# -*- coding: utf-8 -*-
"""DurhamTrees

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1W5gDhKokcuqoA8AK4a6JR7PIeCUdmrTU
"""

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
# URL definitions
_URLS = {
    "first_domain1": {
        "csv_file": "https://drive.google.com/uc?export=download&id=18HmgMbtbntWsvAySoZr4nV1KNu-i7GCy",
        "geojson_file": "https://drive.google.com/uc?export=download&id=1cbn7EY7RofXN7c6Ph0eIGFIZowPZ5lKE",
    },
    "first_domain2": {
        "csv_file2": "https://drive.google.com/uc?export=download&id=1RVdaI5dSTPStjhOHO40ypDv2cAQZpi_Y",
    },
}

# Combined Dataset Class
class DurhamTrees(datasets.GeneratorBasedBuilder):
    VERSION = datasets.Version("1.0.0")

    # Combine BUILDER_CONFIGS from both classes, ensuring they have unique names
    BUILDER_CONFIGS = [
        # Configurations from DatasetClass1
        datasets.BuilderConfig(name="class1_domain1", description="this is combined of csv and geojson"),
        # Configurations from DatasetClass2
        datasets.BuilderConfig(name="class2_domain1", description="this is csv file"),
        # Add other configurations as necessary
    ]

    def _info(self):
        # Combine the features from both classes into one Features dictionary
        return datasets.DatasetInfo(
            description="This dataset combines information from both classes.",
            features=datasets.Features({
                # Features from DatasetClass1
                "feature1_from_class1": Value("string"),
                "geometry": Value("string"),
                "OBJECTID": Value("int64"),
                "X": Value("float64"),
                "Y": Value("float64"),

                # Features from DatasetClass2
                "feature1_from_class2": Value("string"),
                "geometry": Value("string"),
                "OBJECTID": Value("int64"),
                "streetaddress": Value("string"),
                "city": Value("string"),
                "facilityid": Value("int64"),
                "present": Value("string"),
                "genus": Value("string"),
                "species": Value("string"),
                "commonname": Value("string"),
                "diameterin": Value("float64"),
                "condition": Value("string"),
                "contractwork": Value("string"),
                "neighborhood": Value("string"),
                "program": Value("string"),
                "plantingw": Value("string"),
                "plantingcond": Value("string"),
                "underpwerlins": Value("string"),
                "GlobalID": Value("string"),
                "created_user": Value("string"),
                "last_edited_user": Value("string"),
                "isoprene": Value("float64"),
                "monoterpene": Value("float64"),
                # ... add other features as needed
            }),
            supervised_keys=None,
            homepage="https://github.com/AuraMa111?tab=repositories",
            citation="Citation for the combined dataset",
        )

    def _split_generators(self, dl_manager):
        downloaded_files = dl_manager.download_and_extract(_URLS)

        # Combine the split generators from both classes
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    # Arguments for DatasetClass1
                    "class1_data_file": downloaded_files["first_domain1"]["csv_file"],
                    "class1_geojson_file": downloaded_files["first_domain1"]["geojson_file"],

                    # Arguments for DatasetClass2
                    "class2_data_file": downloaded_files["first_domain2"]["csv_file2"],

                    # Additional arguments if needed
                    "split": "train",
                },
            ),
            # Add other splits as necessary, each with its own generator
        ]

    def _generate_examples(self, class1_data_file, class1_geojson_file, class2_data_file, split):
        # Generate examples from the first dataset class
        for example in self._generate_examples_from_class1(class1_data_file, class1_geojson_file):
            yield example

        # Generate examples from the second dataset class
        for example in self._generate_examples_from_class2(class2_data_file):
            yield example

    def _generate_examples_from_class1(self, csv_filepath, geojson_filepath):
        columns_to_extract = ["geometry", "OBJECTID", "X", "Y"]
        csv_data = pd.read_csv(csv_filepath)

        with open(geojson_filepath, 'r') as file:
            geojson_dict = json.load(file)
        gdf = gpd.GeoDataFrame.from_features(geojson_dict['features'])
        merged_data = gdf.merge(csv_data, on='OBJECTID')
        final_data = merged_data[columns_to_extract]
        for id_, row in final_data.iterrows():
            example = row.to_dict()
            yield id_, example

    def _generate_examples_from_class2(self, csv_filepath2):
        columns_to_extract = ["geometry", "OBJECTID", "streetaddress", "city", "facilityid", "present", "genus", "species", "commonname", "diameterin", "condition", "contractwork", "neighborhood", "program", "plantingw", "plantingcond", "underpwerlins", "GlobalID", "created_user", "last_edited_user", "isoprene", "monoterpene", "coremoved_ozperyr", "coremoved_dolperyr", "o3removed_ozperyr", "o3removed_dolperyr", "no2removed_ozperyr", "no2removed_dolperyr", "so2removed_ozperyr", "so2removed_dolperyr", "pm10removed_ozperyr", "pm10removed_dolperyr", "pm25removed_ozperyr", "o2production_lbperyr", "replacevalue_dol", "carbonstorage_lb", "carbonstorage_dol", "grosscarseq_lbperyr", "X", "Y"]

        # Load the CSV data into a pandas DataFrame
        csv_data2 = pd.read_csv(csv_filepath2)

        # Filter the DataFrame to only include the specified columns
        final_data = csv_data2[columns_to_extract]

        # Iterate over the rows of the final DataFrame
        for id_, row in final_data.iterrows():
            # Convert the row to a dictionary
            example = row.to_dict()

            # Yield the example with its index as the identifier
            yield id_, example