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artiq_experiments
Commit 9c6f1d39 authored by Nicolas Nunez Barreto's avatar Nicolas Nunez Barreto
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agrego todo lo medido con artiq

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analisis/plots/20220520_CPTvariandoB/CPT_plotter_20220520.py
View file @ 9c6f1d39
...@@ -138,14 +138,14 @@ freqslongpi_3 = np.arange(min(FreqsDRpi_3), max(FreqsDRpi_3)+FreqsDRpi_3[1]-Freq ...@@ -138,14 +138,14 @@ freqslongpi_3 = np.arange(min(FreqsDRpi_3), max(FreqsDRpi_3)+FreqsDRpi_3[1]-Freq
#[1.71811842e+04 3.34325038e-17] #[1.71811842e+04 3.34325038e-17]
def FitEITpi(freqs, SG, SP, TITADOPPLER, DETDOPPLER): def FitEITpi(freqs, SG, SP, TITADOPPLER, DETDOPPLER, uguess):
TITAPROBE=TITADOPPLER TITAPROBE=TITADOPPLER
temp = 2e-3 temp = 2e-3
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) MeasuredFreq, MeasuredFluo = GenerateNoisyCPT_fit(SG, sr, SP, gPS, gPD, DETDOPPLER, DetRepump, uguess, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, temp, alpha, phidoppler, TITADOPPLER, phiprobe, [TITAPROBE], phirepump, titarepump, freqs, plot=False, solvemode=1, detpvec=None, noiseamplitude=noiseamplitude)
FinalFluo = [f*1.16e5 + 1.18e3 for f in MeasuredFluo] FinalFluo = [f*1.16e5 + 1.18e3 for f in MeasuredFluo]
return FinalFluo return FinalFluo
popt, pcov = curve_fit(FitEITpi, FreqsDRpi_3, CountsDRpi_3, p0=[0.5, 4.5, 60, -10], bounds=((0, 0, 0, -100), (2, 10, 90, 0))) popt, pcov = curve_fit(FitEITpi, FreqsDRpi_3, CountsDRpi_3, p0=[0.5, 4.5, 60, -10, 20e6], bounds=((0, 0, 0, -100, 0e6), (2, 20, 90, 0, 40e6)))
print(popt) print(popt)
......
analisis/plots/20220520_EspectrosUVyCPT_descompensacion/UV_CPT_spectrums.py
View file @ 9c6f1d39
...@@ -14,10 +14,12 @@ Calib_Files = """000007324-UV_Scan_withcalib_Haeffner ...@@ -14,10 +14,12 @@ Calib_Files = """000007324-UV_Scan_withcalib_Haeffner
000007326-UV_Scan_withcalib_Haeffner 000007326-UV_Scan_withcalib_Haeffner
000007327-IR_Scan_withcal_optimized 000007327-IR_Scan_withcal_optimized
000007328-UV_Scan_withcalib_Haeffner 000007328-UV_Scan_withcalib_Haeffner
000007327-IR_Scan_withcal_optimized""" 000007327-IR_Scan_withcal_optimized
000007197-IR_Scan_withcal_optimized
directory = '/home/liaf-murib/Documents/Artiq/artiq_experiments/analisis/plots/20220520_EspectrosUVyCPT_descompensacion/Data/' 000007198-UV_Scan_withcalib_Haeffner"""
#directory = '/home/liaf-murib/Documents/Artiq/artiq_experiments/analisis/plots/20220520_EspectrosUVyCPT_descompensacion/Data/'
directory = '/home/nico/Documents/artiq_experiments/analisis/plots/20220520_EspectrosUVyCPT_descompensacion/Data/'
#carpeta pc nico labo escritorio: #carpeta pc nico labo escritorio:
#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20220503_EspectrosUVnuevos\Data #C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20220503_EspectrosUVnuevos\Data
...@@ -115,7 +117,7 @@ def MicromotionSpectra(det, A, beta, x0, offset): ...@@ -115,7 +117,7 @@ def MicromotionSpectra(det, A, beta, x0, offset):
return P return P
jvec = [2] #UV_cooling en 90 MHz jvec = [7] #UV_cooling en 90 MHz
""" """
plt.figure() plt.figure()
...@@ -152,7 +154,7 @@ for j in jvec: ...@@ -152,7 +154,7 @@ for j in jvec:
portion = 0. portion = 0.
popt, pcov = curve_fit(MicromotionSpectra, FreqsChosen[int(portion*len(FreqsChosen)):], CountsChosen[int(portion*len(FreqsChosen)):],p0=[100000,1.5,220,200], bounds=((70000,0.1,200,-500),(1000000,10,300,500))) popt, pcov = curve_fit(MicromotionSpectra, FreqsChosen[int(portion*len(FreqsChosen)):], CountsChosen[int(portion*len(FreqsChosen)):],p0=[100000,1.5,220,200], bounds=((7000,0.1,200,-500),(1000000,10,300,500)))
freqslong = np.arange(min(FreqsChosen)-10, max(FreqsChosen)+10, (FreqsChosen[1]-FreqsChosen[0])*0.01) freqslong = np.arange(min(FreqsChosen)-10, max(FreqsChosen)+10, (FreqsChosen[1]-FreqsChosen[0])*0.01)
......
