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_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 _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_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 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 _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_angle_variable(height, standard_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"] = standard_name
tu.add_units(variable, "degree")
tu.add_encoding(variable, np.int32, -999999, scale_factor=0.01)
return variable
def _create_geo_angle_uncertainty_variable(standard_name, height, fill_value, orig_name=None):
default_array = DefaultData.create_default_array(SWATH_WIDTH, height, np.float32, fill_value=fill_value)
variable = Variable(["y", "x"], default_array)
tu.add_encoding(variable, np.uint16, fill_value, scale_factor=0.01)
variable.attrs["standard_name"] = standard_name
if orig_name is not None:
variable.attrs["orig_name"] = orig_name
tu.add_units(variable, "degree")
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 add_bt_variable(dataset, height):
# bt
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"
variable.attrs["long_name"] = "Brightness temperature, NOAA/EUMETSAT calibrated"
tu.add_units(variable, "K")
tu.add_encoding(variable, np.int16, FILL_VALUE, 0.01, 150.0, chunksizes=CHUNKING_BT)
tu.add_geolocation_attribute(variable)
variable.attrs["ancilliary_variables"] = "quality_scanline_bitmask quality_channel_bitmask"
dataset["bt"] = variable
def add_original_variables(dataset, height, srf_size=None):
# height is ignored - supplied just for interface compatibility tb 2017-07-19
# latitude_vis
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"] = "latitude"
tu.add_units(variable, "degrees_north")
tu.add_encoding(variable, np.int16, -32768, scale_factor=0.0027466658)
dataset["latitude_vis"] = variable
# longitude_vis
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"] = "longitude"
tu.add_units(variable, "degrees_east")
tu.add_encoding(variable, np.int16, -32768, scale_factor=0.0054933317)
dataset["longitude_vis"] = variable
# latitude_ir_wv
default_array = DefaultData.create_default_array(IR_SIZE, IR_SIZE, np.float32, fill_value=np.NaN)
variable = Variable([IR_X_DIMENSION, IR_X_DIMENSION], default_array)
variable.attrs["standard_name"] = "latitude"
tu.add_units(variable, "degrees_north")
tu.add_encoding(variable, np.int16, -32768, scale_factor=0.0027466658)
dataset["latitude_ir_wv"] = variable
# longitude_ir_wv
default_array = DefaultData.create_default_array(IR_SIZE, IR_SIZE, np.float32, fill_value=np.NaN)
variable = Variable([IR_X_DIMENSION, IR_X_DIMENSION], default_array)
variable.attrs["standard_name"] = "longitude"
tu.add_units(variable, "degrees_east")
tu.add_encoding(variable, np.int16, -32768, scale_factor=0.0054933317)
dataset["longitude_ir_wv"] = 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 _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 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 _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_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 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_full_fcdr_variables(dataset, height):
# c_earth
default_array = DefaultData.create_default_array_3d(SWATH_WIDTH, height, NUM_RAD_CHANNELS, np.uint16, dims_names=["rad_channel", "y", "x"])
variable = Variable(["rad_channel", "y", "x"], default_array)
