How-to guides

These how-to guides will walk you through how to solve specific tasks.

The guides will only assume that you have the knowledge from the getting started guide, any additional information will be linked from the specific how-to guide.

Topics

📄️ Batch mode

For multiple calculation calls ArupCompute works best with batch requests.

📄️ Optional Inputs

To use the default value for an optional input you can either set the value of the input to be None, or leave out the input entirely from your inputs dictionary:

📄️ Reports

If the library you interact with creates reports they can be easily accessed via ArupCompute.

📄️ Using a specific calculation version

The calcID required by arupcomputepy is specific to the calculation version, which is specific to a library version (ie. a calculation gets a new calcID every time its containing library is published). The calcID must be updated in order to access newer versions of a calculation.

📄️ Using the latest calculation version

A persistentCalcID, instead of a calcID, can also be passed to arupcomputepy. Whereas a calcID is specific to the library version, a persistentCalcID (UUID) does not change across library versions. A persistentCalcId is can be used to look up different versions, including the latest version, of a calculation.

📄️ JSON Input 🗄️

Refer to the JSON page in the Explanation section of the documentation to understand what JSON is and why/when it needs to be used with ArupCompute.

📄️ Jupyter Notebook 🪐

Jupyter Notebooks (which run IPython, an interactive shell built with python) can be a great way to use arupcomputepy and have been used on previous projects.

📄️ Plot graph 📈

The code written in this section is executed in a Jupyter Notebook, however the code for use in a python script will be very similar, if not identical.

📄️ Find an optimal solution ⚙️

Example Problem

📄️ Pandas 🐼

pandas is a python library which can be very helpful for working with tables of data with your calculations.