Discretization tools for finite volume and inverse problems
Project description
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
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 discretizeTo install from source
git clone https://github.com/simpeg/discretize.git
python setup.py installCiting 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}
}Links
Website: http://simpeg.xyz
Documentation: http://discretize.simpeg.xyz
Code: https://github.com/simpeg/discretize
Tests: https://travis-ci.org/simpeg/discretize
Bugs & Issues: https://github.com/simpeg/discretize/issues
Questions: https://groups.google.com/forum/#!forum/simpeg
Chat: http://slack.simpeg.xyz/
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
Built Distributions
File details
Details for the file discretize-0.1.13.tar.gz.
File metadata
- Download URL: discretize-0.1.13.tar.gz
- Upload date:
- Size: 201.0 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 3c012be84cd804710c90fe14ff186d8f3d5c956e9adc1351b79a59ef9c8e5cfb |
|
| MD5 | b0aea0771083e41eb667e311749cfaad |
|
| BLAKE2b-256 | 0b55db4ac55f6a6c9817d7f0eab4b91dedf1378378c8052442336047c98b838c |
File details
Details for the file discretize-0.1.13-cp36-cp36m-win_amd64.whl.
File metadata
- Download URL: discretize-0.1.13-cp36-cp36m-win_amd64.whl
- Upload date:
- Size: 211.0 kB
- Tags: CPython 3.6m, Windows x86-64
- Uploaded using Trusted Publishing? No
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | df650af104e1774cb1e4282f1c8bb366f1a1a890b772567d4eb9214106e7b3d3 |
|
| MD5 | 67c578e5f82add93646c5c6f3665d18c |
|
| BLAKE2b-256 | 5e7b33f204b5320f90ddb3e41d4d47972e8f46da1d2eb311f3038cb2715e5b1d |
File details
Details for the file discretize-0.1.13-cp27-cp27m-macosx_10_7_x86_64.whl.
File metadata
- Download URL: discretize-0.1.13-cp27-cp27m-macosx_10_7_x86_64.whl
- Upload date:
- Size: 167.0 kB
- Tags: CPython 2.7m, macOS 10.7+ x86-64
- Uploaded using Trusted Publishing? No
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 1b924746b62452f61291e4eb42a8cd5e376fd78c31b6a82d05fda4d30a9b48b3 |
|
| MD5 | 785a5ccf85d1daf2545df64453eddbae |
|
| BLAKE2b-256 | 4b6ff1b3f31bf46b1292c3a9e839b5333de606a83883e1cfc8093146331e5481 |
