From ff62e1bae47428d97ae71a96bb4d771856bfae10 Mon Sep 17 00:00:00 2001
From: Dan Nixon <dan@dan-nixon.com>
Date: Fri, 27 Mar 2015 09:16:41 +0000
Subject: [PATCH] Rename algorithm, correct docs
Refs #11437
---
.../{Fury.py => TransformToIqt.py} | 20 ++---
.../CustomInterfaces/src/Indirect/Fury.cpp | 4 +-
.../tests/analysis/ISISIndirectInelastic.py | 12 +--
.../Mantid/docs/source/algorithms/Fury-v1.rst | 24 ------
.../source/algorithms/TransformToIqt-v1.rst | 79 +++++++++++++++++++
.../interfaces/Indirect_DataAnalysis.rst | 72 +----------------
.../scripts/Inelastic/IndirectDataAnalysis.py | 2 +-
7 files changed, 99 insertions(+), 114 deletions(-)
rename Code/Mantid/Framework/PythonInterface/plugins/algorithms/WorkflowAlgorithms/{Fury.py => TransformToIqt.py} (95%)
delete mode 100644 Code/Mantid/docs/source/algorithms/Fury-v1.rst
create mode 100644 Code/Mantid/docs/source/algorithms/TransformToIqt-v1.rst
diff --git a/Code/Mantid/Framework/PythonInterface/plugins/algorithms/WorkflowAlgorithms/Fury.py b/Code/Mantid/Framework/PythonInterface/plugins/algorithms/WorkflowAlgorithms/TransformToIqt.py
similarity index 95%
rename from Code/Mantid/Framework/PythonInterface/plugins/algorithms/WorkflowAlgorithms/Fury.py
rename to Code/Mantid/Framework/PythonInterface/plugins/algorithms/WorkflowAlgorithms/TransformToIqt.py
index ab8aff547ba..5c2cf1fe5ec 100644
--- a/Code/Mantid/Framework/PythonInterface/plugins/algorithms/WorkflowAlgorithms/Fury.py
+++ b/Code/Mantid/Framework/PythonInterface/plugins/algorithms/WorkflowAlgorithms/TransformToIqt.py
@@ -7,7 +7,7 @@ import math
import os
-class Fury(PythonAlgorithm):
+class TransformToIqt(PythonAlgorithm):
_sample = None
_resolution = None
@@ -43,7 +43,7 @@ class Fury(PythonAlgorithm):
self.declareProperty(MatrixWorkspaceProperty('ParameterWorkspace', '',\
direction=Direction.Output, optional=PropertyMode.Optional),
- doc='Table workspace for saving Fury properties')
+ doc='Table workspace for saving TransformToIqt properties')
self.declareProperty(MatrixWorkspaceProperty('OutputWorkspace', '',\
direction=Direction.Output, optional=PropertyMode.Optional),
@@ -76,7 +76,7 @@ class Fury(PythonAlgorithm):
if self._plot:
self._plot_output()
else:
- logger.information('Dry run, will not run Fury')
+ logger.information('Dry run, will not run TransformToIqt')
self.setProperty('ParameterWorkspace', self._parameter_table)
self.setProperty('OutputWorkspace', self._output_workspace)
@@ -97,7 +97,7 @@ class Fury(PythonAlgorithm):
self._parameter_table = self.getPropertyValue('ParameterWorkspace')
if self._parameter_table == '':
- self._parameter_table = getWSprefix(self._sample) + 'FuryParameters'
+ self._parameter_table = getWSprefix(self._sample) + 'TransformToIqtParameters'
self._output_workspace = self.getPropertyValue('OutputWorkspace')
if self._output_workspace == '':
@@ -128,11 +128,11 @@ class Fury(PythonAlgorithm):
def _calculate_parameters(self):
"""
- Calculates the Fury parameters and saves in a table workspace.
+ Calculates the TransformToIqt parameters and saves in a table workspace.