analisis/plots/20220526_CPTvariandoB_v2/CPT_plotter_20220520.py 0 → 100644
View file @ 9c6f1d39
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 de CPT para campo magnetico bajo y terrestre
#/home/nico/Documents/artiq_experiments/analisis/plots/20220526_CPTvariandoB_v2/Data
os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20220526_CPTvariandoB_v2/Data')
ALL_FILES = """000007697-IR_Scan_withcal_optimized
000007696-IR_Scan_withcal_optimized
000007695-IR_Scan_withcal_optimized
000007699-IR_Scan_withcal_optimized
000007700-IR_Scan_withcal_optimized
000007701-IR_Scan_withcal_optimized
000007702-IR_Scan_withcal_optimized
000007703-IR_Scan_withcal_optimized
000007712-IR_Scan_withcal_optimized
000007713-IR_Scan_withcal_optimized
000007714-IR_Scan_withcal_optimized
000007715-IR_Scan_withcal_optimized
000007716-IR_Scan_withcal_optimized
000007717-IR_Scan_withcal_optimized
000007718-IR_Scan_withcal_optimized
000007719-IR_Scan_withcal_optimized
000007720-IR_Scan_withcal_optimized
000007727-IR_Scan_withcal_optimized
000007730-IR_Scan_withcal_optimized
000007731-IR_Scan_withcal_optimized
000007732-IR_Scan_withcal_optimized
000007733-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(ALL_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(ALL_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']['IR_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_B = []
Freqs_B = []
for i, fname in enumerate(ALL_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_B.append(np.array(data['datasets']['IR_Frequencies']))
Counts_B.append(np.array(data['datasets']['counts_spectrum']))
#%% Estos bloques fitean curvas cpt pero se ven feas, no vale la pena mostrarlas
"""
#Barriendo angulo del IR con tisa apagado
jvec = [0, 1, 2, 3, 4, 5, 6, 7]
Bvec = [3, 2, 1, 0, -1, -1.3, -1.5, -1.7]
jsel = [4, 5, 6, 7]
plt.figure()
i = 0
for j in jvec:
if j in jsel:
plt.errorbar([2*f*1e-6 for f in Freqs[j]], Counts[j], yerr=np.sqrt(Counts[j]), fmt='o', label='B: {Bvec[i]}', capsize=2, markersize=2)
#plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', label=f'Amp Tisa: {AmpTisa[i]}', mb arkersize=3)
i = i + 1
#plt.grid()
#plt.xlabel('Frecuencia (MHz)')
#plt.ylabel('counts')
#plt.legend()
#Barriendo angulo del IR con tisa apagado
#60 uW de IR
jvec = [17, 18, 19, 20, 21]
Bvec = [-2.2, -2, -1.75, -1.5, -1.2, -1, 0, 2]
jsel = jvec
plt.figure()
i = 0
for j in jvec:
if j in jsel:
plt.errorbar([2*f*1e-6 for f in Freqs[j]], Counts[j], yerr=np.sqrt(Counts[j]), fmt='o', label=f'B: {Bvec[i]}', capsize=2, markersize=2)
#plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', label=f'Amp Tisa: {AmpTisa[i]}', mb arkersize=3)
i = i + 1
#plt.grid()
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('counts')
plt.legend()
"""
#%%
#Barriendo angulo del IR con tisa apagado
#60 uW de IR
jvec = [10, 9, 11, 12, 13, 14, 15, 16]
Bvec = [2, 0, -1, -1.2, -1.5, -1.75, -2, -2.2]
jsel = [9,11,12,13,14,15,16]
plt.figure()
i = 0
for j in jvec:
if j in jsel:
plt.errorbar([2*f*1e-6 for f in Freqs[j]], Counts[j], yerr=np.sqrt(Counts[j]), fmt='o', label=f'I={Bvec[i]} A', capsize=2, markersize=2)
#plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', label=f'Amp Tisa: {AmpTisa[i]}', mb arkersize=3)
i = i + 1
#plt.grid()
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('counts')
plt.legend()
#%%
"""
Ahora empezamos a intentar fitear de a una mejor
"""
#Ajustamos una curva para obtener escala y offset
phidoppler, titadoppler = 0, 90
phirepump, titarepump = 0, 0
phiprobe = 0
titaprobe = 90
T = 5e-3
sg = 0.544
sp = 4.5
sr = 0
DetRepump = 0
lw = 0.1
DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth = lw, lw, lw #ancho de linea de los laseres
u = 32.5e6
B = (u/(2*np.pi))/c
correccion = -5 #con 8 fitea bien
offsetxpi = 440+1+correccion
DetDoppler = -20.0-correccion
#fitsvec = [9, 11, 12, 13, 14, 15, 16] son las posibilidades
curvafiteada = 13
FreqsDRpi_3 = [2*f*1e-6-offsetxpi+14 for f in Freqs_B[curvafiteada]]
CountsDRpi_3 = Counts_B[curvafiteada]
freqslongpi_3 = np.arange(min(FreqsDRpi_3), max(FreqsDRpi_3)+FreqsDRpi_3[1]-FreqsDRpi_3[0], 0.1*(FreqsDRpi_3[1]-FreqsDRpi_3[0]))
def FitEITpiplotter(freqs, scale, offset, SG, SP, temp):
U=285732
#SG = 1.3
#SP = 3.39e0
#temp = 3.7e-4
#temp=4e-3
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)
FinalFluo = [f*scale + offset for f in MeasuredFluo]
return FinalFluo
popt_tisaoffprueba, pcov_tisaoffprueba = curve_fit(FitEITpiplotter, FreqsDRpi_3, CountsDRpi_3, p0=[1e5, 1e2, 1, 4, 1e-3], bounds=((0,0,0,0,0), (1e6, 1e4, 10, 20, 10e-3)))
FittedEITpi_3 = FitEITpiplotter(freqslongpi_3, *popt_tisaoffprueba)
plt.figure()
plt.errorbar(FreqsDRpi_3, CountsDRpi_3, yerr=2*np.sqrt(CountsDRpi_3), fmt='o', capsize=2, markersize=2)
plt.plot(freqslongpi_3, FittedEITpi_3)
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
#%%
#ESTE CODIGO AJUSTA UNA DE LAS CURVAS, LA 12!!!
