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diff --git a/python-wkcuber.spec b/python-wkcuber.spec new file mode 100644 index 0000000..74c14d8 --- /dev/null +++ b/python-wkcuber.spec @@ -0,0 +1,640 @@ +%global _empty_manifest_terminate_build 0 +Name: python-wkcuber +Version: 0.12.3 +Release: 1 +Summary: Python package to create, cube, and work with WEBKNOSSOS WKW datasets +License: AGPL-3.0 +URL: https://docs.webknossos.org/wkcuber +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/d4/9b/f0a3f675c2ca2de2938150875540e8b79fadc771aea608e81a8d9787758a/wkcuber-0.12.3.tar.gz +BuildArch: noarch + +Requires: python3-attrs +Requires: python3-cattrs +Requires: python3-cluster_tools +Requires: python3-czifile +Requires: python3-GitPython +Requires: python3-imagecodecs +Requires: python3-natsort +Requires: python3-nibabel +Requires: python3-numpy +Requires: python3-pillow +Requires: python3-requests +Requires: python3-scikit-image +Requires: python3-scikit-learn +Requires: python3-scipy +Requires: python3-tifffile +Requires: python3-webknossos +Requires: python3-wkw +Requires: python3-zarr +Requires: python3-universal-pathlib + +%description +# WEBKNOSSOS cuber (wkcuber) +[](https://pypi.python.org/pypi/wkcuber) +[](https://pypi.python.org/pypi/wkcuber) +[](https://github.com/scalableminds/webknossos-libs/actions?query=workflow%3A%22CI%22) +[](https://docs.webknossos.org/wkcuber/index.html) +[](https://github.com/psf/black) + +Python library for creating and working with [WEBKNOSSOS](https://webknossos.org) [WKW](https://github.com/scalableminds/webknossos-wrap) datasets. WKW is a container format for efficiently storing large, scale 3D image data as found in (electron) microscopy. + +The tools are modular components to allow easy integration into existing pipelines and workflows. + +## Features + +* `wkcuber`: Convert supported input files to fully ready WKW datasets (includes type detection, downsampling, compressing and metadata generation) +* `wkcuber.convert_image_stack_to_wkw`: Convert image stacks to fully ready WKW datasets (includes downsampling, compressing and metadata generation) +* `wkcuber.export_wkw_as_tiff`: Convert WKW datasets to a tiff stack (writing as tiles to a `z/y/x.tiff` folder structure is also supported) +* `wkcuber.cubing`: Convert image stacks (e.g., `tiff`, `jpg`, `png`, `bmp`, `dm3`, `dm4`) to WKW cubes +* `wkcuber.tile_cubing`: Convert tiled image stacks (e.g. in `z/y/x.ext` folder structure) to WKW cubes +* `wkcuber.convert_knossos`: Convert KNOSSOS cubes to WKW cubes +* `wkcuber.convert_nifti`: Convert NIFTI files to WKW files (Currently without applying transformations). +* `wkcuber.convert_raw`: Convert RAW binary data (.raw, .vol) files to WKW datasets +* `wkcuber.downsampling`: Create downsampled magnifications (with `median`, `mode` and linear interpolation modes). Downsampling compresses the new magnifications by default (disable via `--no_compress`). +* `wkcuber.compress`: Compress WKW cubes for efficient file storage (especially useful for segmentation data) +* `wkcuber.metadata`: Create (or refresh) metadata (with guessing of most parameters) +* `wkcuber.recubing`: Read existing WKW cubes in and write them again specifying the WKW file length. Useful when dataset was written e.g. with file length 1. +* `wkcuber.check_equality`: Compare two WKW datasets to check whether they are equal (e.g., after compressing a dataset, this task can be useful to double-check that the compressed dataset contains the same data). +* Most modules support multiprocessing + +## Supported input formats + +* Standard image formats, e.g. `tiff`, `jpg`, `png`, `bmp` +* Proprietary image formats, e.g. `dm3` +* Tiled image stacks (used for Catmaid) +* KNOSSOS cubes +* NIFTI files +* Raw binary files + +## Installation +### Python 3 with pip from PyPi +- `wkcuber` requires at least Python 3.8 + +```bash +# Make sure