enh : implement a new feature to perform hierarchical clustering on t…

…he correlation matrix before plotting it

mriqc_learn/viz/metrics.py

@@ -184,6 +184,7 @@ def plot_corrmat(
     cbarlabel="",
     symmetric=True,
     figsize=None,
+    sort=False,
     **kwargs,
 ):
     """
@@ -204,14 +205,40 @@ def plot_corrmat(
         A dictionary with arguments to `matplotlib.Figure.colorbar`.  Optional.
     cbarlabel
         The label for the colorbar.  Optional.
+    sort
+        Flag to perform hierachical clustering on the correlation plot
     **kwargs
         All other arguments are forwarded to `imshow`.
 
     """
     from mpl_toolkits.axes_grid1.inset_locator import inset_axes
 
+    # Cluster rows (if arguments enabled)
+    if sort:
+        import pandas as pd
+        from scipy.cluster.hierarchy import linkage, dendrogram, fcluster
+
+        Z = linkage(data, 'complete', optimal_ordering=True)
+
+        dendrogram(Z, labels=data.columns, no_plot=True)
+
+        # Clusterize the data
+        threshold = 0.1
+        labels = fcluster(Z, threshold, criterion='distance')
+        # Keep the indices to sort labels
+        labels_order = np.argsort(labels)
+
+        # Build a new dataframe with the sorted columns
+        for idx, i in enumerate(data.columns[labels_order]):
+            if idx == 0:
+                clustered = pd.DataFrame(data[i])
+            else:
+                df_to_append = pd.DataFrame(data[i])
+                clustered = pd.concat([clustered, df_to_append], axis=1)
+        data = clustered
+
     if hasattr(data, "columns"):
-        col_labels = data.columns.tolist()
+        col_labels = data.columns
         data = data.values
 
     if figsize is not None:
@@ -220,6 +247,7 @@ def plot_corrmat(
     if not ax:
         ax = plt.gca()
 
+    # If matrix is symmetric, keep only lower triangle
     if symmetric:
         data[np.triu(np.ones(data.shape, dtype=bool))] = np.nan
 
@@ -252,7 +280,7 @@ def plot_corrmat(
     ax.tick_params(top=False, bottom=True, labeltop=False, labelbottom=True)
 
     # Rotate the tick labels and set their alignment.
-    plt.setp(ax.get_xticklabels(), rotation=90, ha="right", rotation_mode="anchor")
+    plt.setp(ax.get_xticklabels(), rotation=90, ha="right", va="center", rotation_mode="anchor")
 
     # Turn spines off and create white grid.
     ax.spines[:].set_visible(False)