import sys, os
import traceback
import pathos.multiprocessing as mp
from collections import Counter, defaultdict
from collections.abc import Collection
import pandas as pd
import numpy as np
from .annotations import Annotations
from .logger import LoggedClass
from .score import Score
from .utils import group_id_tuples, load_tsv, make_id_tuples, metadata2series, no_collections_no_booleans, pretty_dict,\
resolve_dir, scan_directory, string2lines, update_labels_cfg
[docs]class Parse(LoggedClass):
"""
Class for storing and manipulating the information from multiple parses (i.e. :obj:`~ms3.score.Score` objects).
"""
def __init__(self, dir=None, key=None, index=None, file_re=r"\.(mscx|tsv)$", folder_re='.*', exclude_re=r"^(\.|_)",
recursive=True, simulate=False, labels_cfg={}, logger_cfg={}):
"""
Parameters
----------
dir, key, index, file_re, folder_re, exclude_re, recursive : optional
Arguments for the method :py:meth:`~ms3.parse.add_folder`.
If ``dir`` is not passed, no files are added to the new object.
simulate : :obj:`bool`, optional
Pass True if no parsing is actually to be done.
logger_cfg : :obj:`dict`, optional
The following options are available:
'name': LOGGER_NAME -> by default the logger name is based on the parsed file(s)
'level': {'W', 'D', 'I', 'E', 'C', 'WARNING', 'DEBUG', 'INFO', 'ERROR', 'CRITICAL'}
'path': Directory in which log files are stored. If 'file' is relative, this path is used as root, otherwise, it is ignored.
'file': PATH_TO_LOGFILE Pass absolute path to store all log messages in a single log file.
If PATH_TO_LOGFILE is relative, multiple log files are created dynamically, relative to the original MSCX files' paths.
If 'path' is set, the corresponding subdirectory structure is created there.
"""
super().__init__(subclass='Parse', logger_cfg=logger_cfg)
self.simulate=simulate
# defaultdicts with keys as keys, each holding a list with file information (therefore accessed via [key][i] )
self.full_paths = defaultdict(list)
""":obj:`collections.defaultdict`
``{key: [full_path]}`` dictionary of the full paths of all detected files.
"""
self.rel_paths = defaultdict(list)
""":obj:`collections.defaultdict`
``{key: [rel_path]}`` dictionary of the relative (to :obj:`.scan_paths`) paths of all detected files.
"""
self.scan_paths = defaultdict(list)
""":obj:`collections.defaultdict`
``{key: [scan_path]}`` dictionary of the scan_paths from which each file was detected.
"""
self.paths = defaultdict(list)
""":obj:`collections.defaultdict`
``{key: [path]}`` dictionary of the paths of all detected files (without file name).
"""
self.files = defaultdict(list)
""":obj:`collections.defaultdict`
``{key: [file]}`` dictionary of file names with extensions of all detected files.
"""
self.fnames = defaultdict(list)
""":obj:`collections.defaultdict`
``{key: [fname]}`` dictionary of file names without extensions of all detected files.
"""
self.fexts = defaultdict(list)
""":obj:`collections.defaultdict`
``{key: [fext]}`` dictionary of file extensions of all detected files.
"""
self._parsed_mscx = {}
""":obj:`dict`
``{(key, i): :obj:`~ms3.score.Score`}`` dictionary of parsed scores.
"""
self._annotations = {}
""":obj:`dict`
{(key, i): :obj:`~ms3.annotations.Annotations`} dictionary of parsed sets of annotations.
"""
self._notelists = {}
""":obj:`dict`
{(key, i): :obj:`pandas.DataFrame`} dictionary of DataFrames holding :obj:`~ms3.score.Score.notes` tables.
"""
self._restlists = {}
""":obj:`dict`
{(key, i): :obj:`pandas.DataFrame`} dictionary of DataFrames holding :obj:`~ms3.score.Score.rests` tables
"""
self._noterestlists = {}
""":obj:`dict`
{(key, i): :obj:`pandas.DataFrame`} dictionary of DataFrames holding :obj:`~ms3.score.Score.notes_and_rests` tables
"""
self._eventlists = {}
""":obj:`dict`
{(key, i): :obj:`pandas.DataFrame`} dictionary of DataFrames holding :obj:`~ms3.score.Score.events` tables.
"""
self._labellists = {}
""":obj:`dict`
{(key, i): :obj:`pandas.DataFrame`} dictionary of DataFrames holding :obj:`~ms3.score.Score.labels` tables.
"""
self._chordlists = {}
""":obj:`dict`
{(key, i): :obj:`pandas.DataFrame`} dictionary of DataFrames holding :obj:`~ms3.score.Score.chords` tables.
"""
self._expandedlists = {}
""":obj:`dict`
{(key, i): :obj:`pandas.DataFrame`} dictionary of DataFrames holding :obj:`~ms3.score.Score.expanded` tables.
"""
self._index = {}
""":obj:`dict`
{(key, i): :obj:`tuple`} dictionary of index tuples where every element represents one index level.
"""
self._levelnames = {}
""":obj:`dict`
{(key, i): :obj:`tuple`} dictionary of index names for the corresponding index tuple.
"""
self._measurelists = {}
""":obj:`dict`
{(key, i): :obj:`pandas.DataFrame`} dictionary of DataFrames holding :obj:`~ms3.score.Score.measures` tables.
"""
self._parsed_tsv = {}
""":obj:`dict`
{(key, i): :obj:`pandas.DataFrame`} dictionary of all parsed (i.e. loaded as DataFrame) TSV files.
"""
self._tsv_types = {}
""":obj:`dict`
{(key, i): :obj:`str`} dictionary of TSV types as inferred by :py:meth:`._infer_tsv_type`, i.e. one of
``None, 'notes', 'events', 'chords', 'rests', 'measures', 'labels'}``
"""
self.labels_cfg = {
'staff': None,
'voice': None,
'label_type': None,
'positioning': True,
'decode': False,
'column_name': 'label',
}
""":obj:`dict`
Configuration dictionary to determine the output format of :obj:`~ms3.score.Score.labels` and
:obj:`~ms3.score.Score.expanded` tables. The dictonary is passed to :obj:`~ms3.score.Score` upon parsing.
"""
self.labels_cfg.update(update_labels_cfg(labels_cfg, logger=self.logger))
self._lists = {
'notes': self._notelists,
'rests': self._restlists,
'notes_and_rests': self._noterestlists,
'measures': self._measurelists,
'events': self._eventlists,
'labels': self._labellists,
'chords': self._chordlists,
'expanded': self._expandedlists,
}
""":obj:`dict`
Dictionary exposing the different :obj:`dicts<dict>` of :obj:`DataFrames<pandas.DataFrame>`.
"""
self._matches = pd.DataFrame(columns=['mscx', 'annotations']+list(self._lists.keys()))
""":obj:`pandas.DataFrame`
Dataframe that holds the (file name) matches between MuseScore and TSV files.
"""
self.last_scanned_dir = dir
""":obj:`str`
The directory that was scanned for files last.
"""
if dir is not None:
self.add_dir(dir=dir, key=key, index=index, file_re=file_re, folder_re=folder_re, exclude_re=exclude_re, recursive=recursive)
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%% END of __init__() %%%%%%%%%%%%%%%%%%%%%%%%%%%%%#
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%#
@property
def parsed(self):
""":obj:`dict`
Returns an overview of the parsed MuseScore files."""
return {k: score.full_paths['mscx'] for k, score in self._parsed_mscx.items()}
# res = {}
# for k, score in self._parsed.items():
# info = score.full_paths['mscx']
# if 'annotations' in score._annotations:
# info += f" -> {score.annotations.n_labels()} labels"
# res[k] = info
# return res
[docs] def add_detached_annotations(self, mscx_key, tsv_key, new_key=None, match_dict=None):
""" Add :obj:`~ms3.annotations.Annotations` objects generated from TSV files to the :obj:`~ms3.score.Score`
objects to which they are being matched based on their filenames or on ``match_dict``.
