def compare_spatial_means_over_mask(mask = None, label = ''):
"""
timeseries not changed, averaging over the points where mask == 1
"""
start = datetime(1980,01,01,00)
end = datetime(1996, 12, 31,00)
lons = polar_stereographic.lons
lats = polar_stereographic.lats
lons_selected = lons[ mask == 1 ]
lats_selected = lats[ mask == 1 ]
points = [GeoPoint(longitude = lon, latitude = lat) for lon, lat in zip(lons_selected, lats_selected)]
sweObs = SweHolder()
obsData = sweObs.getSpatialMeanDataFromNetCDFforPoints(points, startDate = start, endDate = end)
modelData = getSpatialMeanCCCDataForMask(mask, startDate = start, endDate = end)
plt.figure()
plt.title('SWE' + ' ' + label)
plt.plot(obsData[0], obsData[1], label = 'Obs.', color = 'red', lw = 3)
plt.plot(modelData[0], modelData[1], label = 'Model', color = 'blue', lw = 3)
plt.ylabel('mm')
plt.legend()
plt.savefig('swe_compare_{0}.pdf'.format(label), bbox_inches = 'tight')
def compare_daily_normals_mean_over_mask(mask = None, start = None, end = None, label = ''):
lons = polar_stereographic.lons
lats = polar_stereographic.lats
lons_selected = lons[ mask == 1 ]
lats_selected = lats[ mask == 1 ]
points = [GeoPoint(longitude = lon, latitude = lat) for lon, lat in zip(lons_selected, lats_selected)]
sweObs = SweHolder()
obsData = sweObs.getSpatialMeanDataFromNetCDFforPoints(points, startDate = start, endDate = end)
print 'finished reading observations'
modelData = getSpatialMeanCCCDataForMask(mask, startDate = start, endDate = end)
print 'finished reading model data'
print 'finished reading input mean timeseries'
stamp_year = 2000
obsStamp = map(lambda x: toStampYear(x, stamp_year = stamp_year), obsData[0])
modelStamp = map(lambda x: toStampYear(x, stamp_year = stamp_year), modelData[0])
print 'calculated stamp dates'
##calculate mean for a day of year
obsDict = {}
for stampDate, value in zip(obsStamp, obsData[1]):
if not obsDict.has_key(stampDate):
obsDict[stampDate] = []
obsDict[stampDate].append(value)
for key, theList in obsDict.iteritems():
obsDict[key] = np.mean(theList)
obsDates = sorted(obsDict)
obsMeanValues = [obsDict[d] for d in obsDates]
#do the same thing as for obs for the model data
modelDict = {}
for stampDate, value in zip(modelStamp, modelData[1]):
if not modelDict.has_key(stampDate):
modelDict[stampDate] = []
modelDict[stampDate].append(value)
for key, theList in modelDict.iteritems():
modelDict[key] = np.mean(theList)
modelDates = sorted(modelDict)
modelMeanValues = [modelDict[d] for d in modelDates]
print 'Calculated mean for day of year and over a basin points'
plt.figure(figsize = (8, 6), dpi = 80)
plt.title('SWE {0}'.format(label))
plt.plot(obsDates, obsMeanValues, label = 'Obs.', color = 'red', lw = 3)
plt.plot(modelDates, modelMeanValues, label = 'Model', color = 'blue', lw = 3)
plt.ylabel('mm')
ax = plt.gca()
ax.xaxis.set_major_formatter(mpl.dates.DateFormatter('%b'))
ax.xaxis.set_major_locator(
mpl.dates.MonthLocator(bymonth = range(2,13,2))
)
plt.legend()
plt.savefig(label + '_swe.pdf', bbox_inches = 'tight')
