diff --git a/experiment/meteo_france_SCM_study/main_visualize.py b/experiment/meteo_france_SCM_study/main_visualize.py
index e1a63c51a4dffb5c64f9fd4743713e8b3140f67a..f644c311bd647981eee918b9b9289adf13d15239 100644
--- a/experiment/meteo_france_SCM_study/main_visualize.py
+++ b/experiment/meteo_france_SCM_study/main_visualize.py
@@ -35,8 +35,9 @@ def study_iterator(study_class, only_first_one=False, both_altitude=False, verbo
def extended_visualization():
for study_class in SCM_EXTENDED_STUDIES[:1]:
for study in study_iterator(study_class, only_first_one=True):
- study_visualizer = StudyVisualizer(study, only_first_row=True, save_to_file=False)
- # study_visualizer.visualize_all_kde_graphs()
+ study_visualizer = StudyVisualizer(study, save_to_file=False, only_one_graph=True, vertical_kde_plot=True,
+ year_for_kde_plot=1958)
+ # study_visualizer.visualize_all_mean_and_max_graphs()
study_visualizer.visualize_all_experimental_law()
# for study_class in SCM_EXTENDED_STUDIES[:]:
# for study in study_iterator(study_class, only_first_one=False):
diff --git a/experiment/meteo_france_SCM_study/safran/safran_visualizer.py b/experiment/meteo_france_SCM_study/safran/safran_visualizer.py
index 6d0af70fed45db5614066d46cf23bd0c3c4a7e81..9600169a1128a71773ded5fe0dd064fd0eff6929 100644
--- a/experiment/meteo_france_SCM_study/safran/safran_visualizer.py
+++ b/experiment/meteo_france_SCM_study/safran/safran_visualizer.py
@@ -28,17 +28,22 @@ from utils import get_display_name_from_object_type, VERSION_TIME, float_to_str_
class StudyVisualizer(object):
- def __init__(self, study: AbstractStudy, show=True, save_to_file=False, only_one_graph=False, only_first_row=False):
+ def __init__(self, study: AbstractStudy, show=True, save_to_file=False, only_one_graph=False, only_first_row=False,
+ vertical_kde_plot=False, year_for_kde_plot=None):
self.only_first_row = only_first_row
self.only_one_graph = only_one_graph
self.save_to_file = save_to_file
self.study = study
+ self.plot_name = None
+ # KDE PLOT ARGUMENTS
+ self.vertical_kde_plot=vertical_kde_plot
+ self.year_for_kde_plot = year_for_kde_plot
self.show = False if self.save_to_file else show
self.window_size_for_smoothing = 21
if self.only_one_graph:
self.figsize = (6.0, 4.0)
elif self.only_first_row:
- self.figsize = (16.0, 6.0)
+ self.figsize = (8.0, 6.0)
else:
self.figsize = (16.0, 10.0)
@@ -77,17 +82,22 @@ class StudyVisualizer(object):
def visualize_all_experimental_law(self):
self.visualize_massif_graphs(self.visualize_experimental_law)
- plot_name = ' Empirical distribution with all available data'
- self.show_or_save_to_file(plot_name)
+ self.plot_name = ' Empirical distribution '
+ self.plot_name += 'with all available data' if self.year_for_kde_plot is None else \
+ 'for the year {}'.format(self.year_for_kde_plot)
+ self.show_or_save_to_file()
def visualize_experimental_law(self, ax, massif_id):
# Display the experimental law for a given massif
- all_massif_data = np.concatenate([data[:, massif_id] for data in self.study.year_to_daily_time_serie.values()])
+ if self.year_for_kde_plot is not None:
+ all_massif_data = self.study.year_to_daily_time_serie[self.year_for_kde_plot][:, massif_id]
+ else:
+ all_massif_data = np.concatenate([data[:, massif_id] for data in self.study.year_to_daily_time_serie.values()])
all_massif_data = np.sort(all_massif_data)
# Kde plot, and retrieve the data forming the line
color_kde = 'b'
- sns.kdeplot(all_massif_data, bw=1, ax=ax, color=color_kde).set(xlim=0)
+ sns.kdeplot(all_massif_data, bw=1, ax=ax, color=color_kde, vertical=self.vertical_kde_plot).set(xlim=0)
data_x, data_y = ax.lines[0].get_data()
# Plot the mean point in green
@@ -100,25 +110,33 @@ class StudyVisualizer(object):
x_level_to_color[x_level] = (color, name)
for xi, (color, name) in x_level_to_color.items():
- yi = np.interp(xi, data_x, data_y)
+ if self.vertical_kde_plot:
+ yi = xi
+ xi = np.interp(yi, data_y, data_x)
+ else:
+ yi = np.interp(xi, data_x, data_y)
ax.scatter([xi], [yi], color=color, marker="o", label=name)
- ax.set_ylabel('Probability Density function f(x)', color=color_kde)
+ label_function = ax.set_xlabel if self.vertical_kde_plot else ax.set_ylabel
+ label_function('Probability Density function f(x)', color=color_kde)
+
xlabel = 'x = {}'.format(self.study.title) if self.only_one_graph else 'x'
