import re
import logging
from fractions import Fraction as frac
from collections import defaultdict, ChainMap # for merging dictionaries
import bs4 # python -m pip install beautifulsoup4 lxml
import pandas as pd
import numpy as np
from .bs4_measures import MeasureList
from .logger import get_logger, function_logger
from .utils import fifths2name, ordinal_suffix, resolve_dir
class _MSCX_bs4:
""" This sister class implements MSCX's methods for a score parsed with beautifulsoup4.
Attributes
----------
mscx_src : :obj:`str`
Path to the uncompressed MuseScore 3 file (MSCX) to be parsed.
logger_name : :obj:`str`, optional
If you have defined a logger, pass its name.
level : {'W', 'D', 'I', 'E', 'C', 'WARNING', 'DEBUG', 'INFO', 'ERROR', 'CRITICAL'}, optional
Pass a level name for which (and above which) you want to see log records.
"""
durations = {"measure": frac(1),
"breve": frac(2), # in theory, of course, they could have length 1.5
"long": frac(4), # and 3 as well and other values yet
"whole": frac(1),
"half": frac(1 / 2),
"quarter": frac(1 / 4),
"eighth": frac(1 / 8),
"16th": frac(1 / 16),
"32nd": frac(1 / 32),
"64th": frac(1 / 64),
"128th": frac(1 / 128),
"256th": frac(1 / 256),
"512th": frac(1 / 512), }
def __init__(self, mscx_src, read_only=False, logger_name='_MSCX_bs4', level=None):
self.logger = get_logger(logger_name, level=level)
self.soup = None
self._measures, self._events, self._notes = pd.DataFrame(), pd.DataFrame(), pd.DataFrame()
self.mscx_src = mscx_src
self.read_only = read_only
self.first_mc = 1
self.measure_nodes = {}
self.tags = {} # only used if not self.read_only
self.has_annotations = False
self._ml = None
cols = ['mc', 'onset', 'duration', 'staff', 'voice', 'scalar', 'nominal_duration']
self._nl, self._cl, self._rl, self._nrl = pd.DataFrame(), pd.DataFrame(columns=cols), pd.DataFrame(
columns=cols), pd.DataFrame(columns=cols)
self.parse_measures()
def parse_mscx(self):
assert self.mscx_src is not None, "No MSCX file specified." \
""
with open(self.mscx_src, 'r') as file:
self.soup = bs4.BeautifulSoup(file.read(), 'xml')
if self.version[0] != '3':
# self.logger.exception(f"Cannot parse MuseScore {self.version} file.")
raise ValueError(f"Cannot parse MuseScore {self.version} file.")
# Populate measure_nodes with one {mc: <Measure>} dictionary per staff.
# The <Staff> nodes containing the music are siblings of <Part>
# <Part> contains <Staff> nodes with staff information which is being ignored for now
for staff in self.soup.find('Part').find_next_siblings('Staff'):
staff_id = int(staff['id'])
self.measure_nodes[staff_id] = {}
for mc, measure in enumerate(staff.find_all('Measure'), start=self.first_mc):
self.measure_nodes[staff_id][mc] = measure
def parse_measures(self):
""" Converts the score into the three DataFrame self._measures, self._events, and self._notes
"""
if self.soup is None:
self.parse_mscx()
grace_tags = ['grace4', 'grace4after', 'grace8', 'grace8after', 'grace16', 'grace16after', 'grace32',
'grace32after', 'grace64', 'grace64after', 'appoggiatura', 'acciaccatura']
measure_list, event_list, note_list = [], [], []
staff_ids = tuple(self.measure_nodes.keys())
chord_id = 0
# For every measure: bundle the <Measure> nodes from every staff
for mc, measure_stack in enumerate(
zip(
*[[measure_node for measure_node in measure_dict.values()] for measure_dict in
self.measure_nodes.values()]
),
start=self.first_mc):
if not self.read_only:
self.tags[mc] = {}
# iterate through staves and collect information about each <Measure> node
for staff_id, measure in zip(staff_ids, measure_stack):
if not self.read_only:
self.tags[mc][staff_id] = {}
measure_info = {'mc': mc, 'staff': staff_id}
measure_info.update(recurse_node(measure, exclude_children=['voice']))
# iterate through <voice> tags and run a position counter
voice_nodes = measure.find_all('voice', recursive=False)
# measure_info['voices'] = len(voice_nodes)
for voice_id, voice_node in enumerate(voice_nodes, start=1):
if not self.read_only:
self.tags[mc][staff_id][voice_id] = defaultdict(list)
current_position = frac(0)
duration_multiplier = 1
multiplier_stack = [1]
# iterate through children of <voice> which constitute the note level of one notational layer
for event_node in voice_node.find_all(recursive=False):
event_name = event_node.name
event = {
'mc': mc,
'staff': staff_id,
'voice': voice_id,
'onset': current_position,
'duration': frac(0)}
if event_name == 'Chord':
event['chord_id'] = chord_id
grace = event_node.find(grace_tags)
dur, dot_multiplier = bs4_chord_duration(event_node, duration_multiplier)
if grace:
event['gracenote'] = grace.name
else:
event['duration'] = dur
chord_info = dict(event)
note_event = dict(chord_info)
for chord_child in event_node.find_all(recursive=False):
if chord_child.name == 'Note':
note_event.update(recurse_node(chord_child, prepend=chord_child.name))
note_list.append(note_event)
note_event = dict(chord_info)
else:
event.update(recurse_node(chord_child, prepend='Chord/' + chord_child.name))
chord_id += 1
elif event_name == 'Rest':
event['duration'], dot_multiplier = bs4_rest_duration(event_node, duration_multiplier)
elif event_name == 'location': # <location> tags move the position counter
event['duration'] = frac(event_node.fractions.string)
elif event_name == 'Tuplet':
multiplier_stack.append(duration_multiplier)
duration_multiplier = duration_multiplier * frac(int(event_node.normalNotes.string),
int(event_node.actualNotes.string))
elif event_name == 'endTuplet':
duration_multiplier = multiplier_stack.pop()
# These nodes describe the entire measure and go into measure_list
# All others go into event_list
if event_name in ['TimeSig', 'KeySig', 'BarLine'] or (
