README.md

@@ -1,3 +1,5 @@
+![Junifer logo](docs/images/junifer_logo.png)
+
 # junifer - JUelich NeuroImaging FEature extractoR
 
 ![PyPI](https://img.shields.io/pypi/v/junifer?style=flat-square)

docs/builtin.rst

@@ -14,7 +14,7 @@ Data Grabbers
         - Open with registration
         - Restricted
 
-    Type/config: this should mention weather the class is built-in in the
+    Type/config: this should mention whether the class is built-in in the
     core of junifer or needs to be imported from a specific configuration in
     the `junifer.configs` module.
 
@@ -47,7 +47,7 @@ Available
      - Built-in
      - In Progress
      - :gh:`4`
-   * - :class:`junifer.configs.juseless.JuselessDataladUKBVBM`
+   * - :class:`junifer.configs.juseless.datagrabbers.JuselessDataladUKBVBM`
      - UKB VBM dataset preprocessed with CAT. Available for Juseless only.
      - Restricted
      - ``junifer.configs.juseless``
@@ -56,7 +56,7 @@ Available
    * - :class:`junifer.configs.juseless.datagrabbers.JuselessDataladCamCANVBM`
      - CamCAN VBM dataset preprocessed with CAT. Available for Juseless only.
      - Restricted
-     - ``junifer.configs.juseless.datagrabbers``
+     - ``junifer.configs.juseless``
      - Done
      - 0.0.1
    * - :class:`junifer.datagrabber.DataladAOMICID1000`
@@ -77,7 +77,7 @@ Available
      - Built-in
      - Done
      - 0.0.1
-   * - :class:`junifer.configs.juseless.JuselessDataladAOMICVBM`
+   * - :class:`junifer.configs.juseless.datagrabbers.JuselessDataladAOMICVBM`
      - AOMIC VBM dataset. Available for Juseless only.
      - Restricted
      - ``junifer.configs.juseless``
@@ -86,7 +86,7 @@ Available
    * - :class:`junifer.configs.juseless.datagrabbers.JuselessDataladIXIVBM`
      - `IXI VBM dataset <https://brain-development.org/ixi-dataset/>`_. Available for Juseless only.
      - Restricted
-     - ``junifer.configs.juseless.datagrabbers``
+     - ``junifer.configs.juseless``
      - Done
      - 0.0.1
 
@@ -154,6 +154,10 @@ Available
      - Spherical aggregation using mean
      - Done
      - 0.0.1
+   * - :class:`junifer.markers.FunctionalConnectivitySpheres`
+     - Perform spherical aggregation and compute functional connectivity
+     - Done
+     - 0.0.1
    * - :class:`junifer.markers.RSSETSMarker`
      - Compute root sum of squares of edgewise timeseries
      - Done

docs/conf.py

@@ -64,9 +64,19 @@ exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
 # a list of builtin themes.
 #
 html_theme = "sphinx_rtd_theme"
-
-html_sidebars = {"**": ["globaltoc.html", "sourcelink.html", "searchbox.html"]}
-
+html_theme_options = {
+    "display_version": True,
+    "style_external_links": True,
+    "logo_only": True,
+}
+html_sidebars = {
+    "**": [
+        "globaltoc.html",
+        "sourcelink.html",
+        "searchbox.html",
+    ]
+}
+html_logo = "./images/junifer_logo.png"
 
 # Add any paths that contain custom static files (such as style sheets) here,
 # relative to this directory. They are copied after the builtin static files,

docs/index.rst

@@ -1,5 +1,9 @@
 .. include:: links.inc
 
+.. image:: images/junifer_logo.png
+   :width: 300px
+   :alt: junifer logo
+
 Welcome to junifer's documentation!
 ===================================
 

docs/installation.rst

@@ -9,14 +9,14 @@ Requirements
 
 junifer is compatible with `Python`_ >= 3.8 and requires the following packages:
 
