semvec/pypi-packages huggingface dataset:
pytorch-eo 2025.4.3
Deep Learning for Earth Observation
Requires Python: >=3.12

# Pytorch EO

Deep Learning for Earth Observation applications and research.

## Installation

```
pip install pytorch-eo
```

## Examples

Learn by doing with our [examples](https://github.com/earthpulse/pytorch_eo/tree/main/examples).

- [EuroSAT](examples/eurosat.ipynb).
- [UCMerced](examples/ucmerced.ipynb) Land Use Dataset.
- [BigEarthNet](examples/big_earth_net.ipynb).
- [SEN12FLOODs](examples/sen12floods.ipynb).

### Tutorials

Learn how to build with Pytorch EO with our [tutorials](https://github.com/earthpulse/pytorch_eo/tree/main/tutorials).

- Learn about [data loading](tutorials/00_data_loading.ipynb) is Pytorch EO.
- Learn about [data augmentation](tutorials/00_data_augmentation.ipynb) is Pytorch EO.
- Learn how to [create datasets](tutorials/02_creating_datasets.ipynb) with Pytorch EO.
- Learn about training models with our [tasks](tutorials/03_tasks.ipynb).

## Challenges

PytorchEO has been used in the following challenges:

- [EUROAVIA](./challenges/euroavia_hackathon_21) Mission: European Students Space Hackathon, 2021.
- [On Cloud N](./challenges/OnCloudN): Cloud Cover Detection Challenge (DrivenData, 2021).
- [Solar Panel Detection](./challenges/solar_panel_detection): Solar Panel Detection Using Sentinel-2 (Solafune, 2023).

<!-- ### Build your own Datasets

Using SCAN you can annotate your own data and access it directly through Pytorch EO. -->

<!-- ## Research

Pytorch EO can be a useful tool for research:

- Flexibility: build and experiment with new models for EO applications.
- Reproducibility: use same data splits and random seeds to compare with others.

See the [examples](https://github.com/earthpulse/pytorch_eo/tree/main/examples).

## Production

Pytorch EO was built with production in mind from the beginning:

- Optimize model for production.
- Export models to torchscript.
- Upload models to our Models Universe
- Use models directly through SPAI

See the [examples](https://github.com/earthpulse/pytorch_eo/tree/main/examples). -->

<!-- ## Documentation

Read our [docs](https://earthpulse.github.io/pytorch_eo/). -->

## Contributing

Read the [CONTRIBUTING](https://github.com/earthpulse/pytorch_eo/blob/main/CONTRIBUTING.md) guide.
TFBind 0.6.0
Predict whether the transcription factor protein sequence binds to the DNA gene sequence
Requires Python: >=3.11

# TFBind
Predict whether the transcription factor protein sequence binds to the DNA gene sequence

# Dependencies

TFBind works under Python 3.11.4

The required dependencies for TFBind are as follows：

python==3.11.4

torch==2.0.1

numpy

pandas==2.1.1

scikit-learn

# Input
TFBind Two files are required for input, one is the amino acid protein sequence of the transcription factor, and the other is the DNA nucleotide sequence. The protein sequence does not exceed 1451bp, and the DNA sequence does not exceed 150bp.

# Output
The output is 1 and 0. 1 represents the potential combination possibility in model calculation, and 0 represents the potential non-combination possibility.

# Cite
Liu et al., 2025. Deep Neural Network-Mining of Rice Drought-Responsive TF-TAG Modules by a Combinatorial Analysis of ATAC-Seq and RNA-Seq. First published: 31 March 2025 https://doi.org/10.1111/pce.15489.
meshroom 0.3.4
a commandline Cybersecurity Mesh Assistant
Requires Python: <4.0,>=3.10

<p align="center">
<img src="docs/logo.svg" width=170>
</p>

# Meshroom, the Cybersecurity Mesh Assistant



![release](https://img.shields.io/github/v/release/opencybersecurityalliance/meshroom?)
![build](https://img.shields.io/github/actions/workflow/status/opencybersecurityalliance/meshroom/pytest.yml?branch=master)
![MIT license](https://img.shields.io/github/license/opencybersecurityalliance/meshroom)
[![Python 3.10](https://img.shields.io/badge/python-3.10-blue.svg)](https://www.python.org/downloads/release/python-3100/)
[![Python 3.11](https://img.shields.io/badge/python-3.11-blue.svg)](https://www.python.org/downloads/release/python-3110/)
[![Python 3.12](https://img.shields.io/badge/python-3.12-blue.svg)](https://www.python.org/downloads/release/python-3120/)
[![Python 3.12](https://img.shields.io/badge/python-3.13-blue.svg)](https://www.python.org/downloads/release/python-3130/)

A command-line tool to build and manage Cybersecurity Mesh Architectures (CSMA), initiated by the OXA sub-project under the [Open Cybersecurity Alliance (OCA)](https://opencybersecurityalliance.org/)

See [presentation slides](https://opencybersecurityalliance.github.io/meshroom/presentation-slides.pdf) for in-depth introduction, and [https://opencybersecurityalliance.github.io/meshroom](https://opencybersecurityalliance.github.io/meshroom) for public documentation.

I'm python-based, install me via pip using

```bash
pip install meshroom
```

### What is CSMA ?

A Cybersecurity Mesh Architecture is a graph of interoperated cybersecurity services, each fulfilling a specific functional need (SIEM, EDR, EASM, XDR, TIP, *etc*). Adopting Meshroom's philosophy means promoting an interconnected ecosystem of high-quality products with specialized scopes rather than a captive all-in-one solution.

![CSMA](docs/img/graph.svg)



### Where to start ?

Run

```bash
meshroom --help
```

or browse the documentation to start building meshes.

### Autocompletion

On Linux, you can enable meshroom's autocompletion in your terminal by running

```bash
eval "$(_MESHROOM_COMPLETE=bash_source meshroom)"
```

or adding it to your `.bashrc`