Model Parameters

class pyfuse.ModPar(name, minval, maxval, optguess, pardistribution, *kargs)

Model Parameter class

The parameter is defined by his distribution, boundaries and optimal guess

Parameters:

name : string

Name of the parameter

minval : float

Minimum value of the parameter distribution

maxval : float

Maximum value of the parameter distribution

optguess : float

Optimal guess of the parameter, must be between min and max value

pardistribution : string

choose a distributionfrom: randomUniform, randomTriangular, randomTrapezoidal, randomNormal, randomLogNormal

*kargs :

Extra arguments necessaty for the chosen distribution

Examples

>>> import pyFUSE as pf
>>> par1=pf.ModPar('parameter1',0.0,3.0,2.5,'randomUniform')
>>> par1.aValue()
>>> 2.20263437200365 #random
>>> par1.optguess
>>> 2.5
>>> par1.pardistribution
>>> 'randomUniform

LatinH(nruns)

Return a sample of nMC samples from the par distribution with Latin Hypercube sampling (always randomUniform)

Parameters:

nruns: int

number of Latin HYpercube samples to take

MCSample(nruns)

Give a sample of nMC samples from the par distribution

Parameters:

nruns: int

number of Monte Carlo samples to take

Returns:

mcsample: array

numpy array with the specified number of Monte Carlo samples

aValue()

Sample 1 value of the pardistribution

ahist(nruns, nbins=30, saveit='show', *args, **kwargs)

Returns a histogram from the current parameter distribution

Parameters:

nruns: int

number of Monte Carlo samples

nbins: int

number of bins to use in the histogram

saveit; ‘show’ or True

if True, figure is saved, otherwise the picture is shown

*args:

matplotlib histogram keyword arguments

**kwargs:

kwargs are used to update the properties of the class:~matplotlib.patches.Patch instances returned by hist

Returns:

fig:

figure with the histogram

set_optguess(value)

Change the optimal guess value of the parameter

Parameters:

value: float

New value for optimal guess

pyfuse.reScale(arr, vmin, vmax)

Rescale the sampled values between 0 and 1 towards the real boundaries of the pars

Parameters:

arr: array

array of the sampled values

vmin: float

minimal value to rescale to

vmax: float

maximum value to rescale to

pyfuse.Sobol(ParsIn, nruns, seed=1)

Return a sobol sampling of the parameter space; Sobol is always performed on the entire set of parameters used in the analysis.

Parameters:

ParsIn: list of ModPar instances

List with all the parameters to sample from

nruns: int

number of samples

seed: int

seed to start from, change this when performing multiple samples or to make sure the values are continued by using the last seed

Returns:

Pars: narray

2D array with the rows the different runs and the pars in the columns