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 ea254cd671b23842e70d19a175d1e3b302ca34c7..966b87a5e53a583d95cf991bd3e167cd6ff6aed0 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
@@ -44,9 +44,9 @@ def altitude_trends_significant():
# altitudes that have 20 massifs at least
altitudes = ALL_ALTITUDES[3:-6]
# altitudes = ALL_ALTITUDES[3:5]
- # altitudes = ALL_ALTITUDES[2:4]
+ altitudes = ALL_ALTITUDES[2:4]
for study_class in SCM_STUDIES[:1]:
- trend_test_classes = [MannKendallTrendTest, GevLocationTrendTest, GevScaleTrendTest, GevShapeTrendTest][2:]
+ trend_test_classes = [MannKendallTrendTest, GevLocationTrendTest, GevScaleTrendTest, GevShapeTrendTest][3:]
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)]
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 8ca0156c8e34f92edc85185e92b4677ea15f5baa..810f64d6da18fbfdc395b3b2c4f66c85b1824074 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
@@ -176,7 +176,7 @@ class AltitudeVisualizer(object):
fig, ax = plt.subplots(1, 1, figsize=self.any_study_visualizer.figsize)
# Create one display for each trend test class
- markers = ['o', '+']
+ markers = ['o', '*', 's', 'D']
assert len(markers) >= len(trend_test_classes)
# Add a second legend for the color and to explain the line
@@ -185,7 +185,8 @@ class AltitudeVisualizer(object):
# Add the color legend
handles, labels = ax.get_legend_handles_labels()
- handles_ax, labels_ax = handles[:5], labels[:5]
+ nb_trend_types = len(AbstractTrendTest.trend_type_to_style())
+ handles_ax, labels_ax = handles[:nb_trend_types], labels[:nb_trend_types]
ax.legend(handles_ax, labels_ax, markerscale=0.0, loc=1)
ax.set_xticks(self.altitudes)
ax.set_yticks(list(range(0, 101, 10)))
@@ -193,7 +194,7 @@ 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[::5], names
+ handles_ax2, labels_ax2 = handles[::nb_trend_types], names
ax2 = ax.twinx()
ax2.legend(handles_ax2, labels_ax2, loc=2)
ax2.set_yticks([])
@@ -224,7 +225,7 @@ class AltitudeVisualizer(object):
s = study_visualizer.serie_mean_trend_test_count(trend_test_class, starting_year_to_weights)
altitude_to_serie_with_mean_percentages[altitude] = s
# Plot lines
- for trend_type, style in AbstractTrendTest.TREND_TYPE_TO_STYLE.items():
+ 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
for v in altitude_to_serie_with_mean_percentages.values()]
if set(percentages) == {0.0}:
diff --git a/experiment/trend_analysis/univariate_trend_test/abstract_gev_trend_test.py b/experiment/trend_analysis/univariate_trend_test/abstract_gev_trend_test.py
index b5b3fd3e1c0c669d6d86ab192468fef257fc7881..97caaa58725f4f372b8921d44fff798eee15dee3 100644
--- a/experiment/trend_analysis/univariate_trend_test/abstract_gev_trend_test.py
+++ b/experiment/trend_analysis/univariate_trend_test/abstract_gev_trend_test.py
@@ -6,6 +6,7 @@ from experiment.trend_analysis.univariate_trend_test.abstract_trend_test import
from extreme_estimator.estimator.margin_estimator.abstract_margin_estimator import LinearMarginEstimator
from extreme_estimator.extreme_models.margin_model.temporal_linear_margin_model import StationaryStationModel, \
NonStationaryLocationStationModel, NonStationaryScaleStationModel, NonStationaryShapeStationModel
+from extreme_estimator.extreme_models.utils import SafeRunException
from extreme_estimator.margin_fits.gev.gev_params import GevParams
from spatio_temporal_dataset.coordinates.abstract_coordinates import AbstractCoordinates
from spatio_temporal_dataset.coordinates.temporal_coordinates.abstract_temporal_coordinates import \
@@ -20,24 +21,28 @@ from spatio_temporal_dataset.spatio_temporal_observations.abstract_spatio_tempor
class AbstractGevTrendTest(AbstractTrendTest):
+ RRunTimeError_TREND = 'R RunTimeError trend'
def __init__(self, years_after_change_point, maxima_after_change_point, non_stationary_model_class):
super().__init__(years_after_change_point, maxima_after_change_point)
df = pd.DataFrame({AbstractCoordinates.COORDINATE_T: years_after_change_point})
df_maxima_gev = pd.DataFrame(maxima_after_change_point, index=df.index)
observations = AbstractSpatioTemporalObservations(df_maxima_gev=df_maxima_gev)
- self.coordinates = AbstractTemporalCoordinates.from_df(df, transformation_class=BetweenZeroAndOneNormalization)
