Currently working setup. #3
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This is the currently working code. It includes the requirements file from which one can install all of the dependencies. There are all of the scripts for training and data processing. The process of training a model from root files is as follows.
Create a csv file with names of all of the root files. ExtractDataScript is an example of how the DataExtractor class can be used running the DataExtractor.Interloper() function on the previously created csv will output a set of intermediary csv files with Ecal Energy and TS information. There are pre-processing functions in the CNN_and_Combined_PreProcessing and RNN_Pre_Processing folders. An example of how to run pre-processing is given in CNNEcalPreTrigPar.py running the pre-processing will create a set of individual event files in the output folder. Lastly in an appropriate ANN script the path to data needs to be changed. Afterwards running the training script should create the trained model.