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artiq_experiments
Commit 32f4f986 authored by Nicolas Nunez Barreto's avatar Nicolas Nunez Barreto
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codigos

parent 282ee988
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analisis/plots/20221006_transitoriosv2/Transitorios_02.py
View file @ 32f4f986
......@@ -214,8 +214,9 @@ popt_vec = []
pcov_vec = []
plt.figure()
for j in range(len(SP_Heigths)):
for j in range(len(SP_Heigths)-1):
#for j in [12]:
print(j)
BackgroundVector = SP_Bkgr_builder(np.mean(SP_Heigths[j][0:50]),np.mean(SP_Heigths[j][-50:]),59,67,965)
#CorrectedSP_Height = [SP_Heigths[j][k]-BackgroundVector[k] for k in range(len(BackgroundVector))]
......@@ -234,10 +235,15 @@ for j in range(len(SP_Heigths)):
#%%
plt.figure()
plt.plot(UVpotVec, Taus,'o')
plt.xlabel('UV power (mW)')
plt.ylabel('Characteristic time (us)')
plt.plot(UVpotVec[:-5], Taus[:-6],'o', markersize=10, color='purple')
#plt.errorbar(UVpotVec[:-5], Taus[:-6], yerr=1e1*np.array(ErrorTaus[:-6]), fmt='.', capsize=2, markersize=2)
plt.xlabel('UV power (mW)', fontsize=15)
plt.ylabel('Characteristic time (us)', fontsize=15)
plt.ylim(-0.1,3)
plt.grid()
#%%
plt.figure()
plt.plot(UVpotVec, Amps,'o')
plt.xlabel('UV power (mW)')
......@@ -262,8 +268,8 @@ plt.rcParams.update({
"text.usetex": False,
})
#plt.figure()
#plt.plot(Stat_Bins[0][:-1], Stat_Heigths[0])
plt.figure()
plt.plot(Stat_Bins[0][:-1], Stat_Heigths[0])
#plot figuras papers
......@@ -309,30 +315,36 @@ pcov_vec_v2 = []
ki = 80
selectedj = [2,5,10]
t0 = -0.2
plt.figure()
for j in range(len(SP_Heigths_v2)):
#for j in [1]:
#BackgroundVector = SP_Bkgr_builder(np.mean(SP_Heigths_v2[j][0:50]),np.mean(SP_Heigths_v2[j][-50:]),59,67,965)
#CorrectedSP_Height_v2 = [SP_Heigths_v2[j][k]-BackgroundVector[k] for k in range(len(BackgroundVector))]
CorrectedSP_Height_v2 = SP_Heigths_v2[j]
popt, pcov = curve_fit(expo, RefBins[ki:], CorrectedSP_Height_v2[ki:], p0=(5, 1000, 100), bounds=((0, 0, 0), (50, 1e5, 1e5)))
popt_vec_v2.append(popt)
pcov_vec_v2.append(pcov)
plt.plot(RefBins, CorrectedSP_Height_v2)
plt.plot(RefBins[ki:], [expo(r, *popt) for r in RefBins][ki:])
print(popt[0])
print(pcov[0][0])
Taus_v2.append(popt[0])
Amps_v2.append(popt[1])
Offsets_v2.append(popt[2])
ErrorTaus_v2.append(np.sqrt(pcov)[0][0])
if j in selectedj:
#BackgroundVector = SP_Bkgr_builder(np.mean(SP_Heigths_v2[j][0:50]),np.mean(SP_Heigths_v2[j][-50:]),59,67,965)
#CorrectedSP_Height_v2 = [SP_Heigths_v2[j][k]-BackgroundVector[k] for k in range(len(BackgroundVector))]
CorrectedSP_Height_v2 = SP_Heigths_v2[j]
popt, pcov = curve_fit(expo, RefBins[ki:], CorrectedSP_Height_v2[ki:], p0=(5, 1000, 100), bounds=((0, 0, 0), (50, 1e5, 1e5)))
popt_vec_v2.append(popt)
pcov_vec_v2.append(pcov)
plt.plot([r+t0 for r in RefBins], CorrectedSP_Height_v2)
plt.plot([r+t0 for r in RefBins[ki:]], [expo(r, *popt) for r in RefBins][ki:])
print(popt[0])
print(pcov[0][0])
Taus_v2.append(popt[0])
Amps_v2.append(popt[1])
Offsets_v2.append(popt[2])
ErrorTaus_v2.append(np.sqrt(pcov)[0][0])
plt.xlim(-0.2,2)
#%%
plt.figure()
......@@ -397,7 +409,7 @@ plt.savefig('fig3_01.pdf')
plt.savefig('fig3_01.svg')
#%%
#%%
"""
VEMOS UNA DP CON POTENCIA ALTA A VER SI DA LO QUE TIENE QUE DAR EL ANCHO DE LINEA
......@@ -449,10 +461,16 @@ initi=0.10
popt_UV, pcov_UV = curve_fit(expo, BinsUVhigh[int(initi*len(BinsUVhigh)):], HeightsUVhigh[int(initi*len(BinsUVhigh)):], p0=(1, 1000, 1800, 1), bounds=((0,0,0,0),(20, 1e7, 1e5, 1e3)))
plt.figure()
plt.plot(BinsUVhigh, HeightsUVhigh)
plt.plot(BinsUVhigh, [expo(r, *popt_UV) for r in BinsUVhigh])
plt.plot(BinsUVhigh[int(initi*len(BinsUVhigh))], HeightsUVhigh[int(initi*len(BinsUVhigh))],'o',markersize=5)
plt.plot(BinsUVhigh, HeightsUVhigh,linewidth=4)
plt.plot(BinsUVhigh[80:], [expo(r, *popt_UV) for r in BinsUVhigh][80:], color='fuchsia', linewidth=3, label='Exponential fit')
#plt.plot(BinsUVhigh[int(initi*len(BinsUVhigh))], HeightsUVhigh[int(initi*len(BinsUVhigh))],'o',markersize=5)
#plt.ylim(-1000,20000)
plt.grid()
plt.xlabel(r'Time ($\mu$s)', fontsize=15)
plt.ylabel('Counts', fontsize=15)
plt.xlim(-0.5, 5)
plt.legend(fontsize=15)
tauUV = popt_UV[0]
print(tauUV)
......
analisis/plots/20221006_transitoriosv2/fig3_01.svg
View file @ 32f4f986
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<dc:date>2023-04-27T16:04:44.519667</dc:date>
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......@@ -21,45 +21,43 @@
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analisis/plots/20230321_heatingrate/CPT_plotter_20230321.py
View file @ 32f4f986
......@@ -12,6 +12,8 @@ from scipy import interpolate
#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20220106_CPT_DosLaseres_v08_TISA_DR\Data
os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20230321_heatingrate/Data/')
CPT_FILES = """000010420-IR_Scan_withcal_optimized
000010436-IR_Scan_withcal_optimized
000010437-IR_Scan_withcal_optimized
......@@ -19,6 +21,12 @@ CPT_FILES = """000010420-IR_Scan_withcal_optimized
000010439-IR_Scan_withcal_optimized
000010440-IR_Scan_withcal_optimized
000010441-IR_Scan_withcal_optimized
000010425-IR_Scan_withcal_optimized
000010426-IR_Scan_withcal_optimized
000010427-IR_Scan_withcal_optimized
000010428-IR_Scan_withcal_optimized
000010429-IR_Scan_withcal_optimized
000010430-IR_Scan_withcal_optimized
"""
HEATING_FILES = """000010422-HeatingRate"""
......@@ -81,7 +89,7 @@ for i, fname in enumerate(HEATING_FILES.split()):
#%%
"""
Ploteo la cpt de referencia
Ploteo la cpt de referencia / plotting the reference CPT
"""
jvec = [0]
......@@ -103,6 +111,7 @@ from scipy.optimize import curve_fit
"""
Ajusto un cpt para obtener todos los parámetros relevantes primero.
I fit a cpt curve to retrieve all the relevant parameters first.
"""
phidoppler, titadoppler = 0, 90
......@@ -160,9 +169,10 @@ def FitEITpi(freqs, SG, SP, scale, offset, temp):
#esos valores anteriores dan mal la ordenada al origen, cambie correccion y ahora da
#[4.71134671e-01, 7.63142299e+00, 7.30866544e+04, 1.80899906e+02, 1.20863371e-03]
popt_fullcpt, pcov_fullcpt = curve_fit(FitEITpi, FreqsDRpi, CountsDRpi, p0=[0.5, 4.5, 1e4, 1e3, 1e-3], bounds=((0, 0, 0, 0, 0), (2, 10, 1e5, 1e5, 10e-3)))
print(f'Temperatura: ({round(1e3*popt_fullcpt[-1],2)} +- {round(1e3*np.sqrt(pcov_fullcpt[-1][-1]),2)}) mK')
popt_fullcpt, pcov_fullcpt = curve_fit(FitEITpi, FreqsDRpi, CountsDRpi, p0=[0.5, 4.5, 1e4, 1e3, 1e-3], bounds=((0, 0, 0, 0, 0), (2, 10, 1e5, 1e5, 10e-3)))
print(popt_fullcpt)
......@@ -171,26 +181,44 @@ Det = popt_fullcpt[1]
FittedEITpi = FitEITpi(freqslongpi, *popt_fullcpt)
#%%
"""
Ploteo la CPT de referencia junto al ajuste y a la resonancia oscura de interes
I plot the reference CPT along with the fit to the model and the dark resonance of interest
"""
i_DR = 955
plt.figure()
plt.errorbar(FreqsDRpi, CountsDRpi, yerr=2*np.sqrt(CountsDRpi), fmt='o', capsize=2, markersize=2)
plt.plot(freqslongpi, FittedEITpi)
plt.plot(freqslongpi[i_DR], FittedEITpi[i_DR],'o', color='red', markersize=12)
plt.xlabel('Detuning (MHz)')
plt.ylabel('Counts')
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
#%%
"""
Simulo CPTs con todos esos parámetros para distintas temperaturas
I simulate CPT curves with all the previous parameters but with varying temperatures
"""
TempVecTeorico = list(np.arange(0, 31, 1))
TempVecTeorico = list(np.arange(0.3,1,0.1))+list(np.arange(1, 31, 1))
CurvasTeoricas = []
for tempi in TempVecTeorico:
CurvasTeoricas.append(FitEITpi(freqslongpi, *popt_fullcpt[:-1], tempi*1e-3))
#%%
"""
Acá agarro la primera y busco el valor i_DR que corresponde a la resonancia oscura de interés
With the first one, I look for the value i_DR which corresponds to the dark resonance of interest
"""
curva_ref = CurvasTeoricas[0]
......@@ -201,12 +229,33 @@ plt.figure()
plt.plot(freqslongpi, curva_ref)
plt.plot(freqslongpi[i_DR], curva_ref[i_DR],'o')
#%%
"""
ploteo algunos CPTs teoricos para algunas temperaturas
Plotting some theory cpt curves for some temperatures
"""
plt.plot(freqslongpi, CurvasTeoricas[0])
plt.plot(freqslongpi[i_DR], CurvasTeoricas[0][i_DR],'o',markersize=10)
plt.plot(freqslongpi, CurvasTeoricas[10])
plt.plot(freqslongpi[i_DR], CurvasTeoricas[10][i_DR],'o',markersize=10)
plt.plot(freqslongpi, CurvasTeoricas[20])
plt.plot(freqslongpi[i_DR], CurvasTeoricas[20][i_DR],'o',markersize=10)
plt.xlabel('Detuning (MHz)')
plt.ylabel('Fluorescence')
plt.grid()
#%%
"""
Ahora interpolo los valores teóricos de las profundidades de esas resonancias
y aplico la interpolación a las mediciones para obtener temperaturas.
Luego, grafico las temperaturas en función de los tiempos de calentamiento.
Now I interpolate the theoretical values of the depths of those resonances
and apply the interpolation to the measurements to obtain temperatures.
After that, I plot the temperatures with respect to the heating times
"""
from scipy.interpolate import interp1d
......@@ -235,7 +284,7 @@ plt.ylabel('Temperatura (mK)')
plt.figure()
#plt.plot(Heating_med, Heating_tim, 'o', color='blue')
plt.errorbar(Heating_tim, Heating_med, yerr=ErrorHeating_med, fmt='o', capsize=2, markersize=2)
plt.errorbar([t*1e3 for t in Heating_tim], Heating_med, yerr=ErrorHeating_med, fmt='o', capsize=2, markersize=5)
plt.ylabel('Cuentas de DR medidas')
plt.xlabel('Heating time (s)')
......@@ -250,6 +299,7 @@ p1,p2 = curve_fit(lineal, Heating_tim_ms, Temperaturas_interpoladas)
#%%
"""
Grafico finalmente el plot del heating rate de la trampa
Finally I plot the heating rate of the trap
"""
......@@ -271,6 +321,7 @@ print(f'Heating rate: ({round(p1[0],2)} +- {round(np.sqrt(p2[0][0]),2)}) mK/ms')
#%%
"""
Ahora voy a ver CPT enteras con tiempos de calentamiento distintos.
Now I see whole CPT curves with different heating times
"""
jvec = [3, 4]
......@@ -306,10 +357,14 @@ plt.ylim(1000,2900)
plt.grid()
plt.legend()
#%%
"""
La siguiente curva probablemente no este bien medida ya que inmediatamente
despues, los laseres se deslockearon. La dejo por las dudas.
This curve is probably not well measured...
"""
jvec = [5, 6]
......@@ -330,4 +385,27 @@ plt.legend()
plt.title('Ojo: medicion condicionada por derivas')
#%%
"""
Ahora ploteo 6 curvas cpt para distintos valores de potencia del UV
This is a plot of 6 different cpt curves for 6 different UV powers. I should fit them
to obtain saturation parameters
"""
jvec = [7,8,9,10,11,12]
plt.figure()
for j in jvec:
plt.errorbar([2*f*1e-6 for f in Freqs[j]], Counts[j], yerr=np.sqrt(Counts[j]), fmt='o', capsize=2, markersize=2, label='Without heating')
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('counts')
#plt.ylim(1000,2900)
plt.grid()
#plt.legend()
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analisis/plots/20230421_CPTconmicromocion/CPT_plotter_20230421.py 0 → 100644
View file @ 32f4f986
import h5py
import matplotlib.pyplot as plt
import numpy as np
import sys
import re
import ast
from scipy.optimize import curve_fit
import os
from scipy import interpolate
#Mediciones barriendo angulo del TISA y viendo kicking de resonancias oscuras
#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20220106_CPT_DosLaseres_v08_TISA_DR\Data
os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20230421_CPTconmicromocion/Data/')
CPT_FILES = """000011071-IR_Scan_withcal_optimized
000011072-IR_Scan_withcal_optimized
000011073-IR_Scan_withcal_optimized
"""
def SeeKeys(files):
for i, fname in enumerate(files.split()):
data = h5py.File(fname+'.h5', 'r') # Leo el h5: Recordar que nuestros datos estan en 'datasets'
print(fname)
print(list(data['datasets'].keys()))
print(SeeKeys(CPT_FILES))
#carpeta pc nico labo escritorio:
#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20211101_CPT_DosLaseres_v03\Data
Counts = []
Freqs = []
AmpTisa = []
UVCPTAmp = []
No_measures = []
for i, fname in enumerate(CPT_FILES.split()):
print(str(i) + ' - ' + fname)
#print(fname)
data = h5py.File(fname+'.h5', 'r') # Leo el h5: Recordar que nuestros datos estan en 'datasets'
# Aca hago algo repugnante para poder levantar los strings que dejamos
# que además tenian un error de tipeo al final. Esto no deberá ser necesario
# cuando se solucione el error este del guardado.
Freqs.append(np.array(data['datasets']['IR1_Frequencies']))
Counts.append(np.array(data['datasets']['counts_spectrum']))
#AmpTisa.append(np.array(data['datasets']['TISA_CPT_amp']))
UVCPTAmp.append(np.array(data['datasets']['UV_CPT_amp']))
No_measures.append(np.array(data['datasets']['no_measures']))
#%%
"""
Ploteo la cpt de referencia / plotting the reference CPT
"""
jvec = [0]
plt.figure()
i = 0
for j in jvec:
plt.errorbar([2*f*1e-6 for f in Freqs[j]], Counts[j], yerr=np.sqrt(Counts[j]), fmt='o', capsize=2, markersize=2)
i = i + 1
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('counts')
plt.grid()
plt.legend()
#%%
"""
Intento mergear 0 1 y 2
"""
jvec = [0]
#f1 = 8.8
#f2 = 17.5
f1 = 8
f2 = 18
Freqs0 = Freqs[0]
Freqs1 = [f+f1*1e6 for f in Freqs[1]]
Freqs2 = [f+f2*1e6 for f in Freqs[2]]
plt.figure()
plt.errorbar([2*f*1e-6 for f in Freqs0], Counts[0], yerr=np.sqrt(Counts[0]), fmt='o', capsize=2, markersize=2)
plt.errorbar([2*f*1e-6 for f in Freqs1], Counts[1], yerr=np.sqrt(Counts[1]), fmt='o', capsize=2, markersize=2)
plt.errorbar([2*f*1e-6 for f in Freqs2], Counts[2], yerr=np.sqrt(Counts[2]), fmt='o', capsize=2, markersize=2)
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('counts')
plt.grid()
plt.legend()
Freqs_merged = list(Freqs0) + Freqs1[125:] + Freqs2[126:]
Counts_merged = list(Counts[0]) + list(Counts[1][125:]) + list(Counts[2][126:])
plt.figure()
plt.errorbar([2*f*1e-6 for f in Freqs_merged], Counts_merged, yerr=np.sqrt(np.array(Counts_merged)), fmt='o', capsize=2, markersize=2)
#%%
#from EITfit.MM_eightLevel_2repumps_AnalysisFunctions import PerformExperiment_8levels_MM, GenerateNoisyCPT_MM_fit
from scipy.optimize import curve_fit
"""
Ajusto un cpt para obtener todos los parámetros relevantes primero.
I fit a cpt curve to retrieve all the relevant parameters first.
"""
phidoppler, titadoppler = 0, 90
phirepump, titarepump = 0, 0
phiprobe = 0
titaprobe = 90
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6
alpha = 0
noiseamplitude = 0
T = 0.6e-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 = 33.5e6
beta = 0
drivefreq = 22.135e6
correccion = 12
offsetxpi = 458+correccion
DetDoppler = -2.5 -correccion
FreqsDRpi = [2*f*1e-6-offsetxpi+14 for f in Freqs_merged]
CountsDRpi = Counts_merged
freqslongpi = np.arange(min(FreqsDRpi), max(FreqsDRpi)+FreqsDRpi[1]-FreqsDRpi[0], 0.1*(FreqsDRpi[1]-FreqsDRpi[0]))
#[1.71811842e+04 3.34325038e-17]
def FitEITpi(freqs, SG, SP, BETA, scale, offset):
temp = 1e-3
MeasuredFreq, MeasuredFluo = GenerateNoisyCPT_MM_fit(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, temp, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA, drivefreq, freqs, plot=False, solvemode=1, detpvec=None, noiseamplitude=noiseamplitude)
#scale = 0.3*2e+04
#offset = 0.3*2e+03
FinalFluo = [f*scale + offset for f in MeasuredFluo]
return FinalFluo
popt_fullcpt, pcov_fullcpt = curve_fit(FitEITpi, FreqsDRpi, CountsDRpi, p0=[0.5, 4.5, 1, 1e4, 1e3], bounds=((0, 0, 0, 1e1, 0), (2, 10, 1000, 1e5, 1e5)))
#print(f'Temperatura: ({round(1e3*popt_fullcpt[-1],2)} +- {round(1e3*np.sqrt(pcov_fullcpt[-1][-1]),2)}) mK')
print(popt_fullcpt)
FittedEITpi = FitEITpi(freqslongpi, popt_fullcpt[0], popt_fullcpt[1], 20, popt_fullcpt[3],popt_fullcpt[4])
#FittedEITpi = FitEITpi(freqslongpi, *popt_fullcpt)
"""
Ploteo la CPT de referencia junto al ajuste y a la resonancia oscura de interes
I plot the reference CPT along with the fit to the model and the dark resonance of interest
"""
#%%
#i_DR = 955
DRs = [-31, -22, -16.5, -8]
plt.figure()
plt.errorbar(FreqsDRpi, CountsDRpi, yerr=2*np.sqrt(CountsDRpi), fmt='o', capsize=2, markersize=2)
plt.plot(freqslongpi[:-1], FittedEITpi)
for dr in DRs:
dr = dr+4.7
plt.axvline(dr, color='red',alpha=0.2)
plt.axvline(dr+22.1, color='green',alpha=0.2)
plt.axvline(dr-22.1, color='black',alpha=0.2)
#plt.axvline(DetDoppler-22.1)
#plt.axvline(DetDoppler+22.1)
#plt.plot(freqslongpi[i_DR], FittedEITpi[i_DR],'o', color='red', markersize=12)
plt.xlabel('Detuning (MHz)')
plt.ylabel('Counts')
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
#%%
"""
Simulo CPTs con todos esos parámetros para distintas temperaturas
I simulate CPT curves with all the previous parameters but with varying temperatures
"""
TempVecTeorico = list(np.arange(0.3,1,0.1))+list(np.arange(1, 31, 1))
CurvasTeoricas = []
for tempi in TempVecTeorico:
CurvasTeoricas.append(FitEITpi(freqslongpi, *popt_fullcpt[:-1], tempi*1e-3))
#%%
"""
Acá agarro la primera y busco el valor i_DR que corresponde a la resonancia oscura de interés
With the first one, I look for the value i_DR which corresponds to the dark resonance of interest
"""
curva_ref = CurvasTeoricas[0]
i_DR = 955
plt.figure()
plt.plot(freqslongpi, curva_ref)
plt.plot(freqslongpi[i_DR], curva_ref[i_DR],'o')
#%%
"""
ploteo algunos CPTs teoricos para algunas temperaturas
Plotting some theory cpt curves for some temperatures
"""
plt.plot(freqslongpi, CurvasTeoricas[0])
plt.plot(freqslongpi[i_DR], CurvasTeoricas[0][i_DR],'o',markersize=10)
plt.plot(freqslongpi, CurvasTeoricas[10])
plt.plot(freqslongpi[i_DR], CurvasTeoricas[10][i_DR],'o',markersize=10)
plt.plot(freqslongpi, CurvasTeoricas[20])
plt.plot(freqslongpi[i_DR], CurvasTeoricas[20][i_DR],'o',markersize=10)
plt.xlabel('Detuning (MHz)')
plt.ylabel('Fluorescence')
plt.grid()
#%%
"""
Ahora interpolo los valores teóricos de las profundidades de esas resonancias
y aplico la interpolación a las mediciones para obtener temperaturas.
Luego, grafico las temperaturas en función de los tiempos de calentamiento.
Now I interpolate the theoretical values of the depths of those resonances
and apply the interpolation to the measurements to obtain temperatures.
After that, I plot the temperatures with respect to the heating times
"""
from scipy.interpolate import interp1d
FluosDRTeo = [CurvasTeoricas[k][i_DR] for k in range(len(CurvasTeoricas))]
interpolado = interp1d(FluosDRTeo, TempVecTeorico) #creo funcion que interpola
meas = 0
maxi = 9 #valor maximo, dsp el ion se calento
Heating_tim = Times[meas][:maxi]
Heating_tim_ms = [t*1e3 for t in Heating_tim]
Heating_med = [2*c for c in Counts_heating[meas][:maxi]]
ErrorHeating_med = [2*np.sqrt(c) for c in Counts_heating[meas][:maxi]]
Temperaturas_interpoladas = [float(interpolado(h)) for h in Heating_med]
Error_Temperaturas_interpoladas = [float(interpolado(Heating_med[k]+0.5*ErrorHeating_med[k]))-Temperaturas_interpoladas[k] for k in range(len(Heating_med))]
plt.figure()
plt.plot(FluosDRTeo, [1*t for t in TempVecTeorico], 'o', color='orange')
plt.plot(np.linspace(FluosDRTeo[0],FluosDRTeo[-1],1000), interpolado(np.linspace(FluosDRTeo[0],FluosDRTeo[-1],1000)))
plt.xlabel('Cuentas de DR teoricas')
plt.ylabel('Temperatura (mK)')
plt.figure()
#plt.plot(Heating_med, Heating_tim, 'o', color='blue')
plt.errorbar([t*1e3 for t in Heating_tim], Heating_med, yerr=ErrorHeating_med, fmt='o', capsize=2, markersize=5)
plt.ylabel('Cuentas de DR medidas')
plt.xlabel('Heating time (s)')
def lineal(x,a,b):
return a*x+b
#p1,p2 = curve_fit(lineal, Heating_tim_ms, Temperaturas_interpoladas, sigma=Error_Temperaturas_interpoladas)
p1,p2 = curve_fit(lineal, Heating_tim_ms, Temperaturas_interpoladas)
#%%
"""
Grafico finalmente el plot del heating rate de la trampa
Finally I plot the heating rate of the trap
"""
plt.figure()
#plt.plot(Heating_tim_ms,Temperaturas_interpoladas,'o')
plt.errorbar(Heating_tim_ms,Temperaturas_interpoladas, yerr=np.array(Error_Temperaturas_interpoladas), fmt='o', capsize=2, markersize=7, color='black')
plt.plot(Heating_tim_ms, lineal(np.array(Heating_tim_ms), *p1), color='red')
plt.xlabel('Heating time (ms)', fontname='STIXGeneral', fontsize=15)
plt.ylabel('Temperature (mK)', fontname='STIXGeneral', fontsize=15)
plt.grid()
plt.xticks([0, 5, 10, 15, 20 ,25, 30, 35], fontname='STIXGeneral', fontsize=15)
plt.yticks([0, 5, 10, 15], fontname='STIXGeneral', fontsize=15)
plt.title(f'Heating rate: ({round(p1[0],2)} +- {round(np.sqrt(p2[0][0]),2)}) mK/ms', fontname='STIXGeneral', fontsize=15)
plt.tight_layout()
plt.savefig('Fig_heatingrate.svg')
print(f'Heating rate: ({round(p1[0],2)} +- {round(np.sqrt(p2[0][0]),2)}) mK/ms')
#%%
"""
Ahora voy a ver CPT enteras con tiempos de calentamiento distintos.
Now I see whole CPT curves with different heating times
"""
jvec = [3, 4]
plt.figure()
i = 0
for j in jvec:
if j==4:
plt.errorbar([2*f*1e-6 for f in Freqs[j]], Counts[j], yerr=np.sqrt(Counts[j]), fmt='o', capsize=2, markersize=2, label='Without heating')
elif j==3:
plt.errorbar([2*f*1e-6 for f in Freqs[j]], Counts[j], yerr=np.sqrt(Counts[j]), fmt='o', capsize=2, markersize=2, label='5 ms heating')
i = i + 1
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('counts')
plt.ylim(1000,2900)
plt.grid()
plt.legend()
jvec = [1, 2]
plt.figure()
i = 0
for j in jvec:
if j==2:
plt.errorbar([2*f*1e-6 for f in Freqs[j]], Counts[j], yerr=np.sqrt(Counts[j]), fmt='o', capsize=2, markersize=2, label='Without heating')
elif j==1:
plt.errorbar([2*f*1e-6 for f in Freqs[j]], Counts[j], yerr=np.sqrt(Counts[j]), fmt='o', capsize=2, markersize=2, label='1 ms heating')
i = i + 1
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('counts')
plt.ylim(1000,2900)
plt.grid()
plt.legend()
#%%
"""
La siguiente curva probablemente no este bien medida ya que inmediatamente
despues, los laseres se deslockearon. La dejo por las dudas.
This curve is probably not well measured...
