[SCM] tight subplots for max stable display for advisors'meeting

experiment/meteo_france_SCM_study/abstract_study.py

@@ -138,9 +138,7 @@ class AbstractStudy(object):
                 massif_name = self.coordinate_id_to_massif_name[coordinate_id]
                 fill_kwargs = massif_name_to_fill_kwargs[massif_name] if massif_name_to_fill_kwargs is not None else {}
                 ax.fill(*l, **fill_kwargs)
-        ax.scatter(self.massifs_coordinates.x_coordinates, self.massifs_coordinates.y_coordinates)
-        ax.axis('off')
-
+        ax.scatter(self.massifs_coordinates.x_coordinates, self.massifs_coordinates.y_coordinates, s=1)
         if show:
             plt.show()
 

experiment/meteo_france_SCM_study/crocus/crocus.py

@@ -20,11 +20,13 @@ class Crocus(AbstractStudy):
         suffix = '' if self.altitude == 2400 else ' average of data observed every 6 hours'
         return super().variable_name + suffix
 
+
 class CrocusSwe(Crocus):
 
     def __init__(self, altitude=1800):
         super().__init__(CrocusSweVariable, altitude)
 
+
 class ExtendedCrocusSwe(AbstractExtendedStudy, CrocusSwe):
     pass
 

experiment/meteo_france_SCM_study/main_visualize.py

@@ -35,9 +35,9 @@ def study_iterator(study_class, only_first_one=False, both_altitude=False, verbo
 def extended_visualization():
     save_to_file = True
     only_first_one = True
-    for study_class in SCM_EXTENDED_STUDIES[:1]:
+    for study_class in SCM_EXTENDED_STUDIES[:]:
         for study in study_iterator(study_class, only_first_one=only_first_one):
-            study_visualizer = StudyVisualizer(study, save_to_file=save_to_file, only_one_graph=True, year_for_kde_plot=1958, vertical_kde_plot=True)
+            study_visualizer = StudyVisualizer(study, save_to_file=save_to_file, only_one_graph=True, plot_block_maxima_quantiles=True)
             # study_visualizer.visualize_all_mean_and_max_graphs()
             study_visualizer.visualize_all_experimental_law()
     # for study_class in SCM_EXTENDED_STUDIES[:]:
@@ -54,7 +54,7 @@ def normal_visualization():
         for study in study_iterator(study_class, only_first_one=only_first_one):
             study_visualizer = StudyVisualizer(study, save_to_file=save_to_file)
             # study_visualizer.visualize_independent_margin_fits(threshold=[None, 20, 40, 60][0])
-            study_visualizer.visualize_linear_margin_fit(only_first_max_stable=only_first_one)
+            study_visualizer.visualize_linear_margin_fit(only_first_max_stable=False)
 
 
 def complete_analysis(only_first_one=False):
@@ -73,6 +73,6 @@ def complete_analysis(only_first_one=False):
 
 
 if __name__ == '__main__':
-    # normal_visualization()
-    extended_visualization()
+    normal_visualization()
+    # extended_visualization()
     # complete_analysis()

experiment/meteo_france_SCM_study/safran/safran_visualizer.py

@@ -43,14 +43,18 @@ class StudyVisualizer(object):
         self.year_for_kde_plot = year_for_kde_plot
         self.plot_block_maxima_quantiles = plot_block_maxima_quantiles
 
-        self.show = False if self.save_to_file else show
         self.window_size_for_smoothing = 21
+
+        # PLOT ARGUMENTS
+        self.show = False if self.save_to_file else show
         if self.only_one_graph:
             self.figsize = (6.0, 4.0)
         elif self.only_first_row:
             self.figsize = (8.0, 6.0)
         else:
             self.figsize = (16.0, 10.0)
+        self.subplot_space = 0.05
+        self.coef_zoom_map = 0
 
     @property
     def observations(self):
@@ -74,7 +78,7 @@ class StudyVisualizer(object):
             nb_columns = 5
             nb_rows = 1 if self.only_first_row else math.ceil(len(self.study.safran_massif_names) / nb_columns)
             fig, axes = plt.subplots(nb_rows, nb_columns, figsize=self.figsize)
-            fig.subplots_adjust(hspace=1.0, wspace=1.0)
+            fig.subplots_adjust(hspace=self.subplot_space, wspace=self.subplot_space)
             if self.only_first_row:
                 for massif_id, massif_name in enumerate(self.study.safran_massif_names[:nb_columns]):
                     ax = axes[massif_id]
@@ -149,13 +153,16 @@ class StudyVisualizer(object):
         xlabel = 'x = {}'.format(self.study.title) if self.only_one_graph else 'x'
         label_function = ax.set_ylabel if self.vertical_kde_plot else ax.set_xlabel
         label_function(xlabel)
-        sorted_x_levels = sorted(list([x_level for x_level, _ in name_to_xlevel_and_color.values()]))
 
