def _create_int8_vector(height, standard_name=None, long_name=None, orig_name=None):
default_array = DefaultData.create_default_vector(height, np.int8)
variable = Variable(["y"], default_array)
tu.add_fill_value(variable, DefaultData.get_default_fill_value(np.int8))
HIRS._set_name_attributes(long_name, orig_name, standard_name, variable)
return variable
def test__get_add_offset_missing(self):
default_array = DefaultData.create_default_vector(2, np.float32)
variable = Variable(["y"], default_array)
variable.encoding = dict([('dtype', np.int8), ('_FillValue', -127),
('scale_factor', 0.023)])
add_offset = DataUtility._get_add_offset(variable)
self.assertEqual(0.0, add_offset)
def test__get_min_max(self):
default_array = DefaultData.create_default_vector(2, np.float32)
variable = Variable(["y"], default_array)
variable.encoding = dict([('dtype', np.int8), ('_FillValue', -127),
('scale_factor', 0.023)])
min_max = DataUtility._get_min_max(variable)
self.assertEqual(-128, min_max.min)
self.assertEqual(127, min_max.max)
def test_check_scaling_ranges_int8_vector_unscaled(self):
default_array = DefaultData.create_default_vector(4, np.int8)
default_array[0] = 108
default_array[1] = 109
default_array[2] = -127
default_array[3] = 110
variable = Variable(["y"], default_array)
variable.encoding = dict([('_FillValue', -127)])
DataUtility.check_scaling_ranges(variable)
def add_gridded_geolocation_variables(dataset, width, height):
default_array = DefaultData.create_default_vector(height,
np.float32,
fill_value=np.NaN)
variable = Variable(["y"], default_array)
TemplateUtil.add_fill_value(variable, np.NaN)
variable.attrs["standard_name"] = LAT_NAME
variable.attrs["long_name"] = LAT_NAME
variable.attrs["bounds"] = "lat_bnds"
TemplateUtil.add_units(variable, LATITUDE_UNIT)
dataset["lat"] = variable
default_array = DefaultData.create_default_array(2,
height,
np.float32,
fill_value=np.NaN)
variable = Variable(["y", "bounds"], default_array)
TemplateUtil.add_fill_value(variable, np.NaN)
variable.attrs["long_name"] = "latitude cell boundaries"
TemplateUtil.add_units(variable, LATITUDE_UNIT)
dataset["lat_bnds"] = variable
default_array = DefaultData.create_default_vector(width,
np.float32,
fill_value=np.NaN)
variable = Variable(["x"], default_array)
TemplateUtil.add_fill_value(variable, np.NaN)
variable.attrs["standard_name"] = LON_NAME
variable.attrs["long_name"] = LON_NAME
TemplateUtil.add_units(variable, LONGITUDE_UNIT)
variable.attrs["bounds"] = "lon_bnds"
dataset["lon"] = variable
default_array = DefaultData.create_default_array(2,
width,
np.float32,
fill_value=np.NaN)
variable = Variable(["x", "bounds"], default_array)
TemplateUtil.add_fill_value(variable, np.NaN)
TemplateUtil.add_units(variable, LONGITUDE_UNIT)
variable.attrs["long_name"] = "longitude cell boundaries"
dataset["lon_bnds"] = variable
def _create_float32_vector(fill_value, height, long_name, orig_name):
default_array = DefaultData.create_default_vector(height, np.float32, fill_value=fill_value)
variable = Variable(["y"], default_array)
if fill_value is None:
tu.add_fill_value(variable, DefaultData.get_default_fill_value(np.float32))
else:
tu.add_fill_value(variable, fill_value)
variable.attrs["long_name"] = long_name
if orig_name is not None:
variable.attrs["orig_name"] = orig_name
return variable
def _create_scaled_uint16_vector(height, standard_name=None, original_name=None, long_name=None, dimension_name=None, scale_factor=0.01):
default_array = DefaultData.create_default_vector(height, np.float32)
if dimension_name is None:
variable = Variable(["y"], default_array)
else:
variable = Variable(dimension_name, default_array)
tu.add_encoding(variable, np.uint16, DefaultData.get_default_fill_value(np.uint16), scale_factor)
HIRS._set_name_attributes(long_name, original_name, standard_name, variable)
return variable
def test__get_min_max_missing_type(self):
default_array = DefaultData.create_default_vector(2, np.float32)
variable = Variable(["y"], default_array)
variable.encoding = dict([('_FillValue', -127),
('scale_factor', 0.023)])
try:
DataUtility._get_min_max(variable)
self.fail("ValueError expected")
except ValueError:
pass
def test_check_scaling_ranges_uint8_vector_ok(self):
default_array = DefaultData.create_default_vector(4, np.float32)
default_array[0] = 11.0 # 0
default_array[1] = 16.1 # 255
default_array[2] = np.NaN
default_array[3] = 13.05
variable = Variable(["y"], default_array)
variable.encoding = dict([('dtype', np.uint8), ('_FillValue', 255),
('scale_factor', 0.02), ('add_offset', 11)])
DataUtility.check_scaling_ranges(variable)
def test_check_scaling_ranges_int8_vector_ok(self):
default_array = DefaultData.create_default_vector(4, np.float32)
default_array[0] = 11.872 # -128
default_array[1] = 12.127 # 127
default_array[2] = np.NaN
default_array[3] = 12.04
variable = Variable(["y"], default_array)
variable.encoding = dict([('dtype', np.int8), ('_FillValue', -127),
('scale_factor', 0.001), ('add_offset', 12)])
DataUtility.check_scaling_ranges(variable)
def test_check_scaling_ranges_int32_vector_ok(self):
default_array = DefaultData.create_default_vector(4, np.float32)
default_array[0] = 21487.83647 # 2147483647
default_array[1] = -21461.83648 # -2147483648
default_array[2] = np.NaN
default_array[3] = 14.04
variable = Variable(["y"], default_array)
variable.encoding = dict([('dtype', np.int32),
('_FillValue', -2147483647),
('scale_factor', 0.00001),
('add_offset', 13)])
DataUtility.check_scaling_ranges(variable)
def test_check_scaling_ranges_uint32_only_NaN(self):
default_array = DefaultData.create_default_vector(4, np.float32)
default_array[0] = np.NaN
default_array[1] = np.NaN
default_array[2] = np.NaN
default_array[3] = np.NaN
variable = Variable(["y"], default_array)
variable.encoding = dict([('dtype', np.uint32),
('_FillValue', 4294967295),
('scale_factor', 0.00002),
('add_offset', 14)])
DataUtility.check_scaling_ranges(variable)
def _add_angle_variables(dataset, height):
default_array = DefaultData.create_default_vector(height,
np.float32,
fill_value=np.NaN)
variable = Variable(["y"], default_array)
variable.attrs["standard_name"] = "platform_zenith_angle"
tu.add_units(variable, "degree")
tu.add_geolocation_attribute(variable)
tu.add_encoding(variable, np.uint16,
DefaultData.get_default_fill_value(np.uint16), 0.01,
-180.0)
dataset["satellite_zenith_angle"] = variable
dataset["solar_azimuth_angle"] = HIRS._create_geo_angle_variable(
"solar_azimuth_angle", height, chunking=CHUNKING_2D)
def test_check_scaling_ranges_int8_vector_underflow(self):
default_array = DefaultData.create_default_vector(4, np.float32)
default_array[0] = 11.702 # underflow
default_array[1] = 12.127 # 127
default_array[2] = np.NaN
default_array[3] = 12.04
variable = Variable(["y"], default_array)
variable.encoding = dict([('dtype', np.int8), ('_FillValue', -127),
('scale_factor', 0.001), ('add_offset', 12)])
try:
DataUtility.check_scaling_ranges(variable)
self.fail("ValueError expected")
except ValueError:
pass
def test_check_scaling_ranges_uint8_vector_overflow(self):
default_array = DefaultData.create_default_vector(4, np.float32)
default_array[0] = 11.0 # 0
default_array[1] = 16.2 # overflow
default_array[2] = np.NaN
default_array[3] = 13.05
variable = Variable(["y"], default_array)
variable.encoding = dict([('dtype', np.uint8), ('_FillValue', 255),
('scale_factor', 0.02), ('add_offset', 11)])
try:
DataUtility.check_scaling_ranges(variable)
self.fail("ValueError expected")
except ValueError:
pass
def test_check_scaling_ranges_uint32_vector_overflow(self):
default_array = DefaultData.create_default_vector(4, np.float32)
default_array[0] = 14 # 0
default_array[1] = 85913.4459 # 4294967295
default_array[2] = np.NaN
default_array[3] = 14.01
variable = Variable(["y"], default_array)
variable.encoding = dict([('dtype', np.uint32),
('_FillValue', 4294967295),
('scale_factor', 0.00002),
('add_offset', 14)])
try:
DataUtility.check_scaling_ranges(variable)
self.fail("ValueError expected")
except ValueError:
pass
def test_check_scaling_ranges_int32_vector_underflow(self):
default_array = DefaultData.create_default_vector(4, np.float32)
default_array[0] = 21487.83647 # 2147483647
default_array[1] = -21461.93648 # underflow
default_array[2] = np.NaN
default_array[3] = 14.04
variable = Variable(["y"], default_array)
variable.encoding = dict([('dtype', np.int32),
('_FillValue', -2147483647),
('scale_factor', 0.00001),
('add_offset', 13)])
try:
DataUtility.check_scaling_ranges(variable)
self.fail("ValueError expected")
except ValueError:
pass
def add_variables(dataset, width, height, num_samples=10):
tu.add_geolocation_variables(dataset, width, height, chunksizes=CHUNKING)
tu.add_quality_flags(dataset, width, height, chunksizes=CHUNKING)
default_array = DefaultData.create_default_vector(height, np.int32, fill_value=4294967295)
variable = Variable(["y"], default_array)
tu.add_fill_value(variable, 4294967295)
variable.attrs["standard_name"] = "time"
variable.attrs["long_name"] = "Acquisition time in seconds since 1970-01-01 00:00:00"
tu.add_units(variable, "s")
dataset["time"] = variable
default_array = DefaultData.create_default_array_3d(width, height, num_samples, np.float32, fill_value=np.NaN)
variable = Variable(["samples","y", "x"], default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs["standard_name"] = "sea_surface_temperature"
variable.attrs["units"] = "K"
variable.attrs["coordinates"] = "longitude latitude"
dataset["sst"] = variable
def setUp(self):
self.dataset = xr.Dataset()
self.dataset.attrs["template_key"] = "HIRS2"
default_array = DefaultData.create_default_array(5, 5, np.uint16, fill_value=0)
