhgwZouTk5EzbhIaG8jc0nyOIAE5asmSJJk+enOH+K1euqG/fvgoLCzOuKAB277//vtatW5fhfqvVqhEjRmjNmjUGVgVAkjZv3qxRo0ZluP/MmTPq27cvU+TncwQRwEm9e/fWrFmzNGfOnDT7rly5on79+ik0NFQTJ040vjgAql+/vt566y3t3r07zT5bCFm9erV69uxpQnVAwTZixAgtX75cH3/8cZp9Z8+eVZ8+fRQTE6Ovv/7ahOpgFIII4KQxY8aoXbt2+uSTT7RixQr79qtXr6pfv346deqUPv74Yz333HMmVgkUXN98840qV66sgQMH6tixY/btVqtVb7/9tlatWqUePXpo7NixJlYJFEwDBgzQiy++qLlz52rmzJn27efOnVPv3r0VHR2twMBAPfLIIyZWiZzGrFnAPUhMTNRrr72m4OBgff3116pbt6769u2rU6dOaeLEierUqZPZJQIF2oULF/T8888rMTFRCxYskK+vr0aMGKFVq1bpX//6lz788EOzSwQKtHfeeUcrVqzQ+PHj1bhxY/Xu3VtRUVEKDAxUs2bNzC4POYwgAtyjuLg49enTR6dPn1b58uUVFhamCRMmqHPnzmaXBkC3Brw+//zzKlGihGrXrq3Vq1ere/fuGjdunNmlAQVecnKyBg4cqG3btsnb21sxMTH66quv1Lx5c7NLgwEIIkA2uHLlinr16qWzZ89q/PjxCggIMLskAKkcPHhQ/fr1U3x8vLp27aqPPvrI7JIA/H83btxQ//79FRISQggpYAgiQBb17ds30/2RkZG6dOmSatWq5bDdYrFo7ty5OVkaAEnTp0/PdP/u3bsVEhKi3r17O6zobLFYNGjQoJwuDyjQWrVqlen+mzdv6vr16ypZsqTDdovFkunsd8jbCCJAFt0eMLLKYrEoJCQkm6sBcDteo0Du1bJlS6dv++eff2ZjJchNCCIAgHwhODjY6ds2adIkGysBAGQFQQQw2NGjR3X06FEGswO5VGxsrKKjo1WhQgWzSwFwm+DgYAUHB2vw4MFml4JswDoigMHWrVuX6WqyAMw1Z86cO/ZnB2CO4OBgzZgxw+wykE0IIgAAAAAMRxABAAAAYDiCCAAAAADDEUQAAAAAGI4gAgAAAMBwBBEAAAAAhiOIAAAAADAcQQQAAACA4QgigMF8fX3VqFEjs8sA8j1/f3+99dZbd307q9Uqq9WaAxUBuFe1atVS586dzS4D2cRi5X9bwClZWR29UKFC8vDwkJ+fn5566imVK1fOgMoASFLjxo3Vo0cPjRgxwuxSgAJv1KhRslgsevPNN1W6dOks/Q2VJIvFogkTJuRwdTALQQRwUq1atWSxWCQp3U9PLRaLw3ZXV1e98cYbeuONNwyrESjIXn75ZVksFs2aNcvsUoACz/Y387fffpOfn59q1aqVpdtZLBaFhITkcHUwC0EEcNK5c+c0YcIEHTx4UH379tVDDz2kUqVKKTIyUnv37tW8efNUr149vf766zp27Ji+/vpr/fPPP5o8ebLat29vdvlAvrd//3717t1b48aNU0BAgNnlAAVaRESEJKlcuXJydXW1/5wVvr6+OVUWTEYQAZw0c+ZMzZs3T8uWLVOpUqXS7L906ZI6d+6s/v3765VXXtGFCxfUvn17+fv764cffjChYqBgmT59uvbt26dt27bJ399f9erVU+nSpdO0s1gsGjRokAkVAkDBRhABnNSmTRs98cQTGjNmTIZtxo0bp82bN2vt2rWSpLfeeksbN27U7t27jSoTKLDo+gEAuZur2QUAedX58+fl5uaWaZvChQvr/Pnz9p8rVKigmzdv5nRpACTNmzfP7BIAAJkgiABOKleunP78808NHz5chQsXTrM/ISFBf/75p8NMWZGRkSpRooSRZQIFVpMmTcwuAQCQCdYRAZzUrVs3nTlzRr1799Zff/2lqKgoSVJUVJQ2bNigF154QWfPnlXXrl3tt9mzZ49q1qxpUsUAAAC5B1dEACcNGDBAp06d0vLlyzVw4EBJt9YNSUlJkXRrSt/nnntOr776qiTp8uXLatGihR5//HHTagYAAMgtGKwO3KPt27dr+fLlOnbsmGJjY+Xh4aGaNWuqY8eOatasmdnlAQAA5EoEEQAAAACGY4wIAAAAAMMRRAAAAAAYjiACAAAAwHAEEQAAAACGI4gAAAAAMBzriAAA7lmfPn0UHBx8x3bHjh0zoBpH0dHRmjt3riRpyJAhhp8fAJA+gggAINuUL19e5cuXN7sMB9HR0Zo+fbokgggA5CYEEQBAtunatStv9gEAWcIYEQAAAACG44oIAMAUu3fv1o8//qg9e/boypUrcnd3V506ddSzZ08988wzadonJCTor7/+0oYNG3To0CFduHBB8fHxKlOmjBo3bqyXX35ZNWrUcLjNyJEjFRQUZP+5Zs2aDvsnTpyoLl26KDw8XK1atZKU8TiWadOmafr06QoICNDHH39s3377bf/66y/NmzdPhw8fVlRUlGbMmKHWrVtLklJSUrRixQotW7ZMR44cUWxsrHx8fNS4cWMNGDBAtWvXduKRBIC8iSACADDcZ599pm+//VaS5OnpqWrVquny5cvatm2btm3bpp49e+qDDz5wuE1YWJiGDBmiQoUKqVSpUvL19VViYqIiIiK0dOlS/fbbb5o2bZqefPJJ+22qVq2qunXr6tChQ5Kkhx56yOGYpUqVytb79f3332vSpEny9vZWpUqVVKxYMfu+2NhYDRkyRNu2bZMklSlTRtWrV9fZs2e1atUq/f7775o4caI6duyYrTUBQG5FEAEAGOqnn37St99+Kx8fH/33v/9V+/bt7fu2bt2qt99+Wz///LPq16+vLl262PeVLFlSn3zyiVq0aCFvb2/79oSEBC1evFjjx4/XyJEjtWHDBnsAeP3119WhQwf7FYsFCxbk6H37/PPP9d5776l3795ycXGRJN28eVOSNGbMGG3btk21a9fWhx9+qAcffFDSrask8+fP18cff6zRo0erbt26uv/++3O0TgDIDRgjAgDINtOnT1fNmjXT/bdu3TrFx8fryy+/lCR9+umnDiFEkpo3b673339fkjRz5kyHfaVLl1anTp0cQogkFS5cWC+88ILat2+vq1evasOGDTl2/+6ka9eu6tevnz2ESFKRIkV04MABrV69Wt7e3goMDLSHEEkqVKiQ+vXrpxdeeEEJCQmaM2eOCZUDgPG4IgIAyDaZTd/r7e2tnTt36urVq/L19dXjjz+ebrunnnpKbm5uCg0N1YULF1SuXDmH/du3b9fGjRsVFham2NhYJScnS5L++ecfSdKRI0fSBByjdO3aNd3tq1evlnTrvt1+f2zatGmj+fPna+fOnTlWHwDkJgQRAEC2udP0vYGBgZKkmJgYPf/883c8Xuogcv36dQ0ZMkRbt27N9DZRUVFZLzibVatWLd3ttgHwwcHBGd5vWxeu8+fP50xxAJDLEEQAAIa5du2apFuLDO7du/eO7ePj4+3fT5o0SVu3bpWPj4/eeustNW3aVGXLllXRokUlSV988YW++uorJSUl5UzxWeDu7p7udtv9joiIUERERKbHuHHjRrbXBQC5EUEEAGCY4sWLS5Jat26tGTNmZPl2SUlJWrFihaRbgaRFixZp2tzLlRCLxWL/3mq1OvxsExcX5/TxbQHlvffeU79+/Zw+DgDkJwxWBwAYxrbOx//+9z+lpKRk+XZXrlyxB4GHH3443Tb79u1Ld3t6oeJ2qafZvXz5crptwsLC7nicjNjWL8nKVSAAKCgIIgAAwzz66KPy8vLSpUuXtGjRoizfLnVQuHjxYpr927ZtU0hIyB1vm1G3p5IlS6pEiRKSpP3796fZf+7cOW3ZsiXL9d6ubdu2kqT169fr+PHjTh8HAPITgggAwDAeHh4aNmyYJOmjjz7SnDlz0oSDa9euaenSpZo0aZJ9m6enp2rVqiVJGj9+vH3MhSTt2LFDb775pooUKZLuOX18fOTp6Wlvm5GnnnpKkjRlyhSHcRxnz57VsGHDZLVa7+KeOmrUqJHatm2rxMREvfzyy/rzzz/THC88PFyzZs3S4sWLnT4PAOQljBEBABjqhRdeUFRUlKZNm6aJEyfq888/1/333y83NzdduXJFERERslqtatKkicPt3n77bb366qvasmWLnnzySVWtWlXR0dEKDw+Xv7+/mjVrpu+//z7N+SwWizp16qQffvhBAwcOVPXq1e1XP1555RU98cQTkqQhQ4bor7/+0qlTp/TMM8/Iz89PKSkpOnXqlGrVqqXevXtr9uzZTt/vjz/+WAkJCfrzzz81cOBA++rrKSkpunDhgr1L2ODBg50+BwDkJVwRAQAYbtCgQVq6dKm6d++u++67T6GhoTp58qRcXV31+OOP6z//+Y8+/fRTh9s89thjmjdvnh599FFJ0unTp1W4cGENHDhQCxYsyHDGKkl655139Prrr6tq1aoKCwtTcHCwgoODHcaDVKxYUQsXLlT79u3l4eGh0NBQJSQk6LXXXtOCBQvsA+2dVaxYMX311Vf6+uuv9fTTT6tw4cI6evSoIiIiVLJkSXXo0EGff/65XnzxxXs6DwDkFRbrvVxrBgAAAAAncEUEAAAAgOEIIgAAAAAMRxABAAAAYDiCCAAAAADDEUQAAAAAGI4gAgAAAMBwBBEAAAAAhiOIAAAAADAcQQQAAACA4QgiAAAAAAxHEAEAAABgOIIIAAAAAMMRRAAAAAAY7v8Bm8uAkeRJuJ4AAAAASUVORK5CYII=", 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", 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" ] @@ -761,21 +761,21 @@ }, { "cell_type": "code", - "execution_count": 104, + "execution_count": 14, "id": "39521ac6-0bec-42e7-9062-8fc9ce5edc55", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T21:58:06.568327Z", - "iopub.status.busy": "2025-05-07T21:58:06.567858Z", - "iopub.status.idle": "2025-05-07T21:58:07.237288Z", - "shell.execute_reply": "2025-05-07T21:58:07.236304Z", - "shell.execute_reply.started": "2025-05-07T21:58:06.568289Z" + "iopub.execute_input": "2025-05-07T22:05:47.181972Z", + "iopub.status.busy": "2025-05-07T22:05:47.180976Z", + "iopub.status.idle": "2025-05-07T22:05:47.818553Z", + "shell.execute_reply": "2025-05-07T22:05:47.817379Z", + "shell.execute_reply.started": "2025-05-07T22:05:47.181926Z" } }, "outputs": [ { "data": { - "image/png": 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KPV6jRo0wb948m9BhMWXKFGvwjoyMtLkvPT3d+rWl97Y81emlIqoqBg8iD7dr1y589913yM/PL3WfJEnWsfV9+vQp82LbYvz48davLftClBQWFmYdqnHx4kX861//wkcffQSg+FPLG4e/1MScOXPQoUMH6/969uyJKVOm4MSJEwCK96v43//+V6XJwZbnFBAQUGooUEn33nuvdYJrWa+Dvdjze+NqJYfFFBYW3rR9VlYW4uPjcfHiRev/LBduJYcJ2YOqqkhPT0dcXJzN+SzD9Ox9vsqo6utlYal5z549yMrKsntdZRk9erTdjjVq1Khyh1DpdDrrPIjs7GxERUXZ7byOYHkftmjRosIPXB599NFSjynLiBEjbIYYlhQYGGgNZTfOtSk53HTTpk1QVfWmtRM5EodaEdVy06dPx0svvWRzm8lkQlJSEnbs2IHly5dj48aNOHv2LFauXImQkBBru8TERBiNRgBA9+7dKzxPp06doNfrIYpiqaFdFuPGjcO+ffuwY8cO/PHHHwCKx9d//PHHTlnKtVWrVhg5ciSefPLJKn+qa3lOHTp0KPcPPAD4+PigU6dOOHr0aLmvgz3Y+3vjSiUvnstblejYsWNYvXo1jhw5UuGQj5JDgmpiz549WLNmDY4dO1bhxb29zlcVJespayWv8jz88MM4evQoEhIScM8992D48OG44447cPvttztstaKyVn6qrhvnldwoPDzc+nV0dLTNv92J2Wy2DiksucpeWRo1aoRmzZohOTm5wvfurbfeWuFxLENYb/xZbt68Ofr06YOjR49i5cqV2L9/P4YPH44+ffqgW7duVfr5IrIHBg8iD+Tr64s2bdrghRdeQHh4OJ566ilcvHgRH3zwgc3u5SWHvZQ1FKAkvV6PevXqIT09vcILw3nz5mHPnj3WoRAzZ8686R/Nqiq5c7kgCPDz80P9+vVrtDmd5bUoGczKY5mYmZeXB1VVHbI7uiO+N65S8uK9rDk+Ny5lWpGioqIa1aKqKt5880389NNPTjlfdVheL51OV6Wf6TFjxiApKQnLly9HQUEBNm7caJ3z06xZM9x1112YMGGCXef52HPO1s3ee5b5VIBrAmFllXzvVvb3SXJycoXv3ZstTGEZgmUZ3lXSJ598gpkzZ+LYsWO4dOkSli5diqVLl0Kn06FLly4YNWoUxo4d65Wbe5LzcagVkYcbMGAAbrvtNgDFk08tn6LfqDIXz5Xppl+zZo3N+Gt7LdtZkmXn8vbt26Ndu3Zo0aKF3f5o2ut1sCd3rKkqzp8/b/36xr0GDh8+bA0dLVq0wNtvv40tW7bg2LFjOH/+PKKjoxEdHY0XX3zRLrVs2LDBGjo6duyI999/H7/++iuOHz9uc77KLG/qCJmZmdad5ival6E8M2bMwB9//IF//etfGDBggHXoUnJyMr7//nuMHj0aixYtslu9Wq3Wbse62c+5O/+Ml8cd3ruhoaH4/vvv8e2332Ly5Mno0KEDNBoNJEnCqVOnsGDBAowcORJ//fWXQ+sgAtjjQeQVbr31Vly4cAGiKOLy5cvWVYZKflqZmZlZ4TFEUbR+klfeJMRTp05h6dKlAIqH1BQUFFj38Ci59Kw7qlu3LtLT060TdCtiaRMcHOyQ3g5LPRb2+N640oEDB6xf37jPwLp16wAUP99169aV27tT1qT06rAsCNCqVSv8+OOPZU7Yt+f5qmr//v3Wr2+//fZqHeOWW27Bs88+i2effRayLOPcuXPYsWMH1qxZg4KCAixevBidOnUqtXGgq93svVfyfXDjUErL+7CsT/xLKu+DF3sq+d6tyu8TR793+/bta13pLy8vDxEREfjpp5+wZ88epKenY/r06di5c2eFQ02Jaoo9HkReoORmeJIkWb9u0aKFtQv/Zp92RUVFWXsyyhqqUVhYiFmzZkGSJAQHB2Pjxo3WnpYPP/wQsbGxNX4ejmR5TtHR0RXu8G42m60TWx25NK09vzeulJmZiW3btgEonjR94yaHlp+Lvn37VjikzLIkaE3FxMQAKN5Nu7zQoaoqzp07Z5fzVYWqqjb7hgwfPrzGx9RqtQgPD8esWbOwYsUK6+03bkjpqABdFZYV6Spz/40/55a5Cnl5eRUe4/LlyxXeb4/XwcfHxzrZ+2bPKSMjw7qZpTPfu8HBwbjnnnuwbNky6+IUaWlpbrkqHnkWBg8iD6eqqs1FW8mNwHQ6Hfr06QMAOHr0aIW7D1s+mQZQ5g7Z8+fPtz5+3rx5aNWqFT755BP4+fmhqKgI//rXvyq8oHc1y3MyGAxl7vxs8dtvv1lXCCvrdbB8WljT52rP742ryLKM119/3TpPYsKECaU+1bUEYctSxmU5f/78TcNXZV93Swiv6Hy7du2yWYbUWZYtW2Z9r3bu3BkDBgyw6/F79OhhDbM3zpEo+Sm3q96nv/32W7k9ErIsWzdDrVevHjp27Ghzv2U/l8LCQly6dKnMY6iqat0XpzyWlfBq+hpY3ocJCQmllrgtqeSS3K5675Zcdcud586QZ2DwIPJwP/zwg/UTtU6dOqFx48Y291uGQMmyjDfeeKPMP7h79+61jovv3LlzqSEgv/32GzZt2gQAePDBB3HfffcBKN5p+7XXXgNQvCzpp59+asdnZl9jxoyxjof/+OOPkZSUVKpNUlKSdXK+n5+fzUZ2FpaJ51euXKlxTfb43rhKUlISnnzySevQobZt22LatGml2lmWCT5x4kSZG+ZlZWVZf4YqUtnX3XK+PXv2lDmZ98qVK3jnnXduej57KigowLvvvouFCxcCKO4ZKrmJXmX9/PPPFe5vcezYMeuFffPmzW3us7x+gH1+dqsjIyOj3Oe9aNEixMXFAShegvbGpbItIR2ATc9OSf/73/9u2pNleR0yMzOrtIfKjSZOnGid8P3222+XOXTv3LlzWL58ufW8I0eOrPb5yhMVFWUzx6osJZfxvfHngsjeOMeDqJYra+dys9lsXU7XMsxFo9Fg1qxZpR4/ePBg3H///fjll19w9OhRPPLII3jqqafQrl07FBQUYNeuXdbNsPR6Pf773//aPD4lJQVvvfUWgOKVcyxfWzz++OPYu3cv9u7di2+++QZ33nmnU3eCrqwGDRpg9uzZeOutt5Ceno5HHnkEzzzzjHVOwvHjx7F8+XLrxeprr71ms8qORY8ePRAREYEzZ85g+fLluPPOO62fMvv5+ZUKfhWp6ffGkW78uSsqKkJubi5iY2MRERGBffv2WXsX2rZti2XLlpW5AMBDDz2EPXv2wGAwYNKkSXj22Wetc5BOnjyJb775BhkZGejRowdOnjxZbj2Vfd0feughfPjhh0hLS8P48ePxzDPPoH379jCZTDhy5AhWrVoFs9mMzp072224lcFgsHmtRFFEXl4ekpKScOLECezYscN6kRscHIxPPvnEOkyxKl5//XV8+OGHGDZsGHr27ImWLVvCz88PWVlZiIyMxA8//ACguDet5P4RANC0aVPrjulff/01mjRpgrCwMOvk8ZCQEIevetS1a1esW7cOSUlJmDhxIpo2bYr09HT89NNP2LFjB4Di3zHPP/98qcd27NgRPXv2xIkTJ7Bx40aIoohHHnkEwcHBSEpKwqZNm7Bnzx5rm/L07NkTQPFckbfffhuTJ0+2mU9S0X46JbVr1w7PPPMMvvjiC1y6dAkPP/wwpk6dii5dusBsNuPgwYP45ptvYDQaIQgC/vvf/1Zp36HKioqKwpw5c6w7wHfq1AmNGjWCqqq4evUqfvnlF+vS5507d3bbJYrJczB4ENVya9aswZo1aypsU6dOHfznP/8pd+jGggULIMsytm/fjosXL2L27Nml2gQHB+Ozzz6z7ugMFA9deO2115CbmwutVouPPvqozIuTBQsW4IEHHkBmZiZef/11bNmyxS0nQY8fPx75+fn4v//7P+Tk5Fg3QCxJq9XilVdeKXey/MSJE/Hjjz8iJycHn3zyCT755BPrfX369LEZw18Z1f3eOFplfu6Cg4Px6KOP4qWXXip3PsXIkSMxZswYbNy4EWlpaaXCk1arxZw5c5CXl1dh8Kjs6/7EE0/g0KFDOHDgAOLj4/Hmm2/aHMfPzw8ffPAB9u7da7fgcfbsWTzwwAMVttHr9bj77rsxe/Zs3HLLLdU+V2ZmJtauXYu1a9eWeb+fnx/mz59vDXclPffcc3jnnXeQlJRUahWxBQsWYMyYMdWuqzJeeeUVfPPNNzhw4ECZm+ndcsst+Prrr8vde2LBggWYNGkS0tPTsXXrVmzdutXm/gceeACPPPIIpkyZUm4N/fr1Q/fu3XHq1Cn88ssvpYZmRUdHV/r5zJw5E0ajEd9++y2Sk5PL7Enz9fXF/Pnzcdddd1X6uNVx7ty5Cn+e27dvj8WLF7vFXB/ybAweRB5Ir9cjODgYbdq0wYABA/DII4/YDKW4kY+PDxYuXIgxY8bgp59+wqlTp5CZmQk/Pz+0aNECgwcPxhNPPFFq8u9XX32FiIgIAMUXLeUN8wkJCcF7772H5557DqmpqXjrrbfw+eef2+8J29HUqVMxZMgQfPvttzhy5AhSU1MBFC/h27dvX0yePBnt2rUr9/GNGzfG+vXr8cUXX+Do0aNITU2FyWSqdj3V/d44k0ajQZ06dRAYGIgmTZqgc+fO6NGjB4YNG1Zu4ChpwYIF6NevH9atW2edKN+oUSP06tULkyZNQnh4+E2XgK3s667X6/HFF19gzZo1+Pnnn3Hp0iWoqorGjRvjjjvuwBNPPIE2bdpg79691X49biYgIAB16tRBSEgIOnbsiK5du2L48OEVvkcr47fffsOhQ4dw+PBhxMfHIyMjA/n5+fD390erVq3Qv39/PPbYY2jatGmZj584cSIaNmyIH3/8EVFRUcjLy7NZjMLR9Ho9vvzyS6xbtw4///wz4uLiYDQa0bx5cwwfPhxTp06tsNeldevW2LhxI5YvX44///wTKSkpqFOnDm677TaMHz8e9957r/X3VXk0Gg2++uorrFixAnv27MGVK1dgNBqrteStIAh48803cd9992HNmjWIjIxERkYGtFotmjVrhgEDBuAf//hHud8Pe7j//vvRrFkzHDp0CMePH0dKSgoyMzMhiqJ1rszw4cPx0EMPOWWTVyJBrY0LYxMRERERUa3CyeVERERERORwDB5ERERERORwDB5ERERERORwDB5ERERERORwXNXKSRRFKbXCilar5dJ1RERERFQrqapq3bPJwtfX17qB5o0YPJzEZDLddPdQIiIiIqLarFOnTtYNXG/EoVZERERERORwDB5ERERERORwDB5ERERERORwnOPhJFqtttRtnTp1gk7HbwERERER1T6SJJWaw1zWNa8Fr3qdpKzVq3Q6HfR6vQuqISIiIiKyv4pWbOVQKyIiIiIicjgGDyIiIiIicjgGDyIiIiIicjgGDyIiIiIicjgGDyIiIiIicjgGDyIiIiIicjgGDyIiIiIicjgGDyIiIiIicjgGDyIiIiIicjgGDyIiIiIicjgGDyIiIiIicjgGDyIiIiIicjgGDyIiIiIicjgGDyIiIiIicjgGDyIiIiIicjgGDyIiIiIicjgGDyIiIiIicjgGDyIiIiJyKkVRkJ6e7uoyyMkYPIiIiIjIqU6fPo2JEyciOzvb1aWQEzF4EBEREZFTmUwmAIDZbHZxJeRMDB5ERERE5FSSJAEARFF0cSXkTAweRERERORUluAhy7KLKyFnYvAgIiIiIqdij4d3YvAgIiIiIqeyBA5LACHvwOBBRERERE5lCRwMHt6FwYOIiIiInMrS48GhVt6FwYOIiIiInMp0fRldBg/vwuBBRERERE5lEk2AXmDw8DIMHkRERETkVGbRBPhwjoe3YfAgIiIiIqcyiWaoeoHBw8sweBARERGRU5nMJqh6zvHwNgweRERERORUZtEMRa8yeHgZBg8iIiIicipRFKHoVQ618jIMHkRERETkVKIsQdUDZvZ4eBUGDyIiIiJyKlEUoeqvL6tLXoPBg4iIiIicyhI8RIk9Ht6EwYOIiIiInEqSJPZ4eCEGDyIiIiJyKlmWAS03EPQ2DB5ERERE5FSSLEHVCZBkBg9vwuBBRERERE4lyzJUHXs8vA2DBxERERE5lSxxqJU3YvAgIiIiIqdSFAWqFpAV2dWlkBMxeBARERGRU6mqAmgBRVVcXQo5EYMHERERETmVoihQNYAsM3h4EwYPIiIiInIqRVEBDYdaeRsGDyIiIiJyKlVRAM31/5LXYPAgIiIiIqdSLf9TVVeXQk7E4EFEREREzqWqgFAcPsh7MHgQERERkVOpKoqDB3s8vAqDBxERERE5mcrg4YUYPIiIiIiIyOEYPIiIiIiIyOEYPIiIiIiIyOEYPIiIiIjIqSwzOzjHw7sweBARERGR8wmuLoCcjcGDiIiIiIgcjsGDiIiIiIgcjsGDiIiIiIgcjsGDiIiIiJzLOqmck8u9CYMHERERETmVqoI7l3shBg8iIiIicjIVKle18joMHkRERETkVH/3eLi6EnImBg8iIiIicq7riYNDrbwLgwcREREROZUKABoGD2/D4EFEREREzqWqnFzuhRg8iIiIiMipVLV4cjmDh3dh8CAiIiIip7JMLldUxdWlkBPVyuDx5ZdfokOHDujQoQNOnTpVZpuCggIsWLAAQ4YMQZcuXTBkyBAsWLAABQUF5R5369atGDt2LLp3747evXvjmWeewZkzZ8ptHx8fj5dffhn9+vVDeHg4HnjgAXz33XdQFL6JiIiIiMplGWqlsMfDm9S64HHp0iV8/vnnCAgIKLeNwWDApEmTsHLlSoSFhWHKlClo06YNVq5ciUmTJsFgMJR6zLJlyzBr1ixkZmZiwoQJGDVqFE6cOIHHHnsMERERpdrHxsZi7Nix2LVrFwYOHIjJkycDAObPn4+3337bfk+YiIiIyMOouB48uHO5V9G5uoCqkGUZr7/+Om677Ta0bt0aW7ZsKbPdihUrEBUVhalTp+LVV1+13v75559jyZIlWLFiBWbMmGG9PT4+HosWLULr1q2xYcMGBAUFAQAmT56McePGYe7cudi+fTt0ur9frnnz5iE/Px/Lly/H4MGDAQCvvPIKnnnmGaxbtw733Xcf+vXr54iXgYiIiKj24xwPr1Orejy+/PJLXLhwAe+99x60Wm2ZbVRVxfr16xEQEIBp06bZ3Pfcc8+hbt262LBhg80P+saNGyFJEl544QVr6ACAdu3a4cEHH8SVK1dw5MgR6+1xcXGIjIxE3759raEDAPR6PWbOnAkAWL9+vV2eMxEREZFHEsAdBL1MrQkeFy9exOLFi/HCCy+gXbt25baLj49HWloaevbsWWo4lq+vL3r16oXU1FQkJCRYbz969CgAYMCAAaWON2jQIABAZGRkqfYDBw4s1T48PBzBwcHWNkRERERky5I3mDu8S60YaiVJEmbPno02bdrg2WefrbCtJVC0bt26zPtbtWplbWdpEx8fj4CAADRq1Kjc9vHx8dbbLF9b7itJEAS0bNkSZ8+ehdFohL+/f7m1Go1GiKJY4fMhIiIi8jzFczwURSlz7i3VDpIkVal9rQgey5YtQ3R0NNatWwe9Xl9h2/z8fABAYGBgmfdbbre0A4pXwGrQoEGF7UuuhmX5uuSwrPLOUVHwiImJKfc+IiIiIk8lyzKA4g9ho6KiXFwNOYvbB48LFy5g2bJleOqpp9C5c2dXl2NX7dq1s5mwTkREROQNtFotVAB+/n7o2LGjq8uhapIkqUofpLv9Ve/rr7+OFi1a4KWXXqpUe0svRHn7dZTVWxEYGGjTA1JW+5I9KGX1mtzsMWXx9/e/aQ8OERERkacRBMH634q2SCD3VtUpA24fPC5cuAAA6Nq1a5n3jx8/HgCwZMkSDBs2rMw5GSVZ5oCUnJ/RunVrnDx5Eunp6aXmeZQ1Z8TydckJ6haqquLKlSsIDQ3lG4mIiIioApYAQt7B7YPH2LFjy7z92LFjiI+Px9ChQ9GgQQM0a9YMQHEoCA0NxYkTJ2AwGGwu/k0mE44dO4bQ0FCb4NG7d2+cPHkSBw8exEMPPWRznv3791vbWPTp0wcAcODAgVKT3U+fPo28vDzceeed1X/SRERERB6OkcP7uH3wePfdd8u8ffbs2YiPj8dzzz2H7t27W28XBAHjxo3DkiVLsGTJEpsNBL/44gvk5uZi2rRpNgl7zJgx+Prrr7F06VLcfffd1mFYMTEx2Lx5M1q2bGmzGWBYWBh69+6NiIgI7N2717qXhyiKWLhwIQBg3Lhx9noJiIiIiDyPCgiMH17F7YNHdUydOhW7d++27mDeuXNnXLhwAfv27UPHjh0xdepUm/ZhYWGYPn06Fi5ciNGjR2PEiBEwGAzYtm0bJEnC/PnzS00CnzdvHiZMmIBp06Zh1KhRCA0Nxf79+xEdHY1x48Zx13IiIiKim+BIK+/ikcEjICAAq1evxuLFi/H777/j6NGjaNiwIaZMmYLp06eXOffihRdeQLNmzbBq1SqsWbMGer0ePXr0wIwZMxAeHl6qfdu2bbF+/Xp8+umn2LdvHwwGA1q1aoW5c+fi8ccfd8bTJCIiIqqd1Ov/Y/LwKoKqcs9IZxBFEadPn7a5LTw8nKtaERERkdd5+LExSBxZgH7nbsPiDz93dTlUTVW9vtU4oygiIiIior8JxXM82OHhVRg8iIiIiMipBIBDrbwQgwcREREROZWgsfR4MHh4EwYPIiIiInIyAYICaAReinoTfreJiIiIyKmsHR3s8PAqDB5ERERE5HxcV9XrMHgQERERkXMJAncu90IMHkRERETkVJZVrTi53LsweBARERGRcwkC53d4IQYPIiIiIiJyOAYPIiIiIiJyOAYPIiIiInIulUtaeSMGDyIiIiJyKhUABEBlAPEqDB5ERERE5FyqWhw8uJmHV2HwICIiIiKnsnZ0MHd4FQYPIiIiInI+DrXyOgweRERERORkKlQBUFTF1YWQEzF4EBEREZFTqUrxHA+ubuVdGDyIiIiIyKn+XtXK1ZWQMzF4EBEREZGTcVUrb8TgQURERETOdT1vqAqDhzdh8CAiIiIi5xLAVa28EIMHERERETkfh1p5HQYPIiIiInI6Fezx8DYMHkRERETkVKqqcqiVF2LwICIiIiInEwAweHgbBg8iIiIicj5uIOh1GDyIiIiIyMmKAwdzh3dh8CAiIiIi5+OqVl6HwYOIiIiInMoaN9jl4VUYPIiIiIjIqYTr/8fc4V0YPIiIiIjIqVTgevBQXF0KORGDBxERERG5hKKwy8ObMHgQERERkZOpULmBoNdh8CAiIiIi52Pw8DoMHkRERETkfAKgKpzj4U0YPIiIiIjIqSyTyxX2eHgVBg8iIiIicj72eHgdBg8iIiIici5VLe7x4KpWXoXBg4iIiIicxjKhXOU+Hl6HwYOIiIiInEZRFEAQrg+1Yo+HN2HwICIiIiKnURSl+AqUQ628DoMHERERETmNqqoQBAHQcHK5t2HwICIiIiKnkWUZEIq/VsEeD2/C4EFERERETqOqKqARiud5MHd4FQYPIiIiInKa4snlxV8zd3gXBg8iIiIichpFUSAIln8xengTBg8iIiIichpVVaFaejyYO7wKgwcREREROY11Hw+gRM8HeQMGDyIiIiJyGkVRIPAK1Cvx205ERERETqOqKieXeykGDyIiIiJyGsVm00BGD2/C4EFERERETlOyx4O5w7sweBARERGR0yiKAvX6rHLmDu/C4EFERERETqOqKlez8lIMHkRERETkNMU9Hq6uglyBwYOIiIiInKZ4H4/ir1XuIOhVGDyIiIiIyGlsJpez58OrMHgQERERkdOU7PEQwF4Pb8LgQUREREROUzzH43rY0Ag37OtBnozBg4iIiIicpmSPBwRAlmWX1kPOw+BBRERERE5TclUrQcNJHt6EwYOIiIiInMamxwPs8fAmDB5ERERE5DQ2q1ppBE4u9yIMHkRERETkNDaTywVwcrkXYfAgIiIiIqexWU6XwcOrMHgQERERkdOoqmqdXK4yeHgVBg8iIiIichpZlq1DrQTO8fAqDB5ERERE5DQ2k8u5j4dXYfAgIiIiIqdRFAWWPg5VAHs8vAiDBxERERE5l/D3fxg8vAeDBxERERE5TXGPx9/L6ZL3YPAgIiIiIpdQwVWtvAmDBxERERE5zY2TyznUynsweBARERGRS3CklXdh8CAiIiIiIodj8CAiIiIiIodj8CAiIiIiIodj8CAiIiIil+C8cu/C4EFERERETiMIAlAicGg0vBz1FvxOExEREZHTCILw92q66vUgQl6BwYOIiIiInKa4x4NhwxsxeBARERGRS6hgj4c30bm6gJvJy8vD559/jjNnziApKQm5ubmoX78+wsLC8Pjjj2P48OGlfmALCgqwaNEi7NixA+np6WjUqBGGDx+Ol156CYGBgWWeZ+vWrVi1ahViY2Oh1+vRvXt3zJgxA127di2zfXx8PD799FNERETAYDCgVatWGD9+PCZOnMixikRERETlKDnUChxq5VXc/go5OzsbP/30E/z9/XH33Xfjqaeewp133onY2FjMmDEDb731lk17g8GASZMmYeXKlQgLC8OUKVPQpk0brFy5EpMmTYLBYCh1jmXLlmHWrFnIzMzEhAkTMGrUKJw4cQKPPfYYIiIiSrWPjY3F2LFjsWvXLgwcOBCTJ08GAMyfPx9vv/22Y14IIiIiIg/DyOFd3L7Ho3nz5oiMjIROZ1tqQUEBxo8fj3Xr1uGJJ55Au3btAAArVqxAVFQUpk6dildffdXa/vPPP8eSJUuwYsUKzJgxw3p7fHw8Fi1ahNatW2PDhg0ICgoCAEyePBnjxo3D3LlzsX37dpvzz5s3D/n5+Vi+fDkGDx4MAHjllVfwzDPPYN26dbjvvvvQr18/h70mRERERLWVzapWXE7Xq7h9j4dWqy0VOgAgMDAQAwcOBAAkJCQAAFRVxfr16xEQEIBp06bZtH/uuedQt25dbNiwAWqJRaM3btwISZLwwgsvWEMHALRr1w4PPvggrly5giNHjlhvj4uLQ2RkJPr27WsNHQCg1+sxc+ZMAMD69evt8MyJiIiIPM+NQ6s41Mp7uH3wKI/JZMKRI0cgCALatm0LoLj3Ii0tDT179kRAQIBNe19fX/Tq1QupqanWoAIAR48eBQAMGDCg1DkGDRoEAIiMjCzV3hJ6SgoPD0dwcLC1DRERERERFXP7oVYWeXl5WLVqFRRFQWZmJvbt24dr165h+vTpaN26NYC/ez4s/75Rq1atrO0sbeLj4xEQEIBGjRqV2z4+Pt56m+Vry30lCYKAli1b4uzZszAajfD396/wORmNRoiiWGEbIiIiIk9SVFQE9foYKxUqDAbDTa+ZyD1JklSl9rUqeCxevNj6b71ej9deew1PPfWU9bb8/HwAKHflKsvtlnZA8VyRBg0aVNi+oKDApj0Am2FZ5Z3jZm+imJiYCu8nIiIi8jQJCQmQZQUAIMsyYmNjkZaW5uKqyBlqTfBo3rw5oqOjIcsyrl27hl9//RWffvopTp48iYULF5Y5D8TdtWvXrlbWTURERFRdRqMR2hPFo/21Wi3atm2LkJAQF1dF1SFJUpU+SK91V71arRbNmzfHs88+C41Gg48++gjr1q3DxIkTrb0QJXsoSiqrtyIwMNCmB6Ss9iV7UMrqNbnZY8rj7+8PvV5/03ZEREREnsLX1xe4PqFc0Ajw9fUtNTeXaoeqThmotZPLgb8neFsmc5c1J6MkyxyQkvMzWrduDYPBgPT09HLbl5wzcuN8kpJUVcWVK1cQGhrKNxARERFRGRRFgSpcn+MhwGa1UfJstTp4pKamAijuBQGKQ0FoaChOnDhRaqNAk8mEY8eOITQ01CZ49O7dGwBw8ODBUsffv3+/TRsA6NOnDwDgwIEDpdqfPn0aeXl51jZEREREZEuWZWvwgFAcRMg7uH3wiIqKKnNYU05ODj799FMAwJ133gmgeFWpcePGwWAwYMmSJTbtv/jiC+Tm5mLcuHE260WPGTMGOp0OS5cutTlPTEwMNm/ejJYtW9psBhgWFobevXsjIiICe/futd4uiiIWLlwIABg3blzNnzgRERGRByru8bj+D6E4iJB3cPs5Hhs3bsSGDRvQt29fNG3aFP7+/rh69Sr+/PNPGAwGjBgxAg888IC1/dSpU7F7927rDuadO3fGhQsXsG/fPnTs2BFTp061OX5YWBimT5+OhQsXYvTo0RgxYgQMBgO2bdsGSZIwf/78UhPA582bhwkTJmDatGkYNWoUQkNDsX//fkRHR2PcuHHctZyIiIioHCWHWkEjsMfDi7h98BgxYgQKCgpw6tQpREZGoqioCHXr1sXtt9+Ohx56CPfdd59ND0ZAQABWr16NxYsX4/fff8fRo0fRsGFDTJkyBdOnTy9z7sULL7yAZs2aYdWqVVizZg30ej169OiBGTNmIDw8vFT7tm3bYv369fj000+xb98+GAwGtGrVCnPnzsXjjz/u0NeDiIiIqDazHWqlssfDiwgqZ/Q4hSiKOH36tM1t4eHhXNWKiIiIvMrOnTvx3t6FyO4rosWOIPzfjPfRtm1bV5dF1VDV61u3n+NBRERERJ5DkiQoGsuqVuzx8CYMHkRERETkNLIsQxGK53WomuIgQt6BwYOIiIiInEaSJMgaS/BQGTy8CIMHERERETmNKIl/D7Vi8PAqDB5ERERE5DRm0Wy9AlUYPLwKgwcREREROY1JNEPVFn+taFSIoujagshpGDyIiIiIyGlMZlOJHg+FwcOLMHgQERERkdOYbXo8FA618iIMHkRERETkNCazGbgePGTO8fAqDB5ERERE5DSiaIZ6/QpUZo+HV2HwICIiIiKnMUtiiR4PzvHwJgweREREROQ0JXs8oAWKzEUurYech8GDiIiIiJzGLP7d46Fqrv+bvAKDBxERERE5jSiJ1lWtoAXMosml9ZDzMHgQERERkdPIkmS9AmWPh3dh8CAiIiIipxElyTrUChr2eHgTBg8iIiIichpZkmwml4tcTtdrMHgQERERkdNINwy1EiUOtfIWDB5ERERE5DSSJP/d46Fhj4c3YfAgIiIiIqeRZfnv5XS1gMQeD6/B4EFERERETiPL8t9XoOzx8CoMHkRERETkNIqs2Ay1kmXZpfWQ8zB4EBEREZHTKCV6PFTN9cnm5BUYPIiIiIjIaRRFAYTr/2CPh1dh8CAiIiIip1FVAML15KEBJJk9Ht6CwYOIiIiInEj9+yvheg8IeQUGDyIiIiJyDQ618ioMHkRE5JFiY2Px7ttvIDs729WlEFF52OPhVRg8iIjII0VHR+PPQ5FITk52dSlEVIJacqiVBpAl9nh4CwYPIiLySCaTCQBgNptdXAkRlUsAVJU9Ht5CV9MDxMfHY+XKlThy5AhSU1NhMplw/vx56/0bNmxASkoKnnzySdSpU6empyMiIqoUU5ERWkFl8CByZwKgKurN25FHqFHw+OWXX/Dmm2/CbDZDVYt/aATL8mjX5ebmYsmSJWjTpg1GjRpVk9MRERFVmmgywk8HiKLo6lKIqDwCoKgMHt6i2kOtoqKiMHv2bIiiiMcffxyrV69G586dS7UbOXIkVFXFrl27alQoERFRVYgmE/y0CoMHkZuxiRkCrB9ek+erdo/HihUrIMsy3njjDUyePBkA4OvrW6pds2bN0LBhQ1y8eLH6VRIREVWRJJnhq1G5VCeRu7khaHColfeodo9HZGQkAgMDraGjIo0bN0ZaWlp1T0VERFRlsiRBr1EgSdwVmcitCLZfq2Dw8BbVDh5ZWVlo0aJF5U6i0cBgMFT3VERERFUmywweRO5OBUr1gJDnqnbwCAoKQnp6eqXaXrlyBfXr16/uqYiIiKpMliToBJWbkxG5m5I5Q2Du8CbVDh4dO3ZERkaGzdK5ZdmzZw9yc3PRrVu36p6KiIioymRFgU5QGDyI3EipieScXO5Vqh08HnzwQaiqin//+9/IyMgos01sbCzefvttCIKAMWPGVLtIIiKiqiqe48HJ5URu54atF8h7VHtVq9GjR+Pnn3/G4cOHcf/992PIkCFISUkBAKxevRonTpzAzp07IYoi7r77btx11132qpmIiOimZFmCXlAhiyZXl0JE1xX3bqg33uiSWsj5qt3jIQgCFi9ejJEjRyInJwebNm3C1atXoaoq3nvvPWzfvh2iKGLkyJH45JNP7FkzERHRTVl6PLiPB5H7KA4etj0ejB3eo0Y7l9epUwcLFy7E6dOn8dtvvyE6Ohq5ubkICAhA+/btMWrUKNx+++32qpWIiKjSJEmCr1aBJJpdXQoRlcSRVl6rRsHDIjw8HOHh4fY4FBERkV2IkohgjQqJQ62I3JfADQS9SbWHWhEREbkz0SzCT6dANLPHg8hdKIpSagNBDrbyHgweRETkkcyiGf5aGWZTkatLIaLrVFWFcMOqVpxb7j2qPdTqiSeeqFJ7QRCwatWq6p6OiIioSkSzCH8fBRkmDrUicheqqpbq8eA+Ht6j2sHj6NGjN21jSbRlpVsiIiJHMoki6gQoMLHHg8htlNrQU+DW5d6k2sFjwYIF5d5nMBgQHx+Pbdu2IT8/H9OnT0doaGh1T0VERFRlZrOIOjoOtSJyJ4qilB7oz8+mvUa1g8fDDz980zYvv/wyZs6cibVr12LTpk3VPRUREVGVybICf50Ck4mTy4nchSzLgObGOR7s8fAWDp1cHhgYiPfeew9paWlYtGiRI09FRER0AxU+GhUmM+d4ELmLUqtakVdx+KpWjRo1Qtu2bbF7925Hn4qIiMhKVRX4aNjjQeROZFmGcGOPh4tqIedzynK6JpMJ6enpzjgVERFR8dANVYVeo8JsFl1dDhFdVzzU6oYbOdTKazg8eERHRyMhIQH169d39KmIiIgAAKIoQq8RioOHJLm6HCK6TpZlqDdcfTJ2eI9qTy6/evVqufepqorMzEycPHkSX331FVRVxV133VXdUxEREVWJKIrQaQGtACiy7OpyiOg6SZIA7Y23Mnp4i2oHj7vvvrtS7VRVRYsWLfDyyy9X91RERERVIooidE4ZTExEVSFJUqlVrQDu+eYtqh08brb0mb+/P1q3bo2hQ4fiySefRGBgYHVPRUREVCWSJFmDB5fqJHIfkiRB1dzwntQKkCQJer3eNUWR01Q7eFy4cMGedRAREdmNLMvQXv/wlJ+hErmP4uBhe5ugYfDwFuyIJiIijyPLMjQCezqI3E3x5PIb3ps6oXi1K/J4DB5ERORxFEWx9nRwqBWR+yirxwMaAaLIZa+9AYMHERF5nOKJqq6ugohuVPYcj+uTzsnjVWqOR2VXsKqIIAjYuXNnjY9DRERUGcwdRO5HlmUoNwyDFDTgUCsvUangkZycXOMTcYk0IiIiIu8mSRKUG3o8VIE9Ht6iUsHj22+/dXQdREREDsHPvYjcR5mTy9nj4TUqFTz69Onj6DqIiIjsynJpw7nlRO5DkqRSQ61UBg+vwcnlRETkcQRBsCYP9ngQuQ9ZlqEKiu2NDB5eg8GDiIg8Ejs6iNyPoihQbrj6VIXi28nzVXvn8pKysrJw/vx55OTkVDg56KGHHrLH6YiIiG6KHR1E7qd4VSv2eHirGgWPq1ev4j//+Q/27dtXqQ2aGDyIiIiIvJeiKFBvnOMhqOzx8BLVDh5ZWVmYOHEiUlJS0LhxYxQWFqKwsBC33347cnJyEBcXB1mW4efnh65du9qzZiIiogqVvIjh5HIi91HWPh6qwB4Pb1HtOR5ff/01UlJS8Oijj2Lv3r1o3749AOC7777DL7/8goMHD+K5556D2WxGWFgYVq9ebbeiiYiIKqIoCjTXL2445IrIfSiKAuXGGVic4+E1qt3jsXfvXuj1evzzn/8s8/569eph5syZaNiwId577z10794dY8aMqXahRERElaWqKjSWxHF9GIdGw/VUiFxNUZRSH3urglqpIftU+1X7t3BSUhKaNWuGevXqAfh7Z/IbJ5dPmjQJ9erVw7p166pfJRERURXIsgzt9R4PrSBwGAeRm5BkuVQ3JIdaeY8affwTFBRk/drf3x8AkJ2dbdNGEAQ0a9YMly5dqsmpiIiIKk2SJGiuD+fQcMUcIrchK1IZwYOTy71FtYNHaGgoMjMzrf9u2rQpAOD8+fM27RRFQXJyMsxmc3VPRUREVCW2PR4MHkTuQpJlqDdOvOIcD69R7eDRtm1bZGRkQBRFAECfPn2gqioWLVqE3Nxca7vPPvsM2dnZaNOmTc2rJSIiqgRJkqC5vleAVig9DJiIXENWSg+1Utjj4TWqPbl88ODB2LVrF44cOYJBgwZh+PDhaNasGc6dO4e77roLt956KzIyMpCWlgZBEPD444/bs24iIqJyybIM7fWhVloNgweRu1AUhUOtvFilezzeffddXLhwwfrve+65B//+97+tk8t9fHywfPlyhIWFwWg04ty5c0hNTYVWq8ULL7yARx55xO7FExERlUWWZetyuloO4yByG7Islb765HvUa1S6x2P16tX47rvv0LFjRzzyyCO4//77S/VitGnTBtu2bcPp06eRlJQEPz8/9OjRAw0aNLB74UREROVRFAUaFF/IaHhRQ+Q2pHKGWnEelneodPDo0KEDoqOjcf78eURFReGDDz7AsGHDMGbMGAwYMMC6nK4gCOjWrRu6devmsKKJiIgqIsuydVUrARzGQeQuJEmCWmofD4XBw0tUeqjV5s2bsWnTJkyaNAl169aF2WzGr7/+imeeeQZDhgzBZ599hitXrjiyViIioioTBHBzMiI3UfbkcvZKeosqrWrVsWNHzJ07F/v378fnn3+OwYMHQ6PRICUlBcuWLcOIESMwefJk/PzzzygqKnJUzURERDdV8tqGwYPIPUhS6TkeioY9Ht6iWqta6fV6DB8+HMOHD0dGRgZ+/vln/Pzzz4iNjUVkZCSOHTuG+fPn495778WYMWPQo0cPe9dNRERULkEQYIkaKgRoNDXaL5eI7ESSJag3XH1yjof3qPFv4oYNG2Lq1Kn45ZdfsG7dOowfPx5BQUEoLCzE+vXrMXHiRIwaNQorVqywR71EREQ3pdFooFzv81BUMHgQuQlZLmuoFXs8vEW19/EoS3h4OMLDw/Hmm2/ijz/+wMaNG3H48GHExcXhk08+wdSpU6t8zNTUVGzfvh379u3D5cuXkZGRgbp166Jnz56YOnVqmZPYCwoKsGjRIuzYsQPp6elo1KgRhg8fjpdeegmBgYFlnmfr1q1YtWoVYmNjodfr0b17d8yYMQNdu3Yts318fDw+/fRTREREwGAwoFWrVhg/fjwmTpzIP3BERC6m0WigXN8eWWXwIHIbYhlDrVTuteM1HPKbWK/XIzg4GMHBwdDpapZtVq9ejQULFiAxMRH9+/fHk08+idtvvx27du3ChAkT8Ouvv9q0NxgMmDRpElauXImwsDBMmTIFbdq0wcqVKzFp0iQYDIZS51i2bBlmzZqFzMxMTJgwAaNGjcKJEyfw2GOPISIiolT72NhYjB07Frt27cLAgQMxefJkAMD8+fPx9ttv1+j5EhFRzel0OsjQAgBkFTX+W0RE9iFJIlTtDTdqALNkdkk95Fx2/U18+fJlbNq0CVu2bEFaWhqA4gl9TZo0wYMPPlitY4aHh+P7779Hr169bG4/duwYpkyZgnfeeQfDhg2Dj48PAGDFihWIiorC1KlT8eqrr1rbf/7551iyZAlWrFiBGTNmWG+Pj4/HokWL0Lp1a2zYsAFBQUEAgMmTJ2PcuHGYO3cutm/fbvNHa968ecjPz8fy5csxePBgAMArr7yCZ555BuvWrcN9992Hfv36Vev5EhFRzRUHj+IeD0kBtNobr3SIyBXK6/Ewi6JrCiKnqnGPR0FBAdauXYvx48fjvvvuw4oVK5Camgq9Xo8RI0bgyy+/xJ49ezBz5sxqHX/48OGlQgcA9OrVC3379kVOTg6io6MBFIec9evXIyAgANOmTbNp/9xzz6Fu3brYsGGDzeomGzduhCRJeOGFF6yhAwDatWuHBx98EFeuXMGRI0est8fFxSEyMhJ9+/a1hg6guJfH8hzXr19fredKRET2odPpIF/fLEBSin9HE5HrSZJUao4HNIDIHg+vUK0eD1VVcfDgQWzatAm7du2CyWSyXsxbdjZ/4IEHULduXbsWeyNLL4Tlv/Hx8UhLS8PAgQMREBBg09bX1xe9evXCrl27kJCQgNatWwMAjh49CgAYMGBAqeMPGjQIP/74IyIjIzFw4ECb9pZ/lxQeHo7g4GBrGyIicg29Xg9Jud7jIcPaK05EriXJUplDrUSJPR7eoErBIy4uDps2bcLmzZtthlLVq1cP999/P8aOHYvbbrvNIYXe6OrVqzh06BAaNWqE9u3bAwASEhIAwBoqbtSqVStrO0ub+Ph4BAQEoFGjRuW2j4+Pt95m+dpyX0mCIKBly5Y4e/YsjEYj/P39K3wORqMRIrsWiYjsTpZliNeDh6yqMJlMLq6IiADAbDYDNwQPVQsYioxlzsMl91bVRQEqHTwmTJiAv/76C0Bx2NBoNBgwYADGjBmDYcOGObUbWxRFvPbaazCbzZg1a5Z17G5+fj4AlLtyleV2SzugeKhYgwYNKmxfUFBg0x6AzbCs8s5xs+ARExNT4f1ERFQ9mZmZMEnFy3PKsoKoqCgXV0REQPF1lHrjQH8NkJ6ezvepF6h08Dh16hSA4k/6H374YTz88MNo3Lixo+oql6IoeOONNxAZGYlHH30UDz30kNNrsJd27dpxpRUiIgfIyMjAZqH496tWq0XHjh1dXBERAdeHx5cxubxOYB2+T2shSZKq9EF6pa96H374YTzyyCNlTvR2FlVVMXfuXGzZsgWjR4/GO++8Y3O/pReiZA9FSWX1VgQGBtr0gJTVvmQPSlm9Jjd7THn8/f054ZGIyAGCgoKsGwgKGk2peX9E5BqqopSe46EFFFXl+7QWquqUgUqvarVgwQKXhg5LT8dPP/2E+++/H++//36pDaHKmpNRkmUOSMn5Ga1bt4bBYEB6enq57UvOGbF8bbmvJFVVceXKFYSGhvLNQ0TkQlqtFrJS/LVacVMiciJJlMqc4yGKXNXKG9SKrVwVRcGbb76JjRs34t5778WHH35Y5prsrVu3RmhoKE6cOFFqgpLJZMKxY8cQGhpqEzx69+4NADh48GCp4+3fv9+mDQD06dMHAHDgwIFS7U+fPo28vDxrGyIicg2tVgvFkjiYPIjchiRJZc7x4D4e3sHtg0fJ0DFy5Eh89NFH5W4EJQgCxo0bB4PBgCVLltjc98UXXyA3Nxfjxo2DIPy9gPSYMWOg0+mwdOlSm+FTMTEx2Lx5M1q2bGmzGWBYWBh69+6NiIgI7N2713q7KIpYuHAhAGDcuHH2eOpERFQDlrxR8nc+EbmWLMll93hwOV2v4PYzm5csWYKNGzciICAArVu3xtKlS0u1GTZsmHVC0tSpU7F7927rDuadO3fGhQsXsG/fPnTs2BFTp061eWxYWBimT5+OhQsXYvTo0RgxYgQMBgO2bdsGSZIwf/78UhPA582bhwkTJmDatGkYNWoUQkNDsX//fkRHR2PcuHHctZyIyMUEQYDKng4ityPLcumPvbVVnytAtZPbB4/k5GQAgMFgwLJly8ps06xZM2vwCAgIwOrVq7F48WL8/vvvOHr0KBo2bIgpU6Zg+vTpZc69eOGFF9CsWTOsWrUKa9asgV6vR48ePTBjxgyEh4eXat+2bVusX78en376Kfbt2weDwYBWrVph7ty5ePzxx+347ImIqDpUVQU7Oojcj6oCN745VQ0gMXh4BbcPHu+//z7ef//9Kj0mKCgIc+bMwZw5cyr9mNGjR2P06NGVbh8WFobPP/+8SnUREZFzMHgQuasyuiK1gChWbSM6qp3cfo4HERFRVSmKZTFdQOXsciK3Uda7UdWyx8NbMHgQEZHHkWUZWstfOOYOIrehljX5SguIEns8vAGDBxEReRxJkqCzDrVi8iByB4qiAGUMgVR1gMyhVl6BwYOIiDyOJEnQCtcDB5e3InILoihC0JWRPDjHw2sweBARkceRJAk6/oUjcitmsxkoK3gIAlRVcX5B5HT8tUxERB5HFEXorvd4CIJavHcAEbmU2Wwuu8cDHBDpLRg8iIjI4xQHj+JPUPUagZuTEbkBs9kMVVv2fezx8A4MHkRE5HFKBg8dd0UmcgsmkwnQl3+/ojB8eDoGDyIi8jhms7lEjweDB5E7MJlMUMrZulrQa4rngJBHY/AgIiKPY9PjweBB5BaKioqg6MqezSHoNcU9IuTRGDyIiMjjiKIIHYonlOsFlZ+kErkBk8kERVvOcCodGDy8AIMHERF5nOIej+J9AXQaFRJ3RSZyOaPRCElXdvBQ9cX3k2dj8CAiIo8jiiK013s8dILCHg8iN2A0GiHqyl7aWvEpHopFno3Bg4iIPI4kSdahVjpB5RwPIjdgNBph1pb9XlR0KgwGg5MrImdj8CAiIo9TPMejeHiVVlC4gSCRG8g3FEDxKfs+SSdzqJUXYPAgIiKPUzzUqngsuVbgHA8id5BXkAe1nH08RL3MHg8vwOBBREQeRxZN0ArFy3ZqoTB4ELmB3IJcqOX0eJh1IgoLC51bEDkdgwcREXkc0WwuETxkBg8iN5BfkA/Vt+z7JB8VOQW5zi2InI7Bg4iIPI4kmqG9/hdOK7DHg8gdFBQWlhs8VF8gN5/Bw9MxeBARkceRJclmqBUnlxO5XqGhsNyhVqoPg4c3YPAgIiKPI0l/D7XSMHgQuQVDQSGUCno88grynVsQOR2DBxEReRxJlKAVir/mHA8i92A2mYFyVrVS/YrngJBnY/AgIiKPY9vjweBB5A4URQEEoez7fIHCggInV0TOxuBBREQeRxRFa/DQqRJ3LidyMVEUoWrLv1/1BQwF3EDQ0zF4EBGRx5ElETrLUCuh+N9E5DoFBQUQ/CpIHloBssyeSU/H4EFERB7HpsdDo0I0m1xcEZF3y8/PB/zKHmZloUCFqqpOqohcgcGDiIg8jiRJ0GmuL6crqJBEs4srIvJu+fn5kH0rDhWCjwCDweCkisgVGDyIiMjjSKIInaXHQ1AhMngQuVR+fj4kn5ssa+2nKe4ZIY/F4EFERB5HFEVoNSWGWpmKXFwRkXfLz8+H2afiuVaqHxg8PByDBxEReRzxxh4PzvEgcqm8/DwU3SR4SL4Kg4eHY/AgIiKPYxM8NCrMZg61InKlzJyscncttxB9ZAYPD8fgQUREHkeUJOivD7XSc1UrIpfLys2C6ldxG5NeZPDwcAweRETkccyi+HfwEFSYTQweRK6Uk5978x4PXxlZudnOKYhcgsGDiIg8jija9niY2ONB5FJ5eblQ/Stuo/gV94yQ52LwICIij6MoCjTX9yrTc44Hkcvl5xfctMdD9QWy83KcUg+5BoMHERF5nJK7H+s1KswmBg8iVzIUGG46x0PxA3Lzcp1TELkEgwcREXkcocTXeo3CHg8iF5NlCdAKFbYp3scjz0kVkSsweBARkUdRVRVQFeu/fTQqTGLF+wcQkeMoigIFyk3bqX5AQX6BEyoiV2HwICIijyKKInQlPlnVa1SIouTCioi8W2FhIQRf7U3bqXrAVMSFIDwZgwcREXkUs9kMnxLXOBoBUBS1/AcQkUPl5eUB/pW45BQEKKpqM0eLPAuDBxEReRSz2QzdDX/dVPXmwzyIyDHy8vKg+FUuTAh6AUVFRQ6uiFyFwYOIiDxKWcGDiFwnLy8Poq9cqbaCv6a4h4Q8En81ExGRRxFL7FpuxaEbRC6Tl5cHk0/lFnhQ/IHcXC6p66kYPIiIyKOIogi9YBs0BEGFonC4FZErZGZlosivcsFD9FOQk5Pj2ILIZRg8iGrgl22/Y9I/XsSZM2dcXQoRXSeKInSCbcjQawSIXFKXyCVSs9Og+FeurcnXzB4PD8bgQVQDR4+dRlyqLy5ciHZ1KUR0nSiK0N4QPHRaMHgQuUhGdibUSgYPo6+IrOwsxxZELsPgQVQDmVk5MGlaICUtx9WlENF1ZrMZ+lI9HuDu5UQukpWdVekeDyUAuJaZ6tiCyGUYPIhqIC8vF6JwCzIyc1xdChFdVxw8bFfQYfAgcp2c7GwodSrXVgkA0jPTHFsQuQyDB1ENGArzIWoaIyeHS/8RuQuTyQQ9bHcq12sUmEzcEZnIFQz5Bqh+Zdwhll5tTq0DZGRlOr4ocgkGD6IakEQRslCXa44TuRGTyQS9YDufw1ejMngQuYCiKJChAIJgc7tQqCLkCxVCoW34UAKA3GxOLvdUDB5E1STLMhRVgCwEoqCAwYPIXRQVFcFXtR1W5SPI3A2ZyAVyc3Mh+Je+3NSYAUEu/m9Jqg9gNBidVB05G4MHUTUVFBQA2jpQ4QOziRc0RO7CaCiEr8Z2qJWvRmLwIHKBzMxMqIHCzRtaCAJUrco5WR6KwYOomnJycqBqggBBgKKCm5MRuQlDfi58tbbDN3wFEUYjP0UlcraMjAyYA+SbNyxBCNQiK4tL6noiBg+iasrPz4eE68t0aANgMBhcWxARAQCMhfnw1dh+EOADM9+jRC6Qnp6OQv+q9TbKgSoyMjIcVBG5EoMHUTXl5+fDbFkfUFMH+fn5ri2IiAAABkMh/LS2wcMXJvZ4ELlAUloyxDpVGxFQ5C8yeHgoBg+iajIYDDDLvgAABX4oLCx0cUVEBABFhsLSQ600CgwF/HCAyNmSUpKhBFbtMYUBJlxLueaYgsilGDyIqslkMkFS9AAABXpOhCNyE8YiI3xuGGrlq1FgMhS4qCIi75Wamgo5uGqPUYKAhJRExxRELsXgQVRNxcvpFr+FVGg5uZzITZiKTPDV2PZ4+GhVFBk5x4PI2bIzsyq9a7mFHARcTUl2TEHkUgweRHaiqqV3YCUi5zOZTPC5cY6HRoHRyOGQRM6kKApESQJ0VVhOF8U9HunpnOPhiRg8iKpJo9FAgCVsqNBo+HYicgdmUYT+hh4PnUaFmTuXEzlVdnY2hDpV/9uo+gKGAn5Q4Il4pURUTVqtFhqh+FNVjaBAq9W6uCIiAgBRlKAXbhhqpVFhNjN4EDlTSkpKled3AAAEAfAVuGiLB2LwIKqmksEDUNjjQeQmzKIE3Y09HoIKkcGDyKlSU1NhDBSr9+BgDVJTU+1bELkcr5SIqkkQBAiW4KEyeBC5C1VVoLlhSLlOo0IUJdcUROSlriQnorBO9QJ/UaCElJQUO1dErsYrJaJq0mg0EK5f3AhCcRAhItcra50HnaBCFKv5ySsRVUv81QQo1RlqBaCgThGSr161b0HkcgweRNWkqqrNBQ5XtSJyF6XfizqNCklijweRMyVfTYZct3qPlYJVXE66bN+CyOUYPIiqSVEUqNZ9PDTcx4PIbZQOHlqBwYPI2TIzMqEEVe+xcl3gSvIV+xZELsfgQVRNkiRBVotXslJVDWRZdnFFRKSqapljrfQaFWbO8SByGlEUIakSoK3eMGS5LpCWkmbnqsjVGDyIqkkURUiKDgCgQM/x40RuQJIk6G6cWY7rczzY40HkNKmpqRCCa7DMvF6ASTTzQz0Pw+BBVE3GIhNkVQ8AkFU9TNycjMjlzGYz9GVc62gFQJY5HJLIWZKTk2EKrlloEIK0yMjgDuaehMGDqJoKCoxQ4AsAkBQfFBUVubgiIioqKoJPGcFDEIqX2SUi50hITEB+kLFGxxDrKkhMTLRTReQOGDyIqik3rxCq4AcAMMu+MBgMLq6IiIqKiuCrKW+FOa48R+Qs0XEXIdav2XsuL8iIhEROMPckDB5E1ZSXXwhF8AcAFEl+KCgodHFFRGQ0GuGnLbtnQwtwZSsiJ4lPTIBcv2bHEOsruBh/0T4FkVtg8CCqpvz8QigoDh4K/JGTW+DiioiosLAQftqyw4W/HuyZJHKSzLTqL6VrIdcH4hLi7FMQuQUGD6JqKigshHJ9qJUi+CMnjz0eRK5WWFgIf6HsFeYC9CoKCvgBAZGj5eXlQfVRgTJWmKsKLqnreRg8iKrJaDT+3eMh+KOggJ+kErlafn4+AoSyF3qoo1WQn5/v5IqIvE9cXBykhjULHQAAjQDVV0Bubm7Nj0VugcGDqJpMRUYoQvGqVgp8GTyI3EBebg4CUPZKOgFaM/Ly8pxcEZH3uXT5MvLq2udvohRSHGTIMzB4EFWT2WyCCh8AgCr4wsjldIlcLjcrDYG6svcOqCMYGTyInOBMzFmYQuyzfHVuPQNiL1+yy7HI9Rg8iKpJURRAKN4wQIUOZrPZxRURUU5mBoL0ZQePQBiQk53t5IqIvE9sbAzkhvY5ljlEweno0/Y5GLkcgweRHajQQuGuyEQul5OdhcDygodOQk5mqpMrIvIuZrMZuXl5UALtMMcDgNQYuHiRS+p6CgYPIrsQoHBXZCKXy83NRXA5wSNILyM7M93JFRF5l7i4OKiNtHY7nuonwGAycilsD8HgQVRtqs3XAuzz6Q4RVV9OfkG5czyC9DJys7OcXBGRdzkfdR65IfYNCUoTDWJiYux6THINBg+iahKEv4OGABk6vc6F1RARAMiSBF05f9mC9TJycnKcWg+Rtzl25jiKQssO/9WV3aAQZ6LO2vWY5Bq1Inhs3rwZb731FsaMGYMuXbqgQ4cO2LhxY7ntCwoKsGDBAgwZMgRdunTBkCFDsGDBggo3jtq6dSvGjh2L7t27o3fv3njmmWdw5syZctvHx8fj5ZdfRr9+/RAeHo4HHngA3333XfGEY/IeqqXXQ4ZOy+BB5Epmsxk6QS33/kCdjLwCbvRJ5CiqqiLqwgWIt9j3uKZmKg4fP2Lfg5JL1Irg8dlnn2Ht2rW4evUqQkNDK2xrMBgwadIkrFy5EmFhYZgyZQratGmDlStXYtKkSWWOEVy2bBlmzZqFzMxMTJgwAaNGjcKJEyfw2GOPISIiolT72NhYjB07Frt27cLAgQMxefJkAMD8+fPx9ttv2+dJk9vTanUAij/VEVQZOh2DB5Er5ebmIsi3/Pt1GkAUy97VnIhqLiUlBbK/AujtO/RYbggkXkmEJEl2PS45X60IHv/973+xe/duHDlyBBMmTKiw7YoVKxAVFYWpU6fi66+/xqxZs7BixQpMmzYNUVFRWLFihU37+Ph4LFq0CK1bt8aWLVswe/Zs/Oc//8GPP/4InU6HuXPnlvpBnzdvHvLz87FkyRJ8/PHHePXVV7Fx40bccccdWLduHY4cYSr3BhqNFoIleECGVlsr3k5EHis7O7vcieUWeo3Kpa+JHOTEyRPIu8Vk/wMLAtRQLVe38gC14kqpf//+aNas2U3bqaqK9evXIyAgANOmTbO577nnnkPdunWxYcMGqOrfXfEbN26EJEl44YUXEBQUZL29Xbt2ePDBB3HlyhWbIBEXF4fIyEj07dsXgwcPtt6u1+sxc+ZMAMD69eur/Vyp9io554OInC8nJwdB2ooveoJ8wHkeRA6yP/IgCps6plcxq0kBjp085pBjk/PUiuBRWfHx8UhLS0PPnj0REBBgc5+vry969eqF1NRUJCQkWG8/evQoAGDAgAGljjdo0CAAQGRkZKn2AwcOLNU+PDwcwcHB1jbk2RRFhnr9LaRCA1m272Q6Iqqa7OxsBAoVr6YTrJeQzU0EiexOlmVciLL//A4LU0sVfx7e55iDk9N41KB0S6Bo3bp1mfe3atXK2s7SJj4+HgEBAWjUqFG57ePj4623Wb623FeSIAho2bIlzp49C6PRCH9//wrrNRqNHG9ci0mSCMtbSBV8YDQWcZ1xIhdKvZaMupqK34NBWhNSUlLQokULJ1VF5B2ioqIgNQSgc0zvv1xfQEZ2BlJSUhAcHOyQc1DVVXXejUcFj/z8fABAYGBgmfdbbre0A4pXwGrQoEGF7UuuhmX5uuSwrPLOcbPgwTWpazdJkgFN8S9YBX7Izc1BVFSUi6si8l5xMRcQfpM5HkHIx/lz58r9O0FE1fPzts3IbFkAOHBPq4IWIrZs2YLbb7/dYecgx/Ko4FHbtGvXjish1VKyLEPQ/P29U4QASJKIjh07urAqIu+2RTKjrr7iT9/qak0w6zV8rxLZ2QeffQjT/Y49R36YiKj4aEyaNMmxJ6JKkySpSh+ke9RVr6UXorz9OsrqrQgMDLTpASmrfclPxsrqNbnZY8rj7+8PvV5/03bkfrKzsyHoAoHr1ziq4AtRNJWaW0REzpOTk4O69Sru8ajnIyM2K53vVSI7SkxMRJGPCDXAsYusSLcAl/dcglarha9vBWtnk9NUdcqAR00uL2tORkmWOSAl52e0bt0aBoMB6enp5bYvOWfE8nXJCeoWqqriypUrCA0N5R81D5eTkwNFsB1jqqicYE7kSjl5+Qi+WY+Hj4TM9DQnVUTkHbb/sR2ZtzphjqNGQFFLldsW1GIeFTxat26N0NBQnDhxotQkX5PJhGPHjiE0NNQmePTu3RsAcPDgwVLH279/v00bAOjTpw8A4MCBA6Xanz59Gnl5edY25Lmys7MhqjfM89EGIi8vzzUFERFEUYT+Jn/VgvUyV7UisiNVVfHHnl0wtlWccr6sNkZs+m2zU85F9udRwUMQBIwbNw4GgwFLliyxue+LL75Abm4uxo0bZ7PfwpgxY6DT6bB06VKb4VMxMTHYvHkzWrZsiX79+llvDwsLQ+/evREREYG9e/dabxdFEQsXLgQAjBs3zkHPkNxFbm4ujLLtcDpFCOL+AEQuYjQa4aNRb9ouWC8jN7/s4bhEVHXR0dEoCnL8MCsLqSlwOeFyuUPeyb3Vijke69evx/HjxwHAumvl+vXrrftlDBs2DMOGDQMATJ06Fbt377buYN65c2dcuHAB+/btQ8eOHTF16lSbY4eFhWH69OlYuHAhRo8ejREjRsBgMGDbtm2QJAnz588vNQF83rx5mDBhAqZNm4ZRo0YhNDQU+/fvR3R0NMaNG2cTVMgz5eXlwSjWsXkHSajDHg8iF8nKykI9v5sHD41wfQ8eVeWmn0R2sHXHL0i/1bGrWdkQBBSGSfhz75944P4HnHNOsptaETyOHz+OTZs22dx24sQJnDhxAgDQrFkza/AICAjA6tWrsXjxYvz+++84evQoGjZsiClTpmD69Ollzr144YUX0KxZM6xatQpr1qyBXq9Hjx49MGPGDISHh5dq37ZtW6xfvx6ffvop9u3bB4PBgFatWmHu3Ll4/PHHHfAKkLvJzsmHpNr+LJmVgHIXNiAix8rOzkawrnKTHAP0xQuBlLcsOhFVjtlsxsFDB2F61LnnzW1vxk/bNjF41EK1Ini8//77eP/99yvdPigoCHPmzMGcOXMq/ZjRo0dj9OjRlW4fFhaGzz//vNLtybNk5xZCEUJsbiuS/FFYWOiiioi8W2ZmJoI1RZVqW9dHQVZWFoMHUQ0dOHgAxpYK4OPc3kM5RECWmIO4uDiEhYU59dxUMx41x4PIWfLzC6HAz+Y2k+TL4EHkIpkZGQgWKjfmu65ORGZmpoMrIvJ8azevR2YHo0vOnd6+EBu2/uSSc1P1MXgQVYPBUARV8LG5TYEvCgor94krEdlXRmoy6uorN9QqWChEVlaWgysi8mxXr17FtZwUyI1dM1eqqJ2Kg4cOwmw2u+T8VD0MHkTVYDKZoMB28yIVehiLTC6qiMi7packo4FP6T08THLpi6J6mgKkp15zRllEHmvDlp9c1tsBANALMLSSsXvPHtfVQFXG4EFUDWazCFXQ2tymCnoUFfGTFyJXSEtLRQNf2+CRY9bilcgw5Jht36shvhJSk0pvAktElWM0GrF7724YOjhn747yZHUuwvcbvoeq3nxFO3IPDB5E1SBJIm5cm0GFFqLIncuJXCEzKwf1bujxKJI1kFQNimTbP3UNfCWkXEt2ZnlEHmXHHztQECY5fVL5jeQGAnJ0+Th37pxL66DKY/AgqgZJkqFCe8OtWpjF0kM9iMixzGYzBFWGtpLXQA19RVxLTXNsUUQeSlVVrNm0Ftld3GNocWrnQny7brWry6BKYvAgqgZJkqCW2eNRucmtRGQ/V69eRWhA5Yd8+GpVSKIZssweSqKqOnbsGPIDjVCC3WMDTrEVcOFyNNLS+GFCbcDgQVQNomiGKuhtblMFTi4ncoUrV66gib5qS1k3DlCRnMzhVkRV9eV3XyEt3I2WjhcEZHYpwso1q1xdCVUCgwdRNZhMxlL7eCjwg8HgwhU+iLzUpejzaKqr2vK4TX3yEBcX56CKiDzT+fPncc2YCukW9+jtsDDcpuBgxEFkZ2e7uhS6CQYPomqQRBEQbIdaKUId5OXluagiIu8Ve+EsmgdUbUW5ZvocxESdcVBFRJ7pi1XLkdrdjXo7LHQCsjqZ8N26711dCd0EgwdRFYmiCKXUxHJAFoKRn5fj/IKIvJiqqoi/kohmAVUb5hgWWISos385qCoiz3Pp0iXEpSdAbOHqSspW0FnGzj07kZ+f7+pSqAIMHkRVlJGRAejqlbpdFXxgNpu5njiRE6WmpqKujwJdFf+ahfqJuHothRPMiSrpf18vRUr3QkBwr2FWVnoBOR3NWP0jV7hyZwweRFV07do1mNRGZd6naoORlVW1seZEVH1nzpxBG/+cKj9OIwDNAmXO8yCqhDNnzuBi6iWIYa6upGJ54RJ+3/NH8QeE5JYYPIiqKC4+AdmmxmXeZxaaIDEx0ckVEXmvk0f2oYN/9cJ+e980nDx+zM4VEXkWVVXxf0sXIqWfG/d2WOgFZHQ34n9fL3V1JVQOBg+iKjpz7hKMarMy78squgUxMZedXBGRd1JVFWfOnkXb4OqtJndbUD6OH9ln56qIPMu+/fuQqsuE1MTNQ8d1htsUHDt7HPHx8a4uhcrA4EFURRdjYmHWtizzPgNa4uTpaCdXROSd4uPjEeJjhp+2evOqmgeYkZSYiKKiIjtXRuQZzGYzlqz4H1L7GFxdSuVpBKT0NuCTJf/HOZduiMGDqArMZjPy8wshC8Fl3m/StEJMTIyTqyLyTkcO7kcX/9RqP14jAB3qGnHy5Ek7VkXkOVZ8+xUyWxZCqVc7ejssxNbAJcMV7Nqzy9Wl0A0YPIiqIDY2FpKu/LUEVSEARWYVhYVuuM45kYfZv2cHutXLrdExwgOuYd/OX+1UEZHnuHz5Mn7783dk9araHjluQRCQOsiAJSuWcn8tN8PgQVQF56MuIKOo7GFWFmZNK0RHc7gVkSNdvXoVijEHjfykGh2nc10DTp/+C2ZzLby4InIQWZbxnw/n49ogA6CrXb0dFkqggPRwAz5e9ImrS6ESGDyIquDkXxdhQKsK22QUtcCFaA63InKk3X/8jtsDU2p8HJ0GaB9UiMjISDtUReQZftywFleDMiE1q52hw6Kws4wTcX8h4miEq0uh6xg8iKogLi4OZk3zCtsUCc1x9jxXtiJyFFVVsWvHr+jTIMcux+sXdA2/b9lgl2MR1XYXL17E2i3rkNHPcYsu6FJUBP1SPPFbm+7ACeCCgGuDDfjgs4+4x5abYPAgqiRFUZCfnwdFCKqwnai5BQkJV5xUFZH3OXfuHBrqClDXxz67jrcLNiLhcixycnLscjyi2iovLw9v/vffuDrUANXXcb0ddfao0F3PAQFHHXYaAIBSX8C12/MxZ94bkGX7/M6g6mPwIKqkzMxMQFf/pu1kBCE/L4fL+BE5yLZN63BHoP026tQIQJ966fh9+za7HZOotlEUBW+9+zaudc6D3NixQ6x02X9/ra3Z+hCVUtQeuByQjCVf/s/xJ6MKMXgQVVJiYiJMaHLzhoIAVVeX3bpEDpCXl4ezp0+iWwP7rhw3ICQDv/3yMxRFsetxiWqLVd+vQpR4CYWdPLNXIOMOE3ZE7sK+A/tdXYpXY/AgqqSY2MvIMlUieAAwoSl3TSVygB3bf0WfepnQ2vkD2Xo+Mpro8nD8+HH7HpioFjh0+BA27tyMtEFFgFC7J5SXSyvg2j0GfPK//0NcXJyrq/FaDB5ElXTsZBSMN1nRyiKjqCVOn41ycEVE3kWWZfyyeQMGhaQ55PhD6l/BxjWrHHJsInf1119/4f3FH+LqKAOg99DQcZ0SKODq3YWY9e9Xce3aNVeX45UYPIgqQVVVxFy8AJPm1kq1N2ja49AR7oZMZE+HDx9GS99c1LPTpPIbtQsqQsbVeCQm2m/+CJE7i42NxdsfzEPySAOUQM8OHRZSYwGJA/LwyuyZxXM3yakYPIgq4eLFixCFJlAFfaXai8ItSElJ4w7mRHaiqirWrv4Kd9d3XCgQBODuBolY9/1Kh52DyF0kJSXhtbdfR9KwQij1vSN0WIgtBFzpmY2XX5+J/Px8V5fjVRg8iCphxx/7cLWoe5n3CaqpjBsF5KMrDhw45NjCiLzEX3/9BR9DCloFlvF+s6NeDfJw+vhRZGRkOPQ8RK6UlpaGmW/8C4l35kMO9a7QYVHUBojvkI6Zc/4Jo9Ho6nK8BoMH0U3Isow9f+5DgbZnqfu0Sg5aGV6BVskpdV+a1Bs/b/3DCRUSeb7VXy3FyJB4h59HpwGGhFzFj6u/cfi5iFwhISEBL/5rOq70yan1O5PXVGFHBRebJeOFmS9yHx8nYfAguolTp07BgJZQhDql7tOgCBpI0KD0Dq8mTWskXc1AWppjJsISeYvIyEio2QloF+y4nZRLGtgwC8cO7+PkU/I4586dwytzZiLhzhyYW7u6GveQ31XGxfbX8PwrL/A97wQMHkQ3sWbdL0gq6l/1BwoCUqU7sGXrb/YvishLKIqCFf9biIcbO2/5S50GuLdRAr5a+rnTzknkaIcOH8Kcd99E4sgCSLd4d0/HjYztVVzuk4npr76ES5cuubocj8bgQVSBrKwsRF+8DKO2Y7Ueny3cgV+3/wFZ9swNmYgcbevmTWiKa2hRx+zU8/ZqkI/kmL/w119/OfW8RI7wy/ZtWLD0QyQ/YIDcgKGjLGJLAQlD8vCvf8/CqVOnXF2Ox2LwIKrA5i2/Ik2+AxCq91ZRhEDkq7fiyJEjdq6MyPOlp6djww+r8EiTBKefWyMAjze9hM8+ehdms3NDD5G9yLKMxV8sxtINy5E82gAliKGjInJjAYn3FuKtj+fh562boaqqq0vyOAweROWQZRlbt+1AljCgRsdJNt+J79ZstlNVRN5BVVV8+uF/MTo0AYF6xSU1NK9jRjf/ZHzz5VKXnJ+oJjIyMvDizGnYHPc7ro42QvVj6KgMpZ6ApIcMWP7b13jr3be54pWdMXgQlePo0aMoUFpBEYJqdByT5lYkXs3mpDWiKtiw9gdo086iV4M8l9Zxb+OrOLn/d0REsNeSao8TJ07g2Zefw+lb45Ax0AxoGTqqQvUTcG2kEfvV43h62lQkJDi/19VTMXgQlePH9duQbB5U8wMJAlLE/tj48681PxaRFzh79ix+2/QDHm8eB8HF10t6DTC1ZQwWf/I+V6gjt6coClas+gpvf/YfJIzMR1FbV1dUiwkCcntIiOmbjhlzXsGOnTtcXZFHYPAgKkN2djYuxyWiSNPOLsfL1fTBzl1/cpI50U1cu3YNH/3333iqRSz8tO4xvrqhn4RxjWPx1ux/orCw0NXlEJUpIyMDL7/2Ctad2Yykhw1etxu5o0jNBCQ+VIjP1v8P//3oXRgMBleXVKsxeBCVYdeevchUboe9Pm5VhDowogXOnDljl+MReaLc3FzMfW0mxodGo1mAe03oDq9fiH6+0Zj3xqsQRdHV5RBZKYqCTVt+xtQZz+DYLdFIv8sE6Bg67EkNEHD1PgN2Fh3CE8/+A4ePHHZ1SbUWgwdRGbZu24UstY9dj5lc1Atbftll12MSeQqDwYC5r8/EPcHR6FTPPSdz3tUoA00LT+OD/85j7yW5hStXruC5l1/AF3u+wZWHC1HUwdUVeTBN8dCruJG5ePfrDzDnnTeRnZ3t6qpqHQYPohukpaUhK6cIoqaJXY9r1HbB8ePHIUmSXY9LVNsVFhbi9X9OR0/hDPqF5Li6nAo9dEsytIkHsOA//2b4IJcRRRFfrfoK0+fMwF+3xSH17iKo/uzlcAalnoDkBwzY538cT057Gtu2b+Oyu1XA4EF0g+2/70Kq2Nvux1UFHxg07XH06FG7H5uotsrPz8drr7yIPsJfGNIow9Xl3JQgABOaXYFP0gHMf2sOP0ggpzt//jz+8fyTWBP9MxLHGiC2YuBwOkGAoZOKKw8WYPFvy/HiP6cjOTnZ1VXVCgweRCXIsoytv/yOHNh3mJXFVXN//LB2q0OOTVTbpKam4p8vPYf++jMY1CjL1eVUmiAA45omom7aIbzx6succE5Oce3aNbw5fy5e//ANXOyfhsz+ZkDP0OFKah0BKfcU4fitMXjh1Wn4dMlC5OW5dglwd8fgQVTCnj/3IldqBVlTzyHHL9K0Q3xiFi5fvuyQ4xPVFhcuXMCrM57Hg8F/YUDD2jdOWhCAh29JRqeiCMyc9iyX2iWHyc3NxSeff4LnZr2A3QGRSHrEALkxA4c7EcMEJD5qwKbsHXjiuX9g1fffoqioyNVluSUGD6LrTCYTli3/FlfE+x13EkFAvPkBfPR/SzkmlLzWvr1/4r1/v4pnmp9Hl3qOWZrycr4v/hddPE8rsdDHIecAgLsaZeKB4L8wa8bzOH/+vMPOQ97HaDTim9Xf4Innp+Dn/J1IfNRQPHnc1ZvbUNl0AvK7y7gythDfRq3D409PwtZtWzkX7AYMHkTXLf7fClwz3Q5J08ih5zHquiD2qg5bf+GGguRdJEnC0kUL8eOyD/DPNlFoUcdxS+b+ENcI14y+AIBtSQ0cdh4A6FqvEM82O4cP572GzRs38EMFqhFZlrF562Y8PnUSVl/8CYljC1HQTeHu47WE6isgq5+I+AfysHjPl5g0dTIOHjrI3wvXMXgQAdh/4CB27TuLFHWUU84XL0/El1+v5ZAr8hqZmZmYNeN5FJzaiH+2uYB6Po79FDDF+HcvR7pJ79BzAUDzOma81jYKhzZ9gQXvzIXR6J5LApP7kmUZO3b+gcefnoQl+79C/Oh8ZPcVofoycNRGSqCAtLtMiBmSgfd++AhPT38GkZGRXh9AGDzI68XFxeGj//sCsfKzgKBzyjkVIQiXpCl4/Y35XAecPN6BA/vxyotPY5DmKMY1S4TOQ//yBOgUPN86FiHXdmL6s1MQFRXl6pKoFjCbzdi0eRMmTHkM//fbYsTcnYn0O01Q6zBweAK5gYCrI4040+MK5q16D088NwV79+712iFYzrnKInJTaWlpmPX6PMSKT0LW1nfquU3aWxFrGIWZs+Zi6eKP4e/v79TzEzlaYWEhFv/fB0i+cBSvtL6EEF/PX3pWEIARjdNwW0EuPp43C4PueQCTpkyFTsc/t2SrsLAQG7dswqatm5Dfyoyse01QggQADByeSGoi4OpIA1IzCvH+z58i+JtleGL8ZAwfdg/0esf3yroLD/3ciejmsrOzMWPmHFw0joVJG+aSGvI0fXAhowf++eq/YTY7brw7kbNFRkbixalPICRpO165NdorQkdJrQJNeL1tFNIP/4AZzz+FS5cuubokchM5OTlYumIZHp86Cd9ErUH8w/nIGGC+HjrI08kNBaTcbUTsPZn47M8vMGHKY1j70zqvGZ7Jj2DIK2VnZ2P6jNdxIX8UDNquLq0lXRiGv5JMmPXaW/jog3fg6+vr0nqIaiI9PR1LFn6IrPizeL7ZJdwSILq6JJfx0aoY1ywRF/PS8d4bM9Cj31148tkXUadOHVeXRi6QlpaGVT9+iwOHDyC7kwkF42SoPuzh8FZKXQHpg4qQUWjE8hPfYs2GH/HAyPvx6JhxCAoKcnV5DsPgQV4nLS0NL//zTUTljkS+9nZXlwMASMV9OBb3K17515v4+IN3eGFCtY4oivhp3Rr8+vMG3BeagD635nHVz+vaBxdhTuB57L6QjuefOoQnnn4Ow+4ZAYEvkFe4ePEivlmzCudiziGzSxEM4xVAx8BBxdQ6ArLuEJHd04zVZ3/Clme3YEC/AZj86CTccsstri7P7hg8yKtER0djztz3EFM0DgZtF1eXYyNVuBfHEv/Esy/8E59+PB+hoaGuLonopmRZxu5dO/HDqhXo6JeKOe2S4K/z7lVbyqLTAMMbp6NXvSz89P0n+Hn9D5jy7Evo1asXA4gHUhQFERER+ObHlUgpTENq10KYx+P6Hhz8flNpqq+A3Nsl5HYTkXFxFw68dggdWrfDk49NQadOnVxdnt0weJBXUFUVGzZuxrff/YxYaSrM2hauLqlMWcJdOJ0dgmdf+Bde+9c09O/fz9UlEZVJURTs37cXq79ehhbaDLzY7Aoa+XnXPI7qaOAr45lWsUgoSMTaz/+N7wKb4annZ6Bbt26uLo3swGw24/cdv+O7DT8gv24R0rsWQmrCsEFVoBNg6KTC0LEQKQmncOGzN9FQ2wBPPTYF/fv3h1ardXWFNcLgQR4vNTUV733wGc7HqYhTXoOqDXB1SRUyaLsiSmyK/370FQb2PYwZ059BYGCgq8siAlDcw3Fg/378sHI5QpGOqY0T0MTfe+dxVFerQBOmB0bjUn4CvvnoKnT1W2LS0y+gW7du7AGphXJycrDh5w3YtuNXFLQUkTXcBCWYgYNqQBAgtgaSWhtwLb0Q7238BEFfLsb4h8fj3pGjau1KmILq7TuZOIkoijh9+rTNbeHh4V61hJqzmUwmrF2/ERt++hUJ0kMo0PSEvQed65UUtDD+G4n+8yFqmtj12FBlhOBPNNP/iWefnoThw4fV+k86qPbKzc3Fti2b8Nu2LWgTkIthDZLQLMB9V2KbHnErTErxwo0+GgVL+rr3Zp0X8/ywK7sVstR6eHDsY7j7nuG19sLCm2RkZOCr777BoaMHkXObGfmdJW74VwmTLo3AvcOvb9grAOtNv2FT4m+uLaoW0OSrqHfWB0GX9bh/xP14bNwEl88Jrer1LYOHkzB4OI/ZbMbWrb/iuzU/IUPujmvKKKiC/Xs5fOXLaGRaCR/1GlJ9n0WhrrfdzwEAWiULLXRb0cg/Ec9NnYzBgwdBo+FK2OQcsbGx2Pjjapz56wT610/DwJAMBOkVV5d1U7UteFikF+mwN6sJTuU2wMDBQ/DQ2Alo0sTOH2pQjVkCx8HIg8jsWgRDRwXQMnBUVsj/FAjXO0pVHZA5jX/TqkIwqQg+o0e9aF88MPI+TBjrugDC4OGmGDwcLzs7Gz9t2opt2/5AltIF15RRkIVgh52vqfG/8FMSAABmoRmSAuY57FzA9d4V3TbU903GhHEPYtQofiJKjpGdnY09u3Zi5/Yt0BRlYUi9BHRvUFirrqtqa/CwMMkCjmTWw77spmjQuBnuuW8MBg4ahIAA9x4q6ukyMjLw9fff4EDEQWSGM3BUF4OHffwdQHwwetRojH/kUacHEAYPN8Xg4RiqquLMmTNY99Ov+Ov0OaQrA5CJQVAEx7/xWhdOhwYmAIACH8TXWeLwcwKAVslEY+2faCCcxKCB/fDIw/chLMw1GyCS5zAajTh48CB2btuIlORE3F43A33rZSK0ls7fqO3Bw0JVgSuFvjiaF4q/cuqiQ8eOuOe+MejVqxd3Q3eijIwMfPPDSuw/cqC4h6MTA0dNMHjYV8kekAfvfQCPjnFeAGHwcFMMHvaVlpaGbb/+gW3b/0Ch3BhJRXcUbwQoOG8OhKuCh4WgmhCoHEczn8OoHyhhzEMjMezuIQgOdlwvD3kWk8mEkydPYvdvW3Du7Bl0rZuPvsEpaB1oqvV7cHhK8ChJVoELuf44mt8MsQUB6NO3H+6651506dKF878cJD8/H198sxz7j+xHZlcTCjvK1/fgoJpg8HAMwaQi+LQe9S764sH7RmPS+Mcdfp1Z1etbflxCtUZubi5279mLLb/sQka2Aalib+RgJmRNXa/8SVYFX+Rr++OC3B+67DTErzqEb77diFatmuHhB4ahf/87OBSLSsnLy8ORw4dxYPdviL0Ui9uCjehR5xoevc0AHf/2uzWtAHSuZ0TnerEwyQJOXb6MtZ/uxQcFvggP74qBQ0eiV69e8PPzc3WpHuHy5cuY884buNouBwWPctM/cn+qr4Dc3hLywkV8d2wDDs48iPfnLUDDhg1dXZqVF16uUW1SUFCA/fsPYuuve5CYdA1Zak9kKuMhapoCvEiykjShuIaHcE15EJcuXca5JUcRuPgr3NahA0bfNwR9+/aFj4+Pq8skF0lJScHB/ftwYM/vyMpIQ5fgHAwKzMCUjkXQ8DqqVvLVqujbsAB9EQ1REXAhPRZ/fnMISxfWQVhYGAYMHYk77uiPevXqubrUWmnn7p1YtGIxrg41cB8OqnVUXwFZA0QUxl7Bsy8/j//MmYcuXdxj02QGD3I7BoMBhw4dwuZf9iA+4QpyEY4MaQRMmtbFy+EycJRPEGDStsEVpQ2gjkPcuWiciPkTdZSl6NKlCx64dwh69erFIX4eTpIknDt3DhEH9yLyyCFopAJ0q5OOsXVz0LSDudYPoyJbeo2KrvUN6Fo/DkpTIK4gBn/9dBLrVtZDneD66DtgMPr2H4S2bdtyRbybkGUZi79Ygh0nduPagwaodfhmodrL1Ba4Uj8Pc96fiycf/QcefuAhl+8TxOBBbqGoqAhHjhzB5l92Izb2MvKFrkiThsCkuRUQNACHL1edoIVR2wnxcidAlXD5ZBQiz/8Of2URunfvjgfuHYIePXpwgqqHSE9Px9GICETs34nY2Eu4NVhEJ9+rmN7UgLo+sqvLIyfRCECbIBPaBCVhDJKQVqTD2cNnsXzPT7hm0KNrly7oM+hu9OrVi/PBbpCbm4s577yBGH0iMh4o4uRx8ghyiIDkhw1Y8dtKnI06i9kzX3fpCAhecZDLmM1mREREYMu2PYiOjkaB0Bmp4gAUaaYAYNiwK0EHo64rLstdAVVEXOR5HDmzFf7KQvTqdTvuHzUE4eHhnKBai9zYqyGIhehUJxN3BmbjH52KeM1EAIBQPwlD/bIxFNkwywIuZsYi8vuD+HZZEALrsjekpNffno0zTeNg6KSCQ6vIk6i+Aq6NMuLPQ0fg+7+FeP2V11xWC4MHOZXZbMaxY8fwy/Y/cfbsWRRqOiLV3AtGzePFK1LxutfxBD0Mum64JHWDoJpx+dBZHDyxHn7qJ+jbpxfuHTmEq+S4qYyMDBw9GoGIfbsQExOLsGAzezXKMXzkvbhn5L0AAAEqAqO/RcGJtS6uyrV8tCq61DOgS73i/YdK9YZ07Yp+dw7D7bffjqCgIBdX63x5Bfkw3qqAoYM8kiAgv7WI3LRcl5bB4EEOZzQacezYMWz7bR/Onz8Hg+Y2pJh6wqh9tHgdPV7fuowq+MCg64kYqScEtQiX95/B3si18FM+RJ8+vTFy+J0IDw/ncCwXkWUZUVFRiDi4FxGHDkA1F6BzYBYG1snCE+zVqNCO337F1l9/A+A5y+naW6nekPRYHFl9AN/8Lwj1GjRE3wF3oW//gbj11ltdPi7cGbRaLaC4ugoixxEUQKdz7RxPXk2QQ2RnZ+PQocP47Y+DiI+Ph0HTEanm7jBqxxeHDf7kuR1V8EOhrjdipN4QVBMuHziDvcc2wlf+GJ06dcLIewaid+/eTt8V1dtIkoSTJ09i1/YtOH36L7QKNKOz3zW8eEsh6rFXgxzER6uiS30DutQv7g1JNepxZv8ZLPljHdJNPujb7w4MHXE/Onbs6LEhRKvVQlAAbm5GHksB9C7+IJGXf2QXlk9m9x88ioOHjiE3vwjZSldkyXcXr0alaPjTVouogi8Kdb0QK/Uqnph++iIiog4jUP0KjUMbYfCg3rijX2+0adPGYy9CnEmWZZw+fRq7f9uK48ePo22QEbcHJmMs99YgF2nsL6KxfyaGIRNFsoBTF2Ox8tQepJj80X/gnRg6/F60a9fOo97/Wp0WggFAoKsrIXIMjQEuH8HAS0GqFkVRkJCQgJOnTmPfgeO4fDkWorY1rhV1hEHzNGRNSHFDDqOq/QQdjNpOuKJ0AlQVMdeu4a+15/Djz/+DXklFp85dMKh/D3Tv3g1NmjTxqAsRR7t06RK2bVqHiIgItA4swu0BSXiggwE+Wn7mSu7DT6uiX6MC9MMFFEoanDp7CcuO/oEsyR+DBg/F/Q89gsaNG7u6zBqb+vjT+Ojzj5HR0YD8cAnc5MZxpPqAPq34a7mua2vxCmYVIZG+aJQSjHFvjnVpKYKqqvwL5wRV3VLe3ciyjISEBJw4+RcOHjmFy5diIGubIMPUFnlqh+t7bHhXymhdOB0amAAACnwQX2eJiytyPkE1w0+JRT3tRdTXxUCHPHTs2AmD+ndHeHg4mjZtyiBShry8PKxYughRJw9jWIMEdKtfCD+GDbuaHnErTEpxdxHneDhGvqjB8ay62J3VDMNGjcb4x5+Ar6+vq8uqkYKCAiz+cgkO/nUYKQMtmweSvelSVAT+oUKXBeSNAszt2bXrKD6XgMYR/nj43ocwafzjdr/urOr1LYOHk9S24GE0GhEVFYWTp84i8vhZXL2aBFl3CzJMbZGvtodJ0woQvLvD7MU7t+DeUfcAAFQIWL2rIdbtLHBxVa4lqCb4KZdQVxuDBrpYaJQshIW1Qd9endGtW1e0bdvWq3dQl2UZv2zdjPU/rMLdDZJxZ6NMThB3EAYP5zHJAranNcWpwiZ45sVX0H/AgFr/gUNUVBTe+78FSG2Qg4w+RVB9a/fzcUfabBX1v1WR/YQAuT5fX3vT5KtofCAAt/q3wBv/nIOmTZs65DwMHm7KnYOHqqpITU3FuXPnEHn8HE6fOYf8AiPM2jCkFd0Ko6YtROGW4o38yIo9HpWgSvBVEhGAWDTyi4NOTEBISAh6dO+MXj27oFOnTqhfv76rq3Sad9+eA3NCBMbekoAgPZfPcSQGD+dLM+rxfXIY+owcj8efeMrV5dSYLMtY+9M6rP15HdJ6GVDUTgVqeaByJwweDqKoCDqtQ8Pz/nhx6ou4e8hQh34QUNXrW+/+yNpLybKMS5cu4cTJvxBx7CziLsdCFuohV74V2WIYTJrBkDV1AQn8CaGaEXQwacNgQhiyRQCqCl16Bk78fhmb/9yHOlgJH60Z7Tt0QL/eXdGjeze0aNGi1n9aWp7AoGA0r5PG0EEeKdRfRJs6eQiu18DVpdiFVqvFxEcfw7C77sZHiz5G9I8XUdhaQl6YGVJjMISQ+1BU6JOA+vH+8L0ioH+fOzB9+TQEBrrfSgm8rPQClqBx/MQpHDx8CleuxEPWNUdaUVsU4k6YNJMA4Xoy5U8EOZIgQBIaQdI0QqHct/gmqQgXTsXjz79iEeKzEzolA+3ad8DA/j08Loj0HzwMP5/Zgx4NYjhvlTyOqAg4nR+CCf36u7oUuwoNDcVH8z9Ebm4uDhw6iF93b0fi7kQYW8nICTNBugUMIeR8sgp9IhASHwCfROC2227DvfeNQp8+feDv7+/q6srFy0wPpSgKzp8/j19+3Y0jEUchapoiragtCjACZk0LQNRwxSlyC6rghyLtbSjCbUiTAKgiLp6Lx4GoiwjR74SfNhdDhwzCqBFDERYW5upya6Rbt27Y0fp2vHXOF93r5aBf3TQ0r2N2dVkeqYm/GQmFfgCARr6ii6vxXIoKxOb7ISKvKaLy6uCuIUMRGhrq6rIcom7durhv1L24b9S9KCgowOHDh7Ft96+4vCcORS1V5IQZITYFV8Mix5FU+FwBQuLrQH8V6NqlC+4dMwq9evWqNfMnOcfDSZw5x2PLL9uxctUaGNUmSC7qBYO2G1Shdq804o44x8M5NGo+gpUTuMX3GAJ9izBr5gvo1aunq8uqkcLCQuz980/8sW0j8rNS0Sc4FX0aZHODQDu6nO+LlZdCcc3oi2fbXUPvhoWuLsmjpBj1OJLdECdyQ9Cy9a0Y/sAj6NevX625+LEno9GIiIgIbNv9Ky5evAhTCyCrtQFic4CrR5SPczwqSVThEw80SgiELkVFzx49MWroSHTv3t0t5glzcrmbclbw+H3HLny2dD1ilGlQhCC7HptsMXg4n15JQTvtUrz/39fQuXNnV5djF6mpqdj5+6/YtWM76sCA1n65aOGTg1Z1TGjkJ/LD0xpIMerx71OtML97Apr4s9ejuiQFSDH6IKHQF4lSCC4X1oFPYAPcc99DGDJ0GIKDg11dotswmUyIjIzEr3u249y5cxCbCsi6pRBic0AJ4pu5JAaPcqgqtDmAT6KAkGt1oMsA+vTug5FDRiA8PBxarXsNV+Hkci936XICBIjwUZJQpO3o6nI8mlnTBH5KAgBAEhq5uBovoCrwV+MBKIhPSPKY4NG4cWM8/sSTmDh5CpKSkhATE4Poc6dwJOocrl1OR0N/BS398tFCn4VWdUxo7M8wQo4jKUCywRdXCn2RKDXEFWMA8kUNWjZvhna3d8XAjl3xdIcOaNSIv/PK4uvri4EDB2LgwIEwm804ffo0jhw/gqN/RiKnIBdiUyC7sQHmFoBah29kAqCq0OQCPklASGogNNcUNG4cijt69kPfB/vitttuc7uwURMMHh7mxeefwvBhd+Kj/1uKq6kbYRTCkGFsAZOmNcyapl63yZ8jZfpMRCPTSvio15Dtc5+ry/E4gmqGj5IIPzUBDf2uwFeOQ9fw9njlpU88cgy5IAho0aIFWrRogaFDhwIoXur62rVriImJwcXzp7Hx/BkkxaWgvp+Klv4FaKHLRIs6JjT2E+HLDQipigySBteMeiQa/IpDhsEfRlmLVi1boH3fbhjaqQvatm2LkJAQj1ngwZl8fHzQq1cv9OrVC0DxEMuzZ8/i0LHDOL7zOPKNBTA1VZHdpHhYlhrA19hbaPKuTwxPrQPtNRUNQxqiX8++6DuqDzp27OjRQxYZPDxQ27Zt8cX/PkFmZiZiYmJw+uwFnDr9K5KTrkDR1EWB0hLZpiaQhBBImoaQhBCoguf+kDuKSXsrUv1eRAvjv4sn7FO1CGoR9GoGdEoG9GoGQvyvwU9NgF5jRuvWt6Jn9w7o1HE02rVrh6Ag7xo+KAgCmjZtiqZNm2Lw4MEAisNIWlpacRiJOoPfo88jOe4aJLMJDf1VNPYxIFSbjcZ+ZjTxM6OBr8QeEi8mKUCGSY8Uox6pJn+kKfWRavJDlhEIqBOA5s2aoW2PrhjZsThkeNO+Os5Wp04d9O3bF337Fq/ol5+fjzNnzhQHkd9OwCAaUNRUQU4TY3EQ8ecb11No8ouXu22QUge6ayrq162Pvj37oN89fdGpUyf4+fm5ukSnYfDwYCEhIQgJCUG/fv0AFF+wZGRkIDo6GvEJiYhLiEVS8n5kZqTBZDIDurqQhBAUSCHIN4dAEkIgahpCFupxcjpVm6AWQadmQqdkQq9moK5vFgJ0WdDK6YBcAP+AADQKbYIWzZogrFUT3HprN7Rv3x5169Z1deluSRAENG7cGI0bN8bAgQOtt0uShJSUFCQmJiLx/9u78+ioygT948/NBoEkJARkCSDwAwkMgq0GBVoR6XZBtrA0KDGAoICAwxIcURydZleWaQmQQaZZxGbUGUB2aQRsIUqxiICEPSEQJZCFLJiQpFK/P+jUIWQrYqpuSL6fcziH3KXylJ6i6ql73/e9GKdT50/p75cu6lpSqmq656thrTzd556uBp4ZalgzRw28c1XLg/VEqgKbTcrIc79VLrK8dNVaR4l5Pkq84S6r4a6G99VX0/tbqNn/a6NHmt2vpk2bqn79+nJzY1FYM/n6+qpLly7q0uXW9MPp6en68ccftf/wdzq67QdlWbP1a2Or0htly1oFh9G4pd+6SuuWYru1blgV45EsBSTWkmeCVMe3jjo9HKLHn3pM7du3r9TT3Tobg8tdpDKvXC7dmn43NTVVV65cUWJioi5d/kWxF39RQsIvSk1NVk5Ozq2rIu51lKs6ysqro8xcP+WqjqyGv/KMOrIadWQzqk9rl24Ndm6a9a4uec9QrltDs+O4js0mN2XJ3ZYmd9t1edjS5GWkycczTTXd0+WhNCnvutxkVY2a3gqsV19Nghqqxf2N1CSokRo0aKCGDRvKz8+PWzhcIDMzU5cuXdLly5cVf+Gs4mPP6nJCgrKyslS3pqrsbVq5VptOJeYouIGXPKvo7EI3cg2l3ZTq+PmqSZMmatbyATVr0UpNmjRRUFBQtf6Ac69LTU3Vjz/+qH2HonU1+arZcSpcbk6uzh8/q//3YGt5elWOz0IVqUWT5uryaGe1b99etWvXNjuO0zCrlQsdO3ZMixcv1tGjR5Wbm6tWrVpp2LBh6t27d5FjK3vxcER2drZSUlKUkpKi5ORkJSUlK+GXJP1yJUlJySm6fj1FN7OzZZOn5OGnPNVRttVXN3L9dDPfT1aj4E8dWQ0fybj3L7hVueJhy5W7LV0etnS529LlbktTTfcM1fZMl5dbhjxsaVJemtyMfHnXqi3/gLqqXy9QjRoGqnHDeqpXL1B169ZVYGCgAgICqvR9qlWB1WpVUlKSbt68aXYUp7l586Zq1Ki6V2xr1arFGAzcs7KysijH9zhmtXKRAwcOaOTIkfL09NQLL7wgX19f7dy5UxEREUpISNCYMWPMjljhatasab/fvDQ5OTn2gpKamqqUlBQlXk3Rlavnde1ailJTU5SZkabcPKvkVlNy91Ou/OwlJSffV/n3yJUT9/xUSVLNvJ/k6fazyWkc4277VV5uGfLxSlcNt4x/Xp1Il6FceXp5ys/PXwEBAapfP1CNGgSowX0tFRAQoICAANWtW1cBAQHy8OCfjqrA3d1dDRo0MDsGgGqK0lH9cMWjHPLy8vT888/rypUr+uyzz9SuXTtJt25nGDJkiGJjY7V161Y1b97cfk5VuOLhDFlZWfaCUlBSfr6SrF9/vTe+gc3OztI/9n6lJ596VjVr3hv/gPrXqa0G99W1l4iCP1X5W2EAAFDxuOLhAt9//73i4+PVv39/e+mQJB8fH73++uuaNGmS1q9fr8mTJ5uY8t7g7e2toKAgBQUFmR2l3CZPHMu3NgAAAGVgSotysFgsklRoRpkCXbt2LXQMqj5KBwAAQNm44lEOcXFxkqT777+/yL46deooICBAFy9eLPNxsrKylJubW9HxAAAAAKfLy7u7uZApHuWQmZkpSSUuZubj46MrV66U+Thnz56t0FwAAABAZUXxMFHr1q2ZHQgAAAD3pLy8vLv6Ip1PveXg4+MjScrIyCh2f2ZmZolXQ27n7e1d7We1AgAAwL3pbocMMLi8HAqmyS1uHEdaWppSU1OLHf8BAAAAVFcUj3IICQmRJO3bt6/Ivv3790uSOnXq5NJMAAAAQGVG8SiHzp07q2nTptqyZYtiYmLs2zMzM7V06VJ5eHgoNDTUxIQAAABA5cIYj3Lw8PDQzJkzNWrUKL300kvq1auXfHx8tHPnTl2+fFkTJ05UixYtzI4JAAAAVBoUj3J6/PHH9be//U0fffSRtm/frtzcXLVq1Ur/+q//qj59+pgdDwAAAKhUKB6/QYcOHbRixQqzYwAAAACVHmM8AAAAADgdxQMAAACA01E8AAAAADgdxQMAAACA01E8AAAAADgdxQMAAACA0zGdrovYbLYi2/Ly8kxIAgAAAPx2xX2WLe4zbwGKh4tYrdYi206ePGlCEgAAAMA5ivvMW4BbrQAAAAA4HcUDAAAAgNNRPAAAAAA4nWErbQQIKkx+fr5u3rxZaJu7u7sMwzApEQAAAFB+NputyJiOGjVqyM2t+GsbFA8AAAAATsetVgAAAACcjuIBAAAAwOkoHgAAAACcjuIBAAAAwOkoHkAFy87ONjsCAACVSmmrWd8uOTnZyUlgJooH4KAJEyYoPT291GNOnjyp/v37uygRgNt98sknZR6TmZmpN9980wVpANzuxRdf1KVLl0o9Zu/everTp4+LEsEMFA/AQX//+9/Vp08fWSyWYvevXLlSQ4YM0eXLl12cDIAkzZo1S6NHj1ZKSkqx+48dO6b+/ftr8+bNLk4G4Pjx4woNDdXGjRuL7MvJydHMmTM1duxY5eXluT4cXIbiATho9uzZSk9P1/Dhw7Vw4UL7ZeOkpCSNHDlSH3zwgYKCgvTZZ5+ZnBSonvr166dvvvlGffr00bfffltoX1RUlF566SUlJiZq+vTpJiUEqq+VK1eqVq1amjZtmqZMmaLMzExJ0tmzZzVgwACtXbtWjz76qL788kuTk8KZWEAQuAsXL17UlClTdOLECXXo0EGDBg3SokWLlJKSoj/96U96++23VbNmTbNjAtXWtm3b9N577ykzM1Ph4eF66aWXNH36dB08eFAPPPCAFi5cqFatWpkdE6iW0tLS9M4772jXrl0KCgpS7969tWrVKuXl5emNN97Qq6++KsMwzI4JJ6J4AHfJarXqww8/1KpVq2QYhvz8/DR79mz16NHD7GgAJP3888+aOnWqjhw5IkkyDENhYWGKiIiQl5eXyekAREVF6T//8z9lGIb8/f318ccfq3379mbHggtwqxVwly5cuKD9+/fbf75x44ZOnz4tOjxQOdStW1fNmjWTzWaTzWaTn5+fevToQekAKoHo6Gh9+umnkiRvb29dv35d69atU1ZWlsnJ4AoUD+AufPrppxo0aJBiY2M1ceJEbdmyRa1bt9bixYsVFhamX375xeyIQLUWExNjH8DatWtXvffee8rNzdUrr7yiRYsWOTylJ4CKlZeXpw8++ECjRo1SVlaW5s+fr127dunJJ5/U//3f/6lfv346ceKE2THhZNxqBTho7Nix2rt3r5o0aaIFCxaoQ4cOkqTc3FzNnz9fa9askY+Pj95//3298MILJqcFqp9Vq1Zp4cKFstlsmjx5skaMGCFJio+PV0REhI4dO6YHH3xQCxYsULNmzUxOC1QvAwYM0E8//aSHHnpI8+fPV5MmTez71qxZowULFshqteqNN97Qa6+9ZmJSOBPFA3BQcHCw+vTpo/fee0+1a9cusn/fvn166623lJycrJiYGBMSAtVbcHCwWrRooYULF6pt27aF9lmtVn300Uf6+OOP5e3trcOHD5uUEqie2rVrp9GjR2v8+PFyd3cvsv/06dOKiIjQuXPneA+twigegIM2bdpU5sJGKSkpevvttxUVFeWiVAAKTJ8+Xe+88468vb1LPMZisejf/u3ftGfPHhcmA3Dw4EGFhISUekxOTo7mzZund99910Wp4GoUDwBAtZKeni4/Pz+zYwBAtUPxAAAAAOB0HmYHAO4V06ZNc+g4wzA0e/ZsJ6cBcKeNGzc6fGy/fv2clgNAUeHh4Q4dZxiGVq9e7eQ0MAtXPAAHBQcHl7rfMAzZbDYZhsHAOMAEwcHBZa56zGsUMAfvoZAoHoDDEhISit2ekZGhkydPKioqSm3bttXUqVMLTRMIwDU2bNhQ7PaC1+iWLVv09NNPq3v37goNDXVxOgDFyczM1E8//aRFixbpvvvu06JFi4qd9QpVA8UDqCBJSUnq3bu3xo8fr6FDh5odB8AdfvjhBw0fPlxRUVHq3Lmz2XEA3CYzM1N9+vRR//79NX78eLPjwElYuRyoIPXq1VP37t21du1as6MAKMbvfvc7de/eXX/5y1/MjgLgDj4+PnriiSe0fv16s6PAiSgeQAWqXbt2ibdkATBf48aNdfr0abNjACiGm5ubrl27ZnYMOBHFA6gg6enp+vrrr1WvXj2zowAohs1m06FDh1SjRg2zowC4w6VLl7Rjxw41btzY7ChwIqbTBRwUGRlZ7Har1arExETt3r1baWlpGjdunIuTAZBurYxcnLy8PCUmJurLL7/U8ePH1bdvXxcnA1DSlPQF76GHDx9WXl4e4zuqOAaXAw4qayrAWrVqKSwsTJMmTSpzSk8AFa+s6XRtNpseeughRUVFyd/f33XBAJT5Htq8eXONGDFCgwcPdlEimIHiATjIYrEUu93NzU1+fn5q0aKFPD09XZwKQIHFixcXWzwMw1CdOnXUvn17PfTQQ64PBqDE8Y9ubm7y9fWVj4+PixPBDBQPAAAAAE7H4HIAAAAATsfgcqAEJQ1UdURISEgFJgEA4N6ycePGcp/br1+/CsuByoVbrYASlDVQtTQxMTEVnAbAncr7GjUMQydPnnRCIgAFyvP6tNlsMgyD99AqjCseQAnGjRvH7FRAJcaVRaDymjNnjtkRUAlxxQMAAACA0zG4HAAAAIDTUTyAUkRGRv6mQeYAnOvgwYP6+eefzY4BoBjTpk3T119/XWhbTk6OMjMzTUoEs1E8gFJERkbqwIEDhbYtX75cjz32mEmJANwuPDxc69evL7Rt27ZtGj9+vEmJABTYsGFDkYHiy5cvZ3xWNUbxAO5STk6O0tPTzY4BQLdmwbnThQsXinzLCgAwH8UDAAAAgNNRPAAAAAA4HcUDAAAAgNOxgCBQhsTERB07dsz+85UrVySp0LY7dejQwem5ANzCQp9A5XX27Flt27bN/vOZM2ckSdu3by92jJYk9ezZ0yXZ4HosIAiUIjg4uNgPNTabrdQPO3fO4gHAOYKDg+Xh4SF3d3f7NqvVKqvVKi8vr2LPMQxDR48edVFCoPoq7j204GNnae+tvIdWXVzxAEoRGhpqdgQApWjcuLHZEQCUgGmtcSeueAAAAABwOgaXAwAAAHA6igcAAAAAp2OMB3AXzp07p7Vr1+r48ePKyMiQ1WotcoxhGNq1a5cJ6QDk5ORo165dZb5GZ8+ebUI6oHqLjo7WypUr7a/P/Pz8IscYhqGTJ0+akA6uQPEAHGSxWDRq1Cjl5OTIw8NDgYGBhWbSKcCwKcAcCQkJeuWVVxQfH1/q65DiAbjeV199pUmTJik/P1+NGzdWy5Yti30PRdVG8QActGDBAlmtVs2cOVOhoaH8gwlUMnPmzNHFixfVt29fDRgwQA0bNuR1ClQSS5YsUY0aNbR06VJ17tzZ7DgwCcUDcNCpU6fUs2dPDRw40OwoAIrx/fffq3Pnzpo3b57ZUQDcITY2Vn379qV0VHMMLgccVLt2bQUGBpodA0AJ8vPz1bZtW7NjAChGQECAatasaXYMmIziATioW7duOnTokNkxAJTgd7/7nS5cuGB2DADFeP755/Xdd98pLy/P7CgwEcUDcNDUqVOVkZGhmTNnKisry+w4AO4wZcoUHThwQDt27DA7CoA7TJw4UX5+fpo0aZJ+/vlns+PAJKxcDjgoPDxcGRkZOnXqlLy9vdW8eXP5+PgUOc4wDK1evdqEhED1FhkZqRMnTuibb75RSEiI2rVrV+JrdNy4cSYkBKqvHj16KC8vT1evXpUk+fn5lfj6ZEr6qoviATgoODjYoeMMw1BMTIyT0wC4E69RoPJ6+umnHT529+7dTkwCM1E8AABVgsVicfjYTp06OTEJAKA4FA8AAAAATsc6HkA5/frrr8rMzJSPj49q1apldhwAAO4JeXl5iouLU0ZGhnx8fNSiRQt5ePCRtDrg/zJwF3Jzc/Xf//3f2rBhg+Lj4+3bmzZtqv79++uVV16Rl5eXiQkBHDlyRBs2bFBMTIz9g027du3Ut29fPfroo2bHA6qttLQ0zZ8/X1u2bFF2drZ9e82aNdWrVy9NnjxZAQEBJiaEs3GrFeCg7OxsjRgxQkePHpW7u7uaNm2qevXqKTk5WfHx8bJarerYsaNWrVrFIkmASebNm6dVq1ap4K3Nzc1N+fn5km4NKg8PD9e0adPMjAhUS2lpaRo8eLDi4uLk7++v9u3bq379+kpKStKJEyeUmpqq+++/X5999pn8/f3Njgsn4YoH4KAVK1bohx9+UK9evRQREaGGDRva9yUmJmrBggXatGmTVqxYofHjx5uYFKieNm7cqJUrV6ply5YaP368OnXqZP9y4MCBA1qyZInWrFmjtm3bql+/fmbHBaqVpUuXKi4uTqNHj9aYMWPk7e1t35edna2oqChFRUVp2bJlfDlQhXHFA3DQ888/r9q1a+t///d/Szxm4MCBunHjhrZv3+7CZAAkafDgwbp69ao2b95c7PoAmZmZ6t27t+rXr6/PP//chIRA9dWjRw81adKk1HWuhg8frkuXLunrr792YTK4EiuXAw5KSEhQly5dSj2mc+fOSkhIcFEiALc7c+aMnnnmmWJLhyT5+Pjoj3/8o86ePeviZACuXr2qhx56qNRjOnbsaF9gEFUTxQNwkLe3t1JSUko9JiUlpdDlYwCuVdZFfMMwXJQEwO18fX3L/GIuISFBvr6+LkoEM1A8AAd17NhRW7duLfHb0nPnzmnbtm1lfqMDwDkeeOAB7dy5Uzdu3Ch2f2Zmpnbu3KnWrVu7OBmATp06aceOHYqOji52/3fffacdO3awuGcVxxgPwEFHjhxReHi43N3dNXDgQIWEhKhevXpKSkqSxWLR+vXrlZeXp9WrV+uRRx4xOy5Q7WzYsEHTpk1T69atNX78eIWEhKhu3bpKSUmRxWLRkiVLdO7cOc2ePVuhoaFmxwWqlXPnzmnQoEHKzs5Wt27dFBISosDAQCUnJ8tisegf//iHatasqc8//5wvB6owigdwF7766itNnz5dGRkZhW7ZsNls8vX11YwZM/Tcc8+ZmBCo3mbPnq01a9bYX5+3T6drs9kUFham6dOnmxkRqLaOHDmiadOm6eLFi5Ju3fpY8DG0WbNmmjNnDl/cVXEUD+Au3bhxQ7t27VJMTIx95fK2bduqR48eJQ5qBeA6hw4d0vr163Xq1KlCr9HQ0FAWEARMZrPZdPjw4SLvoY888ghjsKoBigfgoMjISDVt2lR9+/Y1OwqAYhw8eND+IQZA5TJt2jS1adNGw4cPNzsKTMTgcsBBUVFROnPmjNkxAJQgPDyc9TmASmrLli1KTk42OwZMRvEAHNS4cWOlpaWZHQNACQIDA+Xp6Wl2DADFuP/++3Xt2jWzY8BkFA/AQS+88IL27dunjIwMs6MAKMbvf/97HTx4sMy1PAC43sCBA7V3714lJiaaHQUmYowH4KCcnBxNmDBB165d0xtvvKEHH3xQgYGBZscC8E+JiYkaMmSIunbtqoiICPn7+5sdCcA/Xb58WTNmzNCZM2c0atQo+3tocQPKGzdubEJCuALFA3BQwYBVm81W6swbhmHo5MmTrooF4J/Cw8N1/fp1nT17Vp6enmrSpEmxH2wMw9Dq1atNSglUT8HBwfbpc3kPrb48zA4A3CuYhhOo3CwWi/3vOTk5unDhgi5cuFDkOKbsBFyvX79+vPbAFQ8AAAAAzsfgcsBBBasfl4XpAoHKzdHXMoCKk52d7dBxsbGxTk4CM1E8AAdNnz69zGOSk5M1bNgwF6QBcKcvvviizGOsVqumTJnigjQAbjdhwgRZrdZSj4mNjeU9tIqjeAAOWr9+vRYsWFDi/pSUFIWHhysuLs51oQDYvf/++9q1a1eJ+202myIiIrRjxw4XpgIgSd9++62mTZtW4v6LFy8qPDycKeurOIoH4KCwsDCtWLFCq1atKrIvJSVFw4YNU2xsrObMmeP6cADUsWNHTZkyRYcOHSqyr6B0bN++XUOGDDEhHVC9RUREaNOmTZo7d26RffHx8Xr55ZeVkZGhZcuWmZAOrkLxABw0ffp0Pf/88/rggw+0efNm+/bU1FQNGzZM58+f19y5c9W7d28TUwLV13/913+pWbNmGjt2rE6fPm3fbrPZNHXqVG3dulWDBw/We++9Z2JKoHoaNWqURowYodWrV2v58uX27ZcuXVJYWJjS09MVFRWlxx9/3MSUcDZmtQLuQm5urkaPHi2LxaJly5apffv2Cg8P1/nz5zVnzhz17dvX7IhAtZaYmKgXX3xRubm5WrdunYKCghQREaGtW7fqT3/6k/785z+bHRGo1t58801t3rxZs2bNUkhIiMLCwnT9+nVFRUWpc+fOZseDk1E8gLv066+/6uWXX9aFCxfUqFEjxcXFafbs2erXr5/Z0QDo1gDVF198UXXq1FG7du20fft2DRo0SDNmzDA7GlDtWa1WjR07VtHR0fL391dGRoaWLl2qrl27mh0NLkDxAMohJSVFL730kuLj4zVr1iyFhoaaHQnAbY4fP65hw4YpKytLAwYM0MyZM82OBOCfsrOzNXz4cMXExFA6qhmKB1CC8PDwUvcnJyfr2rVrCg4OLrTdMAytXr3amdEASIqMjCx1/6FDhxQTE6OwsLBCKyYbhqFx48Y5Ox5QrfXo0aPU/Tdv3tSNGzdUt27dQtsNwyh1djrc2ygeQAnuLBSOMgxDMTExFZwGwJ14jQKV19NPP13uc3fv3l2BSVCZUDwAAPcki8VS7nM7depUgUkAAI6geABOdurUKZ06dYrB50AllZmZqfT0dDVu3NjsKADuYLFYZLFYNH78eLOjoAKwjgfgZLt27Sp1tVYA5lq1alWZ96MDMIfFYtGSJUvMjoEKQvEAAAAA4HQUDwAAAABOR/EAAAAA4HQUDwAAAABOR/EAAAAA4HQUDwAAAABOR/EAAAAA4HQUDwAAAABOR/EAnCwoKEiPPvqo2TGAKq9t27aaMmXKXZ9ns9lks9mckAjAbxUcHKx+/fqZHQMVxLDxry3gEEdWH3dzc5OPj49atGih7t27q0GDBi5IBkCSQkJCNHjwYEVERJgdBaj2pk2bJsMwNHnyZNWrV8+h91BJMgxDs2fPdnI6mIXiATgoODhYhmFIUrHfjhqGUWi7h4eHXn/9db3++usuywhUZyNHjpRhGFqxYoXZUYBqr+A9c9u2bWrRooWCg4MdOs8wDMXExDg5HcxC8QAcdOnSJc2ePVvHjx9XeHi4Hn74YQUGBio5OVlHjhzRmjVr1KFDB40ZM0anT5/WsmXL9Msvv2jBggXq2bOn2fGBKu/o0aMKCwvTjBkzFBoaanYcoFpLSEiQJDVo0EAeHh72nx0RFBTkrFgwGcUDcNDy5cu1Zs0affnllwoMDCyy/9q1a+rXr5+GDx+uV199VYmJierZs6fatm2rtWvXmpAYqF4iIyP1ww8/KDo6Wm3btlWHDh1Ur169IscZhqFx48aZkBAAqjeKB+CgZ555Rk8++aSmT59e4jEzZszQt99+q507d0qSpkyZom+++UaHDh1yVUyg2uJWDgCo3DzMDgDcK65cuSJPT89Sj/Hy8tKVK1fsPzdu3Fg3b950djQAktasWWN2BABAKSgegIMaNGig3bt3a9KkSfLy8iqyPycnR7t37y40k1VycrLq1KnjyphAtdWpUyezIwAASsE6HoCDBg4cqIsXLyosLEx79+7V9evXJUnXr1/Xnj17NHToUMXHx2vAgAH2cw4fPqw2bdqYlBgAAKDy4IoH4KBRo0bp/Pnz2rRpk8aOHSvp1rod+fn5km5Nsdu7d2+99tprkqSkpCR169ZNTzzxhGmZAQAAKgsGlwN36bvvvtOmTZt0+vRpZWZmysfHR23atFGfPn3UuXNns+MBAABUShQPAAAAAE7HGA8AAAAATkfxAAAAAOB0FA8AAAAATkfxAAAAAOB0FA8AAAAATsc6HgCAu/byyy/LYrGUedzp06ddkKaw9PR0rV69WpI0YcIEl/9+AEDxKB4AgHJr1KiRGjVqZHaMQtLT0xUZGSmJ4gEAlQnFAwBQbgMGDODDPQDAIYzxAAAAAOB0XPEAALjEoUOH9Omnn+rw4cNKSUlRrVq19C//8i8aMmSInn322SLH5+TkaO/evdqzZ49OnDihxMREZWVlqX79+goJCdHIkSP1wAMPFDrnrbfe0oYNG+w/t2nTptD+OXPmqH///rp8+bJ69OghqeRxKIsXL1ZkZKRCQ0M1d+5c+/Y7z927d6/WrFmjn376SdevX9eSJUv0hz/8QZKUn5+vzZs368svv9TJkyeVmZmpgIAAhYSEaNSoUWrXrl05/ksCwL2J4gEAcLr58+fr448/liT5+vqqVatWSkpKUnR0tKKjozVkyBD9x3/8R6Fz4uLiNGHCBLm5uSkwMFBBQUHKzc1VQkKCNm7cqG3btmnx4sV66qmn7Oc0b95c7du314kTJyRJDz/8cKHHDAwMrNDn9de//lXz5s2Tv7+/mjZtKm9vb/u+zMxMTZgwQdHR0ZKk+vXrq3Xr1oqPj9fWrVv11Vdfac6cOerTp0+FZgKAyoriAQBwqr/97W/6+OOPFRAQoH//939Xz5497fv279+vqVOn6n/+53/UsWNH9e/f376vbt26+uCDD9StWzf5+/vbt+fk5OiLL77QrFmz9NZbb2nPnj32D/xjxoxRr1697Fck1q1b59TntnDhQr399tsKCwuTu7u7JOnmzZuSpOnTpys6Olrt2rXTn//8Zz344IOSbl0F+eSTTzR37ly98847at++vVq2bOnUnABQGTDGAwBQbpGRkWrTpk2xf3bt2qWsrCx99NFHkqQPP/ywUOmQpK5du+r999+XJC1fvrzQvnr16qlv376FSockeXl5aejQoerZs6dSU1O1Z88epz2/sgwYMEDDhg2zlw5JqlGjho4dO6bt27fL399fUVFR9tIhSW5ubho2bJiGDh2qnJwcrVq1yoTkAOB6XPEAAJRbadPp+vv768CBA0pNTVVQUJCeeOKJYo/r3r27PD09FRsbq8TERDVo0KDQ/u+++07ffPON4uLilJmZKavVKkn65ZdfJEknT54sUmhcZcCAAcVu3759u6Rbz+3O51PgmWee0SeffKIDBw44LR8AVCYUDwBAuZU1nW5UVJQkKSMjQy+++GKZj3d78bhx44YmTJig/fv3l3rO9evXHQ9cwVq1alXs9oIB6xaLpcTnXXBL1pUrV5wTDgAqGYoHAMBp0tLSJN1a1O/IkSNlHp+VlWX/+7x587R//34FBARoypQpeuyxx3TfffepZs2akqS//OUvWrp0qfLy8pwT3gG1atUqdnvB805ISFBCQkKpj5GdnV3huQCgMqJ4AACcpnbt2pKkP/zhD1qyZInD5+Xl5Wnz5s2SbhWQbt26FTnmt1zpMAzD/nebzVbo5wK//vpruR+/oJC8/fbbGjZsWLkfBwCqEgaXAwCcpmCdjR9//FH5+fkOn5eSkmL/4P/II48Ue8wPP/xQ7PbiSsSdbp/2Nikpqdhj4uLiynyckhSsH+LIVR4AqC4oHgAAp+nSpYv8/Px07do1ff755w6fd3sxuHr1apH90dHRiomJKfPckm5jqlu3rurUqSNJOnr0aJH9ly5d0r59+xzOe6fnnntOkvT111/rzJkz5X4cAKhKKB4AAKfx8fHRxIkTJUkzZ87UqlWripSBtLQ0bdy4UfPmzbNv8/X1VXBwsCRp1qxZ9jETkvT9999r8uTJqlGjRrG/MyAgQL6+vvZjS9K9e3dJ0qJFiwqNw4iPj9fEiRNls9nu4pkW9uijj+q5555Tbm6uRo4cqd27dxd5vMuXL2vFihX64osvyv17AOBewhgPAIBTDR06VNevX9fixYs1Z84cLVy4UC1btpSnp6dSUlKUkJAgm82mTp06FTpv6tSpeu2117Rv3z499dRTat68udLT03X58mW1bdtWnTt31l//+tciv88wDPXt21dr167V2LFj1bp1a/vVjVdffVVPPvmkJGnChAnau3evzp8/r2effVYtWrRQfn6+zp8/r+DgYIWFhWnlypXlft5z585VTk6Odu/erbFjx9pXN8/Pz1diYqL9Fq/x48eX+3cAwL2EKx4AAKcbN26cNm7cqEGDBqlhw4aKjY3VuXPn5OHhoSeeeELvvvuuPvzww0Ln/P73v9eaNWvUpUsXSdKFCxfk5eWlsWPHat26dSXOKCVJb775psaMGaPmzZsrLi5OFotFFoul0HiOJk2a6LPPPlPPnj3l4+Oj2NhY5eTkaPTo0Vq3bp19YHx5eXt7a+nSpVq2bJn++Mc/ysvLS6dOnVJCQoLq1q2rXr16aeHChRoxYsRv+j0AcK8wbL/lWjIAAAAAOIArHgAAAACcjuIBAAAAwOkoHgAAAACcjuIBAAAAwOkoHgAAAACcjuIBAAAAwOkoHgAAAACcjuIBAAAAwOkoHgAAAACcjuIBAAAAwOkoHgAAAACcjuIBAAAAwOkoHgAAAACc7v8Dx8TfMLuZBzwAAAAASUVORK5CYII=", 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", "text/plain": [ "
" ] @@ -809,7 +809,7 @@ "id": "3ec470a9-8db0-403a-89ba-32e0dd9bef15", "metadata": {}, "source": [ - "## 5. Investigate the fluxes and associated flags" + "## 5. Clean the data: Investigate the fluxes and associated flags" ] }, { @@ -822,15 +822,15 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 15, "id": "0be4535d-cc89-45ef-98e9-591b9f459fae", "metadata": { "execution": { - "iopub.execute_input": "2025-05-06T21:16:28.653250Z", - "iopub.status.busy": "2025-05-06T21:16:28.652714Z", - "iopub.status.idle": "2025-05-06T21:16:28.662789Z", - "shell.execute_reply": "2025-05-06T21:16:28.661890Z", - "shell.execute_reply.started": "2025-05-06T21:16:28.653208Z" + "iopub.execute_input": "2025-05-07T22:05:52.420724Z", + "iopub.status.busy": "2025-05-07T22:05:52.420299Z", + "iopub.status.idle": "2025-05-07T22:05:52.429670Z", + "shell.execute_reply": "2025-05-07T22:05:52.428773Z", + "shell.execute_reply.started": "2025-05-07T22:05:52.420687Z" } }, "outputs": [ @@ -843,7 +843,7 @@ "Name: count, dtype: int64" ] }, - "execution_count": 51, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -862,15 +862,15 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "id": "0e66ccb2-3922-471b-8c15-7fb055d02a10", "metadata": { "execution": { - "iopub.execute_input": "2024-12-03T00:04:41.925819Z", - "iopub.status.busy": "2024-12-03T00:04:41.925606Z", - "iopub.status.idle": "2024-12-03T00:04:41.931416Z", - "shell.execute_reply": "2024-12-03T00:04:41.930843Z", - "shell.execute_reply.started": "2024-12-03T00:04:41.925802Z" + "iopub.execute_input": "2025-05-07T22:05:56.102177Z", + "iopub.status.busy": "2025-05-07T22:05:56.101688Z", + "iopub.status.idle": "2025-05-07T22:05:56.110977Z", + "shell.execute_reply": "2025-05-07T22:05:56.110054Z", + "shell.execute_reply.started": "2025-05-07T22:05:56.102091Z" } }, "outputs": [ @@ -883,7 +883,7 @@ "Name: count, dtype: int64" ] }, - "execution_count": 15, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -902,15 +902,15 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "id": "06786c33-2563-4237-9d0f-22d6308c0d7b", "metadata": { "execution": { - "iopub.execute_input": "2024-12-03T00:04:41.932301Z", - "iopub.status.busy": "2024-12-03T00:04:41.932089Z", - "iopub.status.idle": "2024-12-03T00:04:41.970411Z", - "shell.execute_reply": "2024-12-03T00:04:41.969900Z", - "shell.execute_reply.started": "2024-12-03T00:04:41.932283Z" + "iopub.execute_input": "2025-05-07T22:05:57.819779Z", + "iopub.status.busy": "2025-05-07T22:05:57.818811Z", + "iopub.status.idle": "2025-05-07T22:05:57.875856Z", + "shell.execute_reply": "2025-05-07T22:05:57.874913Z", + "shell.execute_reply.started": "2025-05-07T22:05:57.819738Z" } }, "outputs": [ @@ -919,22 +919,22 @@ "output_type": "stream", "text": [ " coord_ra coord_dec g_kronFlux g_kronFlux_flag r_kronFlux \\\n", - "1749 61.916451 -37.018987 232.455339 True 380.415747 \n", - "1758 61.910063 -37.017256 115.486047 True 105.318235 \n", - "1774 61.950787 -37.015252 132.207788 True 193.917364 \n", + "0 62.018897 -37.095671 71.568352 True 91.185588 \n", + "12 62.020649 -37.085250 164.458389 True 83.719543 \n", + "44 62.050118 -37.076587 90.446302 True 127.674283 \n", "... ... ... ... ... ... \n", - "11466 61.956032 -37.074942 59.901381 True 315.077832 \n", - "11471 61.942023 -37.073313 145.759753 True 120.304211 \n", - "11494 61.924542 -37.071842 248.013148 True 273.729756 \n", + "11539 61.964568 -36.904478 NaN True 414.809809 \n", + "11547 61.885858 -36.962398 51.079756 True 138.740430 \n", + "11560 61.975756 -36.903597 66.317754 True 114.603682 \n", "\n", " r_kronFlux_flag i_kronFlux i_kronFlux_flag \n", - "1749 True 562.754481 False \n", - "1758 True 218.924537 True \n", - "1774 True 522.751057 False \n", + "0 True 624.454022 True \n", + "12 True 39.636459 True \n", + "44 True 455.773111 True \n", "... ... ... ... \n", - "11466 True NaN True \n", - "11471 True 243.456290 True \n", - "11494 True 257.108970 True \n", + "11539 True 85.175780 False \n", + "11547 True NaN True \n", + "11560 True 199.928482 True \n", "\n", "[328 rows x 8 columns]\n" ] @@ -961,25 +961,25 @@ "id": "ec13b104-ad8d-4bd6-8a93-b6d1d57b921e", "metadata": {}, "source": [ - "There are six overlapping rows, meaning that in six cases, both photometric bands are flagged. Since the task at hand is a prediction one between three bands ($g$, $r$, and $i$) the bigger concern is the cases where any of these three Kron fluxes are flagged. Exclude rows where this is the case and build a clean DataFrame." + "There are six overlapping rows, meaning that in six cases, both photometric bands are flagged. Since the task at hand is a prediction one between three bands ($g$, $r$, and $i$) the bigger concern is the cases where any of these three Kron fluxes are flagged. Exclude rows where this is the case and build an \"unflagged\" DataFrame." ] }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 46, "id": "e6294681-9c60-4ec6-805c-d378300acaa3", "metadata": { "execution": { - "iopub.execute_input": "2025-05-06T21:24:53.420771Z", - "iopub.status.busy": "2025-05-06T21:24:53.420270Z", - "iopub.status.idle": "2025-05-06T21:24:53.428818Z", - "shell.execute_reply": "2025-05-06T21:24:53.427832Z", - "shell.execute_reply.started": "2025-05-06T21:24:53.420733Z" + "iopub.execute_input": "2025-05-07T22:18:46.381756Z", + "iopub.status.busy": "2025-05-07T22:18:46.381328Z", + "iopub.status.idle": "2025-05-07T22:18:46.389065Z", + "shell.execute_reply": "2025-05-07T22:18:46.388134Z", + "shell.execute_reply.started": "2025-05-07T22:18:46.381719Z" } }, "outputs": [], "source": [ - "clean = results[\n", + "unflagged_df = results[\n", " (results['r_kronFlux_flag'] == False) & \n", " (results['g_kronFlux_flag'] == False) &\n", " (results['i_kronFlux_flag'] == False)\n", @@ -991,26 +991,26 @@ "id": "38a84cda-eedb-4238-9d27-f91cb9f56531", "metadata": {}, "source": [ - "Visualize the relationship between $g$ and $r$ in this clean DataFrame." + "Visualize the relationship between $g$ and $r$ in this unflagged DataFrame." ] }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 47, "id": "61a66274-c3e1-4e41-b743-649fc00d69b7", "metadata": { "execution": { - "iopub.execute_input": "2025-05-06T21:25:25.068316Z", - "iopub.status.busy": "2025-05-06T21:25:25.067860Z", - "iopub.status.idle": "2025-05-06T21:25:25.500866Z", - "shell.execute_reply": "2025-05-06T21:25:25.499927Z", - "shell.execute_reply.started": "2025-05-06T21:25:25.068285Z" + "iopub.execute_input": "2025-05-07T22:18:47.505342Z", + "iopub.status.busy": "2025-05-07T22:18:47.504870Z", + "iopub.status.idle": "2025-05-07T22:18:47.852980Z", + "shell.execute_reply": "2025-05-07T22:18:47.852034Z", + "shell.execute_reply.started": "2025-05-07T22:18:47.505305Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -1020,7 +1020,7 @@ } ], "source": [ - "plt.scatter(clean['g_kronFlux'], clean['r_kronFlux'])\n", + "plt.scatter(unflagged_df['g_kronFlux'], unflagged_df['r_kronFlux'])\n", "plt.xlabel(r'$g-$band kronFlux [nJy]')\n", "plt.ylabel(r'$r-$band kronFlux [nJy]');" ] @@ -1035,21 +1035,21 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 49, "id": "5afedb17-6478-4f2b-bdfc-38e73cd4a65e", "metadata": { "execution": { - "iopub.execute_input": "2025-05-06T21:25:44.109359Z", - "iopub.status.busy": "2025-05-06T21:25:44.108890Z", - "iopub.status.idle": "2025-05-06T21:25:44.400604Z", - "shell.execute_reply": "2025-05-06T21:25:44.399632Z", - "shell.execute_reply.started": "2025-05-06T21:25:44.109321Z" + "iopub.execute_input": "2025-05-07T22:19:01.573175Z", + "iopub.status.busy": "2025-05-07T22:19:01.572728Z", + "iopub.status.idle": "2025-05-07T22:19:01.860892Z", + "shell.execute_reply": "2025-05-07T22:19:01.859964Z", + "shell.execute_reply.started": "2025-05-07T22:19:01.573120Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -1059,7 +1059,7 @@ } ], "source": [ - "plt.scatter(clean['g_kronFlux'], clean['r_kronFlux'])\n", + "plt.scatter(unflagged_df['g_kronFlux'], unflagged_df['r_kronFlux'])\n", "plt.xlabel(r'$g-$band kronFlux [nJy]')\n", "plt.ylabel(r'$r-$band kronFlux [nJy]')\n", "plt.xlim([0,30000])\n", @@ -1074,6 +1074,144 @@ "The relationship between $g-$ and $r-$band Kron fluxes looks roughly linear, so we should be able to do some predictive work here." ] }, + { + "cell_type": "markdown", + "id": "ca8c22a0-485a-4525-af2f-1e4c8bf74cd0", + "metadata": {}, + "source": [ + "Another thing to check before we start predicting is if there are any nans in these Series." + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "01fcf2a8-7f85-4ec0-8ebe-b69b76da7294", + "metadata": { + "execution": { + "iopub.execute_input": "2025-05-07T22:19:15.820758Z", + "iopub.status.busy": "2025-05-07T22:19:15.819249Z", + "iopub.status.idle": "2025-05-07T22:19:15.827303Z", + "shell.execute_reply": "2025-05-07T22:19:15.826279Z", + "shell.execute_reply.started": "2025-05-07T22:19:15.820711Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "False False\n" + ] + } + ], + "source": [ + "print(unflagged_df['g_kronFlux'].isna().any(), unflagged_df['r_kronFlux'].isna().any())" + ] + }, + { + "cell_type": "markdown", + "id": "62479245-028e-4709-9c46-bc244463176c", + "metadata": {}, + "source": [ + "Both outputs should be `False`." + ] + }, + { + "cell_type": "markdown", + "id": "2309022c-3530-4f3c-9f7a-7d51f3537afe", + "metadata": {}, + "source": [ + "Another important step is to check for negative (or zero) values in these data; they are fluxes so they should not be negative or equal to zero. First, count the number of these values in each of the $g-$ and $r-$band Series. (A Series is a single row of a DataFrame)." + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "47a125c4-77fa-4712-be40-241318966774", + "metadata": { + "execution": { + "iopub.execute_input": "2025-05-07T22:22:48.108026Z", + "iopub.status.busy": "2025-05-07T22:22:48.107233Z", + "iopub.status.idle": "2025-05-07T22:22:48.114869Z", + "shell.execute_reply": "2025-05-07T22:22:48.113914Z", + "shell.execute_reply.started": "2025-05-07T22:22:48.107973Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Negative or zero values in g_kronFlux: 23\n", + "Negative or zero values in r_kronFlux: 12\n" + ] + } + ], + "source": [ + "neg_g = (unflagged_df['g_kronFlux'] <= 0).sum()\n", + "neg_r = (unflagged_df['r_kronFlux'] <= 0).sum()\n", + "\n", + "print(f\"Negative or zero values in g_kronFlux: {neg_g}\")\n", + "print(f\"Negative or zero values in r_kronFlux: {neg_r}\")" + ] + }, + { + "cell_type": "markdown", + "id": "7a5d982a-939d-4e84-afbb-abe48975d502", + "metadata": {}, + "source": [ + "Uh oh! This is a big problem, let's mask all rows of the dataframe where any individual flux measurement is negative, making a \"clean\" DataFrame." + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "ffbe5670-6de4-4a92-99f7-4e480789b596", + "metadata": { + "execution": { + "iopub.execute_input": "2025-05-07T22:24:58.138609Z", + "iopub.status.busy": "2025-05-07T22:24:58.138075Z", + "iopub.status.idle": "2025-05-07T22:24:58.146798Z", + "shell.execute_reply": "2025-05-07T22:24:58.145796Z", + "shell.execute_reply.started": "2025-05-07T22:24:58.138568Z" + } + }, + "outputs": [], + "source": [ + "mask = (unflagged_df['g_kronFlux'] > 0) & (unflagged_df['r_kronFlux'] > 0) & (unflagged_df['i_kronFlux'] > 0)\n", + "clean_df = unflagged_df[mask]" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "782e7e2e-372e-4fcb-b837-a19a3bc83511", + "metadata": { + "execution": { + "iopub.execute_input": "2025-05-07T22:25:00.453158Z", + "iopub.status.busy": "2025-05-07T22:25:00.452694Z", + "iopub.status.idle": "2025-05-07T22:25:00.460363Z", + "shell.execute_reply": "2025-05-07T22:25:00.459402Z", + "shell.execute_reply.started": "2025-05-07T22:25:00.453102Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Negative or zero values in g_kronFlux: 0\n", + "Negative or zero values in r_kronFlux: 0\n" + ] + } + ], + "source": [ + "neg_g = (clean_df['g_kronFlux'] <= 0).sum()\n", + "neg_r = (clean_df['r_kronFlux'] <= 0).sum()\n", + "print(f\"Negative or zero values in g_kronFlux: {neg_g}\")\n", + "print(f\"Negative or zero values in r_kronFlux: {neg_r}\")" + ] + }, { "cell_type": "markdown", "id": "4704605a-4665-4ccc-bd7e-cefaf5e09828", @@ -1085,21 +1223,21 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 69, "id": "fc90feca-ede1-44b0-929b-2fec1ddf5ad4", "metadata": { "execution": { - "iopub.execute_input": "2025-05-06T21:48:57.144027Z", - "iopub.status.busy": "2025-05-06T21:48:57.143589Z", - "iopub.status.idle": "2025-05-06T21:48:57.153923Z", - "shell.execute_reply": "2025-05-06T21:48:57.152971Z", - "shell.execute_reply.started": "2025-05-06T21:48:57.143991Z" + "iopub.execute_input": "2025-05-07T22:25:02.790270Z", + "iopub.status.busy": "2025-05-07T22:25:02.789792Z", + "iopub.status.idle": "2025-05-07T22:25:02.799574Z", + "shell.execute_reply": "2025-05-07T22:25:02.798501Z", + "shell.execute_reply.started": "2025-05-07T22:25:02.790231Z" } }, "outputs": [], "source": [ "X_train, X_test, y_train, y_test = train_test_split(\n", - " clean['g_kronFlux'].to_frame(), clean['r_kronFlux'].to_frame(), test_size=0.2, random_state=42)" + " clean_df['g_kronFlux'].to_frame(), clean_df['r_kronFlux'].to_frame(), test_size=0.2, random_state=42)" ] }, { @@ -1120,18 +1258,105 @@ }, { "cell_type": "code", - "execution_count": 66, - "id": "8e675e27-74f0-43e8-91af-8652e2710609", + "execution_count": 77, + "id": "f09a28c9-f868-4cfa-a309-c53b26193e01", + "metadata": { + "execution": { + "iopub.execute_input": "2025-05-07T22:25:38.851010Z", + "iopub.status.busy": "2025-05-07T22:25:38.850578Z", + "iopub.status.idle": "2025-05-07T22:25:38.876912Z", + "shell.execute_reply": "2025-05-07T22:25:38.875876Z", + "shell.execute_reply.started": "2025-05-07T22:25:38.850972Z" + } + }, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import FunctionTransformer, StandardScaler\n", + "from sklearn.pipeline import make_pipeline\n", + "import numpy as np\n", + "\n", + "# Split data first\n", + "X_train, X_test, y_train, y_test = train_test_split(\n", + " clean_df['g_kronFlux'].to_frame(), \n", + " clean_df['r_kronFlux'].to_frame(), \n", + " test_size=0.2, \n", + " random_state=42\n", + ")\n", + "\n", + "# Define log + standard scaler for X\n", + "X_scaler = make_pipeline(\n", + " FunctionTransformer(lambda x: np.log1p(x)),\n", + " StandardScaler()\n", + ")\n", + "\n", + "# Define log + standard scaler for y\n", + "y_scaler = make_pipeline(\n", + " FunctionTransformer(lambda x: np.log1p(x)),\n", + " StandardScaler()\n", + ")\n", + "\n", + "# Fit and transform\n", + "X_train_scaled = X_scaler.fit_transform(X_train)\n", + "X_test_scaled = X_scaler.transform(X_test)\n", + "\n", + "y_train_scaled = y_scaler.fit_transform(y_train)\n", + "y_test_scaled = y_scaler.transform(y_test)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "2c8db726-4e7b-4bd8-ab46-31f25eacdf32", "metadata": { "execution": { - "iopub.execute_input": "2025-05-06T21:50:59.656100Z", - "iopub.status.busy": "2025-05-06T21:50:59.655655Z", - "iopub.status.idle": "2025-05-06T21:50:59.663576Z", - "shell.execute_reply": "2025-05-06T21:50:59.662567Z", - "shell.execute_reply.started": "2025-05-06T21:50:59.656063Z" + "iopub.execute_input": "2025-05-07T22:25:08.817908Z", + "iopub.status.busy": "2025-05-07T22:25:08.816938Z", + "iopub.status.idle": "2025-05-07T22:25:08.822746Z", + "shell.execute_reply": "2025-05-07T22:25:08.821802Z", + "shell.execute_reply.started": "2025-05-07T22:25:08.817865Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(7690, 1)\n" + ] + } + ], + "source": [ + "print(np.shape(X_train_scaled))" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "id": "085dae46-aedd-44f2-9830-ef6cfb788cbc", + "metadata": { + "execution": { + "iopub.execute_input": "2025-05-07T22:25:42.550666Z", + "iopub.status.busy": "2025-05-07T22:25:42.549834Z", + "iopub.status.idle": "2025-05-07T22:25:42.555160Z", + "shell.execute_reply": "2025-05-07T22:25:42.554213Z", + "shell.execute_reply.started": "2025-05-07T22:25:42.550625Z" } }, "outputs": [], + "source": [ + "X_train = X_train_scaled\n", + "X_test = X_test_scaled\n", + "y_train = y_train_scaled\n", + "y_test = y_test_scaled" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8e675e27-74f0-43e8-91af-8652e2710609", + "metadata": {}, + "outputs": [], "source": [ "scaler = StandardScaler()\n", "X_train = scaler.fit_transform(X_train)\n", @@ -1148,15 +1373,15 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 79, "id": "3ee4e732-8688-47fa-b62f-27cb4b9ee9ca", "metadata": { "execution": { - "iopub.execute_input": "2025-05-06T21:52:40.892238Z", - "iopub.status.busy": "2025-05-06T21:52:40.891747Z", - "iopub.status.idle": "2025-05-06T21:52:41.109899Z", - "shell.execute_reply": "2025-05-06T21:52:41.108965Z", - "shell.execute_reply.started": "2025-05-06T21:52:40.892199Z" + "iopub.execute_input": "2025-05-07T22:25:44.500430Z", + "iopub.status.busy": "2025-05-07T22:25:44.500004Z", + "iopub.status.idle": "2025-05-07T22:25:44.712500Z", + "shell.execute_reply": "2025-05-07T22:25:44.711570Z", + "shell.execute_reply.started": "2025-05-07T22:25:44.500395Z" } }, "outputs": [ @@ -1164,13 +1389,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_22441/4138889604.py:1: RuntimeWarning: invalid value encountered in log\n", - " plt.hist(np.log(X_train.flatten()));\n" + "/tmp/ipykernel_25995/1236112267.py:1: RuntimeWarning: invalid value encountered in log\n", + " plt.hist(np.log(X_train_scaled.flatten()));\n" ] }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -1180,7 +1405,44 @@ } ], "source": [ - "plt.hist(np.log(X_train.flatten()));" + "plt.hist(np.log(X_train_scaled.flatten()));" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "id": "40eaabc7-8c97-47dc-9de2-2b505aa32624", + "metadata": { + "execution": { + "iopub.execute_input": "2025-05-07T22:25:45.996991Z", + "iopub.status.busy": "2025-05-07T22:25:45.996231Z", + "iopub.status.idle": "2025-05-07T22:25:46.204265Z", + "shell.execute_reply": "2025-05-07T22:25:46.203327Z", + "shell.execute_reply.started": "2025-05-07T22:25:45.996946Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_25995/2542126211.py:1: RuntimeWarning: invalid value encountered in log\n", + " plt.hist(np.log(y_train_scaled.flatten()));\n" + ] + }, + { + "data": { + "image/png": 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LinearRegression()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + "
LinearRegression()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" ], "text/plain": [ "LinearRegression()" ] }, - "execution_count": 23, + "execution_count": 82, "metadata": {}, "output_type": "execute_result" } @@ -1638,15 +1940,15 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 83, "id": "aca8064a-c94f-4792-86b3-62ec2130471b", "metadata": { "execution": { - "iopub.execute_input": "2024-12-03T00:04:42.431529Z", - "iopub.status.busy": "2024-12-03T00:04:42.431336Z", - "iopub.status.idle": "2024-12-03T00:04:42.438736Z", - "shell.execute_reply": "2024-12-03T00:04:42.438086Z", - "shell.execute_reply.started": "2024-12-03T00:04:42.431514Z" + "iopub.execute_input": "2025-05-07T22:25:53.440319Z", + "iopub.status.busy": "2025-05-07T22:25:53.439863Z", + "iopub.status.idle": "2025-05-07T22:25:53.447572Z", + "shell.execute_reply": "2025-05-07T22:25:53.446653Z", + "shell.execute_reply.started": "2025-05-07T22:25:53.440277Z" } }, "outputs": [ @@ -1654,7 +1956,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "MSE: 81831676.75317723\n" + "MSE: 0.26954356602486723\n" ] } ], @@ -1674,21 +1976,21 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 85, "id": "ee8bd887-928e-4a41-bd77-149b344ab238", "metadata": { "execution": { - "iopub.execute_input": "2024-12-03T00:04:42.439562Z", - "iopub.status.busy": "2024-12-03T00:04:42.439353Z", - "iopub.status.idle": "2024-12-03T00:04:42.606543Z", - "shell.execute_reply": "2024-12-03T00:04:42.605896Z", - "shell.execute_reply.started": "2024-12-03T00:04:42.439546Z" + "iopub.execute_input": "2025-05-07T22:26:08.176359Z", + "iopub.status.busy": "2025-05-07T22:26:08.175337Z", + "iopub.status.idle": "2025-05-07T22:26:08.399256Z", + "shell.execute_reply": "2025-05-07T22:26:08.398296Z", + "shell.execute_reply.started": "2025-05-07T22:26:08.176315Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -1700,11 +2002,11 @@ "source": [ "plt.clf()\n", "plt.scatter(y_test, y_pred)\n", - "plt.plot([0,1e6],[0,1e6], color='black', ls='--')\n", + "#plt.plot([0,1e6],[0,1e6], color='black', ls='--')\n", "plt.xlabel('True')\n", - "plt.ylabel('Predicted')\n", - "plt.xlim([0,2e4])\n", - "plt.ylim([0,2e4]);" + "plt.ylabel('Predicted');\n", + "#plt.xlim([0,2e4])\n", + "#plt.ylim([0,2e4]);" ] }, { @@ -1718,36 +2020,10 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "id": "d80e56eb-6e3d-4110-bee6-3681ee4a923b", - "metadata": { - "execution": { - "iopub.execute_input": "2024-12-03T00:04:43.535589Z", - "iopub.status.busy": "2024-12-03T00:04:43.535315Z", - "iopub.status.idle": "2024-12-03T00:04:43.657195Z", - "shell.execute_reply": "2024-12-03T00:04:43.656652Z", - "shell.execute_reply.started": "2024-12-03T00:04:43.535570Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MSE: 409962.71379404626\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "# Train-test split\n", "X_train, X_test, y_train, y_test = train_test_split(\n", @@ -1803,36 +2079,10 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "id": "efb56f9d-6487-444d-b283-6d60c1694948", - "metadata": { - "execution": { - "iopub.execute_input": "2024-12-03T00:04:45.724451Z", - "iopub.status.busy": "2024-12-03T00:04:45.723900Z", - "iopub.status.idle": "2024-12-03T00:04:48.635415Z", - "shell.execute_reply": "2024-12-03T00:04:48.634885Z", - "shell.execute_reply.started": "2024-12-03T00:04:45.724427Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MSE: 33565886.06951628\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "model = RandomForestRegressor()\n", "model.fit(X_train, y_train)\n", @@ -1868,12 +2118,7 @@ "cell_type": "code", "execution_count": null, "id": "e6420904-44bb-40cf-8ce0-92b22a802e59", - "metadata": { - "execution": { - "iopub.execute_input": "2024-12-03T17:29:45.803252Z", - "iopub.status.busy": "2024-12-03T17:29:45.802663Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import GridSearchCV\n", @@ -1898,43 +2143,10 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": null, "id": "89167a5d-e835-4f8d-8d57-ac82f0df6239", - "metadata": { - "execution": { - "iopub.execute_input": "2024-12-03T00:19:21.191881Z", - "iopub.status.busy": "2024-12-03T00:19:21.191156Z", - "iopub.status.idle": "2024-12-03T00:19:21.944107Z", - "shell.execute_reply": "2024-12-03T00:19:21.943414Z", - "shell.execute_reply.started": "2024-12-03T00:19:21.191857Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Best Model: RandomForestRegressor(n_estimators=1000)\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MSE: 19817809.291009396\n" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ "best_model = grid.best_estimator_\n", "print(\"Best Model:\", best_model)\n", @@ -1963,78 +2175,10 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "id": "5adca7ed-eb36-4e8b-99e3-3b66833fb3e3", - "metadata": { - "execution": { - "iopub.execute_input": "2024-12-03T17:56:30.644538Z", - "iopub.status.busy": "2024-12-03T17:56:30.643733Z", - "iopub.status.idle": "2024-12-03T17:56:30.650634Z", - "shell.execute_reply": "2024-12-03T17:56:30.649954Z", - "shell.execute_reply.started": "2024-12-03T17:56:30.644507Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['brier_score_loss', 'check_scoring', 'coverage_error', 'd2_absolute_error_score', 'd2_pinball_score', 'd2_tweedie_score', 'explained_variance_score', 'label_ranking_loss', 'log_loss', 'max_error', 'mean_absolute_error', 'mean_absolute_percentage_error', 'mean_gamma_deviance', 'mean_pinball_loss', 'mean_poisson_deviance', 'mean_squared_error', 'mean_squared_log_error', 'mean_tweedie_deviance', 'median_absolute_error', 'pairwise_distances', 'r2_score', 'root_mean_squared_error', 'root_mean_squared_log_error']\n", - "--- mean_tweedie_deviance ---\n", - "Help on function mean_tweedie_deviance in module sklearn.metrics._regression:\n", - "\n", - "mean_tweedie_deviance(y_true, y_pred, *, sample_weight=None, power=0)\n", - " Mean Tweedie deviance regression loss.\n", - " \n", - " Read more in the :ref:`User Guide `.\n", - " \n", - " Parameters\n", - " ----------\n", - " y_true : array-like of shape (n_samples,)\n", - " Ground truth (correct) target values.\n", - " \n", - " y_pred : array-like of shape (n_samples,)\n", - " Estimated target values.\n", - " \n", - " sample_weight : array-like of shape (n_samples,), default=None\n", - " Sample weights.\n", - " \n", - " power : float, default=0\n", - " Tweedie power parameter. Either power <= 0 or power >= 1.\n", - " \n", - " The higher `p` the less weight is given to extreme\n", - " deviations between true and predicted targets.\n", - " \n", - " - power < 0: Extreme stable distribution. Requires: y_pred > 0.\n", - " - power = 0 : Normal distribution, output corresponds to\n", - " mean_squared_error. y_true and y_pred can be any real numbers.\n", - " - power = 1 : Poisson distribution. Requires: y_true >= 0 and\n", - " y_pred > 0.\n", - " - 1 < p < 2 : Compound Poisson distribution. Requires: y_true >= 0\n", - " and y_pred > 0.\n", - " - power = 2 : Gamma distribution. Requires: y_true > 0 and y_pred > 0.\n", - " - power = 3 : Inverse Gaussian distribution. Requires: y_true > 0\n", - " and y_pred > 0.\n", - " - otherwise : Positive stable distribution. Requires: y_true > 0\n", - " and y_pred > 0.\n", - " \n", - " Returns\n", - " -------\n", - " loss : float\n", - " A non-negative floating point value (the best value is 0.0).\n", - " \n", - " Examples\n", - " --------\n", - " >>> from sklearn.metrics import mean_tweedie_deviance\n", - " >>> y_true = [2, 0, 1, 4]\n", - " >>> y_pred = [0.5, 0.5, 2., 2.]\n", - " >>> mean_tweedie_deviance(y_true, y_pred, power=1)\n", - " 1.4260...\n", - "\n", - "================================================================================\n" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ "import sklearn.metrics as metrics\n", "import inspect\n", @@ -2058,745 +2202,10 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "de4a41f7-9f4f-4662-9302-2da7d68df434", - "metadata": { - "execution": { - "iopub.execute_input": "2024-12-03T17:40:53.931835Z", - "iopub.status.busy": "2024-12-03T17:40:53.931115Z", - "iopub.status.idle": "2024-12-03T17:40:54.007554Z", - "shell.execute_reply": "2024-12-03T17:40:54.007006Z", - "shell.execute_reply.started": "2024-12-03T17:40:53.931811Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ARDRegression\n", - "AdaBoostRegressor\n", - "BaggingRegressor\n", - "BayesianRidge\n", - "CCA\n", - "DecisionTreeRegressor\n", - "DummyRegressor\n", - "ElasticNet\n", - "ElasticNetCV\n", - "ExtraTreeRegressor\n", - "ExtraTreesRegressor\n", - "GammaRegressor\n", - "GaussianProcessRegressor\n", - "GradientBoostingRegressor\n", - "HistGradientBoostingRegressor\n", - "HuberRegressor\n", - "IsotonicRegression\n", - "KNeighborsRegressor\n", - "KernelRidge\n", - "Lars\n", - "LarsCV\n", - "Lasso\n", - "LassoCV\n", - "LassoLars\n", - "LassoLarsCV\n", - "LassoLarsIC\n", - "LinearRegression\n", - "LinearSVR\n", - "MLPRegressor\n", - "MultiOutputRegressor\n", - "MultiTaskElasticNet\n", - "MultiTaskElasticNetCV\n", - "MultiTaskLasso\n", - "MultiTaskLassoCV\n", - "NuSVR\n", - "OrthogonalMatchingPursuit\n", - "OrthogonalMatchingPursuitCV\n", - "PLSCanonical\n", - "PLSRegression\n", - "PassiveAggressiveRegressor\n", - "PoissonRegressor\n", - "QuantileRegressor\n", - "RANSACRegressor\n", - "RadiusNeighborsRegressor\n", - "RandomForestRegressor\n", - "Help on class RandomForestRegressor in module sklearn.ensemble._forest:\n", - "\n", - "class RandomForestRegressor(ForestRegressor)\n", - " | RandomForestRegressor(n_estimators=100, *, criterion='squared_error', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=1.0, max_leaf_nodes=None, min_impurity_decrease=0.0, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, ccp_alpha=0.0, max_samples=None, monotonic_cst=None)\n", - " | \n", - " | A random forest regressor.\n", - " | \n", - " | A random forest is a meta estimator that fits a number of decision tree\n", - " | regressors on various sub-samples of the dataset and uses averaging to\n", - " | improve the predictive accuracy and control over-fitting.\n", - " | Trees in the forest use the best split strategy, i.e. equivalent to passing\n", - " | `splitter=\"best\"` to the underlying :class:`~sklearn.tree.DecisionTreeRegressor`.\n", - " | The sub-sample size is controlled with the `max_samples` parameter if\n", - " | `bootstrap=True` (default), otherwise the whole dataset is used to build\n", - " | each tree.\n", - " | \n", - " | For a comparison between tree-based ensemble models see the example\n", - " | :ref:`sphx_glr_auto_examples_ensemble_plot_forest_hist_grad_boosting_comparison.py`.\n", - " | \n", - " | Read more in the :ref:`User Guide `.\n", - " | \n", - " | Parameters\n", - " | ----------\n", - " | n_estimators : int, default=100\n", - " | The number of trees in the forest.\n", - " | \n", - " | .. versionchanged:: 0.22\n", - " | The default value of ``n_estimators`` changed from 10 to 100\n", - " | in 0.22.\n", - " | \n", - " | criterion : {\"squared_error\", \"absolute_error\", \"friedman_mse\", \"poisson\"}, default=\"squared_error\"\n", - " | The function to measure the quality of a split. Supported criteria\n", - " | are \"squared_error\" for the mean squared error, which is equal to\n", - " | variance reduction as feature selection criterion and minimizes the L2\n", - " | loss using the mean of each terminal node, \"friedman_mse\", which uses\n", - " | mean squared error with Friedman's improvement score for potential\n", - " | splits, \"absolute_error\" for the mean absolute error, which minimizes\n", - " | the L1 loss using the median of each terminal node, and \"poisson\" which\n", - " | uses reduction in Poisson deviance to find splits.\n", - " | Training using \"absolute_error\" is significantly slower\n", - " | than when using \"squared_error\".\n", - " | \n", - " | .. versionadded:: 0.18\n", - " | Mean Absolute Error (MAE) criterion.\n", - " | \n", - " | .. versionadded:: 1.0\n", - " | Poisson criterion.\n", - " | \n", - " | max_depth : int, default=None\n", - " | The maximum depth of the tree. If None, then nodes are expanded until\n", - " | all leaves are pure or until all leaves contain less than\n", - " | min_samples_split samples.\n", - " | \n", - " | min_samples_split : int or float, default=2\n", - " | The minimum number of samples required to split an internal node:\n", - " | \n", - " | - If int, then consider `min_samples_split` as the minimum number.\n", - " | - If float, then `min_samples_split` is a fraction and\n", - " | `ceil(min_samples_split * n_samples)` are the minimum\n", - " | number of samples for each split.\n", - " | \n", - " | .. versionchanged:: 0.18\n", - " | Added float values for fractions.\n", - " | \n", - " | min_samples_leaf : int or float, default=1\n", - " | The minimum number of samples required to be at a leaf node.\n", - " | A split point at any depth will only be considered if it leaves at\n", - " | least ``min_samples_leaf`` training samples in each of the left and\n", - " | right branches. This may have the effect of smoothing the model,\n", - " | especially in regression.\n", - " | \n", - " | - If int, then consider `min_samples_leaf` as the minimum number.\n", - " | - If float, then `min_samples_leaf` is a fraction and\n", - " | `ceil(min_samples_leaf * n_samples)` are the minimum\n", - " | number of samples for each node.\n", - " | \n", - " | .. versionchanged:: 0.18\n", - " | Added float values for fractions.\n", - " | \n", - " | min_weight_fraction_leaf : float, default=0.0\n", - " | The minimum weighted fraction of the sum total of weights (of all\n", - " | the input samples) required to be at a leaf node. Samples have\n", - " | equal weight when sample_weight is not provided.\n", - " | \n", - " | max_features : {\"sqrt\", \"log2\", None}, int or float, default=1.0\n", - " | The number of features to consider when looking for the best split:\n", - " | \n", - " | - If int, then consider `max_features` features at each split.\n", - " | - If float, then `max_features` is a fraction and\n", - " | `max(1, int(max_features * n_features_in_))` features are considered at each\n", - " | split.\n", - " | - If \"sqrt\", then `max_features=sqrt(n_features)`.\n", - " | - If \"log2\", then `max_features=log2(n_features)`.\n", - " | - If None or 1.0, then `max_features=n_features`.\n", - " | \n", - " | .. note::\n", - " | The default of 1.0 is equivalent to bagged trees and more\n", - " | randomness can be achieved by setting smaller values, e.g. 0.3.\n", - " | \n", - " | .. versionchanged:: 1.1\n", - " | The default of `max_features` changed from `\"auto\"` to 1.0.\n", - " | \n", - " | Note: the search for a split does not stop until at least one\n", - " | valid partition of the node samples is found, even if it requires to\n", - " | effectively inspect more than ``max_features`` features.\n", - " | \n", - " | max_leaf_nodes : int, default=None\n", - " | Grow trees with ``max_leaf_nodes`` in best-first fashion.\n", - " | Best nodes are defined as relative reduction in impurity.\n", - " | If None then unlimited number of leaf nodes.\n", - " | \n", - " | min_impurity_decrease : float, default=0.0\n", - " | A node will be split if this split induces a decrease of the impurity\n", - " | greater than or equal to this value.\n", - " | \n", - " | The weighted impurity decrease equation is the following::\n", - " | \n", - " | N_t / N * (impurity - N_t_R / N_t * right_impurity\n", - " | - N_t_L / N_t * left_impurity)\n", - " | \n", - " | where ``N`` is the total number of samples, ``N_t`` is the number of\n", - " | samples at the current node, ``N_t_L`` is the number of samples in the\n", - " | left child, and ``N_t_R`` is the number of samples in the right child.\n", - " | \n", - " | ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,\n", - " | if ``sample_weight`` is passed.\n", - " | \n", - " | .. versionadded:: 0.19\n", - " | \n", - " | bootstrap : bool, default=True\n", - " | Whether bootstrap samples are used when building trees. If False, the\n", - " | whole dataset is used to build each tree.\n", - " | \n", - " | oob_score : bool or callable, default=False\n", - " | Whether to use out-of-bag samples to estimate the generalization score.\n", - " | By default, :func:`~sklearn.metrics.r2_score` is used.\n", - " | Provide a callable with signature `metric(y_true, y_pred)` to use a\n", - " | custom metric. Only available if `bootstrap=True`.\n", - " | \n", - " | n_jobs : int, default=None\n", - " | The number of jobs to run in parallel. :meth:`fit`, :meth:`predict`,\n", - " | :meth:`decision_path` and :meth:`apply` are all parallelized over the\n", - " | trees. ``None`` means 1 unless in a :obj:`joblib.parallel_backend`\n", - " | context. ``-1`` means using all processors. See :term:`Glossary\n", - " | ` for more details.\n", - " | \n", - " | random_state : int, RandomState instance or None, default=None\n", - " | Controls both the randomness of the bootstrapping of the samples used\n", - " | when building trees (if ``bootstrap=True``) and the sampling of the\n", - " | features to consider when looking for the best split at each node\n", - " | (if ``max_features < n_features``).\n", - " | See :term:`Glossary ` for details.\n", - " | \n", - " | verbose : int, default=0\n", - " | Controls the verbosity when fitting and predicting.\n", - " | \n", - " | warm_start : bool, default=False\n", - " | When set to ``True``, reuse the solution of the previous call to fit\n", - " | and add more estimators to the ensemble, otherwise, just fit a whole\n", - " | new forest. See :term:`Glossary ` and\n", - " | :ref:`tree_ensemble_warm_start` for details.\n", - " | \n", - " | ccp_alpha : non-negative float, default=0.0\n", - " | Complexity parameter used for Minimal Cost-Complexity Pruning. The\n", - " | subtree with the largest cost complexity that is smaller than\n", - " | ``ccp_alpha`` will be chosen. By default, no pruning is performed. See\n", - " | :ref:`minimal_cost_complexity_pruning` for details.\n", - " | \n", - " | .. versionadded:: 0.22\n", - " | \n", - " | max_samples : int or float, default=None\n", - " | If bootstrap is True, the number of samples to draw from X\n", - " | to train each base estimator.\n", - " | \n", - " | - If None (default), then draw `X.shape[0]` samples.\n", - " | - If int, then draw `max_samples` samples.\n", - " | - If float, then draw `max(round(n_samples * max_samples), 1)` samples. Thus,\n", - " | `max_samples` should be in the interval `(0.0, 1.0]`.\n", - " | \n", - " | .. versionadded:: 0.22\n", - " | \n", - " | monotonic_cst : array-like of int of shape (n_features), default=None\n", - " | Indicates the monotonicity constraint to enforce on each feature.\n", - " | - 1: monotonically increasing\n", - " | - 0: no constraint\n", - " | - -1: monotonically decreasing\n", - " | \n", - " | If monotonic_cst is None, no constraints are applied.\n", - " | \n", - " | Monotonicity constraints are not supported for:\n", - " | - multioutput regressions (i.e. when `n_outputs_ > 1`),\n", - " | - regressions trained on data with missing values.\n", - " | \n", - " | Read more in the :ref:`User Guide `.\n", - " | \n", - " | .. versionadded:: 1.4\n", - " | \n", - " | Attributes\n", - " | ----------\n", - " | estimator_ : :class:`~sklearn.tree.DecisionTreeRegressor`\n", - " | The child estimator template used to create the collection of fitted\n", - " | sub-estimators.\n", - " | \n", - " | .. versionadded:: 1.2\n", - " | `base_estimator_` was renamed to `estimator_`.\n", - " | \n", - " | estimators_ : list of DecisionTreeRegressor\n", - " | The collection of fitted sub-estimators.