mtpy-v2 2.0.14

Python toolkit for standard magnetotelluric data processing.

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

Version 2.0.14

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.

Installation

Using Pip

> pip install mtpy-v2

Using conda

> conda install -c conda-forge mtpy-v2

Pardiso Solver

mtpy-v2 now include some tools to model and invert using simpeg. If you want to use the Pardiso solver you will need to install it separately. See the https://github.com/simpeg/pydiso for more information. Below is a snippet from their recommendations.

Installing from source

The wrapper is written in cython and links to the mkl libraries dynamically. Therefore, it needs to find the necessary header files associated with the MKL installation to compile. The meson build backend uses pkg-config to identify the locations of the mkl header files and library dynamic libraries. Most development installations of MKL should provide the necessary pkg-config files for this. For example, conda users can be install the necessary configuration information with mkl-devel package that is available on the default channel, conda-forge channel, the intel channel, or others, e.g.

conda install mkl-devel

If you have installed the configuration files to a non-standard location, you will need to set PKG_CONFIG_PATH to point to that location.

After the necessary MKL files are accessible, you should be able to install by running

pip install .

in the installation directory.

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
  • BIRRP
  • Aurora

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 | [e-mail]

| Alison Kirkby | [e-mail]

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.