variable = Variable(["y", "x"], default_array)
self.dataset["data_quality_bitmask"] = variable
self.dataset["quality_pixel_bitmask"] = variable
default_array = DefaultData.create_default_vector(5, np.int32, fill_value=0)
variable = Variable(["y"], default_array)
self.dataset["quality_scanline_bitmask"] = variable
default_array = DefaultData.create_default_array(19, 5, np.uint8, fill_value=0)
variable = Variable(["y", "channel"], default_array)
self.dataset["quality_channel_bitmask"] = variable
tempDir = tempfile.gettempdir()
self.testDir = os.path.join(tempDir, 'fcdr')
os.mkdir(self.testDir)
def add_variables(dataset, width, height):
# @todo 1 tb/tb add geolocation 2018-06-25
tu.add_quality_flags(dataset, width, height, chunksizes=CHUNKING)
default_array = DefaultData.create_default_vector(height,
np.int32,
fill_value=-1)
variable = Variable(["y"], default_array)
tu.add_fill_value(variable, -1)
variable.attrs["standard_name"] = "time"
variable.attrs[
"long_name"] = "Acquisition time in seconds since 1970-01-01 00:00:00"
tu.add_units(variable, "s")
dataset["time"] = variable
default_array = DefaultData.create_default_array(width,
height,
np.float32,
fill_value=np.NaN)
variable = Variable(["y", "x"], default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs["standard_name"] = "surface_albedo"
variable.attrs["coordinates"] = "longitude latitude"
tu.add_chunking(variable, CHUNKING)
dataset["surface_albedo"] = variable
dataset["u_independent_surface_albedo"] = tu.create_CDR_uncertainty(
width, height,
"Uncertainty of surface_albedo due to independent effects")
dataset["u_structured_surface_albedo"] = tu.create_CDR_uncertainty(
width, height,
"Uncertainty of surface_albedo due to structured effects")
dataset["u_common_surface_albedo"] = tu.create_CDR_uncertainty(
width, height,
"Uncertainty of surface_albedo due to common effects")
def add_full_fcdr_variables(dataset, height):
# height is ignored - supplied just for interface compatibility tb 2017-02-05
# count_vis
default_array = DefaultData.create_default_array(FULL_SIZE, FULL_SIZE, np.uint8)
variable = Variable(["y", "x"], default_array)
tu.add_fill_value(variable, DefaultData.get_default_fill_value(np.uint8))
variable.attrs["long_name"] = "Image counts"
tu.add_units(variable, "count")
tu.add_chunking(variable, CHUNKSIZES)
dataset["count_vis"] = variable
dataset["u_latitude"] = MVIRI._create_angle_variable_int(1.5E-05, long_name="Uncertainty in Latitude", unsigned=True)
MVIRI._add_geo_correlation_attributes(dataset["u_latitude"])
dataset["u_longitude"] = MVIRI._create_angle_variable_int(1.5E-05, long_name="Uncertainty in Longitude", unsigned=True)
MVIRI._add_geo_correlation_attributes(dataset["u_longitude"])
# u_time
default_array = DefaultData.create_default_vector(IR_SIZE, np.float32, fill_value=np.NaN)
variable = Variable([IR_Y_DIMENSION], default_array)
variable.attrs["standard_name"] = "Uncertainty in Time"
tu.add_units(variable, "s")
tu.add_encoding(variable, np.uint16, DefaultData.get_default_fill_value(np.uint16), 0.009155273)
variable.attrs["pdf_shape"] = "rectangle"
dataset["u_time"] = variable
dataset["u_satellite_zenith_angle"] = MVIRI._create_angle_variable_int(7.62939E-05, long_name="Uncertainty in Satellite Zenith Angle", unsigned=True)
dataset["u_satellite_azimuth_angle"] = MVIRI._create_angle_variable_int(7.62939E-05, long_name="Uncertainty in Satellite Azimuth Angle", unsigned=True)
dataset["u_solar_zenith_angle"] = MVIRI._create_angle_variable_int(7.62939E-05, long_name="Uncertainty in Solar Zenith Angle", unsigned=True)
dataset["u_solar_azimuth_angle"] = MVIRI._create_angle_variable_int(7.62939E-05, long_name="Uncertainty in Solar Azimuth Angle", unsigned=True)
dataset["a0_vis"] = tu.create_scalar_float_variable("Calibration Coefficient at Launch", units="Wm^-2sr^-1/count")
dataset["a1_vis"] = tu.create_scalar_float_variable("Time variation of a0", units="Wm^-2sr^-1/count day^-1 10^5")
dataset["a2_vis"] = tu.create_scalar_float_variable("Time variation of a0, quadratic term", units="Wm^-2sr^-1/count year^-2")
dataset["mean_count_space_vis"] = tu.create_scalar_float_variable("Space count", units="count")
# u_a0_vis
variable = tu.create_scalar_float_variable("Uncertainty in a0", units="Wm^-2sr^-1/count")
MVIRI._add_calibration_coeff_correlation_attributes(variable)
dataset["u_a0_vis"] = variable
# u_a1_vis
variable = tu.create_scalar_float_variable("Uncertainty in a1", units="Wm^-2sr^-1/count day^-1 10^5")
MVIRI._add_calibration_coeff_correlation_attributes(variable)
dataset["u_a1_vis"] = variable
# u_a2_vis
variable = tu.create_scalar_float_variable("Uncertainty in a2", units="Wm^-2sr^-1/count year^-2")
MVIRI._add_calibration_coeff_correlation_attributes(variable)
dataset["u_a2_vis"] = variable
# u_zero_vis
variable = tu.create_scalar_float_variable("Uncertainty zero term", units="Wm^-2sr^-1/count")
MVIRI._add_calibration_coeff_correlation_attributes(variable, image_correlation_scale=[-np.inf, np.inf])
dataset["u_zero_vis"] = variable
# covariance_a_vis
variable = tu.create_float_variable(COV_SIZE, COV_SIZE, long_name="Covariance of calibration coefficients from fit to calibration runs", dim_names=["cov_size", "cov_size"], fill_value=np.NaN)
tu.add_fill_value(variable, np.NaN)
tu.add_units(variable, "Wm^-2sr^-1/count")
MVIRI._add_calibration_coeff_correlation_attributes(variable, image_correlation_scale=[-np.inf, np.inf])
dataset["covariance_a_vis"] = variable
dataset["u_electronics_counts_vis"] = tu.create_scalar_float_variable("Uncertainty due to Electronics noise", units="count")
dataset["u_digitization_counts_vis"] = tu.create_scalar_float_variable("Uncertainty due to digitization", units="count")
# allan_deviation_counts_space_vis
variable = tu.create_scalar_float_variable("Uncertainty of space count", units="count")
variable.attrs[corr.SCAN_CORR_FORM] = corr.RECT_ABS
variable.attrs[corr.SCAN_CORR_UNIT] = corr.LINE
variable.attrs[corr.SCAN_CORR_SCALE] = [-np.inf, np.inf]
variable.attrs["pdf_shape"] = "digitised_gaussian"
dataset["allan_deviation_counts_space_vis"] = variable
# u_mean_counts_space_vis
variable = tu.create_scalar_float_variable("Uncertainty of space count", units="count")
variable.attrs[corr.PIX_CORR_FORM] = corr.RECT_ABS
variable.attrs[corr.PIX_CORR_UNIT] = corr.PIXEL
variable.attrs[corr.PIX_CORR_SCALE] = [-np.inf, np.inf]
variable.attrs[corr.SCAN_CORR_FORM] = corr.RECT_ABS
variable.attrs[corr.SCAN_CORR_UNIT] = corr.LINE
variable.attrs[corr.SCAN_CORR_SCALE] = [-np.inf, np.inf]
variable.attrs["pdf_shape"] = "digitised_gaussian"
dataset["u_mean_counts_space_vis"] = variable
# sensitivity_solar_irradiance_vis
variable = tu.create_scalar_float_variable()
variable.attrs["virtual"] = "true"
variable.attrs["dimension"] = "y, x"
variable.attrs[
"expression"] = "distance_sun_earth * distance_sun_earth * PI * (count_vis - mean_count_space_vis) * (a2_vis * years_since_launch * years_since_launch + a1_vis * years_since_launch + a0_vis) / (cos(solar_zenith_angle * PI / 180.0) * solar_irradiance_vis * solar_irradiance_vis)"
dataset["sensitivity_solar_irradiance_vis"] = variable
# sensitivity_count_vis
variable = tu.create_scalar_float_variable()
variable.attrs["virtual"] = "true"
variable.attrs["dimension"] = "y, x"
variable.attrs[
"expression"] = "distance_sun_earth * distance_sun_earth * PI * (a2_vis * years_since_launch * years_since_launch + a1_vis * years_since_launch + a0_vis) / (cos(solar_zenith_angle * PI / 180.0) * solar_irradiance_vis)"
dataset["sensitivity_count_vis"] = variable
# sensitivity_count_space
variable = tu.create_scalar_float_variable()
variable.attrs["virtual"] = "true"
variable.attrs["dimension"] = "y, x"
variable.attrs[
"expression"] = "-1.0 * distance_sun_earth * distance_sun_earth * PI * (a2_vis * years_since_launch * years_since_launch + a1_vis * years_since_launch + a0_vis) / (cos(solar_zenith_angle * PI / 180.0) * solar_irradiance_vis)"
dataset["sensitivity_count_space"] = variable
# sensitivity_a0_vis
variable = tu.create_scalar_float_variable()
variable.attrs["virtual"] = "true"
variable.attrs["dimension"] = "y, x"
variable.attrs["expression"] = "distance_sun_earth * distance_sun_earth * PI * (count_vis - mean_count_space_vis) / (cos(solar_zenith_angle * PI / 180.0) * solar_irradiance_vis)"
dataset["sensitivity_a0_vis"] = variable
# sensitivity_a1_vis
variable = tu.create_scalar_float_variable()
variable.attrs["virtual"] = "true"
variable.attrs["dimension"] = "y, x"
variable.attrs[
"expression"] = "distance_sun_earth * distance_sun_earth * PI * (count_vis - mean_count_space_vis) * years_since_launch / (cos(solar_zenith_angle * PI / 180.0) * solar_irradiance_vis)"
dataset["sensitivity_a1_vis"] = variable
# sensitivity_a2_vis
variable = tu.create_scalar_float_variable()
variable.attrs["virtual"] = "true"
variable.attrs["dimension"] = "y, x"
variable.attrs[
"expression"] = "distance_sun_earth * distance_sun_earth * PI * (count_vis - mean_count_space_vis) * years_since_launch*years_since_launch / (cos(solar_zenith_angle * PI / 180.0) * solar_irradiance_vis)"
dataset["sensitivity_a2_vis"] = variable
effect_names = ["u_solar_irradiance_vis", "u_a0_vis", "u_a1_vis", "u_a2_vis", "u_zero_vis", "u_solar_zenith_angle", "u_mean_count_space_vis"]
dataset["Ne"] = Coordinate("Ne", effect_names)
num_effects = len(effect_names)
default_array = DefaultData.create_default_array(num_effects, num_effects, np.float32, fill_value=np.NaN)
variable = Variable(["Ne", "Ne"], default_array)
tu.add_encoding(variable, np.int16, -32768, 3.05176E-05)
variable.attrs["valid_min"] = -1
variable.attrs["valid_max"] = 1
variable.attrs["long_name"] = "Channel error correlation matrix for structured effects."
