semvec/pypi-packages huggingface dataset:
lci-sim-app-device 0.0.78
Sim App Device package
Requires Python: >=3.7
Keywords: python,sim app device,sim,sim app

Software to run, e.g. on a raspberry pi, to communicate with the SIM800 overlay and connect to the sim-app.ovh controller. Details can be found at https://sim-app.ovh or github. 

 Important! Since version 0.0.62, the application is used in python version 3.11.2, so I also recommend running it in this version.
iqtrade 0.0.5
Python wrapper around Questrade IQ API
Requires Python: >=3.9

[![Build Status](https://travis-ci.com/jpflouret/iqtrade.svg?branch=main)](https://travis-ci.com/jpflouret/iqtrade)
[![PyPI](https://img.shields.io/pypi/v/iqtrade)](https://pypi.org/project/iqtrade/)
[![codecov.io](https://codecov.io/github/jpflouret/iqtrade/coverage.svg?branch=main)](https://codecov.io/github/jpflouret/iqtrade)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
# iqtrade

Python wrapper around Questrade [IQ API](https://www.questrade.com/api/documentation/getting-started).

## Installation

Install via pip

```
pip install iqtrade
```

## Disclaimer

This project is not affiliated with Questrade Wealth Management Inc., Questrade,
Inc., or any members of the Questrade Group of Companies or its affiliates.
hole-filling 1.0.0
A simple Python package for hole filling in triangle meshes.

# HoleFillingPy

A dirty implementation of mesh hole filling with [libigl](https://github.com/libigl/libigl-python-bindings).

<img src="./docs/hole_filling.png" width="600">

## Installation

```bash
pip install hole-filling
```

## Usage

Simply fill all holes.

```python
import numpy as np
import igl
from hole_filling import triangulate_refine_fair

vs, fs, _ = igl.read_off("tests/examples/bunny_holes.off")
out_vs, out_fs = triangulate_refine_fair(vs, fs)

colors = np.ones((len(out_vs), 3))
colors[np.arange(len(vs), len(out_vs))] = [0, 0, 1]  # added vertices are blue

igl.write_off("bunny_hole_filling.off", out_vs, out_fs, colors)
```

You can fill holes with fine controls, e.x. keeping the longest boundary.

```python
import numpy as np
import igl
from hole_filling import close_holes, triangulate_refine_fair

vs, fs, _ = igl.read_off("tests/examples/face_holes.off")
loop = igl.boundary_loop(fs)
length = np.linalg.norm(vs[loop[:-1]] - vs[loop[1:]], axis=1).sum()
# tweak the parameters to see the difference
out_vs, out_fs = triangulate_refine_fair(vs, fs, hole_len_thr=length - 0.1, density_factor=2, fair_alpha=0.5)
igl.write_off("face_hole_filling.off", out_vs, out_fs, np.ones_like(out_vs) * 0.7)
```

## Reference

1. Liepa, Peter. "Filling holes in meshes." Proceedings of the 2003 Eurographics/ACM SIGGRAPH symposium on Geometry
   processing. 2003.
2. Jacobson, Alec, and Daniele Panozzo. "Libigl: Prototyping geometry processing research in c++." SIGGRAPH Asia 2017
   courses. 2017. 1-172.
RegressionVisualizer3D 2.0.0
This is a simple plotting package for visualizing linear regression, Poisson regression and binary logistic regression in 3D, for teaching purposes.
Requires Python: >=3.7
Keywords: regression,linear regression,poisson regression,logistic regression,visualization,3D,visualisation

RegressionVisualizer3D is a simple single-function plotting package for visualizing linear regression, Poisson regression and binary logistic regression in 3D, for teaching purposes.

