Probability distribution function handling

pyfuse.randomUniform(left=0.0, right=1.0, rnsize=None)

link to uniform sampling of numpy, to remain consistency in names of the pyFUSE module

Parameters:

left: float

lower value

right: float

upper value

rnsize: int

number of samples

See also

numpy.random.uniform

pyfuse.randomTriangular(left=0.0, mode=None, right=1.0, rnsize=None)

link to triangular sampling of numpy, to remain consistency in names of the pyFUSE module

Parameters:

left: float

lower value

mode: float

value between left and right, highest probability

right: float

upper value

rnsize: int

number of samples

See also

numpy.random.triangular

pyfuse.randomTrapezoidal(left=0.0, mode1=None, mode2=None, right=1.0, rnsize=None)

random sampling from trapezoidal function

Parameters:

left: float

lower value

mode1: float

value between left and right, highest probability left side

mode2: float

value between left and right, highest probability right side

right: float

upper value

rnsize: int

number of samples

pyfuse.randomNormal(mu=0.0, sigma=1.0, rnsize=None)

link to sampling of normal distribution of numpy, to remain consistency in names of the pyFUSE module

Parameters:

mu: float

mean value

sigma: float

Standard deviation (spread or ‘width’) of the distribution

rnsize: int

number of samples

See also

numpy.random.normal

pyfuse.randomLogNormal(mu=0.0, sigma=1.0, rnsize=None)

link to sampling of lognormal distribution of numpy, to remain consistency in names of the pyFUSE module

Parameters:

mu: float

Mean value of the underlying normal distribution

sigma: float

Standard deviation of the underlying normal distribution

rnsize: int

number of samples

See also

numpy.random.lognormal