def _get_bt_dataset(self, data, calibration_index, wave_number):
"""Get the dataset as brightness temperature.
Apparently we don't use these calibration factors for Rad -> BT::
coeffs = self._get_coefficients(ds_info['calibration_key'], calibration_index)
# coefficients are per-scan, we need to repeat the values for a
# clean alignment
coeffs = np.repeat(coeffs, data.shape[0] // coeffs.shape[1], axis=1)
coeffs = coeffs.rename({
coeffs.dims[0]: 'coefficients', coeffs.dims[1]: 'y'
}) # match data dims
data = coeffs[0] + coeffs[1] * data + coeffs[2] * data**2 + coeffs[3] * data**3
"""
# pass the dask array
bt_data = rad2temp(wave_number,
data.data * 1e-5) # brightness temperature
if isinstance(bt_data, np.ndarray):
# old versions of pyspectral produce numpy arrays
data.data = da.from_array(bt_data, chunks=data.data.chunks)
else:
# new versions of pyspectral can do dask arrays
data.data = bt_data
# additional corrections from the file
corr_coeff_a = float(
self['/attr/TBB_Trans_Coefficient_A'][calibration_index])
corr_coeff_b = float(
self['/attr/TBB_Trans_Coefficient_B'][calibration_index])
if corr_coeff_a != 0:
data = (data - corr_coeff_b) / corr_coeff_a
# Some BT bands seem to have 0 in the first 10 columns
# and it is an invalid Kelvin measurement, so let's mask
data = data.where(data != 0)
return data
def get_dataset(self, dataset_id, ds_info):
file_key = self.geolocation_prefix + ds_info.get(
'file_key', dataset_id.name)
if self.platform_id == 'FY3B':
file_key = file_key.replace('Data/', '')
data = self[file_key]
band_index = ds_info.get('band_index')
if band_index is not None:
data = data[band_index]
data = data.where(
(data >= self[file_key + '/attr/valid_range'][0])
& (data <= self[file_key + '/attr/valid_range'][1]))
if 'E' in dataset_id.name:
slope = self[
self.l1b_prefix +
'Emissive_Radiance_Scales'].data[:, band_index][:,
np.newaxis]
intercept = self[
self.l1b_prefix +
'Emissive_Radiance_Offsets'].data[:,
band_index][:,
np.newaxis]
# Converts cm^-1 (wavenumbers) and (mW/m^2)/(str/cm^-1) (radiance data)
# to SI units m^-1, mW*m^-3*str^-1.
wave_number = self['/attr/' +
self.wave_number][band_index] * 100
bt_data = rad2temp(wave_number,
(data.data * slope + intercept) * 1e-5)
if isinstance(bt_data, np.ndarray):
# old versions of pyspectral produce numpy arrays
data.data = da.from_array(bt_data, chunks=data.data.chunks)
else:
# new versions of pyspectral can do dask arrays
data.data = bt_data
elif 'R' in dataset_id.name:
slope = self['/attr/RefSB_Cal_Coefficients'][0::2]
intercept = self['/attr/RefSB_Cal_Coefficients'][1::2]
data = data * slope[band_index] + intercept[band_index]
else:
data = data.where(
(data >= self[file_key + '/attr/valid_range'][0])
& (data <= self[file_key + '/attr/valid_range'][1]))
data = self[file_key +
'/attr/Intercept'] + self[file_key +
'/attr/Slope'] * data
new_dims = {old: new for old, new in zip(data.dims, ('y', 'x'))}
data = data.rename(new_dims)
data.attrs.update({
'platform_name': self['/attr/Satellite Name'],
'sensor': self['/attr/Sensor Identification Code']
})
data.attrs.update(ds_info)
units = self.get(file_key + '/attr/units')
if units is not None and str(units).lower() != 'none':
data.attrs.update({'units': self.get(file_key + '/attr/units')})
elif data.attrs.get('calibration') == 'reflectance':
data.attrs.update({'units': '%'})
else:
data.attrs.update({'units': '1'})
return data
def get_dataset(self, dataset_id, ds_info):
"""Create DataArray from file content for `dataset_id`."""