event_name == 'Spanner' and 'type' in event_node.attrs and event_node.attrs[
'type'] == 'Volta'):
measure_info.update(recurse_node(event_node, prepend=f"voice/{event_name}"))
else:
event.update({'event': event_name})
if event_name == 'Chord':
event['scalar'] = duration_multiplier * dot_multiplier
for attr, value in event_node.attrs.items():
event[f"Chord:{attr}"] = value
elif event_name == 'Rest':
event['scalar'] = duration_multiplier * dot_multiplier
event.update(recurse_node(event_node, prepend=event_name))
else:
event.update(recurse_node(event_node, prepend=event_name))
event_list.append(event)
if not self.read_only:
remember = {'name': event_name,
'duration': event['duration'],
'tag': event_node, }
position = event['onset']
if event_name == 'location' and event['duration'] < 0:
# this is a backwards pointer: store it where it points to for easy deletion
position += event['duration']
self.tags[mc][staff_id][voice_id][position].append(remember)
current_position += event['duration']
measure_list.append(measure_info)
col_order = ['mc', 'onset', 'event', 'duration', 'staff', 'voice', 'chord_id', 'gracenote', 'scalar', 'tpc',
'pitch']
self._measures = sort_cols(pd.DataFrame(measure_list), col_order)
self._events = sort_cols(pd.DataFrame(event_list), col_order)
if 'chord_id' in self._events.columns:
self._events.chord_id = self._events.chord_id.astype('Int64')
self._notes = sort_cols(pd.DataFrame(note_list), col_order)
if len(self._events) == 0:
self.logger.warning("Empty score?")
elif 'Harmony' in self._events.event.values:
self.has_annotations = True
def output_mscx(self, filepath):
with open(resolve_dir(filepath), 'w') as file:
file.write(bs4_to_mscx(self.soup))
self.logger.info(f"Score written to {filepath}.")
return True
def _make_measure_list(self, section_breaks=True, secure=False, reset_index=True, logger_name=None):
""" Regenerate the measure list from the parsed score with advanced options."""
ln = self.logger.name if logger_name is None else logger_name
return MeasureList(self._measures, section_breaks=section_breaks, secure=secure, reset_index=reset_index, logger_name=ln)
@property
def measures(self):
""" Retrieve a standard measure list from the parsed score.
"""
self._ml = self._make_measure_list()
return self._ml.ml
@property
def ml(self):
"""Like property `measures` but without recomputing."""
if self._ml is None:
return self.measures
return self._ml.ml
@property
def chords(self):
"""A list of <chord> tags (all <note> tags come within one) and attached score information such as
lyrics, dynamics, articulations, slurs, etc."""
return self.get_chords()
@property
def cl(self):
"""Getting self._cl but without recomputing."""
if len(self._cl) == 0:
self.make_standard_chordlist()
return self._cl
@property
def notes(self):
"""A list of all notes with their features."""
self.make_standard_notelist()
return self._nl
@property
def nl(self):
"""Like property `notes` but without recomputing."""
if len(self._nl) == 0:
return self.notes
return self._nl
@property
def rests(self):
"""A list of all rests with their features."""
self.make_standard_restlist()
return self._rl
@property
def rl(self):
"""Like property `rests` but without recomputing."""
if len(self._rl) == 0:
return self.rests
return self._rl
@property
def notes_and_rests(self):
"""Get a combination of properties `notes` and `rests`"""
if len(self._nrl) == 0:
nr = pd.concat([self.nl, self.rl]).astype({col: 'Int64' for col in ['tied', 'tpc', 'midi', 'chord_id']})
self._nrl = sort_note_list(nr.reset_index(drop=True))
return self._nrl
def make_standard_chordlist(self):
""" This chord list has chords only as opposed to the one yielded by selr.get_chords()"""
self._cl = self.add_standard_cols(self._events[self._events.event == 'Chord'])
self._cl = self._cl.astype({'chord_id': int})
self._cl.rename(columns={'Chord/durationType': 'nominal_duration'}, inplace=True)
self._cl.loc[:, 'nominal_duration'] = self._cl.nominal_duration.map(self.durations)
cols = ['mc', 'mn', 'timesig', 'onset', 'staff', 'voice', 'duration', 'gracenote', 'nominal_duration', 'scalar', 'volta', 'chord_id']
for col in cols:
if not col in self._cl.columns:
self._cl[col] = np.nan
self._cl = self._cl[cols]
def make_standard_restlist(self):
self._rl = self.add_standard_cols(self._events[self._events.event == 'Rest'])
if len(self._rl) == 0:
return
self._rl = self._rl.rename(columns={'Rest/durationType': 'nominal_duration'})
self._rl.loc[:, 'nominal_duration'] = self._rl.nominal_duration.map(self.durations)
cols = ['mc', 'mn', 'timesig', 'onset', 'staff', 'voice', 'duration', 'nominal_duration', 'scalar', 'volta']
self._rl = self._rl[cols].reset_index(drop=True)
def make_standard_notelist(self):
cols = {'midi': 'Note/pitch',
'tpc': 'Note/tpc',
}
self._nl = self.add_standard_cols(self._notes)
self._nl.rename(columns={v: k for k, v in cols.items()}, inplace=True)
self._nl = self._nl.astype({'midi': int, 'tpc': int})
self._nl.tpc -= 14
self._nl = self._nl.merge(self.cl[['chord_id', 'nominal_duration', 'scalar']], on='chord_id')
tie_cols = ['Note/Spanner:type', 'Note/Spanner/next/location', 'Note/Spanner/prev/location']
self._nl['tied'] = make_tied_col(self._notes, *tie_cols)
final_cols = [col for col in ['mc', 'mn', 'timesig', 'onset', 'staff', 'voice', 'duration', 'gracenote', 'nominal_duration',
'scalar', 'tied', 'tpc', 'midi', 'volta', 'chord_id'] if col in self._nl.columns]
self._nl = sort_note_list(self._nl[final_cols])
def get_chords(self, staff=None, voice=None, mode='auto', lyrics=False, staff_text=False, dynamics=False, articulation=False, spanners=False, **kwargs):
""" Returns a DataFrame with the score's chords (groups of simultaneous notes in the same layer).
