Metadata-Version: 2.1
Name: swprepost
Version: 0.3.1
Summary: A Python Package for Surface-Wave Inversion Pre- and Post-Processing
Home-page: https://github.com/jpvantassel/swprepost
Author: Joseph P. Vantassel
Author-email: jvantassel@utexas.edu
License: UNKNOWN
Description: # SWprepost - A Python Package for Surface Wave Inversion Pre- and Post-Processing
        
        > Joseph P. Vantassel, The University of Texas at Austin
        
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        ## Table of Contents
        
        ---
        
        - [About _SWprepost_](#About-SWprepost)
        - [A Few Examples](#A-Few-Examples)
        - [Getting Started](#Getting-Started)
        
        ## About _SWprepost_
        
        ---
        
        `SWprepost` is a Python package for performing surface wave inversion pre- and
        post-processing. `SWprepost` was developed by Joseph P. Vantassel under the
        supervision of Professor Brady R. Cox at The University of Texas at Austin. The
        package includes 11 class definitions for interacting with the various
        components required for surface wave inversion. It is designed to integrate
        seamlessly with the Dinver module of the popular open-source software Geopsy,
        however has been written in a general manner to ensure its usefulness with other
        inversion programs. Furthermore, some of the class definitions provided such as
        `GroundModel` may even be of use to those working in the Geotechnical or
        Geophysical fields, but who do not perform surface wave inversions.
        
        If you use `SWprepost` in your research or consulting we ask you please cite the
        following:
        
        > Joseph Vantassel. (2020). jpvantassel/swprepost: latest (Concept). Zenodo.
        > http://doi.org/10.5281/zenodo.3839998
        
        _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 `SWprepost`, please use the citation tool for that specific_
        _version on the `SWprepost` [archive](https://doi.org/10.5281/zenodo.3839998)._
        
        For the motivation behind the development of `SWprepost` and its role in a
        larger project focused on developing a complete workflow for surface wave
        inversion please refer to and consider citing the following:
        
        > Joseph P. Vantassel and Brady R. Cox (2020) SWinvert: A workflow for
        > performing rigorous surface wave inversion. (Submitted).
        
        ## A Few Examples
        
        All examples presented here can be replicated using the Jupyter notebook titled
        `ReadmeExamples.ipynb` in the `examples` directory.
        
        ### Import 100 ground models in less than 0.5 seconds
        
        ```Python
        time_start = time.perf_counter()
        gm_suite = swprepost.GroundModelSuite.from_geopsy(fname="inputs/from_geopsy_100gm.txt")
        time_stop = time.perf_counter()
        print(f"Elapsed Time: {np.round(time_stop - time_start)} seconds.")
        print(gm_suite)
        ```
        
        ```Bash
        Elapsed Time: 0.0 seconds.
        GroundModelSuite with 100 GroundModels.
        ```
        
        ### Plot the ground models
        
        ```Python
        fig, ax = plt.subplots(figsize=(2,4), dpi=150)
        # Plot 100 best
        label = "100 Best"
        for gm in gm_suite:
            ax.plot(gm.vs2, gm.depth, color="#ababab", label=label)
            label=None
        # Plot the single best in different color
        ax.plot(gm_suite[0].vs2, gm_suite[0].depth, color="#00ffff", label="1 Best")
        ax.set_ylim(50,0)
        ax.set_xlabel("Vs (m/s)")
        ax.set_ylabel("Depth (m)")
        ax.legend()
        plt.show()
        ```
        
        ![100bestvs.svg](figs/100bestvs.svg)
        
        ### Compute and plot their uncertainty
        
        ```Python
        fig, ax = plt.subplots(figsize=(2,4), dpi=150)
        # Calculate Median
        disc_depth, siglnvs = gm_suite.sigma_ln()
        ax.plot(siglnvs, disc_depth, color="#00ff00")
        ax.set_xlim(0, 0.2)
        ax.set_ylim(50,0)
        ax.set_xlabel("$\sigma_{ln,Vs}$")
        ax.set_ylabel("Depth (m)")
        plt.show()
        ```
        
        ![siglnvs.svg](figs/siglnvs.svg)
        
        ## Getting Started
        
        ---
        
        ### Installing or Upgrading _SWprepost_
        
        1.  If you do not have Python 3.6 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 `swprepost` previously use
        `pip install swprepost`. 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 `swprepost` use `pip install swprepost --upgrade`.
        
        3.  Confirm that `swprepost` has installed/updated successfully by examining the
        last few lines of text displayed in the console.
        
        ### Using _SWprepost_
        
        1.  Download the contents of the [examples](https://github.com/jpvantassel/swprepost/tree/master/examples)
          directory to any location of your choice.
        
        2.  Explore Jupyter notebooks in the
          [basic](https://github.com/jpvantassel/swprepost/tree/master/examples/basic)
          directory for a no-coding-required introduction to the `swprepost` package.
          If you have not installed `Jupyter`, detailed instructions can be found
          [here](https://jpvantassel.github.io/python3-course/#/intro/installing_jupyter).
        
        3.  Move to the [adv](https://github.com/jpvantassel/swprepost/tree/master/examples/adv)
          directory and follow the Jupyter notebook title `SWinvertWorkflow.ipynb` for
          an example application of the SWinvert workflow.
        
        4.  Enjoy!
        
Keywords: surface-wave inversion geopsy pre-process post-process
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Education
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >3.6
Description-Content-Type: text/markdown
Provides-Extra: dev
