def _create_counts_uncertainty_variable(height, long_name):
variable = tu.create_float_variable(SWATH_WIDTH,
height,
long_name=long_name,
fill_value=np.NaN)
tu.add_units(variable, "count")
tu.add_geolocation_attribute(variable)
tu.add_chunking(variable, CHUNKS_2D)
return variable
def _create_angle_uncertainty_variable(angle_name, height):
variable = tu.create_float_variable(SWATH_WIDTH,
height,
long_name="uncertainty of " +
angle_name,
fill_value=np.NaN)
tu.add_units(variable, "degree")
tu.add_geolocation_attribute(variable)
tu.add_chunking(variable, CHUNKS_2D)
return variable
def _create_counts_variable(height, long_name):
default_array = DefaultData.create_default_array(
SWATH_WIDTH, height, np.int32)
variable = Variable(["y", "x"], default_array)
tu.add_fill_value(variable,
DefaultData.get_default_fill_value(np.int32))
variable.attrs["long_name"] = long_name
tu.add_units(variable, "count")
tu.add_geolocation_attribute(variable)
tu.add_chunking(variable, CHUNKS_2D)
return variable
def add_quality_flags(dataset, height):
tu.add_quality_flags(dataset, SWATH_WIDTH, height, chunksizes=CHUNKING_2D)
default_array = DefaultData.create_default_array(SWATH_WIDTH, height, np.uint16, fill_value=0)
variable = Variable(["y", "x"], default_array)
variable.attrs["flag_masks"] = "1, 2, 4, 8, 16"
variable.attrs["flag_meanings"] = "suspect_mirror suspect_geo suspect_time outlier_nos uncertainty_too_large"
variable.attrs["standard_name"] = "status_flag"
tu.add_chunking(variable, CHUNKING_2D)
tu.add_geolocation_attribute(variable)
dataset["data_quality_bitmask"] = variable
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_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))
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 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"])