variable.attrs["description"] = "Matrix_describing correlations between errors of the uncertainty_effects due to spectral response function errors (determined using Monte Carlo approach)"
dataset["effect_correlation_matrix"] = variable
def test_create_default_vector_int(self):
default_array = DefaultData.create_default_vector(14, np.int32)
self.assertEqual((14, ), default_array.shape)
self.assertEqual(-2147483647, default_array.data[2])
def test_create_default_vector_int_fill_value(self):
default_array = DefaultData.create_default_vector(15,
np.int32,
fill_value=108)
self.assertEqual((15, ), default_array.shape)
self.assertEqual(108, default_array.data[3])
def add_original_variables(dataset, height, srf_size=None):
tu.add_geolocation_variables(dataset,
SWATH_WIDTH,
height,
chunksizes=CHUNKS_2D)
tu.add_quality_flags(dataset,
SWATH_WIDTH,
height,
chunksizes=CHUNKS_2D)
# Time
default_array = DefaultData.create_default_vector(height,
np.float64,
fill_value=np.NaN)
variable = Variable(["y"], default_array)
tu.add_fill_value(variable, np.NaN)
tu.add_units(variable, "s")
variable.attrs["standard_name"] = "time"
variable.attrs[
"long_name"] = "Acquisition time in seconds since 1970-01-01 00:00:00"
dataset["Time"] = variable
# relative_azimuth_angle
default_array = DefaultData.create_default_array(SWATH_WIDTH,
height,
np.float32,
fill_value=np.NaN)
variable = Variable(["y", "x"], default_array)
variable.attrs["standard_name"] = "relative_azimuth_angle"
tu.add_units(variable, "degree")
tu.add_encoding(variable,
np.int16,
DefaultData.get_default_fill_value(np.int16),
0.01,
chunksizes=CHUNKS_2D)
variable.attrs["valid_max"] = 18000
variable.attrs["valid_min"] = -18000
tu.add_geolocation_attribute(variable)
dataset["relative_azimuth_angle"] = variable
# satellite_zenith_angle
default_array = DefaultData.create_default_array(SWATH_WIDTH,
height,
np.float32,
fill_value=np.NaN)
variable = Variable(["y", "x"], default_array)
variable.attrs["standard_name"] = "sensor_zenith_angle"
tu.add_units(variable, "degree")
tu.add_encoding(variable,
np.int16,
DefaultData.get_default_fill_value(np.int16),
0.01,
chunksizes=CHUNKS_2D)
variable.attrs["valid_max"] = 9000
variable.attrs["valid_min"] = 0
tu.add_geolocation_attribute(variable)
dataset["satellite_zenith_angle"] = variable
# solar_zenith_angle
default_array = DefaultData.create_default_array(SWATH_WIDTH,
height,
np.float32,
fill_value=np.NaN)
variable = Variable(["y", "x"], default_array)
variable.attrs["standard_name"] = "solar_zenith_angle"
tu.add_units(variable, "degree")
tu.add_encoding(variable,
np.int16,
DefaultData.get_default_fill_value(np.int16),
0.01,
chunksizes=CHUNKS_2D)
variable.attrs["valid_max"] = 18000
variable.attrs["valid_min"] = 0
tu.add_geolocation_attribute(variable)
dataset["solar_zenith_angle"] = variable
dataset["Ch1"] = AVHRR._create_channel_refl_variable(
height, "Channel 1 Reflectance")
dataset["Ch2"] = AVHRR._create_channel_refl_variable(
height, "Channel 2 Reflectance")
dataset["Ch3a"] = AVHRR._create_channel_refl_variable(
height, "Channel 3a Reflectance")
dataset["Ch3b"] = AVHRR._create_channel_bt_variable(
height, "Channel 3b Brightness Temperature")
dataset["Ch4"] = AVHRR._create_channel_bt_variable(
height, "Channel 4 Brightness Temperature")
dataset["Ch5"] = AVHRR._create_channel_bt_variable(
height, "Channel 5 Brightness Temperature")
# data_quality_bitmask
default_array = DefaultData.create_default_array(SWATH_WIDTH,
height,
np.uint8,
fill_value=0)
variable = Variable(["y", "x"], default_array)
variable.attrs["standard_name"] = 'status_flag'
variable.attrs["long_name"] = 'bitmask for quality per pixel'
variable.attrs["flag_masks"] = '1,2'
variable.attrs[
'flag_meanings'] = 'bad_geolocation_timing_err bad_calibration_radiometer_err'
tu.add_chunking(variable, CHUNKS_2D)
tu.add_geolocation_attribute(variable)
dataset['data_quality_bitmask'] = variable
default_array = DefaultData.create_default_vector(height,
np.uint8,
fill_value=0)
variable = Variable(["y"], default_array)
variable.attrs["long_name"] = 'bitmask for quality per scanline'
variable.attrs["standard_name"] = 'status_flag'
variable.attrs["flag_masks"] = '1,2,4,8,16,32,64'
variable.attrs[
'flag_meanings'] = 'do_not_use bad_time bad_navigation bad_calibration channel3a_present solar_contamination_failure solar_contamination'
dataset['quality_scanline_bitmask'] = variable
default_array = DefaultData.create_default_array(N_CHANS,
height,
np.uint8,
fill_value=0)
variable = Variable(["y", "channel"], default_array)
variable.attrs["long_name"] = 'bitmask for quality per channel'
variable.attrs["standard_name"] = 'status_flag'
variable.attrs["flag_masks"] = '1,2'
variable.attrs[
'flag_meanings'] = 'bad_channel some_pixels_not_detected_2sigma'
dataset['quality_channel_bitmask'] = variable
if srf_size is None:
srf_size = MAX_SRF_SIZE
default_array = DefaultData.create_default_array(srf_size,
N_CHANS,
np.float32,
fill_value=np.NaN)
variable = Variable(["channel", "n_frequencies"], default_array)
variable.attrs["long_name"] = 'Spectral Response Function weights'
variable.attrs[
"description"] = 'Per channel: weights for the relative spectral response function'
tu.add_encoding(variable, np.int16, -32768, 0.000033)
dataset['SRF_weights'] = variable
default_array = DefaultData.create_default_array(srf_size,
N_CHANS,
np.float32,
fill_value=np.NaN)
variable = Variable(["channel", "n_frequencies"], default_array)
variable.attrs["long_name"] = 'Spectral Response Function wavelengths'
variable.attrs[
"description"] = 'Per channel: wavelengths for the relative spectral response function'
tu.add_encoding(variable, np.int32, -2147483648, 0.0001)
tu.add_units(variable, "um")
dataset['SRF_wavelengths'] = variable
default_vector = DefaultData.create_default_vector(height,
np.uint8,
fill_value=255)
variable = Variable(["y"], default_vector)
tu.add_fill_value(variable, 255)
variable.attrs["long_name"] = 'Indicator of original file'
variable.attrs[
"description"] = "Indicator for mapping each line to its corresponding original level 1b file. See global attribute 'source' for the filenames. 0 corresponds to 1st listed file, 1 to 2nd file."
