semvec/pypi-packages huggingface dataset:
basenji2-pytorch 0.1.2
PyTorch implementation of Basenji2
Requires Python: >=3.9

# Basenji2 in PyTorch

This repo provides a PyTorch re-implementation of the Basenji2 model published in ["Cross-species regulatory sequence activity prediction"](https://doi.org/10.1371/journal.pcbi.1008050) by David Kelley. This implementation was checked by verifying that the Tensorflow and PyTorch version yielded the same output on random data. Small deviations were found, likely due to differences in the underlying algorithms used by Tensorflow and PyTorch (e.g. different matrix multiplication algorithms). In addition, [Qixiu Du kindly computed evaluation metrics](https://github.com/d-laub/basenji2-pytorch/issues/1) and found that the PyTorch re-implementation achieves competitive performance on real data, further validating the port.

## Installation

`pip install basenji2-pytorch`

## Usage

```python
import torch
from basenji2_pytorch import Basenji2, basenji2_params, basenji2_weights # or PLBasenji2 to also use training parameters from Kelley et al. 2020

# to use a headless model e.g. for transfer learning
# basenji2_params["model"].pop("head_human", None)

basenji2 = Basenji2(basenji2_params["model"])
basenji2.load_state_dict(torch.load(basenji2_weights()), strict=False)
```

- `basenji2_params` is a dictionary of both training and model parameters matching the implementation in Kelley et al. 2020
- `basenji2_weights` is a function that uses [pooch](https://github.com/fatiando/pooch) to download weights from Zenodo and return the path as a string.
- `Basenji2` is a PyTorch nn.Module that can be initialized from the model parameters of `basenji2_params`
- `PLBasenji2` is a PyTorch Lightning module that can be initialized from `basenji2_params` to match both the training and architectural parameters of Basenji2
dnngior 0.0.5
build high-quality genome-scale metabolic model by using a deep neural network to guide gapfilling
Requires Python: <3.12,>=3.9

# A novel way to gapfill metabolic models

## Installation instructions

To run the `dnngior` gapfiller, the [Gurobi](https://www.gurobi.com/) solver is mandatory.

```bash
pip install gurobipy
```

To use `gurobi`, you need a [license](https://www.gurobi.com/downloads/). If you are an acedemic, you may get a license for free.

Once you have successfully installed `gurobi`, you are ready to install the `dnngior` gapfiller.

```bash
pip install dnngior
```

Optionally, you may need to also get [Tensorflow](https://www.tensorflow.org/install) (or through [conda](https://anaconda.org/conda-forge/tensorflow)) 
in case you would like to use the `NN_Trainer`.

## How to use

Gapfilling models is done using the `Gapfill` class:
```python
import dnngior.gapfill_class.Gapfill  
Gapfill(path_to_model)
```

You may find examples of gap-filling a genome scale reconstruction (GEM) with `dnngior` with a complete or a defined medium in this [example notebook](tutorials/gapfilling_example.ipynb). `dnngior` can gapfill both ModelSEED and BiGG models, to gapfill BiGG models you need to specify `modeltype`. 

```python
Gapfill(path_to_BiGG_model, modeltype='BiGG')
```

## Custom Networks

By default `dnngior` uses an universally trained network capable of accurate predictions under most circumstances. If desired, it is possible to change the Neural Network you want to use during gapfilling:

```python
Gapfill(path_to_model, trainedNNPath=path_to_NN)
```

You can train your own Neural Network following this tutorial: [example training NN](tutorials/NN_training_example.ipynb).

Alternatively you can find additional custom Neural Networks for several taxonomic groups: [Custom Networks](docs/NN/custom_networks/). Upon request additional specially trained networks can be made available for specific biomes or taxonomic groups.


## License


Please see [License](LICENSE)


## Cite

For more about the methods used in this library and in case you are using it, please cite:
Boer, M.D., Melkonian, C., Zafeiropoulos, H., Haas, A.F., Garza, D.R. and Dutilh, B.E., 2024. [Improving genome-scale metabolic models of incomplete genomes with deep learning](https://doi.org/10.1016/j.isci.2024.111349). iScience, 27(12).
image-processing-vosaito 0.0.1
Image processing package using skimage. This project belongs to Karina Tiemi Kato. E-mail:karinatkato@gmail.com.
Requires Python: >=3.5

# Image-processing-package-vosaito

## Description.

Test version - Image processing. This project belongs to Karina Tiemi Kato, Tech Lead, Machine Learning Engineer, Data Scientist Specialist. This package is a demo for simulation of upload on the Pypi and Test Pypi website, and it's from class of the Bootcamp Python. E-mail:karinatkato@gmail.com.

The package image_processing_vosaito is used to:

Processing:
- Histogram matching
- Structural similarity
- Resize image

Utils:
- Read image
- Save image
- Plot image
- Plot result
- Plot histogram

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install package_name

```bash
pip install image_processing_vosaito
```

## Usage example

```python
from image_processing_vosaito import combination
combination.find_difference(image1, image2)
```

## Author (maintainer)
Victor Orui Saito

Projeto: Pacote de Processamento de Imagens\
Autora do Projeto: Karina Tiemi Kato\
PRO - Digital Innovation One\
https://github.com/tiemi/image-processing-package

Tecnologia: Python\
Data: 05/03/2025

## License
[MIT](https://choosealicense.com/licenses/mit/)