Is there a way to tell matplotlib to "normalize" a histogram such that its area equals a specified value (other than 1)?
The option "normed = 0" in
n, bins, patches = plt.hist(x, 50, normed=0, histtype='stepfilled')
just brings it back to a frequency distribution.
Just calculate it and normalize it to any value you'd like, then use
bar to plot the histogram.
On a side note, this will normalize things such that the area of all the bars is
normed_value. The raw sum will not be
normed_value (though it's easy to have that be the case, if you'd like).
import numpy as np import matplotlib.pyplot as plt x = np.random.random(100) normed_value = 2 hist, bins = np.histogram(x, bins=20, density=True) widths = np.diff(bins) hist *= normed_value plt.bar(bins[:-1], hist, widths) plt.show()
So, in this case, if we were to integrate (sum the height multiplied by the width) the bins, we'd get 2.0 instead of 1.0. (i.e.
(hist * widths).sum() will yield
You can pass a
weights argument to
hist instead of using
normed. For example, if your bins cover the interval
[minval, maxval], you have
n bins, and you want to normalize the area to
A, then I think
weights = np.empty_like(x) weights.fill(A * n / (maxval-minval) / x.size) plt.hist(x, bins=n, range=(minval, maxval), weights=weights)
should do the trick.
weights argument must be the same size as
x, and its effect is to make each value in x contribute the corresponding value in
weights towards the bin count, instead of 1.
I think the
hist function could probably do with a greater ability to control normalization, though. For example, I think as it stands, values outside the binned range are ignored when normalizing, which isn't generally what you want.