taigatakano commited on
Commit
145a95e
·
1 Parent(s): 44aae25

Add 1f analyze

Browse files
Files changed (12) hide show
  1. .gitignore +3 -1
  2. Dockerfile +1 -1
  3. lab_tool_webui.py +16 -0
  4. lab_tools/analyze1f.py +69 -0
  5. lab_tools/highpass.py +12 -10
  6. lab_tools/labutils.py +2 -1
  7. lab_tools/wavelet.py +1 -0
  8. poetry.lock +385 -90
  9. pyproject.toml +3 -2
  10. run.py +71 -0
  11. run.sh +6 -3
  12. test.py +14 -17
.gitignore CHANGED
@@ -2,5 +2,7 @@
2
  *.csv
3
  *.xls
4
  *.png
 
5
  __pycache__
6
- flagged
 
 
2
  *.csv
3
  *.xls
4
  *.png
5
+ *.mp3
6
  __pycache__
7
+ flagged
8
+ *.DS_Store
Dockerfile CHANGED
@@ -1,6 +1,6 @@
1
  FROM python:3.10.15-slim-bullseye
2
 
3
- RUN apt-get update && apt-get install -y git curl
4
  RUN git config --global --add safe.directory /app
5
  RUN python3 -m pip install --upgrade pip
6
  RUN python3 -m pip install poetry \
 
1
  FROM python:3.10.15-slim-bullseye
2
 
3
+ RUN apt-get update && apt-get install -y git curl ffmpeg
4
  RUN git config --global --add safe.directory /app
5
  RUN python3 -m pip install --upgrade pip
6
  RUN python3 -m pip install poetry \
lab_tool_webui.py CHANGED
@@ -1,6 +1,8 @@
1
  import gradio as gr
2
  from lab_tools import wavelet
3
  from lab_tools import labutils
 
 
4
 
5
  def update_slider_range(filepath):
6
  timestamp = labutils.load_signal(filepath, "Timestamp")
@@ -33,5 +35,19 @@ with gr.Blocks() as main_ui:
33
  signal_image = gr.Image(type="filepath", label="Signal")
34
 
35
  submit_button.click(wavelet.wavelet_ui, inputs=[file_input, fs_slider, fmax_slider, column_dropdown, start_time, end_time], outputs=[wavelet_image, signal_image])
 
 
 
 
 
 
 
 
 
 
 
 
 
 
36
  if __name__ == "__main__":
37
  main_ui.queue().launch(server_name="0.0.0.0")
 
1
  import gradio as gr
2
  from lab_tools import wavelet
3
  from lab_tools import labutils
4
+ from lab_tools import analyze1f
5
+
6
 
7
  def update_slider_range(filepath):
8
  timestamp = labutils.load_signal(filepath, "Timestamp")
 
35
  signal_image = gr.Image(type="filepath", label="Signal")
36
 
37
  submit_button.click(wavelet.wavelet_ui, inputs=[file_input, fs_slider, fmax_slider, column_dropdown, start_time, end_time], outputs=[wavelet_image, signal_image])
38
+
39
+ with gr.Tab("1f Noise Search"):
40
+ with gr.Row():
41
+ with gr.Column():
42
+ file_input = gr.Text(label="YouTubeのリンクを貼り付けてください。")
43
+ submit_button = gr.Button("計算開始")
44
+
45
+ with gr.Column():
46
+ caption = gr.Text(label="動画タイトル")
47
+ result = gr.Image(type="filepath", label="Wavelet")
48
+
49
+ submit_button.click(analyze1f.analyze_1f_noise, inputs=[file_input], outputs=[caption, result])
50
+
51
+
52
  if __name__ == "__main__":
53
  main_ui.queue().launch(server_name="0.0.0.0")
lab_tools/analyze1f.py ADDED
@@ -0,0 +1,69 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import numpy as np
2
+ import matplotlib.pyplot as plt
3
+ from pydub import AudioSegment
4
+ from scipy.fftpack import fft
5
+ import yt_dlp
6
+ import os
7
+ import tempfile
8
+
9
+
10
+ def download_youtube(youtube_url: str) -> str:
11
+ ydl_opts = {
12
+ 'postprocessors': [
13
+ {
14
+ 'key': 'FFmpegExtractAudio',
15
+ 'preferredcodec': 'mp3',
16
+ 'preferredquality': '128',
17
+ }
18
+ ],
19
+ 'outtmpl': '%(title)s.%(ext)s'
20
+ }
21
+
22
+ with yt_dlp.YoutubeDL(ydl_opts) as ydl:
23
+ info_dict = ydl.extract_info(youtube_url, download=True)
24
+ file_path = ydl.prepare_filename(info_dict)
25
+ filename, _ = os.path.splitext(file_path)
26
+ filename += ".mp3"
27
+ print(f"Downloaded file path: {filename}")
28
+
29
+ return filename
30
+
31
+
32
+ def analyze_1f_noise(youtube_url: str):
33
+ filename = download_youtube(youtube_url)
34
+ audio = AudioSegment.from_mp3(filename)
35
+ os.remove(filename)
36
+ data = np.array(audio.get_array_of_samples())
37
+ sample_rate = audio.frame_rate
38
+
39
+ if audio.channels > 1:
40
+ data = data.reshape((-1, audio.channels)).mean(axis=1)
41
+
42
+ N = len(data)
43
+ T = 1.0 / sample_rate
44
+ yf = fft(data)
45
+ xf = np.fft.fftfreq(N, T)[:N//2]
46
+
47
+ power_spectrum = 2.0/N * np.abs(yf[:N//2])
48
+
49
+ xf_log = xf[1:]
50
+ power_spectrum_log = power_spectrum[1:]
51
+
52
+ graphfile_path = tempfile.NamedTemporaryFile(delete=False, suffix='.png').name
53
+
54
+ # グラフの描画
55
+ plt.figure(figsize=(10, 6))
56
+ plt.plot(xf_log, power_spectrum_log)
57
+ plt.xscale('log')
58
+ plt.yscale('log')
59
+
60
+ plt.title('Power Spectrum (Log Scale)')
61
+ plt.xlabel('Frequency (Hz)')
62
+ plt.ylabel('Power')
63
+ plt.grid(True, which="both", ls="--")
64
+ plt.xlim([1, sample_rate // 2])
65
+ plt.savefig(graphfile_path)
66
+
67
+ filename, _ = os.path.splitext(filename)
68
+
69
+ return filename, graphfile_path
lab_tools/highpass.py CHANGED
@@ -1,7 +1,8 @@
1
  import numpy as np
2
  from scipy import signal
3
  from scipy import fftpack
4
- from typing import List, Tuple
 
