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config.py
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config.py
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import datetime
path_cases_overall_timeseries = 'https://api.covid19india.org/csv/latest/case_time_series.csv'
path_cases_state_wise_timeseries = 'https://api.covid19india.org/csv/latest/states.csv'
path_test_overall_timeseries = 'https://api.covid19india.org/csv/latest/tested_numbers_icmr_data.csv'
path_test_state_wise_timeseries = 'https://api.covid19india.org/csv/latest/statewise_tested_numbers_data.csv'
# path_vaccine_state_wise = 'http://api.covid19india.org/csv/latest/vaccine_doses_statewise.csv'
path_vaccine_state_wise_cowin = 'http://api.covid19india.org/csv/latest/cowin_vaccine_data_statewise.csv'
UNIT = 1000000
POPULATION = 1336459178
TIMEZONE_OFFSET = 5.50 # +5:30 Indian Time # Pacific Standard Time (UTC−08:00)
tzinfo = datetime.timezone(datetime.timedelta(hours=TIMEZONE_OFFSET))
RULE_MAP = {
'Total': 'total',
'Data per 10,00,000': 'percentage',
}
REGION_INDEX = {
'Himachal Pradesh': 1,
'Punjab': 2,
'Uttarakhand': 3,
'Haryana': 4,
'Rajasthan': 5,
'Uttar Pradesh': 6,
'Bihar': 7,
'Sikkim': 8,
'Arunachal Pradesh': 9,
'Nagaland': 10,
'Manipur': 11,
'Mizoram': 12,
'Tripura': 13,
'Meghalaya': 14,
'Assam': 15,
'West Bengal': 16,
'Jharkhand': 17,
'Odisha': 18,
'Chhattisgarh': 19,
'Madhya Pradesh': 20,
'Gujarat': 21,
'Maharashtra': 22,
'Andhra Pradesh': 23,
'Karnataka': 24,
'Goa': 25,
'Kerala': 26,
'Tamil Nadu': 27,
'Telangana': 28,
'Jammu and Kashmir': 29,
'Chandigarh': 30,
'Delhi': 31,
'Dadra and Nagar Haveli and Daman and Diu': 32,
'Lakshadweep': 33,
'Puducherry': 34,
'Andaman and Nicobar Islands': 35,
'Ladakh': 36,
}
POPULATION_MAP = {
'India': 1336459178,
'Himachal Pradesh': 7316708,
'Punjab': 29611935,
'Uttarakhand': 11090425,
'Haryana': 27388008,
'Rajasthan': 78230816,
'Uttar Pradesh': 228959599,
'Bihar': 119461013,
'Sikkim': 671720,
'Arunachal Pradesh': 1528296,
'Nagaland': 2189297,
'Manipur': 3008546,
'Mizoram': 1205974,
'Tripura': 4057847,
'Meghalaya': 3276323,
'Assam': 34586234,
'West Bengal': 97694960,
'Jharkhand': 37329128,
'Odisha': 45429399,
'Chhattisgarh': 28566990,
'Madhya Pradesh': 82342793,
'Gujarat': 63907200,
'Maharashtra': 120837347,
'Andhra Pradesh': 52883163,
'Karnataka': 66165886,
'Goa': 1542750,
'Kerala': 35330888,
'Tamil Nadu': 76481545,
'Telangana': 38472769,
'Jammu and Kashmir': 14046258,
'Chandigarh': 1182104,
'Delhi': 18802494,
'Dadra and Nagar Haveli and Daman and Diu': 657391,
'Lakshadweep': 72210,
'Puducherry': 1397707,
'Andaman and Nicobar Islands': 426251,
'Ladakh': 307204,
}