diff --git a/extreme_fit/utils.py b/extreme_fit/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..21d3db969eebc2aa144ce7dd4494760c0c48ed88
--- /dev/null
+++ b/extreme_fit/utils.py
@@ -0,0 +1,11 @@
+import numpy as np
+from sklearn.linear_model import LinearRegression
+
+
+def fit_linear_regression(x, y):
+ X = np.array(x).reshape(-1, 1)
+ reg = LinearRegression().fit(X, y)
+ r2_score = reg.score(X, y)
+ a = reg.coef_
+ b = reg.intercept_
+ return a, b, r2_score
diff --git a/projects/altitude_spatial_model/altitudes_fit/plots/compute_histogram_change_in_total_snowfall.py b/projects/altitude_spatial_model/altitudes_fit/plots/compute_histogram_change_in_total_snowfall.py
index aa9786142618415488c8aaf1c8d742fa1dab5b76..c7d69c79e2ed661b891acb1a74dc5b8a3a8722cd 100644
--- a/projects/altitude_spatial_model/altitudes_fit/plots/compute_histogram_change_in_total_snowfall.py
+++ b/projects/altitude_spatial_model/altitudes_fit/plots/compute_histogram_change_in_total_snowfall.py
@@ -3,10 +3,9 @@ import matplotlib.pyplot as plt
import numpy as np
from extreme_data.meteo_france_data.scm_models_data.safran.safran import SafranSnowfall
+from extreme_fit.utils import fit_linear_regression
from projects.altitude_spatial_model.altitudes_fit.one_fold_analysis.altitudes_studies_visualizer_for_non_stationary_models import \
AltitudesStudiesVisualizerForNonStationaryModels
-from projects.contrasting_trends_in_snow_loads.article2_snowfall_versus_time_and_altitude.snowfall_plot import \
- fit_linear_regression
def compute_changes_in_total_snowfall(visualizer_list: List[
diff --git a/projects/altitude_spatial_model/delta_graph.py b/projects/altitude_spatial_model/delta_graph.py
deleted file mode 100644
index 92f57de2610784ca79fb0a68d355bcb47ceeacd6..0000000000000000000000000000000000000000
--- a/projects/altitude_spatial_model/delta_graph.py
+++ /dev/null
@@ -1,74 +0,0 @@
-import numpy as np
-import matplotlib.pyplot as plt
-from extreme_data.meteo_france_data.scm_models_data.abstract_study import AbstractStudy
-from extreme_data.meteo_france_data.scm_models_data.safran.safran import SafranSnowfall1Day, SafranTemperature, \
- SafranPrecipitation1Day
-from extreme_data.meteo_france_data.scm_models_data.visualization.main_study_visualizer import \
- STUDY_CLASS_TO_ABBREVIATION
-from extreme_data.meteo_france_data.scm_models_data.visualization.study_visualizer import StudyVisualizer
-from projects.contrasting_trends_in_snow_loads.article2_snowfall_versus_time_and_altitude.snowfall_plot import \
- fit_linear_regression
-
-
-def delta_graph(study_class, altitudes, maxima=True):
- ax = plt.gca()
- all_delta_point_for_regression = []
- # colors = ['orange', 'red', 'blue', 'green', 'yellow']
- for altitude in altitudes:
- delta_points = []
- study = study_class(altitude=altitude) # type : AbstractStudy
- study_temperature = SafranTemperature(altitude=altitude)
- for massif_name in study.study_massif_names:
- if maxima:
- values = study.massif_name_to_annual_maxima[massif_name]
- else:
- values = study.massif_name_to_annual_total[massif_name]
- values_temperature = study_temperature.massif_name_to_annual_total[massif_name]
- delta_point = (compute_delta(values_temperature, relative=False), compute_delta(values, relative=True))
- delta_points.append(delta_point)
- all_delta_point_for_regression.extend(delta_points)
- # Plot part
- x, y = list(zip(*delta_points))
- ax.scatter(x, y, label='{}m'.format(altitude))
- aggregation_str = 'maxima' if maxima else 'total'
- ylabel = 'Delta for {} {}'.format(aggregation_str, STUDY_CLASS_TO_ABBREVIATION[study_class])
- ax.set_ylabel(ylabel)
- ax.set_xlabel('Delta mean Temperature')
- # Plot linear regression
- x_all, y_all = list(zip(*all_delta_point_for_regression))
- a, b, r2_score = fit_linear_regression(x_all, y_all)
- a = a[0]
- x_plot = np.linspace(start=np.min(x_all), stop=np.max(x_all), num=100)
- y_plot = a * x_plot + b
- rounded_number = [str(np.round(e, 2)) for e in [a, b, r2_score]]
- ax.plot(x_plot, y_plot, label='{} x + {} (R2 = {})'.format(*rounded_number))
- visualizer = StudyVisualizer(study=study, show=False, save_to_file=True)
- visualizer.plot_name = ylabel
- ax.legend()
-
- # Show / Save plot
- visualizer.show_or_save_to_file(no_title=True)
- plt.close()
-
-
-def compute_delta(values, relative=True):
- index = 30
- before, after = values[:index], values[index:]
- mean_before, mean_after = np.mean(before), np.mean(after)
- delta = mean_after - mean_before
- if relative:
- delta *= 100 / mean_before
- return delta
-
-
-if __name__ == '__main__':
- fast = False
- if fast is None:
- altitudes = [900, 1800, 2700]
- elif fast:
- altitudes = [900]
- else:
- altitudes = [300, 600, 900, 1200, 1500, 1800, 2100, 2400, 2700, 3000, 3300, 3600][:]
- for study_class in [SafranSnowfall1Day, SafranPrecipitation1Day]:
- for maxima in [True, False]:
- delta_graph(study_class, altitudes, maxima=maxima)
diff --git a/projects/altitude_spatial_model/preliminary_analysis.py b/projects/altitude_spatial_model/preliminary_analysis.py
index dcc3559296f891b259e8e927f1033d16cb17e288..f3642f4b1476ad42c8e3d8d5cf07ff20da627da2 100644
--- a/projects/altitude_spatial_model/preliminary_analysis.py
+++ b/projects/altitude_spatial_model/preliminary_analysis.py
@@ -3,22 +3,18 @@ import os.path as op
from itertools import chain
import numpy as np
-from cached_property import cached_property
from extreme_data.meteo_france_data.adamont_data.abstract_adamont_study import AbstractAdamontStudy
from extreme_data.meteo_france_data.adamont_data.adamont.adamont_snowfall import AdamontSnowfall
from extreme_data.meteo_france_data.adamont_data.adamont_gcm_rcm_couples import _gcm_rcm_couple_adamont_v2_to_full_name
-from extreme_data.meteo_france_data.adamont_data.adamont_scenario import adamont_scenarios_real, \
- get_gcm_rcm_couples, rcp_scenarios
+from extreme_data.meteo_france_data.adamont_data.adamont_scenario import get_gcm_rcm_couples, rcp_scenarios
from extreme_data.meteo_france_data.scm_models_data.safran.safran import SafranSnowfall1Day
from extreme_data.meteo_france_data.scm_models_data.visualization.plot_utils import plot_against_altitude
from extreme_data.meteo_france_data.scm_models_data.visualization.study_visualizer import StudyVisualizer
from extreme_fit.distribution.gev.gev_params import GevParams
+from extreme_fit.utils import fit_linear_regression
from projects.altitude_spatial_model.altitudes_fit.altitudes_studies import AltitudesStudies
from projects.altitude_spatial_model.altitudes_fit.one_fold_analysis.altitude_group import altitudes_for_groups
-from projects.contrasting_trends_in_snow_loads.article2_snowfall_versus_time_and_altitude.snowfall_plot import \
- fit_linear_regression
-from projects.exceeding_snow_loads.utils import paper_altitudes
class PointwiseGevStudyVisualizer(AltitudesStudies):
@@ -193,125 +189,6 @@ class PointwiseGevStudyVisualizer(AltitudesStudies):
legend=legend)
return fit_linear_regression(altitudes, params)
- # plot_against_altitude(altitudes, ax, massif_id, massif_name, confidence_intervals, fill=True)
-
- # Plot against the time
-
- # @property
- # def year_min_and_max_list(self):
- # l = []
- # year_min, year_max = 1959, 1989
- # for shift in range(0, 7):
- # l.append((year_min + 5 * shift, year_max + 5 * shift))
- # return l
-
- # @property
- # def min_years_for_plot_x_axis(self):
- # return [c[0] for c in self.year_min_and_max_list]
- #
- # def plot_gev_params_against_time_for_all_altitudes(self):
- # for altitude in self.altitudes_for_temporal_hypothesis:
- # self._plot_gev_params_against_time_for_one_altitude(altitude)
- #
- # def _plot_gev_params_against_time_for_one_altitude(self, altitude):
- # for param_name in GevParams.PARAM_NAMES[:]:
- # ax = plt.gca()
- # for massif_name in self.study.all_massif_names()[:]:
- # self._plot_gev_params_against_time_for_one_altitude_and_one_massif(ax, massif_name, param_name,
- # altitude,
- # massif_name_as_labels=True)
- # ax.legend(prop={'size': 7}, ncol=3)
- # ax.set_xlabel('Year')
- # ax.set_ylabel(param_name + ' for altitude={}'.format(altitude))
- # xlabels = ['-'.join([str(e) for e in t]) for t in self.year_min_and_max_list]
- # ax.set_xticks(self.min_years_for_plot_x_axis)
- # ax.set_xticklabels(xlabels)
- # # ax.tick_params(labelsize=5)
- # plot_name = '{} change /all with years /for altitude={}'.format(param_name, altitude)
- # self.show_or_save_to_file(plot_name, no_title=True, tight_layout=True, show=False)
- # ax.clear()
- # plt.close()
- #
- # def _plot_gev_params_against_time_for_one_altitude_and_one_massif(self, ax, massif_name, param_name, altitude,
- # massif_name_as_labels):
- # study = self.altitude_to_study[altitude]
- # if massif_name in study.study_massif_names:
- # gev_params_list = study.massif_name_to_gev_param_list(self.year_min_and_max_list)[massif_name]
- # params = [gev_params.to_dict()[param_name] for gev_params in gev_params_list]
- # # params = np.array(params)
- # # param_normalized = params / np.sqrt(np.sum(np.power(params, 2)))
- # # confidence_intervals = [gev_params.param_name_to_confidence_interval[param_name] for gev_params in
- # # gev_params_list]
- # massif_id = self.study.all_massif_names().index(massif_name)
- # plot_against_altitude(self.min_years_for_plot_x_axis, ax, massif_id, massif_name, params,
- # altitude, False,
- # massif_name_as_labels)
- # # plot_against_altitude(self.years, ax, massif_id, massif_name, confidence_intervals, True)
- #
- # # plot for each massif against the time
- #
- # def plot_gev_params_against_time_for_all_massifs(self):
- # for massif_name in self.study.all_massif_names():
- # self._plot_gev_params_against_time_for_one_massif(massif_name)
- #
- # def _plot_gev_params_against_time_for_one_massif(self, massif_name):
- # for param_name in GevParams.PARAM_NAMES[:]:
- # ax = plt.gca()
- # for altitude in self.altitudes_for_temporal_hypothesis:
- # self._plot_gev_params_against_time_for_one_altitude_and_one_massif(ax, massif_name, param_name,
- # altitude,
- # massif_name_as_labels=False)
- # ax.legend()
- # ax.set_xlabel('Year')
- # ax.set_ylabel(param_name + ' for {}'.format(massif_name))
- # xlabels = ['-'.join([str(e) for e in t]) for t in self.year_min_and_max_list]
- # ax.set_xticks(self.min_years_for_plot_x_axis)
- # ax.set_xticklabels(xlabels)
- # plot_name = '{} change /with years /for {}'.format(param_name, massif_name)
- # self.show_or_save_to_file(plot_name, no_title=True, tight_layout=True, show=False)
- # ax.clear()
- # plt.close()
- #
- # # PLot for each massif the derivative against the time for each altitude
- #
- # def plot_time_derivative_against_time(self):
- # for param_name in GevParams.PARAM_NAMES[:]:
- # ax = plt.gca()
- # for massif_name in self.study.all_massif_names()[:]:
- # self._plot_gev_params_time_derivative_against_altitude_one_massif(ax, massif_name, param_name)
- # ax.legend(prop={'size': 7}, ncol=3)
