Start Here#
Choose your background to find a first lesson and a concrete result to work toward. You can start with synthetic data on your own computer; detector access is not a prerequisite. The suggested study times below are planning goals, excluding installation.
Choose a first lesson#
Prerequisites: no Python or spectral-analysis experience assumed; install GWexpy first.
Outcome: run Python code and explain a time-series plot and an ASD plot. Study goal: 20–30 minutes.
Prerequisites: familiar with channels, sample rates, and spectra; Python may be new.
Outcome: connect those concepts to TimeSeries, TimeSeriesDict, and a saved ASD figure. Study goal: 10–20 minutes.
Prerequisites: comfortable choosing channels, time spans, FFT lengths, and reference channels in a GUI.
Outcome: reproduce a saved-data workflow with ASD, coherence, and recorded analysis settings. Study goal: 20–30 minutes.
Prerequisites: NumPy arrays, dictionaries, and basic plotting; no GWpy knowledge needed.
Outcome: attach time and unit metadata to arrays and replace a per-channel spectral loop with a collection method. Study goal: 10–15 minutes.
Prerequisites: existing GWpy scripts or familiarity with its containers.
Outcome: identify useful GWexpy additions and adapt an existing analysis with the migration examples.
Prerequisites: a working GWexpy environment and a specific analysis task.
Outcome: find a task recipe or case study; use the API reference for exact parameters.
Prepare and run#
Follow Installation if GWexpy is not installed yet.
Open the lesson matching your background. Each includes the concepts needed for its first result.
For a short environment check, run the Quickstart: two synthetic channels produce a saved ASD figure.
Continue with your result#
Use core lessons to learn another container, or choose a case study that resembles your measurement. Detailed FFT, GPS-time, and compatibility conventions are available when you need them.