Ubuntu Manpage: pymvpa2-searchlight - traveling ROI analysis

name
synopsis
description
options
author
copyright

NAME

       pymvpa2-searchlight -  traveling ROI analysis

SYNOPSIS

       pymvpa2 searchlight [--version] [-h] -i DATASET [DATASET ...] --payload PAYLOAD --neighbors SPEC [--nproc
       NPROC] [--multiproc-backend {native,hdf5}] [--aggregate-fx AGGREGATE_FX] [--ds-preproc-fx  DS_PREPROC_FX]
       [--enable-ca  NAME [NAME ...]] [--disable-ca NAME [NAME ...]] [--scatter-rois SPEC] [--roi-attr ATTR/EXPR
       [ATTR/EXPR  ...]]  [--cv-learner  CV_LEARNER]  [--cv-learner-space  CV_LEARNER_SPACE]   [--cv-partitioner
       CV_PARTITIONER]     [--cv-errorfx    CV_ERRORFX]    [--cv-avg-datafold-results]    [--cv-balance-training
       CV_BALANCE_TRAINING]     [--cv-sampling-repetitions      CV_SAMPLING_REPETITIONS]      [--cv-permutations
       CV_PERMUTATIONS] [--cv-prob-tail {left,right}] -o OUTPUT [--hdf5-compression TYPE]

DESCRIPTION

       Traveling ROI analysis

OPTIONS

--version
show program's version and license information and exit

-h, --help, --help-np
show this help message and exit. --help-np forcefully disables the use of a pager for displaying
the help.

-i DATASET [DATASET ...], --input DATASET [DATASET ...]
path(s) to one or more PyMVPA dataset files. All datasets will be merged into a single dataset
(vstack'ed) in order of specification. In some cases this option may need to be specified more
than once if multiple, but separate, input datasets are required.

Options for searchlight setup:
--payload PAYLOAD
switch to select a particular analysis type to be run in a searchlight fashion on a dataset.
Depending on the choice the corresponding analysis setup options are evaluated. 'cv' computes a
cross-validation analysis. Alternatively, the argument to this option can also be a script
filename in which a custom measure is built that is then ran as a searchlight.

--neighbors SPEC
define the size and shape of an ROI with respect to a center/seed location. If a single integer
number is given, it is interpreted as the radius (in number of grid elements) around a seed
location. By default grid coordinates for features are taken from a 'voxel_indices' feature
attribute in the input dataset. If coordinates shall be taken from a different attribute, the
radius value can be prefixed with the attribute name, i.e. 'altcoords:2'. For ROI shapes other
than spheres (with potentially additional parameters), the shape name can be specified as well,
i.e. 'voxel_indices:HollowSphere:3:2'. All neighborhood objects from the mvpa2.misc.neighborhood
module are supported. For custom ROI shapes it is also possible to pass a script filename, or an
attribute name plus script filename combination, i.e. 'voxel_indices:myownshape.py' (advanced).
It is possible to specify this option multiple times to define multi-space ROI shapes for, e.g.,
spatiotemporal searchlights.

--nproc NPROC
Use the specific number or worker processes for computing.

--multiproc-backend {native,hdf5}
Specifies the way results are provided back from a processing block in case of --nproc > 1.
'native' is pickling/unpickling of results, while 'hdf5' uses HDF5 based file storage. 'hdf5'
might be more time and memory efficient in some cases.

--aggregate-fx AGGREGATE_FX
use a custom result aggregation function for the searchlight

--ds-preproc-fx DS_PREPROC_FX
custom preprocessing function to be applied immediately after loading the data

Options for conditional attributes:
--enable-ca NAME [NAME ...]
list of conditional attributes to be enabled

--disable-ca NAME [NAME ...]
list of conditional attributes to be disabled

Options for constraining the searchlight:
--scatter-rois SPEC
scatter ROI locations across the available space. The arguments supported by this option are
identical to those of --neighbors. ROI locations are randomly picked from all possible locations
with the constraint that the center coordinates of any ROI is NOT within the neighborhood (as
defined by this option's argument) of a second ROI. Increasing the size of the neighborhood
therefore increases the scarceness of the sampling.