"""
jvec = [5, 6]
plt.figure()
i = 0
for j in jvec:
if j==6:
plt.errorbar([2*f*1e-6 for f in Freqs[j]], Counts[j], yerr=np.sqrt(Counts[j]), fmt='o', capsize=2, markersize=2, label='Without heating')
elif j==5:
plt.errorbar([2*f*1e-6 for f in Freqs[j]], Counts[j], yerr=np.sqrt(Counts[j]), fmt='o', capsize=2, markersize=2, label='10 ms heating')
i = i + 1
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('counts')
plt.ylim(1000,3900)
plt.grid()
plt.legend()
plt.title('Ojo: medicion condicionada por derivas')
#%%
"""
Ahora ploteo 6 curvas cpt para distintos valores de potencia del UV
This is a plot of 6 different cpt curves for 6 different UV powers. I should fit them
to obtain saturation parameters
"""
jvec = [7,8,9,10,11,12]
plt.figure()
for j in jvec:
plt.errorbar([2*f*1e-6 for f in Freqs[j]], Counts[j], yerr=np.sqrt(Counts[j]), fmt='o', capsize=2, markersize=2, label='Without heating')
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('counts')
#plt.ylim(1000,2900)
plt.grid()
#plt.legend()
analisis/plots/20230421_CPTconmicromocion/Data/EITfit/MM_eightLevel_2repumps_AnalysisFunctions.py 0 → 100644
View file @ 32f4f986
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Jul 2 16:30:09 2020
@author: oem
"""
import os
import numpy as np
import time
import matplotlib.pyplot as plt
from scipy.signal import argrelextrema
from EITfit.MM_eightLevel_2repumps_python_scripts import CPTspectrum8levels_MM
import random
from scipy.signal import savgol_filter as sf
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
"""
tinicial = time.time()
ProbeDetuningVectorL, Fluovector = 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)
tfinal = time.time()
#print('Done, Total time: ', round((tfinal-tinicial), 2), "s")
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):
Frequencyvector, 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 Frequencyvector, 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):
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, NoisyFluovector
def SmoothNoisyCPT(Fluo, window=11, poly=3):
SmoothenFluo = sf(Fluo, window, poly)
return SmoothenFluo
analisis/plots/20230421_CPTconmicromocion/Data/EITfit/MM_eightLevel_2repumps_CPTPlotter.py 0 → 100644
View file @ 32f4f986
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Sep 1 17:58:39 2020
@author: nico
"""
import os
import numpy as np
#os.chdir('/home/oem/Nextcloud/G_liaf/liaf-TrampaAnular/Código General/EIT-CPT/Buenos Aires/Experiment Simulations/CPT scripts/Eight Level 2 repumps')
from MM_eightLevel_2repumps_AnalysisFunctions import PerformExperiment_8levels, GenerateNoisyCPT, SmoothNoisyCPT
import matplotlib.pyplot as plt
import time
#from threeLevel_2repumps_AnalysisFunctions import MeasureRelativeFluorescenceFromCPT, IdentifyPolarizationCoincidences, RetrieveAbsoluteCoincidencesBetweenMaps, GetClosestIndex
import seaborn as sns
#C:\Users\Usuario\Nextcloud\G_liaf\liaf-TrampaAnular\Código General\EIT-CPT\Buenos Aires\Experiment Simulations\CPT scripts\Eight Level 2 repumps
ub = 9.27e-24 #magneton de bohr
h = 6.63e-34 #cte de planck
c = (ub/h)*1e-4 #en unidades de MHz/G
u = 32e6 #proportional to the magnetic field of around 5 G
B = (u/(2*np.pi))/c
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6 #anchos de linea de las transiciones
lw = 0.1 #linewidth of the lasers, 0.1 MHz are the actual linewidths of both lasers
DopplerLaserLinewidth, ProbeLaserLinewidth = lw, lw #ancho de linea de los laseres
TempVec = [1e-3, 10e-3] #Temperature vector
alpha = 0 #angle between lasers, which is zero
#Polarization angles (we can keep it fixed in 90)
phidoppler, titadoppler = 0, 90
titaprobe = 90
phiprobe = 0
#este es el desfasaje exp(i.phi) de la componente de la polarizacion y respecto a la x. Con 1 la polarizacion es lineal
CircPr = 1 #this has to do with the circularity of the polarizations and since both are linear it is one
#Simulation parameters
center = -10
span = 120
freqMin = center-span*0.5
freqMax = center+span*0.5
freqStep = 2e-1
noiseamplitude = 0 #i dont know what it is
#parametros de saturacion de los laseres. g: doppler. p: probe (un rebombeo que scanea), r: repump (otro rebombeo fijo)
"""
Good case: sg=0.6, sp=9, DetDoppler=-15
"""
DetDoppler = -15 #nice range: -30 to 0
sgvec = [0.8] #nice range: 0.1 to 10 #g is for green but is the doppler
sp = 9 #nice range: 0.1 to 20 #p is for probe but is the repump
drivefreq=2*np.pi*22.135*1e6 #ignore it
#betavec = np.arange(0,1.1,0.1) #ignore it
betavec=[0] #ignore it
alphavec = [0] #ignore it
fig1, ax1 = plt.subplots()
FrequenciesVec = []
FluorescencesVec = []
for sg in sgvec:
for T in TempVec:
for alpha in alphavec:
for beta in betavec:
Frequencies, Fluorescence = PerformExperiment_8levels(sg, sp, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobe, beta, drivefreq, freqMin, freqMax, freqStep, circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
FrequenciesVec.append(Frequencies)
FluorescencesVec.append(Fluorescence)
ax1.plot(Frequencies, [100*f for f in Fluorescence], label=fr'$\alpha={int(alpha*180/np.pi)}°$')
ax1.set_xlabel('Detuning Rebombeo (MHz)')
ax1.set_ylabel('Fluorescencia (AU)')
ax1.set_title(f'Sdop: {sg}, Spr: {sp}, Temp: {int(T*1e3)} mK')
#ax1.legend()
ax1.grid()
#%%
#Este bloque ajusta a las curvas con un beta de micromocion de 0
from scipy.optimize import curve_fit
def FitEIT_MM(freqs, Temp):
BETA = 0
scale=1
offset=0
Detunings, Fluorescence = PerformExperiment_8levels(sg, sp, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, Temp, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA, drivefreq, freqMin, freqMax, freqStep, circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
ScaledFluo = [f*scale + offset for f in Fluorescence]
return ScaledFluo
TempMedidas = []
FittedEIT_fluosVec = []
for j in range(len(betavec)):
SelectedFluo = FluorescencesVec[j]
SelectedFreqs = FrequenciesVec[j]
popt_mm, pcov_mm = curve_fit(FitEIT_MM, SelectedFreqs, SelectedFluo, p0=[1e-3], bounds=((0), (10e-3)))
TempMedidas.append(1e3*popt_mm[2])
print(popt_mm)
FittedEIT_fluo = FitEIT_MM(SelectedFreqs, *popt_mm)
FittedEIT_fluosVec.append(FittedEIT_fluo)
plt.figure()
plt.plot(SelectedFreqs, SelectedFluo, 'o')
plt.plot(SelectedFreqs, FittedEIT_fluo)
plt.figure()
for i in range(len(FluorescencesVec)):
plt.plot(SelectedFreqs, FluorescencesVec[i], 'o', markersize=3)
plt.plot(SelectedFreqs, FittedEIT_fluosVec[i])
plt.figure()
plt.plot(betavec, TempMedidas, 'o', markersize=10)
plt.xlabel('Beta')
plt.ylabel('Temperatura medida (mK)')
plt.axhline(T*1e3, label='Temperatura real', linestyle='--', color='red')
plt.legend()
plt.grid()
\ No newline at end of file
analisis/plots/20230421_CPTconmicromocion/Data/EITfit/MM_eightLevel_2repumps_python_scripts.py 0 → 100644
View file @ 32f4f986
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Apr 7 22:30:01 2020
@author: nico
"""
#ESTE CODIGO ES EL PRINCIPAL PARA PLOTEAR CPT TEORICOS
import numpy as np
import time
import matplotlib.pyplot as plt
from scipy.signal import argrelextrema
"""
Scripts para el calculo de la curva CPT
"""
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)
return H0
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
def LtempCalculus(beta, drivefreq, forma=1):
Hint = np.zeros((8, 8), dtype=np.complex_)
ampg=beta*drivefreq
ampr=beta*drivefreq
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):
for k in range(8):
for j in range(8):
Ltemp[r*8+q][k*8+j] = (-1j)*(Hint[r,k]*int(j==q) - Hint[j,q]*int(r==k))
"""
"""
for r in range(8):
for q in range(8):
for k in range(8):
for j in range(8):
if r==k and j==q:
Ltemp[r*8+q][k*8+j] = (-1j)*(Hint[r,k] - Hint[j,q])
"""
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])
Ltemp = (-1j)*(np.kron(Hint, deltaKro) - np.kron(deltaKro, Hint))
Omega = np.zeros((64, 64), dtype=np.complex_)
for i in range(64):
Omega[i, i] = (1j)*drivefreq
return np.matrix(Ltemp), np.matrix(Omega)
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))
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))
L1 = 0.5*np.matrix(Ltemp)*(np.matrix(Sp) + np.matrix(Sm))
return L1
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
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.matrix(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
#M[36, 45] = lwp
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
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/(2*mcalcio))
return gammaD
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 + db**2)
lwp = np.sqrt(lwp**2 + 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()
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)
#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
"""
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, 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)]))
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
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)
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.)')
analisis/plots/20230421_CPTconmicromocion/Data/EITfit/threeLevel_2repumps_AnalysisFunctions.py 0 → 100644
View file @ 32f4f986
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Jul 2 16:30:09 2020
@author: oem
"""
import os
import numpy as np
import time
import matplotlib.pyplot as plt
from scipy.signal import argrelextrema
from EITfit.threeLevel_2repumps_linealpol_python_scripts import CPTspectrum8levels, CPTspectrum8levels_fixedRabi
import random
from scipy.signal import savgol_filter as sf
def CalculoTeoricoDarkResonances_8levels(u, titadoppler, detuningdoppler, detuningrepump):
if titadoppler==0:
NegativeDR = [(-7/5)*u, (-3/5)*u, (-1/5)*u, (1/5)*u, (3/5)*u, (7/5)*u]
elif titadoppler==90:
NegativeDR = [(-11/5)*u, (-7/5)*u, (-3/5)*u, (3/5)*u, (7/5)*u, (11/5)*u]
else:
NegativeDR = [(-11/5)*u, (-7/5)*u, (-3/5)*u, (-1/5)*u, (1/5)*u, (3/5)*u, (7/5)*u, (11/5)*u]
PositiveDR = [(-8/5)*u, (-4/5)*u, 0, (4/5)*u, (8/5)*u]
return [detuningdoppler + dr for dr in NegativeDR], [detuningrepump + dr for dr in PositiveDR]
def GetClosestIndex(Vector, value, tolerance=1e-3):
i = 0
while i<len(Vector):
if abs(Vector[i] - value) < tolerance:
return i
else:
i = i + 1
return GetClosestIndex(Vector, value, tolerance=2*tolerance)
def FindDRFrequencies(Freq, Fluo, TeoDR, entorno=3):
"""
Busca los indices y la frecuencia de los minimos en un entorno cercano al de la DR.
Si no encuentra, devuelve el valor teórico.
"""
IndiceDRteo1, IndiceEntornoinicialDRteo1, IndiceEntornofinalDRteo1 = GetClosestIndex(Freq, TeoDR[0]), GetClosestIndex(Freq, TeoDR[0]-entorno), GetClosestIndex(Freq, TeoDR[0]+entorno)
IndiceDRteo2, IndiceEntornoinicialDRteo2, IndiceEntornofinalDRteo2 = GetClosestIndex(Freq, TeoDR[1]), GetClosestIndex(Freq, TeoDR[1]-entorno), GetClosestIndex(Freq, TeoDR[1]+entorno)
IndiceDRteo3, IndiceEntornoinicialDRteo3, IndiceEntornofinalDRteo3 = GetClosestIndex(Freq, TeoDR[2]), GetClosestIndex(Freq, TeoDR[2]-entorno), GetClosestIndex(Freq, TeoDR[2]+entorno)
IndiceDRteo4, IndiceEntornoinicialDRteo4, IndiceEntornofinalDRteo4 = GetClosestIndex(Freq, TeoDR[3]), GetClosestIndex(Freq, TeoDR[3]-entorno), GetClosestIndex(Freq, TeoDR[3]+entorno)
IndiceDRteo5, IndiceEntornoinicialDRteo5, IndiceEntornofinalDRteo5 = GetClosestIndex(Freq, TeoDR[4]), GetClosestIndex(Freq, TeoDR[4]-entorno), GetClosestIndex(Freq, TeoDR[4]+entorno)
IndiceDRteo6, IndiceEntornoinicialDRteo6, IndiceEntornofinalDRteo6 = GetClosestIndex(Freq, TeoDR[5]), GetClosestIndex(Freq, TeoDR[5]-entorno), GetClosestIndex(Freq, TeoDR[5]+entorno)
EntornoFreqDR1, EntornoFreqDR2 = Freq[IndiceEntornoinicialDRteo1:IndiceEntornofinalDRteo1], Freq[IndiceEntornoinicialDRteo2:IndiceEntornofinalDRteo2]
EntornoFreqDR3, EntornoFreqDR4 = Freq[IndiceEntornoinicialDRteo3:IndiceEntornofinalDRteo3], Freq[IndiceEntornoinicialDRteo4:IndiceEntornofinalDRteo4]
EntornoFreqDR5, EntornoFreqDR6 = Freq[IndiceEntornoinicialDRteo5:IndiceEntornofinalDRteo5], Freq[IndiceEntornoinicialDRteo6:IndiceEntornofinalDRteo6]
EntornoFluoDR1, EntornoFluoDR2 = Fluo[IndiceEntornoinicialDRteo1:IndiceEntornofinalDRteo1], Fluo[IndiceEntornoinicialDRteo2:IndiceEntornofinalDRteo2]
EntornoFluoDR3, EntornoFluoDR4 = Fluo[IndiceEntornoinicialDRteo3:IndiceEntornofinalDRteo3], Fluo[IndiceEntornoinicialDRteo4:IndiceEntornofinalDRteo4]
EntornoFluoDR5, EntornoFluoDR6 = Fluo[IndiceEntornoinicialDRteo5:IndiceEntornofinalDRteo5], Fluo[IndiceEntornoinicialDRteo6:IndiceEntornofinalDRteo6]
IndiceFluoMinimaEntorno1, IndiceFluoMinimaEntorno2 = argrelextrema(np.array(EntornoFluoDR1), np.less)[0], argrelextrema(np.array(EntornoFluoDR2), np.less)[0]
IndiceFluoMinimaEntorno3, IndiceFluoMinimaEntorno4 = argrelextrema(np.array(EntornoFluoDR3), np.less)[0], argrelextrema(np.array(EntornoFluoDR4), np.less)[0]
IndiceFluoMinimaEntorno5, IndiceFluoMinimaEntorno6 = argrelextrema(np.array(EntornoFluoDR5), np.less)[0], argrelextrema(np.array(EntornoFluoDR6), np.less)[0]
try:
FreqDR1 = EntornoFreqDR1[int(IndiceFluoMinimaEntorno1)]
IndiceDR1 = GetClosestIndex(Freq, FreqDR1)
except:
FreqDR1 = TeoDR[0]
IndiceDR1 = IndiceDRteo1
try:
FreqDR2 = EntornoFreqDR2[int(IndiceFluoMinimaEntorno2)]
IndiceDR2 = GetClosestIndex(Freq, FreqDR2)
except:
FreqDR2 = TeoDR[1]
IndiceDR2 = IndiceDRteo2
try:
FreqDR3 = EntornoFreqDR3[int(IndiceFluoMinimaEntorno3)]
IndiceDR3 = GetClosestIndex(Freq, FreqDR3)
except:
FreqDR3 = TeoDR[2]
IndiceDR3 = IndiceDRteo3
try:
FreqDR4 = EntornoFreqDR4[int(IndiceFluoMinimaEntorno4)]
IndiceDR4 = GetClosestIndex(Freq, FreqDR4)
except:
FreqDR4 = TeoDR[3]
IndiceDR4 = IndiceDRteo4
try:
FreqDR5 = EntornoFreqDR5[int(IndiceFluoMinimaEntorno5)]
IndiceDR5 = GetClosestIndex(Freq, FreqDR5)
except:
FreqDR5 = TeoDR[4]
IndiceDR5 = IndiceDRteo5
try:
FreqDR6 = EntornoFreqDR6[int(IndiceFluoMinimaEntorno6)]
IndiceDR6 = GetClosestIndex(Freq, FreqDR6)
except:
FreqDR6 = TeoDR[5]
IndiceDR6 = IndiceDRteo6
return [IndiceDR1, IndiceDR2, IndiceDR3, IndiceDR4, IndiceDR5, IndiceDR6], [FreqDR1, FreqDR2, FreqDR3, FreqDR4, FreqDR5, FreqDR6]
def FindRelativeFluorescencesOfDR(Freq, Fluo, IndicesDR, detuningdoppler, NormalizationCriterium=1, frecuenciareferenciacriterioasintotico=-100, getindices=False):
"""
Toma los indices donde estan las DR y evalua su fluorescencia. Esos indices son minimos locales en un entorno
cercano a las DR teoricas y, si no hay ningun minimo, toma la teorica.
Luego, hace el cociente de esa fluorescencia y un factor de normalización segun NormalizationCriterium:
1: Devuelve la fluorescencia absoluta de los minimos
2: Devuelve el cociente entre la fluorescencia del minimo y un valor medio entre dos puntos lejanos, como si no
hubiera una resonancia oscura y hubiera una recta. Ese valor esta a DistanciaFrecuenciaCociente del detuning del azul (el punto medio entre las dos DR en este caso)
3: Devuelve el cociente entre la fluorescencia del minimo y el valor a -100 MHz (si se hizo de -100 a 100),
o el valor limite por izquierda de la curva
4: Deuelve el cociente entre la fluorescencia del minimo y el valor de fluorescencia a detuning 0 MHz
"""
IndiceDR1, IndiceDR2, IndiceDR3, IndiceDR4, IndiceDR5, IndiceDR6 = IndicesDR[0], IndicesDR[1], IndicesDR[2], IndicesDR[3], IndicesDR[4], IndicesDR[5]
FluorescenceOfMinimums = [Fluo[IndiceDR1], Fluo[IndiceDR2], Fluo[IndiceDR3], Fluo[IndiceDR4], Fluo[IndiceDR5], Fluo[IndiceDR6]]
FrequencyOfMinimums = [Freq[IndiceDR1], Freq[IndiceDR2], Freq[IndiceDR3], Freq[IndiceDR4], Freq[IndiceDR5], Freq[IndiceDR6]]
DistanciaFrecuenciaCociente = 25
if NormalizationCriterium==0:
print('che')
return FrequencyOfMinimums, FluorescenceOfMinimums
if NormalizationCriterium==1:
Fluorescenciacerodetuning = Fluo[GetClosestIndex(Freq, 0)]
Fluorescenciaasintotica = Fluo[GetClosestIndex(Freq, frecuenciareferenciacriterioasintotico)]
return FrequencyOfMinimums, np.array([Fluorescenciacerodetuning/Fluorescenciaasintotica, Fluorescenciacerodetuning/Fluorescenciaasintotica, Fluorescenciacerodetuning/Fluorescenciaasintotica, Fluorescenciacerodetuning/Fluorescenciaasintotica, Fluorescenciacerodetuning/Fluorescenciaasintotica, Fluorescenciacerodetuning/Fluorescenciaasintotica])
if NormalizationCriterium==2:
k = 0
while k < len(Freq):
if Freq[k] < detuningdoppler-DistanciaFrecuenciaCociente + 2 and Freq[k] > detuningdoppler-DistanciaFrecuenciaCociente - 2:
FluoIzquierda = Fluo[k]
indiceizquierda = k
print('Izq:', Freq[k])
break
else:
k = k + 1
l = 0
while l < len(Freq):
if Freq[l] < detuningdoppler+DistanciaFrecuenciaCociente + 2 and Freq[l] > detuningdoppler+DistanciaFrecuenciaCociente - 2:
FluoDerecha = Fluo[l]
indicederecha = l
print('Der: ', Freq[l])
break
else:
l = l + 1
FluoNormDivisor = 0.5*(FluoDerecha+FluoIzquierda)
print(FluoNormDivisor)
if NormalizationCriterium==3:
#asintotico
FluoNormDivisor = Fluo[GetClosestIndex(Freq, frecuenciareferenciacriterioasintotico)]
if NormalizationCriterium==4:
#este te tira la fluorescencia de detuning 0
FluoNormDivisor = Fluo[GetClosestIndex(Freq, 0)]
RelativeFluorescenceOfMinimums = np.array([Fluore/FluoNormDivisor for Fluore in FluorescenceOfMinimums])
print('Esto: ', RelativeFluorescenceOfMinimums)
if NormalizationCriterium==2 and getindices==True:
return FrequencyOfMinimums, RelativeFluorescenceOfMinimums, indiceizquierda, indicederecha
return FrequencyOfMinimums, RelativeFluorescenceOfMinimums
def GetFinalMaps(MapasDR1, MapasDR2, MapasDR3, MapasDR4, MapasDR5, MapasDR6):
"""
Nota: esto vale para polarizacion del 397 sigma+ + sigma-. Sino hay que cambiar los coeficientes.
La estructura es:
MapasDRi = [MapaMedido_criterio1_DRi, MapaMedido_criterio2_DRi, MapaMedido_criterio3_DRi, MapaMedido_criterio4_DRi]
"""
Mapa1 = MapasDR1[0]
Mapa2pi = np.sqrt(3)*(MapasDR2[1] + MapasDR5[1])
Mapa2smas = np.sqrt(12/2)*MapasDR3[1] + (2/np.sqrt(2))*MapasDR6[1]
Mapa2smenos = (2/np.sqrt(2))*MapasDR1[1] + np.sqrt(12/2)*MapasDR4[1]
Mapa3pi = np.sqrt(3)*(MapasDR2[2] + MapasDR5[2])
Mapa3smas = np.sqrt(12/2)*MapasDR3[2] + (2/np.sqrt(2))*MapasDR6[2]
Mapa3smenos = (2/np.sqrt(2))*MapasDR1[2] + np.sqrt(12/2)*MapasDR4[2]
return Mapa1, [Mapa2pi, Mapa2smas, Mapa2smenos], [Mapa3pi, Mapa3smas, Mapa3smenos]
def CombinateDRwithCG(RelMinMedido1, RelMinMedido2, RelMinMedido3, RelMinMedido4):
Fluo1 = RelMinMedido1[0]
Fluo2pi = np.sqrt(3)*(RelMinMedido2[1] + RelMinMedido2[4])
Fluo2smas = np.sqrt(12/2)*RelMinMedido2[2] + (2/np.sqrt(2))*RelMinMedido2[5]
Fluo2smenos = (2/np.sqrt(2))*RelMinMedido2[0] + np.sqrt(12/2)*RelMinMedido2[3]
Fluo3pi = np.sqrt(3)*(RelMinMedido3[1] + RelMinMedido3[4])
Fluo3smas = np.sqrt(12/2)*RelMinMedido3[2] + (2/np.sqrt(2))*RelMinMedido3[5]
Fluo3smenos = (2/np.sqrt(2))*RelMinMedido3[0] + np.sqrt(12/2)*RelMinMedido3[3]
return Fluo1, [Fluo2pi, Fluo2smas, Fluo2smenos], [Fluo3pi, Fluo3smas, Fluo3smenos]
def IdentifyPolarizationCoincidences(theoricalmap, target, tolerance=1e-1):
"""
Busca en un mapa 2D la presencia de un valor target (medido) con tolerancia tolerance.
Si lo encuentra, pone un 1. Sino, un 0. Al plotear con pcolor se verá
en blanco la zona donde el valor medido se puede hallar.
"""
CoincidenceMatrix = np.zeros((len(theoricalmap), len(theoricalmap[0])))
i = 0
while i<len(theoricalmap):
j = 0
while j<len(theoricalmap[0]):
if abs(theoricalmap[i][j]-target) < tolerance:
CoincidenceMatrix[i][j] = 1
j=j+1
i=i+1
return CoincidenceMatrix
def RetrieveAbsoluteCoincidencesBetweenMaps(MapsVectors):
MatrixSum = np.zeros((len(MapsVectors[0]), len(MapsVectors[0][0])))
AbsoluteCoincidencesMatrix = np.zeros((len(MapsVectors[0]), len(MapsVectors[0][0])))
MatrixMapsVectors = []
for i in range(len(MapsVectors)):
MatrixMapsVectors.append(np.matrix(MapsVectors[i]))
for i in range(len(MatrixMapsVectors)):
MatrixSum = MatrixSum + MatrixMapsVectors[i]
MaxNumberOfCoincidences = np.max(MatrixSum)
ListMatrixSum = [list(i) for i in list(np.array(MatrixSum))]
for i in range(len(ListMatrixSum)):
for j in range(len(ListMatrixSum[0])):
if ListMatrixSum[i][j] == MaxNumberOfCoincidences:
AbsoluteCoincidencesMatrix[i][j] = 1
return AbsoluteCoincidencesMatrix, MaxNumberOfCoincidences
def MeasureMeanValueOfEstimatedArea(AbsoluteCoincidencesMap, X, Y):
NonZeroIndices = np.nonzero(AbsoluteCoincidencesMap)
Xsum = 0
Xvec = []
Ysum = 0
Yvec = []
N = len(NonZeroIndices[0])
for i in range(N):
Xsum = Xsum + X[NonZeroIndices[1][i]]
Xvec.append(X[NonZeroIndices[1][i]])
Ysum = Ysum + Y[NonZeroIndices[0][i]]
Yvec.append(Y[NonZeroIndices[0][i]])
Xaverage = Xsum/N
Yaverage = Ysum/N
Xspread = np.std(Xvec)
Yspread = np.std(Yvec)
return Xaverage, Yaverage, N, Xspread, Yspread
def MeasureRelativeFluorescenceFromCPT(Freq, Fluo, u, titadoppler, detuningrepump, detuningdoppler, frefasint=-100, entorno=3):
ResonanciasTeoricas, ResonanciasPositivas = CalculoTeoricoDarkResonances_8levels(u, titadoppler, detuningdoppler, detuningrepump)
IndicesDR, FreqsDR = FindDRFrequencies(Freq, Fluo, ResonanciasTeoricas, entorno=entorno)
FrequencyOfMinimums, RelativeFluorescenceOfMinimums0 = FindRelativeFluorescencesOfDR(Freq, Fluo, IndicesDR, detuningdoppler, NormalizationCriterium=0, frecuenciareferenciacriterioasintotico=frefasint)
FrequencyOfMinimums, RelativeFluorescenceOfMinimums1 = FindRelativeFluorescencesOfDR(Freq, Fluo, IndicesDR, detuningdoppler, NormalizationCriterium=1, frecuenciareferenciacriterioasintotico=frefasint)
FrequencyOfMinimums, RelativeFluorescenceOfMinimums2, indiceizquierda, indicederecha = FindRelativeFluorescencesOfDR(Freq, Fluo, IndicesDR, detuningdoppler, NormalizationCriterium=2, frecuenciareferenciacriterioasintotico=frefasint, getindices=True)
FrequencyOfMinimums, RelativeFluorescenceOfMinimums3 = FindRelativeFluorescencesOfDR(Freq, Fluo, IndicesDR, detuningdoppler, NormalizationCriterium=3, frecuenciareferenciacriterioasintotico=frefasint)
FrequencyOfMinimums, RelativeFluorescenceOfMinimums4 = FindRelativeFluorescencesOfDR(Freq, Fluo, IndicesDR, detuningdoppler, NormalizationCriterium=4, frecuenciareferenciacriterioasintotico=frefasint)
print('hola')
print(RelativeFluorescenceOfMinimums0)
return RelativeFluorescenceOfMinimums0, RelativeFluorescenceOfMinimums1, RelativeFluorescenceOfMinimums2, RelativeFluorescenceOfMinimums3, RelativeFluorescenceOfMinimums4, IndicesDR, [indiceizquierda, indicederecha]
def GenerateNoisyCPT(rabG, rabR, rabP, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, phirepump, titarepump, freqMin, freqMax, freqStep, plot=False, solvemode=1, detpvec=None, noiseamplitude=0.001):
Frequencyvector, Fluovector = PerformExperiment_8levels(rabG, rabR, rabP, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, phirepump, titarepump, freqMin, freqMax, freqStep, plot=False, solvemode=1, detpvec=None)
NoisyFluovector = [fluo+noiseamplitude*(2*random.random()-1) for fluo in Fluovector]
return Frequencyvector, NoisyFluovector
def GenerateNoisyCPT_fixedRabi(sg, sr, sp, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, phirepump, titarepump, freqMin, freqMax, freqStep, plot=False, solvemode=1, detpvec=None, noiseamplitude=0.001):
Frequencyvector, Fluovector = PerformExperiment_8levels_fixedRabi(sg, sr, sp, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, phirepump, titarepump, freqMin, freqMax, freqStep, plot=False, solvemode=1, detpvec=None)
NoisyFluovector = [fluo+noiseamplitude*(2*random.random()-1) for fluo in Fluovector]
return Frequencyvector, NoisyFluovector
def GenerateNoisyCPT_fit(sg, sr, sp, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, phirepump, titarepump, freqs, plot=False, solvemode=1, detpvec=None, noiseamplitude=0.001):
Frequencyvector, Fluovector = PerformExperiment_8levels_fixedRabi(sg, sr, sp, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, phirepump, titarepump, min(freqs), max(freqs) + freqs[1]-freqs[0], freqs[1]-freqs[0], plot=False, solvemode=1, detpvec=None)
NoisyFluovector = [fluo+noiseamplitude*(2*random.random()-1) for fluo in Fluovector]
return Frequencyvector, NoisyFluovector
def AddNoiseToCPT(Fluo, noisefactor):
return [f+noisefactor*(2*random.random()-1) for f in Fluo]
def SmoothNoisyCPT(Fluo, window=11, poly=3):
SmoothenFluo = sf(Fluo, window, poly)
return SmoothenFluo
def GetMinimaInfo(Freq, Fluo, u, titadoppler, detuningdoppler, detuningrepump, MinimumCriterium=2, NormalizationCriterium=1):
"""
FUNCION VIEJA
Esta funcion devuelve valores de frecuencias y fluorescencia relativa de los minimos.
