Metadata-Version: 2.1
Name: pitchcontext
Version: 0.1.7
Summary: Library for melody analysis based on pitch context vectors.
Author: Peter van Kranenburg
Author-email: peter.van.kranenburg@meertens.knaw.nl
Requires-Python: >=3.8, !=2.7.*, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*, !=3.7.*
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Requires-Dist: IPython (>=7.19,<8.0)
Requires-Dist: matplotlib (>=3.3,<4.0)
Requires-Dist: music21 (>=8.0,<9.0)
Requires-Dist: numpy (>=1.19,<2.0)
Requires-Dist: seaborn (>=0.12.1,<0.13.0)
Description-Content-Type: text/markdown

---
component-id: pitchcontext
name: pitchcontext
description: Python module for melody analysis based on pitch context vectors.
type: Library
release-date: 2023-03-15
release-number: 0.1.4
work-package: WP3
pilot: TUNES
keywords:
  - melody
  - pitch analysis
changelog:
licence:
release link:
--- 


# pitchcontext
Python module for melody analysis based on pitch context vectors.

## Prerequisites:
- lilypond installed and in command line path
- convert (ImageMagick) installed and in command line path
- kernfiles and corresponding .json files with melodic features

## Installation
The latest release of the pitchcontext module can be installed from pypi:
```
$ pip install pitchcontext
```

The development version can be installed by cloning the repository and by using the provided pyproject.toml and poetry. In root of the rep do:
```
$ poetry install
```
This creates a virtual environment with pitchcontext installed.

## Examples
Requires a Python3 environment with both pitchcontext and streamlit installed.
Two examples are provided:
- apps/st_dissonance.py
- apps/st_novelty.py

To run:
```
$ streamlit run st_dissonance.py -- -krnpath <path_to_kern_files> -jsonpath <path_to_json_files>
```

