> From: MONTAGU Thierry [mailto:[email protected]]
> Sent: Friday, May 21, 2010 09:37
>
> has anyone ever tried to make a quantile-quantile plot with pylab?
> is there any build in function named say "qqplot" available ?
For a plot comparing samples to a theoretical distribution (and if you don't
need masking as in Paul's example), you might be able to use
scipy.stats.probplot, as follows:
import matplotlib.pyplot as plt
import scipy.stats as st
values = st.norm.rvs(size=(100,)) # example data
fig = plt.figure() # set up plot
ax = fig.add_subplot(1, 1, 1)
osm, osr = st.probplot(values, fit=0, dist='norm') # compute
ax.plot(osm, osr, '.') # plot
One way to include the fit line is
(osm, osr), (m, b, r) = st.probplot(values, dist='norm') # compute
osmf = osm.take([0, -1]) # endpoints
osrf = m * osmf + b # fit line
ax.plot(osm, osr, '.', osmf, osrf, '-')
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