Minimumcriterion:
1: Saca los minimos con funcion argelextrema
2: Directamente con las frecuencias teoricas busca las fluorescencias
Normalizationcriterium:
1: Devuelve la fluorescencia absoluta de los minimos
2: Devuelve el cociente entre la fluorescencia del minimo y un valor medio entre dos puntos lejanos, como si no
hubiera una resonancia oscura y hubiera una recta. Ese valor esta a DistanciaFrecuenciaCociente del detuning del azul (el punto medio entre las dos DR en este caso)
3: Devuelve el cociente entre la fluorescencia del minimo y el valor a -100 MHz (si se hizo de -100 a 100),
o el valor limite por izquierda de la curva
"""
FluorescenceOfMaximum = max(Fluo)
FrequencyOfMaximum = Freq[Fluo.index(FluorescenceOfMaximum)]
#criterio para encontrar los minimos
#criterio usando minimos de la fluorescencia calculados con la curva
if MinimumCriterium == 1:
LocationOfMinimums = argrelextrema(np.array(Fluo), np.less)[0]
FluorescenceOfMinimums = np.array([Fluo[i] for i in LocationOfMinimums])
FrequencyOfMinimums = np.array([Freq[j] for j in LocationOfMinimums])
#criterio con las DR teoricas
if MinimumCriterium == 2:
FrecuenciasDRTeoricas, FrecuenciasDRTeoricasPositivas = [darkresonance for darkresonance in CalculoTeoricoDarkResonances_8levels(u, titadoppler, detuningdoppler, detuningrepump)[0]]
FrequencyOfMinimums = []
FluorescenceOfMinimums =[]
print(FrecuenciasDRTeoricas)
k=0
ventanita = 0.001
while k < len(Freq):
if Freq[k] < FrecuenciasDRTeoricas[0] + ventanita and Freq[k] > FrecuenciasDRTeoricas[0] - ventanita:
FrequencyOfMinimums.append(Freq[k])
FluorescenceOfMinimums.append(Fluo[k])
elif Freq[k] < FrecuenciasDRTeoricas[1] + ventanita and Freq[k] > FrecuenciasDRTeoricas[1] - ventanita:
FrequencyOfMinimums.append(Freq[k])
FluorescenceOfMinimums.append(Fluo[k])
elif Freq[k] < FrecuenciasDRTeoricas[2] + ventanita and Freq[k] > FrecuenciasDRTeoricas[2] - ventanita:
FrequencyOfMinimums.append(Freq[k])
FluorescenceOfMinimums.append(Fluo[k])
elif Freq[k] < FrecuenciasDRTeoricas[3] + ventanita and Freq[k] > FrecuenciasDRTeoricas[3] - ventanita:
FrequencyOfMinimums.append(Freq[k])
FluorescenceOfMinimums.append(Fluo[k])
elif Freq[k] < FrecuenciasDRTeoricas[4] + ventanita and Freq[k] > FrecuenciasDRTeoricas[4] - ventanita:
FrequencyOfMinimums.append(Freq[k])
FluorescenceOfMinimums.append(Fluo[k])
elif Freq[k] < FrecuenciasDRTeoricas[5] + ventanita and Freq[k] > FrecuenciasDRTeoricas[5] - ventanita:
FrequencyOfMinimums.append(Freq[k])
FluorescenceOfMinimums.append(Fluo[k])
k = k + 1
print(FrequencyOfMinimums)
if len(FrequencyOfMinimums) != len(FrecuenciasDRTeoricas):
print('NO ANDA BIEN ESTO PAPI, revisalo')
#esto es para establecer un criterio para la fluorescencia relativa
DistanciaFrecuenciaCociente = 15
if NormalizationCriterium==1:
FluoNormDivisor = 1
if NormalizationCriterium==2:
k = 0
while k < len(Freq):
if Freq[k] < detuningdoppler-DistanciaFrecuenciaCociente + 2 and Freq[k] > detuningdoppler-DistanciaFrecuenciaCociente - 2:
FluoIzquierda = Fluo[k]
print('Izq:', Freq[k])
break
else:
k = k + 1
l = 0
while l < len(Freq):
if Freq[l] < detuningdoppler+DistanciaFrecuenciaCociente + 2 and Freq[l] > detuningdoppler+DistanciaFrecuenciaCociente - 2:
FluoDerecha = Fluo[l]
print('Der: ', Freq[l])
break
else:
l = l + 1
FluoNormDivisor = 0.5*(FluoDerecha+FluoIzquierda)
print(FluoNormDivisor)
if NormalizationCriterium==3:
FluoNormDivisor = Fluo[0]
RelativeFluorescenceOfMinimums = np.array([Fluore/FluoNormDivisor for Fluore in FluorescenceOfMinimums])
return FrequencyOfMinimums, RelativeFluorescenceOfMinimums
def GetPlotsofFluovsAngle_8levels(FrequencyOfMinimumsVector, RelativeFluorescenceOfMinimumsVector, u, titadoppler, detuningdoppler, detuningrepump, ventana=0.25, taketheoricalDR=False):
#primero buscamos las frecuencias referencia que se parezcan a las 6:
i = 0
FrecuenciasReferenciaBase = FrequencyOfMinimumsVector[0]
FrecuenciasDRTeoricas = [darkresonance for darkresonance in CalculoTeoricoDarkResonances_8levels(u, titadoppler, detuningdoppler, detuningrepump)[0]]
while i < len(FrequencyOfMinimumsVector):
if len(FrequencyOfMinimumsVector[i])==len(FrecuenciasDRTeoricas):
FrecuenciasReferenciaBase = FrequencyOfMinimumsVector[i]
print('Cool! Taking the DR identified with any curve')
break
else:
i = i + 1
if i==len(FrequencyOfMinimumsVector):
print('No hay ningun plot con 5 resonancias oscuras. Tomo las teóricas')
FrecuenciasReferenciaBase = FrecuenciasDRTeoricas
if taketheoricalDR:
FrecuenciasReferenciaBase = FrecuenciasDRTeoricas
Ventana = abs(ventana*(FrecuenciasReferenciaBase[1] - FrecuenciasReferenciaBase[0])) #ventana separadora de resonancias
print('Ventana = ', Ventana)
DarkResonance1Frequency = []
DarkResonance1Fluorescence = []
DarkResonance2Frequency = []
DarkResonance2Fluorescence = []
DarkResonance3Frequency = []
DarkResonance3Fluorescence = []
DarkResonance4Frequency = []
DarkResonance4Fluorescence = []
DarkResonance5Frequency = []
DarkResonance5Fluorescence = []
DarkResonance6Frequency = []
DarkResonance6Fluorescence = []
i = 0
while i < len(FrequencyOfMinimumsVector):
j = 0
FrecuenciasReferencia = [i for i in FrecuenciasReferenciaBase]
while j < len(FrequencyOfMinimumsVector[i]):
if abs(FrequencyOfMinimumsVector[i][j]) < (abs(FrecuenciasReferencia[0])+Ventana) and abs(FrequencyOfMinimumsVector[i][j]) >= (abs(FrecuenciasReferencia[0])-Ventana):
DarkResonance1Frequency.append(FrequencyOfMinimumsVector[i][j])
DarkResonance1Fluorescence.append(RelativeFluorescenceOfMinimumsVector[i][j])
FrecuenciasReferencia[0] = 0
elif abs(FrequencyOfMinimumsVector[i][j]) < (abs(FrecuenciasReferencia[1])+Ventana) and abs(FrequencyOfMinimumsVector[i][j]) >= (abs(FrecuenciasReferencia[1])-Ventana):
DarkResonance2Frequency.append(FrequencyOfMinimumsVector[i][j])
DarkResonance2Fluorescence.append(RelativeFluorescenceOfMinimumsVector[i][j])
FrecuenciasReferencia[1] = 0
elif abs(FrequencyOfMinimumsVector[i][j]) < (abs(FrecuenciasReferencia[2])+Ventana) and abs(FrequencyOfMinimumsVector[i][j]) >= (abs(FrecuenciasReferencia[2])-Ventana):
DarkResonance3Frequency.append(FrequencyOfMinimumsVector[i][j])
DarkResonance3Fluorescence.append(RelativeFluorescenceOfMinimumsVector[i][j])
FrecuenciasReferencia[2] = 0
elif abs(FrequencyOfMinimumsVector[i][j]) < (abs(FrecuenciasReferencia[3])+Ventana) and abs(FrequencyOfMinimumsVector[i][j]) >= (abs(FrecuenciasReferencia[3])-Ventana):
DarkResonance4Frequency.append(FrequencyOfMinimumsVector[i][j])
DarkResonance4Fluorescence.append(RelativeFluorescenceOfMinimumsVector[i][j])
FrecuenciasReferencia[3] = 0
elif abs(FrequencyOfMinimumsVector[i][j]) < (abs(FrecuenciasReferencia[4])+Ventana) and abs(FrequencyOfMinimumsVector[i][j]) >= (abs(FrecuenciasReferencia[4])-Ventana):
DarkResonance5Frequency.append(FrequencyOfMinimumsVector[i][j])
DarkResonance5Fluorescence.append(RelativeFluorescenceOfMinimumsVector[i][j])
FrecuenciasReferencia[4] = 0
elif abs(FrequencyOfMinimumsVector[i][j]) < (abs(FrecuenciasReferencia[5])+Ventana) and abs(FrequencyOfMinimumsVector[i][j]) >= (abs(FrecuenciasReferencia[5])-Ventana):
DarkResonance6Frequency.append(FrequencyOfMinimumsVector[i][j])
DarkResonance6Fluorescence.append(RelativeFluorescenceOfMinimumsVector[i][j])
FrecuenciasReferencia[5] = 0
else:
#print('Algo anduvo mal, por ahi tenes que cambiar la ventana che')
pass
j = j + 1
if np.count_nonzero(FrecuenciasReferencia) > 0:
if FrecuenciasReferencia[0] != 0:
DarkResonance1Frequency.append(FrecuenciasReferencia[0])
DarkResonance1Fluorescence.append()
if FrecuenciasReferencia[1] != 0:
DarkResonance2Frequency.append(FrecuenciasReferencia[1])
DarkResonance2Fluorescence.append(0)
if FrecuenciasReferencia[2] != 0:
DarkResonance3Frequency.append(FrecuenciasReferencia[2])
DarkResonance3Fluorescence.append(0)
if FrecuenciasReferencia[3] != 0:
DarkResonance4Frequency.append(FrecuenciasReferencia[3])
DarkResonance4Fluorescence.append(0)
if FrecuenciasReferencia[4] != 0:
DarkResonance5Frequency.append(FrecuenciasReferencia[4])
DarkResonance5Fluorescence.append(0)
if FrecuenciasReferencia[5] != 0:
DarkResonance6Frequency.append(FrecuenciasReferencia[5])
DarkResonance6Fluorescence.append(0)
i = i + 1
return DarkResonance1Frequency, DarkResonance1Fluorescence, DarkResonance2Frequency, DarkResonance2Fluorescence, DarkResonance3Frequency, DarkResonance3Fluorescence, DarkResonance4Frequency, DarkResonance4Fluorescence, DarkResonance5Frequency, DarkResonance5Fluorescence, DarkResonance6Frequency, DarkResonance6Fluorescence, FrecuenciasReferenciaBase
def PerformExperiment_8levels(rabG, rabR, rabP, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, phirepump, titarepump, freqMin, freqMax, freqStep, plot=False, solvemode=1, detpvec=None):
"""
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
"""
Fluovectors = []
for titaprobe in titaprobeVec:
tinicial = time.time()
ProbeDetuningVectorL, Fluovector = CPTspectrum8levels(rabG, rabR, rabP, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobe, phirepump, titarepump, freqMin=freqMin, freqMax=freqMax, freqStep=freqStep, plot=False, solvemode=1)
tfinal = time.time()
print('Done angle ', titarepump, ' Total time: ', round((tfinal-tinicial), 2), "s")
if plot:
plt.figure()
plt.xlabel('Repump detuning (MHz')
plt.ylabel('Fluorescence (A.U.)')
plt.plot(ProbeDetuningVectorL, Fluovector, label=str(titarepump)+'º tita repump, T: ' + str(T*1e3) + ' mK')
plt.legend()
Fluovectors.append(Fluovector)
if len(titaprobeVec) == 1: #esto es para que no devuelva un vector de vectores si solo fijamos un angulo
Fluovectors = Fluovector
return ProbeDetuningVectorL, Fluovectors
def PerformExperiment_8levels_fixedRabi(sg, sr, sp, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, phirepump, titarepump, freqMin, freqMax, freqStep, plot=False, solvemode=1, detpvec=None):
"""
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
"""
Fluovectors = []
for titaprobe in titaprobeVec:
tinicial = time.time()
ProbeDetuningVectorL, Fluovector = CPTspectrum8levels_fixedRabi(sg, sr, sp, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobe, phirepump, titarepump, freqMin=freqMin, freqMax=freqMax, freqStep=freqStep, plot=False, solvemode=1)
tfinal = time.time()
print('Done angle ', titarepump, ' Total time: ', round((tfinal-tinicial), 2), "s")
if plot:
plt.figure()
plt.xlabel('Repump detuning (MHz')
plt.ylabel('Fluorescence (A.U.)')
plt.plot(ProbeDetuningVectorL, Fluovector, label=str(titarepump)+'º tita repump, T: ' + str(T*1e3) + ' mK')
plt.legend()
Fluovectors.append(Fluovector)
if len(titaprobeVec) == 1: #esto es para que no devuelva un vector de vectores si solo fijamos un angulo
Fluovectors = Fluovector
return ProbeDetuningVectorL, Fluovectors
analisis/plots/20230421_CPTconmicromocion/Data/EITfit/threeLevel_2repumps_CPTPlotter.py 0 → 100644
View file @ 32f4f986
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Sep 1 17:58:39 2020
@author: oem
"""
import os
import numpy as np
#os.chdir('/home/oem/Nextcloud/G_liaf/liaf-TrampaAnular/Código General/EIT-CPT/Buenos Aires/Experiment Simulations/CPT scripts/Eight Level 2 repumps')
from threeLevel_2repumps_AnalysisFunctions import CalculoTeoricoDarkResonances_8levels, GetMinimaInfo, GetPlotsofFluovsAngle_8levels, PerformExperiment_8levels, FindDRFrequencies, FindRelativeFluorescencesOfDR, GenerateNoisyCPT, SmoothNoisyCPT, GetFinalMaps, GenerateNoisyCPT_fixedRabi, GenerateNoisyCPT_fit
import matplotlib.pyplot as plt
import time
from threeLevel_2repumps_AnalysisFunctions import MeasureRelativeFluorescenceFromCPT, IdentifyPolarizationCoincidences, RetrieveAbsoluteCoincidencesBetweenMaps, GetClosestIndex
#C:\Users\Usuario\Nextcloud\G_liaf\liaf-TrampaAnular\Código General\EIT-CPT\Buenos Aires\Experiment Simulations\CPT scripts\Eight Level 2 repumps
ub = 9.27e-24
h = 6.63e-34
c = (ub/h)*1e-4 #en unidades de MHz/G
#u = 1e6
u = 33.5e6
B = (u/(2*np.pi))/c
#sg, sp = 0.6, 5 #parámetros de control, saturación del doppler y repump
#rabG, rabP = sg*gPS, sp*gPD #frecuencias de rabi
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6 #anchos de linea de las transiciones
lw = 0.1
DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth = lw, lw, lw #ancho de linea de los laseres
DetDoppler = -36 #42
DetRepumpVec = [DetDoppler+29.6]
Tvec = [0.7] #temperatura en mK
alpha = 0*(np.pi/180) #angulo entre los láseres
phidoppler, titadoppler = 0, 90
phirepump, titarepump = 0, 0
phiprobe = 0
titaprobe = 90
#Calculo las resonancias oscuras teóricas
#ResonanciasTeoricas, DRPositivas = CalculoTeoricoDarkResonances_8levels(u/(2*np.pi*1e6), titadoppler, DetDoppler, DetRepump)
#Parametros de la simulacion cpt
center = -45
span = 80
freqMin = center-span*0.5
freqMax = center+span*0.5
""" parametros para tener espectros coherentes
freqMin = -56
freqMax = 14
"""
freqStep = 1e-1
noiseamplitude = 0
RelMinMedido0Vector = []
RelMinMedido1Vector = []
RelMinMedido2Vector = []
RelMinMedido3Vector = []
RelMinMedido4Vector = []
#Sr = np.arange(0, 10, 0.2)
#Sg = np.arange(0.01, 1, 0.05)
#Sp = np.arange(0.1, 6.1, 1)
#Sg = [0.6**2]
#Sp = [2.3**2]
Sg = [1.4]
Sp = [6]
Sr = [11]
i = 0
save = False
showFigures = True
if not showFigures:
plt.ioff()
else:
plt.ion()
fig1, ax1 = plt.subplots()
offsetx = 464
ax1.plot([f-offsetx for f in FreqsDR], CountsDR, 'o')
run = True
Scale = 730
Offset = 600 #600 para 20k cuentas aprox
MaxCoherenceValue = []
for sg in Sg:
for sp in Sp:
rabG, rabP = sg*gPS, sp*gPD
for Ti in Tvec:
T = Ti*1e-3
for DetRepump in DetRepumpVec:
print(T)
for sr in Sr:
rabR = sr*gPD
#MeasuredFreq, MeasuredFluo = GenerateNoisyCPT(rabG, rabR, rabP, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, [titaprobe], phirepump, titarepump, freqMin, freqMax, freqStep, plot=False, solvemode=1, detpvec=None, noiseamplitude=noiseamplitude)
if run:
MeasuredFreq4, MeasuredFluo4 = GenerateNoisyCPT_fixedRabi(sg, sr, sp, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, [titaprobe], phirepump, titarepump, freqMin, freqMax, freqStep, plot=False, solvemode=1, detpvec=None, noiseamplitude=noiseamplitude)
#SmoothFluo = SmoothNoisyCPT(MeasuredFluo, window=9, poly=2)
SmoothFluo4 = MeasuredFluo4
#Scale = max(BestC)/max([100*s for s in SmoothFluo4])
ax1.plot(MeasuredFreq4, [Scale*100*f + Offset for f in SmoothFluo4], label=f'Sr = {sr}')
ax1.axvline(DetDoppler, linestyle='--', linewidth=1)
#if sr != 0:
#ax1.axvline(DetRepump, linestyle='--', linewidth=1)
MaxCoherenceValue.append(np.max(SmoothFluo4))
#print(titaprobe)
ax1.set_xlabel('Detuning Rebombeo (MHz)')
ax1.set_ylabel('Fluorescencia (AU)')
ax1.set_title(f'B: {round(B, 2)} G, Sdop: {round(sg, 2)}, Sp: {round(sp, 2)}, Sr: {round(sr, 2)}, lw: {lw} MHz, T: {Ti} mK')
#ax1.set_ylim(0, 8)
#ax1.axvline(DetDoppler, linestyle='dashed', color='red', linewidth=1)
#ax1.axvline(DetRepump, linestyle='dashed', color='black', linewidth=1)
#ax1.set_title('Pol Doppler y Repump: Sigma+ Sigma-, Pol Probe: PI')
#ax1.legend()
ax1.grid()
print (f'{i+1}/{len(Sg)*len(Sp)}')
i = i + 1
if save:
plt.savefig(f'Mapa_plots_100k_1mk/CPT_SMSM_sdop{round(sg, 2)}_sp{round(sp, 2)}_sr{round(sr, 2)}.jpg')
ax1.legend()
"""
plt.figure()
plt.plot(Sr, MaxCoherenceValue, 'o')
plt.xlabel('Sr')
plt.ylabel('Coherence')
"""
"""
plt.figure()
plt.plot(MeasuredFreq, [100*f for f in SmoothFluo], color='darkred')
plt.xlabel('Desintonía 866 (MHz)')
plt.ylabel('Fluorescencia (A.U.)')
plt.axvline(-30, color='darkblue', linewidth=1.2, linestyle='--')
plt.yticks(np.arange(0.4, 1.8, 0.2))
plt.ylim(0.5, 1.6)
plt.grid()
plt.figure()
plt.plot(MeasuredFreq4, [100*f for f in SmoothFluo4], color='darkred')
plt.xlabel('Desintonía 866 (MHz)')
plt.ylabel('Fluorescencia (A.U.)')
