change version to 1.3.2

README.md

@@ -21,7 +21,7 @@ segmentation, and object tracking.
 
 `Bio-Volumentations` build upon widely used libraries such as Albumentations and TorchIO 
 (see the _Contributions and Acknowledgements_ section below) and are accompanied by 
-[detailed documentation and a user guide](https://biovolumentations.readthedocs.io/1.3.1/). 
+[detailed documentation and a user guide](https://biovolumentations.readthedocs.io/1.3.2/). 
 Therefore, they can easily be adopted by developers.
 
 
@@ -133,7 +133,7 @@ If you call transformations outside of `Compose`, we cannot guarantee the all as
 are checked and enforced, so you might encounter unexpected behaviour.
 
 Below, there are several examples of how to use this library. You are also welcome to check 
-[our documentation pages](https://biovolumentations.readthedocs.io/1.3.1/).
+[our documentation pages](https://biovolumentations.readthedocs.io/1.3.2/).
 
 ### Example: Transforming a Single Image
 
@@ -145,7 +145,7 @@ Optionally, you can specify a datatype conversion transformation that will be ap
 in the list, e.g. from the default `numpy.ndarray` to a `torch.Tensor`. You can also specify the probability
 of actually applying the whole pipeline as a number between 0 and 1. 
 The default probability is 1 (i.e., the pipeline is applied in each call).
-See the [docs](https://biovolumentations.readthedocs.io/1.3.1/examples.html) for more details.
+See the [docs](https://biovolumentations.readthedocs.io/1.3.2/examples.html) for more details.
 
 The `Compose` object is callable. The data is passed as a keyword argument, and the call returns a dictionary 
 with the same keyword and the corresponding transformed image. This might look like an overkill for a single image, 
@@ -186,7 +186,7 @@ To that end, `Bio-Volumentations` define several target types:
 - `value` for non-transformed values.
 
 For more information on the format of individual target types, see the 
-[Getting Started guide](https://biovolumentations.readthedocs.io/1.3.1/examples.html#example-transforming-images-with-annotations)
+[Getting Started guide](https://biovolumentations.readthedocs.io/1.3.2/examples.html#example-transforming-images-with-annotations)
 
 Please note that there must always be an `image`-type target in the sample.
 
@@ -196,7 +196,7 @@ For example, `RandomAffineTransform` applies the same geometric transformation t
 
 Some transformations, such as `RandomGaussianNoise` or `RandomGamma`, are only defined for the `image` target 
 and leave the other targets unchanged. Please consult the 
-[documentation of the individual transforms](https://biovolumentations.readthedocs.io/1.3.1/modules.html) for more details.
+[documentation of the individual transforms](https://biovolumentations.readthedocs.io/1.3.2/modules.html) for more details.
 
 The associated targets are fed to the `Compose` object call as keyword arguments and extracted from the outputted
 dictionary using the same keywords. 

__version__.py

-__version__ = "1.3.1"
+__version__ = "1.3.2"

docs/source/conf.py

@@ -22,7 +22,7 @@ sys.path.insert(0, os.path.abspath(r'../../src/bio_volumentations'))
 project = 'bio-volumentations'
 copyright = '2025, Samuel Sulan, Lucia Hradecka, Filip Lux'
 author = 'Samuel Sulan, Lucia Hradecka, Filip Lux'
-release = '1.3.1'
+release = '1.3.2'
 
 # -- General configuration ---------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration

docs/source/contributions.rst

@@ -6,7 +6,7 @@ Copyright
 Copyright (c) 2025 Samuel Šuľan, Lucia Hradecká, Filip Lux
 
 The `Bio-Volumentations` library is distributed under the MIT License.
-For more details, see `the licence file at GitLab <https://gitlab.fi.muni.cz/cbia/bio-volumentations/-/blob/1.3.1/LICENSE?ref_type=tags>`_.
+For more details, see `the licence file at GitLab <https://gitlab.fi.muni.cz/cbia/bio-volumentations/-/blob/1.3.2/LICENSE?ref_type=tags>`_.
 
