--- a
+++ b/Create_CSV.py
@@ -0,0 +1,193 @@
+import pandas as pd
+import nibabel as nib
+from random import sample
+from tqdm import tqdm
+from Excel_Processing import ProcessSpreadsheets
+
+DATASET_INFO_PATH = "C:\\Users\\leotu\\OneDrive\\Documents\\ImageCHD_dataset\\imageCHD_dataset_info.xlsx"
+SCAN_INFO_PATH = "C:\\Users\\leotu\\OneDrive\\Documents\\ImageCHD_dataset\\imagechd_dataset_image_info.xlsx"
+
+dataset_info = ProcessSpreadsheets(DATASET_INFO_PATH, SCAN_INFO_PATH)
+
+try:
+    dataset_info.to_csv(path_or_buf = "C:\\Users\\leotu\\OneDrive\\Documents\\ImageCHD_dataset\\patient_info.csv",
+                        index = False, mode = 'x')
+    print('Patient CSV saved.')
+except:
+    print('Patient CSV already present. Moving on.')
+
+dataset_info = dataset_info.drop(['ASD', 'VSD', 'AVSD', 'ToF', 'TGA', 'CA',
+                                  'PA', 'PDA', 'COUNT', 'PatientSex',
+                                  'PatientBirthDate', 'AcquisitionDate'],
+                                  axis = 1)
+
+axial_count = [nib.load("C:\\Users\\leotu\\Downloads\\ImageCHD_dataset\\ImageCHD_dataset\\ct_" \
+                         + str(x) + "_image.nii.gz") \
+               .header['dim'][3] for x in dataset_info['index'].sort_values()]
+dataset_info['Axial_count'] = axial_count
+train = list()
+evaluation = list()
+test = list()
+
+for index, row in tqdm(dataset_info.iterrows()):
+    temp = row.copy(deep = True)
+    ax_c = temp['Axial_count']
+    temp = temp.rename({'Axial_count': 'Adjacency_count'})
+
+    temp_sagittal = list()
+    temp_coronal = list()
+    temp_axial = list()
+
+    for i in range(0, 512):
+        temp['Type'] = 'S'
+        temp['Indice'] = i
+        temp_sagittal.append(temp.copy(deep = True))
+    
+    for i in range(0, 512):
+        temp['Type'] = 'C'
+        temp['Indice'] = i
+        temp_coronal.append(temp.copy(deep = True))
+    
+    for i in range(0, ax_c):
+        temp['Type'] = 'A'
+        temp['Indice'] = i
+        temp['Adjacency_count'] = 512
+        temp_axial.append(temp.copy(deep = True))
+    
+    new_train = sample(temp_sagittal, k = round(512*0.7))
+    tmp = list()
+
+    good = False
+    for sag in temp_sagittal:
+        good = True
+        for tr in new_train:
+            if sag.equals(tr):
+                good = False
+                break
+        if good:
+            tmp.append(sag)
+    
+    temp_sagittal = tmp.copy()
+    for tr in new_train:
+        train.append(tr)
+
+    new_train = sample(temp_coronal, k = round(512*0.7))
+    tmp = list()
+
+    good = False
+    for cor in temp_coronal:
+        good = True
+        for tr in new_train:
+            if cor.equals(tr):
+                good = False
+                break
+        if good:
+            tmp.append(cor)
+    
+    temp_coronal = tmp.copy()
+    for tr in new_train:
+        train.append(tr)
+
+    new_train = sample(temp_axial, k = round(ax_c*0.7))
+    tmp = list()
+
+    good = False
+    for ax in temp_axial:
+        good = True
+        for tr in new_train:
+            if ax.equals(tr):
+                good = False
+                break
+        if good:
+            tmp.append(ax)
+    
+    temp_axial = tmp.copy()
+    for tr in new_train:
+        train.append(tr)
+
+    new_eval = sample(temp_sagittal, k = round(512*0.2))
+    tmp = list()
+
+    good = False
+    for sag in temp_sagittal:
+        good = True
+        for ev in new_eval:
+            if sag.equals(ev):
+                good = False
+                break
+        if good:
+            tmp.append(sag)
+    
+    temp_sagittal = tmp.copy()
+    for ev in new_eval:
+        evaluation.append(ev)
+
+    new_eval = sample(temp_coronal, k = round(512*0.2))
+    tmp = list()
+
+    good = False
+    for cor in temp_coronal:
+        good = True
+        for ev in new_eval:
+            if cor.equals(ev):
+                good = False
+                break
+        if good:
+            tmp.append(cor)
+    
+    temp_coronal = tmp.copy()
+    for ev in new_eval:
+        evaluation.append(ev)
+
+    new_eval = sample(temp_axial, k = round(ax_c*0.2))
+    tmp = list()
+
+    good = False
+    for ax in temp_axial:
+        good = True
+        for ev in new_eval:
+            if ax.equals(ev):
+                good = False
+                break
+        if good:
+            tmp.append(ax)
+    
+    temp_axial = tmp.copy()
+    for ev in new_eval:
+        evaluation.append(ev)
+
+    for te in temp_sagittal:
+        test.append(te)
+    for te in temp_coronal:
+        test.append(te)
+    for te in temp_axial:
+        test.append(te)
+
+train_dataset = pd.DataFrame(train).reset_index().drop('level_0', axis = 1)
+eval_dataset = pd.DataFrame(evaluation).reset_index().drop('level_0', axis = 1)
+test_dataset = pd.DataFrame(test).reset_index().drop('level_0', axis = 1)
+
+print("Train: ", str(train_dataset.__len__()))
+print("Eval: ", str(eval_dataset.__len__()))
+print("Test: ", str(test_dataset.__len__()))
+
+try:
+    train_dataset.to_csv(path_or_buf = "C:\\Users\\leotu\\OneDrive\\Documents\\ImageCHD_dataset\\train_dataset_info.csv",
+                         index = False, mode = 'x')
+    print('Train dataset CSV saved.')
+except:
+    print('Train dataset CSV already present.')
+
+try:
+    eval_dataset.to_csv(path_or_buf = "C:\\Users\\leotu\\OneDrive\\Documents\\ImageCHD_dataset\\eval_dataset_info.csv",
+                        index = False, mode = 'x')
+    print('Eval dataset CSV saved.')
+except:
+    print('Eval dataset CSV already present.')
+
+try:
+    test_dataset.to_csv(path_or_buf = "C:\\Users\\leotu\\OneDrive\\Documents\\ImageCHD_dataset\\test_dataset_info.csv",
+                        index = False, mode = 'x')
+    print('Test dataset CSV saved.')
+except:
+    print('Test dataset CSV already present.')
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