phidoppler, titadoppler = 0, 90
phirepump, titarepump = 0, 0
phiprobe = 0
titaprobe = 90
T = 5e-3
sg = 0.544
sp = 4.5
sr = 0
DetRepump = 0
lw = 0.1
DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth = lw, lw, lw #ancho de linea de los laseres
u = 32.5e6
B = (u/(2*np.pi))/c
correccion = -5 #con 8 fitea bien
offsetxpi = 440+1+correccion
DetDoppler = -20.0-correccion
#jvec = [10, 9, 11, 12, 13, 14, 15, 16]
#Bvec = [2, 0, -1, -1.2, -1.5, -1.75, -2, -2.2]
curvafiteada = 12
FreqsDRpi_12 = [2*f*1e-6-offsetxpi+14 for f in Freqs_B[curvafiteada]]
CountsDRpi_12 = Counts_B[curvafiteada]
freqslongpi_12 = np.arange(min(FreqsDRpi_12), max(FreqsDRpi_12)+FreqsDRpi_12[1]-FreqsDRpi_12[0], 0.1*(FreqsDRpi_12[1]-FreqsDRpi_12[0]))
def FitEITpiplotter(freqs, U, SG, SP, DETDOPPLER):
#U=285732
#SG = 1.3
#SP = 3.39e0
#temp = 3.7e-4
temp=0
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)
FinalFluo = [f*2.0917e5 + 7.53e2 for f in MeasuredFluo]
return FinalFluo
popt_12, pcov_12 = curve_fit(FitEITpiplotter, FreqsDRpi_12, CountsDRpi_12, p0=[285000, 1, 4, -15], bounds=((0,0,0,-20), (500000, 10, 20, -10)))
FittedEITpi_12 = FitEITpiplotter(freqslongpi_12, *popt_12)
plt.figure()
plt.errorbar(FreqsDRpi_12, CountsDRpi_12, yerr=2*np.sqrt(CountsDRpi_12), fmt='o', capsize=2, markersize=2)
plt.plot(freqslongpi_12, FittedEITpi_12)
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
#%%
#ESTE CODIGO AJUSTA UNA DE LAS CURVAS, LA 13!!! pero ahora ajustando demas parametros
phidoppler, titadoppler = 0, 90
phirepump, titarepump = 0, 0
phiprobe = 0
titaprobe = 90
T = 5e-3
sg = 0.544
sp = 4.5
sr = 0
DetRepump = 0
lw = 0.1
DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth = lw, lw, lw #ancho de linea de los laseres
u = 32.5e6
B = (u/(2*np.pi))/c
correccion = -5 #con 8 fitea bien
offsetxpi = 440+1+correccion
DetDoppler = -20.0-correccion
#fitsvec = [9, 11, 12, 13, 14, 15, 16] son las posibilidades
#las corrientes son [2, 0, -1, -1.5, -1.75, -2, -2.2]
curvafiteada = 13
FreqsDRpi_13 = [2*f*1e-6-offsetxpi+14 for f in Freqs_B[curvafiteada]]
CountsDRpi_13 = Counts_B[curvafiteada]
freqslongpi_13 = np.arange(min(FreqsDRpi_13), max(FreqsDRpi_13)+FreqsDRpi_13[1]-FreqsDRpi_13[0], 0.1*(FreqsDRpi_13[1]-FreqsDRpi_13[0]))
def FitEITpiplotter(freqs, U, SG, SP, DETDOPPLER):
#U=285732
#SG = 1.3
#SP = 3.39e0
temp = 3.7e-10
#temp=4e-3
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)
FinalFluo = [f*2.0917e5 + 7.53e2 for f in MeasuredFluo]
return FinalFluo
popt_13, pcov_13 = curve_fit(FitEITpiplotter, FreqsDRpi_13, CountsDRpi_13, p0=[285000, 1, 4, -15], bounds=((0,0,0,-20), (500000, 10, 20, 0)))
FittedEITpi_13 = FitEITpiplotter(freqslongpi_13, *popt_13)
plt.figure()
plt.errorbar(FreqsDRpi_13, CountsDRpi_13, yerr=2*np.sqrt(CountsDRpi_13), fmt='o', capsize=2, markersize=2)
plt.plot(freqslongpi_13, FittedEITpi_13)
#%%
#ESTE CODIGO AJUSTA UNA DE LAS CURVAS, LA 14!!!
phidoppler, titadoppler = 0, 90
phirepump, titarepump = 0, 0
phiprobe = 0
titaprobe = 90
T = 5e-3
sg = 0.544
sp = 4.5
sr = 0
DetRepump = 0
lw = 0.1
DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth = lw, lw, lw #ancho de linea de los laseres
u = 32.5e6
B = (u/(2*np.pi))/c
correccion = -5 #con 8 fitea bien
offsetxpi = 440+1+correccion
DetDoppler = -20.0-correccion
#fitsvec = [9, 11, 12, 13, 14, 15, 16] son las posibilidades
#las corrientes son [2, 0, -1, -1.5, -1.75, -2, -2.2]
curvafiteada = 14
FreqsDRpi_14 = [2*f*1e-6-offsetxpi+14 for f in Freqs_B[curvafiteada]]
CountsDRpi_14 = Counts_B[curvafiteada]
freqslongpi_14 = np.arange(min(FreqsDRpi_14), max(FreqsDRpi_14)+FreqsDRpi_14[1]-FreqsDRpi_14[0], 0.1*(FreqsDRpi_14[1]-FreqsDRpi_14[0]))
def FitEITpiplotter(freqs, U, SG, SP, DETDOPPLER):
#U=285732
#SG = 1.3
#SP = 3.39e0
temp = 3.7e-10
#temp=4e-3
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)
FinalFluo = [f*2.0917e5 + 7.53e2 for f in MeasuredFluo]
#FinalFluo = [f*scale + offset for f in MeasuredFluo]
return FinalFluo
popt_14, pcov_14 = curve_fit(FitEITpiplotter, FreqsDRpi_14, CountsDRpi_14, p0=[285000, 1, 2.2, -15], bounds=((0,0,1,-20), (500000, 2.5, 20, 0)))
FittedEITpi_14 = FitEITpiplotter(freqslongpi_14, *popt_14)
#FittedEITpi_14 = FitEITpiplotter(freqslongpi_14, popt_14[0],popt_14[1],2.1,popt_14[3])
plt.figure()
plt.errorbar(FreqsDRpi_14, CountsDRpi_14, yerr=2*np.sqrt(CountsDRpi_14), fmt='o', capsize=2, markersize=2)
plt.plot(freqslongpi_14, FittedEITpi_14)
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
#%%
#ESTE CODIGO AJUSTA UNA DE LAS CURVAS, LA 15!!!
