diff --git a/reproduction_effort/make_test_data_aligned.py b/reproduction_effort/make_test_data_aligned.py
new file mode 100644
index 0000000..afdc035
--- /dev/null
+++ b/reproduction_effort/make_test_data_aligned.py
@@ -0,0 +1,187 @@
+import torch
+import torchvision as tv  # type: ignore
+import numpy as np
+import matplotlib.pyplot as plt
+import scipy.io as sio  # type: ignore
+
+from functions.align_cameras import align_cameras
+
+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)
+dtype: torch.dtype = torch.float32
+
+filename_bin_mat: str = "bin_old/Exp001_Trial001_Part001.mat"
+filename_bin_mat_fake: str = "Exp001_Trial001_Part001_fake.mat"
+fill_value: float = 0.0
+
+mat_data = torch.tensor(
+    sio.loadmat(filename_bin_mat)["nparray"].astype(dtype=np.float32),
+    dtype=dtype,
+    device=device,
+)
+
+angle_refref_target = torch.tensor(
+    [2],
+    dtype=dtype,
+    device=device,
+)
+
+tvec_refref_target = torch.tensor(
+    [10, 3],
+    dtype=dtype,
+    device=device,
+)
+
+
+t = (
+    torch.arange(
+        0,
+        mat_data.shape[-2],
+        dtype=dtype,
+        device=device,
+    )
+    - mat_data.shape[-2] // 2
+) / float(mat_data.shape[-2] // 2)
+
+f_a: float = 8
+A_a: float = 2
+a_target = A_a * torch.sin(2 * torch.pi * t * f_a)
+
+f_x: float = 5
+A_x: float = 10
+x_target = A_x * torch.sin(2.0 * torch.pi * t * f_x)
+
+f_y: float = 7
+A_y: float = 7
+y_target = A_y * torch.sin(2 * torch.pi * t * f_y)
+
+master_images: torch.Tensor = mat_data[:, :, mat_data.shape[-2] // 2, 1]
+
+master_images_2: torch.Tensor = master_images.unsqueeze(-1).tile(
+    (1, 1, mat_data.shape[-1])
+)
+
+# Rotate and move the whole timeseries of the acceptor and oxygenation
+master_images_2[..., 0] = tv.transforms.functional.affine(
+    img=master_images_2[..., 0].unsqueeze(0),
+    angle=-float(angle_refref_target),
+    translate=[0, 0],
+    scale=1.0,
+    shear=0,
+    interpolation=tv.transforms.InterpolationMode.BILINEAR,
+    fill=fill_value,
+).squeeze(0)
+
+master_images_2[..., 0] = tv.transforms.functional.affine(
+    img=master_images_2[..., 0].unsqueeze(0),
+    angle=0,
+    translate=[tvec_refref_target[1], tvec_refref_target[0]],
+    scale=1.0,
+    shear=0,
+    interpolation=tv.transforms.InterpolationMode.BILINEAR,
+    fill=fill_value,
+).squeeze(0)
+
+master_images_2[..., 2] = tv.transforms.functional.affine(
+    img=master_images_2[..., 2].unsqueeze(0),
+    angle=-float(angle_refref_target),
+    translate=[0, 0],
+    scale=1.0,
+    shear=0,
+    interpolation=tv.transforms.InterpolationMode.BILINEAR,
+    fill=fill_value,
+).squeeze(0)
+
+master_images_2[..., 2] = tv.transforms.functional.affine(
+    img=master_images_2[..., 2].unsqueeze(0),
+    angle=0,
+    translate=[tvec_refref_target[1], tvec_refref_target[0]],
+    scale=1.0,
+    shear=0,
+    interpolation=tv.transforms.InterpolationMode.BILINEAR,
+    fill=fill_value,
+).squeeze(0)
+
+fake_data = master_images_2.unsqueeze(-2).tile((1, 1, mat_data.shape[-2], 1)).clone()
+
+for t_id in range(0, fake_data.shape[-2]):
+    for c_id in range(0, fake_data.shape[-1]):
+        fake_data[..., t_id, c_id] = tv.transforms.functional.affine(
+            img=fake_data[..., t_id, c_id].unsqueeze(0),
+            angle=-float(a_target[t_id]),
+            translate=[0, 0],
+            scale=1.0,
+            shear=0,
+            interpolation=tv.transforms.InterpolationMode.BILINEAR,
+            fill=fill_value,
+        ).squeeze(0)
+
+fake_data = fake_data.clone()
+
+for t_id in range(0, fake_data.shape[-2]):
+    for c_id in range(0, fake_data.shape[-1]):
+        fake_data[..., t_id, c_id] = tv.transforms.functional.affine(
+            img=fake_data[..., t_id, c_id].unsqueeze(0),
+            angle=0.0,
+            translate=[y_target[t_id], x_target[t_id]],
+            scale=1.0,
+            shear=0,
+            interpolation=tv.transforms.InterpolationMode.BILINEAR,
+            fill=fill_value,
+        ).squeeze(0)
+
+fake_data_np = fake_data.cpu().numpy()
+mdic = {"nparray": fake_data_np}
+sio.savemat(filename_bin_mat_fake, mdic)
+
+# ----------------------------------------------------
+
+batch_size: int = 200
+filename_raw_json: str = "raw/Exp001_Trial001_Part001_meta.txt"
+
+(
+    acceptor,
+    donor,
+    oxygenation,
+    volume,
+    angle_donor_volume,
+    tvec_donor_volume,
+    angle_refref,
+    tvec_refref,
+) = align_cameras(
+    filename_raw_json=filename_raw_json,
+    filename_bin_mat=filename_bin_mat_fake,
+    device=device,
+    dtype=dtype,
+    batch_size=batch_size,
+    fill_value=-1,
+)
+
+print("References Acceptor <-> Donor:")
+print("Rotation:")
+print(f"target: {float(angle_refref_target):.3f}")
+print(f"found: {-float(angle_refref):.3f}")
+print("Translation")
+print(f"target: {float(tvec_refref_target[0]):.3f}, {float(tvec_refref_target[1]):.3f}")
+print(f"found: {-float(tvec_refref[0]):.3f}, {-float(tvec_refref[1]):.3f}")
+
+plt.subplot(3, 1, 1)
+plt.plot(-angle_donor_volume.cpu(), "g", label="angle found")
+plt.plot(a_target.cpu(), "--k", label="angle target")
+plt.legend()
+
+plt.subplot(3, 1, 2)
+plt.plot(-tvec_donor_volume[:, 0].cpu(), "g", label="x found")
+plt.plot(x_target.cpu(), "k--", label="x target")
+plt.legend()
+
+plt.subplot(3, 1, 3)
+plt.plot(-tvec_donor_volume[:, 1].cpu(), "g", label="y found")
+plt.plot(y_target.cpu(), "k--", label="y target")
+plt.legend()
+
+plt.show()