Exemplo n.º 1
0
def CalibrateWish( run_per_panel_list):
    '''
    :param run_per_panel_list: is a list of tuples with the run number and the associated panel 

    run_per_panel_list =  [ (17706, 'panel01'), (17705, 'panel02'),  (17701, 'panel03'), (17702, 'panel04'), (17695, 'panel05')]
    '''
    # == Set parameters for calibration ==
    previousDefaultInstrument = config['default.instrument']
    config['default.instrument']="WISH"
    
    # definition of the parameters static for the calibration
    lower_tube = numpy.array([-0.41,-0.31,-0.21,-0.11,-0.02, 0.09, 0.18, 0.28, 0.39 ])
    upper_tube = numpy.array(lower_tube+0.003)
    funcForm = 9*[1] # 9 gaussian peaks
    margin = 15
    low_range = range(0,76)
    high_range = range(76,152)
    kwargs = {'margin':margin}

    # it will copy all the data from the runs to have a single instrument with the calibrated data. 
    whole_instrument = LoadRaw(str(run_per_panel_list[0][0]))
    whole_instrument = Integration(whole_instrument)


    for (run_number, panel_name) in run_per_panel_list:        
        panel_name = str(panel_name)
	run_number = str(run_number)
        # load your data and integrate it
        ws = LoadRaw(run_number, OutputWorkspace=panel_name)
        ws = Integration(ws, 1, 20000, OutputWorkspace=panel_name)

        # use the TubeSpec object to be able to copy the data to the whole_instrument
        tube_set = TubeSpec(ws)
        tube_set.setTubeSpecByString(panel_name)
        
        # update kwargs argument before calling calibrate        
        kwargs['rangeList'] = low_range # calibrate only the lower tubes
        calibrationTable = tube.calibrate(ws, tube_set, lower_tube, funcForm, **kwargs)
        
        # update kwargs
        kwargs['calibTable'] = calibrationTable # append calib to calibrationtable
	kwargs['rangeList'] = high_range # calibrate only the upper tubes

        calibrationTable = tube.calibrate(ws, tube_set, upper_tube, funcForm, **kwargs)
        kwargs['calibTable'] = calibrationTable

        ApplyCalibration(ws, calibrationTable)

        # copy data from the current panel to the whole_instrument
        for i in range(tube_set.getNumTubes()):
		for spec_num in tube_set.getTube(i):
			whole_instrument.setY(spec_num,ws.dataY(spec_num))

    # calibrate the whole_instrument with the last calibrated panel which has the calibration accumulation
    # of all the others
    CopyInstrumentParameters(run_per_panel_list[-1][1],whole_instrument)

    config['default.instrument'] = previousDefaultInstrument
Exemplo n.º 2
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def getCalibrationFromPeakFile ( ws, calibTable, iTube,  PeakFile ):
    """
       Get the results the calibration and put them in the calibration table provided.

       @param ws: Integrated Workspace with tubes to be calibrated
       @param calibTable: Calibration table into which the calibration results are placed
       @param  iTube: The ideal tube
       @param PeakFile: File of peaks for calibtation

    """

    # Get Ideal Tube
    idealTube = iTube.getArray()

    # Read Peak File
    PeakArray = readPeakFile( PeakFile )
    nTubes = len(PeakArray)
    print "Number of tubes read from file =",nTubes

    for i in range(nTubes):

        # Deal with (i+1)st tube got from file
        TubeName = PeakArray[i][0] # e.g. 'MERLIN/door3/tube_3_1'
        tube = TubeSpec(ws)
        tube.setTubeSpecByString(TubeName)
        actualTube = PeakArray[i][1] # e.g.  [2.0, 512.5, 1022.0]

        wht, _ = tube.getTube(0)
        print "Calibrating tube", i+1 ,"of", nTubes, TubeName #, " length", tubeSet.getTubeLength(i)
        if  len(wht) < 1 :
            print "Unable to get any workspace indices for this tube. Calibration abandoned."
            return

        detIDList, detPosList = getCalibratedPixelPositions( ws, actualTube, idealTube, wht)

