Metadata-Version: 2.1
Name: eqsig
Version: 1.0.0
Summary: Signal processing for field and experimental data for earthquake engineering
Home-page: UNKNOWN
Author: Maxim Millen
Author-email: mmi46@uclive.ac.nz
License: MIT
Description: .. image:: https://travis-ci.org/eng-tools/eqsig.svg?branch=master
           :target: https://travis-ci.org/eng-tools/eqsig
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        *****
        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
        
        .. code-block:: python
        
            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()
        
        
        Useful material
        ===============
        
        *
        
        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 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:
        
        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:
        
        .. code:: bash
        
            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``
        
        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``
        
        =======
        History
        =======
        
        1.0.0 (2019-07-01)
        -------------------
        
        * First production release
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3
Provides-Extra: test
