Package: pyglimer
A workflow to create a global database for Ps and Sp receiver function imaging of crustal and upper mantle discontinutiesPyGLImERautomates receiver function (RF) processing from download of raw waveform data to common conversion point (CCP) imaging with a minimum amount of user interference.The implementation includes:Functions to download raw waveform data from FDSN providersFunctions to feed in local waveform dataAn adaptable preprocessing scheme, including various rotational algorithmsA variety of deconvolution algorithms (user-defined algorithms possible)An implementation of the iasp91 and GyPSum velocity models for depth migration (user-defined models are accepted)A new, particularly efficient Common Conversion Point Stacking algorithmA variety of plotting tools to explore datasets and to create prublication ready figuresEfficient and fast processing and data management, support multi-processing, MPI, and HDF5As developers, we are particularly concerned to create anautomated, adaptable, efficient, and, yet, easy-to-usetoolkit.The project is largely based on theObsPyproject and can be seen as a more powerful and user-friendly
successor of theGLImERproject.Installation of this packageInstallation from PyPiPyGLImER is now deployed on PyPi and can simply be installed using:pipinstallpyglimerInstallation from source codeTo obtain the latest updates, you can install PyGLImER from the source code, available on GitHub.⚠️Developers should download thedevbranch# Download via wget or web-browserwgethttps://github.com/PyGLImER/PyGLImER/archive/refs/heads/master.zip# For developerswgethttps://github.com/PyGLImER/PyGLImER/archive/refs/heads/dev.zip# unzip the packageunzipmaster.zip# or dev.zip, depending on branch# Change directory to the same directory that this repo is in (i.e., same directory as setup.py)cdPyGLImER-master# That's the standard name the folder should have# Create the conda environment and install dependenciescondaenvcreate-fenvironment.yml# Activate the conda environmentcondaactivatepyglimer# Install your packagepipinstall-e.Optionally, you can test the package by runningpytest-pno:loggingtestsGetting startedAccess PyGLImER's documentationhere.PyGLImER comes with a few tutorials (Jupyter notebooks). You can find those in theexamples/directory.What it looks likeWith PyGLImER, we facilitate processing extremely large amounts of teleseismic data. This enables us to create large scale CCP sections as shown for P-to-S and S-to-P receiver function data in the plot below:FIG: Seismic broadband stations with available receiver functions are plotted as downward-pointing red triangles. The locations of the shown cross-sections are demarked as bold black lines. Cross-sections A, B, and D are created from S receiver functions stacked by common conversion point, whereas cross-section C shows a slice through a P receiver function common conversion point stack. Data begin to fade to grey if the respective gridpoint is hit by fewer than 25 rays. Note that the vertical exaggeration varies from panel to panel.PyGLImER also comes with a toolset to create publication ready figures:FIG: Single station stack and receiver functions sorted by epicentral distance from P receiver function for station GE.DAG.FIG: Distribution of back-azimuth and rayparameter for the P receiver functions from GE.DAG as shown above.Reporting Bugs / Contact the developersThis version is an early release. If you encounter any issues or unexpected behaviour, pleaseopen an issuehere on GitHub orcontact the developers.Citing PyGLImERIf you use PyGLImER to produce content for your publication, please consider citing us. For the time being, please cite ourAGU abstract.LatestWe are happy to announced that PyGLImER has been awarded anORFEUSsoftware development grant and are looking forward to further develop this project.