Parameters
----------
mscx_key : :obj:`str`
A key under which parsed MuseScore files are stored.
tsv_key : :obj:`str`
A key under which parsed TSV files are stored of which the type has been inferred as 'labels'.
new_key : :obj:`str`, optional
The key under which the :obj:`~ms3.annotations.Annotations` objects will be available after attaching
them to the :obj:`~ms3.score.Score` objects (``Parsed.parsed_mscx[ID].key``). By default, ``tsv_key``
is used.
match_dict : :obj:`dict`, optional
Dictionary mapping IDs of parsed :obj:`~ms3.score.Score` objects to IDs of parsed :obj:`~ms3.annotations.Annotations`
objects.
"""
if new_key is None:
new_key = tsv_key
if match_dict is None:
matches = self.match_files(keys=[mscx_key, tsv_key])
match_dict = dict(matches[['mscx', 'labels']].values)
for score_id, labels_id in match_dict.items():
if score_id in self._parsed_mscx and not pd.isnull(labels_id):
if labels_id in self._annotations:
self._parsed_mscx[score_id].load_annotations(anno_obj=self._annotations[labels_id], key=new_key)
else:
k, i = labels_id
self.logger.warning(f"""The TSV {labels_id} has not yet been parsed as Annotations object.
Use parse_tsv(key='{k}') and specify cols={{'label': label_col}}.""")
else:
self.logger.debug(f"Nothing to add to {score_id}. Make sure that it's counterpart has been recognized as tsv_type 'labels'.")
[docs] def add_dir(self, dir, key=None, index=None, file_re=r'\.mscx$', folder_re='.*', exclude_re=r"^(\.|__)", recursive=True):
"""
This function scans the directory ``dir`` for files matching the criteria and adds them (i.e. paths and file names)
to the Parse object without looking at them. It is recommended to add different types of files with different keys,
e.g. 'mscx' for score, 'harmonies' for chord labels, and 'form' for form labels.
Parameters
----------
dir : :obj:`str`
Directory to scan for files.
key : :obj:`str`, optional
| Pass a string to identify the loaded files.
| By default, the relative sub-directories of ``dir`` are used as keys. For example, for files within ``dir``
itself, the key would be ``'.'``, for files in the subfolder ``scores`` it would be ``'scores'``, etc.
index : element or :obj:`~collections.abc.Collection` of {'key', 'fname', 'i', :obj:`~collections.abc.Collection`}
| Change this parameter if you want to create particular indices for multi-piece DataFrames.
| The resulting index must be unique (for identification) and have as many elements as added files.
| Every single element or Collection of elements ∈ {'key', 'fname', 'i', :obj:`~collections.abc.Collection`} stands for an index level.
| In other words, a single level will result in a single index and a collection of levels will result in a
:obj:`~pandas.core.indexes.multi.MultiIndex`.
| If you pass a Collection that does not start with one of {'key', 'fname', 'i'}, it is interpreted as an
index level itself and needs to have at least as many elements as the number of added files.
| The default ``None`` is equivalent to passing ``(key, i)``, i.e. a MultiIndex of IDs.
| 'fname' evokes an index level made from file names.
dir : :obj:`str`
Directory to be scanned for files.
file_re : :obj:`str`, optional
Regular expression for filtering certain file names.
The regEx is checked with search(), not match(), allowing for fuzzy search.
folder_re : :obj:`str`, optional
Regular expression for filtering certain folder names.
The regEx is checked with search(), not match(), allowing for fuzzy search.
recursive : :obj:`bool`, optional
By default, sub-directories are recursively scanned. Pass False to scan only ``dir``.
"""
dir = resolve_dir(dir)
self.last_scanned_dir = dir
if file_re in ['tsv', 'csv']:
file_re = r"\." + file_re + '$'
res = scan_directory(dir, file_re=file_re, folder_re=folder_re, exclude_re=exclude_re, recursive=recursive)
ids = [self._handle_path(p, key) for p in res]
if len(ids) > 0:
selector, added_ids = zip(*[(i, x) for i, x in enumerate(ids) if x[0] is not None])
grouped_ids = group_id_tuples(ids)
exts = {k: self.count_extensions(k, i) for k, i in grouped_ids.items()}
self.logger.debug(f"{len(added_ids)} paths stored.\n{pretty_dict(exts, 'EXTENSIONS')}")
new_index, level_names = self._treat_index_param(index, ids=added_ids, selector=selector)
self._index.update(new_index)
for k in grouped_ids.keys():
if k in self._levelnames:
previous = self._levelnames[k]
if previous != level_names:
replacement_ids = [(k, i) for i in grouped_ids.values()]
if None in previous:
new_levels = [level for level in previous if level is not None]
if len(new_levels) == 0:
new_levels = None
replacement_ix, new_levels = self._treat_index_param(new_levels, ids=replacement_ids)
self.logger.warning(f"""The created index has different levels ({level_names}) than the index that already exists for key '{k}': {previous}.
Since None stands for a custom level, an alternative index with levels {new_levels} has been created.""")
else:
replacement_ix, _ = self._treat_index_param(previous, ids=replacement_ids)
self.logger.info(f"""The created index has different levels ({level_names}) than the index that already exists for key '{k}': {previous}.
Therefore, the index for this key has been adapted.""")
self._index.update(replacement_ix)
else:
self.logger.debug(f"Index level names match the existing ones for key '{k}.'")
else:
self._levelnames[k] = level_names
else:
self.logger.debug("No files added.")
[docs] def attach_labels(self, keys=None, annotation_key=None, staff=None, voice=None, check_for_clashes=True):
""" Attach all :obj:`~ms3.annotations.Annotations` objects that are reachable via ``Score.annotation_key`` to their
respective :obj:`~ms3.score.Score`, changing their current XML. Calling :py:meth:`.store_mscx` will output
MuseScore files where the annotations show in the score.
Parameters
----------
keys : :obj:`str` or :obj:`~collections.abc.Collection`, optional
Key(s) under which parsed MuseScore files are stored. By default, all keys are selected.
annotation_key : :obj:`str` or :obj:`list` or :obj:`tuple`, optional
Key(s) under which the :obj:`~ms3.annotations.Annotations` objects to be attached are stored in the
:obj:`~ms3.score.Score` objects. By default, all keys are selected.
staff : :obj:`int`, optional
If you pass a staff ID, the labels will be attached to that staff where 1 is the upper stuff.
By default, the staves indicated in the 'staff' column of :obj:`ms3.annotations.Annotations.df`
will be used.
voice : {1, 2, 3, 4}, optional
If you pass the ID of a notational layer (where 1 is the upper voice, blue in MuseScore),
the labels will be attached to that one.
By default, the notational layers indicated in the 'voice' column of
:obj:`ms3.annotations.Annotations.df` will be used.
check_for_clashes : :obj:`bool`, optional
By default, warnings are thrown when there already exists a label at a position (and in a notational
layer) where a new one is attached. Pass False to deactivate this warnings.
"""
layers = self.count_annotation_layers(keys, which='detached', per_key=True)
if annotation_key is None:
annotation_key = list(layers.keys())
elif isinstance(annotation_key, str):
annotation_key = [annotation_key]
if any(True for k in annotation_key if k not in layers):
wrong = [k for k in annotation_key if k not in layers]
annotation_key = [k for k in annotation_key if k in layers]
if len(annotation_key) == 0:
self.logger.error(
f"""'{wrong}' are currently not keys for sets of detached labels that have been added to parsed scores.