        #print len(wht)
        if  len(detIDList) == len(wht): # We have corrected positions
            for j in range(len(wht)):
                nextRow = {'Detector ID': detIDList[j], 'Detector Position': detPosList[j] }
                calibTable.addRow ( nextRow )

    if nTubes == 0:
        return

    # Delete temporary workspaces for getting new detector positions
    DeleteWorkspace('PolyFittingWorkspace')
    DeleteWorkspace('QF_NormalisedCovarianceMatrix')
    DeleteWorkspace('QF_Parameters')
    DeleteWorkspace('QF_Workspace')
Exemplo n.º 3
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 def enforce_tube_spec(tube_set, ws):
     if isinstance(tube_set, str):
         selected_tubes = tube_set
         tube_set = TubeSpec(ws)
         tube_set.setTubeSpecByString(selected_tubes)
         return tube_set
     if isinstance(tube_set, list):
         selected_tubes = tube_set
         tube_set = TubeSpec(ws)
         tube_set.setTubeSpecByStringArray(selected_tubes)
         return tube_set
     if isinstance(tube_set, TubeSpec):
         return tube_set
     raise RuntimeError("Wrong argument tubeSet. "
                        "It must be a TubeSpec or a string that defines the set of tubes to be calibrated. "
                        "For example: WISH/panel03")
Exemplo n.º 4
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    def runTest(self):
        # This script calibrates WISH using known peak positions from
        # neutron absorbing bands. The workspace with suffix "_calib"
        # contains calibrated data. The workspace with suxxic "_corrected"
        # contains calibrated data with known problematic tubes also corrected

        ws = mantid.LoadNexusProcessed(Filename="WISH30541_integrated.nxs")

        # This array defines the positions of peaks on the detector in
        # meters from the center (0)

        # For wish this is calculated as follows:
        # Height of all 7 bands = 0.26m => each band is separated by 0.260 / 6 = 0.4333m

        # The bands are on a cylinder diameter 0.923m. So we can work out the angle as
        # (0.4333 * n) / (0.923 / 2) where n is the number of bands above (or below) the
        # center band.

        # Putting this together with the distance to the detector tubes (2.2m) we get
        # the following:  (0.4333n) / 0.4615 * 2200 = Expected peak positions
        # From this we can show there should be 5 peaks (peaks 6 + 7 are too high/low)
        # at: 0, 0.206, 0.413 respectively (this is symmetrical so +/-)

        peak_positions = np.array([-0.413, -0.206, 0, 0.206, 0.413])
        funcForm = 5 * [1]  # 5 gaussian peaks
        fitPar = TubeCalibFitParams([59, 161, 258, 353, 448])
        fitPar.setAutomatic(True)

        instrument = ws.getInstrument()
        spec = TubeSpec(ws)

        spec.setTubeSpecByString(instrument.getFullName())

        idealTube = IdealTube()
        idealTube.setArray(peak_positions)

        # First calibrate all of the detectors
        calibrationTable, peaks = tube.calibrate(ws, spec, peak_positions, funcForm, margin=15,
                                                 outputPeak=True, fitPar=fitPar)
        self.calibration_table = calibrationTable

        def findBadPeakFits(peaksTable, threshold=10):
            """ Find peaks whose fit values fall outside of a given tolerance
            of the mean peak centers across all tubes.

            Tubes are defined as have a bad fit if the absolute difference
            between the fitted peak centers for a specific tube and the
            mean of the fitted peak centers for all tubes differ more than
            the threshold parameter.

            @param peakTable: the table containing fitted peak centers
            @param threshold: the tolerance on the difference from the mean value
            @return A list of expected peak positions and a list of indices of tubes
            to correct
            """
            n = len(peaksTable)
            num_peaks = peaksTable.columnCount() - 1
            column_names = ['Peak%d' % i for i in range(1, num_peaks + 1)]
            data = np.zeros((n, num_peaks))
            for i, row in enumerate(peaksTable):
                data_row = [row[name] for name in column_names]
                data[i, :] = data_row

            # data now has all the peaks positions for each tube
            # the mean value is the expected value for the peak position for each tube
            expected_peak_pos = np.mean(data, axis=0)

            # calculate how far from the expected position each peak position is
            distance_from_expected = np.abs(data - expected_peak_pos)
            check = np.where(distance_from_expected > threshold)[0]
            problematic_tubes = list(set(check))
            print("Problematic tubes are: " + str(problematic_tubes))
            return expected_peak_pos, problematic_tubes

        def correctMisalignedTubes(ws, calibrationTable, peaksTable, spec, idealTube, fitPar, threshold=10):
            """ Correct misaligned tubes due to poor fitting results
            during the first round of calibration.

