Chi2XspecVar

class sherpa.stats.Chi2XspecVar(name: str = 'chi2xspecvar')[source] [edit on github]

Bases: Chi2

Chi Squared with data variance (XSPEC style).

The variance in each bin is estimated from the data value in that bin.

The calculation of the variance is the same as Chi2DataVar except that if the number of counts in a bin is less than 1 then the variance for that bin is set to 1. Note that this does not handle background subtraction, so to match XSPEC extra logic is present in sherpa.astro.data.DataPHA.get_staterror to handle that case.

Methods Summary

`calc_chisqr`(data, model)	Return the chi-square value for each bin.
`calc_stat`(data, model)	Return the statistic value for the data and model.
`calc_staterror`(data)	Return the statistic error values for the data.
`goodness_of_fit`(statval, dof)	Return the reduced statistic and q value.

Methods Documentation

calc_chisqr(data: Data | DataSimulFit, model: Model) → ndarray [edit on github]

Return the chi-square value for each bin.

Parameters:

data (sherpa.data.Data or sherpa.data.DataSimulFit) – The data set, or sets, to use.
model (sherpa.models.model.Model or sherpa.models.model.SimulFitModel) – The model expression, or expressions. If a sherpa.models.model.SimulFitModel is given then it must match the number of data sets in the data parameter.

Returns:

chisqr – The per-bin chi-square values.

Return type:

array of numbers

calc_stat(data: Data | DataSimulFit, model: Model) → tuple[float, ndarray] [edit on github]

Return the statistic value for the data and model.

Parameters:

data (sherpa.data.Data or sherpa.data.DataSimulFit) – The data set, or sets, to use.
model (sherpa.models.model.Model or sherpa.models.model.SimulFitModel) – The model expression, or expressions. If a sherpa.models.model.SimulFitModel is given then it must match the number of data sets in the data parameter.

Returns:

statval (number) – The value of the statistic.
fvec (array of numbers) – The per-bin “statistic” value.

static calc_staterror(data: ndarray) → ndarray[source] [edit on github]

Return the statistic error values for the data.

Parameters:: data (scalar or 1D array of numbers) – The data values.
Returns:: staterror – The errors for the input data values (matches the data argument).
Return type:: scalar or array of numbers

goodness_of_fit(statval: float, dof: int) → tuple[Literal[None], Literal[None]] | tuple[float, float] [edit on github]

Return the reduced statistic and q value.

The reduced statisitc is conceptually simple, as it is just statistic / degrees-of-freedom, but it is not meaningful for all statistics, and it is only valid if there are any degrees of freedom.

Parameters:

statval (float) – The statistic value. It is assumed to be finite.
dof (int) – The number of degrees of freedom, which may be 0 or negative.

Returns:

rstat (float or NaN or None) – The reduced statistic. If the statistic does not support a goodness of fit then the return value is None. If it does then NaN is returned if either the number of degrees of freedom is 0 (or less), or the statistic value is less than 0.
qval (float or NaN or None) – The q value. If the statistic does not support a goodness of fit then the return values are None. If it does then NaN is returned if either the number of degrees of freedom is 0 (or less), or the statistic value is less than 0.