Delete durhamtreesplantingsitesdataset.py

durhamtreesplantingsitesdataset.py

@@ -1,218 +0,0 @@
-# -*- coding: utf-8 -*-
-"""durhamTreesPlantingSitesDataset
-
-Automatically generated by Colaboratory.
-
-Original file is located at
-    https://colab.research.google.com/drive/1Hvt3Y131OjTl7oGQGS55S_v7-aYu1Yj8
-"""
-
-from datasets import DatasetBuilder, DownloadManager, DatasetInfo, SplitGenerator, Split
-from datasets.features import Features, Value, ClassLabel
-import pandas as pd
-import geopandas as gpd
-import matplotlib.pyplot as plt
-import csv
-import json
-import os
-from typing import List
-import datasets
-import logging
-
-_URLS = {
-            "csv": "https://drive.google.com/uc?export=download&id=18HmgMbtbntWsvAySoZr4nV1KNu-i7GCy",
-            "geojson": "https://drive.google.com/uc?export=download&id=1jpFVanNGy7L5tVO-Z_nltbBXKvrcAoDo"
-        }
-
-class durhamTreesPlantingSitesDataset(DatasetBuilder):
-    VERSION = "1.0.0"
-
-    def _info(self):
-        # Specifies the dataset's features
-        return DatasetInfo(
-            description="This dataset contains information about tree planting sites from CSV and GeoJSON files.",
-            features=Features({
-                "geometry": Value("string"),  # Geometry feature, usually spatial data (GeoJSON format)
-                "OBJECTID": Value("int64"),  # Unique identifier for each record
-                "streetaddress": Value("string"),  # Street address of the tree planting site
-                "city": Value("string"),  # City where the tree planting site is located
-                "zipcode": Value("float64"),  # Zip code of the tree planting site (as float due to your data types)
-                "facilityid": Value("int64"),  # Identifier for the facility
-                "present": Value("string"),  # Presence status, assumed to be string
-                "genus": Value("string"),  # Genus of the tree
-                "species": Value("string"),  # Species of the tree
-                "commonname": Value("string"),  # Common name of the tree
-                "plantingdate": Value("string"),  # Planting date of the tree (consider converting to "datetime" for actual dates)
-                "diameterin": Value("float64"),  # Diameter in inches
-                "heightft": Value("string"),  # Height in feet (as a string, consider "float64" for numerical values)
-                "condition": Value("string"),  # Condition of the tree
-                "contractwork": Value("string"),  # Contract work information
-                "neighborhood": Value("string"),  # Neighborhood where the tree is located
-                "program": Value("string"),  # Program under which the tree was planted
-                "plantingw": Value("string"),  # Width available for planting
-                "plantingcond": Value("string"),  # Planting condition
-                "underpwerlins": Value("string"),  # Whether the tree is under power lines
-                "GlobalID": Value("string"),  # Global identifier
-                "created_user": Value("string"),  # User who created the record
-                "created_date": Value("string"),  # Date when the record was created
-                "last_edited_user": Value("string"),  # User who last edited the record
-                "last_edited_date": Value("string"),  # Date when the record was last edited
-                "isoprene": Value("float64"),  # Isoprene emission rate
-                "monoterpene": Value("float64"),  # Monoterpene emission rate
-                 "vocs": Value("float64"),  # Volatile Organic Compounds removed or emitted
-                "coremoved_ozperyr": Value("float64"),  # Carbon monoxide removed, in ounces per year
-                "coremoved_dolperyr": Value("float64"),  # Monetary value of carbon monoxide removal per year
-                "o3removed_ozperyr": Value("float64"),  # Ozone removed, in ounces per year
-                "o3removed_dolperyr": Value("float64"),  # Monetary value of ozone removal per year
-                "no2removed_ozperyr": Value("float64"),  # Nitrogen dioxide removed, in ounces per year
-                "no2removed_dolperyr": Value("float64"),  # Monetary value of nitrogen dioxide removal per year
-                "so2removed_ozperyr": Value("float64"),  # Sulfur dioxide removed, in ounces per year
-                "so2removed_dolperyr": Value("float64"),  # Monetary value of sulfur dioxide removal per year
-                "pm10removed_ozperyr": Value("float64"),  # Particulate matter (10 micrometers or less) removed, in ounces per year
-                "pm10removed_dolperyr": Value("float64"),  # Monetary value of particulate matter removal per year
-                "pm25removed_ozperyr": Value("float64"),  # Particulate matter (2.5 micrometers or less) removed, in ounces per year
-                "o2production_lbperyr": Value("float64"),  # Oxygen production, in pounds per year
