semvec/pypi-packages huggingface dataset:
sphinxext-presentations 0.3.1
Sphinx Extension that turns documentation into presentations.
Requires Python: >=3.6

# sphinx-presentations

sphinx-presentations is a Sphinx extension to convert documentation to Reveal.js presentations.

## Installation

`python -m pip install sphinxext-presentations`

## Usage
Add `sphinxext.presentations` to your extensions list in your `conf.py`

```python
extensions = [
   "sphinxext.presentations",
]
```

Presentations will launch when `?present` is appended to the url of a page. It is recommended to add a way for viewers to launch presentations without modifying the url (a button perhaps?).


## Building js assets

### Installing dependencies
```
npm install
```
### Building js
```
npx webpack
```
rasifal 0.7
A CLI tool to find horoscope in nepali
Requires Python: >=3.6

# Rasifal

Rasifal is a command-line interface (CLI) tool that fetches and displays daily horoscope in Nepali from the Hamro Patro website.

## Installation

You can install Rasifal directly from PyPI:

```bash
pip install rasifal

```

## Usage

After installation, the tool can be invoked with the `rasifal` command:

```bash
rasifal --sign <your-horoscope-sign>
```

Replace `<your-horoscope-sign>` with your horoscope sign in Nepali. For example, you would use mesh if your horoscope sign is Aries.

### Supported Horoscope Signs

The following horoscope signs are supported:

- Mesh (Aries)
- Brish (Taurus)
- Mithun (Gemini)
- Karkat (Cancer)
- Singha (Leo)
- Kanya (Virgo)
- Tula (Libra)
- Brishchik (Scorpio)
- Dhanu (Sagittarius)
- Makar (Capricorn)
- Kumbha (Aquarius)
- Min (Pisces)


### Acknowledgements

- [hamropatro](https://www.hamropatro.com/rashifal)
spikingtorch 0.1.3
Inference and backprop training of spiking neural networks in Pytorch
Keywords: spiking neurons, neural networks, AI, Pytorch, neuromorphic computing, spiking neural networks, neuroscience

# spikingtorch

Training spiking neural networks using Pytorch

## About

`spikingtorch` is a lightweight package for training deep neural
networks using Pytorch. `spikingtorch` includes encoders that transform
standard ML datasets into spike trains, and decoders that transform
the output spikes into values that can be used with loss functions in
Pytorch.

`spikingtorch` implements  spiking neural networks and backpropagation
through spikes for [leaky integrate and fire](https://en.wikipedia.org/wiki/Biological_neuron_model#Leaky_integrate-and-fire) neurons.

In addition to the Python package, this repository also
 implements the methodology
and reproduces the results presented in the paper:

[A. Yanguas-Gil, Coarse scale representation of spiking neural networks:
backpropagation through spikes and application to neuromorphic
hardware, arXiv:2007.06176](https://arxiv.org/abs/2007.06176)

## Status

`spikingtorch` is in active development, with more neuron models coming up
soon.

## Quick install

Through pypi:

```
pip install spikingtorch
```

## Acknowledgements

* Threadwork, U.S. Department of Energy Office of Science, 
  Microelectronics Program.

The original implementation was based on reseach funded through Argonne's Laboratory Directed Research
and Development program.

## Publications

Spikingtorch's backpropagation approach is based on the following work:

[A. Yanguas-Gil, Coarse scale representation of spiking neural networks:
backpropagation through spikes and application to neuromorphic
hardware, arXiv:2007.06176](https://arxiv.org/abs/2007.06176)


## Copyright and license

Copyright © 2023, UChicago Argonne, LLC

spikingtorch is distributed under the terms of BSD License. See 
[LICENSE](https://github.com/anglyan/spikingtorch/blob/master/LICENSE.md)