Python TubeCalibFitParams.setAutomatic方法代码示例

# 需要导入模块: from tube_calib_fit_params import TubeCalibFitParams [as 别名]
# 或者: from tube_calib_fit_params.TubeCalibFitParams import setAutomatic [as 别名]
def provideTheExpectedValue(filename):
    """
    Giving the expected value for the position of the peaks in pixel.

    The :func:`~Examples.minimalInput` let to the calibrate to guess the position of the pixels
    among the tubes. Altough it works nicelly, providing these expected values may improve the results.
    This is done through the **fitPar** parameter.
    """
    from tube_calib_fit_params import TubeCalibFitParams
    CalibInstWS = loadingStep(filename)
    # == Set parameters for calibration ==
    # Set what we want to calibrate (e.g whole intrument or one door )
    CalibratedComponent = 'MAPS'  # Calibrate all
    # define the known positions and function factor (edge, peak, peak, peak, edge)
    knownPos, funcFactor = [-0.50,-0.16,-0.00, 0.16, 0.50 ],[2,1,1,1,2]

    # the expected positions in pixels for the special points
    expectedPositions = [4.0, 85.0, 128.0, 161.0, 252.0]
    fitPar = TubeCalibFitParams(expectedPositions)
    fitPar.setAutomatic(True)

    # == Get the calibration and put results into calibration table ==
    calibrationTable = tube.calibrate(CalibInstWS, CalibratedComponent, knownPos, funcFactor,
                                      fitPar=fitPar)
    # == Apply the Calibation ==
    ApplyCalibration( Workspace=CalibInstWS, PositionTable=calibrationTable)

# 需要导入模块: from tube_calib_fit_params import TubeCalibFitParams [as 别名]
# 或者: from tube_calib_fit_params.TubeCalibFitParams import setAutomatic [as 别名]
    def _get_fit_par(self, args, tube_set, ideal_tube):
        if self.FITPAR in args:
            fit_par = args[self.FITPAR]
            # fitPar must be a TubeCalibFitParams
            if not isinstance(fit_par, TubeCalibFitParams):
                raise RuntimeError(
                    "Wrong argument {0}. This argument, when given, must be a valid TubeCalibFitParams object".
                    format(self.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 = tube_set.getTubeLength(0)
            # ndets = len(wsp_index_for_tube0)
            dummy_id1, ndets, dummy_step = tube_set.getDetectorInfoFromTube(0)

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

            fit_par = 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
            fit_par.setAutomatic(True)

        return fit_par

# 需要导入模块: from tube_calib_fit_params import TubeCalibFitParams [as 别名]
# 或者: from tube_calib_fit_params.TubeCalibFitParams import setAutomatic [as 别名]
def improvingCalibrationOfListOfTubes(filename):
    """
    Analysing the result of provideTheExpectedValue it was seen that the calibration
    of some tubes was not good.

    .. note::
          This method list some of them, there are a group belonging to window B2 that shows
          only 2 peaks that are not dealt with here.

    If first plot the bad ones using the **plotTube** option. It them, find where they fail, and how
    to correct their peaks, using the **overridePeaks**.
    If finally, applies the calibration again with the points corrected.
    """
    from tube_calib_fit_params import TubeCalibFitParams

    not_good = [19,37, 71, 75, 181, 186, 234, 235, 245, 273, 345]

    CalibInstWS = loadingStep(filename)
    # == Set parameters for calibration ==
    # Set what we want to calibrate (e.g whole intrument or one door )
    CalibratedComponent = 'MAPS'  # Calibrate all
    # define the known positions and function factor (edge, peak, peak, peak, edge)
    knownPos, funcFactor = [-0.50,-0.16,-0.00, 0.16, 0.50 ],[2,1,1,1,2]

    # the expected positions in pixels for the special points
    expectedPositions = [4.0, 85.0, 128.0, 161.0, 252.0]
    fitPar = TubeCalibFitParams(expectedPositions)
    fitPar.setAutomatic(True)

    # == Get the calibration and put results into calibration table ==
    #calibrationTable, peakTable= tube.calibrate(CalibInstWS, CalibratedComponent, knownPos, funcFactor,
    #	fitPar=fitPar, outputPeak=True, plotTube=not_good, rangeList=not_good)

    #CalibInstWS = loadingStep(filename)

