def PyInit(self):
arrvalidator = StringArrayMandatoryValidator()
lrg='Input'
self.declareProperty(StringArrayProperty('Workspaces', values=[], validator=arrvalidator,\
direction=Direction.Input), doc='list of input workspaces')
self.declareProperty('LoadErrors', True, direction=Direction.Input,\
doc='Do we load error data contained in the workspaces?')
self.declareProperty(FloatArrayProperty('ParameterValues', values=[],\
validator=FloatArrayMandatoryValidator(),direction=Direction.Input), doc='list of input parameter values')
self.setPropertyGroup('Workspaces', lrg)
self.setPropertyGroup('LoadErrors', lrg)
self.setPropertyGroup('ParameterValues', lrg)
self.declareProperty('LocalRegression', True, direction=Direction.Input, doc='Perform running local-regression?')
condition = EnabledWhenProperty("LocalRegression", PropertyCriterion.IsDefault)
self.declareProperty('RegressionWindow', 6, direction=Direction.Input, doc='window size for the running local-regression')
self.setPropertySettings("RegressionWindow", condition)
regtypes = [ 'linear', 'quadratic']
self.declareProperty('RegressionType', 'quadratic', StringListValidator(regtypes),\
direction=Direction.Input, doc='type of local-regression; linear and quadratic are available')
self.setPropertySettings("RegressionType", condition)
lrg = 'Running Local Regression Options'
self.setPropertyGroup('LocalRegression', lrg)
self.setPropertyGroup('RegressionWindow', lrg)
self.setPropertyGroup('RegressionType', lrg)
lrg='Output'
self.declareProperty(FloatArrayProperty('TargetParameters', values=[], ), doc="Parameters to interpolate the structure factor")
self.declareProperty(StringArrayProperty('OutputWorkspaces', values=[], validator=arrvalidator),\
doc='list of output workspaces to save the interpolated structure factors')
self.setPropertyGroup('TargetParameters', lrg)
self.setPropertyGroup('OutputWorkspaces', lrg)
self.channelgroup = None
def test_dtype_function_calls(self):
"""
Tests the dtype() function call for the data types stored in the array.
"""
# Set up
direc = Direction.Output
validator = NullValidator()
# Create float array
float_input_values = [1.1, 2.5, 5.6, 4.6, 9.0, 6.0]
float_arr = FloatArrayProperty("floats", float_input_values, validator,
direc)
# Create int array
int_input_values = [1, 2, 5, 4, 9, 6]
int_arr = IntArrayProperty("integers", int_input_values, validator,
direc)
# Create string array
str_input_values = ["a", "b", "c", "d", "e"]
str_arr = StringArrayProperty("letters", str_input_values, validator,
direc)
# Test
self.assertEquals(float_arr.dtype(), "f")
self.assertEquals(int_arr.dtype(), "i")
self.assertEquals(str_arr.dtype(), "S1")
def PyInit(self):
self.declareProperty(IMDEventWorkspaceProperty("InputWorkspace", defaultValue="",
optional=PropertyMode.Mandatory,
direction=Direction.Input),
doc="Input MDEvent workspace to convert to a MDHisto in HKL")
self.declareProperty(IPeaksWorkspaceProperty("PeaksWorkspace", defaultValue="", optional=PropertyMode.Optional,
direction=Direction.Input),
doc="Optional peaks workspace to retrieve the UB matrix from, instead of InputWorkspace.")
self.declareProperty(FloatArrayProperty("Uproj", [1, 0, 0], validator=FloatArrayLengthValidator(3),
direction=Direction.Input),
doc="Defines the first projection vector of the target Q coordinate system in HKL mode")
self.declareProperty(FloatArrayProperty("Vproj", [0, 1, 0], validator=FloatArrayLengthValidator(3),
direction=Direction.Input),
doc="Defines the second projection vector of the target Q coordinate system in HKL mode")
self.declareProperty(FloatArrayProperty("Wproj", [0, 0, 1], validator=FloatArrayLengthValidator(3),
direction=Direction.Input),
doc="Defines the third projection vector of the target Q coordinate system in HKL mode")
self.declareProperty(FloatArrayProperty("Extents", [-6.02, 6.02, -6.02, 6.02, -6.02, 6.02],
direction=Direction.Input),
"Binning parameters for each dimension. Enter it as a"
"comma-separated list of values with the"
"format: 'minimum,maximum,'.")
self.declareProperty(IntArrayProperty("Bins", [301, 301, 301],
direction=Direction.Input),
"Number of bins to use for each dimension, Enter it as a"
"comma-separated list of integers.")
self.declareProperty(IMDHistoWorkspaceProperty("OutputWorkspace", "",
optional=PropertyMode.Mandatory,
direction=Direction.Output),
doc="Output MDWorkspace in Q-space, name is prefix if multiple input files were provided.")
