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"""Urban_Tree_Canopy_in_Durham |
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Automatically generated by Colaboratory. |
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Original file is located at |
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https://colab.research.google.com/drive/1X59zPtI7ydiX10ZnfjsNGvnKNTXgwrWs |
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""" |
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! pip install datasets |
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import csv |
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import json |
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import os |
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from typing import List |
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import datasets |
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import logging |
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from datasets import DatasetBuilder, DownloadManager, SplitGenerator, Split |
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import zipfile |
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import json |
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import pandas as pd |
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import geopandas as gpd |
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class Urban_Tree_Canopy_in_Durham(DatasetBuilder): |
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def _info(self): |
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return DatasetInfo( |
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description="A description of the dataset.", |
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features=Features( |
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{ |
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"objectid": Value("int32"), |
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"streetaddr": Value("string"), |
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"city_x": Value("string"), |
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"zipcode_x": Value("string"), |
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"facilityid_x": Value("string"), |
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"present_x": Value("string"), |
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"genus_x": Value("string"), |
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"species_x": Value("string"), |
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"commonname_x": Value("string"), |
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"plantingda": Value("datetime"), |
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"diameterin_x": Value("float"), |
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"heightft_x": Value("float"), |
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"condition_x": Value("string"), |
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"contractwo": Value("string"), |
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"neighborho": Value("string"), |
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"program_x": Value("string"), |
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"plantingw_x": Value("string"), |
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"plantingco": Value("string"), |
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"underpwerl": Value("string"), |
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"matureheig": Value("float"), |
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"globalid_x": Value("string"), |
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"created_us": Value("string"), |
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"created_da": Value("datetime"), |
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"last_edite": Value("string"), |
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"last_edi_1": Value("datetime"), |
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"isoprene_x": Value("float"), |
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"monoterpen": Value("float"), |
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"vocs_x": Value("float"), |
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"coremoved_": Value("float"), |
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"coremove_1": Value("float"), |
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"o3removed_": Value("float"), |
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"o3remove_1": Value("float"), |
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"no2removed": Value("float"), |
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"no2remov_1": Value("float"), |
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"so2removed": Value("float"), |
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"so2remov_1": Value("float"), |
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"pm10remove": Value("float"), |
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"pm10remo_1": Value("float"), |
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"pm25remove": Value("float"), |
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"o2producti": Value("float"), |
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"replaceval": Value("float"), |
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"carbonstor": Value("float"), |
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"carbonst_1": Value("float"), |
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"grosscarse": Value("float"), |
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"grosscar_1": Value("float"), |
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"avoidrunof": Value("float"), |
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"avoidrun_1": Value("float"), |
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"polremoved": Value("float"), |
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"polremov_1": Value("float"), |
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"totannbene": Value("float"), |
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"leafarea_s": Value("float"), |
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"potevapotr": Value("float"), |
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"evaporatio": Value("float"), |
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"transpirat": Value("float"), |
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"h2ointerce": Value("float"), |
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"avoidrunva": Value("float"), |
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"avoidrun_2": Value("float"), |
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"carbonavoi": Value("float"), |
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"carbonav_1": Value("float"), |
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"heating_mb": Value("float"), |
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"heating_do": Value("float"), |
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"heating_kw": Value("float"), |
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"heating__1": Value("float"), |
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"cooling_kw": Value("float"), |
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"cooling_do": Value("float"), |
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"totalenerg": Value("float"), |
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"geometry_x": Value("string"), |
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"x": Value("float"), |
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"y": Value("float"), |
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"streetaddress_x": Value("string"), |
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"city_y": Value("string"), |
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"zipcode_y": Value("string"), |
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"facilityid_y": Value("string"), |
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"present_y": Value("string"), |
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"genus_y": Value("string"), |
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"species_y": Value("string"), |
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"commonname_y": Value("string"), |
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"plantingdate_x": Value("datetime"), |
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"diameterin_y": Value("float"), |
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"heightft_y": Value("float"), |
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"condition_y": Value("string"), |
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"contractwork_x": Value("string"), |
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"neighborhood_x": Value("string"), |
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"program_y": Value("string"), |
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"plantingw_y": Value("string"), |
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"plantingcond_x": Value("string"), |
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"underpwerlins_x": Value("string"), |
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"matureheight_x": Value("float"), |
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"globalid_y": Value("string"), |
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"created_user_x": Value("string"), |
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"created_date_x": Value("datetime"), |
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"last_edited_user_x": Value("string"), |
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"last_edited_date_x": Value("datetime"), |
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"isoprene_y": Value("float"), |
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"monoterpene_x": Value("float"), |
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"vocs_y": Value("float"), |
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"coremoved_ozperyr_x": Value("float"), |
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"coremoved_dolperyr_x": Value("float"), |
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"o3removed_ozperyr_x": Value("float"), |
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"o3removed_dolperyr_x": Value("float"), |
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"no2removed_ozperyr_x": Value("float"), |
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"no2removed_dolperyr_x": Value("float"), |
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"so2removed_ozperyr_x": Value("float"), |
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"so2removed_dolperyr_x": Value("float"), |
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"pm10removed_dolperyr_y":Value("float"), |