    # it is defined as the mean values around the neighbours
    define_peaks = {19:[10, 80.9771, 123.221, 164.993, 245.717], # the first one was bad
                    37: [6.36, 80.9347, 122.941, 165.104, 248.32], # the first one was bad
                    71: [8.62752, 85.074, 124.919, 164.116, 246.82 ], # the last one was bad - check if we can inprove
                    75: [14.4285, 90.087, 128.987, 167.047, 242.62], # the last one was bad - check if we can inprove
                    181: [11.726, 94.0496, 137.816,  180, 255], # the third peak was lost
                    186:[11.9382, 71.5203, 107, 147.727, 239.041], #lost the second peak
                    234: [4.84, 82.7824, 123.125, 163.945, 241.877], # the first one was bad
                    235: [4.84, 80.0077, 121.002, 161.098, 238.502], # the first one was bad
                    245: [9.88089, 93.0593, 136.911, 179.5, 255], # the third peak was bad
                    273: [18.3711, 105.5, 145.5, 181.6, 243.252], # lost first and third peaks
                    345: [4.6084, 87.0351, 128.125, 169.923, 245.3] # the last one was bad
                   }
    calibrationTable, peakTable= tube.calibrate(CalibInstWS, CalibratedComponent, knownPos, funcFactor,
                                                fitPar=fitPar, outputPeak=True, overridePeaks=define_peaks)

    ApplyCalibration( Workspace=CalibInstWS, PositionTable=calibrationTable)

# 需要导入模块: from tube_calib_fit_params import TubeCalibFitParams [as 别名]
# 或者: from tube_calib_fit_params.TubeCalibFitParams import setAutomatic [as 别名]
def changeMarginAndExpectedValue(filename):
    """
    To fit correcly, it is important to have a good window around the peak. This windown is defined
    by the **margin** parameter.

    This examples shows how the results worsen if we change the margin from its default value **15**
    to **10**.

    It shows how to see the fitted values using the **plotTube** parameter.

    It will also output the peaks position and save them, through the **outputPeak** option and
    the :func:`tube.savePeak` method.

    An example of the fitted data compared to the acquired data to find the peaks positions:

    .. image:: /images/calibratePlotFittedData.png

    The result deteriorate, as you can see:

    .. image:: /images/calibrateChangeMarginAndExpectedValue.png

    """
    from tube_calib_fit_params import TubeCalibFitParams
    CalibInstWS = loadingStep(filename)
    # == Set parameters for calibration ==
    # Set what we want to calibrate (e.g whole intrument or one door )
    CalibratedComponent = 'MAPS'  # Calibrate all
    # define the known positions and function factor (edge, peak, peak, peak, edge)
    knownPos, funcFactor = [-0.50,-0.16,-0.00, 0.16, 0.50 ],[2,1,1,1,2]

    # the expected positions in pixels for the special points
    expectedPositions = [4.0, 85.0, 128.0, 161.0, 252.0]
    fitPar = TubeCalibFitParams(expectedPositions)
    fitPar.setAutomatic(True)

    # == Get the calibration and put results into calibration table ==
    calibrationTable, peakTable= tube.calibrate(CalibInstWS, CalibratedComponent, knownPos, funcFactor,
                                                fitPar=fitPar, plotTube=[1,10,100], outputPeak=True, margin=10)
    # == Apply the Calibation ==
    ApplyCalibration( Workspace=CalibInstWS, PositionTable=calibrationTable)

    tube.savePeak(peakTable, 'TubeDemoMaps01.txt')

# 需要导入模块: from tube_calib_fit_params import TubeCalibFitParams [as 别名]
# 或者: from tube_calib_fit_params.TubeCalibFitParams import setAutomatic [as 别名]
def calibrateB2Window(filename):
    """
    There are among the B2 window tubes, some tubes that are showing only 2 strips.

    Those tubes must be calibrated separated, as the known positions are not valid.

    This example calibrate them, using only 4 known values: 2 edges and 2 peaks.

    Run this example, and them see the worksapce in the calibrated instrument and you will see
    how it worked.

    The picture shows the output, look that only a section of the B2 Window was calibrated.

