🧑‍💻 pre-commit autoupdate (TissueImageAnalytics#891)

<!--pre-commit.ci start-->
updates:
- [github.com/executablebooks/mdformat: 0.7.18 → 0.7.19](hukkin/mdformat@0.7.18...0.7.19)

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Shan E Ahmed Raza <13048456+shaneahmed@users.noreply.github.com>

Author: pre-commit-ci[bot]

.pre-commit-config.yaml

@@ -23,7 +23,7 @@ repos:
         - mdformat-black
         - mdformat-myst
   - repo: https://github.com/executablebooks/mdformat
-    rev: 0.7.18
+    rev: 0.7.19
     hooks:
     - id: mdformat
       # Optionally add plugins

HISTORY.md

@@ -64,7 +64,7 @@
 
 ### Major Updates and Feature Improvements
 
-- Adds Python 3.11 support \[experimental\] #500
+- Adds Python 3.11 support [experimental] #500
   - Python 3.11 is not fully supported by `pytorch` https://github.com/pytorch/pytorch/issues/86566 and `openslide` https://github.com/openslide/openslide-python/pull/188
 - Removes Python 3.7 support
   - This allows upgrading all the dependencies which were dependent on an older version of Python.
@@ -181,7 +181,7 @@ None
   - Adds DICE metric
 - Adds [SCCNN](https://doi.org/10.1109/tmi.2016.2525803) architecture. \[[read the docs](https://tia-toolbox.readthedocs.io/en/develop/_autosummary/tiatoolbox.models.architecture.sccnn.SCCNN.html)\]
 - Adds [MapDe](https://arxiv.org/abs/1806.06970) architecture. \[[read the docs](https://tia-toolbox.readthedocs.io/en/develop/_autosummary/tiatoolbox.models.architecture.mapde.MapDe.html)\]
-- Adds support for reading MPP metadata from  NGFF v0.4
+- Adds support for reading MPP metadata from NGFF v0.4
 - Adds enhancements to tiatoolbox.annotation.storage that are useful when using an AnnotationStore for visualization purposes.
 
 ### Changes to API
@@ -196,7 +196,7 @@ None
 - Fixes nucleus_segmentor_engine for boundary artefacts
 - Fixes the colorbar cropping in tests
 - Adds citation in README.md and CITATION.cff to Nature Communications Medicine paper
-- Fixes a bug #452 raised by @rogertrullo  where only the numerator of the TIFF resolution tags was being read.
+- Fixes a bug #452 raised by @rogertrullo where only the numerator of the TIFF resolution tags was being read.
 - Fixes HoVer-Net+ post-processing to be inline with original work.
 - Fixes a bug where an exception would be raised if the OME XML is missing objective power.
 
@@ -337,7 +337,7 @@ None
 ### Major Updates and Feature Improvements
 
 - Adds nucleus instance segmentation base class
-  - Adds  [HoVerNet](https://www.sciencedirect.com/science/article/abs/pii/S1361841519301045) architecture
+  - Adds [HoVerNet](https://www.sciencedirect.com/science/article/abs/pii/S1361841519301045) architecture
 - Adds multi-task segmentor [HoVerNet+](https://arxiv.org/abs/2108.13904) model
 - Adds <a href="https://www.thelancet.com/journals/landig/article/PIIS2589-7500(2100180-1/fulltext">IDaRS</a> pipeline
 - Adds [SlideGraph](https://arxiv.org/abs/2110.06042) pipeline
@@ -358,7 +358,7 @@ None
 
 ### Bug Fixes and Other Changes
 
-- Fixes  Fix `filter_coordinates` read wrong resolutions for patch extraction
+- Fixes `filter_coordinates` read wrong resolutions for patch extraction
 - For `PatchPredictor`
   - `ioconfig` will supersede everything
   - if `ioconfig` is not provided
@@ -410,7 +410,7 @@ None
 - Adds dependencies for tiffile, imagecodecs, zarr.
 - Adds more stringent pre-commit checks
 - Moved local test files into `tiatoolbox/data`.
-- Fixed `Manifest.ini` and added  `tiatoolbox/data`. This means that this directory will be downloaded with the package.
+- Fixed `Manifest.ini` and added `tiatoolbox/data`. This means that this directory will be downloaded with the package.
 - Using `pkg_resources` to properly load bundled resources (e.g. `target_image.png`) in `tiatoolbox.data`.
 - Removed duplicate code in `conftest.py` for downloading remote files. This is now in `tiatoolbox.data._fetch_remote_file`.
 - Fixes errors raised by new flake8 rules.
@@ -513,9 +513,9 @@ ______________________________________________________________________
   - `read_bounds` takes a tuple (left, top, right, bottom) of coordinates in baseline (level 0) reference frame and returns a region bounded by those.
   - `read_rect` takes one coordinate in baseline reference frame and an output size in pixels.
 - Adds `VirtualWSIReader` as a subclass of WSIReader which can be used to read visual fields (tiles).
-  - `VirtualWSIReader`  accepts ndarray or image path as input.
-- Adds MPP fall back to standard TIFF resolution tags  with warning.
-  - If OpenSlide cannot determine microns per pixel (`mpp`) from the metadata, checks the TIFF resolution units (TIFF tags: `ResolutionUnit`, `XResolution` and  `YResolution`) to calculate MPP. Additionally, add function to estimate missing objective power if MPP is known of derived from TIFF resolution tags.
+  - `VirtualWSIReader` accepts ndarray or image path as input.
+- Adds MPP fall back to standard TIFF resolution tags with warning.
+  - If OpenSlide cannot determine microns per pixel (`mpp`) from the metadata, checks the TIFF resolution units (TIFF tags: `ResolutionUnit`, `XResolution` and `YResolution`) to calculate MPP. Additionally, add function to estimate missing objective power if MPP is known of derived from TIFF resolution tags.
 - Estimates missing objective power from MPP with warning.
 - Adds example notebooks for stain normalisation and WSI reader.
 - Adds caching to slide info property. This is done by checking if a private `self._m_info` exists and returning it if so, otherwise `self._info` is called to create the info for the first time (or to force regenerating) and the result is assigned to `self._m_info`. This could in future be made much simpler with the `functools.cached_property` decorator in Python 3.8+.