def PyInit(self):
self.declareProperty(FloatArrayProperty("Masses", direction=Direction.Input),
doc="The masses that make up the sample")
self.declareProperty(FloatArrayProperty("Amplitudes", direction=Direction.Input),
doc="The amplitudes of the peaks")
self.declareProperty("TransmissionGuess", 1.0,
doc="Initial guess for the transmission")
self.declareProperty("Thickness", 5.0,
doc="The thickness of the sample in centimetres (cm)")
self.declareProperty("NumberDensity", 1.0,
doc="The Number Density of the sample material")
self.declareProperty(ITableWorkspaceProperty("DensityWorkspace", "",
direction=Direction.Output),
doc="Output Workspace containing the iterative "
+"approximations for Sample Density. The final "
+"Y value in the first spectrum will be the last iteration")
self.declareProperty(ITableWorkspaceProperty("TransmissionWorkspace", "",
direction=Direction.Output),
doc="Output Workspace containing the iterative "
+"approximation for Transmission.")
def PyInit(self):
self.declareProperty(MatrixWorkspaceProperty('InputWorkspace', '',
Direction.Input),
doc='Workspace to match the spectra between')
self.declareProperty(MatrixWorkspaceProperty('OutputWorkspace', '',
Direction.Output),
doc='Workspace with the spectra matched')
self.declareProperty('ReferenceSpectrum',
1,
doc='Spectrum to match other spectra to')
self.declareProperty('CalculateOffset',
True,
doc='Calculate vertical shift')
self.declareProperty('CalculateScale',
True,
doc='Calculate scale factor')
self.declareProperty(
FloatArrayProperty('Offset', values=[],
direction=Direction.Output),
'Additive factor from matching')
self.declareProperty(
FloatArrayProperty('Scale', values=[], direction=Direction.Output),
'Multiplicitive factor from matching')
self.declareProperty(
FloatArrayProperty('ChiSq', values=[], direction=Direction.Output),
'Unweighted ChiSq between the spectrum and the reference. '
'NaN means that the spectrum was not matched')
def test_name_validator_direction_constructor_gives_correct_object(self):
"""
Test the constructor that takes a name, validator & direction
"""
name = "numbers"
direc = Direction.Output
validator = NullValidator()
arr = FloatArrayProperty(name, validator, direc)
self._check_object_attributes(arr, name, direc)
self.assertEquals(arr.isValid(), "")
def PyInit(self):
""" Declare properties
"""
self.declareProperty(mantid.api.WorkspaceProperty( "Workspace", "",
direction=mantid.kernel.Direction.Input,
validator=mantid.api.InstrumentValidator()),
"Input workspace")
self.declareProperty(IntArrayProperty("UpstreamSpectra", Direction.Output))
self.declareProperty(FloatArrayProperty("UpstreamDetectorDistances", Direction.Output))
self.declareProperty(IntArrayProperty("DownstreamSpectra", Direction.Output))
self.declareProperty(FloatArrayProperty("DownstreamDetectorDistances", Direction.Output))
def PyInit(self):
self.declareProperty(name='Instrument', defaultValue='IRIS',
validator=StringListValidator(['IRIS', 'OSIRIS']),
doc='The name of the instrument.')
self.declareProperty(name='Analyser', defaultValue='',
validator=StringListValidator(['graphite', 'mica', 'fmica']),
doc='The analyser bank used during run.')
self.declareProperty(name='Reflection', defaultValue='',
validator=StringListValidator(['002', '004', '006']),
doc='Reflection number for instrument setup during run.')
self.declareProperty(name="FirstRun", defaultValue=-1,
validator=IntBoundedValidator(lower=0),
doc="First Sample run-number.")
self.declareProperty(name='LastRun', defaultValue=-1,
validator=IntBoundedValidator(lower=0),
doc="Last Sample run-number.")
self.declareProperty(name='NumberSamples', defaultValue=-1,
validator=IntBoundedValidator(lower=0),
doc="Increment for run-number.")
self.declareProperty(IntArrayProperty(name='SpectraRange', values=[0, 1],
validator=IntArrayLengthValidator(2)),
doc='Comma separated range of spectra numbers to use.')
self.declareProperty(FloatArrayProperty(name='ElasticRange',
validator=FloatArrayLengthValidator(2)),
doc='Energy range for the elastic component.')
self.declareProperty(FloatArrayProperty(name='InelasticRange',
validator=FloatArrayLengthValidator(2)),
doc='Energy range for the inelastic component.')
self.declareProperty(FloatArrayProperty(name='TotalRange',
validator=FloatArrayLengthValidator(2)),
doc='Energy range for the total energy component.')
