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artiq_experiments
Commit 085c9da6 authored by Nicolas Nunez Barreto's avatar Nicolas Nunez Barreto
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comento codigo para marce

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analisis/plots/20231123_CPTconmicromocion3/CPT_plotter_20231123.py
View file @ 085c9da6
......@@ -86,822 +86,33 @@ CountsSplit_2ions.append(Split(Counts[4],len(Freqs[4])))
#%%
"""
Ploteo la cpt de referencia / plotting the reference CPT
"""
jvec = [2] # de la 1 a la 9 vale la pena, despues no
drs = [390.5, 399.5, 406, 413.5]
drive=22.1
Frequencies = Freqs[0]
plt.figure()
i = 0
for j in jvec:
plt.errorbar([2*f*1e-6 for f in Frequencies], CountsSplit[0][j], yerr=np.sqrt(CountsSplit[0][j]), fmt='o', capsize=2, markersize=2)
i = i + 1
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('counts')
plt.grid()
#for dr in drs:
# plt.axvline(dr)
#plt.axvline(dr+drive)
plt.legend()
#%%
#from EITfit.MM_eightLevel_2repumps_AnalysisFunctions import PerformExperiment_8levels
from scipy.optimize import curve_fit
import time
"""
MEDICION 1
"""
phidoppler, titadoppler = 0, 90
phirepump, titarepump = 0, 0
phiprobe = 0
titaprobe = 90
Temp = 0.5e-3
sg = 0.544
sp = 4.5
sr = 0
DetRepump = 0
lw = 0.1
DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth = lw, lw, lw #ancho de linea de los laseres
u = 32.5e6
#B = (u/(2*np.pi))/c
correccion = 13
offsetxpi = 419+correccion+3*0.8
DetDoppler = -11.5-correccion
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6
alpha = 0
drivefreq = 2*np.pi*22.135*1e6
selectedcurve = 1
FreqsDR = [2*f*1e-6-offsetxpi for f in Freqs[0]]
CountsDR = CountsSplit[0][selectedcurve]
CountsDR[100]=0.5*(CountsDR[99]+CountsDR[101])
CountsDR[105]=0.5*(CountsDR[104]+CountsDR[106])
freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
CircPr = 1
alpha = 0
def FitEIT_MM_single(freqs, SG, SP, SCALE1, OFFSET, BETA1, TEMP):
#def FitEIT_MM(freqs, SG, SP, SCALE1, OFFSET, BETA1):
#BETA = 1.8
# SG = 0.6
# SP = 8.1
# TEMP = 0.2e-3
Detunings, Fluorescence1 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA1, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
ScaledFluo1 = np.array([f*SCALE1 + OFFSET for f in Fluorescence1])
return ScaledFluo1
#return ScaledFluo1
do_fit = False
if do_fit:
popt_1, pcov_1 = curve_fit(FitEIT_MM_single, FreqsDR, CountsDR, p0=[0.9, 6.2, 3e4, 1.34e3, 2, 1e-3], bounds=((0, 0, 0, 0, 0, 0), (2, 20, 5e4, 5e4, 10, 10e-3)))
FittedEITpi_1 = FitEIT_MM_single(freqslong, *popt_1)
beta1 = popt_1[4]
errorbeta1 = np.sqrt(pcov_1[4,4])
temp1 = popt_1[5]
errortemp1 = np.sqrt(pcov_1[5,5])
plt.figure()
plt.errorbar(FreqsDR, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='darkgreen', alpha=0.5, capsize=2, markersize=2)
plt.plot(freqslong, FittedEITpi_1, color='darkolivegreen', linewidth=3, label='med 1')
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
plt.xlabel('Detuning (MHz)')
plt.ylabel('Counts')
plt.legend(loc='upper left', fontsize=20)
plt.grid()
#%%
#from EITfit.MM_eightLevel_2repumps_AnalysisFunctions import PerformExperiment_8levels
from scipy.optimize import curve_fit
import time
"""
MEDICION 2
"""
phidoppler, titadoppler = 0, 90
phirepump, titarepump = 0, 0
phiprobe = 0
titaprobe = 90
Temp = 0.5e-3
sg = 0.544
sp = 4.5
sr = 0
DetRepump = 0
lw = 0.1
DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth = lw, lw, lw #ancho de linea de los laseres
u = 32.5e6
#B = (u/(2*np.pi))/c
correccion = 13
offsetxpi = 419+correccion+1.6
DetDoppler = -11.5-correccion
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6
alpha = 0
drivefreq = 2*np.pi*22.135*1e6
selectedcurve = 2
FreqsDR = [2*f*1e-6-offsetxpi for f in Freqs[0]]
CountsDR = CountsSplit[0][selectedcurve]
freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
CircPr = 1
alpha = 0
def FitEIT_MM_single(freqs, SG, SP, SCALE1, OFFSET, BETA1, TEMP):
#def FitEIT_MM(freqs, SG, SP, SCALE1, OFFSET, BETA1):
#BETA = 1.8
# SG = 0.6
# SP = 8.1
# TEMP = 0.2e-3
Detunings, Fluorescence1 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA1, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
ScaledFluo1 = np.array([f*SCALE1 + OFFSET for f in Fluorescence1])
return ScaledFluo1
#return ScaledFluo1
do_fit = False
if do_fit:
popt_2, pcov_2 = curve_fit(FitEIT_MM_single, FreqsDR, CountsDR, p0=[0.9, 6.2, 3e4, 1.34e3, 2, 1e-3], bounds=((0, 0, 0, 0, 0, 0), (2, 20, 5e4, 5e4, 10, 10e-3)))
FittedEITpi_2 = FitEIT_MM_single(freqslong, *popt_2)
beta2 = popt_2[4]
errorbeta2 = np.sqrt(pcov_2[4,4])
temp2 = popt_2[5]
errortemp2 = np.sqrt(pcov_2[5,5])
plt.figure()
plt.errorbar(FreqsDR, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='darkgreen', alpha=0.5, capsize=2, markersize=2)
plt.plot(freqslong, FittedEITpi_2, color='darkolivegreen', linewidth=3, label='med 2')
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
plt.xlabel('Detuning (MHz)')
plt.ylabel('Counts')
plt.legend(loc='upper left', fontsize=20)
plt.grid()
#%%
#from EITfit.MM_eightLevel_2repumps_AnalysisFunctions import PerformExperiment_8levels
from scipy.optimize import curve_fit
import time
"""
MEDICION 3
"""
phidoppler, titadoppler = 0, 90
phirepump, titarepump = 0, 0
phiprobe = 0
titaprobe = 90
Temp = 0.5e-3
sg = 0.544
sp = 4.5
sr = 0
DetRepump = 0
lw = 0.1
DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth = lw, lw, lw #ancho de linea de los laseres
u = 32.5e6
#B = (u/(2*np.pi))/c
correccion = 13
offsetxpi = 419+correccion+0.8
DetDoppler = -11.5-correccion
print(offsetxpi,DetDoppler)
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6
alpha = 0
drivefreq = 2*np.pi*22.135*1e6
selectedcurve = 3
FreqsDR = [2*f*1e-6-offsetxpi for f in Freqs[0]]
CountsDR = CountsSplit[0][selectedcurve]
freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
CircPr = 1
alpha = 0
def FitEIT_MM_single(freqs, SG, SP, SCALE1, OFFSET, BETA1, TEMP):
#def FitEIT_MM(freqs, SG, SP, SCALE1, OFFSET, BETA1):
#BETA = 1.8
# SG = 0.6
# SP = 8.1
# TEMP = 0.2e-3
Detunings, Fluorescence1 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA1, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
ScaledFluo1 = np.array([f*SCALE1 + OFFSET for f in Fluorescence1])
return ScaledFluo1
#return ScaledFluo1
do_fit = True
if do_fit:
popt_3, pcov_3 = curve_fit(FitEIT_MM_single, FreqsDR, CountsDR, p0=[0.9, 6.2, 3e4, 1.34e3, 2, 1e-3], bounds=((0, 0, 0, 0, 0, 0), (2, 20, 5e4, 5e4, 10, 10e-3)))
FittedEITpi_3 = FitEIT_MM_single(freqslong, *popt_3)
beta3 = popt_3[4]
errorbeta3 = np.sqrt(pcov_3[4,4])
temp3 = popt_3[5]
errortemp3 = np.sqrt(pcov_3[5,5])
plt.figure()
plt.errorbar(FreqsDR, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='darkgreen', alpha=0.5, capsize=2, markersize=2)
plt.plot(freqslong, FittedEITpi_3, color='darkolivegreen', linewidth=3, label='med 3')
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
plt.xlabel('Detuning (MHz)')
plt.ylabel('Counts')
plt.legend(loc='upper left', fontsize=20)
plt.grid()
#%%
#from EITfit.MM_eightLevel_2repumps_AnalysisFunctions import PerformExperiment_8levels
from scipy.optimize import curve_fit
import time
"""
MEDICION 4
"""
phidoppler, titadoppler = 0, 90
phirepump, titarepump = 0, 0
phiprobe = 0
titaprobe = 90
Temp = 0.5e-3
sg = 0.544
sp = 4.5
sr = 0
DetRepump = 0
lw = 0.1
DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth = lw, lw, lw #ancho de linea de los laseres
u = 32.5e6
#B = (u/(2*np.pi))/c
correccion = 13
offsetxpi = 419+correccion
DetDoppler = -11.5-correccion
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6
alpha = 0
drivefreq = 2*np.pi*22.135*1e6
selectedcurve = 4
FreqsDR = [2*f*1e-6-offsetxpi for f in Freqs[0]]
CountsDR = CountsSplit[0][selectedcurve]
freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
CircPr = 1
alpha = 0
def FitEIT_MM_single(freqs, SG, SP, SCALE1, OFFSET, BETA1, TEMP):
#def FitEIT_MM(freqs, SG, SP, SCALE1, OFFSET, BETA1):
#BETA = 1.8
# SG = 0.6
# SP = 8.1
# TEMP = 0.2e-3
Detunings, Fluorescence1 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA1, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
ScaledFluo1 = np.array([f*SCALE1 + OFFSET for f in Fluorescence1])
return ScaledFluo1
#return ScaledFluo1
do_fit = False
if do_fit:
popt_4, pcov_4 = curve_fit(FitEIT_MM_single, FreqsDR, CountsDR, p0=[0.9, 6.2, 3e4, 1.34e3, 2, 1e-3], bounds=((0, 0, 0, 0, 0, 0), (2, 20, 5e4, 5e4, 10, 10e-3)))
FittedEITpi_4 = FitEIT_MM_single(freqslong, *popt_4)
beta4 = popt_4[4]
errorbeta4 = np.sqrt(pcov_4[4,4])
temp4 = popt_4[5]
errortemp4 = np.sqrt(pcov_4[5,5])
plt.figure()
plt.errorbar(FreqsDR, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='darkgreen', alpha=0.5, capsize=2, markersize=2)
plt.plot(freqslong, FittedEITpi_4, color='darkolivegreen', linewidth=3, label='med 4')
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
plt.xlabel('Detuning (MHz)')
plt.ylabel('Counts')
plt.legend(loc='upper left', fontsize=20)
plt.grid()
#%%
#from EITfit.MM_eightLevel_2repumps_AnalysisFunctions import PerformExperiment_8levels
from scipy.optimize import curve_fit
import time
"""
MEDICION 5
"""
phidoppler, titadoppler = 0, 90
phirepump, titarepump = 0, 0
phiprobe = 0
titaprobe = 90
Temp = 0.5e-3
sg = 0.544
sp = 4.5
sr = 0
DetRepump = 0
lw = 0.1
DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth = lw, lw, lw #ancho de linea de los laseres
u = 32.5e6
#B = (u/(2*np.pi))/c
correccion = 13
offsetxpi = 419+correccion-1
DetDoppler = -11.5-correccion
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6
alpha = 0
drivefreq = 2*np.pi*22.135*1e6
selectedcurve = 5
FreqsDR = [2*f*1e-6-offsetxpi for f in Freqs[0]]
CountsDR = CountsSplit[0][selectedcurve]
freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
CircPr = 1
alpha = 0
def FitEIT_MM_single(freqs, SG, SP, SCALE1, OFFSET, BETA1, TEMP):
#def FitEIT_MM(freqs, SG, SP, SCALE1, OFFSET, BETA1):
#BETA = 1.8
# SG = 0.6
# SP = 8.1
#TEMP = 0.2e-3
Detunings, Fluorescence1 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA1, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
ScaledFluo1 = np.array([f*SCALE1 + OFFSET for f in Fluorescence1])
return ScaledFluo1
#return ScaledFluo1
do_fit = False
if do_fit:
popt_5, pcov_5 = curve_fit(FitEIT_MM_single, FreqsDR, CountsDR, p0=[0.9, 6.2, 3e4, 1.34e3, 2, 1e-3], bounds=((0, 0, 0, 0, 0, 0), (2, 20, 5e4, 5e4, 10, 10e-3)))
FittedEITpi_5 = FitEIT_MM_single(freqslong, *popt_5)
beta5 = popt_5[4]
errorbeta5 = np.sqrt(pcov_5[4,4])
temp5 = popt_5[5]
errortemp5 = np.sqrt(pcov_5[5,5])
plt.figure()
plt.errorbar(FreqsDR, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='darkgreen', alpha=0.5, capsize=2, markersize=2)
plt.plot(freqslong, FittedEITpi_5, color='darkolivegreen', linewidth=3, label='med 5')
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
plt.xlabel('Detuning (MHz)')
plt.ylabel('Counts')
plt.legend(loc='upper left', fontsize=20)
plt.grid()
#%%
#from EITfit.MM_eightLevel_2repumps_AnalysisFunctions import PerformExperiment_8levels
from scipy.optimize import curve_fit
import time
"""
MEDICION 6
"""
phidoppler, titadoppler = 0, 90
phirepump, titarepump = 0, 0
phiprobe = 0
titaprobe = 90
Temp = 0.5e-3
sg = 0.544
sp = 4.5
sr = 0
DetRepump = 0
lw = 0.1
DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth = lw, lw, lw #ancho de linea de los laseres
u = 32.5e6
#B = (u/(2*np.pi))/c
correccion = 13
offsetxpi = 419+correccion-2.2
DetDoppler = -11.5-correccion
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6
alpha = 0
drivefreq = 2*np.pi*22.135*1e6
selectedcurve = 6
FreqsDR = [2*f*1e-6-offsetxpi for f in Freqs[0]]
CountsDR = CountsSplit[0][selectedcurve]
CountsDR[76]=0.5*(CountsDR[75]+CountsDR[77])
CountsDR[1]=0.5*(CountsDR[0]+CountsDR[2])
freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
CircPr = 1
alpha = 0
def FitEIT_MM_single(freqs, SG, SP, SCALE1, OFFSET, BETA1, TEMP):
#def FitEIT_MM(freqs, SG, SP, SCALE1, OFFSET, BETA1):
#BETA = 1.8
# SG = 0.6
# SP = 8.1
# TEMP = 0.2e-3
Detunings, Fluorescence1 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA1, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
ScaledFluo1 = np.array([f*SCALE1 + OFFSET for f in Fluorescence1])
return ScaledFluo1
#return ScaledFluo1
do_fit = True
if do_fit:
popt_6, pcov_6 = curve_fit(FitEIT_MM_single, FreqsDR, CountsDR, p0=[0.9, 6.2, 5e4, 1e3, 2, 1e-3], bounds=((0, 0, 0, 0, 0, 0), (2, 20, 5e4, 5e4, 10, 10e-3)))
FittedEITpi_6 = FitEIT_MM_single(freqslong, *popt_6)
beta6 = popt_6[4]
errorbeta6 = np.sqrt(pcov_6[4,4])
temp6 = popt_6[5]
errortemp6 = np.sqrt(pcov_6[5,5])
plt.figure()
plt.errorbar(FreqsDR, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='darkgreen', alpha=0.5, capsize=2, markersize=2)
plt.plot(freqslong, FittedEITpi_6, color='darkolivegreen', linewidth=3, label='med 6')
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
plt.xlabel('Detuning (MHz)')
plt.ylabel('Counts')
plt.legend(loc='upper left', fontsize=20)
plt.grid()
#%%
#from EITfit.MM_eightLevel_2repumps_AnalysisFunctions import PerformExperiment_8levels
from scipy.optimize import curve_fit
import time
"""
MEDICION 7
"""
phidoppler, titadoppler = 0, 90
phirepump, titarepump = 0, 0
phiprobe = 0
titaprobe = 90
Temp = 0.5e-3
sg = 0.544
sp = 4.5
sr = 0
DetRepump = 0
lw = 0.1
DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth = lw, lw, lw #ancho de linea de los laseres
u = 32.5e6
#B = (u/(2*np.pi))/c
correccion = 13
offsetxpi = 419+correccion-3.7
DetDoppler = -11.5-correccion
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6
alpha = 0
drivefreq = 2*np.pi*22.135*1e6
selectedcurve = 7
FreqsDR = [2*f*1e-6-offsetxpi for f in Freqs[0]]
CountsDR = CountsSplit[0][selectedcurve]
freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
CircPr = 1
alpha = 0
def FitEIT_MM_single(freqs, SG, SP, SCALE1, OFFSET, BETA1, TEMP):
#def FitEIT_MM(freqs, SG, SP, SCALE1, OFFSET, BETA1):
#BETA = 1.8
# SG = 0.6
# SP = 8.1
# TEMP = 0.2e-3
Detunings, Fluorescence1 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA1, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
ScaledFluo1 = np.array([f*SCALE1 + OFFSET for f in Fluorescence1])
return ScaledFluo1
#return ScaledFluo1
do_fit = False
if do_fit:
popt_7, pcov_7 = curve_fit(FitEIT_MM_single, FreqsDR, CountsDR, p0=[0.9, 6.2, 3e4, 1.34e3, 2, 1e-3], bounds=((0, 0, 0, 0, 0, 0), (2, 20, 5e4, 5e4, 10, 10e-3)))
FittedEITpi_7 = FitEIT_MM_single(freqslong, *popt_7)
beta7 = popt_7[4]
errorbeta7 = np.sqrt(pcov_7[4,4])
temp7 = popt_7[5]
errortemp7 = np.sqrt(pcov_7[5,5])
plt.figure()
plt.errorbar(FreqsDR, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='darkgreen', alpha=0.5, capsize=2, markersize=2)
plt.plot(freqslong, FittedEITpi_7, color='darkolivegreen', linewidth=3, label='med 7')
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
plt.xlabel('Detuning (MHz)')
plt.ylabel('Counts')
plt.legend(loc='upper left', fontsize=20)
plt.grid()
#%%
#from EITfit.MM_eightLevel_2repumps_AnalysisFunctions import PerformExperiment_8levels
from scipy.optimize import curve_fit
import time
"""
MEDICION 8
Ploteo curvas para ver que tal son
"""
phidoppler, titadoppler = 0, 90
phirepump, titarepump = 0, 0
phiprobe = 0
titaprobe = 90
Temp = 0.5e-3
sg = 0.544
sp = 4.5
sr = 0
DetRepump = 0
lw = 0.1
DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth = lw, lw, lw #ancho de linea de los laseres
u = 32.5e6
#B = (u/(2*np.pi))/c
correccion = 13
offsetxpi = 419+correccion-4.9