"""
- CropWorkspace(InputWorkspace=self._sample, OutputWorkspace='__Fury_sample_cropped',
+ CropWorkspace(InputWorkspace=self._sample, OutputWorkspace='__TransformToIqt_sample_cropped',
Xmin=self._e_min, Xmax=self._e_max)
- x_data = mtd['__Fury_sample_cropped'].readX(0)
+ x_data = mtd['__TransformToIqt_sample_cropped'].readX(0)
number_input_points = len(x_data) - 1
num_bins = number_input_points / self._number_points_per_bin
self._e_width = (abs(self._e_min) + abs(self._e_max)) / num_bins
@@ -179,7 +179,7 @@ class Fury(PythonAlgorithm):
self._e_min, self._e_max, self._e_width,
resolution, resolution_bins])
- DeleteWorkspace('__Fury_sample_cropped')
+ DeleteWorkspace('__TransformToIqt_sample_cropped')
self.setProperty('ParameterWorkspace', param_table)
@@ -212,7 +212,7 @@ class Fury(PythonAlgorithm):
def _fury(self):
"""
- Run Fury.
+ Run TransformToIqt.
"""
from IndirectCommon import CheckHistZero, CheckHistSame, CheckAnalysers
@@ -261,4 +261,4 @@ class Fury(PythonAlgorithm):
# Register algorithm with Mantid
-AlgorithmFactory.subscribe(Fury)
+AlgorithmFactory.subscribe(TransformToIqt)
diff --git a/Code/Mantid/MantidQt/CustomInterfaces/src/Indirect/Fury.cpp b/Code/Mantid/MantidQt/CustomInterfaces/src/Indirect/Fury.cpp
index 87c3b3416da..f2bd5459329 100644
--- a/Code/Mantid/MantidQt/CustomInterfaces/src/Indirect/Fury.cpp
+++ b/Code/Mantid/MantidQt/CustomInterfaces/src/Indirect/Fury.cpp
@@ -94,7 +94,7 @@ namespace IDA
bool plot = m_uiForm.ckPlot->isChecked();
bool save = m_uiForm.ckSave->isChecked();
- IAlgorithm_sptr furyAlg = AlgorithmManager::Instance().create("Fury", -1);
+ IAlgorithm_sptr furyAlg = AlgorithmManager::Instance().create("TransformToIqt", -1);
furyAlg->initialize();
furyAlg->setProperty("Sample", wsName.toStdString());
@@ -192,7 +192,7 @@ namespace IDA
if(numBins == 0)
return;
- IAlgorithm_sptr furyAlg = AlgorithmManager::Instance().create("Fury");
+ IAlgorithm_sptr furyAlg = AlgorithmManager::Instance().create("TransformToIqt");
furyAlg->initialize();
furyAlg->setProperty("Sample", wsName.toStdString());
diff --git a/Code/Mantid/Testing/SystemTests/tests/analysis/ISISIndirectInelastic.py b/Code/Mantid/Testing/SystemTests/tests/analysis/ISISIndirectInelastic.py
index 573b3654654..17d104c215a 100644
--- a/Code/Mantid/Testing/SystemTests/tests/analysis/ISISIndirectInelastic.py
+++ b/Code/Mantid/Testing/SystemTests/tests/analysis/ISISIndirectInelastic.py
@@ -682,7 +682,7 @@ class OSIRISDiagnostics(ISISIndirectInelasticDiagnostics):
#==============================================================================
class ISISIndirectInelasticMoments(ISISIndirectInelasticBase):
- '''A base class for the ISIS indirect inelastic Fury/FuryFit tests
+ '''A base class for the ISIS indirect inelastic TransformToIqt/TransformToIqtFit tests
The output of Elwin is usually used with MSDFit and so we plug one into
the other in this test.