to have lz4 installed: +# Mac: brew install lz4 +# Ubuntu/Debian: apt-get install liblz4-1 +# CentOS/RHEL: yum install lz4 + +pip install wkcuber +``` + +### Docker +Use the CI-built image: [scalableminds/webknossos-cuber](https://hub.docker.com/r/scalableminds/webknossos-cuber/). Example usage `docker run -v <host path>:/data --rm scalableminds/webknossos-cuber wkcuber --layer_name color --scale 11.24,11.24,25 --name great_dataset /data/source/color /data/target`. + + +## Usage + +```bash +# Convert arbitrary, supported input files into wkw datasets. This sets reasonable defaults, but see other commands for customization. +python -m wkcuber \ + --scale 11.24,11.24,25 \ + data/source data/target + +# Convert image stacks into wkw datasets +python -m wkcuber.convert_image_stack_to_wkw \ + --layer_name color \ + --scale 11.24,11.24,25 \ + --name great_dataset \ + data/source/color data/target + +# Convert image files to wkw cubes +python -m wkcuber.cubing --layer_name color data/source/color data/target +python -m wkcuber.cubing --layer_name segmentation data/source/segmentation data/target + +# Convert tiled image files to wkw cubes +python -m wkcuber.tile_cubing --layer_name color data/source data/target + +# Convert Knossos cubes to wkw cubes +python -m wkcuber.convert_knossos --layer_name color data/source/mag1 data/target + +# Convert NIFTI file to wkw file +python -m wkcuber.convert_nifti --layer_name color --scale 10,10,30 data/source/nifti_file data/target + +# Convert folder with NIFTI files to wkw files +python -m wkcuber.convert_nifti --color_file one_nifti_file --segmentation_file --scale 10,10,30 another_nifti data/source/ data/target + +# Convert RAW file to wkw file +python -m wkcuber.convert_raw --layer_name color --scale 10,10,30 --input_dtype uint8 --shape 2048,2048,1024 data/source/raw_file.raw data/target + +# Create downsampled magnifications +python -m wkcuber.downsampling --layer_name color data/target +python -m wkcuber.downsampling --layer_name segmentation --interpolation_mode mode data/target + +# Compress data in-place (mostly useful for segmentation) +python -m wkcuber.compress --layer_name segmentation data/target + +# Compress data copy (mostly useful for segmentation) +python -m wkcuber.compress --layer_name segmentation data/target data/target_compress + +# Create metadata +python -m wkcuber.metadata --name great_dataset --scale 11.24,11.24,25 data/target + +# Refresh metadata so that new layers and/or magnifications are picked up +python -m wkcuber.metadata --refresh data/target + +# Recubing an existing dataset +python -m wkcuber.recubing --layer_name color --dtype uint8 /data/source/wkw /data/target + +# Check two datasets for equality +python -m wkcuber.check_equality /data/source /data/target +``` + +### Parallelization + +Most tasks can be configured to be executed in a parallelized manner. Via `--distribution_strategy` you can pass `multiprocessing`, `slurm` or `kubernetes`. The first can be further configured with `--jobs` and the latter via `--job_resources='{"mem": "10M"}'`. Use `--help` to get more information. + +### Zarr support + +Most conversion commands can be configured with `--data_format zarr`. This will produce a Zarr-based dataset instead of WKW. Zarr-based datasets can also be stored on remote storage (e.g. S3, GCS, HTTP). For that, storage-specific credentials and configurations need to be passed in as environment variables. + +#### Example S3 + +```bash +export AWS_SECRET_ACCESS_KEY="..." +export AWS_ACCESS_KEY_ID="..." +export AWS_REGION="..." + +python -m wkcuber \ + --scale 11.24,11.24,25 \ + --data_format zarr \ + data/source s3://bucket/data/target +``` + +#### Example HTTPS + +```bash +export HTTP_BASIC_USER="..." +export HTTP_BASIC_PASSWORD="..." + +python -m wkcuber \ + --scale 11.24,11.24,25 \ + --data_format zarr \ + data/source https://example.org/data/target +``` + +Exchange `https://` with `webdav+https://` for WebDAV. + + +## Development +Make sure to install all the required dependencies using Poetry: +```bash +pip install poetry +poetry install +``` + +Please, format, lint, and unit test your code changes before merging them. +```bash +poetry run black . +poetry run pylint -j4 wkcuber +poetry run pytest tests +``` + +Please, run the extended test suite: +```bash +tests/scripts/all_tests.sh +``` + +PyPi releases are automatically pushed when creating a new Git tag/Github release. + +## API documentation +Check out the [latest version of the API documentation](https://docs.webknossos.org/api/wkcuber.html). + +### Generate the API documentation +Run `docs/generate.sh` to open a server displaying the API docs. `docs/generate.sh --persist` persists the html to `docs/api`. + +## Test Data Credits +Excerpts for testing purposes have been sampled from: + +* Dow Jacobo Hossain Siletti Hudspeth (2018). **Connectomics of the zebrafish's lateral-line neuromast reveals wiring and miswiring in a simple microcircuit.** eLife. [DOI:10.7554/eLife.33988](https://elifesciences.org/articles/33988) +* Zheng Lauritzen Perlman Robinson Nichols Milkie Torrens Price Fisher Sharifi Calle-Schuler Kmecova Ali Karsh Trautman Bogovic Hanslovsky Jefferis Kazhdan Khairy Saalfeld Fetter Bock (2018). **A Complete Electron Microscopy Volume of the Brain of Adult Drosophila melanogaster.** Cell. [DOI:10.1016/j.cell.2018.06.019](https://www.cell.com/cell/fulltext/S0092-8674(18)30787-6). License: [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) + +## License +AGPLv3 +Copyright scalable minds + + +%package -n python3-wkcuber +Summary: Python package to create, cube, and work with WEBKNOSSOS WKW datasets +Provides: python-wkcuber +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-wkcuber +# WEBKNOSSOS cuber (wkcuber) +[](https://pypi.python.org/pypi/wkcuber) +[](https://pypi.python.org/pypi/wkcuber) +[](https://github.com/scalableminds/webknossos-libs/actions?query=workflow%3A%22CI%22) +[](https://docs.webknossos.org/wkcuber/index.html) +[](https://github.com/psf/black) + +Python library for creating and working with [WEBKNOSSOS](https://webknossos.org) [WKW](https://github.com/scalableminds/webknossos-wrap) datasets. WKW is a container format for efficiently storing large, scale 3D image data as found in (electron) microscopy. + +The tools are modular components to allow easy integration into existing pipelines and workflows. + +## Features + +* `wkcuber`: Convert supported input files to fully ready WKW datasets (includes type detection, downsampling, compressing and metadata generation) +* `wkcuber.convert_image_stack_to_wkw`: Convert image stacks to fully ready WKW datasets (includes downsampling, compressing and metadata generation) +* `wkcuber.export_wkw_as_tiff`: Convert WKW datasets to a tiff stack (writing as tiles to a `z/y/x.tiff` folder structure is also supported) +* `wkcuber.cubing`: Convert image stacks (e.g., `tiff`, `jpg`, `png`, `bmp`, `dm3`, `dm4`) to WKW cubes +* `wkcuber.tile_cubing`: Convert tiled image stacks (e.g. in `z/y/x.ext` folder structure) to WKW cubes +* `wkcuber.convert_knossos`: Convert KNOSSOS cubes to WKW cubes +* `wkcuber.convert_nifti`: Convert NIFTI files to WKW files (Currently without applying transformations). +* `wkcuber.convert_raw`: Convert RAW binary data (.raw, .vol) files to WKW datasets +* `wkcuber.downsampling`: Create downsampled magnifications (with `median`, `mode` and linear interpolation modes). Downsampling compresses the new magnifications by default (disable via `--no_compress`). +* `wkcuber.compress`: Compress WKW cubes for efficient file storage (especially useful for segmentation data) +* `wkcuber.metadata`: Create (or refresh) metadata (with guessing of most parameters) +* `wkcuber.recubing`: Read existing WKW cubes in and write them again specifying the