Development documentation · 0.2.3 30c2f8ba · Intro examples tested with 0.2.3 · Version details · Known limitations

Tutorials#

Choose a lesson by the result you want to produce. Start Here recommends an entry point for six backgrounds, including readers new to Python. Study times are planning goals, excluding installation.

Start here#

Lesson

Prerequisites

What you will produce

Study goal

Installation

A computer with Python or Conda

An environment that can import GWexpy

Follow the setup steps

Quickstart

GWexpy installed; able to run a terminal command

A saved ASD comparison of two synthetic channels

5 minutes

First analysis

GWexpy installed; no Python or spectral-analysis knowledge assumed

Time-series and ASD figures, with the code and axes explained

20–30 minutes

Commissioner workflow

Familiar with channels, time spans, and GUI spectral settings

A saved-data analysis with ASD, coherence, and recorded settings

20–30 minutes

Scientific Python to GWexpy

NumPy arrays, dictionaries, and basic plotting

Arrays carrying time and unit metadata; spectra computed for a collection

10–15 minutes

GW experimentalists can start at familiar concepts in Python. GWpy users can use the migration guide. Returning GWexpy users can browse task recipes.

Core lessons#

These notebooks extend the first analysis to another container or technique. Open a notebook in Jupyter and run its cells from top to bottom. Use the installation instructions for any additional packages named by a lesson.

Lesson

Prerequisites

Learning outcome

TimeSeries basics

First analysis or equivalent Python experience

Filter a channel, compute spectra, and use time-series operations

FrequencySeries basics

TimeSeries and frequency-domain concepts

Work with spectra and transfer functions

Spectrogram basics

TimeSeries and ASD

Build and interpret a time-frequency representation

Plotting basics

A TimeSeries or spectrum to plot

Customize axes, labels, and saved figures

Fitting basics

Arrays, plotting, and a model to fit

Fit a model and inspect its parameters

Noise generation basics

TimeSeries and ASD

Generate synthetic waveforms and noise with specified parameters

TimeSeriesMatrix basics

TimeSeriesDict and multi-channel analysis

Organize aligned channels in a matrix container

After a lesson, adapt a case study or consult the API reference for parameter details.