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`platerecipy`: a package for PLATE RECognition In PYthon

platerecipy is a tool for detecting candidate plates on global geophysical datasets. It analyzes the surface to identify diffuse and non-conforming regions, as well as regions with low confidence in plate assignment.

Supported platforms

Though platerecipy is fundamentally a Python package, it also relies on backend functionalities that are implemented in C. Linux is the main operating system for platerecipy. However, expect for a number of optimizations (e.g., multithreaded C functions), platerecipy is now available via pip on Windows systems.

Installation

Version releases are made available on PyPI so that platerecipy can be installed using pip on the shell as follows:

pip install platerecipy

User guide

platerecipy's documentation is moved to platerecipy.readthedocs.io!

For a detailed explanation and illustration of platerecipy's core functionalities, please refer to:

Javaheri, P., & Lowman, J. P. (2026). A random walker algorithm for plate boundary detection in spherical mantle convection models and global geophysical data sets: Application to Euler vector determination. Journal of Geophysical Research: Solid Earth, 131, e2025JB032259. https://doi.org/10.1029/2025JB032259

As a demonstration, assuming a given input_xs, input_ys, input_zs, and
input_field (all numpy arrays), using platerecipy is as simple as follows:

from platerecipy.model import PlateModel
from platerecipy.grid import SphericalGrid

input_xs    = # to be specified ...
input_ys    = # to be specified ...
input_zs    = # to be specified ...
input_field = # to be specified ...

# generating a consistent grid for interpolation
grid = SphericalGrid(input_xs, input_ys, input_zs)

# interpolating an input field
field = grid.interpolate_field(input_field)

# initializing a plate model
m = PlateModel(grid)

# stacking the interpolated field
m.stack_field(field, take_log=True)

# finding plates on the stacked field
m.find_plates(
    boundary_quantile     = 0.9,            # threshold for the boundaries 
    separation_tolerance  = 4*3.1416/180.,  # 4 degrees for separation tolerance
    RW_beta               = 200,            # RW beta (for feature sharpness)
    min_marker_size       = 100             # to filter out micro plates
)

# outputting as a ParaView readable .vtk file
from platerecipy import io
io.save_as_vtk(m)

Citing `platerecipy`

If this package has been useful to your research, please cite the original paper:

Javaheri, P., & Lowman, J. P. (2026). A random walker algorithm for plate boundary detection in spherical mantle convection models and global geophysical data sets: Application to Euler vector determination. Journal of Geophysical Research: Solid Earth, 131, e2025JB032259. https://doi.org/10.1029/2025JB032259

@article{platerecipy,
author = {Javaheri, P. and Lowman, J. P.},
title = {{A random walker algorithm for plate boundary detection in spherical mantle convection models and global geophysical data sets: Application to Euler vector determination}},
journal = {Journal of Geophysical Research: Solid Earth},
volume = {131},
number = {3},
pages = {e2025JB032259},
keywords = {plate tectonics, plate modeling, Euler vector, diffuse zones, mantle convection, strain-rate},
doi = {https://doi.org/10.1029/2025JB032259},
url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2025JB032259},
eprint = {https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2025JB032259},
note = {e2025JB032259 2025JB032259},
year = {2026}
}

as well as the software version:

P. Javaheri. (2025). pjavaheri/platerecipy: Release of version 1.0.0 (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.15625716

@software{p_javaheri_2025_15625716,
  author       = {P. Javaheri},
  title        = {pjavaheri/platerecipy: Release of version 1.0.0},
  month        = jun,
  year         = 2025,
  publisher    = {Zenodo},
  version      = {v1.0.0},
  doi          = {10.5281/zenodo.15625716},
  url          = {https://doi.org/10.5281/zenodo.15625716},
}

Please note that platerecipy is a platform under development. Depending on your installed version, the software version citation should point to the most recent prior release on Zenodo.

Developed by Pejvak Javaheri, pejvak.javaheri@mail.utoronto.ca.

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platerecipy 2.0.5

platerecipy: a package for PLATE RECognition In PYthon

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`platerecipy`: a package for PLATE RECognition In PYthon

Citing `platerecipy`

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BLAKE2b-256	`a85d8ef06a011e999d2267d400311988850aaf03b933d24ee8660d02ff780efe`

Algorithm	Hash digest
SHA256	`ead76895b293eacbdd36128939d3c9b11a76a092892ed98ddbc5a8c628b39f54`
MD5	`01bed057dd776503a7ac622e20a4a072`
BLAKE2b-256	`3cfe8b4e496d235569d66f792f2d5802c1ad2ed27c2ead2e2e15665ab74c14b5`

Algorithm	Hash digest
SHA256	`b3dd48336cd73f8e7c616c605948cf85867da71b03af4f2a56c5f929bb62d518`
MD5	`2952b9483e6f5004adfbac24c965d35b`
BLAKE2b-256	`a41092f51c21ca1c0a8d1b39103fefe8c32e3145e1224109e4efb21af4078b66`

Algorithm	Hash digest
SHA256	`62f8890a01b1a3d77e649b7684b4c216ec4de29940243dfb863d7e5cd7fd1497`
MD5	`fee505a67e0e9321d8d51ebf28624467`
BLAKE2b-256	`815e94712155e26a45d6a04b7daf0983f5dfe7e96591956d8e0898a30747677b`

Algorithm	Hash digest
SHA256	`b264fe48eefa420d84685dda026c6fd8288672afd0ff005a1003cf71851aff7b`
MD5	`33c460e58b766be36d84e71409b00e16`
BLAKE2b-256	`e6ff12311750a616761121152259fdcf6b1eea6dca3f87f65b227b36e85a50d3`

Algorithm	Hash digest
SHA256	`a762b07c9c235da2002438abdf3cb0b5b6d4cf9e70a7438ff198c92be5359f3c`
MD5	`90392c964b8c5002fca0553b3d7a9f53`
BLAKE2b-256	`8df8d52d5718f8e2577c5eb335247bdd26a10a3a4e191f021f91729c8294e166`

Algorithm	Hash digest
SHA256	`be240b3adb93087ab00edd73523c06dbb51429560ca9c720d8f3dea619db3291`
MD5	`43ec79d0a7508514b6f3cf0dcde95abd`
BLAKE2b-256	`e0a5b1aa6261cd7e8e7549b7c0d35d5424c1e8c5aefb3aa881ce03ca63fdbe57`

Algorithm	Hash digest
SHA256	`594eea0c0e28b3bfeb07fad3217e57fff9f09e45fff4be67072e6f7f1ca70490`
MD5	`50632f78bce5b10260f4f9837e1156b3`
BLAKE2b-256	`3ec2b9bdc55dbfb720aec25124345bceb18fed203315f4304f75bbc55fd8d78d`