## Description
`PSimPy` (Predictive and probabilistic simulation with Python) implements
a Gaussian process emulation-based framework that enables systematic and
efficient investigation of uncertainties associated with physics-based models
(i.e. simulators).
## Prerequisites
Before installing and using `PSimPy`, please ensure that you have the following
prerequisites:\
(Please note that we will cover number 1 to 3 in our recommended installation
method: [Installation in a Conda Environment](#installation-in-a-conda-environment-recommended).)
1. **Python 3.9 or later**:\
Make sure you have Python installed on your system. You can download the latest
version of Python from the official website:
[Python Downloads](https://www.python.org/downloads/)
2. **R Installed and Added to the PATH Environment Variable**:
- Install R from the official [R Project](https://www.r-project.org/) website.
- Add R to your system's PATH environment variable. This step is crucial for
enabling communication between Python and R.
3. (Optional) **RobustGaSP - R package**:\
The emulator module, `robustgasp.py`, relies on the R package [RobustGaSP](https://cran.r-project.org/web/packages/RobustGaSP/index.html). This has also been initegrated with other PSimPy modules, such as `active_learning.py`. In order to utilize these modules, make sure to install the R package [RobustGaSP](https://cran.r-project.org/web/packages/RobustGaSP/index.html) first.
4. (Optional) **r.avaflow - Mass Flow Simulation Tool**:\
`PSimPy` includes a simulator module, `ravaflow3G.py`, that interfaces
with the open source software [r.avaflow 3G](https://www.landslidemodels.org/r.avaflow/). If you intend to use this module, please refer to the official documentation of [r.avaflow 3G](https://www.landslidemodels.org/r.avaflow/) to for installation guide.
## Installation
`PSimPy` can be easily installed using `pip`.
```bash
$ pip install psimpy
```
This command will install the package along with its dependencies.
## Installation in a Conda Environment (Recommended)
We recommond you to install `PSimPy` in a virtual environment such as a `conda`
environment. In this section, we will ceate a `conda` environment, install prerequisites (number 1 to 3), install python, and lastly, add the conda environment to `Jupyter Notebook`. You may want to first install [Anaconda](https://docs.anaconda.com/free/anaconda/) or [Miniconda](https://docs.conda.io/projects/miniconda/en/latest/) if you haven't. The steps afterwards are as follows:
1. Create a conda environment with Python and R, and activate the environment:
```bash
$ conda create --name your_env_name python R
$ conda activate your_env_name
```
2. Install the R package `RobustGaSP` in the R terminal:
```bash
$ R
...
> install.packages("RobustGaSP",repos="https://cran.r-project.org",version="0.6.4")
```
Make sure it is successfully installed:
```bash
> library("RobustGaSP")
```
3. Next, you need to configure the environment variable `R_HOME` so that `rpy2` knows where to find `R` packages. Find your `R_HOME` using the following command and then quit the R terminal:
```bash
> R.home()
> q()
```
Set the environment variable `R_HOME` in your
conda environment with
```bash
$ conda env config vars set R_HOME=your_R_HOME
```
Reactivate your conda environment to make the change take effect by
```bash
$ conda deactivate
$ conda activate your_env_name
```
4. Install `PSimPy` using `pip` in your conda environment:
```bash
$ pip install psimpy
```
Now you should have `PSimPy` and its dependencies successfully installed in your
conda environment. You can use it in the Python terminal or in your Python IDE.
If you would like to use it with `Jupyter Notebook` (iPython Notebook), there
is one extra step needed to set your conda environment on your Notebook:
5. Install `ipykernel` and install a kernel that points to your conda environment:
```bash
$ conda install -c conda-forge ipykernel
$ python -m ipykernel install --user --name=your_env_name
```
Now you can start your Notebook, change the kernel to your conda environment,
and use `PSimPy`.
## Documentation
Detailed documentation of `PSimPy` is hosted at https://mbd.pages.rwth-aachen.de/psimpy/,
including the API and theory (or reference) of each module.
## Usage
Usage examples are provided by the [Example Gallery](https://mbd.pages.rwth-aachen.de/psimpy/examples.html).
## License
`PSimPy` was created by Hu Zhao at the Chair of Methods for Model-based
Development in Computational Engineering (RWTH Aachen University, Germany). It
is licensed under the terms of the MIT license.
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