self.declareProperty(name='SampleEnvironmentLogName', defaultValue='Position',
doc='Name of the sample environment log entry')
sample_environment_log_values = ['last_value', 'average']
self.declareProperty('SampleEnvironmentLogValue', 'last_value',
StringListValidator(sample_environment_log_values),
doc='Value selection of the sample environment log entry')
self.declareProperty(name='MSDFit', defaultValue=False,
doc='Perform an MSDFit. Do not use with GroupingMethod as "All"')
self.declareProperty(name='WidthFit', defaultValue=False,
doc='Perform a 2 peak width Fit. Do not use with GroupingMethod as "All"')
self.declareProperty(name='Plot', defaultValue=False,
doc='True to plot the output data.')
self.declareProperty(name='Save', defaultValue=False,
doc='True to save the output data.')
def PyInit(self):
self.declareProperty(StringArrayProperty('InputWorkspaces', direction=Direction.Input, validator=ADSValidator()),
doc='List of workspaces or group workspace containing workspaces to be merged.')
self.declareProperty(WorkspaceProperty('OutputWorkspace', '', direction=Direction.Output),
doc='The merged workspace.')
self.declareProperty(FloatArrayProperty('XMin', [], direction=Direction.Input),
doc='Array of minimum X values for each workspace.')
self.declareProperty(FloatArrayProperty('XMax', [], direction=Direction.Input),
doc='Array of maximum X values for each workspace.')
self.declareProperty('CalculateScale', True,
doc='Calculate scale factor when matching spectra.')
self.declareProperty('CalculateOffset', True,
doc='Calculate vertical shift when matching spectra.')
def test_name_string_values_validator_direction_constructor_gives_correct_object(self):
"""
Test the constructor that takes a name, values as string, validator & direction
"""
name = "numbers"
direc = Direction.Output
validator = NullValidator()
values_str = "1.345,34.2,5345.3,4,5.3948"
arr = FloatArrayProperty(name, values_str, validator, direc)
self._check_object_attributes(arr, name, direc, length = 5)
self.assertEquals(arr.isValid(), "")
values = arr.value
self.assertTrue(isinstance(values, np.ndarray))
def test_construct_numpy_array_with_given_dtype_float(self):
# Set up
direc = Direction.Output
validator = NullValidator()
# Create float array
float_input_values = [1.1, 2.5, 5.6, 4.6, 9.0, 6.0]
float_arr = FloatArrayProperty("floats", float_input_values, validator, direc)
# Use the returned dtype() to check it works with numpy arrays
x = np.arange(1, 10, dtype=float_arr.dtype())
self.assertIsInstance(x, np.ndarray)
self.assertEquals(x.dtype, float_arr.dtype())
def test_construct_numpy_array_with_given_dtype_float(self):
# Set up
direc = Direction.Output
validator = NullValidator()
# Create float array
float_input_values = [1.1, 2.5, 5.6, 4.6, 9.0, 6.0]
float_arr = FloatArrayProperty("floats", float_input_values, validator,
direc)
# Use the returned dtype() to check it works with numpy arrays
x = np.arange(1, 10, dtype=float_arr.dtype())
self.assertIsInstance(x, np.ndarray)
self.assertEquals(x.dtype, float_arr.dtype())
def PyInit(self):
self.declareProperty(MatrixWorkspaceProperty(
'InputWorkspace',
'',
direction=Direction.Input,
),
doc='Incident spectrum to be fit.')
self.declareProperty(
MatrixWorkspaceProperty('OutputWorkspace',
'',
direction=Direction.Output),
doc=
'Output workspace containing the fit and it\'s first derivative.')
self.declareProperty(
name='WorkspaceIndex',
defaultValue=0,
doc=
'Workspace index of the spectra to be fitted (Defaults to the first index.)'
)
self.declareProperty(
FloatArrayProperty(name="BinningForCalc",
validator=RebinParamsValidator(AllowEmpty=True),
direction=Direction.Input),
doc=
'Bin range for calculation given as an array of floats in the same format as `Rebin`: '
'[Start],[Increment],[End]. If empty use default binning. The calculated '
'spectrum will use this binning')
self.declareProperty(
FloatArrayProperty(name="BinningForFit",
validator=RebinParamsValidator(AllowEmpty=True),
direction=Direction.Input),
doc=
'Bin range for fitting given as an array of floats in the same format as `Rebin`: '
'[Start],[Increment],[End]. If empty use BinningForCalc. The '
'incident spectrum will be rebined to this range before being fit.'
)
self.declareProperty(
name='FitSpectrumWith',
defaultValue='GaussConvCubicSpline',
validator=StringListValidator([
'GaussConvCubicSpline', 'CubicSpline', 'CubicSplineViaMantid'
]),
doc='The method for fitting the incident spectrum.')
def PyInit(self):
int_validator = IntArrayOrderedPairsValidator()
self.declareProperty(
IntArrayProperty("IntInput", int_validator))
float_validator = FloatArrayOrderedPairsValidator()
self.declareProperty(
FloatArrayProperty("FloatInput", float_validator))
def PyInit(self):
self.declareProperty(MatrixWorkspaceProperty(
'InputWorkspace',
'',
direction=Direction.Input,
validator=WorkspaceUnitValidator("Wavelength")),
doc='Flood weighting measurement')
self.declareProperty(MatrixWorkspaceProperty(
'TransmissionWorkspace',
'',
direction=Direction.Input,
optional=PropertyMode.Optional,
validator=WorkspaceUnitValidator("Wavelength")),
doc='Flood weighting measurement')
validator = FloatArrayBoundedValidator(lower=0.)
