#conda create -n deepeeg

#source activate deepeeg

#chomd +x install.sh

#bash install.sh

#!git clone https://github.com/kylemath/eeg-notebooks_v0.1

#python

from utils import *

data_dir = '/Users/kylemathewson/Desktop/data/'

exp = 'P3'

subs = ['001','002','004','005','006','007','008','010']

subs = [ '008']

sessions = ['ActiveDry','ActiveWet','PassiveWet']

nsesh = len(sessions)

event_id = {'Target': 1, 'Standard': 2}

epochs = []

for sub in subs:

    print('Loading data for subject ' + sub)

    for session in sessions:

        #Load Data

        raw = LoadBVData(sub,session,data_dir,exp)

        #Pre-Process EEG Data

        temp_epochs = PreProcess(raw,event_id,

                            emcp_epochs=True, rereference=True,

                            plot_erp=False, rej_thresh_uV=1000, 

                            epoch_time=(-1,2), baseline=(-.2,0), 

                            epoch_decim=1,filter_range=(1,20))

        if len(temp_epochs) > 0:

            epochs.append(temp_epochs)

        else:

            print('Sub ' + sub + ', Cond ' 

                    + session + 'all trials rejected')

epochs = concatenate_epochs(epochs) 

#Engineer Features for Model

feats = FeatureEngineer(epochs,model_type='CNN',electrode_median=False,

                        normalization=False, frequency_domain=True, 

                        wavelet_decim=10)

#Create Model

model,_ = CreateModel(feats, units=[256,256,256,256])

#Train with validation, then Test

TrainTestVal(model,feats)