How to Access TRNSYS Data Inside Processing Steps

TRNSYS data comes in many shapes. Scalars are available in the .dck file and results come in the form of monthly, hourly, and timestep values.

To access this data, there is a slight difference between processing steps for individual simulations and comparison steps. Individual processing steps only have access to data from a single simulation and comparison steps have access to data from all the provided simulations.

Accessing data in an individual simulation step

Individual simulation steps can be defined as follows:

def step_1(simulation):

During processing a pytrnsys_process.api.Simulation object will automatically be passed into this step. It contains a pandas.DataFrame objects for the hourly, monthly, and timestep data. For individual simulation steps, the scalar data is also a DataFrame with a single row.

Accessing the data thus reduces to:

def processing_step(simulation):
    var_1_monthly_column = simulation.monthly["QSnk60P"]
    var_1_hourly_column = simulation.hourly["QSnk60P"]
    var_1_timestep_column = simulation.step["QSnk60P"]
    var_2_scalar_value = simulation.scalar["ratioDHWtoSH_allSinks"]

Here, single values are returned when accessing simulation.scalar, whereas the other DataFrames return a column of data.

Accessing Data in a Comparison Step

The comparison step allows you to analyze data across multiple simulations. While the same types of data are available (scalar, hourly, monthly, and timestep); the way the data is accessed is slightly different.

Comparison steps can be defined as follows:

def step_1(simulations_data):

During processing a pytrnsys_process.api.SimulationsData object will automatically be passed into this comparison step. It contains a pytrnsys_process.api.SimulationsData object for each simulation, as well as a pandas.DataFrame object for all the scalar data.

You can thus access the data as follows:

def comparison_step(simulations_data):

    var_2_column = simulations_data.scalar["ratioDHWtoSH_allSinks"]

    for sim_name, sim in simulations_data.simulations.items():
        # Get data for current simulation
        var_1_monthly_column = sim.monthly["QSnk60P"]
        var_1_hourly_column = sim.hourly["QSnk60P"]
        var_1_timestep_column = sim.step["QSnk60P"]

Accessing the scalar data here thus returns a column of data, i.e., one value for each of the simulations. For the other data, a DataFrame can be accessed for each simulation.

Advanced data access

Pandas DataFrames provide many ways to access data. For more information on how to use Pandas DataFrames, we refer to the Pandas Tutorials (e.g. How to select a subset of a table) and User’s Guide.