            Misaligned tubes are first identified according to a tolerance
            applied to the absolute difference between the fitted tube
            positions and the mean across all tubes.

            The FindPeaks algorithm is then used to find a better fit
            with the ideal tube positions as starting parameters
            for the peak centers.

            From the refitted peaks the positions of the detectors in the
            tube are recalculated.

            @param ws: the workspace to get the tube geometry from
            @param calibrationTable: the calibration table output from running calibration
            @param peaksTable: the table containing the fitted peak centers from calibration
            @param spec: the tube spec for the instrument
            @param idealTube: the ideal tube for the instrument
            @param fitPar: the fitting parameters for calibration
            @param threshold: tolerance defining is a peak is outside of the acceptable range
            @return table of corrected detector positions
            """
            table_name = calibrationTable.name() + 'Corrected'
            corrections_table = mantid.CreateEmptyTableWorkspace(OutputWorkspace=table_name)
            corrections_table.addColumn('int', "Detector ID")
            corrections_table.addColumn('V3D', "Detector Position")

            mean_peaks, bad_tubes = findBadPeakFits(peaksTable, threshold)

            for index in bad_tubes:
                print("Refitting tube %s" % spec.getTubeName(index))
                tube_dets, _ = spec.getTube(index)
                getPoints(ws, idealTube.getFunctionalForms(), fitPar, tube_dets)
                tube_ws = mantid.mtd['TubePlot']
                fit_ws = mantid.FindPeaks(InputWorkspace=tube_ws, WorkspaceIndex=0,
                                          PeakPositions=fitPar.getPeaks(), PeaksList='RefittedPeaks')
                centers = [row['centre'] for row in fit_ws]
                detIDList, detPosList = getCalibratedPixelPositions(ws, centers, idealTube.getArray(), tube_dets)

                for id, pos in zip(detIDList, detPosList):
                    corrections_table.addRow({'Detector ID': id, 'Detector Position': kernel.V3D(*pos)})

            return corrections_table

        corrected_calibration_table = correctMisalignedTubes(ws, calibrationTable, peaks, spec, idealTube, fitPar)
        self.correction_table = corrected_calibration_table
        tube.saveCalibration(self.correction_table.getName(), out_path=self.calibration_out_path)
        tube.saveCalibration(self.calibration_table.getName(), out_path=self.correction_out_path)
Exemplo n.º 5
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# Set what we want to calibrate (e.g whole intrument or one door )
CalibratedComponent1 = 'C4_window'  # Calibrate C4 window
CalibratedComponent2 = 'C3_window'  # Calibrate C3 window


# Get calibration raw file and integrate it
rawCalibInstWS = Load(filename)  #'raw' in 'rawCalibInstWS' means unintegrated.
print "Integrating Workspace"
CalibInstWS = Integration( rawCalibInstWS, RangeLower=rangeLower, RangeUpper=rangeUpper )
DeleteWorkspace(rawCalibInstWS)
print "Created workspace (CalibInstWS) with integrated data from run and instrument to calibrate"

# == Create Objects needed for calibration ==

# Specify components to calibrate
thisTubeSet = TubeSpec(CalibInstWS)
thisTubeSet.setTubeSpecByString(CalibratedComponent1)
thisTubeSet.setTubeSpecByString(CalibratedComponent2)

# Specify the known positions
knownPos = [-0.50,-0.16,-0.00, 0.16, 0.50 ]
funcForm = [2,1,1,1,2]

print "Created objects needed for calibration."

# == Get the calibration and put results into calibration table ==

calibrationTable, peakTable = tube.calibrate(CalibInstWS, thisTubeSet, knownPos, funcForm,
    outputPeak=True)
print "Got calibration (new positions of detectors) "
Exemplo n.º 6
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CalibInstWS = mantid.Integration( rawCalibInstWS, RangeLower=1, RangeUpper=20000 )
mantid.DeleteWorkspace(rawCalibInstWS)
print("Created workspace (CalibInstWS) with integrated data from run and instrument to calibrate")

CalibratedComponent = 'WISH/panel03/tube038'

