pyvtna.signal

Functions

closest_idx(sig, val)	Get the index of the value in a signal sig that is closest to val.
derivSNR(sig)	Return the ratio of maximum magnitude of the signal derivative to the standard deviation of the signal derivative
drop_neg(t, ss)	Remove all negative points in a signal.
dv_dx(vs[, xs])	Take the derivative of a 1D array vs with spacing xs
filter_shift(sig, time[, win])	Like shift_zero but first smooths, then shifts with a correction for the smoothing.
filter_shift_spear(sig, time[, window])	Remove all points before actual signal starts.
get_best_win(sig[, minwin, maxwin, mode])	Chooses a smoothing window size for maximizing the signal-to-noise ratio.
interp_neg(t, s)	Remove all negative points in a signal and interpolate between the surrounding points.
interp_neg_comp(t, s[, method])	Remove all negative points in a signal and interpolate between the surrounding points.
is_ascending(sig)	Decide whether a signal is generally increasing.
rangeSNR(sig)	Return the ratio of the signal range to its standard deviation.
replace_neg(s[, val])	Replace all negative points in a signal with a set value.
rolling_mean(arr[, win])	Return the rolling average of an array with set window length and uniform weighting
shift_zero(sig, time[, window])	Remove all points before actual signal starts.
shift_zero_spear(sig, t)	Remove all points before actual signal starts.
signaltonoise(sig[, axis, ddof])	Return the signal to noise ratio of a signal s
smooth(x[, window_len, window, general_sig])	smooth the data using a window with requested size.
x_rolling(x[, win])	Return the x_values for the data returned by taking a rolling mean using nan()