scikufu 0.1.1.post1

A Python toolkit for scientific computing and data analysis, featuring parallel processing, batch OpenAI API calls, statistical analysis, and common research utilities.

SciKuFu

中文

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 Processing: High-performance parallel computing with threading, multiprocessing, and asyncio backends
• OpenAI Integration: Batch processing of OpenAI API calls with caching and structured output parsing
• File I/O Operations: Unified text, JSON, and JSON Lines file operations with encoding support
• Statistical Analysis: Comprehensive statistical methods including t-tests with normality checks and visualization
• Clean Architecture: Modular design with optional dependencies for lightweight core usage

Installation

Basic Installation

pip install scikufu

With Optional Features

# Install with parallel processing and OpenAI support
pip install scikufu[parallel,parallel-openai]

# Install with statistical analysis support
pip install scikufu[stats]

# Install with all features
pip install scikufu[parallel,parallel-openai,stats]

From Source

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

Quick Start

Parallel Processing

from scikufu.parallel import run_in_parallel

def process_item(item):
    return item * 2

items = [1, 2, 3, 4, 5]
results = run_in_parallel(
    func=process_item,
    tasks=items,
    n_jobs=4,
    backend="threading"  # or "multiprocessing", "asyncio"
)
print(results)  # [2, 4, 6, 8, 10]

OpenAI API Batch Processing

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?"}],
]

# Simple chat completion
results = client.chat_completion(
    messages=messages,
    model="gpt-4",
    n_jobs=4,
    with_tqdm=True,
    temperature=0.7
)

# Structured output parsing with Pydantic
from pydantic import BaseModel

class Answer(BaseModel):
    language: str
    description: str

structured_results = client.chat_completion_parse(
    messages=messages,
    model="gpt-4",
    response_model=Answer,
    n_jobs=4
)

File I/O Operations

from scikufu.file import text, json, jsonl

# Text file operations
text.write("hello.txt", "Hello, World!")
content = text.read("hello.txt", encoding="utf-8")

# JSON file operations
data = {"name": "SciKuFu", "version": "0.1.0"}
json.write("config.json", data, indent=4)
loaded_data = json.read("config.json")

# JSON Lines operations
records = [{"id": 1, "name": "Alice"}, {"id": 2, "name": "Bob"}]
jsonl.write("data.jsonl", records)
for record in jsonl.read("data.jsonl"):
    print(record)

Statistical Analysis

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

# Generate sample data
group1 = np.random.normal(100, 15, 30)
group2 = np.random.normal(105, 15, 30)

# Comprehensive t-test with visualization
t_stat, p_value, significant = t_test(
    data=(group1, group2),
    alpha=0.05,
    show_plot=True,
    save_path="./t_test_plot.png",
    test_type="welch"  # or "student"
)

print(f"t-statistic: {t_stat}")
print(f"p-value: {p_value}")
print(f"Significant: {significant}")

Modules

🚀 Parallel Processing (scikufu.parallel)

• Core Functions: run_in_parallel(), run_async_in_parallel()
• Backends: Threading, Multiprocessing, AsyncIO
• Features: Disk-based caching, retry mechanisms, progress tracking
• Use Case: CPU-bound tasks, I/O operations, concurrent API calls

🤖 OpenAI Integration (scikufu.parallel.openai)

• Client Class: Wrapper for OpenAI async API
• Features: Batch processing, structured output parsing, caching
• Use Case: Large-scale language model inference, data processing

📁 File I/O (scikufu.file)

• Text Operations: text.read(), text.write(), text.append()
• JSON Operations: json.read(), json.write(), json.append()
• JSONL Operations: jsonl.read(), jsonl.write(), jsonl.append()
• Features: Unicode support, automatic directory creation, memory efficiency

📊 Statistical Analysis (scikufu.stats)

• T-Test: Comprehensive statistical testing with visualization
• Features: Normality checks, effect size calculation, PP/QQ plots
• Input Formats: Tuples, pandas DataFrames, numpy arrays
• Export: Multiple plot formats, detailed statistical reports

Optional Dependencies

# Parallel processing features
pip install diskcache tqdm

# OpenAI API integration
pip install openai

# Statistical analysis and visualization
pip install matplotlib numpy pandas scipy

Project Structure

scikufu/
├── src/scikufu/          # Main package source
│   ├── parallel/         # Parallel processing utilities
│   ├── openai.py        # OpenAI API integration
│   ├── file/            # File I/O operations
│   ├── stats/           # Statistical analysis
│   └── py.typed        # Type annotations support
├── tests/               # Comprehensive test suite
│   ├── parallel/       # Parallel processing tests
│   ├── file/          # File I/O tests
│   └── stats/         # Statistical tests
└── htmlcov/           # Coverage reports

Requirements

• Python: 3.12+
• Core Dependencies: None (lightweight design)
• Optional Dependencies: Feature-based extras for specific functionality

License

MIT

Contributing

All features are developed based on actual research needs. Suggestions, feedback, and contributions are welcome! Please feel free to open issues or submit pull requests.

Note

This toolkit is designed to be modular and extensible. Each module can be used independently, and the core functionality remains lightweight with optional dependencies for specific features.