[](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.
Metadata
Release files for anchor-bio 1.1.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| anchor-bio-1.1.1.tar.gz | 17.6 kB | Without provenance | Details |
Release files / anchor-bio-1.1.1.tar.gz
| Download URL | anchor-bio-1.1.1.tar.gz |
|---|---|
| Size | 17.6 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
27361fea80ffdeb4220b12dbde1c74baf22e208871d4056831387b2f78ac73f4
|
|
BLAKE2b-256 checksum How to use checksums |
3e4190cfb87bed69623713acedb6659a842185549bbbc717d3537277e0885352
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
Security for anchor-bio 1.1.1
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.