- # ax.set_xlabel('Altitude')
- # ax.set_ylabel(param_name)
- # plot_name = '{} change /time derivative with altitude'.format(param_name)
- # self.show_or_save_to_file(plot_name, no_title=True, tight_layout=True, show=False)
- # ax.clear()
- # plt.close()
- #
- # def _plot_gev_params_time_derivative_against_altitude_one_massif(self, ax, massif_name, param_name):
- # altitudes = []
- # time_derivatives = []
- # for altitude, study in self.altitude_to_study.items():
- # if (massif_name in study.study_massif_names) and ("Mercan" not in massif_name):
- # gev_params_list = study.massif_name_to_gev_param_list(self.year_min_and_max_list)[massif_name]
- # params = [gev_params.to_dict()[param_name] for gev_params in gev_params_list]
- # x = list(range(len(params)))
- # y = params
- # a = self.get_robust_slope(x, y)
- # time_derivatives.append(a)
- # altitudes.append(altitude)
- # massif_id = self.study.all_massif_names().index(massif_name)
- # plot_against_altitude(altitudes, ax, massif_id, massif_name, time_derivatives, fill=False)
- #
- # def get_robust_slope(self, x, y):
- # a, *_ = fit_linear_regression(x=x, y=y)
- # a_list = [a]
- # for i in range(len(x)):
- # x_copy, y_copy = x[:], y[:]
- # x_copy.pop(i)
- # y_copy.pop(i)
- # a, *_ = fit_linear_regression(x=x_copy, y=y_copy)
- # a_list.append(a)
- # return np.mean(np.array(a_list))
def main_paper2():
diff --git a/projects/contrasting_trends_in_snow_loads/post thesis committee/__init__.py b/projects/archive/__init__.py
similarity index 100%
rename from projects/contrasting_trends_in_snow_loads/post thesis committee/__init__.py
rename to projects/archive/__init__.py
diff --git a/projects/contrasting_trends_in_snow_loads/spatial trends/__init__.py b/projects/archive/check_mcmc_convergence_for_return_levels/__init__.py
similarity index 100%
rename from projects/contrasting_trends_in_snow_loads/spatial trends/__init__.py
rename to projects/archive/check_mcmc_convergence_for_return_levels/__init__.py
diff --git a/projects/others/check_mcmc_convergence_for_return_levels/gelman_convergence_test.py b/projects/archive/check_mcmc_convergence_for_return_levels/gelman_convergence_test.py
similarity index 100%
rename from projects/others/check_mcmc_convergence_for_return_levels/gelman_convergence_test.py
rename to projects/archive/check_mcmc_convergence_for_return_levels/gelman_convergence_test.py
diff --git a/projects/others/check_mcmc_convergence_for_return_levels/main_bayesian_mcmc.py b/projects/archive/check_mcmc_convergence_for_return_levels/main_bayesian_mcmc.py
similarity index 100%
rename from projects/others/check_mcmc_convergence_for_return_levels/main_bayesian_mcmc.py
rename to projects/archive/check_mcmc_convergence_for_return_levels/main_bayesian_mcmc.py
diff --git a/projects/others/check_mcmc_convergence_for_return_levels/main_gelman_convergence_test.py b/projects/archive/check_mcmc_convergence_for_return_levels/main_gelman_convergence_test.py
similarity index 100%
rename from projects/others/check_mcmc_convergence_for_return_levels/main_gelman_convergence_test.py
rename to projects/archive/check_mcmc_convergence_for_return_levels/main_gelman_convergence_test.py
diff --git a/projects/ogorman/__init__.py b/projects/archive/ogorman/__init__.py
similarity index 100%
rename from projects/ogorman/__init__.py
rename to projects/archive/ogorman/__init__.py
diff --git a/projects/ogorman/gorman_figures/__init__.py b/projects/archive/ogorman/gorman_figures/__init__.py
similarity index 100%
rename from projects/ogorman/gorman_figures/__init__.py
rename to projects/archive/ogorman/gorman_figures/__init__.py
diff --git a/projects/ogorman/gorman_figures/daily_snowfall_fraction.py b/projects/archive/ogorman/gorman_figures/daily_snowfall_fraction.py
similarity index 100%
rename from projects/ogorman/gorman_figures/daily_snowfall_fraction.py
rename to projects/archive/ogorman/gorman_figures/daily_snowfall_fraction.py
diff --git a/projects/ogorman/gorman_figures/figure1/__init__.py b/projects/archive/ogorman/gorman_figures/figure1/__init__.py
similarity index 100%
rename from projects/ogorman/gorman_figures/figure1/__init__.py
rename to projects/archive/ogorman/gorman_figures/figure1/__init__.py
diff --git a/projects/ogorman/gorman_figures/figure1/comparative_curve_wrt_altitude.py b/projects/archive/ogorman/gorman_figures/figure1/comparative_curve_wrt_altitude.py
similarity index 97%
rename from projects/ogorman/gorman_figures/figure1/comparative_curve_wrt_altitude.py
rename to projects/archive/ogorman/gorman_figures/figure1/comparative_curve_wrt_altitude.py
index 475494668a9c9def12c8ce6525bbb0fe681ba89a..6986f685e7e444208cf26013929d3e84b90d0004 100644
--- a/projects/ogorman/gorman_figures/figure1/comparative_curve_wrt_altitude.py
+++ b/projects/archive/ogorman/gorman_figures/figure1/comparative_curve_wrt_altitude.py
@@ -6,7 +6,7 @@ import numpy as np
from cached_property import cached_property
-from projects.ogorman.gorman_figures.figure1.study_visualizer_for_double_stationary_fit import \
+from projects.archive.ogorman.gorman_figures import \
StudyVisualizerForReturnLevelChange
diff --git a/projects/ogorman/gorman_figures/figure1/figure1_mean_ratio_return_level_ratio.py b/projects/archive/ogorman/gorman_figures/figure1/figure1_mean_ratio_return_level_ratio.py
similarity index 92%
rename from projects/ogorman/gorman_figures/figure1/figure1_mean_ratio_return_level_ratio.py
rename to projects/archive/ogorman/gorman_figures/figure1/figure1_mean_ratio_return_level_ratio.py
index 51991de1f88a0fe7ca5565cabde972481a50aab3..7fcd9aef2169b09b0c23f70bc14c07729fda4f74 100644
--- a/projects/ogorman/gorman_figures/figure1/figure1_mean_ratio_return_level_ratio.py
+++ b/projects/archive/ogorman/gorman_figures/figure1/figure1_mean_ratio_return_level_ratio.py
@@ -5,10 +5,10 @@ from extreme_data.meteo_france_data.scm_models_data.safran.safran import SafranS
from extreme_data.meteo_france_data.scm_models_data.visualization.main_study_visualizer import ALL_ALTITUDES_WITHOUT_NAN
from extreme_fit.model.margin_model.utils import \
MarginFitMethod
-from projects.ogorman.gorman_figures.figure1 import \
+from projects.archive.ogorman.gorman_figures import \
ComparativeCurveWrtAltitude
-from projects.ogorman.gorman_figures.figure1.study_visualizer_for_double_stationary_fit import \
+from projects.archive.ogorman.gorman_figures import \
StudyVisualizerForReturnLevelChange
diff --git a/projects/ogorman/gorman_figures/figure1/result_from_stationary_fit.py b/projects/archive/ogorman/gorman_figures/figure1/result_from_stationary_fit.py
similarity index 100%
rename from projects/ogorman/gorman_figures/figure1/result_from_stationary_fit.py
rename to projects/archive/ogorman/gorman_figures/figure1/result_from_stationary_fit.py
diff --git a/projects/ogorman/gorman_figures/figure1/study_visualizer_for_double_stationary_fit.py b/projects/archive/ogorman/gorman_figures/figure1/study_visualizer_for_double_stationary_fit.py
similarity index 92%
rename from projects/ogorman/gorman_figures/figure1/study_visualizer_for_double_stationary_fit.py
rename to projects/archive/ogorman/gorman_figures/figure1/study_visualizer_for_double_stationary_fit.py
index 3fd5d2bd5cda20b4087f1f0d9dbe3d1aa55226d0..cb66916c0f11ca8fb703f065e192d6d204d9b502 100644
--- a/projects/ogorman/gorman_figures/figure1/study_visualizer_for_double_stationary_fit.py
+++ b/projects/archive/ogorman/gorman_figures/figure1/study_visualizer_for_double_stationary_fit.py
@@ -1,15 +1,12 @@
import matplotlib
-from extreme_data.meteo_france_data.scm_models_data.abstract_study import AbstractStudy
-from extreme_data.meteo_france_data.scm_models_data.visualization.create_shifted_cmap import get_shifted_map, \
- get_colors, ticks_values_and_labels_for_percentages
from extreme_data.meteo_france_data.scm_models_data.visualization.study_visualizer import StudyVisualizer
from extreme_fit.model.margin_model.utils import \
- MarginFitMethod, fitmethod_to_str
+ MarginFitMethod
import matplotlib.pyplot as plt
-from projects.ogorman.gorman_figures.figure1.result_from_stationary_fit import ResultFromDoubleStationaryFit
+from projects.archive.ogorman.gorman_figures.figure1.result_from_stationary_fit import ResultFromDoubleStationaryFit
class StudyVisualizerForReturnLevelChange(StudyVisualizer):
diff --git a/projects/ogorman/gorman_figures/temperature_of_snowfall_maxima.py b/projects/archive/ogorman/gorman_figures/temperature_of_snowfall_maxima.py
similarity index 97%
rename from projects/ogorman/gorman_figures/temperature_of_snowfall_maxima.py
rename to projects/archive/ogorman/gorman_figures/temperature_of_snowfall_maxima.py
index bd518ce869a5a9843d8a08860b2693a5cc87fdd8..b1cfb28f85fbc736fcfa6b300fd18b287cb05bcd 100644
--- a/projects/ogorman/gorman_figures/temperature_of_snowfall_maxima.py
+++ b/projects/archive/ogorman/gorman_figures/temperature_of_snowfall_maxima.py
@@ -11,7 +11,7 @@ import numpy as np
from extreme_data.meteo_france_data.scm_models_data.safran.safran import SafranSnowfall1Day, SafranTemperature, \
SafranPrecipitation1Day
-from projects.ogorman.gorman_figures.daily_snowfall_fraction import \
+from projects.archive.ogorman.gorman_figures import \
compute_daily_snowfall_fraction
diff --git a/projects/ogorman/snowfall fraction of total precipitation/__init__.py b/projects/archive/ogorman/snowfall fraction of total precipitation/__init__.py
similarity index 100%
rename from projects/ogorman/snowfall fraction of total precipitation/__init__.py
rename to projects/archive/ogorman/snowfall fraction of total precipitation/__init__.py
diff --git a/projects/ogorman/snowfall fraction of total precipitation/short_snow_load_partition.py b/projects/archive/ogorman/snowfall fraction of total precipitation/short_snow_load_partition.py
similarity index 100%
rename from projects/ogorman/snowfall fraction of total precipitation/short_snow_load_partition.py
rename to projects/archive/ogorman/snowfall fraction of total precipitation/short_snow_load_partition.py
diff --git a/projects/others/__init__.py b/projects/archive/seasonal_analysis/__init__.py
similarity index 100%
rename from projects/others/__init__.py
rename to projects/archive/seasonal_analysis/__init__.py
diff --git a/projects/seasonal_analysis/main_season_repartition_of_maxima.py b/projects/archive/seasonal_analysis/main_season_repartition_of_maxima.py
similarity index 100%
rename from projects/seasonal_analysis/main_season_repartition_of_maxima.py
rename to projects/archive/seasonal_analysis/main_season_repartition_of_maxima.py
diff --git a/projects/others/check_mcmc_convergence_for_return_levels/__init__.py b/projects/archive/thesis_report/__init__.py
similarity index 100%
rename from projects/others/check_mcmc_convergence_for_return_levels/__init__.py
rename to projects/archive/thesis_report/__init__.py
diff --git a/projects/thesis_report/gev_plot.py b/projects/archive/thesis_report/gev_plot.py
