neurodsp.plts.plot_time_series

neurodsp.plts.plot_time_series(times, sigs, labels=None, colors=None, ax=None, **kwargs)[source]

Plot a time series.

Parameters
times1d or 2d array, or list of 1d array, or None

Time definition(s) for the time series to be plotted. If None, time series will be plotted in terms of samples instead of time.

sigs1d or 2d array, or list of 1d array

Time series to plot.

labelslist of str, optional

Labels for each time series.

colorsstr or list of str

Colors to use to plot lines.

axmatplotlib.Axes, optional

Figure axes upon which to plot.

**kwargs

Keyword arguments for customizing the plot.

Examples

Create a time series plot:

>>> from neurodsp.sim import sim_combined
>>> from neurodsp.utils import create_times
>>> sig = sim_combined(n_seconds=10, fs=500,
...                    components={'sim_powerlaw': {'exponent': -1.5, 'f_range': (2, None)},
...                                'sim_oscillation' : {'freq': 10}})
>>> times = create_times(n_seconds=10, fs=500)
>>> plot_time_series(times, sig)

Examples using neurodsp.plts.plot_time_series

Using NeuroDSP with MNE

Using NeuroDSP with MNE

Burst Detection

Burst Detection

Filtering

Filtering

FIR Filters

FIR Filters

IIR Filters

IIR Filters

Filter Checks

Filter Checks

Lagged Coherence

Lagged Coherence

Sliding Window Matching

Sliding Window Matching

Simulating Aperiodic Signals

Simulating Aperiodic Signals

Simulating Combined Signals

Simulating Combined Signals

Modulating Signals

Modulating Signals

Simulating Periodic Signals

Simulating Periodic Signals

Simulating Cycles & Transients

Simulating Cycles & Transients

Spectral Domain Analysis: Power

Spectral Domain Analysis: Power

Spectral Domain Analysis: Variance

Spectral Domain Analysis: Variance

Time-frequency analysis

Time-frequency analysis

Morlet Wavelet Analysis

Morlet Wavelet Analysis