def Taylor_diagram_spatial_pattern_of_multiyear_climatology(
obs_dataset, obs_name, model_datasets, model_names, file_name):
# calculate climatological mean fields
obs_dataset.values = utils.calc_temporal_mean(obs_dataset)
for dataset in model_datasets:
dataset.values = utils.calc_temporal_mean(dataset)
# Metrics (spatial standard deviation and pattern correlation)
# determine the metrics
taylor_diagram = metrics.SpatialPatternTaylorDiagram()
# create the Evaluation object
taylor_evaluation = Evaluation(
obs_dataset, # Reference dataset for the evaluation
model_datasets, # list of target datasets for the evaluation
[taylor_diagram])
# run the evaluation (bias calculation)
taylor_evaluation.run()
taylor_data = taylor_evaluation.results[0]
plotter.draw_taylor_diagram(taylor_data,
model_names,
obs_name,
file_name,
pos='upper right',
frameon=False)
def Taylor_diagram_spatial_pattern_of_multiyear_climatology(
obs_dataset, obs_name, model_datasets, model_names, file_name):
# calculate climatological mean fields
obs_clim_dataset = ds.Dataset(obs_dataset.lats, obs_dataset.lons,
obs_dataset.times,
utils.calc_temporal_mean(obs_dataset))
model_clim_datasets = []
for dataset in model_datasets:
model_clim_datasets.append(
ds.Dataset(dataset.lats, dataset.lons, dataset.times,
utils.calc_temporal_mean(dataset)))
# Metrics (spatial standard deviation and pattern correlation)
# determine the metrics
taylor_diagram = metrics.SpatialPatternTaylorDiagram()
# create the Evaluation object
taylor_evaluation = Evaluation(
obs_clim_dataset, # Climatological mean of reference dataset for the evaluation
model_clim_datasets, # list of climatological means from model datasets for the evaluation
[taylor_diagram])
# run the evaluation (bias calculation)
taylor_evaluation.run()
taylor_data = taylor_evaluation.results[0]
plotter.draw_taylor_diagram(taylor_data,
model_names,
obs_name,
file_name,
pos='upper right',
frameon=False)
def setUp(self):
self.taylor_diagram = metrics.SpatialPatternTaylorDiagram()
self.ref_dataset = Dataset(
np.array([1., 1., 1., 1., 1.]),
np.array([1., 1., 1., 1., 1.]),
np.array([dt.datetime(2000, x, 1) for x in range(1, 13)]),
# Reshapped array with 300 values incremented by 5
np.arange(0, 1500, 5).reshape(12, 5, 5),
'ds1')
self.tar_dataset = Dataset(
np.array([1., 1., 1., 1., 1.]),
np.array([1., 1., 1., 1., 1.]),
np.array([dt.datetime(2000, x, 1) for x in range(1, 13)]),
# Reshapped array with 300 values incremented by 2
np.arange(0, 600, 2).reshape(12, 5, 5),
'ds2')
# Spatially regrid the datasets onto a 1 degree grid.
##########################################################################
# Get the bounds of the reference dataset and use it to create a new
# set of lat/lon values on a 1 degree step
# Using the bounds we will create a new set of lats and lons on 1 degree step
min_lat, max_lat, min_lon, max_lon = knmi_dataset.spatial_boundaries()
new_lons = numpy.arange(min_lon, max_lon, 1)
new_lats = numpy.arange(min_lat, max_lat, 1)
# Spatially regrid datasets using the new_lats, new_lons numpy arrays
knmi_dataset = dsp.spatial_regrid(knmi_dataset, new_lats, new_lons)
wrf_dataset = dsp.spatial_regrid(wrf_dataset, new_lats, new_lons)
# Load the metrics that we want to use for the evaluation.
##########################################################################
taylor_diagram = metrics.SpatialPatternTaylorDiagram()
# Create our new evaluation object. The knmi dataset is the evaluations
# reference dataset. We then provide a list of 1 or more target datasets
# to use for the evaluation. In this case, we only want to use the wrf dataset.
# Then we pass a list of all the metrics that we want to use in the evaluation.
##########################################################################
test_evaluation = evaluation.Evaluation(knmi_dataset, [wrf_dataset],
[taylor_diagram])
test_evaluation.run()
# Pull our the evaluation results and prepare them for drawing a Taylor diagram.
##########################################################################
taylor_data = test_evaluation.results[0]
# Draw our taylor diagram!