@@ -865,7 +865,7 @@ class ISISIndirectInelasticFuryAndFuryFit(ISISIndirectInelasticBase):
LoadNexus(sample, OutputWorkspace=sample)
LoadNexus(self.resolution, OutputWorkspace=self.resolution)
- fury_props, fury_ws = Fury(Sample=self.samples[0],
+ fury_props, fury_ws = TransformToIqt(Sample=self.samples[0],
Resolution=self.resolution,
EnergyMin=self.e_min,
EnergyMax=self.e_max,
@@ -922,7 +922,7 @@ class OSIRISFuryAndFuryFit(ISISIndirectInelasticFuryAndFuryFit):
def __init__(self):
ISISIndirectInelasticFuryAndFuryFit.__init__(self)
- # Fury
+ # TransformToIqt
self.samples = ['osi97935_graphite002_red.nxs']
self.resolution = 'osi97935_graphite002_res.nxs'
self.e_min = -0.4
@@ -947,7 +947,7 @@ class IRISFuryAndFuryFit(ISISIndirectInelasticFuryAndFuryFit):
def __init__(self):
ISISIndirectInelasticFuryAndFuryFit.__init__(self)
- # Fury
+ # TransformToIqt
self.samples = ['irs53664_graphite002_red.nxs']
self.resolution = 'irs53664_graphite002_res.nxs'
self.e_min = -0.4
@@ -1045,7 +1045,7 @@ class OSIRISFuryAndFuryFitMulti(ISISIndirectInelasticFuryAndFuryFitMulti):
def __init__(self):
ISISIndirectInelasticFuryAndFuryFitMulti.__init__(self)
- # Fury
+ # TransformToIqt
self.samples = ['osi97935_graphite002_red.nxs']
self.resolution = 'osi97935_graphite002_res.nxs'
self.e_min = -0.4
@@ -1070,7 +1070,7 @@ class IRISFuryAndFuryFitMulti(ISISIndirectInelasticFuryAndFuryFitMulti):
def __init__(self):
ISISIndirectInelasticFuryAndFuryFitMulti.__init__(self)
- # Fury
+ # TransformToIqt
self.samples = ['irs53664_graphite002_red.nxs']
self.resolution = 'irs53664_graphite002_res.nxs'
self.e_min = -0.4
diff --git a/Code/Mantid/docs/source/algorithms/Fury-v1.rst b/Code/Mantid/docs/source/algorithms/Fury-v1.rst
deleted file mode 100644
index a82a566e5ec..00000000000
--- a/Code/Mantid/docs/source/algorithms/Fury-v1.rst
+++ /dev/null
@@ -1,24 +0,0 @@
-.. algorithm::
-
-.. summary::
-
-.. alias::
-
-.. properties::
-
-Description
------------
-
-The model that is being fitted is that of a delta-function (elastic component) of amplitude :math:`A(0)` and Lorentzians of amplitude :math:`A(j)` and HWHM :math:`W(j)` where :math:`j=1,2,3`. The whole function is then convolved with the resolution function. The -function and Lorentzians are intrinsically
-normalised to unity so that the amplitudes represent their integrated areas.
-
-For a Lorentzian, the Fourier transform does the conversion: :math:`1/(x^{2}+\delta^{2}) \Leftrightarrow exp[-2\pi(\delta k)]`.
-If :math:`x` is identified with energy :math:`E` and :math:`2\pi k` with :math:`t/\hbar` where t is time then: :math:`1/[E^{2}+(\hbar / \tau )^{2}] \Leftrightarrow exp[-t /\tau]` and :math:`\sigma` is identified with :math:`\hbar / \tau`.
-The program estimates the quasielastic components of each of the groups of spectra and requires the resolution file and optionally the normalisation file created by ResNorm.
-
-For a Stretched Exponential, the choice of several Lorentzians is replaced with a single function with the shape : :math:`\psi\beta(x) \Leftrightarrow exp[-2\pi(\sigma k)\beta]`. This, in the energy to time FT transformation, is :math:`\psi\beta(E) \Leftrightarrow exp[-(t/\tau)\beta]`. So \sigma is identified with :math:`(2\pi)\beta\hbar/\tau`.
