Provided by: python-mvpa2_2.6.5-1_all bug

NAME

       __init__.py:36: -  extract (multi-sample) events from a dataset

SYNOPSIS

       pymvpa2  mkevds [--version] [-h] -i DATASET [DATASET ...] [--event-attrs ATTR [ATTR ...] |
       --onsets [TIME [TIME ...]] | --csv-events FILENAME | --fsl-ev3  FILENAME  [FILENAME  ...]]
       [--time-attr  ATTR]  [--onset-column  ATTR]  [--offset VALUE] [--duration VALUE] [--match-
       strategy {prev,next,closest}] [--event-compression {mean,median,min,max}] [--add-sa  VALUE
       [VALUE  ...]]  [--add-fa VALUE [VALUE ...]] [--add-sa-txt VALUE [VALUE ...]] [--add-fa-txt
       VALUE [VALUE ...]] [--add-sa-attr FILENAME] [--add-sa-npy VALUE [VALUE ...]] [--add-fa-npy
       VALUE [VALUE ...]] -o OUTPUT [--hdf5-compression TYPE]

DESCRIPTION

       /usr/lib/python2.7/dist-packages/h5py/__init__.py:36:  FutureWarning:  Conversion  of  the
       second argument of issubdtype from `float` to `np.floating` is deprecated. In  future,  it
       will be treated as `np.float64 == np.dtype(float).type`.

              from ._conv import register_converters as _register_converters

       scatter not available: No module named _tkinter, please install the python-tk package

       Extract (multi-sample) events from a dataset

       An arbitrary number of input datasets is loaded from HDF5 storage. All loaded datasets are
       concatenated along the samples axis. Based on information about onset and  duration  of  a
       sequence  of  events  corresponding  samples  are  extracted  from  the input datasets and
       converted into event samples. It is possible for an event sample to  consist  of  multiple
       input samples (i.e. temporal windows).

       Events  are  defined by onset sample ID and number of consecutive samples that comprise an
       event. However, events can also be defined as temporal onsets and durations, which will be
       translated into sample IDs using time stamp information in the input datasets.

       Analogous  to  the 'mkds' command the event-related dataset can be extended with arbitrary
       feature and sample attributes (one value per event for the latter).

       The finished event-related dataset is written to an HDF5 file.

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 defining events (choose one):
       --event-attrs ATTR [ATTR ...]
              define events as a unique combinations of values from a set of  sample  attributes.
              Going  through  all samples in the order in which they appear in the input dataset,
              onset of events are determined by changes in the combination of  attribute  values.
              The  length  of an event is determined by the number of identical consecutive value
              combinations.

       --onsets [TIME [TIME ...]]
              reads a list of event onsets (float) from the command  line  (space-separated).  If
              this option is given, but no arguments are provided, onsets will be read from STDIN
              (one per line). If --time-attr is also given, onsets will be  interpreted  as  time
              stamps, otherwise they are treated a integer ID of samples.

       --csv-events FILENAME
              read event information from a CSV table. A variety of dialects are supported. A CSV
              file must contain a header line with field names as a first  row.  The  table  must
              include  an  'onset'  column,  and  can  optionally  include an arbitrary number of
              additional columns (e.g. duration,  target).  All  values  are  passed  on  to  the
              event-related samples. If '-' is given as a value the CSV table is read from STDIN.

       --fsl-ev3 FILENAME [FILENAME ...]
              read  event  information  from a text file in FSL's EV3 format (one event per line,
              three columns: onset, duration, intensity). One of more filenames can be given.

   Options for modifying or converting events:
       --time-attr ATTR
              dataset attribute with time stamps for input samples.  Onset and duration  for  all
              events  will  be  converted using this information. All values are assumed to be of
              the same units.

       --onset-column ATTR
              name of the column in the CSV event table that indicates event onsets

       --offset VALUE
              fixed uniform event offset for all events. If no --time-attr option is given,  this
              value  indicates  the number of input samples all event onsets shall be shifted. If
              --time-attr is given, this is treated as a temporal offset that needs to  be  given
              in the same unit as the time stamp attribute (see --time-attr).

