Note
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Create and draw scalar distributions¶
import openturns as ot
import openturns.viewer as viewer
from matplotlib import pylab as plt
ot.Log.Show(ot.Log.NONE)
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 : [ 2.90698 ]
1 : [ 2.4694 ]
2 : [ 2.37417 ]
3 : [ 2.69831 ]
4 : [ 2.28606 ]
5 : [ 2.08412 ]
6 : [ 2.87742 ]
7 : [ 1.80004 ]
8 : [ 1.67943 ]
9 : [ 2.99115 ]
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)
![PDF of a normal distribution with parameters $\mu = 2.2$ and $\sigma = 0.6$](../../_images/sphx_glr_plot_create_and_draw_scalar_distributions_001.png)
graphCDF = distribution.drawCDF()
graphCDF.setTitle(
r"CDF of a normal distribution with parameters $\mu = 2.2$ and $\sigma = 0.6$"
)
view = viewer.View(graphCDF)
![CDF of a normal distribution with parameters $\mu = 2.2$ and $\sigma = 0.6$](../../_images/sphx_glr_plot_create_and_draw_scalar_distributions_002.png)
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 : [ 3 ]
1 : [ 1 ]
2 : [ 1 ]
3 : [ 2 ]
4 : [ 1 ]
5 : [ 2 ]
6 : [ 1 ]
7 : [ 1 ]
8 : [ 1 ]
9 : [ 2 ]
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)
![PDF of a geometric distribution with parameter $p = 0.7$](../../_images/sphx_glr_plot_create_and_draw_scalar_distributions_003.png)
graphCDF = distribution.drawCDF()
graphCDF.setTitle(r"CDF of a geometric distribution with parameter $p = 0.7$")
view = viewer.View(graphCDF)
![CDF of a geometric distribution with parameter $p = 0.7$](../../_images/sphx_glr_plot_create_and_draw_scalar_distributions_004.png)
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()