DetDoppler = -11.5-correccion
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6
alpha = 0
drivefreq = 2*np.pi*22.135*1e6
selectedcurve = 8
FreqsDR = [2*f*1e-6-offsetxpi for f in Freqs[0]]
CountsDR = CountsSplit[0][selectedcurve]
freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
CircPr = 1
alpha = 0
def FitEIT_MM_single(freqs, SG, SP, SCALE1, OFFSET, BETA1, TEMP):
#def FitEIT_MM(freqs, SG, SP, SCALE1, OFFSET, BETA1):
#BETA = 1.8
# SG = 0.6
# SP = 8.1
# TEMP = 0.2e-3
Detunings, Fluorescence1 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA1, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
ScaledFluo1 = np.array([f*SCALE1 + OFFSET for f in Fluorescence1])
return ScaledFluo1
#return ScaledFluo1
do_fit = False
if do_fit:
popt_8, pcov_8 = curve_fit(FitEIT_MM_single, FreqsDR, CountsDR, p0=[0.9, 6.2, 3e4, 1.34e3, 2, 1e-3], bounds=((0, 0, 0, 0, 0, 0), (2, 20, 5e4, 5e4, 10, 10e-3)))
FittedEITpi_8 = FitEIT_MM_single(freqslong, *popt_8)
jvec = [2] # de la 1 a la 9 vale la pena, despues no
beta8 = popt_8[4]
errorbeta8 = np.sqrt(pcov_8[4,4])
temp8 = popt_8[5]
errortemp8 = np.sqrt(pcov_8[5,5])
print()
Frequencies = Freqs[0]
plt.figure()
plt.errorbar(FreqsDR, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='darkgreen', alpha=0.5, capsize=2, markersize=2)
plt.plot(freqslong, FittedEITpi_8, color='darkolivegreen', linewidth=3, label='med 8')
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
plt.xlabel('Detuning (MHz)')
plt.ylabel('Counts')
plt.legend(loc='upper left', fontsize=20)
i = 0
for j in jvec:
plt.errorbar([2*f*1e-6 for f in Frequencies], CountsSplit[0][j], yerr=np.sqrt(CountsSplit[0][j]), fmt='o', capsize=2, markersize=2)
i = i + 1
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('counts')
plt.grid()
plt.legend()
#%%
#from EITfit.MM_eightLevel_2repumps_AnalysisFunctions import PerformExperiment_8levels
from scipy.optimize import curve_fit
import time
"""
MEDICION 9
"""
phidoppler, titadoppler = 0, 90
phirepump, titarepump = 0, 0
phiprobe = 0
titaprobe = 90
Temp = 0.5e-3
sg = 0.544
sp = 4.5
sr = 0
DetRepump = 0
lw = 0.1
DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth = lw, lw, lw #ancho de linea de los laseres
u = 32.5e6
#B = (u/(2*np.pi))/c
correccion = 16
offsetxpi = 419+correccion-6
DetDoppler = -11.5-correccion
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6
alpha = 0
drivefreq = 2*np.pi*22.135*1e6
selectedcurve = 9
FreqsDR = [2*f*1e-6-offsetxpi for f in Freqs[0]]
CountsDR = CountsSplit[0][selectedcurve]
freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
CircPr = 1
alpha = 0
def FitEIT_MM_single(freqs, SG, SP, SCALE1, OFFSET, BETA1, TEMP):
#def FitEIT_MM(freqs, SG, SP, SCALE1, OFFSET, BETA1):
#BETA = 1.8
# SG = 0.6
# SP = 8.1
#TEMP = 0.2e-3
Detunings, Fluorescence1 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA1, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
ScaledFluo1 = np.array([f*SCALE1 + OFFSET for f in Fluorescence1])
return ScaledFluo1
#return ScaledFluo1
do_fit = True
if do_fit:
popt_9, pcov_9 = curve_fit(FitEIT_MM_single, FreqsDR, CountsDR, p0=[0.9, 6.2, 3e4, 1.34e3, 2, 1e-3], bounds=((0, 0, 0, 0, 0, 0), (2, 20, 5e4, 5e4, 10,10e-3)))
FittedEITpi_9 = FitEIT_MM_single(freqslong, *popt_9)
beta9 = popt_9[4]
errorbeta9 = np.sqrt(pcov_9[4,4])
temp9 = popt_9[5]
errortemp9 = np.sqrt(pcov_9[5,5])
plt.figure()
plt.errorbar(FreqsDR, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='darkgreen', alpha=0.5, capsize=2, markersize=2)
plt.plot(freqslong, FittedEITpi_9, color='darkolivegreen', linewidth=3, label='med 9')
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
plt.xlabel('Detuning (MHz)')
plt.ylabel('Counts')
plt.legend(loc='upper left', fontsize=20)
plt.grid()
#%%
"""
AHORA INTENTO SUPER AJUSTES O SEA CON OFFSETXPI Y DETDOPPLER INCLUIDOS
HAGO SUPER AJUSTES O SEA CON OFFSETXPI Y DETDOPPLER INCLUIDOS
La 0 no ajusta bien incluso con todos los parametros libres
De la 1 a la 11 ajustan bien
De la 1 a la 11 ajustan bien. Ignoro las demas
"""
......@@ -932,11 +143,6 @@ DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth = lw, lw, lw #a
u = 32.5e6
#B = (u/(2*np.pi))/c
correccion = 13
#DetDoppler = -11.5-correccion
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6
alpha = 0
......@@ -946,7 +152,6 @@ drivefreq = 2*np.pi*22.135*1e6
SelectedCurveVec = [1,2,3,4,5,6,7,8,9,10,11]
#SelectedCurveVec = [10]
popt_SA_vec = []
pcov_SA_vec = []
......@@ -965,8 +170,6 @@ ErrorDetuningsUV_vec = []
for selectedcurve in SelectedCurveVec:
#selectedcurve = 2 #IMPORTANTE: SELECCIONA LA MEDICION
FreqsDR = Freqs[0]
CountsDR = CountsSplit[0][selectedcurve]
......@@ -989,11 +192,6 @@ for selectedcurve in SelectedCurveVec:
def FitEIT_MM_single(Freqs, offset, DetDoppler, SG, SP, SCALE1, OFFSET, BETA1, TEMP, U, plot=False):
#def FitEIT_MM(freqs, SG, SP, SCALE1, OFFSET, BETA1):
#BETA = 1.8
# SG = 0.6
# SP = 8.1
# TEMP = 0.2e-3
freqs = [2*f*1e-6-offset for f in Freqs]
......@@ -1164,42 +362,40 @@ paleta = sns.color_palette("rocket")
"""
Por si no quiero correr todo de nuevo:
Por si no quiero correr todo de nuevo,
los vectores del grafico son estos
Betas_vec = [1.5063238290734708, 0.901629236713104, 0.5280696724084589, 0.32347492275852474, 0.581352716043054, 0.9655636982224044, 1.6186284658310388, 2.1055294502704047, 2.548008896618126, 3.130666254406969, 3.1665902866111795]
ErrorBetas_vec = [0.034184989382424424, 0.04827624922956116, 0.054704762232730145, 0.06708926613955447, 0.047526038227881616, 0.05210296764897591, 0.03630379898730492, 0.034064021790261405, 0.050405155463964166, 0.10838273932613045, 0.2828605486723366]
Temp_vec = [0.0017384071359855713, 0.0006380145223733723, 0.0007457923288975645, 0.0006442238745101592, 0.0006270680749881928, 0.0008995355804703286, 0.0017799223158665226, 0.002941224610139307, 0.008378628768005558, 0.026250695067608725, 0.09869604401089357]
ErrorTemp_vec = [0.0004229476096548473, 0.00014439375508413987, 0.00013204015146487435, 9.307939678673377e-05, 0.000100129717662808, 0.0001841318633900307, 0.0003595040837509976, 0.0005950353892849986, 0.001866844309182069, 0.012656306714647434, 0.13143081065882864]
"""
def expo(x,tau,A,B):
return A*np.exp(x/tau)+B
def cuadratica(x,a,c):
return a*(x**2)+c
def MicromotionSpectra(beta,det, gamma):
"""
Espectro de transicion doppler considerando micromocion
"""
ftrap=2*np.pi*22.1e6
#gamma=23
P = (jv(0, beta)**2)/(((det)**2)+(0.5*gamma)**2)
i = 1
#print(P)
while i <= 2:
while i <= 2: #numero de bandas de micromocion a considerar
P = P + ((jv(i, beta))**2)/((((det)+i*ftrap)**2)+(0.5*gamma)**2) + ((jv(-i, beta))**2)/((((det)-i*ftrap)**2)+(0.5*gamma)**2)
i = i + 1
#print(P)
return (gamma/2*np.pi)*P
#return P
def polynomial(x,a,b,c,d,e):
b=0
d=0
return a+b*x+c*x*x+d*x*x*x+e*x*x*x*x
def InverseDerivMicromotionSpectra(beta, det, gamma):
"""
La inversa de la derivada del espectro de micromocion
"""
ftrap=2*np.pi*22.1e6
#gamma=23
#det = -gamma/2
......@@ -1216,48 +412,58 @@ def InverseDerivMicromotionSpectra(beta, det, gamma):
def FinalTemp(beta,det, C,D):
"""
Este deja libre un factor C para corregir la primera parte pero
si ajustamos con esto, C da casi 1 entonces es al pedo.
Funcion para ajustar la tmeperatura final en funcion de beta.
Esta funcion tiene un parametro de ajuste C para estudiar posibles desviacioens de la temperatura
doppler teorica para un sistema de dos niveles.
"""
gamma = 23e6
hbar = 1.05e-34
kb = 1.38e-23
return ((hbar/(kb))*C*MicromotionSpectra(beta,2*np.pi*det,2*np.pi*gamma)+D*(beta**2))*InverseDerivMicromotionSpectra(beta, 2*np.pi*det, 2*np.pi*gamma)
def FinalTemp_withoutC(beta,det, D, forgetmicromotion=False):
"""
Funcion para ajustar la temperatura final en funcion de beta.
