semvec/pypi-packages huggingface dataset:
SplitFDM 0.2
1D Finite-Difference Split Newton Solver
License: MIT
Keywords: amr newton python finite-difference armijo optimization pseudotransient splitting

# SplitFDM

[![Downloads](https://pepy.tech/badge/splitfdm)](https://pepy.tech/project/splitfdm)

![img](https://github.com/gpavanb1/SplitFDM/blob/main/assets/logo.jpg)

1D [Finite-Difference](https://en.wikipedia.org/wiki/Finite_difference_method) with [adaptive mesh refinement](https://en.wikipedia.org/wiki/Adaptive_mesh_refinement) and steady-state solver using Newton and [Split-Newton](https://github.com/gpavanb1/SplitNewton) approach

## What does 'split' mean?

The system is divided into two and for ease of communication, let's refer to first set of variables as "outer" and the second as "inner".

* Holding the outer variables fixed, Newton iteration is performed till convergence using the sub-Jacobian

* One Newton step is performed for the outer variables with inner held fixed (using its sub-Jacobian)

* This process is repeated till convergence criterion is met for the full system (same as in Newton)

## How to install and execute?

Just run 
```
pip install splitfdm
```

There is an [examples](https://github.com/gpavanb1/SplitFDM/examples) folder that contains a test model - [Advection-Diffusion](https://en.wikipedia.org/wiki/Convection%E2%80%93diffusion_equation)

You can define your own equations by simply creating a derived class from `Model` and adding to the `_equations` using existing or custom equations!

A basic driver program is as follows
```
# Define the problem
m = AdvectionDiffusion(c=0.2, nu=0.001)

# Define the domain and variables
# ng stands for ghost point count
d = Domain.from_size(20, 2, ["u", "v", "w"]) # nx, ng, variables

# Set IC and BC
ics = {"u": "gaussian", "v": "rarefaction"}
bcs = {
    "u": {
        "left": "periodic",
        "right": "periodic"
    },
    "v": {
        "left": {"dirichlet": 3},
        "right": {"dirichlet": 4}
    },
    "w": {
        "left": {"dirichlet": 2},
        "right": "periodic"
    }
}
s = Simulation(d, m, ics, bcs)

# Advance in time or to steady state
s.evolve(dt=0.1)
bounds = [[-1., -2., 0.], [5., 4., 3.]]
iter = s.steady_state(split=True, split_loc=1, bounds=bounds)

# Visualize
draw(d, "label")
```

## Whom to contact?

Please direct your queries to [gpavanb1](http://github.com/gpavanb1)
for any questions.

## Acknowledgements

Do visit its [Finite-Volume](https://github.com/gpavanb1/SplitFVM) cousin

Special thanks to [Cantera](https://github.com/Cantera/cantera) and [WENO-Scalar](https://github.com/comp-physics/WENO-scalar) for serving as an inspiration for code architecture.
ez_azml 0.1.0
Simplified yaml/jsonargparse driven framework for AzureML interactions
License: MIT
Requires Python: >=3.9

[![pdm-managed](https://img.shields.io/endpoint?url=https%3A%2F%2Fcdn.jsdelivr.net%2Fgh%2Fpdm-project%2F.github%2Fbadge.json)](https://pdm-project.org)
![Static Badge](https://img.shields.io/badge/jsonargparse-d?link=https%3A%2F%2Fgithub.com%2Fomni-us%2Fjsonargparse)


# Easy AzureML (Beta)

This project attempts to reduce the boilerplate code when launching simple azureml jobs from command line.

It aims to:

- Be object oriented to enable end users to extend the SDK for their custom workflows, trying to reuse AzureML SDK V2 native classes as much as possible
- Be config driven (thanks to [jsonargparse](https%3A%2F%2Fgithub.com%2Fomni-us%2Fjsonargparse)) to improve reusability of objects and code
- Be, as it name states, easy to use (hence focused in simple usecases)
- Unify through wrappers the different flows and runs in Azure through a common interface

# Installation

Simply run 
```bash
pip install ezazml
```
# Quickstart
The main entrypoint is the CLI command. The following command will show you the help
```
ez-azml -h
```

You can run an example with
## Command
```
ez-azml --config configs/pytorch/main.yaml run
```
## Pipeline
```
ez-azml --config configs/pipeline/main.yaml run
```