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# Contributing Guide
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**Welcome to the webKnossos-libs contributing guide :sparkles:**
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**Welcome to the WEBKNOSSOS-libs contributing guide :sparkles:**
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Thank you for taking the time to contribute to this project! The following is a set of guidelines for contributing to the different webKnossos related Python libraries, which are part of the [webKnossos-libs repository on GitHub](https://github.com/scalableminds/webknossos-libs). These are mostly guidelines, not rules. Use your best judgment, and feel free to propose changes to this document in a pull request.
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Thank you for taking the time to contribute to this project! The following is a set of guidelines for contributing to the different WEBKNOSSOS related Python libraries, which are part of the [WEBKNOSSOS-libs repository on GitHub](https://github.com/scalableminds/webknossos-libs). These are mostly guidelines, not rules. Use your best judgment, and feel free to propose changes to this document in a pull request.
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## Code of Conduct
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webKnossos-libs and everyone contributing and collaborating on this project is expected to follow the [webKnossos-libs Code of Conduct](CODE_OF_CONDUCT.md). Please report unacceptable behavior to [[email protected]](mailto:[email protected]).
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WEBKNOSSOS-libs and everyone contributing and collaborating on this project is expected to follow the [WEBKNOSSOS-libs Code of Conduct](CODE_OF_CONDUCT.md). Please report unacceptable behavior to [[email protected]](mailto:[email protected]).
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## How can I help?
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## Feedback, Observations and Questions
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We'd love to hear your feedback on the webKnossos Python libraries!
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We'd love to hear your feedback on the WEBKNOSSOS Python libraries!
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We're also interested in hearing if these tools don't work for your usecase,
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or if you have questions regarding their usage.
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Please leave a message on the [image.sc forum](https://forum.image.sc/tag/webknossos)
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using the `webknossos` tag to enable public communication on those topics.
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If you prefer to share information only with the webKnossos team, please write an email
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If you prefer to share information only with the WEBKNOSSOS team, please write an email
reach out to [scalable minds](https://scalableminds.com).
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## Issues: Feature Suggestions and Bug Reports
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We track feature requests and bug reports in the [webKnossos-libs repository issues](https://github.com/scalableminds/webknossos-libs/issues).
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We track feature requests and bug reports in the [WEBKNOSSOS-libs repository issues](https://github.com/scalableminds/webknossos-libs/issues).
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Before opening a new issue, please do a quick search of existing issues to make sure your suggestion hasn’t already been added.
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If your issue doesn’t already exist, and you’re ready to create a new one, make sure to state what you would like to implement, improve or bugfix.
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Please use one of the provided templates to make this process easier for you.
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If you are missing a feature to support your use-case, please consider the following points:
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1. Please verify if this feature is directly related to webKnossos.
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Does it belong into the webKnossos Python libraries?
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1. Please verify if this feature is directly related to WEBKNOSSOS.
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Does it belong into the WEBKNOSSOS Python libraries?
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2. Double-check if an issue for this feature exists already. If there is one with a very similar scope,
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please considering commenting there.
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3. If possible, consider how the implementation might look like (e.g. how would the public API change),
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* Adapt or add the documentation.
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* Consider enhancing the examples.
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The specific coding styles, test frameworks and documentation setup of webKnossos-libs are described
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The specific coding styles, test frameworks and documentation setup of WEBKNOSSOS-libs are described
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in the following sections:
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### Development
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The [webknossos-libs repository](https://github.com/scalableminds/webknossos-libs) is structured as a mono-repo, containing multiple packages:
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The [WEBKNOSSOS-libs repository](https://github.com/scalableminds/webknossos-libs) is structured as a mono-repo, containing multiple packages:
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*`cluster_tools`
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*`webknossos`
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The `webknossos` folder contains examples, which are not part of the package, but are tested via `tests/test_examples.py` and added to the documentation (see `docs/src/webknossos-py/examples`).
