def test_clean_df():
# Default, no arguments. multi-track, with overlaps
# But correct sorting, columns, and index
res = clean_df(CLEAN_INPUT_DF)
assert res.equals(
clean_df(CLEAN_INPUT_DF, single_track=False, non_overlapping=False)
), "clean_df does not default to False, False for optional args."
for track, overlap in itertools.product([True, False], repeat=2):
kwargs = {"single_track": track, "non_overlapping": overlap}
prior = CLEAN_INPUT_DF.copy()
res = clean_df(CLEAN_INPUT_DF, **kwargs)
assert CLEAN_INPUT_DF.equals(prior), "clean_df changed input df"
assert res.equals(
CLEAN_RES_DFS[track]
[overlap]), f"clean_df result incorrect for args: {kwargs}"
def csv_to_df(csv_path, single_track=False, non_overlapping=False):
"""
Read a csv and create a standard note event DataFrame - a `note_df`.
Parameters
----------
csv_path : str
The path of the csv to be imported.
single_track : boolean
True to set the track of every note to 0. This will happen before
overlaps are removed.
non_overlapping : boolean
True to remove overlaps from the resulting dataframe by passing the df
to df_utils.remove_pitch_overlaps. This will create a situation where,
for every (track, pitch) pair, for any point in time which there is a
sustained note present in the input, there will be a sustained note
in the returned df. Likewise for any point with a note onset.
Returns
-------
note_df : pd.DataFrame
A note_df, in mdtk's standard format. With columns:
onset (int): onset time of a note, in ms.
track (int): the track of the note.
pitch (int): the MIDI pitch of the note.
dur (int): the duration of the note, in ms.
Sorting will be first by onset, then track, then pitch, then duration.
"""
df = pd.read_csv(csv_path, names=NOTE_DF_SORT_ORDER)
df = clean_df(df, single_track=single_track, non_overlapping=non_overlapping)
return df
def test_midi_to_df():
df = fileio.midi_to_df(TEST_MID)
# This relies on pretty_midi being correct
m = pretty_midi.PrettyMIDI(TEST_MID)
midi_notes = []
for i, instrument in enumerate(m.instruments):
for note in instrument.notes:
midi_notes.append(
{
"onset": int(round(note.start * 1000)),
"track": i,
"pitch": note.pitch,
"dur": int(round(note.end * 1000) - round(note.start * 1000)),
"velocity": note.velocity,
}
)
midi_df = pd.DataFrame(midi_notes)
df_notes = df.to_dict("records")
# Test that all notes df notes are in the MIDI
for df_note in df_notes:
assert df_note in midi_notes, (
f"DataFrame note {df_note} not in "
+ "list of MIDI notes from pretty_midi "
+ "(or was duplicated)."
)
midi_notes.remove(df_note)
# Test that all MIDI notes were in the df
assert len(midi_notes) == 0, (
"Some MIDI notes (from pretty_midi) were "
+ f"not found in the DataFrame: {midi_notes}"
)
# Check clean_df (args, sorting)
for (track, overlap) in itertools.product([False, True], repeat=2):
kwargs = {"single_track": track, "non_overlapping": overlap}
correct = clean_df(midi_df, **kwargs)
res = fileio.midi_to_df(TEST_MID, **kwargs)
assert res.equals(
correct
), f"csv_to_midi not using args correctly with args={kwargs}"
def midi_to_df(midi_path, single_track=False, non_overlapping=False):
"""
Get the data from a MIDI file and load it into a pandas DataFrame.
Parameters
----------
midi_path : string
The filename of the MIDI file to parse.
single_track : boolean
True to set the track of every note to 0. This will happen before
overlaps are removed.
non_overlapping : boolean
True to remove overlaps from the resulting dataframe by passing the df
to df_utils.remove_pitch_overlaps. This will create a situation where,
for every (track, pitch) pair, for any point in time which there is a
sustained note present in the input, there will be a sustained note
in the returned df. Likewise for any point with a note onset.
Returns
-------
df : DataFrame
A pandas DataFrame containing the notes parsed from the given MIDI
file. There will be 4 columns:
onset: Onset time of the note, in milliseconds.
track: The track number of the instrument the note is from.
pitch: The MIDI pitch number for the note.
dur: The duration of the note (offset - onset), in milliseconds.
Sorting will be first by onset, then track, then pitch, then duration.
"""
try:
midi = pretty_midi.PrettyMIDI(midi_path)
except Exception:
logging.warning(f"Error parsing midi file {midi_path}. Skipping.")
return None
notes = []
for index, instrument in enumerate(midi.instruments):
for note in instrument.notes:
notes.append(
{
"onset": int(round(note.start * 1000)),
"track": index,
"pitch": note.pitch,
"dur": int(round(note.end * 1000) - round(note.start * 1000)),
}
)
if len(notes) == 0:
logging.warning(
f"WARNING: the midi file located at {midi_path} is empty. "
"Returning None.",
)
return None
df = clean_df(
pd.DataFrame(notes), single_track=single_track, non_overlapping=non_overlapping
)
return df