def test_lon_array(self):
"""
Test that the interpolated longitude array has sensible
values.
:notes:
The default image that is used is a 1000 by 1000. This might
be too small for a recursive depth of 7 (default within the
NBAR framework). As such a depth of 3 is used and produced
correct results. If a larger image is used, then the depth
level should probably be increased.
"""
acq = acquisitions(LS5_SCENE1).get_acquisitions()[0]
geobox = acq.gridded_geo_box()
fid = create_lon_lat_grids(acq, depth=5)
dataset_name = ppjoin(GroupName.LON_LAT_GROUP.value,
DatasetName.LON.value)
lon = fid[dataset_name][:]
ids = ut.random_pixel_locations(lon.shape)
# We'll transform (reproject) to WGS84
sr = osr.SpatialReference()
sr.SetFromUserInput(CRS)
# Get a list of x reprojected co-ordinates
reprj = []
for i in range(ids[0].shape[0]):
# Get pixel (x,y)
xy = (ids[1][i], ids[0][i])
# Convert pixel to map
mapXY = geobox.convert_coordinates(xy, centre=True)
# Transform map to another crs
x, _ = geobox.transform_coordinates(mapXY, to_crs=sr)
reprj.append(x)
reprj = numpy.array(reprj)
lon_values = lon[ids]
# A decimal degree co-ordinate should be correct up to the 6th dp
self.assertIsNone(npt.assert_almost_equal(lon_values, reprj,
decimal=5))
def card4l(level1, granule, workflow, vertices, method, pixel_quality, landsea,
tle_path, aerosol, brdf_path, brdf_premodis_path, ozone_path,
water_vapour, dem_path, dsm_fname, invariant_fname, modtran_exe,
out_fname, ecmwf_path=None, rori=0.52, buffer_distance=8000,
compression=H5CompressionFilter.LZF, filter_opts=None,
h5_driver=None, acq_parser_hint=None):
"""
CEOS Analysis Ready Data for Land.
A workflow for producing standardised products that meet the
CARD4L specification.
:param level1:
A string containing the full file pathname to the level1
dataset.
:param granule:
A string containing the granule id to process.
:param workflow:
An enum from wagl.constants.Workflow representing which
workflow workflow to run.
:param vertices:
An integer 2-tuple indicating the number of rows and columns
of sample-locations ("coordinator") to produce.
The vertex columns should be an odd number.
:param method:
An enum from wagl.constants.Method representing the
interpolation method to use during the interpolation
of the atmospheric coefficients.
:param pixel_quality:
A bool indicating whether or not to run pixel quality.
:param landsea:
A string containing the full file pathname to the directory
containing the land/sea mask datasets.
:param tle_path:
A string containing the full file pathname to the directory
containing the two line element datasets.
:param aerosol:
A string containing the full file pathname to the HDF5 file
containing the aerosol data.
:param brdf_path:
A string containing the full file pathname to the directory
containing the BRDF data.
:param brdf_premodis_path:
A string containing the full file pathname to the directory
containing the decadal averaged BRDF data used for acquisitions
prior to TERRA/AQUA satellite operations, or for near real time
applications.
:param ozone_path:
A string containing the full file pathname to the directory
containing the ozone datasets.
:param water_vapour:
A string containing the full file pathname to the directory
containing the water vapour datasets.
:param dem_path:
A string containing the full file pathname to the directory
containing the reduced resolution DEM.
:param dsm_path:
A string containing the full file pathname to the directory
containing the Digital Surface Workflow for use in terrain
illumination correction.
:param invariant_fname:
A string containing the full file pathname to the image file
containing the invariant geo-potential data for use within
the SBT process.
:param modtran_exe:
A string containing the full file pathname to the MODTRAN
executable.
:param out_fname:
A string containing the full file pathname that will contain
the output data from the data standardisation process.
executable.
:param ecmwf_path:
A string containing the full file pathname to the directory
containing the data from the European Centre for Medium Weather
Forcast, for use within the SBT process.
:param rori:
A floating point value for surface reflectance adjustment.
TODO Fuqin to add additional documentation for this parameter.
Default is 0.52.