Esta funcion no tiene ese parametro de ajuste ya que vi que era muy cercano a 1.
"""
gamma = 23e6
hbar = 1.05e-34
kb = 1.38e-23
#C = 0.85
#D=0.5e-27
#det = -12e6
#print(MicromotionSpectra(beta,det,gamma))
#D=0
#C=1
C = 1 #justamente aca dejo fijo ese parametro
if forgetmicromotion:
D=0
return ((hbar/(kb))*C*MicromotionSpectra(beta,2*np.pi*det,2*np.pi*gamma)+D*(beta**2))*InverseDerivMicromotionSpectra(beta, 2*np.pi*det, 2*np.pi*gamma)
#return (C*MicromotionSpectra(beta,det,gamma))*InverseDerivMicromotionSpectra(beta, det, gamma)
def FinalTemp_withoutC(beta,det, D):
def FinalTemp_withoutmicromotion(beta,det):
"""
Este anda barbaro y es el que hay que usar
Funcion para ajustar la temperatura final en funcion de beta.
Esta funcion no tiene ese parametro de ajuste ya que vi que era muy cercano a 1.
"""
gamma = 23e6
hbar = 1.05e-34
kb = 1.38e-23
C = 1
#D=0.5e-27
#det = -12e6
#print(MicromotionSpectra(beta,det,gamma))
#D=0
#C=1
C = 1 #justamente aca dejo fijo ese parametro
D=0
return ((hbar/(kb))*C*MicromotionSpectra(beta,2*np.pi*det,2*np.pi*gamma)+D*(beta**2))*InverseDerivMicromotionSpectra(beta, 2*np.pi*det, 2*np.pi*gamma)
#return (C*MicromotionSpectra(beta,det,gamma))*InverseDerivMicromotionSpectra(beta, det, gamma)
"""
Temperatura vs beta con un ajuste exponencial
"""
#Dos pruebas... ignorar
#popt_rho22_balance, pcov_rho22_balance = curve_fit(FinalTemp,list(Betas_vec[:9]), [t for t in Temp_vec[:9]],p0=(-11e6,0.8,4e-28)) #esto ajusta muy bien
#popt_rho22_balance, pcov_rho22_balance = curve_fit(FinalTemp,list(Betas_vec[:10]), [t for t in Temp_vec[:10]],sigma=ErrorTemp_vec[:10],absolute_sigma=False,p0=(-10e6,0.8,5e-20)) #esto ajusta muy bien
#Buen ajuste final:
popt_rho22_balance, pcov_rho22_balance = curve_fit(FinalTemp_withoutC,list(Betas_vec[:10]), [t for t in Temp_vec[:10]],sigma=ErrorTemp_vec[:10],absolute_sigma=False,p0=(-10e6,5e-20)) #esto ajusta muy bien
popt_rho22_balance_noMM, pcov_rho22_balance_noMM = curve_fit(FinalTemp_withoutmicromotion,list(Betas_vec[:10]), [t for t in Temp_vec[:10]],sigma=ErrorTemp_vec[:10],absolute_sigma=False,p0=(-10e6)) #esto ajusta muy bien
......@@ -1277,7 +483,11 @@ k_plot = 9
plt.figure()
plt.errorbar(Betas_vec[:k_plot],[t*1e3 for t in Temp_vec[:k_plot]],xerr=ErrorBetas_vec[:k_plot], yerr=[t*1e3 for t in ErrorTemp_vec[:k_plot]],fmt='o',capsize=5,markersize=5,color=paleta[3])
#plt.plot(betaslong,[t*1e3 for t in FinalTemp(betaslong,*popt_rho22_balance)],label='Ajuste con espectro modulado')
plt.plot(betaslong,[t*1e3 for t in FinalTemp_withoutC(betaslong,*popt_rho22_balance)],label='Ajuste con espectro modulado')
plt.plot(betaslong,[t*1e3 for t in FinalTemp_withoutC(betaslong,*popt_rho22_balance)],label='With RF heating')
plt.plot(betaslong,[t*1e3 for t in FinalTemp_withoutmicromotion(betaslong,*popt_rho22_balance_noMM)],label='Without RF heating')
#plt.plot(betaslong,FinalTemp_fixedall(betaslong,*popt_rho22_balance),label='Ajuste con espectro modulado')
# plt.xlim(-0.1,1.1)
#plt.ylim(0,1)
......@@ -1285,7 +495,7 @@ plt.plot(betaslong,[t*1e3 for t in FinalTemp_withoutC(betaslong,*popt_rho22_bala
#plt.axhline(0.538)
plt.xlabel('Modulation factor')
plt.ylabel('Temperature (mK)')
plt.title('With RF heating')
plt.legend()
plt.grid()
#%%
......@@ -1389,9 +599,16 @@ print(f'best: {1e3*np.min(Temps_limit)}')
plt.figure()
plt.plot(DetVec*1e-6/(2*np.pi), np.array(Temps_limit)*1e3)
plt.grid()
plt.xlabel('detuning (MHz)')
plt.ylabel('final temperature')
#%%
"""
Calculo del heating rate por micromocion final
"""
hbar=1.05e-34
gammita = 22.1e6
......@@ -1410,83 +627,10 @@ print(f'heating rate due to rf heating: {rfheatrate*1e3} mK/s')
#%%
"""
Esto no es del super ajuste sino de los ajustes anteriores en donde DetDoppler y offset son puestos a mano
Aca grafico los betas con su error en funcion de la tension variada.
Ademas, hago ajuste lineal para primeros y ultimos puntos, ya que espero que
si la tension hace que la posicion del ion varie linealmente, el beta varia proporcional a dicha posicion.
"""
import seaborn as sns
def lineal(x,a,b):
return a*x+b
paleta = sns.color_palette("rocket")
betavector = [beta1,beta2,beta3,beta4,beta5,beta6,beta7,beta8,beta9]
errorbetavector = [errorbeta1,errorbeta2,errorbeta3,errorbeta4,errorbeta5,errorbeta6,errorbeta7,errorbeta8,errorbeta9]
voltages_dcA = Voltages[0][1:10]
poptini,pcovini = curve_fit(lineal,voltages_dcA[0:3],betavector[0:3])
poptfin,pcovfin = curve_fit(lineal,voltages_dcA[4:],betavector[4:])
minimum_voltage = -(poptini[1]-poptfin[1])/(poptini[0]-poptfin[0]) #voltaje donde se intersectan las rectas, es decir, donde deberia estar el minimo de micromocion
minimum_modulationfactor = lineal(minimum_voltage,*poptini) #es lo mismo si pongo *poptfin
xini = np.linspace(-0.23,-0.13,100)
xfin = np.linspace(-0.15,0.005,100)
plt.figure()
plt.errorbar(voltages_dcA,betavector,yerr=errorbetavector,fmt='o',capsize=5,markersize=5,color=paleta[1])
plt.plot(xini,lineal(xini,*poptini))
plt.plot(xfin,lineal(xfin,*poptfin))
plt.axvline(minimum_voltage,linestyle='dashed',color='grey')
plt.xlabel('Endcap voltage (V)')
plt.ylabel('Modulation factor')
plt.grid()
#%%
"""
Aca veo la temperatura del ion en funcion del voltaje del endcap, ya que
al cambiar la cantidad de micromocion, cambia la calidad del enfriado
"""
tempvector = np.array([temp1,temp2,temp3,temp4,temp5,temp6,temp7,temp8,temp9])*1e3
errortempvector = np.array([errortemp1,errortemp2,errortemp3,errortemp4,errortemp5,errortemp6,errortemp7,errortemp8,errortemp9])*1e3
voltages_dcA = Voltages[0][1:10]
plt.figure()
plt.errorbar(voltages_dcA,tempvector,yerr=errortempvector,fmt='o',capsize=5,markersize=5,color=paleta[3])
plt.axvline(minimum_voltage,linestyle='dashed',color='grey')
plt.xlabel('Endcap voltage (V)')
plt.ylabel('Temperature (mK)')
plt.grid()
plt.ylim(0,2)
#%%
"""
Por las dudas, temperatura en funcion de beta
"""
plt.figure()
plt.errorbar(betavector,tempvector,yerr=errortempvector,xerr=errorbetavector,fmt='o',capsize=5,markersize=5)
plt.xlabel('Modulation factor')
plt.ylabel('Temperature (mK)')
plt.grid()
AHORA VAMOS A MEDICIONES CON MAS DE UN ION!!!
#%%
"""
Si quiero ver algun parametro del ajuste puntual. el orden es: 0:SG, 1:SP, 2:SCALE1, 3:OFFSET
"""
ki=2
plt.errorbar(np.arange(0,9,1),[popt_1[ki],popt_2[ki],popt_3[ki],popt_4[ki],popt_5[ki],popt_6[ki],popt_7[ki],popt_8[ki],popt_9[ki]],yerr=[np.sqrt(pcov_1[ki,ki]),np.sqrt(pcov_2[ki,ki]),np.sqrt(pcov_3[ki,ki]),np.sqrt(pcov_4[ki,ki]),np.sqrt(pcov_5[ki,ki]),np.sqrt(pcov_6[ki,ki]),np.sqrt(pcov_7[ki,ki]),np.sqrt(pcov_8[ki,ki]),np.sqrt(pcov_9[ki,ki])], fmt='o',capsize=3,markersize=3)
Las mediciones estan buenas, habria que ver de ajustarlas bien, yo no lo logre.
#%%
"""
AHORA VAMOS A MEDICIONES CON MAS DE UN ION!!!
