scikufu 0.1.0

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SciKuFu

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SciKuFu is a Python toolkit that wraps up the most frequently used utilities from my personal research workflow. It aims to boost productivity and simplify common scientific computing and data analysis tasks.

Features

• Parallel computing and batch processing (e.g., concurrent OpenAI API requests, result caching)
• Common statistical analysis methods (e.g., t-test, normality checks, visualization)
• Clean code structure, easy to extend and integrate into personal projects

Installation

Recommended:

pip install scikufu

Or from source:

git clone https://github.com/Mars160/scikufu.git
cd scikufu
pip install .

Quick Start

Parallel OpenAI API Calls

from scikufu.parallel.openai import Client

client = Client(api_key="your-api-key")
messages = [
    [{"role": "user", "content": "What is Python?"}],
    [{"role": "user", "content": "What is JavaScript?"}],
]
results = client.chat_completion(
    messages=messages,
    model="gpt-4",
    n_jobs=4,
    with_tqdm=True,
    temperature=0.7
)

Statistical T-Test

from scikufu.stats.ttest import t_test
import numpy as np

group1 = np.random.normal(100, 15, 30)
group2 = np.random.normal(105, 15, 30)
t_stat, p_value, significant = t_test(
    data=(group1, group2),
    alpha=0.05,
    show_plot=True,
    save_path="./t_test_plot.png"
)
print(f"t-statistic: {t_stat}")
print(f"p-value: {p_value}")
print(f"Significant: {significant}")

Project Structure

• src/scikufu/: main code
• tests/: test code
• htmlcov/: coverage report

License

MIT

Note

All features are developed based on my own research needs. Suggestions and feedback are welcome!