WKW file length. Useful when dataset was written e.g. with file length 1. +* `wkcuber.check_equality`: Compare two WKW datasets to check whether they are equal (e.g., after compressing a dataset, this task can be useful to double-check that the compressed dataset contains the same data). +* Most modules support multiprocessing + +## Supported input formats + +* Standard image formats, e.g. `tiff`, `jpg`, `png`, `bmp` +* Proprietary image formats, e.g. `dm3` +* Tiled image stacks (used for Catmaid) +* KNOSSOS cubes +* NIFTI files +* Raw binary files + +## Installation +### Python 3 with pip from PyPi +- `wkcuber` requires at least Python 3.8 + +```bash +# Make sure to have lz4 installed: +# Mac: brew install lz4 +# Ubuntu/Debian: apt-get install liblz4-1 +# CentOS/RHEL: yum install lz4 + +pip install wkcuber +``` + +### Docker +Use the CI-built image: [scalableminds/webknossos-cuber](https://hub.docker.com/r/scalableminds/webknossos-cuber/). Example usage `docker run -v <host path>:/data --rm scalableminds/webknossos-cuber wkcuber --layer_name color --scale 11.24,11.24,25 --name great_dataset /data/source/color /data/target`. + + +## Usage + +```bash +# Convert arbitrary, supported input files into wkw datasets. This sets reasonable defaults, but see other commands for customization. +python -m wkcuber \ + --scale 11.24,11.24,25 \ + data/source data/target + +# Convert image stacks into wkw datasets +python -m wkcuber.convert_image_stack_to_wkw \ + --layer_name color \ + --scale 11.24,11.24,25 \ + --name great_dataset \ + data/source/color data/target + +# Convert image files to wkw cubes +python -m wkcuber.cubing --layer_name color data/source/color data/target +python -m wkcuber.cubing --layer_name segmentation data/source/segmentation data/target + +# Convert tiled image files to wkw cubes +python -m wkcuber.tile_cubing --layer_name color data/source data/target + +# Convert Knossos cubes to wkw cubes +python -m wkcuber.convert_knossos --layer_name color data/source/mag1 data/target + +# Convert NIFTI file to wkw file +python -m wkcuber.convert_nifti --layer_name color --scale 10,10,30 data/source/nifti_file data/target + +# Convert folder with NIFTI files to wkw files +python -m wkcuber.convert_nifti --color_file one_nifti_file --segmentation_file --scale 10,10,30 another_nifti data/source/ data/target + +# Convert RAW file to wkw file +python -m wkcuber.convert_raw --layer_name color --scale 10,10,30 --input_dtype uint8 --shape 2048,2048,1024 data/source/raw_file.raw data/target + +# Create downsampled magnifications +python -m wkcuber.downsampling --layer_name color data/target +python -m wkcuber.downsampling --layer_name segmentation --interpolation_mode mode data/target + +# Compress data in-place (mostly useful for segmentation) +python -m wkcuber.compress --layer_name segmentation data/target + +# Compress data copy (mostly useful for segmentation) +python -m wkcuber.compress --layer_name segmentation data/target data/target_compress + +# Create metadata +python -m wkcuber.metadata --name great_dataset --scale 11.24,11.24,25 data/target + +# Refresh metadata so that new layers and/or magnifications are picked up +python -m wkcuber.metadata --refresh data/target + +# Recubing an existing dataset +python -m wkcuber.recubing --layer_name color --dtype uint8 /data/source/wkw /data/target + +# Check two datasets for equality +python -m wkcuber.check_equality /data/source /data/target +``` + +### Parallelization + +Most tasks can be configured to be executed in a parallelized manner. Via `--distribution_strategy` you can pass `multiprocessing`, `slurm` or `kubernetes`. The first can be further configured with `--jobs` and the latter via `--job_resources='{"mem": "10M"}'`. Use `--help` to get more information. + +### Zarr support + +Most conversion commands can be configured with `--data_format zarr`. This will produce a Zarr-based