dataset["scanline_map_to_origl1bfile"] = variable
default_vector = DefaultData.create_default_vector(
height,
np.int16,
fill_value=DefaultData.get_default_fill_value(np.int16))
variable = Variable(["y"], default_vector)
tu.add_fill_value(variable,
DefaultData.get_default_fill_value(np.int16))
variable.attrs["long_name"] = 'Original_Scan_line_number'
variable.attrs[
"description"] = 'Original scan line numbers from corresponding l1b records'
dataset["scanline_origl1b"] = variable
tu.add_coordinates(dataset,
["Ch1", "Ch2", "Ch3a", "Ch3b", "Ch4", "Ch5"])
def add_full_fcdr_variables(dataset, height):
# c_earth
default_array = DefaultData.create_default_array_3d(SWATH_WIDTH, height, NUM_RAD_CHANNELS, np.uint16, dims_names=["rad_channel", "y", "x"])
variable = Variable(["rad_channel", "y", "x"], default_array)
tu.add_fill_value(variable, DefaultData.get_default_fill_value(np.uint16))
variable.attrs["long_name"] = "counts_earth"
tu.add_units(variable, "count")
variable.attrs["ancilliary_variables"] = "scnlinf quality_scanline_bitmask quality_channel_bitmask mnfrqualflags"
dataset["c_earth"] = variable
# L_earth
default_array = DefaultData.create_default_array_3d(SWATH_WIDTH, height, NUM_RAD_CHANNELS, np.float32, np.NaN, ["rad_channel", "y", "x"])
variable = Variable(["rad_channel", "y", "x"], default_array)
tu.add_encoding(variable, np.uint32, DefaultData.get_default_fill_value(np.uint32), 0.0001)
variable.attrs["standard_name"] = "toa_outgoing_inband_radiance"
tu.add_units(variable, "W/Hz/m ** 2/sr")
variable.attrs["long_name"] = "Channel radiance, NOAA/EUMETSAT calibrated"
variable.attrs["ancilliary_variables"] = "scnlinf quality_scanline_bitmask quality_channel_bitmask mnfrqualflags"
dataset["L_earth"] = variable
# u_lat
variable = HIRS._create_angle_variable(height, "uncertainty_latitude")
dataset["u_lat"] = variable
# u_lon
variable = HIRS._create_angle_variable(height, "uncertainty_longitude")
dataset["u_lon"] = variable
# u_time
variable = tu.create_float_variable(SWATH_WIDTH, height, "uncertainty_time")
tu.add_encoding(variable, np.uint16, 65535, 0.01)
tu.add_units(variable, "s")
dataset["u_time"] = variable
# u_c_earth
default_array = DefaultData.create_default_array(NUM_CALIBRATION_CYCLE, NUM_CHANNELS, np.uint16, dims_names=["channel", "calibration_cycle"])
variable = Variable(["channel", "calibration_cycle"], default_array)
tu.add_fill_value(variable, DefaultData.get_default_fill_value(np.uint16))
tu.add_units(variable, "count")
variable.attrs["long_name"] = "uncertainty counts for Earth views"
variable.attrs["ancilliary_variables"] = "u_c_earth_chan_corr"
variable.attrs["channels_affected"] = "all"
variable.attrs["parameter"] = "C_E"
variable.attrs["pdf_shape"] = "gaussian"
dataset["u_c_earth"] = variable
# u_L_earth_independent
variable = HIRS._create_3d_rad_uncertainty_variable(height, "uncertainty_radiance_Earth_random")
tu.add_encoding(variable, np.uint32, DefaultData.get_default_fill_value(np.uint32), 0.01)
tu.add_units(variable, "mW m^-2 sr^-1 cm")
dataset["u_L_earth_independent"] = variable
# u_L_earth_structured
variable = HIRS._create_3d_rad_uncertainty_variable(height, "uncertainty_radiance_Earth_structured")
tu.add_encoding(variable, np.uint32, DefaultData.get_default_fill_value(np.uint32), 0.01)
tu.add_units(variable, "mW m^-2 sr^-1 cm")
dataset["u_L_earth_structured"] = variable
# u_L_earth_systematic
variable = HIRS._create_3d_rad_uncertainty_variable(height, "uncertainty_radiance_Earth_systematic")
tu.add_encoding(variable, np.uint32, DefaultData.get_default_fill_value(np.uint32), 0.01)
tu.add_units(variable, "mW m^-2 sr^-1 cm")
dataset["u_L_earth_systematic"] = variable
# u_L_earth_total
variable = HIRS._create_3d_rad_uncertainty_variable(height, "uncertainty_radiance_Earth_total")
tu.add_encoding(variable, np.uint32, DefaultData.get_default_fill_value(np.uint32), 0.01)
tu.add_units(variable, "mW m^-2 sr^-1 cm")
dataset["u_L_earth_total"] = variable
# S_u_L_earth
variable = tu.create_float_variable(NUM_RAD_CHANNELS, NUM_RAD_CHANNELS, "covariance_radiance_Earth", dim_names=["rad_channel", "rad_channel"])
tu.add_encoding(variable, np.uint32, DefaultData.get_default_fill_value(np.uint32), 0.01)
dataset["S_u_L_earth"] = variable
# u_bt_random
variable = HIRS._create_3d_bt_uncertainty_variable(height, "uncertainty_bt_random")
tu.add_encoding(variable, np.uint16, DefaultData.get_default_fill_value(np.uint16), 0.01)
tu.add_units(variable, "K")
dataset["u_bt_random"] = variable
# u_bt_structured
variable = HIRS._create_3d_bt_uncertainty_variable(height, "uncertainty_bt_structured")
tu.add_encoding(variable, np.uint16, DefaultData.get_default_fill_value(np.uint16), 0.01)
tu.add_units(variable, "K")
dataset["u_bt_structured"] = variable
# u_bt_systematic
variable = HIRS._create_3d_bt_uncertainty_variable(height, "uncertainty_bt_systematic")
tu.add_encoding(variable, np.uint16, DefaultData.get_default_fill_value(np.uint16), 0.01)
tu.add_units(variable, "K")
dataset["u_bt_systematic"] = variable
# u_bt_total
variable = HIRS._create_3d_bt_uncertainty_variable(height, "uncertainty_bt_total")
tu.add_encoding(variable, np.uint16, DefaultData.get_default_fill_value(np.uint16), 0.01)
tu.add_units(variable, "K")
dataset["u_bt_total"] = variable
# S_bt
variable = tu.create_float_variable(NUM_RAD_CHANNELS, NUM_RAD_CHANNELS, "covariance_brightness_temperature", dim_names=["rad_channel", "rad_channel"])
tu.add_encoding(variable, np.uint16, DefaultData.get_default_fill_value(np.uint16), 0.01)
dataset["S_bt"] = variable
# l1b_calcof
default_array = DefaultData.create_default_array(height, NUM_COEFFS, np.float32, dims_names=["coeffs", "y"])
variable = Variable(["coeffs", "y"], default_array)
tu.add_encoding(variable, np.int32, DefaultData.get_default_fill_value(np.int32), 0.01)
variable.attrs["standard_name"] = "calibration_coefficients"
dataset["l1b_calcof"] = variable
# navigation_status
variable = HIRS._create_int32_vector(height, standard_name="status_flag", long_name="Navigation status bit field", orig_name="hrs_navstat")
dataset["navigation_status"] = variable
# quality_flags
variable = HIRS._create_int32_vector(height, standard_name="status_flag", long_name="Quality indicator bit field", orig_name="hrs_qualind")
dataset["quality_flags"] = variable
variable = HIRS._create_scaled_uint16_vector(height, long_name="Platform altitude", original_name="hrs_scalti")
tu.add_units(variable, "km")
dataset["platform_altitude"] = variable
variable = HIRS._create_scaled_int16_vector(height, long_name="Platform pitch angle", original_name="hrs_pitchang")
tu.add_units(variable, "degree")
dataset["platform_pitch_angle"] = variable
variable = HIRS._create_scaled_int16_vector(height, long_name="Platform roll angle", original_name="hrs_rollang")
tu.add_units(variable, "degree")
dataset["platform_roll_angle"] = variable
variable = HIRS._create_scaled_int16_vector(height, long_name="Platform yaw angle", original_name="hrs_yawang")
tu.add_units(variable, "degree")
dataset["platform_yaw_angle"] = variable
# scan_angles
default_array = DefaultData.create_default_array(NUM_SCAN_ANGLES, height, np.float32, dims_names=["y", "num_scan_angles"], fill_value=np.NaN)
variable = Variable(["y", "num_scan_angles"], default_array)
tu.add_encoding(variable, np.uint16, DefaultData.get_default_fill_value(np.uint16), scale_factor=0.01)
tu.add_units(variable, "degree")
variable.attrs["long_name"] = "Scan angles"
variable.attrs["orig_name"] = "hrs_ang"
dataset["scan_angles"] = variable
dataset["l1b_scanline_number"] = HIRS._create_int16_vector(height, long_name="scanline number", orig_name="hrs_scnlin")
dataset["scanline_position"] = HIRS._create_int8_vector(height, long_name="Scanline position number in 32 second cycle", orig_name="hrs_scnpos")
# second_original_calibration_coefficients
default_array = DefaultData.create_default_array(WIDTH_TODO, height, np.float32, fill_value=np.NaN)
variable = Variable(["y", "width_todo"], default_array)
tu.add_encoding(variable, np.int32, DefaultData.get_default_fill_value(np.int32), scale_factor=0.01)
variable.attrs["long_name"] = "Second original calibration coefficients (unsorted)"
variable.attrs["orig_name"] = "hrs_scalcof"
dataset["l1b_second_original_calibration_coefficients"] = variable
dataset["Tc_baseplate"] = HIRS._create_counts_vector(height, "temperature_baseplate_counts")
