def h_integrate(mask, stations_layer, date, output_filename_prefix, date_fmt,
funct, kwargs):
date_fmt_for_filename = date.strftime(date_fmt).replace(' ', '-').replace(
':', '-')
output_filename = '{}-{}.tif'.format(output_filename_prefix,
date.strftime(date_fmt_for_filename))
if not _needs_calculation(output_filename, date, stations_layer):
return
# Read the time series values and add the 'value' attribute to
# stations_layer
stations_layer.CreateField(ogr.FieldDefn('value', ogr.OFTReal))
input_files = []
stations_layer.ResetReading()
for station in stations_layer:
filename = station.GetField('filename')
t = Timeseries()
with open(filename) as f:
t.read_file(f)
value = t.get(date, float('NaN'))
station.SetField('value', value)
if not isnan(value):
input_files.append(filename)
stations_layer.SetFeature(station)
if not input_files:
return
# Create destination data source
output = gdal.GetDriverByName('GTiff').Create(
output_filename, mask.RasterXSize, mask.RasterYSize, 1,
gdal.GDT_Float32)
output.SetMetadataItem('TIMESTAMP', date.strftime(date_fmt))
output.SetMetadataItem('INPUT_FILES', '\n'.join(input_files))
try:
# Set geotransform and projection in the output data source
output.SetGeoTransform(mask.GetGeoTransform())
output.SetProjection(mask.GetProjection())
# Do the integration
integrate(mask, stations_layer, output.GetRasterBand(1), funct, kwargs)
finally:
# Close the dataset
output = None
def _needs_calculation(output_filename, date, stations_layer):
"""
Used by h_integrate to check whether the output file needs to be calculated
or not. It does not need to be calculated if it already exists and has been
calculated from all available data.
"""
# Return immediately if output file does not exist
if not os.path.exists(output_filename):
return True
# Get list of files which were used to calculate the output file
fp = gdal.Open(output_filename)
try:
actual_input_files = fp.GetMetadataItem('INPUT_FILES')
if actual_input_files is None:
raise IOError('{} does not contain the metadata item INPUT_FILES'
.format(output_filename))
finally:
fp = None # Close file
actual_input_files = set(actual_input_files.split('\n'))
# Get list of files available for calculating the output file
stations_layer.ResetReading()
available_input_files = set(
[station.GetField('filename') for station in stations_layer
if os.path.exists(station.GetField('filename'))])
# Which of these files have not been used?
unused_files = available_input_files - actual_input_files
# For each one of these files, check whether it has newly available data.
# Upon finding one that does, the verdict is made: return True
for filename in unused_files:
t = Timeseries()
with open(filename) as f:
t.read_file(f)
value = t.get(date, float('NaN'))
if not isnan(value):
return True
# We were unable to find data that had not already been used
return False
