def add_variables(dataset, width, height):
tu.add_geolocation_variables(dataset,
width,
height,
chunksizes=CHUNKING)
tu.add_quality_flags(dataset, width, height, chunksizes=CHUNKING)
default_array = DefaultData.create_default_vector(
height, np.int32, fill_value=4294967295)
variable = Variable(["y"], default_array)
tu.add_fill_value(variable, 4294967295)
variable.attrs["standard_name"] = "time"
variable.attrs[
"long_name"] = "Acquisition time in seconds since 1970-01-01 00:00:00"
tu.add_units(variable, "s")
dataset["time"] = variable
default_array = DefaultData.create_default_array(width,
height,
np.float32,
fill_value=np.NaN)
variable = Variable(["y", "x"], default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs["coordinates"] = "longitude latitude"
dataset["aot"] = variable
dataset["u_independent_aot"] = tu.create_CDR_uncertainty(
width, height, "Uncertainty of aot due to independent effects")
dataset["u_structured_aot"] = tu.create_CDR_uncertainty(
width, height, "Uncertainty of aot due to structured effects")
dataset["u_common_aot"] = tu.create_CDR_uncertainty(
width, height, "Uncertainty of aot due to common effects")
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 test_add_quality_flags(self):
ds = xr.Dataset()
TemplateUtil.add_quality_flags(ds, 9, 11)
quality = ds.variables["quality_pixel_bitmask"]
self.assertEqual((11, 9), quality.shape)
self.assertEqual(0, quality.data[5, 5])
self.assertEqual(np.uint8, quality.dtype)
self.assertEqual("status_flag", quality.attrs["standard_name"])
self.assertEqual("1, 2, 4, 8, 16, 32, 64, 128",
quality.attrs["flag_masks"])
self.assertEqual(
"invalid use_with_caution invalid_input invalid_geoloc invalid_time sensor_error padded_data incomplete_channel_data",
quality.attrs["flag_meanings"])
def add_variables(dataset, width, height, num_samples=10):
tu.add_geolocation_variables(dataset, width, height, chunksizes=CHUNKING)
tu.add_quality_flags(dataset, width, height, chunksizes=CHUNKING)
default_array = DefaultData.create_default_vector(height, np.int32, fill_value=4294967295)
variable = Variable(["y"], default_array)
tu.add_fill_value(variable, 4294967295)
variable.attrs["standard_name"] = "time"
variable.attrs["long_name"] = "Acquisition time in seconds since 1970-01-01 00:00:00"
tu.add_units(variable, "s")
dataset["time"] = variable
default_array = DefaultData.create_default_array_3d(width, height, num_samples, np.float32, fill_value=np.NaN)
variable = Variable(["samples","y", "x"], default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs["standard_name"] = "sea_surface_temperature"
variable.attrs["units"] = "K"
variable.attrs["coordinates"] = "longitude latitude"
dataset["sst"] = variable
def 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_original_variables(dataset,
height,
srf_size=None,
corr_dx=None,
corr_dy=None,
lut_size=None):
tu.add_geolocation_variables(dataset, SWATH_WIDTH, height)
tu.add_quality_flags(dataset, SWATH_WIDTH, height)
# btemps
default_array = DefaultData.create_default_array_3d(
SWATH_WIDTH, height, NUM_CHANNELS, np.float32, np.NaN)
variable = Variable(["channel", "y", "x"], default_array)
variable.attrs["standard_name"] = "toa_brightness_temperature"
tu.add_encoding(variable, np.int32, -999999, scale_factor=0.01)
tu.add_units(variable, "K")
variable.attrs["ancillary_variables"] = "chanqual qualind scanqual"
dataset["btemps"] = variable
# chanqual
default_array = DefaultData.create_default_array(
height,
NUM_CHANNELS,
np.int32,
dims_names=["channel", "y"],
fill_value=0)
variable = Variable(["channel", "y"], default_array)
variable.attrs["standard_name"] = "status_flag"
variable.attrs["flag_masks"] = "1, 2, 4, 8, 16, 32"
variable.attrs[
"flag_meanings"] = "some_bad_prt_temps some_bad_space_view_counts some_bad_bb_counts no_good_prt_temps no_good_space_view_counts no_good_bb_counts"
