import os
import pandas as pd
import numpy as np
from tqdm import tqdm
import concurrent.futures as cf
from itertools import zip_longest

GITHUB = os.environ.get("GITHUB")

facilities = pd.read_csv('../data/raw/facilities.csv')
accounts = pd.read_csv('../data/raw/accounts.csv')
users = pd.read_csv('../data/raw/users.csv')

# rename ParentId in facitilies to FacilityId
facilities = facilities.rename(columns={'ParentId': 'FacilityId'})
# rename Id in users to CreatedById (to make merging more clean)
users = users.rename(columns={'Id': 'CreatedById'})

# merge the user data and create a Boolean column to represent if the field edit was a mass update or not
# (mass updates are generated by Anthony Zirilli, Steven Han, or Developer, so I identified their user Ids)


def merge_user_data(df):
    df = df.merge(users, on='CreatedById', how='left')
    df['MassUpdated'] = np.where(df['CreatedById'].isin(['005E0000007XSBHIA4', '005E0000007OrqlIAC', '005E0000007QaBkIAK']), True, False)
    return df


facilities = merge_user_data(facilities)
accounts = merge_user_data(accounts)

# Convert date to a timestamp
facilities['CreatedDate'] = pd.to_datetime(facilities['CreatedDate'])
accounts['CreatedDate'] = pd.to_datetime(accounts['CreatedDate'])

# create a Boolean column to represent if the field edit was a "true change":
# if OldValue at first timestamp = NewValue at last timestamp then False, else True
# partition by FacilityId, Field order by CreatedDate

facilities['EditOrder'] = facilities.groupby(['FacilityId', 'Field'])['CreatedDate'].rank(method='first')
accounts['EditOrder'] = accounts.groupby(['AccountId', 'Field'])['CreatedDate'].rank(method='first')

facilities_uniqs = facilities.groupby(['FacilityId', 'Field'])['CreatedDate'].count().reset_index()
facilities_uniqs = facilities_uniqs[facilities_uniqs['CreatedDate'] > 1]
accounts_uniqs = accounts.groupby(['AccountId', 'Field'])['CreatedDate'].count().reset_index()
accounts_uniqs = accounts_uniqs[accounts_uniqs['CreatedDate'] > 1]


def get_truechange(Id, Field, table):
    # returns single boolean value (True or False) representing whether or not the Field for the given Id (facility or account id) has truly changed from the original value
    # input strings for the Id, Field, and table ('facilities' or 'accounts')

    if table == 'facilities':
        idCol = 'FacilityId'
        df = facilities
    else:
        idCol = 'AccountId'
        df = accounts

    df_sub = df[(df[idCol] == Id) & (df['Field'] == Field)]

    orig_val = df_sub[df_sub['EditOrder'] == 1]['OldValue'].values[0]
    latest_val = df_sub[df_sub['EditOrder'] == df_sub['EditOrder'].max()]['NewValue'].values[0]

    return (orig_val != latest_val)


get_true_change = np.vectorize(get_truechange)

#facilities['TrueChange'] = get_true_change(facilities_uniqs['FacilityId'].values, facilities_uniqs['Field'].values, 'facilities')


def tqdm_parallel_map(executor, fn, *iterables, **kwargs):
    """
    Equivalent to executor.map(fn, *iterables),
    but displays a tqdm-based progress bar.
    Does not support timeout or chunksize as executor.submit is used internally
    **kwargs is passed to tqdm.
    """
    futures_list = []
    args = iterables[0]

    final_args = [dict(zip(args.keys(), values)) for values in zip_longest(*args.values())]

    for arg in final_args:
        futures_list += [executor.submit(fn, arg['Id'], arg['Field'], arg['table'])]

    for f in tqdm(cf.as_completed(futures_list), total=len(futures_list), **kwargs):
        pass

    return futures_list


print("Running facilities...")
facilities_uniqs['TrueChange'] = tqdm_parallel_map(cf.ProcessPoolExecutor(), get_truechange, {'Id': facilities_uniqs['FacilityId'].values, 'Field': facilities_uniqs['Field'].values, 'table': ['facilities'] * len(facilities_uniqs['Field'].values)})
facilities_final = facilities.merge(facilities_uniqs, on=['FacilityId','Field'], how='left')
print(facilities_final.head())
facilities_final.to_csv('../data/interim/facilities.csv',index=False)
print("Running accounts...")
accounts_uniqs['TrueChange'] = tqdm_parallel_map(cf.ProcessPoolExecutor(), get_truechange, {'Id': accounts_uniqs['AccountId'].values, 'Field': accounts_uniqs['Field'].values, 'table': ['accounts'] * len(accounts_uniqs['Field'].values)})
accounts_final = accounts.merge(accounts_uniqs, on=['AccountId','Field'], how='left')
accounts_final.to_csv('../data/interim/accounts.csv',index=False)