backfill

.github/workflows/pytest.yml

@@ -1,5 +1,9 @@
 name: Run Pytest and Codecov with Hatch
 
+permissions:
+  id-token: write
+  contents: read
+
 on:
   push:
     branches:
@@ -19,8 +23,8 @@ jobs:
     - name: Run pytest and generate coverage report
       run: docker run --rm -e HATCH_ENV=test -v "${{ github.workspace }}:/app" myapp:hatch cov
     - name: Upload coverage report to Codecov
-      uses: codecov/codecov-action@v3
+      uses: codecov/codecov-action@v4
       with:
-        token: ${{ secrets.CODECOV_TOKEN }}
+        use_oidc: true
         fail_ci_if_error: true
-        files: ${{ github.workspace }}/.coverage
+        files: ${{ github.workspace }}/coverage.xml

README.md

@@ -1,6 +1,42 @@
-TODO: this readme
-=================
-for more info goto: https://lavlab-python-utils.readthedocs.io/en/latest/
-python3 -m unittest
-TODO: convention documentation.
-TODO: pyXNAT support
+# LavLab Python Utils
+
+[![Documentation Status](https://readthedocs.org/projects/lavlab-python-utils/badge/?version=stable)](https://lavlab-python-utils.readthedocs.io/stable)
+[![GitHub release](https://img.shields.io/github/v/release/laviolette-lab/lavlab-python-utils.svg)](https://github.com/laviolette-lab/lavlab-python-utils/releases)
+[![Build Status](https://github.com/laviolette-lab/lavlab-python-utils/actions/workflows/pylint.yml/badge.svg)](https://github.com/laviolette-lab/lavlab-python-utils/actions)
+[![Coverage Status](https://img.shields.io/codecov/c/github/laviolette-lab/lavlab-python-utils.svg)](https://codecov.io/gh/laviolette-lab/lavlab-python-utils)
+
+Welcome to the **LavLab Python Utils** repository! This library is a collection of Python utilities developed by the LaViolette Lab to support our research in RadioPathomics and digital pathology.
+
+<img src="logo.webp" width="256">
+
+## Overview
+
+LavLab Python Utils is designed to streamline common tasks in our research workflow, including data preprocessing, analysis, and visualization. Our goal is to provide a set of tools that are easy to use, well-documented, and performant.
+
+## Features
+
+- **Data Processing:** Functions to handle various data formats used in our research, including DICOM, NIfTI, and more.
+- **Visualization:** Tools to create publication-quality plots and visualizations.
+- **Integration:** Seamless integration with other tools and services used in our lab.
+
+## Installation
+
+You can install LavLab Python Utils via pip:
+
+```bash
+python3 -m pip install https://github.com/laviolette-lab/lavlab-python-utils/releases/latest/download/lavlab_python_utils-latest-py3-none-any.whl
+# optional install targets, must install wheel from github using command above first!
+python3 -m pip install 'lavlab-python-utils[all]'
+```
+
+## Documentation
+
+Comprehensive documentation is available on [Read the Docs](https://lavlab-python-utils.readthedocs.io/stable/). This includes detailed API references, usage examples, and tutorials.
+
+## Contributing
+
+We welcome contributions from the community! If you are interested in contributing, please read our [contributing guide]([CONTRIBUTING.md](https://lavlab-python-utils.readthedocs.io/stable/contributing/)) and check out our [open issues](https://github.com/laviolette-lab/lavlab-python-utils/issues).
+
+---
+
+Thank you for using LavLab Python Utils! We hope you find it useful in your research.

docs/installation.md

@@ -2,11 +2,20 @@
 
 ## Using latest wheel
 
-You can install the latest release from GitHub:
+You can install the latest release from GitHub, once installed you can get the optional dependencies:
 
 ```sh
 python3 -m pip install https://github.com/laviolette-lab/lavlab-python-utils/releases/latest/download/lavlab_python_utils-latest-py3-none-any.whl
+# optional install targets, must install wheel from github using command above first!
