diff --git a/.gitignore b/.gitignore
index 112f2ee..1916182 100644
--- a/.gitignore
+++ b/.gitignore
@@ -2,3 +2,7 @@
 *.csv
 *.xls
 *.png
+*.mp3
+__pycache__
+flagged
+*.DS_Store
diff --git a/Dockerfile b/Dockerfile
index 501f2df..a5423ba 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -1,6 +1,6 @@
 FROM python:3.10.15-slim-bullseye
 
-RUN apt-get update && apt-get install -y git curl
+RUN apt-get update && apt-get install -y git curl ffmpeg
 RUN git config --global --add safe.directory /app
 RUN python3 -m pip install --upgrade pip
 RUN python3 -m pip install poetry \
diff --git a/lab_tool_webui.py b/lab_tool_webui.py
index f226e4a..fdd9fa8 100644
--- a/lab_tool_webui.py
+++ b/lab_tool_webui.py
@@ -1,98 +1,11 @@
-import sys
-import numpy as np
-import matplotlib.pyplot as plt
-import math
-import pandas as pd
 import gradio as gr
-import tempfile
-
-
-# CSVファイルから信号データを読み込む
-def load_signal(file_path, column_name):
-    try:
-        df = pd.read_csv(file_path)
-        signal = df[column_name].values
-        return signal
-    except FileNotFoundError as e:
-        print(f"Error: {e}", file=sys.stderr)
-        return []
-    except KeyError as e:
-        print(f"Column '{column_name}' not found in the file. ({e})", file=sys.stderr)
-        return []
-
-
-# モルレーウェーブレット関数
-def morlet(x, f, width):
-    sf = f / width
-    st = 1 / (2 * math.pi * sf)
-    A = 1 / (st * math.sqrt(2 * math.pi))
-    h = -np.power(x, 2) / (2 * st**2)
-    co1 = 1j * 2 * math.pi * f * x
-    return A * np.exp(co1) * np.exp(h)
-
-
-# 連続ウェーブレット変換
-def continuous_wavelet_transform(Fs, data, fmax, width=48, wavelet_R=0.5):
-    Ts = 1 / Fs
-    wavelet_length = np.arange(-wavelet_R, wavelet_R, Ts)
-    data_length = len(data)
-    cwt_result = np.zeros([fmax, data_length])
-
-    for i in range(fmax):
-        conv_result = np.convolve(data, morlet(wavelet_length, i + 1, width), mode='same')
-        cwt_result[i, :] = (2 * np.abs(conv_result) / Fs) ** 2
-
-    return cwt_result
-
-
-# 連続ウェーブレット変換結果をカラーマップとしてプロット
-def plot_cwt(cwt_result, time_data, fmax):
-    plt.imshow(cwt_result, cmap='jet', aspect='auto',
-               extent=[time_data[0], time_data[-1], 0, fmax],
-               vmax=abs(cwt_result).max(), vmin=-abs(cwt_result).max())
-    plt.xlabel("Time [sec]")
-    plt.ylabel("Frequency [Hz]")
-    plt.colorbar(label="Power")
-    plt.clim(-5, 5)
-
-
-# グラフ描画とCWTの処理を行う関数
-def wavelet_ui(uploaded_file, Fs, fmax, column_name, start_time, end_time):
-    filepath = uploaded_file.name
-    signal = load_signal(filepath, column_name)
-
-    if len(signal) == 0:
-        return None, None
-
-    # 時間データを計算
-    t_data = np.arange(0, len(signal) / Fs, 1 / Fs)
-
-    # スライダーの範囲に基づいてデータをフィルタリング
-    start_idx = int(start_time * Fs)
-    end_idx = int(end_time * Fs)
-    signal = signal[start_idx:end_idx]
-    t_data = t_data[start_idx:end_idx]
-
-    signal_filename = tempfile.NamedTemporaryFile(delete=False, suffix='.png').name
-    plt.figure(dpi=200)
-    plt.title("Signal")
-    plt.plot(t_data, signal)
-    plt.xlim(start_time, end_time)
-    plt.xlabel("Time [sec]")
-    plt.ylabel("Voltage [uV]")
-    plt.savefig(signal_filename)
-
-    cwt_signal_filename = tempfile.NamedTemporaryFile(delete=False, suffix='.png').name
-    cwt_signal = continuous_wavelet_transform(Fs=Fs, data=signal, fmax=fmax)
-    plt.figure(dpi=200)
-    plot_cwt(cwt_signal, t_data, fmax)
-    plt.savefig(cwt_signal_filename)
-
-    return cwt_signal_filename, signal_filename
+from lab_tools import wavelet
+from lab_tools import labutils
+from lab_tools import analyze1f
 
 
 def update_slider_range(filepath):
-    timestamp = load_signal(filepath, "Timestamp")
+    timestamp = labutils.load_signal(filepath, "Timestamp")
     max_value = float(timestamp[len(timestamp) - 1])
     min_value = float(timestamp[0])
 
