diff --git a/temporary_testing_suite/TESTNG-AE-GAN.docx b/temporary_testing_suite/TESTNG-AE-GAN.docx
deleted file mode 100644
index 1bf1c55..0000000
Binary files a/temporary_testing_suite/TESTNG-AE-GAN.docx and /dev/null differ
diff --git a/temporary_testing_suite/ae_visualize_main.py b/temporary_testing_suite/ae_visualize_main.py
deleted file mode 100644
index 691faec..0000000
--- a/temporary_testing_suite/ae_visualize_main.py
+++ /dev/null
@@ -1,57 +0,0 @@
-
-import numpy as np
-import matplotlib.pyplot as plt
-import torch
-from torch.nn.modules.utils import consume_prefix_in_state_dict_if_present
-from nn_architecture.ae_networks import TransformerAutoencoder, TransformerDoubleAutoencoder, TransformerFlattenAutoencoder
-from helpers.dataloader import Dataloader
-
-# another comment
-
-#User input
-data_checkpoint = 'data/ganTrialElectrodeERP_p500_e18_len100.csv'
-ae_checkpoint = 'trained_ae/ae_ddp_4000ep_20230824_145643.pt'
-
-#Load
-ae_dict = torch.load(ae_checkpoint, map_location=torch.device('cuda'))
-dataloader = Dataloader(data_checkpoint, col_label='Condition', channel_label='Electrode')
-dataset = dataloader.get_data()
-sequence_length = dataset.shape[1] - dataloader.labels.shape[1]
-
-#Initiate
-if ae_dict['configuration']['model_class'] == 'TransformerAutoencoder':
-    autoencoder = TransformerAutoencoder(**ae_dict['configuration'], sequence_length=sequence_length)
-elif ae_dict['configuration']['model_class'] == 'TransformerDoubleAutoencoder':
-    autoencoder = TransformerDoubleAutoencoder(**ae_dict['configuration'], sequence_length=sequence_length)
-elif ae_dict['configuration']['model_class'] == 'TransformerFlattenAutoencoder':
-    autoencoder = TransformerFlattenAutoencoder(**ae_dict['configuration'], sequence_length=sequence_length)
-else:
-    raise ValueError(f"Autoencoder class {ae_dict['configuration']['model_class']} not recognized.")
-consume_prefix_in_state_dict_if_present(ae_dict['model'],'module.')
-autoencoder.load_state_dict(ae_dict['model'])
-autoencoder.device = torch.device('cpu')
-print(ae_dict["configuration"]["history"])
-
-#Test
-plt.figure()
-plt.plot(ae_dict['train_loss'], label='Train Loss')
-plt.plot(ae_dict['test_loss'], label = 'Test Loss')
-plt.title('Losses')
-plt.xlabel('Epoch')
-plt.legend()
-plt.show()
-
-def norm(data):
-    return (data-np.min(data)) / (np.max(data) - np.min(data))
-
-dataset = norm(dataset.detach().numpy())
-
-fig, axs = plt.subplots(5,1)
-for i in range(5):
-    sample = np.random.choice(len(dataset), 1)
-    data = dataset[sample,1:,:]
-    axs[i].plot(data[0,:,0], label='Original')
-    axs[i].plot(autoencoder.decode(autoencoder.encode(torch.from_numpy(data)))[0,:,0].detach().numpy(), label='Reconstructed')
-    axs[i].legend()
-plt.show()
-
diff --git a/temporary_testing_suite/gan_visualize_erps_main.py b/temporary_testing_suite/gan_visualize_erps_main.py
deleted file mode 100644
index ac5a7bf..0000000
--- a/temporary_testing_suite/gan_visualize_erps_main.py
+++ /dev/null
@@ -1,43 +0,0 @@
-
-import pandas as pd
-import matplotlib.pyplot as plt
-
-#### Define parameters ####
-gan_type = 'gan' #gan or aegan
-participants = 100 #500 or 100
-epochs = 100 #100, 1000, or 4000
-electrodes = 2 #1, 2, 8
-
-#### Load generated data ####
-filename = f'{gan_type}_p{participants}_ep{epochs}_e{electrodes}'
-c0_syn = pd.read_csv(f'generated_samples/{filename}_c0.csv')
-c1_syn = pd.read_csv(f'generated_samples/{filename}_c1.csv')
-gen_data_index = 2
-
-#### Load empirical data ####
-filename_emp = filename.replace('aegan','ganTrialElectrodeERP').replace('gan','ganTrialElectrodeERP').replace('_ep1000','').replace('_ep100','').replace('_ep4000','')+'_len100.csv'
-c_emp = pd.read_csv(f'data/{filename_emp}')
-c0_emp = c_emp[c_emp['Condition']==0]
-c1_emp = c_emp[c_emp['Condition']==1]
-emp_data_index = 4
-
-#### Plot data ####
-fig, ax = plt.subplots(2,len(c0_syn['Electrode'].unique()))
-
-for electrode in c0_syn['Electrode'].unique():
-    #Empirical
-    ax[0,int(electrode-1)].plot(c0_emp[c0_emp['Electrode']==electrode].mean()[emp_data_index:], label='C0')
-    ax[0,int(electrode-1)].plot(c1_emp[c1_emp['Electrode']==electrode].mean()[emp_data_index:], label='C1')
-    ax[0,int(electrode-1)].set_title(f'Empirical (E: {int(electrode)})')
-    ax[0,int(electrode-1)].get_xaxis().set_visible(False)
-    ax[0,int(electrode-1)].get_yaxis().set_visible(False)
-    ax[0,int(electrode-1)].spines[['right', 'top']].set_visible(False)
-
-    #Synthetic
-    ax[1,int(electrode-1)].plot(c0_syn[c0_syn['Electrode']==electrode].mean()[emp_data_index:], label='C0')
-    ax[1,int(electrode-1)].plot(c1_syn[c1_syn['Electrode']==electrode].mean()[emp_data_index:], label='C1')
-    ax[1,int(electrode-1)].set_title(f'Synthetic (E: {int(electrode)})')
-    ax[1,int(electrode-1)].get_xaxis().set_visible(False)
-    ax[1,int(electrode-1)].get_yaxis().set_visible(False)
-    ax[1,int(electrode-1)].spines[['right', 'top']].set_visible(False)
-plt.show()
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