plt.axvline(-30, color='darkblue', linewidth=1.2, linestyle='--')
plt.yticks(np.arange(0.8, 2.4, 0.4))
plt.grid()
"""
#%%
from scipy.optimize import curve_fit
T = 0.5e-3
sg = 0.7
sp = 6
sr = 0
DetDoppler = -14
DetRepump = 0
FitsSp = []
FitsOffset = []
Sg = [0.87]
def FitEIT(freqs, SP, offset):
MeasuredFreq, MeasuredFluo = GenerateNoisyCPT_fit(0.87, sr, SP, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, [titaprobe], phirepump, titarepump, freqs, plot=False, solvemode=1, detpvec=None, noiseamplitude=noiseamplitude)
FinalFluo = [f*43000 + 2685 for f in MeasuredFluo]
return FinalFluo
freqs = [f-offsetx+32 for f in FreqsDR]
freqslong = np.arange(min(freqs), max(freqs)+freqs[1]-freqs[0], 0.1*(freqs[1]-freqs[0]))
popt, pcov = curve_fit(FitEIT, freqs, CountsDR, p0=[5, 700], bounds=(0, [10, 1e6]))
FitsSp.append(popt[0])
FitsOffset.append(popt[1])
print(popt)
FittedEIT = FitEIT(freqslong, *popt)
plt.figure()
plt.errorbar(freqs, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', capsize=2, markersize=2)
plt.plot(freqslong, FitEIT(freqslong, *popt))
plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {T*1e3} mK, detDop: {DetDoppler} MHz')
np.savetxt('CPT_measured.txt', np.transpose([freqs, CountsDR]))
np.savetxt('CPT_fitted.txt', np.transpose([freqslong, FittedEIT]))
analisis/plots/20230421_CPTconmicromocion/Data/EITfit/threeLevel_2repumps_linealpol_python_scripts.py 0 → 100644
View file @ 32f4f986
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Apr 7 22:30:01 2020
@author: nico
"""
import numpy as np
import time
import matplotlib.pyplot as plt
from scipy.signal import argrelextrema
"""
Scripts para el calculo de la curva CPT
"""
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)
return H0
def HImatrix(rabG, rabP, phidoppler, titadoppler, phiprobe, titaprobe):
"""
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.exp(-1j*phiprobe)
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.exp(1j*phiprobe)
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.exp(-1j*phiprobe)
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.exp(1j*phiprobe)
HI[j-1, i-1] = np.conjugate(HI[i-1, j-1])
return HI
def Lplusminus(detr, detp, phirepump, titarepump, forma=1):
Hintplus = np.zeros((8, 8), dtype=np.complex_)
Hintminus = np.zeros((8, 8), dtype=np.complex_)
Hintplus[4, 2] = (-1/2)*np.sin(titarepump)*np.exp(1j*phirepump)
Hintplus[5, 2] = (-1/np.sqrt(3))*np.cos(titarepump)
Hintplus[6, 2] = (1/(2*np.sqrt(3)))*np.sin(titarepump)*np.exp(-1j*phirepump)
Hintplus[5, 3] = (-1/(2*np.sqrt(3)))*np.sin(titarepump)*np.exp(1j*phirepump)
Hintplus[6, 3] = (-1/np.sqrt(3))*np.cos(titarepump)
Hintplus[7, 3] = (1/2)*np.sin(titarepump)*np.exp(-1j*phirepump)
Hintminus[2, 4] = (-1/2)*np.sin(titarepump)*np.exp(-1j*phirepump)
Hintminus[2, 5] = (-1/np.sqrt(3))*np.cos(titarepump)
Hintminus[2, 6] = (1/(2*np.sqrt(3)))*np.sin(titarepump)*np.exp(1j*phirepump)
Hintminus[3, 5] = (-1/(2*np.sqrt(3)))*np.sin(titarepump)*np.exp(-1j*phirepump)
Hintminus[3, 6] = (-1/np.sqrt(3))*np.cos(titarepump)
Hintminus[3, 7] = (1/2)*np.sin(titarepump)*np.exp(1j*phirepump)
if forma==1:
Lplus = np.zeros((64, 64), dtype=np.complex_)
Lminus = np.zeros((64, 64), dtype=np.complex_)
DeltaBar = 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:
if (k==2 or k==3) and r > 3:
Lplus[r*8+q][k*8+j] = (-1j)*(Hintplus[r,k])
if (r==2 or r==3) and k > 3:
Lminus[r*8+q][k*8+j] = (-1j)*(Hintminus[r,k])
elif r==k:
if (q==2 or q==3) and j > 3:
Lplus[r*8+q][k*8+j] = (-1j)*(- Hintplus[j,q])
if (j==2 or j==3) and q > 3:
Lminus[r*8+q][k*8+j] = (-1j)*(- Hintminus[j,q])
if forma==2:
deltaKro = np.diag([1, 1, 1, 1, 1, 1, 1, 1])
Lplus = (-1j)*(np.kron(Hintplus, deltaKro) - np.kron(deltaKro, Hintplus))
Lminus = (-1j)*(np.kron(Hintminus, deltaKro) - np.kron(deltaKro, Hintminus))
DeltaBar = np.zeros((64, 64), dtype=np.complex_)
for i in range(64):
DeltaBar[i, i] = (1j)*(detr - detp)
return np.matrix(Lminus), np.matrix(Lplus), np.matrix(DeltaBar)
def GetL1(Lplus, Lminus, DeltaBar, L0, rabR, nmax):
"""
Devuelve Splus0 y Sminus0
"""
Sp = (-1)*(0.5*rabR)*(np.matrix(np.linalg.inv(L0 - (nmax+1)*DeltaBar))*np.matrix(Lplus))
Sm = (-1)*(0.5*rabR)*(np.matrix(np.linalg.inv(L0 + (nmax+1)*DeltaBar))*np.matrix(Lminus))
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)*(rabR)*(np.matrix(np.linalg.inv(L0 - n*DeltaBar + rabR*(Lminus*np.matrix(Sp))))*np.matrix(Lplus))
Sm = (-1)*(rabR)*(np.matrix(np.linalg.inv(L0 + n*DeltaBar + rabR*(Lplus*np.matrix(Sm))))*np.matrix(Lminus))
L1 = 0.5*rabR*(np.matrix(Lminus)*np.matrix(Sp) + np.matrix(Lplus)*np.matrix(Sm))
return L1
def EffectiveL(gPS, gPD, lwg, lwr, 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*(lwr + lwp)
Leff[5, 5] = 2*(lwr + lwp)
Leff[6, 6] = 2*(lwr + lwp)
Leff[7, 7] = 2*(lwr + lwp)
return (-0.5j)*Leff
def CalculateSingleMmatrix(gPS, gPD, lwg, lwr, 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.matrix(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
factor1 = 1
factor2 = 1
factor3 = 1
factor4 = 1
#M[36, 45] = lwp
M[36,36] = 2*(lwr + factor1*lwp)
M[37,37] = 2*(lwr + factor1*lwp)
M[38,38] = 2*(lwr + factor1*lwp)
M[39,39] = 2*(lwr + factor1*lwp)
M[44,44] = 2*(lwr + factor2*lwp)
M[45,45] = 2*(lwr + factor2*lwp)
M[46,46] = 2*(lwr + factor2*lwp)
M[47,47] = 2*(lwr + factor2*lwp)
M[52,52] = 2*(lwr + factor3*lwp)
M[53,53] = 2*(lwr + factor3*lwp)
M[54,54] = 2*(lwr + factor3*lwp)
M[55,55] = 2*(lwr + factor3*lwp)
M[60,60] = 2*(lwr + factor4*lwp)
M[61,61] = 2*(lwr + factor4*lwp)
M[62,62] = 2*(lwr + factor4*lwp)
M[63,63] = 2*(lwr + factor4*lwp)
return M
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/(2*mcalcio))
return gammaD
def FullL_efficient(rabG, rabR, rabP, gPS = 0, gPD = 0, Detg = 0, Detr = 0, Detp = 0, u = 0, lwg = 0, lwr=0, lwp = 0,
phidoppler=0, titadoppler=0, phiprobe=0, titaprobe=0, phirepump=0, titarepump=0, T = 0, alpha = 0):
"""
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)
#lwr = np.sqrt(lwr**2 + dopplerBroadening(0.397e-6, 0.866e-6, alpha, T)**2)
lwg = np.sqrt(lwg**2 + db**2)
lwr = np.sqrt(lwr**2 + db**2)
CC = EffectiveL(gPS, gPD, lwg, lwr, lwp)
Heff = H0matrix(Detg, Detp, u) + HImatrix(rabG, rabP, phidoppler, titadoppler, phiprobe, titaprobe) + CC
Heffdaga = np.matrix(Heff).getH()
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, lwr, lwp)
L0 = np.array(np.matrix(Lfullpartial) + M)
nmax = 1
Lminus, Lplus, DeltaBar = Lplusminus(Detr, Detp, phirepump, titarepump)
factor1 = np.exp(1j*0.2*np.pi)
factor2 = np.exp(-1j*0.2*np.pi)
#print(factor)
L1 = GetL1(factor1*Lplus, factor2*Lminus, DeltaBar, L0, rabR, nmax)
Lfull = L0 + L1
#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
"""
def CalculoTeoricoDarkResonances(u, titadoppler):
if titadoppler==0:
NegativeDR = [(-7/5)*u, (-3/5)*u, (-1/5)*u, (1/5)*u, (3/5)*u, (7/5)*u]
elif titadoppler==90:
NegativeDR = [(-11/5)*u, (-7/5)*u, (-3/5)*u, (3/5)*u, (7/5)*u, (11/5)*u]
PositiveDR = [(-8/5)*u, (-4/5)*u, 0, (4/5)*u, (8/5)*u]
return NegativeDR, PositiveDR
def CPTspectrum8levels(rabG, rabR, rabP, gPS, gPD, Detg, Detr, u, lwg, lwr, lwp, Temp, alpha, phidoppler, titadoppler, phiprobe, titaprobe, phirepump, titarepump,
freqMin=-100, freqMax=100, freqStep=1e-1, plot=False, solvemode=1):
"""
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)
phirepump, titarepump = phirepump*(np.pi/180), titarepump*(np.pi/180)
DetProbeVector = 2*np.pi*np.arange(freqMin*1e6, freqMax*1e6, freqStep*1e6)
Detg, Detr = 2*np.pi*Detg*1e6, 2*np.pi*Detr*1e6
lwg, lwr, lwp = 2*np.pi*lwg*1e6, 2*np.pi*lwr*1e6, 2*np.pi*lwp*1e6
#u = 2*np.pi*u*1e6
Fluovector = []
tinicial = time.time()
for Detp in DetProbeVector:
L = FullL_efficient(rabG, rabR, rabP, gPS, gPD, Detg, Detr, Detp, u, lwg, lwr, lwp, phidoppler, titadoppler, phiprobe, titaprobe, phirepump, titarepump, Temp, alpha)
if solvemode == 1:
rhovectorized = np.linalg.solve(L, np.array([int(i==0) for i in range(64)]))
Fluo = np.real(rhovectorized[18] + np.real(rhovectorized[27])) #estos son los rho33 + rho44
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
def CPTspectrum8levels_fixedRabi(sg, sr, sp, gPS, gPD, Detg, Detr, u, lwg, lwr, lwp, Temp, alpha, phidoppler, titadoppler, phiprobe, titaprobe, phirepump, titarepump,
freqMin=-100, freqMax=100, freqStep=1e-1, plot=False, solvemode=1):
"""
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)
phirepump, titarepump = phirepump*(np.pi/180), titarepump*(np.pi/180)
DetProbeVector = 2*np.pi*np.arange(freqMin*1e6, freqMax*1e6, freqStep*1e6)
Detg, Detr = 2*np.pi*Detg*1e6, 2*np.pi*Detr*1e6
#lwg, lwr, lwp = 2*np.pi*lwg*1e6, 2*np.pi*lwr*1e6, 2*np.pi*lwp*1e6
lwg, lwr, lwp = lwg*1e6, lwr*1e6, lwp*1e6
rabG = sg*gPS
rabR = sr*gPD
rabP = sp*gPD
#u = 2*np.pi*u*1e6
Fluovector = []
tinicial = time.time()
for Detp in DetProbeVector:
L = FullL_efficient(rabG, rabR, rabP, gPS, gPD, Detg, Detr, Detp, u, lwg, lwr, lwp, phidoppler, titadoppler, phiprobe, titaprobe, phirepump, titarepump, Temp, alpha)
if solvemode == 1:
coh = 5
rhovectorized = np.linalg.solve(L, np.array([int(i==0) for i in range(64)]))
#Fluo = np.abs(rhovectorized[coh])
Fluo = np.real(rhovectorized[18] + np.real(rhovectorized[27])) #estos son los rho33 + rho44
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
#%%
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(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)
NegativeDR, PositiveDR = CalculoTeoricoDarkResonances(u/(2*np.pi*1e6), titadoppler)
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.)')
for PDR in PositiveDR:
plt.axvline(Detr+PDR, linestyle='--', linewidth=0.5, color='red')
for NDR in NegativeDR:
plt.axvline(Detg+NDR, linestyle='--', linewidth=0.5, color='blue')
#parametros que andan piola:
"""
ub = 9.27e-24
h = 6.63e-34
c = (ub/h)*1e-4 #en unidades de MHz/G
B = 17 #campo magnetico en gauss
u = c*B
#u = 80e6
sr, sp = 0.53, 4.2
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6
rabR, rabP = sr*gPS, sp*gPD
lw = 2*np.pi * 0.33e6
lwr, lwp = lw, lw #ancho de linea de los laseres
dr_spec = - 2*np.pi* 26e6
freqSteps = 500
freqMin = -100e6
freqMax = 100e6
dps = 2*np.pi*np.linspace(freqMin, freqMax, freqSteps)
#dps = [-30e6]
alfar = 90*(np.pi/180)
ex_r, ey_r, ez_r = np.sin(alfar)*np.cos(0), np.sin(alfar)*np.sin(0), np.cos(alfar)
alfap = 90*(np.pi/180)
ex_p, ey_p, ez_p = np.sin(alfap)*np.cos(0), np.sin(alfap)*np.sin(0), np.cos(alfap)
"""
analisis/plots/20230424_CPT_TemperatureSens/CPT_plotter_20230424.py 0 → 100644
View file @ 32f4f986
import h5py
import matplotlib.pyplot as plt
import numpy as np
import sys
import re
import ast
from scipy.optimize import curve_fit
import os
from scipy import interpolate
#Mediciones barriendo angulo del TISA y viendo kicking de resonancias oscuras
#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20220106_CPT_DosLaseres_v08_TISA_DR\Data
os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20230424_CPT_TemperatureSens/Data/')
CPT_FILES = """000011345-IR_Scan_withcal_optimized
000011331-IR_Scan_withcal_optimized
"""
HEATING_FILES = """000011346-IR_Scan_withcal_optimized
000011347-IR_Scan_withcal_optimized
000011348-IR_Scan_withcal_optimized
000011349-IR_Scan_withcal_optimized
000011350-IR_Scan_withcal_optimized
000011334-IR_Scan_withcal_optimized
000011336-IR_Scan_withcal_optimized
000011337-IR_Scan_withcal_optimized
000011341-IR_Scan_withcal_optimized
000011342-IR_Scan_withcal_optimized
000011343-IR_Scan_withcal_optimized
000011344-IR_Scan_withcal_optimized
"""
def SeeKeys(files):
for i, fname in enumerate(files.split()):
data = h5py.File(fname+'.h5', 'r') # Leo el h5: Recordar que nuestros datos estan en 'datasets'
print(fname)
print(list(data['datasets'].keys()))
print(SeeKeys(CPT_FILES))
print(SeeKeys(HEATING_FILES))
#carpeta pc nico labo escritorio:
#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20211101_CPT_DosLaseres_v03\Data
Counts = []
Freqs = []
AmpTisa = []
UVCPTAmp = []
No_measures = []
for i, fname in enumerate(CPT_FILES.split()):
print(str(i) + ' - ' + fname)
#print(fname)
data = h5py.File(fname+'.h5', 'r') # Leo el h5: Recordar que nuestros datos estan en 'datasets'
# Aca hago algo repugnante para poder levantar los strings que dejamos
# que además tenian un error de tipeo al final. Esto no deberá ser necesario
# cuando se solucione el error este del guardado.
Freqs.append(np.array(data['datasets']['IR1_Frequencies']))
Counts.append(np.array(data['datasets']['counts_spectrum']))
#AmpTisa.append(np.array(data['datasets']['TISA_CPT_amp']))
UVCPTAmp.append(np.array(data['datasets']['UV_CPT_amp']))
No_measures.append(np.array(data['datasets']['no_measures']))
Counts_heating = []
HeatTimes = []
AmpTisa_heating = []
UVCPTAmp_heating = []
No_measures_heating = []
Freqs_heating = []
for i, fname in enumerate(HEATING_FILES.split()):
print(str(i) + ' - ' + fname)
#print(fname)
data = h5py.File(fname+'.h5', 'r') # Leo el h5: Recordar que nuestros datos estan en 'datasets'
# Aca hago algo repugnante para poder levantar los strings que dejamos
# que además tenian un error de tipeo al final. Esto no deberá ser necesario
# cuando se solucione el error este del guardado.
HeatTimes.append(np.array(data['datasets']['t_heating']))
Freqs_heating.append(np.array(data['datasets']['IR1_Frequencies']))
Counts_heating.append(np.array(data['datasets']['counts_spectrum']))
#AmpTisa.append(np.array(data['datasets']['TISA_CPT_amp']))
UVCPTAmp_heating.append(np.array(data['datasets']['UV_CPT_amp']))
No_measures_heating.append(np.array(data['datasets']['no_measures']))
#%%
"""
Ploteo las cpt de referencia / plotting the reference CPT
"""
jvec = [0,1]
plt.figure()
i = 0
for j in jvec:
plt.errorbar([2*f*1e-6 for f in Freqs[j]], Counts[j], yerr=np.sqrt(Counts[j]), fmt='o', capsize=2, markersize=2)
i = i + 1
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('counts')
plt.grid()
plt.legend()
#%%
from EITfit.threeLevel_2repumps_AnalysisFunctions import CalculoTeoricoDarkResonances_8levels, GetMinimaInfo, GetPlotsofFluovsAngle_8levels, PerformExperiment_8levels, FindDRFrequencies, FindRelativeFluorescencesOfDR, GenerateNoisyCPT, SmoothNoisyCPT, GetFinalMaps, GenerateNoisyCPT_fixedRabi, GenerateNoisyCPT_fit
from EITfit.threeLevel_2repumps_AnalysisFunctions import MeasureRelativeFluorescenceFromCPT, IdentifyPolarizationCoincidences, RetrieveAbsoluteCoincidencesBetweenMaps, GetClosestIndex
from scipy.optimize import curve_fit
"""
Ajusto un cpt para obtener todos los parámetros relevantes primero.
I fit a cpt curve to retrieve all the relevant parameters first.
"""
phidoppler, titadoppler = 0, 90
phirepump, titarepump = 0, 0
phiprobe = 0
titaprobe = 90
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6
alpha = 0
noiseamplitude = 0
T = 0.6e-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 = 33.5e6
c=3e8
B = (u/(2*np.pi))/c
correccion = 6 #con 3 fitea bien
offsetxpi = 440+1+correccion
DetDoppler = -12-correccion
FreqsCPT = [2*f*1e-6-offsetxpi+14 for f in Freqs[0]]
CountsCPT = Counts[0]
freqslongCPT = np.arange(min(FreqsCPT), max(FreqsCPT)+FreqsCPT[1]-FreqsCPT[0], 0.1*(FreqsCPT[1]-FreqsCPT[0]))
#[1.71811842e+04 3.34325038e-17]
def FitEIT(freqs, SG, SP, scale, offset, temp):
#temp = 2.9e-4
MeasuredFreq, MeasuredFluo = GenerateNoisyCPT_fit(SG, sr, SP, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, temp, alpha, phidoppler, titadoppler, phiprobe, [titaprobe], phirepump, titarepump, freqs, plot=False, solvemode=1, detpvec=None, noiseamplitude=noiseamplitude)
#scale = 2.85164232e+04
#offset = 2.16392191e+03
FinalFluo = [f*scale + offset for f in MeasuredFluo]
return FinalFluo
popt_fullcpt_noheat, pcov_fullcpt_noheat = curve_fit(FitEIT, FreqsCPT, CountsCPT, p0=[0.5, 4.5, 1e4, 1e3, 1e-3], bounds=((0, 0, 0, 0, 0), (2, 10, 1e5, 1e5, 10e-3)))
print(f'Temperatura: ({round(1e3*popt_fullcpt_noheat[-1],2)} +- {round(1e3*np.sqrt(pcov_fullcpt_noheat[-1][-1]),2)}) mK')
print(popt_fullcpt_noheat)
FittedEIT = FitEIT(freqslongCPT, *popt_fullcpt_noheat)
#%%
"""
Ploteo la CPT de referencia junto al ajuste
I plot the reference CPT along with the fit to the model
"""
plt.figure()
plt.errorbar(FreqsCPT, CountsCPT, yerr=2*np.sqrt(CountsCPT), fmt='o', capsize=2, markersize=2)
plt.plot(freqslongCPT, FittedEIT)
plt.xlabel('Detuning (MHz)')
plt.ylabel('Counts')
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
HeatTimes = [0,1,2,3,4]
#plt.figure()
for j in [0,1,2,3,4]:
plt.errorbar([2*f*1e-6-offsetxpi+14 for f in Freqs_heating[j]], Counts_heating[j], yerr=0.0*np.sqrt(Counts_heating[j]), fmt='-', capsize=2, markersize=2, label=f'Heating time {HeatTimes[j]} ms')
plt.legend()
plt.xlim(-25,10)
#%%
#Ahora lo mismo con otro set de mediciones
from EITfit.threeLevel_2repumps_AnalysisFunctions import CalculoTeoricoDarkResonances_8levels, GetMinimaInfo, GetPlotsofFluovsAngle_8levels, PerformExperiment_8levels, FindDRFrequencies, FindRelativeFluorescencesOfDR, GenerateNoisyCPT, SmoothNoisyCPT, GetFinalMaps, GenerateNoisyCPT_fixedRabi, GenerateNoisyCPT_fit
from EITfit.threeLevel_2repumps_AnalysisFunctions import MeasureRelativeFluorescenceFromCPT, IdentifyPolarizationCoincidences, RetrieveAbsoluteCoincidencesBetweenMaps, GetClosestIndex
from scipy.optimize import curve_fit
"""
Ajusto un cpt para obtener todos los parámetros relevantes primero.
I fit a cpt curve to retrieve all the relevant parameters first.
"""
phidoppler, titadoppler = 0, 90
phirepump, titarepump = 0, 0
phiprobe = 0
titaprobe = 90
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6
alpha = 0
noiseamplitude = 0
T = 0.6e-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 = 33.5e6
c=3e8
B = (u/(2*np.pi))/c
correccion = 6 #con 3 fitea bien
offsetxpi = 440+1+correccion
DetDoppler = -12-correccion
FreqsCPT = [2*f*1e-6-offsetxpi+14 for f in Freqs[1]]
CountsCPT = Counts[1]
freqslongCPT = np.arange(min(FreqsCPT), max(FreqsCPT)+FreqsCPT[1]-FreqsCPT[0], 0.1*(FreqsCPT[1]-FreqsCPT[0]))
#[1.71811842e+04 3.34325038e-17]
def FitEIT(freqs, SG, SP, scale, offset, temp):
#temp = 2.9e-4
MeasuredFreq, MeasuredFluo = GenerateNoisyCPT_fit(SG, sr, SP, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, temp, alpha, phidoppler, titadoppler, phiprobe, [titaprobe], phirepump, titarepump, freqs, plot=False, solvemode=1, detpvec=None, noiseamplitude=noiseamplitude)
#scale = 2.85164232e+04
#offset = 2.16392191e+03
FinalFluo = [f*scale + offset for f in MeasuredFluo]
return FinalFluo
popt_fullcpt_noheat, pcov_fullcpt_noheat = curve_fit(FitEIT, FreqsCPT, CountsCPT, p0=[0.5, 4.5, 1e4, 1e3, 1e-3], bounds=((0, 0, 0, 0, 0), (2, 10, 1e5, 1e5, 10e-3)))
print(f'Temperatura: ({round(1e3*popt_fullcpt_noheat[-1],2)} +- {round(1e3*np.sqrt(pcov_fullcpt_noheat[-1][-1]),2)}) mK')
print(popt_fullcpt_noheat)
FittedEIT = FitEIT(freqslongCPT, *popt_fullcpt_noheat)
#%%
"""
Simulo CPTs con todos esos parámetros para distintas temperaturas
I simulate CPT curves with all the previous parameters but with varying temperatures
"""
TempVecTeorico = list(np.arange(0.3,1,0.1))+list(np.arange(1, 31, 1))
CurvasTeoricas = []
for tempi in TempVecTeorico:
CurvasTeoricas.append(FitEITpi(freqslongpi, *popt_fullcpt[:-1], tempi*1e-3))
#%%
"""
Acá agarro la primera y busco el valor i_DR que corresponde a la resonancia oscura de interés
With the first one, I look for the value i_DR which corresponds to the dark resonance of interest
"""
curva_ref = CurvasTeoricas[0]
i_DR = 955
plt.figure()
plt.plot(freqslongpi, curva_ref)
plt.plot(freqslongpi[i_DR], curva_ref[i_DR],'o')
#%%
"""
ploteo algunos CPTs teoricos para algunas temperaturas
Plotting some theory cpt curves for some temperatures
"""
plt.plot(freqslongpi, CurvasTeoricas[0])
plt.plot(freqslongpi[i_DR], CurvasTeoricas[0][i_DR],'o',markersize=10)
plt.plot(freqslongpi, CurvasTeoricas[10])
plt.plot(freqslongpi[i_DR], CurvasTeoricas[10][i_DR],'o',markersize=10)
plt.plot(freqslongpi, CurvasTeoricas[20])
plt.plot(freqslongpi[i_DR], CurvasTeoricas[20][i_DR],'o',markersize=10)
plt.xlabel('Detuning (MHz)')
plt.ylabel('Fluorescence')
plt.grid()
#%%
"""
Ahora interpolo los valores teóricos de las profundidades de esas resonancias
y aplico la interpolación a las mediciones para obtener temperaturas.
Luego, grafico las temperaturas en función de los tiempos de calentamiento.
Now I interpolate the theoretical values of the depths of those resonances
and apply the interpolation to the measurements to obtain temperatures.
After that, I plot the temperatures with respect to the heating times
"""
from scipy.interpolate import interp1d
FluosDRTeo = [CurvasTeoricas[k][i_DR] for k in range(len(CurvasTeoricas))]
interpolado = interp1d(FluosDRTeo, TempVecTeorico) #creo funcion que interpola
meas = 0
maxi = 9 #valor maximo, dsp el ion se calento
Heating_tim = Times[meas][:maxi]
Heating_tim_ms = [t*1e3 for t in Heating_tim]
Heating_med = [2*c for c in Counts_heating[meas][:maxi]]
ErrorHeating_med = [2*np.sqrt(c) for c in Counts_heating[meas][:maxi]]
Temperaturas_interpoladas = [float(interpolado(h)) for h in Heating_med]
Error_Temperaturas_interpoladas = [float(interpolado(Heating_med[k]+0.5*ErrorHeating_med[k]))-Temperaturas_interpoladas[k] for k in range(len(Heating_med))]
plt.figure()
plt.plot(FluosDRTeo, [1*t for t in TempVecTeorico], 'o', color='orange')
plt.plot(np.linspace(FluosDRTeo[0],FluosDRTeo[-1],1000), interpolado(np.linspace(FluosDRTeo[0],FluosDRTeo[-1],1000)))
plt.xlabel('Cuentas de DR teoricas')
plt.ylabel('Temperatura (mK)')
plt.figure()
#plt.plot(Heating_med, Heating_tim, 'o', color='blue')
plt.errorbar([t*1e3 for t in Heating_tim], Heating_med, yerr=ErrorHeating_med, fmt='o', capsize=2, markersize=5)
plt.ylabel('Cuentas de DR medidas')
plt.xlabel('Heating time (s)')
def lineal(x,a,b):
return a*x+b
#p1,p2 = curve_fit(lineal, Heating_tim_ms, Temperaturas_interpoladas, sigma=Error_Temperaturas_interpoladas)
p1,p2 = curve_fit(lineal, Heating_tim_ms, Temperaturas_interpoladas)
#%%
"""
Grafico finalmente el plot del heating rate de la trampa
Finally I plot the heating rate of the trap
"""
plt.figure()
#plt.plot(Heating_tim_ms,Temperaturas_interpoladas,'o')
plt.errorbar(Heating_tim_ms,Temperaturas_interpoladas, yerr=np.array(Error_Temperaturas_interpoladas), fmt='o', capsize=2, markersize=7, color='black')
plt.plot(Heating_tim_ms, lineal(np.array(Heating_tim_ms), *p1), color='red')
plt.xlabel('Heating time (ms)', fontname='STIXGeneral', fontsize=15)
plt.ylabel('Temperature (mK)', fontname='STIXGeneral', fontsize=15)
plt.grid()
plt.xticks([0, 5, 10, 15, 20 ,25, 30, 35], fontname='STIXGeneral', fontsize=15)
plt.yticks([0, 5, 10, 15], fontname='STIXGeneral', fontsize=15)
plt.title(f'Heating rate: ({round(p1[0],2)} +- {round(np.sqrt(p2[0][0]),2)}) mK/ms', fontname='STIXGeneral', fontsize=15)
plt.tight_layout()
plt.savefig('Fig_heatingrate.svg')
print(f'Heating rate: ({round(p1[0],2)} +- {round(np.sqrt(p2[0][0]),2)}) mK/ms')
#%%
"""
Ahora voy a ver CPT enteras con tiempos de calentamiento distintos.
Now I see whole CPT curves with different heating times
"""
jvec = [3, 4]
plt.figure()
i = 0
for j in jvec:
if j==4:
plt.errorbar([2*f*1e-6 for f in Freqs[j]], Counts[j], yerr=np.sqrt(Counts[j]), fmt='o', capsize=2, markersize=2, label='Without heating')
elif j==3:
plt.errorbar([2*f*1e-6 for f in Freqs[j]], Counts[j], yerr=np.sqrt(Counts[j]), fmt='o', capsize=2, markersize=2, label='5 ms heating')
i = i + 1
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('counts')
plt.ylim(1000,2900)
plt.grid()
plt.legend()
jvec = [1, 2]
plt.figure()
i = 0
for j in jvec:
if j==2:
plt.errorbar([2*f*1e-6 for f in Freqs[j]], Counts[j], yerr=np.sqrt(Counts[j]), fmt='o', capsize=2, markersize=2, label='Without heating')
elif j==1:
plt.errorbar([2*f*1e-6 for f in Freqs[j]], Counts[j], yerr=np.sqrt(Counts[j]), fmt='o', capsize=2, markersize=2, label='1 ms heating')
i = i + 1
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('counts')
plt.ylim(1000,2900)
plt.grid()
plt.legend()
#%%
"""
La siguiente curva probablemente no este bien medida ya que inmediatamente
despues, los laseres se deslockearon. La dejo por las dudas.
This curve is probably not well measured...
"""
jvec = [5, 6]
plt.figure()
i = 0
for j in jvec:
if j==6:
plt.errorbar([2*f*1e-6 for f in Freqs[j]], Counts[j], yerr=np.sqrt(Counts[j]), fmt='o', capsize=2, markersize=2, label='Without heating')
elif j==5:
plt.errorbar([2*f*1e-6 for f in Freqs[j]], Counts[j], yerr=np.sqrt(Counts[j]), fmt='o', capsize=2, markersize=2, label='10 ms heating')
i = i + 1
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('counts')
plt.ylim(1000,3900)
plt.grid()
plt.legend()
plt.title('Ojo: medicion condicionada por derivas')
#%%
"""
Ahora ploteo 6 curvas cpt para distintos valores de potencia del UV
This is a plot of 6 different cpt curves for 6 different UV powers. I should fit them
to obtain saturation parameters
"""
jvec = [7,8,9,10,11,12]
plt.figure()
for j in jvec:
plt.errorbar([2*f*1e-6 for f in Freqs[j]], Counts[j], yerr=np.sqrt(Counts[j]), fmt='o', capsize=2, markersize=2, label='Without heating')
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('counts')
#plt.ylim(1000,2900)
plt.grid()
#plt.legend()
analisis/plots/20230424_CPT_TemperatureSens/Data/EITfit/threeLevel_2repumps_AnalysisFunctions.py 0 → 100644
View file @ 32f4f986
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Jul 2 16:30:09 2020
@author: oem
"""
import os
import numpy as np
import time
import matplotlib.pyplot as plt
from scipy.signal import argrelextrema
from EITfit.threeLevel_2repumps_linealpol_python_scripts import CPTspectrum8levels, CPTspectrum8levels_fixedRabi
import random
from scipy.signal import savgol_filter as sf
def CalculoTeoricoDarkResonances_8levels(u, titadoppler, detuningdoppler, detuningrepump):
if titadoppler==0:
NegativeDR = [(-7/5)*u, (-3/5)*u, (-1/5)*u, (1/5)*u, (3/5)*u, (7/5)*u]
elif titadoppler==90:
NegativeDR = [(-11/5)*u, (-7/5)*u, (-3/5)*u, (3/5)*u, (7/5)*u, (11/5)*u]
else:
NegativeDR = [(-11/5)*u, (-7/5)*u, (-3/5)*u, (-1/5)*u, (1/5)*u, (3/5)*u, (7/5)*u, (11/5)*u]
PositiveDR = [(-8/5)*u, (-4/5)*u, 0, (4/5)*u, (8/5)*u]
return [detuningdoppler + dr for dr in NegativeDR], [detuningrepump + dr for dr in PositiveDR]
def GetClosestIndex(Vector, value, tolerance=1e-3):
i = 0
while i<len(Vector):
if abs(Vector[i] - value) < tolerance:
return i
else:
i = i + 1
return GetClosestIndex(Vector, value, tolerance=2*tolerance)
def FindDRFrequencies(Freq, Fluo, TeoDR, entorno=3):
"""
Busca los indices y la frecuencia de los minimos en un entorno cercano al de la DR.
