def main():
#calculate and plot boundary forcing errors
param_file = 'gev_params_stationary'
years = [10, 30]
postfixes = ['low', 'high']
limits = { 10 : 135 , 30 : 770 }
plt.subplots_adjust(wspace=0.05)
for i, return_period in enumerate(years):
for j, postfix in enumerate(postfixes):
bfe = calculate_forcing_differences(return_period, prefix = param_file, postfix = '_' + postfix)
plt.subplot( len(postfixes), len(years), i * len(postfixes) + j + 1 )
gevfit.plot_data(data = bfe, imagefile = None, units = '${\\rm m^3/s}$',
minmax = (-limits[return_period], limits[return_period]))
plt.title( '%s flow, return period %d years' % (postfix, return_period), {'fontsize' : 25} )
plt.savefig('forcing_uncert.png')
def plot(member = 'aex', low_return_periods = [], high_return_periods = []):
for extreme in ['high', 'low']:
data_path = member + '_' + extreme + '_std_dev'
if not os.path.isfile(data_path):
print('Warning: no data for (%s, %s) ' % (member, extreme))
continue
if not os.path.isfile(data_path):
print('Warning can\'t find file %s for plotting' % data_path)
continue
if extreme == 'high':
return_periods = high_return_periods
else:
return_periods = low_return_periods
stds = pickle.load(open(data_path))
for return_period in return_periods:
imagefile = ( member + '_std_devs_%dyr_%s.png' ) % (return_period, extreme)
gevfit.plot_data(data = stds[return_period],
imagefile = imagefile,
color_map = mpl.cm.get_cmap('RdBu',20))
def plot(self):
basemap = polar_stereographic.basemap
plt.subplot(3, 1, 1)
gevfit.plot_data(
self.ret_level_2yr,
imagefile=None,
units="m**3/s",
minmax=(0, None),
i_list=self._i_indices,
j_list=self._j_indices,
)
plt.title("2 year return levels")
plt.subplot(3, 1, 2)
gevfit.plot_data(
self.median_field,
imagefile=None,
units="m**3/s",
minmax=(0, None),
i_list=self._i_indices,
j_list=self._j_indices,
)
plt.title("median")
plt.subplot(3, 1, 3)
gevfit.plot_data(
np.array(self.median_field) - np.array(self.ret_level_2yr),
imagefile=None,
units="m**3/s",
minmax=(None, None),
i_list=self._i_indices,
j_list=self._j_indices,
)
plt.title("difference: median - 2 year return level")
plot_basin_boundaries_from_shape(basemap, plotter=plt, linewidth=0.1)
basemap.drawcoastlines()
plt.savefig("median_test.png")
pass