dataset["chanqual"] = variable
# instrtemp
default_array = DefaultData.create_default_vector(height,
np.float32,
fill_value=np.NaN)
variable = Variable(["y"], default_array)
tu.add_units(variable, "K")
tu.add_encoding(variable,
np.int32,
DefaultData.get_default_fill_value(np.int32),
scale_factor=0.01)
variable.attrs["long_name"] = "instrument_temperature"
dataset["instrtemp"] = variable
# qualind
default_array = DefaultData.create_default_vector(height,
np.int32,
fill_value=0)
variable = Variable(["y"], default_array)
variable.attrs["standard_name"] = "status_flag"
variable.attrs[
"flag_masks"] = "33554432, 67108864, 134217728, 268435456, 536870912, 1073741824, 2147483648"
variable.attrs[
"flag_meanings"] = "instr_status_changed first_good_clock_update no_earth_loc no_calib data_gap_precedes time_seq_error not_use_scan"
dataset["qualind"] = variable
# scanqual
default_array = DefaultData.create_default_vector(height,
np.int32,
fill_value=0)
variable = Variable(["y"], default_array)
variable.attrs["standard_name"] = "status_flag"
variable.attrs[
"flag_masks"] = "8, 16, 32, 64, 128, 1024, 2048, 4096, 8192, 16384, 32768, 1048576, 2097152, 4194304, 8388608"
variable.attrs[
"flag_meanings"] = "earth_loc_quest_ant_pos earth_loc_quest_reas earth_loc_quest_margin earth_loc_quest_time no_earth_loc_time uncalib_instr_mode uncalib_channels calib_marg_prt uncalib_bad_prt calib_few_scans uncalib_bad_time repeat_scan_times inconsistent_time time_field_bad time_field_inferred"
dataset["scanqual"] = variable
# scnlin
default_array = DefaultData.create_default_vector(height, np.int32)
variable = Variable(["y"], default_array)
tu.add_fill_value(variable,
DefaultData.get_default_fill_value(np.int32))
variable.attrs["long_name"] = "scanline"
dataset["scnlin"] = variable
# scnlindy
default_array = DefaultData.create_default_vector(height, np.int32)
variable = Variable(["y"], default_array)
tu.add_fill_value(variable,
DefaultData.get_default_fill_value(np.int32))
variable.attrs["long_name"] = "Acquisition day of year of scan"
dataset["scnlindy"] = variable
# scnlintime
default_array = DefaultData.create_default_vector(height, np.int32)
variable = Variable(["y"], default_array)
tu.add_fill_value(variable,
DefaultData.get_default_fill_value(np.int32))
variable.attrs[
"long_name"] = "Acquisition time of scan in milliseconds since beginning of the day"
tu.add_units(variable, "ms")
dataset["scnlintime"] = variable
# scnlinyr
default_array = DefaultData.create_default_vector(height, np.int32)
variable = Variable(["y"], default_array)
tu.add_fill_value(variable,
DefaultData.get_default_fill_value(np.int32))
variable.attrs["long_name"] = "Acquisition year of scan"
dataset["scnlinyr"] = variable
# satellite_azimuth_angle
variable = AMSUB_MHS.create_angle_variable(height,
"sensor_azimuth_angle")
dataset["satellite_azimuth_angle"] = variable
# satellite_zenith_angle
variable = AMSUB_MHS.create_angle_variable(height,
"sensor_zenith_angle")
dataset["satellite_zenith_angle"] = variable
# solar_azimuth_angle
variable = AMSUB_MHS.create_angle_variable(height,
"solar_azimuth_angle")
dataset["solar_azimuth_angle"] = variable
# solar_zenith_angle
variable = AMSUB_MHS.create_angle_variable(height,
"solar_zenith_angle")
dataset["solar_zenith_angle"] = variable
# acquisition_time
default_array = DefaultData.create_default_vector(height, np.int32)
variable = Variable(["y"], default_array)
tu.add_fill_value(variable,
DefaultData.get_default_fill_value(np.int32))
variable.attrs["standard_name"] = "time"
variable.attrs[
"long_name"] = "Acquisition time in seconds since 1970-01-01 00:00:00"
tu.add_units(variable, "s")
dataset["acquisition_time"] = variable
def add_original_variables(dataset, height, srf_size=None):
