semvec/pypi-packages huggingface dataset:
REMIND-PyPSA-coupling 0.1.3
Tools to couple the REMIND IAM and PyPSA power models
Requires Python: >=3.12

# Tools to couple REMIND & PySPA

## Overview
This is a collection of tools to couple remind and pypsa for different regions.

The tools are currently in development, with as test cases PyPSA-EUR for Germany and PyPSA-China-PIK for China.

## quick start
1. install from pypi `pip install remind-pypsa-coupling`
2. import with `import rpycpl`

## Documentation
https://pik-piam.github.io/Remind-PyPSA-coupling/

# Installation (development)
We recommend using `uv`. 
1. install uv
2. make a venv `uv venv` at `project/.venv`
3. Activate the venv with `source .venv/bin/activate`
4. option a) In the project folder run `uv pip install -e .` Then use as a package
4. option b) In the project workspace update the venv with `uv sync` to have all the package requirements. You can then use the src files as standalone.

> [!NOTE]
> `uv` sometimes causes issues at steps 4. In this case 
> - run `uv pip install pip` after step 3
> - run `pip install -e .` in the project worspace

# Usage
This package is intended for use in combination with REMIND and PyPSA, as part of a snakemake workflow

Examples: Coming at some point

Activate the venv with `source .venv/bin/activate`
e-pandas-stock-quant 0.1.1
A pandas-based stock quantitative analysis library
Requires Python: >=3.7
Keywords: pandas, stock, quant, finance, analysis

# E-Pandas Stock Quant

A pandas-based stock quantitative analysis library for feature extraction from stock market data.

## Features

- Intraday basic feature calculation
- Cross-day trend feature extraction  
- Multi-process support for large datasets

## Installation
pyscsopt 0.1.52
A Python library for self-concordant smooth optimization (Python port of SelfConcordantSmoothOptimization.jl)
Requires Python: >=3.8

# pySCSOpt: Self-Concordant Smooth Optimization in Python

<p align="center">
    <a style="text-decoration:none !important;" href="https://arxiv.org/abs/2309.01781" alt="arXiv" target="_blank"><img src="https://img.shields.io/badge/paper-arXiv-red" /></a>
    <a style="text-decoration:none !important;" href="https://opensource.org/licenses/apache-2-0" alt="License" target="_blank"><img src="https://img.shields.io/badge/license-Apache 2.0-blue.svg" /></a>
</p>

This package is a Python port of most parts of the Julia package [SelfConcordantSmoothOptimization.jl](https://github.com/adeyemiadeoye/SelfConcordantSmoothOptimization.jl). It includes:

- `ProxLQNSCORE` a limited-memory version of ProxQNSCORE of the Julia package
- `ProxGGNSCORE`
- `ProxNSCORE`
- Smoothing and regularization (utility) functions

## Installation

Install with pip:

```sh
pip install pyscsopt
```

## Usage

See the [`examples/`](https://github.com/adeyemiadeoye/pySCSOpt/tree/main/examples) directory for a usage example.

For more information on how to set up problems (especially choosing regularizers), see Julia's [SelfConcordantSmoothOptimization.jl](https://github.com/adeyemiadeoye/SelfConcordantSmoothOptimization.jl).

## Tests

Run tests with:

```sh
pytest pyscsopt/test/
```

## Citation

If you use this package for research, please cite:

```bibtex
@article{adeoye2023self,
  title={Self-concordant Smoothing for Large-Scale Convex Composite Optimization},
  author={Adeoye, Adeyemi D and Bemporad, Alberto},
  journal={arXiv preprint arXiv:2309.01781},
  year={2024}
}
```