From 07091db861e41f48cb8b50b3ec5da137493dcfa9 Mon Sep 17 00:00:00 2001
From: Le Roux Erwan <erwan.le-roux@irstea.fr>
Date: Wed, 22 May 2019 16:22:51 +0200
Subject: [PATCH] [SCM][HYPERCUBE] add trend_strength to the
hypercube_visualization.
---
.../abstract_hypercube_visualizer.py | 22 ++--
.../altitude_hypercube_visualizer.py | 111 +++++++++---------
.../main_hypercube_visualization.py | 19 +--
.../quantity_altitude_visualizer.py | 5 +-
.../study_visualization/study_visualizer.py | 29 +++--
.../abstract_gev_trend_test.py | 19 +--
.../abstract_trend_test.py | 13 ++
.../margin_function/linear_margin_function.py | 2 +-
8 files changed, 121 insertions(+), 99 deletions(-)
diff --git a/experiment/meteo_france_SCM_study/visualization/hypercube_visualization/abstract_hypercube_visualizer.py b/experiment/meteo_france_SCM_study/visualization/hypercube_visualization/abstract_hypercube_visualizer.py
index 13524c33..32ff017e 100644
--- a/experiment/meteo_france_SCM_study/visualization/hypercube_visualization/abstract_hypercube_visualizer.py
+++ b/experiment/meteo_france_SCM_study/visualization/hypercube_visualization/abstract_hypercube_visualizer.py
@@ -41,21 +41,21 @@ class AbstractHypercubeVisualizer(object):
def tuple_values(self, idx):
return sorted(set([t[idx] if isinstance(t, tuple) else t for t in self.tuple_to_study_visualizer.keys()]))
+ @cached_property
+ def df_trends_spatio_temporal(self):
+ return [study_visualizer.df_trend_spatio_temporal(self.trend_test_class, self.starting_years,
+ self.nb_data_for_fast_mode)
+ for study_visualizer in self.tuple_to_study_visualizer.values()]
+
@cached_property
def df_hypercube_trend_type(self) -> pd.DataFrame:
- df_spatio_temporal_trend_types = [
- study_visualizer.df_trend_spatio_temporal(self.trend_test_class, self.starting_years,
- self.nb_data_for_fast_mode)
- for study_visualizer in self.tuple_to_study_visualizer.values()]
+ df_spatio_temporal_trend_types = [e[0] for e in self.df_trends_spatio_temporal]
return pd.concat(df_spatio_temporal_trend_types, keys=list(self.tuple_to_study_visualizer.keys()), axis=0)
@cached_property
def df_hypercube_trend_strength(self) -> pd.DataFrame:
- df_spatio_temporal_trend_types = [
- study_visualizer.df_trend_spatio_temporal(self.trend_test_class, self.starting_years,
- self.nb_data_for_fast_mode)
- for study_visualizer in self.tuple_to_study_visualizer.values()]
- return pd.concat(df_spatio_temporal_trend_types, keys=list(self.tuple_to_study_visualizer.keys()), axis=0)
+ df_spatio_temporal_trend_strength = [e[1] for e in self.df_trends_spatio_temporal]
+ return pd.concat(df_spatio_temporal_trend_strength, keys=list(self.tuple_to_study_visualizer.keys()), axis=0)
# Some properties
@@ -89,7 +89,3 @@ class AbstractHypercubeVisualizer(object):
# Load uniform weights by default
uniform_weight = 1 / len(self.starting_years)
return {year: uniform_weight for year in self.starting_years}
-
-
-
-
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 5c692a91..89b9943a 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
@@ -22,64 +22,66 @@ class AltitudeHypercubeVisualizer(AbstractHypercubeVisualizer):
def trend_types(self):
return self.trend_type_to_style.keys()
- def trend_type_to_s_percentages(self, reduction_function):
+ def trend_type_to_series(self, reduction_function):
# Map each trend type to its serie with percentages
- trend_type_to_s_percentages = {}
+ trend_type_to_series = {}
for trend_type in self.trend_types:
- df_bool = (self.df_hypercube_trend_type == trend_type)
- # Reduce the entire dataframe to a serie