similarity index 100%
rename from projects/thesis_report/gev_plot.py
rename to projects/archive/thesis_report/gev_plot.py
diff --git a/projects/thesis_report/simulation_for_quantile_gap.py b/projects/archive/thesis_report/simulation_for_quantile_gap.py
similarity index 100%
rename from projects/thesis_report/simulation_for_quantile_gap.py
rename to projects/archive/thesis_report/simulation_for_quantile_gap.py
diff --git a/projects/contrasting_trends_in_snow_loads/article2_snowfall_versus_time_and_altitude/data.py b/projects/contrasting_trends_in_snow_loads/article2_snowfall_versus_time_and_altitude/data.py
deleted file mode 100644
index aff3b1848ffe9c1918ed2dd546360539a18b72fc..0000000000000000000000000000000000000000
--- a/projects/contrasting_trends_in_snow_loads/article2_snowfall_versus_time_and_altitude/data.py
+++ /dev/null
@@ -1,76 +0,0 @@
-from collections import OrderedDict
-
-import matplotlib.pyplot as plt
-
-from extreme_data.meteo_france_data.scm_models_data.crocus.crocus import CrocusSnowLoadTotal
-from extreme_data.meteo_france_data.scm_models_data.safran.safran import SafranSnowfall1Day
-from extreme_data.meteo_france_data.scm_models_data.visualization.main_study_visualizer import \
- study_iterator_global, SCM_STUDY_CLASS_TO_ABBREVIATION
-from extreme_data.meteo_france_data.scm_models_data.visualization.study_visualizer import \
- StudyVisualizer
-from projects.exceeding_snow_loads.utils import dpi_paper1_figure
-
-
-def tuples_for_examples_paper1(examples_for_the_paper=True):
- if examples_for_the_paper:
-
- marker_altitude_massif_name_for_paper1 = [
- ('mediumpurple', 900, 'Ubaye'),
- ('darkmagenta', 1800, 'Vercors'),
- # ('mediumpurple', 2700, 'Beaufortain'),
- ]
- else:
- marker_altitude_massif_name_for_paper1 = [
- ('magenta', 600, 'Parpaillon'),
- ('darkmagenta', 300, 'Devoluy'),
- ('mediumpurple', 300, 'Aravis'),
- ]
- return marker_altitude_massif_name_for_paper1
-
-
-def max_graph_annual_maxima_poster():
- """
- We choose these massif because each represents a different eurocode region
- we also choose them because they belong to a different climatic area
- :return:
- """
- save_to_file = False
- study_class = SafranSnowfall1Day
-
- examples_for_the_paper = True
-
- ax = plt.gca()
- if examples_for_the_paper:
- # ax.set_ylim([0, 20])
- # ax.set_yticks(list(range(0, 21, 2)))
- linewidth = 5
- else:
- linewidth = 3
-
- marker_altitude_massif_name_for_paper1 = tuples_for_examples_paper1(examples_for_the_paper)
-
- altitude_to_linestyle = OrderedDict()
- first_altitude = 900
- second_altitude = 2100
- altitude_to_linestyle[first_altitude] = 'dashed'
- altitude_to_linestyle[second_altitude] = 'dotted'
- for altitude, linestyle in altitude_to_linestyle.items():
- for study in study_iterator_global([study_class], altitudes=[altitude]):
- study_visualizer = StudyVisualizer(study, save_to_file=save_to_file,
- verbose=True,
- multiprocessing=True)
- snow_abbreviation = SCM_STUDY_CLASS_TO_ABBREVIATION[study_class]
- tight_pad = {'h_pad': 0.2}
- snow_abbreviation = 'max ' + snow_abbreviation
- for color, _, massif_name in marker_altitude_massif_name_for_paper1[::-1]:
- last_plot = massif_name == 'Ubaye' and altitude == second_altitude
- label = '{} massif at {}m'.format(massif_name, altitude)
- study_visualizer.visualize_max_graphs_poster(massif_name, altitude, snow_abbreviation, color, label,
- last_plot, ax, tight_pad=tight_pad,
- dpi=dpi_paper1_figure,
- linewidth=linewidth,
- linestyle=linestyle)
-
-
-if __name__ == '__main__':
- max_graph_annual_maxima_poster()
diff --git a/projects/contrasting_trends_in_snow_loads/article2_snowfall_versus_time_and_altitude/main_snowfall_article.py b/projects/contrasting_trends_in_snow_loads/article2_snowfall_versus_time_and_altitude/main_snowfall_article.py
deleted file mode 100644
index 7d5c0100b544335d80f03ca07d2186fbaefc5261..0000000000000000000000000000000000000000
--- a/projects/contrasting_trends_in_snow_loads/article2_snowfall_versus_time_and_altitude/main_snowfall_article.py
+++ /dev/null
@@ -1,158 +0,0 @@
-from multiprocessing.pool import Pool
-
-import matplotlib as mpl
-import numpy as np
-
-from extreme_data.meteo_france_data.scm_models_data.safran.safran import SafranSnowfall1Day, SafranPrecipitation1Day, \
- SafranPrecipitation3Days, SafranSnowfall3Days
-from extreme_data.meteo_france_data.scm_models_data.utils import Season
-from extreme_trend.abstract_gev_trend_test import AbstractGevTrendTest
-from projects.contrasting_trends_in_snow_loads.article2_snowfall_versus_time_and_altitude.plot_selection_curves_paper2 import \
- plot_selection_curves_paper2
-from projects.contrasting_trends_in_snow_loads.article2_snowfall_versus_time_and_altitude.shape_plot import shape_plot
-from projects.contrasting_trends_in_snow_loads.article2_snowfall_versus_time_and_altitude.snowfall_plot import \
- plot_snowfall_mean, plot_snowfall_change_mean
-from projects.contrasting_trends_in_snow_loads.article2_snowfall_versus_time_and_altitude.study_visualizer_for_mean_values import \
- StudyVisualizerForMeanValues
-from projects.contrasting_trends_in_snow_loads.article2_snowfall_versus_time_and_altitude.study_visualizer_for_mean_values_with_mean_aic import \
- StudyVisualizerForMeanValuesWithMeanAic
-from projects.contrasting_trends_in_snow_loads.article2_snowfall_versus_time_and_altitude.validation_plot import \
- validation_plot
-from projects.exceeding_snow_loads.section_results.plot_selection_curves import plot_selection_curves
-from projects.exceeding_snow_loads.section_results.plot_trend_curves import plot_trend_map
-
-mpl.rcParams['text.usetex'] = True
-mpl.rcParams['text.latex.preamble'] = [r'\usepackage{amsmath}']
-
-from extreme_data.meteo_france_data.scm_models_data.crocus.crocus import CrocusSnowLoadTotal
-from extreme_fit.model.result_from_model_fit.result_from_extremes.confidence_interval_method import \
- ConfidenceIntervalMethodFromExtremes
-from extreme_trend.visualizers.study_visualizer_for_non_stationary_trends import \
- StudyVisualizerForNonStationaryTrends
-from extreme_trend.visualizers.utils import load_altitude_to_visualizer
-from projects.exceeding_snow_loads.section_results.plot_uncertainty_curves import plot_uncertainty_massifs
-from projects.exceeding_snow_loads.utils import paper_study_classes, paper_altitudes
-from root_utils import NB_CORES
-
-import matplotlib.pyplot as plt
-
-
-def minor_result(altitude):
- """Plot trends for a single altitude to be fast"""
- visualizer = StudyVisualizerForNonStationaryTrends(CrocusSnowLoadTotal(altitude=altitude), multiprocessing=True,
- )
- visualizer.plot_trends()
- plt.show()
-
-
-def compute_minimized_aic(visualizer):
- if isinstance(visualizer, StudyVisualizerForMeanValuesWithMeanAic):
- _ = visualizer.massif_name_and_trend_test_class_to_trend_test
- else:
- _ = visualizer.massif_name_to_trend_test_that_minimized_aic
- return True
-
-
-def intermediate_result(altitudes, massif_names=None,
- model_subsets_for_uncertainty=None,
- uncertainty_methods=None,
- study_class=SafranSnowfall1Day,
- multiprocessing=False, study_visualizer_class=StudyVisualizerForMeanValues,
- season=Season.annual):
- """
- Plot all the trends for all altitudes
- And enable to plot uncertainty plot for some specific massif_names, uncertainty methods to be fast
- :param altitudes:
- :param massif_names:
- :param non_stationary_uncertainty:
- :param uncertainty_methods:
- :param study_class:
- :return:
- """
- # Load altitude to visualizer
- altitude_to_visualizer = load_altitude_to_visualizer(altitudes, massif_names, model_subsets_for_uncertainty,
- study_class, uncertainty_methods,
- study_visualizer_class=study_visualizer_class,
- season=season)
- # Load variable object efficiently
- for v in altitude_to_visualizer.values():
- _ = v.study.year_to_variable_object
- # Compute minimized value efficiently
- visualizers = list(altitude_to_visualizer.values())
- if multiprocessing:
- with Pool(4) as p:
- _ = p.imap(compute_minimized_aic, visualizers)
- else:
- for visualizer in visualizers:
- _ = compute_minimized_aic(visualizer)
- # Aggregate the choice for the minimizer
- aggregate(visualizers)
-
- # Plots
- validation_plot(altitude_to_visualizer, order_derivative=0)
- # validation_plot(altitude_to_visualizer, order_derivative=1)
- plot_snowfall_mean(altitude_to_visualizer)
- # plot_selection_curves_paper2(altitude_to_visualizer)
- plot_snowfall_change_mean(altitude_to_visualizer)
- shape_plot(altitude_to_visualizer)
-
-
-def major_result():
- uncertainty_methods = [ConfidenceIntervalMethodFromExtremes.ci_mle][:]
- # massif_names = ['Beaufortain', 'Vercors']
- massif_names = None
- study_classes = [SafranSnowfall1Day, SafranPrecipitation1Day][::-1][:]
- # study_classes = [SafranSnowfall3Days, SafranPrecipitation3Days][::-1]
- model_subsets_for_uncertainty = None
- altitudes = paper_altitudes
- altitudes = [900, 1200, 1500, 1800, 2100, 2400, 2700, 3000]
- # altitudes = [900, 1200, 1500, 1800][:2]
- # altitudes = [1800, 2100, 2400, 2700][:3]
- # altitudes = [900, 1200, 1500, 1800, 2100, 2400, 2700, 3000]
- # altitudes = draft_altitudes
- # for significance_level in [0.1, 0.05][]:
- AbstractGevTrendTest.SIGNIFICANCE_LEVEL = 0.05
- study_visualizer_class = [StudyVisualizerForMeanValues, StudyVisualizerForMeanValuesWithMeanAic][0]
- season = [Season.annual, Season.winter_extended][1]
- for study_class in study_classes:
- intermediate_result(altitudes, massif_names, model_subsets_for_uncertainty,
- uncertainty_methods, study_class, multiprocessing=False,
- study_visualizer_class=study_visualizer_class,
- season=season)
-
-
-def aggregate(visualizers):
- visualizer = visualizers[0]
- if not isinstance(visualizer, StudyVisualizerForMeanValuesWithMeanAic):
- return
- massif_names = set.union(*[set(v.massif_names) for v in visualizers])
- massif_names = list(massif_names)
- trend_tests = visualizer.non_stationary_trend_test
-
- massif_name_to_trend_test_to_aic_list = {m: {t: [] for t in trend_tests}
- for m in massif_names}
- for v in visualizers:
- for (m, t), t2 in v.massif_name_and_trend_test_class_to_trend_test.items():
- massif_name_to_trend_test_to_aic_list[m][t].append(t2.aic)
-
- massif_name_to_trend_test_with_minimial_mean_aic = {}
- for m in massif_names:
- trend_test_and_mean_aic = [(t, np.array(massif_name_to_trend_test_to_aic_list[m][t]).mean())
- for t in trend_tests]
- sorted_l = sorted(trend_test_and_mean_aic, key=lambda e: e[1])
- trend_test_with_minimial_mean_aic = sorted_l[0][0]
- massif_name_to_trend_test_with_minimial_mean_aic[m] = trend_test_with_minimial_mean_aic
- print(massif_name_to_trend_test_with_minimial_mean_aic)
- for v in visualizers:
- v.massif_name_to_trend_test_with_minimial_mean_aic = {}
- for m, t in massif_name_to_trend_test_with_minimial_mean_aic.items():
- if (m, t) in v.massif_name_and_trend_test_class_to_trend_test:
