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First batch up v2 updates
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@cdburley
cdburley committed Jan 17, 2025
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20 changes: 12 additions & 8 deletions notebooks/tell_data_preprocessing.ipynb
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},
"outputs": [],
"source": [
"# Execute the mapping function for all years from 2015 to 2020:\n",
"# Execute the mapping function for all years from 2015 to 2023:\n",
"tell.map_ba_service_territory(start_year = 2015,\n",
" end_year = 2020,\n",
" data_input_dir = tell_data_dir)\n"
" end_year = 2023,\n",
" fips_codes_data_input_dir = '/Users/burl878/Documents/Code/code_repos/tell/tell/tell_data/tell_raw_data',\n",
" eia_861_data_input_dir = '/Users/burl878/Documents/Code/code_repos/tell/tell/tell_data/tell_raw_data/EIA_861',\n",
" data_output_dir = '/Users/burl878/Documents/Code/code_repos/tell/tell/tell_data/tell_quickstarter_data/outputs/ba_service_territory')\n"
]
},
{
Expand All @@ -132,8 +134,10 @@
"source": [
"# See the output of the mapping step by plotting the service territory for a given BA (e.g., PJM, CISO, ERCO, etc.) in a given year (e.g., 2015-2020):\n",
"tell.plot_ba_service_territory(ba_to_plot = 'PACE',\n",
" year_to_plot = '2019',\n",
" data_input_dir = tell_data_dir, \n",
" year_to_plot = '2023',\n",
" ba_mapping_input_dir = '/Users/burl878/Documents/Code/code_repos/tell/tell/tell_data/tell_quickstarter_data/outputs/ba_service_territory', \n",
" population_input_dir = '/Users/burl878/Documents/Code/code_repos/tell/tell/tell_data/tell_raw_data/Population',\n",
" shapefile_input_dir = '/Users/burl878/Documents/Code/code_repos/tell/tell/tell_data/tell_raw_data/County_Shapefiles',\n",
" image_output_dir = tell_image_dir,\n",
" image_resolution = 150,\n",
" save_images = False)\n"
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],
"metadata": {
"kernelspec": {
"display_name": "py3.9.15_std",
"display_name": "tell",
"language": "python",
"name": "py3.9.15_std"
"name": "tell"
},
"language_info": {
"codemirror_mode": {
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"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.15"
"version": "3.10.8"
}
},
"nbformat": 4,
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292 changes: 292 additions & 0 deletions notebooks/tell_plot_ba_service_territory.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"id": "dd40f4f4-5a80-40b7-94b3-aed136daa9d8",
"metadata": {},
"source": [
"# Plot the Evolution of Service Territory Maps\n",
"\n",
"This notebook plots the evolution of the map of service territory for a given BA."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "27e597e9-538f-41d4-afa9-a268b10ab1df",
"metadata": {},
"outputs": [],
"source": [
"# Start by importing the packages we need:\n",
"import os\n",
"\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import geopandas as gpd\n",
"\n",
"from glob import glob\n",
"from mpl_toolkits.axes_grid1 import make_axes_locatable\n"
]
},
{
"cell_type": "markdown",
"id": "795f6fe5-0769-4298-887e-7c4cffc64a0d",
"metadata": {},
"source": [
"## Set the Directory Structure"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bd887600-32da-46dc-b411-fa63c75f564f",
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"# Identify the top-level directory and the subdirectory where the data will be stored:\n",
