class xspec.FitManager

Xspec fitting class.

PyXspec automatically creates a single object of this class, named Fit.


  • error
  • ftest
  • goodness
  • improve
  • perform
  • renorm
  • show
  • steppar
  • stepparResults

Attributes ((*) = get-only)

  • bayes
  • covariance*
  • criticalDelta
  • delta
  • dof*
  • method
  • nIterations
  • query
  • statMethod
  • statTest
  • statistic*
  • testStatistic*
  • weight

Determine confidence intervals of a fit.


A string with identical syntax to the standard interactive XSPEC error command.

“[[stopat <ntrial> <toler>] [maximum <redchi>] [<delta fit statistic>] [<model param range>...]]”

where <model param range> =::[<modelName>:]<first param> -
<last param>

See the XSPEC manual for a more detailed description.

The results of the error command are stored in the error attributes of the individual Parameter objects.


# Estimate the 90% confidence ranges for parameters 1-3
# Repeat but with delta fit statistic = 9.0, equivalent to the
# 3 sigma range.
# Estimate for parameter 3 after setting the number of trials to 20.
# Note that the tolerance field has to be included (or skipped over).
Fit.error("stop 20,,3")
ftest(chisq2, dof2, chisq1, dof1)

Calculate the F-statistic and its probability given new and old values of chisq and number of degrees of freedom (DOF).


chisq2: [float]

dof2: [int]

chisq1: [float]

dof1: [int]

chisq2 and dof2 should come from a new fit, in which an extra model component was added to (or a frozen parameter thawed from) the model which gave chisq1 and dof1. If the F-test probability is low then it is reasonable to add the extra model component.


It is not correct to use the F-test statistic to test for the presence of a line (see Protassov et al 2002, ApJ 571, 545).

Returns: The F-test probability [float].

goodness(nRealizations=100, sim=False)

Perform a Monte Carlo calculation of the goodness-of-fit.


nRealizations: Number of spectra to simulate [int].

sim: flag [bool]

If False (default), all simulations are drawn from the best fit model parameter values. If True, parameters will be drawn from a Gaussian centered on the best fit.

Try to find a new minimum.

When Fit.method is set to one of the MINUIT algorithms, this will run the MINUIT ‘improve’ command. This does nothing when Fit.method is set to Levenberg-Marquardt.


Perform a fit.


Renormalize the model to minimize statistic with current parameters.


setting: [string]

If None, this will perform an explicit immediate renormalization. Other options determine when renormalization will be performed automatically. They are the following strings:

  • ‘auto’: Renormalize after a model command or parameter change, and at the beginning of a fit.
  • ‘prefit’: Renormalize only at the beginning of a fit.
  • ‘none’: Perform no automatic renormalizations.

Show fit information.


Perform a steppar run.

Generate the statistic “surface” for 1 or more parameters.


argString: [string]

This uses identical syntax to the standard interactive XSPEC steppar command. “<step spec> [<step spec> ...]” where:

<step spec> ::= [<log|nolog>] [<current|best>]
[<modName>:]<param index> <low value> <high value> <# steps>

See the XSPEC manual for a more detailed description of specs.


# Step parameter 3 from 1.5 to 2.5 in 10 linear steps
Fit.steppar("3 1.5 2.5 10")
# Repeat the above but with logarithmic steps
# Step parameter 2 linearly from -.2 to .2 in steps of .02
Fit.steppar("nolog 2 -.2 .2 20")

Retrieve values from the most recent steppar run.



argument should either be ‘statistic’, ‘delstat’, or a parameter specifier. A parameter specifier should be a string of the form:


or simply an integer <parNum>.

Returns the requested values as a list of floats.


Turn Bayesian inference on or off [string].

Valid settings are ‘on’, ‘off’ (default), or ‘cons’. ‘cons’ turns Bayesian inference on AND gives ALL parameters a constant prior. Priors can be set for parameters individually through the Parameter object’s prior attribute.


The covariance matrix from the most recent fit [tuple of floats] (GET only).

As with standard XSPEC’s “tclout covar”, this only returns the diagonal and below-diagonal matrix elements.


Critical delta for fit statistic convergence [float].

The absolute change in the fit statistic between iterations, less than which the fit is deemed to have converged.


Set fit delta values to be proportional to the parameter value [float].

Returns the current proportional setting, or 0.0 if currently using the fixed fit delta values.
Enter the constant factor which will multiply the parameter value to produce a fit delta. A constant factor of 0.0 or negative will turn off the use of proportional fit deltas.

The degrees of freedom for the fit [int] (GET only).


The fitting algorithm to use [string].

Choices are: ‘leven’, ‘migrad’, ‘minimize’, ‘monte’, ‘simplex’. The default is ‘leven’.

When setting the method, additional arguments for <nFitIterations> and <fit critical delta> may also be entered. Valid formats for entering multiple arguments are:

# Single string
Fit.method = "migrad  100 .05"
# List of strings
Fit.method = ["migrad","100",".05"]
# List of strings and numbers
Fit.method = ["migrad", 100, .05]

The maximum number of fit iterations prior to query [int].


The fit query setting [string].

  • ‘yes’: Fit will continue through query.
  • ‘no’ : Fit will end at query.
  • ‘on’ : User will be prompted for “y/n” response.

The type of fit statistic in use [string].

Valid names: ‘chi’ | ‘cstat’ | ‘lstat’ | ‘pgstat’ | ‘pstat’ | ‘whittle’. To set for individual spectra, add a spectrum number (or range) to the string: ie.

>>> Fit.statMethod = "cstat 2"

The type of test statistic in use [string].

Valid names: ‘ad’ | ‘chi’ | ‘cvm’ | ‘ks’ | ‘pchi’ | ‘runs’. To set for individual spectra, add a spectrum number (or range) to the string: ie.

>>> Fit.statTest = "ad 2"

Fit statistic value from the most recent fit [float] (GET only).


Test statistic value from the most recent fit [float] (GET only).


Change the weighting function used in the calculation of chi-sq [string].

Available functions: ‘standard’, ‘gehrels’, ‘churazov’, ‘model’