Such a list is needed for extracting certain types of information which is attached to chords rather than notes.
Parameters
----------
staff : :obj:`int`
Get information from a particular staff only (1 = upper staff)
voice : :obj:`int`
Get information from a particular voice only (1 = only the first layer of every staff)
mode : {'auto', 'all', 'strict'}, optional
Defaults to 'auto', meaning that those aspects are automatically included that occur in the score; the resulting
DataFrame has no empty columns except for those parameters that are set to True.
'all': Columns for all aspects are created, even if they don't occur in the score (e.g. lyrics).
'strict': Create columns for exactly those parameters that are set to True, regardless which aspects occur in the score.
lyrics : :obj:`bool`, optional
Include lyrics.
staff_text : :obj:`bool`, optional
Include staff text such as tempo markings.
dynamics : :obj:`bool`, optional
Include dynamic markings such as f or p.
articulation : :obj:`bool`, optional
Include articulation such as arpeggios.
spanners : :obj:`bool`, optional
Include spanners such as slurs, 8va lines, pedal lines etc.
**kwargs : :obj:`bool`, optional
Set a particular keyword to True in order to include all columns from the _events DataFrame
whose names include that keyword. Column names include the tag names from the MSCX source code.
Returns
-------
"""
cols = {'nominal_duration': 'Chord/durationType',
'lyrics': 'Chord/Lyrics/text',
'syllabic': 'Chord/Lyrics/syllabic',
'articulation': 'Chord/Articulation/subtype',
'dynamics': 'Dynamic/subtype'}
sel = self._events.event == 'Chord'
aspects = ['lyrics', 'staff_text', 'dynamics', 'articulation', 'spanners']
if mode == 'all':
params = {p: True for p in aspects}
else:
l = locals()
params = {p: l[p] for p in aspects}
spanner_sel = self._events.event == 'Spanner'
staff_text_sel = self._events.event == 'StaffText'
dynamics_sel = self._events.event == 'Dynamic'
if mode == 'auto':
if not params['spanners'] and spanner_sel.any():
params['spanners'] = True
if not params['staff_text'] and staff_text_sel.any():
params['staff_text'] = True
if not params['dynamics'] and dynamics_sel.any():
params['dynamics'] = True
if params['spanners']:
sel = sel | spanner_sel
if params['staff_text']:
sel = sel | staff_text_sel
if params['dynamics']:
sel = sel | dynamics_sel
if staff:
sel = sel & (self._events.staff == staff)
if voice:
sel = sel & self._events.voice == voice
df = self.add_standard_cols(self._events[sel])
df = df.astype({'chord_id': 'Int64' if df.chord_id.isna().any() else int})
df.rename(columns={v: k for k, v in cols.items() if v in df.columns}, inplace=True)
if mode == 'auto':
if 'lyrics' in df.columns:
params['lyrics'] = True
if 'articulation' in df.columns:
params['articulation'] = True
if any(c in df.columns for c in ('Spanner:type', 'Chord/Spanner:type')):
params['spanners'] = True
df.loc[:, 'nominal_duration'] = df.nominal_duration.map(self.durations)
main_cols = ['mc', 'mn', 'timesig', 'onset', 'staff', 'voice', 'duration', 'gracenote', 'nominal_duration', 'scalar',
'volta', 'chord_id']
if params['staff_text']:
main_cols.append('staff_text')
text_cols = ['StaffText/text', 'StaffText/text/b', 'StaffText/text/i']
existing_cols = [c for c in text_cols if c in df.columns]
if len(existing_cols) > 0:
df.loc[:, 'staff_text'] = df[existing_cols].fillna('').sum(axis=1).replace('', np.nan)
else:
df.loc[:, 'staff_text'] = np.nan
if params['lyrics']:
main_cols.append('lyrics')
if 'syllabic' in df:
# turn the 'syllabic' column into the typical dashs
sy = df.syllabic
empty = pd.Series(np.nan, index=df.index)
syl_start, syl_mid, syl_end = [empty.where(sy != which, '-').fillna('') for which in
['begin', 'middle', 'end']]
lyrics_col = syl_end + syl_mid + df.lyrics + syl_mid + syl_start
elif 'lyrics' in df:
lyrics_col = df.lyrics
else:
lyrics_col = pd.Series(np.nan, index=df.index)
df.loc[:, 'lyrics'] = lyrics_col
if params['articulation']:
main_cols.append('articulation')
if params['dynamics']:
main_cols.append('dynamics')
for col in main_cols:
if not col in df.columns:
df[col] = np.nan
additional_cols = []
if params['spanners']:
spanner_ids = make_spanner_cols(df, logger=self.logger)
if len(spanner_ids.columns) > 0:
additional_cols.extend(spanner_ids.columns.to_list())
df = pd.concat([df, spanner_ids], axis=1)
for feature in kwargs.keys():
additional_cols.extend([c for c in df.columns if feature in c and c not in main_cols])
return df[main_cols + additional_cols]
def get_annotations(self):
""" Returns a list of harmony tags from the parsed score.
