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eqsig-1.2.5
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مشاهده بیشترتوضیحات
Signal processing for field and experimental data for earthquake engineering
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | eqsig-1.2.5 |
| نام | eqsig |
| نسخه کتابخانه | 1.2.5 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Maxim Millen |
| ایمیل نویسنده | mmi46@uclive.ac.nz |
| آدرس صفحه اصلی | - |
| آدرس اینترنتی | https://pypi.org/project/eqsig/ |
| مجوز | MIT |
.. image:: https://travis-ci.org/eng-tools/eqsig.svg?branch=master
:target: https://travis-ci.org/eng-tools/eqsig
:alt: Testing Status
.. image:: https://img.shields.io/pypi/v/eqsig.svg
:target: https://pypi.python.org/pypi/eqsig
:alt: PyPi version
.. image:: https://coveralls.io/repos/github/eng-tools/eqsig/badge.svg
:target: https://coveralls.io/github/eng-tools/eqsig
.. image:: https://img.shields.io/badge/license-MIT-blue.svg
:target: https://github.com/eng-tools/eqsig/blob/master/LICENSE
:alt: License
.. image:: https://eng-tools.github.io/static/img/ecp-badge.svg
:target: https://eng-tools.github.io
:alt: ECP project
.. image:: https://zenodo.org/badge/125842866.svg
:target: https://zenodo.org/badge/latestdoi/125842866
:alt: DOI
.. image:: https://pepy.tech/badge/eqsig
:target: https://pepy.tech/project/eqsig
*****
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()
Generate Stockwell transform
____________________________
.. code-block:: python
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()
.. image:: ./examples/stockwell-example.png
:width: 400
:alt: Output from example
Useful material
===============
*
Contributing
============
How do I get set up?
--------------------
1. 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:
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``
Release instructions
--------------------
On zenodo.org use the github integration tool, click on the eqsig package and click create new release.
History
=======
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
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3 | Python |
نحوه نصب
نصب پکیج whl eqsig-1.2.5:
pip install eqsig-1.2.5.whl
نصب پکیج tar.gz eqsig-1.2.5:
pip install eqsig-1.2.5.tar.gz