Currently available annotation keys are {list(layers.keys())}""")
return
else:
self.logger.warning(
f"""'{wrong}' are currently not keys for sets of detached labels that have been added to parsed scores.
Continuing with {annotation_key}.""")
ids = list(self._iterids(keys, filter_detached_annotations=True))
reached, goal = 0, 0
for id in ids:
for anno_key in annotation_key:
if anno_key in self._parsed_mscx[id]._annotations:
r, g = self._parsed_mscx[id].attach_labels(anno_key, staff=staff, voice=voice, check_for_clashes=check_for_clashes)
self.logger.info(f"{r}/{g} labels successfully added to {self.files[id[0]][id[1]]}")
reached += r
goal += g
self.logger.info(f"{reached}/{goal} labels successfully added to {len(ids)} files.")
self._collect_annotations_objects_references(ids=ids)
[docs] def change_labels_cfg(self, labels_cfg={}, staff=None, voice=None, label_type=None, positioning=None, decode=None):
""" Update :obj:`Parse.labels_cfg` and retrieve new 'labels' tables accordingly.
Parameters
----------
labels_cfg : :obj:`dict`
Using an entire dictionary or, to change only particular options, choose from:
staff, voice, label_type, positioning, decode
Arguments as they will be passed to :py:meth:`~ms3.annotations.Annotations.get_labels`
"""
for k in self.labels_cfg.keys():
val = locals()[k]
if val is not None:
labels_cfg[k] = val
self.labels_cfg.update(update_labels_cfg(labels_cfg), logger=self.logger)
ids = list(self._labellists.keys())
if len(ids) > 0:
self.collect_lists(ids=ids, labels=True)
[docs] def collect_lists(self, keys=None, ids=None, notes=False, rests=False, notes_and_rests=False, measures=False, events=False,
labels=False, chords=False, expanded=False, only_new=True):
""" Extracts DataFrames from the parsed scores in ``keys`` and stores them in dictionaries.
Parameters
----------
keys : :obj:`str` or :obj:`~collections.abc.Collection`, optional
Key(s) under which parsed MuseScore files are stored. By default, all keys are selected.
ids : :obj:`~collections.abc.Collection`
If you pass a collection of IDs, ``keys`` is ignored and ``only_new`` is set to False.
notes, rests, notes_and_rests, measures, events, labels, chords, expanded : :obj:`bool`, optional
only_new : :obj:`bool`, optional
Set to True to also retrieve lists that had already been retrieved.
"""
if len(self._parsed_mscx) == 0:
self.logger.debug("No scores have been parsed so far. Use parse_mscx()")
return
if ids is None:
only_new = False
ids = list(self._iterids(keys, only_parsed_mscx=True))
scores = {id: self._parsed_mscx[id] for id in ids}
bool_params = list(self._lists.keys())
l = locals()
params = {p: l[p] for p in bool_params}
for i, score in scores.items():
for param, li in self._lists.items():
if params[param] and (i not in li or not only_new):
if self.simulate:
df = pd.DataFrame()
else:
df = score.mscx.__getattribute__(param)
if df is not None:
li[i] = df
[docs] def count_annotation_layers(self, keys=None, which='attached', per_key=False):
""" Counts the labels for each annotation layer defined as (staff, voice, label_type).
By default, only labels attached to a score are counted.
Parameters
----------
keys : :obj:`str` or :obj:`~collections.abc.Collection`, optional
Key(s) for which to count annotation layers. By default, all keys are selected.
which : {'attached', 'detached', 'tsv'}, optional
'attached': Counts layers from annotations attached to a score.
'detached': Counts layers from annotations that are in a Score object, but detached from the score.
'tsv': Counts layers from Annotation objects that have been loaded from or into annotation tables.
per_key : :obj:`bool`, optional
If set to True, the results are returned as a dict {key: Counter}, otherwise the counts are summed up in one Counter.
If ``which='detached'``, the keys are keys from Score objects, otherwise they are keys from this Parse object.
Returns
-------
:obj:`dict` or :obj:`collections.Counter`
By default, the function returns a Counter of labels for every annotation layer (staff, voice, label_type)
If ``per_key`` is set to True, a dictionary {key: Counter} is returned, separating the counts.
"""
res_dict = defaultdict(Counter)
if which == 'detached':
for id in self._iterids(keys, filter_detached_annotations=True):
for key, annotations in self._parsed_mscx[id]._annotations.items():
if key != 'annotations':
_, layers = annotations.annotation_layers
res_dict[key].update(layers.to_dict())
elif which in ['attached', 'tsv']:
for key, i in self._iterids(keys):
if (key, i) in self._annotations:
ext = self.fexts[key][i]
if (which == 'attached' and ext == '.mscx') or (which == 'tsv' and ext != '.mscx'):
_, layers = self._annotations[(key, i)].annotation_layers
res_dict[key].update(layers.to_dict())
else:
self.logger.error(f"Parameter 'which' needs to be one of {{'attached', 'detached', 'tsv'}}, not {which}.")
return {} if per_key else pd.Series()
def make_series(counts):
if len(counts) == 0:
return pd.Series()
data = counts.values()
ix = pd.Index(counts.keys(), names=['staff', 'voice', 'label_type'])
return pd.Series(data, ix)
if per_key:
res = {k: make_series(v) for k, v in res_dict.items()}
else:
res = make_series(sum(res_dict.values(), Counter()))
if len(res) == 0:
self.logger.info("No annotations found. Maybe no scores have been parsed using parse_mscx()?")
return res
[docs] def count_extensions(self, keys=None, per_key=False):
""" Count file extensions.
Parameters
----------
keys : :obj:`str` or :obj:`~collections.abc.Collection`, optional
Key(s) for which to count file extensions. By default, all keys are selected.
per_key : :obj:`bool`, optional
If set to True, the results are returned as a dict {key: Counter},
otherwise the counts are summed up in one Counter.
Returns
-------
:obj:`dict` or :obj:`collections.Counter`
By default, the function returns a Counter of file extensions.
If ``per_key`` is set to True, a dictionary {key: Counter} is returned, separating the counts.
"""
keys = self._treat_key_param(keys)
res_dict = {}
for key in keys:
res_dict[key] = Counter(self.fexts[key])
if per_key:
return {k: dict(v) for k, v in res_dict.items()}
return dict(sum(res_dict.values(), Counter()))
[docs] def count_label_types(self, keys=None, per_key=False):
""" Count label types.
Parameters
----------
keys : :obj:`str` or :obj:`~collections.abc.Collection`, optional
Key(s) for which to count label types. By default, all keys are selected.
per_key : :obj:`bool`, optional
If set to True, the results are returned as a dict {key: Counter},
otherwise the counts are summed up in one Counter.
Returns
-------
:obj:`dict` or :obj:`collections.Counter`
By default, the function returns a Counter of label types.
If ``per_key`` is set to True, a dictionary {key: Counter} is returned, separating the counts.
"""
annotated = [id for id in self._iterids(keys) if id in self._annotations]
res_dict = defaultdict(Counter)
for key, i in annotated:
res_dict[key].update(self._annotations[(key, i)]._label_types)
if len(res_dict) == 0:
if len(self._parsed_mscx) == 0:
self.logger.error("No scores have been parsed so far. Use parse_mscx().")
else:
self.logger.info("None of the scores contain annotations.")
if per_key:
return {k: dict(v) for k, v in res_dict.items()}
return dict(sum(res_dict.values(), Counter()))
[docs] def count_tsv_types(self, keys=None, per_key=False):
""" Count inferred TSV types.
