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calc_lockin.py
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calc_lockin.py
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import re
from scipy.interpolate import interp1d
import numpy as np
from scipy.optimize import curve_fit
from scipy import signal
from scipy import stats
import peakutils
import matplotlib.pyplot as plt
#from numba import jit
from scipy import ndimage
#@jit(nopython=True)
def lockin_filter(signal, reference):
width = signal.shape[0]
n = signal.shape[1]
y = np.zeros(width)
for i in range(width):
for j in range(n):
y[i] += signal[i,j]*reference[j]
y[i] /= len(reference)
return y
#@jit()
def interpolate_data(x,x_new,signal):
width = signal.shape[0]
res = np.zeros((signal.shape[0],len(x_new)))
for ind in range(width):
spline = interp1d(x, signal[ind, :], kind='cubic')
res[ind, :] = spline(x_new)
return res
def plot_lockin(series,savedir):
ts = series[1:, 1:]
t = series[0, 1:]
wl = series[1:,0]
t -= t.min()
maxwl = 1000
minwl = 400
mask = (wl >= minwl) & (wl <= maxwl)
n = ts.shape[1]
width = ts.shape[0]
t2 = np.linspace(0, t.max(), n*5)
for ind in range(width):
slope, intercept, r_value, p_value, std_err = stats.linregress(t, ts[ind,:])
ts[ind, :] -= slope*t+intercept
ts = ndimage.median_filter(ts, 7)
plt.imshow(ts.T, extent=(wl.min(), wl.max(), t2.max(), t2.min()), aspect=1,cmap=plt.get_cmap("seismic"))
plt.xlabel("Wavelength / nm")
plt.ylabel("t / s")
#plt.show()
plt.savefig(savedir + "signal_filt.png",dpi=400)
plt.close()
ts = interpolate_data(t,t2,ts)
@jit()
def func(x,a,f,p,c):
return a*np.sin(2 * np.pi * x * f + p) + c
res = np.zeros(width)
res_phase = np.zeros(width)
freqs = np.zeros(width)
for ind in range(width):
fft = np.fft.rfft(ts[ind,:],norm="ortho")
d = np.absolute(fft)
p = np.angle(fft)
res[ind] = d[1:].max()
res_phase[ind] = p[d[1:].argmax()+1]
f_buf = np.fft.rfftfreq(t2.shape[0], d=t2[1] - t2[0])
freqs[ind] = f_buf[d[1:].argmax()+1]
freqs = freqs
freqs = freqs[np.argwhere(res > np.max(res)*0.9)]
freqs = freqs[:,0]
freqs = freqs[np.argwhere(freqs > 0)]
res_phase += np.pi
phase = res_phase[np.argmax(res)]
f = freqs[0]
print("f1: "+str(f))
maxind = np.argmax(res)
y = ts[maxind, :]
fft = np.fft.rfft(y, norm="ortho")
d = np.absolute(fft)
p = np.angle(fft)
f_buf = np.fft.rfftfreq(t2.shape[0], d=t2[1] - t2[0])
print("f fft: "+str(f_buf[d[1:].argmax()]))
initial_guess = [y.max() - y.min(), f, 0, np.mean(y)]
popt, pcov = curve_fit(func, t2, y, p0=initial_guess)
y2 = func(t2,popt[0],popt[1],popt[2],popt[3])
indexes = peakutils.indexes(y2, thres=np.mean(y2), min_dist=30)
min_ind = indexes.min()
#indexes = peakutils.indexes(-y2, thres=np.mean(y2), min_dist=30)
max_ind = indexes.max()
print((min_ind,max_ind))
t2 = t2[min_ind:max_ind]
ts = ts[:,min_ind:max_ind]
n = ts.shape[1]
width = ts.shape[0]
fig = plt.figure()
y = y[min_ind:max_ind]
plt.plot(t2,y)
y2 = y2[min_ind:max_ind]
plt.plot(t2,y2)
plt.savefig(savedir + "fit.png",dpi=300)
plt.close()
f = popt[1]
phase = popt[2]
print("f2: "+str(f))
fig = plt.figure()
ax = fig.add_subplot(111)
res_phase = res_phase * 180/np.pi
p1 = ax.plot(wl[mask], res[mask], alpha=0.5) # /lamp[mask])
res2 = signal.savgol_filter(res, 71, 1)
p2 = ax.plot(wl[mask], res2[mask]) # /lamp[mask])
ax.tick_params('y', colors=p2[0].get_color())
ax2 = ax.twinx()
next(ax2._get_lines.prop_cycler)
next(ax2._get_lines.prop_cycler)
