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-0.7.2.tar.gz (778.6 kB view details)

Uploaded Source

Built Distributions

discretize-0.7.2-cp39-cp39-win_amd64.whl (658.6 kB view details)

Uploaded CPython 3.9 Windows x86-64

discretize-0.7.2-cp38-cp38-win_amd64.whl (664.2 kB view details)

Uploaded CPython 3.8 Windows x86-64

discretize-0.7.2-cp37-cp37m-win_amd64.whl (637.7 kB view details)

Uploaded CPython 3.7m Windows x86-64

discretize-0.7.2-cp36-cp36m-win_amd64.whl (637.0 kB view details)

Uploaded CPython 3.6m Windows x86-64

File details

Details for the file discretize-0.7.2.tar.gz.

File metadata

  • Download URL: discretize-0.7.2.tar.gz
  • Upload date:
  • Size: 778.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.7.0 importlib_metadata/4.8.2 pkginfo/1.8.2 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.8.12

File hashes

Hashes for discretize-0.7.2.tar.gz
Algorithm Hash digest
SHA256 27da13d7389b232faf86185d148c9ed2c3fc150d2a69df16b19d7e536cda34f6
MD5 2a5b88a3e894ef64afa4465f41c51d92
BLAKE2b-256 9ba6cef0aec4c492269f907b9d9e2a4972fde0dced63de4a95fd5e927596cc3c

See more details on using hashes here.

File details

Details for the file discretize-0.7.2-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: discretize-0.7.2-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 658.6 kB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.7.0 importlib_metadata/4.8.2 pkginfo/1.8.2 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.9

File hashes

Hashes for discretize-0.7.2-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 34377554e7025f4b727cf2141d6a48858c75145eeca55fd90ec706b496a71d1f
MD5 f2d6543f9232e4ac1da11b6edebbc520
BLAKE2b-256 3ff24d26132e78f8bebbeeccb6c18f704c1ec3825530c997a48391bbcfad0089

See more details on using hashes here.

File details

Details for the file discretize-0.7.2-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: discretize-0.7.2-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 664.2 kB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.7.0 importlib_metadata/4.8.2 pkginfo/1.8.2 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.8.10

File hashes

Hashes for discretize-0.7.2-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 360f2b2218cfdc6ef9c84d3d339aa2b7cbb390ecf56e1ceada8f7cd70719280f
MD5 e302003bd1769647442a483535b966a1
BLAKE2b-256 121cfd55d6cc1ae86393d71ee1846f4ea3d252a52e58e8bcc199ff0ae8885553

See more details on using hashes here.

File details

Details for the file discretize-0.7.2-cp37-cp37m-win_amd64.whl.

File metadata

  • Download URL: discretize-0.7.2-cp37-cp37m-win_amd64.whl
  • Upload date:
  • Size: 637.7 kB
  • Tags: CPython 3.7m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.7.0 importlib_metadata/4.8.2 pkginfo/1.8.2 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.7.9

File hashes

Hashes for discretize-0.7.2-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 751d7c636eb626fc2cadd38715d72f0e603247d0eef0f5838e436bcadf920edb
MD5 5ab4112401882e3802b4c1a69c679bd4
BLAKE2b-256 a95dc0225fb9aca8e52288d3b3646b7fc168b4e1450be065c4bbbd551f0cfce3

See more details on using hashes here.

File details

Details for the file discretize-0.7.2-cp36-cp36m-win_amd64.whl.

File metadata

  • Download URL: discretize-0.7.2-cp36-cp36m-win_amd64.whl
  • Upload date:
  • Size: 637.0 kB
  • Tags: CPython 3.6m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.7.0 importlib_metadata/4.8.2 pkginfo/1.8.2 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.6.8

File hashes

Hashes for discretize-0.7.2-cp36-cp36m-win_amd64.whl
Algorithm Hash digest
SHA256 32b6c84abdb0a128873b6d6c43986e2c21b1a3ea26bbb7ea5ddc2a0ba7df9e4b
MD5 36232ee898569f41faeee9da94a56162
BLAKE2b-256 38ccf010c289e379a52de88ee394dcccd690ea6f1d458840f8e9ac3efd6fa676

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