| Name | damast JSON |
| Version |
0.1.12
JSON |
| download |
| home_page | None |
| Summary | Package to improve the development of transparent, replicable data processing pipelines |
| upload_time | 2025-08-26 09:33:22 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | 3-Clause BSD License / New BSD License
Copyright (c) 2023-2025 Simula Research Laboratory, Oslo, Norway
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
3. Neither the name of the copyright holder nor the names of its contributors
may be used to endorse or promote products derived from this software without
specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
| keywords |
data processing
pipeline
machine learning
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
absl-py
astunparse
blosc2
cachetools
certifi
charset-normalizer
click
cloudpickle
commonmark
contourpy
cycler
Cython
flatbuffers
fonttools
fsspec
gast
google-auth
google-auth-oauthlib
google-pasta
grpcio
h5py
idna
joblib
kaleido
keras
kiwisolver
libclang
llvmlite
locket
Markdown
MarkupSafe
matplotlib
msgpack
numba
numexpr
numpy
oauthlib
opt-einsum
packaging
pandas
partd
Pillow
plotly
protobuf
py-cpuinfo
pyasn1
pyasn1-modules
Pygments
pyparsing
python-dateutil
pytz
PyYAML
requests
requests-oauthlib
rich
rsa
scikit-learn
scipy
six
tables
tenacity
tensorboard
tensorboard-data-server
tensorboard-plugin-wit
tensorflow
tensorflow-estimator
tensorflow-io-gcs-filesystem
termcolor
threadpoolctl
toolz
typing_extensions
urllib3
Werkzeug
wrapt
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
[](https://pypi.org/project/damast/)
# damast: Creation of reproducible data processing pipelines
The main purpose of this library is to faciliate the reusability of data and data processing pipelines.
For this, damast introduces a means to associate metadata with data frames and enables consistency checking.
To ensure semantic consistency, transformation steps in a pipeline can be annotated with
allowed data ranges for inputs and outputs, as well as units.
```
class LatLonTransformer(PipelineElement):
"""
The LatLonTransformer will consume a lat(itude) and a lon(gitude) column and perform
cyclic normalization. It will add four columns to a dataframe, namely lat_x, lat_y, lon_x, lon_y.
"""
@damast.core.describe("Lat/Lon cyclic transformation")
@damast.core.input({
"lat": {"unit": "deg"},
"lon": {"unit": "deg"}
})
@damast.core.output({
"lat_x": {"value_range": MinMax(-1.0, 1.0)},
"lat_y": {"value_range": MinMax(-1.0, 1.0)},
"lon_x": {"value_range": MinMax(-1.0, 1.0)},
"lon_y": {"value_range": MinMax(-1.0, 1.0)}
})
def transform(self, df: AnnotatedDataFrame) -> AnnotatedDataFrame:
lat_cyclic_transformer = CycleTransformer(features=["lat"], n=180.0)
lon_cyclic_transformer = CycleTransformer(features=["lon"], n=360.0)
_df = lat_cyclic_transformer.fit_transform(df=df)
_df = lon_cyclic_transformer.fit_transform(df=_df)
return _df
```
For detailed examples, check the documentation at: https://simula.github.io/damast
## Installation and Development Setup
Firstly, you will want to create you an isolated development environment for Python, that being conda or venv-based.
The following will go through a venv based setup.
Let us assume you operate with a 'workspace' directory for this project:
```
cd workspace
```
Here, you will create a virtual environment.
Get an overview over venv (command):
```
python -m venv --help
```
Create your venv and activate it:
```
python -m venv damast-venv
source damast-venv/bin/activate
```
Clone the repo and install:
```
git clone https://github.com/simula/damast
cd damast
pip install -e ".[test,dev]"
```
or alternatively:
```
pip install damast[test,dev]
```
## Docker Container
If you prefer to work or start with a docker container you can build it using the provided [Dockerfile](https://github.com/simula/damast/blob/main/Dockerfile)
```
docker build -t damast:latest -f Dockerfile .
```
To enter the container:
```
docker run -it --rm damast:latest /bin/bash
```
## Usage
To get the usage documentation it is easiest to check the published documentation [here](https://simula.github.io/damast/README.html).
Otherwise, you can also locally generate the latest documentation once you installed the package:
```
tox -e build_docs
```
Then open the documentation with a browser:
```
<yourbrowser> _build/html/index.html
```
## Testing
Install the project and use the predefined default test environment:
tox -e py
## Contributing
This project is open to contributions. For details on how to contribute please check the [Contribution Guidelines](https://github.com/simula/damast/blob/main/CONTRIBUTING.md)
## License
This project is licensed under the [BSD-3-Clause License](https://github.com/simula/damast/blob/main/LICENSE).
