def setup(self, opts):
FetchDriver.setup(self, opts) # set up the getter
self.xslt = opts.get('Xslt', None) # transformation to be applied
self.timefmt = opts.get("Timeformat", None)
self.timezone = opts.get("Timezone", 'UTC')
self.ignore_time = opts.get('IgnoreTimestamps', False)
if self.xslt:
with open(self.xslt, "r") as fp:
self.xslt = etree.XSLT(etree.XML(fp.read()))
def setup(self, opts):
FetchDriver.setup(self, opts) # set up the getter
self.xslt = opts.get('Xslt', None) # transformation to be applied
self.timefmt = opts.get("Timeformat", None)
self.timezone = opts.get("Timezone", 'UTC')
self.ignore_time = opts.get('IgnoreTimestamps', False)
if self.xslt:
with open(self.xslt, "r") as fp:
self.xslt = etree.XSLT(etree.XML(fp.read()))
def setup(self, opts):
"""
We setup the driver by getting the parameters of scraper first and then setting up the timeseries
"""
FetchDriver.setup(self, opts)
data = get_air_quality_init(opts={})
for i in range(0,len(data)):
safar_city = self.add_timeseries('/'+data[i]['area']+'/pm2.5','AQI',description = 'PM2.5 readings for '+data[i]['area'])
safar_city['Properties']['Timezone'] = 'India/Mumbai'
safar_city['Metadata'] = {}
safar_city['Metadata']['SourceName'] = data[i]['area']
safar_city['Metadata']['Area'] = data[i]['area']
safar_city['Metadata']['Latitude'] = data[i]['latitude']
safar_city['Metadata']['Longitude'] = data[i]['longtitude']
