def write_age(self,
data,
outkey='Z_Age',
missing=-1,
dtype='int8',
filename=None):
logging.debug('Writing ' + filename + ' for ' + str(outkey))
ensure_dir(filename)
try:
f = AugmentedNetcdfDataset(filename, 'a', format='NETCDF4')
f.createDimensionIfNotExists('x', self.xoutputsize)
f.createDimensionIfNotExists('y', self.youtputsize)
except OSError:
f = AugmentedNetcdfDataset(filename, 'w', format='NETCDF4')
f.createDimensionIfNotExists('x', self.xoutputsize)
f.createDimensionIfNotExists('y', self.youtputsize)
self._set_date_and_version(f, self.date, __version__,
self.model_id)
f.setncattr('institution', 'IPMA')
outvar = f.createVariableIfNotExists(outkey,
data.dtype, ('y', 'x'),
zlib=True,
complevel=5,
fletcher32=True)
data = data.astype(dtype)
outvar[self.xslice, self.yslice] = data
f.close()
def write_age(self,
data,
outkey='Z_Age',
missing=-1,
dtype='int8',
filename=None):
logging.debug('Writing ' + filename + ' for ' + str(outkey))
ensure_dir(filename)
f = h5py.File(filename, 'a')
if outkey in f.keys():
dataset = f[outkey]
else:
dataset = f.create_dataset(outkey,
chunks=True,
compression='gzip',
fletcher32=True,
shape=(self.xoutputsize,
self.youtputsize),
dtype=dtype)
dataset.attrs.create('CLASS', 'Data'.encode('ASCII'))
dataset.attrs.create('PRODUCT', long_names[outkey].encode('ASCII'))
dataset.attrs.create('PRODUCT_ID', product_id[outkey])
dataset.attrs.create('N_COLS', self.youtputsize)
dataset.attrs.create('N_LINES', self.xoutputsize)
dataset.attrs.create('NB_BYTES', 1)
dataset.attrs.create('UNITS', 'days'.encode('ASCII'))
dataset.attrs.create('CAL_SLOPE', 1.0)
dataset.attrs.create('CAL_OFFSET', 0.0)
dataset.attrs.create("MISSING_VALUE", missing)
dataset.attrs.create("SCALING_FACTOR", 1.)
dataset.attrs.create("OFFSET", 0.)
data = data.astype(dtype)
dataset[self.xslice, self.yslice] = data
f.close()
def write_brdf(self,
alldata,
configkey,
scale=10000.,
offset=0.,
missing=-32768):
""" Write data in hdf file for the K012 kernel coefficients of the brdf model """
for ichannel in range(0, self.n_channels_ref):
for iparam in range(0, self.model_len):
filename = self.config[configkey][f'band{ichannel+1}'][
'filename']
outkey = f'K{iparam}'
logging.debug('Writing ' + filename + ' for ' + str(outkey))
ensure_dir(filename)
try:
f = AugmentedNetcdfDataset(filename, 'a', format='NETCDF4')
except OSError:
f = AugmentedNetcdfDataset(filename, 'w', format='NETCDF4')
f.createDimensionIfNotExists('longitude', self.xoutputsize)
f.createDimensionIfNotExists('latitude', self.youtputsize)
f.createDimensionIfNotExists('NBAND', self.n_channels_ref)
f.createDimensionIfNotExists('KERNEL_INDEX',
self.model_len)
f.createDimensionIfNotExists('KERNEL_INDEX2',
self.model_len)
self._set_date_and_version(f, self.date, __version__,
self.model_id)
data = alldata[:, :, ichannel, iparam]
logging.debug('Average : ' + str(np.mean(data[:])))
data_int = ((data * scale) + offset).round().astype(self.dtype)
data_int[np.isnan(data)] = missing
#~ dataset[self.xslice,self.yslice] = data_int
missing = np.int16(missing)
outvar = f.createVariableIfNotExists(outkey,
data_int.dtype,
('latitude', 'longitude'),
zlib=True,
complevel=5,
fletcher32=True,
attributes={
'units':
'',
'offset':
offset,
'scale_factor':
scale,
'_FillValue':
missing,
'long_name':
f'BRDF {outkey}'
})
outvar[self.xslice, self.yslice] = data_int