dataset instead of WKW. Zarr-based datasets can also be stored on remote storage (e.g. S3, GCS, HTTP). For that, storage-specific credentials and configurations need to be passed in as environment variables. + +#### Example S3 + +```bash +export AWS_SECRET_ACCESS_KEY="..." +export AWS_ACCESS_KEY_ID="..." +export AWS_REGION="..." + +python -m wkcuber \ + --scale 11.24,11.24,25 \ + --data_format zarr \ + data/source s3://bucket/data/target +``` + +#### Example HTTPS + +```bash +export HTTP_BASIC_USER="..." +export HTTP_BASIC_PASSWORD="..." + +python -m wkcuber \ + --scale 11.24,11.24,25 \ + --data_format zarr \ + data/source https://example.org/data/target +``` + +Exchange `https://` with `webdav+https://` for WebDAV. + + +## Development +Make sure to install all the required dependencies using Poetry: +```bash +pip install poetry +poetry install +``` + +Please, format, lint, and unit test your code changes before merging them. +```bash +poetry run black . +poetry run pylint -j4 wkcuber +poetry run pytest tests +``` + +Please, run the extended test suite: +```bash +tests/scripts/all_tests.sh +``` + +PyPi releases are automatically pushed when creating a new Git tag/Github release. + +## API documentation +Check out the [latest version of the API documentation](https://docs.webknossos.org/api/wkcuber.html). + +### Generate the API documentation +Run `docs/generate.sh` to open a server displaying the API docs. `docs/generate.sh --persist` persists the html to `docs/api`. + +## Test Data Credits +Excerpts for testing purposes have been sampled from: + +* Dow Jacobo Hossain Siletti Hudspeth (2018). **Connectomics of the zebrafish's lateral-line neuromast reveals wiring and miswiring in a simple microcircuit.** eLife. [DOI:10.7554/eLife.33988](https://elifesciences.org/articles/33988) +* Zheng Lauritzen Perlman Robinson Nichols Milkie Torrens Price Fisher Sharifi Calle-Schuler Kmecova Ali Karsh Trautman Bogovic Hanslovsky Jefferis Kazhdan Khairy Saalfeld Fetter Bock (2018). **A Complete Electron Microscopy Volume of the Brain of Adult Drosophila melanogaster.** Cell. [DOI:10.1016/j.cell.2018.06.019](https://www.cell.com/cell/fulltext/S0092-8674(18)30787-6). License: [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) + +## License +AGPLv3 +Copyright scalable minds + + +%package help +Summary: Development documents and examples for wkcuber +Provides: python3-wkcuber-doc +%description help +# WEBKNOSSOS cuber (wkcuber) +[](https://pypi.python.org/pypi/wkcuber) +[](https://pypi.python.org/pypi/wkcuber) +[](https://github.com/scalableminds/webknossos-libs/actions?query=workflow%3A%22CI%22) +[](https://docs.webknossos.org/wkcuber/index.html) +[](https://github.com/psf/black) + +Python library for creating and working with [WEBKNOSSOS](https://webknossos.org) [WKW](https://github.com/scalableminds/webknossos-wrap) datasets. WKW is a container format for efficiently storing large, scale 3D image data as found in (electron) microscopy. + +The tools are modular components to allow easy integration into existing pipelines and workflows. + +## Features + +* `wkcuber`: Convert supported input files to fully ready WKW datasets (includes type detection, downsampling, compressing and metadata generation) +* `wkcuber.convert_image_stack_to_wkw`: Convert image stacks to fully ready WKW datasets (includes downsampling, compressing and metadata generation) +* `wkcuber.export_wkw_as_tiff`: Convert WKW datasets to a tiff stack (writing as tiles to a `z/y/x.tiff` folder structure is also supported) +* `wkcuber.cubing`: Convert image stacks (e.g., `tiff`, `jpg`, `png`, `bmp`, `dm3`, `dm4`) to WKW cubes +* `wkcuber.tile_cubing`: Convert tiled image stacks (e.g. in `z/y/x.ext` folder structure) to WKW cubes +* `wkcuber.convert_knossos`: Convert KNOSSOS cubes to WKW cubes +* `wkcuber.convert_nifti`: Convert NIFTI files to WKW files (Currently without applying