Si no encuentra, devuelve el valor teórico.
"""
IndiceDRteo1, IndiceEntornoinicialDRteo1, IndiceEntornofinalDRteo1 = GetClosestIndex(Freq, TeoDR[0]), GetClosestIndex(Freq, TeoDR[0]-entorno), GetClosestIndex(Freq, TeoDR[0]+entorno)
IndiceDRteo2, IndiceEntornoinicialDRteo2, IndiceEntornofinalDRteo2 = GetClosestIndex(Freq, TeoDR[1]), GetClosestIndex(Freq, TeoDR[1]-entorno), GetClosestIndex(Freq, TeoDR[1]+entorno)
IndiceDRteo3, IndiceEntornoinicialDRteo3, IndiceEntornofinalDRteo3 = GetClosestIndex(Freq, TeoDR[2]), GetClosestIndex(Freq, TeoDR[2]-entorno), GetClosestIndex(Freq, TeoDR[2]+entorno)
IndiceDRteo4, IndiceEntornoinicialDRteo4, IndiceEntornofinalDRteo4 = GetClosestIndex(Freq, TeoDR[3]), GetClosestIndex(Freq, TeoDR[3]-entorno), GetClosestIndex(Freq, TeoDR[3]+entorno)
IndiceDRteo5, IndiceEntornoinicialDRteo5, IndiceEntornofinalDRteo5 = GetClosestIndex(Freq, TeoDR[4]), GetClosestIndex(Freq, TeoDR[4]-entorno), GetClosestIndex(Freq, TeoDR[4]+entorno)
IndiceDRteo6, IndiceEntornoinicialDRteo6, IndiceEntornofinalDRteo6 = GetClosestIndex(Freq, TeoDR[5]), GetClosestIndex(Freq, TeoDR[5]-entorno), GetClosestIndex(Freq, TeoDR[5]+entorno)
EntornoFreqDR1, EntornoFreqDR2 = Freq[IndiceEntornoinicialDRteo1:IndiceEntornofinalDRteo1], Freq[IndiceEntornoinicialDRteo2:IndiceEntornofinalDRteo2]
EntornoFreqDR3, EntornoFreqDR4 = Freq[IndiceEntornoinicialDRteo3:IndiceEntornofinalDRteo3], Freq[IndiceEntornoinicialDRteo4:IndiceEntornofinalDRteo4]
EntornoFreqDR5, EntornoFreqDR6 = Freq[IndiceEntornoinicialDRteo5:IndiceEntornofinalDRteo5], Freq[IndiceEntornoinicialDRteo6:IndiceEntornofinalDRteo6]
EntornoFluoDR1, EntornoFluoDR2 = Fluo[IndiceEntornoinicialDRteo1:IndiceEntornofinalDRteo1], Fluo[IndiceEntornoinicialDRteo2:IndiceEntornofinalDRteo2]
EntornoFluoDR3, EntornoFluoDR4 = Fluo[IndiceEntornoinicialDRteo3:IndiceEntornofinalDRteo3], Fluo[IndiceEntornoinicialDRteo4:IndiceEntornofinalDRteo4]
EntornoFluoDR5, EntornoFluoDR6 = Fluo[IndiceEntornoinicialDRteo5:IndiceEntornofinalDRteo5], Fluo[IndiceEntornoinicialDRteo6:IndiceEntornofinalDRteo6]
IndiceFluoMinimaEntorno1, IndiceFluoMinimaEntorno2 = argrelextrema(np.array(EntornoFluoDR1), np.less)[0], argrelextrema(np.array(EntornoFluoDR2), np.less)[0]
IndiceFluoMinimaEntorno3, IndiceFluoMinimaEntorno4 = argrelextrema(np.array(EntornoFluoDR3), np.less)[0], argrelextrema(np.array(EntornoFluoDR4), np.less)[0]
IndiceFluoMinimaEntorno5, IndiceFluoMinimaEntorno6 = argrelextrema(np.array(EntornoFluoDR5), np.less)[0], argrelextrema(np.array(EntornoFluoDR6), np.less)[0]
try:
FreqDR1 = EntornoFreqDR1[int(IndiceFluoMinimaEntorno1)]
IndiceDR1 = GetClosestIndex(Freq, FreqDR1)
except:
FreqDR1 = TeoDR[0]
IndiceDR1 = IndiceDRteo1
try:
FreqDR2 = EntornoFreqDR2[int(IndiceFluoMinimaEntorno2)]
IndiceDR2 = GetClosestIndex(Freq, FreqDR2)
except:
FreqDR2 = TeoDR[1]
IndiceDR2 = IndiceDRteo2
try:
FreqDR3 = EntornoFreqDR3[int(IndiceFluoMinimaEntorno3)]
IndiceDR3 = GetClosestIndex(Freq, FreqDR3)
except:
FreqDR3 = TeoDR[2]
IndiceDR3 = IndiceDRteo3
try:
FreqDR4 = EntornoFreqDR4[int(IndiceFluoMinimaEntorno4)]
IndiceDR4 = GetClosestIndex(Freq, FreqDR4)
except:
FreqDR4 = TeoDR[3]
IndiceDR4 = IndiceDRteo4
try:
FreqDR5 = EntornoFreqDR5[int(IndiceFluoMinimaEntorno5)]
IndiceDR5 = GetClosestIndex(Freq, FreqDR5)
except:
FreqDR5 = TeoDR[4]
IndiceDR5 = IndiceDRteo5
try:
FreqDR6 = EntornoFreqDR6[int(IndiceFluoMinimaEntorno6)]
IndiceDR6 = GetClosestIndex(Freq, FreqDR6)
except:
FreqDR6 = TeoDR[5]
IndiceDR6 = IndiceDRteo6
return [IndiceDR1, IndiceDR2, IndiceDR3, IndiceDR4, IndiceDR5, IndiceDR6], [FreqDR1, FreqDR2, FreqDR3, FreqDR4, FreqDR5, FreqDR6]
def FindRelativeFluorescencesOfDR(Freq, Fluo, IndicesDR, detuningdoppler, NormalizationCriterium=1, frecuenciareferenciacriterioasintotico=-100, getindices=False):
"""
Toma los indices donde estan las DR y evalua su fluorescencia. Esos indices son minimos locales en un entorno
cercano a las DR teoricas y, si no hay ningun minimo, toma la teorica.
Luego, hace el cociente de esa fluorescencia y un factor de normalización segun NormalizationCriterium:
1: Devuelve la fluorescencia absoluta de los minimos
2: Devuelve el cociente entre la fluorescencia del minimo y un valor medio entre dos puntos lejanos, como si no
hubiera una resonancia oscura y hubiera una recta. Ese valor esta a DistanciaFrecuenciaCociente del detuning del azul (el punto medio entre las dos DR en este caso)
3: Devuelve el cociente entre la fluorescencia del minimo y el valor a -100 MHz (si se hizo de -100 a 100),
o el valor limite por izquierda de la curva
4: Deuelve el cociente entre la fluorescencia del minimo y el valor de fluorescencia a detuning 0 MHz
"""
IndiceDR1, IndiceDR2, IndiceDR3, IndiceDR4, IndiceDR5, IndiceDR6 = IndicesDR[0], IndicesDR[1], IndicesDR[2], IndicesDR[3], IndicesDR[4], IndicesDR[5]
FluorescenceOfMinimums = [Fluo[IndiceDR1], Fluo[IndiceDR2], Fluo[IndiceDR3], Fluo[IndiceDR4], Fluo[IndiceDR5], Fluo[IndiceDR6]]
FrequencyOfMinimums = [Freq[IndiceDR1], Freq[IndiceDR2], Freq[IndiceDR3], Freq[IndiceDR4], Freq[IndiceDR5], Freq[IndiceDR6]]
DistanciaFrecuenciaCociente = 25
if NormalizationCriterium==0:
print('che')
return FrequencyOfMinimums, FluorescenceOfMinimums
if NormalizationCriterium==1:
Fluorescenciacerodetuning = Fluo[GetClosestIndex(Freq, 0)]
Fluorescenciaasintotica = Fluo[GetClosestIndex(Freq, frecuenciareferenciacriterioasintotico)]
return FrequencyOfMinimums, np.array([Fluorescenciacerodetuning/Fluorescenciaasintotica, Fluorescenciacerodetuning/Fluorescenciaasintotica, Fluorescenciacerodetuning/Fluorescenciaasintotica, Fluorescenciacerodetuning/Fluorescenciaasintotica, Fluorescenciacerodetuning/Fluorescenciaasintotica, Fluorescenciacerodetuning/Fluorescenciaasintotica])
if NormalizationCriterium==2:
k = 0
while k < len(Freq):
if Freq[k] < detuningdoppler-DistanciaFrecuenciaCociente + 2 and Freq[k] > detuningdoppler-DistanciaFrecuenciaCociente - 2:
FluoIzquierda = Fluo[k]
indiceizquierda = k
print('Izq:', Freq[k])
break
else:
k = k + 1
l = 0
while l < len(Freq):
if Freq[l] < detuningdoppler+DistanciaFrecuenciaCociente + 2 and Freq[l] > detuningdoppler+DistanciaFrecuenciaCociente - 2:
FluoDerecha = Fluo[l]
indicederecha = l
print('Der: ', Freq[l])
break
else:
l = l + 1
FluoNormDivisor = 0.5*(FluoDerecha+FluoIzquierda)
print(FluoNormDivisor)
if NormalizationCriterium==3:
#asintotico
FluoNormDivisor = Fluo[GetClosestIndex(Freq, frecuenciareferenciacriterioasintotico)]
if NormalizationCriterium==4:
#este te tira la fluorescencia de detuning 0
FluoNormDivisor = Fluo[GetClosestIndex(Freq, 0)]
RelativeFluorescenceOfMinimums = np.array([Fluore/FluoNormDivisor for Fluore in FluorescenceOfMinimums])
print('Esto: ', RelativeFluorescenceOfMinimums)
if NormalizationCriterium==2 and getindices==True:
return FrequencyOfMinimums, RelativeFluorescenceOfMinimums, indiceizquierda, indicederecha
return FrequencyOfMinimums, RelativeFluorescenceOfMinimums
def GetFinalMaps(MapasDR1, MapasDR2, MapasDR3, MapasDR4, MapasDR5, MapasDR6):
"""
Nota: esto vale para polarizacion del 397 sigma+ + sigma-. Sino hay que cambiar los coeficientes.
La estructura es:
MapasDRi = [MapaMedido_criterio1_DRi, MapaMedido_criterio2_DRi, MapaMedido_criterio3_DRi, MapaMedido_criterio4_DRi]
"""
Mapa1 = MapasDR1[0]
Mapa2pi = np.sqrt(3)*(MapasDR2[1] + MapasDR5[1])
Mapa2smas = np.sqrt(12/2)*MapasDR3[1] + (2/np.sqrt(2))*MapasDR6[1]
Mapa2smenos = (2/np.sqrt(2))*MapasDR1[1] + np.sqrt(12/2)*MapasDR4[1]
Mapa3pi = np.sqrt(3)*(MapasDR2[2] + MapasDR5[2])
Mapa3smas = np.sqrt(12/2)*MapasDR3[2] + (2/np.sqrt(2))*MapasDR6[2]
Mapa3smenos = (2/np.sqrt(2))*MapasDR1[2] + np.sqrt(12/2)*MapasDR4[2]
return Mapa1, [Mapa2pi, Mapa2smas, Mapa2smenos], [Mapa3pi, Mapa3smas, Mapa3smenos]
def CombinateDRwithCG(RelMinMedido1, RelMinMedido2, RelMinMedido3, RelMinMedido4):
Fluo1 = RelMinMedido1[0]
Fluo2pi = np.sqrt(3)*(RelMinMedido2[1] + RelMinMedido2[4])
Fluo2smas = np.sqrt(12/2)*RelMinMedido2[2] + (2/np.sqrt(2))*RelMinMedido2[5]
Fluo2smenos = (2/np.sqrt(2))*RelMinMedido2[0] + np.sqrt(12/2)*RelMinMedido2[3]
Fluo3pi = np.sqrt(3)*(RelMinMedido3[1] + RelMinMedido3[4])
Fluo3smas = np.sqrt(12/2)*RelMinMedido3[2] + (2/np.sqrt(2))*RelMinMedido3[5]
Fluo3smenos = (2/np.sqrt(2))*RelMinMedido3[0] + np.sqrt(12/2)*RelMinMedido3[3]
return Fluo1, [Fluo2pi, Fluo2smas, Fluo2smenos], [Fluo3pi, Fluo3smas, Fluo3smenos]
def IdentifyPolarizationCoincidences(theoricalmap, target, tolerance=1e-1):
"""
Busca en un mapa 2D la presencia de un valor target (medido) con tolerancia tolerance.
Si lo encuentra, pone un 1. Sino, un 0. Al plotear con pcolor se verá
en blanco la zona donde el valor medido se puede hallar.
"""
CoincidenceMatrix = np.zeros((len(theoricalmap), len(theoricalmap[0])))
i = 0
while i<len(theoricalmap):
j = 0
while j<len(theoricalmap[0]):
if abs(theoricalmap[i][j]-target) < tolerance:
CoincidenceMatrix[i][j] = 1
j=j+1
i=i+1
return CoincidenceMatrix
def RetrieveAbsoluteCoincidencesBetweenMaps(MapsVectors):
MatrixSum = np.zeros((len(MapsVectors[0]), len(MapsVectors[0][0])))
AbsoluteCoincidencesMatrix = np.zeros((len(MapsVectors[0]), len(MapsVectors[0][0])))
MatrixMapsVectors = []
for i in range(len(MapsVectors)):
MatrixMapsVectors.append(np.matrix(MapsVectors[i]))
for i in range(len(MatrixMapsVectors)):
MatrixSum = MatrixSum + MatrixMapsVectors[i]
MaxNumberOfCoincidences = np.max(MatrixSum)
ListMatrixSum = [list(i) for i in list(np.array(MatrixSum))]
for i in range(len(ListMatrixSum)):
for j in range(len(ListMatrixSum[0])):
if ListMatrixSum[i][j] == MaxNumberOfCoincidences:
AbsoluteCoincidencesMatrix[i][j] = 1
return AbsoluteCoincidencesMatrix, MaxNumberOfCoincidences
def MeasureMeanValueOfEstimatedArea(AbsoluteCoincidencesMap, X, Y):
NonZeroIndices = np.nonzero(AbsoluteCoincidencesMap)
Xsum = 0
Xvec = []
Ysum = 0
Yvec = []
N = len(NonZeroIndices[0])
for i in range(N):
Xsum = Xsum + X[NonZeroIndices[1][i]]
Xvec.append(X[NonZeroIndices[1][i]])
Ysum = Ysum + Y[NonZeroIndices[0][i]]
Yvec.append(Y[NonZeroIndices[0][i]])
Xaverage = Xsum/N
Yaverage = Ysum/N
Xspread = np.std(Xvec)
Yspread = np.std(Yvec)
return Xaverage, Yaverage, N, Xspread, Yspread
def MeasureRelativeFluorescenceFromCPT(Freq, Fluo, u, titadoppler, detuningrepump, detuningdoppler, frefasint=-100, entorno=3):
ResonanciasTeoricas, ResonanciasPositivas = CalculoTeoricoDarkResonances_8levels(u, titadoppler, detuningdoppler, detuningrepump)
IndicesDR, FreqsDR = FindDRFrequencies(Freq, Fluo, ResonanciasTeoricas, entorno=entorno)
FrequencyOfMinimums, RelativeFluorescenceOfMinimums0 = FindRelativeFluorescencesOfDR(Freq, Fluo, IndicesDR, detuningdoppler, NormalizationCriterium=0, frecuenciareferenciacriterioasintotico=frefasint)
FrequencyOfMinimums, RelativeFluorescenceOfMinimums1 = FindRelativeFluorescencesOfDR(Freq, Fluo, IndicesDR, detuningdoppler, NormalizationCriterium=1, frecuenciareferenciacriterioasintotico=frefasint)
FrequencyOfMinimums, RelativeFluorescenceOfMinimums2, indiceizquierda, indicederecha = FindRelativeFluorescencesOfDR(Freq, Fluo, IndicesDR, detuningdoppler, NormalizationCriterium=2, frecuenciareferenciacriterioasintotico=frefasint, getindices=True)
FrequencyOfMinimums, RelativeFluorescenceOfMinimums3 = FindRelativeFluorescencesOfDR(Freq, Fluo, IndicesDR, detuningdoppler, NormalizationCriterium=3, frecuenciareferenciacriterioasintotico=frefasint)
FrequencyOfMinimums, RelativeFluorescenceOfMinimums4 = FindRelativeFluorescencesOfDR(Freq, Fluo, IndicesDR, detuningdoppler, NormalizationCriterium=4, frecuenciareferenciacriterioasintotico=frefasint)
print('hola')
print(RelativeFluorescenceOfMinimums0)
return RelativeFluorescenceOfMinimums0, RelativeFluorescenceOfMinimums1, RelativeFluorescenceOfMinimums2, RelativeFluorescenceOfMinimums3, RelativeFluorescenceOfMinimums4, IndicesDR, [indiceizquierda, indicederecha]
def GenerateNoisyCPT(rabG, rabR, rabP, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, phirepump, titarepump, freqMin, freqMax, freqStep, plot=False, solvemode=1, detpvec=None, noiseamplitude=0.001):
Frequencyvector, Fluovector = PerformExperiment_8levels(rabG, rabR, rabP, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, phirepump, titarepump, freqMin, freqMax, freqStep, plot=False, solvemode=1, detpvec=None)
NoisyFluovector = [fluo+noiseamplitude*(2*random.random()-1) for fluo in Fluovector]
return Frequencyvector, NoisyFluovector
def GenerateNoisyCPT_fixedRabi(sg, sr, sp, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, phirepump, titarepump, freqMin, freqMax, freqStep, plot=False, solvemode=1, detpvec=None, noiseamplitude=0.001):
Frequencyvector, Fluovector = PerformExperiment_8levels_fixedRabi(sg, sr, sp, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, phirepump, titarepump, freqMin, freqMax, freqStep, plot=False, solvemode=1, detpvec=None)
NoisyFluovector = [fluo+noiseamplitude*(2*random.random()-1) for fluo in Fluovector]
return Frequencyvector, NoisyFluovector
def GenerateNoisyCPT_fit(sg, sr, sp, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, phirepump, titarepump, freqs, plot=False, solvemode=1, detpvec=None, noiseamplitude=0.001):
Frequencyvector, Fluovector = PerformExperiment_8levels_fixedRabi(sg, sr, sp, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, phirepump, titarepump, min(freqs), max(freqs) + freqs[1]-freqs[0], freqs[1]-freqs[0], plot=False, solvemode=1, detpvec=None)
NoisyFluovector = [fluo+noiseamplitude*(2*random.random()-1) for fluo in Fluovector]
return Frequencyvector, NoisyFluovector
def AddNoiseToCPT(Fluo, noisefactor):
return [f+noisefactor*(2*random.random()-1) for f in Fluo]
def SmoothNoisyCPT(Fluo, window=11, poly=3):
SmoothenFluo = sf(Fluo, window, poly)
return SmoothenFluo
def GetMinimaInfo(Freq, Fluo, u, titadoppler, detuningdoppler, detuningrepump, MinimumCriterium=2, NormalizationCriterium=1):
"""
FUNCION VIEJA
Esta funcion devuelve valores de frecuencias y fluorescencia relativa de los minimos.
Minimumcriterion:
1: Saca los minimos con funcion argelextrema
2: Directamente con las frecuencias teoricas busca las fluorescencias
Normalizationcriterium:
1: Devuelve la fluorescencia absoluta de los minimos
2: Devuelve el cociente entre la fluorescencia del minimo y un valor medio entre dos puntos lejanos, como si no
hubiera una resonancia oscura y hubiera una recta. Ese valor esta a DistanciaFrecuenciaCociente del detuning del azul (el punto medio entre las dos DR en este caso)
3: Devuelve el cociente entre la fluorescencia del minimo y el valor a -100 MHz (si se hizo de -100 a 100),
o el valor limite por izquierda de la curva
"""
FluorescenceOfMaximum = max(Fluo)
FrequencyOfMaximum = Freq[Fluo.index(FluorescenceOfMaximum)]
#criterio para encontrar los minimos
#criterio usando minimos de la fluorescencia calculados con la curva
if MinimumCriterium == 1:
LocationOfMinimums = argrelextrema(np.array(Fluo), np.less)[0]
FluorescenceOfMinimums = np.array([Fluo[i] for i in LocationOfMinimums])
FrequencyOfMinimums = np.array([Freq[j] for j in LocationOfMinimums])
#criterio con las DR teoricas
if MinimumCriterium == 2:
FrecuenciasDRTeoricas, FrecuenciasDRTeoricasPositivas = [darkresonance for darkresonance in CalculoTeoricoDarkResonances_8levels(u, titadoppler, detuningdoppler, detuningrepump)[0]]
FrequencyOfMinimums = []
FluorescenceOfMinimums =[]
print(FrecuenciasDRTeoricas)
k=0
ventanita = 0.001
while k < len(Freq):
if Freq[k] < FrecuenciasDRTeoricas[0] + ventanita and Freq[k] > FrecuenciasDRTeoricas[0] - ventanita:
FrequencyOfMinimums.append(Freq[k])
FluorescenceOfMinimums.append(Fluo[k])
elif Freq[k] < FrecuenciasDRTeoricas[1] + ventanita and Freq[k] > FrecuenciasDRTeoricas[1] - ventanita:
FrequencyOfMinimums.append(Freq[k])
FluorescenceOfMinimums.append(Fluo[k])
elif Freq[k] < FrecuenciasDRTeoricas[2] + ventanita and Freq[k] > FrecuenciasDRTeoricas[2] - ventanita:
FrequencyOfMinimums.append(Freq[k])
FluorescenceOfMinimums.append(Fluo[k])
elif Freq[k] < FrecuenciasDRTeoricas[3] + ventanita and Freq[k] > FrecuenciasDRTeoricas[3] - ventanita:
FrequencyOfMinimums.append(Freq[k])
FluorescenceOfMinimums.append(Fluo[k])
elif Freq[k] < FrecuenciasDRTeoricas[4] + ventanita and Freq[k] > FrecuenciasDRTeoricas[4] - ventanita:
FrequencyOfMinimums.append(Freq[k])
FluorescenceOfMinimums.append(Fluo[k])
elif Freq[k] < FrecuenciasDRTeoricas[5] + ventanita and Freq[k] > FrecuenciasDRTeoricas[5] - ventanita:
FrequencyOfMinimums.append(Freq[k])
FluorescenceOfMinimums.append(Fluo[k])
k = k + 1
print(FrequencyOfMinimums)
if len(FrequencyOfMinimums) != len(FrecuenciasDRTeoricas):
print('NO ANDA BIEN ESTO PAPI, revisalo')
#esto es para establecer un criterio para la fluorescencia relativa
DistanciaFrecuenciaCociente = 15
if NormalizationCriterium==1:
FluoNormDivisor = 1
if NormalizationCriterium==2:
k = 0
while k < len(Freq):
if Freq[k] < detuningdoppler-DistanciaFrecuenciaCociente + 2 and Freq[k] > detuningdoppler-DistanciaFrecuenciaCociente - 2:
FluoIzquierda = Fluo[k]
print('Izq:', Freq[k])
break
else:
k = k + 1
l = 0
while l < len(Freq):
if Freq[l] < detuningdoppler+DistanciaFrecuenciaCociente + 2 and Freq[l] > detuningdoppler+DistanciaFrecuenciaCociente - 2:
FluoDerecha = Fluo[l]
print('Der: ', Freq[l])
break
else:
l = l + 1
FluoNormDivisor = 0.5*(FluoDerecha+FluoIzquierda)
print(FluoNormDivisor)
if NormalizationCriterium==3:
FluoNormDivisor = Fluo[0]
RelativeFluorescenceOfMinimums = np.array([Fluore/FluoNormDivisor for Fluore in FluorescenceOfMinimums])
return FrequencyOfMinimums, RelativeFluorescenceOfMinimums
def GetPlotsofFluovsAngle_8levels(FrequencyOfMinimumsVector, RelativeFluorescenceOfMinimumsVector, u, titadoppler, detuningdoppler, detuningrepump, ventana=0.25, taketheoricalDR=False):
#primero buscamos las frecuencias referencia que se parezcan a las 6:
i = 0
FrecuenciasReferenciaBase = FrequencyOfMinimumsVector[0]
FrecuenciasDRTeoricas = [darkresonance for darkresonance in CalculoTeoricoDarkResonances_8levels(u, titadoppler, detuningdoppler, detuningrepump)[0]]
while i < len(FrequencyOfMinimumsVector):
if len(FrequencyOfMinimumsVector[i])==len(FrecuenciasDRTeoricas):
FrecuenciasReferenciaBase = FrequencyOfMinimumsVector[i]
print('Cool! Taking the DR identified with any curve')
break
else:
i = i + 1
if i==len(FrequencyOfMinimumsVector):
print('No hay ningun plot con 5 resonancias oscuras. Tomo las teóricas')
FrecuenciasReferenciaBase = FrecuenciasDRTeoricas
if taketheoricalDR:
FrecuenciasReferenciaBase = FrecuenciasDRTeoricas
Ventana = abs(ventana*(FrecuenciasReferenciaBase[1] - FrecuenciasReferenciaBase[0])) #ventana separadora de resonancias
print('Ventana = ', Ventana)
DarkResonance1Frequency = []
DarkResonance1Fluorescence = []
DarkResonance2Frequency = []
DarkResonance2Fluorescence = []
DarkResonance3Frequency = []
DarkResonance3Fluorescence = []
DarkResonance4Frequency = []
DarkResonance4Fluorescence = []
DarkResonance5Frequency = []
DarkResonance5Fluorescence = []
DarkResonance6Frequency = []
DarkResonance6Fluorescence = []
i = 0
while i < len(FrequencyOfMinimumsVector):
j = 0
FrecuenciasReferencia = [i for i in FrecuenciasReferenciaBase]
while j < len(FrequencyOfMinimumsVector[i]):
if abs(FrequencyOfMinimumsVector[i][j]) < (abs(FrecuenciasReferencia[0])+Ventana) and abs(FrequencyOfMinimumsVector[i][j]) >= (abs(FrecuenciasReferencia[0])-Ventana):
DarkResonance1Frequency.append(FrequencyOfMinimumsVector[i][j])
DarkResonance1Fluorescence.append(RelativeFluorescenceOfMinimumsVector[i][j])
FrecuenciasReferencia[0] = 0
elif abs(FrequencyOfMinimumsVector[i][j]) < (abs(FrecuenciasReferencia[1])+Ventana) and abs(FrequencyOfMinimumsVector[i][j]) >= (abs(FrecuenciasReferencia[1])-Ventana):
DarkResonance2Frequency.append(FrequencyOfMinimumsVector[i][j])
DarkResonance2Fluorescence.append(RelativeFluorescenceOfMinimumsVector[i][j])
FrecuenciasReferencia[1] = 0
elif abs(FrequencyOfMinimumsVector[i][j]) < (abs(FrecuenciasReferencia[2])+Ventana) and abs(FrequencyOfMinimumsVector[i][j]) >= (abs(FrecuenciasReferencia[2])-Ventana):
DarkResonance3Frequency.append(FrequencyOfMinimumsVector[i][j])
DarkResonance3Fluorescence.append(RelativeFluorescenceOfMinimumsVector[i][j])
FrecuenciasReferencia[2] = 0
elif abs(FrequencyOfMinimumsVector[i][j]) < (abs(FrecuenciasReferencia[3])+Ventana) and abs(FrequencyOfMinimumsVector[i][j]) >= (abs(FrecuenciasReferencia[3])-Ventana):
DarkResonance4Frequency.append(FrequencyOfMinimumsVector[i][j])
DarkResonance4Fluorescence.append(RelativeFluorescenceOfMinimumsVector[i][j])
FrecuenciasReferencia[3] = 0
elif abs(FrequencyOfMinimumsVector[i][j]) < (abs(FrecuenciasReferencia[4])+Ventana) and abs(FrequencyOfMinimumsVector[i][j]) >= (abs(FrecuenciasReferencia[4])-Ventana):
DarkResonance5Frequency.append(FrequencyOfMinimumsVector[i][j])
DarkResonance5Fluorescence.append(RelativeFluorescenceOfMinimumsVector[i][j])
FrecuenciasReferencia[4] = 0
elif abs(FrequencyOfMinimumsVector[i][j]) < (abs(FrecuenciasReferencia[5])+Ventana) and abs(FrequencyOfMinimumsVector[i][j]) >= (abs(FrecuenciasReferencia[5])-Ventana):
DarkResonance6Frequency.append(FrequencyOfMinimumsVector[i][j])
DarkResonance6Fluorescence.append(RelativeFluorescenceOfMinimumsVector[i][j])
FrecuenciasReferencia[5] = 0
else:
#print('Algo anduvo mal, por ahi tenes que cambiar la ventana che')
pass
j = j + 1
if np.count_nonzero(FrecuenciasReferencia) > 0:
if FrecuenciasReferencia[0] != 0:
DarkResonance1Frequency.append(FrecuenciasReferencia[0])
DarkResonance1Fluorescence.append()
if FrecuenciasReferencia[1] != 0:
DarkResonance2Frequency.append(FrecuenciasReferencia[1])
DarkResonance2Fluorescence.append(0)
if FrecuenciasReferencia[2] != 0:
DarkResonance3Frequency.append(FrecuenciasReferencia[2])
DarkResonance3Fluorescence.append(0)
if FrecuenciasReferencia[3] != 0:
DarkResonance4Frequency.append(FrecuenciasReferencia[3])
DarkResonance4Fluorescence.append(0)
if FrecuenciasReferencia[4] != 0:
DarkResonance5Frequency.append(FrecuenciasReferencia[4])
DarkResonance5Fluorescence.append(0)
if FrecuenciasReferencia[5] != 0:
DarkResonance6Frequency.append(FrecuenciasReferencia[5])
DarkResonance6Fluorescence.append(0)
i = i + 1
return DarkResonance1Frequency, DarkResonance1Fluorescence, DarkResonance2Frequency, DarkResonance2Fluorescence, DarkResonance3Frequency, DarkResonance3Fluorescence, DarkResonance4Frequency, DarkResonance4Fluorescence, DarkResonance5Frequency, DarkResonance5Fluorescence, DarkResonance6Frequency, DarkResonance6Fluorescence, FrecuenciasReferenciaBase
def PerformExperiment_8levels(rabG, rabR, rabP, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, phirepump, titarepump, freqMin, freqMax, freqStep, plot=False, solvemode=1, detpvec=None):
"""
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
"""
Fluovectors = []
for titaprobe in titaprobeVec:
tinicial = time.time()
ProbeDetuningVectorL, Fluovector = CPTspectrum8levels(rabG, rabR, rabP, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobe, phirepump, titarepump, freqMin=freqMin, freqMax=freqMax, freqStep=freqStep, plot=False, solvemode=1)
tfinal = time.time()
print('Done angle ', titarepump, ' Total time: ', round((tfinal-tinicial), 2), "s")
if plot:
plt.figure()
plt.xlabel('Repump detuning (MHz')
plt.ylabel('Fluorescence (A.U.)')