# Set fitting parameters
eP = [65.0, 113.0, 161.0, 209.0, 257.0, 305.0, 353.0, 401.0, 449.0]
ExpectedHeight = 2000.0 # Expected Height of Gaussian Peaks (initial value of fit parameter)
ExpectedWidth = 32.0 # Expected width of Gaussian peaks in pixels  (initial value of fit parameter)
fitPar = TubeCalibFitParams( eP, ExpectedHeight, ExpectedWidth )
fitPar.setAutomatic(True)
print("Created objects needed for calibration.")
func_form = 9*[1]

# Use first tube as ideal tube
tube1 = TubeSpec(CalibInstWS)
tube1.setTubeSpecByString('WISH/panel03/tube038')
iTube = tube_calib.constructIdealTubeFromRealTube( CalibInstWS, tube1, fitPar, func_form)

known_pos = iTube.getArray()
print(known_pos)

# Get the calibration and put it into the calibration table
calibrationTable = tube.calibrate( CalibInstWS, 'WISH/panel03', known_pos, func_form, fitPar=fitPar)
print("Got calibration (new positions of detectors)")

#Apply the calibration
mantid.ApplyCalibration(Workspace=CalibInstWS, PositionTable=calibrationTable)
print("Applied calibration")
Exemplo n.º 7
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def calibrate(ws, tubeSet, knownPositions, funcForm, **kwargs):
    """

      Define the calibrated positions of the detectors inside the tubes defined
      in tubeSet.

      Tubes may be considered a list of detectors alined that may be considered
      as pixels for the analogy when they values are displayed.

      The position of these pixels are provided by the manufactor, but its real
      position depends on the electronics inside the tube and varies slightly
      from tube to tube. The calibrate method, aims to find the real positions
      of the detectors (pixels) inside the tube.

      For this, it will receive an Integrated workspace, where a special
      measurement was performed so to have a
      pattern of peaks or through. Where gaussian peaks or edges can be found.


      The calibration follows the following steps

      1. Finding the peaks on each tube
      2. Fitting the peaks agains the Known Positions
      3. Defining the new position for the pixels(detectors)

      Let's consider the simplest way of calling calibrate:

      .. code-block:: python

         from tube import calibrate
         ws = Load('WISH17701')
         ws = Integration(ws)
         known_pos = [-0.41,-0.31,-0.21,-0.11,-0.02, 0.09, 0.18, 0.28, 0.39 ]
         peaks_form = 9*[1] # all the peaks are gaussian peaks
         calibTable = calibrate(ws,'WISH/panel03',known_pos, peaks_form)

      In this example, the calibrate framework will consider all the
      tubes (152) from WISH/panel03.
      You may decide to look for a subset of the tubes, by passing the
      **rangeList** option.

      .. code-block:: python

         # This code will calibrate only the tube indexed as number 3
         # (usually tube0004)
         calibTable = calibrate(ws,'WISH/panel03',known_pos,
                                peaks_form, rangeList=[3])

      **Finding the peaks on each tube**

      * Dynamically fitting peaks

       The framework expects that for each tube, it will find a peak pattern
       around the pixels corresponding to the known_pos positions.

       The way it will work out the estimated peak position (in pixel) is

       1. Get the length of the tube: distance(first_detector,last_detector) in the tube.
       2. Get the number of detectors in the tube (nDets)
       3. It will be assumed that the center of the tube correspond to the origin (0)

       .. code-block:: python

          centre_pixel = known_pos * nDets/tube_length + nDets/2

       It will them look for the real peak around the estimated value as:

       .. code-block:: python

          # consider tube_values the array of counts, and peak the estimated
          # position for the peak
          real_peak_pos = argmax(tube_values[peak-margin:peak+margin])

       After finding the real_peak_pos, it will try to fit the region around
       the peak to find the best expected position of the peak in a continuous
       space. It will do this by fitting the region around the peak to a
       Gaussian Function, and them extract the PeakCentre returned by the
       Fitting.