+python3 -m pip install 'lavlab-python-utils[all]'
 ```
+We support the following install targets:
+* omero
+  * installs omero-py for omero api access
+* jupyter
+  * installs dash for performant image viewing
+* all
+  * installs all optional dependencies
 
 ## Using versioned wheel
 
@@ -22,4 +31,4 @@ Install using pip+git:
 
 ```sh
 python3 -m pip install git+https://github.com/laviolette-lab/lavlab-python-utils.git
-```
+```

pyproject.toml

@@ -53,6 +53,9 @@ jupyter = [
   "dash-slicer",
   "dash-bootstrap-components"
 ]
+all = [
+  "lavlab-python-utils[omero, jupyter]"
+]
 
 [project.entry-points."lavlab_python_utils.service_providers"]
 OMERO = "lavlab.omero:OmeroServiceProvider"
@@ -98,7 +101,7 @@ dependencies = [
 ]
 [tool.hatch.envs.test.scripts]
 test = "pytest {args:test}"
-cov = "pytest --cov=src --cov-report=term-missing {args:test}"
+cov = "pytest --cov=src --cov-report=xml {args:test}"
 
 [tool.hatch.envs.lint]
 features = [ "omero", "jupyter" ]
@@ -163,4 +166,4 @@ module = [
     "omero",
     "scipy"
 ]
-ignore_missing_imports = true
+ignore_missing_imports = true

requirements/requirements-docs.txt

@@ -8,6 +8,7 @@
 # - mkdocs-minify-plugin
 # - mkdocs-material-extensions
 # - mkdocs-git-revision-date-localized-plugin
+# - highdicom
 # - keyring
 # - matplotlib
 # - nibabel
@@ -512,6 +513,10 @@ h11==0.14.0 \
     --hash=sha256:8f19fbbe99e72420ff35c00b27a34cb9937e902a8b810e2c88300c6f0a3b699d \
     --hash=sha256:e3fe4ac4b851c468cc8363d500db52c2ead036020723024a109d37346efaa761
     # via httpcore
+highdicom==0.22.0 \
+    --hash=sha256:2f9c7f468924afafb57d49a24c3c96477fe4c5d1e8b6849097d193f00eab6cda \
+    --hash=sha256:62442fb9e77c05489c8ff99e6a30ed88fa03842a1fb70616dcf934fe4e3b1ca3
+    # via hatch.envs.docs
 htmlmin2==0.1.13 \
     --hash=sha256:75609f2a42e64f7ce57dbff28a39890363bde9e7e5885db633317efbdf8c79a2
     # via mkdocs-minify-plugin
@@ -1058,6 +1063,7 @@ numpy==1.26.4 \
     # via
     #   contourpy
     #   dash-slicer
+    #   highdicom
     #   imageio
     #   matplotlib
     #   nibabel
@@ -1181,10 +1187,48 @@ pillow==10.3.0 \
     --hash=sha256:ff61bfd9253c3915e6d41c651d5f962da23eda633cf02262990094a18a55371a
     # via
     #   dash-slicer
+    #   highdicom
     #   imageio
     #   matplotlib
     #   omero-py
+    #   pillow-jpls
     #   scikit-image
+pillow-jpls==1.3.2 \
+    --hash=sha256:0307b8506862892139a8ce3c63f34e7fd6dfed59bd6c96b677dcf7e03df815a4 \
+    --hash=sha256:058e8ad9fce542673703ae1032dc3f4fa3aaf39a8114ce087bce0bcb3a684d95 \
+    --hash=sha256:0eadbab278b1c54d6fa79085e0331e0db8799813e7009a6709bce4ad484e0711 \
