matplotlib
diff --git a/‎examples/statistics/customized_violin_demo.py
Copy file name to clipboardExpand all lines: examples/statistics/customized_violin_demo.py
+39-67Lines changed: 39 additions & 67 deletions b/‎examples/statistics/customized_violin_demo.py
Copy file name to clipboardExpand all lines: examples/statistics/customized_violin_demo.py
+39-67Lines changed: 39 additions & 67 deletions
@@ -18,87 +18,59 @@
 import numpy as np
 
 
-# functions to calculate percentiles and adjacent values
-def percentile(vals, p):
-    N = len(vals)
-    n = p*(N+1)
-    k = int(n)
-    d = n-k
-    if k <= 0:
-        return vals[0]
-    if k >= N:
-        return vals[N-1]
-    return vals[k-1] + d*(vals[k] - vals[k-1])
-
-
-def adjacent_values(vals):
-    q1 = percentile(vals, 0.25)
-    q3 = percentile(vals, 0.75)
-    iqr = q3 - q1  # inter-quartile range
-
-    # upper adjacent values
-    uav = q3 + iqr * 1.5
-    if uav > vals[-1]:
-        uav = vals[-1]
-    if uav < q3:
-        uav = q3
-
-    # lower adjacent values
-    lav = q1 - iqr * 1.5
-    if lav < vals[0]:
-        lav = vals[0]
-    if lav > q1:
-        lav = q1
-    return [lav, uav]
+def adjacent_values(vals, q1, q3):
+    upper_adjacent_value = q3 + (q3 - q1) * 1.5
+    upper_adjacent_value = np.clip(upper_adjacent_value, q3, vals[-1])
+
+    lower_adjacent_value = q1 - (q3 - q1) * 1.5
+    lower_adjacent_value = np.clip(lower_adjacent_value, vals[0], q1)
+    return lower_adjacent_value, upper_adjacent_value
+
+
+def set_axis_style(ax, labels):
+    ax.get_xaxis().set_tick_params(direction='out')
+    ax.xaxis.set_ticks_position('bottom')
+    ax.set_xticks(np.arange(1, len(labels) + 1))
+    ax.set_xticklabels(labels)
+    ax.set_xlim(0.25, len(labels) + 0.75)
+    ax.set_xlabel('Sample name')
 
 
 # create test data
 np.random.seed(123)
-dat = [np.random.normal(0, std, 100) for std in range(1, 5)]
-lab = ['A', 'B', 'C', 'D']    # labels
-med = []    # medians
-iqr = []    # inter-quantile ranges
-avs = []    # upper and lower adjacent values
-for arr in dat:
-    sarr = sorted(arr)
-    med.append(percentile(sarr, 0.5))
-    iqr.append([percentile(sarr, 0.25), percentile(sarr, 0.75)])
-    avs.append(adjacent_values(sarr))
-
-# plot the violins
-fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(9, 4),
-                               sharey=True)
-_ = ax1.violinplot(dat)
-parts = ax2.violinplot(dat, showmeans=False, showmedians=False,
-                       showextrema=False)
+data = [sorted(np.random.normal(0, std, 100)) for std in range(1, 5)]
+
+fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(9, 4), sharey=True)
 
 ax1.set_title('Default violin plot')
-ax2.set_title('Customized violin plot')
+ax1.set_ylabel('Observed values')
+ax1.violinplot(data)
 
-# plot whiskers as thin lines, quartiles as fat lines,
-# and medians as points
-for i in range(len(med)):
-    # whiskers
-    ax2.plot([i + 1, i + 1], avs[i], '-', color='black', linewidth=1)
-    ax2.plot([i + 1, i + 1], iqr[i], '-', color='black', linewidth=5)
-    ax2.plot(i + 1, med[i], 'o', color='white',
-             markersize=6, markeredgecolor='none')
+ax2.set_title('Customized violin plot')
+parts = ax2.violinplot(
+        data, showmeans=False, showmedians=False,
+        showextrema=False)
 
-# customize colors
 for pc in parts['bodies']:
     pc.set_facecolor('#D43F3A')
     pc.set_edgecolor('black')
     pc.set_alpha(1)
 
-ax1.set_ylabel('Observed values')
+quartile1, medians, quartile3 = np.percentile(data, [25, 50, 75], axis=1)
+whiskers = np.array([
+    adjacent_values(sorted_array, q1, q3)
+    for sorted_array, q1, q3 in zip(data, quartile1, quartile3)])
+whiskersMin, whiskersMax = whiskers[:, 0], whiskers[:, 1]
+
+inds = np.arange(1, len(medians) + 1)
+ax2.scatter(inds, medians, marker='o', color='white', s=30, zorder=3)
+ax2.vlines(inds, quartile1, quartile3, color='k', linestyle='-', lw=5)
+ax2.vlines(inds, whiskersMin, whiskersMax, color='k', linestyle='-', lw=1)
+
+# set style for the axes
+labels = ['A', 'B', 'C', 'D']
 for ax in [ax1, ax2]:
-    ax.get_xaxis().set_tick_params(direction='out')
-    ax.xaxis.set_ticks_position('bottom')
-    ax.set_xticks(np.arange(1, len(lab) + 1))
-    ax.set_xticklabels(lab)
-    ax.set_xlim(0.25, len(lab) + 0.75)
-    ax.set_xlabel('Sample name')
+    set_axis_style(ax, labels)
 
 plt.subplots_adjust(bottom=0.15, wspace=0.05)
-
 plt.show()