Python:面积归一化为 1 以外的值的直方图 [英] Python: Histogram with area normalized to something other than 1

问题描述

有没有办法告诉 matplotlib 对直方图进行标准化"，使其面积等于指定值(不是 1)?

Is there a way to tell matplotlib to "normalize" a histogram such that its area equals a specified value (other than 1)?

选项中的normed = 0"

The option "normed = 0" in

n, bins, patches = plt.hist(x, 50, normed=0, histtype='stepfilled')

只是将其带回频率分布.

just brings it back to a frequency distribution.

推荐答案

只需计算它并将其标准化为您想要的任何值，然后使用 bar 绘制直方图.

Just calculate it and normalize it to any value you'd like, then use bar to plot the histogram.

顺便说一句，这将规范化所有条形的 area 是 normed_value.原始总和将不是normed_value(尽管如果您愿意，很容易做到这一点).

On a side note, this will normalize things such that the area of all the bars is normed_value. The raw sum will not be normed_value (though it's easy to have that be the case, if you'd like).

例如

import numpy as np
import matplotlib.pyplot as plt

x = np.random.random(100)
normed_value = 2

hist, bins = np.histogram(x, bins=20, density=True)
widths = np.diff(bins)
hist *= normed_value

plt.bar(bins[:-1], hist, widths)
plt.show()

因此，在这种情况下，如果我们要对 bin 进行积分(将高度乘以宽度相加)，我们将得到 2.0 而不是 1.0.(即 (hist * widths).sum() 将产生 2.0)

So, in this case, if we were to integrate (sum the height multiplied by the width) the bins, we'd get 2.0 instead of 1.0. (i.e. (hist * widths).sum() will yield 2.0)

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