mcsas.mcsas.backgroundscalingfit module¶
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class
BackgroundScalingFit(findBackground, *args)[source]¶ Bases:
objectChi-squared convergence calculation happens here. Optimizes the scaling and background factor to match intCalc closest to intObs. Returns an array with scaling factors. Input initial guess sc has to be a two-element array with the scaling and background.
Input arguments:
Parameters: - intObs – An array of measured, respectively observed intensities
- intCalc – An array of intensities which should be scaled to match intObs
- intError – An array of uncertainties to match intObs
- sc – A 2-element array of initial guesses for scaling factor and background
- ver – (optional) Can be set to 1 for old version, more robust but slow, default 2 for new version, 10x faster than version 1, requires decent starting values
- outputIntensity – (optional) Return the scaled intensity as third output argument, default: False
- background – (optional) Enables a flat background contribution, default: True
Returns: (sc, conval): A tuple of an array containing the intensity scaling factor and background and the reduced chi-squared value.
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static
aGoFsAlpha(dataMeas, dataErr, dataCalc)[source]¶ The alternative Goodness-of-Fit value without alpha, i.e. multiplied by alpha, according to [Henn 2016] ( http://dx.doi.org/10.1107/S2053273316013206 ).
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calc(data, modelData, sc, ver=2)[source]¶ Warning
method ‘mcsas.backgroundscalingfit.BackgroundScalingFit.calc’ undocumented
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static
chi(sc, dataMeas, dataErr, dataCalc)[source]¶ Chi calculation, difference of measured and calculated signal.
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static
chiNoBg(sc, dataMeas, dataErr, dataCalc)[source]¶ Chi calculation, difference of measured and calculated signal, scaling only, no backgrund.
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static
chiSqr(dataMeas, dataErr, dataCalc)[source]¶ Reduced Chi-squared calculation, size of parameter-space not taken into account; for data with known intError.
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dataScaled(data, sc)[source]¶ Returns the input data scaled by the provided factor and background level applied if requested.