\n", - " | \n", - " | feature_importances_ : ndarray of shape (n_features,)\n", - " | The impurity-based feature importances.\n", - " | The higher, the more important the feature.\n", - " | The importance of a feature is computed as the (normalized)\n", - " | total reduction of the criterion brought by that feature. It is also\n", - " | known as the Gini importance.\n", - " | \n", - " | Warning: impurity-based feature importances can be misleading for\n", - " | high cardinality features (many unique values). See\n", - " | :func:`sklearn.inspection.permutation_importance` as an alternative.\n", - " | \n", - " | n_features_in_ : int\n", - " | Number of features seen during :term:`fit`.\n", - " | \n", - " | .. versionadded:: 0.24\n", - " | \n", - " | feature_names_in_ : ndarray of shape (`n_features_in_`,)\n", - " | Names of features seen during :term:`fit`. Defined only when `X`\n", - " | has feature names that are all strings.\n", - " | \n", - " | .. versionadded:: 1.0\n", - " | \n", - " | n_outputs_ : int\n", - " | The number of outputs when ``fit`` is performed.\n", - " | \n", - " | oob_score_ : float\n", - " | Score of the training dataset obtained using an out-of-bag estimate.\n", - " | This attribute exists only when ``oob_score`` is True.\n", - " | \n", - " | oob_prediction_ : ndarray of shape (n_samples,) or (n_samples, n_outputs)\n", - " | Prediction computed with out-of-bag estimate on the training set.\n", - " | This attribute exists only when ``oob_score`` is True.\n", - " | \n", - " | estimators_samples_ : list of arrays\n", - " | The subset of drawn samples (i.e., the in-bag samples) for each base\n", - " | estimator. Each subset is defined by an array of the indices selected.\n", - " | \n", - " | .. versionadded:: 1.4\n", - " | \n", - " | See Also\n", - " | --------\n", - " | sklearn.tree.DecisionTreeRegressor : A decision tree regressor.\n", - " | sklearn.ensemble.ExtraTreesRegressor : Ensemble of extremely randomized\n", - " | tree regressors.\n", - " | sklearn.ensemble.HistGradientBoostingRegressor : A Histogram-based Gradient\n", - " | Boosting Regression Tree, very fast for big datasets (n_samples >=\n", - " | 10_000).\n", - " | \n", - " | Notes\n", - " | -----\n", - " | The default values for the parameters controlling the size of the trees\n", - " | (e.g. ``max_depth``, ``min_samples_leaf``, etc.) lead to fully grown and\n", - " | unpruned trees which can potentially be very large on some data sets. To\n", - " | reduce memory consumption, the complexity and size of the trees should be\n", - " | controlled by setting those parameter values.\n", - " | \n", - " | The features are always randomly permuted at each split. Therefore,\n", - " | the best found split may vary, even with the same training data,\n", - " | ``max_features=n_features`` and ``bootstrap=False``, if the improvement\n", - " | of the criterion is identical for several splits enumerated during the\n", - " | search of the best split. To obtain a deterministic behaviour during\n", - " | fitting, ``random_state`` has to be fixed.\n", - " | \n", - " | The default value ``max_features=1.0`` uses ``n_features``\n", - " | rather than ``n_features / 3``. The latter was originally suggested in\n", - " | [1], whereas the former was more recently justified empirically in [2].\n", - " | \n", - " | References\n", - " | ----------\n", - " | .. [1] L. Breiman, \"Random Forests\", Machine Learning, 45(1), 5-32, 2001.\n", - " | \n", - " | .. [2] P. Geurts, D. Ernst., and L. Wehenkel, \"Extremely randomized\n", - " | trees\", Machine Learning, 63(1), 3-42, 2006.\n", - " | \n", - " | Examples\n", - " | --------\n", - " | >>> from sklearn.ensemble import RandomForestRegressor\n", - " | >>> from sklearn.datasets import make_regression\n", - " | >>> X, y = make_regression(n_features=4, n_informative=2,\n", - " | ... random_state=0, shuffle=False)\n", - " | >>> regr = RandomForestRegressor(max_depth=2, random_state=0)\n", - " | >>> regr.fit(X, y)\n", - " | RandomForestRegressor(...)\n", - " | >>> print(regr.predict([[0, 0, 0, 0]]))\n", - " | [-8.32987858]\n", - " | \n", - " | Method resolution order:\n", - " | RandomForestRegressor\n", - " | ForestRegressor\n", - " | sklearn.base.RegressorMixin\n", - " | BaseForest\n", - " | sklearn.base.MultiOutputMixin\n", - " | sklearn.ensemble._base.BaseEnsemble\n", - " | sklearn.base.MetaEstimatorMixin\n", - " | sklearn.base.BaseEstimator\n", - " | sklearn.utils._estimator_html_repr._HTMLDocumentationLinkMixin\n", - " | sklearn.utils._metadata_requests._MetadataRequester\n", - " | builtins.object\n", - " | \n", - " | Methods defined here:\n", - " | \n", - " | __init__(self, n_estimators=100, *, criterion='squared_error', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=1.0, max_leaf_nodes=None, min_impurity_decrease=0.0, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, ccp_alpha=0.0, max_samples=None, monotonic_cst=None)\n", - " | Initialize self. See help(type(self)) for accurate signature.\n", - " | \n", - " | set_fit_request(self: sklearn.ensemble._forest.RandomForestRegressor, *, sample_weight: Union[bool, NoneType, str] = '$UNCHANGED$') -> sklearn.ensemble._forest.RandomForestRegressor from sklearn.utils._metadata_requests.RequestMethod.__get__.\n", - " | Request metadata passed to the ``fit`` method.\n", - " | \n", - " | Note that this method is only relevant if\n", - " | ``enable_metadata_routing=True`` (see :func:`sklearn.set_config`).\n", - " | Please see :ref:`User Guide ` on how the routing\n", - " | mechanism works.\n", - " | \n", - " | The options for each parameter are:\n", - " | \n", - " | - ``True``: metadata is requested, and passed to ``fit`` if provided. The request is ignored if metadata is not provided.\n", - " | \n", - " | - ``False``: metadata is not requested and the meta-estimator will not pass it to ``fit``.\n", - " | \n", - " | - ``None``: metadata is not requested, and the meta-estimator will raise an error if the user provides it.\n", - " | \n", - " | - ``str``: metadata should be passed to the meta-estimator with this given alias instead of the original name.\n", - " | \n", - " | The default (``sklearn.utils.metadata_routing.UNCHANGED``) retains the\n", - " | existing request. This allows you to change the request for some\n", - " | parameters and not others.\n", - " | \n", - " | .. versionadded:: 1.3\n", - " | \n", - " | .. note::\n", - " | This method is only relevant if this estimator is used as a\n", - " | sub-estimator of a meta-estimator, e.g. used inside a\n", - " | :class:`~sklearn.pipeline.Pipeline`. Otherwise it has no effect.\n", - " | \n", - " | Parameters\n", - " | ----------\n", - " | sample_weight : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED\n", - " | Metadata routing for ``sample_weight`` parameter in ``fit``.\n", - " | \n", - " | Returns\n", - " | -------\n", - " | self : object\n", - " | The updated object.\n", - " | \n", - " | set_score_request(self: sklearn.ensemble._forest.RandomForestRegressor, *, sample_weight: Union[bool, NoneType, str] = '$UNCHANGED$') -> sklearn.ensemble._forest.RandomForestRegressor from sklearn.utils._metadata_requests.RequestMethod.__get__.\n", - " | Request metadata passed to the ``score`` method.\n", - " | \n", - " | Note that this method is only relevant if\n", - " | ``enable_metadata_routing=True`` (see :func:`sklearn.set_config`).\n", - " | Please see :ref:`User Guide ` on how the routing\n", - " | mechanism works.\n", - " | \n", - " | The options for each parameter are:\n", - " | \n", - " | - ``True``: metadata is requested, and passed to ``score`` if provided. The request is ignored if metadata is not provided.\n", - " | \n", - " | - ``False``: metadata is not requested and the meta-estimator will not pass it to ``score``.\n", - " | \n", - " | - ``None``: metadata is not requested, and the meta-estimator will raise an error if the user provides it.\n", - " | \n", - " | - ``str``: metadata should be passed to the meta-estimator with this given alias instead of the original name.\n", - " | \n", - " | The default (``sklearn.utils.metadata_routing.UNCHANGED``) retains the\n", - " | existing request. This allows you to change the request for some\n", - " | parameters and not others.\n", - " | \n", - " | .. versionadded:: 1.3\n", - " | \n", - " | .. note::\n", - " | This method is only relevant if this estimator is used as a\n", - " | sub-estimator of a meta-estimator, e.g. used inside a\n", - " | :class:`~sklearn.pipeline.Pipeline`. Otherwise it has no effect.\n", - " | \n", - " | Parameters\n", - " | ----------\n", - " | sample_weight : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED\n", - " | Metadata routing for ``sample_weight`` parameter in ``score``.\n", - " | \n", - " | Returns\n", - " | -------\n", - " | self : object\n", - " | The updated object.\n", - " | \n", - " | ----------------------------------------------------------------------\n", - " | Data and other attributes defined here:\n", - " | \n", - " | __abstractmethods__ = frozenset()\n", - " | \n", - " | __annotations__ = {'_parameter_constraints': }\n", - " | \n", - " | ----------------------------------------------------------------------\n", - " | Methods inherited from ForestRegressor:\n", - " | \n", - " | predict(self, X)\n", - " | Predict regression target for X.\n", - " | \n", - " | The predicted regression target of an input sample is computed as the\n", - " | mean predicted regression targets of the trees in the forest.\n", - " | \n", - " | Parameters\n", - " | ----------\n", - " | X : {array-like, sparse matrix} of shape (n_samples, n_features)\n", - " | The input samples. Internally, its dtype will be converted to\n", - " | ``dtype=np.float32``. If a sparse matrix is provided, it will be\n", - " | converted into a sparse ``csr_matrix``.\n", - " | \n", - " | Returns\n", - " | -------\n", - " | y : ndarray of shape (n_samples,) or (n_samples, n_outputs)\n", - " | The predicted values.\n", - " | \n", - " | ----------------------------------------------------------------------\n", - " | Methods inherited from sklearn.base.RegressorMixin:\n", - " | \n", - " | score(self, X, y, sample_weight=None)\n", - " | Return the coefficient of determination of the prediction.\n", - " | \n", - " | The coefficient of determination :math:`R^2` is defined as\n", - " | :math:`(1 - \\frac{u}{v})`, where :math:`u` is the residual\n", - " | sum of squares ``((y_true - y_pred)** 2).sum()`` and :math:`v`\n", - " | is the total sum of squares ``((y_true - y_true.mean()) ** 2).sum()``.\n", - " | The best possible score is 1.0 and it can be negative (because the\n", - " | model can be arbitrarily worse). A constant model that always predicts\n", - " | the expected value of `y`, disregarding the input features, would get\n", - " | a :math:`R^2` score of 0.0.\n", - " | \n", - " | Parameters\n", - " | ----------\n", - " | X : array-like of shape (n_samples, n_features)\n", - " | Test samples. For some estimators this may be a precomputed\n", - " | kernel matrix or a list of generic objects instead with shape\n", - " | ``(n_samples, n_samples_fitted)``, where ``n_samples_fitted``\n", - " | is the number of samples used in the fitting for the estimator.\n", - " | \n", - " | y : array-like of shape (n_samples,) or (n_samples, n_outputs)\n", - " | True values for `X`.\n", - " | \n", - " | sample_weight : array-like of shape (n_samples,), default=None\n", - " | Sample weights.\n", - " | \n", - " | Returns\n", - " | -------\n", - " | score : float\n", - " | :math:`R^2` of ``self.predict(X)`` w.r.t. `y`.\n", - " | \n", - " | Notes\n", - " | -----\n", - " | The :math:`R^2` score used when calling ``score`` on a regressor uses\n", - " | ``multioutput='uniform_average'`` from version 0.23 to keep consistent\n", - " | with default value of :func:`~sklearn.metrics.r2_score`.\n", - " | This influences the ``score`` method of all the multioutput\n", - " | regressors (except for\n", - " | :class:`~sklearn.multioutput.MultiOutputRegressor`).\n", - " | \n", - " | ----------------------------------------------------------------------\n", - " | Data descriptors inherited from sklearn.base.RegressorMixin:\n", - " | \n", - " | __dict__\n", - " | dictionary for instance variables\n", - " | \n", - " | __weakref__\n", - " | list of weak references to the object\n", - " | \n", - " | ----------------------------------------------------------------------\n", - " | Methods inherited from BaseForest:\n", - " | \n", - " | apply(self, X)\n", - " | Apply trees in the forest to X, return leaf indices.\n", - " | \n", - " | Parameters\n", - " | ----------\n", - " | X : {array-like, sparse matrix} of shape (n_samples, n_features)\n", - " | The input samples. Internally, its dtype will be converted to\n", - " | ``dtype=np.float32``. If a sparse matrix is provided, it will be\n", - " | converted into a sparse ``csr_matrix``.\n", - " | \n", - " | Returns\n", - " | -------\n", - " | X_leaves : ndarray of shape (n_samples, n_estimators)\n", - " | For each datapoint x in X and for each tree in the forest,\n", - " | return the index of the leaf x ends up in.\n", - " | \n", - " | decision_path(self, X)\n", - " | Return the decision path in the forest.\n", - " | \n", - " | .. versionadded:: 0.18\n", - " | \n", - " | Parameters\n", - " | ----------\n", - " | X : {array-like, sparse matrix} of shape (n_samples, n_features)\n", - " | The input samples. Internally, its dtype will be converted to\n", - " | ``dtype=np.float32``. If a sparse matrix is provided, it will be\n", - " | converted into a sparse ``csr_matrix``.\n", - " | \n", - " | Returns\n", - " | -------\n", - " | indicator : sparse matrix of shape (n_samples, n_nodes)\n", - " | Return a node indicator matrix where non zero elements indicates\n", - " | that the samples goes through the nodes. The matrix is of CSR\n", - " | format.\n", - " | \n", - " | n_nodes_ptr : ndarray of shape (n_estimators + 1,)\n", - " | The columns from indicator[n_nodes_ptr[i]:n_nodes_ptr[i+1]]\n", - " | gives the indicator value for the i-th estimator.\n", - " | \n", - " | fit(self, X, y, sample_weight=None)\n", - " | Build a forest of trees from the training set (X, y).\n", - " | \n", - " | Parameters\n", - " | ----------\n", - " | X : {array-like, sparse matrix} of shape (n_samples, n_features)\n", - " | The training input samples. Internally, its dtype will be converted\n", - " | to ``dtype=np.float32``. If a sparse matrix is provided, it will be\n", - " | converted into a sparse ``csc_matrix``.\n", - " | \n", - " | y : array-like of shape (n_samples,) or (n_samples, n_outputs)\n", - " | The target values (class labels in classification, real numbers in\n", - " | regression).\n", - " | \n", - " | sample_weight : array-like of shape (n_samples,), default=None\n", - " | Sample weights. If None, then samples are equally weighted. Splits\n", - " | that would create child nodes with net zero or negative weight are\n", - " | ignored while searching for a split in each node. In the case of\n", - " | classification, splits are also ignored if they would result in any\n", - " | single class carrying a negative weight in either child node.\n", - " | \n", - " | Returns\n", - " | -------\n", - " | self : object\n", - " | Fitted estimator.\n", - " | \n", - " | ----------------------------------------------------------------------\n", - " | Readonly properties inherited from BaseForest:\n", - " | \n", - " | estimators_samples_\n", - " | The subset of drawn samples for each base estimator.\n", - " | \n", - " | Returns a dynamically generated list of indices identifying\n", - " | the samples used for fitting each member of the ensemble, i.e.,\n", - " | the in-bag samples.\n", - " | \n", - " | Note: the list is re-created at each call to the property in order\n", - " | to reduce the object memory footprint by not storing the sampling\n", - " | data. Thus fetching the property may be slower than expected.\n", - " | \n", - " | feature_importances_\n", - " | The impurity-based feature importances.\n", - " | \n", - " | The higher, the more important the feature.\n", - " | The importance of a feature is computed as the (normalized)\n", - " | total reduction of the criterion brought by that feature. It is also\n", - " | known as the Gini importance.\n", - " | \n", - " | Warning: impurity-based feature importances can be misleading for\n", - " | high cardinality features (many unique values). See\n", - " | :func:`sklearn.inspection.permutation_importance` as an alternative.\n", - " | \n", - " | Returns\n", - " | -------\n", - " | feature_importances_ : ndarray of shape (n_features,)\n", - " | The values of this array sum to 1, unless all trees are single node\n", - " | trees consisting of only the root node, in which case it will be an\n", - " | array of zeros.\n", - " | \n", - " | ----------------------------------------------------------------------\n", - " | Methods inherited from sklearn.ensemble._base.BaseEnsemble:\n", - " | \n", - " | __getitem__(self, index)\n", - " | Return the index'th estimator in the ensemble.\n", - " | \n", - " | __iter__(self)\n", - " | Return iterator over estimators in the ensemble.\n", - " | \n", - " | __len__(self)\n", - " | Return the number of estimators in the ensemble.\n", - " | \n", - " | ----------------------------------------------------------------------\n", - " | Methods inherited from sklearn.base.BaseEstimator:\n", - " | \n", - " | __getstate__(self)\n", - " | Helper for pickle.\n", - " | \n", - " | __repr__(self, N_CHAR_MAX=700)\n", - " | Return repr(self).\n", - " | \n", - " | __setstate__(self, state)\n", - " | \n", - " | __sklearn_clone__(self)\n", - " | \n", - " | get_params(self, deep=True)\n", - " | Get parameters for this estimator.\n", - " | \n", - " | Parameters\n", - " | ----------\n", - " | deep : bool, default=True\n", - " | If True, will return the parameters for this estimator and\n", - " | contained subobjects that are estimators.\n", - " | \n", - " | Returns\n", - " | -------\n", - " | params : dict\n", - " | Parameter names mapped to their values.\n", - " | \n", - " | set_params(self, **params)\n", - " | Set the parameters of this estimator.\n", - " | \n", - " | The method works on simple estimators as well as on nested objects\n", - " | (such as :class:`~sklearn.pipeline.Pipeline`). The latter have\n", - " | parameters of the form ``__`` so that it's\n", - " | possible to update each component of a nested object.\n", - " | \n", - " | Parameters\n", - " | ----------\n", - " | **params : dict\n", - " | Estimator parameters.\n", - " | \n", - " | Returns\n", - " | -------\n", - " | self : estimator instance\n", - " | Estimator instance.\n", - " | \n", - " | ----------------------------------------------------------------------\n", - " | Methods inherited from sklearn.utils._metadata_requests._MetadataRequester:\n", - " | \n", - " | get_metadata_routing(self)\n", - " | Get metadata routing of this object.\n", - " | \n", - " | Please check :ref:`User Guide ` on how the routing\n", - " | mechanism works.\n", - " | \n", - " | Returns\n", - " | -------\n", - " | routing : MetadataRequest\n", - " | A :class:`~sklearn.utils.metadata_routing.MetadataRequest` encapsulating\n", - " | routing information.\n", - " | \n", - " | ----------------------------------------------------------------------\n", - " | Class methods inherited from sklearn.utils._metadata_requests._MetadataRequester:\n", - " | \n", - " | __init_subclass__(**kwargs)\n", - " | Set the ``set_{method}_request`` methods.\n", - " | \n", - " | This uses PEP-487 [1]_ to set the ``set_{method}_request`` methods. It\n", - " | looks for the information available in the set default values which are\n", - " | set using ``__metadata_request__*`` class attributes, or inferred\n", - " | from method signatures.\n", - " | \n", - " | The ``__metadata_request__*`` class attributes are used when a method\n", - " | does not explicitly accept a metadata through its arguments or if the\n", - " | developer would like to specify a request value for those metadata\n", - " | which are different from the default ``None``.\n", - " | \n", - " | References\n", - " | ----------\n", - " | .. [1] https://www.python.org/dev/peps/pep-0487\n", - "\n", - "None\n", - "RegressorChain\n", - "Ridge\n", - "RidgeCV\n", - "SGDRegressor\n", - "SVR\n", - "StackingRegressor\n", - "TheilSenRegressor\n", - "TransformedTargetRegressor\n", - "TweedieRegressor\n", - "VotingRegressor\n" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ "from sklearn.utils import all_estimators\n", "\n", From f82a32fd01515d4adc9d514df5d0a5359b206736 Mon Sep 17 00:00:00 2001 From: beckynevin Date: Wed, 7 May 2025 22:44:22 +0000 Subject: [PATCH 08/13] struggling to get inverse log transformation --- DP0.2/20_Introduction_to_Data_Science.ipynb | 470 ++++++++++---------- 1 file changed, 247 insertions(+), 223 deletions(-) diff --git a/DP0.2/20_Introduction_to_Data_Science.ipynb b/DP0.2/20_Introduction_to_Data_Science.ipynb index aba78a7f..596a79f7 100644 --- a/DP0.2/20_Introduction_to_Data_Science.ipynb +++ b/DP0.2/20_Introduction_to_Data_Science.ipynb @@ -902,15 +902,15 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 107, "id": "06786c33-2563-4237-9d0f-22d6308c0d7b", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T22:05:57.819779Z", - "iopub.status.busy": "2025-05-07T22:05:57.818811Z", - "iopub.status.idle": "2025-05-07T22:05:57.875856Z", - "shell.execute_reply": "2025-05-07T22:05:57.874913Z", - "shell.execute_reply.started": "2025-05-07T22:05:57.819738Z" + "iopub.execute_input": "2025-05-07T22:34:05.242363Z", + "iopub.status.busy": "2025-05-07T22:34:05.241899Z", + "iopub.status.idle": "2025-05-07T22:34:05.299783Z", + "shell.execute_reply": "2025-05-07T22:34:05.298861Z", + "shell.execute_reply.started": "2025-05-07T22:34:05.242324Z" } }, "outputs": [ @@ -966,15 +966,15 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 108, "id": "e6294681-9c60-4ec6-805c-d378300acaa3", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T22:18:46.381756Z", - "iopub.status.busy": "2025-05-07T22:18:46.381328Z", - "iopub.status.idle": "2025-05-07T22:18:46.389065Z", - "shell.execute_reply": "2025-05-07T22:18:46.388134Z", - "shell.execute_reply.started": "2025-05-07T22:18:46.381719Z" + "iopub.execute_input": "2025-05-07T22:34:06.251575Z", + "iopub.status.busy": "2025-05-07T22:34:06.251095Z", + "iopub.status.idle": "2025-05-07T22:34:06.258983Z", + "shell.execute_reply": "2025-05-07T22:34:06.258037Z", + "shell.execute_reply.started": "2025-05-07T22:34:06.251533Z" } }, "outputs": [], @@ -996,15 +996,15 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 109, "id": "61a66274-c3e1-4e41-b743-649fc00d69b7", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T22:18:47.505342Z", - "iopub.status.busy": "2025-05-07T22:18:47.504870Z", - "iopub.status.idle": "2025-05-07T22:18:47.852980Z", - "shell.execute_reply": "2025-05-07T22:18:47.852034Z", - "shell.execute_reply.started": "2025-05-07T22:18:47.505305Z" + "iopub.execute_input": "2025-05-07T22:34:06.872304Z", + "iopub.status.busy": "2025-05-07T22:34:06.871269Z", + "iopub.status.idle": "2025-05-07T22:34:07.218707Z", + "shell.execute_reply": "2025-05-07T22:34:07.217814Z", + "shell.execute_reply.started": "2025-05-07T22:34:06.872257Z" } }, "outputs": [ @@ -1035,15 +1035,15 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 110, "id": "5afedb17-6478-4f2b-bdfc-38e73cd4a65e", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T22:19:01.573175Z", - "iopub.status.busy": "2025-05-07T22:19:01.572728Z", - "iopub.status.idle": "2025-05-07T22:19:01.860892Z", - 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"execution_count": 56, + "execution_count": 112, "id": "47a125c4-77fa-4712-be40-241318966774", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T22:22:48.108026Z", - "iopub.status.busy": "2025-05-07T22:22:48.107233Z", - "iopub.status.idle": "2025-05-07T22:22:48.114869Z", - "shell.execute_reply": "2025-05-07T22:22:48.113914Z", - "shell.execute_reply.started": "2025-05-07T22:22:48.107973Z" + "iopub.execute_input": "2025-05-07T22:34:11.919025Z", + "iopub.status.busy": "2025-05-07T22:34:11.917951Z", + "iopub.status.idle": "2025-05-07T22:34:11.925377Z", + "shell.execute_reply": "2025-05-07T22:34:11.924448Z", + "shell.execute_reply.started": "2025-05-07T22:34:11.918960Z" } }, "outputs": [ @@ -1165,15 +1165,15 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 113, "id": "ffbe5670-6de4-4a92-99f7-4e480789b596", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T22:24:58.138609Z", - "iopub.status.busy": "2025-05-07T22:24:58.138075Z", - "iopub.status.idle": "2025-05-07T22:24:58.146798Z", - "shell.execute_reply": "2025-05-07T22:24:58.145796Z", - "shell.execute_reply.started": "2025-05-07T22:24:58.138568Z" + "iopub.execute_input": "2025-05-07T22:34:13.161754Z", + "iopub.status.busy": "2025-05-07T22:34:13.161331Z", + "iopub.status.idle": "2025-05-07T22:34:13.169261Z", + "shell.execute_reply": "2025-05-07T22:34:13.168322Z", + "shell.execute_reply.started": "2025-05-07T22:34:13.161713Z" } }, "outputs": [], @@ -1184,15 +1184,15 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 114, "id": "782e7e2e-372e-4fcb-b837-a19a3bc83511", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T22:25:00.453158Z", - "iopub.status.busy": "2025-05-07T22:25:00.452694Z", - "iopub.status.idle": "2025-05-07T22:25:00.460363Z", - "shell.execute_reply": "2025-05-07T22:25:00.459402Z", - "shell.execute_reply.started": "2025-05-07T22:25:00.453102Z" + "iopub.execute_input": "2025-05-07T22:34:14.067231Z", + "iopub.status.busy": "2025-05-07T22:34:14.066763Z", + "iopub.status.idle": "2025-05-07T22:34:14.074436Z", + "shell.execute_reply": "2025-05-07T22:34:14.073483Z", + "shell.execute_reply.started": "2025-05-07T22:34:14.067193Z" } }, "outputs": [ @@ -1223,15 +1223,15 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 115, "id": "fc90feca-ede1-44b0-929b-2fec1ddf5ad4", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T22:25:02.790270Z", - "iopub.status.busy": "2025-05-07T22:25:02.789792Z", - "iopub.status.idle": "2025-05-07T22:25:02.799574Z", - "shell.execute_reply": "2025-05-07T22:25:02.798501Z", - "shell.execute_reply.started": "2025-05-07T22:25:02.790231Z" + "iopub.execute_input": "2025-05-07T22:34:14.965643Z", + "iopub.status.busy": "2025-05-07T22:34:14.965212Z", + "iopub.status.idle": "2025-05-07T22:34:14.975192Z", + "shell.execute_reply": "2025-05-07T22:34:14.974232Z", + "shell.execute_reply.started": "2025-05-07T22:34:14.965603Z" } }, "outputs": [], @@ -1258,23 +1258,34 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 142, "id": "f09a28c9-f868-4cfa-a309-c53b26193e01", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T22:25:38.851010Z", - "iopub.status.busy": "2025-05-07T22:25:38.850578Z", - "iopub.status.idle": "2025-05-07T22:25:38.876912Z", - "shell.execute_reply": "2025-05-07T22:25:38.875876Z", - "shell.execute_reply.started": "2025-05-07T22:25:38.850972Z" + "iopub.execute_input": "2025-05-07T22:43:35.007191Z", + "iopub.status.busy": "2025-05-07T22:43:35.006104Z", + "iopub.status.idle": "2025-05-07T22:43:35.034421Z", + "shell.execute_reply": "2025-05-07T22:43:35.033548Z", + "shell.execute_reply.started": "2025-05-07T22:43:35.007129Z" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'\\n# Inverse transform\\ny_pred_original = y_scaler.inverse_transform(y_pred) # Correct this line\\ny_test_original = y_scaler.inverse_transform(y_test_scaled) # Correct this line\\nX_pred_original = X_scaler.inverse_transform(X_pred) # If needed for X\\nX_test_original = X_scaler.inverse_transform(X_test_scaled) # If needed for X\\n'" + ] + }, + "execution_count": 142, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "from sklearn.model_selection import train_test_split\n", "from sklearn.preprocessing import FunctionTransformer, StandardScaler\n", "from sklearn.pipeline import make_pipeline\n", "import numpy as np\n", + "from sklearn.model_selection import train_test_split\n", "\n", "# Split data first\n", "X_train, X_test, y_train, y_test = train_test_split(\n", @@ -1286,13 +1297,13 @@ "\n", "# Define log + standard scaler for X\n", "X_scaler = make_pipeline(\n", - " FunctionTransformer(lambda x: np.log1p(x)),\n", + " FunctionTransformer(lambda x: np.log(x), validate=True),\n", " StandardScaler()\n", ")\n", "\n", "# Define log + standard scaler for y\n", "y_scaler = make_pipeline(\n", - " FunctionTransformer(lambda x: np.log1p(x)),\n", + " FunctionTransformer(lambda x: np.log(x), validate=True),\n", " StandardScaler()\n", ")\n", "\n", @@ -1301,20 +1312,28 @@ "X_test_scaled = X_scaler.transform(X_test)\n", "\n", "y_train_scaled = y_scaler.fit_transform(y_train)\n", - "y_test_scaled = y_scaler.transform(y_test)\n" + "y_test_scaled = y_scaler.transform(y_test)\n", + "\n", + "'''\n", + "# Inverse transform\n", + "y_pred_original = y_scaler.inverse_transform(y_pred) # Correct this line\n", + "y_test_original = y_scaler.inverse_transform(y_test_scaled) # Correct this line\n", + "X_pred_original = X_scaler.inverse_transform(X_pred) # If needed for X\n", + "X_test_original = X_scaler.inverse_transform(X_test_scaled) # If needed for X\n", + "'''" ] }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 143, "id": "2c8db726-4e7b-4bd8-ab46-31f25eacdf32", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T22:25:08.817908Z", - "iopub.status.busy": "2025-05-07T22:25:08.816938Z", - "iopub.status.idle": "2025-05-07T22:25:08.822746Z", - "shell.execute_reply": "2025-05-07T22:25:08.821802Z", - "shell.execute_reply.started": "2025-05-07T22:25:08.817865Z" + "iopub.execute_input": "2025-05-07T22:43:36.383979Z", + "iopub.status.busy": "2025-05-07T22:43:36.382953Z", + "iopub.status.idle": "2025-05-07T22:43:36.388612Z", + "shell.execute_reply": "2025-05-07T22:43:36.387665Z", + "shell.execute_reply.started": "2025-05-07T22:43:36.383934Z" } }, "outputs": [ @@ -1322,7 +1341,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "(7690, 1)\n" + "(7639, 1)\n" ] } ], @@ -1332,70 +1351,21 @@ }, { "cell_type": "code", - "execution_count": 78, - "id": "085dae46-aedd-44f2-9830-ef6cfb788cbc", - "metadata": { - "execution": { - "iopub.execute_input": "2025-05-07T22:25:42.550666Z", - "iopub.status.busy": "2025-05-07T22:25:42.549834Z", - "iopub.status.idle": "2025-05-07T22:25:42.555160Z", - "shell.execute_reply": "2025-05-07T22:25:42.554213Z", - "shell.execute_reply.started": "2025-05-07T22:25:42.550625Z" - } - }, - "outputs": [], - "source": [ - "X_train = X_train_scaled\n", - "X_test = X_test_scaled\n", - "y_train = y_train_scaled\n", - "y_test = y_test_scaled" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8e675e27-74f0-43e8-91af-8652e2710609", - "metadata": {}, - "outputs": [], - "source": [ - "scaler = StandardScaler()\n", - "X_train = scaler.fit_transform(X_train)\n", - "X_test = scaler.transform(X_test)" - ] - }, - { - "cell_type": "markdown", - "id": "f27ee6f3-713e-4b60-a905-5d04dc7950d8", - "metadata": {}, - "source": [ - "The values of the training and test set should be scaled between values of 0 and 1 by default. Check this using a `seaborn` histogram." - ] - }, - { - "cell_type": "code", - "execution_count": 79, + "execution_count": 144, "id": "3ee4e732-8688-47fa-b62f-27cb4b9ee9ca", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T22:25:44.500430Z", - "iopub.status.busy": "2025-05-07T22:25:44.500004Z", - "iopub.status.idle": "2025-05-07T22:25:44.712500Z", - "shell.execute_reply": "2025-05-07T22:25:44.711570Z", - "shell.execute_reply.started": "2025-05-07T22:25:44.500395Z" + "iopub.execute_input": "2025-05-07T22:43:37.749833Z", + "iopub.status.busy": "2025-05-07T22:43:37.749396Z", + "iopub.status.idle": "2025-05-07T22:43:37.976968Z", + "shell.execute_reply": "2025-05-07T22:43:37.976042Z", + "shell.execute_reply.started": "2025-05-07T22:43:37.749790Z" } }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_25995/1236112267.py:1: RuntimeWarning: invalid value encountered in log\n", - " plt.hist(np.log(X_train_scaled.flatten()));\n" - ] - }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -1405,34 +1375,26 @@ } ], "source": [ - "plt.hist(np.log(X_train_scaled.flatten()));" + "plt.hist(X_train_scaled);" ] }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 145, "id": "40eaabc7-8c97-47dc-9de2-2b505aa32624", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T22:25:45.996991Z", - "iopub.status.busy": "2025-05-07T22:25:45.996231Z", - "iopub.status.idle": "2025-05-07T22:25:46.204265Z", - "shell.execute_reply": "2025-05-07T22:25:46.203327Z", - "shell.execute_reply.started": "2025-05-07T22:25:45.996946Z" + "iopub.execute_input": "2025-05-07T22:43:49.310089Z", + "iopub.status.busy": "2025-05-07T22:43:49.309277Z", + "iopub.status.idle": "2025-05-07T22:43:49.539986Z", + "shell.execute_reply": "2025-05-07T22:43:49.539077Z", + "shell.execute_reply.started": "2025-05-07T22:43:49.310045Z" } }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_25995/2542126211.py:1: RuntimeWarning: invalid value encountered in log\n", - " plt.hist(np.log(y_train_scaled.flatten()));\n" - ] - }, { "data": { - "image/png": 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", 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"text/plain": [ "
" ] @@ -1442,7 +1404,7 @@ } ], "source": [ - "plt.hist(np.log(y_train_scaled.flatten()));" + "plt.hist(y_train_scaled.flatten());" ] }, { @@ -1463,15 +1425,15 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 146, "id": "9a0c0864-ca7a-43fa-9d18-7c873a8ca7a0", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T22:25:48.716313Z", - "iopub.status.busy": "2025-05-07T22:25:48.715834Z", - "iopub.status.idle": "2025-05-07T22:25:48.722991Z", - "shell.execute_reply": "2025-05-07T22:25:48.722098Z", - "shell.execute_reply.started": "2025-05-07T22:25:48.716274Z" + "iopub.execute_input": "2025-05-07T22:43:52.432974Z", + "iopub.status.busy": "2025-05-07T22:43:52.432524Z", + "iopub.status.idle": "2025-05-07T22:43:52.439434Z", + "shell.execute_reply": "2025-05-07T22:43:52.438494Z", + "shell.execute_reply.started": "2025-05-07T22:43:52.432920Z" } }, "outputs": [ @@ -1481,33 +1443,33 @@ "np.False_" ] }, - "execution_count": 81, + "execution_count": 146, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "np.isnan(X_train).any()" + "np.isnan(X_train_scaled).any()" ] }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 147, "id": "e02ca479-6105-442b-9879-2eb215dc4d66", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T22:25:50.087748Z", - "iopub.status.busy": "2025-05-07T22:25:50.087091Z", - "iopub.status.idle": "2025-05-07T22:25:50.101293Z", - "shell.execute_reply": "2025-05-07T22:25:50.100353Z", - "shell.execute_reply.started": "2025-05-07T22:25:50.087710Z" + "iopub.execute_input": "2025-05-07T22:43:54.128094Z", + "iopub.status.busy": "2025-05-07T22:43:54.127064Z", + "iopub.status.idle": "2025-05-07T22:43:54.137551Z", + "shell.execute_reply": "2025-05-07T22:43:54.136632Z", + "shell.execute_reply.started": "2025-05-07T22:43:54.128048Z" } }, "outputs": [ { "data": { "text/html": [ - "
LinearRegression()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + "
LinearRegression()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" ], "text/plain": [ "LinearRegression()" ] }, - "execution_count": 82, + "execution_count": 147, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model = LinearRegression()\n", - "model.fit(X_train, y_train)" + "model.fit(X_train_scaled, y_train_scaled)" ] }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 148, "id": "aca8064a-c94f-4792-86b3-62ec2130471b", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T22:25:53.440319Z", - "iopub.status.busy": "2025-05-07T22:25:53.439863Z", - "iopub.status.idle": "2025-05-07T22:25:53.447572Z", - "shell.execute_reply": "2025-05-07T22:25:53.446653Z", - "shell.execute_reply.started": "2025-05-07T22:25:53.440277Z" + "iopub.execute_input": "2025-05-07T22:43:54.568184Z", + "iopub.status.busy": "2025-05-07T22:43:54.567701Z", + "iopub.status.idle": "2025-05-07T22:43:54.575503Z", + "shell.execute_reply": "2025-05-07T22:43:54.574526Z", + "shell.execute_reply.started": "2025-05-07T22:43:54.568103Z" } }, "outputs": [ @@ -1956,13 +1918,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "MSE: 0.26954356602486723\n" + "MSE: 0.2765946838901815\n" ] } ], "source": [ - "y_pred = model.predict(X_test)\n", - "mse = mean_squared_error(y_test, y_pred)\n", + "y_pred = model.predict(X_test_scaled)\n", + "mse = mean_squared_error(y_test_scaled, y_pred)\n", "print(\"MSE:\", mse)" ] }, @@ -1976,21 +1938,21 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 149, "id": "ee8bd887-928e-4a41-bd77-149b344ab238", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T22:26:08.176359Z", - "iopub.status.busy": "2025-05-07T22:26:08.175337Z", - "iopub.status.idle": "2025-05-07T22:26:08.399256Z", - "shell.execute_reply": "2025-05-07T22:26:08.398296Z", - "shell.execute_reply.started": "2025-05-07T22:26:08.176315Z" + "iopub.execute_input": "2025-05-07T22:43:56.604700Z", + "iopub.status.busy": "2025-05-07T22:43:56.603683Z", + "iopub.status.idle": "2025-05-07T22:43:56.833352Z", + "shell.execute_reply": "2025-05-07T22:43:56.830398Z", + "shell.execute_reply.started": "2025-05-07T22:43:56.604648Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -2001,8 +1963,70 @@ ], "source": [ "plt.clf()\n", - "plt.scatter(y_test, y_pred)\n", - "#plt.plot([0,1e6],[0,1e6], color='black', ls='--')\n", + "plt.scatter(y_test_scaled, y_pred)\n", + "plt.plot([-4,6],[-4,6], color='black', ls='--')\n", + "plt.xlabel('True')\n", + "plt.ylabel('Predicted');\n", + "#plt.xlim([0,2e4])\n", + "#plt.ylim([0,2e4]);" + ] + }, + { + "cell_type": "markdown", + "id": "8367d3db-3527-4cab-abdb-51391259ec02", + "metadata": {}, + "source": [ + "Transform back to the true values (remember the current scaling is logarithmic) and look at the results in that space." + ] + }, + { + "cell_type": "code", + "execution_count": 150, + "id": "6770c381-1f34-44a7-b93b-b7f915013620", + "metadata": { + "execution": { + "iopub.execute_input": "2025-05-07T22:44:05.377368Z", + "iopub.status.busy": "2025-05-07T22:44:05.376863Z", + "iopub.status.idle": "2025-05-07T22:44:05.384480Z", + "shell.execute_reply": "2025-05-07T22:44:05.383362Z", + "shell.execute_reply.started": "2025-05-07T22:44:05.377325Z" + } + }, + "outputs": [], + "source": [ + "y_pred_original = y_scaler.inverse_transform(y_pred) # Correct this line\n", + "y_test_original = y_scaler.inverse_transform(y_test_scaled) # Correct this line" + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "id": "f83cf984-ffa7-4486-98ec-bce890b91bad", + "metadata": { + "execution": { + "iopub.execute_input": "2025-05-07T22:44:07.346379Z", + "iopub.status.busy": "2025-05-07T22:44:07.345906Z", + "iopub.status.idle": "2025-05-07T22:44:07.576732Z", + "shell.execute_reply": "2025-05-07T22:44:07.575840Z", + "shell.execute_reply.started": "2025-05-07T22:44:07.346338Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.clf()\n", + "plt.scatter(y_test_original, y_pred_original)\n", + "#plt.plot([-4,6],[-4,6], color='black', ls='--')\n", "plt.xlabel('True')\n", "plt.ylabel('Predicted');\n", "#plt.xlim([0,2e4])\n", From d1512f61ba523e39f130522bcbf068f89ed137d6 Mon Sep 17 00:00:00 2001 From: beckynevin Date: Fri, 9 May 2025 13:38:56 +0000 Subject: [PATCH 09/13] finished up transform section, added text for optional log, for now just doing standard scaler --- DP0.2/20_Introduction_to_Data_Science.ipynb | 715 ++++++++++---------- 1 file changed, 369 insertions(+), 346 deletions(-) diff --git a/DP0.2/20_Introduction_to_Data_Science.ipynb b/DP0.2/20_Introduction_to_Data_Science.ipynb index 596a79f7..bfadce8f 100644 --- a/DP0.2/20_Introduction_to_Data_Science.ipynb +++ b/DP0.2/20_Introduction_to_Data_Science.ipynb @@ -105,15 +105,15 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 34, "id": "3f4900a4-3358-472a-b9ba-c42e3f2f0771", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T22:05:03.838119Z", - "iopub.status.busy": "2025-05-07T22:05:03.837815Z", - "iopub.status.idle": "2025-05-07T22:05:07.659466Z", - "shell.execute_reply": "2025-05-07T22:05:07.658406Z", - "shell.execute_reply.started": "2025-05-07T22:05:03.838088Z" + "iopub.execute_input": "2025-05-09T13:28:12.164357Z", + "iopub.status.busy": "2025-05-09T13:28:12.163889Z", + "iopub.status.idle": "2025-05-09T13:28:12.170613Z", + "shell.execute_reply": "2025-05-09T13:28:12.169575Z", + "shell.execute_reply.started": "2025-05-09T13:28:12.164320Z" } }, "outputs": [], @@ -127,7 +127,8 @@ "from astropy.coordinates import SkyCoord\n", "\n", "from sklearn.model_selection import train_test_split\n", - "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.preprocessing import FunctionTransformer, StandardScaler\n", + "from sklearn.pipeline import make_pipeline\n", "from sklearn.linear_model import LinearRegression\n", "from sklearn.metrics import mean_squared_error\n", "from sklearn.ensemble import RandomForestRegressor\n", @@ -149,11 +150,11 @@ "id": "94acc9f6-2033-4ace-aefd-d036a35f4221", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T22:05:07.664107Z", - "iopub.status.busy": "2025-05-07T22:05:07.663771Z", - "iopub.status.idle": "2025-05-07T22:05:07.669508Z", - "shell.execute_reply": "2025-05-07T22:05:07.668536Z", - "shell.execute_reply.started": "2025-05-07T22:05:07.664073Z" + "iopub.execute_input": "2025-05-08T15:50:38.409359Z", + "iopub.status.busy": "2025-05-08T15:50:38.408575Z", + "iopub.status.idle": "2025-05-08T15:50:38.415082Z", + "shell.execute_reply": "2025-05-08T15:50:38.414076Z", + "shell.execute_reply.started": "2025-05-08T15:50:38.409318Z" } }, "outputs": [], @@ -179,11 +180,11 @@ "id": "caf56589-100a-4481-8f24-5f5058b6671f", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T22:05:07.673906Z", - "iopub.status.busy": "2025-05-07T22:05:07.673591Z", - "iopub.status.idle": "2025-05-07T22:05:07.724463Z", - "shell.execute_reply": "2025-05-07T22:05:07.723437Z", - "shell.execute_reply.started": "2025-05-07T22:05:07.673877Z" + "iopub.execute_input": "2025-05-08T15:50:39.404092Z", + "iopub.status.busy": "2025-05-08T15:50:39.403614Z", + "iopub.status.idle": "2025-05-08T15:50:39.450009Z", + "shell.execute_reply": "2025-05-08T15:50:39.449018Z", + "shell.execute_reply.started": "2025-05-08T15:50:39.404054Z" } }, "outputs": [], @@ -206,11 +207,11 @@ "id": "2b7b6002-2457-4c20-a03e-6bfa24a0aa27", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T22:05:07.785841Z", - "iopub.status.busy": "2025-05-07T22:05:07.785517Z", - "iopub.status.idle": "2025-05-07T22:05:07.790060Z", - "shell.execute_reply": "2025-05-07T22:05:07.789215Z", - "shell.execute_reply.started": "2025-05-07T22:05:07.785810Z" + "iopub.execute_input": "2025-05-08T15:50:40.248771Z", + "iopub.status.busy": "2025-05-08T15:50:40.248346Z", + "iopub.status.idle": "2025-05-08T15:50:40.253523Z", + "shell.execute_reply": "2025-05-08T15:50:40.252555Z", + "shell.execute_reply.started": "2025-05-08T15:50:40.248733Z" } }, "outputs": [], @@ -224,11 +225,9 @@ "metadata": {}, "source": [ "## 2. Query for Kron fluxes around extended (galaxy) objects.\n", - "I forget why I chose this specific statistic for the demo.\n", + "The Kron flux is a measurement of the total flux (or brightness) of an astronomical object, typically a galaxy or extended source, obtained using an elliptical aperture that scales with the object's light profile. It’s designed to include most of the object’s light while minimizing background contamination.\n", "\n", - "Kron radius: A radius that is calculated using the light profile of the object, typically as the first moment (i.e., a weighted average of radius with brightness) of the light distribution.