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 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 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 _draw_taylor_diagram(evaluation, plot_config):
""""""
plot_name = plot_config['output_name']
ref_dataset_name = evaluation.ref_dataset.name
target_dataset_names = [t.name for t in evaluation.target_datasets]
if len(plot_config['stddev_results_indices'][0]) == 2:
stddev_results = [
evaluation.results[tar][met]
for (tar, met) in plot_config['stddev_results_indices']
]
pattern_corr_results = [
evaluation.results[tar][met]
for (tar, met) in plot_config['pattern_corr_results_indices']
]
elif len(plot_config['stddev_results_indices'][0]) == 3:
stddev_results = [
evaluation.results[tar][met][sub]
for (tar, met, sub) in plot_config['stddev_results_indices']
]
pattern_corr_results = [
evaluation.results[tar][met][sub]
for (tar, met, sub) in plot_config['pattern_corr_results_indices']
]
plot_data = np.array([stddev_results, pattern_corr_results]).transpose()
plots.draw_taylor_diagram(plot_data,
target_dataset_names,
ref_dataset_name,
fname=plot_name,
**plot_config.get('optional_args', {}))
def _draw_taylor_diagram(evaluation, plot_config):
""""""
plot_name = plot_config['output_name']
ref_dataset_name = evaluation.ref_dataset.name
target_dataset_names = [t.name for t in evaluation.target_datasets]
if len(plot_config['stddev_results_indices'][0]) == 2:
stddev_results = [
evaluation.results[tar][met]
for (tar, met) in plot_config['stddev_results_indices']
]
pattern_corr_results = [
evaluation.results[tar][met]
for (tar, met) in plot_config['pattern_corr_results_indices']
]
elif len(plot_config['stddev_results_indices'][0]) == 3:
stddev_results = [
evaluation.results[tar][met][sub]
for (tar, met, sub) in plot_config['stddev_results_indices']
]
pattern_corr_results = [
evaluation.results[tar][met][sub]
for (tar, met, sub) in plot_config['pattern_corr_results_indices']
]
plot_data = np.array([stddev_results, pattern_corr_results]).transpose()
plots.draw_taylor_diagram(plot_data,
target_dataset_names,
ref_dataset_name,
fname=plot_name,
**plot_config.get('optional_args', {}))
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)
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!
##########################################################################
plotter.draw_taylor_diagram(taylor_data, [wrf_dataset.name],
knmi_dataset.name,
fname='taylor_plot',
fmt='png',
frameon=False)
pattern_correlation = metrics.SeasonalPatternCorrelation(
month_start=SEASON_MONTH_START, month_end=SEASON_MONTH_END)
# Create our example evaluation.
example_eval = evaluation.Evaluation(
ref_dataset, # Reference dataset for the evaluation
# 1 or more target datasets for the evaluation
[target_dataset],
# 1 ore more metrics to use in the evaluation
[mean_bias, spatial_std_dev_ratio, pattern_correlation])
example_eval.run()
plotter.draw_contour_map(example_eval.results[0][0],
new_lats,
new_lons,
'lund_example_time_averaged_bias',
gridshape=(1, 1),
ptitle='Time Averaged Bias')
spatial_stddev_ratio = example_eval.results[0][1]
# Pattern correlation results are a tuple, so we need to index and grab
# the component we care about.
spatial_correlation = example_eval.results[0][2][0]
taylor_data = np.array([[spatial_stddev_ratio],
[spatial_correlation]]).transpose()
plotter.draw_taylor_diagram(taylor_data, [target_dataset.name],
ref_dataset.name,
fname='lund_example_taylor_diagram',
frameon=False)
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!