@@ -121,6 +34,20 @@ def update_slider_range(filepath):
                 wavelet_image = gr.Image(type="filepath", label="Wavelet")
                 signal_image = gr.Image(type="filepath", label="Signal")
 
-        submit_button.click(wavelet_ui, inputs=[file_input, fs_slider, fmax_slider, column_dropdown, start_time, end_time], outputs=[wavelet_image, signal_image])
+        submit_button.click(wavelet.wavelet_ui, inputs=[file_input, fs_slider, fmax_slider, column_dropdown, start_time, end_time], outputs=[wavelet_image, signal_image])
+
+    with gr.Tab("1f Noise Search"):
+        with gr.Row():
+            with gr.Column():
+                file_input = gr.Text(label="YouTubeのリンクを貼り付けてください。")
+                submit_button = gr.Button("計算開始")
+
+            with gr.Column():
+                caption = gr.Text(label="動画タイトル")
+                result = gr.Image(type="filepath", label="Wavelet")
+
+        submit_button.click(analyze1f.analyze_1f_noise, inputs=[file_input], outputs=[caption, result])
+
+
 if __name__ == "__main__":
     main_ui.queue().launch(server_name="0.0.0.0")
diff --git a/lab_tools/analyze1f.py b/lab_tools/analyze1f.py
new file mode 100644
index 0000000..bb2c82c
--- /dev/null
+++ b/lab_tools/analyze1f.py
@@ -0,0 +1,69 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from pydub import AudioSegment
+from scipy.fftpack import fft
+import yt_dlp
+import os
+import tempfile
+
+
+def download_youtube(youtube_url: str) -> str:
+    ydl_opts = {
+        'postprocessors': [
+            {
+                'key': 'FFmpegExtractAudio',
+                'preferredcodec': 'mp3',
+                'preferredquality': '128',
+            }
+        ],
+        'outtmpl': '%(title)s.%(ext)s'
+    }
+
+    with yt_dlp.YoutubeDL(ydl_opts) as ydl:
+        info_dict = ydl.extract_info(youtube_url, download=True)
+        file_path = ydl.prepare_filename(info_dict)
+        filename, _ = os.path.splitext(file_path)
+        filename += ".mp3"
+        print(f"Downloaded file path: {filename}")
+
+    return filename
+
+
+def analyze_1f_noise(youtube_url: str):
+    filename = download_youtube(youtube_url)
+    audio = AudioSegment.from_mp3(filename)
+    os.remove(filename)
+    data = np.array(audio.get_array_of_samples())
+    sample_rate = audio.frame_rate
+
+    if audio.channels > 1:
+        data = data.reshape((-1, audio.channels)).mean(axis=1)
+
+    N = len(data)
+    T = 1.0 / sample_rate
+    yf = fft(data)
+    xf = np.fft.fftfreq(N, T)[:N//2]
+
+    power_spectrum = 2.0/N * np.abs(yf[:N//2])
+
+    xf_log = xf[1:]
+    power_spectrum_log = power_spectrum[1:]
+
+    graphfile_path = tempfile.NamedTemporaryFile(delete=False, suffix='.png').name
+
+    # グラフの描画
+    plt.figure(figsize=(10, 6))
+    plt.plot(xf_log, power_spectrum_log)
+    plt.xscale('log')
+    plt.yscale('log')
+
+    plt.title('Power Spectrum (Log Scale)')
+    plt.xlabel('Frequency (Hz)')
+    plt.ylabel('Power')
+    plt.grid(True, which="both", ls="--")
+    plt.xlim([1, sample_rate // 2])
+    plt.savefig(graphfile_path)
+
+    filename, _ = os.path.splitext(filename)
+
+    return filename, graphfile_path
diff --git a/lab_tools/highpass.py b/lab_tools/highpass.py
new file mode 100644
index 0000000..fc8ea29
--- /dev/null
+++ b/lab_tools/highpass.py
@@ -0,0 +1,62 @@
+import numpy as np
+from scipy import signal
+from scipy import fftpack
+from typing import Tuple
+
+
+def highpass_filter(signal_data: np.ndarray, samplerate: float, fp: float, fs: float, gpass: float, gstop: float) -> np.ndarray:
+    fn = samplerate / 2
+    wp = fp / fn
+    ws = fs / fn
+
+    N, Wn = signal.buttord(wp, ws, gpass, gstop)
+    b, a = signal.butter(N, Wn, "high")
+
+    filtered_signal = signal.filtfilt(b, a, signal_data)
+
+    return filtered_signal
+
+
+def overlap_frames(signal_data: np.ndarray, samplerate: float, frame_size: int, overlap: float) -> Tuple[np.ndarray, int]:
+    total_duration = len(signal_data) / samplerate
+    frame_duration = frame_size / samplerate
+    step_size = frame_size * (1 - overlap / 100)
+
+    num_frames = int((total_duration - (frame_duration * overlap / 100)) / (frame_duration * (1 - overlap / 100)))
+
+    frames = []
+
+    for i in range(num_frames):
+        start_idx = int(step_size * i)
+        frames.append(signal_data[start_idx:start_idx + frame_size])
+
+    return np.array(frames), num_frames
+
+