file_key = self.geolocation_prefix + ds_info.get('file_key', dataset_id['name'])
if self.platform_id == 'FY3B':
file_key = file_key.replace('Data/', '')
data = self[file_key]
band_index = ds_info.get('band_index')
valid_range = data.attrs.pop('valid_range', None)
if isinstance(valid_range, np.ndarray):
valid_range = valid_range.tolist()
if band_index is not None:
data = data[band_index]
if valid_range:
data = data.where((data >= valid_range[0]) &
(data <= valid_range[1]))
if 'Emissive' in file_key:
slope = self._correct_slope(self[self.l1b_prefix + 'Emissive_Radiance_Scales'].
data[:, band_index][:, np.newaxis])
intercept = self[self.l1b_prefix + 'Emissive_Radiance_Offsets'].data[:, band_index][:, np.newaxis]
# Converts cm^-1 (wavenumbers) and (mW/m^2)/(str/cm^-1) (radiance data)
# to SI units m^-1, mW*m^-3*str^-1.
wave_number = self['/attr/' + self.wave_number][band_index] * 100
bt_data = rad2temp(wave_number, (data.data * slope + intercept) * 1e-5)
if isinstance(bt_data, np.ndarray):
# old versions of pyspectral produce numpy arrays
data.data = da.from_array(bt_data, chunks=data.data.chunks)
else:
# new versions of pyspectral can do dask arrays
data.data = bt_data
elif 'RefSB' in file_key:
if self.platform_id == 'FY3B':
coeffs = da.from_array(FY3B_REF_COEFFS, chunks=-1)
else:
coeffs = self['/attr/RefSB_Cal_Coefficients']
slope = self._correct_slope(coeffs[0::2])
intercept = coeffs[1::2]
data = data * slope[band_index] + intercept[band_index]
else:
slope = self._correct_slope(self[file_key + '/attr/Slope'])
intercept = self[file_key + '/attr/Intercept']
if valid_range:
data = data.where((data >= valid_range[0]) &
(data <= valid_range[1]))
data = data * slope + intercept
new_dims = {old: new for old, new in zip(data.dims, ('y', 'x'))}
data = data.rename(new_dims)
# use lowercase sensor name to be consistent with the rest of satpy
data.attrs.update({'platform_name': self['/attr/Satellite Name'],
'sensor': self['/attr/Sensor Identification Code'].lower()})
data.attrs.update(ds_info)
units = self.get(file_key + '/attr/units')
if units is not None and str(units).lower() != 'none':
data.attrs.update({'units': self.get(file_key + '/attr/units')})
elif data.attrs.get('calibration') == 'reflectance':
data.attrs.update({'units': '%'})
else:
data.attrs.update({'units': '1'})
return data
def radiance2tb(rad, wavelength):
"""Get the Tb from the radiance using the Planck function.
rad:
Radiance in SI units
wavelength:
Wavelength in SI units (meter)
"""
from pyspectral.blackbody import blackbody_rad2temp as rad2temp
return rad2temp(wavelength, rad)
def radiance2tb(self, rad, wavelength, **kwargs):
"""Get the Tb from the radiance using the Planck function, and optionally the
relative spectral response function
rad:
Radiance in SI units
"""
from pyspectral.blackbody import blackbody_rad2temp as rad2temp
return rad2temp(wavelength, rad)
def radiance2tb(rad, wavelength):
"""
Get the Tb from the radiance using the Planck function
rad:
Radiance in SI units
wavelength:
Wavelength in SI units (meter)
"""
from pyspectral.blackbody import blackbody_rad2temp as rad2temp
return rad2temp(wavelength, rad)
def _calibrate_emissive(self, data, band_index):
slope = self._correct_slope(
self[self.l1b_prefix +
'Emissive_Radiance_Scales'].data[:, band_index][:,
np.newaxis])
intercept = self[
self.l1b_prefix +
'Emissive_Radiance_Offsets'].data[:, band_index][:, np.newaxis]
