# Poisson¶

class sherpa.models.basic.Poisson(name='poisson')[source]

One-dimensional Poisson function.

A model expressing the ratio of two Poisson distributions of mean mu, one for which the random variable is x, and the other for which the random variable is equal to mu itself.

mean

The mean of the first distribution.

ampl

The amplitude of the model.

Notes

The functional form of the model for points is:

f(x) = ampl * mean! exp((x - mean) * log(mean)) / x!


The grid version is evaluated by numerically intgerating the function over each bin using a non-adaptive Gauss-Kronrod scheme suited for smooth functions [1], falling over to a simple trapezoid scheme if this fails.

References

Attributes Summary

Methods Summary

 apply(outer, *otherargs, **otherkwargs) calc(pars, xlo, *args, **kwargs) get_center() guess(dep, *args, **kwargs) Set an initial guess for the parameter values. regrid(*arrays) reset() set_center(*args, **kwargs) startup() Called before a model may be evaluated multiple times. teardown() Called after a model may be evaluated multiple times.

Attributes Documentation

thawedparhardmaxes
thawedparhardmins
thawedparmaxes
thawedparmins
thawedpars

Methods Documentation

apply(outer, *otherargs, **otherkwargs)
calc(pars, xlo, *args, **kwargs)
get_center()
guess(dep, *args, **kwargs)[source]

Set an initial guess for the parameter values.

Attempt to set the parameter values, and ranges, for the model to match the data values. This is intended as a rough guess, so it is expected that the model is only evaluated a small number of times, if at all.

regrid(*arrays)
reset()
set_center(*args, **kwargs)
startup()

Called before a model may be evaluated multiple times.

teardown()

Called after a model may be evaluated multiple times.

setup()