get_int_unc

sherpa.astro.ui.get_int_unc(par: str | Parameter | None = None, id: IdType | None = None, otherids: IdTypes | None = None, recalc: bool = False, min: float | None = None, max: float | None = None, nloop: int = 20, delv: float | None = None, fac: float = 1, log: bool = False, numcores: int | None = None, copy: bool = True)

Return the interval-uncertainty object.

This returns (and optionally calculates) the data used to display the int_unc plot. Note that if the the recalc parameter is False (the default value) then all other parameters are ignored and the results of the last int_unc call are returned.

Changed in version 4.19.0: The plot object is now copied by default. To get the previous behaviour set the copy attribute to False.

Changed in version 4.16.1: The log parameter can now be set to True.

Parameters:

par

The parameter to plot. This argument is only used if recalc is set to True.

idstr, int, or None, optional

The data set that provides the data. If not given then all data sets with an associated model are used simultaneously.

otheridslist of str or int, or None, optional

Other data sets to use in the calculation.

recalcbool, optional

The default value (False) means that the results from the last call to int_proj (or get_int_proj) are returned, ignoring all other parameter values. Otherwise, the statistic curve is re-calculated, but not plotted.

minnumber, 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.

maxnumber, 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.

nloopint, optional

The number of steps to use. This is used when delv is set to None.

delvnumber, optional

The step size for the parameter. Setting this overrides the nloop parameter. The default is None.

facnumber, 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).

logbool, optional

Should the step size be logarithmically spaced? The default (False) is to use a linear grid.

numcoresoptional

The number of CPU cores to use. The default is to use all the cores on the machine.

copybool, optional

Is the plot object copied before being returned? If so then the plot attributes will not be changed by multiple calls to this routine.

Returns:

iunca sherpa.plot.IntervalUncertainty instance

The fields of this object can be used to re-create the plot created by int_unc.

See also

conf

Estimate parameter confidence intervals using the confidence method.

covar

Estimate the confidence intervals using the covariance method.

int_proj

Calculate and plot the fit statistic versus fit parameter value.

int_unc

Calculate and plot the fit statistic versus fit parameter value.

reg_proj

Plot the statistic value as two parameters are varied.

Examples

Return the results of the int_unc run:

>>> int_unc(src.xpos)
>>> iunc = get_int_unc()
>>> min(iunc.y)
119.55942437129544

Since the recalc parameter has not been changed to True, the following will return the results for the last call to int_unc, which may not have been for the src.ypos parameter:

>>> iunc = get_int_unc(src.ypos)

Create the data without creating a plot:

>>> iunc = get_int_unc(pl.gamma, recalc=True)

Specify the range and step size for the parameter, in this case varying linearly between 12 and 14 with 51 values:

>>> iunc = get_int_unc(src.r0, id="src", min=12, max=14,
...                    nloop=51, recalc=True)