Quickstart

Pretrained model variants

Two pretrained variants of DeepExtractor_257 are available, each paired with a specific scaler:

Variant	Checkpoint constant	Scaler file	Use case
Bilby noise	CHECKPOINT_BILBY	assets/scaler_bilby.pkl	Simulated LIGO/Virgo noise
Real O3 noise	CHECKPOINT_REAL	assets/scaler.pkl	Real LIGO O3 detector data

Load a pretrained model and reconstruct

Weights are downloaded automatically from Hugging Face Hub on first use and cached locally.

import numpy as np
import deepextractor

# Load model (bilby noise variant by default)
model = deepextractor.DeepExtractorModel()

# Extract the transient signal from a noisy strain array
noisy_strain = np.random.randn(8192)              # replace with real data
reconstructed = model.reconstruct(noisy_strain)   # extracted signal
background    = model.background(noisy_strain)    # noise estimate

# One-liner convenience function (reloads model on each call)
reconstructed = deepextractor.reconstruct(noisy_strain)

To use the real O3 noise variant:

from deepextractor.utils.checkpoints import CHECKPOINT_REAL

model = deepextractor.DeepExtractorModel(
    checkpoint_filename=CHECKPOINT_REAL,
    scaler_path="assets/scaler.pkl",
)

Notebooks

• DeepExtractor: Minimal Example — minimal end-to-end example: simulate noise, inject a sine-Gaussian wavelet, and reconstruct with DeepExtractorModel

• DeepExtractor — Glitch Reconstruction Tutorial — reconstruct real LIGO O3a glitches using the bundled GravitySpy sample dataset

Bundled dataset

The package ships a sample of the GravitySpy LIGO O3a high-confidence catalogue at assets/data_o3a_sample.csv. It contains 10 H1 examples per glitch class (17 classes, 170 rows total), randomly sampled from high-confidence GravitySpy classifications with SNR > 15. It is used by the glitch reconstruction tutorial notebook.

import pandas as pd
import importlib.resources as resources

with resources.path("deepextractor", "assets") as assets:
    df = pd.read_csv(assets / "data_o3a_sample.csv")

print(df["label"].value_counts())

CLI tools

After installation, the following CLI entry points are available:

# Train a model
deepextractor-train --model DeepExtractor_257 --data-dir data/pycbc_noise/spectrogram_domain/

# Generate training data
deepextractor-generate --output-dir data/ --num-train 250000

# Convert time-domain data to spectrograms
deepextractor-specgen --input-dir data/pycbc_noise/time_domain/ --output-dir data/pycbc_noise/spectrogram_domain/

# Evaluate a trained model
deepextractor-evaluate --model DeepExtractor_257 --checkpoint-dir checkpoints/ --data-dir data/