get_int_unc

sherpa.astro.ui.get_int_unc(par=None, id=None, otherids=None, recalc=False, min=None, max=None, nloop=20, delv=None, fac=1, log=False, numcores=None)

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.

Parameters
  • par – The parameter to plot. This argument is only used if recalc is set to True.

  • id (str or int, optional) – The data set that provides the data. If not given then all data sets with an associated model are used simultaneously.

  • otherids (list of str or int, optional) – Other data sets to use in the calculation.

  • recalc (bool, 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.

  • min (number, optional) – The minimum parameter value for the calcutation. 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 calcutation. 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 over-rides 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.

Returns

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

Return type

a sherpa.plot.IntervalUncertainty instance

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)