.. _grabbing_and_sinking:

==================================
DataGrabber and DataSink explained
==================================

In this chapter we will try to explain the concepts behind DataGrabber and
DataSink.

Why do we need these interfaces?
================================

A typical workflow takes data as input and produces data as the result of one or
more operations. One can set the data required by a workflow directly as
illustrated below. 

::

  from fsl_tutorial2 import preproc
  preproc.base_dir = os.path.abspath('.')
  preproc.inputs.inputspec.func = os.path.abspath('data/s1/f3.nii')
  preproc.inputs.inputspec.struct = os.path.abspath('data/s1/struct.nii')
  preproc.run()

Typical neuroimaging studies require running workflows on multiple subjects or
different parameterizations of algorithms. One simple approach to that would be
to simply iterate over subjects.

::

  from fsl_tutorial2 import preproc
  for name in subjects:
    preproc.base_dir = os.path.abspath('.')
    preproc.inputs.inputspec.func = os.path.abspath('data/%s/f3.nii'%name)
    preproc.inputs.inputspec.struct = os.path.abspath('data/%s/struct.nii'%name)
    preproc.run()

However, in the context of complex workflows and given that users typically
arrange their imaging and other data in a semantically hierarchical data store,
an alternate mechanism for reading and writing the data generated by a workflow
is often necessary. As the names suggest DataGrabber is used to get at data
stored in a shared file system while DataSink is used to store the data
generated by a workflow into a hierarchical structure on disk.


DataGrabber
===========

Datagrabber is an interface for collecting files from hard drive. It is very
flexible and supports almost any file organisation of your data you can imagine.

You can use it as a trivial use case of getting a fixed file. By default,
DataGrabber stores its outputs in a field called outfiles.

::

  import nipype.interfaces.io as nio
  datasource1 = nio.DataGrabber()
  datasource1.inputs.base_directory = os.getcwd()
  datasource1.inputs.template = 'data/s1/f3.nii'
  results = datasource1.run()

Or you can get at all uncompressed nifti files starting with the letter 'f' in
all directories starting with the letter 's'.

::

  datasource2.inputs.base_directory = '/mass'
  datasource2.inputs.template = 'data/s*/f*.nii'

Two special inputs were used in these previous cases. The input `base_directory`
indicates in which directory to search, while the input `template` indicates the
string template to match. So in the previous case datagrabber is looking for
path matches of the form `/mass/data/s*/f*`.

.. note::
  
  When used with wildcards (e.g., s* and f* above) Datagrabber does not return
  data in sorted order. In order to force it to return data in sorted order, one
  needs to set the input `sorted = True`. However, when explicitly specifying an
  order as we will see below, `sorted` should be set to `False`.

More useful cases arise when the template can be filled by other inputs. In the
example below, we define an input field for `datagrabber` called `run`. This is
then used to set the template (see %d in the template).

::

  datasource3 = nio.DataGrabber(infields=['run'])
  datasource3.inputs.base_directory = os.getcwd()
  datasource3.inputs.template = 'data/s1/f%d.nii'
  datasource3.inputs.run = [3, 7]

This will return files `basedir/data/s1/f3.nii` and `basedir/data/s1/f7.nii`. We
can take this a step further and pair subjects with runs.

::

  datasource4 = nio.DataGrabber(infields=['subject_id', 'run'])
  datasource4.inputs.template = 'data/%s/f%d.nii'
  datasource4.inputs.run = [3, 7]
  datasource4.inputs.subject_id = ['s1', 's3']

This will return files `basedir/data/s1/f3.nii` and `basedir/data/s3/f7.nii`.

A more realistic use-case
-------------------------

In a typical study one often wants to grab different files for a given subject
and store them in semantically meaningful outputs. In the following example, we
wish to retrieve all the functional runs and the structural image for subject 's1'.