         # Take all the ticks
+        # sorted_x_levels = sorted(list([x_level for x_level, _ in name_to_xlevel_and_color.values()]))
         # extraticks = [float(float_to_str_with_only_some_significant_digits(x, nb_digits=2))
         #               for x in sorted_x_levels]
         # Display only some specific ticks
-        extraticks = [name_to_xlevel_and_color['mean'][0], name_to_xlevel_and_color[AbstractParams.QUANTILE_100][0]]
+        extraticks_names = ['mean', AbstractParams.QUANTILE_100]
+        if self.plot_block_maxima_quantiles:
+            extraticks_names += [name for name in name_to_xlevel_and_color.keys() if BLOCK_MAXIMA_DISPLAY_NAME in name]
+        extraticks = [name_to_xlevel_and_color[name][0] for name in extraticks_names]
 
         set_ticks_function = ax.set_yticks if self.vertical_kde_plot else ax.set_xticks
         # Round up the ticks with a given number of significative digits
@@ -192,13 +199,15 @@ class StudyVisualizer(object):
         ax.set_title(self.study.safran_massif_names[massif_id])
 
     def visualize_linear_margin_fit(self, only_first_max_stable=False):
-        self.plot_name = 'Full Likelihood with Linear marginals and max stable dependency structure'
         default_covariance_function = CovarianceFunction.cauchy
+        plot_name = 'Full Likelihood with Linear marginals and max stable dependency structure'
+        plot_name += '\n(with {} covariance structure when a covariance is needed)'.format(str(default_covariance_function).split('.')[-1])
+        self.plot_name = plot_name
         max_stable_models = load_test_max_stable_models(default_covariance_function=default_covariance_function)
         if only_first_max_stable:
             max_stable_models = max_stable_models[:1]
         fig, axes = plt.subplots(len(max_stable_models) + 1, len(GevParams.SUMMARY_NAMES), figsize=self.figsize)
-        fig.subplots_adjust(hspace=1.0, wspace=1.0)
+        fig.subplots_adjust(hspace=self.subplot_space, wspace=self.subplot_space)
         margin_class = LinearAllParametersAllDimsMarginModel
         # Plot the smooth margin only
         margin_model = margin_class(coordinates=self.coordinates)
@@ -209,16 +218,44 @@ class StudyVisualizer(object):
             margin_model = margin_class(coordinates=self.coordinates)
             estimator = FullEstimatorInASingleStepWithSmoothMargin(self.dataset, margin_model, max_stable_model)
             title = get_display_name_from_object_type(type(max_stable_model))
-            if isinstance(max_stable_model, AbstractMaxStableModelWithCovarianceFunction):
-                title += ' ' + str(default_covariance_function).split('.')[-1]
+            # if isinstance(max_stable_model, AbstractMaxStableModelWithCovarianceFunction):
+            #     title += ' ' + str(default_covariance_function).split('.')[-1]
             self.fit_and_visualize_estimator(estimator, axes[i], title=title)
+        # Add the label
         self.show_or_save_to_file()
 
     def fit_and_visualize_estimator(self, estimator, axes=None, title=None):
         estimator.fit()
-        axes = estimator.margin_function_fitted.visualize_function(show=False, axes=axes, title=title)
+
+        margin_fct = estimator.margin_function_fitted
+        axes = margin_fct.visualize_function(show=False, axes=axes, title='')
+
+        def get_lim_array(ax):
+            return np.array([np.array(ax.get_xlim()), np.array(ax.get_ylim())])
+
         for ax in axes:
+            old_lim = get_lim_array(ax)
             self.study.visualize(ax, fill=False, show=False)
+            new_lim = get_lim_array(ax)
+            assert 0 <= self.coef_zoom_map <= 1
+            updated_lim = new_lim * self.coef_zoom_map + (1 - self.coef_zoom_map) * old_lim
+            for i, method in enumerate([ax.set_xlim, ax.set_ylim]):
+                method(updated_lim[i, 0], updated_lim[i, 1])
+            ax.tick_params(axis=u'both', which=u'both', length=0)
+            ax.spines['top'].set_visible(False)
+            ax.spines['right'].set_visible(False)
+            ax.spines['bottom'].set_visible(False)
+            ax.spines['left'].set_visible(False)
+            ax.get_xaxis().set_visible(False)
+            ax.get_yaxis().set_visible(False)
+            ax.set_aspect('equal')
+        ax0 = axes[0]
+        ax0.get_yaxis().set_visible(True)
+        # todo: manage to remove ticks on ylabel
+        # finally it's good because it differntiate it from the other labels
+        #  maybe i could put it in bold
+        ax0.set_ylabel(title)
+        ax0.tick_params(axis=u'both', which=u'both', length=0)
 
     def show_or_save_to_file(self):
         assert self.plot_name is not None
@@ -252,7 +289,7 @@ class StudyVisualizer(object):
 
         if axes is None:
             fig, axes = plt.subplots(1, len(params_names))
-            fig.subplots_adjust(hspace=1.0, wspace=1.0)
+            fig.subplots_adjust(hspace=self.subplot_space, wspace=self.subplot_space)
 
         for i, gev_param_name in enumerate(params_names):
             ax = axes[i]

extreme_estimator/margin_fits/plot/create_shifted_cmap.py

@@ -30,7 +30,7 @@ def plot_extreme_param(ax, gev_param_name, values):
     shifted_cmap = shiftedColorMap(cmap, midpoint=midpoint, name='shifted')
     norm = mpl.colors.Normalize(vmin=vmin, vmax=vmax)
     divider = make_axes_locatable(ax)
-    cax = divider.append_axes('right', size='5%', pad=0.05)
+    cax = divider.append_axes('right', size='5%', pad=0.03)
     cb = cbar.ColorbarBase(cax, cmap=shifted_cmap, norm=norm)
     cb.set_label(gev_param_name)
     return norm, shifted_cmap