- v.massif_name_to_trend_test_with_minimial_mean_aic[m] = v.massif_name_and_trend_test_class_to_trend_test[(m, t)]
-
-
-if __name__ == '__main__':
- major_result()
- # intermediate_result(altitudes=[300], massif_names=None,
- # uncertainty_methods=[ConfidenceIntervalMethodFromExtremes.my_bayes,
- # ConfidenceIntervalMethodFromExtremes.ci_mle][1:],
- # multiprocessing=True)
diff --git a/projects/contrasting_trends_in_snow_loads/article2_snowfall_versus_time_and_altitude/plot_selection_curves_paper2.py b/projects/contrasting_trends_in_snow_loads/article2_snowfall_versus_time_and_altitude/plot_selection_curves_paper2.py
deleted file mode 100644
index 8e54d150acea0d12fb7a304d39439ea60fb0bb45..0000000000000000000000000000000000000000
--- a/projects/contrasting_trends_in_snow_loads/article2_snowfall_versus_time_and_altitude/plot_selection_curves_paper2.py
+++ /dev/null
@@ -1,73 +0,0 @@
-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 extreme_trend.abstract_gev_trend_test import AbstractGevTrendTest
-from projects.contrasting_trends_in_snow_loads.article2_snowfall_versus_time_and_altitude.study_visualizer_for_mean_values import \
- StudyVisualizerForMeanValues
-from projects.exceeding_snow_loads.section_results.plot_selection_curves import merge_counter, \
- get_ordered_list_from_counter, permute
-from projects.exceeding_snow_loads.utils import dpi_paper1_figure, get_trend_test_name
-from extreme_trend.visualizers.study_visualizer_for_non_stationary_trends import StudyVisualizerForNonStationaryTrends
-
-
-def plot_selection_curves_paper2(altitude_to_visualizer: Dict[int, StudyVisualizerForMeanValues]):
- visualizer = list(altitude_to_visualizer.values())[0]
-
- ax = create_adjusted_axes(1, 1)
-
- vs = [v for v in altitude_to_visualizer.values()]
-
- selected_counter = merge_counter([v.selected_trend_test_class_counter for v in vs])
- selected_and_anderson_counter = merge_counter([v.selected_and_anderson_trend_test_class_counter for v in vs])
- selected_and_anderson_and_likelihood_counter = merge_counter(
- [v.selected_and_anderson_and_likelihood_ratio_trend_test_class_counter() for v in vs])
-
- 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_anderson_list = get_ordered_list_from_counter(selected_and_anderson_counter, total_of_selected_models,
- visualizer, permutation)
- selected_and_anderson_and_likelihood_list = get_ordered_list_from_counter(
- selected_and_anderson_and_likelihood_counter, total_of_selected_models, visualizer, permutation)
-
- labels = [get_trend_test_name(t) for t in visualizer.non_stationary_trend_test]
- labels = permute(labels, permutation)
- print(select_list, sum(select_list))
-
- nb_selected_models = sum(select_list)
- nb_selected_and_anderson_models = sum(selected_and_anderson_list)
- nb_selected_and_anderson_and_likelihood_models = sum(selected_and_anderson_and_likelihood_list)
- nb_selected_models_not_passing_any_test = nb_selected_models - nb_selected_and_anderson_models
- nb_selected_models_just_passing_anderson = nb_selected_and_anderson_models - nb_selected_and_anderson_and_likelihood_models
-
- # parameters
- width = 5
- size = 30
- legend_fontsize = 30
- labelsize = 15
- linewidth = 3
- x = [10 * (i + 1) for i in range(len(select_list))]
- for l, color, label, nb in zip([select_list, selected_and_anderson_list, selected_and_anderson_and_likelihood_list],
- ['white', 'grey', 'black'],
- ['Not passing any test', 'Just passing anderson test ',
- 'Passing both anderson and likelihood tests '],
- [nb_selected_models_not_passing_any_test, nb_selected_models_just_passing_anderson, nb_selected_and_anderson_and_likelihood_models]):
- label += ' ({} \%)'.format(round(nb, 1))
- ax.bar(x, l, width=width, color=color, edgecolor='black', label=label, 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)
-
- # Save plot
- visualizer.plot_name = 'Selection curves with significance level = {} '.format(AbstractGevTrendTest.SIGNIFICANCE_LEVEL)
- visualizer.show_or_save_to_file(no_title=True, dpi=dpi_paper1_figure)
- plt.close()
diff --git a/projects/contrasting_trends_in_snow_loads/article2_snowfall_versus_time_and_altitude/shape_plot.py b/projects/contrasting_trends_in_snow_loads/article2_snowfall_versus_time_and_altitude/shape_plot.py
deleted file mode 100644
index 657bf026d53dfd0f9596d5cd95fd38fe6caf6a09..0000000000000000000000000000000000000000
--- a/projects/contrasting_trends_in_snow_loads/article2_snowfall_versus_time_and_altitude/shape_plot.py
+++ /dev/null
@@ -1,17 +0,0 @@
-from typing import Dict
-
-import matplotlib
-import matplotlib.pyplot as plt
-
-from projects.contrasting_trends_in_snow_loads.article2_snowfall_versus_time_and_altitude.study_visualizer_for_mean_values import \
- StudyVisualizerForMeanValues
-
-
-def shape_plot(altitude_to_visualizer: Dict[int, StudyVisualizerForMeanValues]):
- # Plot map for the repartition of the difference
- for altitude, visualizer in altitude_to_visualizer.items():
- label = ' shape parameter'
- visualizer.plot_abstract_fast(massif_name_to_value=visualizer.massif_name_to_model_shape_last_year,
- label='Model' + label, negative_and_positive_values=True, add_text=True,
- cmap=matplotlib.cm.get_cmap('BrBG_r'), graduation=0.1)
- plt.close()
diff --git a/projects/contrasting_trends_in_snow_loads/article2_snowfall_versus_time_and_altitude/snowfall_plot.py b/projects/contrasting_trends_in_snow_loads/article2_snowfall_versus_time_and_altitude/snowfall_plot.py
deleted file mode 100644
index 03b9c51e3a39ffd2d447ccb80bcdad0735cb4998..0000000000000000000000000000000000000000
--- a/projects/contrasting_trends_in_snow_loads/article2_snowfall_versus_time_and_altitude/snowfall_plot.py
+++ /dev/null
@@ -1,176 +0,0 @@
-from typing import Dict
-import matplotlib.pyplot as plt
-
-import numpy as np
-from sklearn.linear_model import LinearRegression
-
-from extreme_data.meteo_france_data.scm_models_data.visualization.main_study_visualizer import \
- SCM_STUDY_CLASS_TO_ABBREVIATION
-from extreme_data.meteo_france_data.scm_models_data.visualization.plot_utils import plot_against_altitude
-from projects.contrasting_trends_in_snow_loads.article2_snowfall_versus_time_and_altitude.study_visualizer_for_mean_values import \
- StudyVisualizerForMeanValues
-
-
-def fit_linear_regression(x, y):
- X = np.array(x).reshape(-1, 1)
- reg = LinearRegression().fit(X, y)
- r2_score = reg.score(X, y)
- a = reg.coef_
- b = reg.intercept_
- return a, b, r2_score
-
-
-def plot_snowfall_change_mean(altitude_to_visualizer: Dict[int, StudyVisualizerForMeanValues]):
- visualizer = list(altitude_to_visualizer.values())[0]
- study = visualizer.study
-
- variables = ['mean annual maxima', '50 year return level']
- mean_indicators = [True, False]
- l = list(zip(variables, mean_indicators))
- for variable, mean_indicator in l[:1]:
- for relative in [False, True]:
- if relative:
- variable += str(' (relative)')
- # Plot the curve for the evolution of the mean
- massif_name_to_a, massif_name_to_b, massif_name_to_r2_score = plot_mean(altitude_to_visualizer,
- derivative=True, mean_indicator=mean_indicator,
- relative=relative)
- # Augmentation every km
- massif_name_to_augmentation_every_km = {m: a * 1000 for m, a in massif_name_to_a.items()}
- massif_to_r2_score_text = {k: str(round(v, 2)) for k, v in massif_name_to_r2_score.items()}
-
- visualizer.plot_abstract_fast(massif_name_to_augmentation_every_km,
- label='Slope for changes of {} of {}\n for every km of elevation ({})'.format(
- variable, SCM_STUDY_CLASS_TO_ABBREVIATION[type(study)], study.variable_unit),
- add_x_label=False,
- add_text=True, massif_name_to_text=massif_to_r2_score_text)
- # Value at 2000 m
- massif_name_to_mean_at_2000 = {m: a * 2000 + massif_name_to_b[m] for m, a in massif_name_to_a.items()}
- visualizer.plot_abstract_fast(massif_name_to_mean_at_2000,
- label='Changes of {} \nof {} at 2000 m ({})'.format(
- variable, SCM_STUDY_CLASS_TO_ABBREVIATION[type(study)], study.variable_unit),
- add_x_label=False,
- add_text=True, massif_name_to_text=massif_to_r2_score_text)
- # Altitude for the change of dynamic
- massif_name_to_altitude_change_dynamic = {m: - massif_name_to_b[m] / a for m, a in massif_name_to_a.items()}
- # Keep only those that are in a reasonable range
- massif_name_to_altitude_change_dynamic = {m: d for m, d in massif_name_to_altitude_change_dynamic.items()
- if 0 < d < 3000}
- visualizer.plot_abstract_fast(massif_name_to_altitude_change_dynamic,
- label=('Altitude when the changes equal zero for %s (m)' % variable),
- add_x_label=False, graduation=500,
- add_text=True, massif_name_to_text=massif_to_r2_score_text)
-
-
-def plot_snowfall_mean(altitude_to_visualizer: Dict[int, StudyVisualizerForMeanValues]):
- visualizer = list(altitude_to_visualizer.values())[0]
- study = visualizer.study
-
- variables = ['mean annual maxima', '50 year return level']
- mean_indicators = [True, False]
- for variable, mean_indicator in zip(variables, mean_indicators):
- # Plot the curve for the evolution of the mean
- massif_name_to_a, massif_name_to_b, massif_name_to_r2_score = plot_mean(altitude_to_visualizer,
- derivative=False,
- mean_indicator=mean_indicator)
- # Compute values of interest
- massif_name_to_mean_at_2000 = {m: a * 2000 + massif_name_to_b[m] for m, a in massif_name_to_a.items()}
- massif_name_to_augmentation_every_km = {m: a * 1000 for m, a in massif_name_to_a.items()}
- massif_name_to_relative_augmentation_between_2000_and_3000_every_km = {
- m: 100 * (a * 1000) / (a * 2000 + massif_name_to_b[m]) for m, a in massif_name_to_a.items()}
- massif_name_to_relative_augmentation_between_1000_and_2000_every_km = {
- m: 100 * (a * 1000) / (a * 1000 + massif_name_to_b[m]) for m, a in massif_name_to_a.items()}
- massif_to_r2_score_text = {k: str(round(v, 2)) for k, v in massif_name_to_r2_score.items()}
- # Augmentation every km
- ds = [massif_name_to_augmentation_every_km, massif_name_to_relative_augmentation_between_1000_and_2000_every_km,
- massif_name_to_relative_augmentation_between_2000_and_3000_every_km]
- prefixs = ['Augmentation', 'Relative augmentation between 1000m and 2000m',
- 'Relative augmentation between 2000m and 3000m']
- graduations = [1.0, 10.0, 10.0]
- for d, prefix, graduation in zip(ds, prefixs, graduations):
- visualizer.plot_abstract_fast(d,
- graduation=10.0,
- label=prefix + ' of {} of {} \nfor every km of elevation ({})'.format(
- variable, SCM_STUDY_CLASS_TO_ABBREVIATION[type(study)],
- study.variable_unit),
- add_x_label=False, negative_and_positive_values=False,
- add_text=True,
- massif_name_to_text=massif_to_r2_score_text
- )
- # Value at 2000 m
- visualizer.plot_abstract_fast(massif_name_to_mean_at_2000, label='{} of {} at 2000 m ()'.format(
- variable, SCM_STUDY_CLASS_TO_ABBREVIATION[type(study)], study.variable_unit),
- add_x_label=False, negative_and_positive_values=False,
- add_text=True, massif_name_to_text=massif_to_r2_score_text)
-
-
