Algorithm equalivence from Matlab to Python -

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i've plotted 3-d mesh in matlab below little m-file:

[x,n] = meshgrid(0:0.1:20, 1:1:100);  mu = 0; sigma = sqrt(2)./n;  f = normcdf(x,mu,sigma);  mesh(x,n,f);

i going acquire same result utilization of python , corresponding modules, below code snippet:

import numpy np scipy.integrate import quad import matplotlib.pyplot plt  sigma = 1  def integrand(x, n):     return (n/(2*sigma*np.sqrt(np.pi)))*np.exp(-(n**2*x**2)/(4*sigma**2))  tt = np.linspace(0, 20, 2000) nn = np.linspace(1, 100, 100)    t = np.zeros([len(tt), len(nn)])  i,t in enumerate(tt):     j,n in enumerate(nn):         t[i, j], _ = quad(integrand, -np.inf, t, args=(n,))  x, y = np.mgrid[0:20:0.01, 1:101:1]  plt.pcolormesh(x, y, t)  plt.show()

but output of python is considerably different matlab one, , matter of fact unacceptable. afraid of wrong utilization of functions linespace, enumerate or mgrid...

does have idea about?!...

ps. unfortunately, couldn't insert output plots within thread...!

best

..............................

edit: changed linespace , mgrid intervals , replaced plot_surface method... output 3d suitable accuracy , smoothness...

from see equivalent solution be:

import numpy np scipy.stats import norm import matplotlib.pyplot plt mpl_toolkits.mplot3d import axes3d  x, n = np.mgrid[0:20:0.01, 1:100:1]  mu = 0 sigma = np.sqrt(2)/n  f = norm.cdf(x, mu, sigma)  fig = plt.figure() ax = fig.gca(projection='3d') ax.plot_surface(x, n, f, rstride=x.shape[0]//20, cstride=x.shape[1]//20, alpha=0.3)  plt.show()

unfortunately 3d plotting matplotlib not straight forward matlab.

here plot code: matplotlib result of code


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