Is there a python equivalent function similar to normplot from MATLAB?
Perhaps in matplotlib?
MATLAB syntax:
x = normrnd(10,1,25,1);
normplot(x)
Gives:
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 pltdata =[-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()
Gives:
Else, how could the scales be adjusted as in the first plot?
Thanks.
A late answer, but I just came across the same problem and found a solution, that is worth sharing. I guess.
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 -> percentile
stats.'distribution function'.cdf(cdf_value)
percentile -> cdf
stats.'distribution function'.ppf(percentile_value)
for example:
stats.norm.ppf(percentile)
To get an equivalent y-axis, like normplot, you can replace the cdf-ticks:
from scipy import stats
import matplotlib.pyplot as pltnsample=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()
The result:
