semvec/pypi-packages huggingface dataset:
sungazer 0.2.5
An API client that reads data from the internal network of a SunPower PVS6 monitoring device.
Requires Python: >=3.10
Keywords: aws

# sungazer

**Documentation**: https://sungazer.readthedocs.org

`sungazer` is a command line interface and client library for accessing the the
internal API of the SunPower PVS6 monitoring device installed with a SunPower
solar insallation.

## Installation

`sungazer` supports Python 3.10+.

To install from PyPI:

```shell
pip install sungazer
```

## Usage

Example usage of the command line interface:

```bash
# Get help
sungazer --help
sungazer device --help
sungazer network list --help

# Use JSON output (default)
sungazer session start

# Use table output
sungazer session start --output table
```

## Autocomplete

To enable autocomplete of the `sungazer` command line interface, do:

```bash
# For bash
echo 'eval "$(_SUNGAZER_COMPLETE=bash_source sungazer)"' >> ~/.bashrc

# For zsh
echo 'eval "$(_SUNGAZER_COMPLETE=zsh_source sungazer)"' >> ~/.zshrc

# For fish
echo 'eval (env _SUNGAZER_COMPLETE=fish_source sungazer)' >> ~/.config/fish/config.fish
```
pdaqp 0.6.7
A multi-parametric quadratic programming solver
Requires Python: >=3.8

**pdaqp** is a Python package for solving multi-parametric quadratic programs of the form

$$
\begin{align}
\min_{z} &  ~\frac{1}{2}z^{T}Hz+(f+F \theta)^{T}z \\
\text{s.t.} & ~A z \leq b + B \theta \\
& ~\theta \in \Theta
\end{align}
$$

where $H \succ 0$ and $\Theta \triangleq \lbrace l \leq \theta \leq u : A_{\theta} \theta \leq b_{\theta}\rbrace$.

**pdaqp** is based on the Julia package [ParametricDAQP.jl](https://github.com/darnstrom/ParametricDAQP.jl/) and the Python module [juliacall](https://juliapy.github.io/PythonCall.jl/stable/juliacall/).  More information about the underlying algorithm and numerical experiments can be found in the paper ["A High-Performant Multi-Parametric Quadratic Programming Solver"](https://arxiv.org/abs/2404.05511).

**pdaqp** is also the used in [CVXPYgen](https://github.com/cvxgrp/cvxpygen#explicitly-solving-problems) to compute explicit solutions. For more information, see the following [manuscript](https://stanford.edu/~boyd/papers/cvxpygen_mpqp.html).
 

## Installation
```bash
pip install pdaqp
```
## Citation
If you use the package in your work, consider citing the following paper

```
@inproceedings{arnstrom2024pdaqp,
  author={Arnström, Daniel and Axehill, Daniel},
  booktitle={2024 IEEE 63rd Conference on Decision and Control (CDC)}, 
  title={A High-Performant Multi-Parametric Quadratic Programming Solver}, 
  year={2024},
  volume={},
  number={},
  pages={303-308},
}
```

## Example
The following code solves the mpQP in Section 7.1 in Bemporad et al. 2002
```python
import numpy

H =  numpy.array([[1.5064, 0.4838], [0.4838, 1.5258]])
f = numpy.zeros((2,1))
F = numpy.array([[9.6652, 5.2115], [7.0732, -7.0879]])
A = numpy.array([[1.0, 0], [-1, 0], [0, 1], [0, -1]])
b = 2*numpy.ones((4,1));
B = numpy.zeros((4,2));

thmin = -1.5*numpy.ones(2)
thmax = 1.5*numpy.ones(2)

from pdaqp import MPQP
mpQP = MPQP(H,f,F,A,b,B,thmin,thmax)
mpQP.solve()
```
To construct a binary search tree for point location, and to generate corresponding C-code, run 
```python
mpQP.codegen(dir="codegen", fname="pointlocation")
```
which will create the following directory:
```bash
├── codegen
│   ├── pointlocation.c
│   └── pointlocation.h
```
The critical regions and the optimal solution can be plotted with the commands
```python
mpQP.plot_regions()
mpQP.plot_solution()
```
which create the following plots
<p align="center">
  <img src="https://github.com/darnstrom/pdaqp/blob/main/docs/imgs/example_regions.png?raw=true" width="600" alt="critical_regions" align="center"/>
</p>
<p align="center">
  <img src="https://github.com/darnstrom/pdaqp/blob/main/docs/imgs/example_solution.png?raw=true" width="600" alt="solution_component" align="center"/>
</p>
aleya-alpaca 0.0.3a1.dev57
Aleya Linux PAckage Assistant

# Alpaca - Aleya Linux Package Assistant
This is the official package manager for the Alpaca linux distribution

This package manager is work in progress and will contain bugs. While there are safeguards in place to disable package installation on any other distribution, please do not try running this script on any other system than Aleya Linux unless you know what you're doing.

The current recommendation is to use the aleyalinux/aleya docker image.
routelit-silicon 0.2.3
RouteLit Silicon is a Python library that extends RouteLit Builder with pre-built UI components and layouts.
Requires Python: <4.0,>=3.9
Keywords: builder, components, python, react, routelit, silicon.css, ui

# routelit-silicon

[![Release](https://img.shields.io/github/v/release/routelit/routelit-silicon)](https://img.shields.io/github/v/release/routelit/routelit-silicon)
[![Build status](https://img.shields.io/github/actions/workflow/status/routelit/routelit-silicon/main.yml?branch=main)](https://github.com/routelit/routelit-silicon/actions/workflows/main.yml?query=branch%3Amain)
[![Commit activity](https://img.shields.io/github/commit-activity/m/routelit/routelit-silicon)](https://img.shields.io/github/commit-activity/m/routelit/routelit-silicon)
[![License](https://img.shields.io/github/license/routelit/routelit-silicon)](https://img.shields.io/github/license/routelit/routelit-silicon)

RouteLit Silicon is a Python library that extends RouteLit Builder with pre-built UI components and layouts. It provides a showcase of how to create web applications with common UI patterns like sidebars, footers, and other structural elements.

The library currently includes:

- A components builder that supports sidebar layouts
- Integration with silicon.css for styling
- Easy configuration of UI structure through a builder pattern
- Static asset management for frontend resources

## Installation

```bash
pip install routelit-silicon
```

## Usage

```python
from flask import Flask

from routelit import RouteLit
from routelit_flask import RouteLitFlaskAdapter
from routelit_silicon import RLBuilder

app = Flask(__name__)

routelit = RouteLit(BuilderClass=RLBuilder)
routelit_adapter = RouteLitFlaskAdapter(
    routelit,
    ### TO USE LOCAL VITE DEV SERVER, UNCOMMENT THE FOLLOWING LINES
    # run_mode="dev_components",
    # local_components_server="http://localhost:5173"
).configure(app)


def view(ui: RLBuilder):
    ui.set_config(use_sidebar=True)
    with ui.sidebar:
        ui.title("Sidebar")

    ui.title("Main")
    ui.text("Hello, world! from main")

    ui.sidebar.text("Hello, world! from sidebar")


@app.route("/", methods=["GET", "POST"])
def index():
    return routelit_adapter.response(view)


if __name__ == "__main__":
    app.run(debug=True)
```