"ba_mapping_input_dir = '/Users/burl878/Documents/Code/code_repos/tell/tell/tell_data/tell_quickstarter_data/outputs/ba_service_territory'\n",
"population_input_dir = '/Users/burl878/Documents/Code/code_repos/tell/tell/tell_data/tell_raw_data/Population'\n",
"county_shapefile_input_dir = '/Users/burl878/Documents/Code/code_repos/tell/tell/tell_data/tell_raw_data/County_Shapefiles'\n",
"state_shapefile_input_dir = '/Users/burl878/Documents/Code/code_repos/tell/tell/tell_data/tell_raw_data/State_Shapefiles/'\n",
"image_output_dir = '/Users/burl878/Documents/Code/code_repos/tell/tell/tell_data/visualizations/ba_service_territory'\n"
]
},
{
"cell_type": "markdown",
"id": "7f055d71-411a-4b85-b0b8-5fd3f9059796",
"metadata": {},
"source": [
"## Process the Data"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4b04f656-5fab-4fcb-86db-63c64db9c2c0",
"metadata": {},
"outputs": [],
"source": [
"# Define a function process the mapping file over a range of years:\n",
"def process_ba_mapping_evolution(ba_mapping_input_dir: str, population_input_dir: str, ba_to_process: str, start_year: int, end_year: int):\n",
" # Loop over the years from the start_year to the end_year and read in the mapping data:\n",
" for year in range(start_year, end_year, 1):\n",
" # Read in the BA-to-county mapping file for that year:\n",
" ba_mapping_df = pd.read_csv((os.path.join(ba_mapping_input_dir, f'ba_service_territory_{str(year)}.csv')), index_col=None, header=0)\n",
" \n",
" # Subset the data to only the BA you want to plot:\n",
" ba_mapping_df = ba_mapping_df.loc[(ba_mapping_df['BA_Code'] == ba_to_process)]\n",
"\n",
" # Read in county populations file:\n",
" population_df = pd.read_csv(os.path.join(population_input_dir, r'county_populations_2000_to_2023.csv'))\n",
"\n",
" # Keep only the columns we need:\n",
" population_df = population_df[['county_FIPS', ('pop_' + str(year))]].copy(deep=False)\n",
"\n",
" # Rename the columns:\n",
" population_df.rename(columns={\"county_FIPS\": \"County_FIPS\", ('pop_' + str(year)): \"Population\"}, inplace=True)\n",
"\n",
" # Merge the ba_mapping_df and population_df together using county FIPS codes to join them:\n",
" ba_mapping_df = ba_mapping_df.merge(population_df, on='County_FIPS', how='left')\n",
" \n",
" # Concatenate all the years into a single dataframe:\n",
" if year == start_year:\n",
" output_df = ba_mapping_df.copy()\n",
" else:\n",
" output_df = pd.concat([output_df, ba_mapping_df])\n",
"\n",
" return output_df\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "37a9d4be-aa9d-446d-b31e-d640e9525019",
"metadata": {},
"outputs": [],
"source": [
"mapping_df = process_ba_mapping_evolution(ba_mapping_input_dir = ba_mapping_input_dir,\n",
" population_input_dir = population_input_dir,\n",
" ba_to_process = 'CISO',\n",
" start_year = 2015,\n",
" end_year = 2024)\n",
"\n",
"mapping_df\n"
]
},
{
"cell_type": "markdown",
"id": "0dc65f89-cd8f-4a78-9cb9-c93e4f6b91ac",
"metadata": {},
"source": [
"## Make the Plot"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "25c2d2d2-dadf-45cf-b0e1-40b8856595f9",
"metadata": {},
"outputs": [],
"source": [
"# Define a function to plot the BA-to-county maps:\n",
"def plot_ba_mapping_evolution(ba_mapping_input_dir: str, population_input_dir: str, county_shapefile_input_dir: str, state_shapefile_input_dir: str,\n",
" ba_to_process: str, start_year: int, end_year: int, image_output_dir: str, image_resolution: int, save_images=False):\n",
"\n",