Parameters
----------
keys : :obj:`str` or :obj:`~collections.abc.Collection`, optional
Key(s) for which to count inferred TSV types. By default, all keys are selected.
per_key : :obj:`bool`, optional
If set to True, the results are returned as a dict {key: Counter},
otherwise the counts are summed up in one Counter.
Returns
-------
:obj:`dict` or :obj:`collections.Counter`
By default, the function returns a Counter of inferred TSV types.
If ``per_key`` is set to True, a dictionary {key: Counter} is returned, separating the counts.
"""
res_dict = defaultdict(Counter)
for key, i in self._iterids(keys, only_parsed_mscx=True):
t = self._tsv_types[(key, i)] if (key, i) in self._tsv_types else None
res_dict[key].update([t])
if per_key:
return {k: dict(v) for k, v in res_dict.items()}
return dict(sum(res_dict.values(), Counter()))
[docs] def detach_labels(self, keys=None, annotation_key='detached', staff=None, voice=None, label_type=None, delete=True):
""" Calls :py:meth:`Score.detach_labels<ms3.score.Score.detach_labels` on every parsed score with key ``key``.
"""
assert annotation_key != 'annotations', "The key 'annotations' is reserved, please choose a different one."
ids = list(self._iterids(keys, filter_attached_annotations=True))
prev_logger = self.logger
for id in ids:
score = self._parsed_mscx[id]
self.logger = score.logger
try:
score.detach_labels(key=annotation_key, staff=staff, voice=voice, label_type=label_type, delete=delete)
except:
self.logger.error(f"Detaching labels failed with the following error:\n{sys.exc_info()[1]}")
finally:
self.logger = prev_logger
self._collect_annotations_objects_references(ids=ids)
[docs] def get_labels(self, keys=None, staff=None, voice=None, label_type=None, positioning=True, decode=False, column_name=None, concat=True):
""" This function does not take into account self.labels_cfg """
if len(self._annotations) == 0:
self.logger.error("No labels available so far. Add files using add_dir() and parse them using parse().")
return pd.DataFrame()
keys = self._treat_key_param(keys)
label_type = self._treat_label_type_param(label_type)
self.collect_lists(labels=True, only_new=True)
l = locals()
params = {p: l[p] for p in self.labels_cfg.keys()}
ids = [id for id in self._iterids(keys) if id in self._annotations]
if len(ids) == 0:
self.logger.info(f"No labels match the criteria.")
return pd.DataFrame()
annotation_tables = [self._annotations[id].get_labels(**params, warnings=False) for id in ids]
idx, names = self.ids2idx(ids)
if names is None:
names = (None,) * len(idx[0])
names += tuple(annotation_tables[0].index.names)
if concat:
return pd.concat(annotation_tables, keys=idx, names=names)
return annotation_tables
def get_lists(self, keys=None, notes=False, rests=False, notes_and_rests=False, measures=False, events=False,
labels=False, chords=False, expanded=False, simulate=False):
if len(self._parsed_mscx) == 0 and len(self._annotations) == 0:
self.logger.error("No scores or annotation files have been parsed so far.")
return {}
keys = self._treat_key_param(keys)
bool_params = list(self._lists.keys())
l = locals()
params = {p: l[p] for p in bool_params}
self.collect_lists(keys, only_new=True, **params)
res = {}
for param, li in self._lists.items():
if params[param]:
for id in (i for i in self._iterids(keys) if i in li):
res[id + (param,)] = li[id]
return res
def ids2idx(self, ids, pandas_index=False):
idx = [self._index[id] for id in ids]
levels = [len(ix) for ix in idx]
error = False
if not all(l == levels[0] for l in levels[1:]):
self.logger.warning(
f"Could not create index because the index values have different numbers of levels: {set(levels)}")
idx = ids
error = True
if error:
names = ['key', 'i']
else:
grouped_ids = group_id_tuples(ids)
level_names = {k: self._levelnames[k] for k in grouped_ids}
if len(set(level_names.values())) > 1:
self.logger.warning(
f"Could not set level names because they differ for the different keys:\n{pretty_dict(level_names, 'LEVEL_NAMES')}")
names = None
else:
names = tuple(level_names.values())[0]
if pandas_index:
idx = pd.Index(idx, names=names)
return idx
return idx, names
def index(self, keys=None, per_key=False):
if per_key:
keys = self._treat_key_param(keys)
return {k: self.index(k) for k in keys}
return [self._index[id] for id in self._iterids(keys)]
def info(self, keys=None, return_str=False):
ids = list(self._iterids(keys))
info = f"{len(ids)} files.\n"
exts = self.count_extensions(keys, per_key=True)
info += pretty_dict(exts, heading='EXTENSIONS')
parsed_mscx_ids = [id for id in ids if id in self._parsed_mscx]
parsed_mscx = len(parsed_mscx_ids)
ext_counts = self.count_extensions(keys, per_key=False)
others = sum(v for k, v in ext_counts.items() if k != '.mscx')
if parsed_mscx > 0:
mscx = ext_counts['.mscx']
if parsed_mscx == mscx:
info += f"\n\nAll {mscx} MSCX files have been parsed."
else:
info += f"\n\n{parsed_mscx}/{mscx} MSCX files have been parsed."
annotated = sum(True for id in parsed_mscx_ids if id in self._annotations)
if annotated == mscx:
info += f"\n\nThey all have annotations attached."
else:
info += f"\n\n{annotated} of them have annotations attached."
if annotated > 0:
layers = self.count_annotation_layers(keys, which='attached', per_key=True)
info += f"\n{pretty_dict(layers, heading='ANNOTATION LAYERS')}"
detached = sum(True for id in parsed_mscx_ids if self._parsed_mscx[id].has_detached_annotations)
if detached > 0:
info += f"\n\n{detached} of them have detached annotations:"
layers = self.count_annotation_layers(keys, which='detached', per_key=True)
info += f"\n{pretty_dict(layers, heading='ANNOTATION LAYERS')}"
elif '.mscx' in ext_counts:
info += f"\n\nNo mscx files have been parsed."
parsed_tsv_ids = [id for id in ids if id in self._parsed_tsv]
parsed_tsv = len(parsed_tsv_ids)
if parsed_tsv > 0:
annotations = sum(True for id in parsed_tsv_ids if id in self._annotations)
if parsed_tsv == others:
info += f"\n\nAll {others} tabular files have been parsed, {annotations} of them as Annotations object(s)."
else:
info += f"\n\n{parsed_tsv}/{others} tabular files have been parsed, {annotations} of them as Annotations object(s)."
if annotations > 0:
layers = self.count_annotation_layers(keys, which='tsv', per_key=True)
info += f"\n{pretty_dict(layers, heading='ANNOTATION LAYERS')}"
if return_str:
return info
print(info)
def match_files(self, keys=None, what=['mscx', 'labels'], only_new=True):
lists = dict(self._lists)
lists['mscx'] = self._parsed_mscx
lists['annotations'] = self._annotations
if isinstance(what, str):
what = [what]
assert all(True for wh in what if wh in lists), f"Unknown matching parameter(s) for 'what': {[wh for wh in what if wh not in lists]}"
if len(what) == 1:
what.extend([wh for wh in lists if wh != what[0]])
for wh in what:
if wh not in self._matches.columns:
self._matches[wh] = np.nan
start = what[0]
existing = lists[start]
ids = list(self._iterids(keys))
ids_to_match = [id for id in ids if id in existing]
matching_candidates = {wh: {(key, i): self.fnames[key][i] for key, i in ids if (key, i) in lists[wh]} for wh in what[1:]}
remove = []
for i, wh in enumerate(what[1:], 1):
if len(matching_candidates[wh]) == 0:
self.logger.warning(f"There are no candidates for '{wh}' in the keys {keys}.")
