def pull(self,
date_time_range: api.DateTimeRange = None,
include_wt: bool = False,
tsi_api: str = "GetEvents") -> SignalData:
validate_tsi_api_options(tsi_api)
signals = self.amphoraeApi.amphorae_signals_get_signals(self._id)
signal = utils.filter_by_id(signals,
self._id) # filter for just 1 signal
if (date_time_range is None):
date_time_range = default_date_time_range()
ts_api = api.TimeSeriesApi(
self._apiClient) # the API for interacting with time series
# Create a variable object for getting temperature data
variables = get_inline_variables([signal], include_wt)
if tsi_api == tsi_api_get_series:
get_series = api.GetSeries([self._id],
search_span=date_time_range,
inline_variables=variables)
qr = api.QueryRequest(get_series=get_series)
elif tsi_api == tsi_api_get_events:
get_events = api.GetEvents([self._id], search_span=date_time_range)
qr = api.QueryRequest(get_events=get_events)
elif tsi_api == tsi_api_aggregate_series:
# this might not work
aggregate_series = api.AggregateSeries([self._id],
search_span=date_time_range,
inline_variables=variables)
qr = api.QueryRequest(aggregate_series=aggregate_series)
time_series_data = ts_api.time_series_query_time_series(qr)
return SignalData(time_series_data)
def pull(self,
date_time_range: api.DateTimeRange = None,
include_wt: bool = False,
tsi_api: str = "GetSeries") -> SignalData:
'''
Downloads the data to a local object
params:
date_time_range [UTC] (default to 1 day) amphora_api_client.DateTimeRange
include_wt (default TRUE) bool, includes the Write Time (wt)
tsi_api (options: GetSeries, GetEvents, AggregateSeries) str
returns:
amphora.SignalData
'''
validate_tsi_api_options(tsi_api)
signals = self.amphoraeApi.amphorae_signals_get_signals(self._id)
if (date_time_range is None):
date_time_range = default_date_time_range()
ts_api = api.TimeSeriesApi(
self._apiClient) # the API for interacting with time series
# Create a variable object for getting temperature data
variables = get_inline_variables(signals, include_wt)
if tsi_api == tsi_api_get_series:
get_series = api.GetSeries([self._id],
search_span=date_time_range,
inline_variables=variables)
qr = api.QueryRequest(get_series=get_series)
elif tsi_api == tsi_api_get_events:
get_events = api.GetEvents([self._id], search_span=date_time_range)
qr = api.QueryRequest(get_events=get_events)
elif tsi_api == tsi_api_aggregate_series:
# this might not work
aggregate_series = api.AggregateSeries([self._id],
search_span=date_time_range,
inline_variables=variables)
qr = api.QueryRequest(aggregate_series=aggregate_series)
time_series_data = ts_api.time_series_query_time_series(qr)
return SignalData(time_series_data)
ts_api = a10a.TimeSeriesApi(
client.apiClient) # the API for interacting with time series
tomorrow = datetime.now() + timedelta(hours=24)
# Create a DateTimeRange to describe over what period we want data.
time_range = a10a.DateTimeRange(_from=datetime.now(), to=tomorrow)
# Create a variable object for getting temperature data
temperatureVariable = a10a.NumericVariable(
kind="numeric",
value=a10a.Tsx(tsx="$event.temperature"),
aggregation=a10a.Tsx(tsx="it doesn't matter"))
get_series = a10a.GetSeries(
[id],
search_span=time_range,
inline_variables={"temperature": temperatureVariable})
time_series_data = ts_api.time_series_query_time_series(
a10a.QueryRequest(get_series=get_series))
print(
f'Got {len(time_series_data.timestamps)} datapoints and {len(time_series_data.properties)} properties'
)
# access the data in time_series_data.properties
print("-----------")
# get average of tomorrow's rainfall probablility, filtered by description = Possible shower or Mostly cloudy
variable = a10a.AggregateVariable(
kind="aggregate", aggregation=a10a.Tsx("avg($event.rainProb)"))
aggregate_series = a10a.AggregateSeries(
[id],
search_span=time_range,
inline_variables={"rainProbAvg": variable},
# filter is optional, for numeric filtering use `$event.rainProb.Double > 10` etc.
aggregation=a10a.Tsx(tsx="it doesn't matter")),
"recoveredCases":
a10a.NumericVariable(kind="numeric",
value=a10a.Tsx(tsx="$event.recoveredCases"),
aggregation=a10a.Tsx(tsx="it doesn't matter")),
"deaths":
a10a.NumericVariable(kind="numeric",
value=a10a.Tsx(tsx="$event.deaths"),
aggregation=a10a.Tsx(tsx="it doesn't matter"))
}
get_series_query = a10a.GetSeries(
[amphora_id], search_span=time_range,
inline_variables=variables) # the complete query
time_series_data = ts_api.time_series_query_time_series(
a10a.QueryRequest(get_series=get_series_query))
S0 = 1352640000 #country population
data_array, unique_dt, data_name = flatten_time_series_data(time_series_data)
print(data_name)
for i in range(4):
if data_name[i] == 'activeCases':
I = data_array[i]
elif data_name[i] == 'recoveredCases':
R = data_array[i]
elif data_name[i] == 'deaths':
D = data_array[i]
print(I)
print(R)