       --duration VALUE
              fixed uniform duration for all events. If no --timeattr option is given, this value
              indicates the number of consecutive input samples following an onset that belong to
              an  event.  If  --time-attr  is  given, this is treated as a temporal duration that
              needs to be given in the same unit as the time stamp attribute (see --time-attr).

       --match-strategy {prev,next,closest}
              strategy used to match time-based onsets to  sample  indices.  'prev'  chooses  the
              closes  preceding  samples,  'next'  the  closest following sample and 'closest' to
              absolute closest sample. Default: 'prev'

       --event-compression {mean,median,min,max}
              specify whether and how events spanning multiple input samples shall be compressed.
              A  number  of  methods can be chosen. Selecting, for example, 'mean' will yield the
              mean of all relevant input samples for an event.  By default (when this  option  is
              not given) an event will comprise of all concatenated input samples.

   Options for attributes from the command line:
       --add-sa VALUE [VALUE ...]
              compose  a  sample  attribute  from  the command line input. The first value is the
              desired attribute name, the second value is a comma-separated  list  (appropriately
              quoted) of actual attribute values. An optional third value can be given to specify
              a data type. Additional information on defining dataset attributes on  the  command
              line are given in the section "Compose attributes on the command line.

       --add-fa VALUE [VALUE ...]
              compose  a  feature  attribute  from the command line input. The first value is the
              desired attribute name, the second value is a comma-separated  list  (appropriately
              quoted) of actual attribute values. An optional third value can be given to specify
              a data type. Additional information on defining dataset attributes on  the  command
              line are given in the section "Compose attributes on the command line.

   Options for attributes from text files:
       --add-sa-txt VALUE [VALUE ...]
              load  sample  attribute  from a text file. The first value is the desired attribute
              name, the second  value  is  the  filename  the  attribute  will  be  loaded  from.
              Additional values modifying the way the data is loaded are described in the section
              "Load data from text files".

       --add-fa-txt VALUE [VALUE ...]
              load feature attribute from a text file. The first value is the  desired  attribute
              name,  the  second  value  is  the  filename  the  attribute  will  be loaded from.
              Additional values modifying the way the data is loaded are described in the section
              "Load data from text files".

       --add-sa-attr FILENAME
              load  sample attribute values from an legacy 'attributes file'. Column data is read
              as "literal".  Only two column files ('targets' +  'chunks')  without  headers  are
              supported.  This  option  allows  for  reading  attributes  files from early PyMVPA
              versions.

   Options for attributes from stored Numpy arrays:
       --add-sa-npy VALUE [VALUE ...]
              load sample attribute from a Numpy .npy file.  Compressed files (i.e. .npy.gz)  are
              supported as well.  The first value is the desired attribute name, the second value
              is the filename the data will be loaded from. Additional values modifying  the  way
              the data is loaded are described in the section "Load data from Numpy NPY files".

       --add-fa-npy VALUE [VALUE ...]
              load feature attribute from a Numpy .npy file.  Compressed files (i.e. .npy.gz) are
              supported as well.  The first value is the desired attribute name, the second value
              is  the  filename the data will be loaded from. Additional values modifying the way
              the data is loaded are described in the section "Load data from Numpy NPY files".

   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.

              from ._conv import register_converters as _register_converters

       scatter not available: No module named _tkinter,  please  install  the  python-tk  package
       pymvpa2-mkevds 2.6.5

EXAMPLES

       Extract two events comprising of four consecutive samples from a dataset.

              $ pymvpa2 mkevds --onsets 3 9 --duration 4 -o evds.hdf5 -i 'mydata*.hdf5'

AUTHOR

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

COPYRIGHT

       Copyright © 2006-2016 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.

__init__.py:36: FutureWarning: Conversion ofJune 2018nd argument of issubdtype frINITfl.PY:36:(1)p.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.