-The model that is fitted is that of an elastic component and the stretched exponential and the program gives the best estimate for the :math:`\beta` parameter and the width for each group of spectra.
-
-This routine was originally part of the MODES package.
-
-.. categories::
diff --git a/Code/Mantid/docs/source/algorithms/TransformToIqt-v1.rst b/Code/Mantid/docs/source/algorithms/TransformToIqt-v1.rst
new file mode 100644
index 00000000000..e3b9ecf1d4b
--- /dev/null
+++ b/Code/Mantid/docs/source/algorithms/TransformToIqt-v1.rst
@@ -0,0 +1,79 @@
+.. algorithm::
+
+.. summary::
+
+.. alias::
+
+.. properties::
+
+Description
+-----------
+
+The measured spectrum :math:`I(Q, \omega)` is proportional to the four
+dimensional convolution of the scattering law :math:`S(Q, \omega)` with the
+resolution function :math:`R(Q, \omega)` of the spectrometer via :math:`I(Q,
+\omega) = S(Q, \omega) ⊗ R(Q, \omega)`, so :math:`S(Q, \omega)` can be obtained,
+in principle, by a deconvolution in :math:`Q` and :math:`\omega`. The method
+employed here is based on the Fourier Transform (FT) technique [6,7]. On Fourier
+transforming the equation becomes :math:`I(Q, t) = S(Q, t) x R(Q, t)` where the
+convolution in :math:`\omega`-space is replaced by a simple multiplication in
+:math:`t`-space. The intermediate scattering law :math:`I(Q, t)` is then
+obtained by simple division and the scattering law :math:`S(Q, \omega)` itself
+can be obtained by back transformation. The latter however is full of pitfalls
+for the unwary. The advantage of this technique over that of a fitting procedure
+such as SWIFT is that a functional form for :math:`I(Q, t)` does not have to be
+assumed. On IRIS the resolution function is close to a Lorentzian and the
+scattering law is often in the form of one or more Lorentzians. The FT of a
+Lorentzian is a decaying exponential, :math:`exp(-\alpha t)` , so that plots of
+:math:`ln(I(Q, t))` against t would be straight lines thus making interpretation
+easier.
+
+In general, the origin in energy for the sample run and the resolution run need
+not necessarily be the same or indeed be exactly zero in the conversion of the
+RAW data from time-of-flight to energy transfer. This will depend, for example,
+on the sample and vanadium shapes and positions and whether the analyser
+temperature has changed between the runs. The procedure takes this into account
+automatically, without using an arbitrary fitting procedure, in the following
+way. From the general properties of the FT, the transform of an offset
+Lorentzian has the form :math:`(cos(\omega_{0}t) + isin(\omega_{0}t))exp(-\Gamma
+t)` , thus taking the modulus produces the exponential :math:`exp(-\Gamma t)`
+which is the required function. If this is carried out for both sample and
+resolution, the difference in the energy origin is automatically removed. The
+results of this procedure should however be treated with some caution when
+applied to more complicated spectra in which it is possible for :math:`I(Q, t)`
+to become negative, for example, when inelastic side peaks are comparable in
+height to the elastic peak.
+
+The interpretation of the data must also take into account the propagation of
+statistical errors (counting statistics) in the measured data as discussed by
+Wild et al [1]. If the count in channel :math:`k` is :math:`X_{k}` , then
+:math:`X_{k}=<X_{k}>+\Delta X_{k}` where :math:`<X_{k}>` is the mean value and
+:math:`\Delta X_{k}` the error. The standard deviation for channel :math:`k` is
+:math:`\sigma k` :math:`2=<\Delta X_{k}>2` which is assumed to be given by
+:math:`\sigma k=<X_{k}>`. The FT of :math:`X_{k}` is defined by
+:math:`X_{j}=<X_{j}>+\Delta X_{j}` and the real and imaginary parts denoted by
+:math:`X_{j} I` and :math:`X_{j} I` respectively. The standard deviations on
+:math:`X_{j}` are then given by :math:`\sigma 2(X_{j} R)=1/2 X0 R + 1/2 X2j R`
+and :math:`\sigma 2(X_{j} I)=1/2 X0 I - 1/2 X2j I`.
