pyflakes

birdvoxdetect/cli.py

@@ -1,12 +1,13 @@
 from __future__ import print_function
-import os
-import sys
-from birdvoxdetect.birdvoxdetect_exceptions import BirdVoxDetectError
-import birdvoxdetect
 from argparse import ArgumentParser, RawDescriptionHelpFormatter,\
     ArgumentTypeError
 from collections import Iterable
+import os
 from six import string_types
+import sys
+
+import birdvoxdetect
+from birdvoxdetect.birdvoxdetect_exceptions import BirdVoxDetectError
 
 
 def positive_float(value):
@@ -83,7 +84,7 @@ def parse_args(args):
 
     parser.add_argument('--export-clips', '-c', action='store_true',
         help='Export detected events as audio clips in WAV format.')
-    
+
     parser.add_argument('--export-likelihood', '-l', action='store_true',
         help='Export the time series of event likelihood of events in HDF5 format.')
 
@@ -108,24 +109,24 @@ def parse_args(args):
 
     parser.add_argument('--quiet', '-q', action='store_true',
         help='Suppress all non-error messages to stdout.')
-    
+
     parser.add_argument('--verbose', '-v', action='store_true',
         help='Print timestamps of detected events.')
 
     args = parser.parse_args(args)
-    
+
     if args.quiet and args.verbose:
         raise BirdVoxDetectError(
             'Command-line flags --quiet (-q) and --verbose (-v) '
             'are mutually exclusive.')
-        
+
     if args.clip_duration is None:
         args.clip_duration = 1.0
     elif not args.export_clips:
         raise BirdVoxDetectError(
             'The --export-clips (-c) flag should be present '
             'if the --clip-duration (-d) flag is present.')
-    
+
     return args
 
 
@@ -135,11 +136,11 @@ def main():
     deep learning model (Lostanlen et al. 2019).
     """
     args = parse_args(sys.argv[1:])
-    
+
     if args.inputs[0] == "-V":
         print(birdvoxdetect.version.version)
         return
-        
+
 
     run(args.inputs,
         output_dir=args.output_dir,

birdvoxdetect/core.py

@@ -3,6 +3,9 @@
 import os
 import scipy.signal
 import soundfile as sf
+import traceback
+
+from birdvoxdetect.birdvoxdetect_exceptions import BirdVoxDetectError
 
 
 def pad_audio(audio, padding_length):
@@ -20,59 +23,62 @@ def process_file(filepath,
         frame_rate=20.0,
         clip_duration=1.0,
         logger_level=20):
-    
+
     # Check for existence of the file.
     if not os.path.exists(filepath):
-        raise BirdVoxDetectError('File "{}" could not be found.'.format(filepath))
-
+        raise BirdVoxDetectError(
+            'File "{}" could not be found.'.format(filepath))
+
     # Try loading the file as NumPy array.
     try:
         audio, sr = sf.read(filepath)
     except Exception:
-        raise OpenL3Error('Could not open file "{}":\n{}'.format(filepath, traceback.format_exc()))
-
+        raise BirdVoxDetectError(
+            'Could not open file "{}":\n{}'.format(filepath,
+            traceback.format_exc()))
+
     # Compute likelihood curve.
     likelihood = get_likelihood(audio, sr, frame_rate=frame_rate)
-    
+
     # Find peaks.
-    
+
     # Export timestamps.
     timestamps_path = get_output_path(
         filepath, suffix + "_timestamps.csv", output_dir=output_dir)
-    
+
     # Export likelihood curve.
     if export_likelihood:
         likelihood_path = get_output_path(
             filepath, suffix + "_likelihood.hdf5", output_dir=output_dir)
-        
+
     # Export clips.
     if export_clips:
         clips_dir = get_output_path(
             filepath, suffix + "_clips", output_dir=output_dir)
 
 
 def get_likelihood(audio, sr, frame_rate):
+    # Load settings.
+    pcen_settings = get_pcen_settings()
+
     # Check audio array dimension
     if audio.ndim > 2:
         raise OpenL3Error('Audio array can only be be 1D or 2D')
     elif audio.ndim == 2:
         # Downmix if multichannel
         audio = np.mean(audio, axis=1)
-        
+
     # Resample to 22,050 kHz
     if not sr == pcen_settings["sr"]:
         audio = librosa.resample(audio, sr, pcen_settings["sr"])
-        
+
     # Pad.
     padding_length = int(np.round(0.5 * sr / frame_rate))
     audio = pad_audio(audio, padding_length)
-
-    # Load settings.
-    pcen_settings = get_pcen_settings()
-
+
     # Compute Short-Term Fourier Transform (STFT).
     stft = librosa.stft(
-        chunk_waveform,
+        audio,
         n_fft=pcen_settings["n_fft"],
         win_length=pcen_settings["win_length"],
         hop_length=pcen_settings["hop_length"],
@@ -114,10 +120,10 @@ def get_likelihood(audio, sr, frame_rate):
     audio_duration = len(audio) / pcen_settings["sr"]
     likelihood_x = np.arange(0.0, audio_duration, 1.0/frame_rate)
     likelihood_y = median_likelihood[likelihood_x]
-    
+
     # Return.
     return likelihood_y
-    
+
 
 def get_output_path(filepath, suffix, output_dir=None):
     """