# import logging
# import pytest
#
# from app.api_client import ApiClient
# from ui_automation_framework.utils import AppConfig
# from ui_automation_framework.utils import logger as cl
# import pandas
# import numpy as np
# import time
#
#
# # dates = [("2019-04-01", "2019-06-30")]
# # isrcs = ["USSM10804556"]
# # markets = ["us"]
#
# # dates = [("2017-07-01", "2018-12-11")]
# # isrcs = ['USSM10804556']
# # markets = ["us"]
# #
# # dates = [("2019-07-01", "2019-12-31"), ("2020-01-01", "2020-09-01")]
# # isrcs = ["QM6P41952433"]
# # markets = ["jp"]
# #
# # dates = [("2020-04-01", "2020-09-01")]
# # isrcs = ["QZES71982312"]
# # markets = [ "au"]
# #
# # dates = [("2020-01-01", "2020-09-01")]
# # isrcs = ["USSM11914962"]
# # markets = ["gl"]
# #
# dates = [("2020-08-31", "2020-08-31")]
# isrcs = ['USSD12000190']
# markets = ["worldwide"]
#
# log = cl.Logger(logging.DEBUG)
#
# api_client = ApiClient(AppConfig.get("api_auth_url"), AppConfig.get("qa_api_url"), 5000)
#
# # @pytest.mark.skip(reason="test run on a request with specific data")
# @pytest.mark.parametrize(("start_date", "end_date"), dates)
# @pytest.mark.parametrize("market", markets)
# @pytest.mark.parametrize("isrc", isrcs)
# def test_spotify_demographics_and_consumer_analytics_comparsion(market, start_date, end_date, isrc):
#     token_mobile = api_client.authorize()
#
#     delphi_data = api_client.get_spotify_demographics_delphi(isrc, start_date, end_date, market, token_mobile)[
#         'body'][0]['age_bands']
#
#     cons_analytics = api_client.get_spotify_demographics_consumer_analytics(isrc, start_date, end_date,
#                                                                                  'gl', token_mobile)
#     cons_analytics_data_raw = calculate_spotify_ca__output(cons_analytics['body'])
#     cons_analytics_data = {}
#
#     for k, v in cons_analytics_data_raw.items():
#         cons_analytics_data.update({v.get('header'): v.get('value')})
#
#     result_dict = {}
#     for key, value in delphi_data.items():
#         try:
#             result_dict.update({key: [delphi_data[key], cons_analytics_data[key]]})
#         except KeyError:
#             result_dict.update({key: [delphi_data[key], None]})
#
#     ca_values = []
#     delphi_values = []
#     for key, value in result_dict.items():
#         ca_values.append(value[1])
#     for key, value in result_dict.items():
#         delphi_values.append(value[0])
#
#     df = pandas.DataFrame({'consumer analytics': ca_values, 'delphi': delphi_values}, index=list(result_dict.keys()))
#
#     conditions = [
#         df['consumer analytics'] > df['delphi'],
#         df['consumer analytics'] < df['delphi'],
#         df['consumer analytics'] == df['delphi'],
#         df['consumer analytics'].isnull()]
#     choices = [((df['consumer analytics'] - df['delphi']) / df['consumer analytics']) * 100, ((df['delphi'] - df['consumer analytics']) / df['consumer analytics']) * 100, 'no change', 'no CA data for the field']
#
#     df['% diff'] = np.select(conditions, choices, default='NaN')
#     df.sort_values(by=df.columns[0], inplace=True)
#     df.to_excel('spotify_result_{}_{}_{}-{}.xlsx'.format(isrc, market, start_date, end_date))
#
#     # time.sleep(10)
#
# # @pytest.mark.skip(reason="test run on a request with specific data")
# @pytest.mark.parametrize(("start_date", "end_date"), dates)
# @pytest.mark.parametrize("market", markets)
# @pytest.mark.parametrize("isrc", isrcs)
# def test_apple_demographics_and_consumer_analytics_comparsion(market, start_date, end_date, isrc):
#     token_mobile = api_client.authorize()
#     delphi_data = api_client.get_apple_demographics_delphi(isrc, start_date, end_date, market, token_mobile)[
#         'body'][0]['age_bands']
#     ca_data = api_client.get_apple_demographics_consumer_analytics(isrc, start_date, end_date, 'gl', token_mobile)['body']['items']
#     cons_analytics_data = calculate_apple_ca__output(ca_data, market)
#
#     result_dict = {}
#     for k, v in delphi_data.items():
#         key=''
#         if k == 'female_18_24':
#             key = 'streams_gender_female_age_18_24'
#         elif k == 'male_18_24':
#             key = 'streams_gender_male_age_18_24'
#         elif k == 'unknown_18_24':
#             key = 'streams_gender_unknown_age_18_24'
#         elif k == 'female_25_34':
#             key = 'streams_gender_female_age_25_34'
#         elif k == 'male_25_34':
#             key = 'streams_gender_male_age_25_34'
#         elif k == 'unknown_25_34':
#             key = 'streams_gender_unknown_age_25_34'
#         elif k == 'female_35_44':
#             key = 'streams_gender_female_age_35_44'
#         elif k == 'male_35_44':
#             key = 'streams_gender_male_age_35_44'
#         elif k == 'unknown_35_44':
#             key = 'streams_gender_unknown_age_35_44'
#         elif k == 'female_45_54':
#             key = 'streams_gender_female_age_45_54'
#         elif k == 'male_45_54':
#             key = 'streams_gender_male_age_45_54'
#         elif k == 'unknown_45_54':
#             key = 'streams_gender_unknown_age_45_54'
#         elif k == 'female_55_64':
#             key = 'streams_gender_female_age_55_64'
#         elif k == 'male_55_64':
#             key = 'streams_gender_male_age_55_64'
#         elif k == 'unknown_55_64':
#             key = 'streams_gender_unknown_age_55_64'
#         