"""
"""
......
analisis/plots/20231214_CPTconmicromocioncristals/CPT_plotter_20231214.py
View file @ 085c9da6
......@@ -103,7 +103,7 @@ for kk in range(len(MultiCounts)):
Ploteo la cpt de referencia / plotting the reference CPT
"""
jvec = [0] # de la 1 a la 9 vale la pena, despues no
jvec = [1]
plt.figure()
......@@ -134,8 +134,8 @@ meds:
"""
med=0
jvec = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21]
med=1
jvec = [10]
kk=9
......@@ -162,7 +162,7 @@ print(CountsSplit[med][j][12])
#%%
#from EITfit.MM_eightLevel_2repumps_AnalysisFunctions import PerformExperiment_8levels
from EITfit.lolo_modelo_full_8niveles import PerformExperiment_8levels_MM
from scipy.optimize import curve_fit
import time
......@@ -206,16 +206,16 @@ freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(Fre
CircPr = 1
alpha = 0
def FitEIT_MM_1ion(Freqs, offset, DetDoppler, SG, SP, SCALE1, OFFSET, BETA1, TEMP, plot=False):
def FitEIT_MM_1ion(Freqs, offset, DetDoppler, SG, SP, SCALE1, OFFSET, BETA1, TEMP, U, plot=False):
#def FitEIT_MM(freqs, SG, SP, SCALE1, OFFSET, BETA1):
#BETA = 1.8
# SG = 0.6
# SP = 8.1
# TEMP = 0.2e-3
#U=32.5e6
freqs = [2*f*1e-6-offset for f in Freqs]
Detunings, Fluorescence1 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA1, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
Detunings, Fluorescence1 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, U, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA1, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
ScaledFluo1 = np.array([f*SCALE1 + OFFSET for f in Fluorescence1])
if plot:
......@@ -226,7 +226,7 @@ def FitEIT_MM_1ion(Freqs, offset, DetDoppler, SG, SP, SCALE1, OFFSET, BETA1, TEM
do_fit = True
if do_fit:
popt_1, pcov_1 = curve_fit(FitEIT_MM_1ion, FreqsDR, CountsDR, p0=[430, -25, 0.9, 6.2, 3e4, 1.34e3, 2, (np.pi**2)*1e-3], bounds=((0, -50, 0, 0, 0, 0, 0, 0), (1000, 0, 2, 20, 5e6, 5e4, 10, (np.pi**2)*10e-3)))
popt_1, pcov_1 = curve_fit(FitEIT_MM_1ion, FreqsDR, CountsDR, p0=[430, -25, 0.9, 6.2, 3e4, 200, 2, (np.pi**2)*1e-3,32.5e6], bounds=((0, -50, 0, 0, 0, 0, 0, 0,30e6), (1000, 0, 2, 20, 5e6, 5e4, 10, (np.pi**2)*10e-3,35e6)))
FittedEITpi_1_short, Detunings_1_short = FitEIT_MM_1ion(FreqsDR, *popt_1, plot=True)
freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
......@@ -289,7 +289,7 @@ CountsDR = SingleCounts[selectedcurve]
CircPr = 1
alpha = 0
def FitEIT_MM_2ion(Freqs, offset, DetDoppler, SG, SP, SCALE1, SCALE2, OFFSET1, OFFSET2, BETA1, BETA2, TEMP, plot=False):
def FitEIT_MM_2ion(Freqs, offset, DetDoppler, SG, SP, SCALE1, SCALE2, BETA1, BETA2, TEMP, U, plot=False, plotsingles=False):
#def FitEIT_MM(freqs, SG, SP, SCALE1, OFFSET, BETA1):
#BETA = 1.8
# SG = 0.6
......@@ -298,36 +298,55 @@ def FitEIT_MM_2ion(Freqs, offset, DetDoppler, SG, SP, SCALE1, SCALE2, OFFSET1, O
freqs = [2*f*1e-6-offset for f in Freqs]
Detunings, Fluorescence1 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA1, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
Detunings, Fluorescence2 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA2, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
Detunings, Fluorescence1 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, U, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA1, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
Detunings, Fluorescence2 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, U, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA2, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
OFFSET1 = 300
ScaledFluo1 = np.array([f*SCALE1 + OFFSET1 for f in Fluorescence1])
ScaledFluo2 = np.array([f*SCALE2 + OFFSET2 for f in Fluorescence2])
ScaledFluo1 = np.array([f*SCALE1 + 0.5*OFFSET1 for f in Fluorescence1])
ScaledFluo2 = np.array([f*SCALE2 + 0.5*OFFSET1 for f in Fluorescence2])
if plot:
return ScaledFluo1+ScaledFluo2, Detunings
if plotsingles:
return ScaledFluo1, ScaledFluo2, Detunings
else:
return ScaledFluo1+ScaledFluo2
#return ScaledFluo1
do_fit = True
if do_fit:
popt_1_2ion, pcov_1_2ion = curve_fit(FitEIT_MM_2ion, FreqsDR, CountsDR, p0=[445, -32, 0.5, 7, 2e4, 1e4, 2e3, 1.5e3, 2, 1, 0.5e-3], bounds=((0, -50, 0, 0, 0, 0, 0,0, 0,0, 0), (1000, 0, 2, 20, 5e6, 5e6, 5e4,5e4, 10, 10,20e-3)))
popt_1_2ion, pcov_1_2ion = curve_fit(FitEIT_MM_2ion, FreqsDR, CountsDR, p0=[445, -32, 0.5, 7, 2e4, 1e4, 2, 1, 0.5e-3, 32e6], bounds=((0, -50, 0, 0, 0, 0, 0,0, 0,30e6), (1000, 0, 2, 20, 5e6, 5e6, 10, 10,20e-3,35e6)))
FittedEITpi_1_short_2ion, Detunings_1_short_2ion = FitEIT_MM_2ion(FreqsDR, *popt_1_2ion, plot=True)
freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
FittedEITpi_1_long_2ion, Detunings_1_long_2ion = FitEIT_MM_2ion(freqslong, *popt_1_2ion, plot=True)
plt.figure()
plt.errorbar(Detunings_1_short_2ion, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='darkgreen', alpha=0.5, capsize=2, markersize=2)
plt.plot(Detunings_1_long_2ion, FittedEITpi_1_long_2ion, color='darkolivegreen', linewidth=3, label='med 1')
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
plt.xlabel('Detuning (MHz)')
plt.ylabel('Counts')
#plt.xlim(-80,50)
plt.legend(loc='upper left', fontsize=20)
plt.grid()
plot_singles=False
if plot_singles:
FittedEITpi_1_short_2ion_ion1, FittedEITpi_1_short_2ion_ion2, Detunings_1_short_2ion = FitEIT_MM_2ion(FreqsDR, *popt_1_2ion, plot=False, plotsingles=True)
plt.figure()
#plt.errorbar(Detunings_1_short_2ion, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='darkgreen', alpha=0.5, capsize=2, markersize=2)
plt.plot(Detunings_1_short_2ion, FittedEITpi_1_short_2ion_ion1, color='darkolivegreen', linewidth=3, label='med 1')
plt.plot(Detunings_1_short_2ion, FittedEITpi_1_short_2ion_ion2, color='darkolivegreen', linewidth=3, label='med 1')
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
plt.xlabel('Detuning (MHz)')
plt.ylabel('Counts')
#plt.xlim(-80,50)
plt.legend(loc='upper left', fontsize=20)
plt.grid()
else:
plt.figure()
plt.errorbar(Detunings_1_short_2ion, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='darkgreen', alpha=0.5, capsize=2, markersize=2)
plt.plot(Detunings_1_long_2ion, FittedEITpi_1_long_2ion, color='darkolivegreen', linewidth=3, label='med 1')
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
plt.xlabel('Detuning (MHz)')
plt.ylabel('Counts')
#plt.xlim(-80,50)
plt.legend(loc='upper left', fontsize=20)
plt.grid()
......@@ -391,13 +410,13 @@ for selectedcurve in selectedcurvevec:
def FitEIT_MM_2ion(Freqs, offset, DetDoppler, SG, SP, SCALE1, SCALE2, OFFSET, BETA1, BETA2, TEMP, U, plot=False):
def FitEIT_MM_2ion(Freqs, offset, DetDoppler, SG, SP, SCALE1, SCALE2, BETA1, BETA2, TEMP, U, plot=False):
#def FitEIT_MM(freqs, SG, SP, SCALE1, OFFSET, BETA1):
#BETA = 1.8
# SG = 0.6
# SP = 8.1
# TEMP = 0.2e-3
OFFSET = 300
freqs = [2*f*1e-6-offset for f in Freqs]
Detunings, Fluorescence1 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, U, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA1, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
......@@ -415,46 +434,45 @@ for selectedcurve in selectedcurvevec:
def FitEIT_MM_1ion(Freqs, offset1, offset2, DetDoppler, SG, SP, SCALE1, OFFSET, BETA1, TEMP, plot=False):
def FitEIT_MM_1ion(Freqs, offset, DetDoppler, SG, SP, SCALE1, BETA1, TEMP, U, plot=False):
#def FitEIT_MM(freqs, SG, SP, SCALE1, OFFSET, BETA1):
#BETA = 1.8
# SG = 0.6
# SP = 8.1
# TEMP = 0.2e-3
freqs = [2*f*1e-6-offset1 for f in Freqs]
OFFSET = 300
freqs = [2*f*1e-6-offset for f in Freqs]
Detunings, Fluorescence1 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA1, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
#Detunings, Fluorescence2 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA2, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
Detunings, Fluorescence1 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, U, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA1, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
ScaledFluo1 = np.array([f*SCALE1 + 1*OFFSET for f in Fluorescence1])
#ScaledFluo2 = np.array([f*SCALE2 + 0.5*OFFSET for f in Fluorescence2])
if plot:
return ScaledFluo1, Detunings
else:
return ScaledFluo1
