multi_time_series#

class arte.time_series.multi_time_series.MultiTimeSeries(*args)#

Bases: TimeSeries

Join multiple TimeSeries objects with incompatible time sampling.

After being initialized with a list of TimeSeries objects, which can have different ensemble sizes and length, it behaves as if all the data was part of a single ensemble.

Time operations (like time_mean, time_std) work in any case.

Ensemble operations (like ensemble_mean, ensemble_std) only work if all series have the same sampling rate (delta_time), and raise an Exception otherwise. Use is_homogeneous() to check compatibility before calling ensemble operations.

Examples

>>> # Create multi-series with compatible sampling
>>> multi = MultiTimeSeries(series1_1khz, series2_1khz)
>>> multi.is_homogeneous()  # True - same sampling rates
True
>>> mean = multi.ensemble_mean.value  # Works!
>>>
>>> # Incompatible sampling raises exception
>>> multi2 = MultiTimeSeries(series_1khz, series_500hz)
>>> multi2.ensemble_mean  # Raises Exception - incompatible rates
>>>
>>> # Time operations always work
>>> time_avg = multi2.time_mean.value  # Always works

Notes

Chainable API: ensemble operations return TimeSeries objects that can be chained with time operations and vice versa.

Attributes:

axes

Data series shape

delta_time

Property with the interval between samples.

If no interval can be determined (time vector too short), returns 1 with the correct unit if applicable.

ensemble_mean

Mean over ensemble dimensions (chainable property).

ensemble_median

Median over ensemble dimensions (chainable property).

ensemble_ptp

Peak-to-peak over ensemble dimensions (chainable property).

ensemble_rms

RMS over ensemble dimensions (chainable property).

ensemble_std

Standard deviation over ensemble dimensions (chainable property).

shape

Shape of the underlying data array.

time_mean

Mean over time dimension (chainable property).

time_median

Median over time dimension (chainable property).

time_ptp

Peak-to-peak over time dimension (chainable property).

time_rms

RMS over time dimension (chainable property).

time_std

Standard deviation over time dimension (chainable property).

value

Extract final value(s) from TimeSeries (convenience accessor).

Methods

`add_series`(series)	Adds a new series to this MultiTimeSeries instance
`data_label`()	Override to return a string with a readable unit name
`data_unit`()	Override to return a string with a compact unit notation
`delta_times`(args, *kwargs)	Returns a vector of delta times
`ensemble_size`()	Returns the total ensemble size
`filter`(args, *kwargs)	Create filtered TimeSeries by applying ensemble and/or time selection.
`get_data`(*args[, times, axes])	Retrieve time series data with optional filtering and indexing.
`get_ensemble_average`(*args[, times])	Average across series at each sampling time (legacy method).
`get_ensemble_median`(*args[, times])	Median across series at each sampling time (legacy method).
`get_ensemble_ptp`(*args[, times])	Peak-to-peak (max - min) across series at each sampling time (legacy method).
`get_ensemble_rms`(*args[, times])	Root-Mean-Square across series at each sampling time (legacy method).
`get_ensemble_std`(*args[, times])	Standard deviation across series at each sampling time (legacy method).
`get_time_average`(*args[, times])	Average value over time for each series
`get_time_median`(*args[, times])	Median over time for each series
`get_time_ptp`(*args[, times])	Peak-to-peak (max - min) over time for each series
`get_time_rms`(*args[, times])	Root-Mean-Square value over time for each series
`get_time_std`(*args[, times])	Standard deviation over time for each series
`get_time_vector`()	Return the series time vector
`help`([search, prefix])	Interactive help
`is_homogeneous`(args, *kwargs)	Check if selected series have compatible sampling rates.
`power`(*args[, from_freq, to_freq, ...])	Compute Power Spectral Density using Welch's method.
`time_size`()	Number of time samples in this time ensemble
`with_times`(times)	Filter to specific time interval (convenience alias for filter(times=...)).

frequency
get_index_of
last_cut_frequency

add_series(series)#

Adds a new series to this MultiTimeSeries instance

Parameters:: series (TimeSeries or TimeSeriesWithInterpolation instance) – the series to be added

delta_times(*args, **kwargs)#: Returns a vector of delta times

property ensemble_mean#

Mean over ensemble dimensions (chainable property).

Computes the mean across all series at each time step. Requires all series to have compatible sampling rates.

Returns:: Mean across ensemble at each time step (chainable)
Return type:: TimeSeries
Raises:: Exception – If series have incompatible sampling rates. Use is_homogeneous() to check compatibility first.

Examples

>>> # Check compatibility before using
>>> if multi.is_homogeneous():
...     mean = multi.ensemble_mean.value
>>>
>>> # Chain with time operations
>>> overall_mean = multi.ensemble_mean.time_mean.value

See also

is_homogeneous: Check if series have compatible sampling rates
ensemble_std: Standard deviation across ensemble
ensemble_median: Median across ensemble

property ensemble_median#

Median over ensemble dimensions (chainable property).