    .. image:: /images/calibrateB2Window.png

    """
    from tube_calib_fit_params import TubeCalibFitParams
    # b2 with 2 peaks range
    b2_range = list(range(196, 212)) + list(range(222, 233))

    CalibInstWS = loadingStep(filename)
    # == Set parameters for calibration ==
    # Set what we want to calibrate (e.g whole instrument or one door )
    CalibratedComponent = 'MAPS'  # Calibrate all
    # define the known positions and function factor (edge, peak, peak, peak, edge)
    knownPos, funcFactor = [-0.50, -0.16, 0.16, 0.50], [2, 1, 1, 2]

    # the expected positions in pixels for the special points
    expectedPositions = [4.0, 85.0, 161.0, 252.0]
    fitPar = TubeCalibFitParams(expectedPositions)
    fitPar.setAutomatic(True)

    # == Get the calibration and put results into calibration table ==
    calibrationTable, peakTable = tube.calibrate(CalibInstWS, CalibratedComponent, knownPos, funcFactor,
                                                 fitPar=fitPar, outputPeak=True, plotTube=[b2_range[0], b2_range[-1]],
                                                 rangeList=b2_range)

    mantid.ApplyCalibration(Workspace=CalibInstWS, PositionTable=calibrationTable)

# 需要导入模块: from tube_calib_fit_params import TubeCalibFitParams [as 别名]
# 或者: from tube_calib_fit_params.TubeCalibFitParams import setAutomatic [as 别名]
# == Create Objects needed for calibration ==

# The positions of the shadows and ends here are an intelligent guess.
# First array gives positions in Metres and second array gives type 1=Gaussian peak 2=edge.

knownPos = [-0.65, -0.22, -0.00, 0.22, 0.65]
funcForm = [2, 1, 1, 1, 2]

# Get fitting parameters
# Set initial parameters for peak finding
ExpectedHeight = -1000.0  # Expected Height of Gaussian Peaks (initial value of fit parameter)
ExpectedWidth = 8.0  # Expected width of Gaussian peaks in pixels  (initial value of fit parameter)
ExpectedPositions = [4.0, 85.0, 128.0, 161.0, 252.0]  # Expected positions of the edges and Gaussian peaks
# in pixels (initial values of fit parameters)
fitPar = TubeCalibFitParams(ExpectedPositions, ExpectedHeight, ExpectedWidth)
fitPar.setAutomatic(True)

print("Created objects needed for calibration.")

# == Get the calibration and put results into calibration table ==
calibrationTable = tube.calibrate(CalibInstWS, CalibratedComponent, knownPos, funcForm,
                                  fitPar=fitPar)
print("Got calibration (new positions of detectors) ")

# == Apply the Calibation ==
mantid.ApplyCalibration(Workspace=CalibInstWS, PositionTable=calibrationTable)
print("Applied calibration")

# == Save workspace ==
# mantid.SaveNexusProcessed(CalibInstWS, 'TubeCalibDemoMapsResult.nxs', "Result of Running TCDemoMaps.py")
# print("saved calibrated workspace (CalibInstWS) into Nexus file TubeCalibDemoMapsResult.nxs")

# 需要导入模块: from tube_calib_fit_params import TubeCalibFitParams [as 别名]
# 或者: from tube_calib_fit_params.TubeCalibFitParams import setAutomatic [as 别名]
    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)
#.........这里部分代码省略.........

# 需要导入模块: from tube_calib_fit_params import TubeCalibFitParams [as 别名]
# 或者: from tube_calib_fit_params.TubeCalibFitParams import setAutomatic [as 别名]
def completeCalibration(filename):
    """
    This example shows how to use some properties of calibrate method to
    join together the calibration done in :func:`provideTheExpectedValue`,
    and improved in :func:`calibrateB2Window`, and :func:`improvingCalibrationOfListOfTubes`.