       .. code-block:: python

          centre = real_peak_pos
          fit_start, fit_stop = centre-margin, centre+margin
          values = tube_values[fit_start,fit_stop]
          background = min(values)
          peak = max(values) - background
          width = len(where(values > peak/2+background))
          # It will fit to something like:
          # Fit(function=LinerBackground,A0=background;Gaussian,
          # Height=peak, PeakCentre=centre, Sigma=width,fit_start,fit_end)

      * Force Fitting Parameters


       These dinamically values can be avoided by defining the **fitPar** for
       the calibrate function

       .. code-block:: python

          eP = [57.5, 107.0, 156.5, 206.0, 255.5, 305.0, 354.5, 404.0, 453.5]
          # Expected Height of Gaussian Peaks (initial value of fit parameter)
          ExpectedHeight = 1000.0
          # Expected width of Gaussian peaks in pixels
          # (initial value of fit parameter)
          ExpectedWidth = 10.0
          fitPar = TubeCalibFitParams( eP, ExpectedHeight, ExpectedWidth )
          calibTable = calibrate(ws, 'WISH/panel03', known_pos, peaks_form, fitPar=fitPar)

       Different Function Factors


       Although the examples consider only Gaussian peaks, it is possible to
       change the function factors to edges by passing the index of the
       known_position through the **funcForm**. Hence, considering three special
       points, where there are one gaussian peak and thow edges, the calibrate
       could be configured as:

       .. code-block:: python

          known_pos = [-0.1 2 2.3]
          # gaussian peak followed by two edges (through)
          form_factor = [1 2 2]
          calibTable = calibrate(ws,'WISH/panel03',known_pos,
                                 form_factor)

      * Override Peaks


       It is possible to scape the finding peaks position steps by providing the
       peaks through the **overridePeaks** parameters. The example below tests
       the calibration of a single tube (30) but scapes the finding peaks step.

       .. code-block:: python

          known_pos = [-0.41,-0.31,-0.21,-0.11,-0.02, 0.09, 0.18, 0.28, 0.39 ]
          define_peaks = [57.5, 107.0, 156.5, 206.0, 255.5, 305.0, 354.5,
                         404.0, 453.5]
          calibTable = calibrate(ws, 'WISH/panel03', known_pos, peaks_form,
                           overridePeaks={30:define_peaks}, rangeList=[30])

      * Output Peaks Positions

       Enabling the option **outputPeak** a WorkspaceTable will be produced with
       the first column as tube name and the following columns with the position
       where corresponding peaks were found. Like the table below.

       +-------+-------+-----+-------+
       |TubeId | Peak1 | ... | PeakM |
       +=======+=======+=====+=======+
       |tube0  | 15.5  | ... | 370.3 |
       +-------+-------+-----+-------+
       |  ...  |  ...  | ... |  ...  |
       +-------+-------+-----+-------+
       |tubeN  | 14.9  | ... | 371.2 |
       +-------+-------+-----+-------+

       The signature changes to:

       .. code-block:: python

          calibTable, peakTable = calibrate(...)

       It is possible to give a peakTable directly to the **outputPeak** option,
       which will make the calibration to append the peaks to the given table.

       .. hint::

         It is possible to save the peakTable to a file using the
         :meth:`savePeak` method.

      **Find the correct position along the tube**


       The second step of the calibration is to define the correct position of
       pixels along the tube. This is done by fitting the peaks positions found
       at the previous step against the known_positions provided.

       ::

        known       |              *
        positions   |           *
                    |      *
                    |  *
                    |________________
                      pixels positions

       The default operation is to fit the pixels positions against the known
       positions with a quadratic function in order to define an operation to
       move all the pixels to their real positions. If necessary, the user may
       select to fit using a polinomial of 3rd order, through the parameter
       **fitPolyn**.

       .. note::

         The known positions are given in the same unit as the spacial position
         (3D) and having the center of the tube as the origin.

       Hence, this section will define a function that:

       .. math:: F(pix) = RealRelativePosition

      **Define the new position for the detectors**

       Finally, the position of the detectors are defined as a vector operation
       like

       .. math::

         \\vec{p} = \\vec{c} + v \\vec{u}

       Where :math:`\\vec{p}` is the position in the 3D space, **v** is the
       RealRelativePosition deduced from the last session, and finally,
       :math:`\\vec{u}` is the unitary vector in the direction of the tube.



      :param ws: Integrated workspace with tubes to be calibrated.
      :param tubeSet: Specification of Set of tubes to be calibrated. If a string is passed, a TubeSpec will be created passing the string as the setTubeSpecByString.

       This will be the case for TubeSpec as string

       .. code-block:: python

         self.tube_spec = TubeSpec(ws)
         self.tube_spec.setTubeSpecByString(tubeSet)

       If a list of strings is passed, the TubeSpec will be created with this list:

       .. code-block:: python

          self.tube_spec = TubeSpec(ws)
          self.tube_spec.setTubeSpecByStringArray(tubeSet)

       If a :class:`~tube_spec.TubeSpec` object is passed, it will be used as it is.