#return ScaledFluo1
do_fit = True
if do_fit:
try:
#popt_multi1_1ion_test, pcov_multi1_1ion_test = curve_fit(FitEIT_MM_1ion, FreqsDR, CountsDR, p0=[448.2, -44.8, 0.5, 6.6, 3.8e4, 1.5e-1, 4.2, 1.4e-3], bounds=((0, -50, 0, 0, 0, 0, 0, 0), (1000, 0, 2, 20, 5e6, 5e4, 10, 20e-3)))
popt_multi1_2ion_test, pcov_multi1_2ion_test = curve_fit(FitEIT_MM_2ion, FreqsDR, CountsDR, p0=[448.2, -44.8, 0.5, 6.6, 3.8e4, 1.26e5, 1000, 4.2, 1.3, 1.4e-3, 32e6], bounds=((0, -50, 0, 0, 0, 0, 800, 0,0, 0, 28e6), (1000, 0, 2, 20, 5e6, 5e6, 2000, 10, 10,20e-3,40e6)))
popt_multi1_1ion_test, pcov_multi1_1ion_test = curve_fit(FitEIT_MM_1ion, FreqsDR, CountsDR, p0=[448.2, -44.8, 0.5, 6.6, 3.8e4, 1, 1e-3, 32e6], bounds=((0, -50, 0, 0, 0, 0, 0, 30e6), (1000, 0, 2, 20, 5e6, 10, 10e-3, 35e6)))
#popt_multi1_2ion_test, pcov_multi1_2ion_test = curve_fit(FitEIT_MM_2ion, FreqsDR, CountsDR, p0=[448.2, -44.8, 0.5, 6.6, 3.8e4, 1.26e5, 4.2, 1.3, 1.4e-3, 32e6], bounds=((0, -50, 0, 0, 0, 0, 0,0, 0, 28e6), (1000, 0, 2, 20, 5e6, 5e6, 10, 10,20e-3,40e6)))
except:
popt_multi1_2ion_test = [0,0,0,0,0,0,0,0,0,0]
pcov_multi1_2ion_test = [0]
# FittedEITpi_multi1_short_1ion, Detunings_multi1_short_1ion = FitEIT_MM_1ion(FreqsDR, *popt_multi1_1ion_test, plot=True)
# freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
# FittedEITpi_multi1_long_1ion, Detunings_multi1_long_1ion = FitEIT_MM_1ion(freqslong, *popt_multi1_1ion_test, plot=True)
FittedEITpi_multi1_short_1ion, Detunings_multi1_short_1ion = FitEIT_MM_1ion(FreqsDR, *popt_multi1_1ion_test, plot=True)
freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
FittedEITpi_multi1_long_1ion, Detunings_multi1_long_1ion = FitEIT_MM_1ion(freqslong, *popt_multi1_1ion_test, plot=True)
FittedEITpi_multi1_short_2ion, Detunings_multi1_short_2ion = FitEIT_MM_2ion(FreqsDR, *popt_multi1_2ion_test, plot=True)
freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
FittedEITpi_multi1_long_2ion, Detunings_multi1_long_2ion = FitEIT_MM_2ion(freqslong, *popt_multi1_2ion_test, plot=True)
# FittedEITpi_multi1_short_2ion, Detunings_multi1_short_2ion = FitEIT_MM_2ion(FreqsDR, *popt_multi1_2ion_test, plot=True)
# freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
# FittedEITpi_multi1_long_2ion, Detunings_multi1_long_2ion = FitEIT_MM_2ion(freqslong, *popt_multi1_2ion_test, plot=True)
#popt_vecs.append(popt_multi1_2ion)
......@@ -462,24 +480,24 @@ for selectedcurve in selectedcurvevec:
print(f'Listo {selectedcurve}')
# plt.figure()
# plt.errorbar(Detunings_multi1_short_1ion, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='darkgreen', alpha=0.5, capsize=2, markersize=2)
# plt.plot(Detunings_multi1_long_1ion, FittedEITpi_multi1_long_1ion, color='red', linewidth=3, label=f'selcurve: {selectedcurve}')
# plt.title('1 ion model')
# plt.xlabel('Detuning (MHz)')
# plt.ylabel('Counts')
# plt.legend(loc='upper left', fontsize=20)
# plt.grid()
plt.figure()
plt.errorbar(Detunings_multi1_short_2ion, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='darkgreen', alpha=0.5, capsize=2, markersize=2)
plt.plot(Detunings_multi1_long_2ion, FittedEITpi_multi1_long_2ion, color='darkolivegreen', linewidth=3, label=f'selcurve: {selectedcurve}')
plt.title('2 ion model')
plt.errorbar(Detunings_multi1_short_1ion, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='darkgreen', alpha=0.5, capsize=2, markersize=2)
plt.plot(Detunings_multi1_long_1ion, FittedEITpi_multi1_long_1ion, color='red', linewidth=3, label=f'selcurve: {selectedcurve}')
plt.title('1 ion model')
plt.xlabel('Detuning (MHz)')
plt.ylabel('Counts')
plt.legend(loc='upper left', fontsize=20)
plt.grid()
# plt.figure()
# plt.errorbar(Detunings_multi1_short_2ion, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='darkgreen', alpha=0.5, capsize=2, markersize=2)
# plt.plot(Detunings_multi1_long_2ion, FittedEITpi_multi1_long_2ion, color='darkolivegreen', linewidth=3, label=f'selcurve: {selectedcurve}')
# plt.title('2 ion model')
# plt.xlabel('Detuning (MHz)')
# plt.ylabel('Counts')
# plt.legend(loc='upper left', fontsize=20)
# plt.grid()
# plt.plot(detunings,'o')
......
analisis/plots/20231214_CPTconmicromocioncristals/Data/EITfit/lolo_modelo_full_8niveles.py 0 → 100644
View file @ 085c9da6
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
20 de dic 2023
@author: lolo
reingenieria del código que anda
MAPA de FUNCIONES
CPTspectrum8levels_MM
|--> FullL_MM => ndarray(64,64,np.complex_)
|--> dopplerBroadening => float
|--> EffectiveL => ndarray(8,8,np.complex_)
|--> H0matrix => ndarray(8,8,np.complex_)
|--> HImatrix => ndarray(8,8,np.complex_)
|--> CalculateSingleMmatrix => ndarray(64,64,np.complex_)
|--> LtempCalculus => ndarray,ndarray
|--> GetL1 => ndarray
"""
# pylint: disable=C0301,R0913,R0914,W0621
import time
import random
import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import savgol_filter as sf
from numba import jit,njit
@njit
def PerformExperiment_8levels_MM(sg, sp, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobe, beta, drivefreq, freqMin, freqMax, freqStep, circularityprobe=1, plot=False, solvemode=1, detpvec=None):
"""
solvemode=1: resuelve con np.linalg.solve
solvemode=2: resuelve invirtiendo L con la funcion np.linalg.inv
"""
rta = CPTspectrum8levels_MM(sg, sp, gPS, gPD, DetDoppler, u,
DopplerLaserLinewidth, ProbeLaserLinewidth,
T, alpha, phidoppler, titadoppler, phiprobe,
titaprobe, circularityprobe, beta, drivefreq,
freqMin=freqMin, freqMax=freqMax, freqStep=freqStep,
plot=False, solvemode=1)
ProbeDetuningVectorL, Fluovector = rta
return ProbeDetuningVectorL, Fluovector
def GenerateNoisyCPT_MM(sg, sp, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, kg, kr, v0, drivefreq, freqMin, freqMax, freqStep, circularityprobe=1, plot=False, solvemode=1, detpvec=None, noiseamplitude=0.001):
"""
Genera un resultado de PerformExperiment_8levels_MM con ruido normal agregado
"""
nFrequencyvector, Fluovector = PerformExperiment_8levels_MM(sg, sp, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, kg, kr, v0, drivefreq, freqMin, freqMax, freqStep, circularityprobe, plot=False, solvemode=1, detpvec=None)
NoisyFluovector = [fluo+noiseamplitude*(2*random.random()-1) for fluo in Fluovector]
return nFrequencyvector, NoisyFluovector
def GenerateNoisyCPT_MM_fit(sg, sp, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, beta, drivefreq, freqs, circularityprobe=1, plot=False, solvemode=1, detpvec=None, noiseamplitude=0.001):
"""
Este no se qué hace
"""
Frequencyvector, Fluovector = PerformExperiment_8levels_MM(sg, sp, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, beta, drivefreq, freqs[0], freqs[-1], freqs[1]-freqs[0], circularityprobe, plot=False, solvemode=1, detpvec=None)
#NoisyFluovector = [fluo+noiseamplitude*(2*random.random()-1) for fluo in Fluovector]
return Frequencyvector, Fluovector
def SmoothNoisyCPT(Fluo, window=11, poly=3):
SmoothenFluo = sf(Fluo, window, poly)
return SmoothenFluo
#%% Estos son los auxiliares ###################################################
"""
Esta parte es la del modelo
"""
@njit
def make_diag(vec):
"Construye matris diagonal desde una lista o vector"
return np.eye(len(vec))*np.array(vec).reshape(-1,1)
# @njit
# def kron(a,b):
# "Hago el producto de Kronecker a mano"
# return np.vstack( [ np.hstack( [ a[k,j]*b for j in range(a.shape[1]) ] ) for k in range(a.shape[0])])
# @njit
# def kron(A, B):
# cola = A.shape[1]
# rowa = A.shape[0]
# colb = B.shape[1]
# rowb = B.shape[0]
#
# C = [[0] * (cola * colb) for _ in range(rowa * rowb) ]
#
# for i in range(rowa):
# for k in range(cola):
# for j in range(rowb):
# for l in range(colb):
# C[i * rowb + k][j * colb + l] = A[i][j] * B[k][l]
# return np.array(C)
import numba
@jit
def kron(A,B):
out=np.empty((A.shape[0],B.shape[0],A.shape[1],B.shape[1]),dtype=A.dtype)
for i in numba.prange(A.shape[0]):
for j in range(B.shape[0]):
for k in range(A.shape[1]):
for l in range(B.shape[1]):
out[i,j,k,l]=A[i,k]*B[j,l]
return out
@njit
def H0matrix(Detg, Detp, u):
"""
Calcula la matriz H0 en donde dr es el detuning del doppler, dp es el retuning
del repump y u es el campo magnético en Hz/Gauss.
