semvec/pypi-packages huggingface dataset:
thinkai-quantum 1.0.5
Think AI - Quantum Consciousness AI Library for Python
Requires Python: >=3.8
Keywords: ai, consciousness, quantum, machine-learning, nlp, chat, intelligence, think-ai

# thinkai-quantum

Python library for Think AI - O(1) AI System.

## Installation

```bash
pip install thinkai-quantum
```

## Usage

```python
from thinkai_quantum import ThinkAI

ai = ThinkAI()
response = ai.query('What is consciousness?')
print(response)
```

## CLI Usage

```bash
think-ai chat
```

## Author

- **champi-dev** - [danielsarcor@gmail.com](mailto:danielsarcor@gmail.com)
- GitHub: [https://github.com/champi-dev/think_ai](https://github.com/champi-dev/think_ai)

## License

MIT
quicfire-tools 0.11.0
Input and output management tools for the QUIC-Fire fire model
Requires Python: >=3.9

# quicfire-tools

## Quick-Links

[Documentation](https://silvxlabs.github.io/quicfire-tools/) - [PyPi Package](https://pypi.org/project/quicfire-tools/) [Conda](https://anaconda.org/conda-forge/quicfire-tools)

## What is quicfire-tools?

quicfire-tools is a Python package that provides a convenient interface for programmatically creating and managing
QUIC-Fire input file decks and processing QUIC-Fire output files into standard Python array data structures.

The goals of quicfire-tools are to:

1. Make it easy to write Python code to work with QUIC-Fire input and output files.
2. Unify code, scripts, and workflows across the QUIC-Fire ecosystem into a single package to support the development of
   new QUIC-Fire tools and applications.
3. Provide a platform for collaboration among QUIC-Fire developers and users.

## What is QUIC-Fire?

QUIC-Fire is a fast-running, coupled fire-atmospheric modeling tool developed by Los Alamos National Laboratory for wildland fire behavior prediction and prescribed fire planning.
It combines a 3D wind solver (QUIC-URB) with a physics-based cellular automata fire spread model (Fire-CA) to rapidly simulate the complex interactions between fire, fuels, and atmospheric conditions.

**Important Licensing Note**: QUIC-Fire is a closed-source simulation tool. The maintainers of quicfire-tools are not responsible for QUIC-Fire licensing. Users must obtain QUIC-Fire access through appropriate channels.


## Installation

quicfire-tools can be installed using `pip` or `conda`.

### pip

```bash
pip install quicfire-tools
```

### conda

```bash
conda install conda-forge::quicfire-tools
```

## Issues

If you encounter any issues with the quicfire-tools package, please submit an issue on the quicfire-tools GitHub
repository [issues page](https://github.com/silvxlabs/quicfire-tools/issues).
bcitoolbox 0.1.0.6
A zero-programming package for Bayesian causal inference model

BCI Toolbox is a Python implementation of the hierarchical Bayesian Causal Inference (BCI) model for multisensory research. BCI model is a statistical framework for understanding the causal relationships between sensory inputs and prior expectations of a common cause, which can account for human perception in a number of tasks, including temporal numerosity judgment (Shams et al., 2005; Wozny et al., 2008), spatial localization judgment (Körding et al., 2007; Wozny & Shams, 2011), size-weight illusion paradigm (Peters et al., 2016), rubber-hand illusion paradigm (Chancel et al., 2022; Chancel & Ehrsson, 2023).
protdata 0.1.4
Proteomics data loaders for AnnData format.
Requires Python: <4.0,>=3.9

# protdata

[![Test](https://github.com/czbiohub-sf/protdata/actions/workflows/test.yml/badge.svg)](https://github.com/czbiohub-sf/protdata/actions/workflows/test.yml)
![PyPI - Version](https://img.shields.io/pypi/v/protdata)
[![docs online](https://img.shields.io/badge/docs-online-blue)](https://protdata-czbiohub.vercel.app/)

Proteomics data loaders for the [AnnData](https://anndata.readthedocs.io/) format.

