winbbi 0.1.3

Windows 平台高性能 BigWig 文件读取库（基于 Go 实现，支持原始信号和缩放信号）

winbbi 🚀 | blazingly fast BigWig for Windows-Python

A blazingly fast BigWig parser tailored for Windows Python environments. Implemented in Go with cgo → CPython C-API bindings to deliver native .pyd extensions — ideal for ChIP-seq, ATAC-seq and genomic data pipelines. 🧬

Designed specifically for bioinformatics researchers and developers working on Windows, winbbi delivers high-speed access to raw and zoomed signals from BigWig files. With Go-powered performance, native Windows optimization, and zero external dependencies, it’s the ultimate tool for handling large-scale genomic datasets (even GB-sized files) with minimal setup.

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🌟 Core Features

• 🚀 Blazing Fast: Go-under-the-hood acceleration outperforms pure Python libraries—process GB-sized BigWig files in seconds, not minutes!
• 🪟 Windows-Native: Optimized for 64-bit Windows 10/11 with built-in DLLs—no extra compilers, dependencies, or system tweaks required.
• 📊 Sparse Storage Aware: Natively adapts to BigWig’s sparse format, returning only valid signal intervals to save memory and speed up computations.
• 🎯 Flexible Zooming: Customize bin count and reduction ratio for visualization-ready signals—perfect for plotting, downstream analysis, or rapid data exploration.
• 📦 Zero Dependencies: Install with a single pip command—no numpy, pandas, or external tools needed (works with pure Python 3.7+).
• 🌐 Unicode Path Support: Seamlessly handle file paths with Chinese, special characters, or spaces—no more encoding headaches!
• 🛡️ Memory-Safe: Automatic C memory management and file handle cleanup (via with statements or destructors)—avoid leaks and crashes.

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📦 Installation

Get started in 10 seconds with pip—no complex setup, no build steps!

pip install winbbi

✨ Pro Tip: Use Python 3.7+ (64-bit) for full compatibility. Works with conda environments too!

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🚀 Quick Start

Basic Usage (Recommended: with Statement for Auto-Cleanup)

import winbbi

# Initialize reader + auto-close file (best practice)
with winbbi.BigWigReader() as reader:
    # 1. Open a BigWig file (supports absolute/relative/Unicode paths)
    reader.open(r"D:\BioData\RAD21_ENCFF000WCT.bigWig")
    
    # 2. Fetch file metadata (version, signal stats, coverage)
    metadata = reader.get_metadata()
    print("=== File Metadata ===")
    for key, value in metadata.items():
        print(f"{key}: {value}")
    
    # 3. Read raw signal (case-sensitive chromosome name!)
    raw_signal = reader.read_raw_signal(chrom="chr1", start=1000000, end=1001000)
    print(f"\n=== Raw Signal (chr1:1000000-1001000) ===")
    print(f"Signal length: {len(raw_signal)}")
    print(f"First 5 values: {raw_signal[:5] if raw_signal else 'No signal in interval'}")
    
    # 4. Read zoomed signal (100 bins for smooth visualization)
    zoom_signal = reader.read_zoom_signal(
        chrom="chr1",
        start=100000,
        end=200000,
        num_bins=100,          # Output resolution
        use_closest=True,      # Pick nearest zoom level
        desired_reduction=100  # Target compression ratio
    )
    print(f"\n=== Zoomed Signal (chr1:100000-200000, 100 bins) ===")
    print(f"Signal length: {len(zoom_signal)}")
    print(f"First 5 values: {zoom_signal[:5]}")

Standard Usage (Manual Resource Management)

import winbbi

reader = winbbi.BigWigReader()
try:
    reader.open("experiment_data.bw")
    signal = reader.read_raw_signal(chrom="chr2", start=500000, end=501000)
    print(f"Raw signal (first 10 values): {signal[:10]}")
finally:
    reader.close()  # Critical: Release resources!

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📚 Complete API Documentation

1. winbbi.BigWigReader()

• Purpose: Initialize the reader and load the built-in winbbi.dll
• Exceptions:
  • OSError: Non-Windows OS or 32-bit Windows (64-bit required)
  • FileNotFoundError: Missing winbbi.dll (reinstall the package)
  • RuntimeError: Failed to load DLL (missing MinGW’s libgcc_s_seh-1.dll—see Troubleshooting)

2. open(bw_file_path: str)

• Purpose: Open a BigWig file
• Parameters: bw_file_path → Absolute/relative path (supports Unicode)
• Exceptions:
  • FileNotFoundError: File does not exist
  • RuntimeError: Another file is open or invalid BigWig format

3. close()

• Purpose: Close the file and release handles/memory
• Note: Always call explicitly (or use with statements) to avoid leaks

4. get_metadata() -> dict

• Purpose: Retrieve core file statistics
• Returns:

  Field Name	Description	Type
  Version	BigWig format version	uint16
  Number of Zoom Levels	Built-in zoom levels count	uint16
  Bases Covered	Total bases with signal	uint64
  Minimum Value	Global minimum signal intensity	float64
  Maximum Value	Global maximum signal intensity	float64
  Sum of Data	Sum of all signal values	float64
  Sum of Squares	Sum of squared signal values	float64
  Buffer Size	File read buffer size (bytes)	uint32