remove.append(i)
for i in reversed(remove):
del(what[i])
res_ix = []
for key, i in ids_to_match:
ix = self._index[(key, i)]
if ix in self._matches.index:
row = self._matches.loc[ix].copy()
else:
row = pd.Series(np.nan, index=lists.keys(), name=ix)
row[start] = (key, i)
for wh in what[1:]:
if not pd.isnull(row[wh]) and only_new:
self.logger.debug(f"{ix} had already been matched to {wh} {row[wh]}")
else:
row[wh] = np.nan
fname = self.fnames[key][i]
file = self.files[key][i]
matches = {id: os.path.commonprefix([fname, c]) for id, c in matching_candidates[wh].items()}
lengths = {id: len(prefix) for id, prefix in matches.items()}
longest = {id: prefix for id, prefix in matches.items() if lengths[id] == max(lengths.values())}
if len(longest) == 0:
self.logger.info(f"No match found for {file} among the candidates\n{pretty_dict(matching_candidates[wh])}")
elif len(longest) > 1:
ambiguity = {f"{key}: {self.full_paths[key][i]}": prefix for (key, i), prefix in longest.items()}
self.logger.info(f"Matching {file} is ambiguous. Disambiguate using keys:\n{pretty_dict(ambiguity)}")
else:
id = list(longest.keys())[0]
row[wh] = id
match_file = self.files[id[0]][id[1]]
self.logger.debug(f"Matched {file} to {match_file} based on the prefix {longest[id]}")
if ix in self._matches.index:
self._matches.loc[ix, :] = row
else:
self._matches = self._matches.append(row)
if len(self._matches) == 1:
self._matches.index = pd.MultiIndex.from_tuples(self._matches.index)
res_ix.append(ix)
return self._matches.loc[res_ix]
def metadata(self, keys=None):
parsed_ids = [id for id in self._iterids(keys) if id in self._parsed_mscx]
if len(parsed_ids) > 0:
ids, meta_series = zip(*[(id, metadata2series(self._parsed_mscx[id].mscx.metadata)) for id in parsed_ids])
idx = self.ids2idx(ids, pandas_index=True)
return pd.DataFrame(meta_series, index=idx)
if len(self._parsed_mscx) == 0:
self.logger.info("No scores have been parsed so far. Use parse_mscx()")
return pd.DataFrame()
[docs] def parse(self, keys=None, read_only=True, level=None, parallel=True, only_new=True, labels_cfg={}, fexts=None,
cols={}, infer_types={}, simulate=None, **kwargs):
""" Shorthand for executing parse_mscx and parse_tsv at a time."""
if simulate is not None:
self.simulate = simulate
self.parse_mscx(keys=keys, read_only=read_only, level=level, parallel=parallel, only_new=only_new, labels_cfg=labels_cfg)
self.parse_tsv(keys=keys, fexts=fexts, cols=cols, infer_types=infer_types, level=level, **kwargs)
[docs] def parse_mscx(self, keys=None, read_only=True, level=None, parallel=True, only_new=True, labels_cfg={}, simulate=False):
""" Parse uncompressed MuseScore 3 files (MSCX) and store the resulting read-only Score objects. If they need
to be writeable, e.g. for removing or adding labels, pass ``parallel=False`` which takes longer but prevents
having to re-parse at a later point.
Parameters
----------
keys : :obj:`str` or :obj:`~collections.abc.Collection`, optional
For which key(s) to parse all MSCX files.
read_only : :obj:`bool`, optional
If ``parallel=False``, you can increase speed and lower memory requirements by passing ``read_only=True``.
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.
parallel : :obj:`bool`, optional
Defaults to True, meaning that all CPU cores are used simultaneously to speed up the parsing. It implies
that the resulting Score objects are in read-only mode and that you might not be able to use the computer
during parsing. Set to False to parse one score after the other.
only_new : :obj:`bool`, optional
By default, score which already have been parsed, are not parsed again. Pass False to parse them, too.
Returns
-------
None
"""
if simulate is not None:
self.simulate = simulate
self.labels_cfg.update(update_labels_cfg(labels_cfg), logger=self.logger)
if parallel and not read_only:
read_only = True
self.logger.info("When pieces are parsed in parallel, the resulting objects are always in read_only mode.")
if only_new:
paths = [(key, i) for key, i in self._iterids(keys) if
self.fexts[key][i] == '.mscx' and (key, i) not in self._parsed_mscx]
else:
paths = [(key, i) for key, i in self._iterids(keys) if
self.fexts[key][i] == '.mscx']
if level is None:
level = self.logger.logger.level
cfg = {'level': level}
### If log files are going to be created, compute their paths and configure loggers for individual parses
if self.logger_cfg['file'] is not None or self.logger_cfg['path'] is not None:
file = None if self.logger_cfg['file'] is None else os.path.expanduser(self.logger_cfg['file'])
path = None if self.logger_cfg['path'] is None else os.path.expanduser(self.logger_cfg['path'])
if file is not None:
file_path, file_name = os.path.split(file)
_, fext = os.path.splitext(file_name)
if fext == '':
file_path = file
file_name = None
self.logger.debug(f"{file} was interpreted as a directory rather than as a file name.")
else:
file_path, file_name = None, None
if file_path is not None:
if os.path.isabs(file_path):
if file_name is None:
self.logger.error(f"Logger is configured with 'file' = '{file}' which is an absolute directory without specified file name. Make directory relative or add file name.")
configs = [cfg for i in range(len(paths))]
else:
cfg['file'] = file
configs = [cfg for i in range(len(paths))]
elif file_name is None:
if path is None:
configs = [dict(cfg, file=os.path.abspath(
os.path.join(self.paths[k][i], file_path, f"{self.fnames[k][i]}.log")
)) for k, i in paths]
else:
configs = [dict(cfg, file=os.path.abspath(
os.path.join(path, self.rel_paths[k][i], file_path, f"{self.fnames[k][i]}.log")
)) for k, i in paths]
else:
if path is None:
configs = [dict(cfg, file=os.path.abspath(
os.path.join(self.paths[k][i], file_path, file_name)
)) for k, i in paths]
else:
configs = [dict(cfg, file=os.path.abspath(
os.path.join(path, self.rel_paths[k][i], file_path, file_name)
)) for k, i in paths]
elif path is not None:
configs = [dict(cfg, file=os.path.abspath(
os.path.join(path, f"{self.fnames[k][i]}.log")
)) for k, i in paths]
else:
configs = [cfg for i in range(len(paths))]
else:
configs = [cfg for i in range(len(paths))]
### collect argument tuples for calling self._parse
parse_this = [t + (c, self.labels_cfg, read_only) for t, c in zip(paths, configs)]
target = len(parse_this)
successful = 0
modus = 'would ' if self.simulate else ''
try:
ids = [t[:2] for t in parse_this]
if self.simulate:
logger_cfg = {'level': level}
for key, i, _, _, read_only in parse_this:
logger_cfg['name'] = self.logger_names[(key, i)]
path = self.full_paths[key][i]
try:
score_object = Score(path, read_only=read_only, logger_cfg=logger_cfg)
except:
self.logger.exception(traceback.format_exc())
score_object = None
if score_object is not None:
self._parsed_mscx[(key, i)] = score_object
successful += 1
self.logger.debug(f"Successfully parsed {path}")
else:
self.logger.debug(f"Errors while parsing {path}")
elif parallel:
pool = mp.Pool(mp.cpu_count())
res = pool.starmap(self._parse, parse_this)
pool.close()
pool.join()
successful_results = {id: score for id, score in zip(ids, res) if score is not None}
self._parsed_mscx.update(successful_results)
successful = len(successful_results)
else:
for params in parse_this:
score_object = self._parse(*params)
if score_object is not None:
self._parsed_mscx[params[:2]] = score_object
successful += 1
if successful > 0:
if successful == target:
self.logger.info(f"All {target} files {modus}have been parsed successfully.")
else:
self.logger.info(f"Only {successful} of the {target} files {modus}have been parsed successfully.")
else:
self.logger.info(f"None of the {target} files {modus}have been parsed successfully.")
except KeyboardInterrupt:
self.logger.info("Parsing interrupted by user.")
finally:
self._collect_annotations_objects_references(ids=ids)
[docs] def parse_tsv(self, keys=None, fexts=None, cols={}, infer_types={}, level=None, **kwargs):
""" Parse TSV files (or other value-separated files such as CSV) to be able to do something with them.
