Ladybug Heatmap Error - UTCI/MRT analysis

I am fairly new to Grasshopper, trying to create a heatmap. I am trying to evaluate MRT impact of some masses (set as breps) on my site (set as surface) but once I inputted the ‘sky exposure’ from ‘HumantoSky’ node to the ‘OutdoorSolarMRT’ node, the Heatmap shows an error that reads: “1. Solution exception:Expected the number of data set values (20736) to align with the number of faces (144) or the number of vertices (169).
Consider flattening the _values input and using the “Mesh Join” component to join the _mesh input.”
I’ve already flattened the UTCI values. If the solution is using ‘Mesh Join’ I am not sure which nodes to attach to the ‘Mesh Join’ component (I have tried using it to connect the mesh output from the ‘GenPoint’ component and the ‘Heatmap’ component mesh input, no luck.)

How do I resolve this?

Please ignore the scribble notes, the project is still in-progress.

I attached a snip of my script to demonstrate.

Hi @daniatabka ,

what I assume is happening here is that you are testing for a period (more than 1hr of the year). This will create a tree of outputs in your UTCI component which will represent a UTCI result for each point, for each step of the set period. (example screenshot below)

Depending on what you are after, you can post-process your results to either show a single step of the analysis, average of the period, or percentage satisfied - as a few examples.

Hope this is helpful

@Byron Thanks for your response!
I have set my start/end month, start/end day, and start/end hour as the same value (respectively). Cropped, but vaguely shown in snip.

So not sure if this this the issue.

One thing I just saw from your screenshot, why are you breaking your fract_body_exp output to raw data. Try connecting that directly to your OutdoorSolarMRT component?

@bryanmardas When connecting those nodes directly an error message is generated from the OutdoorSolarMRT component reading: ‘1. Solution exception:Direct Normal Radiation Data Collection is not aligned with Fraction Data Collection.’