Poisson

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

Bases: sherpa.models.model.RegriddableModel1D

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

[1]	https://www.gnu.org/software/gsl/manual/html_node/QNG-non_002dadaptive-Gauss_002dKronrod-integration.html

Attributes Summary

thawedparhardmaxes
thawedparhardmins
thawedparmaxes
thawedparmins
thawedpars

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, **kwargs)	The class RegriddableModel1D allows the user to evaluate in the requested space then interpolate onto the data space.
reset()
set_center(*args, **kwargs)
startup(cache)	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)

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, **kwargs)

The class RegriddableModel1D allows the user to evaluate in the requested space then interpolate onto the data space. An optional argument ‘interp’ enables the user to change the interpolation method.

Examples

>>> import numpy as np
>>> from sherpa.models.basic import Box1D
>>> mybox = Box1D()
>>> request_space = np.arange(1, 10, 0.1)
>>> regrid_model = mybox.regrid(request_space, interp=linear_interp)

reset()

set_center(*args, **kwargs)

startup(cache)

Called before a model may be evaluated multiple times.

See also

teardown()

teardown()

Called after a model may be evaluated multiple times.

See also

setup()