From acdcf96ffb674ac25c98ea454cbdb10e39c35701 Mon Sep 17 00:00:00 2001
From: Le Roux Erwan <erwan.le-roux@irstea.fr>
Date: Mon, 27 May 2019 10:22:06 +0200
Subject: [PATCH] [SCM][HYPERCUBE] refactor trend types for hypercube
visualization. add the difference between real_trend_type &
display_trend_type
---
.../altitude_hypercube_visualizer.py | 28 +++++++--------
.../main_hypercube_visualization.py | 4 +--
.../studies_visualizer.py | 2 +-
.../abstract_gev_change_point_test.py | 11 ++++++
.../abstract_univariate_test.py | 34 ++++++++++++-------
5 files changed, 50 insertions(+), 29 deletions(-)
diff --git a/experiment/meteo_france_SCM_study/visualization/hypercube_visualization/altitude_hypercube_visualizer.py b/experiment/meteo_france_SCM_study/visualization/hypercube_visualization/altitude_hypercube_visualizer.py
index 25ef2966..cb8557e2 100644
--- a/experiment/meteo_france_SCM_study/visualization/hypercube_visualization/altitude_hypercube_visualizer.py
+++ b/experiment/meteo_france_SCM_study/visualization/hypercube_visualization/altitude_hypercube_visualizer.py
@@ -16,12 +16,12 @@ class AltitudeHypercubeVisualizer(AbstractHypercubeVisualizer):
return self.tuple_values(idx=0)
@property
- def trend_type_to_style(self):
- return self.trend_test_class.trend_type_to_style()
+ def display_trend_type_to_style(self):
+ return self.trend_test_class.display_trend_type_to_style()
@property
- def trend_types(self):
- return self.trend_type_to_style.keys()
+ def display_trend_types(self):
+ return self.display_trend_type_to_style.keys()
@property
def nb_axes(self):
@@ -29,12 +29,12 @@ class AltitudeHypercubeVisualizer(AbstractHypercubeVisualizer):
def trend_type_to_series(self, reduction_function):
# Map each trend type to its serie with percentages
- return {trend_type: self.trend_type_reduction(reduction_function, trend_type)[0]
- for trend_type in self.trend_types}
+ return {display_trend_type: self.trend_type_reduction(reduction_function, display_trend_type)[0]
+ for display_trend_type in self.display_trend_types}
- def trend_type_reduction(self, reduction_function, trend_type):
+ def trend_type_reduction(self, reduction_function, display_trend_type):
# Reduce df_bool df to a serie s_trend_type_percentage
- df_bool = self.df_hypercube_trend_type.isin(AbstractUnivariateTest.get_trend_types(trend_type))
+ df_bool = self.df_hypercube_trend_type.isin(AbstractUnivariateTest.get_real_trend_types(display_trend_type))
s_trend_type_percentage = reduction_function(df_bool)
assert isinstance(s_trend_type_percentage, pd.Series)
assert not isinstance(s_trend_type_percentage.index, pd.MultiIndex)
@@ -75,8 +75,8 @@ class AltitudeHypercubeVisualizer(AbstractHypercubeVisualizer):
trend_type_to_series = self.trend_type_to_series(reduction_function)
for ax_idx, ax in enumerate(axes):
- for trend_type in self.trend_types:
- style = self.trend_type_to_style[trend_type]
+ for trend_type in self.display_trend_types:
+ style = self.display_trend_type_to_style[trend_type]
percentages_values = trend_type_to_series[trend_type][ax_idx]
ax.plot(xlabel_values, percentages_values, style + marker, label=trend_type)
@@ -101,7 +101,7 @@ class AltitudeHypercubeVisualizer(AbstractHypercubeVisualizer):
def visualize_trend_test_repartition(self, reduction_function, axes=None, subtitle=''):
