NAME

       scatter -  create a PyMVPA dataset from various sources

SYNOPSIS

       pymvpa2  mkds  [--version]  [-h]  [-i [dataset [dataset ...]]] [--txt-data VALUE [VALUE ...] | --npy-data
       VALUE [VALUE ...] | --mri-data IMAGE [IMAGE ...] | --openfmri-modelbold SPEC SPEC  SPEC  SPEC]  [--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  ...]]
       [--mask IMAGE] [--add-vol-attr ARG ARG] [--add-fsl-mcpar FILENAME] -o OUTPUT [--hdf5-compression TYPE]

DESCRIPTION

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

       Create a PyMVPA dataset from various sources.

       This  command converts data from various sources, such as text files, NumPy's NPY files, and MR (magnetic
       resonance) images into a PyMVPA dataset that gets stored in HDF5 format. An arbitrary  number  of  sample
       and  feature  attributes  can  be  added  to  a  dataset,  and  individual  attributes  can  be read from
       heterogeneous sources (e.g. they do not have to be all from text files).

       For datasets from MR images this command also supports automatic conversion  of  additional  images  into
       (volumetric)  feature  attributes.  This  can  be useful for describing features with, for example, atlas
       labels.

       COMPOSE ATTRIBUTES ON THE COMMAND LINE

       Options --add-sa and --add-fa  can be used to compose dataset attributes directly on  The  command  line.
       The syntax is:

       ... --add-sa <attribute name> <comma-separated values> [DTYPE]

       where  the optional 'DTYPE' is any identifier of a NumPy data type (e.g. 'int', or 'float32'). If no data
       type is specified the attribute values will be strings.

       If only one attribute value is given, it will copied and assigned to all entries in the dataset.

       LOAD DATA FROM TEXT FILES

       All options for loading data from text files support optional parameters to Tweak the conversion:

       ... --add-sa-txt <mandatory values> [DELIMITER [DTYPE [SKIPROWS [COMMENTS]]]]

       where 'DELIMITER' is the string that is used to separate  values  in  the  input  file,  'DTYPE'  is  any
       identifier  of a NumPy data type (e.g. 'int', or 'float32'), 'SKIPROWS' is an integer indicating how many
       lines at the beginning of the respective file shall be ignored, and 'COMMENTS' is a string indicating how
       to-be-ignored comment lines are prefixed in the file.

       LOAD DATA FROM NUMPY NPY FILES

       All options for loading data from NumPy NPY files support an optional parameter:

       ... --add-fa-npy <mandatory values> [MEMMAP]

       where 'MEMMAP' is a flag  that triggers whether the respective file shall be read by memory-mapping, i.e.
       not read (immediately) into memory. Enable by with on of: yes|1|true|enable|on'.

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.

   Input data sources:
       --txt-data VALUE [VALUE ...]
              load  samples  from  a  text  file.  The first 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 text files".

       --npy-data VALUE [VALUE ...]
              load  samples  from  a Numpy .npy file. Compressed files (i.e. .npy.gz) are supported as well. The
              first 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".

       --mri-data IMAGE [IMAGE ...]
              load data from an MR image, such as a NIfTI file. This can either be a single 4D image, or a  list
              of 3D images, or a combination of both.

       --openfmri-modelbold SPEC SPEC SPEC SPEC
              load  all  data  associated with a stimulation model in an OpenFMRI-compliant dataset. This option
              needs 4 argument values: <path> <model ID> <subj ID>  <flavor>.   The  first  value  is  the  base
              directory  of  the  dataset.   The  next  two  are (integer) ID for the desired stimulus model and
              subject. The last argument is either a string indicating the data flavor  to  load,  or  an  empty
              string for the default image (bold.nii.gz).

   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".

   Options for input from MR images:
       --mask IMAGE
              mask  image  file  with  the  same dimensions as an input data sample. All voxels corresponding to
              non-zero mask elements will be permitted into the dataset.

       --add-vol-attr ARG ARG
              attribute name (1st argument) and image file with the same dimensions as an input data sample (2nd
              argument). The image data will be added as a feature attribute under the specified name.

       --add-fsl-mcpar FILENAME
              6-column motion parameter file in FSL's McFlirt format. Six additional sample attributes  will  be
              created: mc_{x,y,z} and mc_rot{1-3}, for translation and rotation estimates respectively.

   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.

       pymvpa2-mkds 2.6.4

EXAMPLES

       Load  4D MRI image, assign atlas labels to a feature attribute, and attach class labels from a text file.
       The resulting dataset is stored as 'ds.hdf5' in the current directory.

              $ pymvpa2 mkds -o ds --mri-data bold.nii.gz --vol-attr  area  harvox.nii.gz  --add-sa-txt  targets
              labels.txt

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.

scatter not available: No module named _tkinte... January 2018                                        SCATTER(1)