Parameters
----------
keys : : :obj:`str` or :obj:`~collections.abc.Collection`, optional
Key(s) for which to parse all non-MSCX files. By default, all keys are selected.
fexts : :obj:`str` or :obj:`~collections.abc.Collection`, optional
If you want to parse only files with one or several particular file extension(s), pass the extension(s)
annotations : :obj:`str` or :obj:`~collections.abc.Collection`, optional
By default, if a column called ``'label'`` is found, the TSV is treated as an annotation table and turned into
an Annotations object. Pass one or several column name(s) to treat *them* as label columns instead. If you
pass ``None`` or no label column is found, the TSV is parsed as a "normal" table, i.e. a DataFrame.
infer_types : :obj:`dict`, optional
To recognize one or several custom label type(s), pass ``{name: regEx}``.
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.
**kwargs:
Arguments for :py:meth:`pandas.DataFrame.to_csv`. Defaults to ``{'sep': '\t', 'index': False}``. In particular,
you might want to update the default dictionaries for ``dtypes`` and ``converters`` used in :py:func:`load_tsv`.
Returns
-------
None
"""
if self.simulate:
return
if fexts is None:
ids = [(key, i) for key, i in self._iterids(keys) if self.fexts[key][i] != '.mscx']
else:
if isinstance(fexts, str):
fexts = [fexts]
fexts = [ext if ext[0] == '.' else f".{ext}" for ext in fexts]
ids = [(key, i) for key, i in self._iterids(keys) if self.fexts[key][i] in fexts]
for key, i in ids:
rel_path = os.path.join(self.rel_paths[key][i], self.files[key][i])
path = self.full_paths[key][i]
try:
df = load_tsv(path, **kwargs)
except:
self.logger.error(path)
raise
label_col = cols['label'] if 'label' in cols else 'label'
try:
self._parsed_tsv[(key, i)] = df
if label_col in df.columns:
tsv_type = 'labels'
else:
tsv_type = self._infer_tsv_type(df)
if tsv_type is None:
self.logger.warning(
f"No label column '{label_col}' was found in {rel_path} and its content could not be inferred. Columns: {df.columns.to_list()}")
else:
self._tsv_types[(key, i)] = tsv_type
self._lists[tsv_type][(key, i)] = self._parsed_tsv[(key, i)]
if tsv_type == 'labels':
if label_col in df.columns:
logger_name = self.files[key][i]
self._annotations[(key, i)] = Annotations(df=df, cols=cols, infer_types=infer_types,
logger_name=logger_name, level=level)
self.logger.debug(
f"{rel_path} parsed as a list of labels and an Annotations object was created.")
else:
self.logger.info(
f"""The file {rel_path} was recognized to contain labels but no label column '{label_col}' was found in {df.columns.to_list()}
Specify parse_tsv(key='{key}', cols={{'label'=label_column_name}}).""")
else:
self.logger.info(f"{rel_path} parsed as a list of {tsv_type}.")
except:
self.logger.error(f"Parsing {rel_path} failed with the following error:\n{sys.exc_info()[1]}")
def store_lists(self, keys=None, root_dir=None, notes_folder=None, notes_suffix='',
rests_folder=None, rests_suffix='',
notes_and_rests_folder=None, notes_and_rests_suffix='',
measures_folder=None, measures_suffix='',
events_folder=None, events_suffix='',
labels_folder=None, labels_suffix='',
chords_folder=None, chords_suffix='',
expanded_folder=None, expanded_suffix='',
simulate=None):
if simulate is None:
simulate = self.simulate
else:
self.simulate = simulate
l = locals()
list_types = list(self._lists)
folder_vars = [t + '_folder' for t in list_types]
suffix_vars = [t + '_suffix' for t in list_types]
folder_params = {t: l[p] for t, p in zip(list_types, folder_vars) if l[p] is not None}
if len(folder_params) == 0:
self.logger.warning("Pass at least one parameter to store files.")
return [] if simulate else None
suffix_params = {t: l[p] for t, p in zip(list_types, suffix_vars) if t in folder_params}
list_params = {p: True for p in folder_params.keys()}
lists = self.get_lists(keys, **list_params)
modus = 'would ' if simulate else ''
if len(lists) == 0:
self.logger.info(f"No files {modus}have been written.")
return [] if simulate else None
paths = {}
warnings, infos = [], []
prev_logger = self.logger.name
for (key, i, what), li in lists.items():
self.update_logger_cfg(name=self.logger_names[(key, i)])
new_path = self._store_tsv(df=li, key=key, i=i, folder=folder_params[what], suffix=suffix_params[what], root_dir=root_dir, what=what, simulate=simulate)
if new_path in paths:
warnings.append(f"The {paths[new_path]} at {new_path} {modus}have been overwritten with {what}.")
else:
infos.append(f"{what} {modus}have been stored as {new_path}.")
paths[new_path] = what
self.update_logger_cfg(name=prev_logger)
if len(warnings) > 0:
self.logger.warning('\n'.join(warnings))
l_infos = len(infos)
l_target = len(lists)
if l_infos > 0:
if l_infos < l_target:
msg = f"\n\nOnly {l_infos} out of {l_target} files {modus}have been stored."
else:
msg = f"\n\nAll {l_infos} {modus}have been written."
self.logger.info('\n'.join(infos) + msg)
else:
self.logger.info(f"\n\nNone of the {l_target} {modus}have been written.")
#self.logger = prev_logger
if simulate:
return list(set(paths.keys()))
[docs] def store_mscx(self, keys=None, root_dir=None, folder='.', suffix='', simulate=False):
""" Stores the parsed MuseScore files in their current state, e.g. after detaching or attaching annotations.
"""
ids = [id for id in self._iterids(keys) if id in self._parsed_mscx]
paths = []
for key, i in ids:
new_path = self._store_mscx(key=key, i=i, folder=folder, suffix=suffix, root_dir=root_dir, simulate=simulate)
if new_path in paths:
modus = 'would ' if simulate else ''
self.logger.warning(f"The score at {new_path} {modus}have been overwritten.")
else:
paths.append(new_path)
if simulate:
return list(set(paths))
def _calculate_path(self, key, i, root_dir, folder):
""" Constructs a path and file name from a loaded file based on the arguments.
Parameters
----------
key, i : (:obj:`str`, :obj:`int`)
ID from which to construct the new path and filename.
folder : :obj:`str`
Where to store the file. Can be relative to ``root_dir`` or absolute, in which case ``root_dir`` is ignored.
