diff --git a/reproduction_effort/heartbeatanalyse.py b/reproduction_effort/heartbeatanalyse.py
deleted file mode 100644
index 8a26474..0000000
--- a/reproduction_effort/heartbeatanalyse.py
+++ /dev/null
@@ -1,122 +0,0 @@
-import torch
-import matplotlib.pyplot as plt
-
-
-from functions.preprocessing import preprocessing
-from functions.bandpass import bandpass
-
-
-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
-
-    lower_freqency_bandpass: float = 5.0
-    upper_freqency_bandpass: float = 14.0
-
-    target_camera: list[str] = ["acceptor", "donor"]
-    regressor_cameras: list[str] = ["oxygenation", "volume"]
-
-    ratio_sequence_a, ratio_sequence_b, 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_a = bandpass(
-        data=ratio_sequence_a,
-        device=ratio_sequence_a.device,
-        low_frequency=lower_freqency_bandpass,
-        high_frequency=upper_freqency_bandpass,
-        fs=100.0,
-        filtfilt_chuck_size=10,
-    )
-
-    ratio_sequence_b = bandpass(
-        data=ratio_sequence_b,
-        device=ratio_sequence_b.device,
-        low_frequency=lower_freqency_bandpass,
-        high_frequency=upper_freqency_bandpass,
-        fs=100.0,
-        filtfilt_chuck_size=10,
-    )
-
-    original_shape = ratio_sequence_a.shape
-
-    ratio_sequence_a = ratio_sequence_a.flatten(start_dim=0, end_dim=-2)
-    ratio_sequence_b = ratio_sequence_b.flatten(start_dim=0, end_dim=-2)
-
-    mask = mask.flatten(start_dim=0, end_dim=-1)
-    ratio_sequence_a = ratio_sequence_a[mask, :]
-    ratio_sequence_b = ratio_sequence_b[mask, :]
-
-    ratio_sequence_a = ratio_sequence_a.movedim(0, -1)
-    ratio_sequence_b = ratio_sequence_b.movedim(0, -1)
-
-    ratio_sequence_a -= ratio_sequence_a.mean(dim=0, keepdim=True)
-    ratio_sequence_b -= ratio_sequence_b.mean(dim=0, keepdim=True)
-
-    u_a, s_a, Vh_a = torch.linalg.svd(ratio_sequence_a, full_matrices=False)
-    u_a = u_a[:, 0]
-    s_a = s_a[0]
-    Vh_a = Vh_a[0, :]
-
-    heartbeatactivitmap_a = torch.zeros(
-        (original_shape[0], original_shape[1]), device=Vh_a.device, dtype=Vh_a.dtype
-    ).flatten(start_dim=0, end_dim=-1)
-
-    heartbeatactivitmap_a *= torch.nan
-    heartbeatactivitmap_a[mask] = s_a * Vh_a
-    heartbeatactivitmap_a = heartbeatactivitmap_a.reshape(
-        (original_shape[0], original_shape[1])
-    )
-
-    u_b, s_b, Vh_b = torch.linalg.svd(ratio_sequence_b, full_matrices=False)
-    u_b = u_b[:, 0]
-    s_b = s_b[0]
-    Vh_b = Vh_b[0, :]
-
-    heartbeatactivitmap_b = torch.zeros(
-        (original_shape[0], original_shape[1]), device=Vh_b.device, dtype=Vh_b.dtype
-    ).flatten(start_dim=0, end_dim=-1)
-
-    heartbeatactivitmap_b *= torch.nan
-    heartbeatactivitmap_b[mask] = s_b * Vh_b
-    heartbeatactivitmap_b = heartbeatactivitmap_b.reshape(
-        (original_shape[0], original_shape[1])
-    )
-
-    plt.subplot(2, 1, 1)
-    plt.plot(u_a.cpu(), label="aceptor")
-    plt.plot(u_b.cpu(), label="donor")
-    plt.legend()
-    plt.subplot(2, 1, 2)
-    plt.imshow(
-        torch.cat(
-            (
-                heartbeatactivitmap_a,
-                heartbeatactivitmap_b,
-            ),
-            dim=1,
-        ).cpu()
-    )
-    plt.colorbar()
-    plt.show()