def _assert_angle_variables(self, ds): satellite_zenith_angle = ds.variables["satellite_zenith_angle"] self.assertEqual((6, ), satellite_zenith_angle.shape) self.assertTrue(np.isnan(satellite_zenith_angle.data[3])) self.assertEqual(np.uint16, satellite_zenith_angle.encoding['dtype']) self.assertEqual(DefaultData.get_default_fill_value(np.uint16), satellite_zenith_angle.encoding['_FillValue']) self.assertEqual(0.01, satellite_zenith_angle.encoding['scale_factor']) self.assertEqual(-180.0, satellite_zenith_angle.encoding['add_offset']) self.assertEqual("platform_zenith_angle", satellite_zenith_angle.attrs["standard_name"]) self.assertEqual("degree", satellite_zenith_angle.attrs["units"]) self.assertEqual("longitude latitude", satellite_zenith_angle.attrs["coordinates"]) solar_azimuth_angle = ds.variables["solar_azimuth_angle"] self.assertEqual((6, 56), solar_azimuth_angle.shape) self.assertTrue(np.isnan(solar_azimuth_angle.data[4, 4])) self.assertEqual(np.uint16, solar_azimuth_angle.encoding['dtype']) self.assertEqual(DefaultData.get_default_fill_value(np.uint16), solar_azimuth_angle.encoding['_FillValue']) self.assertEqual(0.01, solar_azimuth_angle.encoding['scale_factor']) self.assertEqual(-180.0, solar_azimuth_angle.encoding['add_offset']) self.assertEqual(CHUNKING_2D, solar_azimuth_angle.encoding['chunksizes']) self.assertEqual("solar_azimuth_angle", solar_azimuth_angle.attrs["standard_name"]) self.assertEqual("degree", solar_azimuth_angle.attrs["units"]) self.assertEqual("longitude latitude", solar_azimuth_angle.attrs["coordinates"])
def _create_refl_uncertainty_variable(height, long_name=None, structured=False): default_array = DefaultData.create_default_array(SWATH_WIDTH, height, np.float32, fill_value=np.NaN) variable = Variable(["y", "x"], default_array) tu.add_units(variable, "percent") tu.add_geolocation_attribute(variable) variable.attrs["long_name"] = long_name if structured: tu.add_encoding(variable, np.int16, DefaultData.get_default_fill_value(np.int16), 0.01, chunksizes=CHUNKS_2D) variable.attrs["valid_min"] = 3 variable.attrs["valid_max"] = 5 else: tu.add_encoding(variable, np.int16, DefaultData.get_default_fill_value(np.int16), 0.00001, chunksizes=CHUNKS_2D) variable.attrs["valid_max"] = 1000 variable.attrs["valid_min"] = 10 return variable
def assert_common_angles(self, ds, chunking=None): satellite_zenith_angle = ds.variables["satellite_zenith_angle"] self.assertEqual((6, 56), satellite_zenith_angle.shape) self.assertTrue(np.isnan(satellite_zenith_angle.data[2, 2])) self.assertEqual(np.uint16, satellite_zenith_angle.encoding['dtype']) self.assertEqual(DefaultData.get_default_fill_value(np.uint16), satellite_zenith_angle.encoding['_FillValue']) self.assertEqual(0.01, satellite_zenith_angle.encoding['scale_factor']) self.assertEqual(0, satellite_zenith_angle.encoding['add_offset']) if chunking is not None: self.assertEqual(chunking, satellite_zenith_angle.encoding['chunksizes']) self.assertEqual("platform_zenith_angle", satellite_zenith_angle.attrs["standard_name"]) self.assertEqual("degree", satellite_zenith_angle.attrs["units"]) self.assertEqual("longitude latitude", satellite_zenith_angle.attrs["coordinates"]) self.assertEqual([0, 180], satellite_zenith_angle.attrs["valid_range"]) solar_zenith_angle = ds.variables["solar_zenith_angle"] self.assertEqual((6, 56), solar_zenith_angle.shape) self.assertTrue(np.isnan(solar_zenith_angle.data[3, 3])) self.assertEqual(np.uint16, solar_zenith_angle.encoding['dtype']) self.assertEqual(DefaultData.get_default_fill_value(np.uint16), solar_zenith_angle.encoding['_FillValue']) self.assertEqual(0.01, solar_zenith_angle.encoding['scale_factor']) self.assertEqual(0, solar_zenith_angle.encoding['add_offset']) if chunking is not None: self.assertEqual(chunking, solar_zenith_angle.encoding['chunksizes']) self.assertEqual("solar_zenith_angle", solar_zenith_angle.attrs["standard_name"]) self.assertEqual("solar_zenith_angle", solar_zenith_angle.attrs["orig_name"]) self.assertEqual("degree", solar_zenith_angle.attrs["units"]) self.assertEqual("longitude latitude", solar_zenith_angle.attrs["coordinates"]) self.assertEqual([0, 180], solar_zenith_angle.attrs["valid_range"]) satellite_azimuth_angle = ds.variables["satellite_azimuth_angle"] self.assertEqual((6, 