phidoppler, titadoppler = 0, 90
phirepump, titarepump = 0, 0
phiprobe = 0
titaprobe = 90
T = 5e-3
sg = 0.544
sp = 4.5
sr = 0
DetRepump = 0
lw = 0.1
DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth = lw, lw, lw #ancho de linea de los laseres
u = 32.5e6
B = (u/(2*np.pi))/c
correccion = -5 #con 8 fitea bien
offsetxpi = 440+1+correccion
DetDoppler = -20.0-correccion
#fitsvec = [9, 11, 12, 13, 14, 15, 16] son las posibilidades
#las corrientes son [2, 0, -1, -1.5, -1.75, -2, -2.2]
curvafiteada = 15
FreqsDRpi_15 = [2*f*1e-6-offsetxpi+14 for f in Freqs_B[curvafiteada]]
CountsDRpi_15 = Counts_B[curvafiteada]
freqslongpi_15 = np.arange(min(FreqsDRpi_15), max(FreqsDRpi_15)+FreqsDRpi_15[1]-FreqsDRpi_15[0], 0.1*(FreqsDRpi_15[1]-FreqsDRpi_15[0]))
def FitEITpiplotter(freqs, U, SG, SP, DETDOPPLER):
#U=285732
#SG = 1.3
#SP = 3.39e0
temp = 3.7e-10
#temp=4e-3
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)
FinalFluo = [f*2.0917e5 + 7.53e2 for f in MeasuredFluo]
#FinalFluo = [f*2.6917e5 + 3.53e2 for f in MeasuredFluo]
return FinalFluo
popt_15, pcov_15 = curve_fit(FitEITpiplotter, FreqsDRpi_15, CountsDRpi_15, p0=[285000, 1, 4, -15], bounds=((0,0,0,-20), (500000, 10, 20, 0)))
print('B:')
print(popt_15[0])
FittedEITpi_15 = FitEITpiplotter(freqslongpi_15, *popt_15)
plt.figure()
plt.errorbar(FreqsDRpi_15, CountsDRpi_15, yerr=2*np.sqrt(CountsDRpi_15), fmt='o', capsize=2, markersize=2)
plt.plot(freqslongpi_15, FittedEITpi_15)
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
#%%
Detuningsfits = [popt_12[-1], popt_13[-1], popt_14[-1], popt_15[-1]]
PotIRfits = [popt_12[-2], popt_13[-2], popt_14[-2], popt_15[-2]]
PotUVfits = [popt_12[-3], popt_13[-3], popt_14[-3], popt_15[-3]]
ErrorDetuningfits = [np.sqrt(pcov_12)[3,3], np.sqrt(pcov_13)[3,3],np.sqrt(pcov_14)[3,3], np.sqrt(pcov_15)[3,3]]
ErrorPotIRfits = [np.sqrt(pcov_12)[2,2], np.sqrt(pcov_13)[2,2],np.sqrt(pcov_14)[2,2], np.sqrt(pcov_15)[2,2]]
ErrorPotUVfits = [np.sqrt(pcov_12)[1,1], np.sqrt(pcov_13)[1,1],np.sqrt(pcov_14)[1,1], np.sqrt(pcov_15)[1,1]]
print(Detuningsfits)
print(PotIRfits)
print(PotUVfits)
plt.figure()
plt.errorbar([1,2,3,4],Detuningsfits, yerr=ErrorDetuningfits, fmt='o', capsize=2, markersize=15)
plt.figure()
plt.errorbar([1,2,3,4],PotIRfits, yerr=ErrorPotIRfits, fmt='o', capsize=2, markersize=15)
plt.figure()
plt.errorbar([1,2,3,4],PotUVfits, yerr=ErrorPotUVfits, fmt='o', capsize=2, markersize=15)
print('Media de detuning: ', np.mean(Detuningsfits), 'MHz', 'con media de error:', np.mean(ErrorDetuningfits),'MHz')
print('Media de potencia UV: ', np.mean(PotUVfits), 'con media de error:', np.mean(ErrorPotUVfits))
print('Media de potencia IR ', np.mean(PotIRfits), 'con media de error:', np.mean(ErrorPotIRfits))
#%%
#corrijo glitches xdxd
#12
CountsDRpi_12[97]=0.5*(CountsDRpi_12[96]+CountsDRpi_12[98])
#13
CountsDRpi_13[126]=(CountsDRpi_13[125]-CountsDRpi_13[128])*0.33+CountsDRpi_13[125]
CountsDRpi_13[127]=(CountsDRpi_13[125]-CountsDRpi_13[128])*0.66+CountsDRpi_13[125]
#14 ok
#15
CountsDRpi_15[65]=(CountsDRpi_15[64]-CountsDRpi_15[67])*0.33+CountsDRpi_15[64]
CountsDRpi_15[66]=(CountsDRpi_15[64]-CountsDRpi_15[67])*0.66+CountsDRpi_15[64]
CountsDRpi_15[83]=(CountsDRpi_15[82]+CountsDRpi_15[84])*0.5
#%%
#plot raw sin ajustar escalas
plt.figure()
plt.errorbar(FreqsDRpi_12, CountsDRpi_12, yerr=2*np.sqrt(CountsDRpi_12), fmt='o', capsize=2, markersize=2)
plt.plot(freqslongpi_12, FittedEITpi_12)
plt.errorbar(FreqsDRpi_13, CountsDRpi_13, yerr=2*np.sqrt(CountsDRpi_13), fmt='o', capsize=2, markersize=2)
plt.plot(freqslongpi_13, FittedEITpi_13)
plt.errorbar(FreqsDRpi_14, CountsDRpi_14, yerr=2*np.sqrt(CountsDRpi_14), fmt='o', capsize=2, markersize=2)
plt.plot(freqslongpi_14, FittedEITpi_14)
plt.errorbar(FreqsDRpi_15, CountsDRpi_15, yerr=2*np.sqrt(CountsDRpi_15), fmt='o', capsize=2, markersize=2)
plt.plot(freqslongpi_15, FittedEITpi_15)
#%%
#filtro y mejoro el plot
from scipy.signal import savgol_filter as sf
import seaborn as sns
offs = 7.53e2
scal = 2.0917e5
winl = 11
po = 3
FiltCounts12 = np.array([(c-offs)*100/scal for c in sf(CountsDRpi_12, winl, po)])
#ErrorCounts12 = np.array([0.5*np.sqrt(c/scal) for c in CountsDRpi_12])
ErrorCounts12 = np.array([0.5*np.sqrt(c/scal) for c in sf(CountsDRpi_12, winl, po)])
FiltCounts13 = np.array([(c-offs)*100/scal for c in sf(CountsDRpi_13, winl, po)])
#ErrorCounts13 = np.array([0.5*np.sqrt(c/scal) for c in CountsDRpi_13])
ErrorCounts13 = np.array([0.5*np.sqrt(c/scal) for c in sf(CountsDRpi_13, winl, po)])
FiltCounts14 = np.array([(c-offs)*100/scal for c in sf(CountsDRpi_14, winl, po)])
#ErrorCounts14 = np.array([0.5*np.sqrt(c/scal) for c in CountsDRpi_14])
ErrorCounts14 = np.array([0.5*np.sqrt(c/scal) for c in sf(CountsDRpi_14, winl, po)])
