def __init__(self) -> None:
""" Initiate DataFrames """
self.df = get_data_daily_reports()
self.df_time_series = get_data_time_series()
self.df_US_time_series = get_US_time_series()
self.lookup_table = get_data_lookup_table()
concerned_columns = ['Confirmed', 'Deaths', 'Recovered', 'Active']
self.df_grp_by_country = self.df.groupby(
'Country_Region')[concerned_columns].sum()
self.df_grp_by_country[concerned_columns] = self.df_grp_by_country[
concerned_columns].astype(int)
self.datetime = max(self.df['Last_Update'].tolist())
self.timestamp = datetime.strptime(self.datetime,
'%Y-%m-%d %H:%M:%S').timestamp()
self.scheme = {'data': None, 'dt': self.datetime, 'ts': self.timestamp}
class CovidAPIv2:
""" Covid-19 API v2 model and its methods
SCHEMA: {
"data": Any,
"dt": str = "{datetime}",
"ts": int = "{timestamp}
}
"""
def __init__(self) -> None:
""" Initiate DataFrames """
self.lookup_table = get_data_lookup_table()
self.scheme = {'data': None, 'dt': None, 'ts': None}
def wrap_data(func) -> Dict[str, Any]:
""" Wrap a result in a schemed data """
@wraps(func)
def wrapper(self, *args, **kwargs) -> Dict[str, Any]:
packed_data = self.scheme
packed_data['data'] = []
try:
packed_data['data'] = func(self, *args, **kwargs)
except Exception as e:
print(e)
finally:
time_format = '%m-%d-%Y'
packed_data['dt'] = datetime.utcnow().strftime(time_format)
packed_data['ts'] = datetime.strptime(packed_data['dt'],
time_format).timestamp()
return packed_data
return wrapper
@wrap_data
def get_current(self) -> Dict[str, Any]:
""" Current data from all locations (Lastest date) """
concerned_columns = ['Confirmed', 'Deaths', 'Recovered', 'Active']
self.df = get_data_daily_reports() # Get base data
self.df_grp_by_country = self.df.groupby(
'Country_Region')[concerned_columns].sum()
self.df_grp_by_country[concerned_columns] = self.df_grp_by_country[
concerned_columns].astype(int)
df_grp_by_country = self.df_grp_by_country.sort_values(by='Confirmed',
ascending=False)
df_grp_by_country = df_grp_by_country.reset_index()
df_grp_by_country.columns = [
'location', 'confirmed', 'deaths', 'recovered', 'active'
]
data = [v for v in df_grp_by_country.to_dict('index').values()]
return data
@wrap_data
def get_current_US(self) -> Dict[str, Any]:
""" Get current data for USA's situation """
self.df_US = get_data_daily_reports_us() # Get base data
concerned_columns = [
'Confirmed', 'Deaths', 'Recovered', 'Active', 'People_Tested',
'People_Hospitalized'
]
df = self.df_US.groupby([
'Province_State'
])[concerned_columns].sum().sort_values(by='Confirmed',
ascending=False)
df = df[concerned_columns].astype(int)
df = df.reset_index()
df.columns = ['Province_State'] + concerned_columns
data = [v for v in df.to_dict('index').values()]
return data
@wrap_data
def get_country(self, country_name: str) -> Dict[str, Any]:
""" Get a country data from its name or ISO 2 """
all_country_data = self.get_current()['data']
# Check input
if not isinstance(country_name, str) or not country_name.isalpha():
return {}
# Search for a given country
country_name = country_name.lower()
country_name_from_code = self.lookup_table.get(country_name.upper(),
'').lower()
data = [
country_data for country_data in all_country_data
if country_data['location'].lower() in
[country_name, country_name_from_code]
]
data = data[0] if data else {}
return data
@wrap_data
def get_confirmed(self) -> Dict[str, Any]:
""" Summation of all confirmed cases """
self.df = get_data_daily_reports() # Get base data
data = int(self.df['Confirmed'].sum())
return data
@wrap_data
def get_deaths(self) -> Dict[str, Any]:
""" Summation of all deaths """
self.df = get_data_daily_reports() # Get base data
data = int(self.df['Deaths'].sum())
return data
@wrap_data
def get_recovered(self) -> Dict[str, Any]:
""" Summation of all recovers """
self.df = get_data_daily_reports() # Get base data
data = int(self.df['Recovered'].sum())
return data
@wrap_data
def get_active(self) -> Dict[str, Any]:
""" Summation of all actives """
self.df = get_data_daily_reports() # Get base data
data = int(self.df['Active'].sum())
return data
@wrap_data
def get_total(self) -> Dict[str, Any]:
""" Summation of Confirmed, Deaths, Recovered, Active """
self.df = get_data_daily_reports() # Get base data
data = {
'confirmed': int(self.df['Confirmed'].sum()),
'deaths': int(self.df['Deaths'].sum()),
'recovered': int(self.df['Recovered'].sum()),
'active': int(self.df['Active'].sum())
}
return data
def __extract_time_series(self, time_series: Dict) -> List[Dict]:
