int_unc
- sherpa.astro.ui.int_unc(par, id: IdType | None = None, otherids: Sequence[IdType] | None = None, replot=False, min=None, max=None, nloop=20, delv=None, fac=1, log=False, numcores=None, overplot=False) None
Calculate and plot the fit statistic versus fit parameter value.
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 identify the best-fit location.
Changed in version 4.16.1: The log parameter can now be set to
True
.- Parameters:
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 toint_proj
. The default isFalse
.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 thefac
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 thefac
value.nloop (int, optional) – The number of steps to use. This is used when
delv
is set toNone
.delv (number, optional) – The step size for the parameter. Setting this overrides the
nloop
parameter. The default isNone
.fac (number, optional) – When
min
ormax
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 isFalse
.
See also
conf
Estimate parameter confidence intervals using the confidence method.
covar
Estimate the confidence intervals using the covariance method.
get_int_unc
Return the interval-uncertainty object.
int_proj
Calculate and plot the fit statistic versus fit parameter value.
reg_unc
Plot the statistic value as two parameters are varied.
Notes
The difference to
int_proj
is that at each step only the single parameter value is varied while all 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 ofint_proj
, which fits the model to the remaining thawed parameters at each step. If there are no free parameters in the source expression, other than the parameter being plotted, then the results will be the same.Examples
Vary the
gamma
parameter of thep1
model component for all data sets with a source expression.>>> int_unc(p1.gamma)
Use only the data in data set 1:
>>> int_unc(p1.gamma, id=1)
Use two data sets (‘obs1’ and ‘obs2’):
>>> int_unc(clus.kt, id='obs1', otherids=['obs2'])
Vary the
bgnd.c0
parameter between 1e-4 and 2e-4, using 41 points:>>> int_unc(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_unc(bgnd.c0, min=1e-4, max=2e-4, delv=2e-6)
Overplot the
int_unc
results for the parameter on top of theint_proj
values:>>> int_proj(mdl.xpos) >>> int_unc(mdl.xpos, overplot=True)