transformations). +* `wkcuber.convert_raw`: Convert RAW binary data (.raw, .vol) files to WKW datasets +* `wkcuber.downsampling`: Create downsampled magnifications (with `median`, `mode` and linear interpolation modes). Downsampling compresses the new magnifications by default (disable via `--no_compress`). +* `wkcuber.compress`: Compress WKW cubes for efficient file storage (especially useful for segmentation data) +* `wkcuber.metadata`: Create (or refresh) metadata (with guessing of most parameters) +* `wkcuber.recubing`: Read existing WKW cubes in and write them again specifying the WKW file length. Useful when dataset was written e.g. with file length 1. +* `wkcuber.check_equality`: Compare two WKW datasets to check whether they are equal (e.g., after compressing a dataset, this task can be useful to double-check that the compressed dataset contains the same data). +* Most modules support multiprocessing + +## Supported input formats + +* Standard image formats, e.g. `tiff`, `jpg`, `png`, `bmp` +* Proprietary image formats, e.g. `dm3` +* Tiled image stacks (used for Catmaid) +* KNOSSOS cubes +* NIFTI files +* Raw binary files + +## Installation +### Python 3 with pip from PyPi +- `wkcuber` requires at least Python 3.8 + +```bash +# Make sure to have lz4 installed: +# Mac: brew install lz4 +# Ubuntu/Debian: apt-get install liblz4-1 +# CentOS/RHEL: yum install lz4 + +pip install wkcuber +``` + +### Docker +Use the CI-built image: [scalableminds/webknossos-cuber](https://hub.docker.com/r/scalableminds/webknossos-cuber/). Example usage `docker run -v <host path>:/data --rm scalableminds/webknossos-cuber wkcuber --layer_name color --scale 11.24,11.24,25 --name great_dataset /data/source/color /data/target`. + + +## Usage + +```bash +# Convert arbitrary, supported input files into wkw datasets. This sets reasonable defaults, but see other commands for customization. +python -m wkcuber \ + --scale 11.24,11.24,25 \ + data/source data/target + +# Convert image stacks into wkw datasets +python -m wkcuber.convert_image_stack_to_wkw \ + --layer_name color \ + --scale 11.24,11.24,25 \ + --name great_dataset \ + data/source/color data/target + +# Convert image files to wkw cubes +python -m wkcuber.cubing --layer_name color data/source/color data/target +python -m wkcuber.cubing --layer_name segmentation data/source/segmentation data/target + +# Convert tiled image files to wkw cubes +python -m wkcuber.tile_cubing --layer_name color data/source data/target + +# Convert Knossos cubes to wkw cubes +python -m wkcuber.convert_knossos --layer_name color data/source/mag1 data/target + +# Convert NIFTI file to wkw file +python -m wkcuber.convert_nifti --layer_name color --scale 10,10,30 data/source/nifti_file data/target + +# Convert folder with NIFTI files to wkw files +python -m wkcuber.convert_nifti --color_file one_nifti_file --segmentation_file --scale 10,10,30 another_nifti data/source/ data/target + +# Convert RAW file to wkw file +python -m wkcuber.convert_raw --layer_name color --scale 10,10,30 --input_dtype uint8 --shape 2048,2048,1024 data/source/raw_file.raw data/target + +# Create downsampled magnifications +python -m wkcuber.downsampling --layer_name color data/target +python -m wkcuber.downsampling --layer_name segmentation --interpolation_mode mode data/target + +# Compress data in-place (mostly useful for segmentation) +python -m wkcuber.compress --layer_name segmentation data/target + +# Compress data copy (mostly useful for segmentation) +python -m wkcuber.compress --layer_name segmentation data/target data/target_compress + +# Create metadata +python -m wkcuber.metadata --name great_dataset --scale 11.24,11.24,25 data/target + +# Refresh metadata so that new layers and/or magnifications are picked up +python -m wkcuber.metadata --refresh data/target + +# Recubing an existing dataset +python -m wkcuber.recubing --layer_name