import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import numpy as np
import pandas as pd
import os
import seaborn as sns
import json
from matplotlib.colors import LinearSegmentedColormap


def read_data(file_name: str):
    with open(file_name, 'r') as f:
        data = json.load(f)
    for key, value in data.items():
        for subkey, subvalue in value.items():
            data[key][subkey] = float(subvalue)
    df = pd.DataFrame.from_dict(data, orient='index')
    df.index = pd.to_numeric(df.index)
    df.columns = pd.to_numeric(df.columns)
    return df

def draw_results(results, filename, title, annot_benefit=False, figure_size=(10, 10)):
    df= results
    df = df.astype(float)
    df.index = df.index / 1000
    df.columns = df.columns / 1000
    min_value = df.min().min()
    max_value = df.max().max()
    max_scale = max(abs(min_value/1000), abs(max_value/1000))
    plt.figure(figsize=figure_size)
    cmap = sns.color_palette("coolwarm", as_cmap=True)
    ax = sns.heatmap(df/1000, fmt=".1f", cmap=cmap, vmin=-max_scale, vmax=max_scale, annot=annot_benefit)
    ax.xaxis.set_major_formatter(ticker.FuncFormatter(lambda x, _: f"{x:.2f}"))
    # ax.yaxis.set_major_formatter(ticker.FormatStrFormatter('%.1f'))
    plt.title(title)
    plt.gca().invert_yaxis()
    plt.xlabel('ESS Capacity (MWh)')
    plt.ylabel('PV Capacity (MW)')
    plt.savefig(filename)

def draw_cost(costs, filename, title_cost, annot_cost=False, figure_size=(10, 10)):
    df = costs
    df = df.astype(int)
    print(df.index)
    df.index = df.index / 1000
    print(df.columns)
    df.columns = df.columns / 1000

    plt.figure(figsize=figure_size)
    sns.heatmap(df/1000000,  fmt=".1f", cmap='viridis', annot=annot_cost)
    plt.title(title_cost)
    plt.gca().invert_yaxis()
    plt.xlabel('ESS Capacity (MWh)')
    plt.ylabel('PV Capacity (MW)')
    print(filename)
    plt.savefig(filename)

def draw_overload(overload_cnt, filename, title_unmet, annot_unmet=False, figure_size=(10, 10)):
    df = overload_cnt
    df = df.astype(int)
    df.index = df.index / 1000
    df.columns = df.columns / 1000
    min_value = df.min().min()
    max_value = df.max().max()
    max_scale = max(abs(min_value/1000), abs(max_value/1000))

    plt.figure(figsize=figure_size)
    cmap = LinearSegmentedColormap.from_list("", ["white", "blue"])
    ax = sns.heatmap(df/(4*24*365), fmt=".00%", cmap=cmap, vmin=0, vmax=1, annot=annot_unmet)
    cbar = ax.collections[0].colorbar
    cbar.set_ticks([0, 0.25, 0.5, 0.75, 1])
    cbar.set_ticklabels(['0%', '25%', '50%', '75%', '100%'])
    cbar.ax.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, pos: f'{x:.0%}'))

    plt.title(title_unmet)
    plt.gca().invert_yaxis()
    plt.xlabel('ESS Capacity (MWh)')
    plt.ylabel('PV Capacity (MW)')
    plt.savefig(filename)

directory = 'data/'

file_list = [f for f in os.listdir(directory) if os.path.isfile(os.path.join(directory, f))]


split_files = [f.split('-') for f in file_list]

for f in split_files:
    print(f[-1])
costs_files = [f for f in split_files if f[-1].endswith('costs.json')]
print(f'find costs files: {costs_files}')
overload_files = [f for f in split_files if f[-1].endswith('overload_cnt.json')]
print(f'find overload files: {overload_files}')
results_files = [f for f in split_files if f[-1].endswith('results.json')]
print(f'find results files: {results_files}')

costs_dfs = [read_data(directory + '-'.join(f)) for f in costs_files]
overload_dfs = [read_data(directory + '-'.join(f)) for f in overload_files]
results_dfs = [read_data(directory + '-'.join(f)) for f in results_files]

for costs_df, overload_df, results_df in zip(costs_dfs, overload_dfs, results_dfs):
    # print(costs_df.index)
    # print(pd.to_numeric(costs_df.columns))
    # costs_df.index = pd.to_numeric(costs_df.columns )
    # costs_df.columns = pd.to_numeric(costs_df.index)
    print(costs_df)
    draw_cost(costs_df, f'plots/costs-{int(costs_df.columns[-1])}.png', f'Costs for PV-{int(costs_df.columns[-1])}MW ESS-{int(costs_df.index[-1])}MWh', annot_cost=True)
    # overload_df.index = pd.to_numeric(overload_df.columns, errors='coerce')
    # overload_df.columns = pd.to_numeric(overload_df.columns, errors='coerce')
    print(overload_df)
    # draw_overload(overload_df, f'plots/overload-{overload_df.columns[-1]}', f'Overload for PV-{overload_df.columns[-1]}MW ESS-{overload_df.index[-1]}MWh', annot_unmet=True)
    # results_df.index = pd.to_numeric(results_df.columns, errors='coerce')
    # results_df.columns = pd.to_numeric(results_df.columns, errors='coerce')
    # draw_results(results_df, f'plots/results-{results_df.columns[-1]}', f'Results for PV-{results_df.columns[-1]}MW ESS-{results_df.index[-1]}MWh', annot_benefit=True)