tu.add_fill_value(variable, DefaultData.get_default_fill_value(np.uint16))
variable.attrs["long_name"] = "counts_earth"
tu.add_units(variable, "count")
variable.attrs["ancilliary_variables"] = "scnlinf quality_scanline_bitmask quality_channel_bitmask mnfrqualflags"
dataset["c_earth"] = variable
# L_earth
default_array = DefaultData.create_default_array_3d(SWATH_WIDTH, height, NUM_RAD_CHANNELS, np.float32, np.NaN, ["rad_channel", "y", "x"])
variable = Variable(["rad_channel", "y", "x"], default_array)
tu.add_encoding(variable, np.uint32, DefaultData.get_default_fill_value(np.uint32), 0.0001)
variable.attrs["standard_name"] = "toa_outgoing_inband_radiance"
tu.add_units(variable, "W/Hz/m ** 2/sr")
variable.attrs["long_name"] = "Channel radiance, NOAA/EUMETSAT calibrated"
variable.attrs["ancilliary_variables"] = "scnlinf quality_scanline_bitmask quality_channel_bitmask mnfrqualflags"
dataset["L_earth"] = variable
# u_lat
variable = HIRS._create_angle_variable(height, "uncertainty_latitude")
dataset["u_lat"] = variable
# u_lon
variable = HIRS._create_angle_variable(height, "uncertainty_longitude")
dataset["u_lon"] = variable
# u_time
variable = tu.create_float_variable(SWATH_WIDTH, height, "uncertainty_time")
tu.add_encoding(variable, np.uint16, 65535, 0.01)
tu.add_units(variable, "s")
dataset["u_time"] = variable
# u_c_earth
default_array = DefaultData.create_default_array(NUM_CALIBRATION_CYCLE, NUM_CHANNELS, np.uint16, dims_names=["channel", "calibration_cycle"])
variable = Variable(["channel", "calibration_cycle"], default_array)
tu.add_fill_value(variable, DefaultData.get_default_fill_value(np.uint16))
tu.add_units(variable, "count")
variable.attrs["long_name"] = "uncertainty counts for Earth views"
variable.attrs["ancilliary_variables"] = "u_c_earth_chan_corr"
variable.attrs["channels_affected"] = "all"
variable.attrs["parameter"] = "C_E"
variable.attrs["pdf_shape"] = "gaussian"
dataset["u_c_earth"] = variable
# u_L_earth_independent
variable = HIRS._create_3d_rad_uncertainty_variable(height, "uncertainty_radiance_Earth_random")
tu.add_encoding(variable, np.uint32, DefaultData.get_default_fill_value(np.uint32), 0.01)
tu.add_units(variable, "mW m^-2 sr^-1 cm")
dataset["u_L_earth_independent"] = variable
# u_L_earth_structured
variable = HIRS._create_3d_rad_uncertainty_variable(height, "uncertainty_radiance_Earth_structured")
tu.add_encoding(variable, np.uint32, DefaultData.get_default_fill_value(np.uint32), 0.01)
tu.add_units(variable, "mW m^-2 sr^-1 cm")
dataset["u_L_earth_structured"] = variable
# u_L_earth_systematic
variable = HIRS._create_3d_rad_uncertainty_variable(height, "uncertainty_radiance_Earth_systematic")
tu.add_encoding(variable, np.uint32, DefaultData.get_default_fill_value(np.uint32), 0.01)
tu.add_units(variable, "mW m^-2 sr^-1 cm")
dataset["u_L_earth_systematic"] = variable
# u_L_earth_total
variable = HIRS._create_3d_rad_uncertainty_variable(height, "uncertainty_radiance_Earth_total")
tu.add_encoding(variable, np.uint32, DefaultData.get_default_fill_value(np.uint32), 0.01)
tu.add_units(variable, "mW m^-2 sr^-1 cm")
dataset["u_L_earth_total"] = variable
# S_u_L_earth
variable = tu.create_float_variable(NUM_RAD_CHANNELS, NUM_RAD_CHANNELS, "covariance_radiance_Earth", dim_names=["rad_channel", "rad_channel"])
tu.add_encoding(variable, np.uint32, DefaultData.get_default_fill_value(np.uint32), 0.01)
dataset["S_u_L_earth"] = variable