tu.add_geolocation_variables(dataset,
SWATH_WIDTH,
height,
chunksizes=CHUNKS_2D)
tu.add_quality_flags(dataset,
SWATH_WIDTH,
height,
chunksizes=CHUNKS_2D)
# Time
default_array = DefaultData.create_default_vector(height,
np.float64,
fill_value=np.NaN)
variable = Variable(["y"], default_array)
tu.add_fill_value(variable, np.NaN)
tu.add_units(variable, "s")
variable.attrs["standard_name"] = "time"
variable.attrs[
"long_name"] = "Acquisition time in seconds since 1970-01-01 00:00:00"
dataset["Time"] = variable
# relative_azimuth_angle
default_array = DefaultData.create_default_array(SWATH_WIDTH,
height,
np.float32,
fill_value=np.NaN)
variable = Variable(["y", "x"], default_array)
variable.attrs["standard_name"] = "relative_azimuth_angle"
tu.add_units(variable, "degree")
tu.add_encoding(variable,
np.int16,
DefaultData.get_default_fill_value(np.int16),
0.01,
chunksizes=CHUNKS_2D)
variable.attrs["valid_max"] = 18000
variable.attrs["valid_min"] = -18000
tu.add_geolocation_attribute(variable)
dataset["relative_azimuth_angle"] = variable
# satellite_zenith_angle
default_array = DefaultData.create_default_array(SWATH_WIDTH,
height,
np.float32,
fill_value=np.NaN)
variable = Variable(["y", "x"], default_array)
variable.attrs["standard_name"] = "sensor_zenith_angle"
tu.add_units(variable, "degree")
tu.add_encoding(variable,
np.int16,
DefaultData.get_default_fill_value(np.int16),
0.01,
chunksizes=CHUNKS_2D)
variable.attrs["valid_max"] = 9000
variable.attrs["valid_min"] = 0
tu.add_geolocation_attribute(variable)
dataset["satellite_zenith_angle"] = variable
# solar_zenith_angle
default_array = DefaultData.create_default_array(SWATH_WIDTH,
height,
np.float32,
fill_value=np.NaN)
variable = Variable(["y", "x"], default_array)
variable.attrs["standard_name"] = "solar_zenith_angle"
tu.add_units(variable, "degree")
tu.add_encoding(variable,
np.int16,
DefaultData.get_default_fill_value(np.int16),
0.01,
chunksizes=CHUNKS_2D)
variable.attrs["valid_max"] = 18000
variable.attrs["valid_min"] = 0
tu.add_geolocation_attribute(variable)
dataset["solar_zenith_angle"] = variable
dataset["Ch1"] = AVHRR._create_channel_refl_variable(
height, "Channel 1 Reflectance")
dataset["Ch2"] = AVHRR._create_channel_refl_variable(
height, "Channel 2 Reflectance")
dataset["Ch3a"] = AVHRR._create_channel_refl_variable(
height, "Channel 3a Reflectance")
dataset["Ch3b"] = AVHRR._create_channel_bt_variable(
height, "Channel 3b Brightness Temperature")
dataset["Ch4"] = AVHRR._create_channel_bt_variable(
height, "Channel 4 Brightness Temperature")
dataset["Ch5"] = AVHRR._create_channel_bt_variable(
height, "Channel 5 Brightness Temperature")
# data_quality_bitmask
default_array = DefaultData.create_default_array(SWATH_WIDTH,
height,
np.uint8,
fill_value=0)
variable = Variable(["y", "x"], default_array)
variable.attrs["standard_name"] = 'status_flag'
variable.attrs["long_name"] = 'bitmask for quality per pixel'
variable.attrs["flag_masks"] = '1,2'
variable.attrs[
'flag_meanings'] = 'bad_geolocation_timing_err bad_calibration_radiometer_err'
tu.add_chunking(variable, CHUNKS_2D)
tu.add_geolocation_attribute(variable)
dataset['data_quality_bitmask'] = variable
default_array = DefaultData.create_default_vector(height,
np.uint8,
fill_value=0)
variable = Variable(["y"], default_array)
variable.attrs["long_name"] = 'bitmask for quality per scanline'
variable.attrs["standard_name"] = 'status_flag'
variable.attrs["flag_masks"] = '1,2,4,8,16,32,64'
variable.attrs[
'flag_meanings'] = 'do_not_use bad_time bad_navigation bad_calibration channel3a_present solar_contamination_failure solar_contamination'
dataset['quality_scanline_bitmask'] = variable
default_array = DefaultData.create_default_array(N_CHANS,
height,
np.uint8,
fill_value=0)