:param buffer_distance:
A number representing the desired distance (in the same
units as the acquisition) in which to calculate the extra
number of pixels required to buffer an image.
Default is 8000, which for an acquisition using metres would
equate to 8000 metres.
:param compression:
An enum from hdf5.compression.H5CompressionFilter representing
the desired compression filter to use for writing H5 IMAGE and
TABLE class datasets to disk.
Default is H5CompressionFilter.LZF.
:param filter_opts:
A dict containing any additional keyword arguments when
generating the configuration for the given compression Filter.
Default is None.
:param h5_driver:
The specific HDF5 file driver to use when creating the output
HDF5 file.
See http://docs.h5py.org/en/latest/high/file.html#file-drivers
for more details.
Default is None; which writes direct to disk using the
appropriate driver for the underlying OS.
:param acq_parser_hint:
A string containing any hints to provide the acquisitions
loader with.
"""
tp5_fmt = pjoin(POINT_FMT, ALBEDO_FMT, ''.join([POINT_ALBEDO_FMT, '.tp5']))
nvertices = vertices[0] * vertices[1]
container = acquisitions(level1, hint=acq_parser_hint)
# TODO: pass through an acquisitions container rather than pathname
with h5py.File(out_fname, 'w', driver=h5_driver) as fid:
fid.attrs['level1_uri'] = level1
for grp_name in container.supported_groups:
log = STATUS_LOGGER.bind(level1=container.label, granule=granule,
granule_group=grp_name)
# root group for a given granule and resolution group
root = fid.create_group(ppjoin(granule, grp_name))
acqs = container.get_acquisitions(granule=granule, group=grp_name)
# longitude and latitude
log.info('Latitude-Longitude')
create_lon_lat_grids(acqs[0], root, compression, filter_opts)
# satellite and solar angles
log.info('Satellite-Solar-Angles')
calculate_angles(acqs[0], root[GroupName.LON_LAT_GROUP.value],
root, compression, filter_opts, tle_path)
if workflow == Workflow.STANDARD or workflow == Workflow.NBAR:
# DEM
log.info('DEM-retriveal')
get_dsm(acqs[0], dsm_fname, buffer_distance, root, compression,
filter_opts)
# slope & aspect
log.info('Slope-Aspect')
slope_aspect_arrays(acqs[0],
root[GroupName.ELEVATION_GROUP.value],
buffer_distance, root, compression,
filter_opts)
# incident angles
log.info('Incident-Angles')
incident_angles(root[GroupName.SAT_SOL_GROUP.value],
root[GroupName.SLP_ASP_GROUP.value],
root, compression, filter_opts)
# exiting angles
log.info('Exiting-Angles')
exiting_angles(root[GroupName.SAT_SOL_GROUP.value],
root[GroupName.SLP_ASP_GROUP.value],
root, compression, filter_opts)
# relative azimuth slope
log.info('Relative-Azimuth-Angles')
incident_group_name = GroupName.INCIDENT_GROUP.value
exiting_group_name = GroupName.EXITING_GROUP.value
relative_azimuth_slope(root[incident_group_name],
root[exiting_group_name],
root, compression, filter_opts)
# self shadow
log.info('Self-Shadow')
self_shadow(root[incident_group_name],
root[exiting_group_name], root, compression,
filter_opts)
# cast shadow solar source direction
log.info('Cast-Shadow-Solar-Direction')
dsm_group_name = GroupName.ELEVATION_GROUP.value
calculate_cast_shadow(acqs[0], root[dsm_group_name],
root[GroupName.SAT_SOL_GROUP.value],
buffer_distance, root, compression,
filter_opts)
# cast shadow satellite source direction
log.info('Cast-Shadow-Satellite-Direction')
calculate_cast_shadow(acqs[0], root[dsm_group_name],
root[GroupName.SAT_SOL_GROUP.value],
buffer_distance, root, compression,
filter_opts, False)
# combined shadow masks
log.info('Combined-Shadow')
combine_shadow_masks(root[GroupName.SHADOW_GROUP.value],
root[GroupName.SHADOW_GROUP.value],
root[GroupName.SHADOW_GROUP.value],
root, compression, filter_opts)