self.declareProperty(
FloatArrayProperty('Bands', [],
direction=Direction.Input,
validator=validator),
doc='Wavelength bands to use. Single pair min to max.')
self.declareProperty(MatrixWorkspaceProperty(
'OutputWorkspace', '', direction=Direction.Output),
doc='Normalized flood weighting measurement')
self.declareProperty("SolidAngleCorrection",
True,
direction=Direction.Input,
doc="Perform final solid angle correction")
def PyInit(self):
# State
self.declareProperty(PropertyManagerProperty('SANSState'),
doc='A property manager which fulfills the SANSState contract.')
# Workspace which is to be moved
self.declareProperty(MatrixWorkspaceProperty("Workspace", '',
optional=PropertyMode.Mandatory, direction=Direction.InOut),
doc='The sample scatter workspace. This workspace does not contain monitors.')
# Move Type
move_types = StringListValidator(list(self._make_move_type_map().keys()))
self.declareProperty('MoveType', 'ElementaryDisplacement', validator=move_types, direction=Direction.Input,
doc='The type of movement. This can be: '
'1) InitialMove for freshly workspaces, '
'2) ElementaryDisplacement of the instrument component, '
'3) SetToZero resets a component to the initial position.')
# Coordinates of the beam
self.declareProperty(FloatArrayProperty(name='BeamCoordinates', values=[]),
doc='The coordinates which are used to position the instrument component(s). If nothing '
'is specified, then the coordinates from SANSState are used')
# Components which are to be moved
self.declareProperty('Component', '', direction=Direction.Input, doc='Component that should be moved.')
# If is a transmission workspace
self.declareProperty('IsTransmissionWorkspace', False, direction=Direction.Input,
doc='If the input workspace is a transmission or direct workspace')
def test_attrs_with_dims():
from mantid.kernel import FloatArrayProperty
import mantid.simpleapi as sapi
dataX = [1, 2, 3]
dataY = [1, 2, 3]
ws = sapi.CreateWorkspace(DataX=dataX,
DataY=dataY,
NSpec=1,
UnitX="Wavelength")
# Time series property
sapi.AddSampleLog(ws, 'attr0', LogText='1', LogType='Number Series')
# Single value property
sapi.AddSampleLog(ws, 'attr1', LogText='1', LogType='Number')
# Array property (not time series)
p = FloatArrayProperty('attr2', np.arange(10))
run = ws.mutableRun()
run.addProperty('attr2', p, replace=True)
ds = scn.from_mantid(ws)
# Variable (single value) wrapped DataArray
assert isinstance(ds.attrs['attr0'].value, sc.DataArray)
assert 'time' in ds.attrs['attr0'].value.coords
# Variable (single value)
assert isinstance(ds.attrs['attr1'].value, int)
# Variable (single value) wrapped Variable
assert isinstance(ds.attrs['attr2'].value, sc.Variable)
assert ds.attrs['attr2'].shape == [] # outer wrapper
assert ds.attrs['attr2'].value.shape == [10] # inner held
def PyInit(self):
self.declareProperty(
FloatArrayProperty("FloatInput", FloatArrayMandatoryValidator()))
self.declareProperty(
IntArrayProperty("IntInput", IntArrayMandatoryValidator()))
self.declareProperty(
StringArrayProperty("StringInput",
StringArrayMandatoryValidator()))
def PyInit(self):
#
# Properties
#
self.declareProperty('RunNumbers', '', 'Sample run numbers')
self.declareProperty(
FloatArrayProperty(
'MomentumTransferBins',
[0.1, 0.0025, 2.5], # invers A
direction=Direction.Input),
'Momentum transfer binning scheme')
self.declareProperty(WorkspaceProperty('OutputWorkspace',
'',
optional=PropertyMode.Mandatory,
direction=Direction.Output),
doc='Output reduced workspace')
#
# Common Properties
#
required_title = 'Required Properties'
self.declareProperty(FileProperty(name='MaskFile',
defaultValue=self._mask_file,
action=FileAction.OptionalLoad,
extensions=['.xml']),
doc='See documentation for latest mask files.')
self.declareProperty(
'MonitorNormalization', True,
'Normalization with wavelength-dependent '
'monitor counts')
for a_property in ('MaskFile', 'MonitorNormalization'):
self.setPropertyGroup(a_property, required_title)
#
# Background for the sample runs
#
background_title = 'Background runs'
self.declareProperty('BackgroundRuns', '', 'Background run numbers')
self.setPropertyGroup('BackgroundRuns', background_title)
self.declareProperty("BackgroundScale",
1.0,
doc='The background will be scaled by this ' +
'number before being subtracted.')
