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Plots with BuildSim API library

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Visualization is critical for most of the energy modelers. We know the equations and how things work together, but without a useful tool to deliver our knowledge to our clients, all the hard work is in vain.


Therefore, data visualization is always part of the mission in the development of BuildSim API library. The BuildSim API library is an open-source, Python-based library that helps modelers to build up automated modeling workflows, saving time and cost for their business. In this blog, I would like to share a few built-in graphing functions in the API library.


It should be noted that all the functions require the latest Plotly package. The installation of Plotly python can be found in here:


1. Heat Map for Hourly date visualization (link)

Once a cloud simulation completed, you can call the hourly_data(variable_name) to retrieve the list of hourly variables - a sample code is given below:

The code requests the 'Site Outdoor Air Drybulb Temperature:Environment' hourly variable. Lastly, utilize the built-in HourlyPlot object to do a heat map plot. Run the script and this is what you will get:


2. Line graph for hourly data visualization (link)

You can also use a line graph to plot data visualization - changing the heat_map_plot() to line_chart_plot() in the above code:

One difference between line chart and heat map is line chart accept multiple variables with up to two different units:



3. Zone Load Plot (link)

The zone loads can be extracted from any simulations conducted on the BuildSim Cloud.

 Alternatively, you can dive a little deeper to see the load components in each zone:


With browsers, there are flexibilities on arranging the views to help the development further. Adding this line to your script will help you open up the 3d geometry.

4. HTML table extract (link)

The HTML table allows package user to extract any table to make graphs. Currently available graphs include bar chart and pie chart. For example, if we want to extract End Uses table from the HTML file:

This creates a pandas data frame for the table. We then can utilize it to do some plotting

You can also plot this with bar charts:












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