From 81f75bc5f2fadf2b6377194dfa6bc73476be8aaa Mon Sep 17 00:00:00 2001
From: Le Roux Erwan <erwan.le-roux@irstea.fr>
Date: Thu, 26 Mar 2020 13:36:29 +0100
Subject: [PATCH] [contrasting project] change some plot parameters
---
.../scm_models_data/abstract_study.py | 3 +-
...dy_visualizer_for_non_stationary_trends.py | 3 +-
.../main_result.py | 4 +-
.../plot_contrasting_trend_curves.py | 5 +-
.../main_distribution_wps.py | 60 +++++++++----------
5 files changed, 38 insertions(+), 37 deletions(-)
diff --git a/extreme_data/meteo_france_data/scm_models_data/abstract_study.py b/extreme_data/meteo_france_data/scm_models_data/abstract_study.py
index ffd7d542..bf0824c3 100644
--- a/extreme_data/meteo_france_data/scm_models_data/abstract_study.py
+++ b/extreme_data/meteo_france_data/scm_models_data/abstract_study.py
@@ -151,7 +151,8 @@ class AbstractStudy(object):
df.drop(columns=drop_columns, inplace=True)
df.rename(columns={'50%': 'median'}, inplace=True)
df = df.astype(int)
- df.index.name = 'Top {} maxima ({} -{})'.format(nb_top, self.year_min, self.year_max)
+ start_year, end_year = self.start_year_and_stop_year
+ df.index.name = 'Top {} maxima ({} -{})'.format(nb_top, start_year, end_year)
if limit_for_the_percentage is not None:
ind = df['%'] > limit_for_the_percentage
df = df.loc[ind]
diff --git a/extreme_trend/visualizers/study_visualizer_for_non_stationary_trends.py b/extreme_trend/visualizers/study_visualizer_for_non_stationary_trends.py
index dc2561dc..05bcf688 100644
--- a/extreme_trend/visualizers/study_visualizer_for_non_stationary_trends.py
+++ b/extreme_trend/visualizers/study_visualizer_for_non_stationary_trends.py
@@ -459,14 +459,13 @@ class StudyVisualizerForNonStationaryTrends(StudyVisualizer):
# mean_decreases.append(compute_mean_decrease(change_values))
return (self.altitude, percentage_decrease, percentage_decrease_significative, *mean_decreases)
- def trend_summary_contrasting_values(self):
+ def trend_summary_contrasting_values(self, all_regions = False):
# trend_tests = list(self.massif_name_to_trend_test_that_minimized_aic.values())
# decreasing_trend_tests = [t for t in trend_tests if t.time_derivative_of_return_level < 0]
# percentage_decrease = 100 * len(decreasing_trend_tests) / len(trend_tests)
# significative_decrease_trend_tests = [t for t in decreasing_trend_tests if t.is_significant]
# percentage_decrease_significative = 100 * len(significative_decrease_trend_tests) / len(trend_tests)
compute_mean_change = lambda l: np.mean(np.array(list(l))) if len(l) > 0 else 0
- all_regions = False
massif_name_to_region_name = AbstractExtendedStudy.massif_name_to_region_name
if all_regions:
mean_changes = [compute_mean_change(self.massif_name_to_relative_change_value.values())]
diff --git a/projects/contrasting_trends_in_snow_loads/main_result.py b/projects/contrasting_trends_in_snow_loads/main_result.py
index 75b13547..a77384cb 100644
--- a/projects/contrasting_trends_in_snow_loads/main_result.py
+++ b/projects/contrasting_trends_in_snow_loads/main_result.py
@@ -57,7 +57,7 @@ def intermediate_result(altitudes, massif_names=None,
_ = compute_minimized_aic(visualizer)
# Plots
- plot_contrasting_trend_curves(altitude_to_visualizer)
+ plot_contrasting_trend_curves(altitude_to_visualizer, all_regions=True)
def major_result():
@@ -75,7 +75,7 @@ def major_result():
rainfall_classes = [SafranRainfall1Day, SafranRainfall3Days, SafranRainfall5Days, SafranRainfall7Days]
study_classes = precipitation_classes + snow_load_classes
# study_classes = snowfall_classes + rainfall_classes
- for study_class in study_classes[:]:
+ for study_class in [SafranPrecipitation3Days, CrocusSnowLoad3Days]:
intermediate_result(altitudes, massif_names, model_subsets_for_uncertainty,
uncertainty_methods, study_class, multiprocessing=True)
diff --git a/projects/contrasting_trends_in_snow_loads/plot_contrasting_trend_curves.py b/projects/contrasting_trends_in_snow_loads/plot_contrasting_trend_curves.py