Returns
-------
"""
cols = {'label_type': 'Harmony/harmonyType',
'label': 'Harmony/name',
'nashville': 'Harmony/function',
'root': 'Harmony/root',
'base': 'Harmony/base',
'leftParen': 'Harmony/leftParen',
'rightParen': 'Harmony/rightParen'}
std_cols = ['mc', 'mn', 'timesig', 'onset', 'staff', 'voice', 'label',]
main_cols = std_cols + ['nashville', 'root', 'base', 'leftParen', 'rightParen', 'label_type']
sel = self._events.event == 'Harmony'
df = self.add_standard_cols(self._events[sel]).dropna(axis=1, how='all')
if len(df.index) == 0:
return pd.DataFrame(columns=std_cols)
df.rename(columns={v: k for k, v in cols.items() if v in df.columns}, inplace=True)
if 'label_type' in df.columns:
df.label_type.fillna(0, inplace=True)
columns = [c for c in main_cols if c in df.columns]
additional_cols = {c: c[8:] for c in df.columns if c[:8] == 'Harmony/' if c[8:] not in main_cols}
df.rename(columns=additional_cols, inplace=True)
columns += list(additional_cols.values())
return df[columns]
def get_metadata(self):
data = {tag['name']: tag.string for tag in self.soup.find_all('metaTag')}
last_measure = self.ml.iloc[-1]
data['last_mc'] = last_measure.mc
data['last_mn'] = last_measure.mn
data['label_count'] = len(self.get_annotations())
data['TimeSig'] = dict(self.ml.loc[self.ml.timesig != self.ml.timesig.shift(), ['mc', 'timesig']].itertuples(index=False, name=None))
data['KeySig'] = dict(self.ml.loc[self.ml.keysig != self.ml.keysig.shift(), ['mc', 'keysig']].itertuples(index=False, name=None))
first_label = self.soup.find('Harmony')
first_label_name = first_label.find('name') if first_label is not None else None
if first_label_name is not None:
m = re.match(r"^\.?([A-Ga-g](#+|b+)?)", first_label_name.string)
if m.group(1) is not None:
data['annotated_key'] = m.group(1)
staff_groups = self.nl.groupby('staff').midi
ambitus = {t.staff: {'min_midi': t.midi, 'min_name': fifths2name(t.tpc, t.midi)} for t in
self.nl.loc[staff_groups.idxmin(), ['staff', 'tpc', 'midi', ]].itertuples(index=False)}
for t in self.nl.loc[staff_groups.idxmax(), ['staff', 'tpc', 'midi', ]].itertuples(index=False):
ambitus[t.staff]['max_midi'] = t.midi
ambitus[t.staff]['max_name'] = fifths2name(t.tpc, t.midi)
data['parts'] = {
part.trackName.string: {int(staff['id']): ambitus[int(staff['id'])] if int(staff['id']) in ambitus else {} for staff in
part.find_all('Staff')} for part in self.soup.find_all('Part')}
data['musescore'] = self.version
return data
@property
def version(self):
return self.soup.find('programVersion').string
def add_standard_cols(self, df):
df = df.merge(self.ml[['mc', 'mn', 'timesig', 'mc_offset', 'volta']], on='mc', how='left')
# df.onset += df.mc_offset
return df[[col for col in df.columns if not col == 'mc_offset']]
def delete_label(self, mc, staff, voice, onset):
self.make_writeable()
measure = self.tags[mc][staff][voice]
if onset not in measure:
self.logger.warning(f"MC {mc} has no onset {onset} in staff {staff}, voice {voice} where a harmony could be deleted.")
return False
elements = measure[onset]
names = [e['name'] for e in elements]
if not 'Harmony' in names:
self.logger.warning(f"No harmony found at MC {mc}, onset {onset}, staff {staff}, voice {voice}.")
return False
if 'Chord' in names and 'location' in names:
NotImplementedError(f"Check MC {mc}, onset {onset}, staff {staff}, voice {voice}:\n{elements}")
_, name = get_duration_event(elements)
if name is None:
# this label is not attached to a chord or rest and depends on <location> tags, i.e. <location> tags on
# previous and subsequent onsets might have to be adapted
onsets = sorted(measure)
ix = onsets.index(onset)
if ix == 0:
all_dur_ev = sum(True for os, tag_list in measure.items() if get_duration_event(tag_list)[0] is not None)
if all_dur_ev > 0:
raise NotImplementedError(
f"The label on MC {mc}, onset {onset}, staff {staff}, voice {voice} is the first onset but not attached to an event.")
prv_onset = onsets[ix - 1]
prv_elements = measure[prv_onset]
prv_names = [e['name'] for e in prv_elements]
n_locs = names.count('location')
prv_n_locs = prv_names.count('location')
is_last = ix == len(onsets) - 1
if n_locs == 0:
# The current onset has no <location> tag. This presumes that it is the last onset in the measure.
if not is_last:
raise NotImplementedError(
f"The label on MC {mc}, onset {onset}, staff {staff}, voice {voice} is not on the last onset but has no <location> tag.")
if prv_n_locs > 0 and len(names) == 1:
# this harmony is the only event on the last onset, therefore the previous <location> tag can be deleted
if prv_names[-1] != 'location':
raise NotImplementedError(
f"Location tag is not the last element in MC {mc}, onset {onsets[ix-1]}, staff {staff}, voice {voice}.")
prv_elements[-1]['tag'].decompose()
del(measure[prv_onset][-1])
if len(measure[prv_onset]) == 0:
del(measure[prv_onset])
self.logger.debug(f"""Removed <location> tag in MC {mc}, onset {prv_onset}, staff {staff}, voice {voice}
because it precedes the label to be deleted which is the voice's last onset, {onset}.""")
elif n_locs == 1:
if not is_last:
# This presumes that the previous onset has at least one <location> tag which needs to be adapted
assert prv_n_locs > 0, f"""The label on MC {mc}, onset {onset}, staff {staff}, voice {voice} locs forward
but the previous onset {prv_onset} has no <location> tag."""
if prv_names[-1] != 'location':
raise NotImplementedError(
f"Location tag is not the last element in MC {mc}, onset {prv_onset}, staff {staff}, voice {voice}.")
