Note

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Draw the empirical CDFΒΆ

In this example we are going to draw the empirical CDF of an unidimensional sample.

import openturns as ot
import openturns.viewer as viewer
from matplotlib import pylab as plt

ot.Log.Show(ot.Log.NONE)

Then create a sample from a Gaussian distribution.

size = 100
normal = ot.Normal(1)
sample = normal.getSample(size)

We draw the empirical CDF based on the UserDefined distribution. By default, the drawCDF method requires no input argument.

distribution = ot.UserDefined(sample)
graph = distribution.drawCDF()
view = viewer.View(graph)
X0 CDF

If required, we can specify the interval that we want to draw. In the following example, these bounds are computed from the minimum and the maximum of the sample.

xmin = sample.getMin()[0] - 2.0
xmax = sample.getMax()[0] + 2.0
graph = ot.UserDefined(sample).drawCDF(xmin, xmax)
view = viewer.View(graph)
plt.show()
X0 CDF