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"pm25removed_ozperyr_y":Value("float"), |
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"o2production_lbperyr_y":Value("float"), |
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"replacevalue_dol_y":Value("float"), |
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"carbonstorage_lb_y":Value("float"), |
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"carbonstorage_dol_y":Value("float"), |
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"grosscarseq_lbperyr_y":Value("float"), |
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"grosscarseq_dolperyr_y":Value("float"), |
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"avoidrunoff_ft2peryr":Value("float"), |
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} |
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), |
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supervised_keys=None, |
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homepage="https://github.com/AuraMa111/Urban_Tree_Canopy_in_Durham", |
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citation="A citation or reference to the source of the dataset.", |
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) |
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def _split_generators(self, dl_manager: DownloadManager): |
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def _split_generators(self, dl_manager: tfds.download.DownloadManager): |
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downloaded_files = dl_manager.download_and_extract({ |
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"csv": "https://raw.githubusercontent.com/AuraMa111/Urban_Tree_Canopy_in_Durham/main/Trees_%2526_Planting_Sites.csv", |
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"geojson_zip": "https://raw.githubusercontent.com/AuraMa111/Urban_Tree_Canopy_in_Durham/main/Trees_%2526_Planting_Sites.geojson.zip", |
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"zip": "https://raw.githubusercontent.com/AuraMa111/Urban_Tree_Canopy_in_Durham/main/TreesPlanting_Sites.zip" |
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}) |
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return [ |
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tfds.core.SplitGenerator( |
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name=tfds.Split.TRAIN, |
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gen_kwargs={ |
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"file_path_csv": downloaded_files["csv"], |
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"file_path_zip": downloaded_files["zip"], |
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"file_path_geojson_zip": downloaded_files["geojson_zip"], |
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}, |
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), |
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] |
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def _generate_examples(self, file_path_csv, file_path_zip, file_path_geojson_zip): |
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csv_df = self.process_csv_file(file_path_csv) |
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shp_gdf = self.process_zip_shapefiles(file_path_zip) |
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geojson_gdf = self.process_zip_geojson(file_path_geojson_zip) |
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combined_gdf = self.merge_dataframes(csv_df, shp_gdf, geojson_gdf) |
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for idx, example in self.generate_examples_from_merged_data(combined_gdf): |
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yield idx, example |
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def process_csv_file(self, file_path): |
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with open(file_path, 'r') as f: |
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csv_df = pd.read_csv(f) |
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csv_df.drop_duplicates(inplace=True) |
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csv_df.fillna(method='bfill', inplace=True) |
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csv_df.columns = csv_df.columns.str.lower().str.replace(' ', '_') |
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csv_df['objectid'] = csv_df['objectid'].astype(int) |
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return csv_df |
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def process_zip_shapefiles(self, file_path): |
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with zipfile.ZipFile(file_path, 'r') as z: |
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for file_name in z.namelist(): |
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if file_name.endswith(".shp"): |
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with z.open(file_name) as file: |
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shp_gdf = gpd.read_file(file) |
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shp_gdf.columns = shp_gdf.columns.str.lower().str.replace(' ', '_') |
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shp_gdf['objectid'] = shp_gdf['objectid'].astype(int) |
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return shp_gdf |
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def process_zip_geojson(self, file_path): |
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with zipfile.ZipFile(file_path, 'r') as z: |
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for file_name in z.namelist(): |
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if file_name.endswith(".geojson"): |
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with z.open(file_name) as file: |
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geojson_data = json.load(file) |
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geojson_gdf = gpd.GeoDataFrame.from_features(geojson_data['features']) |
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geojson_gdf.columns = geojson_gdf.columns.str.lower().str.replace(' ', '_') |
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geojson_gdf['objectid'] = geojson_gdf['objectid'].astype(int) |
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return geojson_gdf |
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def merge_dataframes(self, csv_df, shp_gdf, geojson_gdf): |
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combined_gdf = shp_gdf.merge(csv_df, on='objectid', how='inner') |
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combined_gdf = combined_gdf.merge(geojson_gdf, on='objectid', how='left') |
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return combined_gdf |
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def generate_examples_from_merged_data(self, combined_gdf): |
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for idx, row in combined_gdf.iterrows(): |
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example = row.to_dict() |
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if 'geometry' in row and row['geometry'] is not None: |
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example['geometry'] = json.loads(gpd.GeoSeries([row['geometry']]).to_json())['features'][0]['geometry'] |
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yield idx, example |
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def plot_spatial_distribution(self, gdf, lat_col='latitude', lon_col='longitude', color_col='species', hover_col='species'): |
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""" |
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Visualize the spatial distribution of the data using Plotly. |
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Parameters: |
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- gdf: GeoDataFrame to be visualized. |
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- lat_col: String, name of the column with latitude values. |
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- lon_col: String, name of the column with longitude values. |
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- color_col: String, name of the column to determine the color of points. |
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- hover_col: String, name of the column to show when hovering over points. |
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""" |
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center_lat = gdf[lat_col].mean() |
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center_lon = gdf[lon_col].mean() |
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fig = px.scatter_mapbox(gdf, |
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lat=lat_col, |
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lon=lon_col, |
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color=color_col, |
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hover_name=hover_col, |
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center={"lat": center_lat, "lon": center_lon}, |
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zoom=10, |
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height=600, |
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width=800) |
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fig.update_layout(mapbox_style="open-street-map") |
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fig.show() |
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def plot_correlation_heatmap(self, gdf, columns, figsize=(10, 8), cmap='coolwarm'): |
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""" |
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Plot a heatmap of the correlation matrix for selected columns in the GeoDataFrame. |
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Parameters: |
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- gdf: GeoDataFrame containing the data. |
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- columns: List of columns to include in the correlation matrix. |
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- figsize: Tuple of figure size dimensions (width, height). |
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- cmap: Colormap for the heatmap. |
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""" |
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env_data = gdf[columns] |
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corr = env_data.corr() |
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plt.figure(figsize=figsize) |
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sns.heatmap(corr, annot=True, fmt=".2f", cmap=cmap, square=True, linewidths=.5, cbar_kws={"shrink": .5}) |
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plt.tight_layout() |
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plt.show() |
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