dataset["Tc_ch"] = HIRS._create_counts_vector(height, "temperature_coolerhousing_counts")
dataset["Tc_elec"] = HIRS._create_counts_vector(height, "temperature_electronics_counts")
dataset["Tc_fsr"] = HIRS._create_counts_vector(height, "temperature_first_stage_radiator_counts")
dataset["Tc_fwh"] = HIRS._create_counts_vector(height, "temperature_filter_wheel_housing_counts")
dataset["Tc_fwm"] = HIRS._create_counts_vector(height, "temperature_filter_wheel_monitor_counts")
dataset["Tc_icct"] = HIRS._create_counts_vector(height, "temperature_internal_cold_calibration_target_counts")
dataset["Tc_iwct"] = HIRS._create_counts_vector(height, "temperature_internal_warm_calibration_target_counts")
dataset["Tc_patch_exp"] = HIRS._create_counts_vector(height, "temperature_patch_expanded_scale_counts")
dataset["Tc_patch_full"] = HIRS._create_counts_vector(height, "temperature_patch_full_range_counts")
dataset["Tc_tlscp_prim"] = HIRS._create_counts_vector(height, "temperature_telescope_primary_counts")
dataset["Tc_tlscp_sec"] = HIRS._create_counts_vector(height, "temperature_telescope_secondary_counts")
dataset["Tc_tlscp_tert"] = HIRS._create_counts_vector(height, "temperature_telescope_tertiary_counts")
dataset["Tc_scanmirror"] = HIRS._create_counts_vector(height, "temperature_scanmirror_counts")
dataset["Tc_scanmotor"] = HIRS._create_counts_vector(height, "temperature_scanmotor_counts")
dataset["u_Tc_baseplate"] = HIRS._create_counts_uncertainty_vector(height, "uncertainty_temperature_baseplate_counts")
dataset["u_Tc_ch"] = HIRS._create_counts_uncertainty_vector(height, "uncertainty_temperature_coolerhousing_counts")
dataset["u_Tc_elec"] = HIRS._create_counts_uncertainty_vector(height, "uncertainty_temperature_electronics_counts")
dataset["u_Tc_fsr"] = HIRS._create_counts_uncertainty_vector(height, "uncertainty_temperature_first_stage_radiator_counts")
dataset["u_Tc_fwh"] = HIRS._create_counts_uncertainty_vector(height, "uncertainty_temperature_filter_wheel_housing_counts")
dataset["u_Tc_fwm"] = HIRS._create_counts_uncertainty_vector(height, "uncertainty_temperature_filter_wheel_monitor_counts")
dataset["u_Tc_icct"] = HIRS._create_counts_uncertainty_vector_uint32(height, "uncertainty_temperature_internal_cold_calibration_target_counts")
dataset["u_Tc_iwct"] = HIRS._create_counts_uncertainty_vector_uint32(height, "uncertainty_temperature_internal_warm_calibration_target_counts")
dataset["u_Tc_patch_exp"] = HIRS._create_counts_uncertainty_vector_uint32(height, "uncertainty_temperature_patch_expanded_scale_counts")
dataset["u_Tc_patch_full"] = HIRS._create_counts_uncertainty_vector_uint32(height, "uncertainty_temperature_patch_full_range_counts")
dataset["u_Tc_tlscp_prim"] = HIRS._create_counts_uncertainty_vector_uint32(height, "uncertainty_temperature_telescope_primary_counts")
dataset["u_Tc_tlscp_sec"] = HIRS._create_counts_uncertainty_vector_uint32(height, "uncertainty_temperature_telescope_secondary_counts")
dataset["u_Tc_tlscp_tert"] = HIRS._create_counts_uncertainty_vector_uint32(height, "uncertainty_temperature_telescope_tertiary_counts")
dataset["u_Tc_scanmirror"] = HIRS._create_counts_uncertainty_vector_uint32(height, "uncertainty_temperature_scanmirror_counts")
dataset["u_Tc_scanmotor"] = HIRS._create_counts_uncertainty_vector_uint32(height, "uncertainty_temperature_scanmotor_counts")
dataset["u_sol_za"] = HIRS._create_geo_angle_uncertainty_variable("uncertainty_solar_zenith_angle", height, FILL_VALUE)
dataset["u_sol_aa"] = HIRS._create_geo_angle_uncertainty_variable("uncertainty_solar_azimuth_angle", height, FILL_VALUE)
dataset["u_sat_za"] = HIRS._create_geo_angle_uncertainty_variable("uncertainty_satellite_zenith_angle", height, FILL_VALUE)
dataset["u_sat_aa"] = HIRS._create_geo_angle_uncertainty_variable("uncertainty_local_azimuth_angle", height, FILL_VALUE)
# u_c_earth_chan_corr
dataset["u_c_earth_chan_corr"] = HIRS._create_channel_correlation_variable("u_c_earth channel correlations", np.int16)
# u_c_space
default_array = DefaultData.create_default_array(NUM_CALIBRATION_CYCLE, NUM_CHANNELS, np.uint16, dims_names=["channel", "calibration_cycle"])
variable = Variable(["channel", "calibration_cycle"], default_array)
tu.add_fill_value(variable, DefaultData.get_default_fill_value(np.uint16))
tu.add_units(variable, "count")
tu.add_scale_factor(variable, 0.005)
variable.attrs["long_name"] = "uncertainty counts for space views"
variable.attrs["ancilliary_variables"] = "u_c_space_chan_corr"
variable.attrs["channels_affected"] = "all"
variable.attrs["parameter"] = "C_s"
variable.attrs["pdf_shape"] = "gaussian"
dataset["u_c_space"] = variable
# u_c_space_chan_corr
dataset["u_c_space_chan_corr"] = HIRS._create_channel_correlation_variable("u_c_space channel correlations", np.uint16)
# u_Earthshine
dataset["u_Earthshine"] = HIRS._create_channel_uncertainty_uint16(height)
# u_O_Re
dataset["u_O_Re"] = HIRS._create_channel_uncertainty_uint16(height)
# u_O_TIWCT
default_array = DefaultData.create_default_vector(height, np.float32, np.NaN)
variable = Variable(["y"], default_array)
tu.add_encoding(variable, np.uint16, DefaultData.get_default_fill_value(np.uint16), 0.01)
dataset["u_O_TIWCT"] = variable
# u_O_TPRT
default_array = DefaultData.create_default_vector(height, np.uint16, DefaultData.get_default_fill_value(np.uint16))
variable = Variable(["y"], default_array)
tu.add_fill_value(variable, 65535)
tu.add_scale_factor(variable, 0.01)
tu.add_units(variable, "K")
variable.attrs["channels_affected"] = "all"
variable.attrs[corr.PIX_CORR_FORM] = corr.RECT_ABS
variable.attrs[corr.PIX_CORR_UNIT] = corr.PIXEL
variable.attrs[corr.PIX_CORR_SCALE] = [-np.inf, np.inf]
variable.attrs[corr.SCAN_CORR_FORM] = corr.RECT_ABS
variable.attrs[corr.SCAN_CORR_UNIT] = corr.LINE
variable.attrs[corr.SCAN_CORR_SCALE] = [-np.inf, np.inf]
variable.attrs[corr.IMG_CORR_FORM] = corr.RECT_ABS
variable.attrs[corr.IMG_CORR_UNIT] = corr.IMG
variable.attrs[corr.IMG_CORR_SCALE] = [-np.inf, np.inf]
variable.attrs["parameter"] = "O_TPRT"
variable.attrs["pdf_shape"] = "gaussian"
variable.attrs["short_name"] = "O_TPRT"
variable.attrs["ancilliary_variables"] = "u_O_TPRT_chan_corr"
dataset["u_O_TPRT"] = variable
dataset["u_Rself"] = HIRS._create_channel_uncertainty_uint16(height)
dataset["u_SRF_calib"] = HIRS._create_channel_uncertainty_uint16(height)
default_array = DefaultData.create_default_array(PRT_NUMBER_IWT, PRT_READING, dtype=np.float32, dims_names=["prt_number_iwt", "prt_reading"], fill_value=np.NaN)
variable = Variable(["prt_number_iwt", "prt_reading"], default_array)
tu.add_encoding(variable, np.uint16, DefaultData.get_default_fill_value(np.uint16), 0.01)
dataset["u_d_PRT"] = variable
dataset["u_electronics"] = HIRS._create_channel_uncertainty_uint16(height)
dataset["u_periodic_noise"] = HIRS._create_channel_uncertainty_uint16(height)
dataset["u_nonlinearity"] = HIRS._create_scaled_uint16_vector(NUM_CHANNELS, dimension_name=["channel"], scale_factor=0.01)
dataset["emissivity"] = tu.create_scalar_float_variable("emissivity", units="1")
dataset["temp_corr_slope"] = tu.create_scalar_float_variable("Slope for effective temperature correction", units="1")
dataset["temp_corr_offset"] = tu.create_scalar_float_variable("Offset for effective temperature correction", units="1")
# mnfrqualflags
default_array = DefaultData.create_default_array(NUM_MINOR_FRAME, height, np.int32, dims_names=["y", "minor_frame"], fill_value=0)
variable = Variable(["y", "minor_frame"], default_array)
variable.attrs["standard_name"] = "status_flag"
variable.attrs["long_name"] = "minor_frame_quality_flags_bitfield"
dataset["mnfrqualflags"] = variable
# scnlintime
variable = HIRS._create_int32_vector(height, standard_name="time", long_name="Scan line time of day", orig_name="hrs_scnlintime")
tu.add_units(variable, "ms")
dataset["scnlintime"] = variable
# scnlinf
default_array = DefaultData.create_default_vector(height, np.int16, fill_value=0)
variable = Variable(["y"], default_array)
variable.attrs["standard_name"] = "status_flag"
variable.attrs["long_name"] = "scanline_bitfield"
variable.attrs["flag_masks"] = "16384, 32768"
variable.attrs["flag_meanings"] = "clock_drift_correction southbound_data"
dataset["scnlinf"] = variable
# scantype
default_array = DefaultData.create_default_vector(height, np.int8, fill_value=0)
