Started documenting code

audio_methods.py

@@ -21,8 +21,17 @@ _SCALING_FACTORS = pd.Series(
 ) # Factors used to normalize song maps
 
 def midi_to_notes(midi_file: str) -> pd.DataFrame:
-  # Convert midi file to "song map" (dataframe where each note is broken
-  # into its components)
+  """
+  Convert midi file to "song map" (dataframe where each note is broken
+  into its components)
+
+  Parameters:
+  midi_file (str): Path to the midi file.
+
+  Returns:
+  pd.Dataframe: 3xN matrix where each column is a note, composed of
+  pitch, duration and step.
+  """
     
   pm = pretty_midi.PrettyMIDI(midi_file)
   instrument = pm.instruments[0]
@@ -48,17 +57,46 @@ def midi_to_notes(midi_file: str) -> pd.DataFrame:
   return notes_df / _SCALING_FACTORS # Scale
 
 
-def display_audio(pm: pretty_midi.PrettyMIDI, seconds=120):
+def display_audio(pm: pretty_midi.PrettyMIDI, seconds=-1):
+  """
+  Display a song in PrettyMIDI format as a display.Audio object.
+  This method specially comes in useful in a jupyter notebook.
+
+  Parameters:
+  pm (str): PrettyMidi object containing a song.
+  seconds (int): Time fraction of the song to be displayed. When
+                 set to -1, the full length is taken.
+
+  Returns:
+  display.Audio: Song as an object allowing for display.
+  """
+    
   waveform = pm.fluidsynth(fs=_SAMPLING_RATE)
   # Take a sample of the generated waveform to mitigate kernel resets
-  waveform_short = waveform[:seconds*_SAMPLING_RATE]
+  if seconds == -1: 
+      waveform_short = waveform[:]
+  else:
+      waveform_short = waveform[:seconds*_SAMPLING_RATE]
+  
   return display.Audio(waveform_short, rate=_SAMPLING_RATE)
 
 
 # Define function to convert song map to wav
 
 def map_to_wav(song_map: pd.DataFrame, out_file: str, velocity: int=100):
-  # Convert "song map" to midi file (reverse process with respect to midi_to_notes)
+  """
+  Convert "song map" to midi file (reverse process with respect to midi_to_notes)
+
+  Parameters:
+  song_map (pd.DataFrame): Path to the midi file.
+  out_file (str): Name of the [CONTINUE].
+
+  Returns:
+  pd.Dataframe: 3xN matrix as a dataframe where each column is a note,
+                composed of pitch, duration and step.
+  """
+    
+  # 
     
   contracted_map = tf.squeeze(song_map)
   song_map_T = contracted_map.numpy().T