From 54c2c62a501b63b354da59d92e1137c828cfc078 Mon Sep 17 00:00:00 2001
From: Le Roux Erwan <erwan.le-roux@irstea.fr>
Date: Wed, 15 May 2019 18:29:49 +0200
Subject: [PATCH] [SCM][SCORE TREND] add score graph depending on starting year
---
.../main_study_visualizer.py | 22 +++-
.../study_visualization/study_visualizer.py | 113 +++++++++++++++---
2 files changed, 115 insertions(+), 20 deletions(-)
diff --git a/experiment/meteo_france_SCM_study/visualization/study_visualization/main_study_visualizer.py b/experiment/meteo_france_SCM_study/visualization/study_visualization/main_study_visualizer.py
index ace9884b..27012848 100644
--- a/experiment/meteo_france_SCM_study/visualization/study_visualization/main_study_visualizer.py
+++ b/experiment/meteo_france_SCM_study/visualization/study_visualization/main_study_visualizer.py
@@ -104,9 +104,8 @@ def normal_visualization(temporal_non_stationarity=False):
for study in study_iterator(study_class, only_first_one=only_first_one, altitudes=[300]):
study_visualizer = StudyVisualizer(study, save_to_file=save_to_file,
temporal_non_stationarity=temporal_non_stationarity)
- study_visualizer.window_size_for_smoothing = 1
- study_visualizer.number_of_top_values = 10
- study_visualizer.visualize_all_mean_and_max_graphs()
+ print(study_visualizer.massif_name_to_scores)
+ # study_visualizer.visualize_all_mean_and_max_graphs()
# study_visualizer.visualize_independent_margin_fits(threshold=[None, 20, 40, 60][0])
# study_visualizer.visualize_annual_mean_values()
@@ -117,6 +116,18 @@ def all_normal_vizu():
study_visualizer = StudyVisualizer(study, save_to_file=True, temporal_non_stationarity=True)
study_visualizer.visualize_all_mean_and_max_graphs()
+def scores_vizu():
+ for study in study_iterator_global(study_classes=ALL_STUDIES, only_first_one=True, altitudes=[1800]):
+ study_visualizer = StudyVisualizer(study, save_to_file=False, temporal_non_stationarity=True)
+ # study_visualizer.visualize_all_score_wrt_starting_year()
+ study_visualizer.visualize_all_score_wrt_starting_year()
+
+
+def all_scores_vizu():
+ for study in study_iterator_global(study_classes=[SafranSnowfall], only_first_one=False, altitudes=ALL_ALTITUDES):
+ study_visualizer = StudyVisualizer(study, save_to_file=True, temporal_non_stationarity=True)
+ study_visualizer.visualize_all_score_wrt_starting_year()
+
def complete_analysis(only_first_one=False):
"""An overview of everything that is possible with study OR extended study"""
for study_class, extended_study_class in list(SCM_STUDY_TO_EXTENDED_STUDY.items())[:]:
@@ -157,14 +168,15 @@ def trend_analysis():
def main_run():
# normal_visualization(temporal_non_stationarity=True)
# trend_analysis()
- all_normal_vizu()
+ # all_normal_vizu()
# annual_mean_vizu_compare_durand_study(safran=True, take_mean_value=True, altitude=2100)
# max_stable_process_vizu_compare_gaume_study(altitude=1800, nb_days=1)
# extended_visualization()
# complete_analysis()
-
+ # scores_vizu()
+ all_scores_vizu()
if __name__ == '__main__':
start = time.time()
diff --git a/experiment/meteo_france_SCM_study/visualization/study_visualization/study_visualizer.py b/experiment/meteo_france_SCM_study/visualization/study_visualization/study_visualizer.py
index af6b1a6e..affa1088 100644
--- a/experiment/meteo_france_SCM_study/visualization/study_visualization/study_visualizer.py
+++ b/experiment/meteo_france_SCM_study/visualization/study_visualization/study_visualizer.py
@@ -166,7 +166,7 @@ class StudyVisualizer(object):
# PLOT FOR SEVERAL MASSIFS
- def visualize_massif_graphs(self, visualize_function, use_ordered_massif_names=False):
+ def visualize_massif_graphs(self, visualize_function, specified_massif_ids=None):
if self.only_one_graph:
fig, ax = plt.subplots(1, 1, figsize=self.figsize)
visualize_function(ax, 0)
@@ -180,12 +180,9 @@ class StudyVisualizer(object):
ax = axes[massif_id]
visualize_function(ax, massif_id)
else:
- if use_ordered_massif_names:
- massif_ids = [self.study.study_massif_names.index(massif_name) for massif_name in
- self.ordered_massif_names]
- else:
- massif_ids = list(range(len(self.study.study_massif_names)))
- for j, massif_id in enumerate(massif_ids):
+ if specified_massif_ids is None:
+ specified_massif_ids = list(range(len(self.study.study_massif_names)))
+ for j, massif_id in enumerate(specified_massif_ids):
row_id, column_id = j // nb_columns, j % nb_columns
ax = axes[row_id, column_id]
visualize_function(ax, massif_id)
@@ -328,15 +325,16 @@ class StudyVisualizer(object):
return all_massif_data
@cached_property
- def massif_name_to_score(self):
+ def massif_name_to_score(self, starting_year=1958):
# Ordered massif by scores
massif_name_to_score = {}
for massif_id, massif_name in enumerate(self.study.study_massif_names):
years, smooth_maxima = self.smooth_maxima_x_y(massif_id)
- start_year = years[0]
+ idx_starting_year = years.index(starting_year)
+ smooth_maxima = smooth_maxima[idx_starting_year:]
sorted_indices = [i for i, e in sorted(enumerate(smooth_maxima), key=lambda s: s[1])]
- mean_max_year = np.mean(sorted_indices[-self.number_of_top_values:]) + start_year
- mean_min_years = np.mean(sorted_indices[:self.number_of_top_values]) + start_year
+ mean_max_year = np.mean(sorted_indices[-self.number_of_top_values:]) + starting_year
+ mean_min_years = np.mean(sorted_indices[:self.number_of_top_values]) + starting_year
score = mean_max_year - mean_min_years
massif_name_to_score[massif_name] = (score, mean_max_year, mean_min_years)
return massif_name_to_score
@@ -345,11 +343,96 @@ class StudyVisualizer(object):
def ordered_massif_names(self):
return sorted(self.study.study_massif_names[:], key=lambda s: self.massif_name_to_score[s][0])
+ @property
+ def starting_years(self):
+ start_year, stop_year = self.study.start_year_and_stop_year
+ return list(range(start_year, stop_year - 2 * self.number_of_top_values))
+
+ @cached_property
+ def massif_name_to_scores(self):
+ """
+ This score respect the following property.