This package provides loader functions to import proteomics data (e.g., MaxQuant) into the AnnData structure for downstream analysis and integration with single-cell and multi-omics workflows.

## Features

- **Multiple formats**: Support for MaxQuant, FragPipe, DIA-NN, and mzTab files
- **Reads metadata**: Automatically extracts and organizes sample and protein metadata

## Installation

Protdata has minimal dependencies listed in [pyproject.toml](https://github.com/czbiohub-sf/protdata/blob/main/pyproject.toml)

To install the latest release from PyPI, run:

```bash
pip install protdata
```

Or install from source:
```bash
git clone https://github.com/czbiohub-sf/protdata.git
cd protdata
pip install -e . # or make setup-develop for developers
```

## Usage Example

### MaxQuant Import

You can download an example proteinGroups [file here](https://github.com/czbiohub-sf/protdata/raw/main/data/proteinGroups.txt)
```python
import protdata

adata = load_maxquant_to_anndata("/path/to/proteinGroups.txt")
print(adata)
```

### DIA-NN Import

You can download an example DIA-NN report [file here](https://github.com/czbiohub-sf/protdata/raw/main/data/report.pg_matrix.tsv)

```python
from protdata.io import read_diann

adata = read_diann("/path/to/report.pg_matrix.tsv")
print(adata)
```

### FragPipe Import

You can download an example FragPipe output [file here](https://github.com/czbiohub-sf/protdata/raw/main/data/combined_protein.tsv)

```python
from protdata.io import read_fragpipe

adata = read_fragpipe("/path/to/combined_protein.tsv")
print(adata)
```

### mzTab Import

You can download an example mzTab [file here](https://github.com/czbiohub-sf/protdata/raw/main/data/SILAC_SQ.mzTab)

```python
from protdata.io import read_mztab

adata = read_mztab("/path/to/SILAC_SQ.mzTab")
print(adata)
```
## Authors

`protdata` is created and maintained by the [Computational Biology Platform](https://www.czbiohub.org/comp-biology/) at the [Chan Zuckerberg Biohub San Francisco](https://www.czbiohub.org/sf/).

To get in touch please use the [GihHub issues](https://github.com/czbiohub-sf/protdata/issues) page.

## Contributing

If you want to contribute to `protdata`, please read the [Contribution Guide](https://prodata.sf.czbiohub.org/contributing)

## Changelog
See [Release Notes](https://prodata.sf.czbiohub.org/release_notes)
sleepfake 1.1.2
Fake the time.sleep/asyncio.sleep function during tests.
Requires Python: >=3.10

<!-- Shield Badges -->
<p align="center">
  <img src="./logo.svg" alt="SleepFake Logo" width="120"/>
</p>
<p align="center">
  <a href="https://pypi.org/project/sleepfake/"><img src="https://img.shields.io/pypi/v/sleepfake.svg?color=blue" alt="PyPI version"></a>
  <a href="https://github.com/spulec/freezegun"><img src="https://img.shields.io/badge/dependency-freezegun-blue" alt="freezegun"></a>
  <img src="https://img.shields.io/badge/pytest%20fixture-alpha-orange" alt="pytest fixture alpha"/>
</p>

# 💤 SleepFake: Time Travel for Your Tests

Ever wish your tests could skip the wait? **SleepFake** lets you fake `time.sleep` and `asyncio.sleep` so your tests run at lightning speed—no more wasting time waiting for the clock!

## 🚀 Features

- Instantly skip over `sleep` calls in both sync and async code
- Works with `time.sleep` and `asyncio.sleep`
- Compatible with pytest and pytest-asyncio
- Supports context manager and async context manager usage
- No more slow tests—get results fast!