+def hanning(signal_data: np.ndarray, frame_size: int, num_frames: int) -> Tuple[np.ndarray, float]:
+    han = signal.get_window('hann', frame_size)
+    acf = 1 / (sum(han) / frame_size)
+
+    for i in range(num_frames):
+        signal_data[i] *= han
+
+    return signal_data, acf
+
+
+def fft_ave(signal_data: np.ndarray, samplerate: float, frame_size: int, num_frames: int, acf: float):
+    fft_array = []
+    for i in range(num_frames):
+        fft_result = fftpack.fft(signal_data[i]) / frame_size
+        fft_array.append(acf * np.abs(fft_result))
+
+    fft_axis = np.linspace(0, samplerate / 2, frame_size // 2)
+    fft_array = np.array(fft_array)[:, :frame_size // 2]
+    fft_mean = np.mean(fft_array, axis=0)
+
+    return fft_array, fft_mean, fft_axis
+
+
+def linear_to_db(x: float, y: float) -> float:
+    if y == 0:
+        raise ValueError("y cannot be zero in logarithmic conversion")
+    return 20 * np.log10(x / y)
diff --git a/lab_tools/labutils.py b/lab_tools/labutils.py
new file mode 100644
index 0000000..82940ca
--- /dev/null
+++ b/lab_tools/labutils.py
@@ -0,0 +1,24 @@
+import sys
+import pandas as pd
+
+
+# CSVファイルから信号データを読み込む
+def load_signal(file_path, column_name):
+    try:
+        with open(file_path, 'r') as file:
+            # データ部分が始まる行を見つける
+            for i, line in enumerate(file):
+                if 'Timestamp' in line:
+                    header_line = i
+                    break
+
+        # 見つけたヘッダー行からデータを読み込む
+        df = pd.read_csv(file_path, skiprows=header_line)
+        signal = df[column_name].values
+        return signal
+    except FileNotFoundError as e:
+        print(f"Error: {e}", file=sys.stderr)
+        return []
+    except KeyError as e:
+        print(f"Column '{column_name}' not found in the file. ({e})", file=sys.stderr)
+        return []
diff --git a/lab_tools/wavelet.py b/lab_tools/wavelet.py
new file mode 100644
index 0000000..b0aab5f
--- /dev/null
+++ b/lab_tools/wavelet.py
@@ -0,0 +1,76 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import math
+import tempfile
+
+from lab_tools import labutils
+
+
+# モルレーウェーブレット関数
+def morlet(x, f, width):
+    sf = f / width
+    st = 1 / (2 * math.pi * sf)
+    A = 1 / (st * math.sqrt(2 * math.pi))
+    h = -np.power(x, 2) / (2 * st**2)
+    co1 = 1j * 2 * math.pi * f * x
+    return A * np.exp(co1) * np.exp(h)
+
+
+# 連続ウェーブレット変換
+def continuous_wavelet_transform(Fs, data, fmax, width=48, wavelet_R=0.5):
+    Ts = 1 / Fs
+    wavelet_length = np.arange(-wavelet_R, wavelet_R, Ts)
+    data_length = len(data)
+    cwt_result = np.zeros([fmax, data_length])
+
+    for i in range(fmax):
+        conv_result = np.convolve(data, morlet(wavelet_length, i + 1, width), mode='same')
+        cwt_result[i, :] = (2 * np.abs(conv_result) / Fs) ** 2
+
+    return cwt_result
+
+
+# 連続ウェーブレット変換結果をカラーマップとしてプロット
+def plot_cwt(cwt_result, time_data, fmax):
+    plt.imshow(cwt_result, cmap='jet', aspect='auto',
+               extent=[time_data[0], time_data[-1], 0, fmax],
+               vmax=abs(cwt_result).max(), vmin=-abs(cwt_result).max())
+    plt.xlabel("Time [sec]")
+    plt.ylabel("Frequency [Hz]")
+    plt.colorbar(label="Power")
+    plt.clim(-5, 5)
+
+
+# グラフ描画とCWTの処理を行う関数
+def wavelet_ui(uploaded_file, Fs, fmax, column_name, start_time, end_time):
+    filepath = uploaded_file.name
+    signal = labutils.load_signal(filepath, column_name)
+
+    if len(signal) == 0:
+        return None, None
+
+    # 時間データを計算
+    t_data = np.arange(0, len(signal) / Fs, 1 / Fs)
+
+    # スライダーの範囲に基づいてデータをフィルタリング
+    start_idx = int(start_time * Fs)
+    end_idx = int(end_time * Fs)
+    signal = signal[start_idx:end_idx]
+    t_data = t_data[start_idx:end_idx]
+
+    signal_filename = tempfile.NamedTemporaryFile(delete=False, suffix='.png').name
+    plt.figure(dpi=200)
+    plt.title("Signal")
+    plt.plot(t_data, signal)
+    plt.xlim(start_time, end_time)
+    plt.xlabel("Time [sec]")
+    plt.ylabel("Voltage [uV]")
+    plt.savefig(signal_filename)
+
+    cwt_signal_filename = tempfile.NamedTemporaryFile(delete=False, suffix='.png').name
+    cwt_signal = continuous_wavelet_transform(Fs=Fs, data=signal, fmax=fmax)
+    plt.figure(dpi=200)
+    plot_cwt(cwt_signal, t_data, fmax)
+    plt.savefig(cwt_signal_filename)
+
+    return cwt_signal_filename, signal_filename
diff --git a/poetry.lock b/poetry.lock
index d60b057..0aa031d 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand.
 
