152 lines
5.2 KiB
HTML
152 lines
5.2 KiB
HTML
<HTML>
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<HEAD>
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<TITLE>Autocloud</TITLE>
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</HEAD>
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<BODY>
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<H1>Autocloud</H1>
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<P>
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With the advent of position sensitive detectors in X-ray and neutron
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diffraction the problem arises how integrated reflection intensities
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may be extratcted from the collected volumes of data. Typically a
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series of frames is measured while rotating the crystal under
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investigation in omega. Autocloud implements a novel approach for the
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extraction of reflection intensities from such data. Other currently
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used integration packages use a UB-matrix to predict the position of a
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reflection on the detector and then integrate the intensity in a box
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around the predicted position. In contrast autocloud tries to
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determine reflection
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positions and intensities directly from the data. In order to do so a
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template matching algorithm is used. One advantage of this approach is
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that crystals with magnetic or incommensurate structures can be easily
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analysed. Typically packages for intensity integration do not have
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facilities for predicting such reflections. The other advantage is ease
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of use. Data analysis with autocloud requires only two steps:
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Integration followed by indexing.
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</P>
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<h2>Running Autocloud</h2>
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<p>
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The syntax is:
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<pre>autocloud options datafile
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</pre>
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The following options are known:
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<dL>
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<dt>-a val
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<dd>Selects the algorithm to use. The following algorithms are
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currently supported:
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<dl>
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<dt>max
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<dd>perform only a local maximum search
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<dt>template
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<dd>Perform template matching. This is the default.
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<dt>cross
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<dd>Perform template matching using the cross correlation function.
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</dl>
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<dt>-b AAxBBxCC
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<dd>For the evaluation of the initial template a preliminary box size
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is needed. This can be specified through this option. Three values
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separated by the character 'x' are required, one for each dimension in
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the order x, y, z.
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<dt>-d val
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<dd>After the correlation of the data volume with the template another
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maximum search is started in order to locate the reflections. In order
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to suppress spurious peaks, a minimum steepness of the candidate peak
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can be set with the -d option.
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<dt>-e val
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<dd>Some systems store frames a single files. With the -e option the
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end file number of the frame files can be set.
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<dt>-m val
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<dd>When the maximum search only option is set a, a threshold is
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required for suppressing spurious peaks. This threshold can be set
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with the -m option.
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<dt>-o file
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<dd>Redirects output to the file name specified. By default all output
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is written to stdout.
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<dt>-s val
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<dd>Some systems store frames a single files. With the -s option the
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start file number of the frame files can be set.
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<dt>-t type
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<dd>This option sets the type of the data file. Currently understood
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are:
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<dl>
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<dt>sxd
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<dd>For NeXus data from SXD at ISIS.
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<dt>trics
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<dd>For NeXus data files from TRICS, SINQ
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<dt>debug
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<dd>An internal format used during software testing.
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</dl>
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<dt>-v val
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<dd>Increases the verbosity of the output.
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</dl>
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</p>
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<h2>The Autocloud Algorithm</h2>
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<p>
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The autocloud algorithm has the following steps:
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<ol>
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<li>Location of strong peaks for template evaluation.
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<li>Background Subtraction.
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<li>Evaluation of a template for volume matching.
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<li>Correlation of the template with the data volume.
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<li>Location of maxima in the correlated data.
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<li>Integration of the reflections found.
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</ol>
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</p>
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<h3>Location of Strong Peaks for Template Evaluation</h3>
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<p>
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This is basically a local maximum detection scheme. A local maxima
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must be the strongest intensity within a 7 by 7 by 7 volume. All
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maxima smaller then 10% of the largest maximum found are discarded.
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</p>
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<h3>Background Subtraction</h3>
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<p>
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Background subtraction is done with essentially the same algorithm XDS
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uses. For each x, y coordinate in the frame values are summed along
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the third dimension. Points belonging to a local maimum are
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excluded. The background
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for this x,y coordinate is then the average of the values
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summed. The data volume is then corrected for the background with
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these values. This works well as long as the assumption holds that the
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background varies mostly across the detector and not much with the
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third dimension.
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</p>
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<h3>Template Evaluation</h3>
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<p>
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The template to be used for template matching later on is calculated
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by summing all local maxima first. Then the limits of the reflection
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are calculated for each scanline using the Lehmann-Larsen
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algorithm. The reflection thus found is scaled to a value of 1 and
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used as the template.
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</p>
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<h3>Template Matching</h3>
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<p>
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For the actual correlation of the template with the data two variantes
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can be used: Normal simple correlation or cross correlation.
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</p>
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<h3>Peak Detection</h3>
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<p>
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This is again a local maximum detection within a 7 by 7 by 7
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box. Another criterium for the supression of wrong identifications is
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a minimum steepness. This means that the candidate local maximum must
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at least be higher by a certain amount (the steepness) then the points
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at the border of its 7 by 7 by 7 box.
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</p>
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<h3>Peak Integration</h3>
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<p>
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A scale factor is calculated for each candidate reflection between the
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data and the template. The intensity is derived from this scale factor
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and the standard deviation is calculated as the squared difference
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between the scaled template and the data. This scheme is the same as
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learnt profile fitting as described by Ford for the 1- and 2d cases.
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</p>
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</BODY>
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</HTML>
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