def createTemplateEasy(sensorType,
height,
srf_size=None,
corr_dx=None,
corr_dy=None,
lut_size=None):
"""
Create a template dataset in EASY FCDR format for the sensor given as argument.
:param sensorType: the sensor type to create the template for
:param height: the height in pixels of the data product
:param srf_size: if set, the length of the spectral response function in frequency steps
:param corr_dx: correlation length across track
:param corr_dy: correlation length along track
:param lut_size: size of a BT/radiance conversion lookup table
:return the template dataset
"""
dataset = xr.Dataset()
WriterUtils.add_standard_global_attributes(dataset)
template_factory = TemplateFactory()
sensor_template = template_factory.get_sensor_template(sensorType)
sensor_template.add_original_variables(dataset, height, srf_size)
sensor_template.add_easy_fcdr_variables(dataset, height, corr_dx,
corr_dy, lut_size)
sensor_template.add_template_key(dataset)
return dataset
def createTemplateFull(sensorType, height):
"""
Create a template dataset in FULL FCDR format for the sensor given as argument.
:param sensorType: the sensor type to create the template for
:param height the hheight in pixels of the data product
:return the template dataset
"""
dataset = xr.Dataset()
WriterUtils.add_standard_global_attributes(dataset)
template_factory = TemplateFactory()
sensor_template = template_factory.get_sensor_template(sensorType)
sensor_template.add_original_variables(dataset, height)
sensor_template.add_full_fcdr_variables(dataset, height)
sensor_template.add_template_key(dataset)
return dataset
def createTemplate(data_type, width, height, num_samples=None):
"""
Create a template dataset in CDR format for the data type given as argument.
:param data_type: the data type to create the template for
:param width: the width in pixels of the data product
:param height: the height in pixels of the data product
:return the template dataset
"""
dataset = xr.Dataset()
WriterUtils.add_standard_global_attributes(dataset)
WriterUtils.add_cdr_global_attributes(dataset)
template_factory = CDR_TemplateFactory()
sensor_template = template_factory.get_cdr_template(data_type)
if num_samples is None:
sensor_template.add_variables(dataset, width, height)
else:
sensor_template.add_variables(dataset, width, height, num_samples)
return dataset
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 test_add_gridded_global_attributes(self):
dataset = xr.Dataset()
WriterUtils.add_gridded_global_attributes(dataset)
Assertions.assert_gridded_global_attributes(self, dataset.attrs)