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 966b87a5e53a583d95cf991bd3e167cd6ff6aed0..3cad2ef64d91c3ececa6deeed2da9445971998ab 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
@@ -46,13 +46,13 @@ def altitude_trends_significant():
# altitudes = ALL_ALTITUDES[3:5]
altitudes = ALL_ALTITUDES[2:4]
for study_class in SCM_STUDIES[:1]:
- trend_test_classes = [MannKendallTrendTest, GevLocationTrendTest, GevScaleTrendTest, GevShapeTrendTest][3:]
+ trend_test_classes = [MannKendallTrendTest, GevLocationTrendTest, GevScaleTrendTest, GevShapeTrendTest][:1]
visualizers = [StudyVisualizer(study, temporal_non_stationarity=True, verbose=False)
for study in study_iterator_global(study_classes=[study_class], only_first_one=only_first_one,
altitudes=altitudes)]
altitude_to_visualizer = OrderedDict(zip(altitudes, visualizers))
visualizer = AltitudeVisualizer(altitude_to_visualizer, multiprocessing=False, save_to_file=save_to_file)
- visualizer.trend_tests_percentage_evolution(trend_test_classes, starting_year_to_weights=None)
+ visualizer.trend_tests_percentage_evolution_with_altitude(trend_test_classes, starting_year_to_weights=None)
if __name__ == '__main__':
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 810f64d6da18fbfdc395b3b2c4f66c85b1824074..6a25564aa3de79f7aeccdfc860b482dbc6dfc4bd 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
@@ -164,12 +164,15 @@ class AltitudeVisualizer(object):
# Trend tests evolution
- def trend_tests_percentage_evolution(self, trend_test_classes, starting_year_to_weights: None):
+ def trend_tests_percentage_evolution_with_altitude(self, trend_test_classes, starting_year_to_weights: None):
# Load uniform weights by default
if starting_year_to_weights is None:
startings_years = self.any_study_visualizer.starting_years
uniform_weight = 1 / len(startings_years)
starting_year_to_weights = {year: uniform_weight for year in startings_years}
+
+ # To get a single year, I could do:
+ # starting_year_to_weights = {1980: 1.0}
else:
uniform_weight = 0.0
@@ -181,7 +184,7 @@ class AltitudeVisualizer(object):
# Add a second legend for the color and to explain the line
for marker, trend_test_class in zip(markers, trend_test_classes):
- self.trend_test_percentages_evolution(ax, marker, trend_test_class, starting_year_to_weights)
+ self.trend_test_weighted_percentages(ax, marker, trend_test_class, starting_year_to_weights)
# Add the color legend
handles, labels = ax.get_legend_handles_labels()
@@ -195,6 +198,8 @@ class AltitudeVisualizer(object):
# Add the marker legend
names = [get_display_name_from_object_type(c) for c in trend_test_classes]
handles_ax2, labels_ax2 = handles[::nb_trend_types], names
+ for handle in handles_ax2:
+ handle.set_color('k')
ax2 = ax.twinx()
ax2.legend(handles_ax2, labels_ax2, loc=2)
ax2.set_yticks([])
@@ -210,25 +215,18 @@ class AltitudeVisualizer(object):
ax.set_title(title)
self.show_or_save_to_file(specific_title=title)
- def trend_test_percentages_evolution(self, ax, marker, trend_test_class, starting_year_to_weights):
- """
- Positive trend in green
- Negative trend in red
- Non significative trend with dotted line
- Significative trend with real line
-
- :return:
- """
+ def trend_test_weighted_percentages(self, ax, marker, trend_test_class, starting_year_to_weights):
# Build OrderedDict mapping altitude to a mean serie
altitude_to_serie_with_mean_percentages = OrderedDict()
for altitude, study_visualizer in self.altitude_to_study_visualizer.items():
- s = study_visualizer.serie_mean_trend_test_count(trend_test_class, starting_year_to_weights)
+ s = study_visualizer.df_trend_test_count(trend_test_class, starting_year_to_weights).mean(axis=1)
altitude_to_serie_with_mean_percentages[altitude] = s
- # Plot lines
- for trend_type, style in AbstractTrendTest.trend_type_to_style().items():
- percentages = [v.loc[trend_type] if trend_type in v.index else 0.0