--roi-attr ATTR/EXPR [ATTR/EXPR ...]
name of a feature attribute whose non-zero values define possible ROI seeds/centers.
Alternatively, this can also be an expression like: parcellation_roi eq 16 (see the 'select'
command on information what expressions are supported).

Options for cross-validation setup:
--cv-learner CV_LEARNER
select a learner (trainable node) via its description in the learner warehouse (see 'info' command
for a listing), a colon-separated list of capabilities, or by a file path to a Python script that
creates a classifier instance (advanced).

--cv-learner-space CV_LEARNER_SPACE
name of a sample attribute that defines the model to be learned by a learner. By default this is
an attribute named 'targets'.

--cv-partitioner CV_PARTITIONER
select a data folding scheme. Supported arguments are: 'half' for split-half partitioning,
'oddeven' for partitioning into odd and even chunks, 'group-X' where X can be any positive integer
for partitioning in X groups, 'n-X' where X can be any positive integer for leave-X-chunks out
partitioning. By default partitioners operate on dataset chunks that are defined by a 'chunks'
sample attribute. The name of the "chunking" attribute can be changed by appending a colon and the
name of the attribute (e.g. 'oddeven:run'). optionally an argument to this option can also be a
file path to a Python script that creates a custom partitioner instance (advanced).

--cv-errorfx CV_ERRORFX
error function to be applied to the targets and predictions of each cross-validation data fold.
This can either be a name of any error function in PyMVPA's mvpa2.misc.errorfx module, or a file
path to a Python script that creates a custom error function (advanced).

--cv-avg-datafold-results
average result values across data folds generated by the partitioner. For example to compute a
mean prediction error across all folds of a crossvalidation procedure.

--cv-balance-training CV_BALANCE_TRAINING
If enabled, training samples are balanced within each data fold. If the keyword 'equal' is given
as argument an equal number of random samples for each unique target value is chosen. The number
of samples per category is determined by the category with the least number of samples in the
respective training set. An integer argument will cause the a corresponding number of samples per
category to be randomly selected. A floating point number argument (interval [0,1]) indicates what
fraction of the available samples shall be selected.

--cv-sampling-repetitions CV_SAMPLING_REPETITIONS
If training set balancing is enabled, how often should random sample selection be performed for
each data fold. Default: 1

--cv-permutations CV_PERMUTATIONS
Number of Monte-Carlo permutation runs to be computed for estimating an H0 distribution for all
crossvalidation results. Enabling this option will make reports of corresponding p-values
available in the result summary and output.

--cv-prob-tail {left,right}
which tail of the probability distribution to report p-values from when evaluating permutation
test results. For example, a cross-validation computing mean prediction error could report
left-tail p-value for a single-sided test.

Output options:
-o OUTPUT, --output OUTPUT
output filename ('.hdf5' extension is added automatically if necessary). NOTE: The output format
is suitable for data exchange between PyMVPA commands, but is not recommended for long-term
storage or exchange as its specific content may vary depending on the actual software environment.
For long-term storage consider conversion into other data formats (see 'dump' command).

--hdf5-compression TYPE
compression type for HDF5 storage. Available values depend on the specific HDF5 installation.
Typical values are: 'gzip', 'lzf', 'szip', or integers from 1 to 9 indicating gzip compression
levels.

AUTHOR

       Written by Michael Hanke & Yaroslav Halchenko, and numerous other contributors.

COPYRIGHT

       Copyright © 2006-2015 PyMVPA developers

       Permission is hereby granted, free of charge, to any  person  obtaining  a  copy  of  this  software  and
       associated  documentation  files (the "Software"), to deal in the Software without restriction, including
       without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense,  and/or  sell
       copies  of the Software, and to permit persons to whom the Software is furnished to do so, subject to the
       following conditions:

       The above copyright notice and this permission notice shall be included  in  all  copies  or  substantial
       portions of the Software.

       THE  SOFTWARE  IS  PROVIDED  "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT
       LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO
       EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
       IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE  SOFTWARE  OR
       THE USE OR OTHER DEALINGS IN THE SOFTWARE.