################################################################################
plotter.draw_taylor_diagram(taylor_data,
[wrf_dataset.name],
knmi_dataset.name,
fname='taylor_plot',
fmt='png',
frameon=False)
for member, each_target_dataset in enumerate(target_datasets):
target_datasets[member].values = utils.calc_temporal_mean(
target_datasets[member])
allNames = []
for target in target_datasets:
allNames.append(target.name)
#calculate the metrics
taylor_diagram = metrics.SpatialPatternTaylorDiagram()
#create the Evaluation object
RCMs_to_CRU_evaluation = evaluation.Evaluation(
CRU31, # Reference dataset for the evaluation
# 1 or more target datasets for the evaluation
target_datasets,
# 1 or more metrics to use in the evaluation
[taylor_diagram
]) #, mean_bias,spatial_std_dev_ratio, pattern_correlation])
RCMs_to_CRU_evaluation.run()
taylor_data = RCMs_to_CRU_evaluation.results[0]
plotter.draw_taylor_diagram(taylor_data,
allNames,
"CRU31",
fname=OUTPUT_PLOT,
fmt='png',
frameon=False)
# 1 ore more metrics to use in the evaluation
[mean_bias, spatial_std_dev_ratio, pattern_correlation])
example_eval.run()
spatial_stddev_ratio = []
spatial_correlation = []
taylor_dataset_names = [] # List of datasets names for the Taylor diagram
for i, target in enumerate(example_eval.target_datasets):
# For each target dataset in the evaluation, draw a contour map
plotter.draw_contour_map(example_eval.results[i][0],
new_lats,
new_lons,
'lund_{}_{}_time_averaged_bias'.format(
ref_dataset.name, target.name),
gridshape=(1, 1),
ptitle='Time Averaged Bias')
taylor_dataset_names.append(target.name)
# Grab Data for a Taylor Diagram
spatial_stddev_ratio.append(example_eval.results[i][1])
# Pattern correlation results are a tuple, so we need to index and grab
# the component we care about.
spatial_correlation.append(example_eval.results[i][2][0])
taylor_data = np.array([spatial_stddev_ratio, spatial_correlation]).transpose()
plotter.draw_taylor_diagram(taylor_data,
taylor_dataset_names,
ref_dataset.name,
fname='lund_taylor_diagram',
frameon=False)
#calculate the metrics
pattern_correlation = metrics.PatternCorrelation()
spatial_std_dev = metrics.StdDevRatio()
#create the Evaluation object
RCMs_to_CRU_evaluation = evaluation.Evaluation(CRU31, # Reference dataset for the evaluation
# 1 or more target datasets for the evaluation
target_datasets,
# 1 or more metrics to use in the evaluation
[spatial_std_dev, pattern_correlation])#, mean_bias,spatial_std_dev_ratio, pattern_correlation])
RCMs_to_CRU_evaluation.run()
rcm_std_dev = [results[0] for results in RCMs_to_CRU_evaluation.results]
rcm_pat_cor = [results[1] for results in RCMs_to_CRU_evaluation.results]
taylor_data = np.array([rcm_std_dev, rcm_pat_cor]).transpose()
new_taylor_data = np.squeeze(np.array(taylor_data))
plotter.draw_taylor_diagram(new_taylor_data,
allNames,
"CRU31",
fname=OUTPUT_PLOT,
fmt='png',
frameon=False)
def calculate_metrics_and_make_plots(varName, workdir, lons, lats, obsData, mdlData, obsRgn, mdlRgn, obsList, mdlList, subRegions, \
subRgnLon0, subRgnLon1, subRgnLat0, subRgnLat1):
'''
Purpose::
Calculate all the metrics used in Kim et al. [2013] paper and plot them
Input::
varName - evaluating variable
workdir -
lons -
lats -
obsData -
mdlData -
obsRgn -
mdlRgn -
obsList -
mdlList -
subRegions -
subRgnLon0, subRgnLat0 - southwest boundary of sub-regions [numSubRgn]
subRgnLon1, subRgnLat1 - northeast boundary of sub-regions [numSubRgn]
Output::
png files
'''
nobs, nt, ny, nx = obsData.shape
nmodel = mdlData.shape[0]
### TODO: unit conversion (K to C)
if varName == 'temp':
obsData[0, :, :, :] = obsData[0, :, :, :] - 273.15
if subRegions:
obsRgn[0, :, :] = obsRgn[0, :, :] - 273.15
if varName == 'prec' and obsData.max() > mdlData.max()*1000.:
mdlData[:, :, :, :] = mdlData[:, :, :, :]*86400.