FiltCounts15 = np.array([(c-offs)*100/scal for c in sf(CountsDRpi_15, winl, po)])
#ErrorCounts15 = np.array([0.5*np.sqrt(c/scal) for c in CountsDRpi_15])
ErrorCounts15 = np.array([0.5*np.sqrt(c/scal) for c in sf(CountsDRpi_15, winl, po)])
#plot con escalas atomicas y lindo
colors=sns.color_palette("rocket", 10)
color1=colors[1]
color2=colors[2]
color3=colors[4]
color4=colors[7]
ms = 4
plt.figure(figsize=(3.5, 3))
plt.plot(FreqsDRpi_12, FiltCounts12, 'o', markersize=ms, color=color1, label=r'$S_{rep}=6.31$', alpha=0.3)
plt.plot(FreqsDRpi_13, FiltCounts13, 'o', markersize=ms, color=color2, label=r'$S_{rep}=6.31$', alpha=0.3)
plt.plot(FreqsDRpi_14, FiltCounts14, 'o', markersize=ms, color=color3, label=r'$S_{rep}=6.31$', alpha=0.3)
plt.plot(FreqsDRpi_15, FiltCounts15, 'o', markersize=ms, color=color4, label=r'$S_{rep}=6.31$', alpha=0.3)
plt.plot(freqslongpi_12, [(c-offs)*100/scal for c in FittedEITpi_12], color=color1, linewidth=3)
plt.plot(freqslongpi_13, [(c-offs)*100/scal for c in FittedEITpi_13], color=color2, linewidth=3)
plt.plot(freqslongpi_14, [(c-offs)*100/scal for c in FittedEITpi_14], color=color3, linewidth=3)
plt.plot(freqslongpi_15, [(c-offs)*100/scal for c in FittedEITpi_15], color=color4, linewidth=3)
plt.fill_between(FreqsDRpi_12, FiltCounts12+ErrorCounts12, FiltCounts12-ErrorCounts12, color=color1, alpha=0.2)
plt.fill_between(FreqsDRpi_13, FiltCounts13+ErrorCounts13, FiltCounts13-ErrorCounts13, color=color2, alpha=0.2)
plt.fill_between(FreqsDRpi_14, FiltCounts14+ErrorCounts14, FiltCounts14-ErrorCounts14, color=color3, alpha=0.2)
plt.fill_between(FreqsDRpi_15, FiltCounts15+ErrorCounts15, FiltCounts15-ErrorCounts15, color=color4, alpha=0.2)
plt.xlim(-40,30)
plt.xlabel('Repump detuning (MHz)', fontsize=11)
plt.ylabel('Normalized fluorescence', fontsize=11)
plt.grid()
plt.tight_layout()
plt.savefig('/home/nico/Nextcloud/G_liaf/Publicaciones/Work/2022 B vs k race/Figuras/Figuras jpg trabajadas/CPT_exp.png',dpi=500)
#%%
from scipy.optimize import curve_fit
def LinearLarmortoCurrent(I, a, b):
Larmor = a*I+b
return Larmor
def ConvertLarmortoBfield(u):
c = 1398190.0452488689
return u/(2*np.pi)/c
"""
mediciones = [10, 9, 11, 12, 13, 14, 15, 16]
corrientes = [2, 0, -1, -1.2, -1.5, -1.75, -2, -2.2]
"""
"""
#Esto anda:
IFit = [-1, -1.5, -1.75]
LarmorFit = [popt_13[0], popt_14[0], popt_15[0]]
"""
IFit = [-1.2, -1.5, -1.75, -2]
LarmorFit = [popt_12[0], popt_13[0], popt_14[0], popt_15[0]]
#LarmorFit = [287282.374931893, 203998.09641511342, 158507.0255951109] por si fallan los ajustes
MeanError = np.mean([np.sqrt(pcov_12[0,0]),np.sqrt(pcov_13[0,0]),np.sqrt(pcov_14[0,0]),np.sqrt(pcov_15[0,0])])
#MeanError = 11139.353180216529 por si fallan los ajustes
Ilong = np.arange(2, -3, -0.01)
popt_larmor, pcov_larmor = curve_fit(LinearLarmortoCurrent, IFit, LarmorFit)
LarmorLong = LinearLarmortoCurrent(Ilong, *popt_larmor)
print(popt_larmor)
plt.figure()
plt.plot(IFit, LarmorFit, 'o', markersize=5)
plt.plot(Ilong, LarmorLong)
Bfitted = [ConvertLarmortoBfield(u) for u in LarmorFit]
BLong = [ConvertLarmortoBfield(u) for u in LarmorLong]
plt.figure()
plt.plot(Ilong, BLong)
plt.plot(IFit, Bfitted, 'o', markersize=8)
plt.xlabel('Corriente (A)')
plt.ylabel('Campo magnetico (G)')
plt.grid()
analisis/plots/20220526_CPTvariandoB_v2/Data/EITfit/threeLevel_2repumps_AnalysisFunctions.py 0 → 100644
View file @ 9c6f1d39
#!/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 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/20220526_CPTvariandoB_v2/Data/EITfit/threeLevel_2repumps_CPTPlotter.py 0 → 100644
View file @ 9c6f1d39
#!/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/20220526_CPTvariandoB_v2/Data/EITfit/threeLevel_2repumps_linealpol_python_scripts.py 0 → 100644
View file @ 9c6f1d39
#!/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)
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/20220527_CPTvariandoB_barriendopotenciaIR/Bvsk_Saturation_Measurements.py 0 → 100644
View file @ 9c6f1d39
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
# Solo levanto algunos experimentos
Calib_Files_IR = """000007808-IR_Saturation
000007809-IR_Saturation
000007810-IR_Saturation
000007811-IR_Saturation
000007812-IR_Saturation
000007813-IR_Saturation
000007814-IR_Saturation
000007815-IR_Saturation
000007816-IR_Saturation
000007817-IR_Saturation
000007820-IR_Saturation
000007828-IR_Saturation
000007829-IR_Saturation
000007830-IR_Saturation
000007831-IR_Saturation"""
os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20220527_CPTvariandoB_barriendopotenciaIR/Data')
#carpeta pc nico labo escritorio:
#/home/nico/Documents/artiq_experiments/analisis/plots/20220527_CPTvariandoB_barriendopotenciaIR/Data
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()))
