semvec/pypi-packages huggingface dataset:
yzip 0.1.0
A compact Python library for archiving and extracting ZIP files.
Requires Python: >=3.8

# yzip

![Zip Archiver Logo](https://img.shields.io/badge/yzip%20-v0.1.0-blue?style=for-the-badge&logo=python)  
[![PyPI version](https://badge.fury.io/py/yzip.svg)](https://badge.fury.io/py/yzip)  
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)  
[![Python Version](https://img.shields.io/badge/python-3.8%2B-blue.svg)](https://www.python.org/downloads/)  

A compact and easy-to-use Python library for archiving files and directories into ZIP files, as well as extracting ZIP archives. Built with standard Python libraries—no external dependencies required! 🚀

## Features ✨
- **Archive Files & Directories**: Recursively zip files or entire folders with customizable compression.
- **Extract ZIPs**: Easily unpack archives to any directory.
- **Lightweight**: Uses only `zipfile` and `os` from the standard library.
- **Flexible Compression**: Supports various ZIP compression methods (e.g., DEFLATED, STORED).
- **Cross-Platform**: Works on Windows, macOS, and Linux.

## Installation 📦
#### Install via pip from PyPI:

```bash
pip install yzip
```

## Quick Start ⚡
### Archiving Files
#### Create an instance of archiver and use the archive method:
```Python
from yzip import archiver

zip = archiver()  # Default compression: ZIP_DEFLATED

# Archive a file and a directory
zip.archive('my_archive.zip', ['example.txt', 'my_folder/'])
```
### Extracting Archives
#### Use the extract method to unpack:
```Python
from yzip import archiver

zip = archiver()

# Extract to current directory
zip.extract('my_archive.zip')

# Or to a specific folder
zip.extract('my_archive.zip', extract_to='output/')
```

### Custom Compression
#### Specify compression during initialization:
```Python
import zipfile
from yzip import archiver

zip = archiver(compression=zipfile.ZIP_STORED)  # No compression
zip.archive('fast_archive.zip', ['large_file.bin'])
```
## API Reference 📖
### Class: archiver
- `__init__(self, compression: int = zipfile.ZIP_DEFLATED)`: Initializes with optional compression type.
- `archive(self, zip_path: str, paths: list[str]) -> None`: Archives files/directories to a ZIP.
- `extract(self, zip_path: str, extract_to: str = '.') -> None`: Extracts ZIP to a directory.

## Error Handling ⚠️
Wrap calls in `try-except` for robustness:
```Python
try:
    archiver.archive('archive.zip', ['nonexistent.txt'])
except FileNotFoundError as e:
    print(f"Error: {e}")
```
## License 📄
This project is licensed under the MIT License - see the LICENSE file for details.
sborf 0.0.3
A neural network that predicts and designs alternative DNA encodings for proteins, aiming to fine-tune their expression

![logo](docs/_static/logo_small.png)

---

## About sborf



## Installation

Package installation should only take a few minutes with any of these methods (pip, source).

### Installing sborf with pip:

We suggest to create a local conda environment where to install sborf. it can be done with:

```sh
conda create -n sborf
```
and activated with

```sh
conda activate sborf
```

or

```sh
source activate sborf
```

We also suggest to install pytorch separately following the instructions from https://pytorch.org/get-started/locally/

```sh
pip install sborf
```

The procedure will install sborf in your computer.

### Installing sborf from source:

If you want to install sborf from this repository, you need to install the dependencies first.
First, install [PyTorch](https://pytorch.org/get-started/locally/) separately following the instructions from https://pytorch.org/get-started/locally/.

Then install the other required libraries:

```sh
pip install numpy scikit-learn requests
```

Finally, you can clone the repository with the following command:

```sh
git clone https://github.com/grogdrinker/sborf/
```

## Usage

the pip installation will install a script called sborf_standalone that is directly usable from command line (at least on linux and mac. Most probably on windows as well if you use a conda environemnt).

### Using the standalone
The script can take a fasta file or a sequence as input and provide a prediction as output

```sh
sborf_standalone AWESAMEPRTEINSEQENCEASINPT
```

or, for multiple sequences, do

```sh
sborf_standalone fastaFile.fasta
```

To write the output in a file, do

```sh
sborf_standalone fastaFile.fasta -o outputFilePath
```

### Using sborf into a python script

sborf can be imported as a python module

```python
from sborf.run_prediction import predict
proteinSeq1 = "ASDASDASDASDASDASDDDDASD"
proteinSeq2 = "ASDADDDDDDDDDDDDDASDASDDDDASD"
proteinSeq2 = "ASDADFFFFFFFFFDDDDDDDDFFFFFFFFFASD"
inputSequences = {"ID1":proteinSeq1,"ID2":proteinSeq2,"ID3":proteinSeq3}

sborf_output = predict(inputSequences) # which is a dict containig the predictions

```


## Help

For bug reports, features addition and technical questions please contact gabriele.orlando@kuleuven.be
readlog 1.2.3
Read themo info from lammps output file or log file

## readlog

- A python code to read thermo info from the log file of LAMMPS output

### Installation 

```bash
git clone https://github.com/eastsheng/readlog.git
cd readlog
pip install .
# or
pip install readlog
```

### Requirements

- numpy
- pandas
- matplotlib

### Usage 

- run `plot_themo.py` in `demo` folder:

  ```bash
  python plot_thermo.py
  ```

- out:
- ![](./demo/imgs/PotEng.png)



### Fixed

- 2023-10-11: v1.2.2
  - [x] a great modification
- 2023-09-16: v1.2.1
  - [x] Fixed `UnicodeDecodeError: 'utf-8' codec can't decode` problem.
- 2023-06-20: v1.2.0

  - [x] Fixed a read error in the complete message frame under incomplete message;

  - [x] add `print_readlog` function;

  - [x] add `ReadRunTime`function.