clean up steps

noise_processing.py

@@ -1,38 +1,39 @@
+# importing general Python libraries
 import pandas as pd
-from orcasound_noise.pipeline.pipeline import NoiseAnalysisPipeline
-from orcasound_noise.utils import Hydrophone
 import datetime as dt
 import os
-
-import pandas as pd
 import matplotlib.pyplot as plt
 import pytz
+import plotly.graph_objects as go
 
-
+# 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
 
 
-import plotly.graph_objects as go
-
-#Example 1: Port Townsend, 1 Hz Frequency, 60-second samples
+# Set Location and Resolution
+# Port Townsend, 1 Hz Frequency, 60-second samples
 if __name__ == '__main__':
     pipeline = NoiseAnalysisPipeline(Hydrophone.PORT_TOWNSEND,
                                      delta_f=10, bands=None,
                                      delta_t=60, mode='safe')
 
 
-now = dt.datetime.now(pytz.timezone('US/Pacific'))
 
+
+# Generate parquet dataframes with noise levels for a time period
+
+now = dt.datetime.now(pytz.timezone('US/Pacific'))
 psd_path, broadband_path = pipeline.generate_parquet_file(now - dt.timedelta(hours = 6), 
                                                           now - dt.timedelta(hours = 1), 
                                                           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 because it does not exist
+# Create a new directory if it does not exist
 if not os.path.exists('img'):
    os.makedirs('img')