" # Process the mapping file using the function defined above:\n",
" mapping_df = process_ba_mapping_evolution(ba_mapping_input_dir = ba_mapping_input_dir,\n",
" population_input_dir = population_input_dir,\n",
" ba_to_process = ba_to_process,\n",
" start_year = start_year,\n",
" end_year = end_year)\n",
"\n",
" # Read in the county shapefile and reassign the 'FIPS' variable as integers:\n",
" counties_df = gpd.read_file(os.path.join(county_shapefile_input_dir, r'tl_2020_us_county.shp')).rename(columns={'GEOID': 'County_FIPS'})\n",
" counties_df['County_FIPS'] = counties_df['County_FIPS'].astype(int)\n",
"\n",
" # Merge the ba_mapping_df and counties_df together using county FIPS codes to join them:\n",
" counties_df = counties_df.merge(mapping_df, on='County_FIPS', how='left')\n",
"\n",
" # Subset to only the BA you want to plot:\n",
" counties_subset_df = counties_df.loc[counties_df['BA_Code'] == ba_to_process]\n",
" \n",
" # Read in the state shapefile:\n",
" states_df = gpd.read_file(os.path.join(state_shapefile_input_dir, 'tl_2020_us_state.shp')).rename(columns={'NAME': 'State_Name'})\n",
" \n",
" # Subset the state shapefile to just the states contained in the maping file:\n",
" state_subset_df = states_df[states_df['State_Name'].isin(mapping_df['State_Name'].unique().tolist())]\n",
" \n",
" # Set the colormap and colorlimit:\n",
" colors = plt.get_cmap('YlGnBu', 15)\n",
" pop_max = counties_subset_df['Population'].max()\n",
" \n",
" # Subset based on year:\n",
" counties_a_df = counties_subset_df.loc[counties_subset_df['Year'] == 2016]\n",
" counties_b_df = counties_subset_df.loc[counties_subset_df['Year'] == 2017]\n",
" counties_c_df = counties_subset_df.loc[counties_subset_df['Year'] == 2018]\n",
" counties_d_df = counties_subset_df.loc[counties_subset_df['Year'] == 2019]\n",
" counties_e_df = counties_subset_df.loc[counties_subset_df['Year'] == 2020]\n",
" counties_f_df = counties_subset_df.loc[counties_subset_df['Year'] == 2021]\n",
" counties_g_df = counties_subset_df.loc[counties_subset_df['Year'] == 2022]\n",
" counties_h_df = counties_subset_df.loc[counties_subset_df['Year'] == 2023]\n",
" \n",
" # Create the figure:\n",
" fig, axes = plt.subplots(nrows=2, ncols=4, figsize=(25,13))\n",
" \n",
" # Plot each year using subplots starting in 2016\n",
" ax1 = counties_a_df.plot(ax=axes[0,0], column='Population', cmap=colors, vmin=0, vmax=pop_max, edgecolor='k', linewidth=1)\n",
" state_subset_df.boundary.plot(ax=axes[0,0], linewidth=2, color='k')\n",
" ax1.set_xlabel('Longitude', fontsize=14); \n",
" ax1.set_ylabel('Latitude', fontsize=14);\n",
" ax1.set_title((ba_to_process + ' Territory: 2016'), fontsize=16)\n",
" ax1.set_title('a)', loc='left', fontsize=16)\n",
" \n",
" ax2 = counties_b_df.plot(ax=axes[0,1], column='Population', cmap=colors, vmin=0, vmax=pop_max, edgecolor='k', linewidth=1)\n",
" state_subset_df.boundary.plot(ax=axes[0,1], linewidth=2, color='k')\n",
" ax2.set_xlabel('Longitude', fontsize=14); \n",
" ax2.set_ylabel('Latitude', fontsize=14);\n",
" ax2.set_title((ba_to_process + ' Territory: 2017'), fontsize=16)\n",
" ax2.set_title('b)', loc='left', fontsize=16) \n",
"\n",
" ax3 = counties_c_df.plot(ax=axes[0,2], column='Population', cmap=colors, vmin=0, vmax=pop_max, edgecolor='k', linewidth=1)\n",
" state_subset_df.boundary.plot(ax=axes[0,2], linewidth=2, color='k')\n",
" ax3.set_xlabel('Longitude', fontsize=14); \n",
" ax3.set_ylabel('Latitude', fontsize=14);\n",
