diff --git a/stage_1_get_ref_image.py b/stage_1_get_ref_image.py
deleted file mode 100644
index 0e5b6da..0000000
--- a/stage_1_get_ref_image.py
+++ /dev/null
@@ -1,129 +0,0 @@
-import os
-import torch
-import numpy as np
-import argh
-
-from functions.get_experiments import get_experiments
-from functions.get_trials import get_trials
-from functions.bandpass import bandpass
-from functions.create_logger import create_logger
-from functions.get_torch_device import get_torch_device
-from functions.load_config import load_config
-from functions.data_raw_loader import data_raw_loader
-
-
-def main(*, config_filename: str = "config.json") -> None:
-    mylogger = create_logger(
-        save_logging_messages=True,
-        display_logging_messages=True,
-        log_stage_name="stage_1",
-    )
-
-    config = load_config(mylogger=mylogger, filename=config_filename)
-
-    if config["binning_enable"] and (config["binning_at_the_end"] is False):
-        device: torch.device = torch.device("cpu")
-    else:
-        device = get_torch_device(mylogger, config["force_to_cpu"])
-
-    raw_data_path: str = os.path.join(
-        config["basic_path"],
-        config["recoding_data"],
-        config["mouse_identifier"],
-        config["raw_path"],
-    )
-
-    mylogger.info(f"Using data path: {raw_data_path}")
-
-    first_experiment_id: int = int(get_experiments(raw_data_path).min())
-    first_trial_id: int = int(get_trials(raw_data_path, first_experiment_id).min())
-
-    meta_channels: list[str]
-    meta_mouse_markings: str
-    meta_recording_date: str
-    meta_stimulation_times: dict
-    meta_experiment_names: dict
-    meta_trial_recording_duration: float
-    meta_frame_time: float
-    meta_mouse: str
-    data: torch.Tensor
-
-    if config["binning_enable"] and (config["binning_at_the_end"] is False):
-        force_to_cpu_memory: bool = True
-    else:
-        force_to_cpu_memory = False
-
-    mylogger.info("Loading data")
-
-    (
-        meta_channels,
-        meta_mouse_markings,
-        meta_recording_date,
-        meta_stimulation_times,
-        meta_experiment_names,
-        meta_trial_recording_duration,
-        meta_frame_time,
-        meta_mouse,
-        data,
-    ) = data_raw_loader(
-        raw_data_path=raw_data_path,
-        mylogger=mylogger,
-        experiment_id=first_experiment_id,
-        trial_id=first_trial_id,
-        device=device,
-        force_to_cpu_memory=force_to_cpu_memory,
-        config=config,
-    )
-    mylogger.info("-==- Done -==-")
-
-    output_path = config["ref_image_path"]
-    mylogger.info(f"Create directory {output_path} in the case it does not exist")
-    os.makedirs(output_path, exist_ok=True)
-
-    mylogger.info("Reference images")
-    for i in range(0, len(meta_channels)):
-        temp_path: str = os.path.join(output_path, meta_channels[i] + ".npy")
-        mylogger.info(f"Extract and save: {temp_path}")
-        frame_id: int = data.shape[-2] // 2
-        mylogger.info(f"Will use frame id: {frame_id}")
-        ref_image: np.ndarray = (
-            data[:, :, frame_id, meta_channels.index(meta_channels[i])]
-            .clone()
-            .cpu()
-            .numpy()
-        )
-        np.save(temp_path, ref_image)
-    mylogger.info("-==- Done -==-")
-
-    sample_frequency: float = 1.0 / meta_frame_time
-    mylogger.info(
-        (
-            f"Heartbeat power {config['lower_freqency_bandpass']} Hz"
-            f" - {config['upper_freqency_bandpass']} Hz,"
-            f" sample-rate: {sample_frequency},"
-            f" skipping the first {config['skip_frames_in_the_beginning']} frames"
-        )
-    )
-
-    for i in range(0, len(meta_channels)):
-        temp_path = os.path.join(output_path, meta_channels[i] + "_var.npy")
-        mylogger.info(f"Extract and save: {temp_path}")
-
-        heartbeat_ts: torch.Tensor = bandpass(
-            data=data[..., i],
-            low_frequency=config["lower_freqency_bandpass"],
-            high_frequency=config["upper_freqency_bandpass"],
-            fs=sample_frequency,
-            filtfilt_chuck_size=10,
-        )
-
-        heartbeat_power = heartbeat_ts[
-            ..., config["skip_frames_in_the_beginning"] :
-        ].var(dim=-1)
-        np.save(temp_path, heartbeat_power)
-
-    mylogger.info("-==- Done -==-")
-
-
-if __name__ == "__main__":
-    argh.dispatch_command(main)