百分位分布图

Question

有没有人知道如何更改 X 轴比例和刻度以显示如下图所示的百分比分布？此图像来自 MATLAB，但我想使用 Python（通过 Matplotlib 或 Seaborn）生成。

从@paulh 的指针来看，我现在更接近了。这段代码

import matplotlib
matplotlib.use('Agg')

import numpy as np
import matplotlib.pyplot as plt
import probscale
import seaborn as sns

clear_bkgd = {'axes.facecolor':'none', 'figure.facecolor':'none'}
sns.set(style='ticks', context='notebook', palette="muted", rc=clear_bkgd)

fig, ax = plt.subplots(figsize=(8, 4))

x = [30, 60, 80, 90, 95, 97, 98, 98.5, 98.9, 99.1, 99.2, 99.3, 99.4]
y = np.arange(0, 12.1, 1)

ax.set_xlim(40, 99.5)
ax.set_xscale('prob')

ax.plot(x, y)
sns.despine(fig=fig)

生成以下图（注意重新分布的 X 轴）

我发现它比标准量表更有用：

我联系了原始图表的作者，他们给了我一些建议。它实际上是一个对数刻度图，x 轴反转，值为 [100-val]，手动标记 x 轴刻度。下面的代码使用与此处其他图形相同的示例数据重新创建原始图像。

import matplotlib
matplotlib.use('Agg')

import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

clear_bkgd = {'axes.facecolor':'none', 'figure.facecolor':'none'}
sns.set(style='ticks', context='notebook', palette="muted", rc=clear_bkgd)

x = [30, 60, 80, 90, 95, 97, 98, 98.5, 98.9, 99.1, 99.2, 99.3, 99.4]
y = np.arange(0, 12.1, 1)

# Number of intervals to display.
# Later calculations add 2 to this number to pad it to align with the reversed axis
num_intervals = 3
x_values = 1.0 - 1.0/10**np.arange(0,num_intervals+2)

# Start with hard-coded lengths for 0,90,99
# Rest of array generated to display correct number of decimal places as precision increases
lengths = [1,2,2] + [int(v)+1 for v in list(np.arange(3,num_intervals+2))]

# Build the label string by trimming on the calculated lengths and appending %
labels = [str(100*v)[0:l] + "%" for v,l in zip(x_values, lengths)]


fig, ax = plt.subplots(figsize=(8, 4))

ax.set_xscale('log')
plt.gca().invert_xaxis()
# Labels have to be reversed because axis is reversed
ax.xaxis.set_ticklabels( labels[::-1] )

ax.plot([100.0 - v for v in x], y)

ax.grid(True, linewidth=0.5, zorder=5)
ax.grid(True, which='minor', linewidth=0.5, linestyle=':')

sns.despine(fig=fig)

plt.savefig("test.png", dpi=300, format='png')

这是结果图：

Answer 1

这些类型的图表在低延迟社区中很流行，用于绘制延迟分布。在处理延迟时，大多数有趣的信息往往位于较高的百分位中，因此对数视图往往效果更好。我第一次看到这些图表是在https://github.com/giltene/jHiccup和https://github.com/HdrHistogram/中使用的。

引用的图表是由以下代码生成的

n = ceil(log10(length(values)));          
p = 1 - 1./10.^(0:0.01:n);
percentiles = prctile(values, p * 100);
semilogx(1./(1-p), percentiles);

x 轴标有以下代码

labels = cell(n+1, 1);
for i = 1:n+1
  labels{i} = getPercentileLabel(i-1);
end
set(gca, 'XTick', 10.^(0:n));
set(gca, 'XTickLabel', labels);

% {'0%' '90%' '99%' '99.9%' '99.99%' '99.999%' '99.999%' '99.9999%'}
function label = getPercentileLabel(i)
    switch(i)
        case 0
            label = '0%';
        case 1
            label = '90%';
        case 2
            label = '99%';
        otherwise
            label = '99.';
            for k = 1:i-2
                label = [label '9'];
            end
            label = [label '%'];
    end
end