- s_percentages = reduction_function(df_bool)
- assert isinstance(s_percentages, pd.Series)
- assert not isinstance(s_percentages.index, pd.MultiIndex)
- s_percentages *= 100
- trend_type_to_s_percentages[trend_type] = s_percentages
- # Post processing - Add the significant trend into the count of normal trend
- trend_type_to_s_percentages[AbstractTrendTest.POSITIVE_TREND] += trend_type_to_s_percentages[
- AbstractTrendTest.SIGNIFICATIVE_POSITIVE_TREND]
- trend_type_to_s_percentages[AbstractTrendTest.NEGATIVE_TREND] += trend_type_to_s_percentages[
- AbstractTrendTest.SIGNIFICATIVE_NEGATIVE_TREND]
- return trend_type_to_s_percentages
-
- def subtitle_to_reduction_function(self, reduction_function, level=None, add_detailed_plot=False):
+ # Reduce df_bool df to a serie s_trend_type_percentage
+ df_bool = self.df_hypercube_trend_type.isin(AbstractTrendTest.get_trend_types(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)
+ s_trend_type_percentage *= 100
+ # Reduce df_strength to a serie s_trend_strength
+ df_strength = self.df_hypercube_trend_strength[df_bool]
+ s_trend_strength = reduction_function(df_strength)
+ # Store results
+ trend_type_to_series[trend_type] = (s_trend_type_percentage, s_trend_strength)
+ return trend_type_to_series
+
+ def subtitle_to_reduction_function(self, reduction_function, level=None, add_detailed_plot=False, subtitle=None):
def reduction_function_with_level(df_bool):
return reduction_function(df_bool) if level is None else reduction_function(df_bool, level)
+ if subtitle is None:
+ subtitle = self.study.variable_name
+ return {subtitle: reduction_function_with_level}
- return {'global': reduction_function_with_level}
-
- def visualize_trend_test_evolution(self, reduction_function, xlabel, xlabel_values, ax=None, marker='o',
+ def visualize_trend_test_evolution(self, reduction_function, xlabel, xlabel_values, axes=None, marker='o',
subtitle=''):
- if ax is None:
- fig, ax = plt.subplots(1, 1, figsize=self.study_visualizer.figsize)
-
- trend_type_to_percentages_values = {k: s.values for k, s in
- self.trend_type_to_s_percentages(reduction_function).items()}
- for trend_type in self.trend_types:
- style = self.trend_type_to_style[trend_type]
- percentages_values = trend_type_to_percentages_values[trend_type]
- ax.plot(xlabel_values, percentages_values, style + marker, label=trend_type)
-
- # Plot the total value of significative values
- significative_values = trend_type_to_percentages_values[AbstractTrendTest.SIGNIFICATIVE_NEGATIVE_TREND] \
- + trend_type_to_percentages_values[AbstractTrendTest.SIGNIFICATIVE_POSITIVE_TREND]
- ax.plot(xlabel_values, significative_values, 'y-' + marker, label=AbstractTrendTest.SIGNIFICATIVE + ' trends')
+ if axes is None:
+ fig, axes = plt.subplots(2, 1, figsize=self.study_visualizer.figsize)
+
+ trend_type_to_series = self.trend_type_to_series(reduction_function)
+ for i, ax in enumerate(axes):
+ for trend_type in self.trend_types:
+ style = self.trend_type_to_style[trend_type]
+ percentages_values = trend_type_to_series[trend_type][i]
+ ax.plot(xlabel_values, percentages_values, style + marker, label=trend_type)
+
+ if i == 0:
+ # Plot the total value of significative values
+ significative_values = trend_type_to_series[AbstractTrendTest.SIGNIFICATIVE_NEGATIVE_TREND][i] \
+ + trend_type_to_series[AbstractTrendTest.SIGNIFICATIVE_POSITIVE_TREND][i]
+ ax.plot(xlabel_values, significative_values, 'y-' + marker, label=AbstractTrendTest.SIGNIFICATIVE + ' trends')
+
+ # Global information
+ added_str = 'weighted '
