Awesome
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 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)
import anchor
bm = anchor.BayesianModalities()
modalities = bm.fit_transform(data)
If you want to see all the intermediate Bayes factors, then you can do:
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.