Metadata-Version: 2.1
Name: ptplot
Version: 0.2.0
Summary: Make beautiful plots of player tracking data
Home-page: https://github.com/AndrewRook/ptplot
Author: Andrew Schechtman-Rook
Author-email: footballastronomer@gmail.com
License: GNU General Public License v3
Keywords: ptplot
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Requires-Dist: bokeh (>=2.3.3)
Requires-Dist: numpy (>=1.19.5)
Requires-Dist: pandas (>=1.2.0)
Requires-Dist: patsy (>=0.5.1)
Requires-Dist: pip (>=20.3.3)
Requires-Dist: svgpathtools (>=1.4.1)
Requires-Dist: versioneer (>=0.19)
Provides-Extra: dev
Requires-Dist: black (>=20.8b1) ; extra == 'dev'
Requires-Dist: flake8 (>=3.8.4) ; extra == 'dev'
Requires-Dist: mypy (>=0.910) ; extra == 'dev'
Requires-Dist: notebook (>=6.2.0) ; extra == 'dev'
Requires-Dist: pytest (>=6.2.1) ; extra == 'dev'
Requires-Dist: pytest-cov (>=2.11.1) ; extra == 'dev'
Requires-Dist: tox (>=3.21.4) ; extra == 'dev'

# ptplot
`ptplot` makes it easy to turn player-tracking data into beautiful,
interactive visualizations — including animations! These visualizations can be used to guide
data exploration/analysis work, or to embed in webpages to share with
the world. 

![example animation](ptplot.gif)

## Installation

`ptplot` can be installed via pip:

```bash
$ pip install ptplot
```

It is strongly recommended that you install `ptplot` into a virtual
environment, such as with [`conda`](https://docs.conda.io/en/latest/):

```bash
[After installing conda]
$ conda create -n player_tracking python=3
$ conda activate player_tracking
$ pip install ptplot
```

You may wish to install some of `ptplot`'s dependencies
via conda, specifically `pandas` and `bokeh`:

```bash
[After installing conda]
$ conda create -n player_tracking python=3 pandas bokeh
$ conda activate player_tracking
$ pip install ptplot
```

## Getting Started

Making your first plot can be as simple as

```python
import pandas as pd

from bokeh.plotting import show

from ptplot import PTPlot
from ptplot.nfl import Field
from ptplot.plot import Positions

data = pd.read_csv("YOUR PLAYER TRACKING DATA")
plot = PTPlot(data) + Field() + Positions("X_COORDINATE_COLUMN", "Y_COORDINATE_COLUMN")
show(plot.draw())
```

For additional documentation and examples, check out the
notebooks in the `notebooks/` directory, which can be viewed
online with all of the plots correctly rendered via nbviewer:
1. [Basic Plots](https://nbviewer.jupyter.org/github/AndrewRook/ptplot/blob/main/notebooks/1-Basic_Plots.ipynb)
2. [Animations](https://nbviewer.jupyter.org/github/AndrewRook/ptplot/blob/main/notebooks/2-Animations.ipynb)
3. [Faceting](https://nbviewer.jupyter.org/github/AndrewRook/ptplot/blob/main/notebooks/3-Faceting.ipynb)
4. [Tips and Tricks](https://nbviewer.jupyter.org/github/AndrewRook/ptplot/blob/main/notebooks/4-Tips_and_Tricks.ipynb)

Additionally, layers within `ptplot` have docstrings with
more usage details. Those can be accessed either by reading the
source code or running `help([FUNCTION])` inside of Python. 

## Development Docs

See [here](development.md)