    It also improves the result of the calibration because it deals with the E door. The
    aquired data cannot be used to calibrate the E door, and trying to do so, produces a bad
    result. In this example, the tubes inside the E door are excluded to the calibration.
    Using the '''rangeList''' option.
    """

    # first step, load the workspace
    from tube_calib_fit_params import TubeCalibFitParams
    CalibInstWS = loadingStep(filename)


    # == Set parameters for calibration ==
    # Set what we want to calibrate (e.g whole intrument or one door )
    CalibratedComponent = 'MAPS'  # Calibrate all

    # define the known positions and function factor (edge, peak, peak, peak, edge)
    knownPos, funcFactor = [-0.50,-0.16,-0.00, 0.16, 0.50 ],[2,1,1,1,2]

    # the expected positions in pixels for the special points
    expectedPositions = [4.0, 85.0, 128.0, 161.0, 252.0]
    fitPar = TubeCalibFitParams(expectedPositions)
    fitPar.setAutomatic(True)


    #execute the improvingCalibrationOfListOfTubes excluding the range of b2 window
    # correct the definition of the peaks for the folowing indexes
    #define_peaks = {19:[10, 80.9771, 123.221, 164.993, 245.717], # the first one was bad
    #	37: [6.36, 80.9347, 122.941, 165.104, 248.32], # the first one was bad
    #	71: [8.62752, 85.074, 124.919, 164.116, 246.82 ], # the last one was bad - check if we can inprove
    #	75: [14.4285, 90.087, 128.987, 167.047, 242.62], # the last one was bad - check if we can inprove
    #	181: [11.726, 94.0496, 137.816,  180, 255], # the third peak was lost
    #	186:[11.9382, 71.5203, 107, 147.727, 239.041], #lost the second peak
    #	234: [4.84, 82.7824, 123.125, 163.945, 241.877], # the first one was bad
    #	235: [4.84, 80.0077, 121.002, 161.098, 238.502], # the first one was bad
    #	245: [9.88089, 93.0593, 136.911, 179.5, 255], # the third peak was bad
    #	273: [18.3711, 105.5, 145.5, 181.6, 243.252],# lost first and third peaks
    #	345: [4.6084, 87.0351, 128.125, 169.923, 245.3]} # the last one was bad
    define_peaks = {19:[10, 80.9771, 123.221, 164.993, 245.717],\
    	37: [6.36, 80.9347, 122.941, 165.104, 248.32],\
    	71: [8.62752, 85.074, 124.919, 164.116, 246.82 ],\
    	75: [14.4285, 90.087, 128.987, 167.047, 242.62],\
    	181: [11.726, 94.0496, 137.816,  180, 255],\
    	186:[11.9382, 71.5203, 107, 147.727, 239.041],\
    	234: [4.84, 82.7824, 123.125, 163.945, 241.877],\
    	235: [4.84, 80.0077, 121.002, 161.098, 238.502],\
    	245: [9.88089, 93.0593, 136.911, 179.5, 255],\
    	273: [18.3711, 105.5, 145.5, 181.6, 243.252],\
    	345: [4.6084, 87.0351, 128.125, 169.923, 245.3]}

    b2_window = range(196,212) + range(222,233)

    complete_range = range(648)

    # this data can not be used to calibrate the E1 window, so, let's remove it.
    e1_window = range(560,577)
    aux = numpy.setdiff1d(complete_range, b2_window)
    # the group that have 3 stripts are all the tubes except the b2 window and e window.
    range_3_strips = numpy.setdiff1d(aux, e1_window)

    calibrationTable, peak3Table= tube.calibrate(CalibInstWS, CalibratedComponent, knownPos, funcFactor,\
    	fitPar=fitPar, outputPeak=True, overridePeaks=define_peaks, rangeList=range_3_strips)

    # now calibrate the b2_window REMOVE SECOND PEAK
    # define the known positions and function factor (edge, peak, peak, edge)
    knownPos, funcFactor = [-0.50,-0.16, 0.16, 0.50 ],[2,1,1,2]

    # the expected positions in pixels for the special points
    expectedPositions = [4.0, 85.0, 161.0, 252.0]
    fitPar = TubeCalibFitParams(expectedPositions)
    fitPar.setAutomatic(True)

    # apply the calibration for the b2_window 2 strips values
    calibrationTable, peak2Table = tube.calibrate(CalibInstWS, CalibratedComponent,
                                                  knownPos,  #these parameters now have only 4 points
                                                  funcFactor,
                                                  fitPar=fitPar,
                                                  outputPeak=True,
                                                  calibTable = calibrationTable, # it will append to the calibTable
                                                  rangeList = b2_window)

    ApplyCalibration( Workspace=CalibInstWS, PositionTable=calibrationTable)

# 需要导入模块: from tube_calib_fit_params import TubeCalibFitParams [as 别名]
# 或者: from tube_calib_fit_params.TubeCalibFitParams import setAutomatic [as 别名]
def findThoseTubesThatNeedSpecialCareForCalibration(filename):
    """
    The example :func:`provideTheExpectedValue` has shown its capability to calibrate almost
    all tubes, but,    as explored in the :func:`improvingCalibrationOfListOfTubes` and
    :func:`improvingCalibrationSingleTube` there are
    some tubes that could not be calibrated using that method.

