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https://github.com/yeolab/bonvoyage
:triangular_ruler: Transform percentage-based units into a 2d space to evaluate changes in distribution with both magnitude and direction.
https://github.com/yeolab/bonvoyage
Last synced: 2 months ago
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:triangular_ruler: Transform percentage-based units into a 2d space to evaluate changes in distribution with both magnitude and direction.
- Host: GitHub
- URL: https://github.com/yeolab/bonvoyage
- Owner: YeoLab
- License: bsd-3-clause
- Created: 2015-11-20T20:13:09.000Z (about 9 years ago)
- Default Branch: master
- Last Pushed: 2017-06-29T14:31:07.000Z (over 7 years ago)
- Last Synced: 2024-08-02T16:44:02.073Z (6 months ago)
- Language: Python
- Homepage: http://yeolab.github.io/bonvoyage/
- Size: 8.35 MB
- Stars: 7
- Watchers: 38
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: HISTORY.rst
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Authors: AUTHORS.md
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README
![Bonvoyage logo: A bottle of champagne as the y=-x + 1 line on a Cartesian plane](https://raw.githubusercontent.com/YeoLab/bonvoyage/master/logo/v2/logo-128.png)
[![Build Status](https://travis-ci.org/YeoLab/bonvoyage.svg?branch=master)](https://travis-ci.org/YeoLab/bonvoyage)[![](https://img.shields.io/pypi/v/bonvoyage.svg)](https://pypi.python.org/pypi/bonvoyage)
## What is `bonvoyage`?
Transform percentage-based units into a 2d space to evaluate changes in distribution with both magnitude and direction.
* Free software: BSD license
* Documentation: https://yeolab.github.io/bonvoyage## Installation
To install `anchor`, we recommend using the
[Anaconda Python Distribution](http://anaconda.org/) and creating an
environment, so the `anchor` code and dependencies don't interfere with
anything else. Here is the command to create an environment:```
conda create -n anchor-env pandas numpy matplotlib seaborn scikit-learn
```### Stable (recommended)
To install this code from the Python Package Index, you can install on the
command line via `pip`:```
pip install bonvoyage
```### Bleeding-edge (for the brave)
To install this code, clone this github repository and use `pip` to install
git clone [email protected]:yeolab/bonvoyage
cd bonvoyage
pip install . # The "." means "install *this*, the folder where I am now"## Usage
To use `bonvoyage` to get waypoints, you want your `data` to be a `pandas`
DataFrame of shape (n_samples, n_features)```python
import bonvoyagewp = bonvoyage.Waypoints()
waypoints = wp.fit_transform(data)
````bonvoyage` is modeled after `scikit-learn` in is method of creating a
transforming object and then running `fit_transform()` to perform the computation.To plot the waypoints, use a `waypointplot`, which can do either `"scatter"` or
`"hex"` plot types. By default, `hexbin` plots are used:```python
import bonvoyagebonvoyage.waypointplot(waypoints)
```![Hexbin waypoints](figures/iPSC_hexbin.png)
You can also specify to use `scatter`:
```python
import bonvoyagebonvoyage.waypointplot(waypoints, kind='scatter')
```![Scatter waypoints](figures/iPSC_scatter.png)
To add color, give a series or other `groupby`-able object:
```python
import bonvoyagebonvoyage.waypointplot(waypoints, kind='scatter', features_groupby=modalities)
```![Scatter, colored by modality waypoints](figures/iPSC_scatter_modality.png)
## History
### 1.0.0 (2017-06-28)
* Added tests and examples
### 0.1.0 (2015-09-15)
* First release on PyPI.