Delete urban_tree_canopy_in_durham2.py

urban_tree_canopy_in_durham2.py

@@ -1,301 +0,0 @@
-# -*- coding: utf-8 -*-
-"""Urban_Tree_Canopy_in_Durham2
-
-Automatically generated by Colaboratory.
-
-Original file is located at
-    https://colab.research.google.com/drive/1X59zPtI7ydiX10ZnfjsNGvnKNTXgwrWs
-"""
-
-! pip install datasets
-import csv
-import json
-import os
-from typing import List
-import datasets
-import logging
-from datasets import DatasetBuilder, DownloadManager, SplitGenerator, Split
-
-import zipfile
-import json
-import pandas as pd
-import geopandas as gpd
-
-import os
-import pandas as pd
-import geopandas as gpd
-import zipfile
-import tempfile
-import shutil
-
-
-
-class Urban_Tree_Canopy_in_Durham2(DatasetBuilder):
-  # Define the `_info` method, which provides dataset metadata
-    def _info(self):
-        return DatasetInfo(
-            description="A description of the dataset.",
-            features=Features(
-                {
-                    "objectid": Value("int32"),
-                    "streetaddr": Value("string"),
-                    "city_x": Value("string"),
-                    "zipcode_x": Value("string"),
-                    "facilityid_x": Value("string"),
-                    "present_x": Value("string"),
-                    "genus_x": Value("string"),
-                    "species_x": Value("string"),
-                    "commonname_x": Value("string"),
-                    "plantingda": Value("datetime"),
-                    "diameterin_x": Value("float"),
-                    "heightft_x": Value("float"),
-                    "condition_x": Value("string"),
-                    "contractwo": Value("string"),
-                    "neighborho": Value("string"),
-                    "program_x": Value("string"),
-                    "plantingw_x": Value("string"),
-                    "plantingco": Value("string"),
-                    "underpwerl": Value("string"),
-                    "matureheig": Value("float"),
-                    "globalid_x": Value("string"),
-                    "created_us": Value("string"),
-                    "created_da": Value("datetime"),
-                    "last_edite": Value("string"),
-                    "last_edi_1": Value("datetime"),
-                    "isoprene_x": Value("float"),
-                    "monoterpen": Value("float"),
-                    "vocs_x": Value("float"),
-                    "coremoved_": Value("float"),
-                    "coremove_1": Value("float"),
-                    "o3removed_": Value("float"),
-                    "o3remove_1": Value("float"),
-                    "no2removed": Value("float"),
-                    "no2remov_1": Value("float"),
-                    "so2removed": Value("float"),
-                    "so2remov_1": Value("float"),
-                    "pm10remove": Value("float"),
-                    "pm10remo_1": Value("float"),
-                    "pm25remove": Value("float"),
-                    "o2producti": Value("float"),
-                    "replaceval": Value("float"),
-                    "carbonstor": Value("float"),
-                    "carbonst_1": Value("float"),
-                    "grosscarse": Value("float"),
-                    "grosscar_1": Value("float"),
-                    "avoidrunof": Value("float"),
-                    "avoidrun_1": Value("float"),
-                    "polremoved": Value("float"),
-                    "polremov_1": Value("float"),
-                    "totannbene": Value("float"),
-                    "leafarea_s": Value("float"),
-                    "potevapotr": Value("float"),
-                    "evaporatio": Value("float"),
-                    "transpirat": Value("float"),
-                    "h2ointerce": Value("float"),
-                    "avoidrunva": Value("float"),
-                    "avoidrun_2": Value("float"),
-                    "carbonavoi": Value("float"),
-                    "carbonav_1": Value("float"),
-                    "heating_mb": Value("float"),
-                    "heating_do": Value("float"),
-                    "heating_kw": Value("float"),
-                    "heating__1": Value("float"),
-                    "cooling_kw": Value("float"),
-                    "cooling_do": Value("float"),
-                    "totalenerg": Value("float"),
-                    "geometry_x": Value("string"),
-                    "x": Value("float"),
-                    "y": Value("float"),
-                    "streetaddress_x": Value("string"),
-                    "city_y": Value("string"),
-                    "zipcode_y": Value("string"),
