.. AUTO-GENERATED FILE -- DO NOT EDIT!

.. _example_cmdline_start_easy:

A simple start (on the command line)
====================================
This script is the exact equivalent of the :ref:`example_start_easy` example,
but using the command line interface.
First we load an fMRI dataset with some attributes for each volume, only
considering voxels that are non-zero in a mask image.

.. code-block:: sh

  pymvpa2 mkds --mri-data "$dataroot"/bold.nii.gz \
              --add-sa-attr "$dataroot"/attributes_literal.txt  \
              --mask "$dataroot"/mask.nii.gz \
              -o "$outdir"/bold_ds.hdf5
  
Next we remove linear trends by polynomial regression for each voxel and
each chunk (recording run) of the dataset individually.

.. code-block:: sh

  pymvpa2 preproc --chunks chunks \
                  --poly-detrend 1 \
                  -i "$outdir"/bold_ds.hdf5 \
                  -o "$outdir"/preproced.hdf5
  
For this example we are only interested in data samples that correspond
to the ``face`` or to the ``house`` condition.

.. code-block:: sh

  pymvpa2 select --samples-by-attr targets eq face or targets eq house \
                 -i "$outdir"/preproced.hdf5 \
                 -o "$outdir"/facehouse.hdf5
  
The setup for our cross-validation analysis include the selection of a
classifier from the "warehouse", a partitioning scheme, and an error function
to convert literal predictions into a quantitative performance metric.

.. code-block:: sh

  pymvpa2 crossval --learner 'SMLR(lm=1.0)' \
                   --partitioner oddeven:chunks \
                   --errorfx mean_mismatch_error \
                   --avg-datafold-results \
                   -i "$outdir"/facehouse.hdf5 \
                   -o "$outdir"/crossval_results.hdf5
  
The resulting dataset contains the computed accuracy.

.. code-block:: sh

  echo -n "Error for cross-validation problem: "
  pymvpa2 dump -s -i "$outdir"/crossval_results.hdf5
  

.. seealso::
  The full source code of this example is included in the PyMVPA source distribution (`doc/examples/cmdline/start_easy.sh`).
