semvec/pypi-packages huggingface dataset:
alaspo 0.3
ALASPO: ASP + Large-Neighborhood Search
Requires Python: >=3.8

# ALASPO

An (adaptive) LNS framework for the for ASP systems. Currently, the system only supports solvers based on [clingo](https://potassco.org/). 

## Installation

It should work out-of-the-box in a conda env after running the following commands:
```
conda create -n alaspo python=3.9
conda activate alaspo
conda install -c potassco clingo clingo-dl clingcon
python -m pip install alaspo
```

The command-line options of the problem-independent LNS can be shown as follows:
```
alaspo -h
```

Examples for portfolio config files can be found in the [git repository](https://gitlab.tuwien.ac.at/kbs/BAI/alaspo).
sigrok 0.1.2
Python-API for libsigrok
Requires Python: >=3.10

# python-sigrok

Python-API for [libsigrok](https://sigrok.org/wiki/Libsigrok)

## Installation
```bash
pip install sigrok
```

### Requirements
#### Ubuntu
```bash
apt install libsigrok4 libsigrok-dev
```

### Fedora
```bash
dnf install libsigrok libsigrok-devel
```

#### macOS
```bash
brew install libsigrok
```

#### Windows
pre-built libsigrok dlls are shipped with this package.

## Usage
```python
from sigrok import Sigrok, ConfigKey

with (
    Sigrok() as sr,
    sr.get_driver("demo") as driver,
    driver.get_device() as device,
):
    device.set_config_uint64(ConfigKey.SR_CONF_SAMPLERATE, 1_000)
    device.set_config_uint64(ConfigKey.SR_CONF_LIMIT_SAMPLES, 10)
    device.enable_channels("D0")

    with sr.session(devices=[device]) as session:
        print(session.next_packet(timeout=1.0))
        print(session.next_packet(timeout=1.0))
        print(session.next_packet(timeout=1.0))
```
promptly-whatsapp 0.1.0
Send custom prompted messages with beautiful images via WhatsApp automatically
Requires Python: >=3.6

# Promptly 🌅

**Send fun, warm "Custom Prompted" messages with beautiful images to your loved ones via WhatsApp—automatically, every day.**

## ✨ Features

- 📸 **Fetch Beautiful Images**: Random high-quality images from Unsplash
- 💬 **Automated Messaging**: Send scheduled messages via WhatsApp
- 📝 **Comprehensive Logging**: Track events and errors
- ⚙️ **Full Customization**: Personalize messages, timing, and images

## 🛠️ Tech Stack

- **Python**: Core application logic
- **Unsplash API**: For fetching beautiful images
- **PyWhatKit**: WhatsApp automation
- **Schedule**: Task scheduling

## 🚀 Getting Started

### Prerequisites
- Python 3.6+
- WhatsApp Web access
- Unsplash API key

### Installation

1. Clone the repository:
```bash
git clone https://github.com/itsmeved24/Promptly.git
cd Promptly
```

2. Install dependencies:
```bash
pip install -r requirements.txt
```

3. Create a `.env` file:
```
PHONE_NUMBER=+911234567890
UNSPLASH_API_KEY=your_unsplash_api_key_here
```

4. Run the application:
```bash
python main.py
```

## ⚙️ Customization

- **Message Text**: Edit message templates in `main.py`
- **Image Categories**: Modify keywords in `image_fetcher.py`
- **Scheduling**: Adjust timing with `schedule.every().day.at("HH:MM")` in `main.py`
- **Custom Images**: Add personal images to `images/` directory

## 📋 Project Structure

```
.
├── images/               # Directory for storing images
├── .env                  # Environment variables
├── image_fetcher.py      # Image fetching module
├── main.py               # Core application file
├── image_fetcher.log     # Logs for debugging
├── requirements.txt      # Dependencies
└── README.md             # This file
```

## 📊 Logging

The application creates detailed logs (`image_fetcher.log`) that include:
- Timestamps for each operation
- Success/failure status
- Image URLs and query terms
- Detailed error information
- Debug information

## Made with ❤️ by Vedank

## 📜 License

This project is licensed under the MIT License.
docdown 0.5.0
DocDown is a Markdown extension for source code documentation.
Requires Python: >=3.11
Keywords: docdown, documentation, markdown

===============================
Python DocDown
===============================

Python DocDown is a suite of extensions for `Python Markdown`_.

.. image:: https://travis-ci.org/livio/DocDown-Python.svg?branch=master
    :target: https://travis-ci.org/livio/DocDown-Python


Documentation
----------------

Run ``make docs`` to build the HTML documentation for Python DocDown

Credits
---------

This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.

.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage
.. _`Python Markdown`: https://pypi.python.org/pypi/Markdown
pbdm 0.0.3
CASAS PBDM Python implementation
Requires Python: >=3.10

# CASAS-PBDM repository

CASAS-PBDM is a general tritrophic system modeling library that uses the
physiologically based demographic modeling (PBDM) approach and unifies
existing CASAS-PBDM system models.

CASAS-PBDM implements the physiologically based demographic modeling (PBDM)
approach of Gutierrez (1996), and generalizes from an existing set of
CASAS-PBDM system models coded in Pascal that have been developed for decades
in the Laboratory of Andrew Paul Gutierrez at University of California,
Berkeley and by international collaborators for published PBDM system
analyses and are now owned by CASAS Global (https://www.casasglobal.org/).

In terms of implementation, the main building block of the CASAS-PBDM general
population modeling library is be the concept of functional population
(see Gutierrez 1996).

## References

Gutierrez, A.P., 1996. Applied population ecology: A supply-demand approach.
John Wiley and Sons, New York, ISBN: 978-0-471-13586-9.