From 3e4d965cfc2d0de7bd4c81953e6c97cba72f8a63 Mon Sep 17 00:00:00 2001
From: Le Roux Erwan <erwan.le-roux@irstea.fr>
Date: Wed, 15 May 2019 15:12:51 +0200
Subject: [PATCH] [SCM][MAXIMA TREND] add score based on the difference between
top 10 argmax and top 10 argmin
---
.../main_study_visualizer.py | 34 +++++++----
.../study_visualization/study_visualizer.py | 58 ++++++++++++-------
2 files changed, 61 insertions(+), 31 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 e5259df5..ace9884b 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
@@ -17,8 +17,15 @@ SCM_STUDIES = [SafranSnowfall, CrocusSwe, CrocusDepth]
SCM_EXTENDED_STUDIES = [ExtendedSafranSnowfall, ExtendedCrocusSwe, ExtendedCrocusDepth]
SCM_STUDY_TO_EXTENDED_STUDY = OrderedDict(zip(SCM_STUDIES, SCM_EXTENDED_STUDIES))
+ALL_ALTITUDES = [0, 300, 600, 900, 1200, 1500, 1800, 2100, 2400, 2700, 3000, 3300, 3600, 3900, 4200, 4500, 4800]
+full_altitude_with_at_least_2_stations = [0, 300, 600, 900, 1200, 1500, 1800, 2100, 2400, 2700, 3000, 3300, 3600, 3900,
+ 4200]
-def study_iterator_global(study_classes, only_first_one=False, both_altitude=False, verbose=True, altitudes=None) -> List[AbstractStudy]:
+ALL_STUDIES = SCM_STUDIES + [SafranTemperature, SafranRainfall]
+
+
+def study_iterator_global(study_classes, only_first_one=False, both_altitude=False, verbose=True, altitudes=None) -> \
+List[AbstractStudy]:
for study_class in study_classes:
for study in study_iterator(study_class, only_first_one, both_altitude, verbose, altitudes):
yield study
@@ -26,7 +33,8 @@ def study_iterator_global(study_classes, only_first_one=False, both_altitude=Fal
break
-def study_iterator(study_class, only_first_one=False, both_altitude=False, verbose=True, altitudes=None) -> List[AbstractStudy]:
+def study_iterator(study_class, only_first_one=False, both_altitude=False, verbose=True, altitudes=None) -> List[
+ AbstractStudy]:
all_studies = []
is_safran_study = study_class in [SafranSnowfall, ExtendedSafranSnowfall]
nb_days = [1] if is_safran_study else [1]
@@ -93,15 +101,21 @@ def normal_visualization(temporal_non_stationarity=False):
only_first_one = True
# for study_class in SCM_STUDIES[:1]:
for study_class in [CrocusDepth, SafranSnowfall, SafranRainfall, SafranTemperature][1:2]:
- for study in study_iterator(study_class, only_first_one=only_first_one):
+ 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()
+
# study_visualizer.visualize_independent_margin_fits(threshold=[None, 20, 40, 60][0])
# study_visualizer.visualize_annual_mean_values()
- study_visualizer.visualize_all_mean_and_max_graphs()
- # study_visualizer.visualize_linear_margin_fit(only_first_max_stable=None)
+def all_normal_vizu():
+ for study in study_iterator_global(study_classes=ALL_STUDIES, only_first_one=False, altitudes=ALL_ALTITUDES):
+ study_visualizer = StudyVisualizer(study, save_to_file=True, temporal_non_stationarity=True)
+ study_visualizer.visualize_all_mean_and_max_graphs()
def complete_analysis(only_first_one=False):
"""An overview of everything that is possible with study OR extended study"""
@@ -122,7 +136,8 @@ def trend_analysis():
save_to_file = False
only_first_one = True
short_altitudes = [300, 1200, 2100, 3000][:1]
- full_altitude_with_at_least_2_stations = [0, 300, 600, 900, 1200, 1500, 1800, 2100, 2400, 2700, 3000, 3300, 3600, 3900, 4200][:]
+ full_altitude_with_at_least_2_stations = [0, 300, 600, 900, 1200, 1500, 1800, 2100, 2400, 2700, 3000, 3300, 3600,
+ 3900, 4200][:]
durand_altitude = [1800]
altitudes = durand_altitude
normalization_class = [None, BetweenMinusOneAndOneNormalization, BetweenZeroAndOneNormalization][-1]
@@ -141,17 +156,16 @@ def trend_analysis():
def main_run():
# normal_visualization(temporal_non_stationarity=True)
- trend_analysis()
-
-
+ # trend_analysis()
+ 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()
+
if __name__ == '__main__':
start = time.time()
main_run()
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 4f1eb446..af6b1a6e 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
@@ -27,6 +27,7 @@ from extreme_estimator.extreme_models.max_stable_model.abstract_max_stable_model
from extreme_estimator.margin_fits.abstract_params import AbstractParams
from extreme_estimator.margin_fits.gev.gev_params import GevParams
from extreme_estimator.margin_fits.gev.gevmle_fit import GevMleFit
+from extreme_estimator.margin_fits.gev.ismev_gev_fit import IsmevGevFit
from extreme_estimator.margin_fits.gpd.gpd_params import GpdParams
from extreme_estimator.margin_fits.gpd.gpdmle_fit import GpdMleFit
