def _draw_time_series_plot(evaluation, plot_config):
""""""
time_range_info = plot_config['time_range']
ref_ds = evaluation.ref_dataset
target_ds = evaluation.target_datasets
if time_range_info == 'monthly':
ref_ds.values, ref_ds.times = utils.calc_climatology_monthly(ref_ds)
for t in target_ds:
t.values, t.times = utils.calc_climatology_monthly(t)
else:
logger.error('Invalid time range provided. Only monthly is supported '
'at the moment')
return
if evaluation.subregions:
for bound_count, bound in enumerate(evaluation.subregions):
results = []
labels = []
subset = dsp.subset(bound,
ref_ds,
subregion_name="R{}_{}".format(
bound_count, ref_ds.name))
results.append(utils.calc_time_series(subset))
labels.append(subset.name)
for t in target_ds:
subset = dsp.subset(bound,
t,
subregion_name="R{}_{}".format(
bound_count, t.name))
results.append(utils.calc_time_series(subset))
labels.append(subset.name)
plots.draw_time_series(np.array(results), ref_ds.times, labels,
'R{}'.format(bound_count),
**plot_config.get('optional_args', {}))
else:
results = []
labels = []
results.append(utils.calc_time_series(ref_ds))
labels.append(ref_ds.name)
for t in target_ds:
results.append(utils.calc_time_series(t))
labels.append(t.name)
plots.draw_time_series(np.array(results), ref_ds.times, labels,
'time_series',
**plot_config.get('optional_args', {}))
def test_calc_climatology_monthly(self):
expected_result = np.ones(300).reshape(12, 5, 5)
expected_times = np.array([datetime.datetime(1, 1, 1) + relativedelta(months = x)
for x in range(12)])
actual_result, actual_times = utils.calc_climatology_monthly(self.dataset)
np.testing.assert_array_equal(actual_result, expected_result)
np.testing.assert_array_equal(actual_times, expected_times)
def test_calc_climatology_monthly(self):
expected_result = np.ones(300).reshape(12, 5, 5)
expected_times = np.array([datetime.datetime(1, 1, 1) + relativedelta(months = x)
for x in range(12)])
actual_result, actual_times = utils.calc_climatology_monthly(self.dataset)
np.testing.assert_array_equal(actual_result, expected_result)
np.testing.assert_array_equal(actual_times, expected_times)
member])
target_datasets[member] = dsp.normalize_dataset_datetimes(
target_datasets[member], 'monthly')
print("... spatial regridding")
new_lats = np.arange(LAT_MIN, LAT_MAX, gridLatStep)
new_lons = np.arange(LON_MIN, LON_MAX, gridLonStep)
CRU31 = dsp.spatial_regrid(CRU31, new_lats, new_lons)
for member, each_target_dataset in enumerate(target_datasets):
target_datasets[member] = dsp.spatial_regrid(
target_datasets[member], new_lats, new_lons)
# find climatology monthly for obs and models
CRU31.values, CRU31.times = utils.calc_climatology_monthly(CRU31)
for member, each_target_dataset in enumerate(target_datasets):
target_datasets[member].values, target_datasets[
member].times = utils.calc_climatology_monthly(target_datasets[member])
# make the model ensemble
target_datasets_ensemble = dsp.ensemble(target_datasets)
target_datasets_ensemble.name = "ENS"
# append to the target_datasets for final analysis
target_datasets.append(target_datasets_ensemble)
""" Step 4: Subregion stuff """
list_of_regions = [
Bounds(-10.0, 0.0, 29.0, 36.5),
def test_invalid_time_shape(self):
flat_array = np.array(range(350))
self.dataset.values = flat_array.reshape(14, 5, 5)
with self.assertRaises(ValueError):
utils.calc_climatology_monthly(self.dataset)
target_datasets[member] = dsp.water_flux_unit_conversion(
target_datasets[member])
target_datasets[member] = dsp.normalize_dataset_datetimes(
target_datasets[member], 'monthly')
print("... spatial regridding")
new_lats = np.arange(LAT_MIN, LAT_MAX, gridLatStep)
new_lons = np.arange(LON_MIN, LON_MAX, gridLonStep)
CRU31 = dsp.spatial_regrid(CRU31, new_lats, new_lons)
for member, each_target_dataset in enumerate(target_datasets):
target_datasets[member] = dsp.spatial_regrid(target_datasets[member],
new_lats, new_lons)
#find climatology monthly for obs and models
CRU31.values, CRU31.times = utils.calc_climatology_monthly(CRU31)
for member, each_target_dataset in enumerate(target_datasets):
target_datasets[member].values, target_datasets[
member].times = utils.calc_climatology_monthly(target_datasets[member])
#make the model ensemble
target_datasets_ensemble = dsp.ensemble(target_datasets)
target_datasets_ensemble.name = "ENS"
#append to the target_datasets for final analysis
target_datasets.append(target_datasets_ensemble)
""" Step 4: Subregion stuff """
list_of_regions = [
Bounds(-10.0, 0.0, 29.0, 36.5),
Bounds(0.0, 10.0, 29.0, 37.5),
def test_calc_climatology_monthly(self):
expected_result = np.ones(300).reshape(12, 5, 5)
actual_result = utils.calc_climatology_monthly(self.dataset)
np.testing.assert_array_equal(actual_result, expected_result)
def _draw_time_series_plot(evaluation, plot_config):
""""""
time_range_info = plot_config['time_range']
ref_ds = evaluation.ref_dataset
target_ds = evaluation.target_datasets
if time_range_info == 'monthly':
ref_ds.values, ref_ds.times = utils.calc_climatology_monthly(ref_ds)
for t in target_ds:
t.values, t.times = utils.calc_climatology_monthly(t)
else:
logger.error(
'Invalid time range provided. Only monthly is supported '
'at the moment'
)
return
if evaluation.subregions:
for bound_count, bound in enumerate(evaluation.subregions):
results = []
labels = []
subset = dsp.subset(
bound,
ref_ds,
subregion_name="R{}_{}".format(bound_count, ref_ds.name)
)
results.append(utils.calc_time_series(subset))
labels.append(subset.name)
for t in target_ds:
subset = dsp.subset(
bound,
t,
subregion_name="R{}_{}".format(bound_count, t.name)
)
results.append(utils.calc_time_series(subset))
labels.append(subset.name)
plots.draw_time_series(np.array(results),
ref_ds.times,
labels,
'R{}'.format(bound_count),
**plot_config.get('optional_args', {}))
else:
results = []
labels = []
results.append(utils.calc_time_series(ref_ds))
labels.append(ref_ds.name)
for t in target_ds:
results.append(utils.calc_time_series(t))
labels.append(t.name)
plots.draw_time_series(np.array(results),
ref_ds.times,
labels,
'time_series',
**plot_config.get('optional_args', {}))