""" Extract time series from a given case """
def __unpack_inner_time_series(
time_series: Dict[str, Any]) -> Dict[str, Any]:
for data in time_series.values():
excluded_cols = [
'Province/State', 'Country/Region', 'Lat', 'Long'
]
temp_dict = {}
temp_dict['Province/State'] = data['Province/State']
temp_dict['Country/Region'] = data['Country/Region']
temp_dict['Coordinates'] = {
'Lat': float(data['Lat']),
'Long': float(data['Long'])
}
temp_time_series_dict = {
k: int(v)
for k, v in data.items() if k not in excluded_cols
}
temp_dict['TimeSeries'] = [{
'date': k,
'value': v
} for k, v in temp_time_series_dict.items()]
yield temp_dict
# Extract the time series data
time_series_data = []
for data in __unpack_inner_time_series(time_series):
time_series_data.append(data)
return time_series_data
def __extract_US_time_series(self, time_series: Dict[str,
Any]) -> List[Dict]:
""" Extract USA time series """
def __unpack_US_inner_time_series(
time_series: Dict[str, Any]) -> Dict[str, Any]:
for data in time_series.values():
excluded_cols = [
'UID', 'iso2', 'iso3', 'code3', 'FIPS', 'Admin2',
'Province_State', 'Country_Region', 'Lat', 'Long_',
'Combined_Key', 'Population'
]
temp_dict = {}
temp_dict['Province_State'] = data['Province_State']
temp_dict['Country_Region'] = data['Country_Region']
temp_dict['Info'] = {
'UID': data['UID'],
'iso2': data['iso2'],
'iso3': data['iso3'],
'code3': data['code3'],
'FIPS': data['FIPS'],
'Admin2': data['Admin2']
}
temp_dict['Coordinates'] = {
'Lat': float(data['Lat']),
'Long': float(data['Long_'])
}
temp_time_series_dict = {
k: int(v)
for k, v in data.items() if k not in excluded_cols
}
temp_dict['TimeSeries'] = [{
'date': k,
'value': v
} for k, v in temp_time_series_dict.items()]
yield temp_dict
# Extract the time series data
time_series_data = []
for data in __unpack_US_inner_time_series(time_series):
time_series_data.append(data)
return time_series_data
def __extract_time_series_global(
self, dataframe_dict: Dict[str, pd.DataFrame]) -> List[Dict]:
""" Extract time series for global case
Iterating all cases from all time series
"""
global_df_list = []
for key, df in dataframe_dict.items():
df_temp = pd.DataFrame(
df.iloc[:, 4:].astype('int32').sum(axis=0)
) # Slice to select time series data (exclude country info)
df_temp.columns = [
key
] # A dataframe with one column named by a key (case), rows are time series
global_df_list.append(df_temp)
# Combine DataFrames
global_dict = pd.concat(global_df_list, axis=1, sort=False).T.to_dict()
data = [{k: v} for k, v in global_dict.items()]
return data
@wrap_data
def get_time_series(self, case: str) -> Dict[str, Any]:
""" Get time series data from a given case
1.) global
2.) confirmed, deaths, recovered
"""
self.df_time_series = get_data_time_series() # Get base data
if case not in ['global']:
raw_data = self.df_time_series[case].T.to_dict()
data = self.__extract_time_series(raw_data)
else:
raw_data = self.df_time_series
data = self.__extract_time_series_global(raw_data)
return data
class CovidAPIv2Integrator:
""" Covid-19 API v2 methods
SCHEMA: {
"data": Any,
"dt": str = "{datetime}",
"ts": int = "{timestamp}
}
"""
def __init__(self) -> None:
""" Initiate DataFrames """
self.lookup_table = get_data_lookup_table()
self.scheme = {
'data': None,
'dt': None,
'ts': None
}
def wrap_data(func) -> ResponseModel:
""" Wrap a result in a schemed data """
@wraps(func)
def wrapper(self, *args, **kwargs):
packed_data = self.scheme
packed_data['data'] = []
try:
packed_data['data'] = func(self, *args, **kwargs)
except Exception as e:
print(e)
finally:
time_format = '%m-%d-%Y'
packed_data['dt'] = datetime.utcnow().strftime(time_format)
packed_data['ts'] = datetime.strptime(packed_data['dt'], time_format).timestamp()
reponse_model = ResponseModel(**packed_data)
return reponse_model
return wrapper
#######################################################################################
# GET - Current
#######################################################################################
@wrap_data
def get_current(self) -> List[CurrentModel]:
""" Current data from all locations (Lastest date) """
concerned_columns = ['Confirmed', 'Deaths', 'Recovered', 'Active']
self.df = get_data_daily_reports() # Get base data
self.df_grp_by_country = self.df.groupby('Country_Region')[concerned_columns].sum()
self.df_grp_by_country[concerned_columns] = self.df_grp_by_country[concerned_columns].astype(int)
df_grp_by_country = self.df_grp_by_country.sort_values(by='Confirmed', ascending=False)
