Predictive and probabilistic simulation tools.
Project description
Description
PSimPy
(Predictive and probabilistic simulation with Python) implements
a Gaussian process emulation-based framework that enables systematically and
efficiently inverstigating uncertainties associated with physics-based models
(i.e. simulators).
Installation
PSimPy
is a pure Python package and can be easily installed using pip
. All
Python-related dependencies are automatically taken care of. It should be noted
that some modules of PSimPy
rely on / take advantage of non-Python package and
software. More specifically, the emulator module robustgasp.py
relies on the R
package RobustGaSP
; the simulator module ravaflow.py
relies on the open
source software r.avaflow 2.4
. If you want to use these modules or any other
modules relying on these modules, corresponding non-Python dependencies need to
be installed.
You can find how to install r.avaflow 2.4
following its official documentation
under https://www.landslidemodels.org/r.avaflow/.
We recommond you to install PSimPy
in a virtual environment such as a conda
environment. You may want to first install Anaconda
or Miniconda
if you
haven't. The steps afterwards are as follows:
- Create a conda environment with Python 3.9:
conda create --name your_env_name python=3.9
- Install
R
if you don't have it on your machine (if you haveR
, you can skip this step; alternatively, you can follow this step to installR
in the conda environment):
conda activate your_env_name
conda install -c conda-forge r-base=3.6
- Install the R package
RobustGaSP
in the R terminal:
R
install.packages("RobustGaSP",repos="https://cran.r-project.org",version="0.6.4")
q()
- Configure the environment variable
R_HOME
so thatrpy2
knows where to findR
packages. You can find the value of yourR_HOME
by typing the following command in the R terminal:
R.home()
Then set R_HOME
in your conda environment by
conda env config vars set R_HOME=your_R_HOME_value
- Install
PSimPy
usingpip
in your conda environment by
pip install psimpy
Now you should have PSimPy
and its dependencies successfully installed.
Usage
Examples are currently in preparation and will be available soon in coming
versions. You may want to have a look at the tests which are currently available
at https://git-ce.rwth-aachen.de/mbd/psimpy. They give a glimpse of how PSimpy
can be used.
Documentation
Documentation is currently in preparation and will be available soon.
License
PSimPy
was created by Hu Zhao at the Chair of Methods for Model-based
Development in Computational Engineering. It is licensed under the terms of
the MIT license.
Project details
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