 [[package]]
 name = "aiofiles"
@@ -22,9 +22,6 @@ files = [
     {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
 ]
 
-[package.dependencies]
-typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.9\""}
-
 [[package]]
 name = "anyio"
 version = "4.5.0"
@@ -47,6 +44,179 @@ doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)",
 test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.21.0b1)"]
 trio = ["trio (>=0.26.1)"]
 
+[[package]]
+name = "brotli"
+version = "1.1.0"
+description = "Python bindings for the Brotli compression library"
+optional = false
+python-versions = "*"
+files = [
+    {file = "Brotli-1.1.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:e1140c64812cb9b06c922e77f1c26a75ec5e3f0fb2bf92cc8c58720dec276752"},
+    {file = "Brotli-1.1.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:c8fd5270e906eef71d4a8d19b7c6a43760c6abcfcc10c9101d14eb2357418de9"},
+    {file = "Brotli-1.1.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1ae56aca0402a0f9a3431cddda62ad71666ca9d4dc3a10a142b9dce2e3c0cda3"},
+    {file = "Brotli-1.1.0-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:43ce1b9935bfa1ede40028054d7f48b5469cd02733a365eec8a329ffd342915d"},
+    {file = "Brotli-1.1.0-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:7c4855522edb2e6ae7fdb58e07c3ba9111e7621a8956f481c68d5d979c93032e"},
+    {file = "Brotli-1.1.0-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:38025d9f30cf4634f8309c6874ef871b841eb3c347e90b0851f63d1ded5212da"},
+    {file = "Brotli-1.1.0-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:e6a904cb26bfefc2f0a6f240bdf5233be78cd2488900a2f846f3c3ac8489ab80"},
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+    {file = "Brotli-1.1.0-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:5dab0844f2cf82be357a0eb11a9087f70c5430b2c241493fc122bb6f2bb0917c"},
+    {file = "Brotli-1.1.0-cp310-cp310-musllinux_1_2_i686.whl", hash = "sha256:e4fe605b917c70283db7dfe5ada75e04561479075761a0b3866c081d035b01c1"},
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+    {file = "Brotli-1.1.0-cp310-cp310-win_amd64.whl", hash = "sha256:0c6244521dda65ea562d5a69b9a26120769b7a9fb3db2fe9545935ed6735b128"},
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 [[package]]
 name = "charset-normalizer"
 version = "3.4.0"
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-
-[package.dependencies]
-numpy = ">=1.16"
-
-[package.extras]
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-docs = ["furo", "sphinx-copybutton"]
-mypy = ["contourpy[bokeh,docs]", "docutils-stubs", "mypy (==1.2.0)", "types-Pillow"]
-test = ["Pillow", "contourpy[test-no-images]", "matplotlib"]
-test-no-images = ["pytest", "pytest-cov", "wurlitzer"]
-
 [[package]]
 name = "contourpy"
 version = "1.1.1"
@@ -693,9 +880,6 @@ files = [
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-[package.dependencies]
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-
 [package.extras]
 check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"]
 cover = ["pytest-cov"]
@@ -997,7 +1181,6 @@ files = [
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 cycler = ">=0.10"
 fonttools = ">=4.22.0"
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 kiwisolver = ">=1.0.1"
 numpy = ">=1.20,<2"
 packaging = ">=20.0"
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 ]
 
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+optional = false
+python-versions = ">=3.7"
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+
 [[package]]
 name = "numpy"
 version = "1.24.4"
@@ -1166,9 +1360,8 @@ files = [
 
 [package.dependencies]
 numpy = [
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     {version = ">=1.23.2", markers = "python_version >= \"3.11\""},
+    {version = ">=1.21.0", markers = "python_version >= \"3.10\" and python_version < \"3.11\""},
 ]
 python-dateutil = ">=2.8.2"
 pytz = ">=2020.1"
@@ -1294,6 +1487,58 @@ tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "pa
 typing = ["typing-extensions"]
 xmp = ["defusedxml"]
 