- # self.coordinates = AbstractTemporalCoordinates.from_df(df, transformation_class=CenteredScaledNormalization)
+ self.coordinates = AbstractTemporalCoordinates.from_df(df, transformation_class=CenteredScaledNormalization)
self.dataset = AbstractDataset(observations=observations, coordinates=self.coordinates)
- # Fit stationary model
- self.stationary_estimator = LinearMarginEstimator(self.dataset, StationaryStationModel(self.coordinates))
- self.stationary_estimator.fit()
+ try:
+ # Fit stationary model
+ self.stationary_estimator = LinearMarginEstimator(self.dataset, StationaryStationModel(self.coordinates))
+ self.stationary_estimator.fit()
- # Fit non stationary model
- self.non_stationary_estimator = LinearMarginEstimator(self.dataset,
- non_stationary_model_class(self.coordinates))
- self.non_stationary_estimator.fit()
+ # Fit non stationary model
+ self.non_stationary_estimator = LinearMarginEstimator(self.dataset,
+ non_stationary_model_class(self.coordinates))
+ self.non_stationary_estimator.fit()
+ self.crashed = False
+ except SafeRunException:
+ self.crashed = True
@property
def likelihood_ratio(self):
@@ -48,6 +53,21 @@ class AbstractGevTrendTest(AbstractTrendTest):
def is_significant(self) -> bool:
return self.likelihood_ratio > chi2.ppf(q=1 - self.SIGNIFICANCE_LEVEL, df=1)
+ # Add a trend type that correspond to run that crashed
+
+ @classmethod
+ def trend_type_to_style(cls):
+ trend_type_to_style = super().trend_type_to_style()
+ trend_type_to_style[cls.RRunTimeError_TREND] = 'b:'
+ return trend_type_to_style
+
+ @property
+ def test_trend_type(self) -> str:
+ if self.crashed:
+ return self.RRunTimeError_TREND
+ else:
+ return super().test_trend_type
+
class GevLocationTrendTest(AbstractGevTrendTest):
diff --git a/experiment/trend_analysis/univariate_trend_test/abstract_trend_test.py b/experiment/trend_analysis/univariate_trend_test/abstract_trend_test.py
index a87598b3136d3f66bfc5fdf1bd9cef9b79d5cbd4..c2afeb6ee594f7f4bcf87a84c9058ffda4904763 100644
--- a/experiment/trend_analysis/univariate_trend_test/abstract_trend_test.py
+++ b/experiment/trend_analysis/univariate_trend_test/abstract_trend_test.py
@@ -1,4 +1,5 @@
import random
+from collections import OrderedDict
import numpy as np
@@ -17,16 +18,15 @@ class AbstractTrendTest(object):
SIGNIFICANCE_LEVEL = 0.05
- # todo: maybe create ordered dict
- TREND_TYPE_TO_STYLE = {
- NO_TREND: 'k--',
- POSITIVE_TREND: 'g--',
- SIGNIFICATIVE_POSITIVE_TREND: 'g-',
- SIGNIFICATIVE_NEGATIVE_TREND: 'r-',
- NEGATIVE_TREND: 'r--',
- }
-
- TREND_TYPES = list(TREND_TYPE_TO_STYLE.keys())
+ @classmethod
+ def trend_type_to_style(cls):
+ d = OrderedDict()
+ d[cls.POSITIVE_TREND] = 'g--'
+ d[cls.NEGATIVE_TREND] = 'r--'
+ d[cls.SIGNIFICATIVE_POSITIVE_TREND] = 'g-'
+ d[cls.SIGNIFICATIVE_NEGATIVE_TREND] = 'r-'
+ d[cls.NO_TREND] = 'k--'
+ return d
def __init__(self, years_after_change_point, maxima_after_change_point):
self.years_after_change_point = years_after_change_point
@@ -47,7 +47,7 @@ class AbstractTrendTest(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.trend_type_to_style()
return trend_type
@property
diff --git a/extreme_estimator/extreme_models/utils.py b/extreme_estimator/extreme_models/utils.py
index 6b4fe7baf14bf9aee8a6ab6475e3d7af91ee0c94..7928ca73ea369fa6813fe06ee8ab7b9b9d01b0fd 100644
--- a/extreme_estimator/extreme_models/utils.py
+++ b/extreme_estimator/extreme_models/utils.py
@@ -59,6 +59,9 @@ class OptimizationConstants(object):
USE_MAXIT = False
+class SafeRunException(Exception):
+ pass
+
def safe_run_r_estimator(function, data=None, use_start=False, threshold_max_abs_value=100, maxit=1000000,
**parameters) -> robjects.ListVector:
@@ -96,7 +99,7 @@ def safe_run_r_estimator(function, data=None, use_start=False, threshold_max_abs
use_start = True
continue
elif isinstance(e, RRuntimeError):
- raise Exception('Some R exception have been launched at RunTime: \n {}'.format(e.__repr__()))
+ raise SafeRunException('Some R exception have been launched at RunTime: \n {}'.format(e.__repr__()))
if isinstance(e, RRuntimeWarning):
warnings.warn(e.__repr__(), WarningWhileRunningR)
return res