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

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 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-0.1.19.tar.gz (203.1 kB view details)

Uploaded Source

Built Distributions

discretize-0.1.19-cp36-cp36m-win_amd64.whl (220.7 kB view details)

Uploaded CPython 3.6m Windows x86-64

discretize-0.1.19-cp36-cp36m-macosx_10_7_x86_64.whl (171.1 kB view details)

Uploaded CPython 3.6m macOS 10.7+ x86-64

discretize-0.1.19-cp27-cp27m-macosx_10_6_x86_64.whl (178.1 kB view details)

Uploaded CPython 2.7m macOS 10.6+ x86-64

File details

Details for the file discretize-0.1.19.tar.gz.

File metadata

  • Download URL: discretize-0.1.19.tar.gz
  • Upload date:
  • Size: 203.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for discretize-0.1.19.tar.gz
Algorithm Hash digest
SHA256 025807b43e3db2ec7b6924fa94438cb870444a79a26c58d9c38d6c5ef531e7a9
MD5 9e26b61b7f826f3623bddaf4dfc70f46
BLAKE2b-256 2ae46a2bb28852b0a493481d8401bbc6aa69fb187b09bca7ccb63e9d3375a3b9

See more details on using hashes here.

File details

Details for the file discretize-0.1.19-cp36-cp36m-win_amd64.whl.

File metadata

File hashes

Hashes for discretize-0.1.19-cp36-cp36m-win_amd64.whl
Algorithm Hash digest
SHA256 78b3d103b3348da2c50a9874acbd4b021a7449dd37753c25b5e8f42ae670edec
MD5 4bc0f6d49ec82e07e81dfafb515d3289
BLAKE2b-256 97211b029c069ad003e6afa221cb7249ebab5e08d01cabcd7b3c981aa035a54a

See more details on using hashes here.

File details

Details for the file discretize-0.1.19-cp36-cp36m-macosx_10_7_x86_64.whl.

File metadata

File hashes

Hashes for discretize-0.1.19-cp36-cp36m-macosx_10_7_x86_64.whl
Algorithm Hash digest
SHA256 a19895a2ff70c9b0cc0ffb1c176f0ea9f5819c47477e2f5a586786b5bd6dde9a
MD5 2290d1fca964e96d2449f0fb14e871a0
BLAKE2b-256 b2cbf2dd515e03a9ed693ebb4968adcf281d4a73224981daefc4aee8565d0b25

See more details on using hashes here.

File details

Details for the file discretize-0.1.19-cp27-cp27m-macosx_10_6_x86_64.whl.

File metadata

File hashes

Hashes for discretize-0.1.19-cp27-cp27m-macosx_10_6_x86_64.whl
Algorithm Hash digest
SHA256 5f76ce86ec53723f5182fa00644deed4a936a5cfd41cb0454c5b7186b1203f0e
MD5 baba4145f200c0c940b3f3cdc2b04a90
BLAKE2b-256 f26f233b759183561430870c89b0d3bfed037f1e9cc88f8b679c3eb5689ce066

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