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', {}))
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')
results = []
tSeries = []
labels = []
region_counter += 1
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,
regions[0],
ptitle=regions[0],
fmt='png')
results = []
tSeries = []
labels = []
region_counter += 1
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)
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', {}))
