def run_geocode(inps):
"""geocode all input files"""
start_time = time.time()
# Prepare geometry for geocoding
res_obj = resample(lookupFile=inps.lookupFile,
dataFile=inps.file[0],
SNWE=inps.SNWE,
laloStep=inps.laloStep,
processor=inps.processor)
res_obj.open()
if not inps.nprocs:
inps.nprocs = multiprocessing.cpu_count()
# resample input files one by one
for infile in inps.file:
print('-' * 50+'\nresampling file: {}'.format(infile))
atr = readfile.read_attribute(infile, datasetName=inps.dset)
outfile = auto_output_filename(infile, inps)
if inps.updateMode and ut.run_or_skip(outfile, in_file=[infile, inps.lookupFile]) == 'skip':
print('update mode is ON, skip geocoding.')
continue
# read source data and resample
dsNames = readfile.get_dataset_list(infile, datasetName=inps.dset)
maxDigit = max([len(i) for i in dsNames])
dsResDict = dict()
for dsName in dsNames:
print('reading {d:<{w}} from {f} ...'.format(d=dsName,
w=maxDigit,
f=os.path.basename(infile)))
data = readfile.read(infile,
datasetName=dsName,
print_msg=False)[0]
if atr['FILE_TYPE'] == 'timeseries' and len(data.shape) == 2:
data = np.reshape(data, (1, data.shape[0], data.shape[1]))
res_data = res_obj.run_resample(src_data=data,
interp_method=inps.interpMethod,
fill_value=inps.fillValue,
nprocs=inps.nprocs,
print_msg=True)
dsResDict[dsName] = res_data
# update metadata
if inps.radar2geo:
atr = metadata_radar2geo(atr, res_obj)
else:
atr = metadata_geo2radar(atr, res_obj)
#if len(dsNames) == 1 and dsName not in ['timeseries']:
# atr['FILE_TYPE'] = dsNames[0]
# infile = None
writefile.write(dsResDict, out_file=outfile, metadata=atr, ref_file=infile)
m, s = divmod(time.time()-start_time, 60)
print('time used: {:02.0f} mins {:02.1f} secs.\n'.format(m, s))
return outfile
def run_resample(inps):
"""resample all input files"""
start_time = time.time()
# Prepare geometry for geocoding
res_obj = resample(lookupFile=inps.lookupFile, dataFile=inps.file[0],
SNWE=inps.SNWE, laloStep=inps.laloStep)
res_obj.get_geometry_definition()
inps.nprocs = multiprocessing.cpu_count()
# resample input files one by one
for infile in inps.file:
print('-' * 50+'\nresampling file: {}'.format(infile))
outfile = auto_output_filename(infile, inps)
if inps.updateMode and not ut.update_file(outfile, [infile, inps.lookupFile]):
print('update mode is ON, skip geocoding.')
return outfile
# read source data and resample
dsNames = readfile.get_dataset_list(infile, datasetName=inps.dset)
maxDigit = max([len(i) for i in dsNames])
dsResDict = dict()
for dsName in dsNames:
print('resampling {d:<{w}} from {f} using {n} processor cores ...'.format(
d=dsName, w=maxDigit, f=os.path.basename(infile), n=inps.nprocs))
data = readfile.read(infile, datasetName=dsName, print_msg=False)[0]
res_data = resample_data(data, inps, res_obj)
dsResDict[dsName] = res_data
# update metadata
atr = readfile.read_attribute(infile, datasetName=inps.dset)
if inps.radar2geo:
atr = metadata_radar2geo(atr, res_obj)
else:
atr = metadata_geo2radar(atr, res_obj)
if len(dsNames) == 1 and dsName not in ['timeseries']:
atr['FILE_TYPE'] = dsNames[0]
infile = None
writefile.write(dsResDict, out_file=outfile, metadata=atr, ref_file=infile)
m, s = divmod(time.time()-start_time, 60)
print('\ntime used: {:02.0f} mins {:02.1f} secs\nDone.'.format(m, s))
return outfile