Renamed vp_coverage baseline images, added bw_method paramater to violinplot and updated demo

Author: khchan

examples/statistics/violinplot_demo.py

@@ -11,42 +11,33 @@
 pos = [1,2,4,5,7,8]
 data = [np.random.normal(size=100) for i in pos]
 
-# TODO: future customizability dicts go here
-
-# (From boxplot demo)
-# demonstrate how to customize the display different elements: 
-# boxprops = dict(linestyle='--', linewidth=3, color='darkgoldenrod')
-# flierprops = dict(marker='o', markerfacecolor='green', markersize=12,
-#                   linestyle='none')
-# medianprops = dict(linestyle='-.', linewidth=2.5, color='firebrick')
-# meanpointprops = dict(marker='D', markeredgecolor='black',
-#                       markerfacecolor='firebrick')
-# meanlineprops = dict(linestyle='--', linewidth=2.5, color='purple')
-
 fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(6,6))
 
-axes[0, 0].violinplot(data, pos, points=20, widths=0.1, showmeans=True,
-                      showextrema=True, showmedians=True)
+axes[0, 0].violinplot(data, pos, points=20, widths=0.1, 
+                      showmeans=True, showextrema=True, showmedians=True)
 axes[0, 0].set_title('Custom violinplot 1', fontsize=fs)
 
-axes[0, 1].violinplot(data, pos, points=40, widths=0.3, showmeans=True,
-                      showextrema=True, showmedians=True)
+axes[0, 1].violinplot(data, pos, points=40, widths=0.3, 
+                      showmeans=True, showextrema=True, showmedians=True,
+                      bw_method='silverman')
 axes[0, 1].set_title('Custom violinplot 2', fontsize=fs)
 
 axes[0, 2].violinplot(data, pos, points=60, widths=0.5, showmeans=True,
-                      showextrema=True, showmedians=True)
+                      showextrema=True, showmedians=True, bw_method=0.5)
 axes[0, 2].set_title('Custom violinplot 3', fontsize=fs)
 
 axes[1, 0].violinplot(data, pos, points=80, vert=False, widths=0.7,
                       showmeans=True, showextrema=True, showmedians=True)
 axes[1, 0].set_title('Custom violinplot 4', fontsize=fs)
 
 axes[1, 1].violinplot(data, pos, points=100, vert=False, widths=0.9,
-                      showmeans=True, showextrema=True, showmedians=True)
+                      showmeans=True, showextrema=True, showmedians=True,
+                      bw_method='silverman')
 axes[1, 1].set_title('Custom violinplot 5', fontsize=fs)
 
 axes[1, 2].violinplot(data, pos, points=200, vert=False, widths=1.1,
-                      showmeans=True, showextrema=True, showmedians=True)
+                      showmeans=True, showextrema=True, showmedians=True,
+                      bw_method=0.5)
 axes[1, 2].set_title('Custom violinplot 6', fontsize=fs)
 
 for ax in axes.flatten():

lib/matplotlib/axes/_axes.py

@@ -6727,7 +6727,7 @@ def matshow(self, Z, **kwargs):
 
     def violinplot(self, dataset, positions=None, vert=True, widths=0.5,
                    showmeans=False, showextrema=True, showmedians=False,
-                   points=100):
+                   points=100, bw_method=None):
         """
         Make a violin plot.
 
@@ -6774,6 +6774,13 @@ def violinplot(self, dataset, positions=None, vert=True, widths=0.5,
             Defines the number of points to evaluate each of the gaussian
             kernel density estimations at.
 
+          bw_method : str, scalar or callable, optional
+            The method used to calculate the estimator bandwidth.  This can be
+            'scott', 'silverman', a scalar constant or a callable.  If a
+            scalar, this will be used directly as `kde.factor`.  If a 
+            callable, it should take a `GaussianKDE` instance as only 
+            parameter and return a scalar. If None (default), 'scott' is used.
+
         Returns
         -------
 
@@ -6852,7 +6859,7 @@ def violinplot(self, dataset, positions=None, vert=True, widths=0.5,
         # Render violins
         for data, pos, width in zip(dataset, positions, widths):
             # Calculate the kernel density
-            kde = mlab.GaussianKDE(data)
+            kde = mlab.GaussianKDE(data, bw_method)
             min_val = kde.dataset.min()
             max_val = kde.dataset.max()
             mean = np.mean(kde.dataset)

lib/matplotlib/mlab.py

@@ -3669,32 +3669,41 @@ class GaussianKDE(object):
     dataset : array_like
         Datapoints to estimate from. In case of univariate data this is a 1-D
         array, otherwise a 2-D array with shape (# of dims, # of data).
-    bw_method : str or scalar, optional
+
+    bw_method : str, scalar or callable, optional
         The method used to calculate the estimator bandwidth.  This can be
-        'scott', 'silverman', or a scalar constant.  If a scalar, this will
-        be used directly as `kde.factor`. If None (default), 'scott' is used.
+        'scott', 'silverman', a scalar constant or a callable.  If a
+        scalar, this will be used directly as `kde.factor`.  If a 
+        callable, it should take a `GaussianKDE` instance as only 
+        parameter and return a scalar. If None (default), 'scott' is used.
 
     Attributes
     ----------
     dataset : ndarray
         The dataset with which `gaussian_kde` was initialized.
+
     dim : int
         Number of dimensions.
+
     num_dp : int
         Number of datapoints.
+
     factor : float
         The bandwidth factor, obtained from `kde.covariance_factor`, with which
         the covariance matrix is multiplied.
+
     covariance : ndarray
         The covariance matrix of `dataset`, scaled by the calculated bandwidth
         (`kde.factor`).
+
     inv_cov : ndarray
         The inverse of `covariance`.
 
     Methods
     -------
     kde.evaluate(points) : ndarray
         Evaluate the estimated pdf on a provided set of points.
+        
     kde(points) : ndarray
         Same as kde.evaluate(points)