Skip to main content

Discretization tools for finite volume and inverse problems

Project description

Discretize Logo

discretize

Latest PyPI version Latest conda-forge version MIT license Azure pipelines build status Coverage status https://zenodo.org/badge/DOI/10.5281/zenodo.596411.svg https://img.shields.io/discourse/users?server=http%3A%2F%2Fsimpeg.discourse.group%2F https://img.shields.io/badge/Slack-simpeg-4B0082.svg?logo=slack https://img.shields.io/badge/Youtube%20channel-GeoSci.xyz-FF0000.svg?logo=youtube

discretize - A python package for finite volume discretization.

The vision is to create a package for finite volume simulation with a focus on large scale inverse problems. This package has the following features:

  • modular with respect to the spacial discretization

  • built with the inverse problem in mind

  • supports 1D, 2D and 3D problems

  • access to sparse matrix operators

  • access to derivatives to mesh variables

https://raw.githubusercontent.com/simpeg/figures/master/finitevolume/cell-anatomy-tensor.png

Currently, discretize supports:

  • Tensor Meshes (1D, 2D and 3D)

  • Cylindrically Symmetric Meshes

  • QuadTree and OcTree Meshes (2D and 3D)

  • Logically Rectangular Meshes (2D and 3D)

Installing

discretize is on conda-forge

conda install -c conda-forge discretize

discretize is on pypi

pip install discretize

To install from source

git clone https://github.com/simpeg/discretize.git
python setup.py install

Citing discretize

Please cite the SimPEG paper when using discretize in your work:

Cockett, R., Kang, S., Heagy, L. J., Pidlisecky, A., & Oldenburg, D. W. (2015). SimPEG: An open source framework for simulation and gradient based parameter estimation in geophysical applications. Computers & Geosciences.

BibTex:

@article{cockett2015simpeg,
  title={SimPEG: An open source framework for simulation and gradient based parameter estimation in geophysical applications},
  author={Cockett, Rowan and Kang, Seogi and Heagy, Lindsey J and Pidlisecky, Adam and Oldenburg, Douglas W},
  journal={Computers \& Geosciences},
  year={2015},
  publisher={Elsevier}
}

Project details


Release history Release notifications | RSS feed

Download files

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

Source Distribution

discretize-0.8.2.tar.gz (1.1 MB view details)

Uploaded Source

Built Distributions

discretize-0.8.2-cp310-cp310-win_amd64.whl (839.8 kB view details)

Uploaded CPython 3.10 Windows x86-64

discretize-0.8.2-cp39-cp39-win_amd64.whl (893.4 kB view details)

Uploaded CPython 3.9 Windows x86-64

discretize-0.8.2-cp38-cp38-win_amd64.whl (899.0 kB view details)

Uploaded CPython 3.8 Windows x86-64

discretize-0.8.2-cp37-cp37m-win_amd64.whl (876.1 kB view details)

Uploaded CPython 3.7m Windows x86-64

File details

Details for the file discretize-0.8.2.tar.gz.

File metadata

  • Download URL: discretize-0.8.2.tar.gz
  • Upload date:
  • Size: 1.1 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.8.13

File hashes

Hashes for discretize-0.8.2.tar.gz
Algorithm Hash digest
SHA256 19b2bcb57cf769ca2c225875db5d3017dc58ca8cd3ad7f44ee89ba0e1622e4a6
MD5 0fe637d5c1983ae42208ec8903f422b0
BLAKE2b-256 59046390535e33c83298ba20038f9e2b78429d2fe0a255cefbf13969a8fd03e2

See more details on using hashes here.

Provenance

File details

Details for the file discretize-0.8.2-cp310-cp310-win_amd64.whl.

File metadata

File hashes

Hashes for discretize-0.8.2-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 b7a8b62e8abcf01b76c2f4f66610cd06f9e73b49ceb6ede2eb712a1d33f1df7f
MD5 3f6965ad5a5a786dccaab4576808e517
BLAKE2b-256 d205f59b260cb9cb7ecb09cd7a4635006b16459e9aac54dd4f204578ec067cae

See more details on using hashes here.

Provenance

File details

Details for the file discretize-0.8.2-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: discretize-0.8.2-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 893.4 kB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.13

File hashes

Hashes for discretize-0.8.2-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 8f5ec9a7560f11e342fd521c13ddb81d24284844a4036ca2a9c26e2c70a687ae
MD5 1b019d0e197e88267e48fa302140c090
BLAKE2b-256 7a76135409f24660fef2ff18b53b9d635bc23de05378a2f992f2f2e3075bcfa2

See more details on using hashes here.

Provenance

File details

Details for the file discretize-0.8.2-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: discretize-0.8.2-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 899.0 kB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.8.10

File hashes

Hashes for discretize-0.8.2-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 ddff64e437ef0c4a9acaef7b02d0eb36955684219638f5b3769089161958aa70
MD5 449fd22b9628057bf56b4e61fc75f6c0
BLAKE2b-256 e7d4bf3a904228303c2a046d1b8e33659f35e649b39c302630d2df02d9916eaa

See more details on using hashes here.

Provenance

File details

Details for the file discretize-0.8.2-cp37-cp37m-win_amd64.whl.

File metadata

File hashes

Hashes for discretize-0.8.2-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 271db691fdfd86ba357e29c0a006e3dd582c48739bcad6db6a578c25bfcbceef
MD5 e7a7fde88cbe2b1fe17a31c2c0946702
BLAKE2b-256 a9cafb2f6cc41185ad2ef39bbd6ae1cfdbb718b2af029479e2cfd2f7a69520c1

See more details on using hashes here.

Provenance

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