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PyCurious

PyCurious: A Python module for computing the Curie depth from the magnetic anomaly. - Published in JOSS (2019)

https://github.com/brmather/pycurious

Science Score: 93.0%

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    Found 10 DOI reference(s) in README and JOSS metadata
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Keywords

bayesian-inference curie-depth magnetic-anomaly magnetism python

Keywords from Contributors

geophysics geoscience earth-science fatiando-a-terra interpolation scipy-stack meshes spherical-geometry triangulation-delaunay

Scientific Fields

Mathematics Computer Science - 84% confidence
Engineering Computer Science - 60% confidence
Last synced: 4 months ago · JSON representation

Repository

Python package for computing the Curie depth from the magnetic anomaly

Basic Info
Statistics
  • Stars: 42
  • Watchers: 5
  • Forks: 19
  • Open Issues: 3
  • Releases: 7
Topics
bayesian-inference curie-depth magnetic-anomaly magnetism python
Created almost 8 years ago · Last pushed over 3 years ago
Metadata Files
Readme Contributing License

README.md

PyCurious

Docker Cloud Automated build PyPI DOI Build Status

Magnetic data is one of the most common geophysics datasets available on the surface of the Earth. Curie depth is the depth at which rocks lose their magnetism. The most prevalent magnetic mineral is magnetite, which has a Curie point of 580°C, thus the Curie depth is often interpreted as the 580°C isotherm.

Current methods to derive Curie depth first compute the (fast) Fourier transform over a square window of a magnetic anomaly that has been reduced to the pole. The depth and thickness of magnetic sources is estimated from the slope of the radial power spectrum. pycurious implements the Tanaka et al. (1999) and Bouligand et al. (2009) methods for computing the thickness of a buried magnetic source. pycurious ingests maps of the magnetic anomaly and distributes the computation of Curie depth across multiple CPUs. Common computational workflows and geospatial manipulation of magnetic data are covered in the Jupyter notebooks bundled with this package.

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Citation

DOI

Mather, B. and Delhaye, R. (2019). PyCurious: A Python module for computing the Curie depth from the magnetic anomaly. Journal of Open Source Software, 4(39), 1544, https://doi.org/10.21105/joss.01544

Navigation / Notebooks

There are two matching sets of Jupyter notebooks - one set for the Tanaka and one for Bouligand implementations. The Bouligand set of noteboks are a natural choice for Bayesian inference applications.

Note, these examples can be installed from the package itself by running:

python import pycurious pycurious.install_documentation(path="Notebooks")

Tanaka

Bouligand

Installation

Dependencies

You will need Python 2.7 or 3.5+. Also, the following packages are required:

Optional dependencies for mapping module and running the Notebooks:

Installing using pip

You can install pycurious using the pip package manager with either version of Python:

bash python2 -m pip install pycurious python3 -m pip install pycurious All the dependencies will be automatically installed by pip.

Installing with conda

You can install pycurious using the conda package manager. Its required dependencies can be easily installed with:

bash conda install numpy scipy cython

And the full set of dependencies with:

bash conda install numpy scipy cython matplotlib pyproj cartopy

Then pycurious can be installed with pip:

bash pip install pycurious

Conda environment

Alternatively, you can create a custom conda environment where pycurious can be installed along with its dependencies.

Clone the repository: bash git clone https://github.com/brmather/pycurious cd pycurious

Create the environment from the environment.yml file: bash conda env create -f environment.yml

Activate the newly created environment: bash conda activate pycurious

And install pycurious with pip: bash pip install pycurious

Issue with gcc

If the pycurious installation fails due to an issue with gcc and Anaconda, you just need to install gxx_linux-64 with conda:

bash conda install gxx_linux-64

And then install pycurious normally.

Installing using Docker

A more straightforward installation for pycurious and all of its dependencies may be deployed with Docker. To install the docker image and start the Jupyter notebook examples:

bash docker run --name pycurious -p 127.0.0.1:8888:8888 brmather/pycurious:latest

Usage

PyCurious consists of 2 classes:

  • CurieGrid: base class that computes radial power spectrum, centroids for processing, decomposition of subgrids.
  • CurieOptimise: optimisation module for fitting the synthetic power spectrum (inherits CurieGrid).

Also included is a mapping module for gridding scattered data points, and converting between coordinate reference systems (CRS).