logging.debug('Average : ' + str(np.mean(data_int[:])))
f.close()
def write_tocr(self, data, xslice, yslice, date, key, typ):
logging.info('Writing ' + typ + ' to ' + self.outfilename)
try:
ensure_dir(self.outfilename)
try:
f = AugmentedNetcdfDataset(self.outfilename,'a', format='NETCDF4')
except OSError:
f = AugmentedNetcdfDataset(self.outfilename,'w', format='NETCDF4')
f.createDimensionIfNotExists('latitude', self.xoutputsize)
f.createDimensionIfNotExists('longitude', self.youtputsize)
f.createDimensionIfNotExists('NBAND', self.n_channels_ref)
self._set_date_and_version(f, date, __version__, self.model_id)
if typ == 'TOC-R' or typ == 'TOC-R-ERR':
scale_factor = 1./10000.0
missing_value = -32767
outvar = f.createVariableIfNotExists(key, 'int16', ('latitude','longitude','NBAND'), zlib=True, complevel=5, fletcher32=True,
attributes = {'units': '', 'offset':0., 'scale_factor':scale_factor } )
missing = np.isnan(data)
data[data < 0] = 0.0
data[data > 1.01] = 1.0
data = data / scale_factor
data = data.astype(np.int16)
data[missing] = missing_value
outvar[xslice, yslice,:] = data[:,:,:]
elif typ=='Z-QFLAG':
outvar=f.createVariableIfNotExists(key, 'uint8', ('latitude','longitude','NBAND'), zlib=True, complevel=5, fletcher32=True)
outvar[xslice,yslice,:]=data[:,:,:]
elif typ == 'solzenith':
outvar = f.createVariableIfNotExists(key, data.dtype, ('latitude','longitude'), zlib=True, complevel=5, fletcher32=True)
outvar[xslice, yslice] = data[:,:]
elif typ == 'n_valid_obs':
data = data.astype('int8')
outvar = f.createVariableIfNotExists(key, data.dtype, ('latitude','longitude'), zlib=True, complevel=5, fletcher32=True,
attributes = {'units': '',
'long_name' : 'NMOD for {key}'.format(key=key) } )
outvar[xslice, yslice] = data[:,:]
elif typ == 'latitude':
outvar = f.createVariableIfNotExists(key, data.dtype, ('latitude'), complevel=5, fletcher32=True, zlib=True,
attributes = {'units': 'degrees',
'title' : 'latitude',
'long_name' : 'latitude' } )
outvar[xslice] = data[:,0] # as per VITO's request, take only first column]
elif typ == 'longitude':
outvar = f.createVariableIfNotExists(key, data.dtype, ('longitude'), complevel=5, fletcher32=True, zlib=True,
attributes = {'units': 'degrees',
'title' : 'longitude',
'long_name' : 'longitude' } )
outvar[yslice] = data[0,:] # as peer VITO's request, take only first row
else:
raise Exception('Unknown type of data to write : typ = ' + str(typ))
f.close()
except Exception as e:
logging.error('Problem writing ' + key + ' on ' + self.outfilename + ' : ' + str(e))
raise(e)
def write_brdf_covariance(self,
alldata,
configkey,
scale=10000.,
offset=0.,
missing=-32768):
""" Write covariance in hdf file for the K012 kernel coefficients of the brdf model """
for ichannel in range(0, self.n_channels_ref):
for iparam in range(0, self.model_len):
for jparam in range(iparam, self.model_len):
filename = self.config[configkey][f'band{ichannel+1}'][
'cov']
ensure_dir(filename)
outkey = f'C{iparam}{jparam}'
logging.debug('Writing ' + filename + ' for ' +
str(outkey))
f = h5py.File(filename, 'a')
if outkey in f.keys():
dataset = f[outkey]
else:
dataset = f.create_dataset(outkey,
chunks=True,
compression='gzip',
fletcher32=True,
shape=(self.xoutputsize,
self.youtputsize),
dtype=self.dtype)
dataset.attrs.create('CLASS', 'Data'.encode('ASCII'))
dataset.attrs.create(
'PRODUCT',
'Covariance Matrix Element {iparam}{jparam}'.
