PlateCurie | Estimating Curie depth from magnetic anomaly data
kandi X-RAY | PlateCurie Summary
kandi X-RAY | PlateCurie Summary
PlateCurie is a Jupyter Notebook library. PlateCurie has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.
Crustal magnetic anomalies carry information on the source distribution of magnetization in the Earth's crust (Blakely, 1995). The Curie point corresponds to the depth at which crustal rocks loose their magnetization where they reach their Curie temperature, and is obtained by fitting the power spectral density (PSD) of magnetic anomaly data with a model where magnetic anomalies are confined within a layer (Bouligand et al., 2009; Audet and Gosselin, 2019; Mather and Fullea, 2019). Mapping the Curie point provides important information on geothermal gradients in the Earth; however, mapping Curie depth is a spatio-spectral localization problem because the PSD needs to be calculated within moving windows at wavelengths long enough to capture the greatest possible depth to the bottom of the magnetic layer. The wavelet transform is particularly well suited to overcome this problem because it avoids splitting the grids into small windows and can therefore produce PSD functions at each point of the input grid (Gaudreau et al., 2019). This package extends the package plateflex, which contains python modules to calculate the wavelet transform and scalogram of 2D gridded data, by providing a new class MagGrid that inherits from plateflex.classes.Grid with methods to estimate the properties of the magnetic layer (depth to top of layer (zt), thickness of layer (dz), and power-law exponent of fractal magnetization (β)) using Bayesian inference. Common computational workflows are covered in the Jupyter notebooks bundled with this package. The software contains methods to make beautiful and insightful plots using the seaborn package. Author: Pascal Audet (Developer and Maintainer).
Crustal magnetic anomalies carry information on the source distribution of magnetization in the Earth's crust (Blakely, 1995). The Curie point corresponds to the depth at which crustal rocks loose their magnetization where they reach their Curie temperature, and is obtained by fitting the power spectral density (PSD) of magnetic anomaly data with a model where magnetic anomalies are confined within a layer (Bouligand et al., 2009; Audet and Gosselin, 2019; Mather and Fullea, 2019). Mapping the Curie point provides important information on geothermal gradients in the Earth; however, mapping Curie depth is a spatio-spectral localization problem because the PSD needs to be calculated within moving windows at wavelengths long enough to capture the greatest possible depth to the bottom of the magnetic layer. The wavelet transform is particularly well suited to overcome this problem because it avoids splitting the grids into small windows and can therefore produce PSD functions at each point of the input grid (Gaudreau et al., 2019). This package extends the package plateflex, which contains python modules to calculate the wavelet transform and scalogram of 2D gridded data, by providing a new class MagGrid that inherits from plateflex.classes.Grid with methods to estimate the properties of the magnetic layer (depth to top of layer (zt), thickness of layer (dz), and power-law exponent of fractal magnetization (β)) using Bayesian inference. Common computational workflows are covered in the Jupyter notebooks bundled with this package. The software contains methods to make beautiful and insightful plots using the seaborn package. Author: Pascal Audet (Developer and Maintainer).
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Support
PlateCurie has a low active ecosystem.
It has 5 star(s) with 4 fork(s). There are 3 watchers for this library.
It had no major release in the last 12 months.
There are 1 open issues and 1 have been closed. On average issues are closed in 5 days. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of PlateCurie is v0.0.1
Quality
PlateCurie has 0 bugs and 0 code smells.
Security
PlateCurie has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
PlateCurie code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
PlateCurie is licensed under the MIT License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.
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PlateCurie releases are available to install and integrate.
Installation instructions are not available. Examples and code snippets are available.
It has 3790 lines of code, 27 functions and 26 files.
It has high code complexity. Code complexity directly impacts maintainability of the code.
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PlateCurie Key Features
No Key Features are available at this moment for PlateCurie.
PlateCurie Examples and Code Snippets
conda install numpy pymc3 matplotlib seaborn -c conda-forge
pip install platecurie
conda create -n curie python=3.7 numpy pymc3 matplotlib seaborn -c conda-forge
git clone https://github.com/paudetseis/PlateCurie.git
cd PlateCurie
conda env create
git clone https://github.com/paudetseis/PlateCurie.git
cd PlateCurie
pip install plateflex
pip install .
pip install -e .
from platecurie import doc
doc.install_doc(path='Notebooks')
conda install jupyter
jupyter notebook
Community Discussions
No Community Discussions are available at this moment for PlateCurie.Refer to stack overflow page for discussions.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install PlateCurie
You can download it from GitHub.
Support
The documentation for all classes and functions in platecurie can be accessed from https://paudetseis.github.io/PlateCurie/.
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