- V0.0.1 - 2021/10/06: first addition of the version counter. Proceeding the correction of a bug that prevented the use of custom reflect_fn function and the correction of the interpolation points for the background gas so they correspond to the middle of each cell. Small changes in the documentation too.
LPPy-DSMC is both a library containing useful procedures and a script meant to be run using configuration files. It uses Python programming language.
It was designed as a Direct Simulation Monte Carlo tool, initially to simulate a rarefied gas flow throught a somewhat complex geometry. It was then improved until its current state so it can offer a more comprehensive approach. The Documentation introduced more thoroughly its functionnalities and limits.
- LPPy-DSMC
Several versions of the environment is available, depending on weather you want to use only the basic tools, the poisson solver with fenics or the plotting tools too.
conda create --name NAME_ENV python=3.7 pandas numpy tqdm numexpr pytables configobj
conda create --name NAME_ENV python=3.7 pandas numpy tqdm numexpr pytables configobj matplotlib seaborn
conda create --name NAME_ENV -c conda-forge fenics=2018 mshr=2018 pandas numpy tqdm numexpr pytables configobj
Note : On 25th July 2021, the 2019 install using conda, for fenics and mshr, did not work properly. The 2018 versions forces the use of python 3.7 which is the reasons why it is forced also on the other build versions. If you install the 2019 version in another way, the code should still work for the fenics part, but there is no guarantee no parts of it will break.
conda create --name NAME_ENV -c conda-forge fenics=2018 mshr=2018 pandas numpy tqdm numexpr pytables configobj seaborn matplotlib
Please add to the command :
notebookif you want to have jupyter notebook available,jupyterlabif you want to have jupyter lab available
The best way to make a package available and usable from a notebook or terminal and from any directory is to install it in the environment, at least locally.
To install lppydsmc:
git clone https://[email protected]/rhodecode/GIT_REPOSITORIES/LPP/Users/Calot/lppydsmc
cd lppydsmc
git checkout main
conda activate NAME_ENV
python -m pip install -e .At this point, the package lppydsmc will be available in the environment NAME_ENV and you can import it like you would do with NumPy and from anywhere:
import lppydsmc as ldSince it was installed using pip, you can uninstall it simply by doing pip uninstall lppydsmc-taltos in a terminal.
Note: I plan on releasing it to
Pypias a package that could be installed usingpipdirectly.
Using the configuration files and the very high level lppydsmc.main.
Please, see the specifications files or the examples.
Available options :
- Saving name and directory
- Simulation parameters
- System
- Species
- Particles initialization (optional)
- Injection (optional)
- DSMC (optional)
- background gas (optional)
- Reactions (optional)
- Poisson solver (optional, requires fenics install)
- Monitoring (optional)
- Verbose (optional)
- Command line (you need to be in the folder where
run.pyis):conda activate ENV_NAME python run.py -p <path_to_cfg_file> -s
The -s flag simply tells the algorithm to save the parameters at the end of the setup phase, right before the simulation begins.
- Jupyter notebook / lab with the right environment:
import lppydsmc as ld path = <path_to_cfg_file> ld.main(path, save = True) # save is True by default
At this point, you should see a progress bar with the current iteration and estimated remaining time before completion (depending on what you chose in the options - this is the default case).
If you chose to monitor the simulations in your options in the configuration file, then an hdf5 will be available under the path <dir_path>/<name>/monitoring.h5 where directory_path and name comes from the configuration file.
You can load the hdf5 file of your simulation this way :
import pandas as pd
store = pd.HDFStore(results_path)
print(store.keys()) # to see what has been saved (depends on what you activated amongst the optional options)You can then explore each dataframes.
Depending on the options you chose, you will find a directory images with plots of the state of the system and of the velocity distribution and a file params.ini that summarizes the parameters used in the simulation. It encompasses the computed values from the "raw" configuration file.
The module takes advantage of the ConfigObj module. A good tutorial is available here and you can find the documentation here.
You can find the specification file here.
Note : it will be heavily commented in the future, once the code is under less development.
(IN PROGRESS)
The current state of the project, the available functionalities and limitations along with other useful information are presented here.
This code was initially designed to simulate an flow of a rarefied gas made of neutrals atoms through a somewhat complex geometry.
It was then designed to be a closer to a generic tool but design choices were made in agreement with the final goal aforementioned.
The code was from the start designed with flexibility in mind. For that reason, lots of options are available which makes the configuration files slightly harder to digest.
Also note that while most functionnalities were unit tested, especially the more important ones, the code has not passed enough integration benchmarks, nor mezzanine ones, to be considered with any degree of certainty correct.