semvec/pypi-packages huggingface dataset:
leddimmerserver 0.1.3.dev0
A server for controlling LED dimmers
Requires Python: >=3.8

# LEDDimmerServer
[![codecov](https://codecov.io/gh/terminator-ger/LEDDimmerServer/branch/master/graph/badge.svg?token=9B8U9NB1JJ)](https://codecov.io/gh/terminator-ger/LEDDimmerServer)

Is a Python based REST-server on your Raspi that acts as control-devices
between the LED-Strip and your [Android App](https://github.com/terminator-ger/Sunriser).

## Installation
# Install using pypi
```bash
python -m pip install LEDDimmerServer
```

# Setup 
```bash
sudo cp LEDDimmerServer.service /etc/system.d/system
sudo systemctl daemon-reload
sudo systemctl start LEDDimmerServer
```

Start PI-GPIOD on boot, root is important here
```bash
sudo crontab -e
```
and then add the lines
```bash
@reboot     /usr/local/bin/pigpiod
```
to start the gpio daemon and the LEDDimmer.
ennvee-commons 0.1.1
Common utilities for ennVee organization
Requires Python: >=3.8

# ennvee_commons

A collection of reusable Python utility modules designed to simplify common tasks across projects. This package includes utilities for email sending, SMS, captcha handling, logging, error handling, and middleware support.


---

## Modules Overview

### 1. `email_utils.py`  
Send emails through Microsoft Graph API with support for multiple recipients, attachments, HTML or plain text content, and optional saving to Sent Items.  
**Key features:** Azure AD authentication, multiple recipients (To, CC, BCC), attachment handling.

### 2. `sms_utils.py`  
Utility for sending SMS messages via external APIs (implementation details not shown). Designed for easy integration and reuse.

### 3. `captcha_utils.py`  
Functions to generate and validate captchas for enhancing user verification flows. Supports image captchas and related security measures.

### 4. `logging_framework.py`  
Custom logging framework for standardized log formatting, levels, and output destinations to support consistent application logging.

### 5. `common_error_handler.py`  
Comprehensive function-based error handling utilities that provide consistent error response formatting across services, covering validation, authentication, API errors, resource issues, and more.

### 6. `res_middleware_utils.py`  
Middleware helper utilities to streamline resource processing and response handling in web applications or API services.

---

## Installation

Currently, this package is intended for internal use. Simply copy the `ennvee_commons` directory into your project and import the utilities as needed.
jupyter-slurm-ssh-provisioner 0.2.0
Start a remote Kernel via ssh in a slurm job

# Slurm SSH Provisioner

## What is this package

This package is a Jupyter Kernel Provisioner.

It uses ssh tunnels to launch a remote kernel in a slurm job and connect to it.

Once configured, on the Jupyter Notebook / Lab interface, you can just select a kernel using this provisioner and it will set up everything accordingly.

## Setup

### 1 - Build a remote wrapper script

This remote wrapper will be called when we want to launch the kernel.
Thus, it should:

- Start a slurm job that will start a kernel (the connection file should be named .../kernel-{slurm_job_id}.json, thus the command inside a batch script could be `python -m ipykernel_launcher -f=/tmp/kernel-${SLURM_JOB_ID}.json`)
- Wait for the job to start
- Return the slurm job id

### 2 - Setup a kernel

This is the kernel that will use the slurm-ssh-provisioner. It will be displayed in the Notebook / Lab interface.

```
#~/.local/share/jupyter/kernels/slurm-ssh/kernel.json
{
  "display_name": "Python 3 (Slurm SSH)",
  "language": "python",
  "metadata": {
    "kernel_provisioner": {
      "provisioner_name": "slurm-ssh-provisioner",
      "config": {
        "host": "xxx.xxx.xxx.xxx",
        "username": "debian",
        "first_local_port": 9000,
        "wrapper_cmd": "bash wrapper.sh"
      }
    }
  }
}
```

### 3 - Voilà

You should now be able to use this new kernel.

Use `jupyter kernelspec list` to check your kernel is detected.
Use `jupyter kernelspec provisioners` to check that slurm-ssh-provisioner is installed.
slowarg 0.2.3
A slow CLI tool demonstrating argparse with argcomplete and fastargsnap
Requires Python: >=3.8
Keywords: cli, argparse, argcomplete, tab-completion, performance, fastargsnap

# SlowArg

A deliberately slow CLI tool that demonstrates the performance impact of heavy imports on argparse tab completion, now with fastargsnap integration for instant completions.

## Purpose

This package is designed to simulate a real-world CLI application with heavy dependencies that slow down tab completion. It's useful for:

- Testing tab completion performance
- Demonstrating the benefits of fast completion libraries like fastargsnap
- Benchmarking completion speed improvements
- Educational purposes for understanding CLI performance issues

## Features

- Multiple subcommands with various argument types
- Heavy dependencies (matplotlib, numpy, pandas, scikit-learn, requests, click)
- Deliberately slow import times (5-second delay) to demonstrate completion performance issues
- **NEW**: Fastargsnap integration for instant tab completions
- Full argparse integration with argcomplete fallback
- Simple and clean command structure

## Installation

```bash
pip install slowarg
```

## Usage

After installation, you can use the `slowarg` command:

```bash
# Basic usage
slowarg --help

# Run foo command
slowarg foo --bar 42 --baz a

# Run data command
slowarg data --file input.csv --mode fast

# With tab completion (now fast with fastargsnap!)
slowarg <TAB>
```

## Fast Completions with FastArgSnap

Slowarg now includes fastargsnap integration for instant tab completions:

- **Before**: 5+ second delays due to heavy imports
- **After**: Instant completions using pre-generated snapshots
- **Fallback**: Automatically falls back to regular argcomplete if snapshot is unavailable

The snapshot is generated automatically during development and included in the package.

## Commands

### `foo` command
- `--bar`: Integer value
- `--baz`: Choice between "a", "b", or "c"

### `data` command
- `--file`: Input file path
- `--mode`: Choice between "fast" or "slow"

## Performance Impact

This tool intentionally includes heavy imports and a 5-second delay to simulate real-world scenarios where CLI tools have slow startup times due to:

- Large dependency trees
- Complex initialization processes
- Heavy computational libraries
- Network or database connections

**With fastargsnap integration**, completions are now instant regardless of the heavy dependencies!

## Development

To install in development mode:

```bash
git clone https://github.com/udayayya/slowarg
cd slowarg
pip install -e '.[dev]'
```

## Testing

```bash
# Run tests
pytest

# Format code
black .

# Lint code
flake8
```

## License

MIT License - see [LICENSE](LICENSE) file for details.

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.