+ # Plot weighted percentages over the years
+ for trend_type, style in trend_test_class.trend_type_to_style().items():
+
+ weighted_percentages = [v.loc[trend_type] if trend_type in v.index else 0.0
for v in altitude_to_serie_with_mean_percentages.values()]
- if set(percentages) == {0.0}:
- continue
+ if set(weighted_percentages) == {0.0}:
+ ax.plot([], [], style + marker, label=trend_type)
else:
- ax.plot(self.altitudes, percentages, style + marker, label=trend_type)
+ ax.plot(self.altitudes, weighted_percentages, style + marker, label=trend_type)
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 4c2ef1809a4377392b3a71eb94033a0b03cf110a..647623d7e7194a3783703908751a9e75f5f991bb 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
@@ -362,34 +362,46 @@ class StudyVisualizer(object):
massif_name_to_scores[massif_name] = np.array(detailed_scores)
return massif_name_to_scores
- def massif_name_to_trend_test_count(self, trend_test_class, starting_year_to_weight):
- massif_name_to_serie_percentages = {}
+ def massif_name_to_df_trend_type(self, trend_test_class, starting_year_to_weight):
+ """
+ Create a DataFrame with massif as index
+ :param trend_test_class:
+ :param starting_year_to_weight:
+ :return:
+ """
+ massif_name_to_df_trend_type = {}
for massif_id, massif_name in enumerate(self.study.study_massif_names):
trend_type_and_weight = []
years, smooth_maxima = self.smooth_maxima_x_y(massif_id)
for starting_year, weight in starting_year_to_weight.items():
idx = years.index(starting_year)
- years, smooth_maxima = years[idx:], smooth_maxima[idx:]
- assert years[0] == starting_year, "{} {}".format(years[0], starting_year)
- trend_test = trend_test_class(years, smooth_maxima) # type: AbstractTrendTest
+ # assert years[0] == starting_year, "{} {}".format(years[0], starting_year)
+ trend_test = trend_test_class(years[:][idx:], smooth_maxima[:][idx:]) # type: AbstractTrendTest
trend_type_and_weight.append((trend_test.test_trend_type, weight))
df = pd.DataFrame(trend_type_and_weight, columns=['trend type', 'weight'])
- serie = df.groupby(['trend type']).sum()
- massif_name_to_serie_percentages[massif_name] = serie * 100
- return massif_name_to_serie_percentages
+ massif_name_to_df_trend_type[massif_name] = df
+ return massif_name_to_df_trend_type
- def serie_mean_trend_test_count(self, trend_test_class, starting_year_to_weight):
- massif_name_to_trend_test_count = self.massif_name_to_trend_test_count(trend_test_class, starting_year_to_weight)
- df = pd.concat(list(massif_name_to_trend_test_count.values()), axis=1, sort=False)
+ def df_trend_test_count(self, trend_test_class, starting_year_to_weight):
+ """
+ Index are the trend type
+ Columns are the massif
+
+ :param starting_year_to_weight:
+ :param trend_test_class:
+ :return:
+ """
+ massif_name_to_df_trend_type = self.massif_name_to_df_trend_type(trend_test_class, starting_year_to_weight)
+ df = pd.concat([100 * v.groupby(['trend type']).sum()
+ for v in massif_name_to_df_trend_type.values()], axis=1, sort=False)
df.fillna(0.0, inplace=True)
- s = df.mean(axis=1)
- assert np.allclose(df.sum(), 100)
+ assert np.allclose(df.sum(axis=0), 100)
# Add the significant trend into the count of normal trend
- if AbstractTrendTest.SIGNIFICATIVE_POSITIVE_TREND in s.index:
- s[AbstractTrendTest.POSITIVE_TREND] += s[AbstractTrendTest.SIGNIFICATIVE_POSITIVE_TREND]
- if AbstractTrendTest.SIGNIFICATIVE_NEGATIVE_TREND in s.index:
- s[AbstractTrendTest.NEGATIVE_TREND] += s[AbstractTrendTest.SIGNIFICATIVE_NEGATIVE_TREND]
- return s
+ if AbstractTrendTest.SIGNIFICATIVE_POSITIVE_TREND in df.index:
+ df.loc[AbstractTrendTest.POSITIVE_TREND] += df.loc[AbstractTrendTest.SIGNIFICATIVE_POSITIVE_TREND]
+ if AbstractTrendTest.SIGNIFICATIVE_NEGATIVE_TREND in df.index:
+ df.loc[AbstractTrendTest.NEGATIVE_TREND] += df.loc[AbstractTrendTest.SIGNIFICATIVE_NEGATIVE_TREND]
+ return df
@cached_property
def massif_name_to_scores(self):