add fishers transformation; tweak more font sizes

AFQ/viz/utils.py

@@ -509,6 +509,10 @@ def visualize_tract_profiles(tract_profiles, scalar="dti_fa", ylim=None,
     return df
 
 
+def display_scalar(scalar_name):
+    return scalar_name.replace("_", " ").upper()
+
+
 class BrainAxes():
     '''
     Helper class.
@@ -970,7 +974,7 @@ def tract_profiles(self, names=None, scalar="dti_fa",
                 profile = profile[profile['tractID'] == bundle]
                 ba.plot_line(
                     bundle, "nodeID", scalar, profile,
-                    scalar, ylim, n_boot, self._alpha(0.6 + 0.2 * i),
+                    display_scalar(scalar), ylim, n_boot, self._alpha(0.6 + 0.2 * i),
                     {
                         "dashes": [(2**i, 2**i)],
                         "hue": "tractID",
@@ -1219,19 +1223,22 @@ def reliability_plots(self, names=None,
             return None
 
         # extract relevant statistics / data from profiles
+        N = len(self.subjects)
+        err_prime = 1.95/np.sqrt(N-3)
         all_sub_coef = np.zeros((len(scalars), len(self.bundles)))
+        all_sub_coef_err = np.zeros((len(scalars), len(self.bundles), 2))
         all_sub_means = np.zeros(
-            (len(scalars), len(self.bundles), 2, len(self.subjects)))
+            (len(scalars), len(self.bundles), 2, N))
         all_profile_coef = \
-            np.zeros((len(scalars), len(self.bundles), len(self.subjects)))
+            np.zeros((len(scalars), len(self.bundles), N))
         all_node_coef = np.zeros(
             (len(scalars), len(self.bundles), self.prof_len))
         miss_counts = pd.DataFrame(0, index=self.bundles, columns=[
             f"miss_count{names[0]}", f"miss_count{names[1]}"])
         for m, scalar in enumerate(scalars):
             for k, bundle in enumerate(self.bundles):
                 bundle_profiles =\
-                    np.zeros((2, len(self.subjects), self.prof_len))
+                    np.zeros((2, N, self.prof_len))
                 for j, name in enumerate(names):
                     for i, subject in enumerate(self.subjects):
                         single_profile = self._get_profile(
@@ -1245,9 +1252,12 @@ def reliability_plots(self, names=None,
 
                 all_sub_means[m, k] = np.nanmean(bundle_profiles, axis=2)
                 all_sub_coef[m, k] = self.masked_corr(all_sub_means[m, k])
+                corr_prime = np.arctanh(all_sub_coef[m, k])  # uses Fisher Transformation
+                all_sub_coef_err[m, k, 0] = np.tanh(corr_prime + err_prime) - all_sub_coef[m, k]
+                all_sub_coef_err[m, k, 1] = all_sub_coef[m, k] - np.tanh(corr_prime - err_prime)
 
-                bundle_coefs = np.zeros(len(self.subjects))
-                for i in range(len(self.subjects)):
+                bundle_coefs = np.zeros(N)
+                for i in range(N):
                     bundle_coefs[i] = \
                         self.masked_corr(bundle_profiles[:, i, :])
                 all_profile_coef[m, k] = bundle_coefs
@@ -1403,7 +1413,7 @@ def reliability_plots(self, names=None,
         fig, axes = plt.subplots(2, 1)
         fig.set_size_inches((8, 8))
         bundle_prof_means = np.nanmean(all_profile_coef, axis=2)
-        bundle_prof_stds = sem(all_profile_coef, axis=2, nan_policy='omit')
+        bundle_prof_stds = 1.95*sem(all_profile_coef, axis=2, nan_policy='omit')
         if ylims is None:
             maxi = np.maximum(bundle_prof_means.max(), all_sub_coef.max())
             mini = np.minimum(bundle_prof_means.min(), all_sub_coef.min())
@@ -1428,10 +1438,15 @@ def reliability_plots(self, names=None,
                 all_sub_coef,
                 removal_idx,
                 axis=1)
+            all_sub_coef_err_removed = np.delete(
+                all_sub_coef_err,
+                removal_idx,
+                axis=1)
         else:
             is_removed_bundle = [False]*len(self.bundles)
             bundle_prof_means_removed = bundle_prof_means
             bundle_prof_stds_removed = bundle_prof_stds
+            all_sub_coef_err_removed = all_sub_coef_err
             all_sub_coef_removed = all_sub_coef
 
         df_bundle_prof_means = pd.DataFrame(
@@ -1468,6 +1483,7 @@ def reliability_plots(self, names=None,
                 ignore_index=True)
 
         sns.set(style="whitegrid")
+        print(np.transpose([*all_sub_coef_err_removed[m], np.asarray([0, 0])]))
         sns.barplot(
             data=df_bundle_prof_means, x='tractID', y='value', hue='scalar',
             palette=tableau_20_sns[:len(scalars) * 2 + 2:2],
@@ -1477,6 +1493,7 @@ def reliability_plots(self, names=None,
         sns.barplot(
             data=df_all_sub_coef, x='tractID', y='value', hue='scalar',
             palette=tableau_20_sns[:len(scalars) * 2 + 2:2],
+            yerr=np.transpose([*all_sub_coef_err_removed[m], np.asarray([0, 0])]),
             ax=axes[1])
         axes[1].legend_.remove()
 
@@ -1591,9 +1608,17 @@ def compare_reliability(self, reliability_df1, reliability_df2,
             hue='tractID',
             palette=self.color_dict,
             ax=ax)
-        g.set(ylim=(0, 1))
-        g.set(xlim=(0, 1))
-        plt.legend(bbox_to_anchor=(1.1, 1),borderaxespad=0)
+        ax.set_xlabel(f"{analysis_label1} {rtype}",
+                      fontsize=medium_font)
+        ax.set_ylabel(f"{analysis_label2} {rtype}",
+                      fontsize=medium_font)
+        ax.tick_params(
+            axis='x', which='major', labelsize=medium_font)
+        ax.tick_params(
+            axis='y', which='major', labelsize=medium_font)
+        g.set(ylim=(0.2, 1))
+        g.set(xlim=(0.2, 1))
+        plt.legend(loc="lower left", ncol=7, bbox_to_anchor=(0., -0.8))
         ax.plot([[0, 0], [1, 1]], [[0, 0], [1, 1]], '--')
 
         return fig, ax