zlw 0.1.1

Zero Latency Whitening Utilities

zlw — Zero‑Latency Whitening utilities

zlw is a focused package for zero‑latency whitening in gravitational‑wave data analysis. It provides rigorous tools for:

• Whitening Kernels: Computing Minimum‑Phase (MP) filters that whiten data without introducing acausal latency.
• Perturbative Corrections: Geometric drift correction terms for the MP–MP scheme. These calculate the precise timing ($dt$) and phase ($d\phi$) biases introduced when the template PSD differs slightly from the real noise PSD.

Installation

pip install zlw

Structure

• zlw.kernels: Construction of MP whitening filters and frequency response utilities.
• zlw.corrections: Perturbative MP–MP correction terms (PerturbativeMPCorrection).
• zlw.fourier, zlw.window: FFT and windowing backend helpers.
• zlw.bin: Simulation and QA scripts.

Part 3: Example 1 (Whitening Filters)

Quick Examples

1. Computing a Whitening Filter

from zlw.kernels import MPWhiteningFilter

# psd: One‑sided PSD array (Hz^-1)
# fs: Sampling rate (Hz)
# n_fft: FFT length (e.g., 4 * fs)
wf = MPWhiteningFilter(psd, fs, n_fft)

# Get the one‑sided complex frequency response
# This response is minimum-phase (causal)
Wf = wf.frequency_response()

2. Computing Perturbative Corrections

If your template PSD (psd2) differs from the actual noise PSD (psd1), you can calculate the resulting geometric drift corrections.

import numpy as np
from zlw.corrections import PertMpCorrection

# freqs:  One‑sided frequency grid
# psd1:   Realization/Noise PSD (The truth)
# psd2:   Template PSD (The model)
# htilde: Template waveform (frequency domain)

corr = PertMpCorrection(
    freqs=freqs,
    psd1=psd1,
    psd2=psd2,
    htilde=htilde,
    fs=4096.0
)

# Calculate first-order geometric corrections
results = corr.correction()

print(f"Time Drift: {results.dt1:.4e} s")
print(f"SNR Loss:   {results.dsnr1:.4f}")

Development

To install development dependencies and run the test suite (requires lalsuite):

# Install in editable mode with dev dependencies
pip install -e .[dev]

# Run tests
pytest