# u_bt_random
variable = HIRS._create_3d_bt_uncertainty_variable(height, "uncertainty_bt_random")
tu.add_encoding(variable, np.uint16, DefaultData.get_default_fill_value(np.uint16), 0.01)
tu.add_units(variable, "K")
dataset["u_bt_random"] = variable
# u_bt_structured
variable = HIRS._create_3d_bt_uncertainty_variable(height, "uncertainty_bt_structured")
tu.add_encoding(variable, np.uint16, DefaultData.get_default_fill_value(np.uint16), 0.01)
tu.add_units(variable, "K")
dataset["u_bt_structured"] = variable
# u_bt_systematic
variable = HIRS._create_3d_bt_uncertainty_variable(height, "uncertainty_bt_systematic")
tu.add_encoding(variable, np.uint16, DefaultData.get_default_fill_value(np.uint16), 0.01)
tu.add_units(variable, "K")
dataset["u_bt_systematic"] = variable
# u_bt_total
variable = HIRS._create_3d_bt_uncertainty_variable(height, "uncertainty_bt_total")
tu.add_encoding(variable, np.uint16, DefaultData.get_default_fill_value(np.uint16), 0.01)
tu.add_units(variable, "K")
dataset["u_bt_total"] = variable
# S_bt
variable = tu.create_float_variable(NUM_RAD_CHANNELS, NUM_RAD_CHANNELS, "covariance_brightness_temperature", dim_names=["rad_channel", "rad_channel"])
tu.add_encoding(variable, np.uint16, DefaultData.get_default_fill_value(np.uint16), 0.01)
dataset["S_bt"] = variable
# l1b_calcof
default_array = DefaultData.create_default_array(height, NUM_COEFFS, np.float32, dims_names=["coeffs", "y"])
variable = Variable(["coeffs", "y"], default_array)
tu.add_encoding(variable, np.int32, DefaultData.get_default_fill_value(np.int32), 0.01)
variable.attrs["standard_name"] = "calibration_coefficients"
dataset["l1b_calcof"] = variable
# navigation_status
variable = HIRS._create_int32_vector(height, standard_name="status_flag", long_name="Navigation status bit field", orig_name="hrs_navstat")
dataset["navigation_status"] = variable
# quality_flags
variable = HIRS._create_int32_vector(height, standard_name="status_flag", long_name="Quality indicator bit field", orig_name="hrs_qualind")
dataset["quality_flags"] = variable
variable = HIRS._create_scaled_uint16_vector(height, long_name="Platform altitude", original_name="hrs_scalti")
tu.add_units(variable, "km")
dataset["platform_altitude"] = variable
variable = HIRS._create_scaled_int16_vector(height, long_name="Platform pitch angle", original_name="hrs_pitchang")
tu.add_units(variable, "degree")
dataset["platform_pitch_angle"] = variable
variable = HIRS._create_scaled_int16_vector(height, long_name="Platform roll angle", original_name="hrs_rollang")
tu.add_units(variable, "degree")
dataset["platform_roll_angle"] = variable
variable = HIRS._create_scaled_int16_vector(height, long_name="Platform yaw angle", original_name="hrs_yawang")
tu.add_units(variable, "degree")
dataset["platform_yaw_angle"] = variable
# scan_angles
default_array = DefaultData.create_default_array(NUM_SCAN_ANGLES, height, np.float32, dims_names=["y", "num_scan_angles"], fill_value=np.NaN)
variable = Variable(["y", "num_scan_angles"], default_array)
tu.add_encoding(variable, np.uint16, DefaultData.get_default_fill_value(np.uint16), scale_factor=0.01)
tu.add_units(variable, "degree")
variable.attrs["long_name"] = "Scan angles"
variable.attrs["orig_name"] = "hrs_ang"
dataset["scan_angles"] = variable
dataset["l1b_scanline_number"] = HIRS._create_int16_vector(height, long_name="scanline number", orig_name="hrs_scnlin")
dataset["scanline_position"] = HIRS._create_int8_vector(height, long_name="Scanline position number in 32 second cycle", orig_name="hrs_scnpos")