-* click>=8.1.3,<8.2
-* numpy>=1.22,<1.23
-* datalad>=0.15.4,<0.18
-* pandas>=1.4.0,<1.5
-* nibabel>=3.2.0,<4.1
-* nilearn>=0.9.0,<1.0
-* sqlalchemy>=1.4.27,<= 1.5.0
-* pyyaml>=5.1.2,<7.0
+* ``click>=8.1.3,<8.2``
+* ``numpy>=1.22,<1.23``
+* ``datalad>=0.15.4,<0.18``
+* ``pandas>=1.4.0,<1.5``
+* ``nibabel>=3.2.0,<4.1``
+* ``nilearn>=0.9.0,<1.0``
+* ``sqlalchemy>=1.4.27,<= 1.5.0``
+* ``pyyaml>=5.1.2,<7.0``
 
 Depending on the installation method, these packages might be installed automatically.
 

docs/understanding/data.rst

@@ -1,5 +1,7 @@
 .. include:: ../links.inc
 
+.. _data_object:
+
 The Data Object
 ===============
 
@@ -7,7 +9,7 @@ Description
 ^^^^^^^^^^^
 
 This is the *object* that traverses the steps of the pipeline. It is indeed a
-dictionary of dictionaries. The first level of keys are the :ref:`data_types`
+dictionary of dictionaries. The first level of keys are the :ref:`data types <data_types>`
 and a special key named ``meta`` that contains all the information on the data
 object including source and previous transformation steps.
 
@@ -16,17 +18,19 @@ The second level of keys are the actual data. So far, there are two keys used:
 - ``path``: path to the file containing the data.
 - ``data``: the data loaded in memory.
 
-The :ref:`datagrabber` step will only fill the ``path`` value.
-The ``data`` value will be filled by the :ref:`datareader` step, if it is one of the possible file types
+The :ref:`DataGrabber <datagrabber>` step will only fill the ``path`` value.
+The ``data`` value will be filled by the :ref:`DataReader <datareader>` step, if it is one of the possible file types
 that the datareader can read.
 
+A point to note is that you never directly interact with the *data object* but it's important to know where and how the object is being manipulated to reason about your pipeine.
+
 .. _data_types:
 
 Data types
 ^^^^^^^^^^
 
-.. list-table:: Built-in data types
-   :widths: 30 80 40
+.. list-table::
+   :widths: auto
    :header-rows: 1
 
    * - Name

docs/understanding/datagrabber.rst

@@ -2,50 +2,52 @@
 
 .. _datagrabber:
 
-Data Grabber
-============
-
+DataGrabber
+===========
 
 Description
 ^^^^^^^^^^^
-A datagrabber is an object that can provide datasets you want to junifer.
-For example, a DataladDataGrabber can provide data from a Datalad dataset to junifer.
-Of course, datagrabbers are not only possible for Datalad but any origin of a dataset.
-It is intended to use them as context managers.
-If you are interested in just using already provided datagrabbers please go to :doc:`../builtin`.
-If you want to implement your own Data Grabbers you need to inherit from different types of 
-Data Grabbers we already provide.
 
-Typical Data Grabbers
-^^^^^^^^^^^^^^^^^^^^^
-In this section we will showcase different types of datagrabber classes you might want to use
-to implement your own datagrabbers for your own data.
+The ``DataGrabber`` is an object that can provide an interface to datasets you want to work with in junifer.
+Every concrete implementation of a datagrabber is aware of a particular dataset's structure and thus allows
+you to fetch specific elements of interest from the dataset. It adds the ``path`` key to each :ref:`data type <data_types>`
+in the :ref:`Data object <data_object>`.
 
-.. list-table:: Data Grabbers Type
-   :widths: 25 35
+Datagrabbers are intended to be used as context managers. When used within a context, a datagrabber takes care
+of any pre and post steps for interacting with the dataset, for example, downloading and cleaning up. As the interface
+is consistent, you always use the same procedure to interact with the datagrabber.
+
+For example, a concrete implementation of :class:`junifer.datagrabber.DataladDataGrabber` can provide junifer
+with data from a Datalad dataset. Of course, datagrabbers are not only meant to work with Datalad datasets but
+any dataset.
+
+If you are interested in using already provided datagrabbers, please go to :doc:`../builtin`. And, if you want
+to implement your own datagrabber, you need to provide concrete implementations of base classes already
+provided.
+
+Base classes
+^^^^^^^^^^^^
+In this section, we showcase different abstract base classes you might want to use to implement your own datagrabber.
+
+.. list-table::
+   :widths: auto
    :header-rows: 1
 