examples/README.md

@@ -28,7 +28,7 @@ We now give a list of our Jupyter notebooks, giving a brief description of the T
 
 ### 1- Reading Whole Slide Images ([01-wsi-reading](./01-wsi-reading.ipynb))
 
-This notebook shows how to use TIAToolbox to read different kinds of WSIs. TIAToolbox provides a uniform interface to various WSI formats. To see what formats are dealt with, click [here](https://tia-toolbox.readthedocs.io/en/latest/usage.html?highlight=wsiread#tiatoolbox.wsicore.wsireader.WSIReader)  and then search for _format_. In this notebook, you will learn some well-known techniques for WSI masking and patch extraction.
+This notebook shows how to use TIAToolbox to read different kinds of WSIs. TIAToolbox provides a uniform interface to various WSI formats. To see what formats are dealt with, click [here](https://tia-toolbox.readthedocs.io/en/latest/usage.html?highlight=wsiread#tiatoolbox.wsicore.wsireader.WSIReader) and then search for _format_. In this notebook, you will learn some well-known techniques for WSI masking and patch extraction.
 
 [![image](../docs/images/wsi-reading.png)](./01-wsi-reading.ipynb)
 
@@ -77,7 +77,7 @@ This notebook is aimed at advanced users of TIAToolbox, familiar with object-ori
 
 ### 8- Nucleus instance segmentation in whole slide images using the HoVer-Net model ([08-nucleus-instance-segmentation](./08-nucleus-instance-segmentation.ipynb))
 
-Each WSI can contain up to a million nuclei of various types. These can  analysed systematically and used for predicting clinical outcomes. Nucleus segmentation and classification must be carried out before using nuclear features in downstream analysis. In this example, we will demonstrate the use of the TIAToolbox implementation of the [HoVer-Net model](https://www.sciencedirect.com/science/article/pii/S1361841519301045) to solve the problem of nucleus instance segmentation and classification.
+Each WSI can contain up to a million nuclei of various types. These can analysed systematically and used for predicting clinical outcomes. Nucleus segmentation and classification must be carried out before using nuclear features in downstream analysis. In this example, we will demonstrate the use of the TIAToolbox implementation of the [HoVer-Net model](https://www.sciencedirect.com/science/article/pii/S1361841519301045) to solve the problem of nucleus instance segmentation and classification.
 
 [![image](../docs/images/hovernet.png)](./08-nucleus-instance-segmentation.ipynb)
 
@@ -89,11 +89,11 @@ Each WSI consists of a multitude of different tissue types, each containing many
 
 ### 10- Image Alignment ([10-wsi_registration](./10-wsi-registration.ipynb))
 
-This notebook presents an example to show how to use TIAToolbox for registration of an image pair using [Deep Feature Based Registration](https://arxiv.org/pdf/2202.09971.pdf) (DFBR) \[1\], followed by non-rigid alignment using [SimpleITK](https://simpleitk.readthedocs.io/en/master/registrationOverview.html). The registration tool in the TIAToolbox also comprises a pre-alignment step, a pre-requisite to DFBR. In particular, we will introduce the use of our registration tool `wsi_registration`.
+This notebook presents an example to show how to use TIAToolbox for registration of an image pair using [Deep Feature Based Registration](https://arxiv.org/pdf/2202.09971.pdf) (DFBR) [1], followed by non-rigid alignment using [SimpleITK](https://simpleitk.readthedocs.io/en/master/registrationOverview.html). The registration tool in the TIAToolbox also comprises a pre-alignment step, a pre-requisite to DFBR. In particular, we will introduce the use of our registration tool `wsi_registration`.
 
 In this example, the affine transformation is computed using thumbnails of the fixed and moving images. The estimated transformation is then used to extract corresponding tiles from both fixed and moving images at a higher magnification level. The non-rigid deformation between the two tiles is then dealt with using the SimpleITK.
 
-\[1\] Awan, Ruqayya, et al. "Deep Feature based Cross-slide Registration." arXiv preprint arXiv:2202.09971 (2022).
+[1] Awan, Ruqayya, et al. "Deep Feature based Cross-slide Registration." arXiv preprint arXiv:2202.09971 (2022).
 
 [![image](../docs/images/wsi-registration.png)](./10-wsi-registration.ipynb)