self.setPropertyGroup('BackgroundScale', background_title)
self.declareProperty(WorkspaceProperty('OutputBackground',
'',
optional=PropertyMode.Optional,
direction=Direction.Output),
doc='Reduced workspace for background runs')
self.setPropertyGroup('OutputBackground', background_title)
#
# Vanadium
#
vanadium_title = 'Vanadium runs'
self.declareProperty('VanadiumRuns', '', 'Vanadium run numbers')
self.setPropertyGroup('VanadiumRuns', vanadium_title)
def PyInit(self):
self.declareProperty(MatrixWorkspaceProperty('InputWorkspace', "", direction=Direction.Input))
self.declareProperty('Axis1', "", StringMandatoryValidator())
self.declareProperty('Axis2', "", StringMandatoryValidator())
self.declareProperty('Units', "", StringMandatoryValidator())
self.declareProperty(FloatArrayProperty('Limits', direction=Direction.Input))
self.declareProperty('EMode', "Direct", StringMandatoryValidator())
self.declareProperty('ProjectionType', "QE", StringMandatoryValidator(), doc='Q or Theta projection')
self.declareProperty(IMDEventWorkspaceProperty('OutputWorkspace', "", direction=Direction.Output))
def test_name_direction_constructor_gives_correct_object(self):
"""
Tests the constructor that takes
only a name & direction
"""
name = "numbers"
direc = Direction.Output
arr = FloatArrayProperty(name, direc)
self._check_object_attributes(arr, name, direc)
def test_name_only_constructor_gives_correct_object(self):
"""
Tests the simplest constructor that takes
only a name
"""
name = "numbers"
arr = FloatArrayProperty(name)
self.assertTrue(isinstance(arr, FloatArrayProperty))
self._check_object_attributes(arr, name, Direction.Input)
def PyInit(self):
self.declareProperty(MultipleFileProperty('Run', extensions=['nxs']),
doc='File path of run(s).')
self.declareProperty(name='NormaliseTo',
defaultValue='Monitor',
validator=StringListValidator(['None', 'Monitor']),
doc='Normalise to monitor, or skip normalisation.')
self.declareProperty(FileProperty('CalibrationFile', '', action=FileAction.OptionalLoad, extensions=['nxs']),
doc='File containing the detector efficiencies.')
self.declareProperty(name='UseCalibratedData',
defaultValue=True,
doc='Whether or not to use the calibrated data in the NeXus files.')
self.declareProperty(name='Output2DTubes',
defaultValue=False,
doc='Output a 2D workspace of height along tube against tube scattering angle.')
self.declareProperty(name='Output2D',
defaultValue=False,
doc='Output a 2D workspace of height along tube against the real scattering angle.')
self.declareProperty(name='Output1D',
defaultValue=True,
doc='Output a 1D workspace with counts against scattering angle.')
self.declareProperty(name='CropNegativeScatteringAngles', defaultValue=True,
doc='Whether or not to crop the negative scattering angles.')
self.declareProperty(FloatArrayProperty(name='HeightRange', values=[],
validator=CompositeValidator([FloatArrayOrderedPairsValidator(),
FloatArrayLengthValidator(0, 2)])),
doc='A pair of values, comma separated, to give the minimum and maximum height range (in m). If not specified '
'the full height range is used.')
self.declareProperty(WorkspaceGroupProperty('OutputWorkspace', '',
direction=Direction.Output),
doc='Output workspace containing the reduced data.')
self.declareProperty(name='InitialMask', defaultValue=20, validator=IntBoundedValidator(lower=0, upper=64),
doc='Number of pixels to mask from the bottom and the top of each tube before superposition.')
self.declareProperty(name='FinalMask', defaultValue=30, validator=IntBoundedValidator(lower=0, upper=70),
doc='Number of spectra to mask from the bottom and the top of the result of 2D options.')
self.declareProperty(name='ComponentsToMask', defaultValue='',
doc='Comma separated list of component names to mask, for instance: tube_1, tube_2')
self.declareProperty(name='ComponentsToReduce', defaultValue='',
doc='Comma separated list of component names to output the reduced data for; for example tube_1')
self.declareProperty(name='AlignTubes', defaultValue=True,
doc='Align the tubes vertically and horizontally according to IPF.')