if axes is None:
- nb_trend_type = len(self.trend_test_class.trend_type_to_style())
+ nb_trend_type = len(self.display_trend_type_to_style)
fig, axes = plt.subplots(self.nb_axes, nb_trend_type, figsize=self.study_visualizer.figsize)
for i, axes_row in enumerate(axes):
@@ -109,7 +109,7 @@ class AltitudeHypercubeVisualizer(AbstractHypercubeVisualizer):
vmax = max([s.max() for s in trend_type_to_serie.values()])
vmin = min([s.min() for s in trend_type_to_serie.values()])
vmax = max(vmax, 0.01)
- for ax, trend_type in zip(axes_row, self.trend_types):
+ for ax, trend_type in zip(axes_row, self.display_trend_types):
s_percentages = trend_type_to_serie[trend_type]
massif_to_value = dict(s_percentages)
cmap = self.trend_test_class.get_cmap_from_trend_type(trend_type)
@@ -183,8 +183,8 @@ class Altitude_Hypercube_Year_Visualizer(AltitudeHypercubeVisualizer):
# Take the mean with respect to the level of interest
return df.mean(level=level)
- def trend_type_reduction(self, reduction_function, trend_type):
- series, df_bool = super().trend_type_reduction(reduction_function, trend_type)
+ def trend_type_reduction(self, reduction_function, display_trend_type):
+ series, df_bool = super().trend_type_reduction(reduction_function, display_trend_type)
# Create df argmax
df = df_bool.copy()
df = (df * df.columns)[df_bool]
diff --git a/experiment/meteo_france_SCM_study/visualization/hypercube_visualization/main_hypercube_visualization.py b/experiment/meteo_france_SCM_study/visualization/hypercube_visualization/main_hypercube_visualization.py
index 07a1c6b9..f274e0ab 100644
--- a/experiment/meteo_france_SCM_study/visualization/hypercube_visualization/main_hypercube_visualization.py
+++ b/experiment/meteo_france_SCM_study/visualization/hypercube_visualization/main_hypercube_visualization.py
@@ -132,8 +132,8 @@ def fast_quantity_altitude_hypercube():
def main_run():
# fast_altitude_hypercube()
- # fast_altitude_year_hypercube()
- full_altitude_year_hypercube()
+ fast_altitude_year_hypercube()
+ # full_altitude_year_hypercube()
# fast_quantity_altitude_hypercube()
# full_quantity_altitude_hypercube()
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 3b618abd..664506f8 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
@@ -237,7 +237,7 @@ class AltitudeVisualizer(object):
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 weighted percentages over the years
- for trend_type, style in trend_test_class.trend_type_to_style().items():
+ for trend_type, style in trend_test_class.display_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()]
diff --git a/experiment/trend_analysis/univariate_test/abstract_gev_change_point_test.py b/experiment/trend_analysis/univariate_test/abstract_gev_change_point_test.py
index cc46b8a8..c326ac2d 100644
--- a/experiment/trend_analysis/univariate_test/abstract_gev_change_point_test.py
+++ b/experiment/trend_analysis/univariate_test/abstract_gev_change_point_test.py
@@ -44,6 +44,17 @@ class AbstractGevChangePointTest(AbstractUnivariateTest):
except SafeRunException:
self.crashed = True
+ @classmethod
+ def real_trend_types(cls):
+ return super().real_trend_types() + [cls.RRunTimeError_TREND]
+
+ @classmethod
+ def get_real_trend_types(cls, display_trend_type):