    The goal of this method is to show one way to find the tubes that will require special care.

    It will first perform the same calibration seen in :func:`provideTheExpectedValue`,
    them, it will process the **peakTable** output of the calibrate method when enabling the
    parameter **outputPeak**.

    It them creates the Peaks workspace, that is the diffence of the peaks position from the
    expected values of the peaks positions for all the tubes. This allows to spot what are the
    tubes whose fitting are outliers in relation to the others.

    .. image:: /images/plotingPeaksDifference.png

    The final result for this method is to output using **plotTube** the result of the fitting
    to all the 'outliers' tubes.
    """
    from tube_calib_fit_params import TubeCalibFitParams
    CalibInstWS = loadingStep(filename)
    # == Set parameters for calibration ==
    # Set what we want to calibrate (e.g whole intrument or one door )
    CalibratedComponent = 'MAPS'  # Calibrate all
    # define the known positions and function factor (edge, peak, peak, peak, edge)
    knownPos, funcFactor = [-0.50,-0.16,-0.00, 0.16, 0.50 ],[2,1,1,1,2]

    # the expected positions in pixels for the special points
    expectedPositions = [4.0, 85.0, 128.0, 161.0, 252.0]
    fitPar = TubeCalibFitParams(expectedPositions)
    fitPar.setAutomatic(True)

    # == Get the calibration and put results into calibration table ==
    calibrationTable, peakTable= tube.calibrate(CalibInstWS, CalibratedComponent, knownPos, funcFactor,
                                                fitPar=fitPar, outputPeak=True)

    # == now, lets investigate the peaks

    #parsing the peakTable to produce a numpy array with dimension (number_of_tubes x number_of_peaks)
    print 'parsing the peak table'
    n = len(peakTable)
    peaksId = n*['']
    data = numpy.zeros((n,5))
    line = 0
    for row in peakTable:
        data_row = [row['Peak%d'%(i)] for i in [1,2,3,4,5]]
        data[line,:] = data_row
        peaksId[line] = row['TubeId']
        line+=1
    # 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 = numpy.mean(data,axis=0)
    #calculate how far from the expected position each peak position is
    distance_from_expected =  numpy.abs(data - expected_peak_pos)

    print 'Creating the Peaks Workspace that shows the distance from the expected value for all peaks for each tube'
    # Let's see these peaks:
    Peaks = CreateWorkspace(range(n),distance_from_expected,NSpec=5)

    # plot all the 5 peaks for Peaks Workspace. You will see that most of the tubes differ
    # at most 12 pixels from the expected values.

    #so let's investigate those that differ more than 12
    # return an array with the indexes for the first axis which is the tube indentification
    check = numpy.where(distance_from_expected > 12)[0]

    #remove repeated values
    #select only those tubes inside the problematic_tubes
    problematic_tubes = list(set(check))

    print 'Tubes whose distance is far from the expected value: ', problematic_tubes

    print 'Calibrating again only these tubes'
    #let's confir that our suspect works
    CalibInstWS = loadingStep(filename)
    calibrationTable = tube.calibrate(CalibInstWS, CalibratedComponent, knownPos, funcFactor,\
    	fitPar=fitPar, rangeList= problematic_tubes, plotTube=problematic_tubes)

# 需要导入模块: from tube_calib_fit_params import TubeCalibFitParams [as 别名]
# 或者: from tube_calib_fit_params.TubeCalibFitParams import setAutomatic [as 别名]
def improvingCalibrationSingleTube(filename):
    """
    The :func:`~Examples.provideTheExpectedValue` provided a good solution, but there are few
    tubes whose calibration was not so good.

    This method explores how to deal with these tubes.

    First of all, it is important to identify the tubes that did not work well.

    From the outputs of provideTheExpectedValue, looking inside the instrument tree,
    it is possible to list all the tubes that are not so good.

