Polynom1D

class sherpa.models.basic.Polynom1D(name='polynom1d')

Bases: sherpa.models.model.RegriddableModel1D

One-dimensional polynomial function of order 8.

The maximum order of the polynomial is 8. The default setting has all parameters frozen except for c0, which means that the model acts as a constant.

c0

The constant term.

c1

The amplitude of the (x-offset) term.

c2

The amplitude of the (x-offset)^2 term.

c3

The amplitude of the (x-offset)^3 term.

c4

The amplitude of the (x-offset)^4 term.

c5

The amplitude of the (x-offset)^5 term.

c6

The amplitude of the (x-offset)^6 term.

c7

The amplitude of the (x-offset)^7 term.

c8

The amplitude of the (x-offset)^8 term.

offset

There is a degeneracy between c0 and offset, so it is recommended that at least one of these remains frozen.

See also

PowLaw1D, Polynom2D

Notes

The functional form of the model for points is:

f(x) = sum_(i=0)^(i=8) c_i * (x - offset)^i

and for an integrated grid it is the integral of this over the bin.

Attributes Summary

cache	The maximum size of the cache.
ndim	A one-dimensional model.
thawedparhardmaxes	The hard maximum values for the thawed parameters.
thawedparhardmins	The hard minimum values for the thawed parameters.
thawedparmaxes	Access to the maximum limits for the thawed parameters
thawedparmins	Access to the minimum limits for the thawed parameters
thawedpars	Access to the thawed parameters of the model

Methods Summary

apply(outer, *otherargs, **otherkwargs)
cache_clear()	Clear the cache.
cache_status()	Display the cache status.
calc(*args, **kwargs)	Evaluate the model on a grid.
get_center()
guess(dep, *args, **kwargs)	Set an initial guess for the parameter values.
regrid(*args, **kwargs)	The class RegriddableModel1D allows the user to evaluate in the requested space then interpolate onto the data space.
reset()	Reset the parameter values.
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

cache = 5

The maximum size of the cache.

ndim = 1

A one-dimensional model.

thawedparhardmaxes

The hard maximum values for the thawed parameters.

thawedparhardmins

The hard minimum values for the thawed parameters.

thawedparmaxes

Access to the maximum limits for the thawed parameters

thawedparmins

Access to the minimum limits for the thawed parameters

thawedpars

Access to the thawed parameters of the model

Methods Documentation

apply(outer, *otherargs, **otherkwargs)

cache_clear()

Clear the cache.

cache_status()

Display the cache status.

Information on the cache - the number of “hits”, “misses”, and “requests” - is displayed at the INFO logging level.

Example

>>> pl.cache_status()
 powlaw1d.pl                size:    5  hits:   633  misses:   240  check=  873

calc(*args, **kwargs)

Evaluate the model on a grid.

Parameters

• p (sequence of numbers) – The parameter values to use. The order matches the pars field.

• *args – The model grid. The values can be scalar or arrays, and the number depends on the dimensionality of the model and whether it is being evaluated over an integrated grid or at a point (or points).

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(*args, **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()

Reset the parameter values.

set_center(*args, **kwargs)

startup(cache=False)

Called before a model may be evaluated multiple times.

Parameters

cache (bool, optional) – Should a cache be used when evaluating the models.

See also

teardown

teardown()

Called after a model may be evaluated multiple times.

See also

startup