+[[package]]
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+version = "2.22"
+description = "C parser in Python"
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+
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+description = "Cryptographic library for Python"
+optional = false
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
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+    {file = "pycryptodomex-3.21.0-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0df2608682db8279a9ebbaf05a72f62a321433522ed0e499bc486a6889b96bf3"},
+    {file = "pycryptodomex-3.21.0-pp310-pypy310_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:5823d03e904ea3e53aebd6799d6b8ec63b7675b5d2f4a4bd5e3adcb512d03b37"},
+    {file = "pycryptodomex-3.21.0-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:27e84eeff24250ffec32722334749ac2a57a5fd60332cd6a0680090e7c42877e"},
+    {file = "pycryptodomex-3.21.0-pp39-pypy39_pp73-macosx_10_15_x86_64.whl", hash = "sha256:8ef436cdeea794015263853311f84c1ff0341b98fc7908e8a70595a68cefd971"},
+    {file = "pycryptodomex-3.21.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7a1058e6dfe827f4209c5cae466e67610bcd0d66f2f037465daa2a29d92d952b"},
+    {file = "pycryptodomex-3.21.0-pp39-pypy39_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:9ba09a5b407cbb3bcb325221e346a140605714b5e880741dc9a1e9ecf1688d42"},
+    {file = "pycryptodomex-3.21.0-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:8a9d8342cf22b74a746e3c6c9453cb0cfbb55943410e3a2619bd9164b48dc9d9"},
+    {file = "pycryptodomex-3.21.0.tar.gz", hash = "sha256:222d0bd05381dd25c32dd6065c071ebf084212ab79bab4599ba9e6a3e0009e6c"},
+]
+
 [[package]]
 name = "pydantic"
 version = "2.9.2"
@@ -1308,10 +1553,7 @@ files = [
 [package.dependencies]
 annotated-types = ">=0.6.0"
 pydantic-core = "2.23.4"
-typing-extensions = [
-    {version = ">=4.6.1", markers = "python_version < \"3.13\""},
-    {version = ">=4.12.2", markers = "python_version >= \"3.13\""},
-]
+typing-extensions = {version = ">=4.6.1", markers = "python_version < \"3.13\""}
 
 [package.extras]
 email = ["email-validator (>=2.0.0)"]
@@ -1622,6 +1864,48 @@ files = [
     {file = "ruff-0.6.9.tar.gz", hash = "sha256:b076ef717a8e5bc819514ee1d602bbdca5b4420ae13a9cf61a0c0a4f53a2baa2"},
 ]
 
+[[package]]
+name = "scipy"
+version = "1.14.0"
+description = "Fundamental algorithms for scientific computing in Python"
+optional = false
+python-versions = ">=3.10"
+files = [
+    {file = "scipy-1.14.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:7e911933d54ead4d557c02402710c2396529540b81dd554fc1ba270eb7308484"},
+    {file = "scipy-1.14.0-cp310-cp310-macosx_12_0_arm64.whl", hash = "sha256:687af0a35462402dd851726295c1a5ae5f987bd6e9026f52e9505994e2f84ef6"},
+    {file = "scipy-1.14.0-cp310-cp310-macosx_14_0_arm64.whl", hash = "sha256:07e179dc0205a50721022344fb85074f772eadbda1e1b3eecdc483f8033709b7"},
+    {file = "scipy-1.14.0-cp310-cp310-macosx_14_0_x86_64.whl", hash = "sha256:6a9c9a9b226d9a21e0a208bdb024c3982932e43811b62d202aaf1bb59af264b1"},
+    {file = "scipy-1.14.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:076c27284c768b84a45dcf2e914d4000aac537da74236a0d45d82c6fa4b7b3c0"},
+    {file = "scipy-1.14.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:42470ea0195336df319741e230626b6225a740fd9dce9642ca13e98f667047c0"},
+    {file = "scipy-1.14.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:176c6f0d0470a32f1b2efaf40c3d37a24876cebf447498a4cefb947a79c21e9d"},
+    {file = "scipy-1.14.0-cp310-cp310-win_amd64.whl", hash = "sha256:ad36af9626d27a4326c8e884917b7ec321d8a1841cd6dacc67d2a9e90c2f0359"},
+    {file = "scipy-1.14.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:6d056a8709ccda6cf36cdd2eac597d13bc03dba38360f418560a93050c76a16e"},
+    {file = "scipy-1.14.0-cp311-cp311-macosx_12_0_arm64.whl", hash = "sha256:f0a50da861a7ec4573b7c716b2ebdcdf142b66b756a0d392c236ae568b3a93fb"},
+    {file = "scipy-1.14.0-cp311-cp311-macosx_14_0_arm64.whl", hash = "sha256:94c164a9e2498e68308e6e148646e486d979f7fcdb8b4cf34b5441894bdb9caf"},
+    {file = "scipy-1.14.0-cp311-cp311-macosx_14_0_x86_64.whl", hash = "sha256:a7d46c3e0aea5c064e734c3eac5cf9eb1f8c4ceee756262f2c7327c4c2691c86"},
+    {file = "scipy-1.14.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9eee2989868e274aae26125345584254d97c56194c072ed96cb433f32f692ed8"},
+    {file = "scipy-1.14.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9e3154691b9f7ed73778d746da2df67a19d046a6c8087c8b385bc4cdb2cfca74"},
+    {file = "scipy-1.14.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:c40003d880f39c11c1edbae8144e3813904b10514cd3d3d00c277ae996488cdb"},
+    {file = "scipy-1.14.0-cp311-cp311-win_amd64.whl", hash = "sha256:5b083c8940028bb7e0b4172acafda6df762da1927b9091f9611b0bcd8676f2bc"},
+    {file = "scipy-1.14.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:bff2438ea1330e06e53c424893ec0072640dac00f29c6a43a575cbae4c99b2b9"},
+    {file = "scipy-1.14.0-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:bbc0471b5f22c11c389075d091d3885693fd3f5e9a54ce051b46308bc787e5d4"},
+    {file = "scipy-1.14.0-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:64b2ff514a98cf2bb734a9f90d32dc89dc6ad4a4a36a312cd0d6327170339eb0"},
+    {file = "scipy-1.14.0-cp312-cp312-macosx_14_0_x86_64.whl", hash = "sha256:7d3da42fbbbb860211a811782504f38ae7aaec9de8764a9bef6b262de7a2b50f"},
+    {file = "scipy-1.14.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d91db2c41dd6c20646af280355d41dfa1ec7eead235642178bd57635a3f82209"},
+    {file = "scipy-1.14.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a01cc03bcdc777c9da3cfdcc74b5a75caffb48a6c39c8450a9a05f82c4250a14"},
+    {file = "scipy-1.14.0-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:65df4da3c12a2bb9ad52b86b4dcf46813e869afb006e58be0f516bc370165159"},
+    {file = "scipy-1.14.0-cp312-cp312-win_amd64.whl", hash = "sha256:4c4161597c75043f7154238ef419c29a64ac4a7c889d588ea77690ac4d0d9b20"},
+    {file = "scipy-1.14.0.tar.gz", hash = "sha256:b5923f48cb840380f9854339176ef21763118a7300a88203ccd0bdd26e58527b"},
+]
+
+[package.dependencies]
+numpy = ">=1.23.5,<2.3"
+
+[package.extras]
+dev = ["cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy (==1.10.0)", "pycodestyle", "pydevtool", "rich-click", "ruff (>=0.0.292)", "types-psutil", "typing_extensions"]
+doc = ["jupyterlite-pyodide-kernel", "jupyterlite-sphinx (>=0.13.1)", "jupytext", "matplotlib (>=3.5)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (>=0.15.2)", "sphinx (>=5.0.0)", "sphinx-design (>=0.4.0)"]
+test = ["Cython", "array-api-strict", "asv", "gmpy2", "hypothesis (>=6.30)", "meson", "mpmath", "ninja", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
+
 [[package]]
 name = "semantic-version"
 version = "2.10.0"
@@ -1683,7 +1967,6 @@ files = [
 