# Converts cm^-1 (wavenumbers) and (mW/m^2)/(str/cm^-1) (radiance data)
# to SI units m^-1, mW*m^-3*str^-1.
wave_number = self['/attr/' + self.wave_number][band_index] * 100
bt_data = rad2temp(wave_number, (data.data * slope + intercept) * 1e-5)
if isinstance(bt_data, np.ndarray):
# old versions of pyspectral produce numpy arrays
data.data = da.from_array(bt_data, chunks=data.data.chunks)
else:
# new versions of pyspectral can do dask arrays
data.data = bt_data
def get_dataset(self, dataset_id, ds_info):
file_key = self.geolocation_prefix + ds_info.get('file_key', dataset_id.name)
if self.platform_id == 'FY3B':
file_key = file_key.replace('Data/', '')
data = self.get(file_key)
if data is None:
logging.error('File key "{0}" could not be found in file {1}'.format(file_key, self.filename))
band_index = ds_info.get('band_index')
if band_index is not None:
data = data[band_index]
data = data.where((data >= self[file_key + '/attr/valid_range'][0]) &
(data <= self[file_key + '/attr/valid_range'][1]))
if 'E' in dataset_id.name:
slope = self[self.l1b_prefix + 'Emissive_Radiance_Scales'].data[:, band_index][:, np.newaxis]
intercept = self[self.l1b_prefix + 'Emissive_Radiance_Offsets'].data[:, band_index][:, np.newaxis]
radiance_data = rad2temp(self['/attr/' + self.wave_number][band_index] * 100,
(data * slope + intercept) * 1e-5)
data = xr.DataArray(da.from_array(radiance_data, data.chunks),
coords=data.coords, dims=data.dims, name=data.name, attrs=data.attrs)
elif 'R' in dataset_id.name:
slope = self['/attr/RefSB_Cal_Coefficients'][0::2]
intercept = self['/attr/RefSB_Cal_Coefficients'][1::2]
data = data * slope[band_index] + intercept[band_index]
else:
data = data.where((data >= self[file_key + '/attr/valid_range'][0]) &
(data <= self[file_key + '/attr/valid_range'][1]))
data = self[file_key + '/attr/Intercept'] + self[file_key + '/attr/Slope'] * data
new_dims = {old: new for old, new in zip(data.dims, ('y', 'x'))}
data = data.rename(new_dims)
data.attrs.update({'platform_name': self['/attr/Satellite Name'],
'sensor': self['/attr/Sensor Identification Code']})
data.attrs.update(ds_info)
units = self.get(file_key + '/attr/units')
if units is not None and str(units).lower() != 'none':
data.attrs.update({'units': self.get(file_key + '/attr/units')})
elif data.attrs.get('calibration') == 'reflectance':
data.attrs.update({'units': '%'})
else:
data.attrs.update({'units': '1'})
return data
def _calibrate_ir(self, dataset_id, data):
"""Calibrate radiance data to BTs using either pyspectral or in-file coefficients."""
if self.calib_mode == 'PYSPECTRAL':
# depends on the radiance calibration above
# Convert um to m^-1 (SI units for pyspectral)
wn = 1 / (dataset_id.wavelength[1] / 1e6)
# Convert cm^-1 (wavenumbers) and (mW/m^2)/(str/cm^-1) (radiance data)
# to SI units m^-1, mW*m^-3*str^-1.
bt_data = rad2temp(wn, data.data * 1e-5)
if isinstance(bt_data, np.ndarray):
# old versions of pyspectral produce numpy arrays
data.data = da.from_array(bt_data, chunks=data.data.chunks)
else:
# new versions of pyspectral can do dask arrays
data.data = bt_data
else:
# IR coefficients from the file
# Channel specific
c0 = self.nc.attrs['Teff_to_Tbb_c0']
c1 = self.nc.attrs['Teff_to_Tbb_c1']
c2 = self.nc.attrs['Teff_to_Tbb_c2']
# These should be fixed, but load anyway
cval = self.nc.attrs['light_speed']
kval = self.nc.attrs['Boltzmann_constant_k']
hval = self.nc.attrs['Plank_constant_h']
# Compute wavenumber as cm-1
wn = (10000 / dataset_id.wavelength[1]) * 100
# Convert radiance to effective brightness temperature
e1 = (2 * hval * cval * cval) * np.power(wn, 3)
e2 = (data.data * 1e-5)
t_eff = ((hval * cval / kval) * wn) / np.log((e1 / e2) + 1)
# Now convert to actual brightness temperature
bt_data = c0 + c1 * t_eff + c2 * t_eff * t_eff
data.data = bt_data
return data
def get_dataset(self, dataset_id, ds_info):
"""Load data variable and metadata and calibrate if needed."""
