diff --git a/inspection_30fps_no_glow_main_svd_removed.py b/inspection_30fps_no_glow_main_svd_removed.py
new file mode 100644
index 0000000..fdf508f
--- /dev/null
+++ b/inspection_30fps_no_glow_main_svd_removed.py
@@ -0,0 +1,156 @@
+import numpy as np
+import torch
+import matplotlib.pyplot as plt
+import skimage
+from scipy.stats import skew
+from svd import calculate_svd, to_remove, calculate_translation
+import torchvision as tv
+
+from ImageAlignment import ImageAlignment
+
+# from Anime import Anime
+
+filename: str = "example_data_crop"
+use_svd: bool = True
+
+torch_device: torch.device = torch.device(
+    "cuda:0" if torch.cuda.is_available() else "cpu"
+)
+
+with torch.no_grad():
+    print("Load data")
+    input = np.load(filename + str(".npy"))  # str("_decorrelated.npy"))
+    data = torch.tensor(input, device=torch_device)
+    # del input
+    print("loading done")
+
+    fill_value: float = 0.0
+
+    print("Movement compensation [BROKEN!!!!]")
+    print("During development, information about what could move was missing.")
+    print("Thus the preprocessing before shift determination may not work.")
+    data -= data.min(dim=0)[0]
+    data /= data.std(dim=0, keepdim=True) + 1e-20
+
+    image_alignment = ImageAlignment(default_dtype=torch.float32, device=torch_device)
+
+    tvec = calculate_translation(
+        input=data,
+        reference_image=data[0, ...].clone(),
+        image_alignment=image_alignment,
+    )
+    tvec_media = tvec.median(dim=0)[0]
+    print(f"Median of movement: {tvec_media[0]}, {tvec_media[1]}")
+
+    data = torch.tensor(input, device=torch_device)
+    data -= data.min(dim=0, keepdim=True)[0]
+    for id in range(0, data.shape[0]):
+        data[id, ...] = tv.transforms.functional.affine(
+            img=data[id, ...].unsqueeze(0),
+            angle=0,
+            translate=[tvec[id, 1], tvec[id, 0]],
+            scale=1.0,
+            shear=0,
+            fill=fill_value,
+        ).squeeze(0)
+
+    print("SVD")
+    whiten_mean, whiten_k, eigenvalues = calculate_svd(data)
+    # ----
+    data = torch.tensor(input, device=torch_device)
+    for id in range(0, data.shape[0]):
+        data[id, ...] = tv.transforms.functional.affine(
+            img=data[id, ...].unsqueeze(0),
+            angle=0,
+            translate=[tvec[id, 1], tvec[id, 0]],
+            scale=1.0,
+            shear=0,
+            fill=fill_value,
+        ).squeeze(0)
+    data -= data.min(dim=0, keepdim=True)[0]
+    to_remove_data = to_remove(data, whiten_k, whiten_mean)
+
+    data -= to_remove_data
+    del to_remove_data
+
+    stored_contours = np.load("cells.npy", allow_pickle=True)
+
+    if use_svd:
+        data_flat = torch.flatten(
+            data.nan_to_num(nan=0.0).movedim(0, -1),
+            start_dim=0,
+            end_dim=1,
+        )
+
+    to_plot = torch.zeros(
+        (int(data.shape[0]), int(stored_contours.shape[0])),
+        device=torch_device,
+        dtype=torch.float32,
+    )
+
+    print("Calculate cell's time series")
+
+    for id in range(0, stored_contours.shape[0]):
+        mask = torch.tensor(
+            skimage.draw.polygon2mask(
+                (int(data.shape[1]), int(data.shape[2])), stored_contours[id]
+            ),
+            device=torch_device,
+            dtype=torch.float32,
+        )
+        if use_svd:
+            mask_flat = torch.flatten(
+                mask.unsqueeze(0).nan_to_num(nan=0.0).movedim(0, -1),
+                start_dim=0,
+                end_dim=1,
+            )
+            idx = torch.where(mask_flat > 0)[0]
+            temp = data_flat[idx, :].clone()
+            whiten_mean = torch.mean(temp, dim=-1)
+            temp -= whiten_mean.unsqueeze(-1)
+            svd_u, svd_s, _ = torch.svd_lowrank(temp, q=6)
+
+            whiten_k = (
+                torch.sign(svd_u[0, :]).unsqueeze(0)
+                * svd_u
+                / (svd_s.unsqueeze(0) + 1e-20)
+            )[:, 0]
+
+            temp = temp * whiten_k.unsqueeze(-1)
+            data_svd = temp.movedim(-1, 0).sum(dim=-1)
+            to_plot[:, id] = data_svd
+        else:
+            ts = (data * mask.unsqueeze(0)).nan_to_num(nan=0.0).sum(
+                dim=(-2, -1)
+            ) / mask.sum()
+            to_plot[:, id] = ts
+
+
+skew_value = skew(to_plot.cpu().numpy(), axis=0)
+skew_idx = np.flip(skew_value.argsort())
+skew_value = skew_value[skew_idx]
+
+to_plot_np = to_plot.cpu().numpy()
+to_plot_np = to_plot_np[:, skew_idx]
+
+
+plt.imshow(to_plot_np.T, cmap="gray_r", interpolation="nearest")
+plt.colorbar()
+plt.show()
+
+
+# plt.plot(to_plot[:, 0:5].cpu())
+# plt.show()
+
+# block_size: int = 8
+# # print(to_plot.shape[1] // block_size)
+# for i in range(0, 4 * 8):
+#     plt.subplot(8, 4, i + 1)
+#     plt.plot(to_plot[:, i * block_size : (i + 1) * block_size].cpu())
+#     plt.ylim(
+#         [
+#             to_plot.min().cpu(),
+#             to_plot.max().cpu(),
+#         ]
+#     )
+# plt.show()