相当于MATLAB的normplot的Python吗? [英] Python equivalent for MATLAB's normplot?

问题描述

是否存在与MATLAB中的normplot类似的python等效函数? 也许在matplotlib中?

Is there a python equivalent function similar to normplot from MATLAB? Perhaps in matplotlib?

MATLAB语法:

x = normrnd(10,1,25,1);
normplot(x)

赠予:

我尝试使用matplotlib& numpy模块确定数组中值的概率/百分位数，但输出图的y轴比例与MATLAB中的图相比是线性的.

I have tried using matplotlib & numpy module to determine the probability/percentile of the values in array but the output plot y-axis scales are linear as compared to the plot from MATLAB.

import numpy as np
import matplotlib.pyplot as plt

data =[-11.83,-8.53,-2.86,-6.49,-7.53,-9.74,-9.44,-3.58,-6.68,-13.26,-4.52]
plot_percentiles = range(0, 110, 10) 

x = np.percentile(data, plot_percentiles)
plt.plot(x, plot_percentiles, 'ro-')
plt.xlabel('Value')
plt.ylabel('Probability')  
plt.show() 

赠予:

否则，如何像第一幅图那样调整比例?

Else, how could the scales be adjusted as in the first plot?

谢谢.

推荐答案

一个较新的答案，但我遇到了同样的问题，找到了一个值得分享的解决方案.我想.

A late answer, but I just came across the same problem and found a solution, that is worth sharing. I guess.

正如joris指出的那样，probplot函数与normplot等效，但是结果分布采用累积密度函数的形式. Scipy.stats还提供了转换这些值的功能.

As joris pointed out the probplot function is an equivalent to normplot, but the resulting distribution is in form of the cumulative density function. Scipy.stats also offers a function, to convert these values.

cdf->百分位数

stats.'distribution function'.cdf(cdf_value)

百分位数-> cdf

percentile -> cdf

stats.'distribution function'.ppf(percentile_value)

例如:

stats.norm.ppf(percentile)

要获得等效的y轴(如normplot)，可以替换cdf-ticks:

To get an equivalent y-axis, like normplot, you can replace the cdf-ticks:

from scipy import stats
import matplotlib.pyplot as plt

nsample=500

#create list of random variables
x=stats.t.rvs(100, size=nsample)

# Calculate quantiles and least-square-fit curve
(quantiles, values), (slope, intercept, r) = stats.probplot(x, dist='norm')

#plot results
plt.plot(values, quantiles,'ob')
plt.plot(quantiles * slope + intercept, quantiles, 'r')

#define ticks
ticks_perc=[1, 5, 10, 20, 50, 80, 90, 95, 99]

#transfrom them from precentile to cumulative density
ticks_quan=[stats.norm.ppf(i/100.) for i in ticks_perc]

#assign new ticks
plt.yticks(ticks_quan,ticks_perc)

#show plot
plt.grid()
plt.show()

结果:

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