Create and draw scalar distributions

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

A continuous distribution

We build a Normal distribution with parameters:

distribution = ot.Normal(2.2, 0.6)
print(distribution)

Normal(mu = 2.2, sigma = 0.6)

We can draw a sample following this distribution with the getSample method :

size = 10
sample = distribution.getSample(size)
print(sample)

    [ X0       ]
0 : [ 2.56492  ]
1 : [ 1.4403   ]
2 : [ 1.93704  ]
3 : [ 2.92329  ]
4 : [ 0.891169 ]
5 : [ 2.41003  ]
6 : [ 1.987    ]
7 : [ 3.06235  ]
8 : [ 2.6864   ]
9 : [ 2.67589  ]

We draw its PDF and CDF :

graphPDF = distribution.drawPDF()
graphPDF.setTitle(
    r"PDF of a normal distribution with parameters $\mu = 2.2$ and $\sigma = 0.6$"
)
view = viewer.View(graphPDF)

graphCDF = distribution.drawCDF()
graphCDF.setTitle(
    r"CDF of a normal distribution with parameters $\mu = 2.2$ and $\sigma = 0.6$"
)
view = viewer.View(graphCDF)

A discrete distribution

We define a geometric distribution with parameter .

p = 0.7
distribution = ot.Geometric(p)
print(distribution)

Geometric(p = 0.7)

We draw a sample of it :

size = 10
sample = distribution.getSample(size)
print(sample)

    [ X0 ]
0 : [ 1  ]
1 : [ 1  ]
2 : [ 1  ]
3 : [ 2  ]
4 : [ 3  ]
5 : [ 1  ]
6 : [ 2  ]
7 : [ 1  ]
8 : [ 4  ]
9 : [ 1  ]

We draw its PDF and its CDF :

graphPDF = distribution.drawPDF()
graphPDF.setTitle(r"PDF of a geometric distribution with parameter $p = 0.7$")
view = viewer.View(graphPDF)

graphCDF = distribution.drawCDF()
graphCDF.setTitle(r"CDF of a geometric distribution with parameter $p = 0.7$")
view = viewer.View(graphCDF)

Conclusion

The two previous examples look very similar despite their continuous and discrete nature. In the library there is no distinction between continuous and discrete distributions.

Display all figures

plt.show()