def plot_cv_for_return_levels_current_and_future_and_change():
low_periods = [2, 5]
high_periods = [10, 30]
extreme_types = ['high', 'low']
extreme_type_to_periods = dict(list(zip(extreme_types, [high_periods, low_periods])))
i_indices, j_indices = data_select.get_indices_from_file()
current_cvs = {}
future_cvs = {}
for extreme_type in extreme_types:
current_cvs[extreme_type] = {} #to save return level fields for a given extreme type
future_cvs[extreme_type] = {}
for return_period in extreme_type_to_periods[extreme_type]:
c_rl_fields = np.zeros((len(members.current_ids), len(i_indices)))
f_rl_fields = np.zeros((len(members.current_ids), len(i_indices)))
for member_num, the_member in enumerate(members.current_ids):
pars_current = csfb.get_pars_for_member_and_type(the_member, level_type = extreme_type)
pars_future = csfb.get_pars_for_member_and_type(members.current2future[the_member], level_type = extreme_type)
#calculate changes for each pair of members and each position
npos = len(pars_current)
for pos, pc, pf in zip(range(npos), pars_current, pars_future):
if extreme_type == 'high':
c = gevfit.get_high_ret_level_stationary(pc, return_period)
f = gevfit.get_high_ret_level_stationary(pf, return_period)
else:
c = gevfit.get_low_ret_level_stationary(pc, return_period)
f = gevfit.get_low_ret_level_stationary(pf, return_period)
c_rl_fields[member_num, pos] = c
f_rl_fields[member_num, pos] = f
pass
current_cvs[extreme_type][return_period] = np.std( c_rl_fields, axis = 0 ) / np.mean( c_rl_fields, axis = 0 )
future_cvs[extreme_type][return_period] = np.std( f_rl_fields, axis = 0 ) / np.mean( f_rl_fields, axis = 0 )
#do plotting
_plot_map_as_subplots(i_indices, j_indices, current_cvs, title = " Current Climate, CV ")
plt.savefig('cv_for_return_levels_current.png')
_plot_map_as_subplots(i_indices, j_indices, future_cvs, title = "Future Climate, CV")
plt.savefig('cv_for_return_levels_future.png')
pass
def plot_signal_to_noise_ratio():
low_periods = [2, 5]
high_periods = [10, 30]
plot_utils.apply_plot_params(width_pt=None, font_size=9, aspect_ratio=2.5)
extreme_types = ['high', 'low']
extreme_type_to_periods = dict(list(zip(extreme_types, [high_periods, low_periods])))
i_indices, j_indices = data_select.get_indices_from_file()
i_subplot = 0
#plt.subplots_adjust(wspace = 0.1)
cMap = mpl.cm.get_cmap('jet', 3)
cMap.set_over(color = '#FF8C00')
gs = gridspec.GridSpec(3,2)
for extreme_type in extreme_types:
for return_period in extreme_type_to_periods[extreme_type]:
changes = np.zeros((len(members.current_ids), len(i_indices)))
for member_num, the_member in enumerate(members.current_ids):
pars_current = csfb.get_pars_for_member_and_type(the_member, level_type = extreme_type)
pars_future = csfb.get_pars_for_member_and_type(members.current2future[the_member], level_type = extreme_type)
#calculate changes for each pair of members and each position
npos = len(pars_current)
for pos, pc, pf in zip(range(npos), pars_current, pars_future):
if extreme_type == 'high':
c = gevfit.get_high_ret_level_stationary(pc, return_period)
f = gevfit.get_high_ret_level_stationary(pf, return_period)
else:
c = gevfit.get_low_ret_level_stationary(pc, return_period)
f = gevfit.get_low_ret_level_stationary(pf, return_period)
changes[member_num, pos] = f - c
pass
#calculate mean and stdev of the obtained changes
the_mean = np.mean(changes, axis = 0)
the_std = np.std(changes, axis = 0)
#change if you want signal to noise ratio, currently it is cv (coefficient of variation 1/(signal-to-noise-ratio))
the_values = the_std / np.abs(the_mean)
print(the_values.min(), the_values.max())
#the_values = the_mean
to_plot = np.ma.masked_all(csfb.xs.shape)
max_value = 1.5
for i, j, value, dev in zip(i_indices, j_indices, the_values, the_std):
to_plot[i, j] = value
#shaded = np.ma.masked_where(to_plot != 0, shaded)
#to_plot = np.ma.masked_where(to_plot == 0, to_plot)
plot_axes = plt.subplot(gs[i_subplot // 2, i_subplot % 2])
i_subplot += 1
print('just before plotting')
basin_boundaries.plot_basin_boundaries_from_shape(csfb.basemap, plotter = plt, linewidth = 1.)
image = csfb.basemap.pcolormesh(csfb.xs, csfb.ys, to_plot, vmin = 0, vmax = max_value, cmap = cMap,
ax = plot_axes)
csfb.basemap.drawcoastlines(linewidth = 0.2)
plot_axes.set_title('T = {0}-year'.format(return_period))
x_min, x_max, y_min, y_max = plot_utils.get_ranges(csfb.xs[i_indices, j_indices], csfb.ys[i_indices, j_indices])
plot_axes.set_xlim(x_min, x_max)
plot_axes.set_ylim(y_min, y_max)
divider = make_axes_locatable(plot_axes)
cax = divider.append_axes("right", "8%", pad="3%")
# extend choices are "both", "min", "max" and "neither"
cb = plt.colorbar(image, extend = 'max',
format = "%.1f", drawedges = True, cax = cax)
cb.set_ticks(LinearLocator(numticks = 4))
cb.outline.set_visible(False)
#plt.show()
plt.tight_layout()
plt.savefig('cv_for_changes.png')
