reg_unc

sherpa.astro.ui.reg_unc(par0: str | Parameter, par1: str | Parameter, id: IdType | None = None, otherids: IdTypes | None = None, replot: bool = False, min=None, max=None, nloop=(10, 10), delv=None, fac: float = 4, log=(False, False), sigma=(1, 2, 3), levels=None, numcores: int | None = None, overplot: bool = False) → None

Plot the statistic value as two parameters are varied.

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 statistic evaluated while holding the other parameters fixed. It is expected that this is run after a successful fit, so that the parameter values are at the best-fit location.

Parameters:

par0, par1: The parameters to plot on the X and Y axes, respectively.
idint, 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.
otheridssequence of int or str, or None, optional: Other data sets to use in the calculation.
replotbool, optional: Set to True to use the values calculated by the last call to int_proj. The default is False.
minpair of numbers, 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.
maxpair of 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.
nlooppair of int, optional: The number of steps to use. This is used when delv is set to None.
delvpair of number, 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).
logpair of bool, optional: Should the step size be logarithmically spaced? The default (False) is to use a linear grid.
sigmasequence of number, optional: The levels at which to draw the contours. The units are the change in significance relative to the starting value, in units of sigma.
levelssequence of number, optional: The numeric values at which to draw the contours. This overrides the sigma parameter, if set (the default is None).
numcoresoptional: The number of CPU cores to use. The default is to use all the cores on the machine.
overplotbool, optional: If True then add the data to an existing plot, otherwise create a new plot. The default is False.

See also

conf: Estimate parameter confidence intervals using the confidence method.
covar: Estimate the confidence intervals using the covariance method.
get_reg_unc: Return the interval-uncertainty object.
int_unc: Calculate and plot the fit statistic versus fit parameter value.
reg_proj: Plot the statistic value as two parameters are varied.

Notes

The difference to reg_proj is that at each step only the pair of parameters are varied, while all the other parameters remain at their starting value. This makes the result a less-accurate rendering of the projected shape of the hypersurface formed by the statistic, but the run-time is likely shorter than, the results of reg_proj, which fits the model to the remaining thawed parameters at each step. If there are no free parameters in the model, other than the parameters being plotted, then the results will be the same.

Examples

Vary the xpos and ypos parameters of the gsrc model component for all data sets with a source expression.

>>> reg_unc(gsrc.xpos, gsrc.ypos)

Use only the data in data set 1:

>>> reg_unc(gsrc.xpos, gsrc.ypos, id=1)

Only display the one- and three-sigma contours:

>>> reg_unc(gsrc.xpos, gsrc.ypos, sigma=(1, 3))

Display contours at values of 5, 10, and 20 more than the statistic value of the source model for data set 1:

>>> s0 = calc_stat(id=1)
>>> lvls = s0 + np.asarray([5, 10, 20])
>>> reg_unc(gsrc.xpos, gsrc.ypos, levels=lvls, id=1)

Increase the limits of the plot and the number of steps along each axis:

>>> reg_unc(gsrc.xpos, gsrc.ypos, id=1, fac=6, nloop=(41, 41))

Compare the ampl parameters of the g and b model components, for data sets ‘core’ and ‘jet’, over the given ranges:

>>> reg_unc(g.ampl, b.ampl, min=(0, 1e-4), max=(0.2, 5e-4),
...         nloop=(51, 51), id='core', otherids=['jet'])

Overplot the results on the reg_proj plot:

>>> reg_proj(s1.c0, s2.xpos)
>>> reg_unc(s1.c0, s2.xpos, overplot=True)