A lightweight, Apache 2.0 distribution of Matthieu Ancellin`s Capytaine BEM code.
Project description
LiteBEM
A lightweight, Apache 2.0 distribution of Matthieu Ancellin's Capytaine BEM code.
Requirements
- Conda is recommended for managing your Python distribution, dependencies and environment:
- https://docs.conda.io/en/latest/miniconda.html
- current development efforts are based on Python 3.9
- Microsoft Visual Studio is required for linking the fortran binaries
- https://visualstudio.microsoft.com/downloads/
- during installation check the box to include "Desktop development with C++"
- Intel oneAPI HPC toolkit is required for compiling the fortran binaries (you do not need the base kit)
- https://www.intel.com/content/www/us/en/developer/tools/oneapi/hpc-toolkit-download.html
- install to the default file location
- create "LIB" environment variable to point towards the intel directory for compiler ".lib" files
- if oneAPI is installed to the default location, assign the LIB user variable a value of: "C:\Program Files (x86)\Intel\oneAPI\compiler\2022.1.0\windows\compiler\lib\intel64_win"
- if oneAPI is installed to a different location then adjust the path above as necessary
Installation for Users
Recommended approach:
- Open the anaconda powershell and create a new environment for the LiteBEM project (e.g. "liteBemProject")
> conda create --name liteBemProject python
- Install LiteBEM from PyPI by entering the following command within your new environment
-
> conda activate liteBemProject > python -m pip install litebem
Installation for Developers
Recommended approach:
- Open the anaconda powershell and create a new environment for LiteBEM-related development (e.g. "liteBemDev")
> conda create --name liteBemDev python
- Install numpy (numpy's f2py is required to compile Fortran code) within your LiteBEM development environment:
> conda activate liteBemDev > pip install numpy
- Clone the LiteBEM repo to your preferred location (e.g. "C:/code/")
> cd C:/code/ > git clone https://github.com/dav-og/LiteBEM.git
- Install LiteBEM as a developer!
> cd LiteBEM > pip install -e .
- Be sure to check setup.py => install_requires = [...] to ensure that your environment has all required packages installed. You can check your environment's packages using:
> conda list
- If any packages are missing simply install them using:
> pip install <package name>
- If any packages are missing simply install them using:
Run Tests
-
Make sure
pytestis installed in your working environment:(liteBemDev) > conda list
- if its not installed then do:
(liteBemDev) > pip install pytest
- if its not installed then do:
-
Navigate to
LiteBEMand run:(liteBemDev) > pytest tests/unit/preprocessor_unit_tests.py (liteBemDev) > pytest tests/unit/solver_unit_tests.py
Tutorials
- For a tutorial on how to use LiteBEM, it is currently recommended that users utilize Capytaine's documentation, as it remains largely consistent with LiteBEM
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
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file litebem-1.0.2.tar.gz.
File metadata
- Download URL: litebem-1.0.2.tar.gz
- Upload date:
- Size: 198.0 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.1 CPython/3.9.10
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
22500a9210b69bf70d6f811bef1166552d26c6c1f6b74b197ae8b168e7b3d075
|
|
| MD5 |
67b3994297cb86299b1900904a7da8ac
|
|
| BLAKE2b-256 |
9878f3929ef79b28473b31eba16f4a51972a799172caa6e9f964219f904cc02c
|
File details
Details for the file litebem-1.0.2-cp39-cp39-win_amd64.whl.
File metadata
- Download URL: litebem-1.0.2-cp39-cp39-win_amd64.whl
- Upload date:
- Size: 82.6 kB
- Tags: CPython 3.9, Windows x86-64
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.1 CPython/3.9.10
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
5e68991aef776145b418e866fd6e7692cfb61a7cc5a46f40aeb31d7394ab0d84
|
|
| MD5 |
7f72f6812084fb0d1c08fa9380a5b582
|
|
| BLAKE2b-256 |
87145989ea92530de808a3875c770cbd34bcff891a258a4843849e14b0a335ea
|