format(iparam=iparam,
jparam=jparam).encode('ASCII'))
dataset.attrs.create('PRODUCT_ID', 128)
dataset.attrs.create('N_COLS', self.youtputsize)
dataset.attrs.create('N_LINES', self.xoutputsize)
dataset.attrs.create('NB_BYTES', 2)
dataset.attrs.create('SCALING_FACTOR', 1.0)
dataset.attrs.create('UNITS',
'dimensionless'.encode('ASCII'))
dataset.attrs.create('CAL_SLOPE', 1.0)
dataset.attrs.create("MISSING_VALUE", missing)
dataset.attrs.create("CAL_OFFSET", 0.)
dataset.attrs.create("OFFSET", 0.)
dataset.attrs.create("SCALING_FACTOR", scale)
dataset.attrs.create("OFFSET", offset)
data = alldata[:, :, ichannel, iparam, jparam]
data_int = ((data * scale) + offset).round().astype(
self.dtype)
data_int[np.isnan(data)] = missing
dataset[self.xslice, self.yslice] = data_int
f.close()
def write_albedo_after_spectral_integration(self,
alldata,
typ,
filename,
missing=-1,
scale=10000.,
offset=0.):
dtype = '<i2' # beware there is rounding of the value below
for iout, outname in enumerate(self.outalbedos_names):
for iin, inname in enumerate(self.inalbedos_names):
f = h5py.File(filename, 'a')
fullname = outname + '-' + inname
logging.debug('Writing ' + filename + ' for albedo ' +
fullname)
ensure_dir(filename)
if typ == 'albedo':
outkey = 'AL-' + fullname
elif typ == 'albedo-err':
outkey = 'AL-' + fullname + '-ERR'
if outkey == 'AL-NI-BH' or outkey == 'AL-NI-BH-ERR' or outkey == 'AL-VI-BH' or outkey == 'AL-VI-BH-ERR':
continue
if outkey in f.keys():
dataset = f[outkey]
else:
dataset = f.create_dataset(outkey,
chunks=True,
compression='gzip',
fletcher32=True,
shape=(self.xoutputsize,
self.youtputsize),
dtype=dtype)
dataset.attrs.create('CLASS', 'Data'.encode('ASCII'))
dataset.attrs.create('PRODUCT',
long_names[outkey].encode('ASCII'))
dataset.attrs.create('PRODUCT_ID', product_id[outkey])
dataset.attrs.create('N_COLS', self.youtputsize)
dataset.attrs.create('N_LINES', self.xoutputsize)
dataset.attrs.create('NB_BYTES', 2)
dataset.attrs.create('UNITS',
'dimensionless'.encode('ASCII'))
dataset.attrs.create('CAL_SLOPE', 1.0)
dataset.attrs.create('CAL_OFFSET', 0.0)
dataset.attrs.create("MISSING_VALUE", missing)
dataset.attrs.create("SCALING_FACTOR", scale)
dataset.attrs.create("OFFSET", offset)
data = alldata[:, :, iin, iout]
data_int = ((data * scale) + offset).round().astype(dtype)
data_int[np.isnan(data)] = missing
dataset[self.xslice, self.yslice] = data_int
f.close()
def write_all_brdf(self, al2runner):
print('writing to ', self.config)
for ichannel in range(0, self.n_channels_ref):
filename = self.config['brdf'][f'band{ichannel+1}']['filename']
ensure_dir(filename)
self._write_global_attributes(self.date, filename, options=None)
data = al2runner.age.values[:, :, ichannel]
self.write_age(data, filename=filename)
for ichannel in range(0, self.n_channels_ref):
filename = self.config['brdf'][f'band{ichannel+1}']['filename']
ensure_dir(filename)
self._write_global_attributes(self.date, filename, options=None)