\n", - "\n", - "Kron flux: The total flux measured within a certain multiple (often 2.5×) of the Kron radius, typically capturing about 90–95% of the total light for extended sources like galaxies." + "The aperture is defined based on the Kron radius, which is calculated from the first moment of the light distribution. The resulting aperture is adaptive - it changes in size and shape depending on the morphology of the source." ] }, { @@ -245,11 +244,11 @@ "id": "7ddd0344-b354-45a0-9e5a-755149c9bc54", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T22:05:10.643647Z", - "iopub.status.busy": "2025-05-07T22:05:10.643208Z", - "iopub.status.idle": "2025-05-07T22:05:10.648607Z", - "shell.execute_reply": "2025-05-07T22:05:10.647556Z", - "shell.execute_reply.started": "2025-05-07T22:05:10.643609Z" + "iopub.execute_input": "2025-05-08T15:50:42.669499Z", + "iopub.status.busy": "2025-05-08T15:50:42.668489Z", + "iopub.status.idle": "2025-05-08T15:50:42.674127Z", + "shell.execute_reply": "2025-05-08T15:50:42.673137Z", + "shell.execute_reply.started": "2025-05-08T15:50:42.669457Z" } }, "outputs": [], @@ -267,7 +266,7 @@ "id": "dd80babb-ee05-49e9-9f9c-923d5c0cee31", "metadata": {}, "source": [ - "Start with the same query as used in the beginner TAP tutorial notebook 02a." + "Start with the same query as used in the beginner TAP tutorial notebook 02a. Note that the extendedness flag in the $g-$band is used to select for galaxies." ] }, { @@ -276,11 +275,11 @@ "id": "985e3b62-8065-42ec-a40c-1232c4c45f17", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T22:05:11.807873Z", - "iopub.status.busy": "2025-05-07T22:05:11.807426Z", - "iopub.status.idle": "2025-05-07T22:05:11.813478Z", - "shell.execute_reply": "2025-05-07T22:05:11.812520Z", - "shell.execute_reply.started": "2025-05-07T22:05:11.807835Z" + "iopub.execute_input": "2025-05-08T15:50:44.432293Z", + "iopub.status.busy": "2025-05-08T15:50:44.431809Z", + "iopub.status.idle": "2025-05-08T15:50:44.437788Z", + "shell.execute_reply": "2025-05-08T15:50:44.436804Z", + "shell.execute_reply.started": "2025-05-08T15:50:44.432255Z" } }, "outputs": [ @@ -316,11 +315,11 @@ "id": "c02adc91-5f5e-418b-87a3-cba8beba7dd2", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T22:05:12.604063Z", - "iopub.status.busy": "2025-05-07T22:05:12.603619Z", - "iopub.status.idle": "2025-05-07T22:05:13.791167Z", - "shell.execute_reply": "2025-05-07T22:05:13.790094Z", - "shell.execute_reply.started": "2025-05-07T22:05:12.604025Z" + "iopub.execute_input": "2025-05-08T15:50:45.904416Z", + "iopub.status.busy": "2025-05-08T15:50:45.903914Z", + "iopub.status.idle": "2025-05-08T15:50:47.182530Z", + "shell.execute_reply": "2025-05-08T15:50:47.181526Z", + "shell.execute_reply.started": "2025-05-08T15:50:45.904381Z" } }, "outputs": [ @@ -354,7 +353,7 @@ "id": "07d1cfb1-589b-402b-8b8f-c2c70652b6c6", "metadata": {}, "source": [ - "Return the results as a `pandas` dataframe, and then delete the query to save space." + "Return the results as a `pandas` DataFrame, and then delete the query to save space." ] }, { @@ -363,11 +362,11 @@ "id": "8cd2f538-c2d7-44ca-ab4d-825120b8f2e7", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T22:05:14.347205Z", - "iopub.status.busy": "2025-05-07T22:05:14.346714Z", - "iopub.status.idle": "2025-05-07T22:05:15.261252Z", - "shell.execute_reply": "2025-05-07T22:05:15.260192Z", - "shell.execute_reply.started": "2025-05-07T22:05:14.347162Z" + "iopub.execute_input": "2025-05-08T15:50:49.547916Z", + "iopub.status.busy": "2025-05-08T15:50:49.547418Z", + "iopub.status.idle": "2025-05-08T15:50:50.326158Z", + "shell.execute_reply": "2025-05-08T15:50:50.325069Z", + "shell.execute_reply.started": "2025-05-08T15:50:49.547880Z" } }, "outputs": [], @@ -391,11 +390,11 @@ "id": "ee4d121e-6b4d-4371-afae-4f7587b95d51", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T22:05:15.389946Z", - "iopub.status.busy": "2025-05-07T22:05:15.389502Z", - "iopub.status.idle": "2025-05-07T22:05:15.413383Z", - "shell.execute_reply": "2025-05-07T22:05:15.412410Z", - "shell.execute_reply.started": "2025-05-07T22:05:15.389907Z" + "iopub.execute_input": "2025-05-08T15:50:52.216555Z", + "iopub.status.busy": "2025-05-08T15:50:52.215445Z", + "iopub.status.idle": "2025-05-08T15:50:52.241861Z", + "shell.execute_reply": "2025-05-08T15:50:52.241057Z", + "shell.execute_reply.started": "2025-05-08T15:50:52.216511Z" } }, "outputs": [ @@ -550,11 +549,11 @@ "id": "db2168fe-593a-423d-b2f4-26ac0db60e8c", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T22:05:15.818183Z", - "iopub.status.busy": "2025-05-07T22:05:15.817048Z", - "iopub.status.idle": "2025-05-07T22:05:15.823850Z", - "shell.execute_reply": "2025-05-07T22:05:15.822948Z", - "shell.execute_reply.started": "2025-05-07T22:05:15.818117Z" + "iopub.execute_input": "2025-05-08T15:50:53.124214Z", + "iopub.status.busy": "2025-05-08T15:50:53.123725Z", + "iopub.status.idle": "2025-05-08T15:50:53.130819Z", + "shell.execute_reply": "2025-05-08T15:50:53.129785Z", + "shell.execute_reply.started": "2025-05-08T15:50:53.124156Z" } }, "outputs": [ @@ -589,11 +588,11 @@ "id": "eec25f58-d3f3-4ef4-b3e2-ab105c4718fd", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T22:05:17.628778Z", - "iopub.status.busy": "2025-05-07T22:05:17.627960Z", - "iopub.status.idle": "2025-05-07T22:05:17.667654Z", - "shell.execute_reply": "2025-05-07T22:05:17.666625Z", - "shell.execute_reply.started": "2025-05-07T22:05:17.628732Z" + "iopub.execute_input": "2025-05-08T15:50:55.299389Z", + "iopub.status.busy": "2025-05-08T15:50:55.298909Z", + "iopub.status.idle": "2025-05-08T15:50:55.339125Z", + "shell.execute_reply": "2025-05-08T15:50:55.338156Z", + "shell.execute_reply.started": "2025-05-08T15:50:55.299348Z" } }, "outputs": [ @@ -657,7 +656,7 @@ "id": "1b37fc18-e00b-4ef2-8d18-85d823d60d9e", "metadata": {}, "source": [ - "## 4. Visualize using `seaborn`\n", + "## 4. Visualize the data using `seaborn`\n", "Use the boxplot tool from `seaborn` to visualize the distribution of the values in each column of the DataFrame." ] }, @@ -667,11 +666,11 @@ "id": "4fc9b578-2be4-4fb2-8d74-ebca809ea99f", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T22:05:18.635581Z", - "iopub.status.busy": "2025-05-07T22:05:18.635131Z", - "iopub.status.idle": "2025-05-07T22:05:19.692427Z", - "shell.execute_reply": "2025-05-07T22:05:19.691484Z", - "shell.execute_reply.started": "2025-05-07T22:05:18.635542Z" + "iopub.execute_input": "2025-05-08T15:50:56.647957Z", + "iopub.status.busy": "2025-05-08T15:50:56.647507Z", + "iopub.status.idle": "2025-05-08T15:50:57.986424Z", + "shell.execute_reply": "2025-05-08T15:50:57.985342Z", + "shell.execute_reply.started": "2025-05-08T15:50:56.647918Z" } }, "outputs": [ @@ -701,7 +700,7 @@ "id": "ef5f20de-0fd6-4ba1-9cab-9d59cd05df99", "metadata": {}, "source": [ - "The outliers are dominant in the visualization. Hide these and also only plot the kron Flux values." + "The outliers (points far from the majority of the distribution) are dominant in the visualization. Hide these and also only plot the Kron Flux values." ] }, { @@ -710,11 +709,11 @@ "id": "bacf5114-6a64-4100-8eb6-f1d9ddc36f89", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T22:05:34.059685Z", - "iopub.status.busy": "2025-05-07T22:05:34.059225Z", - "iopub.status.idle": "2025-05-07T22:05:34.518113Z", - "shell.execute_reply": "2025-05-07T22:05:34.517180Z", - "shell.execute_reply.started": "2025-05-07T22:05:34.059647Z" + "iopub.execute_input": "2025-05-08T15:50:58.892134Z", + "iopub.status.busy": "2025-05-08T15:50:58.891640Z", + "iopub.status.idle": "2025-05-08T15:50:59.327923Z", + "shell.execute_reply": "2025-05-08T15:50:59.326984Z", + "shell.execute_reply.started": "2025-05-08T15:50:58.892091Z" } }, "outputs": [ @@ -765,11 +764,11 @@ "id": "39521ac6-0bec-42e7-9062-8fc9ce5edc55", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T22:05:47.181972Z", - "iopub.status.busy": "2025-05-07T22:05:47.180976Z", - "iopub.status.idle": "2025-05-07T22:05:47.818553Z", - "shell.execute_reply": "2025-05-07T22:05:47.817379Z", - "shell.execute_reply.started": "2025-05-07T22:05:47.181926Z" + "iopub.execute_input": "2025-05-08T15:51:02.488157Z", + "iopub.status.busy": "2025-05-08T15:51:02.487689Z", + "iopub.status.idle": "2025-05-08T15:51:03.008439Z", + "shell.execute_reply": "2025-05-08T15:51:03.007484Z", + "shell.execute_reply.started": "2025-05-08T15:51:02.488101Z" } }, "outputs": [ @@ -817,7 +816,7 @@ "id": "18dba188-ea4c-47e9-8faf-62e3552add1e", "metadata": {}, "source": [ - "Use `pandas` to investigate if there are any flags on the `kronFlux` measurement. The `.value_counts()` method will show the number of True and False columns, where True are rows for which the `g_kronFlux` measurement was flagged for a variety of reasons. There are many other columns that investigate specific reasons why this measurement is untrustworthy; the `g_kronFlux_flag` is a way to combine all of the individual flags." + "Use `pandas` to investigate if there are any flags on the `kronFlux` measurement. The `.value_counts()` method will show the number of True and False columns, where True are rows for which the `g_kronFlux` measurement was flagged for a variety of reasons. There are many other columns that investigate specific reasons why this measurement is untrustworthy; the `g_kronFlux_flag` is a way to combine all of the individual flags. When this flag is set to `True`, the row is flagged." ] }, { @@ -826,11 +825,11 @@ "id": "0be4535d-cc89-45ef-98e9-591b9f459fae", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T22:05:52.420724Z", - "iopub.status.busy": "2025-05-07T22:05:52.420299Z", - "iopub.status.idle": "2025-05-07T22:05:52.429670Z", - "shell.execute_reply": "2025-05-07T22:05:52.428773Z", - "shell.execute_reply.started": "2025-05-07T22:05:52.420687Z" + "iopub.execute_input": "2025-05-08T15:51:07.063820Z", + "iopub.status.busy": "2025-05-08T15:51:07.063346Z", + "iopub.status.idle": "2025-05-08T15:51:07.072825Z", + "shell.execute_reply": "2025-05-08T15:51:07.071888Z", + "shell.execute_reply.started": "2025-05-08T15:51:07.063762Z" } }, "outputs": [ @@ -866,11 +865,11 @@ "id": "0e66ccb2-3922-471b-8c15-7fb055d02a10", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T22:05:56.102177Z", - "iopub.status.busy": "2025-05-07T22:05:56.101688Z", - "iopub.status.idle": "2025-05-07T22:05:56.110977Z", - "shell.execute_reply": "2025-05-07T22:05:56.110054Z", - "shell.execute_reply.started": "2025-05-07T22:05:56.102091Z" + "iopub.execute_input": "2025-05-08T15:51:09.208070Z", + "iopub.status.busy": "2025-05-08T15:51:09.207602Z", + "iopub.status.idle": "2025-05-08T15:51:09.218270Z", + "shell.execute_reply": "2025-05-08T15:51:09.217235Z", + "shell.execute_reply.started": "2025-05-08T15:51:09.208022Z" } }, "outputs": [ @@ -902,15 +901,15 @@ }, { "cell_type": "code", - "execution_count": 107, + "execution_count": 17, "id": "06786c33-2563-4237-9d0f-22d6308c0d7b", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T22:34:05.242363Z", - "iopub.status.busy": "2025-05-07T22:34:05.241899Z", - "iopub.status.idle": "2025-05-07T22:34:05.299783Z", - "shell.execute_reply": "2025-05-07T22:34:05.298861Z", - "shell.execute_reply.started": "2025-05-07T22:34:05.242324Z" + "iopub.execute_input": "2025-05-08T15:51:12.117284Z", + "iopub.status.busy": "2025-05-08T15:51:12.116205Z", + "iopub.status.idle": "2025-05-08T15:51:12.171978Z", + "shell.execute_reply": "2025-05-08T15:51:12.170939Z", + "shell.execute_reply.started": "2025-05-08T15:51:12.117236Z" } }, "outputs": [ @@ -961,20 +960,20 @@ "id": "ec13b104-ad8d-4bd6-8a93-b6d1d57b921e", "metadata": {}, "source": [ - "There are six overlapping rows, meaning that in six cases, both photometric bands are flagged. Since the task at hand is a prediction one between three bands ($g$, $r$, and $i$) the bigger concern is the cases where any of these three Kron fluxes are flagged. Exclude rows where this is the case and build an \"unflagged\" DataFrame." + "There are many overlapping rows, meaning that in these cases, both photometric bands are flagged. Since the task at hand is a prediction one between three bands ($g$, $r$, and $i$) the bigger concern is the cases where any of these individual three Kron fluxes are flagged. Exclude rows where this is the case and build an \"unflagged\" DataFrame." ] }, { "cell_type": "code", - "execution_count": 108, + "execution_count": 18, "id": "e6294681-9c60-4ec6-805c-d378300acaa3", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T22:34:06.251575Z", - "iopub.status.busy": "2025-05-07T22:34:06.251095Z", - "iopub.status.idle": "2025-05-07T22:34:06.258983Z", - "shell.execute_reply": "2025-05-07T22:34:06.258037Z", - "shell.execute_reply.started": "2025-05-07T22:34:06.251533Z" + "iopub.execute_input": "2025-05-08T15:51:15.547801Z", + "iopub.status.busy": "2025-05-08T15:51:15.547302Z", + "iopub.status.idle": "2025-05-08T15:51:15.555768Z", + "shell.execute_reply": "2025-05-08T15:51:15.554642Z", + "shell.execute_reply.started": "2025-05-08T15:51:15.547757Z" } }, "outputs": [], @@ -996,15 +995,15 @@ }, { "cell_type": "code", - "execution_count": 109, + "execution_count": 19, "id": "61a66274-c3e1-4e41-b743-649fc00d69b7", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T22:34:06.872304Z", - "iopub.status.busy": "2025-05-07T22:34:06.871269Z", - "iopub.status.idle": "2025-05-07T22:34:07.218707Z", - "shell.execute_reply": "2025-05-07T22:34:07.217814Z", - "shell.execute_reply.started": "2025-05-07T22:34:06.872257Z" + "iopub.execute_input": "2025-05-08T15:51:17.167573Z", + "iopub.status.busy": "2025-05-08T15:51:17.167087Z", + "iopub.status.idle": "2025-05-08T15:51:17.587121Z", + "shell.execute_reply": "2025-05-08T15:51:17.586163Z", + "shell.execute_reply.started": "2025-05-08T15:51:17.167536Z" } }, "outputs": [ @@ -1035,15 +1034,15 @@ }, { "cell_type": "code", - "execution_count": 110, + "execution_count": 20, "id": "5afedb17-6478-4f2b-bdfc-38e73cd4a65e", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T22:34:08.619236Z", - "iopub.status.busy": "2025-05-07T22:34:08.618760Z", - "iopub.status.idle": "2025-05-07T22:34:08.905048Z", - "shell.execute_reply": "2025-05-07T22:34:08.904158Z", - "shell.execute_reply.started": "2025-05-07T22:34:08.619193Z" + "iopub.execute_input": "2025-05-08T15:51:23.567876Z", + "iopub.status.busy": "2025-05-08T15:51:23.567418Z", + "iopub.status.idle": "2025-05-08T15:51:23.852986Z", + "shell.execute_reply": "2025-05-08T15:51:23.852016Z", + "shell.execute_reply.started": "2025-05-08T15:51:23.567837Z" } }, "outputs": [ @@ -1071,7 +1070,7 @@ "id": "2f503394-3816-4d31-9cf0-9de88e229f87", "metadata": {}, "source": [ - "The relationship between $g-$ and $r-$band Kron fluxes looks roughly linear, so we should be able to do some predictive work here." + "There does seem to be a relationship between $g-$ and $r-$band Kron fluxes, meaning that it should be possible to do some predictive work here." ] }, { @@ -1079,20 +1078,20 @@ "id": "ca8c22a0-485a-4525-af2f-1e4c8bf74cd0", "metadata": {}, "source": [ - "Another thing to check before we start predicting is if there are any nans in these Series." + "Another thing to check before starting to fit is if there are any nans in these Series. (A Series is a single column of a DataFrame.)" ] }, { "cell_type": "code", - "execution_count": 111, + "execution_count": 21, "id": "01fcf2a8-7f85-4ec0-8ebe-b69b76da7294", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T22:34:10.390855Z", - "iopub.status.busy": "2025-05-07T22:34:10.390417Z", - "iopub.status.idle": "2025-05-07T22:34:10.397175Z", - "shell.execute_reply": "2025-05-07T22:34:10.396214Z", - "shell.execute_reply.started": "2025-05-07T22:34:10.390814Z" + "iopub.execute_input": "2025-05-08T15:51:27.644191Z", + "iopub.status.busy": "2025-05-08T15:51:27.643095Z", + "iopub.status.idle": "2025-05-08T15:51:27.650162Z", + "shell.execute_reply": "2025-05-08T15:51:27.649214Z", + "shell.execute_reply.started": "2025-05-08T15:51:27.644129Z" } }, "outputs": [ @@ -1121,20 +1120,20 @@ "id": "2309022c-3530-4f3c-9f7a-7d51f3537afe", "metadata": {}, "source": [ - "Another important step is to check for negative (or zero) values in these data; they are fluxes so they should not be negative or equal to zero. First, count the number of these values in each of the $g-$ and $r-$band Series. (A Series is a single row of a DataFrame)." + "Another important step is to check for negative (or zero) values in these data; they are fluxes so they should not be negative or equal to zero. First, count the number of these values in each of the $g-$ and $r-$band Series." ] }, { "cell_type": "code", - "execution_count": 112, + "execution_count": 22, "id": "47a125c4-77fa-4712-be40-241318966774", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T22:34:11.919025Z", - "iopub.status.busy": "2025-05-07T22:34:11.917951Z", - "iopub.status.idle": "2025-05-07T22:34:11.925377Z", - "shell.execute_reply": "2025-05-07T22:34:11.924448Z", - "shell.execute_reply.started": "2025-05-07T22:34:11.918960Z" + "iopub.execute_input": "2025-05-08T15:51:29.748114Z", + "iopub.status.busy": "2025-05-08T15:51:29.747213Z", + "iopub.status.idle": "2025-05-08T15:51:29.755113Z", + "shell.execute_reply": "2025-05-08T15:51:29.754029Z", + "shell.execute_reply.started": "2025-05-08T15:51:29.748069Z" } }, "outputs": [ @@ -1165,15 +1164,15 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": 23, "id": "ffbe5670-6de4-4a92-99f7-4e480789b596", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T22:34:13.161754Z", - "iopub.status.busy": "2025-05-07T22:34:13.161331Z", - "iopub.status.idle": "2025-05-07T22:34:13.169261Z", - "shell.execute_reply": "2025-05-07T22:34:13.168322Z", - "shell.execute_reply.started": "2025-05-07T22:34:13.161713Z" + "iopub.execute_input": "2025-05-08T15:51:31.827991Z", + "iopub.status.busy": "2025-05-08T15:51:31.827410Z", + "iopub.status.idle": "2025-05-08T15:51:31.835759Z", + "shell.execute_reply": "2025-05-08T15:51:31.834754Z", + "shell.execute_reply.started": "2025-05-08T15:51:31.827945Z" } }, "outputs": [], @@ -1182,17 +1181,25 @@ "clean_df = unflagged_df[mask]" ] }, + { + "cell_type": "markdown", + "id": "20799ed0-9d75-4a85-8a7c-57cebbda85f6", + "metadata": {}, + "source": [ + "It's always good to double check if the above mask worked. Below, there should be zero values in the `clean_df`." + ] + }, { "cell_type": "code", - "execution_count": 114, + "execution_count": 24, "id": "782e7e2e-372e-4fcb-b837-a19a3bc83511", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T22:34:14.067231Z", - "iopub.status.busy": "2025-05-07T22:34:14.066763Z", - "iopub.status.idle": "2025-05-07T22:34:14.074436Z", - "shell.execute_reply": "2025-05-07T22:34:14.073483Z", - "shell.execute_reply.started": "2025-05-07T22:34:14.067193Z" + "iopub.execute_input": "2025-05-08T15:51:32.487714Z", + "iopub.status.busy": "2025-05-08T15:51:32.487243Z", + "iopub.status.idle": "2025-05-08T15:51:32.494953Z", + "shell.execute_reply": "2025-05-08T15:51:32.494012Z", + "shell.execute_reply.started": "2025-05-08T15:51:32.487658Z" } }, "outputs": [ @@ -1223,21 +1230,23 @@ }, { "cell_type": "code", - "execution_count": 115, + "execution_count": 45, "id": "fc90feca-ede1-44b0-929b-2fec1ddf5ad4", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T22:34:14.965643Z", - "iopub.status.busy": "2025-05-07T22:34:14.965212Z", - "iopub.status.idle": "2025-05-07T22:34:14.975192Z", - "shell.execute_reply": "2025-05-07T22:34:14.974232Z", - "shell.execute_reply.started": "2025-05-07T22:34:14.965603Z" + "iopub.execute_input": "2025-05-09T13:33:45.064393Z", + "iopub.status.busy": "2025-05-09T13:33:45.063919Z", + "iopub.status.idle": "2025-05-09T13:33:45.074011Z", + "shell.execute_reply": "2025-05-09T13:33:45.073062Z", + "shell.execute_reply.started": "2025-05-09T13:33:45.064359Z" } }, "outputs": [], "source": [ "X_train, X_test, y_train, y_test = train_test_split(\n", - " clean_df['g_kronFlux'].to_frame(), clean_df['r_kronFlux'].to_frame(), test_size=0.2, random_state=42)" + " clean_df['g_kronFlux'].to_frame(),\n", + " clean_df['r_kronFlux'].to_frame(),\n", + " test_size=0.2, random_state=42)" ] }, { @@ -1253,119 +1262,94 @@ "id": "ecb487df-ce82-4d2d-9821-64620e1e922b", "metadata": {}, "source": [ - "It's practice to use a standard scaler when training machine learning models. Transform the training and test data." + "It's best practice to use a standard scaler when training machine learning models. Transform the training and test data, scaling to a mean of zero and a standard deviation of one by default.\n", + "\n", + "**Note to future developer of this notebook: I (Becky) was wondering if a log transform is also necessary because the data seems logarithmically scaled. I'm not sure if it's needed, but I've set this up to also include an invertible log transform if you want to test it.**" ] }, { "cell_type": "code", - "execution_count": 142, + "execution_count": 46, "id": "f09a28c9-f868-4cfa-a309-c53b26193e01", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T22:43:35.007191Z", - "iopub.status.busy": "2025-05-07T22:43:35.006104Z", - "iopub.status.idle": "2025-05-07T22:43:35.034421Z", - "shell.execute_reply": "2025-05-07T22:43:35.033548Z", - "shell.execute_reply.started": "2025-05-07T22:43:35.007129Z" + "iopub.execute_input": "2025-05-09T13:33:45.996941Z", + "iopub.status.busy": "2025-05-09T13:33:45.995872Z", + "iopub.status.idle": "2025-05-09T13:33:46.001661Z", + "shell.execute_reply": "2025-05-09T13:33:46.000736Z", + "shell.execute_reply.started": "2025-05-09T13:33:45.996895Z" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "'\\n# Inverse transform\\ny_pred_original = y_scaler.inverse_transform(y_pred) # Correct this line\\ny_test_original = y_scaler.inverse_transform(y_test_scaled) # Correct this line\\nX_pred_original = X_scaler.inverse_transform(X_pred) # If needed for X\\nX_test_original = X_scaler.inverse_transform(X_test_scaled) # If needed for X\\n'" - ] - }, - "execution_count": 142, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "from sklearn.preprocessing import FunctionTransformer, StandardScaler\n", - "from sklearn.pipeline import make_pipeline\n", - "import numpy as np\n", - "from sklearn.model_selection import train_test_split\n", - "\n", - "# Split data first\n", - "X_train, X_test, y_train, y_test = train_test_split(\n", - " clean_df['g_kronFlux'].to_frame(), \n", - " clean_df['r_kronFlux'].to_frame(), \n", - " test_size=0.2, \n", - " random_state=42\n", - ")\n", + "log_scaler = FunctionTransformer(func=np.log, inverse_func=np.exp, validate=True)\n", "\n", - "# Define log + standard scaler for X\n", "X_scaler = make_pipeline(\n", - " FunctionTransformer(lambda x: np.log(x), validate=True),\n", + " #log_scaler,\n", " StandardScaler()\n", ")\n", "\n", - "# Define log + standard scaler for y\n", "y_scaler = make_pipeline(\n", - " FunctionTransformer(lambda x: np.log(x), validate=True),\n", + " #log_scaler,\n", " StandardScaler()\n", - ")\n", - "\n", - "# Fit and transform\n", - "X_train_scaled = X_scaler.fit_transform(X_train)\n", - "X_test_scaled = X_scaler.transform(X_test)\n", - "\n", - "y_train_scaled = y_scaler.fit_transform(y_train)\n", - "y_test_scaled = y_scaler.transform(y_test)\n", - "\n", - "'''\n", - "# Inverse transform\n", - "y_pred_original = y_scaler.inverse_transform(y_pred) # Correct this line\n", - "y_test_original = y_scaler.inverse_transform(y_test_scaled) # Correct this line\n", - "X_pred_original = X_scaler.inverse_transform(X_pred) # If needed for X\n", - "X_test_original = X_scaler.inverse_transform(X_test_scaled) # If needed for X\n", - "'''" + ")" + ] + }, + { + "cell_type": "markdown", + "id": "9c8aeb31-3cbd-461f-88de-3e60e9acb4a7", + "metadata": {}, + "source": [ + "Fit to the transform and then also transform the test data according to the scaler." ] }, { "cell_type": "code", - "execution_count": 143, - "id": "2c8db726-4e7b-4bd8-ab46-31f25eacdf32", + "execution_count": 47, + "id": "ec1efab7-be4c-4bdc-9ead-78fd6b400345", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T22:43:36.383979Z", - "iopub.status.busy": "2025-05-07T22:43:36.382953Z", - "iopub.status.idle": "2025-05-07T22:43:36.388612Z", - "shell.execute_reply": "2025-05-07T22:43:36.387665Z", - "shell.execute_reply.started": "2025-05-07T22:43:36.383934Z" + "iopub.execute_input": "2025-05-09T13:33:47.032314Z", + "iopub.status.busy": "2025-05-09T13:33:47.031824Z", + "iopub.status.idle": "2025-05-09T13:33:47.050988Z", + "shell.execute_reply": "2025-05-09T13:33:47.050020Z", + "shell.execute_reply.started": "2025-05-09T13:33:47.032275Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(7639, 1)\n" - ] - } - ], + "outputs": [], "source": [ - "print(np.shape(X_train_scaled))" + "X_train_scaled = X_scaler.fit_transform(X_train)\n", + "X_test_scaled = X_scaler.transform(X_test)\n", + "\n", + "y_train_scaled = y_scaler.fit_transform(y_train)\n", + "y_test_scaled = y_scaler.transform(y_test)" + ] + }, + { + "cell_type": "markdown", + "id": "f990ce31-fb46-4c64-b56c-b26781c8e571", + "metadata": {}, + "source": [ + "Check that the resultant distribution for training data has a center of roughly zero." ] }, { "cell_type": "code", - "execution_count": 144, - "id": "3ee4e732-8688-47fa-b62f-27cb4b9ee9ca", + "execution_count": 55, + "id": "5be7b1b1-2177-46e5-ad41-607e55d12949", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T22:43:37.749833Z", - "iopub.status.busy": "2025-05-07T22:43:37.749396Z", - "iopub.status.idle": "2025-05-07T22:43:37.976968Z", - "shell.execute_reply": "2025-05-07T22:43:37.976042Z", - "shell.execute_reply.started": "2025-05-07T22:43:37.749790Z" + "iopub.execute_input": "2025-05-09T13:34:59.961016Z", + "iopub.status.busy": "2025-05-09T13:34:59.960567Z", + "iopub.status.idle": "2025-05-09T13:35:00.198956Z", + "shell.execute_reply": "2025-05-09T13:35:00.197908Z", + "shell.execute_reply.started": "2025-05-09T13:34:59.960978Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -1375,26 +1359,73 @@ } ], "source": [ - "plt.hist(X_train_scaled);" + "plt.clf()\n", + "plt.hist(X_train_scaled, range=[-2,2]);" + ] + }, + { + "cell_type": "markdown", + "id": "5e0d7b9a-b383-43c2-bb4a-826586326484", + "metadata": {}, + "source": [ + "> A histogram with a x-axis range from -2 to 2 for the distribution of the rescaled `X_train` data." + ] + }, + { + "cell_type": "markdown", + "id": "8dfc5412-e4a5-4f32-a27c-25acb1a8c54c", + "metadata": {}, + "source": [ + "Perform the inverse transform to check if the twice transformed data is the same as the original." ] }, { "cell_type": "code", - "execution_count": 145, - "id": "40eaabc7-8c97-47dc-9de2-2b505aa32624", + "execution_count": 63, + "id": "f4086f5f-7c2d-4d6c-a2d4-8dff42e2fc84", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T22:43:49.310089Z", - "iopub.status.busy": "2025-05-07T22:43:49.309277Z", - "iopub.status.idle": "2025-05-07T22:43:49.539986Z", - "shell.execute_reply": "2025-05-07T22:43:49.539077Z", - "shell.execute_reply.started": "2025-05-07T22:43:49.310045Z" + "iopub.execute_input": "2025-05-09T13:37:44.884323Z", + "iopub.status.busy": "2025-05-09T13:37:44.883776Z", + "iopub.status.idle": "2025-05-09T13:37:44.893331Z", + "shell.execute_reply": "2025-05-09T13:37:44.892372Z", + "shell.execute_reply.started": "2025-05-09T13:37:44.884278Z" + } + }, + "outputs": [], + "source": [ + "X_train_tt = X_scaler.inverse_transform(X_train_scaled)\n", + "X_test_tt = X_scaler.inverse_transform(X_test_scaled)\n", + "\n", + "y_train_tt = y_scaler.inverse_transform(y_train_scaled)\n", + "y_test_tt = y_scaler.inverse_transform(y_test_scaled)" + ] + }, + { + "cell_type": "markdown", + "id": "46ca963a-c142-438b-91a7-c6e30904e45e", + "metadata": {}, + "source": [ + "Check that the original `X_train` is the same as the twice transformed `X_train_tt`." + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "fc4cecc6-f256-4f55-8d54-f585630e8b4f", + "metadata": { + "execution": { + "iopub.execute_input": "2025-05-09T13:37:45.567934Z", + "iopub.status.busy": "2025-05-09T13:37:45.567476Z", + "iopub.status.idle": "2025-05-09T13:37:45.857123Z", + "shell.execute_reply": "2025-05-09T13:37:45.856221Z", + "shell.execute_reply.started": "2025-05-09T13:37:45.567892Z" } }, "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -1404,72 +1435,54 @@ } ], "source": [ - "plt.hist(y_train_scaled.flatten());" + "plt.clf()\n", + "plt.hist(X_train_tt, range=[0,1500], label='twice transformed', alpha=0.5, color='red')\n", + "plt.hist(X_train, range=[0,1500], label='original', alpha=0.5, color='orange')\n", + "plt.legend();" ] }, { "cell_type": "markdown", - "id": "6011b2e6-5197-4475-8eb4-3a8841b3bd28", + "id": "ad501069-4509-40d2-b05d-03d800e40296", "metadata": {}, "source": [ - "## 7. Model (using `scikit-learn`)" + "> Overlapping histograms for `X_train` and `X_train_tt` with an x-axis range of 0 to 1500." ] }, { "cell_type": "markdown", - "id": "48c8d648-288f-4635-a961-248f127fe524", + "id": "6011b2e6-5197-4475-8eb4-3a8841b3bd28", "metadata": {}, "source": [ - "## 7.1 Start with a linear regression" + "## 7. Model (using `scikit-learn`)" ] }, { - "cell_type": "code", - "execution_count": 146, - "id": "9a0c0864-ca7a-43fa-9d18-7c873a8ca7a0", - "metadata": { - "execution": { - "iopub.execute_input": "2025-05-07T22:43:52.432974Z", - "iopub.status.busy": "2025-05-07T22:43:52.432524Z", - "iopub.status.idle": "2025-05-07T22:43:52.439434Z", - "shell.execute_reply": "2025-05-07T22:43:52.438494Z", - "shell.execute_reply.started": "2025-05-07T22:43:52.432920Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "np.False_" - ] - }, - "execution_count": 146, - "metadata": {}, - "output_type": "execute_result" - } - ], + "cell_type": "markdown", + "id": "48c8d648-288f-4635-a961-248f127fe524", + "metadata": {}, "source": [ - "np.isnan(X_train_scaled).any()" + "## 7.1 Start with a linear regression" ] }, { "cell_type": "code", - "execution_count": 147, + "execution_count": 29, "id": "e02ca479-6105-442b-9879-2eb215dc4d66", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T22:43:54.128094Z", - "iopub.status.busy": "2025-05-07T22:43:54.127064Z", - "iopub.status.idle": "2025-05-07T22:43:54.137551Z", - "shell.execute_reply": "2025-05-07T22:43:54.136632Z", - "shell.execute_reply.started": "2025-05-07T22:43:54.128048Z" + "iopub.execute_input": "2025-05-08T15:54:51.604838Z", + "iopub.status.busy": "2025-05-08T15:54:51.604287Z", + "iopub.status.idle": "2025-05-08T15:54:51.618400Z", + "shell.execute_reply": "2025-05-08T15:54:51.617461Z", + "shell.execute_reply.started": "2025-05-08T15:54:51.604795Z" } }, "outputs": [ { "data": { "text/html": [ - "
LinearRegression()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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" + "
LinearRegression()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" ], "text/plain": [ "LinearRegression()" ] }, - "execution_count": 147, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -1902,15 +1915,15 @@ }, { "cell_type": "code", - "execution_count": 148, + "execution_count": 30, "id": "aca8064a-c94f-4792-86b3-62ec2130471b", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T22:43:54.568184Z", - "iopub.status.busy": "2025-05-07T22:43:54.567701Z", - "iopub.status.idle": "2025-05-07T22:43:54.575503Z", - "shell.execute_reply": "2025-05-07T22:43:54.574526Z", - "shell.execute_reply.started": "2025-05-07T22:43:54.568103Z" + "iopub.execute_input": "2025-05-08T15:54:55.412402Z", + "iopub.status.busy": "2025-05-08T15:54:55.411935Z", + "iopub.status.idle": "2025-05-08T15:54:55.419771Z", + "shell.execute_reply": "2025-05-08T15:54:55.418755Z", + "shell.execute_reply.started": "2025-05-08T15:54:55.412367Z" } }, "outputs": [ @@ -1938,15 +1951,15 @@ }, { "cell_type": "code", - "execution_count": 149, + "execution_count": 31, "id": "ee8bd887-928e-4a41-bd77-149b344ab238", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T22:43:56.604700Z", - "iopub.status.busy": "2025-05-07T22:43:56.603683Z", - "iopub.status.idle": "2025-05-07T22:43:56.833352Z", - "shell.execute_reply": "2025-05-07T22:43:56.830398Z", - "shell.execute_reply.started": "2025-05-07T22:43:56.604648Z" + "iopub.execute_input": "2025-05-08T15:55:02.919616Z", + "iopub.status.busy": "2025-05-08T15:55:02.919164Z", + "iopub.status.idle": "2025-05-08T15:55:03.149756Z", + "shell.execute_reply": "2025-05-08T15:55:03.148773Z", + "shell.execute_reply.started": "2025-05-08T15:55:02.919578Z" } }, "outputs": [ @@ -1976,45 +1989,55 @@ "id": "8367d3db-3527-4cab-abdb-51391259ec02", "metadata": {}, "source": [ - "Transform back to the true values (remember the current scaling is logarithmic) and look at the results in that space." + "Transform the predictions back to the true values (remember the current scaling is logarithmic and standardized) and look at the results in that space." ] }, { "cell_type": "code", - "execution_count": 150, + "execution_count": 32, "id": "6770c381-1f34-44a7-b93b-b7f915013620", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T22:44:05.377368Z", - "iopub.status.busy": "2025-05-07T22:44:05.376863Z", - "iopub.status.idle": "2025-05-07T22:44:05.384480Z", - "shell.execute_reply": "2025-05-07T22:44:05.383362Z", - "shell.execute_reply.started": "2025-05-07T22:44:05.377325Z" + "iopub.execute_input": "2025-05-08T15:55:48.240962Z", + "iopub.status.busy": "2025-05-08T15:55:48.240500Z", + "iopub.status.idle": "2025-05-08T15:55:48.251408Z", + "shell.execute_reply": "2025-05-08T15:55:48.250499Z", + "shell.execute_reply.started": "2025-05-08T15:55:48.240923Z" } }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/lib/python3.12/site-packages/sklearn/utils/validation.py:2739: UserWarning: X does not have valid feature names, but FunctionTransformer was fitted with feature names\n", + " warnings.warn(\n", + "/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/lib/python3.12/site-packages/sklearn/preprocessing/_function_transformer.py:387: RuntimeWarning: invalid value encountered in log\n", + " return func(X, **(kw_args if kw_args else {}))\n" + ] + } + ], "source": [ - "y_pred_original = y_scaler.inverse_transform(y_pred) # Correct this line\n", - "y_test_original = y_scaler.inverse_transform(y_test_scaled) # Correct this line" + "y_pred_orig = y_scaler.transform(y_pred)" ] }, { "cell_type": "code", - "execution_count": 151, + "execution_count": 33, "id": "f83cf984-ffa7-4486-98ec-bce890b91bad", "metadata": { "execution": { - "iopub.execute_input": "2025-05-07T22:44:07.346379Z", - "iopub.status.busy": "2025-05-07T22:44:07.345906Z", - "iopub.status.idle": "2025-05-07T22:44:07.576732Z", - "shell.execute_reply": "2025-05-07T22:44:07.575840Z", - "shell.execute_reply.started": "2025-05-07T22:44:07.346338Z" + "iopub.execute_input": "2025-05-08T15:55:49.528942Z", + "iopub.status.busy": "2025-05-08T15:55:49.528053Z", + "iopub.status.idle": "2025-05-08T15:55:49.799725Z", + "shell.execute_reply": "2025-05-08T15:55:49.798661Z", + "shell.execute_reply.started": "2025-05-08T15:55:49.528895Z" } }, "outputs": [ { "data": { - "image/png": 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urZmx2gSPe9Q0vs7uk2Ue3Vr64kSZrIXQ+UXlkg4UdYQnlpNaGHaIiBRmtQl4Pb/I7WyNuGamYzt5syuOrrO7yLOt3zm7TmL4yn+j25KdknZneRNY2BiQ1MKwQ0SkoDxLCbot2Yl5249Ien7JlToYlLiz4/kOdQDSt6N7ElgMkHeiOpHSGHaIiDzgqB+OJ71n4iNCcaHSs9tCjQ3rEY3wEKPHr29+a82ZwUnRSIw0QWo+Y2NA0gLuxiIiksnZDquaBqvkCZbGxyDke3gLShQVFozSynpU1lu9uo6U7ejGIAOWj0vFxDV77T1zXBG3mrMxIKmJYYeISAZn27vlLg4WALw8JgXGIIN9tqS4otaju1EP/6IbHv7Q4sErHdtWeN7ltvAsczw2T+vfIvCxMSBpFcMOEVEz4uGWxw6eQ0JkW/sPbG86Bzvy6LZCGIMMyDLHY9nYFExat0/2NaLDghERakRZVYP7J0u0fv9ZvHQ9iDmTZY5HRkocvjxZiv9YvsXPzckY0iOWwYY0iWGHiKgRR4dbJrQPxcyBt6DBKih6FpS4KHjukCSs3XvG4+vM//ioYjUBQGlVg6TOysYgA4Z0j0bklTCkdecMDmkXww4R0XVOj2y4XIeFnx5X/P3E91n2ZZHs10aEGnGlztrkfCklsScO6Ql3YxERwbvDLf0pPCQIv+vbGZV13i1Gdoc9cUhPGHaIiAB8ecqzDsSNGQC0Dfb1P6sGvHvgnM9CGXvikB4x7BBRq5dnKcGktfIXBzcmrlYxKtIh0Dlvt5e7wp44pFcMO0TUqonrdC7VeLf2JaZdCLLM8T4NI0prH9q0CWFiBxM2T+vPnjikO1ygTESthtUmIL+o3N4DZlDXKK/X6Yw3x+HL64djfiDhbCktuVxnxcI7e+LW2HD2xCFd03TYuXr1Kl5//XVs374dZWVliI2NRWZmJh544AEEBXFSiohaah5oxB/gjroex7QLQZmHJ3gD13rc5FnOexyWDAZAUHlF9F+/+QFFT41kyCFd03TYWbVqFd5//3288MIL6NGjBwoKCvDkk08iIiIC06ZNU7s8ItIYZ8c43Ns3AUt3n2oRSrwJOgBQd9XmUdCJCDXi0aHdYbMJWPz5Ca9q8Ja74yGI9EDTYefgwYMYMWIEhg4dCgBITEzEJ598goKCAnULIyLNcXWMw8u7T/nkPT1dn3OlzopFnx7Hxux+iA4L9lmvHKnYU4f0TtNhp1+/fnj//fdx+vRpdOvWDceOHcO+ffvw1FNPyb6W1Ro4iwbFWgOpZrVwrKTT81j5skeOlMMuPTVvWyEeGHQLnt150kfvIE2ndsEefy70/LlSGsdKOqXHyCAIat8xdk4QBCxbtgyrVq2C0WiE1WrF3Llzcf/990u+htVqxcGDB31XJBGpbt+FOsza5d3J4YFuym1hWH+sWvbrbgo14P8bF8c1O6RZaWlpMBqN7p/ogqZndnbs2IFt27Zh6dKl6NGjB44ePYqcnBx07NgRmZmZsq5lNpu9Hix/sVqtsFgsAVWzWjhW0ul5rI4dPAfxHKvWJqF9KF7JSEFmahwyCs5j7keFKL4s/bbUWxPS0K+351vN9fy5UhrHSjpxrJSi6bDz4osv4r777sOoUaMAAMnJyTh37hxyc3Nlhx2j0RhwH65ArFktHCvp9DhWCZFt1S5BFQt/3RMLRvS0z8pM6JOATHNnLNl5HAs/c3+W1/yh3TGpb6Iitejxc+UrHCv/0/T+7draWhiadSM1Go3Q8J03IlLB4KRoJEaa0JpuxBgA/PW/P7R43BhkwDN3JmPLtP5IjDQ5fG1suxBsyu6HF0f38nGVRNqg6ZmdYcOGYeXKlejcubP9Ntbq1asxfvx4tUsjIhec9brxFWOQAff2TfDZristEuB623iWOR4ZKXHILypHcUUtSivrEBseioRIE5sHUquj6bDz9NNP49VXX8WiRYtQXl6Ojh074p577sGDDz6odmlE5ISzXjfLx6UqdgxB8zBVWlWPpa0o6DTmatu4McjA/jlE0HjYCQ8Px4IFC7BgwQK1SyEiCZz1uimuqMXENXu9OndJDDjbCs/j3f1nUVZ1ozeN0eC77eFaFx8RqnYJRJqn6bBDRIHDVa8bAdfWmMz9qAAZKfK3OTuaLWry3q0w6Rhw7eDOwUnRapdCpHmaXqBMRIEjv6jcaRgBmq4xkUOcLXJ17UBjwLVztaLaevb7phgVX8lI5dobIgkYdohIEVKPHJBzNIEvOyOr7a3xvREW4lnYSexg8uqWIFFrw9tYRKQIqWtH5KwxcTdbFIgSI0OxfJwZUW2DZf/Z5gzuhrEpcdxNRSQTww4RKULsdVNcUetwJsaTNSZ6O6By4Z09sWDktSaAGw4US36d0rvZiFobhh0iUoQxyIDl41Ixcc3eFodnerrG5NuLlUqWqJrYdiF4I8uMTHO8fcv8hcvSZnWWjemFhwcncSaHyAsMO0SkmCxzPDZP69+yz04HE17JcD0zIW4tFxvg7TxRhh3HLvqjbJ8ICw5CdYMNAFBaVY/fbzyIP2w6iMt1N05zNhqc7yQTZ8IYdIi8x7BDRIpq3LlXagdld1vLA5EYdESV9dYWz3EVdADutiJSCsMOESlOTudeZ40IW5PmMzxSZsKISDqGHSLyG6tNwO6TZdhdVA4IwC+TovHHrZZWHXSAa0Fn2Zhe6NTe5JezxIhaG8lh59ixY5Ivetttt3lUDBHpV56lBPdvPoTy6hvHPCz54oSKFWlLp/YmTO6boHYZRLokOeyMGzcOBoMBgiDAYHD9G8fRo0e9LoyIAlfjgzo7hYfgn6fLsfgzBhtXeMYVke9IDjtffPGF/b+PHj2KF154ATNmzEBaWhoA4ODBg1i9ejUee+wxxYsk0pvmp3br6baFHhcb+xLPuCLyPclhJyHhxvTqH//4Rzz99NMYMmSI/bHbbrsN8fHxePXVVzFy5EhlqyTSEUdhQC9N47jY2DPcdUXkWx6djXX8+HEkJia2eDwxMREnT570uigivXJ2qGVxRS0mrtmLPEuJSpV5T8/nWPlKF55xReQXHoWd7t27480330Rd3Y1W7vX19XjzzTfRvXt3xYoj0hNXYUB8bO5HBbDa1IsL4m6pDQeKsftkmaxa9HiOlbfG945HVFhwk8di24VgzuBu2DVrIIqeGsmgQ+QHHm09X7RoEWbNmoUhQ4bYd14dO3YMBoMBubm5ihZI5G++Wk/jLgwIAM78VIv8onLJPWqU5MnttcZjVVBy2V+lBowHB3XF+7/rp9v1WUSBwqOw07t3b3zxxRfYtm0bioqKIAgC7r77bowePRphYWFK10jkN75cTyP1UEs1Dr90ttZGvL3m6FbL5kPn8MAHh1BefdV/hQaQmLA29mCjRnglohs8birYtm1b3HPPPUrWQqQqT37gy9GxXYiiz1OKu9trBly7vZaREmefkXj84yN4efcpf5YZcN4Y34czOEQa4dGaHQD48MMPMXnyZKSnp6O4uBgA8M4772Dnzp2KFUfkL/5YT+OmPZWdTYDH62Y8Ief2GgBsOXSOQceN+UO7Y2KfzmqXQUTXeRR23nvvPTz//PP45S9/icuXL8Nmu3bgXfv27bFmzRpFCyTyB7k/8D1xobJe0vMmrNmD4Sv/jSnr92P4yn+j25KdPtulZbUJ+OJkmaTn/vWb77Hmvz/gf98/4JNaAkViZCg2ZvdDdLOFxwAQEWrEpux+eHF0LxUqIyJnPLqN9e677+K5557DyJEj8fbbb9sfT01NxQsvvKBYcUT+4o/1NFI75F5pdjr2WYVuozUnt/nf+v3nsH7/OcXeP1D94ee3YGKfzsgyxzc552tYj2gM6R7DW1dEGuRR2Dl79ixuv/32Fo+HhISgpqbG66KI/E1qEJH6PEc7ugYnRSMx0oTiilrZvWgEtFw3482uMTb/89ytMeEArp3sPqJnLEb0jFW5IiJyx6Owk5iYiKNHjzbpqgwA//znP9GjRw9FCiPyJ3dBRE5Lf1c7upaPS8XENXthAGQHjcbb0r3ZNcbmf97pFO7fBeRE5D2P1uzMmDEDixcvxo4dOwAAhw8fxltvvYVXXnkFM2bMULRAIn8wBhmwfFwqgGvBpjHxaykt/V11SJ6wZi8Kzl/GHwd3Q4yHO662FZ73ugvzV6cvsfmfF6ZtOBDQna6JWiOPZnbGjx8Pq9WKl156CTU1NXj00UfRqVMnPPXUUxg1apTSNRIpztEtoCxzPDZP699yxqSDCa9keDdjIj628NPj9sdi2gXjd3ckol1IGyz5QtqJ4MvzT2PdvrNut4mPvr0T/vXdpSZ/PpEafXz05NzlOp+soSIi3/G4z86kSZMwadIkXLp0CYIgIDqaJ/ZSYHB3CygjJc7lWhhna2XkHpdQXtWAV/NP48+/7imr/vLqBqffE3eNdXn2c5RW3dj9ldA+FH8YcDOCK6thimbY8Yaz3kNEpF0ehZ2pU6dixYoVaN++PaKiouyPV1ZW4oEHHsDatWsVK5BISVIbBzrreOsqKNVdtcmqRfyh+f/+8z0SI02K3lpqHHQAoPhyHRZ9Ls4e/aTY+7RWah/tQUTyeLRm55tvvkFDQ8vfLuvq6rBv3z6viyLyBW8bB7pbK3OitFJ2TQKAsxV1+MOAm1usFSLt4y1BosAga2bn2LFj9v8+efIkSktL7V/bbDbk5+ejU6dOylVHpCBvDuKUcqTC//vvD0hoH4riy/J/AN4aG47N0/pj9pbDLWZlSLuktiIgInXJCjvjxo2DwWCAwWDAtGnTWnzfZDLh6aefVqw4Ijnc9Z3xpnGglKB0tqIWWalxyCs4L7v2+IhQDO0Rg9G3d0Lis5+hrMr5uhxSn5xWBESkPllh54svvoAgCBg5ciQ2b97cZL1OcHAwoqOjYTQaFS+SyJ2tBecxb9sRl31nvGkcKDUoeRJ0osOC7T80Q9oEYeWEPpi4Zi8A+b14yPfktCIgIm2QtWYnISEBiYmJOHbsGMxmMxISEuz/69ixI4MOqWLXmRrcs26/274zYuNAZz+eDAC6OPlt3Ze3K8qrG/BR4Y2QlGWOx8bsfh734iHviZ+RR4d0Q2Kkqcn3EjuYuO2cKMB4tBsrNzcX0dHRmDBhQpPHt2zZgkuXLuG+++5TpDgid6w2AUv3VbjtOyNuEXbVwVgA8PKYFIe/rZdW1SPIcO1Ecl+Y8+GNGvMsJZi3rZBrd1SUEGnCw+a2mHf37Xh+VIrHx3IQkTZ4tBtr48aNSEpKavH4rbfeivfff9/rokjfrDYBu0+WYcOBYuw+WeZ095MUX52+hIs1zrd8Nz+tXGwcmNDst3XRo9sKW3THzbOU4J51+3wWdIBr632WfHHc6Y4v8q2otm2w8Nc9sX7KHdg1ayBOPjEMw7u0BXCtu/bQHjGY3DcBQ3vwoE+iQOTRzE5paSliY1sefhcVFdVkhxZRc96c6eSI1LU0X5wos/9GnmWOh80mYNK6lm0SmvfbEXdh+cPCT48jOiyY63T84OUxt+PH6quAARiaFN0ixFitVhevJqJA41HYiY+Px/79+9GlS5cmj+/btw8dO3ZUpDDSH6kN/eSQupZmyRcnsGbvGSwfl4qMlDjM21bo8HnNb33J7YrsLVfdkcl74i6qPw7uzhkaolbEo9tYEyZMwF/+8hd88MEHKC4uRnFxMbZs2YKcnBxMmjRJ6RpJB7xt6OdMercotA+R9kNLDFVLvjguud+ON03jDABi24VgzT1piOViY7/z5kBXItIXj2Z2Zs6ciYqKCixatMjeSTk0NBR/+MMfcP/99ytaIOmDNw393JH6Y0uctXkt/7Sk55dcqcOJMvldkRvX9NaE3sgyx6OdqQ0mXN9OTr43tHs0TpZVeXSgKxHpj0dhx2Aw4LHHHsMDDzyAU6dOwWQyoWvXrggJ4W+v5Jg3Df1c+er0JVTUS58NEgBcknir6ERZZZNTyuVo/oM1yxyPhXf2xMLPPLseybP7VDk2Xd++z11UROTxqecA0K5dO/Tu3VupWkjHvGno54qnt5miwoLxY3WD08XAMe2CsfzLUx5dG7i2hb35DMKCkT2xdPdJXKmXd2AoeebR7YUoemokAw4RSQ87Dz30EJ5//nmEh4fjoYcecvncFStWeF0Y6YvY0K+4otZhwPC0/b6nzf4eSe+GRZ8dd9hvB4BXxzUYAMzfXogsczyMQQb7MRbbCs+j9ir3WvkLTyUnIpHksBMREeHwv4mkcNXQz5uFo+ndotCxbRBKa2yStmyLoWrByJ5IjW/fYhu8EsT1R6/nF+GHn2rw7v6zPOtKJTyVnIgAGWEnJyfH4X/72oULF/DSSy8hPz8ftbW16Nq1K5YsWYLU1FS/1UDKEBv6teiz48XCUWOQAY/2i8QTX/3odJZG1DxUZZnjkZEShyU7j+O1/NO4VKNsIJm3/Yii1yP5eCo5EQFertnxtYqKCkyePBkDBgzAqlWrEBUVhTNnzqB9+/Zql6ao5qd1D7qlg9ol+YwYMJRsvz+8S1tszO7W4iBQowGwNko/jkLVR4Xnseiz42zkpzM8lZyIGpMcdsaNGweDQdoPpK1bt3pcUGOrVq1CXFxck5mkxMRERa6tFc46Cj9sbou0NPXq8iWx/b6SMlPjkGnu3DQ0do3Cv7675DBUiUdW3Lf5EIOOzrCfDhE1JznsjBw50v7fdXV1eO+999CjRw+kXf+JfOjQIZw4cQK//e1vFStu165dSE9PxyOPPII9e/agU6dO+O1vf+tR40Ittn/fWnAe96zb77Cj8J++qsUtN5/D+D6dValNCVabgK9O3wgb6d2iFP/hI/69Wq1WGI3A4G43Nfqu0PRrwQar9dq4N58FosDV3mTE5dob//9OiDRh2dheyOjV0eP/3zf+XJFrHCvpOFbSKT1GBkEQZP9iu2DBAsTGxmLOnDlNHn/ttddQUlKi2Joes9kMAJg+fTp+85vf4PDhw/jLX/6CxYsXY9y4cZKuYbVacfDgQUXqUZLVJmDstgsuD7HsFBaEj8Z0CsjfTnedqcHSfRVN/nwd2wbh0X6R9gMW1arrT1/96NFrTUHAvcnt8OGpavwko7cP+dbCAZGIa9cGZTVWxLQ1Ii02JCD/P0NEjqWlpcFoNHp1DY/W7Pz973/HBx980OLxsWPHYvz48YqFHUEQkJqainnz5gEAevXqhZMnT2LDhg2Sw47IbDZ7PVhK+vJUOS7WlLh8zoVqGyojb8aQ7oG17mBrwXk88VXLGavSGhue+OpHbMzuhszUOEXey2q1wmKxSPr7tdoEZO74h8fvNXPgLchIicM7R//r8TVIeel9blP8/yNyPletHcdKOo6VdOJYKcWjsGMymbBv3z507dq1yeP79u1DaKhyux9iY2PRvXv3Jo8lJSXh008/lX0to9GoqQ/XBYlbkS9UNWiqbnesNgHzth1xegaWAcCj248g09xZ0d++pfz95p8u8+rW1dGLVSiu+N7j15PyunQwYUiPWJ/N5Gjt3w0t41hJx7HyP4/CzrRp07Bw4UIUFhaiT58+AK6t2fnggw/w4IMPKlbcHXfcgdOnm55j9N133yEhIUGx91CLrzoKq82XZ2B5y9ueKztPlClUCSnBAC5CJiJpPAo79913HxITE7F27Vp8/PHHAK7NuOTk5ODuu+9WrLhp06Zh8uTJWLlyJe666y4cPnwYmzZtwuLFixV7D7W46ygMXNuVFWhbZ311BpYSAi040g3RYcEob3SmWRce6klEMnjcZ+fuu+9WNNg40rt3b6xYsQLLli3DG2+8gcTERDz11FMYO3asT9/XH9x1FBYALBvbK+B+a9XyjJWUgEnaYwDQNjgIO+//OS5U1vNQTyKSzeOwc/nyZXz66ac4c+YMfv/736NDhw4oLCxETEwMOnXqpFiBw4YNw7BhwxS7npY46yiccL3PjlKLeP3JV2dgKcFVwCTtEgCcrahDkMGAyX0D/xY2EfmfR2Hn2LFjmD59OiIiIlBcXIyJEyeiQ4cO+Pzzz3Hu3Dm8+OKLStepW446Cg+6pQMshw+pXZpHlD4Dq3l3aW9/oxcD5v2bDzW5LULax3OuiMhTHoWd559/HpmZmXj88cfRt29f++O//OUvMX/+fMWKay2adxQO9IZTSp2B5ay79PJx3q3VsNoE/KjwOVjke1xzRUSe8ijsWCwWh4uEO3XqhNLSUq+LosDn7RlYeZYSTFyz12F36Ylr9mLztP4eBZ48SwnuWbdP9utIPTznioi85VHYCQ0NRWVlZYvHT58+jaioKK+LIn3w9Awsq03AnA8LXPbqmftRATJS5K1pEq9LgYPnXBGREoI8edGIESPwxhtvoKHhxq2Ac+fOYenSpbjzzjsVK45aJzm9epoTD/jccKAYu0+WwWq7EZncXZd8IzqsDaLDgp1+33D9f/OHdkdipKnJ9xI7mDyexSMiEnk0s/OnP/0JM2fOxKBBg1BXV4fs7GyUlZUhLS0Nc+fOVbpGamU87dXjbo0PF7iqo21wG0xK64y1e8+gzEHn8MZruXLuvl3RBelERICHYSc8PBwbNmzAv//9bxw5cgQ2mw0pKSkYNGiQ0vVRKyLuvMo77PrMMFHjBavOTpA/W1GLCWv2YsOUO1BSUaNgtSTV2YpaLPuyyOn3Xx6TYp+58fTWJxGRK7LDztWrV9G7d298+OGHGDhwIAYOHOiLuqiVcTQr405ZVT0A1+dxiSav3+9lheQLBgDztxciyxzPGRwi8hnZa3batGmDzp07w2az+aIe0hFX62caE3deyV1P8+j2QlhtAg6W1nMtToBytf6KiEgpHt3Gmj17NpYuXYqXXnoJHTp0ULgk0gOpPXJc7bxy58xPtfjq9CWU1QR2XyJiw0Ai8i2Pws66devw/fffY/DgwejcuTPCwsKafH/r1q2KFEeBSU6PHG93SJVcqUNMW6MX1ZIWsGEgEfmSR2Fn5MiRStdBOiGnR44xyOD1b/Q7j5eie7AV0WFtUF591atrUVPtTUZcrvXtrBkbBhKRP8gKOzU1NXjxxRexc+dOXL16FQMHDsTTTz/NRoJkJ6dHztAeMV7/Rr9mX7FXryfHEiNNWDo2xafdptkwkIj8RdYC5ddeew1bt27F0KFDMWrUKPzrX//CwoULfVQaBSK5PXLEU9L5o05blo1NwcQ+nbEpux+Mbv5y3OUUowGYNySJDQOJSDWyZnY+//xzLFmyBKNGjQIAjB07FpMnT4bVaoXRyHUTJH3thfg8V6ekk3rmbStEUJABE/p0hgC4nOFxssnOzioAo2/vhBdG9WLDQCJShayZnfPnz6N///72r3v37g2j0YiLFy8qXhgFJnczNQYAXZqt0RBPSU9o9ps/qUdcTJ5nKcHEPp2xZVp/l0c+uFNypc7eMHBy3wQM7RHDoENEfiMr7FitVgQHN/0Hz2g04upVLgyla8SZGgAtAk/zNRqN+/BEtQ3GySdHYNesgZgzuBvah3q0dp4UIk7WzP2oAFabgIyUOLQN9nz2lrutiEhNsn6iCIKAJ554AiEhIfbH6uvrsXDhQrRt29b+2IoVK5SrkAKOOFPTos9OozOQnPXhubdvApbnn1ajbGqmecM/T1oEuNttJR4RwltbRORLssJOZmZmi8fGjh2rWDGkH1nmeGSkxNl/kHUKD4EgABer6rH4s2+x6LPjDvvwvLz7lCr1knOetgdwt9tKauNJIiJvyQo7OTk5vqqDdEhco5FnKcH/vn/Q7cwAFydrk6e3oBrP5DUnp/EkEZG3uDCCfMrZDzXSvua3oBIjTSiuqHX4d2kAkBAZinfu7YsLlfUub0nJbTxJROQthh3yGW/OvSJ1iRFjxv/cjE2HziE+IhTLrjcZbN4iQHzu8nFmDL811u215TaeJCLyFsNOAAqURZ3enntF6om6vs184WfH7Y8lRprw6NDueP9AscOF5xkpcdh9sszt51Ju40kiIm8x7AQYrS/qbBzEjpy/onY5JNODv+iK2PAQLPz0eIvvFVfUYunuU3g/ux9i24U0CTUfFZ5HtyU7JX0u5TaeJCLyFsNOANH6ok5HQYwCy9henTDlvf0Ovyeup5m/vRBFT420z9rI/VyKjSddrf/h4aBEpCRZTQVJPe4WdQI3GsCpQfyB54ug0/xOiLuzmkg+A4DosGBMee8AyqoanD6vee8dTz6XchpPEhEpgTM7AULLizp9tRBZ/FH3/u/6IabRbZNBXaPw5tenMW/7EYXfsXUSFxyXVzsPOc2J62k8/VxKaTxJRKQUhp0AoeVFnb5aiOzqB9/Dg5Ow7J9FTm+FkHQJkaGoabDJCjviehpvPpfNG09qebE9EQU2hp0AoeVFnb4IWMvG9MLDg5Oc/uATb4VMWLNX8ffWu/cmpyEusq09YNgEASNz/yP59VFhwbAJAqw2wevPpdh4kojIlxh2POTv7d9aXtQp9QdebLsQlFXVu52J6dLB5DLoiDJS4hAdFixrRqI1i2kXjMf6hmNiWmcYjTcO9dxwoFjWdS5VN2Bk7n+QGGnCsrEpmv1cEhGJuEDZA3mWEnRbshPDV/4bU9bvx/CV/0a3JTuRZynx2XtqeVGnGMRcvXNiZCjeyDK7vZYB0v8c+UXlDDoyLB3dC8O7tG3xuKezgcUVtbhn3T7c2zcBgPY+l0REIoYdmZztOhK32foy8IiLOhMiTU0eT+xgUnXbuasgJqppsCEoyIDN0/ojsVn9oi4y/xxsOidP88+NSEpYdUScydl4sBjvZ/fT3OeSiEjE21gyaOFMH60u6hSD2P2bDzmcbblU3WDvuXJ6wUjkF5WjuKIWpZV1iA0PRUKkSfafg03npBFvJaV3i4Ll8JkW3xfD6sQ1e1scBeGOuNsqtl2I/e9VS59LIiKAYUcWrWz/1uqizoyUOMz5sABAy7DTPAwqUb84I6HXJoaZKZ2wtfCC19cR4P5WkrOt4FFhwbgk4VZhyZU6zX4uiYgYdmTQ8vZvLfB1GHS0KHzZ2F6YtG6/7BmJQPDw4CR0i2mHZV8WSX5NEABbs8eir59z5Y6jWUOpO7U4y0ZEWsawI4OWt39rgS/DoLMzwZaN7YUX0m/Ca4erUXz5xnUd/dAPJLHtgjE4KRqDk6Jx9qdabD50zmWYE8Oeoz+zeAtxY/Yd6ObmfZvPzlhtAndbEVHA4wJlGdwt5DTg2iLb1voPv6/CoKtF4fes24+CsnrA0PRvJT7ShEl94hEeYkQgmnJHov1wzU2Ngk67ECPS4iNwU9umv6ckRIY6ncERXztv2xHZx4loeRcgEZFUnNmRwdVCTv7D75teQFLOXlp3rKrF94orarHpkO92xvlapCnY4eGa1fVWHCq5go3ZTY/QcHe7SQBwtqIWB0vr0U9mLTzagYgCHcOOTPyH3zlfhEFfHUWhdW/86zuXu/4ebXbyuNTGgGU1Vo/q0eouQCIiKRh2PMB/+J1TOgy21sXeZVX1Tr/naKH3idJKSdeNaev5bT3utiKiQMWw4yH+w++ckmGwtS72lkIMgnmWEiz67LjL5xpwralgWmyIHyojItIWhh3yCaXCoN576TgSaWqDitqrbp8XHxHqck1TYwKAZWN7wXj1vCI1EhEFEu7GIk0zBhnsZy+1Ftn9EhDbzvkMTONdf1LXNC38dU9kpsYpWCURUeAIqLCTm5uL5ORkLFmyRO1SyE/yLCVYuvuU2mX41caDJSh1sman+UJvqWuabo0JV6g6IqLAEzBh5/Dhw9i4cSOSk5PVLoX8ROotGr1xFnSAlodrstElEZF7ARF2qqqq8Nhjj+G5555DZGSk2uWQTFabgN0ny7DhQDF2nyyT3NiutW47dya2XTBOPDGiyY42NrokInIvIBYoL168GEOGDMGgQYPw1ltveXQNq9Wz/iJqEGsNpJqd2VpwHvO2HWlxzMPLo29v0hQvvVtUi91axRU1/i5X00qrGrDiqyJ0ightMmbLxvbCPQ7OBxNHc+mYXoBg09Xnytc4VtJxrKTjWEmn9BgZBEHQ9F2CTz75BCtXrsSWLVsQGhqK7Oxs3HbbbViwYIGk11utVhw8eNC3RZJDu87U4E9f/SjpuR3bBmHuHe1xU6gRZTVWxLQ1wioIePAfl3xcZeDq2DYIj/aLxPAubbHrTA2W7qvAxZobp2N1CgvCvDuufZ+IKFClpaXBaPTu6B9Nz+yUlJRgyZIl+Nvf/obQUO/WHJjNZq8Hy1+sVissFktA1dyc1SYgc8c/JD//Yo0NT379U5PHEtpfO+/pUnWDrtftJEaaMOiWm7DpsLzjLUprbHjiqx+xMbsb5o2Jwx9HCfjq9CWns2V6+Fz5C8dKOo6VdBwr6cSxUoqmw05hYSHKy8uRlZVlf8xqtWLPnj1Yv349LBaL5A+M0WgMuA9XINYsyj9d5vV6m3OX6+whp/ktGj14ckQP/OrWWJRW1eOedftkv/7G0RFHkGnujJBgA4b37Oj2dYH8ufI3jpV0HCvpOFb+p+mw8/Of/xzbt29v8tiTTz6JpKQkzJw5kx8WDVPimAcx3ISHGGEKNro8QsFXggCY49vjUMllRa8bHRaMxb++DQDQbclOj6/j6OgIIiJqStNhJzw8HD179mzyWFhYGDp06NDicdIWJbc6V9ZbUVlvRUy7YPzujkTc3KEt5m0/otj1XbEBigcdAMid2AfGIAN2n/R+BgxovWeIERFJERBbzynwuNsS7Ynyqga8mn8aCR3aKn5tf5ozuJt9+7hSIYV9dIiInAu4sLNu3TrJO7HI/8SeOpsOncMfBtwMAIqFEvG21vzthVg2NkXRa3srKiwYL4++XdJzx6bcOLbB25DCPjpERO5p+jYWBZY8SwnmfFjQ5LZMdFgwAKC8ukGR9xDXqMS0C8Hmaf1bvJ9afv8/N+OPv+yO5fmn3dbTeO2ROANWXFHrcAG2AdeC1KXr4+eoj454dAQRETkWcDM7pE15lhJMXLO3xQ/6S9UNKK9uwMJf98T6KXdg16yB2JTdD4mRJq/er+RKHbLM8Tj5xDCsHB6NdZPTnF47tl0I5gzuhmVjenn1nq5sPFgMAFh6fcbJlUe3F9q7SBuDDFg+LhVAy1kq8evciX2weVp/JDT7czU/OoKIiBzjzA55zdUZVuL26L/+9wcUPTXSPgORaY5HflG5vSdMaVU9Ht1WKHmWRrz9YwwyoF+nUKSldbbvzmt+7cFJ0TAGGWC1CVj2zyKnsyjeEHdEuTqtvPlzxd1TWeZ4h7NUiR1MeCUj1R5mMlLiHP65iIjINYYd8pq7M6wcbY82BhlabJXOMsfjy1NlmLR2Hy7VOL7tZcC1EOBqjYqja4uPLx+Xiolr9rr/Q3lAzmLj5s/NMse7DTPO/lxEROQab2OR16T+kHf3PGOQAcNvjcXbk/rAAOe3dbxZoyLOosS0C/bo9a7EXz+zSupzmxPDzOS+CRjaI4azNkRECmHYIa958wPeETGQOFujkpESZz9F/ctT5ZJPUW98/bP/d6ekW043mdxPfjbeEcVTyImItIe3schrUnYUubv11Jyz2zofFZ5HtyU7m9w269g2CCtCzmNCnwTJ1w9pE4S3JvTGBDe3tKYPuBmjb++EbYXnsTz/tNOTxcXZJqtNwB8G3IyFnx1vcS3uniIiUgfDDnmt8VoYd2FA7nUbr1HZcugcJjk4Q+pijQ33rNuPzUFBbncmWW2CPUB1Cg9BVNs2uFRz1enzl31ZhJ/ffBOWZaQiPSna5SJiR1vvG2u+4JiIiPyDYYcUIXVHkac2HzqH377r+rDMuR8VICMlzmmochdGnHkwz4JMc7zLRcTi1ntnN9QW/ronFozoyRkdIiIVMOyQYqTsKPJEnqXE7ang7g7EdBdGXCmtqrdf19GOKFdb70Wv5Z/GL26J4sJjIiIVMOyQopTeHi0GCakc7fiSEkY8ua7I3dZ74FpzxV+9/R8kRpqwfBxvZRER+RN3Y5GmSQkSjTna8SX3GlKvKyqWce3iilpMXLMXeZYSr+ohIiLpGHZI0+Q06nO2pdvbk8XdbRUvrZR+fXF2ae5HBbK3zBMRkWcYdkjT5JwK7mzHlzcnixtcXFcUGy7v+o3XFxERke8x7JCmuWvSBwBBBuD9KX2droOR0ugvOiwYMWFNuyp3kXjQZvPmh1J5O+NERETScIEyaZqrHj6iJYM6YHxv54FEvIazBoICrp0s7ulOMjFMyV0X5M2MExERSceZHdI8Z8dHdOlgwqbsOzDy5jBF3sfTs6nEMCV1QzmPjCAi8i/O7FBAcNbDB4INBw+ed/lad9vXDXDfkFBKfY6aKjp6L4BHRhAR+RPDDgUMhw39rO5f527rubuGhFI1D2Qnyiqx6t/fo/jyjbU5PDKCiMj/GHZI96QuBPZmwXDjM7fiI0IxqU9nGIMMWDCip+IdpYmISB6GHdI9qQuBPV0w7OjMrcadkpXsKE1ERPJxgTLpnpSt554uGBbP3Gp+m4ydkomItINhh3RP3C3lrF+xAM8WDLs6c4udkomItINhhwjAV0Xl2H2yTFYwkbPwmYiI1MOwQ7on5eT05fmnMXzlv9FtyU7Jt578sfCZiIi8x7BDmmO1Cdh9sgwbDhTLnm1xRM6p53LW2vh64TMRESmDu7FIU9ztbPKEnJkVAdKbDIoLn4srah2u2zHgWl8ddkomIlIXZ3ZIM3y1s0nuzIrUtTbiwmcALXZ6sVMyEZF2MOyQJvhyZ5OUk9MdkTIj5OzcrkSJJ6YTEZHv8TYWaYIvj3SQcnK6Ixcu18JqE1zOzFhtAqLaBiPn7ttRWlmH2PBQJESa2CmZiEhDGHZIE3y9s0nqQZ2Nzdt+BMv+WeR0vZCr9UUMOkRE2sHbWKQJ/tjZlGWOx+kFI7Fr1kDMGdxN0mucrRdi52QiosDBsEOa4MsjHRoTT05flpGKLdP6I7HZWpvmHK0XYudkIqLAwrBDmqDGziZxpmfZmF4un9d8dxY7JxMRBRaGHdIMNXY2GYMM6NTe9eyOSFwvxM7JRESBhQuUSVOyzPHISIlDflE5Sq7UIT4i1OOdTVabIOk6ctcLsXMyEVFgYdghzRHX1XhDTidmuZ2Q2TmZiCiw8DYW6Y7cnVJy1wuxczIRUWBh2CFd8XSnlNz1QuycTEQUOHgbqxWSupYlEHnTiVnueiEl1xcREZHvMOy0Mr44VVxLvN0pJXe9kBLri4iIyLd4G6sVaQ1df7lTioiImmPYaSVaS9dff3ViJiKiwKHpsJObm4vx48ejb9++GDhwIB544AEUFRWpXVZAai1df7lTioiImtN02Pnmm28wZcoUbNq0CatXr4bVasWMGTNQXV2tdmkBpzV1/eVOKSIiakzTC5T/+te/Nvk6JycHAwcORGFhIX72s5+pVFVgam1rWbhTioiIRJoOO81duXIFABAZGSn7tVarVelyfEasVcmaB93SwW3X34RIEwbd0kFXYzW42003vhBsCKA/muJ88bnSK46VdBwr6ThW0ik9RgZBEAJiRaogCJg9ezYuX76M9957T/LrrFYrDh486LvCAsiuMzX401c/Ov3+C+k3YXiXtn6siIiIyLW0tDQYjUavrhEwMzuLFy/G8ePHZQWdxsxms9eD5S9WqxUWi0XxmtPSgG7dzmPetiMt+uwsG9sLmalxir2Xv/hqrPSIYyUdx0o6jpV0HCvpxLFSSkCEnWeffRa7du3Cu+++i7g4z34gG43GgPtw+aLmCX0SkGnurLu1LIH496sWjpV0HCvpOFbScaz8T9NhRxAEPPvss/j888+xbt06dOnSRe2SdIFdf4mIqDXRdNhZtGgRPv74Y7z55pto164dSktLAQAREREwmUxuXk1ERESk8bCzYcMGAEB2dnaTx3NycpCVlaVGSURERBRgNB12vv32W7VLICIiogCn6Q7KRERERN5i2CEiIiJdY9ghIiIiXWPYISIiIl1j2CEiIiJdY9ghIiIiXWPYISIiIl1j2CEiIiJdY9ghIiIiXWPYISIiIl1j2CEiIiJdY9ghIiIiXWPYISIiIl1j2CEiIiJdY9ghIiIiXWPYISIiIl1j2CEiIiJdY9ghIiIiXWPYISIiIl1j2CEiIiJdY9ghIiIiXWPYISIiIl1j2CEiIiJdY9ghIiIiXWPYISIiIl1j2CEiIiJdY9ghIiIiXWPYISIiIl1j2CEiIiJdY9ghIiIiXWPYISIiIl1j2CEiIiJdY9ghIiIiXWPYISIiIl1j2CEiIiJdY9ghIiIiXWPYISIiIl1j2CEiIiJdY9ghIiIiXWujdgFE3rLaBHx5qhwXqhoQHxGKwUnRMAYZ1C6LiIg0gmGHAtrWgvN4aNsFXKwpsT+WGGnC8nGpyDLHq1gZERFpBW9jUcDKs5TgnnX7cbHG1uTx4opaTFyzF3mWEievJCKi1oRhhwKS1SZgzocFEBx8T3xs7kcFsNocPYOIiFqTgAg769evx/Dhw2E2m5GVlYW9e/eqXRKpLL+oHGcrap1+XwBw5qda5BeV+68oIiLSJM2HnR07diAnJwezZ8/Ghx9+iH79+mHmzJk4d+6c2qWRikqu1Cn6PCIi0i/Nh53Vq1dj/PjxmDhxIrp3744FCxYgLi4OGzZsULs0UlF8RKiizyMiIv3S9G6s+vp6FBYW4r777mvy+C9+8QscOHBA1rWsVquSpfmUWGsg1exvg27pgMRIE4orah2u2zEASIg0YdAtHTiO1/FzJR3HSjqOlXQcK+mUHiNNh50ff/wRVqsV0dHRTR6PiYlBaWmprGtZLBYlS/OLQKzZnx42t8WfvnK8bke4/n3L4UP+LSoA8HMlHcdKOo6VdBwr/9N02BEZDE0bxAmC0OIxd8xmM4xGo5Jl+YzVaoXFYgmomtWQlgbccvM5PLL1cJPt54mRJiwb2wuZqXHqFadB/FxJx7GSjmMlHcdKOnGslKLpsHPTTTfBaDSirKysyePl5eWIiYmRdS2j0RhwH65ArNnfxvfpjK7WC6iMvJkdlCXi50o6jpV0HCvpOFb+p+mwExISgpSUFHz99df41a9+ZX/8X//6F0aMGKFiZaQlxiADhnSP5j8eRETkkKbDDgBMnz4djz/+OFJTU9G3b19s3LgRJSUluPfee9UujYiIiAKA5sPO3XffjR9//BFvvvkmLl68iJ49e+Ltt99GQkKC2qURERFRANB82AGAKVOmYMqUKWqXQURERAFI800FiYiIiLzBsENERES6xrBDREREusawQ0RERLrGsENERES6xrBDREREuhYQW8+9IQjXzsQOpFNmeTKudBwr6ThW0nGspONYScexkq7xGIk/x71hEJS4iobV19fzhFkiIqIAZTabERIS4tU1dB92bDYbrl69iqCgINknpRMREZE6BEGAzWZDmzZtEBTk3aob3YcdIiIiat24QJmIiIh0jWGHiIiIdI1hh4iIiHSNYYeIiIh0jWGHiIiIdI1hh4iIiHSNYYeIiIh0jWGHiIiIdI1hR0Nyc3Mxfvx49O3bFwMHDsQDDzyAoqIitcvSvNzcXCQnJ2PJkiVql6JZFy5cwPz58zFgwAD06dMHGRkZKCgoULsszbl69SpeeeUVDB8+HL1798aIESOwYsUK2Gw2tUtT3Z49ezBr1iykp6cjOTkZO3fubPJ9QRDw+uuvIz09Hb1790Z2djZOnDihUrXqcjVWDQ0NeOmllzBmzBikpaUhPT0djz/+OC5cuKBixepx97lq7JlnnkFycjLeeecd2e/DsKMh33zzDaZMmYJNmzZh9erVsFqtmDFjBqqrq9UuTbMOHz6MjRs3Ijk5We1SNKuiogKTJ09GcHAwVq1ahU8++QRPPPEE2rdvr3ZpmrNq1Sq8//77eOaZZ7Bjxw489thj+Otf/4p169apXZrqqqurkZycjGeeecbh91etWoXVq1fjmWeewZYtWxATE4Pp06ejsrLSz5Wqz9VY1dbW4siRI5g9ezby8vKwYsUKfPfdd5g9e7YKlarP3edKtHPnThw6dAgdO3b07I0E0qzy8nKhZ8+ewjfffKN2KZpUWVkp3HnnncLXX38t/O53vxOee+45tUvSpJdeekmYPHmy2mUEhPvuu0948sknmzz20EMPCfPnz1epIm3q2bOn8Pnnn9u/ttlswi9+8QshNzfX/lhdXZ3Qr18/YcOGDWqUqBnNx8qRQ4cOCT179hSKi4v9VJU2ORur8+fPC4MHDxaOHz8uDBs2TFi9erXsa3NmR8OuXLkCAIiMjFS5Em1avHgxhgwZgkGDBqldiqbt2rULqampeOSRRzBw4ECMGzcOmzZtUrssTerXrx/+85//4PTp0wCAY8eOYd++fRgyZIjKlWnb2bNnUVpaivT0dPtjISEh+NnPfoYDBw6oWFlgqKyshMFg4GyrAzabDY899hhmzJiBW2+91ePrtFGwJlKQIAjIyclBv3790LNnT7XL0ZxPPvkER44cwZYtW9QuRfPOnDmDDRs2YPr06Zg1axYOHz6M5557DiEhIRg3bpza5WnKzJkzceXKFdx1110wGo2wWq2YO3cuRo8erXZpmlZaWgoAiI6ObvJ4TEwMzp07p0ZJAaOurg4vv/wyRo8ejfDwcLXL0ZxVq1ahTZs2mDp1qlfXYdjRqMWLF+P48eN477331C5Fc0pKSrBkyRL87W9/Q2hoqNrlaJ4gCEhNTcW8efMAAL169cLJkyexYcMGhp1mduzYgW3btmHp0qXo0aMHjh49ipycHHTs2BGZmZlql6d5BoOhydeCIKhUSWBoaGjA3LlzIQgCFi5cqHY5mlNQUIC1a9ciLy+vxWdLLoYdDXr22Wexa9cuvPvuu4iLi1O7HM0pLCxEeXk5srKy7I9ZrVbs2bMH69evh8VigdFoVLFCbYmNjUX37t2bPJaUlIRPP/1UpYq068UXX8R9992HUaNGAQCSk5Nx7tw55ObmMuy4EBsbCwAoKytrsoC0vLwcMTExapWlaQ0NDZgzZw7Onj2LNWvWcFbHgb1796K8vBzDhg2zP2a1WvHCCy9g7dq12LVrl+RrMexoiCAIePbZZ/H5559j3bp16NKli9oladLPf/5zbN++vcljTz75JJKSkjBz5kwGnWbuuOMO+xoU0XfffYeEhASVKtKu2traFr9BGo1GzlC4kZiYiNjYWHz99dfo1asXAKC+vh579uzB/PnzVa5Oe8Sg8/3332Pt2rW46aab1C5JkzIyMlqsyZwxYwYyMjKa/LIrBcOOhixatAgff/wx3nzzTbRr185+HzwiIgImk0nl6rQjPDy8xTqmsLAwdOjQgeubHJg2bRomT56MlStX4q677sLhw4exadMmLF68WO3SNGfYsGFYuXIlOnfubL+NtXr1aowfP17t0lRXVVWFH374wf712bNncfToUURGRqJz586YOnUqcnNz0bVrV9xyyy3Izc2FyWRqleudXI1Vx44d8cgjj+DIkSPIzc2F1Wq1/1sfGRmJkJAQtcpWhbvPVfMgGBwcjJiYGCQlJcl6H4PAX1k0w1mvmJycHNkptrXJzs7GbbfdhgULFqhdiib94x//wLJly/Ddd98hMTER06dPx6RJk9QuS3MqKyvx6quvYufOnSgvL0fHjh0xatQoPPjgg63uh1Bz//3vfx0uEs3MzMTzzz8PQRCwYsUKbNy4ERUVFejTpw+eeeaZVvkLiKuxeuihhzBixAiHr1u7di0GDBjg6/I0xd3nqrnhw4dj6tSp+N///V9Z78OwQ0RERLrGPjtERESkaww7REREpGsMO0RERKRrDDtERESkaww7REREpGsMO0RERKRrDDtERESkaww7REREpGsMO0RERKRrPBuLiFTn7KgUkbPW8UREUvC4CCJSnXgQIgDs2LEDr732Gv7+97/bHzOZTIiIiLB/3dDQgODgYL/WSESBi7exiEh1sbGx9v9FRETAYDDYv66rq0P//v2xY8cOZGdnw2w2Y9u2bXj99deRkZHR5DrvvPMOhg8f3uSxDz74AHfddRfMZjN+85vfYP369f78oxGRBjDsEFFAePnll5GdnY0dO3YgPT1d0ms2bdqEV155BXPnzsWOHTswb948vPbaa9i6dauPqyUiLeGaHSIKCNOmTcOdd94p6zVvvvkmnnjiCfvrunTpgpMnT2Ljxo3IzMz0RZlEpEEMO0QUEFJTU2U9/9KlSygpKcGCBQvwf//3f/bHr1692mT9DxHpH8MOEQWEsLCwJl8bDAY0319x9epV+3/bbDYAwLPPPos+ffo0eV5QEO/gE7UmDDtEFJCioqJQVlYGQRBgMBgAAEePHrV/PyYmBp06dcKZM2cwduxYtcokIg3grzdEFJAGDBiAS5cuYdWqVfjhhx+wfv165OfnN3nOww8/jLfffhtr1qzB6dOn8e233+KDDz7A6tWrVaqaiNTAsENEAal79+7485//jPfeew8ZGRk4fPgwfv/73zd5zsSJE/Hcc89h69atGDNmDLKzs7F161YkJiaqVDURqYFNBYmIiEjXOLNDREREusawQ0RERLrGsENERES6xrBDREREusawQ0RERLrGsENERES6xrBDREREusawQ0RERLrGsENERES6xrBDREREusawQ0RERLr2/wOO7aUdJZh9xgAAAABJRU5ErkJggg==", + "image/png": 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", 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" ] @@ -2025,7 +2048,7 @@ ], "source": [ "plt.clf()\n", - "plt.scatter(y_test_original, y_pred_original)\n", + "plt.scatter(y_test_orig, y_pred_orig)\n", "#plt.plot([-4,6],[-4,6], color='black', ls='--')\n", "plt.xlabel('True')\n", "plt.ylabel('Predicted');\n", From a6ff72cfcd76537d8c68080a89a03c93063dc720 Mon Sep 17 00:00:00 2001 From: beckynevin Date: Fri, 9 May 2025 13:52:09 +0000 Subject: [PATCH 10/13] adding captions --- DP0.2/20_Introduction_to_Data_Science.ipynb | 138 +++++++++++++++----- 1 file changed, 104 insertions(+), 34 deletions(-) diff --git a/DP0.2/20_Introduction_to_Data_Science.ipynb b/DP0.2/20_Introduction_to_Data_Science.ipynb index bfadce8f..1cd68884 100644 --- a/DP0.2/20_Introduction_to_Data_Science.ipynb +++ b/DP0.2/20_Introduction_to_Data_Science.ipynb @@ -83,7 +83,7 @@ "id": "ca5378d1-00fe-44ad-be88-5f1a0a85b404", "metadata": {}, "source": [ - "## 1. Introduction\n", + "# 1. Introduction\n", "\n", "This notebook provides an intermediate-level demonstration of how to use the Table Access Protocol (TAP) server and ADQL (Astronomy Data Query Language) to query and retrieve data from the DP0.2 catalogs.