file_key = ds_info.get('file_key', dataset_id['name'])
band_index = ds_info.get('band_index')
data = self[file_key]
if band_index is not None:
data = data[band_index]
if data.ndim >= 2:
data = data.rename({data.dims[-2]: 'y', data.dims[-1]: 'x'})
attrs = data.attrs.copy() # avoid contaminating other band loading
attrs.update(ds_info)
if 'rows_per_scan' in self.filetype_info:
attrs.setdefault('rows_per_scan',
self.filetype_info['rows_per_scan'])
fill_value = attrs.pop('FillValue', np.nan) # covered by valid_range
valid_range = attrs.pop('valid_range', None)
if dataset_id.get('calibration') == 'counts':
# preserve integer type of counts if possible
attrs['_FillValue'] = fill_value
new_fill = fill_value
else:
new_fill = np.nan
if valid_range is not None:
# Due to a bug in the valid_range upper limit in the 10.8(24) and 12.0(25)
# in the HDF data, this is hardcoded here.
if dataset_id['name'] in ['24', '25'] and valid_range[1] == 4095:
valid_range[1] = 25000
# typically bad_values == 65535, saturated == 65534
# dead detector == 65533
data = data.where(
(data >= valid_range[0]) & (data <= valid_range[1]), new_fill)
slope = attrs.pop('Slope', None)
intercept = attrs.pop('Intercept', None)
if slope is not None and dataset_id.get('calibration') != 'counts':
if band_index is not None:
slope = slope[band_index]
intercept = intercept[band_index]
data = data * slope + intercept
if dataset_id.get('calibration') == "reflectance":
# some bands have 0 counts for the first N columns and
# seem to be invalid data points
data = data.where(data != 0)
coeffs = self._get_coefficients(ds_info['calibration_key'],
ds_info['calibration_index'])
data = coeffs[0] + coeffs[1] * data + coeffs[2] * data**2
elif dataset_id.get('calibration') == "brightness_temperature":
cal_index = ds_info['calibration_index']