A notebook demonstrating the package is here: https://nbviewer.org/github/pxr687/RegressionVisualizer3D_demo/blob/main/RV3D_demo.ipynb

The user supplies parameters for the data-generating process and a population of observations is created through those parameters. The population data consists of two continuous predictor variables and one outcome variable. The type of outcome variable depends on the type of regression model being visualized (continuous for linear regression, binary for logistic regression etc.)..

A random sample (of a size specified by the user) is drawn from the population data. A regression model is then fit to the sample data.

3D visualisations are then shown, which depict:

* The population data
* The population regression surface
* The sample data
* The sample regression surface

An optional regression table (with slopes and p-values etc.) is also shown, alongside the true regression equation used to generate the data. 

To aid understanding, the user can also specify the names of the predictor variables and the outcome variable.

If the user does not supply population parameters, defaults are used.
the-todo 0.1.1
A simple command line todo application

# The-Todo

<!-- https://user-images.githubusercontent.com/27226348/230737157-7273264b-276c-4feb-87c4-3cb384307d99.mov -->

### Table of Contents

1. [Installation](#installation)
2. [Description of Project](#description)
3. [Customization](#customization)
4. [File Descriptions](#files)
5. [Licensing](#licensing)

## Installation <a name="installation"></a>
To install the app, you can use `pip` :

```bash
pip install the-todo
```

To use the todo, all you have to do is open terminal and type:
```bash
python -m todo
```

The todo app has following options available to manage tasks :

## Description <a name="description"></a>
This is a simple command-line todo app built in Python. It allows you to add, view, mark as completed, and remove tasks from a todo list using a terminal interface.

### Options :
```markdown
1. add  : Add task
2. ls   : View tasks
3. cplt : Mark task as completed
4. rm   : Remove task
5. exit : Exit
```
You can then enter one of these commands to perform an action.

### Examples :

Add a new task :
```bash
todo >> add Buy groceries
Task added successfully!
```

View all tasks :
```bash
todo >> ls
Tasks:
1. Buy groceries
```

Mark a task as completed :
```bash
todo >> cplt 1
Task marked as completed!
```

Remove a task :
```bash
todo >> rm 1
Task removed successfully!
```

## Customization <a name="customization"></a>

By default, the app creates a todo list called "todo" and stores it in a SQLite database at ~/.todos/todo.db. However, you can specify a different name for the list by using the -n or --name option:

```bash
python -m todo --name mylist
```
This will create a new todo list called "mylist" and store it in a SQLite database at ~/.todos/mylist.db.

## File Description <a name="files"></a>

```
.
├── README.md
├── LICENCE : contains MIT LICENCE
├── setup.py : Setup file to create distribution packages
└── todo : The todo package
    ├── __init__.py
    ├── __main__.py 
    ├── todo.py : Todo class with modular code
    └── todo_prompt.py : Todo Prompt
```

## Licensing <a name="licensing"></a>
This app is released under the MIT License. Feel free to use, modify, and distribute it as you like.
AIFunction 1.0.9
Generate realtime AI Functions!
Requires Python: >=3.6

# AI Function

Generate realtime AI Functions!

## Installation

To install AI Function, run:

```bash
pip install AIFunction
```

## Usage

To use AIFunction, you must have an OpenAI API key.

Firstly, add a .env file to your project, and add the following:

```
OPENAI_API_KEY="key_here"
```

Then, import the aiFunction function:

```python
from AIFunction import aiFunction
```

Now, you can use the aiFunction function to generate AI Functions!

```python
print(aiFunction("bitcoin_price", "Returns the current price of bitcoin."));
# --> 27002.055
```

## What is an AI Function?

Let's say you want to write a function to get the current time in python. All you have to do is:

```python
from AIFunction import aiFunction

print(aiFunction("time", "Returns the current time."));
# --> 12:00:00
```

## How does it work?

AI function uses OpenAI's GPT model to generate the code for the function. It then runs the code and returns the output. If specified, it will also install the required packages for the code.

## Security

AI Functions are not very secure, as they run code without any user testing, and can be used to run malicious code.