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