+    --hash=sha256:20fb78195f70ace9cdfd863cb5b3e76d9c9d2ad20aab1b4c87c0bad9aab90563 \
+    --hash=sha256:28bdb039cddb68933024330453c2692c6c595e546d0ce9305217413045fca8d4 \
+    --hash=sha256:2d1a223a842dd97e3af324e80d07d4dbb9aa19ccab3b46b1c39e2ea231e7dd00 \
+    --hash=sha256:34a8d6783a46f8f41c7c4dcd0235b3c0a893818492e8f2500b4a4e70886b5959 \
+    --hash=sha256:3aa7e3e48f5e29c22f565e677e27d029e3a302cf56b98c1115835f7c08875453 \
+    --hash=sha256:4125954831a91f1f6484f21ef6e521ca214955322e207abb487ebb7cb7ad1b21 \
+    --hash=sha256:463ee0eb8c6004fbca535f044b8f7969fa324c2ea4d992158f8bad0ff8ca91dc \
+    --hash=sha256:5a87864a95f0e36b32908f12251b5e8b7fa14551a89f27c916cc0e69fdc3382f \
+    --hash=sha256:5f87c1cef6de984f07c08d34a84e35e69643d3bc6a3e39241089f4be38f864da \
+    --hash=sha256:6bbb20d063fc9efe2474ba7594f203288e5bab75b0cfca32d2d4432fb9053de3 \
+    --hash=sha256:7610b9aad5295eb6adc2e658df0dd995b38fef7d4c72c4ef63c1977989f5e701 \
+    --hash=sha256:85a42e28cac15b9e25a65588a68a1ba65ed5334a7f7a9b57bcab1223ddf65dae \
+    --hash=sha256:8a6c1e8c76b94620e0070948b91771461f7d49cc6be2575e233f6056ffe1ec26 \
+    --hash=sha256:8b4740afc7555fb7bce95d6c507069fc0cc9800892e336dba18c5302ef7364a5 \
+    --hash=sha256:91553669c4ecaf92ffa53426870190cb1b5bdf2795cddbeac1ae6fc3120c939a \
+    --hash=sha256:97d3265f8fcdd2add41a4ca49f464fd6e0982c0225ecbc80f30175fa04ce4e3c \
+    --hash=sha256:99a4c3598cf0a82885e3691307fd222b763c69c5b4a15223998679377247bc50 \
+    --hash=sha256:9cb643dd50c8fa848c16fe5e41350049ca762499e22cf5fb4cd5f67ae2c027d1 \
+    --hash=sha256:a0e04d6ca24dea9763a544518fc06c737499ab9075d216776b86e054be83599c \
+    --hash=sha256:a1f1d47544bf824a6262678e5884815f6a2df8cef410bcd9365e213450a4853e \
+    --hash=sha256:a79106cc1094385ac1d8670a3bb4d5fe67a7e49d405673d241bf19535d557318 \
+    --hash=sha256:ac607508232cdafb09e77f5280211e25d7678152edb0885f0dd374e0b651e2c2 \
+    --hash=sha256:ad0afb2c1676bc7d1618cf3837e9fc58997c8f0527464d9f524c19a36d666ce9 \
+    --hash=sha256:c822fafda531bc047068663722f86fb67083b572957b38fbc818b6339a817302 \
+    --hash=sha256:d1a00f77bd2d33a93082b9496c6891151fed1e1e1b915b60bdcad5f28085a9fc \
+    --hash=sha256:d64f4df3c385466aa897a1c70ffe4ecf91c611ca77f524321ddaf3969daede92 \
+    --hash=sha256:dea160c6e100b05c896a8434b0b9021870d7c4b055051ec637536437df218da6 \
+    --hash=sha256:e837011ad129ff9d2f0c17087856eeb493b208816f41ef82daa21d7cd21fcd53 \
+    --hash=sha256:f1ea5f90a3e30e12a5844e850393ee43acc281712bca97fd6545252d652c6f0e \
+    --hash=sha256:f346cc2f957c7b726266d46749028832351599a737bb27671eda5853784d02c3 \
+    --hash=sha256:f8956774bb9bc23aa85df733b21eaf128c1e115feedf6f37d88c1955d52c444d
