Prepare for 0.2.0

.github/workflows/pull-request.yml

@@ -18,9 +18,8 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        # python-version: ["3.8", "3.9", "3.10"]
         python-version: ["3.8", "3.9"]
-        poetry-version: [1.3.2]
+        poetry-version: [1.7.1]
         # os: [ubuntu-20.04, macos-latest, windows-latest]
     steps:
       - uses: actions/checkout@v2

CITATION.cff

@@ -12,14 +12,14 @@ authors:
     family-names: Chlap
     email: [email protected]
     affiliation: University of New South Wales
-    orcid: 'https://orcid.org/0000-0002-6517-8745'
+    orcid: "https://orcid.org/0000-0002-6517-8745"
   - given-names: Daniel
     family-names: Al Mouiee
     affiliation: Ingham Institute
   - given-names: Robert N.
     family-names: Finnegan
     affiliation: University of Sydney
-    orcid: 'https://orcid.org/0000-0003-4728-8462'
+    orcid: "https://orcid.org/0000-0003-4728-8462"
   - given-names: Xinyi
     family-names: Cui
     affiliation: University of New South Wales
@@ -34,12 +34,12 @@ authors:
   - given-names: Lois
     family-names: Holloway
     affiliation: University of New South Wales
-repository-code: 'https://github.com/AustralianCancerDataNetwork/pydicer'
-url: 'https://australiancancerdatanetwork.github.io/pydicer/'
+repository-code: "https://github.com/AustralianCancerDataNetwork/pydicer"
+url: "https://australiancancerdatanetwork.github.io/pydicer/"
 keywords:
   - medical imaging
   - DICOM
   - radiotherapy
 license: Apache-2.0
-version: 0.1.0
-date-released: '2023-06-04'
+version: 0.2.0
+date-released: "2023-12-20"

README.md

@@ -1,6 +1,13 @@
 # PyDicer: PYthon Dicom Image ConvertER
 
-Welcome to pydicer, a tool to ease the process of converting DICOM data objects into a format typically used for research purposes. In addition to data conversion, functionality is provided to help analyse the data such as the computing radiomic features or radiotherapy dose metrics. PyDicer uses the NIfTI format to store data is a well defined file system structure. Tracking of these data objects in CSV files, also stored on the file system, provides an easy and flexible way to work with the converted data in your research.
+Welcome to PyDicer, a tool to ease the process of converting Radiotherapy DICOM data objects into a format typically used for research purposes. In addition to data conversion, functionality is provided to help analyse the data. This includes computing radiomic features, radiotherapy dose metrics and auto-segmentation metrics. PyDicer uses the NIfTI format to store data is a well defined file system structure. Tracking of these data objects in CSV files, also stored on the file system, provides an easy and flexible way to work with the converted data in your research.
+
+The [PyDicer documentation](https://australiancancerdatanetwork.github.io/pydicer/index.html) provides several examples and guides to help you get started with the tool. Here are a few **PyDicer principles** to keep in mind as you get started:
+
+- The [working directory structure](https://australiancancerdatanetwork.github.io/pydicer/index.html#directory-structure) is standardised and generalisable for use with any DICOM dataset.
+- Use [Pandas DataFrame's](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html) to work with converted data objects.
+- [SimpleITK](https://simpleitk.readthedocs.io/en/master/) and [PlatiPy](https://pyplati.github.io/platipy/) are used under the hood for the image conversion, visualisation and analysis tasks.
+- Always inspect visualisations, plots and metrics produced by PyDicer in your working directory. Remember, PyDicer is a research tool so only use it for research purposes and expect the unexpected!
 
