wfdb-python
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Rounding errors in to_dataframe
The wfdb.Record.to_dataframe
function generates a DataFrame from a Record object. The index
of the resulting DataFrame is the elapsed or absolute time of each sample.
This code, however, will have significant rounding errors over a long record:
if self.base_datetime is not None:
index = pd.date_range(
start=self.base_datetime,
periods=self.sig_len,
freq=pd.Timedelta(seconds=1 / self.fs),
)
else:
index = pd.timedelta_range(
start=pd.Timedelta(0),
periods=self.sig_len,
freq=pd.Timedelta(seconds=1 / self.fs),
)
For example:
$ python3
>>> import wfdb
>>> r = wfdb.rdrecord('81739927', pn_dir='mimic4wdb/0.1.0/waves/p100/p10014354/81739927')
>>> str(r.base_datetime)
'2148-08-16 09:00:17.566000'
>>> r.fs
62.4725
>>> r.sig_len
6661120
>>> r.to_dataframe()
I II III V aVR Pleth Resp
2148-08-16 09:00:17.566000 NaN NaN NaN NaN NaN NaN -0.751374
2148-08-16 09:00:17.582007 NaN NaN NaN NaN NaN NaN -0.751374
2148-08-16 09:00:17.598014 NaN NaN NaN NaN NaN NaN -0.751374
2148-08-16 09:00:17.614021 NaN NaN NaN NaN NaN NaN -0.751374
2148-08-16 09:00:17.630028 NaN NaN NaN NaN NaN NaN -0.751374
... .. ... ... ... ... ... ...
2148-08-17 14:37:22.033805 NaN -0.220 -0.285 -0.025 NaN 0.404297 0.487477
2148-08-17 14:37:22.049812 NaN -0.030 0.005 0.025 NaN 0.396484 0.530238
2148-08-17 14:37:22.065819 NaN -0.065 -0.030 -0.015 NaN 0.386475 0.574832
2148-08-17 14:37:22.081826 NaN -0.265 -0.255 -0.125 NaN 0.375977 0.621258
2148-08-17 14:37:22.097833 NaN -0.550 -0.610 -0.355 NaN 0.366211 0.664020
[6661120 rows x 7 columns]
>>> str(r.get_absolute_time(6661119)
'2148-08-17 14:37:22.384920'
$ wfdbtime -r mimic4wdb/0.1.0/waves/p100/p10014354/81739927/ s6661119
s6661119 29:37:04.819 [14:37:22.385 17/08/2148]
Here, get_absolute_time
is correct to the nearest microsecond and the wfdbtime
command is correct to the nearest millisecond. to_dataframe
, however, is off by 0.287 seconds.
I think this would be avoided by using start
and end
arguments to date_range
or timedelta_range
, rather than using start
and freq
.