semvec/pypi-packages huggingface dataset:
ornl-asgard 0.9.0b1
Library for high-dimensional PDEs using sparse grids and discontinuous Galerkin method
License: MIT


The ASGarD project has the goal of building a solver specifically targeting
high-dimensional PDEs where the "curse-of-dimensionality" has previously
precluded useful continuum / Eularian (grid or mesh based as opposed to
Monte-Carlo sampling) simulation. Our approach is based on a
Discontinuous-Galerkin finite-element solver build atop an adaptive hierarchical
sparse-grid (note this is different from the "combination technique" when applied
to sparse-grids).

To cite the ASGarD code in your work, please use:
* [![DOI](https://joss.theoj.org/papers/10.21105/joss.06766/status.svg)](https://doi.org/10.21105/joss.06766)
* [doi:10.11578/dc.20201125.5](https://www.osti.gov/doecode/biblio/48752)

Papers using ASGarD:
* [Sparse-grid Discontinuous Galerkin Methods for the Vlasov-Poisson-Lenard-Bernstein Model](https://arxiv.org/abs/2402.06493)

[Documentation of usage: https://project-asgard.github.io/asgard/](https://project-asgard.github.io/asgard/)

The [developer documentation](https://github.com/project-asgard/ASGarD/wiki/developing)
contains information about how to contribute to the ASGarD project.

### Quick Install
 ASGarD supports `--user` and venv install only.

user install: python3 -m pip install ornl-asgard==0.9.0b1 --user

venv install: python3 -m pip install ornl-asgard==0.9.0b1
imagefilters-apoorvamdeval 0.1.1
A simple image filtering package with Averaging, Gaussian, Weighted, and Median filters.
License: MIT
Requires Python: >=3.7

# imagefilters-apoorvamdeval

A simple image filtering package supporting five filters:
- Averaging
- Gaussian
- Weighted Average
- Median

## Installation
pip install imagefilters-apoorvamdeval

## Usage
from imagefilters import apply_filter

# Basic usage:
apply_filter("path/to/image.jpg")

# Specify filter type (averaging / gaussian / median / weighted / average):

apply_filter("path/to/image.jpg", filter_type=" ")

# Specify filter and kernel size (use odd sizes like 3, 5, 7):
apply_filter("path/to/image.jpg", filter_type=" ", kernel_size=x)