Installation
Installation
Installing BPTK within your Python enviroment is simple, all you need to call is
pip install BPTK-Py
If you are new to BPTK-Py and maybe even Python, you should install the entire BPTK-Py tutorial – the tutorial contains a number of Juypter notebooks and both System Dynamics and Agent-based Models built with BPTK.
It is the best place to see BPTK in action.
If you are reading this documentation online at bptk.transentis.com: This documentation was generated from the BPTK-Py Tutorial using quarto.
Installing The BPTK-Py Tutorial Starting From Scratch
Assuming you are starting from scratch, you need to perform the following steps:
- Install Python
- Clone the BPTK-Py tutorial
- Set up a virtual environment
- Install BPTK-Py and JupyterLab
- Start JupyterLab
Install Python
First of all, you need Python. Download the latest version for your operating system.
BPTK-Py was tested with Python 3.10, but should also run fine with Python 3.9
Clone the BPTK-Py tutorial
On the command line, move into a directory where you would like to store the BPTK-Py tutorial.
Clone the BPTK-Py tutorial repository using git clone
:
//github.com/transentis/bptk_py_tutorial.git git clone https:
Set up a virtual environment
A virtual environment is a local copy of your Python distribution that stores all packages required and does not interfere with your system's packages.
Following steps are required to set up a virtual environment in a folder called venv
:
-m venv venv python3
Enter the virtual environment using one of the following commands appropriate:
OS | Command |
---|---|
UNIX/Linux/MacOS |
source venv/bin/activate |
Windows |
venv.bat |
Now you should see "(venv)" at the beginning of your command prompt.
Install BPTK-Py and JupyterLab
Now we have a virtual environment, we can install BPTK-Py and JupyterLab:
-r requirements.txt pip install
Start JupyterLab
Now you have a functioning version of JupyterLab and can start working interactively using jupyter notebooks.
Just type jupyter lab
in the terminal to get started. This will automatically open your browser with JupyterLab running in it, pointing at the directory of the tutorial
Open the notebook readme.ipynb
from within JupyterLab.
Once you are finished, close your browser and kill the JupyterLab process in your terminal.
Keeping BPTK-Py up-to-date
Software evolves. We regularly release new versions to add functionality, improve the code and fix bugs.
If you are on the command line using pip, you can update BPTK-Py as follows:
pip install --upgrade BPTK-Py
We also offer a seamless way for checking for updates and installing new ones from within a notebook environment such as Jupyter Lab, simply run the following code:
from BPTK_Py import bptk
= bptk()
bptk bptk.update()
The update mechanism automatically checks for a newer version and (if necessary) downloads and installs it.
To check for the currently installed version, simple run these commands:
from BPTK_Py import bptk
= bptk()
bptk print(bptk.version)
Package dependencies
If for any reason, you want to install the requirements manually or need to know why we need the packages, here comes the list.
If you observe malfunctions in the framework and believe the reason may be incompatibilities with newer versions of the packages, please inform us.
We have tested the framework with Python 3.9 and above. BPTK Server requires a version of Python >= 3.9, other parts of the framework should work fine with older versions of Python.
Package name | What we use it for |
---|---|
pandas | DataFrames and internal results storage |
matplotlib | Plotting environment |
ipywidgets | Widget environment for notebooks |
jinja2 | Generating python classes for XMILE SD models |
parsimonious | Parsing XMILE models |
pyyaml | Using YAML to specify scenarios (instead of JSON) |
scipy | Linear interpolation for graphical functions |
numpy | Linear interpolation and required by pandas |
xlsxwriter | Exporting simulation results to CSV files |
xmltodict | Reading XMILE files |
distlib | Update checks |
flask | REST API |