def Portrait_diagram_subregion(obs_subregion_mean,
obs_name,
model_subregion_mean,
model_names,
seasonal_cycle,
file_name,
normalize=True):
nmodel, nt, nregion = model_subregion_mean.shape
if seasonal_cycle:
obs_data = ma.mean(
obs_subregion_mean.reshape([1, nt / 12, 12, nregion]), axis=1)
model_data = ma.mean(
model_subregion_mean.reshape([nmodel, nt / 12, 12, nregion]),
axis=1)
nt = 12
else:
obs_data = obs_subregion_mean
model_data = model_subregion_mean
subregion_metrics = ma.zeros([4, nregion, nmodel])
for imodel in np.arange(nmodel):
for iregion in np.arange(nregion):
# First metric: bias
subregion_metrics[0, iregion, imodel] = metrics.calc_bias(
model_data[imodel, :, iregion],
obs_data[0, :, iregion],
average_over_time=True)
# Second metric: standard deviation
subregion_metrics[1, iregion, imodel] = metrics.calc_stddev_ratio(
model_data[imodel, :, iregion], obs_data[0, :, iregion])
# Third metric: RMSE
subregion_metrics[2, iregion, imodel] = metrics.calc_rmse(
model_data[imodel, :, iregion], obs_data[0, :, iregion])
# Fourth metric: correlation
subregion_metrics[3, iregion, imodel] = metrics.calc_correlation(
model_data[imodel, :, iregion], obs_data[0, :, iregion])
if normalize:
for iregion in np.arange(nregion):
subregion_metrics[0, iregion, :] = subregion_metrics[
0, iregion, :] / ma.std(obs_data[0, :, iregion]) * 100.
subregion_metrics[
1, iregion, :] = subregion_metrics[1, iregion, :] * 100.
subregion_metrics[2, iregion, :] = subregion_metrics[
2, iregion, :] / ma.std(obs_data[0, :, iregion]) * 100.
region_names = ['R%02d' % i for i in np.arange(nregion) + 1]
for imetric, metric in enumerate(['bias', 'std', 'RMSE', 'corr']):
plotter.draw_portrait_diagram(
subregion_metrics[imetric, :, :],
region_names,
model_names,
file_name + '_' + metric,
xlabel='model',
ylabel='region')
def Portrait_diagram_subregion(obs_subregion_mean,
obs_name,
model_subregion_mean,
model_names,
seasonal_cycle,
file_name,
normalize=True):
nmodel, nt, nregion = model_subregion_mean.shape
if seasonal_cycle:
obs_data = ma.mean(obs_subregion_mean.reshape(
[1, nt / 12, 12, nregion]),
axis=1)
model_data = ma.mean(model_subregion_mean.reshape(
[nmodel, nt / 12, 12, nregion]),
axis=1)
nt = 12
else:
obs_data = obs_subregion_mean
model_data = model_subregion_mean
subregion_metrics = ma.zeros([4, nregion, nmodel])
for imodel in np.arange(nmodel):
for iregion in np.arange(nregion):
# First metric: bias
subregion_metrics[0, iregion, imodel] = metrics.calc_bias(
model_data[imodel, :, iregion],
obs_data[0, :, iregion],
average_over_time=True)
# Second metric: standard deviation
subregion_metrics[1, iregion, imodel] = metrics.calc_stddev_ratio(
model_data[imodel, :, iregion], obs_data[0, :, iregion])
# Third metric: RMSE
subregion_metrics[2, iregion, imodel] = metrics.calc_rmse(
model_data[imodel, :, iregion], obs_data[0, :, iregion])
# Fourth metric: correlation
subregion_metrics[3, iregion, imodel] = metrics.calc_correlation(
model_data[imodel, :, iregion], obs_data[0, :, iregion])
if normalize:
for iregion in np.arange(nregion):
subregion_metrics[0, iregion, :] = subregion_metrics[
0, iregion, :] / ma.std(obs_data[0, :, iregion]) * 100.
subregion_metrics[
1, iregion, :] = subregion_metrics[1, iregion, :] * 100.
subregion_metrics[2, iregion, :] = subregion_metrics[
2, iregion, :] / ma.std(obs_data[0, :, iregion]) * 100.
region_names = ['R%02d' % i for i in np.arange(nregion) + 1]
for imetric, metric in enumerate(['bias', 'std', 'RMSE', 'corr']):
plotter.draw_portrait_diagram(subregion_metrics[imetric, :, :],
region_names,
model_names,
file_name + '_' + metric,
xlabel='model',
ylabel='region')
def _draw_portrait_diagram(evaluation, plot_config):
""""""
metric_index = plot_config['metric_index']
diagram_data = np.array(evaluation.results[:][metric_index][:])
subregion_names = ["R{}".format(i) for i in range(len(evaluation.subregions))]
target_names = [t.name for t in evaluation.target_datasets]
plots.draw_portrait_diagram(diagram_data,
target_names,
subregion_names,
fname=plot_config['output_name'],
**plot_config.get('optional_args', {}))
def _draw_portrait_diagram(evaluation, plot_config):
""""""
metric_index = plot_config['metric_index']
diagram_data = np.array(evaluation.results[:][metric_index][:])
subregion_names = [
"R{}".format(i) for i in range(len(evaluation.subregions))
]
target_names = [t.name for t in evaluation.target_datasets]
plots.draw_portrait_diagram(diagram_data,
target_names,
subregion_names,
fname=plot_config['output_name'],
**plot_config.get('optional_args', {}))
Bounds(33.0, 40.0, 25.0, 35.00)
]
region_list = ["R" + str(i + 1) for i in xrange(13)]
# metrics
pattern_correlation = metrics.PatternCorrelation()
# 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
[pattern_correlation],
# list of subregion Bounds
# Objects
list_of_regions)
RCMs_to_CRU_evaluation.run()
new_patcor = np.squeeze(np.array(RCMs_to_CRU_evaluation.results), axis=1)
plotter.draw_portrait_diagram(np.transpose(new_patcor),
allNames,
region_list,
fname=OUTPUT_PLOT,
fmt='png',
cmap='coolwarm_r')
Bounds( 15.0, 30.0, 15.0, 25.0),
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, 35.00)]
region_list=["R"+str(i+1) for i in xrange(13)]
#metrics
pattern_correlation = metrics.PatternCorrelation()
#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
[pattern_correlation],
# list of subregion Bounds Objects
list_of_regions)
RCMs_to_CRU_evaluation.run()
new_patcor = np.squeeze(np.array(RCMs_to_CRU_evaluation.results), axis=1)
plotter.draw_portrait_diagram(np.transpose(new_patcor),allNames, region_list, fname=OUTPUT_PLOT, fmt='png', cmap='coolwarm_r')
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)