elif k == 'male_65_plus':
#             key = 'streams_gender_male_age_65_plus'
#         elif k == 'female_65_plus':
#             key = 'streams_gender_female_age_65_plus'
#         elif k == 'unknown_65_plus':
#             key = 'streams_gender_unknown_age_65_plus'
#         elif k == 'unknown_unknown':
#             key = 'streams_gender_unknown_age_unknown'
#         elif k == 'unknown_0_17':
#             key = 'streams_gender_unknown_age_0_17'
#         elif k == 'male_0_17':
#             key = 'streams_gender_male_age_0_17'
#         elif k == 'female_0_17':
#             key = 'streams_gender_female_age_0_17'
#
#         try:
#             result_dict.update({k: [delphi_data[k], cons_analytics_data[key]]})
#         except KeyError:
#             result_dict.update({k: [delphi_data[k], None]})
#
#     ca_values = []
#     delphi_values = []
#     for key, value in result_dict.items():
#         ca_values.append(value[1])
#     for key, value in result_dict.items():
#         delphi_values.append(value[0])
#
#     df = pandas.DataFrame({'consumer analytics': ca_values, 'delphi': delphi_values}, index=list(result_dict.keys()))
#
#     conditions = [
#         df['consumer analytics'] > df['delphi'],
#         df['consumer analytics'] < df['delphi'],
#         df['consumer analytics'] == df['delphi'],
#         df['consumer analytics'].isnull()]
#     choices = [((df['consumer analytics'] - df['delphi']) / df['consumer analytics']) * 100, ((df['delphi'] - df['consumer analytics']) / df['consumer analytics']) * 100, 'no change', 'no CA data for the field']
#
#     df['% diff'] = np.select(conditions, choices, default='NaN')
#     df.sort_values(by=df.columns[0], inplace=True)
#     df.to_excel('apple_result_{}_{}_{}-{}.xlsx'.format(isrc, market, start_date, end_date))
# #
# #     time.sleep(10)
#
# # @pytest.mark.skip(reason="test run on a request with specific data")
# # @pytest.mark.parametrize(("start_date", "end_date"), dates)
# # @pytest.mark.parametrize("market", markets)
# # @pytest.mark.parametrize("isrc", isrcs)
# # def test_apple_2_demographics_and_consumer_analytics_comparsion(market, start_date, end_date, isrc):
# #     token_mobile = api_client.authorize()
# #
# #     delphi_data = api_client.get_apple_demographics_delphi(isrc, start_date, end_date, market, token_mobile)[
# #         'body'][0]['age_bands']
# #
# #     cons_analytics = api_client.get_apple_demographics_consumer_analytics(isrc, start_date, end_date,
# #                                                                                  market, token_mobile)
# #     ca_data = \
# #     api_client.get_apple_demographics_consumer_analytics(isrc, start_date,
# #                                                          end_date, market,
# #                                                          token_mobile)['body'][
# #         'items']
# #     cons_analytics_data_raw = calculate_apple_ca__output(ca_data, market)
# #     cons_analytics_data = {}
# #
# #     for k, v in cons_analytics_data_raw.items():
# #         cons_analytics_data.update({v.get('header'): v.get('value')})
# #
# #     result_dict = {}
# #     for key, value in delphi_data.items():
# #         try:
# #             result_dict.update({key: [delphi_data[key], cons_analytics_data[key]]})
# #         except KeyError:
# #             result_dict.update({key: [delphi_data[key], None]})
# #
# #     ca_values = []
# #     delphi_values = []
# #     for key, value in result_dict.items():
# #         ca_values.append(value[1])
# #     for key, value in result_dict.items():
# #         delphi_values.append(value[0])
# #
# #     df = pandas.DataFrame({'consumer analytics': ca_values, 'delphi': delphi_values}, index=list(result_dict.keys()))
# #
# #     conditions = [
# #         df['consumer analytics'] > df['delphi'],
# #         df['consumer analytics'] < df['delphi'],
# #         df['consumer analytics'] == df['delphi'],
# #         df['consumer analytics'].isnull()]
# #     choices = [((df['consumer analytics'] - df['delphi']) / df['consumer analytics']) * 100, ((df['delphi'] - df['consumer analytics']) / df['consumer analytics']) * 100, 'no change', 'no CA data for the field']
# #
# #     df['% diff'] = np.select(conditions, choices, default='NaN')
# #     df.sort_values(by=df.columns[0], inplace=True)
# #     df.to_excel('apple_result_{}_{}_{}-{}.xlsx'.format(isrc, market, start_date, end_date))
#
# def calculate_spotify_ca__output(json_raw):
#     data = json_raw
#     object_len = data[0]
#     i = 1
#     stack = {}
#     while i < len(data):
#         if i > object_len:
#             if isinstance(data[i], int):
#                 key = i
#
#                 while key > object_len:
#                     key -= object_len
#
#                 stack[key]["value"] += data[i]
#         else:
#             stack[i] = {
#                 "header": data[i],
#                 "value": 0
#             }
#         i += 1
#     return stack
#
# def calculate_apple_ca__output(json_raw, market):
#     stack = {}
#     for item in json_raw:
#         if market in item['countryCode']:
#             for k, v in item.items():
#                 if k == 'countryCode' or k == 'date' or k == 'isrc':
#                     pass
#                 else:
#                     stack[k] = stack.get(k, 0) + v
#         else:
#             pass
#     return stack