 
 Contributions

docs/source/examples.rst

@@ -46,7 +46,7 @@ The shape of the output image will be either [C, Z, Y, X] (for cases 1 & 2) or [
 The images are type-casted to a floating-point datatype before being transformed, irrespective of their actual datatype.
 
 For the specification of image annotation conventions, please see
-`the respective section below <https://biovolumentations.readthedocs.io/1.3.1/examples.html#example-transforming-images-with-annotations>`_.
+`the respective section below <https://biovolumentations.readthedocs.io/1.3.2/examples.html#example-transforming-images-with-annotations>`_.
 
 All transformations are implemented as callable classes inheriting from an abstract :class:`Transform` class.
 Upon instantiating a transformation object, one has to specify the parameters of the transformation.
@@ -65,7 +65,7 @@ If you call transformations outside of :class:`Compose`, we cannot guarantee the
 are checked and enforced, so you might encounter unexpected behaviour.
 
 Below, there are several examples of how to use the `Bio-Volumentations` library. You are also welcome to check
-`the API reference <https://biovolumentations.readthedocs.io/1.3.1/modules.html>`_ to learn more about the individual transforms.
+`the API reference <https://biovolumentations.readthedocs.io/1.3.2/modules.html>`_ to learn more about the individual transforms.
 
 Example: Transforming a Single Image
 ************************************
@@ -78,7 +78,7 @@ Optionally, you can specify a datatype conversion transformation that will be ap
 in the list, for example from the default :class:`numpy.ndarray` to a PyTorch :class:`torch.Tensor`.
 You can also specify the probability of applying the whole pipeline as a number between 0 and 1.
 The default probability is 1 (i.e., the pipeline is applied in each call). See the :class:`Compose`
-`docs <https://biovolumentations.readthedocs.io/1.3.1/bio_volumentations.core.html#module-bio_volumentations.core.composition>`_
+`docs <https://biovolumentations.readthedocs.io/1.3.2/bio_volumentations.core.html#module-bio_volumentations.core.composition>`_
 for more details.
 
 Note: You can also toggle the probability of applying the individual transforms. To do so, you can
@@ -133,7 +133,7 @@ images of shape ``[150, 300, 300]``, ``[1, 150, 300, 300]``, as well as ``[4, 15
 If you want to use a multi-channel :class:`mask` or :class:`float_mask`, you have to split it into
 a set of single-channel :class:`mask` or :class:`float_mask` targets, respectively, and input them
 as stand-alone targets (see
-`the respective section below <https://biovolumentations.readthedocs.io/1.3.1/examples.html#example-transforming-multiple-targets-of-the-same-type>`_
+`the respective section below <https://biovolumentations.readthedocs.io/1.3.2/examples.html#example-transforming-multiple-targets-of-the-same-type>`_
 on transforming multiple masks with a single image).
 
 The :class:`keypoints` target is represented as a list of tuples. Each tuple represents
@@ -185,13 +185,13 @@ For example, :class:`RandomAffineTransform` applies the same geometric transform
 Some transformations, such as :class:`RandomGaussianNoise` or :class:`RandomGamma`,
 are only defined for the :class:`image` target
 and leave the other target types unchanged. Please consult the
-`documentation of the individual transforms <https://biovolumentations.readthedocs.io/1.3.1/modules.html>`_
+`documentation of the individual transforms <https://biovolumentations.readthedocs.io/1.3.2/modules.html>`_
 for more details.
 
 Another example of transforming an annotated image is available
-`at the project's GitLab page <https://gitlab.fi.muni.cz/cbia/bio-volumentations/-/tree/1.3.1/example?ref_type=tags>`_,
+`at the project's GitLab page <https://gitlab.fi.muni.cz/cbia/bio-volumentations/-/tree/1.3.2/example?ref_type=tags>`_,
 where a runnable Python script and a test data sample are provided.
-See `the readme at GitLab <https://gitlab.fi.muni.cz/cbia/bio-volumentations/-/blob/1.3.1/README.md?ref_type=tags#the-first-example>`_ for more details.
+See `the readme at GitLab <https://gitlab.fi.muni.cz/cbia/bio-volumentations/-/blob/1.3.2/README.md?ref_type=tags#the-first-example>`_ for more details.
 
