[SCM][HYPERCUBE] refactor hypercube_visualization folder.

experiment/meteo_france_SCM_study/visualization/hypercube_visualization/abstract_hypercube_visualizer.py

+import os
+import os.path as op
+from typing import Dict, Tuple
+
+import matplotlib.pyplot as plt
+import pandas as pd
+
+from experiment.meteo_france_SCM_study.visualization.study_visualization.study_visualizer import StudyVisualizer
+from utils import cached_property, VERSION_TIME, get_display_name_from_object_type
+
+
+class AbstractHypercubeVisualizer(object):
+    """
+    A study visualizer contain some massifs and years. This forms the base DataFrame of the hypercube
+    Additional index will come from the tuple.
+    Tuple could contain altitudes, type of snow quantity
+    """
+
+    def __init__(self, tuple_to_study_visualizer: Dict[Tuple, StudyVisualizer],
+                 trend_test_class,
+                 fast=False,
+                 save_to_file=False):
+        self.nb_data_for_fast_mode = 7 if fast else None
+        self.save_to_file = save_to_file
+        self.trend_test_class = trend_test_class
+        self.tuple_to_study_visualizer = tuple_to_study_visualizer  # type: Dict[Tuple, StudyVisualizer]
+
+    # Main attributes defining the hypercube
+
+    @property
+    def trend_test_name(self):
+        return get_display_name_from_object_type(self.trend_test_class)
+
+    @cached_property
+    def starting_years(self):
+        starting_years = self.study_visualizer.starting_years
+        if self.nb_data_for_fast_mode is not None:
+            starting_years = starting_years[:self.nb_data_for_fast_mode]
+        return starting_years
+
+    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_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()]
+        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)
+
+    # Some properties
+
+    @property
+    def study_title(self):
+        return self.study.title
+
+    def show_or_save_to_file(self, specific_title=''):
+        if self.save_to_file:
+            main_title, *_ = '_'.join(self.study_title.split()).split('/')
+            filename = "{}/{}/".format(VERSION_TIME, main_title)
+            filename += specific_title
+            filepath = op.join(self.study.result_full_path, filename + '.png')
+            dirname = op.dirname(filepath)
+            if not op.exists(dirname):
+                os.makedirs(dirname, exist_ok=True)
+            plt.savefig(filepath)
+        else:
+            plt.show()
+
+    @property
+    def study_visualizer(self) -> StudyVisualizer:
+        return list(self.tuple_to_study_visualizer.values())[0]
+
+    @property
+    def study(self):
+        return self.study_visualizer.study
+
+    @property
+    def starting_year_to_weights(self):
+        # Load uniform weights by default
+        uniform_weight = 1 / len(self.starting_years)
+        return {year: uniform_weight for year in self.starting_years}
+
+
+
+

experiment/meteo_france_SCM_study/visualization/hypercube_visualization/hypercube_visualizer.py → experiment/meteo_france_SCM_study/visualization/hypercube_visualization/altitude_hypercube_visualizer.py

-import os
-import os.path as op
-from typing import Dict, Tuple
-
 import matplotlib.pyplot as plt
 import pandas as pd
 
+from experiment.meteo_france_SCM_study.visualization.hypercube_visualization.abstract_hypercube_visualizer import \
+    AbstractHypercubeVisualizer
 from experiment.meteo_france_SCM_study.visualization.study_visualization.study_visualizer import StudyVisualizer
 from experiment.trend_analysis.univariate_trend_test.abstract_trend_test import AbstractTrendTest
-from utils import cached_property, VERSION_TIME, get_display_name_from_object_type
-
-
-class HypercubeVisualizer(object):
-    """
-    A study visualizer contain some massifs and years. This forms the base DataFrame of the hypercube
-    Additional index will come from the tuple.
-    Tuple could contain altitudes, type of snow quantity
-    """
-
-    def __init__(self, tuple_to_study_visualizer: Dict[Tuple, StudyVisualizer],
-                 trend_test_class,
-                 fast=False,
-                 save_to_file=False):
-        self.nb_data_for_fast_mode = 7 if fast else None
-        self.save_to_file = save_to_file
-        self.trend_test_class = trend_test_class
-        self.tuple_to_study_visualizer = tuple_to_study_visualizer  # type: Dict[Tuple, StudyVisualizer]
-
-    # Main attributes defining the hypercube
-
-    @property
-    def trend_test_name(self):
-        return get_display_name_from_object_type(self.trend_test_class)
-
-    @cached_property
-    def starting_years(self):
-        starting_years = self.study_visualizer.starting_years
-        if self.nb_data_for_fast_mode is not None:
-            starting_years = starting_years[:self.nb_data_for_fast_mode]
-        return starting_years
-
-    @cached_property
-    def tuple_to_df_trend_type(self):
-        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 dict(zip(self.tuple_to_study_visualizer.keys(), df_spatio_temporal_trend_types))
-
-    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_hypercube(self) -> pd.DataFrame:
-        keys = list(self.tuple_to_df_trend_type.keys())
-        values = list(self.tuple_to_df_trend_type.values())
-        df = pd.concat(values, keys=keys, axis=0)
-        return df
+from utils import get_display_name_from_object_type
 