Para esto se toma la energía del nivel P como 0
"""
eigenEnergies = (Detg-u, Detg+u, -u/3, u/3, Detp-6*u/5,
Detp-2*u/5, Detp+2*u/5, Detp+6*u/5)
#pagina 26 de Oberst. los lande del calcio son iguales a Bario.
# H0 = np.diag(eigenEnergies)
# H0 = np.eye(len(eigenEnergies))*np.array(eigenEnergies).reshape(-1,1)
H0 = make_diag(eigenEnergies)
return H0
@njit
def HImatrix(rabG, rabP, phidoppler, titadoppler, phiprobe, titaprobe, circularityprobe=1):
"""
Calcula la matriz de interacción Hsp + Hpd, en donde rabR es la frecuencia de rabi de
la transición Doppler SP, rabP es la frecuencia de rabi de la transición repump DP,
y las componentes ei_r y ei_p son las componentes de la polarización
del campo eléctrico incidente de doppler y repump respectivamente. Deben
estar normalizadas a 1
"""
HI = np.zeros((8, 8), dtype=np.complex_)
i, j = 1, 3
HI[i-1, j-1] = (rabG/np.sqrt(3)) * np.cos(titadoppler)
HI[j-1, i-1] = np.conjugate(HI[i-1, j-1])
i, j = 1, 4
HI[i-1, j-1] = -(rabG/np.sqrt(3)) * np.sin(titadoppler)*np.exp(1j*phidoppler)
HI[j-1, i-1] = np.conjugate(HI[i-1, j-1])
i, j = 2, 3
HI[i-1, j-1] = -(rabG/np.sqrt(3)) * np.sin(titadoppler)*np.exp(-1j*phidoppler)
HI[j-1, i-1] = np.conjugate(HI[i-1, j-1])
i, j = 2, 4
HI[i-1, j-1] = -(rabG/np.sqrt(3)) * np.cos(titadoppler)
HI[j-1, i-1] = np.conjugate(HI[i-1, j-1])
i, j = 3, 5
HI[i-1, j-1] = -(rabP/2) * np.sin(titaprobe)*(np.cos(phiprobe)-1j*np.sin(phiprobe)*circularityprobe)
HI[j-1, i-1] = np.conjugate(HI[i-1, j-1])
i, j = 3, 6
HI[i-1, j-1] = -(rabP/np.sqrt(3)) * np.cos(titaprobe)
HI[j-1, i-1] = np.conjugate(HI[i-1, j-1])
i, j = 3, 7
HI[i-1, j-1] = rabP/np.sqrt(12) * np.sin(titaprobe)*(np.cos(phiprobe)+1j*np.sin(phiprobe)*circularityprobe)
HI[j-1, i-1] = np.conjugate(HI[i-1, j-1])
i, j = 4, 6
HI[i-1, j-1] = -(rabP/np.sqrt(12)) * np.sin(titaprobe)*(np.cos(phiprobe)-1j*np.sin(phiprobe)*circularityprobe)
HI[j-1, i-1] = np.conjugate(HI[i-1, j-1])
i, j = 4, 7
HI[i-1, j-1] = -(rabP/np.sqrt(3)) * np.cos(titaprobe)
HI[j-1, i-1] = np.conjugate(HI[i-1, j-1])
i, j = 4, 8
HI[i-1, j-1] = (rabP/2) * np.sin(titaprobe)*(np.cos(phiprobe)+1j*np.sin(phiprobe)*circularityprobe)
HI[j-1, i-1] = np.conjugate(HI[i-1, j-1])
return HI
# @jit
# def LtempCalculus(beta:float, drivefreq:float, forma=1):
# Hint = np.zeros((8, 8), dtype=np.complex_)
# ampg=beta*drivefreq
# ampr=beta*drivefreq*(397/866)
# Hint[0,0] = ampg
# Hint[1,1] = ampg
# Hint[4,4] = ampr
# Hint[5,5] = ampr
# Hint[6,6] = ampr
# Hint[7,7] = ampr
# if forma==1:
# Ltemp = np.zeros((64, 64), dtype=np.complex_)
# for r in range(8):
# for q in range(8):
# if r!=q:
# Ltemp[r*8+q][r*8+q] = (-1j)*(Hint[r,r] - Hint[q,q])
# if forma==2:
# # deltaKro = np.diag([1, 1, 1, 1, 1, 1, 1, 1])
# deltaKro = make_diag([1., 1., 1., 1., 1., 1., 1., 1.]).astype(np.complex_)
# # Ltemp = (-1j)*(np.kron(Hint, deltaKro) - np.kron(deltaKro, Hint))
# Ltemp = (-1j)*(kron(Hint, deltaKro) - kron(deltaKro, Hint))
# Omega = np.zeros((64, 64), dtype=np.complex_)
# for i in range(64):
# Omega[i, i] = (1j)*drivefreq
# return Ltemp, Omega
@njit
def LtempCalculus(beta:float, drivefreq:float, forma=1):
Hint = np.zeros((8, 8), dtype=np.complex_)
ampg=beta*drivefreq
ampr=beta*drivefreq*(397/866)
Hint[0,0] = ampg
Hint[1,1] = ampg
Hint[4,4] = ampr
Hint[5,5] = ampr
Hint[6,6] = ampr
Hint[7,7] = ampr
Ltemp = np.zeros((64, 64), dtype=np.complex_)
for r in range(8):
for q in range(8):
if r!=q:
Ltemp[r*8+q][r*8+q] = (-1j)*(Hint[r,r] - Hint[q,q])
Omega = np.zeros((64, 64), dtype=np.complex_)
for i in range(64):
Omega[i, i] = (1j)*drivefreq
return Ltemp, Omega
# LtempCalculus(0,1)
# raise ValueError('aaa')
@njit
def GetL1(Ltemp, L0, Omega, nmax):
"""
Devuelve Splus0 y Sminus0
"""
# Sp = (-1)*(np.matrix(np.linalg.inv(L0 - (nmax+1)*Omega))*0.5*np.matrix(Ltemp))
# Sm = (-1)*(np.matrix(np.linalg.inv(L0 + (nmax+1)*Omega))*0.5*np.matrix(Ltemp))
Sp = (-1)*np.linalg.inv(L0 - (nmax+1)*Omega).dot(0.5*Ltemp)
Sm = (-1)*np.linalg.inv(L0 + (nmax+1)*Omega).dot(0.5*Ltemp)
for n in list(range(nmax+1))[(nmax+1)::-1][0:len(list(range(nmax+1))[(nmax+1)::-1])-1]: #jaja esto solo es para que vaya de nmax a 1 bajando. debe haber algo mas facil pero kcio
# Sp = (-1)*(np.matrix(np.linalg.inv(L0 - n*Omega + (0.5*Ltemp*np.matrix(Sp))))*0.5*np.matrix(Ltemp))
# Sm = (-1)*(np.matrix(np.linalg.inv(L0 + n*Omega + (0.5*Ltemp*np.matrix(Sm))))*0.5*np.matrix(Ltemp))
Sp = (-1)*np.linalg.inv(L0 - n*Omega + (0.5*Ltemp.dot(Sp))).dot(0.5*Ltemp)
Sm = (-1)*np.linalg.inv(L0 + n*Omega + (0.5*Ltemp.dot(Sm))).dot(0.5*Ltemp)
# L1 = 0.5*np.matrix(Ltemp)*(np.matrix(Sp) + np.matrix(Sm))
L1 = 0.5*Ltemp.dot(Sp + Sm)
return L1
@njit
def EffectiveL(gPS, gPD, lwg, lwp):
"""
Siendo Heff = H + EffectiveL, calcula dicho EffectiveL que es (-0.5j)*sumatoria(CmDaga*Cm) que luego sirve para calcular el Liouvilliano
"""
Leff = np.zeros((8, 8), dtype=np.complex_)
Leff[0, 0] = 2*lwg
Leff[1, 1] = 2*lwg
Leff[2, 2] = ((2/3)+(1/3))*gPS + ((1/2) + (1/6) + (1/3))*gPD
Leff[3, 3] = ((2/3)+(1/3))*gPS + ((1/2) + (1/6) + (1/3))*gPD
Leff[4, 4] = 2*lwp
Leff[5, 5] = 2*lwp
Leff[6, 6] = 2*lwp
Leff[7, 7] = 2*lwp
return (-0.5j)*Leff
@njit
def CalculateSingleMmatrix(gPS, gPD, lwg, lwp):
"""
Si tomamos el Liuvilliano como L = (-j)*(Heff*deltak - Heffdaga*deltak) + sum(Mm),
esta funcion calcula dichos Mm, que tienen dimensión 64x64 ya que esa es la dimensión del L. Estas componentes
salen de hacer la cuenta a mano conociendo los Cm y considerando que Mm[8*(r-1)+s, 8*(k-1)+j] = Cm[r,l] + Cmdaga[j,s] = Cm[r,l] + Cm[s,j]
ya que los componentes de Cm son reales.
Esta M es la suma de las 8 matrices M.