# nbar and sbt ancillary
log = STATUS_LOGGER.bind(level1=container.label, granule=granule,
granule_group=None)
# granule root group
root = fid[granule]
# get the highest resoltion group cotaining supported bands
acqs, grp_name = container.get_highest_resolution(granule=granule)
grn_con = container.get_granule(granule=granule, container=True)
res_group = root[grp_name]
log.info('Ancillary-Retrieval')
nbar_paths = {'aerosol_dict': aerosol,
'water_vapour_dict': water_vapour,
'ozone_path': ozone_path,
'dem_path': dem_path,
'brdf_path': brdf_path,
'brdf_premodis_path': brdf_premodis_path}
collect_ancillary(grn_con, res_group[GroupName.SAT_SOL_GROUP.value],
nbar_paths, ecmwf_path, invariant_fname,
vertices, root, compression, filter_opts)
# atmospherics
log.info('Atmospherics')
ancillary_group = root[GroupName.ANCILLARY_GROUP.value]
# satellite/solar angles and lon/lat for a resolution group
sat_sol_grp = res_group[GroupName.SAT_SOL_GROUP.value]
lon_lat_grp = res_group[GroupName.LON_LAT_GROUP.value]
# TODO: supported acqs in different groups pointing to different response funcs
# tp5 files
tp5_data, _ = format_tp5(acqs, ancillary_group, sat_sol_grp,
lon_lat_grp, workflow, root)
# atmospheric inputs group
inputs_grp = root[GroupName.ATMOSPHERIC_INPUTS_GRP.value]
# radiative transfer for each point and albedo
for key in tp5_data:
point, albedo = key
log.info('Radiative-Transfer', point=point, albedo=albedo.value)
with tempfile.TemporaryDirectory() as tmpdir:
prepare_modtran(acqs, point, [albedo], tmpdir, modtran_exe)
# tp5 data
fname = pjoin(tmpdir,
tp5_fmt.format(p=point, a=albedo.value))
with open(fname, 'w') as src:
src.writelines(tp5_data[key])
run_modtran(acqs, inputs_grp, workflow, nvertices, point,
[albedo], modtran_exe, tmpdir, root, compression,
filter_opts)
# atmospheric coefficients
log.info('Coefficients')
results_group = root[GroupName.ATMOSPHERIC_RESULTS_GRP.value]
calculate_coefficients(results_group, root, compression, filter_opts)
# interpolate coefficients
for grp_name in container.supported_groups:
log = STATUS_LOGGER.bind(level1=container.label, granule=granule,
granule_group=grp_name)
log.info('Interpolation')
# acquisitions and available bands for the current group level
acqs = container.get_acquisitions(granule=granule, group=grp_name)
nbar_acqs = [acq for acq in acqs if
acq.band_type == BandType.REFLECTIVE]
sbt_acqs = [acq for acq in acqs if
acq.band_type == BandType.THERMAL]
res_group = root[grp_name]
sat_sol_grp = res_group[GroupName.SAT_SOL_GROUP.value]
comp_grp = root[GroupName.COEFFICIENTS_GROUP.value]
for coefficient in workflow.atmos_coefficients:
if coefficient in Workflow.NBAR.atmos_coefficients:
band_acqs = nbar_acqs
else:
band_acqs = sbt_acqs
for acq in band_acqs:
log.info('Interpolate', band_id=acq.band_id,
coefficient=coefficient.value)
interpolate(acq, coefficient, ancillary_group, sat_sol_grp,
comp_grp, res_group, compression, filter_opts,
method)
# standardised products
band_acqs = []
if workflow == Workflow.STANDARD or workflow == Workflow.NBAR:
band_acqs.extend(nbar_acqs)
if workflow == Workflow.STANDARD or workflow == Workflow.SBT:
band_acqs.extend(sbt_acqs)
for acq in band_acqs:
interp_grp = res_group[GroupName.INTERP_GROUP.value]
if acq.band_type == BandType.THERMAL:
log.info('SBT', band_id=acq.band_id)
surface_brightness_temperature(acq, interp_grp, res_group,
compression, filter_opts)
else:
slp_asp_grp = res_group[GroupName.SLP_ASP_GROUP.value]
rel_slp_asp = res_group[GroupName.REL_SLP_GROUP.value]
incident_grp = res_group[GroupName.INCIDENT_GROUP.value]
exiting_grp = res_group[GroupName.EXITING_GROUP.value]
shadow_grp = res_group[GroupName.SHADOW_GROUP.value]
log.info('Surface-Reflectance', band_id=acq.band_id)
