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.

pymvpa2-searchlight 2.4.1                         November 2015                           PYMVPA2-SEARCHLIGHT(1)