semvec/pypi-packages huggingface dataset:
sta-converter 0.1.2
A CLI tool for converting .sta (MT940) files to CSV and merging CSV files.
License: MIT
Requires Python: <4.0,>=3.9
Keywords: mt940, sta, bank statement, converter, csv


# sta-converter

`sta-converter` is a command-line tool for converting `.sta` bank statement files (MT940 format) to CSV format and merging multiple CSVs into one for easier financial analysis and reporting.

## Features

- **Convert `.sta` files to CSV**: Extracts transaction data from `.sta` files into structured CSV files.
- **Batch Processing**: Automatically processes all `.sta` files in a given directory.
- **Merge CSVs**: Combines multiple CSV files into a single output file.

## Requirements

- Python 3.9+
- Dependencies managed by [Poetry](https://python-poetry.org/)

## Installation

1. Install Poetry if you haven't already:

   ```bash
   curl -sSL https://install.python-poetry.org | python3 -
   ```

2. Clone the repository:

   ```bash
   git clone https://github.com/bravelab/sta-converter.git
   cd sta-converter
   ```

3. Install the dependencies:

   ```bash
   poetry install
   ```

## Usage

Once installed, you can use the `sta-converter` CLI tool to convert `.sta` files to CSV and merge the results.

### Convert `.sta` Files to CSV

To convert `.sta` files into CSV files, use the following command:

```bash
poetry run sta_converter <input_dir> <merged_output_file>
```


- **Install Dependencies**:

  ```bash
  poetry install
  ```

- **Run the CLI**:

  ```bash
  poetry run sta_converter <input_dir> <merged_output_file>
  ```

## License

This project is licensed under the MIT License.

## Author

Developed by Mariusz Smenżyk. You can reach out to me at mariusz@bravelab.io.
# sta-converter
DMM-PyTorch 0.1
Debiased Mapping for Full-Reference Image Quality Assessment
License: MIT
Keywords: pytorch, Quality, Debiased, Full-reference, metric

# Debiased Mapping for Full-Reference Image Quality Assessment

This is the repository of paper [Debiased Mapping for Full-Reference Image Quality Assessment](https://arxiv.org/abs/2302.11464). 

### Highlights:
* The perception bias of existing deep-feature based FR-IQA measures is explored, which may cause inferior performance on misaligned features and restored content.
* We propose an SVD-based debiased mapping to mitigate the perception bias. Specifically, the SVs distance and base angle consistency are designed to capture and measure the feature distortion reliably.


### ====== PyTorch Implementation ======
**Installation:** 
- ```pip install DMM-PyTorch```

**Requirements:**  
- Python >= 3.6
- PyTorch >= 1.0

**Usage:** 
```python

from DMM_PyTorch import DMM
from torchvision import transforms
from PIL import Image

def prepare_PIL_Image(PIL_Image):
    msize = min(PIL_Image.size)
    if  msize>128:
        tar_size = max(int(msize/(1.0*48))*32,128)
        image =transforms.functional.resize(PIL_Image,tar_size)
    image = transforms.ToTensor()(image)
    return image.unsqueeze(0)

model = DMM().cuda()

ref_pth =  './Images/I04.BMP'  
dist_pth =  './Images/i04_24_2.bmp' 
  
ref =  prepare_PIL_Image(Image.open(ref_pth).convert("RGB")).cuda()
dist = prepare_PIL_Image(Image.open(dist_pth).convert("RGB")).cuda()

dmm_score = model(ref, dist)
print(dmm_score)
```
or

```bash
git clone https://github.com/Baoliang93/DMM
cd DMM_PyTorch
python DMM.py --ref <ref_path> --dist <dist_path>
```