def __init__(self,plugin): self.connection = sqlite.connect(config.DATABASE) self.cursor = self.connection.cursor() self.plugin = plugin self.coeff = sg_filter.calc_coeff(config.LISSAGE_NUM_POINTS,config.LISSAGE_COEFF) self.speed = False self.real_data = []
(step, t) = find_min_delta(time)
end_time = time[len(time)-1]
while t <= end_time:
itime.append(t)
t = t + step
icompounds = []
# linear piecewise interpolation
for c in compounds:
icompounds.append(arrayfns.interp(c, time, itime))
compounds = icompounds
time = itime
if smoothing:
############ smoothing (Savitzky-Golay) ############
### TODO choose between sg_filter and savitzky_golay
coeff = sg_filter.calc_coeff(6,3)
smoothed_compounds = []
for i in range(len(compounds)):
#smoothed_compounds.append(sg_filter.smooth(compounds[i], coeff))
smoothed_compounds.append(savitzky_golay(compounds[i]))
############ derivative ############
coeff = sg_filter.calc_coeff(6,3,1)
smoothed_dcompounds = []
for i in range(len(compounds)):
smoothed_dcompounds.append(sg_filter.smooth(compounds[i], coeff))
#smoothed_dcompounds.append(deriv(compounds[i], time))
if plot:
plot_figs('smoothed.png', time, \
compounds=compounds, \
smoothed_compounds=smoothed_compounds, \
smoothed_derivative_of_compounds=smoothed_dcompounds\
def smooth(signal):
""" Smoothing a signal using the Savitzky–Golay filter. """
coeff = sg_filter.calc_coeff(len(signal)/500, 5)
return sg_filter.smooth(signal, coeff)
# generate chirp signal
tvec = arange(0, 6.28, .02)
signal = sin(tvec*(2.0+tvec))
# add noise to signal
noise = random.normal(size=signal.shape)
signal += (2000.+.15 * noise)
# plot signal
subplot(311)
plot(signal)
title('signal')
# smooth and plot signal
subplot(312)
coeff = calc_coeff(8, 4)
s_signal = smooth(signal, coeff)
plot(s_signal)
title('smoothed signal')
# smooth derivative of signal and plot it
subplot(313)
coeff = calc_coeff(8, 1, 1)
d_signal = smooth(signal, coeff)
plot(d_signal)
title('smoothed derivative of signal')
# show plot
savefig("savitzky.png")