plt.plot(ProbeDetuningVectorL, Fluovector, label=str(titarepump)+'º tita repump, T: ' + str(T*1e3) + ' mK')
plt.legend()
Fluovectors.append(Fluovector)
if len(titaprobeVec) == 1: #esto es para que no devuelva un vector de vectores si solo fijamos un angulo
Fluovectors = Fluovector
return ProbeDetuningVectorL, Fluovectors
def PerformExperiment_8levels_fixedRabi(sg, sr, sp, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, phirepump, titarepump, freqMin, freqMax, freqStep, plot=False, solvemode=1, detpvec=None):
"""
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
"""
Fluovectors = []
for titaprobe in titaprobeVec:
tinicial = time.time()
ProbeDetuningVectorL, Fluovector = CPTspectrum8levels_fixedRabi(sg, sr, sp, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobe, phirepump, titarepump, freqMin=freqMin, freqMax=freqMax, freqStep=freqStep, plot=False, solvemode=1)
tfinal = time.time()
print('Done angle ', titarepump, ' Total time: ', round((tfinal-tinicial), 2), "s")
if plot:
plt.figure()
plt.xlabel('Repump detuning (MHz')
plt.ylabel('Fluorescence (A.U.)')
plt.plot(ProbeDetuningVectorL, Fluovector, label=str(titarepump)+'º tita repump, T: ' + str(T*1e3) + ' mK')
plt.legend()
Fluovectors.append(Fluovector)
if len(titaprobeVec) == 1: #esto es para que no devuelva un vector de vectores si solo fijamos un angulo
Fluovectors = Fluovector
return ProbeDetuningVectorL, Fluovectors
analisis/plots/20230424_CPT_TemperatureSens/Data/EITfit/threeLevel_2repumps_CPTPlotter.py 0 → 100644
View file @ 32f4f986
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Sep 1 17:58:39 2020
@author: oem
"""
import os
import numpy as np
#os.chdir('/home/oem/Nextcloud/G_liaf/liaf-TrampaAnular/Código General/EIT-CPT/Buenos Aires/Experiment Simulations/CPT scripts/Eight Level 2 repumps')
from threeLevel_2repumps_AnalysisFunctions import CalculoTeoricoDarkResonances_8levels, GetMinimaInfo, GetPlotsofFluovsAngle_8levels, PerformExperiment_8levels, FindDRFrequencies, FindRelativeFluorescencesOfDR, GenerateNoisyCPT, SmoothNoisyCPT, GetFinalMaps, GenerateNoisyCPT_fixedRabi, GenerateNoisyCPT_fit
import matplotlib.pyplot as plt
import time
from threeLevel_2repumps_AnalysisFunctions import MeasureRelativeFluorescenceFromCPT, IdentifyPolarizationCoincidences, RetrieveAbsoluteCoincidencesBetweenMaps, GetClosestIndex
#C:\Users\Usuario\Nextcloud\G_liaf\liaf-TrampaAnular\Código General\EIT-CPT\Buenos Aires\Experiment Simulations\CPT scripts\Eight Level 2 repumps
ub = 9.27e-24
h = 6.63e-34
c = (ub/h)*1e-4 #en unidades de MHz/G
#u = 1e6
u = 33.5e6
B = (u/(2*np.pi))/c
#sg, sp = 0.6, 5 #parámetros de control, saturación del doppler y repump
#rabG, rabP = sg*gPS, sp*gPD #frecuencias de rabi
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6 #anchos de linea de las transiciones
lw = 0.1
DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth = lw, lw, lw #ancho de linea de los laseres
DetDoppler = -36 #42
DetRepumpVec = [DetDoppler+29.6]
Tvec = [0.7] #temperatura en mK
alpha = 0*(np.pi/180) #angulo entre los láseres
phidoppler, titadoppler = 0, 90
phirepump, titarepump = 0, 0
phiprobe = 0
titaprobe = 90
#Calculo las resonancias oscuras teóricas
#ResonanciasTeoricas, DRPositivas = CalculoTeoricoDarkResonances_8levels(u/(2*np.pi*1e6), titadoppler, DetDoppler, DetRepump)
#Parametros de la simulacion cpt
center = -45
span = 80
freqMin = center-span*0.5
freqMax = center+span*0.5
""" parametros para tener espectros coherentes
freqMin = -56
freqMax = 14
"""
freqStep = 1e-1
noiseamplitude = 0
RelMinMedido0Vector = []
RelMinMedido1Vector = []
RelMinMedido2Vector = []
RelMinMedido3Vector = []
RelMinMedido4Vector = []
#Sr = np.arange(0, 10, 0.2)
#Sg = np.arange(0.01, 1, 0.05)
#Sp = np.arange(0.1, 6.1, 1)
#Sg = [0.6**2]
#Sp = [2.3**2]
Sg = [1.4]
Sp = [6]
Sr = [11]
i = 0
save = False
showFigures = True
if not showFigures:
plt.ioff()
else:
plt.ion()
fig1, ax1 = plt.subplots()
offsetx = 464
ax1.plot([f-offsetx for f in FreqsDR], CountsDR, 'o')
run = True
Scale = 730
Offset = 600 #600 para 20k cuentas aprox
MaxCoherenceValue = []
for sg in Sg:
for sp in Sp:
rabG, rabP = sg*gPS, sp*gPD
for Ti in Tvec:
T = Ti*1e-3
for DetRepump in DetRepumpVec:
print(T)
for sr in Sr:
rabR = sr*gPD
#MeasuredFreq, MeasuredFluo = GenerateNoisyCPT(rabG, rabR, rabP, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, [titaprobe], phirepump, titarepump, freqMin, freqMax, freqStep, plot=False, solvemode=1, detpvec=None, noiseamplitude=noiseamplitude)
if run:
MeasuredFreq4, MeasuredFluo4 = GenerateNoisyCPT_fixedRabi(sg, sr, sp, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, [titaprobe], phirepump, titarepump, freqMin, freqMax, freqStep, plot=False, solvemode=1, detpvec=None, noiseamplitude=noiseamplitude)
#SmoothFluo = SmoothNoisyCPT(MeasuredFluo, window=9, poly=2)
SmoothFluo4 = MeasuredFluo4
#Scale = max(BestC)/max([100*s for s in SmoothFluo4])
ax1.plot(MeasuredFreq4, [Scale*100*f + Offset for f in SmoothFluo4], label=f'Sr = {sr}')
ax1.axvline(DetDoppler, linestyle='--', linewidth=1)
#if sr != 0:
#ax1.axvline(DetRepump, linestyle='--', linewidth=1)
MaxCoherenceValue.append(np.max(SmoothFluo4))
#print(titaprobe)
ax1.set_xlabel('Detuning Rebombeo (MHz)')
ax1.set_ylabel('Fluorescencia (AU)')
ax1.set_title(f'B: {round(B, 2)} G, Sdop: {round(sg, 2)}, Sp: {round(sp, 2)}, Sr: {round(sr, 2)}, lw: {lw} MHz, T: {Ti} mK')
#ax1.set_ylim(0, 8)
#ax1.axvline(DetDoppler, linestyle='dashed', color='red', linewidth=1)
#ax1.axvline(DetRepump, linestyle='dashed', color='black', linewidth=1)
#ax1.set_title('Pol Doppler y Repump: Sigma+ Sigma-, Pol Probe: PI')
#ax1.legend()
ax1.grid()
print (f'{i+1}/{len(Sg)*len(Sp)}')
i = i + 1
if save:
plt.savefig(f'Mapa_plots_100k_1mk/CPT_SMSM_sdop{round(sg, 2)}_sp{round(sp, 2)}_sr{round(sr, 2)}.jpg')
ax1.legend()
"""
plt.figure()
plt.plot(Sr, MaxCoherenceValue, 'o')
plt.xlabel('Sr')
plt.ylabel('Coherence')
"""
"""
plt.figure()
plt.plot(MeasuredFreq, [100*f for f in SmoothFluo], color='darkred')
plt.xlabel('Desintonía 866 (MHz)')
plt.ylabel('Fluorescencia (A.U.)')
plt.axvline(-30, color='darkblue', linewidth=1.2, linestyle='--')
plt.yticks(np.arange(0.4, 1.8, 0.2))
plt.ylim(0.5, 1.6)
plt.grid()
plt.figure()
plt.plot(MeasuredFreq4, [100*f for f in SmoothFluo4], color='darkred')
plt.xlabel('Desintonía 866 (MHz)')
plt.ylabel('Fluorescencia (A.U.)')
plt.axvline(-30, color='darkblue', linewidth=1.2, linestyle='--')
plt.yticks(np.arange(0.8, 2.4, 0.4))
plt.grid()
"""
#%%
from scipy.optimize import curve_fit
T = 0.5e-3
sg = 0.7
sp = 6
sr = 0
DetDoppler = -14
DetRepump = 0
FitsSp = []
FitsOffset = []
Sg = [0.87]
def FitEIT(freqs, SP, offset):
MeasuredFreq, MeasuredFluo = GenerateNoisyCPT_fit(0.87, sr, SP, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, [titaprobe], phirepump, titarepump, freqs, plot=False, solvemode=1, detpvec=None, noiseamplitude=noiseamplitude)
FinalFluo = [f*43000 + 2685 for f in MeasuredFluo]
return FinalFluo
freqs = [f-offsetx+32 for f in FreqsDR]
freqslong = np.arange(min(freqs), max(freqs)+freqs[1]-freqs[0], 0.1*(freqs[1]-freqs[0]))
popt, pcov = curve_fit(FitEIT, freqs, CountsDR, p0=[5, 700], bounds=(0, [10, 1e6]))
FitsSp.append(popt[0])
FitsOffset.append(popt[1])
print(popt)
FittedEIT = FitEIT(freqslong, *popt)
plt.figure()
plt.errorbar(freqs, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', capsize=2, markersize=2)
plt.plot(freqslong, FitEIT(freqslong, *popt))
plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {T*1e3} mK, detDop: {DetDoppler} MHz')
np.savetxt('CPT_measured.txt', np.transpose([freqs, CountsDR]))
np.savetxt('CPT_fitted.txt', np.transpose([freqslong, FittedEIT]))
analisis/plots/20230424_CPT_TemperatureSens/Data/EITfit/threeLevel_2repumps_linealpol_python_scripts.py 0 → 100644
View file @ 32f4f986
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Apr 7 22:30:01 2020
@author: nico
"""
import numpy as np
import time
import matplotlib.pyplot as plt
from scipy.signal import argrelextrema
"""
Scripts para el calculo de la curva CPT
"""
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)
return H0
def HImatrix(rabG, rabP, phidoppler, titadoppler, phiprobe, titaprobe):
"""
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.exp(-1j*phiprobe)
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.exp(1j*phiprobe)
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.exp(-1j*phiprobe)
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.exp(1j*phiprobe)
HI[j-1, i-1] = np.conjugate(HI[i-1, j-1])
return HI
def Lplusminus(detr, detp, phirepump, titarepump, forma=1):
Hintplus = np.zeros((8, 8), dtype=np.complex_)
Hintminus = np.zeros((8, 8), dtype=np.complex_)
Hintplus[4, 2] = (-1/2)*np.sin(titarepump)*np.exp(1j*phirepump)
Hintplus[5, 2] = (-1/np.sqrt(3))*np.cos(titarepump)
Hintplus[6, 2] = (1/(2*np.sqrt(3)))*np.sin(titarepump)*np.exp(-1j*phirepump)
Hintplus[5, 3] = (-1/(2*np.sqrt(3)))*np.sin(titarepump)*np.exp(1j*phirepump)
Hintplus[6, 3] = (-1/np.sqrt(3))*np.cos(titarepump)
Hintplus[7, 3] = (1/2)*np.sin(titarepump)*np.exp(-1j*phirepump)
Hintminus[2, 4] = (-1/2)*np.sin(titarepump)*np.exp(-1j*phirepump)
Hintminus[2, 5] = (-1/np.sqrt(3))*np.cos(titarepump)
Hintminus[2, 6] = (1/(2*np.sqrt(3)))*np.sin(titarepump)*np.exp(1j*phirepump)
Hintminus[3, 5] = (-1/(2*np.sqrt(3)))*np.sin(titarepump)*np.exp(-1j*phirepump)
Hintminus[3, 6] = (-1/np.sqrt(3))*np.cos(titarepump)
Hintminus[3, 7] = (1/2)*np.sin(titarepump)*np.exp(1j*phirepump)
if forma==1:
Lplus = np.zeros((64, 64), dtype=np.complex_)
Lminus = np.zeros((64, 64), dtype=np.complex_)
DeltaBar = 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:
if (k==2 or k==3) and r > 3:
Lplus[r*8+q][k*8+j] = (-1j)*(Hintplus[r,k])
if (r==2 or r==3) and k > 3:
Lminus[r*8+q][k*8+j] = (-1j)*(Hintminus[r,k])
elif r==k:
if (q==2 or q==3) and j > 3:
Lplus[r*8+q][k*8+j] = (-1j)*(- Hintplus[j,q])
if (j==2 or j==3) and q > 3:
Lminus[r*8+q][k*8+j] = (-1j)*(- Hintminus[j,q])
if forma==2:
deltaKro = np.diag([1, 1, 1, 1, 1, 1, 1, 1])
Lplus = (-1j)*(np.kron(Hintplus, deltaKro) - np.kron(deltaKro, Hintplus))
Lminus = (-1j)*(np.kron(Hintminus, deltaKro) - np.kron(deltaKro, Hintminus))
DeltaBar = np.zeros((64, 64), dtype=np.complex_)
for i in range(64):
DeltaBar[i, i] = (1j)*(detr - detp)
return np.matrix(Lminus), np.matrix(Lplus), np.matrix(DeltaBar)
def GetL1(Lplus, Lminus, DeltaBar, L0, rabR, nmax):
"""
Devuelve Splus0 y Sminus0
"""
Sp = (-1)*(0.5*rabR)*(np.matrix(np.linalg.inv(L0 - (nmax+1)*DeltaBar))*np.matrix(Lplus))
Sm = (-1)*(0.5*rabR)*(np.matrix(np.linalg.inv(L0 + (nmax+1)*DeltaBar))*np.matrix(Lminus))
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)*(rabR)*(np.matrix(np.linalg.inv(L0 - n*DeltaBar + rabR*(Lminus*np.matrix(Sp))))*np.matrix(Lplus))
Sm = (-1)*(rabR)*(np.matrix(np.linalg.inv(L0 + n*DeltaBar + rabR*(Lplus*np.matrix(Sm))))*np.matrix(Lminus))
L1 = 0.5*rabR*(np.matrix(Lminus)*np.matrix(Sp) + np.matrix(Lplus)*np.matrix(Sm))
return L1
def EffectiveL(gPS, gPD, lwg, lwr, 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*(lwr + lwp)
Leff[5, 5] = 2*(lwr + lwp)
Leff[6, 6] = 2*(lwr + lwp)
Leff[7, 7] = 2*(lwr + lwp)
return (-0.5j)*Leff
def CalculateSingleMmatrix(gPS, gPD, lwg, lwr, 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.matrix(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
factor1 = 1
factor2 = 1
factor3 = 1
factor4 = 1
#M[36, 45] = lwp
M[36,36] = 2*(lwr + factor1*lwp)
M[37,37] = 2*(lwr + factor1*lwp)
M[38,38] = 2*(lwr + factor1*lwp)
M[39,39] = 2*(lwr + factor1*lwp)
M[44,44] = 2*(lwr + factor2*lwp)
M[45,45] = 2*(lwr + factor2*lwp)
M[46,46] = 2*(lwr + factor2*lwp)
M[47,47] = 2*(lwr + factor2*lwp)
M[52,52] = 2*(lwr + factor3*lwp)
M[53,53] = 2*(lwr + factor3*lwp)
M[54,54] = 2*(lwr + factor3*lwp)
M[55,55] = 2*(lwr + factor3*lwp)
M[60,60] = 2*(lwr + factor4*lwp)
M[61,61] = 2*(lwr + factor4*lwp)
M[62,62] = 2*(lwr + factor4*lwp)
M[63,63] = 2*(lwr + factor4*lwp)
return M
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/(2*mcalcio))
return gammaD
def FullL_efficient(rabG, rabR, rabP, gPS = 0, gPD = 0, Detg = 0, Detr = 0, Detp = 0, u = 0, lwg = 0, lwr=0, lwp = 0,
phidoppler=0, titadoppler=0, phiprobe=0, titaprobe=0, phirepump=0, titarepump=0, T = 0, alpha = 0):
"""
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)
#lwr = np.sqrt(lwr**2 + dopplerBroadening(0.397e-6, 0.866e-6, alpha, T)**2)
lwg = np.sqrt(lwg**2 + db**2)
lwr = np.sqrt(lwr**2 + db**2)
CC = EffectiveL(gPS, gPD, lwg, lwr, lwp)
Heff = H0matrix(Detg, Detp, u) + HImatrix(rabG, rabP, phidoppler, titadoppler, phiprobe, titaprobe) + CC
Heffdaga = np.matrix(Heff).getH()
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, lwr, lwp)
L0 = np.array(np.matrix(Lfullpartial) + M)
nmax = 1
Lminus, Lplus, DeltaBar = Lplusminus(Detr, Detp, phirepump, titarepump)
factor1 = np.exp(1j*0.2*np.pi)
factor2 = np.exp(-1j*0.2*np.pi)
#print(factor)
L1 = GetL1(factor1*Lplus, factor2*Lminus, DeltaBar, L0, rabR, nmax)
Lfull = L0 + L1
#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
"""
def CalculoTeoricoDarkResonances(u, titadoppler):
if titadoppler==0:
NegativeDR = [(-7/5)*u, (-3/5)*u, (-1/5)*u, (1/5)*u, (3/5)*u, (7/5)*u]
elif titadoppler==90:
NegativeDR = [(-11/5)*u, (-7/5)*u, (-3/5)*u, (3/5)*u, (7/5)*u, (11/5)*u]
PositiveDR = [(-8/5)*u, (-4/5)*u, 0, (4/5)*u, (8/5)*u]
return NegativeDR, PositiveDR
def CPTspectrum8levels(rabG, rabR, rabP, gPS, gPD, Detg, Detr, u, lwg, lwr, lwp, Temp, alpha, phidoppler, titadoppler, phiprobe, titaprobe, phirepump, titarepump,
freqMin=-100, freqMax=100, freqStep=1e-1, plot=False, solvemode=1):
"""
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)
phirepump, titarepump = phirepump*(np.pi/180), titarepump*(np.pi/180)
DetProbeVector = 2*np.pi*np.arange(freqMin*1e6, freqMax*1e6, freqStep*1e6)
Detg, Detr = 2*np.pi*Detg*1e6, 2*np.pi*Detr*1e6
lwg, lwr, lwp = 2*np.pi*lwg*1e6, 2*np.pi*lwr*1e6, 2*np.pi*lwp*1e6
#u = 2*np.pi*u*1e6
Fluovector = []
tinicial = time.time()
for Detp in DetProbeVector:
L = FullL_efficient(rabG, rabR, rabP, gPS, gPD, Detg, Detr, Detp, u, lwg, lwr, lwp, phidoppler, titadoppler, phiprobe, titaprobe, phirepump, titarepump, Temp, alpha)
if solvemode == 1:
rhovectorized = np.linalg.solve(L, np.array([int(i==0) for i in range(64)]))
Fluo = np.real(rhovectorized[18] + np.real(rhovectorized[27])) #estos son los rho33 + rho44
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
def CPTspectrum8levels_fixedRabi(sg, sr, sp, gPS, gPD, Detg, Detr, u, lwg, lwr, lwp, Temp, alpha, phidoppler, titadoppler, phiprobe, titaprobe, phirepump, titarepump,
freqMin=-100, freqMax=100, freqStep=1e-1, plot=False, solvemode=1):
"""
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)
phirepump, titarepump = phirepump*(np.pi/180), titarepump*(np.pi/180)
DetProbeVector = 2*np.pi*np.arange(freqMin*1e6, freqMax*1e6, freqStep*1e6)
Detg, Detr = 2*np.pi*Detg*1e6, 2*np.pi*Detr*1e6
#lwg, lwr, lwp = 2*np.pi*lwg*1e6, 2*np.pi*lwr*1e6, 2*np.pi*lwp*1e6
lwg, lwr, lwp = lwg*1e6, lwr*1e6, lwp*1e6
rabG = sg*gPS
rabR = sr*gPD
rabP = sp*gPD
#u = 2*np.pi*u*1e6
Fluovector = []
tinicial = time.time()
for Detp in DetProbeVector:
L = FullL_efficient(rabG, rabR, rabP, gPS, gPD, Detg, Detr, Detp, u, lwg, lwr, lwp, phidoppler, titadoppler, phiprobe, titaprobe, phirepump, titarepump, Temp, alpha)
if solvemode == 1:
coh = 5
rhovectorized = np.linalg.solve(L, np.array([int(i==0) for i in range(64)]))
#Fluo = np.abs(rhovectorized[coh])
Fluo = np.real(rhovectorized[18] + np.real(rhovectorized[27])) #estos son los rho33 + rho44
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
#%%
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(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)
NegativeDR, PositiveDR = CalculoTeoricoDarkResonances(u/(2*np.pi*1e6), titadoppler)
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.)')
for PDR in PositiveDR:
plt.axvline(Detr+PDR, linestyle='--', linewidth=0.5, color='red')
for NDR in NegativeDR:
plt.axvline(Detg+NDR, linestyle='--', linewidth=0.5, color='blue')
#parametros que andan piola:
"""
ub = 9.27e-24
h = 6.63e-34
c = (ub/h)*1e-4 #en unidades de MHz/G
B = 17 #campo magnetico en gauss
u = c*B
#u = 80e6
sr, sp = 0.53, 4.2
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6
rabR, rabP = sr*gPS, sp*gPD
lw = 2*np.pi * 0.33e6
lwr, lwp = lw, lw #ancho de linea de los laseres
dr_spec = - 2*np.pi* 26e6
freqSteps = 500
freqMin = -100e6
freqMax = 100e6
dps = 2*np.pi*np.linspace(freqMin, freqMax, freqSteps)
#dps = [-30e6]
alfar = 90*(np.pi/180)
ex_r, ey_r, ez_r = np.sin(alfar)*np.cos(0), np.sin(alfar)*np.sin(0), np.cos(alfar)
alfap = 90*(np.pi/180)
ex_p, ey_p, ez_p = np.sin(alfap)*np.cos(0), np.sin(alfap)*np.sin(0), np.cos(alfap)
"""
analisis/plots/20230510_MotionalSpectrum/CPT_plotter_20230510.py 0 → 100644
View file @ 32f4f986
import h5py
import matplotlib.pyplot as plt
import numpy as np
import sys
import re
import ast
from scipy.optimize import curve_fit
import os
from scipy import interpolate
#Mediciones barriendo angulo del TISA y viendo kicking de resonancias oscuras
#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20220106_CPT_DosLaseres_v08_TISA_DR\Data
os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20230510_MotionalSpectrum/Data/')
MOTIONAL_FILES = """000011490-AD9910RAM
000011491-AD9910RAM
000011492-AD9910RAM
000011493-AD9910RAM
000011496-AD9910RAM
000011499-AD9910RAM
000011500-AD9910RAM
000011501-AD9910RAM
000011502-AD9910RAM
000011503-AD9910RAM
000011504-AD9910RAM
000011505-AD9910RAM
000011506-AD9910RAM
000011507-AD9910RAM
000011511-AD9910RAM
000011512-AD9910RAM
000011513-AD9910RAM
000011514-AD9910RAM
000011515-AD9910RAM
000011516-AD9910RAM
000011517-AD9910RAM
000011518-AD9910RAM
000011519-AD9910RAM
000011521-AD9910RAM
000011522-AD9910RAM
000011560-AD9910RAM
000011561-AD9910RAM
000011562-AD9910RAM
000011563-AD9910RAM
000011564-AD9910RAM
000011566-AD9910RAM
000011567-AD9910RAM
000011569-AD9910RAM
000011571-AD9910RAM
000011572-AD9910RAM
000011574-AD9910RAM
000011575-AD9910RAM
000011576-AD9910RAM
000011577-AD9910RAM
000011580-AD9910RAM
000011581-AD9910RAM
000011582-AD9910RAM
"""
def SeeKeys(files):
for i, fname in enumerate(files.split()):
data = h5py.File(fname+'.h5', 'r') # Leo el h5: Recordar que nuestros datos estan en 'datasets'
print(fname)
print(list(data['datasets'].keys()))
print(SeeKeys(MOTIONAL_FILES))
#%%
#carpeta pc nico labo escritorio:
#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20211101_CPT_DosLaseres_v03\Data
Counts = []
RealFreqs = []
for i, fname in enumerate(MOTIONAL_FILES.split()):
print(str(i) + ' - ' + fname)
data = h5py.File(fname+'.h5', 'r')
RealFreqs.append(np.array(data['datasets']['real_freq']))
Counts.append(np.array(data['datasets']['counts']))
#%%
"""
Ploteo las curvas de referencia
"""
jvec = [29,30]
#jvec = [16]
plt.figure()
i = 0
for j in jvec:
if i==0:
plt.errorbar([1*f*1e-3 for f in RealFreqs[j]], Counts[j], yerr=np.sqrt(Counts[j]), color='red', fmt='-o', capsize=2, markersize=2)
else:
plt.errorbar([1*f*1e-3 for f in RealFreqs[j]], Counts[j], yerr=np.sqrt(Counts[j]), fmt='-o', capsize=2, markersize=2)
i = i + 1
plt.xlabel('Frecuencia (kHz)')
plt.ylabel('counts')
plt.xlim(851,856)
plt.ylim(600,1700)
plt.grid()
plt.legend()
analisis/plots/20230510_MotionalSpectrum/Data/EITfit/threeLevel_2repumps_AnalysisFunctions.py 0 → 100644
View file @ 32f4f986
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Jul 2 16:30:09 2020
@author: oem
"""
import os
import numpy as np
import time
import matplotlib.pyplot as plt
from scipy.signal import argrelextrema
from EITfit.threeLevel_2repumps_linealpol_python_scripts import CPTspectrum8levels, CPTspectrum8levels_fixedRabi
import random
from scipy.signal import savgol_filter as sf
def CalculoTeoricoDarkResonances_8levels(u, titadoppler, detuningdoppler, detuningrepump):
if titadoppler==0:
NegativeDR = [(-7/5)*u, (-3/5)*u, (-1/5)*u, (1/5)*u, (3/5)*u, (7/5)*u]
elif titadoppler==90:
NegativeDR = [(-11/5)*u, (-7/5)*u, (-3/5)*u, (3/5)*u, (7/5)*u, (11/5)*u]
else:
NegativeDR = [(-11/5)*u, (-7/5)*u, (-3/5)*u, (-1/5)*u, (1/5)*u, (3/5)*u, (7/5)*u, (11/5)*u]
PositiveDR = [(-8/5)*u, (-4/5)*u, 0, (4/5)*u, (8/5)*u]
return [detuningdoppler + dr for dr in NegativeDR], [detuningrepump + dr for dr in PositiveDR]
def GetClosestIndex(Vector, value, tolerance=1e-3):
i = 0
while i<len(Vector):
if abs(Vector[i] - value) < tolerance:
return i
else:
i = i + 1
return GetClosestIndex(Vector, value, tolerance=2*tolerance)
def FindDRFrequencies(Freq, Fluo, TeoDR, entorno=3):
"""
Busca los indices y la frecuencia de los minimos en un entorno cercano al de la DR.
