Merge b063dfd into 8ff3178

code/stat159lambda/reproduction/analyze_similarity.py

@@ -104,6 +104,18 @@ def save_correlation_percentiles(aggregation):
 
 
 def correlation_brain_image(outside_brain_value):
+    """
+    Uses a mask to plot only the areas that are the brain itself and saves
+    that image into the figures folder.
+
+    Parameters
+    ----------
+    outside_brain_values : int
+
+    Returns
+    -------
+    None
+    """
     if outside_brain_value < 0:
         title = 'light'
     else:

code/stat159lambda/reproduction/brain_mask.py

@@ -7,6 +7,18 @@
 
 
 def plot_vol_mean_histogram(subj_num):
+    """
+    Plots the mean volume for a particular subject and saves that histogram
+    into the figures folder
+
+    Parameters
+    ----------
+    subj_num : int
+
+    Returns
+    -------
+    None
+    """
     data = np.load(dp.get_concatenated_path(subj_num))[...,
                                                        NUM_OFFSET_VOLUMES:]
     mean_vol = np.mean(data, axis=-1)
@@ -18,6 +30,17 @@ def plot_vol_mean_histogram(subj_num):
 
 
 def get_brain_mask():
+    """
+    Retrieves the path to the brain mask and loads that data
+
+    Parameters
+    ----------
+    None
+
+    Returns
+    -------
+    brain_mask : array
+    """
     brain_mask_path = dp.get_brain_mask_path()
     if not exists(brain_mask_path):
         data = np.load(dp.get_concatenated_path(1))
@@ -28,6 +51,18 @@ def get_brain_mask():
 
 
 def plot_brain_mask(slice):
+    """
+    Plots the brain mask at a particular slice and saves that into the figures
+    folder
+
+    Parameters
+    ----------
+    slice : int
+
+    Returns
+    -------
+    None
+    """
     image = get_brain_mask()[:, :, slice]
     plt.imshow(image, cmap='gray')
     plot_path = '{0}/figures/brain_mask.png'.format(REPO_HOME_PATH)