-    # Some properties
 
-    @property
-    def study_title(self):
-        return self.study.title
-
-    def show_or_save_to_file(self, specific_title=''):
-        if self.save_to_file:
-            main_title, *_ = '_'.join(self.study_title.split()).split('/')
-            filename = "{}/{}/".format(VERSION_TIME, main_title)
-            filename += specific_title
-            filepath = op.join(self.study.result_full_path, filename + '.png')
-            dirname = op.dirname(filepath)
-            if not op.exists(dirname):
-                os.makedirs(dirname, exist_ok=True)
-            plt.savefig(filepath)
-        else:
-            plt.show()
-
-    @property
-    def study_visualizer(self) -> StudyVisualizer:
-        return list(self.tuple_to_study_visualizer.values())[0]
-
-    @property
-    def study(self):
-        return self.study_visualizer.study
-
-    @property
-    def starting_year_to_weights(self):
-        # Load uniform weights by default
-        uniform_weight = 1 / len(self.starting_years)
-        return {year: uniform_weight for year in self.starting_years}
-
-
-class AltitudeHypercubeVisualizer(HypercubeVisualizer):
+class AltitudeHypercubeVisualizer(AbstractHypercubeVisualizer):
 
     @property
     def altitudes(self):
@@ -109,7 +26,7 @@ class AltitudeHypercubeVisualizer(HypercubeVisualizer):
         # Map each trend type to its serie with percentages
         trend_type_to_s_percentages = {}
         for trend_type in self.trend_types:
-            df_bool = (self.df_hypercube == trend_type)
+            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)
@@ -125,10 +42,8 @@ class AltitudeHypercubeVisualizer(HypercubeVisualizer):
 
     def subtitle_to_reduction_function(self, reduction_function, level=None, add_detailed_plot=False):
         def reduction_function_with_level(df_bool):
-            if level is None:
-                return reduction_function(df_bool)
-            else:
-                return reduction_function(df_bool, level)
+            return reduction_function(df_bool) if level is None else reduction_function(df_bool, level)
+
         return {'global': reduction_function_with_level}
 
     def visualize_trend_test_evolution(self, reduction_function, xlabel, xlabel_values, ax=None, marker='o',
@@ -214,10 +129,10 @@ class AltitudeHypercubeVisualizer(HypercubeVisualizer):
             # Take the mean with respect to the years
             df_bool = df_bool.mean(axis=1)
             # Take the mean with respect the massifs
-            print(df_bool.head())
             return df_bool.mean(level=level)
 
-        for subtitle, reduction_function in self.subtitle_to_reduction_function(altitude_reduction, level=self.altitude_index_level,
+        for subtitle, reduction_function in self.subtitle_to_reduction_function(altitude_reduction,
+                                                                                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,
@@ -230,51 +145,7 @@ class AltitudeHypercubeVisualizer(HypercubeVisualizer):
             # Take the mean with respect the altitude
             return df_bool.mean(level=level)
 
-        for subtitle, reduction_function in self.subtitle_to_reduction_function(massif_reduction,level=self.massif_index_level,
+        for subtitle, reduction_function in self.subtitle_to_reduction_function(massif_reduction,
+                                                                                level=self.massif_index_level,
                                                                                 add_detailed_plot=add_detailed_plots).items():
             self.visualize_trend_test_repartition(reduction_function, axes, subtitle=subtitle)
-
-
-class QuantityAltitudeHypercubeVisualizer(AltitudeHypercubeVisualizer):
-
-    @property
-    def study_title(self):
-        return 'Quantity Altitude Study'
-
-    def subtitle_to_reduction_function(self, reduction_function, level=None, add_detailed_plot=False):
-        subtitle_to_reduction_function = super().subtitle_to_reduction_function(reduction_function, level, add_detailed_plot)
-
-        def get_function_from_tuple(tuple_for_axis_0):
-            def f(df_bool: pd.DataFrame):
-                # Loc with a tuple with respect the axis 0
-                df_bool = df_bool.loc[tuple_for_axis_0, :].copy()
-                # Apply the reduction function
-                if level is None:
-                    return reduction_function(df_bool)
-                else:
-                    return reduction_function(df_bool, level-1)
-
-            return f
-
-        # Add the detailed plot, taken by loc with respect to the first index
-        if add_detailed_plot:
-            tuples_axis_0 = self.tuple_values(idx=0)
-            for tuple_axis_0 in tuples_axis_0:
-                subtitle_to_reduction_function[tuple_axis_0] = get_function_from_tuple(tuple_axis_0)
-        return subtitle_to_reduction_function
-
-    @property
-    def quantities(self):
-        return self.tuple_values(idx=0)
-
-    @property
-    def altitudes(self):
-        return self.tuple_values(idx=1)
-
-    @property
-    def altitude_index_level(self):
-        return 1
-
-    @property
-    def massif_index_level(self):
-        return 2

experiment/meteo_france_SCM_study/visualization/hypercube_visualization/main_hypercube_visualization.py

@@ -2,8 +2,10 @@ import time
 from itertools import product
 from collections import OrderedDict
 
-from experiment.meteo_france_SCM_study.visualization.hypercube_visualization.hypercube_visualizer import \
-    AltitudeHypercubeVisualizer, QuantityAltitudeHypercubeVisualizer
+from experiment.meteo_france_SCM_study.visualization.hypercube_visualization.altitude_hypercube_visualizer import \
+    AltitudeHypercubeVisualizer
+from experiment.meteo_france_SCM_study.visualization.hypercube_visualization.quantity_altitude_visualizer import \
+    QuantityAltitudeHypercubeVisualizer
 from experiment.meteo_france_SCM_study.visualization.study_visualization.main_study_visualizer import ALL_ALTITUDES, \
     SCM_STUDIES, study_iterator, study_iterator_global
 from experiment.meteo_france_SCM_study.visualization.study_visualization.study_visualizer import StudyVisualizer
@@ -90,9 +92,9 @@ 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__':

experiment/meteo_france_SCM_study/visualization/hypercube_visualization/quantity_altitude_visualizer.py

+import pandas as pd
+
+from experiment.meteo_france_SCM_study.visualization.hypercube_visualization.altitude_hypercube_visualizer import \
+    AltitudeHypercubeVisualizer
+
+
+class QuantityAltitudeHypercubeVisualizer(AltitudeHypercubeVisualizer):
+
+    @property
+    def study_title(self):
+        return 'Quantity Altitude Study'
+
+    def subtitle_to_reduction_function(self, reduction_function, level=None, add_detailed_plot=False):
+        subtitle_to_reduction_function = super().subtitle_to_reduction_function(reduction_function,
+                                                                                level, add_detailed_plot)
+
+        def get_function_from_tuple(tuple_for_axis_0):
+            def f(df_bool: pd.DataFrame):
+                # Loc with a tuple with respect the axis 0
+                df_bool = df_bool.loc[tuple_for_axis_0, :].copy()
+                # Apply the reduction function
+                return reduction_function(df_bool) if level is None else reduction_function(df_bool, level-1)
+            return f
+
+        # Add the detailed plot, taken by loc with respect to the first index
+        if add_detailed_plot:
+            tuples_axis_0 = self.tuple_values(idx=0)
+            for tuple_axis_0 in tuples_axis_0:
+                subtitle_to_reduction_function[tuple_axis_0] = get_function_from_tuple(tuple_axis_0)
+        return subtitle_to_reduction_function
+
+    @property
+    def quantities(self):
+        return self.tuple_values(idx=0)
+
+    @property
+    def altitudes(self):
+        return self.tuple_values(idx=1)
+
+    @property
+    def altitude_index_level(self):
+        return 1
+
+    @property
+    def massif_index_level(self):
+        return 2

experiment/meteo_france_SCM_study/visualization/studies_visualization/main_studies_visualizer.py

 import time
-
-from experiment.meteo_france_SCM_study.visualization.hypercube_visualization.hypercube_visualizer import \
-    AltitudeHypercubeVisualizer
 from experiment.trend_analysis.abstract_score import MannKendall, WeigthedScore, MeanScore, MedianScore
 from experiment.trend_analysis.univariate_trend_test.abstract_gev_trend_test import GevLocationTrendTest, \
     GevScaleTrendTest, GevShapeTrendTest
@@ -11,7 +8,7 @@ from experiment.meteo_france_SCM_study.visualization.studies_visualization.studi
 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, SCM_STUDIES, study_iterator
+    study_iterator_global, SCM_STUDIES
 
 from experiment.meteo_france_SCM_study.visualization.study_visualization.study_visualizer import StudyVisualizer
 from collections import OrderedDict