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interactive_sideband_calibration_fit.py
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interactive_sideband_calibration_fit.py
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# -*- coding: utf-8 -*-
"""
Created on Sun Mar 4 20:02:04 2018
@author: Ivan
"""
###############################################################################
# Import python repository from QMPL
###############################################################################
from __future__ import print_function
from __future__ import division
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
from scipy.optimize import curve_fit
plt.ioff()
# File to load
filename = r"escobar-SB-scan-1.3456MHz.csv"
# Modulation frequency in Hz
mod_f = 1.3456e6
# Estimated FWHM
FWHM_guess = 115e3
data = np.genfromtxt(filename, delimiter=',', comments='#')
tdata = data[:,0]
Vdata = data[:,1]
# Normalize the time data and intensity data for improved fitting
tdata = (tdata-tdata.mean())/(tdata.max() - tdata.min())
Vdata = (Vdata - Vdata.min())/(Vdata.max() - Vdata.min())
# If there is a control voltage input, then use it
if data.shape[1] == 3:
pzVdata = data[:,2]
# Otherwise, create mock data to not break the algorithm
elif data.shape[1] == 2:
pzVdata = tdata.copy()
# Peak centers
peaks = []
# Fitting region
fit_region = []
# Ignored region
ignored_region = []
# Handler to get mouse click coordinates from a plot
def onclick(event, maxclicks, arr, nfig):
global ix, iy
ix, iy = event.xdata, event.ydata
print("x = {0:.2e}, y = {0:.2e}".format(ix, iy))
arr.append((ix, iy))
if len(arr) == maxclicks:
fig.canvas.mpl_disconnect(cid)
plt.close(nfig)
return
# Pick the linear voltage region
fig = plt.figure(1)
ax = fig.add_subplot(111)
ax.plot(tdata, Vdata)
plt.xlabel("Time (a.u.)")
plt.ylabel("Control voltage (V)")
plt.title("Select the fitting range")
cid = fig.canvas.mpl_connect('button_press_event', lambda ev: onclick(ev, 2, fit_region, 1))
plt.show()
fit_region = np.array(fit_region)[:,0]
fit_region.sort()
print(fit_region)
# Now pick the peak centers
fig = plt.figure(2)
ax = fig.add_subplot(111)
ax.plot(tdata, Vdata)
plt.xlabel("Time (a.u.)")
plt.ylabel("Detector voltage (a.u.)")
plt.title("Select the center and sideband peaks")
cid = fig.canvas.mpl_connect('button_press_event', lambda ev: onclick(ev, 3, peaks, 2))
plt.show()
peaks = np.array(peaks)
peaks = peaks[peaks[:,0].argsort()] # Sort along the first column
print(peaks)
## Now pick the ignored region boundaries
#fig = plt.figure(3)
#ax = fig.add_subplot(111)
#ax.plot(tdata, Vdata)
#
#plt.xlabel("Time (a.u.)")
#plt.ylabel("Detector voltage (a.u.)")
#plt.title("Select the bounds of the ignored region")
#cid = fig.canvas.mpl_connect('button_press_event', lambda ev: onclick(ev, 2, ignored_region, 3))
#
#plt.show()
#ignored_region = np.array(ignored_region)[:,0]
ignored_region = np.array([1,2])
ignored_region.sort()
# Create the final array on which we will be fitting (i.e. the fitting range
# minus ignored area)
fit_ind = np.logical_and(tdata > fit_region[0], tdata < fit_region[1])
ign_ind = np.logical_not(np.logical_and(ignored_region[0] < tdata, tdata < ignored_region[1]))
indices = np.logical_and(fit_ind, ign_ind)
# Now, let's find a conversion between V and t
V_per_t, intercept, r_value, p_value, std_err = stats.linregress(tdata[indices], pzVdata[indices])
# Plot the linear fit to the piezo voltage
fig = plt.figure(3)
ax = fig.add_subplot(111)
ax.plot(tdata[indices], pzVdata[indices], ".", tdata[indices], intercept + V_per_t*tdata[indices], "-")
plt.xlabel("Time (a.u.)")
plt.ylabel("Control voltage (V)")
plt.title("Linear fit to control voltage")
plt.show()
# Plot the data that excludes the ignored region
fig = plt.figure(4)
ax = fig.add_subplot(111)
ax.plot(tdata[indices], Vdata[indices], ".")
plt.xlabel("Time (a.u.)")
plt.ylabel("Control voltage")
plt.title("Selected data")
plt.show()
## Now we define the function to be fitted on the averaged signal:
def triple_lorentzian(t, mainA, sideA, FWHM, f_per_t, center, A_offset):
detun = (t-center)*f_per_t
return A_offset + mainA / (1+(2*detun/FWHM)**2) + sideA * (1/(1+(2*(detun-mod_f)/FWHM)**2) + 1/(1+(2*(detun+mod_f)/FWHM)**2))
# Time to do the fitting
# First, we supply the initial guesses based on clicked results
mainA_guess = peaks[1,1]
sideA_guess = (peaks[0,1] + peaks[2,1]) / 2
center_guess = peaks[1,0]
A_offset_guess = np.min(Vdata[indices])
f_per_t_guess = 2 * mod_f / (peaks[2,0] - peaks[0,0])
guess = np.array([mainA_guess, sideA_guess,
FWHM_guess, f_per_t_guess, center_guess, A_offset_guess])
# Set the scale for better fitting
scale = guess.copy()
scale[-2:] = 1
scale = np.abs(scale)
guess_n = guess/scale
# Create an aux function with normalized parameters
def aux(t, *params):
p_n = np.array(params)
p = p_n * scale
return triple_lorentzian(t, *p)
popt, pcov = curve_fit(aux, tdata[indices], Vdata[indices],
p0=guess_n)
popt = popt*scale
fit = lambda t: triple_lorentzian(t, *popt)
# Plot the data and the fit
fig = plt.figure(5)
ax = fig.add_subplot(111)
xdata = (tdata-popt[4])*popt[3]
ax.plot(xdata/1e6, Vdata, "-",
xdata/1e6, fit(tdata), "-")
plt.xlabel("Frequency (MHz)")
plt.ylabel("Control voltage")
plt.title("Fit")
plt.show()
# Finally, get some results
print("FWHM: {0:.3f} MHz".format(popt[2]/1e6))
print("Conversion: {0:.3f} MHz per Volt".format(popt[3]/V_per_t/1e6))