import json
import logging
import os

import dateutil.parser
import matplotlib.pyplot as plt
import numpy as np


# Gets all files in tracks folder, returns them in parsed JSON
def get_files():
    folder = os.path.join(os.path.dirname(os.path.abspath(__file__)), "tracks")
    files = []
    for file in os.listdir(folder):
        with open(os.path.join(os.path.join(folder, file))) as file:
            files.append(json.load(file))
    return files


# Simple function to combine a bunch of items from different files
def combine_files(files):
    items = []
    for file in files:
        items.extend(file["items"])
    return items


# Prints the data in a interesting format
def print_data(data):
    for i, item in enumerate(data):
        date = dateutil.parser.parse(item["added_at"])
        explicit = "!" if item["track"]["explicit"] else " "
        track_name = item["track"]["name"]
        artists = " & ".join(artist["name"] for artist in item["track"]["artists"])
        print('[{}] {} "{}" by {}'.format(date, explicit, track_name, artists))


def process_data(data):
    # Process the data by Month/Year, then by Clean/Explicit
    scores = {}
    for item in data:
        date = dateutil.parser.parse(item["added_at"]).strftime("%b %Y")
        if date not in scores.keys():
            scores[date] = [0, 0]
        scores[date][1 if item["track"]["explicit"] else 0] += 1

    # Create simplified arrays for each piece of data
    months = list(scores.keys())[::-1]
    clean, explicit = [], []
    for item in list(scores.values())[::-1]:
        clean.append(item[0])
        explicit.append(item[1])

    # Done processing date properly, start plotting work
    logging.info("Processed data, creating plot from data")
    # Weird numpy stuff
    n = len(scores.values())
    ind = np.arange(n)
    width = 0.55
    # Resizer figuresize to be 2.0 wider
    plt.figure(figsize=(10.0, 6.0))
    # Stacked Bars
    p1 = plt.bar(ind, explicit, width)
    p2 = plt.bar(
        ind, clean, width, bottom=explicit
    )  # bottom= just has the bar sit on top of the explicit
    # Plot labeling
    plt.title("Song Count by Clean/Explicit")
    plt.ylabel("Song Count")
    plt.xlabel("Month")
    plt.xticks(ind, months, rotation=270)  # Rotation 90 will have the
    plt.legend((p1[0], p2[0]), ("Explicit", "Clean"))
    fig = plt.gcf()  # Magic to save to image and then show

    # Save the figure, overwriting anything in your way
    logging.info("Saving the figure to the 'export' folder")
    export_folder = os.path.join(os.path.dirname(os.path.abspath(__file__)), "export")
    if not os.path.exists(export_folder):
        os.makedirs(export_folder)
    plt.tight_layout()
    fig.savefig(
        os.path.join(
            export_folder,
            "export"
            # datetime.datetime.now().strftime('%Y-%m-%d %H-%M-%S')
        ),
        dpi=100,
        quality=95,
    )

    # Finally show the figure to
    logging.info("Showing plot to User")
    # plt.show()

    # Copy the figure to your clipboard to paste in Excel
    # logging.info('Copying the plot data to clipboard')
    # copy(months, clean, explicit)


# Simple method for exporting data to a table like format
# Will paste into Excel very easily
def copy(months, clean, explicit):
    from pyperclip import copy

    top = "Period\tClean\tExplicit\n"
    copy(
        top
        + "\n".join(
            [
                f"{item[0]}\t{item[1]}\t{item[2]}"
                for item in zip(months, clean, explicit)
            ]
        )
    )


def main():
    # logging.basicConfig(level=logging.INFO)
    logging.info("Reading track files")
    files = get_files()
    logging.info(f"Read and parse {len(files)} track files")
    logging.info("Combining into single track file for ease of access")
    data = combine_files(files)
    data.sort(
        key=lambda item: dateutil.parser.parse(item["added_at"]).timestamp(),
        reverse=True,
    )
    logging.info(f"File combined with {len(data)} items")
    logging.info("Processing file...")
    process_data(data)