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        "author": "Maxim Millen",
        "author_email": "mmi46@uclive.ac.nz",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 5 - Production/Stable",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering"
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        "description": ".. image:: https://travis-ci.org/eng-tools/eqsig.svg?branch=master\n   :target: https://travis-ci.org/eng-tools/eqsig\n   :alt: Testing Status\n\n.. image:: https://img.shields.io/pypi/v/eqsig.svg\n   :target: https://pypi.python.org/pypi/eqsig\n   :alt: PyPi version\n\n.. image:: https://coveralls.io/repos/github/eng-tools/eqsig/badge.svg\n   :target: https://coveralls.io/github/eng-tools/eqsig\n\n.. image:: https://img.shields.io/badge/license-MIT-blue.svg\n    :target: https://github.com/eng-tools/eqsig/blob/master/LICENSE\n    :alt: License\n\n.. image:: https://eng-tools.github.io/static/img/ecp-badge.svg\n    :target: https://eng-tools.github.io\n    :alt: ECP project\n\n.. image:: https://zenodo.org/badge/125842866.svg\n   :target: https://zenodo.org/badge/latestdoi/125842866\n   :alt: DOI\n\n.. image:: https://pepy.tech/badge/eqsig\n   :target: https://pepy.tech/project/eqsig\n\n*****\neqsig\n*****\n\nA Python package for seismic signal processing.\n\nFeatures\n========\n\nThis package provides common functions for computing ground motion parameters and performing signal processing.\nThe functions are implemented on either numpy arrays or on a signal object that uses caching to avoid expensive\nrecalculation of widely used parameters.\n\n* Compute the acceleration response spectrum and elastic response time series using the fast Nigam and Jennings (1968) algorithm.\n* Compute the Fourier amplitude spectrum (using the scipy.signal.fft algorithm)\n* Compute the smooth Fourier amplitude spectrum according to Konno and Ohmachi (1998)\n* Compute velocity and displacement from acceleration time series\n* Compute peak ground motion quantities (PGA, PGV, PGD)\n* Compute common ground motion intensity measures (Arias intensity, CAV, CAV_dp5, significant duration, bracketed duration, dominant period)\n* Compute signal features (zero crossings, global peaks, local peaks)\n* Compute rotated ground motion or intensity measure from two ground motion components\n* Resampling of ground motion through interpolation or periodic resampling\n* Butterworth filter (using scipy), running average, polynomial fitting\n* Fast loading of, and saving of, plain text to and from Signal objects\n\n\nHow to Use\n==========\n\n[Eqsig documentation](https://eqsig.readthedocs.io)\n\nExamples\n--------\n\nGenerate response spectra\n_________________________\n\n.. code-block:: python\n\n    import numpy as np\n    import matplotlib.pyplot as plt\n    import eqsig.single\n\n    bf, sub_fig = plt.subplots()\n    a = np.loadtxt(\"<path-to-acceleration-time-series>\")\n    dt = 0.005  # time step of acceleration time series\n    periods = np.linspace(0.2, 5, 100)  # compute the response for 100 periods between T=0.2s and 5.0s\n    record = eqsig.AccSignal(a * 9.8, dt)\n    record.generate_response_spectrum(response_times=periods)\n    times = record.response_times\n\n    sub_fig.plot(times, record.s_a, label=\"eqsig\")\n    plt.show()\n\n\nGenerate Stockwell transform\n____________________________\n\n.. code-block:: python\n\n    import numpy as np\n    import matplotlib.pyplot as plt\n    import eqsig\n\n    from matplotlib import rc\n    rc('font', family='Helvetica', size=9, weight='light')\n    plt.rcParams['pdf.fonttype'] = 42\n\n\n    dt = 0.01\n    time = np.arange(0, 10, dt)\n    f1 = 0.5\n    factor = 10.\n    f2 = f1 * factor\n    acc = np.cos(2 * np.pi * time * f1) + factor / 5 * np.cos(2 * np.pi * time * f2)\n\n    asig = eqsig.AccSignal(acc, dt)\n\n    asig.swtf = eqsig.stockwell.transform(asig.values)\n\n    bf, ax = plt.subplots(nrows=2, sharex=True, figsize=(5.0, 4.0))\n\n    ax[0].plot(asig.time, asig.values, lw=0.7, c='b', label='Signal')\n\n    in_pcm = eqsig.stockwell.plot_stock(ax[1], asig)\n    ax[1].set_ylim([0.0, 10])\n    ax[0].set_xlim([0, 10])\n\n    ax[0].set_ylabel('Amplitude [$m/s^2$]', fontsize=8)\n    ax[1].set_ylabel('$\\it{Stockwell}$\\nFrequency [Hz]', fontsize=8)\n    ax[-1].set_xlabel('Time [s]', fontsize=8)\n\n    from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n    cbaxes = inset_axes(ax[1], width=\"20%\", height=\"3%\", loc='upper right')\n    cbaxes.set_facecolor([1, 1, 1])\n    cb = plt.colorbar(in_pcm, cax=cbaxes, orientation='horizontal')\n    cb.outline.set_edgecolor('white')\n    cbaxes.tick_params(axis='both', colors='white')\n\n    ax[0].legend(loc='upper right')\n    for sp in ax:\n        sp.tick_params(axis='both', which='major', labelsize=8)\n\n    plt.tight_layout()\n    plt.show()\n\n.. image:: ./examples/stockwell-example.png\n  :width: 400\n  :alt: Output from example\n\nUseful material\n===============\n\n*\n\nContributing\n============\n\nHow do I get set up?