If ``folder`` is relative, the behaviour depends on whether it starts with a dot ``.`` or not: If it does,
the folder is created at every end point of the relative tree structure under ``root_dir``. If it doesn't,
it is created only once, relative to ``root_dir``, and the relative tree structure is build below.
root_dir : :obj:`str`, optional
Defaults to None, meaning that the original root directory is used that was added to the Parse object.
Otherwise, pass a directory to rebuild the original substructure. If ``folder`` is an absolute path,
``root_dir`` is ignored.
"""
if os.path.isabs(folder) or '~' in folder:
folder = resolve_dir(folder)
path = folder
else:
root = self.scan_paths[key][i] if root_dir is None else resolve_dir(root_dir)
if folder[0] == '.':
path = os.path.abspath(os.path.join(root, self.rel_paths[key][i], folder))
else:
path = os.path.abspath(os.path.join(root, folder, self.rel_paths[key][i]))
base, _ = os.path.split(root)
if path[:len(base)] != base:
self.logger.error(f"Not allowed to store files above the level of root {root}.\nErroneous path: {path}")
return None
return path
def _collect_annotations_objects_references(self, keys=None, ids=None):
""" Updates the dictionary self._annotations with all parsed Scores that have labels attached (or not any more). """
if ids is None:
ids = list(self._iterids(keys, only_parsed_mscx=True))
updated = {}
for id in ids:
if id in self._parsed_mscx:
score = self._parsed_mscx[id]
if score is not None:
if 'annotations' in score._annotations:
updated[id] = score.annotations
elif id in self._annotations:
del (self._annotations[id])
else:
del (self._parsed_mscx[id])
self._annotations.update(updated)
def _handle_path(self, full_path, key=None):
full_path = resolve_dir(full_path)
if os.path.isfile(full_path):
file_path, file = os.path.split(full_path)
file_name, file_ext = os.path.splitext(file)
rel_path = os.path.relpath(file_path, self.last_scanned_dir)
if key is None:
key = rel_path
if file in self.files[key]:
same_name = [i for i, f in enumerate(self.files[key]) if f == file]
if any(True for i in same_name if self.rel_paths[key][i] == rel_path):
self.logger.error(
f"""The file name {file} is already registered for key '{key}' and both files have the relative path {rel_path}.
Load one of the identically named files with a different key using add_dir(key='KEY').""")
return (None, None)
self.logger.debug(
f"The file {file} is already registered for key '{key}' but can be distinguished via the relative path.")
i = len(self.full_paths[key])
self.full_paths[key].append(full_path)
self.scan_paths[key].append(self.last_scanned_dir)
self.rel_paths[key].append(rel_path)
self.paths[key].append(file_path)
self.files[key].append(file)
self.logger_names[(key, i)] = file.replace('.', '')
self.fnames[key].append(file_name)
self.fexts[key].append(file_ext)
return key, len(self.paths[key]) - 1
else:
self.logger.error("No file found at this path: " + full_path)
return (None, None)
def _infer_tsv_type(self, df):
type2cols = {
'notes': ['tpc', 'midi'],
'events': ['event'],
'chords': ['chord_id'],
'rests': ['nominal_duration'],
'measures': ['act_dur'],
'labels': ['label_type', 'mc', 'mn'],
}
res = None
for t, columns in type2cols.items():
if any(True for c in columns if c in df.columns):
res = t
break
return res
def _iterids(self, keys=None, only_parsed_mscx=False, filter_attached_annotations=False, filter_detached_annotations=False):
"""Iterator through IDs for a given set of keys.
Parameters
----------
keys
only_parsed_mscx
filter_attached_annotations
filter_detached_annotations
Yields
------
:obj:`tuple`
(str, int)
"""
keys = self._treat_key_param(keys)
for key in sorted(keys):
for id in make_id_tuples(key, len(self.fnames[key])):
if only_parsed_mscx or filter_attached_annotations or filter_detached_annotations:
if id not in self._parsed_mscx:
continue
if filter_attached_annotations:
if 'annotations' in self._parsed_mscx[id]._annotations:
pass
else:
continue
if filter_detached_annotations:
if self._parsed_mscx[id].has_detached_annotations:
pass
else:
continue
yield id
def _itersel(self, collectio, selector, opposite=False):
if selector is None:
for i, e in enumerate(collectio):
yield e
if opposite:
for i, e in enumerate(collectio):
if i not in selector:
yield e
else:
for i, e in enumerate(collectio):
if i in selector:
yield e
def _make_index_level(self, level, ids, selector=None):
if level == 'key':
return {id: id[0] for id in ids}
if level == 'i':
return {id: id[1] for id in ids}
if level == 'fname':
return {(key, i): self.fnames[key][i] for key, i in ids}
ll, li = len(level), len(ids)
ls = 0 if selector is None else len(selector)
if ll < li:
self.logger.error(f"Index level (length {ll}) has not enough values for {li} ids.")
return {}
if ll > li:
if ls == 0:
res = {i: l for i, l in self._itersel(zip(ids, level), tuple(range(li)))}
discarded = [l for l in self._itersel(level, tuple(range(li, ll)))]
self.logger.warning(
f"""Index level (length {ll}) has more values than needed for {li} ids and no selector has been passed.
Using the first {li} elements, discarding {discarded}""")
elif ls != li:
self.logger.error(
f"The selector for picking elements from the overlong index level (length {ll}) should have length {li}, not {ls},")
res = {}
else:
if ls != ll:
discarded = [l for l in self._itersel(level, selector, opposite=True)]
plural_s = 's' if len(discarded) > 1 else ''
self.logger.debug(
f"Selector {selector} was applied, leaving out the index value{plural_s} {discarded}")
res = {i: l for i, l in zip(ids, self._itersel(level, selector))}
else:
res = {i: l for i, l in zip(ids, level)}
return res
def _parse(self, key, i, logger_cfg={}, labels_cfg={}, read_only=False):
"""Performs a single parse and returns the resulting Score object or None."""
path = self.full_paths[key][i]
file = self.files[key][i]
self.logger.debug(f"Attempting to parse {file}")
try:
score = Score(path, read_only=read_only, labels_cfg=labels_cfg, logger_cfg=logger_cfg)
if score is None:
self.logger.debug(f"Encountered errors when parsing {file}")
else:
self.logger.debug(f"Successfully parsed {file}")
# self._parsed_mscx[(key, i)] = score
return score
except (KeyboardInterrupt, SystemExit):
self.logger.info("Process aborted.")
raise
except:
self.logger.exception(traceback.format_exc())
return None
def _store_mscx(self, key, i, folder, suffix='', root_dir=None, simulate=False):
""" Creates a MuseScore 3 file from the Score object at the given ID (key, i).
Parameters
----------
key, i : (:obj:`str`, :obj:`int`)
ID from which to construct the new path and filename.
folder, root_dir : :obj:`str`
Parameters passed to :py:meth:`_calculate_path`.
suffix : :obj:`str`, optional
Suffix to append to the original file name.
simulate : :obj:`bool`, optional
Set to True if no files are to be written.
Returns
-------
:obj:`str`
Path of the stored file.
"""
def restore_logger(val):
nonlocal prev_logger
self.logger = prev_logger
return val
prev_logger = self.logger
fname = self.fnames[key][i]
self.update_logger_cfg(name= self.logger_names[(key, i)])
id = (key, i)
if id not in self._parsed_mscx:
self.logger.error(f"No Score object found. Call parse_mscx() first.")
return restore_logger(None)
path = self._calculate_path(key=key, i=i, root_dir=root_dir, folder=folder)
if path is None:
return restore_logger(None)
fname = fname + suffix + '.mscx'
file_path = os.path.join(path, fname)
if simulate:
self.logger.debug(f"Would have written score to {file_path}.")
else:
os.makedirs(path, exist_ok=True)
self._parsed_mscx[id].store_mscx(file_path)
self.logger.debug(f"Score written to {file_path}.")
return restore_logger(file_path)
def _store_tsv(self, df, key, i, folder, suffix='', root_dir=None, what='DataFrame', simulate=False, **kwargs):
""" Stores a given DataFrame by constructing path and file name from a loaded file based on the arguments.