## Copyright
Copyright (c) 2023-2025 [Simula Research Laboratory, Oslo, Norway](https://www.simula.no/research/research-departments)
## Acknowledgments
This work has been derived from work that is part of the [T-SAR project](https://www.simula.no/research/projects/t-sar)
Some derived work is mainly part of the specific data processing for the 'maritime' domain.
The development of this library is part of the EU-project [AI4COPSEC](https://ai4copsec.eu) which receives funding
from the Horizon Europe framework programme under Grant Agreement N. 101190021.
Raw data
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"description": "[](https://pypi.org/project/damast/)\n\n\n\n# damast: Creation of reproducible data processing pipelines\n\nThe main purpose of this library is to faciliate the reusability of data and data processing pipelines.\nFor this, damast introduces a means to associate metadata with data frames and enables consistency checking.\n\nTo ensure semantic consistency, transformation steps in a pipeline can be annotated with\nallowed data ranges for inputs and outputs, as well as units.\n\n```\nclass LatLonTransformer(PipelineElement):\n \"\"\"\n The LatLonTransformer will consume a lat(itude) and a lon(gitude) column and perform\n cyclic normalization. It will add four columns to a dataframe, namely lat_x, lat_y, lon_x, lon_y.\n \"\"\"\n @damast.core.describe(\"Lat/Lon cyclic transformation\")\n @damast.core.input({\n \"lat\": {\"unit\": \"deg\"},\n \"lon\": {\"unit\": \"deg\"}\n })\n @damast.core.output({\n \"lat_x\": {\"value_range\": MinMax(-1.0, 1.0)},\n \"lat_y\": {\"value_range\": MinMax(-1.0, 1.0)},\n \"lon_x\": {\"value_range\": MinMax(-1.0, 1.0)},\n \"lon_y\": {\"value_range\": MinMax(-1.0, 1.0)}\n })\n def transform(self, df: AnnotatedDataFrame) -> AnnotatedDataFrame:\n lat_cyclic_transformer = CycleTransformer(features=[\"lat\"], n=180.0)\n lon_cyclic_transformer = CycleTransformer(features=[\"lon\"], n=360.0)\n\n _df = lat_cyclic_transformer.fit_transform(df=df)\n _df = lon_cyclic_transformer.fit_transform(df=_df)\n return _df\n```\n\nFor detailed examples, check the documentation at: https://simula.github.io/damast\n\n## Installation and Development Setup\n\nFirstly, you will want to create you an isolated development environment for Python, that being conda or venv-based.\nThe following will go through a venv based setup.\n\nLet us assume you operate with a 'workspace' directory for this project:\n\n```\n cd workspace\n```\n\nHere, you will create a virtual environment.\nGet an overview over venv (command):\n\n```\n python -m venv --help\n```\n\nCreate your venv and activate it:\n```\n python -m venv damast-venv\n source damast-venv/bin/activate\n```\n\nClone the repo and install:\n\n```\n git clone https://github.com/simula/damast\n cd damast\n pip install -e \".[test,dev]\"\n```\n\nor alternatively:\n```\n pip install damast[test,dev]\n```\n\n## Docker Container\n\nIf you prefer to work or start with a docker container you can build it using the provided [Dockerfile](https://github.com/simula/damast/blob/main/Dockerfile)\n```\n docker build -t damast:latest -f Dockerfile .\n```\n\nTo enter the container:\n```\n docker run -it --rm damast:latest /bin/bash\n```\n\n## Usage\n\nTo get the usage documentation it is easiest to check the published documentation [here](https://simula.github.io/damast/README.html).\n\nOtherwise, you can also locally generate the latest documentation once you installed the package:\n```\n tox -e build_docs\n```\nThen open the documentation with a browser:\n```\n <yourbrowser> _build/html/index.html\n```\n\n\n## Testing\n\nInstall the project and use the predefined default test environment:\n\n tox -e py\n\n## Contributing\n\nThis project is open to contributions. For details on how to contribute please check the [Contribution Guidelines](https://github.com/simula/damast/blob/main/CONTRIBUTING.md)\n\n## License\nThis project is licensed under the [BSD-3-Clause License](https://github.com/simula/damast/blob/main/LICENSE).\n\n## Copyright\n\nCopyright (c) 2023-2025 [Simula Research Laboratory, Oslo, Norway](https://www.simula.no/research/research-departments)\n\n## Acknowledgments\n\nThis work has been derived from work that is part of the [T-SAR project](https://www.simula.no/research/projects/t-sar)\nSome derived work is mainly part of the specific data processing for the 'maritime' domain.\n\nThe development of this library is part of the EU-project [AI4COPSEC](https://ai4copsec.eu) which receives funding\n from the Horizon Europe framework programme under Grant Agreement N. 101190021.\n",
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}