+ ylabel = '% averaged on massifs & {}averaged on {}'.format(added_str, xlabel)
+ ylabel += ' (with uniform weights)'
+ ax.set_ylabel(ylabel)
+
+ ax.set_yticks(list(range(0, 101, 10)))
+
+ # Common function functions
+ ax.set_xlabel(xlabel)
+ ax.grid()
+ ax.set_xticks(xlabel_values)
+ ax.legend()
- # Global information
- added_str = 'weighted '
- ylabel = '% averaged on massifs & {}averaged on {}'.format(added_str, xlabel)
- ylabel += ' (with uniform weights)'
- ax.set_ylabel(ylabel)
- ax.set_xlabel(xlabel)
- ax.set_xticks(xlabel_values)
- ax.set_yticks(list(range(0, 101, 10)))
- ax.grid()
- ax.legend()
-
- variable_name = self.study.variable_class.NAME
name = get_display_name_from_object_type(self.trend_test_class)
- title = 'Evolution of {} trends (significative or not) wrt to the {} with {}'.format(variable_name, xlabel,
+ title = 'Evolution of {} trends (significative or not) wrt to the {} with {}'.format(subtitle, xlabel,
name)
- title += 'with {} data'.format(subtitle)
- ax.set_title(title)
+ plt.suptitle(title)
self.show_or_save_to_file(specific_title=title)
def visualize_trend_test_repartition(self, reduction_function, axes=None, subtitle=''):
@@ -88,7 +90,8 @@ class AltitudeHypercubeVisualizer(AbstractHypercubeVisualizer):
fig, axes = plt.subplots(1, nb_trend_type, figsize=self.study_visualizer.figsize)
# Plot weighted percentages over the years
- trend_type_to_s_percentages = self.trend_type_to_s_percentages(reduction_function)
+ # todo: implement strength plot spatially
+ trend_type_to_s_percentages = {k: v[0] for k, v in self.trend_type_to_series(reduction_function).items()}
for ax, trend_type in zip(axes, self.trend_types):
s_percentages = trend_type_to_s_percentages[trend_type]
massif_to_value = dict(s_percentages)
@@ -113,7 +116,7 @@ class AltitudeHypercubeVisualizer(AbstractHypercubeVisualizer):
def massif_index_level(self):
return 1
- def visualize_year_trend_test(self, ax=None, marker='o', add_detailed_plots=False):
+ def visualize_year_trend_test(self, axes=None, marker='o', add_detailed_plots=False):
def year_reduction(df_bool):
# Take the mean with respect to all the first axis indices
return df_bool.mean(axis=0)
@@ -121,10 +124,10 @@ class AltitudeHypercubeVisualizer(AbstractHypercubeVisualizer):
for subtitle, reduction_function in self.subtitle_to_reduction_function(year_reduction,
add_detailed_plot=add_detailed_plots).items():
self.visualize_trend_test_evolution(reduction_function=reduction_function, xlabel='starting years',
- xlabel_values=self.starting_years, ax=ax, marker=marker,
+ xlabel_values=self.starting_years, axes=axes, marker=marker,
subtitle=subtitle)
- def visualize_altitude_trend_test(self, ax=None, marker='o', add_detailed_plots=False):
+ def visualize_altitude_trend_test(self, axes=None, marker='o', add_detailed_plots=False):
def altitude_reduction(df_bool, level):
# Take the mean with respect to the years
df_bool = df_bool.mean(axis=1)
@@ -135,7 +138,7 @@ class AltitudeHypercubeVisualizer(AbstractHypercubeVisualizer):
level=self.altitude_index_level,
add_detailed_plot=add_detailed_plots).items():
self.visualize_trend_test_evolution(reduction_function=reduction_function, xlabel='altitude',
- xlabel_values=self.altitudes, ax=ax, marker=marker,
+ xlabel_values=self.altitudes, axes=axes, marker=marker,
subtitle=subtitle)
def visualize_massif_trend_test(self, axes=None, add_detailed_plots=False):
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 28e8e3bc..534541c8 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
@@ -37,9 +37,10 @@ def full_trends_with_quantity_altitude_hypercube():
