Skip to main content

Python interface to TRIPACK and STRIPACK fortran code for triangulation/interpolation in Cartesian coordinates and on a sphere

Project description

Stripy

Docker Cloud Automated build PyPI

pip builds

Conda Deployment

A Python interface to TRIPACK and STRIPACK Fortran code for (constrained) triangulation in Cartesian coordinates and on a sphere. Stripy is an object-oriented package and includes routines from SRFPACK and SSRFPACK for interpolation (nearest neighbor, linear and hermite cubic) and to evaluate derivatives (Renka 1996a,b and 1997a,b).

stripy is bundled with litho1pt0 which is a python interface to the crust 1.0 dataset and the lithospheric part of the litho 1.0 dataset (Laske et al, 2013 and Pasyanos et al, 2014) which both requires and demonstrates the triangulation / searching and interpolation on the sphere that is provided by stripy.

Examples

Sample images created with stripy illustrating the meshing capability: ocean age data can be triangulated on the sphere with no need for points on land. Once stripy ingests your data points, you can sample another dataset to your grid (bathymetry on the right), smooth, find the derivatives of your data, or interpolate to another set of points.

Documentation

There are two matching sets of stripy notebooks - one set for Cartesian Triangulations and one for Spherical Triangulations. For most geographical applications, the spherical triangulations are the natural choice as they expect longitude and latitude coordinates (admittedly in radians). There are some worked examples which use the companion package litho1pt0

Stable code

Bleeding edge code

For previous versions, see the changelog

Installation & Running in the cloud

Binder

Launch the demonstration

Binder

(This is the development branch)

Binder-dev

Citation

DOI

Moresi, L. and Mather, B.R., (2019). Stripy: A Python module for (constrained) triangulation in Cartesian coordinates and on a sphere.. Journal of Open Source Software, 4(38), 1410, https://doi.org/10.21105/joss.01410

Navigation / Notebooks

There are two matching sets of stripy notebooks - one set for Cartesian Triangulations and one for Spherical Triangulations. For most geographical applications, the spherical triangulations are the natural choice as they expect longitude and latitude coordinates (admittedly in radians).

Note: the Cartesian and Spherical notebooks can be obtained / installed from stripy itself as follows:

   python -c 'import stripy; stripy.documentation.install_documentation(path="Notebooks")'

Cartesian

Spherical

Examples

Note, these examples are the notebooks from litho1pt0 which are installed from the package itself:

   python -c 'import litho1pt0; litho1pt0.documentation.install_documentation(path="Notebooks")'

The first three notebooks are an introduction to litho1pt0 that does not explicitly mention stripy but the next two worked examples show how to search, interpolate and plot with the help of stripy routines.

Installation

Dependencies

You will need Python 3.6+. Also, the following packages are required:

Recommended Packages for running the notebooks:

All of which should be available from pip or anaconda (conda-forge) for most platforms.

Installing using pip

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

python3 -m pip install stripy

All the dependencies will be automatically installed by pip, except for gfortran (or any Fortran compiler). It must be installed in your system before installing stripy with pip.

If you change the Fortran compiler, you may have to add the flags config_fc --fcompiler=<compiler name> when setup.py is run (see docs for numpy.distutils).

Installing with conda

If you use the anaconda packaging system, then you should be able to

conda install -c geo-down-under stripy

Usage

Two classes are included as part of the Stripy package:

  • sTriangulation (Spherical coordinates)
  • Triangulation (Cartesian coordinates)

These classes share similar methods and can be easily interchanged. In addition, there are many helper functions provided for building meshes.

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

git checkout https://github.com/underworldcode/stripy.git
cd stripy
pytest -v

References

  1. Laske, G., G. Masters, and Z. Ma (2013), Update on CRUST1. 0—A 1-degree global model of Earth's crust, Geophys Research Abstracts, 15, EGU2013–2658.

  2. Pasyanos, M. E., T. G. Masters, G. Laske, and Z. Ma (2014), LITHO1.0: An updated crust and lithospheric model of the Earth, Journal of Geophysical Research-Solid Earth, 119(3), 2153–2173, doi:10.1002/2013JB010626.

  3. R. J. Renka, "ALGORITHM 751: TRIPACK: A Constrained Two- Dimensional Delaunay Triangulation Package" ACM Trans. Math. Software, Vol. 22, No. 1, 1996, pp. 1-8.

  4. R. J. Renka, "ALGORITHM 752: SRFPACK: Software for Scattered Data Fitting with a Constrained Surface under Tension", ACM Trans. Math. Software, Vol. 22, No. 1, 1996, pp. 9-17.

