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', {}))
for dataset in model_dataset
]
model_dataset_season = [
dsp.temporal_subset(dataset,
month_start,
month_end,
average_each_year=True)
for dataset in model_dataset_subset
]
""" Spatial subset of obs_dataset and generate time series """
obs_timeseries = np.zeros([nyear, n_region
]) # region index 0-6: NW, SW, NGP, SGP, MW, NE, SE
model_timeseries = np.zeros([nmodel, nyear, n_region])
for iregion in np.arange(n_region):
obs_timeseries[:, iregion] = utils.calc_time_series(
dsp.subset(obs_dataset_season, regional_bounds[iregion]))
for imodel in np.arange(nmodel):
model_timeseries[imodel, :, iregion] = utils.calc_time_series(
dsp.subset(model_dataset_season[imodel], regional_bounds[iregion]))
year = np.arange(nyear)
regional_trends_obs = np.zeros(n_region)
regional_trends_obs_error = np.zeros(n_region)
regional_trends_model = np.zeros([nmodel, n_region])
regional_trends_model_error = np.zeros([nmodel, n_region])
regional_trends_ens = np.zeros(n_region)
regional_trends_ens_error = np.zeros(n_region)
for iregion in np.arange(n_region):
regional_trends_obs[iregion], regional_trends_obs_error[
Bounds(-10.0, 10.0, 7.3, 15.0),
Bounds(-10.9, 10.0, 5.0, 7.3),
Bounds(33.9, 40.0, 6.9, 15.0),
Bounds(10.0, 25.0, 0.0, 10.0),
Bounds(10.0, 25.0, -10.0, 0.0),
Bounds(30.0, 40.0, -15.0, 0.0),
Bounds(33.0, 40.0, 25.0, 35.0)]
region_list = [["R" + str(i + 1)] for i in xrange(13)]
for regions in region_list:
firstTime = True
subset_name = regions[0] + "_CRU31"
# labels.append(subset_name) #for legend, uncomment this line
subset = dsp.subset(CRU31, list_of_regions[region_counter], subset_name)
tSeries = utils.calc_time_series(subset)
results.append(tSeries)
tSeries = []
firstTime = False
for member, each_target_dataset in enumerate(target_datasets):
subset_name = regions[0] + "_" + target_datasets[member].name
# labels.append(subset_name) #for legend, uncomment this line
subset = dsp.subset(target_datasets[member],
list_of_regions[region_counter],
subset_name)
tSeries = utils.calc_time_series(subset)
results.append(tSeries)
tSeries = []
plotter.draw_time_series(np.array(results), CRU31.times, labels, regions[
0], ptitle=regions[0], fmt='png')
def test_calc_time_series(self):
expected_result = np.ones(12)
np.testing.assert_array_equal(utils.calc_time_series(self.dataset), expected_result)
def test_calc_time_series(self):
expected_result = np.ones(12)
np.testing.assert_array_equal(
utils.calc_time_series(self.dataset), expected_result)
Bounds(-10.9, 10.0, 5.0, 7.3),
Bounds(33.9, 40.0, 6.9, 15.0),
Bounds(10.0, 25.0, 0.0, 10.0),
Bounds(10.0, 25.0, -10.0, 0.0),
Bounds(30.0, 40.0, -15.0, 0.0),
Bounds(33.0, 40.0, 25.0, 35.0)
]
region_list = [["R" + str(i + 1)] for i in xrange(13)]
for regions in region_list:
firstTime = True
subset_name = regions[0] + "_CRU31"
#labels.append(subset_name) #for legend, uncomment this line
subset = dsp.subset(list_of_regions[region_counter], CRU31, subset_name)
tSeries = utils.calc_time_series(subset)
results.append(tSeries)
tSeries = []
firstTime = False
for member, each_target_dataset in enumerate(target_datasets):
subset_name = regions[0] + "_" + target_datasets[member].name
#labels.append(subset_name) #for legend, uncomment this line
subset = dsp.subset(list_of_regions[region_counter],
target_datasets[member], subset_name)
tSeries = utils.calc_time_series(subset)
results.append(tSeries)
tSeries = []
plotter.draw_time_series(np.array(results),
CRU31.times,
labels,
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', {}))
obs_dataset_season = dsp.temporal_subset(obs_dataset_subset, month_start, month_end,
average_each_year=True)
""" Temporal subset of model_dataset """
model_dataset_subset = [dsp.temporal_slice(dataset,start_time=start_date, end_time=end_date)
for dataset in model_dataset]
model_dataset_season = [dsp.temporal_subset(dataset, month_start, month_end,
average_each_year=True) for dataset in model_dataset_subset]
""" Spatial subset of obs_dataset and generate time series """
obs_timeseries = np.zeros([nyear, n_region]) # region index 0-6: NW, SW, NGP, SGP, MW, NE, SE
model_timeseries = np.zeros([nmodel, nyear, n_region])
for iregion in np.arange(n_region):
obs_timeseries[:, iregion] = utils.calc_time_series(
dsp.subset(obs_dataset_season, regional_bounds[iregion]))
for imodel in np.arange(nmodel):
model_timeseries[imodel, :, iregion] = utils.calc_time_series(
dsp.subset(model_dataset_season[imodel], regional_bounds[iregion]))
year = np.arange(nyear)
regional_trends_obs = np.zeros(n_region)
regional_trends_obs_error = np.zeros(n_region)
regional_trends_model = np.zeros([nmodel, n_region])
regional_trends_model_error = np.zeros([nmodel, n_region])
regional_trends_ens = np.zeros(n_region)
regional_trends_ens_error = np.zeros(n_region)
for iregion in np.arange(n_region):
regional_trends_obs[iregion], regional_trends_obs_error[iregion] = utils.calculate_temporal_trend_of_time_series(