 [package.dependencies]
 anyio = ">=3.4.0,<5"
-typing-extensions = {version = ">=3.10.0", markers = "python_version < \"3.10\""}
 
 [package.extras]
 full = ["httpx (>=0.22.0)", "itsdangerous", "jinja2", "python-multipart (>=0.0.7)", "pyyaml"]
@@ -1876,25 +2159,37 @@ files = [
 ]
 
 [[package]]
-name = "zipp"
-version = "3.20.2"
-description = "Backport of pathlib-compatible object wrapper for zip files"
+name = "yt-dlp"
+version = "2024.7.7"
+description = "A feature-rich command-line audio/video downloader"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "zipp-3.20.2-py3-none-any.whl", hash = "sha256:a817ac80d6cf4b23bf7f2828b7cabf326f15a001bea8b1f9b49631780ba28350"},
-    {file = "zipp-3.20.2.tar.gz", hash = "sha256:bc9eb26f4506fda01b81bcde0ca78103b6e62f991b381fec825435c836edbc29"},
+    {file = "yt_dlp-2024.7.7-py3-none-any.whl", hash = "sha256:2e90abeadc0199c787b1b4a3e0a1c8ed9d7c9f824f58da88467a1b30ed745e07"},
+    {file = "yt_dlp-2024.7.7.tar.gz", hash = "sha256:2a0f89423d25d47db949925db5bd2c6f651960ae93dbbf5b3ed61cf3a4078ce5"},
 ]
 
+[package.dependencies]
+brotli = {version = "*", markers = "implementation_name == \"cpython\""}
+brotlicffi = {version = "*", markers = "implementation_name != \"cpython\""}
+certifi = "*"
+mutagen = "*"
+pycryptodomex = "*"
+requests = ">=2.32.2,<3"
+urllib3 = ">=1.26.17,<3"
+websockets = ">=12.0"
+
 [package.extras]
-check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"]
-cover = ["pytest-cov"]
-doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
-enabler = ["pytest-enabler (>=2.2)"]
-test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"]
-type = ["pytest-mypy"]
+build = ["build", "hatchling", "pip", "setuptools", "wheel"]
+curl-cffi = ["curl-cffi (==0.5.10)"]
+dev = ["autopep8 (>=2.0,<3.0)", "pre-commit", "pytest (>=8.1,<9.0)", "ruff (>=0.5.0,<0.6.0)"]
+py2exe = ["py2exe (>=0.12)"]
+pyinstaller = ["pyinstaller (>=6.7.0)"]
+secretstorage = ["cffi", "secretstorage"]
+static-analysis = ["autopep8 (>=2.0,<3.0)", "ruff (>=0.5.0,<0.6.0)"]
+test = ["pytest (>=8.1,<9.0)"]
 