    Unfortunatelly, they do not have a single name identifier.
    So, locating them it is a little bit trickier.
    The :func:`~Examples.findThoseTubesThatNeedSpecialCareForCalibration` shows one way of finding those
    tubes.     The index is the position inside the PeakTable.

    For this example, we have used inspection from the Instrument View.
    One of them is inside the A1_Window, 3rd PSD_TUBE_STRIP 8 pack up, 4th PSD_TUBE_STRIP: Index = 8+8+4 - 1 = 19.

    In this example, we will ask the calibration to run the calibration only for 3 tubes
    (indexes 18,19,20). Them, we will check why the 19 is not working well. Finally, we will try to
    provide another peaks position for this tube,
    and run the calibration again for these tubes, to improve the results.

    This example shows how to use **overridePeaks** option
    """
    from tube_calib_fit_params import TubeCalibFitParams
    import time
    CalibInstWS = loadingStep(filename)
    # == Set parameters for calibration ==
    # Set what we want to calibrate (e.g whole intrument or one door )
    CalibratedComponent = 'MAPS'  # Calibrate all
    # define the known positions and function factor (edge, peak, peak, peak, edge)
    knownPos, funcFactor = [-0.50,-0.16,-0.00, 0.16, 0.50 ],[2,1,1,1,2]

    # the expected positions in pixels for the special points
    expectedPositions = [4.0, 85.0, 128.0, 161.0, 252.0]
    fitPar = TubeCalibFitParams(expectedPositions)
    fitPar.setAutomatic(True)

    # == Get the calibration and put results into calibration table ==
    calibrationTable, peakTable= tube.calibrate(CalibInstWS, CalibratedComponent, knownPos, funcFactor,
                                                fitPar=fitPar, outputPeak=True, plotTube=[18,19,20], rangeList=[18,19,20])

    ApplyCalibration( Workspace=CalibInstWS, PositionTable=calibrationTable)


    # reload to reset the calibration applied
    CalibInstWS = loadingStep(filename)
    # looking into the second line of calibrationTable, you will see that it defines the peaks for the first position
    # as 14.9788, -0.303511, 9.74828
    # let's change the peak from  -0.303511 to 8.14

    #to override the peaks definition, we use the overridePeaks
    overridePeaks = {19: [8.14, 80.9771, 123.221, 164.993, 245.717]}

    # == Get the calibration and put results into calibration table ==
    # we will not plot anymore, because it will not plot the overrided peaks
    calibrationTable, peakTable= tube.calibrate(CalibInstWS, CalibratedComponent, knownPos, funcFactor,
                                                fitPar=fitPar, outputPeak=True, rangeList=[18,19,20], overridePeaks=overridePeaks)

    ApplyCalibration( Workspace=CalibInstWS, PositionTable=calibrationTable)

# 需要导入模块: from tube_calib_fit_params import TubeCalibFitParams [as 别名]
# 或者: from tube_calib_fit_params.TubeCalibFitParams import setAutomatic [as 别名]
def calibrateMerlin(filename):
  # == Set parameters for calibration ==

    rangeLower = 3000 # Integrate counts in each spectra from rangeLower to rangeUpper
    rangeUpper = 20000 #

  # Get calibration raw file and integrate it
    rawCalibInstWS = LoadRaw(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"

  # the known positions are given in pixels inside the tubes and transformed to provide the positions
  # with the center of the tube as the origin
    knownPositions = 2.92713867188*(numpy.array([ 27.30074322, 92.5,    294.65178585,    362.37861919 , 512.77103043    ,663.41425323, 798.3223896,     930.9, 997.08480835])/1024 - 0.5)
    funcForm = numpy.array([2,2,1,1,1,1,1,2,2],numpy.int8)
  # The calibration will follow different steps for sets of tubes

  # For the door9, the best points to define the known positions are the 1st edge, 5 peaks, last edge.
    points7 = knownPositions[[0,2,3,4,5,6,8]]
    points7func = funcForm[[0,2,3,4,5,6,8]]

    door9pos = points7
    door9func = points7func
    CalibratedComponent = 'MERLIN/door9'    # door9
  # == Get the calibration and put results into calibration table ==
  # also put peaks into PeakFile
    calibrationTable, peakTable = tube.calibrate(CalibInstWS, CalibratedComponent, door9pos, door9func,
        outputPeak=True,
        margin=30,
        rangeList=range(20) # because 20, 21, 22, 23 are defective detectors
        )
    print "Got calibration (new positions of detectors) and put slit peaks into file TubeDemoMerlin01.txt"
    analisePeakTable(peakTable, 'door9_tube1_peaks')