def PyInit(self):
if not allow_empty:
if not allow_ranges:
validator = RebinParamsValidator()
else:
if not allow_ranges:
validator = RebinParamsValidator(allow_empty)
else:
validator = RebinParamsValidator(allow_empty, allow_ranges)
self.declareProperty(FloatArrayProperty("Input", validator))
def test_name_validator_direction_constructor_gives_correct_object(self):
"""
Test the constructor that takes a name, validator & direction
"""
name = "numbers"
direc = Direction.Output
validator = NullValidator()
arr = FloatArrayProperty(name, validator, direc)
self._check_object_attributes(arr, name, direc)
self.assertEquals(arr.isValid, "")
def test_name_values_from_array_validator_direction_constructor_gives_correct_object(
self):
"""
Test the constructor that takes a name, values from python object,
validator & direction
"""
name = "numbers"
direc = Direction.Output
validator = NullValidator()
input_values = np.array([1.1, 2.5, 5.6, 4.6, 9.0, 6.0])
arr = FloatArrayProperty(name, input_values, validator, direc)
self._check_object_attributes(arr, name, direc, length=6)
def PyInit(self):
self.declareProperty(MultipleFileProperty('Run', extensions=['nxs']),
doc='File path of run(s).')
self.declareProperty(
name='NormaliseTo',
defaultValue='Monitor',
validator=StringListValidator(['None', 'Monitor']),
doc='Normalise to monitor, or skip normalisation.')
self.declareProperty(
name='UseCalibratedData',
defaultValue=True,
doc='Whether or not to use the calibrated data in the NeXus files.'
)
self.declareProperty(
name='Output2DTubes',
defaultValue=False,
doc=
'Output a 2D workspace of height along tube against tube scattering angle.'
)
self.declareProperty(
name='Output2D',
defaultValue=False,
doc=
'Output a 2D workspace of height along tube against the real scattering angle.'
)
self.declareProperty(
name='Output1D',
defaultValue=True,
doc='Output a 1D workspace with counts against scattering angle.')
self.declareProperty(
FloatArrayProperty(name='HeightRange',
values=[],
validator=CompositeValidator([
FloatArrayOrderedPairsValidator(),
FloatArrayLengthValidator(0, 2)
])),
doc=
'A pair of values, comma separated, to give the minimum and maximum height range (in m). If not specified '
'the full height range is used.')
self.declareProperty(
WorkspaceGroupProperty('OutputWorkspace',
'',
direction=Direction.Output),
doc='Output workspace containing the reduced data.')
def PyInit(self):
self.declareProperty(MultipleFileProperty('Run', extensions=['nxs']),
doc='File path of run(s).')
self.declareProperty(FileProperty('CalibrationFile', '',
action=FileAction.OptionalLoad, extensions=['nxs']),
doc='File containing the detector efficiencies.')
self.declareProperty(FileProperty('ROCCorrectionFile', '',
action=FileAction.OptionalLoad, extensions=['nxs']),
doc='File containing the radial oscillating collimator (ROC) corrections.')
self.declareProperty(name='NormaliseTo',
defaultValue='None',
validator=StringListValidator(['None', 'Time', 'Monitor', 'ROI']),
doc='Normalise to time, monitor or ROI counts.')
thetaRangeValidator = FloatArrayOrderedPairsValidator()
self.declareProperty(FloatArrayProperty(name='ROI', values=[0, 153.6], validator=thetaRangeValidator),
doc='Regions of interest for normalisation [in scattering angle in degrees].')
normaliseToROI = VisibleWhenProperty('NormaliseTo', PropertyCriterion.IsEqualTo, 'ROI')
self.setPropertySettings('ROI', normaliseToROI)
self.declareProperty(name='Observable',
defaultValue='sample.temperature',
doc='Scanning observable, a Sample Log entry.')
self.declareProperty(name='SortObservableAxis',
defaultValue=False,
doc='Whether or not to sort the scanning observable axis.')
self.declareProperty(name='ScanAxisBinWidth', defaultValue=0., validator=FloatBoundedValidator(lower=0.),
doc='Rebin the observable axis to this width. Default is to not rebin.')
self.declareProperty(name='CropNegative2Theta', defaultValue=True,
doc='Whether or not to crop out the bins corresponding to negative scattering angle.')
self.declareProperty(name='ZeroCountingCells', defaultValue='Interpolate',
validator=StringListValidator(['Crop','Interpolate','Leave']),
doc='Crop out the zero counting cells or interpolate the counts from the neighbours.')
self.declareProperty(name='Unit',
defaultValue='ScatteringAngle',
validator=StringListValidator(['ScatteringAngle', 'MomentumTransfer', 'dSpacing']),
doc='The unit of the reduced diffractogram.')
self.declareProperty(MatrixWorkspaceProperty('OutputWorkspace', '',
direction=Direction.Output),
doc='Output workspace containing the reduced data.')
def PyInit(self):
""" Algorithm properties
"""
self.declareProperty(MatrixWorkspaceProperty("InputWorkspace", "",
Direction.Input),
doc="Input QENS data as MatrixWorkspace")
self.declareProperty(
"RaiseMode",
False,
doc=
"Set to True if an Exception, instead of an empty list of Q values, is desired."