 ## Installation
 
@@ -11,9 +18,20 @@ environment using `pip`:
 pip install pydicer
 ```
 
+## Supported Modalities
+
+PyDicer currently supports converting and analysing the following DICOM modalities:
+
+- CT
+- MR
+- PT (Experimental)
+- RTSTRUCT
+- RTPLAN (Not converted since this only consists of meta data)
+- RTDOSE
+
 ## Directory Structure
 
-pydicer will place converted and intermediate files into a specific directory structure. Within the configured working directory `[working]`, the following directories will be generated:
+PyDicer will place converted and intermediate files into a specific directory structure. Visualisation, metrics computed and plots are also stored along side the converted data objects. Within the configured working directory `[working]`, the following directories will be generated:
 
 - `[working]/data`: Directory in which converted data will be placed
 - `[working]/quarantine`: Files which couldn't be preprocessed or converted will be placed in here for you to investigate further
@@ -66,31 +84,19 @@ pydicer.run_pipeline()
 
 ## Contributing
 
-PyDicer is a research tool and adding to its functionality is encouraged. All GitHub Pull Requests
-are welcome. We do ask that you abide by our code of conduct and follow our coding standards.
-
-### Coding standards
-
-Code in pydicer must conform to Python's PEP-8 standards to ensure consistent formatting between contributors. To ensure this, pylint is used to check code conforms to these standards before a Pull Request can be merged. You can run pylint from the command line using the following command:
+PyDicer is an open-source tool and contributions are welcome! Here are some ways you might consider contributing to the project:
 
-```bash
-pylint pydicer
-```
-
-But a better idea is to ensure you are using a Python IDE which supports linting (such as [VSCode](https://code.visualstudio.com/docs/python/linting) or PyCharm). Make sure you resolve all suggestions from pylint before submitting your pull request.
-
-If you're new to using pylint, you may like to [read this guide](https://docs.pylint.org/en/v2.11.1/tutorial.html).
+- Reporting issues on GitHub.
+- Correcting/extending the documentation.
+- Contributing a bug fix or extending some functionality.
+- Providing functionality to support additional DICOM modalities.
+- Giving the [PyDicer project](https://github.com/AustralianCancerDataNetwork/pydicer) a star on GitHub.
 
-### Automated tests
+For more information, see the [Contributing documentation](https://australiancancerdatanetwork.github.io/pydicer/contributing.html).
 
-A test suite is included in pydicer which ensures that code contributed to the repository functions as expected and continues to function as further development takes place. Any code submitted via a pull request should include appropriate automated tests for the new code.
+## Authors
 
-pytest is used as a testing library. Running the tests from the command line is really easy:
-
-```bash
-pytest
-```
+PyDicer was developed by the [Ingham Medical Physics team](https://www.unsw.edu.au/medicine-health/our-schools/clinical-medicine/research-impact/research-groups/cancer/ingham-medical-physics) in South-Western Sydney. It was developed as part of the [Australian Cancer Data Network](https://australian-cancer-data.network/) supported by the [Australian Research Data Commons](https://ardc.edu.au/).
 
-Add your tests to the appropriate file in the `tests/` directory. See the [pytest documention](https://docs.pytest.org/en/6.2.x/getting-started.html) for more information.
-or check out the [Getting Started Example](https://australiancancerdatanetwork.github.io/pydicer/_examples/GettingStarted.html).
-[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/australiancancerdatanetwork/pydicer/blob/main/examples/GettingStarted.ipynb)
+- **Phillip Chlap** - [[email protected]]([email protected])
+- **Daniel Al Mouiee** - [[email protected]]([email protected])

docs/_static/custom.css

@@ -1,16 +1,16 @@
-table.dataframe {
+table {
     color: var(--color-foreground-primary) !important;
 }
 
-table.dataframe thead {
+table thead {
     background-color: var(--color-card-marginals-background) !important;
 }
 
-table.dataframe tr:nth-child(odd) {
+table tr:nth-child(odd) {
     background: var(--color-background-border) !important;
 }
 
-table.dataframe tr:nth-child(even) {
+table tr:nth-child(even) {
     background: var(--color-card-marginals-background) !important;
 }
 

docs/analyse.rst

@@ -4,3 +4,6 @@ Analyse
 
 .. automodule:: pydicer.analyse.data
     :members:
+
+.. automodule:: pydicer.analyse.compare
+    :members:

docs/conf.py

@@ -28,9 +28,6 @@
 copyright = f"{year}, Ingham Medical Physics"
 author = "Ingham Medical Physics"
 
-# The full version, including alpha/beta/rc tags
-release = "0.1.0"
-
 
 # -- General configuration ---------------------------------------------------
 
@@ -85,9 +82,13 @@ def setup(app):
 shutil.copy("../examples/VisualiseData.ipynb", "_examples/VisualiseData.ipynb")
 shutil.copy("../examples/DoseMetrics.ipynb", "_examples/DoseMetrics.ipynb")
 shutil.copy("../examples/Radiomics.ipynb", "_examples/Radiomics.ipynb")
-shutil.copy("../examples/DatasetPreparation.ipynb", "_examples/DatasetPreparation.ipynb")
+shutil.copy(
+    "../examples/DatasetPreparation.ipynb", "_examples/DatasetPreparation.ipynb"
+)
 shutil.copy("../examples/WorkingWithData.ipynb", "_examples/WorkingWithData.ipynb")
-shutil.copy("../examples/WorkingWithStructures.ipynb", "_examples/WorkingWithStructures.ipynb")
+shutil.copy(
+    "../examples/WorkingWithStructures.ipynb", "_examples/WorkingWithStructures.ipynb"
+)
 shutil.copy("../examples/Configuration.ipynb", "_examples/Configuration.ipynb")
 shutil.copy("../examples/ObjectGeneration.ipynb", "_examples/ObjectGeneration.ipynb")
 shutil.copy("../examples/AutoSegmentation.ipynb", "_examples/AutoSegmentation.ipynb")