  We present a new Python package, gwbench, implementing the well-established
Fisher information formalism as a fast and straightforward tool for the purpose
of gravitational-wave benchmarking, i.e. the estimation of signal-to-noise
ratios and measurement errors of gravitational waves observed by a network of
detectors. Such an infrastructure is necessary due to the high computational
cost of Bayesian parameter estimation methods which renders them less effective
for the scientific assessment of gravitational waveforms, detectors, and
networks of detectors, especially when determining their effects on large
populations of gravitational-wave sources spread throughout the universe.
gwbench further gives quick access to detector locations and sensitivities,
while including the effects of Earth's rotation on the latter, as well as
waveform models and their derivatives, while giving access to the host of
waveforms available in the LSC Algorithm Library. With the provided
functionality, gwbench is relevant for a wide variety of applications in
gravitational-wave astronomy such as waveform modeling, detector development,
cosmology, and tests of general relativity.
