semvec/pypi-packages huggingface dataset:
tracemate 0.1.1
🔍 Tracemate: A comprehensive tracing and logging toolkit for Python CLI applications.
License: MIT

# Tracemate :mag_right:

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_Automated logging setup for Python applications_

Tracemate is a Python library designed to make logging hassle-free. Just import it into your `main.py`, and Tracemate takes care of setting up logging for all your functions. It's lightweight, easy to integrate, and designed to have minimal impact on application performance.

![Tracemate Logo](assets/logo.png) <!-- replace with your actual logo -->

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## :rocket: Features

- **Automated Logging**: Just import Tracemate in your `main.py` and it will automatically set up logging for all your functions.
- **High Performance**: Designed for minimal performance overhead.
- **Asynchronous Support**: Full support for asynchronous Python applications.
- **Simple Integration**: Easy to integrate into existing projects without modifying your codebase.

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## :package: Installation

To install Tracemate, you can use pip:

```bash
pip install tracemate
```

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## :hammer_and_wrench: Usage

Here's how to get started:

Use this in your main.py (or equivalent) file to autonomously add logging and obtain logger and console objects from a centralized location. You can use setup_logging from multiple modules and it works fine.

```python
from tracemate import setup_logging, apply_logger_to_all_functions

backend_logger, ui_logger, console = setup_logging()
apply_logger_to_all_functions(backend_logger)
```

For a more comprehensive guide, please refer to the [Documentation](#-documentation).

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## 📖 Documentation

Detailed documentation is available on our [Wiki](https://github.com/alexfigueroa-solutions/Tracemate/wiki).

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## :handshake: Contributing

Contributions, issues, and feature requests are welcome! See [CONTRIBUTING.md](CONTRIBUTING.md) for how to get started.

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## :memo: License

This project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details.

---

## :telephone_receiver: Contact

Created by [@alexfigueroa-solutions](https://github.com/alexfigueroa-solutions) - feel free to reach out!
MultiPropReg 0.1.1
Image registration network using deformation field
License: MIT
Requires Python: >=3.5.5
Keywords: deformation,registration,imaging,mri

# MultiPropReg: Learning Deformable Image Registration from Optimization: Perspective, Modules, Bilevel Training and Beyond

**MultiPropReg** is a general purpose library for learning-based tools for alignment/registration, and more generally modelling with deformations.




## Pre-trained models

See list of pre-trained models available [here](models/pretrained_model_epoch400.ckpt).

## Training

If you would like to train your own model, you will likely need to customize some of the data-loading code in `MultiPropReg/datagenerators.py` for your own datasets and data formats. Training data can be in the NII, MGZ, or npz (numpy) format, and it's assumed that each npz file in your data list has a `vol` parameter, which points to the image data to be registered, and an optional `seg` variable, which points to a corresponding discrete segmentation (for semi-supervised learning). The training data we provide has been skull removed and affine registered, if you need to process your own data, you can use the tools we provide to do the data processing.

We have defined three training modes:  
`train_src_arcitecture.py`: architecture-search training;  
`train_src_hyper.py`: hyper parameter-search training (without segmentation data);  
`train_src_hyper_seg.py`: hyper parameter-search training (with segmentation data);

Detailed usage instructions can be found in scripts/README.md.


## Testing

If you want to test the training results, you can use our example:

```
python MultiPropReg/scripts/test.py  \
        --model MultiPropReg/models/pretrained_model_epoch400.ckpt \
        --atlas MultiPropReg/data/1yr_t1_atlas.nii  \
        --atlas_seg  /path/to/atlas_seg_file  \
        --image_path MultiPropReg/data/divided_dataset/Test_Affine  \
        --result_save MultiPropReg/output_test_400  \
        --gpu 0
        --hyper_parameters
```

If you have a dataset with segmented images, you have the option to manually specify the `atlas_seg` parameter. Additionally, we offer a selection of pre-trained models for a rapid assessment of registration outcomes.
Detailed usage instructions can be found in scripts/README.md.