index 47c732bd..a84ca2f9 100644
--- a/projects/contrasting_trends_in_snow_loads/plot_contrasting_trend_curves.py
+++ b/projects/contrasting_trends_in_snow_loads/plot_contrasting_trend_curves.py
@@ -8,7 +8,8 @@ from extreme_trend.visualizers.study_visualizer_for_non_stationary_trends import
StudyVisualizerForNonStationaryTrends
-def plot_contrasting_trend_curves(altitude_to_visualizer: Dict[int, StudyVisualizerForNonStationaryTrends]):
+def plot_contrasting_trend_curves(altitude_to_visualizer: Dict[int, StudyVisualizerForNonStationaryTrends],
+ all_regions=False):
"""
Plot a single trend curves
:return:
@@ -20,7 +21,7 @@ def plot_contrasting_trend_curves(altitude_to_visualizer: Dict[int, StudyVisuali
ax_twinx = ax
ax_twiny = ax.twiny()
- trend_summary_values = list(zip(*[v.trend_summary_contrasting_values() for v in altitude_to_visualizer.values()]))
+ trend_summary_values = list(zip(*[v.trend_summary_contrasting_values(all_regions=all_regions) for v in altitude_to_visualizer.values()]))
altitudes, *mean_changes = trend_summary_values
# parameters
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
index 52946ba3..570785ed 100644
--- 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
@@ -8,7 +8,7 @@ from extreme_data.meteo_france_data.scm_models_data.safran.safran import SafranP
from extreme_data.meteo_france_data.scm_models_data.utils import SeasonForTheMaxima
-def main_spatial_distribution_wps(study_class, year_min=1954, year_max=2008, limit_for_the_percentage=None):
+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, season=SeasonForTheMaxima.winter_extended)
for region_name in AbstractExtendedStudy.region_names:
massif_names = AbstractExtendedStudy.region_name_to_massif_names[region_name]
@@ -47,56 +47,56 @@ Process finished with exit code 0
"""
-def main_temporal_distribution_wps(study_class, year_min=1954, year_max=2008, limit_for_the_percentage=None):
+def main_temporal_distribution_wps(study_class, year_min=1959, year_max=2008, limit_for_the_percentage=None):
altitude = 1800
- study_before = study_class(altitude=altitude, year_min=year_min, year_max=1981, season=SeasonForTheMaxima.winter_extended)
- study_after = study_class(altitude=altitude, year_min=1981, year_max=year_max, season=SeasonForTheMaxima.winter_extended)
- # todo: same min and max year ?
+ 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, season=SeasonForTheMaxima.winter_extended)
+ study_after = study_class(altitude=altitude, year_min=intermediate_year+1, year_max=year_max, season=SeasonForTheMaxima.winter_extended)
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:]:
+ 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 (1954 -1981)
-Steady Oceanic 94 66 130 102 127 202
+Top 10 maxima (1959 -1983)
+Steady Oceanic 92 65 131 106 128 202
-Top 10 maxima (1981 -2008)
-Steady Oceanic 82 58 151 104 134 282
+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 (1954 -1981)
-Steady Oceanic 71 50 110 76 107 190
+Top 10 maxima (1959 -1983)
+Steady Oceanic 68 48 111 76 109 190
-Top 10 maxima (1981 -2008)
-Steady Oceanic 74 52 125 87 115 235
-South Circulation 14 10 123 100 120 161
+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 min median max
-Top 10 maxima (1954 -1981)
-South Circulation 43 26 113 67 112 197
-Steady Oceanic 16 10 95 82 93 122
-Southwest Circulation 15 9 102 68 95 140
-
-Top 10 maxima (1981 -2008)
-South Circulation 63 38 134 73 127 235
-Steady Oceanic 21 13 110 80 105 187
-
-
+ % 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 (1954 -1981)
-South Circulation 63 19 136 84 139 194
+Top 10 maxima (1959 -1983)
+South Circulation 66 20 141 84 146 212
Southwest Circulation 13 4 122 95 123 146
-Top 10 maxima (1981 -2008)
-South Circulation 76 23 165 78 159 306
+Top 10 maxima (1984 -2008)
+South Circulation 76 23 158 74 138 306
+
--
GitLab