swprocess


Nameswprocess JSON
Version 0.3.0 PyPI version JSON
download
home_pagehttps://github.com/jpvantassel/swprocess
SummaryPackage for Surface Wave Processing
upload_time2024-07-13 15:08:18
maintainerNone
docs_urlNone
authorJoseph P. Vantassel
requires_python>=3.8
licenseNone
keywords surface-wave dispersion processing geopsy active passive masw mam
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls.
            # _swprocess_ - A Python Package for Surface Wave Processing

> Joseph P. Vantassel, [jpvantassel.com](https://www.jpvantassel.com/)

[![DOI](https://zenodo.org/badge/202217252.svg)](https://zenodo.org/badge/latestdoi/202217252)
[![PyPI - License](https://img.shields.io/pypi/l/swprocess)](https://github.com/jpvantassel/swprocess/blob/main/LICENSE.txt)
[![CircleCI](https://circleci.com/gh/jpvantassel/swprocess.svg?style=svg)](https://circleci.com/gh/jpvantassel/swprocess)
[![Documentation Status](https://readthedocs.org/projects/swprocess/badge/?version=latest)](https://swprocess.readthedocs.io/en/latest/?badge=latest)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/swprocess)
[![Codacy Badge](https://app.codacy.com/project/badge/Grade/8faa1913edd84e4b9ba77807ab5583fd)](https://www.codacy.com/gh/jpvantassel/swprocess/dashboard?utm_source=github.com&utm_medium=referral&utm_content=jpvantassel/swprocess&utm_campaign=Badge_Grade)
[![codecov](https://codecov.io/gh/jpvantassel/swprocess/branch/main/graph/badge.svg?token=XCDW6HMGBR)](https://codecov.io/gh/jpvantassel/swprocess)

## Table of Contents

-   [About _swprocess_](#about-swprocess)
-   [Why use _swprocess_](#why-use-swprocess)
-   [Examples](#examples)
-   [Getting Started](#getting-started)

## About _swprocess_

_swprocess_ is a Python package for surface wave processing. _swprocess_ was
developed by [Joseph P. Vantassel](https://www.jpvantassel.com/) under the
supervision of Professor Brady R. Cox at The University of Texas at Austin.
_swprocess_ continues to be developed and maintained by
[Joseph P. Vantassel and his research group at Virginia Tech](https://geoimaging-research.org/).

If you use _swprocess_ in your research or consulting, we ask you please cite
the following:

> Vantassel, J. P. (2021). jpvantassel/swprocess: latest (Concept). Zenodo.
> [https://doi.org/10.5281/zenodo.4584128](https://doi.org/10.5281/zenodo.4584128)

> Vantassel, J. P. & Cox, B. R. (2022). "SWprocess: a workflow for developing robust
> estimates of surface wave dispersion uncertainty". Journal of Seismology.
> [https://doi.org/10.1007/s10950-021-10035-y](https://doi.org/10.1007/s10950-021-10035-y)

_Note: For software, version specific citations should be preferred to
general concept citations, such as that listed above. To generate a version
specific citation for _swprocess_, please use the citation tool on the _swprocess_
[archive](https://doi.org/10.5281/zenodo.4584128)._

## Why use _swprocess_

_swprocess_ contains features not currently available in any other open-source
software, including:

-   Multiple pre-processing workflows for active-source [i.e., Multichannel
Analysis of Surface Waves (MASW)] measurements including:
    -   time-domain muting,
    -   frequency-domain stacking, and
    -   time-domain stacking.
-   Multiple wavefield transformations for active-source (i.e., MASW) measurements
including:
    -   frequency-wavenumber (Nolet and Panza, 1976),
    -   phase-shift (Park, 1998),
    -   slant-stack (McMechan and Yedlin, 1981), and
    -   frequency domain beamformer (Zywicki 1999).
-   Post-processing of active-source and passive-wavefield [i.e., microtremor
array measurements (MAM)] data from _swprocess_ and _Geopsy_, respectively.
-   Interactive trimming to remove low quality dispersion data.
-   Rigorous calculation of dispersion statistics to quantify epistemic and
aleatory uncertainty in surface wave measurements.

## Examples

### Active-source processing

<img src="https://github.com/jpvantassel/swprocess/blob/main/figs/nz_wghs_rayleigh_-20.0m.png?raw=true" width="775">

### Interactive trimming

<img src="https://github.com/jpvantassel/swprocess/blob/main/figs/nz_wghs_rayleigh_masw_int-trim.gif?raw=true" width="775">

### Calculation of dispersion statistics

<img src="https://github.com/jpvantassel/swprocess/blob/main/figs/nz_wghs_rayleigh.png?raw=true" width="775">

## Getting Started

### Installing or Upgrading _swprocess_

1.  If you do not have Python 3.8 or later installed, you will need to do
so. A detailed set of instructions can be found
[here](https://jpvantassel.github.io/python3-course/#/intro/installing_python).

2.  If you have not installed _swprocess_ previously use `pip install swprocess`.
If you are not familiar with `pip`, a useful tutorial can be found
[here](https://jpvantassel.github.io/python3-course/#/intro/pip). If you have
an earlier version and would like to upgrade to the latest version of
_swprocess_ use `pip install swprocess --upgrade`.

3.  Confirm that _swprocess_ has installed/updated successfully by examining the
last few lines of the text displayed in the console.

### Using _swprocess_

1.  Download the contents of the
  [examples](https://github.com/jpvantassel/swprocess/tree/main/examples)
  directory to any location of your choice.

2.  Start by processing the provided active-source data using the
  Jupyter notebook (`masw.ipynb`). If you have not installed `Jupyter`,
  detailed instructions can be found
  [here](https://jpvantassel.github.io/python3-course/#/intro/installing_jupyter).

3.  Post-process the provided passive-wavefield data using the
  Jupyter notebook (`mam_fk.ipynb`).

4.  Perform interactive trimming and calculate dispersion statistics for the
  example data using the Jupyter notebook (`stats.ipynb`). Compare your results
  to those shown in the figure above.

5.  Enjoy!

            