Si no encuentra, devuelve el valor teórico.
"""
IndiceDRteo1, IndiceEntornoinicialDRteo1, IndiceEntornofinalDRteo1 = GetClosestIndex(Freq, TeoDR[0]), GetClosestIndex(Freq, TeoDR[0]-entorno), GetClosestIndex(Freq, TeoDR[0]+entorno)
IndiceDRteo2, IndiceEntornoinicialDRteo2, IndiceEntornofinalDRteo2 = GetClosestIndex(Freq, TeoDR[1]), GetClosestIndex(Freq, TeoDR[1]-entorno), GetClosestIndex(Freq, TeoDR[1]+entorno)
IndiceDRteo3, IndiceEntornoinicialDRteo3, IndiceEntornofinalDRteo3 = GetClosestIndex(Freq, TeoDR[2]), GetClosestIndex(Freq, TeoDR[2]-entorno), GetClosestIndex(Freq, TeoDR[2]+entorno)
IndiceDRteo4, IndiceEntornoinicialDRteo4, IndiceEntornofinalDRteo4 = GetClosestIndex(Freq, TeoDR[3]), GetClosestIndex(Freq, TeoDR[3]-entorno), GetClosestIndex(Freq, TeoDR[3]+entorno)
IndiceDRteo5, IndiceEntornoinicialDRteo5, IndiceEntornofinalDRteo5 = GetClosestIndex(Freq, TeoDR[4]), GetClosestIndex(Freq, TeoDR[4]-entorno), GetClosestIndex(Freq, TeoDR[4]+entorno)
IndiceDRteo6, IndiceEntornoinicialDRteo6, IndiceEntornofinalDRteo6 = GetClosestIndex(Freq, TeoDR[5]), GetClosestIndex(Freq, TeoDR[5]-entorno), GetClosestIndex(Freq, TeoDR[5]+entorno)
EntornoFreqDR1, EntornoFreqDR2 = Freq[IndiceEntornoinicialDRteo1:IndiceEntornofinalDRteo1], Freq[IndiceEntornoinicialDRteo2:IndiceEntornofinalDRteo2]
EntornoFreqDR3, EntornoFreqDR4 = Freq[IndiceEntornoinicialDRteo3:IndiceEntornofinalDRteo3], Freq[IndiceEntornoinicialDRteo4:IndiceEntornofinalDRteo4]
EntornoFreqDR5, EntornoFreqDR6 = Freq[IndiceEntornoinicialDRteo5:IndiceEntornofinalDRteo5], Freq[IndiceEntornoinicialDRteo6:IndiceEntornofinalDRteo6]
EntornoFluoDR1, EntornoFluoDR2 = Fluo[IndiceEntornoinicialDRteo1:IndiceEntornofinalDRteo1], Fluo[IndiceEntornoinicialDRteo2:IndiceEntornofinalDRteo2]
EntornoFluoDR3, EntornoFluoDR4 = Fluo[IndiceEntornoinicialDRteo3:IndiceEntornofinalDRteo3], Fluo[IndiceEntornoinicialDRteo4:IndiceEntornofinalDRteo4]
EntornoFluoDR5, EntornoFluoDR6 = Fluo[IndiceEntornoinicialDRteo5:IndiceEntornofinalDRteo5], Fluo[IndiceEntornoinicialDRteo6:IndiceEntornofinalDRteo6]
IndiceFluoMinimaEntorno1, IndiceFluoMinimaEntorno2 = argrelextrema(np.array(EntornoFluoDR1), np.less)[0], argrelextrema(np.array(EntornoFluoDR2), np.less)[0]
IndiceFluoMinimaEntorno3, IndiceFluoMinimaEntorno4 = argrelextrema(np.array(EntornoFluoDR3), np.less)[0], argrelextrema(np.array(EntornoFluoDR4), np.less)[0]
IndiceFluoMinimaEntorno5, IndiceFluoMinimaEntorno6 = argrelextrema(np.array(EntornoFluoDR5), np.less)[0], argrelextrema(np.array(EntornoFluoDR6), np.less)[0]
try:
FreqDR1 = EntornoFreqDR1[int(IndiceFluoMinimaEntorno1)]
IndiceDR1 = GetClosestIndex(Freq, FreqDR1)
except:
FreqDR1 = TeoDR[0]
IndiceDR1 = IndiceDRteo1
try:
FreqDR2 = EntornoFreqDR2[int(IndiceFluoMinimaEntorno2)]
IndiceDR2 = GetClosestIndex(Freq, FreqDR2)
except:
FreqDR2 = TeoDR[1]
IndiceDR2 = IndiceDRteo2
try:
FreqDR3 = EntornoFreqDR3[int(IndiceFluoMinimaEntorno3)]
IndiceDR3 = GetClosestIndex(Freq, FreqDR3)
except:
FreqDR3 = TeoDR[2]
IndiceDR3 = IndiceDRteo3
try:
FreqDR4 = EntornoFreqDR4[int(IndiceFluoMinimaEntorno4)]
IndiceDR4 = GetClosestIndex(Freq, FreqDR4)
except:
FreqDR4 = TeoDR[3]
IndiceDR4 = IndiceDRteo4
try:
FreqDR5 = EntornoFreqDR5[int(IndiceFluoMinimaEntorno5)]
IndiceDR5 = GetClosestIndex(Freq, FreqDR5)
except:
FreqDR5 = TeoDR[4]
IndiceDR5 = IndiceDRteo5
try:
FreqDR6 = EntornoFreqDR6[int(IndiceFluoMinimaEntorno6)]
IndiceDR6 = GetClosestIndex(Freq, FreqDR6)
except:
FreqDR6 = TeoDR[5]
IndiceDR6 = IndiceDRteo6
return [IndiceDR1, IndiceDR2, IndiceDR3, IndiceDR4, IndiceDR5, IndiceDR6], [FreqDR1, FreqDR2, FreqDR3, FreqDR4, FreqDR5, FreqDR6]
def FindRelativeFluorescencesOfDR(Freq, Fluo, IndicesDR, detuningdoppler, NormalizationCriterium=1, frecuenciareferenciacriterioasintotico=-100, getindices=False):
"""
Toma los indices donde estan las DR y evalua su fluorescencia. Esos indices son minimos locales en un entorno
cercano a las DR teoricas y, si no hay ningun minimo, toma la teorica.
Luego, hace el cociente de esa fluorescencia y un factor de normalización segun NormalizationCriterium:
1: Devuelve la fluorescencia absoluta de los minimos
2: Devuelve el cociente entre la fluorescencia del minimo y un valor medio entre dos puntos lejanos, como si no
hubiera una resonancia oscura y hubiera una recta. Ese valor esta a DistanciaFrecuenciaCociente del detuning del azul (el punto medio entre las dos DR en este caso)
3: Devuelve el cociente entre la fluorescencia del minimo y el valor a -100 MHz (si se hizo de -100 a 100),
o el valor limite por izquierda de la curva
4: Deuelve el cociente entre la fluorescencia del minimo y el valor de fluorescencia a detuning 0 MHz
"""
IndiceDR1, IndiceDR2, IndiceDR3, IndiceDR4, IndiceDR5, IndiceDR6 = IndicesDR[0], IndicesDR[1], IndicesDR[2], IndicesDR[3], IndicesDR[4], IndicesDR[5]
FluorescenceOfMinimums = [Fluo[IndiceDR1], Fluo[IndiceDR2], Fluo[IndiceDR3], Fluo[IndiceDR4], Fluo[IndiceDR5], Fluo[IndiceDR6]]
FrequencyOfMinimums = [Freq[IndiceDR1], Freq[IndiceDR2], Freq[IndiceDR3], Freq[IndiceDR4], Freq[IndiceDR5], Freq[IndiceDR6]]
DistanciaFrecuenciaCociente = 25
if NormalizationCriterium==0:
print('che')
return FrequencyOfMinimums, FluorescenceOfMinimums
if NormalizationCriterium==1:
Fluorescenciacerodetuning = Fluo[GetClosestIndex(Freq, 0)]
Fluorescenciaasintotica = Fluo[GetClosestIndex(Freq, frecuenciareferenciacriterioasintotico)]
return FrequencyOfMinimums, np.array([Fluorescenciacerodetuning/Fluorescenciaasintotica, Fluorescenciacerodetuning/Fluorescenciaasintotica, Fluorescenciacerodetuning/Fluorescenciaasintotica, Fluorescenciacerodetuning/Fluorescenciaasintotica, Fluorescenciacerodetuning/Fluorescenciaasintotica, Fluorescenciacerodetuning/Fluorescenciaasintotica])
if NormalizationCriterium==2:
k = 0
while k < len(Freq):
if Freq[k] < detuningdoppler-DistanciaFrecuenciaCociente + 2 and Freq[k] > detuningdoppler-DistanciaFrecuenciaCociente - 2:
FluoIzquierda = Fluo[k]
indiceizquierda = k
print('Izq:', Freq[k])
break
else:
k = k + 1
l = 0
while l < len(Freq):
if Freq[l] < detuningdoppler+DistanciaFrecuenciaCociente + 2 and Freq[l] > detuningdoppler+DistanciaFrecuenciaCociente - 2:
FluoDerecha = Fluo[l]
indicederecha = l
print('Der: ', Freq[l])
break
else:
l = l + 1
FluoNormDivisor = 0.5*(FluoDerecha+FluoIzquierda)
print(FluoNormDivisor)
if NormalizationCriterium==3:
#asintotico
FluoNormDivisor = Fluo[GetClosestIndex(Freq, frecuenciareferenciacriterioasintotico)]
if NormalizationCriterium==4:
#este te tira la fluorescencia de detuning 0
FluoNormDivisor = Fluo[GetClosestIndex(Freq, 0)]
RelativeFluorescenceOfMinimums = np.array([Fluore/FluoNormDivisor for Fluore in FluorescenceOfMinimums])
print('Esto: ', RelativeFluorescenceOfMinimums)
if NormalizationCriterium==2 and getindices==True:
return FrequencyOfMinimums, RelativeFluorescenceOfMinimums, indiceizquierda, indicederecha
return FrequencyOfMinimums, RelativeFluorescenceOfMinimums
def GetFinalMaps(MapasDR1, MapasDR2, MapasDR3, MapasDR4, MapasDR5, MapasDR6):
"""
Nota: esto vale para polarizacion del 397 sigma+ + sigma-. Sino hay que cambiar los coeficientes.
La estructura es:
MapasDRi = [MapaMedido_criterio1_DRi, MapaMedido_criterio2_DRi, MapaMedido_criterio3_DRi, MapaMedido_criterio4_DRi]
"""
Mapa1 = MapasDR1[0]
Mapa2pi = np.sqrt(3)*(MapasDR2[1] + MapasDR5[1])
Mapa2smas = np.sqrt(12/2)*MapasDR3[1] + (2/np.sqrt(2))*MapasDR6[1]
Mapa2smenos = (2/np.sqrt(2))*MapasDR1[1] + np.sqrt(12/2)*MapasDR4[1]
Mapa3pi = np.sqrt(3)*(MapasDR2[2] + MapasDR5[2])
Mapa3smas = np.sqrt(12/2)*MapasDR3[2] + (2/np.sqrt(2))*MapasDR6[2]
Mapa3smenos = (2/np.sqrt(2))*MapasDR1[2] + np.sqrt(12/2)*MapasDR4[2]
return Mapa1, [Mapa2pi, Mapa2smas, Mapa2smenos], [Mapa3pi, Mapa3smas, Mapa3smenos]
def CombinateDRwithCG(RelMinMedido1, RelMinMedido2, RelMinMedido3, RelMinMedido4):
Fluo1 = RelMinMedido1[0]
Fluo2pi = np.sqrt(3)*(RelMinMedido2[1] + RelMinMedido2[4])
Fluo2smas = np.sqrt(12/2)*RelMinMedido2[2] + (2/np.sqrt(2))*RelMinMedido2[5]
Fluo2smenos = (2/np.sqrt(2))*RelMinMedido2[0] + np.sqrt(12/2)*RelMinMedido2[3]
Fluo3pi = np.sqrt(3)*(RelMinMedido3[1] + RelMinMedido3[4])
Fluo3smas = np.sqrt(12/2)*RelMinMedido3[2] + (2/np.sqrt(2))*RelMinMedido3[5]
Fluo3smenos = (2/np.sqrt(2))*RelMinMedido3[0] + np.sqrt(12/2)*RelMinMedido3[3]
return Fluo1, [Fluo2pi, Fluo2smas, Fluo2smenos], [Fluo3pi, Fluo3smas, Fluo3smenos]
def IdentifyPolarizationCoincidences(theoricalmap, target, tolerance=1e-1):
"""
Busca en un mapa 2D la presencia de un valor target (medido) con tolerancia tolerance.
Si lo encuentra, pone un 1. Sino, un 0. Al plotear con pcolor se verá
en blanco la zona donde el valor medido se puede hallar.
"""
CoincidenceMatrix = np.zeros((len(theoricalmap), len(theoricalmap[0])))
i = 0
while i<len(theoricalmap):
j = 0
while j<len(theoricalmap[0]):
if abs(theoricalmap[i][j]-target) < tolerance:
CoincidenceMatrix[i][j] = 1
j=j+1
i=i+1
return CoincidenceMatrix
def RetrieveAbsoluteCoincidencesBetweenMaps(MapsVectors):
MatrixSum = np.zeros((len(MapsVectors[0]), len(MapsVectors[0][0])))
AbsoluteCoincidencesMatrix = np.zeros((len(MapsVectors[0]), len(MapsVectors[0][0])))
MatrixMapsVectors = []
for i in range(len(MapsVectors)):
MatrixMapsVectors.append(np.matrix(MapsVectors[i]))
for i in range(len(MatrixMapsVectors)):
MatrixSum = MatrixSum + MatrixMapsVectors[i]
MaxNumberOfCoincidences = np.max(MatrixSum)
ListMatrixSum = [list(i) for i in list(np.array(MatrixSum))]
for i in range(len(ListMatrixSum)):
for j in range(len(ListMatrixSum[0])):
if ListMatrixSum[i][j] == MaxNumberOfCoincidences:
AbsoluteCoincidencesMatrix[i][j] = 1
return AbsoluteCoincidencesMatrix, MaxNumberOfCoincidences
def MeasureMeanValueOfEstimatedArea(AbsoluteCoincidencesMap, X, Y):
NonZeroIndices = np.nonzero(AbsoluteCoincidencesMap)
Xsum = 0
Xvec = []
Ysum = 0
Yvec = []
N = len(NonZeroIndices[0])
for i in range(N):
Xsum = Xsum + X[NonZeroIndices[1][i]]
Xvec.append(X[NonZeroIndices[1][i]])
Ysum = Ysum + Y[NonZeroIndices[0][i]]
Yvec.append(Y[NonZeroIndices[0][i]])
Xaverage = Xsum/N
Yaverage = Ysum/N
Xspread = np.std(Xvec)
Yspread = np.std(Yvec)
return Xaverage, Yaverage, N, Xspread, Yspread
def MeasureRelativeFluorescenceFromCPT(Freq, Fluo, u, titadoppler, detuningrepump, detuningdoppler, frefasint=-100, entorno=3):
ResonanciasTeoricas, ResonanciasPositivas = CalculoTeoricoDarkResonances_8levels(u, titadoppler, detuningdoppler, detuningrepump)
IndicesDR, FreqsDR = FindDRFrequencies(Freq, Fluo, ResonanciasTeoricas, entorno=entorno)
FrequencyOfMinimums, RelativeFluorescenceOfMinimums0 = FindRelativeFluorescencesOfDR(Freq, Fluo, IndicesDR, detuningdoppler, NormalizationCriterium=0, frecuenciareferenciacriterioasintotico=frefasint)
FrequencyOfMinimums, RelativeFluorescenceOfMinimums1 = FindRelativeFluorescencesOfDR(Freq, Fluo, IndicesDR, detuningdoppler, NormalizationCriterium=1, frecuenciareferenciacriterioasintotico=frefasint)
FrequencyOfMinimums, RelativeFluorescenceOfMinimums2, indiceizquierda, indicederecha = FindRelativeFluorescencesOfDR(Freq, Fluo, IndicesDR, detuningdoppler, NormalizationCriterium=2, frecuenciareferenciacriterioasintotico=frefasint, getindices=True)
FrequencyOfMinimums, RelativeFluorescenceOfMinimums3 = FindRelativeFluorescencesOfDR(Freq, Fluo, IndicesDR, detuningdoppler, NormalizationCriterium=3, frecuenciareferenciacriterioasintotico=frefasint)
FrequencyOfMinimums, RelativeFluorescenceOfMinimums4 = FindRelativeFluorescencesOfDR(Freq, Fluo, IndicesDR, detuningdoppler, NormalizationCriterium=4, frecuenciareferenciacriterioasintotico=frefasint)
print('hola')
print(RelativeFluorescenceOfMinimums0)
return RelativeFluorescenceOfMinimums0, RelativeFluorescenceOfMinimums1, RelativeFluorescenceOfMinimums2, RelativeFluorescenceOfMinimums3, RelativeFluorescenceOfMinimums4, IndicesDR, [indiceizquierda, indicederecha]
def GenerateNoisyCPT(rabG, rabR, rabP, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, phirepump, titarepump, freqMin, freqMax, freqStep, plot=False, solvemode=1, detpvec=None, noiseamplitude=0.001):
Frequencyvector, Fluovector = PerformExperiment_8levels(rabG, rabR, rabP, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, phirepump, titarepump, freqMin, freqMax, freqStep, plot=False, solvemode=1, detpvec=None)
NoisyFluovector = [fluo+noiseamplitude*(2*random.random()-1) for fluo in Fluovector]
return Frequencyvector, NoisyFluovector
def GenerateNoisyCPT_fixedRabi(sg, sr, sp, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, phirepump, titarepump, freqMin, freqMax, freqStep, plot=False, solvemode=1, detpvec=None, noiseamplitude=0.001):
Frequencyvector, Fluovector = PerformExperiment_8levels_fixedRabi(sg, sr, sp, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, phirepump, titarepump, freqMin, freqMax, freqStep, plot=False, solvemode=1, detpvec=None)
NoisyFluovector = [fluo+noiseamplitude*(2*random.random()-1) for fluo in Fluovector]
return Frequencyvector, NoisyFluovector
def GenerateNoisyCPT_fit(sg, sr, sp, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, phirepump, titarepump, freqs, plot=False, solvemode=1, detpvec=None, noiseamplitude=0.001):
Frequencyvector, Fluovector = PerformExperiment_8levels_fixedRabi(sg, sr, sp, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, phirepump, titarepump, min(freqs), max(freqs) + freqs[1]-freqs[0], freqs[1]-freqs[0], plot=False, solvemode=1, detpvec=None)
NoisyFluovector = [fluo+noiseamplitude*(2*random.random()-1) for fluo in Fluovector]
return Frequencyvector, NoisyFluovector
def AddNoiseToCPT(Fluo, noisefactor):
return [f+noisefactor*(2*random.random()-1) for f in Fluo]
def SmoothNoisyCPT(Fluo, window=11, poly=3):
SmoothenFluo = sf(Fluo, window, poly)
return SmoothenFluo
def GetMinimaInfo(Freq, Fluo, u, titadoppler, detuningdoppler, detuningrepump, MinimumCriterium=2, NormalizationCriterium=1):
"""
FUNCION VIEJA
Esta funcion devuelve valores de frecuencias y fluorescencia relativa de los minimos.
Minimumcriterion:
1: Saca los minimos con funcion argelextrema
2: Directamente con las frecuencias teoricas busca las fluorescencias
Normalizationcriterium:
1: Devuelve la fluorescencia absoluta de los minimos
2: Devuelve el cociente entre la fluorescencia del minimo y un valor medio entre dos puntos lejanos, como si no
hubiera una resonancia oscura y hubiera una recta. Ese valor esta a DistanciaFrecuenciaCociente del detuning del azul (el punto medio entre las dos DR en este caso)
3: Devuelve el cociente entre la fluorescencia del minimo y el valor a -100 MHz (si se hizo de -100 a 100),
o el valor limite por izquierda de la curva
"""
FluorescenceOfMaximum = max(Fluo)
FrequencyOfMaximum = Freq[Fluo.index(FluorescenceOfMaximum)]
#criterio para encontrar los minimos
#criterio usando minimos de la fluorescencia calculados con la curva
if MinimumCriterium == 1:
LocationOfMinimums = argrelextrema(np.array(Fluo), np.less)[0]
FluorescenceOfMinimums = np.array([Fluo[i] for i in LocationOfMinimums])
FrequencyOfMinimums = np.array([Freq[j] for j in LocationOfMinimums])
#criterio con las DR teoricas
if MinimumCriterium == 2:
FrecuenciasDRTeoricas, FrecuenciasDRTeoricasPositivas = [darkresonance for darkresonance in CalculoTeoricoDarkResonances_8levels(u, titadoppler, detuningdoppler, detuningrepump)[0]]
FrequencyOfMinimums = []
FluorescenceOfMinimums =[]
print(FrecuenciasDRTeoricas)
k=0
ventanita = 0.001
while k < len(Freq):
if Freq[k] < FrecuenciasDRTeoricas[0] + ventanita and Freq[k] > FrecuenciasDRTeoricas[0] - ventanita:
FrequencyOfMinimums.append(Freq[k])
FluorescenceOfMinimums.append(Fluo[k])
elif Freq[k] < FrecuenciasDRTeoricas[1] + ventanita and Freq[k] > FrecuenciasDRTeoricas[1] - ventanita:
FrequencyOfMinimums.append(Freq[k])
FluorescenceOfMinimums.append(Fluo[k])
elif Freq[k] < FrecuenciasDRTeoricas[2] + ventanita and Freq[k] > FrecuenciasDRTeoricas[2] - ventanita:
FrequencyOfMinimums.append(Freq[k])
FluorescenceOfMinimums.append(Fluo[k])
elif Freq[k] < FrecuenciasDRTeoricas[3] + ventanita and Freq[k] > FrecuenciasDRTeoricas[3] - ventanita:
FrequencyOfMinimums.append(Freq[k])
FluorescenceOfMinimums.append(Fluo[k])
elif Freq[k] < FrecuenciasDRTeoricas[4] + ventanita and Freq[k] > FrecuenciasDRTeoricas[4] - ventanita:
FrequencyOfMinimums.append(Freq[k])
FluorescenceOfMinimums.append(Fluo[k])
elif Freq[k] < FrecuenciasDRTeoricas[5] + ventanita and Freq[k] > FrecuenciasDRTeoricas[5] - ventanita:
FrequencyOfMinimums.append(Freq[k])
FluorescenceOfMinimums.append(Fluo[k])
k = k + 1
print(FrequencyOfMinimums)
if len(FrequencyOfMinimums) != len(FrecuenciasDRTeoricas):
print('NO ANDA BIEN ESTO PAPI, revisalo')
#esto es para establecer un criterio para la fluorescencia relativa
DistanciaFrecuenciaCociente = 15
if NormalizationCriterium==1:
FluoNormDivisor = 1
if NormalizationCriterium==2:
k = 0
while k < len(Freq):
if Freq[k] < detuningdoppler-DistanciaFrecuenciaCociente + 2 and Freq[k] > detuningdoppler-DistanciaFrecuenciaCociente - 2:
FluoIzquierda = Fluo[k]
print('Izq:', Freq[k])
break
else:
k = k + 1
l = 0
while l < len(Freq):
if Freq[l] < detuningdoppler+DistanciaFrecuenciaCociente + 2 and Freq[l] > detuningdoppler+DistanciaFrecuenciaCociente - 2:
FluoDerecha = Fluo[l]
print('Der: ', Freq[l])
break
else:
l = l + 1
FluoNormDivisor = 0.5*(FluoDerecha+FluoIzquierda)
print(FluoNormDivisor)
if NormalizationCriterium==3:
FluoNormDivisor = Fluo[0]
RelativeFluorescenceOfMinimums = np.array([Fluore/FluoNormDivisor for Fluore in FluorescenceOfMinimums])
return FrequencyOfMinimums, RelativeFluorescenceOfMinimums
def GetPlotsofFluovsAngle_8levels(FrequencyOfMinimumsVector, RelativeFluorescenceOfMinimumsVector, u, titadoppler, detuningdoppler, detuningrepump, ventana=0.25, taketheoricalDR=False):
#primero buscamos las frecuencias referencia que se parezcan a las 6:
i = 0
FrecuenciasReferenciaBase = FrequencyOfMinimumsVector[0]
FrecuenciasDRTeoricas = [darkresonance for darkresonance in CalculoTeoricoDarkResonances_8levels(u, titadoppler, detuningdoppler, detuningrepump)[0]]
while i < len(FrequencyOfMinimumsVector):
if len(FrequencyOfMinimumsVector[i])==len(FrecuenciasDRTeoricas):
FrecuenciasReferenciaBase = FrequencyOfMinimumsVector[i]
print('Cool! Taking the DR identified with any curve')
break
else:
i = i + 1
if i==len(FrequencyOfMinimumsVector):
print('No hay ningun plot con 5 resonancias oscuras. Tomo las teóricas')
FrecuenciasReferenciaBase = FrecuenciasDRTeoricas
if taketheoricalDR:
FrecuenciasReferenciaBase = FrecuenciasDRTeoricas
Ventana = abs(ventana*(FrecuenciasReferenciaBase[1] - FrecuenciasReferenciaBase[0])) #ventana separadora de resonancias
print('Ventana = ', Ventana)
DarkResonance1Frequency = []
DarkResonance1Fluorescence = []
DarkResonance2Frequency = []
DarkResonance2Fluorescence = []
DarkResonance3Frequency = []
DarkResonance3Fluorescence = []
DarkResonance4Frequency = []
DarkResonance4Fluorescence = []
DarkResonance5Frequency = []
DarkResonance5Fluorescence = []
DarkResonance6Frequency = []
DarkResonance6Fluorescence = []
i = 0
while i < len(FrequencyOfMinimumsVector):
j = 0
FrecuenciasReferencia = [i for i in FrecuenciasReferenciaBase]
while j < len(FrequencyOfMinimumsVector[i]):
if abs(FrequencyOfMinimumsVector[i][j]) < (abs(FrecuenciasReferencia[0])+Ventana) and abs(FrequencyOfMinimumsVector[i][j]) >= (abs(FrecuenciasReferencia[0])-Ventana):
DarkResonance1Frequency.append(FrequencyOfMinimumsVector[i][j])
DarkResonance1Fluorescence.append(RelativeFluorescenceOfMinimumsVector[i][j])
FrecuenciasReferencia[0] = 0
elif abs(FrequencyOfMinimumsVector[i][j]) < (abs(FrecuenciasReferencia[1])+Ventana) and abs(FrequencyOfMinimumsVector[i][j]) >= (abs(FrecuenciasReferencia[1])-Ventana):
DarkResonance2Frequency.append(FrequencyOfMinimumsVector[i][j])
DarkResonance2Fluorescence.append(RelativeFluorescenceOfMinimumsVector[i][j])
FrecuenciasReferencia[1] = 0
elif abs(FrequencyOfMinimumsVector[i][j]) < (abs(FrecuenciasReferencia[2])+Ventana) and abs(FrequencyOfMinimumsVector[i][j]) >= (abs(FrecuenciasReferencia[2])-Ventana):
DarkResonance3Frequency.append(FrequencyOfMinimumsVector[i][j])
DarkResonance3Fluorescence.append(RelativeFluorescenceOfMinimumsVector[i][j])
FrecuenciasReferencia[2] = 0
elif abs(FrequencyOfMinimumsVector[i][j]) < (abs(FrecuenciasReferencia[3])+Ventana) and abs(FrequencyOfMinimumsVector[i][j]) >= (abs(FrecuenciasReferencia[3])-Ventana):
DarkResonance4Frequency.append(FrequencyOfMinimumsVector[i][j])
DarkResonance4Fluorescence.append(RelativeFluorescenceOfMinimumsVector[i][j])
FrecuenciasReferencia[3] = 0
elif abs(FrequencyOfMinimumsVector[i][j]) < (abs(FrecuenciasReferencia[4])+Ventana) and abs(FrequencyOfMinimumsVector[i][j]) >= (abs(FrecuenciasReferencia[4])-Ventana):
DarkResonance5Frequency.append(FrequencyOfMinimumsVector[i][j])
DarkResonance5Fluorescence.append(RelativeFluorescenceOfMinimumsVector[i][j])
FrecuenciasReferencia[4] = 0
elif abs(FrequencyOfMinimumsVector[i][j]) < (abs(FrecuenciasReferencia[5])+Ventana) and abs(FrequencyOfMinimumsVector[i][j]) >= (abs(FrecuenciasReferencia[5])-Ventana):
DarkResonance6Frequency.append(FrequencyOfMinimumsVector[i][j])
DarkResonance6Fluorescence.append(RelativeFluorescenceOfMinimumsVector[i][j])
FrecuenciasReferencia[5] = 0
else:
#print('Algo anduvo mal, por ahi tenes que cambiar la ventana che')
pass
j = j + 1
if np.count_nonzero(FrecuenciasReferencia) > 0:
if FrecuenciasReferencia[0] != 0:
DarkResonance1Frequency.append(FrecuenciasReferencia[0])
DarkResonance1Fluorescence.append()
if FrecuenciasReferencia[1] != 0:
DarkResonance2Frequency.append(FrecuenciasReferencia[1])
DarkResonance2Fluorescence.append(0)
if FrecuenciasReferencia[2] != 0:
DarkResonance3Frequency.append(FrecuenciasReferencia[2])
DarkResonance3Fluorescence.append(0)
if FrecuenciasReferencia[3] != 0:
DarkResonance4Frequency.append(FrecuenciasReferencia[3])
DarkResonance4Fluorescence.append(0)
if FrecuenciasReferencia[4] != 0:
DarkResonance5Frequency.append(FrecuenciasReferencia[4])
DarkResonance5Fluorescence.append(0)
if FrecuenciasReferencia[5] != 0:
DarkResonance6Frequency.append(FrecuenciasReferencia[5])
DarkResonance6Fluorescence.append(0)
i = i + 1
return DarkResonance1Frequency, DarkResonance1Fluorescence, DarkResonance2Frequency, DarkResonance2Fluorescence, DarkResonance3Frequency, DarkResonance3Fluorescence, DarkResonance4Frequency, DarkResonance4Fluorescence, DarkResonance5Frequency, DarkResonance5Fluorescence, DarkResonance6Frequency, DarkResonance6Fluorescence, FrecuenciasReferenciaBase
def PerformExperiment_8levels(rabG, rabR, rabP, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, phirepump, titarepump, freqMin, freqMax, freqStep, plot=False, solvemode=1, detpvec=None):
"""
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
"""
Fluovectors = []
for titaprobe in titaprobeVec:
tinicial = time.time()
ProbeDetuningVectorL, Fluovector = CPTspectrum8levels(rabG, rabR, rabP, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobe, phirepump, titarepump, freqMin=freqMin, freqMax=freqMax, freqStep=freqStep, plot=False, solvemode=1)
tfinal = time.time()
print('Done angle ', titarepump, ' Total time: ', round((tfinal-tinicial), 2), "s")
if plot:
plt.figure()
plt.xlabel('Repump detuning (MHz')
plt.ylabel('Fluorescence (A.U.)')
