def test_adapters_with_ascat():
ascat_data_folder = os.path.join(
os.path.dirname(__file__),
"..",
"test-data",
"sat",
"ascat",
"netcdf",
"55R22",
)
ascat_grid_folder = os.path.join(
os.path.dirname(__file__),
"..",
"test-data",
"sat",
"ascat",
"netcdf",
"grid",
)
grid_fname = os.path.join(ascat_grid_folder, "TUW_WARP5_grid_info_2_1.nc")
ascat_reader = AscatGriddedNcTs(
ascat_data_folder,
"TUW_METOP_ASCAT_WARP55R22_{:04d}",
grid_filename=grid_fname,
)
ascat_anom = AnomalyAdapter(ascat_reader, window_size=35, columns=["sm"])
data = ascat_anom.read_ts(12.891455, 45.923004)
assert data is not None
assert np.any(data["sm"].values != 0)
data = ascat_anom.read(12.891455, 45.923004)
assert data is not None
assert np.any(data["sm"].values != 0)
ascat_self = SelfMaskingAdapter(ascat_reader, ">", 0, "sm")
data2 = ascat_self.read_ts(12.891455, 45.923004)
assert data2 is not None
assert np.all(data2["sm"].values > 0)
data2 = ascat_self.read(12.891455, 45.923004)
assert data2 is not None
assert np.all(data2["sm"].values > 0)
ascat_mask = MaskingAdapter(ascat_reader, ">", 0, "sm")
data3 = ascat_mask.read_ts(12.891455, 45.923004)
assert data3 is not None
assert np.any(data3["sm"].values)
data3 = ascat_mask.read(12.891455, 45.923004)
assert data3 is not None
assert np.any(data3["sm"].values)
ascat_clim = AnomalyClimAdapter(ascat_reader, columns=["sm"])
data4 = ascat_clim.read_ts(12.891455, 45.923004)
assert data4 is not None
assert np.any(data["sm"].values != 0)
data4 = ascat_clim.read(12.891455, 45.923004)
assert data4 is not None
assert np.any(data["sm"].values != 0)
def test_anomaly_clim_adapter():
ds = TestDataset('', n=20)
ds_anom = AnomalyClimAdapter(ds)
data_anom = ds_anom.read_ts()
data_anom2 = ds_anom.read()
nptest.assert_almost_equal(data_anom['x'].values[4], -5.5)
nptest.assert_almost_equal(data_anom['y'].values[4], -2.75)
nptest.assert_almost_equal(data_anom2['x'].values[4], -5.5)
nptest.assert_almost_equal(data_anom2['y'].values[4], -2.75)
def test_timezone_removal():
tz_reader = TestTimezoneReader()
reader_anom = AnomalyAdapter(tz_reader, window_size=35, columns=["data"])
assert reader_anom.read_ts(0) is not None
reader_self = SelfMaskingAdapter(tz_reader, ">", 0, "data")
assert reader_self.read_ts(0) is not None
reader_mask = MaskingAdapter(tz_reader, ">", 0, "data")
assert reader_mask.read_ts(0) is not None
reader_clim = AnomalyClimAdapter(tz_reader, columns=["data"])
assert reader_clim.read_ts(0) is not None
def test_adapters_with_ascat():
ascat_data_folder = os.path.join(os.path.dirname(__file__), '..',
'test-data', 'sat', 'ascat', 'netcdf',
'55R22')
ascat_grid_folder = os.path.join(os.path.dirname(__file__), '..',
'test-data', 'sat', 'ascat', 'netcdf',
'grid')
ascat_reader = AscatSsmCdr(ascat_data_folder,
ascat_grid_folder,
grid_filename='TUW_WARP5_grid_info_2_1.nc')
ascat_anom = AnomalyAdapter(ascat_reader, window_size=35, columns=['sm'])
data = ascat_anom.read_ts(12.891455, 45.923004)
assert data is not None
assert np.any(data['sm'].values != 0)
data = ascat_anom.read(12.891455, 45.923004)
assert data is not None
assert np.any(data['sm'].values != 0)
ascat_self = SelfMaskingAdapter(ascat_reader, '>', 0, 'sm')
data2 = ascat_self.read_ts(12.891455, 45.923004)
assert data2 is not None
assert np.all(data2['sm'].values > 0)
data2 = ascat_self.read(12.891455, 45.923004)
assert data2 is not None
assert np.all(data2['sm'].values > 0)
ascat_mask = MaskingAdapter(ascat_reader, '>', 0, 'sm')
data3 = ascat_mask.read_ts(12.891455, 45.923004)
assert data3 is not None
assert np.any(data3['sm'].values)
data3 = ascat_mask.read(12.891455, 45.923004)
assert data3 is not None
assert np.any(data3['sm'].values)
ascat_clim = AnomalyClimAdapter(ascat_reader, columns=['sm'])
data4 = ascat_clim.read_ts(12.891455, 45.923004)
assert data4 is not None
assert np.any(data['sm'].values != 0)