Raw data

            {
    "_id": null,
    "home_page": "https://github.com/jpvantassel/swprocess",
    "name": "swprocess",
    "maintainer": null,
    "docs_url": null,
    "requires_python": ">=3.8",
    "maintainer_email": null,
    "keywords": "surface-wave dispersion processing geopsy active passive masw mam",
    "author": "Joseph P. Vantassel",
    "author_email": "joseph.p.vantassel@gmail.com",
    "download_url": "https://files.pythonhosted.org/packages/df/76/a2e3a622cd94563c61bd69b7695c77e7f8e194e2594202c234d4e4bd9b01/swprocess-0.3.0.tar.gz",
    "platform": null,
    "description": "# _swprocess_ - A Python Package for Surface Wave Processing\n\n> Joseph P. Vantassel, [jpvantassel.com](https://www.jpvantassel.com/)\n\n[![DOI](https://zenodo.org/badge/202217252.svg)](https://zenodo.org/badge/latestdoi/202217252)\n[![PyPI - License](https://img.shields.io/pypi/l/swprocess)](https://github.com/jpvantassel/swprocess/blob/main/LICENSE.txt)\n[![CircleCI](https://circleci.com/gh/jpvantassel/swprocess.svg?style=svg)](https://circleci.com/gh/jpvantassel/swprocess)\n[![Documentation Status](https://readthedocs.org/projects/swprocess/badge/?version=latest)](https://swprocess.readthedocs.io/en/latest/?badge=latest)\n![PyPI - Python Version](https://img.shields.io/pypi/pyversions/swprocess)\n[![Codacy Badge](https://app.codacy.com/project/badge/Grade/8faa1913edd84e4b9ba77807ab5583fd)](https://www.codacy.com/gh/jpvantassel/swprocess/dashboard?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=jpvantassel/swprocess&amp;utm_campaign=Badge_Grade)\n[![codecov](https://codecov.io/gh/jpvantassel/swprocess/branch/main/graph/badge.svg?token=XCDW6HMGBR)](https://codecov.io/gh/jpvantassel/swprocess)\n\n## Table of Contents\n\n-   [About _swprocess_](#about-swprocess)\n-   [Why use _swprocess_](#why-use-swprocess)\n-   [Examples](#examples)\n-   [Getting Started](#getting-started)\n\n## About _swprocess_\n\n_swprocess_ is a Python package for surface wave processing. _swprocess_ was\ndeveloped by [Joseph P. Vantassel](https://www.jpvantassel.com/) under the\nsupervision of Professor Brady R. Cox at The University of Texas at Austin.\n_swprocess_ continues to be developed and maintained by\n[Joseph P. Vantassel and his research group at Virginia Tech](https://geoimaging-research.org/).\n\nIf you use _swprocess_ in your research or consulting, we ask you please cite\nthe following:\n\n> Vantassel, J. P. (2021). jpvantassel/swprocess: latest (Concept). Zenodo.\n> [https://doi.org/10.5281/zenodo.4584128](https://doi.org/10.5281/zenodo.4584128)\n\n> Vantassel, J. P. & Cox, B. R. (2022). \"SWprocess: a workflow for developing robust\n> estimates of surface wave dispersion uncertainty\". Journal of Seismology.\n> [https://doi.org/10.1007/s10950-021-10035-y](https://doi.org/10.1007/s10950-021-10035-y)\n\n_Note: For software, version specific citations should be preferred to\ngeneral concept citations, such as that listed above. To generate a version\nspecific citation for _swprocess_, please use the citation tool on the _swprocess_\n[archive](https://doi.org/10.5281/zenodo.4584128)._\n\n## Why use _swprocess_\n\n_swprocess_ contains features not currently available in any other open-source\nsoftware, including:\n\n-   Multiple pre-processing workflows for active-source [i.e., Multichannel\nAnalysis of Surface Waves (MASW)] measurements including:\n    -   time-domain muting,\n    -   frequency-domain stacking, and\n    -   time-domain stacking.\n-   Multiple wavefield transformations for active-source (i.e., MASW) measurements\nincluding:\n    -   frequency-wavenumber (Nolet and Panza, 1976),\n    -   phase-shift (Park, 1998),\n    -   slant-stack (McMechan and Yedlin, 1981), and\n    -   frequency domain beamformer (Zywicki 1999).\n-   Post-processing of active-source and passive-wavefield [i.e., microtremor\narray measurements (MAM)] data from _swprocess_ and _Geopsy_, respectively.\n-   Interactive trimming to remove low quality dispersion data.\n-   Rigorous calculation of dispersion statistics to quantify epistemic and\naleatory uncertainty in surface wave measurements.\n\n## Examples\n\n### Active-source processing\n\n<img src=\"https://github.com/jpvantassel/swprocess/blob/main/figs/nz_wghs_rayleigh_-20.0m.png?raw=true\" width=\"775\">\n\n### Interactive trimming\n\n<img src=\"https://github.com/jpvantassel/swprocess/blob/main/figs/nz_wghs_rayleigh_masw_int-trim.gif?raw=true\" width=\"775\">\n\n### Calculation of dispersion statistics\n\n<img src=\"https://github.com/jpvantassel/swprocess/blob/main/figs/nz_wghs_rayleigh.png?raw=true\" width=\"775\">\n\n## Getting Started\n\n### Installing or Upgrading _swprocess_\n\n1.  