Developer’s guide

Get the source code from github

Pytrnsys is available as open source code under the MIT license. If you want to run pytrnsys from source, it is recommended to use a designated virtual environment. Make sure that in your python environment the following dependencies are installed:

  • numpy
  • scipy
  • pandas
  • matplotlib
  • seaborn
  • bokeh

If you would like to contribute to pytrnsys and the documentation, you also need the following python packages:

  • sphinx
  • recommonmark
  • sphinx_rtd_theme

You can fork the source code at (toBeFilledInWhenPublic) or by the following command:

git clone (toBeFilledInWhenPublic)

Once you have you local copy of the pytrnsys source code you can add the pytrnsys package subfolder to your Python project source paths. Once you have the pytrnsys package installed from source and you should be able to execute:

import pytrnsys

in your Python environment.

Run the example systems from source

You can run pytrnsys with the following minimal code example

import pytrnsys.rsim.runParallelTrnsys as runTrnsys

pathConfig  = "pathToTheConfigFile"
configFile = "run_solar_dhw.config"
runTool = runTrnsys.RunParallelTrnsys(pathConfig,configFile=configFile)

The “pathToTHeConfigFile” should be replaced with the full path to the folder examples/solar_dhw in your local repository. This script replaces the pytrnsys-run command and starts a pytrnsys run with the given configuration file. Similarly the processing can be started with the following minimal example

from pytrnsys.psim import processParallelTrnsys as pParallelTrnsys

pathConfig = "pathToTheConfigFile"
configFile = "process_solar_dhw.config"
tool = pParallelTrnsys.ProcessParallelTrnsys()
tool.readConfig(pathConfig,configFile)
tool.process()

If you would like to continue to modify the config file as described in the tutorial, make a local copy of the example folders such that tha changes will not be tracked by GIT.

Create your own processing class

Pytrnsys provides a large number of possibilities to process and plot results with the processing configuration file. But sometimes this is not enough! If you would like to add your own Python code to the processing you can create your own processing class that inherits from the class pytrnsys.psim.processTrnsysDf.

import pytrnsys.psim.processTrnsysDf as processTrnsys

class MyProcess(processTrnsys.ProcessTrnsysDf):

    def __init__(self,pathFolder, fileName):
        processTrnsys.ProcessTrnsysDf.__init__(self,pathFolder, fileName)

    #define your own functions
    def myCalculation()
        myValue=foo+bar

    # overwrite this function and fill it with your content
    def customCalculations()
        self.myCalculation

This class can then be saved in your preferred location. In order to use the custom processing class the pytrnsys.rsim.runParallelTrnsys class has to be modified such that it instantiates the new class. This can be done by replacing the run script in the following way.

from pytrnsys.psim import processParallelTrnsys as pParallelTrnsys
import yourCustomClassFile

class MyProcessParallelTrnsys(pParallelTrnsys.ProcessParallelTrnsys):

    def __init__(self):
        pParallelTrnsys.ProcessParallelTrnsys.__init__(self)

    #The definition of this class is a must
    def getBaseClass(self, classProcessing, pathFolder, fileName):
       return yourCustomClassFile.MyProcess(pathFolder, fileName)

if __name__ == '__main__':
    pathConfig = "pathToTheConfigFile"
    configFile = "process_solar_dhw.config"
    tool = MyProcessParallelTrnsys()
    tool.readConfig(pathConfig,configFile)
    tool.process()

General guidelines for developers

Pytrnsys is open source and developers are invited to submit their own contributions. If you would like to develop for pytrnsys, we are interested in who you are. We are happy about a short message by mail. Please discuss new ideas first in the issue board. You are invited to work on the issues and create a pull request when finished. When working on the code, please consider the following style guidelines:

  • we use the UpperCamelCase convention for Class names and the lowerCamelCase convention for everything else
  • Please use Numpy/Scipy inline code documentation as much as possible
  • Please chose meaningful variable names and use in line comments only where really needed.