\n--------------------\n\n1. Run ``pip install -r requirements.txt``\n\n\nPackage conventions\n-------------------\n\n* A function that calculates a property that takes a Signal object as an input, should be named as `calc_<property>`,\n  if the calculation has multiple different implementations, then include the citation as author and year\n  as well `calc_<property>_<author>_<year>`\n* If the function takes a raw array then it should contain the word array (or values or vals).\n\n\nTesting\n-------\n\nTests are run with pytest\n\n* Locally run: ``pytest`` on the command line.\n\n* Tests are run on every push using travis, see the ``.travis.yml`` file\n\n\nDeployment\n----------\n\nTo deploy the package to pypi.com you need to:\n\n1. Push to the *pypi* branch. This executes the tests on circleci.com\n\n2. Create a git tag and push to github, run: ``trigger_deploy.py`` or manually:\n\n.. code:: bash\n\n    git tag 0.5.2 -m \"version 0.5.2\"\n    git push --tags origin pypi\n\n\nDocumentation\n-------------\n\nBuilt via Sphinx following: https://codeandchaos.wordpress.com/2012/07/30/sphinx-autodoc-tutorial-for-dummies/\n\nFor development mode\n\n 1. cd to docs\n 2. Run ``make html``\n\nDocstrings follow numpy convention (in progress): https://numpydoc.readthedocs.io/en/latest/format.html\n\nTo fix long_description in setup.py: ``pip install collective.checkdocs``, ``python setup.py checkdocs``\n\n\nRelease instructions\n--------------------\n\nOn zenodo.org use the github integration tool, click on the eqsig package and click create new release.\n\nHistory\n=======\n\n1.2.10 (2020-11-24)\n-------------------\n* Adjusted `eqsig.stockwell.plot_stock`, since min freq was out by factor of 0.5.\n\n1.2.5 (2020-11-24)\n-------------------\n* Added `gen_ricker_wavelet_asig` to create an acceleration signal that is a Ricker wavelet\n* Added `eqsig.sdof.calc_input_energy_spectrum` to compute the input energy into an SDOF\n* Can now load a Signal with a scale factor by passing in the keyword `m=<scale factor>`\n* 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)\n\n1.2.4 (2020-07-20)\n-------------------\n* Fixed issue with computation of surface energy spectra\n* Support for numpy==1.19\n\n1.2.3 (2020-05-05)\n-------------------\n* Fixed docs for generation of FAS, changed kwarg `n_plus` to `p2_plus` since this adds to the power of 2.\n\n1.2.2 (2020-05-05)\n-------------------\n* Switched to numpy for computing the Fourier amplitude spectrum\n\n1.2.1 (2020-05-05)\n-------------------\n\n* Added `response_period_range` to AccSignal object initial inputs to define response periods using an upper and lower limit\n* Improved speed of surface energy calculation `calc_surface_energy` and returns correct size based on input dimensions\n* Removed global import of scipy - done at function level\n* Added an `interp_left` function to interpolate an array and take lower value\n* Fixed issue with inverse of stockwell transform `stockwell.itransform`, it no longer doubles the time step\n* Increased speed of stockwell transform `stockwell.transform`.\n* Added `remove_poly` function to remove a polynomial fit from an array\n* Added option to access `fa_frequencies` and `smooth_fa_frequencies` as `fa_freqs` and `smooth_fa_freqs`.\n* Added option for computing smoothed FAS with extra zero padding\n* Added function for computing smoothed fas using a custom smoothing matrix.\n\n\n1.2.0 (2019-11-03)\n-------------------\n\n* Added `interp2d` fast interpolation of a 2D array to obtain a new 2D array\n* No longer raises warning when period is 0.0 for computing response spectrum\n* Fixed issue with computation of smoothed response spectrum for dealing with zeroth frequency\n* Increased speed of`generate_smooth_fa_spectrum`\n* Can now directly set `AccSignal.smooth_fa_frequencies`\n* Deprecated `AccSignal.smooth_freq_points` and `AccSignal.smooth_freq_range` will be removed in later version\n\n1.1.2 (2019-10-31)\n-------------------\n\n* More accuracy in `calc_surface_energy` - now interpolates between time steps. More tests added.\n\n\n1.1.1 (2019-10-29)\n-------------------\n\n* Fixed issue in `get_zero_crossings_array_indices` where it would fail if array did not contain any zeros.\n* Added calculation of equivalent number of cycles and equivalent uniform amplitude using power law relationship as intensity measures\n* Added function `get_n_cyc_array` to compute number of cycles series from a loading series\n* Added intensity measure `im.calc_unit_kinetic_energy()` to compute the cumulative change in kinetic energy according to Millen et al. (2019)\n* Added `surface.py` with calculation of surface energy and cumulative change in surface energy time series versus depth from surface\n\n\n1.1.0 (2019-10-08)\n-------------------\n\n* Fixed issue with second order term in sdof response spectrum calculation which effected high frequency response, updated example to show difference\n\n1.0.0 (2019-07-01)\n-------------------\n\n* First production release",
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