56), satellite_azimuth_angle.shape) self.assertTrue(np.isnan(satellite_azimuth_angle.data[5, 5])) self.assertEqual(np.uint16, satellite_azimuth_angle.encoding['dtype']) self.assertEqual(DefaultData.get_default_fill_value(np.uint16), satellite_azimuth_angle.encoding['_FillValue']) self.assertEqual(0.01, satellite_azimuth_angle.encoding['scale_factor']) self.assertEqual(0, satellite_azimuth_angle.encoding['add_offset']) if chunking is not None: self.assertEqual(chunking, satellite_azimuth_angle.encoding['chunksizes']) self.assertEqual("sensor_azimuth_angle", satellite_azimuth_angle.attrs["standard_name"]) self.assertEqual([0, 360], satellite_azimuth_angle.attrs["valid_range"]) self.assertEqual("clockwise from north", satellite_azimuth_angle.attrs["comment"]) self.assertEqual("degree", satellite_azimuth_angle.attrs["units"]) self.assertEqual("longitude latitude", satellite_azimuth_angle.attrs["coordinates"]) solar_azimuth_angle = ds.variables["solar_azimuth_angle"] self.assertEqual((6, 56), solar_azimuth_angle.shape) self.assertTrue(np.isnan(solar_azimuth_angle.data[4, 4])) self.assertEqual(np.uint16, solar_azimuth_angle.encoding['dtype']) self.assertEqual(DefaultData.get_default_fill_value(np.uint16), solar_azimuth_angle.encoding['_FillValue']) self.assertEqual(0.01, solar_azimuth_angle.encoding['scale_factor']) self.assertEqual(0, solar_azimuth_angle.encoding['add_offset']) if chunking is not None: self.assertEqual(chunking, solar_azimuth_angle.encoding['chunksizes']) self.assertEqual("solar_azimuth_angle", solar_azimuth_angle.attrs["standard_name"]) self.assertEqual([0, 360], solar_azimuth_angle.attrs["valid_range"]) self.assertEqual("clockwise from north", solar_azimuth_angle.attrs["comment"]) self.assertEqual("degree", solar_azimuth_angle.attrs["units"]) self.assertEqual("longitude latitude", solar_azimuth_angle.attrs["coordinates"])
def add_common_sensor_variables(dataset, height, srf_size): # scanline default_array = DefaultData.create_default_vector(height, np.int16) variable = Variable(["y"], default_array) tu.add_fill_value(variable, DefaultData.get_default_fill_value(np.int16)) variable.attrs["long_name"] = "scanline_number" tu.add_units(variable, "count") dataset["scanline"] = variable # time default_array = DefaultData.create_default_vector(height, np.datetime64) 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" # do not set 'units' of "_FillValue" here, xarray sets this from encoding upon storing the file tu.add_encoding(variable, np.uint32, None, scale_factor=0.1) variable.encoding["units"] = "seconds since 1970-01-01 00:00:00" # encoding 'add_offset' varies per file and either needs to be set # by the user or intelligently in fiduceo.fcdr.writer.fcdr_writer.FCDRWriter.write dataset["time"] = variable # quality_scanline_bitmask 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["long_name"] = "quality_indicator_bitfield" variable.attrs[ "flag_masks"] = "1, 2, 4, 8, 16" variable.attrs["flag_meanings"] = "do_not_use_scan reduced_context bad_temp_no_rself suspect_geo suspect_time" dataset["quality_scanline_bitmask"] = variable default_array = DefaultData.create_default_array(srf_size, NUM_CHANNELS, np.float32, fill_value=np.NaN) variable = Variable(["channel", "n_wavelengths"], 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_wavelengths"], 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
def add_common_sensor_variables(dataset, height, srf_size): # scanline default_array = DefaultData.create_default_vector(height, np.int16) variable = Variable(["y"], default_array) tu.add_fill_value(variable, DefaultData.get_default_fill_value(np.int16)) variable.attrs["long_name"] = "scanline_number" tu.add_units(variable, "count") dataset["scanline"] = variable # time default_array = DefaultData.create_default_vector(height, np.uint32) variable = Variable(["y"], 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 in seconds since 1970-01-01 00:00:00" tu.add_units(variable, "s") dataset["time"] = variable # quality_scanline_bitmask 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["long_name"] = "quality_indicator_bitfield" variable.attrs[ "flag_masks"] = "1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 65536, 131072, 262144, 524288, 1048576, 2097152, 4194304, 8388608, 16777216, 33554432, 67108864, 134217728, 268435456, 536870912 