from datetime import datetime
from datetime import timedelta
import sys
import os
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
import numpy as np
import matplotlib.pyplot as plt
from collections import defaultdict
from loader import load, dmt, cms
import math
from common import calc_intervals

printnoln = lambda text: print(text, end='', flush=True)
rprint = lambda text: print('\r' + text)

DAYS_NEW_USER = 7
OLD_USER_YEAR = 3

analyser = SentimentIntensityAnalyzer()
colors = ['red', 'green', 'blue', 'orange', 'deeppink']


def main(folder):
    users, posts, firstcontrib, sumcontrib = load(folder)
    intervals = calc_intervals(posts)

    for (option_date_from, option_date_to) in intervals:
        print((option_date_from.strftime("%d-%m-%Y"), option_date_to.strftime("%d-%m-%Y")))

        # filter posts by option_date_from <= creation date <= option_date_to
        newusers = set(dmt(users).filter(lambda u: option_date_from <= u['CreationDate'] < option_date_to, "filtering users by creation").map(lambda u: u['Id'], "getting user ids").getresults())
        newposts = dmt(posts).filter(lambda p: p['OwnerUserId'] in newusers, "filtering posts by users").getresults()

        postcounts = defaultdict(list)
        i = 0
        for p in newposts:
            postcounts[p['OwnerUserId']].append(p)
            i = i + 1
        postcounts = {id: len(pc) for (id, pc) in postcounts.items()}
        # print("i: " + str(i) + " expected: " + str(len(newposts)) + " is: " + str(sum([pc for pc in postcounts.values()])))

        os.system("mkdir -p " + folder + "/output")
        histfilename = folder + "/output/posthist_" + folder.split("/")[-1] + "_" + option_date_from.strftime("%d-%m-%Y") + "_" + option_date_to.strftime("%d-%m-%Y")
        countfilename = folder + "/output/postcount_" + folder.split("/")[-1] + "_" + option_date_from.strftime("%d-%m-%Y") + "_" + option_date_to.strftime("%d-%m-%Y")

        # fig = plt.figure(figsize=(16, 12))
        # plt.plot(userids, [len(pc) for pc in postcounts])
        # plt.title("Post count for users between " + option_date_from.strftime("%d-%m-%Y") + " and " + option_date_to.strftime("%d-%m-%Y"))
        # plt.xticks(rotation=90)
        # fig.savefig(countfilename + ".png", bbox_inches='tight')
        # plt.close(fig)

        histdata = [pc for pc in postcounts.values()]
        fig = plt.figure(figsize=(16, 12))
        plt.hist(histdata, range(max(histdata, default=0) + 1))
        plt.yscale('log')
        plt.ylim(bottom=0)
        plt.title("Histogram for user post count registered between " + option_date_from.strftime("%d-%m-%Y") + " and " + option_date_to.strftime("%d-%m-%Y"))
        fig.savefig(histfilename + ".png", bbox_inches='tight')
        plt.close(fig)


def computeToxLevel(text):
    return analyser.polarity_scores(text)


def flatmap(arr):
    return [item for sublist in arr for item in sublist]


def dumptoxlevels(lvls, filename):
    with open(filename, "w") as file:
        file.write("from collections import defaultdict\n\n")
        file.write("toxlevels = " + str(lvls).replace("<class 'list'>", "list", 1) + "\n")


if __name__ == "__main__":
    # execute only if run as a script
    usage = sys.argv[0] + " <folder>"
    if len(sys.argv) < 2:
        print(usage)
        sys.exit(1)
    folder = sys.argv[1]
    if not os.path.isdir(folder):
        print(folder + " is not a folder")
        sys.exit(1)

    main(folder)