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_swe_2d():
"""
Compare seasonal mean
"""
start = datetime(1980,01,01,00)
end = datetime(1996, 12, 31,00)
months = [12,1,2]
#calculate mean for ccc data accounting for the mask
domain_mask = get_domain_mask()
cccData = getTemporalMeanCCCDataForMask(domain_mask,
startDate = start,
endDate = end, months = months)
lon_selected = polar_stereographic.lons[domain_mask == 1]
lat_selected = polar_stereographic.lats[domain_mask == 1]
geopointList = []
for lon, lat in zip(lon_selected, lat_selected):
geopointList.append(GeoPoint(longitude = lon, latitude = lat))
print 'Selecting obs data'
sweObs = SweHolder()
obsData = sweObs.getTemporalMeanDataFromNetCDFforPoints(geopointList, startDate = start,
endDate = end, months = months)
to_plot = np.ma.masked_all(polar_stereographic.lons.shape)
condition = domain_mask == 1
to_plot[condition] = (cccData - obsData) / obsData * 100
xs = polar_stereographic.xs
ys = polar_stereographic.ys
basemap = polar_stereographic.basemap
plot_utils.apply_plot_params()
basemap.pcolormesh(xs, ys, to_plot, cmap = mpl.cm.get_cmap('jet', 9), vmin = -60, vmax = 120)
basemap.drawcoastlines()
plt.colorbar(ticks = LinearLocator(numticks = 10), format = '%.1f')
plt.title('Snow Water Equivalent (%) \n $(S_{\\rm CRCM4} - S_{\\rm obs.})/S_{\\rm obs.}\\cdot100\%$\n')
#zoom to domain
selected_x = xs[~to_plot.mask]
selected_y = ys[~to_plot.mask]
marginx = abs(np.min(selected_x) * 5.0e-2)
marginy = abs(np.min(selected_y) * 5.0e-2)
plt.xlim(np.min(selected_x) - marginx, np.max(selected_x) + marginx)
plt.ylim(np.min(selected_y) - marginy, np.max(selected_y) + marginy)
bb.plot_basin_boundaries_from_shape(basemap, plotter = plt, linewidth = 2, edge_color = 'k')
##overlay flow directions
cells = cpe.get_connected_cells('data/hydrosheds/directions_for_streamflow9.nc')
read_infocell.plot_directions(cells, basemap = basemap, domain_mask = get_domain_mask())
selected_stations = [
104001, 103715, 93806, 93801,
92715, 81006, 61502, 80718,
40830, 42607
]
selected_ids = map(lambda x: "%06d" % x, selected_stations)
print selected_ids
cpe.plot_station_positions(id_list=selected_ids, use_warpimage=False,
save_to_file=False, the_basemap=basemap)
plt.savefig('swe_djf_validation.pdf', bbox_inches = 'tight')
pass
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")
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')
