timeseries.TimeSeries

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

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.

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

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”

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: Union[numpy.ndarray, List, Tuple] = [], periodic: bool = False, t_start: Optional[float] = None, t_stop: Optional[float] = None, plotting_backend: Optional[str] = None, name: str = 'unnamed')[source]

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: Union[numpy.ndarray, List, Tuple, float, int]) Union[numpy.ndarray, List, Tuple, float, int][source]

_modulo_period - Calculate provided times modulo self.duration

classmethod concatenate_t(series: Iterable[timeseries.TS], offset: Optional[Union[float, Iterable[Optional[float]]]] = None) timeseries.TS[source]

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: Union[int, float, numpy.ndarray, List, Tuple]) bool[source]

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() timeseries.TS[source]

Return a deep copy of this time series

Return TimeSeries

copy of self

delay(offset: Union[int, float], inplace: bool = False) timeseries.TS[source]

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[source]

Test if this TimeSeries object is empty

Return bool

True iff the TimeSeries object contains no samples

classmethod load(path: Union[str, pathlib.Path], expected_type: Optional[str] = None) timeseries.TS[source]

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()[source]

Print an overview of the time series

set_plotting_backend(backend: Optional[str], verbose: bool = True)[source]

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) timeseries.TS[source]

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) timeseries.TS[source]

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