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Quick Start

Welcome to the gwmock-signal documentation.

Before you begin

  1. Follow Installation to create an environment and install the package.
  2. Read the User guide overview for the recommended workflow (waveforms → projection → injection → multichannel).

Verify the install

gwmock-signal --help
gwmock-signal inject --help

python -c "import gwmock_signal; print(gwmock_signal.__version__)"
python -c "from gwmock_signal import list_registered_source_types; print(list_registered_source_types())"

You can also run the package as a module to print version information:

python -m gwmock_signal

Configuring logging

The package uses Python's standard logging module under the "gwmock_signal" logger. In the CLI, use --verbose DEBUG to increase log level. From Python:

from gwmock_signal.utils import setup_logger, get_version_information

# Set up console logging at INFO level
setup_logger(log_level="INFO", print_version=True)

# Or direct version query
print(get_version_information())

setup_logger accepts outdir and label arguments to write a log file alongside console output.

End-to-end demo

Once installed, run a quick CBC injection with the bundled example parameters. This uses the GW150914-like parameters shipped with the package under examples/gw150914_like.json:

gwmock-signal inject cbc \
    --params examples/gw150914_like.json \
    --network H1L1

This prints the per-detector RMS strain to stdout. Add --output injected.h5 to write the result to an HDF5 file instead.

The same workflow in Python:

import json, numpy as np
from pathlib import Path
from gwpy.timeseries import TimeSeries
from gwmock_signal.pipeline import inject_cbc_signal
from gwmock_signal.network import Network

# Load bundled example parameters
params = json.loads(Path("examples/gw150914_like.json").read_text())

# Resolve a two-detector network
net = Network.from_name("H1L1")

# Build a zero-noise background centred on coa_time
fs = 4096.0
duration = 8.0  # seconds
n = int(duration * fs)
t0 = params["coa_time"] - duration / 2
background = {name: TimeSeries(np.zeros(n), t0=t0, sample_rate=fs) for name in net.detector_names}

# Run the injection pipeline
result = inject_cbc_signal(
    "IMRPhenomD", params, net.detector_names, background,
    sampling_frequency=fs, minimum_frequency=20.0,
)

# Print RMS per detector
for name in result.detector_names:
    rms = float(np.sqrt(np.mean(result[name].value**2)))
    print(f"{name}: rms={rms:.4e}")

The repository also ships additional example files:

  • examples/gw150914_like.json — GW150914-like CBC parameters
  • examples/networks/hlvk.yaml — 4-detector LIGO-Virgo-KAGRA network
  • examples/networks/et_triangle.yaml — Einstein Telescope triangle network

Next steps