5
 
6
  def highpass_filter(signal_data: np.ndarray, samplerate: float, fp: float, fs: float, gpass: float, gstop: float) -> np.ndarray:
7
  fn = samplerate / 2
@@ -10,9 +11,9 @@ def highpass_filter(signal_data: np.ndarray, samplerate: float, fp: float, fs: f
10
 
11
  N, Wn = signal.buttord(wp, ws, gpass, gstop)
12
  b, a = signal.butter(N, Wn, "high")
13
-
14
  filtered_signal = signal.filtfilt(b, a, signal_data)
15
-
16
  return filtered_signal
17
 
18
 
@@ -20,25 +21,25 @@ def overlap_frames(signal_data: np.ndarray, samplerate: float, frame_size: int,
20
  total_duration = len(signal_data) / samplerate
21
  frame_duration = frame_size / samplerate
22
  step_size = frame_size * (1 - overlap / 100)
23
-
24
  num_frames = int((total_duration - (frame_duration * overlap / 100)) / (frame_duration * (1 - overlap / 100)))
25
-
26
  frames = []
27
-
28
  for i in range(num_frames):
29
  start_idx = int(step_size * i)
30
  frames.append(signal_data[start_idx:start_idx + frame_size])
31
-
32
  return np.array(frames), num_frames
33
 
34
 
35
  def hanning(signal_data: np.ndarray, frame_size: int, num_frames: int) -> Tuple[np.ndarray, float]:
36
  han = signal.get_window('hann', frame_size)
37
  acf = 1 / (sum(han) / frame_size)
38
-
39
  for i in range(num_frames):
40
  signal_data[i] *= han
41
-
42
  return signal_data, acf
43
 
44
 
@@ -51,9 +52,10 @@ def fft_ave(signal_data: np.ndarray, samplerate: float, frame_size: int, num_fra
51
  fft_axis = np.linspace(0, samplerate / 2, frame_size // 2)
52
  fft_array = np.array(fft_array)[:, :frame_size // 2]
53
  fft_mean = np.mean(fft_array, axis=0)
54
-
55
  return fft_array, fft_mean, fft_axis
56
 
 
57
  def linear_to_db(x: float, y: float) -> float:
58
  if y == 0:
59
  raise ValueError("y cannot be zero in logarithmic conversion")
 
1
  import numpy as np
2
  from scipy import signal
3
  from scipy import fftpack
4
+ from typing import Tuple
5
+
6
 
7
  def highpass_filter(signal_data: np.ndarray, samplerate: float, fp: float, fs: float, gpass: float, gstop: float) -> np.ndarray:
8
  fn = samplerate / 2
 
11
 
12
  N, Wn = signal.buttord(wp, ws, gpass, gstop)
13
  b, a = signal.butter(N, Wn, "high")
14
+
15
  filtered_signal = signal.filtfilt(b, a, signal_data)
16
+
17
  return filtered_signal
18
 
19
 
 
21
  total_duration = len(signal_data) / samplerate
22
  frame_duration = frame_size / samplerate
23
  step_size = frame_size * (1 - overlap / 100)
24
+
25
  num_frames = int((total_duration - (frame_duration * overlap / 100)) / (frame_duration * (1 - overlap / 100)))
26
+
27
  frames = []
28
+
29
  for i in range(num_frames):
30
  start_idx = int(step_size * i)
31
  frames.append(signal_data[start_idx:start_idx + frame_size])
32
+
33
  return np.array(frames), num_frames
34
 
35
 
36
  def hanning(signal_data: np.ndarray, frame_size: int, num_frames: int) -> Tuple[np.ndarray, float]:
37
  han = signal.get_window('hann', frame_size)
38
  acf = 1 / (sum(han) / frame_size)
39
+
40
  for i in range(num_frames):
41
  signal_data[i] *= han
42
+
43
  return signal_data, acf
44
 
45
 
 
52
  fft_axis = np.linspace(0, samplerate / 2, frame_size // 2)
53
  fft_array = np.array(fft_array)[:, :frame_size // 2]
54
  fft_mean = np.mean(fft_array, axis=0)
55
+
56
  return fft_array, fft_mean, fft_axis
57
 
58
+
59
  def linear_to_db(x: float, y: float) -> float:
60
  if y == 0:
61
  raise ValueError("y cannot be zero in logarithmic conversion")
lab_tools/labutils.py CHANGED
@@ -1,6 +1,7 @@
1
  import sys
2
  import pandas as pd
3
 
 
4
  # CSVファイルから信号データを読み込む
5
  def load_signal(file_path, column_name):
6
  try:
@@ -10,7 +11,7 @@ def load_signal(file_path, column_name):
10
  if 'Timestamp' in line:
11
  header_line = i
12
  break
13
-
14
  # 見つけたヘッダー行からデータを読み込む
15
  df = pd.read_csv(file_path, skiprows=header_line)
16
  signal = df[column_name].values
 
1
  import sys
2
  import pandas as pd
3
 
4
+
5
  # CSVファイルから信号データを読み込む
6
  def load_signal(file_path, column_name):
7
  try:
 
11
  if 'Timestamp' in line:
12
  header_line = i
13
  break
14
+
15
  # 見つけたヘッダー行からデータを読み込む
16
  df = pd.read_csv(file_path, skiprows=header_line)
17
  signal = df[column_name].values
lab_tools/wavelet.py CHANGED
@@ -5,6 +5,7 @@ import tempfile
5
 
6
  from lab_tools import labutils
7
 
 
8
  # モルレーウェーブレット関数
9
  def morlet(x, f, width):
10
  sf = f / width
 
5
 
6
  from lab_tools import labutils
7
 
8
+
9
  # モルレーウェーブレット関数
10
  def morlet(x, f, width):
11
  sf = f / width
poetry.lock CHANGED
@@ -1,4 +1,4 @@
1
- # This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
2
 
3
  [[package]]
4
  name = "aiofiles"
@@ -22,9 +22,6 @@ files = [
22
  {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
23
  ]
24
 
25
- [package.dependencies]
26
- typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.9\""}
27
-
28
  [[package]]
29
  name = "anyio"
30
  version = "4.5.0"
@@ -47,6 +44,179 @@ doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)",
47
  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)"]
48
  trio = ["trio (>=0.26.1)"]
49
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
50
  [[package]]
51
  name = "certifi"
52
  version = "2024.8.30"
@@ -58,6 +228,85 @@ files = [
58
  {file = "certifi-2024.8.30.tar.gz", hash = "sha256:bec941d2aa8195e248a60b31ff9f0558284cf01a52591ceda73ea9afffd69fd9"},
59
  ]
60
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
61
  [[package]]
62
  name = "charset-normalizer"
63
  version = "3.4.0"
@@ -197,68 +446,6 @@ files = [
197
  {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"},
198
  ]
199
 