calculate_reflectance(acq, interp_grp, sat_sol_grp,
slp_asp_grp, rel_slp_asp,
incident_grp, exiting_grp,
shadow_grp, ancillary_group,
rori, res_group, compression,
filter_opts)
# metadata yaml's
if workflow == Workflow.STANDARD or workflow == Workflow.NBAR:
create_ard_yaml(band_acqs, ancillary_group, res_group)
if workflow == Workflow.STANDARD or workflow == Workflow.SBT:
create_ard_yaml(band_acqs, ancillary_group, res_group, True)
# pixel quality
sbt_only = workflow == Workflow.SBT
if pixel_quality and can_pq(level1, acq_parser_hint) and not sbt_only:
run_pq(level1, res_group, landsea, res_group, compression, filter_opts, AP.NBAR, acq_parser_hint)
run_pq(level1, res_group, landsea, res_group, compression, filter_opts, AP.NBART, acq_parser_hint)
def card4l(
level1,
granule,
workflow,
vertices,
method,
pixel_quality,
landsea,
tle_path,
aerosol,
brdf,
ozone_path,
water_vapour,
dem_path,
dsm_fname,
invariant_fname,
modtran_exe,
out_fname,
ecmwf_path=None,
rori=0.52,
buffer_distance=8000,
compression=H5CompressionFilter.LZF,
filter_opts=None,
h5_driver=None,
acq_parser_hint=None,
normalized_solar_zenith=45.0,
):
"""
CEOS Analysis Ready Data for Land.
A workflow for producing standardised products that meet the
CARD4L specification.
:param level1:
A string containing the full file pathname to the level1
dataset.
:param granule:
A string containing the granule id to process.
:param workflow:
An enum from wagl.constants.Workflow representing which
workflow workflow to run.
:param vertices:
An integer 2-tuple indicating the number of rows and columns
of sample-locations ("coordinator") to produce.
The vertex columns should be an odd number.
:param method:
An enum from wagl.constants.Method representing the
interpolation method to use during the interpolation
of the atmospheric coefficients.
:param pixel_quality:
A bool indicating whether or not to run pixel quality.
:param landsea:
A string containing the full file pathname to the directory
containing the land/sea mask datasets.
:param tle_path:
A string containing the full file pathname to the directory
containing the two line element datasets.
:param aerosol:
A string containing the full file pathname to the HDF5 file
containing the aerosol data.
:param brdf:
A dict containing either user-supplied BRDF values, or the
full file pathname to the directory containing the BRDF data
and the decadal averaged BRDF data used for acquisitions
prior to TERRA/AQUA satellite operations.
:param ozone_path:
A string containing the full file pathname to the directory
containing the ozone datasets.
:param water_vapour:
A string containing the full file pathname to the directory
containing the water vapour datasets.
:param dem_path:
A string containing the full file pathname to the directory
containing the reduced resolution DEM.
:param dsm_path:
A string containing the full file pathname to the directory
containing the Digital Surface Workflow for use in terrain
illumination correction.
:param invariant_fname:
A string containing the full file pathname to the image file
containing the invariant geo-potential data for use within
the SBT process.
:param modtran_exe:
A string containing the full file pathname to the MODTRAN
executable.
:param out_fname:
A string containing the full file pathname that will contain
the output data from the data standardisation process.
executable.
:param ecmwf_path:
A string containing the full file pathname to the directory
containing the data from the European Centre for Medium Weather
Forcast, for use within the SBT process.
:param rori:
A floating point value for surface reflectance adjustment.
TODO Fuqin to add additional documentation for this parameter.
Default is 0.52.