+
+Note that :math:`\sigma 2(X_{0} R) = X_{0} R` and from the properties of FT
+:math:`X_{0} R = X_{k}`. Thus the standard deviation of the first coefficient
+of the FT is the square root of the integrated intensity of the spectrum. In
+practice, apart from the first few coefficients, the error is nearly constant
+and close to :math:`X_{0} R`. A further point to note is that the errors make
+the imaginary part of :math:`I(Q, t)` non-zero and that, although these will be
+distributed about zero, on taking the modulus of :math:`I(Q, t)`, they become
+positive at all times and are distributed about a non-zero positive value. When
+:math:`I(Q, t)` is plotted on a log-scale the size of the error bars increases
+with time (coefficient) and for the resolution will reach a point where the
+error on a coefficient is comparable to its value. This region must therefore be
+treated with caution. For a true deconvolution by back transforming, the data
+would be truncated to remove this poor region before back transforming. If the
+truncation is severe the back transform may contain added ripples, so an
+automatic back transform is not provided.
+
+References:
+
+1. U P Wild, R Holzwarth & H P Good, Rev Sci Instr 48 1621 (1977)
+
+.. categories::
diff --git a/Code/Mantid/docs/source/interfaces/Indirect_DataAnalysis.rst b/Code/Mantid/docs/source/interfaces/Indirect_DataAnalysis.rst
index 30201258338..604af290896 100644
--- a/Code/Mantid/docs/source/interfaces/Indirect_DataAnalysis.rst
+++ b/Code/Mantid/docs/source/interfaces/Indirect_DataAnalysis.rst
@@ -144,7 +144,7 @@ Fury
:widget: tabFury
Given sample and resolution inputs, carries out a fit as per the theory detailed
-below.
+in the :ref:`TransformToIqt <algm-TransformToIqt>` algorithm.
Options
~~~~~~~
@@ -194,76 +194,6 @@ ResolutionBins
Number of bins in the resolution after rebinning, typically this should be at
least 5 and a warning will be shown if it is less.
-Theory
-~~~~~~
-
-The measured spectrum :math:`I(Q, \omega)` is proportional to the four
-dimensional convolution of the scattering law :math:`S(Q, \omega)` with the
-resolution function :math:`R(Q, \omega)` of the spectrometer via :math:`I(Q,
-\omega) = S(Q, \omega) ⊗ R(Q, \omega)`, so :math:`S(Q, \omega)` can be obtained,
-in principle, by a deconvolution in :math:`Q` and :math:`\omega`. The method
-employed here is based on the Fourier Transform (FT) technique [6,7]. On Fourier
-transforming the equation becomes :math:`I(Q, t) = S(Q, t) x R(Q, t)` where the
-convolution in :math:`\omega`-space is replaced by a simple multiplication in
-:math:`t`-space. The intermediate scattering law :math:`I(Q, t)` is then
-obtained by simple division and the scattering law :math:`S(Q, \omega)` itself
-can be obtained by back transformation. The latter however is full of pitfalls
-for the unwary. The advantage of this technique over that of a fitting procedure
-such as SWIFT is that a functional form for :math:`I(Q, t)` does not have to be
-assumed. On IRIS the resolution function is close to a Lorentzian and the
-scattering law is often in the form of one or more Lorentzians. The FT of a
-Lorentzian is a decaying exponential, :math:`exp(-\alpha t)` , so that plots of
-:math:`ln(I(Q, t))` against t would be straight lines thus making interpretation
-easier.
