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 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-0.7.1.tar.gz (775.7 kB view details)

Uploaded Source

Built Distributions

discretize-0.7.1-cp39-cp39-win_amd64.whl (657.2 kB view details)

Uploaded CPython 3.9 Windows x86-64

discretize-0.7.1-cp38-cp38-win_amd64.whl (663.2 kB view details)

Uploaded CPython 3.8 Windows x86-64

discretize-0.7.1-cp37-cp37m-win_amd64.whl (636.8 kB view details)

Uploaded CPython 3.7m Windows x86-64

discretize-0.7.1-cp36-cp36m-win_amd64.whl (636.0 kB view details)

Uploaded CPython 3.6m Windows x86-64

File details

Details for the file discretize-0.7.1.tar.gz.

File metadata

  • Download URL: discretize-0.7.1.tar.gz
  • Upload date:
  • Size: 775.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.2 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.8.11

File hashes

Hashes for discretize-0.7.1.tar.gz
Algorithm Hash digest
SHA256 507cda526a6555db034b652373953ea497f1cd98769825cfc9d1af14a17e4df9
MD5 b80c1db446482e2a005c4b757d72ef27
BLAKE2b-256 368db2cc1e0814a309afe6108afb33abf4cfe2293b9547abeb9249e37da4b9cc

See more details on using hashes here.

File details

Details for the file discretize-0.7.1-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: discretize-0.7.1-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 657.2 kB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.2 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.7

File hashes

Hashes for discretize-0.7.1-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 dbbdc4cc5e803826e193b5b8a54d7d766669e2374b7c37829b89f9ce339e1d02
MD5 b40afb2df647278bee9ba6da9157a03f
BLAKE2b-256 ce350c98a65945f4c16b93b6de062e7041e7a6fe1f0101fe408246a150464081

See more details on using hashes here.

File details

Details for the file discretize-0.7.1-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: discretize-0.7.1-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 663.2 kB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.2 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.8.10

File hashes

Hashes for discretize-0.7.1-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 eea2c41b6c81d7efcf6b0f1165c6be2db404dba35b3af737fae68a28055df0c5
MD5 3fb0d391585bdccb8f1e607ed9d9ac5c
BLAKE2b-256 7af70d4c748bc26c4ae18ae78673b863cb8498bdf9667a4ab4555e1cd3ffc8bf

See more details on using hashes here.

File details

Details for the file discretize-0.7.1-cp37-cp37m-win_amd64.whl.

File metadata

  • Download URL: discretize-0.7.1-cp37-cp37m-win_amd64.whl
  • Upload date:
  • Size: 636.8 kB
  • Tags: CPython 3.7m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.2 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.7.9

File hashes

Hashes for discretize-0.7.1-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 395462cc6e4b0f24d0294c0d7c9b5770c71ee1bf40dccc2ed69a5519367078f9
MD5 5e213242a4257062750357b576fb973c
BLAKE2b-256 c14e39e2277422fcb46cc336d6cc35effc51c9acd03545a3082165eeca7068be

See more details on using hashes here.

File details

Details for the file discretize-0.7.1-cp36-cp36m-win_amd64.whl.

File metadata

  • Download URL: discretize-0.7.1-cp36-cp36m-win_amd64.whl
  • Upload date:
  • Size: 636.0 kB
  • Tags: CPython 3.6m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.2 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.6.8

File hashes

Hashes for discretize-0.7.1-cp36-cp36m-win_amd64.whl
Algorithm Hash digest
SHA256 50efd8f5d9fabc95da919e25686d56959148d5e1b5fc7e5f79cd520efa6636bf
MD5 2fea5dcfa67f7969c3fe98d2c7cd2c9e
BLAKE2b-256 f52de1e38c276ed2609c792872c62d7491246e67669faf2bcb6c0e9b09a74458

See more details on using hashes here.

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