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 add_easy_fcdr_variables(dataset,
height,
corr_dx=None,
corr_dy=None,
lut_size=None):
default_array = DefaultData.create_default_array_3d(SWATH_WIDTH,
height,
NUM_CHANNELS,
np.float32,
fill_value=np.NaN)
variable = Variable(["channel", "y", "x"], default_array)
tu.add_fill_value(variable, np.NaN)
tu.add_units(variable, "K")
variable.attrs["long_name"] = "independent uncertainty per pixel"
dataset["u_independent_tb"] = variable
default_array = DefaultData.create_default_array_3d(SWATH_WIDTH,
height,
NUM_CHANNELS,
np.float32,
fill_value=np.NaN)
variable = Variable(["channel", "y", "x"], default_array)
tu.add_fill_value(variable, np.NaN)
tu.add_units(variable, "K")
variable.attrs["long_name"] = "structured uncertainty per pixel"
dataset["u_structured_tb"] = 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_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 _create_angle_variable_int(scale_factor,
standard_name=None,
long_name=None,
unsigned=False,
fill_value=None):
default_array = DefaultData.create_default_array(TIE_SIZE,
TIE_SIZE,
np.float32,
fill_value=np.NaN)
variable = Variable(["y_tie", "x_tie"], default_array)
if unsigned is True:
data_type = np.uint16
else:
data_type = np.int16
if fill_value is None:
fill_value = DefaultData.get_default_fill_value(data_type)
if standard_name is not None:
variable.attrs["standard_name"] = standard_name
if long_name is not None:
variable.attrs["long_name"] = long_name
tu.add_units(variable, "degree")
variable.attrs["tie_points"] = "true"
tu.add_encoding(variable,
data_type,
fill_value,
scale_factor,
chunksizes=CHUNKSIZES)
return variable
def add_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_angle_uncertainty_variable(angle_name, height):
variable = tu.create_float_variable(SWATH_WIDTH,
height,
long_name="uncertainty of " +
angle_name,
fill_value=np.NaN)
tu.add_units(variable, "degree")
return variable
def _create_temperature_array_3d(height, long_name, orig_name, dim_names):
default_array = DefaultData.create_default_array_3d(PRT_READING, height, PRT_NUMBER, np.float32, fill_value=np.NaN, dims_names=dim_names)
variable = Variable(["prt_number", "y", "prt_reading"], default_array)
tu.add_fill_value(variable, np.NaN)
tu.add_units(variable, "K")
variable.attrs["long_name"] = long_name
variable.attrs["orig_name"] = orig_name
return variable
def _create_time_ranges_variable(height, width, description):
default_array = DefaultData.create_default_array_3d(
width, height, 2, np.int32, fill_value=4294967295)
variable = Variable(["bounds", "y", "x"], default_array)
tu.add_fill_value(variable, 4294967295)
tu.add_units(variable, "s")
variable.attrs["description"] = description
variable.attrs["coordinates"] = "lon lat"
return variable
def _create_counts_uncertainty_variable(height, long_name):
variable = tu.create_float_variable(SWATH_WIDTH,
height,
long_name=long_name,
fill_value=np.NaN)
tu.add_units(variable, "count")
tu.add_geolocation_attribute(variable)
tu.add_chunking(variable, CHUNKS_2D)
return variable
def create_angle_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_angle_uncertainty_variable(angle_name, height):
variable = tu.create_float_variable(SWATH_WIDTH,
height,
long_name="uncertainty of " +
angle_name,
fill_value=np.NaN)
tu.add_units(variable, "degree")
tu.add_geolocation_attribute(variable)
tu.add_chunking(variable, CHUNKS_2D)
return variable
def _create_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 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 _create_geo_angle_variable(standard_name, height, orig_name=None, chunking=None):
default_array = DefaultData.create_default_array(SWATH_WIDTH, height, np.float32, fill_value=np.NaN)
variable = Variable(["y", "x"], default_array)
variable.attrs["standard_name"] = standard_name
if orig_name is not None:
variable.attrs["orig_name"] = orig_name
tu.add_units(variable, "degree")
tu.add_geolocation_attribute(variable)
tu.add_encoding(variable, np.uint16, DefaultData.get_default_fill_value(np.uint16), 0.01, -180.0, chunking)
return variable
def _create_counts_variable(height, long_name):
default_array = DefaultData.create_default_array(
SWATH_WIDTH, height, np.int32)