-
-In general, the origin in energy for the sample run and the resolution run need
-not necessarily be the same or indeed be exactly zero in the conversion of the
-RAW data from time-of-flight to energy transfer. This will depend, for example,
-on the sample and vanadium shapes and positions and whether the analyser
-temperature has changed between the runs. The procedure takes this into account
-automatically, without using an arbitrary fitting procedure, in the following
-way. From the general properties of the FT, the transform of an offset
-Lorentzian has the form :math:`(cos(\omega_{0}t) + isin(\omega_{0}t))exp(-\Gamma
-t)` , thus taking the modulus produces the exponential :math:`exp(-\Gamma t)`
-which is the required function. If this is carried out for both sample and
-resolution, the difference in the energy origin is automatically removed. The
-results of this procedure should however be treated with some caution when
-applied to more complicated spectra in which it is possible for :math:`I(Q, t)`
-to become negative, for example, when inelastic side peaks are comparable in
-height to the elastic peak.
-
-The interpretation of the data must also take into account the propagation of
-statistical errors (counting statistics) in the measured data as discussed by
-Wild et al [1]. If the count in channel :math:`k` is :math:`X_{k}` , then
-:math:`X_{k}=<X_{k}>+\Delta X_{k}` where :math:`<X_{k}>` is the mean value and
-:math:`\Delta X_{k}` the error. The standard deviation for channel :math:`k` is
-:math:`\sigma k` :math:`2=<\Delta X_{k}>2` which is assumed to be given by
-:math:`\sigma k=<X_{k}>`. The FT of :math:`X_{k}` is defined by
-:math:`X_{j}=<X_{j}>+\Delta X_{j}` and the real and imaginary parts denoted by
-:math:`X_{j} I` and :math:`X_{j} I` respectively. The standard deviations on
-:math:`X_{j}` are then given by :math:`\sigma 2(X_{j} R)=1/2 X0 R + 1/2 X2j R`
-and :math:`\sigma 2(X_{j} I)=1/2 X0 I - 1/2 X2j I`.
-
-Note that :math:`\sigma 2(X_{0} R) = X_{0} R` and from the properties of FT
-:math:`X_{0} R = X_{k}`. Thus the standard deviation of the first coefficient
-of the FT is the square root of the integrated intensity of the spectrum. In
-practice, apart from the first few coefficients, the error is nearly constant
-and close to :math:`X_{0} R`. A further point to note is that the errors make
-the imaginary part of :math:`I(Q, t)` non-zero and that, although these will be
-distributed about zero, on taking the modulus of :math:`I(Q, t)`, they become
-positive at all times and are distributed about a non-zero positive value. When
-:math:`I(Q, t)` is plotted on a log-scale the size of the error bars increases
-with time (coefficient) and for the resolution will reach a point where the
-error on a coefficient is comparable to its value. This region must therefore be
-treated with caution. For a true deconvolution by back transforming, the data
-would be truncated to remove this poor region before back transforming. If the
-truncation is severe the back transform may contain added ripples, so an
-automatic back transform is not provided.
-
-References:
-
-1. U P Wild, R Holzwarth & H P Good, Rev Sci Instr 48 1621 (1977)
-
Fury Fit
--------
diff --git a/Code/Mantid/scripts/Inelastic/IndirectDataAnalysis.py b/Code/Mantid/scripts/Inelastic/IndirectDataAnalysis.py
index c258965efba..a1d11b69033 100644
--- a/Code/Mantid/scripts/Inelastic/IndirectDataAnalysis.py
+++ b/Code/Mantid/scripts/Inelastic/IndirectDataAnalysis.py
@@ -288,7 +288,7 @@ def furyfitMult(inputWS, function, ftype, startx, endx, spec_min=0, spec_max=Non
if Plot != 'None':
furyfitPlotSeq(result_workspace, Plot)
- EndTime('FuryFit Multi')
+ EndTime('TransformToIqtFit Multi')
return result_workspace
--
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