+ real_trend_types = super().get_real_trend_types(display_trend_type)
+ if display_trend_type is cls.NON_SIGNIFICATIVE_TREND:
+ real_trend_types.append(cls.RRunTimeError_TREND)
+ return real_trend_types
+
@property
def likelihood_ratio(self):
return 2 * (self.non_stationary_estimator.result_from_fit.deviance -
diff --git a/experiment/trend_analysis/univariate_test/abstract_univariate_test.py b/experiment/trend_analysis/univariate_test/abstract_univariate_test.py
index 2bb3baf3..b646301a 100644
--- a/experiment/trend_analysis/univariate_test/abstract_univariate_test.py
+++ b/experiment/trend_analysis/univariate_test/abstract_univariate_test.py
@@ -21,7 +21,9 @@ class AbstractUnivariateTest(object):
SIGNIFICATIVE_ALL_TREND = SIGNIFICATIVE + ' ' + ALL_TREND
SIGNIFICATIVE_POSITIVE_TREND = SIGNIFICATIVE + ' ' + POSITIVE_TREND
SIGNIFICATIVE_NEGATIVE_TREND = SIGNIFICATIVE + ' ' + NEGATIVE_TREND
+ NON_SIGNIFICATIVE_TREND = 'non ' + SIGNIFICATIVE + ' trend'
+ # this is the most common level of significance
SIGNIFICANCE_LEVEL = 0.05
def __init__(self, years, maxima, starting_year):
@@ -43,29 +45,37 @@ class AbstractUnivariateTest(object):
return self.maxima[self.idx_for_starting_year:]
@classmethod
- def trend_type_to_style(cls):
+ def real_trend_types(cls):
+ return [cls.POSITIVE_TREND, cls.NEGATIVE_TREND,
+ cls.SIGNIFICATIVE_POSITIVE_TREND, cls.SIGNIFICATIVE_NEGATIVE_TREND, cls.NO_TREND]
+
+ @classmethod
+ def display_trend_type_to_style(cls):
d = OrderedDict()
+ # d[cls.POSITIVE_TREND] = 'g--'
+ # d[cls.NEGATIVE_TREND] = 'r--'
d[cls.ALL_TREND] = 'k--'
- d[cls.POSITIVE_TREND] = 'g--'
- d[cls.NEGATIVE_TREND] = 'r--'
- d[cls.SIGNIFICATIVE_ALL_TREND] = 'k-'
+ d[cls.NON_SIGNIFICATIVE_TREND] = 'b--'
+ # d[cls.SIGNIFICATIVE_ALL_TREND] = 'k-'
d[cls.SIGNIFICATIVE_POSITIVE_TREND] = 'g-'
d[cls.SIGNIFICATIVE_NEGATIVE_TREND] = 'r-'
# d[cls.NO_TREND] = 'k--'
return d
@classmethod
- def get_trend_types(cls, trend_type):
- if trend_type is cls.ALL_TREND:
- return cls.get_trend_types(cls.POSITIVE_TREND) + cls.get_trend_types(cls.NEGATIVE_TREND)
- elif trend_type is cls.SIGNIFICATIVE_ALL_TREND:
+ def get_real_trend_types(cls, display_trend_type):
+ if display_trend_type is cls.ALL_TREND:
+ return cls.real_trend_types()
+ elif display_trend_type is cls.SIGNIFICATIVE_ALL_TREND:
return [cls.SIGNIFICATIVE_POSITIVE_TREND, cls.SIGNIFICATIVE_NEGATIVE_TREND]
- if trend_type is cls.POSITIVE_TREND:
+ if display_trend_type is cls.POSITIVE_TREND:
return [cls.POSITIVE_TREND, cls.SIGNIFICATIVE_POSITIVE_TREND]
- elif trend_type is cls.NEGATIVE_TREND:
+ elif display_trend_type is cls.NEGATIVE_TREND:
return [cls.NEGATIVE_TREND, cls.SIGNIFICATIVE_NEGATIVE_TREND]
+ elif display_trend_type is cls.NON_SIGNIFICATIVE_TREND:
+ return [cls.POSITIVE_TREND, cls.NEGATIVE_TREND, cls.NO_TREND]
else:
- return [trend_type]
+ return [display_trend_type]
@classmethod
def get_cmap_from_trend_type(cls, trend_type):
@@ -94,7 +104,7 @@ class AbstractUnivariateTest(object):
trend_type = self.POSITIVE_TREND if test_sign > 0 else self.NEGATIVE_TREND
if self.is_significant:
trend_type = self.SIGNIFICATIVE + ' ' + trend_type
- assert trend_type in self.trend_type_to_style()
+ assert trend_type in self.real_trend_types(), trend_type
return trend_type
@property
--
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