cur_loc_dur = frac(elements[names.index('location')]['duration'])
prv_loc_dur = frac(prv_elements[-1]['duration'])
prv_loc_tag = prv_elements[-1]['tag']
new_loc_dur = prv_loc_dur + cur_loc_dur
prv_loc_tag.fractions.string = str(new_loc_dur)
measure[prv_onset][-1]['duration'] = new_loc_dur
# else: proceed with deletion
elif n_locs == 2:
# this onset has two <location> tags meaning that if the next onset has a <location> tag, too a second
# one needs to be added
assert prv_n_locs == 0, f"""The label on MC {mc}, onset {onset}, staff {staff}, voice {voice} has two
<location> tags but the previous onset {prv_onset} has one, too."""
if not is_last:
nxt_onset = onsets[ix + 1]
nxt_elements = measure[nxt_onset]
nxt_names = [e['name'] for e in nxt_elements]
nxt_n_locs = nxt_names.count('location')
_, nxt_name = get_duration_event(nxt_elements)
if nxt_name is None:
# The next onset is neither a chord nor a rest and therefore it needs to have exactly one
# location tag and a second one needs to be added based on the first one being deleted
nxt_is_last = ix + 1 == len(onsets) - 1
if not nxt_is_last:
assert nxt_n_locs == 1, f"""The label on MC {mc}, onset {onset}, staff {staff}, voice {voice} has two
<location> tags but the next onset {nxt_onset} has {nxt_n_locs if nxt_n_locs > 1 else
"none although it's neither a chord nor a rest, nor the last onset,"}."""
if nxt_names[-1] != 'location':
raise NotImplementedError(
f"Location tag is not the last element in MC {mc}, onset {nxt_onset}, staff {staff}, voice {voice}.")
if names[-1] != 'location':
raise NotImplementedError(
f"Location tag is not the last element in MC {mc}, onset {onset}, staff {staff}, voice {voice}.")
neg_loc_dur = frac(elements[names.index('location')]['duration'])
assert neg_loc_dur < 0, f"""Location tag in MC {mc}, onset {nxt_onset}, staff {staff}, voice {voice}
should be negative but is {neg_loc_dur}."""
pos_loc_dur = frac(elements[-1]['duration'])
new_loc_value = neg_loc_dur + pos_loc_dur
new_tag = self.new_location(new_loc_value)
nxt_elements[0]['tag'].insert_before(new_tag)
remember = {
'name': 'location',
'duration': new_loc_value,
'tag': new_tag
}
measure[nxt_onset].insert(0, remember)
self.logger.debug(f"""Added a new negative <location> tag to the subsequent onset {nxt_onset} in
order to prepare the label deletion on MC {mc}, onset {onset}, staff {staff}, voice {voice}.""")
# else: proceed with deletions because it has no effect on a subsequent onset
else:
raise NotImplementedError(
f"Too many location tags in MC {mc}, onset {prv_onset}, staff {staff}, voice {voice}.")
##### Here the actual removal takes place.
deletions = []
for i, e in enumerate(elements):
if e['name'] in ['Harmony', 'location']:
e['tag'].decompose()
deletions.append(i)
self.logger.debug(f"<{e['name']}>-tag deleted in MC {mc}, onset {onset}, staff {staff}, voice {voice}.")
for i in reversed(deletions):
del(measure[onset][i])
if len(measure[onset]) == 0:
del(measure[onset])
self.remove_empty_voices(mc, staff)
return len(deletions) > 0
def remove_empty_voices(self, mc, staff):
voice_tags = self.measure_nodes[staff][mc].find_all('voice')
dict_keys = sorted(self.tags[mc][staff])
assert len(dict_keys) == len(voice_tags), f"""In MC {mc}, staff {staff}, there are {len(voice_tags)} <voice> tags
but the keys of _MSCX_bs4.tags[{mc}][{staff}] are {dict_keys}."""
for key, tag in zip(reversed(dict_keys), reversed(voice_tags)):
if len(self.tags[mc][staff][key]) == 0:
tag.decompose()
self.logger.debug(f"Empty <voice> tag of voice {key} deleted in MC {mc}, staff {staff}.")
else:
# self.logger.debug(f"No superfluous <voice> tags in MC {mc}, staff {staff}.")
break
def make_writeable(self):
if self.read_only:
self.read_only = False
prev_level = self.logger.getEffectiveLevel()
self.logger.setLevel(logging.CRITICAL)
# This is an automatic re-parse which does not have to be logged again
self.parse_measures()
self.logger.setLevel(prev_level)
def add_label(self, label, mc, onset, staff=1, voice=1, **kwargs):
self.make_writeable()
if mc not in self.tags:
self.logger.error(f"MC {mc} not found.")
return False
if staff not in self.tags[mc]:
self.logger.error(f"Staff {staff} not found.")
return False
if voice not in [1, 2, 3, 4]:
self.logger.error(f"Voice needs to be 1, 2, 3, or 4, not {voice}.")
return False
onset = frac(onset)
label_name = kwargs['decoded'] if 'decoded' in kwargs else label
if voice not in self.tags[mc][staff]:
# Adding label to an unused voice that has to be created
existing_voices = self.measure_nodes[staff][mc].find_all('voice')
n = len(existing_voices)
if not voice <= n:
last = existing_voices[-1]
while voice > n:
last = self.new_tag('voice', after=last)
n += 1
remember = self.insert_label(label=label, loc_before=None if onset == 0 else onset, within=last, **kwargs)
self.tags[mc][staff][voice] = defaultdict(list)
self.tags[mc][staff][voice][onset] = remember
self.logger.debug(f"Added {label_name} to empty {voice}{ordinal_suffix(voice)} voice in MC {mc} at onset {onset}.")
