diff --git a/reproduction_effort/preprocessing.py b/reproduction_effort/preprocessing.py
deleted file mode 100644
index 483a81e..0000000
--- a/reproduction_effort/preprocessing.py
+++ /dev/null
@@ -1,68 +0,0 @@
-import torch
-import numpy as np
-import matplotlib.pyplot as plt
-import h5py  # type: ignore
-
-from functions.preprocessing import preprocessing
-
-
-if __name__ == "__main__":
-
-    if torch.cuda.is_available():
-        device_name: str = "cuda:0"
-    else:
-        device_name = "cpu"
-    print(f"Using device: {device_name}")
-    device: torch.device = torch.device(device_name)
-
-    filename_metadata: str = "raw/Exp001_Trial001_Part001_meta.txt"
-    filename_data: str = "Exp001_Trial001_Part001.mat"
-    filename_mask: str = "2020-12-08maskPixelraw2.mat"
-
-    first_none_ramp_frame: int = 100
-    spatial_width: float = 2
-    temporal_width: float = 0.1
-
-    target_camera: list[str] = ["acceptor", "donor"]
-    regressor_cameras: list[str] = ["oxygenation", "volume"]
-
-    data_acceptor, data_donor, mask = preprocessing(
-        filename_metadata=filename_metadata,
-        filename_data=filename_data,
-        filename_mask=filename_mask,
-        device=device,
-        first_none_ramp_frame=first_none_ramp_frame,
-        spatial_width=spatial_width,
-        temporal_width=temporal_width,
-        target_camera=target_camera,
-        regressor_cameras=regressor_cameras,
-    )
-
-    ratio_sequence: torch.Tensor = data_acceptor / data_donor
-
-    new: np.ndarray = ratio_sequence.cpu().numpy()
-
-    file_handle = h5py.File("old.mat", "r")
-    old: np.ndarray = np.array(file_handle["ratioSequence"])
-    # HDF5 loads everything backwards...
-    old = np.moveaxis(old, 0, -1)
-    old = np.moveaxis(old, 0, -2)
-
-    pos_x = 25
-    pos_y = 75
-
-    plt.subplot(2, 1, 1)
-    new_select = new[pos_x, pos_y, :]
-    old_select = old[pos_x, pos_y, :]
-    plt.plot(new_select, label="New")
-    plt.plot(old_select, "--", label="Old")
-    plt.plot(old_select - new_select + 1.0, label="Old - New + 1")
-    plt.title(f"Position: {pos_x}, {pos_y}")
-    plt.legend()
-
-    plt.subplot(2, 1, 2)
-    differences = (np.abs(new - old)).max(axis=-1)
-    plt.imshow(differences)
-    plt.title("Max of abs(new-old) along time")
-    plt.colorbar()
-    plt.show()