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Efficient implementatins of the Konno Omachi filter in Python

Project description

pykoom

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Konno Omachi filter implemented in Cython.

This code implements Konno-Ohmachi spectral smoothing as defined in:

Konno, K. and Ohmachi, T., 1998. Ground-motion characteristics estimated
from spectral ratio between horizontal and vertical components of
microtremor. Bulletin of the Seismological Society of America, 88(1),
pp.228-241.

This code was originally written for smoothing sub-module in gmprocess by Bruce Worden. Dave Boore has provided notes on this topic, which also may be of interest. Notes regarding the characteristics of the Konno-Ohmachi filter and the implementation are provided in the implementation Jupyter Notebook.

Installation

pykoom is available via pip and can be installed with:

pip install pykoom

By default, pykoom uses numba for the fast implementation of the filter. Performance can be increased by using cython, but this requires a C complier. If a C compiler is available, install cython required dependencies with:

pip install pykoom[cython]

Usage

Smooth a signal using a bandwith of 30.

import pykoom
signal_smooth = pykoom.smooth(freqs, freqs_raw, signal_raw, 30)

Additional examples and comparison with obspy are provided in example.

Citation

Please cite this software using the following DOI.

Revision History

v0.3.0

  • Rename to pykoom
  • Add support for numba
  • Make cython an optonal dependency

v0.2.5

  • Packaging is hard. MANIFEST is fixed now.

v0.2.4

  • Added History to MANIFEST.

v0.2.3

  • Updated badges.
  • Added tests for example and implemenation notebooks.

v0.2.2

  • Moved Cython to a setup_requires

v0.2.1

  • Fixed packaging issue

v0.2

  • Added calculation of effective amplitude spectrum

v0.1.2

  • Initial release

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