DOC: Fixup the percentile methods plot

Author: seberg

numpy/lib/function_base.py

@@ -4084,7 +4084,7 @@ def percentile(a,
     array([7.,  2.])
     >>> assert not np.all(a == b)
 
-    The different types of interpolation can be visualized graphically:
+    The different methods can be visualized graphically:
 
     .. plot::
 
@@ -4094,20 +4094,25 @@ def percentile(a,
         p = np.linspace(0, 100, 6001)
         ax = plt.gca()
         lines = [
-            ('linear', None),
-            ('higher', '--'),
-            ('lower', '--'),
-            ('nearest', '-.'),
-            ('midpoint', '-.'),
-        ]
-        for interpolation, style in lines:
+            ('linear', '-', 'C0'),
+            ('inverted_cdf', ':', 'C1'),
+            # Almost the same as `inverted_cdf`:
+            ('averaged_inverted_cdf', '-.', 'C1'),
+            ('closest_observation', ':', 'C2'),
+            ('interpolated_inverted_cdf', '--', 'C1'),
+            ('hazen', '--', 'C3'),
+            ('weibull', '-.', 'C4'),
+            ('median_unbiased', '--', 'C5'),
+            ('normal_unbiased', '-.', 'C6'),
+            ]
+        for method, style, color in lines:
             ax.plot(
-                p, np.percentile(a, p, interpolation=interpolation),
-                label=interpolation, linestyle=style)
+                p, np.percentile(a, p, method=method),
+                label=method, linestyle=style, color=color)
         ax.set(
-            title='Result for the data: ' + str(a),
+            title='Percentiles for different methods and data: ' + str(a),
             xlabel='Percentile',
-            ylabel='List item returned',
+            ylabel='Estimated percentile value',
             yticks=a)
         ax.legend()
         plt.show()
@@ -4347,6 +4352,8 @@ def quantile(a,
     array([7.,  2.])
     >>> assert not np.all(a == b)
 
+    See also `numpy.percentile` for a visualization of most methods.
+
     References
     ----------
     .. [1] R. J. Hyndman and Y. Fan,