variable = Variable(["y", "channel"], default_array)
variable.attrs["long_name"] = 'bitmask for quality per channel'
variable.attrs["standard_name"] = 'status_flag'
variable.attrs["flag_masks"] = '1,2'
variable.attrs[
'flag_meanings'] = 'bad_channel some_pixels_not_detected_2sigma'
dataset['quality_channel_bitmask'] = variable
if srf_size is None:
srf_size = MAX_SRF_SIZE
default_array = DefaultData.create_default_array(srf_size,
N_CHANS,
np.float32,
fill_value=np.NaN)
variable = Variable(["channel", "n_frequencies"], default_array)
variable.attrs["long_name"] = 'Spectral Response Function weights'
variable.attrs[
"description"] = 'Per channel: weights for the relative spectral response function'
tu.add_encoding(variable, np.int16, -32768, 0.000033)
dataset['SRF_weights'] = variable
default_array = DefaultData.create_default_array(srf_size,
N_CHANS,
np.float32,
fill_value=np.NaN)
variable = Variable(["channel", "n_frequencies"], default_array)
variable.attrs["long_name"] = 'Spectral Response Function wavelengths'
variable.attrs[
"description"] = 'Per channel: wavelengths for the relative spectral response function'
tu.add_encoding(variable, np.int32, -2147483648, 0.0001)
tu.add_units(variable, "um")
dataset['SRF_wavelengths'] = variable
default_vector = DefaultData.create_default_vector(height,
np.uint8,
fill_value=255)
variable = Variable(["y"], default_vector)
tu.add_fill_value(variable, 255)
variable.attrs["long_name"] = 'Indicator of original file'
variable.attrs[
"description"] = "Indicator for mapping each line to its corresponding original level 1b file. See global attribute 'source' for the filenames. 0 corresponds to 1st listed file, 1 to 2nd file."
dataset["scanline_map_to_origl1bfile"] = variable
default_vector = DefaultData.create_default_vector(
height,
np.int16,
fill_value=DefaultData.get_default_fill_value(np.int16))
variable = Variable(["y"], default_vector)
tu.add_fill_value(variable,
DefaultData.get_default_fill_value(np.int16))
variable.attrs["long_name"] = 'Original_Scan_line_number'
variable.attrs[
"description"] = 'Original scan line numbers from corresponding l1b records'
dataset["scanline_origl1b"] = variable
tu.add_coordinates(dataset,
["Ch1", "Ch2", "Ch3a", "Ch3b", "Ch4", "Ch5"])
def add_variables(dataset, width, height):
WriterUtils.add_gridded_global_attributes(dataset)
tu.add_gridded_geolocation_variables(dataset, width, height)
tu.add_quality_flags(dataset, width, height)
dataset["time_ranges_ascend"] = UTH._create_time_ranges_variable(
height, width,
"Minimum and maximum seconds of day pixel contribution time, ascending nodes"
)
dataset["time_ranges_descend"] = UTH._create_time_ranges_variable(
height, width,
"Minimum and maximum seconds of day pixel contribution time, descending nodes"
)
dataset[
"observation_count_ascend"] = UTH._create_observation_counts_variable(
height, width,
"Number of UTH/brightness temperature observations in a grid box for ascending passes"
)
dataset[
"observation_count_descend"] = UTH._create_observation_counts_variable(
height, width,
"Number of UTH/brightness temperature observations in a grid box for descending passes"
)
dataset["overpass_count_ascend"] = UTH._create_overpass_counts_variable(
height, width,
"Number of satellite overpasses in a grid box for ascending passes"
)
dataset["overpass_count_descend"] = UTH._create_overpass_counts_variable(
height, width,
"Number of satellite overpasses in a grid box for descending passes"
)
dataset["uth_ascend"] = UTH._create_uth_variable(
width,
height,
description=
"Monthly average of all UTH retrievals in a grid box for ascending passes (calculated from daily averages)",
)
dataset["uth_descend"] = UTH._create_uth_variable(
width,
height,
description=
