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
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)