variable = Variable(["y"], default_array)
variable.attrs["standard_name"] = "status_flag"
variable.attrs["long_name"] = "scantype_bitfield"
variable.attrs["flag_values"] = "0, 1, 2, 3"
variable.attrs["flag_meanings"] = "earth_view space_view cold_bb_view main_bb_view"
dataset["scantype"] = variable
def add_original_variables(dataset, height, srf_size=None):
tu.add_geolocation_variables(dataset, SWATH_WIDTH, height)
tu.add_quality_flags(dataset, SWATH_WIDTH, height)
# Temperature_misc_housekeeping
default_array = DefaultData.create_default_array(
height,
NUM_THERMISTORS,
np.float32,
dims_names=["housekeeping", "y"],
fill_value=np.NaN)
variable = Variable(["housekeeping", "y"], default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs["long_name"] = "TODO"
variable.attrs["units"] = "TODO"
dataset["Temperature_misc_housekeeping"] = variable
# ancil_data
default_array = DefaultData.create_default_array(
height,
ANCIL_VAL,
np.float64,
dims_names=["ancil_val", "y"],
fill_value=np.NaN)
variable = Variable(["ancil_val", "y"], default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs["long_name"] = "Additional per scan information: year, day_of_year, secs_of_day, sat_lat, " \
"sat_long, sat_alt, sat_heading, year, day_of_year, secs_of_day"
dataset["ancil_data"] = variable
# channel_quality_flag
default_array = DefaultData.create_default_array_3d(
SWATH_WIDTH, height, NUM_CHANNELS, np.float32, np.NaN)
variable = Variable(["channel", "y", "x"], default_array)
tu.add_fill_value(variable, np.NaN)
dataset["channel_quality_flag"] = variable
# cold_counts
default_array = DefaultData.create_default_array_3d(
SWATH_WIDTH, height, CALIB_NUMBER, np.float32, np.NaN)
variable = Variable(["calib_number", "y", "x"], default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs["long_name"] = "TODO"
dataset["cold_counts"] = variable
# counts_to_tb_gain
default_array = DefaultData.create_default_array(
height,
NUM_CHANNELS,
np.float32,
dims_names=["channel", "y"],
fill_value=np.NaN)
variable = Variable([
"channel",
"y",
], default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs["long_name"] = "TODO"
dataset["counts_to_tb_gain"] = variable
# counts_to_tb_offset
default_array = DefaultData.create_default_array(
height,
NUM_CHANNELS,
np.float32,
dims_names=["channel", "y"],
fill_value=np.NaN)
variable = Variable([
"channel",
"y",
], default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs["long_name"] = "TODO"
dataset["counts_to_tb_offset"] = variable
# gain_control
default_array = DefaultData.create_default_array(
height,
NUM_CHANNELS,
np.float32,
dims_names=["channel", "y"],
fill_value=np.NaN)
variable = Variable([
"channel",
"y",
], default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs["long_name"] = "TODO"
dataset["gain_control"] = variable
# tb
default_array = DefaultData.create_default_array_3d(
SWATH_WIDTH, height, NUM_CHANNELS, np.float32, np.NaN)
variable = Variable(["channel", "y", "x"], default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs["long_name"] = "TODO"
variable.attrs["standard_name"] = "toa_brightness_temperature"
tu.add_units(variable, "K")
dataset["tb"] = variable
# thermal_reference
default_array = DefaultData.create_default_vector(
height, np.float32, np.NaN)
variable = Variable(["y"], default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs["long_name"] = "TODO"
tu.add_units(variable, "TODO")
dataset["thermal_reference"] = variable
# warm_counts
default_array = DefaultData.create_default_array_3d(
SWATH_WIDTH, height, CALIB_NUMBER, np.float32, np.NaN)
variable = Variable(["calib_number", "y", "x"], default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs["long_name"] = "TODO"
dataset["warm_counts"] = variable
def add_full_fcdr_variables(dataset, height):
# u_Temperature_misc_housekeeping
default_array = DefaultData.create_default_array(
height,
NUM_THERMISTORS,
np.float32,
dims_names=["housekeeping", "y"],
fill_value=np.NaN)
variable = Variable(["housekeeping", "y"], default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs["long_name"] = "TODO"
variable.attrs["units"] = "TODO"
dataset["u_Temperature_misc_housekeeping"] = variable
# u_cold_counts
default_array = DefaultData.create_default_array_3d(
SWATH_WIDTH, height, CALIB_NUMBER, np.float32, np.NaN)
variable = Variable(["calib_number", "y", "x"], default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs["long_name"] = "TODO"
dataset["u_cold_counts"] = variable
# u_counts_to_tb_gain
default_array = DefaultData.create_default_array(
height,
NUM_CHANNELS,
np.float32,
dims_names=["channel", "y"],
fill_value=np.NaN)
variable = Variable([
"channel",
"y",
], default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs["long_name"] = "TODO"
dataset["u_counts_to_tb_gain"] = variable
# u_counts_to_tb_offset
default_array = DefaultData.create_default_array(
height,
NUM_CHANNELS,
np.float32,
dims_names=["channel", "y"],
fill_value=np.NaN)
variable = Variable([
"channel",
"y",
], default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs["long_name"] = "TODO"
dataset["u_counts_to_tb_offset"] = variable
# u_gain_control
default_array = DefaultData.create_default_array(
height,
NUM_CHANNELS,
np.float32,
dims_names=["channel", "y"],
fill_value=np.NaN)
variable = Variable([
"channel",
"y",
], default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs["long_name"] = "TODO"
dataset["u_gain_control"] = variable
# u_tb
default_array = DefaultData.create_default_array_3d(
SWATH_WIDTH, height, NUM_CHANNELS, np.float32, np.NaN)
variable = Variable(["channel", "y", "x"], default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs["long_name"] = "TODO"
tu.add_units(variable, "K")
dataset["u_tb"] = variable
# u_thermal_reference
default_array = DefaultData.create_default_vector(
height, np.float32, np.NaN)
variable = Variable(["y"], default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs["long_name"] = "TODO"
tu.add_units(variable, "TODO")
dataset["u_thermal_reference"] = variable
# u_warm_counts
default_array = DefaultData.create_default_array_3d(
SWATH_WIDTH, height, CALIB_NUMBER, np.float32, np.NaN)
variable = Variable(["calib_number", "y", "x"], default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs["long_name"] = "TODO"
dataset["u_warm_counts"] = variable
def add_full_fcdr_variables(dataset, height):
# u_latitude
variable = AVHRR._create_angle_uncertainty_variable("latitude", height)
dataset["u_latitude"] = variable
# u_longitude
variable = AVHRR._create_angle_uncertainty_variable(
"longitude", height)
dataset["u_longitude"] = variable
# u_time
default_array = DefaultData.create_default_vector(height,
np.float64,
fill_value=np.NaN)
variable = Variable(["y"], default_array)
tu.add_fill_value(variable, np.NaN)
tu.add_units(variable, "s")
variable.attrs["long_name"] = "uncertainty of acquisition time"
dataset["u_time"] = variable
# u_satellite_azimuth_angle
variable = AVHRR._create_angle_uncertainty_variable(
"satellite azimuth angle", height)
dataset["u_satellite_azimuth_angle"] = variable
# u_satellite_zenith_angle
variable = AVHRR._create_angle_uncertainty_variable(
"satellite zenith angle", height)
dataset["u_satellite_zenith_angle"] = variable
# u_solar_azimuth_angle
variable = AVHRR._create_angle_uncertainty_variable(
"solar azimuth angle", height)
dataset["u_solar_azimuth_angle"] = variable
# u_solar_zenith_angle
variable = AVHRR._create_angle_uncertainty_variable(
"solar zenith angle", height)
dataset["u_solar_zenith_angle"] = variable
# PRT_C
default_array = DefaultData.create_default_array(
PRT_WIDTH, height, np.int16)
variable = Variable(["y", "n_prt"], default_array)
tu.add_fill_value(variable,
DefaultData.get_default_fill_value(np.int16))
variable.attrs["long_name"] = "Prt counts"
tu.add_units(variable, "count")
dataset["PRT_C"] = variable
# u_prt
default_array = DefaultData.create_default_array(PRT_WIDTH,
height,
np.float32,
fill_value=np.NaN)
variable = Variable(["y", "n_prt"], default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs["long_name"] = "Uncertainty on the PRT counts"
tu.add_units(variable, "count")
variable.attrs[corr.PIX_CORR_FORM] = corr.RECT_ABS
variable.attrs[corr.PIX_CORR_UNIT] = corr.PIXEL
variable.attrs[corr.PIX_CORR_SCALE] = [-np.inf, np.inf]