p3 = ax2.plot(wl[mask], res_phase[mask], alpha=0.5) # /lamp[mask])
ax2.tick_params('y', colors=p3[0].get_color())
ax.set_zorder(ax2.get_zorder() + 1) # put ax in front of ax2
ax.patch.set_visible(False) # hide the 'canvas'
plt.savefig(savedir + "lockin_fft.png",dpi=300)
plt.close()
ref = np.sin(2 * np.pi * t2 * f)
ref2 = np.sin(2 * np.pi * t2 * f + np.pi/2)
x2 = lockin_filter(ts,ref)
y2 = lockin_filter(ts,ref2)
res_amp = np.abs(x2 + 1j * y2)
res_phase = np.angle(x2 + 1j * y2)
maxind = np.argmax(signal.savgol_filter(res_amp, 71, 1))
phase = res_phase[maxind]
print("phase1: "+str(phase))
phases = np.linspace(0,2*np.pi,3600)
x1 = np.zeros(len(phases))
for i in range(len(phases)):
ref1 = np.sin(2 * np.pi * t2 * f + phases[i])
x1[i] = lockin_filter(np.matrix(ts[maxind,:]),ref1)
phase = phases[np.argmax(x1)]
print("phase2: "+str(phase))
res_phase = np.angle( (x2*np.cos(phase)-y2*np.sin(phase)) + 1j * ( x2*np.sin(phase) + y2*np.cos(phase) ))
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(t2, ref / np.max(ref) + 0)
ax.plot(t2, ref2 / np.max(ref2) + 1)
indices = [np.argmin(np.abs(wl - 500)), np.argmin(np.abs(wl - 600)), np.argmin(np.abs(wl - 700)),
np.argmin(np.abs(wl - 810))]
for i, ind in enumerate(indices):
buf = ts[ind,:]
buf = buf - np.min(buf)
ax.plot(t2, buf / np.max(buf) + i + 2)
plt.savefig(savedir + "traces.png",dpi=300)
plt.close()
fig = plt.figure()
ax = fig.add_subplot(111)
indices = [np.argmin(np.abs(wl - 500)), np.argmin(np.abs(wl - 600)), np.argmin(np.abs(wl - 700)),
np.argmin(np.abs(wl - 810))]
for i, ind in enumerate(indices):
fft = np.fft.rfft(ts[ind,:])
d = np.absolute(fft)
p = np.abs(np.angle(fft))
f_buf = np.fft.rfftfreq(t2.shape[0])
blub = int(len(f_buf)/4)
ax.plot(f_buf[1:blub], d[1:blub] / d[1:blub].max() + i)
ax.plot(f_buf[1:blub], p[1:blub] / p[1:blub].max() + i, alpha=0.5)
plt.savefig(savedir + "fft.png",dpi=300)
plt.close()
fig = plt.figure()
ax = fig.add_subplot(111)
res_phase = res_phase * 180/np.pi
p1 = ax.plot(wl[mask], res_amp[mask], alpha=0.5)
res2 = signal.savgol_filter(res_amp, 71, 1)
p2 = ax.plot(wl[mask], res2[mask])
ax.tick_params('y', colors=p2[0].get_color())
ax2 = ax.twinx()
next(ax2._get_lines.prop_cycler)
next(ax2._get_lines.prop_cycler)
p3 = ax2.plot(wl[mask], res_phase[mask], alpha=0.5)
ax2.tick_params('y', colors=p3[0].get_color())
ax.set_zorder(ax2.get_zorder() + 1) # put ax in front of ax2
ax.patch.set_visible(False) # hide the 'canvas'
plt.savefig(savedir + "lockin_nopsd.png",dpi=300)
plt.close()
ref = np.sin(2 * np.pi * t2 * f + phase)
ref2 = np.sin(2 * np.pi * t2 * f + phase - np.pi)
if dir == "B1":
x2 = lockin_filter(ts,ref)
x1 = lockin_filter(ts,ref2)
else:
x1 = lockin_filter(ts,ref)
x2 = lockin_filter(ts,ref2)
x1[x1 <0] = 0
x2[x2 < 0] = 0
fig = plt.figure()
ax = fig.add_subplot(111)
p1 = ax.plot(wl[mask], x1[mask], alpha=0.5) # /lamp[mask])
res2 = signal.savgol_filter(x1, 71, 1)
p2 = ax.plot(wl[mask], res2[mask]) # /lamp[mask])
p3 = ax.plot(wl[mask], x2[mask], alpha=0.5) # /lamp[mask])
res2 = signal.savgol_filter(x2, 71, 1)
p4 = ax.plot(wl[mask], res2[mask]) # /lamp[mask])
ax.set_zorder(ax2.get_zorder() + 1) # put ax in front of ax2
ax.patch.set_visible(False) # hide the 'canvas'
plt.savefig(savedir + "lockin_psd.png", dpi=300)
plt.close()
if __name__ == '__main__':
data = np.loadtxt("./Spectra/C3/timeseries.csv")
plot_lockin(data, "./Spectra/C3/")