int_proj

Synopsis

Calculate and plot the fit statistic versus fit parameter value.

Syntax

int_proj(par, id=None, otherids=None, replot=False, fast=True,
min=None, max=None, nloop=20, delv=None, fac=1, log=False,
numcores=None, overplot=False)

Description

Create a confidence plot of the fit statistic as a function of parameter value. Dashed lines are added to indicate the current statistic value and the parameter value at this point. The parameter value is varied over a grid of points and the free parameters re-fit. It is expected that this is run after a successful fit, so that the parameter values identify the best-fit location.

Examples

Example 1

Vary the gamma parameter of the p1 model component for all data sets with a source expression.

>>> int_proj(p1.gamma)

Example 2

Use only the data in data set 1:

>>> int_proj(p1.gamma, id=1)

Example 3

Use two data sets ('obs1' and 'obs2'):

>>> int_proj(clus.kt, id='obs1', otherids=['obs2'])

Example 4

Vary the bgnd.c0 parameter between 1e-4 and 2e-4, using 41 points:

>>> int_proj(bgnd.c0, min=1e-4, max=2e-4, step=41)

Example 5

This time define the step size, rather than the number of steps to use:

>>> int_proj(bgnd.c0, min=1e-4, max=2e-4, delv=2e-6)

Example 6

Overplot the `int_proj` results for the parameter on top of the `int_unc` values:

>>> int_unc(mdl.xpos)
>>> int_proj(mdl.xpos, overplot=True)

PARAMETERS

The parameters for this function are:

Parameter	Type information	Definition
par		The parameter to plot.
id	int, str, or None, optional	The data set that provides the data. If not given then all data sets with an associated model are used simultaneously.
otherids	sequence of int or str, or None, optional	Other data sets to use in the calculation.
replot	bool, optional	Set to True to use the values calculated by the last call to `int_proj` . The default is False .
fast	bool, optional	If True then the fit optimization used may be changed from the current setting (only for the error analysis) to use a faster optimization method. The default is False .
min	number, optional	The minimum parameter value for the calculation. The default value of none means that the limit is calculated from the covariance, using the `fac` value.
max	number, optional	The maximum parameter value for the calculation. The default value of none means that the limit is calculated from the covariance, using the `fac` value.
nloop	int, optional	The number of steps to use. This is used when `delv` is set to none .
delv	number, optional	The step size for the parameter. Setting this overrides the `nloop` parameter. The default is none .
fac	number, optional	When `min` or `max` is not given, multiply the covariance of the parameter by this value to calculate the limit (which is then added or subtracted to the parameter value, as required).
log	bool, optional	Should the step size be logarithmically spaced? The default ( False ) is to use a linear grid.
numcores	optional	The number of CPU cores to use. The default is to use all the cores on the machine.
overplot	bool, optional	If True then add the data to an existing plot, otherwise create a new plot. The default is False .

Notes

The difference to `int_unc` is that at each step, a fit is made to the remaining thawed parameters in the source model. This makes the result a more-accurate rendering of the projected shape of the hypersurface formed by the statistic, but the run-time is longer than, the results of `int_unc` , which does not vary any other parameter. If there are no free parameters in the source expression, other than the parameter being plotted, then the results will be the same.

Changes in CIAO

Changed in CIAO 4.17

The log parameter can now be set to `True` .

Bugs

See the bugs pages on the Sherpa website for an up-to-date listing of known bugs.