psimpy 0.1.2

Predictive and probabilistic simulation tools.

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 ravaflow24.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.

If the simulator module ravaflow.py is desired, you may follow the official documentation of r.avaflow 2.4 under https://www.landslidemodels.org/r.avaflow/ to install it. The installation of the R package RobustGaSP is covered in following steps.

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:

1. Create a conda environment with Python 3.9:

conda create --name your_env_name python=3.9

2. Install R if you don't have it on your machine (if you have R, you can skip this step; alternatively, you can still follow this step to install R in the conda environment):

conda activate your_env_name
conda install -c conda-forge r-base=3.6

3. Install the R package RobustGaSP in the R terminal:

R
install.packages("RobustGaSP",repos="https://cran.r-project.org",version="0.6.4")

Try if it is successfully installed by

library("RobustGaSP")

4. Configure the environment variable R_HOME so that rpy2 knows where to find R packages. You can find your R_HOME by typing the following command in the R terminal:

R.home()
q()

It is a path like ".../lib/R". Set the environment variable R_HOME in your conda environment by

conda env config vars set R_HOME=your_R_HOME

Afterwards reactivate your conda environment to make the change take effect by

conda deactivate
conda activate your_env_name

5. Install PSimPy using pip in your conda environment by

conda install -c conda-forge pip
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. You may download the tests folder onto your local machine, and run any test using pytest.

First install pytest by

conda activate your_env_name
conda install pytest

Then navigate to the folder where tests folder is located on your machine. You may run all the tests by

pytest tests/

or a specific test like test_active_learning by

pytest tests/test_active_learning.py

Some tests save results including plots into temporary folders. The temporary folders will be deleted automatically if test_clear_temp_file is called. If you want to exclude a specific test such as test_clear_temp_file, you may run

pytest tests/ --ignore=tests/test_clear_temp_file.py

Documentation

The source codes of PSimPy contains detailed docstrings which explain how it can be used. More documentation about the theories 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.