from typing import Dict
import matplotlib.pyplot as plt
from extreme_data.meteo_france_data.scm_models_data.visualization.utils import create_adjusted_axes
from projects.exceeding_snow_loads.utils import dpi_paper1_figure
from extreme_trend.visualizers.study_visualizer_for_non_stationary_trends import StudyVisualizerForNonStationaryTrends
def permute(l, permutation):
# permutation = [i//2 if i % 2 == 0 else 4 + i //2 for i in range(8)]
return [l[i] for i in permutation]
def plot_selection_curves(altitude_to_visualizer: Dict[int, StudyVisualizerForNonStationaryTrends]):
"""
Plot a single trend curves
:return:
"""
visualizer = list(altitude_to_visualizer.values())[0]
ax = create_adjusted_axes(1, 1)
selected_counter = merge_counter([v.selected_trend_test_class_counter for v in altitude_to_visualizer.values()])
# selected_and_significative_counter = merge_counter([v.selected_and_significative_trend_test_class_counter for v in altitude_to_visualizer.values()])
selected_and_significative_counter = merge_counter([v.selected_and_anderson_goodness_of_fit_trend_test_class_counter for v in altitude_to_visualizer.values()])
# selected_and_significative_counter = merge_counter([v.selected_and_kstest_goodness_of_fit_trend_test_class_counter for v in altitude_to_visualizer.values()])
total_of_selected_models = sum(selected_counter.values())
l = sorted(enumerate(visualizer.non_stationary_trend_test), key=lambda e: selected_counter[e[1]])
permutation = [i for i, v in l][::-1]
select_list = get_ordered_list_from_counter(selected_counter, total_of_selected_models, visualizer, permutation)
selected_and_signifcative_list = get_ordered_list_from_counter(selected_and_significative_counter, total_of_selected_models, visualizer, permutation)
labels = permute(['${}$'.format(t.label) for t in visualizer.non_stationary_trend_test], permutation)
print(l)
print(sum(select_list), select_list)
print(sum(selected_and_signifcative_list), selected_and_signifcative_list)
# [(5, < class 'data.trend_analysis.univariate_test.extreme_trend.trend_test_two_parameters.gev_trend_test_two_parameters.GevLocationAgainstGumbel'> ), (6, < class 'data.trend_analysis.univariate_test.extreme_trend.trend_test_two_parameters.gev_trend_test_two_parameters.GevScaleAgainstGumbel' > ), (2, < class 'data.trend_analysis.univariate_test.extreme_trend.trend_test_one_parameter.gumbel_trend_test_one_parameter.GumbelScaleTrendTest' > ), (1, < class 'data.trend_analysis.univariate_test.extreme_trend.trend_test_one_parameter.gumbel_trend_test_one_parameter.GumbelLocationTrendTest' > ), (7, < class 'data.trend_analysis.univariate_test.extreme_trend.trend_test_three_parameters.gev_trend_test_three_parameters.GevLocationAndScaleTrendTestAgainstGumbel' > ), (3, < class 'data.trend_analysis.univariate_test.extreme_trend.trend_test_two_parameters.gumbel_test_two_parameters.GumbelLocationAndScaleTrendTest' > ), (4, < class 'data.trend_analysis.univariate_test.extreme_trend.trend_test_one_parameter.gumbel_trend_test_one_parameter.GevStationaryVersusGumbel' > ), (0, < class 'data.trend_analysis.univariate_test.extreme_trend.trend_test_one_parameter.gumbel_trend_test_one_parameter.GumbelVersusGumbel' > )]
# [32.64462809917355, 24.380165289256198, 12.396694214876034, 9.50413223140496, 9.090909090909092, 5.785123966942149, 3.71900826446281, 2.479338842975207]
# [0, 13.223140495867769, 7.851239669421488, 8.264462809917354, 4.958677685950414, 2.479338842975207, 0.8264462809917356, 2.0661157024793386]
# parameters
width = 5
size = 30
legend_fontsize = 30
labelsize = 25
linewidth = 3
x = [10 * (i+1) for i in range(len(select_list))]
ax.bar(x, select_list, width=width, color='grey', edgecolor='grey', label='Non significant model',
linewidth=linewidth)
ax.bar(x, selected_and_signifcative_list, width=width, color='black', edgecolor='black',
label='Significant model',
linewidth=linewidth)
ax.legend(loc='upper right', prop={'size': size})
ax.set_ylabel(' Frequency of selected model w.r.t all time series \n '
'i.e. for all massifs and altitudes (\%)', fontsize=legend_fontsize)
ax.set_xlabel('Models', fontsize=legend_fontsize)
ax.tick_params(axis='both', which='major', labelsize=labelsize)
ax.set_xticks(x)
ax.yaxis.grid()
ax.set_xticklabels(labels)
# for ax_horizontal in [ax, ax_twiny]:
# if ax_horizontal == ax_twiny:
# ax_horizontal.plot(altitudes, [0 for _ in altitudes], linewidth=0)
# else:
# ax_horizontal.set_xlabel('Altitude', fontsize=legend_fontsize)
# ax_horizontal.set_xticks(altitudes)
# ax_horizontal.set_xlim([700, 5000])
# ax_horizontal.tick_params(labelsize=labelsize)
#
# # Set the number of massifs on the upper axis
# ax_twiny.set_xticklabels([v.study.nb_study_massif_names for v in altitude_to_visualizer.values()])
# ax_twiny.set_xlabel('Total number of massifs at each altitude (for the percentage)', fontsize=legend_fontsize)
#
# ax.set_ylabel('Massifs with decreasing trend (\%)', fontsize=legend_fontsize)
# max_percent = int(max(percent_decrease))
# n = 2 + (max_percent // 10)
# ax_ticks = [10 * i for i in range(n)]
# # upper_lim = max_percent + 3
# upper_lim = n + 5
# ax_lim = [0, upper_lim]
# for axis in [ax, ax_twinx]:
# axis.set_ylim(ax_lim)
# axis.set_yticks(ax_ticks)
# axis.tick_params(labelsize=labelsize)
# ax.yaxis.grid()
#
# label_curve = (visualizer.label).replace('change', 'decrease')
# ax_twinx.set_ylabel(label_curve.replace('', ''), fontsize=legend_fontsize)
# for region_name, mean_decrease in zip(AbstractExtendedStudy.region_names, mean_decreases):
# if len(mean_decreases) > 1:
# label = region_name
# else:
# label = 'Mean relative decrease'
# ax_twinx.plot(altitudes, mean_decrease, label=label, linewidth=linewidth, marker='o')
# ax_twinx.legend(loc='upper right', prop={'size': size})
# Save plot
visualizer.plot_name = 'Selection curves'
visualizer.show_or_save_to_file(no_title=True, dpi=dpi_paper1_figure)
plt.close()
def get_ordered_list_from_counter(selected_counter, total_of_selected_models, visualizer, permutation):
return permute([100 * float(selected_counter[t]) / total_of_selected_models if t in selected_counter else 0
for t in visualizer.non_stationary_trend_test], permutation)
def merge_counter(counters_list):
global_counter = counters_list[0]
for c in counters_list[1:]:
global_counter += c
return global_counter