1073741824" variable.attrs[ "flag_meanings"] = "do_not_use_scan time_sequence_error data_gap_preceding_scan no_calibration no_earth_location clock_update status_changed line_incomplete, time_field_bad time_field_bad_not_inf inconsistent_sequence scan_time_repeat uncalib_bad_time calib_few_scans uncalib_bad_prt calib_marginal_prt uncalib_channels uncalib_inst_mode quest_ant_black_body zero_loc bad_loc_time bad_loc_marginal bad_loc_reason bad_loc_ant reduced_context bad_temp_no_rself" dataset["quality_scanline_bitmask"] = 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 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 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
def _assert_correct_counts_variable(self, ds, name, long_name): variable = ds.variables[name] self.assertEqual((5, 409), variable.shape) self.assertEqual(DefaultData.get_default_fill_value(np.int32), variable.data[3, 306]) self.assertEqual(DefaultData.get_default_fill_value(np.int32), variable.attrs["_FillValue"]) self.assertEqual(long_name, variable.attrs["long_name"]) self.assertEqual("count", variable.attrs["units"]) self.assertEqual("longitude latitude", variable.attrs["coordinates"]) self.assertEqual(CHUNKING, variable.encoding["chunksizes"])
def _create_int32_vector(height, standard_name=None, long_name=None, orig_name=None): 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)) HIRS._set_name_attributes(long_name, orig_name, standard_name, variable) return variable
def create_float_variable(width, height, standard_name=None, long_name=None, dim_names=None, fill_value=None): if fill_value is None: default_array = DefaultData.create_default_array( width, height, np.float32) else: default_array = DefaultData.create_default_array( width, height, np.float32, fill_value=fill_value) if dim_names is None: variable = Variable(["y", "x"], default_array) else: variable = Variable(dim_names, default_array) if fill_value is None: variable.attrs["_FillValue"] = DefaultData.get_default_fill_value( np.float32) else: variable.attrs["_FillValue"] = fill_value if standard_name is not None: variable.attrs["standard_name"] = standard_name if long_name is not None: variable.attrs["long_name"] = long_name return variable
def _assert_line_int32_variable(self, ds, name, standard_name=None, long_name=None, orig_name=None): variable = ds.variables[name] self.assertEqual((7, ), variable.shape) self.assertEqual(DefaultData.get_default_fill_value(np.int32), variable.data[4]) self.assertEqual(DefaultData.get_default_fill_value(np.int32), variable.attrs["_FillValue"]) self._assert_name_attributes(variable, standard_name, long_name, orig_name) return variable
def _create_counts_uncertainty_vector_uint32(height, standard_name): default_array = DefaultData.create_default_vector(height, np.float32) variable = Variable(["y"], default_array) tu.add_encoding(variable, np.uint32, DefaultData.get_default_fill_value(np.uint32), 0.01) variable.attrs["standard_name"] = standard_name tu.add_units(variable, "count") return variable
def _create_scaled_int16_vector(height, standard_name=None, original_name=None, long_name=None, scale_factor=0.01): default_array = DefaultData.create_default_vector(height, np.float32) variable = Variable(["y"], default_array) tu.add_encoding(variable, np.int16, DefaultData.get_default_fill_value(np.int16), scale_factor) HIRS._set_name_attributes(long_name, original_name, standard_name, variable) return variable
def _create_angle_variable_int(scale_factor, standard_name=None, long_name=None, unsigned=False, fill_value=None): default_array = DefaultData.create_default_array(TIE_SIZE, TIE_SIZE, np.float32, fill_value=np.NaN) variable = Variable(["y_tie", "x_tie"], default_array) if unsigned is True: data_type = np.uint16 else: data_type = np.int16 if fill_value is None: fill_value = DefaultData.get_default_fill_value(data_type) if standard_name is not None: variable.attrs["standard_name"] = standard_name if long_name is not None: variable.attrs["long_name"] = long_name tu.add_units(variable, "degree") variable.attrs["tie_points"] = "true" tu.add_encoding(variable, data_type, fill_value, scale_factor, chunksizes=CHUNKSIZES) return variable