+    # via highdicom
 platformdirs==4.2.0 \
     --hash=sha256:0614df2a2f37e1a662acbd8e2b25b92ccf8632929bc6d43467e17fe89c75e068 \
     --hash=sha256:ef0cc731df711022c174543cb70a9b5bd22e5a9337c8624ef2c2ceb8ddad8768
@@ -1249,7 +1293,9 @@ pycparser==2.22 \
 pydicom==2.4.4 \
     --hash=sha256:90b4801d851ce65be3df520e16bbfa3d6c767cf2a3a3b1c18f6780e6b670b87a \
     --hash=sha256:f9f8e19b78525be57aa6384484298833e4d06ac1d6226c79459131ddb0bd7c42
-    # via hatch.envs.docs
+    # via
+    #   hatch.envs.docs
+    #   highdicom
 pygments==2.17.2 \
     --hash=sha256:b27c2826c47d0f3219f29554824c30c5e8945175d888647acd804ddd04af846c \
     --hash=sha256:da46cec9fd2de5be3a8a784f434e4c4ab670b4ff54d605c4c2717e9d49c4c367

requirements/requirements-lint.txt

@@ -5,6 +5,7 @@
 # - pytest
 # - pylint
 # - black
+# - highdicom
 # - keyring
 # - matplotlib
 # - nibabel
@@ -512,6 +513,10 @@ h11==0.14.0 \
     --hash=sha256:8f19fbbe99e72420ff35c00b27a34cb9937e902a8b810e2c88300c6f0a3b699d \
     --hash=sha256:e3fe4ac4b851c468cc8363d500db52c2ead036020723024a109d37346efaa761
     # via httpcore
+highdicom==0.22.0 \
+    --hash=sha256:2f9c7f468924afafb57d49a24c3c96477fe4c5d1e8b6849097d193f00eab6cda \
+    --hash=sha256:62442fb9e77c05489c8ff99e6a30ed88fa03842a1fb70616dcf934fe4e3b1ca3
+    # via hatch.envs.lint
 httpcore==1.0.5 \
     --hash=sha256:34a38e2f9291467ee3b44e89dd52615370e152954ba21721378a87b2960f7a61 \
     --hash=sha256:421f18bac248b25d310f3cacd198d55b8e6125c107797b609ff9b7a6ba7991b5
@@ -1007,6 +1012,7 @@ numpy==1.26.4 \
     # via
     #   contourpy
     #   dash-slicer
+    #   highdicom
     #   imageio
     #   matplotlib
     #   nibabel
@@ -1128,10 +1134,48 @@ pillow==10.3.0 \
     --hash=sha256:ff61bfd9253c3915e6d41c651d5f962da23eda633cf02262990094a18a55371a
     # via
     #   dash-slicer
+    #   highdicom
     #   imageio
     #   matplotlib
     #   omero-py
+    #   pillow-jpls
     #   scikit-image
+pillow-jpls==1.3.2 \
+    --hash=sha256:0307b8506862892139a8ce3c63f34e7fd6dfed59bd6c96b677dcf7e03df815a4 \
+    --hash=sha256:058e8ad9fce542673703ae1032dc3f4fa3aaf39a8114ce087bce0bcb3a684d95 \
+    --hash=sha256:0eadbab278b1c54d6fa79085e0331e0db8799813e7009a6709bce4ad484e0711 \
+    --hash=sha256:20fb78195f70ace9cdfd863cb5b3e76d9c9d2ad20aab1b4c87c0bad9aab90563 \
+    --hash=sha256:28bdb039cddb68933024330453c2692c6c595e546d0ce9305217413045fca8d4 \
+    --hash=sha256:2d1a223a842dd97e3af324e80d07d4dbb9aa19ccab3b46b1c39e2ea231e7dd00 \
+    --hash=sha256:34a8d6783a46f8f41c7c4dcd0235b3c0a893818492e8f2500b4a4e70886b5959 \
+    --hash=sha256:3aa7e3e48f5e29c22f565e677e27d029e3a302cf56b98c1115835f7c08875453 \