Computes the median across all series at each time step. Requires all series to have compatible sampling rates.

Returns:: Median across ensemble at each time step (chainable)
Return type:: TimeSeries
Raises:: Exception – If series have incompatible sampling rates. Use is_homogeneous() to check compatibility first.

Examples

>>> if multi.is_homogeneous():
...     median = multi.ensemble_median.value
>>>
>>> # Chain with time operations
>>> median_avg = multi.ensemble_median.time_mean.value

See also

is_homogeneous: Check if series have compatible sampling rates
ensemble_mean: Mean across ensemble

property ensemble_ptp#

Peak-to-peak over ensemble dimensions (chainable property).

Computes the peak-to-peak range across all series at each time step. Requires all series to have compatible sampling rates.

Returns:: Peak-to-peak across ensemble at each time step (chainable)
Return type:: TimeSeries
Raises:: Exception – If series have incompatible sampling rates. Use is_homogeneous() to check compatibility first.

Examples

>>> if multi.is_homogeneous():
...     ptp = multi.ensemble_ptp.value
>>>
>>> # Chain with time operations
>>> mean_ptp = multi.ensemble_ptp.time_mean.value

See also

is_homogeneous: Check if series have compatible sampling rates

property ensemble_rms#

RMS over ensemble dimensions (chainable property).

Computes the RMS across all series at each time step. Requires all series to have compatible sampling rates.

Returns:: RMS across ensemble at each time step (chainable)
Return type:: TimeSeries
Raises:: Exception – If series have incompatible sampling rates. Use is_homogeneous() to check compatibility first.

Examples

>>> if multi.is_homogeneous():
...     rms = multi.ensemble_rms.value
>>>
>>> # Chain with time operations
>>> mean_rms = multi.ensemble_rms.time_mean.value

See also

is_homogeneous: Check if series have compatible sampling rates
ensemble_mean: Mean across ensemble

ensemble_size()#: Returns the total ensemble size

property ensemble_std#

Standard deviation over ensemble dimensions (chainable property).

Computes the std across all series at each time step. Requires all series to have compatible sampling rates.

Returns:: Std across ensemble at each time step (chainable)
Return type:: TimeSeries
Raises:: Exception – If series have incompatible sampling rates. Use is_homogeneous() to check compatibility first.

Examples

>>> if multi.is_homogeneous():
...     std = multi.ensemble_std.value
>>>
>>> # Chain with time operations
>>> mean_std = multi.ensemble_std.time_mean.value

See also

is_homogeneous: Check if series have compatible sampling rates
ensemble_mean: Mean across ensemble

get_ensemble_average(*args, times=None, **kwargs)#

Average across series at each sampling time (legacy method).

Deprecated since version Use: the ensemble_mean property instead. This method will be removed in a future version.

Returns:: Mean values (not chainable)
Return type:: ndarray
Raises:: Exception – If series have incompatible sampling rates (not homogeneous)

get_ensemble_median(*args, times=None, **kwargs)#

Median across series at each sampling time (legacy method).

Deprecated since version Use: the ensemble_median property instead. This method will be removed in a future version.

Returns:: Median values (not chainable)
Return type:: ndarray
Raises:: Exception – If series have incompatible sampling rates (not homogeneous)

get_ensemble_ptp(*args, times=None, **kwargs)#

Peak-to-peak (max - min) across series at each sampling time (legacy method).

Deprecated since version Use: the ensemble_ptp property instead. This method will be removed in a future version.

Returns:: Peak-to-peak values (not chainable)
Return type:: ndarray
Raises:: Exception – If series have incompatible sampling rates (not homogeneous)

get_ensemble_rms(*args, times=None, **kwargs)#

Root-Mean-Square across series at each sampling time (legacy method).

Deprecated since version Use: the ensemble_rms property instead. This method will be removed in a future version.

Returns:: RMS values (not chainable)
Return type:: ndarray
Raises:: Exception – If series have incompatible sampling rates (not homogeneous)

get_ensemble_std(*args, times=None, **kwargs)#

Standard deviation across series at each sampling time (legacy method).

Deprecated since version Use: the ensemble_std property instead. This method will be removed in a future version.

Returns:: Std values (not chainable)
Return type:: ndarray
Raises:: Exception – If series have incompatible sampling rates (not homogeneous)

is_homogeneous(*args, **kwargs)#

Check if selected series have compatible sampling rates.

Parameters:

*args – Passed to get_index_of() for series selection.
**kwargs – Passed to get_index_of() for series selection.

Returns:

True if all selected series have identical delta_time, False otherwise.

Return type:

bool

Notes

This check is necessary before calling ensemble-wise operations like ensemble_mean or ensemble_std, which require uniform time sampling across all series.

Examples

>>> multi = MultiTimeSeries(series_1khz, series_500hz)
>>> multi.is_homogeneous()  # False - different sampling rates
False
>>>
>>> multi2 = MultiTimeSeries(series1_1khz, series2_1khz)
>>> multi2.is_homogeneous()  # True - same sampling
True