)
self.declareProperty(FloatArrayProperty("Qvalues", Direction.Output))
def PyInit(self):
group = "Input"
self.declareProperty(FileProperty(name="Filename",
defaultValue="", action=FileAction.OptionalLoad,
extensions=["_event.nxs", ".nxs.h5"]),
"Event file")
self.declareProperty("MaxChunkSize", 0.0,
"Specify maximum Gbytes of file to read in one chunk. Default is whole file.")
self.declareProperty("FilterBadPulses", 95.,
doc="Filter out events measured while proton " +
"charge is more than 5% below average")
self.declareProperty(MatrixWorkspaceProperty("InputWorkspace", "",
direction=Direction.Input,
optional=PropertyMode.Optional),
doc="Handle to reduced workspace")
self.setPropertyGroup("Filename", group)
self.setPropertyGroup("MaxChunkSize", group)
self.setPropertyGroup("FilterBadPulses", group)
self.setPropertyGroup("InputWorkspace", group)
group = "Output"
self.declareProperty(MatrixWorkspaceProperty("OutputWorkspace", "",
direction=Direction.Output),
doc="Handle to reduced workspace")
self.declareProperty(FileProperty(name="PDFgetNFile", defaultValue="", action=FileAction.Save,
extensions=[".getn"]), "Output filename")
self.setPropertyGroup("OutputWorkspace", group)
self.setPropertyGroup("PDFgetNFile", group)
self.declareProperty(FileProperty(name="CalibrationFile",
defaultValue="", action=FileAction.OptionalLoad,
extensions=[".h5", ".hd5", ".hdf", ".cal"]))
self.declareProperty(FileProperty(name="CharacterizationRunsFile", defaultValue="",
action=FileAction.OptionalLoad,
extensions=["txt"]),
"File with characterization runs denoted")
self.declareProperty("RemovePromptPulseWidth", 0.0,
"Width of events (in microseconds) near the prompt pulse to remove. 0 disables")
self.declareProperty("CropWavelengthMin", 0.,
"Crop the data at this minimum wavelength.")
self.declareProperty("CropWavelengthMax", 0.,
"Crop the data at this maximum wavelength.")
self.declareProperty(FloatArrayProperty("Binning", values=[0., 0., 0.],
direction=Direction.Input),
"Positive is linear bins, negative is logorithmic")
self.declareProperty("ResampleX", 0,
"Number of bins in x-axis. Non-zero value overrides \"Params\" property. " +
"Negative value means logorithmic binning.")
def test_name_string_values_validator_direction_constructor_gives_correct_object(
self):
"""
Test the constructor that takes a name, values as string, validator & direction
"""
name = "numbers"
direc = Direction.Output
validator = NullValidator()
values_str = "1.345,34.2,5345.3,4,5.3948"
arr = FloatArrayProperty(name, values_str, validator, direc)
self._check_object_attributes(arr, name, direc, length=5)
self.assertEquals(arr.isValid, "")
values = arr.value
self.assertTrue(isinstance(values, np.ndarray))
def test_dtype_function_calls(self):
"""
Tests the dtype() function call for the data types stored in the array.
"""
# Set up
direc = Direction.Output
validator = NullValidator()
# Create float array
float_input_values = [1.1, 2.5, 5.6, 4.6, 9.0, 6.0]
float_arr = FloatArrayProperty("floats", float_input_values, validator, direc)
# Create int array
int_input_values = [1, 2, 5, 4, 9, 6]
int_arr = IntArrayProperty("integers", int_input_values, validator, direc)
# Create string array
str_input_values = ["a", "b", "c", "d", "e"]
str_arr = StringArrayProperty("letters", str_input_values, validator, direc)
# Test
self.assertEquals(float_arr.dtype(), "f")
self.assertEquals(int_arr.dtype(), "i")
self.assertEquals(str_arr.dtype(), "S1")
def PyInit(self):
group = "Input"
self.declareProperty(FileProperty(name="Filename",
defaultValue="", action=FileAction.OptionalLoad,
extensions=["_event.nxs", ".nxs.h5"]),
"Event file")
self.copyProperties('AlignAndFocusPowderFromFiles', 'MaxChunkSize')
self.declareProperty("FilterBadPulses", 95.,
doc="Filter out events measured while proton " +
"charge is more than 5% below average")
self.declareProperty(MatrixWorkspaceProperty("InputWorkspace", "",
direction=Direction.Input,
optional=PropertyMode.Optional),
doc="Handle to reduced workspace")
self.setPropertyGroup("Filename", group)
self.setPropertyGroup("MaxChunkSize", group)
self.setPropertyGroup("FilterBadPulses", group)
self.setPropertyGroup("InputWorkspace", group)
group = "Output"
self.declareProperty(MatrixWorkspaceProperty("OutputWorkspace", "",
direction=Direction.Output),
doc="Handle to reduced workspace")
self.copyProperties('AlignAndFocusPowderFromFiles', 'CacheDir')
self.declareProperty(FileProperty(name="PDFgetNFile", defaultValue="", action=FileAction.Save,
extensions=[".getn"]), "Output filename")
self.setPropertyGroup("OutputWorkspace", group)
self.setPropertyGroup("PDFgetNFile", group)
self.declareProperty(FileProperty(name="CalibrationFile",
defaultValue="", action=FileAction.OptionalLoad,
extensions=[".h5", ".hd5", ".hdf", ".cal"]))
self.declareProperty(FileProperty(name="CharacterizationRunsFile", defaultValue="",
action=FileAction.OptionalLoad,
extensions=["txt"]),
"File with characterization runs denoted")
self.copyProperties('AlignAndFocusPowderFromFiles',
['FrequencyLogNames', 'WaveLengthLogNames', 'RemovePromptPulseWidth',
'CropWavelengthMin', 'CropWavelengthMax'])
self.declareProperty(FloatArrayProperty("Binning", values=[0., 0., 0.],
direction=Direction.Input),
"Positive is linear bins, negative is logorithmic")
self.declareProperty("ResampleX", 0,
"Number of bins in x-axis. Non-zero value overrides \"Params\" property. " +
"Negative value means logorithmic binning.")