def add_easy_fcdr_variables(dataset, height, corr_dx=None, corr_dy=None, lut_size=None): # height is ignored - supplied just for interface compatibility tb 2017-02-05 # reflectance default_array = DefaultData.create_default_array(FULL_SIZE, FULL_SIZE, np.float32, fill_value=np.NaN) variable = Variable(["y", "x"], default_array) variable.attrs["standard_name"] = "toa_bidirectional_reflectance_vis" variable.attrs["long_name"] = "top of atmosphere bidirectional reflectance factor per pixel of the visible band with central wavelength 0.7" tu.add_units(variable, "1") tu.add_encoding(variable, np.uint16, DefaultData.get_default_fill_value(np.uint16), 3.05176E-05, chunksizes=CHUNKSIZES) dataset["toa_bidirectional_reflectance_vis"] = variable # u_independent default_array = DefaultData.create_default_array(FULL_SIZE, FULL_SIZE, np.float32, fill_value=np.NaN) variable = Variable(["y", "x"], default_array) variable.attrs["long_name"] = "independent uncertainty per pixel" tu.add_units(variable, "1") tu.add_encoding(variable, np.uint16, DefaultData.get_default_fill_value(np.uint16), 3.05176E-05, chunksizes=CHUNKSIZES) dataset["u_independent_toa_bidirectional_reflectance"] = variable # u_structured default_array = DefaultData.create_default_array(FULL_SIZE, FULL_SIZE, np.float32, fill_value=np.NaN) variable = Variable(["y", "x"], default_array) variable.attrs["long_name"] = "structured uncertainty per pixel" tu.add_units(variable, "1") tu.add_encoding(variable, np.uint16, DefaultData.get_default_fill_value(np.uint16), 3.05176E-05, chunksizes=CHUNKSIZES) dataset["u_structured_toa_bidirectional_reflectance"] = variable # u_common dataset["u_common_toa_bidirectional_reflectance"] = tu.create_scalar_float_variable(long_name="common uncertainty per slot", units="1") dataset["sub_satellite_latitude_start"] = tu.create_scalar_float_variable(long_name="Latitude of the sub satellite point at image start", units="degrees_north") dataset["sub_satellite_longitude_start"] = tu.create_scalar_float_variable(long_name="Longitude of the sub satellite point at image start", units="degrees_east") dataset["sub_satellite_latitude_end"] = tu.create_scalar_float_variable(long_name="Latitude of the sub satellite point at image end", units="degrees_north") dataset["sub_satellite_longitude_end"] = tu.create_scalar_float_variable(long_name="Longitude of the sub satellite point at image end", units="degrees_east") tu.add_correlation_matrices(dataset, NUM_CHANNELS) if lut_size is not None: tu.add_lookup_tables(dataset, NUM_CHANNELS, lut_size=lut_size) if corr_dx is not None and corr_dy is not None: tu.add_correlation_coefficients(dataset, NUM_CHANNELS, corr_dx, corr_dy) tu.add_coordinates(dataset, ["vis", "wv", "ir"])
def _assert_line_uint32_variable(self, ds, name, standard_name=None, long_name=None, orig_name=None): variable = ds.variables[name] self.assertEqual((7, ), variable.shape) self.assertEqual(DefaultData.get_default_fill_value(np.float32), variable.data[4]) self.assertEqual(DefaultData.get_default_fill_value(np.uint32), variable.encoding["_FillValue"]) self.assertEqual(0.01, variable.encoding["scale_factor"]) self.assertEqual(np.uint32, variable.encoding["dtype"]) self._assert_name_attributes(variable, standard_name, long_name, orig_name) return variable
def _create_easy_fcdr_variable(height, long_name): 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_encoding(variable, np.uint16, DefaultData.get_default_fill_value(np.uint16), 0.001, chunksizes=CHUNKING_BT) variable.attrs["long_name"] = long_name tu.add_units(variable, "K") tu.add_geolocation_attribute(variable) variable.attrs["valid_min"] = 1 variable.attrs["valid_max"] = 65534 return variable
def _create_overpass_counts_variable(height, width, description): fill_value = DefaultData.get_default_fill_value(np.uint8) default_array = DefaultData.create_default_array(width, height, np.uint8, fill_value=fill_value) variable = Variable(["y", "x"], default_array) tu.add_fill_value(variable, fill_value) variable.attrs["description"] = description variable.attrs["coordinates"] = "lon lat" return 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_geo_angle_variable(standard_name, height, orig_name=None, chunking=None): 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"] = standard_name if orig_name is not None: variable.attrs["orig_name"] = orig_name 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, chunking) 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 test_get_default_fill_value(self): self.assertEqual(-127, DefaultData.get_default_fill_value(np.int8)) self.assertEqual(-32767, DefaultData.get_default_fill_value(np.int16)) self.assertEqual(np.uint16(-1), DefaultData.get_default_fill_value(np.uint16)) self.assertEqual(-2147483647, DefaultData.get_default_fill_value(np.int32)) self.assertEqual(-9223372036854775806, DefaultData.get_default_fill_value(np.int64)) self.assertEqual(np.float32(9.96921E36), DefaultData.get_default_fill_value(np.float32)) self.assertEqual(9.969209968386869E36, DefaultData.get_default_fill_value(np.float64))