data = al2runner.quality.values[:, :, ichannel]
self.write_qflag(data, filename=filename)
for ichannel in range(0, self.n_channels_ref):
ensure_dir(filename)
filename = self.config['brdf-d01'][f'band{ichannel+1}']['filename']
self._write_global_attributes(self.date, filename, options=None)
data = al2runner.quality1.values[:, :, ichannel]
self.write_qflag(data, filename=filename)
self.write_brdf(al2runner.brdf.values, 'brdf')
self.write_brdf_covariance(al2runner.covariance.values, 'brdf')
self.write_brdf(al2runner.brdf1.values, 'brdf-d01')
self.write_brdf_covariance(al2runner.covariance1.values, 'brdf-d01')
def write_brdf(self, data, key, typ):
""" Will write the numpy array "data", in the file defined above in "self.brdf_file", on the data layer "key".
"typ" should be a known identifier, as the data will be processed differently according its value.
"""
logging.debug('Writing ' + key + ':' + typ + ' to ' + self.brdf_file)
try:
ensure_dir(self.brdf_file)
try:
f = AugmentedNetcdfDataset(self.brdf_file,
'a',
format='NETCDF4')
except OSError:
f = AugmentedNetcdfDataset(self.brdf_file,
'w',
format='NETCDF4')
f.createDimensionIfNotExists('X', self.xoutputsize)
f.createDimensionIfNotExists('Y', self.youtputsize)
f.createDimensionIfNotExists('NBAND', self.n_channels_ref)
f.createDimensionIfNotExists('KERNEL_INDEX', self.model_len)
f.createDimensionIfNotExists('KERNEL_INDEX2', self.model_len)
self._set_date_and_version(f, self.date, __version__,
self.model_id)
if typ == 'brdf':
outvar = f.createVariableIfNotExists(
key,
data.dtype, ('X', 'Y', 'NBAND', 'KERNEL_INDEX'),
zlib=True,
complevel=5,
fletcher32=True)
elif typ == 'covariance':
outvar = f.createVariableIfNotExists(
key,
data.dtype,
('X', 'Y', 'NBAND', 'KERNEL_INDEX', 'KERNEL_INDEX2'),
zlib=True,
complevel=5,
fletcher32=True)
elif typ == 'quality':
outvar = f.createVariableIfNotExists(key,
data.dtype,
('X', 'Y', 'NBAND'),
zlib=True,
complevel=5,
fletcher32=True)
elif typ == 'age':
outvar = f.createVariableIfNotExists(key,
data.dtype,
('X', 'Y', 'NBAND'),
zlib=True,
complevel=5,
fletcher32=True)
elif typ == 'latitude' or typ == 'longitude':
outvar = f.createVariableIfNotExists(key,
data.dtype, ('X', 'Y'),
zlib=True,
complevel=5,
fletcher32=True)
elif typ == 'n_valid_obs':
data = data.astype('int8')
outvar = f.createVariableIfNotExists(
key,
data.dtype, ('X', 'Y'),
zlib=True,
complevel=5,
fletcher32=True,
attributes={
'units': '',
'long_name': 'NMOD for {key}'.format(key=key)
})
else:
raise Exception('Unknown type of data to write : typ = ' +
str(typ))
# here is the actual writing command
outvar[self.xslice, self.yslice, ...] = data[...]
f.close()
except Exception as e:
print(e)
logging.error('Problem writing ' + key + ' on ' + self.brdf_file +
' : ' + str(e))
raise (e)
def write_albedo(self, data, key, typ):
""" Will write the numpy array "data", in the file defined above in "self.albedo_file", on the data layer "key".
