semvec/pypi-packages huggingface dataset:
discord-ext-ipcx 0.4.0
Maintained and updated version of discord-ext-ipc for discord.py v2
Requires Python: <4,>=3.9
Keywords: discord.py, asyncio, networking, discord-ext-ipcx, discord-ext-ipc

<div align=center>

# discord-ext-ipcx

[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/discord-ext-ipcx?logo=python&logoColor=white&label=Python)](https://pypi.org/project/discord-ext-ipcx/) [![CodeQL](https://github.com/No767/discord-ext-ipcx/actions/workflows/codeql.yml/badge.svg)](https://github.com/No767/discord-ext-ipcx/actions/workflows/codeql.yml) [![Build and Publish](https://github.com/No767/discord-ext-ipcx/actions/workflows/build_and_publish.yml/badge.svg)](https://github.com/No767/discord-ext-ipcx/actions/workflows/build_and_publish.yml) [![Lint](https://github.com/No767/discord-ext-ipcx/actions/workflows/lint.yml/badge.svg)](https://github.com/No767/discord-ext-ipcx/actions/workflows/lint.yml) [![PyPI - License](https://img.shields.io/pypi/l/discord-ext-ipcx?logo=github&logoColor=white&label=License)](https://github.com/No767/discord-ext-ipcx/blob/main/LICENSE) [![PyPI - Version](https://img.shields.io/pypi/v/discord-ext-ipcx?logo=pypi&logoColor=white&label=Version&link=https%3A%2F%2Fpypi.org%2Fproject%2Fdiscord-ext-ipcx%2F)](https://pypi.org/project/discord-ext-ipcx/)

An maintained discord.py extension for inter-process communication

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## Installation

**Python 3.9 or higher is required**

To install the library, you can just run the following command:

```bash
# On Linux/MacOS
python3 -m pip install discord-ext-ipcx

# On Windows
py -m pip install discord-ext-ipcx
```

To install the development version, do the following:

```bash
git clone https://github.com/No767/discord-ext-ipcx
cd discord-ext-ipcx
python3 -m pip install -U .
```

## Resources

- [Documentation](https://discord-ext-ipcx.readthedocs.io/en/stable)
- [Examples](https://github.com/No767/discord-ext-ipcx/tree/main/examples)

## Motivation and differences

Originally, I forked and upgraded the discord-ext-ipc library to work with discord.py v2 for my own needs. Alternative libraries were soon either left unmaintained or archived due to various reasons. This forked version aims to maintain support for newer versions of discord.py while keeping the core intact for easy migration. In the future, more may be added or deleted, but this is unlikely to happen.

Nonetheless, some modifications had to be made. Here are some changes worth noting:

- The `start` method is now an coroutine and needs to be awaited.
- Types are now added and improved.
xfold 0.0.1
fold for everyone.

# xfold: Democratize AlphaFold3

xfold is an open-source, PyTorch-based reimplementation of AlphaFold3, designed to accelerate protein structure prediction research and make cutting-edge AI technology more accessible to the scientific community.

Future developments for xfold will focus on integrating cutting-edge performance optimization techniques and advanced parallelization strategies. Our ultimate goal is to democratize AlphaFold3, empowering a broader researcher to contribute to and benefit from this transformative technology.

<div align="center">
    <img src="./assets/comparison.gif" width="400">
    <p><em>Visualization result comparison of 2pv7</em></p>
</div>

## Recent Developments 🚀

* **December 2024**: Successful migration to PyTorch, with validation confirming alignment with the original implementation

## Getting Started

### Step 1: Prepare the Environment

Follow the setup instructions provided in the [AlphaFold3 README](https://github.com/google-deepmind/alphafold3) to ensure dependencies are correctly installed and the AlphaFold 3 model parameters are downloaded.

### Step 2: Install xfold

Install xfold using pip:

```bash
pip install xfold
```

### Step 3: Running Predictions

Execute protein structure predictions with the following command:

```bash
python run_alphafold.py \
    --db_dir=$PATH_TO_AF3_DATASET \
    --json_path=./fold_input.json \
    --model_dir=$$PATH_TO_AF3_MODEL \
    --output_dir=./output
```

## Acknowledgments

We extend our gratitude to AlphaFold3 for open-sourcing their inference code and model weights, which has significantly advanced scientific research. xfold is provided exclusively for educational and research purposes. Users are kindly requested to review and comply with the AlphaFold3 license, available at https://github.com/google-deepmind/alphafold3?tab=readme-ov-file#licence-and-disclaimer.

## Contributing

We welcome contributions from the research community! Open an [issue](https://github.com/shenggan/xfold/issues) or send a [pull request](https://github.com/shenggan/xfold/pulls).
logbasic 0.0.9
A simple package for logging.
Requires Python: >=3.10

# Logbasic

This package contains some simple helper functions for logging. There are 6 log levels:

* `debug()`
* `warning()`
* `info()`
* `error()`
* `special()`
* `success()`

They work the same way as the default `print()` function. To get just the formatted string add `_string` to the function, e.g. `warning_string()`.

Dictionaries, `timedelta`s and `datetime`s are specially formatted so keep that in mind. dictionaries are formatted like JSON files, timedeltas when negative are formatted as if they were positive but with a minus sign (-) prefixed.
randomcontent 1.0.2
A library for generating random text, numbers, and structured data.
Requires Python: >=3.10

This library provides utilities to generate mock data for testing purposes, including numbers, text, identifiers, and structured data.

## Modules Overview

# **Installation**
!pip install randomcontent


1. **Number Generator**
   - Generate random integers, floats, or numbers with custom ranges.
   - Example:
     ```python
     from randomcontent import generate_number
     generate_number(type="int", min=1, max=100)  # Generates an random integer between 1 and 100
     generate_number(type="probability") # Generates an random probability between 0 and 1
     generate_number(type="gaussian",mean=0, std_dev=1) # Generates an random number from Gaussian distribution

     ```

2. **Identifier Generator**
   - Generate unique identifiers like UUIDs, credit card numbers, or custom IDs.
   - Example:
     ```python
     from randomcontent import generate_identifier
     generate_identifier(type="uuid")  # Generates and random UUID-like string
     generate_identifier(type="email") # Generates an email address

     ```

3. **Text Generator**
   - Create random names, email addresses, or custom text strings.
   - Examples:
     ```python
     from randomcontent import generate_text
     generate_text(type="sentence", length=12)  # Generates a sentence
     generate_text(type="poem", num_lines=4)   # Generates a poem
     ```

4. **Structured Data Generator**
   - Generate structured mock data based on a schema definition.
   - Example:
     ```python
     from randomcontent import generate_structured_data, format_data
     schema = {"name": "random_name", "email": "random_email","age": "random_int(min=18, max=60)"}
     generate_structured_data(schema, count=5)  # List of 5 data rows
     ```

## Formatting Data
Structured data can be saved in JSON, CSV, or Parquet formats:
- Example:
  ```python
  from randomcontent import format_data
  format_data(data, format_type="json", file_name="data")  # Save as data.json
  format_data(data, format_type="csv", file_name="data")   # Save as data.csv
  format_data(data, format_type="parquet", file_name="data")  # Save as data.parquet