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The tests also contain functionality for the webKnossos client. There a two modes to run the tests:
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The tests also contain functionality for the WEBKNOSSOS client. There a two modes to run the tests:
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1.`./test.sh --refresh-snapshots`, sending network requests to a webKnossos instance:
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This expects a local webKnossos setup with specific test data, which is shipped with webKnossos. If you're starting and running webKnossos manually, please use port 9000 (the default) and run the `tools/postgres/prepareTestDb.sh` script in the webKnossos repository (⚠️ this overwrites your local webKnossos database). Alternatively, a docker-compose setup is started automatically for the tests, see `./test.sh` and `tests/docker-compose.yml` for details. The network requests & response are recorded as "cassettes" by [vcr.py](https://vcrpy.readthedocs.io), see next point:
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1.`./test.sh --refresh-snapshots`, sending network requests to a WEBKNOSSOS instance:
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This expects a local WEBKNOSSOS setup with specific test data, which is shipped with WEBKNOSSOS. If you're starting and running WEBKNOSSOS manually, please use port 9000 (the default) and run the `tools/postgres/prepareTestDb.sh` script in the WEBKNOSSOS repository (⚠️ this overwrites your local WEBKNOSSOS database). Alternatively, a docker-compose setup is started automatically for the tests, see `./test.sh` and `tests/docker-compose.yml` for details. The network requests & response are recorded as "cassettes" by [vcr.py](https://vcrpy.readthedocs.io), see next point:
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2.`./test.sh` replays responses from previous network snapshots using [vcr.py](https://vcrpy.readthedocs.io) via [pytest-recording](https://github.com/kiwicom/pytest-recording). No additional network requests are allowed in this mode.
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`./test.sh --store-durations` updates the durations for
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### Documentation
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We render a common documentation for webKnossos itself and webknossos-libs from this repository using [mkdocs](https://www.mkdocs.org/). Source-files for the documentation are stored at `docs/src`:
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We render a common documentation for WEBKNOSSOS itself and WEBKNOSSOS-libs from this repository using [mkdocs](https://www.mkdocs.org/). Source-files for the documentation are stored at `docs/src`:
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*`docs/src/webknossos`: Server & Website documentation, linked from the [webknossos repository](https://github.com/scalableminds/webknossos) (must be available under `docs/wk-repo`, see below).
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*`docs/src/api`: Generated using [pdoc](https://pdoc.dev) from Python docstrings.
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*`docs/src/webknossos-py` & `docs/src/wkcuber`: Documentation for the respective Python Packages
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The structure of the documentation page is given by `docs/mkdocs.yml`.
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To generate the documentation locally, clone or link the webKnossos repository to `docs/wk-repo` first and then start the documentation server
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To generate the documentation locally, clone or link the WEBKNOSSOS repository to `docs/wk-repo` first and then start the documentation server
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It downloads a [sample dataset](https://webknossos.org/datasets/scalable_minds/l4_sample_dev/view) with a [sample annotation](https://webknossos.org/annotations/Explorational/6204d2cd010000db0003db91).
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The annotation has been created with "merger mode", where skeleton trees are used to merge segments.
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Read more about the [merger mode in the webKnossos documentation](/webknossos/volume_annotation.html#proof-reading-and-merging-segments).
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Read more about the [merger mode in the WEBKNOSSOS documentation](/webknossos/volume_annotation.html#proof-reading-and-merging-segments).
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The example then uses the [Dataset API](dataset_usage.md) and [fastremap module](https://github.com/seung-lab/fastremap) for reading, remapping and rewriting the `segmentation` layer.
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_This example additionally needs the fastremap package._
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# Download segment masks
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This example [accesses a volume annotation on webKnososs](../../api/webknossos/annotation/annotation.md#Annotation.open_as_remote_dataset) and creates segment masks from selected segments. The segment masks are stored as tiff sequences. The segments have been selected from the "Segments" tab in webKnossos. The [BufferedSliceReader](../../api/webknossos/dataset/view.md#View.get_buffered_slice_reader) is used to efficiently request sections from the remote segmentation data.
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This example [accesses a volume annotation on webKnososs](../../api/webknossos/annotation/annotation.md#Annotation.open_as_remote_dataset) and creates segment masks from selected segments. The segment masks are stored as tiff sequences. The segments have been selected from the "Segments" tab in WEBKNOSSOS. The [BufferedSliceReader](../../api/webknossos/dataset/view.md#View.get_buffered_slice_reader) is used to efficiently request sections from the remote segmentation data.
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# Upload tiff stack
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This example shows how to [create a new dataset from a tiff sequence](../../api/webknossos/dataset/dataset.md#Dataset.from_images), [compress](../../api/webknossos/dataset/dataset.md#Dataset.compress) it, [downsample](../../api/webknossos/dataset/dataset.md#Dataset.downsample) it and [upload](../../api/webknossos/dataset/dataset.md#Dataset.upload) it to webKnossos.
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This example shows how to [create a new dataset from a tiff sequence](../../api/webknossos/dataset/dataset.md#Dataset.from_images), [compress](../../api/webknossos/dataset/dataset.md#Dataset.compress) it, [downsample](../../api/webknossos/dataset/dataset.md#Dataset.downsample) it and [upload](../../api/webknossos/dataset/dataset.md#Dataset.upload) it to WEBKNOSSOS.
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