"typ" should be a known identifier, as the data will be processed differently according its value.
"""
logging.debug('Writing ' + key + ' to ' +
self.albedo_file) # + str(self.date))
try:
ensure_dir(self.albedo_file)
try:
f = AugmentedNetcdfDataset(self.albedo_file,
'a',
format='NETCDF4')
except OSError:
f = AugmentedNetcdfDataset(self.albedo_file,
'w',
format='NETCDF4')
f.createDimensionIfNotExists('longitude', self.xoutputsize)
f.createDimensionIfNotExists('latitude', self.youtputsize)
f.createDimensionIfNotExists('NBAND', self.n_channels_ref)
self._set_date_and_version(f, self.date, __version__,
self.model_id)
f.setncattr('institution', 'VITO')
if typ == 'albedo':
#print(f'--------------')
#print(f'DATA {self.date} :{key}: {data[0, 0]}')
scale_factor = 1. / 10000
missing_value = -32767
dtype = np.int16
outvar = f.createVariableIfNotExists(
key,
dtype, ('latitude', 'longitude'),
complevel=5,
fletcher32=True,
zlib=True,
attributes={
'units': '',
'offset': 0.,
'scale_factor': scale_factor,
'long_name': 'Albedo {key}'.format(key=key)
})
missing = np.isnan(data)
#######with numpy.warning.filterwarnings(divide='ignore'):
####### numpy.float64(1.0) / 0.0
data[data < -3.0] = -3.0
data[data > 3.0] = 3.0
#print(f'{self.date} :{key}: m, s, l: {missing[0, 0]}')
data = data / scale_factor
data = data.astype(dtype)
data[missing] = missing_value
#print(f'DATA {self.date} :{key}: {data[0, 0]}')
outvar[self.xslice, self.yslice] = data[:, :]
#outvar[self.xslice, self.yslice] = 7
##f.close()
##f = AugmentedNetcdfDataset(self.albedo_file,'r', format='NETCDF4')
##var = f[key]
##var.set_auto_maskandscale(False)
###print(f'HERE {self.date} :{key}: {var[self.xslice, self.yslice][0,0]}')
##f.close()
##f = AugmentedNetcdfDataset(self.albedo_file,'r', format='NETCDF4')
##print(f'autoscale {self.date} :{key}: {f[key][self.xslice, self.yslice][0,0]}')
elif typ == 'albedo_cov':
scale_factor = 1. / 10000
missing_value = -32767
dtype = np.int16
outvar = f.createVariableIfNotExists(
key,
dtype, ('latitude', 'longitude'),
complevel=5,
fletcher32=True,
zlib=True,
attributes={
'units': '',
'offset': 0.,
'scale_factor': scale_factor,
'long_name': 'Albedo cov {key}'.format(key=key)
})
missing = np.isnan(data)
data[data < -3.0] = -3.0
data[data > 3.0] = 3.0
data = data / scale_factor
data = data.astype(dtype)
data[missing] = missing_value
outvar[self.xslice, self.yslice] = data[:, :]
elif typ == 'age':
data = data.astype('int8')
outvar = f.createVariableIfNotExists(
key,
data.dtype, ('latitude', 'longitude'),
complevel=5,
fletcher32=True,
zlib=True,
attributes={
'units': 'days',
'long_name': 'Age {key}'.format(key=key)
})
outvar[self.xslice, self.yslice] = data[:, :]
elif typ == 'n_valid_obs':
data = data.astype('int8')
outvar = f.createVariableIfNotExists(
key,
data.dtype, ('latitude', 'longitude'),
complevel=5,
fletcher32=True,
zlib=True,
attributes={
'units': '',
'long_name': 'NMOD for {key}'.format(key=key)
})
outvar[self.xslice, self.yslice] = data[:, :]
elif typ == 'quality':
data = data.astype('uint8')
outvar = f.createVariableIfNotExists(
key,
data.dtype, ('latitude', 'longitude'),
complevel=5,
fletcher32=True,
zlib=True,
attributes={
'units': '',
'long_name': 'Quality flag {key}'.format(key=key)
})
outvar[self.xslice, self.yslice] = data[:, :]
elif typ == 'latitude':
outvar = f.createVariableIfNotExists(key,
data.dtype, ('latitude'),
complevel=5,
fletcher32=True,
zlib=True,
attributes={
'units': 'degrees',
'title': 'latitude',
'long_name':
'latitude'
})
outvar[
self.
