[](https://travis-ci.org/YeoLab/anchor)[](https://pypi.python.org/pypi/anchor)[](https://codecov.io/gh/YeoLab/anchor)
## What is `anchor`?
Anchor is a python package to find unimodal, bimodal, and multimodal features in any data that is normalized between 0 and 1, for example alternative splicing or other percent-based units.
* Free software: BSD license
* Documentation: https://YeoLab.github.io/anchor
## 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 scipy numpy matplotlib seaborn
```
### Stable (recommended)
To install this code from the Python Package Index, you'll need to specify ``anchor-bio`` (``anchor`` was already taken - boo).
```
pip install anchor-bio
```
### Bleeding-edge (for the brave)
If you want the latest and greatest version, clone this github repository and use `pip` to install
```
git clone git@github.com:YeoLab/anchor
cd anchor
pip install . # The "." means "install *this*, the folder where I am now"
```
## Usage
`anchor` was structured like `scikit-learn`, where if you want the "final
answer" of your estimator, you use `fit_transform()`, but if you want to see the
intermediates, you use `fit()`.
If you want the modality assignments for your data, first make sure that you
have a `pandas.DataFrame`, here it is called `data`, in the format (samples,
features). This uses a log2 Bayes Factor cutoff of 5, and the default Beta
distribution parameterizations (shown [here]())
```python
import anchor
bm = anchor.BayesianModalities()
modalities = bm.fit_transform(data)
```
If you want to see all the intermediate Bayes factors, then you can do:
```python
import anchor
bm = anchor.BayesianModalities()
bayes_factors = bm.fit(data)
```
## History
### 1.1.1 (2017-06-29)
- In `infotheory.binify`, round the decimal numbers before they are written as strings
### 1.0.1 (2017-06-28)
- Documentation and build fixes
### 1.0.0 (2017-06-28)
* Updated to Python 3.5, 3.6
### 0.1.0 (2015-07-08)
* First release on PyPI.