-def plot_mean(altitude_to_visualizer: Dict[int, StudyVisualizerForMeanValues], derivative=False, mean_indicator=True,
- relative=False):
- ax = plt.gca()
- massif_name_to_linear_regression_result = {}
-
- visualizers = list(altitude_to_visualizer.values())
- visualizer = visualizers[0]
- study = visualizer.study
-
- for massif_id, massif_name in enumerate(visualizer.study.all_massif_names()):
- altitudes_massif = [a for a, v in altitude_to_visualizer.items()
- if massif_name in v.massif_name_to_trend_test_that_minimized_aic]
- if len(altitudes_massif) >= 2:
- trend_tests = [altitude_to_visualizer[a].massif_name_to_trend_test_that_minimized_aic[massif_name]
- for a in altitudes_massif]
- nb_years = 50
- return_period = 50
- if mean_indicator:
- indicator_str = 'mean'
- else:
- indicator_str = '50 year return level'
- res = [(a, t) for i, (a, t) in enumerate(zip(altitudes_massif, trend_tests))]
- if derivative:
- if mean_indicator:
- if relative:
- res = [(a, t.relative_change_in_mean_for_the_last_x_years(nb_years=nb_years)) for a, t in res]
- else:
- res = [(a, t.change_in_mean_for_the_last_x_years(nb_years=nb_years)) for a, t in res]
- else:
- if relative:
- res = [(a, t.relative_change_in_50_year_return_level_for_the_last_x_years(nb_years=nb_years, return_period=return_period)) for a, t in res]
- else:
- res = [(a, t.change_in_50_year_return_level_for_the_last_x_years(nb_years=nb_years, return_period=return_period)) for a, t in res]
- if len(res) > 0:
- altitudes_values, values = zip(*res)
- else:
- altitudes_values, values = [], []
- indicator_str = 'Change of the {} for the last {} years \nfor non-stationary models'.format(indicator_str, nb_years)
- if relative:
- indicator_str += ' (relative)'
- else:
- if mean_indicator:
- values = [t.unconstrained_estimator_gev_params_last_year.mean for t in trend_tests]
- else:
- values = [t.unconstrained_estimator_gev_params_last_year.return_level(return_period=return_period)
- for t in trend_tests]
- altitudes_values = altitudes_massif
- # Plot
- if len(altitudes_values) >= 2:
- massif_name_to_linear_regression_result[massif_name] = fit_linear_regression(altitudes_values, values)
- plot_values_against_altitudes(ax, altitudes_values, massif_id, massif_name, indicator_str, study, values,
- visualizer)
- ax.legend(prop={'size': 7}, ncol=3)
- visualizer.show_or_save_to_file(dpi=500, add_classic_title=False)
- plt.close()
-
- return [{m: t[i][0] if i == 0 else t[i]
- for m, t in massif_name_to_linear_regression_result.items()} for i in range(3)]
-
-
-def plot_values_against_altitudes(ax, altitudes, massif_id, massif_name, moment, study, values, visualizer):
- plot_against_altitude(x_ticks=altitudes, ax=ax, massif_id=massif_id, massif_name=massif_name, values=values)
- plot_name = '{} annual maxima of {}'.format(moment, SCM_STUDY_CLASS_TO_ABBREVIATION[type(study)])
- ax.set_ylabel('{} ({})'.format(plot_name, study.variable_unit), fontsize=15)
- ax.set_xlabel('altitudes', fontsize=15)
- # lim_down, lim_up = ax.get_ylim()
- # lim_up += (lim_up - lim_down) / 3
- # ax.set_ylim([lim_down, lim_up])
- ax.tick_params(axis='both', which='major', labelsize=13)
- visualizer.plot_name = plot_name
diff --git a/projects/contrasting_trends_in_snow_loads/article2_snowfall_versus_time_and_altitude/study_visualizer_for_mean_values.py b/projects/contrasting_trends_in_snow_loads/article2_snowfall_versus_time_and_altitude/study_visualizer_for_mean_values.py
deleted file mode 100644
index d1a30c5c84ab29232989af25125d48e11186c9a4..0000000000000000000000000000000000000000
--- a/projects/contrasting_trends_in_snow_loads/article2_snowfall_versus_time_and_altitude/study_visualizer_for_mean_values.py
+++ /dev/null
@@ -1,154 +0,0 @@
-from collections import Counter
-from typing import Dict
-
-import matplotlib.pyplot as plt
-import numpy as np
-from cached_property import cached_property
-
-from extreme_data.eurocode_data.utils import YEAR_OF_INTEREST_FOR_RETURN_LEVEL
-from extreme_data.meteo_france_data.scm_models_data.abstract_study import AbstractStudy
-from extreme_fit.model.margin_model.utils import MarginFitMethod
-from extreme_trend.abstract_gev_trend_test import AbstractGevTrendTest
-from extreme_trend.visualizers.study_visualizer_for_non_stationary_trends import StudyVisualizerForNonStationaryTrends
-from projects.exceeding_snow_loads.utils import NON_STATIONARY_TREND_TEST_PAPER_2
-
-
-class StudyVisualizerForMeanValues(StudyVisualizerForNonStationaryTrends):
-
- def __init__(self, study: AbstractStudy, show=True, save_to_file=False, only_one_graph=False, only_first_row=False,
- vertical_kde_plot=False, year_for_kde_plot=None, plot_block_maxima_quantiles=False,
- temporal_non_stationarity=False, transformation_class=None, verbose=False, multiprocessing=False,
- complete_non_stationary_trend_analysis=False, normalization_under_one_observations=True,
- uncertainty_methods=None, model_subsets_for_uncertainty=None, uncertainty_massif_names=None,
- effective_temporal_covariate=YEAR_OF_INTEREST_FOR_RETURN_LEVEL, relative_change_trend_plot=True,
- non_stationary_trend_test_to_marker=None, fit_method=MarginFitMethod.extremes_fevd_mle,
- select_only_acceptable_shape_parameter=True, fit_gev_only_on_non_null_maxima=False,
- fit_only_time_series_with_ninety_percent_of_non_null_values=True):
-
- # Change the type of models used in the study
- non_stationary_trend_test_to_marker = {c: '' for c in NON_STATIONARY_TREND_TEST_PAPER_2}
-
- super().__init__(study, show, save_to_file, only_one_graph, only_first_row, vertical_kde_plot,
- year_for_kde_plot, plot_block_maxima_quantiles, temporal_non_stationarity,
- transformation_class, verbose, multiprocessing, complete_non_stationary_trend_analysis,
- normalization_under_one_observations, uncertainty_methods, model_subsets_for_uncertainty,
- uncertainty_massif_names, effective_temporal_covariate, relative_change_trend_plot,
- non_stationary_trend_test_to_marker, fit_method, select_only_acceptable_shape_parameter,
- fit_gev_only_on_non_null_maxima, fit_only_time_series_with_ninety_percent_of_non_null_values)
- assert len(self.non_stationary_trend_test) == len(NON_STATIONARY_TREND_TEST_PAPER_2)
-
- def plot_abstract_fast(self, massif_name_to_value, label, graduation=10.0, cmap=plt.cm.coolwarm, add_x_label=True,
- negative_and_positive_values=True, add_text=False, massif_name_to_text=None):
- if add_text:
- if massif_name_to_text is None:
- massif_name_to_text = self.massif_name_to_text
- else:
- massif_name_to_text = None
- super().plot_abstract(massif_name_to_value, label, label, self.fit_method, graduation, cmap, add_x_label,
- negative_and_positive_values, massif_name_to_text)
-
- @property
- def massif_name_to_text(self):
- d = {}
- for m, t in self.massif_name_to_trend_test_that_minimized_aic.items():
- latex_command = 'textbf' if t.is_significant else 'textrm'
- d[m] = '$\\' + latex_command + '{' + t.name + '}$'
- return d
-
- # Override the main dict massif_name_to_trend_test_that_minimized_aic
-
- @property
- def super_massif_name_to_trend_test_that_minimized_aic(self):
- return super().massif_name_to_trend_test_that_minimized_aic
-
- @property
- def massif_name_to_trend_test_that_minimized_aic_after_anderson_test(self):
- return {m: t for m, t in super().massif_name_to_trend_test_that_minimized_aic.items()
- if t.goodness_of_fit_anderson_test}
-
- @property
- def massif_name_to_trend_test_that_minimized_aic_after_anderson_test_and_likelihood_ratio_test(self):
- return {m: t for m, t in self.massif_name_to_trend_test_that_minimized_aic_after_anderson_test.items()
- if t.is_significant}
-
- @property
- def massif_name_to_trend_test_that_minimized_aic(self) -> Dict[str, AbstractGevTrendTest]:
- return self.massif_name_to_trend_test_that_minimized_aic_after_anderson_test
-
- # Counter for the selection process
-
- @cached_property
- def selected_trend_test_class_counter(self):
- return Counter([type(t) for t in self.super_massif_name_to_trend_test_that_minimized_aic.values()])
-
- @cached_property
- def selected_and_anderson_trend_test_class_counter(self):
- return Counter([type(t) for t in self.massif_name_to_trend_test_that_minimized_aic_after_anderson_test.values()])
-
- def selected_and_anderson_and_likelihood_ratio_trend_test_class_counter(self):
- return Counter([type(t) for t in self.massif_name_to_trend_test_that_minimized_aic_after_anderson_test_and_likelihood_ratio_test.values()])
-
-# Study of the mean
-
- @cached_property
- def massif_name_to_empirical_mean(self):
- massif_name_to_empirical_value = {}
- for massif_name, maxima in self.study.massif_name_to_annual_maxima.items():
- massif_name_to_empirical_value[massif_name] = np.mean(maxima)
- return massif_name_to_empirical_value
-
- @cached_property
- def massif_name_to_model_shape_last_year(self):
- massif_name_to_model_value_last_year = {}
- for massif_name, trend_test in self.massif_name_to_trend_test_that_minimized_aic.items():
- massif_name_to_model_value_last_year[
- massif_name] = trend_test.unconstrained_estimator_gev_params_last_year.shape
- return massif_name_to_model_value_last_year
-
- @cached_property
- def massif_name_to_model_mean_last_year(self):
- massif_name_to_model_value_last_year = {}
- for massif_name, trend_test in self.massif_name_to_trend_test_that_minimized_aic.items():
- massif_name_to_model_value_last_year[
- massif_name] = trend_test.unconstrained_estimator_gev_params_last_year.mean
- return massif_name_to_model_value_last_year
-
- @cached_property
- def massif_name_to_relative_difference_for_mean(self):
- massif_name_to_relative_difference = {}
- for massif_name in self.massif_name_to_trend_test_that_minimized_aic.keys():
- e = self.massif_name_to_empirical_mean[massif_name]
- m = self.massif_name_to_model_mean_last_year[massif_name]
- relative_diference = 100 * (m - e) / e
- massif_name_to_relative_difference[massif_name] = relative_diference
- return massif_name_to_relative_difference
-
- # Study of the change in the mean
-
- @cached_property
- def massif_name_to_change_ratio_in_empirical_mean(self):
- massif_name_to_empirical_value = {}
- for massif_name, maxima in self.study.massif_name_to_annual_maxima.items():
- index = self.study.ordered_years.index(1989)
- maxima_before, maxima_after = maxima[:index + 1], maxima[index + 1:]
- massif_name_to_empirical_value[massif_name] = np.mean(maxima_after) / np.mean(maxima_before)
- return massif_name_to_empirical_value
-
- @cached_property
- def massif_name_to_change_ratio_in_model_mean(self):
- massif_name_to_parameter_value = {}
- for massif_name, trend_test in self.massif_name_to_trend_test_that_minimized_aic.items():
- model_mean_before = trend_test.unconstrained_average_mean_value(year_min=self.study.year_min, year_max=1989)
- model_mean_after = trend_test.unconstrained_average_mean_value(year_min=1990, year_max=self.study.year_max)
- massif_name_to_parameter_value[massif_name] = model_mean_after / model_mean_before