# Apparently we don't use these calibration factors for Rad -> BT
# coeffs = self._get_coefficients(ds_info['calibration_key'], cal_index)
# # coefficients are per-scan, we need to repeat the values for a
# # clean alignment
# coeffs = np.repeat(coeffs, data.shape[0] // coeffs.shape[1], axis=1)
# coeffs = coeffs.rename({
# coeffs.dims[0]: 'coefficients', coeffs.dims[1]: 'y'
# }) # match data dims
# data = coeffs[0] + coeffs[1] * data + coeffs[2] * data**2 + coeffs[3] * data**3
# Converts um^-1 (wavenumbers) and (mW/m^2)/(str/cm^-1) (radiance data)
# to SI units m^-1, mW*m^-3*str^-1.
wave_number = 1. / (dataset_id['wavelength'][1] / 1e6)
# pass the dask array
bt_data = rad2temp(wave_number,
data.data * 1e-5) # brightness temperature
if isinstance(bt_data, np.ndarray):
# old versions of pyspectral produce numpy arrays
data.data = da.from_array(bt_data, chunks=data.data.chunks)
else:
# new versions of pyspectral can do dask arrays
data.data = bt_data
# additional corrections from the file
corr_coeff_a = float(
self['/attr/TBB_Trans_Coefficient_A'][cal_index])
corr_coeff_b = float(
self['/attr/TBB_Trans_Coefficient_B'][cal_index])
if corr_coeff_a != 0:
data = (data - corr_coeff_b) / corr_coeff_a
# Some BT bands seem to have 0 in the first 10 columns
# and it is an invalid Kelvin measurement, so let's mask
data = data.where(data != 0)
data.attrs = attrs
# convert bytes to str
for key, val in attrs.items():
# python 3 only
if bytes is not str and isinstance(val, bytes):
data.attrs[key] = val.decode('utf8')
data.attrs.update({
'platform_name': self['/attr/Satellite Name'],
'sensor': self.sensor_name,
})
return data
def load_virr(satscene, options):
"""Read the VIRR hdf5 file"""
if "filename" not in options:
raise IOError("No 1km virr filename given, cannot load")
values = {"orbit": satscene.orbit,
"satname": satscene.satname,
"instrument": satscene.instrument_name,
"satellite": satscene.fullname
}
filename = \
os.path.join(satscene.time_slot.strftime(options["dir"]) % values,
satscene.time_slot.strftime(
options["filename"])
% values)
LOGGER.debug("Filename= %s", filename)
datasets = ['EV_Emissive',
'EV_RefSB']
calibrate = options['calibrate']
LOGGER.debug("Calibrate = " + str(calibrate))
h5f = h5py.File(filename, 'r')
# Get geolocation information
lons = h5f['Longitude'][:]
lats = h5f['Latitude'][:]
# Mask out unrealistic values:
mask = np.logical_or(lats > 90., lons > 90.)
lons = np.ma.masked_array(lons, mask=mask)
lats = np.ma.masked_array(lats, mask=mask)
sunz = h5f['SolarZenith'][:]
slope = h5f['SolarZenith'].attrs['Slope'][0]
intercept = h5f['SolarZenith'].attrs['Intercept'][0]
sunz = sunz * slope + intercept
sunz = np.where(np.greater(sunz, 85.0), 85.0, sunz)
# Get the calibration information
# Emissive radiance coefficients:
emis_offs = h5f['Emissive_Radiance_Offsets'][:]
emis_scales = h5f['Emissive_Radiance_Scales'][:]
# Central wave number (unit = cm-1) for the three IR bands
# It is ordered according to decreasing wave number (increasing wavelength):
# 3.7 micron, 10.8 micron, 12 micron
emiss_centroid_wn = h5f.attrs['Emmisive_Centroid_Wave_Number']
# VIS/NIR calibration stuff:
refsb_cal_coeff = h5f.attrs['RefSB_Cal_Coefficients']
visnir_scales = refsb_cal_coeff[0::2]
visnir_offs = refsb_cal_coeff[1::2]
refsb_effective_wl = h5f.attrs['RefSB_Effective_Wavelength']
# Read the band data:
for dset in datasets:
band_data = h5f[dset]
valid_range = band_data.attrs['valid_range']
LOGGER.debug("valid-range = " + str(valid_range))
fillvalue = band_data.attrs['_FillValue']
band_names = band_data.attrs['band_name'].split(',')
slope = band_data.attrs['Slope']
intercept = band_data.attrs['Intercept']
units = band_data.attrs['units']
long_name = band_data.attrs['long_name']
LOGGER.debug('band names = ' + str(band_names))
for (i, band) in enumerate(band_names):
if band not in satscene.channels_to_load:
continue
LOGGER.debug("Reading channel %s, i=%d", band, i)
data = band_data[i]
bandmask = np.logical_or(np.less(data, valid_range[0]),
np.greater(data, valid_range[1]))
if calibrate:
if dset in ['EV_Emissive']:
data = (np.array([emis_offs[:, i]]).transpose() +
data * np.array([emis_scales[:, i]]).transpose())
# Radiance to Tb conversion.
# Pyspectral wants SI units,
# but radiance data are in mW/m^2/str/cm^-1 and wavenumbers are in cm^-1
# Therefore multply wavenumber by 100 and radiances by
# 10^-5
data = rad2temp(emiss_centroid_wn[i] * 100., data * 1e-5)
LOGGER.debug("IR data calibrated")
if dset in ['EV_RefSB']:
data = (visnir_offs[i] +
data * visnir_scales[i]) / np.cos(np.deg2rad(sunz))
satscene[band] = np.ma.masked_array(data,
mask=bandmask,
copy=False)
from pyresample import geometry
satscene.area = geometry.SwathDefinition(lons=lons, lats=lats)
h5f.close()
