get_int_proj

sherpa.ui.get_int_proj(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-projection object.

This returns (and optionally calculates) the data used to display the int_proj plot.

Parameters:
  • par – The parameter to plot.
  • id (str or int, optional) –
  • otherids (list of str or int, optional) – The id and otherids arguments determine which data set or data sets are used. If not given, all data sets which have a defined source model are used.
  • 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 the 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:

iproj – The fields of this object can be used to re-create the plot created by int_proj.
Return type:

a sherpa.plot.IntervalProjection instance

See also

conf()
Estimate the 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_proj run:

>>> int_proj(src.xpos)
>>> iproj = get_int_proj()
>>> min(iproj.y)
119.55942437129544

Create the data without creating a plot:

>>> iproj = get_int_proj(pl.gamma, recalc=True)

Control how the data is created

>>> iproj = get_int_proj(pl.gamma, id="src", min=12, max=14,
                         nloop=51, recalc=True)