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Description
-----------

This algorithm calculates backgrounds for the histograms in a workspace by fitting a polynomial to ranges given by *XRanges*. The fitting is done using :ref:`algm-Fit`. The backgrounds are returned as the *OutputWorkspace* which can be subsequently subtracted from the original workspace. The degree of the polynomial can be given by *Degree*. A zeroth degree polynomial would correspond to fitting a constant value. The *XRanges* property is a list of range start and range end value pairs in the X axis units. Overlapping ranges are merged. If no *XRanges* are given, full histograms are used.

The value of *CostFunction* is passed to :ref:`algm-Fit` as-is. The default option is 'Least squares' which uses the histogram errors as weights. This might not be desirable, e.g. when there are bins with zero counts and zero errors. An 'Unweighted least squares' option is available to deal with such cases.

The property *Minimizer* is passed to :ref:`algm-Fit` as-is. The default for this is 'Levenberg-MarquardtMD'.

Usage
-----

**Example - CalculatePolynomialBackground**

.. testcode:: CalculatePolynomialBackgroundExample

   import numpy

   ws = CreateSampleWorkspace(Function='One Peak')
   # ws has a background of 0.3
   # There is a single peak around X = 10000 micro-s. Lets exclude it from
   # the background fitting using XRanges.
   bkg = CalculatePolynomialBackground(ws, XRanges=[0, 8000, 12000, 20000])
   ws_bkg_subtr = ws - bkg

   peakIndex = numpy.argmax(ws.extractY())
   Y = ws.readY(0)[peakIndex]
   print('Peak height with background: {:.1f}'.format(Y))
   Y = ws_bkg_subtr.readY(0)[peakIndex]
   print('Background subtracted peak height: {:.1f}'.format(Y))

Output:

.. testoutput:: CalculatePolynomialBackgroundExample

   Peak height with background: 10.3
   Background subtracted peak height: 10.0

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