xslice] = data[:,
0] # as per VITO's request, take only first column]
elif typ == 'longitude':
outvar = f.createVariableIfNotExists(key,
data.dtype, ('longitude'),
complevel=5,
fletcher32=True,
zlib=True,
attributes={
'units': 'degrees',
'title': 'longitude',
'long_name':
'longitude'
})
outvar[self.yslice] = data[
0, :] # as peer VITO's request, take only first row
else:
raise Exception('Unknown type of data to write : typ = ' +
str(typ))
f.close()
except Exception as e:
logging.error('Problem writing ' + key + ' : ' + str(e))
raise Exception()
def save_yaml(dic, filename):
ensure_dir(filename)
with open(filename, 'w') as f:
yaml.dump(dic, f, indent=2)
def to_yaml(self, filename=None):
""" This function saves the full state of the object DataStore into an human readable file """
if filename: logging.debug(f'data_store : config written in {filename}')
ensure_dir(filename)
return to_yaml_function(dict(self), filename=filename)
def write_albedo_after_spectral_integration(self,
alldata,
typ,
filename,
missing_val=-1,
scale=10000.,
offset=0.):
dtype = '<i2' # beware there is rounding of the value below
ensure_dir(filename)
try:
f = AugmentedNetcdfDataset(filename, 'a', format='NETCDF4')
except OSError:
f = AugmentedNetcdfDataset(filename, 'w', format='NETCDF4')
f.createDimensionIfNotExists('longitude', self.xoutputsize)
f.createDimensionIfNotExists('latitude', self.youtputsize)
f.createDimensionIfNotExists('time', 1)
for iout, outname in enumerate(self.outalbedos_names):
for iin, inname in enumerate(self.inalbedos_names):
fullname = outname + '-' + inname
if typ == 'albedo':
outkey = 'AL-' + fullname
scale_factor = 1. / 10000
missing_value = np.int16(-32767)
dtype = np.int16
outvar = f.createVariableIfNotExists(
outkey,
dtype, ('time', 'latitude', 'longitude'),
complevel=5,
fletcher32=True,
zlib=True,
attributes={
'units': '',
'offset': 0.,
'scale_factor': scale_factor,
'_FillValue': missing_value,
'long_name': 'Albedo {key}'.format(key=outkey)
})
data = np.array([alldata[:, :, iin, iout]])
missing = np.isnan(data)
#######with numpy.warning.filterwarnings(divide='ignore'):
####### numpy.float64(1.0) / 0.0
data[data < -3.0] = -3.0
data[data > 3.0] = 3.0
#print(f'{self.date} :{key}: m, s, l: {missing[0, 0]}')
data = data / scale_factor
data = data.astype(dtype)
data[missing] = missing_value
#print(f'DATA {self.date} :{key}: {data[0, 0]}')
outvar[:, self.xslice, self.yslice] = data[:, :, :]
elif typ == 'albedo-err':
outkey = 'AL-' + fullname + '-ERR'
scale_factor = 1. / 10000
missing_value = np.int16(-32767)
dtype = np.int16
outvar = f.createVariableIfNotExists(
outkey,
dtype, ('time', 'latitude', 'longitude'),
complevel=5,
fletcher32=True,
zlib=True,
attributes={
'units': '',
'offset': 0.,
'scale_factor': scale_factor,
'_FillValue': missing_value,
'long_name': 'Albedo cov {key}'.format(key=outkey)
})
data = np.array([alldata[:, :, iin, iout]])