-                "replacevalue_dol": Value("float64"),  # Replacement value in dollars
-                "carbonstorage_lb": Value("float64"),  # Carbon storage, in pounds
-                "carbonstorage_dol": Value("float64"),  # Monetary value of carbon storage
-                "grosscarseq_lbperyr": Value("float64"),  # Gross carbon sequestration, in pounds per year
-                "grosscarseq_dolperyr": Value("float64"),  # Monetary value of gross carbon sequestration per year
-                "avoidrunoff_ft2peryr": Value("float64"),  # Avoided runoff, in square feet per year
-                "avoidrunoff_dol2peryr": Value("float64"),  # Monetary value of avoided runoff per year
-                "polremoved_ozperyr": Value("float64"),  # Pollutants removed, in ounces per year
-                "polremoved_dolperyr": Value("float64"),  # Monetary value of pollutant removal per year
-                "totannbenefits_dolperyr": Value("float64"),  # Total annual benefits in dollars per year
-                "leafarea_sqft": Value("float64"),  # Leaf area in square feet
-                "potevapotran_cuftperyr": Value("float64"),  # Potential evapotranspiration, in cubic feet per year
-                "evaporation_cuftperyr": Value("float64"),  # Evaporation, in cubic feet per year
-                "transpiration_cuftperyr": Value("float64"),  # Transpiration, in cubic feet per year
-                "h2ointercept_cuftperyr": Value("float64"),  # Water interception, in cubic feet per year
-                "avoidrunval_cuftperyr": Value("float64"),  # Avoided runoff volume, in cubic feet per year
-                "avoidrunval_dol2peryr": Value("float64"),  # Monetary value of avoided runoff volume per year
-                "carbonavoid_lbperyr": Value("float64"),  # Avoided carbon emissions, in pounds per year
-                "carbonavoid_dolperyr": Value("float64"),  # Monetary value of avoided carbon emissions per year
-                "heating_mbtuperyr": Value("float64"),  # Heating energy saved, in MBTU per year
-                "heating_dolperyrmbtu": Value("float64"),  # Monetary value of heating energy saved per MBTU per year
-                "heating_kwhperyr": Value("float64"),  # Heating energy saved, in kWh per year
-                "heating_dolperyrmwh": Value("float64"),  # Monetary value of heating energy saved per kWh per year
-                "cooling_kwhperyr": Value("float64"),
-                "X": Value("float64"),  # X coordinate (longitude if geographic)
-                "Y": Value("float64"),  # Y coordinate (latitude if geographic)
-            }),
-            supervised_keys=None,
-            homepage="https://github.com/AuraMa111?tab=repositories",
-            citation="Citation for the dataset",
-        )
-
-    def _split_generators(self, dl_manager: DownloadManager):
-        # Downloads the data and defines the splits
-        urls_to_download = self._URLS
-
-        downloaded_files = dl_manager.download_and_extract(urls_to_download)
-
-        return [
-            SplitGenerator(name=Split.TRAIN, gen_kwargs={
-                "csv_path": downloaded_files["csv"],
-                "geojson_path": downloaded_files["geojson"]
-            }),
-            # If you have additional splits, define them here
-        ]
-
-    def _generate_examples(self, csv_path, geojson_path):
-        # Log the information about the CSV file being processed
-        logging.info("Generating examples from CSV = %s", csv_path)
-
-        # Load the CSV data into a pandas DataFrame
-        csv_data = pd.read_csv(csv_path)
-
-        # Log the information about the GeoJSON file being processed
-        logging.info("Generating examples from GeoJSON = %s", geojson_path)
-
-        # Load the GeoJSON data into a GeoDataFrame
-        geojson_data = gpd.read_file(geojson_path)
-
-        # Merge the CSV data with the GeoJSON data on the 'OBJECTID' column
-        merged_data = geojson_data.merge(csv_data, on='OBJECTID')
-
-        # Drop columns with suffix '_y' that might have been created during the merge
-        merged_data.drop(columns=[col for col in merged_data if col.endswith('_y')], inplace=True)
-
-        # Rename columns to remove suffix '_x'
-        merged_data.rename(columns=lambda x: x.rstrip('_x'), inplace=True)
-
-        # Select the desired columns
-        columns_to_extract = [ "geometry",  # Geometry feature, usually spatial data (GeoJSON format)
-                "OBJECTID",  # Unique identifier for each record
-                "streetaddress",  # Street address of the tree planting site
-                "city",  # City where the tree planting site is located
-                "zipcode",  # Zip code of the tree planting site (as float due to your data types)
-                "facilityid",  # Identifier for the facility
-                "present",  # Presence status, assumed to be string