save_to_file = True
only_first_one = False
fast = False
+ add_detailed_plots = False
altitudes = ALL_ALTITUDES[3:-6]
study_classes = SCM_STUDIES
- for trend_test_class in [MannKendallTrendTest, GevLocationTrendTest, GevScaleTrendTest, GevShapeTrendTest][:]:
+ for trend_test_class in [MannKendallTrendTest, GevLocationTrendTest, GevScaleTrendTest, GevShapeTrendTest][1:2]:
visualizers = [StudyVisualizer(study, temporal_non_stationarity=True, verbose=False, multiprocessing=True)
for study in study_iterator_global(study_classes=study_classes, only_first_one=only_first_one,
altitudes=altitudes)]
@@ -48,9 +49,9 @@ def full_trends_with_quantity_altitude_hypercube():
quantity_altitude_to_visualizer = OrderedDict(zip(quantity_altitude_tuples, visualizers))
visualizer = QuantityAltitudeHypercubeVisualizer(quantity_altitude_to_visualizer, save_to_file=save_to_file,
trend_test_class=trend_test_class, fast=fast)
- visualizer.visualize_year_trend_test(add_detailed_plots=True)
- visualizer.visualize_massif_trend_test(add_detailed_plots=True)
- visualizer.visualize_altitude_trend_test(add_detailed_plots=True)
+ visualizer.visualize_year_trend_test(add_detailed_plots=add_detailed_plots)
+ visualizer.visualize_massif_trend_test(add_detailed_plots=add_detailed_plots)
+ visualizer.visualize_altitude_trend_test(add_detailed_plots=add_detailed_plots)
def fast_trends_with_altitude_hypercube():
@@ -59,7 +60,7 @@ def fast_trends_with_altitude_hypercube():
fast = True
altitudes = ALL_ALTITUDES[2:4]
for study_class in SCM_STUDIES[:1]:
- for trend_test_class in [MannKendallTrendTest, GevLocationTrendTest, GevScaleTrendTest, GevShapeTrendTest][:1]:
+ for trend_test_class in [MannKendallTrendTest, GevLocationTrendTest, GevScaleTrendTest, GevShapeTrendTest][1:2]:
visualizers = [StudyVisualizer(study, temporal_non_stationarity=True, verbose=False, multiprocessing=True)
for study in study_iterator(study_class=study_class, only_first_one=only_first_one,
altitudes=altitudes)]
@@ -67,8 +68,8 @@ def fast_trends_with_altitude_hypercube():
visualizer = AltitudeHypercubeVisualizer(altitude_to_visualizer, save_to_file=save_to_file,
trend_test_class=trend_test_class, fast=fast)
visualizer.visualize_year_trend_test()
- # visualizer.visualize_massif_trend_test()
- # visualizer.visualize_altitude_trend_test()
+ visualizer.visualize_massif_trend_test()
+ visualizer.visualize_altitude_trend_test()
def fast_trends_with_quantity_altitude_hypercube():
@@ -92,9 +93,9 @@ def fast_trends_with_quantity_altitude_hypercube():
def main_run():
- fast_trends_with_altitude_hypercube()
+ # fast_trends_with_altitude_hypercube()
# fast_trends_with_quantity_altitude_hypercube()
- # full_trends_with_quantity_altitude_hypercube()
+ full_trends_with_quantity_altitude_hypercube()
if __name__ == '__main__':
diff --git a/experiment/meteo_france_SCM_study/visualization/hypercube_visualization/quantity_altitude_visualizer.py b/experiment/meteo_france_SCM_study/visualization/hypercube_visualization/quantity_altitude_visualizer.py
index 92d20988..f4e29b8b 100644
--- a/experiment/meteo_france_SCM_study/visualization/hypercube_visualization/quantity_altitude_visualizer.py
+++ b/experiment/meteo_france_SCM_study/visualization/hypercube_visualization/quantity_altitude_visualizer.py
@@ -10,9 +10,10 @@ class QuantityAltitudeHypercubeVisualizer(AltitudeHypercubeVisualizer):
def study_title(self):
return 'Quantity Altitude Study'
- def subtitle_to_reduction_function(self, reduction_function, level=None, add_detailed_plot=False):
