semvec/pypi-packages huggingface dataset:
factory-djoy 2.2.2
Factories for Django, creating valid model instances every time.
License: MIT
Requires Python: >=3.7


.. image:: https://img.shields.io/github/workflow/status/jamescooke/factory_djoy/Build
    :alt: GitHub Workflow Status
    :target: https://github.com/jamescooke/factory_djoy/actions?query=branch%3Amaster

.. image:: https://img.shields.io/readthedocs/factory-djoy.svg
    :alt: Read the Docs
    :target: https://factory-djoy.readthedocs.io/

.. image:: https://img.shields.io/pypi/v/factory-djoy.svg
    :alt: PyPI version
    :target: https://pypi.org/project/factory-djoy/

.. image:: https://img.shields.io/pypi/pyversions/factory-djoy.svg
    :alt: Python version
    :target: https://pypi.org/project/factory-djoy/

.. image:: https://img.shields.io/pypi/djversions/factory-djoy
    :alt: PyPI - Django Version
    :target: https://pypi.org/project/factory-djoy/

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    :alt: factory-djoy is licensed under the MIT License
    :target: https://raw.githubusercontent.com/jamescooke/factory_djoy/master/LICENSE

Factory Djoy
============

    Factories for Django, creating valid model instances every time.

Factory Djoy provides two simple classes, ``UserFactory`` and
``CleanModelFactory``, which wrap Factory Boy. They call ``full_clean()`` when
creating Django model instances to ensure that only valid data enters your
Django database.


Compatibility
-------------

Factory Djoy is compatible with:

* Django 2.2, 3.0 and 3.1

* Python 3 (3.7, 3.8, 3.9, 3.10, 3.11)

* Factory Boy version 2.11 or greater.


Resources
---------

* `Documentation on ReadTheDocs <https://factory-djoy.readthedocs.io/>`_

* `Package on PyPI <https://pypi.python.org/pypi/factory_djoy>`_

* `Source code on GitHub <https://github.com/jamescooke/factory_djoy>`_

* `Licensed on MIT <https://raw.githubusercontent.com/jamescooke/factory_djoy/master/LICENSE>`_

* `Changelog <https://github.com/jamescooke/factory_djoy/blob/master/CHANGELOG.rst>`_
gameoflife-ndimage 0.1.3
Quick simulation of arbitrary rulesets for nearest-neighbour cellular automata. Uses scipy.ndimage.correlate, and can export videos via ffmpeg-python.
License: MIT
Requires Python: >=3.10,<4.0
Keywords: gameoflife,cellular,automata,scipy

# Cellular Automata Simulator

Uses `numpy` and `scipy.ndimage` to quickly simulate arbitrary rulesets for nearest-neighbours cellular automata. Can
generate videos and images of the results via `ffmpeg-python` and `pillow`.

### Usage example:

```python
import gameoflife_ndimage.simulation as sim
from gameoflife_ndimage.video import Recorder

if __name__ == '__main__':
    rules = sim.Rules2D.classic()
    size = (256, 256)
    draw_params = sim.DrawParams(dead_color=[0, 0, 0], alive_color=[255, 255, 255], resize_factor=4)

    state = sim.State2D.random(rules, size)
    input_wh = tuple(a * draw_params.resize_factor for a in state.wh)
    recorder = Recorder(
        framerate=5,
        input_wh=input_wh,
        output_path="output/gol_classic_{}x{}_from_random.mp4".format(*size),
    )
    state.run_and_record(100, draw_params, recorder)
    recorder.close()
```