def constructIdealTubeFromRealTube(ws, tube, fitPar, funcForm):
"""
Construct an ideal tube from an actual tube (assumed ideal)
:param ws: integrated workspace
:param tube: specification of one tube (if several tubes, only first tube is used)
:param fitPar: initial fit parameters for peak of the tube
:param funcForm: listing the type of known positions 1=Gaussian; 2=edge
:rtype: IdealTube
"""
# Get workspace indices
idealTube = IdealTube()
nTubes = tube.getNumTubes()
if nTubes < 1:
raise RuntimeError(
"Invalid tube specification received by constructIdealTubeFromRealTube"
)
elif nTubes > 1:
print "Specification has several tubes. The ideal tube will be based on the first tube", tube.getTubeName(
0)
wht, _ = tube.getTube(0)
# print wht
# Check tube
if len(wht) < 1:
raise RuntimeError(
"Unable to get any workspace indices for this tube. Cannot use as ideal tube."
)
# Get actual tube on which ideal tube is based
actualTube = getPoints(ws, funcForm, fitPar, wht)
print "Actual tube that ideal tube is to be based upon", actualTube
# Get ideal tube based on this actual tube
try:
idealTube.setArray(actualTube)
except:
msg = "Attempted to create ideal tube based on actual tube" + str(
actualTube)
msg += "Unable to create ideal tube."
msg += "Please choose another tube for constructIdealTubeFromRealTube()."
raise RuntimeError(msg)
return idealTube
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 aligned that may be considered
as pixels for the analogy when they values are displayed.
The position of these pixels are provided by the manufacturer, 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 against 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 dynamically 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 two 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 skips 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
The fitting framework of Mantid stores values and errors for the optimized coefficients of the polynomial
in a table (of type TableWorkspace). These tables can be grouped into a WorkspaceGroup by passing
the name of this workspace to option **parameters_table_group**. The name of each table workspace will
the string **parameters_table_group** plus a suffix which is the index of the tube in the input **tubeSet**.
**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 polynomial to fit the pixels positions against 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 useful 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).
"""
# Legacy code requires kwargs to contain only the list of parameters specify below. Thus, we pop other
# arguments into temporary variables, such as `parameters_table_group`
parameters_table_group = kwargs.pop(
'parameters_table_group'
) if 'parameters_table_group' in kwargs else None
FITPAR = 'fitPar'
MARGIN = 'margin'
RANGELIST = 'rangeList'
CALIBTABLE = 'calibTable'
PLOTTUBE = 'plotTube'
EXCLUDESHORT = 'excludeShortTubes'
OVERRIDEPEAKS = 'overridePeaks'
FITPOLIN = 'fitPolyn'
OUTPUTPEAK = 'outputPeak'
param_helper = _CalibrationParameterHelper(FITPAR, MARGIN, RANGELIST,
CALIBTABLE, PLOTTUBE,
EXCLUDESHORT, OVERRIDEPEAKS,
FITPOLIN, OUTPUTPEAK)
# check that only valid arguments were passed through kwargs
param_helper.ensure_no_unknown_kwargs(kwargs)
# check parameter ws: if it was given as string, transform it in
# mantid object
ws = _CalibrationParameterHelper.enforce_matrix_ws(ws)
# check parameter tubeSet. It accepts string or preferable a TubeSpec
tubeSet = _CalibrationParameterHelper.enforce_tube_spec(tubeSet, ws)
# check the known_positions parameter
# for old version compatibility, it also accepts IdealTube, even though
# they should only be used internally
_CalibrationParameterHelper.validate_known_positions(knownPositions)
ideal_tube = IdealTube()
ideal_tube.setArray(numpy.array(knownPositions))
n_peaks = len(ideal_tube.getArray())
# deal with funcForm parameter
_CalibrationParameterHelper.validate_func_form(funcForm, n_peaks)
# apply the functional form to the ideal Tube
ideal_tube.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
fit_par = param_helper.get_parameter(FITPAR,
kwargs,
tube_set=tubeSet,
ideal_tube=ideal_tube)
if MARGIN in kwargs:
margin = param_helper.get_parameter(MARGIN, kwargs)
fit_par.setMargin(margin)
range_list = param_helper.get_parameter(RANGELIST,
kwargs,
default_range_list=list(
range(tubeSet.getNumTubes())))
calib_table = param_helper.get_parameter(CALIBTABLE, kwargs)
plot_tube = param_helper.get_parameter(PLOTTUBE, kwargs)
exclude_short_tubes = param_helper.get_parameter(EXCLUDESHORT, kwargs)
override_peaks = param_helper.get_parameter(OVERRIDEPEAKS,
kwargs,
tube_set=tubeSet,
ideal_tube=ideal_tube)
polin_fit = param_helper.get_parameter(
FITPOLIN, kwargs) # order of the fiting polynomial. Default is 2
output_peak, delete_peak_table_after = param_helper.get_parameter(
OUTPUTPEAK, kwargs, ideal_tube=ideal_tube)
getCalibration(ws,
tubeSet,
calib_table,
fit_par,
ideal_tube,
output_peak,
override_peaks,
exclude_short_tubes,
plot_tube,
range_list,
polin_fit,
parameters_table_group=parameters_table_group)
if delete_peak_table_after:
DeleteWorkspace(str(output_peak))
return calib_table
else:
return calib_table, output_peak
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
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)