Signal processing for field and experimental data for earthquake engineering

eqsig

A Python package for seismic signal processing.

Features

This package provides common functions for computing ground motion parameters and performing signal processing. The functions are implemented on either numpy arrays or on a signal object that uses caching to avoid expensive recalculation of widely used parameters.

• Compute the acceleration response spectrum and elastic response time series using the fast Nigam and Jennings (1968) algorithm.
• Compute the Fourier amplitude spectrum (using the scipy.signal.fft algorithm)
• Compute the smooth Fourier amplitude spectrum according to Konno and Ohmachi (1998)
• Compute velocity and displacement from acceleration time series
• Compute peak ground motion quantities (PGA, PGV, PGD)
• Compute common ground motion intensity measures (Arias intensity, CAV, CAV_dp5, significant duration, bracketed duration, dominant period)
• Compute signal features (zero crossings, global peaks, local peaks)
• Compute rotated ground motion or intensity measure from two ground motion components
• Resampling of ground motion through interpolation or periodic resampling
• Butterworth filter (using scipy), running average, polynomial fitting
• Fast loading of, and saving of, plain text to and from Signal objects

How to Use

Examples

Generate response spectra

```import numpy as np
import matplotlib.pyplot as plt
import eqsig.single

bf, sub_fig = plt.subplots()
dt = 0.005  # time step of acceleration time series
periods = np.linspace(0.2, 5, 100)  # compute the response for 100 periods between T=0.2s and 5.0s
record = eqsig.AccSignal(a * 9.8, dt)
record.generate_response_spectrum(response_times=periods)
times = record.response_times

sub_fig.plot(times, record.s_a, label="eqsig")
plt.show()
```

Contributing

How do I get set up?

1. Run pip install -r requirements.txt

Package conventions

• A function that calculates a property the takes a signal as an input, should be named as calc_<property>, if the calculation has multiple different implementations, then include the citation as author and year as well calc_<property>_<author>_<year>
• If the function takes a raw array then it should contain the word array (or values).

Testing

Tests are run with pytest

• Locally run: pytest on the command line.
• Tests are run on every push using travis, see the .travis.yml file

Deployment

To deploy the package to pypi.com you need to:

1. Push to the pypi branch. This executes the tests on circleci.com
2. Create a git tag and push to github, run: trigger_deploy.py or manually:
```git tag 0.5.2 -m "version 0.5.2"
git push --tags origin pypi
```

Documentation

Built via Sphinx following: https://codeandchaos.wordpress.com/2012/07/30/sphinx-autodoc-tutorial-for-dummies/

For development mode

1. cd to docs
2. Run make html

To fix long_description in setup.py: pip install collective.checkdocs, python setup.py checkdocs