variable.attrs[corr.SCAN_CORR_FORM] = corr.RECT_ABS
variable.attrs[corr.SCAN_CORR_UNIT] = corr.LINE
variable.attrs[corr.SCAN_CORR_SCALE] = [-np.inf, np.inf]
variable.attrs["pdf_shape"] = "rectangle"
variable.attrs["pdf_parameter"] = 0.1
dataset["u_prt"] = variable
# R_ICT
default_array = DefaultData.create_default_array(PRT_WIDTH,
height,
np.float32,
fill_value=np.NaN)
variable = Variable(["y", "n_prt"], default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs["long_name"] = "Radiance of the PRT"
tu.add_units(variable, "mW m^-2 sr^-1 cm")
dataset["R_ICT"] = variable
# T_instr
default_array = DefaultData.create_default_vector(height,
np.float32,
fill_value=np.NaN)
variable = Variable(["y"], default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs["long_name"] = "Instrument temperature"
tu.add_units(variable, "K")
dataset["T_instr"] = variable
# Chx_Csp
standard_names = [
"Ch1 Space counts", "Ch2 Space counts", "Ch3a Space counts",
"Ch3b Space counts", "Ch4 Space counts", "Ch5 Space counts"
]
names = [
"Ch1_Csp", "Ch2_Csp", "Ch3a_Csp", "Ch3b_Csp", "Ch4_Csp", "Ch5_Csp"
]
AVHRR._add_counts_variables(dataset, height, names, standard_names)
# Chx_Cict
standard_names = [
"Ch3b ICT counts", "Ch4 ICT counts", "Ch5 ICT counts"
]
names = ["Ch3b_Cict", "Ch4_Cict", "Ch5_Cict"]
AVHRR._add_counts_variables(dataset, height, names, standard_names)
# Chx_Ce
standard_names = [
"Ch1 Earth counts", "Ch2 Earth counts", "Ch3a Earth counts",
"Ch3b Earth counts", "Ch4 Earth counts", "Ch5 Earth counts"
]
names = ["Ch1_Ce", "Ch2_Ce", "Ch3a_Ce", "Ch3b_Ce", "Ch4_Ce", "Ch5_Ce"]
AVHRR._add_counts_variables(dataset, height, names, standard_names)
# Chx_u_Csp
standard_names = [
"Ch1 Uncertainty on space counts",
"Ch2 Uncertainty on space counts",
"Ch3a Uncertainty on space counts",
"Ch3b Uncertainty on space counts",
"Ch4 Uncertainty on space counts",
"Ch5 Uncertainty on space counts"
]
names = [
"Ch1_u_Csp", "Ch2_u_Csp", "Ch3a_u_Csp", "Ch3b_u_Csp", "Ch4_u_Csp",
"Ch5_u_Csp"
]
AVHRR._add_counts_uncertainties_variables(
dataset, height, names, standard_names,
COUNT_CORRELATION_ATTRIBUTES)
# Chx_Cict
standard_names = [
"Ch3b Uncertainty on ICT counts", "Ch4 Uncertainty on ICT counts",
"Ch5 Uncertainty on ICT counts"
]
names = ["Ch3b_u_Cict", "Ch4_u_Cict", "Ch5_u_Cict"]
AVHRR._add_counts_uncertainties_variables(
dataset, height, names, standard_names,
COUNT_CORRELATION_ATTRIBUTES)
# Chx_u_Ce
standard_names = [
"Ch1 Uncertainty on earth counts",
"Ch2 Uncertainty on earth counts",
"Ch3a Uncertainty on earth counts",
"Ch3b Uncertainty on earth counts",
"Ch4 Uncertainty on earth counts",
"Ch5 Uncertainty on earth counts"
]
names = [
"Ch1_u_Ce", "Ch2_u_Ce", "Ch3a_u_Ce", "Ch3b_u_Ce", "Ch4_u_Ce",
"Ch5_u_Ce"
]
attributes = {"pdf_shape": "digitised_gaussian"}
AVHRR._add_counts_uncertainties_variables(dataset, height, names,
standard_names, attributes)
# Chx_u_Refl
long_names = [
"Ch1 Total uncertainty on toa reflectance",
"Ch2 Total uncertainty on toa reflectance",
"Ch3a Total uncertainty on toa reflectance"
]
names = ["Ch1_u_Refl", "Ch2_u_Refl", "Ch3a_u_Refl"]
AVHRR._add_refl_uncertainties_variables(dataset, height, names,
long_names)
# Chx_u_Bt
standard_names = [
"Ch3b Total uncertainty on brightness temperature",
"Ch4 Total uncertainty on brightness temperature",
"Ch5 Total uncertainty on brightness temperature"
]
names = ["Ch3b_u_Bt", "Ch4_u_Bt", "Ch5_u_Bt"]
AVHRR._add_bt_uncertainties_variables(dataset, height, names,
standard_names)
# Chx_ur_Bt
standard_names = [
"Ch3b Random uncertainty on brightness temperature",
"Ch4 Random uncertainty on brightness temperature",
"Ch5 Random uncertainty on brightness temperature"
]
names = ["Ch3b_ur_Bt", "Ch4_ur_Bt", "Ch5_ur_Bt"]
AVHRR._add_bt_uncertainties_variables(dataset, height, names,
standard_names)
# Chx_us_Bt
standard_names = [
"Ch3b Systematic uncertainty on brightness temperature",
"Ch4 Systematic uncertainty on brightness temperature",
"Ch5 Systematic uncertainty on brightness temperature"
]
names = ["Ch3b_us_Bt", "Ch4_us_Bt", "Ch5_us_Bt"]
AVHRR._add_bt_uncertainties_variables(dataset, height, names,
standard_names)
def add_original_variables(dataset,
height,
srf_size=None,
corr_dx=None,
corr_dy=None,
lut_size=None):
tu.add_geolocation_variables(dataset, SWATH_WIDTH, height)
tu.add_quality_flags(dataset, SWATH_WIDTH, height)
# btemps
default_array = DefaultData.create_default_array_3d(
SWATH_WIDTH, height, NUM_CHANNELS, np.float32, np.NaN)
variable = Variable(["channel", "y", "x"], default_array)
variable.attrs["standard_name"] = "toa_brightness_temperature"
tu.add_encoding(variable, np.int32, -999999, scale_factor=0.01)
tu.add_units(variable, "K")
variable.attrs["ancillary_variables"] = "chanqual qualind scanqual"
dataset["btemps"] = variable
# chanqual
default_array = DefaultData.create_default_array(
height,
NUM_CHANNELS,
np.int32,
dims_names=["channel", "y"],
fill_value=0)
variable = Variable(["channel", "y"], default_array)
variable.attrs["standard_name"] = "status_flag"
variable.attrs["flag_masks"] = "1, 2, 4, 8, 16, 32"
variable.attrs[
"flag_meanings"] = "some_bad_prt_temps some_bad_space_view_counts some_bad_bb_counts no_good_prt_temps no_good_space_view_counts no_good_bb_counts"
dataset["chanqual"] = variable
# instrtemp
default_array = DefaultData.create_default_vector(height,
np.float32,
fill_value=np.NaN)
variable = Variable(["y"], default_array)
tu.add_units(variable, "K")
tu.add_encoding(variable,
np.int32,
DefaultData.get_default_fill_value(np.int32),
scale_factor=0.01)
variable.attrs["long_name"] = "instrument_temperature"
dataset["instrtemp"] = variable
# qualind
default_array = DefaultData.create_default_vector(height,
np.int32,
fill_value=0)
variable = Variable(["y"], default_array)
variable.attrs["standard_name"] = "status_flag"
variable.attrs[
"flag_masks"] = "33554432, 67108864, 134217728, 268435456, 536870912, 1073741824, 2147483648"
variable.attrs[
"flag_meanings"] = "instr_status_changed first_good_clock_update no_earth_loc no_calib data_gap_precedes time_seq_error not_use_scan"
dataset["qualind"] = variable
# scanqual
default_array = DefaultData.create_default_vector(height,
np.int32,
fill_value=0)
variable = Variable(["y"], default_array)
variable.attrs["standard_name"] = "status_flag"
variable.attrs[
"flag_masks"] = "8, 16, 32, 64, 128, 1024, 2048, 4096, 8192, 16384, 32768, 1048576, 2097152, 4194304, 8388608"
variable.attrs[
"flag_meanings"] = "earth_loc_quest_ant_pos earth_loc_quest_reas earth_loc_quest_margin earth_loc_quest_time no_earth_loc_time uncalib_instr_mode uncalib_channels calib_marg_prt uncalib_bad_prt calib_few_scans uncalib_bad_time repeat_scan_times inconsistent_time time_field_bad time_field_inferred"
dataset["scanqual"] = variable
# scnlin
default_array = DefaultData.create_default_vector(height, np.int32)
variable = Variable(["y"], default_array)
tu.add_fill_value(variable,
DefaultData.get_default_fill_value(np.int32))
variable.attrs["long_name"] = "scanline"
dataset["scnlin"] = variable
# scnlindy
default_array = DefaultData.create_default_vector(height, np.int32)
variable = Variable(["y"], default_array)
tu.add_fill_value(variable,
DefaultData.get_default_fill_value(np.int32))
variable.attrs["long_name"] = "Acquisition day of year of scan"
dataset["scnlindy"] = variable
# scnlintime
default_array = DefaultData.create_default_vector(height, np.int32)
variable = Variable(["y"], default_array)
tu.add_fill_value(variable,
DefaultData.get_default_fill_value(np.int32))
variable.attrs[
"long_name"] = "Acquisition time of scan in milliseconds since beginning of the day"
tu.add_units(variable, "ms")
dataset["scnlintime"] = variable
# scnlinyr
default_array = DefaultData.create_default_vector(height, np.int32)
variable = Variable(["y"], default_array)
tu.add_fill_value(variable,
DefaultData.get_default_fill_value(np.int32))
variable.attrs["long_name"] = "Acquisition year of scan"
dataset["scnlinyr"] = variable
# satellite_azimuth_angle
variable = AMSUB_MHS.create_angle_variable(height,
"sensor_azimuth_angle")
dataset["satellite_azimuth_angle"] = variable
# satellite_zenith_angle
variable = AMSUB_MHS.create_angle_variable(height,
"sensor_zenith_angle")
dataset["satellite_zenith_angle"] = variable
# solar_azimuth_angle
variable = AMSUB_MHS.create_angle_variable(height,
"solar_azimuth_angle")
