# Probability distributions based on SciPy¶

In this example, we seek to create a probability distribution based on the SciPy library.

from __future__ import annotations

from gemseo import configure_logger
from gemseo.uncertainty import create_distribution
from gemseo.uncertainty import get_available_distributions

configure_logger()

<RootLogger root (INFO)>


First of all, we can access the names of the available probability distributions from the API:

all_distributions = get_available_distributions()
all_distributions

['OTComposedDistribution', 'OTDiracDistribution', 'OTDistribution', 'OTExponentialDistribution', 'OTNormalDistribution', 'OTTriangularDistribution', 'OTUniformDistribution', 'OTWeibullDistribution', 'SPComposedDistribution', 'SPDistribution', 'SPExponentialDistribution', 'SPNormalDistribution', 'SPTriangularDistribution', 'SPUniformDistribution', 'SPWeibullDistribution']


and filter the ones based on the SciPy library (their names start with the acronym ‘SP’):

sp_distributions = get_available_distributions("SPDistribution")
sp_distributions

['SPDistribution', 'SPExponentialDistribution', 'SPNormalDistribution', 'SPTriangularDistribution', 'SPUniformDistribution', 'SPWeibullDistribution']


## Create a distribution¶

Then, we can create a probability distribution for a two-dimensional random variable with independent components that follow a normal distribution.

### Case 1: the SciPy distribution has a GEMSEO class¶

For the standard normal distribution (mean = 0 and standard deviation = 1):

distribution_0_1 = create_distribution("x", "SPNormalDistribution", 2)
distribution_0_1

norm[2](mu=0.0, sigma=1.0)


For a normal with mean = 1 and standard deviation = 2:

distribution_1_2 = create_distribution(
"x", "SPNormalDistribution", 2, mu=1.0, sigma=2.0
)
distribution_1_2

norm[2](mu=1.0, sigma=2.0)


### Case 2: the SciPy distribution has no GEMSEO class¶

When GEMSEO does not offer a class for the SciPy distribution, we can use the generic GEMSEO class SPDistribution to create any SciPy distribution by setting interfaced_distribution to its SciPy name and parameters as a dictionary of SciPy parameter names and values (see the documentation of SciPy).

distribution_1_2 = create_distribution(
"x",
"SPDistribution",
2,
interfaced_distribution="norm",
parameters={"loc": 1.0, "scale": 2.0},
)
distribution_1_2

norm[2](loc=1.0, scale=2.0)


## Plot the distribution¶

We can plot both cumulative and probability density functions for the first marginal:

distribution_0_1.plot()

<Figure size 640x320 with 2 Axes>


Note

We can provide a marginal index as first argument of the Distribution.plot() method but in the current version of GEMSEO, all components have the same distributions and so the plot will be the same.

## Get statistics¶

### Mean¶

We can access the mean of the distribution:

distribution_0_1.mean

array([0., 0.])


### Standard deviation¶

We can access the standard deviation of the distribution:

distribution_0_1.standard_deviation

array([1., 1.])


### Numerical range¶

We can access the range, i.e. the difference between the numerical minimum and maximum, of the distribution:

distribution_0_1.range

[array([-7.03448383,  7.03448691]), array([-7.03448383,  7.03448691])]


### Mathematical support¶

We can access the range, i.e. the difference between the minimum and maximum, of the distribution:

distribution_0_1.support

[array([-inf,  inf]), array([-inf,  inf])]


## Compute CDF¶

We can compute the cumulative density function component per component (here the probability that the first component is lower than 0. and that the second one is lower than 1.):

distribution_0_1.compute_cdf([0.0, 1.0])

array([0.5       , 0.84134475])


## Compute inverse CDF¶

We can compute the inverse cumulative density function component per component (here the quantile at 50% for the first component and the quantile at 97.5% for the second one):

distribution_0_1.compute_inverse_cdf([0.5, 0.975])

array([0.        , 1.95996398])


## Generate samples¶

We can generate 10 samples of the distribution:

distribution_0_1.compute_samples(10)

array([[ 1.27208414, -1.34835352],
[ 0.25609371, -0.12737194],
[-0.24924166, -0.08644922],
[ 0.90707346,  0.44694913],
[-0.60255455, -0.02787926],
[-0.08714685, -0.29432329],
[ 0.60522988,  1.97986948],
[ 0.05872685, -0.29765557],
[-0.21300853,  0.23752916],
[-0.17590181,  0.40000415]])


Total running time of the script: (0 minutes 0.186 seconds)

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