semvec/pypi-packages huggingface dataset:
laser-core 1.0.0
Core functionality for the Light Agent Spatial modeling for ERadication toolkit (LASER).
Requires Python: <3.14,>=3.9
Keywords: spatial modeling, disease eradication, agent-based modeling

========
Overview
========

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    * - docs
      - |docs|
    * - tests
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      - | |version| |wheel| |supported-versions| |supported-implementations|
        | |commits-since|
.. |docs| image:: https://readthedocs.org/projects/idmlaser/badge/?style=flat
    :target: https://docs.idmod.org/projects/laser/en/latest/
    :alt: Documentation Status

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    :alt: GitHub Actions Build Status
    :target: https://github.com/InstituteforDiseaseModeling/laser/actions

.. |codecov| image:: https://codecov.io/gh/InstituteforDiseaseModeling/laser/branch/main/graphs/badge.svg?branch=main
    :alt: Coverage Status
    :target: https://app.codecov.io/github/InstituteforDiseaseModeling/laser

.. |version| image:: https://img.shields.io/pypi/v/laser-core.svg
    :alt: PyPI Package latest release
    :target: https://pypi.org/project/laser-core/

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    :target: https://pypi.org/project/laser-core/

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    :target: https://pypi.org/project/laser-core/

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    :alt: Supported implementations
    :target: https://pypi.org/project/laser-core/

.. |commits-since| image:: https://img.shields.io/github/commits-since/InstituteforDiseaseModeling/laser/v1.0.0.svg
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    :target: https://github.com/InstituteforDiseaseModeling/laser/compare/v1.0.0...main



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Light Agent Spatial modeling for ERadication.

* Free software: MIT license

Installation
============

::

    pip install laser-core

You can also install the in-development version with::

    pip install https://github.com/InstituteforDiseaseModeling/laser/archive/main.zip


Documentation
=============


https://docs.idmod.org/projects/laser/en/latest/


Development
===========

To run all the tests run::

    tox

Note, to combine the coverage data from all the tox environments run:

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    :widths: 10 90
    :stub-columns: 1

    - - Windows
      - ::

            set PYTEST_ADDOPTS=--cov-append
            tox

    - - Other
      - ::

            PYTEST_ADDOPTS=--cov-append tox
wagtail-block-fields 0.2.0
Allows using StructBlock and ListBlock as model fields
Requires Python: >=3.10

# wagtail-block-fields

Use Wagtail's `StructBlock` and `ListBlock` as standalone model fields.

Just like `StreamField` wraps `StreamBlock`, this package provides:
- **`StructField`** - wraps a `StructBlock`
- **`ListField`** - wraps a `ListBlock`

## Installation

```bash
pip install wagtail-block-fields
```

## Usage

### StructField

```python
from wagtail.blocks import CharBlock, StructBlock
from wagtail_block_fields import StructField

class AddressBlock(StructBlock):
    street = CharBlock()
    city = CharBlock()
    postal_code = CharBlock()

class MyPage(Page):
    address = StructField(AddressBlock())
    
    contact = StructField([
        ('email', CharBlock()),
        ('phone', CharBlock()),
    ])
```

### ListField

```python
from wagtail.blocks import CharBlock
from wagtail_block_fields import ListField

class MyPage(Page):
    tags = ListField(CharBlock())
    categories = ListField(CharBlock(), min_num=1, max_num=5)
    addresses = ListField(AddressBlock())
```

### In templates

```html
<p>{{ page.address.street }}, {{ page.address.city }}</p>

<ul>
{% for tag in page.tags %}
    <li>{{ tag }}</li>
{% endfor %}
</ul>

{% for address in page.addresses %}
    <p>{{ address.street }}, {{ address.city }}</p>
{% endfor %}
```

## Why?

Sometimes you need structured JSON data in a single field without the complexity of StreamField. These fields:

- Store data as JSON in a single database column
- Provide full Wagtail admin editing UI
- Support validation, search indexing, and reference extraction
- Work with migrations just like StreamField

## License

MIT
nova-galaxy 0.11.6
Utilties for accessing the ORNL Galaxy instance
Requires Python: <3.14,>=3.10
Keywords: Galaxy, NOVA, python

# nova-galaxy
=======================

# Introduction

This is the repository for the nova-galaxy project. This project creates a python package that makes it easier to work with the ORNL Galaxy instance.

## Documentation

A user guide, examples, and a full API for this package can be found at https://nova-galaxy.readthedocs.io/en/stable/.

## Installation
You can install this package directly with

```commandline
pip install nova-galaxy
```

or with [Pixi](https://pixi.sh/latest/):


```commandline
pixi add --pypi nova-galaxy
```

## Formatting
```commandline
pixi run ruff format
```

## Linting
```commandline
pixi run ruff check
pixi run mypy .
```

## Testing
You can run the tests for this package with the following command from the base directory:
```commandline
NOVA_GALAXY_TEST_GALAXY_URL=galaxy-url NOVA_GALAXY_TEST_GALAXY_KEY=key pixi run pytest tests/
```
with `NOVA_GALAXY_TEST_GALAXY_URL` being the url of your Galaxy instance and `NOVA_GALAXY_TEST_GALAXY_KEY` being your
Galaxy API Key.

To run tests with coverage (include the above environment variables):
```commandline
pixi run coverage run
pixi run coverage report
```

## CI/CD in GitHub

Take a look at the [`.github/workflows`](.github/workflows) folder.
Actions to lint and test your code will run automatically on each commit.
The action for building and releasing this package needs to be triggered manually.

### Publishing docs to readthedocs.io

This repo has a [webhook](https://github.com/nova-model/nova-galaxy/settings/hooks) that automatically triggers documentation builds on readthedocs.