200
- [[package]]
201
- name = "contourpy"
202
- version = "1.1.0"
203
- description = "Python library for calculating contours of 2D quadrilateral grids"
204
- optional = false
205
- python-versions = ">=3.8"
206
- files = [
207
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250
- ]
251
-
252
- [package.dependencies]
253
- numpy = ">=1.16"
254
-
255
- [package.extras]
256
- bokeh = ["bokeh", "selenium"]
257
- docs = ["furo", "sphinx-copybutton"]
258
- mypy = ["contourpy[bokeh,docs]", "docutils-stubs", "mypy (==1.2.0)", "types-Pillow"]
259
- test = ["Pillow", "contourpy[test-no-images]", "matplotlib"]
260
- test-no-images = ["pytest", "pytest-cov", "wurlitzer"]
261
-
262
  [[package]]
263
  name = "contourpy"
264
  version = "1.1.1"
@@ -693,9 +880,6 @@ files = [
693
  {file = "importlib_resources-6.4.5.tar.gz", hash = "sha256:980862a1d16c9e147a59603677fa2aa5fd82b87f223b6cb870695bcfce830065"},
694
  ]
695
 
696
- [package.dependencies]
697
- zipp = {version = ">=3.1.0", markers = "python_version < \"3.10\""}
698
-
699
  [package.extras]
700
  check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"]
701
  cover = ["pytest-cov"]
@@ -997,7 +1181,6 @@ files = [
997
  contourpy = ">=1.0.1"
998
  cycler = ">=0.10"
999
  fonttools = ">=4.22.0"
1000
- importlib-resources = {version = ">=3.2.0", markers = "python_version < \"3.10\""}
1001
  kiwisolver = ">=1.0.1"
1002
  numpy = ">=1.20,<2"
1003
  packaging = ">=20.0"
@@ -1016,6 +1199,17 @@ files = [
1016
  {file = "mdurl-0.1.2.tar.gz", hash = "sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba"},
1017
  ]
1018
 
 
 
 
 
 
 
 
 
 
 
 
1019
  [[package]]
1020
  name = "numpy"
1021
  version = "1.24.4"
@@ -1166,9 +1360,8 @@ files = [
1166
 
1167
  [package.dependencies]
1168
  numpy = [
1169
- {version = ">=1.20.3", markers = "python_version < \"3.10\""},
1170
- {version = ">=1.21.0", markers = "python_version >= \"3.10\" and python_version < \"3.11\""},
1171
  {version = ">=1.23.2", markers = "python_version >= \"3.11\""},
 
1172
  ]
1173
  python-dateutil = ">=2.8.2"
1174
  pytz = ">=2020.1"
@@ -1294,6 +1487,58 @@ tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "pa
1294
  typing = ["typing-extensions"]
1295
  xmp = ["defusedxml"]
1296
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1297
  [[package]]
1298
  name = "pydantic"
1299
  version = "2.9.2"
@@ -1308,10 +1553,7 @@ files = [
1308
  [package.dependencies]
1309
  annotated-types = ">=0.6.0"
1310
  pydantic-core = "2.23.4"
1311
- typing-extensions = [
1312
- {version = ">=4.6.1", markers = "python_version < \"3.13\""},
1313
- {version = ">=4.12.2", markers = "python_version >= \"3.13\""},
1314
- ]
1315
 
1316
  [package.extras]
1317
  email = ["email-validator (>=2.0.0)"]
@@ -1622,6 +1864,48 @@ files = [
1622
  {file = "ruff-0.6.9.tar.gz", hash = "sha256:b076ef717a8e5bc819514ee1d602bbdca5b4420ae13a9cf61a0c0a4f53a2baa2"},
1623
  ]
1624
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1625
  [[package]]
1626
  name = "semantic-version"
1627
  version = "2.10.0"
@@ -1683,7 +1967,6 @@ files = [
1683
 
1684
  [package.dependencies]
1685
  anyio = ">=3.4.0,<5"
1686
- typing-extensions = {version = ">=3.10.0", markers = "python_version < \"3.10\""}
1687
 
1688
  [package.extras]
1689
  full = ["httpx (>=0.22.0)", "itsdangerous", "jinja2", "python-multipart (>=0.0.7)", "pyyaml"]
@@ -1876,25 +2159,37 @@ files = [
1876
  ]
1877
 
1878
  [[package]]
1879
- name = "zipp"
1880
- version = "3.20.2"
1881
- description = "Backport of pathlib-compatible object wrapper for zip files"
1882
  optional = false
1883
  python-versions = ">=3.8"
1884
  files = [
1885
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1887
  ]
1888
 
 
 
 
 
 
 
 
 
 
 
1889
  [package.extras]
1890
- check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"]
1891
- cover = ["pytest-cov"]
1892
- doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
1893
- enabler = ["pytest-enabler (>=2.2)"]
1894
- test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"]
1895
- type = ["pytest-mypy"]
 
 
1896
 
1897
  [metadata]
1898
  lock-version = "2.0"
1899
- python-versions = "^3.8"
1900
- content-hash = "d72cefc6dd324993f4733c95f2fb04bdef56d998121325db81a68627ae8062db"
 
1
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3
  [[package]]
4
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22
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25
  [[package]]
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27
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44
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45
  trio = ["trio (>=0.26.1)"]
46
 
47
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+ name = "brotli"
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+
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  [[package]]
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  name = "certifi"
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  version = "2024.8.30"
 
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  ]
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+ {file = "cffi-1.17.1.tar.gz", hash = "sha256:1c39c6016c32bc48dd54561950ebd6836e1670f2ae46128f67cf49e789c52824"},
305
+ ]
306
+
307
+ [package.dependencies]
308
+ pycparser = "*"
309
+
310
  [[package]]
311
  name = "charset-normalizer"
312
  version = "3.4.0"
 
446
  {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"},
447
  ]
448
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
449
  [[package]]
450
  name = "contourpy"
451
  version = "1.1.1"
 
880
  {file = "importlib_resources-6.4.5.tar.gz", hash = "sha256:980862a1d16c9e147a59603677fa2aa5fd82b87f223b6cb870695bcfce830065"},
881
  ]
882
 
 
 
 
883
  [package.extras]
884
  check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"]
885
  cover = ["pytest-cov"]
 
1181
  contourpy = ">=1.0.1"
1182
  cycler = ">=0.10"
1183
  fonttools = ">=4.22.0"
 