:param buffer_distance:
A number representing the desired distance (in the same
units as the acquisition) in which to calculate the extra
number of pixels required to buffer an image.
Default is 8000, which for an acquisition using metres would
equate to 8000 metres.
:param compression:
An enum from hdf5.compression.H5CompressionFilter representing
the desired compression filter to use for writing H5 IMAGE and
TABLE class datasets to disk.
Default is H5CompressionFilter.LZF.
:param filter_opts:
A dict containing any additional keyword arguments when
generating the configuration for the given compression Filter.
Default is None.
:param h5_driver:
The specific HDF5 file driver to use when creating the output
HDF5 file.
See http://docs.h5py.org/en/latest/high/file.html#file-drivers
for more details.
Default is None; which writes direct to disk using the
appropriate driver for the underlying OS.
:param acq_parser_hint:
A string containing any hints to provide the acquisitions
loader with.
:param normalized_solar_zenith:
Solar zenith angle to normalize for (in degrees). Default is 45 degrees.
"""
json_fmt = pjoin(POINT_FMT, ALBEDO_FMT,
"".join([POINT_ALBEDO_FMT, ".json"]))
nvertices = vertices[0] * vertices[1]
container = acquisitions(level1, hint=acq_parser_hint)
# TODO: pass through an acquisitions container rather than pathname
with h5py.File(out_fname, "w", driver=h5_driver) as fid:
fid.attrs["level1_uri"] = level1
for grp_name in container.supported_groups:
log = STATUS_LOGGER.bind(level1=container.label,
granule=granule,
granule_group=grp_name)
# root group for a given granule and resolution group
root = fid.create_group(ppjoin(granule, grp_name))
acqs = container.get_acquisitions(granule=granule, group=grp_name)
# include the resolution as a group attribute
root.attrs["resolution"] = acqs[0].resolution
# longitude and latitude
log.info("Latitude-Longitude")
create_lon_lat_grids(acqs[0], root, compression, filter_opts)
# satellite and solar angles
log.info("Satellite-Solar-Angles")
calculate_angles(
acqs[0],
root[GroupName.LON_LAT_GROUP.value],
root,
compression,
filter_opts,
tle_path,
)
if workflow in (Workflow.STANDARD, Workflow.NBAR):
# DEM
log.info("DEM-retriveal")
get_dsm(acqs[0], dsm_fname, buffer_distance, root, compression,
filter_opts)
# slope & aspect
log.info("Slope-Aspect")
slope_aspect_arrays(
acqs[0],
root[GroupName.ELEVATION_GROUP.value],
buffer_distance,
root,
compression,
filter_opts,
)
# incident angles
log.info("Incident-Angles")
incident_angles(
root[GroupName.SAT_SOL_GROUP.value],
root[GroupName.SLP_ASP_GROUP.value],
root,
compression,
filter_opts,
)
# exiting angles
log.info("Exiting-Angles")
exiting_angles(
root[GroupName.SAT_SOL_GROUP.value],
root[GroupName.SLP_ASP_GROUP.value],
root,
compression,
filter_opts,
)
# relative azimuth slope
log.info("Relative-Azimuth-Angles")
incident_group_name = GroupName.INCIDENT_GROUP.value
exiting_group_name = GroupName.EXITING_GROUP.value
relative_azimuth_slope(
root[incident_group_name],
root[exiting_group_name],
root,
compression,
filter_opts,
)
# self shadow
log.info("Self-Shadow")
self_shadow(
root[incident_group_name],
root[exiting_group_name],
root,
compression,
filter_opts,
)
# cast shadow solar source direction
log.info("Cast-Shadow-Solar-Direction")
dsm_group_name = GroupName.ELEVATION_GROUP.value