\n", "\n", @@ -98,7 +98,7 @@ "The [documentation for Data Preview 0.2](https://dp0-2.lsst.io/) includes definitions\n", "of the data products, descriptions of catalog contents, and ADQL recipes.\n", "\n", - "### 1.1. Package imports\n", + "## 1.1. Package imports\n", "\n", "Import general python packages, the Rubin TAP service utilities, and various scikit-learn utilities." ] @@ -169,7 +169,7 @@ "id": "ca1e28f4-805e-4480-a7c6-0473b7e2b088", "metadata": {}, "source": [ - "### 1.2. Define functions and parameters\n", + "## 1.2. Define functions and parameters\n", "\n", "Instantiate the TAP service." ] @@ -224,7 +224,7 @@ "id": "cd325fe4-6c7c-4803-ad79-f30d7edc23e3", "metadata": {}, "source": [ - "## 2. Query for Kron fluxes around extended (galaxy) objects.\n", + "# 2. Query for Kron fluxes around extended (galaxy) objects.\n", "The Kron flux is a measurement of the total flux (or brightness) of an astronomical object, typically a galaxy or extended source, obtained using an elliptical aperture that scales with the object's light profile. It’s designed to include most of the object’s light while minimizing background contamination.\n", "\n", "The aperture is defined based on the Kron radius, which is calculated from the first moment of the light distribution. The resulting aperture is adaptive - it changes in size and shape depending on the morphology of the source." @@ -343,7 +343,7 @@ "id": "80b28a39-bd12-49d6-9cce-f8ddfc31296c", "metadata": {}, "source": [ - "## 3. Explore the data using a `pandas` DataFrame object.\n", + "# 3. Explore the data using a `pandas` DataFrame object.\n", "From the `pandas` docs:\n", "> A DataFrame is a two-dimensional, size-mutable, heterogeneous tabular data structure with labeled axes (rows and columns)." ] @@ -656,7 +656,7 @@ "id": "1b37fc18-e00b-4ef2-8d18-85d823d60d9e", "metadata": {}, "source": [ - "## 4. Visualize the data using `seaborn`\n", + "# 4. Visualize the data using `seaborn`\n", "Use the boxplot tool from `seaborn` to visualize the distribution of the values in each column of the DataFrame." ] }, @@ -695,6 +695,14 @@ "plt.show()" ] }, + { + "cell_type": "markdown", + "id": "5a1199ac-f06b-46be-8a98-9b6ca175eb8f", + "metadata": {}, + "source": [ + "> A boxplot of the following columns: `coord_ra`, `coord_dec`, `g_kronFlux`, `g_kronflux_flag`, `r_kronFlux`, `r_kronFlux_flag`, `i_kronFlux`, and `i_kronFlux_flag`. The scaling only allows us to see the outlier circles for all of these columns, not the actual boxplot. This will need to be rescaled." + ] + }, { "cell_type": "markdown", "id": "ef5f20de-0fd6-4ba1-9cab-9d59cd05df99", @@ -738,6 +746,14 @@ "plt.show()" ] }, + { + "cell_type": "markdown", + "id": "22a861da-38af-47c9-a29a-782fea694615", + "metadata": {}, + "source": [ + "> A boxplot of the following columns: `g_kronFlux`, `r_kronFlux`, and `i_kronFlux`. The boxplots have greater weight to lower values (<1000), with the whiskers extending for all columns to negative values." + ] + }, { "cell_type": "markdown", "id": "75d6336b-1068-46cf-8105-b945fd18020e", @@ -795,6 +811,14 @@ "plt.show()" ] }, + { + "cell_type": "markdown", + "id": "9e2aa0ec-7ea6-4d65-94ee-b44ef7a673b6", + "metadata": {}, + "source": [ + "> A violinplot of the following columns: `g_kronFlux`, `r_kronFlux`, and `i_kronFlux`. The violinplots have greater weight to lower values (<1000), and also include boxplots inside." + ] + }, { "cell_type": "markdown", "id": "4f2c50a8-098e-4230-b122-3880c8bb1883", @@ -808,7 +832,7 @@ "id": "3ec470a9-8db0-403a-89ba-32e0dd9bef15", "metadata": {}, "source": [ - "## 5. Clean the data: Investigate the fluxes and associated flags" + "# 5. Clean the data" ] }, { @@ -1024,6 +1048,14 @@ "plt.ylabel(r'$r-$band kronFlux [nJy]');" ] }, + { + "cell_type": "markdown", + "id": "ae55eb3a-8a47-44e5-8242-38467b215cde", + "metadata": {}, + "source": [ + "> Scatter plot of $g-$gand kronFlux [nJy] (x-axis) versus $r-$band kronFlux [nJy] (y-axis). The blue points appear to be roungly linear in this space with more concentration towards lower values and spread at higher values. The few points at high value have values on order 1e6 and there is a concentration around values less than 0.5e5 (in both axes)." + ] + }, { "cell_type": "markdown", "id": "1b76242e-6a0d-4f0c-ac35-fa27d4fef24a", @@ -1065,6 +1097,14 @@ "plt.ylim([0,0.5e5]);" ] }, + { + "cell_type": "markdown", + "id": "efffb2cf-1874-42f3-a646-fbe8753774c6", + "metadata": {}, + "source": [ + "> Scatter plot of $g-$gand kronFlux [nJy] (x-axis) versus $r-$band kronFlux [nJy] (y-axis). The blue points appear to be roungly linear in this space with more concentration towards lower values and spread at higher values." + ] + }, { "cell_type": "markdown", "id": "2f503394-3816-4d31-9cf0-9de88e229f87", @@ -1224,7 +1264,7 @@ "id": "4704605a-4665-4ccc-bd7e-cefaf5e09828", "metadata": {}, "source": [ - "## 6. Prepare the training and test sets\n", + "# 6. Prepare the training and test sets\n", "The goal is to predict the $r-$band Kron flux using the $g-$band Kron flux. The first step is to define the training and validation data." ] }, @@ -1454,7 +1494,7 @@ "id": "6011b2e6-5197-4475-8eb4-3a8841b3bd28", "metadata": {}, "source": [ - "## 7. Model (using `scikit-learn`)" + "# 7. Model (using `scikit-learn`)" ] }, { @@ -1465,6 +1505,14 @@ "## 7.1 Start with a linear regression" ] }, + { + "cell_type": "markdown", + "id": "b99ff86e-f364-4edb-8c4f-bdd645a525ae", + "metadata": {}, + "source": [ + "Instantiate the model and then fit it with the training data." + ] + }, { "cell_type": "code", "execution_count": 29, @@ -1913,6 +1961,14 @@ "model.fit(X_train_scaled, y_train_scaled)" ] }, + { + "cell_type": "markdown", + "id": "b2fe11b4-a9ad-45f9-9c1e-e25bb3a0301c", + "metadata": {}, + "source": [ + "Now predict the test data. Compare the prediction to the true value." + ] + }, { "cell_type": "code", "execution_count": 30, @@ -1979,9 +2035,15 @@ "plt.scatter(y_test_scaled, y_pred)\n", "plt.plot([-4,6],[-4,6], color='black', ls='--')\n", "plt.xlabel('True')\n", - "plt.ylabel('Predicted');\n", - "#plt.xlim([0,2e4])\n", - "#plt.ylim([0,2e4]);" + "plt.ylabel('Predicted');" + ] + }, + { + "cell_type": "markdown", + "id": "f33c5476-7662-4e5d-95e5-a2103e6ab2d8", + "metadata": {}, + "source": [ + "> Scatter plot of true versus predicted value for the scaled $r-$band Kron flux. A 1:1 line shows the expected value if the linear regression were a perfect predictor." ] }, { @@ -1989,20 +2051,20 @@ "id": "8367d3db-3527-4cab-abdb-51391259ec02", "metadata": {}, "source": [ - "Transform the predictions back to the true values (remember the current scaling is logarithmic and standardized) and look at the results in that space." + "Transform the predictions back to the true values (remember the current scaling is standardized) and look at the results in that space." ] }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 65, "id": "6770c381-1f34-44a7-b93b-b7f915013620", "metadata": { "execution": { - "iopub.execute_input": "2025-05-08T15:55:48.240962Z", - "iopub.status.busy": "2025-05-08T15:55:48.240500Z", - "iopub.status.idle": "2025-05-08T15:55:48.251408Z", - "shell.execute_reply": "2025-05-08T15:55:48.250499Z", - "shell.execute_reply.started": "2025-05-08T15:55:48.240923Z" + "iopub.execute_input": "2025-05-09T13:49:01.712342Z", + "iopub.status.busy": "2025-05-09T13:49:01.711782Z", + "iopub.status.idle": "2025-05-09T13:49:01.719668Z", + "shell.execute_reply": "2025-05-09T13:49:01.718634Z", + "shell.execute_reply.started": "2025-05-09T13:49:01.712298Z" } }, "outputs": [ @@ -2010,10 +2072,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/lib/python3.12/site-packages/sklearn/utils/validation.py:2739: UserWarning: X does not have valid feature names, but FunctionTransformer was fitted with feature names\n", - " warnings.warn(\n", - "/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/lib/python3.12/site-packages/sklearn/preprocessing/_function_transformer.py:387: RuntimeWarning: invalid value encountered in log\n", - " return func(X, **(kw_args if kw_args else {}))\n" + "/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/lib/python3.12/site-packages/sklearn/utils/validation.py:2739: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n", + " warnings.warn(\n" ] } ], @@ -2023,21 +2083,21 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 66, "id": "f83cf984-ffa7-4486-98ec-bce890b91bad", "metadata": { "execution": { - "iopub.execute_input": "2025-05-08T15:55:49.528942Z", - "iopub.status.busy": "2025-05-08T15:55:49.528053Z", - "iopub.status.idle": "2025-05-08T15:55:49.799725Z", - "shell.execute_reply": "2025-05-08T15:55:49.798661Z", - "shell.execute_reply.started": "2025-05-08T15:55:49.528895Z" + "iopub.execute_input": "2025-05-09T13:49:08.376163Z", + "iopub.status.busy": "2025-05-09T13:49:08.375716Z", + "iopub.status.idle": "2025-05-09T13:49:08.661021Z", + "shell.execute_reply": "2025-05-09T13:49:08.660087Z", + "shell.execute_reply.started": "2025-05-09T13:49:08.376107Z" } }, "outputs": [ { "data": { - "image/png": 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+ "image/png": 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hIVi3bh0SEhIAAK+99homTZqk88DG1KlTsWrVKjz99NOQSCSYP38+0tLS4ObmhpEjRyItLQ0ymQzTp08HAFy+fBm5ubkIDQ3FsGHDcPHiRSQlJcHLywuBgYEA7j0c4uDggDVr1mDJkiUYPHgwDh48iCtXrmDixInisauqqvDjjz+ipqYGd+7cwfnz58WxS6VSHDp0CIMGDYKXlxckEgk+/fRT7Nmzp8vwSURERGQIZhH4AGDLli1ITEzEggULAACTJ09GfHy8Tk1lZSU0Go34eeHChWhqasKGDRvQ0NAAPz8/7Nq1S7w0a2dnh/z8fOzZswe3bt2Cq6srQkNDsXTpUvEUrFwuR3p6OrZt24bIyEi0tLRg9OjR2L59uzhNC3BvWpYvvvhC/Dxr1iwAwCeffAKVSgUA2LFjB65evQobGxu4ublh06ZNfbp/j4iIiKg3zCbwOTk5YcuWLV3WPDj/nUQiwbJly7Bs2TK99a6urti7d2+3x/bx8cHOnTu7rNmzZ0+X62fPno3Zs2d3eywiIiIiQzObV6sRERERUd8w8BERERFZOAY+IiIiIgvHwEdERERk4czmoQ0iQ2ptE3Ciog7XNE1wtR+MkFEK2Np0/dYWIiIic8XAR1bnw+JreDnnG1Q33BGXqRxl2DbLG8/6uBpxZERERAODl3TJqnxYfA2/z/xSJ+wBwJWGO/h95pf4sPiakUZGREQ0cBj4yGq0tgl4OecbCHrWtS9b8Y9v0Nqmr4KIiMh8MfCR1ThRUdfhzN79BADf/fcOTlbWP7xBERERPQQMfGQ1rmmaDFpHRERkLhj4yGq42g82aB0REZG5YOAjqxEySgGVowydTb4iATDCSYbgX8of5rCIiIgGHAMfWQ1bGwm2zfIGgA6hr/3z1pnenI+PiIgsDgMfWZVnfVyRFfkYhjvKdJarnGTIinyM8/AREZFF4sTLNGBM9W0Wz/q4Yqb6ZyY5NiIiooHAwEcDwtTfZmFrI8HER5XGHgYREdFDwUu6ZHB8mwUREZFpYeAjg+LbLIiIiEwPAx8ZVE/fZnGiou7hDYqIiMjKMfCRQfFtFkRERKaHgY8M6pGh0h7V8W0WREREDw8DHxnMh8XXEPl+YZc17W+zCBmleDiDIiIiIk7LQobR/mRuV49i9OZtFqY6hx8REZE5YuCjfuvqydz7De/hPHymPocfERGRueElXeq37p7MbZcx179HYY9z+BERERkWz/BRv/X0idsfbjV3WHb/pdtHhkrx0qHiTufwk+DeHH4z1T/j5V0iIqJeYOCjfuvpE7cP1um7dNuV++fw42vRiIiIeo6Bj/otZJQCKkcZrjTc0Xt2TgJA9cCTuT15yKMznMOPiIiod3gPH/WbrY0E22Z5A9A+idtO35O5PX3IozOcw4+IiKh3GPjIIJ71ccXfI4Kg+KmdznKVkwxZkY/pPKzR04c8HsQ5/IiIiPqGgY8M4sPia1iVew61t1rEZc4/lSJ5hrrDk7l9uSTbmzn8iIiISBcDH/VbZ1Op1Nxqxtw9X3WYSqUvl2T1nSkkIiKinjGbwNfQ0IC4uDgEBQUhKCgIcXFxaGxs7HIbQRCQkpKC4OBg+Pr6IiIiAmVlZTo1ERER8PT01PlZsWKFTk1lZSViY2Mxbtw4BAYGIjw8HPn5+eL6kpISrFy5EqGhofD19cVvf/tbZGZmdhhPaWkpnn/+efj6+iIkJASpqakQhL7eyWYaursfT8C9qVRa27QV7Q95dHaeToJ7Ey3nLXoc++YF4ljME6j481MMe0RERH1kNk/prlq1CtevX0d6ejoAID4+Hq+88greeeedTrd59913kZGRgaSkJLi5uWHHjh2IiorC0aNHMXToULEuLCwMy5cvFz/LZDKd/URHR8PNzQ2ZmZmQyWTIzMxETEwM8vLy4OzsjG+++QZyuRxvvvkmXF1d8fXXXyM+Ph62trZ4/vnnAQA3b97EggULMG7cOGRnZ+PSpUtYs2YNhgwZggULFhiyVQOis1ed9eR+vAenUml/yOP3mV9CAuiExfYQuG2WN37j4Twg34WIiMjamEXgu3jxIk6cOIGDBw/Cz88PALBx40bMnTsXFRUVGDVqVIdtBEHA7t27ERMTgylTpgAANm/ejPHjx+PIkSMIDw8Xa2UyGZyd9YeL+vp6VFVV4fXXX8eYMWMA3Auf+/fvR3l5OZydnfHcc8/pbDNixAgUFRXh448/FgNfbm4umpqakJSUBKlUCg8PD1y6dAkZGRmIioqCRGK696V19aqzzy/W9mgfVxrudAiNByKCsDL3nO5+nWTYOpOvUCMiIjIkswh8hYWFsLe3F8MeAPj7+8Pe3h6FhYV6A191dTVqamoQHBwsLpNKpRg7diwKCwt1At/hw4eRm5sLpVKJCRMmYMmSJeIZwGHDhsHd3R05OTnw8vKCVCrFgQMHoFQqoVarOx2zRqOBk5OT+LmoqAhjx46FVCoVlwUHByM5ORnV1dUYMWJEr3rS2traq/qe7u/B/R765nuE7fm6Q311wx08l/llj/f/7ws/YO2/zncIjVum/wrKn0rFEBj8SzlsbSQG/3690VkvrBF7ocVeaLEXWuyFFnuhpa8Xxu6LWQS+2tpaKBQdp+JQKBSordV/hqmmpkasuZ9SqcTVq1fFzzNmzIBKpYJSqURZWRmSk5NRUlKCjIwMAIBEIkFGRgZiY2MRGBgIGxsbKBQKpKenw8HBQe+xCwsLcfToUaSlpel8h+HDh3cYf/u63ga+4uLiXtX3Zb+tbQIWHvreIPvN/OpKh2XVDXcQvq8Qm4OHYfKInwAaoPjsdwY5niEMVI/NEXuhxV5osRda7IUWe6FlSr0wauBLSUlBampqlzXZ2dmdrhMEodtLoQ+uf/AhibCwMPF3Dw8PjBw5EnPmzMG5c+egVqshCALWr18PhUKBffv2QSaTISsrC9HR0cjOzoaLi4vO/srKyrB48WIsXrwYTz75ZJdj6W55V3x8fGBra9vr7TrT2tqK4uJinf0eK69FQ/O1brbsHwmAlOLbeOl3j5vMdCv6emGt2Ast9kKLvdBiL7TYCy19vWhfZixGDXzz5s3DtGnTuqxRqVQoLS1FXV1dh3X19fV6z/wBEO/Jq62t1QlldXV1UCo7fw+rWq2GnZ0dqqqqoFarkZ+fj+PHj6OgoEC8zKtWq3H69Gnk5ORg0aJF4rbl5eWIjIxEWFgYFi9erLNfpVIpnnW8fyxAx7OQPWFrazsgf6Hu3+/nFTcMvv8HCbh3pu901X9N7v24A9Vjc8ReaLEXWuyFFnuhxV5omVIvjBr45HI55HJ5t3UBAQHQaDQ4e/YsfH19AQBnzpyBRqNBQECA3m1UKhWcnZ1x6tQpeHl5AQCam5tRUFCA1atXd3qssrIytLS0iIHx9u3bADqehZNIJGhra9PZLjIyErNmzeowrQtw757DrVu3orm5WbyP7+TJk3BxcYFKpeq2B0bxEE+48f24REREA8cs5uFzd3dHSEgI1q1bh6KiIhQVFWHdunWYNGmSzgMbU6dORV5eHoB7gWz+/PlIS0tDXl4eLly4gLVr10Imk2H69OkAgMuXLyM1NRXFxcWorq7GZ599hpdeegleXl4IDAwEcC+oOTg4YM2aNSgpKUFlZSU2b96MK1euYOLEiQDuhb358+dj/PjxiIqKQk1NDWpqalBfXy+ObcaMGZBKpVi7di0uXLiAvLw8pKWlmfQTuhP7+QozWwkQGTS8+0Lw/bhEREQDySwe2gCALVu2IDExUZyzbvLkyYiPj9epqayshEajET8vXLgQTU1N2LBhAxoaGuDn54ddu3aJl2bt7OyQn5+PPXv24NatW3B1dUVoaCiWLl0qnoKVy+VIT0/Htm3bEBkZiZaWFowePRrbt28Xp2k5evQo6uvrcfjwYRw+fFg8/vDhw3Hs2DEAgL29PXbt2oWEhATMmTMHjo6OiIqKQlRU1MA1rZ8mPqqEYogd6n5s6b5YDzsbid6HNR7E9+MSERENLLMJfE5OTtiyZUuXNaWlpTqfJRIJli1bhmXLlumtd3V1xd69e7s9to+PD3bu3Nnp+q6OcT9PT0/s27ev2zpTYWsjQdrv/bqcfuUZ9SPIPXdd77o7rT17i8iWGWqTeWCDiIjIEpnFJV0ynmd9XJEd+RiGO+heclU53ps4+evqhn4fw/mn0u6LiIiIqM/M5gwfGc+zPq6Yqf5Zh1er9eS1aj3BBzaIiIgGFgMf9YitjaTDtCmGCmp8YIOIiGhgMfBRtx58B27IKAVsbST9DmoS3Ht3Lh/YICIiGlgMfNSlD4uv4eWcbzq8A3fbLG+0tgmwlQA9fDZDr60zvfnABhER0QBj4CO9WtsEbPr3Baz/+EKHdVca7nT55G5PjHCSYetMbzzr49qv/RAREVH3GPiogw+Lr+GlQ8W40qj/Hr2+ntBz/qkU8wKH4xn1z8TLwkRERDTwGPhIx6FvvkfYnq8Ntj+HwbZY8OtfMOQREREZEQMfiVrbBMR+UGzQfTrI7PAmJ1YmIiIyKk68TKKMc5o+v0atM9UNd3Cios6g+yQiIqLeYeAjAPfO7r1/4daA7JsTKxMRERkXAx8BAE5W1qOxuR/zq3SBEysTEREZFwMfARi4s3AjOLEyERGR0THwEQCgvHZgLudyYmUiIiLjY+AjtLYJSP/fywbdp2KIHbIjH+PEykRERCaA07IQTlTUdTrJcm/Jh9hhefAv8epTHjyzR0REZCIY+AhX7ntPbl84ymyROtsXwx1lnFyZiIjIBDHwEWpu9u/sXsOdVgx3lGHio0oDjYiIiIgMiffwES7duN3vfXCuPSIiItPFwGflWtsEZHxR1e/9cK49IiIi08XAZ+WOl9dC09zWr31wrj0iIiLTxsBn5Y4b4D23nGuPiIjItDHwWbt+vk0tcqyKc+0RERGZOAY+Kzehn5dip3q4GGgkRERENFAY+Kxcf6/EvvLPb9Ha1s/ThERERDSgGPis3A+3mvu1/Xf/vYMTBrgPkIiIiAYOA5+VK/3hZr/3wTn4iIiITBsDnxVrbROQerKi3/vhHHxERESmjYHPip2oqEP97bv92oejbBDn4CMiIjJxDHxW7ErDnX7vI22OL+fgIyIiMnEMfFas5mb/7r17Rv0IwgKGG2g0RERENFAY+KyY89C+3XtnIwFWho5CTtSvDTwiIiIiGghmE/gaGhoQFxeHoKAgBAUFIS4uDo2NjV1uIwgCUlJSEBwcDF9fX0RERKCsrEynJiIiAp6enjo/K1as0KmprKxEbGwsxo0bh8DAQISHhyM/P19cX1JSgpUrVyI0NBS+vr747W9/i8zMTJ19VFdXdziOp6cnPv/88352pu9+1seHLQ5H/RpbZqgNPBoiIiIaKIOMPYCeWrVqFa5fv4709HQAQHx8PF555RW88847nW7z7rvvIiMjA0lJSXBzc8OOHTsQFRWFo0ePYujQoWJdWFgYli9fLn6WyWQ6+4mOjoabmxsyMzMhk8mQmZmJmJgY5OXlwdnZGd988w3kcjnefPNNuLq64uuvv0Z8fDxsbW3x/PPP6+zrvffew6OPPip+dnR07Fdf+kPSx1vvTlbV47dejxh2MERERDRgzCLwXbx4ESdOnMDBgwfh5+cHANi4cSPmzp2LiooKjBo1qsM2giBg9+7diImJwZQpUwAAmzdvxvjx43HkyBGEh4eLtTKZDM7OznqPXV9fj6qqKrz++usYM2YMgHvhc//+/SgvL4ezszOee+45nW1GjBiBoqIifPzxxx0Cn5OTU6fHetiuNfbtHr7vbtw28EiIiIhoIJlF4CssLIS9vb0Y9gDA398f9vb2KCws1Bv4qqurUVNTg+DgYHGZVCrF2LFjUVhYqBP4Dh8+jNzcXCiVSkyYMAFLliwRzwAOGzYM7u7uyMnJgZeXF6RSKQ4cOAClUgm1uvPLmhqNBk5OTh2Wx8bGoqmpCSNHjsQLL7yAqVOn9qUlaG1t7dN297vy3x/7tJ3KUWaQ45uq9u9myd+xp9gLLfZCi73QYi+02Astfb0wdl/MIvDV1tZCoeg415tCoUBtba3ebWpqasSa+ymVSly9elX8PGPGDKhUKiiVSpSVlSE5ORklJSXIyMgAAEgkEmRkZCA2NhaBgYGwsbGBQqFAeno6HBwc9B67sLAQR48eRVpamrhsyJAhWLt2LQIDAyGRSHDs2DGsWLECTU1NmDlzZu8aAqC4uLjX2zzo03N9eyXaCNxAUVFRv49v6gzRY0vBXmixF1rshRZ7ocVeaJlSL4wa+FJSUpCamtplTXZ2dqfrBEGApJsb0R5cLwiCzuewsDDxdw8PD4wcORJz5szBuXPnoFarIQgC1q9fD4VCgX379kEmkyErKwvR0dHIzs6Gi4uLzv7KysqwePFiLF68GE8++aS4XC6X44UXXhA/+/j4oLGxEenp6X0KfD4+PrC1te31dvfTnDoFoHeXdRVD7PDilHEWPfdea2sriouLDdJjc8deaLEXWuyFFnuhxV5o6etF+zJjMWrgmzdvHqZNm9ZljUqlQmlpKerqOp6Nqq+v13vmD4B4n1xtba1OKKurq4NSqez0eGq1GnZ2dqiqqoJarUZ+fj6OHz+OgoIC8TKvWq3G6dOnkZOTg0WLFonblpeXIzIyEmFhYVi8eHGX3wsA/Pz8kJWV1W2dPra2tv3+CyWz6/3//MuCfwlpH7YzR4bosaVgL7TYCy32Qou90GIvtEypF0b9l1sul0Mul3dbFxAQAI1Gg7Nnz8LX1xcAcObMGWg0GgQEBOjdRqVSwdnZGadOnYKXlxcAoLm5GQUFBVi9enWnxyorK0NLS4sYGG/fvveAwoNnCiUSCdra2nS2i4yMxKxZszpM69KZ8+fPG/UBDhd7aa+3Ge08tPsiIiIiMilmcarG3d0dISEhWLduHRISEgAAr732GiZNmqTzwMbUqVOxatUqPP3005BIJJg/fz7S0tLg5uaGkSNHIi0tDTKZDNOnTwcAXL58Gbm5uQgNDcWwYcNw8eJFJCUlwcvLC4GBgQDuPRzi4OCANWvWYMmSJRg8eDAOHjyIK1euYOLEiQDuhb358+fjySefRFRUlHj/oK2trRhoDx06hEGDBsHLywsSiQSffvop9uzZ02X4HGgS9P6yrGsf5+4jIiIi4zGLwAcAW7ZsQWJiIhYsWAAAmDx5MuLj43VqKisrodFoxM8LFy5EU1MTNmzYgIaGBvj5+WHXrl3ipVk7Ozvk5+djz549uHXrFlxdXREaGoqlS5eKp2DlcjnS09Oxbds2REZGoqWlBaNHj8b27dvFaVqOHj2K+vp6HD58GIcPHxaPP3z4cBw7dkz8vGPHDly9ehU2NjZwc3PDpk2b+nT/nqG0Qei+6D7OP5UiZJT+S+hERERkuswm8Dk5OWHLli1d1pSWlup8lkgkWLZsGZYtW6a33tXVFXv37u322D4+Pti5c2en67s6RrvZs2dj9uzZ3R7rYbrWeKdX9eN+4WTRD2sQERFZKrN5tRoZXsl1TfdF98mvuoHWtt6dFSQiIiLjY+CzYj+2tHVfdJ/aH1twoqJvc/cRERGR8fT4km5JSUmPd9p+bxuZtkF9uDx7TdO317ERERGR8fQ48M2aNQsSiaRHkx2fP3++3wOjgffITwfhv3d696oXPqVLRERkfnoc+D755BPx9/Pnz2Pz5s148cUX4e/vDwAoKipCRkYG4uLiDD5IGhjNvXytn8NgWz6lS0REZIZ6HPiGDx8u/v7SSy9h3bp1CA0NFZeNGTMGrq6u+L//9//iqaeeMuwoaUA0NvUu8S150o1P6RIREZmhPj20ceHCBahUqg7LVSoVysvL+z0oejju3O1d4LtUf3uARkJEREQDqU+Bz93dHW+//TaamrQ38Dc3N+Ptt9+Gu7u7wQZHA6e1TcCtlt5NsVL8fe+mcSEiIiLT0KeJlzds2ICYmBiEhoaKT+SWlJRAIpEgLS3NoAOkgdGX6VUcB5vNPN1ERER0nz79C+7r64tPPvkEubm5qKiogCAImDZtGqZPn44hQ4YYeow0APoyvcpM70cGYCREREQ00Pp8yuYnP/kJ5s6da8ix0EOk+Ildr7dZHsLL9UREROaoz2/ayMnJwR/+8AcEBwfjypUrAID33nsP//73vw02OBo4Z6819qr+1yMcIR3EF7MQERGZoz79C75//34kJSVhwoQJaGxsRFvbvVd0OTg4IDMz06ADpIFx+tKNXtVv+P/49hQiIiJz1afAt3fvXiQmJiI2Nha2trbicm9vb1y4cMFgg6OBM3SwbfdF9zn3fe/OCBIREZHp6FPgq66uxq9+9asOy6VSKW7f5lxt5uAP/sO7L7pPRd2PAzQSIiIiGmh9CnwqlUrv+3I///xzPProo/0eFA28prttvapvE3o3Zx8RERGZjj49pfviiy8iISEBzc3NAICzZ8/iyJEj+Nvf/obExESDDpAGxiv//LZX9U59eKqXiIiITEOfAt+cOXPQ2tqKN998E7dv38aqVavwyCOP4M9//jN+97vfGXqMNACuNd7pVb2tDZ/QJSIiMld9nocvLCwMYWFhqK+vhyAIUCgUhhwXDTChrXeXdIN/KR+gkRAREdFA69Npm/nz56Ox8d5Tm3K5XAx7N2/exPz58w03Ohowrb3Lezhz9b8DMg4iIiIaeH0KfF988QVaWlo6LG9qasJXX33V70HRwGvu5TMY//jm+sAMhIiIiAZcry7plpSUiL+Xl5ejpqZG/NzW1oYTJ07gkUf4vlVz0Ntnbr/vw7t3iYiIyDT0KvDNmjULEokEEokEkZGRHdbLZDKsW7fOYIMj0/Eze5mxh0BERER91KvA98knn0AQBDz11FPIysqCXK69kd/Ozg4KhULnzRtkOWb78MwtERGRuepV4Bs+/N7bGe6/tEvWYXmIu7GHQERERH3Up4c20tLSkJ2d3WF5dnY2/va3v/V7UGR6pIM4Dx8REZG56tO/4gcOHMCoUaM6LB89ejT+/ve/93tQRERERGQ4fQp8NTU1cHZ27rBcLpfrPLlLRERERMbXp8Dn6uqKr7/+usPyr776Ci4uLv0eFBEREREZTp9erfbcc8/h9ddfx927d/H4448DAP7zn//gzTffxIIFCww6QCIiIiLqnz4FvoULF6KhoQEbNmwQ37gxePBg/J//838QHR1t0AESERERUf/0KfBJJBLExcVh8eLFuHjxImQyGdzc3CCVSg09PiIiIiLqp37NtfHTn/4Uvr6+8PDwGPCw19DQgLi4OAQFBSEoKAhxcXFobGzschtBEJCSkoLg4GD4+voiIiICZWVlOjURERHw9PTU+VmxYoVOTWVlJWJjYzFu3DgEBgYiPDwc+fn54vobN27gxRdfRHBwMLy9vREaGoqEhATcvHlTZz+lpaV4/vnn4evri5CQEKSmpkIQevuSMyIiIqLe6fEZvqVLlyIpKQlDhw7F0qVLu6xNTU3t98AetGrVKly/fh3p6ekAgPj4eLzyyit45513Ot3m3XffRUZGBpKSkuDm5oYdO3YgKioKR48exdChQ8W6sLAwLF++XPwsk+m+Riw6Ohpubm7IzMyETCZDZmYmYmJikJeXB2dnZ9jY2OA3v/kNXn75Zcjlcly+fBkbNmxAQ0MDkpOTAQA3b97EggULMG7cOGRnZ+PSpUtYs2YNhgwZwvseiYiIaED1OPDZ29vr/f1huHjxIk6cOIGDBw/Cz88PALBx40bMnTsXFRUVeucEFAQBu3fvRkxMDKZMmQIA2Lx5M8aPH48jR44gPDxcrJXJZHqnmQGA+vp6VFVV4fXXX8eYMWMA3Auf+/fvR3l5OZydneHo6Ig//vGP4jbDhw/HH//4R+zcuVNclpubi6amJiQlJUEqlcLDwwOXLl1CRkYGoqKiIJFI+t8oIiIiIj16HPjeeOMNvb8/DIWFhbC3txfDHgD4+/vD3t4ehYWFegNfdXU1ampqEBwcLC6TSqUYO3YsCgsLdQLf4cOHkZubC6VSiQkTJmDJkiXiGcBhw4bB3d0dOTk58PLyglQqxYEDB6BUKqFWq/WO9/r168jLy8PYsWPFZUVFRRg7dqzOpe/g4GAkJyejuroaI0aM6FVPWltbe1XfXw/7eMbU/l2t6Tt3hr3QYi+02Ast9kKLvdDS1wtj96VPD208bLW1tVAoFB2WKxQK1NbW6t2mfQLoB7dTKpW4evWq+HnGjBlQqVRQKpUoKytDcnIySkpKkJGRAeDeAyoZGRmIjY1FYGAgbGxsoFAokJ6eDgcHB519r1y5Ep988gnu3LmDSZMmYdOmTTrfof1dxPePv31dbwNfcXFxr+r7q6io6KEezxQ87B6bMvZCi73QYi+02Ast9kLLlHrR48A3a9asHl92PHToUI/qUlJSur3fT987e9sJgtDtmB5c/+BDEmFhYeLvHh4eGDlyJObMmYNz585BrVZDEASsX78eCoUC+/btg0wmQ1ZWFqKjo5Gdna0z0fTatWuxZMkSVFZWYuvWrXjjjTewfv36TsfS3fKu+Pj4wNbWttfbid6/2n3Nffz9/ft+LDPT2tqK4uLi/vfYArAXWuyFFnuhxV5osRda+nrRvsxYehz4nnrqKfH3pqYm7N+/H48++qgYBM6cOYOysjKde9m6M2/ePEybNq3LGpVKhdLSUtTV1XVYV19fr/fMHwDxnrza2lqdUFZXVwelUtnp8dRqNezs7FBVVQW1Wo38/HwcP34cBQUF4mVetVqN06dPIycnB4sWLdI5prOzM9zd3eHk5IR58+Zh8eLFcHFxgVKp7PDaufbv1Nl36Iqtre1D+wsl+X/HszYPs8emjr3QYi+02Ast9kKLvdAypV706inddq+++ioiIiLw8ssv69S89dZbuHbtWo8PLpfLIZfLu60LCAiARqPB2bNn4evrC+BewNRoNAgICNC7jUqlgrOzM06dOgUvLy8AQHNzMwoKCrB69epOj1VWVoaWlhYxMN6+fRtAx7NwEokEbW1t3Y69ubkZwL0zZFu3bkVzc7N4H9/Jkyfh4uIClUrV7X6MyY7PkxAREZm1Ps3Dd/ToUcyaNavD8meeeQYff/xxf8fUgbu7O0JCQrBu3ToUFRWhqKgI69atw6RJk3Qe2Jg6dSry8vIA3Atk8+fPR1paGvLy8nDhwgWsXbsWMpkM06dPBwBcvnwZqampKC4uRnV1NT777DO89NJL8PLyQmBgIIB7Qc3BwQFr1qxBSUkJKisrsXnzZly5cgUTJ04EAHz22Wf44IMPcOHCBXE/69evR2BgoBjmZsyYAalUirVr1+LChQvIy8tDWlqaWTyh28qpAomIiMxanx7akMlk+Oqrr+Dm5qaz/KuvvsLgwYMNMa4OtmzZgsTERHHOusmTJyM+Pl6nprKyEhqNRvy8cOFCNDU1iXPi+fn5YdeuXeKlWTs7O+Tn52PPnj24desWXF1dERoaiqVLl4qnYOVyOdLT07Ft2zZERkaipaUFo0ePxvbt28VpWgYPHoysrCy88cYbaG5uhqurK55++mmdy7329vbYtWsXEhISMGfOHDg6OiIqKgpRUVED0i9DsjHtPEpERETd6FPgi4yMxPr163Hu3DlxqpQzZ87ggw8+wJIlSww6wHZOTk7YsmVLlzWlpaU6nyUSCZYtW4Zly5bprXd1dcXevXu7PbaPj4/OnHoPevzxx/H3v/+92/14enpi37593daZmmE/MYuHuYmIiKgTffqXfNGiRVCpVNi9ezeOHDkCABg1ahTeeOONbh/CIPMTqHLovoiIiIhMVp9P3UybNo3hzmrwmi4REZE569NDGwDQ2NiIrKws/PWvf8V///tfAMC5c+dw/fp1Q42NTMSX1Y3GHgIRERH1Q5/O8JWUlCAqKgr29va4cuUKfv/738PJyQl5eXm4evUq/ud//sfQ4yQjam7tfvoZIiIiMl19OsOXlJSE2bNn4+OPP9Z5N+yECRPw5ZdfGmxwZBrsB/OhDSIiInPWp8BXXFyM8PDwDssfeeSRDm+TIPMX7j+8+yIiIiIyWX0KfIMHD8bNmzc7LK+srOzRmzPIvEwd49J9EREREZmsPgW+3/zmN9i+fTtaWlrEZVevXkVycjKmTJlisMGRaZj4aOfvHiYiIiLT16fA96c//Qn19fUYP348mpqaEBERgSlTpuCnP/0pVqxYYegxkpHZ8lUbREREZq1Pd+MPHToU77//Pv7zn//g22+/RVtbG9RqNcaPH2/o8ZEJaL7bBumgPs/gQ0REREbW68B39+5d+Pr6IicnB0888QSeeOKJgRgXmZC3Tl7E6omjjT0MIiIi6qNen7YZNGgQfv7zn6OtjXOzWYt/fMPJtImIiMxZn67TxcbGIjk5WXzDBhERERGZrj7dw7dnzx5UVVUhJCQEP//5zzFkyBCd9YcOHTLI4Mg0POP1M2MPgYiIiPqhT4HvqaeeMvQ4yIS9NGGUsYdARERE/dCrwHf79m38z//8D/7973/j7t27eOKJJ7Bu3TpOtmzh+IQuERGReevVv+RvvfUWDh06hIkTJ+J3v/sdTp8+jfXr1w/Q0MhUtLYJxh4CERER9UOvzvDl5eVh06ZN+N3vfgcAeOaZZ/CHP/wBra2tsLW1HZABkvGdqKjj2zaIiIjMWK/O8H3//fd47LHHxM++vr6wtbXFDz/8YPCBkem4pmky9hCIiIioH3oV+FpbW2FnZ6ezzNbWFnfv3jXooMi0PDJUauwhEBERUT/06pKuIAhYs2YNpFJtAGhubsb69evxk5/8RFyWmppquBGS0Qm8hY+IiMis9SrwzZ49u8OyZ555xmCDIdP0w61mYw+BiIiI+qFXge+NN94YqHGQCXO1H2zsIRAREVE/cII16tZ4N86zSEREZM4Y+Khbpy/VG3sIRERE1A8MfNQtTstCRERk3hj4qFu8h4+IiMi8MfBRt0JGKYw9BCIiIuoHBj4iIiIiC8fAR906UVFn7CEQERFRPzDwUbf40AYREZF5Y+CjbvGhDSIiIvPGwEfd4kMbRERE5s1sAl9DQwPi4uIQFBSEoKAgxMXFobGxscttBEFASkoKgoOD4evri4iICJSVlenUREREwNPTU+dnxYoVOjWVlZWIjY3FuHHjEBgYiPDwcOTn54vrb9y4gRdffBHBwcHw9vZGaGgoEhIScPPmTbGmurq6w3E8PT3x+eefG6A7A8vWRmLsIRAREVE/9Opdusa0atUqXL9+Henp6QCA+Ph4vPLKK3jnnXc63ebdd99FRkYGkpKS4Obmhh07diAqKgpHjx7F0KFDxbqwsDAsX75c/CyTyXT2Ex0dDTc3N2RmZkImkyEzMxMxMTHIy8uDs7MzbGxs8Jvf/AYvv/wy5HI5Ll++jA0bNqChoQHJyck6+3rvvffw6KOPip8dHR371RciIiKi7phF4Lt48SJOnDiBgwcPws/PDwCwceNGzJ07FxUVFRg1alSHbQRBwO7duxETE4MpU6YAADZv3ozx48fjyJEjCA8PF2tlMhmcnZ31Hru+vh5VVVV4/fXXMWbMGAD3wuf+/ftRXl4OZ2dnODo64o9//KO4zfDhw/HHP/4RO3fu7LA/JyenTo/VG62trf3ehykeyxS0f19r+976sBda7IUWe6HFXmixF1r6emHsvphF4CssLIS9vb0Y9gDA398f9vb2KCws1Bv4qqurUVNTg+DgYHGZVCrF2LFjUVhYqBP4Dh8+jNzcXCiVSkyYMAFLliwRzwAOGzYM7u7uyMnJgZeXF6RSKQ4cOAClUgm1Wq13vNevX0deXh7Gjh3bYV1sbCyampowcuRIvPDCC5g6dWqfelJcXNyn7fqiqKjooR3LlDzMHps69kKLvdBiL7TYCy32QsuUemEWga+2thYKRccHBxQKBWpra/VuU1NTI9bcT6lU4urVq+LnGTNmQKVSQalUoqysDMnJySgpKUFGRgYAQCKRICMjA7GxsQgMDISNjQ0UCgXS09Ph4OCgs++VK1fik08+wZ07dzBp0iRs2rRJXDdkyBCsXbsWgYGBkEgkOHbsGFasWIGmpibMnDmz1z3x8fGBra1tr7cTvX+1+5r/x9/fv+/HMUOtra0oLi7uf48tAHuhxV5osRda7IUWe6Glrxfty4zFqIEvJSUFqampXdZkZ2d3uk4QBEgkXT9Q8OB6QRB0PoeFhYm/e3h4YOTIkZgzZw7OnTsHtVoNQRCwfv16KBQK7Nu3DzKZDFlZWYiOjkZ2djZcXFzE7deuXYslS5agsrISW7duxRtvvIH169cDAORyOV544QWx1sfHB42NjUhPT+9T4LO1tX1of6Gs9S/uw+yxqWMvtNgLLfZCi73QYi+0TKkXRg188+bNw7Rp07qsUalUKC0tRV1dx7c91NfX6z3zB0C8T662tlYnlNXV1UGpVHZ6PLVaDTs7O1RVVUGtViM/Px/Hjx9HQUGBeJlXrVbj9OnTyMnJwaJFi3SO6ezsDHd3dzg5OWHevHlYvHixzvHv5+fnh6ysrC6/PxEREVF/GTXwyeVyyOXybusCAgKg0Whw9uxZ+Pr6AgDOnDkDjUaDgIAAvduoVCo4Ozvj1KlT8PLyAgA0NzejoKAAq1ev7vRYZWVlaGlpEQPj7du3AXQ8UyiRSNDW1tbt2Jubmztdd/78eYM8wEFERETUFbO4h8/d3R0hISFYt24dEhISAACvvfYaJk2apPPAxtSpU7Fq1So8/fTTkEgkmD9/PtLS0uDm5oaRI0ciLS0NMpkM06dPBwBcvnwZubm5CA0NxbBhw3Dx4kUkJSXBy8sLgYGBAO7dv+bg4IA1a9ZgyZIlGDx4MA4ePIgrV65g4sSJAIDPPvsMtbW18PHxwZAhQ3Dx4kW8+eabCAwMhEqlAgAcOnQIgwYNgpeXFyQSCT799FPs2bOny/BJREREZAhmEfgAYMuWLUhMTMSCBQsAAJMnT0Z8fLxOTWVlJTQajfh54cKFaGpqEufE8/Pzw65du8RLs3Z2dsjPz8eePXtw69YtuLq6IjQ0FEuXLhWvucvlcqSnp2Pbtm2IjIxES0sLRo8eje3bt4vTtAwePBhZWVl444030NzcDFdXVzz99NM6l3sBYMeOHbh69SpsbGzg5uaGTZs29en+PSIiIqLeMJvA5+TkhC1btnRZU1paqvNZIpFg2bJlWLZsmd56V1dX7N27t9tj+/j46J1Tr93jjz+Ov//9713uY/bs2Zg9e3a3xyIiIiIyNLN5tRoRERER9Q0DHxEREZGFY+AjIiIisnAMfFaq6+mqe19HREREpouBz0rZ9jDJ9bSOiIiITBcDn5W6K3Rf05s6IiIiMl0MfEREREQWjoGPiIiIyMIx8Fmpnv4Pzz8gRERE5o//nlspPrRBRERkPRj4rFSbgeuIiIjIdDHwWamf2vXsf/qe1hEREZHp4r/mVspDKTNoHREREZkuBj4rZWs7yKB1REREZLoY+KyU5k6rQeuIiIjIdDHwWamm1p49jtHTOiIiIjJdDHxWysamZ/Ot9LSOiIiITBcDn5X6hdNPDFpHREREpouBz0qtCnU3aB0RERGZLgY+K/W0pwvsurlca2cjwdOeLg9pRERERDRQGPismG03ga+79URERGQeGPis1CcXanDnbtdP4N6524ZPLtQ8pBERERHRQGHgs1K7v/rOoHVERERkuhj4rFRl3Y8GrSMiIiLTxcBnpZq6uZzb2zoiIiIyXQx8VmrwoJ79T9/TOiIiIjJd/NfcSg2R2hq0joiIiEwXA5+VGjvCyaB1REREZLoY+KzUb0Y7G7SOiIiITBcDn5Wa+KgSQ7u5XDtUaouJjyof0oiIiIhooDDwWbHuHsjgAxtERESWgf+iW6kTFXWo+7Gly5q6H1twoqLuIY2IiIiIBorZBL6GhgbExcUhKCgIQUFBiIuLQ2NjY5fbCIKAlJQUBAcHw9fXFxERESgrK9OpiYiIgKenp87PihUrdGoqKysRGxuLcePGITAwEOHh4cjPz9d7zBs3bmDChAnw9PTsML7S0lI8//zz8PX1RUhICFJTUyEIQh+60X/XNE0GrSMiIiLTZTaBb9WqVSgpKUF6ejrS09NRUlKCV155pctt3n33XWRkZCA+Ph7Z2dlQKpWIiorCzZs3derCwsJw8uRJ8SchIUFnfXR0NFpbW5GZmYkPP/wQv/rVrxATE4Oamo7vmX311Vfh6enZYfnNmzexYMECuLi4IDs7G6+99hp27dqFjIyMPnSj/1ztBxu0joiIiEzXIGMPoCcuXryIEydO4ODBg/Dz8wMAbNy4EXPnzkVFRQVGjRrVYRtBELB7927ExMRgypQpAIDNmzdj/PjxOHLkCMLDw8VamUwGZ2f9T6PW19ejqqoKr7/+OsaMGQPgXvjcv38/ysvLdbbbv38/NBoNFi9ejM8//1xnP7m5uWhqakJSUhKkUik8PDxw6dIlZGRkICoqChKJpFc9aW1t7VX9g8aPdIJiiF2Xl3UVQ+wwfqRTv49lbtq/r7V9b33YCy32Qou90GIvtNgLLX29MHZfzCLwFRYWwt7eXgx7AODv7w97e3sUFhbqDXzV1dWoqalBcHCwuEwqlWLs2LEoLCzUCXyHDx9Gbm4ulEolJkyYgCVLlmDo0KEAgGHDhsHd3R05OTnw8vKCVCrFgQMHoFQqoVarxX2Ul5fj7bffxsGDB/Hdd991GE9RURHGjh0LqVQqLgsODkZycjKqq6sxYsSIXvWkuLi4V/UPam0TcPfu3S5r7t69i6KiItja9C6MWor+9tiSsBda7IUWe6HFXmixF1qm1AuzCHy1tbVQKBQdlisUCtTW1urdpv1y64PbKZVKXL16Vfw8Y8YMqFQqKJVKlJWVITk5GSUlJeKlVolEgoyMDMTGxiIwMBA2NjZQKBRIT0+Hg4MDAKC5uRkrV65EXFwcfv7zn+sNfLW1tRg+fHiH8bev623g8/Hxga1t39+C8dnFOjQ0X+uypqFZwE3HXyDUvWPvLVlrayuKi4v73WNLwF5osRda7IUWe6HFXmjp60X7MmMxauBLSUlBampqlzXZ2dmdrhMEodtLoQ+uf/AhibCwMPF3Dw8PjBw5EnPmzMG5c+egVqshCALWr18PhUKBffv2QSaTISsrC9HR0cjOzoaLiwuSk5Ph7u6OmTNn9mos3S3viq2tbb/+Ql3TNPe4zlr/4va3x5aEvdBiL7TYCy32Qou90DKlXhg18M2bNw/Tpk3rskalUqG0tBR1dR2nB6mvr9d75g+AeG9dbW0tXFxcxOV1dXVQKjufTFitVsPOzg5VVVVQq9XIz8/H8ePHUVBQIF7mVavVOH36NHJycrBo0SLk5+fjwoUL+OijjwBoQ+Xjjz+OmJgYLF++HEqlssNDHu3fqbPvMJBqbvbs6due1hEREZHpMmrgk8vlkMvl3dYFBARAo9Hg7Nmz8PX1BQCcOXMGGo0GAQEBerdRqVRwdnbGqVOn4OXlBeDepdeCggKsXr2602OVlZWhpaVFDIy3b98G0PEsnEQiQVtbG4B7Zyrv3LkjrisuLsaf//xn7Nu3D7/4xS8A3LvncOvWrWhubhbv4zt58iRcXFygUqm67YGhKYZIuy/qRR0RERGZLrOYlsXd3R0hISFYt24dioqKUFRUhHXr1mHSpEk6D2xMnToVeXl5AO4Fsvnz5yMtLQ15eXm4cOEC1q5dC5lMhunTpwMALl++jNTUVBQXF6O6uhqfffYZXnrpJXh5eSEwMBDAvaDm4OCANWvWoKSkBJWVldi8eTOuXLmCiRMnAgB+8YtfwMPDQ/xpD3Du7u7i2bsZM2ZAKpVi7dq1uHDhAvLy8pCWltanJ3QNoe7Hnl3S7WkdERERmS6zeGgDALZs2YLExEQsWLAAADB58mTEx8fr1FRWVkKj0YifFy5ciKamJmzYsAENDQ3w8/PDrl27xEuzdnZ2yM/Px549e3Dr1i24uroiNDQUS5cuFa+5y+VypKenY9u2bYiMjERLSwtGjx6N7du3i9O09IS9vT127dqFhIQEzJkzB46OjoiKikJUVFR/W9MnPMNHRERkPcwm8Dk5OWHLli1d1pSWlup8lkgkWLZsGZYtW6a33tXVFXv37u322D4+Pti5c2ePxzpu3LgOYwEAT09P7Nu3r8f7GUg8w0dERGQ9zOKSLhme89CevUGjp3VERERkuhj4rNRwR5lB64iIiMh0MfBZqXG/GGbQOiIiIjJdDHxWKu0/lwxaR0RERKaLgc9KXaz/0aB1REREZLoY+KyUu3yIQeuIiIjIdDHwWanFT/4Stt3M92wruVdHRERE5o2Bz0pJB9ngd16PdFnzO69HIB3EPyJERETmjv+aW6nWNgFfVzd0WVN4pQGtbcJDGhERERENFAY+K3Wiog7VDXe6rPnuv3dwoqLuIY2IiIiIBgoDn5W6pmkyaB0RERGZLgY+K+Vq37NXpvW0joiIiEwXA5+VGu8m79FTuuPd5A9nQERERDRgGPis1OlL9Wjt5nmMVuFeHREREZk3Bj4rxXv4iIiIrAcDn5XiPXxERETWg4HPSoWMUkDlKENnt/FJAIxwkiFklOJhDouIiIgGAAOflbK1kWDbLG8A6BD62j9vnekNW5tunuwgIiIik8fAZ8We9XFFVuRjGO4o01mucpIhK/IxPOvjaqSRERERkSENMvYAyLie9XHFTPXP8Fl5DfKLS/G4jydCH3XmmT0iIiILwsBHsLWRINRdAUfNEPi7Kxj2iIiILAwv6RIRERFZOAY+IiIiIgvHwEdERERk4Rj4iIiIiCwcAx8RERGRhWPgIyIiIrJwDHxEREREFo6Bj4iIiMjCMfARERERWTgGPiIiIiILx8BHREREZOHMJvA1NDQgLi4OQUFBCAoKQlxcHBobG7vcRhAEpKSkIDg4GL6+voiIiEBZWZlOTUREBDw9PXV+VqxYoVNTWVmJ2NhYjBs3DoGBgQgPD0d+fr7eY964cQMTJkyAp6enzviqq6s7HMfT0xOff/55HztCRERE1DODjD2Anlq1ahWuX7+O9PR0AEB8fDxeeeUVvPPOO51u8+677yIjIwNJSUlwc3PDjh07EBUVhaNHj2Lo0KFiXVhYGJYvXy5+lslkOvuJjo6Gm5sbMjMzIZPJkJmZiZiYGOTl5cHZ2Vmn9tVXX4WnpyeuX7+ud0zvvfceHn30UfGzo6Njz5tARERE1AdmEfguXryIEydO4ODBg/Dz8wMAbNy4EXPnzkVFRQVGjRrVYRtBELB7927ExMRgypQpAIDNmzdj/PjxOHLkCMLDw8VamUzWIbi1q6+vR1VVFV5//XWMGTMGwL3wuX//fpSXl+tst3//fmg0GixevLjTM3dOTk6dHqs3Wltb+70Pffsz9H7NEXuhxV5osRda7IUWe6HFXmjp64Wx+2IWga+wsBD29vZi2AMAf39/2Nvbo7CwUG/gq66uRk1NDYKDg8VlUqkUY8eORWFhoU7gO3z4MHJzc6FUKjFhwgQsWbJEPAM4bNgwuLu7IycnB15eXpBKpThw4ACUSiXUarW4j/Lycrz99ts4ePAgvvvuu06/S2xsLJqamjBy5Ei88MILmDp1ap96Ulxc3KftjLVfc8ReaLEXWuyFFnuhxV5osRdaptQLswh8tbW1UCgUHZYrFArU1tbq3aampkasuZ9SqcTVq1fFzzNmzIBKpYJSqURZWRmSk5NRUlKCjIwMAIBEIkFGRgZiY2MRGBgIGxsbKBQKpKenw8HBAQDQ3NyMlStXIi4uDj//+c/1Br4hQ4Zg7dq1CAwMhEQiwbFjx7BixQo0NTVh5syZve6Jj48PbG1te71dZ1pbW1FcXGzw/Zoj9kKLvdBiL7TYCy32Qou90NLXi/ZlxmLUwJeSkoLU1NQua7KzsztdJwgCJBJJl9s/uF4QBJ3PYWFh4u8eHh4YOXIk5syZg3PnzkGtVkMQBKxfvx4KhQL79u2DTCZDVlYWoqOjkZ2dDRcXFyQnJ8Pd3b3L4CaXy/HCCy+In318fNDY2Ij09PQ+BT5bW9sB+Qs1UPs1R+yFFnuhxV5osRda7IUWe6FlSr0wauCbN28epk2b1mWNSqVCaWkp6urqOqyrr6/Xe+YPgHifXG1tLVxcXMTldXV1UCqVnR5PrVbDzs4OVVVVUKvVyM/Px/Hjx1FQUCBe5lWr1Th9+jRycnKwaNEi5Ofn48KFC/joo48AaEPl448/jpiYGJ0HQu7n5+eHrKysLr8/ERERUX8ZNfDJ5XLI5fJu6wICAqDRaHD27Fn4+voCAM6cOQONRoOAgAC926hUKjg7O+PUqVPw8vICcO/Sa0FBAVavXt3pscrKytDS0iIGxtu3bwPoeKZQIpGgra0NwL0zlXfu3BHXFRcX489//jP27duHX/ziF50e6/z58wZ5gIOIiIioK2ZxD5+7uztCQkKwbt06JCQkAABee+01TJo0SeeBjalTp2LVqlV4+umnIZFIMH/+fKSlpcHNzQ0jR45EWloaZDIZpk+fDgC4fPkycnNzERoaimHDhuHixYtISkqCl5cXAgMDAdx7OMTBwQFr1qzBkiVLMHjwYBw8eBBXrlzBxIkTAaBDqLtx44Y47vb7/A4dOoRBgwbBy8sLEokEn376Kfbs2dNl+CQiIiIyBLMIfACwZcsWJCYmYsGCBQCAyZMnIz4+XqemsrISGo1G/Lxw4UI0NTVhw4YNaGhogJ+fH3bt2iVemrWzs0N+fj727NmDW7duwdXVFaGhoVi6dKl4zV0ulyM9PR3btm1DZGQkWlpaMHr0aGzfvl2cpqWnduzYgatXr8LGxgZubm7YtGlTn+7fIyIiIuoNswl8Tk5O2LJlS5c1paWlOp8lEgmWLVuGZcuW6a13dXXF3r17uz22j48Pdu7c2eOxjhs3rsNYZs+ejdmzZ/d4H0RERESGYjavViMiIiKivmHgIyIiIrJwDHxEREREFo6Bj4iIiMjCMfARERERWTgGPiIiIiILx8BHREREZOEY+IiIiIgsHAMfERERkYVj4CMiIiKycAx8RERERBaOgY+IiIjIwjHwEREREVk4Bj4iIiIiC8fAR0RERGThGPiIiIiILBwDHxEREZGFY+AjIiIisnAMfEREREQWjoGPiIiIyMIx8BFa2wR8drEOH136EZ9drENrm2DsIREREZEBDTL2AMi4Piy+hpdzvkF1w517C/7zv1A5yrBtljee9XE17uCIiIjIIHiGz4p9WHwNv8/8Uhv2/p8rDXfw+8wv8WHxNSONjIiIiAyJgc9KtbYJeDnnG+i7eNu+bMU/vuHlXSIiIgvAwGelTlTUdTizdz8BwHf/vYMTFXUPb1BEREQ0IBj4rNQ1TZNB64iIiMh0MfBZKVf7wQatIyIiItPFwGelQkYpoHKUQdLJegmAEU4yhIxSPMxhERER0QBg4LNStjYSbJvlDQAdQl/7560zvWFr01kkJCIiInPBwGfFnvVxRVbkYxjuKNNZrnKSISvyMc7DR0REZCE48bKVe9bHFTPVP8Nn5TXILy7F4z6eCH3UmWf2iIiILAgDH8HWRoJQdwUcNUPg765g2CMiIrIwZnNJt6GhAXFxcQgKCkJQUBDi4uLQ2NjY5TaCICAlJQXBwcHw9fVFREQEysrKdGoiIiLg6emp87NixQqdmsrKSsTGxmLcuHEIDAxEeHg48vPzdWoe3Ienpyfef/99nZrS0lI8//zz8PX1RUhICFJTUyEInNiYiIiIBpbZnOFbtWoVrl+/jvT0dABAfHw8XnnlFbzzzjudbvPuu+8iIyMDSUlJcHNzw44dOxAVFYWjR49i6NChYl1YWBiWL18ufpbJdO9pi46OhpubGzIzMyGTyZCZmYmYmBjk5eXB2dlZrHvjjTcQEhIifra3txd/v3nzJhYsWIBx48YhOzsbly5dwpo1azBkyBAsWLCg740hIiIi6oZZBL6LFy/ixIkTOHjwIPz8/AAAGzduxNy5c1FRUYFRo0Z12EYQBOzevRsxMTGYMmUKAGDz5s0YP348jhw5gvDwcLFWJpPpBLf71dfXo6qqCq+//jrGjBkD4F743L9/P8rLy3W2c3Bw6HQ/ubm5aGpqQlJSEqRSKTw8PHDp0iVkZGQgKioKEknvLqO2trb2qr6n+zP0fs0Re6HFXmixF1rshRZ7ocVeaOnrhbH7YhaBr7CwEPb29mLYAwB/f3/Y29ujsLBQb+Crrq5GTU0NgoODxWVSqRRjx45FYWGhTuA7fPgwcnNzoVQqMWHCBCxZskQ8Azhs2DC4u7sjJycHXl5ekEqlOHDgAJRKJdRqtc4xExIS8Oqrr0KlUuG5557D3LlzYWNz76p5UVERxo4dC6lUKtYHBwcjOTkZ1dXVGDFiRK96Ulxc3Kt6Y+/XHLEXWuyFFnuhxV5osRda7IWWKfXCLAJfbW0tFIqOEwArFArU1tbq3aampkasuZ9SqcTVq1fFzzNmzIBKpYJSqURZWRmSk5NRUlKCjIwMAIBEIkFGRgZiY2MRGBgIGxsbKBQKpKenw8HBQdzPSy+9hCeeeAIymQz/+c9/sHnzZty4cQOLFy8Wv8Pw4cM7jL99XW8Dn4+PD2xtbXu1TVdaW1tRXFxs8P2aI/ZCi73QYi+02Ast9kKLvdDS14v2ZcZi1MCXkpKC1NTULmuys7M7XScIQreXQh9c/+BDEmFhYeLvHh4eGDlyJObMmYNz585BrVZDEASsX78eCoUC+/btg0wmQ1ZWFqKjo5GdnQ0XFxcAEIMdAPzqV78CAGzfvl1neWdj7e3lXACwtbUdkL9QA7Vfc8ReaLEXWuyFFnuhxV5osRdaptQLowa+efPmYdq0aV3WqFQqlJaWoq6ursO6+vp6vWf+AIj30tXW1oqhDADq6uqgVCo7PZ5arYadnR2qqqqgVquRn5+P48ePo6CgQLzMq1arcfr0aeTk5GDRokV69+Pn54ebN2+itrYWSqUSSqVSPOt4/1iAjmchiYiIiAzJqIFPLpdDLpd3WxcQEACNRoOzZ8/C19cXAHDmzBloNBoEBATo3UalUsHZ2RmnTp2Cl5cXAKC5uRkFBQVYvXp1p8cqKytDS0uLGBhv374NoONZOIlEgra2tk73c/78eQwePFi87Ovv74+tW7eiublZvI/v5MmTcHFxgUql6rYHRERERH1lFvPwubu7IyQkBOvWrUNRURGKioqwbt06TJo0SeeBjalTpyIvLw/AvUA2f/58pKWlIS8vDxcuXMDatWshk8kwffp0AMDly5eRmpqK4uJiVFdX47PPPsNLL70ELy8vBAYGArgX1BwcHLBmzRqUlJSgsrISmzdvxpUrVzBx4kQAwLFjx3Dw4EFcuHABly9fRlZWFrZu3YqwsDAx3M2YMQNSqRRr167FhQsXkJeXh7S0tD49oUtERETUG2bx0AYAbNmyBYmJieKcdZMnT0Z8fLxOTWVlJTQajfh54cKFaGpqwoYNG9DQ0AA/Pz/s2rVLvDRrZ2eH/Px87NmzB7du3YKrqytCQ0OxdOlS8Zq7XC5Heno6tm3bhsjISLS0tGD06NHYvn27OE3LoEGDsH//frzxxhsQBAEjRozA8uXLMW/ePHEs9vb22LVrFxISEjBnzhw4OjoiKioKUVFRvepD+z2InJZl4LAXWuyFFnuhxV5osRda7IVWV9OyGOuFCxKBr3owK83NzSb1mDcRERH1nI+Pj84UbQ8LA5+ZaWtrw927d2FjY8NLwURERGZCEAS0tbVh0KBB4hy9DxMDHxEREZGFM4uHNoiIiIio7xj4iIiIiCwcAx8RERGRhWPgIyIiIrJwDHxEREREFo6Bj4iIiMjCMfARERERWTgGPiIiIiILx8BHAIB9+/Zh8uTJ8PHxwbPPPosvv/zS2EPqVEFBAWJiYhAcHAxPT0/8+9//1lkvCAJSUlIQHBwMX19fREREoKysTKemubkZGzduxLhx4+Dv74+YmBh8//33OjUNDQ2Ii4tDUFAQgoKCEBcXh8bGRp2aq1evIiYmBv7+/hg3bhwSExPR3NysU1NaWornn38evr6+CAkJQWpqqkHepZiWloY5c+YgICAATzzxBBYvXoyKigqr7MX+/fsxY8YMBAYGIjAwEHPnzsVnn31mdX3QJy0tDZ6enti0aZO4zFr6kZKSAk9PT52fJ5980ur60O769etYvXo1xo0bBz8/P8ycORPffPON1fVj8uTJHf5ceHp6YsOGDZbdB4Gs3j//+U9BrVYLBw8eFMrLy4XExETB399fuHLlirGHptfx48eFv/71r8JHH30keHh4CHl5eTrr09LShICAAOGjjz4SSktLhZdffll48sknBY1GI9bEx8cLISEhwqlTp4Rz584JERERwjPPPCPcvXtXrHnxxReF6dOnC19//bXw9ddfC9OnTxeio6PF9Xfv3hWmT58uRERECOfOnRNOnTolBAcHCwkJCWKNRqMRxo8fL6xYsUIoLS0VPvroIyEgIEDYuXNnv/uwYMEC4YMPPhAuXLggnD9/Xli0aJEwceJE4datW1bXi08++UQ4fvy4UFFRIVRUVAh//etfBbVaLVy4cMGq+vCgM2fOCJMmTRJmzJghJCYmisutpR9vvfWW8Lvf/U744YcfxJ+6ujqr64MgCMJ///tfYdKkScKaNWuEM2fOCN99951w+vRpoaqqyur6UVdXp/Nn4tSpU4KHh4eQn59v0X1g4CPhueeeE+Lj43WWTZ06VdiyZYuRRtRzDwa+trY24cknnxTS0tLEZU1NTUJQUJDw/vvvC4IgCI2NjYJarRb++c9/ijXff/+9MGbMGOHzzz8XBEEQysvLBQ8PD6GoqEisKSwsFDw8PISLFy8KgnAveI4ZM0b4/vvvxZojR44I3t7e4v8x7Nu3TwgKChKamprEmrS0NCE4OFhoa2szZCuEuro6wcPDQ/jiiy+svheCIAhjx44VDh48aLV9uHnzpjBlyhTh1KlTwvPPPy8GPmvqx1tvvSU888wzetdZUx8EQRDefPNN4Q9/+EOn662tH/dLTEwUnnrqKaGtrc2i+8BLulauubkZ586dQ3BwsM7yJ598EoWFhUYaVd9VV1ejpqZG5/tIpVKMHTtW/D7ffPMNWlpadC7tPPLIIxg9erRYU1hYCHt7e/j5+Yk1/v7+sLe3F2uKioowevRoPPLII2JNcHAwmpubxcskRUVFGDt2LKRSqU7NDz/8gOrqaoN+d41GAwBwdHS06l60trbin//8J3788UcEBARYbR8SEhIQGhqK8ePH6yy3tn5UVVUhODgYkydPxooVK/Ddd99ZZR+OHTsGb29vLF++HE888QRmzZqFgwcPiuutrR/tmpubkZubizlz5kAikVh0Hxj4rNyNGzfQ2toKhUKhs1ypVKKmpsZIo+q79jHr+z61tbUAgNraWtjZ2YnBqLOaB/fRvt/7a5RKpc56R0dH2NnZdVnTvt/2GkMQBAFvvPEGgoKC4OHhAcD6elFaWoqAgAD4+PjgL3/5C7Zv345HH33U6voAAP/85z/x7bffYtWqVR3WWVM/fH19sXnzZuzcuROJiYmora1FeHg4bty4YVV9AIDvvvsO77//Ptzc3LBz506Eh4cjMTEROTk5AKzrz8X9/v3vf0Oj0WD27NkALLsPg3pVTRZLIpHofBYEocMyc6Lv+3SnpzX377uzHvW2pr8SEhJw4cIF7N+/v9vjWGovfvnLXyInJweNjY34+OOP8ac//Ql79+7t9BiW2odr165h06ZN2LVrFwYPHtxpnTX0IzQ0VOezv78/nn76aeTk5IhnXqyhD+3j8fb2xsqVKwEAXl5eKC8vx/vvv49Zs2Z1eixL7Ue7Dz74ABMmTNA5y6bvGJbQB57hs3LDhg2Dra1th/9SqKur6/BfFebA2dkZQMf/8rn/+yiVSrS0tKChoaHLmrq6ug77r6+vF//rSt9Z0IaGBrS0tHRZ075fff/11xcbN27EsWPHkJmZiZ/97GficmvrhVQqxciRI+Hj44NVq1ZhzJgx2L17t9X14dy5c6irq8Ozzz4LLy8veHl54YsvvsCePXvg5eUlfh9r6cf9hgwZAg8PD1y6dMnq/lw4OzvD3d1dZ9moUaNw9epVcT1gPf0AgCtXruD06dN47rnnxGWW3AcGPisnlUqhVqtx6tQpneWnT59GQECAkUbVdyqVCs7Ozjrfp7m5GQUFBeL38fb2hp2dnU7NDz/8gLKyMrEmICAAGo0GZ8+eFWvOnDkDjUYj1vj7+6OsrAw//PCDWHPq1ClIpVJ4e3uLNV9++aXOY/YnT56Ei4sLVCpVv76rIAhISEjAxx9/jMzMTIwYMcJqe6GPIAhobm62uj48/vjjOHz4MHJycsQfb29vzJgxAzk5ORgxYoRV9eN+zc3NuHjxIpydna3uz0VgYCAqKyt1ll26dAnDhw8HYJ3/f/Hhhx9CoVBg4sSJ4jKL7kOvHvEgi9Q+LUtWVpZQXl4ubNq0SfD39xeqq6uNPTS9bt68KXz77bfCt99+K3h4eAgZGRnCt99+K04jk5aWJgQFBQkff/yxUFpaKqxcuVLvI/UTJkwQTp8+LZw7d06YP3++3kfqZ8yYIRQWFgqFhYWdPlIfGRkpnDt3Tjh9+rQwYcIEnUfqGxsbhfHjxwsrV64USktLhY8//lgIDAw0yNQCf/nLX4SgoCDhf//3f3WmGLh9+7ZYYy29SE5OFgoKCoTvvvtOKCkpEf76178KY8aMEU6ePGlVfejM/U/pWlM/kpKShP/93/8VLl++LBQVFQnR0dFCQECA+P9t1tIHQbg3RY+Xl5ewY8cO4dKlS0Jubq7g5+cn/OMf/xBrrKkfra2twsSJE4U333yzwzpL7QMDHwmCIAh79+4VJk2aJKjVamH27Nni1B6mKD8/X/Dw8Ojw86c//UkQhHvTC7z11lvCk08+KXh7ewvz5s0TSktLdfZx584dISEhQfj1r38t+Pr6CtHR0cLVq1d1am7cuCGsWrVKCAgIEAICAoRVq1YJDQ0NOjVXrlwRFi1aJPj6+gq//vWvhYSEBJ3H5wVBEEpKSoQ//vGPgre3t/Dkk08KKSkpBplWQF8PPDw8hA8++ECssZZerF27Vvzz+/jjjwuRkZFi2LOmPnTmwcBnLf1onz9NrVYLwcHBwtKlS4WysjKr60O7Y8eOCdOnTxe8vb2FqVOnCgcOHNBZb039OHHihODh4SFUVFR0WGepfZAIwgBN705EREREJoH38BERERFZOAY+IiIiIgvHwEdERERk4Rj4iIiIiCwcAx8RERGRhWPgIyIiIrJwDHxEREREFo6Bj4iIiMjCMfARERERWbhBxh4AEZGl8fT07HL97NmzkZSU9JBGQ0QE8NVqREQGVlNTI/7+r3/9C2+99RaOHj0qLpPJZLC3txc/t7S0wM7O7qGOkYisCy/pEhEZmLOzs/hjb28PiUQifm5qasJjjz2Gf/3rX4iIiICPjw9yc3ORkpKCmTNn6uznvffew+TJk3WWffDBB/jtb38LHx8fTJ06Ffv27XuYX42IzBQDHxGREWzZsgURERH417/+heDg4B5tc/DgQWzduhUrVqzAv/71L6xcuRJvvfUWDh06NMCjJSJzx3v4iIiMIDIyElOmTOnVNm+//TbWrFkjbjdixAiUl5fjwIEDmD179kAMk4gsBAMfEZEReHt796q+vr4e165dw6uvvorXXntNXH737l2d+wGJiPRh4CMiMoIhQ4bofJZIJHjwGbq7d++Kv7e1tQEANm7cCD8/P506GxvenUNEXWPgIyIyAXK5HLW1tRAEARKJBABw/vx5cb1SqcQjjzyC7777Ds8884yxhklEZor/WUhEZALGjRuH+vp6vPvuu7h8+TL27duHEydO6NQsW7YMf/vb35CZmYnKykqUlpbigw8+QEZGhpFGTUTmgoGPiMgEuLu74y9/+Qv279+PmTNn4uzZs1iwYIFOze9//3skJibi0KFDmDFjBiIiInDo0CGoVCojjZqIzAUnXiYiIiKycDzDR0RERGThGPiIiIiILBwDHxEREZGFY+AjIiIisnAMfEREREQWjoGPiIiIyMIx8BERERFZOAY+IiIiIgvHwEdERERk4Rj4iIiIiCwcAx8RERGRhfv/Aa0tIPOUce4yAAAAAElFTkSuQmCC", "text/plain": [ "
" ] @@ -2157,7 +2217,7 @@ "id": "4ed0b4e6-904a-4af8-8bfe-cad402de09a4", "metadata": {}, "source": [ - "## 8. Hyperparameter tuning\n", + "# 8. Hyperparameter tuning\n", "With any `scikit-learn` model, it's possible to tune the hyperparameters to achieve better performance." ] }, @@ -2185,7 +2245,7 @@ "id": "8445ad22-4275-4a56-ace7-ce1a678dd900", "metadata": {}, "source": [ - "### 8.2 Now retrieve the best fit model" + "## 8.2 Now retrieve the best fit model" ] }, { @@ -2216,7 +2276,7 @@ "id": "d5c33bd9-b95e-4ec8-a44d-cf3f219339e8", "metadata": {}, "source": [ - "## 9. Other available `scikit-learn` choices\n", + "# 9. Other available `scikit-learn` choices\n", "The below two cells explore available options from `scikit-learn` for regression metrics and regression models, respectively. The metric cell is truncated with a `break` statement to only print details of the first metric. The model cell demonstrates printing the class information for the `RandomForestRegressor` class." ] }, @@ -2268,10 +2328,20 @@ " print(help(estimator))" ] }, + { + "cell_type": "markdown", + "id": "2e205cc2-26f1-4b51-bb36-3ce2ed8cece4", + "metadata": {}, + "source": [ + "# Exercise for the learner\n", + "\n", + "Uncertainty." + ] + }, { "cell_type": "code", "execution_count": null, - "id": "be44f0da-e766-4bd0-9312-4b1977397c8d", + "id": "7877e1b2-e1c8-4d1f-a2b8-60c758cdf61d", "metadata": {}, "outputs": [], "source": [] From 8f18b0b1ae6c0e8f0a11446a7aa0ad2aa5b8b09d Mon Sep 17 00:00:00 2001 From: beckynevin Date: Fri, 9 May 2025 16:02:31 +0000 Subject: [PATCH 11/13] adding section for prediction of flagged r-band values --- DP0.2/20_Introduction_to_Data_Science.ipynb | 727 +++++++++++++++----- 1 file changed, 540 insertions(+), 187 deletions(-) diff --git a/DP0.2/20_Introduction_to_Data_Science.ipynb b/DP0.2/20_Introduction_to_Data_Science.ipynb index 1cd68884..35672f94 100644 --- a/DP0.2/20_Introduction_to_Data_Science.ipynb +++ b/DP0.2/20_Introduction_to_Data_Science.ipynb @@ -105,15 +105,15 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 107, "id": "3f4900a4-3358-472a-b9ba-c42e3f2f0771", "metadata": { "execution": { - "iopub.execute_input": "2025-05-09T13:28:12.164357Z", - "iopub.status.busy": "2025-05-09T13:28:12.163889Z", - "iopub.status.idle": "2025-05-09T13:28:12.170613Z", - "shell.execute_reply": "2025-05-09T13:28:12.169575Z", - "shell.execute_reply.started": "2025-05-09T13:28:12.164320Z" + "iopub.execute_input": "2025-05-09T15:57:33.387374Z", + "iopub.status.busy": "2025-05-09T15:57:33.386908Z", + "iopub.status.idle": "2025-05-09T15:57:33.393705Z", + "shell.execute_reply": "2025-05-09T15:57:33.392659Z", + "shell.execute_reply.started": "2025-05-09T15:57:33.387334Z" } }, "outputs": [], @@ -126,12 +126,16 @@ "from astropy import units as u\n", "from astropy.coordinates import SkyCoord\n", "\n", - "from sklearn.model_selection import train_test_split\n", + "from sklearn.model_selection import train_test_split, GridSearchCV\n", "from sklearn.preprocessing import FunctionTransformer, StandardScaler\n", "from sklearn.pipeline import make_pipeline\n", "from sklearn.linear_model import LinearRegression\n", "from sklearn.metrics import mean_squared_error\n", "from sklearn.ensemble import RandomForestRegressor\n", + "import sklearn.metrics as metrics\n", + "import inspect\n", + "from sklearn.utils import all_estimators\n", + "\n", "\n", "from lsst.rsp import get_tap_service, retrieve_query" ] @@ -1270,15 +1274,15 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 73, "id": "fc90feca-ede1-44b0-929b-2fec1ddf5ad4", "metadata": { "execution": { - "iopub.execute_input": "2025-05-09T13:33:45.064393Z", - "iopub.status.busy": "2025-05-09T13:33:45.063919Z", - "iopub.status.idle": "2025-05-09T13:33:45.074011Z", - "shell.execute_reply": "2025-05-09T13:33:45.073062Z", - "shell.execute_reply.started": "2025-05-09T13:33:45.064359Z" + "iopub.execute_input": "2025-05-09T15:31:53.035002Z", + "iopub.status.busy": "2025-05-09T15:31:53.034550Z", + "iopub.status.idle": "2025-05-09T15:31:53.044698Z", + "shell.execute_reply": "2025-05-09T15:31:53.043727Z", + "shell.execute_reply.started": "2025-05-09T15:31:53.034964Z" } }, "outputs": [], @@ -1309,15 +1313,15 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 74, "id": "f09a28c9-f868-4cfa-a309-c53b26193e01", "metadata": { "execution": { - "iopub.execute_input": "2025-05-09T13:33:45.996941Z", - "iopub.status.busy": "2025-05-09T13:33:45.995872Z", - "iopub.status.idle": "2025-05-09T13:33:46.001661Z", - "shell.execute_reply": "2025-05-09T13:33:46.000736Z", - "shell.execute_reply.started": "2025-05-09T13:33:45.996895Z" + "iopub.execute_input": "2025-05-09T15:31:54.951546Z", + "iopub.status.busy": "2025-05-09T15:31:54.951042Z", + "iopub.status.idle": "2025-05-09T15:31:54.956764Z", + "shell.execute_reply": "2025-05-09T15:31:54.955828Z", + "shell.execute_reply.started": "2025-05-09T15:31:54.951504Z" } }, "outputs": [], @@ -1345,15 +1349,15 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 75, "id": "ec1efab7-be4c-4bdc-9ead-78fd6b400345", "metadata": { "execution": { - "iopub.execute_input": "2025-05-09T13:33:47.032314Z", - "iopub.status.busy": "2025-05-09T13:33:47.031824Z", - "iopub.status.idle": "2025-05-09T13:33:47.050988Z", - "shell.execute_reply": "2025-05-09T13:33:47.050020Z", - "shell.execute_reply.started": "2025-05-09T13:33:47.032275Z" + "iopub.execute_input": "2025-05-09T15:31:59.510886Z", + "iopub.status.busy": "2025-05-09T15:31:59.509993Z", + "iopub.status.idle": "2025-05-09T15:31:59.529093Z", + "shell.execute_reply": "2025-05-09T15:31:59.528057Z", + "shell.execute_reply.started": "2025-05-09T15:31:59.510841Z" } }, "outputs": [], @@ -1375,15 +1379,15 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 76, "id": "5be7b1b1-2177-46e5-ad41-607e55d12949", "metadata": { "execution": { - "iopub.execute_input": "2025-05-09T13:34:59.961016Z", - "iopub.status.busy": "2025-05-09T13:34:59.960567Z", - "iopub.status.idle": "2025-05-09T13:35:00.198956Z", - "shell.execute_reply": "2025-05-09T13:35:00.197908Z", - "shell.execute_reply.started": "2025-05-09T13:34:59.960978Z" + "iopub.execute_input": "2025-05-09T15:32:00.747937Z", + "iopub.status.busy": "2025-05-09T15:32:00.747516Z", + "iopub.status.idle": "2025-05-09T15:32:00.991754Z", + "shell.execute_reply": "2025-05-09T15:32:00.990817Z", + "shell.execute_reply.started": "2025-05-09T15:32:00.747900Z" } }, "outputs": [ @@ -1421,15 +1425,15 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 77, "id": "f4086f5f-7c2d-4d6c-a2d4-8dff42e2fc84", "metadata": { "execution": { - "iopub.execute_input": "2025-05-09T13:37:44.884323Z", - "iopub.status.busy": "2025-05-09T13:37:44.883776Z", - "iopub.status.idle": "2025-05-09T13:37:44.893331Z", - "shell.execute_reply": "2025-05-09T13:37:44.892372Z", - "shell.execute_reply.started": "2025-05-09T13:37:44.884278Z" + "iopub.execute_input": "2025-05-09T15:32:02.603014Z", + "iopub.status.busy": "2025-05-09T15:32:02.602111Z", + "iopub.status.idle": "2025-05-09T15:32:02.611130Z", + "shell.execute_reply": "2025-05-09T15:32:02.610226Z", + "shell.execute_reply.started": "2025-05-09T15:32:02.602972Z" } }, "outputs": [], @@ -1451,15 +1455,15 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 78, "id": "fc4cecc6-f256-4f55-8d54-f585630e8b4f", "metadata": { "execution": { - "iopub.execute_input": "2025-05-09T13:37:45.567934Z", - "iopub.status.busy": "2025-05-09T13:37:45.567476Z", - "iopub.status.idle": "2025-05-09T13:37:45.857123Z", - "shell.execute_reply": "2025-05-09T13:37:45.856221Z", - "shell.execute_reply.started": "2025-05-09T13:37:45.567892Z" + "iopub.execute_input": "2025-05-09T15:32:03.910513Z", + "iopub.status.busy": "2025-05-09T15:32:03.909979Z", + "iopub.status.idle": "2025-05-09T15:32:04.202681Z", + "shell.execute_reply": "2025-05-09T15:32:04.201749Z", + "shell.execute_reply.started": "2025-05-09T15:32:03.910472Z" } }, "outputs": [ @@ -1515,22 +1519,22 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 79, "id": "e02ca479-6105-442b-9879-2eb215dc4d66", "metadata": { "execution": { - "iopub.execute_input": "2025-05-08T15:54:51.604838Z", - "iopub.status.busy": "2025-05-08T15:54:51.604287Z", - "iopub.status.idle": "2025-05-08T15:54:51.618400Z", - "shell.execute_reply": "2025-05-08T15:54:51.617461Z", - "shell.execute_reply.started": "2025-05-08T15:54:51.604795Z" + "iopub.execute_input": "2025-05-09T15:32:05.964941Z", + "iopub.status.busy": "2025-05-09T15:32:05.964522Z", + "iopub.status.idle": "2025-05-09T15:32:05.975326Z", + "shell.execute_reply": "2025-05-09T15:32:05.974452Z", + "shell.execute_reply.started": "2025-05-09T15:32:05.964902Z" } }, "outputs": [ { "data": { "text/html": [ - "
LinearRegression()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + "
LinearRegression()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" ], "text/plain": [ "LinearRegression()" ] }, - "execution_count": 29, + "execution_count": 79, "metadata": {}, "output_type": "execute_result" } @@ -1966,20 +1970,20 @@ "id": "b2fe11b4-a9ad-45f9-9c1e-e25bb3a0301c", "metadata": {}, "source": [ - "Now predict the test data. Compare the prediction to the true value." + "Now predict the test data. Compare the prediction to the true value. Use the mean squared error function to get a diagnostic on how well the fit is behaving for the test set." ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 80, "id": "aca8064a-c94f-4792-86b3-62ec2130471b", "metadata": { "execution": { - "iopub.execute_input": "2025-05-08T15:54:55.412402Z", - "iopub.status.busy": "2025-05-08T15:54:55.411935Z", - "iopub.status.idle": "2025-05-08T15:54:55.419771Z", - "shell.execute_reply": "2025-05-08T15:54:55.418755Z", - "shell.execute_reply.started": "2025-05-08T15:54:55.412367Z" + "iopub.execute_input": "2025-05-09T15:32:06.450956Z", + "iopub.status.busy": "2025-05-09T15:32:06.450489Z", + "iopub.status.idle": "2025-05-09T15:32:06.458261Z", + "shell.execute_reply": "2025-05-09T15:32:06.457343Z", + "shell.execute_reply.started": "2025-05-09T15:32:06.450901Z" } }, "outputs": [ @@ -1987,7 +1991,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "MSE: 0.2765946838901815\n" + "MSE: 0.10849655661786743\n" ] } ], @@ -2007,21 +2011,21 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 82, "id": "ee8bd887-928e-4a41-bd77-149b344ab238", "metadata": { "execution": { - "iopub.execute_input": "2025-05-08T15:55:02.919616Z", - "iopub.status.busy": "2025-05-08T15:55:02.919164Z", - "iopub.status.idle": "2025-05-08T15:55:03.149756Z", - "shell.execute_reply": "2025-05-08T15:55:03.148773Z", - "shell.execute_reply.started": "2025-05-08T15:55:02.919578Z" + "iopub.execute_input": "2025-05-09T15:32:32.423693Z", + "iopub.status.busy": "2025-05-09T15:32:32.423222Z", + "iopub.status.idle": "2025-05-09T15:32:32.635256Z", + "shell.execute_reply": "2025-05-09T15:32:32.634305Z", + "shell.execute_reply.started": "2025-05-09T15:32:32.423656Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -2035,7 +2039,9 @@ "plt.scatter(y_test_scaled, y_pred)\n", "plt.plot([-4,6],[-4,6], color='black', ls='--')\n", "plt.xlabel('True')\n", - "plt.ylabel('Predicted');" + "plt.ylabel('Predicted')\n", + "plt.xlim([-5,5])\n", + "plt.ylim([-5,5]);" ] }, { @@ -2056,48 +2062,39 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 83, "id": "6770c381-1f34-44a7-b93b-b7f915013620", "metadata": { "execution": { - "iopub.execute_input": "2025-05-09T13:49:01.712342Z", - "iopub.status.busy": "2025-05-09T13:49:01.711782Z", - "iopub.status.idle": "2025-05-09T13:49:01.719668Z", - "shell.execute_reply": "2025-05-09T13:49:01.718634Z", - "shell.execute_reply.started": "2025-05-09T13:49:01.712298Z" + "iopub.execute_input": "2025-05-09T15:32:47.862687Z", + "iopub.status.busy": "2025-05-09T15:32:47.862230Z", + "iopub.status.idle": "2025-05-09T15:32:47.868252Z", + "shell.execute_reply": "2025-05-09T15:32:47.867305Z", + "shell.execute_reply.started": "2025-05-09T15:32:47.862648Z" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.0.0/lib/python3.12/site-packages/sklearn/utils/validation.py:2739: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n", - " warnings.warn(\n" - ] - } - ], + "outputs": [], "source": [ - "y_pred_orig = y_scaler.transform(y_pred)" + "y_pred_tt = y_scaler.inverse_transform(y_pred)" ] }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 89, "id": "f83cf984-ffa7-4486-98ec-bce890b91bad", "metadata": { "execution": { - "iopub.execute_input": "2025-05-09T13:49:08.376163Z", - "iopub.status.busy": "2025-05-09T13:49:08.375716Z", - "iopub.status.idle": "2025-05-09T13:49:08.661021Z", - "shell.execute_reply": "2025-05-09T13:49:08.660087Z", - "shell.execute_reply.started": "2025-05-09T13:49:08.376107Z" + "iopub.execute_input": "2025-05-09T15:33:37.708917Z", + "iopub.status.busy": "2025-05-09T15:33:37.708417Z", + "iopub.status.idle": "2025-05-09T15:33:37.983120Z", + "shell.execute_reply": "2025-05-09T15:33:37.982160Z", + "shell.execute_reply.started": "2025-05-09T15:33:37.708876Z" } }, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -2108,12 +2105,55 @@ ], "source": [ "plt.clf()\n", - "plt.scatter(y_test_orig, y_pred_orig)\n", - "#plt.plot([-4,6],[-4,6], color='black', ls='--')\n", + "plt.scatter(y_test_tt, y_pred_tt)\n", + "plt.plot([-1e3,2e4],[-1e3,2e4], color='black', ls='--')\n", "plt.xlabel('True')\n", "plt.ylabel('Predicted');\n", - "#plt.xlim([0,2e4])\n", - "#plt.ylim([0,2e4]);" + "plt.xlim([-1e3,2e4])\n", + "plt.ylim([-1e3,2e4]);" + ] + }, + { + "cell_type": "markdown", + "id": "c2b12a25-236e-4a1b-804b-03b97960712c", + "metadata": {}, + "source": [ + "> A scatterplot of the true versus predicted value for the $r-$band Kron flux scaled back to the original values. tHe x- and y-axes scale between -1000 to 2e4." + ] + }, + { + "cell_type": "markdown", + "id": "da4c11bc-53bb-48b5-a89f-aeb2628fd77d", + "metadata": {}, + "source": [ + "Also get the MSE for the rescaled y value." + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "id": "fb653dbd-4e77-4099-80ac-781f20cb38eb", + "metadata": { + "execution": { + "iopub.execute_input": "2025-05-09T15:40:21.967711Z", + "iopub.status.busy": "2025-05-09T15:40:21.967269Z", + "iopub.status.idle": "2025-05-09T15:40:21.974355Z", + "shell.execute_reply": "2025-05-09T15:40:21.973440Z", + "shell.execute_reply.started": "2025-05-09T15:40:21.967672Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE: 80347168.49722074\n" + ] + } + ], + "source": [ + "mse = mean_squared_error(y_test_tt, y_pred_tt)\n", + "print(\"MSE:\", mse)" ] }, { @@ -2122,41 +2162,61 @@ "metadata": {}, "source": [ "## 7.2 Improve the model with more features\n", - "Let's see if this will improve with more predictive values, this time including the i band information." + "Let's see if this will improve with more predictive values, this time including the $i-$band Kron flux. Split the training/test sets, perform the standard scaling, and linear regression. This time, do not scale the y-value $r-$band Kron flux." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 99, "id": "d80e56eb-6e3d-4110-bee6-3681ee4a923b", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2025-05-09T15:47:08.185341Z", + "iopub.status.busy": "2025-05-09T15:47:08.184859Z", + "iopub.status.idle": "2025-05-09T15:47:08.424243Z", + "shell.execute_reply": "2025-05-09T15:47:08.423297Z", + "shell.execute_reply.started": "2025-05-09T15:47:08.185305Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE: 252763.24546072097\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# Train-test split\n", - "X_train, X_test, y_train, y_test = train_test_split(\n", - " clean[['g_kronFlux', 'i_kronFlux']], # Use these two features\n", - " clean['r_kronFlux'], # Target variable\n", + "X_train_i, X_test_i, y_train_i, y_test_i = train_test_split(\n", + " clean_df[['g_kronFlux', 'i_kronFlux']], # Use these two features\n", + " clean_df['r_kronFlux'], # Target variable\n", " test_size=0.2,\n", " random_state=42\n", ")\n", "\n", - "# Standardize the features\n", "scaler = StandardScaler()\n", - "X_train = scaler.fit_transform(X_train)\n", - "X_test = scaler.transform(X_test)\n", + "X_train_i_scaled = scaler.fit_transform(X_train_i)\n", + "X_test_i_scaled = scaler.transform(X_test_i)\n", "\n", - "# Train the model\n", "model = LinearRegression()\n", - "model.fit(X_train, y_train)\n", + "model.fit(X_train_i_scaled, y_train)\n", "\n", - "# Make predictions\n", "y_pred = model.predict(X_test)\n", "\n", - "# Evaluate the model\n", "mse = mean_squared_error(y_test, y_pred)\n", "print(\"MSE:\", mse)\n", "\n", - "# Scatter plot: True vs Predicted\n", "plt.clf()\n", "plt.scatter(y_test, y_pred)\n", "plt.plot([0, 1e6], [0, 1e6], color='black', ls='--')\n", @@ -2167,12 +2227,20 @@ "plt.show()" ] }, + { + "cell_type": "markdown", + "id": "5f72c9ad-547a-455a-8b0e-39674e94fe43", + "metadata": {}, + "source": [ + "> A scatter plot demonstrating a tighter fit when the $i-$band Kron flux is included as a predictive feature." + ] + }, { "cell_type": "markdown", "id": "30cd4c78-35e5-4459-84bf-9642068da002", "metadata": {}, "source": [ - "Test for the reader: try to improve this further by including more features." + "Test for the reader: try to improve this further by including more features. The MSE has already improved from the above value!" ] }, { @@ -2181,35 +2249,74 @@ "metadata": {}, "source": [ "## 7.3 Random forest regressor\n", - "These are great" + "These are a great type of model for regression or classification. Use it here to perform regression, again, also including the $i-$band Kron fluxes." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 101, "id": "efb56f9d-6487-444d-b283-6d60c1694948", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2025-05-09T15:47:31.495369Z", + "iopub.status.busy": "2025-05-09T15:47:31.494829Z", + "iopub.status.idle": "2025-05-09T15:47:35.882482Z", + "shell.execute_reply": "2025-05-09T15:47:35.881498Z", + "shell.execute_reply.started": "2025-05-09T15:47:31.495300Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE: 252763.24546072097\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "model = RandomForestRegressor()\n", - "model.fit(X_train, y_train)\n", + "model_rf = RandomForestRegressor()\n", + "model_rf.fit(X_train_i_scaled, y_train_i)\n", "\n", - "# Make predictions\n", - "y_pred = model.predict(X_test)\n", + "y_pred_i = model_rf.predict(X_test_i_scaled)\n", "\n", - "# Evaluate the model\n", - "mse = mean_squared_error(y_test, y_pred)\n", + "\n", + "mse = mean_squared_error(y_test_i, y_pred)\n", "print(\"MSE:\", mse)\n", "\n", - "# Scatter plot: True vs Predicted\n", "plt.clf()\n", - "plt.scatter(y_test, y_pred)\n", + "plt.scatter(y_test_i, y_pred_i)\n", "plt.plot([0, 1e6], [0, 1e6], color='black', ls='--')\n", "plt.xlabel('True')\n", "plt.ylabel('Predicted')\n", "plt.xlim([0, 2e4])\n", - "plt.ylim([0, 2e4])\n", - "plt.show()" + "plt.ylim([0, 2e4]);" + ] + }, + { + "cell_type": "markdown", + "id": "21b186b9-80b7-455e-8433-78c531050100", + "metadata": {}, + "source": [ + "> Another scatterplot of true versus predicted y-values." + ] + }, + { + "cell_type": "markdown", + "id": "13845e03-08fe-4f80-8883-a671e8afd9d5", + "metadata": {}, + "source": [ + "**Strange that the MSE is exactly the same?**" ] }, { @@ -2218,25 +2325,51 @@ "metadata": {}, "source": [ "# 8. Hyperparameter tuning\n", - "With any `scikit-learn` model, it's possible to tune the hyperparameters to achieve better performance." + "With any `scikit-learn` model, it's possible to tune the hyperparameters to achieve better performance. Define the grid to search over, here test the `n_estimators` parameter from random forest estimators, which defines the number of trees in the random forest. The default setting is 100." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 102, "id": "e6420904-44bb-40cf-8ce0-92b22a802e59", + "metadata": { + "execution": { + "iopub.execute_input": "2025-05-09T15:48:55.192267Z", + "iopub.status.busy": "2025-05-09T15:48:55.191172Z", + "iopub.status.idle": "2025-05-09T15:48:55.196894Z", + "shell.execute_reply": "2025-05-09T15:48:55.195919Z", + "shell.execute_reply.started": "2025-05-09T15:48:55.192220Z" + } + }, + "outputs": [], + "source": [ + "param_grid = {'n_estimators': [1, 10, 50, 100, 200, 1000, 10000]}" + ] + }, + { + "cell_type": "markdown", + "id": "e337134d-22fe-42d3-83d2-9f22bb93e70e", "metadata": {}, + "source": [ + "Now define the `GridSearchCV` object and fit the model, wrapped in this grid search object." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "48a166c2-10b9-470f-bd03-3d7e6d51a1c7", + "metadata": { + "execution": { + "iopub.execute_input": "2025-05-09T15:58:49.884008Z", + "iopub.status.busy": "2025-05-09T15:58:49.883539Z" + } + }, "outputs": [], "source": [ - "from sklearn.model_selection import GridSearchCV\n", - "param_grid = {'n_estimators': [1, 10, 50, 100, 200, 1000, 10000]} # default 100 for n_estimators\n", - "\n", - "# Create GridSearchCV object\n", "grid = GridSearchCV(model, param_grid, cv=5)\n", "\n", - "grid.fit(X_train, y_train)\n", + "grid.fit(X_train_i_scaled, y_train_i)\n", "\n", - "# Get the best parameters\n", "print(grid.best_params_)" ] }, @@ -2245,7 +2378,7 @@ "id": "8445ad22-4275-4a56-ace7-ce1a678dd900", "metadata": {}, "source": [ - "## 8.2 Now retrieve the best fit model" + "Now retrieve the best model and print out model diagnostics." ] }, { @@ -2257,9 +2390,9 @@ "source": [ "best_model = grid.best_estimator_\n", "print(\"Best Model:\", best_model)\n", - "y_pred = best_model.predict(X_test)\n", + "y_pred_grid = best_model.predict(X_test_i_scaled)\n", "plt.clf()\n", - "plt.scatter(y_test, y_pred)\n", + "plt.scatter(y_test_i, y_pred_grid)\n", "plt.plot([0, 1e6], [0, 1e6], color='black', ls='--')\n", "plt.xlabel('True')\n", "plt.ylabel('Predicted')\n", @@ -2267,16 +2400,240 @@ "plt.ylim([0, 2e4])\n", "plt.show()\n", "\n", - "mse = mean_squared_error(y_test, y_pred)\n", + "mse = mean_squared_error(y_test_i, y_pred_grid)\n", "print(\"MSE:\", mse)" ] }, + { + "cell_type": "markdown", + "id": "ec3e2b39-f86e-4dfa-a97a-9e018fc2208c", + "metadata": {}, + "source": [ + "# 9. Fit the best model to rows where the $r-$band Kron flux is missing." + ] + }, + { + "cell_type": "markdown", + "id": "98f817da-d4cd-40d2-be10-ba9c1669e68e", + "metadata": {}, + "source": [ + "First, return to the dataframe and snag rows where the $r-$band flux is flagged but the $g-$ and $i-$band Kron fluxes are intact." + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "id": "a020b226-289d-4587-9e42-47f64574dbf6", + "metadata": { + "execution": { + "iopub.execute_input": "2025-05-09T15:54:10.459305Z", + "iopub.status.busy": "2025-05-09T15:54:10.458743Z", + "iopub.status.idle": "2025-05-09T15:54:10.480127Z", + "shell.execute_reply": "2025-05-09T15:54:10.479093Z", + "shell.execute_reply.started": "2025-05-09T15:54:10.459262Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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176 rows × 8 columns

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" + ], + "text/plain": [ + " coord_ra coord_dec g_kronFlux g_kronFlux_flag r_kronFlux \\\n", + "10 62.010663 -37.088064 303.684022 False 232.540549 \n", + "22 62.006350 -37.090115 58.774248 False 135.041368 \n", + "41 62.014721 -37.080369 317.485708 False 450.916090 \n", + "... ... ... ... ... ... \n", + "11461 61.882326 -36.990083 310.036393 False 250.901590 \n", + "11475 61.924031 -36.998350 104.742902 False 110.345845 \n", + "11559 61.935633 -36.950795 495.811802 False 128.753038 \n", + "\n", + " r_kronFlux_flag i_kronFlux i_kronFlux_flag \n", + "10 True 310.371904 False \n", + "22 True 305.676263 False \n", + "41 True 134.658024 False \n", + "... ... ... ... \n", + "11461 True 228.085994 False \n", + "11475 True 262.059423 False \n", + "11559 True 218.522159 False \n", + "\n", + "[176 rows x 8 columns]" + ] + }, + "execution_count": 105, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "r_missing_df = results[\n", + " (results['r_kronFlux_flag'] == True) & \n", + " (results['g_kronFlux_flag'] == False) &\n", + " (results['i_kronFlux_flag'] == False)\n", + "]\n", + "r_missing_df" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "id": "dd7a891e-5a56-440e-a721-91af73d74269", + "metadata": { + "execution": { + "iopub.execute_input": "2025-05-09T15:56:26.275670Z", + "iopub.status.busy": "2025-05-09T15:56:26.275203Z", + "iopub.status.idle": "2025-05-09T15:56:26.860172Z", + "shell.execute_reply": "2025-05-09T15:56:26.858874Z", + "shell.execute_reply.started": "2025-05-09T15:56:26.275634Z" + } + }, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'best_model' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[106], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m X_r_missing \u001b[38;5;241m=\u001b[39m r_missing_df[[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mg_kronFlux\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mi_kronFlux\u001b[39m\u001b[38;5;124m'\u001b[39m]]\n\u001b[1;32m 2\u001b[0m X_r_missing_scaled \u001b[38;5;241m=\u001b[39m scaler\u001b[38;5;241m.\u001b[39mfit_transform(X_r_missing)\n\u001b[0;32m----> 3\u001b[0m y_pred_r_missing \u001b[38;5;241m=\u001b[39m \u001b[43mbest_model\u001b[49m\u001b[38;5;241m.\u001b[39mpredict(X_r_missing_scaled)\n", + "\u001b[0;31mNameError\u001b[0m: name 'best_model' is not defined" + ] + } + ], + "source": [ + "X_r_missing = r_missing_df[['g_kronFlux', 'i_kronFlux']]\n", + "X_r_missing_scaled = scaler.fit_transform(X_r_missing)\n", + "y_pred_r_missing = best_model.predict(X_r_missing_scaled)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0e438d24-75ba-4888-b2c9-cb4fa6c6db85", + "metadata": {}, + "outputs": [], + "source": [ + "plt.scatter(r_missing_df['r_kronFlux'].values, y_pred_r_missing)\n", + "plt.xlabel(r'Flagged $r-$band values')\n", + "plt.ylabel('Predicted $r-$band values');" + ] + }, { "cell_type": "markdown", "id": "d5c33bd9-b95e-4ec8-a44d-cf3f219339e8", "metadata": {}, "source": [ - "# 9. Other available `scikit-learn` choices\n", + "# 10. Other available `scikit-learn` choices\n", "The below two cells explore available options from `scikit-learn` for regression metrics and regression models, respectively. The metric cell is truncated with a `break` statement to only print details of the first metric. The model cell demonstrates printing the class information for the `RandomForestRegressor` class." ] }, @@ -2287,8 +2644,6 @@ "metadata": {}, "outputs": [], "source": [ - "import sklearn.metrics as metrics\n", - "import inspect\n", "regression_metrics = [\n", " name for name, obj in inspect.getmembers(metrics)\n", " if inspect.isfunction(obj)\n", @@ -2314,8 +2669,6 @@ "metadata": {}, "outputs": [], "source": [ - "from sklearn.utils import all_estimators\n", - "\n", "# Get all regression models\n", "regressors = all_estimators(type_filter='regressor')\n", "\n", @@ -2335,7 +2688,7 @@ "source": [ "# Exercise for the learner\n", "\n", - "Uncertainty." + "The uncertainty values are also available for each column of Kron fluxes. Use those to run an uncertainty-aware model fit for the $r-$band Kron fluxes." ] }, { From 3cbc7a4bcaa40f9b70dd9cbf1ef3729710287b58 Mon Sep 17 00:00:00 2001 From: beckynevin Date: Fri, 9 May 2025 16:28:31 +0000 Subject: [PATCH 12/13] Ran all the way through with printouts --- DP0.2/20_Introduction_to_Data_Science.ipynb | 1529 +++++++++++++++---- 1 file changed, 1203 insertions(+), 326 deletions(-) diff --git a/DP0.2/20_Introduction_to_Data_Science.ipynb b/DP0.2/20_Introduction_to_Data_Science.ipynb index 35672f94..e706f611 100644 --- a/DP0.2/20_Introduction_to_Data_Science.ipynb +++ b/DP0.2/20_Introduction_to_Data_Science.ipynb @@ -105,15 +105,15 @@ }, { "cell_type": "code", - "execution_count": 107, + "execution_count": 1, "id": "3f4900a4-3358-472a-b9ba-c42e3f2f0771", "metadata": { "execution": { - "iopub.execute_input": "2025-05-09T15:57:33.387374Z", - "iopub.status.busy": "2025-05-09T15:57:33.386908Z", - "iopub.status.idle": "2025-05-09T15:57:33.393705Z", - "shell.execute_reply": "2025-05-09T15:57:33.392659Z", - "shell.execute_reply.started": "2025-05-09T15:57:33.387334Z" + "iopub.execute_input": "2025-05-09T16:18:06.633183Z", + "iopub.status.busy": "2025-05-09T16:18:06.632108Z", + "iopub.status.idle": "2025-05-09T16:18:10.294306Z", + "shell.execute_reply": "2025-05-09T16:18:10.293237Z", + "shell.execute_reply.started": "2025-05-09T16:18:06.633120Z" } }, "outputs": [], @@ -136,7 +136,6 @@ "import inspect\n", "from sklearn.utils import all_estimators\n", "\n", - "\n", "from lsst.rsp import get_tap_service, retrieve_query" ] }, @@ -154,11 +153,11 @@ "id": "94acc9f6-2033-4ace-aefd-d036a35f4221", "metadata": { "execution": { - "iopub.execute_input": "2025-05-08T15:50:38.409359Z", - "iopub.status.busy": "2025-05-08T15:50:38.408575Z", - "iopub.status.idle": "2025-05-08T15:50:38.415082Z", - "shell.execute_reply": "2025-05-08T15:50:38.414076Z", - "shell.execute_reply.started": "2025-05-08T15:50:38.409318Z" + "iopub.execute_input": "2025-05-09T16:18:10.297176Z", + "iopub.status.busy": "2025-05-09T16:18:10.295985Z", + "iopub.status.idle": "2025-05-09T16:18:10.302384Z", + "shell.execute_reply": "2025-05-09T16:18:10.301449Z", + "shell.execute_reply.started": "2025-05-09T16:18:10.297111Z" } }, "outputs": [], @@ -184,11 +183,11 @@ "id": "caf56589-100a-4481-8f24-5f5058b6671f", "metadata": { "execution": { - "iopub.execute_input": "2025-05-08T15:50:39.404092Z", - "iopub.status.busy": "2025-05-08T15:50:39.403614Z", - "iopub.status.idle": "2025-05-08T15:50:39.450009Z", - "shell.execute_reply": "2025-05-08T15:50:39.449018Z", - "shell.execute_reply.started": "2025-05-08T15:50:39.404054Z" + "iopub.execute_input": "2025-05-09T16:18:10.303800Z", + "iopub.status.busy": "2025-05-09T16:18:10.303440Z", + "iopub.status.idle": "2025-05-09T16:18:10.376181Z", + "shell.execute_reply": "2025-05-09T16:18:10.375133Z", + "shell.execute_reply.started": "2025-05-09T16:18:10.303766Z" } }, "outputs": [], @@ -211,11 +210,11 @@ "id": "2b7b6002-2457-4c20-a03e-6bfa24a0aa27", "metadata": { "execution": { - "iopub.execute_input": "2025-05-08T15:50:40.248771Z", - "iopub.status.busy": "2025-05-08T15:50:40.248346Z", - "iopub.status.idle": "2025-05-08T15:50:40.253523Z", - "shell.execute_reply": "2025-05-08T15:50:40.252555Z", - "shell.execute_reply.started": "2025-05-08T15:50:40.248733Z" + "iopub.execute_input": "2025-05-09T16:18:10.378538Z", + "iopub.status.busy": "2025-05-09T16:18:10.378198Z", + "iopub.status.idle": "2025-05-09T16:18:10.382986Z", + "shell.execute_reply": "2025-05-09T16:18:10.382055Z", + "shell.execute_reply.started": "2025-05-09T16:18:10.378505Z" } }, "outputs": [], @@ -248,11 +247,11 @@ "id": "7ddd0344-b354-45a0-9e5a-755149c9bc54", "metadata": { "execution": { - "iopub.execute_input": "2025-05-08T15:50:42.669499Z", - "iopub.status.busy": "2025-05-08T15:50:42.668489Z", - "iopub.status.idle": "2025-05-08T15:50:42.674127Z", - "shell.execute_reply": "2025-05-08T15:50:42.673137Z", - "shell.execute_reply.started": "2025-05-08T15:50:42.669457Z" + "iopub.execute_input": "2025-05-09T16:18:10.384792Z", + "iopub.status.busy": "2025-05-09T16:18:10.384175Z", + "iopub.status.idle": "2025-05-09T16:18:10.400375Z", + "shell.execute_reply": "2025-05-09T16:18:10.399370Z", + "shell.execute_reply.started": "2025-05-09T16:18:10.384756Z" } }, "outputs": [], @@ -279,11 +278,11 @@ "id": "985e3b62-8065-42ec-a40c-1232c4c45f17", "metadata": { "execution": { - "iopub.execute_input": "2025-05-08T15:50:44.432293Z", - "iopub.status.busy": "2025-05-08T15:50:44.431809Z", - "iopub.status.idle": "2025-05-08T15:50:44.437788Z", - "shell.execute_reply": "2025-05-08T15:50:44.436804Z", - "shell.execute_reply.started": "2025-05-08T15:50:44.432255Z" + "iopub.execute_input": "2025-05-09T16:18:10.401789Z", + "iopub.status.busy": "2025-05-09T16:18:10.401429Z", + "iopub.status.idle": "2025-05-09T16:18:10.417575Z", + "shell.execute_reply": "2025-05-09T16:18:10.416625Z", + "shell.execute_reply.started": "2025-05-09T16:18:10.401754Z" } }, "outputs": [ @@ -319,11 +318,11 @@ "id": "c02adc91-5f5e-418b-87a3-cba8beba7dd2", "metadata": { "execution": { - "iopub.execute_input": "2025-05-08T15:50:45.904416Z", - "iopub.status.busy": "2025-05-08T15:50:45.903914Z", - "iopub.status.idle": "2025-05-08T15:50:47.182530Z", - "shell.execute_reply": "2025-05-08T15:50:47.181526Z", - "shell.execute_reply.started": "2025-05-08T15:50:45.904381Z" + "iopub.execute_input": "2025-05-09T16:18:10.418946Z", + "iopub.status.busy": "2025-05-09T16:18:10.418589Z", + "iopub.status.idle": "2025-05-09T16:18:11.624283Z", + "shell.execute_reply": "2025-05-09T16:18:11.623334Z", + "shell.execute_reply.started": "2025-05-09T16:18:10.418912Z" } }, "outputs": [ @@ -366,11 +365,11 @@ "id": "8cd2f538-c2d7-44ca-ab4d-825120b8f2e7", "metadata": { "execution": { - "iopub.execute_input": "2025-05-08T15:50:49.547916Z", - "iopub.status.busy": "2025-05-08T15:50:49.547418Z", - "iopub.status.idle": "2025-05-08T15:50:50.326158Z", - "shell.execute_reply": "2025-05-08T15:50:50.325069Z", - "shell.execute_reply.started": "2025-05-08T15:50:49.547880Z" + "iopub.execute_input": "2025-05-09T16:18:11.626676Z", + "iopub.status.busy": "2025-05-09T16:18:11.626330Z", + "iopub.status.idle": "2025-05-09T16:18:12.597894Z", + "shell.execute_reply": "2025-05-09T16:18:12.596916Z", + "shell.execute_reply.started": "2025-05-09T16:18:11.626643Z" } }, "outputs": [], @@ -394,11 +393,11 @@ "id": "ee4d121e-6b4d-4371-afae-4f7587b95d51", "metadata": { "execution": { - "iopub.execute_input": "2025-05-08T15:50:52.216555Z", - "iopub.status.busy": "2025-05-08T15:50:52.215445Z", - "iopub.status.idle": "2025-05-08T15:50:52.241861Z", - "shell.execute_reply": "2025-05-08T15:50:52.241057Z", - "shell.execute_reply.started": "2025-05-08T15:50:52.216511Z" + "iopub.execute_input": "2025-05-09T16:18:12.599653Z", + "iopub.status.busy": "2025-05-09T16:18:12.599299Z", + "iopub.status.idle": "2025-05-09T16:18:12.620797Z", + "shell.execute_reply": "2025-05-09T16:18:12.619962Z", + "shell.execute_reply.started": "2025-05-09T16:18:12.599617Z" } }, "outputs": [ @@ -553,11 +552,11 @@ "id": "db2168fe-593a-423d-b2f4-26ac0db60e8c", "metadata": { "execution": { - "iopub.execute_input": "2025-05-08T15:50:53.124214Z", - "iopub.status.busy": "2025-05-08T15:50:53.123725Z", - "iopub.status.idle": "2025-05-08T15:50:53.130819Z", - "shell.execute_reply": "2025-05-08T15:50:53.129785Z", - "shell.execute_reply.started": "2025-05-08T15:50:53.124156Z" + "iopub.execute_input": "2025-05-09T16:18:12.622350Z", + "iopub.status.busy": "2025-05-09T16:18:12.621983Z", + "iopub.status.idle": "2025-05-09T16:18:12.628248Z", + "shell.execute_reply": "2025-05-09T16:18:12.627342Z", + "shell.execute_reply.started": "2025-05-09T16:18:12.622315Z" } }, "outputs": [ @@ -592,11 +591,11 @@ "id": "eec25f58-d3f3-4ef4-b3e2-ab105c4718fd", "metadata": { "execution": { - "iopub.execute_input": "2025-05-08T15:50:55.299389Z", - "iopub.status.busy": "2025-05-08T15:50:55.298909Z", - "iopub.status.idle": "2025-05-08T15:50:55.339125Z", - "shell.execute_reply": "2025-05-08T15:50:55.338156Z", - "shell.execute_reply.started": "2025-05-08T15:50:55.299348Z" + "iopub.execute_input": "2025-05-09T16:18:12.630935Z", + "iopub.status.busy": "2025-05-09T16:18:12.630113Z", + "iopub.status.idle": "2025-05-09T16:18:12.680196Z", + "shell.execute_reply": "2025-05-09T16:18:12.679227Z", + "shell.execute_reply.started": "2025-05-09T16:18:12.630893Z" } }, "outputs": [ @@ -670,11 +669,11 @@ "id": "4fc9b578-2be4-4fb2-8d74-ebca809ea99f", "metadata": { "execution": { - "iopub.execute_input": "2025-05-08T15:50:56.647957Z", - "iopub.status.busy": "2025-05-08T15:50:56.647507Z", - "iopub.status.idle": "2025-05-08T15:50:57.986424Z", - "shell.execute_reply": "2025-05-08T15:50:57.985342Z", - "shell.execute_reply.started": "2025-05-08T15:50:56.647918Z" + "iopub.execute_input": "2025-05-09T16:18:12.681639Z", + "iopub.status.busy": "2025-05-09T16:18:12.681295Z", + "iopub.status.idle": "2025-05-09T16:18:13.813196Z", + "shell.execute_reply": "2025-05-09T16:18:13.812203Z", + "shell.execute_reply.started": "2025-05-09T16:18:12.681607Z" } }, "outputs": [ @@ -695,8 +694,7 @@ "plt.title('Box Plot of Data Distributions')\n", "plt.xlabel('Feature')\n", "plt.ylabel('Value')\n", - "plt.xticks(rotation=90)\n", - "plt.show()" + "plt.xticks(rotation=90);" ] }, { @@ -721,11 +719,11 @@ "id": "bacf5114-6a64-4100-8eb6-f1d9ddc36f89", "metadata": { "execution": { - "iopub.execute_input": "2025-05-08T15:50:58.892134Z", - "iopub.status.busy": "2025-05-08T15:50:58.891640Z", - "iopub.status.idle": "2025-05-08T15:50:59.327923Z", - "shell.execute_reply": "2025-05-08T15:50:59.326984Z", - "shell.execute_reply.started": "2025-05-08T15:50:58.892091Z" + "iopub.execute_input": "2025-05-09T16:18:13.815218Z", + "iopub.status.busy": "2025-05-09T16:18:13.814841Z", + "iopub.status.idle": "2025-05-09T16:18:14.274795Z", + "shell.execute_reply": "2025-05-09T16:18:14.273877Z", + "shell.execute_reply.started": "2025-05-09T16:18:13.815182Z" } }, "outputs": [ @@ -746,8 +744,7 @@ "plt.title('Box Plot of Data Distributions')\n", "plt.xlabel('Feature')\n", "plt.ylabel('Value')\n", - "plt.xticks(rotation=90)\n", - "plt.show()" + "plt.xticks(rotation=90);" ] }, { @@ -784,11 +781,11 @@ "id": "39521ac6-0bec-42e7-9062-8fc9ce5edc55", "metadata": { "execution": { - "iopub.execute_input": "2025-05-08T15:51:02.488157Z", - "iopub.status.busy": "2025-05-08T15:51:02.487689Z", - "iopub.status.idle": "2025-05-08T15:51:03.008439Z", - "shell.execute_reply": "2025-05-08T15:51:03.007484Z", - "shell.execute_reply.started": "2025-05-08T15:51:02.488101Z" + "iopub.execute_input": "2025-05-09T16:18:14.276565Z", + "iopub.status.busy": "2025-05-09T16:18:14.276205Z", + "iopub.status.idle": "2025-05-09T16:18:14.863604Z", + "shell.execute_reply": "2025-05-09T16:18:14.862594Z", + "shell.execute_reply.started": "2025-05-09T16:18:14.276533Z" } }, "outputs": [ @@ -811,8 +808,7 @@ "plt.title('Box Plot of Data Distributions')\n", "plt.xlabel('Feature')\n", "plt.ylabel('Value')\n", - "plt.xticks(rotation=90)\n", - "plt.show()" + "plt.xticks(rotation=90);" ] }, { @@ -853,11 +849,11 @@ "id": "0be4535d-cc89-45ef-98e9-591b9f459fae", "metadata": { "execution": { - "iopub.execute_input": "2025-05-08T15:51:07.063820Z", - "iopub.status.busy": "2025-05-08T15:51:07.063346Z", - "iopub.status.idle": "2025-05-08T15:51:07.072825Z", - "shell.execute_reply": "2025-05-08T15:51:07.071888Z", - "shell.execute_reply.started": "2025-05-08T15:51:07.063762Z" + "iopub.execute_input": "2025-05-09T16:18:15.468223Z", + "iopub.status.busy": "2025-05-09T16:18:15.467753Z", + "iopub.status.idle": "2025-05-09T16:18:15.478548Z", + "shell.execute_reply": "2025-05-09T16:18:15.477655Z", + "shell.execute_reply.started": "2025-05-09T16:18:15.468182Z" } }, "outputs": [ @@ -893,11 +889,11 @@ "id": "0e66ccb2-3922-471b-8c15-7fb055d02a10", "metadata": { "execution": { - "iopub.execute_input": "2025-05-08T15:51:09.208070Z", - "iopub.status.busy": "2025-05-08T15:51:09.207602Z", - "iopub.status.idle": "2025-05-08T15:51:09.218270Z", - "shell.execute_reply": "2025-05-08T15:51:09.217235Z", - "shell.execute_reply.started": "2025-05-08T15:51:09.208022Z" + "iopub.execute_input": "2025-05-09T16:18:15.985313Z", + "iopub.status.busy": "2025-05-09T16:18:15.984839Z", + "iopub.status.idle": "2025-05-09T16:18:15.993800Z", + "shell.execute_reply": "2025-05-09T16:18:15.992830Z", + "shell.execute_reply.started": "2025-05-09T16:18:15.985271Z" } }, "outputs": [ @@ -933,11 +929,11 @@ "id": "06786c33-2563-4237-9d0f-22d6308c0d7b", "metadata": { "execution": { - "iopub.execute_input": "2025-05-08T15:51:12.117284Z", - "iopub.status.busy": "2025-05-08T15:51:12.116205Z", - "iopub.status.idle": "2025-05-08T15:51:12.171978Z", - "shell.execute_reply": "2025-05-08T15:51:12.170939Z", - "shell.execute_reply.started": "2025-05-08T15:51:12.117236Z" + "iopub.execute_input": "2025-05-09T16:18:16.479307Z", + "iopub.status.busy": "2025-05-09T16:18:16.478811Z", + "iopub.status.idle": "2025-05-09T16:18:16.533260Z", + "shell.execute_reply": "2025-05-09T16:18:16.532360Z", + "shell.execute_reply.started": "2025-05-09T16:18:16.479271Z" } }, "outputs": [ @@ -997,11 +993,11 @@ "id": "e6294681-9c60-4ec6-805c-d378300acaa3", "metadata": { "execution": { - "iopub.execute_input": "2025-05-08T15:51:15.547801Z", - "iopub.status.busy": "2025-05-08T15:51:15.547302Z", - "iopub.status.idle": "2025-05-08T15:51:15.555768Z", - "shell.execute_reply": "2025-05-08T15:51:15.554642Z", - "shell.execute_reply.started": "2025-05-08T15:51:15.547757Z" + "iopub.execute_input": "2025-05-09T16:18:16.996257Z", + "iopub.status.busy": "2025-05-09T16:18:16.995774Z", + "iopub.status.idle": "2025-05-09T16:18:17.004073Z", + "shell.execute_reply": "2025-05-09T16:18:17.003156Z", + "shell.execute_reply.started": "2025-05-09T16:18:16.996219Z" } }, "outputs": [], @@ -1027,11 +1023,11 @@ "id": "61a66274-c3e1-4e41-b743-649fc00d69b7", "metadata": { "execution": { - "iopub.execute_input": "2025-05-08T15:51:17.167573Z", - "iopub.status.busy": "2025-05-08T15:51:17.167087Z", - "iopub.status.idle": "2025-05-08T15:51:17.587121Z", - "shell.execute_reply": "2025-05-08T15:51:17.586163Z", - "shell.execute_reply.started": "2025-05-08T15:51:17.167536Z" + "iopub.execute_input": "2025-05-09T16:18:17.522385Z", + "iopub.status.busy": "2025-05-09T16:18:17.521921Z", + "iopub.status.idle": "2025-05-09T16:18:17.925806Z", + "shell.execute_reply": "2025-05-09T16:18:17.924915Z", + "shell.execute_reply.started": "2025-05-09T16:18:17.522345Z" } }, "outputs": [ @@ -1074,11 +1070,11 @@ "id": "5afedb17-6478-4f2b-bdfc-38e73cd4a65e", "metadata": { "execution": { - "iopub.execute_input": "2025-05-08T15:51:23.567876Z", - "iopub.status.busy": "2025-05-08T15:51:23.567418Z", - "iopub.status.idle": "2025-05-08T15:51:23.852986Z", - "shell.execute_reply": "2025-05-08T15:51:23.852016Z", - "shell.execute_reply.started": "2025-05-08T15:51:23.567837Z" + "iopub.execute_input": "2025-05-09T16:18:18.339902Z", + "iopub.status.busy": "2025-05-09T16:18:18.339398Z", + "iopub.status.idle": "2025-05-09T16:18:18.613409Z", + "shell.execute_reply": "2025-05-09T16:18:18.612464Z", + "shell.execute_reply.started": "2025-05-09T16:18:18.339861Z" } }, "outputs": [ @@ -1131,11 +1127,11 @@ "id": "01fcf2a8-7f85-4ec0-8ebe-b69b76da7294", "metadata": { "execution": { - "iopub.execute_input": "2025-05-08T15:51:27.644191Z", - "iopub.status.busy": "2025-05-08T15:51:27.643095Z", - "iopub.status.idle": "2025-05-08T15:51:27.650162Z", - "shell.execute_reply": "2025-05-08T15:51:27.649214Z", - "shell.execute_reply.started": "2025-05-08T15:51:27.644129Z" + "iopub.execute_input": "2025-05-09T16:18:19.423236Z", + "iopub.status.busy": "2025-05-09T16:18:19.422764Z", + "iopub.status.idle": "2025-05-09T16:18:19.429425Z", + "shell.execute_reply": "2025-05-09T16:18:19.428553Z", + "shell.execute_reply.started": "2025-05-09T16:18:19.423195Z" } }, "outputs": [ @@ -1173,11 +1169,11 @@ "id": "47a125c4-77fa-4712-be40-241318966774", "metadata": { "execution": { - "iopub.execute_input": "2025-05-08T15:51:29.748114Z", - "iopub.status.busy": "2025-05-08T15:51:29.747213Z", - "iopub.status.idle": "2025-05-08T15:51:29.755113Z", - "shell.execute_reply": "2025-05-08T15:51:29.754029Z", - "shell.execute_reply.started": "2025-05-08T15:51:29.748069Z" + "iopub.execute_input": "2025-05-09T16:18:20.210366Z", + "iopub.status.busy": "2025-05-09T16:18:20.209909Z", + "iopub.status.idle": "2025-05-09T16:18:20.217366Z", + "shell.execute_reply": "2025-05-09T16:18:20.216386Z", + "shell.execute_reply.started": "2025-05-09T16:18:20.210329Z" } }, "outputs": [ @@ -1212,11 +1208,11 @@ "id": "ffbe5670-6de4-4a92-99f7-4e480789b596", "metadata": { "execution": { - "iopub.execute_input": "2025-05-08T15:51:31.827991Z", - "iopub.status.busy": "2025-05-08T15:51:31.827410Z", - "iopub.status.idle": "2025-05-08T15:51:31.835759Z", - "shell.execute_reply": "2025-05-08T15:51:31.834754Z", - "shell.execute_reply.started": "2025-05-08T15:51:31.827945Z" + "iopub.execute_input": "2025-05-09T16:18:20.727129Z", + "iopub.status.busy": "2025-05-09T16:18:20.726041Z", + "iopub.status.idle": "2025-05-09T16:18:20.734334Z", + "shell.execute_reply": "2025-05-09T16:18:20.733359Z", + "shell.execute_reply.started": "2025-05-09T16:18:20.727085Z" } }, "outputs": [], @@ -1239,11 +1235,11 @@ "id": "782e7e2e-372e-4fcb-b837-a19a3bc83511", "metadata": { "execution": { - "iopub.execute_input": "2025-05-08T15:51:32.487714Z", - "iopub.status.busy": "2025-05-08T15:51:32.487243Z", - "iopub.status.idle": "2025-05-08T15:51:32.494953Z", - "shell.execute_reply": "2025-05-08T15:51:32.494012Z", - "shell.execute_reply.started": "2025-05-08T15:51:32.487658Z" + "iopub.execute_input": "2025-05-09T16:18:21.293985Z", + "iopub.status.busy": "2025-05-09T16:18:21.292934Z", + "iopub.status.idle": "2025-05-09T16:18:21.300897Z", + "shell.execute_reply": "2025-05-09T16:18:21.299983Z", + "shell.execute_reply.started": "2025-05-09T16:18:21.293940Z" } }, "outputs": [ @@ -1274,15 +1270,15 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 25, "id": "fc90feca-ede1-44b0-929b-2fec1ddf5ad4", "metadata": { "execution": { - "iopub.execute_input": "2025-05-09T15:31:53.035002Z", - "iopub.status.busy": "2025-05-09T15:31:53.034550Z", - "iopub.status.idle": "2025-05-09T15:31:53.044698Z", - "shell.execute_reply": "2025-05-09T15:31:53.043727Z", - "shell.execute_reply.started": "2025-05-09T15:31:53.034964Z" + "iopub.execute_input": "2025-05-09T16:18:21.908571Z", + "iopub.status.busy": "2025-05-09T16:18:21.908104Z", + "iopub.status.idle": "2025-05-09T16:18:21.918120Z", + "shell.execute_reply": "2025-05-09T16:18:21.917110Z", + "shell.execute_reply.started": "2025-05-09T16:18:21.908533Z" } }, "outputs": [], @@ -1313,15 +1309,15 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 26, "id": "f09a28c9-f868-4cfa-a309-c53b26193e01", "metadata": { "execution": { - "iopub.execute_input": "2025-05-09T15:31:54.951546Z", - "iopub.status.busy": "2025-05-09T15:31:54.951042Z", - "iopub.status.idle": "2025-05-09T15:31:54.956764Z", - "shell.execute_reply": "2025-05-09T15:31:54.955828Z", - "shell.execute_reply.started": "2025-05-09T15:31:54.951504Z" + "iopub.execute_input": "2025-05-09T16:18:24.404499Z", + "iopub.status.busy": "2025-05-09T16:18:24.403312Z", + "iopub.status.idle": "2025-05-09T16:18:24.409291Z", + "shell.execute_reply": "2025-05-09T16:18:24.408329Z", + "shell.execute_reply.started": "2025-05-09T16:18:24.404446Z" } }, "outputs": [], @@ -1349,15 +1345,15 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 27, "id": "ec1efab7-be4c-4bdc-9ead-78fd6b400345", "metadata": { "execution": { - "iopub.execute_input": "2025-05-09T15:31:59.510886Z", - "iopub.status.busy": "2025-05-09T15:31:59.509993Z", - "iopub.status.idle": "2025-05-09T15:31:59.529093Z", - "shell.execute_reply": "2025-05-09T15:31:59.528057Z", - "shell.execute_reply.started": "2025-05-09T15:31:59.510841Z" + "iopub.execute_input": "2025-05-09T16:18:25.362815Z", + "iopub.status.busy": "2025-05-09T16:18:25.362388Z", + "iopub.status.idle": "2025-05-09T16:18:25.381427Z", + "shell.execute_reply": "2025-05-09T16:18:25.380399Z", + "shell.execute_reply.started": "2025-05-09T16:18:25.362779Z" } }, "outputs": [], @@ -1379,15 +1375,15 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 28, "id": "5be7b1b1-2177-46e5-ad41-607e55d12949", "metadata": { "execution": { - "iopub.execute_input": "2025-05-09T15:32:00.747937Z", - "iopub.status.busy": "2025-05-09T15:32:00.747516Z", - "iopub.status.idle": "2025-05-09T15:32:00.991754Z", - "shell.execute_reply": "2025-05-09T15:32:00.990817Z", - "shell.execute_reply.started": "2025-05-09T15:32:00.747900Z" + "iopub.execute_input": "2025-05-09T16:18:26.178912Z", + "iopub.status.busy": "2025-05-09T16:18:26.178478Z", + "iopub.status.idle": "2025-05-09T16:18:26.418951Z", + "shell.execute_reply": "2025-05-09T16:18:26.418038Z", + "shell.execute_reply.started": "2025-05-09T16:18:26.178874Z" } }, "outputs": [ @@ -1425,15 +1421,15 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 29, "id": "f4086f5f-7c2d-4d6c-a2d4-8dff42e2fc84", "metadata": { "execution": { - "iopub.execute_input": "2025-05-09T15:32:02.603014Z", - "iopub.status.busy": "2025-05-09T15:32:02.602111Z", - "iopub.status.idle": "2025-05-09T15:32:02.611130Z", - "shell.execute_reply": "2025-05-09T15:32:02.610226Z", - "shell.execute_reply.started": "2025-05-09T15:32:02.602972Z" + "iopub.execute_input": "2025-05-09T16:18:27.749334Z", + "iopub.status.busy": "2025-05-09T16:18:27.748865Z", + "iopub.status.idle": "2025-05-09T16:18:27.757581Z", + "shell.execute_reply": "2025-05-09T16:18:27.756633Z", + "shell.execute_reply.started": "2025-05-09T16:18:27.749299Z" } }, "outputs": [], @@ -1455,15 +1451,15 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 30, "id": "fc4cecc6-f256-4f55-8d54-f585630e8b4f", "metadata": { "execution": { - "iopub.execute_input": "2025-05-09T15:32:03.910513Z", - "iopub.status.busy": "2025-05-09T15:32:03.909979Z", - "iopub.status.idle": "2025-05-09T15:32:04.202681Z", - "shell.execute_reply": "2025-05-09T15:32:04.201749Z", - "shell.execute_reply.started": "2025-05-09T15:32:03.910472Z" + "iopub.execute_input": "2025-05-09T16:18:28.786036Z", + "iopub.status.busy": "2025-05-09T16:18:28.784951Z", + "iopub.status.idle": "2025-05-09T16:18:29.077459Z", + "shell.execute_reply": "2025-05-09T16:18:29.076542Z", + "shell.execute_reply.started": "2025-05-09T16:18:28.785983Z" } }, "outputs": [ @@ -1519,22 +1515,22 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 31, "id": "e02ca479-6105-442b-9879-2eb215dc4d66", "metadata": { "execution": { - "iopub.execute_input": "2025-05-09T15:32:05.964941Z", - "iopub.status.busy": "2025-05-09T15:32:05.964522Z", - "iopub.status.idle": "2025-05-09T15:32:05.975326Z", - "shell.execute_reply": "2025-05-09T15:32:05.974452Z", - "shell.execute_reply.started": "2025-05-09T15:32:05.964902Z" + "iopub.execute_input": "2025-05-09T16:18:30.632898Z", + "iopub.status.busy": "2025-05-09T16:18:30.632409Z", + "iopub.status.idle": "2025-05-09T16:18:30.644873Z", + "shell.execute_reply": "2025-05-09T16:18:30.643977Z", + "shell.execute_reply.started": "2025-05-09T16:18:30.632857Z" } }, "outputs": [ { "data": { "text/html": [ - "
LinearRegression()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + "
LinearRegression()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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" ], "text/plain": [ "LinearRegression()" ] }, - "execution_count": 79, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "model = LinearRegression()\n", - "model.fit(X_train_scaled, y_train_scaled)" + "model_lr = LinearRegression()\n", + "model_lr.fit(X_train_scaled, y_train_scaled)" ] }, { @@ -1975,15 +1971,15 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 32, "id": "aca8064a-c94f-4792-86b3-62ec2130471b", "metadata": { "execution": { - "iopub.execute_input": "2025-05-09T15:32:06.450956Z", - "iopub.status.busy": "2025-05-09T15:32:06.450489Z", - "iopub.status.idle": "2025-05-09T15:32:06.458261Z", - "shell.execute_reply": "2025-05-09T15:32:06.457343Z", - "shell.execute_reply.started": "2025-05-09T15:32:06.450901Z" + "iopub.execute_input": "2025-05-09T16:18:32.396516Z", + "iopub.status.busy": "2025-05-09T16:18:32.395981Z", + "iopub.status.idle": "2025-05-09T16:18:32.403572Z", + "shell.execute_reply": "2025-05-09T16:18:32.402651Z", + "shell.execute_reply.started": "2025-05-09T16:18:32.396475Z" } }, "outputs": [ @@ -1996,7 +1992,7 @@ } ], "source": [ - "y_pred = model.predict(X_test_scaled)\n", + "y_pred = model_lr.predict(X_test_scaled)\n", "mse = mean_squared_error(y_test_scaled, y_pred)\n", "print(\"MSE:\", mse)" ] @@ -2011,15 +2007,15 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 33, "id": "ee8bd887-928e-4a41-bd77-149b344ab238", "metadata": { "execution": { - "iopub.execute_input": "2025-05-09T15:32:32.423693Z", - "iopub.status.busy": "2025-05-09T15:32:32.423222Z", - "iopub.status.idle": "2025-05-09T15:32:32.635256Z", - "shell.execute_reply": "2025-05-09T15:32:32.634305Z", - "shell.execute_reply.started": "2025-05-09T15:32:32.423656Z" + "iopub.execute_input": "2025-05-09T16:18:35.304755Z", + "iopub.status.busy": "2025-05-09T16:18:35.303936Z", + "iopub.status.idle": "2025-05-09T16:18:35.511760Z", + "shell.execute_reply": "2025-05-09T16:18:35.510819Z", + "shell.execute_reply.started": "2025-05-09T16:18:35.304711Z" } }, "outputs": [ @@ -2062,15 +2058,15 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 34, "id": "6770c381-1f34-44a7-b93b-b7f915013620", "metadata": { "execution": { - "iopub.execute_input": "2025-05-09T15:32:47.862687Z", - "iopub.status.busy": "2025-05-09T15:32:47.862230Z", - "iopub.status.idle": "2025-05-09T15:32:47.868252Z", - "shell.execute_reply": "2025-05-09T15:32:47.867305Z", - "shell.execute_reply.started": "2025-05-09T15:32:47.862648Z" + "iopub.execute_input": "2025-05-09T16:18:36.922927Z", + "iopub.status.busy": "2025-05-09T16:18:36.922495Z", + "iopub.status.idle": "2025-05-09T16:18:36.928762Z", + "shell.execute_reply": "2025-05-09T16:18:36.927669Z", + "shell.execute_reply.started": "2025-05-09T16:18:36.922890Z" } }, "outputs": [], @@ -2080,15 +2076,15 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 35, "id": "f83cf984-ffa7-4486-98ec-bce890b91bad", "metadata": { "execution": { - "iopub.execute_input": "2025-05-09T15:33:37.708917Z", - "iopub.status.busy": "2025-05-09T15:33:37.708417Z", - "iopub.status.idle": "2025-05-09T15:33:37.983120Z", - "shell.execute_reply": "2025-05-09T15:33:37.982160Z", - "shell.execute_reply.started": "2025-05-09T15:33:37.708876Z" + "iopub.execute_input": "2025-05-09T16:18:37.323173Z", + "iopub.status.busy": "2025-05-09T16:18:37.322601Z", + "iopub.status.idle": "2025-05-09T16:18:37.597847Z", + "shell.execute_reply": "2025-05-09T16:18:37.596937Z", + "shell.execute_reply.started": "2025-05-09T16:18:37.323102Z" } }, "outputs": [ @@ -2118,7 +2114,7 @@ "id": "c2b12a25-236e-4a1b-804b-03b97960712c", "metadata": {}, "source": [ - "> A scatterplot of the true versus predicted value for the $r-$band Kron flux scaled back to the original values. tHe x- and y-axes scale between -1000 to 2e4." + "> A scatterplot of the true versus predicted value for the $r-$band Kron flux scaled back to the original values. The x- and y-axes scale between -1000 to 2e4." ] }, { @@ -2131,15 +2127,15 @@ }, { "cell_type": "code", - "execution_count": 93, + "execution_count": 36, "id": "fb653dbd-4e77-4099-80ac-781f20cb38eb", "metadata": { "execution": { - "iopub.execute_input": "2025-05-09T15:40:21.967711Z", - "iopub.status.busy": "2025-05-09T15:40:21.967269Z", - "iopub.status.idle": "2025-05-09T15:40:21.974355Z", - "shell.execute_reply": "2025-05-09T15:40:21.973440Z", - "shell.execute_reply.started": "2025-05-09T15:40:21.967672Z" + "iopub.execute_input": "2025-05-09T16:18:39.228469Z", + "iopub.status.busy": "2025-05-09T16:18:39.227972Z", + "iopub.status.idle": "2025-05-09T16:18:39.235162Z", + "shell.execute_reply": "2025-05-09T16:18:39.234208Z", + "shell.execute_reply.started": "2025-05-09T16:18:39.228426Z" } }, "outputs": [ @@ -2167,15 +2163,15 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 40, "id": "d80e56eb-6e3d-4110-bee6-3681ee4a923b", "metadata": { "execution": { - "iopub.execute_input": "2025-05-09T15:47:08.185341Z", - "iopub.status.busy": "2025-05-09T15:47:08.184859Z", - "iopub.status.idle": "2025-05-09T15:47:08.424243Z", - "shell.execute_reply": "2025-05-09T15:47:08.423297Z", - "shell.execute_reply.started": "2025-05-09T15:47:08.185305Z" + "iopub.execute_input": "2025-05-09T16:19:39.630306Z", + "iopub.status.busy": "2025-05-09T16:19:39.629841Z", + "iopub.status.idle": "2025-05-09T16:19:39.915410Z", + "shell.execute_reply": "2025-05-09T16:19:39.914383Z", + "shell.execute_reply.started": "2025-05-09T16:19:39.630268Z" } }, "outputs": [ @@ -2199,8 +2195,8 @@ ], "source": [ "X_train_i, X_test_i, y_train_i, y_test_i = train_test_split(\n", - " clean_df[['g_kronFlux', 'i_kronFlux']], # Use these two features\n", - " clean_df['r_kronFlux'], # Target variable\n", + " clean_df[['g_kronFlux', 'i_kronFlux']],\n", + " clean_df['r_kronFlux'],\n", " test_size=0.2,\n", " random_state=42\n", ")\n", @@ -2209,22 +2205,21 @@ "X_train_i_scaled = scaler.fit_transform(X_train_i)\n", "X_test_i_scaled = scaler.transform(X_test_i)\n", "\n", - "model = LinearRegression()\n", - "model.fit(X_train_i_scaled, y_train)\n", + "model_mlr = LinearRegression()\n", + "model_mlr.fit(X_train_i_scaled, y_train_i)\n", "\n", - "y_pred = model.predict(X_test)\n", + "y_pred_i = model_mlr.predict(X_test_i_scaled)\n", "\n", - "mse = mean_squared_error(y_test, y_pred)\n", + "mse = mean_squared_error(y_test_i, y_pred_i)\n", "print(\"MSE:\", mse)\n", "\n", "plt.clf()\n", - "plt.scatter(y_test, y_pred)\n", + "plt.scatter(y_test_i, y_pred_i)\n", "plt.plot([0, 1e6], [0, 1e6], color='black', ls='--')\n", "plt.xlabel('True')\n", "plt.ylabel('Predicted')\n", "plt.xlim([0, 2e4])\n", - "plt.ylim([0, 2e4])\n", - "plt.show()" + "plt.ylim([0, 2e4]);" ] }, { @@ -2254,15 +2249,15 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 41, "id": "efb56f9d-6487-444d-b283-6d60c1694948", "metadata": { "execution": { - "iopub.execute_input": "2025-05-09T15:47:31.495369Z", - "iopub.status.busy": "2025-05-09T15:47:31.494829Z", - "iopub.status.idle": "2025-05-09T15:47:35.882482Z", - "shell.execute_reply": "2025-05-09T15:47:35.881498Z", - "shell.execute_reply.started": "2025-05-09T15:47:31.495300Z" + "iopub.execute_input": "2025-05-09T16:19:59.530093Z", + "iopub.status.busy": "2025-05-09T16:19:59.529045Z", + "iopub.status.idle": "2025-05-09T16:20:03.716619Z", + "shell.execute_reply": "2025-05-09T16:20:03.715607Z", + "shell.execute_reply.started": "2025-05-09T16:19:59.530036Z" } }, "outputs": [ @@ -2270,12 +2265,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "MSE: 252763.24546072097\n" + "MSE: 3257968.3172415826\n" ] }, { "data": { - "image/png": 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", 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"text/plain": [ "