1184
  kiwisolver = ">=1.0.1"
1185
  numpy = ">=1.20,<2"
1186
  packaging = ">=20.0"
 
1199
  {file = "mdurl-0.1.2.tar.gz", hash = "sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba"},
1200
  ]
1201
 
1202
+ [[package]]
1203
+ name = "mutagen"
1204
+ version = "1.47.0"
1205
+ description = "read and write audio tags for many formats"
1206
+ optional = false
1207
+ python-versions = ">=3.7"
1208
+ files = [
1209
+ {file = "mutagen-1.47.0-py3-none-any.whl", hash = "sha256:edd96f50c5907a9539d8e5bba7245f62c9f520aef333d13392a79a4f70aca719"},
1210
+ {file = "mutagen-1.47.0.tar.gz", hash = "sha256:719fadef0a978c31b4cf3c956261b3c58b6948b32023078a2117b1de09f0fc99"},
1211
+ ]
1212
+
1213
  [[package]]
1214
  name = "numpy"
1215
  version = "1.24.4"
 
1360
 
1361
  [package.dependencies]
1362
  numpy = [
 
 
1363
  {version = ">=1.23.2", markers = "python_version >= \"3.11\""},
1364
+ {version = ">=1.21.0", markers = "python_version >= \"3.10\" and python_version < \"3.11\""},
1365
  ]
1366
  python-dateutil = ">=2.8.2"
1367
  pytz = ">=2020.1"
 
1487
  typing = ["typing-extensions"]
1488
  xmp = ["defusedxml"]
1489
 
1490
+ [[package]]
1491
+ name = "pycparser"
1492
+ version = "2.22"
1493
+ description = "C parser in Python"
1494
+ optional = false
1495
+ python-versions = ">=3.8"
1496
+ files = [
1497
+ {file = "pycparser-2.22-py3-none-any.whl", hash = "sha256:c3702b6d3dd8c7abc1afa565d7e63d53a1d0bd86cdc24edd75470f4de499cfcc"},
1498
+ {file = "pycparser-2.22.tar.gz", hash = "sha256:491c8be9c040f5390f5bf44a5b07752bd07f56edf992381b05c701439eec10f6"},
1499
+ ]
1500
+
1501
+ [[package]]
1502
+ name = "pycryptodomex"
1503
+ version = "3.21.0"
1504
+ description = "Cryptographic library for Python"
1505
+ optional = false
1506
+ python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
1507
+ files = [
1508
+ {file = "pycryptodomex-3.21.0-cp27-cp27m-macosx_10_9_x86_64.whl", hash = "sha256:dbeb84a399373df84a69e0919c1d733b89e049752426041deeb30d68e9867822"},
1509
+ {file = "pycryptodomex-3.21.0-cp27-cp27m-manylinux2010_i686.whl", hash = "sha256:a192fb46c95489beba9c3f002ed7d93979423d1b2a53eab8771dbb1339eb3ddd"},
1510
+ {file = "pycryptodomex-3.21.0-cp27-cp27m-manylinux2010_x86_64.whl", hash = "sha256:1233443f19d278c72c4daae749872a4af3787a813e05c3561c73ab0c153c7b0f"},
1511
+ {file = "pycryptodomex-3.21.0-cp27-cp27m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:bbb07f88e277162b8bfca7134b34f18b400d84eac7375ce73117f865e3c80d4c"},
1512
+ {file = "pycryptodomex-3.21.0-cp27-cp27m-musllinux_1_1_aarch64.whl", hash = "sha256:e859e53d983b7fe18cb8f1b0e29d991a5c93be2c8dd25db7db1fe3bd3617f6f9"},
1513
+ {file = "pycryptodomex-3.21.0-cp27-cp27m-win32.whl", hash = "sha256:ef046b2e6c425647971b51424f0f88d8a2e0a2a63d3531817968c42078895c00"},
1514
+ {file = "pycryptodomex-3.21.0-cp27-cp27m-win_amd64.whl", hash = "sha256:da76ebf6650323eae7236b54b1b1f0e57c16483be6e3c1ebf901d4ada47563b6"},
1515
+ {file = "pycryptodomex-3.21.0-cp27-cp27mu-manylinux2010_i686.whl", hash = "sha256:c07e64867a54f7e93186a55bec08a18b7302e7bee1b02fd84c6089ec215e723a"},
1516
+ {file = "pycryptodomex-3.21.0-cp27-cp27mu-manylinux2010_x86_64.whl", hash = "sha256:56435c7124dd0ce0c8bdd99c52e5d183a0ca7fdcd06c5d5509423843f487dd0b"},
1517
+ {file = "pycryptodomex-3.21.0-cp27-cp27mu-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:65d275e3f866cf6fe891411be9c1454fb58809ccc5de6d3770654c47197acd65"},
1518
+ {file = "pycryptodomex-3.21.0-cp27-cp27mu-musllinux_1_1_aarch64.whl", hash = "sha256:5241bdb53bcf32a9568770a6584774b1b8109342bd033398e4ff2da052123832"},
1519
+ {file = "pycryptodomex-3.21.0-cp36-abi3-macosx_10_9_universal2.whl", hash = "sha256:34325b84c8b380675fd2320d0649cdcbc9cf1e0d1526edbe8fce43ed858cdc7e"},
1520
+ {file = "pycryptodomex-3.21.0-cp36-abi3-macosx_10_9_x86_64.whl", hash = "sha256:103c133d6cd832ae7266feb0a65b69e3a5e4dbbd6f3a3ae3211a557fd653f516"},
1521
+ {file = "pycryptodomex-3.21.0-cp36-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:77ac2ea80bcb4b4e1c6a596734c775a1615d23e31794967416afc14852a639d3"},
1522
+ {file = "pycryptodomex-3.21.0-cp36-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9aa0cf13a1a1128b3e964dc667e5fe5c6235f7d7cfb0277213f0e2a783837cc2"},
1523
+ {file = "pycryptodomex-3.21.0-cp36-abi3-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:46eb1f0c8d309da63a2064c28de54e5e614ad17b7e2f88df0faef58ce192fc7b"},
1524
+ {file = "pycryptodomex-3.21.0-cp36-abi3-musllinux_1_1_aarch64.whl", hash = "sha256:cc7e111e66c274b0df5f4efa679eb31e23c7545d702333dfd2df10ab02c2a2ce"},
1525
+ {file = "pycryptodomex-3.21.0-cp36-abi3-musllinux_1_2_i686.whl", hash = "sha256:770d630a5c46605ec83393feaa73a9635a60e55b112e1fb0c3cea84c2897aa0a"},
1526
+ {file = "pycryptodomex-3.21.0-cp36-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:52e23a0a6e61691134aa8c8beba89de420602541afaae70f66e16060fdcd677e"},
1527
+ {file = "pycryptodomex-3.21.0-cp36-abi3-win32.whl", hash = "sha256:a3d77919e6ff56d89aada1bd009b727b874d464cb0e2e3f00a49f7d2e709d76e"},
1528
+ {file = "pycryptodomex-3.21.0-cp36-abi3-win_amd64.whl", hash = "sha256:b0e9765f93fe4890f39875e6c90c96cb341767833cfa767f41b490b506fa9ec0"},
1529
+ {file = "pycryptodomex-3.21.0-pp27-pypy_73-manylinux2010_x86_64.whl", hash = "sha256:feaecdce4e5c0045e7a287de0c4351284391fe170729aa9182f6bd967631b3a8"},
1530
+ {file = "pycryptodomex-3.21.0-pp27-pypy_73-win32.whl", hash = "sha256:365aa5a66d52fd1f9e0530ea97f392c48c409c2f01ff8b9a39c73ed6f527d36c"},
1531
+ {file = "pycryptodomex-3.21.0-pp310-pypy310_pp73-macosx_10_15_x86_64.whl", hash = "sha256:3efddfc50ac0ca143364042324046800c126a1d63816d532f2e19e6f2d8c0c31"},
1532
+ {file = "pycryptodomex-3.21.0-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0df2608682db8279a9ebbaf05a72f62a321433522ed0e499bc486a6889b96bf3"},
1533
+ {file = "pycryptodomex-3.21.0-pp310-pypy310_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:5823d03e904ea3e53aebd6799d6b8ec63b7675b5d2f4a4bd5e3adcb512d03b37"},
1534
+ {file = "pycryptodomex-3.21.0-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:27e84eeff24250ffec32722334749ac2a57a5fd60332cd6a0680090e7c42877e"},
1535
+ {file = "pycryptodomex-3.21.0-pp39-pypy39_pp73-macosx_10_15_x86_64.whl", hash = "sha256:8ef436cdeea794015263853311f84c1ff0341b98fc7908e8a70595a68cefd971"},
1536
+ {file = "pycryptodomex-3.21.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7a1058e6dfe827f4209c5cae466e67610bcd0d66f2f037465daa2a29d92d952b"},
1537
+ {file = "pycryptodomex-3.21.0-pp39-pypy39_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:9ba09a5b407cbb3bcb325221e346a140605714b5e880741dc9a1e9ecf1688d42"},
1538
+ {file = "pycryptodomex-3.21.0-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:8a9d8342cf22b74a746e3c6c9453cb0cfbb55943410e3a2619bd9164b48dc9d9"},
1539
+ {file = "pycryptodomex-3.21.0.tar.gz", hash = "sha256:222d0bd05381dd25c32dd6065c071ebf084212ab79bab4599ba9e6a3e0009e6c"},
1540
+ ]
1541
+
1542
  [[package]]
1543
  name = "pydantic"
1544
  version = "2.9.2"
 
1553
  [package.dependencies]
1554
  annotated-types = ">=0.6.0"
1555
  pydantic-core = "2.23.4"
1556
+ typing-extensions = {version = ">=4.6.1", markers = "python_version < \"3.13\""}
 
 
 
1557
 
1558
  [package.extras]
1559
  email = ["email-validator (>=2.0.0)"]
 
1864
  {file = "ruff-0.6.9.tar.gz", hash = "sha256:b076ef717a8e5bc819514ee1d602bbdca5b4420ae13a9cf61a0c0a4f53a2baa2"},
1865
  ]
1866
 
1867
+ [[package]]
1868
+ name = "scipy"
1869
+ version = "1.14.0"
1870
+ description = "Fundamental algorithms for scientific computing in Python"
1871
+ optional = false
1872
+ python-versions = ">=3.10"
1873
+ files = [
1874
+ {file = "scipy-1.14.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:7e911933d54ead4d557c02402710c2396529540b81dd554fc1ba270eb7308484"},
1875
+ {file = "scipy-1.14.0-cp310-cp310-macosx_12_0_arm64.whl", hash = "sha256:687af0a35462402dd851726295c1a5ae5f987bd6e9026f52e9505994e2f84ef6"},
1876
+ {file = "scipy-1.14.0-cp310-cp310-macosx_14_0_arm64.whl", hash = "sha256:07e179dc0205a50721022344fb85074f772eadbda1e1b3eecdc483f8033709b7"},
1877
+ {file = "scipy-1.14.0-cp310-cp310-macosx_14_0_x86_64.whl", hash = "sha256:6a9c9a9b226d9a21e0a208bdb024c3982932e43811b62d202aaf1bb59af264b1"},
1878
+ {file = "scipy-1.14.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:076c27284c768b84a45dcf2e914d4000aac537da74236a0d45d82c6fa4b7b3c0"},
1879
+ {file = "scipy-1.14.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:42470ea0195336df319741e230626b6225a740fd9dce9642ca13e98f667047c0"},
1880
+ {file = "scipy-1.14.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:176c6f0d0470a32f1b2efaf40c3d37a24876cebf447498a4cefb947a79c21e9d"},
1881
+ {file = "scipy-1.14.0-cp310-cp310-win_amd64.whl", hash = "sha256:ad36af9626d27a4326c8e884917b7ec321d8a1841cd6dacc67d2a9e90c2f0359"},
1882
+ {file = "scipy-1.14.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:6d056a8709ccda6cf36cdd2eac597d13bc03dba38360f418560a93050c76a16e"},
1883
+ {file = "scipy-1.14.0-cp311-cp311-macosx_12_0_arm64.whl", hash = "sha256:f0a50da861a7ec4573b7c716b2ebdcdf142b66b756a0d392c236ae568b3a93fb"},
1884
+ {file = "scipy-1.14.0-cp311-cp311-macosx_14_0_arm64.whl", hash = "sha256:94c164a9e2498e68308e6e148646e486d979f7fcdb8b4cf34b5441894bdb9caf"},
1885
+ {file = "scipy-1.14.0-cp311-cp311-macosx_14_0_x86_64.whl", hash = "sha256:a7d46c3e0aea5c064e734c3eac5cf9eb1f8c4ceee756262f2c7327c4c2691c86"},
1886
+ {file = "scipy-1.14.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9eee2989868e274aae26125345584254d97c56194c072ed96cb433f32f692ed8"},
1887
+ {file = "scipy-1.14.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9e3154691b9f7ed73778d746da2df67a19d046a6c8087c8b385bc4cdb2cfca74"},
1888
+ {file = "scipy-1.14.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:c40003d880f39c11c1edbae8144e3813904b10514cd3d3d00c277ae996488cdb"},
1889
+ {file = "scipy-1.14.0-cp311-cp311-win_amd64.whl", hash = "sha256:5b083c8940028bb7e0b4172acafda6df762da1927b9091f9611b0bcd8676f2bc"},
1890
+ {file = "scipy-1.14.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:bff2438ea1330e06e53c424893ec0072640dac00f29c6a43a575cbae4c99b2b9"},
1891
+ {file = "scipy-1.14.0-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:bbc0471b5f22c11c389075d091d3885693fd3f5e9a54ce051b46308bc787e5d4"},
1892
+ {file = "scipy-1.14.0-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:64b2ff514a98cf2bb734a9f90d32dc89dc6ad4a4a36a312cd0d6327170339eb0"},
1893
+ {file = "scipy-1.14.0-cp312-cp312-macosx_14_0_x86_64.whl", hash = "sha256:7d3da42fbbbb860211a811782504f38ae7aaec9de8764a9bef6b262de7a2b50f"},
1894
+ {file = "scipy-1.14.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d91db2c41dd6c20646af280355d41dfa1ec7eead235642178bd57635a3f82209"},
1895
+ {file = "scipy-1.14.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a01cc03bcdc777c9da3cfdcc74b5a75caffb48a6c39c8450a9a05f82c4250a14"},
1896
+ {file = "scipy-1.14.0-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:65df4da3c12a2bb9ad52b86b4dcf46813e869afb006e58be0f516bc370165159"},
1897
+ {file = "scipy-1.14.0-cp312-cp312-win_amd64.whl", hash = "sha256:4c4161597c75043f7154238ef419c29a64ac4a7c889d588ea77690ac4d0d9b20"},
1898
+ {file = "scipy-1.14.0.tar.gz", hash = "sha256:b5923f48cb840380f9854339176ef21763118a7300a88203ccd0bdd26e58527b"},
1899
+ ]
1900
+
1901
+ [package.dependencies]
1902
+ numpy = ">=1.23.5,<2.3"
1903
+
1904
+ [package.extras]
1905
+ 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"]
1906
+ 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)"]
1907
+ test = ["Cython", "array-api-strict", "asv", "gmpy2", "hypothesis (>=6.30)", "meson", "mpmath", "ninja", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
1908
+
1909
  [[package]]
1910
  name = "semantic-version"
1911
  version = "2.10.0"
 
1967
 
1968
  [package.dependencies]
1969
  anyio = ">=3.4.0,<5"
 
1970
 
1971
  [package.extras]
1972
  full = ["httpx (>=0.22.0)", "itsdangerous", "jinja2", "python-multipart (>=0.0.7)", "pyyaml"]
 
2159
  ]
2160
 
2161
  [[package]]
2162
+ name = "yt-dlp"
2163
+ version = "2024.7.7"
2164
+ description = "A feature-rich command-line audio/video downloader"
2165
  optional = false
2166
  python-versions = ">=3.8"
2167
  files = [
2168
+ {file = "yt_dlp-2024.7.7-py3-none-any.whl", hash = "sha256:2e90abeadc0199c787b1b4a3e0a1c8ed9d7c9f824f58da88467a1b30ed745e07"},
2169
+ {file = "yt_dlp-2024.7.7.tar.gz", hash = "sha256:2a0f89423d25d47db949925db5bd2c6f651960ae93dbbf5b3ed61cf3a4078ce5"},
2170
  ]
2171
 
2172
+ [package.dependencies]
2173
+ brotli = {version = "*", markers = "implementation_name == \"cpython\""}
2174
+ brotlicffi = {version = "*", markers = "implementation_name != \"cpython\""}
2175
+ certifi = "*"
2176
+ mutagen = "*"
2177
+ pycryptodomex = "*"
2178
+ requests = ">=2.32.2,<3"
2179
+ urllib3 = ">=1.26.17,<3"
2180
+ websockets = ">=12.0"
2181
+
2182
  [package.extras]
2183
+ build = ["build", "hatchling", "pip", "setuptools", "wheel"]
2184
+ curl-cffi = ["curl-cffi (==0.5.10)"]
2185
+ dev = ["autopep8 (>=2.0,<3.0)", "pre-commit", "pytest (>=8.1,<9.0)", "ruff (>=0.5.0,<0.6.0)"]
2186
+ py2exe = ["py2exe (>=0.12)"]
2187
+ pyinstaller = ["pyinstaller (>=6.7.0)"]
2188
+ secretstorage = ["cffi", "secretstorage"]
2189
+ static-analysis = ["autopep8 (>=2.0,<3.0)", "ruff (>=0.5.0,<0.6.0)"]
2190
+ test = ["pytest (>=8.1,<9.0)"]
2191
 
2192
  [metadata]
2193
  lock-version = "2.0"
2194
+ python-versions = ">=3.10,<3.12"
2195
+ content-hash = "2bb4e05993ac956f9ed0c7c5eb5ac3c87c4ceb9bfa3506347ca2e6a7a296b550"
pyproject.toml CHANGED
@@ -5,9 +5,10 @@ description = "research"
5
  authors = ["Taiga Takano <ttttghghnb554z@outlook.jp>"]
6
 
7
  [tool.poetry.dependencies]
8
- python = "^3.8"
9
  gradio = "4.44.0"
10
-
 
11
 
12
  [build-system]
13
  requires = ["poetry-core>=1.0.0"]
 
5
  authors = ["Taiga Takano <ttttghghnb554z@outlook.jp>"]
6
 
7
  [tool.poetry.dependencies]
8
+ python = ">=3.10,<3.12"
9
  gradio = "4.44.0"
10
+ yt-dlp = "2024.07.07"
11
+ scipy = "1.14.0"
12
 
13
  [build-system]
14
  requires = ["poetry-core>=1.0.0"]
run.py ADDED
@@ -0,0 +1,71 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import numpy as np
2
+ import matplotlib.pyplot as plt
3
+ from pydub import AudioSegment
4
+ from scipy.fftpack import fft
5
+ import yt_dlp
6
+ import os
7
+
8
+ url = "https://youtu.be/Ci_zad39Uhw?si=AhB9ArgrWUvbPiv5"
9
+
10
+ ydl_opts = {
11
+ 'postprocessors': [
12
+ {
13
+ 'key': 'FFmpegExtractAudio',
14
+ 'preferredcodec': 'mp3',
15
+ 'preferredquality': '128',
16
+ }
17
+ ],
18
+ 'outtmpl': '%(title)s.%(ext)s' # ファイル名のテンプレート
19
+ }
20
+
21
+ with yt_dlp.YoutubeDL(ydl_opts) as ydl:
22
+ info_dict = ydl.extract_info(url, download=True)
23
+ file_path = ydl.prepare_filename(info_dict)
24
+ filename, ext = os.path.splitext(file_path)
25
+ filename += ".mp3"
26
+ print(f"Downloaded file path: {filename}")
27
+
28
+ # MP3ファイルの読み込みとWAV形式への変換
29
+ audio = AudioSegment.from_mp3(filename)
30
+ os.remove(filename)
31
+ data = np.array(audio.get_array_of_samples())
32
+ sample_rate = audio.frame_rate
33
+
34
+ # モノラル変換(ステレオの場合)
35
+ if audio.channels > 1:
36
+ data = data.reshape((-1, audio.channels)).mean(axis=1)
37
+
38
+ # フーリエ変換の実行
39
+ N = len(data)
40
+ T = 1.0 / sample_rate
41
+ yf = fft(data)
42
+ xf = np.fft.fftfreq(N, T)[:N//2]
43
+
44
+ # パワースペクトルの計算
45
+ power_spectrum = 2.0/N * np.abs(yf[:N//2])
46
+
47
+ # プロット用に周波数とパワーを制限
48
+ xf_log = xf[1:] # 0Hzを除去 (ログスケールでは0が扱えないため)
49
+ power_spectrum_log = power_spectrum[1:]
50
+
51
+ # 縦軸の範囲を指定(例: 0から100まで)
52
+ y_min = 0
53
+ y_max = 1000
54
+
55
+ # グラフの描画
56
+ plt.figure(figsize=(10, 6))
57
+ plt.plot(xf_log, power_spectrum_log)
58
+ plt.xscale('log')
59
+ plt.yscale('log')
60
+
61
+ plt.title('Power Spectrum (Log Scale)')
62
+ plt.xlabel('Frequency (Hz)')
63
+ plt.ylabel('Power')
64
+ plt.grid(True, which="both", ls="--")
65
+ plt.xlim([1, sample_rate // 2]) # 1Hz から Nyquist周波数 (sample_rate/2) まで
66
+
67
+ # 縦軸の範囲を指定
68
+ # plt.ylim([y_min, y_max])
69
+
70
+
71
+ plt.show()
run.sh CHANGED
@@ -8,7 +8,7 @@ ROOT="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
8
  if [[ $(id -u) -eq 0 ]]; then
9
  echo "This script cannot be executed with root privileges."
10
  echo "Please re-run without sudo and follow instructions to configure docker for non-root user if needed."
11
- exit 1
12
  fi
13
 
14
  # Check if user can run docker without root.
@@ -17,14 +17,14 @@ if [[ ! $(groups $USER) =~ $RE ]]; then
17
  echo "User |$USER| is not a member of the 'docker' group and cannot run docker commands without sudo."
18
  echo "Run 'sudo usermod -aG docker \$USER && newgrp docker' to add user to 'docker' group, then re-run this script."
19
  echo "See: https://docs.docker.com/engine/install/linux-postinstall/"
20
- exit 1
21
  fi
22
 
23
  # Check if able to run docker commands.
24
  if [[ -z "$(docker ps)" ]] ; then
25
  echo "Unable to run docker commands. If you have recently added |$USER| to 'docker' group, you may need to log out and log back in for it to take effect."
26
  echo "Otherwise, please check your Docker installation."
27
- exit 1
28
  fi
29
 
30
  PLATFORM="$(uname -m)"
@@ -35,4 +35,7 @@ if [ $PLATFORM = "x86_64" ]; then
35
  docker run -it --rm -v $ROOT:/app -w /app --network host ghcr.io/moriyalab/lab_tool:latest /bin/bash
36
  else
37
  echo "Not Support Platform. Only support x86."
 
 
 
38
  fi
 
8
  if [[ $(id -u) -eq 0 ]]; then
9
  echo "This script cannot be executed with root privileges."
10
  echo "Please re-run without sudo and follow instructions to configure docker for non-root user if needed."
11
+ # exit 1
12
  fi
13
 
14
  # Check if user can run docker without root.
 
17
  echo "User |$USER| is not a member of the 'docker' group and cannot run docker commands without sudo."
18
  echo "Run 'sudo usermod -aG docker \$USER && newgrp docker' to add user to 'docker' group, then re-run this script."
19
  echo "See: https://docs.docker.com/engine/install/linux-postinstall/"
20
+ # exit 1
21
  fi
22
 
23
  # Check if able to run docker commands.
24
  if [[ -z "$(docker ps)" ]] ; then
25
  echo "Unable to run docker commands. If you have recently added |$USER| to 'docker' group, you may need to log out and log back in for it to take effect."
26
  echo "Otherwise, please check your Docker installation."
27
+ # exit 1
28
  fi
29
 
30
  PLATFORM="$(uname -m)"
 
35
  docker run -it --rm -v $ROOT:/app -w /app --network host ghcr.io/moriyalab/lab_tool:latest /bin/bash
36
  else
37
  echo "Not Support Platform. Only support x86."
38
+ docker pull ghcr.io/moriyalab/lab_tool:latest
39
+ docker run -it --rm -v $ROOT:/app -w /app --network host ghcr.io/moriyalab/lab_tool:latest /bin/bash
40
+
41
  fi
test.py CHANGED
@@ -1,11 +1,9 @@
1
  import pandas as pd
2
  import sys
3
- import numpy as np
4
  import matplotlib.pyplot as plt
5
- import sys
6
-
7
  from lab_tools import highpass
8
 
 
9
  def load_signal(file_path, column_name):
10
  try:
11
  with open(file_path, 'r') as file:
@@ -14,7 +12,7 @@ def load_signal(file_path, column_name):
14
  if 'Timestamp' in line:
15
  header_line = i
16
  break
17
-
18
  # 見つけたヘッダー行からデータを読み込む
19
  df = pd.read_csv(file_path, skiprows=header_line)
20
  signal = df[column_name].values
@@ -25,8 +23,7 @@ def load_signal(file_path, column_name):
25
  except KeyError as e:
26
  print(f"Column '{column_name}' not found in the file. ({e})", file=sys.stderr)
27
  return []
28
-
29
- # print(load_signal("./test1_103313.csv", "Fp2"))
30
 
31
  samplerate = 1000
32
  time_data = load_signal("./test2_143809.csv", "Timestamp")
@@ -36,16 +33,16 @@ fs = 10 # 阻止域端周波数[Hz]※ベクトル
36
  gpass = 5 # 通過域端最大損失[dB]
37
  gstop = 40 # 阻止域端最小損失[dB]
38
  Fs = 4096 # フレームサイズ
39
- overlap = 90
40
 
41
  data_filt = highpass.highpass_filter(signal_data, samplerate, fp, fs, gpass, gstop)
42
 
43
  t_array_org, N_ave_org = highpass.overlap_frames(signal_data, samplerate, Fs, overlap)
44
  t_array_filt, N_ave_filt = highpass.overlap_frames(signal_data, samplerate, Fs, overlap)
45
-
46
  t_array_org, acf_org = highpass.hanning(t_array_org, Fs, N_ave_org)
47
  t_array_filt, acf_filt = highpass.hanning(t_array_filt, Fs, N_ave_filt)
48
-
49
  fft_array_org, fft_mean_org, fft_axis_org = highpass.fft_ave(t_array_org, samplerate, Fs, N_ave_org, acf_org)
50
  fft_array_filt, fft_mean_filt, fft_axis_filt = highpass.fft_ave(t_array_filt, samplerate, Fs, N_ave_filt, acf_filt)
51
 
@@ -55,20 +52,20 @@ fft_mean_filt = highpass.linear_to_db(fft_mean_filt, 2e-5)
55
  # フォントの種類とサイズを設定する。
56
  # plt.rcParams['font.size'] = 14
57
  # plt.rcParams['font.family'] = 'Times New Roman'
58
-
59
  # 目盛を内側にする。
60
  plt.rcParams['xtick.direction'] = 'in'
61
  plt.rcParams['ytick.direction'] = 'in'
62
-
63
  # グラフの上下左右に目盛線を付ける。
64
- fig = plt.figure(figsize=(20,10))
65
  ax1 = fig.add_subplot(211)
66
  ax1.yaxis.set_ticks_position('both')
67
  ax1.xaxis.set_ticks_position('both')
68
  ax2 = fig.add_subplot(212)
69
  ax2.yaxis.set_ticks_position('both')
70
  ax2.xaxis.set_ticks_position('both')
71
-
72
  # 軸のラベルを設定する。
73
  ax1.set_xlabel('Time [s]')
74
  ax1.set_ylabel('V[μV]')
@@ -81,16 +78,16 @@ ax1.plot(time_data, data_filt, label='filtered', lw=1)
81
  ax2.plot(fft_axis_org, fft_mean_org, label='original', lw=1)
82
  ax2.plot(fft_axis_filt, fft_mean_filt, label='filtered', lw=1)
83
  plt.legend()
84
-
85
  # 軸のリミットを設定する。
86
  # ax1.set_xlim(0,1200)
87
  # ax1.set_xticks(np.arange(0,1201,100))
88
  # ax2.set_xlim(0, max(fft_axis_org)/2)
89
  # ax2.set_xticks(np.arange(0,501,10))
90
  # ax2.set_ylim(-50, 150)
91
-
92
  # レイアウト設定
93
  fig.tight_layout()
94
-
95
  # グラフを表示する。
96
- plt.savefig("./out.png")
 
1
  import pandas as pd
2
  import sys
 
3
  import matplotlib.pyplot as plt
 
 
4
  from lab_tools import highpass
5
 
6
+
7
  def load_signal(file_path, column_name):
8
  try:
9
  with open(file_path, 'r') as file:
 
12
  if 'Timestamp' in line:
13
  header_line = i
14
  break
15
+
16
  # 見つけたヘッダー行からデータを読み込む
17
  df = pd.read_csv(file_path, skiprows=header_line)
18
  signal = df[column_name].values
 
23
  except KeyError as e:
24
  print(f"Column '{column_name}' not found in the file. ({e})", file=sys.stderr)
25
  return []
26
+
 
27
 
28
  samplerate = 1000
29
  time_data = load_signal("./test2_143809.csv", "Timestamp")
 
33
  gpass = 5 # 通過域端最大損失[dB]
34
  gstop = 40 # 阻止域端最小損失[dB]
35
  Fs = 4096 # フレームサイズ
36
+ overlap = 90
37
 
38
  data_filt = highpass.highpass_filter(signal_data, samplerate, fp, fs, gpass, gstop)
39
 
40
  t_array_org, N_ave_org = highpass.overlap_frames(signal_data, samplerate, Fs, overlap)
41
  t_array_filt, N_ave_filt = highpass.overlap_frames(signal_data, samplerate, Fs, overlap)
42
+
43
  t_array_org, acf_org = highpass.hanning(t_array_org, Fs, N_ave_org)
44
  t_array_filt, acf_filt = highpass.hanning(t_array_filt, Fs, N_ave_filt)
45
+
46
  fft_array_org, fft_mean_org, fft_axis_org = highpass.fft_ave(t_array_org, samplerate, Fs, N_ave_org, acf_org)
47
  fft_array_filt, fft_mean_filt, fft_axis_filt = highpass.fft_ave(t_array_filt, samplerate, Fs, N_ave_filt, acf_filt)
48
 
 
52
  # フォントの種類とサイズを設定する。
53
  # plt.rcParams['font.size'] = 14
54
  # plt.rcParams['font.family'] = 'Times New Roman'
55
+
56
  # 目盛を内側にする。
57
  plt.rcParams['xtick.direction'] = 'in'
58
  plt.rcParams['ytick.direction'] = 'in'
59
+
60
  # グラフの上下左右に目盛線を付ける。
61
+ fig = plt.figure(figsize=(20, 10))
62
  ax1 = fig.add_subplot(211)
63
  ax1.yaxis.set_ticks_position('both')
64
  ax1.xaxis.set_ticks_position('both')
65
  ax2 = fig.add_subplot(212)
66
  ax2.yaxis.set_ticks_position('both')
67
  ax2.xaxis.set_ticks_position('both')
68
+
69
  # 軸のラベルを設定する。
70
  ax1.set_xlabel('Time [s]')
71
  ax1.set_ylabel('V[μV]')
 
78
  ax2.plot(fft_axis_org, fft_mean_org, label='original', lw=1)
79
  ax2.plot(fft_axis_filt, fft_mean_filt, label='filtered', lw=1)
80
  plt.legend()
81
+
82
  # 軸のリミットを設定する。
83
  # ax1.set_xlim(0,1200)
84
  # ax1.set_xticks(np.arange(0,1201,100))
85
  # ax2.set_xlim(0, max(fft_axis_org)/2)
86
  # ax2.set_xticks(np.arange(0,501,10))
87
  # ax2.set_ylim(-50, 150)
88
+
89
  # レイアウト設定
90
  fig.tight_layout()
91
+
92
  # グラフを表示する。
93
+ plt.savefig("./out.png")