semvec/pypi-packages huggingface dataset:
kodo.plugins.cover-test-context 0.1
A Coverage.py plugin module for adding test names as contexts
Requires Python: ~=3.9

[![licence badge]](/LICENCE.txt)
[![pipeline status]][pipeline report]


Coverage.py Test Context Plugin
===============================

**A Coverage.py plugin module for adding test names as contexts**

Coverage reports from *Coverage.py* can include line "contexts"; strings that are attached 
to lines of code that provide some sort of contextual information to a reader.

This package consists of a dynamic context plugin for *Coverage.py* that adds unit test 
names as contexts.


Usage
-----

Add the plugins package to your test dependencies, wherever they are:
  `kodo.plugins.cover-test-context`

Then add the plugin to your `coverage` configuration:

<details><summary><strong>pyproject.toml</strong></summary>

```toml
[tool.coverage.run]
plugins = [
  "kodo.plugins.cover_test_context",
]
```

</details>

<details><summary><strong>setup.cfg, tox.ini</strong></summary>

```ini
[coverage:run]
plugins =
  kodo.plugins.cover_test_context
```

</details>

<details><summary><strong>.coveragerc</strong></summary>

```ini
[run]
plugins =
  kodo.plugins.cover_test_context
```

</details>


---

[licence badge]:
  https://img.shields.io/badge/licence-MPL--2.0-orange.svg
  "Licence: MPL-2.0"

[pipeline status]:
  https://code.kodo.org.uk/dom/cover-plugin-test-context/badges/main/pipeline.svg

[pipeline report]:
  https://code.kodo.org.uk/dom/cover-plugin-test-context/-/pipelines/latest
disco_sound 0.0.2
DISCO Implements Sound Classification Obediently.

[![Generic badge](https://img.shields.io/badge/Contributions-Welcome-brightgreen.svg)](CONTRIBUTING.md)
<a href="https://github.com/psf/black"><img alt="Code style: black" src="https://img.shields.io/badge/code%20style-black-000000.svg"></a>

# DISCO Implements Sound Classification Obediently
This tool annotates sound files using neural networks. It uses a 1D architecture based on U-Net with additional
post-processing heuristics including a Hidden Markov Model.

DISCO is ideal for long streams of sound that need to be classified over time, producing output fully compatible with
The Cornell Lab of Ornithology's sound tool [RAVEN](https://ravensoundsoftware.com/). Work is currently underway to
annotate short samples of data with a single label. DISCO began jointly with the University of Montana's [Emlen Lab](https://hs.umt.edu/dbs/labs/emlen/)
as an annotator for Japanese and Taiwanese Rhinoceros Beetle courtship songs, but it now generalizes to any kind of
recording.

## Quickstart
Install requires python version >=3.8.
Install directly from git with pip:
```
pip install git+https://github.com/TravisWheelerLab/disco.git
```

DISCO contains subcommands useful for training and evaluating models on sound data. Deep learning projects typically follow a series of steps, and DISCO tries to emulate each of these steps:
`label`, `extract`, `shuffle`, `train`, `infer`. 

*NOTE*

Models were updated in the most recent version. Remove them with `rm ~/.cache/disco/*` before running any `disco` commands.

Learn more about how to use the tools provided in this package in the [wiki](https://github.com/TravisWheelerLab/disco/wiki).