    Calculate and plot the fit statistic versus fit parameter value.

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)
id - int or str, optional
otherids - sequence of int or str, optional
replot - bool, optional
fast - bool, optional
min - number, optional
max - number, optional
nloop - int, optional
delv - number, optional
fac - number, optional
log - bool, optional
numcores - optional
overplot - bool, optional

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.

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

>>> int_proj(p1.gamma)

Use only the data in data set 1:

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

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

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

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)

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)

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
Definition
par
The parameter to plot.
id
The data set that provides the data. If not given then all data sets with an associated model are used simultaneously.
otherids
Other data sets to use in the calculation.
replot
Set to True to use the values calculated by the last call to `int_proj` . The default is False .
fast
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
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
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
The number of steps to use. This is used when `delv` is set to none .
delv
The step size for the parameter. Setting this over-rides the `nloop` parameter. The default is none .
fac
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
Should the step size be logarithmically spaced? The default ( False ) is to use a linear grid.
numcores
The number of CPU cores to use. The default is to use all the cores on the machine.
overplot
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.

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

December 2023