def _assert_line_float_variable(self, ds, name, standard_name=None, long_name=None, orig_name=None, fill_value=None): variable = ds.variables[name] self.assertEqual((7, ), variable.shape) if fill_value is None: self.assertEqual(DefaultData.get_default_fill_value(np.float32), variable.data[4]) self.assertEqual(DefaultData.get_default_fill_value(np.float32), variable.attrs["_FillValue"]) elif np.isnan(fill_value): self.assertTrue(np.isnan(variable.data[4])) self.assertTrue(np.isnan(variable.attrs["_FillValue"])) else: self.assertEqual(fill_value, variable.data[4]) self.assertEqual(fill_value, variable.attrs["_FillValue"]) self._assert_name_attributes(variable, standard_name, long_name, orig_name) return 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 _assert_correct_refl_variable(self, variable, long_name): self.assertEqual((5, 409), variable.shape) self.assertTrue(np.isnan(variable.data[0, 8])) self.assertEqual("toa_reflectance", variable.attrs["standard_name"]) self.assertEqual(long_name, variable.attrs["long_name"]) self.assertEqual("1", variable.attrs["units"]) self.assertEqual(np.int16, variable.encoding['dtype']) self.assertEqual(DefaultData.get_default_fill_value(np.int16), variable.encoding['_FillValue']) self.assertEqual(0.0001, variable.encoding['scale_factor']) self.assertEqual(0.0, variable.encoding['add_offset']) self.assertEqual(CHUNKING, variable.encoding["chunksizes"]) self.assertEqual(15000, variable.attrs["valid_max"]) self.assertEqual(0, variable.attrs["valid_min"]) self.assertEqual("longitude latitude", variable.attrs["coordinates"])
def _create_bt_uncertainty_variable(height, long_name): default_array = DefaultData.create_default_array(SWATH_WIDTH, height, np.float32, fill_value=np.NaN) variable = Variable(["y", "x"], default_array) tu.add_units(variable, "K") tu.add_geolocation_attribute(variable) tu.add_encoding(variable, np.int16, DefaultData.get_default_fill_value(np.int16), 0.001, chunksizes=CHUNKS_2D) variable.attrs["valid_max"] = 15000 variable.attrs["valid_min"] = 1 variable.attrs["long_name"] = long_name return variable
def _create_channel_refl_variable(height, long_name): 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"] = "toa_reflectance" variable.attrs["long_name"] = long_name tu.add_units(variable, "1") tu.add_encoding(variable, np.int16, DefaultData.get_default_fill_value(np.int16), 0.0001, chunksizes=CHUNKS_2D) variable.attrs["valid_max"] = 15000 variable.attrs["valid_min"] = 0 tu.add_geolocation_attribute(variable) return variable
def _create_channel_bt_variable(height, long_name): 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"] = "toa_brightness_temperature" variable.attrs["long_name"] = long_name tu.add_units(variable, "K") variable.attrs["valid_max"] = 10000 variable.attrs["valid_min"] = -20000 tu.add_geolocation_attribute(variable) tu.add_encoding(variable, np.int16, DefaultData.get_default_fill_value(np.int16), 0.01, 273.15, chunksizes=CHUNKS_2D) return variable
def _create_refl_uncertainty_variable(height, minmax, scale_factor, long_name=None, units=None): default_array = DefaultData.create_default_array(SWATH_WIDTH, height, np.float32, fill_value=np.NaN) variable = Variable(["y", "x"], default_array) tu.add_units(variable, units) tu.add_geolocation_attribute(variable) variable.attrs["long_name"] = long_name tu.add_encoding(variable, np.int16, DefaultData.get_default_fill_value(np.int16), scale_factor, chunksizes=CHUNKS_2D) variable.attrs["valid_min"] = minmax[0] variable.attrs["valid_max"] = minmax[1] return 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))
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, 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