df_grp_by_country = df_grp_by_country.reset_index()
df_grp_by_country.columns = ['location', 'confirmed', 'deaths', 'recovered', 'active']
data = [CurrentModel(**v) for v in df_grp_by_country.to_dict('index').values()]
return data
#######################################################################################
# GET - Current US
#######################################################################################
@wrap_data
def get_current_US(self) -> List[CurrentUSModel]:
""" Get current data for USA's situation """
self.df_US = get_data_daily_reports_us() # Get base data
concerned_columns = ['Confirmed', 'Deaths', 'Recovered', 'Active']
df = self.df_US.groupby(['Province_State'])[concerned_columns].sum().sort_values(by='Confirmed', ascending=False)
df = df[concerned_columns].astype(int)
df = df.reset_index()
df.columns = ['Province_State'] + concerned_columns
data = [CurrentUSModel(**v) for v in df.to_dict('index').values()]
return data
#######################################################################################
# GET - Country
#######################################################################################
@wrap_data
def get_country(self, country_name: str) -> Dict[str, Any]:
""" Get a country data from its name or ISO 2 """
all_country_data = self.get_current().data
# Check input
if not isinstance(country_name, str) or not country_name.isalpha():
return {}
# Search for a given country
country_name = country_name.lower()
country_name_from_code = self.lookup_table.get(country_name.upper(), '').lower()
data = [country_data for country_data in all_country_data if country_data.location.lower() in [country_name, country_name_from_code]]
data = data[0] if data else {}
return data
#######################################################################################
# GET - Confirm
#######################################################################################
@wrap_data
def get_confirmed(self) -> ConfirmedModel:
""" Summation of all confirmed cases """
self.df = get_data_daily_reports() # Get base data
data = ConfirmedModel(
confirmed=int(self.df['Confirmed'].sum())
)
return data
#######################################################################################
# GET - Deaths
#######################################################################################
@wrap_data
def get_deaths(self) -> DeathsModel:
""" Summation of all deaths """
self.df = get_data_daily_reports() # Get base data
data = DeathsModel(
deaths=int(self.df['Deaths'].sum())
)
return data
#######################################################################################
# GET - Recovered
#######################################################################################
@wrap_data
def get_recovered(self) -> RecoveredModel:
""" Summation of all recovers """
self.df = get_data_daily_reports() # Get base data
data = RecoveredModel(
recovered=int(self.df['Recovered'].sum())
)
return data
#######################################################################################
# GET - Active
#######################################################################################
@wrap_data
def get_active(self) -> ActiveModel:
""" Summation of all actives """
self.df = get_data_daily_reports() # Get base data
data = ActiveModel(
active=int(self.df['Active'].sum())
)
return data
#######################################################################################
# GET - Total
#######################################################################################
@wrap_data
def get_total(self) -> TotalModel:
""" Summation of Confirmed, Deaths, Recovered, Active """
self.df = get_data_daily_reports() # Get base data
data = TotalModel(
confirmed=int(self.df['Confirmed'].sum()),
deaths=int(self.df['Deaths'].sum()),
recovered=int(self.df['Recovered'].sum()),
active=int(self.df['Active'].sum())
)
return data
#######################################################################################
# GET - Timeseries
#######################################################################################
@wrap_data
def get_time_series(self, case: str) -> List[Any]:
""" Get time series data from a given case
1.) global
2.) confirmed, deaths, recovered
"""
self.df_time_series = get_data_time_series() # Get base data
if case not in ['global']:
raw_data = self.df_time_series[case].T.to_dict()
data = self.__extract_time_series(raw_data)
else:
raw_data = self.df_time_series
data = self.__extract_time_series_global(raw_data)
return data