Parameters
----------
df : :obj:`pandas.DataFrame`
DataFrame to store as a TSV.
key, i : (:obj:`str`, :obj:`int`)
ID from which to construct the new path and filename.
folder, root_dir : :obj:`str`
Parameters passed to :py:meth:`_calculate_path`.
suffix : :obj:`str`, optional
Suffix to append to the original file name.
what : :obj:`str`, optional
Descriptor, what the DataFrame contains for more informative log message.
simulate : :obj:`bool`, optional
Set to True if no files are to be written.
**kwargs: Arguments for :py:meth:`pandas.DataFrame.to_csv`. Defaults to ``{'sep': '\t', 'index': False}``.
If 'sep' is changed to a different separator, the file extension(s) will be changed to '.csv' rather than '.tsv'.
Returns
-------
:obj:`str`
Path of the stored file.
"""
def restore_logger(val):
nonlocal prev_logger
self.logger = prev_logger
return val
prev_logger = self.logger
fname = self.fnames[key][i]
self.update_logger_cfg(name=self.logger_names[(key, i)] + f":{what}")
if df is None:
self.logger.debug(f"No DataFrame for {what}.")
return restore_logger(None)
path = self._calculate_path(key=key, i=i, root_dir=root_dir, folder=folder)
if path is None:
return restore_logger(None)
if 'sep' not in kwargs:
kwargs['sep'] = '\t'
if 'index' not in kwargs:
kwargs['index'] = False
ext = '.tsv' if kwargs['sep'] == '\t' else '.csv'
fname = fname + suffix + ext
file_path = os.path.join(path, fname)
if simulate:
self.logger.debug(f"Would have written {what} to {file_path}.")
else:
os.makedirs(path, exist_ok=True)
no_collections_no_booleans(df, logger=self.logger).to_csv(file_path, **kwargs)
self.logger.debug(f"{what} written to {file_path}.")
return restore_logger(file_path)
def _treat_index_param(self, index_param, ids, selector=None):
if index_param is None:
names = ('key', 'i')
return {id: id for id in ids}, names
if isinstance(index_param, str):
index_param = [index_param]
index_levels = []
is_index_level=False
names = []
for i, level in enumerate(index_param):
if isinstance(level, str):
if level in ['key', 'fname', 'i']:
new_level = self._make_index_level(level, ids=ids, selector=selector)
index_levels.append(new_level)
names.append(level)
self.logger.debug(f"Level '{level}' generated: {new_level}")
else:
assert len(index_levels) == 0, f"Failed to create index level '{level}', because it is neither a keyword nor a Collection."
is_index_level = True
break
elif isinstance(level, Collection):
new_level = self._make_index_level(level, ids=ids, selector=selector)
if len(new_level) > 0:
index_levels.append(new_level)
names.append(None)
else:
assert len(index_levels) == 0, f"Failed to create index level '{level}', because it is neither a keyword nor a Collection."
is_index_level = True
break
if is_index_level:
self.logger.debug(f"index_param is interpreted as a single index level rather than a collection of levels.")
new_level = self._make_index_level(index_param, ids=ids, selector=selector)
if len(new_level) > 0:
index_levels.append(new_level)
names = [None]
if len(index_levels) == 0:
self.logger.error(f"No index could be created.")
new_index = {id: ix for id, ix in zip(ids, zip(*[tuple(v.values()) for v in index_levels]))}
existing = [ix for ix in new_index if ix in self._index.keys()]
counts = {k: v for k, v in Counter(new_index.values()).items() if v > 1}
l_counts, l_existing = len(counts), len(existing)
if l_counts > 0 or l_existing > 0:
new_index = self._treat_index_param(None, ids=ids)
if l_counts > 0:
plural_phrase = "These values occur" if l_counts > 1 else "This value occurs"
self.logger.error(f"The generated index is not unique and has been replaced by the standard index (IDs).\n{plural_phrase} several times:\n{pretty_dict(counts)}")
if l_existing > 0:
plural_phrase = "s are" if l_existing > 1 else " is"
self.logger.error(f"The generated index cannot be used because the following element{plural_phrase} already in use:\n{existing}")
return new_index, tuple(names)
def _treat_key_param(self, keys):
if keys is None:
keys = list(self.full_paths.keys())
elif isinstance(keys, str):
keys = [keys]
return list(set(keys))
def _treat_label_type_param(self, label_type):
if label_type is None:
return None
all_types = {str(k): k for k in self.count_label_types().keys()}
if isinstance(label_type, int) or isinstance(label_type, str):
label_type = [label_type]
lt = [str(t) for t in label_type]
not_found = [t for t in lt if t not in all_types]
if len(not_found) > 0:
plural = len(not_found) > 1
plural_s = 's' if plural else ''
self.logger.warning(
f"No labels found with {'these' if plural else 'this'} label{plural_s} label_type{plural_s}: {', '.join(not_found)}")
return [all_types[t] for t in lt if t in all_types]
def __getstate__(self):
""" Override the method of superclass """
return self.__dict__
# def expand_labels(self, keys=None, how='dcml'):
# keys = self._treat_key_param(keys)
# scores = {id: score for id, score in self._parsed.items() if id[0] in keys}
# res = {}
# for id, score in scores.items():
# if score.mscx._annotations is not None:
# exp = score.annotations.expanded
# self._expandedlists[id] = exp
# res[id + ('expanded',)] = exp
# return res
# def __getattr__(self, item):
# if item in self.fexts: # is an existing key
# fexts = self.fexts[item]
# res = {}
# for i, ext in enumerate(fexts):
# id = (item, i)
# ix = str(self._index[id])
# if ext == '.mscx':
# if id in self._parsed_mscx:
# ix += " (parsed)"
# val = str(self._parsed_mscx[id])
# else:
# ix += " (not parsed)"
# val = self.full_paths[item][i]
# else:
# if id in self._parsed_tsv:
# df = self._parsed_tsv[id]
# if isinstance(df, Annotations):
# ix += " (parsed annotations)"
# val = str(df)
# else:
# t = self._tsv_types[id] if id in self._tsv_types else 'unrecognized DataFrame'
# ix += f" (parsed {t}, length {len(df)})"
# val = df.head(5).to_string()
# else:
# ix += " (not parsed)"
# val = self.full_paths[item][i]
# ix += f"\n{'-' * len(ix)}\n"
# if ext != '.mscx':
# ix += f"{self.full_paths[item][i]}\n"
# print(f"{ix}{val}\n")
# else:
# raise AttributeError(item)
def __getitem__(self, item):
if item in self._index:
id = item
elif item in self._index.values():
id = next(k for k, v in self._index.items() if v == item)
else:
if item in self.files:
self.logger.info(f"{item} is neither an ID nor an index, but a key with the following IDs:\n" + string2lines(make_id_tuples(item, len(self.files[item]))))
else:
self.logger.warning(f"{item} is neither an ID nor an index.")
return
if id in self._parsed_mscx:
return self._parsed_mscx[id]
if id in self._annotations:
return self._annotations[id]
if id in self._parsed_tsv:
return self._parsed_tsv[id]
else:
self.logger.warning(f"{self.full_paths[id]} has or could not be parsed.")
def __repr__(self):
return self.info(return_str=True)