    * - Name
      - Description
-   * - :py:class:`~junifer.datagrabber.base.BaseDataGrabber`
-     - | An abstract class providing you an interface to implement for you own datagrabber.
-       | This is not intedent to be used in general.
-       | Instead you should use the DataladDataGrabber or PatternDataGrabber if possible.
-       | You have to at least implement the ``get_elements`` method, but most of the time
-       | you should also overwrite other existing methods like ``__enter__`` and ``__exit__``.
-   * - :py:class:`~junifer.datagrabber.pattern.PatternDataGrabber`
-     - | Implements some functionality to help you to define the pattern of the dataset you want to get. 
-       | E.g. you know that T1 images are found in a directory following this pattern 
-       | inside of the dataset "{subject}/anat/{subject}_T1w.nii.gz".
-       | Now you can provide this to the PatternDataGrabber
-       | and it will be able to get the image to junifer. 
-   * - :py:class:`~junifer.datagrabber.datalad_base.DataladDataGrabber`
-     - | Implements some functionality specific to basic usage of Datalad datasets. 
-       | This mostly takes care of the ``__enter__`` and ``__exit__`` methods to clone your Datalad dataset
-       | and remove it on  ``__exit__``.
-       | This means that even if your code fails inside of the context of the 
-       | datagrabber the DataladDataGrabber cleans up the created Datalad directories. 
-   * - :py:class:`~junifer.datagrabber.pattern_datalad.PatternDataladDataGrabber`
-     - | Combination of PatternDataGrabber and DataladDataGrabber.
-       | Probably the class you are looking for when using Datalad. 
-
+   * - :class:`junifer.datagrabber.BaseDataGrabber`
+     - | The abstract base class providing you an interface to implement your own datagrabber.
+       | You should try to avoid using this directly and instead use
+       | :class:`junifer.datagrabber.PatternDataGrabber` or :class:`junifer.datagrabber.DataladDataGrabber`.
+       | To build your own custom *low-level* datagrabber, you need to at least implement the ``get_elements`` method,
+       | but most of the time you should also override other existing methods like ``__enter__`` and ``__exit__``.
+   * - :class:`junifer.datagrabber.PatternDataGrabber`
+     - | It implements functionality to help you define the pattern of the dataset you want to get. For example,
+       | you know that T1 images are found in a directory following this pattern ``{subject}/anat/{subject}_T1w.nii.gz``
+       | inside of the dataset. Now you can provide this to the **PatternDataGrabber** and it will be able to get the file.
+   * - :class:`junifer.datagrabber.DataladDataGrabber`
+     - | It implements functionality to deal with Datalad datasets. Specifically, the ``__enter__`` and ``__exit__`` methods
+       | take care of cloning and removing the Datalad dataset.
+   * - :class:`junifer.datagrabber.PatternDataladDataGrabber`
+     - | It is a combination of :class:`junifer.datagrabber.PatternPatternDataGrabber` and
+       | :class:`junifer.datagrabber.DataladDataGrabber`. This is probably the class you are looking for when using Datalad.

docs/understanding/datareader.rst

@@ -2,5 +2,45 @@
 
 .. _datareader:
 
-Data Reader
-===========
+DataReader
+==========
+
+Description
+^^^^^^^^^^^
+
+The ``DataReader`` is an object that is responsible for actually reading data files in junifer.
+It reads the value of the key ``path`` for each :ref:`data type <data_types>` in the :ref:`Data object <data_object>`
+and loads them to memory. After reading the data into memory, it adds the key ``data`` to the same level as ``path``
+and the value is the actual data in the memory.
+
+Datareaders are meant to be used inside the datagrabber context but you can operate on them outside the context as long
+as the actual data is in the memory and the Python runtime has not garbage-collected it.
+
+For data formats not supported by junifer yet, you can either make your own ``DataReader`` or open an issue on
+`junifer Github`_ and we can help you out.
+
+Currently supported file-formats
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+We already provide a concrete implementation :class:`junifer.datareader.DefaultDataReader` which knows how to
+read the following file formats:
+
+.. list-table::
+   :widths: auto
+   :header-rows: 1
+
+   * - File extension
+     - File type
+     - Description
+   * - ``.nii``
+     - NIfTI (uncompressed)
+     - Uncompressed NIfTI
+   * - ``.nii.gz``
+     - NIfTI (compressed)
+     - Compressed NIfTI
+   * - ``.csv``
+     - CSV
+     - Comma-separated values file
+   * - ``.tsv``
+     - TSV
+     - Tab-separated values file

docs/understanding/index.rst

@@ -4,7 +4,7 @@ Understanding junifer
 =====================
 
 Before you start, you should understand how junifer works. Junifer is a
-tool conceived to extract features from neuroimaging data in a easy-to-use
+tool conceived to extract features from neuroimaging data in an easy-to-use
 manner, with minimal coding and minimal user expertise in the internal aspects.
 
