semvec/pypi-packages huggingface dataset:
async-gsm-modem 0.1.2
An async GSM modem driver library
Requires Python: >=3.6
Keywords: gsm,modem,asyncio,async,lte,sms,text,usb,serial,phone,mobile,messaging

# async-gsm-modem

async-gsm-modem is YetAnother™ Python library for controlling an AT GSM modem using asyncio.



## Installation

```bash
pip3 install async-gsm-modem
```

## Usage

```python
import asyncio
from async_gsm_modem.quectel_ec25 import Modem

async def example():
  modem = Modem('/dev/ttyUSB2', 115200)
  await modem.connect()
  await modem.ping()
  for message in await modem.list_messages():
    print(message)
  await modem.close()

loop = asyncio.get_event_loop()
loop.run_until_complete(example())

```
## TODO

- Fully implement EC25 module
- Implement generic modem class
- MMS functionality
- Testing
- More examples
- Documentation
- Full typing

## Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.


## License
[Apache 2](https://choosealicense.com/licenses/apache-2.0/)
deepshm 0.0.1
A package for training and applying neural networks aimed to deal with genomic data
Requires Python: >=3.6

# DeepSHM
This repository implements the model from "Deep learning model of somatic hypermutation reveals importance of sequence context beyond targeting of AID and Polη hotspots" by Tang, Krantsevich & MacCarthy. Using DeepSHM you can simply use one of the provided models to make SHM-related predictions for your data, or you can train your own CNN model for any task that uses DNA/RNA one-hot encoded data as input.


## Instalation
### If you want to play with the model 
DeepSHM is on pypi, so it can be installed using pip:

    pip install deepshm

### If you are not familiar with python
You can also just download the deepshm_predict.zip archive and 0nce you extract it you can call the file deepshm_predict.py from the command line. Note that you still will need python installed as wells as several packages: tensorflow, numpy, scipy. deepshm_predict.py can be called in the following way:

    python deepshm_predict.py input.fasta -o output.csv -k 15

where the input fasta file is the required input and the path to the output file and the length of k-mer (5, 9, 15 and 21) are optional. Default value for the output file is output.csv and default k is 15.