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Working with time series data

Concepts

In Rockpool, temporal data (“time series” data) is encapsulated in a set of classes that derive from TimeSeries. Time series come in two basic flavours: “continuous” time series, which have been sampled at some set of time points but which represent values that can exist at any point in time; and “event” time series, which consist of discrete event times.

The TimeSeries subclasses provide methods for extracting, resampling, shifting, trimming and manipulating time series data in a convenient fashion. Since Rockpool naturally deals with temporal dynamics and temporal data, TimeSeries objects are used to pass around time series data both as input and as output.

TimeSeries objects have an implicit shared time-base at \(t_0 = 0\) sec. However, they can easily be offset in time, concatenated, etc.

Housekeeping and import statements

[1]:
# - Import required modules and configure

# - Switch off warnings
import warnings
warnings.filterwarnings('ignore')

# - Required imports
import numpy as np

from rockpool.timeseries import (
    TimeSeries,
    TSContinuous,
    TSEvent,
    set_global_ts_plotting_backend,
)

from IPython.display import Image

# - Use HoloViews for plotting
import colorcet as cc
import holoviews as hv
hv.extension('bokeh')

%opts Curve [width=600]
%opts Scatter [width=600]