From 44aae2569df5d1e4b511de9e0a67d2091de2f730 Mon Sep 17 00:00:00 2001
From: Taiga Takano <ttttghghnb554z@outlook.jp>
Date: Wed, 23 Oct 2024 00:11:01 +0900
Subject: [PATCH 1/2] wip

---
 .gitignore            |  2 +
 lab_tool_webui.py     | 97 ++-----------------------------------------
 lab_tools/highpass.py | 60 ++++++++++++++++++++++++++
 lab_tools/labutils.py | 23 ++++++++++
 lab_tools/wavelet.py  | 75 +++++++++++++++++++++++++++++++++
 test.py               | 96 ++++++++++++++++++++++++++++++++++++++++++
 6 files changed, 260 insertions(+), 93 deletions(-)
 create mode 100644 lab_tools/highpass.py
 create mode 100644 lab_tools/labutils.py
 create mode 100644 lab_tools/wavelet.py
 create mode 100644 test.py

diff --git a/.gitignore b/.gitignore
index 112f2ee..f846f70 100644
--- a/.gitignore
+++ b/.gitignore
@@ -2,3 +2,5 @@
 *.csv
 *.xls
 *.png
+__pycache__
+flagged
\ No newline at end of file
diff --git a/lab_tool_webui.py b/lab_tool_webui.py
index f226e4a..c11cb67 100644
--- a/lab_tool_webui.py
+++ b/lab_tool_webui.py
@@ -1,98 +1,9 @@
-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
 
 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 +32,6 @@ 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])
 if __name__ == "__main__":
     main_ui.queue().launch(server_name="0.0.0.0")
diff --git a/lab_tools/highpass.py b/lab_tools/highpass.py
new file mode 100644
index 0000000..77a5cd8
--- /dev/null
+++ b/lab_tools/highpass.py
@@ -0,0 +1,60 @@
+import numpy as np
+from scipy import signal
+from scipy import fftpack
+from typing import List, 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..97cf40c
--- /dev/null
+++ b/lab_tools/labutils.py
@@ -0,0 +1,23 @@
+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..9552847
--- /dev/null
+++ b/lab_tools/wavelet.py
@@ -0,0 +1,75 @@
+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/test.py b/test.py
new file mode 100644
index 0000000..59f34f7
--- /dev/null
+++ b/test.py
@@ -0,0 +1,96 @@
+import pandas as pd
+import sys
+import numpy as np
+import matplotlib.pyplot as plt
+import sys
+
+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 []
+    
+# print(load_signal("./test1_103313.csv", "Fp2"))
+
+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")
\ No newline at end of file

From 145a95e3b600d4e7dc990816e23be694ed44173e Mon Sep 17 00:00:00 2001
From: Taiga Takano <ttttghghnb554z@outlook.jp>
Date: Wed, 23 Oct 2024 10:04:22 +0900
Subject: [PATCH 2/2] Add 1f analyze

---
 .gitignore             |   4 +-
 Dockerfile             |   2 +-
 lab_tool_webui.py      |  16 ++
 lab_tools/analyze1f.py |  69 ++++++
 lab_tools/highpass.py  |  22 +-
 lab_tools/labutils.py  |   3 +-
 lab_tools/wavelet.py   |   1 +
 poetry.lock            | 475 +++++++++++++++++++++++++++++++++--------
 pyproject.toml         |   5 +-
 run.py                 |  71 ++++++
 run.sh                 |   9 +-
 test.py                |  31 ++-
 12 files changed, 583 insertions(+), 125 deletions(-)
 create mode 100644 lab_tools/analyze1f.py
 create mode 100644 run.py

diff --git a/.gitignore b/.gitignore
index f846f70..1916182 100644
--- a/.gitignore
+++ b/.gitignore
@@ -2,5 +2,7 @@
 *.csv
 *.xls
 *.png
+*.mp3
 __pycache__
-flagged
\ No newline at end of file
+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 c11cb67..fdd9fa8 100644
--- a/lab_tool_webui.py
+++ b/lab_tool_webui.py
@@ -1,6 +1,8 @@
 import gradio as gr
 from lab_tools import wavelet
 from lab_tools import labutils
+from lab_tools import analyze1f
+
 
 def update_slider_range(filepath):
     timestamp = labutils.load_signal(filepath, "Timestamp")
@@ -33,5 +35,19 @@ def update_slider_range(filepath):
                 signal_image = gr.Image(type="filepath", label="Signal")
 
         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
index 77a5cd8..fc8ea29 100644
--- a/lab_tools/highpass.py
+++ b/lab_tools/highpass.py
@@ -1,7 +1,8 @@
 import numpy as np
 from scipy import signal
 from scipy import fftpack
-from typing import List, Tuple
+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
@@ -10,9 +11,9 @@ def highpass_filter(signal_data: np.ndarray, samplerate: float, fp: float, fs: f
 
     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
 
 
@@ -20,25 +21,25 @@ def overlap_frames(signal_data: np.ndarray, samplerate: float, frame_size: 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
 
 
@@ -51,9 +52,10 @@ def fft_ave(signal_data: np.ndarray, samplerate: float, frame_size: int, num_fra
     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")
diff --git a/lab_tools/labutils.py b/lab_tools/labutils.py
index 97cf40c..82940ca 100644
--- a/lab_tools/labutils.py
+++ b/lab_tools/labutils.py
@@ -1,6 +1,7 @@
 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:
@@ -10,7 +11,7 @@ def load_signal(file_path, column_name):
                 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
diff --git a/lab_tools/wavelet.py b/lab_tools/wavelet.py
index 9552847..b0aab5f 100644
--- a/lab_tools/wavelet.py
+++ b/lab_tools/wavelet.py
@@ -5,6 +5,7 @@
 
 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
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 = "*"
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 pytz = ">=2020.1"
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 typing = ["typing-extensions"]
 xmp = ["defusedxml"]
 
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 version = "2.9.2"
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 pydantic-core = "2.23.4"
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 [package.extras]
 email = ["email-validator (>=2.0.0)"]
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+[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
index 59f34f7..5cf94ea 100644
--- a/test.py
+++ b/test.py
@@ -1,11 +1,9 @@
 import pandas as pd
 import sys
-import numpy as np
 import matplotlib.pyplot as plt
-import sys
-
 from lab_tools import highpass
 
+
 def load_signal(file_path, column_name):
     try:
         with open(file_path, 'r') as file:
@@ -14,7 +12,7 @@ def load_signal(file_path, column_name):
                 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
@@ -25,8 +23,7 @@ def load_signal(file_path, column_name):
     except KeyError as e:
         print(f"Column '{column_name}' not found in the file. ({e})", file=sys.stderr)
         return []
-    
-# print(load_signal("./test1_103313.csv", "Fp2"))
+
 
 samplerate = 1000
 time_data = load_signal("./test2_143809.csv", "Timestamp")
@@ -36,16 +33,16 @@ def load_signal(file_path, column_name):
 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 
+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)
 
@@ -55,20 +52,20 @@ def load_signal(file_path, column_name):
 # 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))
+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]')
@@ -81,16 +78,16 @@ def load_signal(file_path, column_name):
 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")
\ No newline at end of file
+plt.savefig("./out.png")