+ Between two successive score, then if the starting year was neither a top10 maxima nor a top10 minima,
+ then the score will not change
+
+ The following case for instance gives a constant score wrt to the starting year
+ because all the maxima and all the minima are at the end
+ smooth_maxima = [0 for _ in years]
+ smooth_maxima[-20:-10] = [i for i in range(10)]
+ smooth_maxima[-10:] = [-i for i in range(10)]
+ :return:
+ """
+ # Ordered massif by scores
+ massif_name_to_scores = {}
+ for massif_id, massif_name in enumerate(self.study.study_massif_names):
+ years, smooth_maxima = self.smooth_maxima_x_y(massif_id)
+ sorted_years = [i + self.study.start_year_and_stop_year[0]
+ for i, e in sorted(enumerate(smooth_maxima), key=lambda s: s[1])]
+ scores = []
+ for j, starting_year in enumerate(self.starting_years):
+ mean_max_year = np.mean(sorted_years[-self.number_of_top_values:])
+ mean_min_years = np.mean(sorted_years[:self.number_of_top_values])
+ score = mean_max_year - mean_min_years
+ scores.append(score)
+ sorted_years.remove(starting_year)
+ massif_name_to_scores[massif_name] = scores
+ return massif_name_to_scores
+
+ def visualize_all_score_wrt_starting_year(self):
+ # Build
+ specified_massif_names = sorted(self.study.study_massif_names,
+ key=lambda s: np.mean(self.massif_name_to_scores[s]))
+ specified_massif_names = sorted(self.study.study_massif_names, key=lambda s: self.massif_name_to_scores[s][0])
+ # Add one graph at the end
+ specified_massif_names += [None]
+ self.visualize_massif_graphs(self.visualize_score_wrt_starting_year,
+ specified_massif_ids=specified_massif_names)
+ plot_name = ''
+ plot_name += '{} top values for each score, abscisse represents starting year for a trend'.format(
+ self.number_of_top_values)
+ self.plot_name = plot_name
+ self.show_or_save_to_file()
+
+ def visualize_score_wrt_starting_year(self, ax, massif_name):
+ if massif_name is None:
+ percentage, title = self.percentage_of_negative_trends()
+ scores = percentage
+ ax.set_ylabel('% of negative trends')
+ else:
+ ax.set_ylabel('max score - min score ')
+ scores = self.massif_name_to_scores[massif_name]
+ title = massif_name
+ ax.plot(self.starting_years, scores)
+ ax.set_title(title)
+ ax.xaxis.set_ticks(self.starting_years[2::20])
+
+ def percentage_of_negative_trends(self):
+ mean = np.mean([np.array(v) < 0 for v in self.massif_name_to_scores.values()], axis=0)
+ percentage = 100 * mean
+ argmin, argmax = np.argmin(mean), np.argmax(mean)
+ top_starting_year_for_positive_trend = self.starting_years[argmin]
+ top_starting_year_for_negative_trend = self.starting_years[argmax]
+ top_percentage_positive_trend = round(100 - percentage[argmin], 0)
+ top_percentage_negative_trend = round(percentage[argmax], 0)
+ title = "Global trend; > 0: {}% in {}; < 0: {}% in {}".format(top_percentage_negative_trend,
+ top_starting_year_for_positive_trend,
+ top_percentage_positive_trend,
+ top_starting_year_for_negative_trend)
+
+ return percentage, title
+
def visualize_all_mean_and_max_graphs(self):
- self.visualize_massif_graphs(self.visualize_mean_and_max_graph, use_ordered_massif_names=True)
- self.plot_name = ' smoothing values temporally with sliding window of size {}' \
- 'and {} top values taking into account'.format(
- self.window_size_for_smoothing, self.number_of_top_values)
+ specified_massif_ids = [self.study.study_massif_names.index(massif_name)
+ for massif_name in
+ sorted(self.study.study_massif_names, key=lambda s: self.massif_name_to_score[s])]
+ self.visualize_massif_graphs(self.visualize_mean_and_max_graph,
+ specified_massif_ids=specified_massif_ids)
+ plot_name = ''
+ if self.window_size_for_smoothing > 1:
+ plot_name += ' smoothing values temporally with sliding window of size {}'.format(
+ self.window_size_for_smoothing)
+ plot_name += '{} top values taken into account for the metric'.format(self.number_of_top_values)
+ self.plot_name = plot_name
self.show_or_save_to_file()
def visualize_mean_and_max_graph(self, ax, massif_id):
--
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