+    --hash=sha256:4125954831a91f1f6484f21ef6e521ca214955322e207abb487ebb7cb7ad1b21 \
+    --hash=sha256:463ee0eb8c6004fbca535f044b8f7969fa324c2ea4d992158f8bad0ff8ca91dc \
+    --hash=sha256:5a87864a95f0e36b32908f12251b5e8b7fa14551a89f27c916cc0e69fdc3382f \
+    --hash=sha256:5f87c1cef6de984f07c08d34a84e35e69643d3bc6a3e39241089f4be38f864da \
+    --hash=sha256:6bbb20d063fc9efe2474ba7594f203288e5bab75b0cfca32d2d4432fb9053de3 \
+    --hash=sha256:7610b9aad5295eb6adc2e658df0dd995b38fef7d4c72c4ef63c1977989f5e701 \
+    --hash=sha256:85a42e28cac15b9e25a65588a68a1ba65ed5334a7f7a9b57bcab1223ddf65dae \
+    --hash=sha256:8a6c1e8c76b94620e0070948b91771461f7d49cc6be2575e233f6056ffe1ec26 \
+    --hash=sha256:8b4740afc7555fb7bce95d6c507069fc0cc9800892e336dba18c5302ef7364a5 \
+    --hash=sha256:91553669c4ecaf92ffa53426870190cb1b5bdf2795cddbeac1ae6fc3120c939a \
+    --hash=sha256:97d3265f8fcdd2add41a4ca49f464fd6e0982c0225ecbc80f30175fa04ce4e3c \
+    --hash=sha256:99a4c3598cf0a82885e3691307fd222b763c69c5b4a15223998679377247bc50 \
+    --hash=sha256:9cb643dd50c8fa848c16fe5e41350049ca762499e22cf5fb4cd5f67ae2c027d1 \
+    --hash=sha256:a0e04d6ca24dea9763a544518fc06c737499ab9075d216776b86e054be83599c \
+    --hash=sha256:a1f1d47544bf824a6262678e5884815f6a2df8cef410bcd9365e213450a4853e \
+    --hash=sha256:a79106cc1094385ac1d8670a3bb4d5fe67a7e49d405673d241bf19535d557318 \
+    --hash=sha256:ac607508232cdafb09e77f5280211e25d7678152edb0885f0dd374e0b651e2c2 \
+    --hash=sha256:ad0afb2c1676bc7d1618cf3837e9fc58997c8f0527464d9f524c19a36d666ce9 \
+    --hash=sha256:c822fafda531bc047068663722f86fb67083b572957b38fbc818b6339a817302 \
+    --hash=sha256:d1a00f77bd2d33a93082b9496c6891151fed1e1e1b915b60bdcad5f28085a9fc \
+    --hash=sha256:d64f4df3c385466aa897a1c70ffe4ecf91c611ca77f524321ddaf3969daede92 \
+    --hash=sha256:dea160c6e100b05c896a8434b0b9021870d7c4b055051ec637536437df218da6 \
+    --hash=sha256:e837011ad129ff9d2f0c17087856eeb493b208816f41ef82daa21d7cd21fcd53 \
+    --hash=sha256:f1ea5f90a3e30e12a5844e850393ee43acc281712bca97fd6545252d652c6f0e \
+    --hash=sha256:f346cc2f957c7b726266d46749028832351599a737bb27671eda5853784d02c3 \
+    --hash=sha256:f8956774bb9bc23aa85df733b21eaf128c1e115feedf6f37d88c1955d52c444d
+    # via highdicom
 platformdirs==4.2.0 \
     --hash=sha256:0614df2a2f37e1a662acbd8e2b25b92ccf8632929bc6d43467e17fe89c75e068 \
     --hash=sha256:ef0cc731df711022c174543cb70a9b5bd22e5a9337c8624ef2c2ceb8ddad8768
@@ -1200,7 +1244,9 @@ pycparser==2.22 \
 pydicom==2.4.4 \
     --hash=sha256:90b4801d851ce65be3df520e16bbfa3d6c767cf2a3a3b1c18f6780e6b670b87a \
