semvec/pypi-packages huggingface dataset:
spectralLayersPyTorch 0.989
PyTorch NN based trainable spectral linear layers
License: MIT

## SpectralLayersPyTorch
Trainable linear spectral layers for PyTorch

Implements trainable spectral layers for PyTorch that can be initialized as 1-D & 2-D DCT and DFT transformations as shown in [paper](https://arxiv.org/abs/1911.05045).

## Install
The package can be installed as follows:

    pip install spectral
    
or

    pip install git+https://github.com/NarayanSchuetz/SpectralLayersPyTorch.git
    

## Attribution

```
@misc{alberti2019trainable,
      title={Trainable Spectrally Initializable Matrix Transformations in Convolutional Neural Networks}, 
      author={Michele Alberti and Angela Botros and Narayan Schuez and Rolf Ingold and Marcus Liwicki and Mathias Seuret},
      year={2019},
      eprint={1911.05045},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
   }
```
yamlfu 0.1.0
Create the skeleton for a python package/project
License: MIT

# YAML Fu

YAML Fu is a YAML templating tool

![PyPI - License](https://img.shields.io/pypi/l/yamlfu)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/yamlfu)
![PyPI](https://img.shields.io/pypi/v/yamlfu)
![PyPI - Downloads](https://img.shields.io/pypi/dm/yamlfu)
![GitHub Workflow Status](https://img.shields.io/github/workflow/status/joaompinto/yamlfu/Test)

## About

This package provides a command line utility and a Python library that generate YAML content fom processing yamlfu documents.

## Requirements
Python 3.6+


## Install

```bash
pip install yamlfu
```

## How to use (command ine tool)
```bash
yamlfu input.yaml
```

You can view examples of the templating capabilities at:
https://joaompinto.github.io/yamlfu/
MFD-Floods 0.1.14
A python script to modelate hidrologic behavior of downstream drainpaths
License: MIT
Keywords: mfdfloods mfd flood drainpaths hidrology downstreams multiple flow direction

# MFD Floods
A python script to model hidrologic behavior of downstream drainpaths. With a DTM cover of your study area you can define longitude and latitude (in the DTM distance unit) as a start point and an income flow to see how the water will flood the territory.

## Installation
With pip ```pip install mfdfloods```

From source ```python setup.py install```

## Dependencies
The script requires GDAL installed on your system and python-gdal as a python dependency.

To install GDAL execute `apt install gdal-bin libgdal-dev`.

## Test
Execute test.py from inside the folder to test the algorithm.

The test.py is a script that call the class MFD and execute its modelization with the datasource from the `data/` folder. There you have to place your GeoJSON files with the modelized line geometry.

`python test.py <area::string> <X::float> <Y::float> <break_flow::int> <base_flow::int> <break_time::int> [cellsize::int]{5} [radius::int]{2000}`

Arguments:

1. **Area** is the file on the `data/` folder with your DTM in GeoTiff format.
2. **X** is the longitude in your reference dtm distance units.
3. **Y** is the latitude in your reference dtm distance units.
4. **break_flow** is the start income flow.
5. **base_flow** is the base income flow after the initial pic.
6. **break_time** is the time that has to pass in seconds to go from the break_flow to the base_flow.
7. **cellsize** is the size of your DTM cellsize.
8. **radius** is the maximum extension of the output flood in your dtm distance units.

The output will be placed in your `data/` floder as three files with the name *drainpaths_{x}-{y}_(draft|flood|speed).tif*.

## Use

Include mfdfloods as a module on your scripts with `from mfdfloods import MFD` then instantiate the class MFD to execute its drainpaths method.