 Example: Transforming Multiple Targets of the Same Type
 *******************************************************

docs/source/index.rst

@@ -19,9 +19,9 @@ and with any major deep learning library.
    contributions
 
 The source codes and more details are also available
-`at the project's GitLab page <https://gitlab.fi.muni.cz/cbia/bio-volumentations/-/tree/1.3.1?ref_type=tags>`_,
+`at the project's GitLab page <https://gitlab.fi.muni.cz/cbia/bio-volumentations/-/tree/1.3.2?ref_type=tags>`_,
 code release is available from the project's
-`GitHub <https://github.com/xluciadh/bio-volumentations/releases/tag/1.3.1>`_
+`GitHub <https://github.com/xluciadh/bio-volumentations/releases>`_
 and from `PyPI <https://pypi.org/project/bio-volumentations/>`_.
 
 Indices and tables

docs/source/intro.rst

@@ -17,7 +17,7 @@ can be used with **any major Python deep learning library**, including PyTorch,
 This library builds upon widely used libraries such as Albumentations and TorchIO, and can thus be easily adopted by developers.
 
 The source codes are available
-`at the project's GitLab page <https://gitlab.fi.muni.cz/cbia/bio-volumentations/-/tree/1.3.1?ref_type=tags>`_.
+`at the project's GitLab page <https://gitlab.fi.muni.cz/cbia/bio-volumentations/-/tree/1.3.2?ref_type=tags>`_.
 The package can be installed via `pip <https://pypi.org/project/bio-volumentations/>`_
-and is archived `at GitHub <https://github.com/xluciadh/bio-volumentations/releases/tag/1.3.1>`_.
+and is archived `at GitHub <https://github.com/xluciadh/bio-volumentations/releases>`_.
 

docs/source/modules.rst

@@ -28,4 +28,4 @@ Runtime
 
 The execution times of individual transformations from our library
 and their comparison to other libraries capable of processing volumetric image data
-are available `at the project's GitLab page <https://gitlab.fi.muni.cz/cbia/bio-volumentations/-/blob/1.3.1/README.md?ref_type=tags#runtime>`_.
+are available `at the project's GitLab page <https://gitlab.fi.muni.cz/cbia/bio-volumentations/-/blob/1.3.2/README.md?ref_type=tags#runtime>`_.

pyproject.toml

@@ -8,7 +8,7 @@ build-backend = "setuptools.build_meta"
 name = "bio-volumentations"
 
 # Put the version of your project. This field is required, although it is often marked as dynamic:
-version = "1.3.1"
+version = "1.3.2"
 #dynamic = ["version"]
 # https://stackoverflow.com/questions/21064581/how-to-overwrite-pypi-package-when-doing-upload-from-command-line
 
@@ -61,6 +61,6 @@ classifiers = [
 
 # A list of URLs associated with your project, displayed on the left sidebar of your PyPI project page.
 [project.urls]
-Homepage = "https://gitlab.fi.muni.cz/cbia/bio-volumentations/-/tree/1.3.1?ref_type=tags"
-Documentation = "https://biovolumentations.readthedocs.io/1.3.1/"
-Repository = "https://gitlab.fi.muni.cz/cbia/bio-volumentations/-/tree/1.3.1?ref_type=tags"
+Homepage = "https://gitlab.fi.muni.cz/cbia/bio-volumentations/-/tree/1.3.2?ref_type=tags"
+Documentation = "https://biovolumentations.readthedocs.io/1.3.2/"
+Repository = "https://gitlab.fi.muni.cz/cbia/bio-volumentations/-/tree/1.3.2?ref_type=tags"