# second_original_calibration_coefficients
default_array = DefaultData.create_default_array(WIDTH_TODO, height, np.float32, fill_value=np.NaN)
variable = Variable(["y", "width_todo"], default_array)
tu.add_encoding(variable, np.int32, DefaultData.get_default_fill_value(np.int32), scale_factor=0.01)
variable.attrs["long_name"] = "Second original calibration coefficients (unsorted)"
variable.attrs["orig_name"] = "hrs_scalcof"
dataset["l1b_second_original_calibration_coefficients"] = variable
dataset["Tc_baseplate"] = HIRS._create_counts_vector(height, "temperature_baseplate_counts")
dataset["Tc_ch"] = HIRS._create_counts_vector(height, "temperature_coolerhousing_counts")
dataset["Tc_elec"] = HIRS._create_counts_vector(height, "temperature_electronics_counts")
dataset["Tc_fsr"] = HIRS._create_counts_vector(height, "temperature_first_stage_radiator_counts")
dataset["Tc_fwh"] = HIRS._create_counts_vector(height, "temperature_filter_wheel_housing_counts")
dataset["Tc_fwm"] = HIRS._create_counts_vector(height, "temperature_filter_wheel_monitor_counts")
dataset["Tc_icct"] = HIRS._create_counts_vector(height, "temperature_internal_cold_calibration_target_counts")
dataset["Tc_iwct"] = HIRS._create_counts_vector(height, "temperature_internal_warm_calibration_target_counts")
dataset["Tc_patch_exp"] = HIRS._create_counts_vector(height, "temperature_patch_expanded_scale_counts")
dataset["Tc_patch_full"] = HIRS._create_counts_vector(height, "temperature_patch_full_range_counts")
dataset["Tc_tlscp_prim"] = HIRS._create_counts_vector(height, "temperature_telescope_primary_counts")
dataset["Tc_tlscp_sec"] = HIRS._create_counts_vector(height, "temperature_telescope_secondary_counts")
dataset["Tc_tlscp_tert"] = HIRS._create_counts_vector(height, "temperature_telescope_tertiary_counts")
dataset["Tc_scanmirror"] = HIRS._create_counts_vector(height, "temperature_scanmirror_counts")
dataset["Tc_scanmotor"] = HIRS._create_counts_vector(height, "temperature_scanmotor_counts")
dataset["u_Tc_baseplate"] = HIRS._create_counts_uncertainty_vector(height, "uncertainty_temperature_baseplate_counts")
dataset["u_Tc_ch"] = HIRS._create_counts_uncertainty_vector(height, "uncertainty_temperature_coolerhousing_counts")
dataset["u_Tc_elec"] = HIRS._create_counts_uncertainty_vector(height, "uncertainty_temperature_electronics_counts")
dataset["u_Tc_fsr"] = HIRS._create_counts_uncertainty_vector(height, "uncertainty_temperature_first_stage_radiator_counts")
dataset["u_Tc_fwh"] = HIRS._create_counts_uncertainty_vector(height, "uncertainty_temperature_filter_wheel_housing_counts")
dataset["u_Tc_fwm"] = HIRS._create_counts_uncertainty_vector(height, "uncertainty_temperature_filter_wheel_monitor_counts")
dataset["u_Tc_icct"] = HIRS._create_counts_uncertainty_vector_uint32(height, "uncertainty_temperature_internal_cold_calibration_target_counts")
dataset["u_Tc_iwct"] = HIRS._create_counts_uncertainty_vector_uint32(height, "uncertainty_temperature_internal_warm_calibration_target_counts")
dataset["u_Tc_patch_exp"] = HIRS._create_counts_uncertainty_vector_uint32(height, "uncertainty_temperature_patch_expanded_scale_counts")
dataset["u_Tc_patch_full"] = HIRS._create_counts_uncertainty_vector_uint32(height, "uncertainty_temperature_patch_full_range_counts")
dataset["u_Tc_tlscp_prim"] = HIRS._create_counts_uncertainty_vector_uint32(height, "uncertainty_temperature_telescope_primary_counts")
