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18 | 18 | import numpy as np
|
19 | 19 |
|
20 | 20 |
|
21 |
| -# functions to calculate percentiles and adjacent values |
22 |
| -def percentile(vals, p): |
23 |
| - N = len(vals) |
24 |
| - n = p*(N+1) |
25 |
| - k = int(n) |
26 |
| - d = n-k |
27 |
| - if k <= 0: |
28 |
| - return vals[0] |
29 |
| - if k >= N: |
30 |
| - return vals[N-1] |
31 |
| - return vals[k-1] + d*(vals[k] - vals[k-1]) |
32 |
| - |
33 |
| - |
34 |
| -def adjacent_values(vals): |
35 |
| - q1 = percentile(vals, 0.25) |
36 |
| - q3 = percentile(vals, 0.75) |
37 |
| - iqr = q3 - q1 # inter-quartile range |
38 |
| - |
39 |
| - # upper adjacent values |
40 |
| - uav = q3 + iqr * 1.5 |
41 |
| - if uav > vals[-1]: |
42 |
| - uav = vals[-1] |
43 |
| - if uav < q3: |
44 |
| - uav = q3 |
45 |
| - |
46 |
| - # lower adjacent values |
47 |
| - lav = q1 - iqr * 1.5 |
48 |
| - if lav < vals[0]: |
49 |
| - lav = vals[0] |
50 |
| - if lav > q1: |
51 |
| - lav = q1 |
52 |
| - return [lav, uav] |
| 21 | +def adjacent_values(vals, q1, q3): |
| 22 | + upper_adjacent_value = q3 + (q3 - q1) * 1.5 |
| 23 | + upper_adjacent_value = np.clip(upper_adjacent_value, q3, vals[-1]) |
| 24 | + |
| 25 | + lower_adjacent_value = q1 - (q3 - q1) * 1.5 |
| 26 | + lower_adjacent_value = np.clip(lower_adjacent_value, vals[0], q1) |
| 27 | + return lower_adjacent_value, upper_adjacent_value |
| 28 | + |
| 29 | + |
| 30 | +def set_axis_style(ax, labels): |
| 31 | + ax.get_xaxis().set_tick_params(direction='out') |
| 32 | + ax.xaxis.set_ticks_position('bottom') |
| 33 | + ax.set_xticks(np.arange(1, len(labels) + 1)) |
| 34 | + ax.set_xticklabels(labels) |
| 35 | + ax.set_xlim(0.25, len(labels) + 0.75) |
| 36 | + ax.set_xlabel('Sample name') |
53 | 37 |
|
54 | 38 |
|
55 | 39 | # create test data
|
56 | 40 | np.random.seed(123)
|
57 |
| -dat = [np.random.normal(0, std, 100) for std in range(1, 5)] |
58 |
| -lab = ['A', 'B', 'C', 'D'] # labels |
59 |
| -med = [] # medians |
60 |
| -iqr = [] # inter-quantile ranges |
61 |
| -avs = [] # upper and lower adjacent values |
62 |
| -for arr in dat: |
63 |
| - sarr = sorted(arr) |
64 |
| - med.append(percentile(sarr, 0.5)) |
65 |
| - iqr.append([percentile(sarr, 0.25), percentile(sarr, 0.75)]) |
66 |
| - avs.append(adjacent_values(sarr)) |
67 |
| - |
68 |
| -# plot the violins |
69 |
| -fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(9, 4), |
70 |
| - sharey=True) |
71 |
| -_ = ax1.violinplot(dat) |
72 |
| -parts = ax2.violinplot(dat, showmeans=False, showmedians=False, |
73 |
| - showextrema=False) |
| 41 | +data = [sorted(np.random.normal(0, std, 100)) for std in range(1, 5)] |
| 42 | + |
| 43 | +fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(9, 4), sharey=True) |
74 | 44 |
|
75 | 45 | ax1.set_title('Default violin plot')
|
76 |
| -ax2.set_title('Customized violin plot') |
| 46 | +ax1.set_ylabel('Observed values') |
| 47 | +ax1.violinplot(data) |
77 | 48 |
|
78 |
| -# plot whiskers as thin lines, quartiles as fat lines, |
79 |
| -# and medians as points |
80 |
| -for i in range(len(med)): |
81 |
| - # whiskers |
82 |
| - ax2.plot([i + 1, i + 1], avs[i], '-', color='black', linewidth=1) |
83 |
| - ax2.plot([i + 1, i + 1], iqr[i], '-', color='black', linewidth=5) |
84 |
| - ax2.plot(i + 1, med[i], 'o', color='white', |
85 |
| - markersize=6, markeredgecolor='none') |
| 49 | +ax2.set_title('Customized violin plot') |
| 50 | +parts = ax2.violinplot( |
| 51 | + data, showmeans=False, showmedians=False, |
| 52 | + showextrema=False) |
86 | 53 |
|
87 |
| -# customize colors |
88 | 54 | for pc in parts['bodies']:
|
89 | 55 | pc.set_facecolor('#D43F3A')
|
90 | 56 | pc.set_edgecolor('black')
|
91 | 57 | pc.set_alpha(1)
|
92 | 58 |
|
93 |
| -ax1.set_ylabel('Observed values') |
| 59 | +quartile1, medians, quartile3 = np.percentile(data, [25, 50, 75], axis=1) |
| 60 | +whiskers = np.array([ |
| 61 | + adjacent_values(sorted_array, q1, q3) |
| 62 | + for sorted_array, q1, q3 in zip(data, quartile1, quartile3)]) |
| 63 | +whiskersMin, whiskersMax = whiskers[:, 0], whiskers[:, 1] |
| 64 | + |
| 65 | +inds = np.arange(1, len(medians) + 1) |
| 66 | +ax2.scatter(inds, medians, marker='o', color='white', s=30, zorder=3) |
| 67 | +ax2.vlines(inds, quartile1, quartile3, color='k', linestyle='-', lw=5) |
| 68 | +ax2.vlines(inds, whiskersMin, whiskersMax, color='k', linestyle='-', lw=1) |
| 69 | + |
| 70 | +# set style for the axes |
| 71 | +labels = ['A', 'B', 'C', 'D'] |
94 | 72 | for ax in [ax1, ax2]:
|
95 |
| - ax.get_xaxis().set_tick_params(direction='out') |
96 |
| - ax.xaxis.set_ticks_position('bottom') |
97 |
| - ax.set_xticks(np.arange(1, len(lab) + 1)) |
98 |
| - ax.set_xticklabels(lab) |
99 |
| - ax.set_xlim(0.25, len(lab) + 0.75) |
100 |
| - ax.set_xlabel('Sample name') |
| 73 | + set_axis_style(ax, labels) |
101 | 74 |
|
102 | 75 | plt.subplots_adjust(bottom=0.15, wspace=0.05)
|
103 |
| - |
104 | 76 | plt.show()
|
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