#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20210824_SaturacionTransiciones\Data_IR
IR_meas_amps_vec = []
IR_fluorescence_vec = []
for i, fname in enumerate(Calib_Files_IR.split()):
print(i)
#print(fname)
data = h5py.File(fname+'.h5', 'r') # Leo el h5: Recordar que nuestros datos estan en 'datasets'
#print(list(data['datasets'].keys()))
meas = np.array(data['datasets']['measurements_IR_sat'])
IR_fluorescence_vec.append(meas)
IR_meas_amps_vec.append(np.array(data['datasets']['IR_amps']))
PotenciasIR = [155, 4.8, 5.4, 6.2, 7.1, 8.0, 9.0, 9.9, 11., 12.1, 13.4, 14.6, 16.1, 17.6, 19.2, 20.9, 22.6, 24.5, 26.5, 28.4, 30.6, 32.7, 34.9, 37.1, 39.6, 41.9, 44.3, 46.9, 49.5, 52.1, 54.8, 57.4, 60.1, 63., 65.7, 68.8, 71.6, 74.5, 77., 80., 82.9]
Ivec = [-2.3, -2.2, -2.1, -2, -1.9, -1.8, -1.7, -1.6]
#%%
#background:
Background = list(IR_fluorescence_vec[11])+list(IR_fluorescence_vec[12])+list(IR_fluorescence_vec[13])+list(IR_fluorescence_vec[14])
bkgr = int(np.mean(Background))
print(f'El background de la medicion es {bkgr} cuentas por segundo')
#%%
FluovsB = []
jselected = [0, 1, 2, 4, 6, 7, 8, 9]
for j in jselected:
FluovsB.append(IR_fluorescence_vec[j])
plt.figure()
for j in range(len(jselected)):
plt.plot(PotenciasIR, [f-bkgr for f in FluovsB[j]], 'o')
plt.xlim(0, 90)
plt.xlabel('Potencia IR (uW)')
plt.ylabel('Cuentas')
#%%
from scipy.signal import savgol_filter as sf
plt.style.use('seaborn-ticks')
colors=sns.color_palette("rocket", 10)
colorsselected=[colors[8],colors[5],colors[3],colors[0]]
FluovsBshort = []
jselected = [0, 1, 4, 8]
for j in jselected:
FluovsBshort.append(IR_fluorescence_vec[j])
bkgr2 = np.min([FluovsBshort[0][1],FluovsBshort[1][1],FluovsBshort[2][1],FluovsBshort[3][1]])
plt.figure(figsize=(3.5, 3))
for j in range(len(jselected)):
rawcuentas = [f-bkgr for f in FluovsBshort[j]]
cuentas = np.array(rawcuentas[0:3]+list(sf(rawcuentas[3:],7,3)))
plt.plot(PotenciasIR[1:], cuentas[1:], 'o', color=colorsselected[j], markersize=3.5)
plt.fill_between(PotenciasIR[1:], cuentas[1:]-np.sqrt(cuentas)[1:], cuentas[1:]+np.sqrt(cuentas)[1:], color=colorsselected[j], alpha=0.4)
#plt.errorbar(PotenciasIR, cuentas, yerr=1*np.sqrt(cuentas), color='r', fmt='o', capsize=2, markersize=4)
plt.xlim(0, 90)
#plt.ylim(-100,3500)
plt.grid()
plt.xlabel(r'Repump power ($\mu$W)', fontsize=11)
plt.ylabel('Counts', fontsize=11)
plt.tight_layout()
plt.savefig('/home/nico/Nextcloud/G_liaf/Publicaciones/Work/2022 B vs k race/Figuras/Figuras jpg trabajadas/Barriendopotencia_exp.png',dpi=500)
#%%
# en funcion del campo magnetico ahora
FluovsBtrans = np.transpose(FluovsB)
potsselected = [5, 26, 31, 36]
plt.figure()
for p in potsselected:
plt.plot(np.flip(np.abs(np.array(Ivec))), np.flip(np.array(FluovsBtrans[p])), 'o')
#%%
#pruebo un suavizado para los datos
from scipy.signal import savgol_filter as sf
FluoSel = IR_fluorescence_vec[0][1:]
PotsSel = PotenciasIR[1:]
plt.plot(PotsSel, FluoSel, 'o')
plt.plot(PotsSel, sf(FluoSel, 13, 3))
#%%
#lo aplico
from scipy.optimize import curve_fit
"""
def LinearLarmortoCurrentCalibrated(I):
Larmor = 379871*I+1145919
return Larmor
"""
def LinearLarmortoCurrentCalibrated(I):
Larmor = 257550*I+667309
return Larmor
def ConvertLarmortoBfield(u):
c = 1398190.0452488689
return u/(2*np.pi)/c
c = 1398190
FluovsB = []
jselected = [0, 1, 2, 4, 6, 7, 8, 9]
Bvec = [ConvertLarmortoBfield(u) for u in LinearLarmortoCurrentCalibrated(np.array(Ivec))]
for j in jselected:
FluovsB.append(IR_fluorescence_vec[j])
"""
plt.figure()
for j in range(len(jselected)):
plt.plot(PotenciasIR, sf([f-bkgr for f in FluovsB[j]], 13, 3), 'o')
plt.xlim(0, 90)
plt.xlabel('Potencia IR (uW)')
plt.ylabel('Cuentas')
"""
MaxsFluosExp = []
MaxsPotsExp = []
for k in range(len(jselected)):
filteredfluo = sf([f-bkgr for f in FluovsB[k]], 13, 3)
maxfluo = np.max(filteredfluo)
j=0
happened=0
while j<len(filteredfluo):
try:
if np.abs(filteredfluo[j]-maxfluo)<10:
MaxsFluosExp.append(filteredfluo[j])
MaxsPotsExp.append(PotenciasIR[j])
break
else:
j=j+1
except:
MaxsFluosExp.append(0)
MaxsPotsExp.append(0)
"""
plt.figure()
plt.plot(Ivec, MaxsPotsExp,'rx')
plt.xlabel('Corriente bobina (A)')
plt.ylabel('Potencia umbral (mW)')
plt.grid()
"""
#longBvec = np.arange(np.min(Bvec), np.max(Bvec), 0.1*Bvec[1]-0.1*Bvec[0])
longBvec = np.arange(0.005, 0.033, 0.01*Bvec[1]-0.01*Bvec[0])
def LinearFitPotvsB(b, pendiente, ordenada):
return pendiente*b + ordenada
#popt_expvspot, pcov_expvspot = curve_fit(LinearFitPotvsB, np.array([0]+Bvec), np.array([0]+MaxsPotsExp))
popt_expvspot, pcov_expvspot = curve_fit(LinearFitPotvsB, np.array(Bvec), np.array(MaxsPotsExp))
print(popt_expvspot)
print(f'Ordenada al origen: {round(popt_expvspot[1],1)} +- {round(np.sqrt(pcov_expvspot[1,1]),1)} mW/G')