data4 = ascat_clim.read(12.891455, 45.923004)
assert data4 is not None
assert np.any(data['sm'].values != 0)
def test_anomaly_clim_adapter_one_column():
ds = TestDataset('', n=20)
ds_anom = AnomalyClimAdapter(ds, columns=['x'])
data_anom = ds_anom.read_ts()
nptest.assert_almost_equal(data_anom['x'].values[4], -5.5)
nptest.assert_almost_equal(data_anom['y'].values[4], 2)
def create_pytesmo_validation(validation_run):
ds_list = []
ref_name = None
scaling_ref_name = None
ds_num = 1
for dataset_config in validation_run.dataset_configurations.all():
reader = create_reader(dataset_config.dataset, dataset_config.version)
reader = setup_filtering(
reader, list(dataset_config.filters.all()),
list(dataset_config.parametrisedfilter_set.all()),
dataset_config.dataset, dataset_config.variable)
if validation_run.anomalies == ValidationRun.MOVING_AVG_35_D:
reader = AnomalyAdapter(
reader,
window_size=35,
columns=[dataset_config.variable.pretty_name])
if validation_run.anomalies == ValidationRun.CLIMATOLOGY:
# make sure our baseline period is in UTC and without timezone information
anomalies_baseline = [
validation_run.anomalies_from.astimezone(tz=pytz.UTC).replace(
tzinfo=None),
validation_run.anomalies_to.astimezone(tz=pytz.UTC).replace(
tzinfo=None)
]
reader = AnomalyClimAdapter(
reader,
columns=[dataset_config.variable.pretty_name],
timespan=anomalies_baseline)
if (validation_run.reference_configuration and
(dataset_config.id == validation_run.reference_configuration.id)):
# reference is always named "0-..."
dataset_name = '{}-{}'.format(0, dataset_config.dataset.short_name)
else:
dataset_name = '{}-{}'.format(ds_num,
dataset_config.dataset.short_name)
ds_num += 1
ds_list.append((dataset_name, {
'class': reader,
'columns': [dataset_config.variable.pretty_name]
}))
if (validation_run.reference_configuration and
(dataset_config.id == validation_run.reference_configuration.id)):
ref_name = dataset_name
ref_short_name = validation_run.reference_configuration.dataset.short_name
if (validation_run.scaling_ref
and (dataset_config.id == validation_run.scaling_ref.id)):
scaling_ref_name = dataset_name
datasets = dict(ds_list)
ds_num = len(ds_list)
period = None
if validation_run.interval_from is not None and validation_run.interval_to is not None:
# while pytesmo can't deal with timezones, normalise the validation period to utc; can be removed once pytesmo can do timezones
startdate = validation_run.interval_from.astimezone(UTC).replace(
tzinfo=None)
enddate = validation_run.interval_to.astimezone(UTC).replace(
tzinfo=None)
period = [startdate, enddate]
upscale_parms = None
if validation_run.upscaling_method != "none":
__logger.debug("Upscaling option is active")
upscale_parms = {
"upscaling_method": validation_run.upscaling_method,
"temporal_stability": validation_run.temporal_stability,
}
upscaling_lut = create_upscaling_lut(
validation_run=validation_run,
datasets=datasets,
ref_name=ref_name,
)
upscale_parms["upscaling_lut"] = upscaling_lut
__logger.debug("Lookup table for non-reference datasets " +
", ".join(upscaling_lut.keys()) + " created")
__logger.debug("{}".format(upscaling_lut))
datamanager = DataManager(
datasets,
ref_name=ref_name,
period=period,
read_ts_names='read',
upscale_parms=upscale_parms,
)
ds_names = get_dataset_names(datamanager.reference_name,
datamanager.datasets,
n=ds_num)
# set value of the metadata template according to what reference dataset is used
if ref_short_name == 'ISMN':
metadata_template = METADATA_TEMPLATE['ismn_ref']
else:
metadata_template = METADATA_TEMPLATE['other_ref']
pairwise_metrics = PairwiseIntercomparisonMetrics(
metadata_template=metadata_template,
calc_kendall=False,
)
metric_calculators = {(ds_num, 2): pairwise_metrics.calc_metrics}
if (len(ds_names) >= 3) and (validation_run.tcol is True):