 [metadata]
 lock-version = "2.0"
-python-versions = "^3.8"
-content-hash = "d72cefc6dd324993f4733c95f2fb04bdef56d998121325db81a68627ae8062db"
+python-versions = ">=3.10,<3.12"
+content-hash = "2bb4e05993ac956f9ed0c7c5eb5ac3c87c4ceb9bfa3506347ca2e6a7a296b550"
diff --git a/pyproject.toml b/pyproject.toml
index afb2004..9718d46 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -5,9 +5,10 @@ description = "research"
 authors = ["Taiga Takano <ttttghghnb554z@outlook.jp>"]
 
 [tool.poetry.dependencies]
-python = "^3.8"
+python = ">=3.10,<3.12"
 gradio = "4.44.0"
-
+yt-dlp = "2024.07.07"
+scipy = "1.14.0"
 
 [build-system]
 requires = ["poetry-core>=1.0.0"]
diff --git a/run.py b/run.py
new file mode 100644
index 0000000..0c1ff56
--- /dev/null
+++ b/run.py
@@ -0,0 +1,71 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from pydub import AudioSegment
+from scipy.fftpack import fft
+import yt_dlp
+import os
+
+url = "https://youtu.be/Ci_zad39Uhw?si=AhB9ArgrWUvbPiv5"
+
+ydl_opts = {
+    'postprocessors': [
+        {
+            'key': 'FFmpegExtractAudio',
+            'preferredcodec': 'mp3',
+            'preferredquality': '128',
+        }
+    ],
+    'outtmpl': '%(title)s.%(ext)s'  # ファイル名のテンプレート
+}
+
+with yt_dlp.YoutubeDL(ydl_opts) as ydl:
+    info_dict = ydl.extract_info(url, download=True)
+    file_path = ydl.prepare_filename(info_dict)
+    filename, ext = os.path.splitext(file_path)
+    filename += ".mp3"
+    print(f"Downloaded file path: {filename}")
+
+# MP3ファイルの読み込みとWAV形式への変換
+audio = AudioSegment.from_mp3(filename)
+os.remove(filename)
+data = np.array(audio.get_array_of_samples())
+sample_rate = audio.frame_rate
+
+# モノラル変換（ステレオの場合）
+if audio.channels > 1:
+    data = data.reshape((-1, audio.channels)).mean(axis=1)
+
+# フーリエ変換の実行
+N = len(data)
+T = 1.0 / sample_rate
+yf = fft(data)
+xf = np.fft.fftfreq(N, T)[:N//2]
+
+# パワースペクトルの計算
+power_spectrum = 2.0/N * np.abs(yf[:N//2])
+
+# プロット用に周波数とパワーを制限
+xf_log = xf[1:]  # 0Hzを除去 (ログスケールでは0が扱えないため)
+power_spectrum_log = power_spectrum[1:]
+
+# 縦軸の範囲を指定（例: 0から100まで）
+y_min = 0
+y_max = 1000
+
+# グラフの描画
+plt.figure(figsize=(10, 6))
+plt.plot(xf_log, power_spectrum_log)
+plt.xscale('log')
+plt.yscale('log')
+
+plt.title('Power Spectrum (Log Scale)')
+plt.xlabel('Frequency (Hz)')
+plt.ylabel('Power')
+plt.grid(True, which="both", ls="--")
+plt.xlim([1, sample_rate // 2])  # 1Hz から Nyquist周波数 (sample_rate/2) まで
+
+# 縦軸の範囲を指定
+# plt.ylim([y_min, y_max])
+
+
+plt.show()
diff --git a/run.sh b/run.sh
index 7af3621..061f6fd 100755
--- a/run.sh
+++ b/run.sh
@@ -8,7 +8,7 @@ ROOT="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
 if [[ $(id -u) -eq 0 ]]; then
     echo "This script cannot be executed with root privileges."
     echo "Please re-run without sudo and follow instructions to configure docker for non-root user if needed."
-    exit 1
+    # exit 1
 fi
 
 # Check if user can run docker without root.
@@ -17,14 +17,14 @@ if [[ ! $(groups $USER) =~ $RE ]]; then
     echo "User |$USER| is not a member of the 'docker' group and cannot run docker commands without sudo."
     echo "Run 'sudo usermod -aG docker \$USER && newgrp docker' to add user to 'docker' group, then re-run this script."
     echo "See: https://docs.docker.com/engine/install/linux-postinstall/"
-    exit 1
+    # exit 1
 fi
 
 # Check if able to run docker commands.
 if [[ -z "$(docker ps)" ]] ;  then
     echo "Unable to run docker commands. If you have recently added |$USER| to 'docker' group, you may need to log out and log back in for it to take effect."
     echo "Otherwise, please check your Docker installation."
-    exit 1
+    # exit 1
 fi
 
