def main():
plot_utils.apply_plot_params(width_pt=None, font_size=9, aspect_ratio=2.5)
gs = mpl.gridspec.GridSpec(3,2)
key_data_list = get_key_data_list()
i_list, j_list = data_select.get_indices_from_file()
subplot_count = 0
for the_type in ["high", "low"]:
for time_window in ["current", "future"]:
selected_data = None
for data in key_data_list:
if data.time_window == time_window and data.type == the_type:
selected_data = data
break
row = subplot_count // 2
col = subplot_count % 2
plt.subplot(gs[row, col])
csfb.plot(selected_data.p_values, i_list, j_list,
polar_stereographic.xs, polar_stereographic.ys,
units = "", basemap = polar_stereographic.basemap,
minmax = (0, 0.25), title = "", # "{0} climate, {1} flow".format(selected_data.time_window, selected_data.type),
colorbar_label_format="%.2f", color_map = mpl.cm.get_cmap("jet", 5), upper_limited=True
)
subplot_count += 1
#TODO:add 2 subplots for mean values
pc = kw_test_for_means()
plt.subplot(gs[2,0])
csfb.plot(pc, i_list, j_list,
polar_stereographic.xs, polar_stereographic.ys,
units = "", basemap = polar_stereographic.basemap,
minmax = (0, 0.25), #title = "{0} climate, {1} flow".format("current", "mean"),
colorbar_label_format="%.2f", color_map = mpl.cm.get_cmap("jet", 5), upper_limited=True
)
pf = kw_test_for_means(current_climate=False)
plt.subplot(gs[2, 1])
csfb.plot(pf, i_list, j_list,
polar_stereographic.xs, polar_stereographic.ys,
units = "", basemap = polar_stereographic.basemap,
minmax = (0, 0.25), #title = "{0} climate, {1} flow".format("future", "mean"),
colorbar_label_format="%.2f", color_map = mpl.cm.get_cmap("jet", 5), upper_limited=True
)
plt.tight_layout()
plt.savefig("p_values_kruskalwallis.png")
def plot_results():
cc = "current"
fc = "future"
climate_types = [cc, fc]
folder_path = 'data/streamflows/hydrosheds_euler9/'
file_path = os.path.join(folder_path, "aex_discharge_1970_01_01_00_00.nc")
i_indices, j_indices = data_select.get_indices_from_file(file_path)
xs = polar_stereographic.xs
ys = polar_stereographic.ys
basemap = polar_stereographic.basemap
hi = "high"
lo = "low"
extreme_types = [hi, lo]
base_std_name = "{0}_std_dev_{1}"
base_par_name = {
cc : "_".join(["{0}"] + members.current_ids),
fc : "_".join(["{0}"] + members.future_ids)
}
extreme_to_return_periods = {
hi : get_high_return_periods(),
lo : get_low_return_periods()
}
sig_coefs = [1.96
# , 1.645
]
sig_levels = ["95 %"
# , "90 %"
]
gs = gridspec.GridSpec(3,2)
for extreme in extreme_types:
pars_path_current = base_par_name[cc].format(extreme)
std_path_current = base_std_name.format(extreme, cc)
pars_path_future = base_par_name[fc].format(extreme)
std_path_future = base_std_name.format(extreme, fc)
pars_current = pickle.load(open(pars_path_current))
stds_current = pickle.load(open(std_path_current))
pars_future = pickle.load(open(pars_path_future))
stds_future = pickle.load(open(std_path_future))
return_periods = extreme_to_return_periods[extreme]
#plot_utils.apply_plot_params(font_size=15, width_pt=900, aspect_ratio=2.5)
#plt.figure()
delta = 50 if extreme == hi else 100
for row, ret_period in enumerate( return_periods ):
#calculate changes in return levels
func = lambda x: gevfit.get_high_ret_level_stationary(x, ret_period)
rl_c = list(map(func, pars_current))
rl_f = list(map(func, pars_future))
rl_c = np.array(rl_c)
rl_f = np.array(rl_f)
std_c = stds_current[ret_period]
std_f = stds_future[ret_period]
in_odf = (std_c > 0) & (std_f > 0) & (rl_c > 0) & (rl_f >= 0)
change = np.ma.masked_all(rl_c.shape)
change[in_odf] = (rl_f[in_odf] - rl_c[in_odf]) / rl_c[in_odf] * 100.0
min_change = np.min((rl_f - rl_c) / rl_c * 100.0)
if min_change >= 0:
low_limit = 0
elif min_change > -10:
low_limit = -10
else:
low_limit = np.floor(min_change / 10.0) * 10
print("min change = {0}, low limit = {1}".format(min_change, low_limit))
for sig_coef, sig_name in zip(sig_coefs, sig_levels):
significance = np.ma.masked_all(rl_c.shape)
sig_cond = (sig_coef * (std_c + std_f) < np.abs(rl_f - rl_c)) & in_odf
significance[~sig_cond] = 0#fill with gray non signifcant areas
change1 = np.ma.masked_where(~sig_cond, change)
if extreme == hi:
plt.subplot(gs[row, 0])
else:
plt.subplot(gs[row, 1])
csfb.plot(change1 , i_indices, j_indices, xs, ys,
title = 'T = {0}-year'.format( ret_period ),
color_map = mycolors.get_red_blue_colormap(ncolors = 10),
units = '%',
basemap = basemap, minmax = (-delta, delta),
colorbar_label_format = '%d',
upper_limited = True,
colorbar_tick_locator = LinearLocator(numticks = 11),
not_significant_mask = significance,
show_colorbar = True, impose_lower_limit=low_limit
)
#subplot_count += 1
plt.tight_layout()
plt.savefig("rl_of_merged_change.png")
pass