diff --git a/stage_2_make_heartbeat_mask.py b/stage_2_make_heartbeat_mask.py
deleted file mode 100644
index dfa8c63..0000000
--- a/stage_2_make_heartbeat_mask.py
+++ /dev/null
@@ -1,163 +0,0 @@
-import matplotlib.pyplot as plt  # type:ignore
-import matplotlib
-import numpy as np
-import torch
-import os
-import argh
-
-from matplotlib.widgets import Slider, Button  # type:ignore
-from functools import partial
-from functions.gauss_smear_individual import gauss_smear_individual
-from functions.create_logger import create_logger
-from functions.get_torch_device import get_torch_device
-from functions.load_config import load_config
-
-
-def main(*, config_filename: str = "config.json") -> None:
-    mylogger = create_logger(
-        save_logging_messages=True,
-        display_logging_messages=True,
-        log_stage_name="stage_2",
-    )
-
-    config = load_config(mylogger=mylogger, filename=config_filename)
-
-    path: str = config["ref_image_path"]
-    use_channel: str = "donor"
-    spatial_width: float = 4.0
-    temporal_width: float = 0.1
-
-    threshold: float = 0.05
-
-    heartbeat_mask_threshold_file: str = os.path.join(
-        path, "heartbeat_mask_threshold.npy"
-    )
-    if os.path.isfile(heartbeat_mask_threshold_file):
-        mylogger.info(
-            f"loading previous threshold file: {heartbeat_mask_threshold_file}"
-        )
-        threshold = float(np.load(heartbeat_mask_threshold_file)[0])
-
-    mylogger.info(f"initial threshold is {threshold}")
-
-    image_ref_file: str = os.path.join(path, use_channel + ".npy")
-    image_var_file: str = os.path.join(path, use_channel + "_var.npy")
-    heartbeat_mask_file: str = os.path.join(path, "heartbeat_mask.npy")
-
-    device = get_torch_device(mylogger, config["force_to_cpu"])
-
-    mylogger.info(f"loading image reference file: {image_ref_file}")
-    image_ref: np.ndarray = np.load(image_ref_file)
-    image_ref /= image_ref.max()
-
-    mylogger.info(f"loading image heartbeat power: {image_var_file}")
-    image_var: np.ndarray = np.load(image_var_file)
-    image_var /= image_var.max()
-
-    mylogger.info("Smear the image heartbeat power patially")
-    temp, _ = gauss_smear_individual(
-        input=torch.tensor(image_var[..., np.newaxis], device=device),
-        spatial_width=spatial_width,
-        temporal_width=temporal_width,
-        use_matlab_mask=False,
-    )
-    temp /= temp.max()
-
-    mylogger.info("-==- DONE -==-")
-
-    image_3color = np.concatenate(
-        (
-            np.zeros_like(image_ref[..., np.newaxis]),
-            image_ref[..., np.newaxis],
-            temp.cpu().numpy(),
-        ),
-        axis=-1,
-    )
-
-    mylogger.info("Prepare image")
-
-    display_image = image_3color.copy()
-    display_image[..., 2] = display_image[..., 0]
-    mask = np.where(image_3color[..., 2] >= threshold, 1.0, np.nan)[..., np.newaxis]
-    display_image *= mask
-    display_image = np.nan_to_num(display_image, nan=1.0)
-
-    value_sort = np.sort(image_var.flatten())
-    value_sort_max = value_sort[int(value_sort.shape[0] * 0.95)] * 3
-    print(value_sort_max)
-    mylogger.info("-==- DONE -==-")
-
-    mylogger.info("Create figure")
-
-    fig: matplotlib.figure.Figure = plt.figure()
-
-    image_handle = plt.imshow(display_image, vmin=0, vmax=1, cmap="hot")
-
-    mylogger.info("Add controls")
-
-    def next_frame(
-        i: float, images: np.ndarray, image_handle: matplotlib.image.AxesImage
-    ) -> None:
-        nonlocal threshold
-        threshold = i
-
-        display_image: np.ndarray = images.copy()
-        display_image[..., 2] = display_image[..., 0]
-        mask: np.ndarray = np.where(images[..., 2] >= i, 1.0, np.nan)[..., np.newaxis]
-        display_image *= mask
-        display_image = np.nan_to_num(display_image, nan=1.0)
-
-        image_handle.set_data(display_image)
-        return
-
-    def on_clicked_accept(event: matplotlib.backend_bases.MouseEvent) -> None:
-        nonlocal threshold
-        nonlocal image_3color
-        nonlocal path
-        nonlocal mylogger
-        nonlocal heartbeat_mask_file
-        nonlocal heartbeat_mask_threshold_file
-
-        mylogger.info(f"Threshold: {threshold}")
-
-        mask: np.ndarray = image_3color[..., 2] >= threshold
-        mylogger.info(f"Save mask to: {heartbeat_mask_file}")
-        np.save(heartbeat_mask_file, mask)
-        mylogger.info(f"Save threshold to: {heartbeat_mask_threshold_file}")
-        np.save(heartbeat_mask_threshold_file, np.array([threshold]))
-        exit()
-
-    def on_clicked_cancel(event: matplotlib.backend_bases.MouseEvent) -> None:
-        exit()
-
-    axfreq = fig.add_axes(rect=(0.4, 0.9, 0.3, 0.03))
-    slice_slider = Slider(
-        ax=axfreq,
-        label="Threshold",
-        valmin=0,
-        valmax=value_sort_max,
-        valinit=threshold,
-        valstep=value_sort_max / 1000.0,
-    )
-    axbutton_accept = fig.add_axes(rect=(0.3, 0.85, 0.2, 0.04))
-    button_accept = Button(
-        ax=axbutton_accept, label="Accept", image=None, color="0.85", hovercolor="0.95"
-    )
-    button_accept.on_clicked(on_clicked_accept)  # type: ignore
-
-    axbutton_cancel = fig.add_axes(rect=(0.55, 0.85, 0.2, 0.04))
-    button_cancel = Button(
-        ax=axbutton_cancel, label="Cancel", image=None, color="0.85", hovercolor="0.95"
-    )
-    button_cancel.on_clicked(on_clicked_cancel)  # type: ignore
-
-    slice_slider.on_changed(
-        partial(next_frame, images=image_3color, image_handle=image_handle)
-    )
-
-    mylogger.info("Display")
-    plt.show()
-
-
-if __name__ == "__main__":
-    argh.dispatch_command(main)