return True
measure = self.tags[mc][staff][voice]
if onset in measure:
# There is an event (chord or rest) with the same onset to attach the label to
elements = measure[onset]
names = [e['name'] for e in elements]
_, name = get_duration_event(elements)
# insert before the first tag that is not in the tags_before_label list
tags_before_label = ['BarLine', 'Dynamic', 'endTuplet', 'FiguredBass', 'KeySig', 'location', 'StaffText', 'Tempo', 'TimeSig']
ix, before = next((i, elements[i]['tag']) for i in range(len(elements)) if elements[i]['name'] not in
tags_before_label )
remember = self.insert_label(label=label, before=before, **kwargs)
measure[onset].insert(ix, remember[0])
old_names = list(names)
names.insert(ix, 'Harmony')
if name is None:
self.logger.debug(f"""MC {mc}, onset {onset}, staff {staff}, voice {voice} had only these tags:
{old_names}\nAfter insertion: {names}""")
else:
self.logger.debug(f"Added {label_name} to {name} in MC {mc}, onset {onset}, staff {staff}, voice {voice}.")
if 'Harmony' in old_names:
self.logger.warning(
f"The chord in MC {mc}, onset {onset}, staff {staff}, voice {voice} was already carrying a label.")
return True
# There is no event to attach the label to
ordered = list(reversed(sorted(measure)))
prv_pos, nxt_pos = next((prv, nxt)
for prv, nxt
in zip(ordered + [None], [None] + ordered)
if prv < onset)
assert prv_pos is not None, f"MC {mc} empty in staff {staff}, voice {voice}?"
prv = measure[prv_pos]
nxt = None if nxt_pos is None else measure[nxt_pos]
prv_names = [e['name'] for e in prv]
prv_ix, prv_name = get_duration_event(prv)
if nxt is not None:
nxt_names = [e['name'] for e in nxt]
_, nxt_name = get_duration_event(nxt)
# distinguish six cases: prv can be [event, location], nxt can be [event, location, None]
if prv_ix is not None:
# prv is event (chord or rest)
if nxt is None:
loc_after = prv_pos + prv[prv_ix]['duration'] - onset
# i.e. the ending of the last event minus the onset
remember = self.insert_label(label=label, loc_before= -loc_after, after=prv[prv_ix]['tag'], **kwargs)
self.logger.debug(f"Added {label_name} at {loc_after} before the ending of MC {mc}'s last {prv_name}.")
elif nxt_name is not None or nxt_names.count('location') == 0:
# nxt is event (chord or rest) or something at onset 1 (after all sounding events, e.g. <Segment>)
loc_after = nxt_pos - onset
remember = self.insert_label(label=label, loc_before=-loc_after, loc_after=loc_after,
after=prv[prv_ix]['tag'], **kwargs)
self.logger.debug(f"Added {label_name} at {loc_after} before the {nxt_name} at onset {nxt_pos}.")
else:
# nxt has location tag(s)
loc_ix = nxt_names.index('location')
loc_dur = nxt[loc_ix]['duration']
assert loc_dur < 0, f"Positive location tag at MC {mc}, when trying to insert {label_name} at onset {onset}: {nxt}"
loc_before = loc_dur - nxt_pos + onset
remember = self.insert_label(label=label, loc_before=loc_before, before=nxt[loc_ix]['tag'], **kwargs)
loc_after = nxt_pos - onset
nxt[loc_ix]['tag'].fractions.string = str(loc_after)
nxt[loc_ix]['duration'] = loc_after
nxt_name = ', '.join(f"<{e}>" for e in nxt_names if e != 'location')
self.logger.debug(f"""Added {label_name} at {-loc_before} before the ending of the {prv_name} at onset {prv_pos}
and {loc_after} before the subsequent {nxt_name}.""")
else:
# prv has location tag(s)
prv_name = ', '.join(f"<{e}>" for e in prv_names if e != 'location')
loc_before = onset - prv_pos
if nxt is None:
remember = self.insert_label(label=label, loc_before=loc_before, after=prv[-1]['tag'], **kwargs)
self.logger.debug(f"MC {mc}: Added {label_name} at {loc_before} after the previous {prv_name} at onset {prv_pos}.")
else:
try:
loc_ix = next(i for i, name in zip(range(len(prv_names) - 1, -1, -1), reversed(prv_names)) if name == 'location')
except:
self.logger.error(f"MC {mc}, staff {staff}, voice {voice}: The tags of onset {prv_pos} should include a <location> tag.")
raise
prv[loc_ix]['tag'].fractions.string = str(loc_before)
prv[loc_ix]['duration'] = loc_before
loc_after = nxt_pos - onset
remember = self.insert_label(label=label, loc_after=loc_after, after=prv[loc_ix]['tag'], **kwargs)
if nxt_name is None:
nxt_name = ', '.join(f"<{e}>" for e in nxt_names if e != 'location')
self.logger.debug(f"""MC {mc}: Added {label_name} at {loc_before} after the previous {prv_name} at onset {prv_pos}
and {loc_after} before the subsequent {nxt_name}.""")
if remember[0]['name'] == 'location':
measure[prv_pos].append(remember[0])
measure[onset] = remember[1:]
else:
measure[onset] = remember
return True
def insert_label(self, label, loc_before=None, before=None, loc_after=None, after=None, within=None, **kwargs):
tag = self.new_label(label, before=before, after=after, within=within, **kwargs)
remember = [dict(
name = 'Harmony',
duration = frac(0),
tag = tag
)]
if loc_before is not None:
location = self.new_location(loc_before)
tag.insert_before(location)
remember.insert(0, dict(
name = 'location',
duration =loc_before,
tag = location
))
if loc_after is not None:
location = self.new_location(loc_after)
tag.insert_after(location)
remember.append(dict(
name = 'location',
duration =loc_after,
tag =location
))
return remember
def new_label(self, label, label_type=None, after=None, before=None, within=None, root=None, base=None, leftParen=None, rightParen=None, offset_x=None, offset_y=None, nashville=None, decoded=None):
tag = self.new_tag('Harmony')
if not pd.isnull(label_type):
# only include <harmonyType> tag for label_type 1 and 2 (MuseScore's Nashville Numbers and Roman Numerals)
if label_type in [1, 2, '1', '2']:
_ = self.new_tag('harmonyType', value=label_type, within=tag)
if not pd.isnull(leftParen):
_ = self.new_tag('leftParen', within=tag)
if not pd.isnull(root):
_ = self.new_tag('root', value=root, within=tag)
if not pd.isnull(label):
_ = self.new_tag('name', value=label, within=tag)
else:
assert not pd.isnull(root), "Either label or root need to be specified."