#%%
'''
Figura paper. Umbral vs campo magnetico con la calibracion
'''
plt.figure(figsize=(3.5, 3))
plt.errorbar([b*1e3 for b in Bvec], MaxsPotsExp, xerr=1e3*MeanError/(2*np.pi)/c, yerr=4, color='purple', fmt="o", markersize=5)
#plt.plot([0],[0],'o', markersize=5)
plt.plot([b*1e3 for b in longBvec], LinearFitPotvsB(longBvec, *popt_expvspot), linewidth=2, color='orangered')
plt.xlabel('Magnetic field (mG)', fontsize=11)
plt.ylabel('Threshold power (mW)', fontsize=11)
plt.xlim(6,32)
plt.ylim(17,81)
plt.grid()
plt.tight_layout()
plt.savefig('/home/nico/Nextcloud/G_liaf/Publicaciones/Work/2022 B vs k race/Figuras/Figuras jpg trabajadas/umbralvsB_exp.png',dpi=500)
analisis/plots/20220527_CPTvariandoB_barriendopotenciaIR/Data/EITfit/threeLevel_2repumps_AnalysisFunctions.py 0 → 100644
View file @ 9c6f1d39
#!/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 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/20220527_CPTvariandoB_barriendopotenciaIR/Data/EITfit/threeLevel_2repumps_CPTPlotter.py 0 → 100644
View file @ 9c6f1d39
#!/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/20220527_CPTvariandoB_barriendopotenciaIR/Data/EITfit/threeLevel_2repumps_linealpol_python_scripts.py 0 → 100644
View file @ 9c6f1d39
#!/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)
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/20220615_CPTvariandocompensacion/CPT_plotter_20220615.py 0 → 100644
View file @ 9c6f1d39
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 de CPT para campo magnetico bajo y terrestre
#/home/nico/Documents/artiq_experiments/analisis/plots/20220615_CPTvariandocompensacion/Data
ALL_FILES = """000007971-IR_Scan_withcal_optimized
000007972-IR_Scan_withcal_optimized
000007973-IR_Scan_withcal_optimized
000007976-IR_Scan_withcal_optimized
000007980-IR_Scan_withcal_optimized
000007981-IR_Scan_withcal_optimized
000007982-IR_Scan_withcal_optimized
000007983-IR_Scan_withcal_optimized
000007984-IR_Scan_withcal_optimized
000007985-IR_Scan_withcal_optimized
000008063-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(ALL_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(ALL_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']['IR_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_B = []
Freqs_B = []
for i, fname in enumerate(ALL_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_B.append(np.array(data['datasets']['IR_Frequencies']))
Counts_B.append(np.array(data['datasets']['counts_spectrum']))
#%%
#barriendo posicion radial del ion en la trampa
jvec = [0, 1, 2, 3]
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)
#plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', label=f'Amp Tisa: {AmpTisa[i]}', mb arkersize=3)
i = i + 1
plt.grid()
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('counts')
#plt.legend()
#%%
import seaborn as sns
#Barriendo angulo del IR con tisa apagado
palette = sns.color_palette("rocket", 8)
jvec = [4,5,6]
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]), color=palette[j-4], fmt='o', capsize=2, markersize=2)
#plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', label=f'Amp Tisa: {AmpTisa[i]}', mb arkersize=3)
i = i + 1
plt.grid()
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('counts')
plt.ylim(2000, 8000)
#plt.legend()
jvec = [7,8,9]
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]), color=palette[j-4], fmt='o', capsize=2, markersize=2)
#plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', label=f'Amp Tisa: {AmpTisa[i]}', mb arkersize=3)
i = i + 1
plt.grid()
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('counts')
plt.ylim(2000, 8000)
#plt.legend()
#%%
#CPT con dos iones
jvec = [10]
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]), color='purple',fmt='o', capsize=2, markersize=2)
#plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', label=f'Amp Tisa: {AmpTisa[i]}', mb arkersize=3)
i = i + 1
plt.grid()
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('Cuentas')
DR = [403.5,412.5,418,426.5]
for dr in DR:
plt.axvline(dr,color='blue',linestyle='--',alpha=0.3)
#plt.axvline(dr+22,color='crimson',linestyle='--',alpha=0.3)
plt.axvline(dr-22,color='red',linestyle='--')
#plt.axvline(dr+44,color='red',linestyle='--')
#plt.legend()
#%%
from EITfit.MM_eightLevel_2repumps_AnalysisFunctions import PerformExperiment_8levels
from scipy.optimize import curve_fit
phidoppler, titadoppler = 0, 90
phirepump, titarepump = 0, 0
phiprobe = 0
titaprobe = 90
Temp = 0.5e-3
sg = 0.544
sp = 4.5
sr = 0
DetRepump = 0
lw = 0.1
DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth = lw, lw, lw #ancho de linea de los laseres
u = 32.5e6
#B = (u/(2*np.pi))/c
correccion = 6
offsetxpi = 440+1+correccion
DetDoppler = -11.5-correccion