calculate_cast_shadow(
acqs[0],
root[dsm_group_name],
root[GroupName.SAT_SOL_GROUP.value],
buffer_distance,
root,
compression,
filter_opts,
)
# cast shadow satellite source direction
log.info("Cast-Shadow-Satellite-Direction")
calculate_cast_shadow(
acqs[0],
root[dsm_group_name],
root[GroupName.SAT_SOL_GROUP.value],
buffer_distance,
root,
compression,
filter_opts,
False,
)
# combined shadow masks
log.info("Combined-Shadow")
combine_shadow_masks(
root[GroupName.SHADOW_GROUP.value],
root[GroupName.SHADOW_GROUP.value],
root[GroupName.SHADOW_GROUP.value],
root,
compression,
filter_opts,
)
# nbar and sbt ancillary
log = STATUS_LOGGER.bind(level1=container.label,
granule=granule,
granule_group=None)
# granule root group
root = fid[granule]
# get the highest resolution group containing supported bands
acqs, grp_name = container.get_highest_resolution(granule=granule)
grn_con = container.get_granule(granule=granule, container=True)
res_group = root[grp_name]
log.info("Ancillary-Retrieval")
nbar_paths = {
"aerosol_dict": aerosol,
"water_vapour_dict": water_vapour,
"ozone_path": ozone_path,
"dem_path": dem_path,
"brdf_dict": brdf,
}
collect_ancillary(
grn_con,
res_group[GroupName.SAT_SOL_GROUP.value],
nbar_paths,
ecmwf_path,
invariant_fname,
vertices,
root,
compression,
filter_opts,
)
# atmospherics
log.info("Atmospherics")
ancillary_group = root[GroupName.ANCILLARY_GROUP.value]
# satellite/solar angles and lon/lat for a resolution group
sat_sol_grp = res_group[GroupName.SAT_SOL_GROUP.value]
lon_lat_grp = res_group[GroupName.LON_LAT_GROUP.value]
# TODO: supported acqs in different groups pointing to different response funcs
json_data, _ = format_json(acqs, ancillary_group, sat_sol_grp,
lon_lat_grp, workflow, root)
# atmospheric inputs group
inputs_grp = root[GroupName.ATMOSPHERIC_INPUTS_GRP.value]
# radiative transfer for each point and albedo
for key in json_data:
point, albedo = key
log.info("Radiative-Transfer", point=point, albedo=albedo.value)
with tempfile.TemporaryDirectory() as tmpdir:
prepare_modtran(acqs, point, [albedo], tmpdir)
point_dir = pjoin(tmpdir, POINT_FMT.format(p=point))
workdir = pjoin(point_dir, ALBEDO_FMT.format(a=albedo.value))
json_mod_infile = pjoin(
tmpdir, json_fmt.format(p=point, a=albedo.value))
with open(json_mod_infile, "w") as src:
json_dict = json_data[key]
if albedo == Albedos.ALBEDO_TH:
json_dict["MODTRAN"][0]["MODTRANINPUT"]["SPECTRAL"][
"FILTNM"] = "%s/%s" % (
workdir,
json_dict["MODTRAN"][0]["MODTRANINPUT"]
["SPECTRAL"]["FILTNM"],
)
json_dict["MODTRAN"][1]["MODTRANINPUT"]["SPECTRAL"][
"FILTNM"] = "%s/%s" % (
workdir,
json_dict["MODTRAN"][1]["MODTRANINPUT"]
["SPECTRAL"]["FILTNM"],
)
else:
json_dict["MODTRAN"][0]["MODTRANINPUT"]["SPECTRAL"][
"FILTNM"] = "%s/%s" % (
workdir,
json_dict["MODTRAN"][0]["MODTRANINPUT"]
["SPECTRAL"]["FILTNM"],
)
json.dump(json_dict, src, cls=JsonEncoder, indent=4)
run_modtran(
acqs,
inputs_grp,
workflow,
nvertices,
point,
[albedo],
modtran_exe,
tmpdir,
root,
compression,
filter_opts,
)
# atmospheric coefficients
log.info("Coefficients")
results_group = root[GroupName.ATMOSPHERIC_RESULTS_GRP.value]
calculate_coefficients(results_group, root, compression, filter_opts)
esun_values = {}
# interpolate coefficients
for grp_name in container.supported_groups:
log = STATUS_LOGGER.bind(level1=container.label,
