import numpy as np
import os
import pandas as pd
from tqdm import tqdm
from model_data.BaseUtility import BaseUtility


def list_subdirectories(directory_path):
    return [d for d in os.listdir(directory_path) if os.path.isdir(os.path.join(directory_path, d))]


DATA_DIRECTORY = os.getcwd() + '/model_data/simulation_system/data/all-domestic-certificates'

FIXED_FEATURES = [
    'PROPERTY_TYPE',
    'BUILT_FORM',
    'CONSTRUCTION_AGE_BAND',
    'NUMBER_HABITABLE_ROOMS',
    'CONSTITUENCY',
    'NUMBER_HEATED_ROOMS',
    'FIXED_LIGHTING_OUTLETS_COUNT',
    'GLAZED_AREA',
    'FLOOR_HEIGHT',
    'FLOOR_LEVEL',
    'TOTAL_FLOOR_AREA',
]

COMPONENT_FEATURES = [
    'TRANSACTION_TYPE',
    'WALLS_DESCRIPTION',
    'FLOOR_DESCRIPTION',
    'LIGHTING_DESCRIPTION',
    'ROOF_DESCRIPTION',
    'MAINHEAT_DESCRIPTION',
    'HOTWATER_DESCRIPTION',
    'MAIN_FUEL',
    'MECHANICAL_VENTILATION',
    'SECONDHEAT_DESCRIPTION',
    'ENERGY_TARIFF',  # Not sure if this is relevant
    'SOLAR_WATER_HEATING_FLAG',
    'PHOTO_SUPPLY',
    'WINDOWS_DESCRIPTION',
    'GLAZED_TYPE',
    'MULTI_GLAZE_PROPORTION',
    'LIGHTING_DESCRIPTION',
    'LOW_ENERGY_LIGHTING',
    'NUMBER_OPEN_FIREPLACES',
    'MAINHEATCONT_DESCRIPTION',
    'EXTENSION_COUNT'
]

AVERAGE_FIXED_FEATURES = [
    "TOTAL_FLOOR_AREA"
]


def app():
    # Get all the files in the directory

    directories = list_subdirectories(DATA_DIRECTORY)

    for directory in tqdm(directories):
        filepath = os.path.join(DATA_DIRECTORY, directory, "certificates.csv")
        df = pd.read_csv(filepath, low_memory=False)
        df = df[~pd.isnull(df["UPRN"])]
        df["UPRN"] = df["UPRN"].astype(int).astype(str)
        counts = df.groupby("UPRN").size().reset_index()
        counts.columns = ["UPRN", "count"]
        counts = counts.sort_values("count", ascending=False)

        # take UPRNS with multiple EPCs
        counts = counts[counts["count"] > 1]
        df = df[df["UPRN"].isin(counts["UPRN"])]
        df = df.sort_values(["UPRN", "LODGEMENT_DATE"], ascending=True)

        for uprn, property_data in df.groupby("UPRN"):

            # Fixed features - these are property attributes that shouldn't change over time

            fixed_data = {}
            for field in FIXED_FEATURES:
                vals = property_data[field].dropna().unique()
                # Remove invalid values
                vals = [v for v in vals if v not in BaseUtility.DATA_ANOMALY_MATCHES]

                if len(vals) > 1:
                    raise ValueError("Fixed feature {} has more than one value - fix me".format(field))

                if field in AVERAGE_FIXED_FEATURES:
                    # Check the values are too far apart
                    if abs(vals[0] - vals[1]) / vals[0] > 0.1:
                        raise ValueError("Large deviation in fixed feature {} - fix me".format(field))

                    field_value = np.mean(vals)
                else:
                    field_value = vals[0] if vals else None

                fixed_data[field] = field_value

            variable_data = property_data[COMPONENT_FEATURES]

            for idx in range(0, property_data.shape[0] - 1):

                if idx >= property_data.shape[0] - 1:
                    break

                starting_record = variable_data.iloc[idx]
                ending_record = variable_data.iloc[idx + 1]