  # For the door8, the best points to define the known positions are the 1st edge, 5 peaks, last_edge
    door8pos = points7
    door8func = points7func
    CalibratedComponent = 'MERLIN/door8'
    calibrationTable, peakTable = tube.calibrate(CalibInstWS, CalibratedComponent, door8pos,
        door8func,
    outputPeak = True, #change to peakTable to append to peakTable
    calibTable = calibrationTable,
    margin = 30)
    analisePeakTable(peakTable, 'door8_peaks')

  # For the doors 7,6,5,4, 2, 1 we may use the 9 points
    doorpos = knownPositions
    doorfunc = funcForm
    CalibratedComponent = ['MERLIN/door%d'%(i) for i in [7,6,5,4, 2, 1]]
    calibrationTable, peakTable = tube.calibrate(CalibInstWS, CalibratedComponent, doorpos,\
        doorfunc,\
    outputPeak = True,\
    calibTable = calibrationTable,\
    margin = 30)
    analisePeakTable(peakTable, 'door1to7_peaks')

  # The door 3 is a special case, because it is composed by diffent kind of tubes.
  # door 3 tubes: 5_8, 5_7, 5_6, 5_5, 5_4, 5_3, 5_2, 5_1, 4_8, 4_7, 4_6, 4_5, 4_4, 4_3, 4_2, 4_1, 3_8, 3_7, 3_6, 3_5, 3_4
  # obeys the same rules as the doors 7, 6, 5, 4, 2, 1
  # For the tubes 3_3, 3_2, 3_1 -> it is better to skip the central peak
  # For the tubes 1_x (smaller tube below), it is better to take the final part of known positions: peak4,peak5,edge6,edge7
  # For the tubes 2_x (smaller tube above, it is better to take the first part of known positions: edge1, edge2, peak1,peak2

  # NOTE: the smaller tubes they have length = 1.22879882813, but 1024 detectors
  # so we have to correct the known positiosn by multiplying by its lenght and dividing by the longer dimension

    from tube_calib_fit_params import TubeCalibFitParams

  # calibrating tubes 1_x
    CalibratedComponent = ['MERLIN/door3/tube_1_%d'%(i) for i in range(1,9)]

    half_diff_center = (2.92713867188 -1.22879882813)/2    # difference among the expected center position for both tubes

  # here a little bit of attempts is necessary. The efective center position and lengh is different for the calibrated tube, that
  # is the reason, the calibrated values of the smaller tube does not seems aligned with the others. By, finding the 'best' half_diff_center
  # value, the alignment occurs nicely.
    half_diff_center = 0.835 #

  # the knownpositions were given with the center of the bigger tube as origin, to convert
  # to the center of the upper tube as origin is necessary to subtract them with  the half_diff_center
    doorpos = knownPositions[[5,6,7,8]] - half_diff_center
    doorfunc = [1,1,2,2]
  # for the smal tubes, automatically searching for the peak position in pixel was not working quite well,
  # so we will give the aproximate position for these tubes through fitPar argument
    fitPar = TubeCalibFitParams([216, 527, 826, 989])
    fitPar.setAutomatic(True)

    calibrationTable, peakTable = tube.calibrate(CalibInstWS, CalibratedComponent, doorpos,\
    doorfunc,\
    outputPeak = True,\
    fitPar = fitPar,\
    calibTable = calibrationTable,\
    margin = 30)
    analisePeakTable(peakTable, 'door3_tube1_peaks')

  # calibrating tubes 2_x
    CalibratedComponent = ['MERLIN/door3/tube_2_%d'%(i) for i in range(1,9)]
  # the knownpositions were given with the center of the bigger tube as origin, to convert
#.........这里部分代码省略.........

# 需要导入模块: from tube_calib_fit_params import TubeCalibFitParams [as 别名]
# 或者: from tube_calib_fit_params.TubeCalibFitParams import setAutomatic [as 别名]

#.........这里部分代码省略.........

    #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