granule=granule,
granule_group=grp_name)
log.info("Interpolation")
# acquisitions and available bands for the current group level
acqs = container.get_acquisitions(granule=granule, group=grp_name)
nbar_acqs = [
acq for acq in acqs if acq.band_type == BandType.REFLECTIVE
]
sbt_acqs = [
acq for acq in acqs if acq.band_type == BandType.THERMAL
]
res_group = root[grp_name]
sat_sol_grp = res_group[GroupName.SAT_SOL_GROUP.value]
comp_grp = root[GroupName.COEFFICIENTS_GROUP.value]
for coefficient in workflow.atmos_coefficients:
if coefficient is AtmosphericCoefficients.ESUN:
continue
if coefficient in Workflow.NBAR.atmos_coefficients:
band_acqs = nbar_acqs
else:
band_acqs = sbt_acqs
for acq in band_acqs:
log.info("Interpolate",
band_id=acq.band_id,
coefficient=coefficient.value)
interpolate(
acq,
coefficient,
ancillary_group,
sat_sol_grp,
comp_grp,
res_group,
compression,
filter_opts,
method,
)
# standardised products
band_acqs = []
if workflow in (Workflow.STANDARD, Workflow.NBAR):
band_acqs.extend(nbar_acqs)
if workflow in (Workflow.STANDARD, Workflow.SBT):
band_acqs.extend(sbt_acqs)
for acq in band_acqs:
interp_grp = res_group[GroupName.INTERP_GROUP.value]
if acq.band_type == BandType.THERMAL:
log.info("SBT", band_id=acq.band_id)
surface_brightness_temperature(acq, interp_grp, res_group,
compression, filter_opts)
else:
atmos_coefs = read_h5_table(
comp_grp, DatasetName.NBAR_COEFFICIENTS.value)
esun_values[acq.band_name] = (
atmos_coefs[atmos_coefs.band_name == acq.band_name][
AtmosphericCoefficients.ESUN.value]).values[0]
slp_asp_grp = res_group[GroupName.SLP_ASP_GROUP.value]
rel_slp_asp = res_group[GroupName.REL_SLP_GROUP.value]
incident_grp = res_group[GroupName.INCIDENT_GROUP.value]
exiting_grp = res_group[GroupName.EXITING_GROUP.value]
shadow_grp = res_group[GroupName.SHADOW_GROUP.value]
log.info("Surface-Reflectance", band_id=acq.band_id)
calculate_reflectance(
acq,
interp_grp,
sat_sol_grp,
slp_asp_grp,
rel_slp_asp,
incident_grp,
exiting_grp,
shadow_grp,
ancillary_group,
rori,
res_group,
compression,
filter_opts,
normalized_solar_zenith,
esun_values[acq.band_name],
)
# pixel quality
sbt_only = workflow == Workflow.SBT
if pixel_quality and can_pq(level1,
acq_parser_hint) and not sbt_only:
run_pq(
level1,
res_group,
landsea,
res_group,
compression,
filter_opts,
AP.NBAR,
acq_parser_hint,
)
run_pq(
level1,
res_group,
landsea,
res_group,
compression,
filter_opts,
AP.NBART,
acq_parser_hint,
)
def get_band_acqs(grp_name):
acqs = container.get_acquisitions(granule=granule, group=grp_name)
nbar_acqs = [
acq for acq in acqs if acq.band_type == BandType.REFLECTIVE
]
sbt_acqs = [
acq for acq in acqs if acq.band_type == BandType.THERMAL
]
band_acqs = []
if workflow in (Workflow.STANDARD, Workflow.NBAR):
band_acqs.extend(nbar_acqs)
if workflow in (Workflow.STANDARD, Workflow.SBT):
band_acqs.extend(sbt_acqs)
return band_acqs
# wagl parameters
parameters = {
"vertices": list(vertices),
"method": method.value,
"rori": rori,
"buffer_distance": buffer_distance,
"normalized_solar_zenith": normalized_solar_zenith,
"esun": esun_values,
}
# metadata yaml's
metadata = root.create_group(DatasetName.METADATA.value)
create_ard_yaml(
{
grp_name: get_band_acqs(grp_name)
for grp_name in container.supported_groups
},
ancillary_group,
metadata,
parameters,
workflow,
)