Skip to main content

Library to generate scan grid measurements from multipole sources and perform numerical inversions

Project description

DOI License: MIT Documentation Status

MMT Numerical Libraries: Multipole Inversion


Library for the numerical inversion of a scan grid detecting the magnetic signal from magnetic sources using a multipole expansion of the magnetic potential of the sources. The mathematical background of this method and its application to experimental data of a synthetic sample is developed in

D. Cortés-Ortuño, K. Fabian, L. V. de Groot Single Particle Multipole Expansions From Micromagnetic Tomography Geochemistry, Geophysics, Geosystems 22(4), e2021GC009663 (2021) DOI: https://doi.org/10.1029/2021GC009663

This library is developed as part of the Mimatom / MMT project.

Library

This library has two main modules

  • multipole_inversion/magnetic_sample.py : contains the MagneticSample class to create a magnetic system with magnetic point sources (dipole or higher order multipole sources) and generate the scan signal. This class also has methods to save the scan signal data in npz format and the scan grid specifications in json format.

  • multipole_inversion/multipole_inversion.py : contains the MultipoleInversion class to perform a numerical inversion from a scan signal data. This class requires scan surface specifications, particle (point source) locations and the scan signal data. These can be passed from the MagneticSample output or be specified manually (useful for combining with other workflows such as micromagnetic simulations).

An additional module to plot results from the inversions is provided in multipole_inversion/plot_tools.py. Magnetic susceptibility and magnetic field functions can be found in the main library as well, although not all of them are documented in the tutorial yet.

Installation

Via PyPI or from this repository using pip . or poetry install or poetry build (which can be used with pip). Poetry is recommended for development.

CUDA

This library contains an optional Nvidia CUDA library to populate the forward matrix in the multipole_inversion code. To build this library you need to set the CUDAHOME or CUDA_PATH environment variables to the location of CUDA in your system. Alternatively, the nvcc compiler path can be set in the PATH variable. The it is only necessary to install or build the library using poetry install or poetry build.

Tutorial

For now you can visualize the Jupyter notebooks from the jupytext scripts in the doc/tutorial/ folder. These notebooks can also be generated from their associated jupytext script, for example,

jupytext --to notebook multipoles_inversion_test.py 

The documentation of the classes can be generated using sphinx and the scripts to do this are located in the doc folder. Future releases of this library will include an online documentation of this code.

Cite

If you find this library useful please cite us (you might need LaTeX's url package)

@Misc{Cortes2022,
  author       = {Cortés-Ortuño, David and Fabian, Karl and de Groot, Lennart V.},
  title        = {{MMT Numerical Libraries: Multipole Inversion}},
  publisher    = {Zenodo},
  note         = {Github: \url{https://github.com/Micromagnetic-Tomography/multipole_inversion}},
  year         = {2022},
  doi          = {10.5281/zenodo.6473257},
  url          = {https://doi.org/10.5281/zenodo.6473257},
}

If you have a new version of biblatex you can also use @Software instead of @Misc, and add a version={} entry. You can also cite the paper with the theoretical framework of this library:

@article{Cortes2021,
author = {Cortés-Ortuño, David and Fabian, Karl and de Groot, Lennart V.},
title = {Single Particle Multipole Expansions From Micromagnetic Tomography},
journal = {Geochemistry, Geophysics, Geosystems},
volume = {22},
number = {4},
pages = {e2021GC009663},
keywords = {magnetism, micromagnetic tomography, multipole, paleomagnetism, rock magnetism},
doi = {https://doi.org/10.1029/2021GC009663},
url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2021GC009663},
eprint = {https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2021GC009663},
note = {e2021GC009663 2021GC009663},
year = {2021}
}

Project details


Release history Release notifications | RSS feed

This version

1.0

Download files

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

Source Distribution

mmt_multipole_inversion-1.0.tar.gz (28.8 kB view details)

Uploaded Source

Built Distributions

mmt_multipole_inversion-1.0-cp310-cp310-win_amd64.whl (33.5 kB view details)

Uploaded CPython 3.10 Windows x86-64

mmt_multipole_inversion-1.0-cp310-cp310-manylinux_2_31_x86_64.whl (33.3 kB view details)

Uploaded CPython 3.10 manylinux: glibc 2.31+ x86-64

mmt_multipole_inversion-1.0-cp310-cp310-macosx_10_16_x86_64.whl (33.3 kB view details)

Uploaded CPython 3.10 macOS 10.16+ x86-64

mmt_multipole_inversion-1.0-cp39-cp39-win_amd64.whl (33.5 kB view details)

Uploaded CPython 3.9 Windows x86-64

mmt_multipole_inversion-1.0-cp39-cp39-manylinux_2_31_x86_64.whl (33.3 kB view details)

Uploaded CPython 3.9 manylinux: glibc 2.31+ x86-64

mmt_multipole_inversion-1.0-cp39-cp39-macosx_10_16_x86_64.whl (33.3 kB view details)

Uploaded CPython 3.9 macOS 10.16+ x86-64

mmt_multipole_inversion-1.0-cp38-cp38-win_amd64.whl (33.5 kB view details)

Uploaded CPython 3.8 Windows x86-64

mmt_multipole_inversion-1.0-cp38-cp38-manylinux_2_31_x86_64.whl (33.3 kB view details)

Uploaded CPython 3.8 manylinux: glibc 2.31+ x86-64

mmt_multipole_inversion-1.0-cp38-cp38-macosx_10_16_x86_64.whl (33.3 kB view details)

Uploaded CPython 3.8 macOS 10.16+ x86-64

File details

Details for the file mmt_multipole_inversion-1.0.tar.gz.