if subRegions:
mdlRgn[:, :, :] = mdlRgn[:, :, :]*86400.
oTser, oClim = calcClimYear( obsData[0, :, :, :])
bias_of_overall_average = ma.zeros([nmodel, ny, nx])
spatial_stdev_ratio = np.zeros([nmodel])
spatial_corr = np.zeros([nmodel])
mdlList.append('ENS')
for imodel in np.arange(nmodel):
mTser, mClim = calcClimYear( mdlData[imodel,:,:,:])
bias_of_overall_average[imodel,:,:] = calcBias(mClim, oClim)
spatial_corr[imodel], sigLev = calcPatternCorrelation(oClim, mClim)
spatial_stdev_ratio[imodel] = calcSpatialStdevRatio(mClim, oClim)
fig_return = plotter.draw_contour_map(oClim, lats, lons, workdir+'/observed_climatology_'+varName, fmt='png', gridshape=(1, 1),
clabel='', ptitle='', subtitles=obsList, cmap=None,
clevs=None, nlevs=10, parallels=None, meridians=None,
extend='neither')
# TODO:
# Be sure to update "gridshape" argument to be the number of sub plots (rows,columns). This should be improved so that the
# gridshape is optimally determined for a given number of models. For example:
# For 3 models, a gridshape of (2,2) would be sensible:
# X X
# X
#
fig_return = plotter.draw_contour_map(bias_of_overall_average, lats, lons, workdir+'/bias_of_climatology_'+varName, fmt='png', gridshape=(6, 2),
clabel='', ptitle='', subtitles=mdlList, cmap=None,
clevs=None, nlevs=10, parallels=None, meridians=None,
extend='neither')
Taylor_data = np.array([spatial_stdev_ratio, spatial_corr]).transpose()
fig_return = plotter.draw_taylor_diagram(Taylor_data, mdlList, refname='CRU', fname = workdir+'/Taylor_'+varName, fmt='png',frameon=False)
if subRegions:
nseason = 2 # (0: summer and 1: winter)
nregion = len(subRgnLon0)
season_name = ['summer','winter']
rowlabels = ['PNw','PNe','CAn','CAs','SWw','SWe','COL','GPn','GPc','GC','GL','NE','SE','FL']
collabels = ['M1','M2','M3','M4','M5','M6','ENS']
collabels[nmodel-1] = 'ENS'
for iseason in [0,1]:
portrait_subregion = np.zeros([4, nregion, nmodel])
portrait_titles = ['(a) Normalized Bias', '(b) Normalized STDV', '(c) Normalized RMSE', '(d) Correlation']
if iseason == 0:
monthBegin=6
monthEnd=8
if iseason == 1:
monthBegin=12
monthEnd=2
obsTser,obsClim = calcClimSeasonSubRegion(6,8,obsRgn[0,:,:])
for imodel in np.arange(nmodel):
mTser, mClim = calcClimSeasonSubRegion(6,8,mdlRgn[imodel,:,:])
for iregion in np.arange(nregion):
portrait_subregion[0,iregion,imodel] = calcBias(mClim[iregion],obsClim[iregion])/calcTemporalStdev(obsTser[iregion,:])
portrait_subregion[1,iregion,imodel] = calcTemporalStdev(mTser[iregion,:])/ calcTemporalStdev(obsTser[iregion,:])
portrait_subregion[2,iregion,imodel] = calcRootMeanSquaredDifferenceAveragedOverTime(mTser[iregion,:], obsTser[iregion,:])/calcTemporalStdev(obsTser[iregion,:])
portrait_subregion[3,iregion, imodel] = calcTemporalCorrelationSubRegion(mTser[iregion,:],obsTser[iregion,:])
portrait_return = plotter.draw_portrait_diagram(portrait_subregion, rowlabels, collabels[0:nmodel], workdir+'/portrait_diagram_'+season_name[iseason]+'_'+varName, fmt='png',
gridshape=(2, 2), xlabel='', ylabel='', clabel='',
