Here is an example, this does something a extra at the end but shows how the
bins can be used.
Regards
Alex Baker.
from scipy.stats import norm
r = norm.rvs(size=10000)
import numpy as np
p, bins = np.histogram(r, width, normed=True)
db = bins[1]-bins[0]
cdf = np.cumsum(p*db)
from pylab import figure, show
fig = figure()
ax = fig.add_subplot(111)
ax.bar(bins[:-1], cdf, width=0.8*db)
show()
o = []
rates = []
for r in np.arange(0, max(bins), db):
G = max(np.cumsum([bin for bin in bins if bin > r]))
L = min(np.cumsum([bin for bin in bins if bin < r]))
o.append(abs(G/L))
rates.append(r)
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2009/8/27 Tim Michelsen <timmichel...@gmx-topmail.de>
> Hello,
> I need some advice on histograms.
> If I interpret the documentation [1, 2] for numpy.histogram correctly, the
> result of the function is a count of the occurences sorted into each bin.
>
> (n, bins) = numpy.histogram(v, bins=50, normed=1)
>
> But how can I apply another function on these values stacked in each bin?
> Like summing them up or building averages?
>
> Thanks,
> Timmie
>
> [1]
> http://docs.scipy.org/doc/numpy/reference/generated/numpy.histogram.html
> [2]
>
> http://www.scipy.org/Tentative_NumPy_Tutorial#head-aa75ec76530ff51a2e98071adb7224a4b793519e
>
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