Metadata-Version: 2.1
Name: psimpy
Version: 0.1.1
Summary: Predictive and probabilistic simulation tools.
Home-page: https://git-ce.rwth-aachen.de/mbd/psimpy
License: MIT
Keywords: emulator,simulator,inference,sensitivity,uncertainty
Author: Hu Zhao
Requires-Python: >=3.9,<3.11
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.9
Requires-Dist: SALib (>=1.4.5,<2.0.0)
Requires-Dist: beartype (>=0.11.0,<0.12.0)
Requires-Dist: numpy (>=1.22.3,<2.0.0)
Requires-Dist: rpy2 (>=3.5.1,<4.0.0)
Requires-Dist: scipy (>=1.8.0,<2.0.0)
Description-Content-Type: text/markdown

## 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:

1. Create a conda environment with Python 3.9:

```bash
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 follow this step
to install `R` in the conda environment):
```bash
conda activate your_env_name
conda install -c conda-forge r-base=3.6
```

3. Install the R package `RobustGaSP` in the R terminal:
```bash
R
install.packages("RobustGaSP",repos="https://cran.r-project.org",version="0.6.4")
q()
```

4. Configure the environment variable `R_HOME` so that `rpy2` knows where to find
`R` packages. You can find the value of your `R_HOME` by typing the following
command in the R terminal:
```bash
R.home()
```
Then set `R_HOME` in your conda environment by
```bash
conda env config vars set R_HOME=your_R_HOME_value
```

5. Install `PSimPy` using `pip` in your conda environment by
```bash
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.
