def compare_daily_normals_integral_over_mask(mask = None, start = None, end = None, label = '',
subplot_count = None, subplot_total = 10
):
"""
"""
lons = polar_stereographic.lons
lats = polar_stereographic.lats
lons_selected = lons[ mask == 1 ]
lats_selected = lats[ mask == 1 ]
global swe_fig
if subplot_count == 1:
swe_fig = plt.figure()
plot_utils.apply_plot_params(font_size=25, width_pt=900, aspect_ratio=2.5)
swe_fig.subplots_adjust(hspace = 0.6, wspace = 0.2, top = 0.9)
points = [GeoPoint(longitude = lon, latitude = lat) for lon, lat in zip(lons_selected, lats_selected)]
sweObs = SweHolder()
obsData = sweObs.getSpatialIntegralFromNetcdfForPoints(points, startDate = start, endDate = end)
print 'finished reading observations'
modelData = getSpatialIntegralCCCDataForMask(mask = mask, path_to_ccc = 'data/ccc_data/aex/aex_p1sno',
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 selected points'
plt.title('Upstream of {0}'.format(label))
line1 = plt.plot(modelDates, modelMeanValues, color = 'blue', lw = 3)
line2 = plt.plot(obsDates, obsMeanValues, color = 'red', 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))
)
if subplot_count == subplot_total:
lines = (line1, line2)
labels = ("CRCM4", "CRU")
swe_fig.legend(lines, labels, 'upper center')
swe_fig.text(0.05, 0.5, 'SWE (mm)',
rotation=90,
ha = 'center', va = 'center'
)
swe_fig.savefig("swe.pdf")