"""
M = np.zeros((64, 64), dtype=np.complex_)
M[0,27] = (2/3)*gPS
M[9,18] = (2/3)*gPS
M[0,18] = (1/3)*gPS
M[1,19] = -(1/3)*gPS
M[8,26] = -(1/3)*gPS
M[9,27] = (1/3)*gPS
M[36,18] = (1/2)*gPD
M[37,19] = (1/np.sqrt(12))*gPD
M[44,26] = (1/np.sqrt(12))*gPD
M[45,27] = (1/6)*gPD
M[54,18] = (1/6)*gPD
M[55,19] = (1/np.sqrt(12))*gPD
M[62,26] = (1/np.sqrt(12))*gPD
M[63,27] = (1/2)*gPD
M[45,18] = (1/3)*gPD
M[46,19] = (1/3)*gPD
M[53,26] = (1/3)*gPD
M[54,27] = (1/3)*gPD
M[0,0] = 2*lwg
M[1,1] = 2*lwg
M[8,8] = 2*lwg
M[9,9] = 2*lwg
for k in [36, 37, 38, 39, 44, 45, 46, 47, 52, 53, 54, 55, 60, 61, 62, 63]:
M[k,k]=2*lwp
return M
@njit
def dopplerBroadening(wlg, wlp, alpha, T, mcalcio = 6.655e-23*1e-3):
"""
Calcula el broadening extra semiclásico por temperatura considerando que el ion atrapado se mueve.
wlg es la longitud de onda doppler, wlp la longitud de onda repump, T la temperatura del ion en kelvin, y alpha (en rads) el ángulo
que forman ambos láseres.
"""
kboltzmann = 1.38e-23 #J/K
gammaD = (2*np.pi)*np.sqrt((1/(wlg*wlg)) + (1/(wlp*wlp)) - 2*(1/(wlg*wlp))*np.cos(alpha))*np.sqrt(kboltzmann*T/(1*mcalcio))
return gammaD
@njit
def FullL_MM(rabG, rabP, gPS = 0, gPD = 0, Detg = 0, Detp = 0, u = 0, lwg = 0, lwp = 0,
phidoppler=0, titadoppler=0, phiprobe=0, titaprobe=0, beta=0,
drivefreq=2*np.pi*22.135*1e6, T = 0, alpha = 0, circularityprobe=1):
"""
Calcula el Liouvilliano total de manera explícita índice a índice. Suma aparte las componentes de las matrices M.
Es la más eficiente hasta ahora.
"""
db = dopplerBroadening(0.397e-6, 0.866e-6, alpha, T)
lwg = np.sqrt(lwg**2 + (0.83*db)**2)
lwp = np.sqrt(lwp**2 + (0.17*db)**2)
CC = EffectiveL(gPS, gPD, lwg, lwp)
Heff = H0matrix(Detg, Detp, u) + HImatrix(rabG, rabP, phidoppler, titadoppler, phiprobe, titaprobe, circularityprobe) + CC
# Heffdaga = np.matrix(Heff).getH()
Heffdaga = np.conj(np.transpose(Heff))
Lfullpartial = np.zeros((64, 64), dtype=np.complex_)
for r in range(8):
for q in range(8):
for k in range(8):
for j in range(8):
if j!=q and r!=k:
pass
elif j==q and r!=k:
if (r < 2 and k > 3) or (k < 2 and r > 3) or (r > 3 and k > 3) or (r==0 and k==1) or (r==1 and k==0) or (r==2 and k==3) or (r==3 and k==2):
#todo esto sale de analizar explicitamente la matriz y tratar de no calcular cosas de más que dan cero
pass
else:
Lfullpartial[r*8+q][k*8+j] = (-1j)*(Heff[r,k])
elif j!=q and r==k:
if (j < 2 and q > 3) or (q < 2 and j > 3) or (j > 3 and q > 3) or (j==0 and q==1) or (j==1 and q==0) or (j==2 and q==3) or (j==3 and q==2):
pass
else:
Lfullpartial[r*8+q][k*8+j] = (-1j)*(-Heffdaga[j,q])
else:
if Heff[r,k] == Heffdaga[j,q]:
pass
else:
Lfullpartial[r*8+q][k*8+j] = (-1j)*(Heff[r,k]-Heffdaga[j,q])
M = CalculateSingleMmatrix(gPS, gPD, lwg, lwp)
# L0 = np.array(np.matrix(Lfullpartial) + M)
L0 = Lfullpartial + M
#ESTA PARTE ES CUANDO AGREGAS MICROMOCION
nmax = 3
#print(nmax)
Ltemp, Omega = LtempCalculus(beta, drivefreq)
#print(factor)
L1 = GetL1(Ltemp, L0, Omega, nmax)
Lfull = L0 + L1 #ESA CORRECCION ESTA EN L1
#HASTA ACA
#NORMALIZACION DE RHO
i = 0
while i < 64:
if i%9 == 0:
Lfull[0, i] = 1
else:
Lfull[0, i] = 0
i = i + 1
return Lfull
"""
Scripts para correr un experimento y hacer el análisis de los datos
"""
@njit
def CPTspectrum8levels_MM(sg, sp, gPS, gPD, Detg, u, lwg, lwp, Temp, alpha, phidoppler, titadoppler, phiprobe, titaprobe, Circularityprobe, beta, drivefreq, freqMin=-100, freqMax=100, freqStep=1e-1, plot=False, solvemode=1):
"""
ESTA ES LA FUNCION QUE ESTAMOS USANDO
Hace un experimento barriendo ángulos de repump con el angulo de doppler fijo.
solvemode=1: resuelve con np.linalg.solve
solvemode=2: resuelve invirtiendo L con la funcion np.linalg.inv
"""
phidoppler, titadoppler = phidoppler*(np.pi/180), titadoppler*(np.pi/180)
phiprobe, titaprobe = phiprobe*(np.pi/180), titaprobe*(np.pi/180)
DetProbeVector = 2*np.pi*np.arange(freqMin*1e6, freqMax*1e6+0*freqStep*1e6, freqStep*1e6)
Detg = 2*np.pi*Detg*1e6
#lwg, lwr, lwp = 2*np.pi*lwg*1e6, 2*np.pi*lwr*1e6, 2*np.pi*lwp*1e6
lwg, lwp = lwg*1e6, lwp*1e6
rabG = sg*gPS
rabP = sp*gPD
#u = 2*np.pi*u*1e6
Fluovector = []
# tinicial = time.time()
for Detp in DetProbeVector:
L = FullL_MM(rabG, rabP, gPS, gPD, Detg, Detp, u, lwg, lwp, phidoppler, titadoppler, phiprobe, titaprobe, beta, drivefreq, Temp, alpha, Circularityprobe)
# if solvemode == 1:
rhovectorized = np.linalg.solve(L, np.array([int(i==0) for i in range(64)],dtype=np.complex_))
Fluo = np.real(rhovectorized[18] + np.real(rhovectorized[27]))
Fluovector.append(Fluo)
# if solvemode == 2:
# Linv = np.linalg.inv(L)
# rhovectorized = [Linv[j][0] for j in range(len(Linv))]
# Fluo = np.real(rhovectorized[18] + np.real(rhovectorized[27])) #estos son los rho33 + rho44
# Fluovector.append(Fluo)
# tfinal = time.time()
# print('Done, Total time: ', round((tfinal-tinicial), 2), "s")
DetProbeVectorMHz = np.arange(freqMin, freqMax, freqStep)
# if plot:
# plt.xlabel('Probe detuning (MHz)')
# plt.ylabel('Fluorescence (A.U.)')
# plt.plot(DetProbeVectorMHz, [100*f for f in Fluovector], label=str(titaprobe) + 'º, T: ' + str(Temp*1e3) + ' mK')
# plt.legend()
return DetProbeVectorMHz, Fluovector
# @njit
# def lolo():
# L = FullL_MM(100,200,12,123,14)
# return np.linalg.solve(L, np.array([int(i==0) for i in range(64)],dtype=np.complex_))
# lolo()
# raise ValueError('áaa')
#%%
if __name__ == "__main__":
ub = 9.27e-24
h = 6.63e-34
c = (ub/h)*1e-4 #en unidades de MHz/G
B = 25 #campo magnetico en gauss
u = c*B
sg, sr, sp = 0.5, 1.5, 4 #parámetros de saturación del doppler y repump
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6 #anchos de linea de las transiciones
rabG, rabR, rabP = sg*gPS, sr*gPD, sp*gPD #frecuencias de rabi
lwg, lwr, lwp = 0.3, 0.3, 0.3 #ancho de linea de los laseres
Detg = -25
Detr = 20 #detuning del doppler y repump
Temp = 0.0e-3 #temperatura en K
alpha = 0*(np.pi/180) #angulo entre los láseres
phidoppler, titadoppler = 0, 90
phirepump, titarepump = 0, 90
phiprobe, titaprobe = 0, 90
plotCPT = False
freqMin = -50
freqMax = 50
freqStep = 5e-2
# Frequencyvector, Fluovector = CPTspectrum8levels_MM(rabG, rabR, rabP, gPS, gPD, Detg, Detr, u, lwg, lwr, lwp, Temp, alpha, phidoppler, titadoppler, phiprobe, titaprobe, phirepump, titarepump, freqMin=freqMin, freqMax=freqMax, freqStep=freqStep, plot=plotCPT, solvemode=1)
Frequencyvector, Fluovector = PerformExperiment_8levels_MM(0.9,6.2,135591138.92893547,8482300.164692441,-24.5,32500000.0,0.1,0.1,0.001,0,0,90,0,90,2.0,139078306.77442014,-54.39999999999998,26.26666666666671,0.6666666666666856,circularityprobe=1,plot=False,solvemode=1,detpvec=None)
plt.plot(Frequencyvector, [100*f for f in Fluovector], label=str(titaprobe) + 'º, T: ' + str(Temp*1e3) + ' mK')
plt.xlabel('Probe detuning (MHz)')
plt.ylabel('Fluorescence (A.U.)')
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