def PyInit(self):
self.declareProperty(
IMDHistoWorkspaceProperty("InputWorkspace",
"",
optional=PropertyMode.Mandatory,
direction=Direction.Input),
"Input Workspace with HKL dimensions centered on zero.")
self.declareProperty(
WorkspaceProperty("IntermediateWorkspace",
"",
optional=PropertyMode.Optional,
direction=Direction.Output),
"The resulting workspace after reflection removal and filters applied. What is the input of the FFT."
)
self.declareProperty(
WorkspaceProperty("OutputWorkspace",
"",
optional=PropertyMode.Mandatory,
direction=Direction.Output), "Output Workspace")
self.declareProperty(
"Method", 'KAREN',
StringListValidator(['None', 'Punch and fill', 'KAREN']),
"Bragg peak removal method")
self.declareProperty(
"WindowFunction", 'Blackman',
StringListValidator(
['None', 'Gaussian', 'Blackman', 'Tukey', 'Kaiser']),
"Apply a window function to the data")
self.declareProperty(
"WindowParameter",
defaultValue=0.5,
validator=FloatBoundedValidator(0.),
doc=
"Parameter for window function, depends on window type, see algorithm docs"
)
# Punch and fill
condition = EnabledWhenProperty("Method", PropertyCriterion.IsEqualTo,
'Punch and fill')
self.declareProperty("Shape",
"sphere",
doc="Shape to punch out reflections",
validator=StringListValidator(['sphere', 'cube']))
self.setPropertySettings("Shape", condition)
val_min_zero = FloatArrayBoundedValidator(lower=0.)
self.declareProperty(
FloatArrayProperty("Size", [0.2], validator=val_min_zero),
"Width of cube/diameter of sphere used to remove reflections, in (HKL) (one or three values)"
)
self.setPropertySettings("Size", condition)
self.declareProperty(
"SpaceGroup",
"",
doc=
"Space group for reflection removal, either full name or number. If empty all HKL's will be removed."
)
self.setPropertySettings("SpaceGroup", condition)
self.declareProperty(
"Convolution", True,
"Apply convolution to fill in removed reflections")
self.setPropertySettings("Convolution", condition)
self.declareProperty("ConvolutionWidth",
2.0,
validator=FloatBoundedValidator(0.),
doc="Width of gaussian convolution in pixels")
self.setPropertySettings("ConvolutionWidth", condition)
self.declareProperty(
"CropSphere", False,
"Limit min/max q values. Can help with edge effects.")
condition = EnabledWhenProperty("CropSphere",
PropertyCriterion.IsNotDefault)
self.declareProperty(
FloatArrayProperty("SphereMin", [Property.EMPTY_DBL],
validator=val_min_zero),
"HKL values below which will be removed (one or three values)")
self.setPropertySettings("SphereMin", condition)
self.declareProperty(
FloatArrayProperty("SphereMax", [Property.EMPTY_DBL],
validator=val_min_zero),
"HKL values above which will be removed (one or three values)")
self.setPropertySettings("SphereMax", condition)
self.declareProperty("FillValue", Property.EMPTY_DBL,
"Value to replace with outside sphere")
self.setPropertySettings("FillValue", condition)
# KAREN
self.declareProperty("KARENWidth", 7, "Size of filter window")
# Reflections
self.setPropertyGroup("Shape", "Punch and fill")
self.setPropertyGroup("Size", "Punch and fill")
self.setPropertyGroup("SpaceGroup", "Punch and fill")
# Sphere
self.setPropertyGroup("CropSphere", "Cropping to a sphere")
self.setPropertyGroup("SphereMin", "Cropping to a sphere")
self.setPropertyGroup("SphereMax", "Cropping to a sphere")
self.setPropertyGroup("FillValue", "Cropping to a sphere")
# Convolution
self.setPropertyGroup("Convolution", "Convolution")
self.setPropertyGroup("ConvolutionWidth", "Convolution")