- return massif_name_to_parameter_value
-
- @cached_property
- def massif_name_to_relative_difference_for_change_ratio_in_mean(self):
- massif_name_to_relative_difference = {}
- for massif_name in self.massif_name_to_trend_test_that_minimized_aic.keys():
- e = self.massif_name_to_change_ratio_in_empirical_mean[massif_name]
- m = self.massif_name_to_change_ratio_in_model_mean[massif_name]
- relative_diference = 100 * (m - e) / e
- massif_name_to_relative_difference[massif_name] = relative_diference
- return massif_name_to_relative_difference
diff --git a/projects/contrasting_trends_in_snow_loads/article2_snowfall_versus_time_and_altitude/study_visualizer_for_mean_values_with_mean_aic.py b/projects/contrasting_trends_in_snow_loads/article2_snowfall_versus_time_and_altitude/study_visualizer_for_mean_values_with_mean_aic.py
deleted file mode 100644
index 7317f7f6f3c65b3de5b0c89d76e1d9fc663c8421..0000000000000000000000000000000000000000
--- a/projects/contrasting_trends_in_snow_loads/article2_snowfall_versus_time_and_altitude/study_visualizer_for_mean_values_with_mean_aic.py
+++ /dev/null
@@ -1,57 +0,0 @@
-from collections import Counter
-from typing import Dict
-
-import matplotlib.pyplot as plt
-import numpy as np
-from cached_property import cached_property
-
-from extreme_data.eurocode_data.utils import YEAR_OF_INTEREST_FOR_RETURN_LEVEL
-from extreme_data.meteo_france_data.scm_models_data.abstract_study import AbstractStudy
-from extreme_fit.model.margin_model.utils import MarginFitMethod
-from extreme_trend.abstract_gev_trend_test import AbstractGevTrendTest
-from extreme_trend.visualizers.study_visualizer_for_non_stationary_trends import StudyVisualizerForNonStationaryTrends
-from projects.contrasting_trends_in_snow_loads.article2_snowfall_versus_time_and_altitude.study_visualizer_for_mean_values import \
- StudyVisualizerForMeanValues
-from projects.exceeding_snow_loads.utils import NON_STATIONARY_TREND_TEST_PAPER_2
-
-
-class StudyVisualizerForMeanValuesWithMeanAic(StudyVisualizerForMeanValues):
-
- def __init__(self, study: AbstractStudy, show=True, save_to_file=False, only_one_graph=False, only_first_row=False,
- vertical_kde_plot=False, year_for_kde_plot=None, plot_block_maxima_quantiles=False,
- temporal_non_stationarity=False, transformation_class=None, verbose=False, multiprocessing=False,
- complete_non_stationary_trend_analysis=False, normalization_under_one_observations=True,
- uncertainty_methods=None, model_subsets_for_uncertainty=None, uncertainty_massif_names=None,
- effective_temporal_covariate=YEAR_OF_INTEREST_FOR_RETURN_LEVEL, relative_change_trend_plot=True,
- non_stationary_trend_test_to_marker=None, fit_method=MarginFitMethod.extremes_fevd_mle,
- select_only_acceptable_shape_parameter=True, fit_gev_only_on_non_null_maxima=False,
- fit_only_time_series_with_ninety_percent_of_non_null_values=True):
- super().__init__(study, show, save_to_file, only_one_graph, only_first_row, vertical_kde_plot,
- year_for_kde_plot, plot_block_maxima_quantiles, temporal_non_stationarity,
- transformation_class, verbose, multiprocessing, complete_non_stationary_trend_analysis,
- normalization_under_one_observations, uncertainty_methods, model_subsets_for_uncertainty,
- uncertainty_massif_names, effective_temporal_covariate, relative_change_trend_plot,
- non_stationary_trend_test_to_marker, fit_method, select_only_acceptable_shape_parameter,
- fit_gev_only_on_non_null_maxima, fit_only_time_series_with_ninety_percent_of_non_null_values)
- self.massif_name_to_trend_test_with_minimial_mean_aic = None
-
- @property
- def massif_name_to_trend_test_that_minimized_aic(self) -> Dict[str, AbstractGevTrendTest]:
- if self.massif_name_to_trend_test_with_minimial_mean_aic is None:
- raise NotImplementedError('Aggregation must be run first')
- else:
- return self.massif_name_to_trend_test_with_minimial_mean_aic
-
- @property
- def massif_names(self):
- return [m for m, _ in self.massif_name_and_trend_test_class_to_trend_test.keys()]
-
- @cached_property
- def massif_name_and_trend_test_class_to_trend_test(self):
- d = {}
- for massif_name in self.massif_name_to_years_and_maxima_for_model_fitting.keys():
- trend_tests = self.get_sorted_trend_test(massif_name)
- for trend_test in trend_tests:
- d[(massif_name, type(trend_test))] = trend_test
- return d
-
diff --git a/projects/contrasting_trends_in_snow_loads/article2_snowfall_versus_time_and_altitude/validation_plot.py b/projects/contrasting_trends_in_snow_loads/article2_snowfall_versus_time_and_altitude/validation_plot.py
deleted file mode 100644
index 7019880de6b644347763d4b6548b8f954c290513..0000000000000000000000000000000000000000
--- a/projects/contrasting_trends_in_snow_loads/article2_snowfall_versus_time_and_altitude/validation_plot.py
+++ /dev/null
@@ -1,79 +0,0 @@
-from multiprocessing import Pool
-from typing import Dict
-
-import matplotlib.pyplot as plt
-
-from extreme_data.meteo_france_data.scm_models_data.visualization.main_study_visualizer import \
- SCM_STUDY_CLASS_TO_ABBREVIATION
-from projects.contrasting_trends_in_snow_loads.article2_snowfall_versus_time_and_altitude.study_visualizer_for_mean_values import \
- StudyVisualizerForMeanValues
-from projects.ogorman.gorman_figures.figure1.study_visualizer_for_double_stationary_fit import \
- StudyVisualizerForReturnLevelChange
-from root_utils import NB_CORES
-
-
-def validation_plot(altitude_to_visualizer: Dict[int, StudyVisualizerForMeanValues], order_derivative=0):
- # Plot the mean empirical, the mean parametric and the relative difference between the two
- altitudes = list(altitude_to_visualizer.keys())
- study_visualizer = list(altitude_to_visualizer.values())[0]
- visualizers = list(altitude_to_visualizer.values())
- if order_derivative == 0:
- plot_function = plot_relative_difference_map_order_zero
- else:
- plot_function = plot_relative_difference_map_order_one
- # Plot map for the repartition of the difference
- altitude_to_relative_differences = {}
- for altitude, visualizer in altitude_to_visualizer.items():
- altitude_to_relative_differences[altitude] = plot_function(visualizer)
- # study_visualizer.show_or_save_to_file(add_classic_title=False, dpi=500)
- # # Shoe plot with respect to the altitude.
- # plot_shoe_relative_differences_distribution(altitude_to_relative_differences, altitudes, study_visualizer,
- # order_derivative)
- # study_visualizer.show_or_save_to_file(add_classic_title=False, dpi=500)
- plt.close()
-
-
-def plot_shoe_relative_differences_distribution(altitude_to_relative_differences, altitudes, visualizer,
- order_derivative):
- study = visualizer.study
- ax = plt.gca()
- width = 150
- ax.boxplot([altitude_to_relative_differences[a] for a in altitudes], positions=altitudes, widths=width)
- ax.set_xlim([min(altitudes) - width, max(altitudes) + width])
- moment = '' if order_derivative == 0 else 'time derivative of '
- ylabel = 'Global relative difference of the {} model mean \n' \
- 'w.r.t. the {}empirical mean of {} (\%)'.format(moment, moment,
- SCM_STUDY_CLASS_TO_ABBREVIATION[type(study)])
- visualizer.plot_trends = ylabel
- ax.set_ylabel(ylabel)
- ax.set_xlabel('Altitude (m)')
- ax.legend()
- ax.grid()
-
-
-def plot_relative_difference_map_order_zero(visualizer: StudyVisualizerForMeanValues):
- study = visualizer.study
- label = ' mean annual maxima of {} ({})'.format(SCM_STUDY_CLASS_TO_ABBREVIATION[type(study)], study.variable_unit)
- # visualizer.plot_abstract_fast(massif_name_to_value=visualizer.massif_name_to_empirical_mean,
- # label='Empirical' + label, negative_and_positive_values=False)
- visualizer.plot_abstract_fast(massif_name_to_value=visualizer.massif_name_to_model_mean_last_year,
- label='Model' + label, negative_and_positive_values=False, add_text=True)
- # visualizer.plot_abstract_fast(massif_name_to_value=visualizer.massif_name_to_relative_difference_for_mean,
- # label='Relative difference of the model mean w.r.t. the empirical mean \n'
- # 'for the ' + label, graduation=1)
- return list(visualizer.massif_name_to_relative_difference_for_mean.values())
-
-
-def plot_relative_difference_map_order_one(visualizer: StudyVisualizerForMeanValues):
- study = visualizer.study
- label = ' time derivative of mean annual maxima of {} ({})'.format(SCM_STUDY_CLASS_TO_ABBREVIATION[type(study)],
- study.variable_unit)
- # visualizer.plot_abstract_fast(massif_name_to_value=visualizer.massif_name_to_change_ratio_in_empirical_mean,
- # label='Empirical' + label, negative_and_positive_values=False, graduation=0.5)
- visualizer.plot_abstract_fast(massif_name_to_value=visualizer.massif_name_to_change_ratio_in_model_mean,
- label='Model' + label, negative_and_positive_values=False, graduation=0.5)
- # visualizer.plot_abstract_fast(
- # massif_name_to_value=visualizer.massif_name_to_relative_difference_for_change_ratio_in_mean,
- # label='Relative difference of the model mean w.r.t. the empirical mean \n'
- # 'for the ' + label, graduation=5)
- return list(visualizer.massif_name_to_relative_difference_for_change_ratio_in_mean.values())
diff --git a/projects/contrasting_trends_in_snow_loads/post thesis committee/pointwise_gev_analysis_cluster_level.py b/projects/contrasting_trends_in_snow_loads/post thesis committee/pointwise_gev_analysis_cluster_level.py
deleted file mode 100644
index 63457fb441b536dd809905057688d35a2bbbd521..0000000000000000000000000000000000000000
--- a/projects/contrasting_trends_in_snow_loads/post thesis committee/pointwise_gev_analysis_cluster_level.py
+++ /dev/null
@@ -1,152 +0,0 @@
-import numpy as np
-from cached_property import cached_property
-import matplotlib.pyplot as plt
-
-from extreme_data.meteo_france_data.scm_models_data.cluster.clustering_total_precip import TotalPrecipCluster
-from extreme_data.meteo_france_data.scm_models_data.safran.safran import SafranSnowfall1Day
-from extreme_fit.distribution.gev.gev_params import GevParams
-from extreme_fit.estimator.margin_estimator.utils import fitted_stationary_gev
-from extreme_fit.model.margin_model.utils import MarginFitMethod
-from projects.altitude_spatial_model.altitudes_fit.altitudes_studies import AltitudesStudies
-
-
-class PointWIseGevAnalysisForCluster(AltitudesStudies):
-
- def __init__(self, study_class, altitudes,
- spatial_transformation_class=None, temporal_transformation_class=None,
- cluster_class=TotalPrecipCluster,
- **kwargs_study):
- super().__init__(study_class, altitudes, spatial_transformation_class, temporal_transformation_class,
- **kwargs_study)
- self.cluster_class = cluster_class
-
- # Plot for the Altitude
-
- def cluster_id_to_annual_maxima_list(self, first_year=1959, last_year=2019, altitudes=None):
- if altitudes is None:
- altitudes = self.altitudes
- cluster_id_to_annual_maxima_list = {}
- for cluster_id in self.cluster_class.cluster_ids:
- annual_maxima_list = []
- massif_names = self.cluster_class.cluster_id_to_massif_names[cluster_id]