" ] @@ -2288,14 +2283,14 @@ "model_rf = RandomForestRegressor()\n", "model_rf.fit(X_train_i_scaled, y_train_i)\n", "\n", - "y_pred_i = model_rf.predict(X_test_i_scaled)\n", + "y_pred_i_rf = model_rf.predict(X_test_i_scaled)\n", "\n", "\n", - "mse = mean_squared_error(y_test_i, y_pred)\n", + "mse = mean_squared_error(y_test_i, y_pred_i_rf)\n", "print(\"MSE:\", mse)\n", "\n", "plt.clf()\n", - "plt.scatter(y_test_i, y_pred_i)\n", + "plt.scatter(y_test_i, y_pred_i_rf)\n", "plt.plot([0, 1e6], [0, 1e6], color='black', ls='--')\n", "plt.xlabel('True')\n", "plt.ylabel('Predicted')\n", @@ -2316,7 +2311,7 @@ "id": "13845e03-08fe-4f80-8883-a671e8afd9d5", "metadata": {}, "source": [ - "**Strange that the MSE is exactly the same?**" + "**Strange that the MSE is actually worse than linear regression.**" ] }, { @@ -2330,20 +2325,20 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": 44, "id": "e6420904-44bb-40cf-8ce0-92b22a802e59", "metadata": { "execution": { - "iopub.execute_input": "2025-05-09T15:48:55.192267Z", - "iopub.status.busy": "2025-05-09T15:48:55.191172Z", - "iopub.status.idle": "2025-05-09T15:48:55.196894Z", - "shell.execute_reply": "2025-05-09T15:48:55.195919Z", - "shell.execute_reply.started": "2025-05-09T15:48:55.192220Z" + "iopub.execute_input": "2025-05-09T16:21:12.136604Z", + "iopub.status.busy": "2025-05-09T16:21:12.136168Z", + "iopub.status.idle": "2025-05-09T16:21:12.141117Z", + "shell.execute_reply": "2025-05-09T16:21:12.140229Z", + "shell.execute_reply.started": "2025-05-09T16:21:12.136569Z" } }, "outputs": [], "source": [ - "param_grid = {'n_estimators': [1, 10, 50, 100, 200, 1000, 10000]}" + "param_grid = {'n_estimators': [1, 10, 50, 100, 200, 1000]}" ] }, { @@ -2356,17 +2351,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "id": "48a166c2-10b9-470f-bd03-3d7e6d51a1c7", "metadata": { "execution": { - "iopub.execute_input": "2025-05-09T15:58:49.884008Z", - "iopub.status.busy": "2025-05-09T15:58:49.883539Z" + "iopub.execute_input": "2025-05-09T16:21:18.230590Z", + "iopub.status.busy": "2025-05-09T16:21:18.230111Z", + "iopub.status.idle": "2025-05-09T16:24:49.175338Z", + "shell.execute_reply": "2025-05-09T16:24:49.174327Z", + "shell.execute_reply.started": "2025-05-09T16:21:18.230555Z" } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'n_estimators': 200}\n" + ] + } + ], "source": [ - "grid = GridSearchCV(model, param_grid, cv=5)\n", + "grid = GridSearchCV(model_rf, param_grid, cv=5)\n", "\n", "grid.fit(X_train_i_scaled, y_train_i)\n", "\n", @@ -2383,10 +2389,43 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "id": "89167a5d-e835-4f8d-8d57-ac82f0df6239", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2025-05-09T16:25:01.097927Z", + "iopub.status.busy": "2025-05-09T16:25:01.097473Z", + "iopub.status.idle": "2025-05-09T16:25:01.506885Z", + "shell.execute_reply": "2025-05-09T16:25:01.505928Z", + "shell.execute_reply.started": "2025-05-09T16:25:01.097885Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best Model: RandomForestRegressor(n_estimators=200)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE: 5916287.221941493\n" + ] + } + ], "source": [ "best_model = grid.best_estimator_\n", "print(\"Best Model:\", best_model)\n", @@ -2404,6 +2443,14 @@ "print(\"MSE:\", mse)" ] }, + { + "cell_type": "markdown", + "id": "e6438d53-e03b-4afa-a417-489064caf51e", + "metadata": {}, + "source": [ + "**To do: The multiple linear regression currently has a better MSE than this model, investigate why that is a little more.**" + ] + }, { "cell_type": "markdown", "id": "ec3e2b39-f86e-4dfa-a97a-9e018fc2208c", @@ -2422,15 +2469,15 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 48, "id": "a020b226-289d-4587-9e42-47f64574dbf6", "metadata": { "execution": { - "iopub.execute_input": "2025-05-09T15:54:10.459305Z", - "iopub.status.busy": "2025-05-09T15:54:10.458743Z", - "iopub.status.idle": "2025-05-09T15:54:10.480127Z", - "shell.execute_reply": "2025-05-09T15:54:10.479093Z", - "shell.execute_reply.started": "2025-05-09T15:54:10.459262Z" + "iopub.execute_input": "2025-05-09T16:25:09.807467Z", + "iopub.status.busy": "2025-05-09T16:25:09.806993Z", + "iopub.status.idle": "2025-05-09T16:25:09.828760Z", + "shell.execute_reply": "2025-05-09T16:25:09.827801Z", + "shell.execute_reply.started": "2025-05-09T16:25:09.807430Z" } }, "outputs": [ @@ -2570,7 +2617,7 @@ "[176 rows x 8 columns]" ] }, - "execution_count": 105, + "execution_count": 48, "metadata": {}, "output_type": "execute_result" } @@ -2586,30 +2633,18 @@ }, { "cell_type": "code", - "execution_count": 106, + "execution_count": 49, "id": "dd7a891e-5a56-440e-a721-91af73d74269", "metadata": { "execution": { - "iopub.execute_input": "2025-05-09T15:56:26.275670Z", - "iopub.status.busy": "2025-05-09T15:56:26.275203Z", - "iopub.status.idle": "2025-05-09T15:56:26.860172Z", - "shell.execute_reply": "2025-05-09T15:56:26.858874Z", - "shell.execute_reply.started": "2025-05-09T15:56:26.275634Z" + "iopub.execute_input": "2025-05-09T16:25:11.324579Z", + "iopub.status.busy": "2025-05-09T16:25:11.324115Z", + "iopub.status.idle": "2025-05-09T16:25:11.370830Z", + "shell.execute_reply": "2025-05-09T16:25:11.369815Z", + "shell.execute_reply.started": "2025-05-09T16:25:11.324542Z" } }, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'best_model' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[106], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m X_r_missing \u001b[38;5;241m=\u001b[39m r_missing_df[[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mg_kronFlux\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mi_kronFlux\u001b[39m\u001b[38;5;124m'\u001b[39m]]\n\u001b[1;32m 2\u001b[0m X_r_missing_scaled \u001b[38;5;241m=\u001b[39m scaler\u001b[38;5;241m.\u001b[39mfit_transform(X_r_missing)\n\u001b[0;32m----> 3\u001b[0m y_pred_r_missing \u001b[38;5;241m=\u001b[39m \u001b[43mbest_model\u001b[49m\u001b[38;5;241m.\u001b[39mpredict(X_r_missing_scaled)\n", - "\u001b[0;31mNameError\u001b[0m: name 'best_model' is not defined" - ] - } - ], + "outputs": [], "source": [ "X_r_missing = r_missing_df[['g_kronFlux', 'i_kronFlux']]\n", "X_r_missing_scaled = scaler.fit_transform(X_r_missing)\n", @@ -2618,16 +2653,51 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 50, "id": "0e438d24-75ba-4888-b2c9-cb4fa6c6db85", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2025-05-09T16:25:13.186405Z", + "iopub.status.busy": "2025-05-09T16:25:13.185950Z", + "iopub.status.idle": "2025-05-09T16:25:13.496043Z", + "shell.execute_reply": "2025-05-09T16:25:13.495130Z", + "shell.execute_reply.started": "2025-05-09T16:25:13.186368Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plt.scatter(r_missing_df['r_kronFlux'].values, y_pred_r_missing)\n", "plt.xlabel(r'Flagged $r-$band values')\n", "plt.ylabel('Predicted $r-$band values');" ] }, + { + "cell_type": "markdown", + "id": "d4c11515-3263-4d8e-bf08-fa60f7cafb9d", + "metadata": {}, + "source": [ + "> A scatterplot of flagged (original) $r-$band Kron flux values against the predicted values from the best fit model. The spread is much greater in the y-axis." + ] + }, + { + "cell_type": "markdown", + "id": "68c59d12-a11f-40b7-b7fb-84276d0c7c78", + "metadata": {}, + "source": [ + "**To do: Also plot against the predictors ($g-$ and $i-$band) Kron fluxes for a better idea of how this model performs.**" + ] + }, { "cell_type": "markdown", "id": "d5c33bd9-b95e-4ec8-a44d-cf3f219339e8", @@ -2639,10 +2709,78 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 51, "id": "5adca7ed-eb36-4e8b-99e3-3b66833fb3e3", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2025-05-09T16:27:33.633390Z", + "iopub.status.busy": "2025-05-09T16:27:33.632891Z", + "iopub.status.idle": "2025-05-09T16:27:33.642842Z", + "shell.execute_reply": "2025-05-09T16:27:33.641977Z", + "shell.execute_reply.started": "2025-05-09T16:27:33.633350Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['brier_score_loss', 'check_scoring', 'coverage_error', 'd2_absolute_error_score', 'd2_pinball_score', 'd2_tweedie_score', 'explained_variance_score', 'label_ranking_loss', 'log_loss', 'max_error', 'mean_absolute_error', 'mean_absolute_percentage_error', 'mean_gamma_deviance', 'mean_pinball_loss', 'mean_poisson_deviance', 'mean_squared_error', 'mean_squared_log_error', 'mean_tweedie_deviance', 'median_absolute_error', 'pairwise_distances', 'r2_score', 'root_mean_squared_error', 'root_mean_squared_log_error']\n", + "--- mean_tweedie_deviance ---\n", + "Help on function mean_tweedie_deviance in module sklearn.metrics._regression:\n", + "\n", + "mean_tweedie_deviance(y_true, y_pred, *, sample_weight=None, power=0)\n", + " Mean Tweedie deviance regression loss.\n", + "\n", + " Read more in the :ref:`User Guide `.\n", + "\n", + " Parameters\n", + " ----------\n", + " y_true : array-like of shape (n_samples,)\n", + " Ground truth (correct) target values.\n", + "\n", + " y_pred : array-like of shape (n_samples,)\n", + " Estimated target values.\n", + "\n", + " sample_weight : array-like of shape (n_samples,), default=None\n", + " Sample weights.\n", + "\n", + " power : float, default=0\n", + " Tweedie power parameter. Either power <= 0 or power >= 1.\n", + "\n", + " The higher `p` the less weight is given to extreme\n", + " deviations between true and predicted targets.\n", + "\n", + " - power < 0: Extreme stable distribution. Requires: y_pred > 0.\n", + " - power = 0 : Normal distribution, output corresponds to\n", + " mean_squared_error. y_true and y_pred can be any real numbers.\n", + " - power = 1 : Poisson distribution. Requires: y_true >= 0 and\n", + " y_pred > 0.\n", + " - 1 < p < 2 : Compound Poisson distribution. Requires: y_true >= 0\n", + " and y_pred > 0.\n", + " - power = 2 : Gamma distribution. Requires: y_true > 0 and y_pred > 0.\n", + " - power = 3 : Inverse Gaussian distribution. Requires: y_true > 0\n", + " and y_pred > 0.\n", + " - otherwise : Positive stable distribution. Requires: y_true > 0\n", + " and y_pred > 0.\n", + "\n", + " Returns\n", + " -------\n", + " loss : float\n", + " A non-negative floating point value (the best value is 0.0).\n", + "\n", + " Examples\n", + " --------\n", + " >>> from sklearn.metrics import mean_tweedie_deviance\n", + " >>> y_true = [2, 0, 1, 4]\n", + " >>> y_pred = [0.5, 0.5, 2., 2.]\n", + " >>> mean_tweedie_deviance(y_true, y_pred, power=1)\n", + " 1.4260...\n", + "\n", + "================================================================================\n" + ] + } + ], "source": [ "regression_metrics = [\n", " name for name, obj in inspect.getmembers(metrics)\n", @@ -2664,10 +2802,749 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 52, "id": "de4a41f7-9f4f-4662-9302-2da7d68df434", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2025-05-09T16:27:35.933017Z", + "iopub.status.busy": "2025-05-09T16:27:35.932581Z", + "iopub.status.idle": "2025-05-09T16:27:37.061758Z", + "shell.execute_reply": "2025-05-09T16:27:37.060814Z", + "shell.execute_reply.started": "2025-05-09T16:27:35.932976Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ARDRegression\n", + "AdaBoostRegressor\n", + "BaggingRegressor\n", + "BayesianRidge\n", + "CCA\n", + "DecisionTreeRegressor\n", + "DummyRegressor\n", + "ElasticNet\n", + "ElasticNetCV\n", + "ExtraTreeRegressor\n", + "ExtraTreesRegressor\n", + "GammaRegressor\n", + "GaussianProcessRegressor\n", + "GradientBoostingRegressor\n", + "HistGradientBoostingRegressor\n", + "HuberRegressor\n", + "IsotonicRegression\n", + "KNeighborsRegressor\n", + "KernelRidge\n", + "Lars\n", + "LarsCV\n", + "Lasso\n", + "LassoCV\n", + "LassoLars\n", + "LassoLarsCV\n", + "LassoLarsIC\n", + "LinearRegression\n", + "LinearSVR\n", + "MLPRegressor\n", + "MultiOutputRegressor\n", + "MultiTaskElasticNet\n", + "MultiTaskElasticNetCV\n", + "MultiTaskLasso\n", + "MultiTaskLassoCV\n", + "NuSVR\n", + "OrthogonalMatchingPursuit\n", + "OrthogonalMatchingPursuitCV\n", + "PLSCanonical\n", + "PLSRegression\n", + "PassiveAggressiveRegressor\n", + "PoissonRegressor\n", + "QuantileRegressor\n", + "RANSACRegressor\n", + "RadiusNeighborsRegressor\n", + "RandomForestRegressor\n", + "RegressorChain\n", + "Ridge\n", + "RidgeCV\n", + "SGDRegressor\n", + "SVR\n", + "StackingRegressor\n", + "TheilSenRegressor\n", + "TransformedTargetRegressor\n", + "TweedieRegressor\n", + "VotingRegressor\n", + "Help on class RandomForestRegressor in module sklearn.ensemble._forest:\n", + "\n", + "class RandomForestRegressor(ForestRegressor)\n", + " | RandomForestRegressor(n_estimators=100, *, criterion='squared_error', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=1.0, max_leaf_nodes=None, min_impurity_decrease=0.0, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, ccp_alpha=0.0, max_samples=None, monotonic_cst=None)\n", + " |\n", + " | A random forest regressor.\n", + " |\n", + " | A random forest is a meta estimator that fits a number of decision tree\n", + " | regressors on various sub-samples of the dataset and uses averaging to\n", + " | improve the predictive accuracy and control over-fitting.\n", + " | Trees in the forest use the best split strategy, i.e. equivalent to passing\n", + " | `splitter=\"best\"` to the underlying :class:`~sklearn.tree.DecisionTreeRegressor`.\n", + " | The sub-sample size is controlled with the `max_samples` parameter if\n", + " | `bootstrap=True` (default), otherwise the whole dataset is used to build\n", + " | each tree.\n", + " |\n", + " | For a comparison between tree-based ensemble models see the example\n", + " | :ref:`sphx_glr_auto_examples_ensemble_plot_forest_hist_grad_boosting_comparison.py`.\n", + " |\n", + " | Read more in the :ref:`User Guide `.\n", + " |\n", + " | Parameters\n", + " | ----------\n", + " | n_estimators : int, default=100\n", + " | The number of trees in the forest.\n", + " |\n", + " | .. versionchanged:: 0.22\n", + " | The default value of ``n_estimators`` changed from 10 to 100\n", + " | in 0.22.\n", + " |\n", + " | criterion : {\"squared_error\", \"absolute_error\", \"friedman_mse\", \"poisson\"}, default=\"squared_error\"\n", + " | The function to measure the quality of a split. Supported criteria\n", + " | are \"squared_error\" for the mean squared error, which is equal to\n", + " | variance reduction as feature selection criterion and minimizes the L2\n", + " | loss using the mean of each terminal node, \"friedman_mse\", which uses\n", + " | mean squared error with Friedman's improvement score for potential\n", + " | splits, \"absolute_error\" for the mean absolute error, which minimizes\n", + " | the L1 loss using the median of each terminal node, and \"poisson\" which\n", + " | uses reduction in Poisson deviance to find splits.\n", + " | Training using \"absolute_error\" is significantly slower\n", + " | than when using \"squared_error\".\n", + " |\n", + " | .. versionadded:: 0.18\n", + " | Mean Absolute Error (MAE) criterion.\n", + " |\n", + " | .. versionadded:: 1.0\n", + " | Poisson criterion.\n", + " |\n", + " | max_depth : int, default=None\n", + " | The maximum depth of the tree. If None, then nodes are expanded until\n", + " | all leaves are pure or until all leaves contain less than\n", + " | min_samples_split samples.\n", + " |\n", + " | min_samples_split : int or float, default=2\n", + " | The minimum number of samples required to split an internal node:\n", + " |\n", + " | - If int, then consider `min_samples_split` as the minimum number.\n", + " | - If float, then `min_samples_split` is a fraction and\n", + " | `ceil(min_samples_split * n_samples)` are the minimum\n", + " | number of samples for each split.\n", + " |\n", + " | .. versionchanged:: 0.18\n", + " | Added float values for fractions.\n", + " |\n", + " | min_samples_leaf : int or float, default=1\n", + " | The minimum number of samples required to be at a leaf node.\n", + " | A split point at any depth will only be considered if it leaves at\n", + " | least ``min_samples_leaf`` training samples in each of the left and\n", + " | right branches. This may have the effect of smoothing the model,\n", + " | especially in regression.\n", + " |\n", + " | - If int, then consider `min_samples_leaf` as the minimum number.\n", + " | - If float, then `min_samples_leaf` is a fraction and\n", + " | `ceil(min_samples_leaf * n_samples)` are the minimum\n", + " | number of samples for each node.\n", + " |\n", + " | .. versionchanged:: 0.18\n", + " | Added float values for fractions.\n", + " |\n", + " | min_weight_fraction_leaf : float, default=0.0\n", + " | The minimum weighted fraction of the sum total of weights (of all\n", + " | the input samples) required to be at a leaf node. Samples have\n", + " | equal weight when sample_weight is not provided.\n", + " |\n", + " | max_features : {\"sqrt\", \"log2\", None}, int or float, default=1.0\n", + " | The number of features to consider when looking for the best split:\n", + " |\n", + " | - If int, then consider `max_features` features at each split.\n", + " | - If float, then `max_features` is a fraction and\n", + " | `max(1, int(max_features * n_features_in_))` features are considered at each\n", + " | split.\n", + " | - If \"sqrt\", then `max_features=sqrt(n_features)`.\n", + " | - If \"log2\", then `max_features=log2(n_features)`.\n", + " | - If None or 1.0, then `max_features=n_features`.\n", + " |\n", + " | .. note::\n", + " | The default of 1.0 is equivalent to bagged trees and more\n", + " | randomness can be achieved by setting smaller values, e.g. 0.3.\n", + " |\n", + " | .. versionchanged:: 1.1\n", + " | The default of `max_features` changed from `\"auto\"` to 1.0.\n", + " |\n", + " | Note: the search for a split does not stop until at least one\n", + " | valid partition of the node samples is found, even if it requires to\n", + " | effectively inspect more than ``max_features`` features.\n", + " |\n", + " | max_leaf_nodes : int, default=None\n", + " | Grow trees with ``max_leaf_nodes`` in best-first fashion.\n", + " | Best nodes are defined as relative reduction in impurity.\n", + " | If None then unlimited number of leaf nodes.\n", + " |\n", + " | min_impurity_decrease : float, default=0.0\n", + " | A node will be split if this split induces a decrease of the impurity\n", + " | greater than or equal to this value.\n", + " |\n", + " | The weighted impurity decrease equation is the following::\n", + " |\n", + " | N_t / N * (impurity - N_t_R / N_t * right_impurity\n", + " | - N_t_L / N_t * left_impurity)\n", + " |\n", + " | where ``N`` is the total number of samples, ``N_t`` is the number of\n", + " | samples at the current node, ``N_t_L`` is the number of samples in the\n", + " | left child, and ``N_t_R`` is the number of samples in the right child.\n", + " |\n", + " | ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,\n", + " | if ``sample_weight`` is passed.\n", + " |\n", + " | .. versionadded:: 0.19\n", + " |\n", + " | bootstrap : bool, default=True\n", + " | Whether bootstrap samples are used when building trees. If False, the\n", + " | whole dataset is used to build each tree.\n", + " |\n", + " | oob_score : bool or callable, default=False\n", + " | Whether to use out-of-bag samples to estimate the generalization score.\n", + " | By default, :func:`~sklearn.metrics.r2_score` is used.\n", + " | Provide a callable with signature `metric(y_true, y_pred)` to use a\n", + " | custom metric. Only available if `bootstrap=True`.\n", + " |\n", + " | n_jobs : int, default=None\n", + " | The number of jobs to run in parallel. :meth:`fit`, :meth:`predict`,\n", + " | :meth:`decision_path` and :meth:`apply` are all parallelized over the\n", + " | trees. ``None`` means 1 unless in a :obj:`joblib.parallel_backend`\n", + " | context. ``-1`` means using all processors. See :term:`Glossary\n", + " | ` for more details.\n", + " |\n", + " | random_state : int, RandomState instance or None, default=None\n", + " | Controls both the randomness of the bootstrapping of the samples used\n", + " | when building trees (if ``bootstrap=True``) and the sampling of the\n", + " | features to consider when looking for the best split at each node\n", + " | (if ``max_features < n_features``).\n", + " | See :term:`Glossary ` for details.\n", + " |\n", + " | verbose : int, default=0\n", + " | Controls the verbosity when fitting and predicting.\n", + " |\n", + " | warm_start : bool, default=False\n", + " | When set to ``True``, reuse the solution of the previous call to fit\n", + " | and add more estimators to the ensemble, otherwise, just fit a whole\n", + " | new forest. See :term:`Glossary ` and\n", + " | :ref:`tree_ensemble_warm_start` for details.\n", + " |\n", + " | ccp_alpha : non-negative float, default=0.0\n", + " | Complexity parameter used for Minimal Cost-Complexity Pruning. The\n", + " | subtree with the largest cost complexity that is smaller than\n", + " | ``ccp_alpha`` will be chosen. By default, no pruning is performed. See\n", + " | :ref:`minimal_cost_complexity_pruning` for details. See\n", + " | :ref:`sphx_glr_auto_examples_tree_plot_cost_complexity_pruning.py`\n", + " | for an example of such pruning.\n", + " |\n", + " | .. versionadded:: 0.22\n", + " |\n", + " | max_samples : int or float, default=None\n", + " | If bootstrap is True, the number of samples to draw from X\n", + " | to train each base estimator.\n", + " |\n", + " | - If None (default), then draw `X.shape[0]` samples.\n", + " | - If int, then draw `max_samples` samples.\n", + " | - If float, then draw `max(round(n_samples * max_samples), 1)` samples. Thus,\n", + " | `max_samples` should be in the interval `(0.0, 1.0]`.\n", + " |\n", + " | .. versionadded:: 0.22\n", + " |\n", + " | monotonic_cst : array-like of int of shape (n_features), default=None\n", + " | Indicates the monotonicity constraint to enforce on each feature.\n", + " | - 1: monotonically increasing\n", + " | - 0: no constraint\n", + " | - -1: monotonically decreasing\n", + " |\n", + " | If monotonic_cst is None, no constraints are applied.\n", + " |\n", + " | Monotonicity constraints are not supported for:\n", + " | - multioutput regressions (i.e. when `n_outputs_ > 1`),\n", + " | - regressions trained on data with missing values.\n", + " |\n", + " | Read more in the :ref:`User Guide `.\n", + " |\n", + " | .. versionadded:: 1.4\n", + " |\n", + " | Attributes\n", + " | ----------\n", + " | estimator_ : :class:`~sklearn.tree.DecisionTreeRegressor`\n", + " | The child estimator template used to create the collection of fitted\n", + " | sub-estimators.\n", + " |\n", + " | .. versionadded:: 1.2\n", + " | `base_estimator_` was renamed to `estimator_`.\n", + " |\n", + " | estimators_ : list of DecisionTreeRegressor\n", + " | The collection of fitted sub-estimators.\n", + " |\n", + " | feature_importances_ : ndarray of shape (n_features,)\n", + " | The impurity-based feature importances.\n", + " | The higher, the more important the feature.\n", + " | The importance of a feature is computed as the (normalized)\n", + " | total reduction of the criterion brought by that feature. It is also\n", + " | known as the Gini importance.\n", + " |\n", + " | Warning: impurity-based feature importances can be misleading for\n", + " | high cardinality features (many unique values). See\n", + " | :func:`sklearn.inspection.permutation_importance` as an alternative.\n", + " |\n", + " | n_features_in_ : int\n", + " | Number of features seen during :term:`fit`.\n", + " |\n", + " | .. versionadded:: 0.24\n", + " |\n", + " | feature_names_in_ : ndarray of shape (`n_features_in_`,)\n", + " | Names of features seen during :term:`fit`. Defined only when `X`\n", + " | has feature names that are all strings.\n", + " |\n", + " | .. versionadded:: 1.0\n", + " |\n", + " | n_outputs_ : int\n", + " | The number of outputs when ``fit`` is performed.\n", + " |\n", + " | oob_score_ : float\n", + " | Score of the training dataset obtained using an out-of-bag estimate.\n", + " | This attribute exists only when ``oob_score`` is True.\n", + " |\n", + " | oob_prediction_ : ndarray of shape (n_samples,) or (n_samples, n_outputs)\n", + " | Prediction computed with out-of-bag estimate on the training set.\n", + " | This attribute exists only when ``oob_score`` is True.\n", + " |\n", + " | estimators_samples_ : list of arrays\n", + " | The subset of drawn samples (i.e., the in-bag samples) for each base\n", + " | estimator. Each subset is defined by an array of the indices selected.\n", + " |\n", + " | .. versionadded:: 1.4\n", + " |\n", + " | See Also\n", + " | --------\n", + " | sklearn.tree.DecisionTreeRegressor : A decision tree regressor.\n", + " | sklearn.ensemble.ExtraTreesRegressor : Ensemble of extremely randomized\n", + " | tree regressors.\n", + " | sklearn.ensemble.HistGradientBoostingRegressor : A Histogram-based Gradient\n", + " | Boosting Regression Tree, very fast for big datasets (n_samples >=\n", + " | 10_000).\n", + " |\n", + " | Notes\n", + " | -----\n", + " | The default values for the parameters controlling the size of the trees\n", + " | (e.g. ``max_depth``, ``min_samples_leaf``, etc.) lead to fully grown and\n", + " | unpruned trees which can potentially be very large on some data sets. To\n", + " | reduce memory consumption, the complexity and size of the trees should be\n", + " | controlled by setting those parameter values.\n", + " |\n", + " | The features are always randomly permuted at each split. Therefore,\n", + " | the best found split may vary, even with the same training data,\n", + " | ``max_features=n_features`` and ``bootstrap=False``, if the improvement\n", + " | of the criterion is identical for several splits enumerated during the\n", + " | search of the best split. To obtain a deterministic behaviour during\n", + " | fitting, ``random_state`` has to be fixed.\n", + " |\n", + " | The default value ``max_features=1.0`` uses ``n_features``\n", + " | rather than ``n_features / 3``. The latter was originally suggested in\n", + " | [1], whereas the former was more recently justified empirically in [2].\n", + " |\n", + " | References\n", + " | ----------\n", + " | .. [1] L. Breiman, \"Random Forests\", Machine Learning, 45(1), 5-32, 2001.\n", + " |\n", + " | .. [2] P. Geurts, D. Ernst., and L. Wehenkel, \"Extremely randomized\n", + " | trees\", Machine Learning, 63(1), 3-42, 2006.\n", + " |\n", + " | Examples\n", + " | --------\n", + " | >>> from sklearn.ensemble import RandomForestRegressor\n", + " | >>> from sklearn.datasets import make_regression\n", + " | >>> X, y = make_regression(n_features=4, n_informative=2,\n", + " | ... random_state=0, shuffle=False)\n", + " | >>> regr = RandomForestRegressor(max_depth=2, random_state=0)\n", + " | >>> regr.fit(X, y)\n", + " | RandomForestRegressor(...)\n", + " | >>> print(regr.predict([[0, 0, 0, 0]]))\n", + " | [-8.32987858]\n", + " |\n", + " | Method resolution order:\n", + " | RandomForestRegressor\n", + " | ForestRegressor\n", + " | sklearn.base.RegressorMixin\n", + " | BaseForest\n", + " | sklearn.base.MultiOutputMixin\n", + " | sklearn.ensemble._base.BaseEnsemble\n", + " | sklearn.base.MetaEstimatorMixin\n", + " | sklearn.base.BaseEstimator\n", + " | sklearn.utils._estimator_html_repr._HTMLDocumentationLinkMixin\n", + " | sklearn.utils._metadata_requests._MetadataRequester\n", + " | builtins.object\n", + " |\n", + " | Methods defined here:\n", + " |\n", + " | __init__(self, n_estimators=100, *, criterion='squared_error', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=1.0, max_leaf_nodes=None, min_impurity_decrease=0.0, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, ccp_alpha=0.0, max_samples=None, monotonic_cst=None)\n", + " | Initialize self. See help(type(self)) for accurate signature.\n", + " |\n", + " | set_fit_request(self: sklearn.ensemble._forest.RandomForestRegressor, *, sample_weight: Union[bool, NoneType, str] = '$UNCHANGED$') -> sklearn.ensemble._forest.RandomForestRegressor from sklearn.utils._metadata_requests.RequestMethod.__get__.\n", + " | Request metadata passed to the ``fit`` method.\n", + " |\n", + " | Note that this method is only relevant if\n", + " | ``enable_metadata_routing=True`` (see :func:`sklearn.set_config`).\n", + " | Please see :ref:`User Guide ` on how the routing\n", + " | mechanism works.\n", + " |\n", + " | The options for each parameter are:\n", + " |\n", + " | - ``True``: metadata is requested, and passed to ``fit`` if provided. The request is ignored if metadata is not provided.\n", + " |\n", + " | - ``False``: metadata is not requested and the meta-estimator will not pass it to ``fit``.\n", + " |\n", + " | - ``None``: metadata is not requested, and the meta-estimator will raise an error if the user provides it.\n", + " |\n", + " | - ``str``: metadata should be passed to the meta-estimator with this given alias instead of the original name.\n", + " |\n", + " | The default (``sklearn.utils.metadata_routing.UNCHANGED``) retains the\n", + " | existing request. This allows you to change the request for some\n", + " | parameters and not others.\n", + " |\n", + " | .. versionadded:: 1.3\n", + " |\n", + " | .. note::\n", + " | This method is only relevant if this estimator is used as a\n", + " | sub-estimator of a meta-estimator, e.g. used inside a\n", + " | :class:`~sklearn.pipeline.Pipeline`. Otherwise it has no effect.\n", + " |\n", + " | Parameters\n", + " | ----------\n", + " | sample_weight : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED\n", + " | Metadata routing for ``sample_weight`` parameter in ``fit``.\n", + " |\n", + " | Returns\n", + " | -------\n", + " | self : object\n", + " | The updated object.\n", + " |\n", + " | set_score_request(self: sklearn.ensemble._forest.RandomForestRegressor, *, sample_weight: Union[bool, NoneType, str] = '$UNCHANGED$') -> sklearn.ensemble._forest.RandomForestRegressor from sklearn.utils._metadata_requests.RequestMethod.__get__.\n", + " | Request metadata passed to the ``score`` method.\n", + " |\n", + " | Note that this method is only relevant if\n", + " | ``enable_metadata_routing=True`` (see :func:`sklearn.set_config`).\n", + " | Please see :ref:`User Guide ` on how the routing\n", + " | mechanism works.\n", + " |\n", + " | The options for each parameter are:\n", + " |\n", + " | - ``True``: metadata is requested, and passed to ``score`` if provided. The request is ignored if metadata is not provided.\n", + " |\n", + " | - ``False``: metadata is not requested and the meta-estimator will not pass it to ``score``.\n", + " |\n", + " | - ``None``: metadata is not requested, and the meta-estimator will raise an error if the user provides it.\n", + " |\n", + " | - ``str``: metadata should be passed to the meta-estimator with this given alias instead of the original name.\n", + " |\n", + " | The default (``sklearn.utils.metadata_routing.UNCHANGED``) retains the\n", + " | existing request. This allows you to change the request for some\n", + " | parameters and not others.\n", + " |\n", + " | .. versionadded:: 1.3\n", + " |\n", + " | .. note::\n", + " | This method is only relevant if this estimator is used as a\n", + " | sub-estimator of a meta-estimator, e.g. used inside a\n", + " | :class:`~sklearn.pipeline.Pipeline`. Otherwise it has no effect.\n", + " |\n", + " | Parameters\n", + " | ----------\n", + " | sample_weight : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED\n", + " | Metadata routing for ``sample_weight`` parameter in ``score``.\n", + " |\n", + " | Returns\n", + " | -------\n", + " | self : object\n", + " | The updated object.\n", + " |\n", + " | ----------------------------------------------------------------------\n", + " | Data and other attributes defined here:\n", + " |\n", + " | __abstractmethods__ = frozenset()\n", + " |\n", + " | __annotations__ = {'_parameter_constraints': }\n", + " |\n", + " | ----------------------------------------------------------------------\n", + " | Methods inherited from ForestRegressor:\n", + " |\n", + " | __sklearn_tags__(self)\n", + " |\n", + " | predict(self, X)\n", + " | Predict regression target for X.\n", + " |\n", + " | The predicted regression target of an input sample is computed as the\n", + " | mean predicted regression targets of the trees in the forest.\n", + " |\n", + " | Parameters\n", + " | ----------\n", + " | X : {array-like, sparse matrix} of shape (n_samples, n_features)\n", + " | The input samples. Internally, its dtype will be converted to\n", + " | ``dtype=np.float32``. If a sparse matrix is provided, it will be\n", + " | converted into a sparse ``csr_matrix``.\n", + " |\n", + " | Returns\n", + " | -------\n", + " | y : ndarray of shape (n_samples,) or (n_samples, n_outputs)\n", + " | The predicted values.\n", + " |\n", + " | ----------------------------------------------------------------------\n", + " | Methods inherited from sklearn.base.RegressorMixin:\n", + " |\n", + " | score(self, X, y, sample_weight=None)\n", + " | Return the coefficient of determination of the prediction.\n", + " |\n", + " | The coefficient of determination :math:`R^2` is defined as\n", + " | :math:`(1 - \\frac{u}{v})`, where :math:`u` is the residual\n", + " | sum of squares ``((y_true - y_pred)** 2).sum()`` and :math:`v`\n", + " | is the total sum of squares ``((y_true - y_true.mean()) ** 2).sum()``.\n", + " | The best possible score is 1.0 and it can be negative (because the\n", + " | model can be arbitrarily worse). A constant model that always predicts\n", + " | the expected value of `y`, disregarding the input features, would get\n", + " | a :math:`R^2` score of 0.0.\n", + " |\n", + " | Parameters\n", + " | ----------\n", + " | X : array-like of shape (n_samples, n_features)\n", + " | Test samples. For some estimators this may be a precomputed\n", + " | kernel matrix or a list of generic objects instead with shape\n", + " | ``(n_samples, n_samples_fitted)``, where ``n_samples_fitted``\n", + " | is the number of samples used in the fitting for the estimator.\n", + " |\n", + " | y : array-like of shape (n_samples,) or (n_samples, n_outputs)\n", + " | True values for `X`.\n", + " |\n", + " | sample_weight : array-like of shape (n_samples,), default=None\n", + " | Sample weights.\n", + " |\n", + " | Returns\n", + " | -------\n", + " | score : float\n", + " | :math:`R^2` of ``self.predict(X)`` w.r.t. `y`.\n", + " |\n", + " | Notes\n", + " | -----\n", + " | The :math:`R^2` score used when calling ``score`` on a regressor uses\n", + " | ``multioutput='uniform_average'`` from version 0.23 to keep consistent\n", + " | with default value of :func:`~sklearn.metrics.r2_score`.\n", + " | This influences the ``score`` method of all the multioutput\n", + " | regressors (except for\n", + " | :class:`~sklearn.multioutput.MultiOutputRegressor`).\n", + " |\n", + " | ----------------------------------------------------------------------\n", + " | Data descriptors inherited from sklearn.base.RegressorMixin:\n", + " |\n", + " | __dict__\n", + " | dictionary for instance variables\n", + " |\n", + " | __weakref__\n", + " | list of weak references to the object\n", + " |\n", + " | ----------------------------------------------------------------------\n", + " | Methods inherited from BaseForest:\n", + " |\n", + " | apply(self, X)\n", + " | Apply trees in the forest to X, return leaf indices.\n", + " |\n", + " | Parameters\n", + " | ----------\n", + " | X : {array-like, sparse matrix} of shape (n_samples, n_features)\n", + " | The input samples. Internally, its dtype will be converted to\n", + " | ``dtype=np.float32``. If a sparse matrix is provided, it will be\n", + " | converted into a sparse ``csr_matrix``.\n", + " |\n", + " | Returns\n", + " | -------\n", + " | X_leaves : ndarray of shape (n_samples, n_estimators)\n", + " | For each datapoint x in X and for each tree in the forest,\n", + " | return the index of the leaf x ends up in.\n", + " |\n", + " | decision_path(self, X)\n", + " | Return the decision path in the forest.\n", + " |\n", + " | .. versionadded:: 0.18\n", + " |\n", + " | Parameters\n", + " | ----------\n", + " | X : {array-like, sparse matrix} of shape (n_samples, n_features)\n", + " | The input samples. Internally, its dtype will be converted to\n", + " | ``dtype=np.float32``. If a sparse matrix is provided, it will be\n", + " | converted into a sparse ``csr_matrix``.\n", + " |\n", + " | Returns\n", + " | -------\n", + " | indicator : sparse matrix of shape (n_samples, n_nodes)\n", + " | Return a node indicator matrix where non zero elements indicates\n", + " | that the samples goes through the nodes. The matrix is of CSR\n", + " | format.\n", + " |\n", + " | n_nodes_ptr : ndarray of shape (n_estimators + 1,)\n", + " | The columns from indicator[n_nodes_ptr[i]:n_nodes_ptr[i+1]]\n", + " | gives the indicator value for the i-th estimator.\n", + " |\n", + " | fit(self, X, y, sample_weight=None)\n", + " | Build a forest of trees from the training set (X, y).\n", + " |\n", + " | Parameters\n", + " | ----------\n", + " | X : {array-like, sparse matrix} of shape (n_samples, n_features)\n", + " | The training input samples. Internally, its dtype will be converted\n", + " | to ``dtype=np.float32``. If a sparse matrix is provided, it will be\n", + " | converted into a sparse ``csc_matrix``.\n", + " |\n", + " | y : array-like of shape (n_samples,) or (n_samples, n_outputs)\n", + " | The target values (class labels in classification, real numbers in\n", + " | regression).\n", + " |\n", + " | sample_weight : array-like of shape (n_samples,), default=None\n", + " | Sample weights. If None, then samples are equally weighted. Splits\n", + " | that would create child nodes with net zero or negative weight are\n", + " | ignored while searching for a split in each node. In the case of\n", + " | classification, splits are also ignored if they would result in any\n", + " | single class carrying a negative weight in either child node.\n", + " |\n", + " | Returns\n", + " | -------\n", + " | self : object\n", + " | Fitted estimator.\n", + " |\n", + " | ----------------------------------------------------------------------\n", + " | Readonly properties inherited from BaseForest:\n", + " |\n", + " | estimators_samples_\n", + " | The subset of drawn samples for each base estimator.\n", + " |\n", + " | Returns a dynamically generated list of indices identifying\n", + " | the samples used for fitting each member of the ensemble, i.e.,\n", + " | the in-bag samples.\n", + " |\n", + " | Note: the list is re-created at each call to the property in order\n", + " | to reduce the object memory footprint by not storing the sampling\n", + " | data. Thus fetching the property may be slower than expected.\n", + " |\n", + " | feature_importances_\n", + " | The impurity-based feature importances.\n", + " |\n", + " | The higher, the more important the feature.\n", + " | The importance of a feature is computed as the (normalized)\n", + " | total reduction of the criterion brought by that feature. It is also\n", + " | known as the Gini importance.\n", + " |\n", + " | Warning: impurity-based feature importances can be misleading for\n", + " | high cardinality features (many unique values). See\n", + " | :func:`sklearn.inspection.permutation_importance` as an alternative.\n", + " |\n", + " | Returns\n", + " | -------\n", + " | feature_importances_ : ndarray of shape (n_features,)\n", + " | The values of this array sum to 1, unless all trees are single node\n", + " | trees consisting of only the root node, in which case it will be an\n", + " | array of zeros.\n", + " |\n", + " | ----------------------------------------------------------------------\n", + " | Methods inherited from sklearn.ensemble._base.BaseEnsemble:\n", + " |\n", + " | __getitem__(self, index)\n", + " | Return the index'th estimator in the ensemble.\n", + " |\n", + " | __iter__(self)\n", + " | Return iterator over estimators in the ensemble.\n", + " |\n", + " | __len__(self)\n", + " | Return the number of estimators in the ensemble.\n", + " |\n", + " | ----------------------------------------------------------------------\n", + " | Methods inherited from sklearn.base.BaseEstimator:\n", + " |\n", + " | __getstate__(self)\n", + " | Helper for pickle.\n", + " |\n", + " | __repr__(self, N_CHAR_MAX=700)\n", + " | Return repr(self).\n", + " |\n", + " | __setstate__(self, state)\n", + " |\n", + " | __sklearn_clone__(self)\n", + " |\n", + " | get_params(self, deep=True)\n", + " | Get parameters for this estimator.\n", + " |\n", + " | Parameters\n", + " | ----------\n", + " | deep : bool, default=True\n", + " | If True, will return the parameters for this estimator and\n", + " | contained subobjects that are estimators.\n", + " |\n", + " | Returns\n", + " | -------\n", + " | params : dict\n", + " | Parameter names mapped to their values.\n", + " |\n", + " | set_params(self, **params)\n", + " | Set the parameters of this estimator.\n", + " |\n", + " | The method works on simple estimators as well as on nested objects\n", + " | (such as :class:`~sklearn.pipeline.Pipeline`). The latter have\n", + " | parameters of the form ``__`` so that it's\n", + " | possible to update each component of a nested object.\n", + " |\n", + " | Parameters\n", + " | ----------\n", + " | **params : dict\n", + " | Estimator parameters.\n", + " |\n", + " | Returns\n", + " | -------\n", + " | self : estimator instance\n", + " | Estimator instance.\n", + " |\n", + " | ----------------------------------------------------------------------\n", + " | Methods inherited from sklearn.utils._metadata_requests._MetadataRequester:\n", + " |\n", + " | get_metadata_routing(self)\n", + " | Get metadata routing of this object.\n", + " |\n", + " | Please check :ref:`User Guide ` on how the routing\n", + " | mechanism works.\n", + " |\n", + " | Returns\n", + " | -------\n", + " | routing : MetadataRequest\n", + " | A :class:`~sklearn.utils.metadata_routing.MetadataRequest` encapsulating\n", + " | routing information.\n", + " |\n", + " | ----------------------------------------------------------------------\n", + " | Class methods inherited from sklearn.utils._metadata_requests._MetadataRequester:\n", + " |\n", + " | __init_subclass__(**kwargs)\n", + " | Set the ``set_{method}_request`` methods.\n", + " |\n", + " | This uses PEP-487 [1]_ to set the ``set_{method}_request`` methods. It\n", + " | looks for the information available in the set default values which are\n", + " | set using ``__metadata_request__*`` class attributes, or inferred\n", + " | from method signatures.\n", + " |\n", + " | The ``__metadata_request__*`` class attributes are used when a method\n", + " | does not explicitly accept a metadata through its arguments or if the\n", + " | developer would like to specify a request value for those metadata\n", + " | which are different from the default ``None``.\n", + " |\n", + " | References\n", + " | ----------\n", + " | .. [1] https://www.python.org/dev/peps/pep-0487\n", + "\n", + "None\n" + ] + } + ], "source": [ "# Get all regression models\n", "regressors = all_estimators(type_filter='regressor')\n", From 3566d77de74c680a1432f28a1e6a03d92e9a60c5 Mon Sep 17 00:00:00 2001 From: beckynevin Date: Fri, 9 May 2025 16:31:29 +0000 Subject: [PATCH 13/13] outputs cleared last run date updated --- DP0.2/20_Introduction_to_Data_Science.ipynb | 2469 +------------------ 1 file changed, 124 insertions(+), 2345 deletions(-) diff --git a/DP0.2/20_Introduction_to_Data_Science.ipynb b/DP0.2/20_Introduction_to_Data_Science.ipynb index e706f611..995e6d81 100644 --- a/DP0.2/20_Introduction_to_Data_Science.ipynb +++ b/DP0.2/20_Introduction_to_Data_Science.ipynb @@ -9,7 +9,7 @@ " \n", "
\n", "Contact author(s): Becky Nevin, Brian Nord
\n", - "Last verified to run: 2025-05-06
\n", + "Last verified to run: 2025-05-09
\n", "LSST Science Pipelines version: Weekly 2025_09
\n", "Container size: small
\n", "Targeted learning level: intermediate
" @@ -105,17 +105,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "3f4900a4-3358-472a-b9ba-c42e3f2f0771", - "metadata": { - "execution": { - "iopub.execute_input": "2025-05-09T16:18:06.633183Z", - "iopub.status.busy": "2025-05-09T16:18:06.632108Z", - "iopub.status.idle": "2025-05-09T16:18:10.294306Z", - "shell.execute_reply": "2025-05-09T16:18:10.293237Z", - "shell.execute_reply.started": "2025-05-09T16:18:06.633120Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -149,17 +141,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "94acc9f6-2033-4ace-aefd-d036a35f4221", - "metadata": { - "execution": { - "iopub.execute_input": "2025-05-09T16:18:10.297176Z", - "iopub.status.busy": "2025-05-09T16:18:10.295985Z", - "iopub.status.idle": "2025-05-09T16:18:10.302384Z", - "shell.execute_reply": "2025-05-09T16:18:10.301449Z", - "shell.execute_reply.started": "2025-05-09T16:18:10.297111Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "sns.set_style(\"whitegrid\")\n", @@ -179,17 +163,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "caf56589-100a-4481-8f24-5f5058b6671f", - "metadata": { - "execution": { - "iopub.execute_input": "2025-05-09T16:18:10.303800Z", - "iopub.status.busy": "2025-05-09T16:18:10.303440Z", - "iopub.status.idle": "2025-05-09T16:18:10.376181Z", - "shell.execute_reply": "2025-05-09T16:18:10.375133Z", - "shell.execute_reply.started": "2025-05-09T16:18:10.303766Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "service = get_tap_service(\"tap\")\n", @@ -206,17 +182,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "2b7b6002-2457-4c20-a03e-6bfa24a0aa27", - "metadata": { - "execution": { - "iopub.execute_input": "2025-05-09T16:18:10.378538Z", - "iopub.status.busy": "2025-05-09T16:18:10.378198Z", - "iopub.status.idle": "2025-05-09T16:18:10.382986Z", - "shell.execute_reply": "2025-05-09T16:18:10.382055Z", - "shell.execute_reply.started": "2025-05-09T16:18:10.378505Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "pandas.set_option(\"display.max_rows\", 6)" @@ -243,17 +211,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "7ddd0344-b354-45a0-9e5a-755149c9bc54", - "metadata": { - "execution": { - "iopub.execute_input": "2025-05-09T16:18:10.384792Z", - "iopub.status.busy": "2025-05-09T16:18:10.384175Z", - "iopub.status.idle": "2025-05-09T16:18:10.400375Z", - "shell.execute_reply": "2025-05-09T16:18:10.399370Z", - "shell.execute_reply.started": "2025-05-09T16:18:10.384756Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "center_ra = 62\n", @@ -274,26 +234,10 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "985e3b62-8065-42ec-a40c-1232c4c45f17", - "metadata": { - "execution": { - "iopub.execute_input": "2025-05-09T16:18:10.401789Z", - "iopub.status.busy": "2025-05-09T16:18:10.401429Z", - "iopub.status.idle": "2025-05-09T16:18:10.417575Z", - "shell.execute_reply": "2025-05-09T16:18:10.416625Z", - "shell.execute_reply.started": "2025-05-09T16:18:10.401754Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "SELECT coord_ra, coord_dec, g_kronFlux, g_kronFlux_flag, r_kronFlux, r_kronFlux_flag, i_kronFlux, i_kronFlux_flag FROM dp02_dc2_catalogs.Object WHERE CONTAINS(POINT('ICRS', coord_ra, coord_dec), CIRCLE('ICRS', 62, -37, 0.1)) = 1 AND detect_isPrimary = 1 AND g_extendedness = 1\n" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ "query = \"SELECT coord_ra, coord_dec, g_kronFlux, g_kronFlux_flag, \"\\\n", " \"r_kronFlux, r_kronFlux_flag, i_kronFlux, i_kronFlux_flag \"\\\n", @@ -314,26 +258,10 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "c02adc91-5f5e-418b-87a3-cba8beba7dd2", - "metadata": { - "execution": { - "iopub.execute_input": "2025-05-09T16:18:10.418946Z", - "iopub.status.busy": "2025-05-09T16:18:10.418589Z", - "iopub.status.idle": "2025-05-09T16:18:11.624283Z", - "shell.execute_reply": "2025-05-09T16:18:11.623334Z", - "shell.execute_reply.started": "2025-05-09T16:18:10.418912Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Job phase is COMPLETED\n" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ "job = service.submit_job(query)\n", "job.run()\n", @@ -361,17 +289,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "8cd2f538-c2d7-44ca-ab4d-825120b8f2e7", - "metadata": { - "execution": { - "iopub.execute_input": "2025-05-09T16:18:11.626676Z", - "iopub.status.busy": "2025-05-09T16:18:11.626330Z", - "iopub.status.idle": "2025-05-09T16:18:12.597894Z", - "shell.execute_reply": "2025-05-09T16:18:12.596916Z", - "shell.execute_reply.started": "2025-05-09T16:18:11.626643Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "results = job.fetch_result().to_table().to_pandas()\n", @@ -389,188 +309,20 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "ee4d121e-6b4d-4371-afae-4f7587b95d51", - 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coord_racoord_decg_kronFluxg_kronFlux_flagr_kronFluxr_kronFlux_flagi_kronFluxi_kronFlux_flag
062.018897-37.09567171.568352True91.185588True624.454022True
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262.000430-37.093196131.680920False137.655812False136.174616True
...........................