-                    "facilityid_y": Value("string"),
-                    "present_y": Value("string"),
-                    "genus_y": Value("string"),
-                    "species_y": Value("string"),
-                    "commonname_y": Value("string"),
-                    "plantingdate_x": Value("datetime"),
-                    "diameterin_y": Value("float"),
-                    "heightft_y": Value("float"),
-                    "condition_y": Value("string"),
-                    "contractwork_x": Value("string"),
-                    "neighborhood_x": Value("string"),
-                    "program_y": Value("string"),
-                    "plantingw_y": Value("string"),
-                    "plantingcond_x": Value("string"),
-                    "underpwerlins_x": Value("string"),
-                    "matureheight_x": Value("float"),
-                    "globalid_y": Value("string"),
-                    "created_user_x": Value("string"),
-                    "created_date_x": Value("datetime"),
-                    "last_edited_user_x": Value("string"),
-                    "last_edited_date_x": Value("datetime"),
-                    "isoprene_y": Value("float"),
-                    "monoterpene_x": Value("float"),
-                    "vocs_y": Value("float"),
-                    "coremoved_ozperyr_x": Value("float"),
-                    "coremoved_dolperyr_x": Value("float"),
-                    "o3removed_ozperyr_x": Value("float"),
-                    "o3removed_dolperyr_x": Value("float"),
-                    "no2removed_ozperyr_x": Value("float"),
-                    "no2removed_dolperyr_x": Value("float"),
-                    "so2removed_ozperyr_x": Value("float"),
-                    "so2removed_dolperyr_x": Value("float"),
-                    "pm10removed_dolperyr_y":Value("float"),
-                    "pm25removed_ozperyr_y":Value("float"),
-                    "o2production_lbperyr_y":Value("float"),
-                    "replacevalue_dol_y":Value("float"),
-                    "carbonstorage_lb_y":Value("float"),
-                    "carbonstorage_dol_y":Value("float"),
-                    "grosscarseq_lbperyr_y":Value("float"),
-                    "grosscarseq_dolperyr_y":Value("float"),
-                    "avoidrunoff_ft2peryr":Value("float"),
-
-                }
-            ),
-            supervised_keys=None,
-            homepage="https://github.com/AuraMa111/Urban_Tree_Canopy_in_Durham",
-            citation="A citation or reference to the source of the dataset.",
-        )
-
-
-
-
-    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
-        """Returns SplitGenerators."""
-
-        # Download files and get their local file paths
-        csv_path = dl_manager.download("https://huggingface.co/datasets/Ziyuan111/Urban_Tree_Canopy_in_Durham2/raw/main/Trees_%2526_Planting_Sites.csv")
-        zip_path = dl_manager.download("https://huggingface.co/datasets/Ziyuan111/Urban_Tree_Canopy_in_Durham2/raw/main/TreesPlanting_Sites.zip")
-        geojson_path = dl_manager.download("https://huggingface.co/datasets/Ziyuan111/Urban_Tree_Canopy_in_Durham2/raw/main/Trees_%2526_Planting_Sites.geojson")
-
-        return [
-            # Define a SplitGenerator for the CSV file
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "filepath": csv_path,
-                    "split": "csv",
-                },
-            ),
-            # Define a SplitGenerator for the ZIP file
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "filepath": zip_path,
-                    "split": "zip",
-                },
-            ),
-            # Define a SplitGenerator for the GEOJSON file
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "filepath": geojson_path,
-                    "split": "geojson",
-                },
-            ),
-        ]
-
-
-
-    def _generate_examples(self, filepaths, split):
-        """Yields examples as (key, example) tuples."""
-
-        # Assuming filepaths is a dictionary like:
-        # {'csv': 'path_to_csv', 'zip': 'path_to_zip', 'geojson': 'path_to_geojson'}
-        csv_df = pd.read_csv(filepaths['csv'])
-        csv_df.columns = csv_df.columns.str.lower().str.replace(' ', '_')
-        csv_df['objectid'] = csv_df['objectid'].astype(int)
-
-        with zipfile.ZipFile(filepaths['zip'], 'r') as z:
-            temp_dir = tempfile.mkdtemp()
-            z.extractall(path=temp_dir)
-            shapefile_path = next((os.path.join(temp_dir, name) for name in z.namelist() if name.endswith('.shp')), None)
-            if shapefile_path is None:
-                raise FileNotFoundError("No shapefile found in the ZIP archive.")