color --dtype uint8 /data/source/wkw /data/target + +# Check two datasets for equality +python -m wkcuber.check_equality /data/source /data/target +``` + +### Parallelization + +Most tasks can be configured to be executed in a parallelized manner. Via `--distribution_strategy` you can pass `multiprocessing`, `slurm` or `kubernetes`. The first can be further configured with `--jobs` and the latter via `--job_resources='{"mem": "10M"}'`. Use `--help` to get more information. + +### Zarr support + +Most conversion commands can be configured with `--data_format zarr`. This will produce a Zarr-based dataset instead of WKW. Zarr-based datasets can also be stored on remote storage (e.g. S3, GCS, HTTP). For that, storage-specific credentials and configurations need to be passed in as environment variables. + +#### Example S3 + +```bash +export AWS_SECRET_ACCESS_KEY="..." +export AWS_ACCESS_KEY_ID="..." +export AWS_REGION="..." + +python -m wkcuber \ + --scale 11.24,11.24,25 \ + --data_format zarr \ + data/source s3://bucket/data/target +``` + +#### Example HTTPS + +```bash +export HTTP_BASIC_USER="..." +export HTTP_BASIC_PASSWORD="..." + +python -m wkcuber \ + --scale 11.24,11.24,25 \ + --data_format zarr \ + data/source https://example.org/data/target +``` + +Exchange `https://` with `webdav+https://` for WebDAV. + + +## Development +Make sure to install all the required dependencies using Poetry: +```bash +pip install poetry +poetry install +``` + +Please, format, lint, and unit test your code changes before merging them. +```bash +poetry run black . +poetry run pylint -j4 wkcuber +poetry run pytest tests +``` + +Please, run the extended test suite: +```bash +tests/scripts/all_tests.sh +``` + +PyPi releases are automatically pushed when creating a new Git tag/Github release. + +## API documentation +Check out the [latest version of the API documentation](https://docs.webknossos.org/api/wkcuber.html). + +### Generate the API documentation +Run `docs/generate.sh` to open a server displaying the API docs. `docs/generate.sh --persist` persists the html to `docs/api`. + +## Test Data Credits +Excerpts for testing purposes have been sampled from: + +* Dow Jacobo Hossain Siletti Hudspeth (2018). **Connectomics of the zebrafish's lateral-line neuromast reveals wiring and miswiring in a simple microcircuit.** eLife. [DOI:10.7554/eLife.33988](https://elifesciences.org/articles/33988) +* Zheng Lauritzen Perlman Robinson Nichols Milkie Torrens Price Fisher Sharifi Calle-Schuler Kmecova Ali Karsh Trautman Bogovic Hanslovsky Jefferis Kazhdan Khairy Saalfeld Fetter Bock (2018). **A Complete Electron Microscopy Volume of the Brain of Adult Drosophila melanogaster.** Cell. [DOI:10.1016/j.cell.2018.06.019](https://www.cell.com/cell/fulltext/S0092-8674(18)30787-6). License: [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) + +## License +AGPLv3 +Copyright scalable minds + + +%prep +%autosetup -n wkcuber-0.12.3 + +%build +%py3_build + +%install +%py3_install +install -d -m755 %{buildroot}/%{_pkgdocdir} +if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi +if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi +if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi +if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi +pushd %{buildroot} +if [ -d usr/lib ]; then + find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/lib64 ]; then + find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/bin ]; then + find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/sbin ]; then + find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst +fi +touch doclist.lst +if [ -d usr/share/man ]; then + find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst +fi +popd +mv %{buildroot}/filelist.lst . +mv %{buildroot}/doclist.lst . + +%files -n python3-wkcuber -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 0.12.3-1 +- Package Spec generated |