variable = Variable(["y", "x"], default_array)
tu.add_fill_value(variable,
DefaultData.get_default_fill_value(np.int32))
variable.attrs["long_name"] = long_name
tu.add_units(variable, "count")
tu.add_geolocation_attribute(variable)
tu.add_chunking(variable, CHUNKS_2D)
return variable
def _create_uth_variable(width, height, description=None):
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"] = "lon lat"
variable.attrs["long_name"] = "upper_tropospheric_humidity"
tu.add_units(variable, "%")
if description is not None:
variable.attrs["description"] = description
return 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_full_fcdr_variables(dataset, height):
# u_btemps
variable = AMSUB_MHS._create_3d_float_variable(height)
variable.attrs[
"long_name"] = "total uncertainty of brightness temperature"
tu.add_units(variable, "K")
dataset["u_btemps"] = variable
# u_syst_btemps
variable = AMSUB_MHS._create_3d_float_variable(height)
variable.attrs[
"long_name"] = "systematic uncertainty of brightness temperature"
tu.add_units(variable, "K")
dataset["u_syst_btemps"] = variable
# u_random_btemps
variable = AMSUB_MHS._create_3d_float_variable(height)
variable.attrs["long_name"] = "noise on brightness temperature"
tu.add_units(variable, "K")
dataset["u_random_btemps"] = variable
# u_instrtemp
default_array = DefaultData.create_default_vector(height,
np.float32,
fill_value=np.NaN)
variable = Variable(["y"], default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs["long_name"] = "uncertainty of instrument temperature"
tu.add_units(variable, "K")
dataset["u_instrtemp"] = variable
# u_latitude
variable = AMSUB_MHS.create_angle_uncertainty_variable(
"latitude", height)
dataset["u_latitude"] = variable
# u_longitude
variable = AMSUB_MHS.create_angle_uncertainty_variable(
"longitude", height)
dataset["u_longitude"] = variable
# u_satellite_azimuth_angle
variable = AMSUB_MHS.create_angle_uncertainty_variable(
"satellite azimuth angle", height)
dataset["u_satellite_azimuth_angle"] = variable
# u_satellite_zenith_angle
variable = AMSUB_MHS.create_angle_uncertainty_variable(
"satellite zenith angle", height)
dataset["u_satellite_zenith_angle"] = variable
# u_solar_azimuth_angle
variable = AMSUB_MHS.create_angle_uncertainty_variable(
"solar azimuth angle", height)
dataset["u_solar_azimuth_angle"] = variable
# u_solar_zenith_angle
variable = AMSUB_MHS.create_angle_uncertainty_variable(
"solar zenith angle", height)
dataset["u_solar_zenith_angle"] = variable
def _create_bt_variable(width, height, description=None):
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"] = "lon lat"
variable.attrs["standard_name"] = "toa_brightness_temperature"
tu.add_units(variable, "K")
if description is not None:
variable.attrs["description"] = description
return variable
def _add_angle_variables(dataset, height):
default_array = DefaultData.create_default_vector(height,
np.float32,
fill_value=np.NaN)
variable = Variable(["y"], default_array)
variable.attrs["standard_name"] = "platform_zenith_angle"
tu.add_units(variable, "degree")
tu.add_geolocation_attribute(variable)
tu.add_encoding(variable, np.uint16,
DefaultData.get_default_fill_value(np.uint16), 0.01,
-180.0)
dataset["satellite_zenith_angle"] = variable
dataset["solar_azimuth_angle"] = HIRS._create_geo_angle_variable(
"solar_azimuth_angle", height, chunking=CHUNKING_2D)
def _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 add_easy_fcdr_variables(dataset,
height,
srf_size=None,
corr_dx=None,
corr_dy=None,
lut_size=None):
# u_independent_btemps
variable = AMSUB_MHS._create_3d_float_variable(height)
tu.add_units(variable, "K")
variable.attrs["long_name"] = "independent uncertainty per pixel"
dataset["u_independent_btemps"] = variable
# u_structured_btemps
variable = AMSUB_MHS._create_3d_float_variable(height)
tu.add_units(variable, "K")
variable.attrs["long_name"] = "structured uncertainty per pixel"
dataset["u_structured_btemps"] = 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_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 _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_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 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")
