frites.conn.define_windows#

frites.conn.define_windows(times, windows=None, slwin_len=None, slwin_start=None, slwin_stop=None, slwin_step=None, sfreq=None, verbose=None)[source]#

Define temporal windows.

This function can be used to either manually define temporal windows either automatic sliding windows. Note that every input parameters should be in the time domain (e.g seconds or milliseconds).

Parameters:

timesnumpy:array_like: Time vector
windowsnumpy:array_like | python:None: Manual windows (e.g (.1, .2) or [(.1, .2), (.4, .5)]).
slwin_lenpython:float | python:None: Length of each sliding (e.g .2 produces 200ms window length).
slwin_startpython:float | python:None: Time point for starting sliding windows (e.g 0.1). If None, sliding windows will start from the first time point.
slwin_stoppython:float | python:None: Time point for ending sliding windows (e.g 1.5). If None, sliding windows will finish at the last time point.
slwin_steppython:float | python:None: Temporal step between each temporal window (e.g .1 means that each consecutive windows are going to be separated by 100ms). This parameter can be used to define either overlapping or non-overlapping windows. If None, slwin_step is going to be set to slwin_step in order to produce consecutive non-overlapping windows.
sfreqpython:float | python:None: The sampling frequency

Returns:

win_samplenumpy:array_like: Array of shape (n_windows, 2) of temporal indexes defining where each window (start, finish)
mean_timenumpy:array_like: Mean time vector inside each defined window of shape (n_windows,)