def load_data(self, data):
"""
MultiStnData data results are arrays without explicit dates;
Infer time series based on start date.
"""
dates = fill_date_range(self.start_date, self.end_date)
for row, date in zip(data, dates):
data = {'date': date}
if self.add:
# If self.add is set, results will contain additional
# attributes (e.g. flags). In that case, create one row per
# result, with attributes "date", "elem", "value", and one for
# each item in self.add.
for elem, vals in zip(self.parameter, row):
data['elem'] = elem
for add, val in zip(['value'] + self.add, vals):
data[add] = val
yield data
else:
# Otherwise, return one row per date, with "date" and each
# element's value as attributes.
for elem, val in zip(self.parameter, row):
# namedtuple doesn't like numeric field names
if elem.isnumeric():
elem = "e%s" % elem
data[elem] = val
yield data
def load_data(self, data):
"""
MultiStnData data results are arrays without explicit dates;
Infer time series based on start date.
"""
dates = fill_date_range(self.start_date, self.end_date)
for row, date in zip(data, dates):
data = {'date': date}
if self.add:
# If self.add is set, results will contain additional
# attributes (e.g. flags). In that case, create one row per
# result, with attributes "date", "elem", "value", and one for
# each item in self.add.
for elem, vals in zip(self.parameter, row):
data['elem'] = elem
for add, val in zip(['value'] + self.add, vals):
data[add] = val
yield data
else:
# Otherwise, return one row per date, with "date" and each
# element's value as attributes.
for elem, val in zip(self.parameter, row):
# namedtuple doesn't like numeric field names
if elem.isdigit():
elem = "e%s" % elem
data[elem] = val
yield data
def parse(self):
data = self.data[0]
if not data or 'values' not in data:
raise NoData
bd = data['beginDate']
ed = data['endDate']
dates = fill_date_range(bd, ed, date_format='%Y-%m-%d %H:%M:%S')
vals = as_list(data['values'])
flags = as_list(data['flags'])
self.data = [{
'date': date,
'value': val,
'flag': flag
} for date, val, flag in zip(dates, vals, flags)]
def parse(self):
data = self.data[0]
if not data or 'values' not in data:
raise NoData
bd = data['beginDate']
ed = data['endDate']
dates = fill_date_range(bd, ed, date_format='%Y-%m-%d %H:%M:%S')
vals = as_list(data['values'])
flags = as_list(data['flags'])
self.data = [{
'date': date,
'value': val,
'flag': flag
} for date, val, flag in zip(dates, vals, flags)]