5. get_chromosomes() -> list[str]

• Purpose: Get list of all chromosomes in the BigWig file
• Returns: List of chromosome names (e.g., ["chr1", "chr2", "chrX", ...])
• Example:

  with winbbi.BigWigReader() as reader:
      reader.open("data.bw")
      chroms = reader.get_chromosomes()
      print(f"Available chromosomes: {chroms}")
      print(f"Total: {len(chroms)} chromosomes")
  

6. read_raw_signal(chrom: str, start: int, end: int) -> list[float]

• Purpose: Read unmodified raw signal from a genomic interval
• Parameters:
  • chrom: Case-sensitive chromosome name (e.g., "chr1", not "Chr1")
  • start: Start position (≥0; genomic data typically starts at 1)
  • end: End position (> start)
• Returns: Raw signal values (empty list if no signal in the interval)

6. read_zoom_signal(chrom: str, start: int, end: int, num_bins=100, use_closest=True, desired_reduction=100) -> list[float]

• Purpose: Read zoomed/compressed signal (NaN → 0 automatically)
• Parameters:
  • num_bins: Output resolution (number of bins, ≥1)
  • use_closest: Use the nearest precomputed zoom level (faster)
  • desired_reduction: Target compression ratio (≥1)
• Returns: Zoomed signal (length = num_bins; all zeros if no data)

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🎯 Advanced Examples

Example 1: Batch Process Multiple Intervals

import winbbi

# List of genomic regions to analyze
regions = [
    ("chr1", 1000000, 1001000),
    ("chr1", 2000000, 2001000),
    ("chr2", 500000, 501000)
]

with winbbi.BigWigReader() as reader:
    reader.open("dataset.bw")
    for chrom, start, end in regions:
        signal = reader.read_raw_signal(chrom, start, end)
        print(f"\n📌 {chrom}:{start}-{end}")
        print(f"Signal length: {len(signal)}")
        if signal:
            print(f"Mean: {sum(signal)/len(signal):.4f} | Min: {min(signal):.2f} | Max: {max(signal):.2f}")

Example 2: Visualize Signals with Matplotlib

import winbbi
import matplotlib.pyplot as plt
import numpy as np

plt.style.use("seaborn-v0_8-darkgrid")

with winbbi.BigWigReader() as reader:
    reader.open(r"D:\BioData\RAD21_ENCFF000WCT.bigWig")
    
    # Read zoomed signal (200 bins for smooth plotting)
    chrom, start, end = "chr1", 500000, 1500000
    zoom_signal = reader.read_zoom_signal(chrom, start, end, num_bins=200)
    
    # Create x-axis (genomic positions)
    x_pos = np.linspace(start, end, len(zoom_signal))
    
    # Plot
    plt.figure(figsize=(14, 5))
    plt.plot(x_pos, zoom_signal, color="#FF6B6B", linewidth=2, alpha=0.8)
    plt.title(f"📊 Zoomed Signal - {chrom}:{start}-{end}", fontsize=16, pad=20)
    plt.xlabel("Genomic Position (bp)", fontsize=12)
    plt.ylabel("Signal Intensity", fontsize=12)
    plt.grid(alpha=0.3)
    plt.tight_layout()
    plt.savefig("signal_plot.png", dpi=300, bbox_inches="tight")
    plt.show()

Example 3: Filter Signals Above a Threshold

import winbbi

THRESHOLD = 10.0  # Keep signals above this intensity

with winbbi.BigWigReader() as reader:
    reader.open("dataset.bw")
    raw_signal = reader.read_raw_signal(chrom="chr3", start=800000, end=805000)
    
    # Filter high-confidence signals
    filtered_signal = [val for val in raw_signal if val > THRESHOLD]
    
    print(f"Original signal length: {len(raw_signal)}")
    print(f"Filtered signal length (values > {THRESHOLD}): {len(filtered_signal)}")
    if filtered_signal:
        print(f"Filtered stats → Min: {min(filtered_signal):.2f} | Max: {max(filtered_signal):.2f}")

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🚨 Troubleshooting

Common Issues & Fixes

Problem	Cause	Solution
Empty raw signal list	No signal in the specified interval	Verify chromosome name case + position range; Try a different region
"File not found" with Unicode paths	Outdated Python or incorrect path formatting	Use Python 3.7+; Use raw strings (e.g., r"C:\中文路径\file.bw")
Case-sensitive chromosome names	BigWig stores names as-is (e.g., "chr1" ≠ "1")	Use exact names from your BigWig (verify with UCSC Genome Browser)

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⚙️ System Requirements

• OS: 64-bit Windows 10 / Windows 11
• Python: 3.7+ (64-bit)
• Disk Space: ≥10MB (package + built-in DLL)
• RAM: Minimal (depends on dataset size—works with 4GB+ RAM)

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📄 License

MIT License © 2025 noesisthink

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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🌟 Contribute

Found a bug? Have a feature request? Want to optimize performance?
Feel free to open an issue or submit a pull request on GitHub!

Let’s make genomic data analysis on Windows faster and easier—together! 🤝