diff --git a/experiment/meteo_france_SCM_study/visualization/studies_visualization/main_studies_visualizer.py b/experiment/meteo_france_SCM_study/visualization/studies_visualization/main_studies_visualizer.py
index 861cf404a73730de86007663a833035a662915dc..689ac9c9b51b1af9b33909d43f61d9f85086b9a7 100644
--- a/experiment/meteo_france_SCM_study/visualization/studies_visualization/main_studies_visualizer.py
+++ b/experiment/meteo_france_SCM_study/visualization/studies_visualization/main_studies_visualizer.py
@@ -1,25 +1,40 @@
+from experiment.meteo_france_SCM_study.abstract_score import MannKendall, WeigthedScore, MeanScore, MedianScore
from experiment.meteo_france_SCM_study.abstract_study import AbstractStudy
from experiment.meteo_france_SCM_study.crocus.crocus import CrocusDepth, CrocusSwe, ExtendedCrocusDepth, \
ExtendedCrocusSwe
from experiment.meteo_france_SCM_study.safran.safran import SafranSnowfall, ExtendedSafranSnowfall, \
ExtendedSafranTotalPrecip
from experiment.meteo_france_SCM_study.visualization.studies_visualization.studies import Studies
-from experiment.meteo_france_SCM_study.visualization.studies_visualization.studies_visualizer import StudiesVisualizer
+from experiment.meteo_france_SCM_study.visualization.studies_visualization.studies_visualizer import StudiesVisualizer, \
+ AltitudeVisualizer
+from experiment.meteo_france_SCM_study.visualization.study_visualization.main_study_visualizer import ALL_ALTITUDES, \
+ study_iterator_global
from experiment.meteo_france_SCM_study.visualization.study_visualization.study_visualizer import StudyVisualizer
from collections import OrderedDict
-SCM_STUDIES = [SafranSnowfall, CrocusSwe, CrocusDepth]
-SCM_EXTENDED_STUDIES = [ExtendedSafranTotalPrecip, ExtendedSafranSnowfall, ExtendedCrocusSwe, ExtendedCrocusDepth]
-SCM_STUDY_TO_EXTENDED_STUDY = OrderedDict(zip(SCM_STUDIES, SCM_EXTENDED_STUDIES))
-
def normal_visualization():
- for study_type in SCM_EXTENDED_STUDIES[:1]:
+ for study_type in [ExtendedSafranTotalPrecip]:
extended_studies = Studies(study_type)
studies_visualizer = StudiesVisualizer(extended_studies)
studies_visualizer.mean_as_a_function_of_altitude(region_only=True)
+def altitude_trends():
+ save_to_file = False
+ only_first_one = False
+ # altitudes that have 20 massifs at least
+ altitudes = ALL_ALTITUDES[3:-6]
+ # altitudes = ALL_ALTITUDES[:2]
+ visualizers = [StudyVisualizer(study, save_to_file=save_to_file, temporal_non_stationarity=True, verbose=True,
+ score_class=MedianScore)
+ for study in study_iterator_global(study_classes=[SafranSnowfall], only_first_one=only_first_one,
+ altitudes=altitudes)]
+ altitude_to_visualizer = OrderedDict(zip(altitudes, visualizers))
+ visualizer = AltitudeVisualizer(altitude_to_visualizer)
+ visualizer.negative_trend_percentages_evolution()
+
+
if __name__ == '__main__':
- normal_visualization()
+ altitude_trends()
diff --git a/experiment/meteo_france_SCM_study/visualization/studies_visualization/studies_visualizer.py b/experiment/meteo_france_SCM_study/visualization/studies_visualization/studies_visualizer.py
index e10979f8c0aaa7b484c819652f6c2f3bd5aa1cc2..2403e3924739ebf551f24e75981ad3973a9afdb9 100644
--- a/experiment/meteo_france_SCM_study/visualization/studies_visualization/studies_visualizer.py
+++ b/experiment/meteo_france_SCM_study/visualization/studies_visualization/studies_visualizer.py
@@ -1,11 +1,17 @@
+from collections import OrderedDict, Counter
+from typing import Dict
+
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
+from matplotlib.lines import Line2D
from experiment.meteo_france_SCM_study.abstract_extended_study import AbstractExtendedStudy
from experiment.meteo_france_SCM_study.visualization.studies_visualization.studies import \
Studies
+from experiment.meteo_france_SCM_study.visualization.study_visualization.study_visualizer import StudyVisualizer
from experiment.meteo_france_SCM_study.visualization.utils import plot_df
+from utils import cached_property, get_display_name_from_object_type
class StudiesVisualizer(object):
@@ -29,11 +35,86 @@ class StudiesVisualizer(object):
for study in self.studies.altitude_to_study.values():
mean_serie = study.df_annual_total.loc[:, massif_names].mean(axis=0)
mean_series.append(mean_serie)
- df_mean = pd.concat(mean_series, axis=1) # type: pd.DataFrame
+ df_mean = pd.concat(mean_series, axis=1) # type: pd.DataFrame
df_mean.columns = self.studies.altitude_list
plot_df(df_mean)
+class AltitudeVisualizer(object):
+
+ def __init__(self, altitude_to_study_visualizer: Dict[int, StudyVisualizer]):
+ assert isinstance(altitude_to_study_visualizer, OrderedDict)
+ self.altitude_to_study_visualizer = altitude_to_study_visualizer
+
+ @property
