pyRaTS


NamepyRaTS JSON
Version 0.21 PyPI version JSON
download
home_pagehttps://github.com/ArvidTrapp/pyRaTS
Summaryprocessing of (RAndom) TimeSeries for vibration fatigue
upload_time2023-05-24 14:53:08
maintainerArvid Trapp, Peter Wolfsteiner
docs_urlNone
authorArvid Trapp
requires_python>=3.6
license
keywords vibration fatigue non-stationarity matrix structural dynamics fatigue damage spectrum
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls.
            pyRaTS - processing of (RAndom) TimeSeries for vibration fatigue
---------------------------------------------

Providing an object-oriented framework to analyze and process time series with the focus on random vibration fatigue. PyRaTS  is capable of handling single- and multi-channel, as well as single- and multi-process time series configurations. 
Implementation of the non-stationarity matrix, the Fatigue Damage Spectrum and quasi-stationary signal definitions to deal with the challenges of non-stationary loading. 

Installing this package
-----------------------

Use `pip` to install it by:

.. code-block:: console

    $ pip install pyRaTS

Simple example
---------------

Here is a simple example for running a basic code. Further examples can be found on: 
https://github.com/ArvidTrapp/pyRaTS

.. code-block:: python

    import pyRaTS as ts
    import numpy as np

    # Defining the series by pseudo-random generator...
    T  = 10
    fs = 1024
    N  = T*fs
    x  = np.random.randn(N)

    # Initialize series and some basic plots...
    sig = ts.timeseries(x,name = 'sample timeseries', fs = fs)
    sig.plot()
    sig.plot_prob()
    sig.plot_psd()

    # derive response series and some further basic plots...
    respsig = sig.der_sdofResponse(fD = 50)
    respsig.plot_psd()
    respsig.plot_ls()
	
Some methods for a statistical analysis of random time series / estimation of statistical descriptors
-----------------------------------------

	* spectral moments (est_specMoms)
	* Dirlik estimator (est_dirlik/est_dirlikD)
	* PSD (est_psd)
	* load spectra (est_ls)
	* Fatigue Damage Spectrum (est_fds) ...accepts list of FLife methods for damage estimation
	* non-stationarity matrix (est_nonstat)

Some methods for plotting
-----------------------------------------

	* time series (plot)
	* PSD (plot_psd)
	* absolute of Fourier transform (plot_X)
	* load spectra (plot_ls) ...accepts list of FLife methods with PDF definition
	* transfer function (plot_tf)
	* Fatigue Damage Spectrum (plot_fds) 
	* non-stationarity matrix (plot_nonstat)

Some methods for processing time series
-----------------------------------------

	* statistical response...PSD & Non-stat.-Matrix (der_statResponse(f,H)) 
	* response timeseries of single-degree-of-freedom system (der_sdofResponse(fD, D, func))
	* response timeseries for linear transfer function (der_response(f,H))
	* quasi-stationary load definition on the basis of the load spectra of the Fatigue Damage Spectrum (der_lsEquivalent())
	* load definition on the basis of the inverse Fatigue Damage Spectrum (der_iFDS())
	* ideal high pass filtered signal (der_highpass(f))
	* ideal low pass filtered signal  (der_lowpass(f))
	* ideal band pass filtered signal (der_bandpass(f))

            

