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-0.4.14.tar.gz (591.5 kB view details)

Uploaded Source

Built Distributions

discretize-0.4.14-cp37-cp37m-win_amd64.whl (478.4 kB view details)

Uploaded CPython 3.7m Windows x86-64

discretize-0.4.14-cp37-cp37m-win32.whl (404.9 kB view details)

Uploaded CPython 3.7m Windows x86

discretize-0.4.14-cp36-cp36m-win_amd64.whl (478.2 kB view details)

Uploaded CPython 3.6m Windows x86-64

discretize-0.4.14-cp36-cp36m-win32.whl (404.8 kB view details)

Uploaded CPython 3.6m Windows x86

File details

Details for the file discretize-0.4.14.tar.gz.

File metadata

  • Download URL: discretize-0.4.14.tar.gz
  • Upload date:
  • Size: 591.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/49.1.0.post20200704 requests-toolbelt/0.9.1 tqdm/4.47.0 CPython/3.7.6

File hashes

Hashes for discretize-0.4.14.tar.gz
Algorithm Hash digest
SHA256 ae6ce22305b90a43c81a1792f11c2e8b4cfb4df3fdf16d3e467a0100e0c8438f
MD5 e8991f1fe634ee22d92e184e3d1dbcc4
BLAKE2b-256 393ae2d81f03cdf1bee6b2a349c57522435a9a45fb8469efffbb1fad69171ea3

See more details on using hashes here.

File details

Details for the file discretize-0.4.14-cp37-cp37m-win_amd64.whl.

File metadata

  • Download URL: discretize-0.4.14-cp37-cp37m-win_amd64.whl
  • Upload date:
  • Size: 478.4 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/47.3.1.post20200622 requests-toolbelt/0.9.1 tqdm/4.47.0 CPython/3.7.4

File hashes

Hashes for discretize-0.4.14-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 1e0b9d264ba53bdd5df2d2a453ff54b6c10dcc8cce9deaca987f7a97b35edec7
MD5 85135c743dee45f4c696ff5a30a9c6c6
BLAKE2b-256 e4e10bc77ea1647cdbc78a2d5e92e7a72998ef898a1a9bd30d359e775c977a8e

See more details on using hashes here.

File details

Details for the file discretize-0.4.14-cp37-cp37m-win32.whl.

File metadata

  • Download URL: discretize-0.4.14-cp37-cp37m-win32.whl
  • Upload date:
  • Size: 404.9 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/47.3.1.post20200622 requests-toolbelt/0.9.1 tqdm/4.47.0 CPython/3.7.4

File hashes

Hashes for discretize-0.4.14-cp37-cp37m-win32.whl
Algorithm Hash digest
SHA256 33671e29ff60f5b09c3798b99925351018e86b5d15253cc49f46f6605e76ba1f
MD5 5b4cc2ac633885ec6ca57120388e32ef
BLAKE2b-256 af0b587ed85caceaa5d631be737407743d2045bc90c559fe528d60903f537805

See more details on using hashes here.

File details

Details for the file discretize-0.4.14-cp36-cp36m-win_amd64.whl.

File metadata

  • Download URL: discretize-0.4.14-cp36-cp36m-win_amd64.whl
  • Upload date:
  • Size: 478.2 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/4.47.0 CPython/3.6.5

File hashes

Hashes for discretize-0.4.14-cp36-cp36m-win_amd64.whl
Algorithm Hash digest
SHA256 9e904a3281456454627da7c45af6a3cea16e08eb7f7108750fbe1c0920190ab3
MD5 a9268bb1e9ce7de798bdaee9035af473
BLAKE2b-256 3794d34bf00d8a20f0b6d002f884d807ca24cc5397d4e9c7001bca33ebb3df39

See more details on using hashes here.

File details

Details for the file discretize-0.4.14-cp36-cp36m-win32.whl.

File metadata

  • Download URL: discretize-0.4.14-cp36-cp36m-win32.whl
  • Upload date:
  • Size: 404.8 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/4.47.0 CPython/3.6.5

File hashes

Hashes for discretize-0.4.14-cp36-cp36m-win32.whl
Algorithm Hash digest
SHA256 fde6a1b49e6bc4d6b6af0ef2f44aba5a98ada3038ddcf8077dd5c0c471d4fcc5
MD5 887eaeb5602236778c587bc01f72525a
BLAKE2b-256 473d4bb123398030f2770c346458ae6b528952db513a56f687d472c57db1753c

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