--- a
+++ b/BV_P3example.py
@@ -0,0 +1,47 @@
+#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)
+
+