If you do not have Python 3.8 or later installed, you will need to do\nso. A detailed set of instructions can be found\n[here](https://jpvantassel.github.io/python3-course/#/intro/installing_python).\n\n2.  If you have not installed _swprocess_ previously use `pip install swprocess`.\nIf you are not familiar with `pip`, a useful tutorial can be found\n[here](https://jpvantassel.github.io/python3-course/#/intro/pip). If you have\nan earlier version and would like to upgrade to the latest version of\n_swprocess_ use `pip install swprocess --upgrade`.\n\n3.  Confirm that _swprocess_ has installed/updated successfully by examining the\nlast few lines of the text displayed in the console.\n\n### Using _swprocess_\n\n1.  Download the contents of the\n  [examples](https://github.com/jpvantassel/swprocess/tree/main/examples)\n  directory to any location of your choice.\n\n2.  Start by processing the provided active-source data using the\n  Jupyter notebook (`masw.ipynb`). If you have not installed `Jupyter`,\n  detailed instructions can be found\n  [here](https://jpvantassel.github.io/python3-course/#/intro/installing_jupyter).\n\n3.  Post-process the provided passive-wavefield data using the\n  Jupyter notebook (`mam_fk.ipynb`).\n\n4.  Perform interactive trimming and calculate dispersion statistics for the\n  example data using the Jupyter notebook (`stats.ipynb`). Compare your results\n  to those shown in the figure above.\n\n5.  Enjoy!\n",
    "bugtrack_url": null,
    "license": null,
    "summary": "Package for Surface Wave Processing",
    "version": "0.3.0",
    "project_urls": {
        "Bug Reports": "https://github.com/jpvantassel/swprocess/issues",
        "Docs": "https://swprocess.readthedocs.io/en/latest/?badge=latest",
        "Homepage": "https://github.com/jpvantassel/swprocess",
        "Source": "https://github.com/jpvantassel/swprocess"
    },
    "split_keywords": [
        "surface-wave",
        "dispersion",
        "processing",
        "geopsy",
        "active",
        "passive",
        "masw",
        "mam"
    ],
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "3b02e6c67eb7e85946e7ab71db8f43a4b790b9b0f1a8d787191c99cd90d57dcb",
                "md5": "327f3524b0ec68d8812d4c26c1331cce",
                "sha256": "8323789df5e02ab03bd567d09586cd64fe9e5f4219b532e7697e00ef33b692a9"
            },
            "downloads": -1,
            "filename": "swprocess-0.3.0-py3-none-any.whl",
            "has_sig": false,
            "md5_digest": "327f3524b0ec68d8812d4c26c1331cce",
            "packagetype": "bdist_wheel",
            "python_version": "py3",
            "requires_python": ">=3.8",
            "size": 94837,
            "upload_time": "2024-07-13T15:08:17",
            "upload_time_iso_8601": "2024-07-13T15:08:17.004609Z",
            "url": "https://files.pythonhosted.org/packages/3b/02/e6c67eb7e85946e7ab71db8f43a4b790b9b0f1a8d787191c99cd90d57dcb/swprocess-0.3.0-py3-none-any.whl",
            "yanked": false,
            "yanked_reason": null
        },
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "df76a2e3a622cd94563c61bd69b7695c77e7f8e194e2594202c234d4e4bd9b01",
                "md5": "a13db73e33a4f6d9c2d83292d0c7222f",
                "sha256": "01c0b4dd7eba0ebfa5274be2859a7eff7b6c7f454d11b726579650270e17b504"
            },
            "downloads": -1,
            "filename": "swprocess-0.3.0.tar.gz",
            "has_sig": false,
            "md5_digest": "a13db73e33a4f6d9c2d83292d0c7222f",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": ">=3.8",
            "size": 92268,
            "upload_time": "2024-07-13T15:08:18",
            "upload_time_iso_8601": "2024-07-13T15:08:18.494364Z",
            "url": "https://files.pythonhosted.org/packages/df/76/a2e3a622cd94563c61bd69b7695c77e7f8e194e2594202c234d4e4bd9b01/swprocess-0.3.0.tar.gz",
            "yanked": false,
            "yanked_reason": null
        }
    ],
    "upload_time": "2024-07-13 15:08:18",
    "github": true,
    "gitlab": false,
    "bitbucket": false,
    "codeberg": false,
    "github_user": "jpvantassel",
    "github_project": "swprocess",
    "travis_ci": false,
    "coveralls": false,
    "github_actions": false,
    "circle": true,
    "requirements": [],
    "tox": true,
    "lcname": "swprocess"
}
        
Elapsed time: 0.29901s