dataset["u_Tc_tlscp_sec"] = HIRS._create_counts_uncertainty_vector_uint32(height, "uncertainty_temperature_telescope_secondary_counts")
dataset["u_Tc_tlscp_tert"] = HIRS._create_counts_uncertainty_vector_uint32(height, "uncertainty_temperature_telescope_tertiary_counts")
dataset["u_Tc_scanmirror"] = HIRS._create_counts_uncertainty_vector_uint32(height, "uncertainty_temperature_scanmirror_counts")
dataset["u_Tc_scanmotor"] = HIRS._create_counts_uncertainty_vector_uint32(height, "uncertainty_temperature_scanmotor_counts")
dataset["u_sol_za"] = HIRS._create_geo_angle_uncertainty_variable("uncertainty_solar_zenith_angle", height, FILL_VALUE)
dataset["u_sol_aa"] = HIRS._create_geo_angle_uncertainty_variable("uncertainty_solar_azimuth_angle", height, FILL_VALUE)
dataset["u_sat_za"] = HIRS._create_geo_angle_uncertainty_variable("uncertainty_satellite_zenith_angle", height, FILL_VALUE)
dataset["u_sat_aa"] = HIRS._create_geo_angle_uncertainty_variable("uncertainty_local_azimuth_angle", height, FILL_VALUE)
# u_c_earth_chan_corr
dataset["u_c_earth_chan_corr"] = HIRS._create_channel_correlation_variable("u_c_earth channel correlations", np.int16)
# u_c_space
default_array = DefaultData.create_default_array(NUM_CALIBRATION_CYCLE, NUM_CHANNELS, np.uint16, dims_names=["channel", "calibration_cycle"])
variable = Variable(["channel", "calibration_cycle"], default_array)
tu.add_fill_value(variable, DefaultData.get_default_fill_value(np.uint16))
tu.add_units(variable, "count")
tu.add_scale_factor(variable, 0.005)
variable.attrs["long_name"] = "uncertainty counts for space views"
variable.attrs["ancilliary_variables"] = "u_c_space_chan_corr"
variable.attrs["channels_affected"] = "all"
variable.attrs["parameter"] = "C_s"
variable.attrs["pdf_shape"] = "gaussian"
dataset["u_c_space"] = variable
# u_c_space_chan_corr
dataset["u_c_space_chan_corr"] = HIRS._create_channel_correlation_variable("u_c_space channel correlations", np.uint16)
# u_Earthshine
dataset["u_Earthshine"] = HIRS._create_channel_uncertainty_uint16(height)
# u_O_Re
dataset["u_O_Re"] = HIRS._create_channel_uncertainty_uint16(height)
# u_O_TIWCT
default_array = DefaultData.create_default_vector(height, np.float32, np.NaN)
variable = Variable(["y"], default_array)
tu.add_encoding(variable, np.uint16, DefaultData.get_default_fill_value(np.uint16), 0.01)
dataset["u_O_TIWCT"] = variable
# u_O_TPRT
default_array = DefaultData.create_default_vector(height, np.uint16, DefaultData.get_default_fill_value(np.uint16))
variable = Variable(["y"], default_array)
tu.add_fill_value(variable, 65535)
tu.add_scale_factor(variable, 0.01)
tu.add_units(variable, "K")
variable.attrs["channels_affected"] = "all"
variable.attrs[corr.PIX_CORR_FORM] = corr.RECT_ABS
variable.attrs[corr.PIX_CORR_UNIT] = corr.PIXEL
variable.attrs[corr.PIX_CORR_SCALE] = [-np.inf, np.inf]
variable.attrs[corr.SCAN_CORR_FORM] = corr.RECT_ABS
variable.attrs[corr.SCAN_CORR_UNIT] = corr.LINE
variable.attrs[corr.SCAN_CORR_SCALE] = [-np.inf, np.inf]
variable.attrs[corr.IMG_CORR_FORM] = corr.RECT_ABS
variable.attrs[corr.IMG_CORR_UNIT] = corr.IMG
variable.attrs[corr.IMG_CORR_SCALE] = [-np.inf, np.inf]
variable.attrs["parameter"] = "O_TPRT"
variable.attrs["pdf_shape"] = "gaussian"
variable.attrs["short_name"] = "O_TPRT"
variable.attrs["ancilliary_variables"] = "u_O_TPRT_chan_corr"
dataset["u_O_TPRT"] = variable
dataset["u_Rself"] = HIRS._create_channel_uncertainty_uint16(height)