      :param knownPositions: The defined position for the peaks/edges, taking the center as the origin and having the same units as the tube length in the 3D space.

      :param funcForm: list with special values to define the format of the peaks/edge (peaks=1, edge=2). If it is not provided, it will be assumed that all the knownPositions are peaks.


      Optionals parameters to tune the calibration:

      :param fitPar: Define the parameters to be used in the fit as a :class:`~tube_calib_fit_params.TubeCalibFitParams`. If not provided, the dynamic mode is used. See :py:func:`~Examples.TubeCalibDemoMaps_All.provideTheExpectedValue`

      :param margin: value in pixesl that will be used around the peaks/edges to fit them. Default = 15. See the code of :py:mod:`~Examples.TubeCalibDemoMerlin` where **margin** is used to calibrate small tubes.

       .. code-block:: python

          fit_start, fit_end = centre - margin, centre + margin

      :param rangeList: list of tubes indexes that will be calibrated. As in the following code (see: :py:func:`~Examples.TubeCalibDemoMaps_All.improvingCalibrationSingleTube`):

       .. code-block:: python

          for index in rangelist:
              do_calibrate(tubeSet.getTube(index))

      :param calibTable: Pass the calibration table, it will them append the values to the provided one and return it. (see: :py:mod:`~Examples.TubeCalibDemoMerlin`)

      :param plotTube: If given, the tube whose index is in plotTube will be ploted as well as its fitted peaks, it can receive a list of indexes to plot.(see: :py:func:`~Examples.TubeCalibDemoMaps_All.changeMarginAndExpectedValue`)

      :param excludeShortTubes: Do not calibrate tubes whose length is smaller than given value. (see at: Examples/TubeCalibDemoMerlin_Adjustable.py)

      :param overridePeaks: dictionary that defines an array of peaks positions (in pixels) to be used for the specific tube(key). (see: :py:func:`~Examples.TubeCalibDemoMaps_All.improvingCalibrationSingleTube`)

       .. code-block:: python

          for index in rangelist:
            if overridePeaks.has_key(index):
              use_this_peaks = overridePeaks[index]
              # skip finding peaks
              fit_peaks_to_position()

      :param fitPolyn: Define the order of the polinomial to fit the pixels positions agains the known positions. The acceptable values are 1, 2 or 3. Default = 2.


      :param outputPeak: Enable the calibrate to output the peak table, relating the tubes with the pixels positions. It may be passed as a boolean value (outputPeak=True) or as a peakTable value. The later case is to inform calibrate to append the new values to the given peakTable. This is usefull when you have to operate in subsets of tubes. (see :py:mod:`~Examples.TubeCalibDemoMerlin` that shows a nice inspection on this table).

       .. code-block:: python

         calibTable, peakTable = calibrate(ws, (omitted), rangeList=[1],
                  outputPeak=True)
         # appending the result to peakTable
         calibTable, peakTable = calibrate(ws, (omitted), rangeList=[2],
                  outputPeak=peakTable)
         # now, peakTable has information for tube[1] and tube[2]

      :rtype: calibrationTable, a TableWorkspace with two columns DetectorID(int) and DetectorPositions(V3D).

    """
    FITPAR = 'fitPar'
    MARGIN = 'margin'
    RANGELIST = 'rangeList'
    CALIBTABLE = 'calibTable'
    PLOTTUBE = 'plotTube'
    EXCLUDESHORT = 'excludeShortTubes'
    OVERRIDEPEAKS = 'overridePeaks'
    FITPOLIN = 'fitPolyn'
    OUTPUTPEAK = 'outputPeak'

    #check that only valid arguments were passed through kwargs
    for key in kwargs.keys():
        if key not in [FITPAR, MARGIN, RANGELIST, CALIBTABLE, PLOTTUBE,
                       EXCLUDESHORT, OVERRIDEPEAKS, FITPOLIN,
                       OUTPUTPEAK]:
            msg = "Wrong argument: '%s'! This argument is not defined in the signature of this function. Hint: remember that arguments are case sensitive" % key
            raise RuntimeError(msg)


    # check parameter ws: if it was given as string, transform it in
    # mantid object
    if isinstance(ws,str):
        ws = mtd[ws]
    if not isinstance(ws,MatrixWorkspace):
        raise RuntimeError("Wrong argument ws = %s. It must be a MatrixWorkspace" % (str(ws)))

