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As far as I know the option Log=True in the histogram function only refers to the y-axis.
P.hist(d,bins=50,log=True,alpha=0.5,color='b',histtype='step') I need the bins to be equally spaced in log10. Is there something that can do this?
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Description. Plots a log-histogram, as in for example Feiller, Flenley and Olbricht (1992). The intended use of the log-histogram is to examine the fit of a particular density to a set of data, as an alternative to a histogram with a density curve.
Logarithmic binning means something totally different to me: usually in statistical physics, "logarithmic binning" is an algorithm when you want to calculate for example earthquake amplitude distribution (or you can call it a kind of histogram). This distribution yields a power-law fit.
To create a histogram the first step is to create bin of the ranges, then distribute the whole range of the values into a series of intervals, and count the values which fall into each of the intervals. Bins are clearly identified as consecutive, non-overlapping intervals of variables. The matplotlib. pyplot.
use logspace() to create a geometric sequence, and pass it to bins parameter. And set the scale of xaxis to log scale.
import pylab as pl import numpy as np data = np.random.normal(size=10000) pl.hist(data, bins=np.logspace(np.log10(0.1),np.log10(1.0), 50)) pl.gca().set_xscale("log") pl.show() 
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The most direct way is to just compute the log10 of the limits, compute linearly spaced bins, and then convert back by raising to the power of 10, as below:
import pylab as pl import numpy as np data = np.random.normal(size=10000) MIN, MAX = .01, 10.0 pl.figure() pl.hist(data, bins = 10 ** np.linspace(np.log10(MIN), np.log10(MAX), 50)) pl.gca().set_xscale("log") pl.show() 
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