-            shp_gdf = gpd.read_file(shapefile_path)
-            shp_gdf.columns = shp_gdf.columns.str.lower().str.replace(' ', '_')
-            shp_gdf['objectid'] = shp_gdf['objectid'].astype(int)
-            shutil.rmtree(temp_dir)
-
-        geojson_gdf = gpd.read_file(filepaths['geojson'])
-        geojson_gdf.columns = geojson_gdf.columns.str.lower().str.replace(' ', '_')
-        geojson_gdf['objectid'] = geojson_gdf['objectid'].astype(int)
-
-        # Merge the dataframes on 'objectid'
-        combined_gdf = shp_gdf.merge(csv_df, on='objectid', how='inner')
-        combined_gdf = combined_gdf.merge(geojson_gdf, on='objectid', how='left')
-
-        # Yield the combined data
-        for idx, row in combined_gdf.iterrows():
-            # Yield each row as an example, using the index as the key
-            yield idx, row.to_dict()
-
-
-    def plot_spatial_distribution(self, gdf, lat_col='latitude', lon_col='longitude', color_col='species', hover_col='species'):
-            """
-            Visualize the spatial distribution of the data using Plotly.
-
-            Parameters:
-            - gdf: GeoDataFrame to be visualized.
-            - lat_col: String, name of the column with latitude values.
-            - lon_col: String, name of the column with longitude values.
-            - color_col: String, name of the column to determine the color of points.
-            - hover_col: String, name of the column to show when hovering over points.
-            """
-            center_lat = gdf[lat_col].mean()
-            center_lon = gdf[lon_col].mean()
-
-            fig = px.scatter_mapbox(gdf,
-                                    lat=lat_col,
-                                    lon=lon_col,
-                                    color=color_col,
-                                    hover_name=hover_col,
-                                    center={"lat": center_lat, "lon": center_lon},
-                                    zoom=10,
-                                    height=600,
-                                    width=800)
-
-            fig.update_layout(mapbox_style="open-street-map")
-            fig.show()
-    def plot_correlation_heatmap(self, gdf, columns, figsize=(10, 8), cmap='coolwarm'):
-        """
-        Plot a heatmap of the correlation matrix for selected columns in the GeoDataFrame.
-
-        Parameters:
-        - gdf: GeoDataFrame containing the data.
-        - columns: List of columns to include in the correlation matrix.
-        - figsize: Tuple of figure size dimensions (width, height).
-        - cmap: Colormap for the heatmap.
-        """
-          # Select only the columns with environmental data
-        env_data = gdf[columns]
-
-          # Compute the correlation matrix
-        corr = env_data.corr()
-
-          # Set up the matplotlib figure
-        plt.figure(figsize=figsize)
-
-          # Generate a heatmap
-        sns.heatmap(corr, annot=True, fmt=".2f", cmap=cmap, square=True, linewidths=.5, cbar_kws={"shrink": .5})
-
-          # Optional: Adjust the layout
-        plt.tight_layout()
-
-          # Show the plot
-        plt.show()
-    def load_dataset():
-        builder = UrbanTreeCanopyInDurham2()
-        return builder.as_dataset()
-# Usage example:
-# data_processor = DataProcessor()
-# for key, example in data_processor._generate_examples(csv_path, zip_path, geojson_zip_path):
-#     # Do something with key and example
-# Usage example:
-# data_processor = DataProcessor()
-# for key, example in data_processor._generate_examples(csv_path, zip_path, geojson_zip_path):
-#     # Do something with key and example
-# combined_gdf = data_processor.merge_dataframes(csv_df, shp_gdf, geojson_gdf)
-# data_processor.plot_spatial_distribution(combined_gdf, lat_col='y', lon_col='x', color_col='species_x', hover_col='species_x')
-