+ def subtitle_to_reduction_function(self, reduction_function, level=None, add_detailed_plot=False, subtitle=None):
subtitle_to_reduction_function = super().subtitle_to_reduction_function(reduction_function,
- level, add_detailed_plot)
+ level, add_detailed_plot,
+ 'global')
def get_function_from_tuple(tuple_for_axis_0):
def f(df_bool: pd.DataFrame):
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 98f0aa6f..fc46ae26 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
@@ -376,7 +376,8 @@ class StudyVisualizer(object):
trend_type_and_weight = []
years, smooth_maxima = self.smooth_maxima_x_y(massif_id)
for starting_year, weight in starting_year_to_weight.items():
- test_trend_type = self.compute_trend_test_type(smooth_maxima, starting_year, trend_test_class, years)
+ test_trend_res = self.compute_trend_test_result(smooth_maxima, starting_year, trend_test_class, years)
+ test_trend_type = test_trend_res[0]
trend_type_and_weight.append((test_trend_type, weight))
df = pd.DataFrame(trend_type_and_weight, columns=['trend type', 'weight'])
massif_name_to_df_trend_type[massif_name] = df
@@ -391,29 +392,35 @@ class StudyVisualizer(object):
:param starting_year_to_weight:
:return:
"""
- massif_name_to_trend_types = {}
+ massif_name_to_trend_res = {}
massif_names = self.study.study_massif_names
if nb_massif_for_fast_mode is not None:
massif_names = massif_names[:nb_massif_for_fast_mode]
for massif_id, massif_name in enumerate(massif_names):
years, smooth_maxima = self.smooth_maxima_x_y(massif_id)
if self.multiprocessing:
- list_args = [(smooth_maxima, starting_year, trend_test_class, years) for starting_year in starting_years]
+ list_args = [(smooth_maxima, starting_year, trend_test_class, years) for starting_year in
+ starting_years]
with Pool(NB_CORES) as p:
- trend_types = p.starmap(self.compute_trend_test_type, list_args)
+ trend_test_res = p.starmap(self.compute_trend_test_result, list_args)
else:
- trend_types = [self.compute_trend_test_type(smooth_maxima, starting_year, trend_test_class, years)
- for starting_year in starting_years]
- massif_name_to_trend_types[massif_name] = trend_types
- df = pd.DataFrame(massif_name_to_trend_types, index=starting_years)
- return df.transpose()
+ trend_test_res = [self.compute_trend_test_result(smooth_maxima, starting_year, trend_test_class, years)
+ for starting_year in starting_years]
+ massif_name_to_trend_res[massif_name] = list(zip(*trend_test_res))
+ # Build one DataFrame per trend test res
+ nb_res = len(list(massif_name_to_trend_res.values())[0])
+ assert nb_res == 2
+ all_massif_name_to_res = [{k: v[idx_res] for k, v in massif_name_to_trend_res.items()}
+ for idx_res in range(nb_res)]
+ return [pd.DataFrame(massif_name_to_res, index=starting_years).transpose()
+ for massif_name_to_res in all_massif_name_to_res]
@staticmethod
- def compute_trend_test_type(smooth_maxima, starting_year, trend_test_class, years):
+ def compute_trend_test_result(smooth_maxima, starting_year, trend_test_class, years):
idx = years.index(starting_year)
# assert years[0] == starting_year, "{} {}".format(years[0], starting_year)
trend_test = trend_test_class(years[:][idx:], smooth_maxima[:][idx:]) # type: AbstractTrendTest
- return trend_test.test_trend_type
+ return trend_test.test_trend_type, trend_test.test_trend_strength
def df_trend_test_count(self, trend_test_class, starting_year_to_weight):
"""
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 42ae3c49..c9811d9e 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
@@ -4,6 +4,7 @@ from scipy.stats import chi2