drivefreq = 2*np.pi*22.135*1e6
FreqsDR = [2*f*1e-6-offsetxpi+14 for f in Freqs[10]]
CountsDR = Counts[10]
freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
def FitEIT_MM(freqs, SCALE, OFFSET, SG, SP, BETA, TEMP):
BETA = 1.8
Detunings, Fluorescence = PerformExperiment_8levels(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
ScaledFluo = [f*SCALE + OFFSET for f in Fluorescence]
return ScaledFluo
popt, pcov = curve_fit(FitEIT_MM, FreqsDR, CountsDR, p0=[1e3, 1e4, 0.7, 5, 2, 5e-3], bounds=((0, 0, 0, 0, 0, 0), (1e5, 1e5, 1.5, 10, 3, 15e-3)))
FittedEITpi = FitEIT_MM(freqslong, *popt)
plt.figure()
plt.errorbar(FreqsDR, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', capsize=2, markersize=2)
plt.plot(freqslong, FittedEITpi)
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
analisis/plots/20220615_CPTvariandocompensacion/Data/EITfit/MM_eightLevel_2repumps_AnalysisFunctions.py 0 → 100644
View file @ 9c6f1d39
#!/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
import random
from scipy.signal import savgol_filter as sf
def PerformExperiment_8levels(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(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(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(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 SmoothNoisyCPT(Fluo, window=11, poly=3):
SmoothenFluo = sf(Fluo, window, poly)
return SmoothenFluo
analisis/plots/20220615_CPTvariandocompensacion/Data/EITfit/MM_eightLevel_2repumps_CPTPlotter.py 0 → 100644
View file @ 9c6f1d39
#!/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 #esto equivale aprox al campo que tenemos
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 #ancho de linea de los laseres en MHz
DopplerLaserLinewidth, ProbeLaserLinewidth = lw, lw #ancho de linea de los laseres
DetDoppler = -15 #detuning doppler en MHz
T = 5e-3 #temperatura en K
alpha = 0 #angulo entre los láseres
#estos son los angulos de la polarizacion de los laseres respecto al campo magnetico
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
#Parametros de la simulacion cpt todo en MHz
center = -10
span = 120
freqMin = center-span*0.5
freqMax = center+span*0.5
freqStep = 2e-1
noiseamplitude = 0
#parametros de saturacion de los laseres. g: doppler. p: probe (un rebombeo que scanea), r: repump (otro rebombeo fijo)
sg = 0.6
sp = 9
drivefreq=2*np.pi*22.135*1e6
#betavec = np.arange(0,1.1,0.1)
betavec=[0, 1, 2]
fig1, ax1 = plt.subplots()
FrequenciesVec = []
FluorescencesVec = []
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'$\beta={beta}$')
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()
#%%
beta=1
Frequencies, Fluorescence1 = 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)
beta=2
Frequencies, Fluorescence2 = 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)
plt.plot(Frequencies, [100*(Fluorescence1[j]+Fluorescence2[j]) for j in range(len(Fluorescence1))], label=fr'$\beta={beta}$')
plt.plot(Frequencies, [100*(Fluorescence1[j]+0*Fluorescence2[j]) for j in range(len(Fluorescence1))], label=fr'$\beta={beta}$')
plt.plot(Frequencies, [100*(0*Fluorescence1[j]+Fluorescence2[j]) for j in range(len(Fluorescence1))], label=fr'$\beta={beta}$')
plt.xlabel('Detuning Rebombeo (MHz)')
plt.ylabel('Fluorescencia (AU)')
#%%
Betas = [0,0.1,0.2]
FluosMM = []
for beta in Betas:
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)
FluosMM.append(np.array(Fluorescence))
plt.plot(Frequencies, 100*sum(FluosMM), label=fr'$\beta={beta}$')
plt.xlabel('Detuning Rebombeo (MHz)')
plt.ylabel('Fluorescencia (AU)')
#%%
#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/20220615_CPTvariandocompensacion/Data/EITfit/MM_eightLevel_2repumps_python_scripts.py 0 → 100644
View file @ 9c6f1d39
#!/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(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 = 7
#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(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(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(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/20220615_CPTvariandocompensacion/Data/EITfit/threeLevel_2repumps_AnalysisFunctions.py 0 → 100644
View file @ 9c6f1d39
#!/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 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/20220615_CPTvariandocompensacion/Data/EITfit/threeLevel_2repumps_CPTPlotter.py 0 → 100644
View file @ 9c6f1d39
#!/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/20220615_CPTvariandocompensacion/Data/EITfit/threeLevel_2repumps_linealpol_python_scripts.py 0 → 100644
View file @ 9c6f1d39
#!/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)
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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