| Name | streaminghub-pydfds JSON |
| Version |
0.1.40
JSON |
| download |
| home_page | None |
| Summary | Parser for Data Flow Description Schema (DFDS) metadata |
| upload_time | 2024-08-20 14:22:18 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | Copyright (c) 2024 Old Dominion University Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: This work shall be cited in the bibliography as: Yasith Jayawardana, Vikas G. Ashok, and Sampath Jayarathna. 2022. StreamingHub: interactive stream analysis workflows. In Proceedings of the 22nd ACM/IEEE Joint Conference on Digital Libraries (JCDL '22). Association for Computing Machinery, New York, NY, USA, Article 15, 1-10. https://doi.org/10.1145/3529372.3530936 The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
dfds
metadata
parser
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# PyDFDS
<img src="https://i.imgur.com/xSieE3V.png" height="100px">
PyDFDS is a parser for Data Flow Description Schema (DFDS) metadata, written using Python.
## Installation
```bash
pip install streaminghub-pydfds==0.1.40
```
## Usage
```python
from streaminghub_pydfds import Parser
from streaminghub_pydfds.typing import Collection, Stream
from util import restream_data
# define a DFDS parser
parser = Parser()
# open a DFDS collection
fp = "/path/to/collection.json"
collection = parser.get_collection_metadata(fp)
dataloader = collection.dataloader()
# list all items in the collection
for attrs in dataloader.ls():
for stream_id, stream in collection.streams.items():
pass
# read an entire recording at once
attrs, data = dataloader.read(stream.attrs)
# or replay the recording as a stream
asyncio.create_task(replay_data(collection, stream))
return streams
```
## Developer Guide
```bash
# create a virtual environment
python -m venv ~/.virtualenvs/pydfds
# activate virtual environment
source ~/.virtualenvs/pydfds/bin/activate
# install pip tools
python -m pip install --upgrade pip-tools
# generate requirements.txt
pip-compile --strip-extras -o requirements.txt pyproject.toml
pip-compile --strip-extras --extra dev -o requirements.dev.txt pyproject.toml
# install dependencies
pip-sync requirements.txt requirements.dev.txt
# update version (--patch or --minor or --major)
bumpver update --patch
# build package
python -m build
# check package
python -m twine check dist/*
# publish package (testpypi)
python -m twine upload -r testpypi dist/*
# publish package (pypi)
python -m twine upload dist/*
```
Raw data
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"description": "# PyDFDS\n\n<img src=\"https://i.imgur.com/xSieE3V.png\" height=\"100px\">\n\nPyDFDS is a parser for Data Flow Description Schema (DFDS) metadata, written using Python.\n\n## Installation\n\n```bash\n\npip install streaminghub-pydfds==0.1.40\n\n```\n\n## Usage\n\n```python\n\nfrom streaminghub_pydfds import Parser\nfrom streaminghub_pydfds.typing import Collection, Stream\nfrom util import restream_data\n\n# define a DFDS parser\nparser = Parser()\n\n# open a DFDS collection\nfp = \"/path/to/collection.json\"\ncollection = parser.get_collection_metadata(fp)\ndataloader = collection.dataloader()\n\n# list all items in the collection\nfor attrs in dataloader.ls():\n for stream_id, stream in collection.streams.items():\n pass\n\n# read an entire recording at once\nattrs, data = dataloader.read(stream.attrs)\n\n# or replay the recording as a stream\nasyncio.create_task(replay_data(collection, stream))\n\nreturn streams\n\n```\n\n## Developer Guide\n\n```bash\n\n# create a virtual environment\npython -m venv ~/.virtualenvs/pydfds\n# activate virtual environment\nsource ~/.virtualenvs/pydfds/bin/activate\n# install pip tools\npython -m pip install --upgrade pip-tools\n# generate requirements.txt\npip-compile --strip-extras -o requirements.txt pyproject.toml\npip-compile --strip-extras --extra dev -o requirements.dev.txt pyproject.toml\n# install dependencies\npip-sync requirements.txt requirements.dev.txt\n# update version (--patch or --minor or --major)\nbumpver update --patch\n# build package\npython -m build\n# check package\npython -m twine check dist/*\n# publish package (testpypi)\npython -m twine upload -r testpypi dist/*\n# publish package (pypi)\npython -m twine upload dist/*\n\n```\n",
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