  5. R. J. Renka, "ALGORITHM 772: STRIPACK: Delaunay Triangulation and Voronoi Diagram on the Surface of a Sphere" ACM Trans. Math. Software, Vol. 23, No. 3, 1997, pp. 416-434.

  6. R. J. Renka, "ALGORITHM 773: SSRFPACK: Interpolation of Scattered Data on the Surface of a Sphere with a Surface under Tension", ACM Trans. Math. Software, Vol. 23, No. 3, 1997, pp. 437-439.

Project details


Download files

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

Source Distribution

stripy-2.3.3.tar.gz (36.5 MB view hashes)

Uploaded Source

Built Distributions

stripy-2.3.3-cp312-cp312-win_amd64.whl (18.7 MB view hashes)

Uploaded CPython 3.12 Windows x86-64

stripy-2.3.3-cp312-cp312-musllinux_1_1_x86_64.whl (18.4 MB view hashes)

Uploaded CPython 3.12 musllinux: musl 1.1+ x86-64

stripy-2.3.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (18.7 MB view hashes)

Uploaded CPython 3.12 manylinux: glibc 2.17+ x86-64

stripy-2.3.3-cp312-cp312-macosx_10_9_x86_64.whl (19.1 MB view hashes)

Uploaded CPython 3.12 macOS 10.9+ x86-64

stripy-2.3.3-cp311-cp311-win_amd64.whl (18.7 MB view hashes)

Uploaded CPython 3.11 Windows x86-64

stripy-2.3.3-cp311-cp311-musllinux_1_1_x86_64.whl (18.4 MB view hashes)

Uploaded CPython 3.11 musllinux: musl 1.1+ x86-64

stripy-2.3.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (18.7 MB view hashes)

Uploaded CPython 3.11 manylinux: glibc 2.17+ x86-64

stripy-2.3.3-cp311-cp311-macosx_10_9_x86_64.whl (19.0 MB view hashes)

Uploaded CPython 3.11 macOS 10.9+ x86-64

stripy-2.3.3-cp310-cp310-win_amd64.whl (18.7 MB view hashes)

Uploaded CPython 3.10 Windows x86-64

stripy-2.3.3-cp310-cp310-musllinux_1_1_x86_64.whl (18.4 MB view hashes)

Uploaded CPython 3.10 musllinux: musl 1.1+ x86-64

stripy-2.3.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (18.7 MB view hashes)

Uploaded CPython 3.10 manylinux: glibc 2.17+ x86-64

stripy-2.3.3-cp310-cp310-macosx_10_9_x86_64.whl (19.0 MB view hashes)

Uploaded CPython 3.10 macOS 10.9+ x86-64

stripy-2.3.3-cp39-cp39-win_amd64.whl (18.7 MB view hashes)

Uploaded CPython 3.9 Windows x86-64

stripy-2.3.3-cp39-cp39-musllinux_1_1_x86_64.whl (18.4 MB view hashes)

Uploaded CPython 3.9 musllinux: musl 1.1+ x86-64

stripy-2.3.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (18.7 MB view hashes)

Uploaded CPython 3.9 manylinux: glibc 2.17+ x86-64

stripy-2.3.3-cp39-cp39-macosx_10_9_x86_64.whl (19.0 MB view hashes)

Uploaded CPython 3.9 macOS 10.9+ x86-64

stripy-2.3.3-cp38-cp38-win_amd64.whl (18.7 MB view hashes)

Uploaded CPython 3.8 Windows x86-64

stripy-2.3.3-cp38-cp38-musllinux_1_1_x86_64.whl (18.4 MB view hashes)

Uploaded CPython 3.8 musllinux: musl 1.1+ x86-64

stripy-2.3.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (18.7 MB view hashes)

Uploaded CPython 3.8 manylinux: glibc 2.17+ x86-64

stripy-2.3.3-cp38-cp38-macosx_10_9_x86_64.whl (19.0 MB view hashes)

Uploaded CPython 3.8 macOS 10.9+ x86-64

stripy-2.3.3-cp37-cp37m-win_amd64.whl (18.7 MB view hashes)

Uploaded CPython 3.7m Windows x86-64

stripy-2.3.3-cp37-cp37m-musllinux_1_1_x86_64.whl (18.4 MB view hashes)

Uploaded CPython 3.7m musllinux: musl 1.1+ x86-64

stripy-2.3.3-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (18.7 MB view hashes)

Uploaded CPython 3.7m manylinux: glibc 2.17+ x86-64

stripy-2.3.3-cp37-cp37m-macosx_10_9_x86_64.whl (19.0 MB view hashes)

Uploaded CPython 3.7m macOS 10.9+ x86-64

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