Metadata-Version: 2.1
Name: mrQA
Version: 0.1.2
Summary: mrQA suite of tools offering automatic evaluation of protocol compliance
Home-page: https://github.com/Open-Minds-Lab/mrQA
Author: Pradeep Raamana
Author-email: raamana@gmail.com
License: Apache Software License 2.0
Keywords: mrQA
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.6
License-File: LICENSE
License-File: AUTHORS.rst

==============================
Quickstart
==============================

.. image:: https://img.shields.io/pypi/v/mrQA.svg
        :target: https://pypi.python.org/pypi/mrQA

.. image:: https://img.shields.io/travis/Open-Minds-Lab/mrQA.svg
        :target: https://travis-ci.com/Open-Minds-Lab/mrQA


mrQA suite of tools offering
 - automatic evaluation of protocol compliance


To use  mrQA in a project::

    import  mrQA

The most important methods for checking protocol compliance is
``mrQA.project.check_compliance``. It calls all the required functions.

* To infer the most frequent values for each acquisition parameter
* Aggregate the non-compliance information to generate an HTML report


First of all, you have to import the relevant modules and classes::

    from MRdataset import import_dataset
    from mrQA import check_compliance

Given a dataset of MR images, (e.g. DICOM images), we create
``MRdataset.base.Project`` object. This can be achieved simply by
``MRdataset.import_dataset`` method, which requires a valid folder path.
For details on ``MRdataset``, please see its documentation. ::

    data_root = '/home/user/datasets/ABCD'
    output_dir = '/home/user/MR_reports/'
    dataset = import_dataset(data_root=data_root,
                             style='xnat',
                             name='ABCD')

    check_compliance(dataset=dataset, output_dir=output_dir)

And that's it! Please note some important points:

* It will generate a corresponding HTML file in the ``output_dir`` which contains the complete report.
* ``style`` denotes the specific format of neuroimaging dataset. For example, use *xnat* for DICOM datasets and *bids* for BIDS datasets
* ``name`` is an identifier which can be used to reload the the cached files later.If no name is specified, it uses a random identifier.



=======
History
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0.1.0 (2022-05-20)
------------------

* First release on PyPI.