::

  datasource = nio.DataGrabber(infields=['subject_id'], outfields=['func', 'struct'])
  datasource.inputs.base_directory = 'data'
  datasource.inputs.template = '*'
  datasource.inputs.field_template = dict(func='%s/f%d.nii',
                                          struct='%s/struct.nii')
  datasource.inputs.template_args = dict(func=[['subject_id', [3,5,7,10]]],
                                         struct=[['subject_id']])
  datasource.inputs.subject_id = 's1'

Two more fields are introduced: `field_template` and `template_args`. These
fields are both dictionaries whose keys correspond to the `outfields`
keyword. The `field_template` reflects the search path for each output field,
while the `template_args` reflect the inputs that satisfy the template. The
inputs can either be one of the named inputs specified by the `infields` keyword
arg or it can be raw strings or integers corresponding to the template. For the
`func` output, the **%s** in the `field_template` is satisfied by `subject_id`
and the **%d** is field in by the list of numbers. 

.. note::

  We have not set `sorted` to `True` as we want the DataGrabber to return the
  functional files in the order it was specified rather than in an alphabetic
  sorted order.
 
DataSink
========

A workflow working directory is like a **cache**. It contains not only the
outputs of various processing stages, it also contains various extraneous
information such as execution reports, hashfiles determining the input state of
processes. All of this is embedded in a hierarchical structure that reflects the
iterables that have been used in the workflow. This makes navigating the working
directory a not so pleasant experience. And typically the user is interested in
preserving only a small percentage of these outputs. The DataSink interface can
be used to extract components from this `cache` and store it at a different
location. For XNAT-based storage, see XNATSink.

.. note:: 

   Unlike other interfaces, a DataSink's inputs are defined and created by using
   the workflow connect statement. Currently disconnecting an input from the
   datasink does not remove that connection port.

Let's assume we have the following workflow.

.. digraph:: simple_workflow

   "InputNode" -> "Realign" -> "DataSink";
   "InputNode" -> "DataSink";

The following code segment defines the DataSink node and sets the `base_directory`
in which all outputs will be stored. The `container` input creates a
subdirectory within the `base_directory`. If you are iterating a workflow over
subjects, it may be useful to save it within a folder with the subject id. 

::

  datasink = pe.Node(nio.DataSink(), name='sinker')
  datasink.inputs.base_directory = '/path/to/output'
  workflow.connect(inputnode, 'subject_id', datasink, 'container')

If we wanted to save the realigned files and the realignment parameters to the
same place the most intuitive option would be:
 
::

  workflow.connect(realigner, 'realigned_files', datasink, 'motion')
  workflow.connect(realigner, 'realignment_parameters', datasink, 'motion')

However, this will not work as only one connection is allowed per input port. So
we need to create a second port. We can store the files in a separate folder.

::

  workflow.connect(realigner, 'realigned_files', datasink, 'motion')
  workflow.connect(realigner, 'realignment_parameters', datasink, 'motion.par')

The period (.) indicates that a subfolder called par should be created. But if
we wanted to store it in the same folder as the realigned files, we would use
the `.@` syntax. The @ tells the datasink interface to not create the
subfolder. This will allow us to create different named input ports for datasink
and allow the user to store the files in the same folder.

::

  workflow.connect(realigner, 'realigned_files', datasink, 'motion')
  workflow.connect(realigner, 'realignment_parameters', datasink, 'motion.@par')

The syntax for the input port of DataSink takes the following form:

::

  string[[.[@]]string[[.[@]]string] ...]
  where parts between paired [] are optional.


Parameterization
----------------

As discussed in :doc:`mapnode_and_iterables`, one can run a workflow iterating
over various inputs using the iterables attribute of nodes. This means that a
given workflow can have multiple outputs depending on how many iterables are
there. Iterables create working directory subfolders such as
`_iterable_name_value`.  The `parameterization` input parameter controls whether
the data stored using DataSink is in a folder structure that contains this
iterable information or not. It is generally recommended to set this to `True`
when using multiple nested iterables.


Substitutions
-------------

The `substitutions` and `substitutions_regexp` inputs allow users to modify the
output destination path and name of a file. Substitutions are a list of 2-tuples
and are carried out in the order in which they were entered. Assuming that the
output path of a file is: 

::

  /root/container/_variable_1/file_subject_realigned.nii

we can use substitutions to clean up the output path.

::

  datasink.inputs.substitutions = [('_variable', 'variable'),
                                   ('file_subject_', '')]

This will rewrite the file as:

::

  /root/container/variable_1/realigned.nii


.. note::

   In order to figure out which substitutions are needed it is often useful to
   run the workflow on a limited set of iterables and then determine the
   substitutions. 

.. include:: ../links_names.txt
