Fix documentation bugs

doc/tests.rst

@@ -32,12 +32,6 @@ avg_std_correlation_test
 
 .. autoclass:: networkunit.tests.avg_std_correlation_test
 
-===================================
-generalized_correlation_matrix_test
-===================================
-
-.. autoclass:: networkunit.tests.generalized_correlation_matrix_test
-
 ===============
 eigenvalue_test
 ===============

networkunit/scores/effect_size.py

@@ -4,6 +4,9 @@
 import matplotlib.pyplot as plt
 
 class effect_size(sciunit.Score):
+    """
+    Calculates the effect size between samples.
+    """
 
     score = np.nan
 

networkunit/scores/students_t.py

@@ -7,11 +7,12 @@
 class students_t(sciunit.Score):
     """
     Student's T-test
-    NOTE: should be named ttest only because if equal_var=False -> Welch's t-test
-    The computation is performed by the scipy.stats.ttest_ind() function.
     """
     score = np.nan
 
+    # TODO: should be named ttest only because if equal_var=False -> Welch's t-test
+    # The computation is performed by the scipy.stats.ttest_ind() function.
+
     @classmethod
     def compute(self, data_sample_1, data_sample_2, equal_var=True, **kwargs):
         # Filter out nans and infs

networkunit/scores/wasserstein_distance.py

@@ -7,26 +7,26 @@
 
 
 class wasserstein_distance(sciunit.Score):
+    """
+    Calculates the Wasserstein distance (Earth mover's distance) between
+    two point clouds. Uses the opencv implementation in the backend and can
+    handle any dimensionality. The observation and prediction must have the
+    same dimensionality.
+
+    norm : string
+        Determines normalization of the input data.
+        * If 'obsv', then all data are normalized based on the observation
+        mean and standard deviation (default)
+        * If 'pred', then the prediction mean and std are used
+        * If 'both', then the observation and prediction are concatenated
+        and the mean and std of the full array are used for normalization.
+    """
     score = np.nan
     _best = 0.
     _worst = np.nan_to_num(np.inf)
 
     @classmethod
     def compute(self, observation, prediction, norm='both', **kwargs):
-        """
-        Calculates the Wasserstein distance (Earth mover's distance) between
-        two point clouds. Uses the opencv implementation in the backend and can
-        handle any dimensionality. The observation and prediction must have the
-        same dimensionality.
-
-        norm : string
-            Determines normalization of the input data.
-            * If 'obsv', then all data are normalized based on the observation
-            mean and standard deviation (default)
-            * If 'pred', then the prediction mean and std are used
-            * If 'both', then the observation and prediction are concatenated
-            and the mean and std of the full array are used for normalization.
-        """
         if type(observation) == list:
             observation = np.array(observation)
         if type(prediction) == list: