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.github/workflows/noise_processing.yml

@@ -5,7 +5,7 @@ on:
   workflow_dispatch:
   #schedule:
     #- cron: '9 * * * *'
-    
+
 jobs:
   process:
     runs-on: ubuntu-latest
@@ -22,12 +22,12 @@ jobs:
       run: |
         pip install -r requirements_noise.txt
 
- 
+
     - name: Install ffmpeg
       run: |
        sudo apt-get update
        sudo apt-get install ffmpeg
-    
+
     - uses: awalsh128/cache-apt-pkgs-action@latest
       with:
         packages: ffmpeg
@@ -53,6 +53,6 @@ jobs:
    #   uses: AnimMouse/setup-rclone@v1
    #   with:
    #     rclone_config: ${{secrets.RCLONE_CONFIG}}
-   #   
+   #
    # - run: |
    #     rclone copy ambient_sound_analysis/img/broadband.png mydrive:rclone_uploads/

noise_processing.py

@@ -1,47 +1,45 @@
 # importing general Python libraries
-import pandas as pd
 import datetime as dt
 import os
+
 import matplotlib.pyplot as plt
-import pytz
+import pandas as pd
 import plotly.graph_objects as go
+import pytz
+from orcasound_noise.pipeline.acoustic_util import plot_bb, plot_spec
 
 # importing orcasound_noise libraries
 from orcasound_noise.pipeline.pipeline import NoiseAnalysisPipeline
 from orcasound_noise.utils import Hydrophone
-from orcasound_noise.pipeline.acoustic_util import plot_spec, plot_bb
-
 
 # Set Location and Resolution
 # Port Townsend, 1 Hz Frequency, 60-second samples
-if __name__ == '__main__':
-    pipeline = NoiseAnalysisPipeline(Hydrophone.BUSH_POINT,
-                                     delta_f=10, bands=None,
-                                     delta_t=60, mode='safe')
-
-
+if __name__ == "__main__":
+    pipeline = NoiseAnalysisPipeline(
+        Hydrophone.BUSH_POINT, delta_f=10, bands=None, delta_t=60, mode="safe"
+    )
 
 
 # Generate parquet dataframes with noise levels for a time period
 
-now = dt.datetime.now(pytz.timezone('US/Pacific'))
+now = dt.datetime.now(pytz.timezone("US/Pacific"))
 now = dt.datetime(2024, 11, 20, 10)
-psd_path, broadband_path = pipeline.generate_parquet_file(now - dt.timedelta(hours = 10), 
-                                                          now - dt.timedelta(hours = 8), 
-                                                          upload_to_s3=False)
+psd_path, broadband_path = pipeline.generate_parquet_file(
+    now - dt.timedelta(hours=10), now - dt.timedelta(hours=8), upload_to_s3=False
+)
 
 # Read the parquet files
 psd_df = pd.read_parquet(psd_path)
 bb_df = pd.read_parquet(broadband_path)
 
 # Create a new directory if it does not exist
-if not os.path.exists('img'):
-   os.makedirs('img')
+if not os.path.exists("img"):
+    os.makedirs("img")
 
-# Create and save psd plot 
+# Create and save psd plot
 fig = plot_spec(psd_df)
-fig.write_image('img/psd.png')
+fig.write_image("img/psd.png")
 
 # Create and save bb plot
 fig = plot_bb(bb_df)
-fig.savefig('img/broadband.png')
+fig.savefig("img/broadband.png")

requirements_noise.txt

@@ -1,6 +1,6 @@
-matplotlib 
-# scipy 
-m3u8 
+matplotlib
+# scipy
+m3u8
 git+https://github.com/kkroening/ffmpeg-python
 orcasound_noise@git+https://github.com/orcasound/ambient-sound-analysis@ffmpeg-quiet
 kaleido

tests/test_ooi.py

@@ -1,4 +1,5 @@
 """Unit tests for OOI workflow"""
+
 import datetime
 import os
 import shutil

tests/test_spectrograms.py

@@ -1,4 +1,5 @@
 """Unit tests for various util functions relating to spectrogram creation"""
+
 import os.path
 
 import matplotlib.pyplot as plt