     --hash=sha256:f9f8e19b78525be57aa6384484298833e4d06ac1d6226c79459131ddb0bd7c42
-    # via hatch.envs.lint
+    # via
+    #   hatch.envs.lint
+    #   highdicom
 pygments==2.18.0 \
     --hash=sha256:786ff802f32e91311bff3889f6e9a86e81505fe99f2735bb6d60ae0c5004f199 \
     --hash=sha256:b8e6aca0523f3ab76fee51799c488e38782ac06eafcf95e7ba832985c8e7b13a

requirements/requirements-test.txt

@@ -7,6 +7,7 @@
 # - pytest-asyncio
 # - coverage[toml]>=6.2
 # - pydicom-data
+# - highdicom
 # - keyring
 # - matplotlib
 # - nibabel
@@ -536,6 +537,10 @@ h11==0.14.0 \
     --hash=sha256:8f19fbbe99e72420ff35c00b27a34cb9937e902a8b810e2c88300c6f0a3b699d \
     --hash=sha256:e3fe4ac4b851c468cc8363d500db52c2ead036020723024a109d37346efaa761
     # via httpcore
+highdicom==0.22.0 \
+    --hash=sha256:2f9c7f468924afafb57d49a24c3c96477fe4c5d1e8b6849097d193f00eab6cda \
+    --hash=sha256:62442fb9e77c05489c8ff99e6a30ed88fa03842a1fb70616dcf934fe4e3b1ca3
+    # via hatch.envs.test
 httpcore==1.0.5 \
     --hash=sha256:34a38e2f9291467ee3b44e89dd52615370e152954ba21721378a87b2960f7a61 \
     --hash=sha256:421f18bac248b25d310f3cacd198d55b8e6125c107797b609ff9b7a6ba7991b5
@@ -1019,6 +1024,7 @@ numpy==1.26.4 \
     # via
     #   contourpy
     #   dash-slicer
+    #   highdicom
     #   imageio
     #   matplotlib
     #   nibabel
@@ -1135,10 +1141,48 @@ pillow==10.3.0 \
     --hash=sha256:ff61bfd9253c3915e6d41c651d5f962da23eda633cf02262990094a18a55371a
     # via
     #   dash-slicer
+    #   highdicom
     #   imageio
     #   matplotlib
     #   omero-py
+    #   pillow-jpls
     #   scikit-image
+pillow-jpls==1.3.2 \
+    --hash=sha256:0307b8506862892139a8ce3c63f34e7fd6dfed59bd6c96b677dcf7e03df815a4 \
+    --hash=sha256:058e8ad9fce542673703ae1032dc3f4fa3aaf39a8114ce087bce0bcb3a684d95 \
+    --hash=sha256:0eadbab278b1c54d6fa79085e0331e0db8799813e7009a6709bce4ad484e0711 \
+    --hash=sha256:20fb78195f70ace9cdfd863cb5b3e76d9c9d2ad20aab1b4c87c0bad9aab90563 \
+    --hash=sha256:28bdb039cddb68933024330453c2692c6c595e546d0ce9305217413045fca8d4 \
+    --hash=sha256:2d1a223a842dd97e3af324e80d07d4dbb9aa19ccab3b46b1c39e2ea231e7dd00 \
+    --hash=sha256:34a8d6783a46f8f41c7c4dcd0235b3c0a893818492e8f2500b4a4e70886b5959 \
+    --hash=sha256:3aa7e3e48f5e29c22f565e677e27d029e3a302cf56b98c1115835f7c08875453 \
+    --hash=sha256:4125954831a91f1f6484f21ef6e521ca214955322e207abb487ebb7cb7ad1b21 \
+    --hash=sha256:463ee0eb8c6004fbca535f044b8f7969fa324c2ea4d992158f8bad0ff8ca91dc \
+    --hash=sha256:5a87864a95f0e36b32908f12251b5e8b7fa14551a89f27c916cc0e69fdc3382f \
+    --hash=sha256:5f87c1cef6de984f07c08d34a84e35e69643d3bc6a3e39241089f4be38f864da \
