Use np.quantile instead of percentile

sasoripathos · sasoripathos · commit a3912a93e15e · 2019-05-06T21:14:46.000-04:00
diff --git a/examples/statistics/violinplot.py b/examples/statistics/violinplot.py
@@ -45,12 +45,13 @@
 
 axes[0, 3].violinplot(data, pos, points=60, widths=0.7, showmeans=True,
                       showextrema=True, showmedians=True, bw_method=0.5,
-                      percentiles=[[10], [], [], [17.5, 95.4], [75], [25]])
+                      percentiles=[[0.1], [], [], [0.175, 0.954], [0.75],
+                                   [0.25]])
 axes[0, 3].set_title('Custom violinplot 4', fontsize=fs)
 
 axes[0, 4].violinplot(data[-1:], pos[-1:], points=60, widths=0.7,
                       showmeans=True, showextrema=True, showmedians=True,
-                      percentiles=[5, 10, 80, 90], bw_method=0.5)
+                      percentiles=[0.05, 0.1, 0.8, 0.9], bw_method=0.5)
 axes[0, 4].set_title('Custom violinplot 5', fontsize=fs)
 
 axes[1, 0].violinplot(data, pos, points=80, vert=False, widths=0.7,
@@ -69,13 +70,14 @@
 
 axes[1, 3].violinplot(data, pos, points=200, vert=False, widths=1.1,
                       showmeans=True, showextrema=True, showmedians=True,
-                      percentiles=[[10], [], [], [17.5, 95.4], [75], [25]],
+                      percentiles=[[0.1], [], [], [0.175, 0.954], [0.75],
+                                   [0.25]],
                       bw_method=0.5)
 axes[1, 3].set_title('Custom violinplot 9', fontsize=fs)
 
 axes[1, 4].violinplot(data[-1:], pos[-1:], points=200, vert=False, widths=1.1,
                       showmeans=True, showextrema=True, showmedians=True,
-                      percentiles=[5, 10, 80, 90], bw_method=0.5)
+                      percentiles=[0.05, 0.1, 0.8, 0.9], bw_method=0.5)
 axes[1, 4].set_title('Custom violinplot 10', fontsize=fs)
 
 for ax in axes.flat:
diff --git a/lib/matplotlib/axes/_axes.py b/lib/matplotlib/axes/_axes.py
@@ -7917,9 +7917,9 @@ def violinplot(self, dataset, positions=None, vert=True, widths=0.5,
           If `True`, will toggle rendering of the medians.
 
         percentiles : array-like, default=None
-          If not None, set a list of floats in interval [0, 100] for each
-          violin, which stands for the percentiles that will be rendered for
-          that violin.
+          If not None, set a list of floats in interval [0, 1] for each violin,
+          which stands for the percentiles that will be rendered for that
+          violin.
 
         points : scalar, default = 100
           Defines the number of points to evaluate each of the
diff --git a/lib/matplotlib/cbook/__init__.py b/lib/matplotlib/cbook/__init__.py
@@ -1456,10 +1456,10 @@ def violin_stats(X, method, points=100, percentiles=None):
         density estimates at.
 
     percentiles : array-like, default = None
-        Defines (if not None) a list of floats in interval [0, 100] for each
+        Defines (if not None) a list of floats in interval [0, 1] for each
         column of data, which represents the percentiles that will be rendered
         for that column of data. Must have 2 or fewer dimensions. 1D array will
-        be returned in a singleton list containing them.
+        be treated as a singleton list containing them.
 
     Returns
     -------
@@ -1506,7 +1506,7 @@ def violin_stats(X, method, points=100, percentiles=None):
         # Calculate basic stats for the distribution
         min_val = np.min(x)
         max_val = np.max(x)
-        p_val = np.percentile(x, p)
+        p_val = np.quantile(x, p)
 
         # Evaluate the kernel density estimate
         coords = np.linspace(min_val, max_val, points)
diff --git a/lib/matplotlib/tests/test_axes.py b/lib/matplotlib/tests/test_axes.py
@@ -2662,7 +2662,7 @@ def test_vert_violinplot_showall():
     data = [np.random.normal(size=100) for i in range(4)]
     ax.violinplot(data, positions=range(4), showmeans=1, showextrema=1,
                   showmedians=1,
-                  percentiles=[[10, 90], [20, 80], [30, 70], [40, 60]])
+                  percentiles=[[0.1, 0.9], [0.2, 0.8], [0.3, 0.7], [0.4, 0.6]])
 
 
 @image_comparison(baseline_images=['violinplot_vert_custompoints_10'],
@@ -2740,7 +2740,7 @@ def test_horiz_violinplot_showall():
     data = [np.random.normal(size=100) for i in range(4)]
     ax.violinplot(data, positions=range(4), vert=False, showmeans=1,
                   showextrema=1, showmedians=1,
-                  percentiles=[[10, 90], [20, 80], [30, 70], [40, 60]])
+                  percentiles=[[0.1, 0.9], [0.2, 0.8], [0.3, 0.7], [0.4, 0.6]])
 
 
 @image_comparison(baseline_images=['violinplot_horiz_custompoints_10'],
@@ -2791,7 +2791,7 @@ def test_violinplot_bad_percentiles():
 
     # Different size percentile list and plots
     with pytest.raises(ValueError):
-        ax.violinplot(data, percentiles=[[10, 20], [50]])
+        ax.violinplot(data, percentiles=[[0.1, 0.2], [0.5]])
 
 
 def test_violinplot_outofrange_percentiles():
@@ -2802,11 +2802,11 @@ def test_violinplot_outofrange_percentiles():
 
     # Percentile value above 100
     with pytest.raises(ValueError):
-        ax.violinplot(data, percentiles=[[10, 20, 30, 105]])
+        ax.violinplot(data, percentiles=[[0.1, 0.2, 0.3, 1.05]])
 
     # Percentile value below 0
     with pytest.raises(ValueError):
-        ax.violinplot(data, percentiles=[[-5, 20, 30, 75]])
+        ax.violinplot(data, percentiles=[[-0.05, 0.2, 0.3, 0.75]])
 
 
 @check_figures_equal(extensions=["png"])
@@ -2818,11 +2818,11 @@ def test_violinplot_single_list_percentiles(fig_test, fig_ref):
 
     # Test image
     ax = fig_test.subplots()
-    ax.violinplot(data, percentiles=[10, 30, 90])
+    ax.violinplot(data, percentiles=[0.1, 0.3, 0.9])
 
     # Reference image
     ax = fig_ref.subplots()
-    ax.violinplot(data, percentiles=[[10, 30, 90]])
+    ax.violinplot(data, percentiles=[[0.1, 0.3, 0.9]])
 
 
 def test_manage_xticks():
