Skip to main content

Python toolkit for standard magnetotelluric data processing.

Project description

MTpy-v2: A Python Toolbox for working with Magnetotelluric (MT) Data

codecov License: MIT Documentation Status Binder

Description

mtpy provides tools for working with magnetotelluric (MT) data. MTpy-v2 is an updated version of mtpy. Many things have changed under the hood and usage is different from mtpy v1. The main difference is that there is a central data type that can hold transfer functions and then read/write to your modeling program, plot, and analyze your data. No longer will you need a directory of EDI files and then read them in everytime you want to do something. You only need to build a project once and save it to an MTH5 file and you are ready to go. All metadata uses mt-metadata.

Functionality

  • Read/write transfer function files (EDI, EMTFXML, J-file, Z-file, AVG-file) using mt-metadata
  • Read/write MTH5 files for full surveys/project in a single file
  • Utility functions for GIS

Plotting

  • Single transfer function
    • apparent resistivity and phase
    • induction vectors
    • phase tensors
    • strike
    • depth of investigation
  • Survey of transfer functions
    • station map
    • phase tensor and induction vector map and pseudosection
    • apparent resistivity and phase maps and pseudosections
    • depth of investigation map

Processing

  • Read/write files for time series processing

Modeling

  • Read/Write files for modeling programs
    • ModEM
    • Occam 1D and 2D
    • Mare2DEM (?)
    • Pek 1D and 2D

What's been Updated in version 2

The main updates in mtpy-v2 are:

  • Remove dependence on EDI files, can be any type of transfer function file
    • Supports (or will support) to/from:
      • EDI (most common format)
      • ZMM (Egberts EMTF output)
      • JFILE (BIRRP output)
      • EMTFXML (Kelbert's format)
      • AVG (Zonge output)
  • Uses mt-metadata to read and write transfer function files where the transfer function data are stored in an xarray
  • The workflow is more centralized by introducing MTCollection and MTData objects which are the databases to hold a collection of transfer functions and manipulate them
    • Includes plotting methods, to/from data file types for modeling, rotations, interpolations, static shifts, etc.
    • Can store a collection as an MTH5 using mth5

Quick Example

Typically MT data are collected as surveys and each station produces a single transfer function. These are provided in various formats like EDI, EMTF XML, etc.

One benefit of mtpy-v2 is reading all these in only needs to be done once and places them in a single MTH5 file.

from pathlib import Path
from mtpy import MTCollection

transfer_function_path = Path("/home/survey_00/transfer_functions")

# write directly to an MTH5 file and close when finished loading TFs
with MTCollection() as mc:
    mc.open_collection(transfer_function_path.joinpath("tf_collection.h5"))
    mc.add_tf(
        mc.make_file_list(
            transfer_function_path,
            file_types=["edi", "xml", "j", "zmm", "zss", "avg"],
        )
    )
 

Now when you want to access your data again, you just need to open a single file.

mc = MTCollection()
mc.open_collection(r"/home/survey_00/transfer_functions/tf_collection.h5")

# plot station locations
station_locations = mc.plot_stations()

How to Cite

If you use this software in a scientific publication, we'd very much appreciate if you could cite the following papers:

  • Kirkby, A.L., Zhang, F., Peacock, J., Hassan, R., Duan, J., 2019. The MTPy software package for magnetotelluric data analysis and visualisation. Journal of Open Source Software, 4(37), 1358. https://doi.org/10.21105/joss.01358

  • Krieger, L., and Peacock, J., 2014. MTpy: A Python toolbox for magnetotellurics. Computers and Geosciences, 72, p167-175. https://doi.org/10.1016/j.cageo.2014.07.013

Contacts

| Jared Peacock | peacock.jared@gmail.com

| Alison Kirkby | alkirkby@gmail.com

System Requirements

  • Python 3.8+

License

MTpy is licensed under the MIT license

The license agreement is contained in the repository and should be kept together with the code.

History

2.0.0 (2022-10-20)

  • major changes under the hood.
    • Now using mt_metadata to read/write transfer function files
    • Now using mth5 to store the transfer functions
    • Introduced MT, MTCollection, and MTData such that operations are more centralized. Now most methods can be called from MT and MTData
    • Removing older modules and group specific modules
    • Added GitActions for testing
    • Updating tests (still lots of work to do)
    • Updated documentation to upload to ReadTheDocs

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

mtpy-v2-2.0.0.tar.gz (515.3 kB view details)

Uploaded Source

Built Distribution

mtpy_v2-2.0.0-py3-none-any.whl (571.8 kB view details)

Uploaded Python 3

File details

Details for the file mtpy-v2-2.0.0.tar.gz.

File metadata

  • Download URL: mtpy-v2-2.0.0.tar.gz
  • Upload date:
  • Size: 515.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.8.13

File hashes

Hashes for mtpy-v2-2.0.0.tar.gz
Algorithm Hash digest
SHA256 b952b3d400149e5534b59dfe79459f3e5d1b8d3d6156d28a315081f7aad2ccf4
MD5 6d70f8bc47646ac52b4b6c677ab57e03
BLAKE2b-256 e334e067fbedb34b56bdd7a9c6c8afdba3cacc81b94604a1a8072f34a841b251

See more details on using hashes here.

File details

Details for the file mtpy_v2-2.0.0-py3-none-any.whl.

File metadata

  • Download URL: mtpy_v2-2.0.0-py3-none-any.whl
  • Upload date:
  • Size: 571.8 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.8.13

File hashes

Hashes for mtpy_v2-2.0.0-py3-none-any.whl
Algorithm Hash digest
SHA256 bd2b5508a87200813c8d5fad5fd46e1de3c4af1fe6de98fde6233fb1112a5059
MD5 ace0f72f1e262daf5497b7a9850eec72
BLAKE2b-256 d4bd9b57c38c14a12cb520328488723f699f2b152650edecff70943fdcda75ee

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page