vitabel.utils.helpers¶

Collection of various auxiliary and helper functions.

Classes¶

NumpyEncoder

Special json encoder for numpy types

Functions¶

`deriv`(time, data)	Calculates the central point first derivative of x=time,y=data,
`integrate`(time, data)	Integrates data after time with trapezoidal rule
`construct_snippets`(acctime, acc, ecgtime, ecg, ...)
`av_mean`(→ numpy.typing.NDArray)	Calculate a centered average mean of data over k elements.
`predict_circulation`(erg)
`area_under_threshold`(→ vitabel.typing.ThresholdMetrics)	Calculates the area and duration where the signal falls
`rename_channels`(dats, new_name_dict)
`determine_gaps_in_recording`(time, data, ...)
`linear_interpolate_gaps_in_recording`(time, data, ...)
`resample_to_common_index`(...)	Aligns two or more Channel objects by their time indices, ensuring
`convert_two_alternating_list`(df)
`find_ROSC_2`(rosctime, roscdata, CC_starts, CC_stops)
`CCF_minute`(t_start, t_stop, CC_starts, CC_stops)
`gaussian_kernel_regression_point`(x0, x, y[, sigma, ...])

Module Contents¶

class vitabel.utils.helpers.NumpyEncoder(*, skipkeys=False, ensure_ascii=True, check_circular=True, allow_nan=True, sort_keys=False, indent=None, separators=None, default=None)¶

Bases: json.JSONEncoder

Special json encoder for numpy types

default(obj)¶

Implement this method in a subclass such that it returns a serializable object for o, or calls the base implementation (to raise a TypeError).

For example, to support arbitrary iterators, you could implement default like this:

def default(self, o):
    try:
        iterable = iter(o)
    except TypeError:
        pass
    else:
        return list(iterable)
    # Let the base class default method raise the TypeError
    return super().default(o)

vitabel.utils.helpers.deriv(time, data)¶

Calculates the central point first derivative of x=time,y=data, and take forward/backward step derivative at the boundaries

Parameters:

timenp.array(): equidistantly spaced x values.
datanp.array(): corresponding y values.

Returns:

ddatnp.array(): array with the derivative values.

vitabel.utils.helpers.integrate(time, data)¶

Integrates data after time with trapezoidal rule

Parameters:

timepd.Series: Timestamps in seconds.
datapd.Series: Datapoints.

Returns:

velpd.Series: Integrated data. vel[0]=0

vitabel.utils.helpers.construct_snippets(acctime, acc, ecgtime, ecg, CC_starts, CC_stops)¶

vitabel.utils.helpers.av_mean(k: int, data: numpy.typing.ArrayLike) → numpy.typing.NDArray¶

Calculate a centered average mean of data over k elements.

The data is zero-padded at the boundaries.

vitabel.utils.helpers.predict_circulation(erg)¶

vitabel.utils.helpers.area_under_threshold(timeseries: pandas.Series, start_time: vitabel.typing.Timestamp | vitabel.typing.Timedelta | None = None, stop_time: vitabel.typing.Timestamp | vitabel.typing.Timedelta | None = None, threshold: int = 0, time_unit: vitabel.typing.TimeUnitChoices = 'minutes') → vitabel.typing.ThresholdMetrics¶

Calculates the area and duration where the signal falls below a threshold.

This function operates on a given timeseries, subtracts a threshold, detects zero-crossings (sign changes), interpolates crossing points, and integrates the area under the threshold using the trapezoidal rule.

Parameters:

timeseries: A pandas.Series holding numerical data indexed by a timeseries.
start_time: Start time for truncating the timeseries (passed to meth:.Vitals.truncate).
stop_time: End time for truncating the timeseries (passed to meth:.Vitals.truncate).
threshold: The threshold of the signal under which the area is calcuated.
time_unit: The time unit according to which the result is scaled. Defaults to "minutes", accepts the same arguments as pandas.to_timedelta.

Returns:

ThresholdMetrics

vitabel.utils.helpers.rename_channels(dats, new_name_dict)¶

vitabel.utils.helpers.determine_gaps_in_recording(time: numpy.typing.ArrayLike, data: numpy.typing.ArrayLike)¶

vitabel.utils.helpers.linear_interpolate_gaps_in_recording(time: numpy.typing.ArrayLike, data: numpy.typing.ArrayLike)¶

vitabel.utils.helpers.resample_to_common_index(*channels: vitabel.timeseries.Channel) → tuple[vitabel.typing.DataSlice, Ellipsis]¶

Aligns two or more Channel objects by their time indices, ensuring all are sampled at the same time points, including zero-crossings. Data are interpolated to a common time base within their overlapping range.

Parameters:

*channels: Two or more Channel objects to be aligned and interpolated.

Returns:

tuple[DataSlice, …]: A tuple of DataSlice objects representing the aligned channels.

Raises:

ValueError: If there is no overlapping time range between the two channels.

Notes

Zero-crossings are inserted to improve alignment, especially for strongly oscillating signals.

vitabel.utils.helpers.convert_two_alternating_list(df)¶

vitabel.utils.helpers.find_ROSC_2(rosctime, roscdata, CC_starts, CC_stops)¶

vitabel.utils.helpers.CCF_minute(t_start, t_stop, CC_starts, CC_stops)¶

vitabel.utils.helpers.gaussian_kernel_regression_point(x0, x, y, sigma=1, max_width_factor=2)¶