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indexamajig - bulk indexing and data reduction program
------------------------------------------------------
The "indexamajig" program takes as input a list of diffraction image files,
currently in HDF5 format. For each image, it attempts to find peaks and then
index the pattern. If successful, it will measure the intensities of the peaks
at Bragg locations and produce a list in the form "h k l I", with some extra
information about the locations of the peaks.
For minimal basic use, you need to provide the list of diffraction patterns,
the method which will be used to index, a file describing the geometry of the
detector, a PDB file which contains the unit cell which will be used for the
indexing, and that you'd like the program to output a list of intensities for
each successfully indexed pattern. Here is what the minimal use looks like on
the command line, with each argument shown on a separate line. In practice,
you'd put this all on one line:
indexamajig
-i mypatternlist.lst
--indexing=dirax
--geometry mygeometry.geom
-p mystructure.pdb
--near-bragg
-o myoutputfile.txt
More typical use includes all the above, but might also include a noise or
common mode filter (--filter-noise or --filter-cm respectively) if detector
noise causes problems for the peak detection. The HDF5 files might be in some
folder a long way from the current directory, so you might want to specify a
full pathname to be added in front of each filename. You'll probably want to
run more than one indexing job at a time (-j <n>), and you might want to correct
the intensities of saturated peaks according to a list stored elsewhere in the
HDF5 file:
indexamajig
-i mypatternlist.lst
--indexing=dirax
--geometry mygeometry.geom
-p mystructure.pdb
--near-bragg
--filter-noise
--prefix=/some/horribly/long/pathname/ending/in/a/slash/
-j 16
--sat-corr
-o myoutputfile.txt
The table of saturation values for --sat-corr should be located in the HDF5 file
as follows: /processing/hitfinder/peakinfo_saturated. It should be an n*3 two
dimensional array, where the first two columns contain x and y coordinates and
the third contains the value which should belong in a peak at location x,y. The
value will be divided by 5 and spread in a small cross centred on that location.
See doc/geometry for information about how to create a geometry description
file.
Peak Detection
--------------
You can control the peak detection on the command line. Firstly, you can choose
the peak detection method using "--peaks=<method>". Currently, two possible
values for "method" are available. "hdf5" will take the peak locations from the
HDF5 file. It expects a two dimensional array at /processing/hitfinder/peakinfo
where size in the first dimension is the number of peaks and the size in the
second dimension is three. The first two columns contain the x and y
coordinate (see the "Note about data orientation" in geometry.txt for details),
the third contains the intensity. However, the intensity will be ignored since
the pattern will always be re-integrated using the unit cell provided by the
indexer on the basis of the peaks.
The "zaef" method uses a simple gradient search after Zaefferer (2000). You can
control the overall threshold and minimum gradient for finding a peak using the
"--threshold" and "--min-gradient" options. Both of these have units of "ADU"
(i.e. units of intensity according to the contents of the HDF5 file).
A minimum peak separation can also be provided in the geometry description file
(see geometry.txt for details). This number serves two purposes. Firstly,
it is the maximum distance allowed between the peak summit and the foot point
(where the gradient exceeds the minimum gradient). Secondly, it is the minimum
distance allowed between one peak and another, before the later peak will be
rejected "by proximity".
You can suppress peak detection altogether for a panel in the geometry file by
specifying the "no_index" value for the panel as non-zero.
Indexing Methods
----------------
You can choose between a variety of indexing methods. You can choose more than
one method, in which case each method will be tried in turn until the later cell
reduction step says that the cell is a "hit". Choose from:
dirax : invoke DirAx
mosflm : invoke MOSFLM (DPS)
Depending on what you have installed. For "dirax" and "mosflm", you need to
have the dirax or ipmosflm binaries in your PATH.
Example: --indexing=dirax,mosflm
Cell Reduction
--------------
You can choose from various options for cell reduction with the
"--cell-reduction=" option. The choices are "none", "reduce" and "compare".
This choice is important because all autoindexing methods produce an "ab
initio" estimate of the unit cell (nine parameters), rather than just finding
the orientation of the target cell (three parameters). It's clear that this is
not optimal, and will hopefully be fixed in future versions.
With "none", the raw cell from the autoindexer will be used. The cell probably
won't match the target cell, but it'll still get used. Use this option to test
whether the patterns are basically "indexable" or not, or if you don't know the
cell parameters. In the latter case, you'll need to plot some kind of histogram
of the resulting parameters from the output stream to see which are the most
popular. If you're lucky, this will reveal the true unit cell.
With "reduce", linear combinations of the raw cell will be checked against the
target cell. If at least one candidate is found for each axis of the target
cell, the angles will be checked to correspondence. If a match is found, this
cell will be used for further processing. This option should generate the most
matches, but might produce spurious results in many cases. The predicted peaks
are always checked to verify that at least 10% of the predicted peaks are close
to peaks located by the peak search. If not, the next candidate unit cell is
tried until there are no more options.
The "compare" method is like "reduce", but linear combinations are not taken.
That means that the cell must either match or match after a simple permutation
of the axes. This is useful when the target cell is subject to reticular
twinning, such as if one cell axis length is close to twice another. With
"reduce", there is a possibility that the axes might be confused in this
situation. This happens for lysozyme (1VDS), so watch out.
The tolerance for matching with "reduce" and "compare" is hardcoded as 5% in
the reciprocal axis lengths and 1.5 degrees in the (reciprocal) angles. Cells
from these reduction routines are further constrained to be right-handed. The
unmatched raw cell might be left-handed: CrystFEL doesn't check this for you.
Always using a right-handed cell means that the Bijvoet pairs can be told
apart.
If the unit cell is centered (i.e. if the space group begins with I, R, C, A or
F), you should be careful when using "compare" for the cell reduction, since
(for example) DirAx will always find a primitive unit cell, and this cell must
be converted to the non-primitive conventional cell from the PDB.
A Note about Unit Cell Settings
-------------------------------
CrystFEL's core symmetry module only knows about one setting for each unit cell.
You must use the same setting. That means that the unique axis (for cells which
have one) must be "c".
Unconventional Use
------------------
There are some less often used options, for example "--dump-peaks" to dump the
peak locations found by the peak search (in turn presented to the indexer).
This might be useful if you want to check the performance of the peak finder.
If you run a large dataset with bot --dump-peaks and --near-bragg enabled,
you'll generate a large amount of data. To separate the peaks from the
indexed peaks, use scripts/stream-split as follows:
scripts/stream-split myoutputfile.txt indexed.txt peaks.txt
.. to generate both indexed.txt and peaks.txt. One of the last two arguments
can be "/dev/null" if you're only interested in the other.
"Gotchas"
---------
Don't run more than one indexamajig jobs simultaneously in the same working
directory - they'll overwrite each other's DirAx files, causing subtle problems
which can't easily be detected.
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