Signal processing for field and experimental data for earthquake engineering

## Project description

## 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

[Eqsig documentation](https://eqsig.readthedocs.io)

#### Examples

##### Generate response spectra

```
import numpy as np
import matplotlib.pyplot as plt
import eqsig.single
bf, sub_fig = plt.subplots()
a = np.loadtxt("<path-to-acceleration-time-series>")
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()
```

##### Generate Stockwell transform

```
import numpy as np
import matplotlib.pyplot as plt
import eqsig
from matplotlib import rc
rc('font', family='Helvetica', size=9, weight='light')
plt.rcParams['pdf.fonttype'] = 42
dt = 0.01
time = np.arange(0, 10, dt)
f1 = 0.5
factor = 10.
f2 = f1 * factor
acc = np.cos(2 * np.pi * time * f1) + factor / 5 * np.cos(2 * np.pi * time * f2)
asig = eqsig.AccSignal(acc, dt)
asig.swtf = eqsig.stockwell.transform(asig.values)
bf, ax = plt.subplots(nrows=2, sharex=True, figsize=(5.0, 4.0))
ax[0].plot(asig.time, asig.values, lw=0.7, c='b', label='Signal')
in_pcm = eqsig.stockwell.plot_stock(ax[1], asig)
ax[1].set_ylim([0.0, 10])
ax[0].set_xlim([0, 10])
ax[0].set_ylabel('Amplitude [$m/s^2$]', fontsize=8)
ax[1].set_ylabel('$\it{Stockwell}$\nFrequency [Hz]', fontsize=8)
ax[-1].set_xlabel('Time [s]', fontsize=8)
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
cbaxes = inset_axes(ax[1], width="20%", height="3%", loc='upper right')
cbaxes.set_facecolor([1, 1, 1])
cb = plt.colorbar(in_pcm, cax=cbaxes, orientation='horizontal')
cb.outline.set_edgecolor('white')
cbaxes.tick_params(axis='both', colors='white')
ax[0].legend(loc='upper right')
for sp in ax:
sp.tick_params(axis='both', which='major', labelsize=8)
plt.tight_layout()
plt.show()
```

### Useful material

### Contributing

#### How do I get set up?

Run pip install -r requirements.txt

#### Package conventions

A function that calculates a property that takes a Signal object 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 or vals).

#### 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:

Push to the

*pypi*branch. This executes the tests on circleci.comCreate 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

cd to docs

Run make html

Docstrings follow numpy convention (in progress): https://numpydoc.readthedocs.io/en/latest/format.html

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

#### Release instructions

On zenodo.org use the github integration tool, click on the eqsig package and click create new release.

### History

#### 1.2.11 (2024-03-28)

Added tol threshold for zero crossing and peak indices algorithms

#### 1.2.10 (2020-11-24)

Adjusted eqsig.stockwell.plot_stock, since min freq was out by factor of 0.5.

#### 1.2.5 (2020-11-24)

Added gen_ricker_wavelet_asig to create an acceleration signal that is a Ricker wavelet

Added eqsig.sdof.calc_input_energy_spectrum to compute the input energy into an SDOF

Can now load a Signal with a scale factor by passing in the keyword m=<scale factor>

The left interpolation function interp_left now returns the same size as x, which can be a scalar, and if y is None then assumes index (0,1,2,…,n)

#### 1.2.4 (2020-07-20)

Fixed issue with computation of surface energy spectra

Support for numpy==1.19

#### 1.2.3 (2020-05-05)

Fixed docs for generation of FAS, changed kwarg n_plus to p2_plus since this adds to the power of 2.

#### 1.2.2 (2020-05-05)

Switched to numpy for computing the Fourier amplitude spectrum

#### 1.2.1 (2020-05-05)

Added response_period_range to AccSignal object initial inputs to define response periods using an upper and lower limit

Improved speed of surface energy calculation calc_surface_energy and returns correct size based on input dimensions

Removed global import of scipy - done at function level

Added an interp_left function to interpolate an array and take lower value

Fixed issue with inverse of stockwell transform stockwell.itransform, it no longer doubles the time step

Increased speed of stockwell transform stockwell.transform.

Added remove_poly function to remove a polynomial fit from an array

Added option to access fa_frequencies and smooth_fa_frequencies as fa_freqs and smooth_fa_freqs.

Added option for computing smoothed FAS with extra zero padding

Added function for computing smoothed fas using a custom smoothing matrix.

#### 1.2.0 (2019-11-03)

Added interp2d fast interpolation of a 2D array to obtain a new 2D array

No longer raises warning when period is 0.0 for computing response spectrum

Fixed issue with computation of smoothed response spectrum for dealing with zeroth frequency

Increased speed of`generate_smooth_fa_spectrum`

Can now directly set AccSignal.smooth_fa_frequencies

Deprecated AccSignal.smooth_freq_points and AccSignal.smooth_freq_range will be removed in later version

#### 1.1.2 (2019-10-31)

More accuracy in calc_surface_energy - now interpolates between time steps. More tests added.

#### 1.1.1 (2019-10-29)

Fixed issue in get_zero_crossings_array_indices where it would fail if array did not contain any zeros.

Added calculation of equivalent number of cycles and equivalent uniform amplitude using power law relationship as intensity measures

Added function get_n_cyc_array to compute number of cycles series from a loading series

Added intensity measure im.calc_unit_kinetic_energy() to compute the cumulative change in kinetic energy according to Millen et al. (2019)

Added surface.py with calculation of surface energy and cumulative change in surface energy time series versus depth from surface

#### 1.1.0 (2019-10-08)

Fixed issue with second order term in sdof response spectrum calculation which effected high frequency response, updated example to show difference

#### 1.0.0 (2019-07-01)

First production release

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