Table of Contents
Runs Maximum Entropy method on every spectrum of an input workspace. Note this algorithm is still in development, and its interface is likely to change. It currently works for the case where the number of data points equals the number of reconstructed (image) points and data and image are related by Fourier transform.
Name | Direction | Type | Default | Description |
---|---|---|---|---|
InputWorkspace | Input | MatrixWorkspace | Mandatory | An input workspace. |
ComplexData | Input | boolean | False | Whether or not the input data are complex. If true, the input workspace is expected to have an even number of histograms, with real and imaginary parts arranged in consecutive workspaces |
A | Input | number | 0.4 | A maximum entropy constant |
ChiTarget | Input | number | 100 | Target value of Chi-square |
ChiEps | Input | number | 0.001 | Required precision for Chi-square |
DistancePenalty | Input | number | 0.1 | Distance penalty applied to the current image |
MaxAngle | Input | number | 0.05 | Maximum degree of non-parallelism between S and C |
MaxIterations | Input | m | 20000 | Maximum number of iterations |
AlphaChopIterations | Input | m | 500 | Maximum number of iterations in alpha chop |
EvolChi | Output | MatrixWorkspace | Mandatory | Output workspace containing the evolution of Chi-sq |
EvolAngle | Output | MatrixWorkspace | Mandatory | Output workspace containing the evolution of non-paralellism between S and C |
ReconstructedImage | Output | MatrixWorkspace | Mandatory | The output workspace containing the reconstructed image. |
ReconstructedData | Output | MatrixWorkspace | Mandatory | The output workspace containing the reconstructed data. |
The maximum entropy method (MEM) is used as a signal processing technique for reconstructing images from noisy data. It selects a single image from the many images which fit the data with the same value of the statistic, . The maximum entropy method selects from this feasible set of images, the one which has minimum information (maximum entropy). More specifically, the algorithm maximizes the entropy subject to the constraint:
where are the experimental data, the associated errors, and the calculated or reconstructed data. The image is the set of numbers which relates to the measured data as:
where the measurement kernel matrix represents a Fourier transform, . At present, nothing is assumed about : it can be either positive or negative and real or complex, and the entropy is defined as
where is a constant. The sensitive of the reconstructed image to reconstructed image will vary depending on the data. In general a smaller value would preduce a sharper image. See section 4.7 in Ref. [1] for recommended strategy to selected .
The implementation used to find the solution where the entropy is maximized subject to the constraint in follows the approach by Skilling & Bryan [2], which is an algorithm that is not explicitly using Lagrange multiplier. Instead, they construct a subspace from a set of search directions to approach the maximum entropy solution. Initially, the image is set to the flat background and the search directions are constructed using the gradients of and :
where stands for a componentwise multiplication. The algorithm next uses a quadratic approximation to determine the increment that moves the image one step closer to the solution:
There are four output workspaces: ReconstructedImage and ReconstructedData contain the image and calculated data respectively, and EvolChi and EvolAngle record the algorithm’s evolution. ReconstructedData can be used to check that the calculated data approaches the experimental, measured data. ReconstructedImage corresponds to its Fourier transform. If the input data are real it contains twice the original number of spectra, as the Fourier transform will be a complex signal in general. The real and imaginary parts are organized as follows (see Table 1): assuming your input workspace has spectra, the real part of the reconstructed image for spectrum corresponds to spectrum in ReconstructedImage, while the imaginary part can be found in spectrum .
When the input data are complex, the input workspace is expected to have spectra, where real and imaginary parts of a specific signal are arranged in consecutive spectra, . Both the ReconstructedData and ReconstructedImage will contain spectra, where spectra correspond to the real and imaginary parts reconstructed from the input signal at in the input workspace (see Table 2 below).
The workspaces EvolChi and EvolAngle record the evolution of and the angle (or non-parallelism) between and . Note that the algorithm runs until a solution is found, and that an image is considered to be a true maximum entropy solution when the following two conditions are met simultaneously:
While one of these conditions is not satisfied the algorithm keeps running until it reaches the maximum number of iterations. When the maximum number of iterations is reached, the last reconstructed image and its corresponding calculated data are returned, even if they do not correspond to a true maximum entropy solution satisfying the conditions above. In this way, the user can check by inspecting the output workspaces whether the algorithm was evolving towards the correct solution or not. On the other hand, the user must always check the validity of the solution by inspecting EvolChi and EvolAngle, whose values will be set to zero once the true maximum entropy solution is found.
