Datasets:

Ziyuan111
/

DurhamTrees

@@ -1,179 +0,0 @@
-# -*- coding: utf-8 -*-
-"""DurhamTrees
-Automatically generated by Colaboratory.
-Original file is located at
-    https://colab.research.google.com/drive/1W5gDhKokcuqoA8AK4a6JR7PIeCUdmrTU
-"""
-!pip install datasets
-import datasets
-import pandas as pd
-import geopandas as gpd
-from datasets import DatasetBuilder, DownloadManager, DatasetInfo, SplitGenerator, Split
-from datasets.features import Features, Value, ClassLabel
-import matplotlib.pyplot as plt
-import csv
-import json
-import os
-from typing import List
-_URLS = {
-    "first_domain": {
-        "csv_file": "https://drive.google.com/uc?export=download&id=18HmgMbtbntWsvAySoZr4nV1KNu-i7GCy",
-        "geojson_file": "https://drive.google.com/uc?export=download&id=1jpFVanNGy7L5tVO-Z_nltbBXKvrcAoDo",
-    },
-    # Add other domains if necessary
-}
-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 (changed to string)
-                "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
-                "diameterin",  # Diameter in inches
-                "heightft",  # Height in feet (changed to "float64")
-                "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
-                "last_edited_user",  # User who last edited the record
-                "isoprene",  # Isoprene emission rate
-                "monoterpene",
-                "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
-                "X",  # X coordinate (longitude if geographic)
-                "Y"]
-class DurhamTrees(datasets.GeneratorBasedBuilder):
-    # ... [other parts of the script] ...
-    VERSION = datasets.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("int64"),  # Zip code of the tree planting site
-                "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
-                "diameterin": Value("float64"),  # Diameter in inches
-                "heightft": Value("float64"),  # Height in feet (changed to "float64")
-                "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
-                "last_edited_user": Value("string"),  # User who last edited the record
-                "isoprene": Value("float64"),  # Isoprene emission rate
-                "monoterpene": Value("float64"),
-                "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
-                "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):
-        downloaded_files = dl_manager.download_and_extract(_URLS[self.config.name])
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "csv_filepath": downloaded_files["first_domain"]["csv_file"],
-                    "geojson_filepath": downloaded_files["first_domain"]["geojson_file"],
-                    "split": "train",
-                },
-            ),
-            # Add additional split generators if available
-        ]
-    def _generate_examples(self, csv_filepath, geojson_filepath, split):
-        # Load the CSV data into a pandas DataFrame
-        csv_data = pd.read_csv(csv_filepath)
-        # Load the GeoJSON data into a GeoDataFrame
-        geojson_data = gpd.read_file(geojson_filepath)
-        # Merge the CSV data with the GeoJSON data on the 'OBJECTID' field
-        merged_data = geojson_data.merge(csv_data, on='OBJECTID')
-        # Convert the merged GeoDataFrame back into a dictionary to yield examples
-        merged_dict = json.loads(merged_data.to_json())
-        final_data = merged_data[columns_to_extract]
-        # Iterate over the rows of the final DataFrame and yield each row as an example
-        for id_, row in final_data.iterrows():
-            # Convert the row to a dictionary
-            example = row.to_dict()
-            # Correct data types for 'zipcode' and 'heightft' if necessary
-            example['zipcode'] = str(example['zipcode'])
-            example['heightft'] = float(example['heightft'])
-            # Yield the example
-            yield id_, example
-        # Iterate over the features and yield each one as an example
-        for id_, feature in enumerate(merged_dict['features']):
-            properties = feature['properties']
-            # You might need additional processing here if you want to include the geometry
-            # or if the properties need further manipulation to fit the datasets.Features structure
-            yield id_, properties