semvec/pypi-packages huggingface dataset:
get-version 3.5.5
A version helper in the spirit of versioneer.
Requires Python: >=3.9

get_version |b-pypi| |b-conda| |b-ci| |b-cover| |b-black|
=========================================================
Automatically use the latest “vX.X.X” Git tag as version in your Python package.

It also supports getting the version from Python source distributions (``sdist``) or,
once your package is installed, via ``importlib.metadata``.

usage
-----
Add the following into ``yourpackage.py`` (or ``__init__.py``):

.. code-block:: python

    from get_version import get_version
    __version__ = get_version(__file__)
    del get_version

contributing
------------
Use |black|_ to ensure proper code style. In order to not forget you can use |pre-commit|_.

.. |b-ci| image:: https://github.com/flying-sheep/get_version/actions/workflows/run_tests.yml/badge.svg
   :target: https://github.com/flying-sheep/get_version/actions/workflows/run_tests.yml
.. |b-cover| image:: https://coveralls.io/repos/github/flying-sheep/get_version/badge.svg
   :target: https://coveralls.io/github/flying-sheep/get_version
.. |b-pypi| image:: https://img.shields.io/pypi/v/get_version.svg
   :target: https://pypi.org/project/get_version
.. |b-conda| image:: https://img.shields.io/conda/vn/conda-forge/get_version.svg
   :target: https://anaconda.org/conda-forge/get_version
.. |b-black| image:: https://img.shields.io/badge/code%20style-black-000000.svg
   :target: https://github.com/ambv/black

.. |black| replace:: ``black .``
.. _black: https://black.readthedocs.io/en/stable/
.. |pre-commit| replace:: ``pre-commit install``
.. _pre-commit: https://pre-commit.com/
ssbkonf 0.0.10
A package for disclosure control in Python, wrapping methods coded in R
Requires Python: >=3.7

# Python package for statistical disclosure control

This package implements a Python wrapper for easy use of the functions in the `GaussSuppression` and `SmallCountRounding` R packages. This package does not support the full functionality of the R packages, and is currently in alpha testing. For some ugly documentation, see [here](https://solid-adventure-n15ynzy.pages.github.io/). Alternatively, see an introduction in [this Jupyter notebook](https://github.com/statisticsnorway/ssbkonf/blob/main/docs/intro.ipynb).
igenius-adapters-sdk 4.0.0
The iGenius Software Development Kit to develop crystal datasource adapters.
Requires Python: >=3.8.1,<4.0.0

# iGenius Adapters SDK

This is the Software Development Kit for iGenius Web Connectors development.  
You can use our SDK in your project to be able to handle correctly the data structures that will be used by iGenius services to call your web connector adapter.
Further information about SDK can be found in the [official documentation](https://webconnectors.crystal.ai/sdk/latest/), instead check [What is a Web Connector](https://webconnectors.crystal.ai/docs/latest/) to know more 
about the data sharing with _Crystal_.

## Installation

With Poetry

```bash
poetry add igenius-adapters-sdk
```

With pip

```bash
pip install igenius-adapters-sdk
```

## Releases

See the [CHANGELOG.md](/CHANGELOG.md) file.

## License

All the content in this repository is licensed under the [Apache license, version 2.0](http://www.apache.org/licenses/LICENSE-2.0.txt). The full license text can be found in the [LICENSE](/LICENSE) file.
j40model 1.1.1
Base simulation and optimization modules for J40 tool

*** Copyright Notice ***

Justice 40 Tool (J40 Tool) Copyright (c) 2023, The Regents of the University
of California, through Lawrence Berkeley National Laboratory (subject to receipt ofany required approvals from the U.S. 
Dept. of Energy). All rights reserved.

If you have questions about your rights to use or distribute this software,
please contact Berkeley Lab's Intellectual Property Office at
IPO@lbl.gov.

NOTICE.  This Software was developed under funding from the U.S. Department
of Energy and the U.S. Government consequently retains certain rights.  As
such, the U.S. Government has been granted for itself and others acting on
its behalf a paid-up, nonexclusive, irrevocable, worldwide license in the
Software to reproduce, distribute copies to the public, prepare derivative 
works, and perform publicly and display publicly, and to permit others to do so.

***********************************************************************************

# J40 Tool model


## Prerequisites 
You need to run python 3.8 or higher. You can install the required packages with

    pip install -r requirements.txt
    next you should install any pyomo-compatible linear optimization solver
    for example, you can install glpk along with pyomo using conda:
    conda install -c conda-forge glpk
    

## Input Files

- The community files and geo files are published separately
StreamLoom 0.1.2
A modular and extensible Python framework for building, managing, and executing pipelines.

# StreamLoom

## Create reusable blocks and weave them into beautiful pipelines.

StreamLoom is a modular and extensible Python framework designed for building, managing, and executing pipelines. With its intuitive block-based structure, users can easily chain tasks and manage data flow, all while seamlessly switching between various executors and transports.

## Features

- **Atomic & Series Data Handling**: StreamLoom can process both singular atomic data and series data streams.

- **Multiple Executors**: Execute blocks using a variety of methods including Process, Async, Threads, Containers, and more.

- **Flexible Transports**: Switch between data transports like sockets, Kafka queues, files, etc., with ease.

- **Extensible & Modular Design**: Easily extend StreamLoom with custom blocks, executors, and transports.

## Installation

Install StreamLoom via pip:

```bash
pip install streamloom
```

## Quick Start

Here's a simple example to get started:

```python
from streamloom import AddBlock, Pipeline, ProcessExecutor, SocketTransport

# Define and connect blocks
add1 = AddBlock(x=10, y=20)
add2 = AddBlock(x=add1.result, y=100)

# Create pipeline with outputs
pipeline = Pipeline(blocks=[add1, add2], outputs={"final_sum": add2.result})

# Execute the pipeline
result = pipeline.execute()
print(result)  # Outputs: {final_sum: 130}
```

For more detailed examples and tutorials, check out our [documentation](documentation_link).

## Documentation

Detailed documentation is available [here](documentation_link).

## Contribution

We welcome contributions to StreamLoom! Please check out our [CONTRIBUTING.md](link_to_contributing_file) for guidelines.

## License

StreamLoom is licensed under the [MIT License](LICENSE).

## Acknowledgements

Special thanks to the community and contributors for their invaluable feedback and contributions.

## Contact

For queries or feedback, please contact [sanygeek@gmail.com](mailto:sanygeek@gmail.com).