--- a
+++ b/tests/dataset/samplers.py
@@ -0,0 +1,32 @@
+import importlib
+import sys
+from src.config import BratsConfiguration
+from src.dataset.loaders.batch_sampler import BratsPatchSampler
+from src.dataset.loaders.brats_dataset_whole_volume import BratsDataset
+from src.dataset.utils import dataset
+from torch.utils.data import DataLoader
+from torchvision import transforms as T
+
+
+def test():
+
+
+    csv = "/Users/lauramora/Documents/MASTER/TFM/Data/2020/train/no_patch/brats20_data.csv"
+    data, data_test = dataset.read_brats(csv)
+
+    modalities_to_use = {BratsDataset.flair_idx: True, BratsDataset.t1_idx: True, BratsDataset.t2_idx: True,
+                         BratsDataset.t1ce_idx: True}
+    transforms = T.Compose([T.ToTensor()])
+    sampling_method = importlib.import_module("src.dataset.patching.random_tumor_distribution")
+    patch_size = (128, 128, 128)
+    n_patches = 10
+
+    data = data * n_patches
+    train_dataset = BratsDataset(data, modalities_to_use, sampling_method, patch_size, transforms)
+    train_loader = DataLoader(dataset=train_dataset,batch_size=16, shuffle=True, num_workers=1)
+
+
+    for idx, b_data, b_labels in train_loader:
+        print(b_data)
+
+