Skip to main content

Discretization tools for finite volume and inverse problems

Project description

Latest PyPI version MIT license Travis CI build status Coverage status codacy 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-0.5.1.tar.gz (652.7 kB view details)

Uploaded Source

Built Distributions

discretize-0.5.1-cp37-cp37m-win_amd64.whl (541.4 kB view details)

Uploaded CPython 3.7m Windows x86-64

discretize-0.5.1-cp37-cp37m-win32.whl (456.3 kB view details)

Uploaded CPython 3.7m Windows x86

discretize-0.5.1-cp36-cp36m-win_amd64.whl (541.3 kB view details)

Uploaded CPython 3.6m Windows x86-64

discretize-0.5.1-cp36-cp36m-win32.whl (456.2 kB view details)

Uploaded CPython 3.6m Windows x86

File details

Details for the file discretize-0.5.1.tar.gz.

File metadata

  • Download URL: discretize-0.5.1.tar.gz
  • Upload date:
  • Size: 652.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/49.6.0.post20200814 requests-toolbelt/0.9.1 tqdm/4.48.2 CPython/3.7.8

File hashes

Hashes for discretize-0.5.1.tar.gz
Algorithm Hash digest
SHA256 8638e8b1c836bce486b6ab6675d058a10f97da2d6501d868e8446d74ce1fcb69
MD5 b624c36b32756b47331b35f9b624999f
BLAKE2b-256 e2d82ab1827a5ea91b314e76fb520c3abe3ea5b029ac45ecebfd22326abd1922

See more details on using hashes here.

Provenance

File details

Details for the file discretize-0.5.1-cp37-cp37m-win_amd64.whl.

File metadata

  • Download URL: discretize-0.5.1-cp37-cp37m-win_amd64.whl
  • Upload date:
  • Size: 541.4 kB
  • Tags: CPython 3.7m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/49.6.0.post20200814 requests-toolbelt/0.9.1 tqdm/4.48.2 CPython/3.7.4

File hashes

Hashes for discretize-0.5.1-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 b40713b094f1845b4340d4a9b4c0b6c1177e1f7f3ef3440f06f81998d2adb048
MD5 aa647e74e37b8e8939e5899f16a7de75
BLAKE2b-256 37eec4c442bdd429f9c5c4d720d45910ede5f4ba4339138ca71cd7f8d09bbde8

See more details on using hashes here.

Provenance

File details

Details for the file discretize-0.5.1-cp37-cp37m-win32.whl.

File metadata

  • Download URL: discretize-0.5.1-cp37-cp37m-win32.whl
  • Upload date:
  • Size: 456.3 kB
  • Tags: CPython 3.7m, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/49.6.0.post20200814 requests-toolbelt/0.9.1 tqdm/4.48.2 CPython/3.7.4

File hashes

Hashes for discretize-0.5.1-cp37-cp37m-win32.whl
Algorithm Hash digest
SHA256 1c5f434b198954cfe70fb061e99f36391b7e3d46a76c6654351a7fc71ce865b6
MD5 220a13f6a5397ae4e06e6e24c06bbbe8
BLAKE2b-256 052177f71432fde4ba90775a300407eeebc0fa54fedb334f269983ab0c13aed8

See more details on using hashes here.

Provenance

File details

Details for the file discretize-0.5.1-cp36-cp36m-win_amd64.whl.

File metadata

  • Download URL: discretize-0.5.1-cp36-cp36m-win_amd64.whl
  • Upload date:
  • Size: 541.3 kB
  • Tags: CPython 3.6m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/39.2.0 requests-toolbelt/0.9.1 tqdm/4.48.2 CPython/3.6.5

File hashes

Hashes for discretize-0.5.1-cp36-cp36m-win_amd64.whl
Algorithm Hash digest
SHA256 fb370c57053822f03563d3f67b7991bf94b8b1976ca30900c6f4ecc0d080c3c1
MD5 fee5379e0459b96fe474046934d78671
BLAKE2b-256 1df26b6a3cf9a3b65d71788af634bfe44576c69724682e0def29d5c7f71b19a2

See more details on using hashes here.

Provenance

File details

Details for the file discretize-0.5.1-cp36-cp36m-win32.whl.

File metadata

  • Download URL: discretize-0.5.1-cp36-cp36m-win32.whl
  • Upload date:
  • Size: 456.2 kB
  • Tags: CPython 3.6m, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/39.2.0 requests-toolbelt/0.9.1 tqdm/4.48.2 CPython/3.6.5

File hashes

Hashes for discretize-0.5.1-cp36-cp36m-win32.whl
Algorithm Hash digest
SHA256 1ebdaaba55c64eba5a3a7ed1c2c2eee022d8f68c6e883d8c7c5969596e038966
MD5 3268a46e9f77b58540b966e6671604fe
BLAKE2b-256 35076395ecbcf51ccd57ee3d66af3242225e3a270150d3346889c424fea546a9

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