+ def altitudes(self):
+ return list(self.altitude_to_study_visualizer.keys())
+
+ @cached_property
+ def all_percentages(self):
+ return [v.percentages_of_negative_trends()[0] for v in self.altitude_to_study_visualizer.values()]
+
+ @property
+ def any_study_visualizer(self) -> StudyVisualizer:
+ return list(self.altitude_to_study_visualizer.values())[0]
+
+ def get_item_fct(self, year):
+ idx = self.any_study_visualizer.starting_years.index(year)
+ f = lambda s: s[idx]
+ return f
+
+ @cached_property
+ def starting_year(self):
+ return self.any_study_visualizer.starting_years[0]
+
+ def get_top_potential_years(self, reverse=False):
+ top_n = 5
+ top_top = 3
+ # keep the top_n for each altitude
+ all_years = [[year for year, _ in sorted(enumerate(p), key=lambda s:s[1], reverse=reverse)[-top_n:]] for p in self.all_percentages]
+ from itertools import chain
+ all_years = list(chain(*all_years))
+ years = [y for y, _ in sorted(Counter(all_years).items(), key=lambda s:s[1])[-top_top:]]
+ years = [y + self.starting_year for y in years]
+ return years
+
+ def negative_trend_percentages_evolution(self):
+ curve_name__metric_and_color = [
+ ('max', np.max, 'r'),
+ ('mean', np.mean, 'b'),
+ ('median', np.median, 'c'),
+ ('min', np.min, 'g'),
+ ]
+ # Add some years
+ # spotted_years = [1963, 1976]
+ # years_to_display = spotted_years
+ str_markers = ['o'] + [m for m in Line2D.markers if isinstance(m, str)][3:]
+ # for year, marker in zip(years_to_display, str_markers):
+ # new = (str(year), self.get_item_fct(year), 'y', marker + ':')
+ # curve_name__metric_and_color.append(new)
+ for year, marker in zip(self.get_top_potential_years(), str_markers):
+ new = (str(year), self.get_item_fct(year), 'm', marker + ':')
+ curve_name__metric_and_color.append(new)
+ for year, marker in zip(self.get_top_potential_years(reverse=True), str_markers):
+ new = (str(year), self.get_item_fct(year), 'y', marker + ':')
+ curve_name__metric_and_color.append(new)
+ fig, ax = plt.subplots(1, 1, figsize=self.any_study_visualizer.figsize)
+ for curve_name, metric, color, *marker in curve_name__metric_and_color[:]:
+ marker, curve_name = (marker[0], curve_name + ' starting year') if marker \
+ else ('-', curve_name + ' over the starting years')
+ values = [metric(p) for p in self.all_percentages]
+ ax.plot(self.altitudes, values, color + marker, label=curve_name)
+ ax.legend()
+ ax.set_xticks(self.altitudes)
+ ax.set_yticks(list(range(0,101, 10)))
+ ax.grid()
+ ax.axhline(y=50, color='k')
+ ax.set_ylabel('% of negative trends')
+ ax.set_xlabel('altitude')
+ scoer_class_name = get_display_name_from_object_type(self.any_study_visualizer.score_class)
+ title = 'Distribution of negative trend for the {}'.format(scoer_class_name)
+ ax.set_title(title)
+ plt.show()
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 23ad3a76837f72049e0b52a5a1213bd879b2726d..6c88b1ba8f4eb009f8b36e4d0501ca64679baaf1 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
@@ -402,7 +402,7 @@ class StudyVisualizer(object):
def visualize_score_wrt_starting_year(self, ax, massif_name):
if massif_name is None:
- percentage, title = self.percentage_of_negative_trends()
+ percentage, title = self.percentages_of_negative_trends()
scores = percentage
ax.set_ylabel('% of negative trends')
# Add two lines of interest
@@ -420,24 +420,27 @@ class StudyVisualizer(object):
ax.set_title(title)
ax.xaxis.set_ticks(self.starting_years[2::20])
- def percentage_of_negative_trends(self):
+ def percentages_of_negative_trends(self):
+ print('start computing percentages negative trends')
# scores = np.median([np.array(v) < 0 for v in self.massif_name_to_scores.values()], axis=0)
+ # Take the mean with respect to the massifs
+ # We obtain an array whose length equal the length of starting years
scores = np.mean([np.array(v) < 0 for v in self.massif_name_to_scores.values()], axis=0)
- percentage = 100 * scores
+ percentages = 100 * scores
# First argmin, first argmax
argmin, argmax = np.argmin(scores), np.argmax(scores)
# Last argmin, last argmax
# argmin, argmax = len(scores) - 1 - np.argmin(scores[::-1]), len(scores) - 1 - np.argmax(scores[::-1])
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)
+ top_percentage_positive_trend = round(100 - percentages[argmin], 0)
+ top_percentage_negative_trend = round(percentages[argmax], 0)
title = "Global trend; > 0: {}% in {}; < 0: {}% in {}".format(top_percentage_positive_trend,
top_starting_year_for_positive_trend,
top_percentage_negative_trend,
top_starting_year_for_negative_trend)
- return percentage, title
+ return percentages, title
def visualize_all_mean_and_max_graphs(self):
specified_massif_ids = [self.study.study_massif_names.index(massif_name)