Raw data

            {
    "_id": null,
    "home_page": "https://github.com/ArvidTrapp/pyRaTS",
    "name": "pyRaTS",
    "maintainer": "Arvid Trapp, Peter Wolfsteiner",
    "docs_url": null,
    "requires_python": ">=3.6",
    "maintainer_email": "arvid.trapp@hm.edu",
    "keywords": "vibration fatigue, non-stationarity matrix, structural dynamics,Fatigue Damage Spectrum",
    "author": "Arvid Trapp",
    "author_email": "arvid.trapp@hm.edu",
    "download_url": "https://files.pythonhosted.org/packages/34/1b/51e300050ced32148eee24afad71a4998e93d4a079c15f14910bf686e3a8/pyRaTS-0.21.tar.gz",
    "platform": null,
    "description": "pyRaTS - processing of (RAndom) TimeSeries for vibration fatigue\r\n---------------------------------------------\r\n\r\nProviding an object-oriented framework to analyze and process time series with the focus on random vibration fatigue. PyRaTS  is capable of handling single- and multi-channel, as well as single- and multi-process time series configurations. \r\nImplementation of the non-stationarity matrix, the Fatigue Damage Spectrum and quasi-stationary signal definitions to deal with the challenges of non-stationary loading. \r\n\r\nInstalling this package\r\n-----------------------\r\n\r\nUse `pip` to install it by:\r\n\r\n.. code-block:: console\r\n\r\n    $ pip install pyRaTS\r\n\r\nSimple example\r\n---------------\r\n\r\nHere is a simple example for running a basic code. Further examples can be found on: \r\nhttps://github.com/ArvidTrapp/pyRaTS\r\n\r\n.. code-block:: python\r\n\r\n    import pyRaTS as ts\r\n    import numpy as np\r\n\r\n    # Defining the series by pseudo-random generator...\r\n    T  = 10\r\n    fs = 1024\r\n    N  = T*fs\r\n    x  = np.random.randn(N)\r\n\r\n    # Initialize series and some basic plots...\r\n    sig = ts.timeseries(x,name = 'sample timeseries', fs = fs)\r\n    sig.plot()\r\n    sig.plot_prob()\r\n    sig.plot_psd()\r\n\r\n    # derive response series and some further basic plots...\r\n    respsig = sig.der_sdofResponse(fD = 50)\r\n    respsig.plot_psd()\r\n    respsig.plot_ls()\r\n\t\r\nSome methods for a statistical analysis of random time series / estimation of statistical descriptors\r\n-----------------------------------------\r\n\r\n\t* spectral moments (est_specMoms)\r\n\t* Dirlik estimator (est_dirlik/est_dirlikD)\r\n\t* PSD (est_psd)\r\n\t* load spectra (est_ls)\r\n\t* Fatigue Damage Spectrum (est_fds) ...accepts list of FLife methods for damage estimation\r\n\t* non-stationarity matrix (est_nonstat)\r\n\r\nSome methods for plotting\r\n-----------------------------------------\r\n\r\n\t* time series (plot)\r\n\t* PSD (plot_psd)\r\n\t* absolute of Fourier transform (plot_X)\r\n\t* load spectra (plot_ls) ...accepts list of FLife methods with PDF definition\r\n\t* transfer function (plot_tf)\r\n\t* Fatigue Damage Spectrum (plot_fds) \r\n\t* non-stationarity matrix (plot_nonstat)\r\n\r\nSome methods for processing time series\r\n-----------------------------------------\r\n\r\n\t* statistical response...PSD & Non-stat.-Matrix (der_statResponse(f,H)) \r\n\t* response timeseries of single-degree-of-freedom system (der_sdofResponse(fD, D, func))\r\n\t* response timeseries for linear transfer function (der_response(f,H))\r\n\t* quasi-stationary load definition on the basis of the load spectra of the Fatigue Damage Spectrum (der_lsEquivalent())\r\n\t* load definition on the basis of the inverse Fatigue Damage Spectrum (der_iFDS())\r\n\t* ideal high pass filtered signal (der_highpass(f))\r\n\t* ideal low pass filtered signal  (der_lowpass(f))\r\n\t* ideal band pass filtered signal (der_bandpass(f))\r\n",
    "bugtrack_url": null,
    "license": "",
    "summary": "processing of (RAndom) TimeSeries for vibration fatigue",
    "version": "0.21",
    "project_urls": {
        "Homepage": "https://github.com/ArvidTrapp/pyRaTS"
    },
    "split_keywords": [
        "vibration fatigue",
        " non-stationarity matrix",
        " structural dynamics",
        "fatigue damage spectrum"
    ],
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "cb039e1dbd3c47de20b5ecf61e1e55b78a2850ed46ceb68b2f71dfb914822798",
                "md5": "1f0ac6384049ab79e7ca32f596d6ee84",
                "sha256": "12efaa77ce3046af563ab2261fa291e4bc464c3afe922cb9cda4c545fd2f80bc"
            },
            "downloads": -1,
            "filename": "pyRaTS-0.21-py3-none-any.whl",
            "has_sig": false,
            "md5_digest": "1f0ac6384049ab79e7ca32f596d6ee84",
            "packagetype": "bdist_wheel",
            "python_version": "py3",
            "requires_python": ">=3.6",
            "size": 23198,
            "upload_time": "2023-05-24T14:53:06",
            "upload_time_iso_8601": "2023-05-24T14:53:06.487198Z",
            "url": "https://files.pythonhosted.org/packages/cb/03/9e1dbd3c47de20b5ecf61e1e55b78a2850ed46ceb68b2f71dfb914822798/pyRaTS-0.21-py3-none-any.whl",
            "yanked": false,
            "yanked_reason": null
        },
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "341b51e300050ced32148eee24afad71a4998e93d4a079c15f14910bf686e3a8",
                "md5": "85411ee9d09f1d13f96faaba609a600b",
                "sha256": "5653f033dd79611fa1b0f1c8dbf2ba211e9bd88358577cd82eb7473f3881764c"
            },
            "downloads": -1,
            "filename": "pyRaTS-0.21.tar.gz",
            "has_sig": false,
            "md5_digest": "85411ee9d09f1d13f96faaba609a600b",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": ">=3.6",
            "size": 23235,
            "upload_time": "2023-05-24T14:53:08",
            "upload_time_iso_8601": "2023-05-24T14:53:08.041473Z",
            "url": "https://files.pythonhosted.org/packages/34/1b/51e300050ced32148eee24afad71a4998e93d4a079c15f14910bf686e3a8/pyRaTS-0.21.tar.gz",
            "yanked": false,
            "yanked_reason": null
        }
    ],
    "upload_time": "2023-05-24 14:53:08",
    "github": true,
    "gitlab": false,
    "bitbucket": false,
    "codeberg": false,
    "github_user": "ArvidTrapp",
    "github_project": "pyRaTS",
    "travis_ci": false,
    "coveralls": false,
    "github_actions": false,
    "lcname": "pyrats"
}
        
Elapsed time: 0.33695s