Delete urbantreecanopyindurham2dataset.py

urbantreecanopyindurham2dataset.py

@@ -1,164 +0,0 @@
-# -*- coding: utf-8 -*-
-"""UrbanTreeCanopyInDurham2Dataset
-
-Automatically generated by Colaboratory.
-
-Original file is located at
-    https://colab.research.google.com/drive/1X59zPtI7ydiX10ZnfjsNGvnKNTXgwrWs
-"""
-
-from datasets import DatasetInfo, Features, Value, load_dataset, BuilderConfig, DatasetBuilder
-import csv
-import json
-import os
-from typing import List
-import datasets
-import logging
-from datasets import DownloadManager, SplitGenerator, Split
-import zipfile
-import pandas as pd
-import geopandas as gpd
-import tempfile
-import shutil
-import plotly.express as px
-from datasets import GeneratorBasedBuilder
-
-class UrbanTreeCanopyInDurham2Dataset(GeneratorBasedBuilder):
-
-    def _info(self):
-        return DatasetInfo(
-            description="Urban_Tree_Canopy_in_Durham2",
-            features=Features(
-                {
-                    "objectid": Value("int32"),
-                    "streetaddr": Value("string"),
-                    "city_x": Value("string"),
-                    "zipcode_x": Value("string"),
-                    "species_x": Value("string"),
-                    "commonname_x": Value("string"),
-                    "plantingda": datasets.Value("timestamp[us]"),
-                    "diameterin_x": Value("float"),
-                    "heightft_x": Value("float"),
-                    "condition_x": Value("string"),
-                    "program_x": Value("string"),
-                    "matureheig": Value("float"),
-                    "created_da": datasets.Value("timestamp[us]"),
-                    "last_edi_1": datasets.Value("timestamp[us]"),
-                    "geometry_x": Value("string"),
-                    "x": Value("float"),
-                    "y": 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"),
-                }
-            ),
-            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):
-        csv_url = "https://drive.google.com/uc?export=download&id=18HmgMbtbntWsvAySoZr4nV1KNu-i7GCy"
-        geojson_url = "https://drive.google.com/uc?export=download&id=1jpFVanNGy7L5tVO-Z_nltbBXKvrcAoDo"
-
-        # Extract the file ID from the SHP Google Drive sharing URL and construct a direct download link
-        shp_file_id = "1DYcug0xiWYlsKZorbbEcrjZWEAB0y4MY"
-        shp_url = f"https://drive.google.com/uc?export=download&id={shp_file_id}"
-
-        # Use dl_manager to download the files
-        csv_path = dl_manager.download_and_extract(csv_url)
-        shp_path = dl_manager.download_and_extract(shp_url)
-        geojson_path = dl_manager.download_and_extract(geojson_url)
-
-        # Assuming the paths are directories, construct the full paths to the files
-        csv_file_path = os.path.join(csv_path, 'Trees_%26_Planting_Sites.csv')
-        shp_file_path = os.path.join(shp_path, 'GS_TreeInventory.shp')  # Adjust if necessary
-        geojson_file_path = os.path.join(geojson_path, 'Trees_%26_Planting_Sites.geojson')
-
-        # Now you can return the paths for use in your data processing
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "csv_path": csv_file_path,
-                    "shp_path": shp_file_path,
-                    "geojson_path": geojson_file_path,
-                },
-            ),
-        ]
-
-    def _generate_examples(self, csv_path, shp_path, geojson_path):
-        """Yields examples as (key, example) tuples."""
-
-        # Load the datasets
-        csv_df = pd.read_csv(csv_path)
-        shp_gdf = gpd.read_file(shp_path)
-        with open(geojson_path, 'r') as f:
-            geojson_data = json.load(f)
-        geojson_gdf = gpd.GeoDataFrame.from_features(geojson_data["features"])
-
-        # Standardize column names
-        csv_df.columns = csv_df.columns.str.lower().str.replace(' ', '_')
-        shp_gdf.columns = shp_gdf.columns.str.lower().str.replace(' ', '_')
-        geojson_gdf.columns = geojson_gdf.columns.str.lower().str.replace(' ', '_')
-
-        # Convert 'objectid' to int
-        csv_df['objectid'] = csv_df['objectid'].astype(int)
-        shp_gdf['objectid'] = shp_gdf['objectid'].astype(int)
-        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='inner')
-        combined_gdf=combined_gdf[["objectid", "streetaddr", "city_x", "zipcode_x",
-                                    "species_x", "commonname_x", "plantingda", "diameterin_x",
-                                    "heightft_x", "condition_x", "program_x", "matureheig",
-                                    "created_da", "last_edi_1", "geometry_x",
-                                    "x", "y",
-                                    "coremoved_", "coremove_1",
-                                    "o3removed_", "o3remove_1",
-                                    "no2removed", "no2remov_1",
-                                    "so2removed", "so2remov_1",
-                                    "pm10remove", "pm10remo_1",
-                                    "pm25remove", "o2producti",
-                                ]]
-
-        # 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()
-
-    @staticmethod
-    def plot_spatial_distribution(combined_gdf, lat_col='x', lon_col='y', color_col='species_x', hover_col='species_x'):
-            # Calculate the mean latitude and longitude for the center of the map
-        center_lat = combined_gdf[lat_col].mean()
-        center_lon = combined_gdf[lon_col].mean()
-
-            # Create a scatter mapbox plot
-        fig = px.scatter_mapbox(combined_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)
-
-            # Set the mapbox style to "open-street-map"
-        fig.update_layout(mapbox_style="open-street-map")
-
-            # Display the figure
-        fig.show()
-