 PLATFORM="$(uname -m)"
@@ -35,4 +35,7 @@ if [ $PLATFORM = "x86_64" ]; then
     docker run -it --rm -v $ROOT:/app -w /app --network host ghcr.io/moriyalab/lab_tool:latest /bin/bash
 else
     echo "Not Support Platform. Only support x86."
+    docker pull ghcr.io/moriyalab/lab_tool:latest
+    docker run -it --rm -v $ROOT:/app -w /app --network host ghcr.io/moriyalab/lab_tool:latest /bin/bash
+
 fi
\ No newline at end of file
diff --git a/test.py b/test.py
new file mode 100644
index 0000000..5cf94ea
--- /dev/null
+++ b/test.py
@@ -0,0 +1,93 @@
+import pandas as pd
+import sys
+import matplotlib.pyplot as plt
+from lab_tools import highpass
+
+
+def load_signal(file_path, column_name):
+    try:
+        with open(file_path, 'r') as file:
+            # データ部分が始まる行を見つける
+            for i, line in enumerate(file):
+                if 'Timestamp' in line:
+                    header_line = i
+                    break
+
+        # 見つけたヘッダー行からデータを読み込む
+        df = pd.read_csv(file_path, skiprows=header_line)
+        signal = df[column_name].values
+        return signal
+    except FileNotFoundError as e:
+        print(f"Error: {e}", file=sys.stderr)
+        return []
+    except KeyError as e:
+        print(f"Column '{column_name}' not found in the file. ({e})", file=sys.stderr)
+        return []
+
+
+samplerate = 1000
+time_data = load_signal("./test2_143809.csv", "Timestamp")
+signal_data = load_signal("./test2_143809.csv", "Fp1")
+fp = 22                            # 通過域端周波数[Hz]※ベクトル
+fs = 10                              # 阻止域端周波数[Hz]※ベクトル
+gpass = 5                                               # 通過域端最大損失[dB]
+gstop = 40                                              # 阻止域端最小損失[dB]
+Fs = 4096                                               # フレームサイズ
+overlap = 90
+
+data_filt = highpass.highpass_filter(signal_data, samplerate, fp, fs, gpass, gstop)
+
+t_array_org, N_ave_org = highpass.overlap_frames(signal_data, samplerate, Fs, overlap)
+t_array_filt, N_ave_filt = highpass.overlap_frames(signal_data, samplerate, Fs, overlap)
+
+t_array_org, acf_org = highpass.hanning(t_array_org, Fs, N_ave_org)
+t_array_filt, acf_filt = highpass.hanning(t_array_filt, Fs, N_ave_filt)
+
+fft_array_org, fft_mean_org, fft_axis_org = highpass.fft_ave(t_array_org, samplerate, Fs, N_ave_org, acf_org)
+fft_array_filt, fft_mean_filt, fft_axis_filt = highpass.fft_ave(t_array_filt, samplerate, Fs, N_ave_filt, acf_filt)
+
+fft_mean_org = highpass.linear_to_db(fft_mean_org, 2e-5)
+fft_mean_filt = highpass.linear_to_db(fft_mean_filt, 2e-5)
+
+# フォントの種類とサイズを設定する。
+# plt.rcParams['font.size'] = 14
+# plt.rcParams['font.family'] = 'Times New Roman'
+
+# 目盛を内側にする。
+plt.rcParams['xtick.direction'] = 'in'
+plt.rcParams['ytick.direction'] = 'in'
+
+# グラフの上下左右に目盛線を付ける。
+fig = plt.figure(figsize=(20, 10))
+ax1 = fig.add_subplot(211)
+ax1.yaxis.set_ticks_position('both')
+ax1.xaxis.set_ticks_position('both')
+ax2 = fig.add_subplot(212)
+ax2.yaxis.set_ticks_position('both')
+ax2.xaxis.set_ticks_position('both')
+
+# 軸のラベルを設定する。
+ax1.set_xlabel('Time [s]')
+ax1.set_ylabel('V[μV]')
+ax2.set_xlabel('Frequency [Hz]')
+ax2.set_ylabel('Amp[dB]')
+
+# データプロットの準備とともに、ラベルと線の太さ、凡例の設置を行う。
+ax1.plot(time_data, signal_data, label='original', lw=1)
+ax1.plot(time_data, data_filt, label='filtered', lw=1)
+ax2.plot(fft_axis_org, fft_mean_org, label='original', lw=1)
+ax2.plot(fft_axis_filt, fft_mean_filt, label='filtered', lw=1)
+plt.legend()
+
+# 軸のリミットを設定する。
+# ax1.set_xlim(0,1200)
+# ax1.set_xticks(np.arange(0,1201,100))
+# ax2.set_xlim(0, max(fft_axis_org)/2)
+# ax2.set_xticks(np.arange(0,501,10))
+# ax2.set_ylim(-50, 150)
+
+# レイアウト設定
+fig.tight_layout()
+
+# グラフを表示する。
+plt.savefig("./out.png")