def test_from_df(annotation):
# Check that we can reconstruct an annotation from a Pandas
# dataframe containing its tracks.
column_names = [PYANNOTE_SEGMENT, PYANNOTE_TRACK, PYANNOTE_LABEL]
df = pd.DataFrame.from_records(
annotation.itertracks(True), columns=column_names)
actual = Annotation.from_df(df)
expected = annotation
assert actual == expected
def read(self, path, uri=None, modality=None, **kwargs):
"""
Parameters
----------
path : str
modality : str, optional
Force all entries to be considered as coming from this modality.
Only taken into account when file format does not provide
any field related to modality (e.g. .seg files)
"""
# load whole file
df = pandas.read_table(path,
delim_whitespace=True,
header=None, names=self.fields(),
comment=self.comment(),
converters=self.converters(),
dtype={PYANNOTE_LABEL: object})
# remove comment lines
# (i.e. lines for which all fields are either None or NaN)
keep = [not all(pandas.isnull(item) for item in row[1:])
for row in df.itertuples()]
df = df[keep]
# add 'segment' column build from start time & duration
df[PYANNOTE_SEGMENT] = [self.get_segment(row)
for row in df.itertuples()]
# add unique track numbers if they are not read from file
if PYANNOTE_TRACK not in self.fields():
df[PYANNOTE_TRACK] = range(df.shape[0])
# add uri column in case it does not exist
if PYANNOTE_URI not in df:
if uri is None:
raise ValueError('missing uri -- use uri=')
df[PYANNOTE_URI] = uri
# obtain list of resources
uris = list(df[PYANNOTE_URI].unique())
# add modality column in case it does not exist
if PYANNOTE_MODALITY not in df:
if modality is None:
raise ValueError('missing modality -- use modality=')
df[PYANNOTE_MODALITY] = modality if modality is not None else ""
# obtain list of modalities
modalities = list(df[PYANNOTE_MODALITY].unique())
self._loaded = {}
# loop on resources
for uri in uris:
# filter based on resource
df_ = df[df[PYANNOTE_URI] == uri]
# loop on modalities
for modality in modalities:
# filter based on modality
modality = modality if modality is not None else ""
df__ = df_[df_[PYANNOTE_MODALITY] == modality]
a = Annotation.from_df(df__, modality=modality, uri=uri)
self._loaded[uri, modality] = a
return self
def read(self, path, uri=None, modality=None, **kwargs):
"""
Parameters
----------
path : str
modality : str, optional
Force all entries to be considered as coming from this modality.
Only taken into account when file format does not provide
any field related to modality (e.g. .seg files)
"""
# load whole file
df = pandas.read_table(path,
delim_whitespace=True,
header=None, names=self.fields(),
comment=self.comment(),
converters=self.converters(),
dtype={PYANNOTE_URI: object,
PYANNOTE_LABEL: object},
keep_default_na=False, na_values=[])
# remove comment lines
# (i.e. lines for which all fields are either None or NaN)
keep = [not all(pandas.isnull(item) for item in row[1:])
for row in df.itertuples()]
df = df[keep]
# add 'segment' column build from start time & duration
df[PYANNOTE_SEGMENT] = [self.get_segment(row)
for row in df.itertuples()]
# add unique track numbers if they are not read from file
if PYANNOTE_TRACK not in self.fields():
df[PYANNOTE_TRACK] = range(df.shape[0])
# add uri column in case it does not exist
if PYANNOTE_URI not in df:
if uri is None:
raise ValueError('missing uri -- use uri=')
df[PYANNOTE_URI] = uri
# obtain list of resources
uris = list(df[PYANNOTE_URI].unique())
# add modality column in case it does not exist
if PYANNOTE_MODALITY not in df:
if modality is None:
raise ValueError('missing modality -- use modality=')
df[PYANNOTE_MODALITY] = modality if modality is not None else ""
# obtain list of modalities
modalities = list(df[PYANNOTE_MODALITY].unique())
self._loaded = {}
# loop on resources
for uri in uris:
# filter based on resource
df_ = df[df[PYANNOTE_URI] == uri]
# loop on modalities
for modality in modalities:
# filter based on modality
modality = modality if modality is not None else ""
df__ = df_[df_[PYANNOTE_MODALITY] == modality]
a = Annotation.from_df(df__, modality=modality, uri=uri)
self._loaded[uri, modality] = a
return self
