diff --git a/olivia_data_plotter_svd.py b/olivia_data_plotter_svd.py
new file mode 100644
index 0000000..956b92a
--- /dev/null
+++ b/olivia_data_plotter_svd.py
@@ -0,0 +1,71 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import os
+from functions.create_logger import create_logger
+from functions.load_config import load_config
+
+from functions.get_trials import get_trials
+import h5py  # type: ignore
+import torch
+
+control_file_handle = h5py.File("ROI_control_49.mat", "r")
+control_roi = (np.array(control_file_handle["roi"]).T) > 0
+control_file_handle.close()
+control_roi = control_roi.reshape((control_roi.shape[0] * control_roi.shape[1]))
+
+s_darken_file_handle = h5py.File("ROI_sDarken_49.mat", "r")
+s_darken_roi = (np.array(s_darken_file_handle["roi"]).T) > 0
+s_darken_file_handle.close()
+s_darken_roi = s_darken_roi.reshape((s_darken_roi.shape[0] * s_darken_roi.shape[1]))
+
+mylogger = create_logger(
+    save_logging_messages=True, display_logging_messages=True, log_stage_name="test_xxx"
+)
+config = load_config(mylogger=mylogger)
+
+experiment_id: int = 1
+
+raw_data_path: str = os.path.join(
+    config["basic_path"],
+    config["recoding_data"],
+    config["mouse_identifier"],
+    config["raw_path"],
+)
+
+data_path: str = "output"
+
+trails = get_trials(path=raw_data_path, experiment_id=experiment_id)
+for i in range(0, trails.shape[0]):
+    trial_id = int(trails[i])
+    experiment_name: str = f"Exp{experiment_id:03d}_Trial{trial_id:03d}"
+    mylogger.info(f"Loading files for {experiment_name}")
+
+    data = np.load(os.path.join(data_path, f"{experiment_name}_ratio_sequence.npz"))
+    rs = data["ratio_sequence"]
+    rs = rs.reshape((rs.shape[0] * rs.shape[1], rs.shape[2]))
+    rs_c = rs[control_roi, :]
+    rs_c_core, _, _ = torch.linalg.svd(torch.tensor(rs_c.T), full_matrices=False)
+    rs_c_core = rs_c_core[:, 0].numpy()
+
+    rs_s = rs[s_darken_roi, :]
+    rs_s_core, _, _ = torch.linalg.svd(torch.tensor(rs_s.T), full_matrices=False)
+    rs_s_core = rs_s_core[:, 0].numpy()
+
+    rs_s_core -= rs_s_core.mean(keepdims=True)
+    rs_c_core -= rs_c_core.mean(keepdims=True)
+
+    rs_c_core *= (rs_s_core * rs_c_core).sum() / (rs_c_core**2).sum()
+
+    if i == 0:
+        ratio_sequence = rs_s_core - rs_c_core
+    else:
+        ratio_sequence += rs_s_core - rs_c_core
+
+ratio_sequence /= float(trails.shape[0])
+
+t = np.arange(0, ratio_sequence.shape[0]) / 100.0
+
+plt.plot(t, ratio_sequence, label="sDarken-control")
+plt.legend()
+plt.xlabel("Time [sec]")
+plt.show()