Below is a simple workflow to calculate the radial power spectrum:

```python import pycurious

initialise CurieOptimise object with 2D magnetic anomaly

grid = pycurious.CurieOptimise(mag_anomaly, xmin, xmax, ymin, ymax)

extract a square window of the magnetic anomaly

subgrid = grid.subgrid(window_size, x, y)

compute the radial power spectrum

k, Phi, sigmaPhi = grid.radialspectrum(subgrid) ```

A series of tests are located in the tests subdirectory. In order to perform these tests, clone the repository and run pytest:

bash git checkout https://github.com/brmather/pycurious.git cd pycurious pytest -v

API Documentation

The API for all functions and classes in pycurious can be accessed from https://brmather.github.io/pycurious/.

References

  1. Bouligand, C., Glen, J. M. G., & Blakely, R. J. (2009). Mapping Curie temperature depth in the western United States with a fractal model for crustal magnetization. Journal of Geophysical Research, 114(B11104), 1–25. https://doi.org/10.1029/2009JB006494
  2. Tanaka, A., Okubo, Y., & Matsubayashi, O. (1999). Curie point depth based on spectrum analysis of the magnetic anomaly data in East and Southeast Asia. Tectonophysics, 306(3–4), 461–470. https://doi.org/10.1016/S0040-1951(99)00072-4

Owner

  • Name: Ben Mather
  • Login: brmather
  • Kind: user
  • Location: Sydney, Australia
  • Company: University of Sydney

Computational Geophysicist

JOSS Publication

PyCurious: A Python module for computing the Curie depth from the magnetic anomaly.
Published
July 26, 2019
DOI
Volume 4, Issue 39, Page 1544
Authors
Ben Mather ORCID
School of Geoscience, The University of Sydney, Sydney, Australia, School of Cosmic Physics, Dublin Institute for Advanced Studies, Dublin, Ireland
Robert Delhaye ORCID
School of Cosmic Physics, Dublin Institute for Advanced Studies, Dublin, Ireland
Editor
Lindsey Heagy ORCID
Tags
Curie depth magnetism magnetic anomaly Bayesian inference
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GitHub Events

Total
  • Issues event: 1
  • Watch event: 3
  • Issue comment event: 1
  • Fork event: 1
Last Year
  • Issues event: 1
  • Watch event: 3
  • Issue comment event: 1
  • Fork event: 1

Committers

Last synced: 5 months ago

All Time
  • Total Commits: 155
  • Total Committers: 8
  • Avg Commits per committer: 19.375
  • Development Distribution Score (DDS): 0.297
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Ben Mather b****1@g****m 109
Ben Mather b****r@c****e 20
Santiago Soler s****r@g****m 9
rdelhaye 3****e 7
Robert Delhaye r****e@p****e 7
Lindsey Heagy l****y@g****m 1
Kyle Niemeyer k****r@g****m 1
Jesse Pisel j****l 1
Committer Domains (Top 20 + Academic)
pollak.cp.dias.ie: 1 cp.dias.ie: 1

Issues and Pull Requests

Last synced: 4 months ago

All Time
  • Total issues: 21
  • Total pull requests: 19
  • Average time to close issues: 4 days
  • Average time to close pull requests: about 17 hours
  • Total issue authors: 7
  • Total pull request authors: 6
  • Average comments per issue: 1.95
  • Average comments per pull request: 0.63
  • Merged pull requests: 18
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 1
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 1
  • Pull request authors: 0
  • Average comments per issue: 0.0
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  • Merged pull requests: 0
  • Bot issues: 0
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Top Authors
Issue Authors
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  • zhongpenggeo (1)
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  • mdtanker (1)
  • brandiscarlier (1)
Pull Request Authors
  • brmather (11)
  • santisoler (4)
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  • kyleniemeyer (1)
  • lheagy (1)
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Packages

  • Total packages: 1
  • Total downloads:
    • pypi 14 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 1
  • Total versions: 13
  • Total maintainers: 1
pypi.org: pycurious

Python tool for computing the Curie depth from magnetic data

  • Versions: 13
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 14 Last month
Rankings
Forks count: 9.3%
Dependent packages count: 10.1%
Stargazers count: 10.5%
Average: 14.8%
Dependent repos count: 21.6%
Downloads: 22.6%
Maintainers (1)
brmather
Last synced: 4 months ago

Dependencies

environment.yml conda
  • cartopy
  • cython
  • jupyter
  • matplotlib
  • numpy
  • pip
  • pyproj
  • pytest
  • python 3.7.*
  • scipy
setup.py pypi
  • numpy *