"Monthly average of all UTH retrievals in a grid box for descending passes (calculated from daily averages)"
)
dataset["u_independent_uth_ascend"] = tu.create_CDR_uncertainty(
width,
height,
"Uncertainty of UTH due to independent effects for ascending passes",
coordinates="lon lat")
dataset["u_independent_uth_descend"] = tu.create_CDR_uncertainty(
width,
height,
"Uncertainty of UTH due to independent effects for descending passes",
coordinates="lon lat")
dataset["u_structured_uth_ascend"] = tu.create_CDR_uncertainty(
width,
height,
"Uncertainty of UTH due to structured effects for ascending passes",
coordinates="lon lat")
dataset["u_structured_uth_descend"] = tu.create_CDR_uncertainty(
width,
height,
"Uncertainty of UTH due to structured effects for descending passes",
coordinates="lon lat")
dataset["u_common_uth_ascend"] = tu.create_CDR_uncertainty(
width,
height,
"Uncertainty of UTH due to common effects for ascending passes",
coordinates="lon lat")
dataset["u_common_uth_descend"] = tu.create_CDR_uncertainty(
width,
height,
"Uncertainty of UTH due to common effects for descending passes",
coordinates="lon lat")
dataset["uth_inhomogeneity_ascend"] = tu.create_CDR_uncertainty(
width,
height,
"Standard deviation of all daily UTH averages which were used to calculate the monthly UTH average in a grid box for ascending passes",
coordinates="lon lat")
dataset["uth_inhomogeneity_descend"] = tu.create_CDR_uncertainty(
width,
height,
"Standard deviation of all daily UTH averages which were used to calculate the monthly UTH average in a grid box for descending passes",
coordinates="lon lat")
dataset["BT_ascend"] = UTH._create_bt_variable(
width,
height,
description=
"Monthly average of all brightness temperatures which were used to retrieve UTH in a grid box for ascending passes (calculated from daily averages)"
)
dataset["BT_descend"] = UTH._create_bt_variable(
width,
height,
description=
"Monthly average of all brightness temperatures which were used to retrieve UTH in a grid box for descending passes (calculated from daily averages)"
)
dataset["u_independent_BT_ascend"] = tu.create_CDR_uncertainty(
width,
height,
"Uncertainty of brightness temperature due to independent effects for ascending passes",
coordinates="lon lat",
units="K")
dataset["u_independent_BT_descend"] = tu.create_CDR_uncertainty(
width,
height,
"Uncertainty of brightness temperature due to independent effects for descending passes",
coordinates="lon lat",
units="K")
dataset["u_structured_BT_ascend"] = tu.create_CDR_uncertainty(
width,
height,
"Uncertainty of brightness temperature due to structured effects for ascending passes",
coordinates="lon lat",
units="K")
dataset["u_structured_BT_descend"] = tu.create_CDR_uncertainty(
width,
height,
"Uncertainty of brightness temperature due to structured effects for descending passes",
coordinates="lon lat",
units="K")
dataset["u_common_BT_ascend"] = tu.create_CDR_uncertainty(
width,
height,
"Uncertainty of brightness temperature due to common effects for ascending passes",
coordinates="lon lat",
units="K")
dataset["u_common_BT_descend"] = tu.create_CDR_uncertainty(
width,
height,
"Uncertainty of brightness temperature due to common effects for descending passes",
coordinates="lon lat",
units="K")
dataset["BT_inhomogeneity_ascend"] = tu.create_CDR_uncertainty(
width,
height,
"Standard deviation of all daily brightness temperature averages which were used to calculate the monthly brightness temperature average for ascending passes",
coordinates="lon lat",
units="K")
dataset["BT_inhomogeneity_descend"] = tu.create_CDR_uncertainty(
width,
height,
"Standard deviation of all daily brightness temperature averages which were used to calculate the monthly brightness temperature average for descending passes",
coordinates="lon lat",
units="K")
dataset[
