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-0.5.0.tar.gz (651.2 kB view details)

Uploaded Source

Built Distributions

discretize-0.5.0-cp37-cp37m-win_amd64.whl (540.3 kB view details)

Uploaded CPython 3.7m Windows x86-64

discretize-0.5.0-cp37-cp37m-win32.whl (455.5 kB view details)

Uploaded CPython 3.7m Windows x86

discretize-0.5.0-cp36-cp36m-win_amd64.whl (540.2 kB view details)

Uploaded CPython 3.6m Windows x86-64

discretize-0.5.0-cp36-cp36m-win32.whl (455.6 kB view details)

Uploaded CPython 3.6m Windows x86

File details

Details for the file discretize-0.5.0.tar.gz.

File metadata

  • Download URL: discretize-0.5.0.tar.gz
  • Upload date:
  • Size: 651.2 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-0.5.0.tar.gz
Algorithm Hash digest
SHA256 81c5c6f48cb09e5eb66eaa65f3f553a9900a53281dd8b09092f007b646c3bfdc
MD5 6f4e1dd7f0d78f9c75c65159b15f4ce8
BLAKE2b-256 655ef87fe6bc8eaa811e799806bc1f8e211acee4a434cdf6d83a27b8a603fcf4

See more details on using hashes here.

Provenance

File details

Details for the file discretize-0.5.0-cp37-cp37m-win_amd64.whl.

File metadata

  • Download URL: discretize-0.5.0-cp37-cp37m-win_amd64.whl
  • Upload date:
  • Size: 540.3 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-0.5.0-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 278fa885c786e74dff816d070da1f31652605343dab59eebee1c5377a9863d43
MD5 db6fdb56d7a9a4f05a94806b67281d0f
BLAKE2b-256 29de7bc91199a46c95e05d911032f1a39a3b3479c2debac5fd199c5ab2a6854e

See more details on using hashes here.

Provenance

File details

Details for the file discretize-0.5.0-cp37-cp37m-win32.whl.

File metadata

  • Download URL: discretize-0.5.0-cp37-cp37m-win32.whl
  • Upload date:
  • Size: 455.5 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-0.5.0-cp37-cp37m-win32.whl
Algorithm Hash digest
SHA256 1bef721edf423e167f49def9b2faea4a1b0b36a12aceca813e09094d2371951c
MD5 da8d6d64a0840652b83a30b128f6592d
BLAKE2b-256 190d85a87db024c9bf405cf10deec07add462182ee0b899ccda6a9e74eb734eb

See more details on using hashes here.

Provenance

File details

Details for the file discretize-0.5.0-cp36-cp36m-win_amd64.whl.

File metadata

  • Download URL: discretize-0.5.0-cp36-cp36m-win_amd64.whl
  • Upload date:
  • Size: 540.2 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-0.5.0-cp36-cp36m-win_amd64.whl
Algorithm Hash digest
SHA256 fa1886ea846c50b8563ab13313cc05afc7812a6fd0a088f58bc63b51c4b4212e
MD5 323fe893cbf60df02f3b371e229fcc61
BLAKE2b-256 5645374c69265bfbdb50b2de278af834d210a2f511ee104194a1e901f6f66e05

See more details on using hashes here.

Provenance

File details

Details for the file discretize-0.5.0-cp36-cp36m-win32.whl.

File metadata

  • Download URL: discretize-0.5.0-cp36-cp36m-win32.whl
  • Upload date:
  • Size: 455.6 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-0.5.0-cp36-cp36m-win32.whl
Algorithm Hash digest
SHA256 964fed0a6541f68b0fb48ad57a22411f21f0ef896c341db2892ffffb9be12ca9
MD5 899115b2f0ad58c8c86667d238f32a60
BLAKE2b-256 8d5ba0165ba6600e191391bbcac7618e6748e588001b874775663c4ca5deb1c7

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