--- a
+++ b/Preprocessing Medical Data Pipeline/writeout_dataset.py
@@ -0,0 +1,104 @@
+# Exporting Training Scan and Mask Data
+import os 
+import numpy as np
+from PIL import Image
+import matplotlib.pyplot as plt
+import re
+import numpy as np
+import nibabel as nib
+import matplotlib.pyplot as plt
+from tqdm import tqdm
+
+def WriteOutTextFile(data_array, save_dir, starting_slice):
+    num_files = data_array.shape[0]
+    
+    # Create the save directory if it doesn't exist
+    if not os.path.exists(save_dir):
+        os.makedirs(save_dir)
+    
+    for i in range(num_files):
+        file_name = f"data_{i + starting_slice}.txt"
+        file_path = save_dir + '/' + file_name
+        np.savetxt(file_path, data_array[i], delimiter=',', fmt='%.4f')
+        print(f"Saved {file_path}")
+
+
+def WriteOutImagePNGFiles(data_array, save_dir, starting_slice):
+    num_files = data_array.shape[0]
+
+    # Create the save directory if it doesn't exist
+    if not os.path.exists(save_dir):
+        os.makedirs(save_dir)
+
+    for i in range(num_files):
+        file_name = f"image_{i + starting_slice}.png"
+        file_path = save_dir + '/' + file_name
+            
+        # Scale the pixel values to the range [0, 255] if necessary
+        image_data = ((data_array[i] * 255).astype(np.uint8)).squeeze()
+        # Create an Image object and save as PNG
+        image = Image.fromarray(image_data)
+
+        if True:
+            image.save(file_path)
+                
+        print(f"Saved {file_path}")
+
+
+def ReadInDatasets(folder_path):
+    files = os.listdir(folder_path)
+    num_files = len(files)
+    result_array = np.empty((num_files, 512, 512))
+
+    for i, file in enumerate(files):
+        file_path = os.path.join(folder_path, file)
+        with open(file_path, 'r') as f:
+            # Read the contents of the text file
+            content = f.read()
+
+            # Convert the content into a 2D NumPy array of floats
+            array_2d = np.array([list(map(float, line.split(','))) for line in content.splitlines()])
+
+            # Store the 2D array in the result array
+            result_array[i] = array_2d
+
+    return result_array
+
+def ReadInDatasetNPY(folder_path):
+    # Get a list of all files in the folder
+    file_list = os.listdir(folder_path)
+
+    # Filter out only the .npy files
+    npy_files = [file for file in file_list if file.endswith('.npy')]
+
+    # Sort the .npy files based on the numeric order in their filenames
+    npy_files.sort(key=lambda x: int(x.split('_')[0]))
+
+    # Initialize an empty list to store arrays from each .npy file
+    arrays_list = []
+
+    # Read in each .npy file and append it to the list
+    for file_name in npy_files:
+        file_path = os.path.join(folder_path, file_name)
+        array = np.load(file_path)
+        arrays_list.append(array)
+
+    # Concatenate the arrays into a single array
+    concatenated_array = np.concatenate(arrays_list, axis=0)
+
+    return concatenated_array
+
+def Export2CompressedNifiti(imgs_train, scans_path, colab_fname, imgs_mask_train, orientation):
+    nii_img_train = nib.Nifti1Image(imgs_train, affine=np.eye(4))
+    scan_idx_fnamecolab = int((colab_fname[0].split('_'))[1])
+    scan_idx_fnamecolab = '{:03d}'.format(scan_idx_fnamecolab)
+    output_file_path = ('{}/nnUNet Data/unprocessed_scans/msk_{}.nii.gz').format(scans_path, scan_idx_fnamecolab)
+    nib.save(nii_img_train, output_file_path)
+
+    nii_img_mask = nib.Nifti1Image(imgs_mask_train, affine=np.eye(4))
+    output_file_path = ('{}/nnUNet Data/masks/{}/{}_{}.nii.gz').format(scans_path, ((colab_fname[0]).split('_'))[0],colab_fname[0], orientation)
+    nib.save(nii_img_mask, output_file_path)   
+
+    print("Conversion to NIfTI complete with shape: ", imgs_mask_train.shape)
+    print('\n')
+    print('Successfully Exported Preprocessed Data!')