" ax3.set_title((ba_to_process + ' Territory: 2018'), fontsize=16)\n",
" ax3.set_title('c)', loc='left', fontsize=16)\n",
"\n",
" ax4 = counties_d_df.plot(ax=axes[0,3], column='Population', cmap=colors, vmin=0, vmax=pop_max, edgecolor='k', linewidth=1)\n",
" state_subset_df.boundary.plot(ax=axes[0,3], linewidth=2, color='k')\n",
" ax4.set_xlabel('Longitude', fontsize=14); \n",
" ax4.set_ylabel('Latitude', fontsize=14);\n",
" ax4.set_title((ba_to_process + ' Territory: 2019'), fontsize=16)\n",
" ax4.set_title('d)', loc='left', fontsize=16)\n",
"\n",
" ax5 = counties_e_df.plot(ax=axes[1,0], column='Population', cmap=colors, vmin=0, vmax=pop_max, edgecolor='k', linewidth=1)\n",
" state_subset_df.boundary.plot(ax=axes[1,0], linewidth=2, color='k')\n",
" ax5.set_xlabel('Longitude', fontsize=14); \n",
" ax5.set_ylabel('Latitude', fontsize=14);\n",
" ax5.set_title((ba_to_process + ' Territory: 2020'), fontsize=16)\n",
" ax5.set_title('e)', loc='left', fontsize=16)\n",
"\n",
" ax6 = counties_f_df.plot(ax=axes[1,1], column='Population', cmap=colors, vmin=0, vmax=pop_max, edgecolor='k', linewidth=1)\n",
" state_subset_df.boundary.plot(ax=axes[1,1], linewidth=2, color='k')\n",
" ax6.set_xlabel('Longitude', fontsize=14); \n",
" ax6.set_ylabel('Latitude', fontsize=14);\n",
" ax6.set_title((ba_to_process + ' Territory: 2021'), fontsize=16)\n",
" ax6.set_title('f)', loc='left', fontsize=16)\n",
"\n",
" ax7 = counties_g_df.plot(ax=axes[1,2], column='Population', cmap=colors, vmin=0, vmax=pop_max, edgecolor='k', linewidth=1)\n",
" state_subset_df.boundary.plot(ax=axes[1,2], linewidth=2, color='k')\n",
" ax7.set_xlabel('Longitude', fontsize=14); \n",
" ax7.set_ylabel('Latitude', fontsize=14);\n",
" ax7.set_title((ba_to_process + ' Territory: 2022'), fontsize=16)\n",
" ax7.set_title('g)', loc='left', fontsize=16)\n",
"\n",
" ax8 = counties_h_df.plot(ax=axes[1,3], column='Population', cmap=colors, vmin=0, vmax=pop_max, edgecolor='k', linewidth=1)\n",
" state_subset_df.boundary.plot(ax=axes[1,3], linewidth=2, color='k')\n",
" ax8.set_xlabel('Longitude', fontsize=14); \n",
" ax8.set_ylabel('Latitude', fontsize=14);\n",
" ax8.set_title((ba_to_process + ' Territory: 2023'), fontsize=16)\n",
" ax8.set_title('h)', loc='left', fontsize=16)\n",
" \n",
" # If the \"save_images\" flag is set to true then save the plot to a .png file:\n",
" if save_images == True:\n",
" filename = ('BA_Service_Territory_Evolution_' + ba_to_process + '.png')\n",
" plt.savefig(os.path.join(image_output_dir, filename), dpi=image_resolution, bbox_inches='tight', facecolor='white')\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c57ac364-4aeb-40e2-adf8-9ece04233c00",
"metadata": {},
"outputs": [],
"source": [
"plot_ba_mapping_evolution(ba_mapping_input_dir = ba_mapping_input_dir,\n",
" population_input_dir = population_input_dir,\n",
" county_shapefile_input_dir = county_shapefile_input_dir,\n",
" state_shapefile_input_dir = state_shapefile_input_dir,\n",
" ba_to_process = 'NEVP',\n",
" start_year = 2015,\n",
" end_year = 2024,\n",
" image_output_dir = image_output_dir,\n",
" image_resolution = 300,\n",
" save_images = True)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e75d231a-c5a3-49ed-9890-ffd5fb436cef",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "tell",
"language": "python",
"name": "tell"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.8"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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