dataset["u_SRF_calib"] = HIRS._create_channel_uncertainty_uint16(height)
default_array = DefaultData.create_default_array(PRT_NUMBER_IWT, PRT_READING, dtype=np.float32, dims_names=["prt_number_iwt", "prt_reading"], fill_value=np.NaN)
variable = Variable(["prt_number_iwt", "prt_reading"], default_array)
tu.add_encoding(variable, np.uint16, DefaultData.get_default_fill_value(np.uint16), 0.01)
dataset["u_d_PRT"] = variable
dataset["u_electronics"] = HIRS._create_channel_uncertainty_uint16(height)
dataset["u_periodic_noise"] = HIRS._create_channel_uncertainty_uint16(height)
dataset["u_nonlinearity"] = HIRS._create_scaled_uint16_vector(NUM_CHANNELS, dimension_name=["channel"], scale_factor=0.01)
dataset["emissivity"] = tu.create_scalar_float_variable("emissivity", units="1")
dataset["temp_corr_slope"] = tu.create_scalar_float_variable("Slope for effective temperature correction", units="1")
dataset["temp_corr_offset"] = tu.create_scalar_float_variable("Offset for effective temperature correction", units="1")
# mnfrqualflags
default_array = DefaultData.create_default_array(NUM_MINOR_FRAME, height, np.int32, dims_names=["y", "minor_frame"], fill_value=0)
variable = Variable(["y", "minor_frame"], default_array)
variable.attrs["standard_name"] = "status_flag"
variable.attrs["long_name"] = "minor_frame_quality_flags_bitfield"
dataset["mnfrqualflags"] = variable
# scnlintime
variable = HIRS._create_int32_vector(height, standard_name="time", long_name="Scan line time of day", orig_name="hrs_scnlintime")
tu.add_units(variable, "ms")
dataset["scnlintime"] = variable
# scnlinf
default_array = DefaultData.create_default_vector(height, np.int16, fill_value=0)
variable = Variable(["y"], default_array)
variable.attrs["standard_name"] = "status_flag"
variable.attrs["long_name"] = "scanline_bitfield"
variable.attrs["flag_masks"] = "16384, 32768"
variable.attrs["flag_meanings"] = "clock_drift_correction southbound_data"
dataset["scnlinf"] = variable
# scantype
default_array = DefaultData.create_default_vector(height, np.int8, fill_value=0)
variable = Variable(["y"], default_array)
variable.attrs["standard_name"] = "status_flag"
variable.attrs["long_name"] = "scantype_bitfield"
variable.attrs["flag_values"] = "0, 1, 2, 3"
variable.attrs["flag_meanings"] = "earth_view space_view cold_bb_view main_bb_view"
dataset["scantype"] = variable
def _create_channel_uncertainty_uint16(height):
default_array = DefaultData.create_default_array(NUM_CHANNELS, height, dtype=np.float32, dims_names=["y", "channel"], fill_value=np.NaN)
variable = Variable(["y", "channel"], default_array)
tu.add_encoding(variable, np.uint16, DefaultData.get_default_fill_value(np.uint16), 0.01)
return variable
def _create_channel_correlation_variable(long_name, data_type):
data_array = DefaultData.create_default_array(NUM_CHANNELS, NUM_CHANNELS, np.float32, fill_value=np.NaN)
variable = Variable(["channel", "channel"], data_array)
tu.add_encoding(variable, data_type, DefaultData.get_default_fill_value(data_type), scale_factor=0.01)
variable.attrs["long_name"] = long_name
return variable
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"])
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 _create_angle_variable(height, standard_name):
variable = tu.create_float_variable(SWATH_WIDTH, height, standard_name)
tu.add_units(variable, "degree")
tu.add_encoding(variable, np.uint16, 65535, 0.01)
return variable