File metadata

  • Download URL: mmt_multipole_inversion-1.0.tar.gz
  • Upload date:
  • Size: 28.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.13

File hashes

Hashes for mmt_multipole_inversion-1.0.tar.gz
Algorithm Hash digest
SHA256 3419b76422ed019d888723f022a3e22679825eb744bce99784d390f5ef71ad02
MD5 b4ea661b93455d4ce4c5cbebe492062b
BLAKE2b-256 2b5eb0d6cb19bcc86a7cb02edac13abd040616d57782e1eb5ca0ac64591a4dcf

See more details on using hashes here.

File details

Details for the file mmt_multipole_inversion-1.0-cp310-cp310-win_amd64.whl.

File metadata

File hashes

Hashes for mmt_multipole_inversion-1.0-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 c5b53a66af64b5c10116aaa612c1cdaf61cc84ef6f066d4c201119837a23f388
MD5 b366b81ae1bf863608f1603429c69f74
BLAKE2b-256 bb662e546ecef0f5be9f2a918affe28b3753664f50876163667b9b58b52f863a

See more details on using hashes here.

File details

Details for the file mmt_multipole_inversion-1.0-cp310-cp310-manylinux_2_31_x86_64.whl.

File metadata

File hashes

Hashes for mmt_multipole_inversion-1.0-cp310-cp310-manylinux_2_31_x86_64.whl
Algorithm Hash digest
SHA256 e36788844701ee7d580515714925e282d3687323d29978379ff868d147da3bbc
MD5 704b65380f2e6c63dc93fd46ddccac5e
BLAKE2b-256 903dca2c55625186c71b4eedd0a5acd0537cb303ee3be55025abce98461705ba

See more details on using hashes here.

File details

Details for the file mmt_multipole_inversion-1.0-cp310-cp310-macosx_10_16_x86_64.whl.

File metadata

File hashes

Hashes for mmt_multipole_inversion-1.0-cp310-cp310-macosx_10_16_x86_64.whl
Algorithm Hash digest
SHA256 e61fd2029c77fd00117e7f96d70a575e8616ffebda6fa08b70cd5c2826a36049
MD5 0f65f5ece4dad1bb062e33fc6677f26a
BLAKE2b-256 2b2c7429f638dc77e7d793ace5d47e5864785a27a343bba1117713ea09e10ad8

See more details on using hashes here.

File details

Details for the file mmt_multipole_inversion-1.0-cp39-cp39-win_amd64.whl.

File metadata

File hashes

Hashes for mmt_multipole_inversion-1.0-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 12bd74f0bd47ad2cf6167e9f932cc4d79c497143442ef5c69df9fd3162c6ad76
MD5 8573defcd6ff0f84275fa2046b17e155
BLAKE2b-256 7b37ceb3028bdf22313398e7dc26524d26479efa5810ace94ee86848b195eff7

See more details on using hashes here.

File details

Details for the file mmt_multipole_inversion-1.0-cp39-cp39-manylinux_2_31_x86_64.whl.

File metadata

File hashes

Hashes for mmt_multipole_inversion-1.0-cp39-cp39-manylinux_2_31_x86_64.whl
Algorithm Hash digest
SHA256 14ada1d5c32663d36f415bd5d12fdf46ed191f5da5fe02cec1f600a96be12262
MD5 87a6dbea338e44570de02f849e3bb6f5
BLAKE2b-256 d2817bab375a8008d8c3bb6c16cb6679f98c60fa5d5951e6c016331c52752307

See more details on using hashes here.

File details

Details for the file mmt_multipole_inversion-1.0-cp39-cp39-macosx_10_16_x86_64.whl.

File metadata

File hashes

Hashes for mmt_multipole_inversion-1.0-cp39-cp39-macosx_10_16_x86_64.whl
Algorithm Hash digest
SHA256 db7de40c474b93623a38114f4e68bd42f8e700193e18eee86acd293de2c3251a
MD5 b4a47c504bfb32b9fa0aca36acf48889
BLAKE2b-256 1227a8acca72ce5933d58942a281ebec515cd5270b0bd1cb7bc13d0d4dd7b45a

See more details on using hashes here.

File details

Details for the file mmt_multipole_inversion-1.0-cp38-cp38-win_amd64.whl.

File metadata

File hashes

Hashes for mmt_multipole_inversion-1.0-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 bfe8d412edfa73a3d35abd2af1ef29369ac47d85c4485c0e4477559fc6b1847e
MD5 8978bd036bce40c4c511ea1f66b20cfc
BLAKE2b-256 843db1e43e829bade4dfb24ef04ad926842d57413a37d52c684075e8556176fa

See more details on using hashes here.

File details

Details for the file mmt_multipole_inversion-1.0-cp38-cp38-manylinux_2_31_x86_64.whl.

File metadata

File hashes

Hashes for mmt_multipole_inversion-1.0-cp38-cp38-manylinux_2_31_x86_64.whl
Algorithm Hash digest
SHA256 f7b35d8d490160dbc1f15f843be7bccc5999b58786e2a9a7a884a1df4290b29a
MD5 9b2151cfcbd58ce2a80df2cf7a00cb19
BLAKE2b-256 f567499ddba11807da00e8203a3dae85e5bbd27194a2b13f7bb466e2e397ea90

See more details on using hashes here.

File details

Details for the file mmt_multipole_inversion-1.0-cp38-cp38-macosx_10_16_x86_64.whl.

File metadata

File hashes

Hashes for mmt_multipole_inversion-1.0-cp38-cp38-macosx_10_16_x86_64.whl
Algorithm Hash digest
SHA256 526d0c2c1f45ace900cf3084817e91be0bb6f6666786347c7795c64a5a6249c2
MD5 9f14113de373683bd30afa6486f50d35
BLAKE2b-256 c1f47ee26e3dfdccad4a013bc3f8b7bb6f430062398544b4f889841643e38c55

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