Question

I have this histogram which counts the array “d” in equally log-spaced bins.

 max_val=np.log10(max(d))
 min_val=np.log10(min(d))
 logspace = np.logspace(min_val, max_val, 50) 


 hist(d,bins=logspace,label='z='+str(redshift),histtype='step')
 show()

The problem is that I want it to be normalized so as the area is one. Using the option Normed=True I didn’t get the result, it might be due to fact that I’m using logarithmic bins. Therefore I tried normalizing the histogram in this way:

 H=hist(d,bins=logspace,label='z='+str(redshift),histtype='step')
 H_norm=H[0]/my_norm_constant

But then I don’t know how to plot H_norm versus the bins

Answer 1

I tried normed=True, and the area is 1:

from pylab import *
d = np.random.normal(loc=20, size=10000)
max_val=np.log10(max(d))
min_val=np.log10(min(d))
logspace = np.logspace(min_val, max_val, 50) 


r = hist(d,bins=logspace,histtype='step', normed=True)
print "area":, sum(np.diff(r[1])*r[0])

can you run the code, and check the output. If it is not 1, check your numpy version. I got this warning message when run the code:

C:\Python26\lib\site-packages\matplotlib\axes.py:7680: UserWarning:
This release fixes a normalization bug in the NumPy histogram
function prior to version 1.5, occuring with non-uniform
bin widths. The returned and plotted value is now a density:
n / (N * bin width),
where n is the bin count and N the total number of points.

to plot the graph yourself:

step(r[1][1:], r[0]/my_norm_constant)