dataset["solar_azimuth_angle"] = variable
# solar_zenith_angle
variable = AMSUB_MHS.create_angle_variable(height,
"solar_zenith_angle")
dataset["solar_zenith_angle"] = variable
# acquisition_time
default_array = DefaultData.create_default_vector(height, np.int32)
variable = Variable(["y"], default_array)
tu.add_fill_value(variable,
DefaultData.get_default_fill_value(np.int32))
variable.attrs["standard_name"] = "time"
variable.attrs[
"long_name"] = "Acquisition time in seconds since 1970-01-01 00:00:00"
tu.add_units(variable, "s")
dataset["acquisition_time"] = variable
def add_original_variables(dataset, height, srf_size=None):
# height is ignored - supplied just for interface compatibility tb 2017-02-05
tu.add_quality_flags(dataset, FULL_SIZE, FULL_SIZE, chunksizes=CHUNKSIZES)
# time
default_array = DefaultData.create_default_array(IR_SIZE, IR_SIZE, np.uint32)
variable = Variable([IR_Y_DIMENSION, IR_X_DIMENSION], default_array)
tu.add_fill_value(variable, DefaultData.get_default_fill_value(np.uint32))
variable.attrs["standard_name"] = "time"
variable.attrs["long_name"] = "Acquisition time of pixel"
tu.add_units(variable, "seconds since 1970-01-01 00:00:00")
tu.add_offset(variable, TIME_FILL_VALUE)
tu.add_chunking(variable, CHUNKSIZES)
dataset["time"] = variable
dataset["solar_azimuth_angle"] = MVIRI._create_angle_variable_int(0.005493164, standard_name="solar_azimuth_angle", unsigned=True)
dataset["solar_zenith_angle"] = MVIRI._create_angle_variable_int(0.005493248, standard_name="solar_zenith_angle")
dataset["satellite_azimuth_angle"] = MVIRI._create_angle_variable_int(0.01, standard_name="sensor_azimuth_angle", long_name="sensor_azimuth_angle", unsigned=True)
dataset["satellite_zenith_angle"] = MVIRI._create_angle_variable_int(0.01, standard_name="platform_zenith_angle", unsigned=True)
# count_ir
default_array = DefaultData.create_default_array(IR_SIZE, IR_SIZE, np.uint8)
variable = Variable([IR_Y_DIMENSION, IR_X_DIMENSION], default_array)
tu.add_fill_value(variable, DefaultData.get_default_fill_value(np.uint8))
variable.attrs["long_name"] = "Infrared Image Counts"
tu.add_units(variable, "count")
tu.add_chunking(variable, CHUNKSIZES)
dataset["count_ir"] = variable
# count_wv
default_array = DefaultData.create_default_array(IR_SIZE, IR_SIZE, np.uint8)
variable = Variable([IR_Y_DIMENSION, IR_X_DIMENSION], default_array)
tu.add_fill_value(variable, DefaultData.get_default_fill_value(np.uint8))
variable.attrs["long_name"] = "WV Image Counts"
tu.add_units(variable, "count")
tu.add_chunking(variable, CHUNKSIZES)
dataset["count_wv"] = variable
default_array = DefaultData.create_default_array(FULL_SIZE, FULL_SIZE, np.uint8, fill_value=0)
variable = Variable(["y", "x"], default_array)
variable.attrs["flag_masks"] = "1, 2, 4, 8, 16, 32"
variable.attrs["flag_meanings"] = "uncertainty_suspicious uncertainty_too_large space_view_suspicious not_on_earth suspect_time suspect_geo"
variable.attrs["standard_name"] = "status_flag"
tu.add_chunking(variable, CHUNKSIZES)
dataset["data_quality_bitmask"] = variable
# distance_sun_earth
dataset["distance_sun_earth"] = tu.create_scalar_float_variable(long_name="Sun-Earth distance", units="au")
# solar_irradiance_vis
dataset["solar_irradiance_vis"] = tu.create_scalar_float_variable(standard_name="solar_irradiance_vis", long_name="Solar effective Irradiance", units="W*m-2")
# u_solar_irradiance_vis
default_array = np.full([], np.NaN, np.float32)
variable = Variable([], default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs["long_name"] = "Uncertainty in Solar effective Irradiance"
tu.add_units(variable, "Wm^-2")
variable.attrs[corr.PIX_CORR_FORM] = corr.RECT_ABS
variable.attrs[corr.PIX_CORR_UNIT] = corr.PIXEL
variable.attrs[corr.PIX_CORR_SCALE] = [-np.inf, np.inf]
variable.attrs[corr.SCAN_CORR_FORM] = corr.RECT_ABS
variable.attrs[corr.SCAN_CORR_UNIT] = corr.LINE
variable.attrs[corr.SCAN_CORR_SCALE] = [-np.inf, np.inf]
variable.attrs[corr.IMG_CORR_FORM] = corr.RECT_ABS
variable.attrs[corr.IMG_CORR_UNIT] = corr.DAYS
variable.attrs[corr.IMG_CORR_SCALE] = [-np.inf, np.inf]
variable.attrs["pdf_shape"] = "rectangle"
dataset["u_solar_irradiance_vis"] = variable
if srf_size is None:
srf_size = SRF_SIZE
default_array = DefaultData.create_default_array(srf_size, NUM_CHANNELS, np.float32, fill_value=np.NaN)
variable = Variable(["channel", "n_frequencies"], default_array)
variable.attrs["long_name"] = 'Spectral Response Function weights'
variable.attrs["description"] = 'Per channel: weights for the relative spectral response function'
tu.add_encoding(variable, np.int16, -32768, 0.000033)
dataset['SRF_weights'] = variable
default_array = DefaultData.create_default_array(srf_size, NUM_CHANNELS, np.float32, fill_value=np.NaN)
variable = Variable(["channel", "n_frequencies"], default_array)
variable.attrs["long_name"] = 'Spectral Response Function frequencies'
variable.attrs["description"] = 'Per channel: frequencies for the relative spectral response function'
tu.add_encoding(variable, np.int32, -2147483648, 0.0001)
tu.add_units(variable, "nm")
variable.attrs["source"] = "Filename of SRF"
variable.attrs["Valid(YYYYDDD)"] = "datestring"
dataset['SRF_frequencies'] = variable
# srf covariance_
default_array = DefaultData.create_default_array(srf_size, srf_size, np.float32, fill_value=np.NaN)
variable = Variable([SRF_VIS_DIMENSION, SRF_VIS_DIMENSION], default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs["long_name"] = "Covariance of the Visible Band Spectral Response Function"
tu.add_chunking(variable, CHUNKSIZES)
dataset["covariance_spectral_response_function_vis"] = variable
# u_srf_ir
default_array = DefaultData.create_default_vector(srf_size, np.float32, fill_value=np.NaN)
variable = Variable([SRF_IR_WV_DIMENSION], default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs["long_name"] = "Uncertainty in Spectral Response Function for IR channel"
dataset["u_spectral_response_function_ir"] = variable
# u_srf_wv
default_array = DefaultData.create_default_vector(srf_size, np.float32, fill_value=np.NaN)
variable = Variable([SRF_IR_WV_DIMENSION], default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs["long_name"] = "Uncertainty in Spectral Response Function for WV channel"
dataset["u_spectral_response_function_wv"] = variable
dataset["a_ir"] = tu.create_scalar_float_variable(long_name="Calibration parameter a for IR Band", units="mWm^-2sr^-1cm^-1")
dataset["b_ir"] = tu.create_scalar_float_variable(long_name="Calibration parameter b for IR Band", units="mWm^-2sr^-1cm^-1/DC")
dataset["u_a_ir"] = tu.create_scalar_float_variable(long_name="Uncertainty of calibration parameter a for IR Band", units="mWm^-2sr^-1cm^-1")
dataset["u_b_ir"] = tu.create_scalar_float_variable(long_name="Uncertainty of calibration parameter b for IR Band", units="mWm^-2sr^-1cm^-1/DC")
dataset["a_wv"] = tu.create_scalar_float_variable(long_name="Calibration parameter a for WV Band", units="mWm^-2sr^-1cm^-1")
dataset["b_wv"] = tu.create_scalar_float_variable(long_name="Calibration parameter b for WV Band", units="mWm^-2sr^-1cm^-1/DC")
dataset["u_a_wv"] = tu.create_scalar_float_variable(long_name="Uncertainty of calibration parameter a for WV Band", units="mWm^-2sr^-1cm^-1")
dataset["u_b_wv"] = tu.create_scalar_float_variable(long_name="Uncertainty of calibration parameter b for WV Band", units="mWm^-2sr^-1cm^-1/DC")
dataset["bt_a_ir"] = tu.create_scalar_float_variable(long_name="IR Band BT conversion parameter A", units="1")
dataset["bt_b_ir"] = tu.create_scalar_float_variable(long_name="IR Band BT conversion parameter B", units="1")
dataset["bt_a_wv"] = tu.create_scalar_float_variable(long_name="WV Band BT conversion parameter A", units="1")
dataset["bt_b_wv"] = tu.create_scalar_float_variable(long_name="WV Band BT conversion parameter B", units="1")
dataset["years_since_launch"] = tu.create_scalar_float_variable(long_name="Fractional year since launch of satellite", units="years")
x_ir_wv_dim = dataset.dims["x_ir_wv"]
dataset["x_ir_wv"] = Coordinate("x_ir_wv", np.arange(x_ir_wv_dim, dtype=np.uint16))
y_ir_wv_dim = dataset.dims["y_ir_wv"]
dataset["y_ir_wv"] = Coordinate("y_ir_wv", np.arange(y_ir_wv_dim, dtype=np.uint16))
srf_size_dim = dataset.dims["srf_size"]
dataset["srf_size"] = Coordinate("srf_size", np.arange(srf_size_dim, dtype=np.uint16))