"observation_count_all_ascend"] = UTH._create_observation_counts_variable(
height, width,
"Number of all observations in a grid box for ascending passes - no filtering done"
)
dataset[
"observation_count_all_descend"] = UTH._create_observation_counts_variable(
height, width,
"Number of all observations in a grid box for descending passes - no filtering done"
)
def add_original_variables(dataset, height, srf_size=None):
tu.add_geolocation_variables(dataset, SWATH_WIDTH, height)
tu.add_quality_flags(dataset, SWATH_WIDTH, height)
# Temperature_misc_housekeeping
default_array = DefaultData.create_default_array(
height,
NUM_THERMISTORS,
np.float32,
dims_names=["housekeeping", "y"],
fill_value=np.NaN)
variable = Variable(["housekeeping", "y"], default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs["long_name"] = "TODO"
variable.attrs["units"] = "TODO"
dataset["Temperature_misc_housekeeping"] = variable
# ancil_data
default_array = DefaultData.create_default_array(
height,
ANCIL_VAL,
np.float64,
dims_names=["ancil_val", "y"],
fill_value=np.NaN)
variable = Variable(["ancil_val", "y"], default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs["long_name"] = "Additional per scan information: year, day_of_year, secs_of_day, sat_lat, " \
"sat_long, sat_alt, sat_heading, year, day_of_year, secs_of_day"
dataset["ancil_data"] = variable
# channel_quality_flag
default_array = DefaultData.create_default_array_3d(
SWATH_WIDTH, height, NUM_CHANNELS, np.float32, np.NaN)
variable = Variable(["channel", "y", "x"], default_array)
tu.add_fill_value(variable, np.NaN)
dataset["channel_quality_flag"] = variable
# cold_counts
default_array = DefaultData.create_default_array_3d(
SWATH_WIDTH, height, CALIB_NUMBER, np.float32, np.NaN)
variable = Variable(["calib_number", "y", "x"], default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs["long_name"] = "TODO"
dataset["cold_counts"] = variable
# counts_to_tb_gain
default_array = DefaultData.create_default_array(
height,
NUM_CHANNELS,
np.float32,
dims_names=["channel", "y"],
fill_value=np.NaN)
variable = Variable([
"channel",
"y",
], default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs["long_name"] = "TODO"
dataset["counts_to_tb_gain"] = variable
# counts_to_tb_offset
default_array = DefaultData.create_default_array(
height,
NUM_CHANNELS,
np.float32,
dims_names=["channel", "y"],
fill_value=np.NaN)
variable = Variable([
"channel",
"y",
], default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs["long_name"] = "TODO"
dataset["counts_to_tb_offset"] = variable
# gain_control
default_array = DefaultData.create_default_array(
height,
NUM_CHANNELS,
np.float32,
dims_names=["channel", "y"],
fill_value=np.NaN)
variable = Variable([
"channel",
"y",
], default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs["long_name"] = "TODO"
dataset["gain_control"] = variable
# tb
default_array = DefaultData.create_default_array_3d(
SWATH_WIDTH, height, NUM_CHANNELS, np.float32, np.NaN)
variable = Variable(["channel", "y", "x"], default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs["long_name"] = "TODO"
variable.attrs["standard_name"] = "toa_brightness_temperature"
tu.add_units(variable, "K")
dataset["tb"] = variable
# thermal_reference
default_array = DefaultData.create_default_vector(
height, np.float32, np.NaN)
variable = Variable(["y"], default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs["long_name"] = "TODO"
tu.add_units(variable, "TODO")
dataset["thermal_reference"] = variable
# warm_counts
default_array = DefaultData.create_default_array_3d(
SWATH_WIDTH, height, CALIB_NUMBER, np.float32, np.NaN)
variable = Variable(["calib_number", "y", "x"], default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs["long_name"] = "TODO"
dataset["warm_counts"] = variable