if not pd.isnull(nashville):
_ = self.new_tag('function', value=nashville, within=tag)
if not pd.isnull(base):
_ = self.new_tag('base', value=base, within=tag)
if not pd.isnull(offset_x) or not pd.isnull(offset_y):
if pd.isnull(offset_x):
offset_x = '0'
if pd.isnull(offset_y):
offset_y = '0'
_ = self.new_tag('offset', attributes={'x': offset_x, 'y': offset_y}, within=tag)
if not pd.isnull(rightParen):
_ = self.new_tag('rightParen', within=tag)
if after is not None:
after.insert_after(tag)
elif before is not None:
before.insert_before(tag)
elif within is not None:
within.append(tag)
return tag
def new_location(self, location):
tag = self.new_tag('location')
_ = self.new_tag('fractions', value=str(location), within=tag)
return tag
def new_tag(self, name, value=None, attributes={}, after=None, before=None, within=None):
tag = self.soup.new_tag(name)
if value is not None:
tag.string = str(value)
for k, v in attributes.items():
tag.attrs[k] = v
if after is not None:
after.insert_after(tag)
elif before is not None:
before.insert_before(tag)
elif within is not None:
within.append(tag)
return tag
def __getstate__(self):
self.soup = None
self.tags, self.measure_nodes = {}, {}
self.read_only = True
return self.__dict__
####################### END OF CLASS DEFINITION #######################
[docs]def get_duration_event(elements):
names = [e['name'] for e in elements]
if 'Chord' in names or 'Rest' in names:
if 'Rest' in names:
ix = names.index('Rest')
name = '<Rest>'
else:
ix = next(i for i, d in enumerate(elements) if d['name'] == 'Chord' and d['duration'] > 0)
name = '<Chord>'
return ix, name
return (None, None)
[docs]@function_logger
def make_spanner_cols(df, spanner_types=None):
""" From a raw chord list as returned by ``get_chords(spanners=True)``
create a DataFrame with Spanner IDs for all chords for all spanner
types they are associated with.
Parameters
----------
spanner_types : :obj:`collection`
If this parameter is passed, only the enlisted
spanner types (e.g. ``Slur`` or ``Pedal``) are included.
"""
cols = {
'nxt_m': 'Spanner/next/location/measures',
'nxt_f': 'Spanner/next/location/fractions',
'prv_m': 'Spanner/prev/location/measures',
'prv_f': 'Spanner/prev/location/fractions',
'type': 'Spanner:type',
}
def get_spanner_ids(spanner_type, subtype=None):
if spanner_type == 'Slur':
f_cols = ['Chord/' + cols[c] for c in ['nxt_m', 'nxt_f', 'prv_m', 'prv_f']]
type_col = 'Chord/' + cols['type']
else:
f_cols = [cols[c] for c in ['nxt_m', 'nxt_f', 'prv_m', 'prv_f']]
type_col = cols['type']
sel = df[type_col] == spanner_type
subtype_col = f"Spanner/{spanner_type}/subtype"
if subtype is None and subtype_col in df:
subtypes = set(df.loc[df[subtype_col].notna(), subtype_col])
results = [get_spanner_ids(spanner_type, st) for st in subtypes]
return dict(ChainMap(*results))
elif subtype:
sel = sel & (df[subtype_col] == subtype)
existing = [c for c in f_cols if c in df.columns]
features = pd.DataFrame('', index=df.index, columns=f_cols)
features.loc[sel, existing] = df.loc[sel, existing]
features = features.apply(lambda col: col.fillna('').str.replace('-', ''))
features.insert(0, 'staff', df.staff)
current_id = -1
column_name = spanner_type
if subtype:
column_name += ':' + subtype
if spanner_type != 'Slur':
staff_stacks = {i: {} for i in df.staff.unique()}
else:
features.insert(1, 'voice', df.voice)
staff_stacks = {(i, v): {} for i in df.staff.unique() for v in range(1, 5)}
def spanner_ids(row, distinguish_voices=False):
nonlocal staff_stacks, current_id
if distinguish_voices:
staff, voice, nxt_m, nxt_f, prv_m, prv_f = row
layer = (staff, voice)
else:
staff, nxt_m, nxt_f, prv_m, prv_f = row
layer = staff
if nxt_m != '' or nxt_f != '':
current_id += 1
staff_stacks[layer][(nxt_m, nxt_f)] = current_id
return ', '.join(str(i) for i in staff_stacks[layer].values())
val = ', '.join(str(i) for i in staff_stacks[layer].values())
if prv_m != '' or prv_f != '':
if len(staff_stacks[layer]) == 0 or (prv_m, prv_f) not in staff_stacks[layer]:
logger.warning(f"Spanner ending (type {spanner_type}{'' if subtype is None else ', subtype: ' + subtype }) could not be matched with a beginning at id {current_id}.")