missing = np.isnan(data)
data[data < -3.0] = -3.0
data[data > 3.0] = 3.0
data = data / scale_factor
data = data.astype(dtype)
data[missing] = missing_value
outvar[:, self.xslice, self.yslice] = data[:, :, :]
if outkey == 'AL-NI-BH' or outkey == 'AL-NI-BH-ERR' or outkey == 'AL-VI-BH' or outkey == 'AL-VI-BH-ERR':
continue
data = alldata[:, :, iin, iout]
data_int = ((data * scale) + offset).round().astype(dtype)
data_int[np.isnan(data)] = missing_val
#~ dataset[self.xslice,self.yslice] = data_int
f.close()
def write_albedo_per_band(self,
alldata,
typ,
filename,
ichannel,
missing=-1,
scale=10000.,
offset=0.):
dtype = '<i2' # beware there is rounding of the value below
logging.debug('Writing ' + filename + ' for albedo err channel ' +
str(ichannel + 1))
ensure_dir(filename)
try:
f = AugmentedNetcdfDataset(filename, 'a', format='NETCDF4')
except OSError:
f = AugmentedNetcdfDataset(filename, 'w', format='NETCDF4')
f.createDimensionIfNotExists('x', self.xoutputsize)
f.createDimensionIfNotExists('y', self.youtputsize)
f.createDimensionIfNotExists('time', 1)
self._set_date_and_version(f, self.date, __version__,
self.model_id)
f.setncattr('institution', 'IPMA')
for j, bhdh in enumerate(self.inalbedos_names):
if typ == 'albedo':
outkey = 'AL-SP-' + bhdh
scale_factor = 1. / 10000
missing_value = np.int16(-32768)
dtype = np.int16
outvar = f.createVariableIfNotExists(
outkey,
dtype, ('time', 'y', 'x'),
complevel=5,
fletcher32=True,
zlib=True,
attributes={
'units': '',
'offset': 0.,
'_FillValue': missing_value,
'scale_factor': scale_factor,
'long_name': 'Albedo {key}'.format(key=outkey)
})
data = np.array([alldata[:, :, ichannel, j]]) # pas beau
missing = np.isnan(data)
#######with numpy.warning.filterwarnings(divide='ignore'):
####### numpy.float64(1.0) / 0.0
data[data < -3.0] = -3.0
data[data > 3.0] = 3.0
#print(f'{self.date} :{key}: m, s, l: {missing[0, 0]}')
data = data / scale_factor
data = data.astype(dtype)
data[missing] = missing_value
#print(f'DATA {self.date} :{key}: {data[0, 0]}')
outvar[0, self.xslice, self.yslice] = data[:, :, :]
elif typ == 'albedo-err':
outkey = 'AL-SP-' + bhdh + '-ERR'
scale_factor = 1. / 10000
missing_value = np.int16(-32768)
dtype = np.int16
outvar = f.createVariableIfNotExists(
outkey,
dtype, ('time', 'y', 'x'),
complevel=5,
fletcher32=True,
zlib=True,
attributes={
'units': '',
'offset': 0.,
'scale_factor': scale_factor,
'_FillValue': missing_value,
'long_name': 'Albedo cov {key}'.format(key=outkey)
})
data = np.array([alldata[:, :, ichannel, j]])
missing = np.isnan(data)
data[data < -3.0] = -3.0
data[data > 3.0] = 3.0
data = data / scale_factor
data = data.astype(dtype)
data[missing] = missing_value
outvar[0, self.xslice, self.yslice] = data[:, :, :]
#~ data = alldata[:,:,ichannel,j]
#~ data_int = ((data * scale) + offset).round().astype(dtype)
#~ data_int[np.isnan(data)] = missing
#~ outvar[self.xslice,self.yslice] = data_int
f.close()