plt.plot(ProbeDetuningVectorL, Fluovector, label=str(titarepump)+'º tita repump, T: ' + str(T*1e3) + ' mK')
plt.legend()
Fluovectors.append(Fluovector)
if len(titaprobeVec) == 1: #esto es para que no devuelva un vector de vectores si solo fijamos un angulo
Fluovectors = Fluovector
return ProbeDetuningVectorL, Fluovectors
def PerformExperiment_8levels_fixedRabi(sg, sr, sp, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, phirepump, titarepump, freqMin, freqMax, freqStep, plot=False, solvemode=1, detpvec=None):
"""
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
"""
Fluovectors = []
for titaprobe in titaprobeVec:
tinicial = time.time()
ProbeDetuningVectorL, Fluovector = CPTspectrum8levels_fixedRabi(sg, sr, sp, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobe, phirepump, titarepump, freqMin=freqMin, freqMax=freqMax, freqStep=freqStep, plot=False, solvemode=1)
tfinal = time.time()
print('Done angle ', titarepump, ' Total time: ', round((tfinal-tinicial), 2), "s")
if plot:
plt.figure()
plt.xlabel('Repump detuning (MHz')
plt.ylabel('Fluorescence (A.U.)')
plt.plot(ProbeDetuningVectorL, Fluovector, label=str(titarepump)+'º tita repump, T: ' + str(T*1e3) + ' mK')
plt.legend()
Fluovectors.append(Fluovector)
if len(titaprobeVec) == 1: #esto es para que no devuelva un vector de vectores si solo fijamos un angulo
Fluovectors = Fluovector
return ProbeDetuningVectorL, Fluovectors
analisis/plots/20230510_MotionalSpectrum/Data/EITfit/threeLevel_2repumps_CPTPlotter.py 0 → 100644
View file @ 32f4f986
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Sep 1 17:58:39 2020
@author: oem
"""
import os
import numpy as np
#os.chdir('/home/oem/Nextcloud/G_liaf/liaf-TrampaAnular/Código General/EIT-CPT/Buenos Aires/Experiment Simulations/CPT scripts/Eight Level 2 repumps')
from threeLevel_2repumps_AnalysisFunctions import CalculoTeoricoDarkResonances_8levels, GetMinimaInfo, GetPlotsofFluovsAngle_8levels, PerformExperiment_8levels, FindDRFrequencies, FindRelativeFluorescencesOfDR, GenerateNoisyCPT, SmoothNoisyCPT, GetFinalMaps, GenerateNoisyCPT_fixedRabi, GenerateNoisyCPT_fit
import matplotlib.pyplot as plt
import time
from threeLevel_2repumps_AnalysisFunctions import MeasureRelativeFluorescenceFromCPT, IdentifyPolarizationCoincidences, RetrieveAbsoluteCoincidencesBetweenMaps, GetClosestIndex
#C:\Users\Usuario\Nextcloud\G_liaf\liaf-TrampaAnular\Código General\EIT-CPT\Buenos Aires\Experiment Simulations\CPT scripts\Eight Level 2 repumps
ub = 9.27e-24
h = 6.63e-34
c = (ub/h)*1e-4 #en unidades de MHz/G
#u = 1e6
u = 33.5e6
B = (u/(2*np.pi))/c
#sg, sp = 0.6, 5 #parámetros de control, saturación del doppler y repump
#rabG, rabP = sg*gPS, sp*gPD #frecuencias de rabi
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6 #anchos de linea de las transiciones
lw = 0.1
DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth = lw, lw, lw #ancho de linea de los laseres
DetDoppler = -36 #42
DetRepumpVec = [DetDoppler+29.6]
Tvec = [0.7] #temperatura en mK
alpha = 0*(np.pi/180) #angulo entre los láseres
phidoppler, titadoppler = 0, 90
phirepump, titarepump = 0, 0
phiprobe = 0
titaprobe = 90
#Calculo las resonancias oscuras teóricas
#ResonanciasTeoricas, DRPositivas = CalculoTeoricoDarkResonances_8levels(u/(2*np.pi*1e6), titadoppler, DetDoppler, DetRepump)
#Parametros de la simulacion cpt
center = -45
span = 80
freqMin = center-span*0.5
freqMax = center+span*0.5
""" parametros para tener espectros coherentes
freqMin = -56
freqMax = 14
"""
freqStep = 1e-1
noiseamplitude = 0
RelMinMedido0Vector = []
RelMinMedido1Vector = []
RelMinMedido2Vector = []
RelMinMedido3Vector = []
RelMinMedido4Vector = []
#Sr = np.arange(0, 10, 0.2)
#Sg = np.arange(0.01, 1, 0.05)
#Sp = np.arange(0.1, 6.1, 1)
#Sg = [0.6**2]
#Sp = [2.3**2]
Sg = [1.4]
Sp = [6]
Sr = [11]
i = 0
save = False
showFigures = True
if not showFigures:
plt.ioff()
else:
plt.ion()
fig1, ax1 = plt.subplots()
offsetx = 464
ax1.plot([f-offsetx for f in FreqsDR], CountsDR, 'o')
run = True
Scale = 730
Offset = 600 #600 para 20k cuentas aprox
MaxCoherenceValue = []
for sg in Sg:
for sp in Sp:
rabG, rabP = sg*gPS, sp*gPD
for Ti in Tvec:
T = Ti*1e-3
for DetRepump in DetRepumpVec:
print(T)
for sr in Sr:
rabR = sr*gPD
#MeasuredFreq, MeasuredFluo = GenerateNoisyCPT(rabG, rabR, rabP, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, [titaprobe], phirepump, titarepump, freqMin, freqMax, freqStep, plot=False, solvemode=1, detpvec=None, noiseamplitude=noiseamplitude)
if run:
MeasuredFreq4, MeasuredFluo4 = GenerateNoisyCPT_fixedRabi(sg, sr, sp, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, [titaprobe], phirepump, titarepump, freqMin, freqMax, freqStep, plot=False, solvemode=1, detpvec=None, noiseamplitude=noiseamplitude)
#SmoothFluo = SmoothNoisyCPT(MeasuredFluo, window=9, poly=2)
SmoothFluo4 = MeasuredFluo4
#Scale = max(BestC)/max([100*s for s in SmoothFluo4])
ax1.plot(MeasuredFreq4, [Scale*100*f + Offset for f in SmoothFluo4], label=f'Sr = {sr}')
ax1.axvline(DetDoppler, linestyle='--', linewidth=1)
#if sr != 0:
#ax1.axvline(DetRepump, linestyle='--', linewidth=1)
MaxCoherenceValue.append(np.max(SmoothFluo4))
#print(titaprobe)
ax1.set_xlabel('Detuning Rebombeo (MHz)')
ax1.set_ylabel('Fluorescencia (AU)')
ax1.set_title(f'B: {round(B, 2)} G, Sdop: {round(sg, 2)}, Sp: {round(sp, 2)}, Sr: {round(sr, 2)}, lw: {lw} MHz, T: {Ti} mK')
#ax1.set_ylim(0, 8)
#ax1.axvline(DetDoppler, linestyle='dashed', color='red', linewidth=1)
#ax1.axvline(DetRepump, linestyle='dashed', color='black', linewidth=1)
#ax1.set_title('Pol Doppler y Repump: Sigma+ Sigma-, Pol Probe: PI')
#ax1.legend()
ax1.grid()
print (f'{i+1}/{len(Sg)*len(Sp)}')
i = i + 1
if save:
plt.savefig(f'Mapa_plots_100k_1mk/CPT_SMSM_sdop{round(sg, 2)}_sp{round(sp, 2)}_sr{round(sr, 2)}.jpg')
ax1.legend()
"""
plt.figure()
plt.plot(Sr, MaxCoherenceValue, 'o')
plt.xlabel('Sr')
plt.ylabel('Coherence')
"""
"""
plt.figure()
plt.plot(MeasuredFreq, [100*f for f in SmoothFluo], color='darkred')
plt.xlabel('Desintonía 866 (MHz)')
plt.ylabel('Fluorescencia (A.U.)')
plt.axvline(-30, color='darkblue', linewidth=1.2, linestyle='--')
plt.yticks(np.arange(0.4, 1.8, 0.2))
plt.ylim(0.5, 1.6)
plt.grid()
plt.figure()
plt.plot(MeasuredFreq4, [100*f for f in SmoothFluo4], color='darkred')
plt.xlabel('Desintonía 866 (MHz)')
plt.ylabel('Fluorescencia (A.U.)')
plt.axvline(-30, color='darkblue', linewidth=1.2, linestyle='--')
plt.yticks(np.arange(0.8, 2.4, 0.4))
plt.grid()
"""
#%%
from scipy.optimize import curve_fit
T = 0.5e-3
sg = 0.7
sp = 6
sr = 0
DetDoppler = -14
DetRepump = 0
FitsSp = []
FitsOffset = []
Sg = [0.87]
def FitEIT(freqs, SP, offset):
MeasuredFreq, MeasuredFluo = GenerateNoisyCPT_fit(0.87, sr, SP, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, [titaprobe], phirepump, titarepump, freqs, plot=False, solvemode=1, detpvec=None, noiseamplitude=noiseamplitude)
FinalFluo = [f*43000 + 2685 for f in MeasuredFluo]
return FinalFluo
freqs = [f-offsetx+32 for f in FreqsDR]
freqslong = np.arange(min(freqs), max(freqs)+freqs[1]-freqs[0], 0.1*(freqs[1]-freqs[0]))
popt, pcov = curve_fit(FitEIT, freqs, CountsDR, p0=[5, 700], bounds=(0, [10, 1e6]))
FitsSp.append(popt[0])
FitsOffset.append(popt[1])
print(popt)
FittedEIT = FitEIT(freqslong, *popt)
plt.figure()
plt.errorbar(freqs, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', capsize=2, markersize=2)
plt.plot(freqslong, FitEIT(freqslong, *popt))
plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {T*1e3} mK, detDop: {DetDoppler} MHz')
np.savetxt('CPT_measured.txt', np.transpose([freqs, CountsDR]))
np.savetxt('CPT_fitted.txt', np.transpose([freqslong, FittedEIT]))
analisis/plots/20230510_MotionalSpectrum/Data/EITfit/threeLevel_2repumps_linealpol_python_scripts.py 0 → 100644
View file @ 32f4f986
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Apr 7 22:30:01 2020
@author: nico
"""
import numpy as np
import time
import matplotlib.pyplot as plt
from scipy.signal import argrelextrema
"""
Scripts para el calculo de la curva CPT
"""
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)
return H0
def HImatrix(rabG, rabP, phidoppler, titadoppler, phiprobe, titaprobe):
"""
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.exp(-1j*phiprobe)
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.exp(1j*phiprobe)
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.exp(-1j*phiprobe)
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.exp(1j*phiprobe)
HI[j-1, i-1] = np.conjugate(HI[i-1, j-1])
return HI
def Lplusminus(detr, detp, phirepump, titarepump, forma=1):
Hintplus = np.zeros((8, 8), dtype=np.complex_)
Hintminus = np.zeros((8, 8), dtype=np.complex_)
Hintplus[4, 2] = (-1/2)*np.sin(titarepump)*np.exp(1j*phirepump)
Hintplus[5, 2] = (-1/np.sqrt(3))*np.cos(titarepump)
Hintplus[6, 2] = (1/(2*np.sqrt(3)))*np.sin(titarepump)*np.exp(-1j*phirepump)
Hintplus[5, 3] = (-1/(2*np.sqrt(3)))*np.sin(titarepump)*np.exp(1j*phirepump)
Hintplus[6, 3] = (-1/np.sqrt(3))*np.cos(titarepump)
Hintplus[7, 3] = (1/2)*np.sin(titarepump)*np.exp(-1j*phirepump)
Hintminus[2, 4] = (-1/2)*np.sin(titarepump)*np.exp(-1j*phirepump)
Hintminus[2, 5] = (-1/np.sqrt(3))*np.cos(titarepump)
Hintminus[2, 6] = (1/(2*np.sqrt(3)))*np.sin(titarepump)*np.exp(1j*phirepump)
Hintminus[3, 5] = (-1/(2*np.sqrt(3)))*np.sin(titarepump)*np.exp(-1j*phirepump)
Hintminus[3, 6] = (-1/np.sqrt(3))*np.cos(titarepump)
Hintminus[3, 7] = (1/2)*np.sin(titarepump)*np.exp(1j*phirepump)
if forma==1:
Lplus = np.zeros((64, 64), dtype=np.complex_)
Lminus = np.zeros((64, 64), dtype=np.complex_)
DeltaBar = 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:
if (k==2 or k==3) and r > 3:
Lplus[r*8+q][k*8+j] = (-1j)*(Hintplus[r,k])
if (r==2 or r==3) and k > 3:
Lminus[r*8+q][k*8+j] = (-1j)*(Hintminus[r,k])
elif r==k:
if (q==2 or q==3) and j > 3:
Lplus[r*8+q][k*8+j] = (-1j)*(- Hintplus[j,q])
if (j==2 or j==3) and q > 3:
Lminus[r*8+q][k*8+j] = (-1j)*(- Hintminus[j,q])
if forma==2:
deltaKro = np.diag([1, 1, 1, 1, 1, 1, 1, 1])
Lplus = (-1j)*(np.kron(Hintplus, deltaKro) - np.kron(deltaKro, Hintplus))
Lminus = (-1j)*(np.kron(Hintminus, deltaKro) - np.kron(deltaKro, Hintminus))
DeltaBar = np.zeros((64, 64), dtype=np.complex_)
for i in range(64):
DeltaBar[i, i] = (1j)*(detr - detp)
return np.matrix(Lminus), np.matrix(Lplus), np.matrix(DeltaBar)
def GetL1(Lplus, Lminus, DeltaBar, L0, rabR, nmax):
"""
Devuelve Splus0 y Sminus0
"""
Sp = (-1)*(0.5*rabR)*(np.matrix(np.linalg.inv(L0 - (nmax+1)*DeltaBar))*np.matrix(Lplus))
Sm = (-1)*(0.5*rabR)*(np.matrix(np.linalg.inv(L0 + (nmax+1)*DeltaBar))*np.matrix(Lminus))
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)*(rabR)*(np.matrix(np.linalg.inv(L0 - n*DeltaBar + rabR*(Lminus*np.matrix(Sp))))*np.matrix(Lplus))
Sm = (-1)*(rabR)*(np.matrix(np.linalg.inv(L0 + n*DeltaBar + rabR*(Lplus*np.matrix(Sm))))*np.matrix(Lminus))
L1 = 0.5*rabR*(np.matrix(Lminus)*np.matrix(Sp) + np.matrix(Lplus)*np.matrix(Sm))
return L1
def EffectiveL(gPS, gPD, lwg, lwr, 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*(lwr + lwp)
Leff[5, 5] = 2*(lwr + lwp)
Leff[6, 6] = 2*(lwr + lwp)
Leff[7, 7] = 2*(lwr + lwp)
return (-0.5j)*Leff
def CalculateSingleMmatrix(gPS, gPD, lwg, lwr, 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.matrix(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
factor1 = 1
factor2 = 1
factor3 = 1
factor4 = 1
#M[36, 45] = lwp
M[36,36] = 2*(lwr + factor1*lwp)
M[37,37] = 2*(lwr + factor1*lwp)
M[38,38] = 2*(lwr + factor1*lwp)
M[39,39] = 2*(lwr + factor1*lwp)
M[44,44] = 2*(lwr + factor2*lwp)
M[45,45] = 2*(lwr + factor2*lwp)
M[46,46] = 2*(lwr + factor2*lwp)
M[47,47] = 2*(lwr + factor2*lwp)
M[52,52] = 2*(lwr + factor3*lwp)
M[53,53] = 2*(lwr + factor3*lwp)
M[54,54] = 2*(lwr + factor3*lwp)
M[55,55] = 2*(lwr + factor3*lwp)
M[60,60] = 2*(lwr + factor4*lwp)
M[61,61] = 2*(lwr + factor4*lwp)
M[62,62] = 2*(lwr + factor4*lwp)
M[63,63] = 2*(lwr + factor4*lwp)
return M
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/(2*mcalcio))
return gammaD
def FullL_efficient(rabG, rabR, rabP, gPS = 0, gPD = 0, Detg = 0, Detr = 0, Detp = 0, u = 0, lwg = 0, lwr=0, lwp = 0,
phidoppler=0, titadoppler=0, phiprobe=0, titaprobe=0, phirepump=0, titarepump=0, T = 0, alpha = 0):
"""
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)
#lwr = np.sqrt(lwr**2 + dopplerBroadening(0.397e-6, 0.866e-6, alpha, T)**2)
lwg = np.sqrt(lwg**2 + db**2)
lwr = np.sqrt(lwr**2 + db**2)
CC = EffectiveL(gPS, gPD, lwg, lwr, lwp)
Heff = H0matrix(Detg, Detp, u) + HImatrix(rabG, rabP, phidoppler, titadoppler, phiprobe, titaprobe) + CC
Heffdaga = np.matrix(Heff).getH()
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, lwr, lwp)
L0 = np.array(np.matrix(Lfullpartial) + M)
nmax = 1
Lminus, Lplus, DeltaBar = Lplusminus(Detr, Detp, phirepump, titarepump)
factor1 = np.exp(1j*0.2*np.pi)
factor2 = np.exp(-1j*0.2*np.pi)
#print(factor)
L1 = GetL1(factor1*Lplus, factor2*Lminus, DeltaBar, L0, rabR, nmax)
Lfull = L0 + L1
#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
"""
def CalculoTeoricoDarkResonances(u, titadoppler):
if titadoppler==0:
NegativeDR = [(-7/5)*u, (-3/5)*u, (-1/5)*u, (1/5)*u, (3/5)*u, (7/5)*u]
elif titadoppler==90:
NegativeDR = [(-11/5)*u, (-7/5)*u, (-3/5)*u, (3/5)*u, (7/5)*u, (11/5)*u]
PositiveDR = [(-8/5)*u, (-4/5)*u, 0, (4/5)*u, (8/5)*u]
return NegativeDR, PositiveDR
def CPTspectrum8levels(rabG, rabR, rabP, gPS, gPD, Detg, Detr, u, lwg, lwr, lwp, Temp, alpha, phidoppler, titadoppler, phiprobe, titaprobe, phirepump, titarepump,
freqMin=-100, freqMax=100, freqStep=1e-1, plot=False, solvemode=1):
"""
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)
phirepump, titarepump = phirepump*(np.pi/180), titarepump*(np.pi/180)
DetProbeVector = 2*np.pi*np.arange(freqMin*1e6, freqMax*1e6, freqStep*1e6)
Detg, Detr = 2*np.pi*Detg*1e6, 2*np.pi*Detr*1e6
lwg, lwr, lwp = 2*np.pi*lwg*1e6, 2*np.pi*lwr*1e6, 2*np.pi*lwp*1e6
#u = 2*np.pi*u*1e6
Fluovector = []
tinicial = time.time()
for Detp in DetProbeVector:
L = FullL_efficient(rabG, rabR, rabP, gPS, gPD, Detg, Detr, Detp, u, lwg, lwr, lwp, phidoppler, titadoppler, phiprobe, titaprobe, phirepump, titarepump, Temp, alpha)
if solvemode == 1:
rhovectorized = np.linalg.solve(L, np.array([int(i==0) for i in range(64)]))
Fluo = np.real(rhovectorized[18] + np.real(rhovectorized[27])) #estos son los rho33 + rho44
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
def CPTspectrum8levels_fixedRabi(sg, sr, sp, gPS, gPD, Detg, Detr, u, lwg, lwr, lwp, Temp, alpha, phidoppler, titadoppler, phiprobe, titaprobe, phirepump, titarepump,
freqMin=-100, freqMax=100, freqStep=1e-1, plot=False, solvemode=1):
"""
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)
phirepump, titarepump = phirepump*(np.pi/180), titarepump*(np.pi/180)
DetProbeVector = 2*np.pi*np.arange(freqMin*1e6, freqMax*1e6, freqStep*1e6)
Detg, Detr = 2*np.pi*Detg*1e6, 2*np.pi*Detr*1e6
#lwg, lwr, lwp = 2*np.pi*lwg*1e6, 2*np.pi*lwr*1e6, 2*np.pi*lwp*1e6
lwg, lwr, lwp = lwg*1e6, lwr*1e6, lwp*1e6
rabG = sg*gPS
rabR = sr*gPD
rabP = sp*gPD
#u = 2*np.pi*u*1e6
Fluovector = []
tinicial = time.time()
for Detp in DetProbeVector:
L = FullL_efficient(rabG, rabR, rabP, gPS, gPD, Detg, Detr, Detp, u, lwg, lwr, lwp, phidoppler, titadoppler, phiprobe, titaprobe, phirepump, titarepump, Temp, alpha)
if solvemode == 1:
coh = 5
rhovectorized = np.linalg.solve(L, np.array([int(i==0) for i in range(64)]))
#Fluo = np.abs(rhovectorized[coh])
Fluo = np.real(rhovectorized[18] + np.real(rhovectorized[27])) #estos son los rho33 + rho44
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
#%%
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(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)
NegativeDR, PositiveDR = CalculoTeoricoDarkResonances(u/(2*np.pi*1e6), titadoppler)
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.)')
for PDR in PositiveDR:
plt.axvline(Detr+PDR, linestyle='--', linewidth=0.5, color='red')
for NDR in NegativeDR:
plt.axvline(Detg+NDR, linestyle='--', linewidth=0.5, color='blue')
#parametros que andan piola:
"""
ub = 9.27e-24
h = 6.63e-34
c = (ub/h)*1e-4 #en unidades de MHz/G
B = 17 #campo magnetico en gauss
u = c*B
#u = 80e6
sr, sp = 0.53, 4.2
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6
rabR, rabP = sr*gPS, sp*gPD
lw = 2*np.pi * 0.33e6
lwr, lwp = lw, lw #ancho de linea de los laseres
dr_spec = - 2*np.pi* 26e6
freqSteps = 500
freqMin = -100e6
freqMax = 100e6
dps = 2*np.pi*np.linspace(freqMin, freqMax, freqSteps)
#dps = [-30e6]
alfar = 90*(np.pi/180)
ex_r, ey_r, ez_r = np.sin(alfar)*np.cos(0), np.sin(alfar)*np.sin(0), np.cos(alfar)
alfap = 90*(np.pi/180)
ex_p, ey_p, ez_p = np.sin(alfap)*np.cos(0), np.sin(alfap)*np.sin(0), np.cos(alfap)
"""
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