 Unlike other tools like FSL, SPM, AFNI, etc., junifer is not a toolbox to
@@ -24,5 +24,6 @@ julearn_).
    data
    datagrabber
    datareader
+   preprocess
    marker
    storage

docs/understanding/marker.rst

@@ -1,4 +1,23 @@
 .. include:: ../links.inc
 
+.. _marker:
+
 Marker
 ======
+
+Description
+^^^^^^^^^^^
+
+The ``Marker`` is an object that is responsible for feature extraction. It primarily operates on data loaded
+in memory by :ref:`datareader <DataReader>` and stored in the ``data`` key of each :ref:`data type <data_types>`
+in the :ref:`Data object <data_object>`. In some cases, it can also operate on pre-processed data as obtained
+from the :ref:`Preprocess <preprocess>` step of the pipeline. It is important to note that this pre-process is
+not similar to pre-processing done by tools like FSL, SPM, AFNI, etc. . For example, one can perform confound
+removal on loaded data and then perform feature extraction.
+
+Markers are meant to be used inside the datagrabber context but you can operate on them outside the context as long
+as the actual data is in the memory and the Python runtime has not garbage-collected it.
+
+If you are interested in using already provided markers, please go to :doc:`../builtin`. And, if you want to implement
+your own marker, you need to provide concrete implementation of :class:`junifer.markers.BaseMarker`. Specifically, you
+need to override ``get_output_kind``, ``store`` and ``compute`` methods.

docs/understanding/preprocess.rst

@@ -0,0 +1,15 @@
+.. include:: ../links.inc
+
+.. _preprocess:
+
+Preprocess
+==========
+
+Description
+^^^^^^^^^^^
+
+The ``Preprocess`` step of the pipeline is meant for pre-processing before or after :ref:`Marker <marker>` step
+depending on the use-case. For example, you might want to perform confound removal on ``BOLD`` data before
+feature extraction.
+
+This step is still under development and is an optional one for the pipeline to work.

docs/understanding/storage.rst

@@ -1,4 +1,41 @@
 .. include:: ../links.inc
 
+.. _storage:
+
 Storage
 =======
+
+Description
+^^^^^^^^^^^
+
+The ``Storage`` is an object that is responsible for storing extracted features as computed from :ref:`Marker <marker>`
+step of the pipeline. If the pipeline is provided with a ``storage-like`` object, the extracted features are stored via
+that object else they are kept in memory.
+
+Storage is meant to be used inside the datagrabber context but you can operate on them outside the context as long
+as the processed data is in the memory and the Python runtime has not garbage-collected it.
+
+The :ref:`Markers <marker>` are responsible for defining what *storage kind* (``matrix``, ``table``, ``timeseries``)
+they support for which :ref:`data type <data_types>` by overriding its ``store`` method. The storage object in turn
+declares and provides implementation for specific *storage kind*. For example, :class:`junifer.storage.SQLiteFeatureStorage`
+supports saving ``matrix``, ``table`` and ``timeseries`` via ``store_matrix``, ``store_table`` and ``store_timeseries``
+methods respectively.
+
+For storage interfaces not supported by junifer yet, you can either make your own ``Storage`` by providing a concrete
+implementation of :class:`junifer.storage.BaseFeatureStorage` or open an issue on `junifer Github`_ and we can help you out.
+
+Currently supported storage interfaces
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. list-table::
+   :widths: auto
+   :header-rows: 1
+
+   * - Storage class
+     - File extension
+     - File type
+     - Storage kinds
+   * - :class:`junifer.storage.SQLiteFeatureStorage`
+     - ``.db``
+     - SQLite
+     - ``matrix``, ``table``, ``timeseries``