--- a
+++ b/tests/dataset/patching/common.py
@@ -0,0 +1,43 @@
+from typing import Tuple
+import os
+import numpy as np
+
+from src.dataset.utils.visualization import plot_3_view
+from src.dataset.utils.nifi_volume import load_nifi_volume
+
+
+dataset_path = "/Users/lauramora/Documents/MASTER/TFM/Data/2020/train/no_patch/"
+patient = "BraTS20_Training_001"
+save = True
+
+def plot(volume: np.ndarray, patch: np.ndarray, volume_slice: int = 100):
+    plot_3_view("flair", volume[0, :, :, :], volume_slice, save=True)
+    plot_3_view("patch_flair", patch[0, :, :, :], volume_slice, save=True)
+
+
+def load_patient() -> Tuple[np.ndarray, np.ndarray]:
+    flair = load_nifi_volume(os.path.join(dataset_path, patient, f"{patient}_flair.nii.gz"))
+    t1 = load_nifi_volume(os.path.join(dataset_path, patient, f"{patient}_t1.nii.gz"))
+    t1ce = load_nifi_volume(os.path.join(dataset_path, patient, f"{patient}_t1ce.nii.gz"))
+    t2 = load_nifi_volume(os.path.join(dataset_path, patient, f"{patient}_t2.nii.gz"))
+    masks = load_nifi_volume(os.path.join(dataset_path, patient, f"{patient}_seg.nii.gz"))
+
+    modalities = np.asarray([t1, t1ce, t2, flair])
+
+    return modalities, masks
+
+def get_brain_mask():
+    data = load_nifi_volume(os.path.join(dataset_path, patient, f"{patient}_flair.nii.gz"), normalize=False)
+    brain_mask = np.zeros(data.shape, np.float)
+    brain_mask[data > 0] = 1
+    return brain_mask
+
+
+def patching_strategy(patching_method, size: tuple) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+    volume, seg = load_patient()
+    brain_mask = get_brain_mask()
+    volume_patches, seg_patches = patching_method(volume, seg, size, brain_mask)
+    return volume, volume_patches, seg_patches
+
+
+