def load_GPM_IMERG_files_with_spatial_filter(file_path=None,
filename_pattern=None,
filelist=None,
variable_name='precipitationCal',
user_mask_file=None,
mask_variable_name='mask',
user_mask_values=[10],
longitude_name='lon',
latitude_name='lat'):
''' Load multiple GPM Level 3 IMEGE files containing calibrated \
precipitation and generate a two-dimensional array \
for the masked grid points.
:param file_path: Directory to the HDF files to load.
:type file_path: :mod:`string`
:param filename_pattern: Path to the HDF files to load.
:type filename_pattern: :mod:`string`
:param filelist: A list of filenames
:type filelist: :mod:`string`
:param variable_name: The variable name to load from the HDF file.
:type variable_name: :mod:`string`
:param name: (Optional) A name for the loaded dataset.
:type name: :mod:`string`
:user_mask_file: user's own gridded mask file(a netCDF file name)
:type name: :mod:`string`
:mask_variable_name: mask variables in user_mask_file
:type name: :mod:`string`
:longitude_name: longitude variable in user_mask_file
:type name: :mod:`string`
:latitude_name: latitude variable in user_mask_file
:type name: :mod:`string`
:param user_mask_values: grid points where mask_variable == user_mask_value will be extracted.
:type user_mask_values: list of strings
:returns: A two-dimensional array with the requested variable's MASKED data from \
the HDF file.
:rtype: :class:`dataset.Dataset`
:raises ValueError:
'''
if not filelist:
GPM_files = []
for pattern in filename_pattern:
GPM_files.extend(glob(file_path + pattern))
else:
GPM_files = [line.rstrip('\n') for line in open(filelist)]
GPM_files.sort()
file_object_first = h5py.File(GPM_files[0])
lats = file_object_first['Grid']['lat'][:]
lons = file_object_first['Grid']['lon'][:]
lons, lats = numpy.meshgrid(lons, lats)
nfile = len(GPM_files)
for ifile, file in enumerate(GPM_files):
if ifile == 0 and user_mask_file:
file_object = netCDF4.Dataset(user_mask_file)
mask_variable = file_object.variables[mask_variable_name][:]
mask_longitude = file_object.variables[longitude_name][:]
mask_latitude = file_object.variables[latitude_name][:]
spatial_mask = utils.regrid_spatial_mask(lons, lats,
mask_longitude,
mask_latitude,
mask_variable,
user_mask_values)
y_index, x_index = numpy.where(spatial_mask == 0)
print('Reading file ' + str(ifile + 1) + '/' + str(nfile), file)
file_object = h5py.File(file)
values0 = ma.transpose(
ma.masked_less(file_object['Grid'][variable_name][:], 0.))
values_masked = values0[y_index, x_index]
values_masked = ma.expand_dims(values_masked, axis=0)
if ifile == 0:
values = values_masked
else:
values = ma.concatenate((values, values_masked))
file_object.close()
return values
def load_GPM_IMERG_files_with_spatial_filter(file_path=None,
filename_pattern=None,
filelist=None,
variable_name='precipitationCal',
user_mask_file=None,
mask_variable_name='mask',
user_mask_values=[10],
longitude_name='lon',
latitude_name='lat'):
''' Load multiple GPM Level 3 IMEGE files containing calibrated \
precipitation and generate a two-dimensional array \
for the masked grid points.
:param file_path: Directory to the HDF files to load.
:type file_path: :mod:`string`
:param filename_pattern: Path to the HDF files to load.
:type filename_pattern: :mod:`string`
:param filelist: A list of filenames
:type filelist: :mod:`string`
:param variable_name: The variable name to load from the HDF file.
:type variable_name: :mod:`string`
:param name: (Optional) A name for the loaded dataset.
:type name: :mod:`string`
:user_mask_file: user's own gridded mask file(a netCDF file name)
:type name: :mod:`string`
:mask_variable_name: mask variables in user_mask_file
:type name: :mod:`string`
:longitude_name: longitude variable in user_mask_file
:type name: :mod:`string`
:latitude_name: latitude variable in user_mask_file
:type name: :mod:`string`
:param user_mask_values: grid points where mask_variable == user_mask_value will be extracted.
:type user_mask_values: list of strings
:returns: A two-dimensional array with the requested variable's MASKED data from \
the HDF file.
:rtype: :class:`dataset.Dataset`
:raises ValueError:
'''
if not filelist:
GPM_files = []
for pattern in filename_pattern:
GPM_files.extend(glob(file_path + pattern))
else:
GPM_files = [line.rstrip('\n') for line in open(filelist)]
GPM_files.sort()
file_object_first = h5py.File(GPM_files[0])
lats = file_object_first['Grid']['lat'][:]
lons = file_object_first['Grid']['lon'][:]
lons, lats = numpy.meshgrid(lons, lats)
nfile = len(GPM_files)
for ifile, file in enumerate(GPM_files):
if ifile == 0 and user_mask_file:
file_object = netCDF4.Dataset(user_mask_file)
mask_variable = file_object.variables[mask_variable_name][:]
mask_longitude = file_object.variables[longitude_name][:]
mask_latitude = file_object.variables[latitude_name][:]
spatial_mask = utils.regrid_spatial_mask(lons,lats,
mask_longitude, mask_latitude,
mask_variable,
user_mask_values)
y_index, x_index = numpy.where(spatial_mask == 0)
print('Reading file ' + str(ifile + 1) + '/' + str(nfile), file)
file_object = h5py.File(file)
values0 = ma.transpose(ma.masked_less(
file_object['Grid'][variable_name][:], 0.))
values_masked = values0[y_index, x_index]
values_masked = ma.expand_dims(values_masked, axis=0)
if ifile == 0:
values = values_masked
else:
values = ma.concatenate((values, values_masked))
file_object.close()
return values