- ax.set_xlabel(xlabel)
+ label_function = ax.set_ylabel if self.vertical_kde_plot else ax.set_xlabel
+ label_function(xlabel)
extraticks = [float(float_to_str_with_only_some_significant_digits(x, nb_digits=2))
for x in sorted(list(x_level_to_color.keys()))]
if not self.only_one_graph:
extraticks = [extraticks[0], extraticks[-1]]
- ax.set_xticks(extraticks)
+ set_ticks_function = ax.set_yticks if self.vertical_kde_plot else ax.set_xticks
+ set_ticks_function(extraticks)
if not self.only_one_graph:
ax.set_title(self.study.safran_massif_names[massif_id])
ax.legend()
def visualize_all_mean_and_max_graphs(self):
self.visualize_massif_graphs(self.visualize_mean_and_max_graph)
- plot_name = ' mean with sliding window of size {}'.format(self.window_size_for_smoothing)
- self.show_or_save_to_file(plot_name)
+ self.plot_name = ' mean with sliding window of size {}'.format(self.window_size_for_smoothing)
+ self.show_or_save_to_file()
def visualize_mean_and_max_graph(self, ax, massif_id):
# Display the graph of the max on top
@@ -137,12 +155,12 @@ class StudyVisualizer(object):
x, y = list(zip(*tuples_x_y))
x, y = average_smoothing_with_sliding_window(x, y, window_size_for_smoothing=self.window_size_for_smoothing)
ax.plot(x, y, color=color_mean)
- ax.set_ylabel('mean', color=color_mean)
+ ax.set_ylabel('mean with sliding window of size {}'.format(self.window_size_for_smoothing), color=color_mean)
ax.set_xlabel('year')
ax.set_title(self.study.safran_massif_names[massif_id])
def visualize_linear_margin_fit(self, only_first_max_stable=False):
- plot_name = 'Full Likelihood with Linear marginals and max stable dependency structure'
+ self.plot_name = 'Full Likelihood with Linear marginals and max stable dependency structure'
default_covariance_function = CovarianceFunction.cauchy
max_stable_models = load_test_max_stable_models(default_covariance_function=default_covariance_function)
if only_first_max_stable:
@@ -162,7 +180,7 @@ class StudyVisualizer(object):
if isinstance(max_stable_model, AbstractMaxStableModelWithCovarianceFunction):
title += ' ' + str(default_covariance_function).split('.')[-1]
self.fit_and_visualize_estimator(estimator, axes[i], title=title)
- self.show_or_save_to_file(plot_name)
+ self.show_or_save_to_file()
def fit_and_visualize_estimator(self, estimator, axes=None, title=None):
estimator.fit()
@@ -170,18 +188,20 @@ class StudyVisualizer(object):
for ax in axes:
self.study.visualize(ax, fill=False, show=False)
- def show_or_save_to_file(self, plot_name):
+ def show_or_save_to_file(self):
+ assert self.plot_name is not None
title = self.study.title
- title += '\n' + plot_name
- if not self.only_one_graph:
+ title += '\n' + self.plot_name
+ if self.only_one_graph:
+ plt.suptitle(self.plot_name)
+ else:
plt.suptitle(title)
if self.show:
plt.show()
if self.save_to_file:
filename = "{}/{}".format(VERSION_TIME, '_'.join(self.study.title.split()))
if not self.only_one_graph:
- filename += "/{}".format('_'.join(plot_name.split()))
-
+ filename += "/{}".format('_'.join(self.plot_name.split()))
filepath = op.join(self.study.result_full_path, filename + '.png')
dir = op.dirname(filepath)
if not op.exists(dir):
@@ -216,7 +236,7 @@ class StudyVisualizer(object):
plt.show()
def visualize_cmap(self, massif_name_to_value):
- orig_cmap = plt.cm.coolwarm
+ orig_cmap = plt.cm. coolwarm
# shifted_cmap = shiftedColorMap(orig_cmap, midpoint=0.75, name='shifted')
massif_name_to_fill_kwargs = {massif_name: {'color': orig_cmap(value)} for massif_name, value in
diff --git a/experiment/utils.py b/experiment/utils.py
index 8b378946153da54fe801df190371dc99057e66c3..fe3083f5e50360cbd26f77e7a43b2a35a150aeb0 100644
--- a/experiment/utils.py
+++ b/experiment/utils.py
@@ -6,7 +6,8 @@ def average_smoothing_with_sliding_window(x, y, window_size_for_smoothing):
kernel = np.ones(window_size_for_smoothing) / window_size_for_smoothing
y = np.convolve(y, kernel, mode='valid')
assert window_size_for_smoothing % 2 == 1
- nb_to_delete = int(window_size_for_smoothing // 2)
- x = np.array(x)[nb_to_delete:-nb_to_delete]
- assert len(x) == len(y)
+ if window_size_for_smoothing > 1:
+ nb_to_delete = int(window_size_for_smoothing // 2)
+ x = np.array(x)[nb_to_delete:-nb_to_delete]
+ assert len(x) == len(y), "{} vs {}".format(len(x), len(y))
return x, y