Current version of code creates AWT dictionaries with different lengths for different models depending on what they predict. This results in a crash when trying to convert to a dataframe due to different length arrays in the dictionaries due to different number of SEP events contained within the forecasts and possibly due to different predicted quantities. Need to rethink how to handle the AWT value. Either break up the AWT calculations for different quantities (All clear, peak flux) into different dictionaries or create one big massive dictionary that has lots of None values as place holders so that all arrays have the same length.
Current version of code creates AWT dictionaries with different lengths for different models depending on what they predict. This results in a crash when trying to convert to a dataframe due to different length arrays in the dictionaries due to different number of SEP events contained within the forecasts and possibly due to different predicted quantities. Need to rethink how to handle the AWT value. Either break up the AWT calculations for different quantities (All clear, peak flux) into different dictionaries or create one big massive dictionary that has lots of None values as place holders so that all arrays have the same length.