correct get_time_freq

get_time_freq_detection.py

@@ -20,7 +20,7 @@ def main(arguments):
     df, dir_path = utils.detection2time_freq(annotations_folder=arguments.path_to_data,
                                              duration=arguments.duration,
                                              outdir=arguments.directory,
-                                             sr=arguments.sr,
+                                             rf=arguments.rf,
                                              names=names,
                                              wav=args.path_to_wav,
                                              raven=args.raven)
@@ -35,7 +35,10 @@ def main(arguments):
                              '"begin time (s)"; "end time (s)" and the species detected'
                              ' in the given files.')
     ds.attrs['date_created'] = pd.Timestamp.now().isoformat()
+    try:
       ds.attrs['creator_name'] = os.getlogin()
+    except Exception:
+      ds.attrs['creator_name'] = input('Your name : ')
 
     # Save Dataset to NetCDF file
     ds.to_netcdf(dir_path)
@@ -51,8 +54,8 @@ if __name__ == "__main__":
                         help='Directory where the dataframe will be stored')
     parser.add_argument('names', type=str,
                         help='path to YOLOv5 custom_data.yaml file')
-    parser.add_argument('-s', '--sr', type=int,
-                        help='Sampling Rate of the spectrogram', required=True)
+    parser.add_argument('--rf', type=int,
+                        help='Resample frequency', required=True)
     parser.add_argument('--duration', type=int,
                         help='Duration of the spectrogram', default=8)
     parser.add_argument('--path_to_wav', type=utils.arg_directory,

utils.py

@@ -355,17 +355,19 @@ def prepare_dataframe(df, args):
     return df, species_list
 
 
-def detection2time_freq(annotations_folder, duration, outdir, sr, names, wav, raven):
+def detection2time_freq(annotations_folder, duration, outdir, rf, names, wav, raven):
     """
     Collect all .txt detection and get time and frequency informations
-    :param annotations_folder (str): Path to the .json files
+    :param annotations_folder (str): Path to the .txt files
     :param duration (int): Directory to save the .txt files
-    :param outfir (str): Directory to save the .txt files
-    :param sr (int): Directory to save the .txt files
-    :param names (str): Directory to save the .txt files
+    :param outdir (str): Directory to save the .txt files
+    :param rf (int): Resampling freq.
+    :param names (str): names of the classes
+    :param wav (str): Path to the wav
+    :param raven (int): Save into Raven format or not
     """
     today = date.today()
-    out_file = f'YOLO_detection_{today.day}_{today.month}_freq_{sr}_duration_{duration}.nc'
+    out_file = f'YOLO_detection_{today.day}_{today.month}_freq_{rf}_duration_{duration}.nc'
 
     # Load and process data
     df = pd.concat({f: pd.read_csv(os.path.join(annotations_folder, f),
@@ -390,8 +392,8 @@ def detection2time_freq(annotations_folder, duration, outdir, sr, names, wav, ra
     df['species'] = df['class'].apply(lambda x: names[int(x)])
 
     df['pos'] = (df['x'] * duration) + df['class'].astype(int)
-    df['Low Freq (Hz)'] = (1 - df['y']) * (sr / 2) - (df['h'] * (sr / 2)) / 2
-    df['High Freq (Hz)'] = (1 - df['y']) * (sr / 2) + (df['h'] * (sr / 2)) / 2
+    df['Low Freq (Hz)'] = (1 - df['y']) * (rf / 2) - (df['h'] * (rf / 2)) / 2
+    df['High Freq (Hz)'] = (1 - df['y']) * (rf / 2) + (df['h'] * (rf / 2)) / 2
     df['Begin Time (s)'] = df['pos'] - (df['w'] * duration) / 2
     df['End Time (s)'] = df['pos'] + (df['w'] * duration) / 2
     df['duration'] = df['End Time (s)'] - df['Begin Time (s)']