- for study in [s for a, s in self.altitude_to_study.items() if a in altitudes]:
- annual_maxima = []
- massif_ids = [study.massif_name_to_massif_id[m] for m in massif_names if
- m in study.massif_name_to_massif_id]
- for year in range(first_year, last_year + 1):
- annual_maxima.extend(study.year_to_annual_maxima[year][massif_ids])
- annual_maxima_list.append(annual_maxima)
- cluster_id_to_annual_maxima_list[cluster_id] = annual_maxima_list
- return cluster_id_to_annual_maxima_list
-
- @property
- def cluster_id_to_gev_params_list(self):
- cluster_id_to_gev_params_list = {}
- for cluster_id, annual_maxima_list in self.cluster_id_to_annual_maxima_list().items():
- gev_params_list = []
- for annual_maxima in annual_maxima_list:
- if len(annual_maxima) > 0:
- gev_params = fitted_stationary_gev(annual_maxima, fit_method=MarginFitMethod.extremes_fevd_mle)
- else:
- gev_params = GevParams(0, 0, 0)
- assert gev_params.has_undefined_parameters
- gev_params_list.append(gev_params)
- cluster_id_to_gev_params_list[cluster_id] = gev_params_list
- return cluster_id_to_gev_params_list
-
- def plot_gev_parameters_against_altitude(self):
- for param_name in GevParams.PARAM_NAMES[:]:
- ax = plt.gca()
- for cluster_id, gev_param_list in self.cluster_id_to_gev_params_list.items():
- params, altitudes = zip(*[(gev_param.to_dict()[param_name], altitude) for gev_param, altitude
- in zip(gev_param_list, self.altitudes) if
- not gev_param.has_undefined_parameters])
- # ax.plot(self.altitudes, params, label=str(cluster_id), linestyle='None', marker='x')
- label = self.cluster_class.cluster_id_to_cluster_name[cluster_id]
- ax.plot(altitudes, params, label=label, marker='x')
- ax.legend()
- ax.set_xlabel('Altitude')
- ax.set_ylabel(param_name)
- plot_name = '{} change with altitude'.format(param_name)
- self.show_or_save_to_file(plot_name, no_title=True, tight_layout=True, show=False)
- ax.clear()
- plt.close()
-
- # Plot for the time
-
- @property
- def year_min_and_max_list(self):
- l = []
- year_min, year_max = 1959, 1989
- for shift in range(0, 7):
- l.append((year_min + 5 * shift, year_max + 5 * shift))
- return l
-
- def cluster_id_to_time_annual_maxima_list(self, altitudes):
- cluster_id_to_time_annual_maxima_list = {cluster_id: [] for cluster_id in self.cluster_class.cluster_ids}
- for year_min, year_max in self.year_min_and_max_list:
- d = self.cluster_id_to_annual_maxima_list(first_year=year_min,
- last_year=year_max,
- altitudes=altitudes)
- for cluster_id, annual_maxima_list in d.items():
- a = np.array(annual_maxima_list)
- a = a.flatten()
- cluster_id_to_time_annual_maxima_list[cluster_id].append(list(a))
- return cluster_id_to_time_annual_maxima_list
-
- def cluster_id_to_time_gev_params_list(self, altitudes):
- cluster_id_to_gev_params_list = {}
- for cluster_id, annual_maxima_list in self.cluster_id_to_time_annual_maxima_list(altitudes=altitudes).items():
- print("cluster", cluster_id)
- gev_params_list = []
- for annual_maxima in annual_maxima_list:
- if len(annual_maxima) > 20:
- print(type(annual_maxima))
- annual_maxima = np.array(annual_maxima)
- print(annual_maxima.shape)
- annual_maxima = annual_maxima[annual_maxima != 0]
- gev_params = fitted_stationary_gev(annual_maxima)
- else:
- print('here all the time')
- gev_params = GevParams(0, 0, 0)
- assert gev_params.has_undefined_parameters
- gev_params_list.append(gev_params)
- cluster_id_to_gev_params_list[cluster_id] = gev_params_list
- return cluster_id_to_gev_params_list
-
- def plot_gev_parameters_against_time(self, altitudes=None):
- for param_name in GevParams.PARAM_NAMES[:]:
- ax = plt.gca()
- for cluster_id, gev_param_list in self.cluster_id_to_time_gev_params_list(altitudes).items():
- print(gev_param_list)
- params, years = zip(*[(gev_param.to_dict()[param_name], years) for gev_param, years
- in zip(gev_param_list, self.year_min_and_max_list) if
- not gev_param.has_undefined_parameters])
- # ax.plot(self.altitudes, params, label=str(cluster_id), linestyle='None', marker='x')
- label = self.cluster_class.cluster_id_to_cluster_name[cluster_id]
- years = [year[0] for year in years]
- ax.plot(years, params, label=label, marker='x')
- ax.legend()
- ax.set_xlabel('Year')
- ax.set_ylabel(param_name)
- xlabels = ['-'.join([str(e) for e in t]) for t in self.year_min_and_max_list]
- ax.set_xticklabels(xlabels)
- plot_name = '{} change with year for altitudes {}'.format(param_name, '+'.join([str(a) for a in altitudes]))
- self.show_or_save_to_file(plot_name, no_title=True, tight_layout=True, show=False)
- ax.clear()
- plt.close()
-
-
-if __name__ == '__main__':
- altitudes = [600, 900, 1200, 1500, 1800, 2100, 2400, 2700, 3000, 3300, 3600]
- # altitudes = [1800, 2100]
- pointwise_gev_analysis = PointWIseGevAnalysisForCluster(SafranSnowfall1Day, altitudes=altitudes)
- # pointwise_gev_analysis.plot_gev_parameters_against_time(altitudes)
-
- # pointwise_gev_analysis.plot_gev_parameters_against_altitude()
- for altitudes in [[600, 900],
- [1200, 1500, 1800],
- [2100, 2400, 2700],
- [3000, 3300, 3600]][3:]:
- print(altitudes)
- pointwise_gev_analysis.plot_gev_parameters_against_time(altitudes)
diff --git a/projects/contrasting_trends_in_snow_loads/spatial trends/main_constrasting_result.py b/projects/contrasting_trends_in_snow_loads/spatial trends/main_constrasting_result.py
deleted file mode 100644
index 57d3cbea1a0522ac5fed6c52922a3abf0c4b53c4..0000000000000000000000000000000000000000
--- a/projects/contrasting_trends_in_snow_loads/spatial trends/main_constrasting_result.py
+++ /dev/null
@@ -1,96 +0,0 @@
-from multiprocessing.pool import Pool
-
-import matplotlib as mpl
-
-mpl.use('Agg')
-mpl.rcParams['text.usetex'] = True
-mpl.rcParams['text.latex.preamble'] = [r'\usepackage{amsmath}']
-
-from extreme_trend.visualizers.utils import load_altitude_to_visualizer
-from projects.exceeding_snow_loads.section_results.plot_trend_curves import plot_trend_map
-
-from extreme_data.meteo_france_data.scm_models_data.abstract_study import AbstractStudy
-from extreme_data.meteo_france_data.scm_models_data.safran.safran import SafranPrecipitation3Days, \
- SafranPrecipitation1Day, SafranPrecipitation5Days, SafranPrecipitation7Days, SafranSnowfall1Day, \
- SafranSnowfall5Days, SafranSnowfall3Days, SafranSnowfall7Days, SafranRainfall1Day, SafranRainfall3Days, \
- SafranRainfall5Days, SafranRainfall7Days
-
-from extreme_data.meteo_france_data.scm_models_data.crocus.crocus import CrocusSnowLoad3Days, \
- CrocusSnowLoad5Days, CrocusSnowLoad7Days, CrocusSnowLoad1Day
-from extreme_fit.model.result_from_model_fit.result_from_extremes.confidence_interval_method import \
- ConfidenceIntervalMethodFromExtremes
-from projects.exceeding_snow_loads.section_results.main_result_trends_and_return_levels import \
- compute_minimized_aic
-
-
-def intermediate_result(altitudes, massif_names=None,
- model_subsets_for_uncertainty=None, uncertainty_methods=None,
- study_class=AbstractStudy,
- multiprocessing=False,
- save_to_file=True):
- """
- Plot all the trends for all altitudes
- And enable to plot uncertainty plot for some specific massif_names, uncertainty methods to be fast
- :param altitudes:
- :param massif_names:
- :param non_stationary_uncertainty:
- :param uncertainty_methods:
- :param study_class:
- :return:
- """
- # Load altitude to visualizer
- altitude_to_visualizer = load_altitude_to_visualizer(altitudes, massif_names, model_subsets_for_uncertainty,
- study_class, uncertainty_methods, save_to_file=save_to_file)
- # Load variable object efficiently
- for v in altitude_to_visualizer.values():
- _ = v.study.year_to_variable_object
- # Compute minimized value efficiently
- visualizers = list(altitude_to_visualizer.values())
- if multiprocessing:
- with Pool(4) as p:
- _ = p.map(compute_minimized_aic, visualizers)
- else:
- for visualizer in visualizers:
- _ = compute_minimized_aic(visualizer)
-
- # Plots
- # plot_contrasting_trend_curves(altitude_to_visualizer, all_regions=True)
- # plot_contrasting_trend_curves_massif(altitude_to_visualizer, all_regions=True)
- # plot_trend_curves(altitude_to_visualizer)
- plot_trend_map(altitude_to_visualizer, )
-
-
-def major_result():
- uncertainty_methods = [ConfidenceIntervalMethodFromExtremes.my_bayes,
- ConfidenceIntervalMethodFromExtremes.ci_mle][1:]
- massif_names = None
- model_subsets_for_uncertainty = None
- # altitudes = paper_altitudes
- # altitudes = paper_altitudes
- altitudes = [300, 600, 900, 1200, 1500, 1800, 2100, 2400, 2700, 3000][:]
- # altitudes = [1200, 1500, 1800, 2100, 2400, 2700][:]
- snow_load_classes = [CrocusSnowLoad1Day, CrocusSnowLoad3Days, CrocusSnowLoad5Days, CrocusSnowLoad7Days][:]
- precipitation_classes = [SafranPrecipitation1Day, SafranPrecipitation3Days, SafranPrecipitation5Days, SafranPrecipitation7Days][:]
- snowfall_classes = [SafranSnowfall1Day, SafranSnowfall3Days, SafranSnowfall5Days, SafranSnowfall7Days]
- rainfall_classes = [SafranRainfall1Day, SafranRainfall3Days, SafranRainfall5Days, SafranRainfall7Days]
- study_classes = precipitation_classes + snow_load_classes
- study_classes = snowfall_classes + rainfall_classes
- for study_class in precipitation_classes[:1]:
- intermediate_result(altitudes, massif_names, model_subsets_for_uncertainty,
- uncertainty_methods, study_class, multiprocessing=True)
-
-
-"""
-
-est ce qu il y a une croissance signifcative en pluie,
-
-est ce qu'il y a une decroissance signifcatieve à partir d'une certaine altitude
-"""
-
-if __name__ == '__main__':
- major_result()
- # intermediate_result(altitudes=[1500, 1800][:], massif_names=None,
- # uncertainty_methods=[ConfidenceIntervalMethodFromExtremes.my_bayes,
- # ConfidenceIntervalMethodFromExtremes.ci_mle][1:],
- # multiprocessing=True,
- # save_to_file=False)
diff --git a/projects/contrasting_trends_in_snow_loads/spatial trends/main_spatial_relative_change_in_maxima_at_fixed_altitude.py b/projects/contrasting_trends_in_snow_loads/spatial trends/main_spatial_relative_change_in_maxima_at_fixed_altitude.py
deleted file mode 100644
index 99425900b880c081d2bf7b755d690ad5d98fdbea..0000000000000000000000000000000000000000
--- a/projects/contrasting_trends_in_snow_loads/spatial trends/main_spatial_relative_change_in_maxima_at_fixed_altitude.py
+++ /dev/null
@@ -1,70 +0,0 @@
-import pandas as pd
-from extreme_data.meteo_france_data.scm_models_data.crocus.crocus import CrocusSnowLoad3Days, \
- CrocusSnowLoadTotal
-from extreme_data.meteo_france_data.scm_models_data.safran.safran import SafranSnowfall, SafranRainfall, \
- SafranPrecipitation, SafranPrecipitation1Day, SafranSnowfall1Day, SafranTemperature, \
- SafranNormalizedPreciptationRateOnWetDays, SafranNormalizedPreciptationRate
-from extreme_data.meteo_france_data.scm_models_data.safran.safran_variable import \
- SafranNormalizedPrecipitationRateOnWetDaysVariable
-from extreme_data.meteo_france_data.scm_models_data.utils import Season
