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

NAME

       __init__.py:36: -  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

       /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

       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.

              from ._conv import register_converters as _register_converters

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

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.

__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`.