| Name | slabdip JSON |
| Version |
4.2.2
JSON |
| download |
| home_page | https://github.com/brmather/Slab-Dip |
| Summary | Method to calculate slab dip using simple plate kinematic parameters |
| upload_time | 2025-01-11 23:37:48 |
| maintainer | None |
| docs_url | None |
| author | Ben Mather |
| requires_python | None |
| license | None |
| keywords |
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| VCS |
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| bugtrack_url |
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| requirements |
No requirements were recorded.
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|
# Predict slab dip
Predict the dip angle of subducting oceanic lithosphere using simple plate kinematic parameters.
#### Cite
```bib
@article{Mather2023,
title = {Kimberlite Eruptions Driven by Slab Flux and Subduction Angle},
author = {Mather, Ben R and M{\"u}ller, R Dietmar and Alfonso, Christopher P. and Seton, Maria and Wright, Nicky M.},
year = {2023},
journal = {Scientific Reports},
volume = {13},
number = {9216},
pages = {1--12},
doi = {10.1038/s41598-023-36250-w},
}
```
> Mather, B. R., Müller, R. D., Alfonso, C. P., Seton, M., & Wright, N. M. (2023). Kimberlite eruptions driven by slab flux and subduction angle. Scientific Reports, 13(9216), 1–12. https://doi.org/10.1038/s41598-023-36250-w
## Dependencies
To run the Jupyter notebooks some dependencies are required:
- [pygplates](https://www.gplates.org/download/)
- [gplately](https://github.com/GPlates/gplately)
- [PlateTectonicTools](https://github.com/EarthByte/PlateTectonicTools/tree/master/ptt)
- [Scikit-Learn](https://scikit-learn.org)
- [cartopy](https://scitools.org.uk/cartopy/docs/latest/installing.html) (for mapping)
- [netCDF4](https://pypi.org/project/netCDF4/) (to extract age grids of the seafloor)
Instructions to install these dependencies can be found within each package above.
Some conda instructions for setting up a Python environment are [here](https://www.benmather.info/post/2022-07-07-python-for-mac-m1/). While these have been written with the Mac M1 architecture in mind, the same instructions should apply equally to other distributions.
## Installation
Most of the Jupyter notebooks can be run without installing this package, however, following these installation instructions will make the slab dip prediction tool available system-wide.
### 1. Using conda (recommended)
You can install the latest stable public release of `slabdip` and all of its dependencies using conda.
This is the preferred method to install `slabdip` which downloads binaries from the conda-forge channel.
```sh
conda install -c conda-forge slabdip
```
#### Creating a new conda environment
We recommend creating a new conda environment inside which to install `slabdip`. This avoids any potential conflicts in your base Python environment. In the example below we create a new environment called "`my-env`":
```sh
conda create -n my-env
conda activate my-env
conda install -c conda-forge slabdip
```
`my-env` needs to be activated whenever you use `GPlately`: i.e. `conda activate my-env`.
### 2. Using pip
From the current directory, run
```sh
pip install .
```
You can also install the most up-to-date version by running
```sh
pip install git+https://github.com/brmather/Slab-Dip.git
```
which will clone the `main` branch and install the latest version.
## Data packages
Plate reconstruction and corresponding age grids of the seafloor are required to predict slab dip. These may be downloaded from https://www.earthbyte.org/gplates-2-3-software-and-data-sets/
The slab dip prediction tool has been tested on [Clennett _et al._ (2020)](https://doi.org/10.1029/2020GC009117) and [Müller _et al._ (2019)](https://doi.org/10.1029/2018TC005462) plate reconstructions but should also work fine for all other plate reconstructions.
## Usage
A series of Jupyter notebooks document the workflow to calculate plate kinematic and rheological information used to predict slab dip. Skip to __notebook 6__ to jump straight into the slab dip estimator. The Python snippet below outlines the usage of the `SlabDipper` object which can be used with little modification to estimate slab dip for a user-defined reconstruction time.