ptitle='', subtitles=portrait_titles, cmap=None, clevs=None,
nlevs=10, extend='neither')
# annual cycle
nmonth = 12
times = np.arange(nmonth)
data_names = [obsList[0]] + list(mdlList)
annual_cycle = np.zeros([nregion, nmonth, nmodel+1])
obsTser, annual_cycle[:, :, 0] = calcAnnualCycleMeansSubRegion(obsRgn[0,:,:])
obsStd = calcAnnualCycleStdevSubRegion(obsRgn[0,:,:])
for imodel in np.arange(nmodel):
mdlTser, annual_cycle[:, :, imodel+1] = calcAnnualCycleMeansSubRegion(mdlRgn[imodel, :, :])
# Make annual_cycle shape compatible with draw_time_series
annual_cycle = annual_cycle.swapaxes(1, 2)
tseries_return = plotter.draw_time_series(annual_cycle, times, data_names, workdir+'/time_series_'+varName, gridshape=(7, 2),
subtitles=rowlabels, label_month=True)
# configuration section.
spatial_std_dev_ratio = metrics.SeasonalSpatialStdDevRatio(month_start=SEASON_MONTH_START, month_end=SEASON_MONTH_END)
pattern_correlation = metrics.SeasonalPatternCorrelation(month_start=SEASON_MONTH_START, month_end=SEASON_MONTH_END)
# Create our example evaluation.
example_eval = evaluation.Evaluation(ref_dataset, # Reference dataset for the evaluation
# 1 or more target datasets for the evaluation
[target_dataset],
# 1 ore more metrics to use in the evaluation
[mean_bias, spatial_std_dev_ratio, pattern_correlation])
example_eval.run()
plotter.draw_contour_map(example_eval.results[0][0],
new_lats,
new_lons,
'lund_example_time_averaged_bias',
gridshape=(1, 1),
ptitle='Time Averaged Bias')
spatial_stddev_ratio = example_eval.results[0][1]
# Pattern correlation results are a tuple, so we need to index and grab
# the component we care about.
spatial_correlation = example_eval.results[0][2][0]
taylor_data = np.array([[spatial_stddev_ratio], [spatial_correlation]]).transpose()
plotter.draw_taylor_diagram(taylor_data,
[target_dataset.name],
ref_dataset.name,
fname='lund_example_taylor_diagram',
frameon=False)
target_datasets,
# 1 ore more metrics to use in the evaluation
[mean_bias, spatial_std_dev_ratio, pattern_correlation])
example_eval.run()
spatial_stddev_ratio = []
spatial_correlation = []
taylor_dataset_names = [] # List of datasets names for the Taylor diagram
for i, target in enumerate(example_eval.target_datasets):
# For each target dataset in the evaluation, draw a contour map
plotter.draw_contour_map(example_eval.results[i][0],
new_lats,
new_lons,
'lund_{}_{}_time_averaged_bias'.format(ref_dataset.name, target.name),
gridshape=(1, 1),
ptitle='Time Averaged Bias')
taylor_dataset_names.append(target.name)
# Grab Data for a Taylor Diagram
spatial_stddev_ratio.append(example_eval.results[i][1])
# Pattern correlation results are a tuple, so we need to index and grab
# the component we care about.
spatial_correlation.append(example_eval.results[i][2][0])
taylor_data = np.array([spatial_stddev_ratio, spatial_correlation]).transpose()
plotter.draw_taylor_diagram(taylor_data,
taylor_dataset_names,
ref_dataset.name,
fname='lund_taylor_diagram',
frameon=False)