-                "genus",  # Genus of the tree
-                "species",  # Species of the tree
-                "commonname",  # Common name of the tree
-                "plantingdate",  # Planting date of the tree (consider converting to "datetime" for actual dates)
-                "diameterin",  # Diameter in inches
-                "heightft",  # Height in feet (as a string, consider "float64" for numerical values)
-                "condition",  # Condition of the tree
-                "contractwork",  # Contract work information
-                "neighborhood",  # Neighborhood where the tree is located
-                "program",  # Program under which the tree was planted
-                "plantingw",  # Width available for planting
-                "plantingcond",  # Planting condition
-                "underpwerlins",  # Whether the tree is under power lines
-                "GlobalID",  # Global identifier
-                "created_user",  # User who created the record
-                "created_date",  # Date when the record was created
-                "last_edited_user",  # User who last edited the record
-                "last_edited_date",  # Date when the record was last edited
-                "isoprene",  # Isoprene emission rate
-                "monoterpene",  # Monoterpene emission rate
-                 "vocs",  # Volatile Organic Compounds removed or emitted
-                "coremoved_ozperyr",  # Carbon monoxide removed, in ounces per year
-                "coremoved_dolperyr",  # Monetary value of carbon monoxide removal per year
-                "o3removed_ozperyr",  # Ozone removed, in ounces per year
-                "o3removed_dolperyr",  # Monetary value of ozone removal per year
-                "no2removed_ozperyr",  # Nitrogen dioxide removed, in ounces per year
-                "no2removed_dolperyr",  # Monetary value of nitrogen dioxide removal per year
-                "so2removed_ozperyr",  # Sulfur dioxide removed, in ounces per year
-                "so2removed_dolperyr",  # Monetary value of sulfur dioxide removal per year
-                "pm10removed_ozperyr",  # Particulate matter (10 micrometers or less) removed, in ounces per year
-                "pm10removed_dolperyr",  # Monetary value of particulate matter removal per year
-                "pm25removed_ozperyr",  # Particulate matter (2.5 micrometers or less) removed, in ounces per year
-                "o2production_lbperyr",  # Oxygen production, in pounds per year
-                "replacevalue_dol",  # Replacement value in dollars
-                "carbonstorage_lb",  # Carbon storage, in pounds
-                "carbonstorage_dol",  # Monetary value of carbon storage
-                "grosscarseq_lbperyr",  # Gross carbon sequestration, in pounds per year
-                "grosscarseq_dolperyr",  # Monetary value of gross carbon sequestration per year
-                "avoidrunoff_ft2peryr",  # Avoided runoff, in square feet per year
-                "avoidrunoff_dol2peryr",  # Monetary value of avoided runoff per year
-                "polremoved_ozperyr",  # Pollutants removed, in ounces per year
-                "polremoved_dolperyr",  # Monetary value of pollutant removal per year
-                "totannbenefits_dolperyr",  # Total annual benefits in dollars per year
-                "leafarea_sqft",  # Leaf area in square feet
-                "potevapotran_cuftperyr",  # Potential evapotranspiration, in cubic feet per year
-                "evaporation_cuftperyr",  # Evaporation, in cubic feet per year
-                "transpiration_cuftperyr",  # Transpiration, in cubic feet per year
-                "h2ointercept_cuftperyr",  # Water interception, in cubic feet per year
-                "avoidrunval_cuftperyr",  # Avoided runoff volume, in cubic feet per year
-                "avoidrunval_dol2peryr",  # Monetary value of avoided runoff volume per year
-                "carbonavoid_lbperyr",  # Avoided carbon emissions, in pounds per year
-                "carbonavoid_dolperyr",  # Monetary value of avoided carbon emissions per year
-                "heating_mbtuperyr",  # Heating energy saved, in MBTU per year
-                "heating_dolperyrmbtu",  # Monetary value of heating energy saved per MBTU per year
-                "heating_kwhperyr",  # Heating energy saved, in kWh per year
-                "heating_dolperyrmwh",  # Monetary value of heating energy saved per kWh per year
-                "cooling_kwhperyr",
-                "X",  # X coordinate (longitude if geographic)
-                "Y"]
-
-        # Create the final DataFrame with the selected columns
-        df = merged_data[columns_to_extract]
-
-        # Iterate over each row in the DataFrame and yield it
-        for index, row in df.iterrows():
-            # Convert the row to a dictionary, it's more convenient for yielding
-            yield index, row.to_dict()
-