```python
# Call GPlately's DataServer object and download the plate model
gdownload = gplately.download.DataServer("Clennett2020")
rotation_model, topology_features, static_polygons = gdownload.get_plate_reconstruction_files()
# Use the PlateReconstruction object to create a plate motion model
model = gplately.PlateReconstruction(rotation_model, topology_features, static_polygons)
# Initialise SlabDipper object
dipper = SlabDipper()
dipper.model = model
# Set the filename (including path) of the seafloor age and spreading rate grids
dipper.set_age_grid_filename(agegrid_filename)
dipper.set_spreading_rate_grid_filename(spreadrate_filename)
# Estimate slab dip across the globe for a specified reconstruction time
# (returned as a Pandas DataFrame)
dataFrame = dipper.tessellate_slab_dip(0)
```
#### References
- Clennett, E. J., Sigloch, K., Mihalynuk, M. G., Seton, M., Henderson, M. A., Hosseini, K., et al. (2020). A Quantitative Tomotectonic Plate Reconstruction of Western North America and the Eastern Pacific Basin. Geochemistry, Geophysics, Geosystems, 21(8), 1–25. https://doi.org/10.1029/2020GC009117
- Müller, R. D., Zahirovic, S., Williams, S. E., Cannon, J., Seton, M., Bower, D. J., et al. (2019). A Global Plate Model Including Lithospheric Deformation Along Major Rifts and Orogens Since the Triassic. Tectonics, 38(6), 1884–1907. https://doi.org/10.1029/2018TC005462
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"description": "# Predict slab dip\n\nPredict the dip angle of subducting oceanic lithosphere using simple plate kinematic parameters.\n\n#### Cite\n\n```bib\n@article{Mather2023,\n title = {Kimberlite Eruptions Driven by Slab Flux and Subduction Angle},\n author = {Mather, Ben R and M{\\\"u}ller, R Dietmar and Alfonso, Christopher P. and Seton, Maria and Wright, Nicky M.},\n year = {2023},\n journal = {Scientific Reports},\n volume = {13},\n number = {9216},\n pages = {1--12},\n doi = {10.1038/s41598-023-36250-w},\n}\n```\n\n> Mather, B. R., M\u00fcller, R. D., Alfonso, C. P., Seton, M., & Wright, N. M. (2023). Kimberlite eruptions driven by slab flux and subduction angle. Scientific Reports, 13(9216), 1\u201312. https://doi.org/10.1038/s41598-023-36250-w\n\n\n\n## Dependencies\n\nTo run the Jupyter notebooks some dependencies are required:\n\n- [pygplates](https://www.gplates.org/download/)\n- [gplately](https://github.com/GPlates/gplately)\n- [PlateTectonicTools](https://github.com/EarthByte/PlateTectonicTools/tree/master/ptt)\n- [Scikit-Learn](https://scikit-learn.org)\n- [cartopy](https://scitools.org.uk/cartopy/docs/latest/installing.html) (for mapping)\n- [netCDF4](https://pypi.org/project/netCDF4/) (to extract age grids of the seafloor)\n\nInstructions to install these dependencies can be found within each package above.\nSome conda instructions for setting up a Python environment are [here](https://www.benmather.info/post/2022-07-07-python-for-mac-m1/). While these have been written with the Mac M1 architecture in mind, the same instructions should apply equally to other distributions.\n\n## Installation\n\nMost of the Jupyter notebooks can be run without installing this package, however, following these installation instructions will make the slab dip prediction tool available system-wide.\n\n### 1. Using conda (recommended)\n\nYou can install the latest stable public release of `slabdip` and all of its dependencies using conda.\nThis is the preferred method to install `slabdip` which downloads binaries from the conda-forge channel.\n\n```sh\nconda install -c conda-forge slabdip\n```\n\n#### Creating a new conda environment\n\nWe recommend creating a new conda environment inside which to install `slabdip`. This avoids any potential conflicts in your base Python environment. In the example below we create a new environment called \"`my-env`\":\n\n```sh\nconda create -n my-env\nconda activate my-env\nconda install -c conda-forge slabdip\n```\n\n`my-env` needs to be activated whenever you use `GPlately`: i.e. `conda activate my-env`.\n\n### 2. Using pip\n\nFrom the current directory, run\n\n```sh\npip install .\n```\n\nYou can also install the most up-to-date version by running\n\n```sh\npip install git+https://github.com/brmather/Slab-Dip.git\n```\n\nwhich will clone the `main` branch and install the latest version.\n\n## Data packages\n\nPlate reconstruction and corresponding age grids of the seafloor are required to predict slab dip. These may be downloaded from https://www.earthbyte.org/gplates-2-3-software-and-data-sets/\n\nThe slab dip prediction tool has been tested on [Clennett _et al._ (2020)](https://doi.org/10.1029/2020GC009117) and [M\u00fcller _et al._ (2019)](https://doi.org/10.1029/2018TC005462) plate reconstructions but should also work fine for all other plate reconstructions.\n\n## Usage\n\nA series of Jupyter notebooks document the workflow to calculate plate kinematic and rheological information used to predict slab dip. Skip to __notebook 6__ to jump straight into the slab dip estimator. The Python snippet below outlines the usage of the `SlabDipper` object which can be used with little modification to estimate slab dip for a user-defined reconstruction time.\n\n```python\n# Call GPlately's DataServer object and download the plate model\ngdownload = gplately.download.DataServer(\"Clennett2020\")\nrotation_model, topology_features, static_polygons = gdownload.get_plate_reconstruction_files()\n\n# Use the PlateReconstruction object to create a plate motion model\nmodel = gplately.PlateReconstruction(rotation_model, topology_features, static_polygons)\n\n# Initialise SlabDipper object\ndipper = SlabDipper()\ndipper.model = model\n\n# Set the filename (including path) of the seafloor age and spreading rate grids\ndipper.set_age_grid_filename(agegrid_filename)\ndipper.set_spreading_rate_grid_filename(spreadrate_filename)\n\n# Estimate slab dip across the globe for a specified reconstruction time\n# (returned as a Pandas DataFrame)\ndataFrame = dipper.tessellate_slab_dip(0)\n```\n\n#### References\n\n- Clennett, E. J., Sigloch, K., Mihalynuk, M. G., Seton, M., Henderson, M. A., Hosseini, K., et al. (2020). A Quantitative Tomotectonic Plate Reconstruction of Western North America and the Eastern Pacific Basin. Geochemistry, Geophysics, Geosystems, 21(8), 1\u201325. https://doi.org/10.1029/2020GC009117\n- M\u00fcller, R. D., Zahirovic, S., Williams, S. E., Cannon, J., Seton, M., Bower, D. J., et al. (2019). A Global Plate Model Including Lithospheric Deformation Along Major Rifts and Orogens Since the Triassic. Tectonics, 38(6), 1884\u20131907. https://doi.org/10.1029/2018TC005462\n",
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