from spatio_temporal_dataset.coordinates.spatial_coordinates.abstract_spatial_coordinates import \
@@ -41,7 +42,8 @@ from spatio_temporal_dataset.spatio_temporal_observations.abstract_spatio_tempor
AbstractSpatioTemporalObservations
from spatio_temporal_dataset.spatio_temporal_observations.annual_maxima_observations import AnnualMaxima
from test.test_utils import load_test_max_stable_models
-from utils import get_display_name_from_object_type, VERSION_TIME, float_to_str_with_only_some_significant_digits
+from utils import get_display_name_from_object_type, VERSION_TIME, float_to_str_with_only_some_significant_digits, \
+ cached_property
BLOCK_MAXIMA_DISPLAY_NAME = 'block maxima '
@@ -83,7 +85,8 @@ class StudyVisualizer(object):
self.year_for_kde_plot = year_for_kde_plot
self.plot_block_maxima_quantiles = plot_block_maxima_quantiles
- self.window_size_for_smoothing = 21
+ self.window_size_for_smoothing = 1 # other value could be
+ self.number_of_top_values = 10 # 1 if we just want the maxima
# PLOT ARGUMENTS
self.show = False if self.save_to_file else show
@@ -163,7 +166,7 @@ class StudyVisualizer(object):
# PLOT FOR SEVERAL MASSIFS
- def visualize_massif_graphs(self, visualize_function, specified_massif_names=None):
+ def visualize_massif_graphs(self, visualize_function, use_ordered_massif_names=False):
if self.only_one_graph:
fig, ax = plt.subplots(1, 1, figsize=self.figsize)
visualize_function(ax, 0)
@@ -177,11 +180,11 @@ class StudyVisualizer(object):
ax = axes[massif_id]
visualize_function(ax, massif_id)
else:
- if specified_massif_names is None:
- massif_ids = list(range(len(self.study.study_massif_names)))
- else:
+ if use_ordered_massif_names:
massif_ids = [self.study.study_massif_names.index(massif_name) for massif_name in
- specified_massif_names]
+ self.ordered_massif_names]
+ else:
+ massif_ids = list(range(len(self.study.study_massif_names)))
for j, massif_id in enumerate(massif_ids):
row_id, column_id = j // nb_columns, j % nb_columns
ax = axes[row_id, column_id]
@@ -324,18 +327,29 @@ class StudyVisualizer(object):
all_massif_data = np.sort(all_massif_data)
return all_massif_data
- #
-
- def visualize_all_mean_and_max_graphs(self):
- # Compute the order of massif names
+ @cached_property
+ def massif_name_to_score(self):
+ # Ordered massif by scores
massif_name_to_score = {}
for massif_id, massif_name in enumerate(self.study.study_massif_names):
- score = self.smooth_maxima_x_y(massif_id)[1].argmax()
- massif_name_to_score[massif_name] = score
- ordered_massif_names = sorted(self.study.study_massif_names[:], key=lambda s: massif_name_to_score[s])
- self.visualize_massif_graphs(self.visualize_mean_and_max_graph, specified_massif_names=ordered_massif_names)
- self.plot_name = ' smoothing values temporally with sliding window of size {}'.format(
- self.window_size_for_smoothing)
+ years, smooth_maxima = self.smooth_maxima_x_y(massif_id)
+ start_year = years[0]
+ 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
+ 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
+
+ @cached_property
+ def ordered_massif_names(self):
+ return sorted(self.study.study_massif_names[:], key=lambda s: self.massif_name_to_score[s][0])
+
+ 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)
self.show_or_save_to_file()
def visualize_mean_and_max_graph(self, ax, massif_id):
@@ -355,9 +369,11 @@ class StudyVisualizer(object):
x, y = average_smoothing_with_sliding_window(x, y, window_size_for_smoothing=self.window_size_for_smoothing)
ax.plot(x, y, color=color_mean)
ax.set_ylabel('mean'.format(self.window_size_for_smoothing), color=color_mean)
- ax.set_xlabel('year')
- ax.set_title(self.study.study_massif_names[massif_id])
- ax.xaxis.set_ticks(x[2::10])
+ massif_name = self.study.study_massif_names[massif_id]
+ title = massif_name
+ title += ' {}={}-{}'.format(*[round(e, 1) for e in list(self.massif_name_to_score[massif_name])])
+ ax.set_title(title)
+ ax.xaxis.set_ticks(x[2::20])
def smooth_maxima_x_y(self, massif_id):
if massif_id not in self.massif_id_to_smooth_maxima:
@@ -536,7 +552,7 @@ class StudyVisualizer(object):
@property
def df_gev_mle(self) -> pd.DataFrame:
# Fit a margin_fits on each massif
- massif_to_gev_mle = {massif_name: GevMleFit(self.df_maxima_gev.loc[massif_name]).gev_params.summary_serie
+ massif_to_gev_mle = {massif_name: IsmevGevFit(self.df_maxima_gev.loc[massif_name]).gev_params.summary_serie
for massif_name in self.study.study_massif_names}
return pd.DataFrame(massif_to_gev_mle, columns=self.study.study_massif_names)
--
GitLab