    # check parameter tubeSet. It accepts string or preferable a TubeSpec
    if isinstance(tubeSet,str):
        selectedTubes = tubeSet
        tubeSet = TubeSpec(ws)
        tubeSet.setTubeSpecByString(selectedTubes)
    elif isinstance(tubeSet, list):
        selectedTubes = tubeSet
        tubeSet = TubeSpec(ws)
        tubeSet.setTubeSpecByStringArray(selectedTubes)
    elif not isinstance(tubeSet,TubeSpec):
        raise RuntimeError("Wrong argument tubeSet. It must be a TubeSpec or a string that defines the set of tubes to be calibrated. For example: WISH/panel03")

    # check the known_positions parameter
    # for old version compatibility, it also accepts IdealTube, eventhough
    # they should only be used internally
    if not (isinstance(knownPositions, list) or
            isinstance(knownPositions, tuple) or
            isinstance(knownPositions, numpy.ndarray)):
        raise RuntimeError("Wrong argument knownPositions. It expects a list of values for the positions expected for the peaks in relation to the center of the tube")
    else:
        idealTube = IdealTube()
        idealTube.setArray(numpy.array(knownPositions))


    #deal with funcForm parameter
    try:
        nPeaks = len(idealTube.getArray())
        if len(funcForm) != nPeaks:
            raise 1
        for val in funcForm:
            if val not in [1,2]:
                raise 2
    except:
        raise RuntimeError("Wrong argument FuncForm. It expects a list of values describing the form of everysingle peaks. So, for example, if there are three peaks where the first is a peak and the followers as edge, funcForm = [1, 2, 2]. Currently, it is defined 1-Gaussian Peak, 2 - Edge. The knownPos has %d elements and the given funcForm has %d."%(nPeaks, len(funcForm)))

    #apply the functional form to the ideal Tube
    idealTube.setForm(funcForm)

    # check the FITPAR parameter (optional)
    # if the FITPAR is given, than it will just pass on, if the FITPAR is
    # not given, it will create a FITPAR 'guessing' the centre positions,
    # and allowing the find peaks calibration methods to adjust the parameter
    # for the peaks automatically
    if kwargs.has_key(FITPAR):
        fitPar = kwargs[FITPAR]
        #fitPar must be a TubeCalibFitParams
        if not isinstance(fitPar, TubeCalibFitParams):
            raise RuntimeError("Wrong argument %s. This argument, when given, must be a valid TubeCalibFitParams object"%FITPAR)
    else:
        # create a fit parameters guessing centre positions
        # the guessing obeys the following rule:
        #
        # centre_pixel = known_pos * ndets/tube_length + ndets / 2
        #
        # Get tube length and number of detectors
        tube_length = tubeSet.getTubeLength(0)
        #ndets = len(wsp_index_for_tube0)
        id1, ndets, step = tubeSet.getDetectorInfoFromTube(0)

        known_pos = idealTube.getArray()
        # position of the peaks in pixels
        centre_pixel = known_pos * ndets/tube_length + ndets * 0.5

        fitPar = TubeCalibFitParams(centre_pixel)
        # make it automatic, it means, that for every tube,
        # the parameters for fit will be re-evaluated, from the first
        # guess positions given by centre_pixel
        fitPar.setAutomatic(True)


    # check the MARGIN paramter (optional)
    if kwargs.has_key(MARGIN):
        try:
            margin = float(kwargs[MARGIN])
        except:
            raise RuntimeError("Wrong argument %s. It was expected a number!"%MARGIN)
        fitPar.setMargin(margin)

    #deal with RANGELIST parameter
    if kwargs.has_key(RANGELIST):
        rangeList = kwargs[RANGELIST]
        if isinstance(rangeList,int):
            rangeList = [rangeList]
        try:
            # this deals with list and tuples and iterables to make sure
            # rangeList becomes a list
            rangeList = list(rangeList)
        except:
            raise RuntimeError("Wrong argument %s. It expects a list of indexes for calibration"%RANGELIST)
    else:
        rangeList = range(tubeSet.getNumTubes())