Workspace | Number of histograms | Number of bins | Description |
---|---|---|---|
EvolChi | M | MaxIterations | Evolution of until the solution is found. Then all values are set to zero. |
EvolAngle | M | MaxIterations | Evolution of the angle between and , until the solution is found. Then all values are set to zero. |
ReconstructedImage | 2M | N | For spectrum in the input workspace, the reconstructed image is stored in spectra (real part) and (imaginary part) |
ReconstructedData | M | N | For spectrum in the input workspace, the reconstructed data are stored in spectrum (only real part, as the input is real) |
Workspace | Number of histograms | Number of bins | Description |
---|---|---|---|
EvolChi | M | MaxIterations | Evolution of until the solution is found. Then all values are set to zero. |
EvolAngle | M | MaxIterations | Evolution of the angle between and , until the solution is found. Then all values are set to zero. |
ReconstructedImage | 2M | For spectrum in the input workspace, the reconstructed image is stored in spectra (real part) and (imaginary part) | |
ReconstructedData | 2M | For spectrum in the input workspace, the reconstructed data are stored in spectra (real part) and (imaginary part) |
[1] Anders Johannes Markvardsen, (2000). Polarised neutron diffraction measurements of PrBa2Cu3O6+x and the Bayesian statistical analysis of such data. DPhil. University of Oxford (http://ora.ox.ac.uk/objects/uuid:bef0c991-4e1c-4b07-952a-a0fe7e4943f7)
[2] Skilling & Bryan, (1984). Maximum entropy image reconstruction: general algorithm. Mon. Not. R. astr. Soc. 211, 111-124.
Example - Reconstruct Fourier coefficients
In the example below, a workspace containing five Fourier coefficients is created and used as input to MaxEnt v1. In the figure we show the original and reconstructed data (left), and the reconstructed image, i.e. Fourier transform (right).
# Create an empty workspace
X = []
Y = []
E = []
N = 200
for i in range(0,N):
x = ((i-N/2) *1./N)
X.append(x)
Y.append(0)
E.append(0.001)
# Fill in five Fourier coefficients
# The input signal must be symmetric
Y[5] = Y[195] = 0.85
Y[10] = Y[190] = 0.85
Y[20] = Y[180] = 0.85
Y[12] = Y[188] = 0.90
Y[14] = Y[186] = 0.90
CreateWorkspace(OutputWorkspace='inputws',DataX=X,DataY=Y,DataE=E,NSpec=1)
evolChi, evolAngle, image, data = MaxEnt(InputWorkspace='inputws', chiTarget=N, A=0.0001)
Example - Reconstruct a real muon dataset
In this example, MaxEnt v1 is run on a pre-analyzed muon dataset. The corresponding figure shows the original and reconstructed data (left), and the real part of the image obtained with MaxEnt v1 and FFT v1 (right).
Load(Filename=r'MUSR00022725.nxs', OutputWorkspace='MUSR00022725')
CropWorkspace(InputWorkspace='MUSR00022725', OutputWorkspace='MUSR00022725', XMin=0.11, XMax=1.6, EndWorkspaceIndex=0)
RemoveExpDecay(InputWorkspace='MUSR00022725', OutputWorkspace='MUSR00022725')
Rebin(InputWorkspace='MUSR00022725', OutputWorkspace='MUSR00022725', Params='0.016')
evolChi, evolAngle, image, data = MaxEnt(InputWorkspace='MUSR00022725', A=0.005, ChiTarget=90)
# Compare MaxEnt to FFT
imageFFT = FFT(InputWorkspace='MUSR00022725')
Next, MaxEnt v1 is run on a different muon dataset. The figure shows the original and reconstructed data (left), the real part of the image (middle) and its imaginary part (right).
Load(Filename=r'EMU00020884.nxs', OutputWorkspace='EMU00020884')
CropWorkspace(InputWorkspace='EMU00020884', OutputWorkspace='EMU00020884', XMin=0.17, XMax=4.5, EndWorkspaceIndex=0)
RemoveExpDecay(InputWorkspace='EMU00020884', OutputWorkspace='EMU00020884')
Rebin(InputWorkspace='EMU00020884', OutputWorkspace='EMU00020884', Params='0.016')
evolChi, evolAngle, image, data = MaxEnt(InputWorkspace='EMU00020884', A=0.0001, ChiTarget=300, MaxIterations=2500)
# Compare MaxEnt to FFT
imageFFT = FFT(InputWorkspace='EMU00020884')
Finally, we show an example where a complex signal is analyzed. In this case, the input workspace contains two spectra corresponding to the real and imaginary part of the same signal. The figure shows the original and reconstructed data (left), and the reconstructed image (right).
from math import pi, sin, cos
from random import random
# Create a test workspace
X = []
YRe = []
YIm = []
E = []
N = 200
w = 3
for i in range(0,N):
x = 2*pi*i/N
X.append(x)
YRe.append(cos(w*x)+(random()-0.5)*0.3)
YIm.append(sin(w*x)+(random()-0.5)*0.3)
E.append(0.1)
CreateWorkspace(OutputWorkspace='ws',DataX=X+X,DataY=YRe+YIm,DataE=E+E,NSpec=2)
evolChi, evolAngle, image, data = MaxEnt(InputWorkspace='ws', ComplexData=True, chiTarget=2*N, A=0.001)
Categories: Algorithms | Arithmetic\FFT