timeseries.TimeSeries

class timeseries.TimeSeries(times: ndarray | List | Tuple = [], periodic: bool = False, t_start: float | None = None, t_stop: float | None = None, plotting_backend: str | None = None, name: str = 'unnamed')

Bases: object

Base class to represent a continuous or event-based time series. You should use the subclasses TSContinuous and TSEvent to represent continuous-time and event-based time series, respectively. See Working with time series data for futher explanation and examples.

Attributes overview

duration	(float) Duration of TimeSeries
name
plotting_backend	(str) Current plotting backend
t_start	(float) Start time of time series
t_stop	(float) Stop time of time series (final sample)
times	(ArrayLike[float]) Array of sample times

Methods overview

__init__([times, periodic, t_start, t_stop, ...])	Represent a continuous or event-based time series
concatenate_t(series[, offset])	Append multiple TimeSeries objects in time to a new series
contains(times)	Does the time series contain the time range specified in the given time trace? Always true for periodic series
copy()	Return a deep copy of this time series
delay(offset[, inplace])	Return a copy of self that is delayed by an offset
isempty()	Test if this TimeSeries object is empty
load(path[, expected_type])	Load TimeSeries object from file.
print()	Print an overview of the time series
set_plotting_backend(backend[, verbose])	Set which plotting backend to use with the plot method
start_at(t_start[, inplace])	Convenience function that calls the delay method such that self.t_start falls at t_start.
start_at_zero([inplace])	Convenience function that calls the delay method such that self.t_start falls at 0.

__init__(times: ndarray | List | Tuple = [], periodic: bool = False, t_start: float | None = None, t_stop: float | None = None, plotting_backend: str | None = None, name: str = 'unnamed')

Represent a continuous or event-based time series

Parameters:

• times (ArrayLike) – [Tx1] vector of time samples

• periodic (bool) – Treat the time series as periodic around the end points. Default: False

• t_start (Optional[float]) – If not None, the series start time is t_start, otherwise times[0]

• t_stop (Optional[float]) – If not None, the series stop time is t_stop, otherwise times[-1]

• plotting_backend (Optional[str]) – Determines plotting backend. If None, backend will be chosen automatically based on what is available.

• name (str) – Name of the TimeSeries object. Default: “unnamed”

_modulo_period(times: ndarray | List | Tuple | float | int) → ndarray | List | Tuple | float | int

_modulo_period - Calculate provided times modulo self.duration

classmethod concatenate_t(series: Iterable[TS], offset: float | Iterable[float | None] | None = None) → TS

Append multiple TimeSeries objects in time to a new series

Parameters:

• series (Iterable) – Time series to be tacked at the end of each other. These series must have the same number of channels.

• offset (Union[None, float, Iterable]) – Offset to be introduced between time traces. First value corresponds to delay of first time series.

Return TimeSeries:

Time series with data from series in series

contains(times: int | float | ndarray | List | Tuple) → bool

Does the time series contain the time range specified in the given time trace? Always true for periodic series

Parameters:

times (ArrayLike) – Array-like containing time points

Return bool:

True iff all specified time points are contained within this time series

copy() → TS

Return a deep copy of this time series

Return TimeSeries:

copy of self

delay(offset: int | float, inplace: bool = False) → TS

Return a copy of self that is delayed by an offset

For delaying self, use the inplace argument, or .times += ... instead.

Parameters:

• Offset (float) – Time by which to offset this time series

• inplace (bool) – If True, conduct operation in-place (Default: False; create a copy)

Return TimeSeries:

New TimeSeries, delayed

property duration: float

(float) Duration of TimeSeries

isempty() → bool

Test if this TimeSeries object is empty

Return bool:

True iff the TimeSeries object contains no samples

classmethod load(path: str | Path, expected_type: str | None = None) → TS

Load TimeSeries object from file. If called from a subclass of :py:class’TimeSeries`, the type of the stored object must match that of the method class.

Parameters:

• path (Union[str, Path]) – Path to load from.

• expected_type (Optional[str]) – Specify expected type of timeseires (TSContinuous or py:class:TSEvent). Can only be set if method is called from py:class:TimeSeries class. Default: None, use whichever type is loaded.

Return TimeSeries:

Loaded time series object

Raises:

• TypeError – Unsupported or unexpected type

• TypeError – Argument expected_type is defined if class is not TimeSeries.

property name: str

property plotting_backend

(str) Current plotting backend

print()

Print an overview of the time series

set_plotting_backend(backend: str | None, verbose: bool = True)

Set which plotting backend to use with the plot method

Parameters:

• backend (str) – Specify a backend to use. Supported: {“holoviews”, “matplotlib”}

• verbose (bool) – If True, print feedback about which backend has been set

start_at(t_start: float, inplace: bool = False) → TS

Convenience function that calls the delay method such that self.t_start falls at t_start.

Parameters:

• t_start (float) – Time to which self.t_start should be shifted;

• inplace (bool) – If True, conduct operation in-place (Default: False; create a copy)

Return TimeSeries:

New TimeSeries, delayed

start_at_zero(inplace: bool = False) → TS

Convenience function that calls the delay method such that self.t_start falls at 0.

Return TimeSeries:

New TimeSeries, with t_start at 0

property t_start: float

(float) Start time of time series

property t_stop: float

(float) Stop time of time series (final sample)

property times

(ArrayLike[float]) Array of sample times