tcol_metrics = TripleCollocationMetrics(
ref_name,
metadata_template=metadata_template,
)
metric_calculators.update({(ds_num, 3): tcol_metrics.calc_metrics})
if validation_run.scaling_method == validation_run.NO_SCALING:
scaling_method = None
else:
scaling_method = validation_run.scaling_method
__logger.debug(f"Scaling method: {scaling_method}")
__logger.debug(f"Scaling dataset: {scaling_ref_name}")
val = Validation(datasets=datamanager,
temporal_matcher=make_combined_temporal_matcher(
pd.Timedelta(12, "H")),
spatial_ref=ref_name,
scaling=scaling_method,
scaling_ref=scaling_ref_name,
metrics_calculators=metric_calculators,
period=period)
return val
def create_pytesmo_validation(validation_run):
ds_list = []
ref_name = None
scaling_ref_name = None
ds_num = 1
for dataset_config in validation_run.dataset_configurations.all():
reader = create_reader(dataset_config.dataset, dataset_config.version)
reader = setup_filtering(
reader, list(dataset_config.filters.all()),
list(dataset_config.parametrisedfilter_set.all()),
dataset_config.dataset, dataset_config.variable)
if validation_run.anomalies == ValidationRun.MOVING_AVG_35_D:
reader = AnomalyAdapter(
reader,
window_size=35,
columns=[dataset_config.variable.pretty_name])
if validation_run.anomalies == ValidationRun.CLIMATOLOGY:
# make sure our baseline period is in UTC and without timezone information
anomalies_baseline = [
validation_run.anomalies_from.astimezone(tz=pytz.UTC).replace(
tzinfo=None),
validation_run.anomalies_to.astimezone(tz=pytz.UTC).replace(
tzinfo=None)
]
reader = AnomalyClimAdapter(
reader,
columns=[dataset_config.variable.pretty_name],
timespan=anomalies_baseline)
if ((validation_run.reference_configuration) and
(dataset_config.id == validation_run.reference_configuration.id)):
# reference is always named "0-..."
dataset_name = '{}-{}'.format(0, dataset_config.dataset.short_name)
else:
dataset_name = '{}-{}'.format(ds_num,
dataset_config.dataset.short_name)
ds_num += 1
ds_list.append((dataset_name, {
'class': reader,
'columns': [dataset_config.variable.pretty_name]
}))
if ((validation_run.reference_configuration) and
(dataset_config.id == validation_run.reference_configuration.id)):
ref_name = dataset_name
if ((validation_run.scaling_ref)
and (dataset_config.id == validation_run.scaling_ref.id)):
scaling_ref_name = dataset_name
datasets = dict(ds_list)
ds_num = len(ds_list)
period = None
if validation_run.interval_from is not None and validation_run.interval_to is not None:
## while pytesmo can't deal with timezones, normalise the validation period to utc; can be removed once pytesmo can do timezones
startdate = validation_run.interval_from.astimezone(UTC).replace(
tzinfo=None)
enddate = validation_run.interval_to.astimezone(UTC).replace(
tzinfo=None)
period = [startdate, enddate]
datamanager = DataManager(datasets,
ref_name=ref_name,
period=period,
read_ts_names='read')
ds_names = get_dataset_names(datamanager.reference_name,
datamanager.datasets,
n=ds_num)
if (len(ds_names) >= 3) and (validation_run.tcol is True):
# if there are 3 or more dataset, do TC, exclude ref metrics
metrics = TCMetrics(
dataset_names=ds_names,
tc_metrics_for_ref=False,
other_names=['k{}'.format(i + 1) for i in range(ds_num - 1)])
else:
metrics = IntercomparisonMetrics(
dataset_names=ds_names,
other_names=['k{}'.format(i + 1) for i in range(ds_num - 1)])
if validation_run.scaling_method == validation_run.NO_SCALING:
scaling_method = None
else:
scaling_method = validation_run.scaling_method
__logger.debug(f"Scaling method: {scaling_method}")
__logger.debug(f"Scaling dataset: {scaling_ref_name}")
val = Validation(datasets=datamanager,
spatial_ref=ref_name,
temporal_window=0.5,
scaling=scaling_method,
scaling_ref=scaling_ref_name,
metrics_calculators={
(ds_num, ds_num): metrics.calc_metrics
},
period=period)
return val