'^ *' # space
'(' # 1st group: value
'[\\-\\+]?[0-9]*[.]?[0-9]*'
')'
' *' # space
'(' # 2nd group: unit
'[a-zA-Zμ]*'
')'
' *$' # space
duration: typing.Optional[
typing.Union[float, int, str, np.timedelta64]
],
and ``sampling_rate`` is always ignored.
If duration is
``None``,
:obj:`numpy.nan`,
:obj:`pandas.NA`,
:obj:`pandas.NaT`,
``''``,
``'None'``,
``'NaN'``,
``'NaT'``,
or any other lower/mixed case version of those strings
:obj:`numpy.nan` is returned.
If duration is
:obj:`numpy.inf`,
``'Inf'``
or any other lower/mixed case version of that string
:obj:`numpy.inf` is returned,
and ``-``:obj:`numpy.inf` for the negative case
>>> duration_in_seconds('Inf')
inf
>>> duration_in_seconds(None)
nan
# none/-inf/inf duration
if duration.lower() in ['', 'none', 'nan', 'nat']:
return np.NaN
elif duration.lower() == '-inf':
return -np.inf
elif duration.lower() == 'inf' or duration.lower() == '+inf':
return np.inf
match = re.match(VALUE_UNIT_PATTERN, duration)
if not value or value == '+':
elif value == '-':
value = -1.0
# handle nan/none durations
elif (
duration is None
or duration.__class__.__name__ == 'NaTType'
or duration.__class__.__name__ == 'NAType'
or np.isnan(duration)
):
return np.NaN
(None, None, np.NaN),
(None, 1000, np.NaN),
('', None, np.NaN),
('', 1000, np.NaN),
('none', None, np.NaN),
('none', 1000, np.NaN),
('None', None, np.NaN),
('None', 1000, np.NaN),
('nan', None, np.NaN),
('nan', 1000, np.NaN),
('NaN', None, np.NaN),
('NaN', 1000, np.NaN),
('nat', None, np.NaN),
('nat', 1000, np.NaN),
('NaT', None, np.NaN),
('NaT', 1000, np.NaN),
(np.NaN, None, np.NaN),
(np.NaN, 1000, np.NaN),
(pd.NaT, None, np.NaN),
(pd.NaT, 1000, np.NaN),
(pd.NA, None, np.NaN),
(pd.NA, 1000, np.NaN),
(np.timedelta64('NaT', 's'), None, np.NaN),
(np.timedelta64('NaT', 's'), 1000, np.NaN),
('inf', None, np.inf),
('inf', 1000, np.inf),
('Inf', None, np.inf),
('Inf', 1000, np.inf),
(np.inf, None, np.inf),
(np.inf, 1000, np.inf),
(np.Inf, None, np.inf),
(np.Inf, 1000, np.inf),
('+inf', None, np.inf),
('+inf', 1000, np.inf),
('+Inf', None, np.inf),
('+Inf', 1000, np.inf),
(+2, None, 2.0),
(+2, 1000, 0.002),
(+2.0, None, 2.0),
(+2.0, 1000, 0.002),
('+s', None, 1.0),
('+s', 1000, 1.0),
(' +s', None, 1.0),
(' +s', 1000, 1.0),
('+2s', None, 2.0),
('+2s', 1000, 2.0),
('+2s ', None, 2.0),
('+2s ', 1000, 2.0),
(' +2s', None, 2.0),
(' +2s', 1000, 2.0),
('+2 s', None, 2.0),
('+2 s', 1000, 2.0),
('+2 s', None, 2.0),
('+2 s', 1000, 2.0),
('+2000ms', None, 2.0),
('+2000ms', 1000, 2.0),
('+2000.0ms', None, 2.0),
('+2000.0ms', 1000, 2.0),
('+2000 ms', None, 2.0),
('+2000 ms', 1000, 2.0),
('+2000.0 ms', None, 2.0),
('+2000.0 ms', 1000, 2.0),
('+2000', None, 2000.0),
('+2000', 1000, 2.0),
('+2000 ', None, 2000.0),
('+2000 ', 1000, 2.0),
('+2000.0', None, 2000.0),
('+2000.0', 1000, 2.0),
('+2000.1', None, 2000.1),
('+2000.1', 1000, 2.0000999999999998),
('+0.5', None, 0.5),
('+0.5', 2, 0.25),
('+3', 1.5, 2.0),
(np.timedelta64(+2, 's'), None, 2.0),
(np.timedelta64(+2, 's'), 1000, 2.0),
(np.timedelta64(+2000, 'ms'), None, 2.0),
(np.timedelta64(+2000, 'ms'), 1000, 2.0),
(pd.to_timedelta(+2, 's'), None, 2.0),
(pd.to_timedelta(+2, 's'), 1000, 2.0),
(pd.to_timedelta(+2000, 'ms'), None, 2.0),
(pd.to_timedelta(+2000, 'ms'), 1000, 2.0),
('-inf', None, -np.inf),
('-inf', 1000, -np.inf),
('-Inf', None, -np.inf),
('-Inf', 1000, -np.inf),
(-2, None, -2.0),
(-2, 1000, -0.002),
(-2.0, None, -2.0),
(-2.0, 1000, -0.002),
('-s', None, -1.0),
('-s', 1000, -1.0),
(' -s', None, -1.0),
(' -s', 1000, -1.0),
('-2s', None, -2.0),
('-2s', 1000, -2.0),
('-2s ', None, -2.0),
('-2s ', 1000, -2.0),
(' -2s', None, -2.0),
(' -2s', 1000, -2.0),
('-2 s', None, -2.0),
('-2 s', 1000, -2.0),
('-2 s', None, -2.0),
('-2 s', 1000, -2.0),
('-2000ms', None, -2.0),
('-2000ms', 1000, -2.0),
('-2000.0ms', None, -2.0),
('-2000.0ms', 1000, -2.0),
('-2000 ms', None, -2.0),
('-2000 ms', 1000, -2.0),
('-2000.0 ms', None, -2.0),
('-2000.0 ms', 1000, -2.0),
('-2000', None, -2000.0),
('-2000', 1000, -2.0),
('-2000 ', None, -2000.0),
('-2000 ', 1000, -2.0),
('-2000.0', None, -2000.0),
('-2000.0', 1000, -2.0),
('-2000.1', None, -2000.1),
('-2000.1', 1000, -2.0000999999999998),
('-0.5', None, -0.5),
('-0.5', 2, -0.25),
('-3', 1.5, -2.0),
(np.timedelta64(-2, 's'), None, -2.0),
(np.timedelta64(-2, 's'), 1000, -2.0),
(np.timedelta64(-2000, 'ms'), None, -2.0),
(np.timedelta64(-2000, 'ms'), 1000, -2.0),
(pd.to_timedelta(-2, 's'), None, -2.0),
(pd.to_timedelta(-2, 's'), 1000, -2.0),
(pd.to_timedelta(-2000, 'ms'), None, -2.0),
(pd.to_timedelta(-2000, 'ms'), 1000, -2.0),
duration_in_seconds = audmath.duration_in_seconds(duration, sampling_rate)
if np.isnan(expected):
assert np.isnan(duration_in_seconds)
else:
assert duration_in_seconds == expected
