def fill_gaps(self, begin=None, end=None):
"""
Detects gaps in data and tries to fill them by downloading and
resampling the data within these periods.
Parameters
----------
begin : datetime
Begin date of intervall to check, defaults to None.
end : datetime
End date of intervall to check, defaults to None.
"""
gaps = []
for region in self.valid_regions:
if self.regions is not None:
if region not in self.regions:
continue
_, _, period = nc.get_properties(self.src_file[region])
if begin is None:
if self.begin_date < self.dest_start_date:
begin = self.dest_start_date
else:
begin = self.begin_date
if end is None:
end = period[1]
drange = dt.get_dtindex(self.dest_temp_res, begin, end)
for date in drange:
nonans = []
for var in self.get_variables():
img, _, _, _ = self.read_img(date, region, var)
if np.nanmean(img) is not np.ma.masked:
nonans.append(1)
if len(nonans) == 0:
if date not in gaps:
gaps.append(date)
if len(gaps) == 0:
print '[INFO] No gaps found.'
else:
print('[INFO] Found ' + str(len(gaps)) +
' gap(s), attempt to fill..')
for date in gaps:
if self.dest_temp_res in ['day', 'daily']:
begin = date
end = date
else:
begin, end = dt.check_period_boundaries(
self.dest_temp_res, date)
self.download_and_resample(begin=begin, end=end)
def fill_gaps(self, begin=None, end=None):
"""
Detects gaps in data and tries to fill them by downloading and
resampling the data within these periods.
Parameters
----------
begin : datetime
Begin date of intervall to check, defaults to None.
end : datetime
End date of intervall to check, defaults to None.
"""
gaps = []
for region in self.valid_regions:
if self.regions is not None:
if region not in self.regions:
continue
_, _, period = nc.get_properties(self.src_file[region])
if begin is None:
if self.begin_date < self.dest_start_date:
begin = self.dest_start_date
else:
begin = self.begin_date
if end is None:
end = period[1]
drange = dt.get_dtindex(self.dest_temp_res, begin, end)
for date in drange:
nonans = []
for var in self.get_variables():
img, _, _, _ = self.read_img(date, region, var)
if np.nanmean(img) is not np.ma.masked:
nonans.append(1)
if len(nonans) == 0:
if date not in gaps:
gaps.append(date)
if len(gaps) == 0:
print '[INFO] No gaps found.'
else:
print('[INFO] Found ' + str(len(gaps)) +
' gap(s), attempt to fill..')
for date in gaps:
if self.dest_temp_res in ['day', 'daily']:
begin = date
end = date
else:
begin, end = dt.check_period_boundaries(self.dest_temp_res,
date)
self.download_and_resample(begin=begin, end=end)
def _check_begin_end(self, begin, end):
"""
Checks begin and end date and returns valid dates.
Parameters
----------
begin : datetime
Begin date to check.
end : datetime
End date to check.
Returns
-------
begin : datetime
Begin date.
end : datetime
End date.
"""
if begin is None:
if self.dest_start_date < self.begin_date:
begin = self.begin_date
else:
begin = self.dest_start_date
if begin < self._get_download_date():
begin = self._get_download_date()
# start one period earlier to close possible gaps
begin = begin - timedelta(days=1)
begin, _ = dt.check_period_boundaries(self.dest_temp_res, begin)
if begin < self.begin_date:
begin = self.begin_date
if begin < self.dest_start_date:
begin = self.dest_start_date
if end is None:
end = datetime.now()
return begin, end
def _check_begin_end(self, begin, end):
"""
Checks begin and end date and returns valid dates.
Parameters
----------
begin : datetime
Begin date to check.
end : datetime
End date to check.
Returns
-------
begin : datetime
Begin date.
end : datetime
End date.
"""
if begin is None:
if self.dest_start_date < self.begin_date:
begin = self.begin_date
else:
begin = self.dest_start_date
if begin < self._get_download_date():
begin = self._get_download_date()
# start one period earlier to close possible gaps
begin = begin - timedelta(days=1)
begin, _ = dt.check_period_boundaries(self.dest_temp_res, begin)
if begin < self.begin_date:
begin = self.begin_date
if begin < self.dest_start_date:
begin = self.dest_start_date
if end is None:
end = datetime.now()
return begin, end