Pymia

Latest version: v0.3.2

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0.2.1

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* New statistics plotting module :mod:`pymia.plotting.statistics` (subject to heavy changes and possibly removal!)
* Bug fixes and code maintenance
* Several improvements to the documentation

0.2.0

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* New :mod:`pymia.deeplearning` package
* New extractor :class:`.PadDataExtractor`, which replaces the ``PadPatchDataExtractor`` (see migration guide below)
* New metrics :class:`.NormalizedRootMeanSquaredError`, :class:`.SurfaceDiceOverlap`, and :class:`.SurfaceOverlap`
* Faster and more generic implementation of :class:`.HausdorffDistance`
* New data augmentation module :mod:`pymia.data.augmentation`
* New filter :class:`.BinaryThreshold`
* Replaced the transformation in :class:`.SubjectAssembler` by a more flexible function (see migration guide below)
* Minor bug fixes and maintenance
* Several improvements to the documentation

We kindly appreciate the help of our contributors:

- Jan Riedo
- Yannick Soom

Migration guide
^^^^^^^^^^^^^^^

The extractor ``PadPatchDataExtractor`` has been replaced by the :class:`.PadDataExtractor` to facilitate the
extraction flexibility. The :class:`.PadDataExtractor` works now with any kind of the three data extractors
(:class:`.DataExtractor`, :class:`.RandomDataExtractor`, and :class:`.SelectiveDataExtractor`),
which are passed as argument. Further, it is now possible to pass a function for the padding as argument to replace the
default zero padding. Suppose you used the ``PadPatchDataExtractor`` like this:

.. code-block:: python

import pymia.data.extraction as pymia_extr
pymia_extr.PadPatchDataExtractor(padding=(10, 10, 10), categories=('images',))

To have the same behaviour, replace it by:

.. code-block:: python

import pymia.data.extraction as pymia_extr
pymia_extr.PadDataExtractor(padding=(10, 10, 10),
extractor=pymia_extr.DataExtractor(categories=('images',)))

The transformation in :meth:`.SubjectAssembler.add_batch` has been removed and replaced by the ``on_sample_fn``
parameter in the constructor. Replacing the transformation by this function should be straight forward by rewriting your
transformation as function:

.. code-block:: python

def on_sample_fn(params: dict):
key = '__prediction'
batch = params['batch']
idx = params['batch_idx']

data = params[key]
index_expr = batch['index_expr'][idx]

manipulate data and index_expr according to your needs

return data, index_expr

0.1.1

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* Improves the documentation
* Mocks the torch dependency to build the docs

0.1.0

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* Initial release on PyPI

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