.. algorithm::

.. summary::

.. relatedalgorithms::

.. properties::

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

After fitting a model to POLDI 2D-data the residuals have to be analysed in order to determine whether the data are described by the model. Since this is very hard to see in the two dimensional residual data, the analysis involves a procedure that is very similar to the correlation method implemented in :ref:`algm-PoldiAutoCorrelation`. The method is slightly different because the residual at a given point may be either positive or negative.

The algorithm iteratively calculates the correlation spectrum of the residual data, distributes the correlation counts over the 2D residual data and normalizes the residuals so that their sum is equal to zero. The correlation spectra of all steps are accumulated and returned as output. In the spectrum it's for example possible to spot additional peaks, which may have been hidden in by larger peaks in a first data analysis.

Usage
-----

.. include:: ../usagedata-note.txt

The following example shows how to calculate the residuals following a fit performed with :ref:`algm-PoldiFitPeaks2D` on Silicon data that had been collected for a calibration measurement.

.. testcode:: ExSiliconMerged

    # Load data from file, load instrument and truncate data to correct size
    data_Si_raw = Load("Poldi2013Silicon.nxs")
    LoadInstrument(data_Si_raw, InstrumentName="POLDI", RewriteSpectraMap=True)
    data_Si = PoldiTruncateData(data_Si_raw)

    # Perform correlation method, find and refine peaks in correlation spectrum
    correlation_Si = PoldiAutoCorrelation(data_Si)
    peaks_Si_raw = PoldiPeakSearch(correlation_Si)

    # Only use the 11 strongest peaks
    DeleteTableRows(TableWorkspace=peaks_Si_raw, Rows="11-20")

    peaks_Si_1D = PoldiFitPeaks1D(correlation_Si, FwhmMultiples=4, PoldiPeakTable="peaks_Si_raw",

                                    FitPlotsWorkspace = "fit_plots_Si",
                                    Version=1)

    # Fit peaks to original 2D data
    fit_result_Si_2D = PoldiFitPeaks2D(data_Si,
				    PoldiPeakWorkspace="peaks_Si_1D", MaximumIterations=100,
				    RefinedPoldiPeakWorkspace="peaks_Si_2D",
				    Calculated1DSpectrum="fit_result_Si_1D")

    # Calculate residuals
    residuals_Si = PoldiAnalyseResiduals(MeasuredCountData=data_Si, FittedCountData="fit_result_Si_2D", MaxIterations=5)

    residual_data = residuals_Si.readY(0)

    print("Residuals are in the range: [ {:.2f} ,  {:.2f} ]".format(round(min(residual_data), 2), round(max(residual_data), 2)))

The output contains the range in which residuals are found:

.. testoutput:: ExSiliconMerged

    Residuals are in the range: [ -2361.45 ,  2651.65 ]

.. categories::

.. sourcelink::