from experiment.trend_analysis.univariate_trend_test.abstract_trend_test import AbstractTrendTest
from extreme_estimator.estimator.margin_estimator.abstract_margin_estimator import LinearMarginEstimator
+from extreme_estimator.extreme_models.margin_model.param_function.linear_coef import LinearCoef
from extreme_estimator.extreme_models.margin_model.temporal_linear_margin_model import StationaryStationModel, \
NonStationaryLocationStationModel, NonStationaryScaleStationModel, NonStationaryShapeStationModel
from extreme_estimator.extreme_models.utils import SafeRunException
@@ -67,24 +68,24 @@ class AbstractGevTrendTest(AbstractTrendTest):
else:
return super().test_trend_type
- def get_coef(self, estimator):
- return estimator.margin_function_fitted.get_coef(self.gev_param_name, AbstractCoordinates.COORDINATE_T)
+ def get_coef(self, estimator, coef_name):
+ return estimator.margin_function_fitted.get_coef(self.gev_param_name, coef_name)
@property
- def stationary_coef(self):
- return self.get_coef(self.stationary_estimator)
+ def non_stationary_intercept_coef(self):
+ return self.get_coef(self.non_stationary_estimator, LinearCoef.INTERCEPT_NAME)
@property
- def non_stationary_coef(self):
- return self.get_coef(self.non_stationary_estimator)
+ def non_stationary_linear_coef(self):
+ return self.get_coef(self.non_stationary_estimator, AbstractCoordinates.COORDINATE_T)
@property
- def ratio_non_stationary_coef(self):
- pass
+ def test_trend_strength(self):
+ return 100 * np.abs(self.non_stationary_linear_coef) / np.abs(self.non_stationary_intercept_coef)
@property
def test_sign(self) -> int:
- return np.sign(self.non_stationary_coef)
+ return np.sign(self.non_stationary_linear_coef)
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 07466f6c..1462db94 100644
--- a/experiment/trend_analysis/univariate_trend_test/abstract_trend_test.py
+++ b/experiment/trend_analysis/univariate_trend_test/abstract_trend_test.py
@@ -31,6 +31,15 @@ class AbstractTrendTest(object):
d[cls.NO_TREND] = 'k--'
return d
+ @classmethod
+ def get_trend_types(cls, trend_type):
+ if trend_type is cls.POSITIVE_TREND:
+ return [cls.POSITIVE_TREND, cls.SIGNIFICATIVE_POSITIVE_TREND]
+ elif trend_type is cls.NEGATIVE_TREND:
+ return [cls.NEGATIVE_TREND, cls.SIGNIFICATIVE_NEGATIVE_TREND]
+ else:
+ return [trend_type]
+
@classmethod
def get_cmap_from_trend_type(cls, trend_type):
if 'positive' in trend_type:
@@ -49,6 +58,10 @@ class AbstractTrendTest(object):
def n(self):
return len(self.years_after_change_point)
+ @property
+ def test_trend_strength(self):
+ return 0.0
+
@property
def test_trend_type(self) -> str:
test_sign = self.test_sign
diff --git a/extreme_estimator/extreme_models/margin_model/margin_function/linear_margin_function.py b/extreme_estimator/extreme_models/margin_model/margin_function/linear_margin_function.py
index 4c81dd5c..4c1b0f2a 100644
--- a/extreme_estimator/extreme_models/margin_model/margin_function/linear_margin_function.py
+++ b/extreme_estimator/extreme_models/margin_model/margin_function/linear_margin_function.py
@@ -83,7 +83,7 @@ class LinearMarginFunction(ParametricMarginFunction):
# Properties for the location parameter
def get_coef(self, gev_param_name, coef_name):
- idx = 1 if coef_name in [AbstractCoordinates.COORDINATE_T] \
+ idx = 1 if coef_name in [AbstractCoordinates.COORDINATE_T, LinearCoef.INTERCEPT_NAME] \
else AbstractCoordinates.COORDINATE_SPATIAL_NAMES.index(coef_name) + 2
return self.coef_dict[LinearCoef.coef_template_str(gev_param_name, coef_name).format(idx)]
--
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