    # check if the user passed the option calibTable
    if kwargs.has_key(CALIBTABLE):
        calibTable = kwargs[CALIBTABLE]
        #ensure the correct type is passed
        # if a string was passed, transform it in mantid object
        if isinstance(calibTable,str):
            calibTable = mtd[calibTable]
        #check that calibTable has the expected form
        try:
            if not isinstance(calibTable,ITableWorkspace):
                raise 1
            if calibTable.columnCount() != 2:
                raise 2
            colNames = calibTable.getColumnNames()
            if colNames[0] != 'Detector ID' or colNames[1] != 'Detector Position':
                raise 3
        except:
            raise RuntimeError("Invalid type for %s. The expected type was ITableWorkspace with 2 columns(Detector ID and Detector Positions)" % CALIBTABLE)
    else:
        calibTable = CreateEmptyTableWorkspace(OutputWorkspace="CalibTable")
        # "Detector ID" column required by ApplyCalibration
        calibTable.addColumn(type="int",name="Detector ID")
        # "Detector Position" column required by ApplyCalibration
        calibTable.addColumn(type="V3D",name="Detector Position")


    #deal with plotTube option
    if kwargs.has_key(PLOTTUBE):
        plotTube = kwargs[PLOTTUBE]
        if isinstance(plotTube, int):
            plotTube = [plotTube]
        try:
            plotTube = list(plotTube)
        except:
            raise RuntimeError("Wrong argument %s. It expects an index (int) or a list of indexes" %PLOTTUBE)
    else:
        plotTube = []

    #deal with minimun tubes sizes
    if kwargs.has_key(EXCLUDESHORT):
        excludeShortTubes = kwargs[EXCLUDESHORT]
        try:
            excludeShortTubes = float(excludeShortTubes)
        except:
            raise RuntimeError("Wrong argument %s. It expects a float value for the minimun size of tubes to be calibrated")
    else:
        #a tube with length 0 can not be calibrated, this is the minimun value
        excludeShortTubes = 0.0

    #deal with OVERRIDEPEAKS parameters
    if kwargs.has_key(OVERRIDEPEAKS):
        overridePeaks = kwargs[OVERRIDEPEAKS]
        try:
            nPeaks = len(idealTube.getArray())
            # check the format of override peaks
            if not isinstance(overridePeaks, dict):
                raise 1
            for key in overridePeaks.keys():
                if not isinstance(key,int):
                    raise 2
                if key < 0 or key >= tubeSet.getNumTubes():
                    raise 3
                if len(overridePeaks[key]) != nPeaks:
                    raise 4
        except:
            raise RuntimeError("Wrong argument %s. It expects a dictionary with key as the tube index and the value as a list of peaks positions. Ex (3 peaks): overridePeaks = {1:[2,5.4,500]}"%OVERRIDEPEAKS)
    else:
        overridePeaks = dict()


    # deal with FITPOLIN parameter
    if kwargs.has_key(FITPOLIN):
        polinFit = kwargs[FITPOLIN]
        if polinFit not in [1, 2,3]:
            raise RuntimeError("Wrong argument %s. It expects a number 1 for linear, 2 for quadratic, or 3 for 3rd polinomial order when fitting the pixels positions agains the known positions" % FITPOLIN)
    else:
        polinFit = 2

    # deal with OUTPUT PEAK
    deletePeakTableAfter = False
    if kwargs.has_key(OUTPUTPEAK):
        outputPeak = kwargs[OUTPUTPEAK]
    else:
        outputPeak = False
    if isinstance(outputPeak, ITableWorkspace):
        if outputPeak.columnCount() < len(idealTube.getArray()):
            raise RuntimeError("Wrong argument %s. It expects a boolean flag, or a ITableWorksapce with columns (TubeId, Peak1,...,PeakM) for M = number of peaks given in knownPositions" % OUTPUTPEAK)
    else:
        if not outputPeak:
            deletePeakTableAfter = True
        # create the output peak table
        outputPeak = CreateEmptyTableWorkspace(OutputWorkspace="PeakTable")
        outputPeak.addColumn(type='str',name='TubeId')
        for i in range(len(idealTube.getArray())):
            outputPeak.addColumn(type='float',name='Peak%d'%(i+1))

    getCalibration(ws, tubeSet, calibTable, fitPar, idealTube, outputPeak,\
        overridePeaks, excludeShortTubes, plotTube, rangeList, polinFit)

    if deletePeakTableAfter:
        DeleteWorkspace(str(outputPeak))
        return calibTable
    else:
        return calibTable, outputPeak