return 'err'
del(staff_stacks[layer][(prv_m, prv_f)])
return val if val != '' else np.nan
return {column_name: [spanner_ids(row, distinguish_voices=(spanner_type == 'Slur')) for row in features.values]}
type_col = cols['type']
types = list(set(df.loc[df[type_col].notna(), type_col])) if type_col in df.columns else []
if 'Chord/' + type_col in df.columns:
types += ['Slur']
if spanner_types is not None:
types = [t for t in types if t in spanner_types]
list_of_dicts = [get_spanner_ids(t) for t in types]
merged_dict = dict(ChainMap(*list_of_dicts))
renaming = {
'HairPin:1': 'decrescendo',
'HairPin:3': 'diminuendo',
}
return pd.DataFrame(merged_dict, index=df.index).rename(columns=renaming)
[docs]def sort_note_list(df, mc_col='mc', onset_col='onset', midi_col='midi', duration_col='duration'):
"""Sort every measure (MC) by ['onset', 'midi', 'duration'] while leaving gracenotes' order (duration=0) intact"""
is_grace = df[duration_col] == 0
grace_ix = {k: v.to_numpy() for k, v in df[is_grace].groupby([mc_col, onset_col]).groups.items()}
has_nan = df[midi_col].isna().any()
if has_nan:
df.loc[:, midi_col] = df[midi_col].fillna(1000)
normal_ix = df.loc[~is_grace, [mc_col, onset_col, midi_col, duration_col]].groupby([mc_col, onset_col]).apply(
lambda gr: gr.index[np.lexsort((gr.values[:, 3], gr.values[:, 2]))].to_numpy())
sorted_ixs = [np.concatenate((grace_ix[mc_onset], ix)) if mc_onset in grace_ix else ix for mc_onset, ix in
normal_ix.iteritems()]
df = df.reindex(np.concatenate(sorted_ixs)).reset_index(drop=True)
if has_nan:
df.loc[:, midi_col] = df[midi_col].replace({1000: np.nan}).astype('Int64')
return df
[docs]def make_tied_col(df, tie_col, next_col, prev_col):
new_col = pd.Series(np.nan, index=df.index, name='tied')
if tie_col not in df.columns:
return new_col
has_tie = df[tie_col].fillna('').str.contains('Tie')
if has_tie.sum() == 0:
return new_col
# merge all columns whose names start with `next_col` and `prev_col` respectively
next_cols = [col for col in df.columns if col[:len(next_col)] == next_col]
nxt = df[next_cols].notna().any(axis=1)
prev_cols = [col for col in df.columns if col[:len(prev_col)] == prev_col]
prv = df[prev_cols].notna().any(axis=1)
new_col = new_col.where(~has_tie, 0).astype('Int64')
tie_starts = has_tie & nxt
tie_ends = has_tie & prv
new_col.loc[tie_ends] -= 1
new_col.loc[tie_starts] += 1
return new_col
[docs]def safe_update(old, new):
""" Update dict without replacing values.
"""
existing = [k for k in new.keys() if k in old]
if len(existing) > 0:
new = dict(new)
for ex in existing:
old[ex] = f"{old[ex]} & {new[ex]}"
del (new[ex])
old.update(new)
[docs]def recurse_node(node, prepend=None, exclude_children=None):
def tag_or_string(c, ignore_empty=False):
nonlocal info, name
if isinstance(c, bs4.element.Tag):
if c.name not in exclude_children:
safe_update(info, {child_prepend + k: v for k, v in recurse_node(c, prepend=c.name).items()})
elif c not in ['\n', None]:
info[name] = str(c)
elif not ignore_empty:
if c == '\n':
info[name] = '∅'
elif c is None:
info[name] = '/'
info = {}
if exclude_children is None:
exclude_children = []
name = node.name if prepend is None else prepend
attr_prepend = name + ':'
child_prepend = '' if prepend is None else prepend + '/'
for attr, value in node.attrs.items():
info[attr_prepend + attr] = value
children = tuple(node.children)
if len(children) > 1:
for c in children:
tag_or_string(c, ignore_empty=True)
elif len(children) == 1:
tag_or_string(children[0], ignore_empty=False)
else:
info[name] = '/'
return info
[docs]def sort_cols(df, first_cols=None):
if first_cols is None:
first_cols = []
cols = df.columns
column_order = [col for col in first_cols if col in cols] + sorted([col for col in cols if col not in first_cols])
return df[column_order]
[docs]def bs4_chord_duration(node, duration_multiplier=1):
durationtype = node.find('durationType').string
if durationtype == 'measure' and node.find('duration'):
nominal_duration = frac(node.find('duration').string)
else:
nominal_duration = _MSCX_bs4.durations[durationtype]
dots = node.find('dots')
dotmultiplier = sum([frac(1 / 2) ** i for i in range(int(dots.string) + 1)]) if dots else 1
return nominal_duration * duration_multiplier * dotmultiplier, dotmultiplier
[docs]def bs4_rest_duration(node, duration_multiplier=1):
return bs4_chord_duration(node, duration_multiplier)
[docs]def opening_tag(node, closed=False):
closing = '/' if closed else ''
result = f"<{node.name}"
attributes = node.attrs.items()
if len(attributes) > 0:
result += ' ' + ' '.join(f'{attr}="{value}"' for attr, value in attributes)
return f"{result}{closing}>"
[docs]def closing_tag(node_name):
return f"</{node_name}>"
[docs]def make_oneliner(node):
result = opening_tag(node)
for c in node.children:
if isinstance(c, bs4.element.Tag):
result += make_oneliner(c)
else:
result += str(c).replace('"', '"')\
.replace('<', '<')\
.replace('>', '>')\
.replace('&', '&')
result += closing_tag(node.name)
return result
[docs]def bs4_to_mscx(soup):
def format_node(node, indent):
nxt_indent = indent + 2
space = indent * ' '
node_name = node.name
# The following tags are exceptionally not abbreviated when empty,
# so for instance you get <metaTag></metaTag> and not <metaTag/>
if node_name in ['continueAt', 'endText', 'text', 'LayerTag', 'metaTag', 'trackName']:
return f"{space}{make_oneliner(node)}\n"
children = node.find_all(recursive=False)
if len(children) > 0:
result = f"{space}{opening_tag(node)}\n"
result += ''.join(format_node(child, nxt_indent) for child in children)
result += f"{nxt_indent * ' '}{closing_tag(node_name)}\n"
return result
if node.string == '\n':
return f"{space}{opening_tag(node)}\n{nxt_indent * ' '}{closing_tag(node_name)}\n"
if node.string is None:
return f"{space}{opening_tag(node, closed=True)}\n"
return f"{space}{make_oneliner(node)}\n"
initial_tag = """<?xml version="1.0" encoding="UTF-8"?>\n"""
first_tag = soup.find()
return initial_tag + format_node(first_tag, indent=0)