-from extreme_data.meteo_france_data.scm_models_data.visualization.study_visualizer import \
- StudyVisualizer
-import matplotlib.pyplot as plt
-
-
-def relative_change_in_maxima_wrt_altitude():
- save_to_file = True
- study_class = [SafranSnowfall1Day, SafranPrecipitation1Day, SafranPrecipitation, SafranRainfall,
- SafranSnowfall, CrocusSnowLoad3Days, CrocusSnowLoadTotal,
- SafranTemperature, SafranNormalizedPreciptationRateOnWetDays,
- SafranNormalizedPreciptationRate][1]
- altitudes = [300, 600, 900, 1200, 1500, 1800, 2100, 2400, 2700, 3000][::-1]
- relative = True
-
- for altitude in altitudes:
-
- ax = plt.gca()
- study = study_class(altitude=altitude, season=Season.winter_extended)
- # study = study_class(altitude=altitude, nb_consecutive_days=3)
- massif_name_to_value = {}
- for massif_name in study.study_massif_names:
- if study_class is SafranTemperature:
- s = study.observations_annual_mean.loc[massif_name]
- else:
- s = study.observations_annual_maxima.df_maxima_gev.loc[massif_name]
- year_limit = 1989
- df_before, df_after = s.loc[:year_limit], s.loc[year_limit + 1:]
- df_before, df_after = df_before.mean(), df_after.mean()
- # df_before, df_after = df_before.median(), df_after.median()
- if relative:
- change_value = 100 * (df_after - df_before) / df_before
- else:
- change_value = (df_after - df_before)
- massif_name_to_value[massif_name] = change_value
- print(massif_name_to_value)
- # Plot
- # massif_name_to_value = {m: i for i, m in enumerate(study.study_massif_names)}
- max_values = max([abs(e) for e in massif_name_to_value.values()]) * 1.05
- print(max_values)
- variable_name = study.variable_name
- prefix = 'Relative' if relative else ''
- str_season = str(study.season).split('.')[-1]
- study.visualize_study(ax=ax, massif_name_to_value=massif_name_to_value,
- vmin=-max_values, vmax=max_values,
- add_colorbar=True,
- replace_blue_by_white=False,
- show=False,
- label='{} changes in mean {} maxima\n'
- 'of {}\n between 1959-1989 and 1990-2019\n at {}m (%)\n'
- ''.format(prefix, str_season, variable_name, study.altitude)
- )
- study_visualizer = StudyVisualizer(study, save_to_file=save_to_file)
- study_visualizer.plot_name = '{}_changes_in_maxima'.format(prefix)
- study_visualizer.show_or_save_to_file()
- ax.clear()
- plt.close()
-
-
-if __name__ == '__main__':
- relative_change_in_maxima_wrt_altitude()
- # test()
diff --git a/projects/contrasting_trends_in_snow_loads/spatial trends/plot_contrasting_trend_curves.py b/projects/contrasting_trends_in_snow_loads/spatial trends/plot_contrasting_trend_curves.py
deleted file mode 100644
index 1a75bb42eff8c49c742feb11bb2b3873b8c0c14e..0000000000000000000000000000000000000000
--- a/projects/contrasting_trends_in_snow_loads/spatial trends/plot_contrasting_trend_curves.py
+++ /dev/null
@@ -1,144 +0,0 @@
-from typing import Dict
-import matplotlib.pyplot as plt
-
-from extreme_data.meteo_france_data.scm_models_data.abstract_extended_study import AbstractExtendedStudy
-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 plot_contrasting_trend_curves_massif(altitude_to_visualizer: Dict[int, StudyVisualizerForNonStationaryTrends],
- all_regions=False):
- """
- Plot a single trend curves
- :return:
- """
- visualizers = list(altitude_to_visualizer.values())
- visualizer = visualizers[0]
- altitudes = list(altitude_to_visualizer.keys())
-
- ax = create_adjusted_axes(1, 1)
- # ax_twinx = ax.twinx()
- ax_twinx = ax
- ax_twiny = ax.twiny()
-
- # parameters
- width = 150
- size = 20
- legend_fontsize = 20
- color = 'white'
- labelsize = 15
- linewidth = 3
-
- 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_twinx.yaxis.grid()
-
- ax_twinx.set_ylabel(visualizer.label, fontsize=legend_fontsize)
- for j, massif_name in enumerate(visualizer.study.study_massif_names):
- massif_visualizers = [v for v in visualizers if massif_name in v.massif_name_to_change_value]
- changes = [v.massif_name_to_relative_change_value[massif_name] for v in massif_visualizers]
- massif_altitudes = [v.study.altitude for v in massif_visualizers]
- label = massif_name.replace('-', '').replace('_', '')
- if j < 10:
- linestyle = 'solid'
- elif j < 20:
- linestyle = 'dashed'
- else:
- linestyle = 'dotted'
- ax_twinx.plot(massif_altitudes, changes, label=label, linewidth=linewidth, marker='o', linestyle=linestyle)
- ax_twinx.legend(loc='upper right', prop={'size': 5})
-
- ax.axhline(y=0, color='k')
-
- # Save plot
- visualizer.plot_name = 'Trend curves for' + visualizer.study.variable_name
- visualizer.show_or_save_to_file(no_title=True, dpi=dpi_paper1_figure, folder_for_variable=False)
- plt.close()
-
-
-def plot_contrasting_trend_curves(altitude_to_visualizer: Dict[int, StudyVisualizerForNonStationaryTrends],
- all_regions=False):
- """
- Plot a single trend curves
- :return:
- """
- visualizer = list(altitude_to_visualizer.values())[0]
-
- ax = create_adjusted_axes(1, 1)
- # ax_twinx = ax.twinx()
- ax_twinx = ax
- ax_twiny = ax.twiny()
-
- trend_summary_values = list(zip(*[v.trend_summary_contrasting_values(regions=all_regions) for v in altitude_to_visualizer.values()]))
- altitudes, *mean_changes = trend_summary_values
-
- # parameters
- width = 150
- size = 20
- legend_fontsize = 20
- color = 'white'
- labelsize = 15
- linewidth = 3
- # ax.bar(altitudes, percent_decrease, width=width, color=color, edgecolor='blue', label='decreasing trend',
- # linewidth=linewidth)
- # ax.bar(altitudes, percent_decrease_signi, width=width, color=color, edgecolor='black',
- # label='significative decreasing trend',
- # linewidth=linewidth)
- # ax.legend(loc='upper left', prop={'size': size})
-
- 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_twinx.yaxis.grid()
-
- ax_twinx.set_ylabel(visualizer.label, fontsize=legend_fontsize)
- for j, (region_name, mean_change) in enumerate(zip(AbstractExtendedStudy.region_names, mean_changes)):
- if len(mean_changes) > 2:
- label = region_name
- elif len(mean_changes) == 2:
- label = 'North' if j == 0 else 'South'
- else:
- label = 'Mean relative change'
- ax_twinx.plot(altitudes, mean_change, label=label, linewidth=linewidth, marker='o')
- ax_twinx.legend(loc='upper right', prop={'size': size})
-
- ax.axhline(y=0, color='k')
-
- # Save plot
- visualizer.plot_name = 'Trend curves for' + visualizer.study.variable_name
- visualizer.show_or_save_to_file(no_title=True, dpi=dpi_paper1_figure, folder_for_variable=False)
- plt.close()
diff --git a/projects/contrasting_trends_in_snow_loads/weather_types_analysis/main_distribution_wps.py b/projects/contrasting_trends_in_snow_loads/weather_types_analysis/main_distribution_wps.py
deleted file mode 100644
index 73863db9bdba02a01455d0bde4f88227466bd4b6..0000000000000000000000000000000000000000
--- a/projects/contrasting_trends_in_snow_loads/weather_types_analysis/main_distribution_wps.py
+++ /dev/null
@@ -1,111 +0,0 @@
-import pandas as pd
-import numpy as np
-
-from extreme_data.meteo_france_data.scm_models_data.abstract_extended_study import AbstractExtendedStudy
-from extreme_data.meteo_france_data.scm_models_data.crocus.crocus import CrocusSnowLoad3Days, CrocusSnowLoad1Day
-from extreme_data.meteo_france_data.scm_models_data.safran.safran import SafranPrecipitation1Day, \
- SafranPrecipitation3Days
-from extreme_data.meteo_france_data.scm_models_data.utils import Season
-
-
-def main_spatial_distribution_wps(study_class, year_min=1959, year_max=2008, limit_for_the_percentage=None):
- study = study_class(altitude=1800, year_min=year_min, year_max=year_max)
- for region_name in AbstractExtendedStudy.region_names:
- massif_names = AbstractExtendedStudy.region_name_to_massif_names[region_name]
- print('\n \n', region_name, '\n')
- for nb_top in [study.nb_years, 10, 1][1:2]:
- print(study.df_for_top_annual_maxima(nb_top=nb_top, massif_names=massif_names, limit_for_the_percentage=limit_for_the_percentage), '\n')
-
-
-"""
- Northern Alps
- % count mean std min median max
-Steady Oceanic 92 65 159 35 121 146 282
-
- Central Alps
- % count mean std min median max
-Steady Oceanic 72 51 136 31 97 130 235
-South Circulation 15 11 128 18 105 126 161
-
- Southern Alps
- % count mean min median max
-South Circulation 61 37 147 79 147 235
-Steady Oceanic 13 8 121 93 114 187
-
- Extreme South Alps
- % count mean min median max
-South Circulation 80 24 174 101 166 306
-
-
-Process finished with exit code 0
-
-
-
-
-
-
-"""
-
-
-def main_temporal_distribution_wps(study_class, year_min=1959, year_max=2008, limit_for_the_percentage=None):
- altitude = 1800
- intermediate_year = year_min + round(float(year_max - year_min) / 2)
- study_before = study_class(altitude=altitude, year_min=year_min, year_max=intermediate_year)
- study_after = study_class(altitude=altitude, year_min=intermediate_year+1, year_max=year_max)
- for region_name in AbstractExtendedStudy.region_names:
- massif_names = AbstractExtendedStudy.region_name_to_massif_names[region_name]
- print('\n \n', '{} ({} massifs)'.format(region_name, len(massif_names)), '\n')
- for nb_top in [study_before.nb_years, 10, 1, 10][3:]:
- print(study_before.df_for_top_annual_maxima(nb_top=nb_top, massif_names=massif_names, limit_for_the_percentage=limit_for_the_percentage), '\n')
- print(study_after.df_for_top_annual_maxima(nb_top=nb_top, massif_names=massif_names, limit_for_the_percentage=limit_for_the_percentage), '\n')
-
-"""
- Northern Alps (7 massifs)
- % count mean min median max
-Top 10 maxima (1959 -1983)
-Steady Oceanic 92 65 131 106 128 202
-
-Top 10 maxima (1984 -2008)
-Steady Oceanic 87 61 149 103 133 282
-
- Central Alps (7 massifs)
- % count mean min median max
-Top 10 maxima (1959 -1983)
-Steady Oceanic 68 48 111 76 109 190
-
-Top 10 maxima (1984 -2008)
-Steady Oceanic 77 54 123 81 107 235
-South Circulation 14 10 122 93 120 161
-
- Southern Alps (6 massifs)
- % count mean std min median max
-Top 10 maxima (1959 -1983)
-South Circulation 50 30 114 32 70 112 197
-Southwest Circulation 13 8 107 24 79 97 140
-Steady Oceanic 13 8 97 14 82 93 122
-
-Top 10 maxima (1984 -2008)
-South Circulation 58 35 135 73 125 235
-Steady Oceanic 23 14 107 69 104 187
-
- Extreme South Alps (3 massifs)
- % count mean min median max
-Top 10 maxima (1959 -1983)
-South Circulation 66 20 141 84 146 212
-Southwest Circulation 13 4 122 95 123 146
-
-Top 10 maxima (1984 -2008)
-South Circulation 76 23 158 74 138 306
-
-
-
-
-
-"""
-
-
-if __name__ == '__main__':
- limit_percentage = 1
- study_class = [CrocusSnowLoad1Day, SafranPrecipitation1Day, SafranPrecipitation3Days][-1]
- main_spatial_distribution_wps(study_class, limit_for_the_percentage=limit_percentage)
- # main_temporal_distribution_wps(study_class, limit_for_the_percentage=limit_percentage)
diff --git a/projects/seasonal_analysis/__init__.py b/projects/seasonal_analysis/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/projects/thesis_report/__init__.py b/projects/thesis_report/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000