semvec/pypi-packages huggingface dataset:
cn2int 0.2.3
Conversion bettwen Chinese number and integer/float
Requires Python: >=3

# cn2int: Chinese Numerals To Int/Float

- Conversion bettwen Chinese numerals and integer/float.
- Conversion bettwen Roman numerals and integer.

cn2int does the format checking during conversion, it can raise `ValueError`, `OverflowError` and `KeyError`. Before doing conversion, you should ensure the following patterns are met.

- `roman2int`: "[IVXLCDM]+"
- `chinese2int`: "[正负負]?[〇一二三四五六七八九十百千万亿零壹贰叁肆伍陆柒捌玖拾佰仟萬億两]+"
- `chinese2float`: "[正负負]?[〇一二三四五六七八九十百千万亿零壹贰叁肆伍陆柒捌玖拾佰仟萬億两点點]+"

## Usage

```python
import cn2int as c2i

# chinese/roman/arab => integer
c2i.convert2int("两亿零六千五")

# chinese => integer
c2i.chinese2int("二十三亿零六百三十万零七十八")

# chinese => float
c2i.chinese2float("二千三百零六万三千点七八")

# integer => chinese
c2i.int2chinese(2306300078)

# float => chinese
c2i.float2chinese(23063000.78)

# rooman => integer
c2i.roman2int("XVI")

# integer => rooman
c2i.int2roman(16)

# performance
c2i.performance()
```
abmap 0.1.4
AbMAP: Antibody Mutagenesis Augmented Processing
Requires Python: >=3.7

# AbMAP: Antibody Mutagenesis-Augmented Processing
![image](https://user-images.githubusercontent.com/6614489/235450484-2ad78557-0deb-43fb-ba8d-6fb570c3a052.png)

*This repository is a work in progress.*

This repository contains code and pre-trained model checkpoints for AbMAP, a Protein Language Model (PLM) customized for antibodies as featured in **Learning the Language of Antibody Hypervariability** ([_Singh, Im et al. 2023_](https://www.biorxiv.org/content/10.1101/2023.04.26.538476)). AbMAP leverages information from foundational PLMs as well as antibody structure and function, offering a multi-functional tool useful for predicting structure, functional properties, and analyzing B-cell repertoires.

### Installation
AbMAP relies on ANARCI to assign IMGT labels to antibody sequences. Please see the [ANARCI](https://github.com/oxpig/ANARCI/blob/master/INSTALL) repo or run the following in a new conda environment: 
```bash
conda install -c biocore hmmer # Can also install using `brew/port/apt/yum install hmmer`
git clone https://github.com/oxpig/ANARCI.git
cd ANARCI
python setup.py install
```

Then install abmap using:
```bash
pip install abmap  # (recommended) latest release from PyPI 
pip install git+https://github.com/rs239/ablm.git  # the live main branch
```

### Usage:
After installation, AbMAP can be easily imported into your python projects or run from the command line. Please see [examples/demo.ipynb](examples/demo.ipynb/) for common use cases. Instructions for running via CLI are below.

## Command Line Usage 
*Instructions In Progress*

### Augment
Given a sequence, generate a foundational PLM embedding augmented with in-silico mutagenesis and CDR isolation.
### Train
Given a dataset of labeled pairs of sequences and their augmented embeddings, train the AbMAP model on downstream prediction tasks.
### Embed
Given fasta sequences and a pre-trained AbMAP model, generate their AbMAP embeddings (fixed or variable).

Please provide feedback on the issues page or by opening a pull request. If AbMAP is useful in your work, please consider citing our [bioRxiv preprint](https://www.biorxiv.org/content/10.1101/2023.04.26.538476).