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"execution_count": 10, + "execution_count": null, "id": "db2168fe-593a-423d-b2f4-26ac0db60e8c", - "metadata": { - "execution": { - "iopub.execute_input": "2025-05-09T16:18:12.622350Z", - "iopub.status.busy": "2025-05-09T16:18:12.621983Z", - "iopub.status.idle": "2025-05-09T16:18:12.628248Z", - "shell.execute_reply": "2025-05-09T16:18:12.627342Z", - "shell.execute_reply.started": "2025-05-09T16:18:12.622315Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "pandas.core.frame.DataFrame" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "type(results)" ] @@ -587,67 +339,10 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "eec25f58-d3f3-4ef4-b3e2-ab105c4718fd", - "metadata": { - "execution": { - "iopub.execute_input": "2025-05-09T16:18:12.630935Z", - "iopub.status.busy": "2025-05-09T16:18:12.630113Z", - "iopub.status.idle": 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- "11556 61.942562 -36.951137 108.966860 False 135.301872 \n", - "... ... ... ... ... ... \n", - "11561 61.950427 -36.946586 51.054369 True 175.646973 \n", - "11562 61.976752 -36.904225 199.039503 False 187.972452 \n", - "11563 61.932319 -36.941077 266.123377 False 218.853195 \n", - "\n", - " r_kronFlux_flag i_kronFlux i_kronFlux_flag \n", - "11554 True 102.094474 True \n", - "11555 True NaN True \n", - "11556 False 191.964068 True \n", - "... ... ... ... \n", - "11561 False 123.073904 True \n", - "11562 False 115.825734 False \n", - "11563 False 481.650950 True \n", - "\n", - "[10 rows x 8 columns]\n", - " coord_ra coord_dec g_kronFlux r_kronFlux i_kronFlux\n", - "count 11564.000000 11564.000000 11447.000000 1.145300e+04 1.129200e+04\n", - "mean 61.999517 -37.001530 761.468963 1.368137e+03 2.071233e+03\n", - "std 0.062390 0.050868 13194.742739 2.395294e+04 3.258561e+04\n", - "... ... ... ... ... ...\n", - "50% 61.999039 -37.001694 182.963268 2.409297e+02 3.517374e+02\n", - "75% 62.049891 -36.960187 340.536311 4.811457e+02 7.569082e+02\n", - "max 62.124349 -36.900195 782163.585831 1.957761e+06 2.870998e+06\n", - "\n", - "[8 rows x 5 columns]\n" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ "print(results.head())\n", "print(results.tail(10))\n", @@ -665,29 +360,10 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "4fc9b578-2be4-4fb2-8d74-ebca809ea99f", - "metadata": { - "execution": { - "iopub.execute_input": "2025-05-09T16:18:12.681639Z", - "iopub.status.busy": "2025-05-09T16:18:12.681295Z", - "iopub.status.idle": "2025-05-09T16:18:13.813196Z", - "shell.execute_reply": "2025-05-09T16:18:13.812203Z", - "shell.execute_reply.started": "2025-05-09T16:18:12.681607Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "plt.figure(figsize=(8, 6))\n", "sns.boxplot(data=results)\n", @@ -715,29 +391,10 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "bacf5114-6a64-4100-8eb6-f1d9ddc36f89", - "metadata": { - "execution": { - "iopub.execute_input": "2025-05-09T16:18:13.815218Z", - "iopub.status.busy": "2025-05-09T16:18:13.814841Z", - "iopub.status.idle": "2025-05-09T16:18:14.274795Z", - "shell.execute_reply": "2025-05-09T16:18:14.273877Z", - "shell.execute_reply.started": "2025-05-09T16:18:13.815182Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "plt.figure(figsize=(8, 6))\n", "sns.boxplot(data=results[['g_kronFlux','r_kronFlux','i_kronFlux']], showfliers=False)\n", @@ -777,29 +434,10 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "39521ac6-0bec-42e7-9062-8fc9ce5edc55", - "metadata": { - "execution": { - "iopub.execute_input": "2025-05-09T16:18:14.276565Z", - "iopub.status.busy": "2025-05-09T16:18:14.276205Z", - "iopub.status.idle": "2025-05-09T16:18:14.863604Z", - "shell.execute_reply": "2025-05-09T16:18:14.862594Z", - "shell.execute_reply.started": "2025-05-09T16:18:14.276533Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "plt.figure(figsize=(8, 6))\n", "filtered_results = results[['g_kronFlux', 'r_kronFlux', 'i_kronFlux']].apply(lambda x: x[(x > x.quantile(0.05)) & (x < x.quantile(0.95))])\n", @@ -845,32 +483,10 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "0be4535d-cc89-45ef-98e9-591b9f459fae", - "metadata": { - "execution": { - "iopub.execute_input": "2025-05-09T16:18:15.468223Z", - "iopub.status.busy": "2025-05-09T16:18:15.467753Z", - "iopub.status.idle": "2025-05-09T16:18:15.478548Z", - "shell.execute_reply": "2025-05-09T16:18:15.477655Z", - "shell.execute_reply.started": "2025-05-09T16:18:15.468182Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "g_kronFlux_flag\n", - "False 10728\n", - "True 836\n", - "Name: count, dtype: int64" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "results['g_kronFlux_flag'].value_counts()" ] @@ -885,32 +501,10 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "0e66ccb2-3922-471b-8c15-7fb055d02a10", - "metadata": { - "execution": { - "iopub.execute_input": "2025-05-09T16:18:15.985313Z", - "iopub.status.busy": "2025-05-09T16:18:15.984839Z", - "iopub.status.idle": "2025-05-09T16:18:15.993800Z", - "shell.execute_reply": "2025-05-09T16:18:15.992830Z", - "shell.execute_reply.started": "2025-05-09T16:18:15.985271Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "r_kronFlux_flag\n", - "False 10723\n", - "True 841\n", - "Name: count, dtype: int64" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "results['r_kronFlux_flag'].value_counts()" ] @@ -925,44 +519,10 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "06786c33-2563-4237-9d0f-22d6308c0d7b", - "metadata": { - "execution": { - "iopub.execute_input": "2025-05-09T16:18:16.479307Z", - "iopub.status.busy": "2025-05-09T16:18:16.478811Z", - "iopub.status.idle": "2025-05-09T16:18:16.533260Z", - "shell.execute_reply": "2025-05-09T16:18:16.532360Z", - "shell.execute_reply.started": "2025-05-09T16:18:16.479271Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " coord_ra coord_dec g_kronFlux g_kronFlux_flag r_kronFlux \\\n", - "0 62.018897 -37.095671 71.568352 True 91.185588 \n", - "12 62.020649 -37.085250 164.458389 True 83.719543 \n", - "44 62.050118 -37.076587 90.446302 True 127.674283 \n", - "... ... ... ... ... ... \n", - "11539 61.964568 -36.904478 NaN True 414.809809 \n", - "11547 61.885858 -36.962398 51.079756 True 138.740430 \n", - "11560 61.975756 -36.903597 66.317754 True 114.603682 \n", - "\n", - " r_kronFlux_flag i_kronFlux i_kronFlux_flag \n", - "0 True 624.454022 True \n", - "12 True 39.636459 True \n", - "44 True 455.773111 True \n", - "... ... ... ... \n", - "11539 True 85.175780 False \n", - "11547 True NaN True \n", - "11560 True 199.928482 True \n", - "\n", - "[328 rows x 8 columns]\n" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ "r_values = set(results['r_kronFlux_flag'].unique())\n", "g_values = set(results['g_kronFlux_flag'].unique())\n", @@ -989,17 +549,9 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "e6294681-9c60-4ec6-805c-d378300acaa3", - "metadata": { - "execution": { - "iopub.execute_input": "2025-05-09T16:18:16.996257Z", - "iopub.status.busy": "2025-05-09T16:18:16.995774Z", - "iopub.status.idle": "2025-05-09T16:18:17.004073Z", - "shell.execute_reply": "2025-05-09T16:18:17.003156Z", - "shell.execute_reply.started": "2025-05-09T16:18:16.996219Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "unflagged_df = results[\n", @@ -1019,29 +571,10 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "id": "61a66274-c3e1-4e41-b743-649fc00d69b7", - "metadata": { - "execution": { - "iopub.execute_input": "2025-05-09T16:18:17.522385Z", - "iopub.status.busy": "2025-05-09T16:18:17.521921Z", - "iopub.status.idle": "2025-05-09T16:18:17.925806Z", - "shell.execute_reply": "2025-05-09T16:18:17.924915Z", - "shell.execute_reply.started": "2025-05-09T16:18:17.522345Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "plt.scatter(unflagged_df['g_kronFlux'], unflagged_df['r_kronFlux'])\n", "plt.xlabel(r'$g-$band kronFlux [nJy]')\n", @@ -1066,29 +599,10 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "id": "5afedb17-6478-4f2b-bdfc-38e73cd4a65e", - "metadata": { - "execution": { - "iopub.execute_input": "2025-05-09T16:18:18.339902Z", - "iopub.status.busy": "2025-05-09T16:18:18.339398Z", - "iopub.status.idle": "2025-05-09T16:18:18.613409Z", - "shell.execute_reply": "2025-05-09T16:18:18.612464Z", - "shell.execute_reply.started": "2025-05-09T16:18:18.339861Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "plt.scatter(unflagged_df['g_kronFlux'], unflagged_df['r_kronFlux'])\n", "plt.xlabel(r'$g-$band kronFlux [nJy]')\n", @@ -1123,26 +637,10 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "id": "01fcf2a8-7f85-4ec0-8ebe-b69b76da7294", - "metadata": { - "execution": { - "iopub.execute_input": "2025-05-09T16:18:19.423236Z", - "iopub.status.busy": "2025-05-09T16:18:19.422764Z", - "iopub.status.idle": "2025-05-09T16:18:19.429425Z", - "shell.execute_reply": "2025-05-09T16:18:19.428553Z", - "shell.execute_reply.started": "2025-05-09T16:18:19.423195Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "False False\n" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ "print(unflagged_df['g_kronFlux'].isna().any(), unflagged_df['r_kronFlux'].isna().any())" ] @@ -1165,27 +663,10 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "id": "47a125c4-77fa-4712-be40-241318966774", - "metadata": { - "execution": { - "iopub.execute_input": "2025-05-09T16:18:20.210366Z", - "iopub.status.busy": "2025-05-09T16:18:20.209909Z", - "iopub.status.idle": "2025-05-09T16:18:20.217366Z", - "shell.execute_reply": "2025-05-09T16:18:20.216386Z", - "shell.execute_reply.started": "2025-05-09T16:18:20.210329Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Negative or zero values in g_kronFlux: 23\n", - "Negative or zero values in r_kronFlux: 12\n" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ "neg_g = (unflagged_df['g_kronFlux'] <= 0).sum()\n", "neg_r = (unflagged_df['r_kronFlux'] <= 0).sum()\n", @@ -1204,17 +685,9 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "id": "ffbe5670-6de4-4a92-99f7-4e480789b596", - "metadata": { - "execution": { - "iopub.execute_input": "2025-05-09T16:18:20.727129Z", - "iopub.status.busy": "2025-05-09T16:18:20.726041Z", - "iopub.status.idle": "2025-05-09T16:18:20.734334Z", - "shell.execute_reply": "2025-05-09T16:18:20.733359Z", - "shell.execute_reply.started": "2025-05-09T16:18:20.727085Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "mask = (unflagged_df['g_kronFlux'] > 0) & (unflagged_df['r_kronFlux'] > 0) & (unflagged_df['i_kronFlux'] > 0)\n", @@ -1231,27 +704,10 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "id": "782e7e2e-372e-4fcb-b837-a19a3bc83511", - "metadata": { - "execution": { - "iopub.execute_input": "2025-05-09T16:18:21.293985Z", - "iopub.status.busy": "2025-05-09T16:18:21.292934Z", - "iopub.status.idle": "2025-05-09T16:18:21.300897Z", - "shell.execute_reply": "2025-05-09T16:18:21.299983Z", - "shell.execute_reply.started": "2025-05-09T16:18:21.293940Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Negative or zero values in g_kronFlux: 0\n", - "Negative or zero values in r_kronFlux: 0\n" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ "neg_g = (clean_df['g_kronFlux'] <= 0).sum()\n", "neg_r = (clean_df['r_kronFlux'] <= 0).sum()\n", @@ -1270,17 +726,9 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "id": "fc90feca-ede1-44b0-929b-2fec1ddf5ad4", - "metadata": { - "execution": { - "iopub.execute_input": "2025-05-09T16:18:21.908571Z", - "iopub.status.busy": "2025-05-09T16:18:21.908104Z", - "iopub.status.idle": "2025-05-09T16:18:21.918120Z", - "shell.execute_reply": "2025-05-09T16:18:21.917110Z", - "shell.execute_reply.started": "2025-05-09T16:18:21.908533Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "X_train, X_test, y_train, y_test = train_test_split(\n", @@ -1309,17 +757,9 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "id": "f09a28c9-f868-4cfa-a309-c53b26193e01", - "metadata": { - "execution": { - "iopub.execute_input": "2025-05-09T16:18:24.404499Z", - "iopub.status.busy": "2025-05-09T16:18:24.403312Z", - "iopub.status.idle": "2025-05-09T16:18:24.409291Z", - "shell.execute_reply": "2025-05-09T16:18:24.408329Z", - "shell.execute_reply.started": "2025-05-09T16:18:24.404446Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "log_scaler = FunctionTransformer(func=np.log, inverse_func=np.exp, validate=True)\n", @@ -1345,17 +785,9 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "id": "ec1efab7-be4c-4bdc-9ead-78fd6b400345", - "metadata": { - "execution": { - "iopub.execute_input": "2025-05-09T16:18:25.362815Z", - "iopub.status.busy": "2025-05-09T16:18:25.362388Z", - "iopub.status.idle": "2025-05-09T16:18:25.381427Z", - "shell.execute_reply": "2025-05-09T16:18:25.380399Z", - "shell.execute_reply.started": "2025-05-09T16:18:25.362779Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "X_train_scaled = X_scaler.fit_transform(X_train)\n", @@ -1375,29 +807,10 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "id": "5be7b1b1-2177-46e5-ad41-607e55d12949", - "metadata": { - "execution": { - "iopub.execute_input": "2025-05-09T16:18:26.178912Z", - "iopub.status.busy": "2025-05-09T16:18:26.178478Z", - "iopub.status.idle": "2025-05-09T16:18:26.418951Z", - "shell.execute_reply": "2025-05-09T16:18:26.418038Z", - "shell.execute_reply.started": "2025-05-09T16:18:26.178874Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "plt.clf()\n", "plt.hist(X_train_scaled, range=[-2,2]);" @@ -1421,17 +834,9 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "id": "f4086f5f-7c2d-4d6c-a2d4-8dff42e2fc84", - "metadata": { - "execution": { - "iopub.execute_input": "2025-05-09T16:18:27.749334Z", - "iopub.status.busy": "2025-05-09T16:18:27.748865Z", - "iopub.status.idle": "2025-05-09T16:18:27.757581Z", - "shell.execute_reply": "2025-05-09T16:18:27.756633Z", - "shell.execute_reply.started": "2025-05-09T16:18:27.749299Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "X_train_tt = X_scaler.inverse_transform(X_train_scaled)\n", @@ -1451,29 +856,10 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "id": "fc4cecc6-f256-4f55-8d54-f585630e8b4f", - "metadata": { - "execution": { - "iopub.execute_input": "2025-05-09T16:18:28.786036Z", - "iopub.status.busy": "2025-05-09T16:18:28.784951Z", - "iopub.status.idle": "2025-05-09T16:18:29.077459Z", - "shell.execute_reply": "2025-05-09T16:18:29.076542Z", - "shell.execute_reply.started": "2025-05-09T16:18:28.785983Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "plt.clf()\n", "plt.hist(X_train_tt, range=[0,1500], label='twice transformed', alpha=0.5, color='red')\n", @@ -1515,447 +901,10 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "id": "e02ca479-6105-442b-9879-2eb215dc4d66", - "metadata": { - "execution": { - "iopub.execute_input": "2025-05-09T16:18:30.632898Z", - "iopub.status.busy": "2025-05-09T16:18:30.632409Z", - "iopub.status.idle": "2025-05-09T16:18:30.644873Z", - "shell.execute_reply": "2025-05-09T16:18:30.643977Z", - "shell.execute_reply.started": "2025-05-09T16:18:30.632857Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
LinearRegression()
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" - ], - "text/plain": [ - "LinearRegression()" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "model_lr = LinearRegression()\n", "model_lr.fit(X_train_scaled, y_train_scaled)" @@ -1971,26 +920,10 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "id": "aca8064a-c94f-4792-86b3-62ec2130471b", - "metadata": { - "execution": { - "iopub.execute_input": "2025-05-09T16:18:32.396516Z", - "iopub.status.busy": "2025-05-09T16:18:32.395981Z", - "iopub.status.idle": "2025-05-09T16:18:32.403572Z", - "shell.execute_reply": "2025-05-09T16:18:32.402651Z", - "shell.execute_reply.started": "2025-05-09T16:18:32.396475Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MSE: 0.10849655661786743\n" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ "y_pred = model_lr.predict(X_test_scaled)\n", "mse = mean_squared_error(y_test_scaled, y_pred)\n", @@ -2007,29 +940,10 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "id": "ee8bd887-928e-4a41-bd77-149b344ab238", - "metadata": { - "execution": { - "iopub.execute_input": "2025-05-09T16:18:35.304755Z", - "iopub.status.busy": "2025-05-09T16:18:35.303936Z", - "iopub.status.idle": "2025-05-09T16:18:35.511760Z", - "shell.execute_reply": "2025-05-09T16:18:35.510819Z", - "shell.execute_reply.started": "2025-05-09T16:18:35.304711Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "plt.clf()\n", "plt.scatter(y_test_scaled, y_pred)\n", @@ -2058,17 +972,9 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "id": "6770c381-1f34-44a7-b93b-b7f915013620", - "metadata": { - "execution": { - "iopub.execute_input": "2025-05-09T16:18:36.922927Z", - "iopub.status.busy": "2025-05-09T16:18:36.922495Z", - "iopub.status.idle": "2025-05-09T16:18:36.928762Z", - "shell.execute_reply": "2025-05-09T16:18:36.927669Z", - "shell.execute_reply.started": "2025-05-09T16:18:36.922890Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "y_pred_tt = y_scaler.inverse_transform(y_pred)" @@ -2076,29 +982,10 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "id": "f83cf984-ffa7-4486-98ec-bce890b91bad", - "metadata": { - "execution": { - "iopub.execute_input": "2025-05-09T16:18:37.323173Z", - "iopub.status.busy": "2025-05-09T16:18:37.322601Z", - "iopub.status.idle": "2025-05-09T16:18:37.597847Z", - "shell.execute_reply": "2025-05-09T16:18:37.596937Z", - "shell.execute_reply.started": "2025-05-09T16:18:37.323102Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "plt.clf()\n", "plt.scatter(y_test_tt, y_pred_tt)\n", @@ -2127,26 +1014,10 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "id": "fb653dbd-4e77-4099-80ac-781f20cb38eb", - "metadata": { - "execution": { - "iopub.execute_input": "2025-05-09T16:18:39.228469Z", - "iopub.status.busy": "2025-05-09T16:18:39.227972Z", - "iopub.status.idle": "2025-05-09T16:18:39.235162Z", - "shell.execute_reply": "2025-05-09T16:18:39.234208Z", - "shell.execute_reply.started": "2025-05-09T16:18:39.228426Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MSE: 80347168.49722074\n" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ "mse = mean_squared_error(y_test_tt, y_pred_tt)\n", "print(\"MSE:\", mse)" @@ -2163,36 +1034,10 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": null, "id": "d80e56eb-6e3d-4110-bee6-3681ee4a923b", - "metadata": { - "execution": { - "iopub.execute_input": "2025-05-09T16:19:39.630306Z", - "iopub.status.busy": "2025-05-09T16:19:39.629841Z", - "iopub.status.idle": "2025-05-09T16:19:39.915410Z", - "shell.execute_reply": "2025-05-09T16:19:39.914383Z", - "shell.execute_reply.started": "2025-05-09T16:19:39.630268Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MSE: 252763.24546072097\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "X_train_i, X_test_i, y_train_i, y_test_i = train_test_split(\n", " clean_df[['g_kronFlux', 'i_kronFlux']],\n", @@ -2249,36 +1094,10 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": null, "id": "efb56f9d-6487-444d-b283-6d60c1694948", - "metadata": { - "execution": { - "iopub.execute_input": "2025-05-09T16:19:59.530093Z", - "iopub.status.busy": "2025-05-09T16:19:59.529045Z", - "iopub.status.idle": "2025-05-09T16:20:03.716619Z", - "shell.execute_reply": "2025-05-09T16:20:03.715607Z", - "shell.execute_reply.started": "2025-05-09T16:19:59.530036Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MSE: 3257968.3172415826\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "model_rf = RandomForestRegressor()\n", "model_rf.fit(X_train_i_scaled, y_train_i)\n", @@ -2325,17 +1144,9 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": null, "id": "e6420904-44bb-40cf-8ce0-92b22a802e59", - "metadata": { - "execution": { - "iopub.execute_input": "2025-05-09T16:21:12.136604Z", - "iopub.status.busy": "2025-05-09T16:21:12.136168Z", - "iopub.status.idle": "2025-05-09T16:21:12.141117Z", - "shell.execute_reply": "2025-05-09T16:21:12.140229Z", - "shell.execute_reply.started": "2025-05-09T16:21:12.136569Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "param_grid = {'n_estimators': [1, 10, 50, 100, 200, 1000]}" @@ -2351,26 +1162,10 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": null, "id": "48a166c2-10b9-470f-bd03-3d7e6d51a1c7", - "metadata": { - "execution": { - "iopub.execute_input": "2025-05-09T16:21:18.230590Z", - "iopub.status.busy": "2025-05-09T16:21:18.230111Z", - "iopub.status.idle": "2025-05-09T16:24:49.175338Z", - "shell.execute_reply": "2025-05-09T16:24:49.174327Z", - "shell.execute_reply.started": "2025-05-09T16:21:18.230555Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'n_estimators': 200}\n" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ "grid = GridSearchCV(model_rf, param_grid, cv=5)\n", "\n", @@ -2389,43 +1184,10 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": null, "id": "89167a5d-e835-4f8d-8d57-ac82f0df6239", - "metadata": { - "execution": { - "iopub.execute_input": "2025-05-09T16:25:01.097927Z", - "iopub.status.busy": "2025-05-09T16:25:01.097473Z", - "iopub.status.idle": "2025-05-09T16:25:01.506885Z", - "shell.execute_reply": "2025-05-09T16:25:01.505928Z", - "shell.execute_reply.started": "2025-05-09T16:25:01.097885Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Best Model: RandomForestRegressor(n_estimators=200)\n" - ] - }, - { - "data": { - "image/png": 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coord_racoord_decg_kronFluxg_kronFlux_flagr_kronFluxr_kronFlux_flagi_kronFluxi_kronFlux_flag
1062.010663-37.088064303.684022False232.540549True310.371904False
2262.006350-37.09011558.774248False135.041368True305.676263False
4162.014721-37.080369317.485708False450.916090True134.658024False
...........................
1146161.882326-36.990083310.036393False250.901590True228.085994False
1147561.924031-36.998350104.742902False110.345845True262.059423False
1155961.935633-36.950795495.811802False128.753038True218.522159False
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176 rows × 8 columns

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" - ], - "text/plain": [ - " coord_ra coord_dec g_kronFlux g_kronFlux_flag r_kronFlux \\\n", - "10 62.010663 -37.088064 303.684022 False 232.540549 \n", - "22 62.006350 -37.090115 58.774248 False 135.041368 \n", - "41 62.014721 -37.080369 317.485708 False 450.916090 \n", - "... ... ... ... ... ... \n", - "11461 61.882326 -36.990083 310.036393 False 250.901590 \n", - "11475 61.924031 -36.998350 104.742902 False 110.345845 \n", - "11559 61.935633 -36.950795 495.811802 False 128.753038 \n", - "\n", - " r_kronFlux_flag i_kronFlux i_kronFlux_flag \n", - "10 True 310.371904 False \n", - "22 True 305.676263 False \n", - "41 True 134.658024 False \n", - "... ... ... ... \n", - "11461 True 228.085994 False \n", - "11475 True 262.059423 False \n", - "11559 True 218.522159 False \n", - "\n", - "[176 rows x 8 columns]" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "r_missing_df = results[\n", " (results['r_kronFlux_flag'] == True) & \n", @@ -2633,17 +1246,9 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": null, "id": "dd7a891e-5a56-440e-a721-91af73d74269", - "metadata": { - "execution": { - "iopub.execute_input": "2025-05-09T16:25:11.324579Z", - "iopub.status.busy": "2025-05-09T16:25:11.324115Z", - "iopub.status.idle": "2025-05-09T16:25:11.370830Z", - "shell.execute_reply": "2025-05-09T16:25:11.369815Z", - "shell.execute_reply.started": "2025-05-09T16:25:11.324542Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "X_r_missing = r_missing_df[['g_kronFlux', 'i_kronFlux']]\n", @@ -2653,29 +1258,10 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": null, "id": "0e438d24-75ba-4888-b2c9-cb4fa6c6db85", - "metadata": { - "execution": { - "iopub.execute_input": "2025-05-09T16:25:13.186405Z", - "iopub.status.busy": "2025-05-09T16:25:13.185950Z", - "iopub.status.idle": "2025-05-09T16:25:13.496043Z", - "shell.execute_reply": "2025-05-09T16:25:13.495130Z", - "shell.execute_reply.started": "2025-05-09T16:25:13.186368Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "plt.scatter(r_missing_df['r_kronFlux'].values, y_pred_r_missing)\n", "plt.xlabel(r'Flagged $r-$band values')\n", @@ -2709,78 +1295,10 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": null, "id": "5adca7ed-eb36-4e8b-99e3-3b66833fb3e3", - "metadata": { - "execution": { - "iopub.execute_input": "2025-05-09T16:27:33.633390Z", - "iopub.status.busy": "2025-05-09T16:27:33.632891Z", - "iopub.status.idle": "2025-05-09T16:27:33.642842Z", - "shell.execute_reply": "2025-05-09T16:27:33.641977Z", - "shell.execute_reply.started": "2025-05-09T16:27:33.633350Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['brier_score_loss', 'check_scoring', 'coverage_error', 'd2_absolute_error_score', 'd2_pinball_score', 'd2_tweedie_score', 'explained_variance_score', 'label_ranking_loss', 'log_loss', 'max_error', 'mean_absolute_error', 'mean_absolute_percentage_error', 'mean_gamma_deviance', 'mean_pinball_loss', 'mean_poisson_deviance', 'mean_squared_error', 'mean_squared_log_error', 'mean_tweedie_deviance', 'median_absolute_error', 'pairwise_distances', 'r2_score', 'root_mean_squared_error', 'root_mean_squared_log_error']\n", - "--- mean_tweedie_deviance ---\n", - "Help on function mean_tweedie_deviance in module sklearn.metrics._regression:\n", - "\n", - "mean_tweedie_deviance(y_true, y_pred, *, sample_weight=None, power=0)\n", - " Mean Tweedie deviance regression loss.\n", - "\n", - " Read more in the :ref:`User Guide `.\n", - "\n", - " Parameters\n", - " ----------\n", - " y_true : array-like of shape (n_samples,)\n", - " Ground truth (correct) target values.\n", - "\n", - " y_pred : array-like of shape (n_samples,)\n", - " Estimated target values.\n", - "\n", - " sample_weight : array-like of shape (n_samples,), default=None\n", - " Sample weights.\n", - "\n", - " power : float, default=0\n", - " Tweedie power parameter. Either power <= 0 or power >= 1.\n", - "\n", - " The higher `p` the less weight is given to extreme\n", - " deviations between true and predicted targets.\n", - "\n", - " - power < 0: Extreme stable distribution. Requires: y_pred > 0.\n", - " - power = 0 : Normal distribution, output corresponds to\n", - " mean_squared_error. y_true and y_pred can be any real numbers.\n", - " - power = 1 : Poisson distribution. Requires: y_true >= 0 and\n", - " y_pred > 0.\n", - " - 1 < p < 2 : Compound Poisson distribution. Requires: y_true >= 0\n", - " and y_pred > 0.\n", - " - power = 2 : Gamma distribution. Requires: y_true > 0 and y_pred > 0.\n", - " - power = 3 : Inverse Gaussian distribution. Requires: y_true > 0\n", - " and y_pred > 0.\n", - " - otherwise : Positive stable distribution. Requires: y_true > 0\n", - " and y_pred > 0.\n", - "\n", - " Returns\n", - " -------\n", - " loss : float\n", - " A non-negative floating point value (the best value is 0.0).\n", - "\n", - " Examples\n", - " --------\n", - " >>> from sklearn.metrics import mean_tweedie_deviance\n", - " >>> y_true = [2, 0, 1, 4]\n", - " >>> y_pred = [0.5, 0.5, 2., 2.]\n", - " >>> mean_tweedie_deviance(y_true, y_pred, power=1)\n", - " 1.4260...\n", - "\n", - "================================================================================\n" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ "regression_metrics = [\n", " name for name, obj in inspect.getmembers(metrics)\n", @@ -2802,749 +1320,10 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": null, "id": "de4a41f7-9f4f-4662-9302-2da7d68df434", - "metadata": { - "execution": { - "iopub.execute_input": "2025-05-09T16:27:35.933017Z", - "iopub.status.busy": "2025-05-09T16:27:35.932581Z", - "iopub.status.idle": "2025-05-09T16:27:37.061758Z", - "shell.execute_reply": "2025-05-09T16:27:37.060814Z", - "shell.execute_reply.started": "2025-05-09T16:27:35.932976Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ARDRegression\n", - "AdaBoostRegressor\n", - "BaggingRegressor\n", - "BayesianRidge\n", - "CCA\n", - "DecisionTreeRegressor\n", - "DummyRegressor\n", - "ElasticNet\n", - "ElasticNetCV\n", - "ExtraTreeRegressor\n", - "ExtraTreesRegressor\n", - "GammaRegressor\n", - "GaussianProcessRegressor\n", - "GradientBoostingRegressor\n", - "HistGradientBoostingRegressor\n", - "HuberRegressor\n", - "IsotonicRegression\n", - "KNeighborsRegressor\n", - "KernelRidge\n", - "Lars\n", - "LarsCV\n", - "Lasso\n", - "LassoCV\n", - "LassoLars\n", - "LassoLarsCV\n", - "LassoLarsIC\n", - "LinearRegression\n", - "LinearSVR\n", - "MLPRegressor\n", - "MultiOutputRegressor\n", - "MultiTaskElasticNet\n", - "MultiTaskElasticNetCV\n", - "MultiTaskLasso\n", - "MultiTaskLassoCV\n", - "NuSVR\n", - "OrthogonalMatchingPursuit\n", - "OrthogonalMatchingPursuitCV\n", - "PLSCanonical\n", - "PLSRegression\n", - "PassiveAggressiveRegressor\n", - "PoissonRegressor\n", - "QuantileRegressor\n", - "RANSACRegressor\n", - "RadiusNeighborsRegressor\n", - "RandomForestRegressor\n", - "RegressorChain\n", - "Ridge\n", - "RidgeCV\n", - "SGDRegressor\n", - "SVR\n", - "StackingRegressor\n", - "TheilSenRegressor\n", - "TransformedTargetRegressor\n", - "TweedieRegressor\n", - "VotingRegressor\n", - "Help on class RandomForestRegressor in module sklearn.ensemble._forest:\n", - "\n", - "class RandomForestRegressor(ForestRegressor)\n", - " | RandomForestRegressor(n_estimators=100, *, criterion='squared_error', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=1.0, max_leaf_nodes=None, min_impurity_decrease=0.0, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, ccp_alpha=0.0, max_samples=None, monotonic_cst=None)\n", - " |\n", - " | A random forest regressor.\n", - " |\n", - " | A random forest is a meta estimator that fits a number of decision tree\n", - " | regressors on various sub-samples of the dataset and uses averaging to\n", - " | improve the predictive accuracy and control over-fitting.\n", - " | Trees in the forest use the best split strategy, i.e. equivalent to passing\n", - " | `splitter=\"best\"` to the underlying :class:`~sklearn.tree.DecisionTreeRegressor`.\n", - " | The sub-sample size is controlled with the `max_samples` parameter if\n", - " | `bootstrap=True` (default), otherwise the whole dataset is used to build\n", - " | each tree.\n", - " |\n", - " | For a comparison between tree-based ensemble models see the example\n", - " | :ref:`sphx_glr_auto_examples_ensemble_plot_forest_hist_grad_boosting_comparison.py`.\n", - " |\n", - " | Read more in the :ref:`User Guide `.\n", - " |\n", - " | Parameters\n", - " | ----------\n", - " | n_estimators : int, default=100\n", - " | The number of trees in the forest.\n", - " |\n", - " | .. versionchanged:: 0.22\n", - " | The default value of ``n_estimators`` changed from 10 to 100\n", - " | in 0.22.\n", - " |\n", - " | criterion : {\"squared_error\", \"absolute_error\", \"friedman_mse\", \"poisson\"}, default=\"squared_error\"\n", - " | The function to measure the quality of a split. Supported criteria\n", - " | are \"squared_error\" for the mean squared error, which is equal to\n", - " | variance reduction as feature selection criterion and minimizes the L2\n", - " | loss using the mean of each terminal node, \"friedman_mse\", which uses\n", - " | mean squared error with Friedman's improvement score for potential\n", - " | splits, \"absolute_error\" for the mean absolute error, which minimizes\n", - " | the L1 loss using the median of each terminal node, and \"poisson\" which\n", - " | uses reduction in Poisson deviance to find splits.\n", - " | Training using \"absolute_error\" is significantly slower\n", - " | than when using \"squared_error\".\n", - " |\n", - " | .. versionadded:: 0.18\n", - " | Mean Absolute Error (MAE) criterion.\n", - " |\n", - " | .. versionadded:: 1.0\n", - " | Poisson criterion.\n", - " |\n", - " | max_depth : int, default=None\n", - " | The maximum depth of the tree. If None, then nodes are expanded until\n", - " | all leaves are pure or until all leaves contain less than\n", - " | min_samples_split samples.\n", - " |\n", - " | min_samples_split : int or float, default=2\n", - " | The minimum number of samples required to split an internal node:\n", - " |\n", - " | - If int, then consider `min_samples_split` as the minimum number.\n", - " | - If float, then `min_samples_split` is a fraction and\n", - " | `ceil(min_samples_split * n_samples)` are the minimum\n", - " | number of samples for each split.\n", - " |\n", - " | .. versionchanged:: 0.18\n", - " | Added float values for fractions.\n", - " |\n", - " | min_samples_leaf : int or float, default=1\n", - " | The minimum number of samples required to be at a leaf node.\n", - " | A split point at any depth will only be considered if it leaves at\n", - " | least ``min_samples_leaf`` training samples in each of the left and\n", - " | right branches. This may have the effect of smoothing the model,\n", - " | especially in regression.\n", - " |\n", - " | - If int, then consider `min_samples_leaf` as the minimum number.\n", - " | - If float, then `min_samples_leaf` is a fraction and\n", - " | `ceil(min_samples_leaf * n_samples)` are the minimum\n", - " | number of samples for each node.\n", - " |\n", - " | .. versionchanged:: 0.18\n", - " | Added float values for fractions.\n", - " |\n", - " | min_weight_fraction_leaf : float, default=0.0\n", - " | The minimum weighted fraction of the sum total of weights (of all\n", - " | the input samples) required to be at a leaf node. Samples have\n", - " | equal weight when sample_weight is not provided.\n", - " |\n", - " | max_features : {\"sqrt\", \"log2\", None}, int or float, default=1.0\n", - " | The number of features to consider when looking for the best split:\n", - " |\n", - " | - If int, then consider `max_features` features at each split.\n", - " | - If float, then `max_features` is a fraction and\n", - " | `max(1, int(max_features * n_features_in_))` features are considered at each\n", - " | split.\n", - " | - If \"sqrt\", then `max_features=sqrt(n_features)`.\n", - " | - If \"log2\", then `max_features=log2(n_features)`.\n", - " | - If None or 1.0, then `max_features=n_features`.\n", - " |\n", - " | .. note::\n", - " | The default of 1.0 is equivalent to bagged trees and more\n", - " | randomness can be achieved by setting smaller values, e.g. 0.3.\n", - " |\n", - " | .. versionchanged:: 1.1\n", - " | The default of `max_features` changed from `\"auto\"` to 1.0.\n", - " |\n", - " | Note: the search for a split does not stop until at least one\n", - " | valid partition of the node samples is found, even if it requires to\n", - " | effectively inspect more than ``max_features`` features.\n", - " |\n", - " | max_leaf_nodes : int, default=None\n", - " | Grow trees with ``max_leaf_nodes`` in best-first fashion.\n", - " | Best nodes are defined as relative reduction in impurity.\n", - " | If None then unlimited number of leaf nodes.\n", - " |\n", - " | min_impurity_decrease : float, default=0.0\n", - " | A node will be split if this split induces a decrease of the impurity\n", - " | greater than or equal to this value.\n", - " |\n", - " | The weighted impurity decrease equation is the following::\n", - " |\n", - " | N_t / N * (impurity - N_t_R / N_t * right_impurity\n", - " | - N_t_L / N_t * left_impurity)\n", - " |\n", - " | where ``N`` is the total number of samples, ``N_t`` is the number of\n", - " | samples at the current node, ``N_t_L`` is the number of samples in the\n", - " | left child, and ``N_t_R`` is the number of samples in the right child.\n", - " |\n", - " | ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,\n", - " | if ``sample_weight`` is passed.\n", - " |\n", - " | .. versionadded:: 0.19\n", - " |\n", - " | bootstrap : bool, default=True\n", - " | Whether bootstrap samples are used when building trees. If False, the\n", - " | whole dataset is used to build each tree.\n", - " |\n", - " | oob_score : bool or callable, default=False\n", - " | Whether to use out-of-bag samples to estimate the generalization score.\n", - " | By default, :func:`~sklearn.metrics.r2_score` is used.\n", - " | Provide a callable with signature `metric(y_true, y_pred)` to use a\n", - " | custom metric. Only available if `bootstrap=True`.\n", - " |\n", - " | n_jobs : int, default=None\n", - " | The number of jobs to run in parallel. :meth:`fit`, :meth:`predict`,\n", - " | :meth:`decision_path` and :meth:`apply` are all parallelized over the\n", - " | trees. ``None`` means 1 unless in a :obj:`joblib.parallel_backend`\n", - " | context. ``-1`` means using all processors. See :term:`Glossary\n", - " | ` for more details.\n", - " |\n", - " | random_state : int, RandomState instance or None, default=None\n", - " | Controls both the randomness of the bootstrapping of the samples used\n", - " | when building trees (if ``bootstrap=True``) and the sampling of the\n", - " | features to consider when looking for the best split at each node\n", - " | (if ``max_features < n_features``).\n", - " | See :term:`Glossary ` for details.\n", - " |\n", - " | verbose : int, default=0\n", - " | Controls the verbosity when fitting and predicting.\n", - " |\n", - " | warm_start : bool, default=False\n", - " | When set to ``True``, reuse the solution of the previous call to fit\n", - " | and add more estimators to the ensemble, otherwise, just fit a whole\n", - " | new forest. See :term:`Glossary ` and\n", - " | :ref:`tree_ensemble_warm_start` for details.\n", - " |\n", - " | ccp_alpha : non-negative float, default=0.0\n", - " | Complexity parameter used for Minimal Cost-Complexity Pruning. The\n", - " | subtree with the largest cost complexity that is smaller than\n", - " | ``ccp_alpha`` will be chosen. By default, no pruning is performed. See\n", - " | :ref:`minimal_cost_complexity_pruning` for details. See\n", - " | :ref:`sphx_glr_auto_examples_tree_plot_cost_complexity_pruning.py`\n", - " | for an example of such pruning.\n", - " |\n", - " | .. versionadded:: 0.22\n", - " |\n", - " | max_samples : int or float, default=None\n", - " | If bootstrap is True, the number of samples to draw from X\n", - " | to train each base estimator.\n", - " |\n", - " | - If None (default), then draw `X.shape[0]` samples.\n", - " | - If int, then draw `max_samples` samples.\n", - " | - If float, then draw `max(round(n_samples * max_samples), 1)` samples. Thus,\n", - " | `max_samples` should be in the interval `(0.0, 1.0]`.\n", - " |\n", - " | .. versionadded:: 0.22\n", - " |\n", - " | monotonic_cst : array-like of int of shape (n_features), default=None\n", - " | Indicates the monotonicity constraint to enforce on each feature.\n", - " | - 1: monotonically increasing\n", - " | - 0: no constraint\n", - " | - -1: monotonically decreasing\n", - " |\n", - " | If monotonic_cst is None, no constraints are applied.\n", - " |\n", - " | Monotonicity constraints are not supported for:\n", - " | - multioutput regressions (i.e. when `n_outputs_ > 1`),\n", - " | - regressions trained on data with missing values.\n", - " |\n", - " | Read more in the :ref:`User Guide `.\n", - " |\n", - " | .. versionadded:: 1.4\n", - " |\n", - " | Attributes\n", - " | ----------\n", - " | estimator_ : :class:`~sklearn.tree.DecisionTreeRegressor`\n", - " | The child estimator template used to create the collection of fitted\n", - " | sub-estimators.\n", - " |\n", - " | .. versionadded:: 1.2\n", - " | `base_estimator_` was renamed to `estimator_`.\n", - " |\n", - " | estimators_ : list of DecisionTreeRegressor\n", - " | The collection of fitted sub-estimators.\n", - " |\n", - " | feature_importances_ : ndarray of shape (n_features,)\n", - " | The impurity-based feature importances.\n", - " | The higher, the more important the feature.\n", - " | The importance of a feature is computed as the (normalized)\n", - " | total reduction of the criterion brought by that feature. It is also\n", - " | known as the Gini importance.\n", - " |\n", - " | Warning: impurity-based feature importances can be misleading for\n", - " | high cardinality features (many unique values). See\n", - " | :func:`sklearn.inspection.permutation_importance` as an alternative.\n", - " |\n", - " | n_features_in_ : int\n", - " | Number of features seen during :term:`fit`.\n", - " |\n", - " | .. versionadded:: 0.24\n", - " |\n", - " | feature_names_in_ : ndarray of shape (`n_features_in_`,)\n", - " | Names of features seen during :term:`fit`. Defined only when `X`\n", - " | has feature names that are all strings.\n", - " |\n", - " | .. versionadded:: 1.0\n", - " |\n", - " | n_outputs_ : int\n", - " | The number of outputs when ``fit`` is performed.\n", - " |\n", - " | oob_score_ : float\n", - " | Score of the training dataset obtained using an out-of-bag estimate.\n", - " | This attribute exists only when ``oob_score`` is True.\n", - " |\n", - " | oob_prediction_ : ndarray of shape (n_samples,) or (n_samples, n_outputs)\n", - " | Prediction computed with out-of-bag estimate on the training set.\n", - " | This attribute exists only when ``oob_score`` is True.\n", - " |\n", - " | estimators_samples_ : list of arrays\n", - " | The subset of drawn samples (i.e., the in-bag samples) for each base\n", - " | estimator. Each subset is defined by an array of the indices selected.\n", - " |\n", - " | .. versionadded:: 1.4\n", - " |\n", - " | See Also\n", - " | --------\n", - " | sklearn.tree.DecisionTreeRegressor : A decision tree regressor.\n", - " | sklearn.ensemble.ExtraTreesRegressor : Ensemble of extremely randomized\n", - " | tree regressors.\n", - " | sklearn.ensemble.HistGradientBoostingRegressor : A Histogram-based Gradient\n", - " | Boosting Regression Tree, very fast for big datasets (n_samples >=\n", - " | 10_000).\n", - " |\n", - " | Notes\n", - " | -----\n", - " | The default values for the parameters controlling the size of the trees\n", - " | (e.g. ``max_depth``, ``min_samples_leaf``, etc.) lead to fully grown and\n", - " | unpruned trees which can potentially be very large on some data sets. To\n", - " | reduce memory consumption, the complexity and size of the trees should be\n", - " | controlled by setting those parameter values.\n", - " |\n", - " | The features are always randomly permuted at each split. Therefore,\n", - " | the best found split may vary, even with the same training data,\n", - " | ``max_features=n_features`` and ``bootstrap=False``, if the improvement\n", - " | of the criterion is identical for several splits enumerated during the\n", - " | search of the best split. To obtain a deterministic behaviour during\n", - " | fitting, ``random_state`` has to be fixed.\n", - " |\n", - " | The default value ``max_features=1.0`` uses ``n_features``\n", - " | rather than ``n_features / 3``. The latter was originally suggested in\n", - " | [1], whereas the former was more recently justified empirically in [2].\n", - " |\n", - " | References\n", - " | ----------\n", - " | .. [1] L. Breiman, \"Random Forests\", Machine Learning, 45(1), 5-32, 2001.\n", - " |\n", - " | .. [2] P. Geurts, D. Ernst., and L. Wehenkel, \"Extremely randomized\n", - " | trees\", Machine Learning, 63(1), 3-42, 2006.\n", - " |\n", - " | Examples\n", - " | --------\n", - " | >>> from sklearn.ensemble import RandomForestRegressor\n", - " | >>> from sklearn.datasets import make_regression\n", - " | >>> X, y = make_regression(n_features=4, n_informative=2,\n", - " | ... random_state=0, shuffle=False)\n", - " | >>> regr = RandomForestRegressor(max_depth=2, random_state=0)\n", - " | >>> regr.fit(X, y)\n", - " | RandomForestRegressor(...)\n", - " | >>> print(regr.predict([[0, 0, 0, 0]]))\n", - " | [-8.32987858]\n", - " |\n", - " | Method resolution order:\n", - " | RandomForestRegressor\n", - " | ForestRegressor\n", - " | sklearn.base.RegressorMixin\n", - " | BaseForest\n", - " | sklearn.base.MultiOutputMixin\n", - " | sklearn.ensemble._base.BaseEnsemble\n", - " | sklearn.base.MetaEstimatorMixin\n", - " | sklearn.base.BaseEstimator\n", - " | sklearn.utils._estimator_html_repr._HTMLDocumentationLinkMixin\n", - " | sklearn.utils._metadata_requests._MetadataRequester\n", - " | builtins.object\n", - " |\n", - " | Methods defined here:\n", - " |\n", - " | __init__(self, n_estimators=100, *, criterion='squared_error', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=1.0, max_leaf_nodes=None, min_impurity_decrease=0.0, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, ccp_alpha=0.0, max_samples=None, monotonic_cst=None)\n", - " | Initialize self. See help(type(self)) for accurate signature.\n", - " |\n", - " | set_fit_request(self: sklearn.ensemble._forest.RandomForestRegressor, *, sample_weight: Union[bool, NoneType, str] = '$UNCHANGED$') -> sklearn.ensemble._forest.RandomForestRegressor from sklearn.utils._metadata_requests.RequestMethod.__get__.\n", - " | Request metadata passed to the ``fit`` method.\n", - " |\n", - " | Note that this method is only relevant if\n", - " | ``enable_metadata_routing=True`` (see :func:`sklearn.set_config`).\n", - " | Please see :ref:`User Guide ` on how the routing\n", - " | mechanism works.\n", - " |\n", - " | The options for each parameter are:\n", - " |\n", - " | - ``True``: metadata is requested, and passed to ``fit`` if provided. The request is ignored if metadata is not provided.\n", - " |\n", - " | - ``False``: metadata is not requested and the meta-estimator will not pass it to ``fit``.\n", - " |\n", - " | - ``None``: metadata is not requested, and the meta-estimator will raise an error if the user provides it.\n", - " |\n", - " | - ``str``: metadata should be passed to the meta-estimator with this given alias instead of the original name.\n", - " |\n", - " | The default (``sklearn.utils.metadata_routing.UNCHANGED``) retains the\n", - " | existing request. This allows you to change the request for some\n", - " | parameters and not others.\n", - " |\n", - " | .. versionadded:: 1.3\n", - " |\n", - " | .. note::\n", - " | This method is only relevant if this estimator is used as a\n", - " | sub-estimator of a meta-estimator, e.g. used inside a\n", - " | :class:`~sklearn.pipeline.Pipeline`. Otherwise it has no effect.\n", - " |\n", - " | Parameters\n", - " | ----------\n", - " | sample_weight : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED\n", - " | Metadata routing for ``sample_weight`` parameter in ``fit``.\n", - " |\n", - " | Returns\n", - " | -------\n", - " | self : object\n", - " | The updated object.\n", - " |\n", - " | set_score_request(self: sklearn.ensemble._forest.RandomForestRegressor, *, sample_weight: Union[bool, NoneType, str] = '$UNCHANGED$') -> sklearn.ensemble._forest.RandomForestRegressor from sklearn.utils._metadata_requests.RequestMethod.__get__.\n", - " | Request metadata passed to the ``score`` method.\n", - " |\n", - " | Note that this method is only relevant if\n", - " | ``enable_metadata_routing=True`` (see :func:`sklearn.set_config`).\n", - " | Please see :ref:`User Guide ` on how the routing\n", - " | mechanism works.\n", - " |\n", - " | The options for each parameter are:\n", - " |\n", - " | - ``True``: metadata is requested, and passed to ``score`` if provided. The request is ignored if metadata is not provided.\n", - " |\n", - " | - ``False``: metadata is not requested and the meta-estimator will not pass it to ``score``.\n", - " |\n", - " | - ``None``: metadata is not requested, and the meta-estimator will raise an error if the user provides it.\n", - " |\n", - " | - ``str``: metadata should be passed to the meta-estimator with this given alias instead of the original name.\n", - " |\n", - " | The default (``sklearn.utils.metadata_routing.UNCHANGED``) retains the\n", - " | existing request. This allows you to change the request for some\n", - " | parameters and not others.\n", - " |\n", - " | .. versionadded:: 1.3\n", - " |\n", - " | .. note::\n", - " | This method is only relevant if this estimator is used as a\n", - " | sub-estimator of a meta-estimator, e.g. used inside a\n", - " | :class:`~sklearn.pipeline.Pipeline`. Otherwise it has no effect.\n", - " |\n", - " | Parameters\n", - " | ----------\n", - " | sample_weight : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED\n", - " | Metadata routing for ``sample_weight`` parameter in ``score``.\n", - " |\n", - " | Returns\n", - " | -------\n", - " | self : object\n", - " | The updated object.\n", - " |\n", - " | ----------------------------------------------------------------------\n", - " | Data and other attributes defined here:\n", - " |\n", - " | __abstractmethods__ = frozenset()\n", - " |\n", - " | __annotations__ = {'_parameter_constraints': }\n", - " |\n", - " | ----------------------------------------------------------------------\n", - " | Methods inherited from ForestRegressor:\n", - " |\n", - " | __sklearn_tags__(self)\n", - " |\n", - " | predict(self, X)\n", - " | Predict regression target for X.\n", - " |\n", - " | The predicted regression target of an input sample is computed as the\n", - " | mean predicted regression targets of the trees in the forest.\n", - " |\n", - " | Parameters\n", - " | ----------\n", - " | X : {array-like, sparse matrix} of shape (n_samples, n_features)\n", - " | The input samples. Internally, its dtype will be converted to\n", - " | ``dtype=np.float32``. If a sparse matrix is provided, it will be\n", - " | converted into a sparse ``csr_matrix``.\n", - " |\n", - " | Returns\n", - " | -------\n", - " | y : ndarray of shape (n_samples,) or (n_samples, n_outputs)\n", - " | The predicted values.\n", - " |\n", - " | ----------------------------------------------------------------------\n", - " | Methods inherited from sklearn.base.RegressorMixin:\n", - " |\n", - " | score(self, X, y, sample_weight=None)\n", - " | Return the coefficient of determination of the prediction.\n", - " |\n", - " | The coefficient of determination :math:`R^2` is defined as\n", - " | :math:`(1 - \\frac{u}{v})`, where :math:`u` is the residual\n", - " | sum of squares ``((y_true - y_pred)** 2).sum()`` and :math:`v`\n", - " | is the total sum of squares ``((y_true - y_true.mean()) ** 2).sum()``.\n", - " | The best possible score is 1.0 and it can be negative (because the\n", - " | model can be arbitrarily worse). A constant model that always predicts\n", - " | the expected value of `y`, disregarding the input features, would get\n", - " | a :math:`R^2` score of 0.0.\n", - " |\n", - " | Parameters\n", - " | ----------\n", - " | X : array-like of shape (n_samples, n_features)\n", - " | Test samples. For some estimators this may be a precomputed\n", - " | kernel matrix or a list of generic objects instead with shape\n", - " | ``(n_samples, n_samples_fitted)``, where ``n_samples_fitted``\n", - " | is the number of samples used in the fitting for the estimator.\n", - " |\n", - " | y : array-like of shape (n_samples,) or (n_samples, n_outputs)\n", - " | True values for `X`.\n", - " |\n", - " | sample_weight : array-like of shape (n_samples,), default=None\n", - " | Sample weights.\n", - " |\n", - " | Returns\n", - " | -------\n", - " | score : float\n", - " | :math:`R^2` of ``self.predict(X)`` w.r.t. `y`.\n", - " |\n", - " | Notes\n", - " | -----\n", - " | The :math:`R^2` score used when calling ``score`` on a regressor uses\n", - " | ``multioutput='uniform_average'`` from version 0.23 to keep consistent\n", - " | with default value of :func:`~sklearn.metrics.r2_score`.\n", - " | This influences the ``score`` method of all the multioutput\n", - " | regressors (except for\n", - " | :class:`~sklearn.multioutput.MultiOutputRegressor`).\n", - " |\n", - " | ----------------------------------------------------------------------\n", - " | Data descriptors inherited from sklearn.base.RegressorMixin:\n", - " |\n", - " | __dict__\n", - " | dictionary for instance variables\n", - " |\n", - " | __weakref__\n", - " | list of weak references to the object\n", - " |\n", - " | ----------------------------------------------------------------------\n", - " | Methods inherited from BaseForest:\n", - " |\n", - " | apply(self, X)\n", - " | Apply trees in the forest to X, return leaf indices.\n", - " |\n", - " | Parameters\n", - " | ----------\n", - " | X : {array-like, sparse matrix} of shape (n_samples, n_features)\n", - " | The input samples. Internally, its dtype will be converted to\n", - " | ``dtype=np.float32``. If a sparse matrix is provided, it will be\n", - " | converted into a sparse ``csr_matrix``.\n", - " |\n", - " | Returns\n", - " | -------\n", - " | X_leaves : ndarray of shape (n_samples, n_estimators)\n", - " | For each datapoint x in X and for each tree in the forest,\n", - " | return the index of the leaf x ends up in.\n", - " |\n", - " | decision_path(self, X)\n", - " | Return the decision path in the forest.\n", - " |\n", - " | .. versionadded:: 0.18\n", - " |\n", - " | Parameters\n", - " | ----------\n", - " | X : {array-like, sparse matrix} of shape (n_samples, n_features)\n", - " | The input samples. Internally, its dtype will be converted to\n", - " | ``dtype=np.float32``. If a sparse matrix is provided, it will be\n", - " | converted into a sparse ``csr_matrix``.\n", - " |\n", - " | Returns\n", - " | -------\n", - " | indicator : sparse matrix of shape (n_samples, n_nodes)\n", - " | Return a node indicator matrix where non zero elements indicates\n", - " | that the samples goes through the nodes. The matrix is of CSR\n", - " | format.\n", - " |\n", - " | n_nodes_ptr : ndarray of shape (n_estimators + 1,)\n", - " | The columns from indicator[n_nodes_ptr[i]:n_nodes_ptr[i+1]]\n", - " | gives the indicator value for the i-th estimator.\n", - " |\n", - " | fit(self, X, y, sample_weight=None)\n", - " | Build a forest of trees from the training set (X, y).\n", - " |\n", - " | Parameters\n", - " | ----------\n", - " | X : {array-like, sparse matrix} of shape (n_samples, n_features)\n", - " | The training input samples. Internally, its dtype will be converted\n", - " | to ``dtype=np.float32``. If a sparse matrix is provided, it will be\n", - " | converted into a sparse ``csc_matrix``.\n", - " |\n", - " | y : array-like of shape (n_samples,) or (n_samples, n_outputs)\n", - " | The target values (class labels in classification, real numbers in\n", - " | regression).\n", - " |\n", - " | sample_weight : array-like of shape (n_samples,), default=None\n", - " | Sample weights. If None, then samples are equally weighted. Splits\n", - " | that would create child nodes with net zero or negative weight are\n", - " | ignored while searching for a split in each node. In the case of\n", - " | classification, splits are also ignored if they would result in any\n", - " | single class carrying a negative weight in either child node.\n", - " |\n", - " | Returns\n", - " | -------\n", - " | self : object\n", - " | Fitted estimator.\n", - " |\n", - " | ----------------------------------------------------------------------\n", - " | Readonly properties inherited from BaseForest:\n", - " |\n", - " | estimators_samples_\n", - " | The subset of drawn samples for each base estimator.\n", - " |\n", - " | Returns a dynamically generated list of indices identifying\n", - " | the samples used for fitting each member of the ensemble, i.e.,\n", - " | the in-bag samples.\n", - " |\n", - " | Note: the list is re-created at each call to the property in order\n", - " | to reduce the object memory footprint by not storing the sampling\n", - " | data. Thus fetching the property may be slower than expected.\n", - " |\n", - " | feature_importances_\n", - " | The impurity-based feature importances.\n", - " |\n", - " | The higher, the more important the feature.\n", - " | The importance of a feature is computed as the (normalized)\n", - " | total reduction of the criterion brought by that feature. It is also\n", - " | known as the Gini importance.\n", - " |\n", - " | Warning: impurity-based feature importances can be misleading for\n", - " | high cardinality features (many unique values). See\n", - " | :func:`sklearn.inspection.permutation_importance` as an alternative.\n", - " |\n", - " | Returns\n", - " | -------\n", - " | feature_importances_ : ndarray of shape (n_features,)\n", - " | The values of this array sum to 1, unless all trees are single node\n", - " | trees consisting of only the root node, in which case it will be an\n", - " | array of zeros.\n", - " |\n", - " | ----------------------------------------------------------------------\n", - " | Methods inherited from sklearn.ensemble._base.BaseEnsemble:\n", - " |\n", - " | __getitem__(self, index)\n", - " | Return the index'th estimator in the ensemble.\n", - " |\n", - " | __iter__(self)\n", - " | Return iterator over estimators in the ensemble.\n", - " |\n", - " | __len__(self)\n", - " | Return the number of estimators in the ensemble.\n", - " |\n", - " | ----------------------------------------------------------------------\n", - " | Methods inherited from sklearn.base.BaseEstimator:\n", - " |\n", - " | __getstate__(self)\n", - " | Helper for pickle.\n", - " |\n", - " | __repr__(self, N_CHAR_MAX=700)\n", - " | Return repr(self).\n", - " |\n", - " | __setstate__(self, state)\n", - " |\n", - " | __sklearn_clone__(self)\n", - " |\n", - " | get_params(self, deep=True)\n", - " | Get parameters for this estimator.\n", - " |\n", - " | Parameters\n", - " | ----------\n", - " | deep : bool, default=True\n", - " | If True, will return the parameters for this estimator and\n", - " | contained subobjects that are estimators.\n", - " |\n", - " | Returns\n", - " | -------\n", - " | params : dict\n", - " | Parameter names mapped to their values.\n", - " |\n", - " | set_params(self, **params)\n", - " | Set the parameters of this estimator.\n", - " |\n", - " | The method works on simple estimators as well as on nested objects\n", - " | (such as :class:`~sklearn.pipeline.Pipeline`). The latter have\n", - " | parameters of the form ``__`` so that it's\n", - " | possible to update each component of a nested object.\n", - " |\n", - " | Parameters\n", - " | ----------\n", - " | **params : dict\n", - " | Estimator parameters.\n", - " |\n", - " | Returns\n", - " | -------\n", - " | self : estimator instance\n", - " | Estimator instance.\n", - " |\n", - " | ----------------------------------------------------------------------\n", - " | Methods inherited from sklearn.utils._metadata_requests._MetadataRequester:\n", - " |\n", - " | get_metadata_routing(self)\n", - " | Get metadata routing of this object.\n", - " |\n", - " | Please check :ref:`User Guide ` on how the routing\n", - " | mechanism works.\n", - " |\n", - " | Returns\n", - " | -------\n", - " | routing : MetadataRequest\n", - " | A :class:`~sklearn.utils.metadata_routing.MetadataRequest` encapsulating\n", - " | routing information.\n", - " |\n", - " | ----------------------------------------------------------------------\n", - " | Class methods inherited from sklearn.utils._metadata_requests._MetadataRequester:\n", - " |\n", - " | __init_subclass__(**kwargs)\n", - " | Set the ``set_{method}_request`` methods.\n", - " |\n", - " | This uses PEP-487 [1]_ to set the ``set_{method}_request`` methods. It\n", - " | looks for the information available in the set default values which are\n", - " | set using ``__metadata_request__*`` class attributes, or inferred\n", - " | from method signatures.\n", - " |\n", - " | The ``__metadata_request__*`` class attributes are used when a method\n", - " | does not explicitly accept a metadata through its arguments or if the\n", - " | developer would like to specify a request value for those metadata\n", - " | which are different from the default ``None``.\n", - " |\n", - " | References\n", - " | ----------\n", - " | .. [1] https://www.python.org/dev/peps/pep-0487\n", - "\n", - "None\n" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ "# Get all regression models\n", "regressors = all_estimators(type_filter='regressor')\n",