## ✨ Example Usage

```python
from sleepfake import SleepFake
import time
import asyncio

# Synchronous example
with SleepFake():
    start = time.time()
    time.sleep(10)  # Instantly skipped!
    end = time.time()
    print(f"Elapsed: {end - start:.2f}s")  # Elapsed: 10.00s

# Asynchronous example
async def main():
    async with SleepFake():
        start = asyncio.get_event_loop().time()
        await asyncio.sleep(5)  # Instantly skipped!
        end = asyncio.get_event_loop().time()
        print(f"Elapsed: {end - start:.2f}s")  # Elapsed: 5.00s

asyncio.run(main())
```

## 🧪 Why Use SleepFake?

- **Speed up your test suite**: No more real waiting!
- **Test time-based logic**: Simulate long waits, retries, and timeouts instantly.
- **Fun to use**: Who doesn't love time travel?

## 📦 Installation

```bash
pip install sleepfake
```

## 🤝 Contributing

PRs and issues welcome! Help make testing even more fun.

---

Made with ❤️ and a dash of impatience.

---

> **Note:** SleepFake uses [freezegun](https://github.com/spulec/freezegun) under the hood for time manipulation magic.
grein-loader 0.0.6
Package for loading datasets from GREIN
Requires Python: >=3.7

[![Test Package](https://github.com/grisslab/grein_loader/actions/workflows/test.yaml/badge.svg?branch=main)](https://github.com/grisslab/grein_loader/actions/workflows/test.yaml)
[![PyPI version](https://badge.fury.io/py/grein-loader.svg)](https://badge.fury.io/py/grein-loader)

# grein_loader

Python package to automatically download datasets from GREIN

http://www.ilincs.org/apps/grein

#### Introduction
Grein Loader enables users to access data from the GREIN website by using the GSE identification number

#### Installation

Install the package from pypi by using: 
```
pip install grein_loader
```


### Usage

The package allows you to download the description, metadata and the raw counts of a GREIN dataset based on the GSE id. 
The datasets from GREIN are publicly available and can be accessed via the [GREIN webpage](http://www.ilincs.org/apps/grein/?gse=). 
Each dataset uses an GEO accession id which allows you to access its data.

#### load_dataset()
```
geo_accession = "GSE112749"
description, metadata, count_matrix = grein_loader.load_dataset(geo_accession)
```

Input/Output parameters
```
Input parameter:
| gse_id | string | GEO accession id

Output parameter: 
| description  | dictionary      | description of dataset
| metadata     | dictionary      | metadata of dataset
| count_matrix | pandas dataframe| numpy array of raw counts
```

#### load_overview()
loads a number of datasets from Grein, the datasets are also listed on the main paige of GREIN
```
number_of_datasets = 10
overview = loader.load_overview(number_of_datasets)
```

The function returns a list of dictionaries, each dictionary contains the GSE id, number of samples, species and description 
provided from GREIN. 

```
Input parameter:
number_of_samples

Output parameter: 
list of dictionaries with, "geo_accession", no_samples", "species","title", "study_summary"
```
mustafaInstaSerchV2 0.2.0
A Python library for Instagram username search.
Requires Python: >=3.6

# mustafaInstaSerch

A Python library for Instagram search and login (placeholder).

## Installation

```bash
pip install mustafaInstaSerch
```

## Usage

### Search Usernames

```python
from mustafaInstaSerch.instagram_search import generate_usernames

usernames = generate_usernames()
for username in usernames:
    print(username)
```

### Login (Placeholder)

```python
from mustafaInstaSerch.instagram_search import login

result = login("your_email@example.com", "your_password")
print(result)
```



# mustafaInstaSerchV2

A Python library for Instagram username search.

## Installation

```bash
pip install mustafaInstaSerchV2
```

## Usage

```python
from mustafaInstaSerchV2.instagram_search import generate_usernames

usernames = generate_usernames()
for username in usernames:
    print(username)
```
pathorstr 2025.7.14.0
Unify Path and str

pathorstr
=========

Provide a union of pathlib's Path and built-in str

The code
--------

Everyone has this, in one form or another, locally.  `Here
<https://gitlab.com/bedhanger/mwe/-/tree/master/python/pathorstr/src/pathorstr/_pathorstr.py>`_ it
is "officially".

Installation
------------

The `project <https://pypi.org/project/pathorstr/>`_ is on PyPI, so simply run

.. code-block:: bash

    $ python -m pip install pathorstr