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"])