+    --hash=sha256:6bbb20d063fc9efe2474ba7594f203288e5bab75b0cfca32d2d4432fb9053de3 \
+    --hash=sha256:7610b9aad5295eb6adc2e658df0dd995b38fef7d4c72c4ef63c1977989f5e701 \
+    --hash=sha256:85a42e28cac15b9e25a65588a68a1ba65ed5334a7f7a9b57bcab1223ddf65dae \
+    --hash=sha256:8a6c1e8c76b94620e0070948b91771461f7d49cc6be2575e233f6056ffe1ec26 \
+    --hash=sha256:8b4740afc7555fb7bce95d6c507069fc0cc9800892e336dba18c5302ef7364a5 \
+    --hash=sha256:91553669c4ecaf92ffa53426870190cb1b5bdf2795cddbeac1ae6fc3120c939a \
+    --hash=sha256:97d3265f8fcdd2add41a4ca49f464fd6e0982c0225ecbc80f30175fa04ce4e3c \
+    --hash=sha256:99a4c3598cf0a82885e3691307fd222b763c69c5b4a15223998679377247bc50 \
+    --hash=sha256:9cb643dd50c8fa848c16fe5e41350049ca762499e22cf5fb4cd5f67ae2c027d1 \
+    --hash=sha256:a0e04d6ca24dea9763a544518fc06c737499ab9075d216776b86e054be83599c \
+    --hash=sha256:a1f1d47544bf824a6262678e5884815f6a2df8cef410bcd9365e213450a4853e \
+    --hash=sha256:a79106cc1094385ac1d8670a3bb4d5fe67a7e49d405673d241bf19535d557318 \
+    --hash=sha256:ac607508232cdafb09e77f5280211e25d7678152edb0885f0dd374e0b651e2c2 \
+    --hash=sha256:ad0afb2c1676bc7d1618cf3837e9fc58997c8f0527464d9f524c19a36d666ce9 \
+    --hash=sha256:c822fafda531bc047068663722f86fb67083b572957b38fbc818b6339a817302 \
+    --hash=sha256:d1a00f77bd2d33a93082b9496c6891151fed1e1e1b915b60bdcad5f28085a9fc \
+    --hash=sha256:d64f4df3c385466aa897a1c70ffe4ecf91c611ca77f524321ddaf3969daede92 \
+    --hash=sha256:dea160c6e100b05c896a8434b0b9021870d7c4b055051ec637536437df218da6 \
+    --hash=sha256:e837011ad129ff9d2f0c17087856eeb493b208816f41ef82daa21d7cd21fcd53 \
+    --hash=sha256:f1ea5f90a3e30e12a5844e850393ee43acc281712bca97fd6545252d652c6f0e \
+    --hash=sha256:f346cc2f957c7b726266d46749028832351599a737bb27671eda5853784d02c3 \
+    --hash=sha256:f8956774bb9bc23aa85df733b21eaf128c1e115feedf6f37d88c1955d52c444d
+    # via highdicom
 platformdirs==4.2.2 \
     --hash=sha256:2d7a1657e36a80ea911db832a8a6ece5ee53d8de21edd5cc5879af6530b1bfee \
     --hash=sha256:38b7b51f512eed9e84a22788b4bce1de17c0adb134d6becb09836e37d8654cd3
@@ -1204,7 +1248,9 @@ pycparser==2.22 \
 pydicom==2.4.4 \
     --hash=sha256:90b4801d851ce65be3df520e16bbfa3d6c767cf2a3a3b1c18f6780e6b670b87a \
     --hash=sha256:f9f8e19b78525be57aa6384484298833e4d06ac1d6226c79459131ddb0bd7c42
-    # via hatch.envs.test
+    # via
+    #   hatch.envs.test
+    #   highdicom
 pydicom-data==1.0.0 \
     --hash=sha256:622ac97265f1d91f76b5120b5bd6eb0f57737c392ef863b72c71779c91e33637 \
     --hash=sha256:93f2094f219b6fec1e6dbed2e5039fb879272f678646a351b46172f6d0bf110f